2025-09-09T14:02:53.2298024Z Current runner version: '2.328.0' 2025-09-09T14:02:53.2303849Z Runner name: 'i-00dd278d0e297a335' 2025-09-09T14:02:53.2304596Z Runner group name: 'default' 2025-09-09T14:02:53.2305430Z Machine name: 'ip-10-0-60-13' 2025-09-09T14:02:53.2308251Z ##[group]GITHUB_TOKEN Permissions 2025-09-09T14:02:53.2310592Z Contents: read 2025-09-09T14:02:53.2311111Z Metadata: read 2025-09-09T14:02:53.2311617Z Packages: read 2025-09-09T14:02:53.2312115Z ##[endgroup] 2025-09-09T14:02:53.2314009Z Secret source: Actions 2025-09-09T14:02:53.2314650Z Prepare workflow directory 2025-09-09T14:02:53.2866934Z Prepare all required actions 2025-09-09T14:02:53.2903576Z Getting action download info 2025-09-09T14:02:53.5947949Z Download action repository 'actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683' (SHA:11bd71901bbe5b1630ceea73d27597364c9af683) 2025-09-09T14:02:53.9317645Z Download action repository 'pytorch/pytorch@main' (SHA:4dd73e659a8fd4872e5f49cfd72e420fa7c4e6c9) 2025-09-09T14:03:08.8403710Z Download action repository 'actions/download-artifact@d3f86a106a0bac45b974a628896c90dbdf5c8093' (SHA:d3f86a106a0bac45b974a628896c90dbdf5c8093) 2025-09-09T14:03:09.2216103Z Download action repository 'pmeier/pytest-results-action@a2c1430e2bddadbad9f49a6f9b879f062c6b19b1' (SHA:a2c1430e2bddadbad9f49a6f9b879f062c6b19b1) 2025-09-09T14:03:09.3704161Z Download action repository 'actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02' (SHA:ea165f8d65b6e75b540449e92b4886f43607fa02) 2025-09-09T14:03:09.9121457Z Getting action download info 2025-09-09T14:03:10.1230811Z Uses: pytorch/test-infra/.github/workflows/linux_job_v2.yml@refs/heads/main (e502b6d9079a2a411c68046e8a7694b851c5df33) 2025-09-09T14:03:10.1234803Z ##[group] Inputs 2025-09-09T14:03:10.1236637Z script: conda create -n venv python=3.9 -y conda activate venv echo "::group::Install newer objcopy that supports --set-section-alignment" dnf install -y gcc-toolset-10-binutils export PATH=/opt/rh/gcc-toolset-10/root/usr/bin/:$PATH python -m pip install --upgrade pip pip install torch==2.8.0 sed -i '' dev-requirements.txt pip install -r dev-requirements.txt pip install . export CONDA=$(dirname $(dirname $(which conda))) export LD_LIBRARY_PATH=$CONDA/lib/:$LD_LIBRARY_PATH pytest test --verbose -s 2025-09-09T14:03:10.1238960Z timeout: 180 2025-09-09T14:03:10.1239207Z runner: linux.g5.12xlarge.nvidia.gpu 2025-09-09T14:03:10.1239488Z upload-artifact: 2025-09-09T14:03:10.1239995Z upload-artifact-to-s3: false 2025-09-09T14:03:10.1240259Z download-artifact: 2025-09-09T14:03:10.1240490Z repository: 2025-09-09T14:03:10.1240719Z fetch-depth: 1 2025-09-09T14:03:10.1240947Z submodules: recursive 2025-09-09T14:03:10.1241174Z ref: 2025-09-09T14:03:10.1241409Z test-infra-repository: pytorch/test-infra 2025-09-09T14:03:10.1241705Z test-infra-ref: 2025-09-09T14:03:10.1241940Z use-custom-docker-registry: true 2025-09-09T14:03:10.1242244Z docker-image: pytorch/almalinux-builder 2025-09-09T14:03:10.1242544Z docker-build-dir: .ci/docker 2025-09-09T14:03:10.1242808Z gpu-arch-type: cuda 2025-09-09T14:03:10.1243053Z gpu-arch-version: 12.6 2025-09-09T14:03:10.1243292Z job-name: linux-job 2025-09-09T14:03:10.1243526Z continue-on-error: false 2025-09-09T14:03:10.1243778Z binary-matrix: 2025-09-09T14:03:10.1244002Z run-with-docker: true 2025-09-09T14:03:10.1244222Z secrets-env: 2025-09-09T14:03:10.1244430Z no-sudo: false 2025-09-09T14:03:10.1244664Z ##[endgroup] 2025-09-09T14:03:10.1245106Z Complete job name: test (CUDA 2.8, linux.g5.12xlarge.nvidia.gpu, torch==2.8.0, cuda, 12.6) / linux-job 2025-09-09T14:03:10.2083801Z A job started hook has been configured by the self-hosted runner administrator 2025-09-09T14:03:10.2220364Z ##[group]Run '/home/ec2-user/runner-scripts/before_job.sh' 2025-09-09T14:03:10.2231881Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-09T14:03:10.2232458Z ##[endgroup] 2025-09-09T14:03:11.6248956Z Runner Type: linux.g5.12xlarge.nvidia.gpu 2025-09-09T14:03:11.6249377Z Instance Type: g5.12xlarge 2025-09-09T14:03:11.6249618Z AMI Name: unknown 2025-09-09T14:03:11.6298742Z AMI ID: ami-05ffe3c48a9991133 2025-09-09T14:03:17.2650548Z ##[group]Run set -euxo pipefail 2025-09-09T14:03:17.2650929Z set -euxo pipefail 2025-09-09T14:03:17.2651245Z if [[ "${NO_SUDO}" == "false" ]]; then 2025-09-09T14:03:17.2651616Z  echo "::group::Cleanup with-sudo debug output" 2025-09-09T14:03:17.2651991Z  sudo rm -rfv "${GITHUB_WORKSPACE}" 2025-09-09T14:03:17.2652286Z else 2025-09-09T14:03:17.2652545Z  echo "::group::Cleanup no-sudo debug output" 2025-09-09T14:03:17.2652883Z  rm -rfv "${GITHUB_WORKSPACE}" 2025-09-09T14:03:17.2653172Z fi 2025-09-09T14:03:17.2653362Z  2025-09-09T14:03:17.2653587Z mkdir -p "${GITHUB_WORKSPACE}" 2025-09-09T14:03:17.2653907Z echo "::endgroup::" 2025-09-09T14:03:17.2668930Z shell: /usr/bin/bash -e {0} 2025-09-09T14:03:17.2669215Z env: 2025-09-09T14:03:17.2669463Z DOCKER_IMAGE: pytorch/almalinux-builder:cuda12.6 2025-09-09T14:03:17.2669814Z REPOSITORY: pytorch/ao 2025-09-09T14:03:17.2670096Z PR_NUMBER: 2963 2025-09-09T14:03:17.2671908Z SCRIPT: conda create -n venv python=3.9 -y conda activate venv echo "::group::Install newer objcopy that supports --set-section-alignment" dnf install -y gcc-toolset-10-binutils export PATH=/opt/rh/gcc-toolset-10/root/usr/bin/:$PATH python -m pip install --upgrade pip pip install torch==2.8.0 sed -i '' dev-requirements.txt pip install -r dev-requirements.txt pip install . export CONDA=$(dirname $(dirname $(which conda))) export LD_LIBRARY_PATH=$CONDA/lib/:$LD_LIBRARY_PATH pytest test --verbose -s 2025-09-09T14:03:17.2673712Z NO_SUDO: false 2025-09-09T14:03:17.2673920Z ##[endgroup] 2025-09-09T14:03:17.2708632Z + [[ false == \f\a\l\s\e ]] 2025-09-09T14:03:17.2718316Z + echo '::group::Cleanup with-sudo debug output' 2025-09-09T14:03:17.2724510Z ##[group]Cleanup with-sudo debug output 2025-09-09T14:03:17.2724936Z + sudo rm -rfv /home/ec2-user/actions-runner/_work/ao/ao 2025-09-09T14:03:17.4192534Z removed directory '/home/ec2-user/actions-runner/_work/ao/ao' 2025-09-09T14:03:17.4216927Z + mkdir -p /home/ec2-user/actions-runner/_work/ao/ao 2025-09-09T14:03:17.4234778Z + echo ::endgroup:: 2025-09-09T14:03:17.4235466Z ##[endgroup] 2025-09-09T14:03:17.4352835Z ##[group]Run actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 2025-09-09T14:03:17.4353249Z with: 2025-09-09T14:03:17.4353478Z repository: pytorch/test-infra 2025-09-09T14:03:17.4353759Z path: test-infra 2025-09-09T14:03:17.4353986Z submodules: recursive 2025-09-09T14:03:17.4354337Z token: *** 2025-09-09T14:03:17.4354547Z ssh-strict: true 2025-09-09T14:03:17.4354760Z ssh-user: git 2025-09-09T14:03:17.4354988Z persist-credentials: true 2025-09-09T14:03:17.4355233Z clean: true 2025-09-09T14:03:17.4355472Z sparse-checkout-cone-mode: true 2025-09-09T14:03:17.4355753Z fetch-depth: 1 2025-09-09T14:03:17.4355978Z fetch-tags: false 2025-09-09T14:03:17.4356206Z show-progress: true 2025-09-09T14:03:17.4356440Z lfs: false 2025-09-09T14:03:17.4356661Z set-safe-directory: true 2025-09-09T14:03:17.4356902Z env: 2025-09-09T14:03:17.4357144Z DOCKER_IMAGE: pytorch/almalinux-builder:cuda12.6 2025-09-09T14:03:17.4357471Z REPOSITORY: pytorch/ao 2025-09-09T14:03:17.4357740Z PR_NUMBER: 2963 2025-09-09T14:03:17.4359581Z SCRIPT: conda create -n venv python=3.9 -y conda activate venv echo "::group::Install newer objcopy that supports --set-section-alignment" dnf install -y gcc-toolset-10-binutils export PATH=/opt/rh/gcc-toolset-10/root/usr/bin/:$PATH python -m pip install --upgrade pip pip install torch==2.8.0 sed -i '' dev-requirements.txt pip install -r dev-requirements.txt pip install . export CONDA=$(dirname $(dirname $(which conda))) export LD_LIBRARY_PATH=$CONDA/lib/:$LD_LIBRARY_PATH pytest test --verbose -s 2025-09-09T14:03:17.4361406Z ##[endgroup] 2025-09-09T14:03:17.5803081Z Syncing repository: pytorch/test-infra 2025-09-09T14:03:17.5803730Z ##[group]Getting Git version info 2025-09-09T14:03:17.5804551Z Working directory is '/home/ec2-user/actions-runner/_work/ao/ao/test-infra' 2025-09-09T14:03:17.5805159Z [command]/usr/bin/git version 2025-09-09T14:03:17.5805426Z git version 2.47.1 2025-09-09T14:03:17.5812417Z ##[endgroup] 2025-09-09T14:03:17.5826575Z Temporarily overriding HOME='/home/ec2-user/actions-runner/_work/_temp/1759523c-0982-4aa1-a7ed-ec6ab8829b8f' before making global git config changes 2025-09-09T14:03:17.5840727Z Adding repository directory to the temporary git global config as a safe directory 2025-09-09T14:03:17.5841436Z [command]/usr/bin/git config --global --add safe.directory /home/ec2-user/actions-runner/_work/ao/ao/test-infra 2025-09-09T14:03:17.5879573Z ##[group]Initializing the repository 2025-09-09T14:03:17.5884028Z [command]/usr/bin/git init /home/ec2-user/actions-runner/_work/ao/ao/test-infra 2025-09-09T14:03:17.5951572Z hint: Using 'master' as the name for the initial branch. This default branch name 2025-09-09T14:03:17.5952142Z hint: is subject to change. To configure the initial branch name to use in all 2025-09-09T14:03:17.5952681Z hint: of your new repositories, which will suppress this warning, call: 2025-09-09T14:03:17.5953065Z hint: 2025-09-09T14:03:17.5953366Z hint: git config --global init.defaultBranch 2025-09-09T14:03:17.5953700Z hint: 2025-09-09T14:03:17.5954019Z hint: Names commonly chosen instead of 'master' are 'main', 'trunk' and 2025-09-09T14:03:17.5954540Z hint: 'development'. The just-created branch can be renamed via this command: 2025-09-09T14:03:17.5954946Z hint: 2025-09-09T14:03:17.5955168Z hint: git branch -m 2025-09-09T14:03:17.5964836Z Initialized empty Git repository in /home/ec2-user/actions-runner/_work/ao/ao/test-infra/.git/ 2025-09-09T14:03:17.5975672Z [command]/usr/bin/git remote add origin https://github.com/pytorch/test-infra 2025-09-09T14:03:17.6053087Z ##[endgroup] 2025-09-09T14:03:17.6053540Z ##[group]Disabling automatic garbage collection 2025-09-09T14:03:17.6057473Z [command]/usr/bin/git config --local gc.auto 0 2025-09-09T14:03:17.6105709Z ##[endgroup] 2025-09-09T14:03:17.6106418Z ##[group]Setting up auth 2025-09-09T14:03:17.6111066Z [command]/usr/bin/git config --local --name-only --get-regexp core\.sshCommand 2025-09-09T14:03:17.6148565Z [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' || :" 2025-09-09T14:03:17.6594840Z [command]/usr/bin/git config --local --name-only --get-regexp http\.https\:\/\/github\.com\/\.extraheader 2025-09-09T14:03:17.6631617Z [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' || :" 2025-09-09T14:03:17.7051088Z [command]/usr/bin/git config --local http.https://github.com/.extraheader AUTHORIZATION: basic *** 2025-09-09T14:03:17.7095974Z ##[endgroup] 2025-09-09T14:03:17.7096424Z ##[group]Determining the default branch 2025-09-09T14:03:17.7099065Z Retrieving the default branch name 2025-09-09T14:03:17.9727970Z Default branch 'main' 2025-09-09T14:03:17.9728497Z ##[endgroup] 2025-09-09T14:03:17.9728890Z ##[group]Fetching the repository 2025-09-09T14:03:17.9733412Z [command]/usr/bin/git -c protocol.version=2 fetch --no-tags --prune --no-recurse-submodules --depth=1 origin +refs/heads/main:refs/remotes/origin/main 2025-09-09T14:03:18.3888851Z From https://github.com/pytorch/test-infra 2025-09-09T14:03:18.3889376Z * [new branch] main -> origin/main 2025-09-09T14:03:18.3922696Z ##[endgroup] 2025-09-09T14:03:18.3923100Z ##[group]Determining the checkout info 2025-09-09T14:03:18.3923607Z ##[endgroup] 2025-09-09T14:03:18.3928833Z [command]/usr/bin/git sparse-checkout disable 2025-09-09T14:03:18.3978136Z [command]/usr/bin/git config --local --unset-all extensions.worktreeConfig 2025-09-09T14:03:18.4014344Z ##[group]Checking out the ref 2025-09-09T14:03:18.4016910Z [command]/usr/bin/git checkout --progress --force -B main refs/remotes/origin/main 2025-09-09T14:03:18.5593634Z Switched to a new branch 'main' 2025-09-09T14:03:18.5605799Z branch 'main' set up to track 'origin/main'. 2025-09-09T14:03:18.5617694Z ##[endgroup] 2025-09-09T14:03:18.5618106Z ##[group]Setting up auth for fetching submodules 2025-09-09T14:03:18.5623653Z [command]/usr/bin/git config --global http.https://github.com/.extraheader AUTHORIZATION: basic *** 2025-09-09T14:03:18.5672038Z [command]/usr/bin/git config --global --unset-all url.https://github.com/.insteadOf 2025-09-09T14:03:18.5720824Z [command]/usr/bin/git config --global --add url.https://github.com/.insteadOf git@github.com: 2025-09-09T14:03:18.5757023Z [command]/usr/bin/git config --global --add url.https://github.com/.insteadOf org-21003710@github.com: 2025-09-09T14:03:18.5791600Z ##[endgroup] 2025-09-09T14:03:18.5791985Z ##[group]Fetching submodules 2025-09-09T14:03:18.5793955Z [command]/usr/bin/git submodule sync --recursive 2025-09-09T14:03:18.6213526Z [command]/usr/bin/git -c protocol.version=2 submodule update --init --force --depth=1 --recursive 2025-09-09T14:03:18.6626462Z [command]/usr/bin/git submodule foreach --recursive git config --local gc.auto 0 2025-09-09T14:03:18.7029193Z ##[endgroup] 2025-09-09T14:03:18.7029621Z ##[group]Persisting credentials for submodules 2025-09-09T14:03:18.7034574Z [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' || :" 2025-09-09T14:03:18.7438415Z [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" 2025-09-09T14:03:18.7840597Z [command]/usr/bin/git submodule foreach --recursive git config --local --add 'url.https://github.com/.insteadOf' 'git@github.com:' 2025-09-09T14:03:18.8242376Z [command]/usr/bin/git submodule foreach --recursive git config --local --add 'url.https://github.com/.insteadOf' 'org-21003710@github.com:' 2025-09-09T14:03:18.8649342Z ##[endgroup] 2025-09-09T14:03:18.8695571Z [command]/usr/bin/git log -1 --format=%H 2025-09-09T14:03:18.8728854Z e502b6d9079a2a411c68046e8a7694b851c5df33 2025-09-09T14:03:18.8935650Z Prepare all required actions 2025-09-09T14:03:18.8936094Z Getting action download info 2025-09-09T14:03:19.0257916Z Download action repository 'pytorch/test-infra@main' (SHA:e502b6d9079a2a411c68046e8a7694b851c5df33) 2025-09-09T14:03:21.0745595Z Getting action download info 2025-09-09T14:03:21.1945297Z Download action repository 'nick-fields/retry@3e91a01664abd3c5cd539100d10d33b9c5b68482' (SHA:3e91a01664abd3c5cd539100d10d33b9c5b68482) 2025-09-09T14:03:21.4894613Z ##[group]Run ./test-infra/.github/actions/setup-linux 2025-09-09T14:03:21.4894946Z env: 2025-09-09T14:03:21.4895200Z DOCKER_IMAGE: pytorch/almalinux-builder:cuda12.6 2025-09-09T14:03:21.4895531Z REPOSITORY: pytorch/ao 2025-09-09T14:03:21.4895800Z PR_NUMBER: 2963 2025-09-09T14:03:21.4897572Z SCRIPT: conda create -n venv python=3.9 -y conda activate venv echo "::group::Install newer objcopy that supports --set-section-alignment" dnf install -y gcc-toolset-10-binutils export PATH=/opt/rh/gcc-toolset-10/root/usr/bin/:$PATH python -m pip install --upgrade pip pip install torch==2.8.0 sed -i '' dev-requirements.txt pip install -r dev-requirements.txt pip install . export CONDA=$(dirname $(dirname $(which conda))) export LD_LIBRARY_PATH=$CONDA/lib/:$LD_LIBRARY_PATH pytest test --verbose -s 2025-09-09T14:03:21.4899371Z ##[endgroup] 2025-09-09T14:03:21.5194016Z ##[group]Run set -euo pipefail 2025-09-09T14:03:21.5194335Z set -euo pipefail 2025-09-09T14:03:21.5194609Z function get_ec2_metadata() { 2025-09-09T14:03:21.5194952Z  # Pulled from instance metadata endpoint for EC2 2025-09-09T14:03:21.5195549Z  # see https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/instancedata-data-retrieval.html 2025-09-09T14:03:21.5196254Z  category=$1 2025-09-09T14:03:21.5197076Z  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}" 2025-09-09T14:03:21.5197894Z } 2025-09-09T14:03:21.5198147Z echo "ami-id: $(get_ec2_metadata ami-id)" 2025-09-09T14:03:21.5198546Z echo "instance-id: $(get_ec2_metadata instance-id)" 2025-09-09T14:03:21.5199063Z echo "instance-type: $(get_ec2_metadata instance-type)" 2025-09-09T14:03:21.5199460Z echo "system info $(uname -a)" 2025-09-09T14:03:21.5208692Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-09T14:03:21.5209044Z env: 2025-09-09T14:03:21.5209297Z DOCKER_IMAGE: pytorch/almalinux-builder:cuda12.6 2025-09-09T14:03:21.5209625Z REPOSITORY: pytorch/ao 2025-09-09T14:03:21.5209849Z PR_NUMBER: 2963 2025-09-09T14:03:21.5211574Z SCRIPT: conda create -n venv python=3.9 -y conda activate venv echo "::group::Install newer objcopy that supports --set-section-alignment" dnf install -y gcc-toolset-10-binutils export PATH=/opt/rh/gcc-toolset-10/root/usr/bin/:$PATH python -m pip install --upgrade pip pip install torch==2.8.0 sed -i '' dev-requirements.txt pip install -r dev-requirements.txt pip install . export CONDA=$(dirname $(dirname $(which conda))) export LD_LIBRARY_PATH=$CONDA/lib/:$LD_LIBRARY_PATH pytest test --verbose -s 2025-09-09T14:03:21.5213341Z ##[endgroup] 2025-09-09T14:03:21.5380739Z ami-id: ami-05ffe3c48a9991133 2025-09-09T14:03:21.5513790Z instance-id: i-00dd278d0e297a335 2025-09-09T14:03:21.5642828Z instance-type: g5.12xlarge 2025-09-09T14:03:21.5659121Z system info Linux ip-10-0-60-13.ec2.internal 6.1.141-155.222.amzn2023.x86_64 #1 SMP PREEMPT_DYNAMIC Tue Jun 17 10:29:47 UTC 2025 x86_64 x86_64 x86_64 GNU/Linux 2025-09-09T14:03:21.5716815Z ##[group]Run echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT" 2025-09-09T14:03:21.5717683Z echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT" 2025-09-09T14:03:21.5728156Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-09T14:03:21.5728514Z env: 2025-09-09T14:03:21.5728759Z DOCKER_IMAGE: pytorch/almalinux-builder:cuda12.6 2025-09-09T14:03:21.5729091Z REPOSITORY: pytorch/ao 2025-09-09T14:03:21.5729331Z PR_NUMBER: 2963 2025-09-09T14:03:21.5731057Z SCRIPT: conda create -n venv python=3.9 -y conda activate venv echo "::group::Install newer objcopy that supports --set-section-alignment" dnf install -y gcc-toolset-10-binutils export PATH=/opt/rh/gcc-toolset-10/root/usr/bin/:$PATH python -m pip install --upgrade pip pip install torch==2.8.0 sed -i '' dev-requirements.txt pip install -r dev-requirements.txt pip install . export CONDA=$(dirname $(dirname $(which conda))) export LD_LIBRARY_PATH=$CONDA/lib/:$LD_LIBRARY_PATH pytest test --verbose -s 2025-09-09T14:03:21.5732834Z ##[endgroup] 2025-09-09T14:03:21.5823300Z ##[group]Run if systemctl is-active --quiet docker; then 2025-09-09T14:03:21.5823724Z if systemctl is-active --quiet docker; then 2025-09-09T14:03:21.5824095Z  echo "Docker daemon is running..."; 2025-09-09T14:03:21.5824398Z else 2025-09-09T14:03:21.5824719Z  echo "Starting docker deamon..." && sudo systemctl start docker; 2025-09-09T14:03:21.5825109Z fi 2025-09-09T14:03:21.5833742Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-09T14:03:21.5834081Z env: 2025-09-09T14:03:21.5834329Z DOCKER_IMAGE: pytorch/almalinux-builder:cuda12.6 2025-09-09T14:03:21.5834647Z REPOSITORY: pytorch/ao 2025-09-09T14:03:21.5834891Z PR_NUMBER: 2963 2025-09-09T14:03:21.5836610Z SCRIPT: conda create -n venv python=3.9 -y conda activate venv echo "::group::Install newer objcopy that supports --set-section-alignment" dnf install -y gcc-toolset-10-binutils export PATH=/opt/rh/gcc-toolset-10/root/usr/bin/:$PATH python -m pip install --upgrade pip pip install torch==2.8.0 sed -i '' dev-requirements.txt pip install -r dev-requirements.txt pip install . export CONDA=$(dirname $(dirname $(which conda))) export LD_LIBRARY_PATH=$CONDA/lib/:$LD_LIBRARY_PATH pytest test --verbose -s 2025-09-09T14:03:21.5838541Z ##[endgroup] 2025-09-09T14:03:21.5935607Z Docker daemon is running... 2025-09-09T14:03:21.5974658Z ##[group]Run AWS_ACCOUNT_ID=$(aws sts get-caller-identity|grep Account|cut -f4 -d\") 2025-09-09T14:03:21.5975291Z AWS_ACCOUNT_ID=$(aws sts get-caller-identity|grep Account|cut -f4 -d\") 2025-09-09T14:03:21.5975788Z retry () { "$@" || (sleep 1 && "$@") || (sleep 2 && "$@") } 2025-09-09T14:03:21.5976361Z retry aws ecr get-login-password --region "$AWS_DEFAULT_REGION" | docker login --username AWS \ 2025-09-09T14:03:21.5977050Z  --password-stdin "$AWS_ACCOUNT_ID.dkr.ecr.$AWS_DEFAULT_REGION.amazonaws.com" 2025-09-09T14:03:21.5986449Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-09T14:03:21.5986801Z env: 2025-09-09T14:03:21.5987060Z DOCKER_IMAGE: pytorch/almalinux-builder:cuda12.6 2025-09-09T14:03:21.5987406Z REPOSITORY: pytorch/ao 2025-09-09T14:03:21.5987676Z PR_NUMBER: 2963 2025-09-09T14:03:21.5989390Z SCRIPT: conda create -n venv python=3.9 -y conda activate venv echo "::group::Install newer objcopy that supports --set-section-alignment" dnf install -y gcc-toolset-10-binutils export PATH=/opt/rh/gcc-toolset-10/root/usr/bin/:$PATH python -m pip install --upgrade pip pip install torch==2.8.0 sed -i '' dev-requirements.txt pip install -r dev-requirements.txt pip install . export CONDA=$(dirname $(dirname $(which conda))) export LD_LIBRARY_PATH=$CONDA/lib/:$LD_LIBRARY_PATH pytest test --verbose -s 2025-09-09T14:03:21.5991159Z AWS_RETRY_MODE: standard 2025-09-09T14:03:21.5991412Z AWS_MAX_ATTEMPTS: 5 2025-09-09T14:03:21.5991655Z AWS_DEFAULT_REGION: us-east-1 2025-09-09T14:03:21.5991913Z ##[endgroup] 2025-09-09T14:03:22.6663440Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json. 2025-09-09T14:03:22.6664043Z Configure a credential helper to remove this warning. See 2025-09-09T14:03:22.6665436Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store 2025-09-09T14:03:22.6665944Z 2025-09-09T14:03:22.6666911Z Login Succeeded 2025-09-09T14:03:22.6722983Z ##[group]Run env | grep '^GITHUB' >> "${RUNNER_TEMP}/github_env_${GITHUB_RUN_ID}" 2025-09-09T14:03:22.6723527Z env | grep '^GITHUB' >> "${RUNNER_TEMP}/github_env_${GITHUB_RUN_ID}" 2025-09-09T14:03:22.6723985Z env | grep '^CI' >> "${RUNNER_TEMP}/github_env_${GITHUB_RUN_ID}" 2025-09-09T14:03:22.6733438Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-09T14:03:22.6733770Z env: 2025-09-09T14:03:22.6734022Z DOCKER_IMAGE: pytorch/almalinux-builder:cuda12.6 2025-09-09T14:03:22.6734346Z REPOSITORY: pytorch/ao 2025-09-09T14:03:22.6734586Z PR_NUMBER: 2963 2025-09-09T14:03:22.6736344Z SCRIPT: conda create -n venv python=3.9 -y conda activate venv echo "::group::Install newer objcopy that supports --set-section-alignment" dnf install -y gcc-toolset-10-binutils export PATH=/opt/rh/gcc-toolset-10/root/usr/bin/:$PATH python -m pip install --upgrade pip pip install torch==2.8.0 sed -i '' dev-requirements.txt pip install -r dev-requirements.txt pip install . export CONDA=$(dirname $(dirname $(which conda))) export LD_LIBRARY_PATH=$CONDA/lib/:$LD_LIBRARY_PATH pytest test --verbose -s 2025-09-09T14:03:22.6738110Z ##[endgroup] 2025-09-09T14:03:22.6860121Z ##[group]Run RUNNER_ARTIFACT_DIR="${RUNNER_TEMP}/artifacts" 2025-09-09T14:03:22.6860550Z RUNNER_ARTIFACT_DIR="${RUNNER_TEMP}/artifacts" 2025-09-09T14:03:22.6860913Z sudo rm -rf "${RUNNER_ARTIFACT_DIR}" 2025-09-09T14:03:22.6861237Z mkdir -p "${RUNNER_ARTIFACT_DIR}" 2025-09-09T14:03:22.6861639Z echo "RUNNER_ARTIFACT_DIR=${RUNNER_ARTIFACT_DIR}" >> "${GITHUB_ENV}" 2025-09-09T14:03:22.6862204Z  2025-09-09T14:03:22.6862483Z RUNNER_TEST_RESULTS_DIR="${RUNNER_TEMP}/test-results" 2025-09-09T14:03:22.6862856Z sudo rm -rf "${RUNNER_TEST_RESULTS_DIR}" 2025-09-09T14:03:22.6863188Z mkdir -p "${RUNNER_TEST_RESULTS_DIR}" 2025-09-09T14:03:22.6863621Z echo "RUNNER_TEST_RESULTS_DIR=${RUNNER_TEST_RESULTS_DIR}" >> "${GITHUB_ENV}" 2025-09-09T14:03:22.6864012Z  2025-09-09T14:03:22.6864225Z RUNNER_DOCS_DIR="${RUNNER_TEMP}/docs" 2025-09-09T14:03:22.6864526Z sudo rm -rf "${RUNNER_DOCS_DIR}" 2025-09-09T14:03:22.6864817Z mkdir -p "${RUNNER_DOCS_DIR}" 2025-09-09T14:03:22.6865168Z echo "RUNNER_DOCS_DIR=${RUNNER_DOCS_DIR}" >> "${GITHUB_ENV}" 2025-09-09T14:03:22.6873402Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-09T14:03:22.6873730Z env: 2025-09-09T14:03:22.6873967Z DOCKER_IMAGE: pytorch/almalinux-builder:cuda12.6 2025-09-09T14:03:22.6874281Z REPOSITORY: pytorch/ao 2025-09-09T14:03:22.6874512Z PR_NUMBER: 2963 2025-09-09T14:03:22.6876229Z SCRIPT: conda create -n venv python=3.9 -y conda activate venv echo "::group::Install newer objcopy that supports --set-section-alignment" dnf install -y gcc-toolset-10-binutils export PATH=/opt/rh/gcc-toolset-10/root/usr/bin/:$PATH python -m pip install --upgrade pip pip install torch==2.8.0 sed -i '' dev-requirements.txt pip install -r dev-requirements.txt pip install . export CONDA=$(dirname $(dirname $(which conda))) export LD_LIBRARY_PATH=$CONDA/lib/:$LD_LIBRARY_PATH pytest test --verbose -s 2025-09-09T14:03:22.6877961Z ##[endgroup] 2025-09-09T14:03:23.2477350Z ##[group]Run needs=0 2025-09-09T14:03:23.2477610Z needs=0 2025-09-09T14:03:23.2477995Z if lspci -v | grep -e 'controller.*NVIDIA' >/dev/null 2>/dev/null; then 2025-09-09T14:03:23.2478455Z  needs=1 2025-09-09T14:03:23.2478683Z fi 2025-09-09T14:03:23.2479022Z echo "does=${needs}" >> $GITHUB_OUTPUT 2025-09-09T14:03:23.2488676Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-09T14:03:23.2489066Z env: 2025-09-09T14:03:23.2489333Z DOCKER_IMAGE: pytorch/almalinux-builder:cuda12.6 2025-09-09T14:03:23.2489696Z REPOSITORY: pytorch/ao 2025-09-09T14:03:23.2489957Z PR_NUMBER: 2963 2025-09-09T14:03:23.2492296Z SCRIPT: conda create -n venv python=3.9 -y conda activate venv echo "::group::Install newer objcopy that supports --set-section-alignment" dnf install -y gcc-toolset-10-binutils export PATH=/opt/rh/gcc-toolset-10/root/usr/bin/:$PATH python -m pip install --upgrade pip pip install torch==2.8.0 sed -i '' dev-requirements.txt pip install -r dev-requirements.txt pip install . export CONDA=$(dirname $(dirname $(which conda))) export LD_LIBRARY_PATH=$CONDA/lib/:$LD_LIBRARY_PATH pytest test --verbose -s 2025-09-09T14:03:23.2494174Z RUNNER_ARTIFACT_DIR: /home/ec2-user/actions-runner/_work/_temp/artifacts 2025-09-09T14:03:23.2494701Z RUNNER_TEST_RESULTS_DIR: /home/ec2-user/actions-runner/_work/_temp/test-results 2025-09-09T14:03:23.2495201Z RUNNER_DOCS_DIR: /home/ec2-user/actions-runner/_work/_temp/docs 2025-09-09T14:03:23.2495546Z ##[endgroup] 2025-09-09T14:03:23.2842871Z ##[group]Run pytorch/test-infra/.github/actions/setup-nvidia@main 2025-09-09T14:03:23.2843246Z with: 2025-09-09T14:03:23.2843465Z driver-version: 580.65.06 2025-09-09T14:03:23.2843722Z env: 2025-09-09T14:03:23.2843976Z DOCKER_IMAGE: pytorch/almalinux-builder:cuda12.6 2025-09-09T14:03:23.2844347Z REPOSITORY: pytorch/ao 2025-09-09T14:03:23.2844606Z PR_NUMBER: 2963 2025-09-09T14:03:23.2846779Z SCRIPT: conda create -n venv python=3.9 -y conda activate venv echo "::group::Install newer objcopy that supports --set-section-alignment" dnf install -y gcc-toolset-10-binutils export PATH=/opt/rh/gcc-toolset-10/root/usr/bin/:$PATH python -m pip install --upgrade pip pip install torch==2.8.0 sed -i '' dev-requirements.txt pip install -r dev-requirements.txt pip install . export CONDA=$(dirname $(dirname $(which conda))) export LD_LIBRARY_PATH=$CONDA/lib/:$LD_LIBRARY_PATH pytest test --verbose -s 2025-09-09T14:03:23.2848883Z RUNNER_ARTIFACT_DIR: /home/ec2-user/actions-runner/_work/_temp/artifacts 2025-09-09T14:03:23.2849422Z RUNNER_TEST_RESULTS_DIR: /home/ec2-user/actions-runner/_work/_temp/test-results 2025-09-09T14:03:23.2849923Z RUNNER_DOCS_DIR: /home/ec2-user/actions-runner/_work/_temp/docs 2025-09-09T14:03:23.2850280Z ##[endgroup] 2025-09-09T14:03:23.2888419Z ##[group]Run nick-fields/retry@3e91a01664abd3c5cd539100d10d33b9c5b68482 2025-09-09T14:03:23.2888801Z with: 2025-09-09T14:03:23.2889011Z timeout_minutes: 10 2025-09-09T14:03:23.2889244Z max_attempts: 3 2025-09-09T14:03:23.2914980Z command: # Is it disgusting to have a full shell script here in this github action? Sure # But is it the best way to make it so that this action relies on nothing else? Absolutely set -eou pipefail DISTRIBUTION=$(. /etc/os-release;echo $ID$VERSION_ID) DRIVER_FN="NVIDIA-Linux-x86_64-${DRIVER_VERSION}.run" install_nvidia_docker2_amzn2() { ( set -x # Needed for yum-config-manager sudo yum install -y yum-utils if [[ "${DISTRIBUTION}" == "amzn2023" ]] ; then YUM_REPO_URL="https://nvidia.github.io/libnvidia-container/stable/rpm/nvidia-container-toolkit.repo" else # Amazon Linux 2 YUM_REPO_URL="https://nvidia.github.io/nvidia-docker/${DISTRIBUTION}/nvidia-docker.repo" fi sudo yum-config-manager --add-repo "${YUM_REPO_URL}" sudo yum install -y \ nvidia-container-toolkit-1.17.8 \ libnvidia-container-tools-1.17.8 \ libnvidia-container1-1.17.8 \ nvidia-container-toolkit-base-1.17.8 sudo systemctl restart docker ) } install_nvidia_docker2_ubuntu20() { ( set -x # Install nvidia-driver package if not installed status="$(dpkg-query -W --showformat='${db:Status-Status}' nvidia-docker2 2>&1)" if [ ! $? = 0 ] || [ ! "$status" = installed ]; then sudo apt-get install -y nvidia-container-toolkit-1.17.8 sudo systemctl restart docker fi ) } pre_install_nvidia_driver_amzn2() { ( # Purge any nvidia driver installed from RHEL repo sudo yum remove -y nvidia-driver-latest-dkms ) } install_nvidia_driver_common() { ( # Try to gather more information about the runner and its existing NVIDIA driver if any echo "Before installing NVIDIA driver" lspci lsmod modinfo nvidia || true HAS_NVIDIA_DRIVER=0 # Check if NVIDIA driver has already been installed if [ -x "$(command -v nvidia-smi)" ]; then set +e # The driver exists, check its version next. Also check only the first GPU if there are more than one of them # so that the same driver version is not print over multiple lines INSTALLED_DRIVER_VERSION=$(nvidia-smi --query-gpu=driver_version --format=csv,noheader --id=0) NVIDIA_SMI_STATUS=$? if [ "$NVIDIA_SMI_STATUS" -ne 0 ] && [ "$NVIDIA_SMI_STATUS" -ne 14 ]; then echo "Failed to get NVIDIA driver version ($INSTALLED_DRIVER_VERSION). Continuing" elif [ "$INSTALLED_DRIVER_VERSION" != "$DRIVER_VERSION" ]; then echo "NVIDIA driver ($INSTALLED_DRIVER_VERSION) has been installed, but we expect to have $DRIVER_VERSION instead. Continuing" # Turn off persistent mode so that the installation script can unload the kernel module sudo killall nvidia-persistenced || true else HAS_NVIDIA_DRIVER=1 echo "NVIDIA driver ($INSTALLED_DRIVER_VERSION) has already been installed. Skipping NVIDIA driver installation" fi set -e fi if [ "$HAS_NVIDIA_DRIVER" -eq 0 ]; then # CAUTION: this may need to be updated in future if [ "${DISTRIBUTION}" != ubuntu20.04 ]; then sudo yum groupinstall -y "Development Tools" # ensure our kernel install is the same as our underlying kernel, # groupinstall "Development Tools" has a habit of mismatching kernel headers sudo yum install -y "kernel-devel-uname-r == $(uname -r)" sudo modprobe backlight fi sudo curl -fsL -o /tmp/nvidia_driver "https://s3.amazonaws.com/ossci-linux/nvidia_driver/$DRIVER_FN" set +e sudo /bin/bash /tmp/nvidia_driver -s --no-drm NVIDIA_INSTALLATION_STATUS=$? RESET_GPU=0 if [ "$NVIDIA_INSTALLATION_STATUS" -ne 0 ]; then sudo cat /var/log/nvidia-installer.log # Fail to install NVIDIA driver, try to reset the GPU RESET_GPU=1 elif [ -x "$(command -v nvidia-smi)" ]; then # Check again if nvidia-smi works even if the driver installation completes successfully INSTALLED_DRIVER_VERSION=$(nvidia-smi --query-gpu=driver_version --format=csv,noheader --id=0) NVIDIA_SMI_STATUS=$? if [ "$NVIDIA_SMI_STATUS" -ne 0 ] && [ "$NVIDIA_SMI_STATUS" -ne 14 ]; then RESET_GPU=1 fi fi if [ "$RESET_GPU" -eq 1 ]; then NVIDIA_DEVICES=$(lspci -D | grep -i NVIDIA | cut -d' ' -f1) # The GPU can get stuck in a failure state if somehow the test crashs the GPU microcode. When this # happens, we'll try to reset all NVIDIA devices https://github.com/pytorch/pytorch/issues/88388 for PCI_ID in $NVIDIA_DEVICES; do DEVICE_ENABLED=$(cat /sys/bus/pci/devices/$PCI_ID/enable) echo "Reseting $PCI_ID (enabled state: $DEVICE_ENABLED)" # This requires sudo permission of course echo "1" | sudo tee /sys/bus/pci/devices/$PCI_ID/reset sleep 1 done fi sudo rm -fv /tmp/nvidia_driver set -e fi ) } post_install_nvidia_driver_common() { ( sudo modprobe nvidia || true echo "After installing NVIDIA driver" lspci lsmod modinfo nvidia || true ( set +e nvidia-smi # NB: Annoyingly, nvidia-smi command returns successfully with return code 0 even in # the case where the driver has already crashed as it still can get the driver version # and some basic information like the bus ID. However, the rest of the information # would be missing (ERR!), for example: # # +-----------------------------------------------------------------------------+ # | NVIDIA-SMI 525.89.02 Driver Version: 525.89.02 CUDA Version: 12.0 | # |-------------------------------+----------------------+----------------------+ # | GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC | # | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. | # | | | MIG M. | # |===============================+======================+======================| # | 0 ERR! Off | 00000000:00:1E.0 Off | ERR! | # |ERR! ERR! ERR! ERR! / ERR! | 4184MiB / 23028MiB | ERR! Default | # | | | ERR! | # +-------------------------------+----------------------+----------------------+ # # +-----------------------------------------------------------------------------+ # | Processes: | # | GPU GI CI PID Type Process name GPU Memory | # | ID ID Usage | # |=============================================================================| # +-----------------------------------------------------------------------------+ # # This should be reported as a failure instead as it will guarantee to fail when # Docker tries to run with --gpus all # # So, the correct check here is to query one of the missing piece of info like # GPU name, so that the command can fail accordingly nvidia-smi --query-gpu=gpu_name --format=csv,noheader --id=0 NVIDIA_SMI_STATUS=$? # Allowable exit statuses for nvidia-smi, see: https://github.com/NVIDIA/gpu-operator/issues/285 if [ "$NVIDIA_SMI_STATUS" -eq 0 ] || [ "$NVIDIA_SMI_STATUS" -eq 14 ]; then echo "INFO: Ignoring allowed status ${NVIDIA_SMI_STATUS}" else echo "ERROR: nvidia-smi exited with unresolved status ${NVIDIA_SMI_STATUS}" exit ${NVIDIA_SMI_STATUS} fi set -e ) ) } install_nvidia_driver_amzn2() { ( set -x pre_install_nvidia_driver_amzn2 install_nvidia_driver_common post_install_nvidia_driver_common ) } install_nvidia_driver_ubuntu20() { ( set -x install_nvidia_driver_common post_install_nvidia_driver_common ) } echo "== Installing nvidia driver ${DRIVER_FN} ==" case "${DISTRIBUTION}" in amzn*) install_nvidia_driver_amzn2 ;; ubuntu20.04) install_nvidia_driver_ubuntu20 ;; *) echo "ERROR: Unknown distribution ${DISTRIBUTION}" exit 1 ;; esac # Install container toolkit based on distribution echo "== Installing nvidia container toolkit for ${DISTRIBUTION} ==" case "${DISTRIBUTION}" in amzn*) install_nvidia_docker2_amzn2 ;; ubuntu20.04) install_nvidia_docker2_ubuntu20 ;; *) echo "ERROR: Unknown distribution ${DISTRIBUTION}" exit 1 ;; esac echo "GPU_FLAG=--gpus all -e NVIDIA_DRIVER_CAPABILITIES=all" >> "${GITHUB_ENV}" # Fix https://github.com/NVIDIA/nvidia-docker/issues/1648 on runners with # more than one GPUs. This just needs to be run once. The command fails # on subsequent runs and complains that the mode is already on, but that's # ok sudo nvidia-persistenced || true # This should show persistence mode ON nvidia-smi # check if the container-toolkit is correctly installed and CUDA is available inside a container docker run --rm -t --gpus=all public.ecr.aws/docker/library/python:3.13 nvidia-smi 2025-09-09T14:03:23.2939166Z retry_wait_seconds: 10 2025-09-09T14:03:23.2939419Z polling_interval_seconds: 1 2025-09-09T14:03:23.2939680Z warning_on_retry: true 2025-09-09T14:03:23.2939921Z continue_on_error: false 2025-09-09T14:03:23.2940164Z env: 2025-09-09T14:03:23.2940400Z DOCKER_IMAGE: pytorch/almalinux-builder:cuda12.6 2025-09-09T14:03:23.2940743Z REPOSITORY: pytorch/ao 2025-09-09T14:03:23.2940982Z PR_NUMBER: 2963 2025-09-09T14:03:23.2942721Z SCRIPT: conda create -n venv python=3.9 -y conda activate venv echo "::group::Install newer objcopy that supports --set-section-alignment" dnf install -y gcc-toolset-10-binutils export PATH=/opt/rh/gcc-toolset-10/root/usr/bin/:$PATH python -m pip install --upgrade pip pip install torch==2.8.0 sed -i '' dev-requirements.txt pip install -r dev-requirements.txt pip install . export CONDA=$(dirname $(dirname $(which conda))) export LD_LIBRARY_PATH=$CONDA/lib/:$LD_LIBRARY_PATH pytest test --verbose -s 2025-09-09T14:03:23.2944685Z RUNNER_ARTIFACT_DIR: /home/ec2-user/actions-runner/_work/_temp/artifacts 2025-09-09T14:03:23.2945229Z RUNNER_TEST_RESULTS_DIR: /home/ec2-user/actions-runner/_work/_temp/test-results 2025-09-09T14:03:23.2945732Z RUNNER_DOCS_DIR: /home/ec2-user/actions-runner/_work/_temp/docs 2025-09-09T14:03:23.2946106Z DRIVER_VERSION: 580.65.06 2025-09-09T14:03:23.2946345Z ##[endgroup] 2025-09-09T14:03:23.3813863Z == Installing nvidia driver NVIDIA-Linux-x86_64-580.65.06.run == 2025-09-09T14:03:23.3815327Z + pre_install_nvidia_driver_amzn2 2025-09-09T14:03:23.3819198Z + sudo yum remove -y nvidia-driver-latest-dkms 2025-09-09T14:03:23.7246288Z No match for argument: nvidia-driver-latest-dkms 2025-09-09T14:03:23.7247222Z No packages marked for removal. 2025-09-09T14:03:23.7311791Z Dependencies resolved. 2025-09-09T14:03:23.7322084Z Nothing to do. 2025-09-09T14:03:23.7323519Z Complete! 2025-09-09T14:03:23.7646308Z + install_nvidia_driver_common 2025-09-09T14:03:23.7651054Z + echo 'Before installing NVIDIA driver' 2025-09-09T14:03:23.7651447Z + lspci 2025-09-09T14:03:23.7652542Z Before installing NVIDIA driver 2025-09-09T14:03:23.7786394Z 00:00.0 Host bridge: Intel Corporation 440FX - 82441FX PMC [Natoma] 2025-09-09T14:03:23.7787036Z 00:01.0 ISA bridge: Intel Corporation 82371SB PIIX3 ISA [Natoma/Triton II] 2025-09-09T14:03:23.7787678Z 00:01.3 Non-VGA unclassified device: Intel Corporation 82371AB/EB/MB PIIX4 ACPI (rev 08) 2025-09-09T14:03:23.7788190Z 00:03.0 VGA compatible controller: Amazon.com, Inc. Device 1111 2025-09-09T14:03:23.7788665Z 00:04.0 Non-Volatile memory controller: Amazon.com, Inc. NVMe EBS Controller 2025-09-09T14:03:23.7789237Z 00:05.0 Ethernet controller: Amazon.com, Inc. Elastic Network Adapter (ENA) 2025-09-09T14:03:23.7789884Z 00:1b.0 3D controller: NVIDIA Corporation GA102GL [A10G] (rev a1) 2025-09-09T14:03:23.7790438Z 00:1c.0 3D controller: NVIDIA Corporation GA102GL [A10G] (rev a1) 2025-09-09T14:03:23.7790854Z 00:1d.0 3D controller: NVIDIA Corporation GA102GL [A10G] (rev a1) 2025-09-09T14:03:23.7791272Z 00:1e.0 3D controller: NVIDIA Corporation GA102GL [A10G] (rev a1) 2025-09-09T14:03:23.7791740Z 00:1f.0 Non-Volatile memory controller: Amazon.com, Inc. NVMe SSD Controller 2025-09-09T14:03:23.7792131Z + lsmod 2025-09-09T14:03:23.7843276Z Module Size Used by 2025-09-09T14:03:23.7843665Z xt_conntrack 16384 1 2025-09-09T14:03:23.7843950Z nft_chain_nat 16384 3 2025-09-09T14:03:23.7844235Z xt_MASQUERADE 20480 1 2025-09-09T14:03:23.7844547Z nf_nat 57344 2 nft_chain_nat,xt_MASQUERADE 2025-09-09T14:03:23.7844870Z nf_conntrack_netlink 57344 0 2025-09-09T14:03:23.7845286Z nf_conntrack 184320 4 xt_conntrack,nf_nat,nf_conntrack_netlink,xt_MASQUERADE 2025-09-09T14:03:23.7845981Z nf_defrag_ipv6 24576 1 nf_conntrack 2025-09-09T14:03:23.7846284Z nf_defrag_ipv4 16384 1 nf_conntrack 2025-09-09T14:03:23.7846555Z xfrm_user 57344 1 2025-09-09T14:03:23.7846808Z xfrm_algo 16384 1 xfrm_user 2025-09-09T14:03:23.7847081Z xt_addrtype 16384 2 2025-09-09T14:03:23.7847317Z nft_compat 20480 4 2025-09-09T14:03:23.7847612Z nf_tables 311296 57 nft_compat,nft_chain_nat 2025-09-09T14:03:23.7847999Z nfnetlink 20480 4 nft_compat,nf_conntrack_netlink,nf_tables 2025-09-09T14:03:23.7848362Z br_netfilter 36864 0 2025-09-09T14:03:23.7848625Z bridge 323584 1 br_netfilter 2025-09-09T14:03:23.7848914Z stp 16384 1 bridge 2025-09-09T14:03:23.7849178Z llc 16384 2 bridge,stp 2025-09-09T14:03:23.7849447Z overlay 167936 0 2025-09-09T14:03:23.7849684Z tls 139264 0 2025-09-09T14:03:23.7849920Z nls_ascii 16384 1 2025-09-09T14:03:23.7850156Z nls_cp437 20480 1 2025-09-09T14:03:23.7850383Z vfat 24576 1 2025-09-09T14:03:23.7850621Z fat 86016 1 vfat 2025-09-09T14:03:23.7850862Z sunrpc 700416 1 2025-09-09T14:03:23.7851091Z i8042 45056 0 2025-09-09T14:03:23.7851317Z ghash_clmulni_intel 16384 0 2025-09-09T14:03:23.7851562Z serio 28672 3 i8042 2025-09-09T14:03:23.7851812Z ena 180224 0 2025-09-09T14:03:23.7852048Z button 24576 0 2025-09-09T14:03:23.7852291Z sch_fq_codel 20480 33 2025-09-09T14:03:23.7852537Z fuse 184320 1 2025-09-09T14:03:23.7852775Z dm_mod 188416 0 2025-09-09T14:03:23.7853157Z loop 36864 0 2025-09-09T14:03:23.7853386Z configfs 57344 1 2025-09-09T14:03:23.7853619Z dmi_sysfs 20480 0 2025-09-09T14:03:23.7853854Z crc32_pclmul 16384 0 2025-09-09T14:03:23.7854092Z crc32c_intel 24576 0 2025-09-09T14:03:23.7854328Z efivarfs 24576 1 2025-09-09T14:03:23.7854551Z + modinfo nvidia 2025-09-09T14:03:23.7867463Z filename: /lib/modules/6.1.141-155.222.amzn2023.x86_64/kernel/drivers/video/nvidia.ko 2025-09-09T14:03:23.7867898Z import_ns: DMA_BUF 2025-09-09T14:03:23.7868149Z alias: char-major-195-* 2025-09-09T14:03:23.7868402Z version: 570.133.07 2025-09-09T14:03:23.7868629Z supported: external 2025-09-09T14:03:23.7868862Z license: Dual MIT/GPL 2025-09-09T14:03:23.7869129Z firmware: nvidia/570.133.07/gsp_tu10x.bin 2025-09-09T14:03:23.7869455Z firmware: nvidia/570.133.07/gsp_ga10x.bin 2025-09-09T14:03:23.7869756Z srcversion: 49515739FD8F721A3F2F714 2025-09-09T14:03:23.7870058Z alias: pci:v000010DEd*sv*sd*bc06sc80i00* 2025-09-09T14:03:23.7870382Z alias: pci:v000010DEd*sv*sd*bc03sc02i00* 2025-09-09T14:03:23.7870690Z alias: pci:v000010DEd*sv*sd*bc03sc00i00* 2025-09-09T14:03:23.7870984Z depends: i2c-core,drm 2025-09-09T14:03:23.7871213Z retpoline: Y 2025-09-09T14:03:23.7871504Z name: nvidia 2025-09-09T14:03:23.7871973Z vermagic: 6.1.141-155.222.amzn2023.x86_64 SMP preempt mod_unload modversions 2025-09-09T14:03:23.7872648Z parm: NvSwitchRegDwords:NvSwitch regkey (charp) 2025-09-09T14:03:23.7873232Z parm: NvSwitchBlacklist:NvSwitchBlacklist=uuid[,uuid...] (charp) 2025-09-09T14:03:23.7873768Z parm: NVreg_ResmanDebugLevel:int 2025-09-09T14:03:23.7874169Z parm: NVreg_RmLogonRC:int 2025-09-09T14:03:23.7874577Z parm: NVreg_ModifyDeviceFiles:int 2025-09-09T14:03:23.7875008Z parm: NVreg_DeviceFileUID:int 2025-09-09T14:03:23.7875402Z parm: NVreg_DeviceFileGID:int 2025-09-09T14:03:23.7875810Z parm: NVreg_DeviceFileMode:int 2025-09-09T14:03:23.7876385Z parm: NVreg_InitializeSystemMemoryAllocations:int 2025-09-09T14:03:23.7877172Z parm: NVreg_UsePageAttributeTable:int 2025-09-09T14:03:23.7877698Z parm: NVreg_EnablePCIeGen3:int 2025-09-09T14:03:23.7878122Z parm: NVreg_EnableMSI:int 2025-09-09T14:03:23.7878535Z parm: NVreg_EnableStreamMemOPs:int 2025-09-09T14:03:23.7879476Z parm: NVreg_RestrictProfilingToAdminUsers:int 2025-09-09T14:03:23.7880062Z parm: NVreg_PreserveVideoMemoryAllocations:int 2025-09-09T14:03:23.7880579Z parm: NVreg_EnableS0ixPowerManagement:int 2025-09-09T14:03:23.7881183Z parm: NVreg_S0ixPowerManagementVideoMemoryThreshold:int 2025-09-09T14:03:23.7881679Z parm: NVreg_DynamicPowerManagement:int 2025-09-09T14:03:23.7895017Z parm: NVreg_DynamicPowerManagementVideoMemoryThreshold:int 2025-09-09T14:03:23.7895458Z parm: NVreg_EnableGpuFirmware:int 2025-09-09T14:03:23.7895818Z parm: NVreg_EnableGpuFirmwareLogs:int 2025-09-09T14:03:23.7896190Z parm: NVreg_OpenRmEnableUnsupportedGpus:int 2025-09-09T14:03:23.7896583Z parm: NVreg_EnableUserNUMAManagement:int 2025-09-09T14:03:23.7896920Z parm: NVreg_MemoryPoolSize:int 2025-09-09T14:03:23.7897247Z parm: NVreg_KMallocHeapMaxSize:int 2025-09-09T14:03:23.7897574Z parm: NVreg_VMallocHeapMaxSize:int 2025-09-09T14:03:23.7897900Z parm: NVreg_IgnoreMMIOCheck:int 2025-09-09T14:03:23.7898216Z parm: NVreg_NvLinkDisable:int 2025-09-09T14:03:23.7898568Z parm: NVreg_EnablePCIERelaxedOrderingMode:int 2025-09-09T14:03:23.7898927Z parm: NVreg_RegisterPCIDriver:int 2025-09-09T14:03:23.7899261Z parm: NVreg_EnableResizableBar:int 2025-09-09T14:03:23.7899596Z parm: NVreg_EnableDbgBreakpoint:int 2025-09-09T14:03:23.7899950Z parm: NVreg_EnableNonblockingOpen:int 2025-09-09T14:03:23.7900455Z parm: NVreg_RegistryDwords:charp 2025-09-09T14:03:23.7900793Z parm: NVreg_RegistryDwordsPerDevice:charp 2025-09-09T14:03:23.7901136Z parm: NVreg_RmMsg:charp 2025-09-09T14:03:23.7901424Z parm: NVreg_GpuBlacklist:charp 2025-09-09T14:03:23.7901755Z parm: NVreg_TemporaryFilePath:charp 2025-09-09T14:03:23.7902080Z parm: NVreg_ExcludedGpus:charp 2025-09-09T14:03:23.7902405Z parm: NVreg_DmaRemapPeerMmio:int 2025-09-09T14:03:23.7902735Z parm: NVreg_RmNvlinkBandwidth:charp 2025-09-09T14:03:23.7903099Z parm: NVreg_RmNvlinkBandwidthLinkCount:int 2025-09-09T14:03:23.7903454Z parm: NVreg_ImexChannelCount:int 2025-09-09T14:03:23.7903768Z parm: NVreg_CreateImexChannel0:int 2025-09-09T14:03:23.7904113Z parm: NVreg_GrdmaPciTopoCheckOverride:int 2025-09-09T14:03:23.7904446Z parm: rm_firmware_active:charp 2025-09-09T14:03:23.7904738Z + HAS_NVIDIA_DRIVER=0 2025-09-09T14:03:23.7904990Z ++ command -v nvidia-smi 2025-09-09T14:03:23.7905268Z + '[' -x /usr/bin/nvidia-smi ']' 2025-09-09T14:03:23.7905523Z + set +e 2025-09-09T14:03:23.7905840Z ++ nvidia-smi --query-gpu=driver_version --format=csv,noheader --id=0 2025-09-09T14:03:27.2046826Z + INSTALLED_DRIVER_VERSION=570.133.07 2025-09-09T14:03:27.2047206Z + NVIDIA_SMI_STATUS=0 2025-09-09T14:03:27.2047451Z + '[' 0 -ne 0 ']' 2025-09-09T14:03:27.2047671Z + '[' 570.133.07 '!=' 580.65.06 ']' 2025-09-09T14:03:27.2048150Z + echo 'NVIDIA driver (570.133.07) has been installed, but we expect to have 580.65.06 instead. Continuing' 2025-09-09T14:03:27.2048645Z + sudo killall nvidia-persistenced 2025-09-09T14:03:27.2049113Z NVIDIA driver (570.133.07) has been installed, but we expect to have 580.65.06 instead. Continuing 2025-09-09T14:03:27.3209995Z nvidia-persistenced: no process found 2025-09-09T14:03:27.3235452Z + true 2025-09-09T14:03:27.3236340Z + set -e 2025-09-09T14:03:27.3236568Z + '[' 0 -eq 0 ']' 2025-09-09T14:03:27.3236834Z + '[' amzn2023 '!=' ubuntu20.04 ']' 2025-09-09T14:03:27.3237154Z + sudo yum groupinstall -y 'Development Tools' 2025-09-09T14:03:27.8157428Z Last metadata expiration check: 0:04:31 ago on Tue Sep 9 13:58:56 2025. 2025-09-09T14:03:27.8490661Z No match for group package "system-rpm-config" 2025-09-09T14:03:27.8505714Z No match for group package "rcs" 2025-09-09T14:03:27.8525388Z No match for group package "pkgconfig" 2025-09-09T14:03:27.8966764Z Dependencies resolved. 2025-09-09T14:03:27.9185614Z ================================================================================ 2025-09-09T14:03:27.9186050Z Package Architecture Version Repository Size 2025-09-09T14:03:27.9186486Z ================================================================================ 2025-09-09T14:03:27.9186794Z Installing Groups: 2025-09-09T14:03:27.9187113Z Development Tools 2025-09-09T14:03:27.9187385Z 2025-09-09T14:03:27.9187484Z Transaction Summary 2025-09-09T14:03:27.9187731Z ================================================================================ 2025-09-09T14:03:27.9187943Z 2025-09-09T14:03:28.0926400Z ================================================================================ 2025-09-09T14:03:28.0926739Z WARNING: 2025-09-09T14:03:28.0927019Z A newer release of "Amazon Linux" is available. 2025-09-09T14:03:28.0927327Z 2025-09-09T14:03:28.0927440Z Available Versions: 2025-09-09T14:03:28.0927621Z 2025-09-09T14:03:28.0927727Z Version 2023.8.20250707: 2025-09-09T14:03:28.0928105Z Run the following command to upgrade to 2023.8.20250707: 2025-09-09T14:03:28.0928430Z 2025-09-09T14:03:28.0928585Z dnf upgrade --releasever=2023.8.20250707 2025-09-09T14:03:28.0928798Z 2025-09-09T14:03:28.0928908Z Release notes: 2025-09-09T14:03:28.0929314Z https://docs.aws.amazon.com/linux/al2023/release-notes/relnotes-2023.8.20250707.html 2025-09-09T14:03:28.0929675Z 2025-09-09T14:03:28.0929772Z Version 2023.8.20250715: 2025-09-09T14:03:28.0930248Z Run the following command to upgrade to 2023.8.20250715: 2025-09-09T14:03:28.0930493Z 2025-09-09T14:03:28.0930619Z dnf upgrade --releasever=2023.8.20250715 2025-09-09T14:03:28.0930820Z 2025-09-09T14:03:28.0930905Z Release notes: 2025-09-09T14:03:28.0931292Z https://docs.aws.amazon.com/linux/al2023/release-notes/relnotes-2023.8.20250715.html 2025-09-09T14:03:28.0931648Z 2025-09-09T14:03:28.0931732Z Version 2023.8.20250721: 2025-09-09T14:03:28.0932028Z Run the following command to upgrade to 2023.8.20250721: 2025-09-09T14:03:28.0932266Z 2025-09-09T14:03:28.0932384Z dnf upgrade --releasever=2023.8.20250721 2025-09-09T14:03:28.0932585Z 2025-09-09T14:03:28.0932669Z Release notes: 2025-09-09T14:03:28.0933051Z https://docs.aws.amazon.com/linux/al2023/release-notes/relnotes-2023.8.20250721.html 2025-09-09T14:03:28.0933405Z 2025-09-09T14:03:28.0933492Z Version 2023.8.20250808: 2025-09-09T14:03:28.0933795Z Run the following command to upgrade to 2023.8.20250808: 2025-09-09T14:03:28.0934043Z 2025-09-09T14:03:28.0934159Z dnf upgrade --releasever=2023.8.20250808 2025-09-09T14:03:28.0934360Z 2025-09-09T14:03:28.0934439Z Release notes: 2025-09-09T14:03:28.0934819Z https://docs.aws.amazon.com/linux/al2023/release-notes/relnotes-2023.8.20250808.html 2025-09-09T14:03:28.0935169Z 2025-09-09T14:03:28.0935252Z Version 2023.8.20250818: 2025-09-09T14:03:28.0935547Z Run the following command to upgrade to 2023.8.20250818: 2025-09-09T14:03:28.0935787Z 2025-09-09T14:03:28.0935895Z dnf upgrade --releasever=2023.8.20250818 2025-09-09T14:03:28.0936101Z 2025-09-09T14:03:28.0936187Z Release notes: 2025-09-09T14:03:28.0936566Z https://docs.aws.amazon.com/linux/al2023/release-notes/relnotes-2023.8.20250818.html 2025-09-09T14:03:28.0936917Z 2025-09-09T14:03:28.0937010Z Version 2023.8.20250908: 2025-09-09T14:03:28.0937296Z Run the following command to upgrade to 2023.8.20250908: 2025-09-09T14:03:28.0937544Z 2025-09-09T14:03:28.0937658Z dnf upgrade --releasever=2023.8.20250908 2025-09-09T14:03:28.0937856Z 2025-09-09T14:03:28.0937932Z Release notes: 2025-09-09T14:03:28.0938441Z https://docs.aws.amazon.com/linux/al2023/release-notes/relnotes-2023.8.20250908.html 2025-09-09T14:03:28.0938795Z 2025-09-09T14:03:28.0938910Z ================================================================================ 2025-09-09T14:03:28.0939212Z Complete! 2025-09-09T14:03:28.1352330Z ++ uname -r 2025-09-09T14:03:28.1366537Z + sudo yum install -y 'kernel-devel-uname-r == 6.1.141-155.222.amzn2023.x86_64' 2025-09-09T14:03:28.6007522Z Last metadata expiration check: 0:04:32 ago on Tue Sep 9 13:58:56 2025. 2025-09-09T14:03:28.6245017Z Using '==' operator in reldeps can result in an undefined behavior. It is deprecated and the support will be dropped in future versions. Use '=' operator instead. 2025-09-09T14:03:28.6355211Z Package kernel-devel-6.1.141-155.222.amzn2023.x86_64 is already installed. 2025-09-09T14:03:28.6835778Z Dependencies resolved. 2025-09-09T14:03:28.7065149Z Nothing to do. 2025-09-09T14:03:28.7065551Z Complete! 2025-09-09T14:03:28.7455854Z + sudo modprobe backlight 2025-09-09T14:03:28.9130206Z + sudo curl -fsL -o /tmp/nvidia_driver https://s3.amazonaws.com/ossci-linux/nvidia_driver/NVIDIA-Linux-x86_64-580.65.06.run 2025-09-09T14:03:33.0323927Z + set +e 2025-09-09T14:03:33.0324195Z + sudo /bin/bash /tmp/nvidia_driver -s --no-drm 2025-09-09T14:03:34.4065601Z Verifying archive integrity... OK 2025-09-09T14:03:37.2290763Z Uncompressing NVIDIA Accelerated Graphics Driver for Linux-x86_64 580.65.06.................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................... 2025-09-09T14:03:37.8316645Z 2025-09-09T14:03:37.8317548Z WARNING: The nvidia-drm module will not be installed. As a result, DRM-KMS will not function with this installation of the NVIDIA driver. 2025-09-09T14:03:37.8318131Z 2025-09-09T14:04:00.9014509Z 2025-09-09T14:04:00.9015746Z WARNING: nvidia-installer was forced to guess the X library path '/usr/lib64' and X module path '/usr/lib64/xorg/modules'; these paths were not queryable from the system. If X fails to find the NVIDIA X driver module, please install the `pkg-config` utility and the X.Org SDK/development package for your distribution and reinstall the driver. 2025-09-09T14:04:00.9016953Z 2025-09-09T14:04:00.9037365Z 2025-09-09T14:04:00.9038470Z WARNING: This NVIDIA driver package includes Vulkan components, but no Vulkan ICD loader was detected on this system. The NVIDIA Vulkan ICD will not function without the loader. Most distributions package the Vulkan loader; try installing the "vulkan-loader", "vulkan-icd-loader", or "libvulkan1" package. 2025-09-09T14:04:00.9039578Z 2025-09-09T14:04:13.6582228Z + NVIDIA_INSTALLATION_STATUS=0 2025-09-09T14:04:13.6582519Z + RESET_GPU=0 2025-09-09T14:04:13.6582728Z + '[' 0 -ne 0 ']' 2025-09-09T14:04:13.6588200Z ++ command -v nvidia-smi 2025-09-09T14:04:13.6590859Z + '[' -x /usr/bin/nvidia-smi ']' 2025-09-09T14:04:13.6596585Z ++ nvidia-smi --query-gpu=driver_version --format=csv,noheader --id=0 2025-09-09T14:04:17.4947232Z + INSTALLED_DRIVER_VERSION=580.65.06 2025-09-09T14:04:17.4948188Z + NVIDIA_SMI_STATUS=0 2025-09-09T14:04:17.4948444Z + '[' 0 -ne 0 ']' 2025-09-09T14:04:17.4948650Z + '[' 0 -eq 1 ']' 2025-09-09T14:04:17.4948868Z + sudo rm -fv /tmp/nvidia_driver 2025-09-09T14:04:17.6904943Z removed '/tmp/nvidia_driver' 2025-09-09T14:04:17.6928935Z + set -e 2025-09-09T14:04:17.6933630Z + post_install_nvidia_driver_common 2025-09-09T14:04:17.6938263Z + sudo modprobe nvidia 2025-09-09T14:04:17.8420341Z + echo 'After installing NVIDIA driver' 2025-09-09T14:04:17.8420730Z + lspci 2025-09-09T14:04:17.8420994Z After installing NVIDIA driver 2025-09-09T14:04:17.8555281Z 00:00.0 Host bridge: Intel Corporation 440FX - 82441FX PMC [Natoma] 2025-09-09T14:04:17.8555930Z 00:01.0 ISA bridge: Intel Corporation 82371SB PIIX3 ISA [Natoma/Triton II] 2025-09-09T14:04:17.8556566Z 00:01.3 Non-VGA unclassified device: Intel Corporation 82371AB/EB/MB PIIX4 ACPI (rev 08) 2025-09-09T14:04:17.8557072Z 00:03.0 VGA compatible controller: Amazon.com, Inc. Device 1111 2025-09-09T14:04:17.8557539Z 00:04.0 Non-Volatile memory controller: Amazon.com, Inc. NVMe EBS Controller 2025-09-09T14:04:17.8558123Z 00:05.0 Ethernet controller: Amazon.com, Inc. Elastic Network Adapter (ENA) 2025-09-09T14:04:17.8558759Z 00:1b.0 3D controller: NVIDIA Corporation GA102GL [A10G] (rev a1) 2025-09-09T14:04:17.8559392Z 00:1c.0 3D controller: NVIDIA Corporation GA102GL [A10G] (rev a1) 2025-09-09T14:04:17.8559808Z 00:1d.0 3D controller: NVIDIA Corporation GA102GL [A10G] (rev a1) 2025-09-09T14:04:17.8560214Z 00:1e.0 3D controller: NVIDIA Corporation GA102GL [A10G] (rev a1) 2025-09-09T14:04:17.8560677Z 00:1f.0 Non-Volatile memory controller: Amazon.com, Inc. NVMe SSD Controller 2025-09-09T14:04:17.8561063Z + lsmod 2025-09-09T14:04:17.8596484Z Module Size Used by 2025-09-09T14:04:17.8597156Z nvidia_uvm 1921024 0 2025-09-09T14:04:17.8597512Z nvidia 14274560 1 nvidia_uvm 2025-09-09T14:04:17.8597815Z drm 602112 1 nvidia 2025-09-09T14:04:17.8598118Z drm_panel_orientation_quirks 32768 1 drm 2025-09-09T14:04:17.8598532Z backlight 24576 1 drm 2025-09-09T14:04:17.8598980Z i2c_core 110592 2 nvidia,drm 2025-09-09T14:04:17.8599364Z xt_conntrack 16384 1 2025-09-09T14:04:17.8599689Z nft_chain_nat 16384 3 2025-09-09T14:04:17.8600022Z xt_MASQUERADE 20480 1 2025-09-09T14:04:17.8600346Z nf_nat 57344 2 nft_chain_nat,xt_MASQUERADE 2025-09-09T14:04:17.8600673Z nf_conntrack_netlink 57344 0 2025-09-09T14:04:17.8601072Z nf_conntrack 184320 4 xt_conntrack,nf_nat,nf_conntrack_netlink,xt_MASQUERADE 2025-09-09T14:04:17.8601502Z nf_defrag_ipv6 24576 1 nf_conntrack 2025-09-09T14:04:17.8601895Z nf_defrag_ipv4 16384 1 nf_conntrack 2025-09-09T14:04:17.8602215Z xfrm_user 57344 1 2025-09-09T14:04:17.8602477Z xfrm_algo 16384 1 xfrm_user 2025-09-09T14:04:17.8602757Z xt_addrtype 16384 2 2025-09-09T14:04:17.8603008Z nft_compat 20480 4 2025-09-09T14:04:17.8603311Z nf_tables 311296 57 nft_compat,nft_chain_nat 2025-09-09T14:04:17.8603714Z nfnetlink 20480 4 nft_compat,nf_conntrack_netlink,nf_tables 2025-09-09T14:04:17.8604084Z br_netfilter 36864 0 2025-09-09T14:04:17.8604350Z bridge 323584 1 br_netfilter 2025-09-09T14:04:17.8604644Z stp 16384 1 bridge 2025-09-09T14:04:17.8604919Z llc 16384 2 bridge,stp 2025-09-09T14:04:17.8605196Z overlay 167936 0 2025-09-09T14:04:17.8605438Z tls 139264 0 2025-09-09T14:04:17.8605682Z nls_ascii 16384 1 2025-09-09T14:04:17.8605928Z nls_cp437 20480 1 2025-09-09T14:04:17.8606161Z vfat 24576 1 2025-09-09T14:04:17.8606410Z fat 86016 1 vfat 2025-09-09T14:04:17.8606669Z sunrpc 700416 1 2025-09-09T14:04:17.8606915Z i8042 45056 0 2025-09-09T14:04:17.8607156Z ghash_clmulni_intel 16384 0 2025-09-09T14:04:17.8607606Z serio 28672 3 i8042 2025-09-09T14:04:17.8607885Z ena 180224 0 2025-09-09T14:04:17.8608176Z button 24576 0 2025-09-09T14:04:17.8608423Z sch_fq_codel 20480 33 2025-09-09T14:04:17.8608680Z fuse 184320 1 2025-09-09T14:04:17.8608931Z dm_mod 188416 0 2025-09-09T14:04:17.8609168Z loop 36864 0 2025-09-09T14:04:17.8609412Z configfs 57344 1 2025-09-09T14:04:17.8609662Z dmi_sysfs 20480 0 2025-09-09T14:04:17.8609908Z crc32_pclmul 16384 0 2025-09-09T14:04:17.8610150Z crc32c_intel 24576 0 2025-09-09T14:04:17.8610396Z efivarfs 24576 1 2025-09-09T14:04:17.8610629Z + modinfo nvidia 2025-09-09T14:04:17.8619178Z filename: /lib/modules/6.1.141-155.222.amzn2023.x86_64/kernel/drivers/video/nvidia.ko 2025-09-09T14:04:17.8619800Z import_ns: DMA_BUF 2025-09-09T14:04:17.8620036Z alias: char-major-195-* 2025-09-09T14:04:17.8620297Z version: 580.65.06 2025-09-09T14:04:17.8620527Z supported: external 2025-09-09T14:04:17.8620779Z license: Dual MIT/GPL 2025-09-09T14:04:17.8621146Z firmware: nvidia/580.65.06/gsp_tu10x.bin 2025-09-09T14:04:17.8621589Z firmware: nvidia/580.65.06/gsp_ga10x.bin 2025-09-09T14:04:17.8621947Z srcversion: A69EBF72FC9D60E11E9A05C 2025-09-09T14:04:17.8622480Z alias: of:N*T*Cnvidia,tegra264-displayC* 2025-09-09T14:04:17.8622937Z alias: of:N*T*Cnvidia,tegra264-display 2025-09-09T14:04:17.8623385Z alias: of:N*T*Cnvidia,tegra234-displayC* 2025-09-09T14:04:17.8623832Z alias: of:N*T*Cnvidia,tegra234-display 2025-09-09T14:04:17.8624252Z alias: pci:v000010DEd*sv*sd*bc06sc80i00* 2025-09-09T14:04:17.8624759Z alias: pci:v000010DEd*sv*sd*bc03sc02i00* 2025-09-09T14:04:17.8625086Z alias: pci:v000010DEd*sv*sd*bc03sc00i00* 2025-09-09T14:04:17.8625381Z depends: i2c-core,drm 2025-09-09T14:04:17.8625633Z retpoline: Y 2025-09-09T14:04:17.8625845Z name: nvidia 2025-09-09T14:04:17.8626196Z vermagic: 6.1.141-155.222.amzn2023.x86_64 SMP preempt mod_unload modversions 2025-09-09T14:04:17.8626794Z parm: NvSwitchRegDwords:NvSwitch regkey (charp) 2025-09-09T14:04:17.8627370Z parm: NvSwitchBlacklist:NvSwitchBlacklist=uuid[,uuid...] (charp) 2025-09-09T14:04:17.8627856Z parm: NVreg_ResmanDebugLevel:int 2025-09-09T14:04:17.8628154Z parm: NVreg_RmLogonRC:int 2025-09-09T14:04:17.8628453Z parm: NVreg_ModifyDeviceFiles:int 2025-09-09T14:04:17.8628758Z parm: NVreg_DeviceFileUID:int 2025-09-09T14:04:17.8629059Z parm: NVreg_DeviceFileGID:int 2025-09-09T14:04:17.8629349Z parm: NVreg_DeviceFileMode:int 2025-09-09T14:04:17.8629714Z parm: NVreg_InitializeSystemMemoryAllocations:int 2025-09-09T14:04:17.8630088Z parm: NVreg_UsePageAttributeTable:int 2025-09-09T14:04:17.8630418Z parm: NVreg_EnablePCIeGen3:int 2025-09-09T14:04:17.8630714Z parm: NVreg_EnableMSI:int 2025-09-09T14:04:17.8631005Z parm: NVreg_EnableStreamMemOPs:int 2025-09-09T14:04:17.8631358Z parm: NVreg_RestrictProfilingToAdminUsers:int 2025-09-09T14:04:17.8631741Z parm: NVreg_PreserveVideoMemoryAllocations:int 2025-09-09T14:04:17.8632112Z parm: NVreg_EnableS0ixPowerManagement:int 2025-09-09T14:04:17.8632509Z parm: NVreg_S0ixPowerManagementVideoMemoryThreshold:int 2025-09-09T14:04:17.8632913Z parm: NVreg_DynamicPowerManagement:int 2025-09-09T14:04:17.8633324Z parm: NVreg_DynamicPowerManagementVideoMemoryThreshold:int 2025-09-09T14:04:17.8633730Z parm: NVreg_EnableGpuFirmware:int 2025-09-09T14:04:17.8634060Z parm: NVreg_EnableGpuFirmwareLogs:int 2025-09-09T14:04:17.8634418Z parm: NVreg_OpenRmEnableUnsupportedGpus:int 2025-09-09T14:04:17.8634783Z parm: NVreg_EnableUserNUMAManagement:int 2025-09-09T14:04:17.8635113Z parm: NVreg_MemoryPoolSize:int 2025-09-09T14:04:17.8635586Z parm: NVreg_KMallocHeapMaxSize:int 2025-09-09T14:04:17.8635910Z parm: NVreg_VMallocHeapMaxSize:int 2025-09-09T14:04:17.8636230Z parm: NVreg_IgnoreMMIOCheck:int 2025-09-09T14:04:17.8636525Z parm: NVreg_NvLinkDisable:int 2025-09-09T14:04:17.8636869Z parm: NVreg_EnablePCIERelaxedOrderingMode:int 2025-09-09T14:04:17.8637232Z parm: NVreg_RegisterPCIDriver:int 2025-09-09T14:04:17.8637575Z parm: NVreg_RegisterPlatformDeviceDriver:int 2025-09-09T14:04:17.8637933Z parm: NVreg_EnableResizableBar:int 2025-09-09T14:04:17.8638256Z parm: NVreg_EnableDbgBreakpoint:int 2025-09-09T14:04:17.8638595Z parm: NVreg_EnableNonblockingOpen:int 2025-09-09T14:04:17.8639033Z parm: NVreg_CoherentGPUMemoryMode:charp 2025-09-09T14:04:17.8639365Z parm: NVreg_RegistryDwords:charp 2025-09-09T14:04:17.8639695Z parm: NVreg_RegistryDwordsPerDevice:charp 2025-09-09T14:04:17.8640021Z parm: NVreg_RmMsg:charp 2025-09-09T14:04:17.8640314Z parm: NVreg_GpuBlacklist:charp 2025-09-09T14:04:17.8640627Z parm: NVreg_TemporaryFilePath:charp 2025-09-09T14:04:17.8640948Z parm: NVreg_ExcludedGpus:charp 2025-09-09T14:04:17.8641252Z parm: NVreg_DmaRemapPeerMmio:int 2025-09-09T14:04:17.8641574Z parm: NVreg_RmNvlinkBandwidth:charp 2025-09-09T14:04:17.8641946Z parm: NVreg_RmNvlinkBandwidthLinkCount:int 2025-09-09T14:04:17.8642297Z parm: NVreg_ImexChannelCount:int 2025-09-09T14:04:17.8642609Z parm: NVreg_CreateImexChannel0:int 2025-09-09T14:04:17.8642955Z parm: NVreg_GrdmaPciTopoCheckOverride:int 2025-09-09T14:04:17.8643283Z parm: rm_firmware_active:charp 2025-09-09T14:04:17.8643687Z + set +e 2025-09-09T14:04:17.8643872Z + nvidia-smi 2025-09-09T14:04:20.1906205Z Tue Sep 9 14:04:20 2025 2025-09-09T14:04:20.1906903Z +-----------------------------------------------------------------------------------------+ 2025-09-09T14:04:20.1907899Z | NVIDIA-SMI 580.65.06 Driver Version: 580.65.06 CUDA Version: 13.0 | 2025-09-09T14:04:20.1908647Z +-----------------------------------------+------------------------+----------------------+ 2025-09-09T14:04:20.1909120Z | GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC | 2025-09-09T14:04:20.1909620Z | Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. | 2025-09-09T14:04:20.1910034Z | | | MIG M. | 2025-09-09T14:04:20.1910357Z |=========================================+========================+======================| 2025-09-09T14:04:20.2244298Z | 0 NVIDIA A10G Off | 00000000:00:1B.0 Off | 0 | 2025-09-09T14:04:20.2245244Z | 0% 27C P0 57W / 300W | 0MiB / 23028MiB | 0% Default | 2025-09-09T14:04:20.2246017Z | | | N/A | 2025-09-09T14:04:20.2246785Z +-----------------------------------------+------------------------+----------------------+ 2025-09-09T14:04:20.2247634Z | 1 NVIDIA A10G Off | 00000000:00:1C.0 Off | 0 | 2025-09-09T14:04:20.2248452Z | 0% 27C P0 57W / 300W | 0MiB / 23028MiB | 2% Default | 2025-09-09T14:04:20.2249084Z | | | N/A | 2025-09-09T14:04:20.2249511Z +-----------------------------------------+------------------------+----------------------+ 2025-09-09T14:04:20.2249937Z | 2 NVIDIA A10G Off | 00000000:00:1D.0 Off | 0 | 2025-09-09T14:04:20.2250336Z | 0% 26C P0 58W / 300W | 0MiB / 23028MiB | 0% Default | 2025-09-09T14:04:20.2250690Z | | | N/A | 2025-09-09T14:04:20.2251321Z +-----------------------------------------+------------------------+----------------------+ 2025-09-09T14:04:20.2251745Z | 3 NVIDIA A10G Off | 00000000:00:1E.0 Off | 0 | 2025-09-09T14:04:20.2252148Z | 0% 27C P0 56W / 300W | 0MiB / 23028MiB | 0% Default | 2025-09-09T14:04:20.2252496Z | | | N/A | 2025-09-09T14:04:20.2252868Z +-----------------------------------------+------------------------+----------------------+ 2025-09-09T14:04:20.2253140Z 2025-09-09T14:04:20.2253313Z +-----------------------------------------------------------------------------------------+ 2025-09-09T14:04:20.2253727Z | Processes: | 2025-09-09T14:04:20.2254150Z | GPU GI CI PID Type Process name GPU Memory | 2025-09-09T14:04:20.2254545Z | ID ID Usage | 2025-09-09T14:04:20.2254884Z |=========================================================================================| 2025-09-09T14:04:20.2271876Z | No running processes found | 2025-09-09T14:04:20.2272418Z +-----------------------------------------------------------------------------------------+ 2025-09-09T14:04:21.9051798Z + nvidia-smi --query-gpu=gpu_name --format=csv,noheader --id=0 2025-09-09T14:04:24.2187551Z NVIDIA A10G 2025-09-09T14:04:25.3164180Z + NVIDIA_SMI_STATUS=0 2025-09-09T14:04:25.3164900Z + '[' 0 -eq 0 ']' 2025-09-09T14:04:25.3165583Z + echo 'INFO: Ignoring allowed status 0' 2025-09-09T14:04:25.3166582Z + set -e 2025-09-09T14:04:25.3166964Z INFO: Ignoring allowed status 0 2025-09-09T14:04:25.3178543Z == Installing nvidia container toolkit for amzn2023 == 2025-09-09T14:04:25.3184025Z + sudo yum install -y yum-utils 2025-09-09T14:04:25.7500676Z Last metadata expiration check: 0:05:29 ago on Tue Sep 9 13:58:56 2025. 2025-09-09T14:04:25.7747906Z Package dnf-utils-4.3.0-13.amzn2023.0.5.noarch is already installed. 2025-09-09T14:04:25.8229820Z Dependencies resolved. 2025-09-09T14:04:25.8455277Z Nothing to do. 2025-09-09T14:04:25.8455660Z Complete! 2025-09-09T14:04:25.8851259Z + [[ amzn2023 == \a\m\z\n\2\0\2\3 ]] 2025-09-09T14:04:25.8851788Z + YUM_REPO_URL=https://nvidia.github.io/libnvidia-container/stable/rpm/nvidia-container-toolkit.repo 2025-09-09T14:04:25.8852627Z + sudo yum-config-manager --add-repo https://nvidia.github.io/libnvidia-container/stable/rpm/nvidia-container-toolkit.repo 2025-09-09T14:04:26.1808659Z Adding repo from: https://nvidia.github.io/libnvidia-container/stable/rpm/nvidia-container-toolkit.repo 2025-09-09T14:04:26.2278758Z + sudo yum install -y nvidia-container-toolkit-1.17.8 libnvidia-container-tools-1.17.8 libnvidia-container1-1.17.8 nvidia-container-toolkit-base-1.17.8 2025-09-09T14:04:26.7590976Z nvidia-container-toolkit 18 kB/s | 833 B 00:00 2025-09-09T14:04:26.7840714Z Package nvidia-container-toolkit-1.17.8-1.x86_64 is already installed. 2025-09-09T14:04:26.7846654Z Package libnvidia-container-tools-1.17.8-1.x86_64 is already installed. 2025-09-09T14:04:26.7850737Z Package libnvidia-container1-1.17.8-1.x86_64 is already installed. 2025-09-09T14:04:26.7857683Z Package nvidia-container-toolkit-base-1.17.8-1.x86_64 is already installed. 2025-09-09T14:04:26.8339614Z Dependencies resolved. 2025-09-09T14:04:26.8564330Z Nothing to do. 2025-09-09T14:04:26.8564724Z Complete! 2025-09-09T14:04:26.8973384Z + sudo systemctl restart docker 2025-09-09T14:04:46.6524206Z Tue Sep 9 14:04:46 2025 2025-09-09T14:04:46.6524618Z +-----------------------------------------------------------------------------------------+ 2025-09-09T14:04:46.6525130Z | NVIDIA-SMI 580.65.06 Driver Version: 580.65.06 CUDA Version: 13.0 | 2025-09-09T14:04:46.6525891Z +-----------------------------------------+------------------------+----------------------+ 2025-09-09T14:04:46.6526409Z | GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC | 2025-09-09T14:04:46.6526939Z | Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. | 2025-09-09T14:04:46.6527361Z | | | MIG M. | 2025-09-09T14:04:46.6527684Z |=========================================+========================+======================| 2025-09-09T14:04:46.6876492Z | 0 NVIDIA A10G On | 00000000:00:1B.0 Off | 0 | 2025-09-09T14:04:46.6877060Z | 0% 27C P0 57W / 300W | 0MiB / 23028MiB | 0% Default | 2025-09-09T14:04:46.6877589Z | | | N/A | 2025-09-09T14:04:46.6878033Z +-----------------------------------------+------------------------+----------------------+ 2025-09-09T14:04:46.6878473Z | 1 NVIDIA A10G On | 00000000:00:1C.0 Off | 0 | 2025-09-09T14:04:46.6878944Z | 0% 27C P0 58W / 300W | 0MiB / 23028MiB | 0% Default | 2025-09-09T14:04:46.6879428Z | | | N/A | 2025-09-09T14:04:46.6879863Z +-----------------------------------------+------------------------+----------------------+ 2025-09-09T14:04:46.6880595Z | 2 NVIDIA A10G On | 00000000:00:1D.0 Off | 0 | 2025-09-09T14:04:46.6881068Z | 0% 26C P0 57W / 300W | 0MiB / 23028MiB | 0% Default | 2025-09-09T14:04:46.6881724Z | | | N/A | 2025-09-09T14:04:46.6882236Z +-----------------------------------------+------------------------+----------------------+ 2025-09-09T14:04:46.6882747Z | 3 NVIDIA A10G On | 00000000:00:1E.0 Off | 0 | 2025-09-09T14:04:46.6883220Z | 0% 27C P0 52W / 300W | 0MiB / 23028MiB | 2% Default | 2025-09-09T14:04:46.6883721Z | | | N/A | 2025-09-09T14:04:46.6894140Z +-----------------------------------------+------------------------+----------------------+ 2025-09-09T14:04:46.6894488Z 2025-09-09T14:04:46.6894674Z +-----------------------------------------------------------------------------------------+ 2025-09-09T14:04:46.6895102Z | Processes: | 2025-09-09T14:04:46.6895549Z | GPU GI CI PID Type Process name GPU Memory | 2025-09-09T14:04:46.6895969Z | ID ID Usage | 2025-09-09T14:04:46.6896328Z |=========================================================================================| 2025-09-09T14:04:46.6905148Z | No running processes found | 2025-09-09T14:04:46.6905691Z +-----------------------------------------------------------------------------------------+ 2025-09-09T14:04:47.3284174Z Unable to find image 'public.ecr.aws/docker/library/python:3.13' locally 2025-09-09T14:04:47.5191470Z 3.13: Pulling from docker/library/python 2025-09-09T14:04:47.6097357Z 15b1d8a5ff03: Pulling fs layer 2025-09-09T14:04:47.6097627Z 22718812f617: Pulling fs layer 2025-09-09T14:04:47.6097890Z 401a98f7495b: Pulling fs layer 2025-09-09T14:04:47.6098158Z ad446e7df19a: Pulling fs layer 2025-09-09T14:04:47.6098404Z 5d32990caa16: Pulling fs layer 2025-09-09T14:04:47.6098679Z a79d633abf9a: Pulling fs layer 2025-09-09T14:04:47.6098930Z 249a56c8e466: Pulling fs layer 2025-09-09T14:04:47.6107340Z ad446e7df19a: Waiting 2025-09-09T14:04:47.6107637Z a79d633abf9a: Waiting 2025-09-09T14:04:47.6107937Z 249a56c8e466: Waiting 2025-09-09T14:04:47.6108492Z 5d32990caa16: Waiting 2025-09-09T14:04:47.7412696Z 22718812f617: Verifying Checksum 2025-09-09T14:04:47.7413018Z 22718812f617: Download complete 2025-09-09T14:04:47.7837424Z 15b1d8a5ff03: Verifying Checksum 2025-09-09T14:04:47.7837868Z 15b1d8a5ff03: Download complete 2025-09-09T14:04:47.8715051Z 5d32990caa16: Verifying Checksum 2025-09-09T14:04:47.8715371Z 5d32990caa16: Download complete 2025-09-09T14:04:47.9316214Z 401a98f7495b: Verifying Checksum 2025-09-09T14:04:47.9317542Z 401a98f7495b: Download complete 2025-09-09T14:04:47.9915137Z 249a56c8e466: Verifying Checksum 2025-09-09T14:04:47.9915872Z 249a56c8e466: Download complete 2025-09-09T14:04:47.9930369Z a79d633abf9a: Verifying Checksum 2025-09-09T14:04:47.9930783Z a79d633abf9a: Download complete 2025-09-09T14:04:48.3667751Z ad446e7df19a: Verifying Checksum 2025-09-09T14:04:48.3668081Z ad446e7df19a: Download complete 2025-09-09T14:04:49.5438422Z 15b1d8a5ff03: Pull complete 2025-09-09T14:04:50.2301623Z 22718812f617: Pull complete 2025-09-09T14:04:52.7437878Z 401a98f7495b: Pull complete 2025-09-09T14:04:59.8521901Z ad446e7df19a: Pull complete 2025-09-09T14:05:00.2124161Z 5d32990caa16: Pull complete 2025-09-09T14:05:00.9590104Z a79d633abf9a: Pull complete 2025-09-09T14:05:00.9734809Z 249a56c8e466: Pull complete 2025-09-09T14:05:00.9819854Z Digest: sha256:74503e0bff6cf811f029590a05e0218cc9ba3e099a4b7df0ab84a67df081e1bc 2025-09-09T14:05:00.9843197Z Status: Downloaded newer image for public.ecr.aws/docker/library/python:3.13 2025-09-09T14:05:06.9184828Z Tue Sep 9 14:05:06 2025 2025-09-09T14:05:06.9185305Z +-----------------------------------------------------------------------------------------+ 2025-09-09T14:05:06.9185800Z | NVIDIA-SMI 580.65.06 Driver Version: 580.65.06 CUDA Version: 13.0 | 2025-09-09T14:05:06.9186624Z +-----------------------------------------+------------------------+----------------------+ 2025-09-09T14:05:06.9187115Z | GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC | 2025-09-09T14:05:06.9187624Z | Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. | 2025-09-09T14:05:06.9188034Z | | | MIG M. | 2025-09-09T14:05:06.9188362Z |=========================================+========================+======================| 2025-09-09T14:05:06.9825964Z | 0 NVIDIA A10G On | 00000000:00:1B.0 Off | 0 | 2025-09-09T14:05:06.9826598Z | 0% 24C P8 10W / 300W | 0MiB / 23028MiB | 0% Default | 2025-09-09T14:05:06.9827125Z | | | N/A | 2025-09-09T14:05:06.9827676Z +-----------------------------------------+------------------------+----------------------+ 2025-09-09T14:05:06.9828290Z | 1 NVIDIA A10G On | 00000000:00:1C.0 Off | 0 | 2025-09-09T14:05:06.9828871Z | 0% 25C P8 10W / 300W | 0MiB / 23028MiB | 0% Default | 2025-09-09T14:05:06.9829386Z | | | N/A | 2025-09-09T14:05:06.9829919Z +-----------------------------------------+------------------------+----------------------+ 2025-09-09T14:05:06.9830511Z | 2 NVIDIA A10G On | 00000000:00:1D.0 Off | 0 | 2025-09-09T14:05:06.9831095Z | 0% 24C P8 10W / 300W | 0MiB / 23028MiB | 0% Default | 2025-09-09T14:05:06.9831592Z | | | N/A | 2025-09-09T14:05:06.9832132Z +-----------------------------------------+------------------------+----------------------+ 2025-09-09T14:05:06.9832741Z | 3 NVIDIA A10G On | 00000000:00:1E.0 Off | 0 | 2025-09-09T14:05:06.9833596Z | 0% 24C P8 10W / 300W | 0MiB / 23028MiB | 0% Default | 2025-09-09T14:05:06.9834117Z | | | N/A | 2025-09-09T14:05:06.9834645Z +-----------------------------------------+------------------------+----------------------+ 2025-09-09T14:05:06.9853345Z 2025-09-09T14:05:06.9853878Z +-----------------------------------------------------------------------------------------+ 2025-09-09T14:05:06.9854330Z | Processes: | 2025-09-09T14:05:06.9854777Z | GPU GI CI PID Type Process name GPU Memory | 2025-09-09T14:05:06.9855197Z | ID ID Usage | 2025-09-09T14:05:06.9855554Z |=========================================================================================| 2025-09-09T14:05:06.9892149Z | No running processes found | 2025-09-09T14:05:06.9892638Z +-----------------------------------------------------------------------------------------+ 2025-09-09T14:05:09.4405407Z Command completed after 1 attempt(s). 2025-09-09T14:05:09.4495914Z ##[group]Run # ignore expansion of "docker ps -q" since it could be empty 2025-09-09T14:05:09.4496461Z # ignore expansion of "docker ps -q" since it could be empty 2025-09-09T14:05:09.4496864Z # shellcheck disable=SC2046 2025-09-09T14:05:09.4497181Z docker stop $(docker ps -q) || true 2025-09-09T14:05:09.4497508Z # Prune all of the docker images 2025-09-09T14:05:09.4497811Z docker system prune -af 2025-09-09T14:05:09.4513072Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-09T14:05:09.4513607Z env: 2025-09-09T14:05:09.4513864Z DOCKER_IMAGE: pytorch/almalinux-builder:cuda12.6 2025-09-09T14:05:09.4514191Z REPOSITORY: pytorch/ao 2025-09-09T14:05:09.4514441Z PR_NUMBER: 2963 2025-09-09T14:05:09.4516186Z SCRIPT: conda create -n venv python=3.9 -y conda activate venv echo "::group::Install newer objcopy that supports --set-section-alignment" dnf install -y gcc-toolset-10-binutils export PATH=/opt/rh/gcc-toolset-10/root/usr/bin/:$PATH python -m pip install --upgrade pip pip install torch==2.8.0 sed -i '' dev-requirements.txt pip install -r dev-requirements.txt pip install . export CONDA=$(dirname $(dirname $(which conda))) export LD_LIBRARY_PATH=$CONDA/lib/:$LD_LIBRARY_PATH pytest test --verbose -s 2025-09-09T14:05:09.4518084Z RUNNER_ARTIFACT_DIR: /home/ec2-user/actions-runner/_work/_temp/artifacts 2025-09-09T14:05:09.4518610Z RUNNER_TEST_RESULTS_DIR: /home/ec2-user/actions-runner/_work/_temp/test-results 2025-09-09T14:05:09.4519185Z RUNNER_DOCS_DIR: /home/ec2-user/actions-runner/_work/_temp/docs 2025-09-09T14:05:09.4519604Z GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all 2025-09-09T14:05:09.4519938Z ##[endgroup] 2025-09-09T14:05:09.4852402Z "docker stop" requires at least 1 argument. 2025-09-09T14:05:09.4852803Z See 'docker stop --help'. 2025-09-09T14:05:09.4852964Z 2025-09-09T14:05:09.4853124Z Usage: docker stop [OPTIONS] CONTAINER [CONTAINER...] 2025-09-09T14:05:09.4853371Z 2025-09-09T14:05:09.4853475Z Stop one or more running containers 2025-09-09T14:05:10.7425801Z Deleted Images: 2025-09-09T14:05:10.7426100Z untagged: public.ecr.aws/docker/library/python:3.13 2025-09-09T14:05:10.7426770Z untagged: public.ecr.aws/docker/library/python@sha256:74503e0bff6cf811f029590a05e0218cc9ba3e099a4b7df0ab84a67df081e1bc 2025-09-09T14:05:10.7427504Z deleted: sha256:77f2b24be2b3987f6d59918787d226acb4e6612644bacb3dd37adc494e477d9e 2025-09-09T14:05:10.7428082Z deleted: sha256:1b9aa91044866f8707424c8fe367f924a48557eac69f7485fd6d2a3a116c74d5 2025-09-09T14:05:10.7428665Z deleted: sha256:b86402d18e73d4825a3bd2a09244a93487ba4687ca7c9dcba0f73e160840845c 2025-09-09T14:05:10.7429230Z deleted: sha256:5755f8963eb047a0086073c3a7dd0731296d6751a7445f3693a52b30020a5b65 2025-09-09T14:05:10.7430012Z deleted: sha256:7f33dbfa9475d25622f49ed51f4164c97de1303331c77dfdc738e084d100f50c 2025-09-09T14:05:10.7430698Z deleted: sha256:19daa38049795ba2c166dd898c81b17e31f4b5f98c1337846c6515fff97d8782 2025-09-09T14:05:10.7431394Z deleted: sha256:483bd23b5d7e66fc0f8a92dbfacc3d72fad97ef47dd4767889979a803bc1f5b8 2025-09-09T14:05:10.7432086Z deleted: sha256:185e04da9d947141fd703dbf36361bdc2ff77cc27cbf500fb9f4881cb5ddbe95 2025-09-09T14:05:10.7432532Z 2025-09-09T14:05:10.7668750Z Total reclaimed space: 1.109GB 2025-09-09T14:05:10.7748603Z ##[group]Run ./test-infra/.github/actions/setup-ssh 2025-09-09T14:05:10.7748941Z with: 2025-09-09T14:05:10.7749544Z github-secret: *** 2025-09-09T14:05:10.7750183Z instructions: All testing is done inside the container, to start an interactive session run: docker exec -it $(docker container ps --format '{{.ID}}') bash 2025-09-09T14:05:10.7750872Z activate-with-label: false 2025-09-09T14:05:10.7751137Z label: with-ssh 2025-09-09T14:05:10.7751368Z remove-existing-keys: true 2025-09-09T14:05:10.7751627Z fail-silently: true 2025-09-09T14:05:10.7751844Z env: 2025-09-09T14:05:10.7752092Z DOCKER_IMAGE: pytorch/almalinux-builder:cuda12.6 2025-09-09T14:05:10.7752428Z REPOSITORY: pytorch/ao 2025-09-09T14:05:10.7752674Z PR_NUMBER: 2963 2025-09-09T14:05:10.7754476Z SCRIPT: conda create -n venv python=3.9 -y conda activate venv echo "::group::Install newer objcopy that supports --set-section-alignment" dnf install -y gcc-toolset-10-binutils export PATH=/opt/rh/gcc-toolset-10/root/usr/bin/:$PATH python -m pip install --upgrade pip pip install torch==2.8.0 sed -i '' dev-requirements.txt pip install -r dev-requirements.txt pip install . export CONDA=$(dirname $(dirname $(which conda))) export LD_LIBRARY_PATH=$CONDA/lib/:$LD_LIBRARY_PATH pytest test --verbose -s 2025-09-09T14:05:10.7756538Z RUNNER_ARTIFACT_DIR: /home/ec2-user/actions-runner/_work/_temp/artifacts 2025-09-09T14:05:10.7757077Z RUNNER_TEST_RESULTS_DIR: /home/ec2-user/actions-runner/_work/_temp/test-results 2025-09-09T14:05:10.7757575Z RUNNER_DOCS_DIR: /home/ec2-user/actions-runner/_work/_temp/docs 2025-09-09T14:05:10.7757994Z GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all 2025-09-09T14:05:10.7758324Z ##[endgroup] 2025-09-09T14:05:10.8860891Z Please see https://github.com/pytorch/pytorch/wiki/Debugging-using-with-ssh-for-Github-Actions for more info. 2025-09-09T14:05:11.4529401Z Grabbing public ssh keys from https://github.com/andrewor14.keys 2025-09-09T14:05:11.5299318Z ~/.ssh/authorized_keys file found on node, removing ~/.ssh and starting fresh 2025-09-09T14:05:11.5313252Z Public keys pulled and installed to /home/ec2-user/.ssh/authorized_keys 2025-09-09T14:05:11.5353477Z Login using: ssh ec2-user@ec2-54-90-240-84.compute-1.amazonaws.com 2025-09-09T14:05:11.5354461Z All testing is done inside the container, to start an interactive session run: 2025-09-09T14:05:11.5355427Z docker exec -it $(docker container ps --format '{{.ID}}') bash 2025-09-09T14:05:11.5507685Z ##[group]Run actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 2025-09-09T14:05:11.5508082Z with: 2025-09-09T14:05:11.5508300Z repository: pytorch/ao 2025-09-09T14:05:11.5508550Z ref: refs/pull/2963/merge 2025-09-09T14:05:11.5508801Z path: pytorch/ao 2025-09-09T14:05:11.5509027Z fetch-depth: 1 2025-09-09T14:05:11.5509243Z submodules: recursive 2025-09-09T14:05:11.5509590Z token: *** 2025-09-09T14:05:11.5509793Z ssh-strict: true 2025-09-09T14:05:11.5510016Z ssh-user: git 2025-09-09T14:05:11.5510240Z persist-credentials: true 2025-09-09T14:05:11.5510489Z clean: true 2025-09-09T14:05:11.5510720Z sparse-checkout-cone-mode: true 2025-09-09T14:05:11.5511004Z fetch-tags: false 2025-09-09T14:05:11.5511227Z show-progress: true 2025-09-09T14:05:11.5511442Z lfs: false 2025-09-09T14:05:11.5511666Z set-safe-directory: true 2025-09-09T14:05:11.5511894Z env: 2025-09-09T14:05:11.5512133Z DOCKER_IMAGE: pytorch/almalinux-builder:cuda12.6 2025-09-09T14:05:11.5512478Z REPOSITORY: pytorch/ao 2025-09-09T14:05:11.5512716Z PR_NUMBER: 2963 2025-09-09T14:05:11.5514463Z SCRIPT: conda create -n venv python=3.9 -y conda activate venv echo "::group::Install newer objcopy that supports --set-section-alignment" dnf install -y gcc-toolset-10-binutils export PATH=/opt/rh/gcc-toolset-10/root/usr/bin/:$PATH python -m pip install --upgrade pip pip install torch==2.8.0 sed -i '' dev-requirements.txt pip install -r dev-requirements.txt pip install . export CONDA=$(dirname $(dirname $(which conda))) export LD_LIBRARY_PATH=$CONDA/lib/:$LD_LIBRARY_PATH pytest test --verbose -s 2025-09-09T14:05:11.5516363Z RUNNER_ARTIFACT_DIR: /home/ec2-user/actions-runner/_work/_temp/artifacts 2025-09-09T14:05:11.5516888Z RUNNER_TEST_RESULTS_DIR: /home/ec2-user/actions-runner/_work/_temp/test-results 2025-09-09T14:05:11.5517392Z RUNNER_DOCS_DIR: /home/ec2-user/actions-runner/_work/_temp/docs 2025-09-09T14:05:11.5517822Z GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all 2025-09-09T14:05:11.5518140Z ##[endgroup] 2025-09-09T14:05:11.6503419Z Syncing repository: pytorch/ao 2025-09-09T14:05:11.6512427Z ##[group]Getting Git version info 2025-09-09T14:05:11.6512854Z Working directory is '/home/ec2-user/actions-runner/_work/ao/ao/pytorch/ao' 2025-09-09T14:05:11.6538936Z [command]/usr/bin/git version 2025-09-09T14:05:11.6591700Z git version 2.47.1 2025-09-09T14:05:11.6617629Z ##[endgroup] 2025-09-09T14:05:11.6632473Z Temporarily overriding HOME='/home/ec2-user/actions-runner/_work/_temp/bd6852f9-1aab-4a6e-bfc6-82a2ea309d4a' before making global git config changes 2025-09-09T14:05:11.6633334Z Adding repository directory to the temporary git global config as a safe directory 2025-09-09T14:05:11.6647953Z [command]/usr/bin/git config --global --add safe.directory /home/ec2-user/actions-runner/_work/ao/ao/pytorch/ao 2025-09-09T14:05:11.6687239Z ##[group]Initializing the repository 2025-09-09T14:05:11.6691837Z [command]/usr/bin/git init /home/ec2-user/actions-runner/_work/ao/ao/pytorch/ao 2025-09-09T14:05:11.6741794Z hint: Using 'master' as the name for the initial branch. This default branch name 2025-09-09T14:05:11.6742341Z hint: is subject to change. To configure the initial branch name to use in all 2025-09-09T14:05:11.6742847Z hint: of your new repositories, which will suppress this warning, call: 2025-09-09T14:05:11.6743218Z hint: 2025-09-09T14:05:11.6743471Z hint: git config --global init.defaultBranch 2025-09-09T14:05:11.6743773Z hint: 2025-09-09T14:05:11.6744072Z hint: Names commonly chosen instead of 'master' are 'main', 'trunk' and 2025-09-09T14:05:11.6744571Z hint: 'development'. The just-created branch can be renamed via this command: 2025-09-09T14:05:11.6744957Z hint: 2025-09-09T14:05:11.6745147Z hint: git branch -m 2025-09-09T14:05:11.6745604Z Initialized empty Git repository in /home/ec2-user/actions-runner/_work/ao/ao/pytorch/ao/.git/ 2025-09-09T14:05:11.6753906Z [command]/usr/bin/git remote add origin https://github.com/pytorch/ao 2025-09-09T14:05:11.6789993Z ##[endgroup] 2025-09-09T14:05:11.6790661Z ##[group]Disabling automatic garbage collection 2025-09-09T14:05:11.6794299Z [command]/usr/bin/git config --local gc.auto 0 2025-09-09T14:05:11.6833402Z ##[endgroup] 2025-09-09T14:05:11.6833795Z ##[group]Setting up auth 2025-09-09T14:05:11.6838463Z [command]/usr/bin/git config --local --name-only --get-regexp core\.sshCommand 2025-09-09T14:05:11.6874402Z [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' || :" 2025-09-09T14:05:11.7303285Z [command]/usr/bin/git config --local --name-only --get-regexp http\.https\:\/\/github\.com\/\.extraheader 2025-09-09T14:05:11.7339329Z [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' || :" 2025-09-09T14:05:11.7745311Z [command]/usr/bin/git config --local http.https://github.com/.extraheader AUTHORIZATION: basic *** 2025-09-09T14:05:11.7791620Z ##[endgroup] 2025-09-09T14:05:11.7792001Z ##[group]Fetching the repository 2025-09-09T14:05:11.7798739Z [command]/usr/bin/git -c protocol.version=2 fetch --no-tags --prune --no-recurse-submodules --depth=1 origin +refs/pull/2963/merge:refs/remotes/pull/2963/merge 2025-09-09T14:05:12.5384772Z From https://github.com/pytorch/ao 2025-09-09T14:05:12.5385667Z * [new ref] refs/pull/2963/merge -> pull/2963/merge 2025-09-09T14:05:12.5412573Z ##[endgroup] 2025-09-09T14:05:12.5413067Z ##[group]Determining the checkout info 2025-09-09T14:05:12.5414954Z ##[endgroup] 2025-09-09T14:05:12.5432535Z [command]/usr/bin/git sparse-checkout disable 2025-09-09T14:05:12.5474736Z [command]/usr/bin/git config --local --unset-all extensions.worktreeConfig 2025-09-09T14:05:12.5510957Z ##[group]Checking out the ref 2025-09-09T14:05:12.5513604Z [command]/usr/bin/git checkout --progress --force refs/remotes/pull/2963/merge 2025-09-09T14:05:12.6881726Z Note: switching to 'refs/remotes/pull/2963/merge'. 2025-09-09T14:05:12.6882089Z 2025-09-09T14:05:12.6882364Z You are in 'detached HEAD' state. You can look around, make experimental 2025-09-09T14:05:12.6883033Z changes and commit them, and you can discard any commits you make in this 2025-09-09T14:05:12.6883686Z state without impacting any branches by switching back to a branch. 2025-09-09T14:05:12.6884100Z 2025-09-09T14:05:12.6884327Z If you want to create a new branch to retain commits you create, you may 2025-09-09T14:05:12.6884779Z do so (now or later) by using -c with the switch command. Example: 2025-09-09T14:05:12.6885036Z 2025-09-09T14:05:12.6885145Z git switch -c 2025-09-09T14:05:12.6885324Z 2025-09-09T14:05:12.6885434Z Or undo this operation with: 2025-09-09T14:05:12.6885601Z 2025-09-09T14:05:12.6885908Z git switch - 2025-09-09T14:05:12.6886038Z 2025-09-09T14:05:12.6886258Z Turn off this advice by setting config variable advice.detachedHead to false 2025-09-09T14:05:12.6886578Z 2025-09-09T14:05:12.6886953Z HEAD is now at 7c05f81 Merge c21284c127b039bc49cc7ffda0e692894ed3b094 into 8b72284fd363b5c096de93fb7ac9cc960a6a601e 2025-09-09T14:05:12.6900955Z ##[endgroup] 2025-09-09T14:05:12.6901468Z ##[group]Setting up auth for fetching submodules 2025-09-09T14:05:12.6906821Z [command]/usr/bin/git config --global http.https://github.com/.extraheader AUTHORIZATION: basic *** 2025-09-09T14:05:12.6955856Z [command]/usr/bin/git config --global --unset-all url.https://github.com/.insteadOf 2025-09-09T14:05:12.6988224Z [command]/usr/bin/git config --global --add url.https://github.com/.insteadOf git@github.com: 2025-09-09T14:05:12.7024234Z [command]/usr/bin/git config --global --add url.https://github.com/.insteadOf org-21003710@github.com: 2025-09-09T14:05:12.7054398Z ##[endgroup] 2025-09-09T14:05:12.7054878Z ##[group]Fetching submodules 2025-09-09T14:05:12.7057791Z [command]/usr/bin/git submodule sync --recursive 2025-09-09T14:05:12.7469449Z [command]/usr/bin/git -c protocol.version=2 submodule update --init --force --depth=1 --recursive 2025-09-09T14:05:12.7874581Z Submodule 'third_party/cutlass' (https://github.com/NVIDIA/cutlass) registered for path 'third_party/cutlass' 2025-09-09T14:05:12.7908827Z Cloning into '/home/ec2-user/actions-runner/_work/ao/ao/pytorch/ao/third_party/cutlass'... 2025-09-09T14:05:14.6040690Z From https://github.com/NVIDIA/cutlass 2025-09-09T14:05:14.6041274Z * branch e51efbfe18fe4f4cbb66ab814c55bf4aa0185491 -> FETCH_HEAD 2025-09-09T14:05:15.3830808Z Submodule path 'third_party/cutlass': checked out 'e51efbfe18fe4f4cbb66ab814c55bf4aa0185491' 2025-09-09T14:05:15.3882375Z [command]/usr/bin/git submodule foreach --recursive git config --local gc.auto 0 2025-09-09T14:05:15.4277779Z Entering 'third_party/cutlass' 2025-09-09T14:05:15.4361056Z ##[endgroup] 2025-09-09T14:05:15.4361440Z ##[group]Persisting credentials for submodules 2025-09-09T14:05:15.4367653Z [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' || :" 2025-09-09T14:05:15.4756800Z Entering 'third_party/cutlass' 2025-09-09T14:05:15.4860771Z [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" 2025-09-09T14:05:15.5244645Z Entering 'third_party/cutlass' 2025-09-09T14:05:15.5315954Z file:/home/ec2-user/actions-runner/_work/ao/ao/pytorch/ao/.git/modules/third_party/cutlass/config remote.origin.url 2025-09-09T14:05:15.5382825Z [command]/usr/bin/git submodule foreach --recursive git config --local --add 'url.https://github.com/.insteadOf' 'git@github.com:' 2025-09-09T14:05:15.5772705Z Entering 'third_party/cutlass' 2025-09-09T14:05:15.5857105Z [command]/usr/bin/git submodule foreach --recursive git config --local --add 'url.https://github.com/.insteadOf' 'org-21003710@github.com:' 2025-09-09T14:05:15.6244541Z Entering 'third_party/cutlass' 2025-09-09T14:05:15.6326986Z ##[endgroup] 2025-09-09T14:05:15.6370053Z [command]/usr/bin/git log -1 --format=%H 2025-09-09T14:05:15.6401100Z 7c05f811b89289f7be3e0e3546626827f2cc1ca4 2025-09-09T14:05:15.6616658Z Prepare all required actions 2025-09-09T14:05:15.6617104Z Getting action download info 2025-09-09T14:05:15.8408070Z Download action repository 'nick-fields/retry@v3.0.0' (SHA:7152eba30c6575329ac0576536151aca5a72780e) 2025-09-09T14:05:16.0333111Z ##[group]Run ./test-infra/.github/actions/calculate-docker-image 2025-09-09T14:05:16.0333475Z with: 2025-09-09T14:05:16.0333701Z use-custom-docker-registry: true 2025-09-09T14:05:16.0334047Z docker-image-name: pytorch/almalinux-builder:cuda12.6 2025-09-09T14:05:16.0334394Z docker-build-dir: .ci/docker 2025-09-09T14:05:16.0334853Z working-directory: pytorch/ao 2025-09-09T14:05:16.0335125Z docker-build-script: ./build.sh 2025-09-09T14:05:16.0335487Z docker-registry: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-09-09T14:05:16.0335848Z force-push: false 2025-09-09T14:05:16.0336059Z env: 2025-09-09T14:05:16.0336309Z DOCKER_IMAGE: pytorch/almalinux-builder:cuda12.6 2025-09-09T14:05:16.0336632Z REPOSITORY: pytorch/ao 2025-09-09T14:05:16.0336897Z PR_NUMBER: 2963 2025-09-09T14:05:16.0338622Z SCRIPT: conda create -n venv python=3.9 -y conda activate venv echo "::group::Install newer objcopy that supports --set-section-alignment" dnf install -y gcc-toolset-10-binutils export PATH=/opt/rh/gcc-toolset-10/root/usr/bin/:$PATH python -m pip install --upgrade pip pip install torch==2.8.0 sed -i '' dev-requirements.txt pip install -r dev-requirements.txt pip install . export CONDA=$(dirname $(dirname $(which conda))) export LD_LIBRARY_PATH=$CONDA/lib/:$LD_LIBRARY_PATH pytest test --verbose -s 2025-09-09T14:05:16.0340530Z RUNNER_ARTIFACT_DIR: /home/ec2-user/actions-runner/_work/_temp/artifacts 2025-09-09T14:05:16.0341064Z RUNNER_TEST_RESULTS_DIR: /home/ec2-user/actions-runner/_work/_temp/test-results 2025-09-09T14:05:16.0341552Z RUNNER_DOCS_DIR: /home/ec2-user/actions-runner/_work/_temp/docs 2025-09-09T14:05:16.0341975Z GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all 2025-09-09T14:05:16.0342288Z ##[endgroup] 2025-09-09T14:05:16.0365189Z ##[group]Run set -ex 2025-09-09T14:05:16.0365474Z set -ex 2025-09-09T14:05:16.0365685Z  2025-09-09T14:05:16.0366055Z # If the docker build directory or the build script doesn't exist, the action will 2025-09-09T14:05:16.0366655Z # gracefully return the docker image name as it is. Pulling docker image in Linux 2025-09-09T14:05:16.0367180Z # job could then download the pre-built image as usual 2025-09-09T14:05:16.0367808Z if [[ -d "${DOCKER_BUILD_DIR}" ]] && [[ -f "${DOCKER_BUILD_DIR}/${DOCKER_BUILD_SCRIPT}" ]] && [[ "${USE_CUSTOM_DOCKER_REGISTRY}" == "true" ]]; then 2025-09-09T14:05:16.0368393Z  echo "skip=false" >> "${GITHUB_OUTPUT}" 2025-09-09T14:05:16.0368700Z else 2025-09-09T14:05:16.0368947Z  echo "skip=true" >> "${GITHUB_OUTPUT}" 2025-09-09T14:05:16.0369376Z  echo "docker-image=${DOCKER_IMAGE_NAME}" >> "${GITHUB_OUTPUT}" 2025-09-09T14:05:16.0369758Z  2025-09-09T14:05:16.0370262Z  echo "Not using custom ECR registry. Either it was not requested or there is no Docker build script in the ${REPO_NAME} repo..." 2025-09-09T14:05:16.0370829Z  exit 0 2025-09-09T14:05:16.0371038Z fi 2025-09-09T14:05:16.0371245Z  2025-09-09T14:05:16.0371556Z if [[ "${DOCKER_IMAGE_NAME}" == *"${DOCKER_REGISTRY}/${REPO_NAME}"* ]]; then 2025-09-09T14:05:16.0372105Z  # The docker image name already includes the ECR prefix and tag, so we can just 2025-09-09T14:05:16.0372588Z  # use it as it is, but first let's extract the tag 2025-09-09T14:05:16.0373035Z  DOCKER_TAG=$(echo "${DOCKER_IMAGE_NAME}" | awk -F '[:,]' '{print $2}') 2025-09-09T14:05:16.0373505Z  echo "docker-tag=${DOCKER_TAG}" >> "${GITHUB_OUTPUT}" 2025-09-09T14:05:16.0373956Z  echo "docker-image=${DOCKER_IMAGE_NAME}" >> "${GITHUB_OUTPUT}" 2025-09-09T14:05:16.0374333Z else 2025-09-09T14:05:16.0374785Z  if [[ "${DOCKER_IMAGE_NAME}" == *:* ]]; then 2025-09-09T14:05:16.0375151Z  CUSTOM_TAG_PREFIX=${DOCKER_IMAGE_NAME#*:} 2025-09-09T14:05:16.0375523Z  DOCKER_IMAGE_NAME=${DOCKER_IMAGE_NAME%%:*} 2025-09-09T14:05:16.0375821Z  fi 2025-09-09T14:05:16.0376233Z  DOCKER_TAG=${CUSTOM_TAG_PREFIX:+${CUSTOM_TAG_PREFIX}-}$(git rev-parse HEAD:"${DOCKER_BUILD_DIR}") 2025-09-09T14:05:16.0376784Z  echo "docker-tag=${DOCKER_TAG}" >> "${GITHUB_OUTPUT}" 2025-09-09T14:05:16.0377368Z  echo "docker-image=${DOCKER_REGISTRY}/${REPO_NAME}/${DOCKER_IMAGE_NAME}:${DOCKER_TAG}" >> "${GITHUB_OUTPUT}" 2025-09-09T14:05:16.0378103Z  echo "custom-tag-prefix=${CUSTOM_TAG_PREFIX}" >> "${GITHUB_OUTPUT}" 2025-09-09T14:05:16.0378503Z fi 2025-09-09T14:05:16.0387697Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-09T14:05:16.0388045Z env: 2025-09-09T14:05:16.0388315Z DOCKER_IMAGE: pytorch/almalinux-builder:cuda12.6 2025-09-09T14:05:16.0388667Z REPOSITORY: pytorch/ao 2025-09-09T14:05:16.0388934Z PR_NUMBER: 2963 2025-09-09T14:05:16.0390685Z SCRIPT: conda create -n venv python=3.9 -y conda activate venv echo "::group::Install newer objcopy that supports --set-section-alignment" dnf install -y gcc-toolset-10-binutils export PATH=/opt/rh/gcc-toolset-10/root/usr/bin/:$PATH python -m pip install --upgrade pip pip install torch==2.8.0 sed -i '' dev-requirements.txt pip install -r dev-requirements.txt pip install . export CONDA=$(dirname $(dirname $(which conda))) export LD_LIBRARY_PATH=$CONDA/lib/:$LD_LIBRARY_PATH pytest test --verbose -s 2025-09-09T14:05:16.0392598Z RUNNER_ARTIFACT_DIR: /home/ec2-user/actions-runner/_work/_temp/artifacts 2025-09-09T14:05:16.0393161Z RUNNER_TEST_RESULTS_DIR: /home/ec2-user/actions-runner/_work/_temp/test-results 2025-09-09T14:05:16.0393679Z RUNNER_DOCS_DIR: /home/ec2-user/actions-runner/_work/_temp/docs 2025-09-09T14:05:16.0394119Z GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all 2025-09-09T14:05:16.0394471Z REPO_NAME: ao 2025-09-09T14:05:16.0394756Z DOCKER_IMAGE_NAME: pytorch/almalinux-builder:cuda12.6 2025-09-09T14:05:16.0395121Z DOCKER_BUILD_DIR: .ci/docker 2025-09-09T14:05:16.0395395Z DOCKER_BUILD_SCRIPT: ./build.sh 2025-09-09T14:05:16.0395771Z DOCKER_REGISTRY: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-09-09T14:05:16.0396163Z USE_CUSTOM_DOCKER_REGISTRY: true 2025-09-09T14:05:16.0396451Z CUSTOM_TAG_PREFIX: 2025-09-09T14:05:16.0396691Z ##[endgroup] 2025-09-09T14:05:16.0431029Z + [[ -d .ci/docker ]] 2025-09-09T14:05:16.0431285Z + echo skip=true 2025-09-09T14:05:16.0431567Z + echo docker-image=pytorch/almalinux-builder:cuda12.6 2025-09-09T14:05:16.0432173Z + echo 'Not using custom ECR registry. Either it was not requested or there is no Docker build script in the ao repo...' 2025-09-09T14:05:16.0432694Z + exit 0 2025-09-09T14:05:16.0433116Z Not using custom ECR registry. Either it was not requested or there is no Docker build script in the ao repo... 2025-09-09T14:05:16.0474627Z ##[group]Run set -eux 2025-09-09T14:05:16.0474996Z set -eux 2025-09-09T14:05:16.0475489Z # It's ok if this steps fails, it would then be an anonymous user like what we used to have 2025-09-09T14:05:16.0491747Z aws secretsmanager get-secret-value --secret-id docker_hub_readonly_token | jq --raw-output '.SecretString' | jq -r .docker_hub_readonly_token | docker login --username pytorchbot --password-stdin || true 2025-09-09T14:05:16.0501510Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-09T14:05:16.0501865Z env: 2025-09-09T14:05:16.0502126Z DOCKER_IMAGE: pytorch/almalinux-builder:cuda12.6 2025-09-09T14:05:16.0502463Z REPOSITORY: pytorch/ao 2025-09-09T14:05:16.0502693Z PR_NUMBER: 2963 2025-09-09T14:05:16.0504653Z SCRIPT: conda create -n venv python=3.9 -y conda activate venv echo "::group::Install newer objcopy that supports --set-section-alignment" dnf install -y gcc-toolset-10-binutils export PATH=/opt/rh/gcc-toolset-10/root/usr/bin/:$PATH python -m pip install --upgrade pip pip install torch==2.8.0 sed -i '' dev-requirements.txt pip install -r dev-requirements.txt pip install . export CONDA=$(dirname $(dirname $(which conda))) export LD_LIBRARY_PATH=$CONDA/lib/:$LD_LIBRARY_PATH pytest test --verbose -s 2025-09-09T14:05:16.0506540Z RUNNER_ARTIFACT_DIR: /home/ec2-user/actions-runner/_work/_temp/artifacts 2025-09-09T14:05:16.0507073Z RUNNER_TEST_RESULTS_DIR: /home/ec2-user/actions-runner/_work/_temp/test-results 2025-09-09T14:05:16.0507557Z RUNNER_DOCS_DIR: /home/ec2-user/actions-runner/_work/_temp/docs 2025-09-09T14:05:16.0508138Z GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all 2025-09-09T14:05:16.0508451Z ##[endgroup] 2025-09-09T14:05:16.0542338Z + aws secretsmanager get-secret-value --secret-id docker_hub_readonly_token 2025-09-09T14:05:16.0543045Z + jq --raw-output .SecretString 2025-09-09T14:05:16.0545208Z + jq -r .docker_hub_readonly_token 2025-09-09T14:05:16.0545901Z + docker login --username pytorchbot --password-stdin 2025-09-09T14:05:16.6419061Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json. 2025-09-09T14:05:16.6419671Z Configure a credential helper to remove this warning. See 2025-09-09T14:05:16.6420190Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store 2025-09-09T14:05:16.6420539Z 2025-09-09T14:05:16.6425882Z Login Succeeded 2025-09-09T14:05:16.6519598Z Prepare all required actions 2025-09-09T14:05:16.6554693Z ##[group]Run ./test-infra/.github/actions/pull-docker-image 2025-09-09T14:05:16.6555039Z with: 2025-09-09T14:05:16.6555289Z docker-image: pytorch/almalinux-builder:cuda12.6 2025-09-09T14:05:16.6555737Z docker-registry: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-09-09T14:05:16.6556086Z env: 2025-09-09T14:05:16.6556334Z DOCKER_IMAGE: pytorch/almalinux-builder:cuda12.6 2025-09-09T14:05:16.6556661Z REPOSITORY: pytorch/ao 2025-09-09T14:05:16.6556911Z PR_NUMBER: 2963 2025-09-09T14:05:16.6558683Z SCRIPT: conda create -n venv python=3.9 -y conda activate venv echo "::group::Install newer objcopy that supports --set-section-alignment" dnf install -y gcc-toolset-10-binutils export PATH=/opt/rh/gcc-toolset-10/root/usr/bin/:$PATH python -m pip install --upgrade pip pip install torch==2.8.0 sed -i '' dev-requirements.txt pip install -r dev-requirements.txt pip install . export CONDA=$(dirname $(dirname $(which conda))) export LD_LIBRARY_PATH=$CONDA/lib/:$LD_LIBRARY_PATH pytest test --verbose -s 2025-09-09T14:05:16.6560679Z RUNNER_ARTIFACT_DIR: /home/ec2-user/actions-runner/_work/_temp/artifacts 2025-09-09T14:05:16.6561219Z RUNNER_TEST_RESULTS_DIR: /home/ec2-user/actions-runner/_work/_temp/test-results 2025-09-09T14:05:16.6561718Z RUNNER_DOCS_DIR: /home/ec2-user/actions-runner/_work/_temp/docs 2025-09-09T14:05:16.6562140Z GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all 2025-09-09T14:05:16.6562464Z ##[endgroup] 2025-09-09T14:05:16.6582286Z ##[group]Run set -x 2025-09-09T14:05:16.6582564Z set -x 2025-09-09T14:05:16.6582804Z set +e 2025-09-09T14:05:16.6583015Z  2025-09-09T14:05:16.6583239Z login() { 2025-09-09T14:05:16.6583693Z  aws ecr get-login-password --region us-east-1 | docker login -u AWS --password-stdin "$1" 2025-09-09T14:05:16.6584211Z } 2025-09-09T14:05:16.6584435Z  2025-09-09T14:05:16.6584646Z retry () { 2025-09-09T14:05:16.6584922Z  $* || (sleep 1 && $*) || (sleep 2 && $*) 2025-09-09T14:05:16.6585215Z } 2025-09-09T14:05:16.6585432Z  2025-09-09T14:05:16.6585657Z retry login "${DOCKER_REGISTRY}" 2025-09-09T14:05:16.6585967Z  2025-09-09T14:05:16.6586415Z IMAGE_SIZE=$(docker manifest inspect "${DOCKER_IMAGE}" | jq '[.layers[].size, .config.size] | add / 1024 / 1024') 2025-09-09T14:05:16.6587047Z echo "Compressed size of image in MB: ${IMAGE_SIZE}" 2025-09-09T14:05:16.6587390Z  2025-09-09T14:05:16.6587595Z set -e 2025-09-09T14:05:16.6587940Z # ignore output since only exit code is used for conditional 2025-09-09T14:05:16.6588403Z # only pull docker image if it's not available locally 2025-09-09T14:05:16.6588921Z if ! docker inspect --type=image "${DOCKER_IMAGE}" >/dev/null 2>/dev/null; then 2025-09-09T14:05:16.6589382Z  retry docker pull "${DOCKER_IMAGE}" 2025-09-09T14:05:16.6589697Z fi 2025-09-09T14:05:16.6599215Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-09T14:05:16.6599571Z env: 2025-09-09T14:05:16.6599821Z DOCKER_IMAGE: pytorch/almalinux-builder:cuda12.6 2025-09-09T14:05:16.6600332Z REPOSITORY: pytorch/ao 2025-09-09T14:05:16.6600579Z PR_NUMBER: 2963 2025-09-09T14:05:16.6602322Z SCRIPT: conda create -n venv python=3.9 -y conda activate venv echo "::group::Install newer objcopy that supports --set-section-alignment" dnf install -y gcc-toolset-10-binutils export PATH=/opt/rh/gcc-toolset-10/root/usr/bin/:$PATH python -m pip install --upgrade pip pip install torch==2.8.0 sed -i '' dev-requirements.txt pip install -r dev-requirements.txt pip install . export CONDA=$(dirname $(dirname $(which conda))) export LD_LIBRARY_PATH=$CONDA/lib/:$LD_LIBRARY_PATH pytest test --verbose -s 2025-09-09T14:05:16.6604231Z RUNNER_ARTIFACT_DIR: /home/ec2-user/actions-runner/_work/_temp/artifacts 2025-09-09T14:05:16.6604928Z RUNNER_TEST_RESULTS_DIR: /home/ec2-user/actions-runner/_work/_temp/test-results 2025-09-09T14:05:16.6605431Z RUNNER_DOCS_DIR: /home/ec2-user/actions-runner/_work/_temp/docs 2025-09-09T14:05:16.6605862Z GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all 2025-09-09T14:05:16.6606279Z DOCKER_REGISTRY: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-09-09T14:05:16.6606648Z ##[endgroup] 2025-09-09T14:05:16.6638660Z + set +e 2025-09-09T14:05:16.6639347Z + retry login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-09-09T14:05:16.6639774Z + login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-09-09T14:05:16.6643791Z + aws ecr get-login-password --region us-east-1 2025-09-09T14:05:16.6644370Z + docker login -u AWS --password-stdin 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-09-09T14:05:17.2563995Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json. 2025-09-09T14:05:17.2564524Z Configure a credential helper to remove this warning. See 2025-09-09T14:05:17.2565057Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store 2025-09-09T14:05:17.2565413Z 2025-09-09T14:05:17.2566249Z Login Succeeded 2025-09-09T14:05:17.2601768Z ++ docker manifest inspect pytorch/almalinux-builder:cuda12.6 2025-09-09T14:05:17.2602919Z ++ jq '[.layers[].size, .config.size] | add / 1024 / 1024' 2025-09-09T14:05:17.4135672Z + IMAGE_SIZE=7943.772059440613 2025-09-09T14:05:17.4136017Z + echo 'Compressed size of image in MB: 7943.772059440613' 2025-09-09T14:05:17.4136345Z + set -e 2025-09-09T14:05:17.4136573Z Compressed size of image in MB: 7943.772059440613 2025-09-09T14:05:17.4136985Z + docker inspect --type=image pytorch/almalinux-builder:cuda12.6 2025-09-09T14:05:17.4297579Z + retry docker pull pytorch/almalinux-builder:cuda12.6 2025-09-09T14:05:17.4297972Z + docker pull pytorch/almalinux-builder:cuda12.6 2025-09-09T14:05:17.6139851Z cuda12.6: Pulling from pytorch/almalinux-builder 2025-09-09T14:05:17.6140199Z 19877a9af8e3: Pulling fs layer 2025-09-09T14:05:17.6140486Z 3b95f7accc18: Pulling fs layer 2025-09-09T14:05:17.6140747Z 09fcdf4cf4fd: Pulling fs layer 2025-09-09T14:05:17.6140996Z 17af5086235f: Pulling fs layer 2025-09-09T14:05:17.6141246Z c3175a707c2d: Pulling fs layer 2025-09-09T14:05:17.6141492Z 550b3c83242f: Pulling fs layer 2025-09-09T14:05:17.6141763Z 018f40a634ae: Pulling fs layer 2025-09-09T14:05:17.6142009Z 4f4fb700ef54: Pulling fs layer 2025-09-09T14:05:17.6142267Z cabce7a916a3: Pulling fs layer 2025-09-09T14:05:17.6142516Z 0b3a66ab554e: Pulling fs layer 2025-09-09T14:05:17.6142767Z 72728e4acc07: Pulling fs layer 2025-09-09T14:05:17.6143013Z 2ca30f8660e0: Pulling fs layer 2025-09-09T14:05:17.6143263Z 45f90a05dbb6: Pulling fs layer 2025-09-09T14:05:17.6143535Z 16125e2dbaa8: Pulling fs layer 2025-09-09T14:05:17.6143784Z 8e08c86db3a1: Pulling fs layer 2025-09-09T14:05:17.6144032Z 550d67135f81: Pulling fs layer 2025-09-09T14:05:17.6144275Z cac5e14b36bd: Pulling fs layer 2025-09-09T14:05:17.6144541Z d9fc50fb0d36: Pulling fs layer 2025-09-09T14:05:17.6144786Z 8b2ffa49399c: Pulling fs layer 2025-09-09T14:05:17.6145044Z 05e4f4570fa0: Pulling fs layer 2025-09-09T14:05:17.6145287Z e16b313a64bb: Pulling fs layer 2025-09-09T14:05:17.6145541Z 87b47e27ca53: Pulling fs layer 2025-09-09T14:05:17.6145776Z 550b3c83242f: Waiting 2025-09-09T14:05:17.6146226Z 282dc51a39ad: Pulling fs layer 2025-09-09T14:05:17.6146464Z 018f40a634ae: Waiting 2025-09-09T14:05:17.6146670Z 4f4fb700ef54: Waiting 2025-09-09T14:05:17.6146879Z 0b3a66ab554e: Waiting 2025-09-09T14:05:17.6147083Z 72728e4acc07: Waiting 2025-09-09T14:05:17.6147289Z cabce7a916a3: Waiting 2025-09-09T14:05:17.6147492Z 2ca30f8660e0: Waiting 2025-09-09T14:05:17.6147700Z 8b2ffa49399c: Waiting 2025-09-09T14:05:17.6147905Z 45f90a05dbb6: Waiting 2025-09-09T14:05:17.6148111Z 05e4f4570fa0: Waiting 2025-09-09T14:05:17.6148324Z 16125e2dbaa8: Waiting 2025-09-09T14:05:17.6148526Z e16b313a64bb: Waiting 2025-09-09T14:05:17.6148733Z 8e08c86db3a1: Waiting 2025-09-09T14:05:17.6148931Z 550d67135f81: Waiting 2025-09-09T14:05:17.6149136Z 87b47e27ca53: Waiting 2025-09-09T14:05:17.6149516Z 282dc51a39ad: Waiting 2025-09-09T14:05:17.6149729Z d9fc50fb0d36: Waiting 2025-09-09T14:05:17.6149935Z cac5e14b36bd: Waiting 2025-09-09T14:05:17.6150145Z 17af5086235f: Waiting 2025-09-09T14:05:17.6150347Z c3175a707c2d: Waiting 2025-09-09T14:05:17.6938550Z 09fcdf4cf4fd: Verifying Checksum 2025-09-09T14:05:17.6939098Z 09fcdf4cf4fd: Download complete 2025-09-09T14:05:18.0606399Z 17af5086235f: Verifying Checksum 2025-09-09T14:05:18.0606690Z 17af5086235f: Download complete 2025-09-09T14:05:18.3975006Z 19877a9af8e3: Download complete 2025-09-09T14:05:18.4394838Z 550b3c83242f: Verifying Checksum 2025-09-09T14:05:18.4395117Z 550b3c83242f: Download complete 2025-09-09T14:05:18.8820708Z 3b95f7accc18: Verifying Checksum 2025-09-09T14:05:18.8821007Z 3b95f7accc18: Download complete 2025-09-09T14:05:18.9159143Z 018f40a634ae: Verifying Checksum 2025-09-09T14:05:18.9159439Z 018f40a634ae: Download complete 2025-09-09T14:05:18.9215370Z 4f4fb700ef54: Verifying Checksum 2025-09-09T14:05:18.9215673Z 4f4fb700ef54: Download complete 2025-09-09T14:05:18.9754997Z cabce7a916a3: Download complete 2025-09-09T14:05:18.9873564Z 0b3a66ab554e: Download complete 2025-09-09T14:05:19.0240199Z 72728e4acc07: Verifying Checksum 2025-09-09T14:05:19.0240540Z 72728e4acc07: Download complete 2025-09-09T14:05:19.0354057Z 2ca30f8660e0: Verifying Checksum 2025-09-09T14:05:19.0354480Z 2ca30f8660e0: Download complete 2025-09-09T14:05:19.1643470Z 16125e2dbaa8: Download complete 2025-09-09T14:05:19.2161226Z 8e08c86db3a1: Verifying Checksum 2025-09-09T14:05:19.2162157Z 8e08c86db3a1: Download complete 2025-09-09T14:05:19.9569514Z c3175a707c2d: Verifying Checksum 2025-09-09T14:05:19.9569832Z c3175a707c2d: Download complete 2025-09-09T14:05:20.0145755Z cac5e14b36bd: Download complete 2025-09-09T14:05:20.0592481Z d9fc50fb0d36: Download complete 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2025-09-09T14:08:06.9072729Z Digest: sha256:be7f2a4c6f467933b154ac0b3ded894ad1bf06ce95f8f8d908dba108e68806f3 2025-09-09T14:08:06.9861574Z Status: Downloaded newer image for pytorch/almalinux-builder:cuda12.6 2025-09-09T14:08:07.0172010Z docker.io/pytorch/almalinux-builder:cuda12.6 2025-09-09T14:08:07.0236329Z ##[group]Run echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT" 2025-09-09T14:08:07.0237203Z echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT" 2025-09-09T14:08:07.0251349Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-09T14:08:07.0251703Z env: 2025-09-09T14:08:07.0251955Z DOCKER_IMAGE: pytorch/almalinux-builder:cuda12.6 2025-09-09T14:08:07.0252298Z REPOSITORY: pytorch/ao 2025-09-09T14:08:07.0252545Z PR_NUMBER: 2963 2025-09-09T14:08:07.0254306Z SCRIPT: conda create -n venv python=3.9 -y conda activate venv echo "::group::Install newer objcopy that supports --set-section-alignment" dnf install -y gcc-toolset-10-binutils export PATH=/opt/rh/gcc-toolset-10/root/usr/bin/:$PATH python -m pip install --upgrade pip pip install torch==2.8.0 sed -i '' dev-requirements.txt pip install -r dev-requirements.txt pip install . export CONDA=$(dirname $(dirname $(which conda))) export LD_LIBRARY_PATH=$CONDA/lib/:$LD_LIBRARY_PATH pytest test --verbose -s 2025-09-09T14:08:07.0256197Z RUNNER_ARTIFACT_DIR: /home/ec2-user/actions-runner/_work/_temp/artifacts 2025-09-09T14:08:07.0256727Z RUNNER_TEST_RESULTS_DIR: /home/ec2-user/actions-runner/_work/_temp/test-results 2025-09-09T14:08:07.0257230Z RUNNER_DOCS_DIR: /home/ec2-user/actions-runner/_work/_temp/docs 2025-09-09T14:08:07.0257659Z GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all 2025-09-09T14:08:07.0257982Z ##[endgroup] 2025-09-09T14:08:07.0401563Z Prepare all required actions 2025-09-09T14:08:07.0401887Z Getting action download info 2025-09-09T14:08:07.2406673Z ##[group]Run ./test-infra/.github/actions/setup-nvidia 2025-09-09T14:08:07.2406999Z with: 2025-09-09T14:08:07.2407205Z driver-version: 580.65.06 2025-09-09T14:08:07.2407444Z env: 2025-09-09T14:08:07.2407677Z DOCKER_IMAGE: pytorch/almalinux-builder:cuda12.6 2025-09-09T14:08:07.2420296Z REPOSITORY: pytorch/ao 2025-09-09T14:08:07.2420586Z PR_NUMBER: 2963 2025-09-09T14:08:07.2422749Z SCRIPT: conda create -n venv python=3.9 -y conda activate venv echo "::group::Install newer objcopy that supports --set-section-alignment" dnf install -y gcc-toolset-10-binutils export PATH=/opt/rh/gcc-toolset-10/root/usr/bin/:$PATH python -m pip install --upgrade pip pip install torch==2.8.0 sed -i '' dev-requirements.txt pip install -r dev-requirements.txt pip install . export CONDA=$(dirname $(dirname $(which conda))) export LD_LIBRARY_PATH=$CONDA/lib/:$LD_LIBRARY_PATH pytest test --verbose -s 2025-09-09T14:08:07.2424666Z RUNNER_ARTIFACT_DIR: /home/ec2-user/actions-runner/_work/_temp/artifacts 2025-09-09T14:08:07.2425218Z RUNNER_TEST_RESULTS_DIR: /home/ec2-user/actions-runner/_work/_temp/test-results 2025-09-09T14:08:07.2425726Z RUNNER_DOCS_DIR: /home/ec2-user/actions-runner/_work/_temp/docs 2025-09-09T14:08:07.2426162Z GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all 2025-09-09T14:08:07.2426500Z ##[endgroup] 2025-09-09T14:08:07.2570937Z ##[group]Run nick-fields/retry@3e91a01664abd3c5cd539100d10d33b9c5b68482 2025-09-09T14:08:07.2571316Z with: 2025-09-09T14:08:07.2571519Z timeout_minutes: 10 2025-09-09T14:08:07.2571978Z max_attempts: 3 2025-09-09T14:08:07.2595873Z command: # Is it disgusting to have a full shell script here in this github action? Sure # But is it the best way to make it so that this action relies on nothing else? Absolutely set -eou pipefail DISTRIBUTION=$(. /etc/os-release;echo $ID$VERSION_ID) DRIVER_FN="NVIDIA-Linux-x86_64-${DRIVER_VERSION}.run" install_nvidia_docker2_amzn2() { ( set -x # Needed for yum-config-manager sudo yum install -y yum-utils if [[ "${DISTRIBUTION}" == "amzn2023" ]] ; then YUM_REPO_URL="https://nvidia.github.io/libnvidia-container/stable/rpm/nvidia-container-toolkit.repo" else # Amazon Linux 2 YUM_REPO_URL="https://nvidia.github.io/nvidia-docker/${DISTRIBUTION}/nvidia-docker.repo" fi sudo yum-config-manager --add-repo "${YUM_REPO_URL}" sudo yum install -y \ nvidia-container-toolkit-1.17.8 \ libnvidia-container-tools-1.17.8 \ libnvidia-container1-1.17.8 \ nvidia-container-toolkit-base-1.17.8 sudo systemctl restart docker ) } install_nvidia_docker2_ubuntu20() { ( set -x # Install nvidia-driver package if not installed status="$(dpkg-query -W --showformat='${db:Status-Status}' nvidia-docker2 2>&1)" if [ ! $? = 0 ] || [ ! "$status" = installed ]; then sudo apt-get install -y nvidia-container-toolkit-1.17.8 sudo systemctl restart docker fi ) } pre_install_nvidia_driver_amzn2() { ( # Purge any nvidia driver installed from RHEL repo sudo yum remove -y nvidia-driver-latest-dkms ) } install_nvidia_driver_common() { ( # Try to gather more information about the runner and its existing NVIDIA driver if any echo "Before installing NVIDIA driver" lspci lsmod modinfo nvidia || true HAS_NVIDIA_DRIVER=0 # Check if NVIDIA driver has already been installed if [ -x "$(command -v nvidia-smi)" ]; then set +e # The driver exists, check its version next. Also check only the first GPU if there are more than one of them # so that the same driver version is not print over multiple lines INSTALLED_DRIVER_VERSION=$(nvidia-smi --query-gpu=driver_version --format=csv,noheader --id=0) NVIDIA_SMI_STATUS=$? if [ "$NVIDIA_SMI_STATUS" -ne 0 ] && [ "$NVIDIA_SMI_STATUS" -ne 14 ]; then echo "Failed to get NVIDIA driver version ($INSTALLED_DRIVER_VERSION). Continuing" elif [ "$INSTALLED_DRIVER_VERSION" != "$DRIVER_VERSION" ]; then echo "NVIDIA driver ($INSTALLED_DRIVER_VERSION) has been installed, but we expect to have $DRIVER_VERSION instead. Continuing" # Turn off persistent mode so that the installation script can unload the kernel module sudo killall nvidia-persistenced || true else HAS_NVIDIA_DRIVER=1 echo "NVIDIA driver ($INSTALLED_DRIVER_VERSION) has already been installed. Skipping NVIDIA driver installation" fi set -e fi if [ "$HAS_NVIDIA_DRIVER" -eq 0 ]; then # CAUTION: this may need to be updated in future if [ "${DISTRIBUTION}" != ubuntu20.04 ]; then sudo yum groupinstall -y "Development Tools" # ensure our kernel install is the same as our underlying kernel, # groupinstall "Development Tools" has a habit of mismatching kernel headers sudo yum install -y "kernel-devel-uname-r == $(uname -r)" sudo modprobe backlight fi sudo curl -fsL -o /tmp/nvidia_driver "https://s3.amazonaws.com/ossci-linux/nvidia_driver/$DRIVER_FN" set +e sudo /bin/bash /tmp/nvidia_driver -s --no-drm NVIDIA_INSTALLATION_STATUS=$? RESET_GPU=0 if [ "$NVIDIA_INSTALLATION_STATUS" -ne 0 ]; then sudo cat /var/log/nvidia-installer.log # Fail to install NVIDIA driver, try to reset the GPU RESET_GPU=1 elif [ -x "$(command -v nvidia-smi)" ]; then # Check again if nvidia-smi works even if the driver installation completes successfully INSTALLED_DRIVER_VERSION=$(nvidia-smi --query-gpu=driver_version --format=csv,noheader --id=0) NVIDIA_SMI_STATUS=$? if [ "$NVIDIA_SMI_STATUS" -ne 0 ] && [ "$NVIDIA_SMI_STATUS" -ne 14 ]; then RESET_GPU=1 fi fi if [ "$RESET_GPU" -eq 1 ]; then NVIDIA_DEVICES=$(lspci -D | grep -i NVIDIA | cut -d' ' -f1) # The GPU can get stuck in a failure state if somehow the test crashs the GPU microcode. When this # happens, we'll try to reset all NVIDIA devices https://github.com/pytorch/pytorch/issues/88388 for PCI_ID in $NVIDIA_DEVICES; do DEVICE_ENABLED=$(cat /sys/bus/pci/devices/$PCI_ID/enable) echo "Reseting $PCI_ID (enabled state: $DEVICE_ENABLED)" # This requires sudo permission of course echo "1" | sudo tee /sys/bus/pci/devices/$PCI_ID/reset sleep 1 done fi sudo rm -fv /tmp/nvidia_driver set -e fi ) } post_install_nvidia_driver_common() { ( sudo modprobe nvidia || true echo "After installing NVIDIA driver" lspci lsmod modinfo nvidia || true ( set +e nvidia-smi # NB: Annoyingly, nvidia-smi command returns successfully with return code 0 even in # the case where the driver has already crashed as it still can get the driver version # and some basic information like the bus ID. However, the rest of the information # would be missing (ERR!), for example: # # +-----------------------------------------------------------------------------+ # | NVIDIA-SMI 525.89.02 Driver Version: 525.89.02 CUDA Version: 12.0 | # |-------------------------------+----------------------+----------------------+ # | GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC | # | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. | # | | | MIG M. | # |===============================+======================+======================| # | 0 ERR! Off | 00000000:00:1E.0 Off | ERR! | # |ERR! ERR! ERR! ERR! / ERR! | 4184MiB / 23028MiB | ERR! Default | # | | | ERR! | # +-------------------------------+----------------------+----------------------+ # # +-----------------------------------------------------------------------------+ # | Processes: | # | GPU GI CI PID Type Process name GPU Memory | # | ID ID Usage | # |=============================================================================| # +-----------------------------------------------------------------------------+ # # This should be reported as a failure instead as it will guarantee to fail when # Docker tries to run with --gpus all # # So, the correct check here is to query one of the missing piece of info like # GPU name, so that the command can fail accordingly nvidia-smi --query-gpu=gpu_name --format=csv,noheader --id=0 NVIDIA_SMI_STATUS=$? # Allowable exit statuses for nvidia-smi, see: https://github.com/NVIDIA/gpu-operator/issues/285 if [ "$NVIDIA_SMI_STATUS" -eq 0 ] || [ "$NVIDIA_SMI_STATUS" -eq 14 ]; then echo "INFO: Ignoring allowed status ${NVIDIA_SMI_STATUS}" else echo "ERROR: nvidia-smi exited with unresolved status ${NVIDIA_SMI_STATUS}" exit ${NVIDIA_SMI_STATUS} fi set -e ) ) } install_nvidia_driver_amzn2() { ( set -x pre_install_nvidia_driver_amzn2 install_nvidia_driver_common post_install_nvidia_driver_common ) } install_nvidia_driver_ubuntu20() { ( set -x install_nvidia_driver_common post_install_nvidia_driver_common ) } echo "== Installing nvidia driver ${DRIVER_FN} ==" case "${DISTRIBUTION}" in amzn*) install_nvidia_driver_amzn2 ;; ubuntu20.04) install_nvidia_driver_ubuntu20 ;; *) echo "ERROR: Unknown distribution ${DISTRIBUTION}" exit 1 ;; esac # Install container toolkit based on distribution echo "== Installing nvidia container toolkit for ${DISTRIBUTION} ==" case "${DISTRIBUTION}" in amzn*) install_nvidia_docker2_amzn2 ;; ubuntu20.04) install_nvidia_docker2_ubuntu20 ;; *) echo "ERROR: Unknown distribution ${DISTRIBUTION}" exit 1 ;; esac echo "GPU_FLAG=--gpus all -e NVIDIA_DRIVER_CAPABILITIES=all" >> "${GITHUB_ENV}" # Fix https://github.com/NVIDIA/nvidia-docker/issues/1648 on runners with # more than one GPUs. This just needs to be run once. The command fails # on subsequent runs and complains that the mode is already on, but that's # ok sudo nvidia-persistenced || true # This should show persistence mode ON nvidia-smi # check if the container-toolkit is correctly installed and CUDA is available inside a container docker run --rm -t --gpus=all public.ecr.aws/docker/library/python:3.13 nvidia-smi 2025-09-09T14:08:07.2620044Z retry_wait_seconds: 10 2025-09-09T14:08:07.2620304Z polling_interval_seconds: 1 2025-09-09T14:08:07.2620568Z warning_on_retry: true 2025-09-09T14:08:07.2620816Z continue_on_error: false 2025-09-09T14:08:07.2621058Z env: 2025-09-09T14:08:07.2621300Z DOCKER_IMAGE: pytorch/almalinux-builder:cuda12.6 2025-09-09T14:08:07.2621753Z REPOSITORY: pytorch/ao 2025-09-09T14:08:07.2622008Z PR_NUMBER: 2963 2025-09-09T14:08:07.2623976Z SCRIPT: conda create -n venv python=3.9 -y conda activate venv echo "::group::Install newer objcopy that supports --set-section-alignment" dnf install -y gcc-toolset-10-binutils export PATH=/opt/rh/gcc-toolset-10/root/usr/bin/:$PATH python -m pip install --upgrade pip pip install torch==2.8.0 sed -i '' dev-requirements.txt pip install -r dev-requirements.txt pip install . export CONDA=$(dirname $(dirname $(which conda))) export LD_LIBRARY_PATH=$CONDA/lib/:$LD_LIBRARY_PATH pytest test --verbose -s 2025-09-09T14:08:07.2625905Z RUNNER_ARTIFACT_DIR: /home/ec2-user/actions-runner/_work/_temp/artifacts 2025-09-09T14:08:07.2626469Z RUNNER_TEST_RESULTS_DIR: /home/ec2-user/actions-runner/_work/_temp/test-results 2025-09-09T14:08:07.2626970Z RUNNER_DOCS_DIR: /home/ec2-user/actions-runner/_work/_temp/docs 2025-09-09T14:08:07.2627404Z GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all 2025-09-09T14:08:07.2627747Z DRIVER_VERSION: 580.65.06 2025-09-09T14:08:07.2627996Z ##[endgroup] 2025-09-09T14:08:07.3966679Z == Installing nvidia driver NVIDIA-Linux-x86_64-580.65.06.run == 2025-09-09T14:08:07.3968298Z + pre_install_nvidia_driver_amzn2 2025-09-09T14:08:07.3972101Z + sudo yum remove -y nvidia-driver-latest-dkms 2025-09-09T14:08:07.7727096Z No match for argument: nvidia-driver-latest-dkms 2025-09-09T14:08:07.7728113Z No packages marked for removal. 2025-09-09T14:08:07.7798060Z Dependencies resolved. 2025-09-09T14:08:07.7808448Z Nothing to do. 2025-09-09T14:08:07.7808735Z Complete! 2025-09-09T14:08:07.8835946Z + install_nvidia_driver_common 2025-09-09T14:08:07.8841137Z + echo 'Before installing NVIDIA driver' 2025-09-09T14:08:07.8841420Z + lspci 2025-09-09T14:08:07.8842899Z Before installing NVIDIA driver 2025-09-09T14:08:07.8986673Z 00:00.0 Host bridge: Intel Corporation 440FX - 82441FX PMC [Natoma] 2025-09-09T14:08:07.8987158Z 00:01.0 ISA bridge: Intel Corporation 82371SB PIIX3 ISA [Natoma/Triton II] 2025-09-09T14:08:07.8987717Z 00:01.3 Non-VGA unclassified device: Intel Corporation 82371AB/EB/MB PIIX4 ACPI (rev 08) 2025-09-09T14:08:07.8988235Z 00:03.0 VGA compatible controller: Amazon.com, Inc. Device 1111 2025-09-09T14:08:07.8988696Z 00:04.0 Non-Volatile memory controller: Amazon.com, Inc. NVMe EBS Controller 2025-09-09T14:08:07.8989212Z 00:05.0 Ethernet controller: Amazon.com, Inc. Elastic Network Adapter (ENA) 2025-09-09T14:08:07.8989684Z 00:1b.0 3D controller: NVIDIA Corporation GA102GL [A10G] (rev a1) 2025-09-09T14:08:07.8990105Z 00:1c.0 3D controller: NVIDIA Corporation GA102GL [A10G] (rev a1) 2025-09-09T14:08:07.8990511Z 00:1d.0 3D controller: NVIDIA Corporation GA102GL [A10G] (rev a1) 2025-09-09T14:08:07.8990917Z 00:1e.0 3D controller: NVIDIA Corporation GA102GL [A10G] (rev a1) 2025-09-09T14:08:07.8991407Z 00:1f.0 Non-Volatile memory controller: Amazon.com, Inc. NVMe SSD Controller 2025-09-09T14:08:07.8991826Z + lsmod 2025-09-09T14:08:07.9046354Z Module Size Used by 2025-09-09T14:08:07.9047251Z veth 36864 0 2025-09-09T14:08:07.9047612Z nvidia_modeset 1740800 0 2025-09-09T14:08:07.9047885Z video 65536 1 nvidia_modeset 2025-09-09T14:08:07.9048182Z wmi 36864 1 video 2025-09-09T14:08:07.9048441Z nvidia_uvm 1921024 0 2025-09-09T14:08:07.9048741Z nvidia 14274560 19 nvidia_uvm,nvidia_modeset 2025-09-09T14:08:07.9049058Z drm 602112 1 nvidia 2025-09-09T14:08:07.9049356Z drm_panel_orientation_quirks 32768 1 drm 2025-09-09T14:08:07.9049720Z backlight 24576 3 video,drm,nvidia_modeset 2025-09-09T14:08:07.9050051Z i2c_core 110592 2 nvidia,drm 2025-09-09T14:08:07.9050347Z xt_conntrack 16384 1 2025-09-09T14:08:07.9050591Z nft_chain_nat 16384 3 2025-09-09T14:08:07.9050840Z xt_MASQUERADE 20480 1 2025-09-09T14:08:07.9051117Z nf_nat 57344 2 nft_chain_nat,xt_MASQUERADE 2025-09-09T14:08:07.9051444Z nf_conntrack_netlink 57344 0 2025-09-09T14:08:07.9052097Z nf_conntrack 184320 4 xt_conntrack,nf_nat,nf_conntrack_netlink,xt_MASQUERADE 2025-09-09T14:08:07.9052524Z nf_defrag_ipv6 24576 1 nf_conntrack 2025-09-09T14:08:07.9052826Z nf_defrag_ipv4 16384 1 nf_conntrack 2025-09-09T14:08:07.9053100Z xfrm_user 57344 1 2025-09-09T14:08:07.9053358Z xfrm_algo 16384 1 xfrm_user 2025-09-09T14:08:07.9053627Z xt_addrtype 16384 2 2025-09-09T14:08:07.9053883Z nft_compat 20480 4 2025-09-09T14:08:07.9054188Z nf_tables 311296 57 nft_compat,nft_chain_nat 2025-09-09T14:08:07.9054584Z nfnetlink 20480 4 nft_compat,nf_conntrack_netlink,nf_tables 2025-09-09T14:08:07.9054961Z br_netfilter 36864 0 2025-09-09T14:08:07.9055227Z bridge 323584 1 br_netfilter 2025-09-09T14:08:07.9055515Z stp 16384 1 bridge 2025-09-09T14:08:07.9055788Z llc 16384 2 bridge,stp 2025-09-09T14:08:07.9056078Z overlay 167936 0 2025-09-09T14:08:07.9056311Z tls 139264 0 2025-09-09T14:08:07.9056549Z nls_ascii 16384 1 2025-09-09T14:08:07.9056789Z nls_cp437 20480 1 2025-09-09T14:08:07.9057022Z vfat 24576 1 2025-09-09T14:08:07.9057264Z fat 86016 1 vfat 2025-09-09T14:08:07.9057514Z sunrpc 700416 1 2025-09-09T14:08:07.9057749Z i8042 45056 0 2025-09-09T14:08:07.9057990Z ghash_clmulni_intel 16384 0 2025-09-09T14:08:07.9058245Z serio 28672 3 i8042 2025-09-09T14:08:07.9058496Z ena 180224 0 2025-09-09T14:08:07.9058900Z button 24576 0 2025-09-09T14:08:07.9059153Z sch_fq_codel 20480 33 2025-09-09T14:08:07.9059563Z fuse 184320 1 2025-09-09T14:08:07.9059801Z dm_mod 188416 0 2025-09-09T14:08:07.9060025Z loop 36864 0 2025-09-09T14:08:07.9060261Z configfs 57344 1 2025-09-09T14:08:07.9060504Z dmi_sysfs 20480 0 2025-09-09T14:08:07.9060741Z crc32_pclmul 16384 0 2025-09-09T14:08:07.9060972Z crc32c_intel 24576 0 2025-09-09T14:08:07.9061211Z efivarfs 24576 1 2025-09-09T14:08:07.9061439Z + modinfo nvidia 2025-09-09T14:08:07.9071599Z filename: /lib/modules/6.1.141-155.222.amzn2023.x86_64/kernel/drivers/video/nvidia.ko 2025-09-09T14:08:07.9072077Z import_ns: DMA_BUF 2025-09-09T14:08:07.9072318Z alias: char-major-195-* 2025-09-09T14:08:07.9072584Z version: 580.65.06 2025-09-09T14:08:07.9072828Z supported: external 2025-09-09T14:08:07.9073071Z license: Dual MIT/GPL 2025-09-09T14:08:07.9073368Z firmware: nvidia/580.65.06/gsp_tu10x.bin 2025-09-09T14:08:07.9073700Z firmware: nvidia/580.65.06/gsp_ga10x.bin 2025-09-09T14:08:07.9074022Z srcversion: A69EBF72FC9D60E11E9A05C 2025-09-09T14:08:07.9074362Z alias: of:N*T*Cnvidia,tegra264-displayC* 2025-09-09T14:08:07.9074715Z alias: of:N*T*Cnvidia,tegra264-display 2025-09-09T14:08:07.9075059Z alias: of:N*T*Cnvidia,tegra234-displayC* 2025-09-09T14:08:07.9075406Z alias: of:N*T*Cnvidia,tegra234-display 2025-09-09T14:08:07.9075744Z alias: pci:v000010DEd*sv*sd*bc06sc80i00* 2025-09-09T14:08:07.9076069Z alias: pci:v000010DEd*sv*sd*bc03sc02i00* 2025-09-09T14:08:07.9076398Z alias: pci:v000010DEd*sv*sd*bc03sc00i00* 2025-09-09T14:08:07.9076707Z depends: i2c-core,drm 2025-09-09T14:08:07.9076965Z retpoline: Y 2025-09-09T14:08:07.9077179Z name: nvidia 2025-09-09T14:08:07.9077537Z vermagic: 6.1.141-155.222.amzn2023.x86_64 SMP preempt mod_unload modversions 2025-09-09T14:08:07.9078005Z parm: NvSwitchRegDwords:NvSwitch regkey (charp) 2025-09-09T14:08:07.9078445Z parm: NvSwitchBlacklist:NvSwitchBlacklist=uuid[,uuid...] (charp) 2025-09-09T14:08:07.9078916Z parm: NVreg_ResmanDebugLevel:int 2025-09-09T14:08:07.9079216Z parm: NVreg_RmLogonRC:int 2025-09-09T14:08:07.9079633Z parm: NVreg_ModifyDeviceFiles:int 2025-09-09T14:08:07.9079943Z parm: NVreg_DeviceFileUID:int 2025-09-09T14:08:07.9080244Z parm: NVreg_DeviceFileGID:int 2025-09-09T14:08:07.9080542Z parm: NVreg_DeviceFileMode:int 2025-09-09T14:08:07.9080904Z parm: NVreg_InitializeSystemMemoryAllocations:int 2025-09-09T14:08:07.9081287Z parm: NVreg_UsePageAttributeTable:int 2025-09-09T14:08:07.9081620Z parm: NVreg_EnablePCIeGen3:int 2025-09-09T14:08:07.9081920Z parm: NVreg_EnableMSI:int 2025-09-09T14:08:07.9082228Z parm: NVreg_EnableStreamMemOPs:int 2025-09-09T14:08:07.9082597Z parm: NVreg_RestrictProfilingToAdminUsers:int 2025-09-09T14:08:07.9082985Z parm: NVreg_PreserveVideoMemoryAllocations:int 2025-09-09T14:08:07.9083359Z parm: NVreg_EnableS0ixPowerManagement:int 2025-09-09T14:08:07.9083764Z parm: NVreg_S0ixPowerManagementVideoMemoryThreshold:int 2025-09-09T14:08:07.9084172Z parm: NVreg_DynamicPowerManagement:int 2025-09-09T14:08:07.9084581Z parm: NVreg_DynamicPowerManagementVideoMemoryThreshold:int 2025-09-09T14:08:07.9084991Z parm: NVreg_EnableGpuFirmware:int 2025-09-09T14:08:07.9085327Z parm: NVreg_EnableGpuFirmwareLogs:int 2025-09-09T14:08:07.9085688Z parm: NVreg_OpenRmEnableUnsupportedGpus:int 2025-09-09T14:08:07.9086056Z parm: NVreg_EnableUserNUMAManagement:int 2025-09-09T14:08:07.9086388Z parm: NVreg_MemoryPoolSize:int 2025-09-09T14:08:07.9086708Z parm: NVreg_KMallocHeapMaxSize:int 2025-09-09T14:08:07.9087028Z parm: NVreg_VMallocHeapMaxSize:int 2025-09-09T14:08:07.9087438Z parm: NVreg_IgnoreMMIOCheck:int 2025-09-09T14:08:07.9087743Z parm: NVreg_NvLinkDisable:int 2025-09-09T14:08:07.9088086Z parm: NVreg_EnablePCIERelaxedOrderingMode:int 2025-09-09T14:08:07.9088443Z parm: NVreg_RegisterPCIDriver:int 2025-09-09T14:08:07.9088793Z parm: NVreg_RegisterPlatformDeviceDriver:int 2025-09-09T14:08:07.9089151Z parm: NVreg_EnableResizableBar:int 2025-09-09T14:08:07.9089484Z parm: NVreg_EnableDbgBreakpoint:int 2025-09-09T14:08:07.9089829Z parm: NVreg_EnableNonblockingOpen:int 2025-09-09T14:08:07.9090171Z parm: NVreg_CoherentGPUMemoryMode:charp 2025-09-09T14:08:07.9090512Z parm: NVreg_RegistryDwords:charp 2025-09-09T14:08:07.9090844Z parm: NVreg_RegistryDwordsPerDevice:charp 2025-09-09T14:08:07.9091174Z parm: NVreg_RmMsg:charp 2025-09-09T14:08:07.9091460Z parm: NVreg_GpuBlacklist:charp 2025-09-09T14:08:07.9091783Z parm: NVreg_TemporaryFilePath:charp 2025-09-09T14:08:07.9092107Z parm: NVreg_ExcludedGpus:charp 2025-09-09T14:08:07.9092411Z parm: NVreg_DmaRemapPeerMmio:int 2025-09-09T14:08:07.9092737Z parm: NVreg_RmNvlinkBandwidth:charp 2025-09-09T14:08:07.9093084Z parm: NVreg_RmNvlinkBandwidthLinkCount:int 2025-09-09T14:08:07.9093440Z parm: NVreg_ImexChannelCount:int 2025-09-09T14:08:07.9093757Z parm: NVreg_CreateImexChannel0:int 2025-09-09T14:08:07.9094102Z parm: NVreg_GrdmaPciTopoCheckOverride:int 2025-09-09T14:08:07.9094441Z parm: rm_firmware_active:charp 2025-09-09T14:08:07.9094733Z + HAS_NVIDIA_DRIVER=0 2025-09-09T14:08:07.9094981Z ++ command -v nvidia-smi 2025-09-09T14:08:07.9095237Z + '[' -x /usr/bin/nvidia-smi ']' 2025-09-09T14:08:07.9095496Z + set +e 2025-09-09T14:08:07.9095805Z ++ nvidia-smi --query-gpu=driver_version --format=csv,noheader --id=0 2025-09-09T14:08:07.9692103Z + INSTALLED_DRIVER_VERSION=580.65.06 2025-09-09T14:08:07.9692417Z + NVIDIA_SMI_STATUS=0 2025-09-09T14:08:07.9692637Z + '[' 0 -ne 0 ']' 2025-09-09T14:08:07.9692844Z + '[' 580.65.06 '!=' 580.65.06 ']' 2025-09-09T14:08:07.9693084Z + HAS_NVIDIA_DRIVER=1 2025-09-09T14:08:07.9693496Z + echo 'NVIDIA driver (580.65.06) has already been installed. Skipping NVIDIA driver installation' 2025-09-09T14:08:07.9693941Z + set -e 2025-09-09T14:08:07.9694296Z + '[' 1 -eq 0 ']' 2025-09-09T14:08:07.9694666Z NVIDIA driver (580.65.06) has already been installed. Skipping NVIDIA driver installation 2025-09-09T14:08:07.9695113Z + post_install_nvidia_driver_common 2025-09-09T14:08:07.9702476Z + sudo modprobe nvidia 2025-09-09T14:08:08.1227660Z + echo 'After installing NVIDIA driver' 2025-09-09T14:08:08.1227964Z + lspci 2025-09-09T14:08:08.1228166Z After installing NVIDIA driver 2025-09-09T14:08:08.1360088Z 00:00.0 Host bridge: Intel Corporation 440FX - 82441FX PMC [Natoma] 2025-09-09T14:08:08.1360567Z 00:01.0 ISA bridge: Intel Corporation 82371SB PIIX3 ISA [Natoma/Triton II] 2025-09-09T14:08:08.1361119Z 00:01.3 Non-VGA unclassified device: Intel Corporation 82371AB/EB/MB PIIX4 ACPI (rev 08) 2025-09-09T14:08:08.1361629Z 00:03.0 VGA compatible controller: Amazon.com, Inc. Device 1111 2025-09-09T14:08:08.1362153Z 00:04.0 Non-Volatile memory controller: Amazon.com, Inc. NVMe EBS Controller 2025-09-09T14:08:08.1362679Z 00:05.0 Ethernet controller: Amazon.com, Inc. Elastic Network Adapter (ENA) 2025-09-09T14:08:08.1363147Z 00:1b.0 3D controller: NVIDIA Corporation GA102GL [A10G] (rev a1) 2025-09-09T14:08:08.1363565Z 00:1c.0 3D controller: NVIDIA Corporation GA102GL [A10G] (rev a1) 2025-09-09T14:08:08.1363969Z 00:1d.0 3D controller: NVIDIA Corporation GA102GL [A10G] (rev a1) 2025-09-09T14:08:08.1364365Z 00:1e.0 3D controller: NVIDIA Corporation GA102GL [A10G] (rev a1) 2025-09-09T14:08:08.1364816Z 00:1f.0 Non-Volatile memory controller: Amazon.com, Inc. NVMe SSD Controller 2025-09-09T14:08:08.1365186Z + lsmod 2025-09-09T14:08:08.1407685Z Module Size Used by 2025-09-09T14:08:08.1408193Z veth 36864 0 2025-09-09T14:08:08.1408439Z nvidia_modeset 1740800 0 2025-09-09T14:08:08.1408714Z video 65536 1 nvidia_modeset 2025-09-09T14:08:08.1408995Z wmi 36864 1 video 2025-09-09T14:08:08.1409259Z nvidia_uvm 1921024 0 2025-09-09T14:08:08.1409549Z nvidia 14274560 19 nvidia_uvm,nvidia_modeset 2025-09-09T14:08:08.1409862Z drm 602112 1 nvidia 2025-09-09T14:08:08.1410142Z drm_panel_orientation_quirks 32768 1 drm 2025-09-09T14:08:08.1410494Z backlight 24576 3 video,drm,nvidia_modeset 2025-09-09T14:08:08.1410828Z i2c_core 110592 2 nvidia,drm 2025-09-09T14:08:08.1411096Z xt_conntrack 16384 1 2025-09-09T14:08:08.1411340Z nft_chain_nat 16384 3 2025-09-09T14:08:08.1411580Z xt_MASQUERADE 20480 1 2025-09-09T14:08:08.1411868Z nf_nat 57344 2 nft_chain_nat,xt_MASQUERADE 2025-09-09T14:08:08.1412178Z nf_conntrack_netlink 57344 0 2025-09-09T14:08:08.1412566Z nf_conntrack 184320 4 xt_conntrack,nf_nat,nf_conntrack_netlink,xt_MASQUERADE 2025-09-09T14:08:08.1412982Z nf_defrag_ipv6 24576 1 nf_conntrack 2025-09-09T14:08:08.1413277Z nf_defrag_ipv4 16384 1 nf_conntrack 2025-09-09T14:08:08.1413554Z xfrm_user 57344 1 2025-09-09T14:08:08.1413802Z xfrm_algo 16384 1 xfrm_user 2025-09-09T14:08:08.1414071Z xt_addrtype 16384 2 2025-09-09T14:08:08.1414308Z nft_compat 20480 4 2025-09-09T14:08:08.1414598Z nf_tables 311296 57 nft_compat,nft_chain_nat 2025-09-09T14:08:08.1414985Z nfnetlink 20480 4 nft_compat,nf_conntrack_netlink,nf_tables 2025-09-09T14:08:08.1415342Z br_netfilter 36864 0 2025-09-09T14:08:08.1415599Z bridge 323584 1 br_netfilter 2025-09-09T14:08:08.1415876Z stp 16384 1 bridge 2025-09-09T14:08:08.1416149Z llc 16384 2 bridge,stp 2025-09-09T14:08:08.1416411Z overlay 167936 0 2025-09-09T14:08:08.1416646Z tls 139264 0 2025-09-09T14:08:08.1416872Z nls_ascii 16384 1 2025-09-09T14:08:08.1417105Z nls_cp437 20480 1 2025-09-09T14:08:08.1417325Z vfat 24576 1 2025-09-09T14:08:08.1417562Z fat 86016 1 vfat 2025-09-09T14:08:08.1417961Z sunrpc 700416 1 2025-09-09T14:08:08.1418194Z i8042 45056 0 2025-09-09T14:08:08.1418443Z ghash_clmulni_intel 16384 0 2025-09-09T14:08:08.1418688Z serio 28672 3 i8042 2025-09-09T14:08:08.1418940Z ena 180224 0 2025-09-09T14:08:08.1419169Z button 24576 0 2025-09-09T14:08:08.1419405Z sch_fq_codel 20480 33 2025-09-09T14:08:08.1419643Z fuse 184320 1 2025-09-09T14:08:08.1419870Z dm_mod 188416 0 2025-09-09T14:08:08.1420099Z loop 36864 0 2025-09-09T14:08:08.1420325Z configfs 57344 1 2025-09-09T14:08:08.1420574Z dmi_sysfs 20480 0 2025-09-09T14:08:08.1420799Z crc32_pclmul 16384 0 2025-09-09T14:08:08.1433080Z crc32c_intel 24576 0 2025-09-09T14:08:08.1433375Z efivarfs 24576 1 2025-09-09T14:08:08.1433661Z + modinfo nvidia 2025-09-09T14:08:08.1434041Z filename: /lib/modules/6.1.141-155.222.amzn2023.x86_64/kernel/drivers/video/nvidia.ko 2025-09-09T14:08:08.1434480Z import_ns: DMA_BUF 2025-09-09T14:08:08.1434730Z alias: char-major-195-* 2025-09-09T14:08:08.1434990Z version: 580.65.06 2025-09-09T14:08:08.1435239Z supported: external 2025-09-09T14:08:08.1435480Z license: Dual MIT/GPL 2025-09-09T14:08:08.1435765Z firmware: nvidia/580.65.06/gsp_tu10x.bin 2025-09-09T14:08:08.1436086Z firmware: nvidia/580.65.06/gsp_ga10x.bin 2025-09-09T14:08:08.1436402Z srcversion: A69EBF72FC9D60E11E9A05C 2025-09-09T14:08:08.1436720Z alias: of:N*T*Cnvidia,tegra264-displayC* 2025-09-09T14:08:08.1437061Z alias: of:N*T*Cnvidia,tegra264-display 2025-09-09T14:08:08.1437562Z alias: of:N*T*Cnvidia,tegra234-displayC* 2025-09-09T14:08:08.1437891Z alias: of:N*T*Cnvidia,tegra234-display 2025-09-09T14:08:08.1438223Z alias: pci:v000010DEd*sv*sd*bc06sc80i00* 2025-09-09T14:08:08.1438540Z alias: pci:v000010DEd*sv*sd*bc03sc02i00* 2025-09-09T14:08:08.1438940Z alias: pci:v000010DEd*sv*sd*bc03sc00i00* 2025-09-09T14:08:08.1439239Z depends: i2c-core,drm 2025-09-09T14:08:08.1439494Z retpoline: Y 2025-09-09T14:08:08.1439703Z name: nvidia 2025-09-09T14:08:08.1440055Z vermagic: 6.1.141-155.222.amzn2023.x86_64 SMP preempt mod_unload modversions 2025-09-09T14:08:08.1440529Z parm: NvSwitchRegDwords:NvSwitch regkey (charp) 2025-09-09T14:08:08.1440968Z parm: NvSwitchBlacklist:NvSwitchBlacklist=uuid[,uuid...] (charp) 2025-09-09T14:08:08.1441386Z parm: NVreg_ResmanDebugLevel:int 2025-09-09T14:08:08.1441686Z parm: NVreg_RmLogonRC:int 2025-09-09T14:08:08.1441994Z parm: NVreg_ModifyDeviceFiles:int 2025-09-09T14:08:08.1442296Z parm: NVreg_DeviceFileUID:int 2025-09-09T14:08:08.1442595Z parm: NVreg_DeviceFileGID:int 2025-09-09T14:08:08.1442892Z parm: NVreg_DeviceFileMode:int 2025-09-09T14:08:08.1443250Z parm: NVreg_InitializeSystemMemoryAllocations:int 2025-09-09T14:08:08.1443636Z parm: NVreg_UsePageAttributeTable:int 2025-09-09T14:08:08.1443961Z parm: NVreg_EnablePCIeGen3:int 2025-09-09T14:08:08.1444257Z parm: NVreg_EnableMSI:int 2025-09-09T14:08:08.1444551Z parm: NVreg_EnableStreamMemOPs:int 2025-09-09T14:08:08.1444908Z parm: NVreg_RestrictProfilingToAdminUsers:int 2025-09-09T14:08:08.1445287Z parm: NVreg_PreserveVideoMemoryAllocations:int 2025-09-09T14:08:08.1445652Z parm: NVreg_EnableS0ixPowerManagement:int 2025-09-09T14:08:08.1446049Z parm: NVreg_S0ixPowerManagementVideoMemoryThreshold:int 2025-09-09T14:08:08.1446446Z parm: NVreg_DynamicPowerManagement:int 2025-09-09T14:08:08.1446857Z parm: NVreg_DynamicPowerManagementVideoMemoryThreshold:int 2025-09-09T14:08:08.1447245Z parm: NVreg_EnableGpuFirmware:int 2025-09-09T14:08:08.1447576Z parm: NVreg_EnableGpuFirmwareLogs:int 2025-09-09T14:08:08.1448190Z parm: NVreg_OpenRmEnableUnsupportedGpus:int 2025-09-09T14:08:08.1448551Z parm: NVreg_EnableUserNUMAManagement:int 2025-09-09T14:08:08.1448868Z parm: NVreg_MemoryPoolSize:int 2025-09-09T14:08:08.1449179Z parm: NVreg_KMallocHeapMaxSize:int 2025-09-09T14:08:08.1449720Z parm: NVreg_VMallocHeapMaxSize:int 2025-09-09T14:08:08.1450036Z parm: NVreg_IgnoreMMIOCheck:int 2025-09-09T14:08:08.1450332Z parm: NVreg_NvLinkDisable:int 2025-09-09T14:08:08.1450656Z parm: NVreg_EnablePCIERelaxedOrderingMode:int 2025-09-09T14:08:08.1451002Z parm: NVreg_RegisterPCIDriver:int 2025-09-09T14:08:08.1451335Z parm: NVreg_RegisterPlatformDeviceDriver:int 2025-09-09T14:08:08.1451688Z parm: NVreg_EnableResizableBar:int 2025-09-09T14:08:08.1452002Z parm: NVreg_EnableDbgBreakpoint:int 2025-09-09T14:08:08.1452339Z parm: NVreg_EnableNonblockingOpen:int 2025-09-09T14:08:08.1453004Z parm: NVreg_CoherentGPUMemoryMode:charp 2025-09-09T14:08:08.1453509Z parm: NVreg_RegistryDwords:charp 2025-09-09T14:08:08.1453851Z parm: NVreg_RegistryDwordsPerDevice:charp 2025-09-09T14:08:08.1454205Z parm: NVreg_RmMsg:charp 2025-09-09T14:08:08.1454514Z parm: NVreg_GpuBlacklist:charp 2025-09-09T14:08:08.1454856Z parm: NVreg_TemporaryFilePath:charp 2025-09-09T14:08:08.1455206Z parm: NVreg_ExcludedGpus:charp 2025-09-09T14:08:08.1455539Z parm: NVreg_DmaRemapPeerMmio:int 2025-09-09T14:08:08.1455893Z parm: NVreg_RmNvlinkBandwidth:charp 2025-09-09T14:08:08.1456273Z parm: NVreg_RmNvlinkBandwidthLinkCount:int 2025-09-09T14:08:08.1456720Z parm: NVreg_ImexChannelCount:int 2025-09-09T14:08:08.1457034Z parm: NVreg_CreateImexChannel0:int 2025-09-09T14:08:08.1457364Z parm: NVreg_GrdmaPciTopoCheckOverride:int 2025-09-09T14:08:08.1457699Z parm: rm_firmware_active:charp 2025-09-09T14:08:08.1457964Z + set +e 2025-09-09T14:08:08.1458154Z + nvidia-smi 2025-09-09T14:08:08.1866263Z Tue Sep 9 14:08:08 2025 2025-09-09T14:08:08.1866636Z +-----------------------------------------------------------------------------------------+ 2025-09-09T14:08:08.1867119Z | NVIDIA-SMI 580.65.06 Driver Version: 580.65.06 CUDA Version: 13.0 | 2025-09-09T14:08:08.1867583Z +-----------------------------------------+------------------------+----------------------+ 2025-09-09T14:08:08.1868071Z | GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC | 2025-09-09T14:08:08.1868583Z | Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. | 2025-09-09T14:08:08.1869025Z | | | MIG M. | 2025-09-09T14:08:08.1869362Z |=========================================+========================+======================| 2025-09-09T14:08:08.2484811Z | 0 NVIDIA A10G On | 00000000:00:1B.0 Off | 0 | 2025-09-09T14:08:08.2485289Z | 0% 22C P8 10W / 300W | 0MiB / 23028MiB | 0% Default | 2025-09-09T14:08:08.2485687Z | | | N/A | 2025-09-09T14:08:08.2486119Z +-----------------------------------------+------------------------+----------------------+ 2025-09-09T14:08:08.2486573Z | 1 NVIDIA A10G On | 00000000:00:1C.0 Off | 0 | 2025-09-09T14:08:08.2487003Z | 0% 22C P8 10W / 300W | 0MiB / 23028MiB | 0% Default | 2025-09-09T14:08:08.2487395Z | | | N/A | 2025-09-09T14:08:08.2487798Z +-----------------------------------------+------------------------+----------------------+ 2025-09-09T14:08:08.2488260Z | 2 NVIDIA A10G On | 00000000:00:1D.0 Off | 0 | 2025-09-09T14:08:08.2488869Z | 0% 21C P8 10W / 300W | 0MiB / 23028MiB | 0% Default | 2025-09-09T14:08:08.2489250Z | | | N/A | 2025-09-09T14:08:08.2489661Z +-----------------------------------------+------------------------+----------------------+ 2025-09-09T14:08:08.2490089Z | 3 NVIDIA A10G On | 00000000:00:1E.0 Off | 0 | 2025-09-09T14:08:08.2490509Z | 0% 22C P8 10W / 300W | 0MiB / 23028MiB | 0% Default | 2025-09-09T14:08:08.2490890Z | | | N/A | 2025-09-09T14:08:08.2491284Z +-----------------------------------------+------------------------+----------------------+ 2025-09-09T14:08:08.2513485Z 2025-09-09T14:08:08.2513691Z +-----------------------------------------------------------------------------------------+ 2025-09-09T14:08:08.2514149Z | Processes: | 2025-09-09T14:08:08.2514591Z | GPU GI CI PID Type Process name GPU Memory | 2025-09-09T14:08:08.2515016Z | ID ID Usage | 2025-09-09T14:08:08.2515357Z |=========================================================================================| 2025-09-09T14:08:08.2552656Z | No running processes found | 2025-09-09T14:08:08.2553519Z +-----------------------------------------------------------------------------------------+ 2025-09-09T14:08:09.3289670Z + nvidia-smi --query-gpu=gpu_name --format=csv,noheader --id=0 2025-09-09T14:08:09.3474610Z NVIDIA A10G 2025-09-09T14:08:09.3750185Z + NVIDIA_SMI_STATUS=0 2025-09-09T14:08:09.3750636Z + '[' 0 -eq 0 ']' 2025-09-09T14:08:09.3751039Z + echo 'INFO: Ignoring allowed status 0' 2025-09-09T14:08:09.3751529Z + set -e 2025-09-09T14:08:09.3751908Z INFO: Ignoring allowed status 0 2025-09-09T14:08:09.3762832Z == Installing nvidia container toolkit for amzn2023 == 2025-09-09T14:08:09.3767802Z + sudo yum install -y yum-utils 2025-09-09T14:08:09.8216175Z Last metadata expiration check: 0:03:43 ago on Tue Sep 9 14:04:26 2025. 2025-09-09T14:08:09.8465655Z Package dnf-utils-4.3.0-13.amzn2023.0.5.noarch is already installed. 2025-09-09T14:08:09.8946875Z Dependencies resolved. 2025-09-09T14:08:09.9172266Z Nothing to do. 2025-09-09T14:08:09.9172755Z Complete! 2025-09-09T14:08:10.0513224Z + [[ amzn2023 == \a\m\z\n\2\0\2\3 ]] 2025-09-09T14:08:10.0513759Z + YUM_REPO_URL=https://nvidia.github.io/libnvidia-container/stable/rpm/nvidia-container-toolkit.repo 2025-09-09T14:08:10.0514758Z + sudo yum-config-manager --add-repo https://nvidia.github.io/libnvidia-container/stable/rpm/nvidia-container-toolkit.repo 2025-09-09T14:08:10.3543755Z Adding repo from: https://nvidia.github.io/libnvidia-container/stable/rpm/nvidia-container-toolkit.repo 2025-09-09T14:08:10.4062490Z + sudo yum install -y nvidia-container-toolkit-1.17.8 libnvidia-container-tools-1.17.8 libnvidia-container1-1.17.8 nvidia-container-toolkit-base-1.17.8 2025-09-09T14:08:10.9492467Z nvidia-container-toolkit 18 kB/s | 833 B 00:00 2025-09-09T14:08:10.9740055Z Package nvidia-container-toolkit-1.17.8-1.x86_64 is already installed. 2025-09-09T14:08:10.9745969Z Package libnvidia-container-tools-1.17.8-1.x86_64 is already installed. 2025-09-09T14:08:10.9749939Z Package libnvidia-container1-1.17.8-1.x86_64 is already installed. 2025-09-09T14:08:10.9756175Z Package nvidia-container-toolkit-base-1.17.8-1.x86_64 is already installed. 2025-09-09T14:08:11.0237209Z Dependencies resolved. 2025-09-09T14:08:11.0461943Z Nothing to do. 2025-09-09T14:08:11.0462309Z Complete! 2025-09-09T14:08:11.1779798Z + sudo systemctl restart docker 2025-09-09T14:08:51.4802449Z nvidia-persistenced failed to initialize. Check syslog for more details. 2025-09-09T14:08:51.5265295Z Tue Sep 9 14:08:51 2025 2025-09-09T14:08:51.5268136Z +-----------------------------------------------------------------------------------------+ 2025-09-09T14:08:51.5268655Z | NVIDIA-SMI 580.65.06 Driver Version: 580.65.06 CUDA Version: 13.0 | 2025-09-09T14:08:51.5269130Z +-----------------------------------------+------------------------+----------------------+ 2025-09-09T14:08:51.5269603Z | GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC | 2025-09-09T14:08:51.5270097Z | Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. | 2025-09-09T14:08:51.5270509Z | | | MIG M. | 2025-09-09T14:08:51.5270841Z |=========================================+========================+======================| 2025-09-09T14:08:51.5879263Z | 0 NVIDIA A10G On | 00000000:00:1B.0 Off | 0 | 2025-09-09T14:08:51.5879711Z | 0% 22C P8 10W / 300W | 0MiB / 23028MiB | 0% Default | 2025-09-09T14:08:51.5880076Z | | | N/A | 2025-09-09T14:08:51.5880455Z +-----------------------------------------+------------------------+----------------------+ 2025-09-09T14:08:51.5880872Z | 1 NVIDIA A10G On | 00000000:00:1C.0 Off | 0 | 2025-09-09T14:08:51.5881279Z | 0% 22C P8 10W / 300W | 0MiB / 23028MiB | 0% Default | 2025-09-09T14:08:51.5881637Z | | | N/A | 2025-09-09T14:08:51.5882219Z +-----------------------------------------+------------------------+----------------------+ 2025-09-09T14:08:51.5882636Z | 2 NVIDIA A10G On | 00000000:00:1D.0 Off | 0 | 2025-09-09T14:08:51.5883026Z | 0% 21C P8 10W / 300W | 0MiB / 23028MiB | 0% Default | 2025-09-09T14:08:51.5883384Z | | | N/A | 2025-09-09T14:08:51.5883749Z +-----------------------------------------+------------------------+----------------------+ 2025-09-09T14:08:51.5884167Z | 3 NVIDIA A10G On | 00000000:00:1E.0 Off | 0 | 2025-09-09T14:08:51.5884563Z | 0% 21C P8 10W / 300W | 0MiB / 23028MiB | 0% Default | 2025-09-09T14:08:51.5884911Z | | | N/A | 2025-09-09T14:08:51.5885280Z +-----------------------------------------+------------------------+----------------------+ 2025-09-09T14:08:51.5908407Z 2025-09-09T14:08:51.5908800Z +-----------------------------------------------------------------------------------------+ 2025-09-09T14:08:51.5909633Z | Processes: | 2025-09-09T14:08:51.5910500Z | GPU GI CI PID Type Process name GPU Memory | 2025-09-09T14:08:51.5911291Z | ID ID Usage | 2025-09-09T14:08:51.5911950Z |=========================================================================================| 2025-09-09T14:08:51.5945783Z | No running processes found | 2025-09-09T14:08:51.5946628Z +-----------------------------------------------------------------------------------------+ 2025-09-09T14:08:52.7209758Z Unable to find image 'public.ecr.aws/docker/library/python:3.13' locally 2025-09-09T14:08:52.9633155Z 3.13: Pulling from docker/library/python 2025-09-09T14:08:53.1747711Z 15b1d8a5ff03: Pulling fs layer 2025-09-09T14:08:53.1748094Z 22718812f617: Pulling fs layer 2025-09-09T14:08:53.1748573Z 401a98f7495b: Pulling fs layer 2025-09-09T14:08:53.1748926Z ad446e7df19a: Pulling fs layer 2025-09-09T14:08:53.1749538Z 5d32990caa16: Pulling fs layer 2025-09-09T14:08:53.1749923Z a79d633abf9a: Pulling fs layer 2025-09-09T14:08:53.1750264Z 249a56c8e466: Pulling fs layer 2025-09-09T14:08:53.1750603Z ad446e7df19a: Waiting 2025-09-09T14:08:53.1750893Z 5d32990caa16: Waiting 2025-09-09T14:08:53.1751168Z 249a56c8e466: Waiting 2025-09-09T14:08:53.1751375Z a79d633abf9a: Waiting 2025-09-09T14:08:53.3240944Z 22718812f617: Verifying Checksum 2025-09-09T14:08:53.3241270Z 22718812f617: Download complete 2025-09-09T14:08:53.3783021Z 15b1d8a5ff03: Verifying Checksum 2025-09-09T14:08:53.3783484Z 15b1d8a5ff03: Download complete 2025-09-09T14:08:53.4748933Z 401a98f7495b: Verifying Checksum 2025-09-09T14:08:53.4749286Z 401a98f7495b: Download complete 2025-09-09T14:08:53.4765580Z 5d32990caa16: Verifying Checksum 2025-09-09T14:08:53.4765853Z 5d32990caa16: Download complete 2025-09-09T14:08:53.5148140Z 249a56c8e466: Verifying Checksum 2025-09-09T14:08:53.5148426Z 249a56c8e466: Download complete 2025-09-09T14:08:53.6067009Z a79d633abf9a: Verifying Checksum 2025-09-09T14:08:53.6067328Z a79d633abf9a: Download complete 2025-09-09T14:08:54.1643618Z ad446e7df19a: Verifying Checksum 2025-09-09T14:08:54.1643928Z ad446e7df19a: Download complete 2025-09-09T14:08:55.1365738Z 15b1d8a5ff03: Pull complete 2025-09-09T14:08:55.8584208Z 22718812f617: Pull complete 2025-09-09T14:08:58.3241200Z 401a98f7495b: Pull complete 2025-09-09T14:09:05.0082479Z ad446e7df19a: Pull complete 2025-09-09T14:09:05.2848608Z 5d32990caa16: Pull complete 2025-09-09T14:09:06.0318671Z a79d633abf9a: Pull complete 2025-09-09T14:09:06.0436230Z 249a56c8e466: Pull complete 2025-09-09T14:09:06.0506186Z Digest: sha256:74503e0bff6cf811f029590a05e0218cc9ba3e099a4b7df0ab84a67df081e1bc 2025-09-09T14:09:06.0526304Z Status: Downloaded newer image for public.ecr.aws/docker/library/python:3.13 2025-09-09T14:09:12.8237622Z Tue Sep 9 14:09:12 2025 2025-09-09T14:09:12.8238032Z +-----------------------------------------------------------------------------------------+ 2025-09-09T14:09:12.8238544Z | NVIDIA-SMI 580.65.06 Driver Version: 580.65.06 CUDA Version: 13.0 | 2025-09-09T14:09:12.8239020Z +-----------------------------------------+------------------------+----------------------+ 2025-09-09T14:09:12.8239599Z | GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC | 2025-09-09T14:09:12.8240105Z | Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. | 2025-09-09T14:09:12.8240512Z | | | MIG M. | 2025-09-09T14:09:12.8240831Z |=========================================+========================+======================| 2025-09-09T14:09:12.8848882Z | 0 NVIDIA A10G On | 00000000:00:1B.0 Off | 0 | 2025-09-09T14:09:12.8849313Z | 0% 22C P8 10W / 300W | 0MiB / 23028MiB | 0% Default | 2025-09-09T14:09:12.8849712Z | | | N/A | 2025-09-09T14:09:12.8850112Z +-----------------------------------------+------------------------+----------------------+ 2025-09-09T14:09:12.8850534Z | 1 NVIDIA A10G On | 00000000:00:1C.0 Off | 0 | 2025-09-09T14:09:12.8850942Z | 0% 22C P8 10W / 300W | 0MiB / 23028MiB | 0% Default | 2025-09-09T14:09:12.8851296Z | | | N/A | 2025-09-09T14:09:12.8851683Z +-----------------------------------------+------------------------+----------------------+ 2025-09-09T14:09:12.8852113Z | 2 NVIDIA A10G On | 00000000:00:1D.0 Off | 0 | 2025-09-09T14:09:12.8852512Z | 0% 21C P8 10W / 300W | 0MiB / 23028MiB | 0% Default | 2025-09-09T14:09:12.8852869Z | | | N/A | 2025-09-09T14:09:12.8853589Z +-----------------------------------------+------------------------+----------------------+ 2025-09-09T14:09:12.8854024Z | 3 NVIDIA A10G On | 00000000:00:1E.0 Off | 0 | 2025-09-09T14:09:12.8854435Z | 0% 22C P8 10W / 300W | 0MiB / 23028MiB | 0% Default | 2025-09-09T14:09:12.8854796Z | | | N/A | 2025-09-09T14:09:12.8855182Z +-----------------------------------------+------------------------+----------------------+ 2025-09-09T14:09:12.8876175Z 2025-09-09T14:09:12.8876480Z +-----------------------------------------------------------------------------------------+ 2025-09-09T14:09:12.8876912Z | Processes: | 2025-09-09T14:09:12.8877346Z | GPU GI CI PID Type Process name GPU Memory | 2025-09-09T14:09:12.8877746Z | ID ID Usage | 2025-09-09T14:09:12.8878076Z |=========================================================================================| 2025-09-09T14:09:12.8911586Z | No running processes found | 2025-09-09T14:09:12.8912054Z +-----------------------------------------------------------------------------------------+ 2025-09-09T14:09:15.3711319Z Command completed after 1 attempt(s). 2025-09-09T14:09:15.3820308Z ##[group]Run set -ex 2025-09-09T14:09:15.3820591Z set -ex 2025-09-09T14:09:15.3820800Z { 2025-09-09T14:09:15.3821211Z  echo "#!/usr/bin/env bash"; 2025-09-09T14:09:15.3821513Z  echo "set -eou pipefail"; 2025-09-09T14:09:15.3821811Z  # shellcheck disable=SC2016 2025-09-09T14:09:15.3822369Z  echo 'eval "$(conda shell.bash hook)"'; 2025-09-09T14:09:15.3822682Z  echo "set -x"; 2025-09-09T14:09:15.3822933Z  echo "${SCRIPT}"; 2025-09-09T14:09:15.3823203Z } > "${RUNNER_TEMP}/exec_script" 2025-09-09T14:09:15.3823530Z chmod +x "${RUNNER_TEMP}/exec_script" 2025-09-09T14:09:15.3824111Z python3 "/home/ec2-user/actions-runner/_work/ao/ao/test-infra/.github/scripts/run_with_env_secrets.py" "" 2025-09-09T14:09:15.3840010Z shell: /usr/bin/bash -e {0} 2025-09-09T14:09:15.3840295Z env: 2025-09-09T14:09:15.3840566Z DOCKER_IMAGE: pytorch/almalinux-builder:cuda12.6 2025-09-09T14:09:15.3840927Z REPOSITORY: pytorch/ao 2025-09-09T14:09:15.3841167Z PR_NUMBER: 2963 2025-09-09T14:09:15.3842888Z SCRIPT: conda create -n venv python=3.9 -y conda activate venv echo "::group::Install newer objcopy that supports --set-section-alignment" dnf install -y gcc-toolset-10-binutils export PATH=/opt/rh/gcc-toolset-10/root/usr/bin/:$PATH python -m pip install --upgrade pip pip install torch==2.8.0 sed -i '' dev-requirements.txt pip install -r dev-requirements.txt pip install . export CONDA=$(dirname $(dirname $(which conda))) export LD_LIBRARY_PATH=$CONDA/lib/:$LD_LIBRARY_PATH pytest test --verbose -s 2025-09-09T14:09:15.3844779Z RUNNER_ARTIFACT_DIR: /home/ec2-user/actions-runner/_work/_temp/artifacts 2025-09-09T14:09:15.3845317Z RUNNER_TEST_RESULTS_DIR: /home/ec2-user/actions-runner/_work/_temp/test-results 2025-09-09T14:09:15.3845804Z RUNNER_DOCS_DIR: /home/ec2-user/actions-runner/_work/_temp/docs 2025-09-09T14:09:15.3846222Z GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all 2025-09-09T14:09:15.3846823Z ALL_SECRETS: { "github_token": "***" } 2025-09-09T14:09:15.3847105Z ##[endgroup] 2025-09-09T14:09:15.3903116Z + echo '#!/usr/bin/env bash' 2025-09-09T14:09:15.3903419Z + echo 'set -eou pipefail' 2025-09-09T14:09:15.3903681Z + echo 'eval "$(conda shell.bash hook)"' 2025-09-09T14:09:15.3903956Z + echo 'set -x' 2025-09-09T14:09:15.3904193Z + echo 'conda create -n venv python=3.9 -y 2025-09-09T14:09:15.3904471Z conda activate venv 2025-09-09T14:09:15.3904829Z echo "::group::Install newer objcopy that supports --set-section-alignment" 2025-09-09T14:09:15.3905242Z dnf install -y gcc-toolset-10-binutils 2025-09-09T14:09:15.3905582Z export PATH=/opt/rh/gcc-toolset-10/root/usr/bin/:$PATH 2025-09-09T14:09:15.3905924Z python -m pip install --upgrade pip 2025-09-09T14:09:15.3906198Z pip install torch==2.8.0 2025-09-09T14:09:15.3906452Z sed -i '\'''\'' dev-requirements.txt 2025-09-09T14:09:15.3906735Z pip install -r dev-requirements.txt 2025-09-09T14:09:15.3906997Z pip install . 2025-09-09T14:09:15.3907239Z export CONDA=$(dirname $(dirname $(which conda))) 2025-09-09T14:09:15.3907594Z export LD_LIBRARY_PATH=$CONDA/lib/:$LD_LIBRARY_PATH 2025-09-09T14:09:15.3907908Z pytest test --verbose -s 2025-09-09T14:09:15.3908132Z ' 2025-09-09T14:09:15.3908403Z + chmod +x /home/ec2-user/actions-runner/_work/_temp/exec_script 2025-09-09T14:09:15.3922718Z + python3 /home/ec2-user/actions-runner/_work/ao/ao/test-infra/.github/scripts/run_with_env_secrets.py '' 2025-09-09T14:09:22.4336451Z Running command: 2025-09-09T14:09:22.4341687Z docker run -e PR_NUMBER -e RUNNER_ARTIFACT_DIR=/artifacts -e RUNNER_DOCS_DIR=/docs -e RUNNER_TEST_RESULTS_DIR=/test-results --env-file="/home/ec2-user/actions-runner/_work/_temp/github_env_17585175130" `# It is unknown why the container sees a different value for this.` -e GITHUB_STEP_SUMMARY -e SECRET_GITHUB_TOKEN --cap-add=SYS_PTRACE --detach --ipc=host --security-opt seccomp=unconfined --shm-size=2g --tty --ulimit stack=10485760:83886080 --ulimit core=0 --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all -v "/home/ec2-user/actions-runner/_work/ao/ao/pytorch/ao:/pytorch/ao" -v "/home/ec2-user/actions-runner/_work/ao/ao/test-infra:/test-infra" -v "/home/ec2-user/actions-runner/_work/_temp/artifacts:/artifacts" -v "/home/ec2-user/actions-runner/_work/_temp/docs:/docs" -v "/home/ec2-user/actions-runner/_work/_temp/test-results:/test-results" -v "/home/ec2-user/actions-runner/_work/_temp/exec_script:/exec" -v "/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/step_summary_3c6dddbb-8357-46ad-817e-02d8d788e867":"/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/step_summary_3c6dddbb-8357-46ad-817e-02d8d788e867" -w /pytorch/ao "pytorch/almalinux-builder:cuda12.6" 2025-09-09T14:09:22.4346485Z 2025-09-09T14:09:22.4346789Z 41711a67c7d99be45e4ff98c9daaa6cc7727d22c0e653d70289eb892e8b8e291 2025-09-09T14:09:22.4347413Z Running command: docker exec -t 41711a67c7d99be45e4ff98c9daaa6cc7727d22c0e653d70289eb892e8b8e291 /exec 2025-09-09T14:09:22.4347940Z + conda create -n venv python=3.9 -y 2025-09-09T14:09:22.4348197Z + local cmd=create 2025-09-09T14:09:22.4348400Z + case "$cmd" in 2025-09-09T14:09:22.4348622Z + __conda_exe create -n venv python=3.9 -y 2025-09-09T14:09:22.4348953Z + /opt/conda/bin/conda create -n venv python=3.9 -y 2025-09-09T14:09:22.4349847Z Collecting package metadata (current_repodata.json): - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ done 2025-09-09T14:09:22.4350429Z Solving environment: / done 2025-09-09T14:09:22.4350605Z 2025-09-09T14:09:22.4350609Z 2025-09-09T14:09:22.4350732Z ==> WARNING: A newer version of conda exists. <== 2025-09-09T14:09:22.4351048Z current version: 23.5.2 2025-09-09T14:09:22.4351297Z latest version: 25.7.0 2025-09-09T14:09:22.4351445Z 2025-09-09T14:09:22.4351555Z Please update conda by running 2025-09-09T14:09:22.4351720Z 2025-09-09T14:09:22.4351829Z $ conda update -n base -c defaults conda 2025-09-09T14:09:22.4352030Z 2025-09-09T14:09:22.4352231Z Or to minimize the number of packages updated during conda update use 2025-09-09T14:09:22.4352514Z 2025-09-09T14:09:22.4352604Z conda install conda=25.7.0 2025-09-09T14:09:22.4352775Z 2025-09-09T14:09:22.4352779Z 2025-09-09T14:09:22.4352783Z 2025-09-09T14:09:22.4352863Z ## Package Plan ## 2025-09-09T14:09:22.4352990Z 2025-09-09T14:09:22.4353110Z environment location: /opt/conda/envs/venv 2025-09-09T14:09:22.4353313Z 2025-09-09T14:09:22.4353398Z added / updated specs: 2025-09-09T14:09:22.4353625Z - python=3.9 2025-09-09T14:09:22.4353748Z 2025-09-09T14:09:22.4353751Z 2025-09-09T14:09:22.4353869Z The following packages will be downloaded: 2025-09-09T14:09:22.4354076Z 2025-09-09T14:09:22.4354185Z package | build 2025-09-09T14:09:22.4354491Z ---------------------------|----------------- 2025-09-09T14:09:22.4354824Z bzip2-1.0.8 | h5eee18b_6 262 KB 2025-09-09T14:09:22.4355199Z ld_impl_linux-64-2.40 | h12ee557_0 710 KB 2025-09-09T14:09:22.4355567Z libffi-3.4.4 | h6a678d5_1 141 KB 2025-09-09T14:09:22.4355928Z libxcb-1.17.0 | h9b100fa_0 430 KB 2025-09-09T14:09:22.4356279Z ncurses-6.5 | h7934f7d_0 1.1 MB 2025-09-09T14:09:22.4356626Z pip-25.2 | pyhc872135_0 1.2 MB 2025-09-09T14:09:22.4356994Z pthread-stubs-0.3 | h0ce48e5_1 5 KB 2025-09-09T14:09:22.4357366Z python-3.9.23 | he99959a_0 24.7 MB 2025-09-09T14:09:22.4357860Z readline-8.3 | hc2a1206_0 471 KB 2025-09-09T14:09:22.4358237Z setuptools-78.1.1 | py39h06a4308_0 1.7 MB 2025-09-09T14:09:22.4358609Z sqlite-3.50.2 | hb25bd0a_1 1.1 MB 2025-09-09T14:09:22.4359027Z tk-8.6.15 | h54e0aa7_0 3.4 MB 2025-09-09T14:09:22.4359482Z tzdata-2025b | h04d1e81_0 116 KB 2025-09-09T14:09:22.4359851Z wheel-0.45.1 | py39h06a4308_0 114 KB 2025-09-09T14:09:22.4360223Z xorg-libx11-1.8.12 | h9b100fa_1 895 KB 2025-09-09T14:09:22.4360613Z xorg-libxau-1.0.12 | h9b100fa_0 13 KB 2025-09-09T14:09:22.4361004Z xorg-libxdmcp-1.1.5 | h9b100fa_0 19 KB 2025-09-09T14:09:22.4361418Z xorg-xorgproto-2024.1 | h5eee18b_1 580 KB 2025-09-09T14:09:22.4361787Z xz-5.6.4 | h5eee18b_1 567 KB 2025-09-09T14:09:22.4362144Z zlib-1.2.13 | h5eee18b_1 111 KB 2025-09-09T14:09:22.4362496Z ------------------------------------------------------------ 2025-09-09T14:09:22.4362864Z Total: 37.6 MB 2025-09-09T14:09:22.4363069Z 2025-09-09T14:09:22.4363242Z The following NEW packages will be INSTALLED: 2025-09-09T14:09:22.4363461Z 2025-09-09T14:09:22.4363647Z _libgcc_mutex pkgs/main/linux-64::_libgcc_mutex-0.1-main 2025-09-09T14:09:22.4364074Z _openmp_mutex pkgs/main/linux-64::_openmp_mutex-5.1-1_gnu 2025-09-09T14:09:22.4364490Z bzip2 pkgs/main/linux-64::bzip2-1.0.8-h5eee18b_6 2025-09-09T14:09:22.4364965Z ca-certificates pkgs/main/linux-64::ca-certificates-2025.7.15-h06a4308_0 2025-09-09T14:09:22.4365436Z expat pkgs/main/linux-64::expat-2.7.1-h6a678d5_0 2025-09-09T14:09:22.4365873Z ld_impl_linux-64 pkgs/main/linux-64::ld_impl_linux-64-2.40-h12ee557_0 2025-09-09T14:09:22.4366318Z libffi pkgs/main/linux-64::libffi-3.4.4-h6a678d5_1 2025-09-09T14:09:22.4366736Z libgcc-ng pkgs/main/linux-64::libgcc-ng-11.2.0-h1234567_1 2025-09-09T14:09:22.4367166Z libgomp pkgs/main/linux-64::libgomp-11.2.0-h1234567_1 2025-09-09T14:09:22.4367617Z libstdcxx-ng pkgs/main/linux-64::libstdcxx-ng-11.2.0-h1234567_1 2025-09-09T14:09:22.4368060Z libxcb pkgs/main/linux-64::libxcb-1.17.0-h9b100fa_0 2025-09-09T14:09:22.4368454Z ncurses pkgs/main/linux-64::ncurses-6.5-h7934f7d_0 2025-09-09T14:09:22.4368856Z openssl pkgs/main/linux-64::openssl-3.0.17-h5eee18b_0 2025-09-09T14:09:22.4369251Z pip pkgs/main/noarch::pip-25.2-pyhc872135_0 2025-09-09T14:09:22.4369671Z pthread-stubs pkgs/main/linux-64::pthread-stubs-0.3-h0ce48e5_1 2025-09-09T14:09:22.4370117Z python pkgs/main/linux-64::python-3.9.23-he99959a_0 2025-09-09T14:09:22.4370518Z readline pkgs/main/linux-64::readline-8.3-hc2a1206_0 2025-09-09T14:09:22.4370975Z setuptools pkgs/main/linux-64::setuptools-78.1.1-py39h06a4308_0 2025-09-09T14:09:22.4371410Z sqlite pkgs/main/linux-64::sqlite-3.50.2-hb25bd0a_1 2025-09-09T14:09:22.4371783Z tk pkgs/main/linux-64::tk-8.6.15-h54e0aa7_0 2025-09-09T14:09:22.4372155Z tzdata pkgs/main/noarch::tzdata-2025b-h04d1e81_0 2025-09-09T14:09:22.4372547Z wheel pkgs/main/linux-64::wheel-0.45.1-py39h06a4308_0 2025-09-09T14:09:22.4372979Z xorg-libx11 pkgs/main/linux-64::xorg-libx11-1.8.12-h9b100fa_1 2025-09-09T14:09:22.4373433Z xorg-libxau pkgs/main/linux-64::xorg-libxau-1.0.12-h9b100fa_0 2025-09-09T14:09:22.4373911Z xorg-libxdmcp pkgs/main/linux-64::xorg-libxdmcp-1.1.5-h9b100fa_0 2025-09-09T14:09:22.4374409Z xorg-xorgproto pkgs/main/linux-64::xorg-xorgproto-2024.1-h5eee18b_1 2025-09-09T14:09:22.4374836Z xz pkgs/main/linux-64::xz-5.6.4-h5eee18b_1 2025-09-09T14:09:22.4375202Z zlib pkgs/main/linux-64::zlib-1.2.13-h5eee18b_1 2025-09-09T14:09:22.4375529Z 2025-09-09T14:09:22.4375535Z 2025-09-09T14:09:22.4375539Z 2025-09-09T14:09:22.4375649Z Downloading and Extracting Packages 2025-09-09T14:09:22.4375912Z 2025-09-09T14:09:22.4376062Z setuptools-78.1.1 | 1.7 MB | : 0% 0/1 [00:00=4.10.0 (from torch==2.8.0) 2025-09-09T14:09:36.1302499Z Downloading typing_extensions-4.15.0-py3-none-any.whl.metadata (3.3 kB) 2025-09-09T14:09:36.1302930Z Collecting sympy>=1.13.3 (from torch==2.8.0) 2025-09-09T14:09:36.1303304Z Downloading sympy-1.14.0-py3-none-any.whl.metadata (12 kB) 2025-09-09T14:09:36.1303671Z Collecting networkx (from torch==2.8.0) 2025-09-09T14:09:36.1304049Z Downloading networkx-3.2.1-py3-none-any.whl.metadata (5.2 kB) 2025-09-09T14:09:36.1304422Z Collecting jinja2 (from torch==2.8.0) 2025-09-09T14:09:36.1304795Z Downloading jinja2-3.1.6-py3-none-any.whl.metadata (2.9 kB) 2025-09-09T14:09:36.1305163Z Collecting fsspec (from torch==2.8.0) 2025-09-09T14:09:36.1305549Z Downloading fsspec-2025.9.0-py3-none-any.whl.metadata (10 kB) 2025-09-09T14:09:36.1305993Z Collecting nvidia-cuda-nvrtc-cu12==12.8.93 (from torch==2.8.0) 2025-09-09T14:09:36.1306628Z Downloading nvidia_cuda_nvrtc_cu12-12.8.93-py3-none-manylinux2010_x86_64.manylinux_2_12_x86_64.whl.metadata (1.7 kB) 2025-09-09T14:09:36.1307267Z Collecting nvidia-cuda-runtime-cu12==12.8.90 (from torch==2.8.0) 2025-09-09T14:09:36.1307900Z Downloading nvidia_cuda_runtime_cu12-12.8.90-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (1.7 kB) 2025-09-09T14:09:36.1308534Z Collecting nvidia-cuda-cupti-cu12==12.8.90 (from torch==2.8.0) 2025-09-09T14:09:36.1309149Z Downloading nvidia_cuda_cupti_cu12-12.8.90-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (1.7 kB) 2025-09-09T14:09:36.1309770Z Collecting nvidia-cudnn-cu12==9.10.2.21 (from torch==2.8.0) 2025-09-09T14:09:36.1310288Z Downloading nvidia_cudnn_cu12-9.10.2.21-py3-none-manylinux_2_27_x86_64.whl.metadata (1.8 kB) 2025-09-09T14:09:36.1310805Z Collecting nvidia-cublas-cu12==12.8.4.1 (from torch==2.8.0) 2025-09-09T14:09:36.1311320Z Downloading nvidia_cublas_cu12-12.8.4.1-py3-none-manylinux_2_27_x86_64.whl.metadata (1.7 kB) 2025-09-09T14:09:36.1311831Z Collecting nvidia-cufft-cu12==11.3.3.83 (from torch==2.8.0) 2025-09-09T14:09:36.1312425Z Downloading nvidia_cufft_cu12-11.3.3.83-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (1.7 kB) 2025-09-09T14:09:36.1313024Z Collecting nvidia-curand-cu12==10.3.9.90 (from torch==2.8.0) 2025-09-09T14:09:36.1313539Z Downloading nvidia_curand_cu12-10.3.9.90-py3-none-manylinux_2_27_x86_64.whl.metadata (1.7 kB) 2025-09-09T14:09:36.1314073Z Collecting nvidia-cusolver-cu12==11.7.3.90 (from torch==2.8.0) 2025-09-09T14:09:36.1314604Z Downloading nvidia_cusolver_cu12-11.7.3.90-py3-none-manylinux_2_27_x86_64.whl.metadata (1.8 kB) 2025-09-09T14:09:36.1315144Z Collecting nvidia-cusparse-cu12==12.5.8.93 (from torch==2.8.0) 2025-09-09T14:09:36.1315753Z Downloading nvidia_cusparse_cu12-12.5.8.93-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (1.8 kB) 2025-09-09T14:09:36.1316401Z Collecting nvidia-cusparselt-cu12==0.7.1 (from torch==2.8.0) 2025-09-09T14:09:36.1316966Z Downloading nvidia_cusparselt_cu12-0.7.1-py3-none-manylinux2014_x86_64.whl.metadata (7.0 kB) 2025-09-09T14:09:36.1317479Z Collecting nvidia-nccl-cu12==2.27.3 (from torch==2.8.0) 2025-09-09T14:09:36.1318049Z Downloading nvidia_nccl_cu12-2.27.3-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (2.0 kB) 2025-09-09T14:09:36.1318617Z Collecting nvidia-nvtx-cu12==12.8.90 (from torch==2.8.0) 2025-09-09T14:09:36.1319252Z Downloading nvidia_nvtx_cu12-12.8.90-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (1.8 kB) 2025-09-09T14:09:36.1319853Z Collecting nvidia-nvjitlink-cu12==12.8.93 (from torch==2.8.0) 2025-09-09T14:09:36.1320549Z Downloading nvidia_nvjitlink_cu12-12.8.93-py3-none-manylinux2010_x86_64.manylinux_2_12_x86_64.whl.metadata (1.7 kB) 2025-09-09T14:09:36.1321158Z Collecting nvidia-cufile-cu12==1.13.1.3 (from torch==2.8.0) 2025-09-09T14:09:36.1321820Z Downloading nvidia_cufile_cu12-1.13.1.3-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (1.7 kB) 2025-09-09T14:09:36.1322634Z Collecting triton==3.4.0 (from torch==2.8.0) 2025-09-09T14:09:36.1323128Z Downloading triton-3.4.0-cp39-cp39-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.metadata (1.7 kB) 2025-09-09T14:09:36.1323996Z Requirement already satisfied: setuptools>=40.8.0 in /opt/conda/envs/venv/lib/python3.9/site-packages (from triton==3.4.0->torch==2.8.0) (78.1.1) 2025-09-09T14:09:36.1324735Z Collecting importlib-metadata (from triton==3.4.0->torch==2.8.0) 2025-09-09T14:09:36.1325230Z Downloading importlib_metadata-8.7.0-py3-none-any.whl.metadata (4.8 kB) 2025-09-09T14:09:36.1325764Z Collecting mpmath<1.4,>=1.1.0 (from sympy>=1.13.3->torch==2.8.0) 2025-09-09T14:09:36.1326242Z Downloading mpmath-1.3.0-py3-none-any.whl.metadata (8.6 kB) 2025-09-09T14:09:36.1326749Z Collecting zipp>=3.20 (from importlib-metadata->triton==3.4.0->torch==2.8.0) 2025-09-09T14:09:36.1327233Z Downloading zipp-3.23.0-py3-none-any.whl.metadata (3.6 kB) 2025-09-09T14:09:36.1327641Z Collecting MarkupSafe>=2.0 (from jinja2->torch==2.8.0) 2025-09-09T14:09:36.1328200Z Downloading MarkupSafe-3.0.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.0 kB) 2025-09-09T14:09:36.1328812Z Downloading torch-2.8.0-cp39-cp39-manylinux_2_28_x86_64.whl (888.0 MB) 2025-09-09T14:09:36.1329872Z [?25l ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0.0/888.0 MB ? eta -:--:-- 2025-09-09T14:09:36.1330568Z  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2025-09-09T14:09:57.1541114Z  ━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━━━━━━━━━━ 42.5/88.0 MB 211.9 MB/s eta 0:00:01 2025-09-09T14:09:57.1541829Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━ 76.5/88.0 MB 190.4 MB/s eta 0:00:01 2025-09-09T14:09:57.1542491Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 87.8/88.0 MB 183.1 MB/s eta 0:00:01 2025-09-09T14:09:57.1543155Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 87.8/88.0 MB 183.1 MB/s eta 0:00:01 2025-09-09T14:09:57.1543770Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 88.0/88.0 MB 103.5 MB/s 0:00:00 2025-09-09T14:09:57.1544500Z [?25hDownloading nvidia_cuda_runtime_cu12-12.8.90-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (954 kB) 2025-09-09T14:09:57.1545239Z [?25l ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0.0/954.8 kB ? eta -:--:-- 2025-09-09T14:09:57.1545943Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 954.8/954.8 kB 72.1 MB/s 0:00:00 2025-09-09T14:09:57.1546585Z [?25hDownloading nvidia_cudnn_cu12-9.10.2.21-py3-none-manylinux_2_27_x86_64.whl (706.8 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━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━━━━━━━━━━━ 312.5/706.8 MB 160.6 MB/s eta 0:00:03 2025-09-09T14:09:57.1555889Z  ━━━━━━━━━━━━━━━━━━━╸━━━━━━━━━━━━━━━━━━━ 355.5/706.8 MB 185.4 MB/s eta 0:00:02 2025-09-09T14:09:57.1556639Z  ━━━━━━━━━━━━━━━━━━━━━╸━━━━━━━━━━━━━━━━━ 394.8/706.8 MB 184.6 MB/s eta 0:00:02 2025-09-09T14:09:57.1557355Z  ━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━━━━━━━━━━ 445.1/706.8 MB 194.4 MB/s eta 0:00:02 2025-09-09T14:09:57.1558090Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━ 489.7/706.8 MB 199.9 MB/s eta 0:00:02 2025-09-09T14:09:57.1558796Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━ 530.1/706.8 MB 206.1 MB/s eta 0:00:01 2025-09-09T14:09:57.1559605Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━━━ 576.7/706.8 MB 218.2 MB/s eta 0:00:01 2025-09-09T14:09:57.1560316Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━ 616.8/706.8 MB 216.4 MB/s eta 0:00:01 2025-09-09T14:09:57.1561155Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━ 655.6/706.8 MB 216.2 MB/s eta 0:00:01 2025-09-09T14:09:57.1561876Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━ 687.1/706.8 MB 198.7 MB/s eta 0:00:01 2025-09-09T14:10:04.4501996Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 706.7/706.8 MB 201.2 MB/s eta 0:00:01 2025-09-09T14:10:04.4502873Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 706.7/706.8 MB 201.2 MB/s eta 0:00:01 2025-09-09T14:10:04.4503541Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 706.7/706.8 MB 201.2 MB/s eta 0:00:01 2025-09-09T14:10:04.4504202Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 706.7/706.8 MB 201.2 MB/s eta 0:00:01 2025-09-09T14:10:04.4504854Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 706.7/706.8 MB 201.2 MB/s eta 0:00:01 2025-09-09T14:10:04.4505550Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 706.7/706.8 MB 201.2 MB/s eta 0:00:01 2025-09-09T14:10:04.4506236Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 706.7/706.8 MB 201.2 MB/s eta 0:00:01 2025-09-09T14:10:04.4506885Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 706.7/706.8 MB 201.2 MB/s eta 0:00:01 2025-09-09T14:10:04.4507550Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 706.7/706.8 MB 201.2 MB/s eta 0:00:01 2025-09-09T14:10:04.4508200Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 706.7/706.8 MB 201.2 MB/s eta 0:00:01 2025-09-09T14:10:04.4508867Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 706.7/706.8 MB 201.2 MB/s eta 0:00:01 2025-09-09T14:10:04.4509547Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 706.7/706.8 MB 201.2 MB/s eta 0:00:01 2025-09-09T14:10:04.4510205Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 706.7/706.8 MB 201.2 MB/s eta 0:00:01 2025-09-09T14:10:04.4510881Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 706.7/706.8 MB 201.2 MB/s eta 0:00:01 2025-09-09T14:10:04.4511544Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 706.7/706.8 MB 201.2 MB/s eta 0:00:01 2025-09-09T14:10:04.4512196Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 706.7/706.8 MB 201.2 MB/s eta 0:00:01 2025-09-09T14:10:04.4512855Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 706.7/706.8 MB 201.2 MB/s eta 0:00:01 2025-09-09T14:10:04.4513794Z  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nvidia_cufile_cu12-1.13.1.3-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (1.2 MB) 2025-09-09T14:10:10.7841180Z [?25l ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0.0/1.2 MB ? eta -:--:-- 2025-09-09T14:10:10.7841785Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1.2/1.2 MB 33.2 MB/s 0:00:00 2025-09-09T14:10:10.7842430Z [?25hDownloading nvidia_curand_cu12-10.3.9.90-py3-none-manylinux_2_27_x86_64.whl (63.6 MB) 2025-09-09T14:10:10.7843109Z [?25l ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0.0/63.6 MB ? eta -:--:-- 2025-09-09T14:10:10.7843770Z  ━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━━━━━ 38.8/63.6 MB 195.8 MB/s eta 0:00:01 2025-09-09T14:10:10.7844684Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 63.4/63.6 MB 230.6 MB/s eta 0:00:01 2025-09-09T14:10:10.7845364Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 63.4/63.6 MB 230.6 MB/s eta 0:00:01 2025-09-09T14:10:10.7846193Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 63.4/63.6 MB 230.6 MB/s eta 0:00:01 2025-09-09T14:10:10.7846827Z  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2025-09-09T14:10:10.7859543Z [?25l ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0.0/288.2 MB ? eta -:--:-- 2025-09-09T14:10:10.7860299Z  ━━━━━━━━━━━━╺━━━━━━━━━━━━━━━━━━━━━━━━━━━ 89.7/288.2 MB 449.9 MB/s eta 0:00:01 2025-09-09T14:10:10.7861035Z  ━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━━━━ 178.0/288.2 MB 444.3 MB/s eta 0:00:01 2025-09-09T14:10:10.7861763Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━━ 243.5/288.2 MB 404.5 MB/s eta 0:00:01 2025-09-09T14:10:10.7862456Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 288.1/288.2 MB 374.3 MB/s eta 0:00:01 2025-09-09T14:10:10.7863160Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 288.1/288.2 MB 374.3 MB/s eta 0:00:01 2025-09-09T14:10:10.7863839Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 288.1/288.2 MB 374.3 MB/s eta 0:00:01 2025-09-09T14:10:10.7864521Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 288.1/288.2 MB 374.3 MB/s eta 0:00:01 2025-09-09T14:10:10.7865200Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 288.1/288.2 MB 374.3 MB/s eta 0:00:01 2025-09-09T14:10:17.8672650Z  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━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 287.0/287.2 MB 212.1 MB/s eta 0:00:01 2025-09-09T14:10:17.8687523Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 287.0/287.2 MB 212.1 MB/s eta 0:00:01 2025-09-09T14:10:17.8688181Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 287.0/287.2 MB 212.1 MB/s eta 0:00:01 2025-09-09T14:10:17.8688829Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 287.0/287.2 MB 212.1 MB/s eta 0:00:01 2025-09-09T14:10:17.8689587Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 287.0/287.2 MB 212.1 MB/s eta 0:00:01 2025-09-09T14:10:17.8690253Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 287.0/287.2 MB 212.1 MB/s eta 0:00:01 2025-09-09T14:10:17.8691014Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 287.0/287.2 MB 212.1 MB/s eta 0:00:01 2025-09-09T14:10:17.8691686Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 287.0/287.2 MB 212.1 MB/s eta 0:00:01 2025-09-09T14:10:17.8692349Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 287.0/287.2 MB 212.1 MB/s eta 0:00:01 2025-09-09T14:10:17.8693019Z  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2025-09-09T14:10:41.8291405Z  ━━━━━━━━━━━━━━━━╸━━━━━━━━━━━━━━━━━━━━━━━ 11/26 [nvidia-cuda-nvrtc-cu12] 2025-09-09T14:10:41.8292020Z  ━━━━━━━━━━━━━━━━╸━━━━━━━━━━━━━━━━━━━━━━━ 11/26 [nvidia-cuda-nvrtc-cu12] 2025-09-09T14:10:41.8292633Z  ━━━━━━━━━━━━━━━━╸━━━━━━━━━━━━━━━━━━━━━━━ 11/26 [nvidia-cuda-nvrtc-cu12] 2025-09-09T14:10:41.8293235Z  ━━━━━━━━━━━━━━━━╸━━━━━━━━━━━━━━━━━━━━━━━ 11/26 [nvidia-cuda-nvrtc-cu12] 2025-09-09T14:10:41.8293843Z  ━━━━━━━━━━━━━━━━╸━━━━━━━━━━━━━━━━━━━━━━━ 11/26 [nvidia-cuda-nvrtc-cu12] 2025-09-09T14:10:41.8294471Z  ━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━━━━━━━━━━━ 12/26 [nvidia-cuda-cupti-cu12] 2025-09-09T14:10:41.8295066Z  ━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━━━━━━━━━ 13/26 [nvidia-cublas-cu12] 2025-09-09T14:10:41.8295679Z  ━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━━━━━━━━━ 13/26 [nvidia-cublas-cu12] 2025-09-09T14:10:41.8296266Z  ━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━━━━━━━━━ 13/26 [nvidia-cublas-cu12] 2025-09-09T14:10:41.8296858Z  ━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━━━━━━━━━ 13/26 [nvidia-cublas-cu12] 2025-09-09T14:10:41.8297448Z  ━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━━━━━━━━━ 13/26 [nvidia-cublas-cu12] 2025-09-09T14:10:41.8298030Z  ━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━━━━━━━━━ 13/26 [nvidia-cublas-cu12] 2025-09-09T14:10:49.0374514Z  ━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━━━━━━━━━ 13/26 [nvidia-cublas-cu12] 2025-09-09T14:10:49.0375200Z  ━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━━━━━━━━━ 13/26 [nvidia-cublas-cu12] 2025-09-09T14:10:49.0375845Z  ━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━━━━━━━━━ 13/26 [nvidia-cublas-cu12] 2025-09-09T14:10:49.0376440Z  ━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━━━━━━━━━ 13/26 [nvidia-cublas-cu12] 2025-09-09T14:10:49.0377029Z  ━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━━━━━━━━━ 13/26 [nvidia-cublas-cu12] 2025-09-09T14:10:49.0377617Z  ━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━━━━━━━━━ 13/26 [nvidia-cublas-cu12] 2025-09-09T14:10:49.0378198Z  ━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━━━━━━━━━ 13/26 [nvidia-cublas-cu12] 2025-09-09T14:10:49.0379224Z  ━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━━━━━━━━━ 13/26 [nvidia-cublas-cu12] 2025-09-09T14:10:49.0379828Z  ━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━━━━━━━━━ 13/26 [nvidia-cublas-cu12] 2025-09-09T14:10:49.0380582Z  ━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━━━━━━━━━ 13/26 [nvidia-cublas-cu12] 2025-09-09T14:10:49.0381174Z  ━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━━━━━━━━━ 13/26 [nvidia-cublas-cu12] 2025-09-09T14:10:49.0381758Z  ━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━━━━━━━━━ 13/26 [nvidia-cublas-cu12] 2025-09-09T14:10:49.0382351Z  ━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━━━━━━━━━ 13/26 [nvidia-cublas-cu12] 2025-09-09T14:10:49.0382947Z  ━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━━━━━━━━━ 13/26 [nvidia-cublas-cu12] 2025-09-09T14:10:49.0383540Z  ━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━━━━━━━━━ 13/26 [nvidia-cublas-cu12] 2025-09-09T14:10:49.0384157Z  ━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━━━━━━━━━ 13/26 [nvidia-cublas-cu12] 2025-09-09T14:10:49.0384720Z  ━━━━━━━━━━━━━━━━━━━━━╸━━━━━━━━━━━━━━━━━━ 14/26 [networkx] 2025-09-09T14:10:49.0385290Z  ━━━━━━━━━━━━━━━━━━━━━╸━━━━━━━━━━━━━━━━━━ 14/26 [networkx] 2025-09-09T14:10:49.0385837Z  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MarkupSafe-3.0.2 filelock-3.19.1 fsspec-2025.9.0 importlib-metadata-8.7.0 jinja2-3.1.6 mpmath-1.3.0 networkx-3.2.1 nvidia-cublas-cu12-12.8.4.1 nvidia-cuda-cupti-cu12-12.8.90 nvidia-cuda-nvrtc-cu12-12.8.93 nvidia-cuda-runtime-cu12-12.8.90 nvidia-cudnn-cu12-9.10.2.21 nvidia-cufft-cu12-11.3.3.83 nvidia-cufile-cu12-1.13.1.3 nvidia-curand-cu12-10.3.9.90 nvidia-cusolver-cu12-11.7.3.90 nvidia-cusparse-cu12-12.5.8.93 nvidia-cusparselt-cu12-0.7.1 nvidia-nccl-cu12-2.27.3 nvidia-nvjitlink-cu12-12.8.93 nvidia-nvtx-cu12-12.8.90 sympy-1.14.0 torch-2.8.0 triton-3.4.0 typing-extensions-4.15.0 zipp-3.23.0 2025-09-09T14:11:18.9994517Z WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager, possibly rendering your system unusable. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv. Use the --root-user-action option if you know what you are doing and want to suppress this warning. 2025-09-09T14:11:18.9995965Z + sed -i '' dev-requirements.txt 2025-09-09T14:11:18.9996289Z + pip install -r dev-requirements.txt 2025-09-09T14:11:18.9996787Z Collecting pytest (from -r dev-requirements.txt (line 2)) 2025-09-09T14:11:19.0005460Z Downloading pytest-8.4.2-py3-none-any.whl.metadata (7.7 kB) 2025-09-09T14:11:19.0006013Z Collecting unittest-xml-reporting (from -r dev-requirements.txt (line 3)) 2025-09-09T14:11:19.0006612Z Downloading unittest_xml_reporting-3.2.0-py2.py3-none-any.whl.metadata (11 kB) 2025-09-09T14:11:19.0007174Z Collecting parameterized (from -r dev-requirements.txt (line 4)) 2025-09-09T14:11:19.0007706Z Downloading parameterized-0.9.0-py2.py3-none-any.whl.metadata (18 kB) 2025-09-09T14:11:19.0008228Z Collecting packaging (from -r dev-requirements.txt (line 5)) 2025-09-09T14:11:19.0008700Z Downloading packaging-25.0-py3-none-any.whl.metadata (3.3 kB) 2025-09-09T14:11:29.4473264Z Collecting transformers (from -r dev-requirements.txt (line 6)) 2025-09-09T14:11:29.4473831Z Downloading transformers-4.56.1-py3-none-any.whl.metadata (42 kB) 2025-09-09T14:11:29.4474515Z Collecting hypothesis (from -r dev-requirements.txt (line 7)) 2025-09-09T14:11:29.4475138Z Downloading hypothesis-6.138.15-py3-none-any.whl.metadata (5.6 kB) 2025-09-09T14:11:29.4475642Z Collecting sentencepiece (from -r dev-requirements.txt (line 8)) 2025-09-09T14:11:29.4476295Z Downloading sentencepiece-0.2.1-cp39-cp39-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.metadata (10 kB) 2025-09-09T14:11:29.4476912Z Collecting expecttest (from -r dev-requirements.txt (line 9)) 2025-09-09T14:11:29.4477400Z Downloading expecttest-0.3.0-py3-none-any.whl.metadata (3.8 kB) 2025-09-09T14:11:29.4477891Z Collecting bitsandbytes (from -r dev-requirements.txt (line 12)) 2025-09-09T14:11:29.4478435Z Downloading bitsandbytes-0.47.0-py3-none-manylinux_2_24_x86_64.whl.metadata (11 kB) 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tabledata-1.3.4-py3-none-any.whl (11 kB) 2025-09-09T14:12:00.6597717Z Downloading tcolorpy-0.1.7-py3-none-any.whl (8.1 kB) 2025-09-09T14:12:00.6598095Z Downloading typepy-1.3.4-py3-none-any.whl (31 kB) 2025-09-09T14:12:00.6598475Z Downloading termcolor-3.1.0-py3-none-any.whl (7.7 kB) 2025-09-09T14:12:00.6598897Z Downloading tqdm_multiprocess-0.0.11-py3-none-any.whl (9.8 kB) 2025-09-09T14:12:00.6599556Z Downloading xxhash-3.5.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (193 kB) 2025-09-09T14:12:00.6600211Z Downloading zstandard-0.24.0-cp39-cp39-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (5.6 MB) 2025-09-09T14:12:00.6600930Z [?25l ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0.0/5.6 MB ? eta -:--:-- 2025-09-09T14:12:00.6601554Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 5.6/5.6 MB 109.7 MB/s 0:00:00 2025-09-09T14:12:00.6602164Z [?25hBuilding wheels for collected packages: rouge-score, sqlitedict, word2number 2025-09-09T14:12:00.6604357Z  DEPRECATION: Building 'rouge-score' using the legacy setup.py bdist_wheel mechanism, which will be removed in a future version. pip 25.3 will enforce this behaviour change. A possible replacement is to use the standardized build interface by setting the `--use-pep517` option, (possibly combined with `--no-build-isolation`), or adding a `pyproject.toml` file to the source tree of 'rouge-score'. Discussion can be found at https://github.com/pypa/pip/issues/6334 2025-09-09T14:12:00.6606199Z  Building wheel for rouge-score (setup.py) ... [?25l- done 2025-09-09T14:12:00.6607139Z [?25h Created wheel for rouge-score: filename=rouge_score-0.1.2-py3-none-any.whl size=24988 sha256=d4c644a1e41ccdfdcff478dd364e0be9fc6fe6904a0f3724a1caf0dcfee95cbd 2025-09-09T14:12:00.6608127Z Stored in directory: /root/.cache/pip/wheels/9b/3d/39/09558097d3119ca0a4d462df68f22c6f3c1b345ac63a09b86e 2025-09-09T14:12:00.6610362Z  DEPRECATION: Building 'sqlitedict' using the legacy setup.py bdist_wheel mechanism, which will be removed in a future version. pip 25.3 will enforce this behaviour change. A possible replacement is to use the standardized build interface by setting the `--use-pep517` option, (possibly combined with `--no-build-isolation`), or adding a `pyproject.toml` file to the source tree of 'sqlitedict'. Discussion can be found at https://github.com/pypa/pip/issues/6334 2025-09-09T14:12:00.6612179Z  Building wheel for sqlitedict (setup.py) ... [?25l- done 2025-09-09T14:12:00.6613107Z [?25h Created wheel for sqlitedict: filename=sqlitedict-2.1.0-py3-none-any.whl size=16958 sha256=29a93c4f0dc23367e6a01d4248a0cc2458a8fee90c5e99b43f8a688ba005e6a6 2025-09-09T14:12:00.6614068Z Stored in directory: /root/.cache/pip/wheels/f6/48/c4/942f7a1d556fddd2348cb9ac262f251873dfd8a39afec5678e 2025-09-09T14:12:00.6616329Z  DEPRECATION: Building 'word2number' using the legacy setup.py bdist_wheel mechanism, which will be removed in a future version. pip 25.3 will enforce this behaviour change. A possible replacement is to use the standardized build interface by setting the `--use-pep517` option, (possibly combined with `--no-build-isolation`), or adding a `pyproject.toml` file to the source tree of 'word2number'. Discussion can be found at https://github.com/pypa/pip/issues/6334 2025-09-09T14:12:00.6618168Z  Building wheel for word2number (setup.py) ... [?25l- done 2025-09-09T14:12:00.6619095Z [?25h Created wheel for word2number: filename=word2number-1.1-py3-none-any.whl size=5658 sha256=cdd65c8a5e2085c75f0587497a6dc542de65abf34a614a07b0aa43aa6d5c4e49 2025-09-09T14:12:00.6620059Z Stored in directory: /root/.cache/pip/wheels/a0/4a/5b/d2f2df5c344ddbecb8bea759872c207ea91d93f57fb54e816e 2025-09-09T14:12:00.6620666Z Successfully built rouge-score sqlitedict word2number 2025-09-09T14:12:00.6625725Z Installing collected packages: word2number, sqlitedict, sortedcontainers, pytz, distlib, zstandard, xxhash, urllib3, tzdata, tqdm, tomli, threadpoolctl, termcolor, tcolorpy, tabulate, six, sentencepiece, safetensors, ruff, regex, pyyaml, pyparsing, pygments, pycryptodomex, pybind11, pyarrow, psutil, propcache, portalocker, pluggy, platformdirs, pillow, pathvalidate, parameterized, packaging, numpy, nodeenv, ninja, multidict, more_itertools, lxml, kiwisolver, joblib, iniconfig, importlib-resources, idna, identify, hf-xet, fsspec, frozenlist, fonttools, expecttest, exceptiongroup, diskcache, dill, cycler, colorama, cmake, click, charset_normalizer, chardet, cfgv, certifi, attrs, async-timeout, aiohappyeyeballs, absl-py, yarl, virtualenv, unittest-xml-reporting, tqdm-multiprocess, scipy, sacrebleu, requests, python-dateutil, pytest, pycocotools, numexpr, nltk, multiprocess, mbstrdecoder, jsonlines, hypothesis, fire, contourpy, blobfile, aiosignal, typepy, tiktoken, scikit-learn, rouge-score, pre-commit, pandas, matplotlib, huggingface-hub, aiohttp, tokenizers, bitsandbytes, accelerate, transformers, datasets, DataProperty, tabledata, peft, evaluate, pytablewriter, lm_eval 2025-09-09T14:12:00.6630333Z [?25l 2025-09-09T14:12:00.6630755Z  ━╺━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━  4/107 [distlib] 2025-09-09T14:12:00.6631332Z  ━━╸━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━  7/107 [urllib3] 2025-09-09T14:12:07.4307877Z  ━━╸━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━  8/107 [tzdata] 2025-09-09T14:12:07.4308465Z  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Uninstalling fsspec-2025.9.0: 2025-09-09T14:12:07.4328874Z ━━━━━━━━━━━━━━━━╸━━━━━━━━━━━━━━━━━━━━━━━  45/107 [idna] 2025-09-09T14:12:07.4329476Z  Successfully uninstalled fsspec-2025.9.0 2025-09-09T14:12:07.4329966Z ━━━━━━━━━━━━━━━━╸━━━━━━━━━━━━━━━━━━━━━━━  45/107 [idna] 2025-09-09T14:12:07.4330645Z  ━━━━━━━━━━━━━━━━━╸━━━━━━━━━━━━━━━━━━━━━━  48/107 [fsspec] 2025-09-09T14:12:07.4331209Z  ━━━━━━━━━━━━━━━━━━╸━━━━━━━━━━━━━━━━━━━━━  50/107 [fonttools] 2025-09-09T14:12:07.4331767Z  ━━━━━━━━━━━━━━━━━━╸━━━━━━━━━━━━━━━━━━━━━  50/107 [fonttools] 2025-09-09T14:12:07.4332327Z  ━━━━━━━━━━━━━━━━━━╸━━━━━━━━━━━━━━━━━━━━━  50/107 [fonttools] 2025-09-09T14:12:07.4332876Z  ━━━━━━━━━━━━━━━━━━╸━━━━━━━━━━━━━━━━━━━━━  50/107 [fonttools] 2025-09-09T14:12:07.4333427Z  ━━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━━━━━━━━  57/107 [cmake] 2025-09-09T14:12:07.4333994Z  ━━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━━━━━━━━  57/107 [cmake] 2025-09-09T14:12:07.4334529Z  ━━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━━━━━━━━  57/107 [cmake] 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2025-09-09T14:12:15.0856143Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━━  89/107 [scikit-learn] 2025-09-09T14:12:15.0856704Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━━  89/107 [scikit-learn] 2025-09-09T14:12:15.0857273Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━━  89/107 [scikit-learn] 2025-09-09T14:12:15.0857859Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━━  89/107 [scikit-learn] 2025-09-09T14:12:15.0858424Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━━  89/107 [scikit-learn] 2025-09-09T14:12:15.0859008Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━━  89/107 [scikit-learn] 2025-09-09T14:12:15.0859573Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━━  89/107 [scikit-learn] 2025-09-09T14:12:15.0860131Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━  92/107 [pandas] 2025-09-09T14:12:15.0860673Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━  92/107 [pandas] 2025-09-09T14:12:15.0861235Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━  92/107 [pandas] 2025-09-09T14:12:15.0861770Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━  92/107 [pandas] 2025-09-09T14:12:15.0862418Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━  92/107 [pandas] 2025-09-09T14:12:22.5163689Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━  92/107 [pandas] 2025-09-09T14:12:22.5165319Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━  92/107 [pandas] 2025-09-09T14:12:22.5166390Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━  92/107 [pandas] 2025-09-09T14:12:22.5167457Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━  92/107 [pandas] 2025-09-09T14:12:22.5168507Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━  92/107 [pandas] 2025-09-09T14:12:22.5169566Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━  92/107 [pandas] 2025-09-09T14:12:22.5170620Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━  92/107 [pandas] 2025-09-09T14:12:22.5171707Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━  92/107 [pandas] 2025-09-09T14:12:22.5172311Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━  92/107 [pandas] 2025-09-09T14:12:22.5172865Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━  92/107 [pandas] 2025-09-09T14:12:22.5173404Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━  92/107 [pandas] 2025-09-09T14:12:22.5173932Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━  92/107 [pandas] 2025-09-09T14:12:22.5174470Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━  92/107 [pandas] 2025-09-09T14:12:22.5175012Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━  92/107 [pandas] 2025-09-09T14:12:22.5175544Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━  92/107 [pandas] 2025-09-09T14:12:22.5176107Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━  93/107 [matplotlib] 2025-09-09T14:12:22.5176659Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━  93/107 [matplotlib] 2025-09-09T14:12:22.5177226Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━  93/107 [matplotlib] 2025-09-09T14:12:22.5177772Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━  93/107 [matplotlib] 2025-09-09T14:12:22.5178312Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━  93/107 [matplotlib] 2025-09-09T14:12:22.5178859Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━  93/107 [matplotlib] 2025-09-09T14:12:22.5179415Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━  94/107 [huggingface-hub] 2025-09-09T14:12:22.5180172Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━  94/107 [huggingface-hub] 2025-09-09T14:12:22.5180747Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━  96/107 [tokenizers] 2025-09-09T14:12:22.5181396Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━  97/107 [bitsandbytes] 2025-09-09T14:12:22.5181961Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━  97/107 [bitsandbytes] 2025-09-09T14:12:22.5182516Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━  97/107 [bitsandbytes] 2025-09-09T14:12:22.5183076Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━  97/107 [bitsandbytes] 2025-09-09T14:12:22.5183634Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━  97/107 [bitsandbytes] 2025-09-09T14:12:22.5184179Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━  98/107 [accelerate] 2025-09-09T14:12:22.5184753Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━  99/107 [transformers] 2025-09-09T14:12:22.5185312Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━  99/107 [transformers] 2025-09-09T14:12:22.5185889Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━  99/107 [transformers] 2025-09-09T14:12:22.5186441Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━  99/107 [transformers] 2025-09-09T14:12:22.5186990Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━  99/107 [transformers] 2025-09-09T14:12:22.5187542Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━  99/107 [transformers] 2025-09-09T14:12:22.5188085Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━  99/107 [transformers] 2025-09-09T14:12:22.5188741Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━  99/107 [transformers] 2025-09-09T14:12:22.5189309Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━  99/107 [transformers] 2025-09-09T14:12:22.5189962Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━  99/107 [transformers] 2025-09-09T14:12:22.5190527Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━  99/107 [transformers] 2025-09-09T14:12:22.5191080Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━  99/107 [transformers] 2025-09-09T14:12:22.5191636Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━  99/107 [transformers] 2025-09-09T14:12:22.5192200Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━  99/107 [transformers] 2025-09-09T14:12:28.5221931Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━  99/107 [transformers] 2025-09-09T14:12:28.5223680Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━  99/107 [transformers] 2025-09-09T14:12:28.5224248Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━  99/107 [transformers] 2025-09-09T14:12:28.5224847Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━  99/107 [transformers] 2025-09-09T14:12:28.5225407Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━  99/107 [transformers] 2025-09-09T14:12:28.5225959Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━  99/107 [transformers] 2025-09-09T14:12:28.5226515Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━  99/107 [transformers] 2025-09-09T14:12:28.5227064Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━  99/107 [transformers] 2025-09-09T14:12:28.5227634Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━  99/107 [transformers] 2025-09-09T14:12:28.5228210Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━  99/107 [transformers] 2025-09-09T14:12:28.5228760Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━  99/107 [transformers] 2025-09-09T14:12:28.5229337Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━  99/107 [transformers] 2025-09-09T14:12:28.5229888Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━  99/107 [transformers] 2025-09-09T14:12:28.5230447Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━  99/107 [transformers] 2025-09-09T14:12:28.5231005Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━  99/107 [transformers] 2025-09-09T14:12:28.5231557Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━  99/107 [transformers] 2025-09-09T14:12:28.5232558Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━ 100/107 [datasets] 2025-09-09T14:12:28.5233111Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━ 102/107 [tabledata] 2025-09-09T14:12:28.5233836Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━ 103/107 [peft] 2025-09-09T14:12:28.5234368Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺ 105/107 [pytablewriter] 2025-09-09T14:12:28.5234887Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 106/107 [lm_eval] 2025-09-09T14:12:28.5235391Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 106/107 [lm_eval] 2025-09-09T14:12:28.5235886Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 106/107 [lm_eval] 2025-09-09T14:12:28.5236388Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 106/107 [lm_eval] 2025-09-09T14:12:28.5236915Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 106/107 [lm_eval] 2025-09-09T14:12:28.5237411Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 106/107 [lm_eval] 2025-09-09T14:12:28.5237936Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 106/107 [lm_eval] 2025-09-09T14:12:28.5238429Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 106/107 [lm_eval] 2025-09-09T14:12:28.5238936Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 106/107 [lm_eval] 2025-09-09T14:12:28.5239556Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 106/107 [lm_eval] 2025-09-09T14:12:28.5240064Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 106/107 [lm_eval] 2025-09-09T14:12:28.5240570Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 106/107 [lm_eval] 2025-09-09T14:12:28.5241070Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 106/107 [lm_eval] 2025-09-09T14:12:28.5241682Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 106/107 [lm_eval] 2025-09-09T14:12:28.5242184Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 106/107 [lm_eval] 2025-09-09T14:12:28.5242786Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 106/107 [lm_eval] 2025-09-09T14:12:28.5243285Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 106/107 [lm_eval] 2025-09-09T14:12:28.5243767Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 107/107 [lm_eval] 2025-09-09T14:12:28.5244113Z [?25h 2025-09-09T14:19:55.6265549Z Successfully installed DataProperty-1.1.0 absl-py-2.3.1 accelerate-1.10.1 aiohappyeyeballs-2.6.1 aiohttp-3.12.15 aiosignal-1.4.0 async-timeout-5.0.1 attrs-25.3.0 bitsandbytes-0.47.0 blobfile-3.1.0 certifi-2025.8.3 cfgv-3.4.0 chardet-5.2.0 charset_normalizer-3.4.3 click-8.1.8 cmake-3.31.6 colorama-0.4.6 contourpy-1.3.0 cycler-0.12.1 datasets-3.6.0 dill-0.3.8 diskcache-5.6.3 distlib-0.4.0 evaluate-0.4.5 exceptiongroup-1.3.0 expecttest-0.3.0 fire-0.7.1 fonttools-4.59.2 frozenlist-1.7.0 fsspec-2025.3.0 hf-xet-1.1.9 huggingface-hub-0.34.4 hypothesis-6.138.15 identify-2.6.14 idna-3.10 importlib-resources-6.5.2 iniconfig-2.1.0 joblib-1.5.2 jsonlines-4.0.0 kiwisolver-1.4.7 lm_eval-0.4.9.1 lxml-6.0.1 matplotlib-3.9.4 mbstrdecoder-1.1.4 more_itertools-10.8.0 multidict-6.6.4 multiprocess-0.70.16 ninja-1.13.0 nltk-3.9.1 nodeenv-1.9.1 numexpr-2.10.2 numpy-2.0.2 packaging-25.0 pandas-2.3.2 parameterized-0.9.0 pathvalidate-3.3.1 peft-0.17.1 pillow-11.3.0 platformdirs-4.4.0 pluggy-1.6.0 portalocker-3.2.0 pre-commit-4.3.0 propcache-0.3.2 psutil-7.0.0 pyarrow-21.0.0 pybind11-3.0.1 pycocotools-2.0.10 pycryptodomex-3.23.0 pygments-2.19.2 pyparsing-3.2.3 pytablewriter-1.2.1 pytest-8.4.2 python-dateutil-2.9.0.post0 pytz-2025.2 pyyaml-6.0.2 regex-2025.9.1 requests-2.32.5 rouge-score-0.1.2 ruff-0.11.6 sacrebleu-2.5.1 safetensors-0.6.2 scikit-learn-1.6.1 scipy-1.13.1 sentencepiece-0.2.1 six-1.17.0 sortedcontainers-2.4.0 sqlitedict-2.1.0 tabledata-1.3.4 tabulate-0.9.0 tcolorpy-0.1.7 termcolor-3.1.0 threadpoolctl-3.6.0 tiktoken-0.11.0 tokenizers-0.22.0 tomli-2.2.1 tqdm-4.67.1 tqdm-multiprocess-0.0.11 transformers-4.56.1 typepy-1.3.4 tzdata-2025.2 unittest-xml-reporting-3.2.0 urllib3-2.5.0 virtualenv-20.34.0 word2number-1.1 xxhash-3.5.0 yarl-1.20.1 zstandard-0.24.0 2025-09-09T14:19:55.6273629Z WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager, possibly rendering your system unusable. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv. Use the --root-user-action option if you know what you are doing and want to suppress this warning. 2025-09-09T14:19:55.6275259Z + pip install . 2025-09-09T14:19:55.6275490Z Processing /pytorch/ao 2025-09-09T14:19:55.6275916Z Preparing metadata (setup.py) ... [?25l- done 2025-09-09T14:19:55.6276364Z [?25hBuilding wheels for collected packages: torchao 2025-09-09T14:19:55.6278620Z  DEPRECATION: Building 'torchao' using the legacy setup.py bdist_wheel mechanism, which will be removed in a future version. pip 25.3 will enforce this behaviour change. A possible replacement is to use the standardized build interface by setting the `--use-pep517` option, (possibly combined with `--no-build-isolation`), or adding a `pyproject.toml` file to the source tree of 'torchao'. Discussion can be found at https://github.com/pypa/pip/issues/6334 2025-09-09T14:19:55.6280864Z  Building wheel for torchao (setup.py) ... [?25l- \ | / - \ | / - \ | / - \ | / - \ | / - \ | done 2025-09-09T14:19:55.6282150Z [?25h Created wheel for torchao: filename=torchao-0.14.0+git7c05f81-cp39-abi3-linux_x86_64.whl size=7965965 sha256=cacaf0074557d3a5657014fcf51513e6a34e5bb6d883004702bff8c926839202 2025-09-09T14:19:55.6283897Z Stored in directory: /tmp/pip-ephem-wheel-cache-k09mpnv6/wheels/4d/54/dc/0c70e60a8677bf126f1486798ebe76c8770ada66c7376b401d 2025-09-09T14:19:55.6284606Z Successfully built torchao 2025-09-09T14:19:55.6284915Z Installing collected packages: torchao 2025-09-09T14:19:55.6285532Z Successfully installed torchao-0.14.0+git7c05f81 2025-09-09T14:19:55.6287503Z WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager, possibly rendering your system unusable. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv. Use the --root-user-action option if you know what you are doing and want to suppress this warning. 2025-09-09T14:19:55.6289100Z ++++ which conda 2025-09-09T14:19:55.6289351Z +++ dirname /opt/conda/condabin/conda 2025-09-09T14:19:55.6289626Z ++ dirname /opt/conda/condabin 2025-09-09T14:19:55.6289934Z + export CONDA=/opt/conda 2025-09-09T14:19:55.6290200Z + CONDA=/opt/conda 2025-09-09T14:19:55.6290724Z + export LD_LIBRARY_PATH=/opt/conda/lib/:/opt/rh/gcc-toolset-11/root/usr/lib64:/opt/rh/gcc-toolset-11/root/usr/lib: 2025-09-09T14:19:55.6291571Z + LD_LIBRARY_PATH=/opt/conda/lib/:/opt/rh/gcc-toolset-11/root/usr/lib64:/opt/rh/gcc-toolset-11/root/usr/lib: 2025-09-09T14:19:55.6292156Z + pytest test --verbose -s 2025-09-09T14:19:55.6292632Z ============================= test session starts ============================== 2025-09-09T14:19:55.6293169Z platform linux -- Python 3.9.23, pytest-8.4.2, pluggy-1.6.0 -- /opt/conda/envs/venv/bin/python3.9 2025-09-09T14:19:55.6293715Z cachedir: .pytest_cache 2025-09-09T14:19:55.6294335Z hypothesis profile 'ci' -> database=None, deadline=None, print_blob=True, derandomize=True, suppress_health_check=(HealthCheck.too_slow,) 2025-09-09T14:19:55.6294953Z rootdir: /pytorch/ao 2025-09-09T14:19:55.6295256Z plugins: hypothesis-6.138.15 2025-09-09T14:19:55.6295576Z collecting ...  2025-09-09T14:19:55.6296007Z collecting 0 items  2025-09-09T14:19:55.6296642Z collecting 26 items  2025-09-09T14:19:55.6297149Z collecting 26 items  2025-09-09T14:19:55.6297689Z collecting 273 items  2025-09-09T14:19:55.6298305Z collecting 682 items / 3 skipped  2025-09-09T14:19:55.6298886Z collecting 1031 items / 3 skipped  2025-09-09T14:19:55.6299462Z collecting 1072 items / 5 skipped  2025-09-09T14:19:55.6300819Z collecting 3100 items / 12 skipped NOTE: Using slow Hadamard transform for SpinQuant. For better performance on GPU, install `fast_hadamard_transform`: `pip install git+https://github.com/Dao-AILab/fast-hadamard-transform.git` 2025-09-09T14:19:55.6301793Z  2025-09-09T14:19:55.6302187Z collecting 4088 items / 12 skipped  2025-09-09T14:19:55.6302770Z collected 7126 items / 12 skipped  2025-09-09T14:19:55.6303078Z 2025-09-09T14:19:55.6303470Z test/core/test_config.py::test_reconstructable_dict_file_round_trip[config0] PASSED 2025-09-09T14:19:55.6304295Z test/core/test_config.py::test_reconstructable_dict_file_round_trip[config1] PASSED 2025-09-09T14:19:55.6305018Z test/core/test_config.py::test_reconstructable_dict_file_round_trip[config2] PASSED 2025-09-09T14:19:55.6305736Z test/core/test_config.py::test_reconstructable_dict_file_round_trip[config3] PASSED 2025-09-09T14:19:55.6306451Z test/core/test_config.py::test_reconstructable_dict_file_round_trip[config4] PASSED 2025-09-09T14:19:55.6307160Z test/core/test_config.py::test_reconstructable_dict_file_round_trip[config5] 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test/core/test_config.py::test_reconstructable_dict_file_round_trip[config14] PASSED 2025-09-09T14:19:55.6314789Z test/core/test_config.py::test_reconstructable_dict_file_round_trip[config15] PASSED 2025-09-09T14:19:55.6315522Z test/core/test_config.py::test_reconstructable_dict_file_round_trip[config16] PASSED 2025-09-09T14:19:55.6316244Z test/core/test_config.py::test_reconstructable_dict_file_round_trip[config17] PASSED 2025-09-09T14:19:55.6316975Z test/core/test_config.py::test_reconstructable_dict_file_round_trip[config18] PASSED 2025-09-09T14:19:55.6317702Z test/core/test_config.py::test_reconstructable_dict_file_round_trip[config19] PASSED 2025-09-09T14:19:55.6318423Z test/core/test_config.py::test_reconstructable_dict_file_round_trip[config20] PASSED 2025-09-09T14:19:55.6319146Z test/core/test_config.py::test_reconstructable_dict_file_round_trip[config21] PASSED 2025-09-09T14:19:55.6319961Z test/core/test_config.py::test_reconstructable_dict_file_round_trip[config22] PASSED 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test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_fp8_linear_variants_float32_mode_weight-only_compile_True_granularity0_sizes1 SKIPPED 2025-09-09T14:20:17.3854295Z test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_fp8_linear_variants_float32_mode_weight-only_compile_True_granularity1_sizes0 SKIPPED 2025-09-09T14:20:17.3855741Z test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_fp8_linear_variants_float32_mode_weight-only_compile_True_granularity1_sizes1 SKIPPED 2025-09-09T14:20:17.3856988Z test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_fp8_weight_dimension_warning SKIPPED 2025-09-09T14:20:17.3858016Z test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_invalid_granularity SKIPPED 2025-09-09T14:20:17.3859041Z test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_mismatched_granularity SKIPPED 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test/dtypes/test_affine_quantized_tensor_parallel.py::TestInt8woAffineQuantizedTensorParallel::test_tp_bfloat16 I0909 14:19:56.346582 938 site-packages/torch/testing/_internal/common_distributed.py:729] Started process 0 with pid 1259 2025-09-09T14:20:17.3910578Z I0909 14:19:56.374963 938 site-packages/torch/testing/_internal/common_distributed.py:729] Started process 1 with pid 1260 2025-09-09T14:20:17.3911404Z I0909 14:19:56.403948 938 site-packages/torch/testing/_internal/common_distributed.py:729] Started process 2 with pid 1261 2025-09-09T14:20:17.3912229Z I0909 14:19:56.433863 938 site-packages/torch/testing/_internal/common_distributed.py:729] Started process 3 with pid 1262 2025-09-09T14:20:17.3912802Z PASSED 2025-09-09T14:20:17.3913740Z test/dtypes/test_affine_quantized_tensor_parallel.py::TestInt8woAffineQuantizedTensorParallel::test_tp_float16 I0909 14:20:17.299333 938 site-packages/torch/testing/_internal/common_distributed.py:729] Started process 0 with pid 3617 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test/dtypes/test_bitpacking.py::test_compile[-1-7] PASSED 2025-09-09T14:22:20.5685683Z test/dtypes/test_bitpacking.py::test_compile[1-1] PASSED 2025-09-09T14:22:20.5686350Z test/dtypes/test_bitpacking.py::test_compile[1-2] PASSED 2025-09-09T14:22:20.5686980Z test/dtypes/test_bitpacking.py::test_compile[1-3] PASSED 2025-09-09T14:22:20.5687621Z test/dtypes/test_bitpacking.py::test_compile[1-4] PASSED 2025-09-09T14:22:20.5688252Z test/dtypes/test_bitpacking.py::test_compile[1-5] PASSED 2025-09-09T14:22:20.5688886Z test/dtypes/test_bitpacking.py::test_compile[1-6] PASSED 2025-09-09T14:22:20.5689515Z test/dtypes/test_bitpacking.py::test_compile[1-7] PASSED 2025-09-09T14:22:20.5690447Z test/dtypes/test_bitpacking.py::test_pack_example tensor([ 0, 105, 151, 37], device='cuda:0', dtype=torch.uint8) tensor([ 39, 146], device='cuda:0', dtype=torch.uint8) 2025-09-09T14:22:20.5700056Z PASSED 2025-09-09T14:22:20.5700758Z test/dtypes/test_bitpacking.py::test_pack_example_CPU tensor([ 0, 105, 151, 37], dtype=torch.uint8) tensor([ 39, 146], dtype=torch.uint8) 2025-09-09T14:22:20.5701547Z PASSED 2025-09-09T14:22:20.5702395Z test/dtypes/test_floatx.py::TestFloatxTensorCoreAQTTensorImpl::test_fpx_weight_only_ebits_2_mbits_2_bias_False_bfloat16 PASSED 2025-09-09T14:22:20.5703766Z test/dtypes/test_floatx.py::TestFloatxTensorCoreAQTTensorImpl::test_fpx_weight_only_ebits_2_mbits_2_bias_False_float16 PASSED 2025-09-09T14:22:20.5705122Z test/dtypes/test_floatx.py::TestFloatxTensorCoreAQTTensorImpl::test_fpx_weight_only_ebits_2_mbits_2_bias_True_bfloat16 PASSED 2025-09-09T14:22:20.5706456Z test/dtypes/test_floatx.py::TestFloatxTensorCoreAQTTensorImpl::test_fpx_weight_only_ebits_2_mbits_2_bias_True_float16 PASSED 2025-09-09T14:22:20.5707800Z test/dtypes/test_floatx.py::TestFloatxTensorCoreAQTTensorImpl::test_fpx_weight_only_ebits_3_mbits_2_bias_False_bfloat16 PASSED 2025-09-09T14:22:20.5709147Z test/dtypes/test_floatx.py::TestFloatxTensorCoreAQTTensorImpl::test_fpx_weight_only_ebits_3_mbits_2_bias_False_float16 PASSED 2025-09-09T14:22:20.5710621Z test/dtypes/test_floatx.py::TestFloatxTensorCoreAQTTensorImpl::test_fpx_weight_only_ebits_3_mbits_2_bias_True_bfloat16 PASSED 2025-09-09T14:22:20.5712116Z test/dtypes/test_floatx.py::TestFloatxTensorCoreAQTTensorImpl::test_fpx_weight_only_ebits_3_mbits_2_bias_True_float16 PASSED 2025-09-09T14:22:20.5713495Z test/dtypes/test_floatx.py::TestFloatxTensorCoreAQTTensorImpl::test_from_scaled_tc_floatx_compile_ebits_2_mbits_2_device_cpu PASSED 2025-09-09T14:22:20.5714917Z test/dtypes/test_floatx.py::TestFloatxTensorCoreAQTTensorImpl::test_from_scaled_tc_floatx_compile_ebits_2_mbits_2_device_cuda PASSED 2025-09-09T14:22:20.5716323Z test/dtypes/test_floatx.py::TestFloatxTensorCoreAQTTensorImpl::test_from_scaled_tc_floatx_compile_ebits_3_mbits_2_device_cpu PASSED 2025-09-09T14:22:20.5717743Z test/dtypes/test_floatx.py::TestFloatxTensorCoreAQTTensorImpl::test_from_scaled_tc_floatx_compile_ebits_3_mbits_2_device_cuda PASSED 2025-09-09T14:22:20.5719243Z test/dtypes/test_floatx.py::TestFloatxTensorCoreAQTTensorImpl::test_from_tc_floatx_correctness_ebits_2_mbits_2_device_cpu PASSED 2025-09-09T14:22:20.5720646Z test/dtypes/test_floatx.py::TestFloatxTensorCoreAQTTensorImpl::test_from_tc_floatx_correctness_ebits_2_mbits_2_device_cuda PASSED 2025-09-09T14:22:20.5722038Z test/dtypes/test_floatx.py::TestFloatxTensorCoreAQTTensorImpl::test_from_tc_floatx_correctness_ebits_3_mbits_2_device_cpu PASSED 2025-09-09T14:22:20.5723643Z test/dtypes/test_floatx.py::TestFloatxTensorCoreAQTTensorImpl::test_from_tc_floatx_correctness_ebits_3_mbits_2_device_cuda PASSED 2025-09-09T14:24:05.2281940Z test/dtypes/test_floatx.py::TestFloatxTensorCoreAQTTensorImpl::test_pack_tc_fp6_correctness_device_cpu PASSED 2025-09-09T14:24:05.2282947Z test/dtypes/test_floatx.py::TestFloatxTensorCoreAQTTensorImpl::test_pack_tc_fp6_correctness_device_cuda PASSED 2025-09-09T14:24:05.2283934Z test/dtypes/test_floatx.py::TestFloatxTensorCoreAQTTensorImpl::test_to_copy_device_ebits_2_mbits_2 PASSED 2025-09-09T14:24:05.2284885Z test/dtypes/test_floatx.py::TestFloatxTensorCoreAQTTensorImpl::test_to_copy_device_ebits_3_mbits_2 PASSED 2025-09-09T14:24:05.2285934Z test/dtypes/test_floatx.py::TestFloatxTensorCoreAQTTensorImpl::test_to_scaled_tc_floatx_compile_ebits_2_mbits_2_device_cpu PASSED 2025-09-09T14:24:05.2287067Z test/dtypes/test_floatx.py::TestFloatxTensorCoreAQTTensorImpl::test_to_scaled_tc_floatx_compile_ebits_2_mbits_2_device_cuda PASSED 2025-09-09T14:24:05.2288175Z test/dtypes/test_floatx.py::TestFloatxTensorCoreAQTTensorImpl::test_to_scaled_tc_floatx_compile_ebits_3_mbits_2_device_cpu PASSED 2025-09-09T14:24:05.2289290Z test/dtypes/test_floatx.py::TestFloatxTensorCoreAQTTensorImpl::test_to_scaled_tc_floatx_compile_ebits_3_mbits_2_device_cuda PASSED 2025-09-09T14:24:05.2290198Z test/dtypes/test_nf4.py::TestNF4Linear::test_backward_dtype_match_bfloat16 PASSED 2025-09-09T14:24:05.2290893Z test/dtypes/test_nf4.py::TestNF4Linear::test_backward_dtype_match_float16 PASSED 2025-09-09T14:24:05.2291584Z test/dtypes/test_nf4.py::TestNF4Linear::test_backward_dtype_match_float32 PASSED 2025-09-09T14:24:05.2292369Z test/dtypes/test_nf4.py::TestNF4Linear::test_chunk_size_equivalence_bfloat16_shape0_chunk_size_16 PASSED 2025-09-09T14:24:05.2293244Z test/dtypes/test_nf4.py::TestNF4Linear::test_chunk_size_equivalence_bfloat16_shape0_chunk_size_32 PASSED 2025-09-09T14:24:05.2294123Z test/dtypes/test_nf4.py::TestNF4Linear::test_chunk_size_equivalence_bfloat16_shape0_chunk_size_8 PASSED 2025-09-09T14:24:05.2294978Z test/dtypes/test_nf4.py::TestNF4Linear::test_chunk_size_equivalence_bfloat16_shape1_chunk_size_16 PASSED 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test/dtypes/test_nf4.py::TestNF4Linear::test_chunk_size_equivalence_float16_shape1_chunk_size_8 PASSED 2025-09-09T14:24:05.2303079Z test/dtypes/test_nf4.py::TestNF4Linear::test_chunk_size_equivalence_float32_shape0_chunk_size_16 PASSED 2025-09-09T14:24:05.2303919Z test/dtypes/test_nf4.py::TestNF4Linear::test_chunk_size_equivalence_float32_shape0_chunk_size_32 PASSED 2025-09-09T14:24:05.2304770Z test/dtypes/test_nf4.py::TestNF4Linear::test_chunk_size_equivalence_float32_shape0_chunk_size_8 PASSED 2025-09-09T14:24:05.2305667Z test/dtypes/test_nf4.py::TestNF4Linear::test_chunk_size_equivalence_float32_shape1_chunk_size_16 PASSED 2025-09-09T14:24:05.2306506Z test/dtypes/test_nf4.py::TestNF4Linear::test_chunk_size_equivalence_float32_shape1_chunk_size_32 PASSED 2025-09-09T14:24:05.2307357Z test/dtypes/test_nf4.py::TestNF4Linear::test_chunk_size_equivalence_float32_shape1_chunk_size_8 PASSED 2025-09-09T14:24:05.2308089Z test/dtypes/test_nf4.py::TestNF4Linear::test_empty_like_input_size0 PASSED 2025-09-09T14:24:05.2308730Z test/dtypes/test_nf4.py::TestNF4Linear::test_empty_like_input_size1 PASSED 2025-09-09T14:24:05.2309421Z test/dtypes/test_nf4.py::TestNF4Linear::test_load_from_nf4_diff_meta_bfloat16 PASSED 2025-09-09T14:24:05.2310125Z test/dtypes/test_nf4.py::TestNF4Linear::test_load_from_nf4_diff_meta_float16 PASSED 2025-09-09T14:24:05.2310830Z test/dtypes/test_nf4.py::TestNF4Linear::test_load_from_nf4_diff_meta_float32 PASSED 2025-09-09T14:24:05.2311527Z test/dtypes/test_nf4.py::TestNF4Linear::test_load_from_nf4_same_meta_bfloat16 PASSED 2025-09-09T14:24:05.2312228Z test/dtypes/test_nf4.py::TestNF4Linear::test_load_from_nf4_same_meta_float16 PASSED 2025-09-09T14:24:05.2312921Z test/dtypes/test_nf4.py::TestNF4Linear::test_load_from_nf4_same_meta_float32 PASSED 2025-09-09T14:24:05.2313610Z test/dtypes/test_nf4.py::TestNF4Linear::test_load_from_state_dicts_bfloat16 PASSED 2025-09-09T14:24:05.2314299Z test/dtypes/test_nf4.py::TestNF4Linear::test_load_from_state_dicts_float16 PASSED 2025-09-09T14:24:05.2314979Z test/dtypes/test_nf4.py::TestNF4Linear::test_load_from_state_dicts_float32 PASSED 2025-09-09T14:24:05.2315688Z test/dtypes/test_nf4.py::TestNF4Linear::test_nf4_bnb_linear_bfloat16 SKIPPED 2025-09-09T14:24:05.2316350Z test/dtypes/test_nf4.py::TestNF4Linear::test_nf4_bnb_linear_float16 SKIPPED 2025-09-09T14:24:05.2316999Z test/dtypes/test_nf4.py::TestNF4Linear::test_nf4_bnb_linear_float32 SKIPPED 2025-09-09T14:24:05.2317656Z test/dtypes/test_nf4.py::TestNF4Linear::test_output_dtype_match_bfloat16 PASSED 2025-09-09T14:24:05.2318319Z test/dtypes/test_nf4.py::TestNF4Linear::test_output_dtype_match_float16 PASSED 2025-09-09T14:24:05.2318987Z test/dtypes/test_nf4.py::TestNF4Linear::test_output_dtype_match_float32 PASSED 2025-09-09T14:24:05.2319762Z test/dtypes/test_nf4.py::TestNF4Linear::test_quantize_api_compile_False PASSED 2025-09-09T14:24:05.2320428Z test/dtypes/test_nf4.py::TestNF4Linear::test_quantize_api_compile_True PASSED 2025-09-09T14:24:05.2321245Z test/dtypes/test_nf4.py::TestNF4Linear::test_reconstruction_qlora_vs_bnb_bfloat16 SKIPPED 2025-09-09T14:24:05.2322008Z test/dtypes/test_nf4.py::TestNF4Linear::test_reconstruction_qlora_vs_bnb_float16 SKIPPED 2025-09-09T14:24:05.2323022Z test/dtypes/test_nf4.py::TestNF4Linear::test_reconstruction_qlora_vs_bnb_float32 SKIPPED 2025-09-09T14:24:05.2323739Z test/dtypes/test_nf4.py::TestNF4Linear::test_register_nf4_as_param_bfloat16 PASSED 2025-09-09T14:24:05.2324432Z test/dtypes/test_nf4.py::TestNF4Linear::test_register_nf4_as_param_float16 PASSED 2025-09-09T14:24:05.2325120Z test/dtypes/test_nf4.py::TestNF4Linear::test_register_nf4_as_param_float32 PASSED 2025-09-09T14:24:05.2325783Z test/dtypes/test_nf4.py::TestNF4Linear::test_smoketest_linear_bfloat16 PASSED 2025-09-09T14:24:05.2326441Z test/dtypes/test_nf4.py::TestNF4Linear::test_smoketest_linear_compile_bfloat16 AUTOTUNE mm(64x32, 32x32) 2025-09-09T14:24:05.2326940Z strides: [32, 1], [1, 32] 2025-09-09T14:24:05.2327192Z dtypes: torch.bfloat16, torch.bfloat16 2025-09-09T14:24:05.2327855Z triton_mm_7 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=32, BLOCK_M=64, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=8 2025-09-09T14:24:05.2328830Z triton_mm_3 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=32, BLOCK_M=64, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=8 2025-09-09T14:24:05.2329781Z triton_mm_4 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=32, BLOCK_M=64, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:24:05.2330722Z triton_mm_6 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=32, BLOCK_M=64, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:24:05.2331676Z triton_mm_8 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=32, BLOCK_M=64, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:24:05.2332630Z triton_mm_10 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=32, BLOCK_M=64, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=4 2025-09-09T14:24:05.2333581Z triton_mm_11 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=32, BLOCK_M=64, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=8 2025-09-09T14:24:05.2334532Z triton_mm_9 0.0267 ms 95.9% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=32, BLOCK_M=64, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=8 2025-09-09T14:24:05.2335527Z triton_mm_0 0.0276 ms 92.6% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=16, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=1, num_warps=2 2025-09-09T14:24:05.2336471Z triton_mm_1 0.0276 ms 92.6% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:24:05.2337314Z SingleProcess AUTOTUNE benchmarking takes 0.1654 seconds and 0.4352 seconds precompiling for 13 choices 2025-09-09T14:24:05.2337847Z PASSED 2025-09-09T14:24:05.2338276Z test/dtypes/test_nf4.py::TestNF4Linear::test_smoketest_linear_compile_float16 AUTOTUNE mm(64x32, 32x32) 2025-09-09T14:24:05.2338769Z strides: [32, 1], [1, 32] 2025-09-09T14:24:05.2339014Z dtypes: torch.float16, torch.float16 2025-09-09T14:24:05.2339647Z triton_mm_17 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=16, BLOCK_M=64, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:24:19.9626294Z triton_mm_18 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=32, BLOCK_M=64, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:24:19.9627795Z triton_mm_13 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:24:19.9628779Z triton_mm_19 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=32, BLOCK_M=64, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=8 2025-09-09T14:24:19.9629904Z triton_mm_20 0.0267 ms 96.0% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=32, BLOCK_M=64, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:24:19.9630853Z triton_mm_12 0.0276 ms 92.6% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=16, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=1, num_warps=2 2025-09-09T14:24:19.9631796Z triton_mm_14 0.0276 ms 92.6% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:24:19.9632751Z triton_mm_15 0.0276 ms 92.6% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=32, BLOCK_M=64, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=8 2025-09-09T14:24:19.9633700Z triton_mm_16 0.0276 ms 92.6% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=32, BLOCK_M=64, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:24:19.9634646Z triton_mm_21 0.0276 ms 92.6% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=32, BLOCK_M=64, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=8 2025-09-09T14:24:19.9635492Z SingleProcess AUTOTUNE benchmarking takes 0.2965 seconds and 0.3359 seconds precompiling for 13 choices 2025-09-09T14:24:19.9636231Z PASSED 2025-09-09T14:24:19.9636690Z test/dtypes/test_nf4.py::TestNF4Linear::test_smoketest_linear_compile_float32 AUTOTUNE mm(64x32, 32x32) 2025-09-09T14:24:19.9637183Z strides: [32, 1], [1, 32] 2025-09-09T14:24:19.9637428Z dtypes: torch.float32, torch.float32 2025-09-09T14:24:19.9638063Z triton_mm_25 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:24:19.9639027Z triton_mm_26 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:24:19.9640092Z triton_mm_29 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=16, BLOCK_M=64, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:24:19.9641052Z triton_mm_34 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=32, BLOCK_M=64, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=4 2025-09-09T14:24:19.9642003Z triton_mm_35 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=32, BLOCK_M=64, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=8 2025-09-09T14:24:19.9642964Z triton_mm_24 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=16, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=1, num_warps=2 2025-09-09T14:24:19.9643917Z triton_mm_28 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=32, BLOCK_M=64, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:24:19.9644863Z triton_mm_30 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=32, BLOCK_M=64, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:24:19.9645807Z triton_mm_31 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=32, BLOCK_M=64, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=8 2025-09-09T14:24:19.9646760Z triton_mm_32 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=32, BLOCK_M=64, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:24:19.9647688Z SingleProcess AUTOTUNE benchmarking takes 0.1642 seconds and 0.4055 seconds precompiling for 13 choices 2025-09-09T14:24:19.9648218Z PASSED 2025-09-09T14:24:19.9648668Z test/dtypes/test_nf4.py::TestNF4Linear::test_smoketest_linear_float16 PASSED 2025-09-09T14:24:19.9649446Z test/dtypes/test_nf4.py::TestNF4Linear::test_smoketest_linear_float32 PASSED 2025-09-09T14:24:19.9650069Z test/dtypes/test_nf4.py::TestNF4Linear::test_to_copy_bfloat16 PASSED 2025-09-09T14:24:19.9650658Z test/dtypes/test_nf4.py::TestNF4Linear::test_to_copy_device PASSED 2025-09-09T14:24:19.9651241Z test/dtypes/test_nf4.py::TestNF4Linear::test_to_copy_float16 PASSED 2025-09-09T14:24:19.9651815Z test/dtypes/test_nf4.py::TestNF4Linear::test_to_copy_float32 PASSED 2025-09-09T14:24:19.9652424Z test/dtypes/test_nf4.py::TestNF4Linear::test_to_dtype_bfloat16 PASSED 2025-09-09T14:24:19.9653012Z test/dtypes/test_nf4.py::TestNF4Linear::test_to_dtype_float16 PASSED 2025-09-09T14:24:19.9653611Z test/dtypes/test_nf4.py::TestNF4Linear::test_to_dtype_float32 PASSED 2025-09-09T14:24:19.9654187Z test/dtypes/test_nf4.py::TestFSDPOps::test_pin_memory PASSED 2025-09-09T14:24:19.9654814Z test/dtypes/test_nf4.py::TestFSDPOps::test_tensor_2d_view_valid_input_size0 PASSED 2025-09-09T14:24:19.9655532Z test/dtypes/test_nf4.py::TestFSDPOps::test_tensor_as_strided_invalid_input_size0 PASSED 2025-09-09T14:24:19.9656258Z test/dtypes/test_nf4.py::TestFSDPOps::test_tensor_as_strided_invalid_input_size1 PASSED 2025-09-09T14:24:19.9656977Z test/dtypes/test_nf4.py::TestFSDPOps::test_tensor_as_strided_valid_input_size1 PASSED 2025-09-09T14:24:19.9657693Z test/dtypes/test_nf4.py::TestFSDPOps::test_tensor_as_strided_valid_input_size2 PASSED 2025-09-09T14:24:19.9658431Z test/dtypes/test_nf4.py::TestFSDPOps::test_tensor_as_strided_valid_input_size_262144 PASSED 2025-09-09T14:24:19.9659139Z test/dtypes/test_nf4.py::TestFSDPOps::test_tensor_deepcopy_input_size1 PASSED 2025-09-09T14:24:19.9659844Z test/dtypes/test_nf4.py::TestFSDPOps::test_tensor_deepcopy_input_size2 PASSED 2025-09-09T14:24:19.9660524Z test/dtypes/test_nf4.py::TestFSDPOps::test_tensor_deepcopy_input_size_262144 PASSED 2025-09-09T14:24:19.9661243Z test/dtypes/test_nf4.py::TestFSDPOps::test_tensor_new_zeros_invalid_input_size1 PASSED 2025-09-09T14:24:19.9661958Z test/dtypes/test_nf4.py::TestFSDPOps::test_tensor_new_zeros_invalid_input_size2 PASSED 2025-09-09T14:24:19.9662705Z test/dtypes/test_nf4.py::TestFSDPOps::test_tensor_new_zeros_invalid_input_size_262144 PASSED 2025-09-09T14:24:19.9663433Z test/dtypes/test_nf4.py::TestFSDPOps::test_tensor_new_zeros_valid_input_size1 PASSED 2025-09-09T14:24:19.9664140Z test/dtypes/test_nf4.py::TestFSDPOps::test_tensor_new_zeros_valid_input_size2 PASSED 2025-09-09T14:24:19.9664868Z test/dtypes/test_nf4.py::TestFSDPOps::test_tensor_new_zeros_valid_input_size_262144 PASSED 2025-09-09T14:24:19.9665562Z test/dtypes/test_nf4.py::TestFSDPOps::test_tensor_slice_1d_invalid PASSED 2025-09-09T14:24:19.9666192Z test/dtypes/test_nf4.py::TestFSDPOps::test_tensor_slice_2d_invalid PASSED 2025-09-09T14:24:19.9666840Z test/dtypes/test_nf4.py::TestFSDPOps::test_tensor_slice_valid_input_size1 PASSED 2025-09-09T14:24:19.9667510Z test/dtypes/test_nf4.py::TestFSDPOps::test_tensor_slice_valid_input_size2 PASSED 2025-09-09T14:24:19.9668205Z test/dtypes/test_nf4.py::TestFSDPOps::test_tensor_slice_valid_input_size_262144 PASSED 2025-09-09T14:24:19.9668899Z test/dtypes/test_nf4.py::TestFSDPOps::test_tensor_view_invalid_input_size0 PASSED 2025-09-09T14:24:19.9669572Z test/dtypes/test_nf4.py::TestFSDPOps::test_tensor_view_valid_input_size0 PASSED 2025-09-09T14:24:19.9670229Z test/dtypes/test_nf4.py::TestFSDPOps::test_tensor_view_valid_input_size1 PASSED 2025-09-09T14:24:19.9670814Z test/dtypes/test_nf4.py::TestFSDPOps::test_to_cpu PASSED 2025-09-09T14:24:19.9671415Z test/dtypes/test_nf4.py::TestFSDPOps::test_to_cuda PASSED 2025-09-09T14:24:19.9671945Z test/dtypes/test_nf4.py::TestFSDPOps::test_to_module PASSED 2025-09-09T14:24:19.9672639Z test/dtypes/test_nf4.py::TestFSDPOps::test_torch_chunk_invalid_3d_input_size0 PASSED 2025-09-09T14:24:19.9673366Z test/dtypes/test_nf4.py::TestFSDPOps::test_torch_chunk_invalid_divide_input_size1 PASSED 2025-09-09T14:24:19.9674102Z test/dtypes/test_nf4.py::TestFSDPOps::test_torch_chunk_invalid_divide_input_size2 PASSED 2025-09-09T14:24:19.9674863Z test/dtypes/test_nf4.py::TestFSDPOps::test_torch_chunk_invalid_divide_input_size_261632 PASSED 2025-09-09T14:24:19.9675584Z test/dtypes/test_nf4.py::TestFSDPOps::test_torch_chunk_valid_input_size1 PASSED 2025-09-09T14:24:19.9676237Z test/dtypes/test_nf4.py::TestFSDPOps::test_torch_chunk_valid_input_size2 PASSED 2025-09-09T14:24:19.9676928Z test/dtypes/test_nf4.py::TestFSDPOps::test_torch_chunk_valid_input_size_262144 PASSED 2025-09-09T14:24:19.9677842Z test/dtypes/test_nf4.py::TestQLoRA::test_qlora_fsdp2 I0909 14:24:14.785942 938 site-packages/torch/testing/_internal/common_distributed.py:729] Started process 0 with pid 13901 2025-09-09T14:24:19.9678844Z I0909 14:24:14.820480 938 site-packages/torch/testing/_internal/common_distributed.py:729] Started process 1 with pid 13902 2025-09-09T14:24:19.9679458Z dist init r=0, world=2 2025-09-09T14:24:19.9679674Z dist init r=1, world=2 2025-09-09T14:24:19.9680531Z /opt/conda/envs/venv/lib/python3.9/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 2025-09-09T14:27:11.6798385Z warnings.warn( # warn only once 2025-09-09T14:27:11.6801466Z /opt/conda/envs/venv/lib/python3.9/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 2025-09-09T14:27:11.6802408Z warnings.warn( # warn only once 2025-09-09T14:27:11.6802899Z PASSED 2025-09-09T14:27:11.6803568Z test/dtypes/test_nf4.py::TestComm::test_comm I0909 14:24:24.670383 938 site-packages/torch/testing/_internal/common_distributed.py:729] Started process 0 with pid 14082 2025-09-09T14:27:11.6804609Z I0909 14:24:24.705611 938 site-packages/torch/testing/_internal/common_distributed.py:729] Started process 1 with pid 14083 2025-09-09T14:27:11.6805164Z dist init r=0, world=2 2025-09-09T14:27:11.6805377Z dist init r=1, world=2 2025-09-09T14:27:11.6806282Z /opt/conda/envs/venv/lib/python3.9/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 2025-09-09T14:27:11.6807222Z warnings.warn( # warn only once 2025-09-09T14:27:11.6808127Z /opt/conda/envs/venv/lib/python3.9/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 2025-09-09T14:27:11.6809028Z warnings.warn( # warn only once 2025-09-09T14:27:11.6809325Z PASSED 2025-09-09T14:27:11.6809743Z test/dtypes/test_uint4.py::TestUInt4::test_basic_tensor_ops SKIPPED 2025-09-09T14:27:11.6810364Z test/dtypes/test_uint4.py::TestUInt4::test_gpu_quant SKIPPED (FAILED...) 2025-09-09T14:27:11.6810993Z test/dtypes/test_uint4.py::TestUInt4::test_pt2e_quant SKIPPED (FAILE...) 2025-09-09T14:27:11.6811667Z test/dtypes/test_uintx.py::test_uintx_quant_on_cpu_then_move_to_cuda[32-dtype0] PASSED 2025-09-09T14:27:11.6812389Z test/dtypes/test_uintx.py::test_uintx_quant_on_cpu_then_move_to_cuda[32-dtype1] PASSED 2025-09-09T14:27:11.6813108Z test/dtypes/test_uintx.py::test_uintx_quant_on_cpu_then_move_to_cuda[32-dtype2] PASSED 2025-09-09T14:27:11.6814334Z test/dtypes/test_uintx.py::test_uintx_quant_on_cpu_then_move_to_cuda[32-dtype3] PASSED 2025-09-09T14:27:11.6815057Z test/dtypes/test_uintx.py::test_uintx_quant_on_cpu_then_move_to_cuda[32-dtype4] PASSED 2025-09-09T14:27:11.6815967Z test/dtypes/test_uintx.py::test_uintx_quant_on_cpu_then_move_to_cuda[32-dtype5] PASSED 2025-09-09T14:27:11.6816676Z test/dtypes/test_uintx.py::test_uintx_quant_on_cpu_then_move_to_cuda[32-dtype6] PASSED 2025-09-09T14:27:11.6817386Z test/dtypes/test_uintx.py::test_uintx_quant_on_cpu_then_move_to_cuda[64-dtype0] PASSED 2025-09-09T14:27:11.6818091Z test/dtypes/test_uintx.py::test_uintx_quant_on_cpu_then_move_to_cuda[64-dtype1] PASSED 2025-09-09T14:27:11.6818811Z test/dtypes/test_uintx.py::test_uintx_quant_on_cpu_then_move_to_cuda[64-dtype2] PASSED 2025-09-09T14:27:11.6819522Z test/dtypes/test_uintx.py::test_uintx_quant_on_cpu_then_move_to_cuda[64-dtype3] PASSED 2025-09-09T14:27:11.6820226Z test/dtypes/test_uintx.py::test_uintx_quant_on_cpu_then_move_to_cuda[64-dtype4] PASSED 2025-09-09T14:27:11.6820945Z test/dtypes/test_uintx.py::test_uintx_quant_on_cpu_then_move_to_cuda[64-dtype5] PASSED 2025-09-09T14:27:11.6821652Z test/dtypes/test_uintx.py::test_uintx_quant_on_cpu_then_move_to_cuda[64-dtype6] PASSED 2025-09-09T14:27:11.6822568Z test/dtypes/test_uintx.py::test_uintx_quant_on_cpu_then_move_to_cuda[128-dtype0] PASSED 2025-09-09T14:27:11.6823308Z test/dtypes/test_uintx.py::test_uintx_quant_on_cpu_then_move_to_cuda[128-dtype1] PASSED 2025-09-09T14:27:11.6824024Z test/dtypes/test_uintx.py::test_uintx_quant_on_cpu_then_move_to_cuda[128-dtype2] PASSED 2025-09-09T14:27:11.6824741Z test/dtypes/test_uintx.py::test_uintx_quant_on_cpu_then_move_to_cuda[128-dtype3] PASSED 2025-09-09T14:27:11.6825448Z test/dtypes/test_uintx.py::test_uintx_quant_on_cpu_then_move_to_cuda[128-dtype4] PASSED 2025-09-09T14:27:11.6826169Z test/dtypes/test_uintx.py::test_uintx_quant_on_cpu_then_move_to_cuda[128-dtype5] PASSED 2025-09-09T14:27:11.6826892Z test/dtypes/test_uintx.py::test_uintx_quant_on_cpu_then_move_to_cuda[128-dtype6] PASSED 2025-09-09T14:27:11.6827599Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cpu-32-dtype0] PASSED 2025-09-09T14:27:11.6828315Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cpu-32-dtype1] PASSED 2025-09-09T14:27:11.6829008Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cpu-32-dtype2] PASSED 2025-09-09T14:27:11.6829715Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cpu-32-dtype3] PASSED 2025-09-09T14:27:11.6830412Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cpu-32-dtype4] PASSED 2025-09-09T14:27:11.6831114Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cpu-32-dtype5] PASSED 2025-09-09T14:27:11.6831808Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cpu-32-dtype6] PASSED 2025-09-09T14:27:11.6832508Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cpu-64-dtype0] PASSED 2025-09-09T14:27:11.6833209Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cpu-64-dtype1] PASSED 2025-09-09T14:27:11.6833907Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cpu-64-dtype2] PASSED 2025-09-09T14:27:11.6834611Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cpu-64-dtype3] PASSED 2025-09-09T14:27:11.6835308Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cpu-64-dtype4] PASSED 2025-09-09T14:27:11.6836000Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cpu-64-dtype5] PASSED 2025-09-09T14:27:11.6836703Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cpu-64-dtype6] PASSED 2025-09-09T14:27:11.6837399Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cpu-128-dtype0] PASSED 2025-09-09T14:27:11.6838249Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cpu-128-dtype1] PASSED 2025-09-09T14:27:11.6838961Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cpu-128-dtype2] PASSED 2025-09-09T14:27:11.6839734Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cpu-128-dtype3] PASSED 2025-09-09T14:27:11.6840557Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cpu-128-dtype4] PASSED 2025-09-09T14:27:11.6841257Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cpu-128-dtype5] PASSED 2025-09-09T14:27:11.6841961Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cpu-128-dtype6] PASSED 2025-09-09T14:27:11.6842667Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cuda-32-dtype0] PASSED 2025-09-09T14:27:11.6843368Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cuda-32-dtype1] PASSED 2025-09-09T14:27:11.6844074Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cuda-32-dtype2] PASSED 2025-09-09T14:27:11.6844779Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cuda-32-dtype3] PASSED 2025-09-09T14:27:11.6845488Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cuda-32-dtype4] PASSED 2025-09-09T14:27:11.6846198Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cuda-32-dtype5] PASSED 2025-09-09T14:27:11.6846954Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cuda-32-dtype6] PASSED 2025-09-09T14:27:11.6847660Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cuda-64-dtype0] PASSED 2025-09-09T14:27:11.6848358Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cuda-64-dtype1] PASSED 2025-09-09T14:27:11.6849062Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cuda-64-dtype2] PASSED 2025-09-09T14:27:11.6849765Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cuda-64-dtype3] PASSED 2025-09-09T14:27:11.6850482Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cuda-64-dtype4] PASSED 2025-09-09T14:27:11.6851185Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cuda-64-dtype5] PASSED 2025-09-09T14:27:11.6851890Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cuda-64-dtype6] PASSED 2025-09-09T14:27:11.6852603Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cuda-128-dtype0] PASSED 2025-09-09T14:27:11.6853314Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cuda-128-dtype1] PASSED 2025-09-09T14:27:11.6854030Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cuda-128-dtype2] PASSED 2025-09-09T14:27:11.6854756Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cuda-128-dtype3] PASSED 2025-09-09T14:27:11.6855465Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cuda-128-dtype4] PASSED 2025-09-09T14:27:11.6856190Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cuda-128-dtype5] PASSED 2025-09-09T14:27:11.6856902Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cuda-128-dtype6] PASSED 2025-09-09T14:27:11.6857601Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cpu-32-dtype0] PASSED 2025-09-09T14:27:11.6858268Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cpu-32-dtype1] PASSED 2025-09-09T14:27:11.6858925Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cpu-32-dtype2] PASSED 2025-09-09T14:27:11.6859582Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cpu-32-dtype3] PASSED 2025-09-09T14:27:11.6860231Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cpu-32-dtype4] PASSED 2025-09-09T14:27:11.6860885Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cpu-32-dtype5] PASSED 2025-09-09T14:27:11.6861541Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cpu-32-dtype6] PASSED 2025-09-09T14:27:11.6862288Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cpu-64-dtype0] PASSED 2025-09-09T14:27:11.6862947Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cpu-64-dtype1] PASSED 2025-09-09T14:29:22.3437274Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cpu-64-dtype2] PASSED 2025-09-09T14:29:22.3439673Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cpu-64-dtype3] PASSED 2025-09-09T14:29:22.3440414Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cpu-64-dtype4] PASSED 2025-09-09T14:29:22.3441075Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cpu-64-dtype5] PASSED 2025-09-09T14:29:22.3441718Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cpu-64-dtype6] PASSED 2025-09-09T14:29:22.3442377Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cpu-128-dtype0] PASSED 2025-09-09T14:29:22.3443031Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cpu-128-dtype1] PASSED 2025-09-09T14:29:22.3443704Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cpu-128-dtype2] PASSED 2025-09-09T14:29:22.3444362Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cpu-128-dtype3] PASSED 2025-09-09T14:29:22.3445024Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cpu-128-dtype4] PASSED 2025-09-09T14:29:22.3445673Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cpu-128-dtype5] PASSED 2025-09-09T14:29:22.3446315Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cpu-128-dtype6] PASSED 2025-09-09T14:29:22.3446968Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cuda-32-dtype0] PASSED 2025-09-09T14:29:22.3447618Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cuda-32-dtype1] PASSED 2025-09-09T14:29:22.3448261Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cuda-32-dtype2] PASSED 2025-09-09T14:29:22.3448913Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cuda-32-dtype3] PASSED 2025-09-09T14:29:22.3449569Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cuda-32-dtype4] PASSED 2025-09-09T14:29:22.3450222Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cuda-32-dtype5] PASSED 2025-09-09T14:29:22.3450879Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cuda-32-dtype6] PASSED 2025-09-09T14:29:22.3451528Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cuda-64-dtype0] PASSED 2025-09-09T14:29:22.3452179Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cuda-64-dtype1] PASSED 2025-09-09T14:29:22.3452823Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cuda-64-dtype2] PASSED 2025-09-09T14:29:22.3453473Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cuda-64-dtype3] PASSED 2025-09-09T14:29:22.3454120Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cuda-64-dtype4] PASSED 2025-09-09T14:29:22.3454778Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cuda-64-dtype5] PASSED 2025-09-09T14:29:22.3455429Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cuda-64-dtype6] PASSED 2025-09-09T14:29:22.3456089Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cuda-128-dtype0] PASSED 2025-09-09T14:29:22.3456774Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cuda-128-dtype1] PASSED 2025-09-09T14:29:22.3457438Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cuda-128-dtype2] PASSED 2025-09-09T14:29:22.3458101Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cuda-128-dtype3] PASSED 2025-09-09T14:29:22.3458762Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cuda-128-dtype4] PASSED 2025-09-09T14:29:22.3459415Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cuda-128-dtype5] PASSED 2025-09-09T14:29:22.3460073Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cuda-128-dtype6] PASSED 2025-09-09T14:29:22.3460982Z test/dtypes/test_uintx.py::test_uintx_target_dtype[dtype0] PASSED 2025-09-09T14:29:22.3461569Z test/dtypes/test_uintx.py::test_uintx_target_dtype[dtype1] PASSED 2025-09-09T14:29:22.3462307Z test/dtypes/test_uintx.py::test_uintx_target_dtype[dtype2] PASSED 2025-09-09T14:29:22.3462930Z test/dtypes/test_uintx.py::test_uintx_target_dtype[dtype3] PASSED 2025-09-09T14:29:22.3463498Z test/dtypes/test_uintx.py::test_uintx_target_dtype[dtype4] PASSED 2025-09-09T14:29:22.3464064Z test/dtypes/test_uintx.py::test_uintx_target_dtype[dtype5] PASSED 2025-09-09T14:29:22.3464636Z test/dtypes/test_uintx.py::test_uintx_target_dtype[dtype6] PASSED 2025-09-09T14:29:22.3465230Z test/dtypes/test_uintx.py::test_uintx_target_dtype_compile[dtype0] PASSED 2025-09-09T14:29:22.3465855Z test/dtypes/test_uintx.py::test_uintx_target_dtype_compile[dtype1] PASSED 2025-09-09T14:29:22.3466478Z test/dtypes/test_uintx.py::test_uintx_target_dtype_compile[dtype2] PASSED 2025-09-09T14:29:22.3467101Z test/dtypes/test_uintx.py::test_uintx_target_dtype_compile[dtype3] PASSED 2025-09-09T14:29:22.3467722Z test/dtypes/test_uintx.py::test_uintx_target_dtype_compile[dtype4] PASSED 2025-09-09T14:29:22.3468336Z test/dtypes/test_uintx.py::test_uintx_target_dtype_compile[dtype5] PASSED 2025-09-09T14:29:22.3468969Z test/dtypes/test_uintx.py::test_uintx_target_dtype_compile[dtype6] PASSED 2025-09-09T14:29:22.3469555Z test/dtypes/test_uintx.py::test_uintx_model_size[dtype0] PASSED 2025-09-09T14:29:22.3470119Z test/dtypes/test_uintx.py::test_uintx_model_size[dtype1] PASSED 2025-09-09T14:29:22.3470680Z test/dtypes/test_uintx.py::test_uintx_model_size[dtype2] PASSED 2025-09-09T14:29:22.3471237Z test/dtypes/test_uintx.py::test_uintx_model_size[dtype3] PASSED 2025-09-09T14:29:22.3471791Z test/dtypes/test_uintx.py::test_uintx_model_size[dtype4] PASSED 2025-09-09T14:29:22.3472350Z test/dtypes/test_uintx.py::test_uintx_model_size[dtype5] PASSED 2025-09-09T14:29:22.3472905Z test/dtypes/test_uintx.py::test_uintx_model_size[dtype6] PASSED 2025-09-09T14:29:22.3473709Z test/float8/test_auto_filter.py::test_end_to_end_filtering[tensorwise-module_dims0-valid.layer-filter_fqns0-True] PASSED 2025-09-09T14:29:22.3474753Z test/float8/test_auto_filter.py::test_end_to_end_filtering[tensorwise-module_dims1-skip_layer.linear-filter_fqns1-False] PASSED 2025-09-09T14:29:22.3475780Z test/float8/test_auto_filter.py::test_end_to_end_filtering[tensorwise-module_dims2-valid.layer-filter_fqns2-False] PASSED 2025-09-09T14:29:22.3476758Z test/float8/test_auto_filter.py::test_end_to_end_filtering[rowwise-module_dims3-valid.layer-filter_fqns3-True] PASSED 2025-09-09T14:29:22.3477754Z test/float8/test_auto_filter.py::test_end_to_end_filtering[rowwise-module_dims4-skip_layer.linear-filter_fqns4-False] PASSED 2025-09-09T14:29:22.3478753Z test/float8/test_auto_filter.py::test_end_to_end_filtering[rowwise-module_dims5-valid.layer-filter_fqns5-False] PASSED 2025-09-09T14:29:22.3479604Z test/float8/test_auto_filter.py::test_exact_boundary_dimensions_rowwise PASSED 2025-09-09T14:29:22.3480286Z test/float8/test_auto_filter.py::test_exact_boundary_dimensions_tensorwise PASSED 2025-09-09T14:29:22.3480908Z test/float8/test_auto_filter.py::test_partial_fqn_matching PASSED 2025-09-09T14:29:22.3481559Z test/float8/test_base.py::TestFloat8TrainingTensor::test_preserves_dtype PASSED 2025-09-09T14:29:22.3482293Z test/float8/test_base.py::TestFloat8TrainingTensor::test_differentiable_casts PASSED 2025-09-09T14:29:22.3482984Z test/float8/test_base.py::TestFloat8TrainingTensor::test_split_cat PASSED 2025-09-09T14:29:22.3483633Z test/float8/test_base.py::TestFloat8TrainingTensor::test_index_put PASSED 2025-09-09T14:29:22.3484260Z test/float8/test_base.py::TestFloat8TrainingTensor::test_copy_ PASSED 2025-09-09T14:29:22.3484994Z test/float8/test_base.py::TestFloat8TrainingTensor::test_transpose PASSED 2025-09-09T14:29:22.3485767Z test/float8/test_base.py::TestFloat8TrainingTensor::test_axiswise_dynamic_cast[True-0-shape0] PASSED 2025-09-09T14:29:22.3486719Z test/float8/test_base.py::TestFloat8TrainingTensor::test_axiswise_dynamic_cast[True-0-shape1] PASSED 2025-09-09T14:29:22.3487580Z test/float8/test_base.py::TestFloat8TrainingTensor::test_axiswise_dynamic_cast[True-0-shape2] PASSED 2025-09-09T14:29:22.3488447Z test/float8/test_base.py::TestFloat8TrainingTensor::test_axiswise_dynamic_cast[True--1-shape0] PASSED 2025-09-09T14:29:22.3489324Z test/float8/test_base.py::TestFloat8TrainingTensor::test_axiswise_dynamic_cast[True--1-shape1] PASSED 2025-09-09T14:29:22.3490187Z test/float8/test_base.py::TestFloat8TrainingTensor::test_axiswise_dynamic_cast[True--1-shape2] PASSED 2025-09-09T14:29:22.3491065Z test/float8/test_base.py::TestFloat8TrainingTensor::test_axiswise_dynamic_cast[False-0-shape0] PASSED 2025-09-09T14:29:22.3491944Z test/float8/test_base.py::TestFloat8TrainingTensor::test_axiswise_dynamic_cast[False-0-shape1] PASSED 2025-09-09T14:29:22.3492867Z test/float8/test_base.py::TestFloat8TrainingTensor::test_axiswise_dynamic_cast[False-0-shape2] PASSED 2025-09-09T14:29:22.3493751Z test/float8/test_base.py::TestFloat8TrainingTensor::test_axiswise_dynamic_cast[False--1-shape0] PASSED 2025-09-09T14:29:22.3494624Z test/float8/test_base.py::TestFloat8TrainingTensor::test_axiswise_dynamic_cast[False--1-shape1] PASSED 2025-09-09T14:29:22.3495503Z test/float8/test_base.py::TestFloat8TrainingTensor::test_axiswise_dynamic_cast[False--1-shape2] PASSED 2025-09-09T14:29:22.3496297Z test/float8/test_base.py::TestFloat8TrainingTensor::test_axiswise_reshape PASSED 2025-09-09T14:29:22.3497340Z test/float8/test_base.py::TestFloat8TrainingTensor::test_axiswise_gemm[ScalingGranularity.AXISWISE-ScalingGranularity.AXISWISE-a_shape0] SKIPPED 2025-09-09T14:29:22.3498677Z test/float8/test_base.py::TestFloat8TrainingTensor::test_axiswise_gemm[ScalingGranularity.AXISWISE-ScalingGranularity.AXISWISE-a_shape1] SKIPPED 2025-09-09T14:29:22.3500000Z test/float8/test_base.py::TestFloat8TrainingTensor::test_axiswise_gemm[ScalingGranularity.AXISWISE-ScalingGranularity.AXISWISE-a_shape2] SKIPPED 2025-09-09T14:29:22.3501332Z test/float8/test_base.py::TestFloat8TrainingTensor::test_axiswise_gemm[ScalingGranularity.AXISWISE-ScalingGranularity.TENSORWISE-a_shape0] SKIPPED 2025-09-09T14:29:22.5896794Z test/float8/test_base.py::TestFloat8TrainingTensor::test_axiswise_gemm[ScalingGranularity.AXISWISE-ScalingGranularity.TENSORWISE-a_shape1] SKIPPED 2025-09-09T14:29:22.5898339Z test/float8/test_base.py::TestFloat8TrainingTensor::test_axiswise_gemm[ScalingGranularity.AXISWISE-ScalingGranularity.TENSORWISE-a_shape2] SKIPPED 2025-09-09T14:29:22.5899696Z test/float8/test_base.py::TestFloat8TrainingTensor::test_axiswise_gemm[ScalingGranularity.TENSORWISE-ScalingGranularity.AXISWISE-a_shape0] SKIPPED 2025-09-09T14:29:22.5901047Z test/float8/test_base.py::TestFloat8TrainingTensor::test_axiswise_gemm[ScalingGranularity.TENSORWISE-ScalingGranularity.AXISWISE-a_shape1] SKIPPED 2025-09-09T14:29:22.5902400Z test/float8/test_base.py::TestFloat8TrainingTensor::test_axiswise_gemm[ScalingGranularity.TENSORWISE-ScalingGranularity.AXISWISE-a_shape2] SKIPPED 2025-09-09T14:29:22.5903380Z test/float8/test_base.py::TestFloat8TrainingTensor::test_fp8_dtype PASSED 2025-09-09T14:29:22.5904591Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_config_params[False-False-linear_dtype0-ScalingType.DYNAMIC-ScalingType.DYNAMIC-ScalingType.DYNAMIC-x_shape0-True] PASSED 2025-09-09T14:29:22.5906228Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_config_params[False-False-linear_dtype0-ScalingType.DYNAMIC-ScalingType.DYNAMIC-ScalingType.DYNAMIC-x_shape1-True] PASSED 2025-09-09T14:29:22.5908116Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_config_params[False-False-linear_dtype0-ScalingType.DYNAMIC-ScalingType.DYNAMIC-ScalingType.DYNAMIC-x_shape2-True] PASSED 2025-09-09T14:29:22.5909867Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_config_params[False-False-linear_dtype1-ScalingType.DYNAMIC-ScalingType.DYNAMIC-ScalingType.DYNAMIC-x_shape0-True] PASSED 2025-09-09T14:29:22.5911492Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_config_params[False-False-linear_dtype1-ScalingType.DYNAMIC-ScalingType.DYNAMIC-ScalingType.DYNAMIC-x_shape1-True] PASSED 2025-09-09T14:29:22.5913158Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_config_params[False-False-linear_dtype1-ScalingType.DYNAMIC-ScalingType.DYNAMIC-ScalingType.DYNAMIC-x_shape2-True] PASSED 2025-09-09T14:29:22.5914787Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_config_params[False-True-linear_dtype0-ScalingType.DYNAMIC-ScalingType.DYNAMIC-ScalingType.DYNAMIC-x_shape0-True] PASSED 2025-09-09T14:29:22.5916448Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_config_params[False-True-linear_dtype0-ScalingType.DYNAMIC-ScalingType.DYNAMIC-ScalingType.DYNAMIC-x_shape1-True] PASSED 2025-09-09T14:29:22.5918061Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_config_params[False-True-linear_dtype0-ScalingType.DYNAMIC-ScalingType.DYNAMIC-ScalingType.DYNAMIC-x_shape2-True] PASSED 2025-09-09T14:29:22.5919763Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_config_params[False-True-linear_dtype1-ScalingType.DYNAMIC-ScalingType.DYNAMIC-ScalingType.DYNAMIC-x_shape0-True] PASSED 2025-09-09T14:29:22.5921374Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_config_params[False-True-linear_dtype1-ScalingType.DYNAMIC-ScalingType.DYNAMIC-ScalingType.DYNAMIC-x_shape1-True] PASSED 2025-09-09T14:29:22.5923203Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_config_params[False-True-linear_dtype1-ScalingType.DYNAMIC-ScalingType.DYNAMIC-ScalingType.DYNAMIC-x_shape2-True] PASSED 2025-09-09T14:29:22.5924812Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_config_params[True-False-linear_dtype0-ScalingType.DYNAMIC-ScalingType.DYNAMIC-ScalingType.DYNAMIC-x_shape0-True] PASSED 2025-09-09T14:29:22.5926431Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_config_params[True-False-linear_dtype0-ScalingType.DYNAMIC-ScalingType.DYNAMIC-ScalingType.DYNAMIC-x_shape1-True] PASSED 2025-09-09T14:29:22.5928032Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_config_params[True-False-linear_dtype0-ScalingType.DYNAMIC-ScalingType.DYNAMIC-ScalingType.DYNAMIC-x_shape2-True] PASSED 2025-09-09T14:29:22.5929653Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_config_params[True-False-linear_dtype1-ScalingType.DYNAMIC-ScalingType.DYNAMIC-ScalingType.DYNAMIC-x_shape0-True] PASSED 2025-09-09T14:29:22.5931266Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_config_params[True-False-linear_dtype1-ScalingType.DYNAMIC-ScalingType.DYNAMIC-ScalingType.DYNAMIC-x_shape1-True] PASSED 2025-09-09T14:29:22.5932879Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_config_params[True-False-linear_dtype1-ScalingType.DYNAMIC-ScalingType.DYNAMIC-ScalingType.DYNAMIC-x_shape2-True] PASSED 2025-09-09T14:29:22.5934495Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_config_params[True-True-linear_dtype0-ScalingType.DYNAMIC-ScalingType.DYNAMIC-ScalingType.DYNAMIC-x_shape0-True] PASSED 2025-09-09T14:29:22.5936091Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_config_params[True-True-linear_dtype0-ScalingType.DYNAMIC-ScalingType.DYNAMIC-ScalingType.DYNAMIC-x_shape1-True] PASSED 2025-09-09T14:29:22.5937813Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_config_params[True-True-linear_dtype0-ScalingType.DYNAMIC-ScalingType.DYNAMIC-ScalingType.DYNAMIC-x_shape2-True] PASSED 2025-09-09T14:29:22.5939415Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_config_params[True-True-linear_dtype1-ScalingType.DYNAMIC-ScalingType.DYNAMIC-ScalingType.DYNAMIC-x_shape0-True] PASSED 2025-09-09T14:29:22.5941117Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_config_params[True-True-linear_dtype1-ScalingType.DYNAMIC-ScalingType.DYNAMIC-ScalingType.DYNAMIC-x_shape1-True] PASSED 2025-09-09T14:29:22.5942708Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_config_params[True-True-linear_dtype1-ScalingType.DYNAMIC-ScalingType.DYNAMIC-ScalingType.DYNAMIC-x_shape2-True] PASSED 2025-09-09T14:29:22.5944079Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype0-True-x_shape0-Float8LinearRecipeName.ROWWISE] SKIPPED 2025-09-09T14:29:22.5945286Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype0-True-x_shape0-Float8LinearRecipeName.ROWWISE_WITH_GW_HP] SKIPPED 2025-09-09T14:29:22.5946487Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype0-True-x_shape1-Float8LinearRecipeName.ROWWISE] SKIPPED 2025-09-09T14:29:22.5947700Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype0-True-x_shape1-Float8LinearRecipeName.ROWWISE_WITH_GW_HP] SKIPPED 2025-09-09T14:29:22.5948891Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype0-True-x_shape2-Float8LinearRecipeName.ROWWISE] SKIPPED 2025-09-09T14:29:22.5950087Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype0-True-x_shape2-Float8LinearRecipeName.ROWWISE_WITH_GW_HP] SKIPPED 2025-09-09T14:29:22.5951293Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype0-False-x_shape0-Float8LinearRecipeName.ROWWISE] SKIPPED 2025-09-09T14:29:22.5952560Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype0-False-x_shape0-Float8LinearRecipeName.ROWWISE_WITH_GW_HP] SKIPPED 2025-09-09T14:29:22.5953772Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype0-False-x_shape1-Float8LinearRecipeName.ROWWISE] SKIPPED 2025-09-09T14:29:22.5954992Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype0-False-x_shape1-Float8LinearRecipeName.ROWWISE_WITH_GW_HP] SKIPPED 2025-09-09T14:29:22.5956200Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype0-False-x_shape2-Float8LinearRecipeName.ROWWISE] SKIPPED 2025-09-09T14:29:22.5957414Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype0-False-x_shape2-Float8LinearRecipeName.ROWWISE_WITH_GW_HP] SKIPPED 2025-09-09T14:29:22.5958611Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype1-True-x_shape0-Float8LinearRecipeName.ROWWISE] SKIPPED 2025-09-09T14:29:22.5959865Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype1-True-x_shape0-Float8LinearRecipeName.ROWWISE_WITH_GW_HP] SKIPPED 2025-09-09T14:29:22.5961063Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype1-True-x_shape1-Float8LinearRecipeName.ROWWISE] SKIPPED 2025-09-09T14:29:22.5962255Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype1-True-x_shape1-Float8LinearRecipeName.ROWWISE_WITH_GW_HP] SKIPPED 2025-09-09T14:29:22.5963459Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype1-True-x_shape2-Float8LinearRecipeName.ROWWISE] SKIPPED 2025-09-09T14:29:22.5964676Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype1-True-x_shape2-Float8LinearRecipeName.ROWWISE_WITH_GW_HP] SKIPPED 2025-09-09T14:29:23.5640668Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype1-False-x_shape0-Float8LinearRecipeName.ROWWISE] SKIPPED 2025-09-09T14:29:23.5641932Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype1-False-x_shape0-Float8LinearRecipeName.ROWWISE_WITH_GW_HP] SKIPPED 2025-09-09T14:29:23.5643246Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype1-False-x_shape1-Float8LinearRecipeName.ROWWISE] SKIPPED 2025-09-09T14:29:23.5644441Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype1-False-x_shape1-Float8LinearRecipeName.ROWWISE_WITH_GW_HP] SKIPPED 2025-09-09T14:29:23.5645642Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype1-False-x_shape2-Float8LinearRecipeName.ROWWISE] SKIPPED 2025-09-09T14:29:23.5646843Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype1-False-x_shape2-Float8LinearRecipeName.ROWWISE_WITH_GW_HP] SKIPPED 2025-09-09T14:29:23.5648035Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype2-True-x_shape0-Float8LinearRecipeName.ROWWISE] SKIPPED 2025-09-09T14:29:23.5649228Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype2-True-x_shape0-Float8LinearRecipeName.ROWWISE_WITH_GW_HP] SKIPPED 2025-09-09T14:29:23.5650452Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype2-True-x_shape1-Float8LinearRecipeName.ROWWISE] SKIPPED 2025-09-09T14:29:23.5651653Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype2-True-x_shape1-Float8LinearRecipeName.ROWWISE_WITH_GW_HP] SKIPPED 2025-09-09T14:29:23.5652853Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype2-True-x_shape2-Float8LinearRecipeName.ROWWISE] SKIPPED 2025-09-09T14:29:23.5654047Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype2-True-x_shape2-Float8LinearRecipeName.ROWWISE_WITH_GW_HP] SKIPPED 2025-09-09T14:29:23.5655256Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype2-False-x_shape0-Float8LinearRecipeName.ROWWISE] SKIPPED 2025-09-09T14:29:23.5656464Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype2-False-x_shape0-Float8LinearRecipeName.ROWWISE_WITH_GW_HP] SKIPPED 2025-09-09T14:29:23.5657663Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype2-False-x_shape1-Float8LinearRecipeName.ROWWISE] SKIPPED 2025-09-09T14:29:23.5658874Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype2-False-x_shape1-Float8LinearRecipeName.ROWWISE_WITH_GW_HP] SKIPPED 2025-09-09T14:29:23.5660075Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype2-False-x_shape2-Float8LinearRecipeName.ROWWISE] SKIPPED 2025-09-09T14:29:23.5661304Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype2-False-x_shape2-Float8LinearRecipeName.ROWWISE_WITH_GW_HP] SKIPPED 2025-09-09T14:29:23.5662485Z test/float8/test_base.py::TestFloat8Linear::test_autocast_outputs[Float8LinearRecipeName.TENSORWISE-linear_dtype0-True] PASSED 2025-09-09T14:29:23.5663605Z test/float8/test_base.py::TestFloat8Linear::test_autocast_outputs[Float8LinearRecipeName.TENSORWISE-linear_dtype1-True] PASSED 2025-09-09T14:29:23.5664720Z test/float8/test_base.py::TestFloat8Linear::test_autocast_outputs[Float8LinearRecipeName.TENSORWISE-linear_dtype2-True] PASSED 2025-09-09T14:29:23.5665809Z test/float8/test_base.py::TestFloat8Linear::test_autocast_outputs[Float8LinearRecipeName.ROWWISE-linear_dtype0-True] PASSED 2025-09-09T14:29:23.5666883Z test/float8/test_base.py::TestFloat8Linear::test_autocast_outputs[Float8LinearRecipeName.ROWWISE-linear_dtype1-True] PASSED 2025-09-09T14:29:23.5667963Z test/float8/test_base.py::TestFloat8Linear::test_autocast_outputs[Float8LinearRecipeName.ROWWISE-linear_dtype2-True] PASSED 2025-09-09T14:29:23.5669171Z test/float8/test_base.py::TestFloat8Linear::test_autocast_outputs[Float8LinearRecipeName.ROWWISE_WITH_GW_HP-linear_dtype0-True] PASSED 2025-09-09T14:29:23.5670403Z test/float8/test_base.py::TestFloat8Linear::test_autocast_outputs[Float8LinearRecipeName.ROWWISE_WITH_GW_HP-linear_dtype1-True] PASSED 2025-09-09T14:29:23.5671546Z test/float8/test_base.py::TestFloat8Linear::test_autocast_outputs[Float8LinearRecipeName.ROWWISE_WITH_GW_HP-linear_dtype2-True] PASSED 2025-09-09T14:29:23.5672377Z test/float8/test_base.py::TestFloat8Linear::test_repr PASSED 2025-09-09T14:29:23.5673025Z test/float8/test_base.py::TestFloat8Linear::test_inference_mode SKIPPED 2025-09-09T14:29:23.5673664Z test/float8/test_base.py::TestFloat8Linear::test_quantize SKIPPED (C...) 2025-09-09T14:29:23.5674381Z test/float8/test_base.py::TestScaledMM::test_scaled_mm_vs_emulated[True-base_dtype0] SKIPPED 2025-09-09T14:29:23.5675170Z test/float8/test_base.py::TestScaledMM::test_scaled_mm_vs_emulated[True-base_dtype1] SKIPPED 2025-09-09T14:29:23.5675951Z test/float8/test_base.py::TestScaledMM::test_scaled_mm_vs_emulated[True-base_dtype2] SKIPPED 2025-09-09T14:29:23.5676731Z test/float8/test_base.py::TestScaledMM::test_scaled_mm_vs_emulated[False-base_dtype0] SKIPPED 2025-09-09T14:29:23.5677515Z test/float8/test_base.py::TestScaledMM::test_scaled_mm_vs_emulated[False-base_dtype1] SKIPPED 2025-09-09T14:29:23.5678300Z test/float8/test_base.py::TestScaledMM::test_scaled_mm_vs_emulated[False-base_dtype2] SKIPPED 2025-09-09T14:29:23.5679006Z test/float8/test_base.py::TestScaledMM::test_different_configs_error SKIPPED 2025-09-09T14:29:23.5679782Z test/float8/test_base.py::TestScaledMM::test_pad_inner_dim[True-base_dtype0] SKIPPED 2025-09-09T14:29:23.5680488Z test/float8/test_base.py::TestScaledMM::test_pad_inner_dim[True-base_dtype1] SKIPPED 2025-09-09T14:29:23.5681203Z test/float8/test_base.py::TestScaledMM::test_pad_inner_dim[True-base_dtype2] SKIPPED 2025-09-09T14:29:23.5681914Z test/float8/test_base.py::TestScaledMM::test_pad_inner_dim[False-base_dtype0] SKIPPED 2025-09-09T14:29:23.5682636Z test/float8/test_base.py::TestScaledMM::test_pad_inner_dim[False-base_dtype1] SKIPPED 2025-09-09T14:29:23.5683350Z test/float8/test_base.py::TestScaledMM::test_pad_inner_dim[False-base_dtype2] SKIPPED 2025-09-09T14:29:23.5684059Z test/float8/test_base.py::TestNumerics::test_small_amax_float16[float8_dtype0] PASSED 2025-09-09T14:29:23.5684766Z test/float8/test_base.py::TestNumerics::test_small_amax_float16[float8_dtype1] PASSED 2025-09-09T14:29:23.5685475Z test/float8/test_base.py::TestNumerics::test_small_amax_float16[float8_dtype2] PASSED 2025-09-09T14:29:23.5686179Z test/float8/test_base.py::TestNumerics::test_small_amax_float16[float8_dtype3] PASSED 2025-09-09T14:29:23.5686898Z test/float8/test_base.py::TestFloat8LinearUtils::test_fp8_tensor_statistics PASSED 2025-09-09T14:29:23.5687628Z test/float8/test_base.py::TestFloat8LinearUtils::test_swap_linears_with_filters PASSED 2025-09-09T14:29:23.5688340Z test/float8/test_base.py::TestFloat8LinearUtils::test_swap_root_linear PASSED 2025-09-09T14:29:23.5689108Z test/float8/test_base.py::TestFloat8LinearUtils::test_swap_root_linear_with_children_raises PASSED 2025-09-09T14:29:23.5689891Z test/float8/test_base.py::TestFloat8LinearUtils::test_swap_submodule_linears PASSED 2025-09-09T14:29:23.5690660Z test/float8/test_base.py::TestFloat8LinearUtils::test_swap_submodule_linears_with_skip PASSED 2025-09-09T14:29:23.5691682Z test/float8/test_compile.py::test_eager_only[dtype0-True-ScalingType.DYNAMIC-ScalingType.DYNAMIC-ScalingType.DYNAMIC-True] PASSED 2025-09-09T14:29:23.5692899Z test/float8/test_compile.py::test_eager_only[dtype1-True-ScalingType.DYNAMIC-ScalingType.DYNAMIC-ScalingType.DYNAMIC-True] PASSED 2025-09-09T14:29:23.5694207Z test/float8/test_compile.py::test_aot_eager[dtype0-ScalingType.DYNAMIC-ScalingType.DYNAMIC-ScalingType.DYNAMIC-True-True] PASSED 2025-09-09T14:29:23.5695405Z test/float8/test_compile.py::test_aot_eager[dtype1-ScalingType.DYNAMIC-ScalingType.DYNAMIC-ScalingType.DYNAMIC-True-True] PASSED 2025-09-09T14:29:23.5696777Z test/float8/test_compile.py::test_inductor_from_config_params[dtype0-ScalingType.DYNAMIC-ScalingType.DYNAMIC-ScalingType.DYNAMIC-False-True] SKIPPED 2025-09-09T14:29:23.5698143Z test/float8/test_compile.py::test_inductor_from_config_params[dtype1-ScalingType.DYNAMIC-ScalingType.DYNAMIC-ScalingType.DYNAMIC-False-True] SKIPPED 2025-09-09T14:29:23.5699236Z test/float8/test_compile.py::test_inductor_from_recipe[Float8LinearRecipeName.ROWWISE] SKIPPED 2025-09-09T14:29:23.5700111Z test/float8/test_compile.py::test_inductor_from_recipe[Float8LinearRecipeName.ROWWISE_WITH_GW_HP] SKIPPED 2025-09-09T14:29:23.5700901Z test/float8/test_compile.py::TestGraphBreaks::test_float8_graph_input SKIPPED 2025-09-09T14:29:23.5701584Z test/float8/test_compile.py::TestGraphBreaks::test_float8_graph_output SKIPPED 2025-09-09T14:29:23.5702341Z test/float8/test_compile.py::TestGraphBreaks::test_float8_with_graph_break_in_the_middle SKIPPED 2025-09-09T14:29:23.5703153Z test/float8/test_compile.py::test_dynamic_scale_numeric_parity[True-dtype0] SKIPPED 2025-09-09T14:29:23.5703864Z test/float8/test_compile.py::test_dynamic_scale_numeric_parity[True-dtype1] SKIPPED 2025-09-09T14:29:23.5704561Z test/float8/test_compile.py::test_dynamic_scale_numeric_parity[True-dtype2] SKIPPED 2025-09-09T14:29:23.5705287Z test/float8/test_compile.py::test_dynamic_scale_numeric_parity[False-dtype0] SKIPPED 2025-09-09T14:29:46.2164948Z test/float8/test_compile.py::test_dynamic_scale_numeric_parity[False-dtype1] SKIPPED 2025-09-09T14:29:46.2166480Z test/float8/test_compile.py::test_dynamic_scale_numeric_parity[False-dtype2] SKIPPED 2025-09-09T14:29:46.2167355Z test/float8/test_float8_utils.py::test_round_scale_down_to_power_of_2_valid_inputs[test_case0] PASSED 2025-09-09T14:29:46.2168188Z test/float8/test_float8_utils.py::test_round_scale_down_to_power_of_2_valid_inputs[test_case1] PASSED 2025-09-09T14:29:46.2169017Z test/float8/test_float8_utils.py::test_round_scale_down_to_power_of_2_valid_inputs[test_case2] PASSED 2025-09-09T14:29:46.2169816Z test/float8/test_float8_utils.py::test_round_scale_down_to_power_of_2_valid_inputs[test_case3] PASSED 2025-09-09T14:29:46.2170617Z test/float8/test_float8_utils.py::test_round_scale_down_to_power_of_2_valid_inputs[test_case4] PASSED 2025-09-09T14:29:46.2171410Z test/float8/test_float8_utils.py::test_round_scale_down_to_power_of_2_valid_inputs[test_case5] PASSED 2025-09-09T14:29:46.2172213Z test/float8/test_float8_utils.py::test_round_scale_down_to_power_of_2_valid_inputs[test_case6] PASSED 2025-09-09T14:29:46.2173019Z test/float8/test_float8_utils.py::test_round_scale_down_to_power_of_2_valid_inputs[test_case7] PASSED 2025-09-09T14:29:46.2173753Z test/float8/test_float8_utils.py::test_non_float32_input[invalid_dtype0] PASSED 2025-09-09T14:29:46.2174446Z test/float8/test_float8_utils.py::test_non_float32_input[invalid_dtype1] PASSED 2025-09-09T14:29:46.2175096Z test/float8/test_float8_utils.py::test_non_float32_input[invalid_dtype2] PASSED 2025-09-09T14:29:46.2175745Z test/float8/test_float8_utils.py::test_non_float32_input[invalid_dtype3] PASSED 2025-09-09T14:29:46.2176397Z test/float8/test_float8_utils.py::test_non_float32_input[invalid_dtype4] PASSED 2025-09-09T14:29:46.2177040Z test/float8/test_float8_utils.py::test_non_float32_input[invalid_dtype5] PASSED 2025-09-09T14:29:46.2177686Z test/float8/test_float8_utils.py::test_non_float32_input[invalid_dtype6] PASSED 2025-09-09T14:29:46.2178607Z test/float8/test_float8_utils.py::test_non_float32_input[invalid_dtype7] PASSED 2025-09-09T14:29:46.2179814Z test/float8/test_numerics_integration.py::TestFloat8NumericsIntegrationTest::test_encoder_fw_bw_from_config_params[ScalingType.DYNAMIC-ScalingType.DYNAMIC-ScalingType.DYNAMIC] SKIPPED 2025-09-09T14:29:46.2181426Z test/float8/test_numerics_integration.py::TestFloat8NumericsIntegrationTest::test_encoder_fw_bw_from_recipe[Float8LinearRecipeName.ROWWISE] SKIPPED 2025-09-09T14:29:46.2182758Z test/float8/test_numerics_integration.py::TestFloat8NumericsIntegrationTest::test_encoder_fw_bw_from_recipe[Float8LinearRecipeName.ROWWISE_WITH_GW_HP] SKIPPED 2025-09-09T14:29:46.2183706Z test/hqq/test_hqq_affine.py::TestHQQ::test_hqq_plain_2bit PASSED 2025-09-09T14:29:46.2184269Z test/hqq/test_hqq_affine.py::TestHQQ::test_hqq_plain_3bit PASSED 2025-09-09T14:29:46.2184828Z test/hqq/test_hqq_affine.py::TestHQQ::test_hqq_plain_4bit PASSED 2025-09-09T14:29:46.2185385Z test/hqq/test_hqq_affine.py::TestHQQ::test_hqq_plain_5bit PASSED 2025-09-09T14:29:46.2185942Z test/hqq/test_hqq_affine.py::TestHQQ::test_hqq_plain_6bit PASSED 2025-09-09T14:29:46.2186497Z test/hqq/test_hqq_affine.py::TestHQQ::test_hqq_plain_7bit PASSED 2025-09-09T14:29:46.2187052Z test/hqq/test_hqq_affine.py::TestHQQ::test_hqq_plain_8bit PASSED 2025-09-09T14:29:46.2187721Z test/integration/test_integration.py::SmoothquantUnitTest::test_debug_x_absmax PASSED 2025-09-09T14:29:46.2188462Z test/integration/test_integration.py::SmoothquantUnitTest::test_figure_4 PASSED 2025-09-09T14:29:46.2189244Z test/integration/test_integration.py::SmoothquantUnitTest::test_selective_torch_compile PASSED 2025-09-09T14:29:46.2190601Z test/integration/test_integration.py::SmoothquantUnitTest::test_smooth_linear_cpu [W909 14:29:29.359192124 qlinear_dynamic.cpp:251] Warning: Currently, qnnpack incorrectly ignores reduce_range when it is set to true; this may change in a future release. (function operator()) 2025-09-09T14:29:46.2191745Z PASSED 2025-09-09T14:29:46.2192253Z test/integration/test_integration.py::SmoothquantUnitTest::test_smooth_linear_cuda PASSED 2025-09-09T14:29:46.2193075Z test/integration/test_integration.py::SmoothquantUnitTest::test_smooth_linear_edge_cases PASSED 2025-09-09T14:29:46.2193836Z test/integration/test_integration.py::SmoothquantUnitTest::test_swap PASSED 2025-09-09T14:29:46.2194533Z test/integration/test_integration.py::SmoothquantUnitTest::test_tensors PASSED 2025-09-09T14:29:46.2195298Z test/integration/test_integration.py::SmoothquantUnitTest::test_weight_t_and_non_t_numerics_match AUTOTUNE int_mm(32x32, 32x16) 2025-09-09T14:29:46.2195880Z strides: [32, 1], [1, 32] 2025-09-09T14:29:46.2196123Z dtypes: torch.int8, torch.int8 2025-09-09T14:29:46.2196723Z triton_mm_38 0.0256 ms 100.0% ACC_TYPE='tl.int32', ALLOW_TF32=True, BLOCK_K=32, BLOCK_M=32, BLOCK_N=16, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=2 2025-09-09T14:29:46.2197680Z triton_mm_36 0.0266 ms 96.2% ACC_TYPE='tl.int32', ALLOW_TF32=True, BLOCK_K=32, BLOCK_M=32, BLOCK_N=16, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=2 2025-09-09T14:29:46.2198615Z triton_mm_37 0.0266 ms 96.2% ACC_TYPE='tl.int32', ALLOW_TF32=True, BLOCK_K=32, BLOCK_M=32, BLOCK_N=16, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=2 2025-09-09T14:29:46.2199613Z triton_mm_39 0.0266 ms 96.2% ACC_TYPE='tl.int32', ALLOW_TF32=True, BLOCK_K=32, BLOCK_M=32, BLOCK_N=16, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=2 2025-09-09T14:29:46.2200216Z _int_mm 0.0379 ms 67.6% 2025-09-09T14:29:46.2200672Z SingleProcess AUTOTUNE benchmarking takes 0.0672 seconds and 0.2280 seconds precompiling for 5 choices 2025-09-09T14:29:46.2201200Z PASSED 2025-09-09T14:29:46.2201677Z test/integration/test_integration.py::PythonQuantUtilOpUnitTest::test__int_mm AUTOTUNE int_mm(32x32, 32x16) 2025-09-09T14:29:46.2202201Z strides: [32, 1], [16, 1] 2025-09-09T14:29:46.2202536Z dtypes: torch.int8, torch.int8 2025-09-09T14:29:46.2203123Z triton_mm_43 0.0256 ms 100.0% ACC_TYPE='tl.int32', ALLOW_TF32=True, BLOCK_K=32, BLOCK_M=32, BLOCK_N=16, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=2 2025-09-09T14:29:46.2204136Z triton_mm_40 0.0266 ms 96.2% ACC_TYPE='tl.int32', ALLOW_TF32=True, BLOCK_K=32, BLOCK_M=32, BLOCK_N=16, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=2 2025-09-09T14:29:46.2205071Z triton_mm_41 0.0266 ms 96.2% ACC_TYPE='tl.int32', ALLOW_TF32=True, BLOCK_K=32, BLOCK_M=32, BLOCK_N=16, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=2 2025-09-09T14:29:46.2205995Z triton_mm_42 0.0267 ms 95.9% ACC_TYPE='tl.int32', ALLOW_TF32=True, BLOCK_K=32, BLOCK_M=32, BLOCK_N=16, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=2 2025-09-09T14:29:46.2206587Z _int_mm 0.0369 ms 69.4% 2025-09-09T14:29:46.2207066Z SingleProcess AUTOTUNE benchmarking takes 0.0667 seconds and 0.2684 seconds precompiling for 5 choices 2025-09-09T14:29:46.2207586Z PASSED 2025-09-09T14:29:46.2208175Z test/integration/test_integration.py::PythonQuantUtilOpUnitTest::test__int_mm_eager_and_torch_compile_numerics AUTOTUNE int_mm(17x1536, 1536x1536) 2025-09-09T14:29:46.2208834Z strides: [s15, 1], [s21, 1] 2025-09-09T14:29:46.2209079Z dtypes: torch.int8, torch.int8 2025-09-09T14:29:46.2209666Z triton_mm_53 0.0717 ms 100.0% ACC_TYPE='tl.int32', ALLOW_TF32=True, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=False, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:29:46.2210616Z triton_mm_56 0.0809 ms 88.6% ACC_TYPE='tl.int32', ALLOW_TF32=True, BLOCK_K=64, BLOCK_M=32, BLOCK_N=64, EVEN_K=False, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=8 2025-09-09T14:29:46.2211553Z triton_mm_54 0.0932 ms 76.9% ACC_TYPE='tl.int32', ALLOW_TF32=True, BLOCK_K=32, BLOCK_M=32, BLOCK_N=64, EVEN_K=False, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=8 2025-09-09T14:29:46.2212484Z triton_mm_52 0.0942 ms 76.1% ACC_TYPE='tl.int32', ALLOW_TF32=True, BLOCK_K=32, BLOCK_M=32, BLOCK_N=64, EVEN_K=False, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=8 2025-09-09T14:29:46.2213425Z triton_mm_48 0.1085 ms 66.0% ACC_TYPE='tl.int32', ALLOW_TF32=True, BLOCK_K=32, BLOCK_M=32, BLOCK_N=64, EVEN_K=False, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:29:46.2214349Z triton_mm_50 0.1085 ms 66.0% ACC_TYPE='tl.int32', ALLOW_TF32=True, BLOCK_K=32, BLOCK_M=32, BLOCK_N=64, EVEN_K=False, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:29:46.2214942Z _int_mm 0.1219 ms 58.8% 2025-09-09T14:29:46.2215525Z triton_mm_58 0.1454 ms 49.3% ACC_TYPE='tl.int32', ALLOW_TF32=True, BLOCK_K=128, BLOCK_M=32, BLOCK_N=128, EVEN_K=False, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=8 2025-09-09T14:29:46.2216481Z triton_mm_51 0.1608 ms 44.6% ACC_TYPE='tl.int32', ALLOW_TF32=True, BLOCK_K=32, BLOCK_M=32, BLOCK_N=128, EVEN_K=False, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=8 2025-09-09T14:29:46.2217424Z triton_mm_55 0.1608 ms 44.6% ACC_TYPE='tl.int32', ALLOW_TF32=True, BLOCK_K=32, BLOCK_M=32, BLOCK_N=128, EVEN_K=False, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=8 2025-09-09T14:29:46.2218267Z SingleProcess AUTOTUNE benchmarking takes 0.2548 seconds and 1.0456 seconds precompiling for 12 choices 2025-09-09T14:29:46.2218765Z AUTOTUNE int_mm(136x4096, 4096x1536) 2025-09-09T14:29:46.2219054Z strides: [s15, 1], [s21, 1] 2025-09-09T14:29:46.2219315Z dtypes: torch.int8, torch.int8 2025-09-09T14:29:46.2219909Z triton_mm_64 0.2181 ms 100.0% ACC_TYPE='tl.int32', ALLOW_TF32=True, BLOCK_K=32, BLOCK_M=64, BLOCK_N=32, EVEN_K=False, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=8 2025-09-09T14:30:28.7992932Z triton_mm_67 0.2314 ms 94.2% ACC_TYPE='tl.int32', ALLOW_TF32=True, BLOCK_K=64, BLOCK_M=64, BLOCK_N=64, EVEN_K=False, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=8 2025-09-09T14:30:28.7996183Z triton_mm_59 0.2703 ms 80.7% ACC_TYPE='tl.int32', ALLOW_TF32=True, BLOCK_K=32, BLOCK_M=64, BLOCK_N=64, EVEN_K=False, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:30:28.7997345Z triton_mm_63 0.2939 ms 74.2% ACC_TYPE='tl.int32', ALLOW_TF32=True, BLOCK_K=32, BLOCK_M=128, BLOCK_N=64, EVEN_K=False, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=8 2025-09-09T14:30:28.7998299Z triton_mm_61 0.3052 ms 71.5% ACC_TYPE='tl.int32', ALLOW_TF32=True, BLOCK_K=32, BLOCK_M=128, BLOCK_N=64, EVEN_K=False, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:30:28.7998917Z _int_mm 0.3154 ms 69.2% 2025-09-09T14:30:28.7999614Z triton_mm_65 0.3512 ms 62.1% ACC_TYPE='tl.int32', ALLOW_TF32=True, BLOCK_K=32, BLOCK_M=32, BLOCK_N=64, EVEN_K=False, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=8 2025-09-09T14:30:28.8000569Z triton_mm_62 0.4403 ms 49.5% ACC_TYPE='tl.int32', ALLOW_TF32=True, BLOCK_K=32, BLOCK_M=64, BLOCK_N=128, EVEN_K=False, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=8 2025-09-09T14:30:28.8001527Z triton_mm_60 0.4444 ms 49.1% ACC_TYPE='tl.int32', ALLOW_TF32=True, BLOCK_K=32, BLOCK_M=64, BLOCK_N=128, EVEN_K=False, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:30:28.8002490Z triton_mm_66 0.4977 ms 43.8% ACC_TYPE='tl.int32', ALLOW_TF32=True, BLOCK_K=32, BLOCK_M=128, BLOCK_N=128, EVEN_K=False, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=8 2025-09-09T14:30:28.8003344Z SingleProcess AUTOTUNE benchmarking takes 0.4391 seconds and 2.1466 seconds precompiling for 12 choices 2025-09-09T14:30:28.8004056Z PASSED 2025-09-09T14:30:28.8004723Z test/integration/test_integration.py::PythonQuantUtilOpUnitTest::test_dynamic_quant_per_channel_numerics_cpu PASSED 2025-09-09T14:30:28.8005765Z test/integration/test_integration.py::PythonQuantUtilOpUnitTest::test_dynamic_quant_per_channel_numerics_cuda SKIPPED 2025-09-09T14:30:28.8006746Z test/integration/test_integration.py::PythonQuantUtilOpUnitTest::test_per_token_linear_cpu PASSED 2025-09-09T14:30:28.8007651Z test/integration/test_integration.py::PythonQuantUtilOpUnitTest::test_per_token_linear_cuda PASSED 2025-09-09T14:30:28.8008551Z test/integration/test_integration.py::PythonQuantUtilOpUnitTest::test_quantize_per_token_cpu PASSED 2025-09-09T14:30:28.8009456Z test/integration/test_integration.py::PythonQuantUtilOpUnitTest::test_quantize_per_token_cuda PASSED 2025-09-09T14:30:28.8010358Z test/integration/test_integration.py::PythonQuantUtilOpUnitTest::test_quantize_per_token_xpu SKIPPED 2025-09-09T14:30:28.8011331Z test/integration/test_integration.py::TestSubclass::test_aq_float8_dynamic_quant_rowwise_scaling_subclass_0_cpu SKIPPED 2025-09-09T14:30:28.8012336Z test/integration/test_integration.py::TestSubclass::test_aq_float8_dynamic_quant_rowwise_scaling_subclass_1_cpu SKIPPED 2025-09-09T14:30:28.8013351Z test/integration/test_integration.py::TestSubclass::test_aq_float8_dynamic_quant_rowwise_scaling_subclass_2_cpu SKIPPED 2025-09-09T14:30:28.8014365Z test/integration/test_integration.py::TestSubclass::test_aq_float8_dynamic_quant_rowwise_scaling_subclass_3_cuda SKIPPED 2025-09-09T14:30:28.8015378Z test/integration/test_integration.py::TestSubclass::test_aq_float8_dynamic_quant_rowwise_scaling_subclass_4_cuda SKIPPED 2025-09-09T14:30:28.8016389Z test/integration/test_integration.py::TestSubclass::test_aq_float8_dynamic_quant_rowwise_scaling_subclass_5_cuda SKIPPED 2025-09-09T14:30:28.8017409Z test/integration/test_integration.py::TestSubclass::test_aq_float8_dynamic_quant_tensorwise_scaling_subclass_0_cpu SKIPPED 2025-09-09T14:30:28.8018442Z test/integration/test_integration.py::TestSubclass::test_aq_float8_dynamic_quant_tensorwise_scaling_subclass_1_cpu SKIPPED 2025-09-09T14:30:28.8019484Z test/integration/test_integration.py::TestSubclass::test_aq_float8_dynamic_quant_tensorwise_scaling_subclass_2_cpu SKIPPED 2025-09-09T14:30:28.8020607Z test/integration/test_integration.py::TestSubclass::test_aq_float8_dynamic_quant_tensorwise_scaling_subclass_3_cuda SKIPPED 2025-09-09T14:30:28.8021650Z test/integration/test_integration.py::TestSubclass::test_aq_float8_dynamic_quant_tensorwise_scaling_subclass_4_cuda SKIPPED 2025-09-09T14:30:28.8023011Z test/integration/test_integration.py::TestSubclass::test_aq_float8_dynamic_quant_tensorwise_scaling_subclass_5_cuda SKIPPED 2025-09-09T14:30:28.8023994Z test/integration/test_integration.py::TestSubclass::test_aq_float8_weight_only_quant_subclass_0_cpu SKIPPED 2025-09-09T14:30:28.8024919Z test/integration/test_integration.py::TestSubclass::test_aq_float8_weight_only_quant_subclass_1_cpu SKIPPED 2025-09-09T14:30:28.8025830Z test/integration/test_integration.py::TestSubclass::test_aq_float8_weight_only_quant_subclass_2_cpu SKIPPED 2025-09-09T14:30:28.8026754Z test/integration/test_integration.py::TestSubclass::test_aq_float8_weight_only_quant_subclass_3_cuda SKIPPED 2025-09-09T14:30:28.8027676Z test/integration/test_integration.py::TestSubclass::test_aq_float8_weight_only_quant_subclass_4_cuda SKIPPED 2025-09-09T14:30:28.8028591Z test/integration/test_integration.py::TestSubclass::test_aq_float8_weight_only_quant_subclass_5_cuda SKIPPED 2025-09-09T14:30:28.8029480Z test/integration/test_integration.py::TestSubclass::test_aq_int8_dynamic_quant_subclass_0_cpu SKIPPED 2025-09-09T14:30:28.8030345Z test/integration/test_integration.py::TestSubclass::test_aq_int8_dynamic_quant_subclass_1_cpu SKIPPED 2025-09-09T14:30:28.8031207Z test/integration/test_integration.py::TestSubclass::test_aq_int8_dynamic_quant_subclass_2_cpu SKIPPED 2025-09-09T14:30:28.8032027Z test/integration/test_integration.py::TestSubclass::test_aq_int8_dynamic_quant_subclass_3_cuda AUTOTUNE int_mm(32x64, 64x32) 2025-09-09T14:30:28.8032588Z strides: [64, 1], [1, 64] 2025-09-09T14:30:28.8032835Z dtypes: torch.int8, torch.int8 2025-09-09T14:30:28.8033441Z triton_mm_73 0.0266 ms 100.0% ACC_TYPE='tl.int32', ALLOW_TF32=True, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:30:28.8034402Z triton_mm_74 0.0266 ms 100.0% ACC_TYPE='tl.int32', ALLOW_TF32=True, BLOCK_K=64, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:30:28.8035356Z triton_mm_71 0.0276 ms 96.3% ACC_TYPE='tl.int32', ALLOW_TF32=True, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:30:28.8036288Z triton_mm_72 0.0276 ms 96.3% ACC_TYPE='tl.int32', ALLOW_TF32=True, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=4 2025-09-09T14:30:28.8045541Z triton_mm_70 0.0287 ms 92.9% ACC_TYPE='tl.int32', ALLOW_TF32=True, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:30:28.8046226Z _int_mm 0.0399 ms 66.7% 2025-09-09T14:30:28.8046708Z SingleProcess AUTOTUNE benchmarking takes 0.0780 seconds and 0.2670 seconds precompiling for 6 choices 2025-09-09T14:30:28.8047274Z PASSED 2025-09-09T14:30:28.8047851Z test/integration/test_integration.py::TestSubclass::test_aq_int8_dynamic_quant_subclass_4_cuda PASSED 2025-09-09T14:30:28.8048730Z test/integration/test_integration.py::TestSubclass::test_aq_int8_dynamic_quant_subclass_5_cuda PASSED 2025-09-09T14:30:28.8049641Z test/integration/test_integration.py::TestSubclass::test_aq_int8_weight_only_quant_2_subclass_0_cpu SKIPPED 2025-09-09T14:30:28.8050562Z test/integration/test_integration.py::TestSubclass::test_aq_int8_weight_only_quant_2_subclass_1_cpu SKIPPED 2025-09-09T14:30:28.8051487Z test/integration/test_integration.py::TestSubclass::test_aq_int8_weight_only_quant_2_subclass_2_cpu SKIPPED 2025-09-09T14:30:28.8052580Z test/integration/test_integration.py::TestSubclass::test_aq_int8_weight_only_quant_2_subclass_3_cuda PASSED 2025-09-09T14:30:28.8053502Z test/integration/test_integration.py::TestSubclass::test_aq_int8_weight_only_quant_2_subclass_4_cuda PASSED 2025-09-09T14:30:28.8054544Z test/integration/test_integration.py::TestSubclass::test_aq_int8_weight_only_quant_2_subclass_5_cuda PASSED 2025-09-09T14:30:28.8055460Z test/integration/test_integration.py::TestSubclass::test_aq_int8_weight_only_quant_3_subclass_0_cpu SKIPPED 2025-09-09T14:30:28.8056384Z test/integration/test_integration.py::TestSubclass::test_aq_int8_weight_only_quant_3_subclass_1_cpu SKIPPED 2025-09-09T14:30:28.8057309Z test/integration/test_integration.py::TestSubclass::test_aq_int8_weight_only_quant_3_subclass_2_cpu SKIPPED 2025-09-09T14:30:28.8058202Z test/integration/test_integration.py::TestSubclass::test_aq_int8_weight_only_quant_3_subclass_3_cuda AUTOTUNE addmm(32x32, 32x64, 64x32) 2025-09-09T14:30:28.8058840Z strides: [0, 1], [64, 1], [32, 1] 2025-09-09T14:30:28.8059165Z dtypes: torch.float32, torch.float32, torch.float32 2025-09-09T14:30:28.8059868Z triton_mm_88 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=64, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:30:28.8060867Z triton_mm_94 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=64, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=4 2025-09-09T14:30:56.6708184Z triton_mm_85 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=16, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=1, num_warps=2 2025-09-09T14:30:56.6710980Z triton_mm_86 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=64, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:30:56.6712215Z triton_mm_87 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:30:56.6713392Z triton_mm_89 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=16, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:30:56.6714545Z triton_mm_90 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:30:56.6715857Z triton_mm_91 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=64, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:30:56.6716824Z triton_mm_93 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=4 2025-09-09T14:30:56.6717788Z triton_mm_92 0.0287 ms 92.9% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:30:56.6718650Z SingleProcess AUTOTUNE benchmarking takes 0.1591 seconds and 0.3395 seconds precompiling for 12 choices 2025-09-09T14:30:56.6719512Z PASSED 2025-09-09T14:30:56.6720131Z test/integration/test_integration.py::TestSubclass::test_aq_int8_weight_only_quant_3_subclass_4_cuda AUTOTUNE addmm(32x32, 32x64, 64x32) 2025-09-09T14:30:56.6720759Z strides: [0, 1], [64, 1], [32, 1] 2025-09-09T14:30:56.6721064Z dtypes: torch.float16, torch.float16, torch.float16 2025-09-09T14:30:56.6721756Z triton_mm_95 0.0276 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=16, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=1, num_warps=2 2025-09-09T14:30:56.6723093Z triton_mm_97 0.0276 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:30:56.6724567Z triton_mm_98 0.0276 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=64, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:30:56.6725722Z triton_mm_100 0.0276 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:30:56.6726739Z triton_mm_101 0.0276 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=64, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:30:56.6727742Z triton_mm_102 0.0276 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:30:56.6728806Z triton_mm_103 0.0276 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=4 2025-09-09T14:30:56.6729810Z triton_mm_104 0.0276 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=64, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=4 2025-09-09T14:30:56.6730807Z triton_mm_99 0.0277 ms 99.8% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=16, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:30:56.6731798Z triton_mm_96 0.0287 ms 96.4% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=64, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:30:56.6732668Z SingleProcess AUTOTUNE benchmarking takes 0.1609 seconds and 0.3048 seconds precompiling for 12 choices 2025-09-09T14:30:56.6733237Z PASSED 2025-09-09T14:30:56.6733812Z test/integration/test_integration.py::TestSubclass::test_aq_int8_weight_only_quant_3_subclass_5_cuda AUTOTUNE addmm(32x32, 32x64, 64x32) 2025-09-09T14:30:56.6734446Z strides: [0, 1], [64, 1], [32, 1] 2025-09-09T14:30:56.6734776Z dtypes: torch.bfloat16, torch.bfloat16, torch.bfloat16 2025-09-09T14:30:56.6735481Z triton_mm_109 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=16, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:30:56.6736503Z triton_mm_110 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:30:56.6737501Z triton_mm_111 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=64, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:30:56.6738489Z triton_mm_106 0.0267 ms 99.9% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=64, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:30:56.6739493Z triton_mm_107 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:30:56.6740488Z triton_mm_108 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=64, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:30:56.6741473Z triton_mm_112 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:30:56.6742461Z triton_mm_113 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=4 2025-09-09T14:30:56.6743447Z triton_mm_114 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=64, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=4 2025-09-09T14:30:56.6744516Z triton_mm_105 0.0287 ms 92.9% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=16, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=1, num_warps=2 2025-09-09T14:30:56.6745469Z SingleProcess AUTOTUNE benchmarking takes 0.1598 seconds and 0.2971 seconds precompiling for 12 choices 2025-09-09T14:30:56.6746013Z PASSED 2025-09-09T14:30:56.6746614Z test/integration/test_integration.py::TestSubclass::test_aq_int8_weight_only_quant_subclass_0_cpu SKIPPED 2025-09-09T14:30:56.6747547Z test/integration/test_integration.py::TestSubclass::test_aq_int8_weight_only_quant_subclass_1_cpu SKIPPED 2025-09-09T14:30:56.6748469Z test/integration/test_integration.py::TestSubclass::test_aq_int8_weight_only_quant_subclass_2_cpu SKIPPED 2025-09-09T14:30:56.6749397Z test/integration/test_integration.py::TestSubclass::test_aq_int8_weight_only_quant_subclass_3_cuda SKIPPED 2025-09-09T14:30:56.6750332Z test/integration/test_integration.py::TestSubclass::test_aq_int8_weight_only_quant_subclass_4_cuda SKIPPED 2025-09-09T14:30:56.6751252Z test/integration/test_integration.py::TestSubclass::test_aq_int8_weight_only_quant_subclass_5_cuda SKIPPED 2025-09-09T14:30:56.6752161Z test/integration/test_integration.py::TestSubclass::test_autoquantizable_flatten_unflatten PASSED 2025-09-09T14:30:56.6753117Z test/integration/test_integration.py::TestSubclass::test_dequantize_int4_weight_only_quant_subclass_0_cpu SKIPPED 2025-09-09T14:30:56.6754121Z test/integration/test_integration.py::TestSubclass::test_dequantize_int4_weight_only_quant_subclass_1_cpu SKIPPED 2025-09-09T14:30:56.6755106Z test/integration/test_integration.py::TestSubclass::test_dequantize_int4_weight_only_quant_subclass_2_cpu SKIPPED 2025-09-09T14:30:56.6756107Z test/integration/test_integration.py::TestSubclass::test_dequantize_int4_weight_only_quant_subclass_3_cuda SKIPPED 2025-09-09T14:30:56.6757098Z test/integration/test_integration.py::TestSubclass::test_dequantize_int4_weight_only_quant_subclass_4_cuda SKIPPED 2025-09-09T14:30:56.6758071Z test/integration/test_integration.py::TestSubclass::test_dequantize_int4_weight_only_quant_subclass_5_cuda PASSED 2025-09-09T14:30:56.6759098Z test/integration/test_integration.py::TestSubclass::test_dequantize_int4_weight_only_quant_subclass_grouped_0_cpu SKIPPED 2025-09-09T14:30:56.6760208Z test/integration/test_integration.py::TestSubclass::test_dequantize_int4_weight_only_quant_subclass_grouped_1_cpu SKIPPED 2025-09-09T14:30:56.6761242Z test/integration/test_integration.py::TestSubclass::test_dequantize_int4_weight_only_quant_subclass_grouped_2_cpu SKIPPED 2025-09-09T14:30:56.6762286Z test/integration/test_integration.py::TestSubclass::test_dequantize_int4_weight_only_quant_subclass_grouped_3_cuda SKIPPED 2025-09-09T14:30:56.6763329Z test/integration/test_integration.py::TestSubclass::test_dequantize_int4_weight_only_quant_subclass_grouped_4_cuda SKIPPED 2025-09-09T14:30:56.6764379Z test/integration/test_integration.py::TestSubclass::test_dequantize_int4_weight_only_quant_subclass_grouped_5_cuda PASSED 2025-09-09T14:31:27.8029915Z test/integration/test_integration.py::TestSubclass::test_dequantize_int8_dynamic_quant_subclass_0_cpu PASSED 2025-09-09T14:31:27.8030919Z test/integration/test_integration.py::TestSubclass::test_dequantize_int8_dynamic_quant_subclass_1_cpu PASSED 2025-09-09T14:31:27.8031869Z test/integration/test_integration.py::TestSubclass::test_dequantize_int8_dynamic_quant_subclass_2_cpu PASSED 2025-09-09T14:31:27.8032807Z test/integration/test_integration.py::TestSubclass::test_dequantize_int8_dynamic_quant_subclass_3_cuda PASSED 2025-09-09T14:31:27.8033762Z test/integration/test_integration.py::TestSubclass::test_dequantize_int8_dynamic_quant_subclass_4_cuda PASSED 2025-09-09T14:31:27.8034712Z test/integration/test_integration.py::TestSubclass::test_dequantize_int8_dynamic_quant_subclass_5_cuda PASSED 2025-09-09T14:31:27.8035968Z test/integration/test_integration.py::TestSubclass::test_dequantize_int8_weight_only_quant_subclass_0_cpu PASSED 2025-09-09T14:31:27.8037120Z test/integration/test_integration.py::TestSubclass::test_dequantize_int8_weight_only_quant_subclass_1_cpu PASSED 2025-09-09T14:31:27.8038092Z test/integration/test_integration.py::TestSubclass::test_dequantize_int8_weight_only_quant_subclass_2_cpu PASSED 2025-09-09T14:31:27.8039056Z test/integration/test_integration.py::TestSubclass::test_dequantize_int8_weight_only_quant_subclass_3_cuda PASSED 2025-09-09T14:31:27.8040106Z test/integration/test_integration.py::TestSubclass::test_dequantize_int8_weight_only_quant_subclass_4_cuda PASSED 2025-09-09T14:31:27.8041070Z test/integration/test_integration.py::TestSubclass::test_dequantize_int8_weight_only_quant_subclass_5_cuda PASSED 2025-09-09T14:31:27.8041943Z test/integration/test_integration.py::TestSubclass::test_gemlite_layout_0_cpu SKIPPED 2025-09-09T14:31:27.8042711Z test/integration/test_integration.py::TestSubclass::test_gemlite_layout_1_cpu SKIPPED 2025-09-09T14:31:27.8043470Z test/integration/test_integration.py::TestSubclass::test_gemlite_layout_2_cpu SKIPPED 2025-09-09T14:31:27.8044253Z test/integration/test_integration.py::TestSubclass::test_gemlite_layout_3_cuda SKIPPED 2025-09-09T14:31:27.8045018Z test/integration/test_integration.py::TestSubclass::test_gemlite_layout_4_cuda SKIPPED 2025-09-09T14:31:27.8045794Z test/integration/test_integration.py::TestSubclass::test_gemlite_layout_5_cuda SKIPPED 2025-09-09T14:31:27.8046669Z test/integration/test_integration.py::TestSubclass::test_int4_weight_only_hqq_quant_subclass_api_0_cpu SKIPPED 2025-09-09T14:31:27.8047660Z test/integration/test_integration.py::TestSubclass::test_int4_weight_only_hqq_quant_subclass_api_1_cpu SKIPPED 2025-09-09T14:31:27.8048609Z test/integration/test_integration.py::TestSubclass::test_int4_weight_only_hqq_quant_subclass_api_2_cpu PASSED 2025-09-09T14:31:27.8049552Z test/integration/test_integration.py::TestSubclass::test_int4_weight_only_hqq_quant_subclass_api_3_cuda SKIPPED 2025-09-09T14:31:27.8050503Z test/integration/test_integration.py::TestSubclass::test_int4_weight_only_hqq_quant_subclass_api_4_cuda SKIPPED 2025-09-09T14:31:27.8051418Z test/integration/test_integration.py::TestSubclass::test_int4_weight_only_hqq_quant_subclass_api_5_cuda AUTOTUNE addmm(16x16, 16x16, 16x16) 2025-09-09T14:31:27.8052036Z strides: [0, 1], [16, 1], [1, 16] 2025-09-09T14:31:27.8052357Z dtypes: torch.bfloat16, torch.bfloat16, torch.bfloat16 2025-09-09T14:31:27.8053059Z triton_mm_117 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=16, BLOCK_M=16, BLOCK_N=16, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=1 2025-09-09T14:31:27.8054058Z triton_mm_118 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=16, BLOCK_M=16, BLOCK_N=16, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=1 2025-09-09T14:31:27.8055041Z triton_mm_119 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=16, BLOCK_M=16, BLOCK_N=16, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=1 2025-09-09T14:31:27.8056022Z triton_mm_115 0.0297 ms 89.7% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=16, BLOCK_M=16, BLOCK_N=16, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=1, num_warps=1 2025-09-09T14:31:27.8056993Z triton_mm_116 0.0297 ms 89.7% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=16, BLOCK_M=16, BLOCK_N=16, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=1 2025-09-09T14:31:27.8057674Z addmm 0.0522 ms 51.0% 2025-09-09T14:31:27.8057901Z bias_addmm 0.0737 ms 36.1% 2025-09-09T14:31:27.8058392Z SingleProcess AUTOTUNE benchmarking takes 0.0993 seconds and 0.2692 seconds precompiling for 7 choices 2025-09-09T14:31:27.8058917Z PASSED 2025-09-09T14:31:27.8059586Z test/integration/test_integration.py::TestSubclass::test_int4_weight_only_quant_subclass_0_cpu SKIPPED 2025-09-09T14:31:27.8060488Z test/integration/test_integration.py::TestSubclass::test_int4_weight_only_quant_subclass_1_cpu SKIPPED 2025-09-09T14:31:27.8061446Z test/integration/test_integration.py::TestSubclass::test_int4_weight_only_quant_subclass_2_cpu SKIPPED 2025-09-09T14:31:27.8062338Z test/integration/test_integration.py::TestSubclass::test_int4_weight_only_quant_subclass_3_cuda SKIPPED 2025-09-09T14:31:27.8063222Z test/integration/test_integration.py::TestSubclass::test_int4_weight_only_quant_subclass_4_cuda SKIPPED 2025-09-09T14:31:27.8064108Z test/integration/test_integration.py::TestSubclass::test_int4_weight_only_quant_subclass_5_cuda PASSED 2025-09-09T14:31:27.8065007Z test/integration/test_integration.py::TestSubclass::test_int4_weight_only_quant_subclass_api_0_cpu SKIPPED 2025-09-09T14:31:27.8065920Z test/integration/test_integration.py::TestSubclass::test_int4_weight_only_quant_subclass_api_1_cpu SKIPPED 2025-09-09T14:31:27.8066830Z test/integration/test_integration.py::TestSubclass::test_int4_weight_only_quant_subclass_api_2_cpu PASSED 2025-09-09T14:31:27.8067748Z test/integration/test_integration.py::TestSubclass::test_int4_weight_only_quant_subclass_api_3_cuda SKIPPED 2025-09-09T14:31:27.8068667Z test/integration/test_integration.py::TestSubclass::test_int4_weight_only_quant_subclass_api_4_cuda SKIPPED 2025-09-09T14:31:27.8069581Z test/integration/test_integration.py::TestSubclass::test_int4_weight_only_quant_subclass_api_5_cuda PASSED 2025-09-09T14:31:27.8070517Z test/integration/test_integration.py::TestSubclass::test_int4_weight_only_quant_subclass_api_grouped_0_cpu SKIPPED 2025-09-09T14:31:27.8071487Z test/integration/test_integration.py::TestSubclass::test_int4_weight_only_quant_subclass_api_grouped_1_cpu SKIPPED 2025-09-09T14:31:27.8072456Z test/integration/test_integration.py::TestSubclass::test_int4_weight_only_quant_subclass_api_grouped_2_cpu PASSED 2025-09-09T14:31:27.8073421Z test/integration/test_integration.py::TestSubclass::test_int4_weight_only_quant_subclass_api_grouped_3_cuda SKIPPED 2025-09-09T14:31:27.8074407Z test/integration/test_integration.py::TestSubclass::test_int4_weight_only_quant_subclass_api_grouped_4_cuda SKIPPED 2025-09-09T14:31:27.8075346Z test/integration/test_integration.py::TestSubclass::test_int4_weight_only_quant_subclass_api_grouped_5_cuda AUTOTUNE addmm(256x16, 256x16, 16x16) 2025-09-09T14:31:27.8075986Z strides: [0, 1], [16, 1], [1, 16] 2025-09-09T14:31:27.8076300Z dtypes: torch.bfloat16, torch.bfloat16, torch.bfloat16 2025-09-09T14:31:27.8077004Z triton_mm_121 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=16, BLOCK_M=32, BLOCK_N=16, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=2 2025-09-09T14:31:27.8078002Z triton_mm_123 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=16, BLOCK_M=64, BLOCK_N=16, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:31:27.8078979Z triton_mm_120 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=16, BLOCK_M=32, BLOCK_N=16, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=1, num_warps=2 2025-09-09T14:31:27.8080017Z triton_mm_122 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=16, BLOCK_M=32, BLOCK_N=16, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=2 2025-09-09T14:31:27.8080988Z triton_mm_124 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=16, BLOCK_M=64, BLOCK_N=16, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:31:27.8081955Z triton_mm_125 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=16, BLOCK_M=64, BLOCK_N=16, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:31:27.8083056Z triton_mm_127 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=16, BLOCK_M=128, BLOCK_N=16, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:31:27.8084049Z triton_mm_129 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=16, BLOCK_M=128, BLOCK_N=16, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=8 2025-09-09T14:31:27.8086436Z triton_mm_130 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=16, BLOCK_M=128, BLOCK_N=16, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=8 2025-09-09T14:31:27.8087422Z triton_mm_128 0.0286 ms 93.0% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=16, BLOCK_M=128, BLOCK_N=16, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=8 2025-09-09T14:31:27.8088289Z SingleProcess AUTOTUNE benchmarking takes 0.1734 seconds and 0.2839 seconds precompiling for 13 choices 2025-09-09T14:31:27.8088798Z AUTOTUNE addmm(256x8, 256x8, 8x8) 2025-09-09T14:31:27.8089058Z strides: [0, 1], [8, 1], [1, 8] 2025-09-09T14:31:27.8089368Z dtypes: torch.bfloat16, torch.bfloat16, torch.bfloat16 2025-09-09T14:31:53.1584928Z triton_mm_165 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=16, BLOCK_M=32, BLOCK_N=16, EVEN_K=False, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=2 2025-09-09T14:31:53.1586014Z triton_mm_168 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=16, BLOCK_M=64, BLOCK_N=16, EVEN_K=False, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:31:53.1587010Z triton_mm_174 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=16, BLOCK_M=128, BLOCK_N=16, EVEN_K=False, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=8 2025-09-09T14:31:53.1588001Z triton_mm_173 0.0277 ms 96.1% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=16, BLOCK_M=128, BLOCK_N=16, EVEN_K=False, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=8 2025-09-09T14:31:53.1589001Z triton_mm_166 0.0287 ms 92.9% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=16, BLOCK_M=32, BLOCK_N=16, EVEN_K=False, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=2 2025-09-09T14:31:53.1590001Z triton_mm_169 0.0287 ms 92.9% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=16, BLOCK_M=64, BLOCK_N=16, EVEN_K=False, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:31:53.1590998Z triton_mm_172 0.0287 ms 92.9% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=16, BLOCK_M=128, BLOCK_N=16, EVEN_K=False, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=8 2025-09-09T14:31:53.1591982Z triton_mm_164 0.0297 ms 89.7% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=16, BLOCK_M=32, BLOCK_N=16, EVEN_K=False, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=1, num_warps=2 2025-09-09T14:31:53.1592969Z triton_mm_167 0.0297 ms 89.7% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=16, BLOCK_M=64, BLOCK_N=16, EVEN_K=False, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:31:53.1594001Z triton_mm_170 0.0297 ms 89.7% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=16, BLOCK_M=64, BLOCK_N=16, EVEN_K=False, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=4 2025-09-09T14:31:53.1594885Z SingleProcess AUTOTUNE benchmarking takes 0.1798 seconds and 0.2893 seconds precompiling for 13 choices 2025-09-09T14:31:53.1595615Z PASSED 2025-09-09T14:31:53.1596253Z test/integration/test_integration.py::TestSubclass::test_int4_weight_only_quant_subclass_grouped_0_cpu SKIPPED 2025-09-09T14:31:53.1597229Z test/integration/test_integration.py::TestSubclass::test_int4_weight_only_quant_subclass_grouped_1_cpu SKIPPED 2025-09-09T14:31:53.1598178Z test/integration/test_integration.py::TestSubclass::test_int4_weight_only_quant_subclass_grouped_2_cpu SKIPPED 2025-09-09T14:31:53.1599122Z test/integration/test_integration.py::TestSubclass::test_int4_weight_only_quant_subclass_grouped_3_cuda SKIPPED 2025-09-09T14:31:53.1600537Z test/integration/test_integration.py::TestSubclass::test_int4_weight_only_quant_subclass_grouped_4_cuda SKIPPED 2025-09-09T14:31:53.1601502Z test/integration/test_integration.py::TestSubclass::test_int4_weight_only_quant_subclass_grouped_5_cuda SKIPPED 2025-09-09T14:31:53.1602715Z test/integration/test_integration.py::TestSubclass::test_int8_dynamic_quant_subclass_0_cpu SKIPPED 2025-09-09T14:31:53.1603593Z test/integration/test_integration.py::TestSubclass::test_int8_dynamic_quant_subclass_1_cpu SKIPPED 2025-09-09T14:31:53.1604498Z test/integration/test_integration.py::TestSubclass::test_int8_dynamic_quant_subclass_2_cpu SKIPPED 2025-09-09T14:31:53.1605356Z test/integration/test_integration.py::TestSubclass::test_int8_dynamic_quant_subclass_3_cuda PASSED 2025-09-09T14:31:53.1606205Z test/integration/test_integration.py::TestSubclass::test_int8_dynamic_quant_subclass_4_cuda PASSED 2025-09-09T14:31:53.1607047Z test/integration/test_integration.py::TestSubclass::test_int8_dynamic_quant_subclass_5_cuda PASSED 2025-09-09T14:31:53.1607923Z test/integration/test_integration.py::TestSubclass::test_int8_dynamic_quant_subclass_api_00_cpu SKIPPED 2025-09-09T14:31:53.1608803Z test/integration/test_integration.py::TestSubclass::test_int8_dynamic_quant_subclass_api_01_cpu SKIPPED 2025-09-09T14:31:53.1609695Z test/integration/test_integration.py::TestSubclass::test_int8_dynamic_quant_subclass_api_02_cpu SKIPPED 2025-09-09T14:31:53.1610581Z test/integration/test_integration.py::TestSubclass::test_int8_dynamic_quant_subclass_api_03_cpu SKIPPED 2025-09-09T14:31:53.1611460Z test/integration/test_integration.py::TestSubclass::test_int8_dynamic_quant_subclass_api_04_cpu SKIPPED 2025-09-09T14:31:53.1612343Z test/integration/test_integration.py::TestSubclass::test_int8_dynamic_quant_subclass_api_05_cpu SKIPPED 2025-09-09T14:31:53.1613228Z test/integration/test_integration.py::TestSubclass::test_int8_dynamic_quant_subclass_api_06_cuda SKIPPED 2025-09-09T14:31:53.1614129Z test/integration/test_integration.py::TestSubclass::test_int8_dynamic_quant_subclass_api_07_cuda SKIPPED 2025-09-09T14:31:53.1615019Z test/integration/test_integration.py::TestSubclass::test_int8_dynamic_quant_subclass_api_08_cuda SKIPPED 2025-09-09T14:31:53.1615906Z test/integration/test_integration.py::TestSubclass::test_int8_dynamic_quant_subclass_api_09_cuda SKIPPED 2025-09-09T14:31:53.1616796Z test/integration/test_integration.py::TestSubclass::test_int8_dynamic_quant_subclass_api_10_cuda SKIPPED 2025-09-09T14:31:53.1617679Z test/integration/test_integration.py::TestSubclass::test_int8_dynamic_quant_subclass_api_11_cuda SKIPPED 2025-09-09T14:31:53.1618567Z test/integration/test_integration.py::TestSubclass::test_int8_weight_only_quant_subclass_0_cpu SKIPPED 2025-09-09T14:31:53.1619450Z test/integration/test_integration.py::TestSubclass::test_int8_weight_only_quant_subclass_1_cpu SKIPPED 2025-09-09T14:31:53.1620331Z test/integration/test_integration.py::TestSubclass::test_int8_weight_only_quant_subclass_2_cpu SKIPPED 2025-09-09T14:31:53.1621160Z test/integration/test_integration.py::TestSubclass::test_int8_weight_only_quant_subclass_3_cuda AUTOTUNE mm(32x64, 64x32) 2025-09-09T14:31:53.1621725Z strides: [64, 1], [32, 1] 2025-09-09T14:31:53.1621983Z dtypes: torch.float32, torch.float32 2025-09-09T14:31:53.1622964Z triton_mm_226 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:31:53.1623964Z triton_mm_227 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:31:53.1624960Z triton_mm_229 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:31:53.1626141Z triton_mm_230 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:31:53.1627260Z triton_mm_231 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=4 2025-09-09T14:31:53.1628258Z triton_mm_232 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=4 2025-09-09T14:31:53.1629249Z triton_mm_223 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=1, num_warps=2 2025-09-09T14:31:53.1630239Z triton_mm_224 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:31:53.1631235Z triton_mm_225 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:31:53.1632221Z triton_mm_228 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:31:53.1633094Z SingleProcess AUTOTUNE benchmarking takes 0.1389 seconds and 0.4186 seconds precompiling for 11 choices 2025-09-09T14:31:53.1633642Z PASSED 2025-09-09T14:31:53.1634207Z test/integration/test_integration.py::TestSubclass::test_int8_weight_only_quant_subclass_4_cuda AUTOTUNE mm(32x64, 64x32) 2025-09-09T14:31:53.1634788Z strides: [64, 1], [32, 1] 2025-09-09T14:31:53.1635038Z dtypes: torch.float16, torch.float16 2025-09-09T14:31:53.1635692Z triton_mm_234 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:31:53.1636689Z triton_mm_236 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:31:53.1637683Z triton_mm_237 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:31:53.1638684Z triton_mm_239 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:32:45.3713091Z triton_mm_240 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:32:45.3715847Z triton_mm_241 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=4 2025-09-09T14:32:45.3717145Z triton_mm_242 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=4 2025-09-09T14:32:45.3718438Z triton_mm_233 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=1, num_warps=2 2025-09-09T14:32:45.3721676Z triton_mm_235 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:32:45.3723115Z triton_mm_238 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:32:45.3724624Z SingleProcess AUTOTUNE benchmarking takes 0.1404 seconds and 0.2828 seconds precompiling for 11 choices 2025-09-09T14:32:45.3725495Z PASSED 2025-09-09T14:32:45.3726147Z test/integration/test_integration.py::TestSubclass::test_int8_weight_only_quant_subclass_5_cuda AUTOTUNE mm(32x64, 64x32) 2025-09-09T14:32:45.3727081Z strides: [64, 1], [32, 1] 2025-09-09T14:32:45.3727383Z dtypes: torch.bfloat16, torch.bfloat16 2025-09-09T14:32:45.3728187Z triton_mm_245 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:32:45.3729432Z triton_mm_244 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:32:45.3730654Z triton_mm_246 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:32:45.3731889Z triton_mm_251 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=4 2025-09-09T14:32:45.3733124Z triton_mm_247 0.0276 ms 92.8% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:32:45.3734344Z triton_mm_243 0.0276 ms 92.6% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=1, num_warps=2 2025-09-09T14:32:45.3735572Z triton_mm_248 0.0276 ms 92.6% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:32:45.3736786Z triton_mm_249 0.0276 ms 92.6% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:32:45.3738019Z triton_mm_250 0.0276 ms 92.6% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:32:45.3739255Z triton_mm_252 0.0276 ms 92.6% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=4 2025-09-09T14:32:45.3740325Z SingleProcess AUTOTUNE benchmarking takes 0.1404 seconds and 0.2886 seconds precompiling for 11 choices 2025-09-09T14:32:45.3740990Z PASSED 2025-09-09T14:32:45.3741711Z test/integration/test_integration.py::TestSubclass::test_int8_weight_only_quant_subclass_api_0_cpu PASSED 2025-09-09T14:32:45.3742848Z test/integration/test_integration.py::TestSubclass::test_int8_weight_only_quant_subclass_api_1_cpu PASSED 2025-09-09T14:32:45.3743976Z test/integration/test_integration.py::TestSubclass::test_int8_weight_only_quant_subclass_api_2_cpu PASSED 2025-09-09T14:32:45.3745049Z test/integration/test_integration.py::TestSubclass::test_int8_weight_only_quant_subclass_api_3_cuda AUTOTUNE mm(32x32, 32x32) 2025-09-09T14:32:45.3745767Z strides: [32, 1], [32, 1] 2025-09-09T14:32:45.3746083Z dtypes: torch.float32, torch.float32 2025-09-09T14:32:45.3746881Z triton_mm_263 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=1, num_warps=2 2025-09-09T14:32:45.3748144Z triton_mm_265 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:32:45.3749492Z triton_mm_266 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:32:45.3750598Z triton_mm_268 0.0276 ms 96.4% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=4 2025-09-09T14:32:45.3751599Z triton_mm_264 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:32:45.3752670Z triton_mm_267 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:32:45.3753304Z mm 0.0399 ms 66.7% 2025-09-09T14:32:45.3753770Z SingleProcess AUTOTUNE benchmarking takes 0.0899 seconds and 0.0002 seconds precompiling for 7 choices 2025-09-09T14:32:45.3754315Z PASSED 2025-09-09T14:32:45.3754851Z test/integration/test_integration.py::TestSubclass::test_int8_weight_only_quant_subclass_api_4_cuda AUTOTUNE mm(32x32, 32x32) 2025-09-09T14:32:45.3755427Z strides: [32, 1], [32, 1] 2025-09-09T14:32:45.3755681Z dtypes: torch.float16, torch.float16 2025-09-09T14:32:45.3756328Z triton_mm_279 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=1, num_warps=2 2025-09-09T14:32:45.3757344Z triton_mm_280 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:32:45.3758344Z triton_mm_283 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:32:45.3759409Z triton_mm_281 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:32:45.3760408Z triton_mm_282 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:32:45.3761405Z triton_mm_284 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=4 2025-09-09T14:32:45.3762025Z mm 0.0410 ms 65.0% 2025-09-09T14:32:45.3762487Z SingleProcess AUTOTUNE benchmarking takes 0.0896 seconds and 0.0002 seconds precompiling for 7 choices 2025-09-09T14:32:45.3763011Z PASSED 2025-09-09T14:32:45.3763544Z test/integration/test_integration.py::TestSubclass::test_int8_weight_only_quant_subclass_api_5_cuda AUTOTUNE mm(32x32, 32x32) 2025-09-09T14:32:45.3764129Z strides: [32, 1], [32, 1] 2025-09-09T14:32:45.3764377Z dtypes: torch.bfloat16, torch.bfloat16 2025-09-09T14:32:45.3765036Z triton_mm_296 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:32:45.3766034Z triton_mm_295 0.0266 ms 99.8% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=1, num_warps=2 2025-09-09T14:32:45.3767025Z triton_mm_297 0.0266 ms 99.8% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:32:45.3768024Z triton_mm_298 0.0266 ms 99.8% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:32:45.3769018Z triton_mm_299 0.0266 ms 99.8% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:32:45.3770014Z triton_mm_300 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=4 2025-09-09T14:32:45.3770636Z mm 0.0399 ms 66.5% 2025-09-09T14:32:45.3771176Z SingleProcess AUTOTUNE benchmarking takes 0.0936 seconds and 0.0003 seconds precompiling for 7 choices 2025-09-09T14:32:45.3771707Z PASSED 2025-09-09T14:32:45.3772374Z test/integration/test_integration.py::TestSubclass::test_int8_weight_only_quant_with_freeze_0_cpu AUTOTUNE packed_linear(32x64, 1459233x1, 32x64) 2025-09-09T14:32:45.3773024Z strides: [64, 1], [1, 0], [64, 1] 2025-09-09T14:32:45.3773323Z dtypes: torch.float32, torch.float32, torch.float32 2025-09-09T14:33:14.5707808Z cpp_CppMicroGemmFP32Vec_0 0.0063 ms 100.0% 2025-09-09T14:33:14.5710300Z _mkl_linear 0.0274 ms 23.1% 2025-09-09T14:33:14.5711243Z SingleProcess AUTOTUNE benchmarking takes 0.2493 seconds and 2.0917 seconds precompiling for 2 choices 2025-09-09T14:33:14.5711812Z AUTOTUNE packed_linear(32x32, 1459233x1, 32x32) 2025-09-09T14:33:14.5712116Z strides: [32, 1], [1, 0], [32, 1] 2025-09-09T14:33:14.5712423Z dtypes: torch.float32, torch.float32, torch.float32 2025-09-09T14:33:14.5712794Z cpp_CppMicroGemmFP32Vec_1 0.0060 ms 100.0% 2025-09-09T14:33:14.5713129Z _mkl_linear 0.0269 ms 22.5% 2025-09-09T14:33:14.5713679Z SingleProcess AUTOTUNE benchmarking takes 0.2491 seconds and 2.0888 seconds precompiling for 2 choices 2025-09-09T14:33:14.5714391Z PASSED 2025-09-09T14:33:14.5714944Z test/integration/test_integration.py::TestSubclass::test_int8_weight_only_quant_with_freeze_1_cpu AUTOTUNE mm(32x64, 64x32) 2025-09-09T14:33:14.5715521Z strides: [64, 1], [1, 64] 2025-09-09T14:33:14.5715776Z dtypes: torch.float16, torch.float16 2025-09-09T14:33:14.5716090Z cpp_CppMicroGemmFP32Vec_2 0.0066 ms 100.0% 2025-09-09T14:33:14.5716420Z mm 0.0326 ms 20.3% 2025-09-09T14:33:14.5716865Z SingleProcess AUTOTUNE benchmarking takes 0.2488 seconds and 2.2341 seconds precompiling for 2 choices 2025-09-09T14:33:14.5717365Z AUTOTUNE mm(32x32, 32x32) 2025-09-09T14:33:14.5717589Z strides: [32, 1], [1, 32] 2025-09-09T14:33:14.5717836Z dtypes: torch.float16, torch.float16 2025-09-09T14:33:14.5718146Z cpp_CppMicroGemmFP32Vec_3 0.0066 ms 100.0% 2025-09-09T14:33:14.5718426Z mm 0.0319 ms 20.6% 2025-09-09T14:33:14.5718873Z SingleProcess AUTOTUNE benchmarking takes 0.2486 seconds and 2.2409 seconds precompiling for 2 choices 2025-09-09T14:33:14.5719503Z PASSED 2025-09-09T14:33:14.5720084Z test/integration/test_integration.py::TestSubclass::test_int8_weight_only_quant_with_freeze_2_cpu AUTOTUNE _weight_int8pack_mm(32x64, 32x64, 32) 2025-09-09T14:33:14.5720724Z strides: [64, 1], [64, 1], [1] 2025-09-09T14:33:14.5721020Z dtypes: torch.bfloat16, torch.int8, torch.bfloat16 2025-09-09T14:33:14.5721366Z cpp_CppMicroGemmFP32Vec_4 0.0072 ms 100.0% 2025-09-09T14:33:14.5721663Z _weight_int8pack_mm 0.0185 ms 39.0% 2025-09-09T14:33:14.5722392Z SingleProcess AUTOTUNE benchmarking takes 0.2494 seconds and 2.2077 seconds precompiling for 2 choices 2025-09-09T14:33:14.5722917Z AUTOTUNE _weight_int8pack_mm(32x32, 32x32, 32) 2025-09-09T14:33:14.5723213Z strides: [32, 1], [32, 1], [1] 2025-09-09T14:33:14.5723499Z dtypes: torch.bfloat16, torch.int8, torch.bfloat16 2025-09-09T14:33:14.5723857Z cpp_CppMicroGemmFP32Vec_5 0.0071 ms 100.0% 2025-09-09T14:33:14.5724157Z _weight_int8pack_mm 0.0177 ms 40.3% 2025-09-09T14:33:14.5724675Z SingleProcess AUTOTUNE benchmarking takes 0.2492 seconds and 2.2031 seconds precompiling for 2 choices 2025-09-09T14:33:14.5725200Z PASSED 2025-09-09T14:33:14.5725712Z test/integration/test_integration.py::TestSubclass::test_int8_weight_only_quant_with_freeze_3_cuda AUTOTUNE mm(32x64, 64x32) 2025-09-09T14:33:14.5726282Z strides: [64, 1], [1, 64] 2025-09-09T14:33:14.5726525Z dtypes: torch.float32, torch.float32 2025-09-09T14:33:14.5727183Z triton_mm_304 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:33:14.5728559Z triton_mm_310 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=4 2025-09-09T14:33:14.5729557Z triton_mm_302 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:33:14.5730707Z triton_mm_303 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:33:14.5731694Z triton_mm_305 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:33:14.5732674Z triton_mm_307 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:33:14.5742026Z triton_mm_308 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:33:14.5743081Z triton_mm_309 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=4 2025-09-09T14:33:14.5744087Z triton_mm_301 0.0267 ms 95.9% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=1, num_warps=2 2025-09-09T14:33:14.5745079Z triton_mm_306 0.0276 ms 92.6% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:33:14.5745943Z SingleProcess AUTOTUNE benchmarking takes 0.1416 seconds and 0.3710 seconds precompiling for 11 choices 2025-09-09T14:33:14.5746464Z AUTOTUNE mm(32x32, 32x32) 2025-09-09T14:33:14.5746709Z strides: [32, 1], [1, 32] 2025-09-09T14:33:14.5746968Z dtypes: torch.float32, torch.float32 2025-09-09T14:33:14.5747613Z triton_mm_311 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=1, num_warps=2 2025-09-09T14:33:14.5748618Z triton_mm_312 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:33:14.5749610Z triton_mm_315 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:33:14.5750584Z triton_mm_313 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:33:14.5751559Z triton_mm_314 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:33:14.5752539Z triton_mm_316 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=4 2025-09-09T14:33:14.5753151Z mm 0.0389 ms 68.4% 2025-09-09T14:33:14.5753602Z SingleProcess AUTOTUNE benchmarking takes 0.1029 seconds and 0.0003 seconds precompiling for 7 choices 2025-09-09T14:33:14.5754161Z PASSED 2025-09-09T14:33:14.5754676Z test/integration/test_integration.py::TestSubclass::test_int8_weight_only_quant_with_freeze_4_cuda AUTOTUNE mm(32x64, 64x32) 2025-09-09T14:33:14.5755248Z strides: [64, 1], [1, 64] 2025-09-09T14:33:14.5755490Z dtypes: torch.float16, torch.float16 2025-09-09T14:33:14.5756127Z triton_mm_321 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:33:14.5757265Z triton_mm_323 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:33:14.5758251Z triton_mm_324 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:33:14.5759384Z triton_mm_319 0.0267 ms 99.9% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:33:14.5760351Z triton_mm_318 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:33:14.5761323Z triton_mm_325 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=4 2025-09-09T14:33:14.5762304Z triton_mm_326 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=4 2025-09-09T14:33:14.5763272Z triton_mm_320 0.0286 ms 93.1% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:33:14.5764258Z triton_mm_317 0.0287 ms 92.9% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=1, num_warps=2 2025-09-09T14:33:14.5765224Z triton_mm_322 0.0297 ms 89.7% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:33:14.5766077Z SingleProcess AUTOTUNE benchmarking takes 0.1530 seconds and 0.1891 seconds precompiling for 11 choices 2025-09-09T14:33:14.5766569Z AUTOTUNE mm(32x32, 32x32) 2025-09-09T14:33:14.5766792Z strides: [32, 1], [1, 32] 2025-09-09T14:33:14.5767033Z dtypes: torch.float16, torch.float16 2025-09-09T14:33:14.5767656Z triton_mm_329 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:33:14.5768639Z triton_mm_331 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:33:14.5769624Z triton_mm_332 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=4 2025-09-09T14:33:37.8115571Z triton_mm_327 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=1, num_warps=2 2025-09-09T14:33:37.8116629Z triton_mm_328 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:33:37.8117626Z triton_mm_330 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:33:37.8118259Z mm 0.0410 ms 65.0% 2025-09-09T14:33:37.8118716Z SingleProcess AUTOTUNE benchmarking takes 0.0919 seconds and 0.0003 seconds precompiling for 7 choices 2025-09-09T14:33:37.8119509Z PASSED 2025-09-09T14:33:37.8120068Z test/integration/test_integration.py::TestSubclass::test_int8_weight_only_quant_with_freeze_5_cuda AUTOTUNE mm(32x64, 64x32) 2025-09-09T14:33:37.8120644Z strides: [64, 1], [32, 1] 2025-09-09T14:33:37.8120898Z dtypes: torch.bfloat16, torch.bfloat16 2025-09-09T14:33:37.8121558Z triton_mm_333 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=1, num_warps=2 2025-09-09T14:33:37.8123094Z triton_mm_334 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:33:37.8124252Z triton_mm_335 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:33:37.8125233Z triton_mm_338 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:33:37.8126208Z triton_mm_342 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=4 2025-09-09T14:33:37.8127187Z triton_mm_341 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=4 2025-09-09T14:33:37.8128170Z triton_mm_336 0.0287 ms 92.9% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:33:37.8129146Z triton_mm_337 0.0287 ms 92.9% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:33:37.8130128Z triton_mm_339 0.0287 ms 92.9% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:33:37.8131106Z triton_mm_340 0.0287 ms 92.9% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:33:37.8131958Z SingleProcess AUTOTUNE benchmarking takes 0.1482 seconds and 0.1819 seconds precompiling for 11 choices 2025-09-09T14:33:37.8132468Z AUTOTUNE mm(32x32, 32x32) 2025-09-09T14:33:37.8132704Z strides: [32, 1], [32, 1] 2025-09-09T14:33:37.8132952Z dtypes: torch.bfloat16, torch.bfloat16 2025-09-09T14:33:37.8133604Z triton_mm_347 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:33:37.8134588Z triton_mm_345 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:33:37.8135566Z triton_mm_348 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=4 2025-09-09T14:33:37.8136552Z triton_mm_343 0.0276 ms 92.6% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=1, num_warps=2 2025-09-09T14:33:37.8137524Z triton_mm_344 0.0276 ms 92.6% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:33:37.8138507Z triton_mm_346 0.0276 ms 92.6% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:33:37.8139119Z mm 0.0420 ms 61.0% 2025-09-09T14:33:37.8139563Z SingleProcess AUTOTUNE benchmarking takes 0.0927 seconds and 0.0003 seconds precompiling for 7 choices 2025-09-09T14:33:37.8140100Z PASSED 2025-09-09T14:33:37.8140578Z test/integration/test_integration.py::TestDynamicQuant::test_dynamic_quant PASSED 2025-09-09T14:33:37.8141478Z test/integration/test_integration.py::TestWeightOnlyInt8Quant::test_weight_only_groupwise_embedding_quant PASSED 2025-09-09T14:33:37.8142432Z test/integration/test_integration.py::TestWeightOnlyInt8Quant::test_weight_only_groupwise_quant PASSED 2025-09-09T14:33:37.8143404Z test/integration/test_integration.py::TestWeightOnlyInt8Quant::test_weight_only_quant PASSED 2025-09-09T14:33:37.8144338Z test/integration/test_integration.py::TestWeightOnlyInt8Quant::test_weight_only_quant_force_mixed_mm_0_cpu SKIPPED 2025-09-09T14:33:37.8145408Z test/integration/test_integration.py::TestWeightOnlyInt8Quant::test_weight_only_quant_force_mixed_mm_1_cpu SKIPPED 2025-09-09T14:33:37.8146404Z test/integration/test_integration.py::TestWeightOnlyInt8Quant::test_weight_only_quant_force_mixed_mm_2_cpu SKIPPED 2025-09-09T14:33:37.8147315Z test/integration/test_integration.py::TestWeightOnlyInt8Quant::test_weight_only_quant_force_mixed_mm_3_cuda AUTOTUNE mm(2x4, 4x5) 2025-09-09T14:33:37.8147914Z strides: [4, 1], [5, 1] 2025-09-09T14:33:37.8148160Z dtypes: torch.float32, torch.float32 2025-09-09T14:33:37.8148810Z triton_mm_353 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=16, BLOCK_N=16, EVEN_K=False, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=1 2025-09-09T14:33:37.8149805Z triton_mm_352 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=16, BLOCK_N=16, EVEN_K=False, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=1 2025-09-09T14:33:37.8150784Z triton_mm_350 0.0276 ms 92.7% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=16, BLOCK_N=16, EVEN_K=False, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=1 2025-09-09T14:33:37.8151767Z triton_mm_349 0.0276 ms 92.6% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=16, BLOCK_N=16, EVEN_K=False, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=1, num_warps=1 2025-09-09T14:33:37.8152754Z triton_mm_351 0.0276 ms 92.6% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=16, BLOCK_N=16, EVEN_K=False, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=1 2025-09-09T14:33:37.8153385Z mm 0.0379 ms 67.6% 2025-09-09T14:33:37.8153864Z SingleProcess AUTOTUNE benchmarking takes 0.0805 seconds and 0.2958 seconds precompiling for 6 choices 2025-09-09T14:33:37.8154358Z AUTOTUNE mm(125x4, 4x5) 2025-09-09T14:33:37.8154571Z strides: [4, 1], [5, 1] 2025-09-09T14:33:37.8154813Z dtypes: torch.float32, torch.float32 2025-09-09T14:33:37.8155457Z triton_mm_363 0.0246 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=128, BLOCK_N=16, EVEN_K=False, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=8 2025-09-09T14:33:37.8156453Z triton_mm_355 0.0256 ms 96.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=32, BLOCK_N=16, EVEN_K=False, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=2 2025-09-09T14:33:37.8157437Z triton_mm_357 0.0256 ms 96.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=64, BLOCK_N=16, EVEN_K=False, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:33:37.8158423Z triton_mm_360 0.0256 ms 96.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=64, BLOCK_N=16, EVEN_K=False, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=4 2025-09-09T14:33:37.8159486Z triton_mm_361 0.0256 ms 96.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=128, BLOCK_N=16, EVEN_K=False, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:33:37.8160484Z triton_mm_364 0.0256 ms 96.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=128, BLOCK_N=16, EVEN_K=False, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=8 2025-09-09T14:33:37.8161464Z triton_mm_354 0.0266 ms 92.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=32, BLOCK_N=16, EVEN_K=False, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=1, num_warps=2 2025-09-09T14:33:37.8162446Z triton_mm_358 0.0266 ms 92.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=64, BLOCK_N=16, EVEN_K=False, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:33:37.8163563Z triton_mm_359 0.0266 ms 92.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=64, BLOCK_N=16, EVEN_K=False, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:33:37.8164557Z triton_mm_362 0.0266 ms 92.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=128, BLOCK_N=16, EVEN_K=False, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=8 2025-09-09T14:33:37.8165494Z SingleProcess AUTOTUNE benchmarking takes 0.1564 seconds and 0.3304 seconds precompiling for 12 choices 2025-09-09T14:33:37.8165986Z AUTOTUNE mm(4x4, 4x5) 2025-09-09T14:34:06.3009873Z strides: [4, 1], [5, 1] 2025-09-09T14:34:06.3010181Z dtypes: torch.float32, torch.float32 2025-09-09T14:34:06.3010864Z triton_mm_366 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=16, BLOCK_N=16, EVEN_K=False, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=1 2025-09-09T14:34:06.3011888Z triton_mm_367 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=16, BLOCK_N=16, EVEN_K=False, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=1 2025-09-09T14:34:06.3012931Z triton_mm_365 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=16, BLOCK_N=16, EVEN_K=False, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=1, num_warps=1 2025-09-09T14:34:06.3013938Z triton_mm_368 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=16, BLOCK_N=16, EVEN_K=False, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=1 2025-09-09T14:34:06.3014930Z triton_mm_369 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=16, BLOCK_N=16, EVEN_K=False, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=1 2025-09-09T14:34:06.3015556Z mm 0.0379 ms 67.6% 2025-09-09T14:34:06.3016020Z SingleProcess AUTOTUNE benchmarking takes 0.0784 seconds and 0.2989 seconds precompiling for 6 choices 2025-09-09T14:34:06.3016743Z PASSED 2025-09-09T14:34:06.3017318Z test/integration/test_integration.py::TestWeightOnlyInt8Quant::test_weight_only_quant_force_mixed_mm_4_cuda AUTOTUNE mm(2x4, 4x5) 2025-09-09T14:34:06.3017954Z strides: [4, 1], [5, 1] 2025-09-09T14:34:06.3018197Z dtypes: torch.float16, torch.float16 2025-09-09T14:34:06.3018848Z triton_mm_371 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=16, BLOCK_N=16, EVEN_K=False, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=1 2025-09-09T14:34:06.3019865Z triton_mm_372 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=16, BLOCK_N=16, EVEN_K=False, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=1 2025-09-09T14:34:06.3020863Z triton_mm_373 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=16, BLOCK_N=16, EVEN_K=False, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=1 2025-09-09T14:34:06.3021856Z triton_mm_370 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=16, BLOCK_N=16, EVEN_K=False, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=1, num_warps=1 2025-09-09T14:34:06.3023093Z triton_mm_374 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=16, BLOCK_N=16, EVEN_K=False, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=1 2025-09-09T14:34:06.3023715Z mm 0.0440 ms 58.1% 2025-09-09T14:34:06.3024181Z SingleProcess AUTOTUNE benchmarking takes 0.0791 seconds and 0.2983 seconds precompiling for 6 choices 2025-09-09T14:34:06.3024675Z AUTOTUNE mm(125x4, 4x5) 2025-09-09T14:34:06.3024902Z strides: [4, 1], [5, 1] 2025-09-09T14:34:06.3025138Z dtypes: torch.float16, torch.float16 2025-09-09T14:34:06.3025797Z triton_mm_381 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=64, BLOCK_N=16, EVEN_K=False, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=4 2025-09-09T14:34:06.3026806Z triton_mm_375 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=32, BLOCK_N=16, EVEN_K=False, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=1, num_warps=2 2025-09-09T14:34:06.3028092Z triton_mm_376 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=32, BLOCK_N=16, EVEN_K=False, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=2 2025-09-09T14:34:06.3029088Z triton_mm_377 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=32, BLOCK_N=16, EVEN_K=False, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=2 2025-09-09T14:34:06.3030240Z triton_mm_379 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=64, BLOCK_N=16, EVEN_K=False, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:34:06.3031239Z triton_mm_380 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=64, BLOCK_N=16, EVEN_K=False, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:34:06.3032244Z triton_mm_384 0.0276 ms 92.8% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=128, BLOCK_N=16, EVEN_K=False, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=8 2025-09-09T14:34:06.3033256Z triton_mm_378 0.0276 ms 92.6% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=64, BLOCK_N=16, EVEN_K=False, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:34:06.3034253Z triton_mm_382 0.0276 ms 92.6% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=128, BLOCK_N=16, EVEN_K=False, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:34:06.3035269Z triton_mm_383 0.0276 ms 92.6% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=128, BLOCK_N=16, EVEN_K=False, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=8 2025-09-09T14:34:06.3036146Z SingleProcess AUTOTUNE benchmarking takes 0.1562 seconds and 0.2992 seconds precompiling for 12 choices 2025-09-09T14:34:06.3036653Z AUTOTUNE mm(4x4, 4x5) 2025-09-09T14:34:06.3036878Z strides: [4, 1], [5, 1] 2025-09-09T14:34:06.3037126Z dtypes: torch.float16, torch.float16 2025-09-09T14:34:06.3037785Z triton_mm_387 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=16, BLOCK_N=16, EVEN_K=False, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=1 2025-09-09T14:34:06.3038784Z triton_mm_386 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=16, BLOCK_N=16, EVEN_K=False, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=1, num_warps=1 2025-09-09T14:34:06.3039862Z triton_mm_388 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=16, BLOCK_N=16, EVEN_K=False, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=1 2025-09-09T14:34:06.3040860Z triton_mm_389 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=16, BLOCK_N=16, EVEN_K=False, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=1 2025-09-09T14:34:06.3041848Z triton_mm_390 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=16, BLOCK_N=16, EVEN_K=False, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=1 2025-09-09T14:34:06.3042486Z mm 0.0451 ms 59.1% 2025-09-09T14:34:06.3042952Z SingleProcess AUTOTUNE benchmarking takes 0.0803 seconds and 0.2824 seconds precompiling for 6 choices 2025-09-09T14:34:06.3043492Z PASSED 2025-09-09T14:34:06.3044056Z test/integration/test_integration.py::TestWeightOnlyInt8Quant::test_weight_only_quant_force_mixed_mm_5_cuda AUTOTUNE mm(2x4, 4x5) 2025-09-09T14:34:06.3044670Z strides: [4, 1], [5, 1] 2025-09-09T14:34:06.3044929Z dtypes: torch.bfloat16, torch.bfloat16 2025-09-09T14:34:06.3045583Z triton_mm_395 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=16, BLOCK_N=16, EVEN_K=False, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=1 2025-09-09T14:34:06.3046591Z triton_mm_392 0.0266 ms 99.9% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=16, BLOCK_N=16, EVEN_K=False, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=1 2025-09-09T14:34:06.3047586Z triton_mm_393 0.0266 ms 99.9% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=16, BLOCK_N=16, EVEN_K=False, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=1 2025-09-09T14:34:06.3048662Z triton_mm_394 0.0266 ms 99.9% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=16, BLOCK_N=16, EVEN_K=False, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=1 2025-09-09T14:34:06.3049733Z triton_mm_391 0.0276 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=16, BLOCK_N=16, EVEN_K=False, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=1, num_warps=1 2025-09-09T14:34:06.3050369Z mm 0.0389 ms 68.3% 2025-09-09T14:34:06.3050820Z SingleProcess AUTOTUNE benchmarking takes 0.0789 seconds and 0.2781 seconds precompiling for 6 choices 2025-09-09T14:34:06.3051322Z AUTOTUNE mm(125x4, 4x5) 2025-09-09T14:34:06.3051538Z strides: [4, 1], [5, 1] 2025-09-09T14:34:06.3051786Z dtypes: torch.bfloat16, torch.bfloat16 2025-09-09T14:34:06.3052444Z triton_mm_398 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=32, BLOCK_N=16, EVEN_K=False, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=2 2025-09-09T14:34:06.3053452Z triton_mm_397 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=32, BLOCK_N=16, EVEN_K=False, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=2 2025-09-09T14:34:06.3054450Z triton_mm_399 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=64, BLOCK_N=16, EVEN_K=False, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:34:06.3055440Z triton_mm_400 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=64, BLOCK_N=16, EVEN_K=False, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:34:06.3056437Z triton_mm_401 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=64, BLOCK_N=16, EVEN_K=False, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:34:06.3057438Z triton_mm_403 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=128, BLOCK_N=16, EVEN_K=False, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:34:06.3058443Z triton_mm_404 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=128, BLOCK_N=16, EVEN_K=False, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=8 2025-09-09T14:35:08.6311830Z triton_mm_405 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=128, BLOCK_N=16, EVEN_K=False, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=8 2025-09-09T14:35:08.6312888Z triton_mm_406 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=128, BLOCK_N=16, EVEN_K=False, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=8 2025-09-09T14:35:08.6313885Z triton_mm_396 0.0276 ms 92.6% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=32, BLOCK_N=16, EVEN_K=False, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=1, num_warps=2 2025-09-09T14:35:08.6314751Z SingleProcess AUTOTUNE benchmarking takes 0.1565 seconds and 0.2875 seconds precompiling for 12 choices 2025-09-09T14:35:08.6315246Z AUTOTUNE mm(4x4, 4x5) 2025-09-09T14:35:08.6315492Z strides: [4, 1], [5, 1] 2025-09-09T14:35:08.6315739Z dtypes: torch.bfloat16, torch.bfloat16 2025-09-09T14:35:08.6316400Z triton_mm_409 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=16, BLOCK_N=16, EVEN_K=False, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=1 2025-09-09T14:35:08.6317406Z triton_mm_410 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=16, BLOCK_N=16, EVEN_K=False, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=1 2025-09-09T14:35:08.6318382Z triton_mm_408 0.0267 ms 99.8% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=16, BLOCK_N=16, EVEN_K=False, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=1 2025-09-09T14:35:08.6319459Z triton_mm_407 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=16, BLOCK_N=16, EVEN_K=False, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=1, num_warps=1 2025-09-09T14:35:08.6320793Z triton_mm_411 0.0277 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=16, BLOCK_N=16, EVEN_K=False, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=1 2025-09-09T14:35:08.6321569Z mm 0.0379 ms 70.3% 2025-09-09T14:35:08.6322019Z SingleProcess AUTOTUNE benchmarking takes 0.0801 seconds and 0.2947 seconds precompiling for 6 choices 2025-09-09T14:35:08.6322876Z PASSED 2025-09-09T14:35:08.6323532Z test/integration/test_integration.py::TestWeightOnlyInt8Quant::test_weight_only_quant_use_mixed_mm_0_cpu SKIPPED 2025-09-09T14:35:08.6324529Z test/integration/test_integration.py::TestWeightOnlyInt8Quant::test_weight_only_quant_use_mixed_mm_1_cpu SKIPPED 2025-09-09T14:35:08.6325501Z test/integration/test_integration.py::TestWeightOnlyInt8Quant::test_weight_only_quant_use_mixed_mm_2_cpu SKIPPED 2025-09-09T14:35:08.6326480Z test/integration/test_integration.py::TestWeightOnlyInt8Quant::test_weight_only_quant_use_mixed_mm_3_cuda PASSED 2025-09-09T14:35:08.6327457Z test/integration/test_integration.py::TestWeightOnlyInt8Quant::test_weight_only_quant_use_mixed_mm_4_cuda PASSED 2025-09-09T14:35:08.6328430Z test/integration/test_integration.py::TestWeightOnlyInt8Quant::test_weight_only_quant_use_mixed_mm_5_cuda PASSED 2025-09-09T14:35:08.6329344Z test/integration/test_integration.py::TestSaveLoadMeta::test_save_load_dqtensors_0_cpu SKIPPED 2025-09-09T14:35:08.6330170Z test/integration/test_integration.py::TestSaveLoadMeta::test_save_load_dqtensors_1_cpu SKIPPED 2025-09-09T14:35:08.6330995Z test/integration/test_integration.py::TestSaveLoadMeta::test_save_load_dqtensors_2_cpu SKIPPED 2025-09-09T14:35:08.6331779Z test/integration/test_integration.py::TestSaveLoadMeta::test_save_load_dqtensors_3_cuda AUTOTUNE int_mm(32x32, 32x32) 2025-09-09T14:35:08.6332327Z strides: [32, 1], [1, 32] 2025-09-09T14:35:08.6332575Z dtypes: torch.int8, torch.int8 2025-09-09T14:35:08.6333185Z triton_mm_480 0.0266 ms 100.0% ACC_TYPE='tl.int32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:35:08.6334149Z triton_mm_482 0.0266 ms 100.0% ACC_TYPE='tl.int32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=4 2025-09-09T14:35:08.6335100Z triton_mm_481 0.0276 ms 96.3% ACC_TYPE='tl.int32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:35:08.6336043Z triton_mm_483 0.0276 ms 96.3% ACC_TYPE='tl.int32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:35:08.6336649Z _int_mm 0.0389 ms 68.4% 2025-09-09T14:35:08.6337102Z SingleProcess AUTOTUNE benchmarking takes 0.0675 seconds and 0.1925 seconds precompiling for 5 choices 2025-09-09T14:35:08.6337609Z AUTOTUNE int_mm(32x64, 64x32) 2025-09-09T14:35:08.6337853Z strides: [64, 1], [1, 64] 2025-09-09T14:35:08.6338137Z dtypes: torch.int8, torch.int8 2025-09-09T14:35:08.6338728Z triton_mm_479 0.0276 ms 100.0% ACC_TYPE='tl.int32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:35:08.6339685Z triton_mm_475 0.0276 ms 99.9% ACC_TYPE='tl.int32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:35:08.6340626Z triton_mm_476 0.0276 ms 99.9% ACC_TYPE='tl.int32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:35:08.6341564Z triton_mm_477 0.0276 ms 99.9% ACC_TYPE='tl.int32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=4 2025-09-09T14:35:08.6342634Z triton_mm_478 0.0276 ms 99.9% ACC_TYPE='tl.int32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:35:08.6343258Z _int_mm 0.0399 ms 69.2% 2025-09-09T14:35:08.6343707Z SingleProcess AUTOTUNE benchmarking takes 0.0787 seconds and 0.0002 seconds precompiling for 6 choices 2025-09-09T14:35:08.6345039Z PASSED 2025-09-09T14:35:08.6345568Z test/integration/test_integration.py::TestSaveLoadMeta::test_save_load_dqtensors_4_cuda PASSED 2025-09-09T14:35:08.6346404Z test/integration/test_integration.py::TestSaveLoadMeta::test_save_load_dqtensors_5_cuda PASSED 2025-09-09T14:35:08.6347253Z test/integration/test_integration.py::TestSaveLoadMeta::test_save_load_int4woqtensors_0_cpu SKIPPED 2025-09-09T14:35:08.6348118Z test/integration/test_integration.py::TestSaveLoadMeta::test_save_load_int4woqtensors_1_cpu SKIPPED 2025-09-09T14:35:08.6348981Z test/integration/test_integration.py::TestSaveLoadMeta::test_save_load_int4woqtensors_2_cpu PASSED 2025-09-09T14:35:08.6349851Z test/integration/test_integration.py::TestSaveLoadMeta::test_save_load_int4woqtensors_3_cuda SKIPPED 2025-09-09T14:35:08.6350722Z test/integration/test_integration.py::TestSaveLoadMeta::test_save_load_int4woqtensors_4_cuda SKIPPED 2025-09-09T14:35:08.6351542Z test/integration/test_integration.py::TestSaveLoadMeta::test_save_load_int4woqtensors_5_cuda AUTOTUNE mm(32x64, 64x32) 2025-09-09T14:35:08.6352087Z strides: [64, 1], [1, 64] 2025-09-09T14:35:08.6352338Z dtypes: torch.bfloat16, torch.bfloat16 2025-09-09T14:35:08.6352983Z triton_mm_502 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=1, num_warps=2 2025-09-09T14:35:08.6353976Z triton_mm_506 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:35:08.6354964Z triton_mm_508 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:35:08.6355936Z triton_mm_509 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:35:08.6356924Z triton_mm_510 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=4 2025-09-09T14:35:08.6357903Z triton_mm_511 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=4 2025-09-09T14:35:08.6358880Z triton_mm_504 0.0267 ms 99.9% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:35:08.6359914Z triton_mm_507 0.0267 ms 99.9% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:35:08.6360891Z triton_mm_503 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:35:08.6361853Z triton_mm_505 0.0287 ms 92.9% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:35:08.6362708Z SingleProcess AUTOTUNE benchmarking takes 0.1484 seconds and 0.1961 seconds precompiling for 11 choices 2025-09-09T14:35:08.6363212Z AUTOTUNE addmm(32x32, 32x32, 32x32) 2025-09-09T14:35:08.6363470Z strides: [0, 1], [32, 1], [1, 32] 2025-09-09T14:35:08.6363783Z dtypes: torch.bfloat16, torch.bfloat16, torch.bfloat16 2025-09-09T14:35:08.6364553Z triton_mm_515 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:35:15.4211267Z triton_mm_512 0.0276 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=1, num_warps=2 2025-09-09T14:35:15.4212778Z triton_mm_513 0.0276 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:35:15.4213877Z triton_mm_514 0.0276 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:35:15.4214963Z triton_mm_516 0.0276 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:35:15.4216112Z triton_mm_517 0.0276 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=4 2025-09-09T14:35:15.4216786Z addmm 0.0512 ms 51.9% 2025-09-09T14:35:15.4217110Z bias_addmm 0.0737 ms 36.1% 2025-09-09T14:35:15.4217608Z SingleProcess AUTOTUNE benchmarking takes 0.1096 seconds and 0.0002 seconds precompiling for 8 choices 2025-09-09T14:35:15.4218405Z PASSED 2025-09-09T14:35:15.4219093Z test/integration/test_integration.py::TestSaveLoadMeta::test_save_load_int8woqtensors_0_cpu PASSED 2025-09-09T14:35:15.4220082Z test/integration/test_integration.py::TestSaveLoadMeta::test_save_load_int8woqtensors_1_cpu PASSED 2025-09-09T14:35:15.4221065Z test/integration/test_integration.py::TestSaveLoadMeta::test_save_load_int8woqtensors_2_cpu PASSED 2025-09-09T14:35:15.4221992Z test/integration/test_integration.py::TestSaveLoadMeta::test_save_load_int8woqtensors_3_cuda AUTOTUNE mm(32x64, 64x32) 2025-09-09T14:35:15.4223007Z strides: [64, 1], [32, 1] 2025-09-09T14:35:15.4223359Z dtypes: torch.float32, torch.float32 2025-09-09T14:35:15.4224106Z triton_mm_521 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:35:15.4225248Z triton_mm_522 0.0257 ms 99.8% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:35:15.4226326Z triton_mm_518 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=1, num_warps=2 2025-09-09T14:35:15.4227416Z triton_mm_520 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:35:15.4228581Z triton_mm_523 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:35:15.4229667Z triton_mm_524 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:35:15.4230735Z triton_mm_525 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:35:15.4231829Z triton_mm_527 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=4 2025-09-09T14:35:15.4232974Z triton_mm_519 0.0267 ms 96.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:35:15.4234279Z triton_mm_526 0.0276 ms 92.6% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=4 2025-09-09T14:35:15.4235264Z SingleProcess AUTOTUNE benchmarking takes 0.1412 seconds and 0.3230 seconds precompiling for 11 choices 2025-09-09T14:35:15.4235991Z AUTOTUNE mm(32x32, 32x32) 2025-09-09T14:35:15.4236251Z strides: [32, 1], [32, 1] 2025-09-09T14:35:15.4236556Z dtypes: torch.float32, torch.float32 2025-09-09T14:35:15.4237263Z triton_mm_528 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=1, num_warps=2 2025-09-09T14:35:15.4238366Z triton_mm_529 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:35:15.4239715Z triton_mm_530 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:35:15.4240878Z triton_mm_531 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:35:15.4241976Z triton_mm_532 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:35:15.4243038Z triton_mm_533 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=4 2025-09-09T14:35:15.4243761Z mm 0.0389 ms 65.8% 2025-09-09T14:35:15.4244297Z SingleProcess AUTOTUNE benchmarking takes 0.0891 seconds and 0.0002 seconds precompiling for 7 choices 2025-09-09T14:35:15.4244860Z PASSED 2025-09-09T14:35:15.4245471Z test/integration/test_integration.py::TestSaveLoadMeta::test_save_load_int8woqtensors_4_cuda AUTOTUNE mm(32x64, 64x32) 2025-09-09T14:35:15.4246112Z strides: [64, 1], [32, 1] 2025-09-09T14:35:15.4246364Z dtypes: torch.float16, torch.float16 2025-09-09T14:35:15.4247103Z triton_mm_534 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=1, num_warps=2 2025-09-09T14:35:15.4248200Z triton_mm_543 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=4 2025-09-09T14:35:15.4249292Z triton_mm_535 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:35:15.4250445Z triton_mm_536 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:35:15.4251535Z triton_mm_537 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:35:15.4252621Z triton_mm_538 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:35:15.4253635Z triton_mm_539 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:35:15.4254719Z triton_mm_540 0.0276 ms 92.6% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:35:15.4255729Z triton_mm_541 0.0276 ms 92.6% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:35:15.4256935Z triton_mm_542 0.0276 ms 92.6% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=4 2025-09-09T14:35:15.4267400Z SingleProcess AUTOTUNE benchmarking takes 0.1442 seconds and 0.1911 seconds precompiling for 11 choices 2025-09-09T14:35:15.4267922Z AUTOTUNE mm(32x32, 32x32) 2025-09-09T14:35:15.4268173Z strides: [32, 1], [32, 1] 2025-09-09T14:35:15.4268435Z dtypes: torch.float16, torch.float16 2025-09-09T14:35:15.4269096Z triton_mm_545 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:35:15.4270100Z triton_mm_544 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=1, num_warps=2 2025-09-09T14:35:15.4271092Z triton_mm_546 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:35:15.4272079Z triton_mm_547 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:35:15.4273069Z triton_mm_548 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:35:15.4274044Z triton_mm_549 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=4 2025-09-09T14:35:15.4274672Z mm 0.0410 ms 62.5% 2025-09-09T14:35:46.2905823Z SingleProcess AUTOTUNE benchmarking takes 0.0894 seconds and 0.0002 seconds precompiling for 7 choices 2025-09-09T14:35:46.2908007Z PASSED 2025-09-09T14:35:46.2908673Z test/integration/test_integration.py::TestSaveLoadMeta::test_save_load_int8woqtensors_5_cuda PASSED 2025-09-09T14:35:46.2909520Z test/integration/test_integration.py::TorchCompileUnitTest::test_fullgraph PASSED 2025-09-09T14:35:46.2910281Z test/integration/test_integration.py::UtilsUnitTest::test_shape_logger PASSED 2025-09-09T14:35:46.2911283Z test/integration/test_integration.py::SmoothquantIntegrationTest::test_non_dynamically_quantizable_linear SKIPPED 2025-09-09T14:35:46.2912256Z test/integration/test_integration.py::SmoothquantIntegrationTest::test_on_dummy_distilbert 2025-09-09T14:35:46.2912880Z tokenizer_config.json: 0% 0.00/48.0 [00:00>time: 0.014ms for , to_beat: infms 2025-09-09T14:35:46.2942654Z AUTOTUNE mm(32x128, 128x128) 2025-09-09T14:35:46.2942910Z strides: [128, 1], [128, 1] 2025-09-09T14:35:46.2943157Z dtypes: torch.float32, torch.float32 2025-09-09T14:35:46.2943907Z triton_mm_583 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=1, num_warps=2 2025-09-09T14:35:46.2944911Z triton_mm_584 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:35:46.2945903Z triton_mm_585 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=8 2025-09-09T14:35:46.2946890Z triton_mm_586 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:35:46.2947898Z triton_mm_587 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:35:46.2948931Z triton_mm_588 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=32, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:35:46.2949940Z triton_mm_589 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:35:46.2950931Z triton_mm_590 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=32, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=8 2025-09-09T14:35:46.2951929Z triton_mm_591 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=32, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:35:46.2952932Z triton_mm_592 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:35:46.2953810Z SingleProcess AUTOTUNE benchmarking takes 0.2208 seconds and 1.8270 seconds precompiling for 18 choices 2025-09-09T14:35:46.2954642Z >>time: 0.014ms for , to_beat: 0.014ms 2025-09-09T14:35:46.2955563Z >>time: 0.005ms for , to_beat: 0.014ms 2025-09-09T14:35:46.2956163Z AUTOTUNE int_mm(32x128, 128x128) 2025-09-09T14:35:46.2956417Z strides: [128, 1], [1, 128] 2025-09-09T14:35:46.2956669Z dtypes: torch.int8, torch.int8 2025-09-09T14:35:46.2957282Z triton_mm_602 0.0256 ms 100.0% ACC_TYPE='tl.int32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:35:46.2958254Z triton_mm_604 0.0256 ms 100.0% ACC_TYPE='tl.int32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=8 2025-09-09T14:35:46.2959272Z triton_mm_606 0.0256 ms 100.0% ACC_TYPE='tl.int32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=8 2025-09-09T14:35:46.2960244Z triton_mm_600 0.0266 ms 96.2% ACC_TYPE='tl.int32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:35:46.2961217Z triton_mm_601 0.0266 ms 96.2% ACC_TYPE='tl.int32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:36:16.1414764Z triton_mm_605 0.0266 ms 96.2% ACC_TYPE='tl.int32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:36:16.1417150Z triton_mm_607 0.0266 ms 96.2% ACC_TYPE='tl.int32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=8 2025-09-09T14:36:16.1418621Z triton_mm_608 0.0266 ms 96.2% ACC_TYPE='tl.int32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=32, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=8 2025-09-09T14:36:16.1419683Z triton_mm_603 0.0267 ms 96.0% ACC_TYPE='tl.int32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=8 2025-09-09T14:36:16.1420754Z triton_mm_609 0.0276 ms 92.6% ACC_TYPE='tl.int32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=32, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=8 2025-09-09T14:36:16.1421709Z SingleProcess AUTOTUNE benchmarking takes 0.1438 seconds and 0.3072 seconds precompiling for 11 choices 2025-09-09T14:36:16.1422906Z >>time: 0.006ms for matmul, to_beat: 0.005ms 2025-09-09T14:36:16.1423870Z best_cls= 2025-09-09T14:36:16.1424291Z 2025-09-09T14:36:16.1424635Z PASSED 2025-09-09T14:36:16.1425215Z test/integration/test_integration.py::TestAutoQuant::test_autoquant_compile_12_cuda SKIPPED 2025-09-09T14:36:16.1426241Z test/integration/test_integration.py::TestAutoQuant::test_autoquant_compile_13_cuda SKIPPED 2025-09-09T14:36:16.1427196Z test/integration/test_integration.py::TestAutoQuant::test_autoquant_compile_14_cuda activation_shapes: torch.Size([32, 128]), times_seen: 2 2025-09-09T14:36:16.1428068Z weight_shape: torch.Size([128, 128]), dtype: torch.float16, bias_shape: torch.Size([128]) 2025-09-09T14:36:16.1428523Z AUTOTUNE addmm(32x128, 32x128, 128x128) 2025-09-09T14:36:16.1428879Z strides: [0, 1], [128, 1], [1, 128] 2025-09-09T14:36:16.1429187Z dtypes: torch.float16, torch.float16, torch.float16 2025-09-09T14:36:16.1429965Z triton_mm_616 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:36:16.1431073Z triton_mm_621 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=32, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:36:16.1432218Z triton_mm_611 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:36:16.1433299Z triton_mm_622 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=32, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=4 2025-09-09T14:36:16.1434369Z triton_mm_624 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=8 2025-09-09T14:36:16.1435444Z triton_mm_625 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=8 2025-09-09T14:36:16.1436623Z triton_mm_623 0.0267 ms 95.9% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:36:16.1437693Z triton_mm_626 0.0276 ms 92.8% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=32, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=8 2025-09-09T14:36:16.1438754Z triton_mm_610 0.0276 ms 92.6% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=1, num_warps=2 2025-09-09T14:36:16.1440147Z triton_mm_612 0.0276 ms 92.6% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=8 2025-09-09T14:36:16.1441229Z SingleProcess AUTOTUNE benchmarking takes 0.2525 seconds and 0.3959 seconds precompiling for 19 choices 2025-09-09T14:36:16.1442038Z >>time: 0.007ms for , to_beat: infms 2025-09-09T14:36:16.1442619Z AUTOTUNE mm(32x128, 128x128) 2025-09-09T14:36:16.1442864Z strides: [128, 1], [128, 1] 2025-09-09T14:36:16.1443149Z dtypes: torch.float16, torch.float16 2025-09-09T14:36:16.1443845Z triton_mm_638 0.0246 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=32, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:36:16.1445004Z triton_mm_639 0.0246 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=32, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=4 2025-09-09T14:36:16.1446092Z triton_mm_633 0.0256 ms 96.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:36:16.1447153Z triton_mm_640 0.0256 ms 96.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:36:16.1448234Z triton_mm_641 0.0256 ms 96.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=8 2025-09-09T14:36:16.1449350Z triton_mm_642 0.0256 ms 96.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=8 2025-09-09T14:36:16.1450425Z triton_mm_628 0.0266 ms 92.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:36:16.1451497Z triton_mm_629 0.0266 ms 92.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=8 2025-09-09T14:36:16.1452629Z triton_mm_630 0.0266 ms 92.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:36:16.1453680Z triton_mm_631 0.0266 ms 92.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:36:16.1454550Z SingleProcess AUTOTUNE benchmarking takes 0.2294 seconds and 0.3531 seconds precompiling for 18 choices 2025-09-09T14:36:16.1455448Z >>time: 0.007ms for , to_beat: 0.007ms 2025-09-09T14:36:16.1456387Z >>time: 0.005ms for , to_beat: 0.007ms 2025-09-09T14:36:16.1457425Z >>time: 0.006ms for matmul, to_beat: 0.005ms 2025-09-09T14:36:16.1458289Z best_cls= 2025-09-09T14:36:16.1458716Z 2025-09-09T14:36:16.1458868Z PASSED 2025-09-09T14:36:16.1459415Z test/integration/test_integration.py::TestAutoQuant::test_autoquant_compile_15_cuda SKIPPED 2025-09-09T14:36:16.1460232Z test/integration/test_integration.py::TestAutoQuant::test_autoquant_compile_16_cuda SKIPPED 2025-09-09T14:36:16.1461147Z test/integration/test_integration.py::TestAutoQuant::test_autoquant_compile_17_cuda activation_shapes: torch.Size([32, 128]), times_seen: 2 2025-09-09T14:36:16.1461971Z weight_shape: torch.Size([128, 128]), dtype: torch.bfloat16, bias_shape: torch.Size([128]) 2025-09-09T14:36:16.1462424Z AUTOTUNE addmm(32x128, 32x128, 128x128) 2025-09-09T14:36:16.1462813Z strides: [0, 1], [128, 1], [1, 128] 2025-09-09T14:36:16.1463142Z dtypes: torch.bfloat16, torch.bfloat16, torch.bfloat16 2025-09-09T14:36:16.1463934Z triton_mm_661 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=32, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=8 2025-09-09T14:36:16.1464940Z triton_mm_664 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=8 2025-09-09T14:36:16.1465945Z triton_mm_667 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:36:16.1466972Z triton_mm_668 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=8 2025-09-09T14:36:16.1468004Z triton_mm_669 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=8 2025-09-09T14:36:16.1469110Z triton_mm_670 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=32, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=8 2025-09-09T14:36:16.1470100Z triton_mm_654 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=1, num_warps=2 2025-09-09T14:36:59.9886213Z triton_mm_655 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:36:59.9887270Z triton_mm_656 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=8 2025-09-09T14:36:59.9888292Z triton_mm_657 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:36:59.9889181Z SingleProcess AUTOTUNE benchmarking takes 0.2476 seconds and 0.4257 seconds precompiling for 19 choices 2025-09-09T14:36:59.9889923Z >>time: 0.005ms for , to_beat: infms 2025-09-09T14:36:59.9890424Z AUTOTUNE mm(32x128, 128x128) 2025-09-09T14:36:59.9890675Z strides: [128, 1], [128, 1] 2025-09-09T14:36:59.9890933Z dtypes: torch.bfloat16, torch.bfloat16 2025-09-09T14:36:59.9891588Z triton_mm_674 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:36:59.9892605Z triton_mm_681 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=8 2025-09-09T14:36:59.9893602Z triton_mm_686 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=8 2025-09-09T14:36:59.9894605Z triton_mm_673 0.0266 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=8 2025-09-09T14:36:59.9895593Z triton_mm_672 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:36:59.9896574Z triton_mm_676 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=32, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:36:59.9897865Z triton_mm_680 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:36:59.9898862Z triton_mm_682 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=32, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:36:59.9901407Z triton_mm_685 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=8 2025-09-09T14:36:59.9902400Z triton_mm_687 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=32, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=8 2025-09-09T14:36:59.9903266Z SingleProcess AUTOTUNE benchmarking takes 0.2285 seconds and 0.3792 seconds precompiling for 18 choices 2025-09-09T14:36:59.9904099Z >>time: 0.005ms for , to_beat: 0.005ms 2025-09-09T14:36:59.9905027Z >>time: 0.005ms for , to_beat: 0.005ms 2025-09-09T14:36:59.9905971Z >>time: 0.006ms for matmul, to_beat: 0.005ms 2025-09-09T14:36:59.9906833Z best_cls= 2025-09-09T14:36:59.9907257Z 2025-09-09T14:36:59.9907566Z PASSED 2025-09-09T14:36:59.9908134Z test/integration/test_integration.py::TestAutoQuant::test_autoquant_double_access_0_cpu SKIPPED 2025-09-09T14:36:59.9908985Z test/integration/test_integration.py::TestAutoQuant::test_autoquant_double_access_1_cpu SKIPPED 2025-09-09T14:36:59.9909824Z test/integration/test_integration.py::TestAutoQuant::test_autoquant_double_access_2_cpu SKIPPED 2025-09-09T14:36:59.9910727Z test/integration/test_integration.py::TestAutoQuant::test_autoquant_double_access_3_cuda activation_shapes: torch.Size([16, 128]), times_seen: 1 2025-09-09T14:36:59.9911528Z weight_shape: torch.Size([128, 128]), dtype: torch.float32, bias_shape: torch.Size([128]) 2025-09-09T14:36:59.9911962Z AUTOTUNE addmm(16x128, 16x128, 128x128) 2025-09-09T14:36:59.9912246Z strides: [0, 1], [128, 1], [1, 128] 2025-09-09T14:36:59.9912553Z dtypes: torch.float32, torch.float32, torch.float32 2025-09-09T14:36:59.9913253Z triton_mm_702 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=16, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=2 2025-09-09T14:36:59.9914262Z triton_mm_703 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:36:59.9915252Z triton_mm_705 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:36:59.9916254Z triton_mm_706 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:36:59.9917250Z triton_mm_698 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=16, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=1, num_warps=2 2025-09-09T14:36:59.9918230Z triton_mm_699 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=16, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=2 2025-09-09T14:36:59.9919300Z triton_mm_700 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:36:59.9920292Z triton_mm_701 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=2 2025-09-09T14:36:59.9921369Z triton_mm_704 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:36:59.9922598Z triton_mm_707 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:36:59.9923470Z SingleProcess AUTOTUNE benchmarking takes 0.2430 seconds and 1.9276 seconds precompiling for 19 choices 2025-09-09T14:36:59.9924207Z >>time: 0.007ms for , to_beat: infms 2025-09-09T14:36:59.9924721Z AUTOTUNE mm(16x128, 128x128) 2025-09-09T14:36:59.9924964Z strides: [128, 1], [128, 1] 2025-09-09T14:36:59.9925230Z dtypes: torch.float32, torch.float32 2025-09-09T14:36:59.9925880Z triton_mm_728 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:36:59.9926877Z triton_mm_729 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=4 2025-09-09T14:36:59.9927885Z triton_mm_730 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=8 2025-09-09T14:36:59.9928874Z triton_mm_715 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=16, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=1, num_warps=2 2025-09-09T14:36:59.9929872Z triton_mm_716 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=16, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=2 2025-09-09T14:36:59.9930868Z triton_mm_717 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:36:59.9931849Z triton_mm_722 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:36:59.9932840Z triton_mm_723 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:36:59.9933835Z triton_mm_725 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=8 2025-09-09T14:36:59.9934832Z triton_mm_726 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=16, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:36:59.9935706Z SingleProcess AUTOTUNE benchmarking takes 0.2209 seconds and 1.0623 seconds precompiling for 18 choices 2025-09-09T14:36:59.9936534Z >>time: 0.011ms for , to_beat: 0.007ms 2025-09-09T14:36:59.9937457Z >>time: 0.004ms for , to_beat: 0.007ms 2025-09-09T14:37:20.6606698Z AUTOTUNE int_mm(16x128, 128x128) 2025-09-09T14:37:20.6607019Z strides: [128, 1], [1, 128] 2025-09-09T14:37:20.6607283Z dtypes: torch.int8, torch.int8 2025-09-09T14:37:20.6607916Z triton_mm_737 0.0266 ms 100.0% ACC_TYPE='tl.int32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=2 2025-09-09T14:37:20.6608895Z triton_mm_733 0.0266 ms 99.9% ACC_TYPE='tl.int32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:37:20.6610235Z triton_mm_732 0.0266 ms 99.8% ACC_TYPE='tl.int32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:37:20.6611199Z triton_mm_734 0.0266 ms 99.8% ACC_TYPE='tl.int32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:37:20.6612312Z triton_mm_735 0.0266 ms 99.8% ACC_TYPE='tl.int32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=8 2025-09-09T14:37:20.6613269Z triton_mm_738 0.0266 ms 99.8% ACC_TYPE='tl.int32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:37:20.6614234Z triton_mm_741 0.0266 ms 99.8% ACC_TYPE='tl.int32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=16, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=8 2025-09-09T14:37:20.6615202Z triton_mm_736 0.0267 ms 99.6% ACC_TYPE='tl.int32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=4 2025-09-09T14:37:20.6616155Z triton_mm_740 0.0287 ms 92.6% ACC_TYPE='tl.int32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:37:20.6617140Z triton_mm_739 0.0297 ms 89.4% ACC_TYPE='tl.int32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=8 2025-09-09T14:37:20.6617988Z SingleProcess AUTOTUNE benchmarking takes 0.1323 seconds and 0.2991 seconds precompiling for 11 choices 2025-09-09T14:37:20.6618850Z >>time: 0.005ms for matmul, to_beat: 0.004ms 2025-09-09T14:37:20.6619702Z best_cls= 2025-09-09T14:37:20.6620135Z 2025-09-09T14:37:20.6620428Z PASSED 2025-09-09T14:37:20.6621053Z test/integration/test_integration.py::TestAutoQuant::test_autoquant_double_access_4_cuda activation_shapes: torch.Size([16, 128]), times_seen: 1 2025-09-09T14:37:20.6621853Z weight_shape: torch.Size([128, 128]), dtype: torch.float16, bias_shape: torch.Size([128]) 2025-09-09T14:37:20.6622477Z AUTOTUNE addmm(16x128, 16x128, 128x128) 2025-09-09T14:37:20.6622747Z strides: [0, 1], [128, 1], [1, 128] 2025-09-09T14:37:20.6623058Z dtypes: torch.float16, torch.float16, torch.float16 2025-09-09T14:37:20.6623746Z triton_mm_746 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=16, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=2 2025-09-09T14:37:20.6624784Z triton_mm_749 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:37:20.6625798Z triton_mm_750 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:37:20.6626797Z triton_mm_754 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=16, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=4 2025-09-09T14:37:20.6627788Z triton_mm_743 0.0276 ms 96.4% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=16, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=2 2025-09-09T14:37:20.6628770Z triton_mm_753 0.0276 ms 96.4% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=16, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:37:20.6629748Z triton_mm_742 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=16, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=1, num_warps=2 2025-09-09T14:37:20.6630857Z triton_mm_744 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:37:20.6631834Z triton_mm_745 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=2 2025-09-09T14:37:20.6632918Z triton_mm_747 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:37:20.6633774Z SingleProcess AUTOTUNE benchmarking takes 0.2515 seconds and 0.3648 seconds precompiling for 19 choices 2025-09-09T14:37:20.6634497Z >>time: 0.004ms for , to_beat: infms 2025-09-09T14:37:20.6635045Z AUTOTUNE mm(16x128, 128x128) 2025-09-09T14:37:20.6635286Z strides: [128, 1], [128, 1] 2025-09-09T14:37:20.6635530Z dtypes: torch.float16, torch.float16 2025-09-09T14:37:20.6636179Z triton_mm_766 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:37:20.6637185Z triton_mm_768 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:37:20.6638174Z triton_mm_770 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=16, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:37:20.6639248Z triton_mm_772 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:37:20.6640240Z triton_mm_773 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=4 2025-09-09T14:37:20.6641215Z triton_mm_759 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=16, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=1, num_warps=2 2025-09-09T14:37:20.6642202Z triton_mm_760 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=16, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=2 2025-09-09T14:37:20.6643185Z triton_mm_762 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=2 2025-09-09T14:37:20.6644158Z triton_mm_763 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=16, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=2 2025-09-09T14:37:20.6645146Z triton_mm_764 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:37:20.6645995Z SingleProcess AUTOTUNE benchmarking takes 0.2288 seconds and 0.3300 seconds precompiling for 18 choices 2025-09-09T14:37:20.6646826Z >>time: 0.004ms for , to_beat: 0.004ms 2025-09-09T14:37:20.6647743Z >>time: 0.004ms for , to_beat: 0.004ms 2025-09-09T14:37:20.6648687Z >>time: 0.004ms for matmul, to_beat: 0.004ms 2025-09-09T14:37:20.6649542Z best_cls= 2025-09-09T14:37:20.6649961Z 2025-09-09T14:37:20.6650087Z PASSED 2025-09-09T14:37:20.6650674Z test/integration/test_integration.py::TestAutoQuant::test_autoquant_double_access_5_cuda activation_shapes: torch.Size([16, 128]), times_seen: 1 2025-09-09T14:37:20.6651565Z weight_shape: torch.Size([128, 128]), dtype: torch.bfloat16, bias_shape: torch.Size([128]) 2025-09-09T14:37:20.6651999Z AUTOTUNE addmm(16x128, 16x128, 128x128) 2025-09-09T14:37:20.6652348Z strides: [0, 1], [128, 1], [1, 128] 2025-09-09T14:37:20.6652670Z dtypes: torch.bfloat16, torch.bfloat16, torch.bfloat16 2025-09-09T14:37:20.6653370Z triton_mm_800 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=4 2025-09-09T14:37:20.6654368Z triton_mm_787 0.0257 ms 99.8% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=16, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=2 2025-09-09T14:37:20.6655396Z triton_mm_789 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=2 2025-09-09T14:37:20.6656386Z triton_mm_795 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:37:46.8833490Z triton_mm_799 0.0267 ms 96.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:37:46.8834573Z triton_mm_786 0.0276 ms 92.6% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=16, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=1, num_warps=2 2025-09-09T14:37:46.8835712Z triton_mm_788 0.0276 ms 92.6% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:37:46.8836832Z triton_mm_790 0.0276 ms 92.6% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=16, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=2 2025-09-09T14:37:46.8837934Z triton_mm_791 0.0276 ms 92.6% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:37:46.8839029Z triton_mm_792 0.0276 ms 92.6% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:37:46.8840087Z SingleProcess AUTOTUNE benchmarking takes 0.2475 seconds and 0.3519 seconds precompiling for 19 choices 2025-09-09T14:37:46.8840904Z >>time: 0.004ms for , to_beat: infms 2025-09-09T14:37:46.8841505Z AUTOTUNE mm(16x128, 128x128) 2025-09-09T14:37:46.8841765Z strides: [128, 1], [128, 1] 2025-09-09T14:37:46.8842114Z dtypes: torch.bfloat16, torch.bfloat16 2025-09-09T14:37:46.8842819Z triton_mm_810 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:37:46.8843983Z triton_mm_812 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:37:46.8845102Z triton_mm_814 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=16, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:37:46.8846203Z triton_mm_819 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=16, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=8 2025-09-09T14:37:46.8847380Z triton_mm_815 0.0266 ms 96.4% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=16, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=4 2025-09-09T14:37:46.8848792Z triton_mm_804 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=16, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=2 2025-09-09T14:37:46.8849987Z triton_mm_805 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:37:46.8851268Z triton_mm_808 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:37:46.8852359Z triton_mm_809 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:37:46.8853525Z triton_mm_813 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=8 2025-09-09T14:37:46.8854487Z SingleProcess AUTOTUNE benchmarking takes 0.2264 seconds and 0.3587 seconds precompiling for 18 choices 2025-09-09T14:37:46.8855437Z >>time: 0.004ms for , to_beat: 0.004ms 2025-09-09T14:37:46.8856456Z >>time: 0.004ms for , to_beat: 0.004ms 2025-09-09T14:37:46.8857511Z >>time: 0.004ms for matmul, to_beat: 0.004ms 2025-09-09T14:37:46.8858476Z best_cls= 2025-09-09T14:37:46.8858981Z 2025-09-09T14:37:46.8859290Z PASSED 2025-09-09T14:37:46.8859939Z test/integration/test_integration.py::TestAutoQuant::test_autoquant_float8_0_cpu SKIPPED 2025-09-09T14:37:46.8860847Z test/integration/test_integration.py::TestAutoQuant::test_autoquant_float8_1_cpu SKIPPED 2025-09-09T14:37:46.8861802Z test/integration/test_integration.py::TestAutoQuant::test_autoquant_float8_2_cpu SKIPPED 2025-09-09T14:37:46.8862715Z test/integration/test_integration.py::TestAutoQuant::test_autoquant_float8_3_cuda SKIPPED 2025-09-09T14:37:46.8863621Z test/integration/test_integration.py::TestAutoQuant::test_autoquant_float8_4_cuda SKIPPED 2025-09-09T14:37:46.8864543Z test/integration/test_integration.py::TestAutoQuant::test_autoquant_float8_5_cuda SKIPPED 2025-09-09T14:37:46.8865497Z test/integration/test_integration.py::TestAutoQuant::test_autoquant_hp_float activation_shapes: torch.Size([128, 128]), times_seen: 1 2025-09-09T14:37:46.8866380Z weight_shape: torch.Size([128, 128]), dtype: torch.float32, bias_shape: torch.Size([128]) 2025-09-09T14:37:46.8867148Z >>time: 0.008ms for , to_beat: infms 2025-09-09T14:37:46.8867884Z best_cls= 2025-09-09T14:37:46.8868263Z 2025-09-09T14:37:46.8868462Z activation_shapes: torch.Size([128, 128]), times_seen: 1 2025-09-09T14:37:46.8868965Z weight_shape: torch.Size([128, 128]), dtype: torch.float32, bias_shape: torch.Size([128]) 2025-09-09T14:37:46.8869831Z >>time: 0.011ms for , to_beat: infms 2025-09-09T14:37:46.8870697Z best_cls= 2025-09-09T14:37:46.8871186Z 2025-09-09T14:37:46.8871528Z activation_shapes: torch.Size([128, 128]), times_seen: 1 2025-09-09T14:37:46.8881015Z weight_shape: torch.Size([128, 128]), dtype: torch.float32, bias_shape: torch.Size([128]) 2025-09-09T14:37:46.8881797Z >>time: 0.011ms for , to_beat: infms 2025-09-09T14:37:46.8882548Z best_cls= 2025-09-09T14:37:46.8882904Z 2025-09-09T14:37:46.8883075Z PASSED 2025-09-09T14:37:46.8883615Z test/integration/test_integration.py::TestAutoQuant::test_autoquant_int4wo_0_cpu SKIPPED 2025-09-09T14:37:46.8884552Z test/integration/test_integration.py::TestAutoQuant::test_autoquant_int4wo_1_cpu SKIPPED 2025-09-09T14:37:46.8885349Z test/integration/test_integration.py::TestAutoQuant::test_autoquant_int4wo_2_cpu SKIPPED 2025-09-09T14:37:46.8886249Z test/integration/test_integration.py::TestAutoQuant::test_autoquant_int4wo_3_cuda SKIPPED 2025-09-09T14:37:46.8887055Z test/integration/test_integration.py::TestAutoQuant::test_autoquant_int4wo_4_cuda SKIPPED 2025-09-09T14:37:46.8887853Z test/integration/test_integration.py::TestAutoQuant::test_autoquant_int4wo_5_cuda SKIPPED 2025-09-09T14:37:46.8888664Z test/integration/test_integration.py::TestAutoQuant::test_autoquant_kwargs_00_cpu SKIPPED 2025-09-09T14:37:46.8889496Z test/integration/test_integration.py::TestAutoQuant::test_autoquant_kwargs_01_cpu SKIPPED 2025-09-09T14:37:46.8890392Z test/integration/test_integration.py::TestAutoQuant::test_autoquant_kwargs_02_cpu SKIPPED 2025-09-09T14:37:46.8891201Z test/integration/test_integration.py::TestAutoQuant::test_autoquant_kwargs_03_cpu SKIPPED 2025-09-09T14:37:46.8891992Z test/integration/test_integration.py::TestAutoQuant::test_autoquant_kwargs_04_cpu SKIPPED 2025-09-09T14:37:46.8892799Z test/integration/test_integration.py::TestAutoQuant::test_autoquant_kwargs_05_cpu SKIPPED 2025-09-09T14:37:46.8893594Z test/integration/test_integration.py::TestAutoQuant::test_autoquant_kwargs_06_cpu SKIPPED 2025-09-09T14:37:46.8894395Z test/integration/test_integration.py::TestAutoQuant::test_autoquant_kwargs_07_cpu SKIPPED 2025-09-09T14:37:46.8895191Z test/integration/test_integration.py::TestAutoQuant::test_autoquant_kwargs_08_cpu SKIPPED 2025-09-09T14:37:46.8895990Z test/integration/test_integration.py::TestAutoQuant::test_autoquant_kwargs_09_cuda SKIPPED 2025-09-09T14:37:46.8896797Z test/integration/test_integration.py::TestAutoQuant::test_autoquant_kwargs_10_cuda SKIPPED 2025-09-09T14:37:46.8897605Z test/integration/test_integration.py::TestAutoQuant::test_autoquant_kwargs_11_cuda PASSED 2025-09-09T14:37:46.8898390Z test/integration/test_integration.py::TestAutoQuant::test_autoquant_kwargs_12_cuda SKIPPED 2025-09-09T14:37:46.8899202Z test/integration/test_integration.py::TestAutoQuant::test_autoquant_kwargs_13_cuda SKIPPED 2025-09-09T14:37:46.8899991Z test/integration/test_integration.py::TestAutoQuant::test_autoquant_kwargs_14_cuda PASSED 2025-09-09T14:37:46.8900791Z test/integration/test_integration.py::TestAutoQuant::test_autoquant_kwargs_15_cuda SKIPPED 2025-09-09T14:37:46.8901619Z test/integration/test_integration.py::TestAutoQuant::test_autoquant_kwargs_16_cuda SKIPPED 2025-09-09T14:37:46.8902424Z test/integration/test_integration.py::TestAutoQuant::test_autoquant_kwargs_17_cuda PASSED 2025-09-09T14:37:46.8903309Z test/integration/test_integration.py::TestAutoQuant::test_autoquant_manual_0_cpu SKIPPED 2025-09-09T14:37:46.8904103Z test/integration/test_integration.py::TestAutoQuant::test_autoquant_manual_1_cpu SKIPPED 2025-09-09T14:37:46.8904888Z test/integration/test_integration.py::TestAutoQuant::test_autoquant_manual_2_cpu SKIPPED 2025-09-09T14:38:20.8350184Z test/integration/test_integration.py::TestAutoQuant::test_autoquant_manual_3_cuda PASSED 2025-09-09T14:38:20.8351104Z test/integration/test_integration.py::TestAutoQuant::test_autoquant_manual_4_cuda PASSED 2025-09-09T14:38:20.8352238Z test/integration/test_integration.py::TestAutoQuant::test_autoquant_manual_5_cuda PASSED 2025-09-09T14:38:20.8353262Z test/integration/test_integration.py::TestAutoQuant::test_autoquant_mha_0_cpu SKIPPED 2025-09-09T14:38:20.8354027Z test/integration/test_integration.py::TestAutoQuant::test_autoquant_mha_1_cpu SKIPPED 2025-09-09T14:38:20.8354786Z test/integration/test_integration.py::TestAutoQuant::test_autoquant_mha_2_cpu SKIPPED 2025-09-09T14:38:20.8355905Z test/integration/test_integration.py::TestAutoQuant::test_autoquant_mha_3_cuda activation_shapes: torch.Size([1, 4096]), times_seen: 1 2025-09-09T14:38:20.8356688Z weight_shape: torch.Size([4096, 4096]), dtype: torch.float32, bias_shape: torch.Size([4096]) 2025-09-09T14:38:20.8357888Z AUTOTUNE addmm(1x4096, 1x4096, 4096x4096) 2025-09-09T14:38:20.8358166Z strides: [0, 1], [4096, 1], [1, 4096] 2025-09-09T14:38:20.8358478Z dtypes: torch.float32, torch.float32, torch.float32 2025-09-09T14:38:20.8358792Z bias_addmm 0.1444 ms 100.0% 2025-09-09T14:38:20.8359030Z addmm 0.1495 ms 96.6% 2025-09-09T14:38:20.8359734Z triton_mm_837 0.1864 ms 77.5% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:38:20.8360728Z triton_mm_832 0.1905 ms 75.8% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:38:20.8361724Z triton_mm_844 0.1905 ms 75.8% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=4 2025-09-09T14:38:20.8362706Z triton_mm_843 0.1925 ms 75.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:38:20.8363681Z triton_mm_833 0.2058 ms 70.1% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=2 2025-09-09T14:38:20.8364661Z triton_mm_834 0.2068 ms 69.8% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=16, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=2 2025-09-09T14:38:20.8365639Z triton_mm_831 0.2232 ms 64.7% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=16, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=2 2025-09-09T14:38:20.8366626Z triton_mm_836 0.2580 ms 56.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:38:20.8367500Z SingleProcess AUTOTUNE benchmarking takes 0.4805 seconds and 1.4605 seconds precompiling for 19 choices 2025-09-09T14:38:20.8368224Z >>time: 0.144ms for , to_beat: infms 2025-09-09T14:38:20.8369047Z >>time: 0.036ms for , to_beat: 0.144ms 2025-09-09T14:38:20.8369960Z >>time: 0.036ms for , to_beat: 0.036ms 2025-09-09T14:38:20.8370572Z AUTOTUNE int_mm(1x4096, 4096x4096) 2025-09-09T14:38:20.8370841Z strides: [4096, 1], [1, 4096] 2025-09-09T14:38:20.8371087Z dtypes: torch.int8, torch.int8 2025-09-09T14:38:20.8371715Z triton_mm_856 0.0532 ms 100.0% ACC_TYPE='tl.int32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=16, BLOCK_N=256, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=8 2025-09-09T14:38:20.8372686Z triton_mm_855 0.0543 ms 98.1% ACC_TYPE='tl.int32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:38:20.8373665Z triton_mm_857 0.0543 ms 98.1% ACC_TYPE='tl.int32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=16, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=8 2025-09-09T14:38:20.8374639Z triton_mm_852 0.0563 ms 94.5% ACC_TYPE='tl.int32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=2 2025-09-09T14:38:20.8375585Z triton_mm_853 0.0573 ms 92.9% ACC_TYPE='tl.int32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:38:20.8376629Z triton_mm_851 0.0635 ms 83.9% ACC_TYPE='tl.int32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=4 2025-09-09T14:38:20.8377586Z triton_mm_849 0.0799 ms 66.7% ACC_TYPE='tl.int32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:38:20.8378618Z triton_mm_848 0.0819 ms 65.0% ACC_TYPE='tl.int32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:38:20.8379586Z triton_mm_850 0.1413 ms 37.7% ACC_TYPE='tl.int32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=8 2025-09-09T14:38:20.8380550Z triton_mm_854 0.1423 ms 37.4% ACC_TYPE='tl.int32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=8 2025-09-09T14:38:20.8381403Z SingleProcess AUTOTUNE benchmarking takes 0.1778 seconds and 0.4584 seconds precompiling for 12 choices 2025-09-09T14:38:20.8382265Z >>time: 0.049ms for matmul, to_beat: 0.036ms 2025-09-09T14:38:20.8383111Z best_cls= 2025-09-09T14:38:20.8383530Z 2025-09-09T14:38:20.8383659Z PASSED 2025-09-09T14:38:20.8384216Z test/integration/test_integration.py::TestAutoQuant::test_autoquant_mha_4_cuda activation_shapes: torch.Size([1, 4096]), times_seen: 1 2025-09-09T14:38:20.8384978Z weight_shape: torch.Size([4096, 4096]), dtype: torch.float16, bias_shape: torch.Size([4096]) 2025-09-09T14:38:20.8385421Z AUTOTUNE addmm(1x4096, 1x4096, 4096x4096) 2025-09-09T14:38:20.8385694Z strides: [0, 1], [4096, 1], [1, 4096] 2025-09-09T14:38:20.8386002Z dtypes: torch.float16, torch.float16, torch.float16 2025-09-09T14:38:20.8386688Z triton_mm_866 0.0799 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:38:20.8387689Z triton_mm_874 0.0829 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=16, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=8 2025-09-09T14:38:20.8388694Z triton_mm_869 0.0850 ms 94.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=16, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:38:20.8389677Z triton_mm_860 0.0870 ms 91.8% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:38:20.8390655Z triton_mm_865 0.0870 ms 91.8% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:38:20.8391283Z addmm 0.0881 ms 90.7% 2025-09-09T14:38:20.8391875Z triton_mm_862 0.0881 ms 90.7% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=16, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=2 2025-09-09T14:38:20.8392863Z triton_mm_872 0.0922 ms 86.7% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=4 2025-09-09T14:38:20.8393844Z triton_mm_868 0.0952 ms 83.9% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=8 2025-09-09T14:38:20.8394467Z bias_addmm 0.0983 ms 81.2% 2025-09-09T14:38:20.8394949Z SingleProcess AUTOTUNE benchmarking takes 0.3459 seconds and 0.5637 seconds precompiling for 19 choices 2025-09-09T14:38:20.8395672Z >>time: 0.081ms for , to_beat: infms 2025-09-09T14:38:20.8396573Z >>time: 0.037ms for , to_beat: 0.081ms 2025-09-09T14:38:20.8397487Z >>time: 0.036ms for , to_beat: 0.037ms 2025-09-09T14:38:20.8398507Z >>time: 0.049ms for matmul, to_beat: 0.036ms 2025-09-09T14:38:20.8399395Z best_cls= 2025-09-09T14:38:20.8399811Z 2025-09-09T14:38:20.8399929Z PASSED 2025-09-09T14:38:20.8400482Z test/integration/test_integration.py::TestAutoQuant::test_autoquant_mha_5_cuda activation_shapes: torch.Size([1, 4096]), times_seen: 1 2025-09-09T14:38:20.8401264Z weight_shape: torch.Size([4096, 4096]), dtype: torch.bfloat16, bias_shape: torch.Size([4096]) 2025-09-09T14:38:20.8401708Z AUTOTUNE addmm(1x4096, 1x4096, 4096x4096) 2025-09-09T14:38:20.8401990Z strides: [0, 1], [4096, 1], [1, 4096] 2025-09-09T14:38:20.8402322Z dtypes: torch.bfloat16, torch.bfloat16, torch.bfloat16 2025-09-09T14:38:43.1125821Z triton_mm_894 0.0809 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:38:43.1127317Z triton_mm_897 0.0840 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=16, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:38:43.1128452Z triton_mm_902 0.0840 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=16, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=8 2025-09-09T14:38:43.1129337Z addmm 0.0881 ms 91.9% 2025-09-09T14:38:43.1130179Z triton_mm_888 0.0881 ms 91.9% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:38:43.1131194Z triton_mm_890 0.0881 ms 91.9% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=16, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=2 2025-09-09T14:38:43.1132201Z triton_mm_893 0.0881 ms 91.9% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:38:43.1133205Z triton_mm_900 0.0932 ms 86.8% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=4 2025-09-09T14:38:43.1134205Z triton_mm_896 0.0963 ms 84.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=8 2025-09-09T14:38:43.1134847Z bias_addmm 0.0973 ms 83.2% 2025-09-09T14:38:43.1135341Z SingleProcess AUTOTUNE benchmarking takes 0.3453 seconds and 0.5388 seconds precompiling for 19 choices 2025-09-09T14:38:43.1136094Z >>time: 0.080ms for , to_beat: infms 2025-09-09T14:38:43.1136937Z >>time: 0.037ms for , to_beat: 0.080ms 2025-09-09T14:38:43.1137866Z >>time: 0.036ms for , to_beat: 0.037ms 2025-09-09T14:38:43.1138829Z >>time: 0.049ms for matmul, to_beat: 0.036ms 2025-09-09T14:38:43.1139698Z best_cls= 2025-09-09T14:38:43.1140123Z 2025-09-09T14:38:43.1140423Z PASSED 2025-09-09T14:38:43.1141029Z test/integration/test_integration.py::TestAutoQuant::test_autoquant_min_sqnr_0_cuda activation_shapes: torch.Size([128, 128]), times_seen: 1 2025-09-09T14:38:43.1141827Z weight_shape: torch.Size([128, 128]), dtype: torch.float32, bias_shape: torch.Size([128]) 2025-09-09T14:38:43.1142536Z AUTOTUNE addmm(128x128, 128x128, 128x128) 2025-09-09T14:38:43.1142825Z strides: [0, 1], [128, 1], [1, 128] 2025-09-09T14:38:43.1143138Z dtypes: torch.float32, torch.float32, torch.float32 2025-09-09T14:38:43.1143977Z triton_mm_916 0.0276 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=32, BLOCK_M=32, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=8 2025-09-09T14:38:43.1144982Z triton_mm_918 0.0276 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=128, BLOCK_M=64, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:38:43.1145985Z triton_mm_920 0.0276 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=32, BLOCK_M=64, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:38:43.1146974Z triton_mm_921 0.0276 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=64, BLOCK_M=64, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=8 2025-09-09T14:38:43.1147974Z triton_mm_923 0.0276 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=32, BLOCK_M=64, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:38:43.1148979Z triton_mm_924 0.0276 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=32, BLOCK_M=64, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=8 2025-09-09T14:38:43.1149989Z triton_mm_927 0.0276 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=32, BLOCK_M=128, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:38:43.1150984Z triton_mm_915 0.0286 ms 96.5% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=128, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:38:43.1151981Z triton_mm_914 0.0287 ms 96.4% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=16, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=1, num_warps=2 2025-09-09T14:38:43.1152963Z triton_mm_917 0.0287 ms 96.4% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=32, BLOCK_M=64, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=8 2025-09-09T14:38:43.1153842Z SingleProcess AUTOTUNE benchmarking takes 0.2449 seconds and 0.9385 seconds precompiling for 21 choices 2025-09-09T14:38:43.1154588Z >>time: 0.006ms for , to_beat: infms 2025-09-09T14:38:43.1155641Z skipping q_cls: because the sqnr is too small, minimum expected sqnr: 60, got 53.82148742675781 2025-09-09T14:38:43.1156987Z skipping q_cls: because the sqnr is too small, minimum expected sqnr: 60, got 54.006710052490234 2025-09-09T14:38:43.1158345Z skipping q_cls: because the sqnr is too small, minimum expected sqnr: 60, got 47.44115447998047 2025-09-09T14:38:43.1159406Z best_cls= 2025-09-09T14:38:43.1159756Z 2025-09-09T14:38:43.1159889Z PASSED 2025-09-09T14:38:43.1160477Z test/integration/test_integration.py::TestAutoQuant::test_autoquant_min_sqnr_1_cuda activation_shapes: torch.Size([128, 128]), times_seen: 1 2025-09-09T14:38:43.1161273Z weight_shape: torch.Size([128, 128]), dtype: torch.float16, bias_shape: torch.Size([128]) 2025-09-09T14:38:43.1161725Z AUTOTUNE addmm(128x128, 128x128, 128x128) 2025-09-09T14:38:43.1162016Z strides: [0, 1], [128, 1], [1, 128] 2025-09-09T14:38:43.1162335Z dtypes: torch.float16, torch.float16, torch.float16 2025-09-09T14:38:43.1163033Z triton_mm_937 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=128, BLOCK_M=64, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:38:43.1164142Z triton_mm_938 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=16, BLOCK_M=64, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:38:43.1165218Z triton_mm_943 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=32, BLOCK_M=64, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=8 2025-09-09T14:38:43.1166208Z triton_mm_933 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=16, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=1, num_warps=2 2025-09-09T14:38:43.1167204Z triton_mm_934 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=128, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:38:43.1168196Z triton_mm_935 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=32, BLOCK_M=32, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=8 2025-09-09T14:38:43.1169186Z triton_mm_936 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=32, BLOCK_M=64, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=8 2025-09-09T14:38:43.1170168Z triton_mm_939 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=32, BLOCK_M=64, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:38:43.1171147Z triton_mm_940 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=64, BLOCK_M=64, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=8 2025-09-09T14:38:43.1172132Z triton_mm_941 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=128, BLOCK_M=64, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:38:43.1173014Z SingleProcess AUTOTUNE benchmarking takes 0.2647 seconds and 0.7220 seconds precompiling for 21 choices 2025-09-09T14:38:43.1173754Z >>time: 0.005ms for , to_beat: infms 2025-09-09T14:38:43.1174773Z skipping q_cls: because the sqnr is too small, minimum expected sqnr: 60, got 54.25 2025-09-09T14:38:43.1176069Z skipping q_cls: because the sqnr is too small, minimum expected sqnr: 60, got 54.40625 2025-09-09T14:38:43.1177366Z skipping q_cls: because the sqnr is too small, minimum expected sqnr: 60, got 48.0625 2025-09-09T14:38:43.1178289Z best_cls= 2025-09-09T14:38:43.1178624Z 2025-09-09T14:38:43.1178743Z PASSED 2025-09-09T14:39:04.7358659Z test/integration/test_integration.py::TestAutoQuant::test_autoquant_min_sqnr_2_cuda activation_shapes: torch.Size([128, 128]), times_seen: 1 2025-09-09T14:39:04.7359591Z weight_shape: torch.Size([128, 128]), dtype: torch.bfloat16, bias_shape: torch.Size([128]) 2025-09-09T14:39:04.7360039Z AUTOTUNE addmm(128x128, 128x128, 128x128) 2025-09-09T14:39:04.7360339Z strides: [0, 1], [128, 1], [1, 128] 2025-09-09T14:39:04.7360658Z dtypes: torch.bfloat16, torch.bfloat16, torch.bfloat16 2025-09-09T14:39:04.7361372Z triton_mm_958 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=32, BLOCK_M=64, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:39:04.7362379Z triton_mm_962 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=32, BLOCK_M=64, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=8 2025-09-09T14:39:04.7363416Z triton_mm_952 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=16, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=1, num_warps=2 2025-09-09T14:39:04.7364688Z triton_mm_955 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=32, BLOCK_M=64, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=8 2025-09-09T14:39:04.7365862Z triton_mm_956 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=128, BLOCK_M=64, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:39:04.7367225Z triton_mm_963 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=64, BLOCK_M=64, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:39:04.7368444Z triton_mm_964 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=128, BLOCK_M=64, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=4 2025-09-09T14:39:04.7369655Z triton_mm_966 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=32, BLOCK_M=128, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=8 2025-09-09T14:39:04.7370879Z triton_mm_968 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=32, BLOCK_M=128, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:39:04.7372094Z triton_mm_954 0.0267 ms 96.0% ACC_TYPE='tl.float32', ALLOW_TF32=True, BLOCK_K=32, BLOCK_M=32, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=8 2025-09-09T14:39:04.7373136Z SingleProcess AUTOTUNE benchmarking takes 0.2627 seconds and 0.7428 seconds precompiling for 21 choices 2025-09-09T14:39:04.7374021Z >>time: 0.007ms for , to_beat: infms 2025-09-09T14:39:04.7375250Z skipping q_cls: because the sqnr is too small, minimum expected sqnr: 60, got 49.0 2025-09-09T14:39:04.7376819Z skipping q_cls: because the sqnr is too small, minimum expected sqnr: 60, got 48.75 2025-09-09T14:39:04.7378400Z skipping q_cls: because the sqnr is too small, minimum expected sqnr: 60, got 46.0 2025-09-09T14:39:04.7379520Z best_cls= 2025-09-09T14:39:04.7379908Z 2025-09-09T14:39:04.7380198Z PASSED 2025-09-09T14:39:04.7380680Z test/integration/test_integration.py::TestAutoQuant::test_autoquant_one_input_00_cpu (m, k, n): (16, 128, 128) 2025-09-09T14:39:04.7381239Z SKIPPED 2025-09-09T14:39:04.7381722Z test/integration/test_integration.py::TestAutoQuant::test_autoquant_one_input_01_cpu (m, k, n): (64, 128, 128) 2025-09-09T14:39:04.7382268Z SKIPPED 2025-09-09T14:39:04.7382737Z test/integration/test_integration.py::TestAutoQuant::test_autoquant_one_input_02_cpu (m, k, n): (16, 128, 256) 2025-09-09T14:39:04.7383289Z SKIPPED 2025-09-09T14:39:04.7383763Z test/integration/test_integration.py::TestAutoQuant::test_autoquant_one_input_03_cpu (m, k, n): (16, 256, 128) 2025-09-09T14:39:04.7384311Z SKIPPED 2025-09-09T14:39:04.7384773Z test/integration/test_integration.py::TestAutoQuant::test_autoquant_one_input_04_cpu (m, k, n): (64, 256, 128) 2025-09-09T14:39:04.7385325Z SKIPPED 2025-09-09T14:39:04.7385783Z test/integration/test_integration.py::TestAutoQuant::test_autoquant_one_input_05_cpu (m, k, n): (16, 128, 128) 2025-09-09T14:39:04.7386333Z SKIPPED 2025-09-09T14:39:04.7386788Z test/integration/test_integration.py::TestAutoQuant::test_autoquant_one_input_06_cpu (m, k, n): (64, 128, 128) 2025-09-09T14:39:04.7387333Z SKIPPED 2025-09-09T14:39:04.7387792Z test/integration/test_integration.py::TestAutoQuant::test_autoquant_one_input_07_cpu (m, k, n): (16, 128, 256) 2025-09-09T14:39:04.7388333Z SKIPPED 2025-09-09T14:39:04.7388798Z test/integration/test_integration.py::TestAutoQuant::test_autoquant_one_input_08_cpu (m, k, n): (16, 256, 128) 2025-09-09T14:39:04.7389337Z SKIPPED 2025-09-09T14:39:04.7389894Z test/integration/test_integration.py::TestAutoQuant::test_autoquant_one_input_09_cpu (m, k, n): (64, 256, 128) 2025-09-09T14:39:04.7390441Z SKIPPED 2025-09-09T14:39:04.7390997Z test/integration/test_integration.py::TestAutoQuant::test_autoquant_one_input_10_cpu (m, k, n): (16, 128, 128) 2025-09-09T14:39:04.7391544Z SKIPPED 2025-09-09T14:39:04.7392001Z test/integration/test_integration.py::TestAutoQuant::test_autoquant_one_input_11_cpu (m, k, n): (64, 128, 128) 2025-09-09T14:39:04.7392548Z SKIPPED 2025-09-09T14:39:04.7393003Z test/integration/test_integration.py::TestAutoQuant::test_autoquant_one_input_12_cpu (m, k, n): (16, 128, 256) 2025-09-09T14:39:04.7393552Z SKIPPED 2025-09-09T14:39:04.7394007Z test/integration/test_integration.py::TestAutoQuant::test_autoquant_one_input_13_cpu (m, k, n): (16, 256, 128) 2025-09-09T14:39:04.7394554Z SKIPPED 2025-09-09T14:39:04.7395028Z test/integration/test_integration.py::TestAutoQuant::test_autoquant_one_input_14_cpu (m, k, n): (64, 256, 128) 2025-09-09T14:39:04.7395573Z SKIPPED 2025-09-09T14:39:04.7396049Z test/integration/test_integration.py::TestAutoQuant::test_autoquant_one_input_15_cuda (m, k, n): (16, 128, 128) 2025-09-09T14:39:04.7396603Z PASSED 2025-09-09T14:39:04.7397069Z test/integration/test_integration.py::TestAutoQuant::test_autoquant_one_input_16_cuda (m, k, n): (64, 128, 128) 2025-09-09T14:39:04.7397657Z activation_shapes: torch.Size([64, 128]), times_seen: 1 2025-09-09T14:39:04.7398143Z weight_shape: torch.Size([128, 128]), dtype: torch.float32, bias_shape: torch.Size([128]) 2025-09-09T14:39:04.7398579Z AUTOTUNE addmm(64x128, 64x128, 128x128) 2025-09-09T14:39:04.7398853Z strides: [0, 1], [128, 1], [1, 128] 2025-09-09T14:39:04.7399223Z dtypes: torch.float32, torch.float32, torch.float32 2025-09-09T14:39:04.7399920Z triton_mm_972 0.0276 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:39:04.7400937Z triton_mm_971 0.0287 ms 96.4% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=1, num_warps=2 2025-09-09T14:39:04.7401944Z triton_mm_973 0.0287 ms 96.4% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=8 2025-09-09T14:39:04.7402924Z triton_mm_974 0.0287 ms 96.4% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=64, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=8 2025-09-09T14:39:04.7403960Z triton_mm_984 0.0307 ms 90.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=64, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:39:04.7404952Z triton_mm_977 0.0317 ms 87.1% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=64, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:39:04.7405930Z triton_mm_976 0.0327 ms 84.5% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=64, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:39:04.7406936Z triton_mm_980 0.0338 ms 81.8% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=64, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:39:04.7407927Z triton_mm_978 0.0389 ms 71.1% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=64, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=8 2025-09-09T14:39:04.7408907Z triton_mm_982 0.0389 ms 71.1% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=64, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:39:04.7409777Z SingleProcess AUTOTUNE benchmarking takes 0.2831 seconds and 10.8467 seconds precompiling for 19 choices 2025-09-09T14:39:04.7410609Z >>time: 0.010ms for , to_beat: infms 2025-09-09T14:39:04.7411125Z AUTOTUNE mm(64x128, 128x128) 2025-09-09T14:39:04.7411452Z strides: [128, 1], [128, 1] 2025-09-09T14:39:04.7411704Z dtypes: torch.float32, torch.float32 2025-09-09T14:39:04.7412350Z triton_mm_991 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=64, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=8 2025-09-09T14:39:04.7413338Z triton_mm_994 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=64, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:39:04.7414323Z triton_mm_988 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=1, num_warps=2 2025-09-09T14:39:24.8894625Z triton_mm_989 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:39:24.8895701Z triton_mm_990 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=8 2025-09-09T14:39:24.8896727Z triton_mm_992 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=64, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:39:24.8897728Z triton_mm_995 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=64, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=8 2025-09-09T14:39:24.8898739Z triton_mm_997 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=64, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:39:24.8899753Z triton_mm_998 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=64, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=8 2025-09-09T14:39:24.8900754Z triton_mm_1001 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=64, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:39:24.8901643Z SingleProcess AUTOTUNE benchmarking takes 0.2366 seconds and 5.0494 seconds precompiling for 18 choices 2025-09-09T14:39:24.8902497Z >>time: 0.016ms for , to_beat: 0.010ms 2025-09-09T14:39:24.8903106Z AUTOTUNE int_mm(64x128, 128x128) 2025-09-09T14:39:24.8903380Z strides: [128, 1], [1, 128] 2025-09-09T14:39:24.8903634Z dtypes: torch.int8, torch.int8 2025-09-09T14:39:24.8904271Z triton_mm_1008 0.0256 ms 100.0% ACC_TYPE='tl.int32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=64, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=8 2025-09-09T14:39:24.8905268Z triton_mm_1014 0.0256 ms 99.9% ACC_TYPE='tl.int32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=64, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=8 2025-09-09T14:39:24.8906263Z triton_mm_1005 0.0266 ms 96.0% ACC_TYPE='tl.int32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=64, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:39:24.8907249Z triton_mm_1006 0.0266 ms 96.0% ACC_TYPE='tl.int32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=64, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:39:24.8908224Z triton_mm_1007 0.0266 ms 96.0% ACC_TYPE='tl.int32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=64, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:39:24.8909469Z triton_mm_1009 0.0266 ms 96.0% ACC_TYPE='tl.int32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=64, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=8 2025-09-09T14:39:24.8910448Z triton_mm_1010 0.0266 ms 96.0% ACC_TYPE='tl.int32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=64, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=8 2025-09-09T14:39:24.8911570Z triton_mm_1011 0.0266 ms 96.0% ACC_TYPE='tl.int32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=8 2025-09-09T14:39:24.8912549Z triton_mm_1012 0.0266 ms 96.0% ACC_TYPE='tl.int32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=64, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=8 2025-09-09T14:39:24.8913525Z triton_mm_1013 0.0266 ms 96.0% ACC_TYPE='tl.int32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=64, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=8 2025-09-09T14:39:24.8914380Z SingleProcess AUTOTUNE benchmarking takes 0.1414 seconds and 0.3306 seconds precompiling for 11 choices 2025-09-09T14:39:24.8915254Z >>time: 0.008ms for matmul, to_beat: 0.010ms 2025-09-09T14:39:24.8916221Z >>time: 0.010ms for , to_beat: 0.022ms 2025-09-09T14:39:24.8917239Z >>time: 0.010ms for interpolated, breakeven constant: 1.00 2025-09-09T14:39:24.8918171Z best_cls= 2025-09-09T14:39:24.8918599Z 2025-09-09T14:39:24.8918895Z PASSED 2025-09-09T14:39:24.8919471Z test/integration/test_integration.py::TestAutoQuant::test_autoquant_one_input_17_cuda (m, k, n): (16, 128, 256) 2025-09-09T14:39:24.8920086Z activation_shapes: torch.Size([16, 128]), times_seen: 1 2025-09-09T14:39:24.8920561Z weight_shape: torch.Size([256, 128]), dtype: torch.float32, bias_shape: torch.Size([256]) 2025-09-09T14:39:24.8921009Z AUTOTUNE addmm(16x256, 16x128, 128x256) 2025-09-09T14:39:24.8921286Z strides: [0, 1], [128, 1], [1, 128] 2025-09-09T14:39:24.8921593Z dtypes: torch.float32, torch.float32, torch.float32 2025-09-09T14:39:24.8922465Z triton_mm_1033 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:39:24.8923487Z triton_mm_1025 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=16, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=1, num_warps=2 2025-09-09T14:39:24.8924485Z triton_mm_1027 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:39:24.8925471Z triton_mm_1028 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=2 2025-09-09T14:39:24.8926475Z triton_mm_1029 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=16, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=2 2025-09-09T14:39:24.8927478Z triton_mm_1030 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:39:24.8928468Z triton_mm_1034 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:39:24.8929468Z triton_mm_1038 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:39:24.8930604Z triton_mm_1039 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=4 2025-09-09T14:39:24.8931605Z triton_mm_1026 0.0287 ms 92.9% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=16, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=2 2025-09-09T14:39:24.8932662Z SingleProcess AUTOTUNE benchmarking takes 0.2421 seconds and 1.9808 seconds precompiling for 19 choices 2025-09-09T14:39:24.8933402Z >>time: 0.010ms for , to_beat: infms 2025-09-09T14:39:24.8933902Z AUTOTUNE mm(16x128, 128x256) 2025-09-09T14:39:24.8934152Z strides: [128, 1], [256, 1] 2025-09-09T14:39:24.8934406Z dtypes: torch.float32, torch.float32 2025-09-09T14:39:24.8935057Z triton_mm_1047 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:39:24.8936067Z triton_mm_1051 0.0266 ms 99.9% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:39:24.8937074Z triton_mm_1054 0.0266 ms 99.9% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=16, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=4 2025-09-09T14:39:24.8938091Z triton_mm_1055 0.0266 ms 99.9% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:39:24.8939089Z triton_mm_1045 0.0267 ms 99.8% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=2 2025-09-09T14:39:24.8940083Z triton_mm_1053 0.0267 ms 99.8% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=16, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:39:24.8941085Z triton_mm_1042 0.0276 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=16, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=1, num_warps=2 2025-09-09T14:39:24.8942080Z triton_mm_1043 0.0276 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=16, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=2 2025-09-09T14:39:24.8943089Z triton_mm_1044 0.0276 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:40:06.2054907Z triton_mm_1046 0.0276 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=16, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=2 2025-09-09T14:40:06.2055989Z SingleProcess AUTOTUNE benchmarking takes 0.2175 seconds and 0.8541 seconds precompiling for 18 choices 2025-09-09T14:40:06.2057046Z >>time: 0.014ms for , to_beat: 0.010ms 2025-09-09T14:40:06.2057967Z >>time: 0.005ms for , to_beat: 0.010ms 2025-09-09T14:40:06.2058670Z AUTOTUNE int_mm(16x128, 128x256) 2025-09-09T14:40:06.2059016Z strides: [128, 1], [1, 128] 2025-09-09T14:40:06.2059354Z dtypes: torch.int8, torch.int8 2025-09-09T14:40:06.2060149Z triton_mm_1059 0.0266 ms 100.0% ACC_TYPE='tl.int32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:40:06.2061136Z triton_mm_1060 0.0266 ms 100.0% ACC_TYPE='tl.int32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:40:06.2062115Z triton_mm_1061 0.0266 ms 100.0% ACC_TYPE='tl.int32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:40:06.2063401Z triton_mm_1062 0.0266 ms 100.0% ACC_TYPE='tl.int32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=8 2025-09-09T14:40:06.2064534Z triton_mm_1063 0.0266 ms 100.0% ACC_TYPE='tl.int32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=4 2025-09-09T14:40:06.2065516Z triton_mm_1064 0.0266 ms 100.0% ACC_TYPE='tl.int32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=2 2025-09-09T14:40:06.2066482Z triton_mm_1065 0.0266 ms 100.0% ACC_TYPE='tl.int32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:40:06.2067466Z triton_mm_1066 0.0266 ms 100.0% ACC_TYPE='tl.int32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=8 2025-09-09T14:40:06.2068439Z triton_mm_1067 0.0266 ms 100.0% ACC_TYPE='tl.int32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:40:06.2069432Z triton_mm_1068 0.0266 ms 100.0% ACC_TYPE='tl.int32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=16, BLOCK_N=256, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=8 2025-09-09T14:40:06.2070348Z SingleProcess AUTOTUNE benchmarking takes 0.1446 seconds and 0.2967 seconds precompiling for 12 choices 2025-09-09T14:40:06.2079576Z >>time: 0.007ms for matmul, to_beat: 0.005ms 2025-09-09T14:40:06.2080542Z best_cls= 2025-09-09T14:40:06.2080977Z 2025-09-09T14:40:06.2081269Z PASSED 2025-09-09T14:40:06.2081768Z test/integration/test_integration.py::TestAutoQuant::test_autoquant_one_input_18_cuda (m, k, n): (16, 256, 128) 2025-09-09T14:40:06.2082390Z activation_shapes: torch.Size([16, 256]), times_seen: 1 2025-09-09T14:40:06.2082876Z weight_shape: torch.Size([128, 256]), dtype: torch.float32, bias_shape: torch.Size([128]) 2025-09-09T14:40:06.2083324Z AUTOTUNE addmm(16x128, 16x256, 256x128) 2025-09-09T14:40:06.2083614Z strides: [0, 1], [256, 1], [1, 256] 2025-09-09T14:40:06.2083928Z dtypes: torch.float32, torch.float32, torch.float32 2025-09-09T14:40:06.2084642Z triton_mm_1071 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=16, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=2 2025-09-09T14:40:06.2085656Z triton_mm_1073 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=2 2025-09-09T14:40:06.2086680Z triton_mm_1074 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=16, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=2 2025-09-09T14:40:06.2087684Z triton_mm_1083 0.0286 ms 93.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:40:06.2088684Z triton_mm_1072 0.0287 ms 92.9% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:40:06.2089684Z triton_mm_1077 0.0287 ms 92.9% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:40:06.2090735Z triton_mm_1084 0.0287 ms 92.9% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=4 2025-09-09T14:40:06.2091844Z triton_mm_1070 0.0297 ms 89.7% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=16, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=1, num_warps=2 2025-09-09T14:40:06.2093090Z triton_mm_1076 0.0307 ms 86.7% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:40:06.2094411Z triton_mm_1075 0.0348 ms 76.5% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:40:06.2095482Z SingleProcess AUTOTUNE benchmarking takes 0.2193 seconds and 1.4261 seconds precompiling for 19 choices 2025-09-09T14:40:06.2096221Z >>time: 0.011ms for , to_beat: infms 2025-09-09T14:40:06.2096732Z AUTOTUNE mm(16x256, 256x128) 2025-09-09T14:40:06.2096981Z strides: [256, 1], [128, 1] 2025-09-09T14:40:06.2097244Z dtypes: torch.float32, torch.float32 2025-09-09T14:40:06.2097897Z triton_mm_1089 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:40:06.2098900Z triton_mm_1087 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=16, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=1, num_warps=2 2025-09-09T14:40:06.2099898Z triton_mm_1088 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=16, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=2 2025-09-09T14:40:06.2100922Z triton_mm_1090 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=2 2025-09-09T14:40:06.2101946Z triton_mm_1091 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=16, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=2 2025-09-09T14:40:06.2102939Z triton_mm_1093 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:40:06.2103936Z triton_mm_1094 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:40:06.2104934Z triton_mm_1096 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:40:06.2105926Z triton_mm_1097 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=8 2025-09-09T14:40:06.2106920Z triton_mm_1100 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:40:06.2107784Z SingleProcess AUTOTUNE benchmarking takes 0.2007 seconds and 1.0905 seconds precompiling for 18 choices 2025-09-09T14:40:06.2108603Z >>time: 0.018ms for , to_beat: 0.011ms 2025-09-09T14:40:06.2109525Z >>time: 0.004ms for , to_beat: 0.011ms 2025-09-09T14:40:06.2110132Z AUTOTUNE int_mm(16x256, 256x128) 2025-09-09T14:40:06.2110385Z strides: [256, 1], [1, 256] 2025-09-09T14:40:06.2110639Z dtypes: torch.int8, torch.int8 2025-09-09T14:40:06.2111255Z triton_mm_1111 0.0256 ms 100.0% ACC_TYPE='tl.int32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=8 2025-09-09T14:40:06.2112233Z triton_mm_1104 0.0266 ms 96.2% ACC_TYPE='tl.int32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:40:06.2113284Z triton_mm_1105 0.0266 ms 96.2% ACC_TYPE='tl.int32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:40:23.7339444Z triton_mm_1108 0.0266 ms 96.2% ACC_TYPE='tl.int32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=4 2025-09-09T14:40:23.7340466Z triton_mm_1109 0.0266 ms 96.2% ACC_TYPE='tl.int32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=2 2025-09-09T14:40:23.7341454Z triton_mm_1112 0.0266 ms 96.2% ACC_TYPE='tl.int32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:40:23.7342458Z triton_mm_1113 0.0266 ms 96.2% ACC_TYPE='tl.int32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=16, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=8 2025-09-09T14:40:23.7343474Z triton_mm_1106 0.0276 ms 92.7% ACC_TYPE='tl.int32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:40:23.7344475Z triton_mm_1107 0.0276 ms 92.6% ACC_TYPE='tl.int32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=8 2025-09-09T14:40:23.7345490Z triton_mm_1110 0.0276 ms 92.6% ACC_TYPE='tl.int32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:40:23.7346383Z SingleProcess AUTOTUNE benchmarking takes 0.1314 seconds and 0.3054 seconds precompiling for 11 choices 2025-09-09T14:40:23.7347271Z >>time: 0.009ms for matmul, to_beat: 0.004ms 2025-09-09T14:40:23.7348145Z best_cls= 2025-09-09T14:40:23.7348579Z 2025-09-09T14:40:23.7348872Z PASSED 2025-09-09T14:40:23.7349380Z test/integration/test_integration.py::TestAutoQuant::test_autoquant_one_input_19_cuda (m, k, n): (64, 256, 128) 2025-09-09T14:40:23.7349985Z activation_shapes: torch.Size([64, 256]), times_seen: 1 2025-09-09T14:40:23.7350474Z weight_shape: torch.Size([128, 256]), dtype: torch.float32, bias_shape: torch.Size([128]) 2025-09-09T14:40:23.7350910Z AUTOTUNE addmm(64x128, 64x256, 256x128) 2025-09-09T14:40:23.7351194Z strides: [0, 1], [256, 1], [1, 256] 2025-09-09T14:40:23.7351493Z dtypes: torch.float32, torch.float32, torch.float32 2025-09-09T14:40:23.7352194Z triton_mm_1115 0.0287 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:40:23.7353213Z triton_mm_1114 0.0369 ms 77.8% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=1, num_warps=2 2025-09-09T14:40:23.7354215Z triton_mm_1116 0.0389 ms 73.7% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=8 2025-09-09T14:40:23.7355219Z triton_mm_1117 0.0389 ms 73.7% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=64, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=8 2025-09-09T14:40:23.7356219Z triton_mm_1127 0.0430 ms 66.7% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=64, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:40:23.7357206Z triton_mm_1120 0.0471 ms 60.9% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=64, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:40:23.7358520Z triton_mm_1119 0.0481 ms 59.6% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=64, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:40:23.7359627Z triton_mm_1121 0.0492 ms 58.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=64, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=8 2025-09-09T14:40:23.7360782Z triton_mm_1123 0.0492 ms 58.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=64, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:40:23.7361787Z triton_mm_1125 0.0492 ms 58.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=64, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:40:23.7362661Z SingleProcess AUTOTUNE benchmarking takes 0.2152 seconds and 4.6519 seconds precompiling for 19 choices 2025-09-09T14:40:23.7363390Z >>time: 0.017ms for , to_beat: infms 2025-09-09T14:40:23.7363897Z AUTOTUNE mm(64x256, 256x128) 2025-09-09T14:40:23.7364144Z strides: [256, 1], [128, 1] 2025-09-09T14:40:23.7364402Z dtypes: torch.float32, torch.float32 2025-09-09T14:40:23.7365054Z triton_mm_1133 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=8 2025-09-09T14:40:23.7366107Z triton_mm_1132 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:40:23.7367104Z triton_mm_1134 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=64, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=8 2025-09-09T14:40:23.7368105Z triton_mm_1144 0.0276 ms 92.7% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=64, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:40:23.7369092Z triton_mm_1131 0.0276 ms 92.6% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=1, num_warps=2 2025-09-09T14:40:23.7370086Z triton_mm_1138 0.0276 ms 92.6% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=64, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=8 2025-09-09T14:40:23.7371079Z triton_mm_1145 0.0276 ms 92.6% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=64, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=8 2025-09-09T14:40:23.7372069Z triton_mm_1137 0.0287 ms 89.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=64, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:40:23.7373060Z triton_mm_1136 0.0317 ms 80.6% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=64, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:40:23.7374050Z triton_mm_1140 0.0328 ms 78.1% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=64, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:40:23.7374924Z SingleProcess AUTOTUNE benchmarking takes 0.1943 seconds and 3.8254 seconds precompiling for 18 choices 2025-09-09T14:40:23.7375807Z >>time: 0.029ms for , to_beat: 0.017ms 2025-09-09T14:40:23.7376401Z AUTOTUNE int_mm(64x256, 256x128) 2025-09-09T14:40:23.7376657Z strides: [256, 1], [1, 256] 2025-09-09T14:40:23.7376909Z dtypes: torch.int8, torch.int8 2025-09-09T14:40:23.7377530Z triton_mm_1149 0.0256 ms 100.0% ACC_TYPE='tl.int32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=64, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:40:23.7378616Z triton_mm_1157 0.0256 ms 100.0% ACC_TYPE='tl.int32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=64, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=8 2025-09-09T14:40:23.7379598Z triton_mm_1148 0.0266 ms 96.2% ACC_TYPE='tl.int32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=64, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:40:23.7380657Z triton_mm_1150 0.0266 ms 96.2% ACC_TYPE='tl.int32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=64, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:40:23.7381633Z triton_mm_1151 0.0266 ms 96.2% ACC_TYPE='tl.int32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=64, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=8 2025-09-09T14:40:23.7382602Z triton_mm_1152 0.0266 ms 96.2% ACC_TYPE='tl.int32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=64, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=8 2025-09-09T14:40:23.7383577Z triton_mm_1153 0.0266 ms 96.2% ACC_TYPE='tl.int32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=64, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=8 2025-09-09T14:40:23.7384547Z triton_mm_1155 0.0266 ms 96.2% ACC_TYPE='tl.int32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=64, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=8 2025-09-09T14:40:23.7385516Z triton_mm_1156 0.0266 ms 96.2% ACC_TYPE='tl.int32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=64, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=8 2025-09-09T14:40:23.7386486Z triton_mm_1154 0.0276 ms 92.6% ACC_TYPE='tl.int32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=8 2025-09-09T14:40:23.7387336Z SingleProcess AUTOTUNE benchmarking takes 0.1414 seconds and 0.3457 seconds precompiling for 11 choices 2025-09-09T14:40:37.5924565Z >>time: 0.009ms for matmul, to_beat: 0.017ms 2025-09-09T14:40:37.5925582Z >>time: 0.011ms for , to_beat: 0.063ms 2025-09-09T14:40:37.5926594Z >>time: 0.011ms for interpolated, breakeven constant: 3.76 2025-09-09T14:40:37.5927508Z best_cls= 2025-09-09T14:40:37.5927929Z 2025-09-09T14:40:37.5928227Z PASSED 2025-09-09T14:40:37.5928724Z test/integration/test_integration.py::TestAutoQuant::test_autoquant_one_input_20_cuda (m, k, n): (16, 128, 128) 2025-09-09T14:40:37.5929278Z PASSED 2025-09-09T14:40:37.5929745Z test/integration/test_integration.py::TestAutoQuant::test_autoquant_one_input_21_cuda (m, k, n): (64, 128, 128) 2025-09-09T14:40:37.5930327Z activation_shapes: torch.Size([64, 128]), times_seen: 1 2025-09-09T14:40:37.5930805Z weight_shape: torch.Size([128, 128]), dtype: torch.float16, bias_shape: torch.Size([128]) 2025-09-09T14:40:37.5931238Z AUTOTUNE addmm(64x128, 64x128, 128x128) 2025-09-09T14:40:37.5931514Z strides: [0, 1], [128, 1], [1, 128] 2025-09-09T14:40:37.5931816Z dtypes: torch.float16, torch.float16, torch.float16 2025-09-09T14:40:37.5932511Z triton_mm_1170 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=8 2025-09-09T14:40:37.5933513Z triton_mm_1171 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=64, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=8 2025-09-09T14:40:37.5934509Z triton_mm_1184 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=64, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=8 2025-09-09T14:40:37.5935808Z triton_mm_1169 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:40:37.5936809Z triton_mm_1173 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=64, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:40:37.5938434Z triton_mm_1176 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=64, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:40:37.5939422Z triton_mm_1177 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=64, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:40:37.5940422Z triton_mm_1178 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=64, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=8 2025-09-09T14:40:37.5941423Z triton_mm_1180 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=64, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=4 2025-09-09T14:40:37.5942412Z triton_mm_1168 0.0276 ms 92.6% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=1, num_warps=2 2025-09-09T14:40:37.5943284Z SingleProcess AUTOTUNE benchmarking takes 0.2481 seconds and 0.5735 seconds precompiling for 19 choices 2025-09-09T14:40:37.5944013Z >>time: 0.006ms for , to_beat: infms 2025-09-09T14:40:37.5944512Z AUTOTUNE mm(64x128, 128x128) 2025-09-09T14:40:37.5944748Z strides: [128, 1], [128, 1] 2025-09-09T14:40:37.5945002Z dtypes: torch.float16, torch.float16 2025-09-09T14:40:37.5945644Z triton_mm_1196 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=64, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:40:37.5946649Z triton_mm_1186 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:40:37.5947640Z triton_mm_1194 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=64, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:40:37.5948626Z triton_mm_1198 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=64, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:40:37.5949618Z triton_mm_1200 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=64, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=8 2025-09-09T14:40:37.5950606Z triton_mm_1187 0.0267 ms 95.9% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=8 2025-09-09T14:40:37.5951588Z triton_mm_1195 0.0276 ms 92.8% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=64, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=8 2025-09-09T14:40:37.5952581Z triton_mm_1185 0.0276 ms 92.6% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=1, num_warps=2 2025-09-09T14:40:37.5953560Z triton_mm_1188 0.0276 ms 92.6% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=64, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=8 2025-09-09T14:40:37.5954539Z triton_mm_1189 0.0276 ms 92.6% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=64, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:40:37.5955398Z SingleProcess AUTOTUNE benchmarking takes 0.2283 seconds and 0.4958 seconds precompiling for 18 choices 2025-09-09T14:40:37.5956312Z >>time: 0.009ms for , to_beat: 0.006ms 2025-09-09T14:40:37.5957249Z >>time: 0.008ms for matmul, to_beat: 0.006ms 2025-09-09T14:40:37.5958094Z best_cls= 2025-09-09T14:40:37.5958419Z 2025-09-09T14:40:37.5958547Z PASSED 2025-09-09T14:40:37.5959025Z test/integration/test_integration.py::TestAutoQuant::test_autoquant_one_input_22_cuda (m, k, n): (16, 128, 256) 2025-09-09T14:40:37.5959704Z activation_shapes: torch.Size([16, 128]), times_seen: 1 2025-09-09T14:40:37.5960169Z weight_shape: torch.Size([256, 128]), dtype: torch.float16, bias_shape: torch.Size([256]) 2025-09-09T14:40:37.5960604Z AUTOTUNE addmm(16x256, 16x128, 128x256) 2025-09-09T14:40:37.5960876Z strides: [0, 1], [128, 1], [1, 128] 2025-09-09T14:40:37.5961180Z dtypes: torch.float16, torch.float16, torch.float16 2025-09-09T14:40:37.5961876Z triton_mm_1215 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=2 2025-09-09T14:40:37.5962890Z triton_mm_1220 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:40:37.5963908Z triton_mm_1221 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:40:37.5964923Z triton_mm_1224 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=16, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=4 2025-09-09T14:40:37.5965932Z triton_mm_1225 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:40:37.5966938Z triton_mm_1226 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=4 2025-09-09T14:40:37.5967934Z triton_mm_1213 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=16, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=2 2025-09-09T14:40:37.5968931Z triton_mm_1216 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=16, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=2 2025-09-09T14:40:37.5969927Z triton_mm_1217 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:40:37.5970912Z triton_mm_1227 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=8 2025-09-09T14:40:37.5971781Z SingleProcess AUTOTUNE benchmarking takes 0.2465 seconds and 0.3853 seconds precompiling for 19 choices 2025-09-09T14:40:37.5972522Z >>time: 0.005ms for , to_beat: infms 2025-09-09T14:40:37.5973028Z AUTOTUNE mm(16x128, 128x256) 2025-09-09T14:40:37.5973271Z strides: [128, 1], [256, 1] 2025-09-09T14:40:37.5973516Z dtypes: torch.float16, torch.float16 2025-09-09T14:40:37.5974163Z triton_mm_1230 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=16, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=2 2025-09-09T14:41:16.4430843Z triton_mm_1231 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:41:16.4432216Z triton_mm_1232 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=2 2025-09-09T14:41:16.4434657Z triton_mm_1237 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:41:16.4436051Z triton_mm_1238 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:41:16.4437064Z triton_mm_1239 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=8 2025-09-09T14:41:16.4438058Z triton_mm_1241 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=16, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=4 2025-09-09T14:41:16.4439065Z triton_mm_1242 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:41:16.4440151Z triton_mm_1245 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=16, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=8 2025-09-09T14:41:16.4441141Z triton_mm_1229 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=16, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=1, num_warps=2 2025-09-09T14:41:16.4442001Z SingleProcess AUTOTUNE benchmarking takes 0.2259 seconds and 0.3559 seconds precompiling for 18 choices 2025-09-09T14:41:16.4442832Z >>time: 0.006ms for , to_beat: 0.005ms 2025-09-09T14:41:16.4443743Z >>time: 0.004ms for , to_beat: 0.005ms 2025-09-09T14:41:16.4444687Z >>time: 0.006ms for matmul, to_beat: 0.004ms 2025-09-09T14:41:16.4445529Z best_cls= 2025-09-09T14:41:16.4445956Z 2025-09-09T14:41:16.4446244Z PASSED 2025-09-09T14:41:16.4446738Z test/integration/test_integration.py::TestAutoQuant::test_autoquant_one_input_23_cuda (m, k, n): (16, 256, 128) 2025-09-09T14:41:16.4447325Z activation_shapes: torch.Size([16, 256]), times_seen: 1 2025-09-09T14:41:16.4447801Z weight_shape: torch.Size([128, 256]), dtype: torch.float16, bias_shape: torch.Size([128]) 2025-09-09T14:41:16.4448228Z AUTOTUNE addmm(16x128, 16x256, 256x128) 2025-09-09T14:41:16.4448507Z strides: [0, 1], [256, 1], [1, 256] 2025-09-09T14:41:16.4448805Z dtypes: torch.float16, torch.float16, torch.float16 2025-09-09T14:41:16.4449500Z triton_mm_1257 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=16, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=1, num_warps=2 2025-09-09T14:41:16.4450509Z triton_mm_1258 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=16, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=2 2025-09-09T14:41:16.4451509Z triton_mm_1260 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=2 2025-09-09T14:41:16.4452504Z triton_mm_1261 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=16, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=2 2025-09-09T14:41:16.4453503Z triton_mm_1263 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:41:16.4454582Z triton_mm_1264 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:41:16.4455583Z triton_mm_1266 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:41:16.4456659Z triton_mm_1268 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=16, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:41:16.4457649Z triton_mm_1270 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:41:16.4458642Z triton_mm_1271 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=4 2025-09-09T14:41:16.4459511Z SingleProcess AUTOTUNE benchmarking takes 0.2389 seconds and 0.4569 seconds precompiling for 19 choices 2025-09-09T14:41:16.4460243Z >>time: 0.012ms for , to_beat: infms 2025-09-09T14:41:16.4460753Z AUTOTUNE mm(16x256, 256x128) 2025-09-09T14:41:16.4460996Z strides: [256, 1], [128, 1] 2025-09-09T14:41:16.4461253Z dtypes: torch.float16, torch.float16 2025-09-09T14:41:16.4461902Z triton_mm_1282 0.0246 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:41:16.4462906Z triton_mm_1278 0.0256 ms 96.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=16, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=2 2025-09-09T14:41:16.4463892Z triton_mm_1280 0.0256 ms 96.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:41:16.4464880Z triton_mm_1281 0.0256 ms 96.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:41:16.4465863Z triton_mm_1288 0.0256 ms 96.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=4 2025-09-09T14:41:16.4466853Z triton_mm_1276 0.0256 ms 95.9% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:41:16.4467833Z triton_mm_1274 0.0266 ms 92.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=16, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=1, num_warps=2 2025-09-09T14:41:16.4468825Z triton_mm_1275 0.0266 ms 92.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=16, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=2 2025-09-09T14:41:16.4469821Z triton_mm_1277 0.0266 ms 92.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=2 2025-09-09T14:41:16.4470802Z triton_mm_1279 0.0266 ms 92.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:41:16.4471661Z SingleProcess AUTOTUNE benchmarking takes 0.2161 seconds and 0.4408 seconds precompiling for 18 choices 2025-09-09T14:41:16.4472478Z >>time: 0.006ms for , to_beat: 0.012ms 2025-09-09T14:41:16.4473400Z >>time: 0.005ms for , to_beat: 0.006ms 2025-09-09T14:41:16.4474344Z >>time: 0.010ms for matmul, to_beat: 0.005ms 2025-09-09T14:41:16.4475276Z best_cls= 2025-09-09T14:41:16.4475701Z 2025-09-09T14:41:16.4475822Z PASSED 2025-09-09T14:41:16.4476290Z test/integration/test_integration.py::TestAutoQuant::test_autoquant_one_input_24_cuda (m, k, n): (64, 256, 128) 2025-09-09T14:41:16.4476958Z activation_shapes: torch.Size([64, 256]), times_seen: 1 2025-09-09T14:41:16.4477433Z weight_shape: torch.Size([128, 256]), dtype: torch.float16, bias_shape: torch.Size([128]) 2025-09-09T14:41:16.4477864Z AUTOTUNE addmm(64x128, 64x256, 256x128) 2025-09-09T14:41:16.4478142Z strides: [0, 1], [256, 1], [1, 256] 2025-09-09T14:41:16.4478444Z dtypes: torch.float16, torch.float16, torch.float16 2025-09-09T14:41:16.4479139Z triton_mm_1312 0.0257 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=64, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:41:16.4480197Z triton_mm_1302 0.0266 ms 96.4% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:41:16.4481181Z triton_mm_1303 0.0266 ms 96.4% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=8 2025-09-09T14:41:27.3764158Z triton_mm_1304 0.0266 ms 96.4% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=64, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=8 2025-09-09T14:41:27.3765327Z triton_mm_1310 0.0266 ms 96.4% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=64, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:41:27.3766332Z triton_mm_1311 0.0266 ms 96.4% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=64, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=8 2025-09-09T14:41:27.3767356Z triton_mm_1314 0.0266 ms 96.4% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=64, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:41:27.3768349Z triton_mm_1315 0.0266 ms 96.4% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=64, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=8 2025-09-09T14:41:27.3769347Z triton_mm_1301 0.0276 ms 92.8% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=1, num_warps=2 2025-09-09T14:41:27.3770341Z triton_mm_1305 0.0276 ms 92.8% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=64, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:41:27.3771219Z SingleProcess AUTOTUNE benchmarking takes 0.2326 seconds and 0.6176 seconds precompiling for 19 choices 2025-09-09T14:41:27.3771953Z >>time: 0.010ms for , to_beat: infms 2025-09-09T14:41:27.3772471Z AUTOTUNE mm(64x256, 256x128) 2025-09-09T14:41:27.3772720Z strides: [256, 1], [128, 1] 2025-09-09T14:41:27.3772980Z dtypes: torch.float16, torch.float16 2025-09-09T14:41:27.3773631Z triton_mm_1331 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=64, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:41:27.3774616Z triton_mm_1318 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=1, num_warps=2 2025-09-09T14:41:27.3775609Z triton_mm_1319 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:41:27.3776591Z triton_mm_1320 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=8 2025-09-09T14:41:27.3777847Z triton_mm_1321 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=64, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=8 2025-09-09T14:41:27.3780116Z triton_mm_1322 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=64, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:41:27.3781107Z triton_mm_1325 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=64, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=8 2025-09-09T14:41:27.3782094Z triton_mm_1327 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=64, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:41:27.3783101Z triton_mm_1328 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=64, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=8 2025-09-09T14:41:27.3784097Z triton_mm_1329 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=64, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:41:27.3784966Z SingleProcess AUTOTUNE benchmarking takes 0.2110 seconds and 0.5897 seconds precompiling for 18 choices 2025-09-09T14:41:27.3785805Z >>time: 0.009ms for , to_beat: 0.010ms 2025-09-09T14:41:27.3786754Z >>time: 0.010ms for matmul, to_beat: 0.009ms 2025-09-09T14:41:27.3787607Z best_cls= 2025-09-09T14:41:27.3788022Z 2025-09-09T14:41:27.3788327Z PASSED 2025-09-09T14:41:27.3788821Z test/integration/test_integration.py::TestAutoQuant::test_autoquant_one_input_25_cuda (m, k, n): (16, 128, 128) 2025-09-09T14:41:27.3789396Z PASSED 2025-09-09T14:41:27.3789874Z test/integration/test_integration.py::TestAutoQuant::test_autoquant_one_input_26_cuda (m, k, n): (64, 128, 128) 2025-09-09T14:41:27.3790476Z activation_shapes: torch.Size([64, 128]), times_seen: 1 2025-09-09T14:41:27.3790964Z weight_shape: torch.Size([128, 128]), dtype: torch.bfloat16, bias_shape: torch.Size([128]) 2025-09-09T14:41:27.3791406Z AUTOTUNE addmm(64x128, 64x128, 128x128) 2025-09-09T14:41:27.3791692Z strides: [0, 1], [128, 1], [1, 128] 2025-09-09T14:41:27.3792019Z dtypes: torch.bfloat16, torch.bfloat16, torch.bfloat16 2025-09-09T14:41:27.3792731Z triton_mm_1347 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=8 2025-09-09T14:41:27.3793742Z triton_mm_1349 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=64, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:41:27.3794757Z triton_mm_1350 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=64, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:41:27.3795768Z triton_mm_1358 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=64, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:41:27.3796763Z triton_mm_1353 0.0276 ms 96.4% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=64, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:41:27.3797761Z triton_mm_1345 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=1, num_warps=2 2025-09-09T14:41:27.3798863Z triton_mm_1346 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:41:27.3799951Z triton_mm_1348 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=64, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=8 2025-09-09T14:41:27.3801027Z triton_mm_1351 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=64, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:41:27.3802025Z triton_mm_1352 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=64, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=8 2025-09-09T14:41:27.3802894Z SingleProcess AUTOTUNE benchmarking takes 0.2494 seconds and 0.5793 seconds precompiling for 19 choices 2025-09-09T14:41:27.3803688Z >>time: 0.006ms for , to_beat: infms 2025-09-09T14:41:27.3804204Z AUTOTUNE mm(64x128, 128x128) 2025-09-09T14:41:27.3804466Z strides: [128, 1], [128, 1] 2025-09-09T14:41:27.3804732Z dtypes: torch.bfloat16, torch.bfloat16 2025-09-09T14:41:27.3805396Z triton_mm_1371 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=64, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:41:27.3806417Z triton_mm_1372 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=64, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=8 2025-09-09T14:41:27.3807427Z triton_mm_1373 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=64, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:41:27.3808434Z triton_mm_1374 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=64, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=4 2025-09-09T14:41:27.3809446Z triton_mm_1375 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=64, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:41:27.3810439Z triton_mm_1376 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=64, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=8 2025-09-09T14:41:27.3811448Z triton_mm_1377 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=64, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=8 2025-09-09T14:41:27.3812456Z triton_mm_1378 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=64, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=8 2025-09-09T14:41:27.3813451Z triton_mm_1363 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:42:00.6338476Z triton_mm_1364 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=8 2025-09-09T14:42:00.6339399Z SingleProcess AUTOTUNE benchmarking takes 0.2257 seconds and 0.5200 seconds precompiling for 18 choices 2025-09-09T14:42:00.6340245Z >>time: 0.008ms for , to_beat: 0.006ms 2025-09-09T14:42:00.6341202Z >>time: 0.008ms for matmul, to_beat: 0.006ms 2025-09-09T14:42:00.6341990Z best_cls= 2025-09-09T14:42:00.6342328Z 2025-09-09T14:42:00.6342619Z PASSED 2025-09-09T14:42:00.6343117Z test/integration/test_integration.py::TestAutoQuant::test_autoquant_one_input_27_cuda (m, k, n): (16, 128, 256) 2025-09-09T14:42:00.6343721Z activation_shapes: torch.Size([16, 128]), times_seen: 1 2025-09-09T14:42:00.6344569Z weight_shape: torch.Size([256, 128]), dtype: torch.bfloat16, bias_shape: torch.Size([256]) 2025-09-09T14:42:00.6345013Z AUTOTUNE addmm(16x256, 16x128, 128x256) 2025-09-09T14:42:00.6345446Z strides: [0, 1], [128, 1], [1, 128] 2025-09-09T14:42:00.6345763Z dtypes: torch.bfloat16, torch.bfloat16, torch.bfloat16 2025-09-09T14:42:00.6346477Z triton_mm_1395 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:42:00.6347489Z triton_mm_1391 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:42:00.6348484Z triton_mm_1393 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=16, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=2 2025-09-09T14:42:00.6349489Z triton_mm_1397 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:42:00.6350497Z triton_mm_1401 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=16, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=4 2025-09-09T14:42:00.6351494Z triton_mm_1403 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=4 2025-09-09T14:42:00.6352485Z triton_mm_1394 0.0267 ms 96.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:42:00.6353468Z triton_mm_1392 0.0267 ms 95.9% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=2 2025-09-09T14:42:00.6354464Z triton_mm_1389 0.0276 ms 92.6% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=16, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=1, num_warps=2 2025-09-09T14:42:00.6355464Z triton_mm_1390 0.0276 ms 92.6% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=16, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=2 2025-09-09T14:42:00.6356328Z SingleProcess AUTOTUNE benchmarking takes 0.2493 seconds and 0.3719 seconds precompiling for 19 choices 2025-09-09T14:42:00.6357079Z >>time: 0.006ms for , to_beat: infms 2025-09-09T14:42:00.6357597Z AUTOTUNE mm(16x128, 128x256) 2025-09-09T14:42:00.6357836Z strides: [128, 1], [256, 1] 2025-09-09T14:42:00.6358096Z dtypes: torch.bfloat16, torch.bfloat16 2025-09-09T14:42:00.6358746Z triton_mm_1409 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=2 2025-09-09T14:42:00.6359828Z triton_mm_1406 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=16, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=1, num_warps=2 2025-09-09T14:42:00.6360825Z triton_mm_1407 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=16, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=2 2025-09-09T14:42:00.6361811Z triton_mm_1408 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:42:00.6362866Z triton_mm_1410 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=16, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=2 2025-09-09T14:42:00.6363952Z triton_mm_1411 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:42:00.6364943Z triton_mm_1417 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=16, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:42:00.6366027Z triton_mm_1418 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=16, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=4 2025-09-09T14:42:00.6367029Z triton_mm_1419 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:42:00.6368012Z triton_mm_1420 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=4 2025-09-09T14:42:00.6378496Z SingleProcess AUTOTUNE benchmarking takes 0.2318 seconds and 0.3508 seconds precompiling for 18 choices 2025-09-09T14:42:00.6379385Z >>time: 0.006ms for , to_beat: 0.006ms 2025-09-09T14:42:00.6380336Z >>time: 0.005ms for , to_beat: 0.006ms 2025-09-09T14:42:00.6381308Z >>time: 0.006ms for matmul, to_beat: 0.005ms 2025-09-09T14:42:00.6382174Z best_cls= 2025-09-09T14:42:00.6382603Z 2025-09-09T14:42:00.6382768Z PASSED 2025-09-09T14:42:00.6383259Z test/integration/test_integration.py::TestAutoQuant::test_autoquant_one_input_28_cuda (m, k, n): (16, 256, 128) 2025-09-09T14:42:00.6383868Z activation_shapes: torch.Size([16, 256]), times_seen: 1 2025-09-09T14:42:00.6384347Z weight_shape: torch.Size([128, 256]), dtype: torch.bfloat16, bias_shape: torch.Size([128]) 2025-09-09T14:42:00.6384799Z AUTOTUNE addmm(16x128, 16x256, 256x128) 2025-09-09T14:42:00.6385084Z strides: [0, 1], [256, 1], [1, 256] 2025-09-09T14:42:00.6385406Z dtypes: torch.bfloat16, torch.bfloat16, torch.bfloat16 2025-09-09T14:42:00.6386134Z triton_mm_1436 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:42:00.6387138Z triton_mm_1438 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=16, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=2 2025-09-09T14:42:00.6388148Z triton_mm_1450 0.0266 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=16, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=8 2025-09-09T14:42:00.6389153Z triton_mm_1437 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=2 2025-09-09T14:42:00.6390133Z triton_mm_1441 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:42:00.6391132Z triton_mm_1443 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:42:00.6392155Z triton_mm_1444 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=8 2025-09-09T14:42:00.6393170Z triton_mm_1445 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=16, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:42:00.6394277Z triton_mm_1447 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:42:00.6395253Z triton_mm_1448 0.0276 ms 96.3% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=4 2025-09-09T14:42:00.6396202Z SingleProcess AUTOTUNE benchmarking takes 0.2397 seconds and 0.4528 seconds precompiling for 19 choices 2025-09-09T14:42:00.6396921Z >>time: 0.006ms for , to_beat: infms 2025-09-09T14:42:00.6397426Z AUTOTUNE mm(16x256, 256x128) 2025-09-09T14:42:00.6397671Z strides: [256, 1], [128, 1] 2025-09-09T14:42:00.6397919Z dtypes: torch.bfloat16, torch.bfloat16 2025-09-09T14:42:31.9729995Z triton_mm_1455 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=16, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=2 2025-09-09T14:42:31.9731127Z triton_mm_1461 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=8 2025-09-09T14:42:31.9732139Z triton_mm_1464 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:42:31.9733163Z triton_mm_1465 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=4 2025-09-09T14:42:31.9734209Z triton_mm_1466 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=8 2025-09-09T14:42:31.9735395Z triton_mm_1451 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=16, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=1, num_warps=2 2025-09-09T14:42:31.9736401Z triton_mm_1454 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=2 2025-09-09T14:42:31.9737500Z triton_mm_1456 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:42:31.9738590Z triton_mm_1457 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:42:31.9739592Z triton_mm_1458 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=16, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:42:31.9740477Z SingleProcess AUTOTUNE benchmarking takes 0.2186 seconds and 0.4220 seconds precompiling for 18 choices 2025-09-09T14:42:31.9741417Z >>time: 0.006ms for , to_beat: 0.006ms 2025-09-09T14:42:31.9742451Z >>time: 0.004ms for , to_beat: 0.006ms 2025-09-09T14:42:31.9743409Z >>time: 0.010ms for matmul, to_beat: 0.004ms 2025-09-09T14:42:31.9744374Z best_cls= 2025-09-09T14:42:31.9744886Z 2025-09-09T14:42:31.9745198Z PASSED 2025-09-09T14:42:31.9745692Z test/integration/test_integration.py::TestAutoQuant::test_autoquant_one_input_29_cuda (m, k, n): (64, 256, 128) 2025-09-09T14:42:31.9746289Z activation_shapes: torch.Size([64, 256]), times_seen: 1 2025-09-09T14:42:31.9746758Z weight_shape: torch.Size([128, 256]), dtype: torch.bfloat16, bias_shape: torch.Size([128]) 2025-09-09T14:42:31.9747197Z AUTOTUNE addmm(64x128, 64x256, 256x128) 2025-09-09T14:42:31.9747471Z strides: [0, 1], [256, 1], [1, 256] 2025-09-09T14:42:31.9748373Z dtypes: torch.bfloat16, torch.bfloat16, torch.bfloat16 2025-09-09T14:42:31.9749197Z triton_mm_1488 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=64, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=8 2025-09-09T14:42:31.9750493Z triton_mm_1489 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=64, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:42:31.9751603Z triton_mm_1491 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=64, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:42:31.9752617Z triton_mm_1479 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:42:31.9753626Z triton_mm_1482 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=64, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:42:31.9754717Z triton_mm_1485 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=64, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=8 2025-09-09T14:42:31.9755817Z triton_mm_1487 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=64, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:42:31.9756901Z triton_mm_1492 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=64, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=8 2025-09-09T14:42:31.9757997Z triton_mm_1481 0.0276 ms 92.8% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=64, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=8 2025-09-09T14:42:31.9759008Z triton_mm_1480 0.0276 ms 92.7% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=8 2025-09-09T14:42:31.9759980Z SingleProcess AUTOTUNE benchmarking takes 0.2286 seconds and 0.6383 seconds precompiling for 19 choices 2025-09-09T14:42:31.9760744Z >>time: 0.010ms for , to_beat: infms 2025-09-09T14:42:31.9761339Z AUTOTUNE mm(64x256, 256x128) 2025-09-09T14:42:31.9761597Z strides: [256, 1], [128, 1] 2025-09-09T14:42:31.9761949Z dtypes: torch.bfloat16, torch.bfloat16 2025-09-09T14:42:31.9762601Z triton_mm_1506 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=64, BLOCK_M=64, BLOCK_N=128, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:42:31.9763708Z triton_mm_1508 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=64, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=3, num_warps=4 2025-09-09T14:42:31.9764802Z triton_mm_1509 0.0256 ms 100.0% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=64, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=4, num_warps=8 2025-09-09T14:42:31.9765811Z triton_mm_1495 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=1, num_warps=2 2025-09-09T14:42:31.9766808Z triton_mm_1496 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=32, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:42:31.9767904Z triton_mm_1497 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=32, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=8 2025-09-09T14:42:31.9769130Z triton_mm_1498 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=64, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=8 2025-09-09T14:42:31.9770227Z triton_mm_1499 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=128, BLOCK_M=64, BLOCK_N=32, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=5, num_warps=4 2025-09-09T14:42:31.9771459Z triton_mm_1500 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=16, BLOCK_M=64, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:42:31.9772463Z triton_mm_1501 0.0266 ms 96.2% ACC_TYPE='tl.float32', ALLOW_TF32=False, BLOCK_K=32, BLOCK_M=64, BLOCK_N=64, EVEN_K=True, GROUP_M=8, USE_FAST_ACCUM=False, num_stages=2, num_warps=4 2025-09-09T14:42:31.9773336Z SingleProcess AUTOTUNE benchmarking takes 0.2096 seconds and 0.5774 seconds precompiling for 18 choices 2025-09-09T14:42:31.9774251Z >>time: 0.011ms for , to_beat: 0.010ms 2025-09-09T14:42:31.9775321Z >>time: 0.009ms for matmul, to_beat: 0.010ms 2025-09-09T14:42:31.9776364Z >>time: 0.012ms for , to_beat: 0.015ms 2025-09-09T14:42:31.9777489Z >>time: 0.012ms for interpolated, breakeven constant: 0.30 2025-09-09T14:42:31.9778348Z best_cls= 2025-09-09T14:42:31.9778686Z 2025-09-09T14:42:31.9778837Z PASSED 2025-09-09T14:42:31.9779291Z test/integration/test_integration.py::TestAOTI::test_aoti_00 SKIPPED 2025-09-09T14:42:31.9779944Z test/integration/test_integration.py::TestAOTI::test_aoti_01 SKIPPED 2025-09-09T14:42:31.9780700Z test/integration/test_integration.py::TestAOTI::test_aoti_02 SKIPPED 2025-09-09T14:42:31.9781434Z test/integration/test_integration.py::TestAOTI::test_aoti_03 SKIPPED 2025-09-09T14:42:31.9782087Z test/integration/test_integration.py::TestAOTI::test_aoti_04 SKIPPED 2025-09-09T14:42:31.9782803Z test/integration/test_integration.py::TestAOTI::test_aoti_05 SKIPPED 2025-09-09T14:42:31.9783459Z test/integration/test_integration.py::TestAOTI::test_aoti_06 SKIPPED 2025-09-09T14:42:31.9784201Z test/integration/test_integration.py::TestAOTI::test_aoti_07 SKIPPED 2025-09-09T14:42:31.9784844Z test/integration/test_integration.py::TestAOTI::test_aoti_08 SKIPPED 2025-09-09T14:42:38.6538056Z test/integration/test_integration.py::TestAOTI::test_aoti_09 SKIPPED 2025-09-09T14:42:38.6539014Z test/integration/test_integration.py::TestAOTI::test_aoti_10 SKIPPED 2025-09-09T14:42:38.6539843Z test/integration/test_integration.py::TestAOTI::test_aoti_11 SKIPPED 2025-09-09T14:42:38.6540484Z test/integration/test_integration.py::TestAOTI::test_aoti_12 SKIPPED 2025-09-09T14:42:38.6541108Z test/integration/test_integration.py::TestAOTI::test_aoti_13 SKIPPED 2025-09-09T14:42:38.6541748Z test/integration/test_integration.py::TestAOTI::test_aoti_14 SKIPPED 2025-09-09T14:42:38.6542378Z test/integration/test_integration.py::TestAOTI::test_aoti_15 SKIPPED 2025-09-09T14:42:38.6543020Z test/integration/test_integration.py::TestAOTI::test_aoti_16 SKIPPED 2025-09-09T14:42:38.6543635Z test/integration/test_integration.py::TestAOTI::test_aoti_17 SKIPPED 2025-09-09T14:42:38.6544274Z test/integration/test_integration.py::TestExport::test_export_00 PASSED 2025-09-09T14:42:38.6544911Z test/integration/test_integration.py::TestExport::test_export_01 PASSED 2025-09-09T14:42:38.6545555Z test/integration/test_integration.py::TestExport::test_export_02 PASSED 2025-09-09T14:42:38.6546195Z test/integration/test_integration.py::TestExport::test_export_03 PASSED 2025-09-09T14:42:38.6546828Z test/integration/test_integration.py::TestExport::test_export_04 PASSED 2025-09-09T14:42:38.6549577Z test/integration/test_integration.py::TestExport::test_export_05 PASSED 2025-09-09T14:42:38.6550236Z test/integration/test_integration.py::TestExport::test_export_06 PASSED 2025-09-09T14:42:38.6551050Z test/integration/test_integration.py::TestExport::test_export_07 PASSED 2025-09-09T14:42:38.6551682Z test/integration/test_integration.py::TestExport::test_export_08 PASSED 2025-09-09T14:42:38.6552325Z test/integration/test_integration.py::TestExport::test_export_09 PASSED 2025-09-09T14:42:38.6552964Z test/integration/test_integration.py::TestExport::test_export_10 PASSED 2025-09-09T14:42:38.6553591Z test/integration/test_integration.py::TestExport::test_export_11 PASSED 2025-09-09T14:42:38.6554234Z test/integration/test_integration.py::TestExport::test_export_12 PASSED 2025-09-09T14:42:38.6554864Z test/integration/test_integration.py::TestExport::test_export_13 PASSED 2025-09-09T14:42:38.6555504Z test/integration/test_integration.py::TestExport::test_export_14 PASSED 2025-09-09T14:42:38.6556132Z test/integration/test_integration.py::TestExport::test_export_15 PASSED 2025-09-09T14:42:38.6556771Z test/integration/test_integration.py::TestExport::test_export_16 PASSED 2025-09-09T14:42:38.6557416Z test/integration/test_integration.py::TestExport::test_export_17 PASSED 2025-09-09T14:42:38.6558048Z test/integration/test_integration.py::TestExport::test_export_18 PASSED 2025-09-09T14:42:38.6558682Z test/integration/test_integration.py::TestExport::test_export_19 PASSED 2025-09-09T14:42:38.6559408Z test/integration/test_integration.py::TestExport::test_export_20 PASSED 2025-09-09T14:42:38.6560051Z test/integration/test_integration.py::TestExport::test_export_21 PASSED 2025-09-09T14:42:38.6560692Z test/integration/test_integration.py::TestExport::test_export_22 PASSED 2025-09-09T14:42:38.6561324Z test/integration/test_integration.py::TestExport::test_export_23 PASSED 2025-09-09T14:42:38.6561994Z test/integration/test_integration.py::TestExport::test_export_float8 SKIPPED 2025-09-09T14:42:38.6562702Z test/integration/test_integration.py::TestUtils::test_get_model_size_aqt_00 SKIPPED 2025-09-09T14:42:38.6563435Z test/integration/test_integration.py::TestUtils::test_get_model_size_aqt_01 SKIPPED 2025-09-09T14:42:38.6564151Z test/integration/test_integration.py::TestUtils::test_get_model_size_aqt_02 SKIPPED 2025-09-09T14:42:38.6564878Z test/integration/test_integration.py::TestUtils::test_get_model_size_aqt_03 SKIPPED 2025-09-09T14:42:38.6565608Z test/integration/test_integration.py::TestUtils::test_get_model_size_aqt_04 SKIPPED 2025-09-09T14:42:38.6566322Z test/integration/test_integration.py::TestUtils::test_get_model_size_aqt_05 PASSED 2025-09-09T14:42:38.6567038Z test/integration/test_integration.py::TestUtils::test_get_model_size_aqt_06 SKIPPED 2025-09-09T14:42:38.6567756Z test/integration/test_integration.py::TestUtils::test_get_model_size_aqt_07 SKIPPED 2025-09-09T14:42:38.6568479Z test/integration/test_integration.py::TestUtils::test_get_model_size_aqt_08 SKIPPED 2025-09-09T14:42:38.6569206Z test/integration/test_integration.py::TestUtils::test_get_model_size_aqt_09 SKIPPED 2025-09-09T14:42:38.6569921Z test/integration/test_integration.py::TestUtils::test_get_model_size_aqt_10 SKIPPED 2025-09-09T14:42:38.6570640Z test/integration/test_integration.py::TestUtils::test_get_model_size_aqt_11 PASSED 2025-09-09T14:42:38.6571354Z test/integration/test_integration.py::TestUtils::test_get_model_size_aqt_12 SKIPPED 2025-09-09T14:42:38.6572077Z test/integration/test_integration.py::TestUtils::test_get_model_size_aqt_13 SKIPPED 2025-09-09T14:42:38.6572853Z test/integration/test_integration.py::TestUtils::test_get_model_size_aqt_14 SKIPPED 2025-09-09T14:42:38.6573668Z test/integration/test_integration.py::TestUtils::test_get_model_size_aqt_15 SKIPPED 2025-09-09T14:42:38.6574390Z test/integration/test_integration.py::TestUtils::test_get_model_size_aqt_16 SKIPPED 2025-09-09T14:42:38.6575108Z test/integration/test_integration.py::TestUtils::test_get_model_size_aqt_17 PASSED 2025-09-09T14:42:38.6575955Z test/integration/test_integration.py::TestBenchmarkModel::test_benchmark_model_cpu PASSED 2025-09-09T14:42:38.6576754Z test/integration/test_integration.py::TestBenchmarkModel::test_benchmark_model_cuda PASSED 2025-09-09T14:42:38.6577694Z test/integration/test_load_and_run_checkpoint.py::TestLoadAndRunCheckpoint::test_deprecated_hf_models_model_info0 SKIPPED 2025-09-09T14:42:38.6579183Z test/integration/test_load_and_run_checkpoint.py::TestLoadAndRunCheckpoint::test_deprecated_single_linear_model_name_torchao-testing/single-linear-Float8DynamicActivationFloat8WeightConfig-v1-0_13_dev SKIPPED 2025-09-09T14:42:38.6580996Z test/integration/test_load_and_run_checkpoint.py::TestLoadAndRunCheckpoint::test_single_linear_model_name_torchao-testing/single-linear-Float8DynamicActivationFloat8WeightConfig-v2-0_13_dev SKIPPED 2025-09-09T14:42:38.6582771Z test/integration/test_load_and_run_checkpoint.py::TestLoadAndRunCheckpoint::test_single_linear_model_name_torchao-testing/single-linear-Int4WeightOnlyConfig-preshuffled-v2-0_13_dev SKIPPED 2025-09-09T14:42:38.6584367Z test/integration/test_load_and_run_checkpoint.py::TestLoadAndRunCheckpoint::test_single_linear_model_name_torchao-testing/single-linear-Int4WeightOnlyConfig-v2-0_13_dev SKIPPED 2025-09-09T14:42:38.6585424Z test/kernel/test_autotuner.py::TestQuantFlow::test_int_mm_0_cuda PASSED 2025-09-09T14:42:38.6586061Z test/kernel/test_autotuner.py::TestQuantFlow::test_int_mm_1_cuda PASSED 2025-09-09T14:42:38.6586721Z test/kernel/test_autotuner.py::TestQuantFlow::test_int_mm_float8_0_cuda SKIPPED 2025-09-09T14:42:38.6587399Z test/kernel/test_autotuner.py::TestQuantFlow::test_int_mm_float8_1_cuda SKIPPED 2025-09-09T14:42:38.6588089Z test/kernel/test_autotuner.py::TestQuantFlow::test_int_scaled_mm_0_cuda PASSED 2025-09-09T14:42:38.6588761Z test/kernel/test_autotuner.py::TestQuantFlow::test_int_scaled_mm_1_cpu PASSED 2025-09-09T14:42:38.6589444Z test/kernel/test_autotuner.py::TestQuantFlow::test_int_scaled_mm_2_cuda PASSED 2025-09-09T14:42:38.6590109Z test/kernel/test_autotuner.py::TestQuantFlow::test_int_scaled_mm_3_cpu PASSED 2025-09-09T14:42:38.6591010Z test/prototype/blockwise_fp8_training/test_blockwise_kernels.py::test_triton_fp8_gemm_1x128_128x128[dtype0-2-512-128] SKIPPED 2025-09-09T14:42:38.6592067Z test/prototype/blockwise_fp8_training/test_blockwise_kernels.py::test_triton_fp8_gemm_1x128_128x128[dtype0-2-5120-1280] SKIPPED 2025-09-09T14:42:38.6593160Z test/prototype/blockwise_fp8_training/test_blockwise_kernels.py::test_triton_fp8_gemm_1x128_128x128[dtype0-3-2048-2048] SKIPPED 2025-09-09T14:42:38.6594214Z test/prototype/blockwise_fp8_training/test_blockwise_kernels.py::test_triton_fp8_gemm_1x128_128x128[dtype0-4-3584-640] SKIPPED 2025-09-09T14:42:38.6595273Z test/prototype/blockwise_fp8_training/test_blockwise_kernels.py::test_triton_fp8_gemm_1x128_128x128[dtype0-13-8704-8576] SKIPPED 2025-09-09T14:42:38.6596335Z test/prototype/blockwise_fp8_training/test_blockwise_kernels.py::test_triton_fp8_gemm_1x128_128x128[dtype0-26-18944-1664] SKIPPED 2025-09-09T14:42:38.6597393Z test/prototype/blockwise_fp8_training/test_blockwise_kernels.py::test_triton_fp8_gemm_1x128_128x128[dtype0-67-6656-1408] SKIPPED 2025-09-09T14:42:38.6598427Z test/prototype/blockwise_fp8_training/test_blockwise_kernels.py::test_triton_fp8_gemm_1x128_128x1[dtype0-2-512-128] SKIPPED 2025-09-09T14:42:38.6599514Z test/prototype/blockwise_fp8_training/test_blockwise_kernels.py::test_triton_fp8_gemm_1x128_128x1[dtype0-2-5120-1280] SKIPPED 2025-09-09T14:42:38.6600669Z test/prototype/blockwise_fp8_training/test_blockwise_kernels.py::test_triton_fp8_gemm_1x128_128x1[dtype0-3-2048-2048] SKIPPED 2025-09-09T14:42:38.6601700Z test/prototype/blockwise_fp8_training/test_blockwise_kernels.py::test_triton_fp8_gemm_1x128_128x1[dtype0-4-3584-640] SKIPPED 2025-09-09T14:42:38.6602876Z test/prototype/blockwise_fp8_training/test_blockwise_kernels.py::test_triton_fp8_gemm_1x128_128x1[dtype0-13-8704-8576] SKIPPED 2025-09-09T14:42:38.6603931Z test/prototype/blockwise_fp8_training/test_blockwise_kernels.py::test_triton_fp8_gemm_1x128_128x1[dtype0-26-18944-1664] SKIPPED 2025-09-09T14:42:38.6604970Z test/prototype/blockwise_fp8_training/test_blockwise_kernels.py::test_triton_fp8_gemm_1x128_128x1[dtype0-67-6656-1408] SKIPPED 2025-09-09T14:42:39.1134984Z test/prototype/blockwise_fp8_training/test_blockwise_kernels.py::test_triton_quantize_fp8_act_quant_lhs[128] SKIPPED 2025-09-09T14:42:39.1136258Z test/prototype/blockwise_fp8_training/test_blockwise_kernels.py::test_triton_quantize_fp8_act_quant_lhs[256] SKIPPED 2025-09-09T14:42:39.1137476Z test/prototype/blockwise_fp8_training/test_blockwise_kernels.py::test_triton_quantize_fp8_act_quant_rhs[128] SKIPPED 2025-09-09T14:42:39.1138715Z test/prototype/blockwise_fp8_training/test_blockwise_kernels.py::test_triton_quantize_fp8_act_quant_rhs[256] SKIPPED 2025-09-09T14:42:39.1140041Z test/prototype/blockwise_fp8_training/test_blockwise_kernels.py::test_triton_quantize_fp8_act_quant_transposed_lhs[4096-1024-128] SKIPPED 2025-09-09T14:42:39.1141433Z test/prototype/blockwise_fp8_training/test_blockwise_kernels.py::test_triton_quantize_fp8_act_quant_transposed_lhs[4096-1024-256] SKIPPED 2025-09-09T14:42:39.1142856Z test/prototype/blockwise_fp8_training/test_blockwise_kernels.py::test_triton_quantize_fp8_act_quant_transposed_lhs[4096-16384-128] SKIPPED 2025-09-09T14:42:39.1144197Z test/prototype/blockwise_fp8_training/test_blockwise_kernels.py::test_triton_quantize_fp8_act_quant_transposed_lhs[4096-16384-256] SKIPPED 2025-09-09T14:42:39.1145313Z test/prototype/blockwise_fp8_training/test_blockwise_kernels.py::test_triton_quantize_fp8_weight_quant_rhs[4096-1024-128] SKIPPED 2025-09-09T14:42:39.1146418Z test/prototype/blockwise_fp8_training/test_blockwise_kernels.py::test_triton_quantize_fp8_weight_quant_rhs[4096-1024-256] SKIPPED 2025-09-09T14:42:39.1147524Z test/prototype/blockwise_fp8_training/test_blockwise_kernels.py::test_triton_quantize_fp8_weight_quant_rhs[4096-16384-128] SKIPPED 2025-09-09T14:42:39.1148616Z test/prototype/blockwise_fp8_training/test_blockwise_kernels.py::test_triton_quantize_fp8_weight_quant_rhs[4096-16384-256] SKIPPED 2025-09-09T14:42:39.1149719Z test/prototype/blockwise_fp8_training/test_blockwise_kernels.py::test_triton_quantize_fp8_weight_quant_transposed_rhs[128] SKIPPED 2025-09-09T14:42:39.1150829Z test/prototype/blockwise_fp8_training/test_blockwise_kernels.py::test_triton_quantize_fp8_weight_quant_transposed_rhs[256] SKIPPED 2025-09-09T14:42:39.1151922Z test/prototype/inductor/test_int8_sdpa_fusion.py::SDPAPatternRewriterCpuTests::test_sdpa_int8_rewriter_cpu SKIPPED 2025-09-09T14:42:39.1153017Z test/prototype/module_swap_quantization/test_kmeans_codebook.py::TestKmeansCodebook::test_kmeans_codebook SKIPPED 2025-09-09T14:42:39.1154016Z test/prototype/module_swap_quantization/test_llm_ptq_data_getter.py::TestPTQDataGetter::test_data_getter SKIPPED 2025-09-09T14:42:39.1154983Z test/prototype/module_swap_quantization/test_module_swap.py::TestEmbeddingSwap::test_embedding_swap PASSED 2025-09-09T14:42:39.1156080Z test/prototype/module_swap_quantization/test_module_swap_quantization_utils.py::TestQuantizedModuleUtils::test_set_bit_widths_by_name PASSED 2025-09-09T14:42:39.1157198Z test/prototype/module_swap_quantization/test_quantized_modules.py::TestQuantizedLinear::test_quantize_dynamic PASSED 2025-09-09T14:42:39.1158473Z test/prototype/module_swap_quantization/test_quantized_modules.py::TestQuantizedLinear::test_quantize_dynamic_vectorized PASSED 2025-09-09T14:42:39.1159623Z test/prototype/module_swap_quantization/test_quantized_modules.py::TestQuantizedLinear::test_quantized_linear PASSED 2025-09-09T14:42:39.1160844Z test/prototype/module_swap_quantization/test_quantized_modules.py::TestQuantizedLinear::test_quantized_linear_init PASSED 2025-09-09T14:42:39.1161972Z test/prototype/module_swap_quantization/test_quantized_modules.py::TestQuantizedLinear::test_quantized_linear_passes_gradients PASSED 2025-09-09T14:42:39.1163213Z test/prototype/module_swap_quantization/test_quantized_modules.py::TestQuantizedLinear::test_quantized_linear_passes_gradients_to_activation_scale PASSED 2025-09-09T14:42:39.1164515Z test/prototype/module_swap_quantization/test_quantized_modules.py::TestQuantizedLinear::test_quantized_linear_passes_gradients_to_weight_scale PASSED 2025-09-09T14:42:39.1165784Z test/prototype/module_swap_quantization/test_quantized_modules.py::TestQuantizedLinear::test_set_weight_scale_to_min_max_test_all_options PASSED 2025-09-09T14:42:39.1166997Z test/prototype/module_swap_quantization/test_quantized_modules.py::TestQuantizedLinear::test_set_weight_scale_to_min_max_test_correct PASSED 2025-09-09T14:42:39.1168150Z test/prototype/module_swap_quantization/test_quantized_modules.py::TestQuantizedEmbedding::test_quantized_embedding PASSED 2025-09-09T14:42:39.1169157Z test/prototype/module_swap_quantization/test_quantizers.py::TestIntQuantizer::test_get_qmin_qmax PASSED 2025-09-09T14:42:39.1170135Z test/prototype/module_swap_quantization/test_quantizers.py::TestIntQuantizer::test_get_scale_from_min_max PASSED 2025-09-09T14:42:39.1171168Z test/prototype/module_swap_quantization/test_quantizers.py::TestIntQuantizer::test_get_scale_from_min_max_vectorized PASSED 2025-09-09T14:42:39.1172225Z test/prototype/module_swap_quantization/test_quantizers.py::TestIntQuantizer::test_get_scale_offset_asymmetric PASSED 2025-09-09T14:42:39.1173268Z test/prototype/module_swap_quantization/test_quantizers.py::TestIntQuantizer::test_get_scale_offset_from_min_max PASSED 2025-09-09T14:42:39.1174363Z test/prototype/module_swap_quantization/test_quantizers.py::TestIntQuantizer::test_get_scale_offset_from_min_max_tensorized PASSED 2025-09-09T14:42:39.1175427Z test/prototype/module_swap_quantization/test_quantizers.py::TestIntQuantizer::test_get_scale_offset_symmetric PASSED 2025-09-09T14:42:39.1176434Z test/prototype/module_swap_quantization/test_quantizers.py::TestIntQuantizer::test_get_scale_param_size PASSED 2025-09-09T14:42:39.1177387Z test/prototype/module_swap_quantization/test_quantizers.py::TestIntQuantizer::test_quantize_forward PASSED 2025-09-09T14:42:39.1178425Z test/prototype/module_swap_quantization/test_quantizers.py::TestIntQuantizer::test_quantize_forward_asymmetric_clipping PASSED 2025-09-09T14:42:39.1179497Z test/prototype/module_swap_quantization/test_quantizers.py::TestIntQuantizer::test_quantize_forward_symmetric PASSED 2025-09-09T14:42:39.1180554Z test/prototype/module_swap_quantization/test_quantizers.py::TestIntQuantizer::test_quantize_forward_symmetric_clipping PASSED 2025-09-09T14:42:39.1181617Z test/prototype/module_swap_quantization/test_quantizers.py::TestCodebookQuantizer::test_codebook_quantizer PASSED 2025-09-09T14:42:39.1182675Z test/prototype/module_swap_quantization/test_quantizers.py::TestCodebookQuantizer::test_vector_quantizer PASSED 2025-09-09T14:42:39.1183723Z test/prototype/module_swap_quantization/test_range_setting_methods.py::TestSetWeightMinMax::test_set_weight_min_max PASSED 2025-09-09T14:42:39.1184831Z test/prototype/module_swap_quantization/test_range_setting_methods.py::TestSetWeightMinMax::test_set_weight_min_max_grouped PASSED 2025-09-09T14:42:39.1185997Z test/prototype/module_swap_quantization/test_range_setting_methods.py::TestSetWeightMSE::test_set_weight_mse PASSED 2025-09-09T14:42:39.1187051Z test/prototype/module_swap_quantization/test_range_setting_methods.py::TestSetWeightMSE::test_set_weight_mse_grouped PASSED 2025-09-09T14:42:39.1188336Z test/prototype/module_swap_quantization/test_range_setting_methods.py::TestSetWeightRangeActivationLoss::test_set_weight_range_activation_loss PASSED 2025-09-09T14:42:39.1189692Z test/prototype/module_swap_quantization/test_range_setting_methods.py::TestSetWeightRangeActivationLoss::test_set_weight_range_activation_loss_progressive PASSED 2025-09-09T14:42:39.1191044Z test/prototype/module_swap_quantization/test_range_setting_methods.py::TestStaticActivationRangeSetting::test_static_activation_range_setting PASSED 2025-09-09T14:42:39.1192373Z test/prototype/module_swap_quantization/test_range_setting_methods.py::TestStaticActivationRangeSetting::test_static_activation_range_setting_no_input PASSED 2025-09-09T14:42:39.1193662Z test/prototype/module_swap_quantization/test_range_setting_methods.py::TestQuantizePerGroupScales::test_quantize_per_group_scales PASSED 2025-09-09T14:42:39.1194949Z test/prototype/module_swap_quantization/test_range_setting_methods.py::TestQuantizePerGroupScales::test_quantize_per_group_scales_dont_change_per_channel PASSED 2025-09-09T14:42:39.1196097Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_mx[True-True-elem_dtype0] SKIPPED 2025-09-09T14:42:39.1197056Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_mx[True-True-elem_dtype1] SKIPPED 2025-09-09T14:42:39.1198015Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_mx[True-False-elem_dtype0] SKIPPED 2025-09-09T14:42:39.1198979Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_mx[True-False-elem_dtype1] SKIPPED 2025-09-09T14:42:39.1200007Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_mx[False-True-elem_dtype0] SKIPPED 2025-09-09T14:42:39.1200954Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_mx[False-True-elem_dtype1] SKIPPED 2025-09-09T14:42:39.1201922Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_mx[False-False-elem_dtype0] SKIPPED 2025-09-09T14:42:39.2013497Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_mx[False-False-elem_dtype1] SKIPPED 2025-09-09T14:42:39.2014697Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x64x256-True-inpt_dtype0-NVFP4MMConfig.DYNAMIC-True-True] SKIPPED 2025-09-09T14:42:39.2016016Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x64x256-True-inpt_dtype0-NVFP4MMConfig.DYNAMIC-True-False] SKIPPED 2025-09-09T14:42:39.2017336Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x64x256-True-inpt_dtype0-NVFP4MMConfig.DYNAMIC-False-True] SKIPPED 2025-09-09T14:42:39.2018656Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x64x256-True-inpt_dtype0-NVFP4MMConfig.DYNAMIC-False-False] SKIPPED 2025-09-09T14:42:39.2019998Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x64x256-True-inpt_dtype0-NVFP4MMConfig.WEIGHT_ONLY-True-True] SKIPPED 2025-09-09T14:42:39.2021327Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x64x256-True-inpt_dtype0-NVFP4MMConfig.WEIGHT_ONLY-True-False] SKIPPED 2025-09-09T14:42:39.2022904Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x64x256-True-inpt_dtype0-NVFP4MMConfig.WEIGHT_ONLY-False-True] PASSED 2025-09-09T14:42:39.2024228Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x64x256-True-inpt_dtype0-NVFP4MMConfig.WEIGHT_ONLY-False-False] PASSED 2025-09-09T14:42:39.2025710Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x64x256-True-inpt_dtype1-NVFP4MMConfig.DYNAMIC-True-True] SKIPPED 2025-09-09T14:42:39.2027153Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x64x256-True-inpt_dtype1-NVFP4MMConfig.DYNAMIC-True-False] SKIPPED 2025-09-09T14:42:39.2028463Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x64x256-True-inpt_dtype1-NVFP4MMConfig.DYNAMIC-False-True] SKIPPED 2025-09-09T14:42:39.2029791Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x64x256-True-inpt_dtype1-NVFP4MMConfig.DYNAMIC-False-False] SKIPPED 2025-09-09T14:42:39.2031110Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x64x256-True-inpt_dtype1-NVFP4MMConfig.WEIGHT_ONLY-True-True] XFAIL 2025-09-09T14:42:39.2032441Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x64x256-True-inpt_dtype1-NVFP4MMConfig.WEIGHT_ONLY-True-False] SKIPPED 2025-09-09T14:42:39.2033773Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x64x256-True-inpt_dtype1-NVFP4MMConfig.WEIGHT_ONLY-False-True] XFAIL 2025-09-09T14:42:39.2035109Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x64x256-True-inpt_dtype1-NVFP4MMConfig.WEIGHT_ONLY-False-False] PASSED 2025-09-09T14:42:39.2036436Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x64x256-False-inpt_dtype0-NVFP4MMConfig.DYNAMIC-True-True] SKIPPED 2025-09-09T14:42:39.2037765Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x64x256-False-inpt_dtype0-NVFP4MMConfig.DYNAMIC-True-False] SKIPPED 2025-09-09T14:42:39.2039098Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x64x256-False-inpt_dtype0-NVFP4MMConfig.DYNAMIC-False-True] SKIPPED 2025-09-09T14:42:39.2040481Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x64x256-False-inpt_dtype0-NVFP4MMConfig.DYNAMIC-False-False] SKIPPED 2025-09-09T14:42:39.2041824Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x64x256-False-inpt_dtype0-NVFP4MMConfig.WEIGHT_ONLY-True-True] SKIPPED 2025-09-09T14:42:39.2043160Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x64x256-False-inpt_dtype0-NVFP4MMConfig.WEIGHT_ONLY-True-False] SKIPPED 2025-09-09T14:42:39.2044507Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x64x256-False-inpt_dtype0-NVFP4MMConfig.WEIGHT_ONLY-False-True] PASSED 2025-09-09T14:42:39.2045854Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x64x256-False-inpt_dtype0-NVFP4MMConfig.WEIGHT_ONLY-False-False] PASSED 2025-09-09T14:42:39.2047177Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x64x256-False-inpt_dtype1-NVFP4MMConfig.DYNAMIC-True-True] SKIPPED 2025-09-09T14:42:39.2048506Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x64x256-False-inpt_dtype1-NVFP4MMConfig.DYNAMIC-True-False] SKIPPED 2025-09-09T14:42:39.2049833Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x64x256-False-inpt_dtype1-NVFP4MMConfig.DYNAMIC-False-True] SKIPPED 2025-09-09T14:42:39.2051159Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x64x256-False-inpt_dtype1-NVFP4MMConfig.DYNAMIC-False-False] SKIPPED 2025-09-09T14:42:39.2052488Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x64x256-False-inpt_dtype1-NVFP4MMConfig.WEIGHT_ONLY-True-True] XFAIL 2025-09-09T14:42:39.2053908Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x64x256-False-inpt_dtype1-NVFP4MMConfig.WEIGHT_ONLY-True-False] SKIPPED 2025-09-09T14:42:39.2055316Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x64x256-False-inpt_dtype1-NVFP4MMConfig.WEIGHT_ONLY-False-True] XFAIL 2025-09-09T14:42:39.2056652Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x64x256-False-inpt_dtype1-NVFP4MMConfig.WEIGHT_ONLY-False-False] PASSED 2025-09-09T14:42:39.2057981Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[256x128x512-True-inpt_dtype0-NVFP4MMConfig.DYNAMIC-True-True] SKIPPED 2025-09-09T14:42:39.2059293Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[256x128x512-True-inpt_dtype0-NVFP4MMConfig.DYNAMIC-True-False] SKIPPED 2025-09-09T14:42:39.2060625Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[256x128x512-True-inpt_dtype0-NVFP4MMConfig.DYNAMIC-False-True] SKIPPED 2025-09-09T14:42:39.2061950Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[256x128x512-True-inpt_dtype0-NVFP4MMConfig.DYNAMIC-False-False] SKIPPED 2025-09-09T14:42:39.2063296Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[256x128x512-True-inpt_dtype0-NVFP4MMConfig.WEIGHT_ONLY-True-True] SKIPPED 2025-09-09T14:42:39.2064639Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[256x128x512-True-inpt_dtype0-NVFP4MMConfig.WEIGHT_ONLY-True-False] SKIPPED 2025-09-09T14:42:39.2065972Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[256x128x512-True-inpt_dtype0-NVFP4MMConfig.WEIGHT_ONLY-False-True] PASSED 2025-09-09T14:42:39.2067315Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[256x128x512-True-inpt_dtype0-NVFP4MMConfig.WEIGHT_ONLY-False-False] PASSED 2025-09-09T14:42:39.2068646Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[256x128x512-True-inpt_dtype1-NVFP4MMConfig.DYNAMIC-True-True] SKIPPED 2025-09-09T14:42:39.2069965Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[256x128x512-True-inpt_dtype1-NVFP4MMConfig.DYNAMIC-True-False] SKIPPED 2025-09-09T14:42:39.2071338Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[256x128x512-True-inpt_dtype1-NVFP4MMConfig.DYNAMIC-False-True] SKIPPED 2025-09-09T14:42:39.2072673Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[256x128x512-True-inpt_dtype1-NVFP4MMConfig.DYNAMIC-False-False] SKIPPED 2025-09-09T14:42:39.2073993Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[256x128x512-True-inpt_dtype1-NVFP4MMConfig.WEIGHT_ONLY-True-True] XFAIL 2025-09-09T14:42:39.2075331Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[256x128x512-True-inpt_dtype1-NVFP4MMConfig.WEIGHT_ONLY-True-False] SKIPPED 2025-09-09T14:42:39.2076666Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[256x128x512-True-inpt_dtype1-NVFP4MMConfig.WEIGHT_ONLY-False-True] XFAIL 2025-09-09T14:42:39.2077989Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[256x128x512-True-inpt_dtype1-NVFP4MMConfig.WEIGHT_ONLY-False-False] PASSED 2025-09-09T14:42:39.2842751Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[256x128x512-False-inpt_dtype0-NVFP4MMConfig.DYNAMIC-True-True] SKIPPED 2025-09-09T14:42:39.2844110Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[256x128x512-False-inpt_dtype0-NVFP4MMConfig.DYNAMIC-True-False] SKIPPED 2025-09-09T14:42:39.2845635Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[256x128x512-False-inpt_dtype0-NVFP4MMConfig.DYNAMIC-False-True] SKIPPED 2025-09-09T14:42:39.2847098Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[256x128x512-False-inpt_dtype0-NVFP4MMConfig.DYNAMIC-False-False] SKIPPED 2025-09-09T14:42:39.2848444Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[256x128x512-False-inpt_dtype0-NVFP4MMConfig.WEIGHT_ONLY-True-True] SKIPPED 2025-09-09T14:42:39.2849789Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[256x128x512-False-inpt_dtype0-NVFP4MMConfig.WEIGHT_ONLY-True-False] SKIPPED 2025-09-09T14:42:39.2851142Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[256x128x512-False-inpt_dtype0-NVFP4MMConfig.WEIGHT_ONLY-False-True] PASSED 2025-09-09T14:42:39.2852501Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[256x128x512-False-inpt_dtype0-NVFP4MMConfig.WEIGHT_ONLY-False-False] PASSED 2025-09-09T14:42:39.2853835Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[256x128x512-False-inpt_dtype1-NVFP4MMConfig.DYNAMIC-True-True] SKIPPED 2025-09-09T14:42:39.2855215Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[256x128x512-False-inpt_dtype1-NVFP4MMConfig.DYNAMIC-True-False] SKIPPED 2025-09-09T14:42:39.2856553Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[256x128x512-False-inpt_dtype1-NVFP4MMConfig.DYNAMIC-False-True] SKIPPED 2025-09-09T14:42:39.2857885Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[256x128x512-False-inpt_dtype1-NVFP4MMConfig.DYNAMIC-False-False] SKIPPED 2025-09-09T14:42:39.2859230Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[256x128x512-False-inpt_dtype1-NVFP4MMConfig.WEIGHT_ONLY-True-True] XFAIL 2025-09-09T14:42:39.2860564Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[256x128x512-False-inpt_dtype1-NVFP4MMConfig.WEIGHT_ONLY-True-False] SKIPPED 2025-09-09T14:42:39.2861920Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[256x128x512-False-inpt_dtype1-NVFP4MMConfig.WEIGHT_ONLY-False-True] XFAIL 2025-09-09T14:42:39.2863271Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[256x128x512-False-inpt_dtype1-NVFP4MMConfig.WEIGHT_ONLY-False-False] PASSED 2025-09-09T14:42:39.2864592Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[145x64x256-True-inpt_dtype0-NVFP4MMConfig.DYNAMIC-True-True] SKIPPED 2025-09-09T14:42:39.2865923Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[145x64x256-True-inpt_dtype0-NVFP4MMConfig.DYNAMIC-True-False] SKIPPED 2025-09-09T14:42:39.2867243Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[145x64x256-True-inpt_dtype0-NVFP4MMConfig.DYNAMIC-False-True] SKIPPED 2025-09-09T14:42:39.2868558Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[145x64x256-True-inpt_dtype0-NVFP4MMConfig.DYNAMIC-False-False] SKIPPED 2025-09-09T14:42:39.2869893Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[145x64x256-True-inpt_dtype0-NVFP4MMConfig.WEIGHT_ONLY-True-True] SKIPPED 2025-09-09T14:42:39.2871233Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[145x64x256-True-inpt_dtype0-NVFP4MMConfig.WEIGHT_ONLY-True-False] SKIPPED 2025-09-09T14:42:39.2872585Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[145x64x256-True-inpt_dtype0-NVFP4MMConfig.WEIGHT_ONLY-False-True] PASSED 2025-09-09T14:42:39.2874035Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[145x64x256-True-inpt_dtype0-NVFP4MMConfig.WEIGHT_ONLY-False-False] PASSED 2025-09-09T14:42:39.2875443Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[145x64x256-True-inpt_dtype1-NVFP4MMConfig.DYNAMIC-True-True] SKIPPED 2025-09-09T14:42:39.2876754Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[145x64x256-True-inpt_dtype1-NVFP4MMConfig.DYNAMIC-True-False] SKIPPED 2025-09-09T14:42:39.2878070Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[145x64x256-True-inpt_dtype1-NVFP4MMConfig.DYNAMIC-False-True] SKIPPED 2025-09-09T14:42:39.2879458Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[145x64x256-True-inpt_dtype1-NVFP4MMConfig.DYNAMIC-False-False] SKIPPED 2025-09-09T14:42:39.2880788Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[145x64x256-True-inpt_dtype1-NVFP4MMConfig.WEIGHT_ONLY-True-True] XFAIL 2025-09-09T14:42:39.2882107Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[145x64x256-True-inpt_dtype1-NVFP4MMConfig.WEIGHT_ONLY-True-False] SKIPPED 2025-09-09T14:42:39.2883482Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[145x64x256-True-inpt_dtype1-NVFP4MMConfig.WEIGHT_ONLY-False-True] XFAIL 2025-09-09T14:42:39.2884812Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[145x64x256-True-inpt_dtype1-NVFP4MMConfig.WEIGHT_ONLY-False-False] PASSED 2025-09-09T14:42:39.2886143Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[145x64x256-False-inpt_dtype0-NVFP4MMConfig.DYNAMIC-True-True] SKIPPED 2025-09-09T14:42:39.2887465Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[145x64x256-False-inpt_dtype0-NVFP4MMConfig.DYNAMIC-True-False] SKIPPED 2025-09-09T14:42:39.2888794Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[145x64x256-False-inpt_dtype0-NVFP4MMConfig.DYNAMIC-False-True] SKIPPED 2025-09-09T14:42:39.2890128Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[145x64x256-False-inpt_dtype0-NVFP4MMConfig.DYNAMIC-False-False] SKIPPED 2025-09-09T14:42:39.2891463Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[145x64x256-False-inpt_dtype0-NVFP4MMConfig.WEIGHT_ONLY-True-True] SKIPPED 2025-09-09T14:42:39.2892857Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[145x64x256-False-inpt_dtype0-NVFP4MMConfig.WEIGHT_ONLY-True-False] SKIPPED 2025-09-09T14:42:39.2894203Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[145x64x256-False-inpt_dtype0-NVFP4MMConfig.WEIGHT_ONLY-False-True] PASSED 2025-09-09T14:42:39.2895557Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[145x64x256-False-inpt_dtype0-NVFP4MMConfig.WEIGHT_ONLY-False-False] PASSED 2025-09-09T14:42:39.2896898Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[145x64x256-False-inpt_dtype1-NVFP4MMConfig.DYNAMIC-True-True] SKIPPED 2025-09-09T14:42:39.2898214Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[145x64x256-False-inpt_dtype1-NVFP4MMConfig.DYNAMIC-True-False] SKIPPED 2025-09-09T14:42:39.2899542Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[145x64x256-False-inpt_dtype1-NVFP4MMConfig.DYNAMIC-False-True] SKIPPED 2025-09-09T14:42:39.2900878Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[145x64x256-False-inpt_dtype1-NVFP4MMConfig.DYNAMIC-False-False] SKIPPED 2025-09-09T14:42:39.2902292Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[145x64x256-False-inpt_dtype1-NVFP4MMConfig.WEIGHT_ONLY-True-True] XFAIL 2025-09-09T14:42:39.2903626Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[145x64x256-False-inpt_dtype1-NVFP4MMConfig.WEIGHT_ONLY-True-False] SKIPPED 2025-09-09T14:42:39.2905053Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[145x64x256-False-inpt_dtype1-NVFP4MMConfig.WEIGHT_ONLY-False-True] XFAIL 2025-09-09T14:42:39.2906384Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[145x64x256-False-inpt_dtype1-NVFP4MMConfig.WEIGHT_ONLY-False-False] PASSED 2025-09-09T14:42:39.2907708Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x96x256-True-inpt_dtype0-NVFP4MMConfig.DYNAMIC-True-True] SKIPPED 2025-09-09T14:42:39.3665032Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x96x256-True-inpt_dtype0-NVFP4MMConfig.DYNAMIC-True-False] SKIPPED 2025-09-09T14:42:39.3667700Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x96x256-True-inpt_dtype0-NVFP4MMConfig.DYNAMIC-False-True] SKIPPED 2025-09-09T14:42:39.3670347Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x96x256-True-inpt_dtype0-NVFP4MMConfig.DYNAMIC-False-False] SKIPPED 2025-09-09T14:42:39.3672714Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x96x256-True-inpt_dtype0-NVFP4MMConfig.WEIGHT_ONLY-True-True] SKIPPED 2025-09-09T14:42:39.3674062Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x96x256-True-inpt_dtype0-NVFP4MMConfig.WEIGHT_ONLY-True-False] SKIPPED 2025-09-09T14:42:39.3675399Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x96x256-True-inpt_dtype0-NVFP4MMConfig.WEIGHT_ONLY-False-True] PASSED 2025-09-09T14:42:39.3676737Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x96x256-True-inpt_dtype0-NVFP4MMConfig.WEIGHT_ONLY-False-False] PASSED 2025-09-09T14:42:39.3678059Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x96x256-True-inpt_dtype1-NVFP4MMConfig.DYNAMIC-True-True] SKIPPED 2025-09-09T14:42:39.3679453Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x96x256-True-inpt_dtype1-NVFP4MMConfig.DYNAMIC-True-False] SKIPPED 2025-09-09T14:42:39.3680772Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x96x256-True-inpt_dtype1-NVFP4MMConfig.DYNAMIC-False-True] SKIPPED 2025-09-09T14:42:39.3682095Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x96x256-True-inpt_dtype1-NVFP4MMConfig.DYNAMIC-False-False] SKIPPED 2025-09-09T14:42:39.3683466Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x96x256-True-inpt_dtype1-NVFP4MMConfig.WEIGHT_ONLY-True-True] XFAIL 2025-09-09T14:42:39.3684792Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x96x256-True-inpt_dtype1-NVFP4MMConfig.WEIGHT_ONLY-True-False] SKIPPED 2025-09-09T14:42:39.3686111Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x96x256-True-inpt_dtype1-NVFP4MMConfig.WEIGHT_ONLY-False-True] XFAIL 2025-09-09T14:42:39.3687440Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x96x256-True-inpt_dtype1-NVFP4MMConfig.WEIGHT_ONLY-False-False] PASSED 2025-09-09T14:42:39.3688767Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x96x256-False-inpt_dtype0-NVFP4MMConfig.DYNAMIC-True-True] SKIPPED 2025-09-09T14:42:39.3690253Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x96x256-False-inpt_dtype0-NVFP4MMConfig.DYNAMIC-True-False] SKIPPED 2025-09-09T14:42:39.3691585Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x96x256-False-inpt_dtype0-NVFP4MMConfig.DYNAMIC-False-True] SKIPPED 2025-09-09T14:42:39.3693034Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x96x256-False-inpt_dtype0-NVFP4MMConfig.DYNAMIC-False-False] SKIPPED 2025-09-09T14:42:39.3694375Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x96x256-False-inpt_dtype0-NVFP4MMConfig.WEIGHT_ONLY-True-True] SKIPPED 2025-09-09T14:42:39.3695712Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x96x256-False-inpt_dtype0-NVFP4MMConfig.WEIGHT_ONLY-True-False] SKIPPED 2025-09-09T14:42:39.3697111Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x96x256-False-inpt_dtype0-NVFP4MMConfig.WEIGHT_ONLY-False-True] PASSED 2025-09-09T14:42:39.3698460Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x96x256-False-inpt_dtype0-NVFP4MMConfig.WEIGHT_ONLY-False-False] PASSED 2025-09-09T14:42:39.3699795Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x96x256-False-inpt_dtype1-NVFP4MMConfig.DYNAMIC-True-True] SKIPPED 2025-09-09T14:42:39.3710137Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x96x256-False-inpt_dtype1-NVFP4MMConfig.DYNAMIC-True-False] SKIPPED 2025-09-09T14:42:39.3711492Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x96x256-False-inpt_dtype1-NVFP4MMConfig.DYNAMIC-False-True] SKIPPED 2025-09-09T14:42:39.3712881Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x96x256-False-inpt_dtype1-NVFP4MMConfig.DYNAMIC-False-False] SKIPPED 2025-09-09T14:42:39.3714204Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x96x256-False-inpt_dtype1-NVFP4MMConfig.WEIGHT_ONLY-True-True] XFAIL 2025-09-09T14:42:39.3715547Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x96x256-False-inpt_dtype1-NVFP4MMConfig.WEIGHT_ONLY-True-False] SKIPPED 2025-09-09T14:42:39.3716882Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x96x256-False-inpt_dtype1-NVFP4MMConfig.WEIGHT_ONLY-False-True] XFAIL 2025-09-09T14:42:39.3718223Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x96x256-False-inpt_dtype1-NVFP4MMConfig.WEIGHT_ONLY-False-False] PASSED 2025-09-09T14:42:39.3719625Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x160x256-True-inpt_dtype0-NVFP4MMConfig.DYNAMIC-True-True] SKIPPED 2025-09-09T14:42:39.3720953Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x160x256-True-inpt_dtype0-NVFP4MMConfig.DYNAMIC-True-False] SKIPPED 2025-09-09T14:42:39.3722457Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x160x256-True-inpt_dtype0-NVFP4MMConfig.DYNAMIC-False-True] SKIPPED 2025-09-09T14:42:39.3723778Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x160x256-True-inpt_dtype0-NVFP4MMConfig.DYNAMIC-False-False] SKIPPED 2025-09-09T14:42:39.3725120Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x160x256-True-inpt_dtype0-NVFP4MMConfig.WEIGHT_ONLY-True-True] SKIPPED 2025-09-09T14:42:39.3726464Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x160x256-True-inpt_dtype0-NVFP4MMConfig.WEIGHT_ONLY-True-False] SKIPPED 2025-09-09T14:42:39.3727975Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x160x256-True-inpt_dtype0-NVFP4MMConfig.WEIGHT_ONLY-False-True] PASSED 2025-09-09T14:42:39.3729324Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x160x256-True-inpt_dtype0-NVFP4MMConfig.WEIGHT_ONLY-False-False] PASSED 2025-09-09T14:42:39.3730775Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x160x256-True-inpt_dtype1-NVFP4MMConfig.DYNAMIC-True-True] SKIPPED 2025-09-09T14:42:39.3732105Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x160x256-True-inpt_dtype1-NVFP4MMConfig.DYNAMIC-True-False] SKIPPED 2025-09-09T14:42:39.3733441Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x160x256-True-inpt_dtype1-NVFP4MMConfig.DYNAMIC-False-True] SKIPPED 2025-09-09T14:42:39.3734785Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x160x256-True-inpt_dtype1-NVFP4MMConfig.DYNAMIC-False-False] SKIPPED 2025-09-09T14:42:39.3736108Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x160x256-True-inpt_dtype1-NVFP4MMConfig.WEIGHT_ONLY-True-True] XFAIL 2025-09-09T14:42:39.3737455Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x160x256-True-inpt_dtype1-NVFP4MMConfig.WEIGHT_ONLY-True-False] SKIPPED 2025-09-09T14:42:39.3738799Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x160x256-True-inpt_dtype1-NVFP4MMConfig.WEIGHT_ONLY-False-True] XFAIL 2025-09-09T14:42:39.3740126Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x160x256-True-inpt_dtype1-NVFP4MMConfig.WEIGHT_ONLY-False-False] PASSED 2025-09-09T14:42:39.3741456Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x160x256-False-inpt_dtype0-NVFP4MMConfig.DYNAMIC-True-True] SKIPPED 2025-09-09T14:42:39.3742794Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x160x256-False-inpt_dtype0-NVFP4MMConfig.DYNAMIC-True-False] SKIPPED 2025-09-09T14:42:39.4480504Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x160x256-False-inpt_dtype0-NVFP4MMConfig.DYNAMIC-False-True] SKIPPED 2025-09-09T14:42:39.4483004Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x160x256-False-inpt_dtype0-NVFP4MMConfig.DYNAMIC-False-False] SKIPPED 2025-09-09T14:42:39.4484342Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x160x256-False-inpt_dtype0-NVFP4MMConfig.WEIGHT_ONLY-True-True] SKIPPED 2025-09-09T14:42:39.4485694Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x160x256-False-inpt_dtype0-NVFP4MMConfig.WEIGHT_ONLY-True-False] SKIPPED 2025-09-09T14:42:39.4487051Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x160x256-False-inpt_dtype0-NVFP4MMConfig.WEIGHT_ONLY-False-True] PASSED 2025-09-09T14:42:39.4488403Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x160x256-False-inpt_dtype0-NVFP4MMConfig.WEIGHT_ONLY-False-False] PASSED 2025-09-09T14:42:39.4489734Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x160x256-False-inpt_dtype1-NVFP4MMConfig.DYNAMIC-True-True] SKIPPED 2025-09-09T14:42:39.4491052Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x160x256-False-inpt_dtype1-NVFP4MMConfig.DYNAMIC-True-False] SKIPPED 2025-09-09T14:42:39.4492387Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x160x256-False-inpt_dtype1-NVFP4MMConfig.DYNAMIC-False-True] SKIPPED 2025-09-09T14:42:39.4493887Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x160x256-False-inpt_dtype1-NVFP4MMConfig.DYNAMIC-False-False] SKIPPED 2025-09-09T14:42:39.4495216Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x160x256-False-inpt_dtype1-NVFP4MMConfig.WEIGHT_ONLY-True-True] XFAIL 2025-09-09T14:42:39.4496667Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x160x256-False-inpt_dtype1-NVFP4MMConfig.WEIGHT_ONLY-True-False] SKIPPED 2025-09-09T14:42:39.4498005Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x160x256-False-inpt_dtype1-NVFP4MMConfig.WEIGHT_ONLY-False-True] XFAIL 2025-09-09T14:42:39.4499336Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[128x160x256-False-inpt_dtype1-NVFP4MMConfig.WEIGHT_ONLY-False-False] PASSED 2025-09-09T14:42:39.4500655Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[64x64x256-True-inpt_dtype0-NVFP4MMConfig.DYNAMIC-True-True] SKIPPED 2025-09-09T14:42:39.4501947Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[64x64x256-True-inpt_dtype0-NVFP4MMConfig.DYNAMIC-True-False] SKIPPED 2025-09-09T14:42:39.4503241Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[64x64x256-True-inpt_dtype0-NVFP4MMConfig.DYNAMIC-False-True] SKIPPED 2025-09-09T14:42:39.4504540Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[64x64x256-True-inpt_dtype0-NVFP4MMConfig.DYNAMIC-False-False] SKIPPED 2025-09-09T14:42:39.4505845Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[64x64x256-True-inpt_dtype0-NVFP4MMConfig.WEIGHT_ONLY-True-True] SKIPPED 2025-09-09T14:42:39.4507149Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[64x64x256-True-inpt_dtype0-NVFP4MMConfig.WEIGHT_ONLY-True-False] SKIPPED 2025-09-09T14:42:39.4508470Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[64x64x256-True-inpt_dtype0-NVFP4MMConfig.WEIGHT_ONLY-False-True] PASSED 2025-09-09T14:42:39.4509776Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[64x64x256-True-inpt_dtype0-NVFP4MMConfig.WEIGHT_ONLY-False-False] PASSED 2025-09-09T14:42:39.4511075Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[64x64x256-True-inpt_dtype1-NVFP4MMConfig.DYNAMIC-True-True] SKIPPED 2025-09-09T14:42:39.4512371Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[64x64x256-True-inpt_dtype1-NVFP4MMConfig.DYNAMIC-True-False] SKIPPED 2025-09-09T14:42:39.4513710Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[64x64x256-True-inpt_dtype1-NVFP4MMConfig.DYNAMIC-False-True] SKIPPED 2025-09-09T14:42:39.4515012Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[64x64x256-True-inpt_dtype1-NVFP4MMConfig.DYNAMIC-False-False] SKIPPED 2025-09-09T14:42:39.4516315Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[64x64x256-True-inpt_dtype1-NVFP4MMConfig.WEIGHT_ONLY-True-True] XFAIL 2025-09-09T14:42:39.4517619Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[64x64x256-True-inpt_dtype1-NVFP4MMConfig.WEIGHT_ONLY-True-False] SKIPPED 2025-09-09T14:42:39.4518935Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[64x64x256-True-inpt_dtype1-NVFP4MMConfig.WEIGHT_ONLY-False-True] XFAIL 2025-09-09T14:42:39.4520302Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[64x64x256-True-inpt_dtype1-NVFP4MMConfig.WEIGHT_ONLY-False-False] PASSED 2025-09-09T14:42:39.4521702Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[64x64x256-False-inpt_dtype0-NVFP4MMConfig.DYNAMIC-True-True] SKIPPED 2025-09-09T14:42:39.4523299Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[64x64x256-False-inpt_dtype0-NVFP4MMConfig.DYNAMIC-True-False] SKIPPED 2025-09-09T14:42:39.4524747Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[64x64x256-False-inpt_dtype0-NVFP4MMConfig.DYNAMIC-False-True] SKIPPED 2025-09-09T14:42:39.4526055Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[64x64x256-False-inpt_dtype0-NVFP4MMConfig.DYNAMIC-False-False] SKIPPED 2025-09-09T14:42:39.4527378Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[64x64x256-False-inpt_dtype0-NVFP4MMConfig.WEIGHT_ONLY-True-True] SKIPPED 2025-09-09T14:42:39.4528707Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[64x64x256-False-inpt_dtype0-NVFP4MMConfig.WEIGHT_ONLY-True-False] SKIPPED 2025-09-09T14:42:39.4530034Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[64x64x256-False-inpt_dtype0-NVFP4MMConfig.WEIGHT_ONLY-False-True] PASSED 2025-09-09T14:42:39.4531369Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[64x64x256-False-inpt_dtype0-NVFP4MMConfig.WEIGHT_ONLY-False-False] PASSED 2025-09-09T14:42:39.4532688Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[64x64x256-False-inpt_dtype1-NVFP4MMConfig.DYNAMIC-True-True] SKIPPED 2025-09-09T14:42:39.4533997Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[64x64x256-False-inpt_dtype1-NVFP4MMConfig.DYNAMIC-True-False] SKIPPED 2025-09-09T14:42:39.4535310Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[64x64x256-False-inpt_dtype1-NVFP4MMConfig.DYNAMIC-False-True] SKIPPED 2025-09-09T14:42:39.4536626Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[64x64x256-False-inpt_dtype1-NVFP4MMConfig.DYNAMIC-False-False] SKIPPED 2025-09-09T14:42:39.4537942Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[64x64x256-False-inpt_dtype1-NVFP4MMConfig.WEIGHT_ONLY-True-True] XFAIL 2025-09-09T14:42:39.4539263Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[64x64x256-False-inpt_dtype1-NVFP4MMConfig.WEIGHT_ONLY-True-False] SKIPPED 2025-09-09T14:42:39.4540581Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[64x64x256-False-inpt_dtype1-NVFP4MMConfig.WEIGHT_ONLY-False-True] XFAIL 2025-09-09T14:42:39.4541907Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[64x64x256-False-inpt_dtype1-NVFP4MMConfig.WEIGHT_ONLY-False-False] PASSED 2025-09-09T14:42:39.4543286Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[200x192x256-True-inpt_dtype0-NVFP4MMConfig.DYNAMIC-True-True] SKIPPED 2025-09-09T14:42:39.4544597Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[200x192x256-True-inpt_dtype0-NVFP4MMConfig.DYNAMIC-True-False] SKIPPED 2025-09-09T14:42:39.4545923Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[200x192x256-True-inpt_dtype0-NVFP4MMConfig.DYNAMIC-False-True] SKIPPED 2025-09-09T14:42:47.9377495Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[200x192x256-True-inpt_dtype0-NVFP4MMConfig.DYNAMIC-False-False] SKIPPED 2025-09-09T14:42:47.9379345Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[200x192x256-True-inpt_dtype0-NVFP4MMConfig.WEIGHT_ONLY-True-True] SKIPPED 2025-09-09T14:42:47.9381039Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[200x192x256-True-inpt_dtype0-NVFP4MMConfig.WEIGHT_ONLY-True-False] SKIPPED 2025-09-09T14:42:47.9382530Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[200x192x256-True-inpt_dtype0-NVFP4MMConfig.WEIGHT_ONLY-False-True] PASSED 2025-09-09T14:42:47.9384066Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[200x192x256-True-inpt_dtype0-NVFP4MMConfig.WEIGHT_ONLY-False-False] PASSED 2025-09-09T14:42:47.9385397Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[200x192x256-True-inpt_dtype1-NVFP4MMConfig.DYNAMIC-True-True] SKIPPED 2025-09-09T14:42:47.9386725Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[200x192x256-True-inpt_dtype1-NVFP4MMConfig.DYNAMIC-True-False] SKIPPED 2025-09-09T14:42:47.9388045Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[200x192x256-True-inpt_dtype1-NVFP4MMConfig.DYNAMIC-False-True] SKIPPED 2025-09-09T14:42:47.9389379Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[200x192x256-True-inpt_dtype1-NVFP4MMConfig.DYNAMIC-False-False] SKIPPED 2025-09-09T14:42:47.9390712Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[200x192x256-True-inpt_dtype1-NVFP4MMConfig.WEIGHT_ONLY-True-True] XFAIL 2025-09-09T14:42:47.9392037Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[200x192x256-True-inpt_dtype1-NVFP4MMConfig.WEIGHT_ONLY-True-False] SKIPPED 2025-09-09T14:42:47.9393374Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[200x192x256-True-inpt_dtype1-NVFP4MMConfig.WEIGHT_ONLY-False-True] XFAIL 2025-09-09T14:42:47.9394702Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[200x192x256-True-inpt_dtype1-NVFP4MMConfig.WEIGHT_ONLY-False-False] PASSED 2025-09-09T14:42:47.9396045Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[200x192x256-False-inpt_dtype0-NVFP4MMConfig.DYNAMIC-True-True] SKIPPED 2025-09-09T14:42:47.9397374Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[200x192x256-False-inpt_dtype0-NVFP4MMConfig.DYNAMIC-True-False] SKIPPED 2025-09-09T14:42:47.9398711Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[200x192x256-False-inpt_dtype0-NVFP4MMConfig.DYNAMIC-False-True] SKIPPED 2025-09-09T14:42:47.9400104Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[200x192x256-False-inpt_dtype0-NVFP4MMConfig.DYNAMIC-False-False] SKIPPED 2025-09-09T14:42:47.9401448Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[200x192x256-False-inpt_dtype0-NVFP4MMConfig.WEIGHT_ONLY-True-True] SKIPPED 2025-09-09T14:42:47.9402797Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[200x192x256-False-inpt_dtype0-NVFP4MMConfig.WEIGHT_ONLY-True-False] SKIPPED 2025-09-09T14:42:47.9404143Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[200x192x256-False-inpt_dtype0-NVFP4MMConfig.WEIGHT_ONLY-False-True] PASSED 2025-09-09T14:42:47.9405495Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[200x192x256-False-inpt_dtype0-NVFP4MMConfig.WEIGHT_ONLY-False-False] PASSED 2025-09-09T14:42:47.9406827Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[200x192x256-False-inpt_dtype1-NVFP4MMConfig.DYNAMIC-True-True] SKIPPED 2025-09-09T14:42:47.9408159Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[200x192x256-False-inpt_dtype1-NVFP4MMConfig.DYNAMIC-True-False] SKIPPED 2025-09-09T14:42:47.9409581Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[200x192x256-False-inpt_dtype1-NVFP4MMConfig.DYNAMIC-False-True] SKIPPED 2025-09-09T14:42:47.9410920Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[200x192x256-False-inpt_dtype1-NVFP4MMConfig.DYNAMIC-False-False] SKIPPED 2025-09-09T14:42:47.9412332Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[200x192x256-False-inpt_dtype1-NVFP4MMConfig.WEIGHT_ONLY-True-True] XFAIL 2025-09-09T14:42:47.9413666Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[200x192x256-False-inpt_dtype1-NVFP4MMConfig.WEIGHT_ONLY-True-False] SKIPPED 2025-09-09T14:42:47.9415044Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[200x192x256-False-inpt_dtype1-NVFP4MMConfig.WEIGHT_ONLY-False-True] XFAIL 2025-09-09T14:42:47.9416416Z test/prototype/mx_formats/test_inference_workflow.py::test_inference_workflow_nvfp4[200x192x256-False-inpt_dtype1-NVFP4MMConfig.WEIGHT_ONLY-False-False] PASSED 2025-09-09T14:42:47.9417404Z test/prototype/mx_formats/test_kernels.py::test_fp32 SKIPPED (TODO d...) 2025-09-09T14:42:47.9418047Z test/prototype/mx_formats/test_kernels.py::test_bf16 SKIPPED (TODO d...) 2025-09-09T14:42:47.9418649Z test/prototype/mx_formats/test_kernels.py::test_fp16 PASSED 2025-09-09T14:42:47.9419230Z test/prototype/mx_formats/test_kernels.py::test_float8_e4m3fn PASSED 2025-09-09T14:42:47.9419837Z test/prototype/mx_formats/test_kernels.py::test_float8_e5m2 PASSED 2025-09-09T14:42:47.9420450Z test/prototype/mx_formats/test_kernels.py::test_float4_e2m1_table PASSED 2025-09-09T14:42:47.9421081Z test/prototype/mx_formats/test_kernels.py::test_float6_e3m2_table PASSED 2025-09-09T14:42:47.9421706Z test/prototype/mx_formats/test_kernels.py::test_float6_e2m3_table PASSED 2025-09-09T14:42:47.9422466Z test/prototype/mx_formats/test_kernels.py::test_fp4_0_0 PASSED 2025-09-09T14:42:47.9423039Z test/prototype/mx_formats/test_kernels.py::test_fp4_0_5 PASSED 2025-09-09T14:42:47.9423592Z test/prototype/mx_formats/test_kernels.py::test_fp4_1_0 PASSED 2025-09-09T14:42:47.9424146Z test/prototype/mx_formats/test_kernels.py::test_fp4_1_5 PASSED 2025-09-09T14:42:47.9424702Z test/prototype/mx_formats/test_kernels.py::test_fp4_2_0 PASSED 2025-09-09T14:42:47.9425259Z test/prototype/mx_formats/test_kernels.py::test_fp4_3_0 PASSED 2025-09-09T14:42:47.9425807Z test/prototype/mx_formats/test_kernels.py::test_fp4_4_0 PASSED 2025-09-09T14:42:47.9426364Z test/prototype/mx_formats/test_kernels.py::test_fp4_6_0 PASSED 2025-09-09T14:42:47.9426956Z test/prototype/mx_formats/test_kernels.py::test_fp4_pack_unpack PASSED 2025-09-09T14:42:47.9427598Z test/prototype/mx_formats/test_kernels.py::test_fp6_values[fp6_e2m3] PASSED 2025-09-09T14:42:47.9428256Z test/prototype/mx_formats/test_kernels.py::test_fp6_values[fp6_e3m2] PASSED 2025-09-09T14:42:47.9428955Z test/prototype/mx_formats/test_kernels.py::test_fp6_e3m2_rounding[29.0-31-cpu] PASSED 2025-09-09T14:42:47.9429684Z test/prototype/mx_formats/test_kernels.py::test_fp6_e3m2_rounding[29.0-31-cuda] PASSED 2025-09-09T14:42:47.9430413Z test/prototype/mx_formats/test_kernels.py::test_fp6_e3m2_rounding[26.0-30-cpu] PASSED 2025-09-09T14:42:47.9431132Z test/prototype/mx_formats/test_kernels.py::test_fp6_e3m2_rounding[26.0-30-cuda] PASSED 2025-09-09T14:42:47.9431858Z test/prototype/mx_formats/test_kernels.py::test_fp6_e3m2_rounding[0.1251-2-cpu] PASSED 2025-09-09T14:42:47.9432580Z test/prototype/mx_formats/test_kernels.py::test_fp6_e3m2_rounding[0.1251-2-cuda] PASSED 2025-09-09T14:42:47.9433312Z test/prototype/mx_formats/test_kernels.py::test_fp6_e3m2_rounding[0.0314-1-cpu] PASSED 2025-09-09T14:42:47.9434041Z test/prototype/mx_formats/test_kernels.py::test_fp6_e3m2_rounding[0.0314-1-cuda] PASSED 2025-09-09T14:42:47.9434889Z test/prototype/mx_formats/test_kernels.py::test_fp6_e3m2_rounding[0.03-0-cpu] PASSED 2025-09-09T14:42:47.9435612Z test/prototype/mx_formats/test_kernels.py::test_fp6_e3m2_rounding[0.03-0-cuda] PASSED 2025-09-09T14:42:47.9436397Z test/prototype/mx_formats/test_kernels.py::test_fp6_e2m3_pack_unpack PASSED 2025-09-09T14:42:47.9437054Z test/prototype/mx_formats/test_kernels.py::test_fp6_e3m2_pack_unpack PASSED 2025-09-09T14:42:47.9437755Z test/prototype/mx_formats/test_kernels.py::test_triton_mxfp8_dim1_randn[256-256] SKIPPED 2025-09-09T14:42:47.9438508Z test/prototype/mx_formats/test_kernels.py::test_triton_mxfp8_dim1_randn[256-2048] SKIPPED 2025-09-09T14:42:47.9439313Z test/prototype/mx_formats/test_kernels.py::test_triton_mxfp8_dim1_randn[2048-256] SKIPPED 2025-09-09T14:42:47.9440058Z test/prototype/mx_formats/test_kernels.py::test_triton_mxfp8_dim1_randn[2048-2048] SKIPPED 2025-09-09T14:42:47.9440761Z test/prototype/mx_formats/test_kernels.py::test_rearrange[shape0] PASSED 2025-09-09T14:42:47.9441404Z test/prototype/mx_formats/test_kernels.py::test_rearrange[shape1] PASSED 2025-09-09T14:42:47.9442040Z test/prototype/mx_formats/test_kernels.py::test_rearrange[shape2] PASSED 2025-09-09T14:42:47.9442680Z test/prototype/mx_formats/test_kernels.py::test_rearrange[shape3] PASSED 2025-09-09T14:42:50.0772426Z test/prototype/mx_formats/test_kernels.py::test_rearrange[shape4] PASSED 2025-09-09T14:42:50.0773254Z test/prototype/mx_formats/test_kernels.py::test_rearrange[shape5] PASSED 2025-09-09T14:42:50.0773898Z test/prototype/mx_formats/test_kernels.py::test_rearrange[shape6] PASSED 2025-09-09T14:42:50.0774542Z test/prototype/mx_formats/test_kernels.py::test_rearrange[shape7] PASSED 2025-09-09T14:42:50.0775421Z test/prototype/mx_formats/test_kernels.py::test_cuda_mx_dim1_numerics[ScaleCalculationMode.FLOOR-input_dtype0-32-32] SKIPPED 2025-09-09T14:42:50.0776505Z test/prototype/mx_formats/test_kernels.py::test_cuda_mx_dim1_numerics[ScaleCalculationMode.FLOOR-input_dtype0-32-64] SKIPPED 2025-09-09T14:42:50.0777578Z test/prototype/mx_formats/test_kernels.py::test_cuda_mx_dim1_numerics[ScaleCalculationMode.FLOOR-input_dtype0-32-2048] SKIPPED 2025-09-09T14:42:50.0778649Z test/prototype/mx_formats/test_kernels.py::test_cuda_mx_dim1_numerics[ScaleCalculationMode.FLOOR-input_dtype0-64-32] SKIPPED 2025-09-09T14:42:50.0779704Z test/prototype/mx_formats/test_kernels.py::test_cuda_mx_dim1_numerics[ScaleCalculationMode.FLOOR-input_dtype0-64-64] SKIPPED 2025-09-09T14:42:50.0780760Z test/prototype/mx_formats/test_kernels.py::test_cuda_mx_dim1_numerics[ScaleCalculationMode.FLOOR-input_dtype0-64-2048] SKIPPED 2025-09-09T14:42:50.0781834Z test/prototype/mx_formats/test_kernels.py::test_cuda_mx_dim1_numerics[ScaleCalculationMode.FLOOR-input_dtype0-2048-32] SKIPPED 2025-09-09T14:42:50.0782909Z test/prototype/mx_formats/test_kernels.py::test_cuda_mx_dim1_numerics[ScaleCalculationMode.FLOOR-input_dtype0-2048-64] SKIPPED 2025-09-09T14:42:50.0783990Z test/prototype/mx_formats/test_kernels.py::test_cuda_mx_dim1_numerics[ScaleCalculationMode.FLOOR-input_dtype0-2048-2048] SKIPPED 2025-09-09T14:42:50.0785083Z test/prototype/mx_formats/test_kernels.py::test_cuda_mx_dim1_numerics[ScaleCalculationMode.FLOOR-input_dtype1-32-32] SKIPPED 2025-09-09T14:42:50.0786190Z test/prototype/mx_formats/test_kernels.py::test_cuda_mx_dim1_numerics[ScaleCalculationMode.FLOOR-input_dtype1-32-64] SKIPPED 2025-09-09T14:42:50.0787244Z test/prototype/mx_formats/test_kernels.py::test_cuda_mx_dim1_numerics[ScaleCalculationMode.FLOOR-input_dtype1-32-2048] SKIPPED 2025-09-09T14:42:50.0788308Z test/prototype/mx_formats/test_kernels.py::test_cuda_mx_dim1_numerics[ScaleCalculationMode.FLOOR-input_dtype1-64-32] SKIPPED 2025-09-09T14:42:50.0789568Z test/prototype/mx_formats/test_kernels.py::test_cuda_mx_dim1_numerics[ScaleCalculationMode.FLOOR-input_dtype1-64-64] SKIPPED 2025-09-09T14:42:50.0790642Z test/prototype/mx_formats/test_kernels.py::test_cuda_mx_dim1_numerics[ScaleCalculationMode.FLOOR-input_dtype1-64-2048] SKIPPED 2025-09-09T14:42:50.0791837Z test/prototype/mx_formats/test_kernels.py::test_cuda_mx_dim1_numerics[ScaleCalculationMode.FLOOR-input_dtype1-2048-32] SKIPPED 2025-09-09T14:42:50.0792903Z test/prototype/mx_formats/test_kernels.py::test_cuda_mx_dim1_numerics[ScaleCalculationMode.FLOOR-input_dtype1-2048-64] SKIPPED 2025-09-09T14:42:50.0793988Z test/prototype/mx_formats/test_kernels.py::test_cuda_mx_dim1_numerics[ScaleCalculationMode.FLOOR-input_dtype1-2048-2048] SKIPPED 2025-09-09T14:42:50.0795056Z test/prototype/mx_formats/test_kernels.py::test_cuda_mx_dim1_numerics[ScaleCalculationMode.RCEIL-input_dtype0-32-32] SKIPPED 2025-09-09T14:42:50.0796106Z test/prototype/mx_formats/test_kernels.py::test_cuda_mx_dim1_numerics[ScaleCalculationMode.RCEIL-input_dtype0-32-64] SKIPPED 2025-09-09T14:42:50.0797176Z test/prototype/mx_formats/test_kernels.py::test_cuda_mx_dim1_numerics[ScaleCalculationMode.RCEIL-input_dtype0-32-2048] SKIPPED 2025-09-09T14:42:50.0798231Z test/prototype/mx_formats/test_kernels.py::test_cuda_mx_dim1_numerics[ScaleCalculationMode.RCEIL-input_dtype0-64-32] SKIPPED 2025-09-09T14:42:50.0799376Z test/prototype/mx_formats/test_kernels.py::test_cuda_mx_dim1_numerics[ScaleCalculationMode.RCEIL-input_dtype0-64-64] SKIPPED 2025-09-09T14:42:50.0800445Z test/prototype/mx_formats/test_kernels.py::test_cuda_mx_dim1_numerics[ScaleCalculationMode.RCEIL-input_dtype0-64-2048] SKIPPED 2025-09-09T14:42:50.0801510Z test/prototype/mx_formats/test_kernels.py::test_cuda_mx_dim1_numerics[ScaleCalculationMode.RCEIL-input_dtype0-2048-32] SKIPPED 2025-09-09T14:42:50.0802598Z test/prototype/mx_formats/test_kernels.py::test_cuda_mx_dim1_numerics[ScaleCalculationMode.RCEIL-input_dtype0-2048-64] SKIPPED 2025-09-09T14:42:50.0803686Z test/prototype/mx_formats/test_kernels.py::test_cuda_mx_dim1_numerics[ScaleCalculationMode.RCEIL-input_dtype0-2048-2048] SKIPPED 2025-09-09T14:42:50.0804764Z test/prototype/mx_formats/test_kernels.py::test_cuda_mx_dim1_numerics[ScaleCalculationMode.RCEIL-input_dtype1-32-32] SKIPPED 2025-09-09T14:42:50.0805823Z test/prototype/mx_formats/test_kernels.py::test_cuda_mx_dim1_numerics[ScaleCalculationMode.RCEIL-input_dtype1-32-64] SKIPPED 2025-09-09T14:42:50.0806880Z test/prototype/mx_formats/test_kernels.py::test_cuda_mx_dim1_numerics[ScaleCalculationMode.RCEIL-input_dtype1-32-2048] SKIPPED 2025-09-09T14:42:50.0807951Z test/prototype/mx_formats/test_kernels.py::test_cuda_mx_dim1_numerics[ScaleCalculationMode.RCEIL-input_dtype1-64-32] SKIPPED 2025-09-09T14:42:50.0809010Z test/prototype/mx_formats/test_kernels.py::test_cuda_mx_dim1_numerics[ScaleCalculationMode.RCEIL-input_dtype1-64-64] SKIPPED 2025-09-09T14:42:50.0810078Z test/prototype/mx_formats/test_kernels.py::test_cuda_mx_dim1_numerics[ScaleCalculationMode.RCEIL-input_dtype1-64-2048] SKIPPED 2025-09-09T14:42:50.0811153Z test/prototype/mx_formats/test_kernels.py::test_cuda_mx_dim1_numerics[ScaleCalculationMode.RCEIL-input_dtype1-2048-32] SKIPPED 2025-09-09T14:42:50.0812219Z test/prototype/mx_formats/test_kernels.py::test_cuda_mx_dim1_numerics[ScaleCalculationMode.RCEIL-input_dtype1-2048-64] SKIPPED 2025-09-09T14:42:50.0813310Z test/prototype/mx_formats/test_kernels.py::test_cuda_mx_dim1_numerics[ScaleCalculationMode.RCEIL-input_dtype1-2048-2048] SKIPPED 2025-09-09T14:42:50.0814202Z test/prototype/mx_formats/test_kernels.py::test_cuda_mx_dim0_not_supported SKIPPED 2025-09-09T14:42:50.0814936Z test/prototype/mx_formats/test_kernels.py::test_cuda_mx_dim1_invalid_block_size SKIPPED 2025-09-09T14:42:50.0816133Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TORCH-input_shape0-True-elem_dtype0] PASSED 2025-09-09T14:42:50.0818583Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TORCH-input_shape0-True-elem_dtype1] PASSED 2025-09-09T14:42:50.0820143Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TORCH-input_shape0-True-elem_dtype2] PASSED 2025-09-09T14:42:50.0821611Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TORCH-input_shape0-True-elem_dtype3] PASSED 2025-09-09T14:42:50.0823333Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TORCH-input_shape0-True-elem_dtype4] PASSED 2025-09-09T14:42:50.0824817Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TORCH-input_shape0-False-elem_dtype0] PASSED 2025-09-09T14:42:50.0826297Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TORCH-input_shape0-False-elem_dtype1] PASSED 2025-09-09T14:42:50.0827770Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TORCH-input_shape0-False-elem_dtype2] PASSED 2025-09-09T14:42:50.0829239Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TORCH-input_shape0-False-elem_dtype3] PASSED 2025-09-09T14:42:50.0830706Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TORCH-input_shape0-False-elem_dtype4] PASSED 2025-09-09T14:42:50.0832166Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TORCH-input_shape1-True-elem_dtype0] PASSED 2025-09-09T14:42:50.0833636Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TORCH-input_shape1-True-elem_dtype1] PASSED 2025-09-09T14:42:50.0835109Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TORCH-input_shape1-True-elem_dtype2] PASSED 2025-09-09T14:42:50.0836577Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TORCH-input_shape1-True-elem_dtype3] PASSED 2025-09-09T14:42:50.0838040Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TORCH-input_shape1-True-elem_dtype4] PASSED 2025-09-09T14:42:50.2385058Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TORCH-input_shape1-False-elem_dtype0] PASSED 2025-09-09T14:42:50.2388098Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TORCH-input_shape1-False-elem_dtype1] PASSED 2025-09-09T14:42:50.2391796Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TORCH-input_shape1-False-elem_dtype2] PASSED 2025-09-09T14:42:50.2395448Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TORCH-input_shape1-False-elem_dtype3] PASSED 2025-09-09T14:42:50.2397341Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TORCH-input_shape1-False-elem_dtype4] PASSED 2025-09-09T14:42:50.2399188Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TORCH-input_shape2-True-elem_dtype0] PASSED 2025-09-09T14:42:50.2400918Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TORCH-input_shape2-True-elem_dtype1] PASSED 2025-09-09T14:42:50.2402498Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TORCH-input_shape2-True-elem_dtype2] PASSED 2025-09-09T14:42:50.2403978Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TORCH-input_shape2-True-elem_dtype3] PASSED 2025-09-09T14:42:50.2405497Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TORCH-input_shape2-True-elem_dtype4] PASSED 2025-09-09T14:42:50.2406979Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TORCH-input_shape2-False-elem_dtype0] PASSED 2025-09-09T14:42:50.2408472Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TORCH-input_shape2-False-elem_dtype1] PASSED 2025-09-09T14:42:50.2409960Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TORCH-input_shape2-False-elem_dtype2] PASSED 2025-09-09T14:42:50.2411431Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TORCH-input_shape2-False-elem_dtype3] PASSED 2025-09-09T14:42:50.2412920Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TORCH-input_shape2-False-elem_dtype4] PASSED 2025-09-09T14:42:50.2414399Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TRITON-input_shape0-True-elem_dtype0] SKIPPED 2025-09-09T14:42:50.2415900Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TRITON-input_shape0-True-elem_dtype1] SKIPPED 2025-09-09T14:42:50.2417398Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TRITON-input_shape0-True-elem_dtype2] SKIPPED 2025-09-09T14:42:50.2418883Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TRITON-input_shape0-True-elem_dtype3] SKIPPED 2025-09-09T14:42:50.2420376Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TRITON-input_shape0-True-elem_dtype4] SKIPPED 2025-09-09T14:42:50.2421881Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TRITON-input_shape0-False-elem_dtype0] SKIPPED 2025-09-09T14:42:50.2423571Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TRITON-input_shape0-False-elem_dtype1] SKIPPED 2025-09-09T14:42:50.2425090Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TRITON-input_shape0-False-elem_dtype2] SKIPPED 2025-09-09T14:42:50.2426648Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TRITON-input_shape0-False-elem_dtype3] SKIPPED 2025-09-09T14:42:50.2428138Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TRITON-input_shape0-False-elem_dtype4] SKIPPED 2025-09-09T14:42:50.2429631Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TRITON-input_shape1-True-elem_dtype0] SKIPPED 2025-09-09T14:42:50.2431248Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TRITON-input_shape1-True-elem_dtype1] SKIPPED 2025-09-09T14:42:50.2432874Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TRITON-input_shape1-True-elem_dtype2] SKIPPED 2025-09-09T14:42:50.2434369Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TRITON-input_shape1-True-elem_dtype3] SKIPPED 2025-09-09T14:42:50.2435863Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TRITON-input_shape1-True-elem_dtype4] SKIPPED 2025-09-09T14:42:50.2437355Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TRITON-input_shape1-False-elem_dtype0] SKIPPED 2025-09-09T14:42:50.2438865Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TRITON-input_shape1-False-elem_dtype1] SKIPPED 2025-09-09T14:42:50.2440438Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TRITON-input_shape1-False-elem_dtype2] SKIPPED 2025-09-09T14:42:50.2441930Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TRITON-input_shape1-False-elem_dtype3] SKIPPED 2025-09-09T14:42:50.2443429Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TRITON-input_shape1-False-elem_dtype4] SKIPPED 2025-09-09T14:42:50.2444936Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TRITON-input_shape2-True-elem_dtype0] SKIPPED 2025-09-09T14:42:50.2446420Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TRITON-input_shape2-True-elem_dtype1] SKIPPED 2025-09-09T14:42:50.2447917Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TRITON-input_shape2-True-elem_dtype2] SKIPPED 2025-09-09T14:42:50.2449400Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TRITON-input_shape2-True-elem_dtype3] SKIPPED 2025-09-09T14:42:50.2450892Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TRITON-input_shape2-True-elem_dtype4] SKIPPED 2025-09-09T14:42:50.2452396Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TRITON-input_shape2-False-elem_dtype0] SKIPPED 2025-09-09T14:42:50.2453894Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TRITON-input_shape2-False-elem_dtype1] SKIPPED 2025-09-09T14:42:50.2455411Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TRITON-input_shape2-False-elem_dtype2] SKIPPED 2025-09-09T14:42:50.2456963Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TRITON-input_shape2-False-elem_dtype3] SKIPPED 2025-09-09T14:42:50.2458465Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TRITON-input_shape2-False-elem_dtype4] SKIPPED 2025-09-09T14:42:50.2460041Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.CUDA-input_shape0-True-elem_dtype0] SKIPPED 2025-09-09T14:42:50.4259774Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.CUDA-input_shape0-True-elem_dtype1] SKIPPED 2025-09-09T14:42:50.4261475Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.CUDA-input_shape0-True-elem_dtype2] SKIPPED 2025-09-09T14:42:50.4262950Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.CUDA-input_shape0-True-elem_dtype3] SKIPPED 2025-09-09T14:42:50.4264508Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.CUDA-input_shape0-True-elem_dtype4] SKIPPED 2025-09-09T14:42:50.4266238Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.CUDA-input_shape0-False-elem_dtype0] SKIPPED 2025-09-09T14:42:50.4267727Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.CUDA-input_shape0-False-elem_dtype1] SKIPPED 2025-09-09T14:42:50.4269221Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.CUDA-input_shape0-False-elem_dtype2] SKIPPED 2025-09-09T14:42:50.4270700Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.CUDA-input_shape0-False-elem_dtype3] SKIPPED 2025-09-09T14:42:50.4272194Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.CUDA-input_shape0-False-elem_dtype4] SKIPPED 2025-09-09T14:42:50.4273692Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.CUDA-input_shape1-True-elem_dtype0] SKIPPED 2025-09-09T14:42:50.4275163Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.CUDA-input_shape1-True-elem_dtype1] SKIPPED 2025-09-09T14:42:50.4276650Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.CUDA-input_shape1-True-elem_dtype2] SKIPPED 2025-09-09T14:42:50.4278131Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.CUDA-input_shape1-True-elem_dtype3] SKIPPED 2025-09-09T14:42:50.4279672Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.CUDA-input_shape1-True-elem_dtype4] SKIPPED 2025-09-09T14:42:50.4281169Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.CUDA-input_shape1-False-elem_dtype0] SKIPPED 2025-09-09T14:42:50.4282664Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.CUDA-input_shape1-False-elem_dtype1] SKIPPED 2025-09-09T14:42:50.4284154Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.CUDA-input_shape1-False-elem_dtype2] SKIPPED 2025-09-09T14:42:50.4285650Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.CUDA-input_shape1-False-elem_dtype3] SKIPPED 2025-09-09T14:42:50.4287197Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.CUDA-input_shape1-False-elem_dtype4] SKIPPED 2025-09-09T14:42:50.4288835Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.CUDA-input_shape2-True-elem_dtype0] SKIPPED 2025-09-09T14:42:50.4290333Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.CUDA-input_shape2-True-elem_dtype1] SKIPPED 2025-09-09T14:42:50.4291892Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.CUDA-input_shape2-True-elem_dtype2] SKIPPED 2025-09-09T14:42:50.4293370Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.CUDA-input_shape2-True-elem_dtype3] SKIPPED 2025-09-09T14:42:50.4294853Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.CUDA-input_shape2-True-elem_dtype4] SKIPPED 2025-09-09T14:42:50.4296351Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.CUDA-input_shape2-False-elem_dtype0] SKIPPED 2025-09-09T14:42:50.4297836Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.CUDA-input_shape2-False-elem_dtype1] SKIPPED 2025-09-09T14:42:50.4299329Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.CUDA-input_shape2-False-elem_dtype2] SKIPPED 2025-09-09T14:42:50.4300818Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.CUDA-input_shape2-False-elem_dtype3] SKIPPED 2025-09-09T14:42:50.4302317Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.CUDA-input_shape2-False-elem_dtype4] SKIPPED 2025-09-09T14:42:50.4303813Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TORCH-input_shape0-True-elem_dtype0] PASSED 2025-09-09T14:42:50.4305284Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TORCH-input_shape0-True-elem_dtype1] PASSED 2025-09-09T14:42:50.4306771Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TORCH-input_shape0-True-elem_dtype2] PASSED 2025-09-09T14:42:50.4308255Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TORCH-input_shape0-True-elem_dtype3] PASSED 2025-09-09T14:42:50.4309727Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TORCH-input_shape0-True-elem_dtype4] PASSED 2025-09-09T14:42:50.4311210Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TORCH-input_shape0-False-elem_dtype0] PASSED 2025-09-09T14:42:50.4312709Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TORCH-input_shape0-False-elem_dtype1] PASSED 2025-09-09T14:42:50.4314192Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TORCH-input_shape0-False-elem_dtype2] PASSED 2025-09-09T14:42:50.4315685Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TORCH-input_shape0-False-elem_dtype3] PASSED 2025-09-09T14:42:50.4317174Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TORCH-input_shape0-False-elem_dtype4] PASSED 2025-09-09T14:42:50.4318646Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TORCH-input_shape1-True-elem_dtype0] PASSED 2025-09-09T14:42:50.4320278Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TORCH-input_shape1-True-elem_dtype1] PASSED 2025-09-09T14:42:50.4321830Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TORCH-input_shape1-True-elem_dtype2] PASSED 2025-09-09T14:42:50.4323549Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TORCH-input_shape1-True-elem_dtype3] PASSED 2025-09-09T14:42:50.4325029Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TORCH-input_shape1-True-elem_dtype4] PASSED 2025-09-09T14:42:50.4326568Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TORCH-input_shape1-False-elem_dtype0] PASSED 2025-09-09T14:42:50.4328059Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TORCH-input_shape1-False-elem_dtype1] PASSED 2025-09-09T14:42:50.5672921Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TORCH-input_shape1-False-elem_dtype2] PASSED 2025-09-09T14:42:50.5674570Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TORCH-input_shape1-False-elem_dtype3] PASSED 2025-09-09T14:42:50.5676031Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TORCH-input_shape1-False-elem_dtype4] PASSED 2025-09-09T14:42:50.5677497Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TORCH-input_shape2-True-elem_dtype0] PASSED 2025-09-09T14:42:50.5678969Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TORCH-input_shape2-True-elem_dtype1] PASSED 2025-09-09T14:42:50.5680486Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TORCH-input_shape2-True-elem_dtype2] PASSED 2025-09-09T14:42:50.5681940Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TORCH-input_shape2-True-elem_dtype3] PASSED 2025-09-09T14:42:50.5683392Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TORCH-input_shape2-True-elem_dtype4] PASSED 2025-09-09T14:42:50.5684838Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TORCH-input_shape2-False-elem_dtype0] PASSED 2025-09-09T14:42:50.5686304Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TORCH-input_shape2-False-elem_dtype1] PASSED 2025-09-09T14:42:50.5687768Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TORCH-input_shape2-False-elem_dtype2] PASSED 2025-09-09T14:42:50.5689219Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TORCH-input_shape2-False-elem_dtype3] PASSED 2025-09-09T14:42:50.5690684Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TORCH-input_shape2-False-elem_dtype4] PASSED 2025-09-09T14:42:50.5692141Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TRITON-input_shape0-True-elem_dtype0] SKIPPED 2025-09-09T14:42:50.5693805Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TRITON-input_shape0-True-elem_dtype1] SKIPPED 2025-09-09T14:42:50.5695400Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TRITON-input_shape0-True-elem_dtype2] SKIPPED 2025-09-09T14:42:50.5696860Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TRITON-input_shape0-True-elem_dtype3] SKIPPED 2025-09-09T14:42:50.5698332Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TRITON-input_shape0-True-elem_dtype4] SKIPPED 2025-09-09T14:42:50.5699822Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TRITON-input_shape0-False-elem_dtype0] SKIPPED 2025-09-09T14:42:50.5701325Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TRITON-input_shape0-False-elem_dtype1] SKIPPED 2025-09-09T14:42:50.5702834Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TRITON-input_shape0-False-elem_dtype2] SKIPPED 2025-09-09T14:42:50.5704325Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TRITON-input_shape0-False-elem_dtype3] SKIPPED 2025-09-09T14:42:50.5705841Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TRITON-input_shape0-False-elem_dtype4] SKIPPED 2025-09-09T14:42:50.5707359Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TRITON-input_shape1-True-elem_dtype0] SKIPPED 2025-09-09T14:42:50.5708853Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TRITON-input_shape1-True-elem_dtype1] SKIPPED 2025-09-09T14:42:50.5710342Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TRITON-input_shape1-True-elem_dtype2] SKIPPED 2025-09-09T14:42:50.5711837Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TRITON-input_shape1-True-elem_dtype3] SKIPPED 2025-09-09T14:42:50.5713318Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TRITON-input_shape1-True-elem_dtype4] SKIPPED 2025-09-09T14:42:50.5714808Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TRITON-input_shape1-False-elem_dtype0] SKIPPED 2025-09-09T14:42:50.5716309Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TRITON-input_shape1-False-elem_dtype1] SKIPPED 2025-09-09T14:42:50.5717806Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TRITON-input_shape1-False-elem_dtype2] SKIPPED 2025-09-09T14:42:50.5719348Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TRITON-input_shape1-False-elem_dtype3] SKIPPED 2025-09-09T14:42:50.5720841Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TRITON-input_shape1-False-elem_dtype4] SKIPPED 2025-09-09T14:42:50.5722554Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TRITON-input_shape2-True-elem_dtype0] SKIPPED 2025-09-09T14:42:50.5724206Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TRITON-input_shape2-True-elem_dtype1] SKIPPED 2025-09-09T14:42:50.5725802Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TRITON-input_shape2-True-elem_dtype2] SKIPPED 2025-09-09T14:42:50.5727278Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TRITON-input_shape2-True-elem_dtype3] SKIPPED 2025-09-09T14:42:50.5728766Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TRITON-input_shape2-True-elem_dtype4] SKIPPED 2025-09-09T14:42:50.5730250Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TRITON-input_shape2-False-elem_dtype0] SKIPPED 2025-09-09T14:42:50.5731747Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TRITON-input_shape2-False-elem_dtype1] SKIPPED 2025-09-09T14:42:50.5733242Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TRITON-input_shape2-False-elem_dtype2] SKIPPED 2025-09-09T14:42:50.5734736Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TRITON-input_shape2-False-elem_dtype3] SKIPPED 2025-09-09T14:42:50.5736219Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TRITON-input_shape2-False-elem_dtype4] SKIPPED 2025-09-09T14:42:50.5737708Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.CUDA-input_shape0-True-elem_dtype0] SKIPPED 2025-09-09T14:42:50.5739179Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.CUDA-input_shape0-True-elem_dtype1] SKIPPED 2025-09-09T14:42:50.5740642Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.CUDA-input_shape0-True-elem_dtype2] SKIPPED 2025-09-09T14:42:50.7856293Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.CUDA-input_shape0-True-elem_dtype3] SKIPPED 2025-09-09T14:42:50.7857782Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.CUDA-input_shape0-True-elem_dtype4] SKIPPED 2025-09-09T14:42:50.7859238Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.CUDA-input_shape0-False-elem_dtype0] SKIPPED 2025-09-09T14:42:50.7860691Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.CUDA-input_shape0-False-elem_dtype1] SKIPPED 2025-09-09T14:42:50.7862151Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.CUDA-input_shape0-False-elem_dtype2] SKIPPED 2025-09-09T14:42:50.7863601Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.CUDA-input_shape0-False-elem_dtype3] SKIPPED 2025-09-09T14:42:50.7865045Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.CUDA-input_shape0-False-elem_dtype4] SKIPPED 2025-09-09T14:42:50.7866491Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.CUDA-input_shape1-True-elem_dtype0] SKIPPED 2025-09-09T14:42:50.7868108Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.CUDA-input_shape1-True-elem_dtype1] SKIPPED 2025-09-09T14:42:50.7878120Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.CUDA-input_shape1-True-elem_dtype2] SKIPPED 2025-09-09T14:42:50.7879657Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.CUDA-input_shape1-True-elem_dtype3] SKIPPED 2025-09-09T14:42:50.7881102Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.CUDA-input_shape1-True-elem_dtype4] SKIPPED 2025-09-09T14:42:50.7882555Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.CUDA-input_shape1-False-elem_dtype0] SKIPPED 2025-09-09T14:42:50.7884024Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.CUDA-input_shape1-False-elem_dtype1] SKIPPED 2025-09-09T14:42:50.7885480Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.CUDA-input_shape1-False-elem_dtype2] SKIPPED 2025-09-09T14:42:50.7886990Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.CUDA-input_shape1-False-elem_dtype3] SKIPPED 2025-09-09T14:42:50.7888455Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.CUDA-input_shape1-False-elem_dtype4] SKIPPED 2025-09-09T14:42:50.7889907Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.CUDA-input_shape2-True-elem_dtype0] SKIPPED 2025-09-09T14:42:50.7891360Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.CUDA-input_shape2-True-elem_dtype1] SKIPPED 2025-09-09T14:42:50.7892809Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.CUDA-input_shape2-True-elem_dtype2] SKIPPED 2025-09-09T14:42:50.7894259Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.CUDA-input_shape2-True-elem_dtype3] SKIPPED 2025-09-09T14:42:50.7895711Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.CUDA-input_shape2-True-elem_dtype4] SKIPPED 2025-09-09T14:42:50.7897163Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.CUDA-input_shape2-False-elem_dtype0] SKIPPED 2025-09-09T14:42:50.7898629Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.CUDA-input_shape2-False-elem_dtype1] SKIPPED 2025-09-09T14:42:50.7900090Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.CUDA-input_shape2-False-elem_dtype2] SKIPPED 2025-09-09T14:42:50.7901548Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.CUDA-input_shape2-False-elem_dtype3] SKIPPED 2025-09-09T14:42:50.7903007Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.CUDA-input_shape2-False-elem_dtype4] SKIPPED 2025-09-09T14:42:50.7904465Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.EVEN-MXFP8Dim1CastKernelChoice.TORCH-input_shape0-True-elem_dtype0] PASSED 2025-09-09T14:42:50.7906027Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.EVEN-MXFP8Dim1CastKernelChoice.TORCH-input_shape0-True-elem_dtype1] PASSED 2025-09-09T14:42:50.7907563Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.EVEN-MXFP8Dim1CastKernelChoice.TORCH-input_shape0-True-elem_dtype2] PASSED 2025-09-09T14:42:50.7909022Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.EVEN-MXFP8Dim1CastKernelChoice.TORCH-input_shape0-True-elem_dtype3] PASSED 2025-09-09T14:42:50.7910469Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.EVEN-MXFP8Dim1CastKernelChoice.TORCH-input_shape0-True-elem_dtype4] PASSED 2025-09-09T14:42:50.7911921Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.EVEN-MXFP8Dim1CastKernelChoice.TORCH-input_shape0-False-elem_dtype0] PASSED 2025-09-09T14:42:50.7913382Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.EVEN-MXFP8Dim1CastKernelChoice.TORCH-input_shape0-False-elem_dtype1] PASSED 2025-09-09T14:42:50.7914838Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.EVEN-MXFP8Dim1CastKernelChoice.TORCH-input_shape0-False-elem_dtype2] PASSED 2025-09-09T14:42:50.7916346Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.EVEN-MXFP8Dim1CastKernelChoice.TORCH-input_shape0-False-elem_dtype3] PASSED 2025-09-09T14:42:50.7917785Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.EVEN-MXFP8Dim1CastKernelChoice.TORCH-input_shape0-False-elem_dtype4] PASSED 2025-09-09T14:42:50.7919282Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.EVEN-MXFP8Dim1CastKernelChoice.TORCH-input_shape1-True-elem_dtype0] PASSED 2025-09-09T14:42:50.7920734Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.EVEN-MXFP8Dim1CastKernelChoice.TORCH-input_shape1-True-elem_dtype1] PASSED 2025-09-09T14:42:50.7922504Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.EVEN-MXFP8Dim1CastKernelChoice.TORCH-input_shape1-True-elem_dtype2] PASSED 2025-09-09T14:42:50.7923963Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.EVEN-MXFP8Dim1CastKernelChoice.TORCH-input_shape1-True-elem_dtype3] PASSED 2025-09-09T14:42:50.7925406Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.EVEN-MXFP8Dim1CastKernelChoice.TORCH-input_shape1-True-elem_dtype4] PASSED 2025-09-09T14:42:50.7926899Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.EVEN-MXFP8Dim1CastKernelChoice.TORCH-input_shape1-False-elem_dtype0] PASSED 2025-09-09T14:42:50.7928360Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.EVEN-MXFP8Dim1CastKernelChoice.TORCH-input_shape1-False-elem_dtype1] PASSED 2025-09-09T14:42:50.7929808Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.EVEN-MXFP8Dim1CastKernelChoice.TORCH-input_shape1-False-elem_dtype2] PASSED 2025-09-09T14:42:50.7931257Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.EVEN-MXFP8Dim1CastKernelChoice.TORCH-input_shape1-False-elem_dtype3] PASSED 2025-09-09T14:42:50.9178652Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.EVEN-MXFP8Dim1CastKernelChoice.TORCH-input_shape1-False-elem_dtype4] PASSED 2025-09-09T14:42:50.9180140Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.EVEN-MXFP8Dim1CastKernelChoice.TORCH-input_shape2-True-elem_dtype0] PASSED 2025-09-09T14:42:50.9181749Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.EVEN-MXFP8Dim1CastKernelChoice.TORCH-input_shape2-True-elem_dtype1] PASSED 2025-09-09T14:42:50.9183209Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.EVEN-MXFP8Dim1CastKernelChoice.TORCH-input_shape2-True-elem_dtype2] PASSED 2025-09-09T14:42:50.9184764Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.EVEN-MXFP8Dim1CastKernelChoice.TORCH-input_shape2-True-elem_dtype3] PASSED 2025-09-09T14:42:50.9186266Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.EVEN-MXFP8Dim1CastKernelChoice.TORCH-input_shape2-True-elem_dtype4] PASSED 2025-09-09T14:42:50.9187715Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.EVEN-MXFP8Dim1CastKernelChoice.TORCH-input_shape2-False-elem_dtype0] PASSED 2025-09-09T14:42:50.9189165Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.EVEN-MXFP8Dim1CastKernelChoice.TORCH-input_shape2-False-elem_dtype1] PASSED 2025-09-09T14:42:50.9190618Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.EVEN-MXFP8Dim1CastKernelChoice.TORCH-input_shape2-False-elem_dtype2] PASSED 2025-09-09T14:42:50.9192075Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.EVEN-MXFP8Dim1CastKernelChoice.TORCH-input_shape2-False-elem_dtype3] PASSED 2025-09-09T14:42:50.9193518Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.EVEN-MXFP8Dim1CastKernelChoice.TORCH-input_shape2-False-elem_dtype4] PASSED 2025-09-09T14:42:50.9194979Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.EVEN-MXFP8Dim1CastKernelChoice.TRITON-input_shape0-True-elem_dtype0] SKIPPED 2025-09-09T14:42:50.9196447Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.EVEN-MXFP8Dim1CastKernelChoice.TRITON-input_shape0-True-elem_dtype1] SKIPPED 2025-09-09T14:42:50.9197907Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.EVEN-MXFP8Dim1CastKernelChoice.TRITON-input_shape0-True-elem_dtype2] SKIPPED 2025-09-09T14:42:50.9199453Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.EVEN-MXFP8Dim1CastKernelChoice.TRITON-input_shape0-True-elem_dtype3] SKIPPED 2025-09-09T14:42:50.9200916Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.EVEN-MXFP8Dim1CastKernelChoice.TRITON-input_shape0-True-elem_dtype4] SKIPPED 2025-09-09T14:42:50.9202399Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.EVEN-MXFP8Dim1CastKernelChoice.TRITON-input_shape0-False-elem_dtype0] SKIPPED 2025-09-09T14:42:50.9203901Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.EVEN-MXFP8Dim1CastKernelChoice.TRITON-input_shape0-False-elem_dtype1] SKIPPED 2025-09-09T14:42:50.9205391Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.EVEN-MXFP8Dim1CastKernelChoice.TRITON-input_shape0-False-elem_dtype2] SKIPPED 2025-09-09T14:42:50.9206872Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.EVEN-MXFP8Dim1CastKernelChoice.TRITON-input_shape0-False-elem_dtype3] SKIPPED 2025-09-09T14:42:50.9208356Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.EVEN-MXFP8Dim1CastKernelChoice.TRITON-input_shape0-False-elem_dtype4] SKIPPED 2025-09-09T14:42:50.9209839Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.EVEN-MXFP8Dim1CastKernelChoice.TRITON-input_shape1-True-elem_dtype0] SKIPPED 2025-09-09T14:42:50.9211407Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.EVEN-MXFP8Dim1CastKernelChoice.TRITON-input_shape1-True-elem_dtype1] SKIPPED 2025-09-09T14:42:50.9212901Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.EVEN-MXFP8Dim1CastKernelChoice.TRITON-input_shape1-True-elem_dtype2] SKIPPED 2025-09-09T14:42:50.9214467Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.EVEN-MXFP8Dim1CastKernelChoice.TRITON-input_shape1-True-elem_dtype3] SKIPPED 2025-09-09T14:42:50.9215999Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.EVEN-MXFP8Dim1CastKernelChoice.TRITON-input_shape1-True-elem_dtype4] SKIPPED 2025-09-09T14:42:50.9217486Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.EVEN-MXFP8Dim1CastKernelChoice.TRITON-input_shape1-False-elem_dtype0] SKIPPED 2025-09-09T14:42:50.9218984Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.EVEN-MXFP8Dim1CastKernelChoice.TRITON-input_shape1-False-elem_dtype1] SKIPPED 2025-09-09T14:42:50.9220474Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.EVEN-MXFP8Dim1CastKernelChoice.TRITON-input_shape1-False-elem_dtype2] SKIPPED 2025-09-09T14:42:50.9221973Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.EVEN-MXFP8Dim1CastKernelChoice.TRITON-input_shape1-False-elem_dtype3] SKIPPED 2025-09-09T14:42:50.9223630Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.EVEN-MXFP8Dim1CastKernelChoice.TRITON-input_shape1-False-elem_dtype4] SKIPPED 2025-09-09T14:42:50.9225122Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.EVEN-MXFP8Dim1CastKernelChoice.TRITON-input_shape2-True-elem_dtype0] SKIPPED 2025-09-09T14:42:50.9226617Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.EVEN-MXFP8Dim1CastKernelChoice.TRITON-input_shape2-True-elem_dtype1] SKIPPED 2025-09-09T14:42:50.9228104Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.EVEN-MXFP8Dim1CastKernelChoice.TRITON-input_shape2-True-elem_dtype2] SKIPPED 2025-09-09T14:42:50.9229591Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.EVEN-MXFP8Dim1CastKernelChoice.TRITON-input_shape2-True-elem_dtype3] SKIPPED 2025-09-09T14:42:50.9231076Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.EVEN-MXFP8Dim1CastKernelChoice.TRITON-input_shape2-True-elem_dtype4] SKIPPED 2025-09-09T14:42:50.9232564Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.EVEN-MXFP8Dim1CastKernelChoice.TRITON-input_shape2-False-elem_dtype0] SKIPPED 2025-09-09T14:42:50.9234054Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.EVEN-MXFP8Dim1CastKernelChoice.TRITON-input_shape2-False-elem_dtype1] SKIPPED 2025-09-09T14:42:50.9235544Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.EVEN-MXFP8Dim1CastKernelChoice.TRITON-input_shape2-False-elem_dtype2] SKIPPED 2025-09-09T14:42:50.9237086Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.EVEN-MXFP8Dim1CastKernelChoice.TRITON-input_shape2-False-elem_dtype3] SKIPPED 2025-09-09T14:42:50.9238582Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.EVEN-MXFP8Dim1CastKernelChoice.TRITON-input_shape2-False-elem_dtype4] SKIPPED 2025-09-09T14:42:50.9240100Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.EVEN-MXFP8Dim1CastKernelChoice.CUDA-input_shape0-True-elem_dtype0] SKIPPED 2025-09-09T14:42:50.9241691Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.EVEN-MXFP8Dim1CastKernelChoice.CUDA-input_shape0-True-elem_dtype1] SKIPPED 2025-09-09T14:42:50.9243300Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.EVEN-MXFP8Dim1CastKernelChoice.CUDA-input_shape0-True-elem_dtype2] SKIPPED 2025-09-09T14:42:50.9244755Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.EVEN-MXFP8Dim1CastKernelChoice.CUDA-input_shape0-True-elem_dtype3] SKIPPED 2025-09-09T14:42:50.9246273Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.EVEN-MXFP8Dim1CastKernelChoice.CUDA-input_shape0-True-elem_dtype4] SKIPPED 2025-09-09T14:42:51.1330912Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.EVEN-MXFP8Dim1CastKernelChoice.CUDA-input_shape0-False-elem_dtype0] SKIPPED 2025-09-09T14:42:51.1332499Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.EVEN-MXFP8Dim1CastKernelChoice.CUDA-input_shape0-False-elem_dtype1] SKIPPED 2025-09-09T14:42:51.1333950Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.EVEN-MXFP8Dim1CastKernelChoice.CUDA-input_shape0-False-elem_dtype2] SKIPPED 2025-09-09T14:42:51.1335437Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.EVEN-MXFP8Dim1CastKernelChoice.CUDA-input_shape0-False-elem_dtype3] SKIPPED 2025-09-09T14:42:51.1336903Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.EVEN-MXFP8Dim1CastKernelChoice.CUDA-input_shape0-False-elem_dtype4] SKIPPED 2025-09-09T14:42:51.1338353Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.EVEN-MXFP8Dim1CastKernelChoice.CUDA-input_shape1-True-elem_dtype0] SKIPPED 2025-09-09T14:42:51.1339813Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.EVEN-MXFP8Dim1CastKernelChoice.CUDA-input_shape1-True-elem_dtype1] SKIPPED 2025-09-09T14:42:51.1341263Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.EVEN-MXFP8Dim1CastKernelChoice.CUDA-input_shape1-True-elem_dtype2] SKIPPED 2025-09-09T14:42:51.1342715Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.EVEN-MXFP8Dim1CastKernelChoice.CUDA-input_shape1-True-elem_dtype3] SKIPPED 2025-09-09T14:42:51.1344166Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.EVEN-MXFP8Dim1CastKernelChoice.CUDA-input_shape1-True-elem_dtype4] SKIPPED 2025-09-09T14:42:51.1345621Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.EVEN-MXFP8Dim1CastKernelChoice.CUDA-input_shape1-False-elem_dtype0] SKIPPED 2025-09-09T14:42:51.1347081Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.EVEN-MXFP8Dim1CastKernelChoice.CUDA-input_shape1-False-elem_dtype1] SKIPPED 2025-09-09T14:42:51.1348542Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.EVEN-MXFP8Dim1CastKernelChoice.CUDA-input_shape1-False-elem_dtype2] SKIPPED 2025-09-09T14:42:51.1350007Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.EVEN-MXFP8Dim1CastKernelChoice.CUDA-input_shape1-False-elem_dtype3] SKIPPED 2025-09-09T14:42:51.1351460Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.EVEN-MXFP8Dim1CastKernelChoice.CUDA-input_shape1-False-elem_dtype4] SKIPPED 2025-09-09T14:42:51.1352915Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.EVEN-MXFP8Dim1CastKernelChoice.CUDA-input_shape2-True-elem_dtype0] SKIPPED 2025-09-09T14:42:51.1354531Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.EVEN-MXFP8Dim1CastKernelChoice.CUDA-input_shape2-True-elem_dtype1] SKIPPED 2025-09-09T14:42:51.1355995Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.EVEN-MXFP8Dim1CastKernelChoice.CUDA-input_shape2-True-elem_dtype2] SKIPPED 2025-09-09T14:42:51.1357610Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.EVEN-MXFP8Dim1CastKernelChoice.CUDA-input_shape2-True-elem_dtype3] SKIPPED 2025-09-09T14:42:51.1359058Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.EVEN-MXFP8Dim1CastKernelChoice.CUDA-input_shape2-True-elem_dtype4] SKIPPED 2025-09-09T14:42:51.1360599Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.EVEN-MXFP8Dim1CastKernelChoice.CUDA-input_shape2-False-elem_dtype0] SKIPPED 2025-09-09T14:42:51.1362067Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.EVEN-MXFP8Dim1CastKernelChoice.CUDA-input_shape2-False-elem_dtype1] SKIPPED 2025-09-09T14:42:51.1363526Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.EVEN-MXFP8Dim1CastKernelChoice.CUDA-input_shape2-False-elem_dtype2] SKIPPED 2025-09-09T14:42:51.1365001Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.EVEN-MXFP8Dim1CastKernelChoice.CUDA-input_shape2-False-elem_dtype3] SKIPPED 2025-09-09T14:42:51.1366465Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.EVEN-MXFP8Dim1CastKernelChoice.CUDA-input_shape2-False-elem_dtype4] SKIPPED 2025-09-09T14:42:51.1367947Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TORCH-input_shape0-True-elem_dtype0] PASSED 2025-09-09T14:42:51.1369466Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TORCH-input_shape0-True-elem_dtype1] PASSED 2025-09-09T14:42:51.1370967Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TORCH-input_shape0-True-elem_dtype2] PASSED 2025-09-09T14:42:51.1372465Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TORCH-input_shape0-True-elem_dtype3] PASSED 2025-09-09T14:42:51.1373961Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TORCH-input_shape0-True-elem_dtype4] PASSED 2025-09-09T14:42:51.1375458Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TORCH-input_shape0-False-elem_dtype0] PASSED 2025-09-09T14:42:51.1376957Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TORCH-input_shape0-False-elem_dtype1] PASSED 2025-09-09T14:42:51.1378465Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TORCH-input_shape0-False-elem_dtype2] PASSED 2025-09-09T14:42:51.1379966Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TORCH-input_shape0-False-elem_dtype3] PASSED 2025-09-09T14:42:51.1381468Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TORCH-input_shape0-False-elem_dtype4] PASSED 2025-09-09T14:42:51.1382974Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TORCH-input_shape1-True-elem_dtype0] PASSED 2025-09-09T14:42:51.1384546Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TORCH-input_shape1-True-elem_dtype1] PASSED 2025-09-09T14:42:51.1386041Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TORCH-input_shape1-True-elem_dtype2] PASSED 2025-09-09T14:42:51.1387609Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TORCH-input_shape1-True-elem_dtype3] PASSED 2025-09-09T14:42:51.1389099Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TORCH-input_shape1-True-elem_dtype4] PASSED 2025-09-09T14:42:51.1390602Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TORCH-input_shape1-False-elem_dtype0] PASSED 2025-09-09T14:42:51.1392119Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TORCH-input_shape1-False-elem_dtype1] PASSED 2025-09-09T14:42:51.1393615Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TORCH-input_shape1-False-elem_dtype2] PASSED 2025-09-09T14:42:51.1395135Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TORCH-input_shape1-False-elem_dtype3] PASSED 2025-09-09T14:42:51.1396715Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TORCH-input_shape1-False-elem_dtype4] PASSED 2025-09-09T14:42:51.1398217Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TORCH-input_shape2-True-elem_dtype0] PASSED 2025-09-09T14:42:51.2413666Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TORCH-input_shape2-True-elem_dtype1] PASSED 2025-09-09T14:42:51.2415199Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TORCH-input_shape2-True-elem_dtype2] PASSED 2025-09-09T14:42:51.2416689Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TORCH-input_shape2-True-elem_dtype3] PASSED 2025-09-09T14:42:51.2418179Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TORCH-input_shape2-True-elem_dtype4] PASSED 2025-09-09T14:42:51.2419663Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TORCH-input_shape2-False-elem_dtype0] PASSED 2025-09-09T14:42:51.2421169Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TORCH-input_shape2-False-elem_dtype1] PASSED 2025-09-09T14:42:51.2422875Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TORCH-input_shape2-False-elem_dtype2] PASSED 2025-09-09T14:42:51.2424365Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TORCH-input_shape2-False-elem_dtype3] PASSED 2025-09-09T14:42:51.2425854Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TORCH-input_shape2-False-elem_dtype4] PASSED 2025-09-09T14:42:51.2427345Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TRITON-input_shape0-True-elem_dtype0] SKIPPED 2025-09-09T14:42:51.2429038Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TRITON-input_shape0-True-elem_dtype1] SKIPPED 2025-09-09T14:42:51.2430541Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TRITON-input_shape0-True-elem_dtype2] SKIPPED 2025-09-09T14:42:51.2432166Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TRITON-input_shape0-True-elem_dtype3] SKIPPED 2025-09-09T14:42:51.2433659Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TRITON-input_shape0-True-elem_dtype4] SKIPPED 2025-09-09T14:42:51.2435164Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TRITON-input_shape0-False-elem_dtype0] SKIPPED 2025-09-09T14:42:51.2436682Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TRITON-input_shape0-False-elem_dtype1] SKIPPED 2025-09-09T14:42:51.2438174Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TRITON-input_shape0-False-elem_dtype2] SKIPPED 2025-09-09T14:42:51.2439763Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TRITON-input_shape0-False-elem_dtype3] SKIPPED 2025-09-09T14:42:51.2441265Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TRITON-input_shape0-False-elem_dtype4] SKIPPED 2025-09-09T14:42:51.2442757Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TRITON-input_shape1-True-elem_dtype0] SKIPPED 2025-09-09T14:42:51.2444257Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TRITON-input_shape1-True-elem_dtype1] SKIPPED 2025-09-09T14:42:51.2445756Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TRITON-input_shape1-True-elem_dtype2] SKIPPED 2025-09-09T14:42:51.2447252Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TRITON-input_shape1-True-elem_dtype3] SKIPPED 2025-09-09T14:42:51.2448759Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TRITON-input_shape1-True-elem_dtype4] SKIPPED 2025-09-09T14:42:51.2450261Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TRITON-input_shape1-False-elem_dtype0] SKIPPED 2025-09-09T14:42:51.2451777Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TRITON-input_shape1-False-elem_dtype1] SKIPPED 2025-09-09T14:42:51.2453277Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TRITON-input_shape1-False-elem_dtype2] SKIPPED 2025-09-09T14:42:51.2454784Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TRITON-input_shape1-False-elem_dtype3] SKIPPED 2025-09-09T14:42:51.2456273Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TRITON-input_shape1-False-elem_dtype4] SKIPPED 2025-09-09T14:42:51.2457769Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TRITON-input_shape2-True-elem_dtype0] SKIPPED 2025-09-09T14:42:51.2459355Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TRITON-input_shape2-True-elem_dtype1] SKIPPED 2025-09-09T14:42:51.2460841Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TRITON-input_shape2-True-elem_dtype2] SKIPPED 2025-09-09T14:42:51.2462414Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TRITON-input_shape2-True-elem_dtype3] SKIPPED 2025-09-09T14:42:51.2463905Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TRITON-input_shape2-True-elem_dtype4] SKIPPED 2025-09-09T14:42:51.2465405Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TRITON-input_shape2-False-elem_dtype0] SKIPPED 2025-09-09T14:42:51.2466920Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TRITON-input_shape2-False-elem_dtype1] SKIPPED 2025-09-09T14:42:51.2468417Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TRITON-input_shape2-False-elem_dtype2] SKIPPED 2025-09-09T14:42:51.2469925Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TRITON-input_shape2-False-elem_dtype3] SKIPPED 2025-09-09T14:42:51.2471428Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TRITON-input_shape2-False-elem_dtype4] SKIPPED 2025-09-09T14:42:51.2472915Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.CUDA-input_shape0-True-elem_dtype0] SKIPPED 2025-09-09T14:42:51.2474403Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.CUDA-input_shape0-True-elem_dtype1] SKIPPED 2025-09-09T14:42:51.2475884Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.CUDA-input_shape0-True-elem_dtype2] SKIPPED 2025-09-09T14:42:51.2477353Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.CUDA-input_shape0-True-elem_dtype3] SKIPPED 2025-09-09T14:42:51.2478830Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.CUDA-input_shape0-True-elem_dtype4] SKIPPED 2025-09-09T14:42:51.2480355Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.CUDA-input_shape0-False-elem_dtype0] SKIPPED 2025-09-09T14:42:51.2481835Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.CUDA-input_shape0-False-elem_dtype1] SKIPPED 2025-09-09T14:43:15.1720772Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.CUDA-input_shape0-False-elem_dtype2] SKIPPED 2025-09-09T14:43:15.1722479Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.CUDA-input_shape0-False-elem_dtype3] SKIPPED 2025-09-09T14:43:15.1723965Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.CUDA-input_shape0-False-elem_dtype4] SKIPPED 2025-09-09T14:43:15.1725429Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.CUDA-input_shape1-True-elem_dtype0] SKIPPED 2025-09-09T14:43:15.1727297Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.CUDA-input_shape1-True-elem_dtype1] SKIPPED 2025-09-09T14:43:15.1728960Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.CUDA-input_shape1-True-elem_dtype2] SKIPPED 2025-09-09T14:43:15.1730415Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.CUDA-input_shape1-True-elem_dtype3] SKIPPED 2025-09-09T14:43:15.1731874Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.CUDA-input_shape1-True-elem_dtype4] SKIPPED 2025-09-09T14:43:15.1733341Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.CUDA-input_shape1-False-elem_dtype0] SKIPPED 2025-09-09T14:43:15.1734811Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.CUDA-input_shape1-False-elem_dtype1] SKIPPED 2025-09-09T14:43:15.1736279Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.CUDA-input_shape1-False-elem_dtype2] SKIPPED 2025-09-09T14:43:15.1737746Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.CUDA-input_shape1-False-elem_dtype3] SKIPPED 2025-09-09T14:43:15.1739204Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.CUDA-input_shape1-False-elem_dtype4] SKIPPED 2025-09-09T14:43:15.1740669Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.CUDA-input_shape2-True-elem_dtype0] SKIPPED 2025-09-09T14:43:15.1742143Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.CUDA-input_shape2-True-elem_dtype1] SKIPPED 2025-09-09T14:43:15.1743656Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.CUDA-input_shape2-True-elem_dtype2] SKIPPED 2025-09-09T14:43:15.1745111Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.CUDA-input_shape2-True-elem_dtype3] SKIPPED 2025-09-09T14:43:15.1746570Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.CUDA-input_shape2-True-elem_dtype4] SKIPPED 2025-09-09T14:43:15.1748034Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.CUDA-input_shape2-False-elem_dtype0] SKIPPED 2025-09-09T14:43:15.1749503Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.CUDA-input_shape2-False-elem_dtype1] SKIPPED 2025-09-09T14:43:15.1750971Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.CUDA-input_shape2-False-elem_dtype2] SKIPPED 2025-09-09T14:43:15.1752436Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.CUDA-input_shape2-False-elem_dtype3] SKIPPED 2025-09-09T14:43:15.1753901Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_vs_hp[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.CUDA-input_shape2-False-elem_dtype4] SKIPPED 2025-09-09T14:43:15.1755156Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_emulated_vs_real_gemm[mkn0-MXLinearRecipeName.MXFP8_CUBLAS] SKIPPED 2025-09-09T14:43:15.1756327Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_emulated_vs_real_gemm[mkn0-MXLinearRecipeName.MXFP4_CUTLASS] SKIPPED 2025-09-09T14:43:15.1757400Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_emulated_vs_real_gemm[mkn1-MXLinearRecipeName.MXFP8_CUBLAS] SKIPPED 2025-09-09T14:43:15.1758541Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_emulated_vs_real_gemm[mkn1-MXLinearRecipeName.MXFP4_CUTLASS] SKIPPED 2025-09-09T14:43:15.1759688Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_emulated_vs_real_gemm[mkn2-MXLinearRecipeName.MXFP8_CUBLAS] SKIPPED 2025-09-09T14:43:15.1760755Z test/prototype/mx_formats/test_mx_linear.py::test_linear_eager_emulated_vs_real_gemm[mkn2-MXLinearRecipeName.MXFP4_CUTLASS] SKIPPED 2025-09-09T14:43:15.1761630Z test/prototype/mx_formats/test_mx_linear.py::test_activation_checkpointing PASSED 2025-09-09T14:43:15.1762824Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TORCH-False-mxfp8_emulated-hp_dtype0] SKIPPED 2025-09-09T14:43:15.1764306Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TORCH-False-mxfp8_emulated-hp_dtype1] SKIPPED 2025-09-09T14:43:15.1765800Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TORCH-False-mxfp4_emulated-hp_dtype0] PASSED 2025-09-09T14:43:15.1767288Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TORCH-False-mxfp4_emulated-hp_dtype1] SKIPPED 2025-09-09T14:43:15.1768762Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TORCH-False-mxfp8_cublas-hp_dtype0] SKIPPED 2025-09-09T14:43:15.1770247Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TORCH-False-mxfp8_cublas-hp_dtype1] SKIPPED 2025-09-09T14:43:15.1771726Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TORCH-False-mxfp4_cutlass-hp_dtype0] SKIPPED 2025-09-09T14:43:15.1773207Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TORCH-False-mxfp4_cutlass-hp_dtype1] SKIPPED 2025-09-09T14:43:15.1774689Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TORCH-True-mxfp8_emulated-hp_dtype0] SKIPPED 2025-09-09T14:43:15.1776171Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TORCH-True-mxfp8_emulated-hp_dtype1] SKIPPED 2025-09-09T14:43:15.1777644Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TORCH-True-mxfp4_emulated-hp_dtype0] PASSED 2025-09-09T14:43:15.1779128Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TORCH-True-mxfp4_emulated-hp_dtype1] SKIPPED 2025-09-09T14:43:15.1780607Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TORCH-True-mxfp8_cublas-hp_dtype0] SKIPPED 2025-09-09T14:43:15.1782058Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TORCH-True-mxfp8_cublas-hp_dtype1] SKIPPED 2025-09-09T14:43:15.1783524Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TORCH-True-mxfp4_cutlass-hp_dtype0] SKIPPED 2025-09-09T14:43:15.1784982Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TORCH-True-mxfp4_cutlass-hp_dtype1] SKIPPED 2025-09-09T14:43:15.1786560Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TRITON-False-mxfp8_emulated-hp_dtype0] SKIPPED 2025-09-09T14:43:15.1788142Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TRITON-False-mxfp8_emulated-hp_dtype1] SKIPPED 2025-09-09T14:43:35.3136403Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TRITON-False-mxfp4_emulated-hp_dtype0] SKIPPED 2025-09-09T14:43:35.3137948Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TRITON-False-mxfp4_emulated-hp_dtype1] SKIPPED 2025-09-09T14:43:35.3139447Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TRITON-False-mxfp8_cublas-hp_dtype0] SKIPPED 2025-09-09T14:43:35.3140971Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TRITON-False-mxfp8_cublas-hp_dtype1] SKIPPED 2025-09-09T14:43:35.3142471Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TRITON-False-mxfp4_cutlass-hp_dtype0] SKIPPED 2025-09-09T14:43:35.3143964Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TRITON-False-mxfp4_cutlass-hp_dtype1] SKIPPED 2025-09-09T14:43:35.3145444Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TRITON-True-mxfp8_emulated-hp_dtype0] SKIPPED 2025-09-09T14:43:35.3146933Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TRITON-True-mxfp8_emulated-hp_dtype1] SKIPPED 2025-09-09T14:43:35.3148425Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TRITON-True-mxfp4_emulated-hp_dtype0] SKIPPED 2025-09-09T14:43:35.3149912Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TRITON-True-mxfp4_emulated-hp_dtype1] SKIPPED 2025-09-09T14:43:35.3151403Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TRITON-True-mxfp8_cublas-hp_dtype0] SKIPPED 2025-09-09T14:43:35.3152879Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TRITON-True-mxfp8_cublas-hp_dtype1] SKIPPED 2025-09-09T14:43:35.3154349Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TRITON-True-mxfp4_cutlass-hp_dtype0] SKIPPED 2025-09-09T14:43:35.3155833Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.TRITON-True-mxfp4_cutlass-hp_dtype1] SKIPPED 2025-09-09T14:43:35.3157308Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.CUDA-False-mxfp8_emulated-hp_dtype0] SKIPPED 2025-09-09T14:43:35.3158853Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.CUDA-False-mxfp8_emulated-hp_dtype1] SKIPPED 2025-09-09T14:43:35.3160395Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.CUDA-False-mxfp4_emulated-hp_dtype0] SKIPPED 2025-09-09T14:43:35.3161867Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.CUDA-False-mxfp4_emulated-hp_dtype1] SKIPPED 2025-09-09T14:43:35.3163699Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.CUDA-False-mxfp8_cublas-hp_dtype0] SKIPPED 2025-09-09T14:43:35.3165170Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.CUDA-False-mxfp8_cublas-hp_dtype1] SKIPPED 2025-09-09T14:43:35.3166805Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.CUDA-False-mxfp4_cutlass-hp_dtype0] SKIPPED 2025-09-09T14:43:35.3168270Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.CUDA-False-mxfp4_cutlass-hp_dtype1] SKIPPED 2025-09-09T14:43:35.3169734Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.CUDA-True-mxfp8_emulated-hp_dtype0] SKIPPED 2025-09-09T14:43:35.3171203Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.CUDA-True-mxfp8_emulated-hp_dtype1] SKIPPED 2025-09-09T14:43:35.3172661Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.CUDA-True-mxfp4_emulated-hp_dtype0] SKIPPED 2025-09-09T14:43:35.3174134Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.CUDA-True-mxfp4_emulated-hp_dtype1] SKIPPED 2025-09-09T14:43:35.3175585Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.CUDA-True-mxfp8_cublas-hp_dtype0] SKIPPED 2025-09-09T14:43:35.3177022Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.CUDA-True-mxfp8_cublas-hp_dtype1] SKIPPED 2025-09-09T14:43:35.3178527Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.CUDA-True-mxfp4_cutlass-hp_dtype0] SKIPPED 2025-09-09T14:43:35.3179973Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.FLOOR-MXFP8Dim1CastKernelChoice.CUDA-True-mxfp4_cutlass-hp_dtype1] SKIPPED 2025-09-09T14:43:35.3181444Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TORCH-False-mxfp8_emulated-hp_dtype0] SKIPPED 2025-09-09T14:43:35.3182917Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TORCH-False-mxfp8_emulated-hp_dtype1] SKIPPED 2025-09-09T14:43:35.3184381Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TORCH-False-mxfp4_emulated-hp_dtype0] PASSED 2025-09-09T14:43:35.3185853Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TORCH-False-mxfp4_emulated-hp_dtype1] SKIPPED 2025-09-09T14:43:35.3187318Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TORCH-False-mxfp8_cublas-hp_dtype0] SKIPPED 2025-09-09T14:43:35.3188835Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TORCH-False-mxfp8_cublas-hp_dtype1] SKIPPED 2025-09-09T14:43:35.3190301Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TORCH-False-mxfp4_cutlass-hp_dtype0] SKIPPED 2025-09-09T14:43:35.3191772Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TORCH-False-mxfp4_cutlass-hp_dtype1] SKIPPED 2025-09-09T14:43:35.3193320Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TORCH-True-mxfp8_emulated-hp_dtype0] SKIPPED 2025-09-09T14:43:35.3194786Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TORCH-True-mxfp8_emulated-hp_dtype1] SKIPPED 2025-09-09T14:43:35.3197130Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TORCH-True-mxfp4_emulated-hp_dtype0] PASSED 2025-09-09T14:43:35.3198586Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TORCH-True-mxfp4_emulated-hp_dtype1] SKIPPED 2025-09-09T14:43:35.3200113Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TORCH-True-mxfp8_cublas-hp_dtype0] SKIPPED 2025-09-09T14:43:35.3201560Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TORCH-True-mxfp8_cublas-hp_dtype1] SKIPPED 2025-09-09T14:43:35.3203013Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TORCH-True-mxfp4_cutlass-hp_dtype0] SKIPPED 2025-09-09T14:43:35.3204478Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TORCH-True-mxfp4_cutlass-hp_dtype1] SKIPPED 2025-09-09T14:43:35.3205942Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TRITON-False-mxfp8_emulated-hp_dtype0] SKIPPED 2025-09-09T14:43:53.7122298Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TRITON-False-mxfp8_emulated-hp_dtype1] SKIPPED 2025-09-09T14:43:53.7123914Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TRITON-False-mxfp4_emulated-hp_dtype0] SKIPPED 2025-09-09T14:43:53.7125436Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TRITON-False-mxfp4_emulated-hp_dtype1] SKIPPED 2025-09-09T14:43:53.7126942Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TRITON-False-mxfp8_cublas-hp_dtype0] SKIPPED 2025-09-09T14:43:53.7128532Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TRITON-False-mxfp8_cublas-hp_dtype1] SKIPPED 2025-09-09T14:43:53.7130022Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TRITON-False-mxfp4_cutlass-hp_dtype0] SKIPPED 2025-09-09T14:43:53.7131521Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TRITON-False-mxfp4_cutlass-hp_dtype1] SKIPPED 2025-09-09T14:43:53.7133034Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TRITON-True-mxfp8_emulated-hp_dtype0] SKIPPED 2025-09-09T14:43:53.7134549Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TRITON-True-mxfp8_emulated-hp_dtype1] SKIPPED 2025-09-09T14:43:53.7136038Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TRITON-True-mxfp4_emulated-hp_dtype0] SKIPPED 2025-09-09T14:43:53.7137520Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TRITON-True-mxfp4_emulated-hp_dtype1] SKIPPED 2025-09-09T14:43:53.7139001Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TRITON-True-mxfp8_cublas-hp_dtype0] SKIPPED 2025-09-09T14:43:53.7140782Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TRITON-True-mxfp8_cublas-hp_dtype1] SKIPPED 2025-09-09T14:43:53.7142259Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TRITON-True-mxfp4_cutlass-hp_dtype0] SKIPPED 2025-09-09T14:43:53.7143902Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.TRITON-True-mxfp4_cutlass-hp_dtype1] SKIPPED 2025-09-09T14:43:53.7145380Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.CUDA-False-mxfp8_emulated-hp_dtype0] SKIPPED 2025-09-09T14:43:53.7146846Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.CUDA-False-mxfp8_emulated-hp_dtype1] SKIPPED 2025-09-09T14:43:53.7148326Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.CUDA-False-mxfp4_emulated-hp_dtype0] SKIPPED 2025-09-09T14:43:53.7149801Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.CUDA-False-mxfp4_emulated-hp_dtype1] SKIPPED 2025-09-09T14:43:53.7151273Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.CUDA-False-mxfp8_cublas-hp_dtype0] SKIPPED 2025-09-09T14:43:53.7152731Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.CUDA-False-mxfp8_cublas-hp_dtype1] SKIPPED 2025-09-09T14:43:53.7154191Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.CUDA-False-mxfp4_cutlass-hp_dtype0] SKIPPED 2025-09-09T14:43:53.7155657Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.CUDA-False-mxfp4_cutlass-hp_dtype1] SKIPPED 2025-09-09T14:43:53.7157121Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.CUDA-True-mxfp8_emulated-hp_dtype0] SKIPPED 2025-09-09T14:43:53.7158575Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.CUDA-True-mxfp8_emulated-hp_dtype1] SKIPPED 2025-09-09T14:43:53.7160130Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.CUDA-True-mxfp4_emulated-hp_dtype0] SKIPPED 2025-09-09T14:43:53.7161589Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.CUDA-True-mxfp4_emulated-hp_dtype1] SKIPPED 2025-09-09T14:43:53.7163047Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.CUDA-True-mxfp8_cublas-hp_dtype0] SKIPPED 2025-09-09T14:43:53.7164505Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.CUDA-True-mxfp8_cublas-hp_dtype1] SKIPPED 2025-09-09T14:43:53.7165964Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.CUDA-True-mxfp4_cutlass-hp_dtype0] SKIPPED 2025-09-09T14:43:53.7167413Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.CEIL-MXFP8Dim1CastKernelChoice.CUDA-True-mxfp4_cutlass-hp_dtype1] SKIPPED 2025-09-09T14:43:53.7168892Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TORCH-False-mxfp8_emulated-hp_dtype0] SKIPPED 2025-09-09T14:43:53.7170489Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TORCH-False-mxfp8_emulated-hp_dtype1] SKIPPED 2025-09-09T14:43:53.7171986Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TORCH-False-mxfp4_emulated-hp_dtype0] PASSED 2025-09-09T14:43:53.7173562Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TORCH-False-mxfp4_emulated-hp_dtype1] SKIPPED 2025-09-09T14:43:53.7175057Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TORCH-False-mxfp8_cublas-hp_dtype0] SKIPPED 2025-09-09T14:43:53.7176551Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TORCH-False-mxfp8_cublas-hp_dtype1] SKIPPED 2025-09-09T14:43:53.7178048Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TORCH-False-mxfp4_cutlass-hp_dtype0] SKIPPED 2025-09-09T14:43:53.7179562Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TORCH-False-mxfp4_cutlass-hp_dtype1] SKIPPED 2025-09-09T14:43:53.7181051Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TORCH-True-mxfp8_emulated-hp_dtype0] SKIPPED 2025-09-09T14:43:53.7182547Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TORCH-True-mxfp8_emulated-hp_dtype1] SKIPPED 2025-09-09T14:43:53.7184080Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TORCH-True-mxfp4_emulated-hp_dtype0] PASSED 2025-09-09T14:43:53.7185570Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TORCH-True-mxfp4_emulated-hp_dtype1] SKIPPED 2025-09-09T14:43:53.7187062Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TORCH-True-mxfp8_cublas-hp_dtype0] SKIPPED 2025-09-09T14:43:53.7188532Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TORCH-True-mxfp8_cublas-hp_dtype1] SKIPPED 2025-09-09T14:43:53.7190018Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TORCH-True-mxfp4_cutlass-hp_dtype0] SKIPPED 2025-09-09T14:43:53.7191506Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TORCH-True-mxfp4_cutlass-hp_dtype1] SKIPPED 2025-09-09T14:43:53.7654188Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TRITON-False-mxfp8_emulated-hp_dtype0] SKIPPED 2025-09-09T14:43:53.7655743Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TRITON-False-mxfp8_emulated-hp_dtype1] SKIPPED 2025-09-09T14:43:53.7657264Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TRITON-False-mxfp4_emulated-hp_dtype0] SKIPPED 2025-09-09T14:43:53.7658779Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TRITON-False-mxfp4_emulated-hp_dtype1] SKIPPED 2025-09-09T14:43:53.7660282Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TRITON-False-mxfp8_cublas-hp_dtype0] SKIPPED 2025-09-09T14:43:53.7661783Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TRITON-False-mxfp8_cublas-hp_dtype1] SKIPPED 2025-09-09T14:43:53.7663494Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TRITON-False-mxfp4_cutlass-hp_dtype0] SKIPPED 2025-09-09T14:43:53.7665015Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TRITON-False-mxfp4_cutlass-hp_dtype1] SKIPPED 2025-09-09T14:43:53.7666644Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TRITON-True-mxfp8_emulated-hp_dtype0] SKIPPED 2025-09-09T14:43:53.7668140Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TRITON-True-mxfp8_emulated-hp_dtype1] SKIPPED 2025-09-09T14:43:53.7669639Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TRITON-True-mxfp4_emulated-hp_dtype0] SKIPPED 2025-09-09T14:43:53.7671141Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TRITON-True-mxfp4_emulated-hp_dtype1] SKIPPED 2025-09-09T14:43:53.7672625Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TRITON-True-mxfp8_cublas-hp_dtype0] SKIPPED 2025-09-09T14:43:53.7674119Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TRITON-True-mxfp8_cublas-hp_dtype1] SKIPPED 2025-09-09T14:43:53.7675604Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TRITON-True-mxfp4_cutlass-hp_dtype0] SKIPPED 2025-09-09T14:43:53.7677091Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.TRITON-True-mxfp4_cutlass-hp_dtype1] SKIPPED 2025-09-09T14:43:53.7678603Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.CUDA-False-mxfp8_emulated-hp_dtype0] SKIPPED 2025-09-09T14:43:53.7688107Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.CUDA-False-mxfp8_emulated-hp_dtype1] SKIPPED 2025-09-09T14:43:53.7689632Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.CUDA-False-mxfp4_emulated-hp_dtype0] SKIPPED 2025-09-09T14:43:53.7691125Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.CUDA-False-mxfp4_emulated-hp_dtype1] SKIPPED 2025-09-09T14:43:53.7692614Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.CUDA-False-mxfp8_cublas-hp_dtype0] SKIPPED 2025-09-09T14:43:53.7694101Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.CUDA-False-mxfp8_cublas-hp_dtype1] SKIPPED 2025-09-09T14:43:53.7695577Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.CUDA-False-mxfp4_cutlass-hp_dtype0] SKIPPED 2025-09-09T14:43:53.7697069Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.CUDA-False-mxfp4_cutlass-hp_dtype1] SKIPPED 2025-09-09T14:43:53.7698551Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.CUDA-True-mxfp8_emulated-hp_dtype0] SKIPPED 2025-09-09T14:43:53.7700040Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.CUDA-True-mxfp8_emulated-hp_dtype1] SKIPPED 2025-09-09T14:43:53.7701636Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.CUDA-True-mxfp4_emulated-hp_dtype0] SKIPPED 2025-09-09T14:43:53.7703117Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.CUDA-True-mxfp4_emulated-hp_dtype1] SKIPPED 2025-09-09T14:43:53.7704724Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.CUDA-True-mxfp8_cublas-hp_dtype0] SKIPPED 2025-09-09T14:43:53.7706186Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.CUDA-True-mxfp8_cublas-hp_dtype1] SKIPPED 2025-09-09T14:43:53.7707653Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.CUDA-True-mxfp4_cutlass-hp_dtype0] SKIPPED 2025-09-09T14:43:53.7709136Z test/prototype/mx_formats/test_mx_linear.py::test_linear_compile[ScaleCalculationMode.RCEIL-MXFP8Dim1CastKernelChoice.CUDA-True-mxfp4_cutlass-hp_dtype1] SKIPPED 2025-09-09T14:43:53.7710161Z test/prototype/mx_formats/test_mx_linear.py::test_filter_fn PASSED 2025-09-09T14:43:53.7710803Z test/prototype/mx_formats/test_mx_linear.py::test_training_print_str PASSED 2025-09-09T14:43:53.7711550Z test/prototype/mx_formats/test_mx_mm.py::test_matrix_multiplication[fp8-128x128x128] SKIPPED 2025-09-09T14:43:53.7712330Z test/prototype/mx_formats/test_mx_mm.py::test_matrix_multiplication[fp8-256x256x256] SKIPPED 2025-09-09T14:43:53.7713110Z test/prototype/mx_formats/test_mx_mm.py::test_matrix_multiplication[fp8-384x384x384] SKIPPED 2025-09-09T14:43:53.7713892Z test/prototype/mx_formats/test_mx_mm.py::test_matrix_multiplication[fp8-512x512x512] SKIPPED 2025-09-09T14:43:53.7714653Z test/prototype/mx_formats/test_mx_mm.py::test_matrix_multiplication[fp8-768x768x768] SKIPPED 2025-09-09T14:43:53.7715449Z test/prototype/mx_formats/test_mx_mm.py::test_matrix_multiplication[fp8-1024x1024x1024] SKIPPED 2025-09-09T14:43:53.7716247Z test/prototype/mx_formats/test_mx_mm.py::test_matrix_multiplication[fp8-8192x8192x8192] SKIPPED 2025-09-09T14:43:53.7717022Z test/prototype/mx_formats/test_mx_mm.py::test_matrix_multiplication[fp8-128x256x384] SKIPPED 2025-09-09T14:43:53.7717784Z test/prototype/mx_formats/test_mx_mm.py::test_matrix_multiplication[fp8-256x384x512] SKIPPED 2025-09-09T14:43:53.7718546Z test/prototype/mx_formats/test_mx_mm.py::test_matrix_multiplication[fp8-129x256x384] SKIPPED 2025-09-09T14:43:53.7719358Z test/prototype/mx_formats/test_mx_mm.py::test_matrix_multiplication[fp8-133x512x528] SKIPPED 2025-09-09T14:43:53.7720118Z test/prototype/mx_formats/test_mx_mm.py::test_matrix_multiplication[fp4-128x128x128] SKIPPED 2025-09-09T14:43:53.7720877Z test/prototype/mx_formats/test_mx_mm.py::test_matrix_multiplication[fp4-256x256x256] SKIPPED 2025-09-09T14:43:53.7721646Z test/prototype/mx_formats/test_mx_mm.py::test_matrix_multiplication[fp4-384x384x384] SKIPPED 2025-09-09T14:43:53.7722590Z test/prototype/mx_formats/test_mx_mm.py::test_matrix_multiplication[fp4-512x512x512] SKIPPED 2025-09-09T14:43:53.7723407Z test/prototype/mx_formats/test_mx_mm.py::test_matrix_multiplication[fp4-768x768x768] SKIPPED 2025-09-09T14:43:53.7724183Z test/prototype/mx_formats/test_mx_mm.py::test_matrix_multiplication[fp4-1024x1024x1024] SKIPPED 2025-09-09T14:43:53.7724959Z test/prototype/mx_formats/test_mx_mm.py::test_matrix_multiplication[fp4-8192x8192x8192] SKIPPED 2025-09-09T14:43:53.7725733Z test/prototype/mx_formats/test_mx_mm.py::test_matrix_multiplication[fp4-128x256x384] SKIPPED 2025-09-09T14:43:53.7726493Z test/prototype/mx_formats/test_mx_mm.py::test_matrix_multiplication[fp4-256x384x512] SKIPPED 2025-09-09T14:43:53.7727243Z test/prototype/mx_formats/test_mx_mm.py::test_matrix_multiplication[fp4-129x256x384] SKIPPED 2025-09-09T14:43:53.7728143Z test/prototype/mx_formats/test_mx_mm.py::test_matrix_multiplication[fp4-133x512x528] SKIPPED 2025-09-09T14:43:53.7729026Z test/prototype/mx_formats/test_mx_tensor.py::test_hello_world[elem_dtype0] PASSED 2025-09-09T14:43:53.7729974Z test/prototype/mx_formats/test_mx_tensor.py::test_hello_world[elem_dtype1] PASSED 2025-09-09T14:43:53.7730793Z test/prototype/mx_formats/test_mx_tensor.py::test_hello_world[fp6_e2m3] PASSED 2025-09-09T14:43:57.7809560Z test/prototype/mx_formats/test_mx_tensor.py::test_hello_world[fp6_e3m2] PASSED 2025-09-09T14:43:57.7810287Z test/prototype/mx_formats/test_mx_tensor.py::test_hello_world[elem_dtype4] PASSED 2025-09-09T14:43:57.7811154Z test/prototype/mx_formats/test_mx_tensor.py::test_realistic_numerics[elem_dtype0-ScaleCalculationMode.FLOOR] PASSED 2025-09-09T14:43:57.7812122Z test/prototype/mx_formats/test_mx_tensor.py::test_realistic_numerics[elem_dtype0-ScaleCalculationMode.RCEIL] PASSED 2025-09-09T14:43:57.7813116Z test/prototype/mx_formats/test_mx_tensor.py::test_realistic_numerics[elem_dtype0-ScaleCalculationMode.CEIL] PASSED 2025-09-09T14:43:57.7814089Z test/prototype/mx_formats/test_mx_tensor.py::test_realistic_numerics[elem_dtype0-ScaleCalculationMode.EVEN] PASSED 2025-09-09T14:43:57.7815067Z test/prototype/mx_formats/test_mx_tensor.py::test_realistic_numerics[elem_dtype1-ScaleCalculationMode.FLOOR] PASSED 2025-09-09T14:43:57.7816038Z test/prototype/mx_formats/test_mx_tensor.py::test_realistic_numerics[elem_dtype1-ScaleCalculationMode.RCEIL] PASSED 2025-09-09T14:43:57.7817002Z test/prototype/mx_formats/test_mx_tensor.py::test_realistic_numerics[elem_dtype1-ScaleCalculationMode.CEIL] PASSED 2025-09-09T14:43:57.7817974Z test/prototype/mx_formats/test_mx_tensor.py::test_realistic_numerics[elem_dtype1-ScaleCalculationMode.EVEN] PASSED 2025-09-09T14:43:57.7818932Z test/prototype/mx_formats/test_mx_tensor.py::test_realistic_numerics[fp6_e2m3-ScaleCalculationMode.FLOOR] PASSED 2025-09-09T14:43:57.7819875Z test/prototype/mx_formats/test_mx_tensor.py::test_realistic_numerics[fp6_e2m3-ScaleCalculationMode.RCEIL] PASSED 2025-09-09T14:43:57.7820820Z test/prototype/mx_formats/test_mx_tensor.py::test_realistic_numerics[fp6_e2m3-ScaleCalculationMode.CEIL] PASSED 2025-09-09T14:43:57.7821751Z test/prototype/mx_formats/test_mx_tensor.py::test_realistic_numerics[fp6_e2m3-ScaleCalculationMode.EVEN] PASSED 2025-09-09T14:43:57.7823176Z test/prototype/mx_formats/test_mx_tensor.py::test_realistic_numerics[fp6_e3m2-ScaleCalculationMode.FLOOR] PASSED 2025-09-09T14:43:57.7824253Z test/prototype/mx_formats/test_mx_tensor.py::test_realistic_numerics[fp6_e3m2-ScaleCalculationMode.RCEIL] PASSED 2025-09-09T14:43:57.7825182Z test/prototype/mx_formats/test_mx_tensor.py::test_realistic_numerics[fp6_e3m2-ScaleCalculationMode.CEIL] PASSED 2025-09-09T14:43:57.7826124Z test/prototype/mx_formats/test_mx_tensor.py::test_realistic_numerics[fp6_e3m2-ScaleCalculationMode.EVEN] PASSED 2025-09-09T14:43:57.7827077Z test/prototype/mx_formats/test_mx_tensor.py::test_realistic_numerics[elem_dtype4-ScaleCalculationMode.FLOOR] PASSED 2025-09-09T14:43:57.7828044Z test/prototype/mx_formats/test_mx_tensor.py::test_realistic_numerics[elem_dtype4-ScaleCalculationMode.RCEIL] PASSED 2025-09-09T14:43:57.7829006Z test/prototype/mx_formats/test_mx_tensor.py::test_realistic_numerics[elem_dtype4-ScaleCalculationMode.CEIL] PASSED 2025-09-09T14:43:57.7829963Z test/prototype/mx_formats/test_mx_tensor.py::test_realistic_numerics[elem_dtype4-ScaleCalculationMode.EVEN] PASSED 2025-09-09T14:43:57.7830774Z test/prototype/mx_formats/test_mx_tensor.py::test_all_zeros[elem_dtype0] PASSED 2025-09-09T14:43:57.7831449Z test/prototype/mx_formats/test_mx_tensor.py::test_all_zeros[elem_dtype1] PASSED 2025-09-09T14:43:57.7832125Z test/prototype/mx_formats/test_mx_tensor.py::test_all_zeros[fp6_e2m3] PASSED 2025-09-09T14:43:57.7833073Z test/prototype/mx_formats/test_mx_tensor.py::test_all_zeros[fp6_e3m2] PASSED 2025-09-09T14:43:57.7833745Z test/prototype/mx_formats/test_mx_tensor.py::test_all_zeros[elem_dtype4] PASSED 2025-09-09T14:43:57.7834581Z test/prototype/mx_formats/test_mx_tensor.py::test_some_zeros[elem_dtype0] PASSED 2025-09-09T14:43:57.7835260Z test/prototype/mx_formats/test_mx_tensor.py::test_some_zeros[elem_dtype1] PASSED 2025-09-09T14:43:57.7835920Z test/prototype/mx_formats/test_mx_tensor.py::test_some_zeros[fp6_e2m3] PASSED 2025-09-09T14:43:57.7836575Z test/prototype/mx_formats/test_mx_tensor.py::test_some_zeros[fp6_e3m2] PASSED 2025-09-09T14:43:57.7837232Z test/prototype/mx_formats/test_mx_tensor.py::test_some_zeros[elem_dtype4] PASSED 2025-09-09T14:43:57.7837877Z test/prototype/mx_formats/test_mx_tensor.py::test_to_mx_rceil SKIPPED 2025-09-09T14:43:57.7838550Z test/prototype/mx_formats/test_mx_tensor.py::test_exponent_nan_in[elem_dtype0] PASSED 2025-09-09T14:43:57.7839360Z test/prototype/mx_formats/test_mx_tensor.py::test_exponent_nan_in[elem_dtype1] PASSED 2025-09-09T14:43:57.7840067Z test/prototype/mx_formats/test_mx_tensor.py::test_exponent_nan_in[fp6_e2m3] PASSED 2025-09-09T14:43:57.7840762Z test/prototype/mx_formats/test_mx_tensor.py::test_exponent_nan_in[fp6_e3m2] PASSED 2025-09-09T14:43:57.7841471Z test/prototype/mx_formats/test_mx_tensor.py::test_exponent_nan_in[elem_dtype4] PASSED 2025-09-09T14:43:57.7842217Z test/prototype/mx_formats/test_mx_tensor.py::test_exponent_nan_out[False-elem_dtype0] PASSED 2025-09-09T14:43:57.7842985Z test/prototype/mx_formats/test_mx_tensor.py::test_exponent_nan_out[False-elem_dtype1] PASSED 2025-09-09T14:43:57.7843743Z test/prototype/mx_formats/test_mx_tensor.py::test_exponent_nan_out[False-fp6_e2m3] PASSED 2025-09-09T14:43:57.7844538Z test/prototype/mx_formats/test_mx_tensor.py::test_exponent_nan_out[False-fp6_e3m2] PASSED 2025-09-09T14:43:57.7845299Z test/prototype/mx_formats/test_mx_tensor.py::test_exponent_nan_out[False-elem_dtype4] PASSED 2025-09-09T14:43:57.7846066Z test/prototype/mx_formats/test_mx_tensor.py::test_exponent_nan_out[True-elem_dtype0] SKIPPED 2025-09-09T14:43:57.7846828Z test/prototype/mx_formats/test_mx_tensor.py::test_exponent_nan_out[True-elem_dtype1] SKIPPED 2025-09-09T14:43:57.7847579Z test/prototype/mx_formats/test_mx_tensor.py::test_exponent_nan_out[True-fp6_e2m3] PASSED 2025-09-09T14:43:57.7848318Z test/prototype/mx_formats/test_mx_tensor.py::test_exponent_nan_out[True-fp6_e3m2] PASSED 2025-09-09T14:43:57.7849071Z test/prototype/mx_formats/test_mx_tensor.py::test_exponent_nan_out[True-elem_dtype4] SKIPPED 2025-09-09T14:43:57.7849775Z test/prototype/mx_formats/test_mx_tensor.py::test_ranks[elem_dtype0] PASSED 2025-09-09T14:43:57.7850422Z test/prototype/mx_formats/test_mx_tensor.py::test_ranks[elem_dtype1] PASSED 2025-09-09T14:43:57.7851068Z test/prototype/mx_formats/test_mx_tensor.py::test_ranks[fp6_e2m3] PASSED 2025-09-09T14:43:57.7851689Z test/prototype/mx_formats/test_mx_tensor.py::test_ranks[fp6_e3m2] PASSED 2025-09-09T14:43:57.7852331Z test/prototype/mx_formats/test_mx_tensor.py::test_ranks[elem_dtype4] PASSED 2025-09-09T14:43:57.7853000Z test/prototype/mx_formats/test_mx_tensor.py::test_block_sizes[1-elem_dtype0] PASSED 2025-09-09T14:43:57.7853706Z test/prototype/mx_formats/test_mx_tensor.py::test_block_sizes[1-elem_dtype1] PASSED 2025-09-09T14:43:57.7854400Z test/prototype/mx_formats/test_mx_tensor.py::test_block_sizes[1-fp6_e2m3] SKIPPED 2025-09-09T14:43:57.7855132Z test/prototype/mx_formats/test_mx_tensor.py::test_block_sizes[1-fp6_e3m2] SKIPPED 2025-09-09T14:43:57.7855833Z test/prototype/mx_formats/test_mx_tensor.py::test_block_sizes[1-elem_dtype4] SKIPPED 2025-09-09T14:43:57.7856535Z test/prototype/mx_formats/test_mx_tensor.py::test_block_sizes[4-elem_dtype0] PASSED 2025-09-09T14:43:57.7857332Z test/prototype/mx_formats/test_mx_tensor.py::test_block_sizes[4-elem_dtype1] PASSED 2025-09-09T14:43:57.7858025Z test/prototype/mx_formats/test_mx_tensor.py::test_block_sizes[4-fp6_e2m3] PASSED 2025-09-09T14:43:57.7859653Z test/prototype/mx_formats/test_mx_tensor.py::test_block_sizes[4-fp6_e3m2] PASSED 2025-09-09T14:43:57.7860342Z test/prototype/mx_formats/test_mx_tensor.py::test_block_sizes[4-elem_dtype4] PASSED 2025-09-09T14:43:57.7861045Z test/prototype/mx_formats/test_mx_tensor.py::test_block_sizes[32-elem_dtype0] PASSED 2025-09-09T14:43:57.7861753Z test/prototype/mx_formats/test_mx_tensor.py::test_block_sizes[32-elem_dtype1] PASSED 2025-09-09T14:43:57.7862444Z test/prototype/mx_formats/test_mx_tensor.py::test_block_sizes[32-fp6_e2m3] PASSED 2025-09-09T14:43:57.7863127Z test/prototype/mx_formats/test_mx_tensor.py::test_block_sizes[32-fp6_e3m2] PASSED 2025-09-09T14:43:57.7863828Z test/prototype/mx_formats/test_mx_tensor.py::test_block_sizes[32-elem_dtype4] PASSED 2025-09-09T14:43:57.7864507Z test/prototype/mx_formats/test_mx_tensor.py::test_transpose[elem_dtype0] PASSED 2025-09-09T14:43:57.7865186Z test/prototype/mx_formats/test_mx_tensor.py::test_transpose[elem_dtype1] PASSED 2025-09-09T14:43:57.7865841Z test/prototype/mx_formats/test_mx_tensor.py::test_transpose[fp6_e2m3] PASSED 2025-09-09T14:43:57.7866492Z test/prototype/mx_formats/test_mx_tensor.py::test_transpose[fp6_e3m2] PASSED 2025-09-09T14:43:57.7867159Z test/prototype/mx_formats/test_mx_tensor.py::test_transpose[elem_dtype4] PASSED 2025-09-09T14:43:57.7867807Z test/prototype/mx_formats/test_mx_tensor.py::test_view[elem_dtype0] PASSED 2025-09-09T14:43:57.7868448Z test/prototype/mx_formats/test_mx_tensor.py::test_view[elem_dtype1] PASSED 2025-09-09T14:43:57.7869073Z test/prototype/mx_formats/test_mx_tensor.py::test_view[fp6_e2m3] PASSED 2025-09-09T14:43:57.7869701Z test/prototype/mx_formats/test_mx_tensor.py::test_view[fp6_e3m2] PASSED 2025-09-09T14:43:57.7870330Z test/prototype/mx_formats/test_mx_tensor.py::test_view[elem_dtype4] PASSED 2025-09-09T14:43:57.7871014Z test/prototype/mx_formats/test_mx_tensor.py::test_fp6_packing[False-fp6_e2m3] PASSED 2025-09-09T14:43:57.7871728Z test/prototype/mx_formats/test_mx_tensor.py::test_fp6_packing[False-fp6_e3m2] PASSED 2025-09-09T14:43:57.7872430Z test/prototype/mx_formats/test_mx_tensor.py::test_fp6_packing[True-fp6_e2m3] PASSED 2025-09-09T14:43:57.7873130Z test/prototype/mx_formats/test_mx_tensor.py::test_fp6_packing[True-fp6_e3m2] PASSED 2025-09-09T14:44:13.3629090Z test/prototype/mx_formats/test_mx_tensor.py::test_to_mx_from_mx_compile_numerics[False-hp_dtype0-elem_dtype0] SKIPPED 2025-09-09T14:44:13.3630080Z test/prototype/mx_formats/test_mx_tensor.py::test_to_mx_from_mx_compile_numerics[False-hp_dtype0-elem_dtype1] SKIPPED 2025-09-09T14:44:13.3631040Z test/prototype/mx_formats/test_mx_tensor.py::test_to_mx_from_mx_compile_numerics[False-hp_dtype0-fp6_e2m3] PASSED 2025-09-09T14:44:13.3631943Z test/prototype/mx_formats/test_mx_tensor.py::test_to_mx_from_mx_compile_numerics[False-hp_dtype0-fp6_e3m2] PASSED 2025-09-09T14:44:13.3632889Z test/prototype/mx_formats/test_mx_tensor.py::test_to_mx_from_mx_compile_numerics[False-hp_dtype0-elem_dtype4] PASSED 2025-09-09T14:44:13.3633818Z test/prototype/mx_formats/test_mx_tensor.py::test_to_mx_from_mx_compile_numerics[False-hp_dtype1-elem_dtype0] SKIPPED 2025-09-09T14:44:13.3634762Z test/prototype/mx_formats/test_mx_tensor.py::test_to_mx_from_mx_compile_numerics[False-hp_dtype1-elem_dtype1] SKIPPED 2025-09-09T14:44:13.3635685Z test/prototype/mx_formats/test_mx_tensor.py::test_to_mx_from_mx_compile_numerics[False-hp_dtype1-fp6_e2m3] PASSED 2025-09-09T14:44:13.3636839Z test/prototype/mx_formats/test_mx_tensor.py::test_to_mx_from_mx_compile_numerics[False-hp_dtype1-fp6_e3m2] PASSED 2025-09-09T14:44:13.3637774Z test/prototype/mx_formats/test_mx_tensor.py::test_to_mx_from_mx_compile_numerics[False-hp_dtype1-elem_dtype4] PASSED 2025-09-09T14:44:13.3638887Z test/prototype/mx_formats/test_mx_tensor.py::test_to_mx_from_mx_compile_numerics[True-hp_dtype0-elem_dtype0] SKIPPED 2025-09-09T14:44:13.3639952Z test/prototype/mx_formats/test_mx_tensor.py::test_to_mx_from_mx_compile_numerics[True-hp_dtype0-elem_dtype1] SKIPPED 2025-09-09T14:44:13.3640896Z test/prototype/mx_formats/test_mx_tensor.py::test_to_mx_from_mx_compile_numerics[True-hp_dtype0-fp6_e2m3] PASSED 2025-09-09T14:44:13.3641792Z test/prototype/mx_formats/test_mx_tensor.py::test_to_mx_from_mx_compile_numerics[True-hp_dtype0-fp6_e3m2] PASSED 2025-09-09T14:44:13.3642711Z test/prototype/mx_formats/test_mx_tensor.py::test_to_mx_from_mx_compile_numerics[True-hp_dtype0-elem_dtype4] PASSED 2025-09-09T14:44:13.3643650Z test/prototype/mx_formats/test_mx_tensor.py::test_to_mx_from_mx_compile_numerics[True-hp_dtype1-elem_dtype0] SKIPPED 2025-09-09T14:44:13.3644582Z test/prototype/mx_formats/test_mx_tensor.py::test_to_mx_from_mx_compile_numerics[True-hp_dtype1-elem_dtype1] SKIPPED 2025-09-09T14:44:13.3645509Z test/prototype/mx_formats/test_mx_tensor.py::test_to_mx_from_mx_compile_numerics[True-hp_dtype1-fp6_e2m3] PASSED 2025-09-09T14:44:13.3646407Z test/prototype/mx_formats/test_mx_tensor.py::test_to_mx_from_mx_compile_numerics[True-hp_dtype1-fp6_e3m2] PASSED 2025-09-09T14:44:13.3647329Z test/prototype/mx_formats/test_mx_tensor.py::test_to_mx_from_mx_compile_numerics[True-hp_dtype1-elem_dtype4] PASSED 2025-09-09T14:44:13.3648164Z test/prototype/mx_formats/test_mx_tensor.py::test_to_mx_inductor_single_kernel SKIPPED 2025-09-09T14:44:13.3648952Z test/prototype/mx_formats/test_mx_tensor.py::test_cast_to_float8_e4m3fn_saturation_behavior SKIPPED 2025-09-09T14:44:13.3649798Z test/prototype/mx_formats/test_mx_tensor.py::test_nvfp4_reconstruction[dtype0-shape0-False] PASSED 2025-09-09T14:44:13.3650641Z test/prototype/mx_formats/test_mx_tensor.py::test_nvfp4_reconstruction[dtype1-shape1-False] PASSED 2025-09-09T14:44:13.3651480Z test/prototype/mx_formats/test_mx_tensor.py::test_nvfp4_reconstruction[dtype2-shape2-False] PASSED 2025-09-09T14:44:13.3652315Z test/prototype/mx_formats/test_mx_tensor.py::test_nvfp4_reconstruction[dtype3-shape3-True] PASSED 2025-09-09T14:44:13.3653165Z test/prototype/mx_formats/test_mx_tensor.py::test_to_blocked_from_blocked_roundtrip[False-shape0] PASSED 2025-09-09T14:44:13.3654040Z test/prototype/mx_formats/test_mx_tensor.py::test_to_blocked_from_blocked_roundtrip[False-shape1] PASSED 2025-09-09T14:44:13.3654907Z test/prototype/mx_formats/test_mx_tensor.py::test_to_blocked_from_blocked_roundtrip[False-shape2] PASSED 2025-09-09T14:44:13.3655774Z test/prototype/mx_formats/test_mx_tensor.py::test_to_blocked_from_blocked_roundtrip[False-shape3] PASSED 2025-09-09T14:44:13.3656641Z test/prototype/mx_formats/test_mx_tensor.py::test_to_blocked_from_blocked_roundtrip[False-shape4] PASSED 2025-09-09T14:44:13.3657505Z test/prototype/mx_formats/test_mx_tensor.py::test_to_blocked_from_blocked_roundtrip[False-shape5] PASSED 2025-09-09T14:44:13.3658363Z test/prototype/mx_formats/test_mx_tensor.py::test_to_blocked_from_blocked_roundtrip[True-shape0] PASSED 2025-09-09T14:44:13.3659222Z test/prototype/mx_formats/test_mx_tensor.py::test_to_blocked_from_blocked_roundtrip[True-shape1] PASSED 2025-09-09T14:44:13.3660070Z test/prototype/mx_formats/test_mx_tensor.py::test_to_blocked_from_blocked_roundtrip[True-shape2] PASSED 2025-09-09T14:44:13.3660923Z test/prototype/mx_formats/test_mx_tensor.py::test_to_blocked_from_blocked_roundtrip[True-shape3] PASSED 2025-09-09T14:44:13.3661769Z test/prototype/mx_formats/test_mx_tensor.py::test_to_blocked_from_blocked_roundtrip[True-shape4] PASSED 2025-09-09T14:44:13.3662717Z test/prototype/mx_formats/test_mx_tensor.py::test_to_blocked_from_blocked_roundtrip[True-shape5] PASSED 2025-09-09T14:44:13.3663589Z test/prototype/mx_formats/test_mx_tensor.py::test_nvfp4_swizzled_scales_construction[shape0-False] PASSED 2025-09-09T14:44:13.3664547Z test/prototype/mx_formats/test_mx_tensor.py::test_nvfp4_swizzled_scales_construction[shape0-True] PASSED 2025-09-09T14:44:13.3665432Z test/prototype/mx_formats/test_mx_tensor.py::test_nvfp4_swizzled_scales_construction[shape1-False] PASSED 2025-09-09T14:44:13.3666308Z test/prototype/mx_formats/test_mx_tensor.py::test_nvfp4_swizzled_scales_construction[shape1-True] PASSED 2025-09-09T14:44:13.3667190Z test/prototype/mx_formats/test_mx_tensor.py::test_nvfp4_swizzled_scales_construction[shape2-False] PASSED 2025-09-09T14:44:13.3668073Z test/prototype/mx_formats/test_mx_tensor.py::test_nvfp4_swizzled_scales_construction[shape2-True] PASSED 2025-09-09T14:44:13.3668981Z test/prototype/mx_formats/test_mx_tensor.py::test_nvfp4_swizzled_scales_construction[shape3-False] PASSED 2025-09-09T14:44:13.3669894Z test/prototype/mx_formats/test_mx_tensor.py::test_nvfp4_swizzled_scales_construction[shape3-True] PASSED 2025-09-09T14:44:13.3670776Z test/prototype/mx_formats/test_mx_tensor.py::test_nvfp4_swizzled_scales_slicing[slice_rows[0:128]] PASSED 2025-09-09T14:44:13.3671658Z test/prototype/mx_formats/test_mx_tensor.py::test_nvfp4_swizzled_scales_slicing[slice_rows[128:256]] PASSED 2025-09-09T14:44:13.3672536Z test/prototype/mx_formats/test_mx_tensor.py::test_nvfp4_swizzled_scales_slicing[slice_cols[0:64]] PASSED 2025-09-09T14:44:13.3673410Z test/prototype/mx_formats/test_mx_tensor.py::test_nvfp4_swizzled_scales_slicing[slice_cols[64:128]] PASSED 2025-09-09T14:44:13.3674344Z test/prototype/mx_formats/test_mx_tensor.py::test_nvfp4_swizzled_scales_slicing[slice_cols[0:128]_full_width] PASSED 2025-09-09T14:44:13.3675333Z test/prototype/mx_formats/test_mx_tensor.py::test_nvfp4_swizzled_scales_slicing[slice_cols[0:2048]_tp_first_half] PASSED 2025-09-09T14:44:13.3676321Z test/prototype/mx_formats/test_mx_tensor.py::test_nvfp4_swizzled_scales_slicing[slice_cols[2048:4096]_tp_second_half] PASSED 2025-09-09T14:44:13.3677301Z test/prototype/mx_formats/test_mx_tensor.py::test_nvfp4_swizzled_scales_slicing[slice_cols[0:1024]_quarter] PASSED 2025-09-09T14:44:13.3678247Z test/prototype/mx_formats/test_mx_tensor.py::test_nvfp4_swizzled_scales_slicing[slice_cols[1024:2048]_quarter] PASSED 2025-09-09T14:44:13.3679256Z test/prototype/mx_formats/test_mx_tensor.py::test_nvfp4_swizzled_scales_slicing_errors[misaligned_row_end] PASSED 2025-09-09T14:44:13.3680219Z test/prototype/mx_formats/test_mx_tensor.py::test_nvfp4_swizzled_scales_slicing_errors[misaligned_row_start] PASSED 2025-09-09T14:44:13.3681166Z test/prototype/mx_formats/test_mx_tensor.py::test_nvfp4_swizzled_scales_slicing_errors[misaligned_col_32] PASSED 2025-09-09T14:44:13.3682104Z test/prototype/mx_formats/test_mx_tensor.py::test_nvfp4_swizzled_scales_slicing_errors[misaligned_col_start] PASSED 2025-09-09T14:44:13.3683047Z test/prototype/mx_formats/test_mx_tensor.py::test_nvfp4_swizzled_scales_slicing_errors[misaligned_col_end] PASSED 2025-09-09T14:44:13.3683950Z test/prototype/mx_formats/test_mx_tensor.py::test_nvfp4_swizzled_scales_slicing_errors[odd_start] PASSED 2025-09-09T14:44:13.3684825Z test/prototype/mx_formats/test_mx_tensor.py::test_nvfp4_swizzled_scales_slicing_errors[odd_end] PASSED 2025-09-09T14:44:13.3685641Z test/prototype/mx_formats/test_mx_tensor.py::test_nvfp4_swizzled_scales_view_semantics PASSED 2025-09-09T14:44:13.3686430Z test/prototype/mx_formats/test_mx_tensor.py::test_nvfp4_swizzled_scales_serialization PASSED 2025-09-09T14:44:13.3687229Z test/prototype/mx_formats/test_mx_tensor.py::test_nvfp4_swizzled_scales_get_scales_method PASSED 2025-09-09T14:44:13.3688227Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N64-M128] SKIPPED 2025-09-09T14:44:13.3689292Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N64-M256] SKIPPED 2025-09-09T14:44:13.3690263Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N64-M512] SKIPPED 2025-09-09T14:44:13.3691253Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N64-M1024] SKIPPED 2025-09-09T14:44:13.3938955Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N64-M100] SKIPPED 2025-09-09T14:44:13.3939952Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N64-M200] SKIPPED 2025-09-09T14:44:13.3940931Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N64-M384] SKIPPED 2025-09-09T14:44:13.3941908Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N128-M128] SKIPPED 2025-09-09T14:44:13.3942892Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N128-M256] SKIPPED 2025-09-09T14:44:13.3943871Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N128-M512] SKIPPED 2025-09-09T14:44:13.3944854Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N128-M1024] SKIPPED 2025-09-09T14:44:13.3945840Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N128-M100] SKIPPED 2025-09-09T14:44:13.3946820Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N128-M200] SKIPPED 2025-09-09T14:44:13.3947799Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N128-M384] SKIPPED 2025-09-09T14:44:13.3948783Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N256-M128] SKIPPED 2025-09-09T14:44:13.3949754Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N256-M256] SKIPPED 2025-09-09T14:44:13.3950735Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N256-M512] SKIPPED 2025-09-09T14:44:13.3951725Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N256-M1024] SKIPPED 2025-09-09T14:44:13.3952702Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N256-M100] SKIPPED 2025-09-09T14:44:13.3953688Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N256-M200] SKIPPED 2025-09-09T14:44:13.3954673Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N256-M384] SKIPPED 2025-09-09T14:44:13.3955650Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N512-M128] SKIPPED 2025-09-09T14:44:13.3956631Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N512-M256] SKIPPED 2025-09-09T14:44:13.3957604Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N512-M512] SKIPPED 2025-09-09T14:44:13.3958596Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N512-M1024] SKIPPED 2025-09-09T14:44:13.3959658Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N512-M100] SKIPPED 2025-09-09T14:44:13.3960822Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N512-M200] SKIPPED 2025-09-09T14:44:13.3961918Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N512-M384] SKIPPED 2025-09-09T14:44:13.3962909Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N32-M128] SKIPPED 2025-09-09T14:44:13.3963885Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N32-M256] SKIPPED 2025-09-09T14:44:13.3964860Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N32-M512] SKIPPED 2025-09-09T14:44:13.3965836Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N32-M1024] SKIPPED 2025-09-09T14:44:13.3966823Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N32-M100] SKIPPED 2025-09-09T14:44:13.3967803Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N32-M200] SKIPPED 2025-09-09T14:44:13.3968784Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N32-M384] SKIPPED 2025-09-09T14:44:13.3969810Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N96-M128] SKIPPED 2025-09-09T14:44:13.3970784Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N96-M256] SKIPPED 2025-09-09T14:44:13.3971774Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N96-M512] SKIPPED 2025-09-09T14:44:13.3972757Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N96-M1024] SKIPPED 2025-09-09T14:44:13.3973749Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N96-M100] SKIPPED 2025-09-09T14:44:13.3974735Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N96-M200] SKIPPED 2025-09-09T14:44:13.3975711Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N96-M384] SKIPPED 2025-09-09T14:44:13.3976690Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N160-M128] SKIPPED 2025-09-09T14:44:13.3977674Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N160-M256] SKIPPED 2025-09-09T14:44:13.3978652Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N160-M512] SKIPPED 2025-09-09T14:44:13.3979704Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N160-M1024] SKIPPED 2025-09-09T14:44:13.3980693Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N160-M100] SKIPPED 2025-09-09T14:44:13.3981678Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N160-M200] SKIPPED 2025-09-09T14:44:13.3982668Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N160-M384] SKIPPED 2025-09-09T14:44:13.3983657Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-tensor_scale-N64-M128] SKIPPED 2025-09-09T14:44:13.3984648Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-tensor_scale-N64-M256] SKIPPED 2025-09-09T14:44:13.3985641Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-tensor_scale-N64-M512] SKIPPED 2025-09-09T14:44:13.3986720Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-tensor_scale-N64-M1024] SKIPPED 2025-09-09T14:44:13.3987788Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-tensor_scale-N64-M100] SKIPPED 2025-09-09T14:44:13.3988787Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-tensor_scale-N64-M200] SKIPPED 2025-09-09T14:44:13.3989767Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-tensor_scale-N64-M384] SKIPPED 2025-09-09T14:44:13.3990752Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-tensor_scale-N128-M128] SKIPPED 2025-09-09T14:44:13.3991740Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-tensor_scale-N128-M256] SKIPPED 2025-09-09T14:44:13.3992750Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-tensor_scale-N128-M512] SKIPPED 2025-09-09T14:44:13.3993753Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-tensor_scale-N128-M1024] SKIPPED 2025-09-09T14:44:13.3994754Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-tensor_scale-N128-M100] SKIPPED 2025-09-09T14:44:13.3995747Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-tensor_scale-N128-M200] SKIPPED 2025-09-09T14:44:13.3996734Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-tensor_scale-N128-M384] SKIPPED 2025-09-09T14:44:13.3997730Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-tensor_scale-N256-M128] SKIPPED 2025-09-09T14:44:13.3998727Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-tensor_scale-N256-M256] SKIPPED 2025-09-09T14:44:13.3999768Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-tensor_scale-N256-M512] SKIPPED 2025-09-09T14:44:13.4000777Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-tensor_scale-N256-M1024] SKIPPED 2025-09-09T14:44:13.4247043Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-tensor_scale-N256-M100] SKIPPED 2025-09-09T14:44:13.4248089Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-tensor_scale-N256-M200] SKIPPED 2025-09-09T14:44:13.4249078Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-tensor_scale-N256-M384] SKIPPED 2025-09-09T14:44:13.4250066Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-tensor_scale-N512-M128] SKIPPED 2025-09-09T14:44:13.4251054Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-tensor_scale-N512-M256] SKIPPED 2025-09-09T14:44:13.4252045Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-tensor_scale-N512-M512] SKIPPED 2025-09-09T14:44:13.4253035Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-tensor_scale-N512-M1024] SKIPPED 2025-09-09T14:44:13.4254053Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-tensor_scale-N512-M100] SKIPPED 2025-09-09T14:44:13.4255047Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-tensor_scale-N512-M200] SKIPPED 2025-09-09T14:44:13.4256045Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-tensor_scale-N512-M384] SKIPPED 2025-09-09T14:44:13.4257187Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-tensor_scale-N32-M128] SKIPPED 2025-09-09T14:44:13.4258176Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-tensor_scale-N32-M256] SKIPPED 2025-09-09T14:44:13.4259257Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-tensor_scale-N32-M512] SKIPPED 2025-09-09T14:44:13.4260245Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-tensor_scale-N32-M1024] SKIPPED 2025-09-09T14:44:13.4261226Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-tensor_scale-N32-M100] SKIPPED 2025-09-09T14:44:13.4262208Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-tensor_scale-N32-M200] SKIPPED 2025-09-09T14:44:13.4263189Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-tensor_scale-N32-M384] SKIPPED 2025-09-09T14:44:13.4264174Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-tensor_scale-N96-M128] SKIPPED 2025-09-09T14:44:13.4265158Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-tensor_scale-N96-M256] SKIPPED 2025-09-09T14:44:13.4266138Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-tensor_scale-N96-M512] SKIPPED 2025-09-09T14:44:13.4267133Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-tensor_scale-N96-M1024] SKIPPED 2025-09-09T14:44:13.4268114Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-tensor_scale-N96-M100] SKIPPED 2025-09-09T14:44:13.4269145Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-tensor_scale-N96-M200] SKIPPED 2025-09-09T14:44:13.4270137Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-tensor_scale-N96-M384] SKIPPED 2025-09-09T14:44:13.4271120Z 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test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-tensor_scale-N160-M384] SKIPPED 2025-09-09T14:44:13.4278046Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-block_scale-N64-M128] SKIPPED 2025-09-09T14:44:13.4279028Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-block_scale-N64-M256] SKIPPED 2025-09-09T14:44:13.4280075Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-block_scale-N64-M512] SKIPPED 2025-09-09T14:44:13.4281049Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-block_scale-N64-M1024] SKIPPED 2025-09-09T14:44:13.4282014Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-block_scale-N64-M100] SKIPPED 2025-09-09T14:44:13.4283076Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-block_scale-N64-M200] SKIPPED 2025-09-09T14:44:13.4284046Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-block_scale-N64-M384] SKIPPED 2025-09-09T14:44:13.4285127Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-block_scale-N128-M128] SKIPPED 2025-09-09T14:44:13.4286104Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-block_scale-N128-M256] SKIPPED 2025-09-09T14:44:13.4287080Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-block_scale-N128-M512] SKIPPED 2025-09-09T14:44:13.4288061Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-block_scale-N128-M1024] SKIPPED 2025-09-09T14:44:13.4289043Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-block_scale-N128-M100] SKIPPED 2025-09-09T14:44:13.4290024Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-block_scale-N128-M200] SKIPPED 2025-09-09T14:44:13.4291002Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-block_scale-N128-M384] SKIPPED 2025-09-09T14:44:13.4291996Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-block_scale-N256-M128] SKIPPED 2025-09-09T14:44:13.4292969Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-block_scale-N256-M256] SKIPPED 2025-09-09T14:44:13.4293958Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-block_scale-N256-M512] SKIPPED 2025-09-09T14:44:13.4294937Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-block_scale-N256-M1024] SKIPPED 2025-09-09T14:44:13.4295923Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-block_scale-N256-M100] SKIPPED 2025-09-09T14:44:13.4296902Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-block_scale-N256-M200] SKIPPED 2025-09-09T14:44:13.4297879Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-block_scale-N256-M384] SKIPPED 2025-09-09T14:44:13.4298889Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-block_scale-N512-M128] SKIPPED 2025-09-09T14:44:13.4299894Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-block_scale-N512-M256] SKIPPED 2025-09-09T14:44:13.4300873Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-block_scale-N512-M512] SKIPPED 2025-09-09T14:44:13.4301860Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-block_scale-N512-M1024] SKIPPED 2025-09-09T14:44:13.4302840Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-block_scale-N512-M100] SKIPPED 2025-09-09T14:44:13.4303828Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-block_scale-N512-M200] SKIPPED 2025-09-09T14:44:13.4304809Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-block_scale-N512-M384] SKIPPED 2025-09-09T14:44:13.4305780Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-block_scale-N32-M128] SKIPPED 2025-09-09T14:44:13.4306758Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-block_scale-N32-M256] SKIPPED 2025-09-09T14:44:13.4307719Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-block_scale-N32-M512] SKIPPED 2025-09-09T14:44:13.4308785Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-block_scale-N32-M1024] SKIPPED 2025-09-09T14:44:13.4548619Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-block_scale-N32-M100] SKIPPED 2025-09-09T14:44:13.4549817Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-block_scale-N32-M200] SKIPPED 2025-09-09T14:44:13.4550796Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-block_scale-N32-M384] SKIPPED 2025-09-09T14:44:13.4551776Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-block_scale-N96-M128] SKIPPED 2025-09-09T14:44:13.4552743Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-block_scale-N96-M256] SKIPPED 2025-09-09T14:44:13.4553721Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-block_scale-N96-M512] SKIPPED 2025-09-09T14:44:13.4554714Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-block_scale-N96-M1024] SKIPPED 2025-09-09T14:44:13.4555687Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-block_scale-N96-M100] SKIPPED 2025-09-09T14:44:13.4556665Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-block_scale-N96-M200] SKIPPED 2025-09-09T14:44:13.4557634Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-block_scale-N96-M384] SKIPPED 2025-09-09T14:44:13.4558633Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-block_scale-N160-M128] SKIPPED 2025-09-09T14:44:13.4559693Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-block_scale-N160-M256] SKIPPED 2025-09-09T14:44:13.4560675Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-block_scale-N160-M512] SKIPPED 2025-09-09T14:44:13.4561664Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-block_scale-N160-M1024] SKIPPED 2025-09-09T14:44:13.4562654Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-block_scale-N160-M100] SKIPPED 2025-09-09T14:44:13.4563639Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-block_scale-N160-M200] SKIPPED 2025-09-09T14:44:13.4564621Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-block_scale-N160-M384] SKIPPED 2025-09-09T14:44:13.4565602Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-tensor_scale-N64-M128] SKIPPED 2025-09-09T14:44:13.4566588Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-tensor_scale-N64-M256] SKIPPED 2025-09-09T14:44:13.4567574Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-tensor_scale-N64-M512] SKIPPED 2025-09-09T14:44:13.4568565Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-tensor_scale-N64-M1024] SKIPPED 2025-09-09T14:44:13.4569552Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-tensor_scale-N64-M100] SKIPPED 2025-09-09T14:44:13.4570528Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-tensor_scale-N64-M200] SKIPPED 2025-09-09T14:44:13.4571511Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-tensor_scale-N64-M384] SKIPPED 2025-09-09T14:44:13.4572500Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-tensor_scale-N128-M128] SKIPPED 2025-09-09T14:44:13.4575018Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-tensor_scale-N128-M256] SKIPPED 2025-09-09T14:44:13.4576027Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-tensor_scale-N128-M512] SKIPPED 2025-09-09T14:44:13.4577105Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-tensor_scale-N128-M1024] SKIPPED 2025-09-09T14:44:13.4578105Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-tensor_scale-N128-M100] SKIPPED 2025-09-09T14:44:13.4579098Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-tensor_scale-N128-M200] SKIPPED 2025-09-09T14:44:13.4580089Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-tensor_scale-N128-M384] SKIPPED 2025-09-09T14:44:13.4581083Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-tensor_scale-N256-M128] SKIPPED 2025-09-09T14:44:13.4582088Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-tensor_scale-N256-M256] SKIPPED 2025-09-09T14:44:13.4583082Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-tensor_scale-N256-M512] SKIPPED 2025-09-09T14:44:13.4584079Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-tensor_scale-N256-M1024] SKIPPED 2025-09-09T14:44:13.4585073Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-tensor_scale-N256-M100] SKIPPED 2025-09-09T14:44:13.4586066Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-tensor_scale-N256-M200] SKIPPED 2025-09-09T14:44:13.4587063Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-tensor_scale-N256-M384] SKIPPED 2025-09-09T14:44:13.4588052Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-tensor_scale-N512-M128] SKIPPED 2025-09-09T14:44:13.4589095Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-tensor_scale-N512-M256] SKIPPED 2025-09-09T14:44:13.4590097Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-tensor_scale-N512-M512] SKIPPED 2025-09-09T14:44:13.4591089Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-tensor_scale-N512-M1024] SKIPPED 2025-09-09T14:44:13.4592084Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-tensor_scale-N512-M100] SKIPPED 2025-09-09T14:44:13.4593070Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-tensor_scale-N512-M200] SKIPPED 2025-09-09T14:44:13.4594076Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-tensor_scale-N512-M384] SKIPPED 2025-09-09T14:44:13.4595069Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-tensor_scale-N32-M128] SKIPPED 2025-09-09T14:44:13.4596065Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-tensor_scale-N32-M256] SKIPPED 2025-09-09T14:44:13.4597059Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-tensor_scale-N32-M512] SKIPPED 2025-09-09T14:44:13.4598052Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-tensor_scale-N32-M1024] SKIPPED 2025-09-09T14:44:13.4607769Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-tensor_scale-N32-M100] SKIPPED 2025-09-09T14:44:13.4608788Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-tensor_scale-N32-M200] SKIPPED 2025-09-09T14:44:13.4609902Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-tensor_scale-N32-M384] SKIPPED 2025-09-09T14:44:13.4610909Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-tensor_scale-N96-M128] SKIPPED 2025-09-09T14:44:13.4612002Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-tensor_scale-N96-M256] SKIPPED 2025-09-09T14:44:13.4612996Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-tensor_scale-N96-M512] SKIPPED 2025-09-09T14:44:13.4614008Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-tensor_scale-N96-M1024] SKIPPED 2025-09-09T14:44:13.4615019Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-tensor_scale-N96-M100] SKIPPED 2025-09-09T14:44:13.4616023Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-tensor_scale-N96-M200] SKIPPED 2025-09-09T14:44:13.4617024Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-tensor_scale-N96-M384] SKIPPED 2025-09-09T14:44:13.4618028Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-tensor_scale-N160-M128] SKIPPED 2025-09-09T14:44:13.4619038Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-tensor_scale-N160-M256] SKIPPED 2025-09-09T14:44:13.4620045Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-tensor_scale-N160-M512] SKIPPED 2025-09-09T14:44:13.4621087Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-tensor_scale-N160-M1024] SKIPPED 2025-09-09T14:44:13.5381139Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-tensor_scale-N160-M100] SKIPPED 2025-09-09T14:44:13.5382170Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-tensor_scale-N160-M200] SKIPPED 2025-09-09T14:44:13.5383158Z test/prototype/mx_formats/test_mx_tensor.py::test_triton_nvfp4_quantize_equivalence[bf16-tensor_scale-N160-M384] SKIPPED 2025-09-09T14:44:13.5384098Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_nvfp4_reconstruction[dtype0-shape0-False] PASSED 2025-09-09T14:44:13.5384961Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_nvfp4_reconstruction[dtype1-shape1-False] PASSED 2025-09-09T14:44:13.5385832Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_nvfp4_reconstruction[dtype2-shape2-False] PASSED 2025-09-09T14:44:13.5386689Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_nvfp4_reconstruction[dtype3-shape3-True] PASSED 2025-09-09T14:44:13.5387569Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_nvfp4_swizzled_scales_construction[shape0-False] PASSED 2025-09-09T14:44:13.5388488Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_nvfp4_swizzled_scales_construction[shape0-True] PASSED 2025-09-09T14:44:13.5389392Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_nvfp4_swizzled_scales_construction[shape1-False] PASSED 2025-09-09T14:44:13.5390299Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_nvfp4_swizzled_scales_construction[shape1-True] PASSED 2025-09-09T14:44:13.5391205Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_nvfp4_swizzled_scales_construction[shape2-False] PASSED 2025-09-09T14:44:13.5392101Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_nvfp4_swizzled_scales_construction[shape2-True] PASSED 2025-09-09T14:44:13.5393008Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_nvfp4_swizzled_scales_construction[shape3-False] PASSED 2025-09-09T14:44:13.5393906Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_nvfp4_swizzled_scales_construction[shape3-True] PASSED 2025-09-09T14:44:13.5394972Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_nvfp4_swizzled_scales_slicing[slice_rows[0:128]] PASSED 2025-09-09T14:44:13.5395885Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_nvfp4_swizzled_scales_slicing[slice_rows[128:256]] PASSED 2025-09-09T14:44:13.5396889Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_nvfp4_swizzled_scales_slicing[slice_cols[0:64]] PASSED 2025-09-09T14:44:13.5397792Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_nvfp4_swizzled_scales_slicing[slice_cols[64:128]] PASSED 2025-09-09T14:44:13.5398729Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_nvfp4_swizzled_scales_slicing[slice_cols[0:128]_full_width] PASSED 2025-09-09T14:44:13.5399802Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_nvfp4_swizzled_scales_slicing[slice_cols[0:2048]_tp_first_half] PASSED 2025-09-09T14:44:13.5400824Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_nvfp4_swizzled_scales_slicing[slice_cols[2048:4096]_tp_second_half] PASSED 2025-09-09T14:44:13.5401806Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_nvfp4_swizzled_scales_slicing[slice_cols[0:1024]_quarter] PASSED 2025-09-09T14:44:13.5402779Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_nvfp4_swizzled_scales_slicing[slice_cols[1024:2048]_quarter] PASSED 2025-09-09T14:44:13.5403757Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_nvfp4_swizzled_scales_slicing_errors[misaligned_row_end] PASSED 2025-09-09T14:44:13.5404728Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_nvfp4_swizzled_scales_slicing_errors[misaligned_row_start] PASSED 2025-09-09T14:44:13.5405700Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_nvfp4_swizzled_scales_slicing_errors[misaligned_col_32] PASSED 2025-09-09T14:44:13.5406656Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_nvfp4_swizzled_scales_slicing_errors[misaligned_col_start] PASSED 2025-09-09T14:44:13.5407634Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_nvfp4_swizzled_scales_slicing_errors[misaligned_col_end] PASSED 2025-09-09T14:44:13.5408561Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_nvfp4_swizzled_scales_slicing_errors[odd_start] PASSED 2025-09-09T14:44:13.5409443Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_nvfp4_swizzled_scales_slicing_errors[odd_end] PASSED 2025-09-09T14:44:13.5410289Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_nvfp4_swizzled_scales_view_semantics PASSED 2025-09-09T14:44:13.5411098Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_nvfp4_swizzled_scales_serialization PASSED 2025-09-09T14:44:13.5411922Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_nvfp4_swizzled_scales_get_scales_method PASSED 2025-09-09T14:44:13.5412855Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N64-M128] SKIPPED 2025-09-09T14:44:13.5413870Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N64-M256] SKIPPED 2025-09-09T14:44:13.5414868Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N64-M512] SKIPPED 2025-09-09T14:44:13.5415889Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N64-M1024] SKIPPED 2025-09-09T14:44:13.5416897Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N64-M100] SKIPPED 2025-09-09T14:44:13.5417893Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N64-M200] SKIPPED 2025-09-09T14:44:13.5418884Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N64-M384] SKIPPED 2025-09-09T14:44:13.5419992Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N128-M128] SKIPPED 2025-09-09T14:44:13.5421008Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N128-M256] SKIPPED 2025-09-09T14:44:13.5422098Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N128-M512] SKIPPED 2025-09-09T14:44:13.5423288Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N128-M1024] SKIPPED 2025-09-09T14:44:13.5424308Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N128-M100] SKIPPED 2025-09-09T14:44:13.5425325Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N128-M200] SKIPPED 2025-09-09T14:44:13.5426339Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N128-M384] SKIPPED 2025-09-09T14:44:13.5427350Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N256-M128] SKIPPED 2025-09-09T14:44:13.5428373Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N256-M256] SKIPPED 2025-09-09T14:44:13.5429442Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N256-M512] SKIPPED 2025-09-09T14:44:13.5430459Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N256-M1024] SKIPPED 2025-09-09T14:44:13.5431483Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N256-M100] SKIPPED 2025-09-09T14:44:13.5432488Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N256-M200] SKIPPED 2025-09-09T14:44:13.5433506Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N256-M384] SKIPPED 2025-09-09T14:44:13.5434517Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N512-M128] SKIPPED 2025-09-09T14:44:13.5435532Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N512-M256] SKIPPED 2025-09-09T14:44:13.5436541Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N512-M512] SKIPPED 2025-09-09T14:44:13.5437564Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N512-M1024] SKIPPED 2025-09-09T14:44:13.5438581Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N512-M100] SKIPPED 2025-09-09T14:44:13.5439695Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N512-M200] SKIPPED 2025-09-09T14:44:13.5440716Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N512-M384] SKIPPED 2025-09-09T14:44:13.5441733Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N32-M128] SKIPPED 2025-09-09T14:44:13.5442751Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N32-M256] SKIPPED 2025-09-09T14:44:13.5443751Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N32-M512] SKIPPED 2025-09-09T14:44:13.5673938Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N32-M1024] SKIPPED 2025-09-09T14:44:13.5675997Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N32-M100] SKIPPED 2025-09-09T14:44:13.5678274Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N32-M200] SKIPPED 2025-09-09T14:44:13.5679611Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N32-M384] SKIPPED 2025-09-09T14:44:13.5680745Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N96-M128] SKIPPED 2025-09-09T14:44:13.5681733Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N96-M256] SKIPPED 2025-09-09T14:44:13.5682731Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N96-M512] SKIPPED 2025-09-09T14:44:13.5683724Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N96-M1024] SKIPPED 2025-09-09T14:44:13.5684735Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N96-M100] SKIPPED 2025-09-09T14:44:13.5685736Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N96-M200] SKIPPED 2025-09-09T14:44:13.5686730Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N96-M384] SKIPPED 2025-09-09T14:44:13.5687729Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N160-M128] SKIPPED 2025-09-09T14:44:13.5688734Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N160-M256] SKIPPED 2025-09-09T14:44:13.5689739Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N160-M512] SKIPPED 2025-09-09T14:44:13.5690752Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N160-M1024] SKIPPED 2025-09-09T14:44:13.5691764Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N160-M100] SKIPPED 2025-09-09T14:44:13.5692772Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N160-M200] SKIPPED 2025-09-09T14:44:13.5693791Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-block_scale-N160-M384] SKIPPED 2025-09-09T14:44:13.5694786Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-tensor_scale-N64-M128] SKIPPED 2025-09-09T14:44:13.5695799Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-tensor_scale-N64-M256] SKIPPED 2025-09-09T14:44:13.5696795Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-tensor_scale-N64-M512] SKIPPED 2025-09-09T14:44:13.5697819Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-tensor_scale-N64-M1024] SKIPPED 2025-09-09T14:44:13.5698831Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-tensor_scale-N64-M100] SKIPPED 2025-09-09T14:44:13.5699836Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-tensor_scale-N64-M200] SKIPPED 2025-09-09T14:44:13.5700850Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-tensor_scale-N64-M384] SKIPPED 2025-09-09T14:44:13.5701861Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-tensor_scale-N128-M128] SKIPPED 2025-09-09T14:44:13.5702884Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-tensor_scale-N128-M256] SKIPPED 2025-09-09T14:44:13.5703990Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-tensor_scale-N128-M512] SKIPPED 2025-09-09T14:44:13.5705018Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-tensor_scale-N128-M1024] SKIPPED 2025-09-09T14:44:13.5706221Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-tensor_scale-N128-M100] SKIPPED 2025-09-09T14:44:13.5707248Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-tensor_scale-N128-M200] SKIPPED 2025-09-09T14:44:13.5708266Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-tensor_scale-N128-M384] SKIPPED 2025-09-09T14:44:13.5709291Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-tensor_scale-N256-M128] SKIPPED 2025-09-09T14:44:13.5710309Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-tensor_scale-N256-M256] SKIPPED 2025-09-09T14:44:13.5711340Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-tensor_scale-N256-M512] SKIPPED 2025-09-09T14:44:13.5712376Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-tensor_scale-N256-M1024] SKIPPED 2025-09-09T14:44:13.5713402Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-tensor_scale-N256-M100] SKIPPED 2025-09-09T14:44:13.5714429Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-tensor_scale-N256-M200] SKIPPED 2025-09-09T14:44:13.5715454Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-tensor_scale-N256-M384] SKIPPED 2025-09-09T14:44:13.5716474Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-tensor_scale-N512-M128] SKIPPED 2025-09-09T14:44:13.5717506Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-tensor_scale-N512-M256] SKIPPED 2025-09-09T14:44:13.5718520Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-tensor_scale-N512-M512] SKIPPED 2025-09-09T14:44:13.5719654Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-tensor_scale-N512-M1024] SKIPPED 2025-09-09T14:44:13.5720684Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-tensor_scale-N512-M100] SKIPPED 2025-09-09T14:44:13.5721704Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-tensor_scale-N512-M200] SKIPPED 2025-09-09T14:44:13.5722888Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-tensor_scale-N512-M384] SKIPPED 2025-09-09T14:44:13.5723909Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-tensor_scale-N32-M128] SKIPPED 2025-09-09T14:44:13.5724924Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-tensor_scale-N32-M256] SKIPPED 2025-09-09T14:44:13.5725936Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-tensor_scale-N32-M512] SKIPPED 2025-09-09T14:44:13.5726943Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-tensor_scale-N32-M1024] SKIPPED 2025-09-09T14:44:13.5727955Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-tensor_scale-N32-M100] SKIPPED 2025-09-09T14:44:13.5728963Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-tensor_scale-N32-M200] SKIPPED 2025-09-09T14:44:13.5729966Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-tensor_scale-N32-M384] SKIPPED 2025-09-09T14:44:13.5731110Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-tensor_scale-N96-M128] SKIPPED 2025-09-09T14:44:13.5732123Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-tensor_scale-N96-M256] SKIPPED 2025-09-09T14:44:13.5733244Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-tensor_scale-N96-M512] SKIPPED 2025-09-09T14:44:13.5734266Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-tensor_scale-N96-M1024] SKIPPED 2025-09-09T14:44:13.5735267Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-tensor_scale-N96-M100] SKIPPED 2025-09-09T14:44:13.5736279Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-tensor_scale-N96-M200] SKIPPED 2025-09-09T14:44:13.5737298Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_triton_nvfp4_quantize_equivalence[fp32-tensor_scale-N96-M384] SKIPPED 2025-09-09T14:44:13.5738307Z 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test/prototype/mx_formats/test_nvfp4_tensor.py::test_nvfp4_matmul_with_amax[64x64x256-True-inpt_dtype1-False-False-NVFP4MMConfig.DYNAMIC-False] SKIPPED 2025-09-09T14:44:13.7217022Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_nvfp4_matmul_with_amax[64x64x256-True-inpt_dtype1-False-False-NVFP4MMConfig.WEIGHT_ONLY-True] SKIPPED 2025-09-09T14:44:13.7218312Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_nvfp4_matmul_with_amax[64x64x256-True-inpt_dtype1-False-False-NVFP4MMConfig.WEIGHT_ONLY-False] SKIPPED 2025-09-09T14:44:13.7219600Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_nvfp4_matmul_with_amax[64x64x256-False-inpt_dtype0-True-False-NVFP4MMConfig.DYNAMIC-True] SKIPPED 2025-09-09T14:44:13.7220873Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_nvfp4_matmul_with_amax[64x64x256-False-inpt_dtype0-True-False-NVFP4MMConfig.DYNAMIC-False] SKIPPED 2025-09-09T14:44:13.7222294Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_nvfp4_matmul_with_amax[64x64x256-False-inpt_dtype0-True-False-NVFP4MMConfig.WEIGHT_ONLY-True] SKIPPED 2025-09-09T14:44:13.7223602Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_nvfp4_matmul_with_amax[64x64x256-False-inpt_dtype0-True-False-NVFP4MMConfig.WEIGHT_ONLY-False] SKIPPED 2025-09-09T14:44:13.7224891Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_nvfp4_matmul_with_amax[64x64x256-False-inpt_dtype0-False-False-NVFP4MMConfig.DYNAMIC-True] SKIPPED 2025-09-09T14:44:13.7226164Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_nvfp4_matmul_with_amax[64x64x256-False-inpt_dtype0-False-False-NVFP4MMConfig.DYNAMIC-False] SKIPPED 2025-09-09T14:44:13.7227614Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_nvfp4_matmul_with_amax[64x64x256-False-inpt_dtype0-False-False-NVFP4MMConfig.WEIGHT_ONLY-True] SKIPPED 2025-09-09T14:44:13.7228922Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_nvfp4_matmul_with_amax[64x64x256-False-inpt_dtype0-False-False-NVFP4MMConfig.WEIGHT_ONLY-False] SKIPPED 2025-09-09T14:44:13.7230337Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_nvfp4_matmul_with_amax[64x64x256-False-inpt_dtype1-True-False-NVFP4MMConfig.DYNAMIC-True] SKIPPED 2025-09-09T14:44:13.7231609Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_nvfp4_matmul_with_amax[64x64x256-False-inpt_dtype1-True-False-NVFP4MMConfig.DYNAMIC-False] SKIPPED 2025-09-09T14:44:13.7232883Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_nvfp4_matmul_with_amax[64x64x256-False-inpt_dtype1-True-False-NVFP4MMConfig.WEIGHT_ONLY-True] SKIPPED 2025-09-09T14:44:13.7234176Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_nvfp4_matmul_with_amax[64x64x256-False-inpt_dtype1-True-False-NVFP4MMConfig.WEIGHT_ONLY-False] SKIPPED 2025-09-09T14:44:13.7235469Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_nvfp4_matmul_with_amax[64x64x256-False-inpt_dtype1-False-False-NVFP4MMConfig.DYNAMIC-True] SKIPPED 2025-09-09T14:44:13.7236754Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_nvfp4_matmul_with_amax[64x64x256-False-inpt_dtype1-False-False-NVFP4MMConfig.DYNAMIC-False] SKIPPED 2025-09-09T14:44:13.7238051Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_nvfp4_matmul_with_amax[64x64x256-False-inpt_dtype1-False-False-NVFP4MMConfig.WEIGHT_ONLY-True] SKIPPED 2025-09-09T14:44:13.7239474Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_nvfp4_matmul_with_amax[64x64x256-False-inpt_dtype1-False-False-NVFP4MMConfig.WEIGHT_ONLY-False] SKIPPED 2025-09-09T14:44:13.7240759Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_nvfp4_matmul_with_amax[200x192x256-True-inpt_dtype0-True-False-NVFP4MMConfig.DYNAMIC-True] SKIPPED 2025-09-09T14:44:13.7242042Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_nvfp4_matmul_with_amax[200x192x256-True-inpt_dtype0-True-False-NVFP4MMConfig.DYNAMIC-False] SKIPPED 2025-09-09T14:44:13.7243332Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_nvfp4_matmul_with_amax[200x192x256-True-inpt_dtype0-True-False-NVFP4MMConfig.WEIGHT_ONLY-True] SKIPPED 2025-09-09T14:44:13.7244629Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_nvfp4_matmul_with_amax[200x192x256-True-inpt_dtype0-True-False-NVFP4MMConfig.WEIGHT_ONLY-False] SKIPPED 2025-09-09T14:44:13.7245925Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_nvfp4_matmul_with_amax[200x192x256-True-inpt_dtype0-False-False-NVFP4MMConfig.DYNAMIC-True] SKIPPED 2025-09-09T14:44:13.7247208Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_nvfp4_matmul_with_amax[200x192x256-True-inpt_dtype0-False-False-NVFP4MMConfig.DYNAMIC-False] SKIPPED 2025-09-09T14:44:13.7248513Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_nvfp4_matmul_with_amax[200x192x256-True-inpt_dtype0-False-False-NVFP4MMConfig.WEIGHT_ONLY-True] SKIPPED 2025-09-09T14:44:13.7249827Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_nvfp4_matmul_with_amax[200x192x256-True-inpt_dtype0-False-False-NVFP4MMConfig.WEIGHT_ONLY-False] SKIPPED 2025-09-09T14:44:13.7251121Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_nvfp4_matmul_with_amax[200x192x256-True-inpt_dtype1-True-False-NVFP4MMConfig.DYNAMIC-True] SKIPPED 2025-09-09T14:44:13.7252395Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_nvfp4_matmul_with_amax[200x192x256-True-inpt_dtype1-True-False-NVFP4MMConfig.DYNAMIC-False] SKIPPED 2025-09-09T14:44:13.7253679Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_nvfp4_matmul_with_amax[200x192x256-True-inpt_dtype1-True-False-NVFP4MMConfig.WEIGHT_ONLY-True] SKIPPED 2025-09-09T14:44:13.7254972Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_nvfp4_matmul_with_amax[200x192x256-True-inpt_dtype1-True-False-NVFP4MMConfig.WEIGHT_ONLY-False] SKIPPED 2025-09-09T14:44:13.7256363Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_nvfp4_matmul_with_amax[200x192x256-True-inpt_dtype1-False-False-NVFP4MMConfig.DYNAMIC-True] SKIPPED 2025-09-09T14:44:13.7257725Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_nvfp4_matmul_with_amax[200x192x256-True-inpt_dtype1-False-False-NVFP4MMConfig.DYNAMIC-False] SKIPPED 2025-09-09T14:44:13.7259024Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_nvfp4_matmul_with_amax[200x192x256-True-inpt_dtype1-False-False-NVFP4MMConfig.WEIGHT_ONLY-True] SKIPPED 2025-09-09T14:44:13.7260341Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_nvfp4_matmul_with_amax[200x192x256-True-inpt_dtype1-False-False-NVFP4MMConfig.WEIGHT_ONLY-False] SKIPPED 2025-09-09T14:44:13.7261637Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_nvfp4_matmul_with_amax[200x192x256-False-inpt_dtype0-True-False-NVFP4MMConfig.DYNAMIC-True] SKIPPED 2025-09-09T14:44:13.7262930Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_nvfp4_matmul_with_amax[200x192x256-False-inpt_dtype0-True-False-NVFP4MMConfig.DYNAMIC-False] SKIPPED 2025-09-09T14:44:13.7264226Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_nvfp4_matmul_with_amax[200x192x256-False-inpt_dtype0-True-False-NVFP4MMConfig.WEIGHT_ONLY-True] SKIPPED 2025-09-09T14:44:13.7265834Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_nvfp4_matmul_with_amax[200x192x256-False-inpt_dtype0-True-False-NVFP4MMConfig.WEIGHT_ONLY-False] SKIPPED 2025-09-09T14:44:13.7267355Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_nvfp4_matmul_with_amax[200x192x256-False-inpt_dtype0-False-False-NVFP4MMConfig.DYNAMIC-True] SKIPPED 2025-09-09T14:44:13.7268710Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_nvfp4_matmul_with_amax[200x192x256-False-inpt_dtype0-False-False-NVFP4MMConfig.DYNAMIC-False] SKIPPED 2025-09-09T14:48:38.4420501Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_nvfp4_matmul_with_amax[200x192x256-False-inpt_dtype0-False-False-NVFP4MMConfig.WEIGHT_ONLY-True] SKIPPED 2025-09-09T14:48:38.4421942Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_nvfp4_matmul_with_amax[200x192x256-False-inpt_dtype0-False-False-NVFP4MMConfig.WEIGHT_ONLY-False] SKIPPED 2025-09-09T14:48:38.4423513Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_nvfp4_matmul_with_amax[200x192x256-False-inpt_dtype1-True-False-NVFP4MMConfig.DYNAMIC-True] SKIPPED 2025-09-09T14:48:38.4424810Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_nvfp4_matmul_with_amax[200x192x256-False-inpt_dtype1-True-False-NVFP4MMConfig.DYNAMIC-False] SKIPPED 2025-09-09T14:48:38.4426121Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_nvfp4_matmul_with_amax[200x192x256-False-inpt_dtype1-True-False-NVFP4MMConfig.WEIGHT_ONLY-True] SKIPPED 2025-09-09T14:48:38.4427457Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_nvfp4_matmul_with_amax[200x192x256-False-inpt_dtype1-True-False-NVFP4MMConfig.WEIGHT_ONLY-False] SKIPPED 2025-09-09T14:48:38.4428770Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_nvfp4_matmul_with_amax[200x192x256-False-inpt_dtype1-False-False-NVFP4MMConfig.DYNAMIC-True] SKIPPED 2025-09-09T14:48:38.4430104Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_nvfp4_matmul_with_amax[200x192x256-False-inpt_dtype1-False-False-NVFP4MMConfig.DYNAMIC-False] SKIPPED 2025-09-09T14:48:38.4431418Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_nvfp4_matmul_with_amax[200x192x256-False-inpt_dtype1-False-False-NVFP4MMConfig.WEIGHT_ONLY-True] SKIPPED 2025-09-09T14:48:38.4432745Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_nvfp4_matmul_with_amax[200x192x256-False-inpt_dtype1-False-False-NVFP4MMConfig.WEIGHT_ONLY-False] SKIPPED 2025-09-09T14:48:38.4433723Z test/prototype/mx_formats/test_nvfp4_tensor.py::test_nvfp4_to_copy PASSED 2025-09-09T14:48:38.4436706Z test/prototype/safetensors/test_safetensors_support.py::TestSafeTensors::test_safetensors SKIPPED 2025-09-09T14:48:38.4437657Z test/prototype/safetensors/test_safetensors_utils.py::TestSafeTensorsUtils::test_metadata_torchao SKIPPED 2025-09-09T14:48:38.4438871Z test/prototype/safetensors/test_safetensors_utils.py::TestSafeTensorsUtils::test_not_metadata_torchao_metadata0 SKIPPED 2025-09-09T14:48:38.4440016Z test/prototype/safetensors/test_safetensors_utils.py::TestSafeTensorsUtils::test_not_metadata_torchao_metadata1 SKIPPED 2025-09-09T14:48:38.4441058Z test/prototype/safetensors/test_safetensors_utils.py::TestSafeTensorsUtils::test_not_metadata_torchao_metadata2 SKIPPED 2025-09-09T14:48:38.4442096Z 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test/prototype/safetensors/test_safetensors_utils.py::TestSafeTensorsUtils::test_not_metadata_torchao_metadata9 SKIPPED 2025-09-09T14:48:38.4449177Z test/prototype/test_awq.py::TestAWQ::test_awq_config SKIPPED (need t...) 2025-09-09T14:48:38.4449786Z test/prototype/test_awq.py::TestAWQ::test_awq_functionality SKIPPED 2025-09-09T14:48:38.4450405Z test/prototype/test_awq.py::TestAWQ::test_awq_loading SKIPPED (need ...) 2025-09-09T14:48:38.4451019Z test/prototype/test_awq.py::TestAWQ::test_awq_loading_vllm SKIPPED (...) 2025-09-09T14:48:38.4451686Z test/prototype/test_blockwise_triton.py::test_blockwise_quant_dequant[dtype0-2-512-128] Relative Error: 0.052430 2025-09-09T14:48:38.4452232Z PASSED 2025-09-09T14:48:38.4452704Z test/prototype/test_blockwise_triton.py::test_blockwise_quant_dequant[dtype0-3-2048-2048] Relative Error: 0.052625 2025-09-09T14:48:38.4453258Z PASSED 2025-09-09T14:48:38.4453716Z test/prototype/test_blockwise_triton.py::test_blockwise_quant_dequant[dtype0-4-3584-640] Relative Error: 0.052573 2025-09-09T14:48:38.4454261Z PASSED 2025-09-09T14:48:38.4454728Z test/prototype/test_blockwise_triton.py::test_blockwise_quant_dequant[dtype0-13-8704-8576] Relative Error: 0.052637 2025-09-09T14:48:38.4455276Z PASSED 2025-09-09T14:48:38.4455748Z test/prototype/test_blockwise_triton.py::test_blockwise_quant_dequant[dtype0-26-18944-1664] Relative Error: 0.052638 2025-09-09T14:48:38.4456302Z PASSED 2025-09-09T14:48:38.4456770Z test/prototype/test_blockwise_triton.py::test_blockwise_quant_dequant[dtype0-67-6656-1408] Relative Error: 0.052663 2025-09-09T14:48:38.4457314Z PASSED 2025-09-09T14:48:38.4457753Z test/prototype/test_blockwise_triton.py::test_blockwise_fp8_gemm[dtype0-2-512-128] Relative Error: 0.076219 2025-09-09T14:48:38.4458268Z PASSED 2025-09-09T14:48:38.4458714Z test/prototype/test_blockwise_triton.py::test_blockwise_fp8_gemm[dtype0-3-2048-2048] Relative Error: 0.073036 2025-09-09T14:48:38.4459237Z PASSED 2025-09-09T14:48:38.4459675Z test/prototype/test_blockwise_triton.py::test_blockwise_fp8_gemm[dtype0-4-3584-640] Relative Error: 0.072511 2025-09-09T14:48:38.4460208Z PASSED 2025-09-09T14:48:38.4460655Z test/prototype/test_blockwise_triton.py::test_blockwise_fp8_gemm[dtype0-13-8704-8576] Relative Error: 0.073267 2025-09-09T14:48:38.4461185Z PASSED 2025-09-09T14:48:38.4461721Z test/prototype/test_blockwise_triton.py::test_blockwise_fp8_gemm[dtype0-26-18944-1664] Relative Error: 0.073289 2025-09-09T14:48:38.4462261Z PASSED 2025-09-09T14:48:38.4462778Z test/prototype/test_blockwise_triton.py::test_blockwise_fp8_gemm[dtype0-67-6656-1408] Relative Error: 0.073508 2025-09-09T14:48:38.4463308Z PASSED 2025-09-09T14:48:38.4463871Z test/prototype/test_codebook_coreml.py::TestCodebookQuantization::test_choose_qparams_codebook SKIPPED 2025-09-09T14:48:38.4464822Z test/prototype/test_codebook_coreml.py::TestCodebookQuantization::test_choose_qparams_codebook_row_grouping SKIPPED 2025-09-09T14:48:38.4465817Z test/prototype/test_codebook_coreml.py::TestCodebookQuantization::test_codebook_quantized_tensor_from_float SKIPPED 2025-09-09T14:48:38.4466811Z test/prototype/test_codebook_coreml.py::TestCodebookQuantization::test_codebook_quantized_tensor_from_float2 SKIPPED 2025-09-09T14:48:38.4467883Z test/prototype/test_codebook_coreml.py::TestCodebookQuantization::test_codebook_quantized_tensor_from_float_row_grouping SKIPPED 2025-09-09T14:48:38.4468809Z test/prototype/test_codebook_coreml.py::TestCodebookQuantization::test_export SKIPPED 2025-09-09T14:48:38.4469611Z test/prototype/test_codebook_coreml.py::TestCodebookQuantization::test_quantize_api SKIPPED 2025-09-09T14:48:38.4470466Z test/prototype/test_codebook_quant.py::TestCodebookQuantization::test_choose_qparams_codebook PASSED 2025-09-09T14:48:38.4471400Z test/prototype/test_codebook_quant.py::TestCodebookQuantization::test_codebook_quantized_tensor_from_float PASSED 2025-09-09T14:48:38.4472385Z test/prototype/test_codebook_quant.py::TestCodebookQuantization::test_codebook_quantized_tensor_from_float2 PASSED 2025-09-09T14:48:38.4473266Z test/prototype/test_codebook_quant.py::TestCodebookQuantization::test_quantize_api PASSED 2025-09-09T14:48:38.4474137Z test/prototype/test_dynamic_activation_lut.py::test_parq_conversion[lead_dim0-1-granularity0-dtype0] SKIPPED 2025-09-09T14:48:38.4475061Z test/prototype/test_dynamic_activation_lut.py::test_parq_conversion[lead_dim0-1-granularity0-dtype1] SKIPPED 2025-09-09T14:48:38.4475982Z test/prototype/test_dynamic_activation_lut.py::test_parq_conversion[lead_dim0-1-granularity1-dtype0] SKIPPED 2025-09-09T14:48:38.4476895Z test/prototype/test_dynamic_activation_lut.py::test_parq_conversion[lead_dim0-1-granularity1-dtype1] SKIPPED 2025-09-09T14:48:38.4477805Z test/prototype/test_dynamic_activation_lut.py::test_parq_conversion[lead_dim0-2-granularity0-dtype0] SKIPPED 2025-09-09T14:48:38.4478712Z test/prototype/test_dynamic_activation_lut.py::test_parq_conversion[lead_dim0-2-granularity0-dtype1] SKIPPED 2025-09-09T14:48:38.4479677Z test/prototype/test_dynamic_activation_lut.py::test_parq_conversion[lead_dim0-2-granularity1-dtype0] SKIPPED 2025-09-09T14:48:38.4480589Z test/prototype/test_dynamic_activation_lut.py::test_parq_conversion[lead_dim0-2-granularity1-dtype1] SKIPPED 2025-09-09T14:48:38.4481503Z test/prototype/test_dynamic_activation_lut.py::test_parq_conversion[lead_dim0-3-granularity0-dtype0] SKIPPED 2025-09-09T14:48:38.4482469Z test/prototype/test_dynamic_activation_lut.py::test_parq_conversion[lead_dim0-3-granularity0-dtype1] SKIPPED 2025-09-09T14:48:38.4483375Z test/prototype/test_dynamic_activation_lut.py::test_parq_conversion[lead_dim0-3-granularity1-dtype0] SKIPPED 2025-09-09T14:48:38.4484289Z test/prototype/test_dynamic_activation_lut.py::test_parq_conversion[lead_dim0-3-granularity1-dtype1] SKIPPED 2025-09-09T14:48:38.4485204Z test/prototype/test_dynamic_activation_lut.py::test_parq_conversion[lead_dim0-4-granularity0-dtype0] SKIPPED 2025-09-09T14:48:38.4486114Z test/prototype/test_dynamic_activation_lut.py::test_parq_conversion[lead_dim0-4-granularity0-dtype1] SKIPPED 2025-09-09T14:48:38.4487115Z test/prototype/test_dynamic_activation_lut.py::test_parq_conversion[lead_dim0-4-granularity1-dtype0] SKIPPED 2025-09-09T14:48:44.9263138Z test/prototype/test_dynamic_activation_lut.py::test_parq_conversion[lead_dim0-4-granularity1-dtype1] SKIPPED 2025-09-09T14:48:44.9264668Z test/prototype/test_dynamic_activation_lut.py::test_parq_conversion[lead_dim1-1-granularity0-dtype0] SKIPPED 2025-09-09T14:48:44.9265590Z test/prototype/test_dynamic_activation_lut.py::test_parq_conversion[lead_dim1-1-granularity0-dtype1] SKIPPED 2025-09-09T14:48:44.9266500Z test/prototype/test_dynamic_activation_lut.py::test_parq_conversion[lead_dim1-1-granularity1-dtype0] SKIPPED 2025-09-09T14:48:44.9267416Z 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test/prototype/test_dynamic_activation_lut.py::test_export[lead_dim1-4-granularity1-dtype1] SKIPPED 2025-09-09T14:48:44.9306537Z test/prototype/test_gguf_quant.py::TestGGUFQuantization::test_choose_qparams_gguf PASSED 2025-09-09T14:48:44.9307386Z test/prototype/test_gguf_quant.py::TestGGUFQuantization::test_gguf_quantized_tensor_from_float PASSED 2025-09-09T14:48:44.9308176Z test/prototype/test_gguf_quant.py::TestGGUFQuantization::test_quantize_api PASSED 2025-09-09T14:48:44.9308983Z test/prototype/test_mixed_precision.py::TestWeightOnlyQuantNaive::test_quantization_intNwo PASSED 2025-09-09T14:48:44.9309770Z test/prototype/test_parametrization.py::TestFakeSparsity::test_jit_trace PASSED 2025-09-09T14:48:44.9310503Z test/prototype/test_parametrization.py::TestFakeSparsity::test_masking_logic PASSED 2025-09-09T14:48:44.9311279Z test/prototype/test_parametrization.py::TestFakeSparsity::test_state_dict_preserved PASSED 2025-09-09T14:48:44.9312093Z test/prototype/test_parametrization.py::TestFakeSparsity::test_weights_parametrized PASSED 2025-09-09T14:48:44.9312834Z test/prototype/test_paretoq.py::TestParetoQ::test_quantize_functions PASSED 2025-09-09T14:48:44.9313574Z test/prototype/test_paretoq.py::TestParetoQ::test_quantized_linear PASSED 2025-09-09T14:48:44.9314548Z test/prototype/test_parq.py::TestPARQuantization::test_parq_train_loop_b_0_unif_quant_False_hard_prox_False_per_group_quantizer_False PASSED 2025-09-09T14:48:44.9315756Z test/prototype/test_parq.py::TestPARQuantization::test_parq_train_loop_b_0_unif_quant_False_hard_prox_False_per_group_quantizer_True PASSED 2025-09-09T14:48:44.9316877Z test/prototype/test_parq.py::TestPARQuantization::test_parq_train_loop_b_0_unif_quant_False_hard_prox_True_per_group_quantizer_False PASSED 2025-09-09T14:48:44.9317987Z test/prototype/test_parq.py::TestPARQuantization::test_parq_train_loop_b_0_unif_quant_False_hard_prox_True_per_group_quantizer_True PASSED 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test/prototype/test_parq.py::TestPARQuantization::test_parq_train_loop_b_1_unif_quant_False_hard_prox_False_per_group_quantizer_True PASSED 2025-09-09T14:48:44.9326116Z test/prototype/test_parq.py::TestPARQuantization::test_parq_train_loop_b_1_unif_quant_False_hard_prox_True_per_group_quantizer_False PASSED 2025-09-09T14:49:12.5392232Z test/prototype/test_parq.py::TestPARQuantization::test_parq_train_loop_b_1_unif_quant_False_hard_prox_True_per_group_quantizer_True PASSED 2025-09-09T14:49:12.5393438Z test/prototype/test_parq.py::TestPARQuantization::test_parq_train_loop_b_1_unif_quant_True_hard_prox_False_per_group_quantizer_False PASSED 2025-09-09T14:49:12.5394590Z test/prototype/test_parq.py::TestPARQuantization::test_parq_train_loop_b_1_unif_quant_True_hard_prox_False_per_group_quantizer_True PASSED 2025-09-09T14:49:12.5395710Z test/prototype/test_parq.py::TestPARQuantization::test_parq_train_loop_b_1_unif_quant_True_hard_prox_True_per_group_quantizer_False PASSED 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test/prototype/test_parq.py::TestPARQuantization::test_parq_train_loop_b_2_unif_quant_True_hard_prox_False_per_group_quantizer_False PASSED 2025-09-09T14:49:12.5403707Z test/prototype/test_parq.py::TestPARQuantization::test_parq_train_loop_b_2_unif_quant_True_hard_prox_False_per_group_quantizer_True PASSED 2025-09-09T14:49:12.5404821Z test/prototype/test_parq.py::TestPARQuantization::test_parq_train_loop_b_2_unif_quant_True_hard_prox_True_per_group_quantizer_False PASSED 2025-09-09T14:49:12.5406302Z test/prototype/test_parq.py::TestPARQuantization::test_parq_train_loop_b_2_unif_quant_True_hard_prox_True_per_group_quantizer_True PASSED 2025-09-09T14:49:12.5407719Z test/prototype/test_parq.py::TestPARQuantization::test_parq_train_loop_b_4_unif_quant_False_hard_prox_False_per_group_quantizer_False PASSED 2025-09-09T14:49:12.5408903Z test/prototype/test_parq.py::TestPARQuantization::test_parq_train_loop_b_4_unif_quant_False_hard_prox_False_per_group_quantizer_True PASSED 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test/prototype/test_parq.py::TestPARQuantization::test_parq_train_loop_b_4_unif_quant_True_hard_prox_True_per_group_quantizer_True PASSED 2025-09-09T14:49:12.5425558Z test/prototype/test_parq.py::TestUnifTorchaoQuantizer::test_int4_weight_only_e2e PASSED 2025-09-09T14:49:12.5426391Z test/prototype/test_parq.py::TestUnifTorchaoQuantizer::test_int4_weight_only_group_size_256 PASSED 2025-09-09T14:49:12.5427263Z test/prototype/test_parq.py::TestUnifTorchaoQuantizer::test_int4_weight_only_group_size_32 PASSED 2025-09-09T14:49:12.5428139Z test/prototype/test_parq.py::TestUnifTorchaoQuantizer::test_intx_weight_only_b_2_group_size_32 PASSED 2025-09-09T14:49:12.5429033Z test/prototype/test_parq.py::TestUnifTorchaoQuantizer::test_intx_weight_only_b_2_group_size_512 PASSED 2025-09-09T14:49:12.5429926Z test/prototype/test_parq.py::TestUnifTorchaoQuantizer::test_intx_weight_only_b_3_group_size_32 PASSED 2025-09-09T14:49:12.5430809Z test/prototype/test_parq.py::TestUnifTorchaoQuantizer::test_intx_weight_only_b_3_group_size_512 PASSED 2025-09-09T14:49:12.5431702Z test/prototype/test_parq.py::TestUnifTorchaoQuantizer::test_intx_weight_only_b_4_group_size_32 PASSED 2025-09-09T14:49:12.5432585Z test/prototype/test_parq.py::TestUnifTorchaoQuantizer::test_intx_weight_only_b_4_group_size_512 PASSED 2025-09-09T14:49:12.5433474Z test/prototype/test_parq.py::TestUnifTorchaoQuantizer::test_intx_weight_only_b_8_group_size_32 PASSED 2025-09-09T14:49:12.5434371Z test/prototype/test_parq.py::TestUnifTorchaoQuantizer::test_intx_weight_only_b_8_group_size_512 PASSED 2025-09-09T14:49:12.5435222Z test/prototype/test_parq.py::TestUnifTorchaoQuantizer::test_intx_weight_only_e2e_b_2 PASSED 2025-09-09T14:49:12.5436049Z test/prototype/test_parq.py::TestUnifTorchaoQuantizer::test_intx_weight_only_e2e_b_3 PASSED 2025-09-09T14:49:12.5436864Z test/prototype/test_parq.py::TestUnifTorchaoQuantizer::test_intx_weight_only_e2e_b_4 PASSED 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test/prototype/test_parq.py::TestInt8DynamicActivationTorchaoQuantizer::test_int8_dynamic_activation_intx_e2e_b_2_float32_group_size_128 PASSED 2025-09-09T14:49:12.5445587Z test/prototype/test_parq.py::TestInt8DynamicActivationTorchaoQuantizer::test_int8_dynamic_activation_intx_e2e_b_2_float32_group_size_32 PASSED 2025-09-09T14:49:12.5446833Z test/prototype/test_parq.py::TestInt8DynamicActivationTorchaoQuantizer::test_int8_dynamic_activation_intx_e2e_b_3_float16_group_size_128 PASSED 2025-09-09T14:49:12.5448093Z test/prototype/test_parq.py::TestInt8DynamicActivationTorchaoQuantizer::test_int8_dynamic_activation_intx_e2e_b_3_float16_group_size_32 PASSED 2025-09-09T14:49:12.5449341Z test/prototype/test_parq.py::TestInt8DynamicActivationTorchaoQuantizer::test_int8_dynamic_activation_intx_e2e_b_3_float32_group_size_128 PASSED 2025-09-09T14:49:12.5450598Z test/prototype/test_parq.py::TestInt8DynamicActivationTorchaoQuantizer::test_int8_dynamic_activation_intx_e2e_b_3_float32_group_size_32 PASSED 2025-09-09T14:49:12.5451853Z test/prototype/test_parq.py::TestInt8DynamicActivationTorchaoQuantizer::test_int8_dynamic_activation_intx_e2e_b_4_float16_group_size_128 PASSED 2025-09-09T14:49:12.5453149Z test/prototype/test_parq.py::TestInt8DynamicActivationTorchaoQuantizer::test_int8_dynamic_activation_intx_e2e_b_4_float16_group_size_32 PASSED 2025-09-09T14:49:12.5454409Z test/prototype/test_parq.py::TestInt8DynamicActivationTorchaoQuantizer::test_int8_dynamic_activation_intx_e2e_b_4_float32_group_size_128 PASSED 2025-09-09T14:49:12.5455666Z test/prototype/test_parq.py::TestInt8DynamicActivationTorchaoQuantizer::test_int8_dynamic_activation_intx_e2e_b_4_float32_group_size_32 PASSED 2025-09-09T14:49:12.5456912Z test/prototype/test_parq.py::TestInt8DynamicActivationTorchaoQuantizer::test_int8_dynamic_activation_intx_e2e_b_8_float16_group_size_128 PASSED 2025-09-09T14:49:12.5458166Z test/prototype/test_parq.py::TestInt8DynamicActivationTorchaoQuantizer::test_int8_dynamic_activation_intx_e2e_b_8_float16_group_size_32 PASSED 2025-09-09T14:49:12.5459411Z test/prototype/test_parq.py::TestInt8DynamicActivationTorchaoQuantizer::test_int8_dynamic_activation_intx_e2e_b_8_float32_group_size_128 PASSED 2025-09-09T14:49:12.5460664Z test/prototype/test_parq.py::TestInt8DynamicActivationTorchaoQuantizer::test_int8_dynamic_activation_intx_e2e_b_8_float32_group_size_32 PASSED 2025-09-09T14:49:12.5461806Z test/prototype/test_quantized_training.py::TestQuantizedTraining::test_bitnet_training_compile_False SKIPPED 2025-09-09T14:49:12.5462756Z test/prototype/test_quantized_training.py::TestQuantizedTraining::test_bitnet_training_compile_True SKIPPED 2025-09-09T14:49:12.5463864Z test/prototype/test_quantized_training.py::TestQuantizedTraining::test_int8_mixed_precision_training_compile_False_config0_module_swap_False PASSED 2025-09-09T14:49:12.5465102Z test/prototype/test_quantized_training.py::TestQuantizedTraining::test_int8_mixed_precision_training_compile_False_config0_module_swap_True PASSED 2025-09-09T14:53:42.8817339Z test/prototype/test_quantized_training.py::TestQuantizedTraining::test_int8_mixed_precision_training_compile_False_config1_module_swap_False PASSED 2025-09-09T14:53:42.8818641Z test/prototype/test_quantized_training.py::TestQuantizedTraining::test_int8_mixed_precision_training_compile_False_config1_module_swap_True PASSED 2025-09-09T14:53:42.8819895Z test/prototype/test_quantized_training.py::TestQuantizedTraining::test_int8_mixed_precision_training_compile_False_config2_module_swap_False PASSED 2025-09-09T14:53:42.8821637Z test/prototype/test_quantized_training.py::TestQuantizedTraining::test_int8_mixed_precision_training_compile_False_config2_module_swap_True PASSED 2025-09-09T14:53:42.8823365Z test/prototype/test_quantized_training.py::TestQuantizedTraining::test_int8_mixed_precision_training_compile_False_config3_module_swap_False PASSED 2025-09-09T14:53:42.8824607Z test/prototype/test_quantized_training.py::TestQuantizedTraining::test_int8_mixed_precision_training_compile_False_config3_module_swap_True PASSED 2025-09-09T14:53:42.8826060Z test/prototype/test_quantized_training.py::TestQuantizedTraining::test_int8_mixed_precision_training_compile_True_config0_module_swap_False PASSED 2025-09-09T14:53:42.8827372Z test/prototype/test_quantized_training.py::TestQuantizedTraining::test_int8_mixed_precision_training_compile_True_config0_module_swap_True PASSED 2025-09-09T14:53:42.8828629Z test/prototype/test_quantized_training.py::TestQuantizedTraining::test_int8_mixed_precision_training_compile_True_config1_module_swap_False PASSED 2025-09-09T14:53:42.8829872Z test/prototype/test_quantized_training.py::TestQuantizedTraining::test_int8_mixed_precision_training_compile_True_config1_module_swap_True PASSED 2025-09-09T14:53:42.8831110Z test/prototype/test_quantized_training.py::TestQuantizedTraining::test_int8_mixed_precision_training_compile_True_config2_module_swap_False PASSED 2025-09-09T14:53:42.8832342Z test/prototype/test_quantized_training.py::TestQuantizedTraining::test_int8_mixed_precision_training_compile_True_config2_module_swap_True PASSED 2025-09-09T14:53:42.8833577Z test/prototype/test_quantized_training.py::TestQuantizedTraining::test_int8_mixed_precision_training_compile_True_config3_module_swap_False PASSED 2025-09-09T14:53:42.8834803Z test/prototype/test_quantized_training.py::TestQuantizedTraining::test_int8_mixed_precision_training_compile_True_config3_module_swap_True PASSED 2025-09-09T14:53:42.8835917Z test/prototype/test_quantized_training.py::TestQuantizedTraining::test_int8_stochastic_rounding_device_cpu PASSED 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PASSED 2025-09-09T14:53:42.8851380Z test/prototype/test_quantized_training.py::TestQuantizedTraining::test_int8_weight_only_compile_leading_dims2_bias_True_device_cuda PASSED 2025-09-09T14:53:42.8852590Z test/prototype/test_quantized_training.py::TestQuantizedTraining::test_int8_weight_only_correctness_leading_dims0_bias_False_device_cpu PASSED 2025-09-09T14:53:42.8853810Z test/prototype/test_quantized_training.py::TestQuantizedTraining::test_int8_weight_only_correctness_leading_dims0_bias_False_device_cuda PASSED 2025-09-09T14:53:42.8855027Z test/prototype/test_quantized_training.py::TestQuantizedTraining::test_int8_weight_only_correctness_leading_dims0_bias_True_device_cpu PASSED 2025-09-09T14:53:42.8856250Z test/prototype/test_quantized_training.py::TestQuantizedTraining::test_int8_weight_only_correctness_leading_dims0_bias_True_device_cuda PASSED 2025-09-09T14:53:42.8857514Z test/prototype/test_quantized_training.py::TestQuantizedTraining::test_int8_weight_only_correctness_leading_dims1_bias_False_device_cpu PASSED 2025-09-09T14:53:42.8858746Z test/prototype/test_quantized_training.py::TestQuantizedTraining::test_int8_weight_only_correctness_leading_dims1_bias_False_device_cuda PASSED 2025-09-09T14:53:42.8859969Z test/prototype/test_quantized_training.py::TestQuantizedTraining::test_int8_weight_only_correctness_leading_dims1_bias_True_device_cpu PASSED 2025-09-09T14:53:42.8861179Z test/prototype/test_quantized_training.py::TestQuantizedTraining::test_int8_weight_only_correctness_leading_dims1_bias_True_device_cuda PASSED 2025-09-09T14:53:42.8862398Z test/prototype/test_quantized_training.py::TestQuantizedTraining::test_int8_weight_only_correctness_leading_dims2_bias_False_device_cpu PASSED 2025-09-09T14:53:42.8863629Z test/prototype/test_quantized_training.py::TestQuantizedTraining::test_int8_weight_only_correctness_leading_dims2_bias_False_device_cuda PASSED 2025-09-09T14:53:42.8864849Z test/prototype/test_quantized_training.py::TestQuantizedTraining::test_int8_weight_only_correctness_leading_dims2_bias_True_device_cpu PASSED 2025-09-09T14:53:42.8866070Z test/prototype/test_quantized_training.py::TestQuantizedTraining::test_int8_weight_only_correctness_leading_dims2_bias_True_device_cuda PASSED 2025-09-09T14:53:42.8867234Z test/prototype/test_quantized_training.py::TestQuantizedTraining::test_int8_weight_only_training_compile_False_device_cpu PASSED 2025-09-09T14:53:42.8868406Z test/prototype/test_quantized_training.py::TestQuantizedTraining::test_int8_weight_only_training_compile_False_device_cuda PASSED 2025-09-09T14:53:42.8869522Z test/prototype/test_quantized_training.py::TestQuantizedTraining::test_int8_weight_only_training_compile_True_device_cpu PASSED 2025-09-09T14:53:42.8870631Z test/prototype/test_quantized_training.py::TestQuantizedTraining::test_int8_weight_only_training_compile_True_device_cuda PASSED 2025-09-09T14:53:42.8871843Z test/prototype/test_quantized_training.py::TestFSDP2::test_fsdp2_correctness I0909 14:53:14.675693 938 site-packages/torch/testing/_internal/common_distributed.py:729] Started process 0 with pid 54491 2025-09-09T14:53:42.8872971Z I0909 14:53:14.735687 938 site-packages/torch/testing/_internal/common_distributed.py:729] Started process 1 with pid 54492 2025-09-09T14:53:42.8873523Z dist init r=0, world=2 2025-09-09T14:53:42.8873752Z dist init r=1, world=2 2025-09-09T14:53:42.8874630Z /opt/conda/envs/venv/lib/python3.9/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 2025-09-09T14:53:42.8875551Z warnings.warn( # warn only once 2025-09-09T14:53:42.8876534Z /opt/conda/envs/venv/lib/python3.9/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 2025-09-09T14:53:42.8877492Z warnings.warn( # warn only once 2025-09-09T14:53:42.8878469Z /opt/conda/envs/venv/lib/python3.9/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 2025-09-09T14:53:42.8879429Z warnings.warn( # warn only once 2025-09-09T14:53:42.8880319Z /opt/conda/envs/venv/lib/python3.9/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 2025-09-09T14:53:42.8881223Z warnings.warn( # warn only once 2025-09-09T14:53:42.8882110Z /opt/conda/envs/venv/lib/python3.9/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 2025-09-09T14:54:28.6318262Z warnings.warn( # warn only once 2025-09-09T14:54:28.6319845Z /opt/conda/envs/venv/lib/python3.9/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 2025-09-09T14:54:28.6320800Z warnings.warn( # warn only once 2025-09-09T14:54:28.6321694Z /opt/conda/envs/venv/lib/python3.9/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 2025-09-09T14:54:28.6322881Z warnings.warn( # warn only once 2025-09-09T14:54:28.6323772Z /opt/conda/envs/venv/lib/python3.9/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 2025-09-09T14:54:28.6324673Z warnings.warn( # warn only once 2025-09-09T14:54:28.6325575Z /opt/conda/envs/venv/lib/python3.9/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 2025-09-09T14:54:28.6326475Z warnings.warn( # warn only once 2025-09-09T14:54:28.6327370Z /opt/conda/envs/venv/lib/python3.9/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 2025-09-09T14:54:28.6328266Z warnings.warn( # warn only once 2025-09-09T14:54:28.6328739Z PASSED 2025-09-09T14:54:28.6329620Z test/prototype/test_quantized_training.py::TestFSDP2::test_precompute_bitnet_scale I0909 14:54:23.295892 938 site-packages/torch/testing/_internal/common_distributed.py:729] Started process 0 with pid 55355 2025-09-09T14:54:28.6330776Z I0909 14:54:23.355411 938 site-packages/torch/testing/_internal/common_distributed.py:729] Started process 1 with pid 55356 2025-09-09T14:54:28.6331333Z dist init r=0, world=2 2025-09-09T14:54:28.6331553Z dist init r=1, world=2 2025-09-09T14:54:28.6331814Z PASSED 2025-09-09T14:54:28.6332280Z test/prototype/test_scheduler.py::TestScheduler::test_constructor PASSED 2025-09-09T14:54:28.6332979Z test/prototype/test_scheduler.py::TestScheduler::test_lambda_scheduler PASSED 2025-09-09T14:54:28.6333679Z test/prototype/test_scheduler.py::TestScheduler::test_order_of_steps PASSED 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test/prototype/test_smoothquant.py::TestSmoothQuant::test_save_load_recipe_alpha0_quant_mode_dynamic_device_cuda_float16 SKIPPED 2025-09-09T14:54:28.6342663Z test/prototype/test_smoothquant.py::TestSmoothQuant::test_save_load_recipe_alpha0_quant_mode_dynamic_device_cuda_float32 SKIPPED 2025-09-09T14:54:28.6343760Z test/prototype/test_smoothquant.py::TestSmoothQuant::test_save_load_recipe_alpha0_quant_mode_static_device_cpu_bfloat16 SKIPPED 2025-09-09T14:54:28.6344837Z test/prototype/test_smoothquant.py::TestSmoothQuant::test_save_load_recipe_alpha0_quant_mode_static_device_cpu_float16 SKIPPED 2025-09-09T14:54:28.6345909Z test/prototype/test_smoothquant.py::TestSmoothQuant::test_save_load_recipe_alpha0_quant_mode_static_device_cpu_float32 SKIPPED 2025-09-09T14:54:28.6347001Z test/prototype/test_smoothquant.py::TestSmoothQuant::test_save_load_recipe_alpha0_quant_mode_static_device_cuda_bfloat16 SKIPPED 2025-09-09T14:54:28.6348077Z test/prototype/test_smoothquant.py::TestSmoothQuant::test_save_load_recipe_alpha0_quant_mode_static_device_cuda_float16 SKIPPED 2025-09-09T14:54:28.6349161Z test/prototype/test_smoothquant.py::TestSmoothQuant::test_save_load_recipe_alpha0_quant_mode_static_device_cuda_float32 SKIPPED 2025-09-09T14:54:28.6350313Z test/prototype/test_smoothquant.py::TestSmoothQuant::test_save_load_recipe_alpha_0_5_quant_mode_dynamic_device_cpu_bfloat16 SKIPPED 2025-09-09T14:54:28.6351418Z test/prototype/test_smoothquant.py::TestSmoothQuant::test_save_load_recipe_alpha_0_5_quant_mode_dynamic_device_cpu_float16 SKIPPED 2025-09-09T14:54:28.6352512Z test/prototype/test_smoothquant.py::TestSmoothQuant::test_save_load_recipe_alpha_0_5_quant_mode_dynamic_device_cpu_float32 SKIPPED 2025-09-09T14:54:28.6353614Z test/prototype/test_smoothquant.py::TestSmoothQuant::test_save_load_recipe_alpha_0_5_quant_mode_dynamic_device_cuda_bfloat16 SKIPPED 2025-09-09T14:54:28.6354715Z test/prototype/test_smoothquant.py::TestSmoothQuant::test_save_load_recipe_alpha_0_5_quant_mode_dynamic_device_cuda_float16 SKIPPED 2025-09-09T14:54:28.6355810Z test/prototype/test_smoothquant.py::TestSmoothQuant::test_save_load_recipe_alpha_0_5_quant_mode_dynamic_device_cuda_float32 SKIPPED 2025-09-09T14:54:28.6356900Z test/prototype/test_smoothquant.py::TestSmoothQuant::test_save_load_recipe_alpha_0_5_quant_mode_static_device_cpu_bfloat16 SKIPPED 2025-09-09T14:54:28.6357993Z test/prototype/test_smoothquant.py::TestSmoothQuant::test_save_load_recipe_alpha_0_5_quant_mode_static_device_cpu_float16 SKIPPED 2025-09-09T14:54:28.6359070Z test/prototype/test_smoothquant.py::TestSmoothQuant::test_save_load_recipe_alpha_0_5_quant_mode_static_device_cpu_float32 SKIPPED 2025-09-09T14:54:28.6360236Z test/prototype/test_smoothquant.py::TestSmoothQuant::test_save_load_recipe_alpha_0_5_quant_mode_static_device_cuda_bfloat16 SKIPPED 2025-09-09T14:54:28.6361333Z 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2025-09-09T14:56:10.0930998Z test/prototype/test_structured_sparsifier.py::TestBaseStructuredSparsifier::test_prune_lstm_layernorm_linear_single_layer PASSED 2025-09-09T14:56:10.0932098Z test/prototype/test_structured_sparsifier.py::TestBaseStructuredSparsifier::test_prune_lstm_linear_multiple_layer PASSED 2025-09-09T14:56:10.0933153Z test/prototype/test_structured_sparsifier.py::TestBaseStructuredSparsifier::test_prune_lstm_linear_single_layer PASSED 2025-09-09T14:56:10.0934137Z test/prototype/test_structured_sparsifier.py::TestBaseStructuredSparsifier::test_step_conv2d PASSED 2025-09-09T14:56:10.0935041Z test/prototype/test_structured_sparsifier.py::TestBaseStructuredSparsifier::test_step_linear PASSED 2025-09-09T14:56:10.0935909Z test/prototype/test_structured_sparsifier.py::TestFPGMPruner::test_compute_distance PASSED 2025-09-09T14:56:10.0936705Z test/prototype/test_structured_sparsifier.py::TestFPGMPruner::test_update_mask PASSED 2025-09-09T14:56:10.0937607Z test/quantization/pt2e/test_arm_inductor_quantizer.py::TestQuantizePT2EArmInductor::test_attention_block SKIPPED 2025-09-09T14:56:10.0938582Z test/quantization/pt2e/test_arm_inductor_quantizer.py::TestQuantizePT2EArmInductor::test_conv2d SKIPPED 2025-09-09T14:56:10.0939551Z test/quantization/pt2e/test_arm_inductor_quantizer.py::TestQuantizePT2EArmInductor::test_conv2d_binary SKIPPED 2025-09-09T14:56:10.0940557Z test/quantization/pt2e/test_arm_inductor_quantizer.py::TestQuantizePT2EArmInductor::test_conv2d_binary2 SKIPPED 2025-09-09T14:56:10.0941587Z test/quantization/pt2e/test_arm_inductor_quantizer.py::TestQuantizePT2EArmInductor::test_dynamic_quant_linear SKIPPED 2025-09-09T14:56:37.5300825Z test/quantization/pt2e/test_arm_inductor_quantizer.py::TestQuantizePT2EArmInductor::test_filter_linear_recipe SKIPPED 2025-09-09T14:56:37.5302098Z test/quantization/pt2e/test_arm_inductor_quantizer.py::TestQuantizePT2EArmInductor::test_linear SKIPPED 2025-09-09T14:56:37.5303291Z test/quantization/pt2e/test_arm_inductor_quantizer.py::TestQuantizePT2EArmInductor::test_linear_unary SKIPPED 2025-09-09T14:56:37.5304568Z test/quantization/pt2e/test_arm_inductor_quantizer.py::TestQuantizePT2EArmInductor::test_linear_unary_dynamic SKIPPED 2025-09-09T14:56:37.5305892Z test/quantization/pt2e/test_arm_inductor_quantizer.py::TestQuantizePT2EArmInductor::test_linear_unary_dynamic_qat SKIPPED 2025-09-09T14:56:37.5307206Z test/quantization/pt2e/test_arm_inductor_quantizer.py::TestQuantizePT2EArmInductor::test_linear_unary_qat SKIPPED 2025-09-09T14:56:37.5308428Z test/quantization/pt2e/test_arm_inductor_quantizer.py::TestQuantizePT2EArmInductor::test_qat_conv2d SKIPPED 2025-09-09T14:56:37.5309671Z test/quantization/pt2e/test_arm_inductor_quantizer.py::TestQuantizePT2EArmInductor::test_qat_conv2d_binary SKIPPED 2025-09-09T14:56:37.5310936Z test/quantization/pt2e/test_arm_inductor_quantizer.py::TestQuantizePT2EArmInductor::test_qat_conv2d_binary2 SKIPPED 2025-09-09T14:56:37.5312232Z test/quantization/pt2e/test_arm_inductor_quantizer.py::TestQuantizePT2EArmInductor::test_qat_dynamic_quant_linear SKIPPED 2025-09-09T14:56:37.5313628Z test/quantization/pt2e/test_arm_inductor_quantizer.py::TestQuantizePT2EArmInductor::test_set_module_name_and_module_type_case1 SKIPPED 2025-09-09T14:56:37.5315399Z test/quantization/pt2e/test_arm_inductor_quantizer.py::TestQuantizePT2EArmInductor::test_set_module_name_and_module_type_case2 SKIPPED 2025-09-09T14:56:37.5316936Z test/quantization/pt2e/test_arm_inductor_quantizer.py::TestQuantizePT2EArmInductor::test_set_module_name_and_module_type_with_mixed_configs SKIPPED 2025-09-09T14:56:37.5318562Z test/quantization/pt2e/test_arm_inductor_quantizer.py::TestQuantizePT2EArmInductor::test_set_module_name_qconfig SKIPPED 2025-09-09T14:56:37.5320050Z test/quantization/pt2e/test_arm_inductor_quantizer.py::TestQuantizePT2EArmInductor::test_set_module_name_qconfig_for_dynamic_quant SKIPPED 2025-09-09T14:56:37.5321526Z test/quantization/pt2e/test_arm_inductor_quantizer.py::TestQuantizePT2EArmInductor::test_set_module_name_qconfig_with_underscores SKIPPED 2025-09-09T14:56:37.5323183Z test/quantization/pt2e/test_arm_inductor_quantizer.py::TestQuantizePT2EArmInductor::test_set_module_name_with_mixed_configs SKIPPED 2025-09-09T14:56:37.5324238Z test/quantization/pt2e/test_duplicate_dq.py::TestDuplicateDQPass::test_avgpool_use_different_qconfig PASSED 2025-09-09T14:56:37.5325164Z test/quantization/pt2e/test_duplicate_dq.py::TestDuplicateDQPass::test_no_add_quant_duplicate_dq PASSED 2025-09-09T14:56:37.5326078Z test/quantization/pt2e/test_duplicate_dq.py::TestDuplicateDQPass::test_no_need_for_duplicate_dq PASSED 2025-09-09T14:56:37.5326962Z test/quantization/pt2e/test_duplicate_dq.py::TestDuplicateDQPass::test_simple_duplicate_dq PASSED 2025-09-09T14:56:37.5327787Z test/quantization/pt2e/test_graph_utils.py::TestGraphUtils::test_conv_bn_conv_relu PASSED 2025-09-09T14:56:37.5328849Z test/quantization/pt2e/test_graph_utils.py::TestGraphUtils::test_conv_bn_relu W0909 14:56:12.830308 938 site-packages/torch/fx/experimental/symbolic_shapes.py:3130] Failed to reduce inequalities: 1/2 2025-09-09T14:56:37.5329711Z PASSED 2025-09-09T14:56:37.5330566Z test/quantization/pt2e/test_graph_utils.py::TestGraphUtils::test_customized_equivalet_types_dict W0909 14:56:12.983762 938 site-packages/torch/fx/experimental/symbolic_shapes.py:3130] Failed to reduce inequalities: 1/2 2025-09-09T14:56:37.5331493Z PASSED 2025-09-09T14:56:37.5332090Z test/quantization/pt2e/test_metadata_porting.py::TestMetaDataPorting::test_metadata_porting_for_dq SKIPPED 2025-09-09T14:56:37.5333087Z test/quantization/pt2e/test_metadata_porting.py::TestMetaDataPorting::test_metadata_porting_for_dq_no_static_q PASSED 2025-09-09T14:56:37.5334085Z test/quantization/pt2e/test_metadata_porting.py::TestMetaDataPorting::test_metadata_porting_for_two_dq PASSED 2025-09-09T14:56:37.5335108Z test/quantization/pt2e/test_metadata_porting.py::TestMetaDataPorting::test_metadata_porting_with_no_quant_inbetween PASSED 2025-09-09T14:56:37.5336084Z test/quantization/pt2e/test_metadata_porting.py::TestMetaDataPorting::test_no_metadata_porting PASSED 2025-09-09T14:56:37.5337094Z test/quantization/pt2e/test_metadata_porting.py::TestMetaDataPorting::test_no_metadata_porting_through_unknown_ops PASSED 2025-09-09T14:56:37.5338085Z test/quantization/pt2e/test_metadata_porting.py::TestMetaDataPorting::test_simple_metadata_porting PASSED 2025-09-09T14:56:37.5339058Z test/quantization/pt2e/test_numeric_debugger.py::TestNumericDebuggerInfra::test_added_node_gets_unique_id PASSED 2025-09-09T14:56:37.5340012Z test/quantization/pt2e/test_numeric_debugger.py::TestNumericDebuggerInfra::test_control_flow SKIPPED 2025-09-09T14:56:37.5340952Z test/quantization/pt2e/test_numeric_debugger.py::TestNumericDebuggerInfra::test_copy_preserve_handle PASSED 2025-09-09T14:56:37.5341936Z test/quantization/pt2e/test_numeric_debugger.py::TestNumericDebuggerInfra::test_deepcopy_preserve_handle PASSED 2025-09-09T14:56:37.5342973Z test/quantization/pt2e/test_numeric_debugger.py::TestNumericDebuggerInfra::test_prepare_for_propagation_comparison PASSED 2025-09-09T14:56:37.5344150Z test/quantization/pt2e/test_numeric_debugger.py::TestNumericDebuggerInfra::test_re_export_preserve_handle PASSED 2025-09-09T14:56:37.5345240Z test/quantization/pt2e/test_numeric_debugger.py::TestNumericDebuggerInfra::test_run_decompositions_map_handle_to_new_nodes PASSED 2025-09-09T14:56:37.5346453Z test/quantization/pt2e/test_numeric_debugger.py::TestNumericDebuggerInfra::test_run_decompositions_same_handle_id PASSED 2025-09-09T14:56:37.5347391Z test/quantization/pt2e/test_numeric_debugger.py::TestNumericDebuggerInfra::test_simple PASSED 2025-09-09T14:56:37.5348291Z test/quantization/pt2e/test_quantize_pt2e.py::TestQuantizePT2E::test_allow_exported_model_train_eval PASSED 2025-09-09T14:56:37.5349265Z test/quantization/pt2e/test_quantize_pt2e.py::TestQuantizePT2E::test_allow_exported_model_train_eval_idempotent PASSED 2025-09-09T14:56:37.5350204Z test/quantization/pt2e/test_quantize_pt2e.py::TestQuantizePT2E::test_allow_implicit_sharing PASSED 2025-09-09T14:56:37.5351059Z test/quantization/pt2e/test_quantize_pt2e.py::TestQuantizePT2E::test_chunked_bn_fusion PASSED 2025-09-09T14:56:37.5352893Z test/quantization/pt2e/test_quantize_pt2e.py::TestQuantizePT2E::test_composable_quantizer_linear_conv [W909 14:56:37.987345091 PyInterpreter.cpp:263] Warning: Deallocating Tensor that still has live PyObject references. This probably happened because you took out a weak reference to Tensor and didn't call _fix_weakref() after dereferencing it. Subsequent accesses to this tensor via the PyObject will now fail. (function decref) 2025-09-09T14:56:37.5355440Z [W909 14:56:37.987381232 PyInterpreter.cpp:263] Warning: Deallocating Tensor that still has live PyObject references. This probably happened because you took out a weak reference to Tensor and didn't call _fix_weakref() after dereferencing it. Subsequent accesses to this tensor via the PyObject will now fail. (function decref) 2025-09-09T14:56:37.5357583Z [W909 14:56:37.987394752 PyInterpreter.cpp:263] Warning: Deallocating Tensor that still has live PyObject references. This probably happened because you took out a weak reference to Tensor and didn't call _fix_weakref() after dereferencing it. Subsequent accesses to this tensor via the PyObject will now fail. (function decref) 2025-09-09T14:56:37.5359779Z [W909 14:56:37.987410022 PyInterpreter.cpp:263] Warning: Deallocating Tensor that still has live PyObject references. This probably happened because you took out a weak reference to Tensor and didn't call _fix_weakref() after dereferencing it. Subsequent accesses to this tensor via the PyObject will now fail. (function decref) 2025-09-09T14:56:37.5361905Z [W909 14:56:37.987422143 PyInterpreter.cpp:263] Warning: Deallocating Tensor that still has live PyObject references. This probably happened because you took out a weak reference to Tensor and didn't call _fix_weakref() after dereferencing it. Subsequent accesses to this tensor via the PyObject will now fail. (function decref) 2025-09-09T14:56:37.5364045Z [W909 14:56:37.987441953 PyInterpreter.cpp:263] Warning: Deallocating Tensor that still has live PyObject references. This probably happened because you took out a weak reference to Tensor and didn't call _fix_weakref() after dereferencing it. Subsequent accesses to this tensor via the PyObject will now fail. (function decref) 2025-09-09T14:56:37.5366190Z [W909 14:56:37.987456453 PyInterpreter.cpp:263] Warning: Deallocating Tensor that still has live PyObject references. This probably happened because you took out a weak reference to Tensor and didn't call _fix_weakref() after dereferencing it. Subsequent accesses to this tensor via the PyObject will now fail. (function decref) 2025-09-09T14:57:24.0516548Z [W909 14:56:37.987469374 PyInterpreter.cpp:263] Warning: Deallocating Tensor that still has live PyObject references. This probably happened because you took out a weak reference to Tensor and didn't call _fix_weakref() after dereferencing it. Subsequent accesses to this tensor via the PyObject will now fail. (function decref) 2025-09-09T14:57:24.0519672Z [W909 14:56:37.987499364 PyInterpreter.cpp:263] Warning: Deallocating Tensor that still has live PyObject references. This probably happened because you took out a weak reference to Tensor and didn't call _fix_weakref() after dereferencing it. Subsequent accesses to this tensor via the PyObject will now fail. (function decref) 2025-09-09T14:57:24.0522824Z [W909 14:56:37.987511334 PyInterpreter.cpp:263] Warning: Deallocating Tensor that still has live PyObject references. This probably happened because you took out a weak reference to Tensor and didn't call _fix_weakref() after dereferencing it. Subsequent accesses to this tensor via the PyObject will now fail. (function decref) 2025-09-09T14:57:24.0525517Z [W909 14:56:37.987529805 PyInterpreter.cpp:263] Warning: Deallocating Tensor that still has live PyObject references. This probably happened because you took out a weak reference to Tensor and didn't call _fix_weakref() after dereferencing it. Subsequent accesses to this tensor via the PyObject will now fail. (function decref) 2025-09-09T14:57:24.0528197Z [W909 14:56:37.987544905 PyInterpreter.cpp:263] Warning: Deallocating Tensor that still has live PyObject references. This probably happened because you took out a weak reference to Tensor and didn't call _fix_weakref() after dereferencing it. Subsequent accesses to this tensor via the PyObject will now fail. (function decref) 2025-09-09T14:57:24.0530872Z [W909 14:56:37.987564035 PyInterpreter.cpp:263] Warning: Deallocating Tensor that still has live PyObject references. This probably happened because you took out a weak reference to Tensor and didn't call _fix_weakref() after dereferencing it. Subsequent accesses to this tensor via the PyObject will now fail. (function decref) 2025-09-09T14:57:24.0533545Z [W909 14:56:37.987573665 PyInterpreter.cpp:263] Warning: Deallocating Tensor that still has live PyObject references. This probably happened because you took out a weak reference to Tensor and didn't call _fix_weakref() after dereferencing it. Subsequent accesses to this tensor via the PyObject will now fail. (function decref) 2025-09-09T14:57:24.0536213Z [W909 14:56:37.987603806 PyInterpreter.cpp:263] Warning: Deallocating Tensor that still has live PyObject references. This probably happened because you took out a weak reference to Tensor and didn't call _fix_weakref() after dereferencing it. Subsequent accesses to this tensor via the PyObject will now fail. (function decref) 2025-09-09T14:57:24.0538937Z [W909 14:56:37.987620236 PyInterpreter.cpp:263] Warning: Deallocating Tensor that still has live PyObject references. This probably happened because you took out a weak reference to Tensor and didn't call _fix_weakref() after dereferencing it. Subsequent accesses to this tensor via the PyObject will now fail. (function decref) 2025-09-09T14:57:24.0540658Z PASSED 2025-09-09T14:57:24.0541395Z test/quantization/pt2e/test_quantize_pt2e.py::TestQuantizePT2E::test_composable_quantizer_throw PASSED 2025-09-09T14:57:24.0542628Z test/quantization/pt2e/test_quantize_pt2e.py::TestQuantizePT2E::test_composable_quantizer_transform_for_annotation PASSED 2025-09-09T14:57:24.0543864Z test/quantization/pt2e/test_quantize_pt2e.py::TestQuantizePT2E::test_constant_prop_preserve_metadata PASSED 2025-09-09T14:57:24.0544935Z test/quantization/pt2e/test_quantize_pt2e.py::TestQuantizePT2E::test_conv3d_bn_relu PASSED 2025-09-09T14:57:24.0545962Z test/quantization/pt2e/test_quantize_pt2e.py::TestQuantizePT2E::test_conv_padding_bn_relu PASSED 2025-09-09T14:57:24.0547032Z test/quantization/pt2e/test_quantize_pt2e.py::TestQuantizePT2E::test_conv_transpose3d_bn_relu PASSED 2025-09-09T14:57:24.0548111Z test/quantization/pt2e/test_quantize_pt2e.py::TestQuantizePT2E::test_conv_transpose_bn_relu PASSED 2025-09-09T14:57:24.0549130Z test/quantization/pt2e/test_quantize_pt2e.py::TestQuantizePT2E::test_derived_qspec PASSED 2025-09-09T14:57:24.0550308Z test/quantization/pt2e/test_quantize_pt2e.py::TestQuantizePT2E::test_derived_qspec_per_channel PASSED 2025-09-09T14:57:24.0551387Z test/quantization/pt2e/test_quantize_pt2e.py::TestQuantizePT2E::test_disallow_eval_train PASSED 2025-09-09T14:57:24.0552565Z test/quantization/pt2e/test_quantize_pt2e.py::TestQuantizePT2E::test_dont_fold_other_constant PASSED 2025-09-09T14:57:24.0553711Z test/quantization/pt2e/test_quantize_pt2e.py::TestQuantizePT2E::test_embedding_conv_linear_quantization PASSED 2025-09-09T14:57:24.0554819Z test/quantization/pt2e/test_quantize_pt2e.py::TestQuantizePT2E::test_embedding_quantizer PASSED 2025-09-09T14:57:24.0555925Z test/quantization/pt2e/test_quantize_pt2e.py::TestQuantizePT2E::test_fixed_qparams_qspec_observer_dedup PASSED 2025-09-09T14:57:24.0557058Z test/quantization/pt2e/test_quantize_pt2e.py::TestQuantizePT2E::test_fixed_qparams_qspec_ptq PASSED 2025-09-09T14:57:24.0558127Z test/quantization/pt2e/test_quantize_pt2e.py::TestQuantizePT2E::test_fixed_qparams_qspec_qat PASSED 2025-09-09T14:57:24.0559297Z test/quantization/pt2e/test_quantize_pt2e.py::TestQuantizePT2E::test_fold_all_ops_before_quantize PASSED 2025-09-09T14:57:24.0560343Z test/quantization/pt2e/test_quantize_pt2e.py::TestQuantizePT2E::test_fold_quantize PASSED 2025-09-09T14:57:24.0561386Z test/quantization/pt2e/test_quantize_pt2e.py::TestQuantizePT2E::test_fold_quantize_per_channel PASSED 2025-09-09T14:57:24.0562496Z test/quantization/pt2e/test_quantize_pt2e.py::TestQuantizePT2E::test_groupwise_per_channel_quant PASSED 2025-09-09T14:57:24.0563581Z test/quantization/pt2e/test_quantize_pt2e.py::TestQuantizePT2E::test_input_edge_sanity_check PASSED 2025-09-09T14:57:24.0564644Z test/quantization/pt2e/test_quantize_pt2e.py::TestQuantizePT2E::test_max_pool2d_quantizer PASSED 2025-09-09T14:57:24.0565690Z test/quantization/pt2e/test_quantize_pt2e.py::TestQuantizePT2E::test_model_is_exported PASSED 2025-09-09T14:57:24.0566588Z test/quantization/pt2e/test_quantize_pt2e.py::TestQuantizePT2E::test_move_exported_model_bn_device_cpu PASSED 2025-09-09T14:57:24.0567540Z test/quantization/pt2e/test_quantize_pt2e.py::TestQuantizePT2E::test_move_exported_model_bn_device_cuda PASSED 2025-09-09T14:57:24.0568477Z test/quantization/pt2e/test_quantize_pt2e.py::TestQuantizePT2E::test_move_exported_model_dropout PASSED 2025-09-09T14:57:24.0569410Z test/quantization/pt2e/test_quantize_pt2e.py::TestQuantizePT2E::test_move_exported_model_dropout_inplace PASSED 2025-09-09T14:57:24.0570383Z test/quantization/pt2e/test_quantize_pt2e.py::TestQuantizePT2E::test_multi_users_without_output_observer PASSED 2025-09-09T14:57:24.0571268Z test/quantization/pt2e/test_quantize_pt2e.py::TestQuantizePT2E::test_observer_callback PASSED 2025-09-09T14:57:24.0572137Z test/quantization/pt2e/test_quantize_pt2e.py::TestQuantizePT2E::test_prepare_obs_or_fq_callback PASSED 2025-09-09T14:57:24.0573033Z test/quantization/pt2e/test_quantize_pt2e.py::TestQuantizePT2E::test_preserve_nn_module_stack PASSED 2025-09-09T14:57:24.0573986Z test/quantization/pt2e/test_quantize_pt2e.py::TestQuantizePT2E::test_quantization_dtype_bfloat16_float8_e4m3fn PASSED 2025-09-09T14:57:24.0575004Z test/quantization/pt2e/test_quantize_pt2e.py::TestQuantizePT2E::test_quantization_dtype_bfloat16_float8_e5m2 PASSED 2025-09-09T14:57:24.0575974Z test/quantization/pt2e/test_quantize_pt2e.py::TestQuantizePT2E::test_quantization_dtype_bfloat16_int16 PASSED 2025-09-09T14:57:24.0576959Z test/quantization/pt2e/test_quantize_pt2e.py::TestQuantizePT2E::test_quantization_dtype_float32_float8_e4m3fn PASSED 2025-09-09T14:57:24.0577960Z test/quantization/pt2e/test_quantize_pt2e.py::TestQuantizePT2E::test_quantization_dtype_float32_float8_e5m2 PASSED 2025-09-09T14:57:24.0578921Z test/quantization/pt2e/test_quantize_pt2e.py::TestQuantizePT2E::test_quantization_dtype_float32_int16 PASSED 2025-09-09T14:57:24.0579848Z test/quantization/pt2e/test_quantize_pt2e.py::TestQuantizePT2E::test_quantize_in_place_ops input_act1 is a node 2025-09-09T14:57:24.0580413Z PASSED 2025-09-09T14:57:24.0580927Z test/quantization/pt2e/test_quantize_pt2e.py::TestQuantizePT2E::test_reentrant PASSED 2025-09-09T14:57:24.0582218Z test/quantization/pt2e/test_quantize_pt2e.py::TestQuantizePT2E::test_save_load W0909 14:57:23.747435 938 site-packages/torch/export/pt2_archive/_package.py:396] Expect archive file to be a file ending in .pt2, or is a buffer. Instead got {/tmp/tmpfas92rtw} 2025-09-09T14:57:24.0583661Z W0909 14:57:23.758127 938 site-packages/torch/export/pt2_archive/_package.py:571] Unable to load package. f must be a buffer or a file ending in .pt2. Instead got {/tmp/tmpfas92rtw} 2025-09-09T14:57:24.0584399Z PASSED 2025-09-09T14:57:24.0584910Z test/quantization/pt2e/test_quantize_pt2e.py::TestQuantizePT2E::test_shared_qspec PASSED 2025-09-09T14:57:24.0585758Z test/quantization/pt2e/test_quantize_pt2e.py::TestQuantizePT2E::test_shared_qspec_transitivity PASSED 2025-09-09T14:57:24.0586687Z test/quantization/pt2e/test_quantize_pt2e.py::TestQuantizePT2E::test_shared_qspec_transitivity_case_2 PASSED 2025-09-09T14:58:08.0526410Z test/quantization/pt2e/test_quantize_pt2e.py::TestQuantizePT2E::test_simple_quantizer PASSED 2025-09-09T14:58:08.0527387Z test/quantization/pt2e/test_quantize_pt2e.py::TestQuantizePT2E::test_speed PASSED 2025-09-09T14:58:08.0528386Z test/quantization/pt2e/test_quantize_pt2e.py::TestQuantizePT2E::test_transform_for_annotation PASSED 2025-09-09T14:58:08.0529522Z test/quantization/pt2e/test_quantize_pt2e.py::TestQuantizePT2E::test_wo_annotate_conv_output_quantizer PASSED 2025-09-09T14:58:08.0530808Z test/quantization/pt2e/test_quantize_pt2e.py::TestQuantizePT2EAffineQuantization::test_channel_group_quantization prepared model: GraphModule( 2025-09-09T14:58:08.0531647Z (linear): Module() 2025-09-09T14:58:08.0532029Z (activation_post_process_1): AffineQuantizedMinMaxObserver() 2025-09-09T14:58:08.0532586Z (activation_post_process_0): AffineQuantizedMinMaxObserver() 2025-09-09T14:58:08.0533004Z ) 2025-09-09T14:58:08.0533119Z 2025-09-09T14:58:08.0533136Z 2025-09-09T14:58:08.0533141Z 2025-09-09T14:58:08.0533253Z def forward(self, x): 2025-09-09T14:58:08.0533584Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:58:08.0533994Z linear_weight = self.linear.weight 2025-09-09T14:58:08.0534577Z activation_post_process_1 = self.activation_post_process_1(linear_weight); linear_weight = None 2025-09-09T14:58:08.0535169Z linear_bias = self.linear.bias 2025-09-09T14:58:08.0535635Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:58:08.0536722Z linear = torch.ops.aten.linear.default(activation_post_process_0, activation_post_process_1, linear_bias); activation_post_process_0 = activation_post_process_1 = linear_bias = None 2025-09-09T14:58:08.0537754Z return pytree.tree_unflatten((linear,), self._out_spec) 2025-09-09T14:58:08.0538137Z 2025-09-09T14:58:08.0538469Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:58:08.0538915Z quantized model GraphModule( 2025-09-09T14:58:08.0539203Z (linear): Module() 2025-09-09T14:58:08.0539438Z ) 2025-09-09T14:58:08.0539556Z 2025-09-09T14:58:08.0539561Z 2025-09-09T14:58:08.0539566Z 2025-09-09T14:58:08.0539662Z def forward(self, x): 2025-09-09T14:58:08.0539991Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:58:08.0540377Z _scale0 = self._scale0 2025-09-09T14:58:08.0540667Z _zero_point0 = self._zero_point0 2025-09-09T14:58:08.0541002Z quantize_affine = self._frozen_param0 2025-09-09T14:58:08.0542010Z dequantize_affine = torch.ops.torchao.dequantize_affine(quantize_affine, (1, 128), _scale0, _zero_point0, torch.uint8, 0, 255, output_dtype = torch.float32); quantize_affine = _scale0 = _zero_point0 = None 2025-09-09T14:58:08.0543003Z linear_bias = self.linear.bias 2025-09-09T14:58:08.0543606Z _scale1 = self._scale1 2025-09-09T14:58:08.0543899Z _zero_point1 = self._zero_point1 2025-09-09T14:58:08.0544532Z quantize_affine_1 = torch.ops.torchao.quantize_affine(x, (1, 128), _scale1, _zero_point1, torch.uint8, 0, 255); x = None 2025-09-09T14:58:08.0546053Z dequantize_affine_1 = torch.ops.torchao.dequantize_affine(quantize_affine_1, (1, 128), _scale1, _zero_point1, torch.uint8, 0, 255, output_dtype = torch.float32); quantize_affine_1 = _scale1 = _zero_point1 = None 2025-09-09T14:58:08.0547541Z linear = torch.ops.aten.linear.default(dequantize_affine_1, dequantize_affine, linear_bias); dequantize_affine_1 = dequantize_affine = linear_bias = None 2025-09-09T14:58:08.0548441Z return pytree.tree_unflatten((linear,), self._out_spec) 2025-09-09T14:58:08.0548831Z 2025-09-09T14:58:08.0549151Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:58:08.0549629Z PASSED 2025-09-09T14:58:08.0550524Z test/quantization/pt2e/test_quantize_pt2e.py::TestQuantizePT2EAffineQuantization::test_dynamic_affine_act_per_channel_weights PASSED 2025-09-09T14:58:08.0551914Z test/quantization/pt2e/test_quantize_pt2e.py::TestQuantizePT2EAffineQuantization::test_dynamic_per_tok_act_per_group_weights prepared model: GraphModule( 2025-09-09T14:58:08.0552791Z (linear): Module() 2025-09-09T14:58:08.0553157Z (activation_post_process_1): AffineQuantizedMinMaxObserver() 2025-09-09T14:58:08.0553720Z (activation_post_process_0): AffineQuantizedPlaceholderObserver() 2025-09-09T14:58:08.0554148Z ) 2025-09-09T14:58:08.0554267Z 2025-09-09T14:58:08.0554272Z 2025-09-09T14:58:08.0554277Z 2025-09-09T14:58:08.0554374Z def forward(self, x): 2025-09-09T14:58:08.0554707Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:58:08.0555105Z linear_weight = self.linear.weight 2025-09-09T14:58:08.0555681Z activation_post_process_1 = self.activation_post_process_1(linear_weight); linear_weight = None 2025-09-09T14:58:08.0556270Z linear_bias = self.linear.bias 2025-09-09T14:58:08.0556729Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:58:08.0557802Z linear = torch.ops.aten.linear.default(activation_post_process_0, activation_post_process_1, linear_bias); activation_post_process_0 = activation_post_process_1 = linear_bias = None 2025-09-09T14:58:08.0558835Z return pytree.tree_unflatten((linear,), self._out_spec) 2025-09-09T14:58:08.0559220Z 2025-09-09T14:58:08.0559628Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:58:08.0560070Z quantized model GraphModule( 2025-09-09T14:58:08.0560355Z (linear): Module() 2025-09-09T14:58:08.0560598Z ) 2025-09-09T14:58:08.0560715Z 2025-09-09T14:58:08.0560720Z 2025-09-09T14:58:08.0560725Z 2025-09-09T14:58:08.0560821Z def forward(self, x): 2025-09-09T14:58:08.0561150Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:58:08.0561533Z _scale0 = self._scale0 2025-09-09T14:58:08.0561818Z _zero_point0 = self._zero_point0 2025-09-09T14:58:08.0562156Z quantize_affine = self._frozen_param0 2025-09-09T14:58:08.0563179Z dequantize_affine = torch.ops.torchao.dequantize_affine(quantize_affine, (1, 128), _scale0, _zero_point0, torch.int8, -127, 127, output_dtype = torch.float32); quantize_affine = _scale0 = _zero_point0 = None 2025-09-09T14:58:08.0564266Z linear_bias = self.linear.bias 2025-09-09T14:58:08.0564950Z choose_qparams_affine = torch.ops.torchao.choose_qparams_affine(x, 'SYMMETRIC', (1, 128), torch.int8, -128, 127, None, None, None) 2025-09-09T14:58:08.0565529Z getitem = choose_qparams_affine[0] 2025-09-09T14:58:08.0565910Z getitem_1 = choose_qparams_affine[1]; choose_qparams_affine = None 2025-09-09T14:58:08.0566524Z quantize_affine_1 = torch.ops.torchao.quantize_affine(x, (1, 128), getitem, getitem_1, torch.int8, -128, 127); x = None 2025-09-09T14:58:08.0567677Z dequantize_affine_1 = torch.ops.torchao.dequantize_affine(quantize_affine_1, (1, 128), getitem, getitem_1, torch.int8, -128, 127, output_dtype = torch.float32); quantize_affine_1 = getitem = getitem_1 = None 2025-09-09T14:58:08.0568872Z linear = torch.ops.aten.linear.default(dequantize_affine_1, dequantize_affine, linear_bias); dequantize_affine_1 = dequantize_affine = linear_bias = None 2025-09-09T14:58:08.0569676Z return pytree.tree_unflatten((linear,), self._out_spec) 2025-09-09T14:58:08.0570000Z 2025-09-09T14:58:08.0570268Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:58:08.0570661Z PASSED 2025-09-09T14:58:08.0571298Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn_Base::test_fold_bn_erases_bn_node SKIPPED 2025-09-09T14:58:08.0572339Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn_Base::test_qat_conv_bn_bias_derived_qspec SKIPPED 2025-09-09T14:58:08.0573363Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn_Base::test_qat_conv_bn_fusion SKIPPED 2025-09-09T14:58:08.0574353Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn_Base::test_qat_conv_bn_fusion_cuda SKIPPED 2025-09-09T14:58:08.0575401Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn_Base::test_qat_conv_bn_fusion_literal_args SKIPPED 2025-09-09T14:58:08.0576460Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn_Base::test_qat_conv_bn_fusion_no_conv_bias SKIPPED 2025-09-09T14:58:08.0577535Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn_Base::test_qat_conv_bn_per_channel_weight_bias SKIPPED 2025-09-09T14:58:08.0578585Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn_Base::test_qat_conv_bn_relu_fusion SKIPPED 2025-09-09T14:58:08.0579606Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn_Base::test_qat_conv_bn_relu_fusion_cuda SKIPPED 2025-09-09T14:58:08.0580681Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn_Base::test_qat_conv_bn_relu_fusion_no_conv_bias SKIPPED 2025-09-09T14:58:08.0581712Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn_Base::test_qat_conv_no_bias SKIPPED 2025-09-09T14:58:08.0582692Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn_Base::test_qat_conv_transpose_bn SKIPPED 2025-09-09T14:58:08.0583715Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn_Base::test_qat_conv_transpose_bn_relu SKIPPED 2025-09-09T14:58:08.0584722Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn_Base::test_qat_inplace_add_relu SKIPPED 2025-09-09T14:58:08.0585769Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn_Base::test_qat_per_channel_weight_custom_dtype SKIPPED 2025-09-09T14:58:08.0586844Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn_Base::test_qat_preserve_source_fn_stack SKIPPED 2025-09-09T14:58:08.0587865Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn_Base::test_qat_update_shared_qspec SKIPPED 2025-09-09T14:58:08.0588868Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn1d::test_fold_bn_erases_bn_node PASSED 2025-09-09T14:58:08.0589870Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn1d::test_qat_conv_bn_bias_derived_qspec PASSED 2025-09-09T14:58:08.0590793Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn1d::test_qat_conv_bn_fusion model pt2e: GraphModule( 2025-09-09T14:58:08.0591376Z (conv): Module() 2025-09-09T14:58:08.0591568Z (bn): Module() 2025-09-09T14:58:08.0591861Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:58:08.0592869Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0104]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:58:08.0594078Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3264806270599365, max_val=1.318617343902588) 2025-09-09T14:58:08.0594581Z ) 2025-09-09T14:58:26.3909086Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:58:26.3910352Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0017, 0.0019, 0.0024]), zero_point=tensor([0, 0, 0], dtype=torch.int32), dtype=torch.int8, quant_min=-127, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False 2025-09-09T14:58:26.3911984Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.2148, -0.0992, -0.2048]), max_val=tensor([0.0771, 0.2459, 0.3011])) 2025-09-09T14:58:26.3912775Z ) 2025-09-09T14:58:26.3913102Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:58:26.3914262Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0111]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:58:26.3915617Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.4139524698257446, max_val=1.4139440059661865) 2025-09-09T14:58:26.3916238Z ) 2025-09-09T14:58:26.3916436Z ) 2025-09-09T14:58:26.3916552Z 2025-09-09T14:58:26.3916557Z 2025-09-09T14:58:26.3916561Z 2025-09-09T14:58:26.3916666Z def forward(self, x): 2025-09-09T14:58:26.3916998Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:58:26.3917408Z conv_weight = self.conv.weight 2025-09-09T14:58:26.3917730Z conv_bias = self.conv.bias 2025-09-09T14:58:26.3918034Z bn_weight = self.bn.weight 2025-09-09T14:58:26.3918323Z bn_bias = self.bn.bias 2025-09-09T14:58:26.3918630Z bn_running_mean = self.bn.running_mean 2025-09-09T14:58:26.3918986Z bn_running_var = self.bn.running_var 2025-09-09T14:58:26.3919493Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T14:58:26.3920026Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:58:26.3920730Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T14:58:26.3921363Z add = torch.ops.aten.add.Tensor(bn_running_var, 1e-05) 2025-09-09T14:58:26.3921816Z sqrt = torch.ops.aten.sqrt.default(add); add = None 2025-09-09T14:58:26.3922488Z div = torch.ops.aten.div.Tensor(bn_weight, sqrt); sqrt = None 2025-09-09T14:58:26.3923003Z reshape = torch.ops.aten.reshape.default(div, [-1, 1, 1]) 2025-09-09T14:58:26.3923598Z mul = torch.ops.aten.mul.Tensor(conv_weight, reshape); conv_weight = reshape = None 2025-09-09T14:58:26.3924269Z activation_post_process_1 = self.activation_post_process_1(mul); mul = None 2025-09-09T14:58:26.3925001Z zeros_like = torch.ops.aten.zeros_like.default(conv_bias, dtype = torch.float32, pin_memory = False) 2025-09-09T14:58:26.3926243Z conv1d_1 = torch.ops.aten.conv1d.default(activation_post_process_0, activation_post_process_1, zeros_like); activation_post_process_0 = activation_post_process_1 = zeros_like = None 2025-09-09T14:58:26.3927308Z reshape_1 = torch.ops.aten.reshape.default(div, [1, -1, 1]); div = None 2025-09-09T14:58:26.3927945Z div_1 = torch.ops.aten.div.Tensor(conv1d_1, reshape_1); conv1d_1 = reshape_1 = None 2025-09-09T14:58:26.3928632Z reshape_2 = torch.ops.aten.reshape.default(conv_bias, [1, -1, 1]); conv_bias = None 2025-09-09T14:58:26.3929276Z add_1 = torch.ops.aten.add.Tensor(div_1, reshape_2); div_1 = reshape_2 = None 2025-09-09T14:58:26.3930333Z batch_norm_1 = torch.ops.aten.batch_norm.default(add_1, bn_weight, bn_bias, bn_running_mean, bn_running_var, True, 0.1, 1e-05, True); add_1 = bn_weight = bn_bias = bn_running_mean = bn_running_var = None 2025-09-09T14:58:26.3931693Z activation_post_process_2 = self.activation_post_process_2(batch_norm_1); batch_norm_1 = None 2025-09-09T14:58:26.3932432Z return pytree.tree_unflatten((activation_post_process_2,), self._out_spec) 2025-09-09T14:58:26.3933046Z 2025-09-09T14:58:26.3933376Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:58:26.3933812Z model fx: GraphModule( 2025-09-09T14:58:26.3934185Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:58:26.3935333Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0104]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:58:26.3936674Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3264806270599365, max_val=1.318617343902588) 2025-09-09T14:58:26.3937289Z ) 2025-09-09T14:58:26.3937496Z (conv): ConvBn1d( 2025-09-09T14:58:26.3937760Z 3, 3, kernel_size=(3,), stride=(1,) 2025-09-09T14:58:26.3938243Z (bn): BatchNorm1d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T14:58:26.3938789Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:58:26.3939959Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0017, 0.0019, 0.0024]), zero_point=tensor([0, 0, 0], dtype=torch.int32), dtype=torch.qint8, quant_min=-127, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False 2025-09-09T14:58:26.3941536Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.2148, -0.0992, -0.2048]), max_val=tensor([0.0771, 0.2459, 0.3011])) 2025-09-09T14:58:26.3942312Z ) 2025-09-09T14:58:26.3942513Z ) 2025-09-09T14:58:26.3942828Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:58:26.3943978Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0111]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:58:26.3945323Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.4139524698257446, max_val=1.4139440059661865) 2025-09-09T14:58:26.3945944Z ) 2025-09-09T14:58:26.3946140Z ) 2025-09-09T14:58:26.3946250Z 2025-09-09T14:58:26.3946255Z 2025-09-09T14:58:26.3946260Z 2025-09-09T14:58:26.3946357Z def forward(self, x): 2025-09-09T14:58:26.3946773Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:58:26.3947400Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T14:58:26.3948070Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T14:58:26.3948571Z return activation_post_process_1 2025-09-09T14:58:26.3948877Z 2025-09-09T14:58:26.3957614Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:58:26.3958056Z diff: tensor([[[0., 0., 0.], 2025-09-09T14:58:26.3958305Z [0., 0., 0.], 2025-09-09T14:58:26.3958552Z [0., 0., 0.]]], grad_fn=) 2025-09-09T14:58:26.3958860Z converted model pt2e: GraphModule( 2025-09-09T14:58:26.3959129Z (conv): Module() 2025-09-09T14:58:26.3959406Z (bn): Module() 2025-09-09T14:58:26.3959596Z ) 2025-09-09T14:58:26.3959695Z 2025-09-09T14:58:26.3959699Z 2025-09-09T14:58:26.3959703Z 2025-09-09T14:58:26.3959795Z def forward(self, x): 2025-09-09T14:58:26.3960077Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:58:26.3960415Z conv_bias = self.conv.bias 2025-09-09T14:58:26.3960710Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T14:58:26.3961419Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.010372933000326157, 0, -128, 127, torch.int8); x = None 2025-09-09T14:58:26.3962770Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.010372933000326157, 0, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T14:58:26.3963803Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T14:58:26.3964356Z _scale_0 = self._scale_0 2025-09-09T14:58:26.3964613Z _zero_point_0 = self._zero_point_0 2025-09-09T14:58:26.3964920Z quantize_per_channel = self._frozen_param0 2025-09-09T14:58:26.3965801Z dequantize_per_channel = torch.ops.quantized_decomposed.dequantize_per_channel.default(quantize_per_channel, _scale_0, _zero_point_0, 0, -127, 127, torch.int8); quantize_per_channel = _scale_0 = _zero_point_0 = None 2025-09-09T14:58:26.3967131Z conv1d_2 = torch.ops.aten.conv1d.default(dequantize_per_tensor_default, dequantize_per_channel, conv_bias); dequantize_per_tensor_default = dequantize_per_channel = conv_bias = None 2025-09-09T14:58:26.3968326Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv1d_2, 0.011089790612459183, 0, -128, 127, torch.int8); conv1d_2 = None 2025-09-09T14:58:26.3969596Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.011089790612459183, 0, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T14:58:26.3970595Z return pytree.tree_unflatten((dequantize_per_tensor_default_1,), self._out_spec) 2025-09-09T14:58:26.3971005Z 2025-09-09T14:58:26.3971283Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:58:26.3971665Z onverted model fx: GraphModule( 2025-09-09T14:58:26.3972039Z (conv): QuantizedConv1d(Reference)(3, 3, kernel_size=(3,), stride=(1,)) 2025-09-09T14:58:26.3972409Z ) 2025-09-09T14:58:26.3972506Z 2025-09-09T14:58:26.3972510Z 2025-09-09T14:58:26.3972514Z 2025-09-09T14:58:26.3972604Z def forward(self, x): 2025-09-09T14:58:26.3973211Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.010372933000326157, 0, -128, 127, torch.int8); x = None 2025-09-09T14:58:26.3974424Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.010372933000326157, 0, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T14:58:26.3975419Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T14:58:26.3976265Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.011089790612459183, 0, -128, 127, torch.int8); conv = None 2025-09-09T14:58:26.3977515Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.011089790612459183, 0, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T14:58:26.3978381Z return dequantize_per_tensor_default_1 2025-09-09T14:58:26.3978652Z 2025-09-09T14:58:26.3978927Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:58:26.3979291Z diff: tensor([[[0., 0., 0.], 2025-09-09T14:58:26.3979530Z [0., 0., 0.], 2025-09-09T14:58:26.3979738Z [0., 0., 0.]]]) 2025-09-09T14:58:26.3979971Z model pt2e: GraphModule( 2025-09-09T14:58:26.3980191Z (conv): Module() 2025-09-09T14:58:26.3980392Z (bn): Module() 2025-09-09T14:58:26.3980683Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:58:50.2901002Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0104]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:58:50.2902618Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3264806270599365, max_val=1.318617343902588) 2025-09-09T14:58:50.2903116Z ) 2025-09-09T14:58:50.2903394Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:58:50.2904611Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0024]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.int8, quant_min=-127, quant_max=127, qscheme=torch.per_tensor_symmetric, reduce_range=False 2025-09-09T14:58:50.2905883Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.214811772108078, max_val=0.30109599232673645) 2025-09-09T14:58:50.2906388Z ) 2025-09-09T14:58:50.2906656Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:58:50.2907569Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0111]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:58:50.2908635Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.4139524698257446, max_val=1.4138529300689697) 2025-09-09T14:58:50.2909133Z ) 2025-09-09T14:58:50.2909292Z ) 2025-09-09T14:58:50.2909383Z 2025-09-09T14:58:50.2909388Z 2025-09-09T14:58:50.2909400Z 2025-09-09T14:58:50.2909479Z def forward(self, x): 2025-09-09T14:58:50.2909760Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:58:50.2910110Z conv_weight = self.conv.weight 2025-09-09T14:58:50.2910375Z conv_bias = self.conv.bias 2025-09-09T14:58:50.2910625Z bn_weight = self.bn.weight 2025-09-09T14:58:50.2910862Z bn_bias = self.bn.bias 2025-09-09T14:58:50.2911116Z bn_running_mean = self.bn.running_mean 2025-09-09T14:58:50.2911408Z bn_running_var = self.bn.running_var 2025-09-09T14:58:50.2911734Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T14:58:50.2912167Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:58:50.2912745Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T14:58:50.2913263Z add = torch.ops.aten.add.Tensor(bn_running_var, 1e-05) 2025-09-09T14:58:50.2913649Z sqrt = torch.ops.aten.sqrt.default(add); add = None 2025-09-09T14:58:50.2914057Z div = torch.ops.aten.div.Tensor(bn_weight, sqrt); sqrt = None 2025-09-09T14:58:50.2914484Z reshape = torch.ops.aten.reshape.default(div, [-1, 1, 1]) 2025-09-09T14:58:50.2914980Z mul = torch.ops.aten.mul.Tensor(conv_weight, reshape); conv_weight = reshape = None 2025-09-09T14:58:50.2915529Z activation_post_process_1 = self.activation_post_process_1(mul); mul = None 2025-09-09T14:58:50.2916132Z zeros_like = torch.ops.aten.zeros_like.default(conv_bias, dtype = torch.float32, pin_memory = False) 2025-09-09T14:58:50.2917088Z conv1d_1 = torch.ops.aten.conv1d.default(activation_post_process_0, activation_post_process_1, zeros_like); activation_post_process_0 = activation_post_process_1 = zeros_like = None 2025-09-09T14:58:50.2917957Z reshape_1 = torch.ops.aten.reshape.default(div, [1, -1, 1]); div = None 2025-09-09T14:58:50.2918486Z div_1 = torch.ops.aten.div.Tensor(conv1d_1, reshape_1); conv1d_1 = reshape_1 = None 2025-09-09T14:58:50.2919049Z reshape_2 = torch.ops.aten.reshape.default(conv_bias, [1, -1, 1]); conv_bias = None 2025-09-09T14:58:50.2919692Z add_1 = torch.ops.aten.add.Tensor(div_1, reshape_2); div_1 = reshape_2 = None 2025-09-09T14:58:50.2920566Z batch_norm_1 = torch.ops.aten.batch_norm.default(add_1, bn_weight, bn_bias, bn_running_mean, bn_running_var, True, 0.1, 1e-05, True); add_1 = bn_weight = bn_bias = bn_running_mean = bn_running_var = None 2025-09-09T14:58:50.2921479Z activation_post_process_2 = self.activation_post_process_2(batch_norm_1); batch_norm_1 = None 2025-09-09T14:58:50.2922076Z return pytree.tree_unflatten((activation_post_process_2,), self._out_spec) 2025-09-09T14:58:50.2922632Z 2025-09-09T14:58:50.2922909Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:58:50.2923271Z model fx: GraphModule( 2025-09-09T14:58:50.2923582Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:58:50.2924640Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0104]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:58:50.2925818Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3264806270599365, max_val=1.318617343902588) 2025-09-09T14:58:50.2926321Z ) 2025-09-09T14:58:50.2926490Z (conv): ConvBn1d( 2025-09-09T14:58:50.2926717Z 3, 3, kernel_size=(3,), stride=(1,) 2025-09-09T14:58:50.2927123Z (bn): BatchNorm1d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T14:58:50.2927573Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:58:50.2928478Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0024]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-127, quant_max=127, qscheme=torch.per_tensor_symmetric, reduce_range=False 2025-09-09T14:58:50.2929574Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.214811772108078, max_val=0.30109599232673645) 2025-09-09T14:58:50.2930073Z ) 2025-09-09T14:58:50.2930240Z ) 2025-09-09T14:58:50.2930506Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:58:50.2931425Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0111]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:58:50.2932493Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.4139524698257446, max_val=1.4138529300689697) 2025-09-09T14:58:50.2933002Z ) 2025-09-09T14:58:50.2933155Z ) 2025-09-09T14:58:50.2933251Z 2025-09-09T14:58:50.2933256Z 2025-09-09T14:58:50.2933260Z 2025-09-09T14:58:50.2933338Z def forward(self, x): 2025-09-09T14:58:50.2933804Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:58:50.2934497Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T14:58:50.2935132Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T14:58:50.2935553Z return activation_post_process_1 2025-09-09T14:58:50.2935805Z 2025-09-09T14:58:50.2936067Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:58:50.2936425Z diff: tensor([[[0., 0., 0.], 2025-09-09T14:58:50.2936650Z [0., 0., 0.], 2025-09-09T14:58:50.2936934Z [0., 0., 0.]]], grad_fn=) 2025-09-09T14:58:50.2937295Z converted model pt2e: GraphModule( 2025-09-09T14:58:50.2937544Z (conv): Module() 2025-09-09T14:58:50.2937739Z (bn): Module() 2025-09-09T14:58:50.2937918Z ) 2025-09-09T14:58:50.2938017Z 2025-09-09T14:58:50.2938021Z 2025-09-09T14:58:50.2938025Z 2025-09-09T14:58:50.2938103Z def forward(self, x): 2025-09-09T14:58:50.2938372Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:58:50.2938705Z conv_bias = self.conv.bias 2025-09-09T14:58:50.2938996Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T14:58:50.2939691Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.010372933000326157, 0, -128, 127, torch.int8); x = None 2025-09-09T14:58:50.2940905Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.010372933000326157, 0, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T14:58:50.2941946Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T14:58:50.2942452Z quantize_per_tensor = self._frozen_param0 2025-09-09T14:58:50.2943226Z dequantize_per_tensor = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor, 0.002370834583416581, 0, -127, 127, torch.int8); quantize_per_tensor = None 2025-09-09T14:58:50.2944557Z conv1d_2 = torch.ops.aten.conv1d.default(dequantize_per_tensor_default, dequantize_per_tensor, conv_bias); dequantize_per_tensor_default = dequantize_per_tensor = conv_bias = None 2025-09-09T14:58:50.2946105Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv1d_2, 0.01108943298459053, 0, -128, 127, torch.int8); conv1d_2 = None 2025-09-09T14:58:50.2947694Z dequantize_per_tensor_default_2 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_2, 0.01108943298459053, 0, -128, 127, torch.int8); quantize_per_tensor_default_2 = None 2025-09-09T14:58:50.2948914Z return pytree.tree_unflatten((dequantize_per_tensor_default_2,), self._out_spec) 2025-09-09T14:58:50.2949397Z 2025-09-09T14:58:50.2949694Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:58:50.2950119Z onverted model fx: GraphModule( 2025-09-09T14:58:50.2950534Z (conv): QuantizedConv1d(Reference)(3, 3, kernel_size=(3,), stride=(1,)) 2025-09-09T14:58:50.2950965Z ) 2025-09-09T14:58:50.2951064Z 2025-09-09T14:58:50.2951068Z 2025-09-09T14:58:50.2951072Z 2025-09-09T14:58:50.2951165Z def forward(self, x): 2025-09-09T14:58:50.2951891Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.010372933000326157, 0, -128, 127, torch.int8); x = None 2025-09-09T14:58:50.2953411Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.010372933000326157, 0, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T14:58:50.2954640Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T14:58:50.2955653Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.01108943298459053, 0, -128, 127, torch.int8); conv = None 2025-09-09T14:58:50.2957217Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.01108943298459053, 0, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T14:58:50.2958278Z return dequantize_per_tensor_default_1 2025-09-09T14:58:50.2958582Z 2025-09-09T14:58:50.2958883Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:58:50.2959287Z diff: tensor([[[0., 0., 0.], 2025-09-09T14:58:50.2959600Z [0., 0., 0.], 2025-09-09T14:58:50.2959811Z [0., 0., 0.]]]) 2025-09-09T14:58:50.2960252Z PASSED 2025-09-09T14:58:50.2960835Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn1d::test_qat_conv_bn_fusion_cuda model pt2e: GraphModule( 2025-09-09T14:58:50.2961451Z (conv): Module() 2025-09-09T14:58:50.2961653Z (bn): Module() 2025-09-09T14:59:05.9516843Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:59:05.9518130Z fake_quant_enabled=tensor([1], device='cuda:0'), observer_enabled=tensor([1], device='cuda:0'), scale=tensor([0.0104], device='cuda:0'), zero_point=tensor([0], device='cuda:0', dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:59:05.9519789Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3264806270599365, max_val=1.318617343902588) 2025-09-09T14:59:05.9520306Z ) 2025-09-09T14:59:05.9520593Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:59:05.9521745Z fake_quant_enabled=tensor([1], device='cuda:0'), observer_enabled=tensor([1], device='cuda:0'), scale=tensor([0.0022, 0.0020, 0.0022], device='cuda:0'), zero_point=tensor([0, 0, 0], device='cuda:0', dtype=torch.int32), dtype=torch.int8, quant_min=-127, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False 2025-09-09T14:59:05.9523924Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.2799, -0.2557, -0.2618], device='cuda:0'), max_val=tensor([0.1970, 0.2308, 0.2775], device='cuda:0')) 2025-09-09T14:59:05.9524887Z ) 2025-09-09T14:59:05.9525172Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:59:05.9526303Z fake_quant_enabled=tensor([1], device='cuda:0'), observer_enabled=tensor([1], device='cuda:0'), scale=tensor([0.0110], device='cuda:0'), zero_point=tensor([-1], device='cuda:0', dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:59:05.9527872Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3958139419555664, max_val=1.4123148918151855) 2025-09-09T14:59:05.9528382Z ) 2025-09-09T14:59:05.9528560Z ) 2025-09-09T14:59:05.9528663Z 2025-09-09T14:59:05.9528667Z 2025-09-09T14:59:05.9528671Z 2025-09-09T14:59:05.9528765Z def forward(self, x): 2025-09-09T14:59:05.9529052Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:59:05.9529413Z conv_weight = self.conv.weight 2025-09-09T14:59:05.9529698Z conv_bias = self.conv.bias 2025-09-09T14:59:05.9529974Z bn_weight = self.bn.weight 2025-09-09T14:59:05.9530229Z bn_bias = self.bn.bias 2025-09-09T14:59:05.9530500Z bn_running_mean = self.bn.running_mean 2025-09-09T14:59:05.9530813Z bn_running_var = self.bn.running_var 2025-09-09T14:59:05.9531158Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T14:59:05.9531611Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:59:05.9532207Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T14:59:05.9532745Z add = torch.ops.aten.add.Tensor(bn_running_var, 1e-05) 2025-09-09T14:59:05.9533136Z sqrt = torch.ops.aten.sqrt.default(add); add = None 2025-09-09T14:59:05.9533547Z div = torch.ops.aten.div.Tensor(bn_weight, sqrt); sqrt = None 2025-09-09T14:59:05.9533984Z reshape = torch.ops.aten.reshape.default(div, [-1, 1, 1]) 2025-09-09T14:59:05.9534499Z mul = torch.ops.aten.mul.Tensor(conv_weight, reshape); conv_weight = reshape = None 2025-09-09T14:59:05.9535066Z activation_post_process_1 = self.activation_post_process_1(mul); mul = None 2025-09-09T14:59:05.9535682Z zeros_like = torch.ops.aten.zeros_like.default(conv_bias, dtype = torch.float32, pin_memory = False) 2025-09-09T14:59:05.9536662Z conv1d_1 = torch.ops.aten.conv1d.default(activation_post_process_0, activation_post_process_1, zeros_like); activation_post_process_0 = activation_post_process_1 = zeros_like = None 2025-09-09T14:59:05.9537537Z reshape_1 = torch.ops.aten.reshape.default(div, [1, -1, 1]); div = None 2025-09-09T14:59:05.9538074Z div_1 = torch.ops.aten.div.Tensor(conv1d_1, reshape_1); conv1d_1 = reshape_1 = None 2025-09-09T14:59:05.9538646Z reshape_2 = torch.ops.aten.reshape.default(conv_bias, [1, -1, 1]); conv_bias = None 2025-09-09T14:59:05.9539188Z add_1 = torch.ops.aten.add.Tensor(div_1, reshape_2); div_1 = reshape_2 = None 2025-09-09T14:59:05.9540056Z batch_norm_1 = torch.ops.aten.batch_norm.default(add_1, bn_weight, bn_bias, bn_running_mean, bn_running_var, True, 0.1, 1e-05, True); add_1 = bn_weight = bn_bias = bn_running_mean = bn_running_var = None 2025-09-09T14:59:05.9540977Z activation_post_process_2 = self.activation_post_process_2(batch_norm_1); batch_norm_1 = None 2025-09-09T14:59:05.9541571Z return pytree.tree_unflatten((activation_post_process_2,), self._out_spec) 2025-09-09T14:59:05.9541953Z 2025-09-09T14:59:05.9542225Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:59:05.9542594Z model fx: GraphModule( 2025-09-09T14:59:05.9542909Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:59:05.9544010Z fake_quant_enabled=tensor([1], device='cuda:0'), observer_enabled=tensor([1], device='cuda:0'), scale=tensor([0.0104], device='cuda:0'), zero_point=tensor([0], device='cuda:0', dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:59:05.9545370Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3264806270599365, max_val=1.318617343902588) 2025-09-09T14:59:05.9545870Z ) 2025-09-09T14:59:05.9546126Z (conv): ConvBn1d( 2025-09-09T14:59:05.9546345Z 3, 3, kernel_size=(3,), stride=(1,) 2025-09-09T14:59:05.9546754Z (bn): BatchNorm1d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T14:59:05.9547212Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:59:05.9548338Z fake_quant_enabled=tensor([1], device='cuda:0'), observer_enabled=tensor([1], device='cuda:0'), scale=tensor([0.0022, 0.0020, 0.0022], device='cuda:0'), zero_point=tensor([0, 0, 0], device='cuda:0', dtype=torch.int32), dtype=torch.qint8, quant_min=-127, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False 2025-09-09T14:59:05.9549887Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.2799, -0.2557, -0.2618], device='cuda:0'), max_val=tensor([0.1970, 0.2308, 0.2775], device='cuda:0')) 2025-09-09T14:59:05.9550613Z ) 2025-09-09T14:59:05.9550787Z ) 2025-09-09T14:59:05.9551054Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:59:05.9552170Z fake_quant_enabled=tensor([1], device='cuda:0'), observer_enabled=tensor([1], device='cuda:0'), scale=tensor([0.0110], device='cuda:0'), zero_point=tensor([-1], device='cuda:0', dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:59:05.9553437Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3958139419555664, max_val=1.4123148918151855) 2025-09-09T14:59:05.9553942Z ) 2025-09-09T14:59:05.9554111Z ) 2025-09-09T14:59:05.9554205Z 2025-09-09T14:59:05.9554209Z 2025-09-09T14:59:05.9554213Z 2025-09-09T14:59:05.9554296Z def forward(self, x): 2025-09-09T14:59:05.9554654Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:59:05.9555194Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T14:59:05.9555735Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T14:59:05.9556168Z return activation_post_process_1 2025-09-09T14:59:05.9556421Z 2025-09-09T14:59:05.9556698Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:59:05.9557084Z diff: tensor([[[0., 0., 0.], 2025-09-09T14:59:05.9557348Z [0., 0., 0.], 2025-09-09T14:59:05.9557633Z [0., 0., 0.]]], device='cuda:0', grad_fn=) 2025-09-09T14:59:05.9557963Z converted model pt2e: GraphModule( 2025-09-09T14:59:05.9558225Z (conv): Module() 2025-09-09T14:59:05.9558424Z (bn): Module() 2025-09-09T14:59:05.9558619Z ) 2025-09-09T14:59:05.9558716Z 2025-09-09T14:59:05.9558720Z 2025-09-09T14:59:05.9558724Z 2025-09-09T14:59:05.9558805Z def forward(self, x): 2025-09-09T14:59:05.9559090Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:59:05.9559505Z conv_bias = self.conv.bias 2025-09-09T14:59:05.9559799Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T14:59:05.9560504Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.010372933931648731, 0, -128, 127, torch.int8); x = None 2025-09-09T14:59:05.9561728Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.010372933931648731, 0, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T14:59:05.9562763Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T14:59:05.9563236Z _scale_0 = self._scale_0 2025-09-09T14:59:05.9563490Z _zero_point_0 = self._zero_point_0 2025-09-09T14:59:05.9563798Z quantize_per_channel = self._frozen_param0 2025-09-09T14:59:05.9564771Z dequantize_per_channel = torch.ops.quantized_decomposed.dequantize_per_channel.default(quantize_per_channel, _scale_0, _zero_point_0, 0, -127, 127, torch.int8); quantize_per_channel = _scale_0 = _zero_point_0 = None 2025-09-09T14:59:05.9566109Z conv1d_2 = torch.ops.aten.conv1d.default(dequantize_per_tensor_default, dequantize_per_channel, conv_bias); dequantize_per_tensor_default = dequantize_per_channel = conv_bias = None 2025-09-09T14:59:05.9567383Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv1d_2, 0.011012271046638489, -1, -128, 127, torch.int8); conv1d_2 = None 2025-09-09T14:59:05.9568665Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.011012271046638489, -1, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T14:59:05.9569671Z return pytree.tree_unflatten((dequantize_per_tensor_default_1,), self._out_spec) 2025-09-09T14:59:05.9570076Z 2025-09-09T14:59:05.9570359Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:59:05.9570738Z onverted model fx: GraphModule( 2025-09-09T14:59:05.9571113Z (conv): QuantizedConv1d(Reference)(3, 3, kernel_size=(3,), stride=(1,)) 2025-09-09T14:59:05.9571489Z ) 2025-09-09T14:59:05.9571584Z 2025-09-09T14:59:05.9571588Z 2025-09-09T14:59:05.9571592Z 2025-09-09T14:59:05.9571674Z def forward(self, x): 2025-09-09T14:59:05.9572286Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.010372933931648731, 0, -128, 127, torch.int8); x = None 2025-09-09T14:59:05.9573505Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.010372933931648731, 0, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T14:59:24.3135703Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T14:59:24.3137454Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.011012271046638489, -1, -128, 127, torch.int8); conv = None 2025-09-09T14:59:24.3139346Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.011012271046638489, -1, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T14:59:24.3140234Z return dequantize_per_tensor_default_1 2025-09-09T14:59:24.3140501Z 2025-09-09T14:59:24.3140778Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:59:24.3141139Z diff: tensor([[[0., 0., 0.], 2025-09-09T14:59:24.3141373Z [0., 0., 0.], 2025-09-09T14:59:24.3141590Z [0., 0., 0.]]], device='cuda:0') 2025-09-09T14:59:24.3141865Z model pt2e: GraphModule( 2025-09-09T14:59:24.3142083Z (conv): Module() 2025-09-09T14:59:24.3142281Z (bn): Module() 2025-09-09T14:59:24.3142574Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:59:24.3143789Z fake_quant_enabled=tensor([1], device='cuda:0'), observer_enabled=tensor([1], device='cuda:0'), scale=tensor([0.0104], device='cuda:0'), zero_point=tensor([0], device='cuda:0', dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:59:24.3145540Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3264806270599365, max_val=1.318617343902588) 2025-09-09T14:59:24.3146054Z ) 2025-09-09T14:59:24.3146328Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:59:24.3147441Z fake_quant_enabled=tensor([1], device='cuda:0'), observer_enabled=tensor([1], device='cuda:0'), scale=tensor([0.0022], device='cuda:0'), zero_point=tensor([0], device='cuda:0', dtype=torch.int32), dtype=torch.int8, quant_min=-127, quant_max=127, qscheme=torch.per_tensor_symmetric, reduce_range=False 2025-09-09T14:59:24.3148812Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.2799264192581177, max_val=0.27745386958122253) 2025-09-09T14:59:24.3149322Z ) 2025-09-09T14:59:24.3149866Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:59:24.3150960Z fake_quant_enabled=tensor([1], device='cuda:0'), observer_enabled=tensor([1], device='cuda:0'), scale=tensor([0.0110], device='cuda:0'), zero_point=tensor([-1], device='cuda:0', dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:59:24.3152350Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3977917432785034, max_val=1.4123148918151855) 2025-09-09T14:59:24.3152860Z ) 2025-09-09T14:59:24.3153030Z ) 2025-09-09T14:59:24.3153133Z 2025-09-09T14:59:24.3153137Z 2025-09-09T14:59:24.3153141Z 2025-09-09T14:59:24.3153227Z def forward(self, x): 2025-09-09T14:59:24.3153512Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:59:24.3153854Z conv_weight = self.conv.weight 2025-09-09T14:59:24.3154134Z conv_bias = self.conv.bias 2025-09-09T14:59:24.3154386Z bn_weight = self.bn.weight 2025-09-09T14:59:24.3154650Z bn_bias = self.bn.bias 2025-09-09T14:59:24.3154898Z bn_running_mean = self.bn.running_mean 2025-09-09T14:59:24.3155195Z bn_running_var = self.bn.running_var 2025-09-09T14:59:24.3155523Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T14:59:24.3155954Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:59:24.3156531Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T14:59:24.3157049Z add = torch.ops.aten.add.Tensor(bn_running_var, 1e-05) 2025-09-09T14:59:24.3157436Z sqrt = torch.ops.aten.sqrt.default(add); add = None 2025-09-09T14:59:24.3157838Z div = torch.ops.aten.div.Tensor(bn_weight, sqrt); sqrt = None 2025-09-09T14:59:24.3158273Z reshape = torch.ops.aten.reshape.default(div, [-1, 1, 1]) 2025-09-09T14:59:24.3158759Z mul = torch.ops.aten.mul.Tensor(conv_weight, reshape); conv_weight = reshape = None 2025-09-09T14:59:24.3159415Z activation_post_process_1 = self.activation_post_process_1(mul); mul = None 2025-09-09T14:59:24.3160035Z zeros_like = torch.ops.aten.zeros_like.default(conv_bias, dtype = torch.float32, pin_memory = False) 2025-09-09T14:59:24.3161099Z conv1d_1 = torch.ops.aten.conv1d.default(activation_post_process_0, activation_post_process_1, zeros_like); activation_post_process_0 = activation_post_process_1 = zeros_like = None 2025-09-09T14:59:24.3161980Z reshape_1 = torch.ops.aten.reshape.default(div, [1, -1, 1]); div = None 2025-09-09T14:59:24.3162511Z div_1 = torch.ops.aten.div.Tensor(conv1d_1, reshape_1); conv1d_1 = reshape_1 = None 2025-09-09T14:59:24.3163084Z reshape_2 = torch.ops.aten.reshape.default(conv_bias, [1, -1, 1]); conv_bias = None 2025-09-09T14:59:24.3163635Z add_1 = torch.ops.aten.add.Tensor(div_1, reshape_2); div_1 = reshape_2 = None 2025-09-09T14:59:24.3164498Z batch_norm_1 = torch.ops.aten.batch_norm.default(add_1, bn_weight, bn_bias, bn_running_mean, bn_running_var, True, 0.1, 1e-05, True); add_1 = bn_weight = bn_bias = bn_running_mean = bn_running_var = None 2025-09-09T14:59:24.3165423Z activation_post_process_2 = self.activation_post_process_2(batch_norm_1); batch_norm_1 = None 2025-09-09T14:59:24.3166025Z return pytree.tree_unflatten((activation_post_process_2,), self._out_spec) 2025-09-09T14:59:24.3166419Z 2025-09-09T14:59:24.3166701Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:59:24.3167067Z model fx: GraphModule( 2025-09-09T14:59:24.3167397Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:59:24.3168493Z fake_quant_enabled=tensor([1], device='cuda:0'), observer_enabled=tensor([1], device='cuda:0'), scale=tensor([0.0104], device='cuda:0'), zero_point=tensor([0], device='cuda:0', dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:59:24.3169844Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3264806270599365, max_val=1.318617343902588) 2025-09-09T14:59:24.3170349Z ) 2025-09-09T14:59:24.3170536Z (conv): ConvBn1d( 2025-09-09T14:59:24.3170874Z 3, 3, kernel_size=(3,), stride=(1,) 2025-09-09T14:59:24.3171283Z (bn): BatchNorm1d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T14:59:24.3171800Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:59:24.3172881Z fake_quant_enabled=tensor([1], device='cuda:0'), observer_enabled=tensor([1], device='cuda:0'), scale=tensor([0.0022], device='cuda:0'), zero_point=tensor([0], device='cuda:0', dtype=torch.int32), dtype=torch.qint8, quant_min=-127, quant_max=127, qscheme=torch.per_tensor_symmetric, reduce_range=False 2025-09-09T14:59:24.3174165Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.2799264192581177, max_val=0.27745386958122253) 2025-09-09T14:59:24.3174680Z ) 2025-09-09T14:59:24.3174850Z ) 2025-09-09T14:59:24.3175132Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:59:24.3176218Z fake_quant_enabled=tensor([1], device='cuda:0'), observer_enabled=tensor([1], device='cuda:0'), scale=tensor([0.0110], device='cuda:0'), zero_point=tensor([-1], device='cuda:0', dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:59:24.3177472Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3977917432785034, max_val=1.4123148918151855) 2025-09-09T14:59:24.3177983Z ) 2025-09-09T14:59:24.3178149Z ) 2025-09-09T14:59:24.3178247Z 2025-09-09T14:59:24.3178251Z 2025-09-09T14:59:24.3178255Z 2025-09-09T14:59:24.3178345Z def forward(self, x): 2025-09-09T14:59:24.3178690Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:59:24.3179219Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T14:59:24.3179757Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T14:59:24.3180182Z return activation_post_process_1 2025-09-09T14:59:24.3180448Z 2025-09-09T14:59:24.3180717Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:59:24.3181086Z diff: tensor([[[0., 0., 0.], 2025-09-09T14:59:24.3181315Z [0., 0., 0.], 2025-09-09T14:59:24.3181577Z [0., 0., 0.]]], device='cuda:0', grad_fn=) 2025-09-09T14:59:24.3181903Z converted model pt2e: GraphModule( 2025-09-09T14:59:24.3182165Z (conv): Module() 2025-09-09T14:59:24.3182370Z (bn): Module() 2025-09-09T14:59:24.3182568Z ) 2025-09-09T14:59:24.3182665Z 2025-09-09T14:59:24.3182669Z 2025-09-09T14:59:24.3182673Z 2025-09-09T14:59:24.3182772Z def forward(self, x): 2025-09-09T14:59:24.3183049Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:59:24.3183383Z conv_bias = self.conv.bias 2025-09-09T14:59:24.3183676Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T14:59:24.3184378Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.010372933931648731, 0, -128, 127, torch.int8); x = None 2025-09-09T14:59:24.3185596Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.010372933931648731, 0, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T14:59:24.3186623Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T14:59:24.3187116Z quantize_per_tensor = self._frozen_param0 2025-09-09T14:59:24.3187895Z dequantize_per_tensor = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor, 0.002204145072028041, 0, -127, 127, torch.int8); quantize_per_tensor = None 2025-09-09T14:59:24.3189222Z conv1d_2 = torch.ops.aten.conv1d.default(dequantize_per_tensor_default, dequantize_per_tensor, conv_bias); dequantize_per_tensor_default = dequantize_per_tensor = conv_bias = None 2025-09-09T14:59:24.3190403Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv1d_2, 0.011020027101039886, -1, -128, 127, torch.int8); conv1d_2 = None 2025-09-09T14:59:24.3191752Z dequantize_per_tensor_default_2 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_2, 0.011020027101039886, -1, -128, 127, torch.int8); quantize_per_tensor_default_2 = None 2025-09-09T14:59:24.3192812Z return pytree.tree_unflatten((dequantize_per_tensor_default_2,), self._out_spec) 2025-09-09T14:59:24.3193225Z 2025-09-09T14:59:24.3193500Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:59:24.3193877Z onverted model fx: GraphModule( 2025-09-09T14:59:24.3194247Z (conv): QuantizedConv1d(Reference)(3, 3, kernel_size=(3,), stride=(1,)) 2025-09-09T14:59:24.3194623Z ) 2025-09-09T14:59:24.3194723Z 2025-09-09T14:59:24.3194727Z 2025-09-09T14:59:24.3194731Z 2025-09-09T14:59:48.1882669Z def forward(self, x): 2025-09-09T14:59:48.1884287Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.010372933931648731, 0, -128, 127, torch.int8); x = None 2025-09-09T14:59:48.1885870Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.010372933931648731, 0, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T14:59:48.1887109Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T14:59:48.1888159Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.011020027101039886, -1, -128, 127, torch.int8); conv = None 2025-09-09T14:59:48.1889729Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.011020027101039886, -1, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T14:59:48.1890816Z return dequantize_per_tensor_default_1 2025-09-09T14:59:48.1891144Z 2025-09-09T14:59:48.1891471Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:59:48.1891919Z diff: tensor([[[0., 0., 0.], 2025-09-09T14:59:48.1892188Z [0., 0., 0.], 2025-09-09T14:59:48.1892453Z [0., 0., 0.]]], device='cuda:0') 2025-09-09T14:59:48.1892996Z PASSED 2025-09-09T14:59:48.1893737Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn1d::test_qat_conv_bn_fusion_literal_args model pt2e: GraphModule( 2025-09-09T14:59:48.1894513Z (conv): Module() 2025-09-09T14:59:48.1894762Z (bn): Module() 2025-09-09T14:59:48.1895109Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:59:48.1896250Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0087]), zero_point=tensor([57], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:59:48.1897609Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.611703872680664, max_val=0.6104744076728821) 2025-09-09T14:59:48.1898229Z ) 2025-09-09T14:59:48.1898558Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:59:48.1899746Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0024, 0.0022, 0.0021]), zero_point=tensor([0, 0, 0], dtype=torch.int32), dtype=torch.int8, quant_min=-127, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False 2025-09-09T14:59:48.1901310Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.3119, -0.2799, -0.2618]), max_val=tensor([0.1970, 0.1855, 0.2308])) 2025-09-09T14:59:48.1902091Z ) 2025-09-09T14:59:48.1902409Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:59:48.1903828Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0119]), zero_point=tensor([-46], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:59:48.1905173Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.9784814715385437, max_val=2.047511577606201) 2025-09-09T14:59:48.1905939Z ) 2025-09-09T14:59:48.1906133Z ) 2025-09-09T14:59:48.1906245Z 2025-09-09T14:59:48.1906250Z 2025-09-09T14:59:48.1906255Z 2025-09-09T14:59:48.1906353Z def forward(self, x): 2025-09-09T14:59:48.1906687Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:59:48.1907082Z conv_weight = self.conv.weight 2025-09-09T14:59:48.1907408Z conv_bias = self.conv.bias 2025-09-09T14:59:48.1907706Z bn_weight = self.bn.weight 2025-09-09T14:59:48.1907997Z bn_bias = self.bn.bias 2025-09-09T14:59:48.1908298Z bn_running_mean = self.bn.running_mean 2025-09-09T14:59:48.1908645Z bn_running_var = self.bn.running_var 2025-09-09T14:59:48.1909049Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T14:59:48.1909568Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:59:48.1910269Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T14:59:48.1910910Z add = torch.ops.aten.add.Tensor(bn_running_var, 1e-05) 2025-09-09T14:59:48.1911366Z sqrt = torch.ops.aten.sqrt.default(add); add = None 2025-09-09T14:59:48.1911852Z div = torch.ops.aten.div.Tensor(bn_weight, sqrt); sqrt = None 2025-09-09T14:59:48.1912361Z reshape = torch.ops.aten.reshape.default(div, [-1, 1, 1]) 2025-09-09T14:59:48.1912952Z mul = torch.ops.aten.mul.Tensor(conv_weight, reshape); conv_weight = reshape = None 2025-09-09T14:59:48.1913617Z activation_post_process_1 = self.activation_post_process_1(mul); mul = None 2025-09-09T14:59:48.1914352Z zeros_like = torch.ops.aten.zeros_like.default(conv_bias, dtype = torch.float32, pin_memory = False) 2025-09-09T14:59:48.1915559Z conv1d_1 = torch.ops.aten.conv1d.default(activation_post_process_0, activation_post_process_1, zeros_like, [2], [4]); activation_post_process_0 = activation_post_process_1 = zeros_like = None 2025-09-09T14:59:48.1916635Z reshape_1 = torch.ops.aten.reshape.default(div, [1, -1, 1]); div = None 2025-09-09T14:59:48.1917263Z div_1 = torch.ops.aten.div.Tensor(conv1d_1, reshape_1); conv1d_1 = reshape_1 = None 2025-09-09T14:59:48.1917938Z reshape_2 = torch.ops.aten.reshape.default(conv_bias, [1, -1, 1]); conv_bias = None 2025-09-09T14:59:48.1918591Z add_1 = torch.ops.aten.add.Tensor(div_1, reshape_2); div_1 = reshape_2 = None 2025-09-09T14:59:48.1919748Z batch_norm_1 = torch.ops.aten.batch_norm.default(add_1, bn_weight, bn_bias, bn_running_mean, bn_running_var, True, 0.1, 1e-05, True); add_1 = bn_weight = bn_bias = bn_running_mean = bn_running_var = None 2025-09-09T14:59:48.1920872Z activation_post_process_2 = self.activation_post_process_2(batch_norm_1); batch_norm_1 = None 2025-09-09T14:59:48.1921597Z return pytree.tree_unflatten((activation_post_process_2,), self._out_spec) 2025-09-09T14:59:48.1922059Z 2025-09-09T14:59:48.1922648Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:59:48.1923090Z model fx: GraphModule( 2025-09-09T14:59:48.1923472Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:59:48.1924617Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0087]), zero_point=tensor([57], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:59:48.1925959Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.611703872680664, max_val=0.6104744076728821) 2025-09-09T14:59:48.1926579Z ) 2025-09-09T14:59:48.1926781Z (conv): ConvBn1d( 2025-09-09T14:59:48.1927074Z 3, 3, kernel_size=(3,), stride=(2,), padding=(4,) 2025-09-09T14:59:48.1927738Z (bn): BatchNorm1d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T14:59:48.1928295Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:59:48.1929465Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0024, 0.0022, 0.0021]), zero_point=tensor([0, 0, 0], dtype=torch.int32), dtype=torch.qint8, quant_min=-127, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False 2025-09-09T14:59:48.1931179Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.3119, -0.2799, -0.2618]), max_val=tensor([0.1970, 0.1855, 0.2308])) 2025-09-09T14:59:48.1931966Z ) 2025-09-09T14:59:48.1932165Z ) 2025-09-09T14:59:48.1932481Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:59:48.1933634Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0119]), zero_point=tensor([-46], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:59:48.1934981Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.9784814715385437, max_val=2.047511577606201) 2025-09-09T14:59:48.1935601Z ) 2025-09-09T14:59:48.1935796Z ) 2025-09-09T14:59:48.1935914Z 2025-09-09T14:59:48.1935919Z 2025-09-09T14:59:48.1935924Z 2025-09-09T14:59:48.1936022Z def forward(self, x): 2025-09-09T14:59:48.1936439Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:59:48.1937070Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T14:59:48.1937750Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T14:59:48.1938300Z return activation_post_process_1 2025-09-09T14:59:48.1938624Z 2025-09-09T14:59:48.1938961Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:59:48.1939327Z diff: tensor([[[0., 0., 0., 0., 0., 0.], 2025-09-09T14:59:48.1939595Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:59:48.1939878Z [0., 0., 0., 0., 0., 0.]]], grad_fn=) 2025-09-09T14:59:48.1940195Z converted model pt2e: GraphModule( 2025-09-09T14:59:48.1940446Z (conv): Module() 2025-09-09T14:59:48.1940652Z (bn): Module() 2025-09-09T14:59:48.1940835Z ) 2025-09-09T14:59:48.1940933Z 2025-09-09T14:59:48.1940938Z 2025-09-09T14:59:48.1940942Z 2025-09-09T14:59:48.1941023Z def forward(self, x): 2025-09-09T14:59:48.1941303Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:59:48.1941628Z conv_bias = self.conv.bias 2025-09-09T14:59:48.1941924Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T14:59:48.1942619Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.008714424446225166, 57, -128, 127, torch.int8); x = None 2025-09-09T14:59:48.1943847Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.008714424446225166, 57, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T14:59:48.1944872Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T14:59:48.1945332Z _scale_0 = self._scale_0 2025-09-09T14:59:48.1945587Z _zero_point_0 = self._zero_point_0 2025-09-09T14:59:48.1945881Z quantize_per_channel = self._frozen_param0 2025-09-09T14:59:48.1946758Z dequantize_per_channel = torch.ops.quantized_decomposed.dequantize_per_channel.default(quantize_per_channel, _scale_0, _zero_point_0, 0, -127, 127, torch.int8); quantize_per_channel = _scale_0 = _zero_point_0 = None 2025-09-09T14:59:48.1948090Z conv1d_2 = torch.ops.aten.conv1d.default(dequantize_per_tensor_default, dequantize_per_channel, conv_bias, [2], [4]); dequantize_per_tensor_default = dequantize_per_channel = conv_bias = None 2025-09-09T14:59:48.1949379Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv1d_2, 0.011866639368236065, -46, -128, 127, torch.int8); conv1d_2 = None 2025-09-09T15:00:06.6164063Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.011866639368236065, -46, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T15:00:06.6165710Z return pytree.tree_unflatten((dequantize_per_tensor_default_1,), self._out_spec) 2025-09-09T15:00:06.6166200Z 2025-09-09T15:00:06.6166533Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:00:06.6166974Z onverted model fx: GraphModule( 2025-09-09T15:00:06.6167474Z (conv): QuantizedConv1d(Reference)(3, 3, kernel_size=(3,), stride=(2,), padding=(4,)) 2025-09-09T15:00:06.6167969Z ) 2025-09-09T15:00:06.6168094Z 2025-09-09T15:00:06.6168099Z 2025-09-09T15:00:06.6168104Z 2025-09-09T15:00:06.6168202Z def forward(self, x): 2025-09-09T15:00:06.6168965Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.008714424446225166, 57, -128, 127, torch.int8); x = None 2025-09-09T15:00:06.6170496Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.008714424446225166, 57, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T15:00:06.6171741Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T15:00:06.6172775Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.011866639368236065, -46, -128, 127, torch.int8); conv = None 2025-09-09T15:00:06.6174349Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.011866639368236065, -46, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T15:00:06.6175425Z return dequantize_per_tensor_default_1 2025-09-09T15:00:06.6175737Z 2025-09-09T15:00:06.6176072Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:00:06.6176505Z diff: tensor([[[0., 0., 0., 0., 0., 0.], 2025-09-09T15:00:06.6176824Z [0., 0., 0., 0., 0., 0.], 2025-09-09T15:00:06.6177115Z [0., 0., 0., 0., 0., 0.]]]) 2025-09-09T15:00:06.6177418Z model pt2e: GraphModule( 2025-09-09T15:00:06.6177688Z (conv): Module() 2025-09-09T15:00:06.6177923Z (bn): Module() 2025-09-09T15:00:06.6178276Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:00:06.6179410Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0087]), zero_point=tensor([57], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:00:06.6180769Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.611703872680664, max_val=0.6104744076728821) 2025-09-09T15:00:06.6181384Z ) 2025-09-09T15:00:06.6181703Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:00:06.6182859Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0024]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.int8, quant_min=-127, quant_max=127, qscheme=torch.per_tensor_symmetric, reduce_range=False 2025-09-09T15:00:06.6184284Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.31192728877067566, max_val=0.23078496754169464) 2025-09-09T15:00:06.6184910Z ) 2025-09-09T15:00:06.6185230Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:00:06.6186365Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0119]), zero_point=tensor([-44], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:00:06.6187697Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.9980934262275696, max_val=2.047511577606201) 2025-09-09T15:00:06.6188303Z ) 2025-09-09T15:00:06.6188501Z ) 2025-09-09T15:00:06.6188614Z 2025-09-09T15:00:06.6188620Z 2025-09-09T15:00:06.6188791Z 2025-09-09T15:00:06.6188891Z def forward(self, x): 2025-09-09T15:00:06.6189234Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T15:00:06.6189717Z conv_weight = self.conv.weight 2025-09-09T15:00:06.6190036Z conv_bias = self.conv.bias 2025-09-09T15:00:06.6190337Z bn_weight = self.bn.weight 2025-09-09T15:00:06.6190623Z bn_bias = self.bn.bias 2025-09-09T15:00:06.6190922Z bn_running_mean = self.bn.running_mean 2025-09-09T15:00:06.6191273Z bn_running_var = self.bn.running_var 2025-09-09T15:00:06.6191667Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T15:00:06.6192186Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T15:00:06.6192887Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T15:00:06.6193517Z add = torch.ops.aten.add.Tensor(bn_running_var, 1e-05) 2025-09-09T15:00:06.6193984Z sqrt = torch.ops.aten.sqrt.default(add); add = None 2025-09-09T15:00:06.6194468Z div = torch.ops.aten.div.Tensor(bn_weight, sqrt); sqrt = None 2025-09-09T15:00:06.6194977Z reshape = torch.ops.aten.reshape.default(div, [-1, 1, 1]) 2025-09-09T15:00:06.6195586Z mul = torch.ops.aten.mul.Tensor(conv_weight, reshape); conv_weight = reshape = None 2025-09-09T15:00:06.6196250Z activation_post_process_1 = self.activation_post_process_1(mul); mul = None 2025-09-09T15:00:06.6196983Z zeros_like = torch.ops.aten.zeros_like.default(conv_bias, dtype = torch.float32, pin_memory = False) 2025-09-09T15:00:06.6198181Z conv1d_1 = torch.ops.aten.conv1d.default(activation_post_process_0, activation_post_process_1, zeros_like, [2], [4]); activation_post_process_0 = activation_post_process_1 = zeros_like = None 2025-09-09T15:00:06.6199358Z reshape_1 = torch.ops.aten.reshape.default(div, [1, -1, 1]); div = None 2025-09-09T15:00:06.6199995Z div_1 = torch.ops.aten.div.Tensor(conv1d_1, reshape_1); conv1d_1 = reshape_1 = None 2025-09-09T15:00:06.6200688Z reshape_2 = torch.ops.aten.reshape.default(conv_bias, [1, -1, 1]); conv_bias = None 2025-09-09T15:00:06.6201337Z add_1 = torch.ops.aten.add.Tensor(div_1, reshape_2); div_1 = reshape_2 = None 2025-09-09T15:00:06.6202390Z batch_norm_1 = torch.ops.aten.batch_norm.default(add_1, bn_weight, bn_bias, bn_running_mean, bn_running_var, True, 0.1, 1e-05, True); add_1 = bn_weight = bn_bias = bn_running_mean = bn_running_var = None 2025-09-09T15:00:06.6203510Z activation_post_process_2 = self.activation_post_process_2(batch_norm_1); batch_norm_1 = None 2025-09-09T15:00:06.6204228Z return pytree.tree_unflatten((activation_post_process_2,), self._out_spec) 2025-09-09T15:00:06.6204689Z 2025-09-09T15:00:06.6205013Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:00:06.6205440Z model fx: GraphModule( 2025-09-09T15:00:06.6205810Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:00:06.6206956Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0087]), zero_point=tensor([57], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:00:06.6208297Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.611703872680664, max_val=0.6104744076728821) 2025-09-09T15:00:06.6208914Z ) 2025-09-09T15:00:06.6209120Z (conv): ConvBn1d( 2025-09-09T15:00:06.6209406Z 3, 3, kernel_size=(3,), stride=(2,), padding=(4,) 2025-09-09T15:00:06.6209935Z (bn): BatchNorm1d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T15:00:06.6210485Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:00:06.6211599Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0024]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-127, quant_max=127, qscheme=torch.per_tensor_symmetric, reduce_range=False 2025-09-09T15:00:06.6213069Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.31192728877067566, max_val=0.23078496754169464) 2025-09-09T15:00:06.6213747Z ) 2025-09-09T15:00:06.6214078Z ) 2025-09-09T15:00:06.6214393Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:00:06.6215538Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0119]), zero_point=tensor([-44], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:00:06.6216874Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.9980934262275696, max_val=2.047511577606201) 2025-09-09T15:00:06.6217484Z ) 2025-09-09T15:00:06.6217682Z ) 2025-09-09T15:00:06.6217796Z 2025-09-09T15:00:06.6217801Z 2025-09-09T15:00:06.6217806Z 2025-09-09T15:00:06.6217901Z def forward(self, x): 2025-09-09T15:00:06.6218340Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T15:00:06.6219049Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T15:00:06.6219698Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T15:00:06.6220134Z return activation_post_process_1 2025-09-09T15:00:06.6220383Z 2025-09-09T15:00:06.6220660Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:00:06.6221018Z diff: tensor([[[0., 0., 0., 0., 0., 0.], 2025-09-09T15:00:06.6221288Z [0., 0., 0., 0., 0., 0.], 2025-09-09T15:00:06.6221570Z [0., 0., 0., 0., 0., 0.]]], grad_fn=) 2025-09-09T15:00:06.6221884Z converted model pt2e: GraphModule( 2025-09-09T15:00:06.6222322Z (conv): Module() 2025-09-09T15:00:06.6222518Z (bn): Module() 2025-09-09T15:00:06.6222706Z ) 2025-09-09T15:00:06.6222798Z 2025-09-09T15:00:06.6222803Z 2025-09-09T15:00:06.6222806Z 2025-09-09T15:00:06.6222886Z def forward(self, x): 2025-09-09T15:00:06.6223173Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T15:00:06.6223499Z conv_bias = self.conv.bias 2025-09-09T15:00:06.6223800Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T15:00:06.6224499Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.008714424446225166, 57, -128, 127, torch.int8); x = None 2025-09-09T15:00:06.6225720Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.008714424446225166, 57, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T15:00:06.6226748Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T15:00:06.6227229Z quantize_per_tensor = self._frozen_param0 2025-09-09T15:00:06.6228013Z dequantize_per_tensor = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor, 0.0024561204481869936, 0, -127, 127, torch.int8); quantize_per_tensor = None 2025-09-09T15:00:06.6229265Z conv1d_2 = torch.ops.aten.conv1d.default(dequantize_per_tensor_default, dequantize_per_tensor, conv_bias, [2], [4]); dequantize_per_tensor_default = dequantize_per_tensor = conv_bias = None 2025-09-09T15:00:30.5042436Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv1d_2, 0.011943548917770386, -44, -128, 127, torch.int8); conv1d_2 = None 2025-09-09T15:00:30.5044161Z dequantize_per_tensor_default_2 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_2, 0.011943548917770386, -44, -128, 127, torch.int8); quantize_per_tensor_default_2 = None 2025-09-09T15:00:30.5045320Z return pytree.tree_unflatten((dequantize_per_tensor_default_2,), self._out_spec) 2025-09-09T15:00:30.5045733Z 2025-09-09T15:00:30.5046015Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:00:30.5046395Z onverted model fx: GraphModule( 2025-09-09T15:00:30.5047069Z (conv): QuantizedConv1d(Reference)(3, 3, kernel_size=(3,), stride=(2,), padding=(4,)) 2025-09-09T15:00:30.5047484Z ) 2025-09-09T15:00:30.5047589Z 2025-09-09T15:00:30.5047593Z 2025-09-09T15:00:30.5047748Z 2025-09-09T15:00:30.5047841Z def forward(self, x): 2025-09-09T15:00:30.5048457Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.008714424446225166, 57, -128, 127, torch.int8); x = None 2025-09-09T15:00:30.5049683Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.008714424446225166, 57, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T15:00:30.5050683Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T15:00:30.5051520Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.011943548917770386, -44, -128, 127, torch.int8); conv = None 2025-09-09T15:00:30.5052786Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.011943548917770386, -44, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T15:00:30.5053673Z return dequantize_per_tensor_default_1 2025-09-09T15:00:30.5053943Z 2025-09-09T15:00:30.5054222Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:00:30.5054585Z diff: tensor([[[0., 0., 0., 0., 0., 0.], 2025-09-09T15:00:30.5054879Z [0., 0., 0., 0., 0., 0.], 2025-09-09T15:00:30.5055130Z [0., 0., 0., 0., 0., 0.]]]) 2025-09-09T15:00:30.5055591Z PASSED 2025-09-09T15:00:30.5056201Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn1d::test_qat_conv_bn_fusion_no_conv_bias model pt2e: GraphModule( 2025-09-09T15:00:30.5056835Z (conv): Module() 2025-09-09T15:00:30.5057042Z (bn): Module() 2025-09-09T15:00:30.5057343Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:00:30.5058281Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0150]), zero_point=tensor([14], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:00:30.5059389Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.1280412673950195, max_val=1.6863642930984497) 2025-09-09T15:00:30.5059896Z ) 2025-09-09T15:00:30.5060176Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:00:30.5061141Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0022, 0.0020, 0.0022]), zero_point=tensor([0, 0, 0], dtype=torch.int32), dtype=torch.int8, quant_min=-127, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False 2025-09-09T15:00:30.5062407Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.2799, -0.2557, -0.2618]), max_val=tensor([0.1970, 0.2308, 0.2775])) 2025-09-09T15:00:30.5063061Z ) 2025-09-09T15:00:30.5063336Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:00:30.5064260Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0146]), zero_point=tensor([8], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:00:30.5065334Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.9912875890731812, max_val=1.733071208000183) 2025-09-09T15:00:30.5065844Z ) 2025-09-09T15:00:30.5066022Z ) 2025-09-09T15:00:30.5066120Z 2025-09-09T15:00:30.5066124Z 2025-09-09T15:00:30.5066128Z 2025-09-09T15:00:30.5066215Z def forward(self, x): 2025-09-09T15:00:30.5066504Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T15:00:30.5066841Z conv_weight = self.conv.weight 2025-09-09T15:00:30.5067123Z bn_weight = self.bn.weight 2025-09-09T15:00:30.5067374Z bn_bias = self.bn.bias 2025-09-09T15:00:30.5067726Z bn_running_mean = self.bn.running_mean 2025-09-09T15:00:30.5068035Z bn_running_var = self.bn.running_var 2025-09-09T15:00:30.5068369Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T15:00:30.5068896Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T15:00:30.5069481Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T15:00:30.5070010Z add = torch.ops.aten.add.Tensor(bn_running_var, 1e-05) 2025-09-09T15:00:30.5070404Z sqrt = torch.ops.aten.sqrt.default(add); add = None 2025-09-09T15:00:30.5070865Z div = torch.ops.aten.div.Tensor(bn_weight, sqrt); sqrt = None 2025-09-09T15:00:30.5071306Z reshape = torch.ops.aten.reshape.default(div, [-1, 1, 1]) 2025-09-09T15:00:30.5071800Z mul = torch.ops.aten.mul.Tensor(conv_weight, reshape); conv_weight = reshape = None 2025-09-09T15:00:30.5072364Z activation_post_process_1 = self.activation_post_process_1(mul); mul = None 2025-09-09T15:00:30.5073195Z conv1d_1 = torch.ops.aten.conv1d.default(activation_post_process_0, activation_post_process_1, None); activation_post_process_0 = activation_post_process_1 = None 2025-09-09T15:00:30.5074018Z reshape_1 = torch.ops.aten.reshape.default(div, [1, -1, 1]); div = None 2025-09-09T15:00:30.5074550Z div_1 = torch.ops.aten.div.Tensor(conv1d_1, reshape_1); conv1d_1 = reshape_1 = None 2025-09-09T15:00:30.5075440Z batch_norm_1 = torch.ops.aten.batch_norm.default(div_1, bn_weight, bn_bias, bn_running_mean, bn_running_var, True, 0.1, 1e-05, True); div_1 = bn_weight = bn_bias = bn_running_mean = bn_running_var = None 2025-09-09T15:00:30.5076363Z activation_post_process_2 = self.activation_post_process_2(batch_norm_1); batch_norm_1 = None 2025-09-09T15:00:30.5076954Z return pytree.tree_unflatten((activation_post_process_2,), self._out_spec) 2025-09-09T15:00:30.5077344Z 2025-09-09T15:00:30.5077623Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:00:30.5077994Z model fx: GraphModule( 2025-09-09T15:00:30.5078320Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:00:30.5079343Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0150]), zero_point=tensor([14], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:00:30.5080442Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.1280412673950195, max_val=1.6863642930984497) 2025-09-09T15:00:30.5080948Z ) 2025-09-09T15:00:30.5081132Z (conv): ConvBn1d( 2025-09-09T15:00:30.5081370Z 3, 3, kernel_size=(3,), stride=(1,), bias=False 2025-09-09T15:00:30.5081798Z (bn): BatchNorm1d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T15:00:30.5082259Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:00:30.5083204Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0022, 0.0020, 0.0022]), zero_point=tensor([0, 0, 0], dtype=torch.int32), dtype=torch.qint8, quant_min=-127, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False 2025-09-09T15:00:30.5084483Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.2799, -0.2557, -0.2618]), max_val=tensor([0.1970, 0.2308, 0.2775])) 2025-09-09T15:00:30.5085125Z ) 2025-09-09T15:00:30.5085297Z ) 2025-09-09T15:00:30.5085574Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:00:30.5086496Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0146]), zero_point=tensor([8], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:00:30.5087578Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.9912875890731812, max_val=1.733071208000183) 2025-09-09T15:00:30.5088080Z ) 2025-09-09T15:00:30.5088253Z ) 2025-09-09T15:00:30.5088439Z 2025-09-09T15:00:30.5088444Z 2025-09-09T15:00:30.5088448Z 2025-09-09T15:00:30.5088544Z def forward(self, x): 2025-09-09T15:00:30.5088893Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T15:00:30.5089501Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T15:00:30.5090035Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T15:00:30.5090460Z return activation_post_process_1 2025-09-09T15:00:30.5090716Z 2025-09-09T15:00:30.5090991Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:00:30.5091361Z diff: tensor([[[0., 0., 0.], 2025-09-09T15:00:30.5091591Z [0., 0., 0.], 2025-09-09T15:00:30.5091801Z [0., 0., 0.]], 2025-09-09T15:00:30.5091935Z 2025-09-09T15:00:30.5092011Z [[0., 0., 0.], 2025-09-09T15:00:30.5092217Z [0., 0., 0.], 2025-09-09T15:00:30.5092416Z [0., 0., 0.]], 2025-09-09T15:00:30.5092557Z 2025-09-09T15:00:30.5092640Z [[0., 0., 0.], 2025-09-09T15:00:30.5092838Z [0., 0., 0.], 2025-09-09T15:00:30.5093073Z [0., 0., 0.]]], grad_fn=) 2025-09-09T15:00:30.5093386Z converted model pt2e: GraphModule( 2025-09-09T15:00:30.5093645Z (conv): Module() 2025-09-09T15:00:30.5093847Z (bn): Module() 2025-09-09T15:00:30.5094039Z ) 2025-09-09T15:00:30.5094137Z 2025-09-09T15:00:30.5094141Z 2025-09-09T15:00:30.5094151Z 2025-09-09T15:00:30.5094233Z def forward(self, x): 2025-09-09T15:00:30.5094509Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T15:00:30.5094890Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T15:00:30.5095591Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.014958452433347702, 14, -128, 127, torch.int8); x = None 2025-09-09T15:00:30.5096825Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.014958452433347702, 14, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T15:00:30.5097856Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T15:00:30.5098322Z _scale_0 = self._scale_0 2025-09-09T15:00:30.5098588Z _zero_point_0 = self._zero_point_0 2025-09-09T15:00:30.5098888Z quantize_per_channel = self._frozen_param0 2025-09-09T15:00:30.5099766Z dequantize_per_channel = torch.ops.quantized_decomposed.dequantize_per_channel.default(quantize_per_channel, _scale_0, _zero_point_0, 0, -127, 127, torch.int8); quantize_per_channel = _scale_0 = _zero_point_0 = None 2025-09-09T15:00:48.9438138Z conv_weight_bias = self.conv.weight_bias 2025-09-09T15:00:48.9439431Z conv1d_2 = torch.ops.aten.conv1d.default(dequantize_per_tensor_default, dequantize_per_channel, conv_weight_bias); dequantize_per_tensor_default = dequantize_per_channel = conv_weight_bias = None 2025-09-09T15:00:48.9440866Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv1d_2, 0.014605328440666199, 8, -128, 127, torch.int8); conv1d_2 = None 2025-09-09T15:00:48.9442159Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.014605328440666199, 8, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T15:00:48.9443148Z return pytree.tree_unflatten((dequantize_per_tensor_default_1,), self._out_spec) 2025-09-09T15:00:48.9443552Z 2025-09-09T15:00:48.9443827Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:00:48.9444197Z onverted model fx: GraphModule( 2025-09-09T15:00:48.9444562Z (conv): QuantizedConv1d(Reference)(3, 3, kernel_size=(3,), stride=(1,)) 2025-09-09T15:00:48.9444921Z ) 2025-09-09T15:00:48.9445017Z 2025-09-09T15:00:48.9445021Z 2025-09-09T15:00:48.9445025Z 2025-09-09T15:00:48.9445107Z def forward(self, x): 2025-09-09T15:00:48.9445965Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.014958452433347702, 14, -128, 127, torch.int8); x = None 2025-09-09T15:00:48.9447338Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.014958452433347702, 14, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T15:00:48.9448320Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T15:00:48.9449152Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.014605328440666199, 8, -128, 127, torch.int8); conv = None 2025-09-09T15:00:48.9450390Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.014605328440666199, 8, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T15:00:48.9451246Z return dequantize_per_tensor_default_1 2025-09-09T15:00:48.9451522Z 2025-09-09T15:00:48.9451792Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:00:48.9452158Z diff: tensor([[[0., 0., 0.], 2025-09-09T15:00:48.9452389Z [0., 0., 0.], 2025-09-09T15:00:48.9452596Z [0., 0., 0.]], 2025-09-09T15:00:48.9452724Z 2025-09-09T15:00:48.9452803Z [[0., 0., 0.], 2025-09-09T15:00:48.9452996Z [0., 0., 0.], 2025-09-09T15:00:48.9453196Z [0., 0., 0.]], 2025-09-09T15:00:48.9453327Z 2025-09-09T15:00:48.9453397Z [[0., 0., 0.], 2025-09-09T15:00:48.9453596Z [0., 0., 0.], 2025-09-09T15:00:48.9453790Z [0., 0., 0.]]]) 2025-09-09T15:00:48.9454016Z model pt2e: GraphModule( 2025-09-09T15:00:48.9454231Z (conv): Module() 2025-09-09T15:00:48.9454445Z (bn): Module() 2025-09-09T15:00:48.9454733Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:00:48.9455655Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0150]), zero_point=tensor([14], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:00:48.9464753Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.1280412673950195, max_val=1.6863642930984497) 2025-09-09T15:00:48.9465278Z ) 2025-09-09T15:00:48.9465560Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:00:48.9466512Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0022]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.int8, quant_min=-127, quant_max=127, qscheme=torch.per_tensor_symmetric, reduce_range=False 2025-09-09T15:00:48.9467618Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.2799264192581177, max_val=0.27745386958122253) 2025-09-09T15:00:48.9468135Z ) 2025-09-09T15:00:48.9468421Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:00:48.9469350Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0146]), zero_point=tensor([8], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:00:48.9470435Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.9912875890731812, max_val=1.733071208000183) 2025-09-09T15:00:48.9470950Z ) 2025-09-09T15:00:48.9471119Z ) 2025-09-09T15:00:48.9471218Z 2025-09-09T15:00:48.9471222Z 2025-09-09T15:00:48.9471226Z 2025-09-09T15:00:48.9471324Z def forward(self, x): 2025-09-09T15:00:48.9471611Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T15:00:48.9471961Z conv_weight = self.conv.weight 2025-09-09T15:00:48.9472239Z bn_weight = self.bn.weight 2025-09-09T15:00:48.9472504Z bn_bias = self.bn.bias 2025-09-09T15:00:48.9472767Z bn_running_mean = self.bn.running_mean 2025-09-09T15:00:48.9473078Z bn_running_var = self.bn.running_var 2025-09-09T15:00:48.9473537Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T15:00:48.9473984Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T15:00:48.9474574Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T15:00:48.9475184Z add = torch.ops.aten.add.Tensor(bn_running_var, 1e-05) 2025-09-09T15:00:48.9475574Z sqrt = torch.ops.aten.sqrt.default(add); add = None 2025-09-09T15:00:48.9475990Z div = torch.ops.aten.div.Tensor(bn_weight, sqrt); sqrt = None 2025-09-09T15:00:48.9476421Z reshape = torch.ops.aten.reshape.default(div, [-1, 1, 1]) 2025-09-09T15:00:48.9476922Z mul = torch.ops.aten.mul.Tensor(conv_weight, reshape); conv_weight = reshape = None 2025-09-09T15:00:48.9477480Z activation_post_process_1 = self.activation_post_process_1(mul); mul = None 2025-09-09T15:00:48.9478317Z conv1d_1 = torch.ops.aten.conv1d.default(activation_post_process_0, activation_post_process_1, None); activation_post_process_0 = activation_post_process_1 = None 2025-09-09T15:00:48.9479132Z reshape_1 = torch.ops.aten.reshape.default(div, [1, -1, 1]); div = None 2025-09-09T15:00:48.9479726Z div_1 = torch.ops.aten.div.Tensor(conv1d_1, reshape_1); conv1d_1 = reshape_1 = None 2025-09-09T15:00:48.9480615Z batch_norm_1 = torch.ops.aten.batch_norm.default(div_1, bn_weight, bn_bias, bn_running_mean, bn_running_var, True, 0.1, 1e-05, True); div_1 = bn_weight = bn_bias = bn_running_mean = bn_running_var = None 2025-09-09T15:00:48.9481527Z activation_post_process_2 = self.activation_post_process_2(batch_norm_1); batch_norm_1 = None 2025-09-09T15:00:48.9482123Z return pytree.tree_unflatten((activation_post_process_2,), self._out_spec) 2025-09-09T15:00:48.9482510Z 2025-09-09T15:00:48.9482786Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:00:48.9483148Z model fx: GraphModule( 2025-09-09T15:00:48.9483468Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:00:48.9484403Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0150]), zero_point=tensor([14], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:00:48.9485507Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.1280412673950195, max_val=1.6863642930984497) 2025-09-09T15:00:48.9486012Z ) 2025-09-09T15:00:48.9486195Z (conv): ConvBn1d( 2025-09-09T15:00:48.9486432Z 3, 3, kernel_size=(3,), stride=(1,), bias=False 2025-09-09T15:00:48.9486863Z (bn): BatchNorm1d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T15:00:48.9487322Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:00:48.9488233Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0022]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-127, quant_max=127, qscheme=torch.per_tensor_symmetric, reduce_range=False 2025-09-09T15:00:48.9489342Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.2799264192581177, max_val=0.27745386958122253) 2025-09-09T15:00:48.9489848Z ) 2025-09-09T15:00:48.9490030Z ) 2025-09-09T15:00:48.9490306Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:00:48.9491229Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0146]), zero_point=tensor([8], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:00:48.9492307Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.9912875890731812, max_val=1.733071208000183) 2025-09-09T15:00:48.9492808Z ) 2025-09-09T15:00:48.9492979Z ) 2025-09-09T15:00:48.9493078Z 2025-09-09T15:00:48.9493083Z 2025-09-09T15:00:48.9493087Z 2025-09-09T15:00:48.9493171Z def forward(self, x): 2025-09-09T15:00:48.9493518Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T15:00:48.9494132Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T15:00:48.9494676Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T15:00:48.9495180Z return activation_post_process_1 2025-09-09T15:00:48.9495437Z 2025-09-09T15:00:48.9495712Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:00:48.9496076Z diff: tensor([[[0., 0., 0.], 2025-09-09T15:00:48.9496312Z [0., 0., 0.], 2025-09-09T15:00:48.9496519Z [0., 0., 0.]], 2025-09-09T15:00:48.9496657Z 2025-09-09T15:00:48.9496734Z [[0., 0., 0.], 2025-09-09T15:00:48.9496938Z [0., 0., 0.], 2025-09-09T15:00:48.9497150Z [0., 0., 0.]], 2025-09-09T15:00:48.9497283Z 2025-09-09T15:00:48.9497369Z [[0., 0., 0.], 2025-09-09T15:00:48.9497569Z [0., 0., 0.], 2025-09-09T15:00:48.9497806Z [0., 0., 0.]]], grad_fn=) 2025-09-09T15:00:48.9498120Z converted model pt2e: GraphModule( 2025-09-09T15:00:48.9498386Z (conv): Module() 2025-09-09T15:00:48.9498585Z (bn): Module() 2025-09-09T15:00:48.9498782Z ) 2025-09-09T15:00:48.9498880Z 2025-09-09T15:00:48.9498889Z 2025-09-09T15:00:48.9498893Z 2025-09-09T15:00:48.9498978Z def forward(self, x): 2025-09-09T15:00:48.9499259Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T15:00:48.9499640Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T15:00:48.9500339Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.014958452433347702, 14, -128, 127, torch.int8); x = None 2025-09-09T15:00:48.9501568Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.014958452433347702, 14, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T15:00:48.9502596Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T15:00:48.9503097Z quantize_per_tensor = self._frozen_param0 2025-09-09T15:00:48.9503880Z dequantize_per_tensor = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor, 0.002204145072028041, 0, -127, 127, torch.int8); quantize_per_tensor = None 2025-09-09T15:00:48.9504672Z conv_weight_bias = self.conv.weight_bias 2025-09-09T15:00:51.7684679Z conv1d_2 = torch.ops.aten.conv1d.default(dequantize_per_tensor_default, dequantize_per_tensor, conv_weight_bias); dequantize_per_tensor_default = dequantize_per_tensor = conv_weight_bias = None 2025-09-09T15:00:51.7686113Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv1d_2, 0.014605328440666199, 8, -128, 127, torch.int8); conv1d_2 = None 2025-09-09T15:00:51.7687532Z dequantize_per_tensor_default_2 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_2, 0.014605328440666199, 8, -128, 127, torch.int8); quantize_per_tensor_default_2 = None 2025-09-09T15:00:51.7688616Z return pytree.tree_unflatten((dequantize_per_tensor_default_2,), self._out_spec) 2025-09-09T15:00:51.7689016Z 2025-09-09T15:00:51.7689305Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:00:51.7689677Z onverted model fx: GraphModule( 2025-09-09T15:00:51.7690045Z (conv): QuantizedConv1d(Reference)(3, 3, kernel_size=(3,), stride=(1,)) 2025-09-09T15:00:51.7690415Z ) 2025-09-09T15:00:51.7690509Z 2025-09-09T15:00:51.7690513Z 2025-09-09T15:00:51.7690517Z 2025-09-09T15:00:51.7690596Z def forward(self, x): 2025-09-09T15:00:51.7691206Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.014958452433347702, 14, -128, 127, torch.int8); x = None 2025-09-09T15:00:51.7692428Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.014958452433347702, 14, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T15:00:51.7693603Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T15:00:51.7694445Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.014605328440666199, 8, -128, 127, torch.int8); conv = None 2025-09-09T15:00:51.7695809Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.014605328440666199, 8, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T15:00:51.7696683Z return dequantize_per_tensor_default_1 2025-09-09T15:00:51.7696958Z 2025-09-09T15:00:51.7697241Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:00:51.7697607Z diff: tensor([[[0., 0., 0.], 2025-09-09T15:00:51.7697838Z [0., 0., 0.], 2025-09-09T15:00:51.7698044Z [0., 0., 0.]], 2025-09-09T15:00:51.7698177Z 2025-09-09T15:00:51.7698251Z [[0., 0., 0.], 2025-09-09T15:00:51.7698465Z [0., 0., 0.], 2025-09-09T15:00:51.7698660Z [0., 0., 0.]], 2025-09-09T15:00:51.7698796Z 2025-09-09T15:00:51.7698866Z [[0., 0., 0.], 2025-09-09T15:00:51.7699069Z [0., 0., 0.], 2025-09-09T15:00:51.7699262Z [0., 0., 0.]]]) 2025-09-09T15:00:51.7699487Z model pt2e: GraphModule( 2025-09-09T15:00:51.7699705Z (conv1): Module() 2025-09-09T15:00:51.7699909Z (bn1): Module() 2025-09-09T15:00:51.7700100Z (conv2): Module() 2025-09-09T15:00:51.7700291Z (bn2): Module() 2025-09-09T15:00:51.7700579Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:00:51.7701504Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0150]), zero_point=tensor([14], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:00:51.7702594Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.1280412673950195, max_val=1.6863642930984497) 2025-09-09T15:00:51.7703102Z ) 2025-09-09T15:00:51.7703379Z (activation_post_process_3): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:00:51.7704341Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0023, 0.0026, 0.0025]), zero_point=tensor([0, 0, 0], dtype=torch.int32), dtype=torch.int8, quant_min=-127, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False 2025-09-09T15:00:51.7705612Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.2878, -0.2584, -0.3162]), max_val=tensor([0.2745, 0.3315, 0.3105])) 2025-09-09T15:00:51.7706270Z ) 2025-09-09T15:00:51.7706564Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:00:51.7707526Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0025, 0.0024, 0.0023]), zero_point=tensor([0, 0, 0], dtype=torch.int32), dtype=torch.int8, quant_min=-127, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False 2025-09-09T15:00:51.7708791Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.3233, -0.3086, -0.2979]), max_val=tensor([0.3026, 0.1712, 0.2405])) 2025-09-09T15:00:51.7709422Z ) 2025-09-09T15:00:51.7709689Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:00:51.7710602Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0161]), zero_point=tensor([-16], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:00:51.7711676Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.805302619934082, max_val=2.310788631439209) 2025-09-09T15:00:51.7712173Z ) 2025-09-09T15:00:51.7712436Z (activation_post_process_4): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:00:51.7713441Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0102]), zero_point=tensor([-11], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:00:51.7714516Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.1982380151748657, max_val=1.4133442640304565) 2025-09-09T15:00:51.7715128Z ) 2025-09-09T15:00:51.7715281Z ) 2025-09-09T15:00:51.7715381Z 2025-09-09T15:00:51.7715385Z 2025-09-09T15:00:51.7715389Z 2025-09-09T15:00:51.7715471Z def forward(self, x): 2025-09-09T15:00:51.7715752Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T15:00:51.7716075Z conv1_weight = self.conv1.weight 2025-09-09T15:00:51.7716354Z bn1_weight = self.bn1.weight 2025-09-09T15:00:51.7716602Z bn1_bias = self.bn1.bias 2025-09-09T15:00:51.7716848Z conv2_weight = self.conv2.weight 2025-09-09T15:00:51.7717116Z conv2_bias = self.conv2.bias 2025-09-09T15:00:51.7717370Z bn2_weight = self.bn2.weight 2025-09-09T15:00:51.7717612Z bn2_bias = self.bn2.bias 2025-09-09T15:00:51.7717876Z bn1_running_mean = self.bn1.running_mean 2025-09-09T15:00:51.7718185Z bn1_running_var = self.bn1.running_var 2025-09-09T15:00:51.7718515Z bn1_num_batches_tracked = self.bn1.num_batches_tracked 2025-09-09T15:00:51.7718864Z bn2_running_mean = self.bn2.running_mean 2025-09-09T15:00:51.7719259Z bn2_running_var = self.bn2.running_var 2025-09-09T15:00:51.7719634Z bn2_num_batches_tracked = self.bn2.num_batches_tracked 2025-09-09T15:00:51.7720069Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T15:00:51.7720659Z add_ = torch.ops.aten.add_.Tensor(bn1_num_batches_tracked, 1); bn1_num_batches_tracked = add_ = None 2025-09-09T15:00:51.7721318Z add__1 = torch.ops.aten.add_.Tensor(bn2_num_batches_tracked, 1); bn2_num_batches_tracked = add__1 = None 2025-09-09T15:00:51.7721853Z add = torch.ops.aten.add.Tensor(bn2_running_var, 1e-05) 2025-09-09T15:00:51.7722509Z sqrt = torch.ops.aten.sqrt.default(add); add = None 2025-09-09T15:00:51.7722932Z div = torch.ops.aten.div.Tensor(bn2_weight, sqrt); sqrt = None 2025-09-09T15:00:51.7723375Z reshape = torch.ops.aten.reshape.default(div, [-1, 1, 1]) 2025-09-09T15:00:51.7723876Z mul = torch.ops.aten.mul.Tensor(conv2_weight, reshape); conv2_weight = reshape = None 2025-09-09T15:00:51.7724437Z activation_post_process_3 = self.activation_post_process_3(mul); mul = None 2025-09-09T15:00:51.7725056Z zeros_like = torch.ops.aten.zeros_like.default(conv2_bias, dtype = torch.float32, pin_memory = False) 2025-09-09T15:00:51.7725603Z add_2 = torch.ops.aten.add.Tensor(bn1_running_var, 1e-05) 2025-09-09T15:00:51.7726008Z sqrt_1 = torch.ops.aten.sqrt.default(add_2); add_2 = None 2025-09-09T15:00:51.7726435Z div_2 = torch.ops.aten.div.Tensor(bn1_weight, sqrt_1); sqrt_1 = None 2025-09-09T15:00:51.7726889Z reshape_3 = torch.ops.aten.reshape.default(div_2, [-1, 1, 1]) 2025-09-09T15:00:51.7727410Z mul_1 = torch.ops.aten.mul.Tensor(conv1_weight, reshape_3); conv1_weight = reshape_3 = None 2025-09-09T15:00:51.7727994Z activation_post_process_1 = self.activation_post_process_1(mul_1); mul_1 = None 2025-09-09T15:00:51.7728837Z conv1d_3 = torch.ops.aten.conv1d.default(activation_post_process_0, activation_post_process_1, None); activation_post_process_0 = activation_post_process_1 = None 2025-09-09T15:00:51.7729656Z reshape_4 = torch.ops.aten.reshape.default(div_2, [1, -1, 1]); div_2 = None 2025-09-09T15:00:51.7730183Z div_3 = torch.ops.aten.div.Tensor(conv1d_3, reshape_4); conv1d_3 = reshape_4 = None 2025-09-09T15:00:51.7731086Z batch_norm_3 = torch.ops.aten.batch_norm.default(div_3, bn1_weight, bn1_bias, bn1_running_mean, bn1_running_var, True, 0.1, 1e-05, True); div_3 = bn1_weight = bn1_bias = bn1_running_mean = bn1_running_var = None 2025-09-09T15:00:51.7732023Z activation_post_process_2 = self.activation_post_process_2(batch_norm_3); batch_norm_3 = None 2025-09-09T15:00:51.7733094Z conv1d_2 = torch.ops.aten.conv1d.default(activation_post_process_2, activation_post_process_3, zeros_like); activation_post_process_2 = activation_post_process_3 = zeros_like = None 2025-09-09T15:00:51.7733952Z reshape_1 = torch.ops.aten.reshape.default(div, [1, -1, 1]); div = None 2025-09-09T15:00:51.7734575Z div_1 = torch.ops.aten.div.Tensor(conv1d_2, reshape_1); conv1d_2 = reshape_1 = None 2025-09-09T15:00:51.7735142Z reshape_2 = torch.ops.aten.reshape.default(conv2_bias, [1, -1, 1]); conv2_bias = None 2025-09-09T15:00:51.7735681Z add_1 = torch.ops.aten.add.Tensor(div_1, reshape_2); div_1 = reshape_2 = None 2025-09-09T15:00:51.7736553Z batch_norm_2 = torch.ops.aten.batch_norm.default(add_1, bn2_weight, bn2_bias, bn2_running_mean, bn2_running_var, True, 0.1, 1e-05, True); add_1 = bn2_weight = bn2_bias = bn2_running_mean = bn2_running_var = None 2025-09-09T15:00:51.7737489Z activation_post_process_4 = self.activation_post_process_4(batch_norm_2); batch_norm_2 = None 2025-09-09T15:00:51.7738065Z return pytree.tree_unflatten((activation_post_process_4,), self._out_spec) 2025-09-09T15:00:51.7738443Z 2025-09-09T15:00:51.7738718Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:00:51.7739075Z model fx: GraphModule( 2025-09-09T15:00:51.7739386Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:01:10.3866622Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0150]), zero_point=tensor([14], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:01:10.3868024Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.1280412673950195, max_val=1.6863642930984497) 2025-09-09T15:01:10.3868656Z ) 2025-09-09T15:01:10.3868883Z (conv1): ConvBn1d( 2025-09-09T15:01:10.3869173Z 3, 3, kernel_size=(3,), stride=(1,), bias=False 2025-09-09T15:01:10.3869702Z (bn): BatchNorm1d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T15:01:10.3870251Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:01:10.3871450Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0025, 0.0024, 0.0023]), zero_point=tensor([0, 0, 0], dtype=torch.int32), dtype=torch.qint8, quant_min=-127, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False 2025-09-09T15:01:10.3873065Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.3233, -0.3086, -0.2979]), max_val=tensor([0.3026, 0.1712, 0.2405])) 2025-09-09T15:01:10.3873849Z ) 2025-09-09T15:01:10.3874080Z ) 2025-09-09T15:01:10.3874412Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:01:10.3875563Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0161]), zero_point=tensor([-16], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:01:10.3876932Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.805302619934082, max_val=2.310788631439209) 2025-09-09T15:01:10.3877551Z ) 2025-09-09T15:01:10.3877753Z (conv2): ConvBn1d( 2025-09-09T15:01:10.3878023Z 3, 3, kernel_size=(3,), stride=(1,) 2025-09-09T15:01:10.3878511Z (bn): BatchNorm1d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T15:01:10.3879069Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:01:10.3880338Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0023, 0.0026, 0.0025]), zero_point=tensor([0, 0, 0], dtype=torch.int32), dtype=torch.qint8, quant_min=-127, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False 2025-09-09T15:01:10.3881976Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.2878, -0.2584, -0.3162]), max_val=tensor([0.2745, 0.3315, 0.3105])) 2025-09-09T15:01:10.3882762Z ) 2025-09-09T15:01:10.3882958Z ) 2025-09-09T15:01:10.3883280Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:01:10.3884725Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0102]), zero_point=tensor([-11], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:01:10.3886275Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.1982380151748657, max_val=1.4133442640304565) 2025-09-09T15:01:10.3886900Z ) 2025-09-09T15:01:10.3887091Z ) 2025-09-09T15:01:10.3887203Z 2025-09-09T15:01:10.3887215Z 2025-09-09T15:01:10.3887219Z 2025-09-09T15:01:10.3887318Z def forward(self, x): 2025-09-09T15:01:10.3887732Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T15:01:10.3888386Z conv1 = self.conv1(activation_post_process_0); activation_post_process_0 = None 2025-09-09T15:01:10.3889068Z activation_post_process_1 = self.activation_post_process_1(conv1); conv1 = None 2025-09-09T15:01:10.3889750Z conv2 = self.conv2(activation_post_process_1); activation_post_process_1 = None 2025-09-09T15:01:10.3890418Z activation_post_process_2 = self.activation_post_process_2(conv2); conv2 = None 2025-09-09T15:01:10.3890934Z return activation_post_process_2 2025-09-09T15:01:10.3891251Z 2025-09-09T15:01:10.3891571Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:01:10.3891997Z diff: tensor([[[0.], 2025-09-09T15:01:10.3892235Z [0.], 2025-09-09T15:01:10.3892458Z [0.]], 2025-09-09T15:01:10.3892593Z 2025-09-09T15:01:10.3892681Z [[0.], 2025-09-09T15:01:10.3892897Z [0.], 2025-09-09T15:01:10.3893119Z [0.]], 2025-09-09T15:01:10.3893251Z 2025-09-09T15:01:10.3893332Z [[0.], 2025-09-09T15:01:10.3893550Z [0.], 2025-09-09T15:01:10.3893790Z [0.]]], grad_fn=) 2025-09-09T15:01:10.3894139Z converted model pt2e: GraphModule( 2025-09-09T15:01:10.3894442Z (conv1): Module() 2025-09-09T15:01:10.3894680Z (bn1): Module() 2025-09-09T15:01:10.3894914Z (conv2): Module() 2025-09-09T15:01:10.3895151Z (bn2): Module() 2025-09-09T15:01:10.3895377Z ) 2025-09-09T15:01:10.3895489Z 2025-09-09T15:01:10.3895494Z 2025-09-09T15:01:10.3895509Z 2025-09-09T15:01:10.3895605Z def forward(self, x): 2025-09-09T15:01:10.3895938Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T15:01:10.3896333Z conv2_bias = self.conv2.bias 2025-09-09T15:01:10.3896707Z bn1_num_batches_tracked = self.bn1.num_batches_tracked 2025-09-09T15:01:10.3897150Z bn2_num_batches_tracked = self.bn2.num_batches_tracked 2025-09-09T15:01:10.3898032Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.014958452433347702, 14, -128, 127, torch.int8); x = None 2025-09-09T15:01:10.3899660Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.014958452433347702, 14, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T15:01:10.3900791Z add_ = torch.ops.aten.add_.Tensor(bn1_num_batches_tracked, 1); bn1_num_batches_tracked = add_ = None 2025-09-09T15:01:10.3901460Z add__1 = torch.ops.aten.add_.Tensor(bn2_num_batches_tracked, 1); bn2_num_batches_tracked = add__1 = None 2025-09-09T15:01:10.3901933Z _scale_0 = self._scale_0 2025-09-09T15:01:10.3902192Z _zero_point_0 = self._zero_point_0 2025-09-09T15:01:10.3902477Z _scale_1 = self._scale_1 2025-09-09T15:01:10.3902725Z _zero_point_1 = self._zero_point_1 2025-09-09T15:01:10.3903032Z quantize_per_channel_1 = self._frozen_param0 2025-09-09T15:01:10.3903935Z dequantize_per_channel_1 = torch.ops.quantized_decomposed.dequantize_per_channel.default(quantize_per_channel_1, _scale_1, _zero_point_1, 0, -127, 127, torch.int8); quantize_per_channel_1 = _scale_1 = _zero_point_1 = None 2025-09-09T15:01:10.3904836Z conv1_weight_bias = self.conv1.weight_bias 2025-09-09T15:01:10.3905793Z conv1d_5 = torch.ops.aten.conv1d.default(dequantize_per_tensor_default, dequantize_per_channel_1, conv1_weight_bias); dequantize_per_tensor_default = dequantize_per_channel_1 = conv1_weight_bias = None 2025-09-09T15:01:10.3907064Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv1d_5, 0.016141533851623535, -16, -128, 127, torch.int8); conv1d_5 = None 2025-09-09T15:01:10.3908452Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.016141533851623535, -16, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T15:01:10.3909335Z quantize_per_channel = self._frozen_param1 2025-09-09T15:01:10.3910211Z dequantize_per_channel = torch.ops.quantized_decomposed.dequantize_per_channel.default(quantize_per_channel, _scale_0, _zero_point_0, 0, -127, 127, torch.int8); quantize_per_channel = _scale_0 = _zero_point_0 = None 2025-09-09T15:01:10.3911631Z conv1d_4 = torch.ops.aten.conv1d.default(dequantize_per_tensor_default_1, dequantize_per_channel, conv2_bias); dequantize_per_tensor_default_1 = dequantize_per_channel = conv2_bias = None 2025-09-09T15:01:10.3912841Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv1d_4, 0.010241499170660973, -11, -128, 127, torch.int8); conv1d_4 = None 2025-09-09T15:01:10.3914156Z dequantize_per_tensor_default_2 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_2, 0.010241499170660973, -11, -128, 127, torch.int8); quantize_per_tensor_default_2 = None 2025-09-09T15:01:10.3915161Z return pytree.tree_unflatten((dequantize_per_tensor_default_2,), self._out_spec) 2025-09-09T15:01:10.3915560Z 2025-09-09T15:01:10.3915837Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:01:10.3916200Z onverted model fx: GraphModule( 2025-09-09T15:01:10.3916576Z (conv1): QuantizedConv1d(Reference)(3, 3, kernel_size=(3,), stride=(1,)) 2025-09-09T15:01:10.3917077Z (conv2): QuantizedConv1d(Reference)(3, 3, kernel_size=(3,), stride=(1,)) 2025-09-09T15:01:10.3917435Z ) 2025-09-09T15:01:10.3917534Z 2025-09-09T15:01:10.3917538Z 2025-09-09T15:01:10.3917542Z 2025-09-09T15:01:10.3917630Z def forward(self, x): 2025-09-09T15:01:10.3918241Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.014958452433347702, 14, -128, 127, torch.int8); x = None 2025-09-09T15:01:10.3919556Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.014958452433347702, 14, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T15:01:10.3920573Z conv1 = self.conv1(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T15:01:10.3921446Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv1, 0.016141533851623535, -16, -128, 127, torch.int8); conv1 = None 2025-09-09T15:01:10.3923031Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.016141533851623535, -16, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T15:01:10.3924092Z conv2 = self.conv2(dequantize_per_tensor_default_1); dequantize_per_tensor_default_1 = None 2025-09-09T15:01:10.3924966Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv2, 0.010241499170660973, -11, -128, 127, torch.int8); conv2 = None 2025-09-09T15:01:10.3926254Z dequantize_per_tensor_default_2 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_2, 0.010241499170660973, -11, -128, 127, torch.int8); quantize_per_tensor_default_2 = None 2025-09-09T15:01:10.3927132Z return dequantize_per_tensor_default_2 2025-09-09T15:01:10.3927412Z 2025-09-09T15:01:10.3927698Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:01:10.3928058Z diff: tensor([[[0.], 2025-09-09T15:01:10.3928421Z [0.], 2025-09-09T15:01:10.3928613Z [0.]], 2025-09-09T15:01:10.3928735Z 2025-09-09T15:01:10.3928817Z [[0.], 2025-09-09T15:01:10.3929003Z [0.], 2025-09-09T15:01:10.3929304Z [0.]], 2025-09-09T15:01:10.3929423Z 2025-09-09T15:01:10.3929498Z [[0.], 2025-09-09T15:01:10.3929690Z [0.], 2025-09-09T15:01:10.3929877Z [0.]]]) 2025-09-09T15:01:10.3930097Z model pt2e: GraphModule( 2025-09-09T15:01:10.3930333Z (conv1): Module() 2025-09-09T15:01:10.3930536Z (bn1): Module() 2025-09-09T15:01:10.3930747Z (conv2): Module() 2025-09-09T15:01:10.3930947Z (bn2): Module() 2025-09-09T15:01:10.3931255Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:01:10.3932220Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0150]), zero_point=tensor([14], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:01:10.3933326Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.1280412673950195, max_val=1.6863642930984497) 2025-09-09T15:01:10.3933847Z ) 2025-09-09T15:01:10.3934127Z (activation_post_process_3): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:01:10.3935083Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0026]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.int8, quant_min=-127, quant_max=127, qscheme=torch.per_tensor_symmetric, reduce_range=False 2025-09-09T15:01:10.3936195Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.3161814510822296, max_val=0.33154603838920593) 2025-09-09T15:01:10.3936715Z ) 2025-09-09T15:01:10.3936994Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:01:10.3937928Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0025]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.int8, quant_min=-127, quant_max=127, qscheme=torch.per_tensor_symmetric, reduce_range=False 2025-09-09T15:01:10.3939036Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.3232710361480713, max_val=0.30256387591362) 2025-09-09T15:01:10.3939552Z ) 2025-09-09T15:01:10.3939823Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:01:10.3940768Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0161]), zero_point=tensor([-16], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:01:10.3941908Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.807204008102417, max_val=2.3096539974212646) 2025-09-09T15:01:10.3942427Z ) 2025-09-09T15:01:10.3942707Z (activation_post_process_4): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:01:10.3943637Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0102]), zero_point=tensor([-11], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:01:10.3944735Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.2001533508300781, max_val=1.4126498699188232) 2025-09-09T15:01:10.3945250Z ) 2025-09-09T15:01:10.3945422Z ) 2025-09-09T15:01:10.3945518Z 2025-09-09T15:01:10.3945522Z 2025-09-09T15:01:10.3945526Z 2025-09-09T15:01:10.3945617Z def forward(self, x): 2025-09-09T15:01:10.3945901Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T15:01:10.3946245Z conv1_weight = self.conv1.weight 2025-09-09T15:01:10.3946525Z bn1_weight = self.bn1.weight 2025-09-09T15:01:10.3946789Z bn1_bias = self.bn1.bias 2025-09-09T15:01:10.3947042Z conv2_weight = self.conv2.weight 2025-09-09T15:01:10.3947325Z conv2_bias = self.conv2.bias 2025-09-09T15:01:10.3947586Z bn2_weight = self.bn2.weight 2025-09-09T15:01:10.3947846Z bn2_bias = self.bn2.bias 2025-09-09T15:01:10.3948115Z bn1_running_mean = self.bn1.running_mean 2025-09-09T15:01:10.3948518Z bn1_running_var = self.bn1.running_var 2025-09-09T15:01:10.3948867Z bn1_num_batches_tracked = self.bn1.num_batches_tracked 2025-09-09T15:01:10.3949217Z bn2_running_mean = self.bn2.running_mean 2025-09-09T15:01:10.3949635Z bn2_running_var = self.bn2.running_var 2025-09-09T15:01:10.3949970Z bn2_num_batches_tracked = self.bn2.num_batches_tracked 2025-09-09T15:01:10.3950422Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T15:01:10.3951018Z add_ = torch.ops.aten.add_.Tensor(bn1_num_batches_tracked, 1); bn1_num_batches_tracked = add_ = None 2025-09-09T15:01:10.3951688Z add__1 = torch.ops.aten.add_.Tensor(bn2_num_batches_tracked, 1); bn2_num_batches_tracked = add__1 = None 2025-09-09T15:01:10.3952262Z add = torch.ops.aten.add.Tensor(bn2_running_var, 1e-05) 2025-09-09T15:01:10.3952681Z sqrt = torch.ops.aten.sqrt.default(add); add = None 2025-09-09T15:01:10.3953100Z div = torch.ops.aten.div.Tensor(bn2_weight, sqrt); sqrt = None 2025-09-09T15:01:10.3953547Z reshape = torch.ops.aten.reshape.default(div, [-1, 1, 1]) 2025-09-09T15:01:10.3954064Z mul = torch.ops.aten.mul.Tensor(conv2_weight, reshape); conv2_weight = reshape = None 2025-09-09T15:01:10.3954645Z activation_post_process_3 = self.activation_post_process_3(mul); mul = None 2025-09-09T15:01:10.3955439Z zeros_like = torch.ops.aten.zeros_like.default(conv2_bias, dtype = torch.float32, pin_memory = False) 2025-09-09T15:01:10.3955994Z add_2 = torch.ops.aten.add.Tensor(bn1_running_var, 1e-05) 2025-09-09T15:01:10.3956402Z sqrt_1 = torch.ops.aten.sqrt.default(add_2); add_2 = None 2025-09-09T15:01:10.3956846Z div_2 = torch.ops.aten.div.Tensor(bn1_weight, sqrt_1); sqrt_1 = None 2025-09-09T15:01:10.3957303Z reshape_3 = torch.ops.aten.reshape.default(div_2, [-1, 1, 1]) 2025-09-09T15:01:10.3957838Z mul_1 = torch.ops.aten.mul.Tensor(conv1_weight, reshape_3); conv1_weight = reshape_3 = None 2025-09-09T15:01:10.3958438Z activation_post_process_1 = self.activation_post_process_1(mul_1); mul_1 = None 2025-09-09T15:01:10.3959364Z conv1d_3 = torch.ops.aten.conv1d.default(activation_post_process_0, activation_post_process_1, None); activation_post_process_0 = activation_post_process_1 = None 2025-09-09T15:01:10.3960199Z reshape_4 = torch.ops.aten.reshape.default(div_2, [1, -1, 1]); div_2 = None 2025-09-09T15:01:10.3960733Z div_3 = torch.ops.aten.div.Tensor(conv1d_3, reshape_4); conv1d_3 = reshape_4 = None 2025-09-09T15:01:10.3961651Z batch_norm_3 = torch.ops.aten.batch_norm.default(div_3, bn1_weight, bn1_bias, bn1_running_mean, bn1_running_var, True, 0.1, 1e-05, True); div_3 = bn1_weight = bn1_bias = bn1_running_mean = bn1_running_var = None 2025-09-09T15:01:10.3962655Z activation_post_process_2 = self.activation_post_process_2(batch_norm_3); batch_norm_3 = None 2025-09-09T15:01:10.3963605Z conv1d_2 = torch.ops.aten.conv1d.default(activation_post_process_2, activation_post_process_3, zeros_like); activation_post_process_2 = activation_post_process_3 = zeros_like = None 2025-09-09T15:01:10.3964489Z reshape_1 = torch.ops.aten.reshape.default(div, [1, -1, 1]); div = None 2025-09-09T15:01:10.3965015Z div_1 = torch.ops.aten.div.Tensor(conv1d_2, reshape_1); conv1d_2 = reshape_1 = None 2025-09-09T15:01:10.3965599Z reshape_2 = torch.ops.aten.reshape.default(conv2_bias, [1, -1, 1]); conv2_bias = None 2025-09-09T15:01:10.3966157Z add_1 = torch.ops.aten.add.Tensor(div_1, reshape_2); div_1 = reshape_2 = None 2025-09-09T15:01:10.3967041Z batch_norm_2 = torch.ops.aten.batch_norm.default(add_1, bn2_weight, bn2_bias, bn2_running_mean, bn2_running_var, True, 0.1, 1e-05, True); add_1 = bn2_weight = bn2_bias = bn2_running_mean = bn2_running_var = None 2025-09-09T15:01:10.3967999Z activation_post_process_4 = self.activation_post_process_4(batch_norm_2); batch_norm_2 = None 2025-09-09T15:01:10.3968588Z return pytree.tree_unflatten((activation_post_process_4,), self._out_spec) 2025-09-09T15:01:10.3968981Z 2025-09-09T15:01:10.3969355Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:01:10.3969717Z model fx: GraphModule( 2025-09-09T15:01:10.3970042Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:01:10.3971057Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0150]), zero_point=tensor([14], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:01:10.3972213Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.1280412673950195, max_val=1.6863642930984497) 2025-09-09T15:01:10.3972730Z ) 2025-09-09T15:01:10.3972911Z (conv1): ConvBn1d( 2025-09-09T15:01:10.3973167Z 3, 3, kernel_size=(3,), stride=(1,), bias=False 2025-09-09T15:01:10.3973589Z (bn): BatchNorm1d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T15:01:10.3974071Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:01:10.3974996Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0025]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-127, quant_max=127, qscheme=torch.per_tensor_symmetric, reduce_range=False 2025-09-09T15:01:10.3976102Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.3232710361480713, max_val=0.30256387591362) 2025-09-09T15:01:10.3984998Z ) 2025-09-09T15:01:10.3985222Z ) 2025-09-09T15:01:10.3985520Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:01:10.3986496Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0161]), zero_point=tensor([-16], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:01:10.3987607Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.807204008102417, max_val=2.3096539974212646) 2025-09-09T15:01:10.3988130Z ) 2025-09-09T15:01:10.3988325Z (conv2): ConvBn1d( 2025-09-09T15:01:10.3988570Z 3, 3, kernel_size=(3,), stride=(1,) 2025-09-09T15:01:10.3988991Z (bn): BatchNorm1d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T15:01:10.3989469Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:01:10.3990399Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0026]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-127, quant_max=127, qscheme=torch.per_tensor_symmetric, reduce_range=False 2025-09-09T15:01:10.3991557Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.3161814510822296, max_val=0.33154603838920593) 2025-09-09T15:01:10.3992088Z ) 2025-09-09T15:01:10.3992268Z ) 2025-09-09T15:01:10.3992536Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:01:10.3993482Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0102]), zero_point=tensor([-11], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:01:10.3994575Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.2001533508300781, max_val=1.4126498699188232) 2025-09-09T15:01:10.3995095Z ) 2025-09-09T15:01:10.3995254Z ) 2025-09-09T15:01:10.3995356Z 2025-09-09T15:01:10.3995360Z 2025-09-09T15:01:10.3995363Z 2025-09-09T15:01:10.3995444Z def forward(self, x): 2025-09-09T15:01:10.3995800Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T15:01:10.3996331Z conv1 = self.conv1(activation_post_process_0); activation_post_process_0 = None 2025-09-09T15:01:51.6608090Z activation_post_process_1 = self.activation_post_process_1(conv1); conv1 = None 2025-09-09T15:01:51.6608810Z conv2 = self.conv2(activation_post_process_1); activation_post_process_1 = None 2025-09-09T15:01:51.6609487Z activation_post_process_2 = self.activation_post_process_2(conv2); conv2 = None 2025-09-09T15:01:51.6610304Z return activation_post_process_2 2025-09-09T15:01:51.6610620Z 2025-09-09T15:01:51.6610956Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:01:51.6611552Z diff: tensor([[[0.], 2025-09-09T15:01:51.6611798Z [0.], 2025-09-09T15:01:51.6612010Z [0.]], 2025-09-09T15:01:51.6612154Z 2025-09-09T15:01:51.6612240Z [[0.], 2025-09-09T15:01:51.6612448Z [0.], 2025-09-09T15:01:51.6612673Z [0.]], 2025-09-09T15:01:51.6612805Z 2025-09-09T15:01:51.6612888Z [[0.], 2025-09-09T15:01:51.6613109Z [0.], 2025-09-09T15:01:51.6613351Z [0.]]], grad_fn=) 2025-09-09T15:01:51.6613692Z converted model pt2e: GraphModule( 2025-09-09T15:01:51.6614003Z (conv1): Module() 2025-09-09T15:01:51.6614230Z (bn1): Module() 2025-09-09T15:01:51.6614467Z (conv2): Module() 2025-09-09T15:01:51.6614696Z (bn2): Module() 2025-09-09T15:01:51.6614926Z ) 2025-09-09T15:01:51.6615038Z 2025-09-09T15:01:51.6615051Z 2025-09-09T15:01:51.6615057Z 2025-09-09T15:01:51.6615158Z def forward(self, x): 2025-09-09T15:01:51.6615493Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T15:01:51.6615908Z conv2_bias = self.conv2.bias 2025-09-09T15:01:51.6616276Z bn1_num_batches_tracked = self.bn1.num_batches_tracked 2025-09-09T15:01:51.6616736Z bn2_num_batches_tracked = self.bn2.num_batches_tracked 2025-09-09T15:01:51.6617610Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.014958452433347702, 14, -128, 127, torch.int8); x = None 2025-09-09T15:01:51.6619146Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.014958452433347702, 14, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T15:01:51.6620429Z add_ = torch.ops.aten.add_.Tensor(bn1_num_batches_tracked, 1); bn1_num_batches_tracked = add_ = None 2025-09-09T15:01:51.6621234Z add__1 = torch.ops.aten.add_.Tensor(bn2_num_batches_tracked, 1); bn2_num_batches_tracked = add__1 = None 2025-09-09T15:01:51.6621840Z quantize_per_tensor_1 = self._frozen_param0 2025-09-09T15:01:51.6623083Z dequantize_per_tensor_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_1, 0.0025454412680119276, 0, -127, 127, torch.int8); quantize_per_tensor_1 = None 2025-09-09T15:01:51.6624089Z conv1_weight_bias = self.conv1.weight_bias 2025-09-09T15:01:51.6625137Z conv1d_5 = torch.ops.aten.conv1d.default(dequantize_per_tensor_default, dequantize_per_tensor_1, conv1_weight_bias); dequantize_per_tensor_default = dequantize_per_tensor_1 = conv1_weight_bias = None 2025-09-09T15:01:51.6626689Z quantize_per_tensor_default_3 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv1d_5, 0.016144542023539543, -16, -128, 127, torch.int8); conv1d_5 = None 2025-09-09T15:01:51.6628297Z dequantize_per_tensor_default_3 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_3, 0.016144542023539543, -16, -128, 127, torch.int8); quantize_per_tensor_default_3 = None 2025-09-09T15:01:51.6629382Z quantize_per_tensor = self._frozen_param1 2025-09-09T15:01:51.6630353Z dequantize_per_tensor = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor, 0.0026105986908078194, 0, -127, 127, torch.int8); quantize_per_tensor = None 2025-09-09T15:01:51.6631909Z conv1d_4 = torch.ops.aten.conv1d.default(dequantize_per_tensor_default_3, dequantize_per_tensor, conv2_bias); dequantize_per_tensor_default_3 = dequantize_per_tensor = conv2_bias = None 2025-09-09T15:01:51.6633457Z quantize_per_tensor_default_4 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv1d_4, 0.01024628710001707, -11, -128, 127, torch.int8); conv1d_4 = None 2025-09-09T15:01:51.6635180Z dequantize_per_tensor_default_4 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_4, 0.01024628710001707, -11, -128, 127, torch.int8); quantize_per_tensor_default_4 = None 2025-09-09T15:01:51.6636429Z return pytree.tree_unflatten((dequantize_per_tensor_default_4,), self._out_spec) 2025-09-09T15:01:51.6637038Z 2025-09-09T15:01:51.6637366Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:01:51.6637814Z onverted model fx: GraphModule( 2025-09-09T15:01:51.6638258Z (conv1): QuantizedConv1d(Reference)(3, 3, kernel_size=(3,), stride=(1,)) 2025-09-09T15:01:51.6638861Z (conv2): QuantizedConv1d(Reference)(3, 3, kernel_size=(3,), stride=(1,)) 2025-09-09T15:01:51.6639408Z ) 2025-09-09T15:01:51.6639534Z 2025-09-09T15:01:51.6639539Z 2025-09-09T15:01:51.6639544Z 2025-09-09T15:01:51.6639642Z def forward(self, x): 2025-09-09T15:01:51.6640396Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.014958452433347702, 14, -128, 127, torch.int8); x = None 2025-09-09T15:01:51.6641923Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.014958452433347702, 14, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T15:01:51.6643178Z conv1 = self.conv1(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T15:01:51.6644300Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv1, 0.016144542023539543, -16, -128, 127, torch.int8); conv1 = None 2025-09-09T15:01:51.6645762Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.016144542023539543, -16, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T15:01:51.6646784Z conv2 = self.conv2(dequantize_per_tensor_default_1); dequantize_per_tensor_default_1 = None 2025-09-09T15:01:51.6647642Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv2, 0.01024628710001707, -11, -128, 127, torch.int8); conv2 = None 2025-09-09T15:01:51.6648906Z dequantize_per_tensor_default_2 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_2, 0.01024628710001707, -11, -128, 127, torch.int8); quantize_per_tensor_default_2 = None 2025-09-09T15:01:51.6649776Z return dequantize_per_tensor_default_2 2025-09-09T15:01:51.6650040Z 2025-09-09T15:01:51.6650319Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:01:51.6650667Z diff: tensor([[[0.], 2025-09-09T15:01:51.6650878Z [0.], 2025-09-09T15:01:51.6651065Z [0.]], 2025-09-09T15:01:51.6651187Z 2025-09-09T15:01:51.6651259Z [[0.], 2025-09-09T15:01:51.6651443Z [0.], 2025-09-09T15:01:51.6651619Z [0.]], 2025-09-09T15:01:51.6651735Z 2025-09-09T15:01:51.6651810Z [[0.], 2025-09-09T15:01:51.6651983Z [0.], 2025-09-09T15:01:51.6652162Z [0.]]]) 2025-09-09T15:01:51.6652565Z PASSED 2025-09-09T15:01:51.6653271Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn1d::test_qat_conv_bn_per_channel_weight_bias PASSED 2025-09-09T15:01:51.6654243Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn1d::test_qat_conv_bn_relu_fusion model pt2e: GraphModule( 2025-09-09T15:01:51.6654853Z (conv): Module() 2025-09-09T15:01:51.6655053Z (bn): Module() 2025-09-09T15:01:51.6655345Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:01:51.6656274Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0104]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:01:51.6657364Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3264806270599365, max_val=1.318617343902588) 2025-09-09T15:01:51.6657864Z ) 2025-09-09T15:01:51.6658148Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:01:51.6659209Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0024, 0.0026, 0.0025]), zero_point=tensor([0, 0, 0], dtype=torch.int32), dtype=torch.int8, quant_min=-127, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False 2025-09-09T15:01:51.6660548Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.2845, -0.3289, -0.3229]), max_val=tensor([0.2989, 0.2870, 0.2939])) 2025-09-09T15:01:51.6661187Z ) 2025-09-09T15:01:51.6661454Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:01:51.6662382Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0055]), zero_point=tensor([-128], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:01:51.6663439Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=1.413901925086975) 2025-09-09T15:01:51.6663919Z ) 2025-09-09T15:01:51.6664085Z ) 2025-09-09T15:01:51.6664183Z 2025-09-09T15:01:51.6664187Z 2025-09-09T15:01:51.6664191Z 2025-09-09T15:01:51.6664276Z def forward(self, x): 2025-09-09T15:01:51.6664564Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T15:01:51.6664888Z conv_weight = self.conv.weight 2025-09-09T15:01:51.6665158Z conv_bias = self.conv.bias 2025-09-09T15:01:51.6665403Z bn_weight = self.bn.weight 2025-09-09T15:01:51.6665650Z bn_bias = self.bn.bias 2025-09-09T15:01:51.6665896Z bn_running_mean = self.bn.running_mean 2025-09-09T15:01:51.6666193Z bn_running_var = self.bn.running_var 2025-09-09T15:01:51.6666515Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T15:01:51.6666947Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T15:01:51.6667526Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T15:01:51.6668047Z add = torch.ops.aten.add.Tensor(bn_running_var, 1e-05) 2025-09-09T15:01:51.6668432Z sqrt = torch.ops.aten.sqrt.default(add); add = None 2025-09-09T15:01:51.6668827Z div = torch.ops.aten.div.Tensor(bn_weight, sqrt); sqrt = None 2025-09-09T15:01:51.6669263Z reshape = torch.ops.aten.reshape.default(div, [-1, 1, 1]) 2025-09-09T15:01:51.6669758Z mul = torch.ops.aten.mul.Tensor(conv_weight, reshape); conv_weight = reshape = None 2025-09-09T15:01:51.6670310Z activation_post_process_1 = self.activation_post_process_1(mul); mul = None 2025-09-09T15:01:51.6670912Z zeros_like = torch.ops.aten.zeros_like.default(conv_bias, dtype = torch.float32, pin_memory = False) 2025-09-09T15:01:51.6671860Z conv1d_1 = torch.ops.aten.conv1d.default(activation_post_process_0, activation_post_process_1, zeros_like); activation_post_process_0 = activation_post_process_1 = zeros_like = None 2025-09-09T15:01:51.6672723Z reshape_1 = torch.ops.aten.reshape.default(div, [1, -1, 1]); div = None 2025-09-09T15:01:51.6673249Z div_1 = torch.ops.aten.div.Tensor(conv1d_1, reshape_1); conv1d_1 = reshape_1 = None 2025-09-09T15:02:10.4570878Z reshape_2 = torch.ops.aten.reshape.default(conv_bias, [1, -1, 1]); conv_bias = None 2025-09-09T15:02:10.4571663Z add_1 = torch.ops.aten.add.Tensor(div_1, reshape_2); div_1 = reshape_2 = None 2025-09-09T15:02:10.4572737Z batch_norm_1 = torch.ops.aten.batch_norm.default(add_1, bn_weight, bn_bias, bn_running_mean, bn_running_var, True, 0.1, 1e-05, True); add_1 = bn_weight = bn_bias = bn_running_mean = bn_running_var = None 2025-09-09T15:02:10.4573757Z relu = torch.ops.aten.relu.default(batch_norm_1); batch_norm_1 = None 2025-09-09T15:02:10.4574393Z activation_post_process_2 = self.activation_post_process_2(relu); relu = None 2025-09-09T15:02:10.4575057Z return pytree.tree_unflatten((activation_post_process_2,), self._out_spec) 2025-09-09T15:02:10.4575518Z 2025-09-09T15:02:10.4575853Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:02:10.4576551Z model fx: GraphModule( 2025-09-09T15:02:10.4576943Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:02:10.4578105Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0104]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:02:10.4579693Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3264806270599365, max_val=1.318617343902588) 2025-09-09T15:02:10.4580312Z ) 2025-09-09T15:02:10.4580529Z (conv): ConvBnReLU1d( 2025-09-09T15:02:10.4580806Z 3, 3, kernel_size=(3,), stride=(1,) 2025-09-09T15:02:10.4581281Z (bn): BatchNorm1d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T15:02:10.4581835Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:02:10.4583012Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0024, 0.0026, 0.0025]), zero_point=tensor([0, 0, 0], dtype=torch.int32), dtype=torch.qint8, quant_min=-127, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False 2025-09-09T15:02:10.4584595Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.2845, -0.3289, -0.3229]), max_val=tensor([0.2989, 0.2870, 0.2939])) 2025-09-09T15:02:10.4585386Z ) 2025-09-09T15:02:10.4585583Z ) 2025-09-09T15:02:10.4585911Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:02:10.4587069Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0055]), zero_point=tensor([-128], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:02:10.4588409Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=1.413901925086975) 2025-09-09T15:02:10.4588970Z ) 2025-09-09T15:02:10.4589160Z ) 2025-09-09T15:02:10.4589292Z 2025-09-09T15:02:10.4589297Z 2025-09-09T15:02:10.4589308Z 2025-09-09T15:02:10.4589406Z def forward(self, x): 2025-09-09T15:02:10.4589830Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T15:02:10.4590474Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T15:02:10.4591137Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T15:02:10.4591642Z return activation_post_process_1 2025-09-09T15:02:10.4591957Z 2025-09-09T15:02:10.4592280Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:02:10.4592710Z diff: tensor([[[0., 0., 0.], 2025-09-09T15:02:10.4592985Z [0., 0., 0.], 2025-09-09T15:02:10.4593256Z [0., 0., 0.]]], grad_fn=) 2025-09-09T15:02:10.4593617Z converted model pt2e: GraphModule( 2025-09-09T15:02:10.4593917Z (conv): Module() 2025-09-09T15:02:10.4594154Z (bn): Module() 2025-09-09T15:02:10.4594370Z ) 2025-09-09T15:02:10.4594489Z 2025-09-09T15:02:10.4594494Z 2025-09-09T15:02:10.4594503Z 2025-09-09T15:02:10.4594601Z def forward(self, x): 2025-09-09T15:02:10.4594931Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T15:02:10.4595328Z conv_bias = self.conv.bias 2025-09-09T15:02:10.4595681Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T15:02:10.4596531Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.010372933000326157, 0, -128, 127, torch.int8); x = None 2025-09-09T15:02:10.4598053Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.010372933000326157, 0, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T15:02:10.4599323Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T15:02:10.4599971Z _scale_0 = self._scale_0 2025-09-09T15:02:10.4600270Z _zero_point_0 = self._zero_point_0 2025-09-09T15:02:10.4600728Z quantize_per_channel = self._frozen_param0 2025-09-09T15:02:10.4601810Z dequantize_per_channel = torch.ops.quantized_decomposed.dequantize_per_channel.default(quantize_per_channel, _scale_0, _zero_point_0, 0, -127, 127, torch.int8); quantize_per_channel = _scale_0 = _zero_point_0 = None 2025-09-09T15:02:10.4603550Z conv1d_2 = torch.ops.aten.conv1d.default(dequantize_per_tensor_default, dequantize_per_channel, conv_bias); dequantize_per_tensor_default = dequantize_per_channel = conv_bias = None 2025-09-09T15:02:10.4604579Z relu = torch.ops.aten.relu.default(conv1d_2); conv1d_2 = None 2025-09-09T15:02:10.4605525Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(relu, 0.005544713232666254, -128, -128, 127, torch.int8); relu = None 2025-09-09T15:02:10.4607103Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.005544713232666254, -128, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T15:02:10.4608404Z return pytree.tree_unflatten((dequantize_per_tensor_default_1,), self._out_spec) 2025-09-09T15:02:10.4608901Z 2025-09-09T15:02:10.4609221Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:02:10.4609669Z onverted model fx: GraphModule( 2025-09-09T15:02:10.4609958Z (conv): ConvReLU1d( 2025-09-09T15:02:10.4610348Z (0): QuantizedConv1d(Reference)(3, 3, kernel_size=(3,), stride=(1,)) 2025-09-09T15:02:10.4610772Z (1): ReLU() 2025-09-09T15:02:10.4610995Z ) 2025-09-09T15:02:10.4611186Z ) 2025-09-09T15:02:10.4611305Z 2025-09-09T15:02:10.4611310Z 2025-09-09T15:02:10.4611315Z 2025-09-09T15:02:10.4611413Z def forward(self, x): 2025-09-09T15:02:10.4612157Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.010372933000326157, 0, -128, 127, torch.int8); x = None 2025-09-09T15:02:10.4613677Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.010372933000326157, 0, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T15:02:10.4614945Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T15:02:10.4616067Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.005544713232666254, -128, -128, 127, torch.int8); conv = None 2025-09-09T15:02:10.4617342Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.005544713232666254, -128, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T15:02:10.4618218Z return dequantize_per_tensor_default_1 2025-09-09T15:02:10.4618480Z 2025-09-09T15:02:10.4618749Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:02:10.4619097Z diff: tensor([[[0., 0., 0.], 2025-09-09T15:02:10.4619325Z [0., 0., 0.], 2025-09-09T15:02:10.4619536Z [0., 0., 0.]]]) 2025-09-09T15:02:10.4619755Z model pt2e: GraphModule( 2025-09-09T15:02:10.4619980Z (conv): Module() 2025-09-09T15:02:10.4620179Z (bn): Module() 2025-09-09T15:02:10.4620470Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:02:10.4621382Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0104]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:02:10.4622612Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3264806270599365, max_val=1.318617343902588) 2025-09-09T15:02:10.4623120Z ) 2025-09-09T15:02:10.4623384Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:02:10.4624459Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0026]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.int8, quant_min=-127, quant_max=127, qscheme=torch.per_tensor_symmetric, reduce_range=False 2025-09-09T15:02:10.4625543Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.3289433717727661, max_val=0.29890719056129456) 2025-09-09T15:02:10.4626156Z ) 2025-09-09T15:02:10.4626428Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:02:10.4627339Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0055]), zero_point=tensor([-128], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:02:10.4628369Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=1.413901925086975) 2025-09-09T15:02:10.4628816Z ) 2025-09-09T15:02:10.4628986Z ) 2025-09-09T15:02:10.4629080Z 2025-09-09T15:02:10.4629084Z 2025-09-09T15:02:10.4629087Z 2025-09-09T15:02:10.4629178Z def forward(self, x): 2025-09-09T15:02:10.4629462Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T15:02:10.4629798Z conv_weight = self.conv.weight 2025-09-09T15:02:10.4630060Z conv_bias = self.conv.bias 2025-09-09T15:02:10.4630308Z bn_weight = self.bn.weight 2025-09-09T15:02:10.4630550Z bn_bias = self.bn.bias 2025-09-09T15:02:10.4630800Z bn_running_mean = self.bn.running_mean 2025-09-09T15:02:10.4631086Z bn_running_var = self.bn.running_var 2025-09-09T15:02:10.4631409Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T15:02:10.4631842Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T15:02:10.4632409Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T15:02:10.4632924Z add = torch.ops.aten.add.Tensor(bn_running_var, 1e-05) 2025-09-09T15:02:10.4633300Z sqrt = torch.ops.aten.sqrt.default(add); add = None 2025-09-09T15:02:10.4633705Z div = torch.ops.aten.div.Tensor(bn_weight, sqrt); sqrt = None 2025-09-09T15:02:10.4634133Z reshape = torch.ops.aten.reshape.default(div, [-1, 1, 1]) 2025-09-09T15:02:10.4634622Z mul = torch.ops.aten.mul.Tensor(conv_weight, reshape); conv_weight = reshape = None 2025-09-09T15:02:10.4635185Z activation_post_process_1 = self.activation_post_process_1(mul); mul = None 2025-09-09T15:02:10.4635775Z zeros_like = torch.ops.aten.zeros_like.default(conv_bias, dtype = torch.float32, pin_memory = False) 2025-09-09T15:02:34.7775041Z conv1d_1 = torch.ops.aten.conv1d.default(activation_post_process_0, activation_post_process_1, zeros_like); activation_post_process_0 = activation_post_process_1 = zeros_like = None 2025-09-09T15:02:34.7775992Z reshape_1 = torch.ops.aten.reshape.default(div, [1, -1, 1]); div = None 2025-09-09T15:02:34.7776547Z div_1 = torch.ops.aten.div.Tensor(conv1d_1, reshape_1); conv1d_1 = reshape_1 = None 2025-09-09T15:02:34.7777137Z reshape_2 = torch.ops.aten.reshape.default(conv_bias, [1, -1, 1]); conv_bias = None 2025-09-09T15:02:34.7777728Z add_1 = torch.ops.aten.add.Tensor(div_1, reshape_2); div_1 = reshape_2 = None 2025-09-09T15:02:34.7778601Z batch_norm_1 = torch.ops.aten.batch_norm.default(add_1, bn_weight, bn_bias, bn_running_mean, bn_running_var, True, 0.1, 1e-05, True); add_1 = bn_weight = bn_bias = bn_running_mean = bn_running_var = None 2025-09-09T15:02:34.7779465Z relu = torch.ops.aten.relu.default(batch_norm_1); batch_norm_1 = None 2025-09-09T15:02:34.7780006Z activation_post_process_2 = self.activation_post_process_2(relu); relu = None 2025-09-09T15:02:34.7780554Z return pytree.tree_unflatten((activation_post_process_2,), self._out_spec) 2025-09-09T15:02:34.7780952Z 2025-09-09T15:02:34.7781235Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:02:34.7781606Z model fx: GraphModule( 2025-09-09T15:02:34.7781929Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:02:34.7783166Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0104]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:02:34.7784294Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3264806270599365, max_val=1.318617343902588) 2025-09-09T15:02:34.7784977Z ) 2025-09-09T15:02:34.7785193Z (conv): ConvBnReLU1d( 2025-09-09T15:02:34.7785440Z 3, 3, kernel_size=(3,), stride=(1,) 2025-09-09T15:02:34.7785852Z (bn): BatchNorm1d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T15:02:34.7786320Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:02:34.7787237Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0026]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-127, quant_max=127, qscheme=torch.per_tensor_symmetric, reduce_range=False 2025-09-09T15:02:34.7788373Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.3289433717727661, max_val=0.29890719056129456) 2025-09-09T15:02:34.7788894Z ) 2025-09-09T15:02:34.7789067Z ) 2025-09-09T15:02:34.7789348Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:02:34.7790296Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0055]), zero_point=tensor([-128], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:02:34.7791355Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=1.413901925086975) 2025-09-09T15:02:34.7791815Z ) 2025-09-09T15:02:34.7791984Z ) 2025-09-09T15:02:34.7792082Z 2025-09-09T15:02:34.7792088Z 2025-09-09T15:02:34.7792092Z 2025-09-09T15:02:34.7792181Z def forward(self, x): 2025-09-09T15:02:34.7792534Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T15:02:34.7793069Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T15:02:34.7793611Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T15:02:34.7794037Z return activation_post_process_1 2025-09-09T15:02:34.7794293Z 2025-09-09T15:02:34.7794570Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:02:34.7794939Z diff: tensor([[[0., 0., 0.], 2025-09-09T15:02:34.7795169Z [0., 0., 0.], 2025-09-09T15:02:34.7795407Z [0., 0., 0.]]], grad_fn=) 2025-09-09T15:02:34.7795707Z converted model pt2e: GraphModule( 2025-09-09T15:02:34.7795992Z (conv): Module() 2025-09-09T15:02:34.7796192Z (bn): Module() 2025-09-09T15:02:34.7796382Z ) 2025-09-09T15:02:34.7796476Z 2025-09-09T15:02:34.7796480Z 2025-09-09T15:02:34.7796484Z 2025-09-09T15:02:34.7796573Z def forward(self, x): 2025-09-09T15:02:34.7796850Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T15:02:34.7797185Z conv_bias = self.conv.bias 2025-09-09T15:02:34.7797483Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T15:02:34.7798207Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.010372933000326157, 0, -128, 127, torch.int8); x = None 2025-09-09T15:02:34.7799521Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.010372933000326157, 0, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T15:02:34.7800561Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T15:02:34.7801062Z quantize_per_tensor = self._frozen_param0 2025-09-09T15:02:34.7801849Z dequantize_per_tensor = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor, 0.002590105403214693, 0, -127, 127, torch.int8); quantize_per_tensor = None 2025-09-09T15:02:34.7803197Z conv1d_2 = torch.ops.aten.conv1d.default(dequantize_per_tensor_default, dequantize_per_tensor, conv_bias); dequantize_per_tensor_default = dequantize_per_tensor = conv_bias = None 2025-09-09T15:02:34.7804046Z relu = torch.ops.aten.relu.default(conv1d_2); conv1d_2 = None 2025-09-09T15:02:34.7804897Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(relu, 0.005544713232666254, -128, -128, 127, torch.int8); relu = None 2025-09-09T15:02:34.7806237Z dequantize_per_tensor_default_2 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_2, 0.005544713232666254, -128, -128, 127, torch.int8); quantize_per_tensor_default_2 = None 2025-09-09T15:02:34.7807419Z return pytree.tree_unflatten((dequantize_per_tensor_default_2,), self._out_spec) 2025-09-09T15:02:34.7807831Z 2025-09-09T15:02:34.7808190Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:02:34.7808581Z onverted model fx: GraphModule( 2025-09-09T15:02:34.7808845Z (conv): ConvReLU1d( 2025-09-09T15:02:34.7809273Z (0): QuantizedConv1d(Reference)(3, 3, kernel_size=(3,), stride=(1,)) 2025-09-09T15:02:34.7809649Z (1): ReLU() 2025-09-09T15:02:34.7809873Z ) 2025-09-09T15:02:34.7810090Z ) 2025-09-09T15:02:34.7810190Z 2025-09-09T15:02:34.7810195Z 2025-09-09T15:02:34.7810198Z 2025-09-09T15:02:34.7810287Z def forward(self, x): 2025-09-09T15:02:34.7810982Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.010372933000326157, 0, -128, 127, torch.int8); x = None 2025-09-09T15:02:34.7812314Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.010372933000326157, 0, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T15:02:34.7813443Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T15:02:34.7814414Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.005544713232666254, -128, -128, 127, torch.int8); conv = None 2025-09-09T15:02:34.7815894Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.005544713232666254, -128, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T15:02:34.7816884Z return dequantize_per_tensor_default_1 2025-09-09T15:02:34.7817222Z 2025-09-09T15:02:34.7817518Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:02:34.7817880Z diff: tensor([[[0., 0., 0.], 2025-09-09T15:02:34.7818201Z [0., 0., 0.], 2025-09-09T15:02:34.7818408Z [0., 0., 0.]]]) 2025-09-09T15:02:34.7818886Z PASSED 2025-09-09T15:02:34.7819548Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn1d::test_qat_conv_bn_relu_fusion_cuda model pt2e: GraphModule( 2025-09-09T15:02:34.7820281Z (conv): Module() 2025-09-09T15:02:34.7820481Z (bn): Module() 2025-09-09T15:02:34.7820857Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:02:34.7822078Z fake_quant_enabled=tensor([1], device='cuda:0'), observer_enabled=tensor([1], device='cuda:0'), scale=tensor([0.0104], device='cuda:0'), zero_point=tensor([0], device='cuda:0', dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:02:34.7823716Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3264806270599365, max_val=1.318617343902588) 2025-09-09T15:02:34.7824264Z ) 2025-09-09T15:02:34.7824603Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:02:34.7825881Z fake_quant_enabled=tensor([1], device='cuda:0'), observer_enabled=tensor([1], device='cuda:0'), scale=tensor([0.0022, 0.0020, 0.0022], device='cuda:0'), zero_point=tensor([0, 0, 0], device='cuda:0', dtype=torch.int32), dtype=torch.int8, quant_min=-127, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False 2025-09-09T15:02:34.7827765Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.2799, -0.2557, -0.2618], device='cuda:0'), max_val=tensor([0.1970, 0.2308, 0.2775], device='cuda:0')) 2025-09-09T15:02:34.7828737Z ) 2025-09-09T15:02:34.7829096Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:02:34.7830310Z fake_quant_enabled=tensor([1], device='cuda:0'), observer_enabled=tensor([1], device='cuda:0'), scale=tensor([0.0055], device='cuda:0'), zero_point=tensor([-128], device='cuda:0', dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:02:34.7831733Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=1.4123148918151855) 2025-09-09T15:02:34.7832216Z ) 2025-09-09T15:02:34.7832421Z ) 2025-09-09T15:02:34.7832563Z 2025-09-09T15:02:34.7832567Z 2025-09-09T15:02:34.7832572Z 2025-09-09T15:02:34.7832652Z def forward(self, x): 2025-09-09T15:02:34.7832955Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T15:02:34.7833350Z conv_weight = self.conv.weight 2025-09-09T15:02:34.7833676Z conv_bias = self.conv.bias 2025-09-09T15:02:34.7833936Z bn_weight = self.bn.weight 2025-09-09T15:02:34.7834237Z bn_bias = self.bn.bias 2025-09-09T15:02:34.7834563Z bn_running_mean = self.bn.running_mean 2025-09-09T15:02:34.7834879Z bn_running_var = self.bn.running_var 2025-09-09T15:02:34.7835297Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T15:02:34.7835745Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T15:02:34.7847228Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T15:02:53.7099695Z add = torch.ops.aten.add.Tensor(bn_running_var, 1e-05) 2025-09-09T15:02:53.7100751Z sqrt = torch.ops.aten.sqrt.default(add); add = None 2025-09-09T15:02:53.7101220Z div = torch.ops.aten.div.Tensor(bn_weight, sqrt); sqrt = None 2025-09-09T15:02:53.7101683Z reshape = torch.ops.aten.reshape.default(div, [-1, 1, 1]) 2025-09-09T15:02:53.7102192Z mul = torch.ops.aten.mul.Tensor(conv_weight, reshape); conv_weight = reshape = None 2025-09-09T15:02:53.7102772Z activation_post_process_1 = self.activation_post_process_1(mul); mul = None 2025-09-09T15:02:53.7103376Z zeros_like = torch.ops.aten.zeros_like.default(conv_bias, dtype = torch.float32, pin_memory = False) 2025-09-09T15:02:53.7104332Z conv1d_1 = torch.ops.aten.conv1d.default(activation_post_process_0, activation_post_process_1, zeros_like); activation_post_process_0 = activation_post_process_1 = zeros_like = None 2025-09-09T15:02:53.7105210Z reshape_1 = torch.ops.aten.reshape.default(div, [1, -1, 1]); div = None 2025-09-09T15:02:53.7105734Z div_1 = torch.ops.aten.div.Tensor(conv1d_1, reshape_1); conv1d_1 = reshape_1 = None 2025-09-09T15:02:53.7106307Z reshape_2 = torch.ops.aten.reshape.default(conv_bias, [1, -1, 1]); conv_bias = None 2025-09-09T15:02:53.7106854Z add_1 = torch.ops.aten.add.Tensor(div_1, reshape_2); div_1 = reshape_2 = None 2025-09-09T15:02:53.7107709Z batch_norm_1 = torch.ops.aten.batch_norm.default(add_1, bn_weight, bn_bias, bn_running_mean, bn_running_var, True, 0.1, 1e-05, True); add_1 = bn_weight = bn_bias = bn_running_mean = bn_running_var = None 2025-09-09T15:02:53.7108549Z relu = torch.ops.aten.relu.default(batch_norm_1); batch_norm_1 = None 2025-09-09T15:02:53.7109064Z activation_post_process_2 = self.activation_post_process_2(relu); relu = None 2025-09-09T15:02:53.7109610Z return pytree.tree_unflatten((activation_post_process_2,), self._out_spec) 2025-09-09T15:02:53.7109979Z 2025-09-09T15:02:53.7110265Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:02:53.7110632Z model fx: GraphModule( 2025-09-09T15:02:53.7110943Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:02:53.7112312Z fake_quant_enabled=tensor([1], device='cuda:0'), observer_enabled=tensor([1], device='cuda:0'), scale=tensor([0.0104], device='cuda:0'), zero_point=tensor([0], device='cuda:0', dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:02:53.7113748Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3264806270599365, max_val=1.318617343902588) 2025-09-09T15:02:53.7114251Z ) 2025-09-09T15:02:53.7114448Z (conv): ConvBnReLU1d( 2025-09-09T15:02:53.7114684Z 3, 3, kernel_size=(3,), stride=(1,) 2025-09-09T15:02:53.7115094Z (bn): BatchNorm1d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T15:02:53.7115560Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:02:53.7116688Z fake_quant_enabled=tensor([1], device='cuda:0'), observer_enabled=tensor([1], device='cuda:0'), scale=tensor([0.0022, 0.0020, 0.0022], device='cuda:0'), zero_point=tensor([0, 0, 0], device='cuda:0', dtype=torch.int32), dtype=torch.qint8, quant_min=-127, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False 2025-09-09T15:02:53.7118235Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.2799, -0.2557, -0.2618], device='cuda:0'), max_val=tensor([0.1970, 0.2308, 0.2775], device='cuda:0')) 2025-09-09T15:02:53.7118973Z ) 2025-09-09T15:02:53.7119152Z ) 2025-09-09T15:02:53.7119500Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:02:53.7120609Z fake_quant_enabled=tensor([1], device='cuda:0'), observer_enabled=tensor([1], device='cuda:0'), scale=tensor([0.0055], device='cuda:0'), zero_point=tensor([-128], device='cuda:0', dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:02:53.7121829Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=1.4123148918151855) 2025-09-09T15:02:53.7122551Z ) 2025-09-09T15:02:53.7122729Z ) 2025-09-09T15:02:53.7122829Z 2025-09-09T15:02:53.7122833Z 2025-09-09T15:02:53.7122844Z 2025-09-09T15:02:53.7122931Z def forward(self, x): 2025-09-09T15:02:53.7123290Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T15:02:53.7124060Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T15:02:53.7124837Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T15:02:53.7125272Z return activation_post_process_1 2025-09-09T15:02:53.7125533Z 2025-09-09T15:02:53.7125819Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:02:53.7126182Z diff: tensor([[[0., 0., 0.], 2025-09-09T15:02:53.7126421Z [0., 0., 0.], 2025-09-09T15:02:53.7126686Z [0., 0., 0.]]], device='cuda:0', grad_fn=) 2025-09-09T15:02:53.7127027Z converted model pt2e: GraphModule( 2025-09-09T15:02:53.7127297Z (conv): Module() 2025-09-09T15:02:53.7127514Z (bn): Module() 2025-09-09T15:02:53.7127788Z ) 2025-09-09T15:02:53.7127925Z 2025-09-09T15:02:53.7127940Z 2025-09-09T15:02:53.7127947Z 2025-09-09T15:02:53.7128046Z def forward(self, x): 2025-09-09T15:02:53.7128339Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T15:02:53.7128681Z conv_bias = self.conv.bias 2025-09-09T15:02:53.7128987Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T15:02:53.7129703Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.010372933931648731, 0, -128, 127, torch.int8); x = None 2025-09-09T15:02:53.7130923Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.010372933931648731, 0, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T15:02:53.7131958Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T15:02:53.7132427Z _scale_0 = self._scale_0 2025-09-09T15:02:53.7132692Z _zero_point_0 = self._zero_point_0 2025-09-09T15:02:53.7133196Z quantize_per_channel = self._frozen_param0 2025-09-09T15:02:53.7134079Z dequantize_per_channel = torch.ops.quantized_decomposed.dequantize_per_channel.default(quantize_per_channel, _scale_0, _zero_point_0, 0, -127, 127, torch.int8); quantize_per_channel = _scale_0 = _zero_point_0 = None 2025-09-09T15:02:53.7135526Z conv1d_2 = torch.ops.aten.conv1d.default(dequantize_per_tensor_default, dequantize_per_channel, conv_bias); dequantize_per_tensor_default = dequantize_per_channel = conv_bias = None 2025-09-09T15:02:53.7136373Z relu = torch.ops.aten.relu.default(conv1d_2); conv1d_2 = None 2025-09-09T15:02:53.7137158Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(relu, 0.005538490135222673, -128, -128, 127, torch.int8); relu = None 2025-09-09T15:02:53.7138446Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.005538490135222673, -128, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T15:02:53.7139448Z return pytree.tree_unflatten((dequantize_per_tensor_default_1,), self._out_spec) 2025-09-09T15:02:53.7139870Z 2025-09-09T15:02:53.7140147Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:02:53.7140523Z onverted model fx: GraphModule( 2025-09-09T15:02:53.7140784Z (conv): ConvReLU1d( 2025-09-09T15:02:53.7141110Z (0): QuantizedConv1d(Reference)(3, 3, kernel_size=(3,), stride=(1,)) 2025-09-09T15:02:53.7141475Z (1): ReLU() 2025-09-09T15:02:53.7141672Z ) 2025-09-09T15:02:53.7141845Z ) 2025-09-09T15:02:53.7141944Z 2025-09-09T15:02:53.7141948Z 2025-09-09T15:02:53.7141952Z 2025-09-09T15:02:53.7142038Z def forward(self, x): 2025-09-09T15:02:53.7142649Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.010372933931648731, 0, -128, 127, torch.int8); x = None 2025-09-09T15:02:53.7143872Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.010372933931648731, 0, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T15:02:53.7144876Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T15:02:53.7145727Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.005538490135222673, -128, -128, 127, torch.int8); conv = None 2025-09-09T15:02:53.7147001Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.005538490135222673, -128, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T15:02:53.7147881Z return dequantize_per_tensor_default_1 2025-09-09T15:02:53.7148165Z 2025-09-09T15:02:53.7148437Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:02:53.7148805Z diff: tensor([[[0., 0., 0.], 2025-09-09T15:02:53.7149045Z [0., 0., 0.], 2025-09-09T15:02:53.7149277Z [0., 0., 0.]]], device='cuda:0') 2025-09-09T15:02:53.7149558Z model pt2e: GraphModule( 2025-09-09T15:02:53.7149801Z (conv): Module() 2025-09-09T15:02:53.7150010Z (bn): Module() 2025-09-09T15:02:53.7150307Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:02:53.7151471Z fake_quant_enabled=tensor([1], device='cuda:0'), observer_enabled=tensor([1], device='cuda:0'), scale=tensor([0.0104], device='cuda:0'), zero_point=tensor([0], device='cuda:0', dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:02:53.7152728Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3264806270599365, max_val=1.318617343902588) 2025-09-09T15:02:53.7153242Z ) 2025-09-09T15:02:53.7153515Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:02:53.7154719Z fake_quant_enabled=tensor([1], device='cuda:0'), observer_enabled=tensor([1], device='cuda:0'), scale=tensor([0.0022], device='cuda:0'), zero_point=tensor([0], device='cuda:0', dtype=torch.int32), dtype=torch.int8, quant_min=-127, quant_max=127, qscheme=torch.per_tensor_symmetric, reduce_range=False 2025-09-09T15:02:53.7156067Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.2799264192581177, max_val=0.27745386958122253) 2025-09-09T15:02:53.7156580Z ) 2025-09-09T15:02:53.7156861Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:03:18.0986582Z fake_quant_enabled=tensor([1], device='cuda:0'), observer_enabled=tensor([1], device='cuda:0'), scale=tensor([0.0055], device='cuda:0'), zero_point=tensor([-128], device='cuda:0', dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:03:18.0988249Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=1.4123148918151855) 2025-09-09T15:03:18.0988899Z ) 2025-09-09T15:03:18.0989164Z ) 2025-09-09T15:03:18.0989292Z 2025-09-09T15:03:18.0989298Z 2025-09-09T15:03:18.0989303Z 2025-09-09T15:03:18.0989429Z def forward(self, x): 2025-09-09T15:03:18.0989822Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T15:03:18.0990173Z conv_weight = self.conv.weight 2025-09-09T15:03:18.0990443Z conv_bias = self.conv.bias 2025-09-09T15:03:18.0990705Z bn_weight = self.bn.weight 2025-09-09T15:03:18.0990955Z bn_bias = self.bn.bias 2025-09-09T15:03:18.0991218Z bn_running_mean = self.bn.running_mean 2025-09-09T15:03:18.0991514Z bn_running_var = self.bn.running_var 2025-09-09T15:03:18.0991853Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T15:03:18.0992294Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T15:03:18.0992880Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T15:03:18.0993415Z add = torch.ops.aten.add.Tensor(bn_running_var, 1e-05) 2025-09-09T15:03:18.0993807Z sqrt = torch.ops.aten.sqrt.default(add); add = None 2025-09-09T15:03:18.0994221Z div = torch.ops.aten.div.Tensor(bn_weight, sqrt); sqrt = None 2025-09-09T15:03:18.0994663Z reshape = torch.ops.aten.reshape.default(div, [-1, 1, 1]) 2025-09-09T15:03:18.0995168Z mul = torch.ops.aten.mul.Tensor(conv_weight, reshape); conv_weight = reshape = None 2025-09-09T15:03:18.0995735Z activation_post_process_1 = self.activation_post_process_1(mul); mul = None 2025-09-09T15:03:18.0996338Z zeros_like = torch.ops.aten.zeros_like.default(conv_bias, dtype = torch.float32, pin_memory = False) 2025-09-09T15:03:18.0997307Z conv1d_1 = torch.ops.aten.conv1d.default(activation_post_process_0, activation_post_process_1, zeros_like); activation_post_process_0 = activation_post_process_1 = zeros_like = None 2025-09-09T15:03:18.0998186Z reshape_1 = torch.ops.aten.reshape.default(div, [1, -1, 1]); div = None 2025-09-09T15:03:18.0998729Z div_1 = torch.ops.aten.div.Tensor(conv1d_1, reshape_1); conv1d_1 = reshape_1 = None 2025-09-09T15:03:18.0999304Z reshape_2 = torch.ops.aten.reshape.default(conv_bias, [1, -1, 1]); conv_bias = None 2025-09-09T15:03:18.0999971Z add_1 = torch.ops.aten.add.Tensor(div_1, reshape_2); div_1 = reshape_2 = None 2025-09-09T15:03:18.1000849Z batch_norm_1 = torch.ops.aten.batch_norm.default(add_1, bn_weight, bn_bias, bn_running_mean, bn_running_var, True, 0.1, 1e-05, True); add_1 = bn_weight = bn_bias = bn_running_mean = bn_running_var = None 2025-09-09T15:03:18.1001687Z relu = torch.ops.aten.relu.default(batch_norm_1); batch_norm_1 = None 2025-09-09T15:03:18.1002216Z activation_post_process_2 = self.activation_post_process_2(relu); relu = None 2025-09-09T15:03:18.1002769Z return pytree.tree_unflatten((activation_post_process_2,), self._out_spec) 2025-09-09T15:03:18.1003160Z 2025-09-09T15:03:18.1003454Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:03:18.1004132Z model fx: GraphModule( 2025-09-09T15:03:18.1004480Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:03:18.1005602Z fake_quant_enabled=tensor([1], device='cuda:0'), observer_enabled=tensor([1], device='cuda:0'), scale=tensor([0.0104], device='cuda:0'), zero_point=tensor([0], device='cuda:0', dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:03:18.1007051Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3264806270599365, max_val=1.318617343902588) 2025-09-09T15:03:18.1007610Z ) 2025-09-09T15:03:18.1007804Z (conv): ConvBnReLU1d( 2025-09-09T15:03:18.1008056Z 3, 3, kernel_size=(3,), stride=(1,) 2025-09-09T15:03:18.1008469Z (bn): BatchNorm1d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T15:03:18.1008952Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:03:18.1010059Z fake_quant_enabled=tensor([1], device='cuda:0'), observer_enabled=tensor([1], device='cuda:0'), scale=tensor([0.0022], device='cuda:0'), zero_point=tensor([0], device='cuda:0', dtype=torch.int32), dtype=torch.qint8, quant_min=-127, quant_max=127, qscheme=torch.per_tensor_symmetric, reduce_range=False 2025-09-09T15:03:18.1011364Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.2799264192581177, max_val=0.27745386958122253) 2025-09-09T15:03:18.1011897Z ) 2025-09-09T15:03:18.1012077Z ) 2025-09-09T15:03:18.1012364Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:03:18.1013489Z fake_quant_enabled=tensor([1], device='cuda:0'), observer_enabled=tensor([1], device='cuda:0'), scale=tensor([0.0055], device='cuda:0'), zero_point=tensor([-128], device='cuda:0', dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:03:18.1014729Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=1.4123148918151855) 2025-09-09T15:03:18.1015215Z ) 2025-09-09T15:03:18.1015390Z ) 2025-09-09T15:03:18.1015501Z 2025-09-09T15:03:18.1015505Z 2025-09-09T15:03:18.1015510Z 2025-09-09T15:03:18.1015604Z def forward(self, x): 2025-09-09T15:03:18.1015969Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T15:03:18.1016510Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T15:03:18.1017072Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T15:03:18.1017508Z return activation_post_process_1 2025-09-09T15:03:18.1017784Z 2025-09-09T15:03:18.1018060Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:03:18.1018442Z diff: tensor([[[0., 0., 0.], 2025-09-09T15:03:18.1018681Z [0., 0., 0.], 2025-09-09T15:03:18.1018957Z [0., 0., 0.]]], device='cuda:0', grad_fn=) 2025-09-09T15:03:18.1019302Z converted model pt2e: GraphModule( 2025-09-09T15:03:18.1019573Z (conv): Module() 2025-09-09T15:03:18.1019788Z (bn): Module() 2025-09-09T15:03:18.1019984Z ) 2025-09-09T15:03:18.1020083Z 2025-09-09T15:03:18.1020093Z 2025-09-09T15:03:18.1020102Z 2025-09-09T15:03:18.1020187Z def forward(self, x): 2025-09-09T15:03:18.1020474Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T15:03:18.1020823Z conv_bias = self.conv.bias 2025-09-09T15:03:18.1021143Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T15:03:18.1021867Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.010372933931648731, 0, -128, 127, torch.int8); x = None 2025-09-09T15:03:18.1023333Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.010372933931648731, 0, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T15:03:18.1024751Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T15:03:18.1025258Z quantize_per_tensor = self._frozen_param0 2025-09-09T15:03:18.1026052Z dequantize_per_tensor = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor, 0.002204145072028041, 0, -127, 127, torch.int8); quantize_per_tensor = None 2025-09-09T15:03:18.1027430Z conv1d_2 = torch.ops.aten.conv1d.default(dequantize_per_tensor_default, dequantize_per_tensor, conv_bias); dequantize_per_tensor_default = dequantize_per_tensor = conv_bias = None 2025-09-09T15:03:18.1028284Z relu = torch.ops.aten.relu.default(conv1d_2); conv1d_2 = None 2025-09-09T15:03:18.1029065Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(relu, 0.005538490135222673, -128, -128, 127, torch.int8); relu = None 2025-09-09T15:03:18.1030363Z dequantize_per_tensor_default_2 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_2, 0.005538490135222673, -128, -128, 127, torch.int8); quantize_per_tensor_default_2 = None 2025-09-09T15:03:18.1031389Z return pytree.tree_unflatten((dequantize_per_tensor_default_2,), self._out_spec) 2025-09-09T15:03:18.1031810Z 2025-09-09T15:03:18.1032098Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:03:18.1032482Z onverted model fx: GraphModule( 2025-09-09T15:03:18.1032749Z (conv): ConvReLU1d( 2025-09-09T15:03:18.1033085Z (0): QuantizedConv1d(Reference)(3, 3, kernel_size=(3,), stride=(1,)) 2025-09-09T15:03:18.1033465Z (1): ReLU() 2025-09-09T15:03:18.1033662Z ) 2025-09-09T15:03:18.1033845Z ) 2025-09-09T15:03:18.1033944Z 2025-09-09T15:03:18.1033948Z 2025-09-09T15:03:18.1033952Z 2025-09-09T15:03:18.1034044Z def forward(self, x): 2025-09-09T15:03:18.1034661Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.010372933931648731, 0, -128, 127, torch.int8); x = None 2025-09-09T15:03:18.1035898Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.010372933931648731, 0, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T15:03:18.1036909Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T15:03:18.1037769Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.005538490135222673, -128, -128, 127, torch.int8); conv = None 2025-09-09T15:03:18.1039059Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.005538490135222673, -128, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T15:03:18.1039996Z return dequantize_per_tensor_default_1 2025-09-09T15:03:18.1040284Z 2025-09-09T15:03:18.1040562Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:03:18.1040933Z diff: tensor([[[0., 0., 0.], 2025-09-09T15:03:18.1041188Z [0., 0., 0.], 2025-09-09T15:03:18.1041415Z [0., 0., 0.]]], device='cuda:0') 2025-09-09T15:03:18.1041902Z PASSED 2025-09-09T15:03:18.1042532Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn1d::test_qat_conv_bn_relu_fusion_no_conv_bias model pt2e: GraphModule( 2025-09-09T15:03:18.1043198Z (conv): Module() 2025-09-09T15:03:18.1043405Z (bn): Module() 2025-09-09T15:03:18.1043714Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:03:18.1044648Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0104]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:03:35.1239515Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3264806270599365, max_val=1.318617343902588) 2025-09-09T15:03:35.1240188Z ) 2025-09-09T15:03:35.1240513Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:03:35.1243716Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0026, 0.0026, 0.0025]), zero_point=tensor([0, 0, 0], dtype=torch.int32), dtype=torch.int8, quant_min=-127, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False 2025-09-09T15:03:35.1245490Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.3276, -0.3045, -0.2418]), max_val=tensor([0.2760, 0.3298, 0.3101])) 2025-09-09T15:03:35.1246269Z ) 2025-09-09T15:03:35.1246605Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:03:35.1247744Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0054]), zero_point=tensor([-128], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:03:35.1249034Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=1.3778926134109497) 2025-09-09T15:03:35.1249602Z ) 2025-09-09T15:03:35.1249800Z ) 2025-09-09T15:03:35.1249914Z 2025-09-09T15:03:35.1249919Z 2025-09-09T15:03:35.1249924Z 2025-09-09T15:03:35.1250035Z def forward(self, x): 2025-09-09T15:03:35.1250375Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T15:03:35.1250774Z conv_weight = self.conv.weight 2025-09-09T15:03:35.1251092Z bn_weight = self.bn.weight 2025-09-09T15:03:35.1251388Z bn_bias = self.bn.bias 2025-09-09T15:03:35.1251694Z bn_running_mean = self.bn.running_mean 2025-09-09T15:03:35.1252089Z bn_running_var = self.bn.running_var 2025-09-09T15:03:35.1252479Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T15:03:35.1253000Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T15:03:35.1253705Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T15:03:35.1254331Z add = torch.ops.aten.add.Tensor(bn_running_var, 1e-05) 2025-09-09T15:03:35.1254799Z sqrt = torch.ops.aten.sqrt.default(add); add = None 2025-09-09T15:03:35.1255282Z div = torch.ops.aten.div.Tensor(bn_weight, sqrt); sqrt = None 2025-09-09T15:03:35.1255804Z reshape = torch.ops.aten.reshape.default(div, [-1, 1, 1]) 2025-09-09T15:03:35.1256404Z mul = torch.ops.aten.mul.Tensor(conv_weight, reshape); conv_weight = reshape = None 2025-09-09T15:03:35.1257073Z activation_post_process_1 = self.activation_post_process_1(mul); mul = None 2025-09-09T15:03:35.1258087Z conv1d_1 = torch.ops.aten.conv1d.default(activation_post_process_0, activation_post_process_1, None); activation_post_process_0 = activation_post_process_1 = None 2025-09-09T15:03:35.1259073Z reshape_1 = torch.ops.aten.reshape.default(div, [1, -1, 1]); div = None 2025-09-09T15:03:35.1259705Z div_1 = torch.ops.aten.div.Tensor(conv1d_1, reshape_1); conv1d_1 = reshape_1 = None 2025-09-09T15:03:35.1260781Z batch_norm_1 = torch.ops.aten.batch_norm.default(div_1, bn_weight, bn_bias, bn_running_mean, bn_running_var, True, 0.1, 1e-05, True); div_1 = bn_weight = bn_bias = bn_running_mean = bn_running_var = None 2025-09-09T15:03:35.1261796Z relu = torch.ops.aten.relu.default(batch_norm_1); batch_norm_1 = None 2025-09-09T15:03:35.1262423Z activation_post_process_2 = self.activation_post_process_2(relu); relu = None 2025-09-09T15:03:35.1263070Z return pytree.tree_unflatten((activation_post_process_2,), self._out_spec) 2025-09-09T15:03:35.1263532Z 2025-09-09T15:03:35.1263855Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:03:35.1264285Z model fx: GraphModule( 2025-09-09T15:03:35.1264660Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:03:35.1265789Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0104]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:03:35.1267227Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3264806270599365, max_val=1.318617343902588) 2025-09-09T15:03:35.1267837Z ) 2025-09-09T15:03:35.1268053Z (conv): ConvBnReLU1d( 2025-09-09T15:03:35.1268440Z 3, 3, kernel_size=(3,), stride=(1,), bias=False 2025-09-09T15:03:35.1268939Z (bn): BatchNorm1d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T15:03:35.1269488Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:03:35.1270643Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0026, 0.0026, 0.0025]), zero_point=tensor([0, 0, 0], dtype=torch.int32), dtype=torch.qint8, quant_min=-127, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False 2025-09-09T15:03:35.1272222Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.3276, -0.3045, -0.2418]), max_val=tensor([0.2760, 0.3298, 0.3101])) 2025-09-09T15:03:35.1273004Z ) 2025-09-09T15:03:35.1273196Z ) 2025-09-09T15:03:35.1273526Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:03:35.1274664Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0054]), zero_point=tensor([-128], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:03:35.1275975Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=1.3778926134109497) 2025-09-09T15:03:35.1276537Z ) 2025-09-09T15:03:35.1276735Z ) 2025-09-09T15:03:35.1276848Z 2025-09-09T15:03:35.1276853Z 2025-09-09T15:03:35.1276858Z 2025-09-09T15:03:35.1276964Z def forward(self, x): 2025-09-09T15:03:35.1277376Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T15:03:35.1278011Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T15:03:35.1278654Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T15:03:35.1279167Z return activation_post_process_1 2025-09-09T15:03:35.1279563Z 2025-09-09T15:03:35.1279888Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:03:35.1280337Z diff: tensor([[[0., 0., 0.], 2025-09-09T15:03:35.1280623Z [0., 0., 0.], 2025-09-09T15:03:35.1280925Z [0., 0., 0.]]], grad_fn=) 2025-09-09T15:03:35.1281297Z converted model pt2e: GraphModule( 2025-09-09T15:03:35.1281626Z (conv): Module() 2025-09-09T15:03:35.1281864Z (bn): Module() 2025-09-09T15:03:35.1282079Z ) 2025-09-09T15:03:35.1282173Z 2025-09-09T15:03:35.1282178Z 2025-09-09T15:03:35.1282182Z 2025-09-09T15:03:35.1282263Z def forward(self, x): 2025-09-09T15:03:35.1282542Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T15:03:35.1282915Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T15:03:35.1283612Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.010372933000326157, 0, -128, 127, torch.int8); x = None 2025-09-09T15:03:35.1284818Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.010372933000326157, 0, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T15:03:35.1285835Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T15:03:35.1286291Z _scale_0 = self._scale_0 2025-09-09T15:03:35.1286542Z _zero_point_0 = self._zero_point_0 2025-09-09T15:03:35.1286833Z quantize_per_channel = self._frozen_param0 2025-09-09T15:03:35.1287695Z dequantize_per_channel = torch.ops.quantized_decomposed.dequantize_per_channel.default(quantize_per_channel, _scale_0, _zero_point_0, 0, -127, 127, torch.int8); quantize_per_channel = _scale_0 = _zero_point_0 = None 2025-09-09T15:03:35.1288548Z conv_weight_bias = self.conv.weight_bias 2025-09-09T15:03:35.1289476Z conv1d_2 = torch.ops.aten.conv1d.default(dequantize_per_tensor_default, dequantize_per_channel, conv_weight_bias); dequantize_per_tensor_default = dequantize_per_channel = conv_weight_bias = None 2025-09-09T15:03:35.1290363Z relu = torch.ops.aten.relu.default(conv1d_2); conv1d_2 = None 2025-09-09T15:03:35.1291202Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(relu, 0.0054035005159676075, -128, -128, 127, torch.int8); relu = None 2025-09-09T15:03:35.1292474Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.0054035005159676075, -128, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T15:03:35.1293473Z return pytree.tree_unflatten((dequantize_per_tensor_default_1,), self._out_spec) 2025-09-09T15:03:35.1293866Z 2025-09-09T15:03:35.1294136Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:03:35.1294494Z onverted model fx: GraphModule( 2025-09-09T15:03:35.1294747Z (conv): ConvReLU1d( 2025-09-09T15:03:35.1295062Z (0): QuantizedConv1d(Reference)(3, 3, kernel_size=(3,), stride=(1,)) 2025-09-09T15:03:35.1295417Z (1): ReLU() 2025-09-09T15:03:35.1295603Z ) 2025-09-09T15:03:35.1295766Z ) 2025-09-09T15:03:35.1295856Z 2025-09-09T15:03:35.1295860Z 2025-09-09T15:03:35.1295864Z 2025-09-09T15:03:35.1295947Z def forward(self, x): 2025-09-09T15:03:35.1296537Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.010372933000326157, 0, -128, 127, torch.int8); x = None 2025-09-09T15:03:35.1297736Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.010372933000326157, 0, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T15:03:35.1298713Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T15:03:35.1299561Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.0054035005159676075, -128, -128, 127, torch.int8); conv = None 2025-09-09T15:03:35.1300837Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.0054035005159676075, -128, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T15:03:35.1301704Z return dequantize_per_tensor_default_1 2025-09-09T15:03:35.1301972Z 2025-09-09T15:03:35.1302235Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:03:35.1302590Z diff: tensor([[[0., 0., 0.], 2025-09-09T15:03:35.1302816Z [0., 0., 0.], 2025-09-09T15:03:35.1303011Z [0., 0., 0.]]]) 2025-09-09T15:03:35.1303229Z model pt2e: GraphModule( 2025-09-09T15:03:35.1303446Z (conv): Module() 2025-09-09T15:03:35.1303644Z (bn): Module() 2025-09-09T15:03:35.1303929Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:03:52.6683398Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0104]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:03:52.6684834Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3264806270599365, max_val=1.318617343902588) 2025-09-09T15:03:52.6685458Z ) 2025-09-09T15:03:52.6685784Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:03:52.6686933Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0026]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.int8, quant_min=-127, quant_max=127, qscheme=torch.per_tensor_symmetric, reduce_range=False 2025-09-09T15:03:52.6688290Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.32764676213264465, max_val=0.3298276662826538) 2025-09-09T15:03:52.6688913Z ) 2025-09-09T15:03:52.6689227Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:03:52.6690651Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0054]), zero_point=tensor([-128], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:03:52.6692112Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=1.3749419450759888) 2025-09-09T15:03:52.6692671Z ) 2025-09-09T15:03:52.6692864Z ) 2025-09-09T15:03:52.6692976Z 2025-09-09T15:03:52.6692981Z 2025-09-09T15:03:52.6692986Z 2025-09-09T15:03:52.6693083Z def forward(self, x): 2025-09-09T15:03:52.6693419Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T15:03:52.6693823Z conv_weight = self.conv.weight 2025-09-09T15:03:52.6694150Z bn_weight = self.bn.weight 2025-09-09T15:03:52.6694451Z bn_bias = self.bn.bias 2025-09-09T15:03:52.6694754Z bn_running_mean = self.bn.running_mean 2025-09-09T15:03:52.6695122Z bn_running_var = self.bn.running_var 2025-09-09T15:03:52.6695517Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T15:03:52.6696046Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T15:03:52.6696792Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T15:03:52.6697448Z add = torch.ops.aten.add.Tensor(bn_running_var, 1e-05) 2025-09-09T15:03:52.6697913Z sqrt = torch.ops.aten.sqrt.default(add); add = None 2025-09-09T15:03:52.6698390Z div = torch.ops.aten.div.Tensor(bn_weight, sqrt); sqrt = None 2025-09-09T15:03:52.6698909Z reshape = torch.ops.aten.reshape.default(div, [-1, 1, 1]) 2025-09-09T15:03:52.6699498Z mul = torch.ops.aten.mul.Tensor(conv_weight, reshape); conv_weight = reshape = None 2025-09-09T15:03:52.6700169Z activation_post_process_1 = self.activation_post_process_1(mul); mul = None 2025-09-09T15:03:52.6701205Z conv1d_1 = torch.ops.aten.conv1d.default(activation_post_process_0, activation_post_process_1, None); activation_post_process_0 = activation_post_process_1 = None 2025-09-09T15:03:52.6702212Z reshape_1 = torch.ops.aten.reshape.default(div, [1, -1, 1]); div = None 2025-09-09T15:03:52.6702843Z div_1 = torch.ops.aten.div.Tensor(conv1d_1, reshape_1); conv1d_1 = reshape_1 = None 2025-09-09T15:03:52.6712569Z batch_norm_1 = torch.ops.aten.batch_norm.default(div_1, bn_weight, bn_bias, bn_running_mean, bn_running_var, True, 0.1, 1e-05, True); div_1 = bn_weight = bn_bias = bn_running_mean = bn_running_var = None 2025-09-09T15:03:52.6713614Z relu = torch.ops.aten.relu.default(batch_norm_1); batch_norm_1 = None 2025-09-09T15:03:52.6714249Z activation_post_process_2 = self.activation_post_process_2(relu); relu = None 2025-09-09T15:03:52.6714909Z return pytree.tree_unflatten((activation_post_process_2,), self._out_spec) 2025-09-09T15:03:52.6715372Z 2025-09-09T15:03:52.6715714Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:03:52.6716149Z model fx: GraphModule( 2025-09-09T15:03:52.6716543Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:03:52.6717691Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0104]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:03:52.6719040Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3264806270599365, max_val=1.318617343902588) 2025-09-09T15:03:52.6719782Z ) 2025-09-09T15:03:52.6720004Z (conv): ConvBnReLU1d( 2025-09-09T15:03:52.6720321Z 3, 3, kernel_size=(3,), stride=(1,), bias=False 2025-09-09T15:03:52.6720846Z (bn): BatchNorm1d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T15:03:52.6721468Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:03:52.6722893Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0026]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-127, quant_max=127, qscheme=torch.per_tensor_symmetric, reduce_range=False 2025-09-09T15:03:52.6724007Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.32764676213264465, max_val=0.3298276662826538) 2025-09-09T15:03:52.6724639Z ) 2025-09-09T15:03:52.6724816Z ) 2025-09-09T15:03:52.6725101Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:03:52.6726046Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0054]), zero_point=tensor([-128], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:03:52.6727093Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=1.3749419450759888) 2025-09-09T15:03:52.6727565Z ) 2025-09-09T15:03:52.6727737Z ) 2025-09-09T15:03:52.6727838Z 2025-09-09T15:03:52.6727842Z 2025-09-09T15:03:52.6727846Z 2025-09-09T15:03:52.6727935Z def forward(self, x): 2025-09-09T15:03:52.6728298Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T15:03:52.6728834Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T15:03:52.6729378Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T15:03:52.6729810Z return activation_post_process_1 2025-09-09T15:03:52.6730074Z 2025-09-09T15:03:52.6730356Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:03:52.6730728Z diff: tensor([[[0., 0., 0.], 2025-09-09T15:03:52.6730966Z [0., 0., 0.], 2025-09-09T15:03:52.6731212Z [0., 0., 0.]]], grad_fn=) 2025-09-09T15:03:52.6731516Z converted model pt2e: GraphModule( 2025-09-09T15:03:52.6731786Z (conv): Module() 2025-09-09T15:03:52.6731988Z (bn): Module() 2025-09-09T15:03:52.6732187Z ) 2025-09-09T15:03:52.6732285Z 2025-09-09T15:03:52.6732289Z 2025-09-09T15:03:52.6732293Z 2025-09-09T15:03:52.6732379Z def forward(self, x): 2025-09-09T15:03:52.6732671Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T15:03:52.6733049Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T15:03:52.6733751Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.010372933000326157, 0, -128, 127, torch.int8); x = None 2025-09-09T15:03:52.6734974Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.010372933000326157, 0, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T15:03:52.6735997Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T15:03:52.6736492Z quantize_per_tensor = self._frozen_param0 2025-09-09T15:03:52.6737277Z dequantize_per_tensor = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor, 0.002597068203613162, 0, -127, 127, torch.int8); quantize_per_tensor = None 2025-09-09T15:03:52.6738066Z conv_weight_bias = self.conv.weight_bias 2025-09-09T15:03:52.6738897Z conv1d_2 = torch.ops.aten.conv1d.default(dequantize_per_tensor_default, dequantize_per_tensor, conv_weight_bias); dequantize_per_tensor_default = dequantize_per_tensor = conv_weight_bias = None 2025-09-09T15:03:52.6739794Z relu = torch.ops.aten.relu.default(conv1d_2); conv1d_2 = None 2025-09-09T15:03:52.6740577Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(relu, 0.0053919292986392975, -128, -128, 127, torch.int8); relu = None 2025-09-09T15:03:52.6741859Z dequantize_per_tensor_default_2 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_2, 0.0053919292986392975, -128, -128, 127, torch.int8); quantize_per_tensor_default_2 = None 2025-09-09T15:03:52.6742861Z return pytree.tree_unflatten((dequantize_per_tensor_default_2,), self._out_spec) 2025-09-09T15:03:52.6743273Z 2025-09-09T15:03:52.6743640Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:03:52.6744022Z onverted model fx: GraphModule( 2025-09-09T15:03:52.6744284Z (conv): ConvReLU1d( 2025-09-09T15:03:52.6744686Z (0): QuantizedConv1d(Reference)(3, 3, kernel_size=(3,), stride=(1,)) 2025-09-09T15:03:52.6745052Z (1): ReLU() 2025-09-09T15:03:52.6745241Z ) 2025-09-09T15:03:52.6745414Z ) 2025-09-09T15:03:52.6745511Z 2025-09-09T15:03:52.6745515Z 2025-09-09T15:03:52.6745520Z 2025-09-09T15:03:52.6745606Z def forward(self, x): 2025-09-09T15:03:52.6746217Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.010372933000326157, 0, -128, 127, torch.int8); x = None 2025-09-09T15:03:52.6747439Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.010372933000326157, 0, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T15:03:52.6748435Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T15:03:52.6749287Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.0053919292986392975, -128, -128, 127, torch.int8); conv = None 2025-09-09T15:03:52.6750580Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.0053919292986392975, -128, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T15:03:52.6751466Z return dequantize_per_tensor_default_1 2025-09-09T15:03:52.6751747Z 2025-09-09T15:03:52.6752024Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:03:52.6752394Z diff: tensor([[[0., 0., 0.], 2025-09-09T15:03:52.6752628Z [0., 0., 0.], 2025-09-09T15:03:52.6752844Z [0., 0., 0.]]]) 2025-09-09T15:03:52.6753268Z PASSED 2025-09-09T15:03:52.6753844Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn1d::test_qat_conv_no_bias model pt2e: GraphModule( 2025-09-09T15:03:52.6754450Z (conv): Module() 2025-09-09T15:03:52.6754752Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:03:54.1133645Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0024, 0.0022, 0.0021]), zero_point=tensor([0, 0, 0], dtype=torch.int32), dtype=torch.int8, quant_min=-127, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False 2025-09-09T15:03:54.1135328Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.3119, -0.2799, -0.2618]), max_val=tensor([0.1970, 0.1855, 0.2308])) 2025-09-09T15:03:54.1136118Z ) 2025-09-09T15:03:54.1136440Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:03:54.1137591Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0104]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:03:54.1138922Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3264806270599365, max_val=1.318617343902588) 2025-09-09T15:03:54.1139540Z ) 2025-09-09T15:03:54.1139863Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:03:54.1141002Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0038]), zero_point=tensor([-128], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:03:54.1142294Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=0.9578298330307007) 2025-09-09T15:03:54.1142860Z ) 2025-09-09T15:03:54.1143048Z ) 2025-09-09T15:03:54.1143163Z 2025-09-09T15:03:54.1143168Z 2025-09-09T15:03:54.1143173Z 2025-09-09T15:03:54.1143281Z def forward(self, x): 2025-09-09T15:03:54.1143610Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T15:03:54.1144289Z conv_weight = self.conv.weight 2025-09-09T15:03:54.1144839Z activation_post_process_1 = self.activation_post_process_1(conv_weight); conv_weight = None 2025-09-09T15:03:54.1145666Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T15:03:54.1146651Z conv1d = torch.ops.aten.conv1d.default(activation_post_process_0, activation_post_process_1); activation_post_process_0 = activation_post_process_1 = None 2025-09-09T15:03:54.1147558Z relu = torch.ops.aten.relu.default(conv1d); conv1d = None 2025-09-09T15:03:54.1148137Z activation_post_process_2 = self.activation_post_process_2(relu); relu = None 2025-09-09T15:03:54.1148785Z return pytree.tree_unflatten((activation_post_process_2,), self._out_spec) 2025-09-09T15:03:54.1149248Z 2025-09-09T15:03:54.1149574Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:03:54.1150007Z model fx: GraphModule( 2025-09-09T15:03:54.1150392Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:03:54.1151522Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0104]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:03:54.1152868Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3264806270599365, max_val=1.318617343902588) 2025-09-09T15:03:54.1153479Z ) 2025-09-09T15:03:54.1153689Z (conv): ConvReLU1d( 2025-09-09T15:03:54.1153984Z 3, 3, kernel_size=(3,), stride=(1,), bias=False 2025-09-09T15:03:54.1154404Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:03:54.1155560Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0024, 0.0022, 0.0021]), zero_point=tensor([0, 0, 0], dtype=torch.int32), dtype=torch.qint8, quant_min=-127, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False 2025-09-09T15:03:54.1157168Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.3119, -0.2799, -0.2618]), max_val=tensor([0.1970, 0.1855, 0.2308])) 2025-09-09T15:03:54.1157968Z ) 2025-09-09T15:03:54.1158166Z ) 2025-09-09T15:03:54.1158489Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:03:54.1159740Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0038]), zero_point=tensor([-128], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:03:54.1161025Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=0.9578298330307007) 2025-09-09T15:03:54.1161592Z ) 2025-09-09T15:03:54.1161780Z ) 2025-09-09T15:03:54.1161897Z 2025-09-09T15:03:54.1161903Z 2025-09-09T15:03:54.1161907Z 2025-09-09T15:03:54.1162005Z def forward(self, x): 2025-09-09T15:03:54.1162420Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T15:03:54.1163058Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T15:03:54.1163709Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T15:03:54.1164219Z return activation_post_process_1 2025-09-09T15:03:54.1164524Z 2025-09-09T15:03:54.1164843Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:03:54.1165284Z diff: tensor([[[0., 0., 0.], 2025-09-09T15:03:54.1165556Z [0., 0., 0.], 2025-09-09T15:03:54.1165825Z [0., 0., 0.]]], grad_fn=) 2025-09-09T15:03:54.1166189Z converted model pt2e: GraphModule( 2025-09-09T15:03:54.1166491Z (conv): Module() 2025-09-09T15:03:54.1166723Z ) 2025-09-09T15:03:54.1166835Z 2025-09-09T15:03:54.1166840Z 2025-09-09T15:03:54.1166845Z 2025-09-09T15:03:54.1166943Z def forward(self, x): 2025-09-09T15:03:54.1167272Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T15:03:54.1167654Z _scale_0 = self._scale_0 2025-09-09T15:03:54.1168046Z _zero_point_0 = self._zero_point_0 2025-09-09T15:03:54.1168434Z quantize_per_channel_default = self._frozen_param0 2025-09-09T15:03:54.1169648Z dequantize_per_channel_default = torch.ops.quantized_decomposed.dequantize_per_channel.default(quantize_per_channel_default, _scale_0, _zero_point_0, 0, -127, 127, torch.int8); quantize_per_channel_default = _scale_0 = _zero_point_0 = None 2025-09-09T15:03:54.1171365Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.010372933000326157, 0, -128, 127, torch.int8); x = None 2025-09-09T15:03:54.1172874Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.010372933000326157, 0, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T15:03:54.1174501Z conv1d = torch.ops.aten.conv1d.default(dequantize_per_tensor_default, dequantize_per_channel_default); dequantize_per_tensor_default = dequantize_per_channel_default = None 2025-09-09T15:03:54.1175498Z relu = torch.ops.aten.relu.default(conv1d); conv1d = None 2025-09-09T15:03:54.1176428Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(relu, 0.0037561955396085978, -128, -128, 127, torch.int8); relu = None 2025-09-09T15:03:54.1178132Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.0037561955396085978, -128, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T15:03:54.1179208Z return pytree.tree_unflatten((dequantize_per_tensor_default_1,), self._out_spec) 2025-09-09T15:03:54.1179615Z 2025-09-09T15:03:54.1179893Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:03:54.1180255Z onverted model fx: GraphModule( 2025-09-09T15:03:54.1180506Z (conv): ConvReLU1d( 2025-09-09T15:03:54.1180855Z (0): QuantizedConv1d(Reference)(3, 3, kernel_size=(3,), stride=(1,), bias=False) 2025-09-09T15:03:54.1181255Z (1): ReLU() 2025-09-09T15:03:54.1181442Z ) 2025-09-09T15:03:54.1181603Z ) 2025-09-09T15:03:54.1181695Z 2025-09-09T15:03:54.1181700Z 2025-09-09T15:03:54.1181710Z 2025-09-09T15:03:54.1181796Z def forward(self, x): 2025-09-09T15:03:54.1182397Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.010372933000326157, 0, -128, 127, torch.int8); x = None 2025-09-09T15:03:54.1183605Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.010372933000326157, 0, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T15:03:54.1184595Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T15:03:54.1185431Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.0037561955396085978, -128, -128, 127, torch.int8); conv = None 2025-09-09T15:03:54.1186702Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.0037561955396085978, -128, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T15:03:54.1187583Z return dequantize_per_tensor_default_1 2025-09-09T15:03:54.1187844Z 2025-09-09T15:03:54.1188114Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:03:54.1188466Z diff: tensor([[[0., 0., 0.], 2025-09-09T15:03:54.1188700Z [0., 0., 0.], 2025-09-09T15:03:54.1188901Z [0., 0., 0.]]]) 2025-09-09T15:03:54.1189128Z model pt2e: GraphModule( 2025-09-09T15:03:54.1189340Z (conv): Module() 2025-09-09T15:03:54.1189636Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:03:54.1190678Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0024]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.int8, quant_min=-127, quant_max=127, qscheme=torch.per_tensor_symmetric, reduce_range=False 2025-09-09T15:03:54.1191775Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.3119288384914398, max_val=0.23078612983226776) 2025-09-09T15:03:54.1192358Z ) 2025-09-09T15:03:54.1192623Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:03:54.1193533Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0104]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:03:54.1194599Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3264806270599365, max_val=1.318617343902588) 2025-09-09T15:03:54.1195092Z ) 2025-09-09T15:03:54.1195361Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:03:54.1196278Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0037]), zero_point=tensor([-128], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:03:54.1197311Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=0.9556508660316467) 2025-09-09T15:03:54.1197777Z ) 2025-09-09T15:03:54.1197941Z ) 2025-09-09T15:03:54.1198034Z 2025-09-09T15:03:54.1198038Z 2025-09-09T15:03:54.1198041Z 2025-09-09T15:03:54.1198128Z def forward(self, x): 2025-09-09T15:03:54.1198405Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T15:03:54.1198735Z conv_weight = self.conv.weight 2025-09-09T15:03:55.5592955Z activation_post_process_1 = self.activation_post_process_1(conv_weight); conv_weight = None 2025-09-09T15:03:55.5593721Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T15:03:55.5594715Z conv1d = torch.ops.aten.conv1d.default(activation_post_process_0, activation_post_process_1); activation_post_process_0 = activation_post_process_1 = None 2025-09-09T15:03:55.5595663Z relu = torch.ops.aten.relu.default(conv1d); conv1d = None 2025-09-09T15:03:55.5596249Z activation_post_process_2 = self.activation_post_process_2(relu); relu = None 2025-09-09T15:03:55.5596919Z return pytree.tree_unflatten((activation_post_process_2,), self._out_spec) 2025-09-09T15:03:55.5597385Z 2025-09-09T15:03:55.5597711Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:03:55.5598173Z model fx: GraphModule( 2025-09-09T15:03:55.5598556Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:03:55.5599814Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0104]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:03:55.5601267Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3264806270599365, max_val=1.318617343902588) 2025-09-09T15:03:55.5601891Z ) 2025-09-09T15:03:55.5602100Z (conv): ConvReLU1d( 2025-09-09T15:03:55.5602414Z 3, 3, kernel_size=(3,), stride=(1,), bias=False 2025-09-09T15:03:55.5602846Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:03:55.5603970Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0024]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-127, quant_max=127, qscheme=torch.per_tensor_symmetric, reduce_range=False 2025-09-09T15:03:55.5605350Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.3119288384914398, max_val=0.23078612983226776) 2025-09-09T15:03:55.5605973Z ) 2025-09-09T15:03:55.5606189Z ) 2025-09-09T15:03:55.5606509Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:03:55.5607717Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0037]), zero_point=tensor([-128], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:03:55.5609455Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=0.9556508660316467) 2025-09-09T15:03:55.5610080Z ) 2025-09-09T15:03:55.5610457Z ) 2025-09-09T15:03:55.5610556Z 2025-09-09T15:03:55.5610560Z 2025-09-09T15:03:55.5610564Z 2025-09-09T15:03:55.5610642Z def forward(self, x): 2025-09-09T15:03:55.5610988Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T15:03:55.5611510Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T15:03:55.5612038Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T15:03:55.5612455Z return activation_post_process_1 2025-09-09T15:03:55.5612701Z 2025-09-09T15:03:55.5612969Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:03:55.5613327Z diff: tensor([[[0., 0., 0.], 2025-09-09T15:03:55.5613554Z [0., 0., 0.], 2025-09-09T15:03:55.5613789Z [0., 0., 0.]]], grad_fn=) 2025-09-09T15:03:55.5614098Z converted model pt2e: GraphModule( 2025-09-09T15:03:55.5614354Z (conv): Module() 2025-09-09T15:03:55.5614537Z ) 2025-09-09T15:03:55.5614639Z 2025-09-09T15:03:55.5614643Z 2025-09-09T15:03:55.5614654Z 2025-09-09T15:03:55.5614732Z def forward(self, x): 2025-09-09T15:03:55.5615011Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T15:03:55.5615382Z quantize_per_tensor_default = self._frozen_param0 2025-09-09T15:03:55.5616276Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.0024561325553804636, 0, -127, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T15:03:55.5617513Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.010372933000326157, 0, -128, 127, torch.int8); x = None 2025-09-09T15:03:55.5618762Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.010372933000326157, 0, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T15:03:55.5620081Z conv1d = torch.ops.aten.conv1d.default(dequantize_per_tensor_default_1, dequantize_per_tensor_default); dequantize_per_tensor_default_1 = dequantize_per_tensor_default = None 2025-09-09T15:03:55.5620903Z relu = torch.ops.aten.relu.default(conv1d); conv1d = None 2025-09-09T15:03:55.5621662Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(relu, 0.003747650422155857, -128, -128, 127, torch.int8); relu = None 2025-09-09T15:03:55.5623208Z dequantize_per_tensor_default_2 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_2, 0.003747650422155857, -128, -128, 127, torch.int8); quantize_per_tensor_default_2 = None 2025-09-09T15:03:55.5624233Z return pytree.tree_unflatten((dequantize_per_tensor_default_2,), self._out_spec) 2025-09-09T15:03:55.5624643Z 2025-09-09T15:03:55.5624918Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:03:55.5625295Z onverted model fx: GraphModule( 2025-09-09T15:03:55.5625553Z (conv): ConvReLU1d( 2025-09-09T15:03:55.5625913Z (0): QuantizedConv1d(Reference)(3, 3, kernel_size=(3,), stride=(1,), bias=False) 2025-09-09T15:03:55.5626300Z (1): ReLU() 2025-09-09T15:03:55.5626492Z ) 2025-09-09T15:03:55.5626653Z ) 2025-09-09T15:03:55.5626759Z 2025-09-09T15:03:55.5626764Z 2025-09-09T15:03:55.5626768Z 2025-09-09T15:03:55.5626849Z def forward(self, x): 2025-09-09T15:03:55.5627463Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.010372933000326157, 0, -128, 127, torch.int8); x = None 2025-09-09T15:03:55.5628683Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.010372933000326157, 0, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T15:03:55.5629819Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T15:03:55.5630674Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.003747650422155857, -128, -128, 127, torch.int8); conv = None 2025-09-09T15:03:55.5632045Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.003747650422155857, -128, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T15:03:55.5632928Z return dequantize_per_tensor_default_1 2025-09-09T15:03:55.5633191Z 2025-09-09T15:03:55.5633464Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:03:55.5633823Z diff: tensor([[[0., 0., 0.], 2025-09-09T15:03:55.5634055Z [0., 0., 0.], 2025-09-09T15:03:55.5634262Z [0., 0., 0.]]]) 2025-09-09T15:03:55.5634483Z model pt2e: GraphModule( 2025-09-09T15:03:55.5634707Z (conv): Module() 2025-09-09T15:03:55.5635010Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:03:55.5635987Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0022, 0.0025, 0.0024]), zero_point=tensor([0, 0, 0], dtype=torch.int32), dtype=torch.int8, quant_min=-127, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False 2025-09-09T15:03:55.5637264Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.2203, -0.3233, -0.3086]), max_val=tensor([0.2796, 0.3026, 0.2405])) 2025-09-09T15:03:55.5637899Z ) 2025-09-09T15:03:55.5638168Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:03:55.5639133Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0104]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:03:55.5640265Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3264806270599365, max_val=1.318617343902588) 2025-09-09T15:03:55.5640767Z ) 2025-09-09T15:03:55.5641041Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:03:55.5641970Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0087]), zero_point=tensor([-25], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:03:55.5643049Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.8935509324073792, max_val=1.3209781646728516) 2025-09-09T15:03:55.5643553Z ) 2025-09-09T15:03:55.5643711Z ) 2025-09-09T15:03:55.5643811Z 2025-09-09T15:03:55.5643815Z 2025-09-09T15:03:55.5643819Z 2025-09-09T15:03:55.5643899Z def forward(self, x): 2025-09-09T15:03:55.5644176Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T15:03:55.5644503Z conv_weight = self.conv.weight 2025-09-09T15:03:55.5644958Z activation_post_process_1 = self.activation_post_process_1(conv_weight); conv_weight = None 2025-09-09T15:03:55.5645524Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T15:03:55.5646322Z conv1d = torch.ops.aten.conv1d.default(activation_post_process_0, activation_post_process_1); activation_post_process_0 = activation_post_process_1 = None 2025-09-09T15:03:55.5647136Z activation_post_process_2 = self.activation_post_process_2(conv1d); conv1d = None 2025-09-09T15:03:55.5647681Z return pytree.tree_unflatten((activation_post_process_2,), self._out_spec) 2025-09-09T15:03:55.5648068Z 2025-09-09T15:03:55.5648372Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:03:55.5648739Z model fx: GraphModule( 2025-09-09T15:03:55.5649044Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:03:55.5650096Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0104]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:03:55.5651181Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3264806270599365, max_val=1.318617343902588) 2025-09-09T15:03:55.5651754Z ) 2025-09-09T15:03:55.5651928Z (conv): Conv1d( 2025-09-09T15:03:55.5652158Z 3, 3, kernel_size=(3,), stride=(1,), bias=False 2025-09-09T15:03:55.5652517Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:03:55.5653452Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0022, 0.0025, 0.0024]), zero_point=tensor([0, 0, 0], dtype=torch.int32), dtype=torch.qint8, quant_min=-127, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False 2025-09-09T15:03:57.1719560Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.2203, -0.3233, -0.3086]), max_val=tensor([0.2796, 0.3026, 0.2405])) 2025-09-09T15:03:57.1720395Z ) 2025-09-09T15:03:57.1720616Z ) 2025-09-09T15:03:57.1720969Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:03:57.1722324Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0087]), zero_point=tensor([-25], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:03:57.1723738Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.8935509324073792, max_val=1.3209781646728516) 2025-09-09T15:03:57.1724378Z ) 2025-09-09T15:03:57.1724577Z ) 2025-09-09T15:03:57.1724699Z 2025-09-09T15:03:57.1724704Z 2025-09-09T15:03:57.1724709Z 2025-09-09T15:03:57.1724811Z def forward(self, x): 2025-09-09T15:03:57.1725259Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T15:03:57.1725908Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T15:03:57.1726564Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T15:03:57.1727086Z return activation_post_process_1 2025-09-09T15:03:57.1727391Z 2025-09-09T15:03:57.1727723Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:03:57.1728171Z diff: tensor([[[0., 0., 0.], 2025-09-09T15:03:57.1728456Z [0., 0., 0.], 2025-09-09T15:03:57.1728732Z [0., 0., 0.]]], grad_fn=) 2025-09-09T15:03:57.1729099Z converted model pt2e: GraphModule( 2025-09-09T15:03:57.1729411Z (conv): Module() 2025-09-09T15:03:57.1729647Z ) 2025-09-09T15:03:57.1729763Z 2025-09-09T15:03:57.1729768Z 2025-09-09T15:03:57.1729773Z 2025-09-09T15:03:57.1729880Z def forward(self, x): 2025-09-09T15:03:57.1730210Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T15:03:57.1730612Z _scale_0 = self._scale_0 2025-09-09T15:03:57.1730913Z _zero_point_0 = self._zero_point_0 2025-09-09T15:03:57.1731304Z quantize_per_channel_default = self._frozen_param0 2025-09-09T15:03:57.1732538Z dequantize_per_channel_default = torch.ops.quantized_decomposed.dequantize_per_channel.default(quantize_per_channel_default, _scale_0, _zero_point_0, 0, -127, 127, torch.int8); quantize_per_channel_default = _scale_0 = _zero_point_0 = None 2025-09-09T15:03:57.1734190Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.010372933000326157, 0, -128, 127, torch.int8); x = None 2025-09-09T15:03:57.1735714Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.010372933000326157, 0, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T15:03:57.1737344Z conv1d = torch.ops.aten.conv1d.default(dequantize_per_tensor_default, dequantize_per_channel_default); dequantize_per_tensor_default = dequantize_per_channel_default = None 2025-09-09T15:03:57.1739195Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv1d, 0.008684427477419376, -25, -128, 127, torch.int8); conv1d = None 2025-09-09T15:03:57.1740816Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.008684427477419376, -25, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T15:03:57.1742224Z return pytree.tree_unflatten((dequantize_per_tensor_default_1,), self._out_spec) 2025-09-09T15:03:57.1742730Z 2025-09-09T15:03:57.1743058Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:03:57.1743504Z onverted model fx: GraphModule( 2025-09-09T15:03:57.1743992Z (conv): QuantizedConv1d(Reference)(3, 3, kernel_size=(3,), stride=(1,), bias=False) 2025-09-09T15:03:57.1744475Z ) 2025-09-09T15:03:57.1744595Z 2025-09-09T15:03:57.1744600Z 2025-09-09T15:03:57.1744605Z 2025-09-09T15:03:57.1744703Z def forward(self, x): 2025-09-09T15:03:57.1745450Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.010372933000326157, 0, -128, 127, torch.int8); x = None 2025-09-09T15:03:57.1746984Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.010372933000326157, 0, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T15:03:57.1748237Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T15:03:57.1749281Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.008684427477419376, -25, -128, 127, torch.int8); conv = None 2025-09-09T15:03:57.1750864Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.008684427477419376, -25, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T15:03:57.1751950Z return dequantize_per_tensor_default_1 2025-09-09T15:03:57.1752272Z 2025-09-09T15:03:57.1752617Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:03:57.1753052Z diff: tensor([[[0., 0., 0.], 2025-09-09T15:03:57.1753330Z [0., 0., 0.], 2025-09-09T15:03:57.1753570Z [0., 0., 0.]]]) 2025-09-09T15:03:57.1753848Z model pt2e: GraphModule( 2025-09-09T15:03:57.1754111Z (conv): Module() 2025-09-09T15:03:57.1754477Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:03:57.1755642Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0025]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.int8, quant_min=-127, quant_max=127, qscheme=torch.per_tensor_symmetric, reduce_range=False 2025-09-09T15:03:57.1757009Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.3232726454734802, max_val=0.30256539583206177) 2025-09-09T15:03:57.1757638Z ) 2025-09-09T15:03:57.1757956Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:03:57.1759101Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0104]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:03:57.1760552Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3264806270599365, max_val=1.318617343902588) 2025-09-09T15:03:57.1761168Z ) 2025-09-09T15:03:57.1761492Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:03:57.1762628Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0087]), zero_point=tensor([-26], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:03:57.1763979Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.887858510017395, max_val=1.3209781646728516) 2025-09-09T15:03:57.1764606Z ) 2025-09-09T15:03:57.1764799Z ) 2025-09-09T15:03:57.1764910Z 2025-09-09T15:03:57.1764923Z 2025-09-09T15:03:57.1764927Z 2025-09-09T15:03:57.1765132Z def forward(self, x): 2025-09-09T15:03:57.1765469Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T15:03:57.1765874Z conv_weight = self.conv.weight 2025-09-09T15:03:57.1766513Z activation_post_process_1 = self.activation_post_process_1(conv_weight); conv_weight = None 2025-09-09T15:03:57.1767218Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T15:03:57.1768212Z conv1d = torch.ops.aten.conv1d.default(activation_post_process_0, activation_post_process_1); activation_post_process_0 = activation_post_process_1 = None 2025-09-09T15:03:57.1769219Z activation_post_process_2 = self.activation_post_process_2(conv1d); conv1d = None 2025-09-09T15:03:57.1769890Z return pytree.tree_unflatten((activation_post_process_2,), self._out_spec) 2025-09-09T15:03:57.1770351Z 2025-09-09T15:03:57.1770704Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:03:57.1771139Z model fx: GraphModule( 2025-09-09T15:03:57.1771518Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:03:57.1772690Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0104]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:03:57.1774167Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3264806270599365, max_val=1.318617343902588) 2025-09-09T15:03:57.1774665Z ) 2025-09-09T15:03:57.1774838Z (conv): Conv1d( 2025-09-09T15:03:57.1775067Z 3, 3, kernel_size=(3,), stride=(1,), bias=False 2025-09-09T15:03:57.1775420Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:03:57.1776320Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0025]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-127, quant_max=127, qscheme=torch.per_tensor_symmetric, reduce_range=False 2025-09-09T15:03:57.1777434Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.3232726454734802, max_val=0.30256539583206177) 2025-09-09T15:03:57.1777945Z ) 2025-09-09T15:03:57.1778119Z ) 2025-09-09T15:03:57.1778414Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:03:57.1779349Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0087]), zero_point=tensor([-26], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:03:57.1780454Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.887858510017395, max_val=1.3209781646728516) 2025-09-09T15:03:57.1789355Z ) 2025-09-09T15:03:57.1789551Z ) 2025-09-09T15:03:57.1789654Z 2025-09-09T15:03:57.1789658Z 2025-09-09T15:03:57.1789662Z 2025-09-09T15:03:57.1789758Z def forward(self, x): 2025-09-09T15:03:57.1790126Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T15:03:57.1790685Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T15:03:57.1791237Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T15:03:57.1791683Z return activation_post_process_1 2025-09-09T15:03:57.1791949Z 2025-09-09T15:03:57.1792240Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:03:57.1792623Z diff: tensor([[[0., 0., 0.], 2025-09-09T15:03:57.1792866Z [0., 0., 0.], 2025-09-09T15:03:57.1793117Z [0., 0., 0.]]], grad_fn=) 2025-09-09T15:03:57.1793427Z converted model pt2e: GraphModule( 2025-09-09T15:03:57.1793706Z (conv): Module() 2025-09-09T15:03:57.1793908Z ) 2025-09-09T15:03:57.1794017Z 2025-09-09T15:03:57.1794020Z 2025-09-09T15:03:57.1794025Z 2025-09-09T15:03:57.1794112Z def forward(self, x): 2025-09-09T15:03:57.1794411Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T15:03:57.1794919Z quantize_per_tensor_default = self._frozen_param0 2025-09-09T15:03:57.1795844Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.002545453840866685, 0, -127, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T15:05:12.2717064Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.010372933000326157, 0, -128, 127, torch.int8); x = None 2025-09-09T15:05:12.2718348Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.010372933000326157, 0, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T15:05:12.2719839Z conv1d = torch.ops.aten.conv1d.default(dequantize_per_tensor_default_1, dequantize_per_tensor_default); dequantize_per_tensor_default_1 = dequantize_per_tensor_default = None 2025-09-09T15:05:12.2721026Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv1d, 0.008662103675305843, -26, -128, 127, torch.int8); conv1d = None 2025-09-09T15:05:12.2722584Z dequantize_per_tensor_default_2 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_2, 0.008662103675305843, -26, -128, 127, torch.int8); quantize_per_tensor_default_2 = None 2025-09-09T15:05:12.2723607Z return pytree.tree_unflatten((dequantize_per_tensor_default_2,), self._out_spec) 2025-09-09T15:05:12.2724001Z 2025-09-09T15:05:12.2724278Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:05:12.2724640Z onverted model fx: GraphModule( 2025-09-09T15:05:12.2725047Z (conv): QuantizedConv1d(Reference)(3, 3, kernel_size=(3,), stride=(1,), bias=False) 2025-09-09T15:05:12.2725440Z ) 2025-09-09T15:05:12.2725544Z 2025-09-09T15:05:12.2725565Z 2025-09-09T15:05:12.2725569Z 2025-09-09T15:05:12.2725651Z def forward(self, x): 2025-09-09T15:05:12.2726263Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.010372933000326157, 0, -128, 127, torch.int8); x = None 2025-09-09T15:05:12.2727481Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.010372933000326157, 0, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T15:05:12.2728472Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T15:05:12.2729317Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.008662103675305843, -26, -128, 127, torch.int8); conv = None 2025-09-09T15:05:12.2730579Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.008662103675305843, -26, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T15:05:12.2731447Z return dequantize_per_tensor_default_1 2025-09-09T15:05:12.2731715Z 2025-09-09T15:05:12.2731989Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:05:12.2732357Z diff: tensor([[[0., 0., 0.], 2025-09-09T15:05:12.2732582Z [0., 0., 0.], 2025-09-09T15:05:12.2732793Z [0., 0., 0.]]]) 2025-09-09T15:05:12.2733228Z PASSED 2025-09-09T15:05:12.2733883Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn1d::test_qat_conv_transpose_bn PASSED 2025-09-09T15:05:12.2734902Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn1d::test_qat_conv_transpose_bn_relu PASSED 2025-09-09T15:05:12.2735835Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn1d::test_qat_inplace_add_relu model pt2e: GraphModule( 2025-09-09T15:05:12.2736430Z (conv): Module() 2025-09-09T15:05:12.2736729Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:05:12.2738143Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0010]), zero_point=tensor([127], dtype=torch.int32), dtype=torch.int8, quant_min=-127, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False 2025-09-09T15:05:12.2739573Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.2457]), max_val=tensor([-0.2457])) 2025-09-09T15:05:12.2740310Z ) 2025-09-09T15:05:12.2740587Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:05:12.2741515Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0089]), zero_point=tensor([41], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:05:12.2742595Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.498010516166687, max_val=0.7672448754310608) 2025-09-09T15:05:12.2743096Z ) 2025-09-09T15:05:12.2743360Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:05:12.2744290Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0022]), zero_point=tensor([46], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:05:12.2745380Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.3805955946445465, max_val=0.17587313055992126) 2025-09-09T15:05:12.2745888Z ) 2025-09-09T15:05:12.2746159Z (activation_post_process_3): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:05:12.2747078Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0015]), zero_point=tensor([-128], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:05:12.2748122Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=0.3842603862285614) 2025-09-09T15:05:12.2748587Z ) 2025-09-09T15:05:12.2748741Z ) 2025-09-09T15:05:12.2748833Z 2025-09-09T15:05:12.2748838Z 2025-09-09T15:05:12.2748842Z 2025-09-09T15:05:12.2748927Z def forward(self, x): 2025-09-09T15:05:12.2749209Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T15:05:12.2749543Z conv_weight = self.conv.weight 2025-09-09T15:05:12.2749997Z activation_post_process_1 = self.activation_post_process_1(conv_weight); conv_weight = None 2025-09-09T15:05:12.2750461Z conv_bias = self.conv.bias 2025-09-09T15:05:12.2750831Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T15:05:12.2751600Z conv1d = torch.ops.aten.conv1d.default(activation_post_process_0, activation_post_process_1, conv_bias); activation_post_process_1 = conv_bias = None 2025-09-09T15:05:12.2752399Z activation_post_process_2 = self.activation_post_process_2(conv1d); conv1d = None 2025-09-09T15:05:12.2753191Z add_ = torch.ops.aten.add_.Tensor(activation_post_process_2, activation_post_process_0); activation_post_process_2 = activation_post_process_0 = None 2025-09-09T15:05:12.2753896Z relu_ = torch.ops.aten.relu_.default(add_); add_ = None 2025-09-09T15:05:12.2754385Z activation_post_process_3 = self.activation_post_process_3(relu_); relu_ = None 2025-09-09T15:05:12.2754924Z return pytree.tree_unflatten((activation_post_process_3,), self._out_spec) 2025-09-09T15:05:12.2755312Z 2025-09-09T15:05:12.2755583Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:05:12.2755949Z model fx: GraphModule( 2025-09-09T15:05:12.2756258Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:05:12.2757189Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0089]), zero_point=tensor([41], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:05:12.2758283Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.498010516166687, max_val=0.7672448754310608) 2025-09-09T15:05:12.2758780Z ) 2025-09-09T15:05:12.2758955Z (conv): Conv1d( 2025-09-09T15:05:12.2759332Z 1, 1, kernel_size=(1,), stride=(1,) 2025-09-09T15:05:12.2759670Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:05:12.2760586Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0010]), zero_point=tensor([127], dtype=torch.int32), dtype=torch.qint8, quant_min=-127, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False 2025-09-09T15:05:12.2762868Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.2457]), max_val=tensor([-0.2457])) 2025-09-09T15:05:12.2763434Z ) 2025-09-09T15:05:12.2763599Z ) 2025-09-09T15:05:12.2763871Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:05:12.2764794Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0022]), zero_point=tensor([46], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:05:12.2765895Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.3805955946445465, max_val=0.17587313055992126) 2025-09-09T15:05:12.2766411Z ) 2025-09-09T15:05:12.2766589Z (relu): ReLU(inplace=True) 2025-09-09T15:05:12.2766929Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:05:12.2767851Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0015]), zero_point=tensor([-128], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:05:12.2768894Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=0.3842603862285614) 2025-09-09T15:05:12.2769353Z ) 2025-09-09T15:05:12.2769507Z ) 2025-09-09T15:05:12.2769598Z 2025-09-09T15:05:12.2769602Z 2025-09-09T15:05:12.2769606Z 2025-09-09T15:05:12.2769691Z def forward(self, x): 2025-09-09T15:05:12.2770033Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T15:05:12.2770469Z conv = self.conv(activation_post_process_0) 2025-09-09T15:05:12.2770894Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T15:05:12.2771575Z add = activation_post_process_1 + activation_post_process_0; activation_post_process_1 = activation_post_process_0 = None 2025-09-09T15:05:12.2772139Z relu = self.relu(add); add = None 2025-09-09T15:05:12.2772540Z activation_post_process_2 = self.activation_post_process_2(relu); relu = None 2025-09-09T15:05:12.2772955Z return activation_post_process_2 2025-09-09T15:05:12.2773198Z 2025-09-09T15:05:12.2773472Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:05:12.2773870Z diff: tensor([[[0., 0., 0.]]], grad_fn=) 2025-09-09T15:05:12.2774195Z converted model pt2e: GraphModule( 2025-09-09T15:05:12.2774448Z (conv): Module() 2025-09-09T15:05:12.2774639Z ) 2025-09-09T15:05:12.2774733Z 2025-09-09T15:05:12.2774737Z 2025-09-09T15:05:12.2774741Z 2025-09-09T15:05:12.2774830Z def forward(self, x): 2025-09-09T15:05:12.2775101Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T15:05:12.2775420Z _scale_0 = self._scale_0 2025-09-09T15:05:12.2775666Z _zero_point_0 = self._zero_point_0 2025-09-09T15:05:12.2775982Z quantize_per_channel_default = self._frozen_param0 2025-09-09T15:05:13.5822927Z dequantize_per_channel_default = torch.ops.quantized_decomposed.dequantize_per_channel.default(quantize_per_channel_default, _scale_0, _zero_point_0, 0, -127, 127, torch.int8); quantize_per_channel_default = _scale_0 = _zero_point_0 = None 2025-09-09T15:05:13.5823964Z conv_bias = self.conv.bias 2025-09-09T15:05:13.5824607Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.008883354254066944, 41, -128, 127, torch.int8); x = None 2025-09-09T15:05:13.5826125Z dequantize_per_tensor_default_4 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.008883354254066944, 41, -128, 127, torch.int8) 2025-09-09T15:05:13.5827465Z dequantize_per_tensor_default_3 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.008883354254066944, 41, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T15:05:13.5829026Z conv1d = torch.ops.aten.conv1d.default(dequantize_per_tensor_default_3, dequantize_per_channel_default, conv_bias); dequantize_per_tensor_default_3 = dequantize_per_channel_default = conv_bias = None 2025-09-09T15:05:13.5830285Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv1d, 0.0021822303533554077, 46, -128, 127, torch.int8); conv1d = None 2025-09-09T15:05:13.5831577Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.0021822303533554077, 46, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T15:05:13.5832892Z add_ = torch.ops.aten.add_.Tensor(dequantize_per_tensor_default_1, dequantize_per_tensor_default_4); dequantize_per_tensor_default_1 = dequantize_per_tensor_default_4 = None 2025-09-09T15:05:13.5833680Z relu_ = torch.ops.aten.relu_.default(add_); add_ = None 2025-09-09T15:05:13.5834444Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(relu_, 0.0015069034416228533, -128, -128, 127, torch.int8); relu_ = None 2025-09-09T15:05:13.5835738Z dequantize_per_tensor_default_2 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_2, 0.0015069034416228533, -128, -128, 127, torch.int8); quantize_per_tensor_default_2 = None 2025-09-09T15:05:13.5836770Z return pytree.tree_unflatten((dequantize_per_tensor_default_2,), self._out_spec) 2025-09-09T15:05:13.5837179Z 2025-09-09T15:05:13.5837462Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:05:13.5837830Z onverted model fx: GraphModule( 2025-09-09T15:05:13.5838218Z (conv): QuantizedConv1d(Reference)(1, 1, kernel_size=(1,), stride=(1,)) 2025-09-09T15:05:13.5838617Z (relu): ReLU(inplace=True) 2025-09-09T15:05:13.5838844Z ) 2025-09-09T15:05:13.5838945Z 2025-09-09T15:05:13.5838950Z 2025-09-09T15:05:13.5838953Z 2025-09-09T15:05:13.5839042Z def forward(self, x): 2025-09-09T15:05:13.5839754Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.008883354254066944, 41, -128, 127, torch.int8); x = None 2025-09-09T15:05:13.5840996Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.008883354254066944, 41, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T15:05:13.5841890Z conv = self.conv(dequantize_per_tensor_default) 2025-09-09T15:05:13.5842618Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.0021822303533554077, 46, -128, 127, torch.int8); conv = None 2025-09-09T15:05:13.5843898Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.0021822303533554077, 46, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T15:05:13.5845114Z add = dequantize_per_tensor_default_1 + dequantize_per_tensor_default; dequantize_per_tensor_default_1 = dequantize_per_tensor_default = None 2025-09-09T15:05:13.5845746Z relu = self.relu(add); add = None 2025-09-09T15:05:13.5846462Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(relu, 0.0015069034416228533, -128, -128, 127, torch.int8); relu = None 2025-09-09T15:05:13.5847758Z dequantize_per_tensor_default_2 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_2, 0.0015069034416228533, -128, -128, 127, torch.int8); quantize_per_tensor_default_2 = None 2025-09-09T15:05:13.5848659Z return dequantize_per_tensor_default_2 2025-09-09T15:05:13.5848935Z 2025-09-09T15:05:13.5849354Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:05:13.5849739Z diff: tensor([[[0., 0., 0.]]]) 2025-09-09T15:05:13.5849988Z model pt2e: GraphModule( 2025-09-09T15:05:13.5850298Z (conv): Module() 2025-09-09T15:05:13.5850597Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:05:13.5851553Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0010]), zero_point=tensor([127], dtype=torch.int32), dtype=torch.int8, quant_min=-127, quant_max=127, qscheme=torch.per_tensor_symmetric, reduce_range=False 2025-09-09T15:05:13.5852693Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.24565386772155762, max_val=-0.24565386772155762) 2025-09-09T15:05:13.5853228Z ) 2025-09-09T15:05:13.5853506Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:05:13.5854440Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0089]), zero_point=tensor([41], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:05:13.5855542Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.498010516166687, max_val=0.7672448754310608) 2025-09-09T15:05:13.5856050Z ) 2025-09-09T15:05:13.5856327Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:05:13.5857260Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0022]), zero_point=tensor([46], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:05:13.5858360Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.3805955946445465, max_val=0.17587313055992126) 2025-09-09T15:05:13.5858876Z ) 2025-09-09T15:05:13.5859144Z (activation_post_process_3): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:05:13.5860088Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0015]), zero_point=tensor([-128], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:05:13.5861163Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=0.3842603862285614) 2025-09-09T15:05:13.5861636Z ) 2025-09-09T15:05:13.5861805Z ) 2025-09-09T15:05:13.5861900Z 2025-09-09T15:05:13.5861904Z 2025-09-09T15:05:13.5861908Z 2025-09-09T15:05:13.5861990Z def forward(self, x): 2025-09-09T15:05:13.5862277Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T15:05:13.5862611Z conv_weight = self.conv.weight 2025-09-09T15:05:13.5863076Z activation_post_process_1 = self.activation_post_process_1(conv_weight); conv_weight = None 2025-09-09T15:05:13.5863549Z conv_bias = self.conv.bias 2025-09-09T15:05:13.5863917Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T15:05:13.5864711Z conv1d = torch.ops.aten.conv1d.default(activation_post_process_0, activation_post_process_1, conv_bias); activation_post_process_1 = conv_bias = None 2025-09-09T15:05:13.5865512Z activation_post_process_2 = self.activation_post_process_2(conv1d); conv1d = None 2025-09-09T15:05:13.5866326Z add_ = torch.ops.aten.add_.Tensor(activation_post_process_2, activation_post_process_0); activation_post_process_2 = activation_post_process_0 = None 2025-09-09T15:05:13.5867030Z relu_ = torch.ops.aten.relu_.default(add_); add_ = None 2025-09-09T15:05:13.5867504Z activation_post_process_3 = self.activation_post_process_3(relu_); relu_ = None 2025-09-09T15:05:13.5868058Z return pytree.tree_unflatten((activation_post_process_3,), self._out_spec) 2025-09-09T15:05:13.5868442Z 2025-09-09T15:05:13.5868728Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:05:13.5869083Z model fx: GraphModule( 2025-09-09T15:05:13.5869407Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:05:13.5870439Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0089]), zero_point=tensor([41], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:05:13.5871616Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.498010516166687, max_val=0.7672448754310608) 2025-09-09T15:05:13.5872119Z ) 2025-09-09T15:05:13.5872288Z (conv): Conv1d( 2025-09-09T15:05:13.5872507Z 1, 1, kernel_size=(1,), stride=(1,) 2025-09-09T15:05:13.5872838Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:05:13.5873776Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0010]), zero_point=tensor([127], dtype=torch.int32), dtype=torch.qint8, quant_min=-127, quant_max=127, qscheme=torch.per_tensor_symmetric, reduce_range=False 2025-09-09T15:05:13.5874920Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.24565386772155762, max_val=-0.24565386772155762) 2025-09-09T15:05:13.5875447Z ) 2025-09-09T15:05:13.5875624Z ) 2025-09-09T15:05:13.5875890Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:05:13.5876830Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0022]), zero_point=tensor([46], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:05:13.5877936Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.3805955946445465, max_val=0.17587313055992126) 2025-09-09T15:05:13.5878450Z ) 2025-09-09T15:05:13.5878643Z (relu): ReLU(inplace=True) 2025-09-09T15:05:13.5878974Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:05:54.1823160Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0015]), zero_point=tensor([-128], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:05:54.1824548Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=0.3842603862285614) 2025-09-09T15:05:54.1825116Z ) 2025-09-09T15:05:54.1825327Z ) 2025-09-09T15:05:54.1825441Z 2025-09-09T15:05:54.1825446Z 2025-09-09T15:05:54.1825450Z 2025-09-09T15:05:54.1825561Z def forward(self, x): 2025-09-09T15:05:54.1825982Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T15:05:54.1826498Z conv = self.conv(activation_post_process_0) 2025-09-09T15:05:54.1827019Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T15:05:54.1827857Z add = activation_post_process_1 + activation_post_process_0; activation_post_process_1 = activation_post_process_0 = None 2025-09-09T15:05:54.1828535Z relu = self.relu(add); add = None 2025-09-09T15:05:54.1829034Z activation_post_process_2 = self.activation_post_process_2(relu); relu = None 2025-09-09T15:05:54.1829546Z return activation_post_process_2 2025-09-09T15:05:54.1829848Z 2025-09-09T15:05:54.1830179Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:05:54.1830661Z diff: tensor([[[0., 0., 0.]]], grad_fn=) 2025-09-09T15:05:54.1831062Z converted model pt2e: GraphModule( 2025-09-09T15:05:54.1831363Z (conv): Module() 2025-09-09T15:05:54.1831598Z ) 2025-09-09T15:05:54.1831714Z 2025-09-09T15:05:54.1831719Z 2025-09-09T15:05:54.1831724Z 2025-09-09T15:05:54.1831832Z def forward(self, x): 2025-09-09T15:05:54.1832160Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T15:05:54.1832610Z quantize_per_tensor_default = self._frozen_param0 2025-09-09T15:05:54.1833720Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.0019342823652550578, 0, -127, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T15:05:54.1834785Z conv_bias = self.conv.bias 2025-09-09T15:05:54.1835837Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.008883354254066944, 41, -128, 127, torch.int8); x = None 2025-09-09T15:05:54.1837240Z dequantize_per_tensor_default_5 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.008883354254066944, 41, -128, 127, torch.int8) 2025-09-09T15:05:54.1839034Z dequantize_per_tensor_default_4 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.008883354254066944, 41, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T15:05:54.1840860Z conv1d = torch.ops.aten.conv1d.default(dequantize_per_tensor_default_4, dequantize_per_tensor_default, conv_bias); dequantize_per_tensor_default_4 = dequantize_per_tensor_default = conv_bias = None 2025-09-09T15:05:54.1842392Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv1d, 0.0021822303533554077, 46, -128, 127, torch.int8); conv1d = None 2025-09-09T15:05:54.1843986Z dequantize_per_tensor_default_2 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_2, 0.0021822303533554077, 46, -128, 127, torch.int8); quantize_per_tensor_default_2 = None 2025-09-09T15:05:54.1845599Z add_ = torch.ops.aten.add_.Tensor(dequantize_per_tensor_default_2, dequantize_per_tensor_default_5); dequantize_per_tensor_default_2 = dequantize_per_tensor_default_5 = None 2025-09-09T15:05:54.1846556Z relu_ = torch.ops.aten.relu_.default(add_); add_ = None 2025-09-09T15:05:54.1847479Z quantize_per_tensor_default_3 = torch.ops.quantized_decomposed.quantize_per_tensor.default(relu_, 0.0015069034416228533, -128, -128, 127, torch.int8); relu_ = None 2025-09-09T15:05:54.1849062Z dequantize_per_tensor_default_3 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_3, 0.0015069034416228533, -128, -128, 127, torch.int8); quantize_per_tensor_default_3 = None 2025-09-09T15:05:54.1850322Z return pytree.tree_unflatten((dequantize_per_tensor_default_3,), self._out_spec) 2025-09-09T15:05:54.1850813Z 2025-09-09T15:05:54.1851132Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:05:54.1851579Z onverted model fx: GraphModule( 2025-09-09T15:05:54.1852030Z (conv): QuantizedConv1d(Reference)(1, 1, kernel_size=(1,), stride=(1,)) 2025-09-09T15:05:54.1852542Z (relu): ReLU(inplace=True) 2025-09-09T15:05:54.1852823Z ) 2025-09-09T15:05:54.1852947Z 2025-09-09T15:05:54.1852952Z 2025-09-09T15:05:54.1852958Z 2025-09-09T15:05:54.1853062Z def forward(self, x): 2025-09-09T15:05:54.1853734Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.008883354254066944, 41, -128, 127, torch.int8); x = None 2025-09-09T15:05:54.1854964Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.008883354254066944, 41, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T15:05:54.1855837Z conv = self.conv(dequantize_per_tensor_default) 2025-09-09T15:05:54.1856555Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.0021822303533554077, 46, -128, 127, torch.int8); conv = None 2025-09-09T15:05:54.1857824Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.0021822303533554077, 46, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T15:05:54.1859011Z add = dequantize_per_tensor_default_1 + dequantize_per_tensor_default; dequantize_per_tensor_default_1 = dequantize_per_tensor_default = None 2025-09-09T15:05:54.1859626Z relu = self.relu(add); add = None 2025-09-09T15:05:54.1860318Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(relu, 0.0015069034416228533, -128, -128, 127, torch.int8); relu = None 2025-09-09T15:05:54.1861685Z dequantize_per_tensor_default_2 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_2, 0.0015069034416228533, -128, -128, 127, torch.int8); quantize_per_tensor_default_2 = None 2025-09-09T15:05:54.1862633Z return dequantize_per_tensor_default_2 2025-09-09T15:05:54.1862904Z 2025-09-09T15:05:54.1863169Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:05:54.1863530Z diff: tensor([[[0., 0., 0.]]]) 2025-09-09T15:05:54.1863973Z PASSED 2025-09-09T15:05:54.1864668Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn1d::test_qat_per_channel_weight_custom_dtype PASSED 2025-09-09T15:05:54.1865725Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn1d::test_qat_preserve_source_fn_stack PASSED 2025-09-09T15:05:54.1866665Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn1d::test_qat_update_shared_qspec model pt2e: GraphModule( 2025-09-09T15:05:54.1867271Z (conv): Module() 2025-09-09T15:05:54.1867473Z (bn): Module() 2025-09-09T15:05:54.1867771Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:05:54.1868691Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0104]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:05:54.1869771Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3264806270599365, max_val=1.318617343902588) 2025-09-09T15:05:54.1870269Z ) 2025-09-09T15:05:54.1870534Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:05:54.1871551Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0025, 0.0024, 0.0026]), zero_point=tensor([0, 0, 0], dtype=torch.int32), dtype=torch.int8, quant_min=-127, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False 2025-09-09T15:05:54.1872819Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.3156, -0.1982, -0.3261]), max_val=tensor([0.2827, 0.2978, 0.2359])) 2025-09-09T15:05:54.1873448Z ) 2025-09-09T15:05:54.1873720Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:05:54.1874627Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0106]), zero_point=tensor([-5], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:05:54.1875698Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3043057918548584, max_val=1.399786114692688) 2025-09-09T15:05:54.1876195Z ) 2025-09-09T15:05:54.1876458Z (activation_post_process_3): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:05:54.1877371Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0106]), zero_point=tensor([-5], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:05:54.1878427Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3043057918548584, max_val=1.399786114692688) 2025-09-09T15:05:54.1878929Z ) 2025-09-09T15:05:54.1879087Z ) 2025-09-09T15:05:54.1879185Z 2025-09-09T15:05:54.1879189Z 2025-09-09T15:05:54.1879193Z 2025-09-09T15:05:54.1879345Z def forward(self, x): 2025-09-09T15:05:54.1879626Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T15:05:54.1879949Z conv_weight = self.conv.weight 2025-09-09T15:05:54.1880216Z conv_bias = self.conv.bias 2025-09-09T15:05:54.1880458Z bn_weight = self.bn.weight 2025-09-09T15:05:54.1880703Z bn_bias = self.bn.bias 2025-09-09T15:05:54.1880949Z bn_running_mean = self.bn.running_mean 2025-09-09T15:05:54.1881242Z bn_running_var = self.bn.running_var 2025-09-09T15:05:54.1881562Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T15:05:54.1882090Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T15:05:54.1882671Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T15:05:54.1883258Z add = torch.ops.aten.add.Tensor(bn_running_var, 1e-05) 2025-09-09T15:05:54.1883643Z sqrt = torch.ops.aten.sqrt.default(add); add = None 2025-09-09T15:05:54.1884041Z div = torch.ops.aten.div.Tensor(bn_weight, sqrt); sqrt = None 2025-09-09T15:05:54.1884472Z reshape = torch.ops.aten.reshape.default(div, [-1, 1, 1]) 2025-09-09T15:05:54.1884964Z mul = torch.ops.aten.mul.Tensor(conv_weight, reshape); conv_weight = reshape = None 2025-09-09T15:05:54.1885508Z activation_post_process_1 = self.activation_post_process_1(mul); mul = None 2025-09-09T15:06:11.1932594Z zeros_like = torch.ops.aten.zeros_like.default(conv_bias, dtype = torch.float32, pin_memory = False) 2025-09-09T15:06:11.1933828Z conv1d_1 = torch.ops.aten.conv1d.default(activation_post_process_0, activation_post_process_1, zeros_like); activation_post_process_0 = activation_post_process_1 = zeros_like = None 2025-09-09T15:06:11.1934938Z reshape_1 = torch.ops.aten.reshape.default(div, [1, -1, 1]); div = None 2025-09-09T15:06:11.1935588Z div_1 = torch.ops.aten.div.Tensor(conv1d_1, reshape_1); conv1d_1 = reshape_1 = None 2025-09-09T15:06:11.1936330Z reshape_2 = torch.ops.aten.reshape.default(conv_bias, [1, -1, 1]); conv_bias = None 2025-09-09T15:06:11.1936984Z add_1 = torch.ops.aten.add.Tensor(div_1, reshape_2); div_1 = reshape_2 = None 2025-09-09T15:06:11.1938047Z batch_norm_1 = torch.ops.aten.batch_norm.default(add_1, bn_weight, bn_bias, bn_running_mean, bn_running_var, True, 0.1, 1e-05, True); add_1 = bn_weight = bn_bias = bn_running_mean = bn_running_var = None 2025-09-09T15:06:11.1939177Z activation_post_process_2 = self.activation_post_process_2(batch_norm_1); batch_norm_1 = None 2025-09-09T15:06:11.1940073Z hardtanh = torch.ops.aten.hardtanh.default(activation_post_process_2, -1.0, 1.0); activation_post_process_2 = None 2025-09-09T15:06:11.1940935Z activation_post_process_3 = self.activation_post_process_3(hardtanh); hardtanh = None 2025-09-09T15:06:11.1941621Z return pytree.tree_unflatten((activation_post_process_3,), self._out_spec) 2025-09-09T15:06:11.1942087Z 2025-09-09T15:06:11.1942411Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:06:11.1942847Z model fx: GraphModule( 2025-09-09T15:06:11.1943230Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:06:11.1944372Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0104]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:06:11.1945727Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3264806270599365, max_val=1.318617343902588) 2025-09-09T15:06:11.1946344Z ) 2025-09-09T15:06:11.1946556Z (conv): ConvBn1d( 2025-09-09T15:06:11.1946824Z 3, 3, kernel_size=(3,), stride=(1,) 2025-09-09T15:06:11.1947314Z (bn): BatchNorm1d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T15:06:11.1947880Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:06:11.1949037Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0025, 0.0024, 0.0026]), zero_point=tensor([0, 0, 0], dtype=torch.int32), dtype=torch.qint8, quant_min=-127, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False 2025-09-09T15:06:11.1950624Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.3156, -0.1982, -0.3261]), max_val=tensor([0.2827, 0.2978, 0.2359])) 2025-09-09T15:06:11.1951416Z ) 2025-09-09T15:06:11.1951611Z ) 2025-09-09T15:06:11.1951950Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:06:11.1962193Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0106]), zero_point=tensor([-5], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:06:11.1963706Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3043057918548584, max_val=1.399786114692688) 2025-09-09T15:06:11.1964339Z ) 2025-09-09T15:06:11.1964608Z (hardtanh): Hardtanh(min_val=-1.0, max_val=1.0) 2025-09-09T15:06:11.1965077Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:06:11.1966228Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0106]), zero_point=tensor([-5], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:06:11.1967572Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3043057918548584, max_val=1.399786114692688) 2025-09-09T15:06:11.1968198Z ) 2025-09-09T15:06:11.1968396Z ) 2025-09-09T15:06:11.1968521Z 2025-09-09T15:06:11.1968526Z 2025-09-09T15:06:11.1968539Z 2025-09-09T15:06:11.1968642Z def forward(self, x): 2025-09-09T15:06:11.1969074Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T15:06:11.1969728Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T15:06:11.1970396Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T15:06:11.1971141Z hardtanh = self.hardtanh(activation_post_process_1); activation_post_process_1 = None 2025-09-09T15:06:11.1971933Z activation_post_process_2 = self.activation_post_process_2(hardtanh); hardtanh = None 2025-09-09T15:06:11.1972415Z return activation_post_process_2 2025-09-09T15:06:11.1972665Z 2025-09-09T15:06:11.1972946Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:06:11.1973309Z diff: tensor([[[0., 0., 0.], 2025-09-09T15:06:11.1973543Z [0., 0., 0.], 2025-09-09T15:06:11.1973781Z [0., 0., 0.]]], grad_fn=) 2025-09-09T15:06:11.1974084Z converted model pt2e: GraphModule( 2025-09-09T15:06:11.1974332Z (conv): Module() 2025-09-09T15:06:11.1974531Z (bn): Module() 2025-09-09T15:06:11.1974720Z ) 2025-09-09T15:06:11.1974816Z 2025-09-09T15:06:11.1974820Z 2025-09-09T15:06:11.1974831Z 2025-09-09T15:06:11.1974917Z def forward(self, x): 2025-09-09T15:06:11.1975189Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T15:06:11.1975517Z conv_bias = self.conv.bias 2025-09-09T15:06:11.1975807Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T15:06:11.1976507Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.010372933000326157, 0, -128, 127, torch.int8); x = None 2025-09-09T15:06:11.1977718Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.010372933000326157, 0, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T15:06:11.1978740Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T15:06:11.1979191Z _scale_0 = self._scale_0 2025-09-09T15:06:11.1979440Z _zero_point_0 = self._zero_point_0 2025-09-09T15:06:11.1979738Z quantize_per_channel = self._frozen_param0 2025-09-09T15:06:11.1980603Z dequantize_per_channel = torch.ops.quantized_decomposed.dequantize_per_channel.default(quantize_per_channel, _scale_0, _zero_point_0, 0, -127, 127, torch.int8); quantize_per_channel = _scale_0 = _zero_point_0 = None 2025-09-09T15:06:11.1981918Z conv1d_2 = torch.ops.aten.conv1d.default(dequantize_per_tensor_default, dequantize_per_channel, conv_bias); dequantize_per_tensor_default = dequantize_per_channel = conv_bias = None 2025-09-09T15:06:11.1983102Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv1d_2, 0.010604282841086388, -5, -128, 127, torch.int8); conv1d_2 = None 2025-09-09T15:06:11.1984470Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.010604282841086388, -5, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T15:06:11.1985750Z hardtanh = torch.ops.aten.hardtanh.default(dequantize_per_tensor_default_1, -1.0, 1.0); dequantize_per_tensor_default_1 = None 2025-09-09T15:06:11.1986762Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(hardtanh, 0.010604282841086388, -5, -128, 127, torch.int8); hardtanh = None 2025-09-09T15:06:11.1988048Z dequantize_per_tensor_default_2 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_2, 0.010604282841086388, -5, -128, 127, torch.int8); quantize_per_tensor_default_2 = None 2025-09-09T15:06:11.1989041Z return pytree.tree_unflatten((dequantize_per_tensor_default_2,), self._out_spec) 2025-09-09T15:06:11.1989451Z 2025-09-09T15:06:11.1989732Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:06:11.1990110Z onverted model fx: GraphModule( 2025-09-09T15:06:11.1990486Z (conv): QuantizedConv1d(Reference)(3, 3, kernel_size=(3,), stride=(1,)) 2025-09-09T15:06:11.1990913Z (hardtanh): Hardtanh(min_val=-1.0, max_val=1.0) 2025-09-09T15:06:11.1991203Z ) 2025-09-09T15:06:11.1991302Z 2025-09-09T15:06:11.1991306Z 2025-09-09T15:06:11.1991310Z 2025-09-09T15:06:11.1991394Z def forward(self, x): 2025-09-09T15:06:11.1992003Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.010372933000326157, 0, -128, 127, torch.int8); x = None 2025-09-09T15:06:11.1993209Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.010372933000326157, 0, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T15:06:11.1994203Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T15:06:11.1995055Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.010604282841086388, -5, -128, 127, torch.int8); conv = None 2025-09-09T15:06:11.1996360Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.010604282841086388, -5, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T15:06:11.1997413Z hardtanh = self.hardtanh(dequantize_per_tensor_default_1); dequantize_per_tensor_default_1 = None 2025-09-09T15:06:11.1998323Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(hardtanh, 0.010604282841086388, -5, -128, 127, torch.int8); hardtanh = None 2025-09-09T15:06:11.1999651Z dequantize_per_tensor_default_2 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_2, 0.010604282841086388, -5, -128, 127, torch.int8); quantize_per_tensor_default_2 = None 2025-09-09T15:06:11.2000528Z return dequantize_per_tensor_default_2 2025-09-09T15:06:11.2000795Z 2025-09-09T15:06:11.2001074Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:06:11.2001442Z diff: tensor([[[0., 0., 0.], 2025-09-09T15:06:11.2001671Z [0., 0., 0.], 2025-09-09T15:06:11.2001881Z [0., 0., 0.]]]) 2025-09-09T15:06:11.2002107Z model pt2e: GraphModule( 2025-09-09T15:06:11.2002340Z (conv): Module() 2025-09-09T15:06:11.2002542Z (bn): Module() 2025-09-09T15:06:11.2002847Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:06:27.7323398Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0104]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:06:27.7324785Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3264806270599365, max_val=1.318617343902588) 2025-09-09T15:06:27.7325403Z ) 2025-09-09T15:06:27.7326068Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:06:27.7327231Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0026]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.int8, quant_min=-127, quant_max=127, qscheme=torch.per_tensor_symmetric, reduce_range=False 2025-09-09T15:06:27.7328759Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.3261372148990631, max_val=0.297783762216568) 2025-09-09T15:06:27.7329390Z ) 2025-09-09T15:06:27.7329713Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:06:27.7330853Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0106]), zero_point=tensor([-4], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:06:27.7332191Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3146827220916748, max_val=1.399786114692688) 2025-09-09T15:06:27.7332822Z ) 2025-09-09T15:06:27.7333142Z (activation_post_process_3): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:06:27.7334282Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0106]), zero_point=tensor([-4], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:06:27.7335633Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3146827220916748, max_val=1.399786114692688) 2025-09-09T15:06:27.7336248Z ) 2025-09-09T15:06:27.7336448Z ) 2025-09-09T15:06:27.7336562Z 2025-09-09T15:06:27.7336567Z 2025-09-09T15:06:27.7336571Z 2025-09-09T15:06:27.7336678Z def forward(self, x): 2025-09-09T15:06:27.7337012Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T15:06:27.7337417Z conv_weight = self.conv.weight 2025-09-09T15:06:27.7337739Z conv_bias = self.conv.bias 2025-09-09T15:06:27.7338044Z bn_weight = self.bn.weight 2025-09-09T15:06:27.7338347Z bn_bias = self.bn.bias 2025-09-09T15:06:27.7338658Z bn_running_mean = self.bn.running_mean 2025-09-09T15:06:27.7339012Z bn_running_var = self.bn.running_var 2025-09-09T15:06:27.7339413Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T15:06:27.7339960Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T15:06:27.7340694Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T15:06:27.7341327Z add = torch.ops.aten.add.Tensor(bn_running_var, 1e-05) 2025-09-09T15:06:27.7341785Z sqrt = torch.ops.aten.sqrt.default(add); add = None 2025-09-09T15:06:27.7342269Z div = torch.ops.aten.div.Tensor(bn_weight, sqrt); sqrt = None 2025-09-09T15:06:27.7342779Z reshape = torch.ops.aten.reshape.default(div, [-1, 1, 1]) 2025-09-09T15:06:27.7343375Z mul = torch.ops.aten.mul.Tensor(conv_weight, reshape); conv_weight = reshape = None 2025-09-09T15:06:27.7344072Z activation_post_process_1 = self.activation_post_process_1(mul); mul = None 2025-09-09T15:06:27.7344803Z zeros_like = torch.ops.aten.zeros_like.default(conv_bias, dtype = torch.float32, pin_memory = False) 2025-09-09T15:06:27.7345993Z conv1d_1 = torch.ops.aten.conv1d.default(activation_post_process_0, activation_post_process_1, zeros_like); activation_post_process_0 = activation_post_process_1 = zeros_like = None 2025-09-09T15:06:27.7347063Z reshape_1 = torch.ops.aten.reshape.default(div, [1, -1, 1]); div = None 2025-09-09T15:06:27.7347686Z div_1 = torch.ops.aten.div.Tensor(conv1d_1, reshape_1); conv1d_1 = reshape_1 = None 2025-09-09T15:06:27.7348367Z reshape_2 = torch.ops.aten.reshape.default(conv_bias, [1, -1, 1]); conv_bias = None 2025-09-09T15:06:27.7349019Z add_1 = torch.ops.aten.add.Tensor(div_1, reshape_2); div_1 = reshape_2 = None 2025-09-09T15:06:27.7350163Z batch_norm_1 = torch.ops.aten.batch_norm.default(add_1, bn_weight, bn_bias, bn_running_mean, bn_running_var, True, 0.1, 1e-05, True); add_1 = bn_weight = bn_bias = bn_running_mean = bn_running_var = None 2025-09-09T15:06:27.7351298Z activation_post_process_2 = self.activation_post_process_2(batch_norm_1); batch_norm_1 = None 2025-09-09T15:06:27.7352272Z hardtanh = torch.ops.aten.hardtanh.default(activation_post_process_2, -1.0, 1.0); activation_post_process_2 = None 2025-09-09T15:06:27.7353131Z activation_post_process_3 = self.activation_post_process_3(hardtanh); hardtanh = None 2025-09-09T15:06:27.7353808Z return pytree.tree_unflatten((activation_post_process_3,), self._out_spec) 2025-09-09T15:06:27.7354267Z 2025-09-09T15:06:27.7354597Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:06:27.7355023Z model fx: GraphModule( 2025-09-09T15:06:27.7355397Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:06:27.7356539Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0104]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:06:27.7357875Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3264806270599365, max_val=1.318617343902588) 2025-09-09T15:06:27.7358499Z ) 2025-09-09T15:06:27.7358701Z (conv): ConvBn1d( 2025-09-09T15:06:27.7358962Z 3, 3, kernel_size=(3,), stride=(1,) 2025-09-09T15:06:27.7359540Z (bn): BatchNorm1d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T15:06:27.7360093Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:06:27.7361199Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0026]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-127, quant_max=127, qscheme=torch.per_tensor_symmetric, reduce_range=False 2025-09-09T15:06:27.7362554Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.3261372148990631, max_val=0.297783762216568) 2025-09-09T15:06:27.7363178Z ) 2025-09-09T15:06:27.7363375Z ) 2025-09-09T15:06:27.7363700Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:06:27.7364838Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0106]), zero_point=tensor([-4], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:06:27.7366178Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3146827220916748, max_val=1.399786114692688) 2025-09-09T15:06:27.7366786Z ) 2025-09-09T15:06:27.7367044Z (hardtanh): Hardtanh(min_val=-1.0, max_val=1.0) 2025-09-09T15:06:27.7367513Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:06:27.7368642Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0106]), zero_point=tensor([-4], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:06:27.7369983Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3146827220916748, max_val=1.399786114692688) 2025-09-09T15:06:27.7370593Z ) 2025-09-09T15:06:27.7370790Z ) 2025-09-09T15:06:27.7370900Z 2025-09-09T15:06:27.7370905Z 2025-09-09T15:06:27.7370910Z 2025-09-09T15:06:27.7371015Z def forward(self, x): 2025-09-09T15:06:27.7371421Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T15:06:27.7372054Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T15:06:27.7372699Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T15:06:27.7373394Z hardtanh = self.hardtanh(activation_post_process_1); activation_post_process_1 = None 2025-09-09T15:06:27.7374121Z activation_post_process_2 = self.activation_post_process_2(hardtanh); hardtanh = None 2025-09-09T15:06:27.7374660Z return activation_post_process_2 2025-09-09T15:06:27.7375109Z 2025-09-09T15:06:27.7375428Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:06:27.7375861Z diff: tensor([[[0., 0., 0.], 2025-09-09T15:06:27.7376215Z [0., 0., 0.], 2025-09-09T15:06:27.7376495Z [0., 0., 0.]]], grad_fn=) 2025-09-09T15:06:27.7376848Z converted model pt2e: GraphModule( 2025-09-09T15:06:27.7377178Z (conv): Module() 2025-09-09T15:06:27.7377369Z (bn): Module() 2025-09-09T15:06:27.7377554Z ) 2025-09-09T15:06:27.7377649Z 2025-09-09T15:06:27.7377653Z 2025-09-09T15:06:27.7377657Z 2025-09-09T15:06:27.7377750Z def forward(self, x): 2025-09-09T15:06:27.7378024Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T15:06:27.7378352Z conv_bias = self.conv.bias 2025-09-09T15:06:27.7378641Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T15:06:27.7379347Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.010372933000326157, 0, -128, 127, torch.int8); x = None 2025-09-09T15:06:27.7380601Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.010372933000326157, 0, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T15:06:27.7381622Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T15:06:27.7382105Z quantize_per_tensor = self._frozen_param0 2025-09-09T15:06:27.7382875Z dequantize_per_tensor = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor, 0.002568009542301297, 0, -127, 127, torch.int8); quantize_per_tensor = None 2025-09-09T15:06:27.7384110Z conv1d_2 = torch.ops.aten.conv1d.default(dequantize_per_tensor_default, dequantize_per_tensor, conv_bias); dequantize_per_tensor_default = dequantize_per_tensor = conv_bias = None 2025-09-09T15:06:27.7385290Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv1d_2, 0.010644976049661636, -4, -128, 127, torch.int8); conv1d_2 = None 2025-09-09T15:06:27.7386550Z dequantize_per_tensor_default_2 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_2, 0.010644976049661636, -4, -128, 127, torch.int8); quantize_per_tensor_default_2 = None 2025-09-09T15:06:27.7387706Z hardtanh = torch.ops.aten.hardtanh.default(dequantize_per_tensor_default_2, -1.0, 1.0); dequantize_per_tensor_default_2 = None 2025-09-09T15:07:27.9014039Z quantize_per_tensor_default_3 = torch.ops.quantized_decomposed.quantize_per_tensor.default(hardtanh, 0.010644976049661636, -4, -128, 127, torch.int8); hardtanh = None 2025-09-09T15:07:27.9015698Z dequantize_per_tensor_default_3 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_3, 0.010644976049661636, -4, -128, 127, torch.int8); quantize_per_tensor_default_3 = None 2025-09-09T15:07:27.9016985Z return pytree.tree_unflatten((dequantize_per_tensor_default_3,), self._out_spec) 2025-09-09T15:07:27.9017489Z 2025-09-09T15:07:27.9017816Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:07:27.9018295Z onverted model fx: GraphModule( 2025-09-09T15:07:27.9018737Z (conv): QuantizedConv1d(Reference)(3, 3, kernel_size=(3,), stride=(1,)) 2025-09-09T15:07:27.9019245Z (hardtanh): Hardtanh(min_val=-1.0, max_val=1.0) 2025-09-09T15:07:27.9019587Z ) 2025-09-09T15:07:27.9019712Z 2025-09-09T15:07:27.9019717Z 2025-09-09T15:07:27.9019722Z 2025-09-09T15:07:27.9019819Z def forward(self, x): 2025-09-09T15:07:27.9020575Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.010372933000326157, 0, -128, 127, torch.int8); x = None 2025-09-09T15:07:27.9022311Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.010372933000326157, 0, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T15:07:27.9023990Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T15:07:27.9025041Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.010644976049661636, -4, -128, 127, torch.int8); conv = None 2025-09-09T15:07:27.9026827Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.010644976049661636, -4, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T15:07:27.9028154Z hardtanh = self.hardtanh(dequantize_per_tensor_default_1); dequantize_per_tensor_default_1 = None 2025-09-09T15:07:27.9029291Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(hardtanh, 0.010644976049661636, -4, -128, 127, torch.int8); hardtanh = None 2025-09-09T15:07:27.9030921Z dequantize_per_tensor_default_2 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_2, 0.010644976049661636, -4, -128, 127, torch.int8); quantize_per_tensor_default_2 = None 2025-09-09T15:07:27.9032015Z return dequantize_per_tensor_default_2 2025-09-09T15:07:27.9032343Z 2025-09-09T15:07:27.9032680Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:07:27.9033121Z diff: tensor([[[0., 0., 0.], 2025-09-09T15:07:27.9033402Z [0., 0., 0.], 2025-09-09T15:07:27.9033641Z [0., 0., 0.]]]) 2025-09-09T15:07:27.9034134Z PASSED 2025-09-09T15:07:27.9034915Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn2d::test_fold_bn_erases_bn_node PASSED 2025-09-09T15:07:27.9036208Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_conv_bn_bias_derived_qspec PASSED 2025-09-09T15:07:27.9037381Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_conv_bn_fusion model pt2e: GraphModule( 2025-09-09T15:07:27.9038108Z (conv): Module() 2025-09-09T15:07:27.9038352Z (bn): Module() 2025-09-09T15:07:27.9038702Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:07:27.9039961Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0183]), zero_point=tensor([10], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:07:27.9041330Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.526270866394043, max_val=2.143237352371216) 2025-09-09T15:07:27.9041942Z ) 2025-09-09T15:07:27.9042271Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:07:27.9043474Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0015, 0.0015, 0.0015]), zero_point=tensor([0, 0, 0], dtype=torch.int32), dtype=torch.int8, quant_min=-127, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False 2025-09-09T15:07:27.9045073Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.1894, -0.1850, -0.1857]), max_val=tensor([0.1520, 0.1622, 0.1895])) 2025-09-09T15:07:27.9045866Z ) 2025-09-09T15:07:27.9046191Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:07:27.9047338Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0161]), zero_point=tensor([-4], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:07:27.9048682Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.9920659065246582, max_val=2.112386465072632) 2025-09-09T15:07:27.9049304Z ) 2025-09-09T15:07:27.9049502Z ) 2025-09-09T15:07:27.9049614Z 2025-09-09T15:07:27.9049619Z 2025-09-09T15:07:27.9049624Z 2025-09-09T15:07:27.9049721Z def forward(self, x): 2025-09-09T15:07:27.9050060Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T15:07:27.9050460Z conv_weight = self.conv.weight 2025-09-09T15:07:27.9050893Z conv_bias = self.conv.bias 2025-09-09T15:07:27.9051192Z bn_weight = self.bn.weight 2025-09-09T15:07:27.9051485Z bn_bias = self.bn.bias 2025-09-09T15:07:27.9053742Z bn_running_mean = self.bn.running_mean 2025-09-09T15:07:27.9054091Z bn_running_var = self.bn.running_var 2025-09-09T15:07:27.9054496Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T15:07:27.9055022Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T15:07:27.9055758Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T15:07:27.9056391Z add = torch.ops.aten.add.Tensor(bn_running_var, 1e-05) 2025-09-09T15:07:27.9056853Z sqrt = torch.ops.aten.sqrt.default(add); add = None 2025-09-09T15:07:27.9057339Z div = torch.ops.aten.div.Tensor(bn_weight, sqrt); sqrt = None 2025-09-09T15:07:27.9057862Z reshape = torch.ops.aten.reshape.default(div, [-1, 1, 1, 1]) 2025-09-09T15:07:27.9058481Z mul = torch.ops.aten.mul.Tensor(conv_weight, reshape); conv_weight = reshape = None 2025-09-09T15:07:27.9059153Z activation_post_process_1 = self.activation_post_process_1(mul); mul = None 2025-09-09T15:07:27.9059908Z zeros_like = torch.ops.aten.zeros_like.default(conv_bias, dtype = torch.float32, pin_memory = False) 2025-09-09T15:07:27.9061108Z conv2d_1 = torch.ops.aten.conv2d.default(activation_post_process_0, activation_post_process_1, zeros_like); activation_post_process_0 = activation_post_process_1 = zeros_like = None 2025-09-09T15:07:27.9062195Z reshape_1 = torch.ops.aten.reshape.default(div, [1, -1, 1, 1]); div = None 2025-09-09T15:07:27.9062858Z div_1 = torch.ops.aten.div.Tensor(conv2d_1, reshape_1); conv2d_1 = reshape_1 = None 2025-09-09T15:07:27.9063551Z reshape_2 = torch.ops.aten.reshape.default(conv_bias, [1, -1, 1, 1]); conv_bias = None 2025-09-09T15:07:27.9064224Z add_1 = torch.ops.aten.add.Tensor(div_1, reshape_2); div_1 = reshape_2 = None 2025-09-09T15:07:27.9065279Z batch_norm_1 = torch.ops.aten.batch_norm.default(add_1, bn_weight, bn_bias, bn_running_mean, bn_running_var, True, 0.1, 1e-05, True); add_1 = bn_weight = bn_bias = bn_running_mean = bn_running_var = None 2025-09-09T15:07:27.9066423Z activation_post_process_2 = self.activation_post_process_2(batch_norm_1); batch_norm_1 = None 2025-09-09T15:07:27.9067149Z return pytree.tree_unflatten((activation_post_process_2,), self._out_spec) 2025-09-09T15:07:27.9067607Z 2025-09-09T15:07:27.9067938Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:07:27.9068375Z model fx: GraphModule( 2025-09-09T15:07:27.9068746Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:07:27.9069925Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0183]), zero_point=tensor([10], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:07:27.9071270Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.526270866394043, max_val=2.143237352371216) 2025-09-09T15:07:27.9071783Z ) 2025-09-09T15:07:27.9071963Z (conv): ConvBn2d( 2025-09-09T15:07:27.9072194Z 3, 3, kernel_size=(3, 3), stride=(1, 1) 2025-09-09T15:07:27.9072615Z (bn): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T15:07:27.9073073Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:07:27.9074036Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0015, 0.0015, 0.0015]), zero_point=tensor([0, 0, 0], dtype=torch.int32), dtype=torch.qint8, quant_min=-127, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False 2025-09-09T15:07:27.9075329Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.1894, -0.1850, -0.1857]), max_val=tensor([0.1520, 0.1622, 0.1895])) 2025-09-09T15:07:27.9075975Z ) 2025-09-09T15:07:27.9076158Z ) 2025-09-09T15:07:27.9076536Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:07:27.9077496Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0161]), zero_point=tensor([-4], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:07:27.9078721Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.9920659065246582, max_val=2.112386465072632) 2025-09-09T15:07:27.9079383Z ) 2025-09-09T15:07:27.9079551Z ) 2025-09-09T15:07:27.9079655Z 2025-09-09T15:07:27.9079659Z 2025-09-09T15:07:27.9079663Z 2025-09-09T15:07:27.9079746Z def forward(self, x): 2025-09-09T15:07:27.9080104Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T15:07:27.9080634Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T15:07:27.9081182Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T15:07:27.9081611Z return activation_post_process_1 2025-09-09T15:07:27.9081877Z 2025-09-09T15:07:27.9082159Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:07:27.9082525Z diff: tensor([[[[0., 0., 0.], 2025-09-09T15:07:27.9082764Z [0., 0., 0.], 2025-09-09T15:07:27.9082971Z [0., 0., 0.]], 2025-09-09T15:07:27.9083110Z 2025-09-09T15:07:27.9083198Z [[0., 0., 0.], 2025-09-09T15:07:27.9083398Z [0., 0., 0.], 2025-09-09T15:07:27.9083605Z [0., 0., 0.]], 2025-09-09T15:07:27.9083740Z 2025-09-09T15:07:27.9083813Z [[0., 0., 0.], 2025-09-09T15:07:27.9084017Z [0., 0., 0.], 2025-09-09T15:07:27.9084250Z [0., 0., 0.]]]], grad_fn=) 2025-09-09T15:07:30.7098994Z converted model pt2e: GraphModule( 2025-09-09T15:07:30.7099351Z (conv): Module() 2025-09-09T15:07:30.7099597Z (bn): Module() 2025-09-09T15:07:30.7099886Z ) 2025-09-09T15:07:30.7100005Z 2025-09-09T15:07:30.7100035Z 2025-09-09T15:07:30.7100050Z 2025-09-09T15:07:30.7100164Z def forward(self, x): 2025-09-09T15:07:30.7100507Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T15:07:30.7100919Z conv_bias = self.conv.bias 2025-09-09T15:07:30.7101699Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.018311796709895134, 10, -128, 127, torch.int8); x = None 2025-09-09T15:07:30.7103239Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.018311796709895134, 10, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T15:07:30.7104287Z _scale_0 = self._scale_0 2025-09-09T15:07:30.7104589Z _zero_point_0 = self._zero_point_0 2025-09-09T15:07:30.7104953Z quantize_per_channel = self._frozen_param0 2025-09-09T15:07:30.7106049Z dequantize_per_channel = torch.ops.quantized_decomposed.dequantize_per_channel.default(quantize_per_channel, _scale_0, _zero_point_0, 0, -127, 127, torch.int8); quantize_per_channel = _scale_0 = _zero_point_0 = None 2025-09-09T15:07:30.7107734Z conv2d_2 = torch.ops.aten.conv2d.default(dequantize_per_tensor_default, dequantize_per_channel, conv_bias); dequantize_per_tensor_default = dequantize_per_channel = conv_bias = None 2025-09-09T15:07:30.7109223Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv2d_2, 0.01609589159488678, -4, -128, 127, torch.int8); conv2d_2 = None 2025-09-09T15:07:30.7110815Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.01609589159488678, -4, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T15:07:30.7112065Z return pytree.tree_unflatten((dequantize_per_tensor_default_1,), self._out_spec) 2025-09-09T15:07:30.7112563Z 2025-09-09T15:07:30.7112893Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:07:30.7113556Z onverted model fx: GraphModule( 2025-09-09T15:07:30.7114022Z (conv): QuantizedConv2d(Reference)(3, 3, kernel_size=(3, 3), stride=(1, 1)) 2025-09-09T15:07:30.7114486Z ) 2025-09-09T15:07:30.7114745Z 2025-09-09T15:07:30.7114750Z 2025-09-09T15:07:30.7114755Z 2025-09-09T15:07:30.7114853Z def forward(self, x): 2025-09-09T15:07:30.7115617Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.018311796709895134, 10, -128, 127, torch.int8); x = None 2025-09-09T15:07:30.7117167Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.018311796709895134, 10, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T15:07:30.7118411Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T15:07:30.7119582Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.01609589159488678, -4, -128, 127, torch.int8); conv = None 2025-09-09T15:07:30.7121156Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.01609589159488678, -4, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T15:07:30.7122441Z return dequantize_per_tensor_default_1 2025-09-09T15:07:30.7122767Z 2025-09-09T15:07:30.7123092Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:07:30.7123533Z diff: tensor([[[[0., 0., 0.], 2025-09-09T15:07:30.7123810Z [0., 0., 0.], 2025-09-09T15:07:30.7124060Z [0., 0., 0.]], 2025-09-09T15:07:30.7124224Z 2025-09-09T15:07:30.7124312Z [[0., 0., 0.], 2025-09-09T15:07:30.7124558Z [0., 0., 0.], 2025-09-09T15:07:30.7124800Z [0., 0., 0.]], 2025-09-09T15:07:30.7124964Z 2025-09-09T15:07:30.7125051Z [[0., 0., 0.], 2025-09-09T15:07:30.7125296Z [0., 0., 0.], 2025-09-09T15:07:30.7125559Z [0., 0., 0.]]]]) 2025-09-09T15:07:30.7125861Z model pt2e: GraphModule( 2025-09-09T15:07:30.7126147Z (conv): Module() 2025-09-09T15:07:30.7126394Z (bn): Module() 2025-09-09T15:07:30.7126732Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:07:30.7127668Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0183]), zero_point=tensor([10], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:07:30.7128773Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.526270866394043, max_val=2.143237352371216) 2025-09-09T15:07:30.7129269Z ) 2025-09-09T15:07:30.7129546Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:07:30.7130485Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0015]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.int8, quant_min=-127, quant_max=127, qscheme=torch.per_tensor_symmetric, reduce_range=False 2025-09-09T15:07:30.7131613Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.18937721848487854, max_val=0.18946029245853424) 2025-09-09T15:07:30.7132131Z ) 2025-09-09T15:07:30.7132398Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:07:30.7133344Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0161]), zero_point=tensor([-4], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:07:30.7134438Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.9922423362731934, max_val=2.1162424087524414) 2025-09-09T15:07:30.7134942Z ) 2025-09-09T15:07:30.7135104Z ) 2025-09-09T15:07:30.7135197Z 2025-09-09T15:07:30.7135201Z 2025-09-09T15:07:30.7135205Z 2025-09-09T15:07:30.7135291Z def forward(self, x): 2025-09-09T15:07:30.7135568Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T15:07:30.7136049Z conv_weight = self.conv.weight 2025-09-09T15:07:30.7136323Z conv_bias = self.conv.bias 2025-09-09T15:07:30.7136579Z bn_weight = self.bn.weight 2025-09-09T15:07:30.7136925Z bn_bias = self.bn.bias 2025-09-09T15:07:30.7137184Z bn_running_mean = self.bn.running_mean 2025-09-09T15:07:30.7137478Z bn_running_var = self.bn.running_var 2025-09-09T15:07:30.7137813Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T15:07:30.7138255Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T15:07:30.7138841Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T15:07:30.7139373Z add = torch.ops.aten.add.Tensor(bn_running_var, 1e-05) 2025-09-09T15:07:30.7139761Z sqrt = torch.ops.aten.sqrt.default(add); add = None 2025-09-09T15:07:30.7140171Z div = torch.ops.aten.div.Tensor(bn_weight, sqrt); sqrt = None 2025-09-09T15:07:30.7140615Z reshape = torch.ops.aten.reshape.default(div, [-1, 1, 1, 1]) 2025-09-09T15:07:30.7141122Z mul = torch.ops.aten.mul.Tensor(conv_weight, reshape); conv_weight = reshape = None 2025-09-09T15:07:30.7141687Z activation_post_process_1 = self.activation_post_process_1(mul); mul = None 2025-09-09T15:07:30.7142305Z zeros_like = torch.ops.aten.zeros_like.default(conv_bias, dtype = torch.float32, pin_memory = False) 2025-09-09T15:07:30.7152096Z conv2d_1 = torch.ops.aten.conv2d.default(activation_post_process_0, activation_post_process_1, zeros_like); activation_post_process_0 = activation_post_process_1 = zeros_like = None 2025-09-09T15:07:30.7153167Z reshape_1 = torch.ops.aten.reshape.default(div, [1, -1, 1, 1]); div = None 2025-09-09T15:07:30.7153777Z div_1 = torch.ops.aten.div.Tensor(conv2d_1, reshape_1); conv2d_1 = reshape_1 = None 2025-09-09T15:07:30.7154378Z reshape_2 = torch.ops.aten.reshape.default(conv_bias, [1, -1, 1, 1]); conv_bias = None 2025-09-09T15:07:30.7154957Z add_1 = torch.ops.aten.add.Tensor(div_1, reshape_2); div_1 = reshape_2 = None 2025-09-09T15:07:30.7155843Z batch_norm_1 = torch.ops.aten.batch_norm.default(add_1, bn_weight, bn_bias, bn_running_mean, bn_running_var, True, 0.1, 1e-05, True); add_1 = bn_weight = bn_bias = bn_running_mean = bn_running_var = None 2025-09-09T15:07:30.7156778Z activation_post_process_2 = self.activation_post_process_2(batch_norm_1); batch_norm_1 = None 2025-09-09T15:07:30.7157392Z return pytree.tree_unflatten((activation_post_process_2,), self._out_spec) 2025-09-09T15:07:30.7157794Z 2025-09-09T15:07:30.7158081Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:07:30.7158455Z model fx: GraphModule( 2025-09-09T15:07:30.7158784Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:07:30.7159809Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0183]), zero_point=tensor([10], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:07:30.7160919Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.526270866394043, max_val=2.143237352371216) 2025-09-09T15:07:30.7161441Z ) 2025-09-09T15:07:30.7161637Z (conv): ConvBn2d( 2025-09-09T15:07:30.7161878Z 3, 3, kernel_size=(3, 3), stride=(1, 1) 2025-09-09T15:07:30.7162309Z (bn): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T15:07:30.7162783Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:07:30.7163716Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0015]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-127, quant_max=127, qscheme=torch.per_tensor_symmetric, reduce_range=False 2025-09-09T15:07:30.7164860Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.18937721848487854, max_val=0.18946029245853424) 2025-09-09T15:07:30.7165388Z ) 2025-09-09T15:07:30.7165572Z ) 2025-09-09T15:07:30.7165965Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:07:30.7166911Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0161]), zero_point=tensor([-4], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:07:30.7168082Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.9922423362731934, max_val=2.1162424087524414) 2025-09-09T15:07:30.7168590Z ) 2025-09-09T15:07:30.7168750Z ) 2025-09-09T15:07:30.7168852Z 2025-09-09T15:07:30.7168856Z 2025-09-09T15:07:30.7168860Z 2025-09-09T15:07:30.7168943Z def forward(self, x): 2025-09-09T15:07:30.7169298Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T15:07:30.7169824Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T15:07:30.7170367Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T15:07:30.7170792Z return activation_post_process_1 2025-09-09T15:07:30.7171054Z 2025-09-09T15:07:30.7171324Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:07:30.7171693Z diff: tensor([[[[0., 0., 0.], 2025-09-09T15:07:30.7171927Z [0., 0., 0.], 2025-09-09T15:07:56.0404853Z [0., 0., 0.]], 2025-09-09T15:07:56.0405043Z 2025-09-09T15:07:56.0405132Z [[0., 0., 0.], 2025-09-09T15:07:56.0405345Z [0., 0., 0.], 2025-09-09T15:07:56.0405568Z [0., 0., 0.]], 2025-09-09T15:07:56.0405708Z 2025-09-09T15:07:56.0405788Z [[0., 0., 0.], 2025-09-09T15:07:56.0406001Z [0., 0., 0.], 2025-09-09T15:07:56.0406249Z [0., 0., 0.]]]], grad_fn=) 2025-09-09T15:07:56.0406676Z converted model pt2e: GraphModule( 2025-09-09T15:07:56.0407014Z (conv): Module() 2025-09-09T15:07:56.0407268Z (bn): Module() 2025-09-09T15:07:56.0407520Z ) 2025-09-09T15:07:56.0407627Z 2025-09-09T15:07:56.0407631Z 2025-09-09T15:07:56.0407659Z 2025-09-09T15:07:56.0407745Z def forward(self, x): 2025-09-09T15:07:56.0408040Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T15:07:56.0408386Z conv_bias = self.conv.bias 2025-09-09T15:07:56.0409035Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.018311796709895134, 10, -128, 127, torch.int8); x = None 2025-09-09T15:07:56.0410253Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.018311796709895134, 10, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T15:07:56.0411127Z quantize_per_tensor = self._frozen_param0 2025-09-09T15:07:56.0411921Z dequantize_per_tensor = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor, 0.0014918133383616805, 0, -127, 127, torch.int8); quantize_per_tensor = None 2025-09-09T15:07:56.0413179Z conv2d_2 = torch.ops.aten.conv2d.default(dequantize_per_tensor_default, dequantize_per_tensor, conv_bias); dequantize_per_tensor_default = dequantize_per_tensor = conv_bias = None 2025-09-09T15:07:56.0414363Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv2d_2, 0.01611170545220375, -4, -128, 127, torch.int8); conv2d_2 = None 2025-09-09T15:07:56.0415681Z dequantize_per_tensor_default_2 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_2, 0.01611170545220375, -4, -128, 127, torch.int8); quantize_per_tensor_default_2 = None 2025-09-09T15:07:56.0416682Z return pytree.tree_unflatten((dequantize_per_tensor_default_2,), self._out_spec) 2025-09-09T15:07:56.0417083Z 2025-09-09T15:07:56.0417367Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:07:56.0417734Z onverted model fx: GraphModule( 2025-09-09T15:07:56.0418116Z (conv): QuantizedConv2d(Reference)(3, 3, kernel_size=(3, 3), stride=(1, 1)) 2025-09-09T15:07:56.0418498Z ) 2025-09-09T15:07:56.0418867Z 2025-09-09T15:07:56.0418872Z 2025-09-09T15:07:56.0418876Z 2025-09-09T15:07:56.0418961Z def forward(self, x): 2025-09-09T15:07:56.0419580Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.018311796709895134, 10, -128, 127, torch.int8); x = None 2025-09-09T15:07:56.0420974Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.018311796709895134, 10, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T15:07:56.0421980Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T15:07:56.0423107Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.01611170545220375, -4, -128, 127, torch.int8); conv = None 2025-09-09T15:07:56.0424375Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.01611170545220375, -4, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T15:07:56.0425306Z return dequantize_per_tensor_default_1 2025-09-09T15:07:56.0425592Z 2025-09-09T15:07:56.0425870Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:07:56.0426239Z diff: tensor([[[[0., 0., 0.], 2025-09-09T15:07:56.0426472Z [0., 0., 0.], 2025-09-09T15:07:56.0426680Z [0., 0., 0.]], 2025-09-09T15:07:56.0426818Z 2025-09-09T15:07:56.0426892Z [[0., 0., 0.], 2025-09-09T15:07:56.0427097Z [0., 0., 0.], 2025-09-09T15:07:56.0427292Z [0., 0., 0.]], 2025-09-09T15:07:56.0427434Z 2025-09-09T15:07:56.0427505Z [[0., 0., 0.], 2025-09-09T15:07:56.0427713Z [0., 0., 0.], 2025-09-09T15:07:56.0427913Z [0., 0., 0.]]]]) 2025-09-09T15:07:56.0428342Z PASSED 2025-09-09T15:07:56.0428927Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_conv_bn_fusion_cuda model pt2e: GraphModule( 2025-09-09T15:07:56.0429546Z (conv): Module() 2025-09-09T15:07:56.0429742Z (bn): Module() 2025-09-09T15:07:56.0430043Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:07:56.0431166Z fake_quant_enabled=tensor([1], device='cuda:0'), observer_enabled=tensor([1], device='cuda:0'), scale=tensor([0.0183], device='cuda:0'), zero_point=tensor([10], device='cuda:0', dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:07:56.0432435Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.526270866394043, max_val=2.143237352371216) 2025-09-09T15:07:56.0432940Z ) 2025-09-09T15:07:56.0433211Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:07:56.0434370Z fake_quant_enabled=tensor([1], device='cuda:0'), observer_enabled=tensor([1], device='cuda:0'), scale=tensor([0.0015, 0.0015, 0.0014], device='cuda:0'), zero_point=tensor([0, 0, 0], device='cuda:0', dtype=torch.int32), dtype=torch.int8, quant_min=-127, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False 2025-09-09T15:07:56.0435907Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.1860, -0.1897, -0.1787], device='cuda:0'), max_val=tensor([0.1824, 0.1870, 0.1478], device='cuda:0')) 2025-09-09T15:07:56.0436634Z ) 2025-09-09T15:07:56.0436913Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:07:56.0438004Z fake_quant_enabled=tensor([1], device='cuda:0'), observer_enabled=tensor([1], device='cuda:0'), scale=tensor([0.0165], device='cuda:0'), zero_point=tensor([2], device='cuda:0', dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:07:56.0439333Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.1394810676574707, max_val=2.0564441680908203) 2025-09-09T15:07:56.0439858Z ) 2025-09-09T15:07:56.0440173Z ) 2025-09-09T15:07:56.0440280Z 2025-09-09T15:07:56.0440284Z 2025-09-09T15:07:56.0440288Z 2025-09-09T15:07:56.0440375Z def forward(self, x): 2025-09-09T15:07:56.0440762Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T15:07:56.0441100Z conv_weight = self.conv.weight 2025-09-09T15:07:56.0441375Z conv_bias = self.conv.bias 2025-09-09T15:07:56.0441626Z bn_weight = self.bn.weight 2025-09-09T15:07:56.0441878Z bn_bias = self.bn.bias 2025-09-09T15:07:56.0442130Z bn_running_mean = self.bn.running_mean 2025-09-09T15:07:56.0442429Z bn_running_var = self.bn.running_var 2025-09-09T15:07:56.0442752Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T15:07:56.0443196Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T15:07:56.0443780Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T15:07:56.0444310Z add = torch.ops.aten.add.Tensor(bn_running_var, 1e-05) 2025-09-09T15:07:56.0444700Z sqrt = torch.ops.aten.sqrt.default(add); add = None 2025-09-09T15:07:56.0445101Z div = torch.ops.aten.div.Tensor(bn_weight, sqrt); sqrt = None 2025-09-09T15:07:56.0445547Z reshape = torch.ops.aten.reshape.default(div, [-1, 1, 1, 1]) 2025-09-09T15:07:56.0446047Z mul = torch.ops.aten.mul.Tensor(conv_weight, reshape); conv_weight = reshape = None 2025-09-09T15:07:56.0446606Z activation_post_process_1 = self.activation_post_process_1(mul); mul = None 2025-09-09T15:07:56.0447214Z zeros_like = torch.ops.aten.zeros_like.default(conv_bias, dtype = torch.float32, pin_memory = False) 2025-09-09T15:07:56.0448179Z conv2d_1 = torch.ops.aten.conv2d.default(activation_post_process_0, activation_post_process_1, zeros_like); activation_post_process_0 = activation_post_process_1 = zeros_like = None 2025-09-09T15:07:56.0449067Z reshape_1 = torch.ops.aten.reshape.default(div, [1, -1, 1, 1]); div = None 2025-09-09T15:07:56.0449611Z div_1 = torch.ops.aten.div.Tensor(conv2d_1, reshape_1); conv2d_1 = reshape_1 = None 2025-09-09T15:07:56.0450196Z reshape_2 = torch.ops.aten.reshape.default(conv_bias, [1, -1, 1, 1]); conv_bias = None 2025-09-09T15:07:56.0450759Z add_1 = torch.ops.aten.add.Tensor(div_1, reshape_2); div_1 = reshape_2 = None 2025-09-09T15:07:56.0451624Z batch_norm_1 = torch.ops.aten.batch_norm.default(add_1, bn_weight, bn_bias, bn_running_mean, bn_running_var, True, 0.1, 1e-05, True); add_1 = bn_weight = bn_bias = bn_running_mean = bn_running_var = None 2025-09-09T15:07:56.0452548Z activation_post_process_2 = self.activation_post_process_2(batch_norm_1); batch_norm_1 = None 2025-09-09T15:07:56.0453140Z return pytree.tree_unflatten((activation_post_process_2,), self._out_spec) 2025-09-09T15:07:56.0453529Z 2025-09-09T15:07:56.0453805Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:07:56.0454164Z model fx: GraphModule( 2025-09-09T15:07:56.0454485Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:07:56.0455593Z fake_quant_enabled=tensor([1], device='cuda:0'), observer_enabled=tensor([1], device='cuda:0'), scale=tensor([0.0183], device='cuda:0'), zero_point=tensor([10], device='cuda:0', dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:07:56.0456856Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.526270866394043, max_val=2.143237352371216) 2025-09-09T15:07:56.0457348Z ) 2025-09-09T15:07:56.0457525Z (conv): ConvBn2d( 2025-09-09T15:07:56.0457749Z 3, 3, kernel_size=(3, 3), stride=(1, 1) 2025-09-09T15:07:56.0458166Z (bn): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T15:07:56.0458631Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:07:56.0459838Z fake_quant_enabled=tensor([1], device='cuda:0'), observer_enabled=tensor([1], device='cuda:0'), scale=tensor([0.0015, 0.0015, 0.0014], device='cuda:0'), zero_point=tensor([0, 0, 0], device='cuda:0', dtype=torch.int32), dtype=torch.qint8, quant_min=-127, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False 2025-09-09T15:07:56.0461458Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.1860, -0.1897, -0.1787], device='cuda:0'), max_val=tensor([0.1824, 0.1870, 0.1478], device='cuda:0')) 2025-09-09T15:07:56.0462189Z ) 2025-09-09T15:07:56.0462356Z ) 2025-09-09T15:07:56.0462634Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:08:13.3141620Z fake_quant_enabled=tensor([1], device='cuda:0'), observer_enabled=tensor([1], device='cuda:0'), scale=tensor([0.0165], device='cuda:0'), zero_point=tensor([2], device='cuda:0', dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:08:13.3144044Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.1394810676574707, max_val=2.0564441680908203) 2025-09-09T15:08:13.3145076Z ) 2025-09-09T15:08:13.3145484Z ) 2025-09-09T15:08:13.3145711Z 2025-09-09T15:08:13.3145722Z 2025-09-09T15:08:13.3145746Z 2025-09-09T15:08:13.3145965Z def forward(self, x): 2025-09-09T15:08:13.3146706Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T15:08:13.3147661Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T15:08:13.3148622Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T15:08:13.3149464Z return activation_post_process_1 2025-09-09T15:08:13.3150010Z 2025-09-09T15:08:13.3150588Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:08:13.3151039Z diff: tensor([[[[0., 0., 0.], 2025-09-09T15:08:13.3151288Z [0., 0., 0.], 2025-09-09T15:08:13.3151506Z [0., 0., 0.]], 2025-09-09T15:08:13.3151643Z 2025-09-09T15:08:13.3151720Z [[0., 0., 0.], 2025-09-09T15:08:13.3151938Z [0., 0., 0.], 2025-09-09T15:08:13.3152134Z [0., 0., 0.]], 2025-09-09T15:08:13.3152268Z 2025-09-09T15:08:13.3152349Z [[0., 0., 0.], 2025-09-09T15:08:13.3152552Z [0., 0., 0.], 2025-09-09T15:08:13.3152813Z [0., 0., 0.]]]], device='cuda:0', grad_fn=) 2025-09-09T15:08:13.3153137Z converted model pt2e: GraphModule( 2025-09-09T15:08:13.3153395Z (conv): Module() 2025-09-09T15:08:13.3153597Z (bn): Module() 2025-09-09T15:08:13.3153784Z ) 2025-09-09T15:08:13.3153874Z 2025-09-09T15:08:13.3153878Z 2025-09-09T15:08:13.3153883Z 2025-09-09T15:08:13.3153967Z def forward(self, x): 2025-09-09T15:08:13.3154243Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T15:08:13.3154570Z conv_bias = self.conv.bias 2025-09-09T15:08:13.3155197Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.018311796709895134, 10, -128, 127, torch.int8); x = None 2025-09-09T15:08:13.3156420Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.018311796709895134, 10, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T15:08:13.3157262Z _scale_0 = self._scale_0 2025-09-09T15:08:13.3157507Z _zero_point_0 = self._zero_point_0 2025-09-09T15:08:13.3157823Z quantize_per_channel = self._frozen_param0 2025-09-09T15:08:13.3158690Z dequantize_per_channel = torch.ops.quantized_decomposed.dequantize_per_channel.default(quantize_per_channel, _scale_0, _zero_point_0, 0, -127, 127, torch.int8); quantize_per_channel = _scale_0 = _zero_point_0 = None 2025-09-09T15:08:13.3160107Z conv2d_2 = torch.ops.aten.conv2d.default(dequantize_per_tensor_default, dequantize_per_channel, conv_bias); dequantize_per_tensor_default = dequantize_per_channel = conv_bias = None 2025-09-09T15:08:13.3161592Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv2d_2, 0.016454609110951424, 2, -128, 127, torch.int8); conv2d_2 = None 2025-09-09T15:08:13.3162862Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.016454609110951424, 2, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T15:08:13.3164031Z return pytree.tree_unflatten((dequantize_per_tensor_default_1,), self._out_spec) 2025-09-09T15:08:13.3164438Z 2025-09-09T15:08:13.3164714Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:08:13.3165084Z onverted model fx: GraphModule( 2025-09-09T15:08:13.3165465Z (conv): QuantizedConv2d(Reference)(3, 3, kernel_size=(3, 3), stride=(1, 1)) 2025-09-09T15:08:13.3165832Z ) 2025-09-09T15:08:13.3165928Z 2025-09-09T15:08:13.3165932Z 2025-09-09T15:08:13.3165936Z 2025-09-09T15:08:13.3166024Z def forward(self, x): 2025-09-09T15:08:13.3166632Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.018311796709895134, 10, -128, 127, torch.int8); x = None 2025-09-09T15:08:13.3167845Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.018311796709895134, 10, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T15:08:13.3168838Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T15:08:13.3169665Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.016454609110951424, 2, -128, 127, torch.int8); conv = None 2025-09-09T15:08:13.3170910Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.016454609110951424, 2, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T15:08:13.3171765Z return dequantize_per_tensor_default_1 2025-09-09T15:08:13.3172033Z 2025-09-09T15:08:13.3172314Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:08:13.3172668Z diff: tensor([[[[0., 0., 0.], 2025-09-09T15:08:13.3172906Z [0., 0., 0.], 2025-09-09T15:08:13.3173104Z [0., 0., 0.]], 2025-09-09T15:08:13.3173250Z 2025-09-09T15:08:13.3173321Z [[0., 0., 0.], 2025-09-09T15:08:13.3173518Z [0., 0., 0.], 2025-09-09T15:08:13.3173718Z [0., 0., 0.]], 2025-09-09T15:08:13.3173850Z 2025-09-09T15:08:13.3173926Z [[0., 0., 0.], 2025-09-09T15:08:13.3174130Z [0., 0., 0.], 2025-09-09T15:08:13.3174348Z [0., 0., 0.]]]], device='cuda:0') 2025-09-09T15:08:13.3174615Z model pt2e: GraphModule( 2025-09-09T15:08:13.3174838Z (conv): Module() 2025-09-09T15:08:13.3175029Z (bn): Module() 2025-09-09T15:08:13.3175321Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:08:13.3176411Z fake_quant_enabled=tensor([1], device='cuda:0'), observer_enabled=tensor([1], device='cuda:0'), scale=tensor([0.0183], device='cuda:0'), zero_point=tensor([10], device='cuda:0', dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:08:13.3177648Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.526270866394043, max_val=2.143237352371216) 2025-09-09T15:08:13.3178150Z ) 2025-09-09T15:08:13.3178413Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:08:13.3179513Z fake_quant_enabled=tensor([1], device='cuda:0'), observer_enabled=tensor([1], device='cuda:0'), scale=tensor([0.0015], device='cuda:0'), zero_point=tensor([0], device='cuda:0', dtype=torch.int32), dtype=torch.int8, quant_min=-127, quant_max=127, qscheme=torch.per_tensor_symmetric, reduce_range=False 2025-09-09T15:08:13.3180764Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.18965020775794983, max_val=0.1870359182357788) 2025-09-09T15:08:13.3181268Z ) 2025-09-09T15:08:13.3181534Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:08:13.3182696Z fake_quant_enabled=tensor([1], device='cuda:0'), observer_enabled=tensor([1], device='cuda:0'), scale=tensor([0.0164], device='cuda:0'), zero_point=tensor([2], device='cuda:0', dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:08:13.3184003Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.137371778488159, max_val=2.0522286891937256) 2025-09-09T15:08:13.3184498Z ) 2025-09-09T15:08:13.3184654Z ) 2025-09-09T15:08:13.3184746Z 2025-09-09T15:08:13.3184750Z 2025-09-09T15:08:13.3184754Z 2025-09-09T15:08:13.3184839Z def forward(self, x): 2025-09-09T15:08:13.3185112Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T15:08:13.3185449Z conv_weight = self.conv.weight 2025-09-09T15:08:13.3185711Z conv_bias = self.conv.bias 2025-09-09T15:08:13.3185959Z bn_weight = self.bn.weight 2025-09-09T15:08:13.3186195Z bn_bias = self.bn.bias 2025-09-09T15:08:13.3186455Z bn_running_mean = self.bn.running_mean 2025-09-09T15:08:13.3186753Z bn_running_var = self.bn.running_var 2025-09-09T15:08:13.3187075Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T15:08:13.3187513Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T15:08:13.3188081Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T15:08:13.3188605Z add = torch.ops.aten.add.Tensor(bn_running_var, 1e-05) 2025-09-09T15:08:13.3188987Z sqrt = torch.ops.aten.sqrt.default(add); add = None 2025-09-09T15:08:13.3189389Z div = torch.ops.aten.div.Tensor(bn_weight, sqrt); sqrt = None 2025-09-09T15:08:13.3189826Z reshape = torch.ops.aten.reshape.default(div, [-1, 1, 1, 1]) 2025-09-09T15:08:13.3190315Z mul = torch.ops.aten.mul.Tensor(conv_weight, reshape); conv_weight = reshape = None 2025-09-09T15:08:13.3190877Z activation_post_process_1 = self.activation_post_process_1(mul); mul = None 2025-09-09T15:08:13.3191476Z zeros_like = torch.ops.aten.zeros_like.default(conv_bias, dtype = torch.float32, pin_memory = False) 2025-09-09T15:08:13.3192439Z conv2d_1 = torch.ops.aten.conv2d.default(activation_post_process_0, activation_post_process_1, zeros_like); activation_post_process_0 = activation_post_process_1 = zeros_like = None 2025-09-09T15:08:13.3193317Z reshape_1 = torch.ops.aten.reshape.default(div, [1, -1, 1, 1]); div = None 2025-09-09T15:08:13.3193842Z div_1 = torch.ops.aten.div.Tensor(conv2d_1, reshape_1); conv2d_1 = reshape_1 = None 2025-09-09T15:08:13.3194420Z reshape_2 = torch.ops.aten.reshape.default(conv_bias, [1, -1, 1, 1]); conv_bias = None 2025-09-09T15:08:13.3194961Z add_1 = torch.ops.aten.add.Tensor(div_1, reshape_2); div_1 = reshape_2 = None 2025-09-09T15:08:13.3195814Z batch_norm_1 = torch.ops.aten.batch_norm.default(add_1, bn_weight, bn_bias, bn_running_mean, bn_running_var, True, 0.1, 1e-05, True); add_1 = bn_weight = bn_bias = bn_running_mean = bn_running_var = None 2025-09-09T15:08:13.3196724Z activation_post_process_2 = self.activation_post_process_2(batch_norm_1); batch_norm_1 = None 2025-09-09T15:08:13.3197307Z return pytree.tree_unflatten((activation_post_process_2,), self._out_spec) 2025-09-09T15:08:13.3197685Z 2025-09-09T15:08:13.3197955Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:08:13.3198308Z model fx: GraphModule( 2025-09-09T15:08:13.3198615Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:08:13.3199763Z fake_quant_enabled=tensor([1], device='cuda:0'), observer_enabled=tensor([1], device='cuda:0'), scale=tensor([0.0183], device='cuda:0'), zero_point=tensor([10], device='cuda:0', dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:08:13.3201002Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.526270866394043, max_val=2.143237352371216) 2025-09-09T15:08:13.3201574Z ) 2025-09-09T15:08:13.3201749Z (conv): ConvBn2d( 2025-09-09T15:08:13.3201965Z 3, 3, kernel_size=(3, 3), stride=(1, 1) 2025-09-09T15:08:38.7633414Z (bn): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T15:08:38.7634023Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:08:38.7635370Z fake_quant_enabled=tensor([1], device='cuda:0'), observer_enabled=tensor([1], device='cuda:0'), scale=tensor([0.0015], device='cuda:0'), zero_point=tensor([0], device='cuda:0', dtype=torch.int32), dtype=torch.qint8, quant_min=-127, quant_max=127, qscheme=torch.per_tensor_symmetric, reduce_range=False 2025-09-09T15:08:38.7636962Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.18965020775794983, max_val=0.1870359182357788) 2025-09-09T15:08:38.7637582Z ) 2025-09-09T15:08:38.7637787Z ) 2025-09-09T15:08:38.7638105Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:08:38.7639554Z fake_quant_enabled=tensor([1], device='cuda:0'), observer_enabled=tensor([1], device='cuda:0'), scale=tensor([0.0164], device='cuda:0'), zero_point=tensor([2], device='cuda:0', dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:08:38.7641116Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.137371778488159, max_val=2.0522286891937256) 2025-09-09T15:08:38.7641722Z ) 2025-09-09T15:08:38.7641917Z ) 2025-09-09T15:08:38.7642029Z 2025-09-09T15:08:38.7642034Z 2025-09-09T15:08:38.7642039Z 2025-09-09T15:08:38.7642140Z def forward(self, x): 2025-09-09T15:08:38.7642564Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T15:08:38.7643203Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T15:08:38.7643869Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T15:08:38.7644388Z return activation_post_process_1 2025-09-09T15:08:38.7644688Z 2025-09-09T15:08:38.7645014Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:08:38.7645449Z diff: tensor([[[[0., 0., 0.], 2025-09-09T15:08:38.7645727Z [0., 0., 0.], 2025-09-09T15:08:38.7645966Z [0., 0., 0.]], 2025-09-09T15:08:38.7646135Z 2025-09-09T15:08:38.7646225Z [[0., 0., 0.], 2025-09-09T15:08:38.7646487Z [0., 0., 0.], 2025-09-09T15:08:38.7646754Z [0., 0., 0.]], 2025-09-09T15:08:38.7646916Z 2025-09-09T15:08:38.7647009Z [[0., 0., 0.], 2025-09-09T15:08:38.7647244Z [0., 0., 0.], 2025-09-09T15:08:38.7647557Z [0., 0., 0.]]]], device='cuda:0', grad_fn=) 2025-09-09T15:08:38.7647943Z converted model pt2e: GraphModule( 2025-09-09T15:08:38.7648254Z (conv): Module() 2025-09-09T15:08:38.7648485Z (bn): Module() 2025-09-09T15:08:38.7648709Z ) 2025-09-09T15:08:38.7648821Z 2025-09-09T15:08:38.7648826Z 2025-09-09T15:08:38.7648831Z 2025-09-09T15:08:38.7648942Z def forward(self, x): 2025-09-09T15:08:38.7649268Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T15:08:38.7649677Z conv_bias = self.conv.bias 2025-09-09T15:08:38.7650457Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.018311796709895134, 10, -128, 127, torch.int8); x = None 2025-09-09T15:08:38.7651990Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.018311796709895134, 10, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T15:08:38.7653103Z quantize_per_tensor = self._frozen_param0 2025-09-09T15:08:38.7654090Z dequantize_per_tensor = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor, 0.0014933086931705475, 0, -127, 127, torch.int8); quantize_per_tensor = None 2025-09-09T15:08:38.7655633Z conv2d_2 = torch.ops.aten.conv2d.default(dequantize_per_tensor_default, dequantize_per_tensor, conv_bias); dequantize_per_tensor_default = dequantize_per_tensor = conv_bias = None 2025-09-09T15:08:38.7656805Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv2d_2, 0.016429806128144264, 2, -128, 127, torch.int8); conv2d_2 = None 2025-09-09T15:08:38.7658218Z dequantize_per_tensor_default_2 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_2, 0.016429806128144264, 2, -128, 127, torch.int8); quantize_per_tensor_default_2 = None 2025-09-09T15:08:38.7659209Z return pytree.tree_unflatten((dequantize_per_tensor_default_2,), self._out_spec) 2025-09-09T15:08:38.7659608Z 2025-09-09T15:08:38.7659883Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:08:38.7660245Z onverted model fx: GraphModule( 2025-09-09T15:08:38.7660625Z (conv): QuantizedConv2d(Reference)(3, 3, kernel_size=(3, 3), stride=(1, 1)) 2025-09-09T15:08:38.7660995Z ) 2025-09-09T15:08:38.7661105Z 2025-09-09T15:08:38.7661109Z 2025-09-09T15:08:38.7661113Z 2025-09-09T15:08:38.7661192Z def forward(self, x): 2025-09-09T15:08:38.7661804Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.018311796709895134, 10, -128, 127, torch.int8); x = None 2025-09-09T15:08:38.7663014Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.018311796709895134, 10, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T15:08:38.7664002Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T15:08:38.7664833Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.016429806128144264, 2, -128, 127, torch.int8); conv = None 2025-09-09T15:08:38.7666070Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.016429806128144264, 2, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T15:08:38.7666928Z return dequantize_per_tensor_default_1 2025-09-09T15:08:38.7667191Z 2025-09-09T15:08:38.7667474Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:08:38.7667838Z diff: tensor([[[[0., 0., 0.], 2025-09-09T15:08:38.7668062Z [0., 0., 0.], 2025-09-09T15:08:38.7668274Z [0., 0., 0.]], 2025-09-09T15:08:38.7668408Z 2025-09-09T15:08:38.7668485Z [[0., 0., 0.], 2025-09-09T15:08:38.7668684Z [0., 0., 0.], 2025-09-09T15:08:38.7668880Z [0., 0., 0.]], 2025-09-09T15:08:38.7669021Z 2025-09-09T15:08:38.7669095Z [[0., 0., 0.], 2025-09-09T15:08:38.7669288Z [0., 0., 0.], 2025-09-09T15:08:38.7669513Z [0., 0., 0.]]]], device='cuda:0') 2025-09-09T15:08:38.7669977Z PASSED 2025-09-09T15:08:38.7670582Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_conv_bn_fusion_literal_args model pt2e: GraphModule( 2025-09-09T15:08:38.7671217Z (conv): Module() 2025-09-09T15:08:38.7671409Z (bn): Module() 2025-09-09T15:08:38.7671702Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:08:38.7672626Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0165]), zero_point=tensor([-18], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:08:38.7673701Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.8076945543289185, max_val=2.388113498687744) 2025-09-09T15:08:38.7674202Z ) 2025-09-09T15:08:38.7674466Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:08:38.7675526Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0015, 0.0015, 0.0015]), zero_point=tensor([0, 0, 0], dtype=torch.int32), dtype=torch.int8, quant_min=-127, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False 2025-09-09T15:08:38.7676831Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.1897, -0.1774, -0.1913]), max_val=tensor([0.1806, 0.1870, 0.1478])) 2025-09-09T15:08:38.7677548Z ) 2025-09-09T15:08:38.7677824Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:08:38.7678740Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0279]), zero_point=tensor([-3], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:08:38.7679890Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-3.481316328048706, max_val=3.622279405593872) 2025-09-09T15:08:38.7680381Z ) 2025-09-09T15:08:38.7680547Z ) 2025-09-09T15:08:38.7680639Z 2025-09-09T15:08:38.7680643Z 2025-09-09T15:08:38.7680647Z 2025-09-09T15:08:38.7680737Z def forward(self, x): 2025-09-09T15:08:38.7681017Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T15:08:38.7681349Z conv_weight = self.conv.weight 2025-09-09T15:08:38.7681622Z conv_bias = self.conv.bias 2025-09-09T15:08:38.7681873Z bn_weight = self.bn.weight 2025-09-09T15:08:38.7682122Z bn_bias = self.bn.bias 2025-09-09T15:08:38.7682379Z bn_running_mean = self.bn.running_mean 2025-09-09T15:08:38.7682670Z bn_running_var = self.bn.running_var 2025-09-09T15:08:38.7682999Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T15:08:38.7683429Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T15:08:38.7684002Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T15:08:38.7684519Z add = torch.ops.aten.add.Tensor(bn_running_var, 1e-05) 2025-09-09T15:08:38.7684897Z sqrt = torch.ops.aten.sqrt.default(add); add = None 2025-09-09T15:08:38.7685303Z div = torch.ops.aten.div.Tensor(bn_weight, sqrt); sqrt = None 2025-09-09T15:08:38.7685742Z reshape = torch.ops.aten.reshape.default(div, [-1, 1, 1, 1]) 2025-09-09T15:08:38.7686243Z mul = torch.ops.aten.mul.Tensor(conv_weight, reshape); conv_weight = reshape = None 2025-09-09T15:08:38.7686817Z activation_post_process_1 = self.activation_post_process_1(mul); mul = None 2025-09-09T15:08:38.7687439Z zeros_like = torch.ops.aten.zeros_like.default(conv_bias, dtype = torch.float32, pin_memory = False) 2025-09-09T15:08:38.7688406Z conv2d_1 = torch.ops.aten.conv2d.default(activation_post_process_0, activation_post_process_1, zeros_like, [2, 2], [4, 4]); activation_post_process_0 = activation_post_process_1 = zeros_like = None 2025-09-09T15:08:38.7689285Z reshape_1 = torch.ops.aten.reshape.default(div, [1, -1, 1, 1]); div = None 2025-09-09T15:08:38.7689822Z div_1 = torch.ops.aten.div.Tensor(conv2d_1, reshape_1); conv2d_1 = reshape_1 = None 2025-09-09T15:08:38.7690394Z reshape_2 = torch.ops.aten.reshape.default(conv_bias, [1, -1, 1, 1]); conv_bias = None 2025-09-09T15:08:38.7690962Z add_1 = torch.ops.aten.add.Tensor(div_1, reshape_2); div_1 = reshape_2 = None 2025-09-09T15:08:38.7699687Z batch_norm_1 = torch.ops.aten.batch_norm.default(add_1, bn_weight, bn_bias, bn_running_mean, bn_running_var, True, 0.1, 1e-05, True); add_1 = bn_weight = bn_bias = bn_running_mean = bn_running_var = None 2025-09-09T15:08:38.7700635Z activation_post_process_2 = self.activation_post_process_2(batch_norm_1); batch_norm_1 = None 2025-09-09T15:08:38.7701239Z return pytree.tree_unflatten((activation_post_process_2,), self._out_spec) 2025-09-09T15:08:38.7701632Z 2025-09-09T15:08:38.7701929Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:08:38.7702293Z model fx: GraphModule( 2025-09-09T15:08:58.5463895Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:08:58.5465623Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0165]), zero_point=tensor([-18], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:08:58.5467044Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.8076945543289185, max_val=2.388113498687744) 2025-09-09T15:08:58.5467893Z ) 2025-09-09T15:08:58.5468120Z (conv): ConvBn2d( 2025-09-09T15:08:58.5468432Z 3, 3, kernel_size=(3, 3), stride=(2, 2), padding=(4, 4) 2025-09-09T15:08:58.5468970Z (bn): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T15:08:58.5469529Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:08:58.5470686Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0015, 0.0015, 0.0015]), zero_point=tensor([0, 0, 0], dtype=torch.int32), dtype=torch.qint8, quant_min=-127, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False 2025-09-09T15:08:58.5472285Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.1897, -0.1774, -0.1913]), max_val=tensor([0.1806, 0.1870, 0.1478])) 2025-09-09T15:08:58.5473067Z ) 2025-09-09T15:08:58.5473263Z ) 2025-09-09T15:08:58.5473585Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:08:58.5474723Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0279]), zero_point=tensor([-3], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:08:58.5476057Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-3.481316328048706, max_val=3.622279405593872) 2025-09-09T15:08:58.5476666Z ) 2025-09-09T15:08:58.5476869Z ) 2025-09-09T15:08:58.5476984Z 2025-09-09T15:08:58.5476989Z 2025-09-09T15:08:58.5476994Z 2025-09-09T15:08:58.5477100Z def forward(self, x): 2025-09-09T15:08:58.5477512Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T15:08:58.5478153Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T15:08:58.5478802Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T15:08:58.5479421Z return activation_post_process_1 2025-09-09T15:08:58.5479736Z 2025-09-09T15:08:58.5480072Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:08:58.5480525Z diff: tensor([[[[0., 0., 0., 0., 0., 0.], 2025-09-09T15:08:58.5480839Z [0., 0., 0., 0., 0., 0.], 2025-09-09T15:08:58.5481132Z [0., 0., 0., 0., 0., 0.], 2025-09-09T15:08:58.5481415Z [0., 0., 0., 0., 0., 0.], 2025-09-09T15:08:58.5481702Z [0., 0., 0., 0., 0., 0.], 2025-09-09T15:08:58.5481986Z [0., 0., 0., 0., 0., 0.]], 2025-09-09T15:08:58.5482190Z 2025-09-09T15:08:58.5482281Z [[0., 0., 0., 0., 0., 0.], 2025-09-09T15:08:58.5482560Z [0., 0., 0., 0., 0., 0.], 2025-09-09T15:08:58.5482845Z [0., 0., 0., 0., 0., 0.], 2025-09-09T15:08:58.5483130Z [0., 0., 0., 0., 0., 0.], 2025-09-09T15:08:58.5483414Z [0., 0., 0., 0., 0., 0.], 2025-09-09T15:08:58.5483703Z [0., 0., 0., 0., 0., 0.]], 2025-09-09T15:08:58.5483902Z 2025-09-09T15:08:58.5483999Z [[0., 0., 0., 0., 0., 0.], 2025-09-09T15:08:58.5484283Z [0., 0., 0., 0., 0., 0.], 2025-09-09T15:08:58.5484566Z [0., 0., 0., 0., 0., 0.], 2025-09-09T15:08:58.5484848Z [0., 0., 0., 0., 0., 0.], 2025-09-09T15:08:58.5485127Z [0., 0., 0., 0., 0., 0.], 2025-09-09T15:08:58.5485464Z [0., 0., 0., 0., 0., 0.]]]], grad_fn=) 2025-09-09T15:08:58.5485843Z converted model pt2e: GraphModule( 2025-09-09T15:08:58.5486146Z (conv): Module() 2025-09-09T15:08:58.5486391Z (bn): Module() 2025-09-09T15:08:58.5486607Z ) 2025-09-09T15:08:58.5486723Z 2025-09-09T15:08:58.5486739Z 2025-09-09T15:08:58.5486744Z 2025-09-09T15:08:58.5486840Z def forward(self, x): 2025-09-09T15:08:58.5487163Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T15:08:58.5487660Z conv_bias = self.conv.bias 2025-09-09T15:08:58.5488456Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.016454149037599564, -18, -128, 127, torch.int8); x = None 2025-09-09T15:08:58.5490113Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.016454149037599564, -18, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T15:08:58.5491163Z _scale_0 = self._scale_0 2025-09-09T15:08:58.5491460Z _zero_point_0 = self._zero_point_0 2025-09-09T15:08:58.5491821Z quantize_per_channel = self._frozen_param0 2025-09-09T15:08:58.5492893Z dequantize_per_channel = torch.ops.quantized_decomposed.dequantize_per_channel.default(quantize_per_channel, _scale_0, _zero_point_0, 0, -127, 127, torch.int8); quantize_per_channel = _scale_0 = _zero_point_0 = None 2025-09-09T15:08:58.5494571Z conv2d_2 = torch.ops.aten.conv2d.default(dequantize_per_tensor_default, dequantize_per_channel, conv_bias, [2, 2], [4, 4]); dequantize_per_tensor_default = dequantize_per_channel = conv_bias = None 2025-09-09T15:08:58.5496069Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv2d_2, 0.027857238426804543, -3, -128, 127, torch.int8); conv2d_2 = None 2025-09-09T15:08:58.5497662Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.027857238426804543, -3, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T15:08:58.5498909Z return pytree.tree_unflatten((dequantize_per_tensor_default_1,), self._out_spec) 2025-09-09T15:08:58.5499406Z 2025-09-09T15:08:58.5499730Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:08:58.5500181Z onverted model fx: GraphModule( 2025-09-09T15:08:58.5500685Z (conv): QuantizedConv2d(Reference)(3, 3, kernel_size=(3, 3), stride=(2, 2), padding=(4, 4)) 2025-09-09T15:08:58.5501197Z ) 2025-09-09T15:08:58.5501319Z 2025-09-09T15:08:58.5501324Z 2025-09-09T15:08:58.5501329Z 2025-09-09T15:08:58.5501435Z def forward(self, x): 2025-09-09T15:08:58.5502189Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.016454149037599564, -18, -128, 127, torch.int8); x = None 2025-09-09T15:08:58.5503712Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.016454149037599564, -18, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T15:08:58.5504938Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T15:08:58.5506041Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.027857238426804543, -3, -128, 127, torch.int8); conv = None 2025-09-09T15:08:58.5507481Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.027857238426804543, -3, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T15:08:58.5508341Z return dequantize_per_tensor_default_1 2025-09-09T15:08:58.5508612Z 2025-09-09T15:08:58.5508882Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:08:58.5509256Z diff: tensor([[[[0., 0., 0., 0., 0., 0.], 2025-09-09T15:08:58.5509523Z [0., 0., 0., 0., 0., 0.], 2025-09-09T15:08:58.5509762Z [0., 0., 0., 0., 0., 0.], 2025-09-09T15:08:58.5510002Z [0., 0., 0., 0., 0., 0.], 2025-09-09T15:08:58.5510231Z [0., 0., 0., 0., 0., 0.], 2025-09-09T15:08:58.5510474Z [0., 0., 0., 0., 0., 0.]], 2025-09-09T15:08:58.5510639Z 2025-09-09T15:08:58.5510714Z [[0., 0., 0., 0., 0., 0.], 2025-09-09T15:08:58.5510950Z [0., 0., 0., 0., 0., 0.], 2025-09-09T15:08:58.5511180Z [0., 0., 0., 0., 0., 0.], 2025-09-09T15:08:58.5511414Z [0., 0., 0., 0., 0., 0.], 2025-09-09T15:08:58.5511743Z [0., 0., 0., 0., 0., 0.], 2025-09-09T15:08:58.5511998Z [0., 0., 0., 0., 0., 0.]], 2025-09-09T15:08:58.5512185Z 2025-09-09T15:08:58.5512276Z [[0., 0., 0., 0., 0., 0.], 2025-09-09T15:08:58.5512583Z [0., 0., 0., 0., 0., 0.], 2025-09-09T15:08:58.5512818Z [0., 0., 0., 0., 0., 0.], 2025-09-09T15:08:58.5513045Z [0., 0., 0., 0., 0., 0.], 2025-09-09T15:08:58.5513287Z [0., 0., 0., 0., 0., 0.], 2025-09-09T15:08:58.5513519Z [0., 0., 0., 0., 0., 0.]]]]) 2025-09-09T15:08:58.5513783Z model pt2e: GraphModule( 2025-09-09T15:08:58.5514012Z (conv): Module() 2025-09-09T15:08:58.5514208Z (bn): Module() 2025-09-09T15:08:58.5514504Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:08:58.5515431Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0165]), zero_point=tensor([-18], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:08:58.5516518Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.8076945543289185, max_val=2.388113498687744) 2025-09-09T15:08:58.5517019Z ) 2025-09-09T15:08:58.5517289Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:08:58.5518218Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0015]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.int8, quant_min=-127, quant_max=127, qscheme=torch.per_tensor_symmetric, reduce_range=False 2025-09-09T15:08:58.5519370Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.19127479195594788, max_val=0.1870359182357788) 2025-09-09T15:08:58.5519890Z ) 2025-09-09T15:08:58.5520152Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:08:58.5521069Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0278]), zero_point=tensor([-3], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:08:58.5522289Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-3.4796082973480225, max_val=3.620413064956665) 2025-09-09T15:08:58.5522795Z ) 2025-09-09T15:08:58.5522960Z ) 2025-09-09T15:08:58.5523055Z 2025-09-09T15:08:58.5523059Z 2025-09-09T15:08:58.5523063Z 2025-09-09T15:08:58.5523147Z def forward(self, x): 2025-09-09T15:08:58.5523435Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T15:08:58.5523775Z conv_weight = self.conv.weight 2025-09-09T15:08:58.5524041Z conv_bias = self.conv.bias 2025-09-09T15:08:58.5524293Z bn_weight = self.bn.weight 2025-09-09T15:08:58.5524535Z bn_bias = self.bn.bias 2025-09-09T15:08:58.5524787Z bn_running_mean = self.bn.running_mean 2025-09-09T15:08:58.5525077Z bn_running_var = self.bn.running_var 2025-09-09T15:08:58.5525403Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T15:08:58.5525834Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T15:08:58.5526409Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T15:08:58.5526939Z add = torch.ops.aten.add.Tensor(bn_running_var, 1e-05) 2025-09-09T15:08:58.5527325Z sqrt = torch.ops.aten.sqrt.default(add); add = None 2025-09-09T15:08:58.5527738Z div = torch.ops.aten.div.Tensor(bn_weight, sqrt); sqrt = None 2025-09-09T15:08:58.5528169Z reshape = torch.ops.aten.reshape.default(div, [-1, 1, 1, 1]) 2025-09-09T15:08:58.5528668Z mul = torch.ops.aten.mul.Tensor(conv_weight, reshape); conv_weight = reshape = None 2025-09-09T15:09:21.2937992Z activation_post_process_1 = self.activation_post_process_1(mul); mul = None 2025-09-09T15:09:21.2938792Z zeros_like = torch.ops.aten.zeros_like.default(conv_bias, dtype = torch.float32, pin_memory = False) 2025-09-09T15:09:21.2940348Z conv2d_1 = torch.ops.aten.conv2d.default(activation_post_process_0, activation_post_process_1, zeros_like, [2, 2], [4, 4]); activation_post_process_0 = activation_post_process_1 = zeros_like = None 2025-09-09T15:09:21.2941480Z reshape_1 = torch.ops.aten.reshape.default(div, [1, -1, 1, 1]); div = None 2025-09-09T15:09:21.2942298Z div_1 = torch.ops.aten.div.Tensor(conv2d_1, reshape_1); conv2d_1 = reshape_1 = None 2025-09-09T15:09:21.2942999Z reshape_2 = torch.ops.aten.reshape.default(conv_bias, [1, -1, 1, 1]); conv_bias = None 2025-09-09T15:09:21.2943674Z add_1 = torch.ops.aten.add.Tensor(div_1, reshape_2); div_1 = reshape_2 = None 2025-09-09T15:09:21.2944724Z batch_norm_1 = torch.ops.aten.batch_norm.default(add_1, bn_weight, bn_bias, bn_running_mean, bn_running_var, True, 0.1, 1e-05, True); add_1 = bn_weight = bn_bias = bn_running_mean = bn_running_var = None 2025-09-09T15:09:21.2945882Z activation_post_process_2 = self.activation_post_process_2(batch_norm_1); batch_norm_1 = None 2025-09-09T15:09:21.2946615Z return pytree.tree_unflatten((activation_post_process_2,), self._out_spec) 2025-09-09T15:09:21.2947091Z 2025-09-09T15:09:21.2947417Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:09:21.2947858Z model fx: GraphModule( 2025-09-09T15:09:21.2948254Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:09:21.2949446Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0165]), zero_point=tensor([-18], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:09:21.2950893Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.8076945543289185, max_val=2.388113498687744) 2025-09-09T15:09:21.2951501Z ) 2025-09-09T15:09:21.2951713Z (conv): ConvBn2d( 2025-09-09T15:09:21.2952016Z 3, 3, kernel_size=(3, 3), stride=(2, 2), padding=(4, 4) 2025-09-09T15:09:21.2952551Z (bn): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T15:09:21.2953116Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:09:21.2954228Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0015]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-127, quant_max=127, qscheme=torch.per_tensor_symmetric, reduce_range=False 2025-09-09T15:09:21.2955608Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.19127479195594788, max_val=0.1870359182357788) 2025-09-09T15:09:21.2956229Z ) 2025-09-09T15:09:21.2956428Z ) 2025-09-09T15:09:21.2956746Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:09:21.2957883Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0278]), zero_point=tensor([-3], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:09:21.2959309Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-3.4796082973480225, max_val=3.620413064956665) 2025-09-09T15:09:21.2959919Z ) 2025-09-09T15:09:21.2960113Z ) 2025-09-09T15:09:21.2960224Z 2025-09-09T15:09:21.2960230Z 2025-09-09T15:09:21.2960234Z 2025-09-09T15:09:21.2960338Z def forward(self, x): 2025-09-09T15:09:21.2960759Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T15:09:21.2961403Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T15:09:21.2962051Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T15:09:21.2962561Z return activation_post_process_1 2025-09-09T15:09:21.2962859Z 2025-09-09T15:09:21.2963182Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:09:21.2963638Z diff: tensor([[[[0., 0., 0., 0., 0., 0.], 2025-09-09T15:09:21.2963966Z [0., 0., 0., 0., 0., 0.], 2025-09-09T15:09:21.2964257Z [0., 0., 0., 0., 0., 0.], 2025-09-09T15:09:21.2964549Z [0., 0., 0., 0., 0., 0.], 2025-09-09T15:09:21.2964928Z [0., 0., 0., 0., 0., 0.], 2025-09-09T15:09:21.2965228Z [0., 0., 0., 0., 0., 0.]], 2025-09-09T15:09:21.2965430Z 2025-09-09T15:09:21.2965605Z [[0., 0., 0., 0., 0., 0.], 2025-09-09T15:09:21.2965892Z [0., 0., 0., 0., 0., 0.], 2025-09-09T15:09:21.2966178Z [0., 0., 0., 0., 0., 0.], 2025-09-09T15:09:21.2966457Z [0., 0., 0., 0., 0., 0.], 2025-09-09T15:09:21.2966741Z [0., 0., 0., 0., 0., 0.], 2025-09-09T15:09:21.2967020Z [0., 0., 0., 0., 0., 0.]], 2025-09-09T15:09:21.2967219Z 2025-09-09T15:09:21.2967323Z [[0., 0., 0., 0., 0., 0.], 2025-09-09T15:09:21.2967607Z [0., 0., 0., 0., 0., 0.], 2025-09-09T15:09:21.2967893Z [0., 0., 0., 0., 0., 0.], 2025-09-09T15:09:21.2968169Z [0., 0., 0., 0., 0., 0.], 2025-09-09T15:09:21.2968456Z [0., 0., 0., 0., 0., 0.], 2025-09-09T15:09:21.2968794Z [0., 0., 0., 0., 0., 0.]]]], grad_fn=) 2025-09-09T15:09:21.2969172Z converted model pt2e: GraphModule( 2025-09-09T15:09:21.2969480Z (conv): Module() 2025-09-09T15:09:21.2969712Z (bn): Module() 2025-09-09T15:09:21.2969936Z ) 2025-09-09T15:09:21.2970055Z 2025-09-09T15:09:21.2970060Z 2025-09-09T15:09:21.2970065Z 2025-09-09T15:09:21.2970161Z def forward(self, x): 2025-09-09T15:09:21.2970490Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T15:09:21.2970877Z conv_bias = self.conv.bias 2025-09-09T15:09:21.2971665Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.016454149037599564, -18, -128, 127, torch.int8); x = None 2025-09-09T15:09:21.2973198Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.016454149037599564, -18, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T15:09:21.2974268Z quantize_per_tensor = self._frozen_param0 2025-09-09T15:09:21.2975307Z dequantize_per_tensor = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor, 0.0015061007579788566, 0, -127, 127, torch.int8); quantize_per_tensor = None 2025-09-09T15:09:21.2976733Z conv2d_2 = torch.ops.aten.conv2d.default(dequantize_per_tensor_default, dequantize_per_tensor, conv_bias, [2, 2], [4, 4]); dequantize_per_tensor_default = dequantize_per_tensor = conv_bias = None 2025-09-09T15:09:21.2977906Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv2d_2, 0.02784322015941143, -3, -128, 127, torch.int8); conv2d_2 = None 2025-09-09T15:09:21.2979172Z dequantize_per_tensor_default_2 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_2, 0.02784322015941143, -3, -128, 127, torch.int8); quantize_per_tensor_default_2 = None 2025-09-09T15:09:21.2980158Z return pytree.tree_unflatten((dequantize_per_tensor_default_2,), self._out_spec) 2025-09-09T15:09:21.2980550Z 2025-09-09T15:09:21.2980832Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:09:21.2981189Z onverted model fx: GraphModule( 2025-09-09T15:09:21.2981613Z (conv): QuantizedConv2d(Reference)(3, 3, kernel_size=(3, 3), stride=(2, 2), padding=(4, 4)) 2025-09-09T15:09:21.2982026Z ) 2025-09-09T15:09:21.2982125Z 2025-09-09T15:09:21.2982129Z 2025-09-09T15:09:21.2982133Z 2025-09-09T15:09:21.2982210Z def forward(self, x): 2025-09-09T15:09:21.2982821Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.016454149037599564, -18, -128, 127, torch.int8); x = None 2025-09-09T15:09:21.2984033Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.016454149037599564, -18, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T15:09:21.2985026Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T15:09:21.2985949Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.02784322015941143, -3, -128, 127, torch.int8); conv = None 2025-09-09T15:09:21.2987203Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.02784322015941143, -3, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T15:09:21.2988140Z return dequantize_per_tensor_default_1 2025-09-09T15:09:21.2988405Z 2025-09-09T15:09:21.2988674Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:09:21.2989035Z diff: tensor([[[[0., 0., 0., 0., 0., 0.], 2025-09-09T15:09:21.2989299Z [0., 0., 0., 0., 0., 0.], 2025-09-09T15:09:21.2989547Z [0., 0., 0., 0., 0., 0.], 2025-09-09T15:09:21.2989779Z [0., 0., 0., 0., 0., 0.], 2025-09-09T15:09:21.2990013Z [0., 0., 0., 0., 0., 0.], 2025-09-09T15:09:21.2990243Z [0., 0., 0., 0., 0., 0.]], 2025-09-09T15:09:21.2990406Z 2025-09-09T15:09:21.2990498Z [[0., 0., 0., 0., 0., 0.], 2025-09-09T15:09:21.2990732Z [0., 0., 0., 0., 0., 0.], 2025-09-09T15:09:21.2990966Z [0., 0., 0., 0., 0., 0.], 2025-09-09T15:09:21.2991200Z [0., 0., 0., 0., 0., 0.], 2025-09-09T15:09:21.2991434Z [0., 0., 0., 0., 0., 0.], 2025-09-09T15:09:21.2991663Z [0., 0., 0., 0., 0., 0.]], 2025-09-09T15:09:21.2991836Z 2025-09-09T15:09:21.2991912Z [[0., 0., 0., 0., 0., 0.], 2025-09-09T15:09:21.2992147Z [0., 0., 0., 0., 0., 0.], 2025-09-09T15:09:21.2992376Z [0., 0., 0., 0., 0., 0.], 2025-09-09T15:09:21.2992611Z [0., 0., 0., 0., 0., 0.], 2025-09-09T15:09:21.2992844Z [0., 0., 0., 0., 0., 0.], 2025-09-09T15:09:21.2993086Z [0., 0., 0., 0., 0., 0.]]]]) 2025-09-09T15:09:21.2993540Z PASSED 2025-09-09T15:09:21.2994145Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_conv_bn_fusion_no_conv_bias model pt2e: GraphModule( 2025-09-09T15:09:21.2994786Z (conv): Module() 2025-09-09T15:09:21.2994981Z (bn): Module() 2025-09-09T15:09:21.2995276Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:09:21.2996207Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0189]), zero_point=tensor([-17], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:09:21.2997298Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.0985729694366455, max_val=2.7226178646087646) 2025-09-09T15:09:21.2997797Z ) 2025-09-09T15:09:21.2998068Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:09:21.2999032Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0015, 0.0015, 0.0014]), zero_point=tensor([0, 0, 0], dtype=torch.int32), dtype=torch.int8, quant_min=-127, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False 2025-09-09T15:09:21.3000467Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.1860, -0.1897, -0.1787]), max_val=tensor([0.1824, 0.1870, 0.1478])) 2025-09-09T15:09:21.3001113Z ) 2025-09-09T15:09:21.3001381Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:09:41.1815313Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0177]), zero_point=tensor([-7], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:09:41.1816725Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.1459269523620605, max_val=2.376943349838257) 2025-09-09T15:09:41.1817339Z ) 2025-09-09T15:09:41.1817539Z ) 2025-09-09T15:09:41.1817658Z 2025-09-09T15:09:41.1817662Z 2025-09-09T15:09:41.1817667Z 2025-09-09T15:09:41.1817766Z def forward(self, x): 2025-09-09T15:09:41.1818108Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T15:09:41.1818858Z conv_weight = self.conv.weight 2025-09-09T15:09:41.1819194Z bn_weight = self.bn.weight 2025-09-09T15:09:41.1819492Z bn_bias = self.bn.bias 2025-09-09T15:09:41.1819792Z bn_running_mean = self.bn.running_mean 2025-09-09T15:09:41.1820325Z bn_running_var = self.bn.running_var 2025-09-09T15:09:41.1820715Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T15:09:41.1821254Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T15:09:41.1821952Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T15:09:41.1822907Z add = torch.ops.aten.add.Tensor(bn_running_var, 1e-05) 2025-09-09T15:09:41.1823371Z sqrt = torch.ops.aten.sqrt.default(add); add = None 2025-09-09T15:09:41.1823852Z div = torch.ops.aten.div.Tensor(bn_weight, sqrt); sqrt = None 2025-09-09T15:09:41.1824380Z reshape = torch.ops.aten.reshape.default(div, [-1, 1, 1, 1]) 2025-09-09T15:09:41.1824982Z mul = torch.ops.aten.mul.Tensor(conv_weight, reshape); conv_weight = reshape = None 2025-09-09T15:09:41.1825651Z activation_post_process_1 = self.activation_post_process_1(mul); mul = None 2025-09-09T15:09:41.1826716Z conv2d_1 = torch.ops.aten.conv2d.default(activation_post_process_0, activation_post_process_1, None); activation_post_process_0 = activation_post_process_1 = None 2025-09-09T15:09:41.1827728Z reshape_1 = torch.ops.aten.reshape.default(div, [1, -1, 1, 1]); div = None 2025-09-09T15:09:41.1828368Z div_1 = torch.ops.aten.div.Tensor(conv2d_1, reshape_1); conv2d_1 = reshape_1 = None 2025-09-09T15:09:41.1829439Z batch_norm_1 = torch.ops.aten.batch_norm.default(div_1, bn_weight, bn_bias, bn_running_mean, bn_running_var, True, 0.1, 1e-05, True); div_1 = bn_weight = bn_bias = bn_running_mean = bn_running_var = None 2025-09-09T15:09:41.1830567Z activation_post_process_2 = self.activation_post_process_2(batch_norm_1); batch_norm_1 = None 2025-09-09T15:09:41.1831286Z return pytree.tree_unflatten((activation_post_process_2,), self._out_spec) 2025-09-09T15:09:41.1831750Z 2025-09-09T15:09:41.1832074Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:09:41.1832507Z model fx: GraphModule( 2025-09-09T15:09:41.1832878Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:09:41.1834063Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0189]), zero_point=tensor([-17], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:09:41.1835430Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.0985729694366455, max_val=2.7226178646087646) 2025-09-09T15:09:41.1836044Z ) 2025-09-09T15:09:41.1836254Z (conv): ConvBn2d( 2025-09-09T15:09:41.1836541Z 3, 3, kernel_size=(3, 3), stride=(1, 1), bias=False 2025-09-09T15:09:41.1837057Z (bn): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T15:09:41.1837612Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:09:41.1838772Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0015, 0.0015, 0.0014]), zero_point=tensor([0, 0, 0], dtype=torch.int32), dtype=torch.qint8, quant_min=-127, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False 2025-09-09T15:09:41.1840443Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.1860, -0.1897, -0.1787]), max_val=tensor([0.1824, 0.1870, 0.1478])) 2025-09-09T15:09:41.1841224Z ) 2025-09-09T15:09:41.1841437Z ) 2025-09-09T15:09:41.1841757Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:09:41.1842894Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0177]), zero_point=tensor([-7], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:09:41.1844380Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.1459269523620605, max_val=2.376943349838257) 2025-09-09T15:09:41.1845000Z ) 2025-09-09T15:09:41.1845193Z ) 2025-09-09T15:09:41.1845425Z 2025-09-09T15:09:41.1845430Z 2025-09-09T15:09:41.1845435Z 2025-09-09T15:09:41.1845536Z def forward(self, x): 2025-09-09T15:09:41.1845955Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T15:09:41.1846594Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T15:09:41.1847242Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T15:09:41.1847755Z return activation_post_process_1 2025-09-09T15:09:41.1848054Z 2025-09-09T15:09:41.1848378Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:09:41.1848808Z diff: tensor([[[[0., 0., 0.], 2025-09-09T15:09:41.1849088Z [0., 0., 0.], 2025-09-09T15:09:41.1849328Z [0., 0., 0.]], 2025-09-09T15:09:41.1849499Z 2025-09-09T15:09:41.1849593Z [[0., 0., 0.], 2025-09-09T15:09:41.1849846Z [0., 0., 0.], 2025-09-09T15:09:41.1850081Z [0., 0., 0.]], 2025-09-09T15:09:41.1850251Z 2025-09-09T15:09:41.1850344Z [[0., 0., 0.], 2025-09-09T15:09:41.1850577Z [0., 0., 0.], 2025-09-09T15:09:41.1850821Z [0., 0., 0.]]], 2025-09-09T15:09:41.1850985Z 2025-09-09T15:09:41.1850990Z 2025-09-09T15:09:41.1851077Z [[[0., 0., 0.], 2025-09-09T15:09:41.1851318Z [0., 0., 0.], 2025-09-09T15:09:41.1851551Z [0., 0., 0.]], 2025-09-09T15:09:41.1851718Z 2025-09-09T15:09:41.1851804Z [[0., 0., 0.], 2025-09-09T15:09:41.1852044Z [0., 0., 0.], 2025-09-09T15:09:41.1852277Z [0., 0., 0.]], 2025-09-09T15:09:41.1852436Z 2025-09-09T15:09:41.1852528Z [[0., 0., 0.], 2025-09-09T15:09:41.1852761Z [0., 0., 0.], 2025-09-09T15:09:41.1853004Z [0., 0., 0.]]], 2025-09-09T15:09:41.1853169Z 2025-09-09T15:09:41.1853174Z 2025-09-09T15:09:41.1853268Z [[[0., 0., 0.], 2025-09-09T15:09:41.1853510Z [0., 0., 0.], 2025-09-09T15:09:41.1853743Z [0., 0., 0.]], 2025-09-09T15:09:41.1853918Z 2025-09-09T15:09:41.1854004Z [[0., 0., 0.], 2025-09-09T15:09:41.1854240Z [0., 0., 0.], 2025-09-09T15:09:41.1854472Z [0., 0., 0.]], 2025-09-09T15:09:41.1854630Z 2025-09-09T15:09:41.1854722Z [[0., 0., 0.], 2025-09-09T15:09:41.1854952Z [0., 0., 0.], 2025-09-09T15:09:41.1855228Z [0., 0., 0.]]]], grad_fn=) 2025-09-09T15:09:41.1855584Z converted model pt2e: GraphModule( 2025-09-09T15:09:41.1855895Z (conv): Module() 2025-09-09T15:09:41.1856127Z (bn): Module() 2025-09-09T15:09:41.1856349Z ) 2025-09-09T15:09:41.1856460Z 2025-09-09T15:09:41.1856465Z 2025-09-09T15:09:41.1856470Z 2025-09-09T15:09:41.1856573Z def forward(self, x): 2025-09-09T15:09:41.1856925Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T15:09:41.1857872Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.01890663057565689, -17, -128, 127, torch.int8); x = None 2025-09-09T15:09:41.1859103Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.01890663057565689, -17, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T15:09:41.1859948Z _scale_0 = self._scale_0 2025-09-09T15:09:41.1860202Z _zero_point_0 = self._zero_point_0 2025-09-09T15:09:41.1860505Z quantize_per_channel = self._frozen_param0 2025-09-09T15:09:41.1861371Z dequantize_per_channel = torch.ops.quantized_decomposed.dequantize_per_channel.default(quantize_per_channel, _scale_0, _zero_point_0, 0, -127, 127, torch.int8); quantize_per_channel = _scale_0 = _zero_point_0 = None 2025-09-09T15:09:41.1862229Z conv_weight_bias = self.conv.weight_bias 2025-09-09T15:09:41.1863161Z conv2d_2 = torch.ops.aten.conv2d.default(dequantize_per_tensor_default, dequantize_per_channel, conv_weight_bias); dequantize_per_tensor_default = dequantize_per_channel = conv_weight_bias = None 2025-09-09T15:09:41.1864390Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv2d_2, 0.01773674599826336, -7, -128, 127, torch.int8); conv2d_2 = None 2025-09-09T15:09:41.1865723Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.01773674599826336, -7, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T15:09:41.1866707Z return pytree.tree_unflatten((dequantize_per_tensor_default_1,), self._out_spec) 2025-09-09T15:09:41.1867105Z 2025-09-09T15:09:41.1867372Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:09:41.1867738Z onverted model fx: GraphModule( 2025-09-09T15:09:41.1868110Z (conv): QuantizedConv2d(Reference)(3, 3, kernel_size=(3, 3), stride=(1, 1)) 2025-09-09T15:09:41.1868488Z ) 2025-09-09T15:09:41.1868582Z 2025-09-09T15:09:41.1868591Z 2025-09-09T15:09:41.1868595Z 2025-09-09T15:09:41.1868676Z def forward(self, x): 2025-09-09T15:09:41.1869288Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.01890663057565689, -17, -128, 127, torch.int8); x = None 2025-09-09T15:09:41.1870508Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.01890663057565689, -17, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T15:09:41.1871488Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T15:09:41.1872334Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.01773674599826336, -7, -128, 127, torch.int8); conv = None 2025-09-09T15:09:41.1873577Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.01773674599826336, -7, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T15:09:41.1874429Z return dequantize_per_tensor_default_1 2025-09-09T15:09:41.1883012Z 2025-09-09T15:09:41.1883319Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:09:41.1883709Z diff: tensor([[[[0., 0., 0.], 2025-09-09T15:09:41.1883959Z [0., 0., 0.], 2025-09-09T15:09:41.1884182Z [0., 0., 0.]], 2025-09-09T15:09:41.1884327Z 2025-09-09T15:09:41.1884410Z [[0., 0., 0.], 2025-09-09T15:09:41.1884629Z [0., 0., 0.], 2025-09-09T15:09:41.1884850Z [0., 0., 0.]], 2025-09-09T15:09:41.1885005Z 2025-09-09T15:09:41.1885092Z [[0., 0., 0.], 2025-09-09T15:09:41.1885333Z [0., 0., 0.], 2025-09-09T15:09:41.1885544Z [0., 0., 0.]]], 2025-09-09T15:09:41.1885694Z 2025-09-09T15:09:41.1885698Z 2025-09-09T15:09:41.1885778Z [[[0., 0., 0.], 2025-09-09T15:09:41.1885989Z [0., 0., 0.], 2025-09-09T15:09:41.1886214Z [0., 0., 0.]], 2025-09-09T15:09:41.1886353Z 2025-09-09T15:09:41.1886440Z [[0., 0., 0.], 2025-09-09T15:09:41.1886648Z [0., 0., 0.], 2025-09-09T15:09:41.1886870Z [0., 0., 0.]], 2025-09-09T15:09:41.1887013Z 2025-09-09T15:09:41.1887093Z [[0., 0., 0.], 2025-09-09T15:09:41.1887310Z [0., 0., 0.], 2025-09-09T15:09:41.1887518Z [0., 0., 0.]]], 2025-09-09T15:09:41.1887670Z 2025-09-09T15:09:41.1887674Z 2025-09-09T15:09:41.1887755Z [[[0., 0., 0.], 2025-09-09T15:09:41.1887962Z [0., 0., 0.], 2025-09-09T15:09:41.1888171Z [0., 0., 0.]], 2025-09-09T15:09:41.1888313Z 2025-09-09T15:09:41.1888393Z [[0., 0., 0.], 2025-09-09T15:09:41.1888603Z [0., 0., 0.], 2025-09-09T15:09:41.1888809Z [0., 0., 0.]], 2025-09-09T15:09:41.1888955Z 2025-09-09T15:09:41.1889034Z [[0., 0., 0.], 2025-09-09T15:09:41.1889236Z [0., 0., 0.], 2025-09-09T15:09:41.1889452Z [0., 0., 0.]]]]) 2025-09-09T15:09:41.1889811Z model pt2e: GraphModule( 2025-09-09T15:09:41.1890046Z (conv): Module() 2025-09-09T15:09:41.1890253Z (bn): Module() 2025-09-09T15:09:55.4551499Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:09:55.4553862Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0189]), zero_point=tensor([-17], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:09:55.4554976Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.0985729694366455, max_val=2.7226178646087646) 2025-09-09T15:09:55.4555490Z ) 2025-09-09T15:09:55.4555778Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:09:55.4556718Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0015]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.int8, quant_min=-127, quant_max=127, qscheme=torch.per_tensor_symmetric, reduce_range=False 2025-09-09T15:09:55.4557830Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.18965020775794983, max_val=0.1870359182357788) 2025-09-09T15:09:55.4558354Z ) 2025-09-09T15:09:55.4558632Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:09:55.4559636Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0177]), zero_point=tensor([-7], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:09:55.4560704Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.1475415229797363, max_val=2.368046283721924) 2025-09-09T15:09:55.4561195Z ) 2025-09-09T15:09:55.4561352Z ) 2025-09-09T15:09:55.4561443Z 2025-09-09T15:09:55.4561448Z 2025-09-09T15:09:55.4561451Z 2025-09-09T15:09:55.4561530Z def forward(self, x): 2025-09-09T15:09:55.4561813Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T15:09:55.4562146Z conv_weight = self.conv.weight 2025-09-09T15:09:55.4562422Z bn_weight = self.bn.weight 2025-09-09T15:09:55.4562667Z bn_bias = self.bn.bias 2025-09-09T15:09:55.4562913Z bn_running_mean = self.bn.running_mean 2025-09-09T15:09:55.4563213Z bn_running_var = self.bn.running_var 2025-09-09T15:09:55.4563533Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T15:09:55.4563965Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T15:09:55.4564534Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T15:09:55.4565053Z add = torch.ops.aten.add.Tensor(bn_running_var, 1e-05) 2025-09-09T15:09:55.4565457Z sqrt = torch.ops.aten.sqrt.default(add); add = None 2025-09-09T15:09:55.4565851Z div = torch.ops.aten.div.Tensor(bn_weight, sqrt); sqrt = None 2025-09-09T15:09:55.4566284Z reshape = torch.ops.aten.reshape.default(div, [-1, 1, 1, 1]) 2025-09-09T15:09:55.4566784Z mul = torch.ops.aten.mul.Tensor(conv_weight, reshape); conv_weight = reshape = None 2025-09-09T15:09:55.4567330Z activation_post_process_1 = self.activation_post_process_1(mul); mul = None 2025-09-09T15:09:55.4568158Z conv2d_1 = torch.ops.aten.conv2d.default(activation_post_process_0, activation_post_process_1, None); activation_post_process_0 = activation_post_process_1 = None 2025-09-09T15:09:55.4568971Z reshape_1 = torch.ops.aten.reshape.default(div, [1, -1, 1, 1]); div = None 2025-09-09T15:09:55.4569508Z div_1 = torch.ops.aten.div.Tensor(conv2d_1, reshape_1); conv2d_1 = reshape_1 = None 2025-09-09T15:09:55.4570385Z batch_norm_1 = torch.ops.aten.batch_norm.default(div_1, bn_weight, bn_bias, bn_running_mean, bn_running_var, True, 0.1, 1e-05, True); div_1 = bn_weight = bn_bias = bn_running_mean = bn_running_var = None 2025-09-09T15:09:55.4571289Z activation_post_process_2 = self.activation_post_process_2(batch_norm_1); batch_norm_1 = None 2025-09-09T15:09:55.4572021Z return pytree.tree_unflatten((activation_post_process_2,), self._out_spec) 2025-09-09T15:09:55.4572395Z 2025-09-09T15:09:55.4572672Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:09:55.4573096Z model fx: GraphModule( 2025-09-09T15:09:55.4573415Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:09:55.4574346Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0189]), zero_point=tensor([-17], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:09:55.4575431Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.0985729694366455, max_val=2.7226178646087646) 2025-09-09T15:09:55.4575935Z ) 2025-09-09T15:09:55.4576106Z (conv): ConvBn2d( 2025-09-09T15:09:55.4576355Z 3, 3, kernel_size=(3, 3), stride=(1, 1), bias=False 2025-09-09T15:09:55.4576785Z (bn): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T15:09:55.4577251Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:09:55.4578154Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0015]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-127, quant_max=127, qscheme=torch.per_tensor_symmetric, reduce_range=False 2025-09-09T15:09:55.4579313Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.18965020775794983, max_val=0.1870359182357788) 2025-09-09T15:09:55.4579824Z ) 2025-09-09T15:09:55.4579994Z ) 2025-09-09T15:09:55.4580265Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:09:55.4581196Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0177]), zero_point=tensor([-7], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:09:55.4582271Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.1475415229797363, max_val=2.368046283721924) 2025-09-09T15:09:55.4582773Z ) 2025-09-09T15:09:55.4582933Z ) 2025-09-09T15:09:55.4583030Z 2025-09-09T15:09:55.4583035Z 2025-09-09T15:09:55.4583044Z 2025-09-09T15:09:55.4583125Z def forward(self, x): 2025-09-09T15:09:55.4583472Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T15:09:55.4584000Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T15:09:55.4584540Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T15:09:55.4584954Z return activation_post_process_1 2025-09-09T15:09:55.4585209Z 2025-09-09T15:09:55.4585480Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:09:55.4585842Z diff: tensor([[[[0., 0., 0.], 2025-09-09T15:09:55.4586070Z [0., 0., 0.], 2025-09-09T15:09:55.4586281Z [0., 0., 0.]], 2025-09-09T15:09:55.4586417Z 2025-09-09T15:09:55.4586495Z [[0., 0., 0.], 2025-09-09T15:09:55.4586696Z [0., 0., 0.], 2025-09-09T15:09:55.4586899Z [0., 0., 0.]], 2025-09-09T15:09:55.4587034Z 2025-09-09T15:09:55.4587105Z [[0., 0., 0.], 2025-09-09T15:09:55.4587309Z [0., 0., 0.], 2025-09-09T15:09:55.4587507Z [0., 0., 0.]]], 2025-09-09T15:09:55.4587652Z 2025-09-09T15:09:55.4587656Z 2025-09-09T15:09:55.4587729Z [[[0., 0., 0.], 2025-09-09T15:09:55.4587930Z [0., 0., 0.], 2025-09-09T15:09:55.4588124Z [0., 0., 0.]], 2025-09-09T15:09:55.4588258Z 2025-09-09T15:09:55.4588337Z [[0., 0., 0.], 2025-09-09T15:09:55.4588531Z [0., 0., 0.], 2025-09-09T15:09:55.4588733Z [0., 0., 0.]], 2025-09-09T15:09:55.4588870Z 2025-09-09T15:09:55.4588957Z [[0., 0., 0.], 2025-09-09T15:09:55.4589189Z [0., 0., 0.], 2025-09-09T15:09:55.4589387Z [0., 0., 0.]]], 2025-09-09T15:09:55.4589529Z 2025-09-09T15:09:55.4589532Z 2025-09-09T15:09:55.4589605Z [[[0., 0., 0.], 2025-09-09T15:09:55.4589891Z [0., 0., 0.], 2025-09-09T15:09:55.4590090Z [0., 0., 0.]], 2025-09-09T15:09:55.4590224Z 2025-09-09T15:09:55.4590303Z [[0., 0., 0.], 2025-09-09T15:09:55.4590577Z [0., 0., 0.], 2025-09-09T15:09:55.4590791Z [0., 0., 0.]], 2025-09-09T15:09:55.4590923Z 2025-09-09T15:09:55.4590995Z [[0., 0., 0.], 2025-09-09T15:09:55.4591196Z [0., 0., 0.], 2025-09-09T15:09:55.4591426Z [0., 0., 0.]]]], grad_fn=) 2025-09-09T15:09:55.4591737Z converted model pt2e: GraphModule( 2025-09-09T15:09:55.4591997Z (conv): Module() 2025-09-09T15:09:55.4592189Z (bn): Module() 2025-09-09T15:09:55.4592377Z ) 2025-09-09T15:09:55.4592473Z 2025-09-09T15:09:55.4592477Z 2025-09-09T15:09:55.4592480Z 2025-09-09T15:09:55.4592567Z def forward(self, x): 2025-09-09T15:09:55.4592844Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T15:09:55.4593559Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.01890663057565689, -17, -128, 127, torch.int8); x = None 2025-09-09T15:09:55.4594785Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.01890663057565689, -17, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T15:09:55.4595666Z quantize_per_tensor = self._frozen_param0 2025-09-09T15:09:55.4596448Z dequantize_per_tensor = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor, 0.0014933086931705475, 0, -127, 127, torch.int8); quantize_per_tensor = None 2025-09-09T15:09:55.4597238Z conv_weight_bias = self.conv.weight_bias 2025-09-09T15:09:55.4598060Z conv2d_2 = torch.ops.aten.conv2d.default(dequantize_per_tensor_default, dequantize_per_tensor, conv_weight_bias); dequantize_per_tensor_default = dequantize_per_tensor = conv_weight_bias = None 2025-09-09T15:09:55.4599356Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv2d_2, 0.01770818792283535, -7, -128, 127, torch.int8); conv2d_2 = None 2025-09-09T15:09:55.4600632Z dequantize_per_tensor_default_2 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_2, 0.01770818792283535, -7, -128, 127, torch.int8); quantize_per_tensor_default_2 = None 2025-09-09T15:09:55.4601618Z return pytree.tree_unflatten((dequantize_per_tensor_default_2,), self._out_spec) 2025-09-09T15:09:55.4602019Z 2025-09-09T15:09:55.4602302Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:09:55.4602667Z onverted model fx: GraphModule( 2025-09-09T15:09:55.4603049Z (conv): QuantizedConv2d(Reference)(3, 3, kernel_size=(3, 3), stride=(1, 1)) 2025-09-09T15:09:55.4603419Z ) 2025-09-09T15:09:55.4603518Z 2025-09-09T15:09:55.4603522Z 2025-09-09T15:09:55.4603525Z 2025-09-09T15:09:55.4603606Z def forward(self, x): 2025-09-09T15:09:55.4604216Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.01890663057565689, -17, -128, 127, torch.int8); x = None 2025-09-09T15:09:55.4605438Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.01890663057565689, -17, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T15:09:55.4606436Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T15:09:55.4607271Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.01770818792283535, -7, -128, 127, torch.int8); conv = None 2025-09-09T15:09:55.4608528Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.01770818792283535, -7, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T15:09:55.4609398Z return dequantize_per_tensor_default_1 2025-09-09T15:09:55.4609659Z 2025-09-09T15:09:55.4610028Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:09:55.4610382Z diff: tensor([[[[0., 0., 0.], 2025-09-09T15:10:01.0354714Z [0., 0., 0.], 2025-09-09T15:10:01.0355353Z [0., 0., 0.]], 2025-09-09T15:10:01.0355527Z 2025-09-09T15:10:01.0355624Z [[0., 0., 0.], 2025-09-09T15:10:01.0355905Z [0., 0., 0.], 2025-09-09T15:10:01.0356106Z [0., 0., 0.]], 2025-09-09T15:10:01.0356248Z 2025-09-09T15:10:01.0356320Z [[0., 0., 0.], 2025-09-09T15:10:01.0356513Z [0., 0., 0.], 2025-09-09T15:10:01.0356716Z [0., 0., 0.]]], 2025-09-09T15:10:01.0356856Z 2025-09-09T15:10:01.0356876Z 2025-09-09T15:10:01.0356959Z [[[0., 0., 0.], 2025-09-09T15:10:01.0357153Z [0., 0., 0.], 2025-09-09T15:10:01.0357351Z [0., 0., 0.]], 2025-09-09T15:10:01.0357484Z 2025-09-09T15:10:01.0357555Z [[0., 0., 0.], 2025-09-09T15:10:01.0357751Z [0., 0., 0.], 2025-09-09T15:10:01.0357945Z [0., 0., 0.]], 2025-09-09T15:10:01.0358108Z 2025-09-09T15:10:01.0358179Z [[0., 0., 0.], 2025-09-09T15:10:01.0358378Z [0., 0., 0.], 2025-09-09T15:10:01.0358573Z [0., 0., 0.]]], 2025-09-09T15:10:01.0358721Z 2025-09-09T15:10:01.0358724Z 2025-09-09T15:10:01.0358807Z [[[0., 0., 0.], 2025-09-09T15:10:01.0359002Z [0., 0., 0.], 2025-09-09T15:10:01.0359203Z [0., 0., 0.]], 2025-09-09T15:10:01.0359441Z 2025-09-09T15:10:01.0359518Z [[0., 0., 0.], 2025-09-09T15:10:01.0359712Z [0., 0., 0.], 2025-09-09T15:10:01.0359907Z [0., 0., 0.]], 2025-09-09T15:10:01.0360062Z 2025-09-09T15:10:01.0360135Z [[0., 0., 0.], 2025-09-09T15:10:01.0360326Z [0., 0., 0.], 2025-09-09T15:10:01.0360531Z [0., 0., 0.]]]]) 2025-09-09T15:10:01.0360767Z model pt2e: GraphModule( 2025-09-09T15:10:01.0360984Z (conv1): Module() 2025-09-09T15:10:01.0361179Z (bn1): Module() 2025-09-09T15:10:01.0361368Z (conv2): Module() 2025-09-09T15:10:01.0361562Z (bn2): Module() 2025-09-09T15:10:01.0361857Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:10:01.0362787Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0189]), zero_point=tensor([-17], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:10:01.0363897Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.0985729694366455, max_val=2.7226178646087646) 2025-09-09T15:10:01.0364401Z ) 2025-09-09T15:10:01.0364682Z (activation_post_process_3): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:10:01.0365645Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0015, 0.0015, 0.0015]), zero_point=tensor([0, 0, 0], dtype=torch.int32), dtype=torch.int8, quant_min=-127, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False 2025-09-09T15:10:01.0366911Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.1921, -0.1899, -0.1895]), max_val=tensor([0.1769, 0.1726, 0.1697])) 2025-09-09T15:10:01.0367547Z ) 2025-09-09T15:10:01.0367818Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:10:01.0368791Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0015, 0.0014, 0.0015]), zero_point=tensor([0, 0, 0], dtype=torch.int32), dtype=torch.int8, quant_min=-127, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False 2025-09-09T15:10:01.0370091Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.1913, -0.1469, -0.1921]), max_val=tensor([0.1740, 0.1746, 0.1810])) 2025-09-09T15:10:01.0370726Z ) 2025-09-09T15:10:01.0370993Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:10:01.0372075Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0174]), zero_point=tensor([-29], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:10:01.0373159Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.7323514223098755, max_val=2.7138354778289795) 2025-09-09T15:10:01.0373736Z ) 2025-09-09T15:10:01.0374005Z (activation_post_process_4): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:10:01.0374919Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0110]), zero_point=tensor([-1], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:10:01.0375985Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.4053945541381836, max_val=1.4082176685333252) 2025-09-09T15:10:01.0376487Z ) 2025-09-09T15:10:01.0376646Z ) 2025-09-09T15:10:01.0376743Z 2025-09-09T15:10:01.0376747Z 2025-09-09T15:10:01.0376766Z 2025-09-09T15:10:01.0376849Z def forward(self, x): 2025-09-09T15:10:01.0377129Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T15:10:01.0377463Z conv1_weight = self.conv1.weight 2025-09-09T15:10:01.0377752Z bn1_weight = self.bn1.weight 2025-09-09T15:10:01.0378002Z bn1_bias = self.bn1.bias 2025-09-09T15:10:01.0378255Z conv2_weight = self.conv2.weight 2025-09-09T15:10:01.0378528Z conv2_bias = self.conv2.bias 2025-09-09T15:10:01.0378776Z bn2_weight = self.bn2.weight 2025-09-09T15:10:01.0379028Z bn2_bias = self.bn2.bias 2025-09-09T15:10:01.0379284Z bn1_running_mean = self.bn1.running_mean 2025-09-09T15:10:01.0379586Z bn1_running_var = self.bn1.running_var 2025-09-09T15:10:01.0379917Z bn1_num_batches_tracked = self.bn1.num_batches_tracked 2025-09-09T15:10:01.0380261Z bn2_running_mean = self.bn2.running_mean 2025-09-09T15:10:01.0380550Z bn2_running_var = self.bn2.running_var 2025-09-09T15:10:01.0380880Z bn2_num_batches_tracked = self.bn2.num_batches_tracked 2025-09-09T15:10:01.0381318Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T15:10:01.0381908Z add_ = torch.ops.aten.add_.Tensor(bn1_num_batches_tracked, 1); bn1_num_batches_tracked = add_ = None 2025-09-09T15:10:01.0382567Z add__1 = torch.ops.aten.add_.Tensor(bn2_num_batches_tracked, 1); bn2_num_batches_tracked = add__1 = None 2025-09-09T15:10:01.0383096Z add = torch.ops.aten.add.Tensor(bn2_running_var, 1e-05) 2025-09-09T15:10:01.0383491Z sqrt = torch.ops.aten.sqrt.default(add); add = None 2025-09-09T15:10:01.0383891Z div = torch.ops.aten.div.Tensor(bn2_weight, sqrt); sqrt = None 2025-09-09T15:10:01.0384329Z reshape = torch.ops.aten.reshape.default(div, [-1, 1, 1, 1]) 2025-09-09T15:10:01.0384836Z mul = torch.ops.aten.mul.Tensor(conv2_weight, reshape); conv2_weight = reshape = None 2025-09-09T15:10:01.0385391Z activation_post_process_3 = self.activation_post_process_3(mul); mul = None 2025-09-09T15:10:01.0386001Z zeros_like = torch.ops.aten.zeros_like.default(conv2_bias, dtype = torch.float32, pin_memory = False) 2025-09-09T15:10:01.0386546Z add_2 = torch.ops.aten.add.Tensor(bn1_running_var, 1e-05) 2025-09-09T15:10:01.0386953Z sqrt_1 = torch.ops.aten.sqrt.default(add_2); add_2 = None 2025-09-09T15:10:01.0387377Z div_2 = torch.ops.aten.div.Tensor(bn1_weight, sqrt_1); sqrt_1 = None 2025-09-09T15:10:01.0387844Z reshape_3 = torch.ops.aten.reshape.default(div_2, [-1, 1, 1, 1]) 2025-09-09T15:10:01.0388375Z mul_1 = torch.ops.aten.mul.Tensor(conv1_weight, reshape_3); conv1_weight = reshape_3 = None 2025-09-09T15:10:01.0388954Z activation_post_process_1 = self.activation_post_process_1(mul_1); mul_1 = None 2025-09-09T15:10:01.0389792Z conv2d_3 = torch.ops.aten.conv2d.default(activation_post_process_0, activation_post_process_1, None); activation_post_process_0 = activation_post_process_1 = None 2025-09-09T15:10:01.0390660Z reshape_4 = torch.ops.aten.reshape.default(div_2, [1, -1, 1, 1]); div_2 = None 2025-09-09T15:10:01.0391207Z div_3 = torch.ops.aten.div.Tensor(conv2d_3, reshape_4); conv2d_3 = reshape_4 = None 2025-09-09T15:10:01.0392206Z batch_norm_3 = torch.ops.aten.batch_norm.default(div_3, bn1_weight, bn1_bias, bn1_running_mean, bn1_running_var, True, 0.1, 1e-05, True); div_3 = bn1_weight = bn1_bias = bn1_running_mean = bn1_running_var = None 2025-09-09T15:10:01.0393220Z activation_post_process_2 = self.activation_post_process_2(batch_norm_3); batch_norm_3 = None 2025-09-09T15:10:01.0394163Z conv2d_2 = torch.ops.aten.conv2d.default(activation_post_process_2, activation_post_process_3, zeros_like); activation_post_process_2 = activation_post_process_3 = zeros_like = None 2025-09-09T15:10:01.0395044Z reshape_1 = torch.ops.aten.reshape.default(div, [1, -1, 1, 1]); div = None 2025-09-09T15:10:01.0395572Z div_1 = torch.ops.aten.div.Tensor(conv2d_2, reshape_1); conv2d_2 = reshape_1 = None 2025-09-09T15:10:01.0396153Z reshape_2 = torch.ops.aten.reshape.default(conv2_bias, [1, -1, 1, 1]); conv2_bias = None 2025-09-09T15:10:01.0396718Z add_1 = torch.ops.aten.add.Tensor(div_1, reshape_2); div_1 = reshape_2 = None 2025-09-09T15:10:01.0397594Z batch_norm_2 = torch.ops.aten.batch_norm.default(add_1, bn2_weight, bn2_bias, bn2_running_mean, bn2_running_var, True, 0.1, 1e-05, True); add_1 = bn2_weight = bn2_bias = bn2_running_mean = bn2_running_var = None 2025-09-09T15:10:01.0398544Z activation_post_process_4 = self.activation_post_process_4(batch_norm_2); batch_norm_2 = None 2025-09-09T15:10:01.0399126Z return pytree.tree_unflatten((activation_post_process_4,), self._out_spec) 2025-09-09T15:10:01.0399589Z 2025-09-09T15:10:01.0399864Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:10:01.0400218Z model fx: GraphModule( 2025-09-09T15:10:01.0400529Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:10:01.0401462Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0189]), zero_point=tensor([-17], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:10:01.0402549Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.0985729694366455, max_val=2.7226178646087646) 2025-09-09T15:10:01.0403050Z ) 2025-09-09T15:10:01.0403223Z (conv1): ConvBn2d( 2025-09-09T15:10:01.0403469Z 3, 3, kernel_size=(3, 3), stride=(1, 1), bias=False 2025-09-09T15:10:01.0403896Z (bn): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T15:10:01.0404353Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:10:01.0405293Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0015, 0.0014, 0.0015]), zero_point=tensor([0, 0, 0], dtype=torch.int32), dtype=torch.qint8, quant_min=-127, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False 2025-09-09T15:10:01.0406563Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.1913, -0.1469, -0.1921]), max_val=tensor([0.1740, 0.1746, 0.1810])) 2025-09-09T15:10:01.0407195Z ) 2025-09-09T15:10:01.0407365Z ) 2025-09-09T15:10:01.0407633Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:10:01.0408567Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0174]), zero_point=tensor([-29], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:10:01.0409682Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.7323514223098755, max_val=2.7138354778289795) 2025-09-09T15:10:01.0410203Z ) 2025-09-09T15:10:01.0410379Z (conv2): ConvBn2d( 2025-09-09T15:10:01.0410606Z 3, 3, kernel_size=(3, 3), stride=(1, 1) 2025-09-09T15:10:01.0411013Z (bn): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T15:10:18.3332006Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:10:18.3333557Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0015, 0.0015, 0.0015]), zero_point=tensor([0, 0, 0], dtype=torch.int32), dtype=torch.qint8, quant_min=-127, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False 2025-09-09T15:10:18.3335340Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.1921, -0.1899, -0.1895]), max_val=tensor([0.1769, 0.1726, 0.1697])) 2025-09-09T15:10:18.3336138Z ) 2025-09-09T15:10:18.3336341Z ) 2025-09-09T15:10:18.3336668Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:10:18.3337825Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0110]), zero_point=tensor([-1], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:10:18.3339169Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.4053945541381836, max_val=1.4082176685333252) 2025-09-09T15:10:18.3339792Z ) 2025-09-09T15:10:18.3339987Z ) 2025-09-09T15:10:18.3340107Z 2025-09-09T15:10:18.3340118Z 2025-09-09T15:10:18.3340124Z 2025-09-09T15:10:18.3340221Z def forward(self, x): 2025-09-09T15:10:18.3340652Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T15:10:18.3341305Z conv1 = self.conv1(activation_post_process_0); activation_post_process_0 = None 2025-09-09T15:10:18.3341979Z activation_post_process_1 = self.activation_post_process_1(conv1); conv1 = None 2025-09-09T15:10:18.3342646Z conv2 = self.conv2(activation_post_process_1); activation_post_process_1 = None 2025-09-09T15:10:18.3343314Z activation_post_process_2 = self.activation_post_process_2(conv2); conv2 = None 2025-09-09T15:10:18.3343831Z return activation_post_process_2 2025-09-09T15:10:18.3344132Z 2025-09-09T15:10:18.3344458Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:10:18.3344881Z diff: tensor([[[[0.]], 2025-09-09T15:10:18.3345048Z 2025-09-09T15:10:18.3345147Z [[0.]], 2025-09-09T15:10:18.3345297Z 2025-09-09T15:10:18.3345380Z [[0.]]], 2025-09-09T15:10:18.3345534Z 2025-09-09T15:10:18.3345539Z 2025-09-09T15:10:18.3345624Z [[[0.]], 2025-09-09T15:10:18.3345771Z 2025-09-09T15:10:18.3345867Z [[0.]], 2025-09-09T15:10:18.3346002Z 2025-09-09T15:10:18.3346086Z [[0.]]], 2025-09-09T15:10:18.3346231Z 2025-09-09T15:10:18.3346236Z 2025-09-09T15:10:18.3346330Z [[[0.]], 2025-09-09T15:10:18.3346466Z 2025-09-09T15:10:18.3346553Z [[0.]], 2025-09-09T15:10:18.3346699Z 2025-09-09T15:10:18.3346817Z [[0.]]]], grad_fn=) 2025-09-09T15:10:18.3347162Z converted model pt2e: GraphModule( 2025-09-09T15:10:18.3347476Z (conv1): Module() 2025-09-09T15:10:18.3347725Z (bn1): Module() 2025-09-09T15:10:18.3347959Z (conv2): Module() 2025-09-09T15:10:18.3348200Z (bn2): Module() 2025-09-09T15:10:18.3348419Z ) 2025-09-09T15:10:18.3348535Z 2025-09-09T15:10:18.3348540Z 2025-09-09T15:10:18.3348544Z 2025-09-09T15:10:18.3348648Z def forward(self, x): 2025-09-09T15:10:18.3348977Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T15:10:18.3349382Z conv2_bias = self.conv2.bias 2025-09-09T15:10:18.3350178Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.01890663057565689, -17, -128, 127, torch.int8); x = None 2025-09-09T15:10:18.3351702Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.01890663057565689, -17, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T15:10:18.3352745Z _scale_0 = self._scale_0 2025-09-09T15:10:18.3353040Z _zero_point_0 = self._zero_point_0 2025-09-09T15:10:18.3353368Z _scale_1 = self._scale_1 2025-09-09T15:10:18.3353656Z _zero_point_1 = self._zero_point_1 2025-09-09T15:10:18.3354015Z quantize_per_channel_1 = self._frozen_param0 2025-09-09T15:10:18.3355219Z dequantize_per_channel_1 = torch.ops.quantized_decomposed.dequantize_per_channel.default(quantize_per_channel_1, _scale_1, _zero_point_1, 0, -127, 127, torch.int8); quantize_per_channel_1 = _scale_1 = _zero_point_1 = None 2025-09-09T15:10:18.3356397Z conv1_weight_bias = self.conv1.weight_bias 2025-09-09T15:10:18.3357453Z conv2d_5 = torch.ops.aten.conv2d.default(dequantize_per_tensor_default, dequantize_per_channel_1, conv1_weight_bias); dequantize_per_tensor_default = dequantize_per_channel_1 = conv1_weight_bias = None 2025-09-09T15:10:18.3359007Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv2d_5, 0.01743602752685547, -29, -128, 127, torch.int8); conv2d_5 = None 2025-09-09T15:10:18.3360700Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.01743602752685547, -29, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T15:10:18.3361779Z quantize_per_channel = self._frozen_param1 2025-09-09T15:10:18.3362857Z dequantize_per_channel = torch.ops.quantized_decomposed.dequantize_per_channel.default(quantize_per_channel, _scale_0, _zero_point_0, 0, -127, 127, torch.int8); quantize_per_channel = _scale_0 = _zero_point_0 = None 2025-09-09T15:10:18.3364539Z conv2d_4 = torch.ops.aten.conv2d.default(dequantize_per_tensor_default_1, dequantize_per_channel, conv2_bias); dequantize_per_tensor_default_1 = dequantize_per_channel = conv2_bias = None 2025-09-09T15:10:18.3366131Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv2d_4, 0.011033773422241211, -1, -128, 127, torch.int8); conv2d_4 = None 2025-09-09T15:10:18.3367388Z dequantize_per_tensor_default_2 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_2, 0.011033773422241211, -1, -128, 127, torch.int8); quantize_per_tensor_default_2 = None 2025-09-09T15:10:18.3368379Z return pytree.tree_unflatten((dequantize_per_tensor_default_2,), self._out_spec) 2025-09-09T15:10:18.3368776Z 2025-09-09T15:10:18.3369049Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:10:18.3369414Z onverted model fx: GraphModule( 2025-09-09T15:10:18.3369793Z (conv1): QuantizedConv2d(Reference)(3, 3, kernel_size=(3, 3), stride=(1, 1)) 2025-09-09T15:10:18.3370305Z (conv2): QuantizedConv2d(Reference)(3, 3, kernel_size=(3, 3), stride=(1, 1)) 2025-09-09T15:10:18.3370670Z ) 2025-09-09T15:10:18.3370768Z 2025-09-09T15:10:18.3370772Z 2025-09-09T15:10:18.3370776Z 2025-09-09T15:10:18.3370857Z def forward(self, x): 2025-09-09T15:10:18.3371466Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.01890663057565689, -17, -128, 127, torch.int8); x = None 2025-09-09T15:10:18.3372669Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.01890663057565689, -17, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T15:10:18.3373667Z conv1 = self.conv1(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T15:10:18.3374518Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv1, 0.01743602752685547, -29, -128, 127, torch.int8); conv1 = None 2025-09-09T15:10:18.3375782Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.01743602752685547, -29, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T15:10:18.3376796Z conv2 = self.conv2(dequantize_per_tensor_default_1); dequantize_per_tensor_default_1 = None 2025-09-09T15:10:18.3377650Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv2, 0.011033773422241211, -1, -128, 127, torch.int8); conv2 = None 2025-09-09T15:10:18.3379003Z dequantize_per_tensor_default_2 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_2, 0.011033773422241211, -1, -128, 127, torch.int8); quantize_per_tensor_default_2 = None 2025-09-09T15:10:18.3379865Z return dequantize_per_tensor_default_2 2025-09-09T15:10:18.3380122Z 2025-09-09T15:10:18.3380479Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:10:18.3380822Z diff: tensor([[[[0.]], 2025-09-09T15:10:18.3380967Z 2025-09-09T15:10:18.3381035Z [[0.]], 2025-09-09T15:10:18.3381151Z 2025-09-09T15:10:18.3381220Z [[0.]]], 2025-09-09T15:10:18.3381344Z 2025-09-09T15:10:18.3381347Z 2025-09-09T15:10:18.3381417Z [[[0.]], 2025-09-09T15:10:18.3381530Z 2025-09-09T15:10:18.3381603Z [[0.]], 2025-09-09T15:10:18.3381716Z 2025-09-09T15:10:18.3381788Z [[0.]]], 2025-09-09T15:10:18.3381906Z 2025-09-09T15:10:18.3381910Z 2025-09-09T15:10:18.3381979Z [[[0.]], 2025-09-09T15:10:18.3382092Z 2025-09-09T15:10:18.3382160Z [[0.]], 2025-09-09T15:10:18.3382279Z 2025-09-09T15:10:18.3382347Z [[0.]]]]) 2025-09-09T15:10:18.3382561Z model pt2e: GraphModule( 2025-09-09T15:10:18.3382780Z (conv1): Module() 2025-09-09T15:10:18.3382979Z (bn1): Module() 2025-09-09T15:10:18.3383165Z (conv2): Module() 2025-09-09T15:10:18.3383364Z (bn2): Module() 2025-09-09T15:10:18.3383651Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:10:18.3384579Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0189]), zero_point=tensor([-17], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:10:18.3385661Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.0985729694366455, max_val=2.7226178646087646) 2025-09-09T15:10:18.3386159Z ) 2025-09-09T15:10:18.3386429Z (activation_post_process_3): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:10:18.3387355Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0015]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.int8, quant_min=-127, quant_max=127, qscheme=torch.per_tensor_symmetric, reduce_range=False 2025-09-09T15:10:18.3388435Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.1921343356370926, max_val=0.1768510341644287) 2025-09-09T15:10:18.3388953Z ) 2025-09-09T15:10:18.3389217Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:10:18.3390137Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0015]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.int8, quant_min=-127, quant_max=127, qscheme=torch.per_tensor_symmetric, reduce_range=False 2025-09-09T15:10:18.3399191Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.19212442636489868, max_val=0.18097376823425293) 2025-09-09T15:10:18.3399887Z ) 2025-09-09T15:10:18.3400186Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:10:18.3401143Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0174]), zero_point=tensor([-29], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:10:18.3402257Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.7288155555725098, max_val=2.7138354778289795) 2025-09-09T15:10:18.3402781Z ) 2025-09-09T15:10:18.3403067Z (activation_post_process_4): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:10:35.4166804Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0110]), zero_point=tensor([-1], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:10:35.4168264Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.4025696516036987, max_val=1.4086220264434814) 2025-09-09T15:10:35.4168903Z ) 2025-09-09T15:10:35.4169095Z ) 2025-09-09T15:10:35.4169217Z 2025-09-09T15:10:35.4169222Z 2025-09-09T15:10:35.4169227Z 2025-09-09T15:10:35.4169324Z def forward(self, x): 2025-09-09T15:10:35.4169992Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T15:10:35.4170420Z conv1_weight = self.conv1.weight 2025-09-09T15:10:35.4170770Z bn1_weight = self.bn1.weight 2025-09-09T15:10:35.4171297Z bn1_bias = self.bn1.bias 2025-09-09T15:10:35.4171603Z conv2_weight = self.conv2.weight 2025-09-09T15:10:35.4171925Z conv2_bias = self.conv2.bias 2025-09-09T15:10:35.4172233Z bn2_weight = self.bn2.weight 2025-09-09T15:10:35.4172530Z bn2_bias = self.bn2.bias 2025-09-09T15:10:35.4172848Z bn1_running_mean = self.bn1.running_mean 2025-09-09T15:10:35.4173206Z bn1_running_var = self.bn1.running_var 2025-09-09T15:10:35.4173612Z bn1_num_batches_tracked = self.bn1.num_batches_tracked 2025-09-09T15:10:35.4174030Z bn2_running_mean = self.bn2.running_mean 2025-09-09T15:10:35.4174388Z bn2_running_var = self.bn2.running_var 2025-09-09T15:10:35.4174783Z bn2_num_batches_tracked = self.bn2.num_batches_tracked 2025-09-09T15:10:35.4175318Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T15:10:35.4176033Z add_ = torch.ops.aten.add_.Tensor(bn1_num_batches_tracked, 1); bn1_num_batches_tracked = add_ = None 2025-09-09T15:10:35.4176844Z add__1 = torch.ops.aten.add_.Tensor(bn2_num_batches_tracked, 1); bn2_num_batches_tracked = add__1 = None 2025-09-09T15:10:35.4177495Z add = torch.ops.aten.add.Tensor(bn2_running_var, 1e-05) 2025-09-09T15:10:35.4177966Z sqrt = torch.ops.aten.sqrt.default(add); add = None 2025-09-09T15:10:35.4178445Z div = torch.ops.aten.div.Tensor(bn2_weight, sqrt); sqrt = None 2025-09-09T15:10:35.4178980Z reshape = torch.ops.aten.reshape.default(div, [-1, 1, 1, 1]) 2025-09-09T15:10:35.4179585Z mul = torch.ops.aten.mul.Tensor(conv2_weight, reshape); conv2_weight = reshape = None 2025-09-09T15:10:35.4180268Z activation_post_process_3 = self.activation_post_process_3(mul); mul = None 2025-09-09T15:10:35.4181007Z zeros_like = torch.ops.aten.zeros_like.default(conv2_bias, dtype = torch.float32, pin_memory = False) 2025-09-09T15:10:35.4181665Z add_2 = torch.ops.aten.add.Tensor(bn1_running_var, 1e-05) 2025-09-09T15:10:35.4182150Z sqrt_1 = torch.ops.aten.sqrt.default(add_2); add_2 = None 2025-09-09T15:10:35.4182666Z div_2 = torch.ops.aten.div.Tensor(bn1_weight, sqrt_1); sqrt_1 = None 2025-09-09T15:10:35.4183220Z reshape_3 = torch.ops.aten.reshape.default(div_2, [-1, 1, 1, 1]) 2025-09-09T15:10:35.4183852Z mul_1 = torch.ops.aten.mul.Tensor(conv1_weight, reshape_3); conv1_weight = reshape_3 = None 2025-09-09T15:10:35.4184557Z activation_post_process_1 = self.activation_post_process_1(mul_1); mul_1 = None 2025-09-09T15:10:35.4185587Z conv2d_3 = torch.ops.aten.conv2d.default(activation_post_process_0, activation_post_process_1, None); activation_post_process_0 = activation_post_process_1 = None 2025-09-09T15:10:35.4186601Z reshape_4 = torch.ops.aten.reshape.default(div_2, [1, -1, 1, 1]); div_2 = None 2025-09-09T15:10:35.4187269Z div_3 = torch.ops.aten.div.Tensor(conv2d_3, reshape_4); conv2d_3 = reshape_4 = None 2025-09-09T15:10:35.4188378Z batch_norm_3 = torch.ops.aten.batch_norm.default(div_3, bn1_weight, bn1_bias, bn1_running_mean, bn1_running_var, True, 0.1, 1e-05, True); div_3 = bn1_weight = bn1_bias = bn1_running_mean = bn1_running_var = None 2025-09-09T15:10:35.4189555Z activation_post_process_2 = self.activation_post_process_2(batch_norm_3); batch_norm_3 = None 2025-09-09T15:10:35.4190719Z conv2d_2 = torch.ops.aten.conv2d.default(activation_post_process_2, activation_post_process_3, zeros_like); activation_post_process_2 = activation_post_process_3 = zeros_like = None 2025-09-09T15:10:35.4191792Z reshape_1 = torch.ops.aten.reshape.default(div, [1, -1, 1, 1]); div = None 2025-09-09T15:10:35.4192442Z div_1 = torch.ops.aten.div.Tensor(conv2d_2, reshape_1); conv2d_2 = reshape_1 = None 2025-09-09T15:10:35.4193143Z reshape_2 = torch.ops.aten.reshape.default(conv2_bias, [1, -1, 1, 1]); conv2_bias = None 2025-09-09T15:10:35.4193918Z add_1 = torch.ops.aten.add.Tensor(div_1, reshape_2); div_1 = reshape_2 = None 2025-09-09T15:10:35.4194999Z batch_norm_2 = torch.ops.aten.batch_norm.default(add_1, bn2_weight, bn2_bias, bn2_running_mean, bn2_running_var, True, 0.1, 1e-05, True); add_1 = bn2_weight = bn2_bias = bn2_running_mean = bn2_running_var = None 2025-09-09T15:10:35.4196243Z activation_post_process_4 = self.activation_post_process_4(batch_norm_2); batch_norm_2 = None 2025-09-09T15:10:35.4196961Z return pytree.tree_unflatten((activation_post_process_4,), self._out_spec) 2025-09-09T15:10:35.4197420Z 2025-09-09T15:10:35.4197750Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:10:35.4198181Z model fx: GraphModule( 2025-09-09T15:10:35.4198555Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:10:35.4199773Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0189]), zero_point=tensor([-17], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:10:35.4201123Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.0985729694366455, max_val=2.7226178646087646) 2025-09-09T15:10:35.4201757Z ) 2025-09-09T15:10:35.4201961Z (conv1): ConvBn2d( 2025-09-09T15:10:35.4202266Z 3, 3, kernel_size=(3, 3), stride=(1, 1), bias=False 2025-09-09T15:10:35.4202783Z (bn): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T15:10:35.4203330Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:10:35.4204448Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0015]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-127, quant_max=127, qscheme=torch.per_tensor_symmetric, reduce_range=False 2025-09-09T15:10:35.4205809Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.19212442636489868, max_val=0.18097376823425293) 2025-09-09T15:10:35.4206452Z ) 2025-09-09T15:10:35.4206652Z ) 2025-09-09T15:10:35.4206966Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:10:35.4208114Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0174]), zero_point=tensor([-29], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:10:35.4209458Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.7288155555725098, max_val=2.7138354778289795) 2025-09-09T15:10:35.4210081Z ) 2025-09-09T15:10:35.4210281Z (conv2): ConvBn2d( 2025-09-09T15:10:35.4210559Z 3, 3, kernel_size=(3, 3), stride=(1, 1) 2025-09-09T15:10:35.4211050Z (bn): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T15:10:35.4211601Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:10:35.4212719Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0015]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-127, quant_max=127, qscheme=torch.per_tensor_symmetric, reduce_range=False 2025-09-09T15:10:35.4214079Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.1921343356370926, max_val=0.1768510341644287) 2025-09-09T15:10:35.4214710Z ) 2025-09-09T15:10:35.4214920Z ) 2025-09-09T15:10:35.4215239Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:10:35.4216382Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0110]), zero_point=tensor([-1], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:10:35.4217723Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.4025696516036987, max_val=1.4086220264434814) 2025-09-09T15:10:35.4218339Z ) 2025-09-09T15:10:35.4218527Z ) 2025-09-09T15:10:35.4218644Z 2025-09-09T15:10:35.4218650Z 2025-09-09T15:10:35.4218749Z 2025-09-09T15:10:35.4218852Z def forward(self, x): 2025-09-09T15:10:35.4219297Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T15:10:35.4220044Z conv1 = self.conv1(activation_post_process_0); activation_post_process_0 = None 2025-09-09T15:10:35.4220779Z activation_post_process_1 = self.activation_post_process_1(conv1); conv1 = None 2025-09-09T15:10:35.4221491Z conv2 = self.conv2(activation_post_process_1); activation_post_process_1 = None 2025-09-09T15:10:35.4222048Z activation_post_process_2 = self.activation_post_process_2(conv2); conv2 = None 2025-09-09T15:10:35.4222686Z return activation_post_process_2 2025-09-09T15:10:35.4222933Z 2025-09-09T15:10:35.4223202Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:10:35.4223564Z diff: tensor([[[[0.]], 2025-09-09T15:10:35.4223708Z 2025-09-09T15:10:35.4223778Z [[0.]], 2025-09-09T15:10:35.4223895Z 2025-09-09T15:10:35.4223981Z [[0.]]], 2025-09-09T15:10:35.4224099Z 2025-09-09T15:10:35.4224103Z 2025-09-09T15:10:35.4224173Z [[[0.]], 2025-09-09T15:10:35.4224287Z 2025-09-09T15:10:35.4224368Z [[0.]], 2025-09-09T15:10:35.4224482Z 2025-09-09T15:10:35.4224557Z [[0.]]], 2025-09-09T15:10:35.4224678Z 2025-09-09T15:10:35.4224681Z 2025-09-09T15:10:35.4224749Z [[[0.]], 2025-09-09T15:10:35.4224864Z 2025-09-09T15:10:35.4224936Z [[0.]], 2025-09-09T15:10:35.4225054Z 2025-09-09T15:10:35.4225157Z [[0.]]]], grad_fn=) 2025-09-09T15:10:35.4225451Z converted model pt2e: GraphModule( 2025-09-09T15:10:35.4225703Z (conv1): Module() 2025-09-09T15:10:35.4225904Z (bn1): Module() 2025-09-09T15:10:35.4226095Z (conv2): Module() 2025-09-09T15:10:35.4226295Z (bn2): Module() 2025-09-09T15:10:35.4226473Z ) 2025-09-09T15:10:35.4226575Z 2025-09-09T15:10:35.4226579Z 2025-09-09T15:10:35.4226582Z 2025-09-09T15:10:35.4226662Z def forward(self, x): 2025-09-09T15:10:35.4226942Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T15:10:35.4227268Z conv2_bias = self.conv2.bias 2025-09-09T15:10:35.4227914Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.01890663057565689, -17, -128, 127, torch.int8); x = None 2025-09-09T15:10:35.4229139Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.01890663057565689, -17, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T15:10:35.4230052Z quantize_per_tensor_1 = self._frozen_param0 2025-09-09T15:10:35.4230849Z dequantize_per_tensor_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_1, 0.001512790797278285, 0, -127, 127, torch.int8); quantize_per_tensor_1 = None 2025-09-09T15:10:35.4231638Z conv1_weight_bias = self.conv1.weight_bias 2025-09-09T15:10:59.4680411Z conv2d_5 = torch.ops.aten.conv2d.default(dequantize_per_tensor_default, dequantize_per_tensor_1, conv1_weight_bias); dequantize_per_tensor_default = dequantize_per_tensor_1 = conv1_weight_bias = None 2025-09-09T15:10:59.4682020Z quantize_per_tensor_default_3 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv2d_5, 0.017422160133719444, -29, -128, 127, torch.int8); conv2d_5 = None 2025-09-09T15:10:59.4683645Z dequantize_per_tensor_default_3 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_3, 0.017422160133719444, -29, -128, 127, torch.int8); quantize_per_tensor_default_3 = None 2025-09-09T15:10:59.4684741Z quantize_per_tensor = self._frozen_param1 2025-09-09T15:10:59.4685710Z dequantize_per_tensor = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor, 0.001512868795543909, 0, -127, 127, torch.int8); quantize_per_tensor = None 2025-09-09T15:10:59.4688260Z conv2d_4 = torch.ops.aten.conv2d.default(dequantize_per_tensor_default_3, dequantize_per_tensor, conv2_bias); dequantize_per_tensor_default_3 = dequantize_per_tensor = conv2_bias = None 2025-09-09T15:10:59.4689766Z quantize_per_tensor_default_4 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv2d_4, 0.011024280451238155, -1, -128, 127, torch.int8); conv2d_4 = None 2025-09-09T15:10:59.4691548Z dequantize_per_tensor_default_4 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_4, 0.011024280451238155, -1, -128, 127, torch.int8); quantize_per_tensor_default_4 = None 2025-09-09T15:10:59.4692788Z return pytree.tree_unflatten((dequantize_per_tensor_default_4,), self._out_spec) 2025-09-09T15:10:59.4693291Z 2025-09-09T15:10:59.4693621Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:10:59.4694083Z onverted model fx: GraphModule( 2025-09-09T15:10:59.4694568Z (conv1): QuantizedConv2d(Reference)(3, 3, kernel_size=(3, 3), stride=(1, 1)) 2025-09-09T15:10:59.4695244Z (conv2): QuantizedConv2d(Reference)(3, 3, kernel_size=(3, 3), stride=(1, 1)) 2025-09-09T15:10:59.4695717Z ) 2025-09-09T15:10:59.4695837Z 2025-09-09T15:10:59.4695842Z 2025-09-09T15:10:59.4695847Z 2025-09-09T15:10:59.4695946Z def forward(self, x): 2025-09-09T15:10:59.4696710Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.01890663057565689, -17, -128, 127, torch.int8); x = None 2025-09-09T15:10:59.4698227Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.01890663057565689, -17, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T15:10:59.4699485Z conv1 = self.conv1(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T15:10:59.4700620Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv1, 0.017422160133719444, -29, -128, 127, torch.int8); conv1 = None 2025-09-09T15:10:59.4702068Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.017422160133719444, -29, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T15:10:59.4703103Z conv2 = self.conv2(dequantize_per_tensor_default_1); dequantize_per_tensor_default_1 = None 2025-09-09T15:10:59.4703959Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv2, 0.011024280451238155, -1, -128, 127, torch.int8); conv2 = None 2025-09-09T15:10:59.4705211Z dequantize_per_tensor_default_2 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_2, 0.011024280451238155, -1, -128, 127, torch.int8); quantize_per_tensor_default_2 = None 2025-09-09T15:10:59.4706077Z return dequantize_per_tensor_default_2 2025-09-09T15:10:59.4706359Z 2025-09-09T15:10:59.4706635Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:10:59.4707016Z diff: tensor([[[[0.]], 2025-09-09T15:10:59.4707163Z 2025-09-09T15:10:59.4707249Z [[0.]], 2025-09-09T15:10:59.4707372Z 2025-09-09T15:10:59.4707442Z [[0.]]], 2025-09-09T15:10:59.4707559Z 2025-09-09T15:10:59.4707563Z 2025-09-09T15:10:59.4707647Z [[[0.]], 2025-09-09T15:10:59.4707761Z 2025-09-09T15:10:59.4707830Z [[0.]], 2025-09-09T15:10:59.4707949Z 2025-09-09T15:10:59.4708021Z [[0.]]], 2025-09-09T15:10:59.4708135Z 2025-09-09T15:10:59.4708139Z 2025-09-09T15:10:59.4708206Z [[[0.]], 2025-09-09T15:10:59.4708327Z 2025-09-09T15:10:59.4708397Z [[0.]], 2025-09-09T15:10:59.4708510Z 2025-09-09T15:10:59.4708588Z [[0.]]]]) 2025-09-09T15:10:59.4709006Z PASSED 2025-09-09T15:10:59.4709762Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_conv_bn_per_channel_weight_bias PASSED 2025-09-09T15:10:59.4710729Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_conv_bn_relu_fusion model pt2e: GraphModule( 2025-09-09T15:10:59.4711431Z (conv): Module() 2025-09-09T15:10:59.4711630Z (bn): Module() 2025-09-09T15:10:59.4711928Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:10:59.4712941Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0183]), zero_point=tensor([10], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:10:59.4714023Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.526270866394043, max_val=2.143237352371216) 2025-09-09T15:10:59.4714536Z ) 2025-09-09T15:10:59.4714807Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:10:59.4715798Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0014, 0.0015, 0.0015]), zero_point=tensor([0, 0, 0], dtype=torch.int32), dtype=torch.int8, quant_min=-127, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False 2025-09-09T15:10:59.4717115Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.1835, -0.1822, -0.1883]), max_val=tensor([0.1799, 0.1856, 0.1719])) 2025-09-09T15:10:59.4717748Z ) 2025-09-09T15:10:59.4718035Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:10:59.4718961Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0049]), zero_point=tensor([-128], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:10:59.4720061Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=1.2505061626434326) 2025-09-09T15:10:59.4720532Z ) 2025-09-09T15:10:59.4720691Z ) 2025-09-09T15:10:59.4720796Z 2025-09-09T15:10:59.4720800Z 2025-09-09T15:10:59.4720804Z 2025-09-09T15:10:59.4720885Z def forward(self, x): 2025-09-09T15:10:59.4721166Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T15:10:59.4721506Z conv_weight = self.conv.weight 2025-09-09T15:10:59.4721792Z conv_bias = self.conv.bias 2025-09-09T15:10:59.4722042Z bn_weight = self.bn.weight 2025-09-09T15:10:59.4722463Z bn_bias = self.bn.bias 2025-09-09T15:10:59.4722721Z bn_running_mean = self.bn.running_mean 2025-09-09T15:10:59.4723025Z bn_running_var = self.bn.running_var 2025-09-09T15:10:59.4723352Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T15:10:59.4723795Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T15:10:59.4724372Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T15:10:59.4724895Z add = torch.ops.aten.add.Tensor(bn_running_var, 1e-05) 2025-09-09T15:10:59.4725293Z sqrt = torch.ops.aten.sqrt.default(add); add = None 2025-09-09T15:10:59.4725697Z div = torch.ops.aten.div.Tensor(bn_weight, sqrt); sqrt = None 2025-09-09T15:10:59.4726141Z reshape = torch.ops.aten.reshape.default(div, [-1, 1, 1, 1]) 2025-09-09T15:10:59.4726642Z mul = torch.ops.aten.mul.Tensor(conv_weight, reshape); conv_weight = reshape = None 2025-09-09T15:10:59.4727203Z activation_post_process_1 = self.activation_post_process_1(mul); mul = None 2025-09-09T15:10:59.4727811Z zeros_like = torch.ops.aten.zeros_like.default(conv_bias, dtype = torch.float32, pin_memory = False) 2025-09-09T15:10:59.4728773Z conv2d_1 = torch.ops.aten.conv2d.default(activation_post_process_0, activation_post_process_1, zeros_like); activation_post_process_0 = activation_post_process_1 = zeros_like = None 2025-09-09T15:10:59.4729651Z reshape_1 = torch.ops.aten.reshape.default(div, [1, -1, 1, 1]); div = None 2025-09-09T15:10:59.4730184Z div_1 = torch.ops.aten.div.Tensor(conv2d_1, reshape_1); conv2d_1 = reshape_1 = None 2025-09-09T15:10:59.4730767Z reshape_2 = torch.ops.aten.reshape.default(conv_bias, [1, -1, 1, 1]); conv_bias = None 2025-09-09T15:10:59.4731318Z add_1 = torch.ops.aten.add.Tensor(div_1, reshape_2); div_1 = reshape_2 = None 2025-09-09T15:10:59.4732304Z batch_norm_1 = torch.ops.aten.batch_norm.default(add_1, bn_weight, bn_bias, bn_running_mean, bn_running_var, True, 0.1, 1e-05, True); add_1 = bn_weight = bn_bias = bn_running_mean = bn_running_var = None 2025-09-09T15:10:59.4733245Z relu = torch.ops.aten.relu.default(batch_norm_1); batch_norm_1 = None 2025-09-09T15:10:59.4733759Z activation_post_process_2 = self.activation_post_process_2(relu); relu = None 2025-09-09T15:10:59.4734295Z return pytree.tree_unflatten((activation_post_process_2,), self._out_spec) 2025-09-09T15:10:59.4734671Z 2025-09-09T15:10:59.4734952Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:10:59.4735310Z model fx: GraphModule( 2025-09-09T15:10:59.4735622Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:10:59.4736560Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0183]), zero_point=tensor([10], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:10:59.4737636Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.526270866394043, max_val=2.143237352371216) 2025-09-09T15:10:59.4738141Z ) 2025-09-09T15:10:59.4738328Z (conv): ConvBnReLU2d( 2025-09-09T15:10:59.4738567Z 3, 3, kernel_size=(3, 3), stride=(1, 1) 2025-09-09T15:10:59.4738980Z (bn): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T15:10:59.4739438Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:10:59.4740384Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0014, 0.0015, 0.0015]), zero_point=tensor([0, 0, 0], dtype=torch.int32), dtype=torch.qint8, quant_min=-127, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False 2025-09-09T15:11:19.5351313Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.1835, -0.1822, -0.1883]), max_val=tensor([0.1799, 0.1856, 0.1719])) 2025-09-09T15:11:19.5352147Z ) 2025-09-09T15:11:19.5352350Z ) 2025-09-09T15:11:19.5352692Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:11:19.5353903Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0049]), zero_point=tensor([-128], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:11:19.5355233Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=1.2505061626434326) 2025-09-09T15:11:19.5355813Z ) 2025-09-09T15:11:19.5356014Z ) 2025-09-09T15:11:19.5356128Z 2025-09-09T15:11:19.5356133Z 2025-09-09T15:11:19.5356138Z 2025-09-09T15:11:19.5356246Z def forward(self, x): 2025-09-09T15:11:19.5356666Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T15:11:19.5357310Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T15:11:19.5357985Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T15:11:19.5358509Z return activation_post_process_1 2025-09-09T15:11:19.5358821Z 2025-09-09T15:11:19.5359154Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:11:19.5359696Z diff: tensor([[[[0., 0., 0.], 2025-09-09T15:11:19.5359982Z [0., 0., 0.], 2025-09-09T15:11:19.5360231Z [0., 0., 0.]], 2025-09-09T15:11:19.5360395Z 2025-09-09T15:11:19.5360484Z [[0., 0., 0.], 2025-09-09T15:11:19.5360729Z [0., 0., 0.], 2025-09-09T15:11:19.5360964Z [0., 0., 0.]], 2025-09-09T15:11:19.5361136Z 2025-09-09T15:11:19.5361223Z [[0., 0., 0.], 2025-09-09T15:11:19.5361489Z [0., 0., 0.], 2025-09-09T15:11:19.5361795Z [0., 0., 0.]]]], grad_fn=) 2025-09-09T15:11:19.5362157Z converted model pt2e: GraphModule( 2025-09-09T15:11:19.5362469Z (conv): Module() 2025-09-09T15:11:19.5362708Z (bn): Module() 2025-09-09T15:11:19.5363191Z ) 2025-09-09T15:11:19.5363311Z 2025-09-09T15:11:19.5363316Z 2025-09-09T15:11:19.5363321Z 2025-09-09T15:11:19.5363422Z def forward(self, x): 2025-09-09T15:11:19.5363918Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T15:11:19.5364314Z conv_bias = self.conv.bias 2025-09-09T15:11:19.5365144Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.018311796709895134, 10, -128, 127, torch.int8); x = None 2025-09-09T15:11:19.5366667Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.018311796709895134, 10, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T15:11:19.5367721Z _scale_0 = self._scale_0 2025-09-09T15:11:19.5368022Z _zero_point_0 = self._zero_point_0 2025-09-09T15:11:19.5368391Z quantize_per_channel = self._frozen_param0 2025-09-09T15:11:19.5369486Z dequantize_per_channel = torch.ops.quantized_decomposed.dequantize_per_channel.default(quantize_per_channel, _scale_0, _zero_point_0, 0, -127, 127, torch.int8); quantize_per_channel = _scale_0 = _zero_point_0 = None 2025-09-09T15:11:19.5371155Z conv2d_2 = torch.ops.aten.conv2d.default(dequantize_per_tensor_default, dequantize_per_channel, conv_bias); dequantize_per_tensor_default = dequantize_per_channel = conv_bias = None 2025-09-09T15:11:19.5372190Z relu = torch.ops.aten.relu.default(conv2d_2); conv2d_2 = None 2025-09-09T15:11:19.5373127Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(relu, 0.004903945606201887, -128, -128, 127, torch.int8); relu = None 2025-09-09T15:11:19.5374711Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.004903945606201887, -128, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T15:11:19.5375963Z return pytree.tree_unflatten((dequantize_per_tensor_default_1,), self._out_spec) 2025-09-09T15:11:19.5376447Z 2025-09-09T15:11:19.5376773Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:11:19.5377218Z onverted model fx: GraphModule( 2025-09-09T15:11:19.5377526Z (conv): ConvReLU2d( 2025-09-09T15:11:19.5377922Z (0): QuantizedConv2d(Reference)(3, 3, kernel_size=(3, 3), stride=(1, 1)) 2025-09-09T15:11:19.5378369Z (1): ReLU() 2025-09-09T15:11:19.5378593Z ) 2025-09-09T15:11:19.5378789Z ) 2025-09-09T15:11:19.5378901Z 2025-09-09T15:11:19.5378906Z 2025-09-09T15:11:19.5378911Z 2025-09-09T15:11:19.5379013Z def forward(self, x): 2025-09-09T15:11:19.5379761Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.018311796709895134, 10, -128, 127, torch.int8); x = None 2025-09-09T15:11:19.5381284Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.018311796709895134, 10, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T15:11:19.5382591Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T15:11:19.5383549Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.004903945606201887, -128, -128, 127, torch.int8); conv = None 2025-09-09T15:11:19.5384817Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.004903945606201887, -128, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T15:11:19.5385677Z return dequantize_per_tensor_default_1 2025-09-09T15:11:19.5385948Z 2025-09-09T15:11:19.5386224Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:11:19.5386587Z diff: tensor([[[[0., 0., 0.], 2025-09-09T15:11:19.5386821Z [0., 0., 0.], 2025-09-09T15:11:19.5387024Z [0., 0., 0.]], 2025-09-09T15:11:19.5387170Z 2025-09-09T15:11:19.5387341Z [[0., 0., 0.], 2025-09-09T15:11:19.5387540Z [0., 0., 0.], 2025-09-09T15:11:19.5387738Z [0., 0., 0.]], 2025-09-09T15:11:19.5387869Z 2025-09-09T15:11:19.5388028Z [[0., 0., 0.], 2025-09-09T15:11:19.5388229Z [0., 0., 0.], 2025-09-09T15:11:19.5388428Z [0., 0., 0.]]]]) 2025-09-09T15:11:19.5388646Z model pt2e: GraphModule( 2025-09-09T15:11:19.5388866Z (conv): Module() 2025-09-09T15:11:19.5389058Z (bn): Module() 2025-09-09T15:11:19.5389353Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:11:19.5390270Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0183]), zero_point=tensor([10], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:11:19.5391343Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.526270866394043, max_val=2.143237352371216) 2025-09-09T15:11:19.5391889Z ) 2025-09-09T15:11:19.5392162Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:11:19.5393087Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0015]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.int8, quant_min=-127, quant_max=127, qscheme=torch.per_tensor_symmetric, reduce_range=False 2025-09-09T15:11:19.5394183Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.1882954239845276, max_val=0.1855725795030594) 2025-09-09T15:11:19.5394689Z ) 2025-09-09T15:11:19.5394957Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:11:19.5395879Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0049]), zero_point=tensor([-128], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:11:19.5396910Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=1.2396948337554932) 2025-09-09T15:11:19.5397363Z ) 2025-09-09T15:11:19.5397535Z ) 2025-09-09T15:11:19.5397626Z 2025-09-09T15:11:19.5397630Z 2025-09-09T15:11:19.5397634Z 2025-09-09T15:11:19.5397724Z def forward(self, x): 2025-09-09T15:11:19.5398001Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T15:11:19.5398347Z conv_weight = self.conv.weight 2025-09-09T15:11:19.5398615Z conv_bias = self.conv.bias 2025-09-09T15:11:19.5398858Z bn_weight = self.bn.weight 2025-09-09T15:11:19.5399108Z bn_bias = self.bn.bias 2025-09-09T15:11:19.5399400Z bn_running_mean = self.bn.running_mean 2025-09-09T15:11:19.5399695Z bn_running_var = self.bn.running_var 2025-09-09T15:11:19.5400018Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T15:11:19.5400449Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T15:11:19.5401026Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T15:11:19.5401546Z add = torch.ops.aten.add.Tensor(bn_running_var, 1e-05) 2025-09-09T15:11:19.5401932Z sqrt = torch.ops.aten.sqrt.default(add); add = None 2025-09-09T15:11:19.5402327Z div = torch.ops.aten.div.Tensor(bn_weight, sqrt); sqrt = None 2025-09-09T15:11:19.5402767Z reshape = torch.ops.aten.reshape.default(div, [-1, 1, 1, 1]) 2025-09-09T15:11:19.5403258Z mul = torch.ops.aten.mul.Tensor(conv_weight, reshape); conv_weight = reshape = None 2025-09-09T15:11:19.5403810Z activation_post_process_1 = self.activation_post_process_1(mul); mul = None 2025-09-09T15:11:19.5404408Z zeros_like = torch.ops.aten.zeros_like.default(conv_bias, dtype = torch.float32, pin_memory = False) 2025-09-09T15:11:19.5405357Z conv2d_1 = torch.ops.aten.conv2d.default(activation_post_process_0, activation_post_process_1, zeros_like); activation_post_process_0 = activation_post_process_1 = zeros_like = None 2025-09-09T15:11:19.5406241Z reshape_1 = torch.ops.aten.reshape.default(div, [1, -1, 1, 1]); div = None 2025-09-09T15:11:19.5406856Z div_1 = torch.ops.aten.div.Tensor(conv2d_1, reshape_1); conv2d_1 = reshape_1 = None 2025-09-09T15:11:19.5407434Z reshape_2 = torch.ops.aten.reshape.default(conv_bias, [1, -1, 1, 1]); conv_bias = None 2025-09-09T15:11:19.5408061Z add_1 = torch.ops.aten.add.Tensor(div_1, reshape_2); div_1 = reshape_2 = None 2025-09-09T15:11:19.5408904Z batch_norm_1 = torch.ops.aten.batch_norm.default(add_1, bn_weight, bn_bias, bn_running_mean, bn_running_var, True, 0.1, 1e-05, True); add_1 = bn_weight = bn_bias = bn_running_mean = bn_running_var = None 2025-09-09T15:11:19.5409731Z relu = torch.ops.aten.relu.default(batch_norm_1); batch_norm_1 = None 2025-09-09T15:11:19.5410239Z activation_post_process_2 = self.activation_post_process_2(relu); relu = None 2025-09-09T15:11:19.5410766Z return pytree.tree_unflatten((activation_post_process_2,), self._out_spec) 2025-09-09T15:11:19.5411142Z 2025-09-09T15:11:19.5411411Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:11:19.5411770Z model fx: GraphModule( 2025-09-09T15:11:19.5412080Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:11:19.5413010Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0183]), zero_point=tensor([10], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:11:19.5414081Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.526270866394043, max_val=2.143237352371216) 2025-09-09T15:11:19.5414579Z ) 2025-09-09T15:11:19.5414758Z (conv): ConvBnReLU2d( 2025-09-09T15:11:19.5414992Z 3, 3, kernel_size=(3, 3), stride=(1, 1) 2025-09-09T15:11:45.1240399Z (bn): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T15:11:45.1240920Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:11:45.1241859Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0015]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-127, quant_max=127, qscheme=torch.per_tensor_symmetric, reduce_range=False 2025-09-09T15:11:45.1243008Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.1882954239845276, max_val=0.1855725795030594) 2025-09-09T15:11:45.1243507Z ) 2025-09-09T15:11:45.1243678Z ) 2025-09-09T15:11:45.1243953Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:11:45.1252701Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0049]), zero_point=tensor([-128], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:11:45.1253779Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=1.2396948337554932) 2025-09-09T15:11:45.1254258Z ) 2025-09-09T15:11:45.1254430Z ) 2025-09-09T15:11:45.1254538Z 2025-09-09T15:11:45.1254542Z 2025-09-09T15:11:45.1254546Z 2025-09-09T15:11:45.1254644Z def forward(self, x): 2025-09-09T15:11:45.1255011Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T15:11:45.1255545Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T15:11:45.1256098Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T15:11:45.1256524Z return activation_post_process_1 2025-09-09T15:11:45.1256801Z 2025-09-09T15:11:45.1257085Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:11:45.1257461Z diff: tensor([[[[0., 0., 0.], 2025-09-09T15:11:45.1257711Z [0., 0., 0.], 2025-09-09T15:11:45.1257923Z [0., 0., 0.]], 2025-09-09T15:11:45.1258067Z 2025-09-09T15:11:45.1258156Z [[0., 0., 0.], 2025-09-09T15:11:45.1258369Z [0., 0., 0.], 2025-09-09T15:11:45.1258611Z [0., 0., 0.]], 2025-09-09T15:11:45.1258769Z 2025-09-09T15:11:45.1258855Z [[0., 0., 0.], 2025-09-09T15:11:45.1259409Z [0., 0., 0.], 2025-09-09T15:11:45.1259654Z [0., 0., 0.]]]], grad_fn=) 2025-09-09T15:11:45.1259970Z converted model pt2e: GraphModule( 2025-09-09T15:11:45.1260394Z (conv): Module() 2025-09-09T15:11:45.1260597Z (bn): Module() 2025-09-09T15:11:45.1260792Z ) 2025-09-09T15:11:45.1260903Z 2025-09-09T15:11:45.1260907Z 2025-09-09T15:11:45.1260911Z 2025-09-09T15:11:45.1260996Z def forward(self, x): 2025-09-09T15:11:45.1261289Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T15:11:45.1261624Z conv_bias = self.conv.bias 2025-09-09T15:11:45.1262273Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.018311796709895134, 10, -128, 127, torch.int8); x = None 2025-09-09T15:11:45.1263509Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.018311796709895134, 10, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T15:11:45.1264401Z quantize_per_tensor = self._frozen_param0 2025-09-09T15:11:45.1265195Z dequantize_per_tensor = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor, 0.0014826410915702581, 0, -127, 127, torch.int8); quantize_per_tensor = None 2025-09-09T15:11:45.1266455Z conv2d_2 = torch.ops.aten.conv2d.default(dequantize_per_tensor_default, dequantize_per_tensor, conv_bias); dequantize_per_tensor_default = dequantize_per_tensor = conv_bias = None 2025-09-09T15:11:45.1267296Z relu = torch.ops.aten.relu.default(conv2d_2); conv2d_2 = None 2025-09-09T15:11:45.1268084Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(relu, 0.004861548542976379, -128, -128, 127, torch.int8); relu = None 2025-09-09T15:11:45.1269367Z dequantize_per_tensor_default_2 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_2, 0.004861548542976379, -128, -128, 127, torch.int8); quantize_per_tensor_default_2 = None 2025-09-09T15:11:45.1270381Z return pytree.tree_unflatten((dequantize_per_tensor_default_2,), self._out_spec) 2025-09-09T15:11:45.1270786Z 2025-09-09T15:11:45.1271078Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:11:45.1271455Z onverted model fx: GraphModule( 2025-09-09T15:11:45.1271709Z (conv): ConvReLU2d( 2025-09-09T15:11:45.1272052Z (0): QuantizedConv2d(Reference)(3, 3, kernel_size=(3, 3), stride=(1, 1)) 2025-09-09T15:11:45.1272422Z (1): ReLU() 2025-09-09T15:11:45.1272620Z ) 2025-09-09T15:11:45.1272791Z ) 2025-09-09T15:11:45.1272892Z 2025-09-09T15:11:45.1272896Z 2025-09-09T15:11:45.1272900Z 2025-09-09T15:11:45.1272990Z def forward(self, x): 2025-09-09T15:11:45.1273599Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.018311796709895134, 10, -128, 127, torch.int8); x = None 2025-09-09T15:11:45.1274830Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.018311796709895134, 10, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T15:11:45.1275833Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T15:11:45.1276685Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.004861548542976379, -128, -128, 127, torch.int8); conv = None 2025-09-09T15:11:45.1277962Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.004861548542976379, -128, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T15:11:45.1278842Z return dequantize_per_tensor_default_1 2025-09-09T15:11:45.1279112Z 2025-09-09T15:11:45.1279506Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:11:45.1279869Z diff: tensor([[[[0., 0., 0.], 2025-09-09T15:11:45.1280108Z [0., 0., 0.], 2025-09-09T15:11:45.1280414Z [0., 0., 0.]], 2025-09-09T15:11:45.1280561Z 2025-09-09T15:11:45.1280640Z [[0., 0., 0.], 2025-09-09T15:11:45.1280852Z [0., 0., 0.], 2025-09-09T15:11:45.1281128Z [0., 0., 0.]], 2025-09-09T15:11:45.1281261Z 2025-09-09T15:11:45.1281338Z [[0., 0., 0.], 2025-09-09T15:11:45.1281529Z [0., 0., 0.], 2025-09-09T15:11:45.1281732Z [0., 0., 0.]]]]) 2025-09-09T15:11:45.1282153Z PASSED 2025-09-09T15:11:45.1282752Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_conv_bn_relu_fusion_cuda model pt2e: GraphModule( 2025-09-09T15:11:45.1283369Z (conv): Module() 2025-09-09T15:11:45.1283568Z (bn): Module() 2025-09-09T15:11:45.1283864Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:11:45.1284964Z fake_quant_enabled=tensor([1], device='cuda:0'), observer_enabled=tensor([1], device='cuda:0'), scale=tensor([0.0183], device='cuda:0'), zero_point=tensor([10], device='cuda:0', dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:11:45.1286212Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.526270866394043, max_val=2.143237352371216) 2025-09-09T15:11:45.1286712Z ) 2025-09-09T15:11:45.1286989Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:11:45.1288140Z fake_quant_enabled=tensor([1], device='cuda:0'), observer_enabled=tensor([1], device='cuda:0'), scale=tensor([0.0015, 0.0015, 0.0014], device='cuda:0'), zero_point=tensor([0, 0, 0], device='cuda:0', dtype=torch.int32), dtype=torch.int8, quant_min=-127, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False 2025-09-09T15:11:45.1289698Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.1860, -0.1897, -0.1787], device='cuda:0'), max_val=tensor([0.1824, 0.1870, 0.1478], device='cuda:0')) 2025-09-09T15:11:45.1290417Z ) 2025-09-09T15:11:45.1290685Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:11:45.1291782Z fake_quant_enabled=tensor([1], device='cuda:0'), observer_enabled=tensor([1], device='cuda:0'), scale=tensor([0.0081], device='cuda:0'), zero_point=tensor([-128], device='cuda:0', dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:11:45.1292994Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=2.0564441680908203) 2025-09-09T15:11:45.1293449Z ) 2025-09-09T15:11:45.1293616Z ) 2025-09-09T15:11:45.1293710Z 2025-09-09T15:11:45.1293714Z 2025-09-09T15:11:45.1293718Z 2025-09-09T15:11:45.1293803Z def forward(self, x): 2025-09-09T15:11:45.1294081Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T15:11:45.1294416Z conv_weight = self.conv.weight 2025-09-09T15:11:45.1294681Z conv_bias = self.conv.bias 2025-09-09T15:11:45.1294932Z bn_weight = self.bn.weight 2025-09-09T15:11:45.1295178Z bn_bias = self.bn.bias 2025-09-09T15:11:45.1295430Z bn_running_mean = self.bn.running_mean 2025-09-09T15:11:45.1295717Z bn_running_var = self.bn.running_var 2025-09-09T15:11:45.1296048Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T15:11:45.1296483Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T15:11:45.1297055Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T15:11:45.1297573Z add = torch.ops.aten.add.Tensor(bn_running_var, 1e-05) 2025-09-09T15:11:45.1297953Z sqrt = torch.ops.aten.sqrt.default(add); add = None 2025-09-09T15:11:45.1298355Z div = torch.ops.aten.div.Tensor(bn_weight, sqrt); sqrt = None 2025-09-09T15:11:45.1298787Z reshape = torch.ops.aten.reshape.default(div, [-1, 1, 1, 1]) 2025-09-09T15:11:45.1299286Z mul = torch.ops.aten.mul.Tensor(conv_weight, reshape); conv_weight = reshape = None 2025-09-09T15:11:45.1299932Z activation_post_process_1 = self.activation_post_process_1(mul); mul = None 2025-09-09T15:11:45.1300537Z zeros_like = torch.ops.aten.zeros_like.default(conv_bias, dtype = torch.float32, pin_memory = False) 2025-09-09T15:11:45.1301583Z conv2d_1 = torch.ops.aten.conv2d.default(activation_post_process_0, activation_post_process_1, zeros_like); activation_post_process_0 = activation_post_process_1 = zeros_like = None 2025-09-09T15:11:45.1302466Z reshape_1 = torch.ops.aten.reshape.default(div, [1, -1, 1, 1]); div = None 2025-09-09T15:11:45.1303009Z div_1 = torch.ops.aten.div.Tensor(conv2d_1, reshape_1); conv2d_1 = reshape_1 = None 2025-09-09T15:11:45.1303589Z reshape_2 = torch.ops.aten.reshape.default(conv_bias, [1, -1, 1, 1]); conv_bias = None 2025-09-09T15:11:45.1304142Z add_1 = torch.ops.aten.add.Tensor(div_1, reshape_2); div_1 = reshape_2 = None 2025-09-09T15:11:45.1305013Z batch_norm_1 = torch.ops.aten.batch_norm.default(add_1, bn_weight, bn_bias, bn_running_mean, bn_running_var, True, 0.1, 1e-05, True); add_1 = bn_weight = bn_bias = bn_running_mean = bn_running_var = None 2025-09-09T15:11:45.1305845Z relu = torch.ops.aten.relu.default(batch_norm_1); batch_norm_1 = None 2025-09-09T15:12:02.4257179Z activation_post_process_2 = self.activation_post_process_2(relu); relu = None 2025-09-09T15:12:02.4257948Z return pytree.tree_unflatten((activation_post_process_2,), self._out_spec) 2025-09-09T15:12:02.4258483Z 2025-09-09T15:12:02.4258845Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:12:02.4259308Z model fx: GraphModule( 2025-09-09T15:12:02.4259695Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:12:02.4261121Z fake_quant_enabled=tensor([1], device='cuda:0'), observer_enabled=tensor([1], device='cuda:0'), scale=tensor([0.0183], device='cuda:0'), zero_point=tensor([10], device='cuda:0', dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:12:02.4262623Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.526270866394043, max_val=2.143237352371216) 2025-09-09T15:12:02.4263146Z ) 2025-09-09T15:12:02.4263364Z (conv): ConvBnReLU2d( 2025-09-09T15:12:02.4263695Z 3, 3, kernel_size=(3, 3), stride=(1, 1) 2025-09-09T15:12:02.4264124Z (bn): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T15:12:02.4264695Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:12:02.4265954Z fake_quant_enabled=tensor([1], device='cuda:0'), observer_enabled=tensor([1], device='cuda:0'), scale=tensor([0.0015, 0.0015, 0.0014], device='cuda:0'), zero_point=tensor([0, 0, 0], device='cuda:0', dtype=torch.int32), dtype=torch.qint8, quant_min=-127, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False 2025-09-09T15:12:02.4267505Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.1860, -0.1897, -0.1787], device='cuda:0'), max_val=tensor([0.1824, 0.1870, 0.1478], device='cuda:0')) 2025-09-09T15:12:02.4268230Z ) 2025-09-09T15:12:02.4268412Z ) 2025-09-09T15:12:02.4268688Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:12:02.4269799Z fake_quant_enabled=tensor([1], device='cuda:0'), observer_enabled=tensor([1], device='cuda:0'), scale=tensor([0.0081], device='cuda:0'), zero_point=tensor([-128], device='cuda:0', dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:12:02.4271006Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=2.0564441680908203) 2025-09-09T15:12:02.4271474Z ) 2025-09-09T15:12:02.4271636Z ) 2025-09-09T15:12:02.4271742Z 2025-09-09T15:12:02.4271746Z 2025-09-09T15:12:02.4271750Z 2025-09-09T15:12:02.4271836Z def forward(self, x): 2025-09-09T15:12:02.4272202Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T15:12:02.4273010Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T15:12:02.4273562Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T15:12:02.4274130Z return activation_post_process_1 2025-09-09T15:12:02.4274397Z 2025-09-09T15:12:02.4274676Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:12:02.4275048Z diff: tensor([[[[0., 0., 0.], 2025-09-09T15:12:02.4275289Z [0., 0., 0.], 2025-09-09T15:12:02.4275507Z [0., 0., 0.]], 2025-09-09T15:12:02.4275647Z 2025-09-09T15:12:02.4275732Z [[0., 0., 0.], 2025-09-09T15:12:02.4275939Z [0., 0., 0.], 2025-09-09T15:12:02.4276151Z [0., 0., 0.]], 2025-09-09T15:12:02.4276287Z 2025-09-09T15:12:02.4276369Z [[0., 0., 0.], 2025-09-09T15:12:02.4276575Z [0., 0., 0.], 2025-09-09T15:12:02.4276834Z [0., 0., 0.]]]], device='cuda:0', grad_fn=) 2025-09-09T15:12:02.4277168Z converted model pt2e: GraphModule( 2025-09-09T15:12:02.4277423Z (conv): Module() 2025-09-09T15:12:02.4277614Z (bn): Module() 2025-09-09T15:12:02.4277804Z ) 2025-09-09T15:12:02.4277905Z 2025-09-09T15:12:02.4277909Z 2025-09-09T15:12:02.4277913Z 2025-09-09T15:12:02.4277995Z def forward(self, x): 2025-09-09T15:12:02.4278272Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T15:12:02.4278594Z conv_bias = self.conv.bias 2025-09-09T15:12:02.4279311Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.018311796709895134, 10, -128, 127, torch.int8); x = None 2025-09-09T15:12:02.4280568Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.018311796709895134, 10, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T15:12:02.4281559Z _scale_0 = self._scale_0 2025-09-09T15:12:02.4281892Z _zero_point_0 = self._zero_point_0 2025-09-09T15:12:02.4282223Z quantize_per_channel = self._frozen_param0 2025-09-09T15:12:02.4283103Z dequantize_per_channel = torch.ops.quantized_decomposed.dequantize_per_channel.default(quantize_per_channel, _scale_0, _zero_point_0, 0, -127, 127, torch.int8); quantize_per_channel = _scale_0 = _zero_point_0 = None 2025-09-09T15:12:02.4284431Z conv2d_2 = torch.ops.aten.conv2d.default(dequantize_per_tensor_default, dequantize_per_channel, conv_bias); dequantize_per_tensor_default = dequantize_per_channel = conv_bias = None 2025-09-09T15:12:02.4285269Z relu = torch.ops.aten.relu.default(conv2d_2); conv2d_2 = None 2025-09-09T15:12:02.4286048Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(relu, 0.008064487017691135, -128, -128, 127, torch.int8); relu = None 2025-09-09T15:12:02.4287527Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.008064487017691135, -128, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T15:12:02.4288532Z return pytree.tree_unflatten((dequantize_per_tensor_default_1,), self._out_spec) 2025-09-09T15:12:02.4288954Z 2025-09-09T15:12:02.4289235Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:12:02.4289618Z onverted model fx: GraphModule( 2025-09-09T15:12:02.4289877Z (conv): ConvReLU2d( 2025-09-09T15:12:02.4290225Z (0): QuantizedConv2d(Reference)(3, 3, kernel_size=(3, 3), stride=(1, 1)) 2025-09-09T15:12:02.4290602Z (1): ReLU() 2025-09-09T15:12:02.4290801Z ) 2025-09-09T15:12:02.4290977Z ) 2025-09-09T15:12:02.4291076Z 2025-09-09T15:12:02.4291080Z 2025-09-09T15:12:02.4291084Z 2025-09-09T15:12:02.4291172Z def forward(self, x): 2025-09-09T15:12:02.4291789Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.018311796709895134, 10, -128, 127, torch.int8); x = None 2025-09-09T15:12:02.4293119Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.018311796709895134, 10, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T15:12:02.4294202Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T15:12:02.4295057Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.008064487017691135, -128, -128, 127, torch.int8); conv = None 2025-09-09T15:12:02.4296324Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.008064487017691135, -128, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T15:12:02.4297212Z return dequantize_per_tensor_default_1 2025-09-09T15:12:02.4297495Z 2025-09-09T15:12:02.4297776Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:12:02.4298151Z diff: tensor([[[[0., 0., 0.], 2025-09-09T15:12:02.4298399Z [0., 0., 0.], 2025-09-09T15:12:02.4298623Z [0., 0., 0.]], 2025-09-09T15:12:02.4298766Z 2025-09-09T15:12:02.4298845Z [[0., 0., 0.], 2025-09-09T15:12:02.4299065Z [0., 0., 0.], 2025-09-09T15:12:02.4299298Z [0., 0., 0.]], 2025-09-09T15:12:02.4299437Z 2025-09-09T15:12:02.4299516Z [[0., 0., 0.], 2025-09-09T15:12:02.4299731Z [0., 0., 0.], 2025-09-09T15:12:02.4299959Z [0., 0., 0.]]]], device='cuda:0') 2025-09-09T15:12:02.4300245Z model pt2e: GraphModule( 2025-09-09T15:12:02.4300477Z (conv): Module() 2025-09-09T15:12:02.4300690Z (bn): Module() 2025-09-09T15:12:02.4300989Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:12:02.4302103Z fake_quant_enabled=tensor([1], device='cuda:0'), observer_enabled=tensor([1], device='cuda:0'), scale=tensor([0.0183], device='cuda:0'), zero_point=tensor([10], device='cuda:0', dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:12:02.4303357Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.526270866394043, max_val=2.143237352371216) 2025-09-09T15:12:02.4303870Z ) 2025-09-09T15:12:02.4304153Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:12:02.4305251Z fake_quant_enabled=tensor([1], device='cuda:0'), observer_enabled=tensor([1], device='cuda:0'), scale=tensor([0.0015], device='cuda:0'), zero_point=tensor([0], device='cuda:0', dtype=torch.int32), dtype=torch.int8, quant_min=-127, quant_max=127, qscheme=torch.per_tensor_symmetric, reduce_range=False 2025-09-09T15:12:02.4306528Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.18965020775794983, max_val=0.1870359182357788) 2025-09-09T15:12:02.4307053Z ) 2025-09-09T15:12:02.4307331Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:12:02.4308439Z fake_quant_enabled=tensor([1], device='cuda:0'), observer_enabled=tensor([1], device='cuda:0'), scale=tensor([0.0080], device='cuda:0'), zero_point=tensor([-128], device='cuda:0', dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:12:02.4309659Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=2.0522286891937256) 2025-09-09T15:12:02.4310127Z ) 2025-09-09T15:12:02.4310308Z ) 2025-09-09T15:12:02.4310407Z 2025-09-09T15:12:02.4310411Z 2025-09-09T15:12:02.4310415Z 2025-09-09T15:12:02.4310503Z def forward(self, x): 2025-09-09T15:12:02.4310789Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T15:12:02.4311125Z conv_weight = self.conv.weight 2025-09-09T15:12:02.4311403Z conv_bias = self.conv.bias 2025-09-09T15:12:02.4311664Z bn_weight = self.bn.weight 2025-09-09T15:12:02.4311915Z bn_bias = self.bn.bias 2025-09-09T15:12:02.4312177Z bn_running_mean = self.bn.running_mean 2025-09-09T15:12:02.4312477Z bn_running_var = self.bn.running_var 2025-09-09T15:12:02.4312902Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T15:12:02.4313341Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T15:12:02.4314001Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T15:12:02.4314518Z add = torch.ops.aten.add.Tensor(bn_running_var, 1e-05) 2025-09-09T15:12:02.4314912Z sqrt = torch.ops.aten.sqrt.default(add); add = None 2025-09-09T15:12:02.4315321Z div = torch.ops.aten.div.Tensor(bn_weight, sqrt); sqrt = None 2025-09-09T15:12:02.4315761Z reshape = torch.ops.aten.reshape.default(div, [-1, 1, 1, 1]) 2025-09-09T15:12:02.4316264Z mul = torch.ops.aten.mul.Tensor(conv_weight, reshape); conv_weight = reshape = None 2025-09-09T15:12:02.4316816Z activation_post_process_1 = self.activation_post_process_1(mul); mul = None 2025-09-09T15:12:28.2353241Z zeros_like = torch.ops.aten.zeros_like.default(conv_bias, dtype = torch.float32, pin_memory = False) 2025-09-09T15:12:28.2354516Z conv2d_1 = torch.ops.aten.conv2d.default(activation_post_process_0, activation_post_process_1, zeros_like); activation_post_process_0 = activation_post_process_1 = zeros_like = None 2025-09-09T15:12:28.2355646Z reshape_1 = torch.ops.aten.reshape.default(div, [1, -1, 1, 1]); div = None 2025-09-09T15:12:28.2356312Z div_1 = torch.ops.aten.div.Tensor(conv2d_1, reshape_1); conv2d_1 = reshape_1 = None 2025-09-09T15:12:28.2357013Z reshape_2 = torch.ops.aten.reshape.default(conv_bias, [1, -1, 1, 1]); conv_bias = None 2025-09-09T15:12:28.2357708Z add_1 = torch.ops.aten.add.Tensor(div_1, reshape_2); div_1 = reshape_2 = None 2025-09-09T15:12:28.2358773Z batch_norm_1 = torch.ops.aten.batch_norm.default(add_1, bn_weight, bn_bias, bn_running_mean, bn_running_var, True, 0.1, 1e-05, True); add_1 = bn_weight = bn_bias = bn_running_mean = bn_running_var = None 2025-09-09T15:12:28.2359922Z relu = torch.ops.aten.relu.default(batch_norm_1); batch_norm_1 = None 2025-09-09T15:12:28.2360566Z activation_post_process_2 = self.activation_post_process_2(relu); relu = None 2025-09-09T15:12:28.2361220Z return pytree.tree_unflatten((activation_post_process_2,), self._out_spec) 2025-09-09T15:12:28.2361693Z 2025-09-09T15:12:28.2362023Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:12:28.2362465Z model fx: GraphModule( 2025-09-09T15:12:28.2362859Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:12:28.2364232Z fake_quant_enabled=tensor([1], device='cuda:0'), observer_enabled=tensor([1], device='cuda:0'), scale=tensor([0.0183], device='cuda:0'), zero_point=tensor([10], device='cuda:0', dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:12:28.2365816Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.526270866394043, max_val=2.143237352371216) 2025-09-09T15:12:28.2366428Z ) 2025-09-09T15:12:28.2366649Z (conv): ConvBnReLU2d( 2025-09-09T15:12:28.2366939Z 3, 3, kernel_size=(3, 3), stride=(1, 1) 2025-09-09T15:12:28.2367429Z (bn): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T15:12:28.2367994Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:12:28.2369326Z fake_quant_enabled=tensor([1], device='cuda:0'), observer_enabled=tensor([1], device='cuda:0'), scale=tensor([0.0015], device='cuda:0'), zero_point=tensor([0], device='cuda:0', dtype=torch.int32), dtype=torch.qint8, quant_min=-127, quant_max=127, qscheme=torch.per_tensor_symmetric, reduce_range=False 2025-09-09T15:12:28.2370918Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.18965020775794983, max_val=0.1870359182357788) 2025-09-09T15:12:28.2371550Z ) 2025-09-09T15:12:28.2371745Z ) 2025-09-09T15:12:28.2372074Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:12:28.2373821Z fake_quant_enabled=tensor([1], device='cuda:0'), observer_enabled=tensor([1], device='cuda:0'), scale=tensor([0.0080], device='cuda:0'), zero_point=tensor([-128], device='cuda:0', dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:12:28.2375505Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=2.0522286891937256) 2025-09-09T15:12:28.2376075Z ) 2025-09-09T15:12:28.2376269Z ) 2025-09-09T15:12:28.2376381Z 2025-09-09T15:12:28.2376386Z 2025-09-09T15:12:28.2376391Z 2025-09-09T15:12:28.2376496Z def forward(self, x): 2025-09-09T15:12:28.2376909Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T15:12:28.2377560Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T15:12:28.2378216Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T15:12:28.2378737Z return activation_post_process_1 2025-09-09T15:12:28.2379053Z 2025-09-09T15:12:28.2379378Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:12:28.2379822Z diff: tensor([[[[0., 0., 0.], 2025-09-09T15:12:28.2380101Z [0., 0., 0.], 2025-09-09T15:12:28.2380346Z [0., 0., 0.]], 2025-09-09T15:12:28.2380513Z 2025-09-09T15:12:28.2380600Z [[0., 0., 0.], 2025-09-09T15:12:28.2380844Z [0., 0., 0.], 2025-09-09T15:12:28.2381080Z [0., 0., 0.]], 2025-09-09T15:12:28.2381248Z 2025-09-09T15:12:28.2381334Z [[0., 0., 0.], 2025-09-09T15:12:28.2381568Z [0., 0., 0.], 2025-09-09T15:12:28.2381880Z [0., 0., 0.]]]], device='cuda:0', grad_fn=) 2025-09-09T15:12:28.2382279Z converted model pt2e: GraphModule( 2025-09-09T15:12:28.2382583Z (conv): Module() 2025-09-09T15:12:28.2382820Z (bn): Module() 2025-09-09T15:12:28.2383037Z ) 2025-09-09T15:12:28.2383160Z 2025-09-09T15:12:28.2383166Z 2025-09-09T15:12:28.2383170Z 2025-09-09T15:12:28.2383270Z def forward(self, x): 2025-09-09T15:12:28.2383609Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T15:12:28.2384011Z conv_bias = self.conv.bias 2025-09-09T15:12:28.2384811Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.018311796709895134, 10, -128, 127, torch.int8); x = None 2025-09-09T15:12:28.2386356Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.018311796709895134, 10, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T15:12:28.2387441Z quantize_per_tensor = self._frozen_param0 2025-09-09T15:12:28.2388415Z dequantize_per_tensor = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor, 0.0014933086931705475, 0, -127, 127, torch.int8); quantize_per_tensor = None 2025-09-09T15:12:28.2389993Z conv2d_2 = torch.ops.aten.conv2d.default(dequantize_per_tensor_default, dequantize_per_tensor, conv_bias); dequantize_per_tensor_default = dequantize_per_tensor = conv_bias = None 2025-09-09T15:12:28.2390842Z relu = torch.ops.aten.relu.default(conv2d_2); conv2d_2 = None 2025-09-09T15:12:28.2391618Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(relu, 0.00804795604199171, -128, -128, 127, torch.int8); relu = None 2025-09-09T15:12:28.2392884Z dequantize_per_tensor_default_2 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_2, 0.00804795604199171, -128, -128, 127, torch.int8); quantize_per_tensor_default_2 = None 2025-09-09T15:12:28.2393897Z return pytree.tree_unflatten((dequantize_per_tensor_default_2,), self._out_spec) 2025-09-09T15:12:28.2394302Z 2025-09-09T15:12:28.2394584Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:12:28.2394951Z onverted model fx: GraphModule( 2025-09-09T15:12:28.2395204Z (conv): ConvReLU2d( 2025-09-09T15:12:28.2395627Z (0): QuantizedConv2d(Reference)(3, 3, kernel_size=(3, 3), stride=(1, 1)) 2025-09-09T15:12:28.2396004Z (1): ReLU() 2025-09-09T15:12:28.2396192Z ) 2025-09-09T15:12:28.2396363Z ) 2025-09-09T15:12:28.2396535Z 2025-09-09T15:12:28.2396539Z 2025-09-09T15:12:28.2396543Z 2025-09-09T15:12:28.2396637Z def forward(self, x): 2025-09-09T15:12:28.2397246Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.018311796709895134, 10, -128, 127, torch.int8); x = None 2025-09-09T15:12:28.2398472Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.018311796709895134, 10, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T15:12:28.2399529Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T15:12:28.2400378Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.00804795604199171, -128, -128, 127, torch.int8); conv = None 2025-09-09T15:12:28.2401650Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.00804795604199171, -128, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T15:12:28.2402528Z return dequantize_per_tensor_default_1 2025-09-09T15:12:28.2402802Z 2025-09-09T15:12:28.2403083Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:12:28.2403444Z diff: tensor([[[[0., 0., 0.], 2025-09-09T15:12:28.2403680Z [0., 0., 0.], 2025-09-09T15:12:28.2403890Z [0., 0., 0.]], 2025-09-09T15:12:28.2404031Z 2025-09-09T15:12:28.2404112Z [[0., 0., 0.], 2025-09-09T15:12:28.2404312Z [0., 0., 0.], 2025-09-09T15:12:28.2404519Z [0., 0., 0.]], 2025-09-09T15:12:28.2404654Z 2025-09-09T15:12:28.2404727Z [[0., 0., 0.], 2025-09-09T15:12:28.2404931Z [0., 0., 0.], 2025-09-09T15:12:28.2405149Z [0., 0., 0.]]]], device='cuda:0') 2025-09-09T15:12:28.2405626Z PASSED 2025-09-09T15:12:28.2406255Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_conv_bn_relu_fusion_no_conv_bias model pt2e: GraphModule( 2025-09-09T15:12:28.2406909Z (conv): Module() 2025-09-09T15:12:28.2407111Z (bn): Module() 2025-09-09T15:12:28.2407407Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:12:28.2408348Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0183]), zero_point=tensor([10], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:12:28.2409444Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.526270866394043, max_val=2.143237352371216) 2025-09-09T15:12:28.2409946Z ) 2025-09-09T15:12:28.2410225Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:12:28.2411204Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0015, 0.0015, 0.0015]), zero_point=tensor([0, 0, 0], dtype=torch.int32), dtype=torch.int8, quant_min=-127, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False 2025-09-09T15:12:28.2412494Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.1866, -0.1825, -0.1912]), max_val=tensor([0.1747, 0.1914, 0.1702])) 2025-09-09T15:12:28.2413141Z ) 2025-09-09T15:12:28.2413413Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:12:28.2414358Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0078]), zero_point=tensor([-128], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:12:28.2415406Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=1.984864354133606) 2025-09-09T15:12:28.2415876Z ) 2025-09-09T15:12:28.2416041Z ) 2025-09-09T15:12:28.2416142Z 2025-09-09T15:12:28.2416236Z 2025-09-09T15:12:28.2416241Z 2025-09-09T15:12:28.2416329Z def forward(self, x): 2025-09-09T15:12:28.2416626Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T15:12:28.2417036Z conv_weight = self.conv.weight 2025-09-09T15:12:28.2417313Z bn_weight = self.bn.weight 2025-09-09T15:12:28.2417566Z bn_bias = self.bn.bias 2025-09-09T15:12:28.2417826Z bn_running_mean = self.bn.running_mean 2025-09-09T15:12:28.2418125Z bn_running_var = self.bn.running_var 2025-09-09T15:12:45.5518390Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T15:12:45.5519610Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T15:12:45.5520764Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T15:12:45.5521793Z add = torch.ops.aten.add.Tensor(bn_running_var, 1e-05) 2025-09-09T15:12:45.5522900Z sqrt = torch.ops.aten.sqrt.default(add); add = None 2025-09-09T15:12:45.5523894Z div = torch.ops.aten.div.Tensor(bn_weight, sqrt); sqrt = None 2025-09-09T15:12:45.5524781Z reshape = torch.ops.aten.reshape.default(div, [-1, 1, 1, 1]) 2025-09-09T15:12:45.5525547Z mul = torch.ops.aten.mul.Tensor(conv_weight, reshape); conv_weight = reshape = None 2025-09-09T15:12:45.5526097Z activation_post_process_1 = self.activation_post_process_1(mul); mul = None 2025-09-09T15:12:45.5526918Z conv2d_1 = torch.ops.aten.conv2d.default(activation_post_process_0, activation_post_process_1, None); activation_post_process_0 = activation_post_process_1 = None 2025-09-09T15:12:45.5527883Z reshape_1 = torch.ops.aten.reshape.default(div, [1, -1, 1, 1]); div = None 2025-09-09T15:12:45.5528417Z div_1 = torch.ops.aten.div.Tensor(conv2d_1, reshape_1); conv2d_1 = reshape_1 = None 2025-09-09T15:12:45.5529296Z batch_norm_1 = torch.ops.aten.batch_norm.default(div_1, bn_weight, bn_bias, bn_running_mean, bn_running_var, True, 0.1, 1e-05, True); div_1 = bn_weight = bn_bias = bn_running_mean = bn_running_var = None 2025-09-09T15:12:45.5530115Z relu = torch.ops.aten.relu.default(batch_norm_1); batch_norm_1 = None 2025-09-09T15:12:45.5530741Z activation_post_process_2 = self.activation_post_process_2(relu); relu = None 2025-09-09T15:12:45.5531286Z return pytree.tree_unflatten((activation_post_process_2,), self._out_spec) 2025-09-09T15:12:45.5531662Z 2025-09-09T15:12:45.5531933Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:12:45.5532295Z model fx: GraphModule( 2025-09-09T15:12:45.5532613Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:12:45.5533536Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0183]), zero_point=tensor([10], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:12:45.5534682Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.526270866394043, max_val=2.143237352371216) 2025-09-09T15:12:45.5535183Z ) 2025-09-09T15:12:45.5535378Z (conv): ConvBnReLU2d( 2025-09-09T15:12:45.5535684Z 3, 3, kernel_size=(3, 3), stride=(1, 1), bias=False 2025-09-09T15:12:45.5536141Z (bn): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T15:12:45.5536598Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:12:45.5537537Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0015, 0.0015, 0.0015]), zero_point=tensor([0, 0, 0], dtype=torch.int32), dtype=torch.qint8, quant_min=-127, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False 2025-09-09T15:12:45.5546625Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.1866, -0.1825, -0.1912]), max_val=tensor([0.1747, 0.1914, 0.1702])) 2025-09-09T15:12:45.5547285Z ) 2025-09-09T15:12:45.5547459Z ) 2025-09-09T15:12:45.5547730Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:12:45.5548916Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0078]), zero_point=tensor([-128], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:12:45.5550108Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=1.984864354133606) 2025-09-09T15:12:45.5550566Z ) 2025-09-09T15:12:45.5550736Z ) 2025-09-09T15:12:45.5550831Z 2025-09-09T15:12:45.5550835Z 2025-09-09T15:12:45.5550839Z 2025-09-09T15:12:45.5550921Z def forward(self, x): 2025-09-09T15:12:45.5551272Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T15:12:45.5551806Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T15:12:45.5552339Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T15:12:45.5552760Z return activation_post_process_1 2025-09-09T15:12:45.5553016Z 2025-09-09T15:12:45.5553291Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:12:45.5553648Z diff: tensor([[[[0., 0., 0.], 2025-09-09T15:12:45.5553893Z [0., 0., 0.], 2025-09-09T15:12:45.5554097Z [0., 0., 0.]], 2025-09-09T15:12:45.5554245Z 2025-09-09T15:12:45.5554318Z [[0., 0., 0.], 2025-09-09T15:12:45.5554525Z [0., 0., 0.], 2025-09-09T15:12:45.5554726Z [0., 0., 0.]], 2025-09-09T15:12:45.5554859Z 2025-09-09T15:12:45.5554932Z [[0., 0., 0.], 2025-09-09T15:12:45.5555134Z [0., 0., 0.], 2025-09-09T15:12:45.5555370Z [0., 0., 0.]]]], grad_fn=) 2025-09-09T15:12:45.5555679Z converted model pt2e: GraphModule( 2025-09-09T15:12:45.5555935Z (conv): Module() 2025-09-09T15:12:45.5556135Z (bn): Module() 2025-09-09T15:12:45.5556317Z ) 2025-09-09T15:12:45.5556409Z 2025-09-09T15:12:45.5556413Z 2025-09-09T15:12:45.5556423Z 2025-09-09T15:12:45.5556502Z def forward(self, x): 2025-09-09T15:12:45.5556778Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T15:12:45.5557497Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.018311796709895134, 10, -128, 127, torch.int8); x = None 2025-09-09T15:12:45.5558723Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.018311796709895134, 10, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T15:12:45.5559623Z _scale_0 = self._scale_0 2025-09-09T15:12:45.5559877Z _zero_point_0 = self._zero_point_0 2025-09-09T15:12:45.5560167Z quantize_per_channel = self._frozen_param0 2025-09-09T15:12:45.5561036Z dequantize_per_channel = torch.ops.quantized_decomposed.dequantize_per_channel.default(quantize_per_channel, _scale_0, _zero_point_0, 0, -127, 127, torch.int8); quantize_per_channel = _scale_0 = _zero_point_0 = None 2025-09-09T15:12:45.5561903Z conv_weight_bias = self.conv.weight_bias 2025-09-09T15:12:45.5562729Z conv2d_2 = torch.ops.aten.conv2d.default(dequantize_per_tensor_default, dequantize_per_channel, conv_weight_bias); dequantize_per_tensor_default = dequantize_per_channel = conv_weight_bias = None 2025-09-09T15:12:45.5563619Z relu = torch.ops.aten.relu.default(conv2d_2); conv2d_2 = None 2025-09-09T15:12:45.5564379Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(relu, 0.007783781737089157, -128, -128, 127, torch.int8); relu = None 2025-09-09T15:12:45.5565684Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.007783781737089157, -128, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T15:12:45.5566695Z return pytree.tree_unflatten((dequantize_per_tensor_default_1,), self._out_spec) 2025-09-09T15:12:45.5567089Z 2025-09-09T15:12:45.5567364Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:12:45.5567814Z onverted model fx: GraphModule( 2025-09-09T15:12:45.5568071Z (conv): ConvReLU2d( 2025-09-09T15:12:45.5568398Z (0): QuantizedConv2d(Reference)(3, 3, kernel_size=(3, 3), stride=(1, 1)) 2025-09-09T15:12:45.5568841Z (1): ReLU() 2025-09-09T15:12:45.5569027Z ) 2025-09-09T15:12:45.5569182Z ) 2025-09-09T15:12:45.5569274Z 2025-09-09T15:12:45.5569278Z 2025-09-09T15:12:45.5569282Z 2025-09-09T15:12:45.5569365Z def forward(self, x): 2025-09-09T15:12:45.5569964Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.018311796709895134, 10, -128, 127, torch.int8); x = None 2025-09-09T15:12:45.5571170Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.018311796709895134, 10, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T15:12:45.5572156Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T15:12:45.5572997Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.007783781737089157, -128, -128, 127, torch.int8); conv = None 2025-09-09T15:12:45.5574268Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.007783781737089157, -128, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T15:12:45.5575135Z return dequantize_per_tensor_default_1 2025-09-09T15:12:45.5575399Z 2025-09-09T15:12:45.5575668Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:12:45.5576036Z diff: tensor([[[[0., 0., 0.], 2025-09-09T15:12:45.5576273Z [0., 0., 0.], 2025-09-09T15:12:45.5576472Z [0., 0., 0.]], 2025-09-09T15:12:45.5576618Z 2025-09-09T15:12:45.5576691Z [[0., 0., 0.], 2025-09-09T15:12:45.5576895Z [0., 0., 0.], 2025-09-09T15:12:45.5577091Z [0., 0., 0.]], 2025-09-09T15:12:45.5577225Z 2025-09-09T15:12:45.5577309Z [[0., 0., 0.], 2025-09-09T15:12:45.5577506Z [0., 0., 0.], 2025-09-09T15:12:45.5577715Z [0., 0., 0.]]]]) 2025-09-09T15:12:45.5577945Z model pt2e: GraphModule( 2025-09-09T15:12:45.5578165Z (conv): Module() 2025-09-09T15:12:45.5578361Z (bn): Module() 2025-09-09T15:12:45.5578655Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:12:45.5579572Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0183]), zero_point=tensor([10], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:12:45.5580646Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.526270866394043, max_val=2.143237352371216) 2025-09-09T15:12:45.5581147Z ) 2025-09-09T15:12:45.5581411Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:12:45.5582345Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0015]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.int8, quant_min=-127, quant_max=127, qscheme=torch.per_tensor_symmetric, reduce_range=False 2025-09-09T15:12:45.5583453Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.19124282896518707, max_val=0.19141820073127747) 2025-09-09T15:12:45.5583956Z ) 2025-09-09T15:12:45.5584227Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:12:45.5585134Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0078]), zero_point=tensor([-128], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:12:45.5586163Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=1.9838534593582153) 2025-09-09T15:12:45.5586622Z ) 2025-09-09T15:12:45.5586774Z ) 2025-09-09T15:12:45.5586866Z 2025-09-09T15:12:45.5586870Z 2025-09-09T15:12:45.5586874Z 2025-09-09T15:12:45.5587045Z def forward(self, x): 2025-09-09T15:12:45.5587319Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T15:12:45.5587646Z conv_weight = self.conv.weight 2025-09-09T15:13:04.3728376Z bn_weight = self.bn.weight 2025-09-09T15:13:04.3728723Z bn_bias = self.bn.bias 2025-09-09T15:13:04.3729203Z bn_running_mean = self.bn.running_mean 2025-09-09T15:13:04.3729568Z bn_running_var = self.bn.running_var 2025-09-09T15:13:04.3729972Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T15:13:04.3730551Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T15:13:04.3731333Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T15:13:04.3731983Z add = torch.ops.aten.add.Tensor(bn_running_var, 1e-05) 2025-09-09T15:13:04.3732451Z sqrt = torch.ops.aten.sqrt.default(add); add = None 2025-09-09T15:13:04.3732951Z div = torch.ops.aten.div.Tensor(bn_weight, sqrt); sqrt = None 2025-09-09T15:13:04.3733510Z reshape = torch.ops.aten.reshape.default(div, [-1, 1, 1, 1]) 2025-09-09T15:13:04.3734128Z mul = torch.ops.aten.mul.Tensor(conv_weight, reshape); conv_weight = reshape = None 2025-09-09T15:13:04.3734831Z activation_post_process_1 = self.activation_post_process_1(mul); mul = None 2025-09-09T15:13:04.3735860Z conv2d_1 = torch.ops.aten.conv2d.default(activation_post_process_0, activation_post_process_1, None); activation_post_process_0 = activation_post_process_1 = None 2025-09-09T15:13:04.3736887Z reshape_1 = torch.ops.aten.reshape.default(div, [1, -1, 1, 1]); div = None 2025-09-09T15:13:04.3737549Z div_1 = torch.ops.aten.div.Tensor(conv2d_1, reshape_1); conv2d_1 = reshape_1 = None 2025-09-09T15:13:04.3738640Z batch_norm_1 = torch.ops.aten.batch_norm.default(div_1, bn_weight, bn_bias, bn_running_mean, bn_running_var, True, 0.1, 1e-05, True); div_1 = bn_weight = bn_bias = bn_running_mean = bn_running_var = None 2025-09-09T15:13:04.3739681Z relu = torch.ops.aten.relu.default(batch_norm_1); batch_norm_1 = None 2025-09-09T15:13:04.3740313Z activation_post_process_2 = self.activation_post_process_2(relu); relu = None 2025-09-09T15:13:04.3740981Z return pytree.tree_unflatten((activation_post_process_2,), self._out_spec) 2025-09-09T15:13:04.3741441Z 2025-09-09T15:13:04.3741776Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:13:04.3742214Z model fx: GraphModule( 2025-09-09T15:13:04.3742587Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:13:04.3743747Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0183]), zero_point=tensor([10], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:13:04.3745111Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.526270866394043, max_val=2.143237352371216) 2025-09-09T15:13:04.3745725Z ) 2025-09-09T15:13:04.3745948Z (conv): ConvBnReLU2d( 2025-09-09T15:13:04.3746262Z 3, 3, kernel_size=(3, 3), stride=(1, 1), bias=False 2025-09-09T15:13:04.3746800Z (bn): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T15:13:04.3747359Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:13:04.3748492Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0015]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-127, quant_max=127, qscheme=torch.per_tensor_symmetric, reduce_range=False 2025-09-09T15:13:04.3749883Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.19124282896518707, max_val=0.19141820073127747) 2025-09-09T15:13:04.3750512Z ) 2025-09-09T15:13:04.3750718Z ) 2025-09-09T15:13:04.3751040Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:13:04.3752452Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0078]), zero_point=tensor([-128], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:13:04.3753776Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=1.9838534593582153) 2025-09-09T15:13:04.3754544Z ) 2025-09-09T15:13:04.3754744Z ) 2025-09-09T15:13:04.3754855Z 2025-09-09T15:13:04.3754860Z 2025-09-09T15:13:04.3754864Z 2025-09-09T15:13:04.3754961Z def forward(self, x): 2025-09-09T15:13:04.3755385Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T15:13:04.3756029Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T15:13:04.3756692Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T15:13:04.3757210Z return activation_post_process_1 2025-09-09T15:13:04.3757512Z 2025-09-09T15:13:04.3757838Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:13:04.3758282Z diff: tensor([[[[0., 0., 0.], 2025-09-09T15:13:04.3758562Z [0., 0., 0.], 2025-09-09T15:13:04.3758807Z [0., 0., 0.]], 2025-09-09T15:13:04.3758984Z 2025-09-09T15:13:04.3759070Z [[0., 0., 0.], 2025-09-09T15:13:04.3759380Z [0., 0., 0.], 2025-09-09T15:13:04.3759625Z [0., 0., 0.]], 2025-09-09T15:13:04.3759787Z 2025-09-09T15:13:04.3759882Z [[0., 0., 0.], 2025-09-09T15:13:04.3760118Z [0., 0., 0.], 2025-09-09T15:13:04.3760411Z [0., 0., 0.]]]], grad_fn=) 2025-09-09T15:13:04.3760811Z converted model pt2e: GraphModule( 2025-09-09T15:13:04.3761147Z (conv): Module() 2025-09-09T15:13:04.3761376Z (bn): Module() 2025-09-09T15:13:04.3761604Z ) 2025-09-09T15:13:04.3761714Z 2025-09-09T15:13:04.3761719Z 2025-09-09T15:13:04.3761724Z 2025-09-09T15:13:04.3761820Z def forward(self, x): 2025-09-09T15:13:04.3762159Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T15:13:04.3763051Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.018311796709895134, 10, -128, 127, torch.int8); x = None 2025-09-09T15:13:04.3764686Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.018311796709895134, 10, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T15:13:04.3765650Z quantize_per_tensor = self._frozen_param0 2025-09-09T15:13:04.3766438Z dequantize_per_tensor = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor, 0.001507229870185256, 0, -127, 127, torch.int8); quantize_per_tensor = None 2025-09-09T15:13:04.3767244Z conv_weight_bias = self.conv.weight_bias 2025-09-09T15:13:04.3768081Z conv2d_2 = torch.ops.aten.conv2d.default(dequantize_per_tensor_default, dequantize_per_tensor, conv_weight_bias); dequantize_per_tensor_default = dequantize_per_tensor = conv_weight_bias = None 2025-09-09T15:13:04.3768978Z relu = torch.ops.aten.relu.default(conv2d_2); conv2d_2 = None 2025-09-09T15:13:04.3769765Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(relu, 0.007779817562550306, -128, -128, 127, torch.int8); relu = None 2025-09-09T15:13:04.3771059Z dequantize_per_tensor_default_2 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_2, 0.007779817562550306, -128, -128, 127, torch.int8); quantize_per_tensor_default_2 = None 2025-09-09T15:13:04.3772072Z return pytree.tree_unflatten((dequantize_per_tensor_default_2,), self._out_spec) 2025-09-09T15:13:04.3772484Z 2025-09-09T15:13:04.3772757Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:13:04.3773134Z onverted model fx: GraphModule( 2025-09-09T15:13:04.3773386Z (conv): ConvReLU2d( 2025-09-09T15:13:04.3773723Z (0): QuantizedConv2d(Reference)(3, 3, kernel_size=(3, 3), stride=(1, 1)) 2025-09-09T15:13:04.3774097Z (1): ReLU() 2025-09-09T15:13:04.3774286Z ) 2025-09-09T15:13:04.3774551Z ) 2025-09-09T15:13:04.3774648Z 2025-09-09T15:13:04.3774652Z 2025-09-09T15:13:04.3774656Z 2025-09-09T15:13:04.3774736Z def forward(self, x): 2025-09-09T15:13:04.3775429Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.018311796709895134, 10, -128, 127, torch.int8); x = None 2025-09-09T15:13:04.3776660Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.018311796709895134, 10, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T15:13:04.3777678Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T15:13:04.3778539Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.007779817562550306, -128, -128, 127, torch.int8); conv = None 2025-09-09T15:13:04.3779822Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.007779817562550306, -128, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T15:13:04.3780721Z return dequantize_per_tensor_default_1 2025-09-09T15:13:04.3781000Z 2025-09-09T15:13:04.3781273Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:13:04.3781687Z diff: tensor([[[[0., 0., 0.], 2025-09-09T15:13:04.3781921Z [0., 0., 0.], 2025-09-09T15:13:04.3782133Z [0., 0., 0.]], 2025-09-09T15:13:04.3782275Z 2025-09-09T15:13:04.3782349Z [[0., 0., 0.], 2025-09-09T15:13:04.3782560Z [0., 0., 0.], 2025-09-09T15:13:04.3782760Z [0., 0., 0.]], 2025-09-09T15:13:04.3782899Z 2025-09-09T15:13:04.3782983Z [[0., 0., 0.], 2025-09-09T15:13:04.3783193Z [0., 0., 0.], 2025-09-09T15:13:04.3783402Z [0., 0., 0.]]]]) 2025-09-09T15:13:04.3783819Z PASSED 2025-09-09T15:13:04.3784394Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_conv_no_bias model pt2e: GraphModule( 2025-09-09T15:13:04.3784991Z (conv): Module() 2025-09-09T15:13:04.3785300Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:13:04.3786300Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0015, 0.0015, 0.0014]), zero_point=tensor([0, 0, 0], dtype=torch.int32), dtype=torch.int8, quant_min=-127, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False 2025-09-09T15:13:04.3787594Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.1860, -0.1897, -0.1787]), max_val=tensor([0.1824, 0.1870, 0.1478])) 2025-09-09T15:13:04.3788243Z ) 2025-09-09T15:13:04.3788512Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:13:04.3789465Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0183]), zero_point=tensor([10], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:13:04.3790555Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.526270866394043, max_val=2.143237352371216) 2025-09-09T15:13:04.3791069Z ) 2025-09-09T15:13:04.3791345Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:13:04.3792278Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0062]), zero_point=tensor([-128], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:13:04.3793340Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=1.5882599353790283) 2025-09-09T15:13:04.3793805Z ) 2025-09-09T15:13:04.3793976Z ) 2025-09-09T15:13:04.3794072Z 2025-09-09T15:13:04.3794076Z 2025-09-09T15:13:04.3794080Z 2025-09-09T15:13:05.8354565Z def forward(self, x): 2025-09-09T15:13:05.8354928Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T15:13:05.8355595Z conv_weight = self.conv.weight 2025-09-09T15:13:05.8356177Z activation_post_process_1 = self.activation_post_process_1(conv_weight); conv_weight = None 2025-09-09T15:13:05.8357029Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T15:13:05.8358035Z conv2d = torch.ops.aten.conv2d.default(activation_post_process_0, activation_post_process_1); activation_post_process_0 = activation_post_process_1 = None 2025-09-09T15:13:05.8358973Z relu = torch.ops.aten.relu.default(conv2d); conv2d = None 2025-09-09T15:13:05.8359705Z activation_post_process_2 = self.activation_post_process_2(relu); relu = None 2025-09-09T15:13:05.8360370Z return pytree.tree_unflatten((activation_post_process_2,), self._out_spec) 2025-09-09T15:13:05.8360837Z 2025-09-09T15:13:05.8361230Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:13:05.8361679Z model fx: GraphModule( 2025-09-09T15:13:05.8362063Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:13:05.8363230Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0183]), zero_point=tensor([10], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:13:05.8364598Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.526270866394043, max_val=2.143237352371216) 2025-09-09T15:13:05.8365217Z ) 2025-09-09T15:13:05.8365425Z (conv): ConvReLU2d( 2025-09-09T15:13:05.8365734Z 3, 3, kernel_size=(3, 3), stride=(1, 1), bias=False 2025-09-09T15:13:05.8366168Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:13:05.8367343Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0015, 0.0015, 0.0014]), zero_point=tensor([0, 0, 0], dtype=torch.int32), dtype=torch.qint8, quant_min=-127, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False 2025-09-09T15:13:05.8368950Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.1860, -0.1897, -0.1787]), max_val=tensor([0.1824, 0.1870, 0.1478])) 2025-09-09T15:13:05.8369748Z ) 2025-09-09T15:13:05.8369956Z ) 2025-09-09T15:13:05.8370282Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:13:05.8371444Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0062]), zero_point=tensor([-128], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:13:05.8372758Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=1.5882599353790283) 2025-09-09T15:13:05.8373328Z ) 2025-09-09T15:13:05.8373523Z ) 2025-09-09T15:13:05.8373633Z 2025-09-09T15:13:05.8373638Z 2025-09-09T15:13:05.8373643Z 2025-09-09T15:13:05.8373745Z def forward(self, x): 2025-09-09T15:13:05.8374157Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T15:13:05.8374811Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T15:13:05.8375466Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T15:13:05.8375994Z return activation_post_process_1 2025-09-09T15:13:05.8376295Z 2025-09-09T15:13:05.8376621Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:13:05.8377068Z diff: tensor([[[[0., 0., 0.], 2025-09-09T15:13:05.8377343Z [0., 0., 0.], 2025-09-09T15:13:05.8377593Z [0., 0., 0.]], 2025-09-09T15:13:05.8377759Z 2025-09-09T15:13:05.8377846Z [[0., 0., 0.], 2025-09-09T15:13:05.8378090Z [0., 0., 0.], 2025-09-09T15:13:05.8378330Z [0., 0., 0.]], 2025-09-09T15:13:05.8378503Z 2025-09-09T15:13:05.8378590Z [[0., 0., 0.], 2025-09-09T15:13:05.8378826Z [0., 0., 0.], 2025-09-09T15:13:05.8379107Z [0., 0., 0.]]]], grad_fn=) 2025-09-09T15:13:05.8379569Z converted model pt2e: GraphModule( 2025-09-09T15:13:05.8379887Z (conv): Module() 2025-09-09T15:13:05.8380117Z ) 2025-09-09T15:13:05.8380230Z 2025-09-09T15:13:05.8380235Z 2025-09-09T15:13:05.8380322Z 2025-09-09T15:13:05.8380420Z def forward(self, x): 2025-09-09T15:13:05.8380751Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T15:13:05.8381140Z _scale_0 = self._scale_0 2025-09-09T15:13:05.8381441Z _zero_point_0 = self._zero_point_0 2025-09-09T15:13:05.8381821Z quantize_per_channel_default = self._frozen_param0 2025-09-09T15:13:05.8383052Z dequantize_per_channel_default = torch.ops.quantized_decomposed.dequantize_per_channel.default(quantize_per_channel_default, _scale_0, _zero_point_0, 0, -127, 127, torch.int8); quantize_per_channel_default = _scale_0 = _zero_point_0 = None 2025-09-09T15:13:05.8384779Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.018311796709895134, 10, -128, 127, torch.int8); x = None 2025-09-09T15:13:05.8386203Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.018311796709895134, 10, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T15:13:05.8387540Z conv2d = torch.ops.aten.conv2d.default(dequantize_per_tensor_default, dequantize_per_channel_default); dequantize_per_tensor_default = dequantize_per_channel_default = None 2025-09-09T15:13:05.8388369Z relu = torch.ops.aten.relu.default(conv2d); conv2d = None 2025-09-09T15:13:05.8389134Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(relu, 0.006228470243513584, -128, -128, 127, torch.int8); relu = None 2025-09-09T15:13:05.8390425Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.006228470243513584, -128, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T15:13:05.8391472Z return pytree.tree_unflatten((dequantize_per_tensor_default_1,), self._out_spec) 2025-09-09T15:13:05.8391878Z 2025-09-09T15:13:05.8392160Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:13:05.8392542Z onverted model fx: GraphModule( 2025-09-09T15:13:05.8392797Z (conv): ConvReLU2d( 2025-09-09T15:13:05.8393166Z (0): QuantizedConv2d(Reference)(3, 3, kernel_size=(3, 3), stride=(1, 1), bias=False) 2025-09-09T15:13:05.8393577Z (1): ReLU() 2025-09-09T15:13:05.8393771Z ) 2025-09-09T15:13:05.8393933Z ) 2025-09-09T15:13:05.8394028Z 2025-09-09T15:13:05.8394032Z 2025-09-09T15:13:05.8394036Z 2025-09-09T15:13:05.8394125Z def forward(self, x): 2025-09-09T15:13:05.8394738Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.018311796709895134, 10, -128, 127, torch.int8); x = None 2025-09-09T15:13:05.8395986Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.018311796709895134, 10, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T15:13:05.8397005Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T15:13:05.8397863Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.006228470243513584, -128, -128, 127, torch.int8); conv = None 2025-09-09T15:13:05.8399160Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.006228470243513584, -128, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T15:13:05.8400102Z return dequantize_per_tensor_default_1 2025-09-09T15:13:05.8400368Z 2025-09-09T15:13:05.8400656Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:13:05.8401015Z diff: tensor([[[[0., 0., 0.], 2025-09-09T15:13:05.8401250Z [0., 0., 0.], 2025-09-09T15:13:05.8401457Z [0., 0., 0.]], 2025-09-09T15:13:05.8401696Z 2025-09-09T15:13:05.8401778Z [[0., 0., 0.], 2025-09-09T15:13:05.8401981Z [0., 0., 0.], 2025-09-09T15:13:05.8402210Z [0., 0., 0.]], 2025-09-09T15:13:05.8402448Z 2025-09-09T15:13:05.8402522Z [[0., 0., 0.], 2025-09-09T15:13:05.8402725Z [0., 0., 0.], 2025-09-09T15:13:05.8402933Z [0., 0., 0.]]]]) 2025-09-09T15:13:05.8403158Z model pt2e: GraphModule( 2025-09-09T15:13:05.8403381Z (conv): Module() 2025-09-09T15:13:05.8403678Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:13:05.8404635Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0015]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.int8, quant_min=-127, quant_max=127, qscheme=torch.per_tensor_symmetric, reduce_range=False 2025-09-09T15:13:05.8405757Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.18965116143226624, max_val=0.18703685700893402) 2025-09-09T15:13:05.8406273Z ) 2025-09-09T15:13:05.8406551Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:13:05.8407476Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0183]), zero_point=tensor([10], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:13:05.8408577Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.526270866394043, max_val=2.143237352371216) 2025-09-09T15:13:05.8409076Z ) 2025-09-09T15:13:05.8409352Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:13:05.8410286Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0062]), zero_point=tensor([-128], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:13:05.8411336Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=1.5892566442489624) 2025-09-09T15:13:05.8411806Z ) 2025-09-09T15:13:05.8411967Z ) 2025-09-09T15:13:05.8412073Z 2025-09-09T15:13:05.8412078Z 2025-09-09T15:13:05.8412083Z 2025-09-09T15:13:05.8412178Z def forward(self, x): 2025-09-09T15:13:05.8412491Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T15:13:05.8412822Z conv_weight = self.conv.weight 2025-09-09T15:13:05.8413282Z activation_post_process_1 = self.activation_post_process_1(conv_weight); conv_weight = None 2025-09-09T15:13:05.8413859Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T15:13:05.8414665Z conv2d = torch.ops.aten.conv2d.default(activation_post_process_0, activation_post_process_1); activation_post_process_0 = activation_post_process_1 = None 2025-09-09T15:13:05.8415413Z relu = torch.ops.aten.relu.default(conv2d); conv2d = None 2025-09-09T15:13:05.8415898Z activation_post_process_2 = self.activation_post_process_2(relu); relu = None 2025-09-09T15:13:05.8416452Z return pytree.tree_unflatten((activation_post_process_2,), self._out_spec) 2025-09-09T15:13:05.8416836Z 2025-09-09T15:13:05.8417121Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:13:05.8417480Z model fx: GraphModule( 2025-09-09T15:13:05.8417797Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:13:07.2915996Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0183]), zero_point=tensor([10], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:13:07.2917403Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.526270866394043, max_val=2.143237352371216) 2025-09-09T15:13:07.2918026Z ) 2025-09-09T15:13:07.2918248Z (conv): ConvReLU2d( 2025-09-09T15:13:07.2918553Z 3, 3, kernel_size=(3, 3), stride=(1, 1), bias=False 2025-09-09T15:13:07.2918990Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:13:07.2920420Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0015]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-127, quant_max=127, qscheme=torch.per_tensor_symmetric, reduce_range=False 2025-09-09T15:13:07.2921964Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.18965116143226624, max_val=0.18703685700893402) 2025-09-09T15:13:07.2922911Z ) 2025-09-09T15:13:07.2923114Z ) 2025-09-09T15:13:07.2923439Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:13:07.2924603Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0062]), zero_point=tensor([-128], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:13:07.2925927Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=1.5892566442489624) 2025-09-09T15:13:07.2926503Z ) 2025-09-09T15:13:07.2926699Z ) 2025-09-09T15:13:07.2926811Z 2025-09-09T15:13:07.2926826Z 2025-09-09T15:13:07.2926830Z 2025-09-09T15:13:07.2926933Z def forward(self, x): 2025-09-09T15:13:07.2927353Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T15:13:07.2928012Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T15:13:07.2928670Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T15:13:07.2929192Z return activation_post_process_1 2025-09-09T15:13:07.2929506Z 2025-09-09T15:13:07.2929830Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:13:07.2930273Z diff: tensor([[[[0., 0., 0.], 2025-09-09T15:13:07.2930553Z [0., 0., 0.], 2025-09-09T15:13:07.2930805Z [0., 0., 0.]], 2025-09-09T15:13:07.2930976Z 2025-09-09T15:13:07.2931068Z [[0., 0., 0.], 2025-09-09T15:13:07.2931316Z [0., 0., 0.], 2025-09-09T15:13:07.2931583Z [0., 0., 0.]], 2025-09-09T15:13:07.2931777Z 2025-09-09T15:13:07.2931869Z [[0., 0., 0.], 2025-09-09T15:13:07.2932107Z [0., 0., 0.], 2025-09-09T15:13:07.2932389Z [0., 0., 0.]]]], grad_fn=) 2025-09-09T15:13:07.2932763Z converted model pt2e: GraphModule( 2025-09-09T15:13:07.2933067Z (conv): Module() 2025-09-09T15:13:07.2933294Z ) 2025-09-09T15:13:07.2933407Z 2025-09-09T15:13:07.2933412Z 2025-09-09T15:13:07.2933417Z 2025-09-09T15:13:07.2933514Z def forward(self, x): 2025-09-09T15:13:07.2933852Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T15:13:07.2934299Z quantize_per_tensor_default = self._frozen_param0 2025-09-09T15:13:07.2935429Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.0014933162601664662, 0, -127, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T15:13:07.2936983Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.018311796709895134, 10, -128, 127, torch.int8); x = None 2025-09-09T15:13:07.2938532Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.018311796709895134, 10, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T15:13:07.2940314Z conv2d = torch.ops.aten.conv2d.default(dequantize_per_tensor_default_1, dequantize_per_tensor_default); dequantize_per_tensor_default_1 = dequantize_per_tensor_default = None 2025-09-09T15:13:07.2941137Z relu = torch.ops.aten.relu.default(conv2d); conv2d = None 2025-09-09T15:13:07.2941902Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(relu, 0.006232379004359245, -128, -128, 127, torch.int8); relu = None 2025-09-09T15:13:07.2943322Z dequantize_per_tensor_default_2 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_2, 0.006232379004359245, -128, -128, 127, torch.int8); quantize_per_tensor_default_2 = None 2025-09-09T15:13:07.2944343Z return pytree.tree_unflatten((dequantize_per_tensor_default_2,), self._out_spec) 2025-09-09T15:13:07.2944748Z 2025-09-09T15:13:07.2945125Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:13:07.2945496Z onverted model fx: GraphModule( 2025-09-09T15:13:07.2945750Z (conv): ConvReLU2d( 2025-09-09T15:13:07.2946114Z (0): QuantizedConv2d(Reference)(3, 3, kernel_size=(3, 3), stride=(1, 1), bias=False) 2025-09-09T15:13:07.2946521Z (1): ReLU() 2025-09-09T15:13:07.2946704Z ) 2025-09-09T15:13:07.2946869Z ) 2025-09-09T15:13:07.2946962Z 2025-09-09T15:13:07.2946966Z 2025-09-09T15:13:07.2946970Z 2025-09-09T15:13:07.2947066Z def forward(self, x): 2025-09-09T15:13:07.2947686Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.018311796709895134, 10, -128, 127, torch.int8); x = None 2025-09-09T15:13:07.2948926Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.018311796709895134, 10, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T15:13:07.2957540Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T15:13:07.2958476Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.006232379004359245, -128, -128, 127, torch.int8); conv = None 2025-09-09T15:13:07.2959820Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.006232379004359245, -128, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T15:13:07.2960713Z return dequantize_per_tensor_default_1 2025-09-09T15:13:07.2960991Z 2025-09-09T15:13:07.2961292Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:13:07.2961670Z diff: tensor([[[[0., 0., 0.], 2025-09-09T15:13:07.2961917Z [0., 0., 0.], 2025-09-09T15:13:07.2962135Z [0., 0., 0.]], 2025-09-09T15:13:07.2962284Z 2025-09-09T15:13:07.2962364Z [[0., 0., 0.], 2025-09-09T15:13:07.2962580Z [0., 0., 0.], 2025-09-09T15:13:07.2962792Z [0., 0., 0.]], 2025-09-09T15:13:07.2962930Z 2025-09-09T15:13:07.2963016Z [[0., 0., 0.], 2025-09-09T15:13:07.2963219Z [0., 0., 0.], 2025-09-09T15:13:07.2963432Z [0., 0., 0.]]]]) 2025-09-09T15:13:07.2963663Z model pt2e: GraphModule( 2025-09-09T15:13:07.2963897Z (conv): Module() 2025-09-09T15:13:07.2964196Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:13:07.2965184Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0015, 0.0014, 0.0015]), zero_point=tensor([0, 0, 0], dtype=torch.int32), dtype=torch.int8, quant_min=-127, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False 2025-09-09T15:13:07.2966472Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.1913, -0.1469, -0.1921]), max_val=tensor([0.1740, 0.1746, 0.1810])) 2025-09-09T15:13:07.2967117Z ) 2025-09-09T15:13:07.2967398Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:13:07.2968340Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0183]), zero_point=tensor([10], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:13:07.2969422Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.526270866394043, max_val=2.143237352371216) 2025-09-09T15:13:07.2969926Z ) 2025-09-09T15:13:07.2970210Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:13:07.2971129Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0080]), zero_point=tensor([-4], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:13:07.2973765Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.9872972965240479, max_val=1.0470484495162964) 2025-09-09T15:13:07.2974303Z ) 2025-09-09T15:13:07.2974547Z ) 2025-09-09T15:13:07.2974647Z 2025-09-09T15:13:07.2974651Z 2025-09-09T15:13:07.2974655Z 2025-09-09T15:13:07.2974747Z def forward(self, x): 2025-09-09T15:13:07.2975030Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T15:13:07.2975369Z conv_weight = self.conv.weight 2025-09-09T15:13:07.2975823Z activation_post_process_1 = self.activation_post_process_1(conv_weight); conv_weight = None 2025-09-09T15:13:07.2976406Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T15:13:07.2977213Z conv2d = torch.ops.aten.conv2d.default(activation_post_process_0, activation_post_process_1); activation_post_process_0 = activation_post_process_1 = None 2025-09-09T15:13:07.2978034Z activation_post_process_2 = self.activation_post_process_2(conv2d); conv2d = None 2025-09-09T15:13:07.2978597Z return pytree.tree_unflatten((activation_post_process_2,), self._out_spec) 2025-09-09T15:13:07.2978981Z 2025-09-09T15:13:07.2979269Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:13:07.2979632Z model fx: GraphModule( 2025-09-09T15:13:07.2979953Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:13:07.2980889Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0183]), zero_point=tensor([10], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:13:07.2981971Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.526270866394043, max_val=2.143237352371216) 2025-09-09T15:13:07.2982477Z ) 2025-09-09T15:13:07.2982652Z (conv): Conv2d( 2025-09-09T15:13:07.2982901Z 3, 3, kernel_size=(3, 3), stride=(1, 1), bias=False 2025-09-09T15:13:07.2983263Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:13:07.2984221Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0015, 0.0014, 0.0015]), zero_point=tensor([0, 0, 0], dtype=torch.int32), dtype=torch.qint8, quant_min=-127, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False 2025-09-09T15:13:07.2985511Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.1913, -0.1469, -0.1921]), max_val=tensor([0.1740, 0.1746, 0.1810])) 2025-09-09T15:13:07.2986153Z ) 2025-09-09T15:13:07.2986333Z ) 2025-09-09T15:13:07.2986607Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:13:08.9371256Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0080]), zero_point=tensor([-4], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:13:08.9372799Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.9872972965240479, max_val=1.0470484495162964) 2025-09-09T15:13:08.9373423Z ) 2025-09-09T15:13:08.9373617Z ) 2025-09-09T15:13:08.9373733Z 2025-09-09T15:13:08.9373739Z 2025-09-09T15:13:08.9373752Z 2025-09-09T15:13:08.9373851Z def forward(self, x): 2025-09-09T15:13:08.9374271Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T15:13:08.9374913Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T15:13:08.9375566Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T15:13:08.9376069Z return activation_post_process_1 2025-09-09T15:13:08.9376373Z 2025-09-09T15:13:08.9376695Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:13:08.9377133Z diff: tensor([[[[0., 0., 0.], 2025-09-09T15:13:08.9377408Z [0., 0., 0.], 2025-09-09T15:13:08.9377655Z [0., 0., 0.]], 2025-09-09T15:13:08.9377823Z 2025-09-09T15:13:08.9377909Z [[0., 0., 0.], 2025-09-09T15:13:08.9378370Z [0., 0., 0.], 2025-09-09T15:13:08.9378622Z [0., 0., 0.]], 2025-09-09T15:13:08.9378782Z 2025-09-09T15:13:08.9378868Z [[0., 0., 0.], 2025-09-09T15:13:08.9379238Z [0., 0., 0.], 2025-09-09T15:13:08.9379508Z [0., 0., 0.]]]], grad_fn=) 2025-09-09T15:13:08.9379869Z converted model pt2e: GraphModule( 2025-09-09T15:13:08.9380169Z (conv): Module() 2025-09-09T15:13:08.9380401Z ) 2025-09-09T15:13:08.9380513Z 2025-09-09T15:13:08.9380518Z 2025-09-09T15:13:08.9380523Z 2025-09-09T15:13:08.9380624Z def forward(self, x): 2025-09-09T15:13:08.9380951Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T15:13:08.9381338Z _scale_0 = self._scale_0 2025-09-09T15:13:08.9381631Z _zero_point_0 = self._zero_point_0 2025-09-09T15:13:08.9382021Z quantize_per_channel_default = self._frozen_param0 2025-09-09T15:13:08.9383247Z dequantize_per_channel_default = torch.ops.quantized_decomposed.dequantize_per_channel.default(quantize_per_channel_default, _scale_0, _zero_point_0, 0, -127, 127, torch.int8); quantize_per_channel_default = _scale_0 = _zero_point_0 = None 2025-09-09T15:13:08.9384888Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.018311796709895134, 10, -128, 127, torch.int8); x = None 2025-09-09T15:13:08.9386413Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.018311796709895134, 10, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T15:13:08.9388027Z conv2d = torch.ops.aten.conv2d.default(dequantize_per_tensor_default, dequantize_per_channel_default); dequantize_per_tensor_default = dequantize_per_channel_default = None 2025-09-09T15:13:08.9389464Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv2d, 0.007977825589478016, -4, -128, 127, torch.int8); conv2d = None 2025-09-09T15:13:08.9391050Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.007977825589478016, -4, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T15:13:08.9392291Z return pytree.tree_unflatten((dequantize_per_tensor_default_1,), self._out_spec) 2025-09-09T15:13:08.9392779Z 2025-09-09T15:13:08.9393098Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:13:08.9393542Z onverted model fx: GraphModule( 2025-09-09T15:13:08.9394033Z (conv): QuantizedConv2d(Reference)(3, 3, kernel_size=(3, 3), stride=(1, 1), bias=False) 2025-09-09T15:13:08.9394525Z ) 2025-09-09T15:13:08.9394636Z 2025-09-09T15:13:08.9394641Z 2025-09-09T15:13:08.9394646Z 2025-09-09T15:13:08.9394759Z def forward(self, x): 2025-09-09T15:13:08.9395499Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.018311796709895134, 10, -128, 127, torch.int8); x = None 2025-09-09T15:13:08.9397021Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.018311796709895134, 10, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T15:13:08.9398260Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T15:13:08.9399376Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.007977825589478016, -4, -128, 127, torch.int8); conv = None 2025-09-09T15:13:08.9400945Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.007977825589478016, -4, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T15:13:08.9402017Z return dequantize_per_tensor_default_1 2025-09-09T15:13:08.9402338Z 2025-09-09T15:13:08.9402664Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:13:08.9403089Z diff: tensor([[[[0., 0., 0.], 2025-09-09T15:13:08.9403470Z [0., 0., 0.], 2025-09-09T15:13:08.9403716Z [0., 0., 0.]], 2025-09-09T15:13:08.9403884Z 2025-09-09T15:13:08.9403970Z [[0., 0., 0.], 2025-09-09T15:13:08.9404294Z [0., 0., 0.], 2025-09-09T15:13:08.9404536Z [0., 0., 0.]], 2025-09-09T15:13:08.9404695Z 2025-09-09T15:13:08.9404782Z [[0., 0., 0.], 2025-09-09T15:13:08.9405024Z [0., 0., 0.], 2025-09-09T15:13:08.9405268Z [0., 0., 0.]]]]) 2025-09-09T15:13:08.9405534Z model pt2e: GraphModule( 2025-09-09T15:13:08.9405801Z (conv): Module() 2025-09-09T15:13:08.9406148Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:13:08.9407302Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0015]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.int8, quant_min=-127, quant_max=127, qscheme=torch.per_tensor_symmetric, reduce_range=False 2025-09-09T15:13:08.9408668Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.19212539494037628, max_val=0.18097467720508575) 2025-09-09T15:13:08.9409295Z ) 2025-09-09T15:13:08.9409620Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:13:08.9410754Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0183]), zero_point=tensor([10], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:13:08.9412080Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.526270866394043, max_val=2.143237352371216) 2025-09-09T15:13:08.9412684Z ) 2025-09-09T15:13:08.9413002Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:13:08.9414129Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0079]), zero_point=tensor([-5], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:13:08.9415469Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.9800506234169006, max_val=1.0470484495162964) 2025-09-09T15:13:08.9416089Z ) 2025-09-09T15:13:08.9416280Z ) 2025-09-09T15:13:08.9416406Z 2025-09-09T15:13:08.9416411Z 2025-09-09T15:13:08.9416416Z 2025-09-09T15:13:08.9416513Z def forward(self, x): 2025-09-09T15:13:08.9416851Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T15:13:08.9417243Z conv_weight = self.conv.weight 2025-09-09T15:13:08.9417796Z activation_post_process_1 = self.activation_post_process_1(conv_weight); conv_weight = None 2025-09-09T15:13:08.9418493Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T15:13:08.9419475Z conv2d = torch.ops.aten.conv2d.default(activation_post_process_0, activation_post_process_1); activation_post_process_0 = activation_post_process_1 = None 2025-09-09T15:13:08.9420470Z activation_post_process_2 = self.activation_post_process_2(conv2d); conv2d = None 2025-09-09T15:13:08.9421141Z return pytree.tree_unflatten((activation_post_process_2,), self._out_spec) 2025-09-09T15:13:08.9421610Z 2025-09-09T15:13:08.9421933Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:13:08.9422575Z model fx: GraphModule( 2025-09-09T15:13:08.9422946Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:13:08.9424084Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0183]), zero_point=tensor([10], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:13:08.9425418Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.526270866394043, max_val=2.143237352371216) 2025-09-09T15:13:08.9426024Z ) 2025-09-09T15:13:08.9426231Z (conv): Conv2d( 2025-09-09T15:13:08.9426510Z 3, 3, kernel_size=(3, 3), stride=(1, 1), bias=False 2025-09-09T15:13:08.9426945Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:13:08.9429430Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0015]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-127, quant_max=127, qscheme=torch.per_tensor_symmetric, reduce_range=False 2025-09-09T15:13:08.9430859Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.19212539494037628, max_val=0.18097467720508575) 2025-09-09T15:13:08.9431475Z ) 2025-09-09T15:13:08.9431653Z ) 2025-09-09T15:13:08.9431955Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:13:08.9433080Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0079]), zero_point=tensor([-5], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:13:08.9434421Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.9800506234169006, max_val=1.0470484495162964) 2025-09-09T15:13:08.9435025Z ) 2025-09-09T15:13:08.9435198Z ) 2025-09-09T15:13:08.9435299Z 2025-09-09T15:13:08.9435303Z 2025-09-09T15:13:08.9435306Z 2025-09-09T15:13:08.9435399Z def forward(self, x): 2025-09-09T15:13:08.9435792Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T15:13:08.9436407Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T15:13:08.9437037Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T15:13:08.9437527Z return activation_post_process_1 2025-09-09T15:13:08.9437811Z 2025-09-09T15:13:08.9438105Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:13:08.9438520Z diff: tensor([[[[0., 0., 0.], 2025-09-09T15:13:08.9438748Z [0., 0., 0.], 2025-09-09T15:13:08.9438955Z [0., 0., 0.]], 2025-09-09T15:13:08.9439089Z 2025-09-09T15:13:08.9439162Z [[0., 0., 0.], 2025-09-09T15:13:08.9439447Z [0., 0., 0.], 2025-09-09T15:13:08.9439652Z [0., 0., 0.]], 2025-09-09T15:13:08.9439793Z 2025-09-09T15:13:08.9439866Z [[0., 0., 0.], 2025-09-09T15:13:08.9440066Z [0., 0., 0.], 2025-09-09T15:13:08.9440296Z [0., 0., 0.]]]], grad_fn=) 2025-09-09T15:13:08.9440602Z converted model pt2e: GraphModule( 2025-09-09T15:13:08.9440851Z (conv): Module() 2025-09-09T15:13:08.9441037Z ) 2025-09-09T15:13:08.9441129Z 2025-09-09T15:13:08.9441133Z 2025-09-09T15:13:08.9441136Z 2025-09-09T15:13:08.9441221Z def forward(self, x): 2025-09-09T15:13:08.9441497Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T15:13:08.9441857Z quantize_per_tensor_default = self._frozen_param0 2025-09-09T15:14:24.8422028Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.0015127983642742038, 0, -127, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T15:14:24.8423491Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.018311796709895134, 10, -128, 127, torch.int8); x = None 2025-09-09T15:14:24.8424741Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.018311796709895134, 10, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T15:14:24.8426068Z conv2d = torch.ops.aten.conv2d.default(dequantize_per_tensor_default_1, dequantize_per_tensor_default); dequantize_per_tensor_default_1 = dequantize_per_tensor_default = None 2025-09-09T15:14:24.8427223Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv2d, 0.007949408143758774, -5, -128, 127, torch.int8); conv2d = None 2025-09-09T15:14:24.8428483Z dequantize_per_tensor_default_2 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_2, 0.007949408143758774, -5, -128, 127, torch.int8); quantize_per_tensor_default_2 = None 2025-09-09T15:14:24.8429842Z return pytree.tree_unflatten((dequantize_per_tensor_default_2,), self._out_spec) 2025-09-09T15:14:24.8430254Z 2025-09-09T15:14:24.8430539Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:14:24.8431073Z onverted model fx: GraphModule( 2025-09-09T15:14:24.8431488Z (conv): QuantizedConv2d(Reference)(3, 3, kernel_size=(3, 3), stride=(1, 1), bias=False) 2025-09-09T15:14:24.8431903Z ) 2025-09-09T15:14:24.8432001Z 2025-09-09T15:14:24.8432005Z 2025-09-09T15:14:24.8432009Z 2025-09-09T15:14:24.8432095Z def forward(self, x): 2025-09-09T15:14:24.8432714Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.018311796709895134, 10, -128, 127, torch.int8); x = None 2025-09-09T15:14:24.8433936Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.018311796709895134, 10, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T15:14:24.8434950Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T15:14:24.8435802Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.007949408143758774, -5, -128, 127, torch.int8); conv = None 2025-09-09T15:14:24.8437061Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.007949408143758774, -5, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T15:14:24.8437938Z return dequantize_per_tensor_default_1 2025-09-09T15:14:24.8438206Z 2025-09-09T15:14:24.8438482Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:14:24.8438852Z diff: tensor([[[[0., 0., 0.], 2025-09-09T15:14:24.8439085Z [0., 0., 0.], 2025-09-09T15:14:24.8439365Z [0., 0., 0.]], 2025-09-09T15:14:24.8439504Z 2025-09-09T15:14:24.8439579Z [[0., 0., 0.], 2025-09-09T15:14:24.8439796Z [0., 0., 0.], 2025-09-09T15:14:24.8439995Z [0., 0., 0.]], 2025-09-09T15:14:24.8440140Z 2025-09-09T15:14:24.8440215Z [[0., 0., 0.], 2025-09-09T15:14:24.8440425Z [0., 0., 0.], 2025-09-09T15:14:24.8440635Z [0., 0., 0.]]]]) 2025-09-09T15:14:24.8441077Z PASSED 2025-09-09T15:14:24.8441730Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_conv_transpose_bn PASSED 2025-09-09T15:14:24.8442763Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_conv_transpose_bn_relu PASSED 2025-09-09T15:14:24.8443704Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_inplace_add_relu model pt2e: GraphModule( 2025-09-09T15:14:24.8444308Z (conv): Module() 2025-09-09T15:14:24.8444609Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:14:24.8445571Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0011]), zero_point=tensor([127], dtype=torch.int32), dtype=torch.int8, quant_min=-127, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False 2025-09-09T15:14:24.8446763Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.2877]), max_val=tensor([-0.2877])) 2025-09-09T15:14:24.8447327Z ) 2025-09-09T15:14:24.8447607Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:14:24.8448533Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0126]), zero_point=tensor([7], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:14:24.8449625Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.7008640766143799, max_val=1.5035617351531982) 2025-09-09T15:14:24.8450145Z ) 2025-09-09T15:14:24.8450416Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:14:24.8451439Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0036]), zero_point=tensor([43], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:14:24.8452649Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.6198297739028931, max_val=0.30200809240341187) 2025-09-09T15:14:24.8453163Z ) 2025-09-09T15:14:24.8453438Z (activation_post_process_3): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:14:24.8454362Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0035]), zero_point=tensor([-128], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:14:24.8455418Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=0.8897914886474609) 2025-09-09T15:14:24.8455881Z ) 2025-09-09T15:14:24.8456054Z ) 2025-09-09T15:14:24.8456150Z 2025-09-09T15:14:24.8456154Z 2025-09-09T15:14:24.8456163Z 2025-09-09T15:14:24.8456252Z def forward(self, x): 2025-09-09T15:14:24.8456533Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T15:14:24.8456883Z conv_weight = self.conv.weight 2025-09-09T15:14:24.8457334Z activation_post_process_1 = self.activation_post_process_1(conv_weight); conv_weight = None 2025-09-09T15:14:24.8457803Z conv_bias = self.conv.bias 2025-09-09T15:14:24.8458171Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T15:14:24.8458946Z conv2d = torch.ops.aten.conv2d.default(activation_post_process_0, activation_post_process_1, conv_bias); activation_post_process_1 = conv_bias = None 2025-09-09T15:14:24.8459750Z activation_post_process_2 = self.activation_post_process_2(conv2d); conv2d = None 2025-09-09T15:14:24.8460541Z add_ = torch.ops.aten.add_.Tensor(activation_post_process_2, activation_post_process_0); activation_post_process_2 = activation_post_process_0 = None 2025-09-09T15:14:24.8461245Z relu_ = torch.ops.aten.relu_.default(add_); add_ = None 2025-09-09T15:14:24.8461719Z activation_post_process_3 = self.activation_post_process_3(relu_); relu_ = None 2025-09-09T15:14:24.8462274Z return pytree.tree_unflatten((activation_post_process_3,), self._out_spec) 2025-09-09T15:14:24.8462663Z 2025-09-09T15:14:24.8462935Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:14:24.8463297Z model fx: GraphModule( 2025-09-09T15:14:24.8463610Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:14:24.8464548Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0126]), zero_point=tensor([7], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:14:24.8465644Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.7008640766143799, max_val=1.5035617351531982) 2025-09-09T15:14:24.8466157Z ) 2025-09-09T15:14:24.8466336Z (conv): Conv2d( 2025-09-09T15:14:24.8466561Z 1, 1, kernel_size=(1, 1), stride=(1, 1) 2025-09-09T15:14:24.8466903Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:14:24.8467822Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0011]), zero_point=tensor([127], dtype=torch.int32), dtype=torch.qint8, quant_min=-127, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False 2025-09-09T15:14:24.8468999Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.2877]), max_val=tensor([-0.2877])) 2025-09-09T15:14:24.8469564Z ) 2025-09-09T15:14:24.8469731Z ) 2025-09-09T15:14:24.8470006Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:14:24.8471024Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0036]), zero_point=tensor([43], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:14:24.8472125Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.6198297739028931, max_val=0.30200809240341187) 2025-09-09T15:14:24.8472722Z ) 2025-09-09T15:14:24.8472905Z (relu): ReLU(inplace=True) 2025-09-09T15:14:24.8473243Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:14:24.8474220Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0035]), zero_point=tensor([-128], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:14:24.8475278Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=0.8897914886474609) 2025-09-09T15:14:24.8475747Z ) 2025-09-09T15:14:24.8475913Z ) 2025-09-09T15:14:24.8476007Z 2025-09-09T15:14:24.8476012Z 2025-09-09T15:14:24.8476015Z 2025-09-09T15:14:24.8476107Z def forward(self, x): 2025-09-09T15:14:24.8476460Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T15:14:24.8476887Z conv = self.conv(activation_post_process_0) 2025-09-09T15:14:24.8477326Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T15:14:24.8478020Z add = activation_post_process_1 + activation_post_process_0; activation_post_process_1 = activation_post_process_0 = None 2025-09-09T15:14:24.8478588Z relu = self.relu(add); add = None 2025-09-09T15:14:24.8478997Z activation_post_process_2 = self.activation_post_process_2(relu); relu = None 2025-09-09T15:14:24.8479512Z return activation_post_process_2 2025-09-09T15:14:24.8479765Z 2025-09-09T15:14:24.8480044Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:14:24.8480405Z diff: tensor([[[[0., 0., 0.], 2025-09-09T15:14:26.3746667Z [0., 0., 0.], 2025-09-09T15:14:26.3746946Z [0., 0., 0.]]]], grad_fn=) 2025-09-09T15:14:26.3747397Z converted model pt2e: GraphModule( 2025-09-09T15:14:26.3747739Z (conv): Module() 2025-09-09T15:14:26.3747981Z ) 2025-09-09T15:14:26.3748104Z 2025-09-09T15:14:26.3748110Z 2025-09-09T15:14:26.3748115Z 2025-09-09T15:14:26.3748216Z def forward(self, x): 2025-09-09T15:14:26.3748540Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T15:14:26.3748857Z _scale_0 = self._scale_0 2025-09-09T15:14:26.3749106Z _zero_point_0 = self._zero_point_0 2025-09-09T15:14:26.3749440Z quantize_per_channel_default = self._frozen_param0 2025-09-09T15:14:26.3750415Z dequantize_per_channel_default = torch.ops.quantized_decomposed.dequantize_per_channel.default(quantize_per_channel_default, _scale_0, _zero_point_0, 0, -127, 127, torch.int8); quantize_per_channel_default = _scale_0 = _zero_point_0 = None 2025-09-09T15:14:26.3751348Z conv_bias = self.conv.bias 2025-09-09T15:14:26.3751963Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.01256637554615736, 7, -128, 127, torch.int8); x = None 2025-09-09T15:14:26.3753053Z dequantize_per_tensor_default_4 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.01256637554615736, 7, -128, 127, torch.int8) 2025-09-09T15:14:26.3754334Z dequantize_per_tensor_default_3 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.01256637554615736, 7, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T15:14:26.3755698Z conv2d = torch.ops.aten.conv2d.default(dequantize_per_tensor_default_3, dequantize_per_channel_default, conv_bias); dequantize_per_tensor_default_3 = dequantize_per_channel_default = conv_bias = None 2025-09-09T15:14:26.3756922Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv2d, 0.003615050343796611, 43, -128, 127, torch.int8); conv2d = None 2025-09-09T15:14:26.3758387Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.003615050343796611, 43, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T15:14:26.3759759Z add_ = torch.ops.aten.add_.Tensor(dequantize_per_tensor_default_1, dequantize_per_tensor_default_4); dequantize_per_tensor_default_1 = dequantize_per_tensor_default_4 = None 2025-09-09T15:14:26.3760696Z relu_ = torch.ops.aten.relu_.default(add_); add_ = None 2025-09-09T15:14:26.3761443Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(relu_, 0.003489378374069929, -128, -128, 127, torch.int8); relu_ = None 2025-09-09T15:14:26.3762695Z dequantize_per_tensor_default_2 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_2, 0.003489378374069929, -128, -128, 127, torch.int8); quantize_per_tensor_default_2 = None 2025-09-09T15:14:26.3763694Z return pytree.tree_unflatten((dequantize_per_tensor_default_2,), self._out_spec) 2025-09-09T15:14:26.3764088Z 2025-09-09T15:14:26.3764366Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:14:26.3764733Z onverted model fx: GraphModule( 2025-09-09T15:14:26.3765103Z (conv): QuantizedConv2d(Reference)(1, 1, kernel_size=(1, 1), stride=(1, 1)) 2025-09-09T15:14:26.3765499Z (relu): ReLU(inplace=True) 2025-09-09T15:14:26.3765716Z ) 2025-09-09T15:14:26.3765816Z 2025-09-09T15:14:26.3765820Z 2025-09-09T15:14:26.3765824Z 2025-09-09T15:14:26.3765905Z def forward(self, x): 2025-09-09T15:14:26.3766495Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.01256637554615736, 7, -128, 127, torch.int8); x = None 2025-09-09T15:14:26.3767695Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.01256637554615736, 7, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T15:14:26.3768564Z conv = self.conv(dequantize_per_tensor_default) 2025-09-09T15:14:26.3769274Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.003615050343796611, 43, -128, 127, torch.int8); conv = None 2025-09-09T15:14:26.3770523Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.003615050343796611, 43, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T15:14:26.3771717Z add = dequantize_per_tensor_default_1 + dequantize_per_tensor_default; dequantize_per_tensor_default_1 = dequantize_per_tensor_default = None 2025-09-09T15:14:26.3772330Z relu = self.relu(add); add = None 2025-09-09T15:14:26.3773025Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(relu, 0.003489378374069929, -128, -128, 127, torch.int8); relu = None 2025-09-09T15:14:26.3774343Z dequantize_per_tensor_default_2 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_2, 0.003489378374069929, -128, -128, 127, torch.int8); quantize_per_tensor_default_2 = None 2025-09-09T15:14:26.3775207Z return dequantize_per_tensor_default_2 2025-09-09T15:14:26.3775474Z 2025-09-09T15:14:26.3775737Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:14:26.3776100Z diff: tensor([[[[0., 0., 0.], 2025-09-09T15:14:26.3776329Z [0., 0., 0.], 2025-09-09T15:14:26.3776536Z [0., 0., 0.]]]]) 2025-09-09T15:14:26.3776758Z model pt2e: GraphModule( 2025-09-09T15:14:26.3776986Z (conv): Module() 2025-09-09T15:14:26.3777284Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:14:26.3778221Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0011]), zero_point=tensor([127], dtype=torch.int32), dtype=torch.int8, quant_min=-127, quant_max=127, qscheme=torch.per_tensor_symmetric, reduce_range=False 2025-09-09T15:14:26.3779334Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.28767645359039307, max_val=-0.28767645359039307) 2025-09-09T15:14:26.3779925Z ) 2025-09-09T15:14:26.3780201Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:14:26.3781117Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0126]), zero_point=tensor([7], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:14:26.3782260Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.7008640766143799, max_val=1.5035617351531982) 2025-09-09T15:14:26.3782764Z ) 2025-09-09T15:14:26.3783027Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:14:26.3783937Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0036]), zero_point=tensor([43], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:14:26.3785065Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.6198297739028931, max_val=0.30200809240341187) 2025-09-09T15:14:26.3785562Z ) 2025-09-09T15:14:26.3785830Z (activation_post_process_3): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:14:26.3786744Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0035]), zero_point=tensor([-128], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:14:26.3787776Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=0.8897914886474609) 2025-09-09T15:14:26.3788233Z ) 2025-09-09T15:14:26.3788386Z ) 2025-09-09T15:14:26.3788479Z 2025-09-09T15:14:26.3788483Z 2025-09-09T15:14:26.3788487Z 2025-09-09T15:14:26.3788570Z def forward(self, x): 2025-09-09T15:14:26.3788840Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T15:14:26.3789167Z conv_weight = self.conv.weight 2025-09-09T15:14:26.3789617Z activation_post_process_1 = self.activation_post_process_1(conv_weight); conv_weight = None 2025-09-09T15:14:26.3790073Z conv_bias = self.conv.bias 2025-09-09T15:14:26.3790437Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T15:14:26.3791209Z conv2d = torch.ops.aten.conv2d.default(activation_post_process_0, activation_post_process_1, conv_bias); activation_post_process_1 = conv_bias = None 2025-09-09T15:14:26.3792002Z activation_post_process_2 = self.activation_post_process_2(conv2d); conv2d = None 2025-09-09T15:14:26.3792782Z add_ = torch.ops.aten.add_.Tensor(activation_post_process_2, activation_post_process_0); activation_post_process_2 = activation_post_process_0 = None 2025-09-09T15:14:26.3793472Z relu_ = torch.ops.aten.relu_.default(add_); add_ = None 2025-09-09T15:14:26.3793948Z activation_post_process_3 = self.activation_post_process_3(relu_); relu_ = None 2025-09-09T15:14:26.3794479Z return pytree.tree_unflatten((activation_post_process_3,), self._out_spec) 2025-09-09T15:14:26.3794871Z 2025-09-09T15:14:26.3795144Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:14:26.3795496Z model fx: GraphModule( 2025-09-09T15:14:26.3795800Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:14:26.3804762Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0126]), zero_point=tensor([7], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:14:26.3805883Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.7008640766143799, max_val=1.5035617351531982) 2025-09-09T15:14:26.3806399Z ) 2025-09-09T15:14:26.3806591Z (conv): Conv2d( 2025-09-09T15:14:26.3806825Z 1, 1, kernel_size=(1, 1), stride=(1, 1) 2025-09-09T15:14:26.3807184Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:14:26.3808226Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0011]), zero_point=tensor([127], dtype=torch.int32), dtype=torch.qint8, quant_min=-127, quant_max=127, qscheme=torch.per_tensor_symmetric, reduce_range=False 2025-09-09T15:14:26.3809351Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.28767645359039307, max_val=-0.28767645359039307) 2025-09-09T15:14:26.3809959Z ) 2025-09-09T15:14:26.3810137Z ) 2025-09-09T15:14:26.3810425Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:15:06.1614995Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0036]), zero_point=tensor([43], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:15:06.1616498Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.6198297739028931, max_val=0.30200809240341187) 2025-09-09T15:15:06.1617127Z ) 2025-09-09T15:15:06.1617346Z (relu): ReLU(inplace=True) 2025-09-09T15:15:06.1617752Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:15:06.1618938Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0035]), zero_point=tensor([-128], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:15:06.1620250Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=0.8897914886474609) 2025-09-09T15:15:06.1620817Z ) 2025-09-09T15:15:06.1621005Z ) 2025-09-09T15:15:06.1621122Z 2025-09-09T15:15:06.1621127Z 2025-09-09T15:15:06.1621132Z 2025-09-09T15:15:06.1621235Z def forward(self, x): 2025-09-09T15:15:06.1621655Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T15:15:06.1622373Z conv = self.conv(activation_post_process_0) 2025-09-09T15:15:06.1622896Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T15:15:06.1623749Z add = activation_post_process_1 + activation_post_process_0; activation_post_process_1 = activation_post_process_0 = None 2025-09-09T15:15:06.1624437Z relu = self.relu(add); add = None 2025-09-09T15:15:06.1624958Z activation_post_process_2 = self.activation_post_process_2(relu); relu = None 2025-09-09T15:15:06.1625502Z return activation_post_process_2 2025-09-09T15:15:06.1625805Z 2025-09-09T15:15:06.1626140Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:15:06.1626581Z diff: tensor([[[[0., 0., 0.], 2025-09-09T15:15:06.1626863Z [0., 0., 0.], 2025-09-09T15:15:06.1627138Z [0., 0., 0.]]]], grad_fn=) 2025-09-09T15:15:06.1627503Z converted model pt2e: GraphModule( 2025-09-09T15:15:06.1627814Z (conv): Module() 2025-09-09T15:15:06.1628037Z ) 2025-09-09T15:15:06.1628154Z 2025-09-09T15:15:06.1628160Z 2025-09-09T15:15:06.1628164Z 2025-09-09T15:15:06.1628265Z def forward(self, x): 2025-09-09T15:15:06.1628585Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T15:15:06.1629041Z quantize_per_tensor_default = self._frozen_param0 2025-09-09T15:15:06.1630148Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.002265168819576502, 0, -127, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T15:15:06.1631206Z conv_bias = self.conv.bias 2025-09-09T15:15:06.1632001Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.01256637554615736, 7, -128, 127, torch.int8); x = None 2025-09-09T15:15:06.1633375Z dequantize_per_tensor_default_5 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.01256637554615736, 7, -128, 127, torch.int8) 2025-09-09T15:15:06.1634985Z dequantize_per_tensor_default_4 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.01256637554615736, 7, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T15:15:06.1638944Z conv2d = torch.ops.aten.conv2d.default(dequantize_per_tensor_default_4, dequantize_per_tensor_default, conv_bias); dequantize_per_tensor_default_4 = dequantize_per_tensor_default = conv_bias = None 2025-09-09T15:15:06.1640752Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv2d, 0.003615050343796611, 43, -128, 127, torch.int8); conv2d = None 2025-09-09T15:15:06.1642333Z dequantize_per_tensor_default_2 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_2, 0.003615050343796611, 43, -128, 127, torch.int8); quantize_per_tensor_default_2 = None 2025-09-09T15:15:06.1643950Z add_ = torch.ops.aten.add_.Tensor(dequantize_per_tensor_default_2, dequantize_per_tensor_default_5); dequantize_per_tensor_default_2 = dequantize_per_tensor_default_5 = None 2025-09-09T15:15:06.1644944Z relu_ = torch.ops.aten.relu_.default(add_); add_ = None 2025-09-09T15:15:06.1645950Z quantize_per_tensor_default_3 = torch.ops.quantized_decomposed.quantize_per_tensor.default(relu_, 0.003489378374069929, -128, -128, 127, torch.int8); relu_ = None 2025-09-09T15:15:06.1647250Z dequantize_per_tensor_default_3 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_3, 0.003489378374069929, -128, -128, 127, torch.int8); quantize_per_tensor_default_3 = None 2025-09-09T15:15:06.1648250Z return pytree.tree_unflatten((dequantize_per_tensor_default_3,), self._out_spec) 2025-09-09T15:15:06.1648649Z 2025-09-09T15:15:06.1648923Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:15:06.1649281Z onverted model fx: GraphModule( 2025-09-09T15:15:06.1649661Z (conv): QuantizedConv2d(Reference)(1, 1, kernel_size=(1, 1), stride=(1, 1)) 2025-09-09T15:15:06.1650051Z (relu): ReLU(inplace=True) 2025-09-09T15:15:06.1650277Z ) 2025-09-09T15:15:06.1650371Z 2025-09-09T15:15:06.1650375Z 2025-09-09T15:15:06.1650379Z 2025-09-09T15:15:06.1650458Z def forward(self, x): 2025-09-09T15:15:06.1651064Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.01256637554615736, 7, -128, 127, torch.int8); x = None 2025-09-09T15:15:06.1652271Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.01256637554615736, 7, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T15:15:06.1653130Z conv = self.conv(dequantize_per_tensor_default) 2025-09-09T15:15:06.1653842Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.003615050343796611, 43, -128, 127, torch.int8); conv = None 2025-09-09T15:15:06.1655081Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.003615050343796611, 43, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T15:15:06.1656304Z add = dequantize_per_tensor_default_1 + dequantize_per_tensor_default; dequantize_per_tensor_default_1 = dequantize_per_tensor_default = None 2025-09-09T15:15:06.1656918Z relu = self.relu(add); add = None 2025-09-09T15:15:06.1657597Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(relu, 0.003489378374069929, -128, -128, 127, torch.int8); relu = None 2025-09-09T15:15:06.1658865Z dequantize_per_tensor_default_2 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_2, 0.003489378374069929, -128, -128, 127, torch.int8); quantize_per_tensor_default_2 = None 2025-09-09T15:15:06.1659731Z return dequantize_per_tensor_default_2 2025-09-09T15:15:06.1659991Z 2025-09-09T15:15:06.1660261Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:15:06.1660615Z diff: tensor([[[[0., 0., 0.], 2025-09-09T15:15:06.1660847Z [0., 0., 0.], 2025-09-09T15:15:06.1661049Z [0., 0., 0.]]]]) 2025-09-09T15:15:06.1661492Z PASSED 2025-09-09T15:15:06.1662326Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_per_channel_weight_custom_dtype PASSED 2025-09-09T15:15:06.1663387Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_preserve_source_fn_stack PASSED 2025-09-09T15:15:06.1664428Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_update_shared_qspec model pt2e: GraphModule( 2025-09-09T15:15:06.1665029Z (conv): Module() 2025-09-09T15:15:06.1665230Z (bn): Module() 2025-09-09T15:15:06.1665520Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:15:06.1666447Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0183]), zero_point=tensor([10], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:15:06.1667542Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.526270866394043, max_val=2.143237352371216) 2025-09-09T15:15:06.1668036Z ) 2025-09-09T15:15:06.1668309Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:15:06.1669271Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0015, 0.0015, 0.0014]), zero_point=tensor([0, 0, 0], dtype=torch.int32), dtype=torch.int8, quant_min=-127, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False 2025-09-09T15:15:06.1670547Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.1640, -0.1903, -0.1739]), max_val=tensor([0.1851, 0.1825, 0.1577])) 2025-09-09T15:15:06.1671184Z ) 2025-09-09T15:15:06.1671450Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:15:06.1672378Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0163]), zero_point=tensor([6], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:15:06.1673461Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.1757256984710693, max_val=1.9743094444274902) 2025-09-09T15:15:06.1673966Z ) 2025-09-09T15:15:06.1674245Z (activation_post_process_3): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:15:06.1675157Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0163]), zero_point=tensor([6], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:15:06.1676233Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.1757256984710693, max_val=1.9743094444274902) 2025-09-09T15:15:06.1676740Z ) 2025-09-09T15:15:06.1676898Z ) 2025-09-09T15:15:06.1676994Z 2025-09-09T15:15:06.1676998Z 2025-09-09T15:15:06.1677003Z 2025-09-09T15:15:06.1677091Z def forward(self, x): 2025-09-09T15:15:06.1677372Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T15:15:06.1677710Z conv_weight = self.conv.weight 2025-09-09T15:15:06.1677984Z conv_bias = self.conv.bias 2025-09-09T15:15:06.1678241Z bn_weight = self.bn.weight 2025-09-09T15:15:06.1678481Z bn_bias = self.bn.bias 2025-09-09T15:15:06.1678741Z bn_running_mean = self.bn.running_mean 2025-09-09T15:15:06.1679040Z bn_running_var = self.bn.running_var 2025-09-09T15:15:06.1679446Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T15:15:20.7382169Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T15:15:20.7382975Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T15:15:20.7383611Z add = torch.ops.aten.add.Tensor(bn_running_var, 1e-05) 2025-09-09T15:15:20.7384073Z sqrt = torch.ops.aten.sqrt.default(add); add = None 2025-09-09T15:15:20.7384576Z div = torch.ops.aten.div.Tensor(bn_weight, sqrt); sqrt = None 2025-09-09T15:15:20.7385097Z reshape = torch.ops.aten.reshape.default(div, [-1, 1, 1, 1]) 2025-09-09T15:15:20.7386184Z mul = torch.ops.aten.mul.Tensor(conv_weight, reshape); conv_weight = reshape = None 2025-09-09T15:15:20.7386873Z activation_post_process_1 = self.activation_post_process_1(mul); mul = None 2025-09-09T15:15:20.7387804Z zeros_like = torch.ops.aten.zeros_like.default(conv_bias, dtype = torch.float32, pin_memory = False) 2025-09-09T15:15:20.7389004Z conv2d_1 = torch.ops.aten.conv2d.default(activation_post_process_0, activation_post_process_1, zeros_like); activation_post_process_0 = activation_post_process_1 = zeros_like = None 2025-09-09T15:15:20.7390108Z reshape_1 = torch.ops.aten.reshape.default(div, [1, -1, 1, 1]); div = None 2025-09-09T15:15:20.7390760Z div_1 = torch.ops.aten.div.Tensor(conv2d_1, reshape_1); conv2d_1 = reshape_1 = None 2025-09-09T15:15:20.7391458Z reshape_2 = torch.ops.aten.reshape.default(conv_bias, [1, -1, 1, 1]); conv_bias = None 2025-09-09T15:15:20.7392133Z add_1 = torch.ops.aten.add.Tensor(div_1, reshape_2); div_1 = reshape_2 = None 2025-09-09T15:15:20.7393198Z batch_norm_1 = torch.ops.aten.batch_norm.default(add_1, bn_weight, bn_bias, bn_running_mean, bn_running_var, True, 0.1, 1e-05, True); add_1 = bn_weight = bn_bias = bn_running_mean = bn_running_var = None 2025-09-09T15:15:20.7394333Z activation_post_process_2 = self.activation_post_process_2(batch_norm_1); batch_norm_1 = None 2025-09-09T15:15:20.7395234Z hardtanh = torch.ops.aten.hardtanh.default(activation_post_process_2, -1.0, 1.0); activation_post_process_2 = None 2025-09-09T15:15:20.7396093Z activation_post_process_3 = self.activation_post_process_3(hardtanh); hardtanh = None 2025-09-09T15:15:20.7396778Z return pytree.tree_unflatten((activation_post_process_3,), self._out_spec) 2025-09-09T15:15:20.7397246Z 2025-09-09T15:15:20.7397571Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:15:20.7398011Z model fx: GraphModule( 2025-09-09T15:15:20.7398385Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:15:20.7399659Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0183]), zero_point=tensor([10], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:15:20.7401032Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.526270866394043, max_val=2.143237352371216) 2025-09-09T15:15:20.7401642Z ) 2025-09-09T15:15:20.7401852Z (conv): ConvBn2d( 2025-09-09T15:15:20.7402115Z 3, 3, kernel_size=(3, 3), stride=(1, 1) 2025-09-09T15:15:20.7402606Z (bn): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T15:15:20.7403151Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:15:20.7404322Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0015, 0.0015, 0.0014]), zero_point=tensor([0, 0, 0], dtype=torch.int32), dtype=torch.qint8, quant_min=-127, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False 2025-09-09T15:15:20.7405904Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.1640, -0.1903, -0.1739]), max_val=tensor([0.1851, 0.1825, 0.1577])) 2025-09-09T15:15:20.7406692Z ) 2025-09-09T15:15:20.7406892Z ) 2025-09-09T15:15:20.7407212Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:15:20.7408360Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0163]), zero_point=tensor([6], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:15:20.7409758Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.1757256984710693, max_val=1.9743094444274902) 2025-09-09T15:15:20.7410377Z ) 2025-09-09T15:15:20.7410632Z (hardtanh): Hardtanh(min_val=-1.0, max_val=1.0) 2025-09-09T15:15:20.7411103Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:15:20.7412347Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0163]), zero_point=tensor([6], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:15:20.7413774Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.1757256984710693, max_val=1.9743094444274902) 2025-09-09T15:15:20.7414392Z ) 2025-09-09T15:15:20.7414586Z ) 2025-09-09T15:15:20.7414702Z 2025-09-09T15:15:20.7414707Z 2025-09-09T15:15:20.7414712Z 2025-09-09T15:15:20.7414810Z def forward(self, x): 2025-09-09T15:15:20.7415228Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T15:15:20.7415865Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T15:15:20.7416523Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T15:15:20.7417220Z hardtanh = self.hardtanh(activation_post_process_1); activation_post_process_1 = None 2025-09-09T15:15:20.7417952Z activation_post_process_2 = self.activation_post_process_2(hardtanh); hardtanh = None 2025-09-09T15:15:20.7418491Z return activation_post_process_2 2025-09-09T15:15:20.7418803Z 2025-09-09T15:15:20.7419131Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:15:20.7419568Z diff: tensor([[[[0., 0., 0.], 2025-09-09T15:15:20.7419846Z [0., 0., 0.], 2025-09-09T15:15:20.7420092Z [0., 0., 0.]], 2025-09-09T15:15:20.7420256Z 2025-09-09T15:15:20.7420343Z [[0., 0., 0.], 2025-09-09T15:15:20.7420585Z [0., 0., 0.], 2025-09-09T15:15:20.7420841Z [0., 0., 0.]], 2025-09-09T15:15:20.7421011Z 2025-09-09T15:15:20.7421098Z [[0., 0., 0.], 2025-09-09T15:15:20.7421335Z [0., 0., 0.], 2025-09-09T15:15:20.7421614Z [0., 0., 0.]]]], grad_fn=) 2025-09-09T15:15:20.7421979Z converted model pt2e: GraphModule( 2025-09-09T15:15:20.7422567Z (conv): Module() 2025-09-09T15:15:20.7422819Z (bn): Module() 2025-09-09T15:15:20.7423040Z ) 2025-09-09T15:15:20.7423151Z 2025-09-09T15:15:20.7423156Z 2025-09-09T15:15:20.7423161Z 2025-09-09T15:15:20.7423263Z def forward(self, x): 2025-09-09T15:15:20.7423765Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T15:15:20.7424166Z conv_bias = self.conv.bias 2025-09-09T15:15:20.7424954Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.018311796709895134, 10, -128, 127, torch.int8); x = None 2025-09-09T15:15:20.7426497Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.018311796709895134, 10, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T15:15:20.7427553Z _scale_0 = self._scale_0 2025-09-09T15:15:20.7427852Z _zero_point_0 = self._zero_point_0 2025-09-09T15:15:20.7428221Z quantize_per_channel = self._frozen_param0 2025-09-09T15:15:20.7429312Z dequantize_per_channel = torch.ops.quantized_decomposed.dequantize_per_channel.default(quantize_per_channel, _scale_0, _zero_point_0, 0, -127, 127, torch.int8); quantize_per_channel = _scale_0 = _zero_point_0 = None 2025-09-09T15:15:20.7430996Z conv2d_2 = torch.ops.aten.conv2d.default(dequantize_per_tensor_default, dequantize_per_channel, conv_bias); dequantize_per_tensor_default = dequantize_per_channel = conv_bias = None 2025-09-09T15:15:20.7432487Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv2d_2, 0.016274645924568176, 6, -128, 127, torch.int8); conv2d_2 = None 2025-09-09T15:15:20.7434089Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.016274645924568176, 6, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T15:15:20.7435588Z hardtanh = torch.ops.aten.hardtanh.default(dequantize_per_tensor_default_1, -1.0, 1.0); dequantize_per_tensor_default_1 = None 2025-09-09T15:15:20.7437025Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(hardtanh, 0.016274645924568176, 6, -128, 127, torch.int8); hardtanh = None 2025-09-09T15:15:20.7438416Z dequantize_per_tensor_default_2 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_2, 0.016274645924568176, 6, -128, 127, torch.int8); quantize_per_tensor_default_2 = None 2025-09-09T15:15:20.7439537Z return pytree.tree_unflatten((dequantize_per_tensor_default_2,), self._out_spec) 2025-09-09T15:15:20.7439946Z 2025-09-09T15:15:20.7440219Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:15:20.7440593Z onverted model fx: GraphModule( 2025-09-09T15:15:20.7440967Z (conv): QuantizedConv2d(Reference)(3, 3, kernel_size=(3, 3), stride=(1, 1)) 2025-09-09T15:15:20.7441399Z (hardtanh): Hardtanh(min_val=-1.0, max_val=1.0) 2025-09-09T15:15:20.7441680Z ) 2025-09-09T15:15:20.7441786Z 2025-09-09T15:15:20.7441790Z 2025-09-09T15:15:20.7441801Z 2025-09-09T15:15:20.7441883Z def forward(self, x): 2025-09-09T15:15:20.7442495Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.018311796709895134, 10, -128, 127, torch.int8); x = None 2025-09-09T15:15:20.7443723Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.018311796709895134, 10, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T15:15:20.7444730Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T15:15:20.7445562Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.016274645924568176, 6, -128, 127, torch.int8); conv = None 2025-09-09T15:15:20.7446827Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.016274645924568176, 6, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T15:15:20.7447886Z hardtanh = self.hardtanh(dequantize_per_tensor_default_1); dequantize_per_tensor_default_1 = None 2025-09-09T15:15:20.7448791Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(hardtanh, 0.016274645924568176, 6, -128, 127, torch.int8); hardtanh = None 2025-09-09T15:15:41.8556602Z dequantize_per_tensor_default_2 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_2, 0.016274645924568176, 6, -128, 127, torch.int8); quantize_per_tensor_default_2 = None 2025-09-09T15:15:41.8557729Z return dequantize_per_tensor_default_2 2025-09-09T15:15:41.8558065Z 2025-09-09T15:15:41.8558411Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:15:41.8558853Z diff: tensor([[[[0., 0., 0.], 2025-09-09T15:15:41.8559270Z [0., 0., 0.], 2025-09-09T15:15:41.8559519Z [0., 0., 0.]], 2025-09-09T15:15:41.8559691Z 2025-09-09T15:15:41.8559800Z [[0., 0., 0.], 2025-09-09T15:15:41.8560039Z [0., 0., 0.], 2025-09-09T15:15:41.8560284Z [0., 0., 0.]], 2025-09-09T15:15:41.8560463Z 2025-09-09T15:15:41.8560549Z [[0., 0., 0.], 2025-09-09T15:15:41.8560795Z [0., 0., 0.], 2025-09-09T15:15:41.8561043Z [0., 0., 0.]]]]) 2025-09-09T15:15:41.8561313Z model pt2e: GraphModule( 2025-09-09T15:15:41.8561583Z (conv): Module() 2025-09-09T15:15:41.8561818Z (bn): Module() 2025-09-09T15:15:41.8562172Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:15:41.8563320Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0183]), zero_point=tensor([10], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:15:41.8564685Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.526270866394043, max_val=2.143237352371216) 2025-09-09T15:15:41.8565310Z ) 2025-09-09T15:15:41.8565887Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:15:41.8567084Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0015]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.int8, quant_min=-127, quant_max=127, qscheme=torch.per_tensor_symmetric, reduce_range=False 2025-09-09T15:15:41.8568628Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.19029980897903442, max_val=0.18509264290332794) 2025-09-09T15:15:41.8569258Z ) 2025-09-09T15:15:41.8569581Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:15:41.8570722Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0163]), zero_point=tensor([6], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:15:41.8572071Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.1751670837402344, max_val=1.979515790939331) 2025-09-09T15:15:41.8572700Z ) 2025-09-09T15:15:41.8573015Z (activation_post_process_3): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:15:41.8574160Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0163]), zero_point=tensor([6], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:15:41.8575510Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.1751670837402344, max_val=1.979515790939331) 2025-09-09T15:15:41.8576122Z ) 2025-09-09T15:15:41.8576317Z ) 2025-09-09T15:15:41.8576428Z 2025-09-09T15:15:41.8576434Z 2025-09-09T15:15:41.8576438Z 2025-09-09T15:15:41.8576536Z def forward(self, x): 2025-09-09T15:15:41.8576873Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T15:15:41.8577268Z conv_weight = self.conv.weight 2025-09-09T15:15:41.8577596Z conv_bias = self.conv.bias 2025-09-09T15:15:41.8577896Z bn_weight = self.bn.weight 2025-09-09T15:15:41.8578193Z bn_bias = self.bn.bias 2025-09-09T15:15:41.8578500Z bn_running_mean = self.bn.running_mean 2025-09-09T15:15:41.8578857Z bn_running_var = self.bn.running_var 2025-09-09T15:15:41.8579257Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T15:15:41.8579787Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T15:15:41.8580501Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T15:15:41.8581144Z add = torch.ops.aten.add.Tensor(bn_running_var, 1e-05) 2025-09-09T15:15:41.8581610Z sqrt = torch.ops.aten.sqrt.default(add); add = None 2025-09-09T15:15:41.8582099Z div = torch.ops.aten.div.Tensor(bn_weight, sqrt); sqrt = None 2025-09-09T15:15:41.8582625Z reshape = torch.ops.aten.reshape.default(div, [-1, 1, 1, 1]) 2025-09-09T15:15:41.8583239Z mul = torch.ops.aten.mul.Tensor(conv_weight, reshape); conv_weight = reshape = None 2025-09-09T15:15:41.8583924Z activation_post_process_1 = self.activation_post_process_1(mul); mul = None 2025-09-09T15:15:41.8584671Z zeros_like = torch.ops.aten.zeros_like.default(conv_bias, dtype = torch.float32, pin_memory = False) 2025-09-09T15:15:41.8585885Z conv2d_1 = torch.ops.aten.conv2d.default(activation_post_process_0, activation_post_process_1, zeros_like); activation_post_process_0 = activation_post_process_1 = zeros_like = None 2025-09-09T15:15:41.8586979Z reshape_1 = torch.ops.aten.reshape.default(div, [1, -1, 1, 1]); div = None 2025-09-09T15:15:41.8587632Z div_1 = torch.ops.aten.div.Tensor(conv2d_1, reshape_1); conv2d_1 = reshape_1 = None 2025-09-09T15:15:41.8588335Z reshape_2 = torch.ops.aten.reshape.default(conv_bias, [1, -1, 1, 1]); conv_bias = None 2025-09-09T15:15:41.8589010Z add_1 = torch.ops.aten.add.Tensor(div_1, reshape_2); div_1 = reshape_2 = None 2025-09-09T15:15:41.8590165Z batch_norm_1 = torch.ops.aten.batch_norm.default(add_1, bn_weight, bn_bias, bn_running_mean, bn_running_var, True, 0.1, 1e-05, True); add_1 = bn_weight = bn_bias = bn_running_mean = bn_running_var = None 2025-09-09T15:15:41.8591305Z activation_post_process_2 = self.activation_post_process_2(batch_norm_1); batch_norm_1 = None 2025-09-09T15:15:41.8592296Z hardtanh = torch.ops.aten.hardtanh.default(activation_post_process_2, -1.0, 1.0); activation_post_process_2 = None 2025-09-09T15:15:41.8593161Z activation_post_process_3 = self.activation_post_process_3(hardtanh); hardtanh = None 2025-09-09T15:15:41.8593857Z return pytree.tree_unflatten((activation_post_process_3,), self._out_spec) 2025-09-09T15:15:41.8594326Z 2025-09-09T15:15:41.8594654Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:15:41.8595090Z model fx: GraphModule( 2025-09-09T15:15:41.8595462Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:15:41.8596636Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0183]), zero_point=tensor([10], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:15:41.8597991Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.526270866394043, max_val=2.143237352371216) 2025-09-09T15:15:41.8598607Z ) 2025-09-09T15:15:41.8598819Z (conv): ConvBn2d( 2025-09-09T15:15:41.8599153Z 3, 3, kernel_size=(3, 3), stride=(1, 1) 2025-09-09T15:15:41.8599653Z (bn): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T15:15:41.8600206Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:15:41.8601332Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0015]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-127, quant_max=127, qscheme=torch.per_tensor_symmetric, reduce_range=False 2025-09-09T15:15:41.8602728Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.19029980897903442, max_val=0.18509264290332794) 2025-09-09T15:15:41.8603356Z ) 2025-09-09T15:15:41.8603559Z ) 2025-09-09T15:15:41.8603882Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:15:41.8605038Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0163]), zero_point=tensor([6], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:15:41.8606390Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.1751670837402344, max_val=1.979515790939331) 2025-09-09T15:15:41.8607005Z ) 2025-09-09T15:15:41.8607267Z (hardtanh): Hardtanh(min_val=-1.0, max_val=1.0) 2025-09-09T15:15:41.8607740Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T15:15:41.8608898Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0163]), zero_point=tensor([6], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T15:15:41.8610279Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.1751670837402344, max_val=1.979515790939331) 2025-09-09T15:15:41.8610961Z ) 2025-09-09T15:15:41.8611166Z ) 2025-09-09T15:15:41.8611273Z 2025-09-09T15:15:41.8611278Z 2025-09-09T15:15:41.8611282Z 2025-09-09T15:15:41.8611362Z def forward(self, x): 2025-09-09T15:15:41.8611712Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T15:15:41.8612240Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T15:15:41.8612783Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T15:15:41.8613361Z hardtanh = self.hardtanh(activation_post_process_1); activation_post_process_1 = None 2025-09-09T15:15:41.8613957Z activation_post_process_2 = self.activation_post_process_2(hardtanh); hardtanh = None 2025-09-09T15:15:41.8614413Z return activation_post_process_2 2025-09-09T15:15:41.8616009Z 2025-09-09T15:15:41.8616297Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:15:41.8616657Z diff: tensor([[[[0., 0., 0.], 2025-09-09T15:15:41.8616979Z [0., 0., 0.], 2025-09-09T15:15:41.8617185Z [0., 0., 0.]], 2025-09-09T15:15:41.8617330Z 2025-09-09T15:15:41.8617404Z [[0., 0., 0.], 2025-09-09T15:15:41.8617608Z [0., 0., 0.], 2025-09-09T15:15:41.8617804Z [0., 0., 0.]], 2025-09-09T15:15:41.8617941Z 2025-09-09T15:15:41.8618022Z [[0., 0., 0.], 2025-09-09T15:15:41.8618217Z [0., 0., 0.], 2025-09-09T15:15:41.8618452Z [0., 0., 0.]]]], grad_fn=) 2025-09-09T15:15:41.8618758Z converted model pt2e: GraphModule( 2025-09-09T15:15:41.8619020Z (conv): Module() 2025-09-09T15:15:41.8619214Z (bn): Module() 2025-09-09T15:15:41.8619406Z ) 2025-09-09T15:15:41.8619504Z 2025-09-09T15:15:41.8619507Z 2025-09-09T15:15:41.8619511Z 2025-09-09T15:15:41.8619604Z def forward(self, x): 2025-09-09T15:15:41.8619881Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T15:15:41.8620214Z conv_bias = self.conv.bias 2025-09-09T15:15:41.8620857Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.018311796709895134, 10, -128, 127, torch.int8); x = None 2025-09-09T15:15:41.8622096Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.018311796709895134, 10, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T15:15:41.8623222Z quantize_per_tensor = self._frozen_param0 2025-09-09T15:15:41.8624016Z dequantize_per_tensor = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor, 0.0014984237495809793, 0, -127, 127, torch.int8); quantize_per_tensor = None 2025-09-09T15:16:11.9170191Z conv2d_2 = torch.ops.aten.conv2d.default(dequantize_per_tensor_default, dequantize_per_tensor, conv_bias); dequantize_per_tensor_default = dequantize_per_tensor = conv_bias = None 2025-09-09T15:16:11.9171445Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv2d_2, 0.0162928756326437, 6, -128, 127, torch.int8); conv2d_2 = None 2025-09-09T15:16:11.9172725Z dequantize_per_tensor_default_2 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_2, 0.0162928756326437, 6, -128, 127, torch.int8); quantize_per_tensor_default_2 = None 2025-09-09T15:16:11.9173887Z hardtanh = torch.ops.aten.hardtanh.default(dequantize_per_tensor_default_2, -1.0, 1.0); dequantize_per_tensor_default_2 = None 2025-09-09T15:16:11.9174903Z quantize_per_tensor_default_3 = torch.ops.quantized_decomposed.quantize_per_tensor.default(hardtanh, 0.0162928756326437, 6, -128, 127, torch.int8); hardtanh = None 2025-09-09T15:16:11.9176183Z dequantize_per_tensor_default_3 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_3, 0.0162928756326437, 6, -128, 127, torch.int8); quantize_per_tensor_default_3 = None 2025-09-09T15:16:11.9177183Z return pytree.tree_unflatten((dequantize_per_tensor_default_3,), self._out_spec) 2025-09-09T15:16:11.9177592Z 2025-09-09T15:16:11.9177875Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:16:11.9178238Z onverted model fx: GraphModule( 2025-09-09T15:16:11.9178620Z (conv): QuantizedConv2d(Reference)(3, 3, kernel_size=(3, 3), stride=(1, 1)) 2025-09-09T15:16:11.9179050Z (hardtanh): Hardtanh(min_val=-1.0, max_val=1.0) 2025-09-09T15:16:11.9179327Z ) 2025-09-09T15:16:11.9179422Z 2025-09-09T15:16:11.9179426Z 2025-09-09T15:16:11.9179435Z 2025-09-09T15:16:11.9179515Z def forward(self, x): 2025-09-09T15:16:11.9180120Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.018311796709895134, 10, -128, 127, torch.int8); x = None 2025-09-09T15:16:11.9181578Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.018311796709895134, 10, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T15:16:11.9182773Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T15:16:11.9183599Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.0162928756326437, 6, -128, 127, torch.int8); conv = None 2025-09-09T15:16:11.9184880Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.0162928756326437, 6, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T15:16:11.9185933Z hardtanh = self.hardtanh(dequantize_per_tensor_default_1); dequantize_per_tensor_default_1 = None 2025-09-09T15:16:11.9186851Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(hardtanh, 0.0162928756326437, 6, -128, 127, torch.int8); hardtanh = None 2025-09-09T15:16:11.9188116Z dequantize_per_tensor_default_2 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_2, 0.0162928756326437, 6, -128, 127, torch.int8); quantize_per_tensor_default_2 = None 2025-09-09T15:16:11.9188983Z return dequantize_per_tensor_default_2 2025-09-09T15:16:11.9197123Z 2025-09-09T15:16:11.9197447Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T15:16:11.9197825Z diff: tensor([[[[0., 0., 0.], 2025-09-09T15:16:11.9198086Z [0., 0., 0.], 2025-09-09T15:16:11.9198312Z [0., 0., 0.]], 2025-09-09T15:16:11.9198459Z 2025-09-09T15:16:11.9198542Z [[0., 0., 0.], 2025-09-09T15:16:11.9198763Z [0., 0., 0.], 2025-09-09T15:16:11.9198980Z [0., 0., 0.]], 2025-09-09T15:16:11.9199212Z 2025-09-09T15:16:11.9199299Z [[0., 0., 0.], 2025-09-09T15:16:11.9199511Z [0., 0., 0.], 2025-09-09T15:16:11.9199735Z [0., 0., 0.]]]]) 2025-09-09T15:16:11.9200212Z PASSED 2025-09-09T15:16:11.9200871Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQATModels::test_qat_mobilenet_v2 SKIPPED 2025-09-09T15:16:11.9201831Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQATModels::test_qat_resnet18 SKIPPED 2025-09-09T15:16:11.9202755Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizeMixQATAndPTQ::test_mixing_qat_ptq PASSED 2025-09-09T15:16:11.9203625Z test/quantization/pt2e/test_representation.py::TestPT2ERepresentation::test_add PASSED 2025-09-09T15:16:11.9204453Z test/quantization/pt2e/test_representation.py::TestPT2ERepresentation::test_add_relu PASSED 2025-09-09T15:16:11.9205291Z test/quantization/pt2e/test_representation.py::TestPT2ERepresentation::test_conv2d PASSED 2025-09-09T15:16:11.9206159Z test/quantization/pt2e/test_representation.py::TestPT2ERepresentation::test_dynamic_linear PASSED 2025-09-09T15:16:11.9207033Z test/quantization/pt2e/test_representation.py::TestPT2ERepresentation::test_maxpool2d PASSED 2025-09-09T15:16:11.9207862Z test/quantization/pt2e/test_representation.py::TestPT2ERepresentation::test_qdq PASSED 2025-09-09T15:16:11.9208722Z test/quantization/pt2e/test_representation.py::TestPT2ERepresentation::test_qdq_per_channel PASSED 2025-09-09T15:16:11.9209628Z test/quantization/pt2e/test_representation.py::TestPT2ERepresentation::test_static_linear PASSED 2025-09-09T15:16:11.9210934Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_bfloat16_dynamic_False_reshape_a_False_M_1_inplace_add_False_expand_a_scale_False PASSED 2025-09-09T15:16:11.9212596Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_bfloat16_dynamic_False_reshape_a_False_M_1_inplace_add_False_expand_a_scale_True PASSED 2025-09-09T15:16:11.9214401Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_bfloat16_dynamic_False_reshape_a_False_M_1_inplace_add_True_expand_a_scale_False PASSED 2025-09-09T15:16:11.9216109Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_bfloat16_dynamic_False_reshape_a_False_M_1_inplace_add_True_expand_a_scale_True PASSED 2025-09-09T15:16:11.9217759Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_bfloat16_dynamic_False_reshape_a_False_M_32_inplace_add_False_expand_a_scale_False PASSED 2025-09-09T15:16:11.9219412Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_bfloat16_dynamic_False_reshape_a_False_M_32_inplace_add_False_expand_a_scale_True PASSED 2025-09-09T15:16:11.9221053Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_bfloat16_dynamic_False_reshape_a_False_M_32_inplace_add_True_expand_a_scale_False PASSED 2025-09-09T15:16:11.9222966Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_bfloat16_dynamic_False_reshape_a_False_M_32_inplace_add_True_expand_a_scale_True PASSED 2025-09-09T15:16:11.9224602Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_bfloat16_dynamic_False_reshape_a_True_M_1_inplace_add_False_expand_a_scale_False PASSED 2025-09-09T15:16:11.9226224Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_bfloat16_dynamic_False_reshape_a_True_M_1_inplace_add_False_expand_a_scale_True PASSED 2025-09-09T15:16:11.9227855Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_bfloat16_dynamic_False_reshape_a_True_M_1_inplace_add_True_expand_a_scale_False PASSED 2025-09-09T15:16:11.9229486Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_bfloat16_dynamic_False_reshape_a_True_M_1_inplace_add_True_expand_a_scale_True PASSED 2025-09-09T15:16:11.9231117Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_bfloat16_dynamic_False_reshape_a_True_M_32_inplace_add_False_expand_a_scale_False PASSED 2025-09-09T15:16:11.9232748Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_bfloat16_dynamic_False_reshape_a_True_M_32_inplace_add_False_expand_a_scale_True PASSED 2025-09-09T15:16:11.9234379Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_bfloat16_dynamic_False_reshape_a_True_M_32_inplace_add_True_expand_a_scale_False PASSED 2025-09-09T15:16:11.9236017Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_bfloat16_dynamic_False_reshape_a_True_M_32_inplace_add_True_expand_a_scale_True PASSED 2025-09-09T15:16:11.9237645Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_bfloat16_dynamic_True_reshape_a_False_M_1_inplace_add_False_expand_a_scale_False PASSED 2025-09-09T15:16:18.3916380Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_bfloat16_dynamic_True_reshape_a_False_M_1_inplace_add_False_expand_a_scale_True PASSED 2025-09-09T15:16:18.3918899Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_bfloat16_dynamic_True_reshape_a_False_M_1_inplace_add_True_expand_a_scale_False PASSED 2025-09-09T15:16:18.3921114Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_bfloat16_dynamic_True_reshape_a_False_M_1_inplace_add_True_expand_a_scale_True PASSED 2025-09-09T15:16:18.3923577Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_bfloat16_dynamic_True_reshape_a_False_M_32_inplace_add_False_expand_a_scale_False PASSED 2025-09-09T15:16:18.3925646Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_bfloat16_dynamic_True_reshape_a_False_M_32_inplace_add_False_expand_a_scale_True PASSED 2025-09-09T15:16:18.3927717Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_bfloat16_dynamic_True_reshape_a_False_M_32_inplace_add_True_expand_a_scale_False PASSED 2025-09-09T15:16:18.3929775Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_bfloat16_dynamic_True_reshape_a_False_M_32_inplace_add_True_expand_a_scale_True PASSED 2025-09-09T15:16:18.3931832Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_bfloat16_dynamic_True_reshape_a_True_M_1_inplace_add_False_expand_a_scale_False PASSED 2025-09-09T15:16:18.3933982Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_bfloat16_dynamic_True_reshape_a_True_M_1_inplace_add_False_expand_a_scale_True PASSED 2025-09-09T15:16:18.3936046Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_bfloat16_dynamic_True_reshape_a_True_M_1_inplace_add_True_expand_a_scale_False PASSED 2025-09-09T15:16:18.3938088Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_bfloat16_dynamic_True_reshape_a_True_M_1_inplace_add_True_expand_a_scale_True PASSED 2025-09-09T15:16:18.3940145Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_bfloat16_dynamic_True_reshape_a_True_M_32_inplace_add_False_expand_a_scale_False PASSED 2025-09-09T15:16:18.3942197Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_bfloat16_dynamic_True_reshape_a_True_M_32_inplace_add_False_expand_a_scale_True PASSED 2025-09-09T15:16:18.3944252Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_bfloat16_dynamic_True_reshape_a_True_M_32_inplace_add_True_expand_a_scale_False PASSED 2025-09-09T15:16:18.3946303Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_bfloat16_dynamic_True_reshape_a_True_M_32_inplace_add_True_expand_a_scale_True PASSED 2025-09-09T15:16:18.3948236Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_float32_dynamic_False_reshape_a_False_M_1_inplace_add_False_expand_a_scale_False frames [('total', 1), ('ok', 1)] 2025-09-09T15:16:18.3949473Z stats [('calls_captured', 4), ('unique_graphs', 1)] 2025-09-09T15:16:18.3949951Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:16:18.3951391Z inductor [('pattern_matcher_nodes', 5), ('qlinear_weight_prepack_matcher_nodes', 4), ('pattern_matcher_count', 2), ('qlinear_weight_prepack_matcher_count', 1), ('fxgraph_cache_bypass', 1), ('qlinear_unary_lower_count', 1), ('qlinear_unary_lower_nodes', 1), ('extern_calls', 1)] 2025-09-09T15:16:18.3952576Z graph_break [] 2025-09-09T15:16:18.3952854Z PASSED 2025-09-09T15:16:18.3953966Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_float32_dynamic_False_reshape_a_False_M_1_inplace_add_False_expand_a_scale_True frames [('total', 1), ('ok', 1)] 2025-09-09T15:16:18.3955295Z stats [('calls_captured', 5), ('unique_graphs', 1)] 2025-09-09T15:16:18.3955757Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:16:18.3956827Z inductor [('qlinear_weight_prepack_matcher_nodes', 4), ('pattern_matcher_nodes', 4), ('qlinear_weight_prepack_matcher_count', 1), ('pattern_matcher_count', 1), ('fxgraph_cache_bypass', 1), ('extern_calls', 1)] 2025-09-09T15:16:18.3957779Z graph_break [] 2025-09-09T15:16:18.3958051Z PASSED 2025-09-09T15:16:18.3959238Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_float32_dynamic_False_reshape_a_False_M_1_inplace_add_True_expand_a_scale_False frames [('total', 1), ('ok', 1)] 2025-09-09T15:16:18.3960450Z stats [('calls_captured', 5), ('unique_graphs', 1)] 2025-09-09T15:16:18.3960919Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:16:18.3962479Z inductor [('pattern_matcher_nodes', 7), ('qlinear_weight_prepack_matcher_nodes', 4), ('pattern_matcher_count', 3), ('qlinear_binary_matcher_nodes', 2), ('qlinear_weight_prepack_matcher_count', 1), ('qlinear_binary_matcher_count', 1), ('fxgraph_cache_bypass', 1), ('qlinear_binary_lower_count', 1), ('qlinear_binary_lower_nodes', 1), ('extern_calls', 1)] 2025-09-09T15:16:18.3963920Z graph_break [] 2025-09-09T15:16:18.3964199Z PASSED 2025-09-09T15:16:18.3965283Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_float32_dynamic_False_reshape_a_False_M_1_inplace_add_True_expand_a_scale_True frames [('total', 1), ('ok', 1)] 2025-09-09T15:16:18.3966486Z stats [('calls_captured', 5), ('unique_graphs', 1)] 2025-09-09T15:16:18.3966951Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:16:18.3968013Z inductor [('qlinear_weight_prepack_matcher_nodes', 4), ('pattern_matcher_nodes', 4), ('qlinear_weight_prepack_matcher_count', 1), ('pattern_matcher_count', 1), ('fxgraph_cache_bypass', 1), ('extern_calls', 1)] 2025-09-09T15:16:18.3968963Z graph_break [] 2025-09-09T15:16:18.3969228Z PASSED 2025-09-09T15:16:18.3970332Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_float32_dynamic_False_reshape_a_False_M_32_inplace_add_False_expand_a_scale_False frames [('total', 1), ('ok', 1)] 2025-09-09T15:16:18.3971545Z stats [('calls_captured', 4), ('unique_graphs', 1)] 2025-09-09T15:16:18.3972004Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:16:18.3973064Z inductor [('qlinear_weight_prepack_matcher_nodes', 4), ('pattern_matcher_nodes', 4), ('qlinear_weight_prepack_matcher_count', 1), ('pattern_matcher_count', 1), ('fxgraph_cache_bypass', 1), ('extern_calls', 1)] 2025-09-09T15:16:18.3974011Z graph_break [] 2025-09-09T15:16:18.3974285Z PASSED 2025-09-09T15:16:18.3975424Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_float32_dynamic_False_reshape_a_False_M_32_inplace_add_False_expand_a_scale_True frames [('total', 1), ('ok', 1)] 2025-09-09T15:16:18.3976626Z stats [('calls_captured', 5), ('unique_graphs', 1)] 2025-09-09T15:16:18.3977096Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:16:18.3978151Z inductor [('qlinear_weight_prepack_matcher_nodes', 4), ('pattern_matcher_nodes', 4), ('qlinear_weight_prepack_matcher_count', 1), ('pattern_matcher_count', 1), ('fxgraph_cache_bypass', 1), ('extern_calls', 1)] 2025-09-09T15:16:18.3979101Z graph_break [] 2025-09-09T15:16:18.3979480Z PASSED 2025-09-09T15:16:18.3980571Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_float32_dynamic_False_reshape_a_False_M_32_inplace_add_True_expand_a_scale_False frames [('total', 1), ('ok', 1)] 2025-09-09T15:16:18.3981864Z stats [('calls_captured', 4), ('unique_graphs', 1)] 2025-09-09T15:16:18.3982320Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:16:18.3983386Z inductor [('qlinear_weight_prepack_matcher_nodes', 4), ('pattern_matcher_nodes', 4), ('qlinear_weight_prepack_matcher_count', 1), ('pattern_matcher_count', 1), ('fxgraph_cache_bypass', 1), ('extern_calls', 1)] 2025-09-09T15:16:18.3984330Z graph_break [] 2025-09-09T15:16:18.3984600Z PASSED 2025-09-09T15:16:18.3985693Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_float32_dynamic_False_reshape_a_False_M_32_inplace_add_True_expand_a_scale_True frames [('total', 1), ('ok', 1)] 2025-09-09T15:16:18.3986881Z stats [('calls_captured', 5), ('unique_graphs', 1)] 2025-09-09T15:16:18.3987350Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:16:28.4427561Z inductor [('qlinear_weight_prepack_matcher_nodes', 4), ('pattern_matcher_nodes', 4), ('qlinear_weight_prepack_matcher_count', 1), ('pattern_matcher_count', 1), ('fxgraph_cache_bypass', 1), ('extern_calls', 1)] 2025-09-09T15:16:28.4428639Z graph_break [] 2025-09-09T15:16:28.4429099Z PASSED 2025-09-09T15:16:28.4430230Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_float32_dynamic_False_reshape_a_True_M_1_inplace_add_False_expand_a_scale_False frames [('total', 1), ('ok', 1)] 2025-09-09T15:16:28.4431460Z stats [('calls_captured', 5), ('unique_graphs', 1)] 2025-09-09T15:16:28.4431938Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:16:28.4433268Z inductor [('pattern_matcher_nodes', 6), ('qlinear_weight_prepack_matcher_nodes', 4), ('pattern_matcher_count', 3), ('qlinear_weight_prepack_matcher_count', 1), ('fxgraph_cache_bypass', 1), ('qlinear_unary_lower_count', 1), ('qlinear_unary_lower_nodes', 1), ('extern_calls', 1)] 2025-09-09T15:16:28.4434448Z graph_break [] 2025-09-09T15:16:28.4434724Z PASSED 2025-09-09T15:16:28.4435802Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_float32_dynamic_False_reshape_a_True_M_1_inplace_add_False_expand_a_scale_True frames [('total', 1), ('ok', 1)] 2025-09-09T15:16:28.4437002Z stats [('calls_captured', 6), ('unique_graphs', 1)] 2025-09-09T15:16:28.4437473Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:16:28.4438536Z inductor [('pattern_matcher_nodes', 5), ('qlinear_weight_prepack_matcher_nodes', 4), ('pattern_matcher_count', 2), ('qlinear_weight_prepack_matcher_count', 1), ('fxgraph_cache_bypass', 1), ('extern_calls', 1)] 2025-09-09T15:16:28.4439630Z graph_break [] 2025-09-09T15:16:28.4439897Z PASSED 2025-09-09T15:16:28.4440982Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_float32_dynamic_False_reshape_a_True_M_1_inplace_add_True_expand_a_scale_False frames [('total', 1), ('ok', 1)] 2025-09-09T15:16:28.4442189Z stats [('calls_captured', 6), ('unique_graphs', 1)] 2025-09-09T15:16:28.4442647Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:16:28.4444206Z inductor [('pattern_matcher_nodes', 8), ('pattern_matcher_count', 4), ('qlinear_weight_prepack_matcher_nodes', 4), ('qlinear_binary_matcher_nodes', 2), ('qlinear_weight_prepack_matcher_count', 1), ('qlinear_binary_matcher_count', 1), ('fxgraph_cache_bypass', 1), ('qlinear_binary_lower_count', 1), ('qlinear_binary_lower_nodes', 1), ('extern_calls', 1)] 2025-09-09T15:16:28.4445637Z graph_break [] 2025-09-09T15:16:28.4446247Z PASSED 2025-09-09T15:16:28.4447324Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_float32_dynamic_False_reshape_a_True_M_1_inplace_add_True_expand_a_scale_True frames [('total', 1), ('ok', 1)] 2025-09-09T15:16:28.4448720Z stats [('calls_captured', 6), ('unique_graphs', 1)] 2025-09-09T15:16:28.4449184Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:16:28.4450232Z inductor [('pattern_matcher_nodes', 5), ('qlinear_weight_prepack_matcher_nodes', 4), ('pattern_matcher_count', 2), ('qlinear_weight_prepack_matcher_count', 1), ('fxgraph_cache_bypass', 1), ('extern_calls', 1)] 2025-09-09T15:16:28.4451177Z graph_break [] 2025-09-09T15:16:28.4451451Z PASSED 2025-09-09T15:16:28.4452543Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_float32_dynamic_False_reshape_a_True_M_32_inplace_add_False_expand_a_scale_False frames [('total', 1), ('ok', 1)] 2025-09-09T15:16:28.4453754Z stats [('calls_captured', 5), ('unique_graphs', 1)] 2025-09-09T15:16:28.4454211Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:16:28.4455272Z inductor [('pattern_matcher_nodes', 5), ('qlinear_weight_prepack_matcher_nodes', 4), ('pattern_matcher_count', 2), ('qlinear_weight_prepack_matcher_count', 1), ('fxgraph_cache_bypass', 1), ('extern_calls', 1)] 2025-09-09T15:16:28.4456208Z graph_break [] 2025-09-09T15:16:28.4456469Z PASSED 2025-09-09T15:16:28.4457548Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_float32_dynamic_False_reshape_a_True_M_32_inplace_add_False_expand_a_scale_True frames [('total', 1), ('ok', 1)] 2025-09-09T15:16:28.4458739Z stats [('calls_captured', 6), ('unique_graphs', 1)] 2025-09-09T15:16:28.4459203Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:16:28.4460260Z inductor [('pattern_matcher_nodes', 5), ('qlinear_weight_prepack_matcher_nodes', 4), ('pattern_matcher_count', 2), ('qlinear_weight_prepack_matcher_count', 1), ('fxgraph_cache_bypass', 1), ('extern_calls', 1)] 2025-09-09T15:16:28.4461195Z graph_break [] 2025-09-09T15:16:28.4461463Z PASSED 2025-09-09T15:16:28.4462540Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_float32_dynamic_False_reshape_a_True_M_32_inplace_add_True_expand_a_scale_False frames [('total', 1), ('ok', 1)] 2025-09-09T15:16:28.4463740Z stats [('calls_captured', 5), ('unique_graphs', 1)] 2025-09-09T15:16:28.4464209Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:16:28.4465251Z inductor [('pattern_matcher_nodes', 5), ('qlinear_weight_prepack_matcher_nodes', 4), ('pattern_matcher_count', 2), ('qlinear_weight_prepack_matcher_count', 1), ('fxgraph_cache_bypass', 1), ('extern_calls', 1)] 2025-09-09T15:16:28.4466197Z graph_break [] 2025-09-09T15:16:28.4466465Z PASSED 2025-09-09T15:16:28.4467539Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_float32_dynamic_False_reshape_a_True_M_32_inplace_add_True_expand_a_scale_True frames [('total', 1), ('ok', 1)] 2025-09-09T15:16:28.4468735Z stats [('calls_captured', 6), ('unique_graphs', 1)] 2025-09-09T15:16:28.4469194Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:16:28.4470249Z inductor [('pattern_matcher_nodes', 5), ('qlinear_weight_prepack_matcher_nodes', 4), ('pattern_matcher_count', 2), ('qlinear_weight_prepack_matcher_count', 1), ('fxgraph_cache_bypass', 1), ('extern_calls', 1)] 2025-09-09T15:16:28.4471179Z graph_break [] 2025-09-09T15:16:28.4471446Z PASSED 2025-09-09T15:16:28.4472647Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_float32_dynamic_True_reshape_a_False_M_1_inplace_add_False_expand_a_scale_False frames [('total', 1), ('ok', 1)] 2025-09-09T15:16:28.4473851Z stats [('calls_captured', 4), ('unique_graphs', 1)] 2025-09-09T15:16:28.4474405Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:16:28.4475688Z inductor [('pattern_matcher_nodes', 5), ('qlinear_weight_prepack_matcher_nodes', 4), ('pattern_matcher_count', 2), ('qlinear_weight_prepack_matcher_count', 1), ('fxgraph_cache_bypass', 1), ('qlinear_unary_lower_count', 1), ('qlinear_unary_lower_nodes', 1), ('extern_calls', 1)] 2025-09-09T15:16:28.4476855Z graph_break [] 2025-09-09T15:16:28.4477121Z PASSED 2025-09-09T15:16:28.4478249Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_float32_dynamic_True_reshape_a_False_M_1_inplace_add_False_expand_a_scale_True frames [('total', 1), ('ok', 1)] 2025-09-09T15:16:28.4479543Z stats [('calls_captured', 6), ('unique_graphs', 1)] 2025-09-09T15:16:28.4480008Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:16:28.4481062Z inductor [('qlinear_weight_prepack_matcher_nodes', 4), ('pattern_matcher_nodes', 4), ('qlinear_weight_prepack_matcher_count', 1), ('pattern_matcher_count', 1), ('fxgraph_cache_bypass', 1), ('extern_calls', 1)] 2025-09-09T15:16:28.4482013Z graph_break [] 2025-09-09T15:16:28.4482277Z PASSED 2025-09-09T15:16:28.4483351Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_float32_dynamic_True_reshape_a_False_M_1_inplace_add_True_expand_a_scale_False frames [('total', 1), ('ok', 1)] 2025-09-09T15:16:28.4484541Z stats [('calls_captured', 4), ('unique_graphs', 1)] 2025-09-09T15:16:28.4485009Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:16:28.4486284Z inductor [('pattern_matcher_nodes', 5), ('qlinear_weight_prepack_matcher_nodes', 4), ('pattern_matcher_count', 2), ('qlinear_weight_prepack_matcher_count', 1), ('fxgraph_cache_bypass', 1), ('qlinear_unary_lower_count', 1), ('qlinear_unary_lower_nodes', 1), ('extern_calls', 1)] 2025-09-09T15:16:28.4487448Z graph_break [] 2025-09-09T15:16:28.4487728Z PASSED 2025-09-09T15:16:28.4488790Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_float32_dynamic_True_reshape_a_False_M_1_inplace_add_True_expand_a_scale_True frames [('total', 1), ('ok', 1)] 2025-09-09T15:16:28.4489975Z stats [('calls_captured', 6), ('unique_graphs', 1)] 2025-09-09T15:16:28.4490430Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:16:28.4491481Z inductor [('qlinear_weight_prepack_matcher_nodes', 4), ('pattern_matcher_nodes', 4), ('qlinear_weight_prepack_matcher_count', 1), ('pattern_matcher_count', 1), ('fxgraph_cache_bypass', 1), ('extern_calls', 1)] 2025-09-09T15:16:28.4492427Z graph_break [] 2025-09-09T15:16:28.4492692Z PASSED 2025-09-09T15:16:28.4493779Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_float32_dynamic_True_reshape_a_False_M_32_inplace_add_False_expand_a_scale_False frames [('total', 1), ('ok', 1)] 2025-09-09T15:16:28.4494986Z stats [('calls_captured', 4), ('unique_graphs', 1)] 2025-09-09T15:16:28.4495448Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:16:38.7978443Z inductor [('qlinear_weight_prepack_matcher_nodes', 4), ('pattern_matcher_nodes', 4), ('qlinear_weight_prepack_matcher_count', 1), ('pattern_matcher_count', 1), ('fxgraph_cache_bypass', 1), ('extern_calls', 1)] 2025-09-09T15:16:38.7979245Z graph_break [] 2025-09-09T15:16:38.7979831Z PASSED 2025-09-09T15:16:38.7981005Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_float32_dynamic_True_reshape_a_False_M_32_inplace_add_False_expand_a_scale_True frames [('total', 1), ('ok', 1)] 2025-09-09T15:16:38.7982715Z stats [('calls_captured', 6), ('unique_graphs', 1)] 2025-09-09T15:16:38.7983237Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:16:38.7984294Z inductor [('qlinear_weight_prepack_matcher_nodes', 4), ('pattern_matcher_nodes', 4), ('qlinear_weight_prepack_matcher_count', 1), ('pattern_matcher_count', 1), ('fxgraph_cache_bypass', 1), ('extern_calls', 1)] 2025-09-09T15:16:38.7985042Z graph_break [] 2025-09-09T15:16:38.7985301Z PASSED 2025-09-09T15:16:38.7986167Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_float32_dynamic_True_reshape_a_False_M_32_inplace_add_True_expand_a_scale_False frames [('total', 1), ('ok', 1)] 2025-09-09T15:16:38.7987125Z stats [('calls_captured', 4), ('unique_graphs', 1)] 2025-09-09T15:16:38.7987517Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:16:38.7988366Z inductor [('qlinear_weight_prepack_matcher_nodes', 4), ('pattern_matcher_nodes', 4), ('qlinear_weight_prepack_matcher_count', 1), ('pattern_matcher_count', 1), ('fxgraph_cache_bypass', 1), ('extern_calls', 1)] 2025-09-09T15:16:38.7989125Z graph_break [] 2025-09-09T15:16:38.7989354Z PASSED 2025-09-09T15:16:38.7990215Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_float32_dynamic_True_reshape_a_False_M_32_inplace_add_True_expand_a_scale_True frames [('total', 1), ('ok', 1)] 2025-09-09T15:16:38.7991167Z stats [('calls_captured', 6), ('unique_graphs', 1)] 2025-09-09T15:16:38.7991549Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:16:38.7992393Z inductor [('qlinear_weight_prepack_matcher_nodes', 4), ('pattern_matcher_nodes', 4), ('qlinear_weight_prepack_matcher_count', 1), ('pattern_matcher_count', 1), ('fxgraph_cache_bypass', 1), ('extern_calls', 1)] 2025-09-09T15:16:38.7993137Z graph_break [] 2025-09-09T15:16:38.7993367Z PASSED 2025-09-09T15:16:38.7994222Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_float32_dynamic_True_reshape_a_True_M_1_inplace_add_False_expand_a_scale_False frames [('total', 1), ('ok', 1)] 2025-09-09T15:16:38.7995180Z stats [('calls_captured', 5), ('unique_graphs', 1)] 2025-09-09T15:16:38.7995567Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:16:38.7996577Z inductor [('pattern_matcher_nodes', 6), ('qlinear_weight_prepack_matcher_nodes', 4), ('pattern_matcher_count', 3), ('qlinear_weight_prepack_matcher_count', 1), ('fxgraph_cache_bypass', 1), ('qlinear_unary_lower_count', 1), ('qlinear_unary_lower_nodes', 1), ('extern_calls', 1)] 2025-09-09T15:16:38.7997506Z graph_break [] 2025-09-09T15:16:38.7997727Z PASSED 2025-09-09T15:16:38.7998587Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_float32_dynamic_True_reshape_a_True_M_1_inplace_add_False_expand_a_scale_True frames [('total', 1), ('ok', 1)] 2025-09-09T15:16:38.7999618Z stats [('calls_captured', 7), ('unique_graphs', 1)] 2025-09-09T15:16:38.8000006Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:16:38.8000874Z inductor [('pattern_matcher_nodes', 5), ('qlinear_weight_prepack_matcher_nodes', 4), ('pattern_matcher_count', 2), ('qlinear_weight_prepack_matcher_count', 1), ('fxgraph_cache_bypass', 1), ('extern_calls', 1)] 2025-09-09T15:16:38.8001646Z graph_break [] 2025-09-09T15:16:38.8001864Z PASSED 2025-09-09T15:16:38.8002720Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_float32_dynamic_True_reshape_a_True_M_1_inplace_add_True_expand_a_scale_False frames [('total', 1), ('ok', 1)] 2025-09-09T15:16:38.8003661Z stats [('calls_captured', 5), ('unique_graphs', 1)] 2025-09-09T15:16:38.8004043Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:16:38.8005156Z inductor [('pattern_matcher_nodes', 6), ('qlinear_weight_prepack_matcher_nodes', 4), ('pattern_matcher_count', 3), ('qlinear_weight_prepack_matcher_count', 1), ('fxgraph_cache_bypass', 1), ('qlinear_unary_lower_count', 1), ('qlinear_unary_lower_nodes', 1), ('extern_calls', 1)] 2025-09-09T15:16:38.8006169Z graph_break [] 2025-09-09T15:16:38.8006395Z PASSED 2025-09-09T15:16:38.8007254Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_float32_dynamic_True_reshape_a_True_M_1_inplace_add_True_expand_a_scale_True frames [('total', 1), ('ok', 1)] 2025-09-09T15:16:38.8008198Z stats [('calls_captured', 7), ('unique_graphs', 1)] 2025-09-09T15:16:38.8008580Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:16:38.8009421Z inductor [('pattern_matcher_nodes', 5), ('qlinear_weight_prepack_matcher_nodes', 4), ('pattern_matcher_count', 2), ('qlinear_weight_prepack_matcher_count', 1), ('fxgraph_cache_bypass', 1), ('extern_calls', 1)] 2025-09-09T15:16:38.8010168Z graph_break [] 2025-09-09T15:16:38.8010384Z PASSED 2025-09-09T15:16:38.8011246Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_float32_dynamic_True_reshape_a_True_M_32_inplace_add_False_expand_a_scale_False frames [('total', 1), ('ok', 1)] 2025-09-09T15:16:38.8012201Z stats [('calls_captured', 5), ('unique_graphs', 1)] 2025-09-09T15:16:38.8012583Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:16:38.8013430Z inductor [('pattern_matcher_nodes', 5), ('qlinear_weight_prepack_matcher_nodes', 4), ('pattern_matcher_count', 2), ('qlinear_weight_prepack_matcher_count', 1), ('fxgraph_cache_bypass', 1), ('extern_calls', 1)] 2025-09-09T15:16:38.8014169Z graph_break [] 2025-09-09T15:16:38.8014393Z PASSED 2025-09-09T15:16:38.8015256Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_float32_dynamic_True_reshape_a_True_M_32_inplace_add_False_expand_a_scale_True frames [('total', 1), ('ok', 1)] 2025-09-09T15:16:38.8016202Z stats [('calls_captured', 7), ('unique_graphs', 1)] 2025-09-09T15:16:38.8016584Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:16:38.8017421Z inductor [('pattern_matcher_nodes', 5), ('qlinear_weight_prepack_matcher_nodes', 4), ('pattern_matcher_count', 2), ('qlinear_weight_prepack_matcher_count', 1), ('fxgraph_cache_bypass', 1), ('extern_calls', 1)] 2025-09-09T15:16:38.8018169Z graph_break [] 2025-09-09T15:16:38.8018396Z PASSED 2025-09-09T15:16:38.8019245Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_float32_dynamic_True_reshape_a_True_M_32_inplace_add_True_expand_a_scale_False frames [('total', 1), ('ok', 1)] 2025-09-09T15:16:38.8020189Z stats [('calls_captured', 5), ('unique_graphs', 1)] 2025-09-09T15:16:38.8020573Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:16:38.8021415Z inductor [('pattern_matcher_nodes', 5), ('qlinear_weight_prepack_matcher_nodes', 4), ('pattern_matcher_count', 2), ('qlinear_weight_prepack_matcher_count', 1), ('fxgraph_cache_bypass', 1), ('extern_calls', 1)] 2025-09-09T15:16:38.8022391Z graph_break [] 2025-09-09T15:16:38.8022628Z PASSED 2025-09-09T15:16:38.8023486Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_float32_dynamic_True_reshape_a_True_M_32_inplace_add_True_expand_a_scale_True frames [('total', 1), ('ok', 1)] 2025-09-09T15:16:38.8024434Z stats [('calls_captured', 7), ('unique_graphs', 1)] 2025-09-09T15:16:38.8024831Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:16:38.8025820Z inductor [('pattern_matcher_nodes', 5), ('qlinear_weight_prepack_matcher_nodes', 4), ('pattern_matcher_count', 2), ('qlinear_weight_prepack_matcher_count', 1), ('fxgraph_cache_bypass', 1), ('extern_calls', 1)] 2025-09-09T15:16:38.8026573Z graph_break [] 2025-09-09T15:16:38.8026813Z PASSED 2025-09-09T15:16:38.8029479Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_bfloat16_dynamic_False_reshape_a_False_M_1_inplace_add_False_expand_a_scale_False PASSED 2025-09-09T15:16:38.8031125Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_bfloat16_dynamic_False_reshape_a_False_M_1_inplace_add_False_expand_a_scale_True PASSED 2025-09-09T15:16:38.8032821Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_bfloat16_dynamic_False_reshape_a_False_M_1_inplace_add_True_expand_a_scale_False PASSED 2025-09-09T15:16:38.8034460Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_bfloat16_dynamic_False_reshape_a_False_M_1_inplace_add_True_expand_a_scale_True PASSED 2025-09-09T15:16:38.8036089Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_bfloat16_dynamic_False_reshape_a_False_M_32_inplace_add_False_expand_a_scale_False PASSED 2025-09-09T15:16:43.0127535Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_bfloat16_dynamic_False_reshape_a_False_M_32_inplace_add_False_expand_a_scale_True PASSED 2025-09-09T15:16:43.0129652Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_bfloat16_dynamic_False_reshape_a_False_M_32_inplace_add_True_expand_a_scale_False PASSED 2025-09-09T15:16:43.0131743Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_bfloat16_dynamic_False_reshape_a_False_M_32_inplace_add_True_expand_a_scale_True PASSED 2025-09-09T15:16:43.0133794Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_bfloat16_dynamic_False_reshape_a_True_M_1_inplace_add_False_expand_a_scale_False PASSED 2025-09-09T15:16:43.0135852Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_bfloat16_dynamic_False_reshape_a_True_M_1_inplace_add_False_expand_a_scale_True PASSED 2025-09-09T15:16:43.0137889Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_bfloat16_dynamic_False_reshape_a_True_M_1_inplace_add_True_expand_a_scale_False PASSED 2025-09-09T15:16:43.0139923Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_bfloat16_dynamic_False_reshape_a_True_M_1_inplace_add_True_expand_a_scale_True PASSED 2025-09-09T15:16:43.0141986Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_bfloat16_dynamic_False_reshape_a_True_M_32_inplace_add_False_expand_a_scale_False PASSED 2025-09-09T15:16:43.0144080Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_bfloat16_dynamic_False_reshape_a_True_M_32_inplace_add_False_expand_a_scale_True PASSED 2025-09-09T15:16:43.0146111Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_bfloat16_dynamic_False_reshape_a_True_M_32_inplace_add_True_expand_a_scale_False PASSED 2025-09-09T15:16:43.0148458Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_bfloat16_dynamic_False_reshape_a_True_M_32_inplace_add_True_expand_a_scale_True PASSED 2025-09-09T15:16:43.0150517Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_bfloat16_dynamic_True_reshape_a_False_M_1_inplace_add_False_expand_a_scale_False PASSED 2025-09-09T15:16:43.0152732Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_bfloat16_dynamic_True_reshape_a_False_M_1_inplace_add_False_expand_a_scale_True PASSED 2025-09-09T15:16:43.0154765Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_bfloat16_dynamic_True_reshape_a_False_M_1_inplace_add_True_expand_a_scale_False PASSED 2025-09-09T15:16:43.0156819Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_bfloat16_dynamic_True_reshape_a_False_M_1_inplace_add_True_expand_a_scale_True PASSED 2025-09-09T15:16:43.0158868Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_bfloat16_dynamic_True_reshape_a_False_M_32_inplace_add_False_expand_a_scale_False PASSED 2025-09-09T15:16:43.0161010Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_bfloat16_dynamic_True_reshape_a_False_M_32_inplace_add_False_expand_a_scale_True PASSED 2025-09-09T15:16:43.0163052Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_bfloat16_dynamic_True_reshape_a_False_M_32_inplace_add_True_expand_a_scale_False PASSED 2025-09-09T15:16:43.0165093Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_bfloat16_dynamic_True_reshape_a_False_M_32_inplace_add_True_expand_a_scale_True PASSED 2025-09-09T15:16:43.0167123Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_bfloat16_dynamic_True_reshape_a_True_M_1_inplace_add_False_expand_a_scale_False PASSED 2025-09-09T15:16:43.0169161Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_bfloat16_dynamic_True_reshape_a_True_M_1_inplace_add_False_expand_a_scale_True PASSED 2025-09-09T15:16:43.0171195Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_bfloat16_dynamic_True_reshape_a_True_M_1_inplace_add_True_expand_a_scale_False PASSED 2025-09-09T15:16:43.0173280Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_bfloat16_dynamic_True_reshape_a_True_M_1_inplace_add_True_expand_a_scale_True PASSED 2025-09-09T15:16:43.0175316Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_bfloat16_dynamic_True_reshape_a_True_M_32_inplace_add_False_expand_a_scale_False PASSED 2025-09-09T15:16:43.0177360Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_bfloat16_dynamic_True_reshape_a_True_M_32_inplace_add_False_expand_a_scale_True PASSED 2025-09-09T15:16:43.0179388Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_bfloat16_dynamic_True_reshape_a_True_M_32_inplace_add_True_expand_a_scale_False PASSED 2025-09-09T15:16:43.0181423Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_bfloat16_dynamic_True_reshape_a_True_M_32_inplace_add_True_expand_a_scale_True PASSED 2025-09-09T15:16:43.0183454Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_float32_dynamic_False_reshape_a_False_M_1_inplace_add_False_expand_a_scale_False frames [('total', 1), ('ok', 1)] 2025-09-09T15:16:43.0184762Z stats [('calls_captured', 5), ('unique_graphs', 1)] 2025-09-09T15:16:43.0185225Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:16:43.0186529Z inductor [('pattern_matcher_nodes', 5), ('qlinear_weight_prepack_matcher_nodes', 4), ('pattern_matcher_count', 2), ('qlinear_weight_prepack_matcher_count', 1), ('fxgraph_cache_bypass', 1), ('qlinear_unary_lower_count', 1), ('qlinear_unary_lower_nodes', 1), ('extern_calls', 1)] 2025-09-09T15:16:43.0187688Z graph_break [] 2025-09-09T15:16:43.0187967Z PASSED 2025-09-09T15:16:43.0189058Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_float32_dynamic_False_reshape_a_False_M_1_inplace_add_False_expand_a_scale_True frames [('total', 1), ('ok', 1)] 2025-09-09T15:16:43.0190252Z stats [('calls_captured', 6), ('unique_graphs', 1)] 2025-09-09T15:16:43.0190719Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:16:43.0191776Z inductor [('qlinear_weight_prepack_matcher_nodes', 4), ('pattern_matcher_nodes', 4), ('qlinear_weight_prepack_matcher_count', 1), ('pattern_matcher_count', 1), ('fxgraph_cache_bypass', 1), ('extern_calls', 1)] 2025-09-09T15:16:43.0192717Z graph_break [] 2025-09-09T15:16:43.0192984Z PASSED 2025-09-09T15:16:43.0194057Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_float32_dynamic_False_reshape_a_False_M_1_inplace_add_True_expand_a_scale_False frames [('total', 1), ('ok', 1)] 2025-09-09T15:16:43.0195253Z stats [('calls_captured', 5), ('unique_graphs', 1)] 2025-09-09T15:16:43.0195711Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:16:43.0196994Z inductor [('pattern_matcher_nodes', 5), ('qlinear_weight_prepack_matcher_nodes', 4), ('pattern_matcher_count', 2), ('qlinear_weight_prepack_matcher_count', 1), ('fxgraph_cache_bypass', 1), ('qlinear_unary_lower_count', 1), ('qlinear_unary_lower_nodes', 1), ('extern_calls', 1)] 2025-09-09T15:16:43.0198166Z graph_break [] 2025-09-09T15:16:43.0198426Z PASSED 2025-09-09T15:16:43.0199606Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_float32_dynamic_False_reshape_a_False_M_1_inplace_add_True_expand_a_scale_True frames [('total', 1), ('ok', 1)] 2025-09-09T15:16:43.0200789Z stats [('calls_captured', 6), ('unique_graphs', 1)] 2025-09-09T15:16:56.6912193Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:16:56.6913337Z inductor [('qlinear_weight_prepack_matcher_nodes', 4), ('pattern_matcher_nodes', 4), ('qlinear_weight_prepack_matcher_count', 1), ('pattern_matcher_count', 1), ('fxgraph_cache_bypass', 1), ('extern_calls', 1)] 2025-09-09T15:16:56.6914298Z graph_break [] 2025-09-09T15:16:56.6914761Z PASSED 2025-09-09T15:16:56.6915881Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_float32_dynamic_False_reshape_a_False_M_32_inplace_add_False_expand_a_scale_False frames [('total', 1), ('ok', 1)] 2025-09-09T15:16:56.6917140Z stats [('calls_captured', 5), ('unique_graphs', 1)] 2025-09-09T15:16:56.6917603Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:16:56.6918666Z inductor [('qlinear_weight_prepack_matcher_nodes', 4), ('pattern_matcher_nodes', 4), ('qlinear_weight_prepack_matcher_count', 1), ('pattern_matcher_count', 1), ('fxgraph_cache_bypass', 1), ('extern_calls', 1)] 2025-09-09T15:16:56.6919761Z graph_break [] 2025-09-09T15:16:56.6920044Z PASSED 2025-09-09T15:16:56.6921540Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_float32_dynamic_False_reshape_a_False_M_32_inplace_add_False_expand_a_scale_True frames [('total', 1), ('ok', 1)] 2025-09-09T15:16:56.6922992Z stats [('calls_captured', 6), ('unique_graphs', 1)] 2025-09-09T15:16:56.6923649Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:16:56.6924711Z inductor [('qlinear_weight_prepack_matcher_nodes', 4), ('pattern_matcher_nodes', 4), ('qlinear_weight_prepack_matcher_count', 1), ('pattern_matcher_count', 1), ('fxgraph_cache_bypass', 1), ('extern_calls', 1)] 2025-09-09T15:16:56.6925656Z graph_break [] 2025-09-09T15:16:56.6925982Z PASSED 2025-09-09T15:16:56.6927086Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_float32_dynamic_False_reshape_a_False_M_32_inplace_add_True_expand_a_scale_False frames [('total', 1), ('ok', 1)] 2025-09-09T15:16:56.6928284Z stats [('calls_captured', 5), ('unique_graphs', 1)] 2025-09-09T15:16:56.6928750Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:16:56.6929817Z inductor [('qlinear_weight_prepack_matcher_nodes', 4), ('pattern_matcher_nodes', 4), ('qlinear_weight_prepack_matcher_count', 1), ('pattern_matcher_count', 1), ('fxgraph_cache_bypass', 1), ('extern_calls', 1)] 2025-09-09T15:16:56.6930769Z graph_break [] 2025-09-09T15:16:56.6931039Z PASSED 2025-09-09T15:16:56.6932111Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_float32_dynamic_False_reshape_a_False_M_32_inplace_add_True_expand_a_scale_True frames [('total', 1), ('ok', 1)] 2025-09-09T15:16:56.6933304Z stats [('calls_captured', 6), ('unique_graphs', 1)] 2025-09-09T15:16:56.6933769Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:16:56.6934825Z inductor [('qlinear_weight_prepack_matcher_nodes', 4), ('pattern_matcher_nodes', 4), ('qlinear_weight_prepack_matcher_count', 1), ('pattern_matcher_count', 1), ('fxgraph_cache_bypass', 1), ('extern_calls', 1)] 2025-09-09T15:16:56.6935771Z graph_break [] 2025-09-09T15:16:56.6936063Z PASSED 2025-09-09T15:16:56.6937140Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_float32_dynamic_False_reshape_a_True_M_1_inplace_add_False_expand_a_scale_False frames [('total', 1), ('ok', 1)] 2025-09-09T15:16:56.6938344Z stats [('calls_captured', 6), ('unique_graphs', 1)] 2025-09-09T15:16:56.6938807Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:16:56.6940083Z inductor [('pattern_matcher_nodes', 6), ('qlinear_weight_prepack_matcher_nodes', 4), ('pattern_matcher_count', 3), ('qlinear_weight_prepack_matcher_count', 1), ('fxgraph_cache_bypass', 1), ('qlinear_unary_lower_count', 1), ('qlinear_unary_lower_nodes', 1), ('extern_calls', 1)] 2025-09-09T15:16:56.6941253Z graph_break [] 2025-09-09T15:16:56.6941519Z PASSED 2025-09-09T15:16:56.6942598Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_float32_dynamic_False_reshape_a_True_M_1_inplace_add_False_expand_a_scale_True frames [('total', 1), ('ok', 1)] 2025-09-09T15:16:56.6943798Z stats [('calls_captured', 7), ('unique_graphs', 1)] 2025-09-09T15:16:56.6944256Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:16:56.6945314Z inductor [('pattern_matcher_nodes', 5), ('qlinear_weight_prepack_matcher_nodes', 4), ('pattern_matcher_count', 2), ('qlinear_weight_prepack_matcher_count', 1), ('fxgraph_cache_bypass', 1), ('extern_calls', 1)] 2025-09-09T15:16:56.6946253Z graph_break [] 2025-09-09T15:16:56.6946525Z PASSED 2025-09-09T15:16:56.6947609Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_float32_dynamic_False_reshape_a_True_M_1_inplace_add_True_expand_a_scale_False frames [('total', 1), ('ok', 1)] 2025-09-09T15:16:56.6948936Z stats [('calls_captured', 6), ('unique_graphs', 1)] 2025-09-09T15:16:56.6949415Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:16:56.6950688Z inductor [('pattern_matcher_nodes', 6), ('qlinear_weight_prepack_matcher_nodes', 4), ('pattern_matcher_count', 3), ('qlinear_weight_prepack_matcher_count', 1), ('fxgraph_cache_bypass', 1), ('qlinear_unary_lower_count', 1), ('qlinear_unary_lower_nodes', 1), ('extern_calls', 1)] 2025-09-09T15:16:56.6951947Z graph_break [] 2025-09-09T15:16:56.6952230Z PASSED 2025-09-09T15:16:56.6953292Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_float32_dynamic_False_reshape_a_True_M_1_inplace_add_True_expand_a_scale_True frames [('total', 1), ('ok', 1)] 2025-09-09T15:16:56.6954480Z stats [('calls_captured', 7), ('unique_graphs', 1)] 2025-09-09T15:16:56.6954936Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:16:56.6956002Z inductor [('pattern_matcher_nodes', 5), ('qlinear_weight_prepack_matcher_nodes', 4), ('pattern_matcher_count', 2), ('qlinear_weight_prepack_matcher_count', 1), ('fxgraph_cache_bypass', 1), ('extern_calls', 1)] 2025-09-09T15:16:56.6956976Z graph_break [] 2025-09-09T15:16:56.6957244Z PASSED 2025-09-09T15:16:56.6958330Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_float32_dynamic_False_reshape_a_True_M_32_inplace_add_False_expand_a_scale_False frames [('total', 1), ('ok', 1)] 2025-09-09T15:16:56.6959646Z stats [('calls_captured', 6), ('unique_graphs', 1)] 2025-09-09T15:16:56.6968542Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:16:56.6969632Z inductor [('pattern_matcher_nodes', 5), ('qlinear_weight_prepack_matcher_nodes', 4), ('pattern_matcher_count', 2), ('qlinear_weight_prepack_matcher_count', 1), ('fxgraph_cache_bypass', 1), ('extern_calls', 1)] 2025-09-09T15:16:56.6970586Z graph_break [] 2025-09-09T15:16:56.6970928Z PASSED 2025-09-09T15:16:56.6972020Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_float32_dynamic_False_reshape_a_True_M_32_inplace_add_False_expand_a_scale_True frames [('total', 1), ('ok', 1)] 2025-09-09T15:16:56.6973243Z stats [('calls_captured', 7), ('unique_graphs', 1)] 2025-09-09T15:16:56.6973727Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:16:56.6974786Z inductor [('pattern_matcher_nodes', 5), ('qlinear_weight_prepack_matcher_nodes', 4), ('pattern_matcher_count', 2), ('qlinear_weight_prepack_matcher_count', 1), ('fxgraph_cache_bypass', 1), ('extern_calls', 1)] 2025-09-09T15:16:56.6975742Z graph_break [] 2025-09-09T15:16:56.6976017Z PASSED 2025-09-09T15:16:56.6977116Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_float32_dynamic_False_reshape_a_True_M_32_inplace_add_True_expand_a_scale_False frames [('total', 1), ('ok', 1)] 2025-09-09T15:16:56.6978324Z stats [('calls_captured', 6), ('unique_graphs', 1)] 2025-09-09T15:16:56.6978792Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:16:56.6979877Z inductor [('pattern_matcher_nodes', 5), ('qlinear_weight_prepack_matcher_nodes', 4), ('pattern_matcher_count', 2), ('qlinear_weight_prepack_matcher_count', 1), ('fxgraph_cache_bypass', 1), ('extern_calls', 1)] 2025-09-09T15:16:56.6980819Z graph_break [] 2025-09-09T15:16:56.6981099Z PASSED 2025-09-09T15:16:56.6982184Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_float32_dynamic_False_reshape_a_True_M_32_inplace_add_True_expand_a_scale_True frames [('total', 1), ('ok', 1)] 2025-09-09T15:16:56.6983382Z stats [('calls_captured', 7), ('unique_graphs', 1)] 2025-09-09T15:16:56.6983855Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:16:56.6985041Z inductor [('pattern_matcher_nodes', 5), ('qlinear_weight_prepack_matcher_nodes', 4), ('pattern_matcher_count', 2), ('qlinear_weight_prepack_matcher_count', 1), ('fxgraph_cache_bypass', 1), ('extern_calls', 1)] 2025-09-09T15:16:56.6986080Z graph_break [] 2025-09-09T15:16:56.6986353Z PASSED 2025-09-09T15:16:56.6987430Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_float32_dynamic_True_reshape_a_False_M_1_inplace_add_False_expand_a_scale_False frames [('total', 1), ('ok', 1)] 2025-09-09T15:16:56.6988641Z stats [('calls_captured', 5), ('unique_graphs', 1)] 2025-09-09T15:16:56.6989106Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:17:11.2071351Z inductor [('pattern_matcher_nodes', 5), ('qlinear_weight_prepack_matcher_nodes', 4), ('pattern_matcher_count', 2), ('qlinear_weight_prepack_matcher_count', 1), ('fxgraph_cache_bypass', 1), ('qlinear_unary_lower_count', 1), ('qlinear_unary_lower_nodes', 1), ('extern_calls', 1)] 2025-09-09T15:17:11.2072602Z graph_break [] 2025-09-09T15:17:11.2073061Z PASSED 2025-09-09T15:17:11.2074163Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_float32_dynamic_True_reshape_a_False_M_1_inplace_add_False_expand_a_scale_True frames [('total', 1), ('ok', 1)] 2025-09-09T15:17:11.2075374Z stats [('calls_captured', 7), ('unique_graphs', 1)] 2025-09-09T15:17:11.2075839Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:17:11.2076909Z inductor [('qlinear_weight_prepack_matcher_nodes', 4), ('pattern_matcher_nodes', 4), ('qlinear_weight_prepack_matcher_count', 1), ('pattern_matcher_count', 1), ('fxgraph_cache_bypass', 1), ('extern_calls', 1)] 2025-09-09T15:17:11.2077850Z graph_break [] 2025-09-09T15:17:11.2078127Z PASSED 2025-09-09T15:17:11.2079348Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_float32_dynamic_True_reshape_a_False_M_1_inplace_add_True_expand_a_scale_False frames [('total', 1), ('ok', 1)] 2025-09-09T15:17:11.2080538Z stats [('calls_captured', 5), ('unique_graphs', 1)] 2025-09-09T15:17:11.2081007Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:17:11.2082283Z inductor [('pattern_matcher_nodes', 5), ('qlinear_weight_prepack_matcher_nodes', 4), ('pattern_matcher_count', 2), ('qlinear_weight_prepack_matcher_count', 1), ('fxgraph_cache_bypass', 1), ('qlinear_unary_lower_count', 1), ('qlinear_unary_lower_nodes', 1), ('extern_calls', 1)] 2025-09-09T15:17:11.2083447Z graph_break [] 2025-09-09T15:17:11.2083719Z PASSED 2025-09-09T15:17:11.2084777Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_float32_dynamic_True_reshape_a_False_M_1_inplace_add_True_expand_a_scale_True frames [('total', 1), ('ok', 1)] 2025-09-09T15:17:11.2085962Z stats [('calls_captured', 7), ('unique_graphs', 1)] 2025-09-09T15:17:11.2086427Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:17:11.2087478Z inductor [('qlinear_weight_prepack_matcher_nodes', 4), ('pattern_matcher_nodes', 4), ('qlinear_weight_prepack_matcher_count', 1), ('pattern_matcher_count', 1), ('fxgraph_cache_bypass', 1), ('extern_calls', 1)] 2025-09-09T15:17:11.2088425Z graph_break [] 2025-09-09T15:17:11.2088686Z PASSED 2025-09-09T15:17:11.2089761Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_float32_dynamic_True_reshape_a_False_M_32_inplace_add_False_expand_a_scale_False frames [('total', 1), ('ok', 1)] 2025-09-09T15:17:11.2090946Z stats [('calls_captured', 5), ('unique_graphs', 1)] 2025-09-09T15:17:11.2091407Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:17:11.2092813Z inductor [('qlinear_weight_prepack_matcher_nodes', 4), ('pattern_matcher_nodes', 4), ('qlinear_weight_prepack_matcher_count', 1), ('pattern_matcher_count', 1), ('fxgraph_cache_bypass', 1), ('extern_calls', 1)] 2025-09-09T15:17:11.2093757Z graph_break [] 2025-09-09T15:17:11.2094218Z PASSED 2025-09-09T15:17:11.2095282Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_float32_dynamic_True_reshape_a_False_M_32_inplace_add_False_expand_a_scale_True frames [('total', 1), ('ok', 1)] 2025-09-09T15:17:11.2096474Z stats [('calls_captured', 7), ('unique_graphs', 1)] 2025-09-09T15:17:11.2096935Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:17:11.2097979Z inductor [('qlinear_weight_prepack_matcher_nodes', 4), ('pattern_matcher_nodes', 4), ('qlinear_weight_prepack_matcher_count', 1), ('pattern_matcher_count', 1), ('fxgraph_cache_bypass', 1), ('extern_calls', 1)] 2025-09-09T15:17:11.2098915Z graph_break [] 2025-09-09T15:17:11.2099178Z PASSED 2025-09-09T15:17:11.2100259Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_float32_dynamic_True_reshape_a_False_M_32_inplace_add_True_expand_a_scale_False frames [('total', 1), ('ok', 1)] 2025-09-09T15:17:11.2101458Z stats [('calls_captured', 5), ('unique_graphs', 1)] 2025-09-09T15:17:11.2101913Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:17:11.2102969Z inductor [('qlinear_weight_prepack_matcher_nodes', 4), ('pattern_matcher_nodes', 4), ('qlinear_weight_prepack_matcher_count', 1), ('pattern_matcher_count', 1), ('fxgraph_cache_bypass', 1), ('extern_calls', 1)] 2025-09-09T15:17:11.2103907Z graph_break [] 2025-09-09T15:17:11.2104181Z PASSED 2025-09-09T15:17:11.2105240Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_float32_dynamic_True_reshape_a_False_M_32_inplace_add_True_expand_a_scale_True frames [('total', 1), ('ok', 1)] 2025-09-09T15:17:11.2106437Z stats [('calls_captured', 7), ('unique_graphs', 1)] 2025-09-09T15:17:11.2106898Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:17:11.2107947Z inductor [('qlinear_weight_prepack_matcher_nodes', 4), ('pattern_matcher_nodes', 4), ('qlinear_weight_prepack_matcher_count', 1), ('pattern_matcher_count', 1), ('fxgraph_cache_bypass', 1), ('extern_calls', 1)] 2025-09-09T15:17:11.2108885Z graph_break [] 2025-09-09T15:17:11.2109146Z PASSED 2025-09-09T15:17:11.2110218Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_float32_dynamic_True_reshape_a_True_M_1_inplace_add_False_expand_a_scale_False frames [('total', 1), ('ok', 1)] 2025-09-09T15:17:11.2111408Z stats [('calls_captured', 6), ('unique_graphs', 1)] 2025-09-09T15:17:11.2111863Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:17:11.2113143Z inductor [('pattern_matcher_nodes', 6), ('qlinear_weight_prepack_matcher_nodes', 4), ('pattern_matcher_count', 3), ('qlinear_weight_prepack_matcher_count', 1), ('fxgraph_cache_bypass', 1), ('qlinear_unary_lower_count', 1), ('qlinear_unary_lower_nodes', 1), ('extern_calls', 1)] 2025-09-09T15:17:11.2114317Z graph_break [] 2025-09-09T15:17:11.2114578Z PASSED 2025-09-09T15:17:11.2115640Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_float32_dynamic_True_reshape_a_True_M_1_inplace_add_False_expand_a_scale_True frames [('total', 1), ('ok', 1)] 2025-09-09T15:17:11.2116833Z stats [('calls_captured', 8), ('unique_graphs', 1)] 2025-09-09T15:17:11.2117294Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:17:11.2118345Z inductor [('pattern_matcher_nodes', 5), ('qlinear_weight_prepack_matcher_nodes', 4), ('pattern_matcher_count', 2), ('qlinear_weight_prepack_matcher_count', 1), ('fxgraph_cache_bypass', 1), ('extern_calls', 1)] 2025-09-09T15:17:11.2119357Z graph_break [] 2025-09-09T15:17:11.2119734Z PASSED 2025-09-09T15:17:11.2120796Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_float32_dynamic_True_reshape_a_True_M_1_inplace_add_True_expand_a_scale_False frames [('total', 1), ('ok', 1)] 2025-09-09T15:17:11.2122072Z stats [('calls_captured', 6), ('unique_graphs', 1)] 2025-09-09T15:17:11.2122781Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:17:11.2124070Z inductor [('pattern_matcher_nodes', 6), ('qlinear_weight_prepack_matcher_nodes', 4), ('pattern_matcher_count', 3), ('qlinear_weight_prepack_matcher_count', 1), ('fxgraph_cache_bypass', 1), ('qlinear_unary_lower_count', 1), ('qlinear_unary_lower_nodes', 1), ('extern_calls', 1)] 2025-09-09T15:17:11.2125234Z graph_break [] 2025-09-09T15:17:11.2125508Z PASSED 2025-09-09T15:17:11.2126577Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_float32_dynamic_True_reshape_a_True_M_1_inplace_add_True_expand_a_scale_True frames [('total', 1), ('ok', 1)] 2025-09-09T15:17:11.2127752Z stats [('calls_captured', 8), ('unique_graphs', 1)] 2025-09-09T15:17:11.2128214Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:17:11.2129266Z inductor [('pattern_matcher_nodes', 5), ('qlinear_weight_prepack_matcher_nodes', 4), ('pattern_matcher_count', 2), ('qlinear_weight_prepack_matcher_count', 1), ('fxgraph_cache_bypass', 1), ('extern_calls', 1)] 2025-09-09T15:17:11.2130202Z graph_break [] 2025-09-09T15:17:11.2130473Z PASSED 2025-09-09T15:17:11.2131533Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_float32_dynamic_True_reshape_a_True_M_32_inplace_add_False_expand_a_scale_False frames [('total', 1), ('ok', 1)] 2025-09-09T15:17:11.2132723Z stats [('calls_captured', 6), ('unique_graphs', 1)] 2025-09-09T15:17:11.2133188Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:17:11.2134230Z inductor [('pattern_matcher_nodes', 5), ('qlinear_weight_prepack_matcher_nodes', 4), ('pattern_matcher_count', 2), ('qlinear_weight_prepack_matcher_count', 1), ('fxgraph_cache_bypass', 1), ('extern_calls', 1)] 2025-09-09T15:17:11.2135184Z graph_break [] 2025-09-09T15:17:11.2135446Z PASSED 2025-09-09T15:17:11.2136509Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_float32_dynamic_True_reshape_a_True_M_32_inplace_add_False_expand_a_scale_True frames [('total', 1), ('ok', 1)] 2025-09-09T15:17:11.2137699Z stats [('calls_captured', 8), ('unique_graphs', 1)] 2025-09-09T15:17:11.2138154Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:19:42.6858561Z inductor [('pattern_matcher_nodes', 5), ('qlinear_weight_prepack_matcher_nodes', 4), ('pattern_matcher_count', 2), ('qlinear_weight_prepack_matcher_count', 1), ('fxgraph_cache_bypass', 1), ('extern_calls', 1)] 2025-09-09T15:19:42.6859397Z graph_break [] 2025-09-09T15:19:42.6859834Z PASSED 2025-09-09T15:19:42.6860738Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_float32_dynamic_True_reshape_a_True_M_32_inplace_add_True_expand_a_scale_False frames [('total', 1), ('ok', 1)] 2025-09-09T15:19:42.6861752Z stats [('calls_captured', 6), ('unique_graphs', 1)] 2025-09-09T15:19:42.6862140Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:19:42.6863056Z inductor [('pattern_matcher_nodes', 5), ('qlinear_weight_prepack_matcher_nodes', 4), ('pattern_matcher_count', 2), ('qlinear_weight_prepack_matcher_count', 1), ('fxgraph_cache_bypass', 1), ('extern_calls', 1)] 2025-09-09T15:19:42.6863830Z graph_break [] 2025-09-09T15:19:42.6864055Z PASSED 2025-09-09T15:19:42.6865384Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_float32_dynamic_True_reshape_a_True_M_32_inplace_add_True_expand_a_scale_True frames [('total', 1), ('ok', 1)] 2025-09-09T15:19:42.6866339Z stats [('calls_captured', 8), ('unique_graphs', 1)] 2025-09-09T15:19:42.6866905Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:19:42.6867752Z inductor [('pattern_matcher_nodes', 5), ('qlinear_weight_prepack_matcher_nodes', 4), ('pattern_matcher_count', 2), ('qlinear_weight_prepack_matcher_count', 1), ('fxgraph_cache_bypass', 1), ('extern_calls', 1)] 2025-09-09T15:19:42.6868512Z graph_break [] 2025-09-09T15:19:42.6868747Z PASSED 2025-09-09T15:19:42.6869355Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_dynamic_qlinear_cpu stats [('calls_captured', 22), ('unique_graphs', 8)] 2025-09-09T15:19:42.6870011Z inline_call [] 2025-09-09T15:19:42.6870218Z frames [('total', 1), ('ok', 1)] 2025-09-09T15:19:42.6870549Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:19:42.6871593Z inductor [('pattern_matcher_nodes', 10), ('qlinear_weight_prepack_matcher_nodes', 8), ('pattern_matcher_count', 4), ('qlinear_weight_prepack_matcher_count', 2), ('qlinear_unary_lower_count', 2), ('qlinear_unary_lower_nodes', 2), ('extern_calls', 2), ('fxgraph_cache_bypass', 1)] 2025-09-09T15:19:42.6872538Z graph_break [] 2025-09-09T15:19:42.6872769Z PASSED 2025-09-09T15:19:42.6873471Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_dynamic_qlinear_input_dim_exceeds_2 stats [('calls_captured', 22), ('unique_graphs', 8)] 2025-09-09T15:19:42.6874179Z inline_call [] 2025-09-09T15:19:42.6874378Z frames [('total', 1), ('ok', 1)] 2025-09-09T15:19:42.6874715Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:19:42.6875754Z inductor [('pattern_matcher_nodes', 18), ('qlinear_weight_prepack_matcher_nodes', 12), ('pattern_matcher_count', 8), ('qlinear_weight_prepack_matcher_count', 2), ('qlinear_unary_lower_count', 2), ('qlinear_unary_lower_nodes', 2), ('extern_calls', 2), ('fxgraph_cache_bypass', 1)] 2025-09-09T15:19:42.6876692Z graph_break [] 2025-09-09T15:19:42.6876919Z PASSED 2025-09-09T15:19:42.6877538Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_dynamic_qlinear_qat_cpu stats [('calls_captured', 22), ('unique_graphs', 8)] 2025-09-09T15:19:42.6878207Z inline_call [] 2025-09-09T15:19:42.6878422Z frames [('total', 1), ('ok', 1)] 2025-09-09T15:19:42.6878758Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:19:42.6879891Z inductor [('pattern_matcher_nodes', 10), ('qlinear_weight_prepack_matcher_nodes', 8), ('pattern_matcher_count', 4), ('qlinear_weight_prepack_matcher_count', 2), ('qlinear_unary_lower_count', 2), ('qlinear_unary_lower_nodes', 2), ('extern_calls', 2), ('fxgraph_cache_bypass', 1)] 2025-09-09T15:19:42.6880833Z graph_break [] 2025-09-09T15:19:42.6881069Z PASSED 2025-09-09T15:19:42.6881683Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_linear_dynamic_fp16 stats [('calls_captured', 20), ('unique_graphs', 16)] 2025-09-09T15:19:42.6882356Z inline_call [] 2025-09-09T15:19:42.6882561Z frames [('total', 1), ('ok', 1)] 2025-09-09T15:19:42.6882900Z aot_autograd [('total', 2), ('autograd_cache_bypass', 2), ('ok', 2)] 2025-09-09T15:19:42.6883762Z inductor [('pattern_matcher_nodes', 15), ('qlinear_weight_prepack_matcher_nodes', 12), ('pattern_matcher_count', 5), ('extern_calls', 4), ('qlinear_weight_prepack_matcher_count', 2), ('fxgraph_cache_bypass', 2)] 2025-09-09T15:19:42.6884520Z graph_break [] 2025-09-09T15:19:42.6884752Z PASSED 2025-09-09T15:19:42.6885378Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_linear_relu_dynamic_fp16 stats [('calls_captured', 24), ('unique_graphs', 16)] 2025-09-09T15:19:42.6886063Z inline_call [] 2025-09-09T15:19:42.6886265Z frames [('total', 1), ('ok', 1)] 2025-09-09T15:19:42.6886600Z aot_autograd [('total', 2), ('autograd_cache_bypass', 2), ('ok', 2)] 2025-09-09T15:19:42.6887557Z inductor [('pattern_matcher_nodes', 17), ('qlinear_weight_prepack_matcher_nodes', 14), ('pattern_matcher_count', 5), ('extern_calls', 4), ('qlinear_weight_prepack_matcher_count', 2), ('fxgraph_cache_bypass', 2)] 2025-09-09T15:19:42.6888396Z graph_break [] 2025-09-09T15:19:42.6888625Z PASSED 2025-09-09T15:19:42.6889214Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qat_qconv2d stats [('calls_captured', 1958), ('unique_graphs', 224)] 2025-09-09T15:19:42.6889854Z inline_call [] 2025-09-09T15:19:42.6890052Z frames [('total', 1), ('ok', 1)] 2025-09-09T15:19:42.6890386Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:19:42.6891576Z inductor [('pattern_matcher_nodes', 7), ('qconv_weight_prepack_matcher_nodes', 4), ('pattern_matcher_count', 3), ('qconv_unary_matcher_nodes', 2), ('qconv_weight_prepack_matcher_count', 1), ('qconv_unary_matcher_count', 1), ('fxgraph_cache_bypass', 1), ('qconv_unary_lower_count', 1), ('qconv_unary_lower_nodes', 1), ('extern_calls', 1)] 2025-09-09T15:19:42.6892653Z graph_break [] 2025-09-09T15:19:42.6892877Z PASSED 2025-09-09T15:19:42.6893525Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qat_qconv2d_add stats [('calls_captured', 1967), ('unique_graphs', 224)] 2025-09-09T15:19:42.6894180Z inline_call [] 2025-09-09T15:19:42.6894383Z frames [('total', 1), ('ok', 1)] 2025-09-09T15:19:42.6894710Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:19:42.6896521Z inductor [('pattern_matcher_nodes', 17), ('qconv_weight_prepack_matcher_nodes', 8), ('pattern_matcher_count', 7), ('qconv2d_binary_matcher_nodes', 4), ('qconv_weight_prepack_matcher_count', 2), ('qconv_unary_matcher_nodes', 2), ('extern_calls', 2), ('dequant_promotion_matcher_count', 1), ('dequant_promotion_matcher_nodes', 1), ('qconv2d_binary_matcher_count', 1), ('qconv_unary_matcher_count', 1), ('fxgraph_cache_bypass', 1), ('qconv_unary_lower_count', 1), ('qconv_unary_lower_nodes', 1), ('qconv2d_binary_lower_count', 1), ('qconv2d_binary_lower_nodes', 1)] 2025-09-09T15:19:42.6898239Z graph_break [] 2025-09-09T15:19:42.6898466Z PASSED 2025-09-09T15:19:42.6899083Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qat_qconv2d_add_relu stats [('calls_captured', 1969), ('unique_graphs', 224)] 2025-09-09T15:19:42.6899751Z inline_call [] 2025-09-09T15:19:42.6899950Z frames [('total', 1), ('ok', 1)] 2025-09-09T15:19:42.6900281Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:19:42.6902087Z inductor [('pattern_matcher_nodes', 18), ('qconv_weight_prepack_matcher_nodes', 8), ('pattern_matcher_count', 7), ('qconv2d_binary_matcher_nodes', 5), ('qconv_weight_prepack_matcher_count', 2), ('qconv_unary_matcher_nodes', 2), ('extern_calls', 2), ('dequant_promotion_matcher_count', 1), ('dequant_promotion_matcher_nodes', 1), ('qconv2d_binary_matcher_count', 1), ('qconv_unary_matcher_count', 1), ('fxgraph_cache_bypass', 1), ('qconv_unary_lower_count', 1), ('qconv_unary_lower_nodes', 1), ('qconv2d_binary_lower_count', 1), ('qconv2d_binary_lower_nodes', 1)] 2025-09-09T15:19:42.6903833Z graph_break [] 2025-09-09T15:19:42.6904065Z PASSED 2025-09-09T15:19:42.6904690Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qat_qconv2d_hardswish stats [('calls_captured', 1968), ('unique_graphs', 224)] 2025-09-09T15:19:42.6905372Z inline_call [] 2025-09-09T15:19:42.6905576Z frames [('total', 1), ('ok', 1)] 2025-09-09T15:19:42.6905905Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:19:42.6907096Z inductor [('pattern_matcher_nodes', 24), ('qconv_unary_matcher_nodes', 14), ('qconv_weight_prepack_matcher_nodes', 8), ('pattern_matcher_count', 6), ('qconv_weight_prepack_matcher_count', 2), ('qconv_unary_matcher_count', 2), ('qconv_unary_lower_count', 2), ('qconv_unary_lower_nodes', 2), ('extern_calls', 2), ('fxgraph_cache_bypass', 1)] 2025-09-09T15:19:42.6908183Z graph_break [] 2025-09-09T15:19:42.6908505Z PASSED 2025-09-09T15:19:42.6909132Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qat_qconv2d_hardtanh stats [('calls_captured', 1968), ('unique_graphs', 224)] 2025-09-09T15:19:42.6909879Z inline_call [] 2025-09-09T15:19:42.6910086Z frames [('total', 1), ('ok', 1)] 2025-09-09T15:19:42.6910414Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:19:42.6911609Z inductor [('pattern_matcher_nodes', 18), ('qconv_weight_prepack_matcher_nodes', 8), ('qconv_unary_matcher_nodes', 8), ('pattern_matcher_count', 6), ('qconv_weight_prepack_matcher_count', 2), ('qconv_unary_matcher_count', 2), ('qconv_unary_lower_count', 2), ('qconv_unary_lower_nodes', 2), ('extern_calls', 2), ('fxgraph_cache_bypass', 1)] 2025-09-09T15:19:42.6912705Z graph_break [] 2025-09-09T15:19:42.6912935Z PASSED 2025-09-09T15:19:42.6913548Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qat_qconv2d_relu stats [('calls_captured', 1968), ('unique_graphs', 224)] 2025-09-09T15:19:42.6914201Z inline_call [] 2025-09-09T15:19:42.6914408Z frames [('total', 1), ('ok', 1)] 2025-09-09T15:19:42.6914743Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:21:17.6356010Z inductor [('pattern_matcher_nodes', 16), ('qconv_weight_prepack_matcher_nodes', 8), ('pattern_matcher_count', 6), ('qconv_unary_matcher_nodes', 6), ('qconv_weight_prepack_matcher_count', 2), ('qconv_unary_matcher_count', 2), ('qconv_unary_lower_count', 2), ('qconv_unary_lower_nodes', 2), ('extern_calls', 2), ('fxgraph_cache_bypass', 1)] 2025-09-09T15:21:17.6357467Z graph_break [] 2025-09-09T15:21:17.6357933Z PASSED 2025-09-09T15:21:17.6358721Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qat_qconv2d_relu6 stats [('calls_captured', 1968), ('unique_graphs', 224)] 2025-09-09T15:21:17.6359729Z inline_call [] 2025-09-09T15:21:17.6359986Z frames [('total', 1), ('ok', 1)] 2025-09-09T15:21:17.6360420Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:21:17.6361922Z inductor [('pattern_matcher_nodes', 18), ('qconv_weight_prepack_matcher_nodes', 8), ('qconv_unary_matcher_nodes', 8), ('pattern_matcher_count', 6), ('qconv_weight_prepack_matcher_count', 2), ('qconv_unary_matcher_count', 2), ('qconv_unary_lower_count', 2), ('qconv_unary_lower_nodes', 2), ('extern_calls', 2), ('fxgraph_cache_bypass', 1)] 2025-09-09T15:21:17.6363316Z graph_break [] 2025-09-09T15:21:17.6363594Z PASSED 2025-09-09T15:21:17.6364356Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qat_qconv2d_silu stats [('calls_captured', 1968), ('unique_graphs', 224)] 2025-09-09T15:21:17.6365183Z inline_call [] 2025-09-09T15:21:17.6365430Z frames [('total', 1), ('ok', 1)] 2025-09-09T15:21:17.6365836Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:21:17.6367335Z inductor [('pattern_matcher_nodes', 18), ('qconv_weight_prepack_matcher_nodes', 8), ('qconv_unary_matcher_nodes', 8), ('pattern_matcher_count', 6), ('qconv_weight_prepack_matcher_count', 2), ('qconv_unary_matcher_count', 2), ('qconv_unary_lower_count', 2), ('qconv_unary_lower_nodes', 2), ('extern_calls', 2), ('fxgraph_cache_bypass', 1)] 2025-09-09T15:21:17.6368717Z graph_break [] 2025-09-09T15:21:17.6368992Z PASSED 2025-09-09T15:21:17.6369676Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qcat stats [('calls_captured', 26), ('unique_graphs', 8)] 2025-09-09T15:21:17.6370427Z inline_call [] 2025-09-09T15:21:17.6370669Z frames [('total', 1), ('ok', 1)] 2025-09-09T15:21:17.6371074Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:21:17.6373037Z inductor [('pattern_matcher_nodes', 18), ('qconv_weight_prepack_matcher_nodes', 8), ('pattern_matcher_count', 7), ('qconv_unary_matcher_nodes', 4), ('qcat_matcher_nodes', 4), ('extern_calls', 4), ('qconv_weight_prepack_matcher_count', 2), ('qconv_unary_matcher_count', 2), ('qconv_unary_lower_count', 2), ('qconv_unary_lower_nodes', 2), ('fxgraph_cache_bypass', 1), ('qcat_matcher_count', 1)] 2025-09-09T15:21:17.6374585Z graph_break [] 2025-09-09T15:21:17.6375058Z PASSED 2025-09-09T15:21:17.6375793Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qconv1d_relu_cpu stats [('calls_captured', 20), ('unique_graphs', 8)] 2025-09-09T15:21:17.6376601Z inline_call [] 2025-09-09T15:21:17.6376844Z frames [('total', 1), ('ok', 1)] 2025-09-09T15:21:17.6377253Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:21:17.6378750Z inductor [('pattern_matcher_nodes', 13), ('qconv_weight_prepack_matcher_nodes', 6), ('pattern_matcher_count', 6), ('qconv_unary_matcher_nodes', 5), ('qconv_weight_prepack_matcher_count', 2), ('qconv_unary_matcher_count', 2), ('qconv_unary_lower_count', 2), ('qconv_unary_lower_nodes', 2), ('extern_calls', 2), ('fxgraph_cache_bypass', 1)] 2025-09-09T15:21:17.6380170Z graph_break [] 2025-09-09T15:21:17.6380446Z PASSED 2025-09-09T15:21:17.6381170Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qconv2d_add_2 stats [('calls_captured', 13), ('unique_graphs', 8)] 2025-09-09T15:21:17.6381970Z inline_call [] 2025-09-09T15:21:17.6382220Z frames [('total', 1), ('ok', 1)] 2025-09-09T15:21:17.6382620Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:21:17.6383882Z inductor [('pattern_matcher_nodes', 5), ('qconv_weight_prepack_matcher_nodes', 4), ('pattern_matcher_count', 2), ('qconv_weight_prepack_matcher_count', 1), ('fxgraph_cache_bypass', 1), ('qconv_unary_lower_count', 1), ('qconv_unary_lower_nodes', 1), ('extern_calls', 1)] 2025-09-09T15:21:17.6385025Z graph_break [] 2025-09-09T15:21:17.6385300Z PASSED 2025-09-09T15:21:17.6386014Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qconv2d_add_3 stats [('calls_captured', 29), ('unique_graphs', 8)] 2025-09-09T15:21:17.6386805Z inline_call [] 2025-09-09T15:21:17.6387082Z frames [('total', 1), ('ok', 1)] 2025-09-09T15:21:17.6387482Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:21:17.6389943Z inductor [('pattern_matcher_nodes', 18), ('pattern_matcher_count', 8), ('qconv_weight_prepack_matcher_nodes', 7), ('qcat_matcher_nodes', 4), ('extern_calls', 4), ('qconv_weight_prepack_matcher_count', 2), ('qconv_unary_matcher_nodes', 2), ('qconv2d_binary_matcher_nodes', 2), ('dequant_promotion_matcher_count', 1), ('dequant_promotion_matcher_nodes', 1), ('qconv_unary_matcher_count', 1), ('qconv2d_binary_matcher_count', 1), ('fxgraph_cache_bypass', 1), ('qconv2d_binary_lower_count', 1), ('qconv2d_binary_lower_nodes', 1), ('qconv_unary_lower_count', 1), ('qconv_unary_lower_nodes', 1), ('qcat_matcher_count', 1)] 2025-09-09T15:21:17.6392277Z graph_break [] 2025-09-09T15:21:17.6392552Z PASSED 2025-09-09T15:21:17.6393360Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qconv2d_add_broadcast_shapes_cpu stats [('calls_captured', 15), ('unique_graphs', 8)] 2025-09-09T15:21:17.6394246Z inline_call [] 2025-09-09T15:21:17.6394488Z frames [('total', 1), ('ok', 1)] 2025-09-09T15:21:17.6394890Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:21:17.6396156Z inductor [('pattern_matcher_nodes', 5), ('qconv_weight_prepack_matcher_nodes', 4), ('pattern_matcher_count', 2), ('qconv_weight_prepack_matcher_count', 1), ('fxgraph_cache_bypass', 1), ('qconv_unary_lower_count', 1), ('qconv_unary_lower_nodes', 1), ('extern_calls', 1)] 2025-09-09T15:21:17.6397301Z graph_break [] 2025-09-09T15:21:17.6397576Z PASSED 2025-09-09T15:21:17.6398149Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qconv2d_add_cpu inline_call [] 2025-09-09T15:21:17.6398866Z stats [('calls_captured', 24), ('unique_graphs', 8)] 2025-09-09T15:21:17.6399319Z frames [('total', 1), ('ok', 1)] 2025-09-09T15:21:17.6399726Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:21:17.6401381Z inductor [('pattern_matcher_nodes', 16), ('qconv_weight_prepack_matcher_nodes', 8), ('pattern_matcher_count', 6), ('qconv2d_binary_matcher_nodes', 6), ('qconv_weight_prepack_matcher_count', 2), ('qconv2d_binary_matcher_count', 2), ('qconv2d_binary_lower_count', 2), ('qconv2d_binary_lower_nodes', 2), ('extern_calls', 2), ('fxgraph_cache_bypass', 1)] 2025-09-09T15:21:17.6402889Z graph_break [] 2025-09-09T15:21:17.6403167Z PASSED 2025-09-09T15:21:17.6403927Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qconv2d_add_int8_mixed_bf16 SKIPPED 2025-09-09T15:21:17.6405008Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qconv2d_add_relu_cpu inline_call [] 2025-09-09T15:21:17.6405814Z stats [('calls_captured', 28), ('unique_graphs', 8)] 2025-09-09T15:21:17.6406213Z frames [('total', 1), ('ok', 1)] 2025-09-09T15:21:17.6406581Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:21:17.6407815Z inductor [('pattern_matcher_nodes', 18), ('qconv_weight_prepack_matcher_nodes', 8), ('qconv2d_binary_matcher_nodes', 8), ('pattern_matcher_count', 6), ('qconv_weight_prepack_matcher_count', 2), ('qconv2d_binary_matcher_count', 2), ('qconv2d_binary_lower_count', 2), ('qconv2d_binary_lower_nodes', 2), ('extern_calls', 2), ('fxgraph_cache_bypass', 1)] 2025-09-09T15:21:17.6408970Z graph_break [] 2025-09-09T15:21:17.6409212Z PASSED 2025-09-09T15:21:17.6409899Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qconv2d_add_relu_int8_mixed_bf16 SKIPPED 2025-09-09T15:21:17.6410861Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qconv2d_cpu stats [('calls_captured', 21), ('unique_graphs', 8)] 2025-09-09T15:21:17.6411483Z inline_call [] 2025-09-09T15:21:17.6411693Z frames [('total', 1), ('ok', 1)] 2025-09-09T15:21:17.6412021Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:21:17.6413216Z inductor [('pattern_matcher_nodes', 19), ('qconv_weight_prepack_matcher_nodes', 12), ('pattern_matcher_count', 8), ('qconv_unary_matcher_nodes', 4), ('qconv_weight_prepack_matcher_count', 3), ('qconv_unary_lower_count', 3), ('qconv_unary_lower_nodes', 3), ('extern_calls', 3), ('qconv_unary_matcher_count', 2), ('fxgraph_cache_bypass', 1)] 2025-09-09T15:21:17.6414304Z graph_break [] 2025-09-09T15:21:17.6414526Z PASSED 2025-09-09T15:21:17.6415173Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qconv2d_dequant_promotion_cpu stats [('calls_captured', 24), ('unique_graphs', 8)] 2025-09-09T15:21:17.6415867Z inline_call [] 2025-09-09T15:21:17.6416076Z frames [('total', 1), ('ok', 1)] 2025-09-09T15:21:17.6416408Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:21:17.6418219Z inductor [('pattern_matcher_nodes', 22), ('qconv_weight_prepack_matcher_nodes', 12), ('pattern_matcher_count', 10), ('qconv_unary_matcher_nodes', 4), ('qconv_weight_prepack_matcher_count', 3), ('extern_calls', 3), ('qconv_unary_matcher_count', 2), ('qconv2d_binary_matcher_nodes', 2), ('qconv_unary_lower_count', 2), ('qconv_unary_lower_nodes', 2), ('dequant_promotion_matcher_count', 1), ('dequant_promotion_matcher_nodes', 1), ('qconv2d_binary_matcher_count', 1), ('fxgraph_cache_bypass', 1), ('qconv2d_binary_lower_count', 1), ('qconv2d_binary_lower_nodes', 1)] 2025-09-09T15:21:17.6419926Z graph_break [] 2025-09-09T15:21:17.6420155Z PASSED 2025-09-09T15:23:34.8446789Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qconv2d_hardswish_cpu stats [('calls_captured', 20), ('unique_graphs', 8)] 2025-09-09T15:23:34.8447652Z inline_call [] 2025-09-09T15:23:34.8447901Z frames [('total', 1), ('ok', 1)] 2025-09-09T15:23:34.8448333Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:23:34.8450188Z inductor [('pattern_matcher_nodes', 23), ('qconv_unary_matcher_nodes', 13), ('qconv_weight_prepack_matcher_nodes', 8), ('pattern_matcher_count', 6), ('qconv_weight_prepack_matcher_count', 2), ('qconv_unary_matcher_count', 2), ('qconv_unary_lower_count', 2), ('qconv_unary_lower_nodes', 2), ('extern_calls', 2), ('fxgraph_cache_bypass', 1)] 2025-09-09T15:23:34.8451763Z graph_break [] 2025-09-09T15:23:34.8452243Z PASSED 2025-09-09T15:23:34.8453071Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qconv2d_hardswish_int8_mixed_bf16_cpu SKIPPED 2025-09-09T15:23:34.8454335Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qconv2d_hardtanh_cpu stats [('calls_captured', 20), ('unique_graphs', 8)] 2025-09-09T15:23:34.8455151Z inline_call [] 2025-09-09T15:23:34.8455392Z frames [('total', 1), ('ok', 1)] 2025-09-09T15:23:34.8455790Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:23:34.8457281Z inductor [('pattern_matcher_nodes', 17), ('qconv_weight_prepack_matcher_nodes', 8), ('qconv_unary_matcher_nodes', 7), ('pattern_matcher_count', 6), ('qconv_weight_prepack_matcher_count', 2), ('qconv_unary_matcher_count', 2), ('qconv_unary_lower_count', 2), ('qconv_unary_lower_nodes', 2), ('extern_calls', 2), ('fxgraph_cache_bypass', 1)] 2025-09-09T15:23:34.8458635Z graph_break [] 2025-09-09T15:23:34.8458917Z PASSED 2025-09-09T15:23:34.8459709Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qconv2d_hardtanh_int8_mixed_bf16_cpu SKIPPED 2025-09-09T15:23:34.8460943Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qconv2d_int8_mixed_bf16 SKIPPED 2025-09-09T15:23:34.8462114Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qconv2d_relu6_cpu stats [('calls_captured', 20), ('unique_graphs', 8)] 2025-09-09T15:23:34.8462901Z inline_call [] 2025-09-09T15:23:34.8463155Z frames [('total', 1), ('ok', 1)] 2025-09-09T15:23:34.8463548Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:23:34.8465037Z inductor [('pattern_matcher_nodes', 17), ('qconv_weight_prepack_matcher_nodes', 8), ('qconv_unary_matcher_nodes', 7), ('pattern_matcher_count', 6), ('qconv_weight_prepack_matcher_count', 2), ('qconv_unary_matcher_count', 2), ('qconv_unary_lower_count', 2), ('qconv_unary_lower_nodes', 2), ('extern_calls', 2), ('fxgraph_cache_bypass', 1)] 2025-09-09T15:23:34.8466396Z graph_break [] 2025-09-09T15:23:34.8466662Z PASSED 2025-09-09T15:23:34.8467390Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qconv2d_relu_cpu stats [('calls_captured', 20), ('unique_graphs', 8)] 2025-09-09T15:23:34.8468173Z inline_call [] 2025-09-09T15:23:34.8468415Z frames [('total', 1), ('ok', 1)] 2025-09-09T15:23:34.8468805Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:23:34.8470287Z inductor [('pattern_matcher_nodes', 15), ('qconv_weight_prepack_matcher_nodes', 8), ('pattern_matcher_count', 6), ('qconv_unary_matcher_nodes', 5), ('qconv_weight_prepack_matcher_count', 2), ('qconv_unary_matcher_count', 2), ('qconv_unary_lower_count', 2), ('qconv_unary_lower_nodes', 2), ('extern_calls', 2), ('fxgraph_cache_bypass', 1)] 2025-09-09T15:23:34.8471641Z graph_break [] 2025-09-09T15:23:34.8471902Z PASSED 2025-09-09T15:23:34.8472688Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qconv2d_relu_int8_mixed_bf16_xpu SKIPPED 2025-09-09T15:23:34.8473912Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qconv2d_silu_cpu stats [('calls_captured', 20), ('unique_graphs', 8)] 2025-09-09T15:23:34.8474695Z inline_call [] 2025-09-09T15:23:34.8474945Z frames [('total', 1), ('ok', 1)] 2025-09-09T15:23:34.8475338Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:23:34.8476912Z inductor [('pattern_matcher_nodes', 17), ('qconv_weight_prepack_matcher_nodes', 8), ('qconv_unary_matcher_nodes', 7), ('pattern_matcher_count', 6), ('qconv_weight_prepack_matcher_count', 2), ('qconv_unary_matcher_count', 2), ('qconv_unary_lower_count', 2), ('qconv_unary_lower_nodes', 2), ('extern_calls', 2), ('fxgraph_cache_bypass', 1)] 2025-09-09T15:23:34.8478277Z graph_break [] 2025-09-09T15:23:34.8478541Z PASSED 2025-09-09T15:23:34.8479401Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qconv2d_silu_int8_mixed_bf16_cpu SKIPPED 2025-09-09T15:23:34.8480723Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qconv2d_with_concat_cpu stats [('calls_captured', 32), ('unique_graphs', 8)] 2025-09-09T15:23:34.8481544Z inline_call [] 2025-09-09T15:23:34.8481780Z frames [('total', 1), ('ok', 1)] 2025-09-09T15:23:34.8482178Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:23:34.8484134Z inductor [('pattern_matcher_nodes', 30), ('pattern_matcher_count', 14), ('qconv_weight_prepack_matcher_nodes', 13), ('qconv_unary_matcher_nodes', 6), ('extern_calls', 6), ('qcat_matcher_nodes', 5), ('qconv_weight_prepack_matcher_count', 4), ('qconv_unary_lower_count', 4), ('qconv_unary_lower_nodes', 4), ('qconv_unary_matcher_count', 3), ('dequant_promotion_matcher_count', 2), ('dequant_promotion_matcher_nodes', 2), ('fxgraph_cache_bypass', 1), ('qcat_matcher_count', 1)] 2025-09-09T15:23:34.8485945Z graph_break [] 2025-09-09T15:23:34.8486222Z PASSED 2025-09-09T15:23:34.8486918Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qflatten stats [('calls_captured', 27), ('unique_graphs', 8)] 2025-09-09T15:23:34.8487671Z inline_call [] 2025-09-09T15:23:34.8487914Z frames [('total', 1), ('ok', 1)] 2025-09-09T15:23:34.8488307Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:23:34.8490014Z inductor [('pattern_matcher_nodes', 12), ('pattern_matcher_count', 5), ('qconv_weight_prepack_matcher_nodes', 4), ('qconv_unary_matcher_nodes', 3), ('qreshape_matcher_nodes', 3), ('qconv_weight_prepack_matcher_count', 1), ('qconv_unary_matcher_count', 1), ('fxgraph_cache_bypass', 1), ('qconv_unary_lower_count', 1), ('qconv_unary_lower_nodes', 1), ('qreshape_matcher_count', 1), ('extern_calls', 1)] 2025-09-09T15:23:34.8491422Z graph_break [] 2025-09-09T15:23:34.8491667Z PASSED 2025-09-09T15:23:34.8492292Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_add_cpu_use_relu_False_is_qat_False_is_dynamic_False inline_call [] 2025-09-09T15:23:34.8493021Z stats [('calls_captured', 56), ('unique_graphs', 16)] 2025-09-09T15:23:34.8493354Z frames [('total', 1), ('ok', 1)] 2025-09-09T15:23:34.8493677Z aot_autograd [('total', 2), ('autograd_cache_bypass', 2), ('ok', 2)] 2025-09-09T15:23:34.8495438Z inductor [('pattern_matcher_nodes', 102), ('pattern_matcher_count', 48), ('qlinear_weight_prepack_matcher_nodes', 48), ('qlinear_binary_matcher_nodes', 10), ('dequant_promotion_matcher_nodes', 8), ('qlinear_weight_prepack_matcher_count', 8), ('extern_calls', 8), ('removed_pointless_view_pair', 4), ('dequant_promotion_matcher_count', 4), ('qlinear_binary_matcher_count', 4), ('qlinear_unary_lower_count', 4), ('qlinear_unary_lower_nodes', 4), ('qlinear_binary_lower_count', 4), ('qlinear_binary_lower_nodes', 4), ('fxgraph_cache_bypass', 2)] 2025-09-09T15:23:34.8497096Z graph_break [] 2025-09-09T15:23:34.8497329Z PASSED 2025-09-09T15:23:34.8497935Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_add_cpu_use_relu_False_is_qat_False_is_dynamic_True inline_call [] 2025-09-09T15:23:34.8498652Z stats [('calls_captured', 60), ('unique_graphs', 16)] 2025-09-09T15:23:34.8498958Z frames [('total', 1), ('ok', 1)] 2025-09-09T15:23:34.8499290Z aot_autograd [('total', 2), ('autograd_cache_bypass', 2), ('ok', 2)] 2025-09-09T15:23:34.8501131Z inductor [('pattern_matcher_nodes', 101), ('pattern_matcher_count', 48), ('qlinear_weight_prepack_matcher_nodes', 48), ('qlinear_binary_matcher_nodes', 9), ('dequant_promotion_matcher_nodes', 8), ('qlinear_weight_prepack_matcher_count', 8), ('extern_calls', 8), ('removed_pointless_view_pair', 4), ('dequant_promotion_matcher_count', 4), ('qlinear_binary_matcher_count', 4), ('qlinear_unary_lower_count', 4), ('qlinear_unary_lower_nodes', 4), ('qlinear_binary_lower_count', 4), ('qlinear_binary_lower_nodes', 4), ('fxgraph_cache_bypass', 2)] 2025-09-09T15:23:34.8502769Z graph_break [] 2025-09-09T15:23:34.8503076Z PASSED 2025-09-09T15:23:34.8503682Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_add_cpu_use_relu_False_is_qat_True_is_dynamic_False inline_call [] 2025-09-09T15:23:34.8504384Z stats [('calls_captured', 56), ('unique_graphs', 16)] 2025-09-09T15:23:34.8504700Z frames [('total', 1), ('ok', 1)] 2025-09-09T15:23:34.8505020Z aot_autograd [('total', 2), ('autograd_cache_bypass', 2), ('ok', 2)] 2025-09-09T15:23:34.8506772Z inductor [('pattern_matcher_nodes', 102), ('pattern_matcher_count', 48), ('qlinear_weight_prepack_matcher_nodes', 48), ('qlinear_binary_matcher_nodes', 10), ('dequant_promotion_matcher_nodes', 8), ('qlinear_weight_prepack_matcher_count', 8), ('extern_calls', 8), ('removed_pointless_view_pair', 4), ('dequant_promotion_matcher_count', 4), ('qlinear_binary_matcher_count', 4), ('qlinear_unary_lower_count', 4), ('qlinear_unary_lower_nodes', 4), ('qlinear_binary_lower_count', 4), ('qlinear_binary_lower_nodes', 4), ('fxgraph_cache_bypass', 2)] 2025-09-09T15:23:34.8508434Z graph_break [] 2025-09-09T15:23:34.8508655Z PASSED 2025-09-09T15:23:34.8509259Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_add_cpu_use_relu_False_is_qat_True_is_dynamic_True inline_call [] 2025-09-09T15:23:34.8509960Z stats [('calls_captured', 60), ('unique_graphs', 16)] 2025-09-09T15:23:34.8510278Z frames [('total', 1), ('ok', 1)] 2025-09-09T15:25:57.3249900Z aot_autograd [('total', 2), ('autograd_cache_bypass', 2), ('ok', 2)] 2025-09-09T15:25:57.3252235Z inductor [('pattern_matcher_nodes', 101), ('pattern_matcher_count', 48), ('qlinear_weight_prepack_matcher_nodes', 48), ('qlinear_binary_matcher_nodes', 9), ('dequant_promotion_matcher_nodes', 8), ('qlinear_weight_prepack_matcher_count', 8), ('extern_calls', 8), ('removed_pointless_view_pair', 4), ('dequant_promotion_matcher_count', 4), ('qlinear_binary_matcher_count', 4), ('qlinear_unary_lower_count', 4), ('qlinear_unary_lower_nodes', 4), ('qlinear_binary_lower_count', 4), ('qlinear_binary_lower_nodes', 4), ('fxgraph_cache_bypass', 2)] 2025-09-09T15:25:57.3254408Z graph_break [] 2025-09-09T15:25:57.3254880Z PASSED 2025-09-09T15:25:57.3255659Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_add_cpu_use_relu_True_is_qat_False_is_dynamic_False inline_call [] 2025-09-09T15:25:57.3256556Z stats [('calls_captured', 64), ('unique_graphs', 16)] 2025-09-09T15:25:57.3256938Z frames [('total', 1), ('ok', 1)] 2025-09-09T15:25:57.3257352Z aot_autograd [('total', 2), ('autograd_cache_bypass', 2), ('ok', 2)] 2025-09-09T15:25:57.3259608Z inductor [('pattern_matcher_nodes', 106), ('pattern_matcher_count', 48), ('qlinear_weight_prepack_matcher_nodes', 48), ('qlinear_binary_matcher_nodes', 14), ('dequant_promotion_matcher_nodes', 8), ('qlinear_weight_prepack_matcher_count', 8), ('extern_calls', 8), ('removed_pointless_view_pair', 4), ('dequant_promotion_matcher_count', 4), ('qlinear_binary_matcher_count', 4), ('qlinear_unary_lower_count', 4), ('qlinear_unary_lower_nodes', 4), ('qlinear_binary_lower_count', 4), ('qlinear_binary_lower_nodes', 4), ('fxgraph_cache_bypass', 2)] 2025-09-09T15:25:57.3261709Z graph_break [] 2025-09-09T15:25:57.3261985Z PASSED 2025-09-09T15:25:57.3262735Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_add_cpu_use_relu_True_is_qat_False_is_dynamic_True inline_call [] 2025-09-09T15:25:57.3263607Z stats [('calls_captured', 68), ('unique_graphs', 16)] 2025-09-09T15:25:57.3263995Z frames [('total', 1), ('ok', 1)] 2025-09-09T15:25:57.3264387Z aot_autograd [('total', 2), ('autograd_cache_bypass', 2), ('ok', 2)] 2025-09-09T15:25:57.3266991Z inductor [('pattern_matcher_nodes', 105), ('pattern_matcher_count', 48), ('qlinear_weight_prepack_matcher_nodes', 48), ('qlinear_binary_matcher_nodes', 13), ('dequant_promotion_matcher_nodes', 8), ('qlinear_weight_prepack_matcher_count', 8), ('extern_calls', 8), ('removed_pointless_view_pair', 4), ('dequant_promotion_matcher_count', 4), ('qlinear_binary_matcher_count', 4), ('qlinear_unary_lower_count', 4), ('qlinear_unary_lower_nodes', 4), ('qlinear_binary_lower_count', 4), ('qlinear_binary_lower_nodes', 4), ('fxgraph_cache_bypass', 2)] 2025-09-09T15:25:57.3269311Z graph_break [] 2025-09-09T15:25:57.3269584Z PASSED 2025-09-09T15:25:57.3270333Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_add_cpu_use_relu_True_is_qat_True_is_dynamic_False inline_call [] 2025-09-09T15:25:57.3271205Z stats [('calls_captured', 64), ('unique_graphs', 16)] 2025-09-09T15:25:57.3271590Z frames [('total', 1), ('ok', 1)] 2025-09-09T15:25:57.3271988Z aot_autograd [('total', 2), ('autograd_cache_bypass', 2), ('ok', 2)] 2025-09-09T15:25:57.3274225Z inductor [('pattern_matcher_nodes', 106), ('pattern_matcher_count', 48), ('qlinear_weight_prepack_matcher_nodes', 48), ('qlinear_binary_matcher_nodes', 14), ('dequant_promotion_matcher_nodes', 8), ('qlinear_weight_prepack_matcher_count', 8), ('extern_calls', 8), ('removed_pointless_view_pair', 4), ('dequant_promotion_matcher_count', 4), ('qlinear_binary_matcher_count', 4), ('qlinear_unary_lower_count', 4), ('qlinear_unary_lower_nodes', 4), ('qlinear_binary_lower_count', 4), ('qlinear_binary_lower_nodes', 4), ('fxgraph_cache_bypass', 2)] 2025-09-09T15:25:57.3276345Z graph_break [] 2025-09-09T15:25:57.3276616Z PASSED 2025-09-09T15:25:57.3277350Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_add_cpu_use_relu_True_is_qat_True_is_dynamic_True inline_call [] 2025-09-09T15:25:57.3278225Z stats [('calls_captured', 68), ('unique_graphs', 16)] 2025-09-09T15:25:57.3278598Z frames [('total', 1), ('ok', 1)] 2025-09-09T15:25:57.3279008Z aot_autograd [('total', 2), ('autograd_cache_bypass', 2), ('ok', 2)] 2025-09-09T15:25:57.3281667Z inductor [('pattern_matcher_nodes', 105), ('pattern_matcher_count', 48), ('qlinear_weight_prepack_matcher_nodes', 48), ('qlinear_binary_matcher_nodes', 13), ('dequant_promotion_matcher_nodes', 8), ('qlinear_weight_prepack_matcher_count', 8), ('extern_calls', 8), ('removed_pointless_view_pair', 4), ('dequant_promotion_matcher_count', 4), ('qlinear_binary_matcher_count', 4), ('qlinear_unary_lower_count', 4), ('qlinear_unary_lower_nodes', 4), ('qlinear_binary_lower_count', 4), ('qlinear_binary_lower_nodes', 4), ('fxgraph_cache_bypass', 2)] 2025-09-09T15:25:57.3283784Z graph_break [] 2025-09-09T15:25:57.3284072Z PASSED 2025-09-09T15:25:57.3285044Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_add_int8_mixed_bf16_use_relu_False_is_qat_False_is_dynamic_False SKIPPED 2025-09-09T15:25:57.3286633Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_add_int8_mixed_bf16_use_relu_False_is_qat_False_is_dynamic_True SKIPPED 2025-09-09T15:25:57.3288255Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_add_int8_mixed_bf16_use_relu_False_is_qat_True_is_dynamic_False SKIPPED 2025-09-09T15:25:57.3289859Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_add_int8_mixed_bf16_use_relu_False_is_qat_True_is_dynamic_True SKIPPED 2025-09-09T15:25:57.3291439Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_add_int8_mixed_bf16_use_relu_True_is_qat_False_is_dynamic_False SKIPPED 2025-09-09T15:25:57.3293014Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_add_int8_mixed_bf16_use_relu_True_is_qat_False_is_dynamic_True SKIPPED 2025-09-09T15:25:57.3294574Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_add_int8_mixed_bf16_use_relu_True_is_qat_True_is_dynamic_False SKIPPED 2025-09-09T15:25:57.3296246Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_add_int8_mixed_bf16_use_relu_True_is_qat_True_is_dynamic_True SKIPPED 2025-09-09T15:25:57.3297589Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_cpu stats [('calls_captured', 16), ('unique_graphs', 8)] 2025-09-09T15:25:57.3298440Z inline_call [] 2025-09-09T15:25:57.3298687Z frames [('total', 1), ('ok', 1)] 2025-09-09T15:25:57.3299081Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:25:57.3300656Z inductor [('pattern_matcher_nodes', 12), ('qlinear_weight_prepack_matcher_nodes', 8), ('pattern_matcher_count', 5), ('qlinear_weight_prepack_matcher_count', 2), ('qlinear_unary_matcher_nodes', 2), ('qlinear_unary_lower_count', 2), ('qlinear_unary_lower_nodes', 2), ('extern_calls', 2), ('qlinear_unary_matcher_count', 1), ('fxgraph_cache_bypass', 1)] 2025-09-09T15:25:57.3302118Z graph_break [] 2025-09-09T15:25:57.3302353Z PASSED 2025-09-09T15:25:57.3303002Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_dequant_promotion_cpu stats [('calls_captured', 22), ('unique_graphs', 8)] 2025-09-09T15:25:57.3303693Z inline_call [] 2025-09-09T15:25:57.3303896Z frames [('total', 1), ('ok', 1)] 2025-09-09T15:25:57.3304237Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:25:57.3306065Z inductor [('pattern_matcher_nodes', 20), ('qlinear_weight_prepack_matcher_nodes', 12), ('pattern_matcher_count', 9), ('qlinear_weight_prepack_matcher_count', 3), ('extern_calls', 3), ('qlinear_unary_matcher_nodes', 2), ('qlinear_binary_matcher_nodes', 2), ('qlinear_unary_lower_count', 2), ('qlinear_unary_lower_nodes', 2), ('dequant_promotion_matcher_count', 1), ('dequant_promotion_matcher_nodes', 1), ('qlinear_unary_matcher_count', 1), ('qlinear_binary_matcher_count', 1), ('fxgraph_cache_bypass', 1), ('qlinear_binary_lower_count', 1), ('qlinear_binary_lower_nodes', 1)] 2025-09-09T15:25:57.3307794Z graph_break [] 2025-09-09T15:25:57.3308021Z PASSED 2025-09-09T15:25:57.3308725Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_dequant_promotion_cpu_input_dim_exceeds_2 stats [('calls_captured', 22), ('unique_graphs', 8)] 2025-09-09T15:25:57.3309482Z inline_call [] 2025-09-09T15:25:57.3309698Z frames [('total', 1), ('ok', 1)] 2025-09-09T15:25:57.3310036Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:25:57.3311877Z inductor [('pattern_matcher_nodes', 33), ('qlinear_weight_prepack_matcher_nodes', 18), ('pattern_matcher_count', 15), ('qlinear_weight_prepack_matcher_count', 3), ('extern_calls', 3), ('dequant_promotion_matcher_nodes', 2), ('qlinear_unary_matcher_nodes', 2), ('qlinear_binary_matcher_nodes', 2), ('qlinear_unary_lower_count', 2), ('qlinear_unary_lower_nodes', 2), ('dequant_promotion_matcher_count', 1), ('qlinear_unary_matcher_count', 1), ('qlinear_binary_matcher_count', 1), ('fxgraph_cache_bypass', 1), ('qlinear_binary_lower_count', 1), ('qlinear_binary_lower_nodes', 1)] 2025-09-09T15:25:57.3313612Z graph_break [] 2025-09-09T15:25:57.3313850Z PASSED 2025-09-09T15:25:57.3314525Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_dequant_promotion_dynamic_cpu stats [('calls_captured', 27), ('unique_graphs', 8)] 2025-09-09T15:25:57.3315245Z inline_call [] 2025-09-09T15:25:57.3315461Z frames [('total', 1), ('ok', 1)] 2025-09-09T15:25:57.3315889Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:26:34.2556464Z inductor [('pattern_matcher_nodes', 18), ('qlinear_weight_prepack_matcher_nodes', 12), ('pattern_matcher_count', 8), ('qlinear_weight_prepack_matcher_count', 3), ('extern_calls', 3), ('qlinear_binary_matcher_nodes', 2), ('qlinear_unary_lower_count', 2), ('qlinear_unary_lower_nodes', 2), ('dequant_promotion_matcher_count', 1), ('dequant_promotion_matcher_nodes', 1), ('qlinear_binary_matcher_count', 1), ('fxgraph_cache_bypass', 1), ('qlinear_binary_lower_count', 1), ('qlinear_binary_lower_nodes', 1)] 2025-09-09T15:26:34.2558514Z graph_break [] 2025-09-09T15:26:34.2559508Z PASSED 2025-09-09T15:26:34.2560403Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_dequant_promotion_int8_mixed_bf16 SKIPPED 2025-09-09T15:26:34.2562072Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_dequant_promotion_int8_mixed_bf16_input_dim_exceeds_2 SKIPPED 2025-09-09T15:26:34.2563429Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_gelu_cpu stats [('calls_captured', 20), ('unique_graphs', 8)] 2025-09-09T15:26:34.2564228Z inline_call [] 2025-09-09T15:26:34.2564478Z frames [('total', 1), ('ok', 1)] 2025-09-09T15:26:34.2564887Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:26:34.2566455Z inductor [('pattern_matcher_nodes', 31), ('qlinear_unary_matcher_nodes', 21), ('qlinear_weight_prepack_matcher_nodes', 8), ('pattern_matcher_count', 6), ('qlinear_weight_prepack_matcher_count', 2), ('qlinear_unary_matcher_count', 2), ('qlinear_unary_lower_count', 2), ('qlinear_unary_lower_nodes', 2), ('extern_calls', 2), ('fxgraph_cache_bypass', 1)] 2025-09-09T15:26:34.2567908Z graph_break [] 2025-09-09T15:26:34.2568195Z PASSED 2025-09-09T15:26:34.2568951Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_gelu_int8_mixed_bf16 SKIPPED 2025-09-09T15:26:34.2570206Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_input_dim_exceeds_2 stats [('calls_captured', 16), ('unique_graphs', 8)] 2025-09-09T15:26:34.2571048Z inline_call [] 2025-09-09T15:26:34.2571298Z frames [('total', 1), ('ok', 1)] 2025-09-09T15:26:34.2571700Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:26:34.2573267Z inductor [('pattern_matcher_nodes', 20), ('qlinear_weight_prepack_matcher_nodes', 12), ('pattern_matcher_count', 9), ('qlinear_weight_prepack_matcher_count', 2), ('qlinear_unary_matcher_nodes', 2), ('qlinear_unary_lower_count', 2), ('qlinear_unary_lower_nodes', 2), ('extern_calls', 2), ('qlinear_unary_matcher_count', 1), ('fxgraph_cache_bypass', 1)] 2025-09-09T15:26:34.2574709Z graph_break [] 2025-09-09T15:26:34.2574985Z PASSED 2025-09-09T15:26:34.2575860Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_input_dim_exceeds_2_and_not_contiguous stats [('calls_captured', 20), ('unique_graphs', 8)] 2025-09-09T15:26:34.2576792Z inline_call [] 2025-09-09T15:26:34.2577035Z frames [('total', 1), ('ok', 1)] 2025-09-09T15:26:34.2577438Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:26:34.2579041Z inductor [('pattern_matcher_nodes', 20), ('qlinear_weight_prepack_matcher_nodes', 12), ('pattern_matcher_count', 9), ('qlinear_weight_prepack_matcher_count', 2), ('qlinear_unary_matcher_nodes', 2), ('qlinear_unary_lower_count', 2), ('qlinear_unary_lower_nodes', 2), ('extern_calls', 2), ('qlinear_unary_matcher_count', 1), ('fxgraph_cache_bypass', 1)] 2025-09-09T15:26:34.2580665Z graph_break [] 2025-09-09T15:26:34.2580954Z PASSED 2025-09-09T15:26:34.2581687Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_int8_mixed_bf16 SKIPPED 2025-09-09T15:26:34.2582976Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_int8_mixed_bf16_input_dim_exceeds_2 SKIPPED 2025-09-09T15:26:34.2584427Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_int8_mixed_bf16_input_dim_exceeds_2_and_not_contiguous SKIPPED 2025-09-09T15:26:34.2585790Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_mul_cpu stats [('calls_captured', 17), ('unique_graphs', 8)] 2025-09-09T15:26:34.2586587Z inline_call [] 2025-09-09T15:26:34.2586830Z frames [('total', 1), ('ok', 1)] 2025-09-09T15:26:34.2587239Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:26:34.2588885Z inductor [('pattern_matcher_nodes', 7), ('qlinear_weight_prepack_matcher_nodes', 4), ('pattern_matcher_count', 3), ('qlinear_unary_matcher_nodes', 2), ('qlinear_weight_prepack_matcher_count', 1), ('qlinear_unary_matcher_count', 1), ('fxgraph_cache_bypass', 1), ('qlinear_unary_lower_count', 1), ('qlinear_unary_lower_nodes', 1), ('extern_calls', 1)] 2025-09-09T15:26:34.2590396Z graph_break [] 2025-09-09T15:26:34.2590673Z PASSED 2025-09-09T15:26:34.2591403Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_relu_cpu stats [('calls_captured', 20), ('unique_graphs', 8)] 2025-09-09T15:26:34.2592209Z inline_call [] 2025-09-09T15:26:34.2592454Z frames [('total', 1), ('ok', 1)] 2025-09-09T15:26:34.2592859Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:26:34.2594417Z inductor [('pattern_matcher_nodes', 15), ('qlinear_weight_prepack_matcher_nodes', 8), ('pattern_matcher_count', 6), ('qlinear_unary_matcher_nodes', 5), ('qlinear_weight_prepack_matcher_count', 2), ('qlinear_unary_matcher_count', 2), ('qlinear_unary_lower_count', 2), ('qlinear_unary_lower_nodes', 2), ('extern_calls', 2), ('fxgraph_cache_bypass', 1)] 2025-09-09T15:26:34.2595845Z graph_break [] 2025-09-09T15:26:34.2596129Z PASSED 2025-09-09T15:26:34.2596924Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_relu_input_dim_exceeds_2 stats [('calls_captured', 20), ('unique_graphs', 8)] 2025-09-09T15:26:34.2597795Z inline_call [] 2025-09-09T15:26:34.2598043Z frames [('total', 1), ('ok', 1)] 2025-09-09T15:26:34.2598436Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:26:34.2600087Z inductor [('pattern_matcher_nodes', 23), ('qlinear_weight_prepack_matcher_nodes', 12), ('pattern_matcher_count', 10), ('qlinear_unary_matcher_nodes', 5), ('qlinear_weight_prepack_matcher_count', 2), ('qlinear_unary_matcher_count', 2), ('qlinear_unary_lower_count', 2), ('qlinear_unary_lower_nodes', 2), ('extern_calls', 2), ('fxgraph_cache_bypass', 1)] 2025-09-09T15:26:34.2601519Z graph_break [] 2025-09-09T15:26:34.2601803Z PASSED 2025-09-09T15:26:34.2602566Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_relu_int8_mixed_bf16 SKIPPED 2025-09-09T15:26:34.2603895Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_relu_int8_mixed_bf16_input_dim_exceeds_2 SKIPPED 2025-09-09T15:26:34.2605161Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qmaxpool2d stats [('calls_captured', 19), ('unique_graphs', 8)] 2025-09-09T15:26:34.2605936Z inline_call [] 2025-09-09T15:26:34.2606186Z frames [('total', 1), ('ok', 1)] 2025-09-09T15:26:34.2606582Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:26:34.2608322Z inductor [('pattern_matcher_nodes', 12), ('qconv_weight_prepack_matcher_nodes', 4), ('pattern_matcher_count', 4), ('qmaxpool2d_matcher_nodes', 4), ('qconv_unary_matcher_nodes', 3), ('extern_calls', 3), ('qconv_weight_prepack_matcher_count', 1), ('qconv_unary_matcher_count', 1), ('fxgraph_cache_bypass', 1), ('qconv_unary_lower_count', 1), ('qconv_unary_lower_nodes', 1), ('qmaxpool2d_matcher_count', 1)] 2025-09-09T15:26:34.2609985Z graph_break [] 2025-09-09T15:26:34.2610260Z PASSED 2025-09-09T15:26:34.2611298Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_smooth_quant_with_int_mm_has_bias_False_bfloat16_per_channel_quant_False_dynamic_False PASSED 2025-09-09T15:26:34.2613022Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_smooth_quant_with_int_mm_has_bias_False_bfloat16_per_channel_quant_False_dynamic_True PASSED 2025-09-09T15:26:34.2614728Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_smooth_quant_with_int_mm_has_bias_False_bfloat16_per_channel_quant_True_dynamic_False PASSED 2025-09-09T15:26:34.2616534Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_smooth_quant_with_int_mm_has_bias_False_bfloat16_per_channel_quant_True_dynamic_True PASSED 2025-09-09T15:26:34.2618153Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_smooth_quant_with_int_mm_has_bias_False_float32_per_channel_quant_False_dynamic_False frames [('total', 1), ('ok', 1)] 2025-09-09T15:26:34.2619328Z stats [('calls_captured', 7), ('unique_graphs', 1)] 2025-09-09T15:26:34.2619800Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:26:34.2620853Z inductor [('pattern_matcher_nodes', 8), ('qlinear_weight_prepack_matcher_nodes', 6), ('pattern_matcher_count', 3), ('qlinear_weight_prepack_matcher_count', 1), ('fxgraph_cache_bypass', 1), ('extern_calls', 1)] 2025-09-09T15:26:34.2621799Z graph_break [] 2025-09-09T15:26:34.2622074Z PASSED 2025-09-09T15:26:34.2623201Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_smooth_quant_with_int_mm_has_bias_False_float32_per_channel_quant_False_dynamic_True frames [('total', 1), ('ok', 1)] 2025-09-09T15:26:34.2624260Z stats [('calls_captured', 10), ('unique_graphs', 1)] 2025-09-09T15:26:34.2624728Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:26:34.2626011Z inductor [('pattern_matcher_nodes', 10), ('qlinear_weight_prepack_matcher_nodes', 7), ('pattern_matcher_count', 4), ('qlinear_weight_prepack_matcher_count', 1), ('fxgraph_cache_bypass', 1), ('qlinear_unary_lower_count', 1), ('qlinear_unary_lower_nodes', 1), ('extern_calls', 1)] 2025-09-09T15:26:34.2627195Z graph_break [] 2025-09-09T15:26:34.2627465Z PASSED 2025-09-09T15:27:13.9073965Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_smooth_quant_with_int_mm_has_bias_False_float32_per_channel_quant_True_dynamic_False frames [('total', 1), ('ok', 1)] 2025-09-09T15:27:13.9075088Z stats [('calls_captured', 7), ('unique_graphs', 1)] 2025-09-09T15:27:13.9075582Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:27:13.9076692Z inductor [('pattern_matcher_nodes', 8), ('qlinear_weight_prepack_matcher_nodes', 6), ('pattern_matcher_count', 3), ('qlinear_weight_prepack_matcher_count', 1), ('fxgraph_cache_bypass', 1), ('extern_calls', 1)] 2025-09-09T15:27:13.9077670Z graph_break [] 2025-09-09T15:27:13.9078148Z PASSED 2025-09-09T15:27:13.9079062Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_smooth_quant_with_int_mm_has_bias_False_float32_per_channel_quant_True_dynamic_True frames [('total', 1), ('ok', 1)] 2025-09-09T15:27:13.9080203Z stats [('calls_captured', 10), ('unique_graphs', 1)] 2025-09-09T15:27:13.9080668Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:27:13.9081736Z inductor [('pattern_matcher_nodes', 9), ('qlinear_weight_prepack_matcher_nodes', 7), ('pattern_matcher_count', 3), ('qlinear_weight_prepack_matcher_count', 1), ('fxgraph_cache_bypass', 1), ('extern_calls', 1)] 2025-09-09T15:27:13.9082681Z graph_break [] 2025-09-09T15:27:13.9092530Z PASSED 2025-09-09T15:27:13.9093681Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_smooth_quant_with_int_mm_has_bias_True_bfloat16_per_channel_quant_False_dynamic_False PASSED 2025-09-09T15:27:13.9095425Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_smooth_quant_with_int_mm_has_bias_True_bfloat16_per_channel_quant_False_dynamic_True PASSED 2025-09-09T15:27:13.9097135Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_smooth_quant_with_int_mm_has_bias_True_bfloat16_per_channel_quant_True_dynamic_False PASSED 2025-09-09T15:27:13.9098850Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_smooth_quant_with_int_mm_has_bias_True_bfloat16_per_channel_quant_True_dynamic_True PASSED 2025-09-09T15:27:13.9100483Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_smooth_quant_with_int_mm_has_bias_True_float32_per_channel_quant_False_dynamic_False frames [('total', 1), ('ok', 1)] 2025-09-09T15:27:13.9101893Z stats [('calls_captured', 10), ('unique_graphs', 1)] 2025-09-09T15:27:13.9102397Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:27:13.9103798Z inductor [('pattern_matcher_nodes', 12), ('qlinear_weight_prepack_matcher_nodes', 7), ('pattern_matcher_count', 5), ('removed_pointless_view_pair', 1), ('qlinear_weight_prepack_matcher_count', 1), ('fxgraph_cache_bypass', 1), ('extern_calls', 1)] 2025-09-09T15:27:13.9104903Z graph_break [] 2025-09-09T15:27:13.9105195Z PASSED 2025-09-09T15:27:13.9106112Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_smooth_quant_with_int_mm_has_bias_True_float32_per_channel_quant_False_dynamic_True frames [('total', 1), ('ok', 1)] 2025-09-09T15:27:13.9107167Z stats [('calls_captured', 14), ('unique_graphs', 1)] 2025-09-09T15:27:13.9107648Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:27:13.9109080Z inductor [('pattern_matcher_nodes', 13), ('qlinear_weight_prepack_matcher_nodes', 7), ('pattern_matcher_count', 6), ('removed_pointless_view_pair', 1), ('qlinear_weight_prepack_matcher_count', 1), ('fxgraph_cache_bypass', 1), ('qlinear_unary_lower_count', 1), ('qlinear_unary_lower_nodes', 1), ('extern_calls', 1)] 2025-09-09T15:27:13.9110397Z graph_break [] 2025-09-09T15:27:13.9110668Z PASSED 2025-09-09T15:27:13.9111584Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_smooth_quant_with_int_mm_has_bias_True_float32_per_channel_quant_True_dynamic_False frames [('total', 1), ('ok', 1)] 2025-09-09T15:27:13.9112627Z stats [('calls_captured', 10), ('unique_graphs', 1)] 2025-09-09T15:27:13.9113101Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:27:13.9114282Z inductor [('pattern_matcher_nodes', 12), ('qlinear_weight_prepack_matcher_nodes', 7), ('pattern_matcher_count', 5), ('removed_pointless_view_pair', 1), ('qlinear_weight_prepack_matcher_count', 1), ('fxgraph_cache_bypass', 1), ('extern_calls', 1)] 2025-09-09T15:27:13.9115366Z graph_break [] 2025-09-09T15:27:13.9115643Z PASSED 2025-09-09T15:27:13.9116551Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_smooth_quant_with_int_mm_has_bias_True_float32_per_channel_quant_True_dynamic_True frames [('total', 1), ('ok', 1)] 2025-09-09T15:27:13.9117598Z stats [('calls_captured', 14), ('unique_graphs', 1)] 2025-09-09T15:27:13.9118061Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:27:13.9119321Z inductor [('pattern_matcher_nodes', 12), ('qlinear_weight_prepack_matcher_nodes', 7), ('pattern_matcher_count', 5), ('removed_pointless_view_pair', 1), ('qlinear_weight_prepack_matcher_count', 1), ('fxgraph_cache_bypass', 1), ('extern_calls', 1)] 2025-09-09T15:27:13.9120404Z graph_break [] 2025-09-09T15:27:13.9120673Z PASSED 2025-09-09T15:27:13.9121252Z test/quantization/pt2e/test_x86inductor_fusion.py::TestDynamicPatternMatcher::test_q_attention_block inline_call [] 2025-09-09T15:27:13.9121879Z stats [('calls_captured', 49), ('unique_graphs', 8)] 2025-09-09T15:27:13.9122496Z frames [('total', 1), ('ok', 1)] 2025-09-09T15:27:13.9122834Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:27:13.9124313Z inductor [('pattern_matcher_nodes', 51), ('pattern_matcher_count', 29), ('qlinear_weight_prepack_matcher_nodes', 18), ('qlinear_unary_matcher_nodes', 6), ('extern_calls', 5), ('qlinear_weight_prepack_matcher_count', 3), ('qlinear_unary_matcher_count', 3), ('qlinear_unary_lower_count', 3), ('qlinear_unary_lower_nodes', 3), ('dequant_promotion_matcher_nodes', 2), ('dequant_promotion_matcher_count', 1), ('fxgraph_cache_bypass', 1)] 2025-09-09T15:27:13.9125695Z graph_break [] 2025-09-09T15:27:13.9125993Z aten_mm_info [('aten.bmm_32_384_384_64', 1), ('aten.bmm_32_384_64_384', 1)] 2025-09-09T15:27:13.9126395Z PASSED 2025-09-09T15:27:13.9127182Z test/quantization/pt2e/test_x86inductor_fusion.py::TestDynamicPatternMatcher::test_qat_bn_conv2d stats [('calls_captured', 1960), ('unique_graphs', 224)] 2025-09-09T15:27:13.9127868Z inline_call [] 2025-09-09T15:27:13.9128088Z frames [('total', 1), ('ok', 1)] 2025-09-09T15:27:13.9128419Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:27:13.9129711Z inductor [('pattern_matcher_nodes', 7), ('qconv_weight_prepack_matcher_nodes', 4), ('pattern_matcher_count', 3), ('qconv_unary_matcher_nodes', 2), ('qconv_weight_prepack_matcher_count', 1), ('qconv_unary_matcher_count', 1), ('fxgraph_cache_bypass', 1), ('qconv_unary_lower_count', 1), ('qconv_unary_lower_nodes', 1), ('extern_calls', 1)] 2025-09-09T15:27:13.9130791Z graph_break [] 2025-09-09T15:27:13.9131026Z PASSED 2025-09-09T15:27:13.9131733Z test/quantization/pt2e/test_x86inductor_fusion.py::TestDynamicPatternMatcher::test_qconv2d_maxpool2d_linear_dynamic_cpu stats [('calls_captured', 30), ('unique_graphs', 8)] 2025-09-09T15:27:13.9132481Z inline_call [] 2025-09-09T15:27:13.9132773Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T15:27:13.9134746Z inductor [('pattern_matcher_nodes', 21), ('pattern_matcher_count', 8), ('qlinear_weight_prepack_matcher_nodes', 4), ('qconv_weight_prepack_matcher_nodes', 4), ('qmaxpool2d_matcher_nodes', 4), ('extern_calls', 4), ('qconv_unary_matcher_nodes', 3), ('qreshape_matcher_nodes', 3), ('qlinear_weight_prepack_matcher_count', 1), ('qconv_weight_prepack_matcher_count', 1), ('qconv_unary_matcher_count', 1), ('fxgraph_cache_bypass', 1), ('qconv_unary_lower_count', 1), ('qconv_unary_lower_nodes', 1), ('qmaxpool2d_matcher_count', 1), ('qreshape_matcher_count', 1), ('qlinear_unary_lower_count', 1), ('qlinear_unary_lower_nodes', 1)] 2025-09-09T15:27:13.9136624Z graph_break [] 2025-09-09T15:27:13.9136849Z PASSED 2025-09-09T15:27:13.9137526Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_adaptive_avg_pool2d_recipe PASSED 2025-09-09T15:27:13.9138592Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_annotate_mul_tensor PASSED 2025-09-09T15:27:13.9139606Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_attention_block PASSED 2025-09-09T15:27:13.9140616Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_avg_pool2d_recipe PASSED 2025-09-09T15:27:13.9141595Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_cat_recipe PASSED 2025-09-09T15:27:13.9142598Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_cat_recipe_same_inputs PASSED 2025-09-09T15:27:13.9143644Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_cat_recipe_single_input PASSED 2025-09-09T15:27:13.9144638Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_conv2d PASSED 2025-09-09T15:27:13.9145600Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_conv2d_binary PASSED 2025-09-09T15:27:13.9146580Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_conv2d_binary2 PASSED 2025-09-09T15:27:13.9147597Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_conv2d_binary_unary PASSED 2025-09-09T15:27:13.9148657Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_conv2d_serials_binary_unary PASSED 2025-09-09T15:32:08.6556915Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_conv2d_unary PASSED 2025-09-09T15:32:08.6558353Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_dynamic_quant_linear PASSED 2025-09-09T15:32:08.6559797Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_filter_conv2d_recipe PASSED 2025-09-09T15:32:08.6561878Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_filter_linear_recipe PASSED 2025-09-09T15:32:08.6563097Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_filter_maxpool2d_recipe PASSED 2025-09-09T15:32:08.6564468Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_flatten_recipe PASSED 2025-09-09T15:32:08.6565464Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_flatten_recipe2 PASSED 2025-09-09T15:32:08.6566417Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_linear PASSED 2025-09-09T15:32:08.6567375Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_linear_binary PASSED 2025-09-09T15:32:08.6568370Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_linear_binary2 PASSED 2025-09-09T15:32:08.6569532Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_linear_binary_dynamic PASSED 2025-09-09T15:32:08.6570591Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_linear_binary_dynamic_qat PASSED 2025-09-09T15:32:08.6571713Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_linear_binary_qat PASSED 2025-09-09T15:32:08.6572797Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_linear_binary_unary PASSED 2025-09-09T15:32:08.6573917Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_linear_binary_unary_dynamic PASSED 2025-09-09T15:32:08.6575021Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_linear_binary_unary_dynamic_qat PASSED 2025-09-09T15:32:08.6576118Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_linear_binary_unary_qat PASSED 2025-09-09T15:32:08.6577262Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_linear_binary_unary_serials PASSED 2025-09-09T15:32:08.6578385Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_linear_dynamic_fp16 PASSED 2025-09-09T15:32:08.6579386Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_linear_unary PASSED 2025-09-09T15:32:08.6580444Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_linear_unary_dynamic PASSED 2025-09-09T15:32:08.6581552Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_linear_unary_dynamic_qat PASSED 2025-09-09T15:32:08.6582571Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_linear_unary_qat PASSED 2025-09-09T15:32:08.6583568Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_lowering_to_x86 SKIPPED 2025-09-09T15:32:08.6584616Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_maxpool2d_recipe PASSED 2025-09-09T15:32:08.6585582Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_qat_conv2d PASSED 2025-09-09T15:32:08.6586693Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_qat_conv2d_binary PASSED 2025-09-09T15:32:08.6587698Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_qat_conv2d_binary2 PASSED 2025-09-09T15:32:08.6588727Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_qat_conv2d_binary_unary PASSED 2025-09-09T15:32:08.6589921Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_qat_conv2d_unary PASSED 2025-09-09T15:32:08.6591069Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_qat_dynamic_quant_linear PASSED 2025-09-09T15:32:08.6592183Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_set_module_name_and_module_type_case1 PASSED 2025-09-09T15:32:08.6593418Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_set_module_name_and_module_type_case2 PASSED 2025-09-09T15:32:08.6594727Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_set_module_name_and_module_type_with_mixed_configs PASSED 2025-09-09T15:32:08.6595916Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_set_module_name_qconfig PASSED 2025-09-09T15:32:08.6597048Z 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test/quantization/quantize_/workflows/float8/test_float8_tensor.py::TestFloat8Tensor::test_fp8_linear_variants_float32_mode_dynamic_compile_True_granularity1_kernel_preference_KernelPreference_FBGEMM_sizes1 SKIPPED 2025-09-09T15:32:08.7245329Z test/quantization/quantize_/workflows/float8/test_float8_tensor.py::TestFloat8Tensor::test_fp8_linear_variants_float32_mode_dynamic_compile_True_granularity1_kernel_preference_KernelPreference_TORCH_sizes0 SKIPPED 2025-09-09T15:32:08.7247389Z test/quantization/quantize_/workflows/float8/test_float8_tensor.py::TestFloat8Tensor::test_fp8_linear_variants_float32_mode_dynamic_compile_True_granularity1_kernel_preference_KernelPreference_TORCH_sizes1 SKIPPED 2025-09-09T15:32:08.7249434Z test/quantization/quantize_/workflows/float8/test_float8_tensor.py::TestFloat8Tensor::test_fp8_linear_variants_float32_mode_weight-only_compile_False_granularity0_kernel_preference_KernelPreference_AUTO_sizes0 SKIPPED 2025-09-09T15:32:08.7251432Z test/quantization/quantize_/workflows/float8/test_float8_tensor.py::TestFloat8Tensor::test_fp8_linear_variants_float32_mode_weight-only_compile_False_granularity0_kernel_preference_KernelPreference_AUTO_sizes1 SKIPPED 2025-09-09T15:32:08.7253738Z test/quantization/quantize_/workflows/float8/test_float8_tensor.py::TestFloat8Tensor::test_fp8_linear_variants_float32_mode_weight-only_compile_False_granularity0_kernel_preference_KernelPreference_FBGEMM_sizes0 SKIPPED 2025-09-09T15:32:08.7255827Z test/quantization/quantize_/workflows/float8/test_float8_tensor.py::TestFloat8Tensor::test_fp8_linear_variants_float32_mode_weight-only_compile_False_granularity0_kernel_preference_KernelPreference_FBGEMM_sizes1 SKIPPED 2025-09-09T15:32:08.7257684Z test/quantization/quantize_/workflows/float8/test_float8_tensor.py::TestFloat8Tensor::test_fp8_linear_variants_float32_mode_weight-only_compile_False_granularity0_kernel_preference_KernelPreference_TORCH_sizes0 SKIPPED 2025-09-09T15:32:08.7259503Z test/quantization/quantize_/workflows/float8/test_float8_tensor.py::TestFloat8Tensor::test_fp8_linear_variants_float32_mode_weight-only_compile_False_granularity0_kernel_preference_KernelPreference_TORCH_sizes1 SKIPPED 2025-09-09T15:32:08.7261542Z test/quantization/quantize_/workflows/float8/test_float8_tensor.py::TestFloat8Tensor::test_fp8_linear_variants_float32_mode_weight-only_compile_False_granularity1_kernel_preference_KernelPreference_AUTO_sizes0 SKIPPED 2025-09-09T15:32:08.7263619Z test/quantization/quantize_/workflows/float8/test_float8_tensor.py::TestFloat8Tensor::test_fp8_linear_variants_float32_mode_weight-only_compile_False_granularity1_kernel_preference_KernelPreference_AUTO_sizes1 SKIPPED 2025-09-09T15:32:08.7265499Z test/quantization/quantize_/workflows/float8/test_float8_tensor.py::TestFloat8Tensor::test_fp8_linear_variants_float32_mode_weight-only_compile_False_granularity1_kernel_preference_KernelPreference_FBGEMM_sizes0 SKIPPED 2025-09-09T15:32:08.7267433Z 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test/quantization/quantize_/workflows/int4/test_int4_tensor.py::TestInt4Tensor::test_moe_weight_reshape_ops SKIPPED 2025-09-09T15:32:44.1006670Z test/quantization/quantize_/workflows/int4/test_int4_tensor.py::TestInt4Tensor::test_slice SKIPPED 2025-09-09T15:32:44.1007661Z test/quantization/quantize_/workflows/int4/test_int4_tensor.py::TestInt4Tensor::test_slice_and_copy_similar_to_vllm SKIPPED 2025-09-09T15:32:44.1193516Z test/quantization/quantize_/workflows/int4/test_int4_tensor.py::TestInt4Tensor::test_slice_preserves_aliasing SKIPPED 2025-09-09T15:32:44.1194671Z test/quantization/quantize_/workflows/int4/test_int4_tensor.py::TestInt4Tensor::test_to_device_sizes0 SKIPPED 2025-09-09T15:32:44.1195651Z test/quantization/quantize_/workflows/int4/test_int4_tensor.py::TestInt4Tensor::test_to_device_sizes1 SKIPPED 2025-09-09T15:32:44.1196848Z test/quantization/quantize_/workflows/int4/test_int4_tensor.py::TestInt4Tensor::test_to_device_sizes2 SKIPPED 2025-09-09T15:32:44.1197999Z test/quantization/quantize_/workflows/int4/test_int4_tile_packed_to_4d_tensor.py::TestInt4TilePackedTo4dTensor::test_cant_initialize_in_cpu SKIPPED 2025-09-09T15:32:44.1199690Z test/quantization/quantize_/workflows/int4/test_int4_tile_packed_to_4d_tensor.py::TestInt4TilePackedTo4dTensor::test_different_group_sizes_group_size_128 SKIPPED 2025-09-09T15:32:44.1201074Z test/quantization/quantize_/workflows/int4/test_int4_tile_packed_to_4d_tensor.py::TestInt4TilePackedTo4dTensor::test_different_group_sizes_group_size_32 SKIPPED 2025-09-09T15:32:44.1202441Z test/quantization/quantize_/workflows/int4/test_int4_tile_packed_to_4d_tensor.py::TestInt4TilePackedTo4dTensor::test_different_group_sizes_group_size_64 SKIPPED 2025-09-09T15:32:44.1203925Z test/quantization/quantize_/workflows/int4/test_int4_tile_packed_to_4d_tensor.py::TestInt4TilePackedTo4dTensor::test_error_conditions SKIPPED 2025-09-09T15:32:44.1205285Z test/quantization/quantize_/workflows/int4/test_int4_tile_packed_to_4d_tensor.py::TestInt4TilePackedTo4dTensor::test_linear_sizes0_config0 SKIPPED 2025-09-09T15:32:44.1206792Z test/quantization/quantize_/workflows/int4/test_int4_tile_packed_to_4d_tensor.py::TestInt4TilePackedTo4dTensor::test_linear_sizes0_config1 SKIPPED 2025-09-09T15:32:44.1208063Z test/quantization/quantize_/workflows/int4/test_int4_tile_packed_to_4d_tensor.py::TestInt4TilePackedTo4dTensor::test_linear_sizes1_config0 SKIPPED 2025-09-09T15:32:44.1209420Z test/quantization/quantize_/workflows/int4/test_int4_tile_packed_to_4d_tensor.py::TestInt4TilePackedTo4dTensor::test_linear_sizes1_config1 SKIPPED 2025-09-09T15:32:44.1210937Z test/quantization/quantize_/workflows/int4/test_int4_tile_packed_to_4d_tensor.py::TestInt4TilePackedTo4dTensor::test_linear_sizes2_config0 SKIPPED 2025-09-09T15:32:44.1212427Z test/quantization/quantize_/workflows/int4/test_int4_tile_packed_to_4d_tensor.py::TestInt4TilePackedTo4dTensor::test_linear_sizes2_config1 SKIPPED 2025-09-09T15:32:44.1214219Z test/quantization/quantize_/workflows/int4/test_int4_tile_packed_to_4d_tensor.py::TestInt4TilePackedTo4dTensor::test_mm_int4wo_device_cuda_bfloat16 SKIPPED 2025-09-09T15:32:44.1215533Z test/quantization/quantize_/workflows/int4/test_int4_tile_packed_to_4d_tensor.py::TestInt4TilePackedTo4dTensor::test_module_path_config0 SKIPPED 2025-09-09T15:32:44.1216785Z test/quantization/quantize_/workflows/int4/test_int4_tile_packed_to_4d_tensor.py::TestInt4TilePackedTo4dTensor::test_module_path_config1 SKIPPED 2025-09-09T15:32:44.1218115Z test/quantization/quantize_/workflows/int4/test_int4_tile_packed_to_4d_tensor.py::TestInt4TilePackedTo4dTensor::test_slice_and_copy_similar_to_vllm_config0 SKIPPED 2025-09-09T15:32:44.1219512Z test/quantization/quantize_/workflows/int4/test_int4_tile_packed_to_4d_tensor.py::TestInt4TilePackedTo4dTensor::test_slice_and_copy_similar_to_vllm_config1 SKIPPED 2025-09-09T15:32:44.1221099Z test/quantization/quantize_/workflows/int4/test_int4_tile_packed_to_4d_tensor.py::TestInt4TilePackedTo4dTensor::test_slice_config0 SKIPPED 2025-09-09T15:32:44.1222603Z test/quantization/quantize_/workflows/int4/test_int4_tile_packed_to_4d_tensor.py::TestInt4TilePackedTo4dTensor::test_slice_config1 SKIPPED 2025-09-09T15:32:44.1224088Z test/quantization/quantize_/workflows/int4/test_int4_tile_packed_to_4d_tensor.py::TestInt4TilePackedTo4dTensor::test_slice_preserves_aliasing_config0 SKIPPED 2025-09-09T15:32:44.1225447Z test/quantization/quantize_/workflows/int4/test_int4_tile_packed_to_4d_tensor.py::TestInt4TilePackedTo4dTensor::test_slice_preserves_aliasing_config1 SKIPPED 2025-09-09T15:32:44.1226720Z test/quantization/quantize_/workflows/int4/test_int4_tile_packed_to_4d_tensor.py::TestInt4TilePackedTo4dTensor::test_to_device SKIPPED 2025-09-09T15:32:44.1229008Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int1, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.1232086Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int1, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.1234959Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int1, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.1238136Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int1, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.1241199Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int2, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.1244328Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int2, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.1247531Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int2, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.1250566Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int2, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.1253677Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int2, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.1256598Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int2, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.1259757Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int3, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.1352251Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int3, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.1355504Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int3, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.1358681Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int3, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.1361919Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int3, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.1365508Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int3, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.1368748Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int4, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.1372340Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int4, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.1375246Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int4, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.1378542Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int4, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.1381852Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int4, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.1385162Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int4, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.1388284Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int5, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.1391399Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int5, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.1394531Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int5, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.1397703Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int5, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.1401035Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int5, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.1404335Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int5, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.1407463Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int6, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.1410503Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int6, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.1413389Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int6, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.1416331Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int6, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.1509127Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int6, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.1512244Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int6, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.1515345Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int7, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.1518423Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int7, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.1521692Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int7, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.1524722Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int7, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.1527735Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int7, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.1530827Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int7, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.1533835Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int8, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.1536804Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int8, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.1539611Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int8, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.1542605Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int8, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.1545565Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int8, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.1548760Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int8, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.1551945Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_kleidiai', 'weight_dtype': torch.int4, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.1554928Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_kleidiai', 'weight_dtype': torch.int4, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.1558056Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_kleidiai', 'weight_dtype': torch.int4, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.1561122Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_kleidiai', 'weight_dtype': torch.int4, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.1564226Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int1, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.1567179Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int1, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.1570131Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int1, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.1663460Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int1, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.1666648Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int2, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.1669719Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int2, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.1672644Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int2, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.1675686Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int2, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.1678564Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int2, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.1681647Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int2, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.1684576Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int3, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.1687392Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int3, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.1690201Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int3, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.1693024Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int3, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.1695967Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int3, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.1699001Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int3, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.1701864Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int4, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.1704890Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int4, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.1707719Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int4, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.1710554Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int4, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.1713424Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int4, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.1716403Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int4, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.1719274Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int5, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.1722470Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int5, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.1822642Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int5, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.1825598Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int5, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.1828446Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int5, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.1831421Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int5, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.1834263Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int6, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.1837060Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int6, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.1839922Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int6, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.1842715Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int6, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.1845597Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int6, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.1848496Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int6, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.1851327Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int7, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.1854185Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int7, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.1857053Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int7, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.1859921Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int7, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.1862763Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int7, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.1865646Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int7, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.1868471Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int8, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.1871256Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int8, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.1874204Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int8, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.1877032Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int8, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.1879967Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int8, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.1979798Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int8, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.1983126Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int1, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.1989880Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int1, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.1993051Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int1, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.1996179Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int1, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.1999441Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int2, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.2002636Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int2, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.2005745Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int2, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.2008937Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int2, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.2012155Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int2, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.2015401Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int2, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.2018780Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int3, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.2022433Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int3, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.2025647Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int3, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.2028631Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int3, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.2031674Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int3, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.2034823Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int3, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.2037764Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int4, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.2040831Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int4, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.2043741Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int4, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.2046625Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int4, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.2136352Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int4, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.2139460Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int4, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.2142354Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int5, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.2145257Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int5, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.2148092Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int5, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.2150929Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int5, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.2153824Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int5, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.2156777Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int5, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.2159748Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int6, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.2162724Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int6, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.2165575Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int6, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.2168500Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int6, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.2171393Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int6, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.2174341Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int6, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.2177233Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int7, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.2180084Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int7, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.2182932Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int7, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.2185840Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int7, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.2188730Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int7, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.2191767Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int7, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.2194678Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int8, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.2290408Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int8, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.2293377Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int8, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.2296207Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int8, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.2299092Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int8, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.2302038Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.bfloat16, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int8, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.2304870Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int1, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.2307750Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int1, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.2310498Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int1, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.2313437Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int1, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.2316214Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int2, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.2319092Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int2, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.2321957Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int2, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.2324913Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int2, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.2327722Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int2, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.2330590Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int2, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.2333514Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int3, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.2336290Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int3, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.2339032Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int3, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.2341783Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int3, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.2344785Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int3, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.2347778Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int3, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.2454125Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int4, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.2457608Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int4, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.2460804Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int4, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.2463783Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int4, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.2466764Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int4, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.2469662Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int4, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.2472610Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int5, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.2475547Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int5, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.2478531Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int5, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.2481647Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int5, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.2484692Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int5, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.2487758Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int5, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.2490726Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int6, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.2493579Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int6, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.2496507Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int6, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.2499380Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int6, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.2502353Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int6, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.2505460Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int6, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.2508485Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int7, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.2511528Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int7, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.2514413Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int7, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.2517354Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int7, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.2609854Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int7, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.2612902Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int7, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.2615888Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int8, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.2618764Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int8, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.2621699Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int8, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.2624787Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int8, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.2627923Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int8, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.2630967Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_auto', 'weight_dtype': torch.int8, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.2634140Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_kleidiai', 'weight_dtype': torch.int4, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.2637110Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_kleidiai', 'weight_dtype': torch.int4, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.2640471Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_kleidiai', 'weight_dtype': torch.int4, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.2643656Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_kleidiai', 'weight_dtype': torch.int4, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.2646635Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int1, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.2649498Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int1, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.2652628Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int1, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.2655705Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int1, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.2658874Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int2, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.2661986Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int2, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.2665277Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int2, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.2668367Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int2, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.2671466Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int2, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.2769037Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int2, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.2771937Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int3, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.2774823Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int3, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.2777639Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int3, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.2780452Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int3, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.2783519Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int3, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.2786471Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int3, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.2789457Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int4, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.2792357Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int4, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.2795240Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int4, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.2798043Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int4, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.2800990Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int4, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.2803923Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int4, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.2806777Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int5, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.2809600Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int5, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.2812409Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int5, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.2815328Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int5, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.2818270Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int5, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.2821187Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int5, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.2824338Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int6, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.2827139Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int6, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.2925156Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int6, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.2927943Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int6, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.2930944Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int6, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.2933819Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int6, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.2936845Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int7, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.2939850Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int7, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.2942844Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int7, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.2945910Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int7, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.2948824Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int7, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.2951846Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int7, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.2954726Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int8, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.2957618Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int8, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.2960696Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int8, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.2964151Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int8, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.2967114Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int8, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.2970124Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': 'torchao_lowbit', 'weight_dtype': torch.int8, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.2973224Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int1, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.2976225Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int1, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.2979154Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int1, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.2982111Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int1, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.2984946Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int2, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.3078777Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int2, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.3081806Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int2, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.3084775Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int2, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.3087649Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int2, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.3090901Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int2, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.3093945Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int3, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.3096840Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int3, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.3099908Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int3, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.3102723Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int3, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.3105868Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int3, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.3109004Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int3, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.3112073Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int4, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.3115403Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int4, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.3119034Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int4, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.3122486Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int4, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.3125437Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int4, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.3128784Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int4, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.3131732Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int5, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.3134808Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int5, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.3137841Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int5, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.3140814Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int5, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.3248822Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int5, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.3251798Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int5, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.3254726Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int6, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.3258433Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int6, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.3261289Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int6, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.3264352Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int6, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.3267500Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int6, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.3270439Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int6, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.3273339Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int7, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.3276179Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int7, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.3278998Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int7, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.3281897Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int7, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.3284771Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int7, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.3288171Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int7, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.3291886Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int8, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.3295444Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int8, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.3298988Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int8, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.3302521Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int8, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.3306127Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int8, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:32:44.3309790Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_accuracy_{'model_dtype': torch.float32, 'packing_format': , 'compute_target': None, 'weight_dtype': torch.int8, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:32:44.3312240Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_export_compile_aoti SKIPPED 2025-09-09T15:32:44.3313599Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_moe_quant_intx SKIPPED 2025-09-09T15:32:44.3315341Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_serialization_{'packing_format': , 'compute_target': 'aten'} SKIPPED 2025-09-09T15:32:44.5803161Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_serialization_{'packing_format': , 'compute_target': 'torchao_auto'} SKIPPED 2025-09-09T15:32:44.5805036Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_embedding PASSED 2025-09-09T15:32:44.5806639Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_export_int8_dyn_act_intx_weight_config PASSED 2025-09-09T15:32:44.5808826Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_export_int8_dyn_act_intx_weight_config_with_unwrap PASSED 2025-09-09T15:32:44.5810579Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_export_intx_weight_only_config PASSED 2025-09-09T15:32:44.5813322Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int1, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:44.5816863Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int1, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:44.5820255Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int1, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:44.5823848Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int1, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:44.5827282Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int1, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:44.5830683Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int1, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:44.5834079Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int1, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:44.5837474Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int1, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:44.5840952Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int1, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:44.5844525Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int1, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:44.5847952Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int1, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:44.5851377Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int1, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:44.5854802Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int1, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:44.5858189Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int1, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:44.5861550Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int1, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:44.5864932Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int1, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:44.5868365Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int1, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:44.5871741Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int1, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:44.5875136Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int1, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:44.6793372Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int1, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:44.6796837Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int1, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:44.6800518Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int1, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:44.6803993Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int1, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:44.6807374Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int1, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:44.6810773Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int1, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:44.6814173Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int1, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:44.6817656Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int1, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:44.6821060Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int2, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:44.6824718Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int2, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:44.6828122Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int2, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:44.6831680Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int2, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:44.6835194Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int2, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:44.6838569Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int2, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:44.6842064Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int2, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:44.6845455Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int2, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:44.6848835Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int2, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:44.6852300Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int2, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:44.6855766Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int2, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:44.6859161Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int2, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:44.6862579Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int2, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:44.6866169Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int2, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:44.7768739Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int2, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:44.7772333Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int2, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:44.7775803Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int2, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:44.7779236Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int2, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:44.7782745Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int2, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:44.7786183Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int2, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:44.7789621Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int2, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:44.7793039Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int2, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:44.7796533Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int2, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:44.7800184Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int2, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:44.7803705Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int2, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:44.7807198Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int2, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:44.7810593Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int2, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:44.7814012Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int3, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:44.7817472Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int3, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:44.7820921Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int3, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:44.7824545Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int3, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:44.7827971Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int3, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:44.7831405Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int3, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:44.7835072Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int3, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:44.7838520Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int3, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:44.7842125Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int3, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:44.8741080Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int3, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:44.8744589Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int3, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:44.8748027Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int3, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:44.8751489Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int3, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:44.8754968Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int3, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:44.8758389Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int3, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:44.8761867Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int3, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:44.8765261Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int3, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:44.8771241Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int3, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:44.8774739Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int3, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:44.8778218Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int3, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:44.8781627Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int3, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:44.8785094Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int3, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:44.8788500Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int3, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:44.8791890Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int3, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:44.8795297Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int3, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:44.8798736Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int3, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:44.8802358Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int3, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:44.8805913Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int4, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:44.8809400Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int4, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:44.8812890Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int4, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:44.8816373Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int4, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:44.9715929Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int4, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:44.9719460Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int4, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:44.9723047Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int4, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:44.9726501Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int4, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:44.9729914Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int4, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:44.9733327Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int4, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:44.9737046Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int4, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:44.9740501Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int4, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:44.9744027Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int4, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:44.9747459Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int4, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:44.9750857Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int4, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:44.9754266Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int4, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:44.9757679Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int4, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:44.9761133Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int4, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:44.9764534Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int4, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:44.9767936Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int4, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:44.9771366Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int4, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:44.9774864Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int4, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:44.9778328Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int4, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:44.9781717Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int4, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:44.9785134Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int4, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:44.9788546Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int4, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:45.0708484Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int4, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:45.0711914Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int5, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:45.0715365Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int5, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:45.0718810Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int5, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:45.0722503Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int5, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:45.0726118Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int5, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:45.0729608Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int5, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:45.0733147Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int5, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:45.0736565Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int5, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:45.0740000Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int5, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:45.0743539Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int5, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:45.0746964Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int5, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:45.0750376Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int5, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:45.0753811Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int5, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:45.0757217Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int5, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:45.0760786Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int5, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:45.0764321Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int5, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:45.0767795Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int5, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:45.0771181Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int5, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:45.0774585Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int5, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:45.0778007Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int5, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:45.0781398Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int5, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:45.1688891Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int5, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:45.1692349Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int5, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:45.1695746Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int5, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:45.1699338Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int5, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:45.1702773Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int5, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:45.1706314Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int5, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:45.1709747Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int6, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:45.1713203Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int6, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:45.1716693Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int6, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:45.1720187Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int6, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:45.1723787Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int6, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:45.1727222Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int6, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:45.1730678Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int6, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:45.1734102Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int6, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:45.1737684Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int6, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:45.1741170Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int6, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:45.1744683Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int6, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:45.1748103Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int6, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:45.1751581Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int6, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:45.1754979Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int6, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:45.1758375Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int6, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:45.1761867Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int6, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:45.2661859Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int6, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:45.2665372Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int6, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:45.2668960Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int6, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:45.2672543Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int6, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:45.2676045Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int6, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:45.2679547Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int6, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:45.2682950Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int6, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:45.2686442Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int6, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:45.2689864Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int6, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:45.2693283Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int6, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:45.2696691Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int6, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:45.2700124Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int7, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:45.2703674Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int7, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:45.2707111Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int7, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:45.2710628Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int7, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:45.2714046Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int7, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:45.2717524Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int7, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:45.2721024Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int7, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:45.2724674Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int7, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:45.2728157Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int7, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:45.2731604Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int7, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:45.2735029Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int7, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:45.3631996Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int7, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:45.3635696Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int7, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:45.3639214Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int7, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:45.3642790Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int7, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:45.3646258Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int7, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:45.3649671Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int7, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:45.3653084Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int7, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:45.3656509Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int7, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:45.3659950Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int7, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:45.3663358Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int7, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:45.3666792Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int7, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:45.3670329Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int7, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:45.3673843Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int7, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:45.3677285Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int7, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:45.3680794Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int7, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:45.3684233Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int7, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:45.3687661Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int8, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:45.3691106Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int8, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:45.3694582Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int8, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:45.3698043Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int8, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:45.3701484Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int8, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:45.3705008Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int8, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:45.4650329Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int8, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:45.4653978Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int8, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:45.4657378Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int8, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:45.4660807Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int8, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:45.4664241Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int8, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:45.4667672Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int8, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:45.4671081Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int8, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:45.4674553Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int8, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:45.4677976Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int8, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:45.4681459Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int8, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:45.4685062Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int8, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:45.4688549Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int8, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:45.4691968Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int8, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:45.4695381Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int8, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:45.4698823Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int8, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:45.4702245Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int8, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:45.4705659Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int8, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:45.4709083Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int8, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:45.4712487Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int8, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:45.4715908Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int8, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:45.4719544Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_intx_unpacked_v2_is_close_to_qdq_v1_{'weight_dtype': torch.int8, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:45.4721935Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_linear PASSED 2025-09-09T15:32:45.7352664Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int1, 'group_size': 128, 'mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:45.7355809Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int1, 'group_size': 128, 'mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:45.7358814Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int1, 'group_size': 128, 'mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:45.7361873Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int1, 'group_size': 128, 'mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:45.7364922Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int1, 'group_size': 128, 'mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:45.7367918Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int1, 'group_size': 128, 'mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:45.7370919Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int1, 'group_size': 128, 'mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:45.7373888Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int1, 'group_size': 128, 'mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:45.7376883Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int1, 'group_size': 128, 'mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:45.7379857Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int1, 'group_size': 32, 'mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:45.7383045Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int1, 'group_size': 32, 'mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:45.7386089Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int1, 'group_size': 32, 'mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:45.7389138Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int1, 'group_size': 32, 'mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:45.7392127Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int1, 'group_size': 32, 'mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:45.7395108Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int1, 'group_size': 32, 'mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:45.7398064Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int1, 'group_size': 32, 'mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:45.7401204Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int1, 'group_size': 32, 'mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:45.7404272Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int1, 'group_size': 32, 'mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:45.7407262Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int1, 'group_size': 64, 'mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:45.7410268Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int1, 'group_size': 64, 'mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:45.7413248Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int1, 'group_size': 64, 'mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:45.7416236Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int1, 'group_size': 64, 'mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:45.7419311Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int1, 'group_size': 64, 'mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:45.7422495Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int1, 'group_size': 64, 'mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:45.7425598Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int1, 'group_size': 64, 'mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:46.0023013Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int1, 'group_size': 64, 'mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:46.0026122Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int1, 'group_size': 64, 'mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:46.0029151Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int2, 'group_size': 128, 'mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:46.0032174Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int2, 'group_size': 128, 'mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:46.0035247Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int2, 'group_size': 128, 'mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:46.0041777Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int2, 'group_size': 128, 'mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:46.0044855Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int2, 'group_size': 128, 'mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:46.0047862Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int2, 'group_size': 128, 'mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:46.0050981Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int2, 'group_size': 128, 'mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:46.0053972Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int2, 'group_size': 128, 'mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:46.0057067Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int2, 'group_size': 128, 'mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:46.0060065Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int2, 'group_size': 32, 'mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:46.0063045Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int2, 'group_size': 32, 'mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:46.0066067Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int2, 'group_size': 32, 'mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:46.0069074Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int2, 'group_size': 32, 'mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:46.0072043Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int2, 'group_size': 32, 'mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:46.0075112Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int2, 'group_size': 32, 'mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:46.0078231Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int2, 'group_size': 32, 'mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:46.0081282Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int2, 'group_size': 32, 'mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:46.0084269Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int2, 'group_size': 32, 'mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:46.0087310Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int2, 'group_size': 64, 'mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:46.0090304Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int2, 'group_size': 64, 'mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:46.0093366Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int2, 'group_size': 64, 'mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:46.0096351Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int2, 'group_size': 64, 'mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:46.0099324Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int2, 'group_size': 64, 'mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:46.2696373Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int2, 'group_size': 64, 'mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:46.2699453Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int2, 'group_size': 64, 'mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:46.2702457Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int2, 'group_size': 64, 'mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:46.2705460Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int2, 'group_size': 64, 'mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:46.2708634Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int3, 'group_size': 128, 'mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:46.2711659Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int3, 'group_size': 128, 'mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:46.2714678Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int3, 'group_size': 128, 'mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:46.2717798Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int3, 'group_size': 128, 'mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:46.2720937Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int3, 'group_size': 128, 'mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:46.2724209Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int3, 'group_size': 128, 'mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:46.2727236Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int3, 'group_size': 128, 'mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:46.2730255Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int3, 'group_size': 128, 'mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:46.2733249Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int3, 'group_size': 128, 'mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:46.2736277Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int3, 'group_size': 32, 'mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:46.2739298Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int3, 'group_size': 32, 'mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:46.2742311Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int3, 'group_size': 32, 'mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:46.2745406Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int3, 'group_size': 32, 'mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:46.2748422Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int3, 'group_size': 32, 'mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:46.2751531Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int3, 'group_size': 32, 'mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:46.2754988Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int3, 'group_size': 32, 'mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:46.2758166Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int3, 'group_size': 32, 'mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:46.2761466Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int3, 'group_size': 32, 'mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:46.2764595Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int3, 'group_size': 64, 'mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:46.2767698Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int3, 'group_size': 64, 'mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:46.2770872Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int3, 'group_size': 64, 'mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:46.5424392Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int3, 'group_size': 64, 'mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:46.5427545Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int3, 'group_size': 64, 'mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:46.5430677Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int3, 'group_size': 64, 'mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:46.5433680Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int3, 'group_size': 64, 'mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:46.5436698Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int3, 'group_size': 64, 'mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:46.5439857Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int3, 'group_size': 64, 'mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:46.5442901Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int4, 'group_size': 128, 'mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:46.5446100Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int4, 'group_size': 128, 'mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:46.5449126Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int4, 'group_size': 128, 'mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:46.5452137Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int4, 'group_size': 128, 'mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:46.5455137Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int4, 'group_size': 128, 'mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:46.5458114Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int4, 'group_size': 128, 'mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:46.5461109Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int4, 'group_size': 128, 'mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:46.5464136Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int4, 'group_size': 128, 'mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:46.5467187Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int4, 'group_size': 128, 'mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:46.5470178Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int4, 'group_size': 32, 'mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:46.5473186Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int4, 'group_size': 32, 'mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:46.5476302Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int4, 'group_size': 32, 'mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:46.5479390Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int4, 'group_size': 32, 'mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:46.5482495Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int4, 'group_size': 32, 'mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:46.5485475Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int4, 'group_size': 32, 'mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:46.5488465Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int4, 'group_size': 32, 'mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:46.5491452Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int4, 'group_size': 32, 'mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:46.5494414Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int4, 'group_size': 32, 'mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:46.5497397Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int4, 'group_size': 64, 'mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:46.8108215Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int4, 'group_size': 64, 'mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:46.8111415Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int4, 'group_size': 64, 'mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:46.8114453Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int4, 'group_size': 64, 'mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:46.8117508Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int4, 'group_size': 64, 'mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:46.8120670Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int4, 'group_size': 64, 'mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:46.8123927Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int4, 'group_size': 64, 'mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:46.8127024Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int4, 'group_size': 64, 'mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:46.8130014Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int4, 'group_size': 64, 'mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:46.8133025Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int5, 'group_size': 128, 'mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:46.8136025Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int5, 'group_size': 128, 'mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:46.8139033Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int5, 'group_size': 128, 'mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:46.8142039Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int5, 'group_size': 128, 'mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:46.8145036Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int5, 'group_size': 128, 'mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:46.8148092Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int5, 'group_size': 128, 'mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:46.8151095Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int5, 'group_size': 128, 'mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:46.8154092Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int5, 'group_size': 128, 'mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:46.8157195Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int5, 'group_size': 128, 'mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:46.8160328Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int5, 'group_size': 32, 'mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:46.8163405Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int5, 'group_size': 32, 'mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:46.8166459Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int5, 'group_size': 32, 'mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:46.8169465Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int5, 'group_size': 32, 'mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:46.8172466Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int5, 'group_size': 32, 'mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:46.8175440Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int5, 'group_size': 32, 'mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:46.8178434Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int5, 'group_size': 32, 'mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:46.8181482Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int5, 'group_size': 32, 'mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:47.0777053Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int5, 'group_size': 32, 'mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:47.0780101Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int5, 'group_size': 64, 'mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:47.0783293Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int5, 'group_size': 64, 'mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:47.0786387Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int5, 'group_size': 64, 'mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:47.0789571Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int5, 'group_size': 64, 'mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:47.0792573Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int5, 'group_size': 64, 'mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:47.0795600Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int5, 'group_size': 64, 'mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:47.0798620Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int5, 'group_size': 64, 'mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:47.0801691Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int5, 'group_size': 64, 'mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:47.0804687Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int5, 'group_size': 64, 'mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:47.0807690Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int6, 'group_size': 128, 'mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:47.0810801Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int6, 'group_size': 128, 'mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:47.0813818Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int6, 'group_size': 128, 'mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:47.0816831Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int6, 'group_size': 128, 'mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:47.0819858Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int6, 'group_size': 128, 'mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:47.0823130Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int6, 'group_size': 128, 'mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:47.0826259Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int6, 'group_size': 128, 'mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:47.0829276Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int6, 'group_size': 128, 'mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:47.0832336Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int6, 'group_size': 128, 'mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:47.0835353Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int6, 'group_size': 32, 'mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:47.0838330Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int6, 'group_size': 32, 'mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:47.0841380Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int6, 'group_size': 32, 'mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:47.0844337Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int6, 'group_size': 32, 'mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:47.0847365Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int6, 'group_size': 32, 'mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:47.0850330Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int6, 'group_size': 32, 'mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:47.3490605Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int6, 'group_size': 32, 'mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:47.3493806Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int6, 'group_size': 32, 'mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:47.3496898Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int6, 'group_size': 32, 'mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:47.3499979Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int6, 'group_size': 64, 'mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:47.3502990Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int6, 'group_size': 64, 'mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:47.3505986Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int6, 'group_size': 64, 'mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:47.3508966Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int6, 'group_size': 64, 'mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:47.3511960Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int6, 'group_size': 64, 'mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:47.3514938Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int6, 'group_size': 64, 'mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:47.3517920Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int6, 'group_size': 64, 'mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:47.3521058Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int6, 'group_size': 64, 'mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:47.3524276Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int6, 'group_size': 64, 'mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:47.3527268Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int7, 'group_size': 128, 'mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:47.3530381Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int7, 'group_size': 128, 'mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:47.3533439Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int7, 'group_size': 128, 'mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:47.3536526Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int7, 'group_size': 128, 'mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:47.3539528Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int7, 'group_size': 128, 'mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:47.3542506Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int7, 'group_size': 128, 'mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:47.3545540Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int7, 'group_size': 128, 'mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:47.3548530Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int7, 'group_size': 128, 'mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:47.3551523Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int7, 'group_size': 128, 'mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:47.3554597Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int7, 'group_size': 32, 'mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:47.3557587Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int7, 'group_size': 32, 'mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:47.3560651Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int7, 'group_size': 32, 'mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:47.3563716Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int7, 'group_size': 32, 'mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:47.6187702Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int7, 'group_size': 32, 'mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:47.6190945Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int7, 'group_size': 32, 'mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:47.6193923Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int7, 'group_size': 32, 'mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:47.6196910Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int7, 'group_size': 32, 'mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:47.6199982Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int7, 'group_size': 32, 'mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:47.6202980Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int7, 'group_size': 64, 'mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:47.6205969Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int7, 'group_size': 64, 'mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:47.6208964Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int7, 'group_size': 64, 'mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:47.6212043Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int7, 'group_size': 64, 'mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:47.6215026Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int7, 'group_size': 64, 'mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:47.6218005Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int7, 'group_size': 64, 'mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:47.6221096Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int7, 'group_size': 64, 'mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:47.6224309Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int7, 'group_size': 64, 'mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:47.6227435Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int7, 'group_size': 64, 'mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:47.6230450Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int8, 'group_size': 128, 'mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:47.6233457Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int8, 'group_size': 128, 'mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:47.6236583Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int8, 'group_size': 128, 'mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:47.6239652Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int8, 'group_size': 128, 'mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:47.6242654Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int8, 'group_size': 128, 'mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:47.6245652Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int8, 'group_size': 128, 'mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:47.6248752Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int8, 'group_size': 128, 'mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:47.6251754Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int8, 'group_size': 128, 'mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:47.6254740Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int8, 'group_size': 128, 'mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:47.6257825Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int8, 'group_size': 32, 'mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:47.6260974Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int8, 'group_size': 32, 'mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:47.8222770Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int8, 'group_size': 32, 'mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:47.8225198Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int8, 'group_size': 32, 'mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:47.8227730Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int8, 'group_size': 32, 'mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:47.8230122Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int8, 'group_size': 32, 'mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:47.8232605Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int8, 'group_size': 32, 'mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:47.8235113Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int8, 'group_size': 32, 'mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:47.8237467Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int8, 'group_size': 32, 'mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:47.8240130Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int8, 'group_size': 64, 'mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:47.8242680Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int8, 'group_size': 64, 'mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:47.8245148Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int8, 'group_size': 64, 'mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:47.8247674Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int8, 'group_size': 64, 'mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:47.8250310Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int8, 'group_size': 64, 'mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:47.8252771Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int8, 'group_size': 64, 'mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:47.8255251Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int8, 'group_size': 64, 'mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} PASSED 2025-09-09T15:32:47.8257639Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int8, 'group_size': 64, 'mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} PASSED 2025-09-09T15:32:47.8260102Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_qat_int8_dyn_act_intx_weight_config_{'weight_dtype': torch.int8, 'group_size': 64, 'mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} PASSED 2025-09-09T15:32:47.8262037Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_serialization_int8_dyn_act_intx_weight_config PASSED 2025-09-09T15:32:47.8263553Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_serialization_intx_weight_only_config PASSED 2025-09-09T15:32:47.8264817Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_slice PASSED 2025-09-09T15:32:47.8265990Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_slice_and_copy_ PASSED 2025-09-09T15:32:47.8267302Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_to_dtype PASSED 2025-09-09T15:32:47.8268406Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_concat_linear_cpu_x_dim_2_bias_False SKIPPED 2025-09-09T15:32:47.8269472Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_concat_linear_cpu_x_dim_2_bias_True SKIPPED 2025-09-09T15:32:47.8270385Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_concat_linear_cpu_x_dim_3_bias_False SKIPPED 2025-09-09T15:32:47.8271278Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_concat_linear_cpu_x_dim_3_bias_True SKIPPED 2025-09-09T15:32:47.8272248Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_bfloat16_x_dim_2_bias_False_bs_160_sym_quant_a_False SKIPPED 2025-09-09T15:32:47.8273324Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_bfloat16_x_dim_2_bias_False_bs_160_sym_quant_a_True SKIPPED 2025-09-09T15:32:47.8274395Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_bfloat16_x_dim_2_bias_False_bs_1_sym_quant_a_False SKIPPED 2025-09-09T15:32:47.8275483Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_bfloat16_x_dim_2_bias_False_bs_1_sym_quant_a_True SKIPPED 2025-09-09T15:32:47.8276698Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_bfloat16_x_dim_2_bias_True_bs_160_sym_quant_a_False SKIPPED 2025-09-09T15:32:47.8277718Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_bfloat16_x_dim_2_bias_True_bs_160_sym_quant_a_True SKIPPED 2025-09-09T15:32:47.8278780Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_bfloat16_x_dim_2_bias_True_bs_1_sym_quant_a_False SKIPPED 2025-09-09T15:32:47.8279922Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_bfloat16_x_dim_2_bias_True_bs_1_sym_quant_a_True SKIPPED 2025-09-09T15:32:47.8281045Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_bfloat16_x_dim_3_bias_False_bs_160_sym_quant_a_False SKIPPED 2025-09-09T15:32:47.8282064Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_bfloat16_x_dim_3_bias_False_bs_160_sym_quant_a_True SKIPPED 2025-09-09T15:32:47.8283187Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_bfloat16_x_dim_3_bias_False_bs_1_sym_quant_a_False SKIPPED 2025-09-09T15:32:47.8284199Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_bfloat16_x_dim_3_bias_False_bs_1_sym_quant_a_True SKIPPED 2025-09-09T15:32:47.8285201Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_bfloat16_x_dim_3_bias_True_bs_160_sym_quant_a_False SKIPPED 2025-09-09T15:33:17.0232445Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_bfloat16_x_dim_3_bias_True_bs_160_sym_quant_a_True SKIPPED 2025-09-09T15:33:17.0233504Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_bfloat16_x_dim_3_bias_True_bs_1_sym_quant_a_False SKIPPED 2025-09-09T15:33:17.0234535Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_bfloat16_x_dim_3_bias_True_bs_1_sym_quant_a_True SKIPPED 2025-09-09T15:33:17.0235728Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float16_x_dim_2_bias_False_bs_160_sym_quant_a_False SKIPPED 2025-09-09T15:33:17.0236962Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float16_x_dim_2_bias_False_bs_160_sym_quant_a_True SKIPPED 2025-09-09T15:33:17.0238103Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float16_x_dim_2_bias_False_bs_1_sym_quant_a_False SKIPPED 2025-09-09T15:33:17.0239111Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float16_x_dim_2_bias_False_bs_1_sym_quant_a_True SKIPPED 2025-09-09T15:33:17.0240184Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float16_x_dim_2_bias_True_bs_160_sym_quant_a_False SKIPPED 2025-09-09T15:33:17.0241413Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float16_x_dim_2_bias_True_bs_160_sym_quant_a_True SKIPPED 2025-09-09T15:33:17.0242548Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float16_x_dim_2_bias_True_bs_1_sym_quant_a_False SKIPPED 2025-09-09T15:33:17.0243549Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float16_x_dim_2_bias_True_bs_1_sym_quant_a_True SKIPPED 2025-09-09T15:33:17.0244692Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float16_x_dim_3_bias_False_bs_160_sym_quant_a_False SKIPPED 2025-09-09T15:33:17.0245785Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float16_x_dim_3_bias_False_bs_160_sym_quant_a_True SKIPPED 2025-09-09T15:33:17.0246780Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float16_x_dim_3_bias_False_bs_1_sym_quant_a_False SKIPPED 2025-09-09T15:33:17.0247778Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float16_x_dim_3_bias_False_bs_1_sym_quant_a_True SKIPPED 2025-09-09T15:33:17.0249008Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float16_x_dim_3_bias_True_bs_160_sym_quant_a_False SKIPPED 2025-09-09T15:33:17.0250132Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float16_x_dim_3_bias_True_bs_160_sym_quant_a_True SKIPPED 2025-09-09T15:33:17.0251266Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float16_x_dim_3_bias_True_bs_1_sym_quant_a_False SKIPPED 2025-09-09T15:33:17.0252422Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float16_x_dim_3_bias_True_bs_1_sym_quant_a_True SKIPPED 2025-09-09T15:33:17.0253414Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float32_x_dim_2_bias_False_bs_160_sym_quant_a_False SKIPPED 2025-09-09T15:33:17.0254407Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float32_x_dim_2_bias_False_bs_160_sym_quant_a_True SKIPPED 2025-09-09T15:33:17.0255413Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float32_x_dim_2_bias_False_bs_1_sym_quant_a_False SKIPPED 2025-09-09T15:33:17.0256400Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float32_x_dim_2_bias_False_bs_1_sym_quant_a_True SKIPPED 2025-09-09T15:33:17.0257389Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float32_x_dim_2_bias_True_bs_160_sym_quant_a_False SKIPPED 2025-09-09T15:33:17.0258513Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float32_x_dim_2_bias_True_bs_160_sym_quant_a_True SKIPPED 2025-09-09T15:33:17.0259508Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float32_x_dim_2_bias_True_bs_1_sym_quant_a_False SKIPPED 2025-09-09T15:33:17.0260629Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float32_x_dim_2_bias_True_bs_1_sym_quant_a_True SKIPPED 2025-09-09T15:33:17.0261661Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float32_x_dim_3_bias_False_bs_160_sym_quant_a_False SKIPPED 2025-09-09T15:33:17.0262661Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float32_x_dim_3_bias_False_bs_160_sym_quant_a_True SKIPPED 2025-09-09T15:33:17.0263664Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float32_x_dim_3_bias_False_bs_1_sym_quant_a_False SKIPPED 2025-09-09T15:33:17.0264772Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float32_x_dim_3_bias_False_bs_1_sym_quant_a_True SKIPPED 2025-09-09T15:33:17.0265773Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float32_x_dim_3_bias_True_bs_160_sym_quant_a_False SKIPPED 2025-09-09T15:33:17.0266769Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float32_x_dim_3_bias_True_bs_160_sym_quant_a_True SKIPPED 2025-09-09T15:33:17.0267914Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float32_x_dim_3_bias_True_bs_1_sym_quant_a_False SKIPPED 2025-09-09T15:33:17.0268971Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float32_x_dim_3_bias_True_bs_1_sym_quant_a_True SKIPPED 2025-09-09T15:33:17.0269843Z test/quantization/test_gptq.py::TestGPTQ::test_gptq_quantizer_int4_weight_only SKIPPED 2025-09-09T15:33:17.0270621Z test/quantization/test_gptq.py::TestMultiTensorFlow::test_multitensor_add_tensors PASSED 2025-09-09T15:33:17.0271571Z test/quantization/test_gptq.py::TestMultiTensorFlow::test_multitensor_inplace_operation PASSED 2025-09-09T15:33:17.0272395Z test/quantization/test_gptq.py::TestMultiTensorFlow::test_multitensor_pad_unpad PASSED 2025-09-09T15:33:17.0273259Z test/quantization/test_gptq.py::TestMultiTensorInputRecorder::test_gptq_with_input_recorder layers.0.attention.wqkv.weight 2025-09-09T15:33:17.0273968Z layers.0.attention.wo.weight 2025-09-09T15:33:17.0274275Z layers.0.feed_forward.w1.weight 2025-09-09T15:33:17.0274549Z layers.0.feed_forward.w3.weight 2025-09-09T15:33:17.0274819Z layers.0.feed_forward.w2.weight 2025-09-09T15:33:17.0275160Z layers.1.attention.wqkv.weight 2025-09-09T15:33:17.0275431Z layers.1.attention.wo.weight 2025-09-09T15:33:17.0275696Z layers.1.feed_forward.w1.weight 2025-09-09T15:33:17.0276028Z layers.1.feed_forward.w3.weight 2025-09-09T15:33:17.0276289Z layers.1.feed_forward.w2.weight 2025-09-09T15:33:17.0276542Z output.weight 2025-09-09T15:33:17.0276792Z PASSED 2025-09-09T15:33:17.0277414Z test/quantization/test_gptq.py::TestMultiTensorInputRecorder::test_multitensor_input_recorder PASSED 2025-09-09T15:33:17.0278604Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_aten SKIPPED 2025-09-09T15:33:17.0279889Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_kleidiai SKIPPED 2025-09-09T15:33:17.0282377Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.AUTO), 'weight_dtype': torch.int1, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:33:17.0285759Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.AUTO), 'weight_dtype': torch.int1, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:33:17.0289123Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.AUTO), 'weight_dtype': torch.int1, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:33:17.0292466Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.AUTO), 'weight_dtype': torch.int1, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:33:17.0295955Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.AUTO), 'weight_dtype': torch.int1, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:33:17.0366877Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.AUTO), 'weight_dtype': torch.int1, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:33:17.0370295Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.AUTO), 'weight_dtype': torch.int2, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:33:17.0373793Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.AUTO), 'weight_dtype': torch.int2, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:33:17.0377283Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.AUTO), 'weight_dtype': torch.int2, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:33:17.0380671Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.AUTO), 'weight_dtype': torch.int2, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:33:17.0384076Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.AUTO), 'weight_dtype': torch.int2, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:33:17.0387618Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.AUTO), 'weight_dtype': torch.int2, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:33:17.0390922Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.AUTO), 'weight_dtype': torch.int3, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:33:17.0394285Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.AUTO), 'weight_dtype': torch.int3, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:33:17.0397535Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.AUTO), 'weight_dtype': torch.int3, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:33:17.0400855Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.AUTO), 'weight_dtype': torch.int3, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:33:17.0404266Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.AUTO), 'weight_dtype': torch.int3, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:33:17.0407675Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.AUTO), 'weight_dtype': torch.int3, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:33:17.0411036Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.AUTO), 'weight_dtype': torch.int4, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:33:17.0414308Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.AUTO), 'weight_dtype': torch.int4, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:33:17.0417553Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.AUTO), 'weight_dtype': torch.int4, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:33:17.0420801Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.AUTO), 'weight_dtype': torch.int4, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:33:17.0424298Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.AUTO), 'weight_dtype': torch.int4, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:33:17.0427743Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.AUTO), 'weight_dtype': torch.int4, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:33:17.0496970Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.AUTO), 'weight_dtype': torch.int5, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:33:17.0500334Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.AUTO), 'weight_dtype': torch.int5, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:33:17.0503677Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.AUTO), 'weight_dtype': torch.int5, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:33:17.0506964Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.AUTO), 'weight_dtype': torch.int5, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:33:17.0510249Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.AUTO), 'weight_dtype': torch.int5, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:33:17.0513594Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.AUTO), 'weight_dtype': torch.int5, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:33:17.0516872Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.AUTO), 'weight_dtype': torch.int6, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:33:17.0520172Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.AUTO), 'weight_dtype': torch.int6, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:33:17.0523717Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.AUTO), 'weight_dtype': torch.int6, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:33:17.0526956Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.AUTO), 'weight_dtype': torch.int6, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:33:17.0530300Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.AUTO), 'weight_dtype': torch.int6, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:33:17.0533702Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.AUTO), 'weight_dtype': torch.int6, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:33:17.0538037Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.AUTO), 'weight_dtype': torch.int7, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:33:17.0542225Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.AUTO), 'weight_dtype': torch.int7, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:33:17.0546385Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.AUTO), 'weight_dtype': torch.int7, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:33:17.0550545Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.AUTO), 'weight_dtype': torch.int7, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:33:17.0554781Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.AUTO), 'weight_dtype': torch.int7, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:33:17.0559144Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.AUTO), 'weight_dtype': torch.int7, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:33:17.0562468Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.AUTO), 'weight_dtype': torch.int8, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:33:17.0625657Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.AUTO), 'weight_dtype': torch.int8, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:33:17.0628970Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.AUTO), 'weight_dtype': torch.int8, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:33:17.0632265Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.AUTO), 'weight_dtype': torch.int8, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:33:17.0635549Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.AUTO), 'weight_dtype': torch.int8, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:33:17.0638901Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.AUTO), 'weight_dtype': torch.int8, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:33:17.0642258Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.UNIVERSAL), 'weight_dtype': torch.int1, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:33:17.0645550Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.UNIVERSAL), 'weight_dtype': torch.int1, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:33:17.0648982Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.UNIVERSAL), 'weight_dtype': torch.int1, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:33:17.0652255Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.UNIVERSAL), 'weight_dtype': torch.int1, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:33:17.0655648Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.UNIVERSAL), 'weight_dtype': torch.int1, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:33:17.0659080Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.UNIVERSAL), 'weight_dtype': torch.int1, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:33:17.0662467Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.UNIVERSAL), 'weight_dtype': torch.int2, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:33:17.0665754Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.UNIVERSAL), 'weight_dtype': torch.int2, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:33:17.0669030Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.UNIVERSAL), 'weight_dtype': torch.int2, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:33:17.0672302Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.UNIVERSAL), 'weight_dtype': torch.int2, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:33:17.0675638Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.UNIVERSAL), 'weight_dtype': torch.int2, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:33:17.0679083Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.UNIVERSAL), 'weight_dtype': torch.int2, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:33:17.0682490Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.UNIVERSAL), 'weight_dtype': torch.int3, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:33:17.0685846Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.UNIVERSAL), 'weight_dtype': torch.int3, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:33:17.0755654Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.UNIVERSAL), 'weight_dtype': torch.int3, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:33:17.0759910Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.UNIVERSAL), 'weight_dtype': torch.int3, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:33:17.0763250Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.UNIVERSAL), 'weight_dtype': torch.int3, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:33:17.0766659Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.UNIVERSAL), 'weight_dtype': torch.int3, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:33:17.0770010Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.UNIVERSAL), 'weight_dtype': torch.int4, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:33:17.0773317Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.UNIVERSAL), 'weight_dtype': torch.int4, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:33:17.0776670Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.UNIVERSAL), 'weight_dtype': torch.int4, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:33:17.0779961Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.UNIVERSAL), 'weight_dtype': torch.int4, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:33:17.0783407Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.UNIVERSAL), 'weight_dtype': torch.int4, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:33:17.0786855Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.UNIVERSAL), 'weight_dtype': torch.int4, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:33:17.0790236Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.UNIVERSAL), 'weight_dtype': torch.int5, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:33:17.0793527Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.UNIVERSAL), 'weight_dtype': torch.int5, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:33:17.0796808Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.UNIVERSAL), 'weight_dtype': torch.int5, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:33:17.0800130Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.UNIVERSAL), 'weight_dtype': torch.int5, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:33:17.0803473Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.UNIVERSAL), 'weight_dtype': torch.int5, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:33:17.0806960Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.UNIVERSAL), 'weight_dtype': torch.int5, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:33:17.0810295Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.UNIVERSAL), 'weight_dtype': torch.int6, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:33:17.0813683Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.UNIVERSAL), 'weight_dtype': torch.int6, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:33:17.0817003Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.UNIVERSAL), 'weight_dtype': torch.int6, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:33:17.0907100Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.UNIVERSAL), 'weight_dtype': torch.int6, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:33:17.0913204Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.UNIVERSAL), 'weight_dtype': torch.int6, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:33:17.0916613Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.UNIVERSAL), 'weight_dtype': torch.int6, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:33:17.0920015Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.UNIVERSAL), 'weight_dtype': torch.int7, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:33:17.0923475Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.UNIVERSAL), 'weight_dtype': torch.int7, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:33:17.0926861Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.UNIVERSAL), 'weight_dtype': torch.int7, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:33:17.0930380Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.UNIVERSAL), 'weight_dtype': torch.int7, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:33:17.0933957Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.UNIVERSAL), 'weight_dtype': torch.int7, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:33:17.0937586Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.UNIVERSAL), 'weight_dtype': torch.int7, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:33:17.0941148Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.UNIVERSAL), 'weight_dtype': torch.int8, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:33:17.0944603Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.UNIVERSAL), 'weight_dtype': torch.int8, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:33:17.0948172Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.UNIVERSAL), 'weight_dtype': torch.int8, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:33:17.0951518Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.UNIVERSAL), 'weight_dtype': torch.int8, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:33:17.0955151Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.UNIVERSAL), 'weight_dtype': torch.int8, 'weight_mapping_type': , 'weight_granularity': PerAxis(axis=0)} SKIPPED 2025-09-09T15:33:17.0958779Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.UNIVERSAL), 'weight_dtype': torch.int8, 'weight_mapping_type': , 'weight_granularity': PerGroup(group_size=128)} SKIPPED 2025-09-09T15:33:17.0961248Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_export_QDQLayout SKIPPED 2025-09-09T15:33:17.0962776Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_export_compile_aoti_PackedLinearInt8DynamicActivationIntxWeightLayout SKIPPED 2025-09-09T15:33:17.0964567Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_export_dynamic_shape_PackedLinearInt8DynamicActivationIntxWeightLayout SKIPPED 2025-09-09T15:33:17.0966597Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_Int8DynActInt4WeightQATQuantizer_{'group_size': 128, 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.0968893Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_Int8DynActInt4WeightQATQuantizer_{'group_size': 128, 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.0971027Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_Int8DynActInt4WeightQATQuantizer_{'group_size': 128, 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.0973198Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_Int8DynActInt4WeightQATQuantizer_{'group_size': 128, 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.1095902Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_Int8DynActInt4WeightQATQuantizer_{'group_size': 128, 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.1097983Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_Int8DynActInt4WeightQATQuantizer_{'group_size': 128, 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.1100424Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_Int8DynActInt4WeightQATQuantizer_{'group_size': 128, 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.1102636Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_Int8DynActInt4WeightQATQuantizer_{'group_size': 128, 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.1104702Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_Int8DynActInt4WeightQATQuantizer_{'group_size': 128, 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.1106835Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_Int8DynActInt4WeightQATQuantizer_{'group_size': 32, 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.1109136Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_Int8DynActInt4WeightQATQuantizer_{'group_size': 32, 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.1111181Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_Int8DynActInt4WeightQATQuantizer_{'group_size': 32, 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.1125430Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_Int8DynActInt4WeightQATQuantizer_{'group_size': 32, 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.1127765Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_Int8DynActInt4WeightQATQuantizer_{'group_size': 32, 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.1129951Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_Int8DynActInt4WeightQATQuantizer_{'group_size': 32, 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.1132171Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_Int8DynActInt4WeightQATQuantizer_{'group_size': 32, 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.1134430Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_Int8DynActInt4WeightQATQuantizer_{'group_size': 32, 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.1136458Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_Int8DynActInt4WeightQATQuantizer_{'group_size': 32, 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.1138581Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_Int8DynActInt4WeightQATQuantizer_{'group_size': 64, 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.1140740Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_Int8DynActInt4WeightQATQuantizer_{'group_size': 64, 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.1142771Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_Int8DynActInt4WeightQATQuantizer_{'group_size': 64, 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.1144855Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_Int8DynActInt4WeightQATQuantizer_{'group_size': 64, 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.1146897Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_Int8DynActInt4WeightQATQuantizer_{'group_size': 64, 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.1149044Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_Int8DynActInt4WeightQATQuantizer_{'group_size': 64, 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.1151158Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_Int8DynActInt4WeightQATQuantizer_{'group_size': 64, 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.1153315Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_Int8DynActInt4WeightQATQuantizer_{'group_size': 64, 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.1155498Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_Int8DynActInt4WeightQATQuantizer_{'group_size': 64, 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.1157782Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_Int8DynamicActivationInt4WeightConfig_{'group_size': 32, 'mapping_type': , 'act_mapping_type': } SKIPPED 2025-09-09T15:33:17.1160321Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_Int8DynamicActivationInt4WeightConfig_{'group_size': 64, 'mapping_type': , 'act_mapping_type': } SKIPPED 2025-09-09T15:33:17.1163146Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.1166258Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.1169348Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.1172280Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.1236594Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.1239715Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.1242907Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.1245941Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.1248993Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.1252102Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.1255295Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.1258312Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.1261337Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.1264473Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.1267507Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.1270586Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.1273758Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.1276853Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.1280010Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.1283161Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.1286138Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.1289378Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.1292311Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.1295365Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.1298416Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.1378597Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.1381752Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.1384892Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.1387901Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.1391025Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.1394108Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.1397234Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.1400754Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.1403804Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.1406740Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.1409833Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.1412863Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.1415999Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.1418999Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.1422083Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.1425401Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.1428534Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.1431573Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.1434587Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.1437648Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.1440657Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.1520875Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.1524082Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.1527170Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.1530270Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.1533435Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.1536424Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.1539420Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.1542469Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.1545464Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.1548500Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.1551590Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.1554642Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.1557670Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.1560885Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.1563893Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.1567103Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.1570007Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.1573107Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.1576061Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.1579067Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.1582171Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.1662634Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.1665658Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.1669103Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.1672469Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.1675805Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.1679120Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.1682412Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.1685687Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.1688889Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.1692117Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.1695293Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.1698401Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.1703158Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.1706700Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.1709740Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.1712967Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.1716024Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.1719151Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.1722576Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.1725705Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.1728974Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.1803142Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.1806397Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.1809948Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.1813554Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.1816743Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.1819859Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.1823195Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.1826205Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.1829557Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.1832606Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.1835857Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.1838996Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.1842478Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.1845468Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.1848652Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.1852131Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.1855425Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.1858364Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.1861619Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.1864670Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int1, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.1867880Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.1944923Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.1948572Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.1951720Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.1955204Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.1958371Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.1961942Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.1965057Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.1968368Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.1971511Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.1974709Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.1977877Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.1981103Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.1984324Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.1987320Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.1990557Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.1993543Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.1996787Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.1999957Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.2003213Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.2006382Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.2009627Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.2086478Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.2089629Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.2092817Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.2095942Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.2099130Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.2102266Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.2105583Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.2108740Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.2111995Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.2115140Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.2118446Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.2121642Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.2125048Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.2128591Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.2131800Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.2135008Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.2138290Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.2141442Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.2144727Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.2147888Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.2151219Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.2226423Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.2229719Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.2232893Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.2236031Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.2239201Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.2242537Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.2245779Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.2249003Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.2252178Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.2255470Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.2258630Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.2261572Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.2264857Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.2267961Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.2271187Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.2274339Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.2277545Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.2280775Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.2283904Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.2287177Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.2290292Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.2368045Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.2371450Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.2374678Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.2377971Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.2381221Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.2384405Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.2387684Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.2390816Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.2394160Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.2397312Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.2400592Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.2404213Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.2407370Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.2410525Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.2413780Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.2416904Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.2420204Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.2423515Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.2427009Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.2430084Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.2433374Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.2508917Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.2512223Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.2515402Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.2518670Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.2522479Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.2525747Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.2528907Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.2531972Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.2535102Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.2538309Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.2541451Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.2544629Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.2547672Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.2551006Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.2554072Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.2557400Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.2560537Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.2563962Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.2566984Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.2570270Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.2573286Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.2653017Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.2656189Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int2, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.2659495Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.2662677Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.2665978Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.2669164Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.2672537Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.2675764Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.2679040Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.2682302Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.2685494Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.2688653Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.2691927Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.2695090Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.2698347Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.2701540Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.2704813Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.2707955Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.2711151Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.2714136Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.2717376Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.2792782Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.2796102Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.2799386Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.2802672Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.2805986Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.2809134Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.2812588Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.2815795Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.2819115Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.2822834Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.2826164Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.2829326Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.2832595Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.2836028Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.2839409Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.2842586Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.2845847Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.2848915Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.2852199Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.2855727Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.2858970Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.2934585Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.2938017Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.2941275Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.2944603Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.2947831Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.2951080Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.2954217Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.2957450Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.2960667Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.2963915Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.2967076Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.2970204Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.2973458Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.2976647Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.2979909Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.2983584Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.2986804Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.2989870Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.2993139Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.2996757Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.2999997Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.3075133Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.3078443Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.3082147Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.3085517Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.3088625Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.3091766Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.3094864Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.3097973Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.3101248Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.3104370Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.3107533Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.3110710Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.3113987Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.3117204Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.3120467Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.3123759Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.3126939Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.3130086Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.3133593Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.3136766Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.3139964Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.3214460Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.3218044Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.3221338Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.3224993Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.3228092Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.3231319Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.3234466Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.3237696Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.3240978Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.3244084Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.3247424Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.3250543Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.3253990Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.3257045Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.3260301Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.3263334Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.3266586Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.3269666Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.3272981Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.3275991Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.3279544Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.3355046Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.3358677Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.3361907Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.3365462Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.3368574Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int3, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.3371779Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.3375031Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.3378285Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.3381414Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.3384775Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.3387966Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.3392097Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.3403889Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.3407089Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.3410227Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.3413369Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.3416427Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.3419658Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.3423098Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.3426357Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.3429437Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.3493908Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.3497209Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.3500393Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.3503492Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.3506774Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.3509912Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.3513183Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.3516223Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.3519647Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.3522923Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.3526274Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.3529229Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.3532437Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.3535534Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.3538742Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.3541958Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.3545181Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.3548272Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.3551549Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.3555170Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.3558700Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.3636153Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.3639424Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.3642600Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.3645720Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.3649018Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.3652139Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.3655347Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.3658556Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.3661878Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.3664991Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.3668136Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.3671248Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.3674769Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.3677729Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.3681037Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.3684158Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.3687363Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.3690512Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.3693877Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.3696947Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.3700161Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.3776459Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.3779765Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.3782908Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.3786119Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.3789293Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.3792400Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.3795709Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.3798964Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.3802180Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.3805289Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.3808397Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.3811479Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.3814667Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.3817856Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.3821018Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.3824346Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.3827669Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.3830918Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.3834130Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.3837240Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.3840490Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.3915149Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.3918645Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.3921955Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.3925564Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.3928660Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.3932033Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.3935401Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.3938519Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.3941675Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.3944771Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.3947923Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.3950954Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.3954305Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.3957373Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.3960751Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.3963858Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.3967237Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.3970277Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.3973591Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.3977223Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.3980422Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.4055384Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.4058889Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.4062009Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.4065533Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.4069045Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.4072394Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.4075472Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.4078656Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int4, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.4081793Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.4085006Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.4088111Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.4091316Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.4094558Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.4098002Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.4101143Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.4104382Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.4107522Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.4110552Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.4113860Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.4116905Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.4120230Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.4195773Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.4199324Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.4202647Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.4206031Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.4209290Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.4212517Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.4215628Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.4218833Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.4222562Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.4225798Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.4228991Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.4232243Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.4235452Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.4238684Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.4242036Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.4245270Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.4248357Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.4251558Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.4254715Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.4257840Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.4261048Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.4334426Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.4337901Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.4341019Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.4344378Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.4347474Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.4350633Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.4354170Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.4357466Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.4360648Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.4363878Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.4367043Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.4370145Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.4373457Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.4376616Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.4379861Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.4382902Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.4386141Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.4389165Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.4392439Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.4395516Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.4398829Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.4474277Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.4477778Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.4481141Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.4484486Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.4487568Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.4490765Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.4493832Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.4496996Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.4500194Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.4503355Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.4506568Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.4509873Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.4513092Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.4516193Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.4519415Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.4522750Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.4525894Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.4529014Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.4532292Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.4535438Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.4538570Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.4612429Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.4615822Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.4619395Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.4622701Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.4625707Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.4628766Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.4631936Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.4635206Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.4638567Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.4641818Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.4645119Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.4648352Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.4651561Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.4654672Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.4657850Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.4660955Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.4664123Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.4667782Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.4670993Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.4674134Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.4677501Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.4754052Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.4757513Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.4760678Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.4764121Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.4767198Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.4770279Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.4773578Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.4776669Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.4779907Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.4783056Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.4786273Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int5, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.4789396Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.4792613Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.4795758Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.4798920Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.4801966Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.4805257Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.4808344Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.4811636Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.4814780Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.4818251Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.4893952Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.4897315Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.4900488Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.4903693Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.4906826Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.4910097Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.4913298Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.4916529Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.4920002Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.4923483Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.4926642Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.4929766Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.4932939Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.4936040Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.4939327Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.4942469Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.4945661Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.4948878Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.4952123Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.4955594Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.4958728Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.5032405Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.5035580Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.5038744Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.5042036Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.5045257Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.5048375Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.5051649Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.5054843Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.5058106Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.5061109Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.5064338Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.5067285Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.5070488Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.5073670Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.5077162Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.5080209Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.5083498Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.5087401Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.5090710Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.5093735Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.5096987Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.5172340Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.5175652Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.5178928Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.5182138Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.5185302Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.5188544Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.5191727Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.5195026Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.5198138Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.5201339Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.5204487Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.5207590Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.5210802Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.5213918Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.5217098Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.5220248Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.5223652Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.5226882Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.5229891Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.5232962Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.5236136Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.5310509Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.5313922Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.5317255Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.5320537Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.5324167Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.5327310Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.5330436Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.5333448Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.5336474Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.5339495Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.5342523Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.5345553Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.5348587Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.5351512Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.5354539Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.5357494Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.5360540Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.5363444Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.5366356Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.5369257Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.5372153Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.5452778Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.5455727Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.5458624Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.5461579Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.5464565Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.5467523Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.5470407Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.5473299Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.5476180Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.5479052Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.5482072Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.5484953Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.5487826Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.5490734Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int6, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.5493671Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.5496629Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.5499540Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.5502451Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.5505407Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.5508301Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.5511258Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.5590702Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.5593633Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.5596625Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.5600205Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.5603177Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.5606088Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.5608987Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.5611890Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.5614795Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.5617750Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.5620644Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.5623710Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.5626710Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.5629761Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.5632674Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.5635759Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.5638768Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.5641842Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.5644861Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.5647973Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.5651472Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.5730556Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.5733732Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.5736867Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.5739877Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.5751095Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.5754138Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.5757166Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.5760221Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.5763397Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.5766446Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.5769454Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.5772519Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.5775977Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.5778960Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.5781938Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.5784894Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.5787947Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.5790888Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.5794152Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.5797134Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.5800318Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.5870259Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.5873813Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.5876782Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.5880154Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.5883569Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.5886740Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.5889731Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.5892864Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.5895896Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.5899069Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.5902082Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.5905184Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.5908200Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.5911208Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.5914237Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.5917249Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.5920241Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.5923533Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.5926549Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.5929613Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.5932586Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.6009938Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.6013175Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.6016234Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.6019248Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.6022377Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.6025418Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.6028514Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.6031510Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.6034602Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.6037620Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.6040847Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.6043768Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.6046826Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.6049734Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.6052791Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.6055678Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.6058717Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.6061707Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.6064733Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.6067714Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.6071165Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.6148356Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.6151631Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.6154652Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.6157664Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.6160709Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.6163787Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.6166808Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.6169867Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.6172873Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.6176002Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.6178897Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.6181887Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.6184864Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.6187832Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.6190809Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.6193879Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.6196908Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int7, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.6200094Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.6203161Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.6206317Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.6209266Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.6288691Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.6291653Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.6294574Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.6297501Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.6300529Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.6303505Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.6306498Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.6309430Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.6312465Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.6315396Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.6318312Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.6321290Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.6324432Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.6327349Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.6330345Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.6333270Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.6336252Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.6339171Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.6342188Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.6345097Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.6348006Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.6426810Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.6429738Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.6432662Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.6435671Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.6438587Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.6441620Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.6444573Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.6447576Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.6450479Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.6453390Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.6456277Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 128, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.6459190Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.6462113Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.6465091Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.6468003Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.6470943Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.6473882Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.6476823Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.6479775Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.6482685Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.6485596Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.6566901Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.6569836Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.6572844Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.6575768Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.6578726Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.6581674Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.6584639Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.6587544Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.6590464Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.6593377Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.6596280Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.6599195Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.6602238Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.6605144Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.6608096Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.6611029Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.6613969Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.6616882Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.6619790Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.6622842Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.6625739Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.6707769Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.6710769Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.6714443Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.6718167Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.6721377Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 32, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.6724539Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.6727478Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.6730385Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.6733292Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.6736203Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.6739099Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.6742061Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.6744975Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.6747931Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.6750889Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.6753855Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.6756761Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.6759714Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.6762612Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.6765512Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.6768407Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.6852199Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.6857369Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.6863812Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.6866796Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.6869769Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.6872696Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.6875599Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.6878506Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.6881476Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.6884377Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.6887325Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.6890231Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.6893172Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.6896107Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.bfloat16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.6899049Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.6901940Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.6904819Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float16, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.6907701Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.bfloat16} SKIPPED 2025-09-09T15:33:17.6910586Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float16} SKIPPED 2025-09-09T15:33:17.6913616Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_identical_to_IntXQuantizationAwareTrainingConfig_{'weight_dtype': torch.int8, 'group_size': 64, 'mapping_type': , 'act_mapping_type': , 'scale_dtype': torch.float32, 'model_dtype': torch.float32} SKIPPED 2025-09-09T15:33:17.6915591Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_moe_quant_intx SKIPPED 2025-09-09T15:33:17.6917447Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_serialization_{'layout': PackedLinearInt8DynamicActivationIntxWeightLayout(group_size=None, bit_width=None, has_weight_zeros=None, has_bias=None, target=Target.AUTO)} SKIPPED 2025-09-09T16:11:35.5080387Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_serialization_{'layout': QDQLayout()} SKIPPED 2025-09-09T16:11:35.5081820Z test/quantization/test_moe_quant.py::TestMoEQuantCompile::test_fp8dq_base_0_single_token SKIPPED 2025-09-09T16:11:35.5082892Z test/quantization/test_moe_quant.py::TestMoEQuantCompile::test_fp8dq_base_1_multiple_tokens SKIPPED 2025-09-09T16:11:35.5084340Z test/quantization/test_moe_quant.py::TestMoEQuantCompile::test_fp8dq_fake_dim_0_single_token SKIPPED 2025-09-09T16:11:35.5085457Z test/quantization/test_moe_quant.py::TestMoEQuantCompile::test_fp8dq_fake_dim_1_multiple_tokens SKIPPED 2025-09-09T16:11:35.5086685Z test/quantization/test_moe_quant.py::TestMoEQuantCompile::test_fp8wo_base_0_single_token SKIPPED 2025-09-09T16:11:35.5087929Z test/quantization/test_moe_quant.py::TestMoEQuantCompile::test_fp8wo_base_1_multiple_tokens SKIPPED 2025-09-09T16:11:35.5089000Z test/quantization/test_moe_quant.py::TestMoEQuantCompile::test_fp8wo_fake_dim_0_single_token SKIPPED 2025-09-09T16:11:35.5090094Z test/quantization/test_moe_quant.py::TestMoEQuantCompile::test_fp8wo_fake_dim_1_multiple_tokens SKIPPED 2025-09-09T16:11:35.5091164Z test/quantization/test_moe_quant.py::TestMoEQuantCompile::test_int4wo_base_0_single_token SKIPPED 2025-09-09T16:11:35.5092228Z test/quantization/test_moe_quant.py::TestMoEQuantCompile::test_int4wo_base_1_multiple_tokens SKIPPED 2025-09-09T16:11:35.5093304Z test/quantization/test_moe_quant.py::TestMoEQuantCompile::test_int4wo_fake_dim_0_single_token PASSED 2025-09-09T16:11:35.5094382Z test/quantization/test_moe_quant.py::TestMoEQuantCompile::test_int4wo_fake_dim_1_multiple_tokens PASSED 2025-09-09T16:11:35.5095459Z test/quantization/test_moe_quant.py::TestMoEQuantCompile::test_int8dq_base_0_multiple_tokens PASSED 2025-09-09T16:11:35.5096545Z test/quantization/test_moe_quant.py::TestMoEQuantCompile::test_int8dq_fake_dim_0_multiple_tokens PASSED 2025-09-09T16:11:35.5097601Z test/quantization/test_moe_quant.py::TestMoEQuantCompile::test_int8wo_base_0_single_token PASSED 2025-09-09T16:11:35.5098645Z test/quantization/test_moe_quant.py::TestMoEQuantCompile::test_int8wo_base_1_multiple_tokens PASSED 2025-09-09T16:11:35.5099699Z test/quantization/test_moe_quant.py::TestMoEQuantCompile::test_int8wo_base_cpu_0_single_token PASSED 2025-09-09T16:11:35.5100794Z test/quantization/test_moe_quant.py::TestMoEQuantCompile::test_int8wo_base_cpu_1_multiple_tokens PASSED 2025-09-09T16:11:35.5101879Z test/quantization/test_moe_quant.py::TestMoEQuantCompile::test_int8wo_fake_dim_0_single_token PASSED 2025-09-09T16:11:35.5102957Z test/quantization/test_moe_quant.py::TestMoEQuantCompile::test_int8wo_fake_dim_1_multiple_tokens PASSED 2025-09-09T16:11:35.5103977Z test/quantization/test_observer.py::TestQuantFlow::test_block_size_calc_success PASSED 2025-09-09T16:11:35.5104898Z test/quantization/test_observer.py::TestQuantFlow::test_block_size_row_errors PASSED 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test/test_ops.py::TestOps::test_scaled_dot_product_int8_op_batch_size_120_n_head_2_q_seq_len_89_kv_seq_len_100_head_dim_32_mask_dtype0 SKIPPED 2025-09-09T16:24:53.1013089Z test/test_ops.py::TestOps::test_scaled_dot_product_int8_op_batch_size_120_n_head_2_q_seq_len_89_kv_seq_len_100_head_dim_64_bfloat16 SKIPPED 2025-09-09T16:24:53.1014120Z test/test_ops.py::TestOps::test_scaled_dot_product_int8_op_batch_size_120_n_head_2_q_seq_len_89_kv_seq_len_100_head_dim_64_float32 SKIPPED 2025-09-09T16:24:53.1015150Z test/test_ops.py::TestOps::test_scaled_dot_product_int8_op_batch_size_120_n_head_2_q_seq_len_89_kv_seq_len_100_head_dim_64_mask_dtype0 SKIPPED 2025-09-09T16:24:53.1016185Z test/test_ops.py::TestOps::test_scaled_dot_product_int8_op_batch_size_120_n_head_2_q_seq_len_89_kv_seq_len_253_head_dim_32_bfloat16 SKIPPED 2025-09-09T16:24:53.1017207Z test/test_ops.py::TestOps::test_scaled_dot_product_int8_op_batch_size_120_n_head_2_q_seq_len_89_kv_seq_len_253_head_dim_32_float32 SKIPPED 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test/test_ops.py::TestOps::test_scaled_dot_product_int8_op_batch_size_56_n_head_16_q_seq_len_89_kv_seq_len_100_head_dim_32_bfloat16 SKIPPED 2025-09-09T16:24:53.8343561Z test/test_ops.py::TestOps::test_scaled_dot_product_int8_op_batch_size_56_n_head_16_q_seq_len_89_kv_seq_len_100_head_dim_32_float32 SKIPPED 2025-09-09T16:24:53.8344580Z test/test_ops.py::TestOps::test_scaled_dot_product_int8_op_batch_size_56_n_head_16_q_seq_len_89_kv_seq_len_100_head_dim_32_mask_dtype0 SKIPPED 2025-09-09T16:24:53.8345637Z test/test_ops.py::TestOps::test_scaled_dot_product_int8_op_batch_size_56_n_head_16_q_seq_len_89_kv_seq_len_100_head_dim_64_bfloat16 SKIPPED 2025-09-09T16:24:53.8346663Z test/test_ops.py::TestOps::test_scaled_dot_product_int8_op_batch_size_56_n_head_16_q_seq_len_89_kv_seq_len_100_head_dim_64_float32 SKIPPED 2025-09-09T16:24:53.8347732Z test/test_ops.py::TestOps::test_scaled_dot_product_int8_op_batch_size_56_n_head_16_q_seq_len_89_kv_seq_len_100_head_dim_64_mask_dtype0 SKIPPED 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test/test_ops.py::TestOps::test_scaled_dot_product_int8_op_batch_size_56_n_head_16_q_seq_len_89_kv_seq_len_253_head_dim_64_mask_dtype0 SKIPPED 2025-09-09T16:24:53.8355246Z test/test_ops.py::TestOps::test_scaled_dot_product_int8_op_batch_size_56_n_head_2_q_seq_len_18_kv_seq_len_100_head_dim_32_bfloat16 SKIPPED 2025-09-09T16:24:53.8356256Z test/test_ops.py::TestOps::test_scaled_dot_product_int8_op_batch_size_56_n_head_2_q_seq_len_18_kv_seq_len_100_head_dim_32_float32 SKIPPED 2025-09-09T16:24:53.8357376Z test/test_ops.py::TestOps::test_scaled_dot_product_int8_op_batch_size_56_n_head_2_q_seq_len_18_kv_seq_len_100_head_dim_32_mask_dtype0 SKIPPED 2025-09-09T16:24:53.8358399Z test/test_ops.py::TestOps::test_scaled_dot_product_int8_op_batch_size_56_n_head_2_q_seq_len_18_kv_seq_len_100_head_dim_64_bfloat16 SKIPPED 2025-09-09T16:24:53.8359608Z test/test_ops.py::TestOps::test_scaled_dot_product_int8_op_batch_size_56_n_head_2_q_seq_len_18_kv_seq_len_100_head_dim_64_float32 SKIPPED 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test/test_ops.py::TestOps::test_scaled_dot_product_int8_op_batch_size_56_n_head_2_q_seq_len_89_kv_seq_len_253_head_dim_64_bfloat16 SKIPPED 2025-09-09T16:24:53.8378096Z test/test_ops.py::TestOps::test_scaled_dot_product_int8_op_batch_size_56_n_head_2_q_seq_len_89_kv_seq_len_253_head_dim_64_float32 SKIPPED 2025-09-09T16:24:53.8379113Z test/test_ops.py::TestOps::test_scaled_dot_product_int8_op_batch_size_56_n_head_2_q_seq_len_89_kv_seq_len_253_head_dim_64_mask_dtype0 SKIPPED 2025-09-09T16:24:53.8380037Z test/test_ops.py::test_unpack_tensor_core_tiled_layout_correctness[shape_4096x4096-tiles_2] PASSED 2025-09-09T16:24:53.8380850Z test/test_ops.py::test_unpack_tensor_core_tiled_layout_correctness[shape_4096x4096-tiles_4] PASSED 2025-09-09T16:24:53.8381710Z test/test_ops.py::test_unpack_tensor_core_tiled_layout_correctness[shape_4096x4096-tiles_8] PASSED 2025-09-09T16:24:53.8382527Z test/test_ops.py::test_unpack_tensor_core_tiled_layout_correctness[shape_4096x11008-tiles_2] PASSED 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test/test_ops.py::test_dequantize_tensor_core_tiled_layout_correctness_unpack_and_dequant[(4096, 11008)-8-32] PASSED 2025-09-09T16:25:02.4729013Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_correctness_unpack_and_dequant[(4096, 11008)-8-64] PASSED 2025-09-09T16:25:02.4729922Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_correctness_unpack_and_dequant[(4096, 11008)-8-128] PASSED 2025-09-09T16:25:02.4730930Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_correctness_unpack_and_dequant[(4096, 11008)-8-256] PASSED 2025-09-09T16:25:02.4731855Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_correctness_unpack_and_dequant[(11008, 4096)-2-32] PASSED 2025-09-09T16:25:02.4732830Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_correctness_unpack_and_dequant[(11008, 4096)-2-64] PASSED 2025-09-09T16:25:02.4733818Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_correctness_unpack_and_dequant[(11008, 4096)-2-128] PASSED 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test/test_ops.py::test_dequantize_tensor_core_tiled_layout_correctness_unpack_and_dequant[(11008, 4096)-8-64] PASSED 2025-09-09T16:25:02.4741214Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_correctness_unpack_and_dequant[(11008, 4096)-8-128] PASSED 2025-09-09T16:25:02.4742139Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_correctness_unpack_and_dequant[(11008, 4096)-8-256] PASSED 2025-09-09T16:25:02.4743075Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_correctness_unpack_and_dequant[(4096, 14336)-2-32] PASSED 2025-09-09T16:25:02.4743998Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_correctness_unpack_and_dequant[(4096, 14336)-2-64] PASSED 2025-09-09T16:25:02.4744916Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_correctness_unpack_and_dequant[(4096, 14336)-2-128] PASSED 2025-09-09T16:25:02.4745849Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_correctness_unpack_and_dequant[(4096, 14336)-2-256] PASSED 2025-09-09T16:25:02.4746773Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_correctness_unpack_and_dequant[(4096, 14336)-4-32] PASSED 2025-09-09T16:25:02.4747695Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_correctness_unpack_and_dequant[(4096, 14336)-4-64] PASSED 2025-09-09T16:25:02.4748623Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_correctness_unpack_and_dequant[(4096, 14336)-4-128] PASSED 2025-09-09T16:25:02.4749629Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_correctness_unpack_and_dequant[(4096, 14336)-4-256] PASSED 2025-09-09T16:25:02.4750572Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_correctness_unpack_and_dequant[(4096, 14336)-8-32] PASSED 2025-09-09T16:25:02.4751499Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_correctness_unpack_and_dequant[(4096, 14336)-8-64] PASSED 2025-09-09T16:25:02.4752420Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_correctness_unpack_and_dequant[(4096, 14336)-8-128] PASSED 2025-09-09T16:25:02.4753350Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_correctness_unpack_and_dequant[(4096, 14336)-8-256] PASSED 2025-09-09T16:25:02.4754263Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_correctness_unpack_and_dequant[(14336, 4096)-2-32] PASSED 2025-09-09T16:25:02.4755236Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_correctness_unpack_and_dequant[(14336, 4096)-2-64] PASSED 2025-09-09T16:25:02.4756169Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_correctness_unpack_and_dequant[(14336, 4096)-2-128] PASSED 2025-09-09T16:25:02.4757136Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_correctness_unpack_and_dequant[(14336, 4096)-2-256] PASSED 2025-09-09T16:25:02.4758059Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_correctness_unpack_and_dequant[(14336, 4096)-4-32] PASSED 2025-09-09T16:25:02.4759009Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_correctness_unpack_and_dequant[(14336, 4096)-4-64] PASSED 2025-09-09T16:25:02.4760030Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_correctness_unpack_and_dequant[(14336, 4096)-4-128] PASSED 2025-09-09T16:25:02.4760968Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_correctness_unpack_and_dequant[(14336, 4096)-4-256] PASSED 2025-09-09T16:25:02.4761885Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_correctness_unpack_and_dequant[(14336, 4096)-8-32] PASSED 2025-09-09T16:25:02.4762802Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_correctness_unpack_and_dequant[(14336, 4096)-8-64] PASSED 2025-09-09T16:25:02.4763716Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_correctness_unpack_and_dequant[(14336, 4096)-8-128] PASSED 2025-09-09T16:25:02.4764646Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_correctness_unpack_and_dequant[(14336, 4096)-8-256] PASSED 2025-09-09T16:25:02.4765460Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(4096, 4096)-2-32] PASSED 2025-09-09T16:25:02.4766163Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(4096, 4096)-2-64] PASSED 2025-09-09T16:25:02.4766870Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(4096, 4096)-2-128] PASSED 2025-09-09T16:25:02.4767584Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(4096, 4096)-2-256] PASSED 2025-09-09T16:25:02.4768286Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(4096, 4096)-4-32] PASSED 2025-09-09T16:25:02.4768995Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(4096, 4096)-4-64] PASSED 2025-09-09T16:25:02.4769693Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(4096, 4096)-4-128] PASSED 2025-09-09T16:25:02.4770407Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(4096, 4096)-4-256] PASSED 2025-09-09T16:25:02.4771105Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(4096, 4096)-8-32] PASSED 2025-09-09T16:25:02.4771802Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(4096, 4096)-8-64] PASSED 2025-09-09T16:25:02.4772505Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(4096, 4096)-8-128] PASSED 2025-09-09T16:25:06.9953736Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(4096, 4096)-8-256] PASSED 2025-09-09T16:25:06.9954768Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(4096, 11008)-2-32] PASSED 2025-09-09T16:25:06.9955481Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(4096, 11008)-2-64] PASSED 2025-09-09T16:25:06.9956200Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(4096, 11008)-2-128] PASSED 2025-09-09T16:25:06.9956924Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(4096, 11008)-2-256] PASSED 2025-09-09T16:25:06.9957634Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(4096, 11008)-4-32] PASSED 2025-09-09T16:25:06.9958330Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(4096, 11008)-4-64] PASSED 2025-09-09T16:25:06.9959034Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(4096, 11008)-4-128] PASSED 2025-09-09T16:25:06.9959998Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(4096, 11008)-4-256] PASSED 2025-09-09T16:25:06.9960707Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(4096, 11008)-8-32] PASSED 2025-09-09T16:25:06.9961411Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(4096, 11008)-8-64] PASSED 2025-09-09T16:25:06.9962197Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(4096, 11008)-8-128] PASSED 2025-09-09T16:25:06.9963001Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(4096, 11008)-8-256] PASSED 2025-09-09T16:25:06.9963696Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(11008, 4096)-2-32] PASSED 2025-09-09T16:25:06.9964399Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(11008, 4096)-2-64] PASSED 2025-09-09T16:25:06.9965103Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(11008, 4096)-2-128] PASSED 2025-09-09T16:25:06.9965803Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(11008, 4096)-2-256] PASSED 2025-09-09T16:25:06.9966514Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(11008, 4096)-4-32] PASSED 2025-09-09T16:25:06.9967210Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(11008, 4096)-4-64] PASSED 2025-09-09T16:25:06.9967916Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(11008, 4096)-4-128] PASSED 2025-09-09T16:25:06.9968624Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(11008, 4096)-4-256] PASSED 2025-09-09T16:25:06.9969328Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(11008, 4096)-8-32] PASSED 2025-09-09T16:25:06.9970025Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(11008, 4096)-8-64] PASSED 2025-09-09T16:25:06.9970721Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(11008, 4096)-8-128] PASSED 2025-09-09T16:25:06.9971427Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(11008, 4096)-8-256] PASSED 2025-09-09T16:25:06.9972126Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(4096, 14336)-2-32] PASSED 2025-09-09T16:25:06.9972825Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(4096, 14336)-2-64] PASSED 2025-09-09T16:25:06.9973531Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(4096, 14336)-2-128] PASSED 2025-09-09T16:25:06.9974234Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(4096, 14336)-2-256] PASSED 2025-09-09T16:25:06.9974940Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(4096, 14336)-4-32] PASSED 2025-09-09T16:25:06.9975637Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(4096, 14336)-4-64] PASSED 2025-09-09T16:25:06.9976345Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(4096, 14336)-4-128] PASSED 2025-09-09T16:25:06.9977059Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(4096, 14336)-4-256] PASSED 2025-09-09T16:25:06.9977822Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(4096, 14336)-8-32] PASSED 2025-09-09T16:25:06.9978533Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(4096, 14336)-8-64] PASSED 2025-09-09T16:25:06.9979236Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(4096, 14336)-8-128] PASSED 2025-09-09T16:25:06.9979968Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(4096, 14336)-8-256] PASSED 2025-09-09T16:25:06.9980676Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(14336, 4096)-2-32] PASSED 2025-09-09T16:25:06.9981427Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(14336, 4096)-2-64] PASSED 2025-09-09T16:25:06.9982137Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(14336, 4096)-2-128] PASSED 2025-09-09T16:25:06.9982840Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(14336, 4096)-2-256] PASSED 2025-09-09T16:25:06.9983600Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(14336, 4096)-4-32] PASSED 2025-09-09T16:25:06.9984304Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(14336, 4096)-4-64] PASSED 2025-09-09T16:25:06.9985045Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(14336, 4096)-4-128] PASSED 2025-09-09T16:25:06.9985804Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(14336, 4096)-4-256] PASSED 2025-09-09T16:25:06.9986504Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(14336, 4096)-8-32] PASSED 2025-09-09T16:25:06.9987209Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(14336, 4096)-8-64] PASSED 2025-09-09T16:25:06.9987916Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(14336, 4096)-8-128] PASSED 2025-09-09T16:25:06.9988620Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(14336, 4096)-8-256] PASSED 2025-09-09T16:25:06.9989253Z test/test_ops.py::test_marlin_24[1-128-512-4--1-(1, 1, 1)] PASSED 2025-09-09T16:25:06.9989780Z test/test_ops.py::test_marlin_24[1-128-512-4--1-(1, 4, 8)] PASSED 2025-09-09T16:25:06.9990310Z test/test_ops.py::test_marlin_24[1-128-512-4--1-(1, 7, 5)] PASSED 2025-09-09T16:25:06.9990858Z test/test_ops.py::test_marlin_24[1-128-512-4--1-(13, 17, 67)] PASSED 2025-09-09T16:25:06.9991409Z 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test/test_ops.py::test_marlin_24[4-128-512-4--1-(1, 1, 1)] PASSED 2025-09-09T16:25:07.0002897Z test/test_ops.py::test_marlin_24[4-128-512-4--1-(1, 4, 8)] PASSED 2025-09-09T16:25:07.0003419Z test/test_ops.py::test_marlin_24[4-128-512-4--1-(1, 7, 5)] PASSED 2025-09-09T16:25:07.0003952Z test/test_ops.py::test_marlin_24[4-128-512-4--1-(13, 17, 67)] PASSED 2025-09-09T16:25:07.0004499Z test/test_ops.py::test_marlin_24[4-128-512-4--1-(26, 37, 13)] PASSED 2025-09-09T16:25:07.0005100Z test/test_ops.py::test_marlin_24[4-128-512-4--1-(67, 13, 11)] PASSED 2025-09-09T16:25:07.0005634Z test/test_ops.py::test_marlin_24[4-128-512-4-128-(1, 1, 1)] PASSED 2025-09-09T16:25:07.0006219Z test/test_ops.py::test_marlin_24[4-128-512-4-128-(1, 4, 8)] PASSED 2025-09-09T16:25:07.0006743Z test/test_ops.py::test_marlin_24[4-128-512-4-128-(1, 7, 5)] PASSED 2025-09-09T16:25:07.0007337Z test/test_ops.py::test_marlin_24[4-128-512-4-128-(13, 17, 67)] PASSED 2025-09-09T16:25:07.0007886Z test/test_ops.py::test_marlin_24[4-128-512-4-128-(26, 37, 13)] PASSED 2025-09-09T16:25:07.0008438Z test/test_ops.py::test_marlin_24[4-128-512-4-128-(67, 13, 11)] PASSED 2025-09-09T16:25:07.0008974Z test/test_ops.py::test_marlin_24[4-128-512-8--1-(1, 1, 1)] PASSED 2025-09-09T16:25:07.0009499Z test/test_ops.py::test_marlin_24[4-128-512-8--1-(1, 4, 8)] PASSED 2025-09-09T16:25:07.0010029Z test/test_ops.py::test_marlin_24[4-128-512-8--1-(1, 7, 5)] PASSED 2025-09-09T16:25:07.0010564Z test/test_ops.py::test_marlin_24[4-128-512-8--1-(13, 17, 67)] PASSED 2025-09-09T16:25:07.0011110Z test/test_ops.py::test_marlin_24[4-128-512-8--1-(26, 37, 13)] PASSED 2025-09-09T16:25:07.0011670Z test/test_ops.py::test_marlin_24[4-128-512-8--1-(67, 13, 11)] PASSED 2025-09-09T16:25:07.0012203Z test/test_ops.py::test_marlin_24[4-128-512-8-128-(1, 1, 1)] PASSED 2025-09-09T16:25:09.8977648Z test/test_ops.py::test_marlin_24[4-128-512-8-128-(1, 4, 8)] PASSED 2025-09-09T16:25:09.8978276Z test/test_ops.py::test_marlin_24[4-128-512-8-128-(1, 7, 5)] PASSED 2025-09-09T16:25:09.8978858Z test/test_ops.py::test_marlin_24[4-128-512-8-128-(13, 17, 67)] PASSED 2025-09-09T16:25:09.8979425Z test/test_ops.py::test_marlin_24[4-128-512-8-128-(26, 37, 13)] PASSED 2025-09-09T16:25:09.8979992Z test/test_ops.py::test_marlin_24[4-128-512-8-128-(67, 13, 11)] PASSED 2025-09-09T16:25:09.8980561Z test/test_ops.py::test_marlin_24[8-128-512-4--1-(1, 1, 1)] PASSED 2025-09-09T16:25:09.8981098Z test/test_ops.py::test_marlin_24[8-128-512-4--1-(1, 4, 8)] PASSED 2025-09-09T16:25:09.8981683Z test/test_ops.py::test_marlin_24[8-128-512-4--1-(1, 7, 5)] PASSED 2025-09-09T16:25:09.8982221Z test/test_ops.py::test_marlin_24[8-128-512-4--1-(13, 17, 67)] PASSED 2025-09-09T16:25:09.8982786Z test/test_ops.py::test_marlin_24[8-128-512-4--1-(26, 37, 13)] PASSED 2025-09-09T16:25:09.8983334Z test/test_ops.py::test_marlin_24[8-128-512-4--1-(67, 13, 11)] PASSED 2025-09-09T16:25:09.8983878Z test/test_ops.py::test_marlin_24[8-128-512-4-128-(1, 1, 1)] PASSED 2025-09-09T16:25:09.8984418Z test/test_ops.py::test_marlin_24[8-128-512-4-128-(1, 4, 8)] PASSED 2025-09-09T16:25:09.8984964Z test/test_ops.py::test_marlin_24[8-128-512-4-128-(1, 7, 5)] PASSED 2025-09-09T16:25:09.8985516Z test/test_ops.py::test_marlin_24[8-128-512-4-128-(13, 17, 67)] PASSED 2025-09-09T16:25:09.8986444Z test/test_ops.py::test_marlin_24[8-128-512-4-128-(26, 37, 13)] PASSED 2025-09-09T16:25:09.8987014Z test/test_ops.py::test_marlin_24[8-128-512-4-128-(67, 13, 11)] PASSED 2025-09-09T16:25:09.8987557Z test/test_ops.py::test_marlin_24[8-128-512-8--1-(1, 1, 1)] PASSED 2025-09-09T16:25:09.8988089Z test/test_ops.py::test_marlin_24[8-128-512-8--1-(1, 4, 8)] PASSED 2025-09-09T16:25:09.8988631Z test/test_ops.py::test_marlin_24[8-128-512-8--1-(1, 7, 5)] PASSED 2025-09-09T16:25:09.8989169Z test/test_ops.py::test_marlin_24[8-128-512-8--1-(13, 17, 67)] PASSED 2025-09-09T16:25:09.8989726Z test/test_ops.py::test_marlin_24[8-128-512-8--1-(26, 37, 13)] PASSED 2025-09-09T16:25:09.8990273Z test/test_ops.py::test_marlin_24[8-128-512-8--1-(67, 13, 11)] PASSED 2025-09-09T16:25:09.8990818Z test/test_ops.py::test_marlin_24[8-128-512-8-128-(1, 1, 1)] PASSED 2025-09-09T16:25:09.8991467Z test/test_ops.py::test_marlin_24[8-128-512-8-128-(1, 4, 8)] PASSED 2025-09-09T16:25:09.8992009Z test/test_ops.py::test_marlin_24[8-128-512-8-128-(1, 7, 5)] PASSED 2025-09-09T16:25:09.8992561Z test/test_ops.py::test_marlin_24[8-128-512-8-128-(13, 17, 67)] PASSED 2025-09-09T16:25:09.8993204Z test/test_ops.py::test_marlin_24[8-128-512-8-128-(26, 37, 13)] PASSED 2025-09-09T16:25:09.8993759Z test/test_ops.py::test_marlin_24[8-128-512-8-128-(67, 13, 11)] PASSED 2025-09-09T16:25:09.8994400Z test/test_ops.py::test_marlin_24[16-128-512-4--1-(1, 1, 1)] PASSED 2025-09-09T16:25:09.8994935Z test/test_ops.py::test_marlin_24[16-128-512-4--1-(1, 4, 8)] PASSED 2025-09-09T16:25:09.8995465Z test/test_ops.py::test_marlin_24[16-128-512-4--1-(1, 7, 5)] PASSED 2025-09-09T16:25:09.8996024Z test/test_ops.py::test_marlin_24[16-128-512-4--1-(13, 17, 67)] PASSED 2025-09-09T16:25:09.8996591Z test/test_ops.py::test_marlin_24[16-128-512-4--1-(26, 37, 13)] PASSED 2025-09-09T16:25:09.8997155Z test/test_ops.py::test_marlin_24[16-128-512-4--1-(67, 13, 11)] PASSED 2025-09-09T16:25:09.8997712Z test/test_ops.py::test_marlin_24[16-128-512-4-128-(1, 1, 1)] PASSED 2025-09-09T16:25:09.8998262Z test/test_ops.py::test_marlin_24[16-128-512-4-128-(1, 4, 8)] PASSED 2025-09-09T16:25:09.8998812Z test/test_ops.py::test_marlin_24[16-128-512-4-128-(1, 7, 5)] PASSED 2025-09-09T16:25:09.8999503Z test/test_ops.py::test_marlin_24[16-128-512-4-128-(13, 17, 67)] PASSED 2025-09-09T16:25:09.9000072Z test/test_ops.py::test_marlin_24[16-128-512-4-128-(26, 37, 13)] PASSED 2025-09-09T16:25:09.9000645Z test/test_ops.py::test_marlin_24[16-128-512-4-128-(67, 13, 11)] PASSED 2025-09-09T16:25:09.9001198Z test/test_ops.py::test_marlin_24[16-128-512-8--1-(1, 1, 1)] PASSED 2025-09-09T16:25:09.9001740Z test/test_ops.py::test_marlin_24[16-128-512-8--1-(1, 4, 8)] PASSED 2025-09-09T16:25:09.9002276Z test/test_ops.py::test_marlin_24[16-128-512-8--1-(1, 7, 5)] PASSED 2025-09-09T16:25:09.9002838Z test/test_ops.py::test_marlin_24[16-128-512-8--1-(13, 17, 67)] PASSED 2025-09-09T16:25:09.9003401Z test/test_ops.py::test_marlin_24[16-128-512-8--1-(26, 37, 13)] PASSED 2025-09-09T16:25:09.9003959Z test/test_ops.py::test_marlin_24[16-128-512-8--1-(67, 13, 11)] PASSED 2025-09-09T16:25:09.9004517Z test/test_ops.py::test_marlin_24[16-128-512-8-128-(1, 1, 1)] PASSED 2025-09-09T16:25:09.9005061Z test/test_ops.py::test_marlin_24[16-128-512-8-128-(1, 4, 8)] PASSED 2025-09-09T16:25:09.9005612Z test/test_ops.py::test_marlin_24[16-128-512-8-128-(1, 7, 5)] PASSED 2025-09-09T16:25:09.9006169Z test/test_ops.py::test_marlin_24[16-128-512-8-128-(13, 17, 67)] PASSED 2025-09-09T16:25:09.9006740Z test/test_ops.py::test_marlin_24[16-128-512-8-128-(26, 37, 13)] PASSED 2025-09-09T16:25:09.9007313Z test/test_ops.py::test_marlin_24[16-128-512-8-128-(67, 13, 11)] PASSED 2025-09-09T16:25:09.9007922Z test/test_ops.py::test_marlin_24[32-128-512-4--1-(1, 1, 1)] PASSED 2025-09-09T16:25:09.9008466Z test/test_ops.py::test_marlin_24[32-128-512-4--1-(1, 4, 8)] PASSED 2025-09-09T16:25:09.9009000Z test/test_ops.py::test_marlin_24[32-128-512-4--1-(1, 7, 5)] PASSED 2025-09-09T16:25:09.9009552Z test/test_ops.py::test_marlin_24[32-128-512-4--1-(13, 17, 67)] PASSED 2025-09-09T16:25:09.9010122Z test/test_ops.py::test_marlin_24[32-128-512-4--1-(26, 37, 13)] PASSED 2025-09-09T16:25:09.9010680Z test/test_ops.py::test_marlin_24[32-128-512-4--1-(67, 13, 11)] PASSED 2025-09-09T16:25:09.9011240Z test/test_ops.py::test_marlin_24[32-128-512-4-128-(1, 1, 1)] PASSED 2025-09-09T16:25:09.9011785Z test/test_ops.py::test_marlin_24[32-128-512-4-128-(1, 4, 8)] PASSED 2025-09-09T16:25:09.9012331Z test/test_ops.py::test_marlin_24[32-128-512-4-128-(1, 7, 5)] PASSED 2025-09-09T16:25:09.9012888Z test/test_ops.py::test_marlin_24[32-128-512-4-128-(13, 17, 67)] PASSED 2025-09-09T16:25:09.9013522Z test/test_ops.py::test_marlin_24[32-128-512-4-128-(26, 37, 13)] PASSED 2025-09-09T16:25:09.9014096Z test/test_ops.py::test_marlin_24[32-128-512-4-128-(67, 13, 11)] PASSED 2025-09-09T16:25:09.9014684Z test/test_ops.py::test_marlin_24[32-128-512-8--1-(1, 1, 1)] PASSED 2025-09-09T16:25:09.9015227Z test/test_ops.py::test_marlin_24[32-128-512-8--1-(1, 4, 8)] PASSED 2025-09-09T16:25:09.9015804Z test/test_ops.py::test_marlin_24[32-128-512-8--1-(1, 7, 5)] PASSED 2025-09-09T16:25:09.9016364Z test/test_ops.py::test_marlin_24[32-128-512-8--1-(13, 17, 67)] PASSED 2025-09-09T16:25:09.9016920Z test/test_ops.py::test_marlin_24[32-128-512-8--1-(26, 37, 13)] PASSED 2025-09-09T16:25:09.9017476Z test/test_ops.py::test_marlin_24[32-128-512-8--1-(67, 13, 11)] PASSED 2025-09-09T16:25:09.9018026Z test/test_ops.py::test_marlin_24[32-128-512-8-128-(1, 1, 1)] PASSED 2025-09-09T16:25:09.9018573Z test/test_ops.py::test_marlin_24[32-128-512-8-128-(1, 4, 8)] PASSED 2025-09-09T16:25:09.9019118Z test/test_ops.py::test_marlin_24[32-128-512-8-128-(1, 7, 5)] PASSED 2025-09-09T16:25:09.9019670Z test/test_ops.py::test_marlin_24[32-128-512-8-128-(13, 17, 67)] PASSED 2025-09-09T16:25:09.9020246Z test/test_ops.py::test_marlin_24[32-128-512-8-128-(26, 37, 13)] PASSED 2025-09-09T16:25:09.9020814Z test/test_ops.py::test_marlin_24[32-128-512-8-128-(67, 13, 11)] PASSED 2025-09-09T16:25:09.9021367Z test/test_ops.py::test_marlin_24[64-128-512-4--1-(1, 1, 1)] PASSED 2025-09-09T16:25:09.9021913Z test/test_ops.py::test_marlin_24[64-128-512-4--1-(1, 4, 8)] PASSED 2025-09-09T16:25:09.9022710Z test/test_ops.py::test_marlin_24[64-128-512-4--1-(1, 7, 5)] PASSED 2025-09-09T16:25:09.9023263Z test/test_ops.py::test_marlin_24[64-128-512-4--1-(13, 17, 67)] PASSED 2025-09-09T16:25:09.9023816Z test/test_ops.py::test_marlin_24[64-128-512-4--1-(26, 37, 13)] PASSED 2025-09-09T16:25:09.9024390Z test/test_ops.py::test_marlin_24[64-128-512-4--1-(67, 13, 11)] PASSED 2025-09-09T16:25:09.9024947Z test/test_ops.py::test_marlin_24[64-128-512-4-128-(1, 1, 1)] PASSED 2025-09-09T16:25:09.9025491Z test/test_ops.py::test_marlin_24[64-128-512-4-128-(1, 4, 8)] PASSED 2025-09-09T16:25:09.9026043Z test/test_ops.py::test_marlin_24[64-128-512-4-128-(1, 7, 5)] PASSED 2025-09-09T16:25:09.9026597Z test/test_ops.py::test_marlin_24[64-128-512-4-128-(13, 17, 67)] PASSED 2025-09-09T16:25:09.9027166Z test/test_ops.py::test_marlin_24[64-128-512-4-128-(26, 37, 13)] PASSED 2025-09-09T16:25:09.9027730Z test/test_ops.py::test_marlin_24[64-128-512-4-128-(67, 13, 11)] PASSED 2025-09-09T16:25:09.9028291Z test/test_ops.py::test_marlin_24[64-128-512-8--1-(1, 1, 1)] PASSED 2025-09-09T16:25:09.9028842Z test/test_ops.py::test_marlin_24[64-128-512-8--1-(1, 4, 8)] PASSED 2025-09-09T16:25:09.9029473Z test/test_ops.py::test_marlin_24[64-128-512-8--1-(1, 7, 5)] PASSED 2025-09-09T16:25:09.9030033Z test/test_ops.py::test_marlin_24[64-128-512-8--1-(13, 17, 67)] PASSED 2025-09-09T16:25:09.9030596Z test/test_ops.py::test_marlin_24[64-128-512-8--1-(26, 37, 13)] PASSED 2025-09-09T16:25:09.9031168Z test/test_ops.py::test_marlin_24[64-128-512-8--1-(67, 13, 11)] PASSED 2025-09-09T16:25:09.9031739Z test/test_ops.py::test_marlin_24[64-128-512-8-128-(1, 1, 1)] PASSED 2025-09-09T16:25:09.9032319Z test/test_ops.py::test_marlin_24[64-128-512-8-128-(1, 4, 8)] PASSED 2025-09-09T16:25:09.9032874Z test/test_ops.py::test_marlin_24[64-128-512-8-128-(1, 7, 5)] PASSED 2025-09-09T16:25:09.9033429Z test/test_ops.py::test_marlin_24[64-128-512-8-128-(13, 17, 67)] PASSED 2025-09-09T16:25:09.9034001Z test/test_ops.py::test_marlin_24[64-128-512-8-128-(26, 37, 13)] PASSED 2025-09-09T16:25:09.9034570Z test/test_ops.py::test_marlin_24[64-128-512-8-128-(67, 13, 11)] PASSED 2025-09-09T16:25:09.9035219Z test/test_ops.py::test_marlin_qqq[1-128-64-4--1-(1, 1, 1)] SKIPPED (...) 2025-09-09T16:25:09.9035807Z test/test_ops.py::test_marlin_qqq[1-128-64-4--1-(1, 4, 8)] SKIPPED (...) 2025-09-09T16:25:09.9036447Z test/test_ops.py::test_marlin_qqq[1-128-64-4--1-(1, 7, 5)] SKIPPED (...) 2025-09-09T16:25:09.9037036Z test/test_ops.py::test_marlin_qqq[1-128-64-4--1-(13, 17, 67)] SKIPPED 2025-09-09T16:25:09.9470509Z test/test_ops.py::test_marlin_qqq[1-128-64-4--1-(26, 37, 13)] SKIPPED 2025-09-09T16:25:09.9471134Z test/test_ops.py::test_marlin_qqq[1-128-64-4--1-(67, 13, 11)] SKIPPED 2025-09-09T16:25:09.9471695Z test/test_ops.py::test_marlin_qqq[1-128-64-4-128-(1, 1, 1)] SKIPPED 2025-09-09T16:25:09.9472239Z test/test_ops.py::test_marlin_qqq[1-128-64-4-128-(1, 4, 8)] SKIPPED 2025-09-09T16:25:09.9472790Z test/test_ops.py::test_marlin_qqq[1-128-64-4-128-(1, 7, 5)] SKIPPED 2025-09-09T16:25:09.9473366Z test/test_ops.py::test_marlin_qqq[1-128-64-4-128-(13, 17, 67)] SKIPPED 2025-09-09T16:25:09.9473941Z test/test_ops.py::test_marlin_qqq[1-128-64-4-128-(26, 37, 13)] SKIPPED 2025-09-09T16:25:09.9474520Z test/test_ops.py::test_marlin_qqq[1-128-64-4-128-(67, 13, 11)] SKIPPED 2025-09-09T16:25:09.9475078Z test/test_ops.py::test_marlin_qqq[1-128-128-4--1-(1, 1, 1)] SKIPPED 2025-09-09T16:25:09.9475636Z test/test_ops.py::test_marlin_qqq[1-128-128-4--1-(1, 4, 8)] SKIPPED 2025-09-09T16:25:09.9476187Z test/test_ops.py::test_marlin_qqq[1-128-128-4--1-(1, 7, 5)] SKIPPED 2025-09-09T16:25:09.9476753Z test/test_ops.py::test_marlin_qqq[1-128-128-4--1-(13, 17, 67)] SKIPPED 2025-09-09T16:25:09.9477324Z test/test_ops.py::test_marlin_qqq[1-128-128-4--1-(26, 37, 13)] SKIPPED 2025-09-09T16:25:09.9477890Z test/test_ops.py::test_marlin_qqq[1-128-128-4--1-(67, 13, 11)] SKIPPED 2025-09-09T16:25:09.9478456Z test/test_ops.py::test_marlin_qqq[1-128-128-4-128-(1, 1, 1)] SKIPPED 2025-09-09T16:25:09.9479020Z test/test_ops.py::test_marlin_qqq[1-128-128-4-128-(1, 4, 8)] SKIPPED 2025-09-09T16:25:09.9479651Z test/test_ops.py::test_marlin_qqq[1-128-128-4-128-(1, 7, 5)] SKIPPED 2025-09-09T16:25:09.9480228Z test/test_ops.py::test_marlin_qqq[1-128-128-4-128-(13, 17, 67)] SKIPPED 2025-09-09T16:25:09.9480804Z test/test_ops.py::test_marlin_qqq[1-128-128-4-128-(26, 37, 13)] SKIPPED 2025-09-09T16:25:09.9481387Z test/test_ops.py::test_marlin_qqq[1-128-128-4-128-(67, 13, 11)] SKIPPED 2025-09-09T16:25:09.9481946Z test/test_ops.py::test_marlin_qqq[1-128-256-4--1-(1, 1, 1)] SKIPPED 2025-09-09T16:25:09.9482502Z test/test_ops.py::test_marlin_qqq[1-128-256-4--1-(1, 4, 8)] SKIPPED 2025-09-09T16:25:09.9483053Z test/test_ops.py::test_marlin_qqq[1-128-256-4--1-(1, 7, 5)] SKIPPED 2025-09-09T16:25:09.9483622Z test/test_ops.py::test_marlin_qqq[1-128-256-4--1-(13, 17, 67)] SKIPPED 2025-09-09T16:25:09.9484307Z test/test_ops.py::test_marlin_qqq[1-128-256-4--1-(26, 37, 13)] SKIPPED 2025-09-09T16:25:09.9484881Z test/test_ops.py::test_marlin_qqq[1-128-256-4--1-(67, 13, 11)] SKIPPED 2025-09-09T16:25:09.9485458Z test/test_ops.py::test_marlin_qqq[1-128-256-4-128-(1, 1, 1)] SKIPPED 2025-09-09T16:25:09.9486019Z test/test_ops.py::test_marlin_qqq[1-128-256-4-128-(1, 4, 8)] SKIPPED 2025-09-09T16:25:09.9486589Z test/test_ops.py::test_marlin_qqq[1-128-256-4-128-(1, 7, 5)] SKIPPED 2025-09-09T16:25:09.9487186Z test/test_ops.py::test_marlin_qqq[1-128-256-4-128-(13, 17, 67)] SKIPPED 2025-09-09T16:25:09.9487758Z test/test_ops.py::test_marlin_qqq[1-128-256-4-128-(26, 37, 13)] SKIPPED 2025-09-09T16:25:09.9488337Z test/test_ops.py::test_marlin_qqq[1-128-256-4-128-(67, 13, 11)] SKIPPED 2025-09-09T16:25:09.9488928Z test/test_ops.py::test_marlin_qqq[4-128-64-4--1-(1, 1, 1)] SKIPPED (...) 2025-09-09T16:25:09.9489528Z test/test_ops.py::test_marlin_qqq[4-128-64-4--1-(1, 4, 8)] SKIPPED (...) 2025-09-09T16:25:09.9490192Z test/test_ops.py::test_marlin_qqq[4-128-64-4--1-(1, 7, 5)] SKIPPED (...) 2025-09-09T16:25:09.9499334Z test/test_ops.py::test_marlin_qqq[4-128-64-4--1-(13, 17, 67)] SKIPPED 2025-09-09T16:25:09.9500146Z test/test_ops.py::test_marlin_qqq[4-128-64-4--1-(26, 37, 13)] SKIPPED 2025-09-09T16:25:09.9500724Z test/test_ops.py::test_marlin_qqq[4-128-64-4--1-(67, 13, 11)] SKIPPED 2025-09-09T16:25:09.9501366Z test/test_ops.py::test_marlin_qqq[4-128-64-4-128-(1, 1, 1)] SKIPPED 2025-09-09T16:25:09.9501929Z test/test_ops.py::test_marlin_qqq[4-128-64-4-128-(1, 4, 8)] SKIPPED 2025-09-09T16:25:09.9502483Z test/test_ops.py::test_marlin_qqq[4-128-64-4-128-(1, 7, 5)] SKIPPED 2025-09-09T16:25:09.9503055Z test/test_ops.py::test_marlin_qqq[4-128-64-4-128-(13, 17, 67)] SKIPPED 2025-09-09T16:25:09.9503631Z test/test_ops.py::test_marlin_qqq[4-128-64-4-128-(26, 37, 13)] SKIPPED 2025-09-09T16:25:09.9504218Z test/test_ops.py::test_marlin_qqq[4-128-64-4-128-(67, 13, 11)] SKIPPED 2025-09-09T16:25:09.9504786Z test/test_ops.py::test_marlin_qqq[4-128-128-4--1-(1, 1, 1)] SKIPPED 2025-09-09T16:25:09.9505340Z test/test_ops.py::test_marlin_qqq[4-128-128-4--1-(1, 4, 8)] SKIPPED 2025-09-09T16:25:09.9505894Z test/test_ops.py::test_marlin_qqq[4-128-128-4--1-(1, 7, 5)] SKIPPED 2025-09-09T16:25:09.9506448Z test/test_ops.py::test_marlin_qqq[4-128-128-4--1-(13, 17, 67)] SKIPPED 2025-09-09T16:25:09.9507016Z test/test_ops.py::test_marlin_qqq[4-128-128-4--1-(26, 37, 13)] SKIPPED 2025-09-09T16:25:09.9507577Z test/test_ops.py::test_marlin_qqq[4-128-128-4--1-(67, 13, 11)] SKIPPED 2025-09-09T16:25:09.9508151Z test/test_ops.py::test_marlin_qqq[4-128-128-4-128-(1, 1, 1)] SKIPPED 2025-09-09T16:25:09.9508718Z test/test_ops.py::test_marlin_qqq[4-128-128-4-128-(1, 4, 8)] SKIPPED 2025-09-09T16:25:09.9509276Z test/test_ops.py::test_marlin_qqq[4-128-128-4-128-(1, 7, 5)] SKIPPED 2025-09-09T16:25:09.9509855Z test/test_ops.py::test_marlin_qqq[4-128-128-4-128-(13, 17, 67)] SKIPPED 2025-09-09T16:25:09.9510433Z test/test_ops.py::test_marlin_qqq[4-128-128-4-128-(26, 37, 13)] SKIPPED 2025-09-09T16:25:09.9511014Z test/test_ops.py::test_marlin_qqq[4-128-128-4-128-(67, 13, 11)] SKIPPED 2025-09-09T16:25:09.9511569Z test/test_ops.py::test_marlin_qqq[4-128-256-4--1-(1, 1, 1)] SKIPPED 2025-09-09T16:25:09.9512127Z test/test_ops.py::test_marlin_qqq[4-128-256-4--1-(1, 4, 8)] SKIPPED 2025-09-09T16:25:09.9512674Z test/test_ops.py::test_marlin_qqq[4-128-256-4--1-(1, 7, 5)] SKIPPED 2025-09-09T16:25:09.9513237Z test/test_ops.py::test_marlin_qqq[4-128-256-4--1-(13, 17, 67)] SKIPPED 2025-09-09T16:25:09.9513813Z test/test_ops.py::test_marlin_qqq[4-128-256-4--1-(26, 37, 13)] SKIPPED 2025-09-09T16:25:09.9514376Z test/test_ops.py::test_marlin_qqq[4-128-256-4--1-(67, 13, 11)] SKIPPED 2025-09-09T16:25:09.9515012Z test/test_ops.py::test_marlin_qqq[4-128-256-4-128-(1, 1, 1)] SKIPPED 2025-09-09T16:25:09.9515567Z test/test_ops.py::test_marlin_qqq[4-128-256-4-128-(1, 4, 8)] SKIPPED 2025-09-09T16:25:09.9516134Z test/test_ops.py::test_marlin_qqq[4-128-256-4-128-(1, 7, 5)] SKIPPED 2025-09-09T16:25:09.9516700Z test/test_ops.py::test_marlin_qqq[4-128-256-4-128-(13, 17, 67)] SKIPPED 2025-09-09T16:25:09.9517280Z test/test_ops.py::test_marlin_qqq[4-128-256-4-128-(26, 37, 13)] SKIPPED 2025-09-09T16:25:09.9517856Z test/test_ops.py::test_marlin_qqq[4-128-256-4-128-(67, 13, 11)] SKIPPED 2025-09-09T16:25:09.9518431Z test/test_ops.py::test_marlin_qqq[8-128-64-4--1-(1, 1, 1)] SKIPPED (...) 2025-09-09T16:25:09.9519018Z test/test_ops.py::test_marlin_qqq[8-128-64-4--1-(1, 4, 8)] SKIPPED (...) 2025-09-09T16:25:09.9519663Z test/test_ops.py::test_marlin_qqq[8-128-64-4--1-(1, 7, 5)] SKIPPED (...) 2025-09-09T16:25:09.9520247Z test/test_ops.py::test_marlin_qqq[8-128-64-4--1-(13, 17, 67)] SKIPPED 2025-09-09T16:25:09.9520877Z test/test_ops.py::test_marlin_qqq[8-128-64-4--1-(26, 37, 13)] SKIPPED 2025-09-09T16:25:09.9521442Z test/test_ops.py::test_marlin_qqq[8-128-64-4--1-(67, 13, 11)] SKIPPED 2025-09-09T16:25:09.9522045Z test/test_ops.py::test_marlin_qqq[8-128-64-4-128-(1, 1, 1)] SKIPPED 2025-09-09T16:25:09.9522918Z test/test_ops.py::test_marlin_qqq[8-128-64-4-128-(1, 4, 8)] SKIPPED 2025-09-09T16:25:09.9523550Z test/test_ops.py::test_marlin_qqq[8-128-64-4-128-(1, 7, 5)] SKIPPED 2025-09-09T16:25:09.9524101Z test/test_ops.py::test_marlin_qqq[8-128-64-4-128-(13, 17, 67)] SKIPPED 2025-09-09T16:25:09.9524671Z test/test_ops.py::test_marlin_qqq[8-128-64-4-128-(26, 37, 13)] SKIPPED 2025-09-09T16:25:09.9525232Z test/test_ops.py::test_marlin_qqq[8-128-64-4-128-(67, 13, 11)] SKIPPED 2025-09-09T16:25:09.9525776Z test/test_ops.py::test_marlin_qqq[8-128-128-4--1-(1, 1, 1)] SKIPPED 2025-09-09T16:25:09.9526335Z test/test_ops.py::test_marlin_qqq[8-128-128-4--1-(1, 4, 8)] SKIPPED 2025-09-09T16:25:09.9526877Z test/test_ops.py::test_marlin_qqq[8-128-128-4--1-(1, 7, 5)] SKIPPED 2025-09-09T16:25:09.9527439Z test/test_ops.py::test_marlin_qqq[8-128-128-4--1-(13, 17, 67)] SKIPPED 2025-09-09T16:25:09.9528001Z test/test_ops.py::test_marlin_qqq[8-128-128-4--1-(26, 37, 13)] SKIPPED 2025-09-09T16:25:09.9528569Z test/test_ops.py::test_marlin_qqq[8-128-128-4--1-(67, 13, 11)] SKIPPED 2025-09-09T16:25:09.9529133Z test/test_ops.py::test_marlin_qqq[8-128-128-4-128-(1, 1, 1)] SKIPPED 2025-09-09T16:25:09.9529686Z test/test_ops.py::test_marlin_qqq[8-128-128-4-128-(1, 4, 8)] SKIPPED 2025-09-09T16:25:09.9530258Z test/test_ops.py::test_marlin_qqq[8-128-128-4-128-(1, 7, 5)] SKIPPED 2025-09-09T16:25:09.9530822Z test/test_ops.py::test_marlin_qqq[8-128-128-4-128-(13, 17, 67)] SKIPPED 2025-09-09T16:25:09.9531455Z test/test_ops.py::test_marlin_qqq[8-128-128-4-128-(26, 37, 13)] SKIPPED 2025-09-09T16:25:09.9532033Z test/test_ops.py::test_marlin_qqq[8-128-128-4-128-(67, 13, 11)] SKIPPED 2025-09-09T16:25:09.9532591Z test/test_ops.py::test_marlin_qqq[8-128-256-4--1-(1, 1, 1)] SKIPPED 2025-09-09T16:25:09.9533143Z test/test_ops.py::test_marlin_qqq[8-128-256-4--1-(1, 4, 8)] SKIPPED 2025-09-09T16:25:09.9533689Z test/test_ops.py::test_marlin_qqq[8-128-256-4--1-(1, 7, 5)] SKIPPED 2025-09-09T16:25:09.9534255Z test/test_ops.py::test_marlin_qqq[8-128-256-4--1-(13, 17, 67)] SKIPPED 2025-09-09T16:25:09.9534819Z test/test_ops.py::test_marlin_qqq[8-128-256-4--1-(26, 37, 13)] SKIPPED 2025-09-09T16:25:09.9535384Z test/test_ops.py::test_marlin_qqq[8-128-256-4--1-(67, 13, 11)] SKIPPED 2025-09-09T16:25:09.9535947Z test/test_ops.py::test_marlin_qqq[8-128-256-4-128-(1, 1, 1)] SKIPPED 2025-09-09T16:25:09.9536505Z test/test_ops.py::test_marlin_qqq[8-128-256-4-128-(1, 4, 8)] SKIPPED 2025-09-09T16:25:09.9537142Z test/test_ops.py::test_marlin_qqq[8-128-256-4-128-(1, 7, 5)] SKIPPED 2025-09-09T16:25:09.9537717Z test/test_ops.py::test_marlin_qqq[8-128-256-4-128-(13, 17, 67)] SKIPPED 2025-09-09T16:25:09.9538303Z test/test_ops.py::test_marlin_qqq[8-128-256-4-128-(26, 37, 13)] SKIPPED 2025-09-09T16:25:09.9944652Z test/test_ops.py::test_marlin_qqq[8-128-256-4-128-(67, 13, 11)] SKIPPED 2025-09-09T16:25:09.9945235Z test/test_ops.py::test_marlin_qqq[16-128-64-4--1-(1, 1, 1)] SKIPPED 2025-09-09T16:25:09.9945925Z test/test_ops.py::test_marlin_qqq[16-128-64-4--1-(1, 4, 8)] SKIPPED 2025-09-09T16:25:09.9946566Z test/test_ops.py::test_marlin_qqq[16-128-64-4--1-(1, 7, 5)] SKIPPED 2025-09-09T16:25:09.9947124Z test/test_ops.py::test_marlin_qqq[16-128-64-4--1-(13, 17, 67)] SKIPPED 2025-09-09T16:25:09.9947700Z test/test_ops.py::test_marlin_qqq[16-128-64-4--1-(26, 37, 13)] SKIPPED 2025-09-09T16:25:09.9948262Z test/test_ops.py::test_marlin_qqq[16-128-64-4--1-(67, 13, 11)] SKIPPED 2025-09-09T16:25:09.9948952Z test/test_ops.py::test_marlin_qqq[16-128-64-4-128-(1, 1, 1)] SKIPPED 2025-09-09T16:25:09.9949514Z test/test_ops.py::test_marlin_qqq[16-128-64-4-128-(1, 4, 8)] SKIPPED 2025-09-09T16:25:09.9950147Z test/test_ops.py::test_marlin_qqq[16-128-64-4-128-(1, 7, 5)] SKIPPED 2025-09-09T16:25:09.9950719Z test/test_ops.py::test_marlin_qqq[16-128-64-4-128-(13, 17, 67)] SKIPPED 2025-09-09T16:25:09.9951415Z test/test_ops.py::test_marlin_qqq[16-128-64-4-128-(26, 37, 13)] SKIPPED 2025-09-09T16:25:09.9951987Z test/test_ops.py::test_marlin_qqq[16-128-64-4-128-(67, 13, 11)] SKIPPED 2025-09-09T16:25:09.9952543Z test/test_ops.py::test_marlin_qqq[16-128-128-4--1-(1, 1, 1)] SKIPPED 2025-09-09T16:25:09.9953108Z test/test_ops.py::test_marlin_qqq[16-128-128-4--1-(1, 4, 8)] SKIPPED 2025-09-09T16:25:09.9953668Z test/test_ops.py::test_marlin_qqq[16-128-128-4--1-(1, 7, 5)] SKIPPED 2025-09-09T16:25:09.9954237Z test/test_ops.py::test_marlin_qqq[16-128-128-4--1-(13, 17, 67)] SKIPPED 2025-09-09T16:25:09.9954810Z test/test_ops.py::test_marlin_qqq[16-128-128-4--1-(26, 37, 13)] SKIPPED 2025-09-09T16:25:09.9955383Z test/test_ops.py::test_marlin_qqq[16-128-128-4--1-(67, 13, 11)] SKIPPED 2025-09-09T16:25:09.9955956Z test/test_ops.py::test_marlin_qqq[16-128-128-4-128-(1, 1, 1)] SKIPPED 2025-09-09T16:25:09.9956527Z test/test_ops.py::test_marlin_qqq[16-128-128-4-128-(1, 4, 8)] SKIPPED 2025-09-09T16:25:09.9957086Z test/test_ops.py::test_marlin_qqq[16-128-128-4-128-(1, 7, 5)] SKIPPED 2025-09-09T16:25:09.9957669Z test/test_ops.py::test_marlin_qqq[16-128-128-4-128-(13, 17, 67)] SKIPPED 2025-09-09T16:25:09.9958251Z test/test_ops.py::test_marlin_qqq[16-128-128-4-128-(26, 37, 13)] SKIPPED 2025-09-09T16:25:09.9958835Z test/test_ops.py::test_marlin_qqq[16-128-128-4-128-(67, 13, 11)] SKIPPED 2025-09-09T16:25:09.9959498Z test/test_ops.py::test_marlin_qqq[16-128-256-4--1-(1, 1, 1)] SKIPPED 2025-09-09T16:25:09.9960062Z test/test_ops.py::test_marlin_qqq[16-128-256-4--1-(1, 4, 8)] SKIPPED 2025-09-09T16:25:09.9960625Z test/test_ops.py::test_marlin_qqq[16-128-256-4--1-(1, 7, 5)] SKIPPED 2025-09-09T16:25:09.9961189Z test/test_ops.py::test_marlin_qqq[16-128-256-4--1-(13, 17, 67)] SKIPPED 2025-09-09T16:25:09.9961818Z test/test_ops.py::test_marlin_qqq[16-128-256-4--1-(26, 37, 13)] SKIPPED 2025-09-09T16:25:09.9962394Z test/test_ops.py::test_marlin_qqq[16-128-256-4--1-(67, 13, 11)] SKIPPED 2025-09-09T16:25:09.9962959Z test/test_ops.py::test_marlin_qqq[16-128-256-4-128-(1, 1, 1)] SKIPPED 2025-09-09T16:25:09.9963512Z test/test_ops.py::test_marlin_qqq[16-128-256-4-128-(1, 4, 8)] SKIPPED 2025-09-09T16:25:09.9964075Z test/test_ops.py::test_marlin_qqq[16-128-256-4-128-(1, 7, 5)] SKIPPED 2025-09-09T16:25:09.9964650Z test/test_ops.py::test_marlin_qqq[16-128-256-4-128-(13, 17, 67)] SKIPPED 2025-09-09T16:25:09.9965305Z test/test_ops.py::test_marlin_qqq[16-128-256-4-128-(26, 37, 13)] SKIPPED 2025-09-09T16:25:09.9965887Z test/test_ops.py::test_marlin_qqq[16-128-256-4-128-(67, 13, 11)] SKIPPED 2025-09-09T16:25:09.9966449Z test/test_ops.py::test_marlin_qqq[32-128-64-4--1-(1, 1, 1)] SKIPPED 2025-09-09T16:25:09.9967007Z test/test_ops.py::test_marlin_qqq[32-128-64-4--1-(1, 4, 8)] SKIPPED 2025-09-09T16:25:09.9967566Z test/test_ops.py::test_marlin_qqq[32-128-64-4--1-(1, 7, 5)] SKIPPED 2025-09-09T16:25:09.9968125Z test/test_ops.py::test_marlin_qqq[32-128-64-4--1-(13, 17, 67)] SKIPPED 2025-09-09T16:25:09.9968693Z test/test_ops.py::test_marlin_qqq[32-128-64-4--1-(26, 37, 13)] SKIPPED 2025-09-09T16:25:09.9969253Z test/test_ops.py::test_marlin_qqq[32-128-64-4--1-(67, 13, 11)] SKIPPED 2025-09-09T16:25:09.9969818Z test/test_ops.py::test_marlin_qqq[32-128-64-4-128-(1, 1, 1)] SKIPPED 2025-09-09T16:25:09.9970430Z test/test_ops.py::test_marlin_qqq[32-128-64-4-128-(1, 4, 8)] SKIPPED 2025-09-09T16:25:09.9970997Z test/test_ops.py::test_marlin_qqq[32-128-64-4-128-(1, 7, 5)] SKIPPED 2025-09-09T16:25:09.9971571Z test/test_ops.py::test_marlin_qqq[32-128-64-4-128-(13, 17, 67)] SKIPPED 2025-09-09T16:25:09.9972234Z test/test_ops.py::test_marlin_qqq[32-128-64-4-128-(26, 37, 13)] SKIPPED 2025-09-09T16:25:09.9972853Z test/test_ops.py::test_marlin_qqq[32-128-64-4-128-(67, 13, 11)] SKIPPED 2025-09-09T16:25:09.9973412Z test/test_ops.py::test_marlin_qqq[32-128-128-4--1-(1, 1, 1)] SKIPPED 2025-09-09T16:25:09.9973977Z test/test_ops.py::test_marlin_qqq[32-128-128-4--1-(1, 4, 8)] SKIPPED 2025-09-09T16:25:09.9974530Z test/test_ops.py::test_marlin_qqq[32-128-128-4--1-(1, 7, 5)] SKIPPED 2025-09-09T16:25:09.9975099Z test/test_ops.py::test_marlin_qqq[32-128-128-4--1-(13, 17, 67)] SKIPPED 2025-09-09T16:25:09.9975676Z test/test_ops.py::test_marlin_qqq[32-128-128-4--1-(26, 37, 13)] SKIPPED 2025-09-09T16:25:09.9976246Z test/test_ops.py::test_marlin_qqq[32-128-128-4--1-(67, 13, 11)] SKIPPED 2025-09-09T16:25:09.9976815Z test/test_ops.py::test_marlin_qqq[32-128-128-4-128-(1, 1, 1)] SKIPPED 2025-09-09T16:25:09.9977382Z test/test_ops.py::test_marlin_qqq[32-128-128-4-128-(1, 4, 8)] SKIPPED 2025-09-09T16:25:09.9977947Z test/test_ops.py::test_marlin_qqq[32-128-128-4-128-(1, 7, 5)] SKIPPED 2025-09-09T16:25:09.9978528Z test/test_ops.py::test_marlin_qqq[32-128-128-4-128-(13, 17, 67)] SKIPPED 2025-09-09T16:25:09.9979107Z test/test_ops.py::test_marlin_qqq[32-128-128-4-128-(26, 37, 13)] SKIPPED 2025-09-09T16:25:09.9979692Z test/test_ops.py::test_marlin_qqq[32-128-128-4-128-(67, 13, 11)] SKIPPED 2025-09-09T16:25:09.9980261Z test/test_ops.py::test_marlin_qqq[32-128-256-4--1-(1, 1, 1)] SKIPPED 2025-09-09T16:25:09.9980821Z test/test_ops.py::test_marlin_qqq[32-128-256-4--1-(1, 4, 8)] SKIPPED 2025-09-09T16:25:09.9981381Z test/test_ops.py::test_marlin_qqq[32-128-256-4--1-(1, 7, 5)] SKIPPED 2025-09-09T16:25:09.9981949Z test/test_ops.py::test_marlin_qqq[32-128-256-4--1-(13, 17, 67)] SKIPPED 2025-09-09T16:25:09.9982525Z test/test_ops.py::test_marlin_qqq[32-128-256-4--1-(26, 37, 13)] SKIPPED 2025-09-09T16:25:09.9983083Z test/test_ops.py::test_marlin_qqq[32-128-256-4--1-(67, 13, 11)] SKIPPED 2025-09-09T16:25:09.9983654Z test/test_ops.py::test_marlin_qqq[32-128-256-4-128-(1, 1, 1)] SKIPPED 2025-09-09T16:25:09.9984212Z test/test_ops.py::test_marlin_qqq[32-128-256-4-128-(1, 4, 8)] SKIPPED 2025-09-09T16:25:09.9984777Z test/test_ops.py::test_marlin_qqq[32-128-256-4-128-(1, 7, 5)] SKIPPED 2025-09-09T16:25:09.9985356Z test/test_ops.py::test_marlin_qqq[32-128-256-4-128-(13, 17, 67)] SKIPPED 2025-09-09T16:25:09.9985932Z test/test_ops.py::test_marlin_qqq[32-128-256-4-128-(26, 37, 13)] SKIPPED 2025-09-09T16:25:09.9986600Z test/test_ops.py::test_marlin_qqq[32-128-256-4-128-(67, 13, 11)] SKIPPED 2025-09-09T16:25:09.9987165Z test/test_ops.py::test_marlin_qqq[64-128-64-4--1-(1, 1, 1)] SKIPPED 2025-09-09T16:25:09.9987720Z test/test_ops.py::test_marlin_qqq[64-128-64-4--1-(1, 4, 8)] SKIPPED 2025-09-09T16:25:09.9988273Z test/test_ops.py::test_marlin_qqq[64-128-64-4--1-(1, 7, 5)] SKIPPED 2025-09-09T16:25:09.9988845Z test/test_ops.py::test_marlin_qqq[64-128-64-4--1-(13, 17, 67)] SKIPPED 2025-09-09T16:25:09.9989428Z test/test_ops.py::test_marlin_qqq[64-128-64-4--1-(26, 37, 13)] SKIPPED 2025-09-09T16:25:09.9989996Z test/test_ops.py::test_marlin_qqq[64-128-64-4--1-(67, 13, 11)] SKIPPED 2025-09-09T16:25:09.9990562Z test/test_ops.py::test_marlin_qqq[64-128-64-4-128-(1, 1, 1)] SKIPPED 2025-09-09T16:25:09.9991125Z test/test_ops.py::test_marlin_qqq[64-128-64-4-128-(1, 4, 8)] SKIPPED 2025-09-09T16:25:09.9991738Z test/test_ops.py::test_marlin_qqq[64-128-64-4-128-(1, 7, 5)] SKIPPED 2025-09-09T16:25:09.9992364Z test/test_ops.py::test_marlin_qqq[64-128-64-4-128-(13, 17, 67)] SKIPPED 2025-09-09T16:25:09.9992948Z test/test_ops.py::test_marlin_qqq[64-128-64-4-128-(26, 37, 13)] SKIPPED 2025-09-09T16:25:09.9993570Z test/test_ops.py::test_marlin_qqq[64-128-64-4-128-(67, 13, 11)] SKIPPED 2025-09-09T16:25:09.9994121Z test/test_ops.py::test_marlin_qqq[64-128-128-4--1-(1, 1, 1)] SKIPPED 2025-09-09T16:25:09.9994721Z test/test_ops.py::test_marlin_qqq[64-128-128-4--1-(1, 4, 8)] SKIPPED 2025-09-09T16:25:09.9995264Z test/test_ops.py::test_marlin_qqq[64-128-128-4--1-(1, 7, 5)] SKIPPED 2025-09-09T16:25:09.9995833Z test/test_ops.py::test_marlin_qqq[64-128-128-4--1-(13, 17, 67)] SKIPPED 2025-09-09T16:25:09.9996401Z test/test_ops.py::test_marlin_qqq[64-128-128-4--1-(26, 37, 13)] SKIPPED 2025-09-09T16:25:09.9996959Z test/test_ops.py::test_marlin_qqq[64-128-128-4--1-(67, 13, 11)] SKIPPED 2025-09-09T16:25:09.9997536Z test/test_ops.py::test_marlin_qqq[64-128-128-4-128-(1, 1, 1)] SKIPPED 2025-09-09T16:25:09.9998087Z test/test_ops.py::test_marlin_qqq[64-128-128-4-128-(1, 4, 8)] SKIPPED 2025-09-09T16:25:09.9998649Z test/test_ops.py::test_marlin_qqq[64-128-128-4-128-(1, 7, 5)] SKIPPED 2025-09-09T16:25:09.9999225Z test/test_ops.py::test_marlin_qqq[64-128-128-4-128-(13, 17, 67)] SKIPPED 2025-09-09T16:25:09.9999891Z test/test_ops.py::test_marlin_qqq[64-128-128-4-128-(26, 37, 13)] SKIPPED 2025-09-09T16:25:10.0000480Z test/test_ops.py::test_marlin_qqq[64-128-128-4-128-(67, 13, 11)] SKIPPED 2025-09-09T16:25:10.0001043Z test/test_ops.py::test_marlin_qqq[64-128-256-4--1-(1, 1, 1)] SKIPPED 2025-09-09T16:25:10.0001612Z test/test_ops.py::test_marlin_qqq[64-128-256-4--1-(1, 4, 8)] SKIPPED 2025-09-09T16:25:10.0002208Z test/test_ops.py::test_marlin_qqq[64-128-256-4--1-(1, 7, 5)] SKIPPED 2025-09-09T16:25:10.0002776Z test/test_ops.py::test_marlin_qqq[64-128-256-4--1-(13, 17, 67)] SKIPPED 2025-09-09T16:25:10.0003353Z test/test_ops.py::test_marlin_qqq[64-128-256-4--1-(26, 37, 13)] SKIPPED 2025-09-09T16:25:10.0381993Z test/test_ops.py::test_marlin_qqq[64-128-256-4--1-(67, 13, 11)] SKIPPED 2025-09-09T16:25:10.0383190Z test/test_ops.py::test_marlin_qqq[64-128-256-4-128-(1, 1, 1)] SKIPPED 2025-09-09T16:25:10.0384315Z test/test_ops.py::test_marlin_qqq[64-128-256-4-128-(1, 4, 8)] SKIPPED 2025-09-09T16:25:10.0385426Z test/test_ops.py::test_marlin_qqq[64-128-256-4-128-(1, 7, 5)] SKIPPED 2025-09-09T16:25:10.0386561Z test/test_ops.py::test_marlin_qqq[64-128-256-4-128-(13, 17, 67)] SKIPPED 2025-09-09T16:25:10.0387717Z test/test_ops.py::test_marlin_qqq[64-128-256-4-128-(26, 37, 13)] SKIPPED 2025-09-09T16:25:10.0388874Z test/test_ops.py::test_marlin_qqq[64-128-256-4-128-(67, 13, 11)] SKIPPED 2025-09-09T16:25:10.0389980Z test/test_ops.py::test_swizzle_mm SKIPPED (ROCm not available) 2025-09-09T16:25:10.0392724Z test/test_ops.py::test_scaled_embedding_bag_cpu[1-1-1-torch.int64] SKIPPED 2025-09-09T16:25:10.0393374Z test/test_ops.py::test_scaled_embedding_bag_cpu[1-1-1-torch.int32] SKIPPED 2025-09-09T16:25:10.0394004Z test/test_ops.py::test_scaled_embedding_bag_cpu[1-1-128-torch.int64] SKIPPED 2025-09-09T16:25:10.0394643Z test/test_ops.py::test_scaled_embedding_bag_cpu[1-1-128-torch.int32] SKIPPED 2025-09-09T16:25:10.0395274Z test/test_ops.py::test_scaled_embedding_bag_cpu[1-1-512-torch.int64] SKIPPED 2025-09-09T16:25:10.0395909Z test/test_ops.py::test_scaled_embedding_bag_cpu[1-1-512-torch.int32] SKIPPED 2025-09-09T16:25:10.0396534Z test/test_ops.py::test_scaled_embedding_bag_cpu[1-2-1-torch.int64] SKIPPED 2025-09-09T16:25:10.0397149Z test/test_ops.py::test_scaled_embedding_bag_cpu[1-2-1-torch.int32] SKIPPED 2025-09-09T16:25:10.0397777Z test/test_ops.py::test_scaled_embedding_bag_cpu[1-2-128-torch.int64] SKIPPED 2025-09-09T16:25:10.0398489Z test/test_ops.py::test_scaled_embedding_bag_cpu[1-2-128-torch.int32] SKIPPED 2025-09-09T16:25:10.0399121Z test/test_ops.py::test_scaled_embedding_bag_cpu[1-2-512-torch.int64] SKIPPED 2025-09-09T16:25:10.0399912Z test/test_ops.py::test_scaled_embedding_bag_cpu[1-2-512-torch.int32] SKIPPED 2025-09-09T16:25:10.0400545Z test/test_ops.py::test_scaled_embedding_bag_cpu[1-128-1-torch.int64] SKIPPED 2025-09-09T16:25:10.0401248Z test/test_ops.py::test_scaled_embedding_bag_cpu[1-128-1-torch.int32] SKIPPED 2025-09-09T16:25:10.0401898Z test/test_ops.py::test_scaled_embedding_bag_cpu[1-128-128-torch.int64] SKIPPED 2025-09-09T16:25:10.0402546Z test/test_ops.py::test_scaled_embedding_bag_cpu[1-128-128-torch.int32] SKIPPED 2025-09-09T16:25:10.0403200Z test/test_ops.py::test_scaled_embedding_bag_cpu[1-128-512-torch.int64] SKIPPED 2025-09-09T16:25:10.0403846Z test/test_ops.py::test_scaled_embedding_bag_cpu[1-128-512-torch.int32] SKIPPED 2025-09-09T16:25:10.0404492Z test/test_ops.py::test_scaled_embedding_bag_cpu[1-1024-1-torch.int64] SKIPPED 2025-09-09T16:25:10.0405139Z test/test_ops.py::test_scaled_embedding_bag_cpu[1-1024-1-torch.int32] SKIPPED 2025-09-09T16:25:10.0405794Z test/test_ops.py::test_scaled_embedding_bag_cpu[1-1024-128-torch.int64] SKIPPED 2025-09-09T16:25:10.0406456Z test/test_ops.py::test_scaled_embedding_bag_cpu[1-1024-128-torch.int32] SKIPPED 2025-09-09T16:25:10.0407111Z test/test_ops.py::test_scaled_embedding_bag_cpu[1-1024-512-torch.int64] SKIPPED 2025-09-09T16:25:10.0407774Z test/test_ops.py::test_scaled_embedding_bag_cpu[1-1024-512-torch.int32] SKIPPED 2025-09-09T16:25:10.0408414Z test/test_ops.py::test_scaled_embedding_bag_cpu[2-1-1-torch.int64] SKIPPED 2025-09-09T16:25:10.0409033Z test/test_ops.py::test_scaled_embedding_bag_cpu[2-1-1-torch.int32] SKIPPED 2025-09-09T16:25:10.0409668Z test/test_ops.py::test_scaled_embedding_bag_cpu[2-1-128-torch.int64] SKIPPED 2025-09-09T16:25:10.0410301Z test/test_ops.py::test_scaled_embedding_bag_cpu[2-1-128-torch.int32] SKIPPED 2025-09-09T16:25:10.0410942Z test/test_ops.py::test_scaled_embedding_bag_cpu[2-1-512-torch.int64] SKIPPED 2025-09-09T16:25:10.0411578Z test/test_ops.py::test_scaled_embedding_bag_cpu[2-1-512-torch.int32] SKIPPED 2025-09-09T16:25:10.0412198Z test/test_ops.py::test_scaled_embedding_bag_cpu[2-2-1-torch.int64] SKIPPED 2025-09-09T16:25:10.0412823Z test/test_ops.py::test_scaled_embedding_bag_cpu[2-2-1-torch.int32] SKIPPED 2025-09-09T16:25:10.0413441Z test/test_ops.py::test_scaled_embedding_bag_cpu[2-2-128-torch.int64] SKIPPED 2025-09-09T16:25:10.0414074Z test/test_ops.py::test_scaled_embedding_bag_cpu[2-2-128-torch.int32] SKIPPED 2025-09-09T16:25:10.0414702Z test/test_ops.py::test_scaled_embedding_bag_cpu[2-2-512-torch.int64] SKIPPED 2025-09-09T16:25:10.0415335Z test/test_ops.py::test_scaled_embedding_bag_cpu[2-2-512-torch.int32] SKIPPED 2025-09-09T16:25:10.0416023Z test/test_ops.py::test_scaled_embedding_bag_cpu[2-128-1-torch.int64] SKIPPED 2025-09-09T16:25:10.0416648Z test/test_ops.py::test_scaled_embedding_bag_cpu[2-128-1-torch.int32] SKIPPED 2025-09-09T16:25:10.0417288Z test/test_ops.py::test_scaled_embedding_bag_cpu[2-128-128-torch.int64] SKIPPED 2025-09-09T16:25:10.0417930Z test/test_ops.py::test_scaled_embedding_bag_cpu[2-128-128-torch.int32] SKIPPED 2025-09-09T16:25:10.0418583Z test/test_ops.py::test_scaled_embedding_bag_cpu[2-128-512-torch.int64] SKIPPED 2025-09-09T16:25:10.0419229Z test/test_ops.py::test_scaled_embedding_bag_cpu[2-128-512-torch.int32] SKIPPED 2025-09-09T16:25:10.0419867Z test/test_ops.py::test_scaled_embedding_bag_cpu[2-1024-1-torch.int64] SKIPPED 2025-09-09T16:25:10.0420510Z test/test_ops.py::test_scaled_embedding_bag_cpu[2-1024-1-torch.int32] SKIPPED 2025-09-09T16:25:10.0421158Z test/test_ops.py::test_scaled_embedding_bag_cpu[2-1024-128-torch.int64] SKIPPED 2025-09-09T16:25:10.0421912Z test/test_ops.py::test_scaled_embedding_bag_cpu[2-1024-128-torch.int32] SKIPPED 2025-09-09T16:25:10.0422848Z test/test_ops.py::test_scaled_embedding_bag_cpu[2-1024-512-torch.int64] SKIPPED 2025-09-09T16:25:10.0423591Z test/test_ops.py::test_scaled_embedding_bag_cpu[2-1024-512-torch.int32] SKIPPED 2025-09-09T16:25:10.0424315Z test/test_ops.py::test_scaled_embedding_bag_cpu[3-1-1-torch.int64] SKIPPED 2025-09-09T16:25:10.0424942Z test/test_ops.py::test_scaled_embedding_bag_cpu[3-1-1-torch.int32] SKIPPED 2025-09-09T16:25:10.0425581Z test/test_ops.py::test_scaled_embedding_bag_cpu[3-1-128-torch.int64] SKIPPED 2025-09-09T16:25:10.0426217Z test/test_ops.py::test_scaled_embedding_bag_cpu[3-1-128-torch.int32] SKIPPED 2025-09-09T16:25:10.0426858Z test/test_ops.py::test_scaled_embedding_bag_cpu[3-1-512-torch.int64] SKIPPED 2025-09-09T16:25:10.0427496Z test/test_ops.py::test_scaled_embedding_bag_cpu[3-1-512-torch.int32] SKIPPED 2025-09-09T16:25:10.0428126Z test/test_ops.py::test_scaled_embedding_bag_cpu[3-2-1-torch.int64] SKIPPED 2025-09-09T16:25:10.0428753Z test/test_ops.py::test_scaled_embedding_bag_cpu[3-2-1-torch.int32] SKIPPED 2025-09-09T16:25:10.0429383Z 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test/test_ops_rowwise_scaled_linear_sparse_cutlass.py::test_rowwise_scaled_linear_sparse_cutlass_f8f8[dtype158-Xq_Wq_dtypes158-4-size_mnk158-False] SKIPPED 2025-09-09T16:25:10.2960131Z test/test_ops_rowwise_scaled_linear_sparse_cutlass.py::test_rowwise_scaled_linear_sparse_cutlass_f8f8[dtype159-Xq_Wq_dtypes159-4-size_mnk159-True] SKIPPED 2025-09-09T16:25:10.2961348Z test/test_ops_rowwise_scaled_linear_sparse_cutlass.py::test_rowwise_scaled_linear_sparse_cutlass_f8f8[dtype160-Xq_Wq_dtypes160-4-size_mnk160-False] SKIPPED 2025-09-09T16:25:10.2962559Z test/test_ops_rowwise_scaled_linear_sparse_cutlass.py::test_rowwise_scaled_linear_sparse_cutlass_f8f8[dtype161-Xq_Wq_dtypes161-4-size_mnk161-True] SKIPPED 2025-09-09T16:25:10.2963769Z test/test_ops_rowwise_scaled_linear_sparse_cutlass.py::test_rowwise_scaled_linear_sparse_cutlass_f8f8[dtype162-Xq_Wq_dtypes162-4-size_mnk162-False] SKIPPED 2025-09-09T16:25:10.2964985Z test/test_ops_rowwise_scaled_linear_sparse_cutlass.py::test_rowwise_scaled_linear_sparse_cutlass_f8f8[dtype163-Xq_Wq_dtypes163-4-size_mnk163-True] SKIPPED 2025-09-09T16:25:10.2966358Z test/test_ops_rowwise_scaled_linear_sparse_cutlass.py::test_rowwise_scaled_linear_sparse_cutlass_f8f8[dtype164-Xq_Wq_dtypes164-4-size_mnk164-False] SKIPPED 2025-09-09T16:25:10.2967580Z test/test_ops_rowwise_scaled_linear_sparse_cutlass.py::test_rowwise_scaled_linear_sparse_cutlass_f8f8[dtype165-Xq_Wq_dtypes165-4-size_mnk165-True] SKIPPED 2025-09-09T16:25:10.2968794Z test/test_ops_rowwise_scaled_linear_sparse_cutlass.py::test_rowwise_scaled_linear_sparse_cutlass_f8f8[dtype166-Xq_Wq_dtypes166-4-size_mnk166-False] SKIPPED 2025-09-09T16:25:10.2970019Z test/test_ops_rowwise_scaled_linear_sparse_cutlass.py::test_rowwise_scaled_linear_sparse_cutlass_f8f8[dtype167-Xq_Wq_dtypes167-4-size_mnk167-True] SKIPPED 2025-09-09T16:25:10.2971425Z test/test_ops_rowwise_scaled_linear_sparse_cutlass.py::test_rowwise_scaled_linear_sparse_cutlass_f8f8[dtype168-Xq_Wq_dtypes168-1-size_mnk168-False] SKIPPED 2025-09-09T16:25:10.2972644Z test/test_ops_rowwise_scaled_linear_sparse_cutlass.py::test_rowwise_scaled_linear_sparse_cutlass_f8f8[dtype169-Xq_Wq_dtypes169-1-size_mnk169-True] SKIPPED 2025-09-09T16:25:10.2973859Z test/test_ops_rowwise_scaled_linear_sparse_cutlass.py::test_rowwise_scaled_linear_sparse_cutlass_f8f8[dtype170-Xq_Wq_dtypes170-1-size_mnk170-False] SKIPPED 2025-09-09T16:25:10.2975073Z test/test_ops_rowwise_scaled_linear_sparse_cutlass.py::test_rowwise_scaled_linear_sparse_cutlass_f8f8[dtype171-Xq_Wq_dtypes171-1-size_mnk171-True] SKIPPED 2025-09-09T16:25:10.2976296Z test/test_ops_rowwise_scaled_linear_sparse_cutlass.py::test_rowwise_scaled_linear_sparse_cutlass_f8f8[dtype172-Xq_Wq_dtypes172-1-size_mnk172-False] SKIPPED 2025-09-09T16:25:10.2977595Z test/test_ops_rowwise_scaled_linear_sparse_cutlass.py::test_rowwise_scaled_linear_sparse_cutlass_f8f8[dtype173-Xq_Wq_dtypes173-1-size_mnk173-True] SKIPPED 2025-09-09T16:25:10.2978807Z test/test_ops_rowwise_scaled_linear_sparse_cutlass.py::test_rowwise_scaled_linear_sparse_cutlass_f8f8[dtype174-Xq_Wq_dtypes174-1-size_mnk174-False] SKIPPED 2025-09-09T16:25:10.2980094Z test/test_ops_rowwise_scaled_linear_sparse_cutlass.py::test_rowwise_scaled_linear_sparse_cutlass_f8f8[dtype175-Xq_Wq_dtypes175-1-size_mnk175-True] SKIPPED 2025-09-09T16:25:10.2981395Z test/test_ops_rowwise_scaled_linear_sparse_cutlass.py::test_rowwise_scaled_linear_sparse_cutlass_f8f8[dtype176-Xq_Wq_dtypes176-1-size_mnk176-False] SKIPPED 2025-09-09T16:25:10.2982603Z test/test_ops_rowwise_scaled_linear_sparse_cutlass.py::test_rowwise_scaled_linear_sparse_cutlass_f8f8[dtype177-Xq_Wq_dtypes177-1-size_mnk177-True] SKIPPED 2025-09-09T16:25:10.2983816Z test/test_ops_rowwise_scaled_linear_sparse_cutlass.py::test_rowwise_scaled_linear_sparse_cutlass_f8f8[dtype178-Xq_Wq_dtypes178-1-size_mnk178-False] SKIPPED 2025-09-09T16:25:10.2985039Z test/test_ops_rowwise_scaled_linear_sparse_cutlass.py::test_rowwise_scaled_linear_sparse_cutlass_f8f8[dtype179-Xq_Wq_dtypes179-1-size_mnk179-True] SKIPPED 2025-09-09T16:25:10.2986246Z test/test_ops_rowwise_scaled_linear_sparse_cutlass.py::test_rowwise_scaled_linear_sparse_cutlass_f8f8[dtype180-Xq_Wq_dtypes180-4-size_mnk180-False] SKIPPED 2025-09-09T16:25:10.2987462Z test/test_ops_rowwise_scaled_linear_sparse_cutlass.py::test_rowwise_scaled_linear_sparse_cutlass_f8f8[dtype181-Xq_Wq_dtypes181-4-size_mnk181-True] SKIPPED 2025-09-09T16:25:10.2988667Z test/test_ops_rowwise_scaled_linear_sparse_cutlass.py::test_rowwise_scaled_linear_sparse_cutlass_f8f8[dtype182-Xq_Wq_dtypes182-4-size_mnk182-False] SKIPPED 2025-09-09T16:25:10.2989882Z test/test_ops_rowwise_scaled_linear_sparse_cutlass.py::test_rowwise_scaled_linear_sparse_cutlass_f8f8[dtype183-Xq_Wq_dtypes183-4-size_mnk183-True] SKIPPED 2025-09-09T16:25:10.2991098Z test/test_ops_rowwise_scaled_linear_sparse_cutlass.py::test_rowwise_scaled_linear_sparse_cutlass_f8f8[dtype184-Xq_Wq_dtypes184-4-size_mnk184-False] SKIPPED 2025-09-09T16:25:10.2992306Z test/test_ops_rowwise_scaled_linear_sparse_cutlass.py::test_rowwise_scaled_linear_sparse_cutlass_f8f8[dtype185-Xq_Wq_dtypes185-4-size_mnk185-True] SKIPPED 2025-09-09T16:25:10.2993522Z test/test_ops_rowwise_scaled_linear_sparse_cutlass.py::test_rowwise_scaled_linear_sparse_cutlass_f8f8[dtype186-Xq_Wq_dtypes186-4-size_mnk186-False] SKIPPED 2025-09-09T16:25:10.2994738Z test/test_ops_rowwise_scaled_linear_sparse_cutlass.py::test_rowwise_scaled_linear_sparse_cutlass_f8f8[dtype187-Xq_Wq_dtypes187-4-size_mnk187-True] SKIPPED 2025-09-09T16:25:10.2995950Z test/test_ops_rowwise_scaled_linear_sparse_cutlass.py::test_rowwise_scaled_linear_sparse_cutlass_f8f8[dtype188-Xq_Wq_dtypes188-4-size_mnk188-False] SKIPPED 2025-09-09T16:25:10.2997219Z test/test_ops_rowwise_scaled_linear_sparse_cutlass.py::test_rowwise_scaled_linear_sparse_cutlass_f8f8[dtype189-Xq_Wq_dtypes189-4-size_mnk189-True] SKIPPED 2025-09-09T16:25:10.2998434Z test/test_ops_rowwise_scaled_linear_sparse_cutlass.py::test_rowwise_scaled_linear_sparse_cutlass_f8f8[dtype190-Xq_Wq_dtypes190-4-size_mnk190-False] SKIPPED 2025-09-09T16:25:10.2999744Z test/test_ops_rowwise_scaled_linear_sparse_cutlass.py::test_rowwise_scaled_linear_sparse_cutlass_f8f8[dtype191-Xq_Wq_dtypes191-4-size_mnk191-True] SKIPPED 2025-09-09T16:25:10.3000656Z test/test_utils.py::TestTorchVersion::test_torch_version_at_least PASSED 2025-09-09T16:25:10.3001303Z test/test_utils.py::TestTorchVersion::test_torch_version_deprecation PASSED 2025-09-09T16:25:10.3001981Z test/test_utils.py::TestTorchAOBaseTensor::test_default_impls PASSED 2025-09-09T16:25:10.3002704Z test/test_utils.py::TestTorchAOBaseTensor::test_default_impls_with_optional_attr PASSED 2025-09-09T16:25:10.3003516Z test/test_utils.py::TestTorchAOBaseTensor::test_default_impls_with_optional_data PASSED 2025-09-09T16:25:10.3004213Z test/test_utils.py::TestTorchAOBaseTensor::test_print_arg_types PASSED 2025-09-09T16:25:10.3004545Z 2025-09-09T16:25:10.3004820Z =============================== warnings summary =============================== 2025-09-09T16:25:10.3005299Z test/core/test_config.py::test_reconstructable_dict_file_round_trip[config8] 2025-09-09T16:25:10.3006478Z /opt/conda/envs/venv/lib/python3.9/site-packages/torchao/core/config.py:250: UserWarning: Stored version is not the same as current default version of the config: stored_version=2, current_default_version=1, please check the deprecation warning 2025-09-09T16:25:10.3007576Z warnings.warn( 2025-09-09T16:25:10.3007716Z 2025-09-09T16:25:10.3007895Z test/dtypes/test_nf4.py::TestNF4Linear::test_to_copy_bfloat16 2025-09-09T16:25:10.3018445Z test/dtypes/test_nf4.py::TestNF4Linear::test_to_copy_float16 2025-09-09T16:25:10.3018888Z test/dtypes/test_nf4.py::TestNF4Linear::test_to_copy_float32 2025-09-09T16:25:10.3020153Z /pytorch/ao/test/dtypes/test_nf4.py:223: FutureWarning: `torch.testing.assert_allclose()` is deprecated since 1.12 and will be removed in a future release. Please use `torch.testing.assert_close()` instead. You can find detailed upgrade instructions in https://github.com/pytorch/pytorch/issues/61844. 2025-09-09T16:25:10.3021456Z torch.testing.assert_allclose(input_tensor, nf4_to_dtype, atol=0.13, rtol=0.13) 2025-09-09T16:25:10.3021792Z 2025-09-09T16:25:10.3021972Z test/dtypes/test_nf4.py::TestNF4Linear::test_to_copy_bfloat16 2025-09-09T16:25:10.3022729Z test/dtypes/test_nf4.py::TestNF4Linear::test_to_copy_float16 2025-09-09T16:25:10.3023159Z test/dtypes/test_nf4.py::TestNF4Linear::test_to_copy_float32 2025-09-09T16:25:10.3024380Z /pytorch/ao/test/dtypes/test_nf4.py:229: FutureWarning: `torch.testing.assert_allclose()` is deprecated since 1.12 and will be removed in a future release. Please use `torch.testing.assert_close()` instead. You can find detailed upgrade instructions in https://github.com/pytorch/pytorch/issues/61844. 2025-09-09T16:25:10.3025540Z torch.testing.assert_allclose( 2025-09-09T16:25:10.3025725Z 2025-09-09T16:25:10.3025831Z test/float8/test_base.py: 36 warnings 2025-09-09T16:25:10.3026970Z /opt/conda/envs/venv/lib/python3.9/site-packages/torchao/float8/float8_linear.py:261: DeprecationWarning: torch.get_autocast_gpu_dtype() is deprecated. Please use torch.get_autocast_dtype('cuda') instead. (Triggered internally at /pytorch/torch/csrc/autograd/init.cpp:852.) 2025-09-09T16:25:10.3028134Z autocast_dtype = torch.get_autocast_gpu_dtype() 2025-09-09T16:25:10.3028361Z 2025-09-09T16:25:10.3028572Z test/float8/test_float8_utils.py::test_non_float32_input[invalid_dtype3] 2025-09-09T16:25:10.3029087Z test/float8/test_float8_utils.py::test_non_float32_input[invalid_dtype4] 2025-09-09T16:25:10.3029637Z test/float8/test_float8_utils.py::test_non_float32_input[invalid_dtype5] 2025-09-09T16:25:10.3030140Z test/float8/test_float8_utils.py::test_non_float32_input[invalid_dtype6] 2025-09-09T16:25:10.3030770Z test/float8/test_float8_utils.py::test_non_float32_input[invalid_dtype7] 2025-09-09T16:25:10.3031779Z /pytorch/ao/test/float8/test_float8_utils.py:67: DeprecationWarning: an integer is required (got type float). Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python. 2025-09-09T16:25:10.3032740Z non_float32_tensor = torch.tensor([3.0], dtype=invalid_dtype) 2025-09-09T16:25:10.3033013Z 2025-09-09T16:25:10.3033304Z test/integration/test_integration.py::SmoothquantIntegrationTest::test_on_dummy_distilbert 2025-09-09T16:25:10.3034175Z /pytorch/ao/test/integration/test_integration.py:1440: DeprecationWarning: torch.ao.quantization is deprecated and will be removed in 2.10. 2025-09-09T16:25:10.3034842Z For migrations of users: 2025-09-09T16:25:10.3035545Z 1. Eager mode quantization (torch.ao.quantization.quantize, torch.ao.quantization.quantize_dynamic), please migrate to use torchao eager mode quantize_ API instead 2025-09-09T16:25:10.3036937Z 2. FX graph mode quantization (torch.ao.quantization.quantize_fx.prepare_fx,torch.ao.quantization.quantize_fx.convert_fx, please migrate to use torchao pt2e quantization API instead (prepare_pt2e, convert_pt2e) 2025-09-09T16:25:10.3038157Z 3. pt2e quantization has been migrated to torchao (https://github.com/pytorch/ao/tree/main/torchao/quantization/pt2e) 2025-09-09T16:25:10.3038859Z see https://github.com/pytorch/ao/issues/2259 for more details 2025-09-09T16:25:10.3039352Z model_copy2 = torch.ao.quantization.quantize_dynamic( 2025-09-09T16:25:10.3039605Z 2025-09-09T16:25:10.3039894Z test/integration/test_integration.py::SmoothquantIntegrationTest::test_on_dummy_distilbert 2025-09-09T16:25:10.3040912Z /opt/conda/envs/venv/lib/python3.9/site-packages/torch/ao/quantization/quantize.py:566: DeprecationWarning: torch.ao.quantization is deprecated and will be removed in 2.10. 2025-09-09T16:25:10.3041770Z For migrations of users: 2025-09-09T16:25:10.3042470Z 1. Eager mode quantization (torch.ao.quantization.quantize, torch.ao.quantization.quantize_dynamic), please migrate to use torchao eager mode quantize_ API instead 2025-09-09T16:25:10.3043786Z 2. FX graph mode quantization (torch.ao.quantization.quantize_fx.prepare_fx,torch.ao.quantization.quantize_fx.convert_fx, please migrate to use torchao pt2e quantization API instead (prepare_pt2e, convert_pt2e) 2025-09-09T16:25:10.3044936Z 3. pt2e quantization has been migrated to torchao (https://github.com/pytorch/ao/tree/main/torchao/quantization/pt2e) 2025-09-09T16:25:10.3045574Z see https://github.com/pytorch/ao/issues/2259 for more details 2025-09-09T16:25:10.3045951Z convert(model, mapping, inplace=True) 2025-09-09T16:25:10.3046149Z 2025-09-09T16:25:10.3046346Z test/kernel/test_autotuner.py::TestQuantFlow::test_int_mm_0_cuda 2025-09-09T16:25:10.3046824Z test/kernel/test_autotuner.py::TestQuantFlow::test_int_mm_1_cuda 2025-09-09T16:25:10.3048095Z /pytorch/ao/test/kernel/test_autotuner.py:50: FutureWarning: `torch.testing.assert_allclose()` is deprecated since 1.12 and will be removed in a future release. Please use `torch.testing.assert_close()` instead. You can find detailed upgrade instructions in https://github.com/pytorch/pytorch/issues/61844. 2025-09-09T16:25:10.3049312Z torch.testing.assert_allclose(out32_1, out32_2) 2025-09-09T16:25:10.3049538Z 2025-09-09T16:25:10.3049754Z test/kernel/test_autotuner.py::TestQuantFlow::test_int_scaled_mm_0_cuda 2025-09-09T16:25:10.3050267Z test/kernel/test_autotuner.py::TestQuantFlow::test_int_scaled_mm_1_cpu 2025-09-09T16:25:10.3050766Z test/kernel/test_autotuner.py::TestQuantFlow::test_int_scaled_mm_2_cuda 2025-09-09T16:25:10.3051272Z test/kernel/test_autotuner.py::TestQuantFlow::test_int_scaled_mm_3_cpu 2025-09-09T16:25:10.3052664Z /pytorch/ao/test/kernel/test_autotuner.py:96: FutureWarning: `torch.testing.assert_allclose()` is deprecated since 1.12 and will be removed in a future release. Please use `torch.testing.assert_close()` instead. You can find detailed upgrade instructions in https://github.com/pytorch/pytorch/issues/61844. 2025-09-09T16:25:10.3053860Z torch.testing.assert_allclose(out32_1, out32_2) 2025-09-09T16:25:10.3054096Z 2025-09-09T16:25:10.3054397Z test/prototype/test_codebook_quant.py::TestCodebookQuantization::test_choose_qparams_codebook 2025-09-09T16:25:10.3055661Z /opt/conda/envs/venv/lib/python3.9/site-packages/torch/testing/_internal/common_utils.py:903: UserWarning: index_reduce() is in beta and the API may change at any time. (Triggered internally at /pytorch/aten/src/ATen/native/TensorAdvancedIndexing.cpp:1517.) 2025-09-09T16:25:10.3056712Z return callable(*args, **kwargs) 2025-09-09T16:25:10.3056903Z 2025-09-09T16:25:10.3057130Z test/prototype/test_parametrization.py::TestFakeSparsity::test_jit_trace 2025-09-09T16:25:10.3058703Z /opt/conda/envs/venv/lib/python3.9/site-packages/torchao/prototype/sparsity/sparsifier/utils.py:134: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs! 2025-09-09T16:25:10.3060150Z assert self.mask.shape == x.shape 2025-09-09T16:25:10.3060340Z 2025-09-09T16:25:10.3060562Z test/prototype/test_scheduler.py::TestScheduler::test_lambda_scheduler 2025-09-09T16:25:10.3061790Z test/prototype/test_scheduler.py::TestCubicScheduler::test_step 2025-09-09T16:25:10.3063052Z /opt/conda/envs/venv/lib/python3.9/site-packages/torchao/prototype/sparsity/scheduler/base_scheduler.py:133: UserWarning: Detected call of `scheduler.step()` before `sparsifier.step()`. You have to make sure you run the sparsifier.step() BEFORE any calls to the scheduler.step(). 2025-09-09T16:25:10.3064187Z warnings.warn( 2025-09-09T16:25:10.3064318Z 2025-09-09T16:25:10.3064634Z test/prototype/test_structured_sparsifier.py::TestBaseStructuredSparsifier::test_complex_conv2d 2025-09-09T16:25:10.3065799Z /opt/conda/envs/venv/lib/python3.9/site-packages/torchao/prototype/sparsity/pruner/prune_functions.py:347: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior. 2025-09-09T16:25:10.3067086Z Consider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.) 2025-09-09T16:25:10.3067736Z flattened_pruned_biases = torch.tensor( 2025-09-09T16:25:10.3067943Z 2025-09-09T16:25:10.3068206Z test/quantization/pt2e/test_graph_utils.py::TestGraphUtils::test_conv_bn_conv_relu 2025-09-09T16:25:10.3069463Z /pytorch/ao/test/quantization/pt2e/test_graph_utils.py:42: FutureWarning: export(f, *args, **kwargs) is deprecated, use export(f)(*args, **kwargs) instead. If you don't migrate, we may break your export call in the future if your user defined kwargs conflict with future kwargs added to export(f). 2025-09-09T16:25:10.3070688Z m, guards = torchdynamo.export( # noqa: F841© 2025-09-09T16:25:10.3070910Z 2025-09-09T16:25:10.3071167Z test/quantization/pt2e/test_graph_utils.py::TestGraphUtils::test_conv_bn_relu 2025-09-09T16:25:10.3072448Z /pytorch/ao/test/quantization/pt2e/test_graph_utils.py:86: FutureWarning: export(f, *args, **kwargs) is deprecated, use export(f)(*args, **kwargs) instead. If you don't migrate, we may break your export call in the future if your user defined kwargs conflict with future kwargs added to export(f). 2025-09-09T16:25:10.3073575Z m, guards = torchdynamo.export( # noqa: F841 2025-09-09T16:25:10.3073792Z 2025-09-09T16:25:10.3074108Z test/quantization/pt2e/test_graph_utils.py::TestGraphUtils::test_customized_equivalet_types_dict 2025-09-09T16:25:10.3075407Z /pytorch/ao/test/quantization/pt2e/test_graph_utils.py:118: FutureWarning: export(f, *args, **kwargs) is deprecated, use export(f)(*args, **kwargs) instead. If you don't migrate, we may break your export call in the future if your user defined kwargs conflict with future kwargs added to export(f). 2025-09-09T16:25:10.3076577Z m, guards = torchdynamo.export( # noqa: F841 2025-09-09T16:25:10.3076795Z 2025-09-09T16:25:10.3076959Z test/quantization/pt2e/test_quantize_pt2e.py: 18 warnings 2025-09-09T16:25:10.3077394Z test/quantization/pt2e/test_quantize_pt2e_qat.py: 91 warnings 2025-09-09T16:25:10.3077830Z test/quantization/pt2e/test_representation.py: 8 warnings 2025-09-09T16:25:10.3078583Z /opt/conda/envs/venv/lib/python3.9/site-packages/torchao/testing/pt2e/_xnnpack_quantizer.py:289: UserWarning: XNNPACKQuantizer is deprecated! 2025-09-09T16:25:10.3079391Z warnings.warn(f"{self.__class__.__name__} is deprecated!") 2025-09-09T16:25:10.3079648Z 2025-09-09T16:25:10.3079969Z test/quantization/pt2e/test_quantize_pt2e.py::TestQuantizePT2E::test_allow_exported_model_train_eval 2025-09-09T16:25:10.3080658Z test/quantization/pt2e/test_quantize_pt2e.py::TestQuantizePT2E::test_disallow_eval_train 2025-09-09T16:25:10.3081391Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_annotate_mul_tensor 2025-09-09T16:25:10.3082244Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_annotate_mul_tensor 2025-09-09T16:25:10.3083052Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_annotate_mul_tensor 2025-09-09T16:25:10.3083897Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_filter_conv2d_recipe 2025-09-09T16:25:10.3084753Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_filter_linear_recipe 2025-09-09T16:25:10.3085866Z /opt/conda/envs/venv/lib/python3.9/site-packages/torchao/quantization/pt2e/quantize_pt2e.py:305: DeprecationWarning: torch.ao.quantization is deprecated and will be removed in 2.10. 2025-09-09T16:25:10.3086707Z For migrations of users: 2025-09-09T16:25:10.3087408Z 1. Eager mode quantization (torch.ao.quantization.quantize, torch.ao.quantization.quantize_dynamic), please migrate to use torchao eager mode quantize_ API instead 2025-09-09T16:25:10.3088735Z 2. FX graph mode quantization (torch.ao.quantization.quantize_fx.prepare_fx,torch.ao.quantization.quantize_fx.convert_fx, please migrate to use torchao pt2e quantization API instead (prepare_pt2e, convert_pt2e) 2025-09-09T16:25:10.3089890Z 3. pt2e quantization has been migrated to torchao (https://github.com/pytorch/ao/tree/main/torchao/quantization/pt2e) 2025-09-09T16:25:10.3090528Z see https://github.com/pytorch/ao/issues/2259 for more details 2025-09-09T16:25:10.3091018Z return torch_convert_pt2e(model, use_reference_representation, fold_quantize) 2025-09-09T16:25:10.3091347Z 2025-09-09T16:25:10.3091685Z test/quantization/pt2e/test_quantize_pt2e.py::TestQuantizePT2E::test_embedding_conv_linear_quantization 2025-09-09T16:25:10.3092394Z test/quantization/pt2e/test_quantize_pt2e.py::TestQuantizePT2E::test_embedding_quantizer 2025-09-09T16:25:10.3093369Z /opt/conda/envs/venv/lib/python3.9/site-packages/torchao/testing/pt2e/utils.py:108: DeprecationWarning: torch.ao.quantization is deprecated and will be removed in 2.10. 2025-09-09T16:25:10.3094165Z For migrations of users: 2025-09-09T16:25:10.3094861Z 1. Eager mode quantization (torch.ao.quantization.quantize, torch.ao.quantization.quantize_dynamic), please migrate to use torchao eager mode quantize_ API instead 2025-09-09T16:25:10.3096192Z 2. FX graph mode quantization (torch.ao.quantization.quantize_fx.prepare_fx,torch.ao.quantization.quantize_fx.convert_fx, please migrate to use torchao pt2e quantization API instead (prepare_pt2e, convert_pt2e) 2025-09-09T16:25:10.3097342Z 3. pt2e quantization has been migrated to torchao (https://github.com/pytorch/ao/tree/main/torchao/quantization/pt2e) 2025-09-09T16:25:10.3097978Z see https://github.com/pytorch/ao/issues/2259 for more details 2025-09-09T16:25:10.3098333Z m_fx = prepare_fx( 2025-09-09T16:25:10.3098476Z 2025-09-09T16:25:10.3098745Z test/quantization/pt2e/test_quantize_pt2e.py::TestQuantizePT2E::test_model_is_exported 2025-09-09T16:25:10.3100062Z /opt/conda/envs/venv/lib/python3.9/site-packages/torch/fx/_symbolic_trace.py:923: UserWarning: Was not able to add assertion to guarantee correct input x to specialized function. It is up to the user to make sure that your inputs match the inputs you specialized the function with. 2025-09-09T16:25:10.3101138Z warnings.warn( 2025-09-09T16:25:10.3101290Z 2025-09-09T16:25:10.3101559Z test/quantization/pt2e/test_quantize_pt2e.py::TestQuantizePT2E::test_reentrant 2025-09-09T16:25:10.3102248Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn1d::test_fold_bn_erases_bn_node 2025-09-09T16:25:10.3103026Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn2d::test_fold_bn_erases_bn_node 2025-09-09T16:25:10.3104106Z /opt/conda/envs/venv/lib/python3.9/site-packages/torchao/quantization/pt2e/utils.py:145: UserWarning: must run observer before calling calculate_qparams. Returning default values. 2025-09-09T16:25:10.3104910Z warnings.warn( 2025-09-09T16:25:10.3105039Z 2025-09-09T16:25:10.3105335Z test/quantization/pt2e/test_quantize_pt2e.py::TestQuantizePT2E::test_reentrant 2025-09-09T16:25:10.3106422Z /opt/conda/envs/venv/lib/python3.9/site-packages/torchao/quantization/pt2e/observer.py:1350: UserWarning: must run observer before calling calculate_qparams. Returning default scale and zero point 2025-09-09T16:25:10.3107450Z warnings.warn( 2025-09-09T16:25:10.3107582Z 2025-09-09T16:25:10.3107960Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn1d::test_qat_conv_bn_bias_derived_qspec 2025-09-09T16:25:10.3108831Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn1d::test_qat_conv_bn_per_channel_weight_bias 2025-09-09T16:25:10.3109712Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn1d::test_qat_per_channel_weight_custom_dtype 2025-09-09T16:25:10.3110581Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_conv_bn_bias_derived_qspec 2025-09-09T16:25:10.3111474Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_conv_bn_per_channel_weight_bias 2025-09-09T16:25:10.3112363Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_per_channel_weight_custom_dtype 2025-09-09T16:25:10.3113679Z /opt/conda/envs/venv/lib/python3.9/site-packages/torchao/quantization/pt2e/observer.py:253: UserWarning: Please use quant_min and quant_max to specify the range for observers. reduce_range will be deprecated in a future release of PyTorch. 2025-09-09T16:25:10.3114687Z warnings.warn( 2025-09-09T16:25:10.3114816Z 2025-09-09T16:25:10.3114996Z test/quantization/pt2e/test_quantize_pt2e_qat.py: 48 warnings 2025-09-09T16:25:10.3115786Z /pytorch/ao/test/quantization/pt2e/test_quantize_pt2e_qat.py:165: DeprecationWarning: torch.ao.quantization is deprecated and will be removed in 2.10. 2025-09-09T16:25:10.3116482Z For migrations of users: 2025-09-09T16:25:10.3117190Z 1. Eager mode quantization (torch.ao.quantization.quantize, torch.ao.quantization.quantize_dynamic), please migrate to use torchao eager mode quantize_ API instead 2025-09-09T16:25:10.3118512Z 2. FX graph mode quantization (torch.ao.quantization.quantize_fx.prepare_fx,torch.ao.quantization.quantize_fx.convert_fx, please migrate to use torchao pt2e quantization API instead (prepare_pt2e, convert_pt2e) 2025-09-09T16:25:10.3118881Z 3. pt2e quantization has been migrated to torchao (https://github.com/pytorch/ao/tree/main/torchao/quantization/pt2e) 2025-09-09T16:25:10.3119054Z see https://github.com/pytorch/ao/issues/2259 for more details 2025-09-09T16:25:10.3119152Z model_fx = prepare_qat_fx( 2025-09-09T16:25:10.3119162Z 2025-09-09T16:25:10.3119413Z test/quantization/pt2e/test_quantize_pt2e_qat.py: 48 warnings 2025-09-09T16:25:10.3120103Z /opt/conda/envs/venv/lib/python3.9/site-packages/torch/ao/quantization/fx/prepare.py:464: DeprecationWarning: torch.ao.quantization is deprecated and will be removed in 2.10. 2025-09-09T16:25:10.3120198Z For migrations of users: 2025-09-09T16:25:10.3120740Z 1. Eager mode quantization (torch.ao.quantization.quantize, torch.ao.quantization.quantize_dynamic), please migrate to use torchao eager mode quantize_ API instead 2025-09-09T16:25:10.3121431Z 2. FX graph mode quantization (torch.ao.quantization.quantize_fx.prepare_fx,torch.ao.quantization.quantize_fx.convert_fx, please migrate to use torchao pt2e quantization API instead (prepare_pt2e, convert_pt2e) 2025-09-09T16:25:10.3121800Z 3. pt2e quantization has been migrated to torchao (https://github.com/pytorch/ao/tree/main/torchao/quantization/pt2e) 2025-09-09T16:25:10.3121973Z see https://github.com/pytorch/ao/issues/2259 for more details 2025-09-09T16:25:10.3122457Z convert(root, mapping=module_to_qat_module, inplace=True, remove_qconfig=False) 2025-09-09T16:25:10.3122463Z 2025-09-09T16:25:10.3122847Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qconv2d_add_3 2025-09-09T16:25:10.3123213Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_filter_conv2d_recipe 2025-09-09T16:25:10.3127066Z /opt/conda/envs/venv/lib/python3.9/site-packages/torchao/quantization/pt2e/quantizer/x86_inductor_quantizer.py:1325: UserWarning: The input of maxpool2d is not quantized, skip annotate maxpool2d with config QuantizationConfig(input_activation=QuantizationSpec(dtype=torch.uint8, observer_or_fake_quant_ctr=functools.partial(, eps=0.000244140625){}, quant_min=0, quant_max=255, qscheme=torch.per_tensor_affine, ch_axis=None, is_dynamic=False), output_activation=QuantizationSpec(dtype=torch.uint8, observer_or_fake_quant_ctr=functools.partial(, eps=0.000244140625){}, quant_min=0, quant_max=255, qscheme=torch.per_tensor_affine, ch_axis=None, is_dynamic=False), weight=QuantizationSpec(dtype=torch.int8, observer_or_fake_quant_ctr=functools.partial(, eps=0.000244140625){}, quant_min=-128, quant_max=127, qscheme=torch.per_channel_symmetric, ch_axis=0, is_dynamic=False), bias=None, is_qat=False). 2025-09-09T16:25:10.3127218Z warnings.warn( 2025-09-09T16:25:10.3127223Z 2025-09-09T16:25:10.3127556Z test/quantization/pt2e/test_x86inductor_fusion.py::TestDynamicPatternMatcher::test_q_attention_block 2025-09-09T16:25:10.3127891Z test/quantization/pt2e/test_x86inductor_fusion.py::TestDynamicPatternMatcher::test_q_attention_block 2025-09-09T16:25:10.3128296Z test/quantization/pt2e/test_x86inductor_fusion.py::TestDynamicPatternMatcher::test_qconv2d_maxpool2d_linear_dynamic_cpu 2025-09-09T16:25:10.3129251Z /opt/conda/envs/venv/lib/python3.9/site-packages/torch/_inductor/mkldnn_lowerings.py:731: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.detach().clone() or sourceTensor.detach().clone().requires_grad_(True), rather than torch.tensor(sourceTensor). 2025-09-09T16:25:10.3129444Z torch.tensor(w_zp_tensor, dtype=torch.int32), name=w_zp.get_name() 2025-09-09T16:25:10.3129448Z 2025-09-09T16:25:10.3129923Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_set_module_name_and_module_type_with_mixed_configs 2025-09-09T16:25:10.3130616Z /opt/conda/envs/venv/lib/python3.9/site-packages/torchao/quantization/pt2e/quantizer/x86_inductor_quantizer.py:484: UserWarning: Mixed dynamic and static quantization config is not supported. 2025-09-09T16:25:10.3130707Z warnings.warn( 2025-09-09T16:25:10.3130711Z 2025-09-09T16:25:10.3131180Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_set_module_name_and_module_type_with_mixed_configs 2025-09-09T16:25:10.3131980Z /opt/conda/envs/venv/lib/python3.9/site-packages/torchao/quantization/pt2e/quantizer/x86_inductor_quantizer.py:383: UserWarning: Skip the quantization config for . 2025-09-09T16:25:10.3132070Z warnings.warn( 2025-09-09T16:25:10.3132074Z 2025-09-09T16:25:10.3132203Z test/quantization/test_moe_quant.py: 10 warnings 2025-09-09T16:25:10.3133109Z /opt/conda/envs/venv/lib/python3.9/site-packages/torch/cuda/graphs.py:104: UserWarning: The CUDA Graph is empty. This usually means that the graph was attempted to be captured on wrong device or stream. (Triggered internally at /pytorch/aten/src/ATen/cuda/CUDAGraph.cpp:139.) 2025-09-09T16:25:10.3133209Z super().capture_end() 2025-09-09T16:25:10.3133213Z 2025-09-09T16:25:10.3133500Z test/quantization/test_moe_quant.py::TestMoEQuantCompile::test_int8dq_base_0_multiple_tokens 2025-09-09T16:25:10.3133813Z /opt/conda/envs/venv/lib/python3.9/site-packages/torch/_inductor/lowering.py:7095: UserWarning: 2025-09-09T16:25:10.3133999Z Online softmax is disabled on the fly since Inductor decides to 2025-09-09T16:25:10.3134159Z split the reduction. Cut an issue to PyTorch if this is an 2025-09-09T16:25:10.3134366Z important use case and you want to speed it up with online 2025-09-09T16:25:10.3134449Z softmax. 2025-09-09T16:25:10.3134523Z 2025-09-09T16:25:10.3134607Z warnings.warn( 2025-09-09T16:25:10.3134655Z 2025-09-09T16:25:10.3134861Z test/quantization/test_qat.py::TestQAT::test_legacy_quantize_api_e2e 2025-09-09T16:25:10.3135699Z /opt/conda/envs/venv/lib/python3.9/site-packages/torchao/quantization/qat/utils.py:84: UserWarning: 'IntXQuantizationAwareTrainingConfig' is deprecated and will be removed in a future release. Please use the following API instead: 2025-09-09T16:25:10.3135816Z 2025-09-09T16:25:10.3136026Z base_config = Int8DynamicActivationInt4WeightConfig(group_size=32) 2025-09-09T16:25:10.3136198Z quantize_(model, QATConfig(base_config, step="prepare")) 2025-09-09T16:25:10.3136288Z # train (not shown) 2025-09-09T16:25:10.3136444Z quantize_(model, QATConfig(base_config, step="convert")) 2025-09-09T16:25:10.3136522Z 2025-09-09T16:25:10.3136719Z Alternatively, if you prefer to pass in fake quantization configs: 2025-09-09T16:25:10.3136793Z 2025-09-09T16:25:10.3137112Z activation_config = IntxFakeQuantizeConfig(torch.int8, "per_token", is_symmetric=False) 2025-09-09T16:25:10.3137374Z weight_config = IntxFakeQuantizeConfig(torch.int4, group_size=32) 2025-09-09T16:25:10.3137498Z qat_config = QATConfig( 2025-09-09T16:25:10.3137648Z activation_config=activation_config, 2025-09-09T16:25:10.3137785Z weight_config=weight_config, 2025-09-09T16:25:10.3137899Z step="prepare", 2025-09-09T16:25:10.3137995Z ) 2025-09-09T16:25:10.3138098Z quantize_(model, qat_config) 2025-09-09T16:25:10.3138193Z 2025-09-09T16:25:10.3138554Z Please see https://github.com/pytorch/ao/issues/2630 for more details. 2025-09-09T16:25:10.3138935Z 2025-09-09T16:25:10.3139124Z warnings.warn( 2025-09-09T16:25:10.3139260Z 2025-09-09T16:25:10.3139467Z test/quantization/test_qat.py::TestQAT::test_legacy_quantize_api_e2e 2025-09-09T16:25:10.3140989Z /opt/conda/envs/venv/lib/python3.9/site-packages/torchao/quantization/qat/utils.py:84: UserWarning: 'FromIntXQuantizationAwareTrainingConfig' is deprecated and will be removed in a future release. Please use the following API instead: 2025-09-09T16:25:10.3142372Z 2025-09-09T16:25:10.3142726Z base_config = Int8DynamicActivationInt4WeightConfig(group_size=32) 2025-09-09T16:25:10.3143197Z quantize_(model, QATConfig(base_config, step="prepare")) 2025-09-09T16:25:10.3143543Z # train (not shown) 2025-09-09T16:25:10.3143849Z quantize_(model, QATConfig(base_config, step="convert")) 2025-09-09T16:25:10.3144170Z 2025-09-09T16:25:10.3144466Z Alternatively, if you prefer to pass in fake quantization configs: 2025-09-09T16:25:10.3144827Z 2025-09-09T16:25:10.3145203Z activation_config = IntxFakeQuantizeConfig(torch.int8, "per_token", is_symmetric=False) 2025-09-09T16:25:10.3145768Z weight_config = IntxFakeQuantizeConfig(torch.int4, group_size=32) 2025-09-09T16:25:10.3146240Z qat_config = QATConfig( 2025-09-09T16:25:10.3146550Z activation_config=activation_config, 2025-09-09T16:25:10.3146852Z weight_config=weight_config, 2025-09-09T16:25:10.3147132Z step="prepare", 2025-09-09T16:25:10.3147357Z ) 2025-09-09T16:25:10.3147549Z quantize_(model, qat_config) 2025-09-09T16:25:10.3147798Z 2025-09-09T16:25:10.3148093Z Please see https://github.com/pytorch/ao/issues/2630 for more details. 2025-09-09T16:25:10.3148464Z 2025-09-09T16:25:10.3148652Z warnings.warn( 2025-09-09T16:25:10.3148786Z 2025-09-09T16:25:10.3148980Z test/quantization/test_qat.py::TestQAT::test_qat_fp8a4w_quantizer 2025-09-09T16:25:10.3152311Z /opt/conda/envs/venv/lib/python3.9/site-packages/torch/autograd/graph.py:829: UserWarning: torchao::dequantize_affine_float8: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at /pytorch/torch/csrc/autograd/autograd_not_implemented_fallback.cpp:62.) 2025-09-09T16:25:10.3155563Z return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass 2025-09-09T16:25:10.3155962Z 2025-09-09T16:25:10.3156224Z test/sparsity/test_marlin.py::SparseMarlin24::test_quant_sparse_marlin_layout_compile 2025-09-09T16:25:10.3156832Z test/sparsity/test_sparse_api.py::TestQuantSemiSparse::test_sparse_marlin_compile_True 2025-09-09T16:25:10.3159939Z /opt/conda/envs/venv/lib/python3.9/site-packages/torch/autograd/graph.py:829: UserWarning: torchao::marlin_24_gemm: an autograd kernel was not registered to the Autograd key(s) but we are trying to backprop through it. This may lead to silently incorrect behavior. This behavior is deprecated and will be removed in a future version of PyTorch. If your operator is differentiable, please ensure you have registered an autograd kernel to the correct Autograd key (e.g. DispatchKey::Autograd, DispatchKey::CompositeImplicitAutograd). If your operator is not differentiable, or to squash this warning and use the previous behavior, please register torch::CppFunction::makeFallthrough() to DispatchKey::Autograd. (Triggered internally at /pytorch/torch/csrc/autograd/autograd_not_implemented_fallback.cpp:62.) 2025-09-09T16:25:10.3163201Z return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass 2025-09-09T16:25:10.3163594Z 2025-09-09T16:25:10.3163904Z test/sparsity/test_sparse_api.py::TestBlockSparseWeight::test_sparse_compile_False_input_shape_1 2025-09-09T16:25:10.3165538Z /opt/conda/envs/venv/lib/python3.9/site-packages/torchao/sparsity/blocksparse.py:198: UserWarning: Sparse BSR 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 /pytorch/aten/src/ATen/SparseCsrTensorImpl.cpp:53.) 2025-09-09T16:25:10.3166988Z bsr_tensor = dense_tensor.to_sparse_bsr(blocksize) 2025-09-09T16:25:10.3167215Z 2025-09-09T16:25:10.3167521Z test/sparsity/test_sparse_api.py::TestBlockSparseWeight::test_sparse_compile_False_input_shape_1 2025-09-09T16:25:10.3168922Z /opt/conda/envs/venv/lib/python3.9/site-packages/torchao/kernel/bsr_triton_ops.py:240: UserWarning: bsr_dense_addmm uses non-optimal triton kernel parameters for M=2048 K=1024 N=1 Ms=64, Ks=64 beta=0 alpha=1 dtype=torch.float16 out_dtype=torch.float16. To find optimal triton kernel parameters, run with BSR_AUTOTUNE=1 2025-09-09T16:25:10.3170087Z warn_once( 2025-09-09T16:25:10.3170258Z 2025-09-09T16:25:10.3170567Z test/sparsity/test_sparse_api.py::TestBlockSparseWeight::test_sparse_compile_False_input_shape_1 2025-09-09T16:25:10.3172011Z /opt/conda/envs/venv/lib/python3.9/site-packages/torchao/kernel/bsr_triton_ops.py:240: UserWarning: bsr_dense_addmm uses non-optimal triton kernel parameters for M=1024 K=2048 N=1 Ms=64, Ks=64 beta=0 alpha=1 dtype=torch.float16 out_dtype=torch.float16. To find optimal triton kernel parameters, run with BSR_AUTOTUNE=1 2025-09-09T16:25:10.3173184Z warn_once( 2025-09-09T16:25:10.3173299Z 2025-09-09T16:25:10.3173618Z test/sparsity/test_sparse_api.py::TestBlockSparseWeight::test_sparse_compile_False_input_shape_1024 2025-09-09T16:25:10.3175113Z /opt/conda/envs/venv/lib/python3.9/site-packages/torchao/kernel/bsr_triton_ops.py:240: UserWarning: bsr_dense_addmm uses non-optimal triton kernel parameters for M=2048 K=1024 N=1024 Ms=64, Ks=64 beta=0 alpha=1 dtype=torch.float16 out_dtype=torch.float16. To find optimal triton kernel parameters, run with BSR_AUTOTUNE=1 2025-09-09T16:25:10.3176297Z warn_once( 2025-09-09T16:25:10.3176412Z 2025-09-09T16:25:10.3176726Z test/sparsity/test_sparse_api.py::TestBlockSparseWeight::test_sparse_compile_False_input_shape_1024 2025-09-09T16:25:10.3178196Z /opt/conda/envs/venv/lib/python3.9/site-packages/torchao/kernel/bsr_triton_ops.py:240: UserWarning: bsr_dense_addmm uses non-optimal triton kernel parameters for M=1024 K=2048 N=1024 Ms=64, Ks=64 beta=0 alpha=1 dtype=torch.float16 out_dtype=torch.float16. To find optimal triton kernel parameters, run with BSR_AUTOTUNE=1 2025-09-09T16:25:10.3179427Z warn_once( 2025-09-09T16:25:10.3179545Z 2025-09-09T16:25:10.3179817Z test/sparsity/test_sparse_api.py::TestQuantBlockSparseWeight::test_sparse_compile_False 2025-09-09T16:25:10.3181191Z /opt/conda/envs/venv/lib/python3.9/site-packages/torchao/kernel/bsr_triton_ops.py:240: UserWarning: bsr_dense_addmm uses non-optimal triton kernel parameters for M=256 K=128 N=256 Ms=64, Ks=64 beta=0 alpha=1 dtype=torch.int8 out_dtype=torch.bfloat16. To find optimal triton kernel parameters, run with BSR_AUTOTUNE=1 2025-09-09T16:25:10.3182357Z warn_once( 2025-09-09T16:25:10.3182479Z 2025-09-09T16:25:10.3182752Z test/sparsity/test_sparse_api.py::TestQuantBlockSparseWeight::test_sparse_compile_False 2025-09-09T16:25:10.3184120Z /opt/conda/envs/venv/lib/python3.9/site-packages/torchao/kernel/bsr_triton_ops.py:240: UserWarning: bsr_dense_addmm uses non-optimal triton kernel parameters for M=128 K=256 N=256 Ms=64, Ks=64 beta=0 alpha=1 dtype=torch.int8 out_dtype=torch.bfloat16. To find optimal triton kernel parameters, run with BSR_AUTOTUNE=1 2025-09-09T16:25:10.3185285Z warn_once( 2025-09-09T16:25:10.3185401Z 2025-09-09T16:25:10.3185620Z test/sparsity/test_wanda.py::TestWandaSparsifier::test_one_layer_mlp_2x4 2025-09-09T16:25:42.9761194Z /opt/conda/envs/venv/lib/python3.9/site-packages/torchao/sparsity/wanda.py:46: UserWarning: WandaSparsifier got semi_structured_bock_size=4, sparsity_level fixed to 50% (2:4) sparsity 2025-09-09T16:25:42.9762064Z warnings.warn( 2025-09-09T16:25:42.9762202Z 2025-09-09T16:25:42.9762439Z test/sparsity/test_wanda.py::TestWandaSparsifier::test_one_layer_mlp_2x4 2025-09-09T16:25:42.9763009Z test/sparsity/test_wanda.py::TestWandaSparsifier::test_one_layer_mlp_unstructured 2025-09-09T16:25:42.9763535Z test/sparsity/test_wanda.py::TestWandaSparsifier::test_prepare 2025-09-09T16:25:42.9764011Z test/sparsity/test_wanda.py::TestWandaSparsifier::test_squash_mask 2025-09-09T16:25:42.9764541Z test/sparsity/test_wanda.py::TestWandaSparsifier::test_two_layer_mlp_unstructured 2025-09-09T16:25:42.9765172Z test/sparsity/test_wanda.py::TestWandaSparsifier::test_two_layer_mlp_unstructured_custom_config 2025-09-09T16:25:42.9766138Z /opt/conda/envs/venv/lib/python3.9/site-packages/torchao/sparsity/wanda.py:75: DeprecationWarning: torch.ao.quantization is deprecated and will be removed in 2.10. 2025-09-09T16:25:42.9766896Z For migrations of users: 2025-09-09T16:25:42.9767874Z 1. Eager mode quantization (torch.ao.quantization.quantize, torch.ao.quantization.quantize_dynamic), please migrate to use torchao eager mode quantize_ API instead 2025-09-09T16:25:42.9769191Z 2. FX graph mode quantization (torch.ao.quantization.quantize_fx.prepare_fx,torch.ao.quantization.quantize_fx.convert_fx, please migrate to use torchao pt2e quantization API instead (prepare_pt2e, convert_pt2e) 2025-09-09T16:25:42.9770356Z 3. pt2e quantization has been migrated to torchao (https://github.com/pytorch/ao/tree/main/torchao/quantization/pt2e) 2025-09-09T16:25:42.9770989Z see https://github.com/pytorch/ao/issues/2259 for more details 2025-09-09T16:25:42.9771408Z torch.ao.quantization.prepare(model, inplace=True) 2025-09-09T16:25:42.9771648Z 2025-09-09T16:25:42.9771851Z -- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html 2025-09-09T16:25:42.9772829Z == 2681 passed, 4429 skipped, 28 xfailed, 334 warnings in 7528.50s (2:05:28) === 2025-09-09T16:25:42.9859822Z ##[group]Run pmeier/pytest-results-action@a2c1430e2bddadbad9f49a6f9b879f062c6b19b1 2025-09-09T16:25:42.9860278Z with: 2025-09-09T16:25:42.9860693Z path: /home/ec2-user/actions-runner/_work/_temp/test-results 2025-09-09T16:25:42.9861047Z fail-on-empty: false 2025-09-09T16:25:42.9861266Z env: 2025-09-09T16:25:42.9861559Z DOCKER_IMAGE: pytorch/almalinux-builder:cuda12.6 2025-09-09T16:25:42.9862030Z REPOSITORY: pytorch/ao 2025-09-09T16:25:42.9862263Z PR_NUMBER: 2963 2025-09-09T16:25:42.9864035Z SCRIPT: conda create -n venv python=3.9 -y conda activate venv echo "::group::Install newer objcopy that supports --set-section-alignment" dnf install -y gcc-toolset-10-binutils export PATH=/opt/rh/gcc-toolset-10/root/usr/bin/:$PATH python -m pip install --upgrade pip pip install torch==2.8.0 sed -i '' dev-requirements.txt pip install -r dev-requirements.txt pip install . export CONDA=$(dirname $(dirname $(which conda))) export LD_LIBRARY_PATH=$CONDA/lib/:$LD_LIBRARY_PATH pytest test --verbose -s 2025-09-09T16:25:42.9865946Z RUNNER_ARTIFACT_DIR: /home/ec2-user/actions-runner/_work/_temp/artifacts 2025-09-09T16:25:42.9866484Z RUNNER_TEST_RESULTS_DIR: /home/ec2-user/actions-runner/_work/_temp/test-results 2025-09-09T16:25:42.9866989Z RUNNER_DOCS_DIR: /home/ec2-user/actions-runner/_work/_temp/docs 2025-09-09T16:25:42.9867421Z GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all 2025-09-09T16:25:42.9867744Z ##[endgroup] 2025-09-09T16:25:43.0474609Z Prepare all required actions 2025-09-09T16:25:43.0509187Z ##[group]Run ./test-infra/.github/actions/chown-directory 2025-09-09T16:25:43.0509501Z with: 2025-09-09T16:25:43.0509753Z directory: /home/ec2-user/actions-runner/_work/ao/ao/ 2025-09-09T16:25:43.0510201Z ALPINE_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/tool/alpine 2025-09-09T16:25:43.0510622Z env: 2025-09-09T16:25:43.0510859Z DOCKER_IMAGE: pytorch/almalinux-builder:cuda12.6 2025-09-09T16:25:43.0511169Z REPOSITORY: pytorch/ao 2025-09-09T16:25:43.0511404Z PR_NUMBER: 2963 2025-09-09T16:25:43.0513156Z SCRIPT: conda create -n venv python=3.9 -y conda activate venv echo "::group::Install newer objcopy that supports --set-section-alignment" dnf install -y gcc-toolset-10-binutils export PATH=/opt/rh/gcc-toolset-10/root/usr/bin/:$PATH python -m pip install --upgrade pip pip install torch==2.8.0 sed -i '' dev-requirements.txt pip install -r dev-requirements.txt pip install . export CONDA=$(dirname $(dirname $(which conda))) export LD_LIBRARY_PATH=$CONDA/lib/:$LD_LIBRARY_PATH pytest test --verbose -s 2025-09-09T16:25:43.0515055Z RUNNER_ARTIFACT_DIR: /home/ec2-user/actions-runner/_work/_temp/artifacts 2025-09-09T16:25:43.0515574Z RUNNER_TEST_RESULTS_DIR: /home/ec2-user/actions-runner/_work/_temp/test-results 2025-09-09T16:25:43.0516071Z RUNNER_DOCS_DIR: /home/ec2-user/actions-runner/_work/_temp/docs 2025-09-09T16:25:43.0516476Z GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all 2025-09-09T16:25:43.0516788Z ##[endgroup] 2025-09-09T16:25:43.0537263Z ##[group]Run docker run --rm -v "${DIRECTORY}":/v -w /v "${ALPINE_IMAGE}" chown -R "$(id -u):$(id -g)" . 2025-09-09T16:25:43.0537902Z docker run --rm -v "${DIRECTORY}":/v -w /v "${ALPINE_IMAGE}" chown -R "$(id -u):$(id -g)" . 2025-09-09T16:25:43.0552487Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-09T16:25:43.0552830Z env: 2025-09-09T16:25:43.0553078Z DOCKER_IMAGE: pytorch/almalinux-builder:cuda12.6 2025-09-09T16:25:43.0553424Z REPOSITORY: pytorch/ao 2025-09-09T16:25:43.0553664Z PR_NUMBER: 2963 2025-09-09T16:25:43.0555378Z SCRIPT: conda create -n venv python=3.9 -y conda activate venv echo "::group::Install newer objcopy that supports --set-section-alignment" dnf install -y gcc-toolset-10-binutils export PATH=/opt/rh/gcc-toolset-10/root/usr/bin/:$PATH python -m pip install --upgrade pip pip install torch==2.8.0 sed -i '' dev-requirements.txt pip install -r dev-requirements.txt pip install . export CONDA=$(dirname $(dirname $(which conda))) export LD_LIBRARY_PATH=$CONDA/lib/:$LD_LIBRARY_PATH pytest test --verbose -s 2025-09-09T16:25:43.0557256Z RUNNER_ARTIFACT_DIR: /home/ec2-user/actions-runner/_work/_temp/artifacts 2025-09-09T16:25:43.0557916Z RUNNER_TEST_RESULTS_DIR: /home/ec2-user/actions-runner/_work/_temp/test-results 2025-09-09T16:25:43.0558411Z RUNNER_DOCS_DIR: /home/ec2-user/actions-runner/_work/_temp/docs 2025-09-09T16:25:43.0558841Z GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all 2025-09-09T16:25:43.0559504Z ALPINE_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/tool/alpine 2025-09-09T16:25:43.0559961Z DIRECTORY: /home/ec2-user/actions-runner/_work/ao/ao/ 2025-09-09T16:25:43.0560285Z ##[endgroup] 2025-09-09T16:25:43.0819877Z Unable to find image '308535385114.dkr.ecr.us-east-1.amazonaws.com/tool/alpine:latest' locally 2025-09-09T16:25:43.3346274Z latest: Pulling from tool/alpine 2025-09-09T16:25:43.3346655Z 540db60ca938: Pulling fs layer 2025-09-09T16:25:43.4214573Z 540db60ca938: Download complete 2025-09-09T16:25:43.5289939Z 540db60ca938: Pull complete 2025-09-09T16:25:43.5342303Z Digest: sha256:def822f9851ca422481ec6fee59a9966f12b351c62ccb9aca841526ffaa9f748 2025-09-09T16:25:43.5364846Z Status: Downloaded newer image for 308535385114.dkr.ecr.us-east-1.amazonaws.com/tool/alpine:latest 2025-09-09T16:25:44.6450823Z Prepare all required actions 2025-09-09T16:25:44.6476975Z ##[group]Run ./test-infra/.github/actions/chown-directory 2025-09-09T16:25:44.6477307Z with: 2025-09-09T16:25:44.6477555Z directory: /home/ec2-user/actions-runner/_work/_temp 2025-09-09T16:25:44.6477990Z ALPINE_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/tool/alpine 2025-09-09T16:25:44.6478375Z env: 2025-09-09T16:25:44.6478607Z DOCKER_IMAGE: pytorch/almalinux-builder:cuda12.6 2025-09-09T16:25:44.6478934Z REPOSITORY: pytorch/ao 2025-09-09T16:25:44.6479168Z PR_NUMBER: 2963 2025-09-09T16:25:44.6481007Z SCRIPT: conda create -n venv python=3.9 -y conda activate venv echo "::group::Install newer objcopy that supports --set-section-alignment" dnf install -y gcc-toolset-10-binutils export PATH=/opt/rh/gcc-toolset-10/root/usr/bin/:$PATH python -m pip install --upgrade pip pip install torch==2.8.0 sed -i '' dev-requirements.txt pip install -r dev-requirements.txt pip install . export CONDA=$(dirname $(dirname $(which conda))) export LD_LIBRARY_PATH=$CONDA/lib/:$LD_LIBRARY_PATH pytest test --verbose -s 2025-09-09T16:25:44.6482917Z RUNNER_ARTIFACT_DIR: /home/ec2-user/actions-runner/_work/_temp/artifacts 2025-09-09T16:25:44.6483443Z RUNNER_TEST_RESULTS_DIR: /home/ec2-user/actions-runner/_work/_temp/test-results 2025-09-09T16:25:44.6483940Z RUNNER_DOCS_DIR: /home/ec2-user/actions-runner/_work/_temp/docs 2025-09-09T16:25:44.6484360Z GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all 2025-09-09T16:25:44.6484666Z ##[endgroup] 2025-09-09T16:25:44.6525081Z ##[group]Run docker run --rm -v "${DIRECTORY}":/v -w /v "${ALPINE_IMAGE}" chown -R "$(id -u):$(id -g)" . 2025-09-09T16:25:44.6525711Z docker run --rm -v "${DIRECTORY}":/v -w /v "${ALPINE_IMAGE}" chown -R "$(id -u):$(id -g)" . 2025-09-09T16:25:44.6538233Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-09T16:25:44.6538576Z env: 2025-09-09T16:25:44.6538817Z DOCKER_IMAGE: pytorch/almalinux-builder:cuda12.6 2025-09-09T16:25:44.6539155Z REPOSITORY: pytorch/ao 2025-09-09T16:25:44.6539386Z PR_NUMBER: 2963 2025-09-09T16:25:44.6541104Z SCRIPT: conda create -n venv python=3.9 -y conda activate venv echo "::group::Install newer objcopy that supports --set-section-alignment" dnf install -y gcc-toolset-10-binutils export PATH=/opt/rh/gcc-toolset-10/root/usr/bin/:$PATH python -m pip install --upgrade pip pip install torch==2.8.0 sed -i '' dev-requirements.txt pip install -r dev-requirements.txt pip install . export CONDA=$(dirname $(dirname $(which conda))) export LD_LIBRARY_PATH=$CONDA/lib/:$LD_LIBRARY_PATH pytest test --verbose -s 2025-09-09T16:25:44.6542996Z RUNNER_ARTIFACT_DIR: /home/ec2-user/actions-runner/_work/_temp/artifacts 2025-09-09T16:25:44.6543511Z RUNNER_TEST_RESULTS_DIR: /home/ec2-user/actions-runner/_work/_temp/test-results 2025-09-09T16:25:44.6544002Z RUNNER_DOCS_DIR: /home/ec2-user/actions-runner/_work/_temp/docs 2025-09-09T16:25:44.6544425Z GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all 2025-09-09T16:25:44.6545003Z ALPINE_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/tool/alpine 2025-09-09T16:25:44.6545454Z DIRECTORY: /home/ec2-user/actions-runner/_work/_temp 2025-09-09T16:25:44.6545868Z ##[endgroup] 2025-09-09T16:25:45.6702806Z ##[group]Run # Only do these steps if we actually want to upload an artifact 2025-09-09T16:25:45.6703365Z # Only do these steps if we actually want to upload an artifact 2025-09-09T16:25:45.6703779Z if [[ -n "${UPLOAD_ARTIFACT_NAME}" ]]; then 2025-09-09T16:25:45.6704277Z  # If the default execution path is followed then we should get a wheel in the dist/ folder 2025-09-09T16:25:45.6704834Z  # attempt to just grab whatever is in there and scoop it all up 2025-09-09T16:25:45.6705279Z  if find "dist/" -name "*.whl" >/dev/null 2>/dev/null; then 2025-09-09T16:25:45.6705670Z  mv -v dist/*.whl "${RUNNER_ARTIFACT_DIR}/" 2025-09-09T16:25:45.6705981Z  fi 2025-09-09T16:25:45.6706243Z  if [[ -d "artifacts-to-be-uploaded" ]]; then 2025-09-09T16:25:45.6706657Z  mv -v artifacts-to-be-uploaded/* "${RUNNER_ARTIFACT_DIR}/" 2025-09-09T16:25:45.6707027Z  fi 2025-09-09T16:25:45.6707251Z fi 2025-09-09T16:25:45.6707438Z  2025-09-09T16:25:45.6707637Z upload_docs=0 2025-09-09T16:25:45.6708004Z # Check if there are files in the documentation folder to upload, note that 2025-09-09T16:25:45.6708420Z # empty folders do not count 2025-09-09T16:25:45.6708841Z if find "${RUNNER_DOCS_DIR}" -mindepth 1 -maxdepth 1 -type f | read -r; then 2025-09-09T16:25:45.6709379Z  # TODO: Add a check here to test if on ec2 because if we're not on ec2 then this 2025-09-09T16:25:45.6709830Z  # upload will probably not work correctly 2025-09-09T16:25:45.6710140Z  upload_docs=1 2025-09-09T16:25:45.6710370Z fi 2025-09-09T16:25:45.6710663Z echo "upload-docs=${upload_docs}" >> "${GITHUB_OUTPUT}" 2025-09-09T16:25:45.6723211Z shell: /usr/bin/bash -e {0} 2025-09-09T16:25:45.6723513Z env: 2025-09-09T16:25:45.6723799Z DOCKER_IMAGE: pytorch/almalinux-builder:cuda12.6 2025-09-09T16:25:45.6724212Z REPOSITORY: pytorch/ao 2025-09-09T16:25:45.6724442Z PR_NUMBER: 2963 2025-09-09T16:25:45.6726156Z SCRIPT: conda create -n venv python=3.9 -y conda activate venv echo "::group::Install newer objcopy that supports --set-section-alignment" dnf install -y gcc-toolset-10-binutils export PATH=/opt/rh/gcc-toolset-10/root/usr/bin/:$PATH python -m pip install --upgrade pip pip install torch==2.8.0 sed -i '' dev-requirements.txt pip install -r dev-requirements.txt pip install . export CONDA=$(dirname $(dirname $(which conda))) export LD_LIBRARY_PATH=$CONDA/lib/:$LD_LIBRARY_PATH pytest test --verbose -s 2025-09-09T16:25:45.6728037Z RUNNER_ARTIFACT_DIR: /home/ec2-user/actions-runner/_work/_temp/artifacts 2025-09-09T16:25:45.6728559Z RUNNER_TEST_RESULTS_DIR: /home/ec2-user/actions-runner/_work/_temp/test-results 2025-09-09T16:25:45.6729041Z RUNNER_DOCS_DIR: /home/ec2-user/actions-runner/_work/_temp/docs 2025-09-09T16:25:45.6729451Z GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all 2025-09-09T16:25:45.6729780Z UPLOAD_ARTIFACT_NAME: 2025-09-09T16:25:45.6730007Z ##[endgroup] 2025-09-09T16:25:45.6829803Z Prepare all required actions 2025-09-09T16:25:45.6863793Z ##[group]Run ./test-infra/.github/actions/teardown-linux 2025-09-09T16:25:45.6864161Z with: 2025-09-09T16:25:45.6864354Z env: 2025-09-09T16:25:45.6864594Z DOCKER_IMAGE: pytorch/almalinux-builder:cuda12.6 2025-09-09T16:25:45.6864925Z REPOSITORY: pytorch/ao 2025-09-09T16:25:45.6865159Z PR_NUMBER: 2963 2025-09-09T16:25:45.6866906Z SCRIPT: conda create -n venv python=3.9 -y conda activate venv echo "::group::Install newer objcopy that supports --set-section-alignment" dnf install -y gcc-toolset-10-binutils export PATH=/opt/rh/gcc-toolset-10/root/usr/bin/:$PATH python -m pip install --upgrade pip pip install torch==2.8.0 sed -i '' dev-requirements.txt pip install -r dev-requirements.txt pip install . export CONDA=$(dirname $(dirname $(which conda))) export LD_LIBRARY_PATH=$CONDA/lib/:$LD_LIBRARY_PATH pytest test --verbose -s 2025-09-09T16:25:45.6869042Z RUNNER_ARTIFACT_DIR: /home/ec2-user/actions-runner/_work/_temp/artifacts 2025-09-09T16:25:45.6869575Z RUNNER_TEST_RESULTS_DIR: /home/ec2-user/actions-runner/_work/_temp/test-results 2025-09-09T16:25:45.6870077Z RUNNER_DOCS_DIR: /home/ec2-user/actions-runner/_work/_temp/docs 2025-09-09T16:25:45.6870493Z GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all 2025-09-09T16:25:45.6870823Z ##[endgroup] 2025-09-09T16:25:45.6892506Z ##[group]Run set -eou pipefail 2025-09-09T16:25:45.6892801Z set -eou pipefail 2025-09-09T16:25:45.6893042Z  2025-09-09T16:25:45.6893371Z echo "Holding runner for 2 hours until all ssh sessions have logged out" 2025-09-09T16:25:45.6893790Z for _ in $(seq 1440); do 2025-09-09T16:25:45.6894096Z  # Break if no ssh session exists anymore 2025-09-09T16:25:45.6894423Z  if [ "$(who)" = "" ]; then 2025-09-09T16:25:45.6894690Z  break 2025-09-09T16:25:45.6894908Z  fi 2025-09-09T16:25:45.6895123Z  echo "." 2025-09-09T16:25:45.6895351Z  sleep 5 2025-09-09T16:25:45.6895568Z done 2025-09-09T16:25:45.6903770Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-09T16:25:45.6904105Z env: 2025-09-09T16:25:45.6904349Z DOCKER_IMAGE: pytorch/almalinux-builder:cuda12.6 2025-09-09T16:25:45.6904675Z REPOSITORY: pytorch/ao 2025-09-09T16:25:45.6904907Z PR_NUMBER: 2963 2025-09-09T16:25:45.6906626Z SCRIPT: conda create -n venv python=3.9 -y conda activate venv echo "::group::Install newer objcopy that supports --set-section-alignment" dnf install -y gcc-toolset-10-binutils export PATH=/opt/rh/gcc-toolset-10/root/usr/bin/:$PATH python -m pip install --upgrade pip pip install torch==2.8.0 sed -i '' dev-requirements.txt pip install -r dev-requirements.txt pip install . export CONDA=$(dirname $(dirname $(which conda))) export LD_LIBRARY_PATH=$CONDA/lib/:$LD_LIBRARY_PATH pytest test --verbose -s 2025-09-09T16:25:45.6908520Z RUNNER_ARTIFACT_DIR: /home/ec2-user/actions-runner/_work/_temp/artifacts 2025-09-09T16:25:45.6909050Z RUNNER_TEST_RESULTS_DIR: /home/ec2-user/actions-runner/_work/_temp/test-results 2025-09-09T16:25:45.6909551Z RUNNER_DOCS_DIR: /home/ec2-user/actions-runner/_work/_temp/docs 2025-09-09T16:25:45.6909969Z GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all 2025-09-09T16:25:45.6910295Z ##[endgroup] 2025-09-09T16:25:45.6961864Z Holding runner for 2 hours until all ssh sessions have logged out 2025-09-09T16:25:45.7054247Z ##[group]Run # ignore expansion of "docker ps -q" since it could be empty 2025-09-09T16:25:45.7054853Z # ignore expansion of "docker ps -q" since it could be empty 2025-09-09T16:25:45.7055269Z # shellcheck disable=SC2046 2025-09-09T16:25:45.7055601Z docker stop $(docker ps -q) || true 2025-09-09T16:25:45.7055936Z # Prune all of the docker images 2025-09-09T16:25:45.7056243Z docker system prune -af 2025-09-09T16:25:45.7065364Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-09T16:25:45.7065703Z env: 2025-09-09T16:25:45.7066181Z DOCKER_IMAGE: pytorch/almalinux-builder:cuda12.6 2025-09-09T16:25:45.7066525Z REPOSITORY: pytorch/ao 2025-09-09T16:25:45.7066769Z PR_NUMBER: 2963 2025-09-09T16:25:45.7068506Z SCRIPT: conda create -n venv python=3.9 -y conda activate venv echo "::group::Install newer objcopy that supports --set-section-alignment" dnf install -y gcc-toolset-10-binutils export PATH=/opt/rh/gcc-toolset-10/root/usr/bin/:$PATH python -m pip install --upgrade pip pip install torch==2.8.0 sed -i '' dev-requirements.txt pip install -r dev-requirements.txt pip install . export CONDA=$(dirname $(dirname $(which conda))) export LD_LIBRARY_PATH=$CONDA/lib/:$LD_LIBRARY_PATH pytest test --verbose -s 2025-09-09T16:25:45.7070493Z RUNNER_ARTIFACT_DIR: /home/ec2-user/actions-runner/_work/_temp/artifacts 2025-09-09T16:25:45.7071047Z RUNNER_TEST_RESULTS_DIR: /home/ec2-user/actions-runner/_work/_temp/test-results 2025-09-09T16:25:45.7071680Z RUNNER_DOCS_DIR: /home/ec2-user/actions-runner/_work/_temp/docs 2025-09-09T16:25:45.7072121Z GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all 2025-09-09T16:25:45.7072450Z ##[endgroup] 2025-09-09T16:25:47.6364421Z 41711a67c7d9 2025-09-09T16:25:56.2637346Z Deleted Containers: 2025-09-09T16:25:56.2637739Z 41711a67c7d99be45e4ff98c9daaa6cc7727d22c0e653d70289eb892e8b8e291 2025-09-09T16:25:56.2638047Z 2025-09-09T16:26:03.9459699Z Deleted Images: 2025-09-09T16:26:03.9460055Z untagged: pytorch/almalinux-builder:cuda12.6 2025-09-09T16:26:03.9460667Z untagged: pytorch/almalinux-builder@sha256:be7f2a4c6f467933b154ac0b3ded894ad1bf06ce95f8f8d908dba108e68806f3 2025-09-09T16:26:03.9461376Z deleted: 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308535385114.dkr.ecr.us-east-1.amazonaws.com/tool/alpine@sha256:def822f9851ca422481ec6fee59a9966f12b351c62ccb9aca841526ffaa9f748 2025-09-09T16:26:03.9483818Z deleted: sha256:6dbb9cc54074106d46d4ccb330f2a40a682d49dda5f4844962b7dce9fe44aaec 2025-09-09T16:26:03.9484399Z deleted: sha256:b2d5eeeaba3a22b9b8aa97261957974a6bd65274ebd43e1d81d0a7b8b752b116 2025-09-09T16:26:03.9484747Z 2025-09-09T16:26:03.9484857Z Total reclaimed space: 31.03GB 2025-09-09T16:26:03.9553096Z ##[group]Run set +e 2025-09-09T16:26:03.9553363Z set +e 2025-09-09T16:26:03.9553602Z if [[ "${NO_SUDO}" == "false" ]]; then 2025-09-09T16:26:03.9553986Z  sudo rm -rf "${GITHUB_WORKSPACE:?}/${REPOSITORY:?}" 2025-09-09T16:26:03.9554320Z else 2025-09-09T16:26:03.9554585Z  rm -rf "${GITHUB_WORKSPACE:?}/${REPOSITORY:?}" 2025-09-09T16:26:03.9554899Z fi 2025-09-09T16:26:03.9555115Z set -e 2025-09-09T16:26:03.9566241Z shell: /usr/bin/bash -e {0} 2025-09-09T16:26:03.9566492Z env: 2025-09-09T16:26:03.9566734Z DOCKER_IMAGE: pytorch/almalinux-builder:cuda12.6 2025-09-09T16:26:03.9567069Z REPOSITORY: pytorch/ao 2025-09-09T16:26:03.9567314Z PR_NUMBER: 2963 2025-09-09T16:26:03.9569235Z SCRIPT: conda create -n venv python=3.9 -y conda activate venv echo "::group::Install newer objcopy that supports --set-section-alignment" dnf install -y gcc-toolset-10-binutils export PATH=/opt/rh/gcc-toolset-10/root/usr/bin/:$PATH python -m pip install --upgrade pip pip install torch==2.8.0 sed -i '' dev-requirements.txt pip install -r dev-requirements.txt pip install . export CONDA=$(dirname $(dirname $(which conda))) export LD_LIBRARY_PATH=$CONDA/lib/:$LD_LIBRARY_PATH pytest test --verbose -s 2025-09-09T16:26:03.9571139Z RUNNER_ARTIFACT_DIR: /home/ec2-user/actions-runner/_work/_temp/artifacts 2025-09-09T16:26:03.9571714Z RUNNER_TEST_RESULTS_DIR: /home/ec2-user/actions-runner/_work/_temp/test-results 2025-09-09T16:26:03.9572217Z RUNNER_DOCS_DIR: /home/ec2-user/actions-runner/_work/_temp/docs 2025-09-09T16:26:03.9572639Z GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all 2025-09-09T16:26:03.9572971Z NO_SUDO: false 2025-09-09T16:26:03.9573185Z ##[endgroup] 2025-09-09T16:26:04.5024544Z Post job cleanup. 2025-09-09T16:26:04.6092657Z Post job cleanup. 2025-09-09T16:26:04.7083507Z [command]/usr/bin/git version 2025-09-09T16:26:04.7136214Z git version 2.47.1 2025-09-09T16:26:04.7181871Z Temporarily overriding HOME='/home/ec2-user/actions-runner/_work/_temp/299327c3-8bb4-4825-955a-40f2dfebff9b' before making global git config changes 2025-09-09T16:26:04.7182737Z Adding repository directory to the temporary git global config as a safe directory 2025-09-09T16:26:04.7187295Z [command]/usr/bin/git config --global --add safe.directory /home/ec2-user/actions-runner/_work/ao/ao/test-infra 2025-09-09T16:26:04.7239029Z [command]/usr/bin/git config --local --name-only --get-regexp core\.sshCommand 2025-09-09T16:26:04.7279320Z [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' || :" 2025-09-09T16:26:04.7709479Z [command]/usr/bin/git config --local --name-only --get-regexp http\.https\:\/\/github\.com\/\.extraheader 2025-09-09T16:26:04.7737747Z http.https://github.com/.extraheader 2025-09-09T16:26:04.7749958Z [command]/usr/bin/git config --local --unset-all http.https://github.com/.extraheader 2025-09-09T16:26:04.7787605Z [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' || :" 2025-09-09T16:26:04.8292290Z A job completed hook has been configured by the self-hosted runner administrator 2025-09-09T16:26:04.8323455Z ##[group]Run '/home/ec2-user/runner-scripts/after_job.sh' 2025-09-09T16:26:04.8331568Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-09T16:26:04.8331930Z ##[endgroup] 2025-09-09T16:26:04.8448555Z [!ALERT!] Swap in detected! [!ALERT!] 2025-09-09T16:26:16.1195547Z [!ALERT!] Swap out detected [!ALERT!] 2025-09-09T16:26:34.3236264Z Cleaning up orphan processes