2025-09-09T14:02:53.3016249Z Current runner version: '2.328.0' 2025-09-09T14:02:53.3023389Z Runner name: 'i-0168113e7d2d9a0cf' 2025-09-09T14:02:53.3024495Z Runner group name: 'default' 2025-09-09T14:02:53.3025334Z Machine name: 'ip-10-0-66-179' 2025-09-09T14:02:53.3028239Z ##[group]GITHUB_TOKEN Permissions 2025-09-09T14:02:53.3030651Z Contents: read 2025-09-09T14:02:53.3031239Z Metadata: read 2025-09-09T14:02:53.3031821Z Packages: read 2025-09-09T14:02:53.3032543Z ##[endgroup] 2025-09-09T14:02:53.3034821Z Secret source: Actions 2025-09-09T14:02:53.3035620Z Prepare workflow directory 2025-09-09T14:02:53.3620839Z Prepare all required actions 2025-09-09T14:02:53.3662253Z Getting action download info 2025-09-09T14:02:53.7754478Z Download action repository 'actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683' (SHA:11bd71901bbe5b1630ceea73d27597364c9af683) 2025-09-09T14:02:54.0861789Z Download action repository 'pytorch/pytorch@main' (SHA:4dd73e659a8fd4872e5f49cfd72e420fa7c4e6c9) 2025-09-09T14:03:07.8739025Z Download action repository 'actions/download-artifact@d3f86a106a0bac45b974a628896c90dbdf5c8093' (SHA:d3f86a106a0bac45b974a628896c90dbdf5c8093) 2025-09-09T14:03:08.2498097Z Download action repository 'pmeier/pytest-results-action@a2c1430e2bddadbad9f49a6f9b879f062c6b19b1' (SHA:a2c1430e2bddadbad9f49a6f9b879f062c6b19b1) 2025-09-09T14:03:08.4024929Z Download action repository 'actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02' (SHA:ea165f8d65b6e75b540449e92b4886f43607fa02) 2025-09-09T14:03:08.9400162Z Getting action download info 2025-09-09T14:03:09.1316821Z Uses: pytorch/test-infra/.github/workflows/linux_job_v2.yml@refs/heads/main (e502b6d9079a2a411c68046e8a7694b851c5df33) 2025-09-09T14:03:09.1321010Z ##[group] Inputs 2025-09-09T14:03:09.1328921Z 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/:$PATH python -m pip install --upgrade pip pip install torch==2.8.0 --index-url https://download.pytorch.org/whl/cpu sed -i '' dev-requirements.txt pip install -r dev-requirements.txt pip install . export CONDA=$(dirname $(dirname $(which conda))) export LD_LIBRARY_PATH=$CONDA/lib/:$LD_LIBRARY_PATH pytest test --verbose -s 2025-09-09T14:03:09.1331836Z timeout: 180 2025-09-09T14:03:09.1332073Z runner: linux.4xlarge 2025-09-09T14:03:09.1332318Z upload-artifact: 2025-09-09T14:03:09.1332877Z upload-artifact-to-s3: false 2025-09-09T14:03:09.1333153Z download-artifact: 2025-09-09T14:03:09.1333382Z repository: 2025-09-09T14:03:09.1333612Z fetch-depth: 1 2025-09-09T14:03:09.1333827Z submodules: recursive 2025-09-09T14:03:09.1334045Z ref: 2025-09-09T14:03:09.1334277Z test-infra-repository: pytorch/test-infra 2025-09-09T14:03:09.1334578Z test-infra-ref: 2025-09-09T14:03:09.1334814Z use-custom-docker-registry: true 2025-09-09T14:03:09.1335126Z docker-image: pytorch/almalinux-builder 2025-09-09T14:03:09.1335503Z docker-build-dir: .ci/docker 2025-09-09T14:03:09.1335775Z gpu-arch-type: cpu 2025-09-09T14:03:09.1336001Z gpu-arch-version: 2025-09-09T14:03:09.1336232Z job-name: linux-job 2025-09-09T14:03:09.1336469Z continue-on-error: false 2025-09-09T14:03:09.1336725Z binary-matrix: 2025-09-09T14:03:09.1336958Z run-with-docker: true 2025-09-09T14:03:09.1337210Z secrets-env: 2025-09-09T14:03:09.1337479Z no-sudo: false 2025-09-09T14:03:09.1337711Z ##[endgroup] 2025-09-09T14:03:09.1338273Z Complete job name: test (CPU 2.8, linux.4xlarge, torch==2.8.0 --index-url https://download.pytorch.org/whl/cpu, cpu) / linux-job 2025-09-09T14:03:09.1818803Z A job started hook has been configured by the self-hosted runner administrator 2025-09-09T14:03:09.1928864Z ##[group]Run '/home/ec2-user/runner-scripts/before_job.sh' 2025-09-09T14:03:09.1937849Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-09T14:03:09.1938494Z ##[endgroup] 2025-09-09T14:03:10.4761940Z Runner Type: linux.4xlarge 2025-09-09T14:03:10.4762429Z Instance Type: c5.4xlarge 2025-09-09T14:03:10.4762994Z AMI Name: unknown 2025-09-09T14:03:10.4793138Z AMI ID: ami-05ffe3c48a9991133 2025-09-09T14:03:15.9884694Z ##[group]Run set -euxo pipefail 2025-09-09T14:03:15.9885099Z set -euxo pipefail 2025-09-09T14:03:15.9885408Z if [[ "${NO_SUDO}" == "false" ]]; then 2025-09-09T14:03:15.9885800Z  echo "::group::Cleanup with-sudo debug output" 2025-09-09T14:03:15.9886178Z  sudo rm -rfv "${GITHUB_WORKSPACE}" 2025-09-09T14:03:15.9886498Z else 2025-09-09T14:03:15.9886762Z  echo "::group::Cleanup no-sudo debug output" 2025-09-09T14:03:15.9887128Z  rm -rfv "${GITHUB_WORKSPACE}" 2025-09-09T14:03:15.9887414Z fi 2025-09-09T14:03:15.9887622Z  2025-09-09T14:03:15.9887856Z mkdir -p "${GITHUB_WORKSPACE}" 2025-09-09T14:03:15.9888204Z echo "::endgroup::" 2025-09-09T14:03:15.9897213Z shell: /usr/bin/bash -e {0} 2025-09-09T14:03:15.9897512Z env: 2025-09-09T14:03:15.9897754Z DOCKER_IMAGE: pytorch/almalinux-builder:cpu 2025-09-09T14:03:15.9898114Z REPOSITORY: pytorch/ao 2025-09-09T14:03:15.9898412Z PR_NUMBER: 2963 2025-09-09T14:03:15.9900668Z SCRIPT: conda create -n venv python=3.9 -y conda activate venv echo "::group::Install newer objcopy that supports --set-section-alignment" dnf install -y gcc-toolset-10-binutils export PATH=/opt/rh/gcc-toolset-10/root/usr/bin/:$PATH python -m pip install --upgrade pip pip install torch==2.8.0 --index-url https://download.pytorch.org/whl/cpu 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/:$LD_LIBRARY_PATH pytest test --verbose -s 2025-09-09T14:03:15.9902951Z NO_SUDO: false 2025-09-09T14:03:15.9903187Z ##[endgroup] 2025-09-09T14:03:15.9931052Z + [[ false == \f\a\l\s\e ]] 2025-09-09T14:03:15.9946088Z ##[group]Cleanup with-sudo debug output 2025-09-09T14:03:15.9949210Z + echo '::group::Cleanup with-sudo debug output' 2025-09-09T14:03:15.9949681Z + sudo rm -rfv /home/ec2-user/actions-runner/_work/ao/ao 2025-09-09T14:03:16.1211771Z removed directory '/home/ec2-user/actions-runner/_work/ao/ao' 2025-09-09T14:03:16.1227130Z + mkdir -p /home/ec2-user/actions-runner/_work/ao/ao 2025-09-09T14:03:16.1242642Z + echo ::endgroup:: 2025-09-09T14:03:16.1243639Z ##[endgroup] 2025-09-09T14:03:16.1370920Z ##[group]Run actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 2025-09-09T14:03:16.1371371Z with: 2025-09-09T14:03:16.1371617Z repository: pytorch/test-infra 2025-09-09T14:03:16.1371904Z path: test-infra 2025-09-09T14:03:16.1372146Z submodules: recursive 2025-09-09T14:03:16.1372601Z token: *** 2025-09-09T14:03:16.1372812Z ssh-strict: true 2025-09-09T14:03:16.1373042Z ssh-user: git 2025-09-09T14:03:16.1373270Z persist-credentials: true 2025-09-09T14:03:16.1373542Z clean: true 2025-09-09T14:03:16.1373777Z sparse-checkout-cone-mode: true 2025-09-09T14:03:16.1374073Z fetch-depth: 1 2025-09-09T14:03:16.1374289Z fetch-tags: false 2025-09-09T14:03:16.1374543Z show-progress: true 2025-09-09T14:03:16.1374766Z lfs: false 2025-09-09T14:03:16.1374992Z set-safe-directory: true 2025-09-09T14:03:16.1375236Z env: 2025-09-09T14:03:16.1375479Z DOCKER_IMAGE: pytorch/almalinux-builder:cpu 2025-09-09T14:03:16.1375817Z REPOSITORY: pytorch/ao 2025-09-09T14:03:16.1376110Z PR_NUMBER: 2963 2025-09-09T14:03:16.1378339Z SCRIPT: conda create -n venv python=3.9 -y conda activate venv echo "::group::Install newer objcopy that supports --set-section-alignment" dnf install -y gcc-toolset-10-binutils export PATH=/opt/rh/gcc-toolset-10/root/usr/bin/:$PATH python -m pip install --upgrade pip pip install torch==2.8.0 --index-url https://download.pytorch.org/whl/cpu 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 2025-09-09T14:03:16.1380610Z ##[endgroup] 2025-09-09T14:03:16.2826417Z Syncing repository: pytorch/test-infra 2025-09-09T14:03:16.2827521Z ##[group]Getting Git version info 2025-09-09T14:03:16.2828014Z Working directory is '/home/ec2-user/actions-runner/_work/ao/ao/test-infra' 2025-09-09T14:03:16.2828678Z [command]/usr/bin/git version 2025-09-09T14:03:16.2828950Z git version 2.47.1 2025-09-09T14:03:16.2840577Z ##[endgroup] 2025-09-09T14:03:16.2860990Z Temporarily overriding HOME='/home/ec2-user/actions-runner/_work/_temp/4ea769a2-a8ba-4eae-9832-5eefa54b54a5' before making global git config changes 2025-09-09T14:03:16.2875278Z Adding repository directory to the temporary git global config as a safe directory 2025-09-09T14:03:16.2876089Z [command]/usr/bin/git config --global --add safe.directory /home/ec2-user/actions-runner/_work/ao/ao/test-infra 2025-09-09T14:03:16.2903444Z ##[group]Initializing the repository 2025-09-09T14:03:16.2907515Z [command]/usr/bin/git init /home/ec2-user/actions-runner/_work/ao/ao/test-infra 2025-09-09T14:03:16.2941194Z hint: Using 'master' as the name for the initial branch. This default branch name 2025-09-09T14:03:16.2942360Z hint: is subject to change. To configure the initial branch name to use in all 2025-09-09T14:03:16.2943174Z hint: of your new repositories, which will suppress this warning, call: 2025-09-09T14:03:16.2943597Z hint: 2025-09-09T14:03:16.2943931Z hint: git config --global init.defaultBranch 2025-09-09T14:03:16.2944287Z hint: 2025-09-09T14:03:16.2944658Z hint: Names commonly chosen instead of 'master' are 'main', 'trunk' and 2025-09-09T14:03:16.2945253Z hint: 'development'. The just-created branch can be renamed via this command: 2025-09-09T14:03:16.2945676Z hint: 2025-09-09T14:03:16.2945919Z hint: git branch -m 2025-09-09T14:03:16.2946428Z Initialized empty Git repository in /home/ec2-user/actions-runner/_work/ao/ao/test-infra/.git/ 2025-09-09T14:03:16.2950768Z [command]/usr/bin/git remote add origin https://github.com/pytorch/test-infra 2025-09-09T14:03:16.2974981Z ##[endgroup] 2025-09-09T14:03:16.2975418Z ##[group]Disabling automatic garbage collection 2025-09-09T14:03:16.2978934Z [command]/usr/bin/git config --local gc.auto 0 2025-09-09T14:03:16.3005710Z ##[endgroup] 2025-09-09T14:03:16.3006435Z ##[group]Setting up auth 2025-09-09T14:03:16.3010453Z [command]/usr/bin/git config --local --name-only --get-regexp core\.sshCommand 2025-09-09T14:03:16.3035480Z [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' || :" 2025-09-09T14:03:16.3370943Z [command]/usr/bin/git config --local --name-only --get-regexp http\.https\:\/\/github\.com\/\.extraheader 2025-09-09T14:03:16.3396294Z [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' || :" 2025-09-09T14:03:16.3700703Z [command]/usr/bin/git config --local http.https://github.com/.extraheader AUTHORIZATION: basic *** 2025-09-09T14:03:16.3743190Z ##[endgroup] 2025-09-09T14:03:16.3744072Z ##[group]Determining the default branch 2025-09-09T14:03:16.3745337Z Retrieving the default branch name 2025-09-09T14:03:16.6332122Z Default branch 'main' 2025-09-09T14:03:16.6332914Z ##[endgroup] 2025-09-09T14:03:16.6333398Z ##[group]Fetching the repository 2025-09-09T14:03:16.6337957Z [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 2025-09-09T14:03:17.0137139Z From https://github.com/pytorch/test-infra 2025-09-09T14:03:17.0137560Z * [new branch] main -> origin/main 2025-09-09T14:03:17.0158877Z ##[endgroup] 2025-09-09T14:03:17.0159286Z ##[group]Determining the checkout info 2025-09-09T14:03:17.0160581Z ##[endgroup] 2025-09-09T14:03:17.0164645Z [command]/usr/bin/git sparse-checkout disable 2025-09-09T14:03:17.0205406Z [command]/usr/bin/git config --local --unset-all extensions.worktreeConfig 2025-09-09T14:03:17.0239974Z ##[group]Checking out the ref 2025-09-09T14:03:17.0243356Z [command]/usr/bin/git checkout --progress --force -B main refs/remotes/origin/main 2025-09-09T14:03:17.1393078Z Switched to a new branch 'main' 2025-09-09T14:03:17.1393522Z branch 'main' set up to track 'origin/main'. 2025-09-09T14:03:17.1400627Z ##[endgroup] 2025-09-09T14:03:17.1401073Z ##[group]Setting up auth for fetching submodules 2025-09-09T14:03:17.1406593Z [command]/usr/bin/git config --global http.https://github.com/.extraheader AUTHORIZATION: basic *** 2025-09-09T14:03:17.1443715Z [command]/usr/bin/git config --global --unset-all url.https://github.com/.insteadOf 2025-09-09T14:03:17.1480542Z [command]/usr/bin/git config --global --add url.https://github.com/.insteadOf git@github.com: 2025-09-09T14:03:17.1506565Z [command]/usr/bin/git config --global --add url.https://github.com/.insteadOf org-21003710@github.com: 2025-09-09T14:03:17.1530585Z ##[endgroup] 2025-09-09T14:03:17.1530996Z ##[group]Fetching submodules 2025-09-09T14:03:17.1533683Z [command]/usr/bin/git submodule sync --recursive 2025-09-09T14:03:17.1822482Z [command]/usr/bin/git -c protocol.version=2 submodule update --init --force --depth=1 --recursive 2025-09-09T14:03:17.2110770Z [command]/usr/bin/git submodule foreach --recursive git config --local gc.auto 0 2025-09-09T14:03:17.2396422Z ##[endgroup] 2025-09-09T14:03:17.2400589Z ##[group]Persisting credentials for submodules 2025-09-09T14:03:17.2401648Z [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' || :" 2025-09-09T14:03:17.2686560Z [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" 2025-09-09T14:03:17.2971976Z [command]/usr/bin/git submodule foreach --recursive git config --local --add 'url.https://github.com/.insteadOf' 'git@github.com:' 2025-09-09T14:03:17.3259000Z [command]/usr/bin/git submodule foreach --recursive git config --local --add 'url.https://github.com/.insteadOf' 'org-21003710@github.com:' 2025-09-09T14:03:17.3539314Z ##[endgroup] 2025-09-09T14:03:17.3571422Z [command]/usr/bin/git log -1 --format=%H 2025-09-09T14:03:17.3592959Z e502b6d9079a2a411c68046e8a7694b851c5df33 2025-09-09T14:03:17.3767743Z Prepare all required actions 2025-09-09T14:03:17.3768236Z Getting action download info 2025-09-09T14:03:17.5360187Z Download action repository 'pytorch/test-infra@main' (SHA:e502b6d9079a2a411c68046e8a7694b851c5df33) 2025-09-09T14:03:19.4159213Z Getting action download info 2025-09-09T14:03:19.5537382Z Download action repository 'nick-fields/retry@3e91a01664abd3c5cd539100d10d33b9c5b68482' (SHA:3e91a01664abd3c5cd539100d10d33b9c5b68482) 2025-09-09T14:03:19.7652119Z ##[group]Run ./test-infra/.github/actions/setup-linux 2025-09-09T14:03:19.7652475Z env: 2025-09-09T14:03:19.7652738Z DOCKER_IMAGE: pytorch/almalinux-builder:cpu 2025-09-09T14:03:19.7653086Z REPOSITORY: pytorch/ao 2025-09-09T14:03:19.7653342Z PR_NUMBER: 2963 2025-09-09T14:03:19.7655566Z 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 --index-url https://download.pytorch.org/whl/cpu 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 2025-09-09T14:03:19.7657812Z ##[endgroup] 2025-09-09T14:03:19.7743560Z ##[group]Run set -euo pipefail 2025-09-09T14:03:19.7743895Z set -euo pipefail 2025-09-09T14:03:19.7744175Z function get_ec2_metadata() { 2025-09-09T14:03:19.7744558Z  # Pulled from instance metadata endpoint for EC2 2025-09-09T14:03:19.7745340Z  # see https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/instancedata-data-retrieval.html 2025-09-09T14:03:19.7745905Z  category=$1 2025-09-09T14:03:19.7746780Z  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}" 2025-09-09T14:03:19.7747681Z } 2025-09-09T14:03:19.7747942Z echo "ami-id: $(get_ec2_metadata ami-id)" 2025-09-09T14:03:19.7748348Z echo "instance-id: $(get_ec2_metadata instance-id)" 2025-09-09T14:03:19.7748819Z echo "instance-type: $(get_ec2_metadata instance-type)" 2025-09-09T14:03:19.7749217Z echo "system info $(uname -a)" 2025-09-09T14:03:19.7755290Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-09T14:03:19.7755651Z env: 2025-09-09T14:03:19.7755903Z DOCKER_IMAGE: pytorch/almalinux-builder:cpu 2025-09-09T14:03:19.7756340Z REPOSITORY: pytorch/ao 2025-09-09T14:03:19.7756602Z PR_NUMBER: 2963 2025-09-09T14:03:19.7758796Z SCRIPT: conda create -n venv python=3.9 -y conda activate venv echo "::group::Install newer objcopy that supports --set-section-alignment" dnf install -y gcc-toolset-10-binutils export PATH=/opt/rh/gcc-toolset-10/root/usr/bin/:$PATH python -m pip install --upgrade pip pip install torch==2.8.0 --index-url https://download.pytorch.org/whl/cpu 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/:$LD_LIBRARY_PATH pytest test --verbose -s 2025-09-09T14:03:19.7760998Z ##[endgroup] 2025-09-09T14:03:19.7903745Z ami-id: ami-05ffe3c48a9991133 2025-09-09T14:03:19.8006006Z instance-id: i-0168113e7d2d9a0cf 2025-09-09T14:03:19.8117278Z instance-type: c5.4xlarge 2025-09-09T14:03:19.8128639Z system info Linux ip-10-0-66-179.ec2.internal 6.1.141-155.222.amzn2023.x86_64 #1 SMP PREEMPT_DYNAMIC Tue Jun 17 10:29:47 UTC 2025 x86_64 x86_64 x86_64 GNU/Linux 2025-09-09T14:03:19.8172145Z ##[group]Run echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT" 2025-09-09T14:03:19.8173246Z echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT" 2025-09-09T14:03:19.8179558Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-09T14:03:19.8179928Z env: 2025-09-09T14:03:19.8180169Z DOCKER_IMAGE: pytorch/almalinux-builder:cpu 2025-09-09T14:03:19.8180508Z REPOSITORY: pytorch/ao 2025-09-09T14:03:19.8180765Z PR_NUMBER: 2963 2025-09-09T14:03:19.8182947Z SCRIPT: conda create -n venv python=3.9 -y conda activate venv echo "::group::Install newer objcopy that supports --set-section-alignment" dnf install -y gcc-toolset-10-binutils export PATH=/opt/rh/gcc-toolset-10/root/usr/bin/:$PATH python -m pip install --upgrade pip pip install torch==2.8.0 --index-url https://download.pytorch.org/whl/cpu 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/:$LD_LIBRARY_PATH pytest test --verbose -s 2025-09-09T14:03:19.8185180Z ##[endgroup] 2025-09-09T14:03:19.8262241Z ##[group]Run if systemctl is-active --quiet docker; then 2025-09-09T14:03:19.8262710Z if systemctl is-active --quiet docker; then 2025-09-09T14:03:19.8263075Z  echo "Docker daemon is running..."; 2025-09-09T14:03:19.8263397Z else 2025-09-09T14:03:19.8263735Z  echo "Starting docker deamon..." && sudo systemctl start docker; 2025-09-09T14:03:19.8264147Z fi 2025-09-09T14:03:19.8269512Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-09T14:03:19.8269880Z env: 2025-09-09T14:03:19.8270128Z DOCKER_IMAGE: pytorch/almalinux-builder:cpu 2025-09-09T14:03:19.8270451Z REPOSITORY: pytorch/ao 2025-09-09T14:03:19.8270858Z PR_NUMBER: 2963 2025-09-09T14:03:19.8273042Z SCRIPT: conda create -n venv python=3.9 -y conda activate venv echo "::group::Install newer objcopy that supports --set-section-alignment" dnf install -y gcc-toolset-10-binutils export PATH=/opt/rh/gcc-toolset-10/root/usr/bin/:$PATH python -m pip install --upgrade pip pip install torch==2.8.0 --index-url https://download.pytorch.org/whl/cpu 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/:$LD_LIBRARY_PATH pytest test --verbose -s 2025-09-09T14:03:19.8275260Z ##[endgroup] 2025-09-09T14:03:19.8387075Z Docker daemon is running... 2025-09-09T14:03:19.8591893Z ##[group]Run AWS_ACCOUNT_ID=$(aws sts get-caller-identity|grep Account|cut -f4 -d\") 2025-09-09T14:03:19.8592532Z AWS_ACCOUNT_ID=$(aws sts get-caller-identity|grep Account|cut -f4 -d\") 2025-09-09T14:03:19.8593044Z retry () { "$@" || (sleep 1 && "$@") || (sleep 2 && "$@") } 2025-09-09T14:03:19.8593664Z retry aws ecr get-login-password --region "$AWS_DEFAULT_REGION" | docker login --username AWS \ 2025-09-09T14:03:19.8594382Z  --password-stdin "$AWS_ACCOUNT_ID.dkr.ecr.$AWS_DEFAULT_REGION.amazonaws.com" 2025-09-09T14:03:19.8600463Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-09T14:03:19.8600833Z env: 2025-09-09T14:03:19.8601086Z DOCKER_IMAGE: pytorch/almalinux-builder:cpu 2025-09-09T14:03:19.8601424Z REPOSITORY: pytorch/ao 2025-09-09T14:03:19.8601667Z PR_NUMBER: 2963 2025-09-09T14:03:19.8603848Z SCRIPT: conda create -n venv python=3.9 -y conda activate venv echo "::group::Install newer objcopy that supports --set-section-alignment" dnf install -y gcc-toolset-10-binutils export PATH=/opt/rh/gcc-toolset-10/root/usr/bin/:$PATH python -m pip install --upgrade pip pip install torch==2.8.0 --index-url https://download.pytorch.org/whl/cpu 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/:$LD_LIBRARY_PATH pytest test --verbose -s 2025-09-09T14:03:19.8606082Z AWS_RETRY_MODE: standard 2025-09-09T14:03:19.8606343Z AWS_MAX_ATTEMPTS: 5 2025-09-09T14:03:19.8606592Z AWS_DEFAULT_REGION: us-east-1 2025-09-09T14:03:19.8606860Z ##[endgroup] 2025-09-09T14:03:20.9189933Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json. 2025-09-09T14:03:20.9190970Z Configure a credential helper to remove this warning. See 2025-09-09T14:03:20.9191854Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store 2025-09-09T14:03:20.9192496Z 2025-09-09T14:03:20.9192644Z Login Succeeded 2025-09-09T14:03:20.9237806Z ##[group]Run env | grep '^GITHUB' >> "${RUNNER_TEMP}/github_env_${GITHUB_RUN_ID}" 2025-09-09T14:03:20.9238407Z env | grep '^GITHUB' >> "${RUNNER_TEMP}/github_env_${GITHUB_RUN_ID}" 2025-09-09T14:03:20.9238902Z env | grep '^CI' >> "${RUNNER_TEMP}/github_env_${GITHUB_RUN_ID}" 2025-09-09T14:03:20.9245356Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-09T14:03:20.9245713Z env: 2025-09-09T14:03:20.9245963Z DOCKER_IMAGE: pytorch/almalinux-builder:cpu 2025-09-09T14:03:20.9246289Z REPOSITORY: pytorch/ao 2025-09-09T14:03:20.9246544Z PR_NUMBER: 2963 2025-09-09T14:03:20.9248740Z SCRIPT: conda create -n venv python=3.9 -y conda activate venv echo "::group::Install newer objcopy that supports --set-section-alignment" dnf install -y gcc-toolset-10-binutils export PATH=/opt/rh/gcc-toolset-10/root/usr/bin/:$PATH python -m pip install --upgrade pip pip install torch==2.8.0 --index-url https://download.pytorch.org/whl/cpu 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/:$LD_LIBRARY_PATH pytest test --verbose -s 2025-09-09T14:03:20.9250963Z ##[endgroup] 2025-09-09T14:03:20.9371605Z ##[group]Run RUNNER_ARTIFACT_DIR="${RUNNER_TEMP}/artifacts" 2025-09-09T14:03:20.9372219Z RUNNER_ARTIFACT_DIR="${RUNNER_TEMP}/artifacts" 2025-09-09T14:03:20.9372614Z sudo rm -rf "${RUNNER_ARTIFACT_DIR}" 2025-09-09T14:03:20.9372966Z mkdir -p "${RUNNER_ARTIFACT_DIR}" 2025-09-09T14:03:20.9373393Z echo "RUNNER_ARTIFACT_DIR=${RUNNER_ARTIFACT_DIR}" >> "${GITHUB_ENV}" 2025-09-09T14:03:20.9373829Z  2025-09-09T14:03:20.9374116Z RUNNER_TEST_RESULTS_DIR="${RUNNER_TEMP}/test-results" 2025-09-09T14:03:20.9374546Z sudo rm -rf "${RUNNER_TEST_RESULTS_DIR}" 2025-09-09T14:03:20.9374904Z mkdir -p "${RUNNER_TEST_RESULTS_DIR}" 2025-09-09T14:03:20.9375386Z echo "RUNNER_TEST_RESULTS_DIR=${RUNNER_TEST_RESULTS_DIR}" >> "${GITHUB_ENV}" 2025-09-09T14:03:20.9375843Z  2025-09-09T14:03:20.9376071Z RUNNER_DOCS_DIR="${RUNNER_TEMP}/docs" 2025-09-09T14:03:20.9376415Z sudo rm -rf "${RUNNER_DOCS_DIR}" 2025-09-09T14:03:20.9376729Z mkdir -p "${RUNNER_DOCS_DIR}" 2025-09-09T14:03:20.9377130Z echo "RUNNER_DOCS_DIR=${RUNNER_DOCS_DIR}" >> "${GITHUB_ENV}" 2025-09-09T14:03:20.9382821Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-09T14:03:20.9383187Z env: 2025-09-09T14:03:20.9383423Z DOCKER_IMAGE: pytorch/almalinux-builder:cpu 2025-09-09T14:03:20.9383759Z REPOSITORY: pytorch/ao 2025-09-09T14:03:20.9384007Z PR_NUMBER: 2963 2025-09-09T14:03:20.9386190Z SCRIPT: conda create -n venv python=3.9 -y conda activate venv echo "::group::Install newer objcopy that supports --set-section-alignment" dnf install -y gcc-toolset-10-binutils export PATH=/opt/rh/gcc-toolset-10/root/usr/bin/:$PATH python -m pip install --upgrade pip pip install torch==2.8.0 --index-url https://download.pytorch.org/whl/cpu 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/:$LD_LIBRARY_PATH pytest test --verbose -s 2025-09-09T14:03:20.9388427Z ##[endgroup] 2025-09-09T14:03:21.5043178Z ##[group]Run needs=0 2025-09-09T14:03:21.5043460Z needs=0 2025-09-09T14:03:21.5043818Z if lspci -v | grep -e 'controller.*NVIDIA' >/dev/null 2>/dev/null; then 2025-09-09T14:03:21.5044251Z  needs=1 2025-09-09T14:03:21.5044469Z fi 2025-09-09T14:03:21.5044723Z echo "does=${needs}" >> $GITHUB_OUTPUT 2025-09-09T14:03:21.5050730Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-09T14:03:21.5051106Z env: 2025-09-09T14:03:21.5051344Z DOCKER_IMAGE: pytorch/almalinux-builder:cpu 2025-09-09T14:03:21.5051682Z REPOSITORY: pytorch/ao 2025-09-09T14:03:21.5051937Z PR_NUMBER: 2963 2025-09-09T14:03:21.5054095Z SCRIPT: conda create -n venv python=3.9 -y conda activate venv echo "::group::Install newer objcopy that supports --set-section-alignment" dnf install -y gcc-toolset-10-binutils export PATH=/opt/rh/gcc-toolset-10/root/usr/bin/:$PATH python -m pip install --upgrade pip pip install torch==2.8.0 --index-url https://download.pytorch.org/whl/cpu 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 2025-09-09T14:03:21.5056462Z RUNNER_ARTIFACT_DIR: /home/ec2-user/actions-runner/_work/_temp/artifacts 2025-09-09T14:03:21.5057041Z RUNNER_TEST_RESULTS_DIR: /home/ec2-user/actions-runner/_work/_temp/test-results 2025-09-09T14:03:21.5057571Z RUNNER_DOCS_DIR: /home/ec2-user/actions-runner/_work/_temp/docs 2025-09-09T14:03:21.5057952Z ##[endgroup] 2025-09-09T14:03:21.5314346Z ##[group]Run # ignore expansion of "docker ps -q" since it could be empty 2025-09-09T14:03:21.5314923Z # ignore expansion of "docker ps -q" since it could be empty 2025-09-09T14:03:21.5315334Z # shellcheck disable=SC2046 2025-09-09T14:03:21.5315664Z docker stop $(docker ps -q) || true 2025-09-09T14:03:21.5316006Z # Prune all of the docker images 2025-09-09T14:03:21.5316641Z docker system prune -af 2025-09-09T14:03:21.5322282Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-09T14:03:21.5322645Z env: 2025-09-09T14:03:21.5322894Z DOCKER_IMAGE: pytorch/almalinux-builder:cpu 2025-09-09T14:03:21.5323213Z REPOSITORY: pytorch/ao 2025-09-09T14:03:21.5323466Z PR_NUMBER: 2963 2025-09-09T14:03:21.5325887Z SCRIPT: conda create -n venv python=3.9 -y conda activate venv echo "::group::Install newer objcopy that supports --set-section-alignment" dnf install -y gcc-toolset-10-binutils export PATH=/opt/rh/gcc-toolset-10/root/usr/bin/:$PATH python -m pip install --upgrade pip pip install torch==2.8.0 --index-url https://download.pytorch.org/whl/cpu 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 2025-09-09T14:03:21.5328236Z RUNNER_ARTIFACT_DIR: /home/ec2-user/actions-runner/_work/_temp/artifacts 2025-09-09T14:03:21.5328821Z RUNNER_TEST_RESULTS_DIR: /home/ec2-user/actions-runner/_work/_temp/test-results 2025-09-09T14:03:21.5329364Z RUNNER_DOCS_DIR: /home/ec2-user/actions-runner/_work/_temp/docs 2025-09-09T14:03:21.5329734Z ##[endgroup] 2025-09-09T14:03:21.5772058Z "docker stop" requires at least 1 argument. 2025-09-09T14:03:21.5772553Z See 'docker stop --help'. 2025-09-09T14:03:21.5772740Z 2025-09-09T14:03:21.5772900Z Usage: docker stop [OPTIONS] CONTAINER [CONTAINER...] 2025-09-09T14:03:21.5773168Z 2025-09-09T14:03:21.5773279Z Stop one or more running containers 2025-09-09T14:03:21.5991721Z Total reclaimed space: 0B 2025-09-09T14:03:21.6068828Z ##[group]Run ./test-infra/.github/actions/setup-ssh 2025-09-09T14:03:21.6069177Z with: 2025-09-09T14:03:21.6069833Z github-secret: *** 2025-09-09T14:03:21.6070524Z instructions: All testing is done inside the container, to start an interactive session run: docker exec -it $(docker container ps --format '{{.ID}}') bash 2025-09-09T14:03:21.6071273Z activate-with-label: false 2025-09-09T14:03:21.6071545Z label: with-ssh 2025-09-09T14:03:21.6071773Z remove-existing-keys: true 2025-09-09T14:03:21.6072047Z fail-silently: true 2025-09-09T14:03:21.6072269Z env: 2025-09-09T14:03:21.6072515Z DOCKER_IMAGE: pytorch/almalinux-builder:cpu 2025-09-09T14:03:21.6072838Z REPOSITORY: pytorch/ao 2025-09-09T14:03:21.6073091Z PR_NUMBER: 2963 2025-09-09T14:03:21.6075309Z SCRIPT: conda create -n venv python=3.9 -y conda activate venv echo "::group::Install newer objcopy that supports --set-section-alignment" dnf install -y gcc-toolset-10-binutils export PATH=/opt/rh/gcc-toolset-10/root/usr/bin/:$PATH python -m pip install --upgrade pip pip install torch==2.8.0 --index-url https://download.pytorch.org/whl/cpu 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 2025-09-09T14:03:21.6077799Z RUNNER_ARTIFACT_DIR: /home/ec2-user/actions-runner/_work/_temp/artifacts 2025-09-09T14:03:21.6078381Z RUNNER_TEST_RESULTS_DIR: /home/ec2-user/actions-runner/_work/_temp/test-results 2025-09-09T14:03:21.6078925Z RUNNER_DOCS_DIR: /home/ec2-user/actions-runner/_work/_temp/docs 2025-09-09T14:03:21.6079296Z ##[endgroup] 2025-09-09T14:03:21.7140496Z Please see https://github.com/pytorch/pytorch/wiki/Debugging-using-with-ssh-for-Github-Actions for more info. 2025-09-09T14:03:22.2785736Z Grabbing public ssh keys from https://github.com/andrewor14.keys 2025-09-09T14:03:22.3710947Z ~/.ssh/authorized_keys file found on node, removing ~/.ssh and starting fresh 2025-09-09T14:03:22.3724884Z Public keys pulled and installed to /home/ec2-user/.ssh/authorized_keys 2025-09-09T14:03:22.3762426Z Login using: ssh ec2-user@ec2-44-222-176-134.compute-1.amazonaws.com 2025-09-09T14:03:22.3763347Z All testing is done inside the container, to start an interactive session run: 2025-09-09T14:03:22.3763935Z docker exec -it $(docker container ps --format '{{.ID}}') bash 2025-09-09T14:03:22.3873799Z ##[group]Run actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 2025-09-09T14:03:22.3874243Z with: 2025-09-09T14:03:22.3874463Z repository: pytorch/ao 2025-09-09T14:03:22.3874716Z ref: refs/pull/2963/merge 2025-09-09T14:03:22.3874980Z path: pytorch/ao 2025-09-09T14:03:22.3875197Z fetch-depth: 1 2025-09-09T14:03:22.3875427Z submodules: recursive 2025-09-09T14:03:22.3875798Z token: *** 2025-09-09T14:03:22.3876014Z ssh-strict: true 2025-09-09T14:03:22.3876333Z ssh-user: git 2025-09-09T14:03:22.3876575Z persist-credentials: true 2025-09-09T14:03:22.3876842Z clean: true 2025-09-09T14:03:22.3877074Z sparse-checkout-cone-mode: true 2025-09-09T14:03:22.3877366Z fetch-tags: false 2025-09-09T14:03:22.3877587Z show-progress: true 2025-09-09T14:03:22.3877826Z lfs: false 2025-09-09T14:03:22.3878037Z set-safe-directory: true 2025-09-09T14:03:22.3878285Z env: 2025-09-09T14:03:22.3878511Z DOCKER_IMAGE: pytorch/almalinux-builder:cpu 2025-09-09T14:03:22.3878897Z REPOSITORY: pytorch/ao 2025-09-09T14:03:22.3879139Z PR_NUMBER: 2963 2025-09-09T14:03:22.3881313Z SCRIPT: conda create -n venv python=3.9 -y conda activate venv echo "::group::Install newer objcopy that supports --set-section-alignment" dnf install -y gcc-toolset-10-binutils export PATH=/opt/rh/gcc-toolset-10/root/usr/bin/:$PATH python -m pip install --upgrade pip pip install torch==2.8.0 --index-url https://download.pytorch.org/whl/cpu 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 2025-09-09T14:03:22.3883668Z RUNNER_ARTIFACT_DIR: /home/ec2-user/actions-runner/_work/_temp/artifacts 2025-09-09T14:03:22.3884237Z RUNNER_TEST_RESULTS_DIR: /home/ec2-user/actions-runner/_work/_temp/test-results 2025-09-09T14:03:22.3884775Z RUNNER_DOCS_DIR: /home/ec2-user/actions-runner/_work/_temp/docs 2025-09-09T14:03:22.3885155Z ##[endgroup] 2025-09-09T14:03:22.4906235Z Syncing repository: pytorch/ao 2025-09-09T14:03:22.4914576Z ##[group]Getting Git version info 2025-09-09T14:03:22.4915013Z Working directory is '/home/ec2-user/actions-runner/_work/ao/ao/pytorch/ao' 2025-09-09T14:03:22.4940988Z [command]/usr/bin/git version 2025-09-09T14:03:22.4978275Z git version 2.47.1 2025-09-09T14:03:22.5002596Z ##[endgroup] 2025-09-09T14:03:22.5022319Z Temporarily overriding HOME='/home/ec2-user/actions-runner/_work/_temp/782119e4-7147-430c-bfcc-32623a5db836' before making global git config changes 2025-09-09T14:03:22.5023251Z Adding repository directory to the temporary git global config as a safe directory 2025-09-09T14:03:22.5027408Z [command]/usr/bin/git config --global --add safe.directory /home/ec2-user/actions-runner/_work/ao/ao/pytorch/ao 2025-09-09T14:03:22.5055041Z ##[group]Initializing the repository 2025-09-09T14:03:22.5059427Z [command]/usr/bin/git init /home/ec2-user/actions-runner/_work/ao/ao/pytorch/ao 2025-09-09T14:03:22.5092419Z hint: Using 'master' as the name for the initial branch. This default branch name 2025-09-09T14:03:22.5093047Z hint: is subject to change. To configure the initial branch name to use in all 2025-09-09T14:03:22.5093610Z hint: of your new repositories, which will suppress this warning, call: 2025-09-09T14:03:22.5094037Z hint: 2025-09-09T14:03:22.5094298Z hint: git config --global init.defaultBranch 2025-09-09T14:03:22.5094641Z hint: 2025-09-09T14:03:22.5094956Z hint: Names commonly chosen instead of 'master' are 'main', 'trunk' and 2025-09-09T14:03:22.5095520Z hint: 'development'. The just-created branch can be renamed via this command: 2025-09-09T14:03:22.5095941Z hint: 2025-09-09T14:03:22.5096160Z hint: git branch -m 2025-09-09T14:03:22.5096646Z Initialized empty Git repository in /home/ec2-user/actions-runner/_work/ao/ao/pytorch/ao/.git/ 2025-09-09T14:03:22.5102259Z [command]/usr/bin/git remote add origin https://github.com/pytorch/ao 2025-09-09T14:03:22.5136777Z ##[endgroup] 2025-09-09T14:03:22.5137409Z ##[group]Disabling automatic garbage collection 2025-09-09T14:03:22.5140633Z [command]/usr/bin/git config --local gc.auto 0 2025-09-09T14:03:22.5166012Z ##[endgroup] 2025-09-09T14:03:22.5166390Z ##[group]Setting up auth 2025-09-09T14:03:22.5171873Z [command]/usr/bin/git config --local --name-only --get-regexp core\.sshCommand 2025-09-09T14:03:22.5198866Z [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' || :" 2025-09-09T14:03:22.5485360Z [command]/usr/bin/git config --local --name-only --get-regexp http\.https\:\/\/github\.com\/\.extraheader 2025-09-09T14:03:22.5513275Z [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' || :" 2025-09-09T14:03:22.5796270Z [command]/usr/bin/git config --local http.https://github.com/.extraheader AUTHORIZATION: basic *** 2025-09-09T14:03:22.5836776Z ##[endgroup] 2025-09-09T14:03:22.5837469Z ##[group]Fetching the repository 2025-09-09T14:03:22.5845337Z [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 2025-09-09T14:03:23.3981661Z From https://github.com/pytorch/ao 2025-09-09T14:03:23.3982074Z * [new ref] refs/pull/2963/merge -> pull/2963/merge 2025-09-09T14:03:23.4002861Z ##[endgroup] 2025-09-09T14:03:23.4003290Z ##[group]Determining the checkout info 2025-09-09T14:03:23.4004985Z ##[endgroup] 2025-09-09T14:03:23.4009214Z [command]/usr/bin/git sparse-checkout disable 2025-09-09T14:03:23.4041177Z [command]/usr/bin/git config --local --unset-all extensions.worktreeConfig 2025-09-09T14:03:23.4065541Z ##[group]Checking out the ref 2025-09-09T14:03:23.4068839Z [command]/usr/bin/git checkout --progress --force refs/remotes/pull/2963/merge 2025-09-09T14:03:23.5136252Z Note: switching to 'refs/remotes/pull/2963/merge'. 2025-09-09T14:03:23.5136558Z 2025-09-09T14:03:23.5136788Z You are in 'detached HEAD' state. You can look around, make experimental 2025-09-09T14:03:23.5137323Z changes and commit them, and you can discard any commits you make in this 2025-09-09T14:03:23.5137863Z state without impacting any branches by switching back to a branch. 2025-09-09T14:03:23.5138177Z 2025-09-09T14:03:23.5138380Z If you want to create a new branch to retain commits you create, you may 2025-09-09T14:03:23.5138888Z do so (now or later) by using -c with the switch command. Example: 2025-09-09T14:03:23.5139171Z 2025-09-09T14:03:23.5139297Z git switch -c 2025-09-09T14:03:23.5139495Z 2025-09-09T14:03:23.5139601Z Or undo this operation with: 2025-09-09T14:03:23.5139788Z 2025-09-09T14:03:23.5139876Z git switch - 2025-09-09T14:03:23.5140006Z 2025-09-09T14:03:23.5140242Z Turn off this advice by setting config variable advice.detachedHead to false 2025-09-09T14:03:23.5140612Z 2025-09-09T14:03:23.5141010Z HEAD is now at 7c05f81 Merge c21284c127b039bc49cc7ffda0e692894ed3b094 into 8b72284fd363b5c096de93fb7ac9cc960a6a601e 2025-09-09T14:03:23.5144982Z ##[endgroup] 2025-09-09T14:03:23.5145391Z ##[group]Setting up auth for fetching submodules 2025-09-09T14:03:23.5150433Z [command]/usr/bin/git config --global http.https://github.com/.extraheader AUTHORIZATION: basic *** 2025-09-09T14:03:23.5191228Z [command]/usr/bin/git config --global --unset-all url.https://github.com/.insteadOf 2025-09-09T14:03:23.5217405Z [command]/usr/bin/git config --global --add url.https://github.com/.insteadOf git@github.com: 2025-09-09T14:03:23.5244060Z [command]/usr/bin/git config --global --add url.https://github.com/.insteadOf org-21003710@github.com: 2025-09-09T14:03:23.5265031Z ##[endgroup] 2025-09-09T14:03:23.5265428Z ##[group]Fetching submodules 2025-09-09T14:03:23.5268513Z [command]/usr/bin/git submodule sync --recursive 2025-09-09T14:03:23.5554405Z [command]/usr/bin/git -c protocol.version=2 submodule update --init --force --depth=1 --recursive 2025-09-09T14:03:23.5833270Z Submodule 'third_party/cutlass' (https://github.com/NVIDIA/cutlass) registered for path 'third_party/cutlass' 2025-09-09T14:03:23.5865535Z Cloning into '/home/ec2-user/actions-runner/_work/ao/ao/pytorch/ao/third_party/cutlass'... 2025-09-09T14:03:25.2840318Z From https://github.com/NVIDIA/cutlass 2025-09-09T14:03:25.2840829Z * branch e51efbfe18fe4f4cbb66ab814c55bf4aa0185491 -> FETCH_HEAD 2025-09-09T14:03:25.8810477Z Submodule path 'third_party/cutlass': checked out 'e51efbfe18fe4f4cbb66ab814c55bf4aa0185491' 2025-09-09T14:03:25.8850591Z [command]/usr/bin/git submodule foreach --recursive git config --local gc.auto 0 2025-09-09T14:03:25.9128354Z Entering 'third_party/cutlass' 2025-09-09T14:03:25.9186896Z ##[endgroup] 2025-09-09T14:03:25.9187451Z ##[group]Persisting credentials for submodules 2025-09-09T14:03:25.9193116Z [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' || :" 2025-09-09T14:03:25.9463906Z Entering 'third_party/cutlass' 2025-09-09T14:03:25.9541102Z [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" 2025-09-09T14:03:25.9814850Z Entering 'third_party/cutlass' 2025-09-09T14:03:25.9866679Z file:/home/ec2-user/actions-runner/_work/ao/ao/pytorch/ao/.git/modules/third_party/cutlass/config remote.origin.url 2025-09-09T14:03:25.9919182Z [command]/usr/bin/git submodule foreach --recursive git config --local --add 'url.https://github.com/.insteadOf' 'git@github.com:' 2025-09-09T14:03:26.0193165Z Entering 'third_party/cutlass' 2025-09-09T14:03:26.0256945Z [command]/usr/bin/git submodule foreach --recursive git config --local --add 'url.https://github.com/.insteadOf' 'org-21003710@github.com:' 2025-09-09T14:03:26.0528521Z Entering 'third_party/cutlass' 2025-09-09T14:03:26.0585278Z ##[endgroup] 2025-09-09T14:03:26.0617853Z [command]/usr/bin/git log -1 --format=%H 2025-09-09T14:03:26.0639370Z 7c05f811b89289f7be3e0e3546626827f2cc1ca4 2025-09-09T14:03:26.0825905Z Prepare all required actions 2025-09-09T14:03:26.0826485Z Getting action download info 2025-09-09T14:03:26.2013680Z Download action repository 'nick-fields/retry@v3.0.0' (SHA:7152eba30c6575329ac0576536151aca5a72780e) 2025-09-09T14:03:26.4584674Z ##[group]Run ./test-infra/.github/actions/calculate-docker-image 2025-09-09T14:03:26.4585074Z with: 2025-09-09T14:03:26.4585319Z use-custom-docker-registry: true 2025-09-09T14:03:26.4585667Z docker-image-name: pytorch/almalinux-builder:cpu 2025-09-09T14:03:26.4586037Z docker-build-dir: .ci/docker 2025-09-09T14:03:26.4586327Z working-directory: pytorch/ao 2025-09-09T14:03:26.4586611Z docker-build-script: ./build.sh 2025-09-09T14:03:26.4587001Z docker-registry: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-09-09T14:03:26.4587396Z force-push: false 2025-09-09T14:03:26.4587620Z env: 2025-09-09T14:03:26.4587848Z DOCKER_IMAGE: pytorch/almalinux-builder:cpu 2025-09-09T14:03:26.4588183Z REPOSITORY: pytorch/ao 2025-09-09T14:03:26.4588473Z PR_NUMBER: 2963 2025-09-09T14:03:26.4590662Z 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 --index-url https://download.pytorch.org/whl/cpu 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/:$LD_LIBRARY_PATH pytest test --verbose -s 2025-09-09T14:03:26.4593010Z RUNNER_ARTIFACT_DIR: /home/ec2-user/actions-runner/_work/_temp/artifacts 2025-09-09T14:03:26.4593593Z RUNNER_TEST_RESULTS_DIR: /home/ec2-user/actions-runner/_work/_temp/test-results 2025-09-09T14:03:26.4594312Z RUNNER_DOCS_DIR: /home/ec2-user/actions-runner/_work/_temp/docs 2025-09-09T14:03:26.4594682Z ##[endgroup] 2025-09-09T14:03:26.4638512Z ##[group]Run set -ex 2025-09-09T14:03:26.4638859Z set -ex 2025-09-09T14:03:26.4639070Z  2025-09-09T14:03:26.4639457Z # If the docker build directory or the build script doesn't exist, the action will 2025-09-09T14:03:26.4640099Z # gracefully return the docker image name as it is. Pulling docker image in Linux 2025-09-09T14:03:26.4640660Z # job could then download the pre-built image as usual 2025-09-09T14:03:26.4641331Z if [[ -d "${DOCKER_BUILD_DIR}" ]] && [[ -f "${DOCKER_BUILD_DIR}/${DOCKER_BUILD_SCRIPT}" ]] && [[ "${USE_CUSTOM_DOCKER_REGISTRY}" == "true" ]]; then 2025-09-09T14:03:26.4641953Z  echo "skip=false" >> "${GITHUB_OUTPUT}" 2025-09-09T14:03:26.4642274Z else 2025-09-09T14:03:26.4642529Z  echo "skip=true" >> "${GITHUB_OUTPUT}" 2025-09-09T14:03:26.4642969Z  echo "docker-image=${DOCKER_IMAGE_NAME}" >> "${GITHUB_OUTPUT}" 2025-09-09T14:03:26.4643365Z  2025-09-09T14:03:26.4643902Z  echo "Not using custom ECR registry. Either it was not requested or there is no Docker build script in the ${REPO_NAME} repo..." 2025-09-09T14:03:26.4644530Z  exit 0 2025-09-09T14:03:26.4644735Z fi 2025-09-09T14:03:26.4644940Z  2025-09-09T14:03:26.4645260Z if [[ "${DOCKER_IMAGE_NAME}" == *"${DOCKER_REGISTRY}/${REPO_NAME}"* ]]; then 2025-09-09T14:03:26.4645871Z  # The docker image name already includes the ECR prefix and tag, so we can just 2025-09-09T14:03:26.4646386Z  # use it as it is, but first let's extract the tag 2025-09-09T14:03:26.4646866Z  DOCKER_TAG=$(echo "${DOCKER_IMAGE_NAME}" | awk -F '[:,]' '{print $2}') 2025-09-09T14:03:26.4647352Z  echo "docker-tag=${DOCKER_TAG}" >> "${GITHUB_OUTPUT}" 2025-09-09T14:03:26.4647839Z  echo "docker-image=${DOCKER_IMAGE_NAME}" >> "${GITHUB_OUTPUT}" 2025-09-09T14:03:26.4648232Z else 2025-09-09T14:03:26.4648494Z  if [[ "${DOCKER_IMAGE_NAME}" == *:* ]]; then 2025-09-09T14:03:26.4649061Z  CUSTOM_TAG_PREFIX=${DOCKER_IMAGE_NAME#*:} 2025-09-09T14:03:26.4649446Z  DOCKER_IMAGE_NAME=${DOCKER_IMAGE_NAME%%:*} 2025-09-09T14:03:26.4649783Z  fi 2025-09-09T14:03:26.4650219Z  DOCKER_TAG=${CUSTOM_TAG_PREFIX:+${CUSTOM_TAG_PREFIX}-}$(git rev-parse HEAD:"${DOCKER_BUILD_DIR}") 2025-09-09T14:03:26.4650824Z  echo "docker-tag=${DOCKER_TAG}" >> "${GITHUB_OUTPUT}" 2025-09-09T14:03:26.4651458Z  echo "docker-image=${DOCKER_REGISTRY}/${REPO_NAME}/${DOCKER_IMAGE_NAME}:${DOCKER_TAG}" >> "${GITHUB_OUTPUT}" 2025-09-09T14:03:26.4652136Z  echo "custom-tag-prefix=${CUSTOM_TAG_PREFIX}" >> "${GITHUB_OUTPUT}" 2025-09-09T14:03:26.4652563Z fi 2025-09-09T14:03:26.4658395Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-09T14:03:26.4658769Z env: 2025-09-09T14:03:26.4659006Z DOCKER_IMAGE: pytorch/almalinux-builder:cpu 2025-09-09T14:03:26.4659342Z REPOSITORY: pytorch/ao 2025-09-09T14:03:26.4659594Z PR_NUMBER: 2963 2025-09-09T14:03:26.4661770Z SCRIPT: conda create -n venv python=3.9 -y conda activate venv echo "::group::Install newer objcopy that supports --set-section-alignment" dnf install -y gcc-toolset-10-binutils export PATH=/opt/rh/gcc-toolset-10/root/usr/bin/:$PATH python -m pip install --upgrade pip pip install torch==2.8.0 --index-url https://download.pytorch.org/whl/cpu 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 2025-09-09T14:03:26.4664113Z RUNNER_ARTIFACT_DIR: /home/ec2-user/actions-runner/_work/_temp/artifacts 2025-09-09T14:03:26.4664691Z RUNNER_TEST_RESULTS_DIR: /home/ec2-user/actions-runner/_work/_temp/test-results 2025-09-09T14:03:26.4665343Z RUNNER_DOCS_DIR: /home/ec2-user/actions-runner/_work/_temp/docs 2025-09-09T14:03:26.4665728Z REPO_NAME: ao 2025-09-09T14:03:26.4665997Z DOCKER_IMAGE_NAME: pytorch/almalinux-builder:cpu 2025-09-09T14:03:26.4666351Z DOCKER_BUILD_DIR: .ci/docker 2025-09-09T14:03:26.4666620Z DOCKER_BUILD_SCRIPT: ./build.sh 2025-09-09T14:03:26.4666995Z DOCKER_REGISTRY: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-09-09T14:03:26.4667396Z USE_CUSTOM_DOCKER_REGISTRY: true 2025-09-09T14:03:26.4667701Z CUSTOM_TAG_PREFIX: 2025-09-09T14:03:26.4667947Z ##[endgroup] 2025-09-09T14:03:26.4693085Z + [[ -d .ci/docker ]] 2025-09-09T14:03:26.4693346Z + echo skip=true 2025-09-09T14:03:26.4693645Z + echo docker-image=pytorch/almalinux-builder:cpu 2025-09-09T14:03:26.4694341Z + echo 'Not using custom ECR registry. Either it was not requested or there is no Docker build script in the ao repo...' 2025-09-09T14:03:26.4695007Z + exit 0 2025-09-09T14:03:26.4695980Z Not using custom ECR registry. Either it was not requested or there is no Docker build script in the ao repo... 2025-09-09T14:03:26.4737559Z ##[group]Run set -eux 2025-09-09T14:03:26.4737848Z set -eux 2025-09-09T14:03:26.4738257Z # It's ok if this steps fails, it would then be an anonymous user like what we used to have 2025-09-09T14:03:26.4739385Z 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 2025-09-09T14:03:26.4745438Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-09T14:03:26.4745795Z env: 2025-09-09T14:03:26.4746049Z DOCKER_IMAGE: pytorch/almalinux-builder:cpu 2025-09-09T14:03:26.4746371Z REPOSITORY: pytorch/ao 2025-09-09T14:03:26.4746624Z PR_NUMBER: 2963 2025-09-09T14:03:26.4748957Z 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 --index-url https://download.pytorch.org/whl/cpu 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/:$LD_LIBRARY_PATH pytest test --verbose -s 2025-09-09T14:03:26.4751309Z RUNNER_ARTIFACT_DIR: /home/ec2-user/actions-runner/_work/_temp/artifacts 2025-09-09T14:03:26.4751891Z RUNNER_TEST_RESULTS_DIR: /home/ec2-user/actions-runner/_work/_temp/test-results 2025-09-09T14:03:26.4752437Z RUNNER_DOCS_DIR: /home/ec2-user/actions-runner/_work/_temp/docs 2025-09-09T14:03:26.4752814Z ##[endgroup] 2025-09-09T14:03:26.4778991Z + aws secretsmanager get-secret-value --secret-id docker_hub_readonly_token 2025-09-09T14:03:26.4780070Z + jq --raw-output .SecretString 2025-09-09T14:03:26.4781407Z + jq -r .docker_hub_readonly_token 2025-09-09T14:03:26.4782793Z + docker login --username pytorchbot --password-stdin 2025-09-09T14:03:27.0636268Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json. 2025-09-09T14:03:27.0636919Z Configure a credential helper to remove this warning. See 2025-09-09T14:03:27.0637486Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store 2025-09-09T14:03:27.0637870Z 2025-09-09T14:03:27.0638330Z Login Succeeded 2025-09-09T14:03:27.0715625Z Prepare all required actions 2025-09-09T14:03:27.0756632Z ##[group]Run ./test-infra/.github/actions/pull-docker-image 2025-09-09T14:03:27.0756995Z with: 2025-09-09T14:03:27.0757246Z docker-image: pytorch/almalinux-builder:cpu 2025-09-09T14:03:27.0757669Z docker-registry: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-09-09T14:03:27.0758051Z env: 2025-09-09T14:03:27.0758295Z DOCKER_IMAGE: pytorch/almalinux-builder:cpu 2025-09-09T14:03:27.0758617Z REPOSITORY: pytorch/ao 2025-09-09T14:03:27.0759030Z PR_NUMBER: 2963 2025-09-09T14:03:27.0761240Z SCRIPT: conda create -n venv python=3.9 -y conda activate venv echo "::group::Install newer objcopy that supports --set-section-alignment" dnf install -y gcc-toolset-10-binutils export PATH=/opt/rh/gcc-toolset-10/root/usr/bin/:$PATH python -m pip install --upgrade pip pip install torch==2.8.0 --index-url https://download.pytorch.org/whl/cpu 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 2025-09-09T14:03:27.0763602Z RUNNER_ARTIFACT_DIR: /home/ec2-user/actions-runner/_work/_temp/artifacts 2025-09-09T14:03:27.0764173Z RUNNER_TEST_RESULTS_DIR: /home/ec2-user/actions-runner/_work/_temp/test-results 2025-09-09T14:03:27.0764716Z RUNNER_DOCS_DIR: /home/ec2-user/actions-runner/_work/_temp/docs 2025-09-09T14:03:27.0765097Z ##[endgroup] 2025-09-09T14:03:27.0789807Z ##[group]Run set -x 2025-09-09T14:03:27.0790102Z set -x 2025-09-09T14:03:27.0790329Z set +e 2025-09-09T14:03:27.0790531Z  2025-09-09T14:03:27.0790738Z login() { 2025-09-09T14:03:27.0791191Z  aws ecr get-login-password --region us-east-1 | docker login -u AWS --password-stdin "$1" 2025-09-09T14:03:27.0791703Z } 2025-09-09T14:03:27.0791894Z  2025-09-09T14:03:27.0792095Z retry () { 2025-09-09T14:03:27.0792357Z  $* || (sleep 1 && $*) || (sleep 2 && $*) 2025-09-09T14:03:27.0792669Z } 2025-09-09T14:03:27.0792869Z  2025-09-09T14:03:27.0793084Z retry login "${DOCKER_REGISTRY}" 2025-09-09T14:03:27.0793383Z  2025-09-09T14:03:27.0793845Z IMAGE_SIZE=$(docker manifest inspect "${DOCKER_IMAGE}" | jq '[.layers[].size, .config.size] | add / 1024 / 1024') 2025-09-09T14:03:27.0794493Z echo "Compressed size of image in MB: ${IMAGE_SIZE}" 2025-09-09T14:03:27.0794846Z  2025-09-09T14:03:27.0795057Z set -e 2025-09-09T14:03:27.0795387Z # ignore output since only exit code is used for conditional 2025-09-09T14:03:27.0795859Z # only pull docker image if it's not available locally 2025-09-09T14:03:27.0796499Z if ! docker inspect --type=image "${DOCKER_IMAGE}" >/dev/null 2>/dev/null; then 2025-09-09T14:03:27.0796982Z  retry docker pull "${DOCKER_IMAGE}" 2025-09-09T14:03:27.0797293Z fi 2025-09-09T14:03:27.0803271Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-09T14:03:27.0803640Z env: 2025-09-09T14:03:27.0803874Z DOCKER_IMAGE: pytorch/almalinux-builder:cpu 2025-09-09T14:03:27.0804209Z REPOSITORY: pytorch/ao 2025-09-09T14:03:27.0804451Z PR_NUMBER: 2963 2025-09-09T14:03:27.0806627Z SCRIPT: conda create -n venv python=3.9 -y conda activate venv echo "::group::Install newer objcopy that supports --set-section-alignment" dnf install -y gcc-toolset-10-binutils export PATH=/opt/rh/gcc-toolset-10/root/usr/bin/:$PATH python -m pip install --upgrade pip pip install torch==2.8.0 --index-url https://download.pytorch.org/whl/cpu 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 2025-09-09T14:03:27.0808987Z RUNNER_ARTIFACT_DIR: /home/ec2-user/actions-runner/_work/_temp/artifacts 2025-09-09T14:03:27.0809727Z RUNNER_TEST_RESULTS_DIR: /home/ec2-user/actions-runner/_work/_temp/test-results 2025-09-09T14:03:27.0810266Z RUNNER_DOCS_DIR: /home/ec2-user/actions-runner/_work/_temp/docs 2025-09-09T14:03:27.0810754Z DOCKER_REGISTRY: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-09-09T14:03:27.0811126Z ##[endgroup] 2025-09-09T14:03:27.0835809Z + set +e 2025-09-09T14:03:27.0836601Z + retry login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-09-09T14:03:27.0837238Z + login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-09-09T14:03:27.0839606Z + aws ecr get-login-password --region us-east-1 2025-09-09T14:03:27.0840956Z + docker login -u AWS --password-stdin 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-09-09T14:03:27.6229134Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json. 2025-09-09T14:03:27.6229753Z Configure a credential helper to remove this warning. See 2025-09-09T14:03:27.6230394Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store 2025-09-09T14:03:27.6231147Z 2025-09-09T14:03:27.6231286Z Login Succeeded 2025-09-09T14:03:27.6248381Z ++ docker manifest inspect pytorch/almalinux-builder:cpu 2025-09-09T14:03:27.6249172Z ++ jq '[.layers[].size, .config.size] | add / 1024 / 1024' 2025-09-09T14:03:27.7891754Z + IMAGE_SIZE=1439.2328958511353 2025-09-09T14:03:27.7892405Z Compressed size of image in MB: 1439.2328958511353 2025-09-09T14:03:27.7893096Z + echo 'Compressed size of image in MB: 1439.2328958511353' 2025-09-09T14:03:27.7893717Z + set -e 2025-09-09T14:03:27.7894033Z + docker inspect --type=image pytorch/almalinux-builder:cpu 2025-09-09T14:03:27.8017671Z + retry docker pull pytorch/almalinux-builder:cpu 2025-09-09T14:03:27.8018088Z + docker pull pytorch/almalinux-builder:cpu 2025-09-09T14:03:27.9487914Z cpu: Pulling from pytorch/almalinux-builder 2025-09-09T14:03:27.9488443Z 19877a9af8e3: Pulling fs layer 2025-09-09T14:03:27.9488756Z fe05152297d3: Pulling fs layer 2025-09-09T14:03:27.9489069Z 9c5a63e97f59: Pulling fs layer 2025-09-09T14:03:27.9489372Z 918715f58173: Pulling fs layer 2025-09-09T14:03:27.9489710Z 692d6799dd80: Pulling fs layer 2025-09-09T14:03:27.9489969Z c6352f35dfa2: Pulling fs layer 2025-09-09T14:03:27.9490237Z 518054e53c81: Pulling fs layer 2025-09-09T14:03:27.9490495Z 4f4fb700ef54: Pulling fs layer 2025-09-09T14:03:27.9490821Z 692d6799dd80: Waiting 2025-09-09T14:03:27.9491055Z 3b571ac2ab3b: Pulling fs layer 2025-09-09T14:03:27.9491354Z 84008f185523: Pulling fs layer 2025-09-09T14:03:27.9491658Z c6352f35dfa2: Waiting 2025-09-09T14:03:27.9491892Z 9ee5aeef32d7: Pulling fs layer 2025-09-09T14:03:27.9492188Z a80ec369bee3: Pulling fs layer 2025-09-09T14:03:27.9492450Z f1417b667e9d: Pulling fs layer 2025-09-09T14:03:27.9492710Z 518054e53c81: Waiting 2025-09-09T14:03:27.9492928Z 3b571ac2ab3b: Waiting 2025-09-09T14:03:27.9493214Z 918715f58173: Waiting 2025-09-09T14:03:27.9493441Z 0c3cc5825672: Pulling fs layer 2025-09-09T14:03:27.9493706Z 9ee5aeef32d7: Waiting 2025-09-09T14:03:27.9494023Z 895a870a9edd: Pulling fs layer 2025-09-09T14:03:27.9494279Z a80ec369bee3: Waiting 2025-09-09T14:03:27.9494531Z b7eb993f501a: Pulling fs layer 2025-09-09T14:03:27.9494845Z 895a870a9edd: Waiting 2025-09-09T14:03:27.9495075Z f1417b667e9d: Waiting 2025-09-09T14:03:27.9495291Z 4f4fb700ef54: Waiting 2025-09-09T14:03:27.9495602Z 4d4d94988ad5: Pulling fs layer 2025-09-09T14:03:27.9495853Z 0c3cc5825672: Waiting 2025-09-09T14:03:27.9496084Z 4d4d94988ad5: Waiting 2025-09-09T14:03:27.9496373Z 84008f185523: Waiting 2025-09-09T14:03:28.0720243Z 9c5a63e97f59: Verifying Checksum 2025-09-09T14:03:28.4417739Z 9c5a63e97f59: Download complete 2025-09-09T14:03:28.4418072Z 918715f58173: Verifying Checksum 2025-09-09T14:03:28.4418362Z 918715f58173: Download complete 2025-09-09T14:03:28.6857688Z 19877a9af8e3: Download complete 2025-09-09T14:03:28.7165004Z c6352f35dfa2: Verifying Checksum 2025-09-09T14:03:28.7165355Z c6352f35dfa2: Download complete 2025-09-09T14:03:29.2128210Z 518054e53c81: Verifying Checksum 2025-09-09T14:03:29.2128557Z 518054e53c81: Download complete 2025-09-09T14:03:29.2311636Z fe05152297d3: Verifying Checksum 2025-09-09T14:03:29.2311993Z fe05152297d3: Download complete 2025-09-09T14:03:29.2732836Z 4f4fb700ef54: Download complete 2025-09-09T14:03:29.3746090Z 84008f185523: Verifying Checksum 2025-09-09T14:03:29.3746442Z 84008f185523: Download complete 2025-09-09T14:03:29.4183166Z 3b571ac2ab3b: Verifying Checksum 2025-09-09T14:03:29.4183513Z 3b571ac2ab3b: Download complete 2025-09-09T14:03:29.4536078Z a80ec369bee3: Verifying Checksum 2025-09-09T14:03:29.4536397Z a80ec369bee3: Download complete 2025-09-09T14:03:29.4960320Z f1417b667e9d: Download complete 2025-09-09T14:03:29.5404813Z 0c3cc5825672: Verifying Checksum 2025-09-09T14:03:29.5405346Z 0c3cc5825672: Download complete 2025-09-09T14:03:29.7251517Z 895a870a9edd: Verifying Checksum 2025-09-09T14:03:29.7251866Z 895a870a9edd: Download complete 2025-09-09T14:03:29.7879851Z b7eb993f501a: Verifying Checksum 2025-09-09T14:03:29.7880254Z b7eb993f501a: Download complete 2025-09-09T14:03:30.3296786Z 692d6799dd80: Verifying Checksum 2025-09-09T14:03:30.3297152Z 692d6799dd80: Download complete 2025-09-09T14:03:30.6702185Z 19877a9af8e3: Pull complete 2025-09-09T14:03:32.6148947Z fe05152297d3: Pull complete 2025-09-09T14:03:32.7788971Z 9c5a63e97f59: Pull complete 2025-09-09T14:03:33.0928950Z 918715f58173: Pull complete 2025-09-09T14:03:33.7580585Z 9ee5aeef32d7: Verifying Checksum 2025-09-09T14:03:33.7580958Z 9ee5aeef32d7: Download complete 2025-09-09T14:03:35.8743863Z 4d4d94988ad5: Verifying Checksum 2025-09-09T14:03:35.8744233Z 4d4d94988ad5: Download complete 2025-09-09T14:03:37.7705813Z 692d6799dd80: Pull complete 2025-09-09T14:03:37.9503071Z c6352f35dfa2: Pull complete 2025-09-09T14:03:38.9367126Z 518054e53c81: Pull complete 2025-09-09T14:03:39.0488275Z 4f4fb700ef54: Pull complete 2025-09-09T14:03:39.3214193Z 3b571ac2ab3b: Pull complete 2025-09-09T14:03:39.4774634Z 84008f185523: Pull complete 2025-09-09T14:03:49.9716099Z 9ee5aeef32d7: Pull complete 2025-09-09T14:03:49.9939502Z a80ec369bee3: Pull complete 2025-09-09T14:03:50.0142599Z f1417b667e9d: Pull complete 2025-09-09T14:03:50.0351218Z 0c3cc5825672: Pull complete 2025-09-09T14:03:50.2846951Z 895a870a9edd: Pull complete 2025-09-09T14:03:50.3265079Z b7eb993f501a: Pull complete 2025-09-09T14:04:03.8370933Z 4d4d94988ad5: Pull complete 2025-09-09T14:04:03.9476689Z Digest: sha256:10f309602e8cd84e21cb6970f97544761dd12a06b141583ab4d45f0bac4bf651 2025-09-09T14:04:03.9719083Z Status: Downloaded newer image for pytorch/almalinux-builder:cpu 2025-09-09T14:04:03.9891874Z docker.io/pytorch/almalinux-builder:cpu 2025-09-09T14:04:03.9936255Z ##[group]Run echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT" 2025-09-09T14:04:03.9937210Z echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT" 2025-09-09T14:04:03.9946145Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-09T14:04:03.9946512Z env: 2025-09-09T14:04:03.9946774Z DOCKER_IMAGE: pytorch/almalinux-builder:cpu 2025-09-09T14:04:03.9947113Z REPOSITORY: pytorch/ao 2025-09-09T14:04:03.9947360Z PR_NUMBER: 2963 2025-09-09T14:04:03.9949595Z SCRIPT: conda create -n venv python=3.9 -y conda activate venv echo "::group::Install newer objcopy that supports --set-section-alignment" dnf install -y gcc-toolset-10-binutils export PATH=/opt/rh/gcc-toolset-10/root/usr/bin/:$PATH python -m pip install --upgrade pip pip install torch==2.8.0 --index-url https://download.pytorch.org/whl/cpu 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/:$LD_LIBRARY_PATH pytest test --verbose -s 2025-09-09T14:04:03.9951955Z RUNNER_ARTIFACT_DIR: /home/ec2-user/actions-runner/_work/_temp/artifacts 2025-09-09T14:04:03.9952540Z RUNNER_TEST_RESULTS_DIR: /home/ec2-user/actions-runner/_work/_temp/test-results 2025-09-09T14:04:03.9953085Z RUNNER_DOCS_DIR: /home/ec2-user/actions-runner/_work/_temp/docs 2025-09-09T14:04:03.9953462Z ##[endgroup] 2025-09-09T14:04:04.0121261Z ##[group]Run set -ex 2025-09-09T14:04:04.0121560Z set -ex 2025-09-09T14:04:04.0121785Z { 2025-09-09T14:04:04.0122013Z  echo "#!/usr/bin/env bash"; 2025-09-09T14:04:04.0122333Z  echo "set -eou pipefail"; 2025-09-09T14:04:04.0122648Z  # shellcheck disable=SC2016 2025-09-09T14:04:04.0122993Z  echo 'eval "$(conda shell.bash hook)"'; 2025-09-09T14:04:04.0123318Z  echo "set -x"; 2025-09-09T14:04:04.0123767Z  echo "${SCRIPT}"; 2025-09-09T14:04:04.0124065Z } > "${RUNNER_TEMP}/exec_script" 2025-09-09T14:04:04.0124664Z chmod +x "${RUNNER_TEMP}/exec_script" 2025-09-09T14:04:04.0125315Z python3 "/home/ec2-user/actions-runner/_work/ao/ao/test-infra/.github/scripts/run_with_env_secrets.py" "" 2025-09-09T14:04:04.0131895Z shell: /usr/bin/bash -e {0} 2025-09-09T14:04:04.0132165Z env: 2025-09-09T14:04:04.0132404Z DOCKER_IMAGE: pytorch/almalinux-builder:cpu 2025-09-09T14:04:04.0132787Z REPOSITORY: pytorch/ao 2025-09-09T14:04:04.0133034Z PR_NUMBER: 2963 2025-09-09T14:04:04.0135221Z SCRIPT: conda create -n venv python=3.9 -y conda activate venv echo "::group::Install newer objcopy that supports --set-section-alignment" dnf install -y gcc-toolset-10-binutils export PATH=/opt/rh/gcc-toolset-10/root/usr/bin/:$PATH python -m pip install --upgrade pip pip install torch==2.8.0 --index-url https://download.pytorch.org/whl/cpu 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/:$LD_LIBRARY_PATH pytest test --verbose -s 2025-09-09T14:04:04.0137596Z RUNNER_ARTIFACT_DIR: /home/ec2-user/actions-runner/_work/_temp/artifacts 2025-09-09T14:04:04.0138187Z RUNNER_TEST_RESULTS_DIR: /home/ec2-user/actions-runner/_work/_temp/test-results 2025-09-09T14:04:04.0138727Z RUNNER_DOCS_DIR: /home/ec2-user/actions-runner/_work/_temp/docs 2025-09-09T14:04:04.0139377Z ALL_SECRETS: { "github_token": "***" } 2025-09-09T14:04:04.0139676Z ##[endgroup] 2025-09-09T14:04:04.0166000Z + echo '#!/usr/bin/env bash' 2025-09-09T14:04:04.0166501Z + echo 'set -eou pipefail' 2025-09-09T14:04:04.0167047Z + echo 'eval "$(conda shell.bash hook)"' 2025-09-09T14:04:04.0167414Z + echo 'set -x' 2025-09-09T14:04:04.0167656Z + echo 'conda create -n venv python=3.9 -y 2025-09-09T14:04:04.0167982Z conda activate venv 2025-09-09T14:04:04.0168363Z echo "::group::Install newer objcopy that supports --set-section-alignment" 2025-09-09T14:04:04.0168852Z dnf install -y gcc-toolset-10-binutils 2025-09-09T14:04:04.0169216Z export PATH=/opt/rh/gcc-toolset-10/root/usr/bin/:$PATH 2025-09-09T14:04:04.0169599Z python -m pip install --upgrade pip 2025-09-09T14:04:04.0170032Z pip install torch==2.8.0 --index-url https://download.pytorch.org/whl/cpu 2025-09-09T14:04:04.0170497Z sed -i '\'''\'' dev-requirements.txt 2025-09-09T14:04:04.0170813Z pip install -r dev-requirements.txt 2025-09-09T14:04:04.0171092Z pip install . 2025-09-09T14:04:04.0171370Z export CONDA=$(dirname $(dirname $(which conda))) 2025-09-09T14:04:04.0171747Z export LD_LIBRARY_PATH=$CONDA/lib/:$LD_LIBRARY_PATH 2025-09-09T14:04:04.0172098Z pytest test --verbose -s 2025-09-09T14:04:04.0172336Z ' 2025-09-09T14:04:04.0172634Z + chmod +x /home/ec2-user/actions-runner/_work/_temp/exec_script 2025-09-09T14:04:04.0178600Z + python3 /home/ec2-user/actions-runner/_work/ao/ao/test-infra/.github/scripts/run_with_env_secrets.py '' 2025-09-09T14:04:31.0374368Z Running command: 2025-09-09T14:04:31.0379478Z docker run -e PR_NUMBER -e RUNNER_ARTIFACT_DIR=/artifacts -e RUNNER_DOCS_DIR=/docs -e RUNNER_TEST_RESULTS_DIR=/test-results --env-file="/home/ec2-user/actions-runner/_work/_temp/github_env_17585175130" `# It is unknown why the container sees a different value for this.` -e GITHUB_STEP_SUMMARY -e SECRET_GITHUB_TOKEN --cap-add=SYS_PTRACE --detach --ipc=host --security-opt seccomp=unconfined --shm-size=2g --tty --ulimit stack=10485760:83886080 --ulimit core=0 -v "/home/ec2-user/actions-runner/_work/ao/ao/pytorch/ao:/pytorch/ao" -v "/home/ec2-user/actions-runner/_work/ao/ao/test-infra:/test-infra" -v "/home/ec2-user/actions-runner/_work/_temp/artifacts:/artifacts" -v "/home/ec2-user/actions-runner/_work/_temp/docs:/docs" -v "/home/ec2-user/actions-runner/_work/_temp/test-results:/test-results" -v "/home/ec2-user/actions-runner/_work/_temp/exec_script:/exec" -v "/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/step_summary_4f8bc9ee-f460-4527-a723-5566b35d1035":"/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/step_summary_4f8bc9ee-f460-4527-a723-5566b35d1035" -w /pytorch/ao "pytorch/almalinux-builder:cpu" 2025-09-09T14:04:31.0384525Z 2025-09-09T14:04:31.0384869Z c20abe3e52959e92a4a1996711464b6b29bb54815bed87faf5f6980eaf0ab30d 2025-09-09T14:04:31.0385544Z Running command: docker exec -t c20abe3e52959e92a4a1996711464b6b29bb54815bed87faf5f6980eaf0ab30d /exec 2025-09-09T14:04:31.0386131Z + conda create -n venv python=3.9 -y 2025-09-09T14:04:31.0386420Z + local cmd=create 2025-09-09T14:04:31.0386651Z + case "$cmd" in 2025-09-09T14:04:31.0386894Z + __conda_exe create -n venv python=3.9 -y 2025-09-09T14:04:31.0387269Z + /opt/conda/bin/conda create -n venv python=3.9 -y 2025-09-09T14:04:31.0388157Z Collecting package metadata (current_repodata.json): - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ done 2025-09-09T14:04:31.0388761Z Solving environment: / done 2025-09-09T14:04:31.0388943Z 2025-09-09T14:04:31.0388962Z 2025-09-09T14:04:31.0389101Z ==> WARNING: A newer version of conda exists. <== 2025-09-09T14:04:31.0389431Z current version: 23.5.2 2025-09-09T14:04:31.0389699Z latest version: 25.7.0 2025-09-09T14:04:31.0389857Z 2025-09-09T14:04:31.0389966Z Please update conda by running 2025-09-09T14:04:31.0390157Z 2025-09-09T14:04:31.0390275Z $ conda update -n base -c defaults conda 2025-09-09T14:04:31.0390491Z 2025-09-09T14:04:31.0390706Z Or to minimize the number of packages updated during conda update use 2025-09-09T14:04:31.0391019Z 2025-09-09T14:04:31.0391120Z conda install conda=25.7.0 2025-09-09T14:04:31.0391309Z 2025-09-09T14:04:31.0391317Z 2025-09-09T14:04:31.0391321Z 2025-09-09T14:04:31.0391411Z ## Package Plan ## 2025-09-09T14:04:31.0391548Z 2025-09-09T14:04:31.0391681Z environment location: /opt/conda/envs/venv 2025-09-09T14:04:31.0391903Z 2025-09-09T14:04:31.0392000Z added / updated specs: 2025-09-09T14:04:31.0392256Z - python=3.9 2025-09-09T14:04:31.0392389Z 2025-09-09T14:04:31.0392472Z 2025-09-09T14:04:31.0392595Z The following packages will be downloaded: 2025-09-09T14:04:31.0392831Z 2025-09-09T14:04:31.0392954Z package | build 2025-09-09T14:04:31.0393281Z ---------------------------|----------------- 2025-09-09T14:04:31.0393651Z bzip2-1.0.8 | h5eee18b_6 262 KB 2025-09-09T14:04:31.0394072Z ld_impl_linux-64-2.40 | h12ee557_0 710 KB 2025-09-09T14:04:31.0394476Z libffi-3.4.4 | h6a678d5_1 141 KB 2025-09-09T14:04:31.0394879Z libxcb-1.17.0 | h9b100fa_0 430 KB 2025-09-09T14:04:31.0395280Z ncurses-6.5 | h7934f7d_0 1.1 MB 2025-09-09T14:04:31.0395675Z pip-25.2 | pyhc872135_0 1.2 MB 2025-09-09T14:04:31.0396080Z pthread-stubs-0.3 | h0ce48e5_1 5 KB 2025-09-09T14:04:31.0396605Z python-3.9.23 | he99959a_0 24.7 MB 2025-09-09T14:04:31.0397014Z readline-8.3 | hc2a1206_0 471 KB 2025-09-09T14:04:31.0397545Z setuptools-78.1.1 | py39h06a4308_0 1.7 MB 2025-09-09T14:04:31.0397970Z sqlite-3.50.2 | hb25bd0a_1 1.1 MB 2025-09-09T14:04:31.0398344Z tk-8.6.15 | h54e0aa7_0 3.4 MB 2025-09-09T14:04:31.0398731Z tzdata-2025b | h04d1e81_0 116 KB 2025-09-09T14:04:31.0399125Z wheel-0.45.1 | py39h06a4308_0 114 KB 2025-09-09T14:04:31.0399548Z xorg-libx11-1.8.12 | h9b100fa_1 895 KB 2025-09-09T14:04:31.0400045Z xorg-libxau-1.0.12 | h9b100fa_0 13 KB 2025-09-09T14:04:31.0400469Z xorg-libxdmcp-1.1.5 | h9b100fa_0 19 KB 2025-09-09T14:04:31.0400924Z xorg-xorgproto-2024.1 | h5eee18b_1 580 KB 2025-09-09T14:04:31.0401323Z xz-5.6.4 | h5eee18b_1 567 KB 2025-09-09T14:04:31.0401700Z zlib-1.2.13 | h5eee18b_1 111 KB 2025-09-09T14:04:31.0402078Z ------------------------------------------------------------ 2025-09-09T14:04:31.0402451Z Total: 37.6 MB 2025-09-09T14:04:31.0402669Z 2025-09-09T14:04:31.0402817Z The following NEW packages will be INSTALLED: 2025-09-09T14:04:31.0403053Z 2025-09-09T14:04:31.0403260Z _libgcc_mutex pkgs/main/linux-64::_libgcc_mutex-0.1-main 2025-09-09T14:04:31.0403736Z _openmp_mutex pkgs/main/linux-64::_openmp_mutex-5.1-1_gnu 2025-09-09T14:04:31.0404174Z bzip2 pkgs/main/linux-64::bzip2-1.0.8-h5eee18b_6 2025-09-09T14:04:31.0404701Z ca-certificates pkgs/main/linux-64::ca-certificates-2025.7.15-h06a4308_0 2025-09-09T14:04:31.0405227Z expat pkgs/main/linux-64::expat-2.7.1-h6a678d5_0 2025-09-09T14:04:31.0405696Z ld_impl_linux-64 pkgs/main/linux-64::ld_impl_linux-64-2.40-h12ee557_0 2025-09-09T14:04:31.0406193Z libffi pkgs/main/linux-64::libffi-3.4.4-h6a678d5_1 2025-09-09T14:04:31.0406647Z libgcc-ng pkgs/main/linux-64::libgcc-ng-11.2.0-h1234567_1 2025-09-09T14:04:31.0407119Z libgomp pkgs/main/linux-64::libgomp-11.2.0-h1234567_1 2025-09-09T14:04:31.0407624Z libstdcxx-ng pkgs/main/linux-64::libstdcxx-ng-11.2.0-h1234567_1 2025-09-09T14:04:31.0408097Z libxcb pkgs/main/linux-64::libxcb-1.17.0-h9b100fa_0 2025-09-09T14:04:31.0408542Z ncurses pkgs/main/linux-64::ncurses-6.5-h7934f7d_0 2025-09-09T14:04:31.0408985Z openssl pkgs/main/linux-64::openssl-3.0.17-h5eee18b_0 2025-09-09T14:04:31.0409428Z pip pkgs/main/noarch::pip-25.2-pyhc872135_0 2025-09-09T14:04:31.0409898Z pthread-stubs pkgs/main/linux-64::pthread-stubs-0.3-h0ce48e5_1 2025-09-09T14:04:31.0410387Z python pkgs/main/linux-64::python-3.9.23-he99959a_0 2025-09-09T14:04:31.0410842Z readline pkgs/main/linux-64::readline-8.3-hc2a1206_0 2025-09-09T14:04:31.0411327Z setuptools pkgs/main/linux-64::setuptools-78.1.1-py39h06a4308_0 2025-09-09T14:04:31.0411822Z sqlite pkgs/main/linux-64::sqlite-3.50.2-hb25bd0a_1 2025-09-09T14:04:31.0412219Z tk pkgs/main/linux-64::tk-8.6.15-h54e0aa7_0 2025-09-09T14:04:31.0412627Z tzdata pkgs/main/noarch::tzdata-2025b-h04d1e81_0 2025-09-09T14:04:31.0413067Z wheel pkgs/main/linux-64::wheel-0.45.1-py39h06a4308_0 2025-09-09T14:04:31.0413534Z xorg-libx11 pkgs/main/linux-64::xorg-libx11-1.8.12-h9b100fa_1 2025-09-09T14:04:31.0414043Z xorg-libxau pkgs/main/linux-64::xorg-libxau-1.0.12-h9b100fa_0 2025-09-09T14:04:31.0414567Z xorg-libxdmcp pkgs/main/linux-64::xorg-libxdmcp-1.1.5-h9b100fa_0 2025-09-09T14:04:31.0415123Z xorg-xorgproto pkgs/main/linux-64::xorg-xorgproto-2024.1-h5eee18b_1 2025-09-09T14:04:31.0415604Z xz pkgs/main/linux-64::xz-5.6.4-h5eee18b_1 2025-09-09T14:04:31.0415991Z zlib pkgs/main/linux-64::zlib-1.2.13-h5eee18b_1 2025-09-09T14:04:31.0416255Z 2025-09-09T14:04:31.0416259Z 2025-09-09T14:04:31.0416263Z 2025-09-09T14:04:31.0416445Z Downloading and Extracting Packages 2025-09-09T14:04:31.0416651Z 2025-09-09T14:04:31.0416827Z setuptools-78.1.1 | 1.7 MB | : 0% 0/1 [00:00=4.10.0 (from torch==2.8.0) 2025-09-09T14:04:44.4166530Z Downloading https://download.pytorch.org/whl/typing_extensions-4.12.2-py3-none-any.whl.metadata (3.0 kB) 2025-09-09T14:04:44.4167149Z Collecting sympy>=1.13.3 (from torch==2.8.0) 2025-09-09T14:04:44.4167710Z Downloading https://download.pytorch.org/whl/sympy-1.13.3-py3-none-any.whl.metadata (12 kB) 2025-09-09T14:04:44.4168378Z Collecting networkx (from torch==2.8.0) 2025-09-09T14:04:44.4168946Z Downloading https://download.pytorch.org/whl/networkx-3.2.1-py3-none-any.whl.metadata (5.2 kB) 2025-09-09T14:04:44.4169511Z Collecting jinja2 (from torch==2.8.0) 2025-09-09T14:04:44.4170063Z Downloading https://download.pytorch.org/whl/Jinja2-3.1.4-py3-none-any.whl.metadata (2.6 kB) 2025-09-09T14:04:44.4170626Z Collecting fsspec (from torch==2.8.0) 2025-09-09T14:04:44.4171167Z Downloading https://download.pytorch.org/whl/fsspec-2024.6.1-py3-none-any.whl.metadata (11 kB) 2025-09-09T14:04:44.4171845Z Collecting mpmath<1.4,>=1.1.0 (from sympy>=1.13.3->torch==2.8.0) 2025-09-09T14:04:44.4172426Z Downloading https://download.pytorch.org/whl/mpmath-1.3.0-py3-none-any.whl (536 kB) 2025-09-09T14:04:44.4173524Z [?25l ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0.0/536.2 kB ? eta -:--:-- 2025-09-09T14:04:44.4174234Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 536.2/536.2 kB 34.0 MB/s 0:00:00 2025-09-09T14:04:44.4174806Z [?25hCollecting MarkupSafe>=2.0 (from jinja2->torch==2.8.0) 2025-09-09T14:04:44.4175717Z Downloading https://download.pytorch.org/whl/MarkupSafe-2.1.5-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (25 kB) 2025-09-09T14:04:44.4176706Z Downloading https://download.pytorch.org/whl/cpu/torch-2.8.0%2Bcpu-cp39-cp39-manylinux_2_28_x86_64.whl (184.0 MB) 2025-09-09T14:04:44.4177567Z [?25l ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0.0/184.0 MB ? eta -:--:-- 2025-09-09T14:04:44.4178335Z  ━━━━━━━━━━━╺━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 52.4/184.0 MB 262.6 MB/s eta 0:00:01 2025-09-09T14:04:44.4179375Z  ━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━━━━━━━━━━ 114.8/184.0 MB 285.7 MB/s eta 0:00:01 2025-09-09T14:04:44.4180158Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 183.8/184.0 MB 340.5 MB/s eta 0:00:01 2025-09-09T14:04:44.4180972Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 183.8/184.0 MB 340.5 MB/s eta 0:00:01 2025-09-09T14:04:44.4181715Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 183.8/184.0 MB 340.5 MB/s eta 0:00:01 2025-09-09T14:04:44.4182423Z  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2025-09-09T14:04:44.4188920Z [?25l ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0.0/1.6 MB ? eta -:--:-- 2025-09-09T14:04:44.4189571Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1.6/1.6 MB 148.1 MB/s 0:00:00 2025-09-09T14:04:52.4260762Z [?25hInstalling collected packages: mpmath, typing-extensions, sympy, networkx, MarkupSafe, fsspec, filelock, jinja2, torch 2025-09-09T14:04:52.4261470Z [?25l 2025-09-09T14:04:52.4261887Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0/9 [mpmath] 2025-09-09T14:04:52.4262467Z  ━━━━━━━━╸━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 2/9 [sympy] 2025-09-09T14:04:52.4263323Z  ━━━━━━━━╸━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 2/9 [sympy] 2025-09-09T14:04:52.4263925Z  ━━━━━━━━╸━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 2/9 [sympy] 2025-09-09T14:04:52.4264511Z  ━━━━━━━━╸━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 2/9 [sympy] 2025-09-09T14:04:52.4265101Z  ━━━━━━━━╸━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 2/9 [sympy] 2025-09-09T14:04:52.4265864Z  ━━━━━━━━╸━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 2/9 [sympy] 2025-09-09T14:04:52.4266450Z 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2025-09-09T14:04:52.4287482Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━ 8/9 [torch] 2025-09-09T14:04:52.4288046Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━ 8/9 [torch] 2025-09-09T14:04:59.7201695Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━ 8/9 [torch] 2025-09-09T14:04:59.7202334Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━ 8/9 [torch] 2025-09-09T14:04:59.7203181Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━ 8/9 [torch] 2025-09-09T14:04:59.7203749Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━ 8/9 [torch] 2025-09-09T14:04:59.7204297Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━ 8/9 [torch] 2025-09-09T14:04:59.7204858Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━ 8/9 [torch] 2025-09-09T14:04:59.7205443Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━ 8/9 [torch] 2025-09-09T14:04:59.7205985Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━ 8/9 [torch] 2025-09-09T14:04:59.7206541Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━ 8/9 [torch] 2025-09-09T14:04:59.7207084Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━ 8/9 [torch] 2025-09-09T14:04:59.7207660Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━ 8/9 [torch] 2025-09-09T14:04:59.7208218Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━ 8/9 [torch] 2025-09-09T14:04:59.7208760Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━ 8/9 [torch] 2025-09-09T14:04:59.7209315Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━ 8/9 [torch] 2025-09-09T14:04:59.7209888Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━ 8/9 [torch] 2025-09-09T14:04:59.7210447Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━ 8/9 [torch] 2025-09-09T14:04:59.7211005Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━ 8/9 [torch] 2025-09-09T14:04:59.7211552Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━ 8/9 [torch] 2025-09-09T14:04:59.7212106Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━ 8/9 [torch] 2025-09-09T14:04:59.7212669Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━ 8/9 [torch] 2025-09-09T14:04:59.7213230Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━ 8/9 [torch] 2025-09-09T14:04:59.7213808Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━ 8/9 [torch] 2025-09-09T14:04:59.7214473Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━ 8/9 [torch] 2025-09-09T14:04:59.7215039Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━ 8/9 [torch] 2025-09-09T14:04:59.7215587Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━ 8/9 [torch] 2025-09-09T14:04:59.7216148Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━ 8/9 [torch] 2025-09-09T14:04:59.7216712Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━ 8/9 [torch] 2025-09-09T14:04:59.7217337Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━ 8/9 [torch] 2025-09-09T14:04:59.7217896Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━ 8/9 [torch] 2025-09-09T14:04:59.7218447Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━ 8/9 [torch] 2025-09-09T14:04:59.7219024Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━ 8/9 [torch] 2025-09-09T14:04:59.7219583Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━ 8/9 [torch] 2025-09-09T14:04:59.7220130Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━ 8/9 [torch] 2025-09-09T14:04:59.7220688Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━ 8/9 [torch] 2025-09-09T14:04:59.7221232Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━ 8/9 [torch] 2025-09-09T14:04:59.7221811Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━ 8/9 [torch] 2025-09-09T14:04:59.7222370Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━ 8/9 [torch] 2025-09-09T14:04:59.7222882Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 9/9 [torch] 2025-09-09T14:04:59.7223253Z [?25h 2025-09-09T14:04:59.7224185Z Successfully installed MarkupSafe-2.1.5 filelock-3.13.1 fsspec-2024.6.1 jinja2-3.1.4 mpmath-1.3.0 networkx-3.2.1 sympy-1.13.3 torch-2.8.0+cpu typing-extensions-4.12.2 2025-09-09T14:04:59.7227693Z 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. 2025-09-09T14:04:59.7229613Z + sed -i '' dev-requirements.txt 2025-09-09T14:04:59.7229939Z + pip install -r dev-requirements.txt 2025-09-09T14:04:59.7230342Z Collecting pytest (from -r dev-requirements.txt (line 2)) 2025-09-09T14:04:59.7230826Z Downloading pytest-8.4.2-py3-none-any.whl.metadata (7.7 kB) 2025-09-09T14:04:59.7231393Z Collecting unittest-xml-reporting (from -r dev-requirements.txt (line 3)) 2025-09-09T14:04:59.7232020Z Downloading unittest_xml_reporting-3.2.0-py2.py3-none-any.whl.metadata (11 kB) 2025-09-09T14:04:59.7232637Z Collecting parameterized (from -r dev-requirements.txt (line 4)) 2025-09-09T14:04:59.7233216Z Downloading parameterized-0.9.0-py2.py3-none-any.whl.metadata (18 kB) 2025-09-09T14:04:59.7233764Z Collecting packaging (from -r dev-requirements.txt (line 5)) 2025-09-09T14:04:59.7234274Z Downloading packaging-25.0-py3-none-any.whl.metadata (3.3 kB) 2025-09-09T14:04:59.7234783Z Collecting transformers (from -r dev-requirements.txt (line 6)) 2025-09-09T14:04:59.7235329Z Downloading transformers-4.56.1-py3-none-any.whl.metadata (42 kB) 2025-09-09T14:04:59.7235853Z Collecting hypothesis (from -r dev-requirements.txt (line 7)) 2025-09-09T14:04:59.7236492Z Downloading hypothesis-6.138.15-py3-none-any.whl.metadata (5.6 kB) 2025-09-09T14:04:59.7237048Z Collecting sentencepiece (from -r dev-requirements.txt (line 8)) 2025-09-09T14:05:03.7725042Z Downloading sentencepiece-0.2.1-cp39-cp39-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.metadata (10 kB) 2025-09-09T14:05:03.7725866Z Collecting expecttest (from -r dev-requirements.txt (line 9)) 2025-09-09T14:05:03.7726423Z Downloading expecttest-0.3.0-py3-none-any.whl.metadata (3.8 kB) 2025-09-09T14:05:03.7726990Z Collecting bitsandbytes (from -r dev-requirements.txt (line 12)) 2025-09-09T14:05:03.7727689Z Downloading bitsandbytes-0.47.0-py3-none-manylinux_2_24_x86_64.whl.metadata (11 kB) 2025-09-09T14:05:03.7728352Z Collecting matplotlib (from -r dev-requirements.txt (line 13)) 2025-09-09T14:05:03.7729067Z Downloading matplotlib-3.9.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (11 kB) 2025-09-09T14:05:03.7729789Z Collecting pandas (from -r dev-requirements.txt (line 14)) 2025-09-09T14:05:03.7730466Z Downloading pandas-2.3.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (91 kB) 2025-09-09T14:05:03.7731073Z Collecting fire (from -r dev-requirements.txt (line 15)) 2025-09-09T14:05:03.7731591Z Downloading fire-0.7.1-py3-none-any.whl.metadata (5.8 kB) 2025-09-09T14:05:03.7732125Z Collecting tabulate (from -r dev-requirements.txt (line 16)) 2025-09-09T14:05:03.7732631Z Downloading tabulate-0.9.0-py3-none-any.whl.metadata (34 kB) 2025-09-09T14:05:03.7733188Z Collecting tiktoken (from -r dev-requirements.txt (line 17)) 2025-09-09T14:05:03.7733878Z Downloading tiktoken-0.11.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (6.7 kB) 2025-09-09T14:05:03.7734590Z Collecting blobfile (from -r dev-requirements.txt (line 18)) 2025-09-09T14:05:03.7735146Z Downloading blobfile-3.1.0-py3-none-any.whl.metadata (15 kB) 2025-09-09T14:05:03.7735647Z Collecting lm_eval (from -r dev-requirements.txt (line 19)) 2025-09-09T14:05:03.7736192Z Downloading lm_eval-0.4.9.1-py3-none-any.whl.metadata (53 kB) 2025-09-09T14:05:03.7736759Z Collecting diskcache (from -r dev-requirements.txt (line 21)) 2025-09-09T14:05:03.7737269Z Downloading diskcache-5.6.3-py3-none-any.whl.metadata (20 kB) 2025-09-09T14:05:03.7737841Z Collecting pycocotools (from -r dev-requirements.txt (line 22)) 2025-09-09T14:05:03.7738781Z Downloading pycocotools-2.0.10-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (1.3 kB) 2025-09-09T14:05:03.7739503Z Collecting tqdm (from -r dev-requirements.txt (line 23)) 2025-09-09T14:05:03.7740032Z Downloading tqdm-4.67.1-py3-none-any.whl.metadata (57 kB) 2025-09-09T14:05:03.7740610Z Collecting importlib_metadata (from -r dev-requirements.txt (line 24)) 2025-09-09T14:05:03.7741353Z Downloading importlib_metadata-8.7.0-py3-none-any.whl.metadata (4.8 kB) 2025-09-09T14:05:03.7742032Z Collecting ninja (from -r dev-requirements.txt (line 27)) 2025-09-09T14:05:03.7742686Z Downloading ninja-1.13.0-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (5.1 kB) 2025-09-09T14:05:03.7743427Z Collecting cmake<4.0.0,>=3.19.0 (from -r dev-requirements.txt (line 30)) 2025-09-09T14:05:03.7744132Z Downloading cmake-3.31.6-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (6.3 kB) 2025-09-09T14:05:03.7744838Z Collecting ruff==0.11.6 (from -r dev-requirements.txt (line 33)) 2025-09-09T14:05:03.7745504Z Downloading ruff-0.11.6-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (25 kB) 2025-09-09T14:05:03.7746191Z Collecting pre-commit (from -r dev-requirements.txt (line 34)) 2025-09-09T14:05:03.7746752Z Downloading pre_commit-4.3.0-py2.py3-none-any.whl.metadata (1.2 kB) 2025-09-09T14:05:03.7747372Z Collecting exceptiongroup>=1 (from pytest->-r dev-requirements.txt (line 2)) 2025-09-09T14:05:03.7748049Z Downloading exceptiongroup-1.3.0-py3-none-any.whl.metadata (6.7 kB) 2025-09-09T14:05:03.7748686Z Collecting iniconfig>=1 (from pytest->-r dev-requirements.txt (line 2)) 2025-09-09T14:05:03.7749298Z Downloading iniconfig-2.1.0-py3-none-any.whl.metadata (2.7 kB) 2025-09-09T14:05:03.7749849Z Collecting pluggy<2,>=1.5 (from pytest->-r dev-requirements.txt (line 2)) 2025-09-09T14:05:03.7750414Z Downloading pluggy-1.6.0-py3-none-any.whl.metadata (4.8 kB) 2025-09-09T14:05:03.7751015Z Collecting pygments>=2.7.2 (from pytest->-r dev-requirements.txt (line 2)) 2025-09-09T14:05:03.7751623Z Downloading pygments-2.19.2-py3-none-any.whl.metadata (2.5 kB) 2025-09-09T14:05:03.7752152Z Collecting tomli>=1 (from pytest->-r dev-requirements.txt (line 2)) 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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 2025-09-09T14:05:13.2195897Z  Building wheel for rouge-score (setup.py) ... [?25l- done 2025-09-09T14:05:13.2197038Z [?25h Created wheel for rouge-score: filename=rouge_score-0.1.2-py3-none-any.whl size=24988 sha256=6873ac9b3bef18d75e3121c33ad0d509820078e52c7e5e8f2f4c85b6494d1127 2025-09-09T14:05:13.2198094Z Stored in directory: /root/.cache/pip/wheels/9b/3d/39/09558097d3119ca0a4d462df68f22c6f3c1b345ac63a09b86e 2025-09-09T14:05:13.2200621Z  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 2025-09-09T14:05:13.2202671Z  Building wheel for sqlitedict (setup.py) ... [?25l- done 2025-09-09T14:05:13.2203758Z [?25h Created wheel for sqlitedict: filename=sqlitedict-2.1.0-py3-none-any.whl size=16958 sha256=bb660dd4d6ae1bf4ef4f45bc06f611276dacc682e4907a34c5d1ec3694afdef3 2025-09-09T14:05:13.2204849Z Stored in directory: /root/.cache/pip/wheels/f6/48/c4/942f7a1d556fddd2348cb9ac262f251873dfd8a39afec5678e 2025-09-09T14:05:13.2207369Z  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 2025-09-09T14:05:13.2209479Z  Building wheel for word2number (setup.py) ... [?25l- done 2025-09-09T14:05:13.2210499Z [?25h Created wheel for word2number: filename=word2number-1.1-py3-none-any.whl size=5658 sha256=035e3884f590897ee0ec1c9c3c21972f370196e2aca8b6b242028d980e339c6d 2025-09-09T14:05:13.2211558Z Stored in directory: /root/.cache/pip/wheels/a0/4a/5b/d2f2df5c344ddbecb8bea759872c207ea91d93f57fb54e816e 2025-09-09T14:05:13.2212216Z Successfully built rouge-score sqlitedict word2number 2025-09-09T14:05:19.1846896Z Installing collected packages: word2number, sqlitedict, sortedcontainers, pytz, distlib, zstandard, zipp, xxhash, urllib3, tzdata, typing-extensions, 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, more_itertools, lxml, kiwisolver, joblib, iniconfig, idna, identify, hf-xet, frozenlist, fonttools, expecttest, diskcache, dill, cycler, colorama, cmake, click, charset_normalizer, chardet, cfgv, certifi, attrs, async-timeout, aiohappyeyeballs, absl-py, virtualenv, unittest-xml-reporting, tqdm-multiprocess, scipy, sacrebleu, requests, python-dateutil, pycocotools, numexpr, nltk, multiprocess, multidict, mbstrdecoder, jsonlines, importlib-resources, importlib_metadata, fire, exceptiongroup, contourpy, blobfile, aiosignal, yarl, typepy, tiktoken, scikit-learn, rouge-score, pytest, pre-commit, pandas, matplotlib, hypothesis, huggingface-hub, bitsandbytes, tokenizers, aiohttp, accelerate, transformers, DataProperty, tabledata, peft, datasets, pytablewriter, evaluate, lm_eval 2025-09-09T14:05:19.1852286Z [?25l 2025-09-09T14:05:19.1852735Z  ━╺━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━  4/109 [distlib] 2025-09-09T14:05:19.1853352Z  ━━╸━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━  8/109 [urllib3] 2025-09-09T14:05:19.1853837Z  Attempting uninstall: typing-extensions 2025-09-09T14:05:19.1854376Z 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[typepy] 2025-09-09T14:05:26.8149249Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━━━  89/109 [scikit-learn] 2025-09-09T14:05:26.8149891Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━━━  89/109 [scikit-learn] 2025-09-09T14:05:26.8150495Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━━━  89/109 [scikit-learn] 2025-09-09T14:05:26.8151115Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━━━  89/109 [scikit-learn] 2025-09-09T14:05:34.3259258Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━━━  89/109 [scikit-learn] 2025-09-09T14:05:34.3259999Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━━━  89/109 [scikit-learn] 2025-09-09T14:05:34.3260632Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━━━  89/109 [scikit-learn] 2025-09-09T14:05:34.3261365Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━━━  89/109 [scikit-learn] 2025-09-09T14:05:34.3262322Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━━━  89/109 [scikit-learn] 2025-09-09T14:05:34.3262945Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━━━  89/109 [scikit-learn] 2025-09-09T14:05:34.3263626Z  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━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━  93/109 [pandas] 2025-09-09T14:05:34.3277616Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━  94/109 [matplotlib] 2025-09-09T14:05:34.3278218Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━  94/109 [matplotlib] 2025-09-09T14:05:34.3278827Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━  94/109 [matplotlib] 2025-09-09T14:05:34.3279461Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━  94/109 [matplotlib] 2025-09-09T14:05:34.3280054Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━  94/109 [matplotlib] 2025-09-09T14:05:34.3280656Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━  94/109 [matplotlib] 2025-09-09T14:05:34.3281272Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━  95/109 [hypothesis] 2025-09-09T14:05:34.3281900Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━  96/109 [huggingface-hub] 2025-09-09T14:05:34.3282524Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━  97/109 [bitsandbytes] 2025-09-09T14:05:34.3283132Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━  97/109 [bitsandbytes] 2025-09-09T14:05:34.3283745Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━  97/109 [bitsandbytes] 2025-09-09T14:05:34.3284371Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━  97/109 [bitsandbytes] 2025-09-09T14:05:34.3284988Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━  97/109 [bitsandbytes] 2025-09-09T14:05:34.3285601Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━  97/109 [bitsandbytes] 2025-09-09T14:05:34.3286266Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━  99/109 [aiohttp] 2025-09-09T14:05:41.7677204Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━ 100/109 [accelerate] 2025-09-09T14:05:41.7677874Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━ 101/109 [transformers] 2025-09-09T14:05:41.7678494Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━ 101/109 [transformers] 2025-09-09T14:05:41.7679385Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━ 101/109 [transformers] 2025-09-09T14:05:41.7679987Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━ 101/109 [transformers] 2025-09-09T14:05:41.7680601Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━ 101/109 [transformers] 2025-09-09T14:05:41.7681232Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━ 101/109 [transformers] 2025-09-09T14:05:41.7681846Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━ 101/109 [transformers] 2025-09-09T14:05:41.7682460Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━ 101/109 [transformers] 2025-09-09T14:05:41.7683065Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━ 101/109 [transformers] 2025-09-09T14:05:41.7683676Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━ 101/109 [transformers] 2025-09-09T14:05:41.7684291Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━ 101/109 [transformers] 2025-09-09T14:05:41.7684904Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━ 101/109 [transformers] 2025-09-09T14:05:41.7685519Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━ 101/109 [transformers] 2025-09-09T14:05:41.7686232Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━ 101/109 [transformers] 2025-09-09T14:05:41.7686857Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━ 101/109 [transformers] 2025-09-09T14:05:41.7687458Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━ 101/109 [transformers] 2025-09-09T14:05:41.7688072Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━ 101/109 [transformers] 2025-09-09T14:05:41.7688776Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━ 101/109 [transformers] 2025-09-09T14:05:41.7689374Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━ 101/109 [transformers] 2025-09-09T14:05:41.7689989Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━ 101/109 [transformers] 2025-09-09T14:05:41.7690608Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━ 101/109 [transformers] 2025-09-09T14:05:41.7691241Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━ 101/109 [transformers] 2025-09-09T14:05:41.7691853Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━ 101/109 [transformers] 2025-09-09T14:05:41.7692457Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━ 101/109 [transformers] 2025-09-09T14:05:41.7693065Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━ 101/109 [transformers] 2025-09-09T14:05:41.7693682Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━ 101/109 [transformers] 2025-09-09T14:05:41.7694301Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━ 101/109 [transformers] 2025-09-09T14:05:41.7694913Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━ 101/109 [transformers] 2025-09-09T14:05:41.7695528Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━ 101/109 [transformers] 2025-09-09T14:05:41.7696143Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━ 101/109 [transformers] 2025-09-09T14:05:41.7696741Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━ 101/109 [transformers] 2025-09-09T14:05:41.7697333Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━ 104/109 [peft] 2025-09-09T14:05:41.7697930Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━ 104/109 [peft] 2025-09-09T14:05:41.7698509Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━ 105/109 [datasets] 2025-09-09T14:05:41.7699076Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺ 107/109 [evaluate] 2025-09-09T14:05:41.7699616Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 108/109 [lm_eval] 2025-09-09T14:05:41.7700244Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 108/109 [lm_eval] 2025-09-09T14:05:41.7700791Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 108/109 [lm_eval] 2025-09-09T14:05:41.7701324Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 108/109 [lm_eval] 2025-09-09T14:05:41.7701874Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 108/109 [lm_eval] 2025-09-09T14:05:41.7702408Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 108/109 [lm_eval] 2025-09-09T14:05:41.7703028Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 108/109 [lm_eval] 2025-09-09T14:05:41.7703570Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 108/109 [lm_eval] 2025-09-09T14:06:04.8428332Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 108/109 [lm_eval] 2025-09-09T14:06:04.8429045Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 108/109 [lm_eval] 2025-09-09T14:06:04.8429589Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 108/109 [lm_eval] 2025-09-09T14:06:04.8430136Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 108/109 [lm_eval] 2025-09-09T14:06:04.8430671Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 108/109 [lm_eval] 2025-09-09T14:06:04.8431211Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 108/109 [lm_eval] 2025-09-09T14:06:04.8431770Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 109/109 [lm_eval] 2025-09-09T14:06:04.8432136Z [?25h 2025-09-09T14:06:04.8440379Z 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 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 importlib_metadata-8.7.0 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 typing-extensions-4.15.0 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 zipp-3.23.0 zstandard-0.24.0 2025-09-09T14:06:04.8448876Z 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. 2025-09-09T14:06:04.8450480Z + pip install . 2025-09-09T14:06:04.8450716Z Processing /pytorch/ao 2025-09-09T14:06:04.8451081Z Preparing metadata (setup.py) ... [?25l- done 2025-09-09T14:06:04.8451521Z [?25hBuilding wheels for collected packages: torchao 2025-09-09T14:06:04.8453910Z  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 2025-09-09T14:06:04.8455954Z  Building wheel for torchao (setup.py) ... [?25l- \ | / done 2025-09-09T14:06:04.8457040Z [?25h Created wheel for torchao: filename=torchao-0.14.0+git7c05f81-py3-none-any.whl size=1043958 sha256=5e37390081acbb38c2f8f2ed4a923571c0b0cccd9a931aade3edfe570fdaea9d 2025-09-09T14:06:04.8458241Z Stored in directory: /tmp/pip-ephem-wheel-cache-kdxsf5_s/wheels/4d/54/dc/0c70e60a8677bf126f1486798ebe76c8770ada66c7376b401d 2025-09-09T14:06:04.8458918Z Successfully built torchao 2025-09-09T14:06:04.8459197Z Installing collected packages: torchao 2025-09-09T14:06:04.8459551Z Successfully installed torchao-0.14.0+git7c05f81 2025-09-09T14:06:04.8461439Z 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. 2025-09-09T14:06:04.8463030Z ++++ which conda 2025-09-09T14:06:04.8463287Z +++ dirname /opt/conda/condabin/conda 2025-09-09T14:06:04.8463585Z ++ dirname /opt/conda/condabin 2025-09-09T14:06:04.8463866Z + export CONDA=/opt/conda 2025-09-09T14:06:04.8464108Z + CONDA=/opt/conda 2025-09-09T14:06:04.8464644Z + export LD_LIBRARY_PATH=/opt/conda/lib/:/opt/rh/gcc-toolset-11/root/usr/lib64:/opt/rh/gcc-toolset-11/root/usr/lib: 2025-09-09T14:06:04.8465476Z + LD_LIBRARY_PATH=/opt/conda/lib/:/opt/rh/gcc-toolset-11/root/usr/lib64:/opt/rh/gcc-toolset-11/root/usr/lib: 2025-09-09T14:06:04.8466045Z + pytest test --verbose -s 2025-09-09T14:06:04.8466461Z ============================= test session starts ============================== 2025-09-09T14:06:04.8467017Z platform linux -- Python 3.9.23, pytest-8.4.2, pluggy-1.6.0 -- /opt/conda/envs/venv/bin/python3.9 2025-09-09T14:06:04.8467527Z cachedir: .pytest_cache 2025-09-09T14:06:04.8468131Z hypothesis profile 'ci' -> database=None, deadline=None, print_blob=True, derandomize=True, suppress_health_check=(HealthCheck.too_slow,) 2025-09-09T14:06:04.8468792Z rootdir: /pytorch/ao 2025-09-09T14:06:04.8469036Z plugins: hypothesis-6.138.15 2025-09-09T14:06:04.8469353Z collecting ...  2025-09-09T14:06:04.8469784Z collecting 0 items  2025-09-09T14:06:04.8470460Z collecting 26 items  2025-09-09T14:06:04.8471027Z collecting 26 items  2025-09-09T14:06:04.8471574Z collecting 264 items  2025-09-09T14:06:04.8472174Z collecting 1022 items / 3 skipped  2025-09-09T14:06:04.8472794Z collecting 1035 items / 6 skipped  2025-09-09T14:06:04.8474268Z collecting 2976 items / 14 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` 2025-09-09T14:06:04.8475329Z  2025-09-09T14:06:04.8475732Z collecting 3072 items / 14 skipped  2025-09-09T14:06:04.8476462Z collecting 4456 items / 14 skipped  2025-09-09T14:06:04.8477087Z collected 6963 items / 14 skipped  2025-09-09T14:06:04.8477429Z 2025-09-09T14:06:04.8477856Z test/core/test_config.py::test_reconstructable_dict_file_round_trip[config0] PASSED 2025-09-09T14:06:04.8478662Z test/core/test_config.py::test_reconstructable_dict_file_round_trip[config1] PASSED 2025-09-09T14:06:04.8479454Z test/core/test_config.py::test_reconstructable_dict_file_round_trip[config2] PASSED 2025-09-09T14:06:04.8480255Z test/core/test_config.py::test_reconstructable_dict_file_round_trip[config3] PASSED 2025-09-09T14:06:04.8481037Z test/core/test_config.py::test_reconstructable_dict_file_round_trip[config4] PASSED 2025-09-09T14:06:04.8481834Z 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test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_choose_scale_float8_bounds_float8_e4m3fn_float32 SKIPPED 2025-09-09T14:06:04.9468629Z test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_choose_scale_float8_bounds_float8_e5m2_bfloat16 SKIPPED 2025-09-09T14:06:04.9469946Z test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_choose_scale_float8_bounds_float8_e5m2_float32 SKIPPED 2025-09-09T14:06:04.9471335Z test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_dequantize_affine_float8_float8_e4m3fn_bfloat16_block_size0 SKIPPED 2025-09-09T14:06:04.9472776Z test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_dequantize_affine_float8_float8_e4m3fn_bfloat16_block_size1 SKIPPED 2025-09-09T14:06:04.9474207Z 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test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_fp8_linear_variants_bfloat16_mode_dynamic_compile_False_granularity0_sizes0 SKIPPED 2025-09-09T14:06:04.9822158Z test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_fp8_linear_variants_bfloat16_mode_dynamic_compile_False_granularity0_sizes1 SKIPPED 2025-09-09T14:06:04.9823753Z test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_fp8_linear_variants_bfloat16_mode_dynamic_compile_False_granularity1_sizes0 SKIPPED 2025-09-09T14:06:04.9825500Z test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_fp8_linear_variants_bfloat16_mode_dynamic_compile_False_granularity1_sizes1 SKIPPED 2025-09-09T14:06:04.9827154Z test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_fp8_linear_variants_bfloat16_mode_dynamic_compile_True_granularity0_sizes0 SKIPPED 2025-09-09T14:06:04.9828740Z 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test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_fp8_linear_variants_bfloat16_mode_static_compile_False_granularity1_sizes0 SKIPPED 2025-09-09T14:06:04.9838741Z test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_fp8_linear_variants_bfloat16_mode_static_compile_False_granularity1_sizes1 SKIPPED 2025-09-09T14:06:04.9840291Z test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_fp8_linear_variants_bfloat16_mode_static_compile_True_granularity0_sizes0 SKIPPED 2025-09-09T14:06:04.9841847Z test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_fp8_linear_variants_bfloat16_mode_static_compile_True_granularity0_sizes1 SKIPPED 2025-09-09T14:06:04.9843475Z test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_fp8_linear_variants_bfloat16_mode_static_compile_True_granularity1_sizes0 SKIPPED 2025-09-09T14:06:04.9845020Z test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_fp8_linear_variants_bfloat16_mode_static_compile_True_granularity1_sizes1 SKIPPED 2025-09-09T14:06:04.9846683Z test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_fp8_linear_variants_bfloat16_mode_weight-only_compile_False_granularity0_sizes0 SKIPPED 2025-09-09T14:06:04.9848291Z test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_fp8_linear_variants_bfloat16_mode_weight-only_compile_False_granularity0_sizes1 SKIPPED 2025-09-09T14:06:04.9849910Z test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_fp8_linear_variants_bfloat16_mode_weight-only_compile_False_granularity1_sizes0 SKIPPED 2025-09-09T14:06:04.9851589Z test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_fp8_linear_variants_bfloat16_mode_weight-only_compile_False_granularity1_sizes1 SKIPPED 2025-09-09T14:06:04.9853244Z test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_fp8_linear_variants_bfloat16_mode_weight-only_compile_True_granularity0_sizes0 SKIPPED 2025-09-09T14:06:04.9854854Z test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_fp8_linear_variants_bfloat16_mode_weight-only_compile_True_granularity0_sizes1 SKIPPED 2025-09-09T14:06:04.9856452Z test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_fp8_linear_variants_bfloat16_mode_weight-only_compile_True_granularity1_sizes0 SKIPPED 2025-09-09T14:06:04.9858099Z test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_fp8_linear_variants_bfloat16_mode_weight-only_compile_True_granularity1_sizes1 SKIPPED 2025-09-09T14:06:04.9859686Z test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_fp8_linear_variants_float32_mode_dynamic_compile_False_granularity0_sizes0 SKIPPED 2025-09-09T14:06:04.9861313Z test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_fp8_linear_variants_float32_mode_dynamic_compile_False_granularity0_sizes1 SKIPPED 2025-09-09T14:06:04.9862868Z test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_fp8_linear_variants_float32_mode_dynamic_compile_False_granularity1_sizes0 SKIPPED 2025-09-09T14:06:04.9864427Z test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_fp8_linear_variants_float32_mode_dynamic_compile_False_granularity1_sizes1 SKIPPED 2025-09-09T14:06:04.9866123Z test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_fp8_linear_variants_float32_mode_dynamic_compile_True_granularity0_sizes0 SKIPPED 2025-09-09T14:06:04.9867680Z test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_fp8_linear_variants_float32_mode_dynamic_compile_True_granularity0_sizes1 SKIPPED 2025-09-09T14:06:04.9869297Z test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_fp8_linear_variants_float32_mode_dynamic_compile_True_granularity1_sizes0 SKIPPED 2025-09-09T14:06:04.9871814Z test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_fp8_linear_variants_float32_mode_dynamic_compile_True_granularity1_sizes1 SKIPPED 2025-09-09T14:06:04.9873400Z test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_fp8_linear_variants_float32_mode_static_compile_False_granularity0_sizes0 SKIPPED 2025-09-09T14:06:04.9874990Z test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_fp8_linear_variants_float32_mode_static_compile_False_granularity0_sizes1 SKIPPED 2025-09-09T14:06:04.9876714Z test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_fp8_linear_variants_float32_mode_static_compile_False_granularity1_sizes0 SKIPPED 2025-09-09T14:06:04.9878262Z test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_fp8_linear_variants_float32_mode_static_compile_False_granularity1_sizes1 SKIPPED 2025-09-09T14:06:04.9879821Z test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_fp8_linear_variants_float32_mode_static_compile_True_granularity0_sizes0 SKIPPED 2025-09-09T14:06:04.9881361Z test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_fp8_linear_variants_float32_mode_static_compile_True_granularity0_sizes1 SKIPPED 2025-09-09T14:06:04.9882908Z test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_fp8_linear_variants_float32_mode_static_compile_True_granularity1_sizes0 SKIPPED 2025-09-09T14:06:05.0227214Z test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_fp8_linear_variants_float32_mode_static_compile_True_granularity1_sizes1 SKIPPED 2025-09-09T14:06:05.0228852Z test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_fp8_linear_variants_float32_mode_weight-only_compile_False_granularity0_sizes0 SKIPPED 2025-09-09T14:06:05.0230634Z test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_fp8_linear_variants_float32_mode_weight-only_compile_False_granularity0_sizes1 SKIPPED 2025-09-09T14:06:05.0232256Z test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_fp8_linear_variants_float32_mode_weight-only_compile_False_granularity1_sizes0 SKIPPED 2025-09-09T14:06:05.0234047Z test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_fp8_linear_variants_float32_mode_weight-only_compile_False_granularity1_sizes1 SKIPPED 2025-09-09T14:06:05.0235637Z test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_fp8_linear_variants_float32_mode_weight-only_compile_True_granularity0_sizes0 SKIPPED 2025-09-09T14:06:05.0237306Z test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_fp8_linear_variants_float32_mode_weight-only_compile_True_granularity0_sizes1 SKIPPED 2025-09-09T14:06:05.0239067Z test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_fp8_linear_variants_float32_mode_weight-only_compile_True_granularity1_sizes0 SKIPPED 2025-09-09T14:06:05.0240659Z test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_fp8_linear_variants_float32_mode_weight-only_compile_True_granularity1_sizes1 SKIPPED 2025-09-09T14:06:05.0242388Z test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_fp8_weight_dimension_warning SKIPPED 2025-09-09T14:06:05.0243537Z test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_invalid_granularity SKIPPED 2025-09-09T14:06:05.0244666Z test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_mismatched_granularity SKIPPED 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test/dtypes/test_bitpacking.py::test_CPU[0-2] PASSED 2025-09-09T14:07:01.4529258Z test/dtypes/test_bitpacking.py::test_CPU[0-3] PASSED 2025-09-09T14:07:01.4530530Z test/dtypes/test_bitpacking.py::test_CPU[0-4] PASSED 2025-09-09T14:07:01.4531518Z test/dtypes/test_bitpacking.py::test_CPU[0-5] PASSED 2025-09-09T14:07:01.4532504Z test/dtypes/test_bitpacking.py::test_CPU[0-6] PASSED 2025-09-09T14:07:01.4533474Z test/dtypes/test_bitpacking.py::test_CPU[0-7] PASSED 2025-09-09T14:07:01.4534463Z test/dtypes/test_bitpacking.py::test_CPU[-1-1] PASSED 2025-09-09T14:07:01.4535250Z test/dtypes/test_bitpacking.py::test_CPU[-1-2] PASSED 2025-09-09T14:07:01.4535806Z test/dtypes/test_bitpacking.py::test_CPU[-1-3] PASSED 2025-09-09T14:07:01.4536341Z test/dtypes/test_bitpacking.py::test_CPU[-1-4] PASSED 2025-09-09T14:07:01.4536888Z test/dtypes/test_bitpacking.py::test_CPU[-1-5] PASSED 2025-09-09T14:07:01.4537421Z test/dtypes/test_bitpacking.py::test_CPU[-1-6] PASSED 2025-09-09T14:07:01.4537963Z test/dtypes/test_bitpacking.py::test_CPU[-1-7] PASSED 2025-09-09T14:07:01.4538539Z test/dtypes/test_bitpacking.py::test_CPU[1-1] PASSED 2025-09-09T14:07:01.4539066Z test/dtypes/test_bitpacking.py::test_CPU[1-2] PASSED 2025-09-09T14:07:01.4539605Z test/dtypes/test_bitpacking.py::test_CPU[1-3] PASSED 2025-09-09T14:07:01.4540129Z test/dtypes/test_bitpacking.py::test_CPU[1-4] PASSED 2025-09-09T14:07:01.4540662Z test/dtypes/test_bitpacking.py::test_CPU[1-5] PASSED 2025-09-09T14:07:01.4541191Z test/dtypes/test_bitpacking.py::test_CPU[1-6] PASSED 2025-09-09T14:07:01.4541733Z test/dtypes/test_bitpacking.py::test_CPU[1-7] PASSED 2025-09-09T14:07:01.4542365Z test/dtypes/test_bitpacking.py::test_GPU[0-1] SKIPPED (CUDA not avai...) 2025-09-09T14:07:01.4543068Z test/dtypes/test_bitpacking.py::test_GPU[0-2] SKIPPED (CUDA not avai...) 2025-09-09T14:07:01.4543771Z test/dtypes/test_bitpacking.py::test_GPU[0-3] SKIPPED (CUDA not avai...) 2025-09-09T14:07:01.4544463Z test/dtypes/test_bitpacking.py::test_GPU[0-4] SKIPPED (CUDA not avai...) 2025-09-09T14:07:01.4545169Z test/dtypes/test_bitpacking.py::test_GPU[0-5] SKIPPED (CUDA not avai...) 2025-09-09T14:07:01.4545851Z test/dtypes/test_bitpacking.py::test_GPU[0-6] SKIPPED (CUDA not avai...) 2025-09-09T14:07:01.4546547Z test/dtypes/test_bitpacking.py::test_GPU[0-7] SKIPPED (CUDA not avai...) 2025-09-09T14:07:01.4547241Z test/dtypes/test_bitpacking.py::test_GPU[-1-1] SKIPPED (CUDA not ava...) 2025-09-09T14:07:01.4547927Z test/dtypes/test_bitpacking.py::test_GPU[-1-2] SKIPPED (CUDA not ava...) 2025-09-09T14:07:01.4548618Z test/dtypes/test_bitpacking.py::test_GPU[-1-3] SKIPPED (CUDA not ava...) 2025-09-09T14:07:01.4549295Z test/dtypes/test_bitpacking.py::test_GPU[-1-4] SKIPPED (CUDA not ava...) 2025-09-09T14:07:01.4549985Z test/dtypes/test_bitpacking.py::test_GPU[-1-5] SKIPPED (CUDA not ava...) 2025-09-09T14:07:01.4550663Z test/dtypes/test_bitpacking.py::test_GPU[-1-6] SKIPPED (CUDA not ava...) 2025-09-09T14:07:01.4551359Z test/dtypes/test_bitpacking.py::test_GPU[-1-7] SKIPPED (CUDA not ava...) 2025-09-09T14:07:01.4552050Z test/dtypes/test_bitpacking.py::test_GPU[1-1] SKIPPED (CUDA not avai...) 2025-09-09T14:07:01.4552730Z test/dtypes/test_bitpacking.py::test_GPU[1-2] SKIPPED (CUDA not avai...) 2025-09-09T14:07:01.4553423Z test/dtypes/test_bitpacking.py::test_GPU[1-3] SKIPPED (CUDA not avai...) 2025-09-09T14:07:01.4554309Z test/dtypes/test_bitpacking.py::test_GPU[1-4] SKIPPED (CUDA not avai...) 2025-09-09T14:07:01.4555017Z test/dtypes/test_bitpacking.py::test_GPU[1-5] SKIPPED (CUDA not avai...) 2025-09-09T14:07:01.4555718Z test/dtypes/test_bitpacking.py::test_GPU[1-6] SKIPPED (CUDA not avai...) 2025-09-09T14:07:01.4556558Z test/dtypes/test_bitpacking.py::test_GPU[1-7] SKIPPED (CUDA not avai...) 2025-09-09T14:07:01.4557272Z test/dtypes/test_bitpacking.py::test_compile[0-1] SKIPPED (unsupport...) 2025-09-09T14:07:01.4558052Z test/dtypes/test_bitpacking.py::test_compile[0-2] SKIPPED (unsupport...) 2025-09-09T14:07:01.4558758Z test/dtypes/test_bitpacking.py::test_compile[0-3] SKIPPED (unsupport...) 2025-09-09T14:07:01.4559451Z test/dtypes/test_bitpacking.py::test_compile[0-4] SKIPPED (unsupport...) 2025-09-09T14:07:01.4560160Z test/dtypes/test_bitpacking.py::test_compile[0-5] SKIPPED (unsupport...) 2025-09-09T14:07:01.4560874Z test/dtypes/test_bitpacking.py::test_compile[0-6] SKIPPED (unsupport...) 2025-09-09T14:07:01.4561570Z test/dtypes/test_bitpacking.py::test_compile[0-7] SKIPPED (unsupport...) 2025-09-09T14:07:01.4562270Z test/dtypes/test_bitpacking.py::test_compile[-1-1] SKIPPED (unsuppor...) 2025-09-09T14:07:01.4562958Z test/dtypes/test_bitpacking.py::test_compile[-1-2] SKIPPED (unsuppor...) 2025-09-09T14:07:01.4563657Z test/dtypes/test_bitpacking.py::test_compile[-1-3] SKIPPED (unsuppor...) 2025-09-09T14:07:01.4564358Z test/dtypes/test_bitpacking.py::test_compile[-1-4] SKIPPED (unsuppor...) 2025-09-09T14:07:01.4565041Z test/dtypes/test_bitpacking.py::test_compile[-1-5] SKIPPED (unsuppor...) 2025-09-09T14:07:01.4565741Z test/dtypes/test_bitpacking.py::test_compile[-1-6] SKIPPED (unsuppor...) 2025-09-09T14:07:01.4566425Z test/dtypes/test_bitpacking.py::test_compile[-1-7] SKIPPED (unsuppor...) 2025-09-09T14:07:01.4567128Z test/dtypes/test_bitpacking.py::test_compile[1-1] SKIPPED (unsupport...) 2025-09-09T14:07:01.4567815Z test/dtypes/test_bitpacking.py::test_compile[1-2] SKIPPED (unsupport...) 2025-09-09T14:07:01.4568516Z test/dtypes/test_bitpacking.py::test_compile[1-3] SKIPPED (unsupport...) 2025-09-09T14:07:01.4569221Z test/dtypes/test_bitpacking.py::test_compile[1-4] SKIPPED (unsupport...) 2025-09-09T14:07:01.4569909Z test/dtypes/test_bitpacking.py::test_compile[1-5] SKIPPED (unsupport...) 2025-09-09T14:07:01.4570611Z test/dtypes/test_bitpacking.py::test_compile[1-6] SKIPPED (unsupport...) 2025-09-09T14:07:01.4571302Z test/dtypes/test_bitpacking.py::test_compile[1-7] SKIPPED (unsupport...) 2025-09-09T14:07:01.4572000Z test/dtypes/test_bitpacking.py::test_pack_example SKIPPED (CUDA not ...) 2025-09-09T14:07:01.4572841Z test/dtypes/test_bitpacking.py::test_pack_example_CPU tensor([ 0, 105, 151, 37], dtype=torch.uint8) tensor([ 39, 146], dtype=torch.uint8) 2025-09-09T14:07:01.4573499Z PASSED 2025-09-09T14:07:01.4574279Z test/dtypes/test_floatx.py::TestFloatxTensorCoreAQTTensorImpl::test_fpx_weight_only_ebits_2_mbits_2_bias_False_bfloat16 SKIPPED 2025-09-09T14:07:01.4575519Z test/dtypes/test_floatx.py::TestFloatxTensorCoreAQTTensorImpl::test_fpx_weight_only_ebits_2_mbits_2_bias_False_float16 SKIPPED 2025-09-09T14:07:01.4576745Z test/dtypes/test_floatx.py::TestFloatxTensorCoreAQTTensorImpl::test_fpx_weight_only_ebits_2_mbits_2_bias_True_bfloat16 SKIPPED 2025-09-09T14:07:01.4577975Z test/dtypes/test_floatx.py::TestFloatxTensorCoreAQTTensorImpl::test_fpx_weight_only_ebits_2_mbits_2_bias_True_float16 SKIPPED 2025-09-09T14:07:01.4579190Z test/dtypes/test_floatx.py::TestFloatxTensorCoreAQTTensorImpl::test_fpx_weight_only_ebits_3_mbits_2_bias_False_bfloat16 SKIPPED 2025-09-09T14:07:01.4580426Z test/dtypes/test_floatx.py::TestFloatxTensorCoreAQTTensorImpl::test_fpx_weight_only_ebits_3_mbits_2_bias_False_float16 SKIPPED 2025-09-09T14:07:01.4581718Z test/dtypes/test_floatx.py::TestFloatxTensorCoreAQTTensorImpl::test_fpx_weight_only_ebits_3_mbits_2_bias_True_bfloat16 SKIPPED 2025-09-09T14:07:01.4582949Z test/dtypes/test_floatx.py::TestFloatxTensorCoreAQTTensorImpl::test_fpx_weight_only_ebits_3_mbits_2_bias_True_float16 SKIPPED 2025-09-09T14:07:01.4584203Z test/dtypes/test_floatx.py::TestFloatxTensorCoreAQTTensorImpl::test_from_scaled_tc_floatx_compile_ebits_2_mbits_2_device_cpu PASSED 2025-09-09T14:07:01.4585537Z test/dtypes/test_floatx.py::TestFloatxTensorCoreAQTTensorImpl::test_from_scaled_tc_floatx_compile_ebits_3_mbits_2_device_cpu PASSED 2025-09-09T14:07:01.4586808Z test/dtypes/test_floatx.py::TestFloatxTensorCoreAQTTensorImpl::test_from_tc_floatx_correctness_ebits_2_mbits_2_device_cpu PASSED 2025-09-09T14:07:01.4588049Z test/dtypes/test_floatx.py::TestFloatxTensorCoreAQTTensorImpl::test_from_tc_floatx_correctness_ebits_3_mbits_2_device_cpu PASSED 2025-09-09T14:07:01.4589227Z test/dtypes/test_floatx.py::TestFloatxTensorCoreAQTTensorImpl::test_pack_tc_fp6_correctness_device_cpu PASSED 2025-09-09T14:07:01.4590314Z test/dtypes/test_floatx.py::TestFloatxTensorCoreAQTTensorImpl::test_to_copy_device_ebits_2_mbits_2 SKIPPED 2025-09-09T14:07:01.4591372Z test/dtypes/test_floatx.py::TestFloatxTensorCoreAQTTensorImpl::test_to_copy_device_ebits_3_mbits_2 SKIPPED 2025-09-09T14:07:01.4592548Z test/dtypes/test_floatx.py::TestFloatxTensorCoreAQTTensorImpl::test_to_scaled_tc_floatx_compile_ebits_2_mbits_2_device_cpu PASSED 2025-09-09T14:07:01.4593810Z test/dtypes/test_floatx.py::TestFloatxTensorCoreAQTTensorImpl::test_to_scaled_tc_floatx_compile_ebits_3_mbits_2_device_cpu PASSED 2025-09-09T14:07:01.4594799Z test/dtypes/test_nf4.py::TestNF4Linear::test_backward_dtype_match_bfloat16 PASSED 2025-09-09T14:07:02.6901194Z test/dtypes/test_nf4.py::TestNF4Linear::test_backward_dtype_match_float16 PASSED 2025-09-09T14:07:02.6902297Z test/dtypes/test_nf4.py::TestNF4Linear::test_backward_dtype_match_float32 PASSED 2025-09-09T14:07:02.6903496Z test/dtypes/test_nf4.py::TestNF4Linear::test_chunk_size_equivalence_bfloat16_shape0_chunk_size_16 SKIPPED 2025-09-09T14:07:02.6904815Z test/dtypes/test_nf4.py::TestNF4Linear::test_chunk_size_equivalence_bfloat16_shape0_chunk_size_32 SKIPPED 2025-09-09T14:07:02.6906136Z test/dtypes/test_nf4.py::TestNF4Linear::test_chunk_size_equivalence_bfloat16_shape0_chunk_size_8 SKIPPED 2025-09-09T14:07:02.6907464Z test/dtypes/test_nf4.py::TestNF4Linear::test_chunk_size_equivalence_bfloat16_shape1_chunk_size_16 SKIPPED 2025-09-09T14:07:02.6908777Z test/dtypes/test_nf4.py::TestNF4Linear::test_chunk_size_equivalence_bfloat16_shape1_chunk_size_32 SKIPPED 2025-09-09T14:07:02.6910095Z test/dtypes/test_nf4.py::TestNF4Linear::test_chunk_size_equivalence_bfloat16_shape1_chunk_size_8 SKIPPED 2025-09-09T14:07:02.6911410Z test/dtypes/test_nf4.py::TestNF4Linear::test_chunk_size_equivalence_float16_shape0_chunk_size_16 SKIPPED 2025-09-09T14:07:02.6912765Z test/dtypes/test_nf4.py::TestNF4Linear::test_chunk_size_equivalence_float16_shape0_chunk_size_32 SKIPPED 2025-09-09T14:07:02.6914070Z test/dtypes/test_nf4.py::TestNF4Linear::test_chunk_size_equivalence_float16_shape0_chunk_size_8 SKIPPED 2025-09-09T14:07:02.6915382Z test/dtypes/test_nf4.py::TestNF4Linear::test_chunk_size_equivalence_float16_shape1_chunk_size_16 SKIPPED 2025-09-09T14:07:02.6916751Z test/dtypes/test_nf4.py::TestNF4Linear::test_chunk_size_equivalence_float16_shape1_chunk_size_32 SKIPPED 2025-09-09T14:07:02.6918051Z test/dtypes/test_nf4.py::TestNF4Linear::test_chunk_size_equivalence_float16_shape1_chunk_size_8 SKIPPED 2025-09-09T14:07:02.6919340Z test/dtypes/test_nf4.py::TestNF4Linear::test_chunk_size_equivalence_float32_shape0_chunk_size_16 SKIPPED 2025-09-09T14:07:02.6920649Z test/dtypes/test_nf4.py::TestNF4Linear::test_chunk_size_equivalence_float32_shape0_chunk_size_32 SKIPPED 2025-09-09T14:07:02.6922248Z test/dtypes/test_nf4.py::TestNF4Linear::test_chunk_size_equivalence_float32_shape0_chunk_size_8 SKIPPED 2025-09-09T14:07:02.6923708Z test/dtypes/test_nf4.py::TestNF4Linear::test_chunk_size_equivalence_float32_shape1_chunk_size_16 SKIPPED 2025-09-09T14:07:02.6925303Z test/dtypes/test_nf4.py::TestNF4Linear::test_chunk_size_equivalence_float32_shape1_chunk_size_32 SKIPPED 2025-09-09T14:07:02.6926600Z test/dtypes/test_nf4.py::TestNF4Linear::test_chunk_size_equivalence_float32_shape1_chunk_size_8 SKIPPED 2025-09-09T14:07:02.6927882Z test/dtypes/test_nf4.py::TestNF4Linear::test_empty_like_input_size0 SKIPPED 2025-09-09T14:07:02.6928856Z test/dtypes/test_nf4.py::TestNF4Linear::test_empty_like_input_size1 SKIPPED 2025-09-09T14:07:02.6929870Z test/dtypes/test_nf4.py::TestNF4Linear::test_load_from_nf4_diff_meta_bfloat16 PASSED 2025-09-09T14:07:02.6930944Z test/dtypes/test_nf4.py::TestNF4Linear::test_load_from_nf4_diff_meta_float16 PASSED 2025-09-09T14:07:02.6931985Z test/dtypes/test_nf4.py::TestNF4Linear::test_load_from_nf4_diff_meta_float32 PASSED 2025-09-09T14:07:02.6933052Z test/dtypes/test_nf4.py::TestNF4Linear::test_load_from_nf4_same_meta_bfloat16 PASSED 2025-09-09T14:07:02.6934113Z test/dtypes/test_nf4.py::TestNF4Linear::test_load_from_nf4_same_meta_float16 PASSED 2025-09-09T14:07:02.6935143Z test/dtypes/test_nf4.py::TestNF4Linear::test_load_from_nf4_same_meta_float32 PASSED 2025-09-09T14:07:02.6936201Z test/dtypes/test_nf4.py::TestNF4Linear::test_load_from_state_dicts_bfloat16 SKIPPED 2025-09-09T14:07:02.6937242Z test/dtypes/test_nf4.py::TestNF4Linear::test_load_from_state_dicts_float16 SKIPPED 2025-09-09T14:07:02.6938297Z test/dtypes/test_nf4.py::TestNF4Linear::test_load_from_state_dicts_float32 SKIPPED 2025-09-09T14:07:02.6939306Z test/dtypes/test_nf4.py::TestNF4Linear::test_nf4_bnb_linear_bfloat16 SKIPPED 2025-09-09T14:07:02.6940270Z test/dtypes/test_nf4.py::TestNF4Linear::test_nf4_bnb_linear_float16 SKIPPED 2025-09-09T14:07:02.6941238Z test/dtypes/test_nf4.py::TestNF4Linear::test_nf4_bnb_linear_float32 SKIPPED 2025-09-09T14:07:02.6942219Z test/dtypes/test_nf4.py::TestNF4Linear::test_output_dtype_match_bfloat16 PASSED 2025-09-09T14:07:02.6943225Z test/dtypes/test_nf4.py::TestNF4Linear::test_output_dtype_match_float16 PASSED 2025-09-09T14:07:02.6944215Z test/dtypes/test_nf4.py::TestNF4Linear::test_output_dtype_match_float32 PASSED 2025-09-09T14:07:02.6945228Z test/dtypes/test_nf4.py::TestNF4Linear::test_quantize_api_compile_False SKIPPED 2025-09-09T14:07:02.6946239Z test/dtypes/test_nf4.py::TestNF4Linear::test_quantize_api_compile_True SKIPPED 2025-09-09T14:07:02.6947316Z test/dtypes/test_nf4.py::TestNF4Linear::test_reconstruction_qlora_vs_bnb_bfloat16 SKIPPED 2025-09-09T14:07:02.6948466Z test/dtypes/test_nf4.py::TestNF4Linear::test_reconstruction_qlora_vs_bnb_float16 SKIPPED 2025-09-09T14:07:02.6949581Z test/dtypes/test_nf4.py::TestNF4Linear::test_reconstruction_qlora_vs_bnb_float32 SKIPPED 2025-09-09T14:07:02.6950690Z test/dtypes/test_nf4.py::TestNF4Linear::test_register_nf4_as_param_bfloat16 PASSED 2025-09-09T14:07:02.6951725Z test/dtypes/test_nf4.py::TestNF4Linear::test_register_nf4_as_param_float16 PASSED 2025-09-09T14:07:02.6952740Z test/dtypes/test_nf4.py::TestNF4Linear::test_register_nf4_as_param_float32 PASSED 2025-09-09T14:07:02.6953934Z test/dtypes/test_nf4.py::TestNF4Linear::test_smoketest_linear_bfloat16 SKIPPED 2025-09-09T14:07:02.6955042Z test/dtypes/test_nf4.py::TestNF4Linear::test_smoketest_linear_compile_bfloat16 SKIPPED 2025-09-09T14:07:02.6956207Z test/dtypes/test_nf4.py::TestNF4Linear::test_smoketest_linear_compile_float16 SKIPPED 2025-09-09T14:07:02.6957306Z test/dtypes/test_nf4.py::TestNF4Linear::test_smoketest_linear_compile_float32 SKIPPED 2025-09-09T14:07:02.6958507Z test/dtypes/test_nf4.py::TestNF4Linear::test_smoketest_linear_float16 SKIPPED 2025-09-09T14:07:02.6959504Z test/dtypes/test_nf4.py::TestNF4Linear::test_smoketest_linear_float32 SKIPPED 2025-09-09T14:07:02.6960430Z test/dtypes/test_nf4.py::TestNF4Linear::test_to_copy_bfloat16 PASSED 2025-09-09T14:07:02.6961316Z test/dtypes/test_nf4.py::TestNF4Linear::test_to_copy_device SKIPPED 2025-09-09T14:07:02.6962172Z test/dtypes/test_nf4.py::TestNF4Linear::test_to_copy_float16 PASSED 2025-09-09T14:07:02.6963130Z test/dtypes/test_nf4.py::TestNF4Linear::test_to_copy_float32 PASSED 2025-09-09T14:07:02.6964013Z test/dtypes/test_nf4.py::TestNF4Linear::test_to_dtype_bfloat16 PASSED 2025-09-09T14:07:02.6964887Z test/dtypes/test_nf4.py::TestNF4Linear::test_to_dtype_float16 PASSED 2025-09-09T14:07:02.6965767Z test/dtypes/test_nf4.py::TestNF4Linear::test_to_dtype_float32 PASSED 2025-09-09T14:07:02.6966668Z test/dtypes/test_nf4.py::TestFSDPOps::test_pin_memory SKIPPED (Need ...) 2025-09-09T14:07:02.6967663Z test/dtypes/test_nf4.py::TestFSDPOps::test_tensor_2d_view_valid_input_size0 PASSED 2025-09-09T14:07:02.6968745Z test/dtypes/test_nf4.py::TestFSDPOps::test_tensor_as_strided_invalid_input_size0 PASSED 2025-09-09T14:07:02.6969836Z test/dtypes/test_nf4.py::TestFSDPOps::test_tensor_as_strided_invalid_input_size1 PASSED 2025-09-09T14:07:02.6970924Z test/dtypes/test_nf4.py::TestFSDPOps::test_tensor_as_strided_valid_input_size1 PASSED 2025-09-09T14:07:02.6971995Z test/dtypes/test_nf4.py::TestFSDPOps::test_tensor_as_strided_valid_input_size2 PASSED 2025-09-09T14:07:02.6973114Z test/dtypes/test_nf4.py::TestFSDPOps::test_tensor_as_strided_valid_input_size_262144 PASSED 2025-09-09T14:07:02.6974170Z test/dtypes/test_nf4.py::TestFSDPOps::test_tensor_deepcopy_input_size1 SKIPPED 2025-09-09T14:07:02.6975175Z test/dtypes/test_nf4.py::TestFSDPOps::test_tensor_deepcopy_input_size2 SKIPPED 2025-09-09T14:07:02.6976217Z test/dtypes/test_nf4.py::TestFSDPOps::test_tensor_deepcopy_input_size_262144 SKIPPED 2025-09-09T14:07:02.6987973Z test/dtypes/test_nf4.py::TestFSDPOps::test_tensor_new_zeros_invalid_input_size1 PASSED 2025-09-09T14:07:02.6990563Z test/dtypes/test_nf4.py::TestFSDPOps::test_tensor_new_zeros_invalid_input_size2 PASSED 2025-09-09T14:07:02.6992112Z test/dtypes/test_nf4.py::TestFSDPOps::test_tensor_new_zeros_invalid_input_size_262144 PASSED 2025-09-09T14:07:02.6993718Z test/dtypes/test_nf4.py::TestFSDPOps::test_tensor_new_zeros_valid_input_size1 PASSED 2025-09-09T14:07:02.6995266Z test/dtypes/test_nf4.py::TestFSDPOps::test_tensor_new_zeros_valid_input_size2 PASSED 2025-09-09T14:07:02.6996858Z test/dtypes/test_nf4.py::TestFSDPOps::test_tensor_new_zeros_valid_input_size_262144 PASSED 2025-09-09T14:07:02.6998398Z test/dtypes/test_nf4.py::TestFSDPOps::test_tensor_slice_1d_invalid PASSED 2025-09-09T14:07:02.6999821Z test/dtypes/test_nf4.py::TestFSDPOps::test_tensor_slice_2d_invalid PASSED 2025-09-09T14:07:02.7001274Z test/dtypes/test_nf4.py::TestFSDPOps::test_tensor_slice_valid_input_size1 PASSED 2025-09-09T14:07:02.7002807Z test/dtypes/test_nf4.py::TestFSDPOps::test_tensor_slice_valid_input_size2 PASSED 2025-09-09T14:07:02.7004386Z test/dtypes/test_nf4.py::TestFSDPOps::test_tensor_slice_valid_input_size_262144 PASSED 2025-09-09T14:07:02.7005946Z test/dtypes/test_nf4.py::TestFSDPOps::test_tensor_view_invalid_input_size0 PASSED 2025-09-09T14:07:02.7007479Z test/dtypes/test_nf4.py::TestFSDPOps::test_tensor_view_valid_input_size0 PASSED 2025-09-09T14:07:02.7008967Z test/dtypes/test_nf4.py::TestFSDPOps::test_tensor_view_valid_input_size1 PASSED 2025-09-09T14:07:02.7010402Z test/dtypes/test_nf4.py::TestFSDPOps::test_to_cpu SKIPPED (Need CUDA...) 2025-09-09T14:07:02.7011772Z test/dtypes/test_nf4.py::TestFSDPOps::test_to_cuda SKIPPED (Need CUD...) 2025-09-09T14:07:02.7013391Z test/dtypes/test_nf4.py::TestFSDPOps::test_to_module SKIPPED (Need C...) 2025-09-09T14:07:02.7014889Z test/dtypes/test_nf4.py::TestFSDPOps::test_torch_chunk_invalid_3d_input_size0 PASSED 2025-09-09T14:07:10.8244792Z test/dtypes/test_nf4.py::TestFSDPOps::test_torch_chunk_invalid_divide_input_size1 PASSED 2025-09-09T14:07:10.8245671Z test/dtypes/test_nf4.py::TestFSDPOps::test_torch_chunk_invalid_divide_input_size2 PASSED 2025-09-09T14:07:10.8246845Z test/dtypes/test_nf4.py::TestFSDPOps::test_torch_chunk_invalid_divide_input_size_261632 PASSED 2025-09-09T14:07:10.8247649Z test/dtypes/test_nf4.py::TestFSDPOps::test_torch_chunk_valid_input_size1 PASSED 2025-09-09T14:07:10.8248409Z test/dtypes/test_nf4.py::TestFSDPOps::test_torch_chunk_valid_input_size2 PASSED 2025-09-09T14:07:10.8249215Z test/dtypes/test_nf4.py::TestFSDPOps::test_torch_chunk_valid_input_size_262144 PASSED 2025-09-09T14:07:10.8250251Z test/dtypes/test_nf4.py::TestQLoRA::test_qlora_fsdp2 I0909 14:07:02.737191 343 site-packages/torch/testing/_internal/common_distributed.py:729] Started process 0 with pid 528 2025-09-09T14:07:10.8251360Z I0909 14:07:02.747656 343 site-packages/torch/testing/_internal/common_distributed.py:729] Started process 1 with pid 529 2025-09-09T14:07:10.8252131Z The 8-bit optimizer is not available on your device, only available on CUDA for now. 2025-09-09T14:07:10.8252739Z The 8-bit optimizer is not available on your device, only available on CUDA for now. 2025-09-09T14:07:10.8253204Z dist init r=0, world=2 2025-09-09T14:07:10.8253439Z dist init r=1, world=2 2025-09-09T14:07:10.8253863Z [Gloo] Rank 1 is connected to 1 peer ranks. Expected number of connected peer ranks is : 1 2025-09-09T14:07:10.8254499Z [Gloo] Rank 0 is connected to 1 peer ranks. Expected number of connected peer ranks is : 1 2025-09-09T14:07:10.8255008Z SKIPPED (Need a...) 2025-09-09T14:07:10.8255771Z test/dtypes/test_nf4.py::TestComm::test_comm I0909 14:07:06.768467 343 site-packages/torch/testing/_internal/common_distributed.py:729] Started process 0 with pid 576 2025-09-09T14:07:10.8256832Z I0909 14:07:06.778197 343 site-packages/torch/testing/_internal/common_distributed.py:729] Started process 1 with pid 577 2025-09-09T14:07:10.8257604Z The 8-bit optimizer is not available on your device, only available on CUDA for now. 2025-09-09T14:07:10.8258224Z The 8-bit optimizer is not available on your device, only available on CUDA for now. 2025-09-09T14:07:10.8258675Z dist init r=0, world=2 2025-09-09T14:07:10.8258923Z dist init r=1, world=2 2025-09-09T14:07:10.8259327Z [Gloo] Rank 1 is connected to 1 peer ranks. Expected number of connected peer ranks is : 1 2025-09-09T14:07:10.8259965Z [Gloo] Rank 0 is connected to 1 peer ranks. Expected number of connected peer ranks is : 1 2025-09-09T14:07:10.8260488Z SKIPPED (Need at least ...) 2025-09-09T14:07:10.8261008Z test/dtypes/test_uint4.py::TestUInt4::test_basic_tensor_ops SKIPPED 2025-09-09T14:07:10.8261701Z test/dtypes/test_uint4.py::TestUInt4::test_gpu_quant SKIPPED (FAILED...) 2025-09-09T14:07:10.8262383Z test/dtypes/test_uint4.py::TestUInt4::test_pt2e_quant SKIPPED (FAILE...) 2025-09-09T14:07:10.8263143Z test/dtypes/test_uintx.py::test_uintx_quant_on_cpu_then_move_to_cuda[32-dtype0] SKIPPED 2025-09-09T14:07:10.8263943Z test/dtypes/test_uintx.py::test_uintx_quant_on_cpu_then_move_to_cuda[32-dtype1] SKIPPED 2025-09-09T14:07:10.8264747Z test/dtypes/test_uintx.py::test_uintx_quant_on_cpu_then_move_to_cuda[32-dtype2] SKIPPED 2025-09-09T14:07:10.8265551Z test/dtypes/test_uintx.py::test_uintx_quant_on_cpu_then_move_to_cuda[32-dtype3] SKIPPED 2025-09-09T14:07:10.8266337Z test/dtypes/test_uintx.py::test_uintx_quant_on_cpu_then_move_to_cuda[32-dtype4] SKIPPED 2025-09-09T14:07:10.8267138Z test/dtypes/test_uintx.py::test_uintx_quant_on_cpu_then_move_to_cuda[32-dtype5] SKIPPED 2025-09-09T14:07:10.8268043Z test/dtypes/test_uintx.py::test_uintx_quant_on_cpu_then_move_to_cuda[32-dtype6] SKIPPED 2025-09-09T14:07:10.8268857Z test/dtypes/test_uintx.py::test_uintx_quant_on_cpu_then_move_to_cuda[64-dtype0] SKIPPED 2025-09-09T14:07:10.8269653Z test/dtypes/test_uintx.py::test_uintx_quant_on_cpu_then_move_to_cuda[64-dtype1] SKIPPED 2025-09-09T14:07:10.8270456Z test/dtypes/test_uintx.py::test_uintx_quant_on_cpu_then_move_to_cuda[64-dtype2] SKIPPED 2025-09-09T14:07:10.8271260Z test/dtypes/test_uintx.py::test_uintx_quant_on_cpu_then_move_to_cuda[64-dtype3] SKIPPED 2025-09-09T14:07:10.8272112Z test/dtypes/test_uintx.py::test_uintx_quant_on_cpu_then_move_to_cuda[64-dtype4] SKIPPED 2025-09-09T14:07:10.8272912Z test/dtypes/test_uintx.py::test_uintx_quant_on_cpu_then_move_to_cuda[64-dtype5] SKIPPED 2025-09-09T14:07:10.8273700Z test/dtypes/test_uintx.py::test_uintx_quant_on_cpu_then_move_to_cuda[64-dtype6] SKIPPED 2025-09-09T14:07:10.8274512Z test/dtypes/test_uintx.py::test_uintx_quant_on_cpu_then_move_to_cuda[128-dtype0] SKIPPED 2025-09-09T14:07:10.8275322Z test/dtypes/test_uintx.py::test_uintx_quant_on_cpu_then_move_to_cuda[128-dtype1] SKIPPED 2025-09-09T14:07:10.8276209Z test/dtypes/test_uintx.py::test_uintx_quant_on_cpu_then_move_to_cuda[128-dtype2] SKIPPED 2025-09-09T14:07:10.8277026Z test/dtypes/test_uintx.py::test_uintx_quant_on_cpu_then_move_to_cuda[128-dtype3] SKIPPED 2025-09-09T14:07:10.8277820Z test/dtypes/test_uintx.py::test_uintx_quant_on_cpu_then_move_to_cuda[128-dtype4] SKIPPED 2025-09-09T14:07:10.8278635Z test/dtypes/test_uintx.py::test_uintx_quant_on_cpu_then_move_to_cuda[128-dtype5] SKIPPED 2025-09-09T14:07:10.8279445Z test/dtypes/test_uintx.py::test_uintx_quant_on_cpu_then_move_to_cuda[128-dtype6] SKIPPED 2025-09-09T14:07:10.8280237Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cpu-32-dtype0] SKIPPED 2025-09-09T14:07:10.8281038Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cpu-32-dtype1] SKIPPED 2025-09-09T14:07:10.8281820Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cpu-32-dtype2] SKIPPED 2025-09-09T14:07:10.8282612Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cpu-32-dtype3] SKIPPED 2025-09-09T14:07:10.8283401Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cpu-32-dtype4] SKIPPED 2025-09-09T14:07:10.8284192Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cpu-32-dtype5] SKIPPED 2025-09-09T14:07:10.8284986Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cpu-32-dtype6] SKIPPED 2025-09-09T14:07:10.8285759Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cpu-64-dtype0] SKIPPED 2025-09-09T14:07:10.8286545Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cpu-64-dtype1] SKIPPED 2025-09-09T14:07:10.8287320Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cpu-64-dtype2] SKIPPED 2025-09-09T14:07:10.8288110Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cpu-64-dtype3] SKIPPED 2025-09-09T14:07:10.8288884Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cpu-64-dtype4] SKIPPED 2025-09-09T14:07:10.8289673Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cpu-64-dtype5] SKIPPED 2025-09-09T14:07:10.8290460Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cpu-64-dtype6] SKIPPED 2025-09-09T14:07:10.8291248Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cpu-128-dtype0] SKIPPED 2025-09-09T14:07:10.8292047Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cpu-128-dtype1] SKIPPED 2025-09-09T14:07:10.8292829Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cpu-128-dtype2] SKIPPED 2025-09-09T14:07:10.8293624Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cpu-128-dtype3] SKIPPED 2025-09-09T14:07:10.8294506Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cpu-128-dtype4] SKIPPED 2025-09-09T14:07:10.8295295Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cpu-128-dtype5] SKIPPED 2025-09-09T14:07:10.8296094Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cpu-128-dtype6] SKIPPED 2025-09-09T14:07:10.8296877Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cuda-32-dtype0] SKIPPED 2025-09-09T14:07:10.8297673Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cuda-32-dtype1] SKIPPED 2025-09-09T14:07:10.8298536Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cuda-32-dtype2] SKIPPED 2025-09-09T14:07:10.8299320Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cuda-32-dtype3] SKIPPED 2025-09-09T14:07:10.8300122Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cuda-32-dtype4] SKIPPED 2025-09-09T14:07:10.8300913Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cuda-32-dtype5] SKIPPED 2025-09-09T14:07:10.8301714Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cuda-32-dtype6] SKIPPED 2025-09-09T14:07:10.8302511Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cuda-64-dtype0] SKIPPED 2025-09-09T14:07:10.8303296Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cuda-64-dtype1] SKIPPED 2025-09-09T14:07:10.8304091Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cuda-64-dtype2] SKIPPED 2025-09-09T14:07:10.8304880Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cuda-64-dtype3] SKIPPED 2025-09-09T14:07:10.8305674Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cuda-64-dtype4] SKIPPED 2025-09-09T14:07:10.8306458Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cuda-64-dtype5] SKIPPED 2025-09-09T14:07:10.8307260Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cuda-64-dtype6] SKIPPED 2025-09-09T14:07:10.8308062Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cuda-128-dtype0] SKIPPED 2025-09-09T14:07:10.8308862Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cuda-128-dtype1] SKIPPED 2025-09-09T14:07:10.8309671Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cuda-128-dtype2] SKIPPED 2025-09-09T14:07:10.8310466Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cuda-128-dtype3] SKIPPED 2025-09-09T14:07:10.8311277Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cuda-128-dtype4] SKIPPED 2025-09-09T14:07:11.8963594Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cuda-128-dtype5] SKIPPED 2025-09-09T14:07:11.8964449Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cuda-128-dtype6] SKIPPED 2025-09-09T14:07:11.8965227Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cpu-32-dtype0] SKIPPED 2025-09-09T14:07:11.8965973Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cpu-32-dtype1] SKIPPED 2025-09-09T14:07:11.8966710Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cpu-32-dtype2] SKIPPED 2025-09-09T14:07:11.8967428Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cpu-32-dtype3] SKIPPED 2025-09-09T14:07:11.8968160Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cpu-32-dtype4] SKIPPED 2025-09-09T14:07:11.8968880Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cpu-32-dtype5] SKIPPED 2025-09-09T14:07:11.8969622Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cpu-32-dtype6] SKIPPED 2025-09-09T14:07:11.8970353Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cpu-64-dtype0] SKIPPED 2025-09-09T14:07:11.8971073Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cpu-64-dtype1] SKIPPED 2025-09-09T14:07:11.8971806Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cpu-64-dtype2] SKIPPED 2025-09-09T14:07:11.8972787Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cpu-64-dtype3] SKIPPED 2025-09-09T14:07:11.8973539Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cpu-64-dtype4] SKIPPED 2025-09-09T14:07:11.8974276Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cpu-64-dtype5] SKIPPED 2025-09-09T14:07:11.8974999Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cpu-64-dtype6] SKIPPED 2025-09-09T14:07:11.8975861Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cpu-128-dtype0] SKIPPED 2025-09-09T14:07:11.8976593Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cpu-128-dtype1] SKIPPED 2025-09-09T14:07:11.8977381Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cpu-128-dtype2] SKIPPED 2025-09-09T14:07:11.8978124Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cpu-128-dtype3] SKIPPED 2025-09-09T14:07:11.8978853Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cpu-128-dtype4] SKIPPED 2025-09-09T14:07:11.8979596Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cpu-128-dtype5] SKIPPED 2025-09-09T14:07:11.8980324Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cpu-128-dtype6] SKIPPED 2025-09-09T14:07:11.8981063Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cuda-32-dtype0] SKIPPED 2025-09-09T14:07:11.8981790Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cuda-32-dtype1] SKIPPED 2025-09-09T14:07:11.8982532Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cuda-32-dtype2] SKIPPED 2025-09-09T14:07:11.8983267Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cuda-32-dtype3] SKIPPED 2025-09-09T14:07:11.8983997Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cuda-32-dtype4] SKIPPED 2025-09-09T14:07:11.8984731Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cuda-32-dtype5] SKIPPED 2025-09-09T14:07:11.8985491Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cuda-32-dtype6] SKIPPED 2025-09-09T14:07:11.8986218Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cuda-64-dtype0] SKIPPED 2025-09-09T14:07:11.8986957Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cuda-64-dtype1] SKIPPED 2025-09-09T14:07:11.8987686Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cuda-64-dtype2] SKIPPED 2025-09-09T14:07:11.8988464Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cuda-64-dtype3] SKIPPED 2025-09-09T14:07:11.8989191Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cuda-64-dtype4] SKIPPED 2025-09-09T14:07:11.8989932Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cuda-64-dtype5] SKIPPED 2025-09-09T14:07:11.8990670Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cuda-64-dtype6] SKIPPED 2025-09-09T14:07:11.8991405Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cuda-128-dtype0] SKIPPED 2025-09-09T14:07:11.8992158Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cuda-128-dtype1] SKIPPED 2025-09-09T14:07:11.8992893Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cuda-128-dtype2] SKIPPED 2025-09-09T14:07:11.8993638Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cuda-128-dtype3] SKIPPED 2025-09-09T14:07:11.8994391Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cuda-128-dtype4] SKIPPED 2025-09-09T14:07:11.8995132Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cuda-128-dtype5] SKIPPED 2025-09-09T14:07:11.8995882Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cuda-128-dtype6] SKIPPED 2025-09-09T14:07:11.8996695Z test/dtypes/test_uintx.py::test_uintx_target_dtype[dtype0] SKIPPED (...) 2025-09-09T14:07:11.8997384Z test/dtypes/test_uintx.py::test_uintx_target_dtype[dtype1] SKIPPED (...) 2025-09-09T14:07:11.8998146Z test/dtypes/test_uintx.py::test_uintx_target_dtype[dtype2] SKIPPED (...) 2025-09-09T14:07:11.8998818Z test/dtypes/test_uintx.py::test_uintx_target_dtype[dtype3] SKIPPED (...) 2025-09-09T14:07:11.8999498Z test/dtypes/test_uintx.py::test_uintx_target_dtype[dtype4] SKIPPED (...) 2025-09-09T14:07:11.9000167Z test/dtypes/test_uintx.py::test_uintx_target_dtype[dtype5] SKIPPED (...) 2025-09-09T14:07:11.9000845Z test/dtypes/test_uintx.py::test_uintx_target_dtype[dtype6] SKIPPED (...) 2025-09-09T14:07:11.9001633Z test/dtypes/test_uintx.py::test_uintx_target_dtype_compile[dtype0] SKIPPED 2025-09-09T14:07:11.9002339Z test/dtypes/test_uintx.py::test_uintx_target_dtype_compile[dtype1] SKIPPED 2025-09-09T14:07:11.9003041Z test/dtypes/test_uintx.py::test_uintx_target_dtype_compile[dtype2] SKIPPED 2025-09-09T14:07:11.9003728Z test/dtypes/test_uintx.py::test_uintx_target_dtype_compile[dtype3] SKIPPED 2025-09-09T14:07:11.9004428Z test/dtypes/test_uintx.py::test_uintx_target_dtype_compile[dtype4] SKIPPED 2025-09-09T14:07:11.9005161Z test/dtypes/test_uintx.py::test_uintx_target_dtype_compile[dtype5] SKIPPED 2025-09-09T14:07:11.9005865Z test/dtypes/test_uintx.py::test_uintx_target_dtype_compile[dtype6] SKIPPED 2025-09-09T14:07:11.9006557Z test/dtypes/test_uintx.py::test_uintx_model_size[dtype0] SKIPPED (Ne...) 2025-09-09T14:07:11.9007226Z test/dtypes/test_uintx.py::test_uintx_model_size[dtype1] SKIPPED (Ne...) 2025-09-09T14:07:11.9007907Z test/dtypes/test_uintx.py::test_uintx_model_size[dtype2] SKIPPED (Ne...) 2025-09-09T14:07:11.9008649Z test/dtypes/test_uintx.py::test_uintx_model_size[dtype3] SKIPPED (Ne...) 2025-09-09T14:07:11.9009332Z test/dtypes/test_uintx.py::test_uintx_model_size[dtype4] SKIPPED (Ne...) 2025-09-09T14:07:11.9009998Z test/dtypes/test_uintx.py::test_uintx_model_size[dtype5] SKIPPED (Ne...) 2025-09-09T14:07:11.9010676Z test/dtypes/test_uintx.py::test_uintx_model_size[dtype6] SKIPPED (Ne...) 2025-09-09T14:07:11.9011611Z test/float8/test_auto_filter.py::test_end_to_end_filtering[tensorwise-module_dims0-valid.layer-filter_fqns0-True] PASSED 2025-09-09T14:07:11.9012763Z test/float8/test_auto_filter.py::test_end_to_end_filtering[tensorwise-module_dims1-skip_layer.linear-filter_fqns1-False] PASSED 2025-09-09T14:07:11.9013914Z test/float8/test_auto_filter.py::test_end_to_end_filtering[tensorwise-module_dims2-valid.layer-filter_fqns2-False] PASSED 2025-09-09T14:07:11.9015014Z test/float8/test_auto_filter.py::test_end_to_end_filtering[rowwise-module_dims3-valid.layer-filter_fqns3-True] PASSED 2025-09-09T14:07:11.9016141Z test/float8/test_auto_filter.py::test_end_to_end_filtering[rowwise-module_dims4-skip_layer.linear-filter_fqns4-False] PASSED 2025-09-09T14:07:11.9017259Z test/float8/test_auto_filter.py::test_end_to_end_filtering[rowwise-module_dims5-valid.layer-filter_fqns5-False] PASSED 2025-09-09T14:07:11.9018146Z test/float8/test_auto_filter.py::test_exact_boundary_dimensions_rowwise PASSED 2025-09-09T14:07:11.9018902Z test/float8/test_auto_filter.py::test_exact_boundary_dimensions_tensorwise PASSED 2025-09-09T14:07:11.9019599Z test/float8/test_auto_filter.py::test_partial_fqn_matching PASSED 2025-09-09T14:07:11.9020316Z test/float8/test_base.py::TestFloat8TrainingTensor::test_preserves_dtype PASSED 2025-09-09T14:07:11.9021134Z test/float8/test_base.py::TestFloat8TrainingTensor::test_differentiable_casts PASSED 2025-09-09T14:07:11.9021926Z test/float8/test_base.py::TestFloat8TrainingTensor::test_split_cat PASSED 2025-09-09T14:07:11.9022670Z test/float8/test_base.py::TestFloat8TrainingTensor::test_index_put PASSED 2025-09-09T14:07:11.9023374Z test/float8/test_base.py::TestFloat8TrainingTensor::test_copy_ PASSED 2025-09-09T14:07:11.9024088Z test/float8/test_base.py::TestFloat8TrainingTensor::test_transpose PASSED 2025-09-09T14:07:11.9025398Z test/float8/test_base.py::TestFloat8TrainingTensor::test_axiswise_dynamic_cast[True-0-shape0] PASSED 2025-09-09T14:07:11.9026390Z test/float8/test_base.py::TestFloat8TrainingTensor::test_axiswise_dynamic_cast[True-0-shape1] PASSED 2025-09-09T14:07:11.9027350Z test/float8/test_base.py::TestFloat8TrainingTensor::test_axiswise_dynamic_cast[True-0-shape2] PASSED 2025-09-09T14:07:11.9028329Z test/float8/test_base.py::TestFloat8TrainingTensor::test_axiswise_dynamic_cast[True--1-shape0] PASSED 2025-09-09T14:07:11.9029407Z test/float8/test_base.py::TestFloat8TrainingTensor::test_axiswise_dynamic_cast[True--1-shape1] PASSED 2025-09-09T14:07:11.9030372Z test/float8/test_base.py::TestFloat8TrainingTensor::test_axiswise_dynamic_cast[True--1-shape2] PASSED 2025-09-09T14:07:11.9031352Z test/float8/test_base.py::TestFloat8TrainingTensor::test_axiswise_dynamic_cast[False-0-shape0] PASSED 2025-09-09T14:07:11.9032347Z test/float8/test_base.py::TestFloat8TrainingTensor::test_axiswise_dynamic_cast[False-0-shape1] PASSED 2025-09-09T14:07:11.9444532Z test/float8/test_base.py::TestFloat8TrainingTensor::test_axiswise_dynamic_cast[False-0-shape2] PASSED 2025-09-09T14:07:11.9445586Z test/float8/test_base.py::TestFloat8TrainingTensor::test_axiswise_dynamic_cast[False--1-shape0] PASSED 2025-09-09T14:07:11.9446585Z test/float8/test_base.py::TestFloat8TrainingTensor::test_axiswise_dynamic_cast[False--1-shape1] PASSED 2025-09-09T14:07:11.9447567Z test/float8/test_base.py::TestFloat8TrainingTensor::test_axiswise_dynamic_cast[False--1-shape2] PASSED 2025-09-09T14:07:11.9448480Z test/float8/test_base.py::TestFloat8TrainingTensor::test_axiswise_reshape PASSED 2025-09-09T14:07:11.9449646Z test/float8/test_base.py::TestFloat8TrainingTensor::test_axiswise_gemm[ScalingGranularity.AXISWISE-ScalingGranularity.AXISWISE-a_shape0] SKIPPED 2025-09-09T14:07:11.9451152Z test/float8/test_base.py::TestFloat8TrainingTensor::test_axiswise_gemm[ScalingGranularity.AXISWISE-ScalingGranularity.AXISWISE-a_shape1] SKIPPED 2025-09-09T14:07:11.9452642Z test/float8/test_base.py::TestFloat8TrainingTensor::test_axiswise_gemm[ScalingGranularity.AXISWISE-ScalingGranularity.AXISWISE-a_shape2] SKIPPED 2025-09-09T14:07:11.9454137Z test/float8/test_base.py::TestFloat8TrainingTensor::test_axiswise_gemm[ScalingGranularity.AXISWISE-ScalingGranularity.TENSORWISE-a_shape0] SKIPPED 2025-09-09T14:07:11.9455662Z test/float8/test_base.py::TestFloat8TrainingTensor::test_axiswise_gemm[ScalingGranularity.AXISWISE-ScalingGranularity.TENSORWISE-a_shape1] SKIPPED 2025-09-09T14:07:11.9457174Z test/float8/test_base.py::TestFloat8TrainingTensor::test_axiswise_gemm[ScalingGranularity.AXISWISE-ScalingGranularity.TENSORWISE-a_shape2] SKIPPED 2025-09-09T14:07:11.9458698Z test/float8/test_base.py::TestFloat8TrainingTensor::test_axiswise_gemm[ScalingGranularity.TENSORWISE-ScalingGranularity.AXISWISE-a_shape0] SKIPPED 2025-09-09T14:07:11.9460222Z test/float8/test_base.py::TestFloat8TrainingTensor::test_axiswise_gemm[ScalingGranularity.TENSORWISE-ScalingGranularity.AXISWISE-a_shape1] SKIPPED 2025-09-09T14:07:11.9461719Z test/float8/test_base.py::TestFloat8TrainingTensor::test_axiswise_gemm[ScalingGranularity.TENSORWISE-ScalingGranularity.AXISWISE-a_shape2] SKIPPED 2025-09-09T14:07:11.9462822Z test/float8/test_base.py::TestFloat8TrainingTensor::test_fp8_dtype SKIPPED 2025-09-09T14:07:11.9464189Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_config_params[False-False-linear_dtype0-ScalingType.DYNAMIC-ScalingType.DYNAMIC-ScalingType.DYNAMIC-x_shape0-True] SKIPPED 2025-09-09T14:07:11.9466051Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_config_params[False-False-linear_dtype0-ScalingType.DYNAMIC-ScalingType.DYNAMIC-ScalingType.DYNAMIC-x_shape1-True] SKIPPED 2025-09-09T14:07:11.9468103Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_config_params[False-False-linear_dtype0-ScalingType.DYNAMIC-ScalingType.DYNAMIC-ScalingType.DYNAMIC-x_shape2-True] SKIPPED 2025-09-09T14:07:11.9469973Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_config_params[False-False-linear_dtype1-ScalingType.DYNAMIC-ScalingType.DYNAMIC-ScalingType.DYNAMIC-x_shape0-True] SKIPPED 2025-09-09T14:07:11.9471816Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_config_params[False-False-linear_dtype1-ScalingType.DYNAMIC-ScalingType.DYNAMIC-ScalingType.DYNAMIC-x_shape1-True] SKIPPED 2025-09-09T14:07:11.9473787Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_config_params[False-False-linear_dtype1-ScalingType.DYNAMIC-ScalingType.DYNAMIC-ScalingType.DYNAMIC-x_shape2-True] SKIPPED 2025-09-09T14:07:11.9475647Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_config_params[False-True-linear_dtype0-ScalingType.DYNAMIC-ScalingType.DYNAMIC-ScalingType.DYNAMIC-x_shape0-True] SKIPPED 2025-09-09T14:07:11.9477617Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_config_params[False-True-linear_dtype0-ScalingType.DYNAMIC-ScalingType.DYNAMIC-ScalingType.DYNAMIC-x_shape1-True] SKIPPED 2025-09-09T14:07:11.9479473Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_config_params[False-True-linear_dtype0-ScalingType.DYNAMIC-ScalingType.DYNAMIC-ScalingType.DYNAMIC-x_shape2-True] SKIPPED 2025-09-09T14:07:11.9481327Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_config_params[False-True-linear_dtype1-ScalingType.DYNAMIC-ScalingType.DYNAMIC-ScalingType.DYNAMIC-x_shape0-True] SKIPPED 2025-09-09T14:07:11.9483179Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_config_params[False-True-linear_dtype1-ScalingType.DYNAMIC-ScalingType.DYNAMIC-ScalingType.DYNAMIC-x_shape1-True] SKIPPED 2025-09-09T14:07:11.9485035Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_config_params[False-True-linear_dtype1-ScalingType.DYNAMIC-ScalingType.DYNAMIC-ScalingType.DYNAMIC-x_shape2-True] SKIPPED 2025-09-09T14:07:11.9486883Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_config_params[True-False-linear_dtype0-ScalingType.DYNAMIC-ScalingType.DYNAMIC-ScalingType.DYNAMIC-x_shape0-True] SKIPPED 2025-09-09T14:07:11.9488720Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_config_params[True-False-linear_dtype0-ScalingType.DYNAMIC-ScalingType.DYNAMIC-ScalingType.DYNAMIC-x_shape1-True] SKIPPED 2025-09-09T14:07:11.9490572Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_config_params[True-False-linear_dtype0-ScalingType.DYNAMIC-ScalingType.DYNAMIC-ScalingType.DYNAMIC-x_shape2-True] SKIPPED 2025-09-09T14:07:11.9492425Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_config_params[True-False-linear_dtype1-ScalingType.DYNAMIC-ScalingType.DYNAMIC-ScalingType.DYNAMIC-x_shape0-True] SKIPPED 2025-09-09T14:07:11.9494268Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_config_params[True-False-linear_dtype1-ScalingType.DYNAMIC-ScalingType.DYNAMIC-ScalingType.DYNAMIC-x_shape1-True] SKIPPED 2025-09-09T14:07:11.9496109Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_config_params[True-False-linear_dtype1-ScalingType.DYNAMIC-ScalingType.DYNAMIC-ScalingType.DYNAMIC-x_shape2-True] SKIPPED 2025-09-09T14:07:11.9497951Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_config_params[True-True-linear_dtype0-ScalingType.DYNAMIC-ScalingType.DYNAMIC-ScalingType.DYNAMIC-x_shape0-True] SKIPPED 2025-09-09T14:07:11.9499774Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_config_params[True-True-linear_dtype0-ScalingType.DYNAMIC-ScalingType.DYNAMIC-ScalingType.DYNAMIC-x_shape1-True] SKIPPED 2025-09-09T14:07:11.9501612Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_config_params[True-True-linear_dtype0-ScalingType.DYNAMIC-ScalingType.DYNAMIC-ScalingType.DYNAMIC-x_shape2-True] SKIPPED 2025-09-09T14:07:11.9503512Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_config_params[True-True-linear_dtype1-ScalingType.DYNAMIC-ScalingType.DYNAMIC-ScalingType.DYNAMIC-x_shape0-True] SKIPPED 2025-09-09T14:07:11.9505337Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_config_params[True-True-linear_dtype1-ScalingType.DYNAMIC-ScalingType.DYNAMIC-ScalingType.DYNAMIC-x_shape1-True] SKIPPED 2025-09-09T14:07:11.9507173Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_config_params[True-True-linear_dtype1-ScalingType.DYNAMIC-ScalingType.DYNAMIC-ScalingType.DYNAMIC-x_shape2-True] SKIPPED 2025-09-09T14:07:11.9508801Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype0-True-x_shape0-Float8LinearRecipeName.ROWWISE] SKIPPED 2025-09-09T14:07:11.9510154Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype0-True-x_shape0-Float8LinearRecipeName.ROWWISE_WITH_GW_HP] SKIPPED 2025-09-09T14:07:11.9511515Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype0-True-x_shape1-Float8LinearRecipeName.ROWWISE] SKIPPED 2025-09-09T14:07:11.9512862Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype0-True-x_shape1-Float8LinearRecipeName.ROWWISE_WITH_GW_HP] SKIPPED 2025-09-09T14:07:11.9514217Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype0-True-x_shape2-Float8LinearRecipeName.ROWWISE] SKIPPED 2025-09-09T14:07:11.9515582Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype0-True-x_shape2-Float8LinearRecipeName.ROWWISE_WITH_GW_HP] SKIPPED 2025-09-09T14:07:11.9517128Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype0-False-x_shape0-Float8LinearRecipeName.ROWWISE] SKIPPED 2025-09-09T14:07:11.9518507Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype0-False-x_shape0-Float8LinearRecipeName.ROWWISE_WITH_GW_HP] SKIPPED 2025-09-09T14:07:11.9519885Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype0-False-x_shape1-Float8LinearRecipeName.ROWWISE] SKIPPED 2025-09-09T14:07:11.9521248Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype0-False-x_shape1-Float8LinearRecipeName.ROWWISE_WITH_GW_HP] SKIPPED 2025-09-09T14:07:12.0064503Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype0-False-x_shape2-Float8LinearRecipeName.ROWWISE] SKIPPED 2025-09-09T14:07:12.0065951Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype0-False-x_shape2-Float8LinearRecipeName.ROWWISE_WITH_GW_HP] SKIPPED 2025-09-09T14:07:12.0067333Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype1-True-x_shape0-Float8LinearRecipeName.ROWWISE] SKIPPED 2025-09-09T14:07:12.0068724Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype1-True-x_shape0-Float8LinearRecipeName.ROWWISE_WITH_GW_HP] SKIPPED 2025-09-09T14:07:12.0070076Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype1-True-x_shape1-Float8LinearRecipeName.ROWWISE] SKIPPED 2025-09-09T14:07:12.0071451Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype1-True-x_shape1-Float8LinearRecipeName.ROWWISE_WITH_GW_HP] SKIPPED 2025-09-09T14:07:12.0072801Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype1-True-x_shape2-Float8LinearRecipeName.ROWWISE] SKIPPED 2025-09-09T14:07:12.0074172Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype1-True-x_shape2-Float8LinearRecipeName.ROWWISE_WITH_GW_HP] SKIPPED 2025-09-09T14:07:12.0075544Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype1-False-x_shape0-Float8LinearRecipeName.ROWWISE] SKIPPED 2025-09-09T14:07:12.0077248Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype1-False-x_shape0-Float8LinearRecipeName.ROWWISE_WITH_GW_HP] SKIPPED 2025-09-09T14:07:12.0078638Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype1-False-x_shape1-Float8LinearRecipeName.ROWWISE] SKIPPED 2025-09-09T14:07:12.0080030Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype1-False-x_shape1-Float8LinearRecipeName.ROWWISE_WITH_GW_HP] SKIPPED 2025-09-09T14:07:12.0081540Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype1-False-x_shape2-Float8LinearRecipeName.ROWWISE] SKIPPED 2025-09-09T14:07:12.0082925Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype1-False-x_shape2-Float8LinearRecipeName.ROWWISE_WITH_GW_HP] SKIPPED 2025-09-09T14:07:12.0084293Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype2-True-x_shape0-Float8LinearRecipeName.ROWWISE] SKIPPED 2025-09-09T14:07:12.0085657Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype2-True-x_shape0-Float8LinearRecipeName.ROWWISE_WITH_GW_HP] SKIPPED 2025-09-09T14:07:12.0087023Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype2-True-x_shape1-Float8LinearRecipeName.ROWWISE] SKIPPED 2025-09-09T14:07:12.0088372Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype2-True-x_shape1-Float8LinearRecipeName.ROWWISE_WITH_GW_HP] SKIPPED 2025-09-09T14:07:12.0089740Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype2-True-x_shape2-Float8LinearRecipeName.ROWWISE] SKIPPED 2025-09-09T14:07:12.0091105Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype2-True-x_shape2-Float8LinearRecipeName.ROWWISE_WITH_GW_HP] SKIPPED 2025-09-09T14:07:12.0092469Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype2-False-x_shape0-Float8LinearRecipeName.ROWWISE] SKIPPED 2025-09-09T14:07:12.0093856Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype2-False-x_shape0-Float8LinearRecipeName.ROWWISE_WITH_GW_HP] SKIPPED 2025-09-09T14:07:12.0095243Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype2-False-x_shape1-Float8LinearRecipeName.ROWWISE] SKIPPED 2025-09-09T14:07:12.0096617Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype2-False-x_shape1-Float8LinearRecipeName.ROWWISE_WITH_GW_HP] SKIPPED 2025-09-09T14:07:12.0097992Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype2-False-x_shape2-Float8LinearRecipeName.ROWWISE] SKIPPED 2025-09-09T14:07:12.0099363Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype2-False-x_shape2-Float8LinearRecipeName.ROWWISE_WITH_GW_HP] SKIPPED 2025-09-09T14:07:12.0100717Z test/float8/test_base.py::TestFloat8Linear::test_autocast_outputs[Float8LinearRecipeName.TENSORWISE-linear_dtype0-True] SKIPPED 2025-09-09T14:07:12.0102012Z test/float8/test_base.py::TestFloat8Linear::test_autocast_outputs[Float8LinearRecipeName.TENSORWISE-linear_dtype1-True] SKIPPED 2025-09-09T14:07:12.0103280Z test/float8/test_base.py::TestFloat8Linear::test_autocast_outputs[Float8LinearRecipeName.TENSORWISE-linear_dtype2-True] SKIPPED 2025-09-09T14:07:12.0104521Z test/float8/test_base.py::TestFloat8Linear::test_autocast_outputs[Float8LinearRecipeName.ROWWISE-linear_dtype0-True] SKIPPED 2025-09-09T14:07:12.0105772Z test/float8/test_base.py::TestFloat8Linear::test_autocast_outputs[Float8LinearRecipeName.ROWWISE-linear_dtype1-True] SKIPPED 2025-09-09T14:07:12.0106996Z test/float8/test_base.py::TestFloat8Linear::test_autocast_outputs[Float8LinearRecipeName.ROWWISE-linear_dtype2-True] SKIPPED 2025-09-09T14:07:12.0108359Z test/float8/test_base.py::TestFloat8Linear::test_autocast_outputs[Float8LinearRecipeName.ROWWISE_WITH_GW_HP-linear_dtype0-True] SKIPPED 2025-09-09T14:07:12.0109686Z test/float8/test_base.py::TestFloat8Linear::test_autocast_outputs[Float8LinearRecipeName.ROWWISE_WITH_GW_HP-linear_dtype1-True] SKIPPED 2025-09-09T14:07:12.0110993Z test/float8/test_base.py::TestFloat8Linear::test_autocast_outputs[Float8LinearRecipeName.ROWWISE_WITH_GW_HP-linear_dtype2-True] SKIPPED 2025-09-09T14:07:12.0111932Z test/float8/test_base.py::TestFloat8Linear::test_repr PASSED 2025-09-09T14:07:12.0112654Z test/float8/test_base.py::TestFloat8Linear::test_inference_mode SKIPPED 2025-09-09T14:07:12.0113366Z test/float8/test_base.py::TestFloat8Linear::test_quantize SKIPPED (C...) 2025-09-09T14:07:12.0114166Z test/float8/test_base.py::TestScaledMM::test_scaled_mm_vs_emulated[True-base_dtype0] SKIPPED 2025-09-09T14:07:12.0115028Z test/float8/test_base.py::TestScaledMM::test_scaled_mm_vs_emulated[True-base_dtype1] SKIPPED 2025-09-09T14:07:12.0115900Z test/float8/test_base.py::TestScaledMM::test_scaled_mm_vs_emulated[True-base_dtype2] SKIPPED 2025-09-09T14:07:12.0116845Z test/float8/test_base.py::TestScaledMM::test_scaled_mm_vs_emulated[False-base_dtype0] SKIPPED 2025-09-09T14:07:12.0117722Z test/float8/test_base.py::TestScaledMM::test_scaled_mm_vs_emulated[False-base_dtype1] SKIPPED 2025-09-09T14:07:12.0118600Z test/float8/test_base.py::TestScaledMM::test_scaled_mm_vs_emulated[False-base_dtype2] SKIPPED 2025-09-09T14:07:12.0119393Z test/float8/test_base.py::TestScaledMM::test_different_configs_error SKIPPED 2025-09-09T14:07:12.0120177Z test/float8/test_base.py::TestScaledMM::test_pad_inner_dim[True-base_dtype0] SKIPPED 2025-09-09T14:07:12.0120969Z test/float8/test_base.py::TestScaledMM::test_pad_inner_dim[True-base_dtype1] SKIPPED 2025-09-09T14:07:12.0121772Z test/float8/test_base.py::TestScaledMM::test_pad_inner_dim[True-base_dtype2] SKIPPED 2025-09-09T14:07:12.0122588Z test/float8/test_base.py::TestScaledMM::test_pad_inner_dim[False-base_dtype0] SKIPPED 2025-09-09T14:07:12.0123383Z test/float8/test_base.py::TestScaledMM::test_pad_inner_dim[False-base_dtype1] SKIPPED 2025-09-09T14:07:12.0124188Z test/float8/test_base.py::TestScaledMM::test_pad_inner_dim[False-base_dtype2] SKIPPED 2025-09-09T14:07:12.0125348Z test/float8/test_base.py::TestNumerics::test_small_amax_float16[float8_dtype0] SKIPPED 2025-09-09T14:07:12.0126164Z test/float8/test_base.py::TestNumerics::test_small_amax_float16[float8_dtype1] SKIPPED 2025-09-09T14:07:12.0126983Z test/float8/test_base.py::TestNumerics::test_small_amax_float16[float8_dtype2] SKIPPED 2025-09-09T14:07:12.0127781Z test/float8/test_base.py::TestNumerics::test_small_amax_float16[float8_dtype3] SKIPPED 2025-09-09T14:07:12.0128590Z test/float8/test_base.py::TestFloat8LinearUtils::test_fp8_tensor_statistics PASSED 2025-09-09T14:07:12.0129410Z test/float8/test_base.py::TestFloat8LinearUtils::test_swap_linears_with_filters PASSED 2025-09-09T14:07:12.0130215Z test/float8/test_base.py::TestFloat8LinearUtils::test_swap_root_linear PASSED 2025-09-09T14:07:12.0131063Z test/float8/test_base.py::TestFloat8LinearUtils::test_swap_root_linear_with_children_raises PASSED 2025-09-09T14:07:12.0131940Z test/float8/test_base.py::TestFloat8LinearUtils::test_swap_submodule_linears PASSED 2025-09-09T14:07:12.0132806Z test/float8/test_base.py::TestFloat8LinearUtils::test_swap_submodule_linears_with_skip PASSED 2025-09-09T14:07:12.0133967Z test/float8/test_compile.py::test_eager_only[dtype0-True-ScalingType.DYNAMIC-ScalingType.DYNAMIC-ScalingType.DYNAMIC-True] SKIPPED 2025-09-09T14:07:12.0135373Z test/float8/test_compile.py::test_eager_only[dtype1-True-ScalingType.DYNAMIC-ScalingType.DYNAMIC-ScalingType.DYNAMIC-True] SKIPPED 2025-09-09T14:07:12.0136907Z test/float8/test_compile.py::test_aot_eager[dtype0-ScalingType.DYNAMIC-ScalingType.DYNAMIC-ScalingType.DYNAMIC-True-True] SKIPPED 2025-09-09T14:07:12.3596373Z test/float8/test_compile.py::test_aot_eager[dtype1-ScalingType.DYNAMIC-ScalingType.DYNAMIC-ScalingType.DYNAMIC-True-True] SKIPPED 2025-09-09T14:07:12.3598778Z test/float8/test_compile.py::test_inductor_from_config_params[dtype0-ScalingType.DYNAMIC-ScalingType.DYNAMIC-ScalingType.DYNAMIC-False-True] SKIPPED 2025-09-09T14:07:12.3601452Z test/float8/test_compile.py::test_inductor_from_config_params[dtype1-ScalingType.DYNAMIC-ScalingType.DYNAMIC-ScalingType.DYNAMIC-False-True] SKIPPED 2025-09-09T14:07:12.3603993Z test/float8/test_compile.py::test_inductor_from_recipe[Float8LinearRecipeName.ROWWISE] SKIPPED 2025-09-09T14:07:12.3605644Z test/float8/test_compile.py::test_inductor_from_recipe[Float8LinearRecipeName.ROWWISE_WITH_GW_HP] SKIPPED 2025-09-09T14:07:12.3607197Z test/float8/test_compile.py::TestGraphBreaks::test_float8_graph_input SKIPPED 2025-09-09T14:07:12.3608531Z test/float8/test_compile.py::TestGraphBreaks::test_float8_graph_output SKIPPED 2025-09-09T14:07:12.3610018Z test/float8/test_compile.py::TestGraphBreaks::test_float8_with_graph_break_in_the_middle SKIPPED 2025-09-09T14:07:12.3611578Z test/float8/test_compile.py::test_dynamic_scale_numeric_parity[True-dtype0] SKIPPED 2025-09-09T14:07:12.3613068Z test/float8/test_compile.py::test_dynamic_scale_numeric_parity[True-dtype1] SKIPPED 2025-09-09T14:07:12.3614537Z test/float8/test_compile.py::test_dynamic_scale_numeric_parity[True-dtype2] SKIPPED 2025-09-09T14:07:12.3615927Z test/float8/test_compile.py::test_dynamic_scale_numeric_parity[False-dtype0] SKIPPED 2025-09-09T14:07:12.3617001Z test/float8/test_compile.py::test_dynamic_scale_numeric_parity[False-dtype1] SKIPPED 2025-09-09T14:07:12.3617810Z test/float8/test_compile.py::test_dynamic_scale_numeric_parity[False-dtype2] SKIPPED 2025-09-09T14:07:12.3618689Z test/float8/test_float8_utils.py::test_round_scale_down_to_power_of_2_valid_inputs[test_case0] SKIPPED 2025-09-09T14:07:12.3619626Z test/float8/test_float8_utils.py::test_round_scale_down_to_power_of_2_valid_inputs[test_case1] SKIPPED 2025-09-09T14:07:12.3620552Z test/float8/test_float8_utils.py::test_round_scale_down_to_power_of_2_valid_inputs[test_case2] SKIPPED 2025-09-09T14:07:12.3621465Z test/float8/test_float8_utils.py::test_round_scale_down_to_power_of_2_valid_inputs[test_case3] SKIPPED 2025-09-09T14:07:12.3622398Z test/float8/test_float8_utils.py::test_round_scale_down_to_power_of_2_valid_inputs[test_case4] SKIPPED 2025-09-09T14:07:12.3623311Z test/float8/test_float8_utils.py::test_round_scale_down_to_power_of_2_valid_inputs[test_case5] SKIPPED 2025-09-09T14:07:12.3624236Z test/float8/test_float8_utils.py::test_round_scale_down_to_power_of_2_valid_inputs[test_case6] SKIPPED 2025-09-09T14:07:12.3625486Z test/float8/test_float8_utils.py::test_round_scale_down_to_power_of_2_valid_inputs[test_case7] SKIPPED 2025-09-09T14:07:12.3626304Z test/float8/test_float8_utils.py::test_non_float32_input[invalid_dtype0] PASSED 2025-09-09T14:07:12.3627051Z test/float8/test_float8_utils.py::test_non_float32_input[invalid_dtype1] PASSED 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test/float8/test_numerics_integration.py::TestFloat8NumericsIntegrationTest::test_encoder_fw_bw_from_recipe[Float8LinearRecipeName.ROWWISE] SKIPPED 2025-09-09T14:07:12.3636307Z test/float8/test_numerics_integration.py::TestFloat8NumericsIntegrationTest::test_encoder_fw_bw_from_recipe[Float8LinearRecipeName.ROWWISE_WITH_GW_HP] SKIPPED 2025-09-09T14:07:12.3637534Z test/hqq/test_hqq_affine.py::TestHQQ::test_hqq_plain_2bit SKIPPED (N...) 2025-09-09T14:07:12.3638209Z test/hqq/test_hqq_affine.py::TestHQQ::test_hqq_plain_3bit SKIPPED (N...) 2025-09-09T14:07:12.3638890Z test/hqq/test_hqq_affine.py::TestHQQ::test_hqq_plain_4bit SKIPPED (N...) 2025-09-09T14:07:12.3639571Z test/hqq/test_hqq_affine.py::TestHQQ::test_hqq_plain_5bit SKIPPED (N...) 2025-09-09T14:07:12.3640244Z test/hqq/test_hqq_affine.py::TestHQQ::test_hqq_plain_6bit SKIPPED (N...) 2025-09-09T14:07:12.3640917Z test/hqq/test_hqq_affine.py::TestHQQ::test_hqq_plain_7bit SKIPPED (N...) 2025-09-09T14:07:12.3641574Z test/hqq/test_hqq_affine.py::TestHQQ::test_hqq_plain_8bit SKIPPED (N...) 2025-09-09T14:07:12.3642349Z test/integration/test_integration.py::SmoothquantUnitTest::test_debug_x_absmax PASSED 2025-09-09T14:07:12.3643179Z test/integration/test_integration.py::SmoothquantUnitTest::test_figure_4 PASSED 2025-09-09T14:07:12.3644049Z test/integration/test_integration.py::SmoothquantUnitTest::test_selective_torch_compile PASSED 2025-09-09T14:07:12.3645526Z test/integration/test_integration.py::SmoothquantUnitTest::test_smooth_linear_cpu [W909 14:07:12.749383496 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()) 2025-09-09T14:07:12.3646858Z PASSED 2025-09-09T14:07:12.3647436Z test/integration/test_integration.py::SmoothquantUnitTest::test_smooth_linear_cuda SKIPPED 2025-09-09T14:07:12.3648374Z test/integration/test_integration.py::SmoothquantUnitTest::test_smooth_linear_edge_cases PASSED 2025-09-09T14:07:12.3649213Z test/integration/test_integration.py::SmoothquantUnitTest::test_swap PASSED 2025-09-09T14:07:12.3649994Z test/integration/test_integration.py::SmoothquantUnitTest::test_tensors PASSED 2025-09-09T14:07:12.3650920Z test/integration/test_integration.py::SmoothquantUnitTest::test_weight_t_and_non_t_numerics_match SKIPPED 2025-09-09T14:07:12.3651857Z test/integration/test_integration.py::PythonQuantUtilOpUnitTest::test__int_mm SKIPPED 2025-09-09T14:07:12.3652886Z test/integration/test_integration.py::PythonQuantUtilOpUnitTest::test__int_mm_eager_and_torch_compile_numerics SKIPPED 2025-09-09T14:07:12.3654031Z test/integration/test_integration.py::PythonQuantUtilOpUnitTest::test_dynamic_quant_per_channel_numerics_cpu PASSED 2025-09-09T14:07:12.3655192Z test/integration/test_integration.py::PythonQuantUtilOpUnitTest::test_dynamic_quant_per_channel_numerics_cuda SKIPPED 2025-09-09T14:07:12.3656263Z test/integration/test_integration.py::PythonQuantUtilOpUnitTest::test_per_token_linear_cpu PASSED 2025-09-09T14:07:12.3657247Z test/integration/test_integration.py::PythonQuantUtilOpUnitTest::test_per_token_linear_cuda SKIPPED 2025-09-09T14:07:12.3658270Z test/integration/test_integration.py::PythonQuantUtilOpUnitTest::test_quantize_per_token_cpu PASSED 2025-09-09T14:07:12.3659275Z test/integration/test_integration.py::PythonQuantUtilOpUnitTest::test_quantize_per_token_cuda SKIPPED 2025-09-09T14:07:12.3660302Z test/integration/test_integration.py::PythonQuantUtilOpUnitTest::test_quantize_per_token_xpu SKIPPED 2025-09-09T14:07:12.3661463Z test/integration/test_integration.py::TestSubclass::test_aq_float8_dynamic_quant_rowwise_scaling_subclass_0_cpu SKIPPED 2025-09-09T14:07:12.3662602Z test/integration/test_integration.py::TestSubclass::test_aq_float8_dynamic_quant_rowwise_scaling_subclass_1_cpu SKIPPED 2025-09-09T14:07:12.3663742Z test/integration/test_integration.py::TestSubclass::test_aq_float8_dynamic_quant_rowwise_scaling_subclass_2_cpu SKIPPED 2025-09-09T14:07:12.3664862Z test/integration/test_integration.py::TestSubclass::test_aq_float8_dynamic_quant_rowwise_scaling_subclass_3_cuda SKIPPED 2025-09-09T14:07:12.3666064Z test/integration/test_integration.py::TestSubclass::test_aq_float8_dynamic_quant_rowwise_scaling_subclass_4_cuda SKIPPED 2025-09-09T14:07:12.3667192Z test/integration/test_integration.py::TestSubclass::test_aq_float8_dynamic_quant_rowwise_scaling_subclass_5_cuda SKIPPED 2025-09-09T14:07:12.3668344Z test/integration/test_integration.py::TestSubclass::test_aq_float8_dynamic_quant_tensorwise_scaling_subclass_0_cpu SKIPPED 2025-09-09T14:07:12.3669544Z test/integration/test_integration.py::TestSubclass::test_aq_float8_dynamic_quant_tensorwise_scaling_subclass_1_cpu SKIPPED 2025-09-09T14:07:12.3670778Z test/integration/test_integration.py::TestSubclass::test_aq_float8_dynamic_quant_tensorwise_scaling_subclass_2_cpu SKIPPED 2025-09-09T14:07:12.3671945Z test/integration/test_integration.py::TestSubclass::test_aq_float8_dynamic_quant_tensorwise_scaling_subclass_3_cuda SKIPPED 2025-09-09T14:07:12.3673109Z test/integration/test_integration.py::TestSubclass::test_aq_float8_dynamic_quant_tensorwise_scaling_subclass_4_cuda SKIPPED 2025-09-09T14:07:12.3674275Z test/integration/test_integration.py::TestSubclass::test_aq_float8_dynamic_quant_tensorwise_scaling_subclass_5_cuda SKIPPED 2025-09-09T14:07:12.3675368Z test/integration/test_integration.py::TestSubclass::test_aq_float8_weight_only_quant_subclass_0_cpu SKIPPED 2025-09-09T14:07:12.3676474Z test/integration/test_integration.py::TestSubclass::test_aq_float8_weight_only_quant_subclass_1_cpu SKIPPED 2025-09-09T14:07:14.6354251Z test/integration/test_integration.py::TestSubclass::test_aq_float8_weight_only_quant_subclass_2_cpu SKIPPED 2025-09-09T14:07:14.6355342Z test/integration/test_integration.py::TestSubclass::test_aq_float8_weight_only_quant_subclass_3_cuda SKIPPED 2025-09-09T14:07:14.6356425Z test/integration/test_integration.py::TestSubclass::test_aq_float8_weight_only_quant_subclass_4_cuda SKIPPED 2025-09-09T14:07:14.6357487Z test/integration/test_integration.py::TestSubclass::test_aq_float8_weight_only_quant_subclass_5_cuda SKIPPED 2025-09-09T14:07:14.6358491Z test/integration/test_integration.py::TestSubclass::test_aq_int8_dynamic_quant_subclass_0_cpu SKIPPED 2025-09-09T14:07:14.6359447Z test/integration/test_integration.py::TestSubclass::test_aq_int8_dynamic_quant_subclass_1_cpu SKIPPED 2025-09-09T14:07:14.6360424Z test/integration/test_integration.py::TestSubclass::test_aq_int8_dynamic_quant_subclass_2_cpu SKIPPED 2025-09-09T14:07:14.6361382Z test/integration/test_integration.py::TestSubclass::test_aq_int8_dynamic_quant_subclass_3_cuda SKIPPED 2025-09-09T14:07:14.6362354Z test/integration/test_integration.py::TestSubclass::test_aq_int8_dynamic_quant_subclass_4_cuda SKIPPED 2025-09-09T14:07:14.6363326Z test/integration/test_integration.py::TestSubclass::test_aq_int8_dynamic_quant_subclass_5_cuda SKIPPED 2025-09-09T14:07:14.6364311Z test/integration/test_integration.py::TestSubclass::test_aq_int8_weight_only_quant_2_subclass_0_cpu SKIPPED 2025-09-09T14:07:14.6365333Z test/integration/test_integration.py::TestSubclass::test_aq_int8_weight_only_quant_2_subclass_1_cpu SKIPPED 2025-09-09T14:07:14.6366335Z test/integration/test_integration.py::TestSubclass::test_aq_int8_weight_only_quant_2_subclass_2_cpu SKIPPED 2025-09-09T14:07:14.6367353Z test/integration/test_integration.py::TestSubclass::test_aq_int8_weight_only_quant_2_subclass_3_cuda SKIPPED 2025-09-09T14:07:14.6368629Z test/integration/test_integration.py::TestSubclass::test_aq_int8_weight_only_quant_2_subclass_4_cuda SKIPPED 2025-09-09T14:07:14.6369653Z test/integration/test_integration.py::TestSubclass::test_aq_int8_weight_only_quant_2_subclass_5_cuda SKIPPED 2025-09-09T14:07:14.6370673Z test/integration/test_integration.py::TestSubclass::test_aq_int8_weight_only_quant_3_subclass_0_cpu SKIPPED 2025-09-09T14:07:14.6371674Z test/integration/test_integration.py::TestSubclass::test_aq_int8_weight_only_quant_3_subclass_1_cpu SKIPPED 2025-09-09T14:07:14.6372798Z test/integration/test_integration.py::TestSubclass::test_aq_int8_weight_only_quant_3_subclass_2_cpu SKIPPED 2025-09-09T14:07:14.6373821Z test/integration/test_integration.py::TestSubclass::test_aq_int8_weight_only_quant_3_subclass_3_cuda SKIPPED 2025-09-09T14:07:14.6374836Z test/integration/test_integration.py::TestSubclass::test_aq_int8_weight_only_quant_3_subclass_4_cuda SKIPPED 2025-09-09T14:07:14.6375871Z test/integration/test_integration.py::TestSubclass::test_aq_int8_weight_only_quant_3_subclass_5_cuda SKIPPED 2025-09-09T14:07:14.6376888Z test/integration/test_integration.py::TestSubclass::test_aq_int8_weight_only_quant_subclass_0_cpu SKIPPED 2025-09-09T14:07:14.6377907Z test/integration/test_integration.py::TestSubclass::test_aq_int8_weight_only_quant_subclass_1_cpu SKIPPED 2025-09-09T14:07:14.6378910Z test/integration/test_integration.py::TestSubclass::test_aq_int8_weight_only_quant_subclass_2_cpu SKIPPED 2025-09-09T14:07:14.6379902Z test/integration/test_integration.py::TestSubclass::test_aq_int8_weight_only_quant_subclass_3_cuda SKIPPED 2025-09-09T14:07:14.6380916Z test/integration/test_integration.py::TestSubclass::test_aq_int8_weight_only_quant_subclass_4_cuda SKIPPED 2025-09-09T14:07:14.6381928Z test/integration/test_integration.py::TestSubclass::test_aq_int8_weight_only_quant_subclass_5_cuda SKIPPED 2025-09-09T14:07:14.6382898Z test/integration/test_integration.py::TestSubclass::test_autoquantizable_flatten_unflatten PASSED 2025-09-09T14:07:14.6383923Z test/integration/test_integration.py::TestSubclass::test_dequantize_int4_weight_only_quant_subclass_0_cpu SKIPPED 2025-09-09T14:07:14.6385712Z test/integration/test_integration.py::TestSubclass::test_dequantize_int4_weight_only_quant_subclass_1_cpu SKIPPED 2025-09-09T14:07:14.6387412Z test/integration/test_integration.py::TestSubclass::test_dequantize_int4_weight_only_quant_subclass_2_cpu SKIPPED 2025-09-09T14:07:14.6389134Z test/integration/test_integration.py::TestSubclass::test_dequantize_int4_weight_only_quant_subclass_3_cuda SKIPPED 2025-09-09T14:07:14.6390844Z test/integration/test_integration.py::TestSubclass::test_dequantize_int4_weight_only_quant_subclass_4_cuda SKIPPED 2025-09-09T14:07:14.6392569Z test/integration/test_integration.py::TestSubclass::test_dequantize_int4_weight_only_quant_subclass_5_cuda SKIPPED 2025-09-09T14:07:14.6394102Z test/integration/test_integration.py::TestSubclass::test_dequantize_int4_weight_only_quant_subclass_grouped_0_cpu SKIPPED 2025-09-09T14:07:14.6395656Z test/integration/test_integration.py::TestSubclass::test_dequantize_int4_weight_only_quant_subclass_grouped_1_cpu SKIPPED 2025-09-09T14:07:14.6397283Z test/integration/test_integration.py::TestSubclass::test_dequantize_int4_weight_only_quant_subclass_grouped_2_cpu SKIPPED 2025-09-09T14:07:14.6398841Z test/integration/test_integration.py::TestSubclass::test_dequantize_int4_weight_only_quant_subclass_grouped_3_cuda SKIPPED 2025-09-09T14:07:14.6400416Z test/integration/test_integration.py::TestSubclass::test_dequantize_int4_weight_only_quant_subclass_grouped_4_cuda SKIPPED 2025-09-09T14:07:14.6401990Z test/integration/test_integration.py::TestSubclass::test_dequantize_int4_weight_only_quant_subclass_grouped_5_cuda SKIPPED 2025-09-09T14:07:14.6403856Z test/integration/test_integration.py::TestSubclass::test_dequantize_int8_dynamic_quant_subclass_0_cpu PASSED 2025-09-09T14:07:14.6406627Z test/integration/test_integration.py::TestSubclass::test_dequantize_int8_dynamic_quant_subclass_1_cpu PASSED 2025-09-09T14:07:14.6408541Z test/integration/test_integration.py::TestSubclass::test_dequantize_int8_dynamic_quant_subclass_2_cpu PASSED 2025-09-09T14:07:14.6410194Z test/integration/test_integration.py::TestSubclass::test_dequantize_int8_dynamic_quant_subclass_3_cuda SKIPPED 2025-09-09T14:07:14.6411948Z test/integration/test_integration.py::TestSubclass::test_dequantize_int8_dynamic_quant_subclass_4_cuda SKIPPED 2025-09-09T14:07:14.6413632Z test/integration/test_integration.py::TestSubclass::test_dequantize_int8_dynamic_quant_subclass_5_cuda SKIPPED 2025-09-09T14:07:14.6415306Z test/integration/test_integration.py::TestSubclass::test_dequantize_int8_weight_only_quant_subclass_0_cpu PASSED 2025-09-09T14:07:14.6416994Z test/integration/test_integration.py::TestSubclass::test_dequantize_int8_weight_only_quant_subclass_1_cpu PASSED 2025-09-09T14:07:14.6418673Z test/integration/test_integration.py::TestSubclass::test_dequantize_int8_weight_only_quant_subclass_2_cpu PASSED 2025-09-09T14:07:14.6420364Z test/integration/test_integration.py::TestSubclass::test_dequantize_int8_weight_only_quant_subclass_3_cuda SKIPPED 2025-09-09T14:07:14.6421831Z test/integration/test_integration.py::TestSubclass::test_dequantize_int8_weight_only_quant_subclass_4_cuda SKIPPED 2025-09-09T14:07:14.6423485Z test/integration/test_integration.py::TestSubclass::test_dequantize_int8_weight_only_quant_subclass_5_cuda SKIPPED 2025-09-09T14:07:14.6424928Z test/integration/test_integration.py::TestSubclass::test_gemlite_layout_0_cpu SKIPPED 2025-09-09T14:07:14.6426059Z test/integration/test_integration.py::TestSubclass::test_gemlite_layout_1_cpu SKIPPED 2025-09-09T14:07:14.6427194Z test/integration/test_integration.py::TestSubclass::test_gemlite_layout_2_cpu SKIPPED 2025-09-09T14:07:14.6428322Z test/integration/test_integration.py::TestSubclass::test_gemlite_layout_3_cuda SKIPPED 2025-09-09T14:07:14.6429474Z test/integration/test_integration.py::TestSubclass::test_gemlite_layout_4_cuda SKIPPED 2025-09-09T14:07:14.6430622Z test/integration/test_integration.py::TestSubclass::test_gemlite_layout_5_cuda SKIPPED 2025-09-09T14:07:14.6431906Z test/integration/test_integration.py::TestSubclass::test_int4_weight_only_hqq_quant_subclass_api_0_cpu SKIPPED 2025-09-09T14:07:14.6433334Z test/integration/test_integration.py::TestSubclass::test_int4_weight_only_hqq_quant_subclass_api_1_cpu SKIPPED 2025-09-09T14:07:14.6434744Z test/integration/test_integration.py::TestSubclass::test_int4_weight_only_hqq_quant_subclass_api_2_cpu PASSED 2025-09-09T14:07:14.6436221Z test/integration/test_integration.py::TestSubclass::test_int4_weight_only_hqq_quant_subclass_api_3_cuda SKIPPED 2025-09-09T14:07:14.6437660Z test/integration/test_integration.py::TestSubclass::test_int4_weight_only_hqq_quant_subclass_api_4_cuda SKIPPED 2025-09-09T14:07:14.6439078Z test/integration/test_integration.py::TestSubclass::test_int4_weight_only_hqq_quant_subclass_api_5_cuda SKIPPED 2025-09-09T14:07:14.6440463Z test/integration/test_integration.py::TestSubclass::test_int4_weight_only_quant_subclass_0_cpu SKIPPED 2025-09-09T14:07:14.6441798Z test/integration/test_integration.py::TestSubclass::test_int4_weight_only_quant_subclass_1_cpu SKIPPED 2025-09-09T14:07:14.6443148Z test/integration/test_integration.py::TestSubclass::test_int4_weight_only_quant_subclass_2_cpu SKIPPED 2025-09-09T14:07:14.6444483Z test/integration/test_integration.py::TestSubclass::test_int4_weight_only_quant_subclass_3_cuda SKIPPED 2025-09-09T14:07:14.6445813Z test/integration/test_integration.py::TestSubclass::test_int4_weight_only_quant_subclass_4_cuda SKIPPED 2025-09-09T14:07:41.7570104Z test/integration/test_integration.py::TestSubclass::test_int4_weight_only_quant_subclass_5_cuda SKIPPED 2025-09-09T14:07:41.7571250Z test/integration/test_integration.py::TestSubclass::test_int4_weight_only_quant_subclass_api_0_cpu SKIPPED 2025-09-09T14:07:41.7572270Z test/integration/test_integration.py::TestSubclass::test_int4_weight_only_quant_subclass_api_1_cpu SKIPPED 2025-09-09T14:07:41.7573288Z test/integration/test_integration.py::TestSubclass::test_int4_weight_only_quant_subclass_api_2_cpu PASSED 2025-09-09T14:07:41.7574425Z test/integration/test_integration.py::TestSubclass::test_int4_weight_only_quant_subclass_api_3_cuda SKIPPED 2025-09-09T14:07:41.7575450Z 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test/integration/test_integration.py::TestSubclass::test_int4_weight_only_quant_subclass_api_grouped_5_cuda SKIPPED 2025-09-09T14:07:41.7584012Z test/integration/test_integration.py::TestSubclass::test_int4_weight_only_quant_subclass_grouped_0_cpu SKIPPED 2025-09-09T14:07:41.7585066Z test/integration/test_integration.py::TestSubclass::test_int4_weight_only_quant_subclass_grouped_1_cpu SKIPPED 2025-09-09T14:07:41.7586108Z test/integration/test_integration.py::TestSubclass::test_int4_weight_only_quant_subclass_grouped_2_cpu SKIPPED 2025-09-09T14:07:41.7587167Z test/integration/test_integration.py::TestSubclass::test_int4_weight_only_quant_subclass_grouped_3_cuda SKIPPED 2025-09-09T14:07:41.7588215Z test/integration/test_integration.py::TestSubclass::test_int4_weight_only_quant_subclass_grouped_4_cuda SKIPPED 2025-09-09T14:07:41.7589277Z test/integration/test_integration.py::TestSubclass::test_int4_weight_only_quant_subclass_grouped_5_cuda SKIPPED 2025-09-09T14:07:41.7590280Z test/integration/test_integration.py::TestSubclass::test_int8_dynamic_quant_subclass_0_cpu SKIPPED 2025-09-09T14:07:41.7591224Z test/integration/test_integration.py::TestSubclass::test_int8_dynamic_quant_subclass_1_cpu SKIPPED 2025-09-09T14:07:41.7592177Z test/integration/test_integration.py::TestSubclass::test_int8_dynamic_quant_subclass_2_cpu SKIPPED 2025-09-09T14:07:41.7593119Z test/integration/test_integration.py::TestSubclass::test_int8_dynamic_quant_subclass_3_cuda SKIPPED 2025-09-09T14:07:41.7594076Z test/integration/test_integration.py::TestSubclass::test_int8_dynamic_quant_subclass_4_cuda SKIPPED 2025-09-09T14:07:41.7595031Z test/integration/test_integration.py::TestSubclass::test_int8_dynamic_quant_subclass_5_cuda SKIPPED 2025-09-09T14:07:41.7595994Z test/integration/test_integration.py::TestSubclass::test_int8_dynamic_quant_subclass_api_00_cpu SKIPPED 2025-09-09T14:07:41.7597090Z test/integration/test_integration.py::TestSubclass::test_int8_dynamic_quant_subclass_api_01_cpu SKIPPED 2025-09-09T14:07:41.7598072Z test/integration/test_integration.py::TestSubclass::test_int8_dynamic_quant_subclass_api_02_cpu SKIPPED 2025-09-09T14:07:41.7599141Z test/integration/test_integration.py::TestSubclass::test_int8_dynamic_quant_subclass_api_03_cpu SKIPPED 2025-09-09T14:07:41.7600133Z test/integration/test_integration.py::TestSubclass::test_int8_dynamic_quant_subclass_api_04_cpu SKIPPED 2025-09-09T14:07:41.7601116Z test/integration/test_integration.py::TestSubclass::test_int8_dynamic_quant_subclass_api_05_cpu SKIPPED 2025-09-09T14:07:41.7602114Z test/integration/test_integration.py::TestSubclass::test_int8_dynamic_quant_subclass_api_06_cuda SKIPPED 2025-09-09T14:07:41.7603170Z test/integration/test_integration.py::TestSubclass::test_int8_dynamic_quant_subclass_api_07_cuda SKIPPED 2025-09-09T14:07:41.7604166Z test/integration/test_integration.py::TestSubclass::test_int8_dynamic_quant_subclass_api_08_cuda SKIPPED 2025-09-09T14:07:41.7605471Z test/integration/test_integration.py::TestSubclass::test_int8_dynamic_quant_subclass_api_09_cuda SKIPPED 2025-09-09T14:07:41.7607084Z test/integration/test_integration.py::TestSubclass::test_int8_dynamic_quant_subclass_api_10_cuda SKIPPED 2025-09-09T14:07:41.7608655Z test/integration/test_integration.py::TestSubclass::test_int8_dynamic_quant_subclass_api_11_cuda SKIPPED 2025-09-09T14:07:41.7610468Z test/integration/test_integration.py::TestSubclass::test_int8_weight_only_quant_subclass_0_cpu SKIPPED 2025-09-09T14:07:41.7612258Z test/integration/test_integration.py::TestSubclass::test_int8_weight_only_quant_subclass_1_cpu SKIPPED 2025-09-09T14:07:41.7614154Z test/integration/test_integration.py::TestSubclass::test_int8_weight_only_quant_subclass_2_cpu SKIPPED 2025-09-09T14:07:41.7616085Z test/integration/test_integration.py::TestSubclass::test_int8_weight_only_quant_subclass_3_cuda SKIPPED 2025-09-09T14:07:41.7617676Z test/integration/test_integration.py::TestSubclass::test_int8_weight_only_quant_subclass_4_cuda SKIPPED 2025-09-09T14:07:41.7619225Z test/integration/test_integration.py::TestSubclass::test_int8_weight_only_quant_subclass_5_cuda SKIPPED 2025-09-09T14:07:41.7620805Z test/integration/test_integration.py::TestSubclass::test_int8_weight_only_quant_subclass_api_0_cpu PASSED 2025-09-09T14:07:41.7622386Z test/integration/test_integration.py::TestSubclass::test_int8_weight_only_quant_subclass_api_1_cpu PASSED 2025-09-09T14:07:41.7624268Z test/integration/test_integration.py::TestSubclass::test_int8_weight_only_quant_subclass_api_2_cpu PASSED 2025-09-09T14:07:41.7626904Z test/integration/test_integration.py::TestSubclass::test_int8_weight_only_quant_subclass_api_3_cuda SKIPPED 2025-09-09T14:07:41.7628667Z test/integration/test_integration.py::TestSubclass::test_int8_weight_only_quant_subclass_api_4_cuda SKIPPED 2025-09-09T14:07:41.7630280Z test/integration/test_integration.py::TestSubclass::test_int8_weight_only_quant_subclass_api_5_cuda SKIPPED 2025-09-09T14:07:41.7631896Z test/integration/test_integration.py::TestSubclass::test_int8_weight_only_quant_with_freeze_0_cpu AUTOTUNE packed_linear(32x64, 1982689x1, 32x64) 2025-09-09T14:07:41.7633003Z strides: [64, 1], [1, 0], [64, 1] 2025-09-09T14:07:41.7633507Z dtypes: torch.float32, torch.float32, torch.float32 2025-09-09T14:07:41.7634071Z cpp_CppMicroGemmFP32Vec_0 0.0034 ms 100.0% 2025-09-09T14:07:41.7634655Z _mkl_linear 0.0187 ms 18.3% 2025-09-09T14:07:41.7635364Z SingleProcess AUTOTUNE benchmarking takes 0.2510 seconds and 2.4025 seconds precompiling for 2 choices 2025-09-09T14:07:41.7636233Z AUTOTUNE packed_linear(32x32, 1982689x1, 32x32) 2025-09-09T14:07:41.7636671Z strides: [32, 1], [1, 0], [32, 1] 2025-09-09T14:07:41.7637084Z dtypes: torch.float32, torch.float32, torch.float32 2025-09-09T14:07:41.7637583Z cpp_CppMicroGemmFP32Vec_1 0.0033 ms 100.0% 2025-09-09T14:07:41.7637996Z _mkl_linear 0.0183 ms 17.9% 2025-09-09T14:07:41.7638692Z SingleProcess AUTOTUNE benchmarking takes 0.2508 seconds and 2.3975 seconds precompiling for 2 choices 2025-09-09T14:07:41.7639687Z PASSED 2025-09-09T14:07:41.7640450Z test/integration/test_integration.py::TestSubclass::test_int8_weight_only_quant_with_freeze_1_cpu AUTOTUNE mm(32x64, 64x32) 2025-09-09T14:07:41.7641308Z strides: [64, 1], [1, 64] 2025-09-09T14:07:41.7641650Z dtypes: torch.float16, torch.float16 2025-09-09T14:07:41.7642085Z cpp_CppMicroGemmFP32Vec_2 0.0041 ms 100.0% 2025-09-09T14:07:41.7642489Z mm 0.0274 ms 14.8% 2025-09-09T14:07:41.7643145Z SingleProcess AUTOTUNE benchmarking takes 0.2498 seconds and 2.5323 seconds precompiling for 2 choices 2025-09-09T14:07:41.7643985Z AUTOTUNE mm(32x32, 32x32) 2025-09-09T14:07:41.7644314Z strides: [32, 1], [1, 32] 2025-09-09T14:07:41.7644652Z dtypes: torch.float16, torch.float16 2025-09-09T14:07:41.7645442Z cpp_CppMicroGemmFP32Vec_3 0.0037 ms 100.0% 2025-09-09T14:07:41.7645934Z mm 0.0275 ms 13.3% 2025-09-09T14:07:41.7646674Z SingleProcess AUTOTUNE benchmarking takes 0.2501 seconds and 2.5437 seconds precompiling for 2 choices 2025-09-09T14:07:41.7647580Z PASSED 2025-09-09T14:07:41.7648564Z test/integration/test_integration.py::TestSubclass::test_int8_weight_only_quant_with_freeze_2_cpu AUTOTUNE _weight_int8pack_mm(32x64, 32x64, 32) 2025-09-09T14:07:41.7649686Z strides: [64, 1], [64, 1], [1] 2025-09-09T14:07:41.7650146Z dtypes: torch.bfloat16, torch.int8, torch.bfloat16 2025-09-09T14:07:41.7650719Z cpp_CppMicroGemmFP32Vec_4 0.0040 ms 100.0% 2025-09-09T14:07:41.7651224Z _weight_int8pack_mm 0.0173 ms 22.9% 2025-09-09T14:07:41.7652068Z SingleProcess AUTOTUNE benchmarking takes 0.2508 seconds and 2.5086 seconds precompiling for 2 choices 2025-09-09T14:07:41.7653000Z AUTOTUNE _weight_int8pack_mm(32x32, 32x32, 32) 2025-09-09T14:07:41.7653416Z strides: [32, 1], [32, 1], [1] 2025-09-09T14:07:41.7653829Z dtypes: torch.bfloat16, torch.int8, torch.bfloat16 2025-09-09T14:07:41.7654309Z cpp_CppMicroGemmFP32Vec_5 0.0037 ms 100.0% 2025-09-09T14:07:41.7654748Z _weight_int8pack_mm 0.0166 ms 22.3% 2025-09-09T14:07:41.7655478Z SingleProcess AUTOTUNE benchmarking takes 0.2508 seconds and 2.5199 seconds precompiling for 2 choices 2025-09-09T14:07:41.7656256Z PASSED 2025-09-09T14:07:41.7657119Z test/integration/test_integration.py::TestSubclass::test_int8_weight_only_quant_with_freeze_3_cuda SKIPPED 2025-09-09T14:07:41.7658860Z test/integration/test_integration.py::TestSubclass::test_int8_weight_only_quant_with_freeze_4_cuda SKIPPED 2025-09-09T14:07:41.7660453Z test/integration/test_integration.py::TestSubclass::test_int8_weight_only_quant_with_freeze_5_cuda SKIPPED 2025-09-09T14:07:48.0452924Z test/integration/test_integration.py::TestDynamicQuant::test_dynamic_quant PASSED 2025-09-09T14:07:48.0453977Z test/integration/test_integration.py::TestWeightOnlyInt8Quant::test_weight_only_groupwise_embedding_quant PASSED 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SKIPPED 2025-09-09T14:07:48.0478794Z test/integration/test_integration.py::TestSaveLoadMeta::test_save_load_int4woqtensors_2_cpu PASSED 2025-09-09T14:07:48.0479760Z test/integration/test_integration.py::TestSaveLoadMeta::test_save_load_int4woqtensors_3_cuda SKIPPED 2025-09-09T14:07:48.0480744Z test/integration/test_integration.py::TestSaveLoadMeta::test_save_load_int4woqtensors_4_cuda SKIPPED 2025-09-09T14:07:48.0481767Z test/integration/test_integration.py::TestSaveLoadMeta::test_save_load_int4woqtensors_5_cuda SKIPPED 2025-09-09T14:07:48.0482735Z test/integration/test_integration.py::TestSaveLoadMeta::test_save_load_int8woqtensors_0_cpu PASSED 2025-09-09T14:07:48.0483765Z test/integration/test_integration.py::TestSaveLoadMeta::test_save_load_int8woqtensors_1_cpu PASSED 2025-09-09T14:07:48.0484719Z test/integration/test_integration.py::TestSaveLoadMeta::test_save_load_int8woqtensors_2_cpu PASSED 2025-09-09T14:07:48.0485691Z test/integration/test_integration.py::TestSaveLoadMeta::test_save_load_int8woqtensors_3_cuda SKIPPED 2025-09-09T14:07:48.0486660Z test/integration/test_integration.py::TestSaveLoadMeta::test_save_load_int8woqtensors_4_cuda SKIPPED 2025-09-09T14:07:48.0487635Z test/integration/test_integration.py::TestSaveLoadMeta::test_save_load_int8woqtensors_5_cuda SKIPPED 2025-09-09T14:07:48.0488542Z test/integration/test_integration.py::TorchCompileUnitTest::test_fullgraph SKIPPED 2025-09-09T14:07:48.0489395Z test/integration/test_integration.py::UtilsUnitTest::test_shape_logger PASSED 2025-09-09T14:07:48.0490365Z test/integration/test_integration.py::SmoothquantIntegrationTest::test_non_dynamically_quantizable_linear SKIPPED 2025-09-09T14:07:48.0491246Z test/integration/test_integration.py::SmoothquantIntegrationTest::test_on_dummy_distilbert 2025-09-09T14:07:48.0491886Z tokenizer_config.json: 0% 0.00/48.0 [00:00) 2025-09-09T14:10:48.8537619Z converted model pt2e: GraphModule( 2025-09-09T14:10:48.8537916Z (conv): Module() 2025-09-09T14:10:48.8538129Z (bn): Module() 2025-09-09T14:10:48.8538341Z ) 2025-09-09T14:10:48.8538444Z 2025-09-09T14:10:48.8538448Z 2025-09-09T14:10:48.8538452Z 2025-09-09T14:10:48.8538542Z def forward(self, x): 2025-09-09T14:10:48.8538850Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:10:48.8539204Z conv_bias = self.conv.bias 2025-09-09T14:10:48.8539532Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T14:10:48.8540315Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.010372933000326157, 0, -128, 127, torch.int8); x = None 2025-09-09T14:10:48.8541667Z 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 2025-09-09T14:10:48.8542958Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T14:10:48.8543466Z _scale_0 = self._scale_0 2025-09-09T14:10:48.8543752Z _zero_point_0 = self._zero_point_0 2025-09-09T14:10:48.8544070Z quantize_per_channel = self._frozen_param0 2025-09-09T14:10:48.8545058Z 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 2025-09-09T14:10:48.8546635Z 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 2025-09-09T14:10:48.8547956Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv1d_2, 0.010256201960146427, -10, -128, 127, torch.int8); conv1d_2 = None 2025-09-09T14:10:48.8549406Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.010256201960146427, -10, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T14:10:48.8550529Z return pytree.tree_unflatten((dequantize_per_tensor_default_1,), self._out_spec) 2025-09-09T14:10:48.8550970Z 2025-09-09T14:10:48.8551275Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:10:48.8551674Z onverted model fx: GraphModule( 2025-09-09T14:10:48.8552079Z (conv): QuantizedConv1d(Reference)(3, 3, kernel_size=(3,), stride=(1,)) 2025-09-09T14:10:48.8552490Z ) 2025-09-09T14:10:48.8552591Z 2025-09-09T14:10:48.8552595Z 2025-09-09T14:10:48.8552599Z 2025-09-09T14:10:48.8552687Z def forward(self, x): 2025-09-09T14:10:48.8553364Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.010372933000326157, 0, -128, 127, torch.int8); x = None 2025-09-09T14:10:48.8554726Z 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 2025-09-09T14:10:48.8555842Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T14:10:48.8556883Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.010256201960146427, -10, -128, 127, torch.int8); conv = None 2025-09-09T14:10:48.8558290Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.010256201960146427, -10, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T14:10:48.8559270Z return dequantize_per_tensor_default_1 2025-09-09T14:10:48.8559562Z 2025-09-09T14:10:48.8559869Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:10:48.8560270Z diff: tensor([[[0., 0., 0.], 2025-09-09T14:10:48.8560514Z [0., 0., 0.], 2025-09-09T14:10:48.8560744Z [0., 0., 0.]]]) 2025-09-09T14:10:48.8560990Z model pt2e: GraphModule( 2025-09-09T14:10:48.8561241Z (conv): Module() 2025-09-09T14:10:48.8561453Z (bn): Module() 2025-09-09T14:10:48.8561781Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:10:48.8562798Z 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 2025-09-09T14:10:48.8564023Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3264806270599365, max_val=1.318617343902588) 2025-09-09T14:10:48.8564588Z ) 2025-09-09T14:10:48.8564877Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:10:48.8565916Z 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 2025-09-09T14:10:48.8567210Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.3201889097690582, max_val=0.3243715763092041) 2025-09-09T14:10:48.8567780Z ) 2025-09-09T14:10:48.8568084Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:10:48.8569107Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0103]), zero_point=tensor([-10], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:10:48.8570369Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.20903742313385, max_val=1.4068148136138916) 2025-09-09T14:10:48.8570918Z ) 2025-09-09T14:10:48.8571105Z ) 2025-09-09T14:10:48.8571206Z 2025-09-09T14:10:48.8571210Z 2025-09-09T14:10:48.8571214Z 2025-09-09T14:10:48.8571319Z def forward(self, x): 2025-09-09T14:10:48.8571621Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:10:48.8572004Z conv_weight = self.conv.weight 2025-09-09T14:10:48.8572294Z conv_bias = self.conv.bias 2025-09-09T14:10:48.8572577Z bn_weight = self.bn.weight 2025-09-09T14:10:48.8572840Z bn_bias = self.bn.bias 2025-09-09T14:10:48.8573121Z bn_running_mean = self.bn.running_mean 2025-09-09T14:10:48.8573437Z bn_running_var = self.bn.running_var 2025-09-09T14:10:48.8573796Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T14:10:48.8574276Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:10:48.8574913Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T14:10:48.8575493Z add = torch.ops.aten.add.Tensor(bn_running_var, 1e-05) 2025-09-09T14:10:48.8575911Z sqrt = torch.ops.aten.sqrt.default(add); add = None 2025-09-09T14:10:48.8576363Z div = torch.ops.aten.div.Tensor(bn_weight, sqrt); sqrt = None 2025-09-09T14:10:48.8576833Z reshape = torch.ops.aten.reshape.default(div, [-1, 1, 1]) 2025-09-09T14:10:48.8577388Z mul = torch.ops.aten.mul.Tensor(conv_weight, reshape); conv_weight = reshape = None 2025-09-09T14:10:48.8578002Z activation_post_process_1 = self.activation_post_process_1(mul); mul = None 2025-09-09T14:11:01.4435528Z zeros_like = torch.ops.aten.zeros_like.default(conv_bias, dtype = torch.float32, pin_memory = False) 2025-09-09T14:11:01.4437072Z 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 2025-09-09T14:11:01.4438418Z reshape_1 = torch.ops.aten.reshape.default(div, [1, -1, 1]); div = None 2025-09-09T14:11:01.4439192Z div_1 = torch.ops.aten.div.Tensor(conv1d_1, reshape_1); conv1d_1 = reshape_1 = None 2025-09-09T14:11:01.4440038Z reshape_2 = torch.ops.aten.reshape.default(conv_bias, [1, -1, 1]); conv_bias = None 2025-09-09T14:11:01.4440839Z add_1 = torch.ops.aten.add.Tensor(div_1, reshape_2); div_1 = reshape_2 = None 2025-09-09T14:11:01.4442136Z 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 2025-09-09T14:11:01.4443513Z activation_post_process_2 = self.activation_post_process_2(batch_norm_1); batch_norm_1 = None 2025-09-09T14:11:01.4444380Z return pytree.tree_unflatten((activation_post_process_2,), self._out_spec) 2025-09-09T14:11:01.4444963Z 2025-09-09T14:11:01.4445352Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:11:01.4445890Z model fx: GraphModule( 2025-09-09T14:11:01.4446349Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:11:01.4447758Z 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 2025-09-09T14:11:01.4449740Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3264806270599365, max_val=1.318617343902588) 2025-09-09T14:11:01.4450508Z ) 2025-09-09T14:11:01.4450753Z (conv): ConvBn1d( 2025-09-09T14:11:01.4451070Z 3, 3, kernel_size=(3,), stride=(1,) 2025-09-09T14:11:01.4451642Z (bn): BatchNorm1d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T14:11:01.4452219Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:11:01.4453323Z 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 2025-09-09T14:11:01.4454557Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.3201889097690582, max_val=0.3243715763092041) 2025-09-09T14:11:01.4455133Z ) 2025-09-09T14:11:01.4455312Z ) 2025-09-09T14:11:01.4455620Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:11:01.4456642Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0103]), zero_point=tensor([-10], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:11:01.4457861Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.20903742313385, max_val=1.4068148136138916) 2025-09-09T14:11:01.4458426Z ) 2025-09-09T14:11:01.4458607Z ) 2025-09-09T14:11:01.4458707Z 2025-09-09T14:11:01.4458724Z 2025-09-09T14:11:01.4458728Z 2025-09-09T14:11:01.4458818Z def forward(self, x): 2025-09-09T14:11:01.4459187Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:11:01.4459774Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T14:11:01.4460370Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T14:11:01.4460827Z return activation_post_process_1 2025-09-09T14:11:01.4461113Z 2025-09-09T14:11:01.4461400Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:11:01.4461803Z diff: tensor([[[0., 0., 0.], 2025-09-09T14:11:01.4462047Z [0., 0., 0.], 2025-09-09T14:11:01.4462306Z [0., 0., 0.]]], grad_fn=) 2025-09-09T14:11:01.4462623Z converted model pt2e: GraphModule( 2025-09-09T14:11:01.4462912Z (conv): Module() 2025-09-09T14:11:01.4463128Z (bn): Module() 2025-09-09T14:11:01.4463344Z ) 2025-09-09T14:11:01.4463446Z 2025-09-09T14:11:01.4463450Z 2025-09-09T14:11:01.4463453Z 2025-09-09T14:11:01.4463560Z def forward(self, x): 2025-09-09T14:11:01.4463854Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:11:01.4464220Z conv_bias = self.conv.bias 2025-09-09T14:11:01.4464536Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T14:11:01.4465316Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.010372933000326157, 0, -128, 127, torch.int8); x = None 2025-09-09T14:11:01.4466682Z 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 2025-09-09T14:11:01.4467818Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T14:11:01.4468352Z quantize_per_tensor = self._frozen_param0 2025-09-09T14:11:01.4469216Z dequantize_per_tensor = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor, 0.002554106991738081, 0, -127, 127, torch.int8); quantize_per_tensor = None 2025-09-09T14:11:01.4470605Z 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 2025-09-09T14:11:01.4472009Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv1d_2, 0.010258244350552559, -10, -128, 127, torch.int8); conv1d_2 = None 2025-09-09T14:11:01.4473442Z dequantize_per_tensor_default_2 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_2, 0.010258244350552559, -10, -128, 127, torch.int8); quantize_per_tensor_default_2 = None 2025-09-09T14:11:01.4474563Z return pytree.tree_unflatten((dequantize_per_tensor_default_2,), self._out_spec) 2025-09-09T14:11:01.4475084Z 2025-09-09T14:11:01.4475376Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:11:01.4475871Z onverted model fx: GraphModule( 2025-09-09T14:11:01.4476274Z (conv): QuantizedConv1d(Reference)(3, 3, kernel_size=(3,), stride=(1,)) 2025-09-09T14:11:01.4476687Z ) 2025-09-09T14:11:01.4476788Z 2025-09-09T14:11:01.4476793Z 2025-09-09T14:11:01.4476797Z 2025-09-09T14:11:01.4476886Z def forward(self, x): 2025-09-09T14:11:01.4477570Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.010372933000326157, 0, -128, 127, torch.int8); x = None 2025-09-09T14:11:01.4478936Z 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 2025-09-09T14:11:01.4480043Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T14:11:01.4480989Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.010258244350552559, -10, -128, 127, torch.int8); conv = None 2025-09-09T14:11:01.4482403Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.010258244350552559, -10, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T14:11:01.4483364Z return dequantize_per_tensor_default_1 2025-09-09T14:11:01.4483665Z 2025-09-09T14:11:01.4483956Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:11:01.4484357Z diff: tensor([[[0., 0., 0.], 2025-09-09T14:11:01.4484606Z [0., 0., 0.], 2025-09-09T14:11:01.4484835Z [0., 0., 0.]]]) 2025-09-09T14:11:01.4485249Z PASSED 2025-09-09T14:11:01.4485979Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn1d::test_qat_conv_bn_fusion_cuda SKIPPED 2025-09-09T14:11:01.4487064Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn1d::test_qat_conv_bn_fusion_literal_args model pt2e: GraphModule( 2025-09-09T14:11:01.4487765Z (conv): Module() 2025-09-09T14:11:01.4487994Z (bn): Module() 2025-09-09T14:11:01.4488314Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:11:01.4489353Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0161]), zero_point=tensor([14], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:11:01.4490577Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.276310682296753, max_val=1.8198994398117065) 2025-09-09T14:11:01.4491128Z ) 2025-09-09T14:11:01.4491429Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:11:01.4492502Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0026, 0.0026, 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 2025-09-09T14:11:01.4493931Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.3263, -0.3276, -0.3045]), max_val=tensor([0.2760, 0.3011, 0.3298])) 2025-09-09T14:11:01.4494649Z ) 2025-09-09T14:11:01.4494938Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:11:01.4496061Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0152]), zero_point=tensor([-12], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:11:01.4497263Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.7719206809997559, max_val=2.111994981765747) 2025-09-09T14:11:01.4497844Z ) 2025-09-09T14:11:01.4498040Z ) 2025-09-09T14:11:01.4498141Z 2025-09-09T14:11:01.4498146Z 2025-09-09T14:11:01.4498150Z 2025-09-09T14:11:01.4498308Z def forward(self, x): 2025-09-09T14:11:01.4498623Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:11:01.4498984Z conv_weight = self.conv.weight 2025-09-09T14:11:01.4499288Z conv_bias = self.conv.bias 2025-09-09T14:11:01.4499558Z bn_weight = self.bn.weight 2025-09-09T14:11:01.4499840Z bn_bias = self.bn.bias 2025-09-09T14:11:01.4500127Z bn_running_mean = self.bn.running_mean 2025-09-09T14:11:01.4500451Z bn_running_var = self.bn.running_var 2025-09-09T14:11:01.4500825Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T14:11:01.4510259Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:11:01.4511037Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T14:11:01.4511618Z add = torch.ops.aten.add.Tensor(bn_running_var, 1e-05) 2025-09-09T14:11:01.4512055Z sqrt = torch.ops.aten.sqrt.default(add); add = None 2025-09-09T14:11:11.2014054Z div = torch.ops.aten.div.Tensor(bn_weight, sqrt); sqrt = None 2025-09-09T14:11:11.2014773Z reshape = torch.ops.aten.reshape.default(div, [-1, 1, 1]) 2025-09-09T14:11:11.2015507Z mul = torch.ops.aten.mul.Tensor(conv_weight, reshape); conv_weight = reshape = None 2025-09-09T14:11:11.2016338Z activation_post_process_1 = self.activation_post_process_1(mul); mul = None 2025-09-09T14:11:11.2017291Z zeros_like = torch.ops.aten.zeros_like.default(conv_bias, dtype = torch.float32, pin_memory = False) 2025-09-09T14:11:11.2018797Z 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 2025-09-09T14:11:11.2020112Z reshape_1 = torch.ops.aten.reshape.default(div, [1, -1, 1]); div = None 2025-09-09T14:11:11.2020904Z div_1 = torch.ops.aten.div.Tensor(conv1d_1, reshape_1); conv1d_1 = reshape_1 = None 2025-09-09T14:11:11.2021733Z reshape_2 = torch.ops.aten.reshape.default(conv_bias, [1, -1, 1]); conv_bias = None 2025-09-09T14:11:11.2022565Z add_1 = torch.ops.aten.add.Tensor(div_1, reshape_2); div_1 = reshape_2 = None 2025-09-09T14:11:11.2023856Z 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 2025-09-09T14:11:11.2025399Z activation_post_process_2 = self.activation_post_process_2(batch_norm_1); batch_norm_1 = None 2025-09-09T14:11:11.2026285Z return pytree.tree_unflatten((activation_post_process_2,), self._out_spec) 2025-09-09T14:11:11.2026846Z 2025-09-09T14:11:11.2027249Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:11:11.2027765Z model fx: GraphModule( 2025-09-09T14:11:11.2028228Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:11:11.2029626Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0161]), zero_point=tensor([14], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:11:11.2031292Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.276310682296753, max_val=1.8198994398117065) 2025-09-09T14:11:11.2032059Z ) 2025-09-09T14:11:11.2032319Z (conv): ConvBn1d( 2025-09-09T14:11:11.2032665Z 3, 3, kernel_size=(3,), stride=(2,), padding=(4,) 2025-09-09T14:11:11.2033702Z (bn): BatchNorm1d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T14:11:11.2034374Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:11:11.2035873Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0026, 0.0026, 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 2025-09-09T14:11:11.2037459Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.3263, -0.3276, -0.3045]), max_val=tensor([0.2760, 0.3011, 0.3298])) 2025-09-09T14:11:11.2038289Z ) 2025-09-09T14:11:11.2038495Z ) 2025-09-09T14:11:11.2038787Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:11:11.2039839Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0152]), zero_point=tensor([-12], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:11:11.2041056Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.7719206809997559, max_val=2.111994981765747) 2025-09-09T14:11:11.2041622Z ) 2025-09-09T14:11:11.2041812Z ) 2025-09-09T14:11:11.2041914Z 2025-09-09T14:11:11.2041920Z 2025-09-09T14:11:11.2041924Z 2025-09-09T14:11:11.2042016Z def forward(self, x): 2025-09-09T14:11:11.2042398Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:11:11.2042973Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T14:11:11.2043576Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T14:11:11.2044045Z return activation_post_process_1 2025-09-09T14:11:11.2044318Z 2025-09-09T14:11:11.2044621Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:11:11.2045016Z diff: tensor([[[0., 0., 0., 0., 0., 0.], 2025-09-09T14:11:11.2045317Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:11:11.2045616Z [0., 0., 0., 0., 0., 0.]]], grad_fn=) 2025-09-09T14:11:11.2045972Z converted model pt2e: GraphModule( 2025-09-09T14:11:11.2046248Z (conv): Module() 2025-09-09T14:11:11.2046476Z (bn): Module() 2025-09-09T14:11:11.2046681Z ) 2025-09-09T14:11:11.2046798Z 2025-09-09T14:11:11.2046802Z 2025-09-09T14:11:11.2046806Z 2025-09-09T14:11:11.2046897Z def forward(self, x): 2025-09-09T14:11:11.2047203Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:11:11.2047563Z conv_bias = self.conv.bias 2025-09-09T14:11:11.2047898Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T14:11:11.2048676Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.016063569113612175, 14, -128, 127, torch.int8); x = None 2025-09-09T14:11:11.2050059Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.016063569113612175, 14, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T14:11:11.2051221Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T14:11:11.2051726Z _scale_0 = self._scale_0 2025-09-09T14:11:11.2052011Z _zero_point_0 = self._zero_point_0 2025-09-09T14:11:11.2052332Z quantize_per_channel = self._frozen_param0 2025-09-09T14:11:11.2053313Z 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 2025-09-09T14:11:11.2054831Z 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 2025-09-09T14:11:11.2056237Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv1d_2, 0.015231042169034481, -12, -128, 127, torch.int8); conv1d_2 = None 2025-09-09T14:11:11.2057687Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.015231042169034481, -12, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T14:11:11.2058816Z return pytree.tree_unflatten((dequantize_per_tensor_default_1,), self._out_spec) 2025-09-09T14:11:11.2059273Z 2025-09-09T14:11:11.2059644Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:11:11.2060063Z onverted model fx: GraphModule( 2025-09-09T14:11:11.2060509Z (conv): QuantizedConv1d(Reference)(3, 3, kernel_size=(3,), stride=(2,), padding=(4,)) 2025-09-09T14:11:11.2060974Z ) 2025-09-09T14:11:11.2061079Z 2025-09-09T14:11:11.2061084Z 2025-09-09T14:11:11.2061088Z 2025-09-09T14:11:11.2061198Z def forward(self, x): 2025-09-09T14:11:11.2061880Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.016063569113612175, 14, -128, 127, torch.int8); x = None 2025-09-09T14:11:11.2063262Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.016063569113612175, 14, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T14:11:11.2064377Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T14:11:11.2065307Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.015231042169034481, -12, -128, 127, torch.int8); conv = None 2025-09-09T14:11:11.2066724Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.015231042169034481, -12, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T14:11:11.2067695Z return dequantize_per_tensor_default_1 2025-09-09T14:11:11.2067997Z 2025-09-09T14:11:11.2068427Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:11:11.2068829Z diff: tensor([[[0., 0., 0., 0., 0., 0.], 2025-09-09T14:11:11.2069123Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:11:11.2069381Z [0., 0., 0., 0., 0., 0.]]]) 2025-09-09T14:11:11.2069672Z model pt2e: GraphModule( 2025-09-09T14:11:11.2069916Z (conv): Module() 2025-09-09T14:11:11.2070141Z (bn): Module() 2025-09-09T14:11:11.2070457Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:11:11.2071496Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0161]), zero_point=tensor([14], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:11:11.2072712Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.276310682296753, max_val=1.8198994398117065) 2025-09-09T14:11:11.2073264Z ) 2025-09-09T14:11:11.2073567Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:11:11.2074606Z 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 2025-09-09T14:11:11.2075902Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.32764676213264465, max_val=0.3298276662826538) 2025-09-09T14:11:11.2076477Z ) 2025-09-09T14:11:11.2076768Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:11:11.2077808Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0152]), zero_point=tensor([-12], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:11:11.2079009Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.7719206809997559, max_val=2.113234519958496) 2025-09-09T14:11:11.2079575Z ) 2025-09-09T14:11:11.2079767Z ) 2025-09-09T14:11:11.2079940Z 2025-09-09T14:11:11.2079945Z 2025-09-09T14:11:11.2079949Z 2025-09-09T14:11:11.2080042Z def forward(self, x): 2025-09-09T14:11:11.2080356Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:11:11.2080721Z conv_weight = self.conv.weight 2025-09-09T14:11:11.2081030Z conv_bias = self.conv.bias 2025-09-09T14:11:11.2081300Z bn_weight = self.bn.weight 2025-09-09T14:11:11.2081577Z bn_bias = self.bn.bias 2025-09-09T14:11:11.2081905Z bn_running_mean = self.bn.running_mean 2025-09-09T14:11:11.2082233Z bn_running_var = self.bn.running_var 2025-09-09T14:11:11.2082591Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T14:11:23.8123224Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:11:23.8124125Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T14:11:23.8125080Z add = torch.ops.aten.add.Tensor(bn_running_var, 1e-05) 2025-09-09T14:11:23.8125686Z sqrt = torch.ops.aten.sqrt.default(add); add = None 2025-09-09T14:11:23.8126266Z div = torch.ops.aten.div.Tensor(bn_weight, sqrt); sqrt = None 2025-09-09T14:11:23.8126899Z reshape = torch.ops.aten.reshape.default(div, [-1, 1, 1]) 2025-09-09T14:11:23.8127620Z mul = torch.ops.aten.mul.Tensor(conv_weight, reshape); conv_weight = reshape = None 2025-09-09T14:11:23.8128430Z activation_post_process_1 = self.activation_post_process_1(mul); mul = None 2025-09-09T14:11:23.8129340Z zeros_like = torch.ops.aten.zeros_like.default(conv_bias, dtype = torch.float32, pin_memory = False) 2025-09-09T14:11:23.8130784Z 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 2025-09-09T14:11:23.8132113Z reshape_1 = torch.ops.aten.reshape.default(div, [1, -1, 1]); div = None 2025-09-09T14:11:23.8132900Z div_1 = torch.ops.aten.div.Tensor(conv1d_1, reshape_1); conv1d_1 = reshape_1 = None 2025-09-09T14:11:23.8133727Z reshape_2 = torch.ops.aten.reshape.default(conv_bias, [1, -1, 1]); conv_bias = None 2025-09-09T14:11:23.8134532Z add_1 = torch.ops.aten.add.Tensor(div_1, reshape_2); div_1 = reshape_2 = None 2025-09-09T14:11:23.8135804Z 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 2025-09-09T14:11:23.8137182Z activation_post_process_2 = self.activation_post_process_2(batch_norm_1); batch_norm_1 = None 2025-09-09T14:11:23.8138055Z return pytree.tree_unflatten((activation_post_process_2,), self._out_spec) 2025-09-09T14:11:23.8138611Z 2025-09-09T14:11:23.8139007Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:11:23.8139521Z model fx: GraphModule( 2025-09-09T14:11:23.8139984Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:11:23.8141279Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0161]), zero_point=tensor([14], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:11:23.8142514Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.276310682296753, max_val=1.8198994398117065) 2025-09-09T14:11:23.8143083Z ) 2025-09-09T14:11:23.8143283Z (conv): ConvBn1d( 2025-09-09T14:11:23.8143561Z 3, 3, kernel_size=(3,), stride=(2,), padding=(4,) 2025-09-09T14:11:23.8144026Z (bn): BatchNorm1d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T14:11:23.8144544Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:11:23.8145833Z 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 2025-09-09T14:11:23.8147066Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.32764676213264465, max_val=0.3298276662826538) 2025-09-09T14:11:23.8147643Z ) 2025-09-09T14:11:23.8147824Z ) 2025-09-09T14:11:23.8148133Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:11:23.8149173Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0152]), zero_point=tensor([-12], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:11:23.8150488Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.7719206809997559, max_val=2.113234519958496) 2025-09-09T14:11:23.8151055Z ) 2025-09-09T14:11:23.8151236Z ) 2025-09-09T14:11:23.8151352Z 2025-09-09T14:11:23.8151357Z 2025-09-09T14:11:23.8151361Z 2025-09-09T14:11:23.8151451Z def forward(self, x): 2025-09-09T14:11:23.8151841Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:11:23.8152411Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T14:11:23.8153003Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T14:11:23.8153456Z return activation_post_process_1 2025-09-09T14:11:23.8153740Z 2025-09-09T14:11:23.8154026Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:11:23.8154434Z diff: tensor([[[0., 0., 0., 0., 0., 0.], 2025-09-09T14:11:23.8154716Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:11:23.8155023Z [0., 0., 0., 0., 0., 0.]]], grad_fn=) 2025-09-09T14:11:23.8155375Z converted model pt2e: GraphModule( 2025-09-09T14:11:23.8155647Z (conv): Module() 2025-09-09T14:11:23.8155869Z (bn): Module() 2025-09-09T14:11:23.8156148Z ) 2025-09-09T14:11:23.8156263Z 2025-09-09T14:11:23.8156267Z 2025-09-09T14:11:23.8156271Z 2025-09-09T14:11:23.8156361Z def forward(self, x): 2025-09-09T14:11:23.8156658Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:11:23.8157022Z conv_bias = self.conv.bias 2025-09-09T14:11:23.8157349Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T14:11:23.8158118Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.016063569113612175, 14, -128, 127, torch.int8); x = None 2025-09-09T14:11:23.8159495Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.016063569113612175, 14, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T14:11:23.8160641Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T14:11:23.8161182Z quantize_per_tensor = self._frozen_param0 2025-09-09T14:11:23.8162064Z 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 2025-09-09T14:11:23.8163454Z 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 2025-09-09T14:11:23.8164782Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv1d_2, 0.0152359027415514, -12, -128, 127, torch.int8); conv1d_2 = None 2025-09-09T14:11:23.8166221Z dequantize_per_tensor_default_2 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_2, 0.0152359027415514, -12, -128, 127, torch.int8); quantize_per_tensor_default_2 = None 2025-09-09T14:11:23.8167321Z return pytree.tree_unflatten((dequantize_per_tensor_default_2,), self._out_spec) 2025-09-09T14:11:23.8167775Z 2025-09-09T14:11:23.8168068Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:11:23.8168492Z onverted model fx: GraphModule( 2025-09-09T14:11:23.8169014Z (conv): QuantizedConv1d(Reference)(3, 3, kernel_size=(3,), stride=(2,), padding=(4,)) 2025-09-09T14:11:23.8169474Z ) 2025-09-09T14:11:23.8169579Z 2025-09-09T14:11:23.8169583Z 2025-09-09T14:11:23.8169587Z 2025-09-09T14:11:23.8169693Z def forward(self, x): 2025-09-09T14:11:23.8170368Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.016063569113612175, 14, -128, 127, torch.int8); x = None 2025-09-09T14:11:23.8171808Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.016063569113612175, 14, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T14:11:23.8172919Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T14:11:23.8173864Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.0152359027415514, -12, -128, 127, torch.int8); conv = None 2025-09-09T14:11:23.8175267Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.0152359027415514, -12, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T14:11:23.8176227Z return dequantize_per_tensor_default_1 2025-09-09T14:11:23.8176537Z 2025-09-09T14:11:23.8176830Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:11:23.8177246Z diff: tensor([[[0., 0., 0., 0., 0., 0.], 2025-09-09T14:11:23.8177547Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:11:23.8177810Z [0., 0., 0., 0., 0., 0.]]]) 2025-09-09T14:11:23.8178277Z PASSED 2025-09-09T14:11:23.8178937Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn1d::test_qat_conv_bn_fusion_no_conv_bias model pt2e: GraphModule( 2025-09-09T14:11:23.8179655Z (conv): Module() 2025-09-09T14:11:23.8179866Z (bn): Module() 2025-09-09T14:11:23.8180199Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:11:23.8181233Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0188]), zero_point=tensor([-45], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:11:23.8182452Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.5603605508804321, max_val=3.2356624603271484) 2025-09-09T14:11:23.8183021Z ) 2025-09-09T14:11:23.8183315Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:11:23.8184399Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0022, 0.0026, 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 2025-09-09T14:11:23.8185822Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.2639, -0.2941, -0.2608]), max_val=tensor([0.2795, 0.3227, 0.2891])) 2025-09-09T14:11:23.8186525Z ) 2025-09-09T14:11:23.8186824Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:11:23.8187975Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0169]), zero_point=tensor([-13], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:11:23.8189192Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.9445278644561768, max_val=2.3592891693115234) 2025-09-09T14:11:23.8189765Z ) 2025-09-09T14:11:23.8189937Z ) 2025-09-09T14:11:23.8190038Z 2025-09-09T14:11:23.8190042Z 2025-09-09T14:11:23.8190057Z 2025-09-09T14:11:23.8190145Z def forward(self, x): 2025-09-09T14:11:23.8190442Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:11:33.5928837Z conv_weight = self.conv.weight 2025-09-09T14:11:33.5929351Z bn_weight = self.bn.weight 2025-09-09T14:11:33.5929713Z bn_bias = self.bn.bias 2025-09-09T14:11:33.5930404Z bn_running_mean = self.bn.running_mean 2025-09-09T14:11:33.5930832Z bn_running_var = self.bn.running_var 2025-09-09T14:11:33.5931311Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T14:11:33.5931951Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:11:33.5932802Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T14:11:33.5933694Z add = torch.ops.aten.add.Tensor(bn_running_var, 1e-05) 2025-09-09T14:11:33.5934242Z sqrt = torch.ops.aten.sqrt.default(add); add = None 2025-09-09T14:11:33.5934835Z div = torch.ops.aten.div.Tensor(bn_weight, sqrt); sqrt = None 2025-09-09T14:11:33.5935456Z reshape = torch.ops.aten.reshape.default(div, [-1, 1, 1]) 2025-09-09T14:11:33.5936190Z mul = torch.ops.aten.mul.Tensor(conv_weight, reshape); conv_weight = reshape = None 2025-09-09T14:11:33.5937019Z activation_post_process_1 = self.activation_post_process_1(mul); mul = None 2025-09-09T14:11:33.5938247Z 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 2025-09-09T14:11:33.5939474Z reshape_1 = torch.ops.aten.reshape.default(div, [1, -1, 1]); div = None 2025-09-09T14:11:33.5940239Z div_1 = torch.ops.aten.div.Tensor(conv1d_1, reshape_1); conv1d_1 = reshape_1 = None 2025-09-09T14:11:33.5941555Z 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 2025-09-09T14:11:33.5942929Z activation_post_process_2 = self.activation_post_process_2(batch_norm_1); batch_norm_1 = None 2025-09-09T14:11:33.5943790Z return pytree.tree_unflatten((activation_post_process_2,), self._out_spec) 2025-09-09T14:11:33.5944359Z 2025-09-09T14:11:33.5944747Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:11:33.5945273Z model fx: GraphModule( 2025-09-09T14:11:33.5945718Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:11:33.5947119Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0188]), zero_point=tensor([-45], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:11:33.5948798Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.5603605508804321, max_val=3.2356624603271484) 2025-09-09T14:11:33.5949559Z ) 2025-09-09T14:11:33.5949818Z (conv): ConvBn1d( 2025-09-09T14:11:33.5950148Z 3, 3, kernel_size=(3,), stride=(1,), bias=False 2025-09-09T14:11:33.5950763Z (bn): BatchNorm1d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T14:11:33.5951264Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:11:33.5952329Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0022, 0.0026, 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 2025-09-09T14:11:33.5953756Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.2639, -0.2941, -0.2608]), max_val=tensor([0.2795, 0.3227, 0.2891])) 2025-09-09T14:11:33.5954464Z ) 2025-09-09T14:11:33.5954653Z ) 2025-09-09T14:11:33.5954944Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:11:33.5955987Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0169]), zero_point=tensor([-13], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:11:33.5957307Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.9445278644561768, max_val=2.3592891693115234) 2025-09-09T14:11:33.5957863Z ) 2025-09-09T14:11:33.5958149Z ) 2025-09-09T14:11:33.5958252Z 2025-09-09T14:11:33.5958257Z 2025-09-09T14:11:33.5958260Z 2025-09-09T14:11:33.5958348Z def forward(self, x): 2025-09-09T14:11:33.5958732Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:11:33.5959313Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T14:11:33.5959900Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T14:11:33.5960431Z return activation_post_process_1 2025-09-09T14:11:33.5960703Z 2025-09-09T14:11:33.5961005Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:11:33.5961402Z diff: tensor([[[0., 0., 0.], 2025-09-09T14:11:33.5961661Z [0., 0., 0.], 2025-09-09T14:11:33.5961892Z [0., 0., 0.]], 2025-09-09T14:11:33.5962036Z 2025-09-09T14:11:33.5962124Z [[0., 0., 0.], 2025-09-09T14:11:33.5962353Z [0., 0., 0.], 2025-09-09T14:11:33.5962576Z [0., 0., 0.]], 2025-09-09T14:11:33.5962723Z 2025-09-09T14:11:33.5962817Z [[0., 0., 0.], 2025-09-09T14:11:33.5963031Z [0., 0., 0.], 2025-09-09T14:11:33.5963284Z [0., 0., 0.]]], grad_fn=) 2025-09-09T14:11:33.5963605Z converted model pt2e: GraphModule( 2025-09-09T14:11:33.5963894Z (conv): Module() 2025-09-09T14:11:33.5964106Z (bn): Module() 2025-09-09T14:11:33.5964319Z ) 2025-09-09T14:11:33.5964423Z 2025-09-09T14:11:33.5964431Z 2025-09-09T14:11:33.5964435Z 2025-09-09T14:11:33.5964536Z def forward(self, x): 2025-09-09T14:11:33.5964832Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:11:33.5965247Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T14:11:33.5966061Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.01880793273448944, -45, -128, 127, torch.int8); x = None 2025-09-09T14:11:33.5967435Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.01880793273448944, -45, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T14:11:33.5968591Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T14:11:33.5969093Z _scale_0 = self._scale_0 2025-09-09T14:11:33.5969385Z _zero_point_0 = self._zero_point_0 2025-09-09T14:11:33.5969709Z quantize_per_channel = self._frozen_param0 2025-09-09T14:11:33.5970697Z 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 2025-09-09T14:11:33.5971677Z conv_weight_bias = self.conv.weight_bias 2025-09-09T14:11:33.5972596Z 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 2025-09-09T14:11:33.5973987Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv1d_2, 0.016877712681889534, -13, -128, 127, torch.int8); conv1d_2 = None 2025-09-09T14:11:33.5975434Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.016877712681889534, -13, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T14:11:33.5976540Z return pytree.tree_unflatten((dequantize_per_tensor_default_1,), self._out_spec) 2025-09-09T14:11:33.5976995Z 2025-09-09T14:11:33.5977288Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:11:33.5977703Z onverted model fx: GraphModule( 2025-09-09T14:11:33.5978112Z (conv): QuantizedConv1d(Reference)(3, 3, kernel_size=(3,), stride=(1,)) 2025-09-09T14:11:33.5978508Z ) 2025-09-09T14:11:33.5978609Z 2025-09-09T14:11:33.5978614Z 2025-09-09T14:11:33.5978618Z 2025-09-09T14:11:33.5978784Z def forward(self, x): 2025-09-09T14:11:33.5979459Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.01880793273448944, -45, -128, 127, torch.int8); x = None 2025-09-09T14:11:33.5980831Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.01880793273448944, -45, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T14:11:33.5981947Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T14:11:33.5982934Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.016877712681889534, -13, -128, 127, torch.int8); conv = None 2025-09-09T14:11:33.5984349Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.016877712681889534, -13, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T14:11:33.5985332Z return dequantize_per_tensor_default_1 2025-09-09T14:11:33.5985620Z 2025-09-09T14:11:33.5985919Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:11:33.5986308Z diff: tensor([[[0., 0., 0.], 2025-09-09T14:11:33.5986561Z [0., 0., 0.], 2025-09-09T14:11:33.5986777Z [0., 0., 0.]], 2025-09-09T14:11:33.5986931Z 2025-09-09T14:11:33.5987010Z [[0., 0., 0.], 2025-09-09T14:11:33.5987222Z [0., 0., 0.], 2025-09-09T14:11:33.5987450Z [0., 0., 0.]], 2025-09-09T14:11:33.5987589Z 2025-09-09T14:11:33.5987667Z [[0., 0., 0.], 2025-09-09T14:11:33.5987891Z [0., 0., 0.], 2025-09-09T14:11:33.5988121Z [0., 0., 0.]]]) 2025-09-09T14:11:33.5988367Z model pt2e: GraphModule( 2025-09-09T14:11:33.5988619Z (conv): Module() 2025-09-09T14:11:33.5988830Z (bn): Module() 2025-09-09T14:11:33.5989156Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:11:33.5990184Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0188]), zero_point=tensor([-45], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:11:33.5991409Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.5603605508804321, max_val=3.2356624603271484) 2025-09-09T14:11:33.5991974Z ) 2025-09-09T14:11:33.5992261Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:11:33.5993313Z 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 2025-09-09T14:11:33.5994527Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.2940981984138489, max_val=0.32268622517585754) 2025-09-09T14:11:33.5995101Z ) 2025-09-09T14:11:33.5995402Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:11:33.5996517Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0169]), zero_point=tensor([-13], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:11:42.6785489Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.9461743831634521, max_val=2.3577661514282227) 2025-09-09T14:11:42.6786293Z ) 2025-09-09T14:11:42.6786550Z ) 2025-09-09T14:11:42.6786714Z 2025-09-09T14:11:42.6786720Z 2025-09-09T14:11:42.6786749Z 2025-09-09T14:11:42.6786885Z def forward(self, x): 2025-09-09T14:11:42.6787285Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:11:42.6787791Z conv_weight = self.conv.weight 2025-09-09T14:11:42.6788183Z bn_weight = self.bn.weight 2025-09-09T14:11:42.6788549Z bn_bias = self.bn.bias 2025-09-09T14:11:42.6788913Z bn_running_mean = self.bn.running_mean 2025-09-09T14:11:42.6789331Z bn_running_var = self.bn.running_var 2025-09-09T14:11:42.6790134Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T14:11:42.6790753Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:11:42.6791401Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T14:11:42.6791971Z add = torch.ops.aten.add.Tensor(bn_running_var, 1e-05) 2025-09-09T14:11:42.6792403Z sqrt = torch.ops.aten.sqrt.default(add); add = None 2025-09-09T14:11:42.6792959Z div = torch.ops.aten.div.Tensor(bn_weight, sqrt); sqrt = None 2025-09-09T14:11:42.6793426Z reshape = torch.ops.aten.reshape.default(div, [-1, 1, 1]) 2025-09-09T14:11:42.6793976Z mul = torch.ops.aten.mul.Tensor(conv_weight, reshape); conv_weight = reshape = None 2025-09-09T14:11:42.6794578Z activation_post_process_1 = self.activation_post_process_1(mul); mul = None 2025-09-09T14:11:42.6795505Z 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 2025-09-09T14:11:42.6796464Z reshape_1 = torch.ops.aten.reshape.default(div, [1, -1, 1]); div = None 2025-09-09T14:11:42.6797053Z div_1 = torch.ops.aten.div.Tensor(conv1d_1, reshape_1); conv1d_1 = reshape_1 = None 2025-09-09T14:11:42.6798032Z 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 2025-09-09T14:11:42.6799045Z activation_post_process_2 = self.activation_post_process_2(batch_norm_1); batch_norm_1 = None 2025-09-09T14:11:42.6799706Z return pytree.tree_unflatten((activation_post_process_2,), self._out_spec) 2025-09-09T14:11:42.6800124Z 2025-09-09T14:11:42.6800434Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:11:42.6800835Z model fx: GraphModule( 2025-09-09T14:11:42.6801180Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:11:42.6802227Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0188]), zero_point=tensor([-45], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:11:42.6803459Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.5603605508804321, max_val=3.2356624603271484) 2025-09-09T14:11:42.6804032Z ) 2025-09-09T14:11:42.6804224Z (conv): ConvBn1d( 2025-09-09T14:11:42.6804486Z 3, 3, kernel_size=(3,), stride=(1,), bias=False 2025-09-09T14:11:42.6804955Z (bn): BatchNorm1d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T14:11:42.6805452Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:11:42.6806478Z 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 2025-09-09T14:11:42.6807720Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.2940981984138489, max_val=0.32268622517585754) 2025-09-09T14:11:42.6808297Z ) 2025-09-09T14:11:42.6808479Z ) 2025-09-09T14:11:42.6808787Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:11:42.6809814Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0169]), zero_point=tensor([-13], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:11:42.6811047Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.9461743831634521, max_val=2.3577661514282227) 2025-09-09T14:11:42.6811606Z ) 2025-09-09T14:11:42.6811799Z ) 2025-09-09T14:11:42.6811901Z 2025-09-09T14:11:42.6811905Z 2025-09-09T14:11:42.6811909Z 2025-09-09T14:11:42.6812016Z def forward(self, x): 2025-09-09T14:11:42.6812467Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:11:42.6813054Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T14:11:42.6813646Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T14:11:42.6814121Z return activation_post_process_1 2025-09-09T14:11:42.6814395Z 2025-09-09T14:11:42.6814702Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:11:42.6815173Z diff: tensor([[[0., 0., 0.], 2025-09-09T14:11:42.6815417Z [0., 0., 0.], 2025-09-09T14:11:42.6815657Z [0., 0., 0.]], 2025-09-09T14:11:42.6815797Z 2025-09-09T14:11:42.6815877Z [[0., 0., 0.], 2025-09-09T14:11:42.6816101Z [0., 0., 0.], 2025-09-09T14:11:42.6816316Z [0., 0., 0.]], 2025-09-09T14:11:42.6816470Z 2025-09-09T14:11:42.6816574Z [[0., 0., 0.], 2025-09-09T14:11:42.6816800Z [0., 0., 0.], 2025-09-09T14:11:42.6817044Z [0., 0., 0.]]], grad_fn=) 2025-09-09T14:11:42.6817375Z converted model pt2e: GraphModule( 2025-09-09T14:11:42.6817653Z (conv): Module() 2025-09-09T14:11:42.6817879Z (bn): Module() 2025-09-09T14:11:42.6818076Z ) 2025-09-09T14:11:42.6818189Z 2025-09-09T14:11:42.6818193Z 2025-09-09T14:11:42.6818197Z 2025-09-09T14:11:42.6818285Z def forward(self, x): 2025-09-09T14:11:42.6818588Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:11:42.6818987Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T14:11:42.6819773Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.01880793273448944, -45, -128, 127, torch.int8); x = None 2025-09-09T14:11:42.6821142Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.01880793273448944, -45, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T14:11:42.6822298Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T14:11:42.6822835Z quantize_per_tensor = self._frozen_param0 2025-09-09T14:11:42.6823706Z dequantize_per_tensor = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor, 0.0025408363435417414, 0, -127, 127, torch.int8); quantize_per_tensor = None 2025-09-09T14:11:42.6824808Z conv_weight_bias = self.conv.weight_bias 2025-09-09T14:11:42.6825716Z 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 2025-09-09T14:11:42.6827109Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv1d_2, 0.016878198832273483, -13, -128, 127, torch.int8); conv1d_2 = None 2025-09-09T14:11:42.6828553Z dequantize_per_tensor_default_2 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_2, 0.016878198832273483, -13, -128, 127, torch.int8); quantize_per_tensor_default_2 = None 2025-09-09T14:11:42.6829662Z return pytree.tree_unflatten((dequantize_per_tensor_default_2,), self._out_spec) 2025-09-09T14:11:42.6830112Z 2025-09-09T14:11:42.6830415Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:11:42.6830814Z onverted model fx: GraphModule( 2025-09-09T14:11:42.6831221Z (conv): QuantizedConv1d(Reference)(3, 3, kernel_size=(3,), stride=(1,)) 2025-09-09T14:11:42.6831622Z ) 2025-09-09T14:11:42.6831734Z 2025-09-09T14:11:42.6831739Z 2025-09-09T14:11:42.6831742Z 2025-09-09T14:11:42.6831831Z def forward(self, x): 2025-09-09T14:11:42.6832498Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.01880793273448944, -45, -128, 127, torch.int8); x = None 2025-09-09T14:11:42.6834012Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.01880793273448944, -45, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T14:11:42.6835137Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T14:11:42.6836068Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.016878198832273483, -13, -128, 127, torch.int8); conv = None 2025-09-09T14:11:42.6837566Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.016878198832273483, -13, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T14:11:42.6838633Z return dequantize_per_tensor_default_1 2025-09-09T14:11:42.6838924Z 2025-09-09T14:11:42.6839233Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:11:42.6839627Z diff: tensor([[[0., 0., 0.], 2025-09-09T14:11:42.6839884Z [0., 0., 0.], 2025-09-09T14:11:42.6840100Z [0., 0., 0.]], 2025-09-09T14:11:42.6840253Z 2025-09-09T14:11:42.6840336Z [[0., 0., 0.], 2025-09-09T14:11:42.6840550Z [0., 0., 0.], 2025-09-09T14:11:42.6840774Z [0., 0., 0.]], 2025-09-09T14:11:42.6840914Z 2025-09-09T14:11:42.6841006Z [[0., 0., 0.], 2025-09-09T14:11:42.6841217Z [0., 0., 0.], 2025-09-09T14:11:42.6841442Z [0., 0., 0.]]]) 2025-09-09T14:11:42.6841679Z model pt2e: GraphModule( 2025-09-09T14:11:42.6841930Z (conv1): Module() 2025-09-09T14:11:42.6842140Z (bn1): Module() 2025-09-09T14:11:42.6842363Z (conv2): Module() 2025-09-09T14:11:42.6842570Z (bn2): Module() 2025-09-09T14:11:42.6842895Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:11:42.6843937Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0188]), zero_point=tensor([-45], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:11:42.6845149Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.5603605508804321, max_val=3.2356624603271484) 2025-09-09T14:11:42.6845714Z ) 2025-09-09T14:11:42.6846001Z (activation_post_process_3): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:11:42.6847090Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0025, 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 2025-09-09T14:11:42.6848509Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.3119, -0.2563, -0.2799]), max_val=tensor([0.3101, 0.1970, 0.1855])) 2025-09-09T14:11:42.6849215Z ) 2025-09-09T14:11:42.6849513Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:11:51.0637890Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0026, 0.0026, 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 2025-09-09T14:11:51.0639471Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.3263, -0.3276, -0.3045]), max_val=tensor([0.1376, 0.2760, 0.3298])) 2025-09-09T14:11:51.0640179Z ) 2025-09-09T14:11:51.0640492Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:11:51.0641523Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0132]), zero_point=tensor([-3], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:11:51.0642756Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.6533392667770386, max_val=1.7188055515289307) 2025-09-09T14:11:51.0643330Z ) 2025-09-09T14:11:51.0643622Z (activation_post_process_4): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:11:51.0644956Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0110]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:11:51.0646155Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.403315544128418, max_val=1.3918161392211914) 2025-09-09T14:11:51.0646724Z ) 2025-09-09T14:11:51.0646904Z ) 2025-09-09T14:11:51.0647023Z 2025-09-09T14:11:51.0647027Z 2025-09-09T14:11:51.0647031Z 2025-09-09T14:11:51.0647121Z def forward(self, x): 2025-09-09T14:11:51.0647541Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:11:51.0647905Z conv1_weight = self.conv1.weight 2025-09-09T14:11:51.0648219Z bn1_weight = self.bn1.weight 2025-09-09T14:11:51.0648495Z bn1_bias = self.bn1.bias 2025-09-09T14:11:51.0648781Z conv2_weight = self.conv2.weight 2025-09-09T14:11:51.0649074Z conv2_bias = self.conv2.bias 2025-09-09T14:11:51.0649363Z bn2_weight = self.bn2.weight 2025-09-09T14:11:51.0649645Z bn2_bias = self.bn2.bias 2025-09-09T14:11:51.0649963Z bn1_running_mean = self.bn1.running_mean 2025-09-09T14:11:51.0650305Z bn1_running_var = self.bn1.running_var 2025-09-09T14:11:51.0650663Z bn1_num_batches_tracked = self.bn1.num_batches_tracked 2025-09-09T14:11:51.0651051Z bn2_running_mean = self.bn2.running_mean 2025-09-09T14:11:51.0651384Z bn2_running_var = self.bn2.running_var 2025-09-09T14:11:51.0651738Z bn2_num_batches_tracked = self.bn2.num_batches_tracked 2025-09-09T14:11:51.0652227Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:11:51.0652864Z add_ = torch.ops.aten.add_.Tensor(bn1_num_batches_tracked, 1); bn1_num_batches_tracked = add_ = None 2025-09-09T14:11:51.0653601Z add__1 = torch.ops.aten.add_.Tensor(bn2_num_batches_tracked, 1); bn2_num_batches_tracked = add__1 = None 2025-09-09T14:11:51.0654178Z add = torch.ops.aten.add.Tensor(bn2_running_var, 1e-05) 2025-09-09T14:11:51.0654611Z sqrt = torch.ops.aten.sqrt.default(add); add = None 2025-09-09T14:11:51.0655065Z div = torch.ops.aten.div.Tensor(bn2_weight, sqrt); sqrt = None 2025-09-09T14:11:51.0655537Z reshape = torch.ops.aten.reshape.default(div, [-1, 1, 1]) 2025-09-09T14:11:51.0656095Z mul = torch.ops.aten.mul.Tensor(conv2_weight, reshape); conv2_weight = reshape = None 2025-09-09T14:11:51.0656704Z activation_post_process_3 = self.activation_post_process_3(mul); mul = None 2025-09-09T14:11:51.0657382Z zeros_like = torch.ops.aten.zeros_like.default(conv2_bias, dtype = torch.float32, pin_memory = False) 2025-09-09T14:11:51.0657993Z add_2 = torch.ops.aten.add.Tensor(bn1_running_var, 1e-05) 2025-09-09T14:11:51.0658434Z sqrt_1 = torch.ops.aten.sqrt.default(add_2); add_2 = None 2025-09-09T14:11:51.0658921Z div_2 = torch.ops.aten.div.Tensor(bn1_weight, sqrt_1); sqrt_1 = None 2025-09-09T14:11:51.0659415Z reshape_3 = torch.ops.aten.reshape.default(div_2, [-1, 1, 1]) 2025-09-09T14:11:51.0660005Z mul_1 = torch.ops.aten.mul.Tensor(conv1_weight, reshape_3); conv1_weight = reshape_3 = None 2025-09-09T14:11:51.0660646Z activation_post_process_1 = self.activation_post_process_1(mul_1); mul_1 = None 2025-09-09T14:11:51.0661586Z 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 2025-09-09T14:11:51.0662507Z reshape_4 = torch.ops.aten.reshape.default(div_2, [1, -1, 1]); div_2 = None 2025-09-09T14:11:51.0663091Z div_3 = torch.ops.aten.div.Tensor(conv1d_3, reshape_4); conv1d_3 = reshape_4 = None 2025-09-09T14:11:51.0664111Z 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 2025-09-09T14:11:51.0665157Z activation_post_process_2 = self.activation_post_process_2(batch_norm_3); batch_norm_3 = None 2025-09-09T14:11:51.0666294Z 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 2025-09-09T14:11:51.0667271Z reshape_1 = torch.ops.aten.reshape.default(div, [1, -1, 1]); div = None 2025-09-09T14:11:51.0667847Z div_1 = torch.ops.aten.div.Tensor(conv1d_2, reshape_1); conv1d_2 = reshape_1 = None 2025-09-09T14:11:51.0668490Z reshape_2 = torch.ops.aten.reshape.default(conv2_bias, [1, -1, 1]); conv2_bias = None 2025-09-09T14:11:51.0669157Z add_1 = torch.ops.aten.add.Tensor(div_1, reshape_2); div_1 = reshape_2 = None 2025-09-09T14:11:51.0670149Z 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 2025-09-09T14:11:51.0671208Z activation_post_process_4 = self.activation_post_process_4(batch_norm_2); batch_norm_2 = None 2025-09-09T14:11:51.0671857Z return pytree.tree_unflatten((activation_post_process_4,), self._out_spec) 2025-09-09T14:11:51.0672292Z 2025-09-09T14:11:51.0672591Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:11:51.0672996Z model fx: GraphModule( 2025-09-09T14:11:51.0673352Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:11:51.0674383Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0188]), zero_point=tensor([-45], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:11:51.0675614Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.5603605508804321, max_val=3.2356624603271484) 2025-09-09T14:11:51.0676171Z ) 2025-09-09T14:11:51.0676494Z (conv1): ConvBn1d( 2025-09-09T14:11:51.0676757Z 3, 3, kernel_size=(3,), stride=(1,), bias=False 2025-09-09T14:11:51.0677232Z (bn): BatchNorm1d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T14:11:51.0677754Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:11:51.0678807Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0026, 0.0026, 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 2025-09-09T14:11:51.0680248Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.3263, -0.3276, -0.3045]), max_val=tensor([0.1376, 0.2760, 0.3298])) 2025-09-09T14:11:51.0680972Z ) 2025-09-09T14:11:51.0681151Z ) 2025-09-09T14:11:51.0681452Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:11:51.0682475Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0132]), zero_point=tensor([-3], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:11:51.0683697Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.6533392667770386, max_val=1.7188055515289307) 2025-09-09T14:11:51.0684252Z ) 2025-09-09T14:11:51.0684447Z (conv2): ConvBn1d( 2025-09-09T14:11:51.0684695Z 3, 3, kernel_size=(3,), stride=(1,) 2025-09-09T14:11:51.0685127Z (bn): BatchNorm1d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T14:11:51.0685636Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:11:51.0686680Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0025, 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 2025-09-09T14:11:51.0688110Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.3119, -0.2563, -0.2799]), max_val=tensor([0.3101, 0.1970, 0.1855])) 2025-09-09T14:11:51.0688829Z ) 2025-09-09T14:11:51.0689007Z ) 2025-09-09T14:11:51.0689392Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:11:51.0690415Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0110]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:11:51.0691631Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.403315544128418, max_val=1.3918161392211914) 2025-09-09T14:11:51.0692246Z ) 2025-09-09T14:11:51.0692437Z ) 2025-09-09T14:11:51.0692539Z 2025-09-09T14:11:51.0692543Z 2025-09-09T14:11:51.0692547Z 2025-09-09T14:11:51.0692653Z def forward(self, x): 2025-09-09T14:11:51.0693025Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:11:51.0693618Z conv1 = self.conv1(activation_post_process_0); activation_post_process_0 = None 2025-09-09T14:11:51.0694218Z activation_post_process_1 = self.activation_post_process_1(conv1); conv1 = None 2025-09-09T14:11:51.0694832Z conv2 = self.conv2(activation_post_process_1); activation_post_process_1 = None 2025-09-09T14:11:51.0695441Z activation_post_process_2 = self.activation_post_process_2(conv2); conv2 = None 2025-09-09T14:11:51.0695903Z return activation_post_process_2 2025-09-09T14:11:51.0696188Z 2025-09-09T14:11:51.0696475Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:11:51.0696867Z diff: tensor([[[0.], 2025-09-09T14:11:51.0697091Z [0.], 2025-09-09T14:11:51.0697299Z [0.]], 2025-09-09T14:11:51.0697422Z 2025-09-09T14:11:51.0697501Z [[0.], 2025-09-09T14:11:51.0697705Z [0.], 2025-09-09T14:11:51.0697897Z [0.]], 2025-09-09T14:11:51.0698034Z 2025-09-09T14:11:51.0698112Z [[0.], 2025-09-09T14:11:51.0698315Z [0.], 2025-09-09T14:11:51.0698534Z [0.]]], grad_fn=) 2025-09-09T14:11:51.0698857Z converted model pt2e: GraphModule( 2025-09-09T14:11:51.0699134Z (conv1): Module() 2025-09-09T14:11:51.0699362Z (bn1): Module() 2025-09-09T14:11:51.0699572Z (conv2): Module() 2025-09-09T14:11:51.0699795Z (bn2): Module() 2025-09-09T14:11:51.0699992Z ) 2025-09-09T14:11:51.0700124Z 2025-09-09T14:11:51.0700129Z 2025-09-09T14:11:51.0700132Z 2025-09-09T14:11:52.5772704Z def forward(self, x): 2025-09-09T14:11:52.5773183Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:11:52.5773715Z conv2_bias = self.conv2.bias 2025-09-09T14:11:52.5774191Z bn1_num_batches_tracked = self.bn1.num_batches_tracked 2025-09-09T14:11:52.5774752Z bn2_num_batches_tracked = self.bn2.num_batches_tracked 2025-09-09T14:11:52.5775825Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.01880793273448944, -45, -128, 127, torch.int8); x = None 2025-09-09T14:11:52.5777688Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.01880793273448944, -45, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T14:11:52.5779253Z add_ = torch.ops.aten.add_.Tensor(bn1_num_batches_tracked, 1); bn1_num_batches_tracked = add_ = None 2025-09-09T14:11:52.5780227Z add__1 = torch.ops.aten.add_.Tensor(bn2_num_batches_tracked, 1); bn2_num_batches_tracked = add__1 = None 2025-09-09T14:11:52.5780930Z _scale_0 = self._scale_0 2025-09-09T14:11:52.5781299Z _zero_point_0 = self._zero_point_0 2025-09-09T14:11:52.5781682Z _scale_1 = self._scale_1 2025-09-09T14:11:52.5782048Z _zero_point_1 = self._zero_point_1 2025-09-09T14:11:52.5782471Z quantize_per_channel_1 = self._frozen_param0 2025-09-09T14:11:52.5783809Z 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 2025-09-09T14:11:52.5785244Z conv1_weight_bias = self.conv1.weight_bias 2025-09-09T14:11:52.5786769Z 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 2025-09-09T14:11:52.5788188Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv1d_5, 0.013224096968770027, -3, -128, 127, torch.int8); conv1d_5 = None 2025-09-09T14:11:52.5789628Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.013224096968770027, -3, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T14:11:52.5790709Z quantize_per_channel = self._frozen_param1 2025-09-09T14:11:52.5791811Z 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 2025-09-09T14:11:52.5793314Z 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 2025-09-09T14:11:52.5794658Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv1d_4, 0.010961300693452358, 0, -128, 127, torch.int8); conv1d_4 = None 2025-09-09T14:11:52.5796086Z dequantize_per_tensor_default_2 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_2, 0.010961300693452358, 0, -128, 127, torch.int8); quantize_per_tensor_default_2 = None 2025-09-09T14:11:52.5797259Z return pytree.tree_unflatten((dequantize_per_tensor_default_2,), self._out_spec) 2025-09-09T14:11:52.5797715Z 2025-09-09T14:11:52.5798019Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:11:52.5798416Z onverted model fx: GraphModule( 2025-09-09T14:11:52.5798825Z (conv1): QuantizedConv1d(Reference)(3, 3, kernel_size=(3,), stride=(1,)) 2025-09-09T14:11:52.5799365Z (conv2): QuantizedConv1d(Reference)(3, 3, kernel_size=(3,), stride=(1,)) 2025-09-09T14:11:52.5799775Z ) 2025-09-09T14:11:52.5799878Z 2025-09-09T14:11:52.5799882Z 2025-09-09T14:11:52.5799886Z 2025-09-09T14:11:52.5799976Z def forward(self, x): 2025-09-09T14:11:52.5800654Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.01880793273448944, -45, -128, 127, torch.int8); x = None 2025-09-09T14:11:52.5802029Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.01880793273448944, -45, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T14:11:52.5803297Z conv1 = self.conv1(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T14:11:52.5804255Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv1, 0.013224096968770027, -3, -128, 127, torch.int8); conv1 = None 2025-09-09T14:11:52.5805681Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.013224096968770027, -3, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T14:11:52.5806810Z conv2 = self.conv2(dequantize_per_tensor_default_1); dequantize_per_tensor_default_1 = None 2025-09-09T14:11:52.5807769Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv2, 0.010961300693452358, 0, -128, 127, torch.int8); conv2 = None 2025-09-09T14:11:52.5809179Z dequantize_per_tensor_default_2 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_2, 0.010961300693452358, 0, -128, 127, torch.int8); quantize_per_tensor_default_2 = None 2025-09-09T14:11:52.5810147Z return dequantize_per_tensor_default_2 2025-09-09T14:11:52.5810451Z 2025-09-09T14:11:52.5810743Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:11:52.5811230Z diff: tensor([[[0.], 2025-09-09T14:11:52.5811453Z [0.], 2025-09-09T14:11:52.5811659Z [0.]], 2025-09-09T14:11:52.5811782Z 2025-09-09T14:11:52.5811860Z [[0.], 2025-09-09T14:11:52.5812067Z [0.], 2025-09-09T14:11:52.5812260Z [0.]], 2025-09-09T14:11:52.5812396Z 2025-09-09T14:11:52.5812474Z [[0.], 2025-09-09T14:11:52.5812675Z [0.], 2025-09-09T14:11:52.5812866Z [0.]]]) 2025-09-09T14:11:52.5813096Z model pt2e: GraphModule( 2025-09-09T14:11:52.5813413Z (conv1): Module() 2025-09-09T14:11:52.5813637Z (bn1): Module() 2025-09-09T14:11:52.5813844Z (conv2): Module() 2025-09-09T14:11:52.5814067Z (bn2): Module() 2025-09-09T14:11:52.5814386Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:11:52.5815431Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0188]), zero_point=tensor([-45], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:11:52.5816661Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.5603605508804321, max_val=3.2356624603271484) 2025-09-09T14:11:52.5817217Z ) 2025-09-09T14:11:52.5817519Z (activation_post_process_3): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:11:52.5818545Z 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 2025-09-09T14:11:52.5819779Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.31192728877067566, max_val=0.31014329195022583) 2025-09-09T14:11:52.5820351Z ) 2025-09-09T14:11:52.5820637Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:11:52.5821677Z 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 2025-09-09T14:11:52.5822890Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.32764676213264465, max_val=0.3298276662826538) 2025-09-09T14:11:52.5823459Z ) 2025-09-09T14:11:52.5823781Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:11:52.5824986Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0132]), zero_point=tensor([-3], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:11:52.5826196Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.652099370956421, max_val=1.720017671585083) 2025-09-09T14:11:52.5826754Z ) 2025-09-09T14:11:52.5827043Z (activation_post_process_4): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:11:52.5828072Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0109]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:11:52.5829271Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.4020289182662964, max_val=1.3896838426589966) 2025-09-09T14:11:52.5829841Z ) 2025-09-09T14:11:52.5830015Z ) 2025-09-09T14:11:52.5830126Z 2025-09-09T14:11:52.5830130Z 2025-09-09T14:11:52.5830134Z 2025-09-09T14:11:52.5830221Z def forward(self, x): 2025-09-09T14:11:52.5830534Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:11:52.5830891Z conv1_weight = self.conv1.weight 2025-09-09T14:11:52.5831205Z bn1_weight = self.bn1.weight 2025-09-09T14:11:52.5831478Z bn1_bias = self.bn1.bias 2025-09-09T14:11:52.5831757Z conv2_weight = self.conv2.weight 2025-09-09T14:11:52.5832048Z conv2_bias = self.conv2.bias 2025-09-09T14:11:52.5832333Z bn2_weight = self.bn2.weight 2025-09-09T14:11:52.5832600Z bn2_bias = self.bn2.bias 2025-09-09T14:11:52.5833050Z bn1_running_mean = self.bn1.running_mean 2025-09-09T14:11:52.5833389Z bn1_running_var = self.bn1.running_var 2025-09-09T14:11:52.5833750Z bn1_num_batches_tracked = self.bn1.num_batches_tracked 2025-09-09T14:11:52.5834137Z bn2_running_mean = self.bn2.running_mean 2025-09-09T14:11:52.5834461Z bn2_running_var = self.bn2.running_var 2025-09-09T14:11:52.5834827Z bn2_num_batches_tracked = self.bn2.num_batches_tracked 2025-09-09T14:11:52.5835299Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:11:52.5836055Z add_ = torch.ops.aten.add_.Tensor(bn1_num_batches_tracked, 1); bn1_num_batches_tracked = add_ = None 2025-09-09T14:11:52.5836927Z add__1 = torch.ops.aten.add_.Tensor(bn2_num_batches_tracked, 1); bn2_num_batches_tracked = add__1 = None 2025-09-09T14:11:52.5837509Z add = torch.ops.aten.add.Tensor(bn2_running_var, 1e-05) 2025-09-09T14:11:52.5837942Z sqrt = torch.ops.aten.sqrt.default(add); add = None 2025-09-09T14:11:52.5838392Z div = torch.ops.aten.div.Tensor(bn2_weight, sqrt); sqrt = None 2025-09-09T14:11:52.5838882Z reshape = torch.ops.aten.reshape.default(div, [-1, 1, 1]) 2025-09-09T14:11:52.5839432Z mul = torch.ops.aten.mul.Tensor(conv2_weight, reshape); conv2_weight = reshape = None 2025-09-09T14:12:00.9759502Z activation_post_process_3 = self.activation_post_process_3(mul); mul = None 2025-09-09T14:12:00.9760374Z zeros_like = torch.ops.aten.zeros_like.default(conv2_bias, dtype = torch.float32, pin_memory = False) 2025-09-09T14:12:00.9761010Z add_2 = torch.ops.aten.add.Tensor(bn1_running_var, 1e-05) 2025-09-09T14:12:00.9761458Z sqrt_1 = torch.ops.aten.sqrt.default(add_2); add_2 = None 2025-09-09T14:12:00.9761928Z div_2 = torch.ops.aten.div.Tensor(bn1_weight, sqrt_1); sqrt_1 = None 2025-09-09T14:12:00.9762432Z reshape_3 = torch.ops.aten.reshape.default(div_2, [-1, 1, 1]) 2025-09-09T14:12:00.9763010Z mul_1 = torch.ops.aten.mul.Tensor(conv1_weight, reshape_3); conv1_weight = reshape_3 = None 2025-09-09T14:12:00.9763654Z activation_post_process_1 = self.activation_post_process_1(mul_1); mul_1 = None 2025-09-09T14:12:00.9764589Z 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 2025-09-09T14:12:00.9765499Z reshape_4 = torch.ops.aten.reshape.default(div_2, [1, -1, 1]); div_2 = None 2025-09-09T14:12:00.9766097Z div_3 = torch.ops.aten.div.Tensor(conv1d_3, reshape_4); conv1d_3 = reshape_4 = None 2025-09-09T14:12:00.9767122Z 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 2025-09-09T14:12:00.9768304Z activation_post_process_2 = self.activation_post_process_2(batch_norm_3); batch_norm_3 = None 2025-09-09T14:12:00.9769367Z 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 2025-09-09T14:12:00.9770334Z reshape_1 = torch.ops.aten.reshape.default(div, [1, -1, 1]); div = None 2025-09-09T14:12:00.9770911Z div_1 = torch.ops.aten.div.Tensor(conv1d_2, reshape_1); conv1d_2 = reshape_1 = None 2025-09-09T14:12:00.9771547Z reshape_2 = torch.ops.aten.reshape.default(conv2_bias, [1, -1, 1]); conv2_bias = None 2025-09-09T14:12:00.9772144Z add_1 = torch.ops.aten.add.Tensor(div_1, reshape_2); div_1 = reshape_2 = None 2025-09-09T14:12:00.9773144Z 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 2025-09-09T14:12:00.9774262Z activation_post_process_4 = self.activation_post_process_4(batch_norm_2); batch_norm_2 = None 2025-09-09T14:12:00.9775219Z return pytree.tree_unflatten((activation_post_process_4,), self._out_spec) 2025-09-09T14:12:00.9775658Z 2025-09-09T14:12:00.9775953Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:12:00.9776355Z model fx: GraphModule( 2025-09-09T14:12:00.9776708Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:12:00.9777746Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0188]), zero_point=tensor([-45], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:12:00.9779126Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.5603605508804321, max_val=3.2356624603271484) 2025-09-09T14:12:00.9779687Z ) 2025-09-09T14:12:00.9779890Z (conv1): ConvBn1d( 2025-09-09T14:12:00.9780150Z 3, 3, kernel_size=(3,), stride=(1,), bias=False 2025-09-09T14:12:00.9780623Z (bn): BatchNorm1d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T14:12:00.9781140Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:12:00.9782149Z 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 2025-09-09T14:12:00.9783388Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.32764676213264465, max_val=0.3298276662826538) 2025-09-09T14:12:00.9783952Z ) 2025-09-09T14:12:00.9784146Z ) 2025-09-09T14:12:00.9784447Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:12:00.9785467Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0132]), zero_point=tensor([-3], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:12:00.9786676Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.652099370956421, max_val=1.720017671585083) 2025-09-09T14:12:00.9787229Z ) 2025-09-09T14:12:00.9787429Z (conv2): ConvBn1d( 2025-09-09T14:12:00.9787667Z 3, 3, kernel_size=(3,), stride=(1,) 2025-09-09T14:12:00.9788110Z (bn): BatchNorm1d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T14:12:00.9788623Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:12:00.9789628Z 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 2025-09-09T14:12:00.9790876Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.31192728877067566, max_val=0.31014329195022583) 2025-09-09T14:12:00.9791441Z ) 2025-09-09T14:12:00.9791633Z ) 2025-09-09T14:12:00.9791933Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:12:00.9792957Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0109]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:12:00.9794169Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.4020289182662964, max_val=1.3896838426589966) 2025-09-09T14:12:00.9794725Z ) 2025-09-09T14:12:00.9794912Z ) 2025-09-09T14:12:00.9795014Z 2025-09-09T14:12:00.9795018Z 2025-09-09T14:12:00.9795022Z 2025-09-09T14:12:00.9795132Z def forward(self, x): 2025-09-09T14:12:00.9795502Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:12:00.9796088Z conv1 = self.conv1(activation_post_process_0); activation_post_process_0 = None 2025-09-09T14:12:00.9796772Z activation_post_process_1 = self.activation_post_process_1(conv1); conv1 = None 2025-09-09T14:12:00.9797385Z conv2 = self.conv2(activation_post_process_1); activation_post_process_1 = None 2025-09-09T14:12:00.9798074Z activation_post_process_2 = self.activation_post_process_2(conv2); conv2 = None 2025-09-09T14:12:00.9798555Z return activation_post_process_2 2025-09-09T14:12:00.9798848Z 2025-09-09T14:12:00.9799140Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:12:00.9799535Z diff: tensor([[[0.], 2025-09-09T14:12:00.9799760Z [0.], 2025-09-09T14:12:00.9799967Z [0.]], 2025-09-09T14:12:00.9800091Z 2025-09-09T14:12:00.9800174Z [[0.], 2025-09-09T14:12:00.9800447Z [0.], 2025-09-09T14:12:00.9800640Z [0.]], 2025-09-09T14:12:00.9800773Z 2025-09-09T14:12:00.9800851Z [[0.], 2025-09-09T14:12:00.9801043Z [0.], 2025-09-09T14:12:00.9801274Z [0.]]], grad_fn=) 2025-09-09T14:12:00.9801596Z converted model pt2e: GraphModule( 2025-09-09T14:12:00.9801872Z (conv1): Module() 2025-09-09T14:12:00.9802095Z (bn1): Module() 2025-09-09T14:12:00.9802304Z (conv2): Module() 2025-09-09T14:12:00.9802531Z (bn2): Module() 2025-09-09T14:12:00.9802735Z ) 2025-09-09T14:12:00.9802849Z 2025-09-09T14:12:00.9802853Z 2025-09-09T14:12:00.9802858Z 2025-09-09T14:12:00.9802949Z def forward(self, x): 2025-09-09T14:12:00.9803251Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:12:00.9803629Z conv2_bias = self.conv2.bias 2025-09-09T14:12:00.9803975Z bn1_num_batches_tracked = self.bn1.num_batches_tracked 2025-09-09T14:12:00.9804378Z bn2_num_batches_tracked = self.bn2.num_batches_tracked 2025-09-09T14:12:00.9805203Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.01880793273448944, -45, -128, 127, torch.int8); x = None 2025-09-09T14:12:00.9806570Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.01880793273448944, -45, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T14:12:00.9807732Z add_ = torch.ops.aten.add_.Tensor(bn1_num_batches_tracked, 1); bn1_num_batches_tracked = add_ = None 2025-09-09T14:12:00.9808477Z add__1 = torch.ops.aten.add_.Tensor(bn2_num_batches_tracked, 1); bn2_num_batches_tracked = add__1 = None 2025-09-09T14:12:00.9809024Z quantize_per_tensor_1 = self._frozen_param0 2025-09-09T14:12:00.9809925Z dequantize_per_tensor_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_1, 0.002597068203613162, 0, -127, 127, torch.int8); quantize_per_tensor_1 = None 2025-09-09T14:12:00.9810817Z conv1_weight_bias = self.conv1.weight_bias 2025-09-09T14:12:00.9811769Z 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 2025-09-09T14:12:00.9813174Z quantize_per_tensor_default_3 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv1d_5, 0.013223988004028797, -3, -128, 127, torch.int8); conv1d_5 = None 2025-09-09T14:12:00.9814605Z dequantize_per_tensor_default_3 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_3, 0.013223988004028797, -3, -128, 127, torch.int8); quantize_per_tensor_default_3 = None 2025-09-09T14:12:00.9815591Z quantize_per_tensor = self._frozen_param1 2025-09-09T14:12:00.9816474Z 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 2025-09-09T14:12:00.9817884Z 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 2025-09-09T14:12:00.9819227Z quantize_per_tensor_default_4 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv1d_4, 0.010947893373668194, 0, -128, 127, torch.int8); conv1d_4 = None 2025-09-09T14:12:00.9820728Z dequantize_per_tensor_default_4 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_4, 0.010947893373668194, 0, -128, 127, torch.int8); quantize_per_tensor_default_4 = None 2025-09-09T14:12:00.9821831Z return pytree.tree_unflatten((dequantize_per_tensor_default_4,), self._out_spec) 2025-09-09T14:12:00.9822290Z 2025-09-09T14:12:22.8781043Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:12:22.8781621Z onverted model fx: GraphModule( 2025-09-09T14:12:22.8782319Z (conv1): QuantizedConv1d(Reference)(3, 3, kernel_size=(3,), stride=(1,)) 2025-09-09T14:12:22.8784021Z (conv2): QuantizedConv1d(Reference)(3, 3, kernel_size=(3,), stride=(1,)) 2025-09-09T14:12:22.8784440Z ) 2025-09-09T14:12:22.8784544Z 2025-09-09T14:12:22.8784550Z 2025-09-09T14:12:22.8784553Z 2025-09-09T14:12:22.8784658Z def forward(self, x): 2025-09-09T14:12:22.8785344Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.01880793273448944, -45, -128, 127, torch.int8); x = None 2025-09-09T14:12:22.8786732Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.01880793273448944, -45, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T14:12:22.8787846Z conv1 = self.conv1(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T14:12:22.8788801Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv1, 0.013223988004028797, -3, -128, 127, torch.int8); conv1 = None 2025-09-09T14:12:22.8790225Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.013223988004028797, -3, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T14:12:22.8791361Z conv2 = self.conv2(dequantize_per_tensor_default_1); dequantize_per_tensor_default_1 = None 2025-09-09T14:12:22.8792326Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv2, 0.010947893373668194, 0, -128, 127, torch.int8); conv2 = None 2025-09-09T14:12:22.8793737Z dequantize_per_tensor_default_2 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_2, 0.010947893373668194, 0, -128, 127, torch.int8); quantize_per_tensor_default_2 = None 2025-09-09T14:12:22.8794698Z return dequantize_per_tensor_default_2 2025-09-09T14:12:22.8794999Z 2025-09-09T14:12:22.8795338Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:12:22.8795726Z diff: tensor([[[0.], 2025-09-09T14:12:22.8795958Z [0.], 2025-09-09T14:12:22.8796152Z [0.]], 2025-09-09T14:12:22.8796290Z 2025-09-09T14:12:22.8796450Z [[0.], 2025-09-09T14:12:22.8796642Z [0.], 2025-09-09T14:12:22.8796846Z [0.]], 2025-09-09T14:12:22.8796969Z 2025-09-09T14:12:22.8797063Z [[0.], 2025-09-09T14:12:22.8797254Z [0.], 2025-09-09T14:12:22.8797458Z [0.]]]) 2025-09-09T14:12:22.8797862Z PASSED 2025-09-09T14:12:22.8798666Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn1d::test_qat_conv_bn_per_channel_weight_bias PASSED 2025-09-09T14:12:22.8799750Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn1d::test_qat_conv_bn_relu_fusion model pt2e: GraphModule( 2025-09-09T14:12:22.8800436Z (conv): Module() 2025-09-09T14:12:22.8800650Z (bn): Module() 2025-09-09T14:12:22.8800987Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:12:22.8802036Z 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 2025-09-09T14:12:22.8803255Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3264806270599365, max_val=1.318617343902588) 2025-09-09T14:12:22.8803820Z ) 2025-09-09T14:12:22.8804284Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:12:22.8805383Z 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 2025-09-09T14:12:22.8806814Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.2935, -0.3313, -0.3129]), max_val=tensor([0.2532, 0.1628, 0.3013])) 2025-09-09T14:12:22.8807592Z ) 2025-09-09T14:12:22.8807897Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:12:22.8808922Z 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 2025-09-09T14:12:22.8810096Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=1.410499095916748) 2025-09-09T14:12:22.8810626Z ) 2025-09-09T14:12:22.8810816Z ) 2025-09-09T14:12:22.8810918Z 2025-09-09T14:12:22.8810923Z 2025-09-09T14:12:22.8810940Z 2025-09-09T14:12:22.8811030Z def forward(self, x): 2025-09-09T14:12:22.8811330Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:12:22.8811702Z conv_weight = self.conv.weight 2025-09-09T14:12:22.8811996Z conv_bias = self.conv.bias 2025-09-09T14:12:22.8812275Z bn_weight = self.bn.weight 2025-09-09T14:12:22.8812558Z bn_bias = self.bn.bias 2025-09-09T14:12:22.8812826Z bn_running_mean = self.bn.running_mean 2025-09-09T14:12:22.8813154Z bn_running_var = self.bn.running_var 2025-09-09T14:12:22.8813503Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T14:12:22.8813982Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:12:22.8814613Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T14:12:22.8815201Z add = torch.ops.aten.add.Tensor(bn_running_var, 1e-05) 2025-09-09T14:12:22.8815637Z sqrt = torch.ops.aten.sqrt.default(add); add = None 2025-09-09T14:12:22.8816078Z div = torch.ops.aten.div.Tensor(bn_weight, sqrt); sqrt = None 2025-09-09T14:12:22.8816557Z reshape = torch.ops.aten.reshape.default(div, [-1, 1, 1]) 2025-09-09T14:12:22.8817105Z mul = torch.ops.aten.mul.Tensor(conv_weight, reshape); conv_weight = reshape = None 2025-09-09T14:12:22.8817720Z activation_post_process_1 = self.activation_post_process_1(mul); mul = None 2025-09-09T14:12:22.8818380Z zeros_like = torch.ops.aten.zeros_like.default(conv_bias, dtype = torch.float32, pin_memory = False) 2025-09-09T14:12:22.8819452Z 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 2025-09-09T14:12:22.8820424Z reshape_1 = torch.ops.aten.reshape.default(div, [1, -1, 1]); div = None 2025-09-09T14:12:22.8821006Z div_1 = torch.ops.aten.div.Tensor(conv1d_1, reshape_1); conv1d_1 = reshape_1 = None 2025-09-09T14:12:22.8821639Z reshape_2 = torch.ops.aten.reshape.default(conv_bias, [1, -1, 1]); conv_bias = None 2025-09-09T14:12:22.8822235Z add_1 = torch.ops.aten.add.Tensor(div_1, reshape_2); div_1 = reshape_2 = None 2025-09-09T14:12:22.8823189Z 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 2025-09-09T14:12:22.8832244Z relu = torch.ops.aten.relu.default(batch_norm_1); batch_norm_1 = None 2025-09-09T14:12:22.8832851Z activation_post_process_2 = self.activation_post_process_2(relu); relu = None 2025-09-09T14:12:22.8833445Z return pytree.tree_unflatten((activation_post_process_2,), self._out_spec) 2025-09-09T14:12:22.8833879Z 2025-09-09T14:12:22.8834365Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:12:22.8834781Z model fx: GraphModule( 2025-09-09T14:12:22.8835128Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:12:22.8836175Z 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 2025-09-09T14:12:22.8837537Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3264806270599365, max_val=1.318617343902588) 2025-09-09T14:12:22.8838224Z ) 2025-09-09T14:12:22.8838444Z (conv): ConvBnReLU1d( 2025-09-09T14:12:22.8838700Z 3, 3, kernel_size=(3,), stride=(1,) 2025-09-09T14:12:22.8839150Z (bn): BatchNorm1d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T14:12:22.8839658Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:12:22.8840736Z 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 2025-09-09T14:12:22.8842177Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.2935, -0.3313, -0.3129]), max_val=tensor([0.2532, 0.1628, 0.3013])) 2025-09-09T14:12:22.8842890Z ) 2025-09-09T14:12:22.8843092Z ) 2025-09-09T14:12:22.8843387Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:12:22.8844447Z 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 2025-09-09T14:12:22.8845628Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=1.410499095916748) 2025-09-09T14:12:22.8846137Z ) 2025-09-09T14:12:22.8846332Z ) 2025-09-09T14:12:22.8846437Z 2025-09-09T14:12:22.8846446Z 2025-09-09T14:12:22.8846450Z 2025-09-09T14:12:22.8846541Z def forward(self, x): 2025-09-09T14:12:22.8846932Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:12:22.8847520Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T14:12:22.8848108Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T14:12:22.8848580Z return activation_post_process_1 2025-09-09T14:12:22.8848861Z 2025-09-09T14:12:22.8849166Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:12:22.8849562Z diff: tensor([[[0., 0., 0.], 2025-09-09T14:12:22.8849832Z [0., 0., 0.], 2025-09-09T14:12:22.8850079Z [0., 0., 0.]]], grad_fn=) 2025-09-09T14:12:22.8850416Z converted model pt2e: GraphModule( 2025-09-09T14:12:22.8850696Z (conv): Module() 2025-09-09T14:12:22.8850922Z (bn): Module() 2025-09-09T14:12:22.8851130Z ) 2025-09-09T14:12:22.8851236Z 2025-09-09T14:12:22.8851240Z 2025-09-09T14:12:22.8851243Z 2025-09-09T14:12:22.8851333Z def forward(self, x): 2025-09-09T14:12:22.8851645Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:12:22.8852000Z conv_bias = self.conv.bias 2025-09-09T14:12:22.8852330Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T14:12:22.8853096Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.010372933000326157, 0, -128, 127, torch.int8); x = None 2025-09-09T14:12:22.8854463Z 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 2025-09-09T14:12:32.0060334Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T14:12:32.0061188Z _scale_0 = self._scale_0 2025-09-09T14:12:32.0062010Z _zero_point_0 = self._zero_point_0 2025-09-09T14:12:32.0062508Z quantize_per_channel = self._frozen_param0 2025-09-09T14:12:32.0063998Z 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 2025-09-09T14:12:32.0066282Z 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 2025-09-09T14:12:32.0068016Z relu = torch.ops.aten.relu.default(conv1d_2); conv1d_2 = None 2025-09-09T14:12:32.0069376Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(relu, 0.005531368777155876, -128, -128, 127, torch.int8); relu = None 2025-09-09T14:12:32.0071780Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.005531368777155876, -128, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T14:12:32.0073789Z return pytree.tree_unflatten((dequantize_per_tensor_default_1,), self._out_spec) 2025-09-09T14:12:32.0074474Z 2025-09-09T14:12:32.0074932Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:12:32.0075612Z onverted model fx: GraphModule( 2025-09-09T14:12:32.0076035Z (conv): ConvReLU1d( 2025-09-09T14:12:32.0076732Z (0): QuantizedConv1d(Reference)(3, 3, kernel_size=(3,), stride=(1,)) 2025-09-09T14:12:32.0077352Z (1): ReLU() 2025-09-09T14:12:32.0077669Z ) 2025-09-09T14:12:32.0077964Z ) 2025-09-09T14:12:32.0078151Z 2025-09-09T14:12:32.0078159Z 2025-09-09T14:12:32.0078166Z 2025-09-09T14:12:32.0078315Z def forward(self, x): 2025-09-09T14:12:32.0079488Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.010372933000326157, 0, -128, 127, torch.int8); x = None 2025-09-09T14:12:32.0081720Z 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 2025-09-09T14:12:32.0083641Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T14:12:32.0085297Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.005531368777155876, -128, -128, 127, torch.int8); conv = None 2025-09-09T14:12:32.0087887Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.005531368777155876, -128, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T14:12:32.0089655Z return dequantize_per_tensor_default_1 2025-09-09T14:12:32.0090147Z 2025-09-09T14:12:32.0090679Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:12:32.0091364Z diff: tensor([[[0., 0., 0.], 2025-09-09T14:12:32.0091810Z [0., 0., 0.], 2025-09-09T14:12:32.0092202Z [0., 0., 0.]]]) 2025-09-09T14:12:32.0092619Z model pt2e: GraphModule( 2025-09-09T14:12:32.0093089Z (conv): Module() 2025-09-09T14:12:32.0093451Z (bn): Module() 2025-09-09T14:12:32.0093968Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:12:32.0095743Z 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 2025-09-09T14:12:32.0097838Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3264806270599365, max_val=1.318617343902588) 2025-09-09T14:12:32.0098800Z ) 2025-09-09T14:12:32.0099321Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:12:32.0101404Z 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 2025-09-09T14:12:32.0103508Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.3312976360321045, max_val=0.3013271391391754) 2025-09-09T14:12:32.0104556Z ) 2025-09-09T14:12:32.0105082Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:12:32.0106853Z 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 2025-09-09T14:12:32.0109062Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=1.4099854230880737) 2025-09-09T14:12:32.0109976Z ) 2025-09-09T14:12:32.0110274Z ) 2025-09-09T14:12:32.0110470Z 2025-09-09T14:12:32.0110478Z 2025-09-09T14:12:32.0110485Z 2025-09-09T14:12:32.0110628Z def forward(self, x): 2025-09-09T14:12:32.0111163Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:12:32.0111765Z conv_weight = self.conv.weight 2025-09-09T14:12:32.0112267Z conv_bias = self.conv.bias 2025-09-09T14:12:32.0112707Z bn_weight = self.bn.weight 2025-09-09T14:12:32.0113171Z bn_bias = self.bn.bias 2025-09-09T14:12:32.0113653Z bn_running_mean = self.bn.running_mean 2025-09-09T14:12:32.0114175Z bn_running_var = self.bn.running_var 2025-09-09T14:12:32.0114719Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T14:12:32.0115550Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:12:32.0116694Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T14:12:32.0117629Z add = torch.ops.aten.add.Tensor(bn_running_var, 1e-05) 2025-09-09T14:12:32.0118402Z sqrt = torch.ops.aten.sqrt.default(add); add = None 2025-09-09T14:12:32.0119188Z div = torch.ops.aten.div.Tensor(bn_weight, sqrt); sqrt = None 2025-09-09T14:12:32.0119927Z reshape = torch.ops.aten.reshape.default(div, [-1, 1, 1]) 2025-09-09T14:12:32.0120921Z mul = torch.ops.aten.mul.Tensor(conv_weight, reshape); conv_weight = reshape = None 2025-09-09T14:12:32.0121896Z activation_post_process_1 = self.activation_post_process_1(mul); mul = None 2025-09-09T14:12:32.0122986Z zeros_like = torch.ops.aten.zeros_like.default(conv_bias, dtype = torch.float32, pin_memory = False) 2025-09-09T14:12:32.0124894Z 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 2025-09-09T14:12:32.0126532Z reshape_1 = torch.ops.aten.reshape.default(div, [1, -1, 1]); div = None 2025-09-09T14:12:32.0127504Z div_1 = torch.ops.aten.div.Tensor(conv1d_1, reshape_1); conv1d_1 = reshape_1 = None 2025-09-09T14:12:32.0128664Z reshape_2 = torch.ops.aten.reshape.default(conv_bias, [1, -1, 1]); conv_bias = None 2025-09-09T14:12:32.0129725Z add_1 = torch.ops.aten.add.Tensor(div_1, reshape_2); div_1 = reshape_2 = None 2025-09-09T14:12:32.0131452Z 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 2025-09-09T14:12:32.0133046Z relu = torch.ops.aten.relu.default(batch_norm_1); batch_norm_1 = None 2025-09-09T14:12:32.0133970Z activation_post_process_2 = self.activation_post_process_2(relu); relu = None 2025-09-09T14:12:32.0134958Z return pytree.tree_unflatten((activation_post_process_2,), self._out_spec) 2025-09-09T14:12:32.0135611Z 2025-09-09T14:12:32.0136038Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:12:32.0136685Z model fx: GraphModule( 2025-09-09T14:12:32.0137209Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:12:32.0139234Z 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 2025-09-09T14:12:32.0141502Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3264806270599365, max_val=1.318617343902588) 2025-09-09T14:12:32.0142505Z ) 2025-09-09T14:12:32.0142853Z (conv): ConvBnReLU1d( 2025-09-09T14:12:32.0143292Z 3, 3, kernel_size=(3,), stride=(1,) 2025-09-09T14:12:32.0144120Z (bn): BatchNorm1d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T14:12:32.0144963Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:12:32.0146591Z 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 2025-09-09T14:12:32.0148637Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.3312976360321045, max_val=0.3013271391391754) 2025-09-09T14:12:32.0149575Z ) 2025-09-09T14:12:32.0149858Z ) 2025-09-09T14:12:32.0150323Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:12:32.0152256Z 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 2025-09-09T14:12:32.0154287Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=1.4099854230880737) 2025-09-09T14:12:32.0155126Z ) 2025-09-09T14:12:32.0155432Z ) 2025-09-09T14:12:32.0155608Z 2025-09-09T14:12:32.0155617Z 2025-09-09T14:12:32.0155624Z 2025-09-09T14:12:32.0155787Z def forward(self, x): 2025-09-09T14:12:32.0156536Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:12:32.0157459Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T14:12:32.0158477Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T14:12:32.0159210Z return activation_post_process_1 2025-09-09T14:12:32.0159655Z 2025-09-09T14:12:32.0160146Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:12:32.0160827Z diff: tensor([[[0., 0., 0.], 2025-09-09T14:12:32.0161281Z [0., 0., 0.], 2025-09-09T14:12:32.0161695Z [0., 0., 0.]]], grad_fn=) 2025-09-09T14:12:32.0162178Z converted model pt2e: GraphModule( 2025-09-09T14:12:32.0162620Z (conv): Module() 2025-09-09T14:12:32.0162962Z (bn): Module() 2025-09-09T14:12:32.0163283Z ) 2025-09-09T14:12:32.0163457Z 2025-09-09T14:12:32.0163464Z 2025-09-09T14:12:32.0163470Z 2025-09-09T14:12:32.0163608Z def forward(self, x): 2025-09-09T14:12:32.0164073Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:12:32.0164629Z conv_bias = self.conv.bias 2025-09-09T14:12:32.0165161Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T14:12:32.0166406Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.010372933000326157, 0, -128, 127, torch.int8); x = None 2025-09-09T14:12:44.9958199Z 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 2025-09-09T14:12:44.9959412Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T14:12:44.9959960Z quantize_per_tensor = self._frozen_param0 2025-09-09T14:12:44.9960827Z dequantize_per_tensor = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor, 0.002608642913401127, 0, -127, 127, torch.int8); quantize_per_tensor = None 2025-09-09T14:12:44.9962554Z 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 2025-09-09T14:12:44.9963501Z relu = torch.ops.aten.relu.default(conv1d_2); conv1d_2 = None 2025-09-09T14:12:44.9964352Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(relu, 0.00552935479208827, -128, -128, 127, torch.int8); relu = None 2025-09-09T14:12:44.9965876Z dequantize_per_tensor_default_2 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_2, 0.00552935479208827, -128, -128, 127, torch.int8); quantize_per_tensor_default_2 = None 2025-09-09T14:12:44.9967113Z return pytree.tree_unflatten((dequantize_per_tensor_default_2,), self._out_spec) 2025-09-09T14:12:44.9967563Z 2025-09-09T14:12:44.9967877Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:12:44.9968283Z onverted model fx: GraphModule( 2025-09-09T14:12:44.9968572Z (conv): ConvReLU1d( 2025-09-09T14:12:44.9968921Z (0): QuantizedConv1d(Reference)(3, 3, kernel_size=(3,), stride=(1,)) 2025-09-09T14:12:44.9969317Z (1): ReLU() 2025-09-09T14:12:44.9969515Z ) 2025-09-09T14:12:44.9969704Z ) 2025-09-09T14:12:44.9969803Z 2025-09-09T14:12:44.9969807Z 2025-09-09T14:12:44.9969812Z 2025-09-09T14:12:44.9969914Z def forward(self, x): 2025-09-09T14:12:44.9970595Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.010372933000326157, 0, -128, 127, torch.int8); x = None 2025-09-09T14:12:44.9971958Z 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 2025-09-09T14:12:44.9973078Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T14:12:44.9974027Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.00552935479208827, -128, -128, 127, torch.int8); conv = None 2025-09-09T14:12:44.9975436Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.00552935479208827, -128, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T14:12:44.9976456Z return dequantize_per_tensor_default_1 2025-09-09T14:12:44.9976761Z 2025-09-09T14:12:44.9977056Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:12:44.9977473Z diff: tensor([[[0., 0., 0.], 2025-09-09T14:12:44.9977722Z [0., 0., 0.], 2025-09-09T14:12:44.9977961Z [0., 0., 0.]]]) 2025-09-09T14:12:44.9978393Z PASSED 2025-09-09T14:12:44.9979140Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn1d::test_qat_conv_bn_relu_fusion_cuda SKIPPED 2025-09-09T14:12:44.9980292Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn1d::test_qat_conv_bn_relu_fusion_no_conv_bias model pt2e: GraphModule( 2025-09-09T14:12:44.9981007Z (conv): Module() 2025-09-09T14:12:44.9981239Z (bn): Module() 2025-09-09T14:12:44.9981556Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:12:44.9982602Z 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 2025-09-09T14:12:44.9983918Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3264806270599365, max_val=1.318617343902588) 2025-09-09T14:12:44.9984472Z ) 2025-09-09T14:12:44.9984777Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:12:44.9985953Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0022, 0.0026, 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 2025-09-09T14:12:44.9987363Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.2639, -0.2941, -0.2608]), max_val=tensor([0.2795, 0.3227, 0.2891])) 2025-09-09T14:12:44.9988084Z ) 2025-09-09T14:12:44.9988373Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:12:44.9989413Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0039]), zero_point=tensor([-128], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:12:44.9990647Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=0.9981018900871277) 2025-09-09T14:12:44.9991154Z ) 2025-09-09T14:12:44.9991338Z ) 2025-09-09T14:12:44.9991439Z 2025-09-09T14:12:44.9991443Z 2025-09-09T14:12:44.9991447Z 2025-09-09T14:12:44.9991535Z def forward(self, x): 2025-09-09T14:12:44.9991845Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:12:44.9992226Z conv_weight = self.conv.weight 2025-09-09T14:12:44.9992513Z bn_weight = self.bn.weight 2025-09-09T14:12:44.9992789Z bn_bias = self.bn.bias 2025-09-09T14:12:44.9993057Z bn_running_mean = self.bn.running_mean 2025-09-09T14:12:44.9993386Z bn_running_var = self.bn.running_var 2025-09-09T14:12:44.9993732Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T14:12:44.9994216Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:12:44.9994854Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T14:12:44.9995435Z add = torch.ops.aten.add.Tensor(bn_running_var, 1e-05) 2025-09-09T14:12:44.9995867Z sqrt = torch.ops.aten.sqrt.default(add); add = None 2025-09-09T14:12:44.9996415Z div = torch.ops.aten.div.Tensor(bn_weight, sqrt); sqrt = None 2025-09-09T14:12:44.9996904Z reshape = torch.ops.aten.reshape.default(div, [-1, 1, 1]) 2025-09-09T14:12:44.9997449Z mul = torch.ops.aten.mul.Tensor(conv_weight, reshape); conv_weight = reshape = None 2025-09-09T14:12:44.9998064Z activation_post_process_1 = self.activation_post_process_1(mul); mul = None 2025-09-09T14:12:44.9998985Z 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 2025-09-09T14:12:44.9999880Z reshape_1 = torch.ops.aten.reshape.default(div, [1, -1, 1]); div = None 2025-09-09T14:12:45.0000471Z div_1 = torch.ops.aten.div.Tensor(conv1d_1, reshape_1); conv1d_1 = reshape_1 = None 2025-09-09T14:12:45.0001445Z 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 2025-09-09T14:12:45.0002374Z relu = torch.ops.aten.relu.default(batch_norm_1); batch_norm_1 = None 2025-09-09T14:12:45.0002950Z activation_post_process_2 = self.activation_post_process_2(relu); relu = None 2025-09-09T14:12:45.0003533Z return pytree.tree_unflatten((activation_post_process_2,), self._out_spec) 2025-09-09T14:12:45.0003959Z 2025-09-09T14:12:45.0004252Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:12:45.0004653Z model fx: GraphModule( 2025-09-09T14:12:45.0004992Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:12:45.0006025Z 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 2025-09-09T14:12:45.0007246Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3264806270599365, max_val=1.318617343902588) 2025-09-09T14:12:45.0007797Z ) 2025-09-09T14:12:45.0008000Z (conv): ConvBnReLU1d( 2025-09-09T14:12:45.0008265Z 3, 3, kernel_size=(3,), stride=(1,), bias=False 2025-09-09T14:12:45.0008814Z (bn): BatchNorm1d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T14:12:45.0009317Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:12:45.0010380Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0022, 0.0026, 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 2025-09-09T14:12:45.0011876Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.2639, -0.2941, -0.2608]), max_val=tensor([0.2795, 0.3227, 0.2891])) 2025-09-09T14:12:45.0012583Z ) 2025-09-09T14:12:45.0012775Z ) 2025-09-09T14:12:45.0013064Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:12:45.0014119Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0039]), zero_point=tensor([-128], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:12:45.0015291Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=0.9981018900871277) 2025-09-09T14:12:45.0015797Z ) 2025-09-09T14:12:45.0015981Z ) 2025-09-09T14:12:45.0016080Z 2025-09-09T14:12:45.0016084Z 2025-09-09T14:12:45.0016088Z 2025-09-09T14:12:45.0016177Z def forward(self, x): 2025-09-09T14:12:45.0016556Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:12:45.0017139Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T14:12:45.0017722Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T14:12:45.0018190Z return activation_post_process_1 2025-09-09T14:12:45.0018462Z 2025-09-09T14:12:45.0018763Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:12:45.0019152Z diff: tensor([[[0., 0., 0.], 2025-09-09T14:12:45.0019412Z [0., 0., 0.], 2025-09-09T14:12:45.0019658Z [0., 0., 0.]]], grad_fn=) 2025-09-09T14:12:45.0019992Z converted model pt2e: GraphModule( 2025-09-09T14:12:45.0020278Z (conv): Module() 2025-09-09T14:12:45.0020487Z (bn): Module() 2025-09-09T14:12:45.0020699Z ) 2025-09-09T14:12:45.0020808Z 2025-09-09T14:12:45.0020813Z 2025-09-09T14:12:45.0020816Z 2025-09-09T14:12:45.0020906Z def forward(self, x): 2025-09-09T14:12:45.0021217Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:12:45.0021630Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T14:12:55.1040818Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.010372933000326157, 0, -128, 127, torch.int8); x = None 2025-09-09T14:12:55.1042323Z 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 2025-09-09T14:12:55.1043698Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T14:12:55.1044543Z _scale_0 = self._scale_0 2025-09-09T14:12:55.1045064Z _zero_point_0 = self._zero_point_0 2025-09-09T14:12:55.1045385Z quantize_per_channel = self._frozen_param0 2025-09-09T14:12:55.1046370Z 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 2025-09-09T14:12:55.1047409Z conv_weight_bias = self.conv.weight_bias 2025-09-09T14:12:55.1048324Z 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 2025-09-09T14:12:55.1049329Z relu = torch.ops.aten.relu.default(conv1d_2); conv1d_2 = None 2025-09-09T14:12:55.1050462Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(relu, 0.0039141252636909485, -128, -128, 127, torch.int8); relu = None 2025-09-09T14:12:55.1051886Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.0039141252636909485, -128, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T14:12:55.1053024Z return pytree.tree_unflatten((dequantize_per_tensor_default_1,), self._out_spec) 2025-09-09T14:12:55.1053588Z 2025-09-09T14:12:55.1053883Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:12:55.1054297Z onverted model fx: GraphModule( 2025-09-09T14:12:55.1054567Z (conv): ConvReLU1d( 2025-09-09T14:12:55.1054927Z (0): QuantizedConv1d(Reference)(3, 3, kernel_size=(3,), stride=(1,)) 2025-09-09T14:12:55.1055314Z (1): ReLU() 2025-09-09T14:12:55.1055522Z ) 2025-09-09T14:12:55.1055702Z ) 2025-09-09T14:12:55.1055817Z 2025-09-09T14:12:55.1055825Z 2025-09-09T14:12:55.1055829Z 2025-09-09T14:12:55.1055920Z def forward(self, x): 2025-09-09T14:12:55.1056593Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.010372933000326157, 0, -128, 127, torch.int8); x = None 2025-09-09T14:12:55.1057947Z 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 2025-09-09T14:12:55.1059067Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T14:12:55.1060007Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.0039141252636909485, -128, -128, 127, torch.int8); conv = None 2025-09-09T14:12:55.1061428Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.0039141252636909485, -128, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T14:12:55.1062418Z return dequantize_per_tensor_default_1 2025-09-09T14:12:55.1062705Z 2025-09-09T14:12:55.1063009Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:12:55.1063400Z diff: tensor([[[0., 0., 0.], 2025-09-09T14:12:55.1063658Z [0., 0., 0.], 2025-09-09T14:12:55.1063885Z [0., 0., 0.]]]) 2025-09-09T14:12:55.1064122Z model pt2e: GraphModule( 2025-09-09T14:12:55.1064377Z (conv): Module() 2025-09-09T14:12:55.1064587Z (bn): Module() 2025-09-09T14:12:55.1064922Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:12:55.1065943Z 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 2025-09-09T14:12:55.1067171Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3264806270599365, max_val=1.318617343902588) 2025-09-09T14:12:55.1067738Z ) 2025-09-09T14:12:55.1068031Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:12:55.1069075Z 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 2025-09-09T14:12:55.1070286Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.2940981984138489, max_val=0.32268622517585754) 2025-09-09T14:12:55.1070860Z ) 2025-09-09T14:12:55.1071160Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:12:55.1072182Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0040]), zero_point=tensor([-128], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:12:55.1073447Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=1.0074307918548584) 2025-09-09T14:12:55.1073956Z ) 2025-09-09T14:12:55.1074145Z ) 2025-09-09T14:12:55.1074246Z 2025-09-09T14:12:55.1074250Z 2025-09-09T14:12:55.1074255Z 2025-09-09T14:12:55.1074359Z def forward(self, x): 2025-09-09T14:12:55.1074659Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:12:55.1075037Z conv_weight = self.conv.weight 2025-09-09T14:12:55.1075377Z bn_weight = self.bn.weight 2025-09-09T14:12:55.1075768Z bn_bias = self.bn.bias 2025-09-09T14:12:55.1076043Z bn_running_mean = self.bn.running_mean 2025-09-09T14:12:55.1076461Z bn_running_var = self.bn.running_var 2025-09-09T14:12:55.1076813Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T14:12:55.1077300Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:12:55.1077949Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T14:12:55.1078522Z add = torch.ops.aten.add.Tensor(bn_running_var, 1e-05) 2025-09-09T14:12:55.1078951Z sqrt = torch.ops.aten.sqrt.default(add); add = None 2025-09-09T14:12:55.1079391Z div = torch.ops.aten.div.Tensor(bn_weight, sqrt); sqrt = None 2025-09-09T14:12:55.1079871Z reshape = torch.ops.aten.reshape.default(div, [-1, 1, 1]) 2025-09-09T14:12:55.1080408Z mul = torch.ops.aten.mul.Tensor(conv_weight, reshape); conv_weight = reshape = None 2025-09-09T14:12:55.1081024Z activation_post_process_1 = self.activation_post_process_1(mul); mul = None 2025-09-09T14:12:55.1081943Z 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 2025-09-09T14:12:55.1082837Z reshape_1 = torch.ops.aten.reshape.default(div, [1, -1, 1]); div = None 2025-09-09T14:12:55.1083418Z div_1 = torch.ops.aten.div.Tensor(conv1d_1, reshape_1); conv1d_1 = reshape_1 = None 2025-09-09T14:12:55.1084389Z 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 2025-09-09T14:12:55.1085317Z relu = torch.ops.aten.relu.default(batch_norm_1); batch_norm_1 = None 2025-09-09T14:12:55.1085883Z activation_post_process_2 = self.activation_post_process_2(relu); relu = None 2025-09-09T14:12:55.1086460Z return pytree.tree_unflatten((activation_post_process_2,), self._out_spec) 2025-09-09T14:12:55.1086890Z 2025-09-09T14:12:55.1087180Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:12:55.1087578Z model fx: GraphModule( 2025-09-09T14:12:55.1087915Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:12:55.1088949Z 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 2025-09-09T14:12:55.1090169Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3264806270599365, max_val=1.318617343902588) 2025-09-09T14:12:55.1090721Z ) 2025-09-09T14:12:55.1090924Z (conv): ConvBnReLU1d( 2025-09-09T14:12:55.1091190Z 3, 3, kernel_size=(3,), stride=(1,), bias=False 2025-09-09T14:12:55.1091659Z (bn): BatchNorm1d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T14:12:55.1092158Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:12:55.1093171Z 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 2025-09-09T14:12:55.1094403Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.2940981984138489, max_val=0.32268622517585754) 2025-09-09T14:12:55.1094965Z ) 2025-09-09T14:12:55.1095239Z ) 2025-09-09T14:12:55.1095534Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:12:55.1096582Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0040]), zero_point=tensor([-128], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:12:55.1097767Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=1.0074307918548584) 2025-09-09T14:12:55.1098339Z ) 2025-09-09T14:12:55.1098528Z ) 2025-09-09T14:12:55.1098628Z 2025-09-09T14:12:55.1098633Z 2025-09-09T14:12:55.1098637Z 2025-09-09T14:12:55.1098725Z def forward(self, x): 2025-09-09T14:12:55.1099104Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:12:55.1099681Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T14:12:55.1100267Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T14:12:55.1100732Z return activation_post_process_1 2025-09-09T14:12:55.1101006Z 2025-09-09T14:12:55.1101306Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:12:55.1101714Z diff: tensor([[[0., 0., 0.], 2025-09-09T14:12:55.1101971Z [0., 0., 0.], 2025-09-09T14:12:55.1102217Z [0., 0., 0.]]], grad_fn=) 2025-09-09T14:12:55.1102556Z converted model pt2e: GraphModule( 2025-09-09T14:12:55.1102855Z (conv): Module() 2025-09-09T14:12:55.1103067Z (bn): Module() 2025-09-09T14:12:55.1103280Z ) 2025-09-09T14:12:55.1103385Z 2025-09-09T14:12:55.1103388Z 2025-09-09T14:12:55.1103392Z 2025-09-09T14:12:55.1103484Z def forward(self, x): 2025-09-09T14:12:55.1103798Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:12:55.1104201Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T14:12:56.0050538Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.010372933000326157, 0, -128, 127, torch.int8); x = None 2025-09-09T14:12:56.0051962Z 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 2025-09-09T14:12:56.0053121Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T14:12:56.0053682Z quantize_per_tensor = self._frozen_param0 2025-09-09T14:12:56.0054559Z dequantize_per_tensor = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor, 0.0025408363435417414, 0, -127, 127, torch.int8); quantize_per_tensor = None 2025-09-09T14:12:56.0055440Z conv_weight_bias = self.conv.weight_bias 2025-09-09T14:12:56.0056364Z 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 2025-09-09T14:12:56.0057341Z relu = torch.ops.aten.relu.default(conv1d_2); conv1d_2 = None 2025-09-09T14:12:56.0058201Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(relu, 0.003950709011405706, -128, -128, 127, torch.int8); relu = None 2025-09-09T14:12:56.0059629Z dequantize_per_tensor_default_2 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_2, 0.003950709011405706, -128, -128, 127, torch.int8); quantize_per_tensor_default_2 = None 2025-09-09T14:12:56.0060750Z return pytree.tree_unflatten((dequantize_per_tensor_default_2,), self._out_spec) 2025-09-09T14:12:56.0061205Z 2025-09-09T14:12:56.0061500Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:12:56.0061912Z onverted model fx: GraphModule( 2025-09-09T14:12:56.0062195Z (conv): ConvReLU1d( 2025-09-09T14:12:56.0062782Z (0): QuantizedConv1d(Reference)(3, 3, kernel_size=(3,), stride=(1,)) 2025-09-09T14:12:56.0063184Z (1): ReLU() 2025-09-09T14:12:56.0063384Z ) 2025-09-09T14:12:56.0063578Z ) 2025-09-09T14:12:56.0063679Z 2025-09-09T14:12:56.0063684Z 2025-09-09T14:12:56.0063689Z 2025-09-09T14:12:56.0063778Z def forward(self, x): 2025-09-09T14:12:56.0064457Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.010372933000326157, 0, -128, 127, torch.int8); x = None 2025-09-09T14:12:56.0065826Z 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 2025-09-09T14:12:56.0067037Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T14:12:56.0068021Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.003950709011405706, -128, -128, 127, torch.int8); conv = None 2025-09-09T14:12:56.0069446Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.003950709011405706, -128, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T14:12:56.0070426Z return dequantize_per_tensor_default_1 2025-09-09T14:12:56.0070729Z 2025-09-09T14:12:56.0071041Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:12:56.0071441Z diff: tensor([[[0., 0., 0.], 2025-09-09T14:12:56.0071704Z [0., 0., 0.], 2025-09-09T14:12:56.0071927Z [0., 0., 0.]]]) 2025-09-09T14:12:56.0072367Z PASSED 2025-09-09T14:12:56.0072985Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn1d::test_qat_conv_no_bias model pt2e: GraphModule( 2025-09-09T14:12:56.0073654Z (conv): Module() 2025-09-09T14:12:56.0073975Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:12:56.0075072Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0021, 0.0023, 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 2025-09-09T14:12:56.0076706Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.1720, -0.2918, -0.2941]), max_val=tensor([0.2663, 0.2795, 0.3227])) 2025-09-09T14:12:56.0077430Z ) 2025-09-09T14:12:56.0077719Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:12:56.0078771Z 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 2025-09-09T14:12:56.0079971Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3264806270599365, max_val=1.318617343902588) 2025-09-09T14:12:56.0080541Z ) 2025-09-09T14:12:56.0080835Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:12:56.0081874Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0006]), zero_point=tensor([-128], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:12:56.0083047Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=0.16202233731746674) 2025-09-09T14:12:56.0083561Z ) 2025-09-09T14:12:56.0083750Z ) 2025-09-09T14:12:56.0083858Z 2025-09-09T14:12:56.0083862Z 2025-09-09T14:12:56.0083866Z 2025-09-09T14:12:56.0083957Z def forward(self, x): 2025-09-09T14:12:56.0084272Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:12:56.0084646Z conv_weight = self.conv.weight 2025-09-09T14:12:56.0085136Z activation_post_process_1 = self.activation_post_process_1(conv_weight); conv_weight = None 2025-09-09T14:12:56.0085777Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:12:56.0086754Z conv1d = torch.ops.aten.conv1d.default(activation_post_process_0, activation_post_process_1); activation_post_process_0 = activation_post_process_1 = None 2025-09-09T14:12:56.0087599Z relu = torch.ops.aten.relu.default(conv1d); conv1d = None 2025-09-09T14:12:56.0088132Z activation_post_process_2 = self.activation_post_process_2(relu); relu = None 2025-09-09T14:12:56.0088715Z return pytree.tree_unflatten((activation_post_process_2,), self._out_spec) 2025-09-09T14:12:56.0089216Z 2025-09-09T14:12:56.0089506Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:12:56.0089907Z model fx: GraphModule( 2025-09-09T14:12:56.0090247Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:12:56.0091292Z 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 2025-09-09T14:12:56.0092506Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3264806270599365, max_val=1.318617343902588) 2025-09-09T14:12:56.0093059Z ) 2025-09-09T14:12:56.0093260Z (conv): ConvReLU1d( 2025-09-09T14:12:56.0093530Z 3, 3, kernel_size=(3,), stride=(1,), bias=False 2025-09-09T14:12:56.0093920Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:12:56.0094961Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0021, 0.0023, 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 2025-09-09T14:12:56.0096397Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.1720, -0.2918, -0.2941]), max_val=tensor([0.2663, 0.2795, 0.3227])) 2025-09-09T14:12:56.0097116Z ) 2025-09-09T14:12:56.0097295Z ) 2025-09-09T14:12:56.0097599Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:12:56.0098636Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0006]), zero_point=tensor([-128], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:12:56.0099814Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=0.16202233731746674) 2025-09-09T14:12:56.0100337Z ) 2025-09-09T14:12:56.0100513Z ) 2025-09-09T14:12:56.0100618Z 2025-09-09T14:12:56.0100622Z 2025-09-09T14:12:56.0100637Z 2025-09-09T14:12:56.0100727Z def forward(self, x): 2025-09-09T14:12:56.0101098Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:12:56.0101682Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T14:12:56.0102281Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T14:12:56.0102739Z return activation_post_process_1 2025-09-09T14:12:56.0103033Z 2025-09-09T14:12:56.0103322Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:12:56.0103731Z diff: tensor([[[0., 0., 0.], 2025-09-09T14:12:56.0103984Z [0., 0., 0.], 2025-09-09T14:12:56.0104246Z [0., 0., 0.]]], grad_fn=) 2025-09-09T14:12:56.0104572Z converted model pt2e: GraphModule( 2025-09-09T14:12:56.0104861Z (conv): Module() 2025-09-09T14:12:56.0105066Z ) 2025-09-09T14:12:56.0105185Z 2025-09-09T14:12:56.0105194Z 2025-09-09T14:12:56.0105198Z 2025-09-09T14:12:56.0105289Z def forward(self, x): 2025-09-09T14:12:56.0105596Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:12:56.0105946Z _scale_0 = self._scale_0 2025-09-09T14:12:56.0106225Z _zero_point_0 = self._zero_point_0 2025-09-09T14:12:56.0106570Z quantize_per_channel_default = self._frozen_param0 2025-09-09T14:12:56.0107774Z 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 2025-09-09T14:12:56.0109261Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.010372933000326157, 0, -128, 127, torch.int8); x = None 2025-09-09T14:12:56.0110619Z 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 2025-09-09T14:12:56.0112148Z conv1d = torch.ops.aten.conv1d.default(dequantize_per_tensor_default, dequantize_per_channel_default); dequantize_per_tensor_default = dequantize_per_channel_default = None 2025-09-09T14:12:56.0113066Z relu = torch.ops.aten.relu.default(conv1d); conv1d = None 2025-09-09T14:12:56.0113910Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(relu, 0.0006353817298077047, -128, -128, 127, torch.int8); relu = None 2025-09-09T14:12:56.8760900Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.0006353817298077047, -128, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T14:12:56.8762321Z return pytree.tree_unflatten((dequantize_per_tensor_default_1,), self._out_spec) 2025-09-09T14:12:56.8762771Z 2025-09-09T14:12:56.8763085Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:12:56.8763532Z onverted model fx: GraphModule( 2025-09-09T14:12:56.8763805Z (conv): ConvReLU1d( 2025-09-09T14:12:56.8764205Z (0): QuantizedConv1d(Reference)(3, 3, kernel_size=(3,), stride=(1,), bias=False) 2025-09-09T14:12:56.8764630Z (1): ReLU() 2025-09-09T14:12:56.8764850Z ) 2025-09-09T14:12:56.8765032Z ) 2025-09-09T14:12:56.8765152Z 2025-09-09T14:12:56.8765156Z 2025-09-09T14:12:56.8765161Z 2025-09-09T14:12:56.8765251Z def forward(self, x): 2025-09-09T14:12:56.8765926Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.010372933000326157, 0, -128, 127, torch.int8); x = None 2025-09-09T14:12:56.8767306Z 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 2025-09-09T14:12:56.8768422Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T14:12:56.8769366Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.0006353817298077047, -128, -128, 127, torch.int8); conv = None 2025-09-09T14:12:56.8770801Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.0006353817298077047, -128, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T14:12:56.8771794Z return dequantize_per_tensor_default_1 2025-09-09T14:12:56.8772091Z 2025-09-09T14:12:56.8772401Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:12:56.8772797Z diff: tensor([[[0., 0., 0.], 2025-09-09T14:12:56.8773063Z [0., 0., 0.], 2025-09-09T14:12:56.8773287Z [0., 0., 0.]]]) 2025-09-09T14:12:56.8773544Z model pt2e: GraphModule( 2025-09-09T14:12:56.8773787Z (conv): Module() 2025-09-09T14:12:56.8774125Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:12:56.8775184Z 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 2025-09-09T14:12:56.8776422Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.2940996587276459, max_val=0.3226878345012665) 2025-09-09T14:12:56.8776991Z ) 2025-09-09T14:12:56.8777586Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:12:56.8778631Z 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 2025-09-09T14:12:56.8779840Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3264806270599365, max_val=1.318617343902588) 2025-09-09T14:12:56.8780395Z ) 2025-09-09T14:12:56.8780806Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:12:56.8781841Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0006]), zero_point=tensor([-128], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:12:56.8783010Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=0.1632835566997528) 2025-09-09T14:12:56.8783529Z ) 2025-09-09T14:12:56.8783708Z ) 2025-09-09T14:12:56.8783813Z 2025-09-09T14:12:56.8783832Z 2025-09-09T14:12:56.8783835Z 2025-09-09T14:12:56.8783925Z def forward(self, x): 2025-09-09T14:12:56.8784224Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:12:56.8784597Z conv_weight = self.conv.weight 2025-09-09T14:12:56.8785093Z activation_post_process_1 = self.activation_post_process_1(conv_weight); conv_weight = None 2025-09-09T14:12:56.8785717Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:12:56.8786609Z conv1d = torch.ops.aten.conv1d.default(activation_post_process_0, activation_post_process_1); activation_post_process_0 = activation_post_process_1 = None 2025-09-09T14:12:56.8787429Z relu = torch.ops.aten.relu.default(conv1d); conv1d = None 2025-09-09T14:12:56.8787955Z activation_post_process_2 = self.activation_post_process_2(relu); relu = None 2025-09-09T14:12:56.8788541Z return pytree.tree_unflatten((activation_post_process_2,), self._out_spec) 2025-09-09T14:12:56.8788975Z 2025-09-09T14:12:56.8789276Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:12:56.8789664Z model fx: GraphModule( 2025-09-09T14:12:56.8790017Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:12:56.8791045Z 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 2025-09-09T14:12:56.8792263Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3264806270599365, max_val=1.318617343902588) 2025-09-09T14:12:56.8792827Z ) 2025-09-09T14:12:56.8793017Z (conv): ConvReLU1d( 2025-09-09T14:12:56.8793292Z 3, 3, kernel_size=(3,), stride=(1,), bias=False 2025-09-09T14:12:56.8793677Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:12:56.8794703Z 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 2025-09-09T14:12:56.8795931Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.2940996587276459, max_val=0.3226878345012665) 2025-09-09T14:12:56.8796625Z ) 2025-09-09T14:12:56.8796823Z ) 2025-09-09T14:12:56.8797110Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:12:56.8798161Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0006]), zero_point=tensor([-128], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:12:56.8799322Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=0.1632835566997528) 2025-09-09T14:12:56.8799838Z ) 2025-09-09T14:12:56.8800011Z ) 2025-09-09T14:12:56.8800134Z 2025-09-09T14:12:56.8800139Z 2025-09-09T14:12:56.8800143Z 2025-09-09T14:12:56.8800319Z def forward(self, x): 2025-09-09T14:12:56.8800707Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:12:56.8801285Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T14:12:56.8801894Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T14:12:56.8802354Z return activation_post_process_1 2025-09-09T14:12:56.8802650Z 2025-09-09T14:12:56.8803005Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:12:56.8803413Z diff: tensor([[[0., 0., 0.], 2025-09-09T14:12:56.8803678Z [0., 0., 0.], 2025-09-09T14:12:56.8803928Z [0., 0., 0.]]], grad_fn=) 2025-09-09T14:12:56.8804261Z converted model pt2e: GraphModule( 2025-09-09T14:12:56.8804537Z (conv): Module() 2025-09-09T14:12:56.8804753Z ) 2025-09-09T14:12:56.8804876Z 2025-09-09T14:12:56.8804880Z 2025-09-09T14:12:56.8804884Z 2025-09-09T14:12:56.8804981Z def forward(self, x): 2025-09-09T14:12:56.8805289Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:12:56.8805689Z quantize_per_tensor_default = self._frozen_param0 2025-09-09T14:12:56.8806694Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.0025408491492271423, 0, -127, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T14:12:56.8808066Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.010372933000326157, 0, -128, 127, torch.int8); x = None 2025-09-09T14:12:56.8809450Z 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 2025-09-09T14:12:56.8810935Z 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 2025-09-09T14:12:56.8811836Z relu = torch.ops.aten.relu.default(conv1d); conv1d = None 2025-09-09T14:12:56.8812695Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(relu, 0.0006403276929631829, -128, -128, 127, torch.int8); relu = None 2025-09-09T14:12:56.8814138Z dequantize_per_tensor_default_2 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_2, 0.0006403276929631829, -128, -128, 127, torch.int8); quantize_per_tensor_default_2 = None 2025-09-09T14:12:56.8815259Z return pytree.tree_unflatten((dequantize_per_tensor_default_2,), self._out_spec) 2025-09-09T14:12:56.8815724Z 2025-09-09T14:12:56.8816020Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:12:56.8816443Z onverted model fx: GraphModule( 2025-09-09T14:12:56.8816717Z (conv): ConvReLU1d( 2025-09-09T14:12:56.8817120Z (0): QuantizedConv1d(Reference)(3, 3, kernel_size=(3,), stride=(1,), bias=False) 2025-09-09T14:12:56.8817557Z (1): ReLU() 2025-09-09T14:12:56.8817756Z ) 2025-09-09T14:12:56.8817946Z ) 2025-09-09T14:12:56.8818047Z 2025-09-09T14:12:56.8818051Z 2025-09-09T14:12:56.8818055Z 2025-09-09T14:12:56.8818143Z def forward(self, x): 2025-09-09T14:12:56.8818818Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.010372933000326157, 0, -128, 127, torch.int8); x = None 2025-09-09T14:12:56.8820186Z 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 2025-09-09T14:12:56.8821286Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T14:12:56.8822293Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.0006403276929631829, -128, -128, 127, torch.int8); conv = None 2025-09-09T14:12:56.8823716Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.0006403276929631829, -128, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T14:12:56.8824903Z return dequantize_per_tensor_default_1 2025-09-09T14:12:56.8825207Z 2025-09-09T14:12:56.8825497Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:12:58.4137742Z diff: tensor([[[0., 0., 0.], 2025-09-09T14:12:58.4138313Z [0., 0., 0.], 2025-09-09T14:12:58.4138575Z [0., 0., 0.]]]) 2025-09-09T14:12:58.4138823Z model pt2e: GraphModule( 2025-09-09T14:12:58.4139076Z (conv): Module() 2025-09-09T14:12:58.4139396Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:12:58.4140527Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0026, 0.0026, 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 2025-09-09T14:12:58.4141983Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.3263, -0.3276, -0.3045]), max_val=tensor([0.1376, 0.2760, 0.3298])) 2025-09-09T14:12:58.4142693Z ) 2025-09-09T14:12:58.4142996Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:12:58.4144021Z 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 2025-09-09T14:12:58.4145249Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3264806270599365, max_val=1.318617343902588) 2025-09-09T14:12:58.4145815Z ) 2025-09-09T14:12:58.4146103Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:12:58.4147157Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0079]), zero_point=tensor([34], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:12:58.4148414Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.284900426864624, max_val=0.7360976338386536) 2025-09-09T14:12:58.4148976Z ) 2025-09-09T14:12:58.4149169Z ) 2025-09-09T14:12:58.4149270Z 2025-09-09T14:12:58.4149275Z 2025-09-09T14:12:58.4149279Z 2025-09-09T14:12:58.4149374Z def forward(self, x): 2025-09-09T14:12:58.4149736Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:12:58.4150095Z conv_weight = self.conv.weight 2025-09-09T14:12:58.4150593Z activation_post_process_1 = self.activation_post_process_1(conv_weight); conv_weight = None 2025-09-09T14:12:58.4151222Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:12:58.4152108Z conv1d = torch.ops.aten.conv1d.default(activation_post_process_0, activation_post_process_1); activation_post_process_0 = activation_post_process_1 = None 2025-09-09T14:12:58.4153023Z activation_post_process_2 = self.activation_post_process_2(conv1d); conv1d = None 2025-09-09T14:12:58.4153619Z return pytree.tree_unflatten((activation_post_process_2,), self._out_spec) 2025-09-09T14:12:58.4154046Z 2025-09-09T14:12:58.4154338Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:12:58.4154767Z model fx: GraphModule( 2025-09-09T14:12:58.4155123Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:12:58.4156212Z 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 2025-09-09T14:12:58.4157434Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3264806270599365, max_val=1.318617343902588) 2025-09-09T14:12:58.4157999Z ) 2025-09-09T14:12:58.4158455Z (conv): Conv1d( 2025-09-09T14:12:58.4158720Z 3, 3, kernel_size=(3,), stride=(1,), bias=False 2025-09-09T14:12:58.4159100Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:12:58.4160165Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0026, 0.0026, 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 2025-09-09T14:12:58.4161736Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.3263, -0.3276, -0.3045]), max_val=tensor([0.1376, 0.2760, 0.3298])) 2025-09-09T14:12:58.4162444Z ) 2025-09-09T14:12:58.4162638Z ) 2025-09-09T14:12:58.4162927Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:12:58.4163974Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0079]), zero_point=tensor([34], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:12:58.4165183Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.284900426864624, max_val=0.7360976338386536) 2025-09-09T14:12:58.4165754Z ) 2025-09-09T14:12:58.4165944Z ) 2025-09-09T14:12:58.4166045Z 2025-09-09T14:12:58.4166050Z 2025-09-09T14:12:58.4166054Z 2025-09-09T14:12:58.4166146Z def forward(self, x): 2025-09-09T14:12:58.4166528Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:12:58.4167101Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T14:12:58.4167695Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T14:12:58.4168161Z return activation_post_process_1 2025-09-09T14:12:58.4168433Z 2025-09-09T14:12:58.4168734Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:12:58.4169123Z diff: tensor([[[0., 0., 0.], 2025-09-09T14:12:58.4169382Z [0., 0., 0.], 2025-09-09T14:12:58.4169634Z [0., 0., 0.]]], grad_fn=) 2025-09-09T14:12:58.4169966Z converted model pt2e: GraphModule( 2025-09-09T14:12:58.4170241Z (conv): Module() 2025-09-09T14:12:58.4170461Z ) 2025-09-09T14:12:58.4170563Z 2025-09-09T14:12:58.4170568Z 2025-09-09T14:12:58.4170572Z 2025-09-09T14:12:58.4170677Z def forward(self, x): 2025-09-09T14:12:58.4170970Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:12:58.4171336Z _scale_0 = self._scale_0 2025-09-09T14:12:58.4171601Z _zero_point_0 = self._zero_point_0 2025-09-09T14:12:58.4171971Z quantize_per_channel_default = self._frozen_param0 2025-09-09T14:12:58.4173059Z 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 2025-09-09T14:12:58.4174526Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.010372933000326157, 0, -128, 127, torch.int8); x = None 2025-09-09T14:12:58.4175893Z 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 2025-09-09T14:12:58.4177349Z conv1d = torch.ops.aten.conv1d.default(dequantize_per_tensor_default, dequantize_per_channel_default); dequantize_per_tensor_default = dequantize_per_channel_default = None 2025-09-09T14:12:58.4178654Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv1d, 0.007925482466816902, 34, -128, 127, torch.int8); conv1d = None 2025-09-09T14:12:58.4180085Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.007925482466816902, 34, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T14:12:58.4181258Z return pytree.tree_unflatten((dequantize_per_tensor_default_1,), self._out_spec) 2025-09-09T14:12:58.4181719Z 2025-09-09T14:12:58.4182012Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:12:58.4182431Z onverted model fx: GraphModule( 2025-09-09T14:12:58.4182878Z (conv): QuantizedConv1d(Reference)(3, 3, kernel_size=(3,), stride=(1,), bias=False) 2025-09-09T14:12:58.4183316Z ) 2025-09-09T14:12:58.4183523Z 2025-09-09T14:12:58.4183527Z 2025-09-09T14:12:58.4183532Z 2025-09-09T14:12:58.4183637Z def forward(self, x): 2025-09-09T14:12:58.4184303Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.010372933000326157, 0, -128, 127, torch.int8); x = None 2025-09-09T14:12:58.4185667Z 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 2025-09-09T14:12:58.4186792Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T14:12:58.4187723Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.007925482466816902, 34, -128, 127, torch.int8); conv = None 2025-09-09T14:12:58.4189131Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.007925482466816902, 34, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T14:12:58.4190114Z return dequantize_per_tensor_default_1 2025-09-09T14:12:58.4190402Z 2025-09-09T14:12:58.4190703Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:12:58.4191092Z diff: tensor([[[0., 0., 0.], 2025-09-09T14:12:58.4191351Z [0., 0., 0.], 2025-09-09T14:12:58.4191572Z [0., 0., 0.]]]) 2025-09-09T14:12:58.4191822Z model pt2e: GraphModule( 2025-09-09T14:12:58.4192059Z (conv): Module() 2025-09-09T14:12:58.4192392Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:12:58.4193427Z 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 2025-09-09T14:12:58.4194663Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.327648401260376, max_val=0.32982930541038513) 2025-09-09T14:12:58.4195244Z ) 2025-09-09T14:12:58.4195542Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:12:58.4196666Z 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 2025-09-09T14:12:58.4197888Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3264806270599365, max_val=1.318617343902588) 2025-09-09T14:12:58.4198443Z ) 2025-09-09T14:12:58.4198750Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:12:58.4199769Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0079]), zero_point=tensor([34], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:12:58.4200973Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.284900426864624, max_val=0.7398542761802673) 2025-09-09T14:12:58.4201541Z ) 2025-09-09T14:12:58.4201716Z ) 2025-09-09T14:12:58.4201816Z 2025-09-09T14:12:58.4201820Z 2025-09-09T14:12:58.4201824Z 2025-09-09T14:12:58.4201925Z def forward(self, x): 2025-09-09T14:12:58.4202222Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:13:37.7584417Z conv_weight = self.conv.weight 2025-09-09T14:13:37.7585190Z activation_post_process_1 = self.activation_post_process_1(conv_weight); conv_weight = None 2025-09-09T14:13:37.7587180Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:13:37.7588131Z conv1d = torch.ops.aten.conv1d.default(activation_post_process_0, activation_post_process_1); activation_post_process_0 = activation_post_process_1 = None 2025-09-09T14:13:37.7589044Z activation_post_process_2 = self.activation_post_process_2(conv1d); conv1d = None 2025-09-09T14:13:37.7589642Z return pytree.tree_unflatten((activation_post_process_2,), self._out_spec) 2025-09-09T14:13:37.7590184Z 2025-09-09T14:13:37.7590485Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:13:37.7590893Z model fx: GraphModule( 2025-09-09T14:13:37.7591238Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:13:37.7592297Z 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 2025-09-09T14:13:37.7593641Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3264806270599365, max_val=1.318617343902588) 2025-09-09T14:13:37.7594201Z ) 2025-09-09T14:13:37.7594405Z (conv): Conv1d( 2025-09-09T14:13:37.7594656Z 3, 3, kernel_size=(3,), stride=(1,), bias=False 2025-09-09T14:13:37.7595056Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:13:37.7596077Z 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 2025-09-09T14:13:37.7597376Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.327648401260376, max_val=0.32982930541038513) 2025-09-09T14:13:37.7597949Z ) 2025-09-09T14:13:37.7598131Z ) 2025-09-09T14:13:37.7598434Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:13:37.7599464Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0079]), zero_point=tensor([34], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:13:37.7600935Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.284900426864624, max_val=0.7398542761802673) 2025-09-09T14:13:37.7601504Z ) 2025-09-09T14:13:37.7601683Z ) 2025-09-09T14:13:37.7601802Z 2025-09-09T14:13:37.7601807Z 2025-09-09T14:13:37.7601810Z 2025-09-09T14:13:37.7601937Z def forward(self, x): 2025-09-09T14:13:37.7602396Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:13:37.7602978Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T14:13:37.7603618Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T14:13:37.7604075Z return activation_post_process_1 2025-09-09T14:13:37.7604359Z 2025-09-09T14:13:37.7604676Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:13:37.7605069Z diff: tensor([[[0., 0., 0.], 2025-09-09T14:13:37.7605327Z [0., 0., 0.], 2025-09-09T14:13:37.7605576Z [0., 0., 0.]]], grad_fn=) 2025-09-09T14:13:37.7605906Z converted model pt2e: GraphModule( 2025-09-09T14:13:37.7606181Z (conv): Module() 2025-09-09T14:13:37.7606398Z ) 2025-09-09T14:13:37.7606499Z 2025-09-09T14:13:37.7606508Z 2025-09-09T14:13:37.7606512Z 2025-09-09T14:13:37.7606617Z def forward(self, x): 2025-09-09T14:13:37.7606913Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:13:37.7607324Z quantize_per_tensor_default = self._frozen_param0 2025-09-09T14:13:37.7608318Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.0025970812421292067, 0, -127, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T14:13:37.7609848Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.010372933000326157, 0, -128, 127, torch.int8); x = None 2025-09-09T14:13:37.7611219Z 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 2025-09-09T14:13:37.7612708Z 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 2025-09-09T14:13:37.7614082Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv1d, 0.007940215058624744, 34, -128, 127, torch.int8); conv1d = None 2025-09-09T14:13:37.7615505Z dequantize_per_tensor_default_2 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_2, 0.007940215058624744, 34, -128, 127, torch.int8); quantize_per_tensor_default_2 = None 2025-09-09T14:13:37.7616628Z return pytree.tree_unflatten((dequantize_per_tensor_default_2,), self._out_spec) 2025-09-09T14:13:37.7617088Z 2025-09-09T14:13:37.7617387Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:13:37.7617808Z onverted model fx: GraphModule( 2025-09-09T14:13:37.7618241Z (conv): QuantizedConv1d(Reference)(3, 3, kernel_size=(3,), stride=(1,), bias=False) 2025-09-09T14:13:37.7618692Z ) 2025-09-09T14:13:37.7618801Z 2025-09-09T14:13:37.7618805Z 2025-09-09T14:13:37.7618809Z 2025-09-09T14:13:37.7618901Z def forward(self, x): 2025-09-09T14:13:37.7619583Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.010372933000326157, 0, -128, 127, torch.int8); x = None 2025-09-09T14:13:37.7620957Z 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 2025-09-09T14:13:37.7622063Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T14:13:37.7623007Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.007940215058624744, 34, -128, 127, torch.int8); conv = None 2025-09-09T14:13:37.7624724Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.007940215058624744, 34, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T14:13:37.7625707Z return dequantize_per_tensor_default_1 2025-09-09T14:13:37.7626010Z 2025-09-09T14:13:37.7626303Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:13:37.7626708Z diff: tensor([[[0., 0., 0.], 2025-09-09T14:13:37.7626955Z [0., 0., 0.], 2025-09-09T14:13:37.7627184Z [0., 0., 0.]]]) 2025-09-09T14:13:37.7627613Z PASSED 2025-09-09T14:13:37.7628343Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn1d::test_qat_conv_transpose_bn PASSED 2025-09-09T14:13:37.7629482Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn1d::test_qat_conv_transpose_bn_relu PASSED 2025-09-09T14:13:37.7630519Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn1d::test_qat_inplace_add_relu model pt2e: GraphModule( 2025-09-09T14:13:37.7631186Z (conv): Module() 2025-09-09T14:13:37.7631509Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:13:37.7632568Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0021]), zero_point=tensor([127], dtype=torch.int32), dtype=torch.int8, quant_min=-127, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False 2025-09-09T14:13:37.7633876Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.5429]), max_val=tensor([-0.5429])) 2025-09-09T14:13:37.7634489Z ) 2025-09-09T14:13:37.7634931Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:13:37.7635959Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0012]), zero_point=tensor([127], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:13:37.7637275Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.31662631034851074, max_val=-0.1489601731300354) 2025-09-09T14:13:37.7637952Z ) 2025-09-09T14:13:37.7638242Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:13:37.7639285Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0014]), zero_point=tensor([-128], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:13:37.7640506Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.26761165261268616, max_val=0.3586132824420929) 2025-09-09T14:13:37.7641067Z ) 2025-09-09T14:13:37.7641366Z (activation_post_process_3): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:13:37.7642391Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0005]), zero_point=tensor([-128], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:13:37.7643606Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.04198697209358215, max_val=0.11820143461227417) 2025-09-09T14:13:37.7644178Z ) 2025-09-09T14:13:37.7644353Z ) 2025-09-09T14:13:37.7644454Z 2025-09-09T14:13:37.7644458Z 2025-09-09T14:13:37.7644462Z 2025-09-09T14:13:37.7644563Z def forward(self, x): 2025-09-09T14:13:37.7644862Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:13:37.7645234Z conv_weight = self.conv.weight 2025-09-09T14:13:37.7645726Z activation_post_process_1 = self.activation_post_process_1(conv_weight); conv_weight = None 2025-09-09T14:13:37.7646241Z conv_bias = self.conv.bias 2025-09-09T14:13:37.7646635Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:13:37.7647497Z conv1d = torch.ops.aten.conv1d.default(activation_post_process_0, activation_post_process_1, conv_bias); activation_post_process_1 = conv_bias = None 2025-09-09T14:13:37.7648386Z activation_post_process_2 = self.activation_post_process_2(conv1d); conv1d = None 2025-09-09T14:13:37.7649254Z add_ = torch.ops.aten.add_.Tensor(activation_post_process_2, activation_post_process_0); activation_post_process_2 = activation_post_process_0 = None 2025-09-09T14:13:37.7650033Z relu_ = torch.ops.aten.relu_.default(add_); add_ = None 2025-09-09T14:13:37.7650543Z activation_post_process_3 = self.activation_post_process_3(relu_); relu_ = None 2025-09-09T14:13:37.7651143Z return pytree.tree_unflatten((activation_post_process_3,), self._out_spec) 2025-09-09T14:13:37.7651572Z 2025-09-09T14:13:38.6545762Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:13:38.6546218Z model fx: GraphModule( 2025-09-09T14:13:38.6546566Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:13:38.6547655Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0012]), zero_point=tensor([127], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:13:38.6548974Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.31662631034851074, max_val=-0.1489601731300354) 2025-09-09T14:13:38.6549553Z ) 2025-09-09T14:13:38.6549739Z (conv): Conv1d( 2025-09-09T14:13:38.6549980Z 1, 1, kernel_size=(1,), stride=(1,) 2025-09-09T14:13:38.6550336Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:13:38.6551670Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0021]), zero_point=tensor([127], dtype=torch.int32), dtype=torch.qint8, quant_min=-127, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False 2025-09-09T14:13:38.6552972Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.5429]), max_val=tensor([-0.5429])) 2025-09-09T14:13:38.6553610Z ) 2025-09-09T14:13:38.6553807Z ) 2025-09-09T14:13:38.6554100Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:13:38.6555148Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0014]), zero_point=tensor([-128], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:13:38.6556530Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.26761165261268616, max_val=0.3586132824420929) 2025-09-09T14:13:38.6557113Z ) 2025-09-09T14:13:38.6557313Z (relu): ReLU(inplace=True) 2025-09-09T14:13:38.6557694Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:13:38.6558743Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0005]), zero_point=tensor([-128], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:13:38.6559962Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.04198697209358215, max_val=0.11820143461227417) 2025-09-09T14:13:38.6560537Z ) 2025-09-09T14:13:38.6560721Z ) 2025-09-09T14:13:38.6560837Z 2025-09-09T14:13:38.6560842Z 2025-09-09T14:13:38.6560846Z 2025-09-09T14:13:38.6560936Z def forward(self, x): 2025-09-09T14:13:38.6561320Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:13:38.6561777Z conv = self.conv(activation_post_process_0) 2025-09-09T14:13:38.6562254Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T14:13:38.6563007Z add = activation_post_process_1 + activation_post_process_0; activation_post_process_1 = activation_post_process_0 = None 2025-09-09T14:13:38.6563627Z relu = self.relu(add); add = None 2025-09-09T14:13:38.6564063Z activation_post_process_2 = self.activation_post_process_2(relu); relu = None 2025-09-09T14:13:38.6564532Z return activation_post_process_2 2025-09-09T14:13:38.6564816Z 2025-09-09T14:13:38.6565105Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:13:38.6565546Z diff: tensor([[[0., 0., 0.]]], grad_fn=) 2025-09-09T14:13:38.6573639Z converted model pt2e: GraphModule( 2025-09-09T14:13:38.6573940Z (conv): Module() 2025-09-09T14:13:38.6574151Z ) 2025-09-09T14:13:38.6574255Z 2025-09-09T14:13:38.6574260Z 2025-09-09T14:13:38.6574264Z 2025-09-09T14:13:38.6574367Z def forward(self, x): 2025-09-09T14:13:38.6574666Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:13:38.6575031Z _scale_0 = self._scale_0 2025-09-09T14:13:38.6575299Z _zero_point_0 = self._zero_point_0 2025-09-09T14:13:38.6575667Z quantize_per_channel_default = self._frozen_param0 2025-09-09T14:13:38.6576772Z 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 2025-09-09T14:13:38.6577820Z conv_bias = self.conv.bias 2025-09-09T14:13:38.6578537Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.0012416718527674675, 127, -128, 127, torch.int8); x = None 2025-09-09T14:13:38.6579796Z dequantize_per_tensor_default_4 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.0012416718527674675, 127, -128, 127, torch.int8) 2025-09-09T14:13:38.6581397Z dequantize_per_tensor_default_3 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.0012416718527674675, 127, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T14:13:38.6582975Z 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 2025-09-09T14:13:38.6584364Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv1d, 0.0014063265407457948, -128, -128, 127, torch.int8); conv1d = None 2025-09-09T14:13:38.6585882Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.0014063265407457948, -128, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T14:13:38.6587338Z 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 2025-09-09T14:13:38.6588192Z relu_ = torch.ops.aten.relu_.default(add_); add_ = None 2025-09-09T14:13:38.6589043Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(relu_, 0.00046353504876606166, -128, -128, 127, torch.int8); relu_ = None 2025-09-09T14:13:38.6590489Z dequantize_per_tensor_default_2 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_2, 0.00046353504876606166, -128, -128, 127, torch.int8); quantize_per_tensor_default_2 = None 2025-09-09T14:13:38.6591610Z return pytree.tree_unflatten((dequantize_per_tensor_default_2,), self._out_spec) 2025-09-09T14:13:38.6592068Z 2025-09-09T14:13:38.6592364Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:13:38.6592778Z onverted model fx: GraphModule( 2025-09-09T14:13:38.6593187Z (conv): QuantizedConv1d(Reference)(1, 1, kernel_size=(1,), stride=(1,)) 2025-09-09T14:13:38.6593606Z (relu): ReLU(inplace=True) 2025-09-09T14:13:38.6593863Z ) 2025-09-09T14:13:38.6593966Z 2025-09-09T14:13:38.6593971Z 2025-09-09T14:13:38.6593975Z 2025-09-09T14:13:38.6594071Z def forward(self, x): 2025-09-09T14:13:38.6594763Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.0012416718527674675, 127, -128, 127, torch.int8); x = None 2025-09-09T14:13:38.6596260Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.0012416718527674675, 127, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T14:13:38.6597251Z conv = self.conv(dequantize_per_tensor_default) 2025-09-09T14:13:38.6598068Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.0014063265407457948, -128, -128, 127, torch.int8); conv = None 2025-09-09T14:13:38.6599486Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.0014063265407457948, -128, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T14:13:38.6600840Z add = dequantize_per_tensor_default_1 + dequantize_per_tensor_default; dequantize_per_tensor_default_1 = dequantize_per_tensor_default = None 2025-09-09T14:13:38.6601539Z relu = self.relu(add); add = None 2025-09-09T14:13:38.6602301Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(relu, 0.00046353504876606166, -128, -128, 127, torch.int8); relu = None 2025-09-09T14:13:38.6603772Z dequantize_per_tensor_default_2 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_2, 0.00046353504876606166, -128, -128, 127, torch.int8); quantize_per_tensor_default_2 = None 2025-09-09T14:13:38.6604771Z return dequantize_per_tensor_default_2 2025-09-09T14:13:38.6605062Z 2025-09-09T14:13:38.6605374Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:13:38.6605774Z diff: tensor([[[0., 0., 0.]]]) 2025-09-09T14:13:38.6606050Z model pt2e: GraphModule( 2025-09-09T14:13:38.6606365Z (conv): Module() 2025-09-09T14:13:38.6606702Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:13:38.6607757Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0021]), zero_point=tensor([127], dtype=torch.int32), dtype=torch.int8, quant_min=-127, quant_max=127, qscheme=torch.per_tensor_symmetric, reduce_range=False 2025-09-09T14:13:38.6608991Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.5428858995437622, max_val=-0.5428858995437622) 2025-09-09T14:13:38.6609642Z ) 2025-09-09T14:13:38.6609933Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:13:38.6610972Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0012]), zero_point=tensor([127], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:13:38.6612201Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.31662631034851074, max_val=-0.1489601731300354) 2025-09-09T14:13:38.6612764Z ) 2025-09-09T14:13:38.6613075Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:13:38.6614101Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0014]), zero_point=tensor([-128], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:13:38.6615320Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.26761165261268616, max_val=0.3586132824420929) 2025-09-09T14:13:38.6615901Z ) 2025-09-09T14:13:38.6616191Z (activation_post_process_3): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:13:57.3179480Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0005]), zero_point=tensor([-128], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:13:57.3180841Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.04198697209358215, max_val=0.11820143461227417) 2025-09-09T14:13:57.3181424Z ) 2025-09-09T14:13:57.3181623Z ) 2025-09-09T14:13:57.3181727Z 2025-09-09T14:13:57.3181733Z 2025-09-09T14:13:57.3181736Z 2025-09-09T14:13:57.3181825Z def forward(self, x): 2025-09-09T14:13:57.3182139Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:13:57.3182517Z conv_weight = self.conv.weight 2025-09-09T14:13:57.3183011Z activation_post_process_1 = self.activation_post_process_1(conv_weight); conv_weight = None 2025-09-09T14:13:57.3183540Z conv_bias = self.conv.bias 2025-09-09T14:13:57.3183938Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:13:57.3184793Z conv1d = torch.ops.aten.conv1d.default(activation_post_process_0, activation_post_process_1, conv_bias); activation_post_process_1 = conv_bias = None 2025-09-09T14:13:57.3185675Z activation_post_process_2 = self.activation_post_process_2(conv1d); conv1d = None 2025-09-09T14:13:57.3186556Z add_ = torch.ops.aten.add_.Tensor(activation_post_process_2, activation_post_process_0); activation_post_process_2 = activation_post_process_0 = None 2025-09-09T14:13:57.3187334Z relu_ = torch.ops.aten.relu_.default(add_); add_ = None 2025-09-09T14:13:57.3187841Z activation_post_process_3 = self.activation_post_process_3(relu_); relu_ = None 2025-09-09T14:13:57.3188443Z return pytree.tree_unflatten((activation_post_process_3,), self._out_spec) 2025-09-09T14:13:57.3188868Z 2025-09-09T14:13:57.3189171Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:13:57.3189557Z model fx: GraphModule( 2025-09-09T14:13:57.3189908Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:13:57.3191215Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0012]), zero_point=tensor([127], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:13:57.3192442Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.31662631034851074, max_val=-0.1489601731300354) 2025-09-09T14:13:57.3193021Z ) 2025-09-09T14:13:57.3193204Z (conv): Conv1d( 2025-09-09T14:13:57.3193444Z 1, 1, kernel_size=(1,), stride=(1,) 2025-09-09T14:13:57.3193811Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:13:57.3194821Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0021]), zero_point=tensor([127], dtype=torch.int32), dtype=torch.qint8, quant_min=-127, quant_max=127, qscheme=torch.per_tensor_symmetric, reduce_range=False 2025-09-09T14:13:57.3196251Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.5428858995437622, max_val=-0.5428858995437622) 2025-09-09T14:13:57.3196817Z ) 2025-09-09T14:13:57.3197009Z ) 2025-09-09T14:13:57.3197298Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:13:57.3198355Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0014]), zero_point=tensor([-128], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:13:57.3199588Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.26761165261268616, max_val=0.3586132824420929) 2025-09-09T14:13:57.3200148Z ) 2025-09-09T14:13:57.3200356Z (relu): ReLU(inplace=True) 2025-09-09T14:13:57.3200720Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:13:57.3201768Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0005]), zero_point=tensor([-128], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:13:57.3202988Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.04198697209358215, max_val=0.11820143461227417) 2025-09-09T14:13:57.3203547Z ) 2025-09-09T14:13:57.3203736Z ) 2025-09-09T14:13:57.3203839Z 2025-09-09T14:13:57.3203844Z 2025-09-09T14:13:57.3203848Z 2025-09-09T14:13:57.3203937Z def forward(self, x): 2025-09-09T14:13:57.3204318Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:13:57.3204788Z conv = self.conv(activation_post_process_0) 2025-09-09T14:13:57.3205254Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T14:13:57.3206013Z add = activation_post_process_1 + activation_post_process_0; activation_post_process_1 = activation_post_process_0 = None 2025-09-09T14:13:57.3206625Z relu = self.relu(add); add = None 2025-09-09T14:13:57.3207080Z activation_post_process_2 = self.activation_post_process_2(relu); relu = None 2025-09-09T14:13:57.3207534Z return activation_post_process_2 2025-09-09T14:13:57.3207815Z 2025-09-09T14:13:57.3208120Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:13:57.3208562Z diff: tensor([[[0., 0., 0.]]], grad_fn=) 2025-09-09T14:13:57.3208926Z converted model pt2e: GraphModule( 2025-09-09T14:13:57.3209201Z (conv): Module() 2025-09-09T14:13:57.3209414Z ) 2025-09-09T14:13:57.3209514Z 2025-09-09T14:13:57.3209518Z 2025-09-09T14:13:57.3209522Z 2025-09-09T14:13:57.3209611Z def forward(self, x): 2025-09-09T14:13:57.3209915Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:13:57.3210312Z quantize_per_tensor_default = self._frozen_param0 2025-09-09T14:13:57.3211314Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.004274691920727491, 0, -127, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T14:13:57.3212265Z conv_bias = self.conv.bias 2025-09-09T14:13:57.3212971Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.0012416718527674675, 127, -128, 127, torch.int8); x = None 2025-09-09T14:13:57.3214326Z dequantize_per_tensor_default_5 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.0012416718527674675, 127, -128, 127, torch.int8) 2025-09-09T14:13:57.3215822Z dequantize_per_tensor_default_4 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.0012416718527674675, 127, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T14:13:57.3217377Z 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 2025-09-09T14:13:57.3219318Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv1d, 0.0014063265407457948, -128, -128, 127, torch.int8); conv1d = None 2025-09-09T14:13:57.3220769Z dequantize_per_tensor_default_2 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_2, 0.0014063265407457948, -128, -128, 127, torch.int8); quantize_per_tensor_default_2 = None 2025-09-09T14:13:57.3222214Z 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 2025-09-09T14:13:57.3223080Z relu_ = torch.ops.aten.relu_.default(add_); add_ = None 2025-09-09T14:13:57.3223917Z quantize_per_tensor_default_3 = torch.ops.quantized_decomposed.quantize_per_tensor.default(relu_, 0.00046353504876606166, -128, -128, 127, torch.int8); relu_ = None 2025-09-09T14:13:57.3225558Z dequantize_per_tensor_default_3 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_3, 0.00046353504876606166, -128, -128, 127, torch.int8); quantize_per_tensor_default_3 = None 2025-09-09T14:13:57.3226689Z return pytree.tree_unflatten((dequantize_per_tensor_default_3,), self._out_spec) 2025-09-09T14:13:57.3227130Z 2025-09-09T14:13:57.3227441Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:13:57.3227843Z onverted model fx: GraphModule( 2025-09-09T14:13:57.3228252Z (conv): QuantizedConv1d(Reference)(1, 1, kernel_size=(1,), stride=(1,)) 2025-09-09T14:13:57.3228681Z (relu): ReLU(inplace=True) 2025-09-09T14:13:57.3228924Z ) 2025-09-09T14:13:57.3229027Z 2025-09-09T14:13:57.3229031Z 2025-09-09T14:13:57.3229047Z 2025-09-09T14:13:57.3229138Z def forward(self, x): 2025-09-09T14:13:57.3229812Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.0012416718527674675, 127, -128, 127, torch.int8); x = None 2025-09-09T14:13:57.3231203Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.0012416718527674675, 127, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T14:13:57.3232194Z conv = self.conv(dequantize_per_tensor_default) 2025-09-09T14:13:57.3232996Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.0014063265407457948, -128, -128, 127, torch.int8); conv = None 2025-09-09T14:13:57.3234430Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.0014063265407457948, -128, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T14:13:57.3235771Z add = dequantize_per_tensor_default_1 + dequantize_per_tensor_default; dequantize_per_tensor_default_1 = dequantize_per_tensor_default = None 2025-09-09T14:13:57.3236539Z relu = self.relu(add); add = None 2025-09-09T14:13:57.3237314Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(relu, 0.00046353504876606166, -128, -128, 127, torch.int8); relu = None 2025-09-09T14:13:57.3238868Z dequantize_per_tensor_default_2 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_2, 0.00046353504876606166, -128, -128, 127, torch.int8); quantize_per_tensor_default_2 = None 2025-09-09T14:13:57.3239867Z return dequantize_per_tensor_default_2 2025-09-09T14:13:57.3240171Z 2025-09-09T14:13:57.3240466Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:13:57.3240878Z diff: tensor([[[0., 0., 0.]]]) 2025-09-09T14:13:57.3241328Z PASSED 2025-09-09T14:13:57.3242120Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn1d::test_qat_per_channel_weight_custom_dtype PASSED 2025-09-09T14:14:07.6550856Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn1d::test_qat_preserve_source_fn_stack PASSED 2025-09-09T14:14:07.6552021Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn1d::test_qat_update_shared_qspec model pt2e: GraphModule( 2025-09-09T14:14:07.6552700Z (conv): Module() 2025-09-09T14:14:07.6552930Z (bn): Module() 2025-09-09T14:14:07.6553266Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:14:07.6554316Z 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 2025-09-09T14:14:07.6555614Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3264806270599365, max_val=1.318617343902588) 2025-09-09T14:14:07.6556268Z ) 2025-09-09T14:14:07.6556563Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:14:07.6557673Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0024, 0.0016, 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 2025-09-09T14:14:07.6559104Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.3010, -0.2094, -0.2957]), max_val=tensor([0.2519, 0.1882, 0.3171])) 2025-09-09T14:14:07.6559828Z ) 2025-09-09T14:14:07.6560129Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:14:07.6561145Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0109]), zero_point=tensor([1], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:14:07.6562344Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.407715082168579, max_val=1.379811406135559) 2025-09-09T14:14:07.6562897Z ) 2025-09-09T14:14:07.6563200Z (activation_post_process_3): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:14:07.6564230Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0109]), zero_point=tensor([1], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:14:07.6565421Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.407715082168579, max_val=1.379811406135559) 2025-09-09T14:14:07.6565980Z ) 2025-09-09T14:14:07.6566156Z ) 2025-09-09T14:14:07.6566271Z 2025-09-09T14:14:07.6566275Z 2025-09-09T14:14:07.6566279Z 2025-09-09T14:14:07.6566369Z def forward(self, x): 2025-09-09T14:14:07.6566669Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:14:07.6567052Z conv_weight = self.conv.weight 2025-09-09T14:14:07.6567357Z conv_bias = self.conv.bias 2025-09-09T14:14:07.6567626Z bn_weight = self.bn.weight 2025-09-09T14:14:07.6567913Z bn_bias = self.bn.bias 2025-09-09T14:14:07.6568183Z bn_running_mean = self.bn.running_mean 2025-09-09T14:14:07.6568517Z bn_running_var = self.bn.running_var 2025-09-09T14:14:07.6568881Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T14:14:07.6569353Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:14:07.6570003Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T14:14:07.6570875Z add = torch.ops.aten.add.Tensor(bn_running_var, 1e-05) 2025-09-09T14:14:07.6571319Z sqrt = torch.ops.aten.sqrt.default(add); add = None 2025-09-09T14:14:07.6571765Z div = torch.ops.aten.div.Tensor(bn_weight, sqrt); sqrt = None 2025-09-09T14:14:07.6572251Z reshape = torch.ops.aten.reshape.default(div, [-1, 1, 1]) 2025-09-09T14:14:07.6572806Z mul = torch.ops.aten.mul.Tensor(conv_weight, reshape); conv_weight = reshape = None 2025-09-09T14:14:07.6573518Z activation_post_process_1 = self.activation_post_process_1(mul); mul = None 2025-09-09T14:14:07.6574199Z zeros_like = torch.ops.aten.zeros_like.default(conv_bias, dtype = torch.float32, pin_memory = False) 2025-09-09T14:14:07.6575264Z 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 2025-09-09T14:14:07.6576248Z reshape_1 = torch.ops.aten.reshape.default(div, [1, -1, 1]); div = None 2025-09-09T14:14:07.6576840Z div_1 = torch.ops.aten.div.Tensor(conv1d_1, reshape_1); conv1d_1 = reshape_1 = None 2025-09-09T14:14:07.6577462Z reshape_2 = torch.ops.aten.reshape.default(conv_bias, [1, -1, 1]); conv_bias = None 2025-09-09T14:14:07.6578069Z add_1 = torch.ops.aten.add.Tensor(div_1, reshape_2); div_1 = reshape_2 = None 2025-09-09T14:14:07.6579011Z 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 2025-09-09T14:14:07.6580035Z activation_post_process_2 = self.activation_post_process_2(batch_norm_1); batch_norm_1 = None 2025-09-09T14:14:07.6580852Z hardtanh = torch.ops.aten.hardtanh.default(activation_post_process_2, -1.0, 1.0); activation_post_process_2 = None 2025-09-09T14:14:07.6581625Z activation_post_process_3 = self.activation_post_process_3(hardtanh); hardtanh = None 2025-09-09T14:14:07.6582251Z return pytree.tree_unflatten((activation_post_process_3,), self._out_spec) 2025-09-09T14:14:07.6582672Z 2025-09-09T14:14:07.6582976Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:14:07.6583365Z model fx: GraphModule( 2025-09-09T14:14:07.6583715Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:14:07.6584751Z 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 2025-09-09T14:14:07.6585956Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3264806270599365, max_val=1.318617343902588) 2025-09-09T14:14:07.6586522Z ) 2025-09-09T14:14:07.6586709Z (conv): ConvBn1d( 2025-09-09T14:14:07.6586953Z 3, 3, kernel_size=(3,), stride=(1,) 2025-09-09T14:14:07.6587401Z (bn): BatchNorm1d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T14:14:07.6587904Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:14:07.6588968Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0024, 0.0016, 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 2025-09-09T14:14:07.6590386Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.3010, -0.2094, -0.2957]), max_val=tensor([0.2519, 0.1882, 0.3171])) 2025-09-09T14:14:07.6591114Z ) 2025-09-09T14:14:07.6591303Z ) 2025-09-09T14:14:07.6591591Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:14:07.6592628Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0109]), zero_point=tensor([1], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:14:07.6593889Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.407715082168579, max_val=1.379811406135559) 2025-09-09T14:14:07.6594454Z ) 2025-09-09T14:14:07.6594679Z (hardtanh): Hardtanh(min_val=-1.0, max_val=1.0) 2025-09-09T14:14:07.6595114Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:14:07.6596247Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0109]), zero_point=tensor([1], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:14:07.6597555Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.407715082168579, max_val=1.379811406135559) 2025-09-09T14:14:07.6598125Z ) 2025-09-09T14:14:07.6598306Z ) 2025-09-09T14:14:07.6598423Z 2025-09-09T14:14:07.6598428Z 2025-09-09T14:14:07.6598432Z 2025-09-09T14:14:07.6598524Z def forward(self, x): 2025-09-09T14:14:07.6598916Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:14:07.6599489Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T14:14:07.6600090Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T14:14:07.6600714Z hardtanh = self.hardtanh(activation_post_process_1); activation_post_process_1 = None 2025-09-09T14:14:07.6601382Z activation_post_process_2 = self.activation_post_process_2(hardtanh); hardtanh = None 2025-09-09T14:14:07.6601884Z return activation_post_process_2 2025-09-09T14:14:07.6602159Z 2025-09-09T14:14:07.6602458Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:14:07.6602848Z diff: tensor([[[0., 0., 0.], 2025-09-09T14:14:07.6603108Z [0., 0., 0.], 2025-09-09T14:14:07.6603356Z [0., 0., 0.]]], grad_fn=) 2025-09-09T14:14:07.6603693Z converted model pt2e: GraphModule( 2025-09-09T14:14:07.6603973Z (conv): Module() 2025-09-09T14:14:07.6604196Z (bn): Module() 2025-09-09T14:14:07.6604399Z ) 2025-09-09T14:14:07.6604512Z 2025-09-09T14:14:07.6604517Z 2025-09-09T14:14:07.6604520Z 2025-09-09T14:14:07.6604610Z def forward(self, x): 2025-09-09T14:14:07.6604915Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:14:07.6605268Z conv_bias = self.conv.bias 2025-09-09T14:14:07.6605594Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T14:14:07.6606435Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.010372933000326157, 0, -128, 127, torch.int8); x = None 2025-09-09T14:14:07.6607849Z 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 2025-09-09T14:14:07.6608999Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T14:14:07.6609499Z _scale_0 = self._scale_0 2025-09-09T14:14:07.6609784Z _zero_point_0 = self._zero_point_0 2025-09-09T14:14:07.6610102Z quantize_per_channel = self._frozen_param0 2025-09-09T14:14:07.6611075Z 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 2025-09-09T14:14:07.6612569Z 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 2025-09-09T14:14:09.1632924Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv1d_2, 0.010931476950645447, 1, -128, 127, torch.int8); conv1d_2 = None 2025-09-09T14:14:09.1634739Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.010931476950645447, 1, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T14:14:09.1636034Z hardtanh = torch.ops.aten.hardtanh.default(dequantize_per_tensor_default_1, -1.0, 1.0); dequantize_per_tensor_default_1 = None 2025-09-09T14:14:09.1637247Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(hardtanh, 0.010931476950645447, 1, -128, 127, torch.int8); hardtanh = None 2025-09-09T14:14:09.1638701Z dequantize_per_tensor_default_2 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_2, 0.010931476950645447, 1, -128, 127, torch.int8); quantize_per_tensor_default_2 = None 2025-09-09T14:14:09.1639928Z return pytree.tree_unflatten((dequantize_per_tensor_default_2,), self._out_spec) 2025-09-09T14:14:09.1640390Z 2025-09-09T14:14:09.1640693Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:14:09.1641115Z onverted model fx: GraphModule( 2025-09-09T14:14:09.1641550Z (conv): QuantizedConv1d(Reference)(3, 3, kernel_size=(3,), stride=(1,)) 2025-09-09T14:14:09.1642081Z (hardtanh): Hardtanh(min_val=-1.0, max_val=1.0) 2025-09-09T14:14:09.1642476Z ) 2025-09-09T14:14:09.1642611Z 2025-09-09T14:14:09.1642615Z 2025-09-09T14:14:09.1642619Z 2025-09-09T14:14:09.1642727Z def forward(self, x): 2025-09-09T14:14:09.1643398Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.010372933000326157, 0, -128, 127, torch.int8); x = None 2025-09-09T14:14:09.1644773Z 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 2025-09-09T14:14:09.1645880Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T14:14:09.1646818Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.010931476950645447, 1, -128, 127, torch.int8); conv = None 2025-09-09T14:14:09.1648223Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.010931476950645447, 1, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T14:14:09.1649388Z hardtanh = self.hardtanh(dequantize_per_tensor_default_1); dequantize_per_tensor_default_1 = None 2025-09-09T14:14:09.1650409Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(hardtanh, 0.010931476950645447, 1, -128, 127, torch.int8); hardtanh = None 2025-09-09T14:14:09.1651847Z dequantize_per_tensor_default_2 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_2, 0.010931476950645447, 1, -128, 127, torch.int8); quantize_per_tensor_default_2 = None 2025-09-09T14:14:09.1652807Z return dequantize_per_tensor_default_2 2025-09-09T14:14:09.1653114Z 2025-09-09T14:14:09.1653406Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:14:09.1653819Z diff: tensor([[[0., 0., 0.], 2025-09-09T14:14:09.1654066Z [0., 0., 0.], 2025-09-09T14:14:09.1654297Z [0., 0., 0.]]]) 2025-09-09T14:14:09.1654538Z model pt2e: GraphModule( 2025-09-09T14:14:09.1654792Z (conv): Module() 2025-09-09T14:14:09.1655014Z (bn): Module() 2025-09-09T14:14:09.1655328Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:14:09.1656361Z 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 2025-09-09T14:14:09.1657583Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3264806270599365, max_val=1.318617343902588) 2025-09-09T14:14:09.1658152Z ) 2025-09-09T14:14:09.1658444Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:14:09.1659582Z 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 2025-09-09T14:14:09.1660823Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.30097201466560364, max_val=0.3171221613883972) 2025-09-09T14:14:09.1661382Z ) 2025-09-09T14:14:09.1661682Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:14:09.1662698Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0109]), zero_point=tensor([1], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:14:09.1663978Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.407715082168579, max_val=1.3807092905044556) 2025-09-09T14:14:09.1664547Z ) 2025-09-09T14:14:09.1664837Z (activation_post_process_3): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:14:09.1665871Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0109]), zero_point=tensor([1], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:14:09.1667076Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.407715082168579, max_val=1.3807092905044556) 2025-09-09T14:14:09.1667631Z ) 2025-09-09T14:14:09.1667817Z ) 2025-09-09T14:14:09.1667918Z 2025-09-09T14:14:09.1667923Z 2025-09-09T14:14:09.1667931Z 2025-09-09T14:14:09.1668020Z def forward(self, x): 2025-09-09T14:14:09.1668328Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:14:09.1668687Z conv_weight = self.conv.weight 2025-09-09T14:14:09.1668990Z conv_bias = self.conv.bias 2025-09-09T14:14:09.1669273Z bn_weight = self.bn.weight 2025-09-09T14:14:09.1669537Z bn_bias = self.bn.bias 2025-09-09T14:14:09.1669819Z bn_running_mean = self.bn.running_mean 2025-09-09T14:14:09.1670142Z bn_running_var = self.bn.running_var 2025-09-09T14:14:09.1670507Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T14:14:09.1670978Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:14:09.1671620Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T14:14:09.1672193Z add = torch.ops.aten.add.Tensor(bn_running_var, 1e-05) 2025-09-09T14:14:09.1672624Z sqrt = torch.ops.aten.sqrt.default(add); add = None 2025-09-09T14:14:09.1673080Z div = torch.ops.aten.div.Tensor(bn_weight, sqrt); sqrt = None 2025-09-09T14:14:09.1673555Z reshape = torch.ops.aten.reshape.default(div, [-1, 1, 1]) 2025-09-09T14:14:09.1674106Z mul = torch.ops.aten.mul.Tensor(conv_weight, reshape); conv_weight = reshape = None 2025-09-09T14:14:09.1674711Z activation_post_process_1 = self.activation_post_process_1(mul); mul = None 2025-09-09T14:14:09.1675389Z zeros_like = torch.ops.aten.zeros_like.default(conv_bias, dtype = torch.float32, pin_memory = False) 2025-09-09T14:14:09.1676574Z 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 2025-09-09T14:14:09.1677540Z reshape_1 = torch.ops.aten.reshape.default(div, [1, -1, 1]); div = None 2025-09-09T14:14:09.1678138Z div_1 = torch.ops.aten.div.Tensor(conv1d_1, reshape_1); conv1d_1 = reshape_1 = None 2025-09-09T14:14:09.1678766Z reshape_2 = torch.ops.aten.reshape.default(conv_bias, [1, -1, 1]); conv_bias = None 2025-09-09T14:14:09.1679488Z add_1 = torch.ops.aten.add.Tensor(div_1, reshape_2); div_1 = reshape_2 = None 2025-09-09T14:14:09.1680459Z 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 2025-09-09T14:14:09.1681568Z activation_post_process_2 = self.activation_post_process_2(batch_norm_1); batch_norm_1 = None 2025-09-09T14:14:09.1682391Z hardtanh = torch.ops.aten.hardtanh.default(activation_post_process_2, -1.0, 1.0); activation_post_process_2 = None 2025-09-09T14:14:09.1683166Z activation_post_process_3 = self.activation_post_process_3(hardtanh); hardtanh = None 2025-09-09T14:14:09.1683788Z return pytree.tree_unflatten((activation_post_process_3,), self._out_spec) 2025-09-09T14:14:09.1684218Z 2025-09-09T14:14:09.1684570Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:14:09.1684970Z model fx: GraphModule( 2025-09-09T14:14:09.1685305Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:14:09.1686345Z 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 2025-09-09T14:14:09.1687556Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3264806270599365, max_val=1.318617343902588) 2025-09-09T14:14:09.1688120Z ) 2025-09-09T14:14:09.1688320Z (conv): ConvBn1d( 2025-09-09T14:14:09.1688551Z 3, 3, kernel_size=(3,), stride=(1,) 2025-09-09T14:14:09.1689000Z (bn): BatchNorm1d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T14:14:09.1689496Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:14:09.1690520Z 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 2025-09-09T14:14:09.1691762Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.30097201466560364, max_val=0.3171221613883972) 2025-09-09T14:14:09.1692323Z ) 2025-09-09T14:14:09.1692522Z ) 2025-09-09T14:14:09.1692813Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:14:09.1693852Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0109]), zero_point=tensor([1], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:14:09.1695054Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.407715082168579, max_val=1.3807092905044556) 2025-09-09T14:14:09.1695623Z ) 2025-09-09T14:14:40.6099238Z (hardtanh): Hardtanh(min_val=-1.0, max_val=1.0) 2025-09-09T14:14:40.6099751Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:14:40.6103356Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0109]), zero_point=tensor([1], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:14:40.6105569Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.407715082168579, max_val=1.3807092905044556) 2025-09-09T14:14:40.6106620Z ) 2025-09-09T14:14:40.6106933Z ) 2025-09-09T14:14:40.6107101Z 2025-09-09T14:14:40.6107108Z 2025-09-09T14:14:40.6107115Z 2025-09-09T14:14:40.6107276Z def forward(self, x): 2025-09-09T14:14:40.6107878Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:14:40.6108851Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T14:14:40.6109881Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T14:14:40.6110995Z hardtanh = self.hardtanh(activation_post_process_1); activation_post_process_1 = None 2025-09-09T14:14:40.6112192Z activation_post_process_2 = self.activation_post_process_2(hardtanh); hardtanh = None 2025-09-09T14:14:40.6113012Z return activation_post_process_2 2025-09-09T14:14:40.6113461Z 2025-09-09T14:14:40.6113946Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:14:40.6114635Z diff: tensor([[[0., 0., 0.], 2025-09-09T14:14:40.6115400Z [0., 0., 0.], 2025-09-09T14:14:40.6115849Z [0., 0., 0.]]], grad_fn=) 2025-09-09T14:14:40.6116440Z converted model pt2e: GraphModule( 2025-09-09T14:14:40.6116878Z (conv): Module() 2025-09-09T14:14:40.6117213Z (bn): Module() 2025-09-09T14:14:40.6117545Z ) 2025-09-09T14:14:40.6117693Z 2025-09-09T14:14:40.6117700Z 2025-09-09T14:14:40.6117706Z 2025-09-09T14:14:40.6117858Z def forward(self, x): 2025-09-09T14:14:40.6118303Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:14:40.6119249Z conv_bias = self.conv.bias 2025-09-09T14:14:40.6119822Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T14:14:40.6121068Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.010372933000326157, 0, -128, 127, torch.int8); x = None 2025-09-09T14:14:40.6123314Z 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 2025-09-09T14:14:40.6125662Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T14:14:40.6126587Z quantize_per_tensor = self._frozen_param0 2025-09-09T14:14:40.6128209Z dequantize_per_tensor = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor, 0.0024970248341560364, 0, -127, 127, torch.int8); quantize_per_tensor = None 2025-09-09T14:14:40.6130848Z 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 2025-09-09T14:14:40.6133374Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv1d_2, 0.010934998281300068, 1, -128, 127, torch.int8); conv1d_2 = None 2025-09-09T14:14:40.6136065Z dequantize_per_tensor_default_2 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_2, 0.010934998281300068, 1, -128, 127, torch.int8); quantize_per_tensor_default_2 = None 2025-09-09T14:14:40.6138388Z hardtanh = torch.ops.aten.hardtanh.default(dequantize_per_tensor_default_2, -1.0, 1.0); dequantize_per_tensor_default_2 = None 2025-09-09T14:14:40.6140493Z quantize_per_tensor_default_3 = torch.ops.quantized_decomposed.quantize_per_tensor.default(hardtanh, 0.010934998281300068, 1, -128, 127, torch.int8); hardtanh = None 2025-09-09T14:14:40.6142853Z dequantize_per_tensor_default_3 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_3, 0.010934998281300068, 1, -128, 127, torch.int8); quantize_per_tensor_default_3 = None 2025-09-09T14:14:40.6144816Z return pytree.tree_unflatten((dequantize_per_tensor_default_3,), self._out_spec) 2025-09-09T14:14:40.6145600Z 2025-09-09T14:14:40.6146123Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:14:40.6146806Z onverted model fx: GraphModule( 2025-09-09T14:14:40.6147482Z (conv): QuantizedConv1d(Reference)(3, 3, kernel_size=(3,), stride=(1,)) 2025-09-09T14:14:40.6148303Z (hardtanh): Hardtanh(min_val=-1.0, max_val=1.0) 2025-09-09T14:14:40.6148788Z ) 2025-09-09T14:14:40.6148952Z 2025-09-09T14:14:40.6148958Z 2025-09-09T14:14:40.6148964Z 2025-09-09T14:14:40.6149108Z def forward(self, x): 2025-09-09T14:14:40.6150237Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.010372933000326157, 0, -128, 127, torch.int8); x = None 2025-09-09T14:14:40.6152558Z 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 2025-09-09T14:14:40.6154500Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T14:14:40.6156309Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.010934998281300068, 1, -128, 127, torch.int8); conv = None 2025-09-09T14:14:40.6158776Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.010934998281300068, 1, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T14:14:40.6160639Z hardtanh = self.hardtanh(dequantize_per_tensor_default_1); dequantize_per_tensor_default_1 = None 2025-09-09T14:14:40.6162661Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(hardtanh, 0.010934998281300068, 1, -128, 127, torch.int8); hardtanh = None 2025-09-09T14:14:40.6165288Z dequantize_per_tensor_default_2 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_2, 0.010934998281300068, 1, -128, 127, torch.int8); quantize_per_tensor_default_2 = None 2025-09-09T14:14:40.6166876Z return dequantize_per_tensor_default_2 2025-09-09T14:14:40.6167299Z 2025-09-09T14:14:40.6167729Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:14:40.6168321Z diff: tensor([[[0., 0., 0.], 2025-09-09T14:14:40.6168725Z [0., 0., 0.], 2025-09-09T14:14:40.6169060Z [0., 0., 0.]]]) 2025-09-09T14:14:40.6169691Z PASSED 2025-09-09T14:14:40.6170865Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn2d::test_fold_bn_erases_bn_node PASSED 2025-09-09T14:14:40.6172926Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_conv_bn_bias_derived_qspec PASSED 2025-09-09T14:14:40.6174822Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_conv_bn_fusion model pt2e: GraphModule( 2025-09-09T14:14:40.6176021Z (conv): Module() 2025-09-09T14:14:40.6176363Z (bn): Module() 2025-09-09T14:14:40.6176831Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:14:40.6178515Z 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 2025-09-09T14:14:40.6180500Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.526270866394043, max_val=2.143237352371216) 2025-09-09T14:14:40.6181429Z ) 2025-09-09T14:14:40.6181903Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:14:40.6183762Z 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 2025-09-09T14:14:40.6186234Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.1822, -0.1883, -0.1585]), max_val=tensor([0.1856, 0.1719, 0.1858])) 2025-09-09T14:14:40.6187392Z ) 2025-09-09T14:14:40.6187871Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:14:40.6189555Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0139]), zero_point=tensor([-11], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:14:40.6191558Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.628747820854187, max_val=1.9105255603790283) 2025-09-09T14:14:40.6192535Z ) 2025-09-09T14:14:40.6192813Z ) 2025-09-09T14:14:40.6192956Z 2025-09-09T14:14:40.6192963Z 2025-09-09T14:14:40.6192968Z 2025-09-09T14:14:40.6193102Z def forward(self, x): 2025-09-09T14:14:40.6193577Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:14:40.6194158Z conv_weight = self.conv.weight 2025-09-09T14:14:40.6194631Z conv_bias = self.conv.bias 2025-09-09T14:14:40.6195046Z bn_weight = self.bn.weight 2025-09-09T14:14:40.6195468Z bn_bias = self.bn.bias 2025-09-09T14:14:40.6196075Z bn_running_mean = self.bn.running_mean 2025-09-09T14:14:40.6196725Z bn_running_var = self.bn.running_var 2025-09-09T14:14:40.6197278Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T14:14:40.6197972Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:14:40.6199030Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T14:14:40.6199964Z add = torch.ops.aten.add.Tensor(bn_running_var, 1e-05) 2025-09-09T14:14:40.6200831Z sqrt = torch.ops.aten.sqrt.default(add); add = None 2025-09-09T14:14:40.6201598Z div = torch.ops.aten.div.Tensor(bn_weight, sqrt); sqrt = None 2025-09-09T14:14:40.6202404Z reshape = torch.ops.aten.reshape.default(div, [-1, 1, 1, 1]) 2025-09-09T14:14:40.6203275Z mul = torch.ops.aten.mul.Tensor(conv_weight, reshape); conv_weight = reshape = None 2025-09-09T14:14:40.6204237Z activation_post_process_1 = self.activation_post_process_1(mul); mul = None 2025-09-09T14:14:40.6205384Z zeros_like = torch.ops.aten.zeros_like.default(conv_bias, dtype = torch.float32, pin_memory = False) 2025-09-09T14:14:40.6207079Z 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 2025-09-09T14:14:40.6208752Z reshape_1 = torch.ops.aten.reshape.default(div, [1, -1, 1, 1]); div = None 2025-09-09T14:14:50.9261560Z div_1 = torch.ops.aten.div.Tensor(conv2d_1, reshape_1); conv2d_1 = reshape_1 = None 2025-09-09T14:14:50.9262432Z reshape_2 = torch.ops.aten.reshape.default(conv_bias, [1, -1, 1, 1]); conv_bias = None 2025-09-09T14:14:50.9263060Z add_1 = torch.ops.aten.add.Tensor(div_1, reshape_2); div_1 = reshape_2 = None 2025-09-09T14:14:50.9264041Z 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 2025-09-09T14:14:50.9265066Z activation_post_process_2 = self.activation_post_process_2(batch_norm_1); batch_norm_1 = None 2025-09-09T14:14:50.9265723Z return pytree.tree_unflatten((activation_post_process_2,), self._out_spec) 2025-09-09T14:14:50.9266142Z 2025-09-09T14:14:50.9266446Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:14:50.9266834Z model fx: GraphModule( 2025-09-09T14:14:50.9267197Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:14:50.9268226Z 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 2025-09-09T14:14:50.9269451Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.526270866394043, max_val=2.143237352371216) 2025-09-09T14:14:50.9270012Z ) 2025-09-09T14:14:50.9270245Z (conv): ConvBn2d( 2025-09-09T14:14:50.9270614Z 3, 3, kernel_size=(3, 3), stride=(1, 1) 2025-09-09T14:14:50.9271068Z (bn): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T14:14:50.9271767Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:14:50.9272991Z 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 2025-09-09T14:14:50.9274578Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.1822, -0.1883, -0.1585]), max_val=tensor([0.1856, 0.1719, 0.1858])) 2025-09-09T14:14:50.9275303Z ) 2025-09-09T14:14:50.9275484Z ) 2025-09-09T14:14:50.9275783Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:14:50.9277167Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0139]), zero_point=tensor([-11], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:14:50.9278379Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.628747820854187, max_val=1.9105255603790283) 2025-09-09T14:14:50.9278942Z ) 2025-09-09T14:14:50.9279121Z ) 2025-09-09T14:14:50.9279235Z 2025-09-09T14:14:50.9279275Z 2025-09-09T14:14:50.9279279Z 2025-09-09T14:14:50.9279573Z def forward(self, x): 2025-09-09T14:14:50.9279962Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:14:50.9280537Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T14:14:50.9281142Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T14:14:50.9281616Z return activation_post_process_1 2025-09-09T14:14:50.9281893Z 2025-09-09T14:14:50.9282200Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:14:50.9282594Z diff: tensor([[[[0., 0., 0.], 2025-09-09T14:14:50.9282855Z [0., 0., 0.], 2025-09-09T14:14:50.9283078Z [0., 0., 0.]], 2025-09-09T14:14:50.9283236Z 2025-09-09T14:14:50.9283317Z [[0., 0., 0.], 2025-09-09T14:14:50.9283532Z [0., 0., 0.], 2025-09-09T14:14:50.9283758Z [0., 0., 0.]], 2025-09-09T14:14:50.9283904Z 2025-09-09T14:14:50.9283983Z [[0., 0., 0.], 2025-09-09T14:14:50.9284212Z [0., 0., 0.], 2025-09-09T14:14:50.9284471Z [0., 0., 0.]]]], grad_fn=) 2025-09-09T14:14:50.9284793Z converted model pt2e: GraphModule( 2025-09-09T14:14:50.9285078Z (conv): Module() 2025-09-09T14:14:50.9285285Z (bn): Module() 2025-09-09T14:14:50.9285500Z ) 2025-09-09T14:14:50.9285601Z 2025-09-09T14:14:50.9285605Z 2025-09-09T14:14:50.9285609Z 2025-09-09T14:14:50.9285698Z def forward(self, x): 2025-09-09T14:14:50.9286002Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:14:50.9286361Z conv_bias = self.conv.bias 2025-09-09T14:14:50.9287071Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.018311796709895134, 10, -128, 127, torch.int8); x = None 2025-09-09T14:14:50.9288454Z 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 2025-09-09T14:14:50.9289390Z _scale_0 = self._scale_0 2025-09-09T14:14:50.9289667Z _zero_point_0 = self._zero_point_0 2025-09-09T14:14:50.9289988Z quantize_per_channel = self._frozen_param0 2025-09-09T14:14:50.9290964Z 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 2025-09-09T14:14:50.9292454Z 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 2025-09-09T14:14:50.9293765Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv2d_2, 0.0138795031234622, -11, -128, 127, torch.int8); conv2d_2 = None 2025-09-09T14:14:50.9295193Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.0138795031234622, -11, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T14:14:50.9296300Z return pytree.tree_unflatten((dequantize_per_tensor_default_1,), self._out_spec) 2025-09-09T14:14:50.9296741Z 2025-09-09T14:14:50.9297043Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:14:50.9297442Z onverted model fx: GraphModule( 2025-09-09T14:14:50.9297862Z (conv): QuantizedConv2d(Reference)(3, 3, kernel_size=(3, 3), stride=(1, 1)) 2025-09-09T14:14:50.9298271Z ) 2025-09-09T14:14:50.9298465Z 2025-09-09T14:14:50.9298470Z 2025-09-09T14:14:50.9298474Z 2025-09-09T14:14:50.9298567Z def forward(self, x): 2025-09-09T14:14:50.9299254Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.018311796709895134, 10, -128, 127, torch.int8); x = None 2025-09-09T14:14:50.9300615Z 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 2025-09-09T14:14:50.9301789Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T14:14:50.9302724Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.0138795031234622, -11, -128, 127, torch.int8); conv = None 2025-09-09T14:14:50.9304122Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.0138795031234622, -11, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T14:14:50.9305092Z return dequantize_per_tensor_default_1 2025-09-09T14:14:50.9305378Z 2025-09-09T14:14:50.9305680Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:14:50.9306081Z diff: tensor([[[[0., 0., 0.], 2025-09-09T14:14:50.9306331Z [0., 0., 0.], 2025-09-09T14:14:50.9306562Z [0., 0., 0.]], 2025-09-09T14:14:50.9306713Z 2025-09-09T14:14:50.9306795Z [[0., 0., 0.], 2025-09-09T14:14:50.9307022Z [0., 0., 0.], 2025-09-09T14:14:50.9307236Z [0., 0., 0.]], 2025-09-09T14:14:50.9307397Z 2025-09-09T14:14:50.9307479Z [[0., 0., 0.], 2025-09-09T14:14:50.9307693Z [0., 0., 0.], 2025-09-09T14:14:50.9307929Z [0., 0., 0.]]]]) 2025-09-09T14:14:50.9308172Z model pt2e: GraphModule( 2025-09-09T14:14:50.9308427Z (conv): Module() 2025-09-09T14:14:50.9308649Z (bn): Module() 2025-09-09T14:14:50.9308963Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:14:50.9309999Z 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 2025-09-09T14:14:50.9311199Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.526270866394043, max_val=2.143237352371216) 2025-09-09T14:14:50.9311768Z ) 2025-09-09T14:14:50.9312067Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:14:50.9313099Z 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 2025-09-09T14:14:50.9314324Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.1882954239845276, max_val=0.18581794202327728) 2025-09-09T14:14:50.9314883Z ) 2025-09-09T14:14:50.9315212Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:14:50.9316348Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0139]), zero_point=tensor([-11], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:14:50.9317571Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.6255242824554443, max_val=1.9112863540649414) 2025-09-09T14:14:50.9318136Z ) 2025-09-09T14:14:50.9318329Z ) 2025-09-09T14:14:50.9318431Z 2025-09-09T14:14:50.9318435Z 2025-09-09T14:14:50.9318439Z 2025-09-09T14:14:50.9318545Z def forward(self, x): 2025-09-09T14:14:50.9318845Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:14:50.9319220Z conv_weight = self.conv.weight 2025-09-09T14:14:50.9319514Z conv_bias = self.conv.bias 2025-09-09T14:14:50.9319803Z bn_weight = self.bn.weight 2025-09-09T14:14:50.9320152Z bn_bias = self.bn.bias 2025-09-09T14:14:50.9320438Z bn_running_mean = self.bn.running_mean 2025-09-09T14:14:50.9320757Z bn_running_var = self.bn.running_var 2025-09-09T14:14:50.9321122Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T14:14:50.9321604Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:14:50.9322234Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T14:14:50.9322881Z add = torch.ops.aten.add.Tensor(bn_running_var, 1e-05) 2025-09-09T14:14:50.9323302Z sqrt = torch.ops.aten.sqrt.default(add); add = None 2025-09-09T14:14:50.9323755Z div = torch.ops.aten.div.Tensor(bn_weight, sqrt); sqrt = None 2025-09-09T14:14:50.9324231Z reshape = torch.ops.aten.reshape.default(div, [-1, 1, 1, 1]) 2025-09-09T14:14:50.9325001Z mul = torch.ops.aten.mul.Tensor(conv_weight, reshape); conv_weight = reshape = None 2025-09-09T14:14:50.9325630Z activation_post_process_1 = self.activation_post_process_1(mul); mul = None 2025-09-09T14:14:50.9326293Z zeros_like = torch.ops.aten.zeros_like.default(conv_bias, dtype = torch.float32, pin_memory = False) 2025-09-09T14:15:03.1375487Z 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 2025-09-09T14:15:03.1376736Z reshape_1 = torch.ops.aten.reshape.default(div, [1, -1, 1, 1]); div = None 2025-09-09T14:15:03.1377460Z div_1 = torch.ops.aten.div.Tensor(conv2d_1, reshape_1); conv2d_1 = reshape_1 = None 2025-09-09T14:15:03.1378689Z reshape_2 = torch.ops.aten.reshape.default(conv_bias, [1, -1, 1, 1]); conv_bias = None 2025-09-09T14:15:03.1379370Z add_1 = torch.ops.aten.add.Tensor(div_1, reshape_2); div_1 = reshape_2 = None 2025-09-09T14:15:03.1380355Z 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 2025-09-09T14:15:03.1381366Z activation_post_process_2 = self.activation_post_process_2(batch_norm_1); batch_norm_1 = None 2025-09-09T14:15:03.1382026Z return pytree.tree_unflatten((activation_post_process_2,), self._out_spec) 2025-09-09T14:15:03.1382442Z 2025-09-09T14:15:03.1382750Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:15:03.1383155Z model fx: GraphModule( 2025-09-09T14:15:03.1383500Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:15:03.1384539Z 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 2025-09-09T14:15:03.1385825Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.526270866394043, max_val=2.143237352371216) 2025-09-09T14:15:03.1386494Z ) 2025-09-09T14:15:03.1386699Z (conv): ConvBn2d( 2025-09-09T14:15:03.1386939Z 3, 3, kernel_size=(3, 3), stride=(1, 1) 2025-09-09T14:15:03.1387491Z (bn): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T14:15:03.1388037Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:15:03.1389051Z 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 2025-09-09T14:15:03.1390413Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.1882954239845276, max_val=0.18581794202327728) 2025-09-09T14:15:03.1391151Z ) 2025-09-09T14:15:03.1391344Z ) 2025-09-09T14:15:03.1391631Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:15:03.1392935Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0139]), zero_point=tensor([-11], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:15:03.1394164Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.6255242824554443, max_val=1.9112863540649414) 2025-09-09T14:15:03.1394722Z ) 2025-09-09T14:15:03.1394916Z ) 2025-09-09T14:15:03.1395021Z 2025-09-09T14:15:03.1395026Z 2025-09-09T14:15:03.1395030Z 2025-09-09T14:15:03.1396024Z def forward(self, x): 2025-09-09T14:15:03.1396525Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:15:03.1397114Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T14:15:03.1397708Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T14:15:03.1398173Z return activation_post_process_1 2025-09-09T14:15:03.1398445Z 2025-09-09T14:15:03.1398754Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:15:03.1399165Z diff: tensor([[[[0., 0., 0.], 2025-09-09T14:15:03.1399416Z [0., 0., 0.], 2025-09-09T14:15:03.1399652Z [0., 0., 0.]], 2025-09-09T14:15:03.1399803Z 2025-09-09T14:15:03.1399883Z [[0., 0., 0.], 2025-09-09T14:15:03.1400115Z [0., 0., 0.], 2025-09-09T14:15:03.1400336Z [0., 0., 0.]], 2025-09-09T14:15:03.1400495Z 2025-09-09T14:15:03.1400579Z [[0., 0., 0.], 2025-09-09T14:15:03.1400795Z [0., 0., 0.], 2025-09-09T14:15:03.1401058Z [0., 0., 0.]]]], grad_fn=) 2025-09-09T14:15:03.1401399Z converted model pt2e: GraphModule( 2025-09-09T14:15:03.1401675Z (conv): Module() 2025-09-09T14:15:03.1401893Z (bn): Module() 2025-09-09T14:15:03.1402095Z ) 2025-09-09T14:15:03.1402196Z 2025-09-09T14:15:03.1402200Z 2025-09-09T14:15:03.1402217Z 2025-09-09T14:15:03.1402306Z def forward(self, x): 2025-09-09T14:15:03.1402600Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:15:03.1402972Z conv_bias = self.conv.bias 2025-09-09T14:15:03.1403671Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.018311796709895134, 10, -128, 127, torch.int8); x = None 2025-09-09T14:15:03.1405050Z 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 2025-09-09T14:15:03.1406038Z quantize_per_tensor = self._frozen_param0 2025-09-09T14:15:03.1406902Z 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 2025-09-09T14:15:03.1408299Z 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 2025-09-09T14:15:03.1409633Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv2d_2, 0.013869845308363438, -11, -128, 127, torch.int8); conv2d_2 = None 2025-09-09T14:15:03.1411060Z dequantize_per_tensor_default_2 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_2, 0.013869845308363438, -11, -128, 127, torch.int8); quantize_per_tensor_default_2 = None 2025-09-09T14:15:03.1412180Z return pytree.tree_unflatten((dequantize_per_tensor_default_2,), self._out_spec) 2025-09-09T14:15:03.1412641Z 2025-09-09T14:15:03.1412935Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:15:03.1413347Z onverted model fx: GraphModule( 2025-09-09T14:15:03.1413754Z (conv): QuantizedConv2d(Reference)(3, 3, kernel_size=(3, 3), stride=(1, 1)) 2025-09-09T14:15:03.1414175Z ) 2025-09-09T14:15:03.1414279Z 2025-09-09T14:15:03.1414283Z 2025-09-09T14:15:03.1414287Z 2025-09-09T14:15:03.1414377Z def forward(self, x): 2025-09-09T14:15:03.1415158Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.018311796709895134, 10, -128, 127, torch.int8); x = None 2025-09-09T14:15:03.1416541Z 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 2025-09-09T14:15:03.1417646Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T14:15:03.1418667Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.013869845308363438, -11, -128, 127, torch.int8); conv = None 2025-09-09T14:15:03.1420085Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.013869845308363438, -11, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T14:15:03.1421057Z return dequantize_per_tensor_default_1 2025-09-09T14:15:03.1421374Z 2025-09-09T14:15:03.1421667Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:15:03.1422078Z diff: tensor([[[[0., 0., 0.], 2025-09-09T14:15:03.1422333Z [0., 0., 0.], 2025-09-09T14:15:03.1422576Z [0., 0., 0.]], 2025-09-09T14:15:03.1422728Z 2025-09-09T14:15:03.1422825Z [[0., 0., 0.], 2025-09-09T14:15:03.1423047Z [0., 0., 0.], 2025-09-09T14:15:03.1423279Z [0., 0., 0.]], 2025-09-09T14:15:03.1423429Z 2025-09-09T14:15:03.1423510Z [[0., 0., 0.], 2025-09-09T14:15:03.1423739Z [0., 0., 0.], 2025-09-09T14:15:03.1423955Z [0., 0., 0.]]]]) 2025-09-09T14:15:03.1424702Z PASSED 2025-09-09T14:15:03.1425483Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_conv_bn_fusion_cuda SKIPPED 2025-09-09T14:15:03.1426577Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_conv_bn_fusion_literal_args model pt2e: GraphModule( 2025-09-09T14:15:03.1427291Z (conv): Module() 2025-09-09T14:15:03.1427503Z (bn): Module() 2025-09-09T14:15:03.1427831Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:15:03.1428858Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0147]), zero_point=tensor([-28], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:15:03.1430091Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.4721859693527222, max_val=2.2869999408721924) 2025-09-09T14:15:03.1430648Z ) 2025-09-09T14:15:03.1430954Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:15:03.1432042Z 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 2025-09-09T14:15:03.1433467Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.1897, -0.1787, -0.1913]), max_val=tensor([0.1870, 0.1478, 0.1740])) 2025-09-09T14:15:03.1434182Z ) 2025-09-09T14:15:03.1434473Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:15:03.1435505Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0313]), zero_point=tensor([1], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:15:03.1436788Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-4.046965599060059, max_val=3.922553539276123) 2025-09-09T14:15:03.1437339Z ) 2025-09-09T14:15:03.1437532Z ) 2025-09-09T14:15:03.1437632Z 2025-09-09T14:15:03.1437637Z 2025-09-09T14:15:03.1437641Z 2025-09-09T14:15:03.1437745Z def forward(self, x): 2025-09-09T14:15:03.1438048Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:15:03.1438635Z conv_weight = self.conv.weight 2025-09-09T14:15:03.1438930Z conv_bias = self.conv.bias 2025-09-09T14:15:03.1439216Z bn_weight = self.bn.weight 2025-09-09T14:15:03.1439492Z bn_bias = self.bn.bias 2025-09-09T14:15:03.1439782Z bn_running_mean = self.bn.running_mean 2025-09-09T14:15:03.1440103Z bn_running_var = self.bn.running_var 2025-09-09T14:15:03.1440469Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T14:15:03.1441029Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:15:03.1441674Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T14:15:03.1442263Z add = torch.ops.aten.add.Tensor(bn_running_var, 1e-05) 2025-09-09T14:15:13.5564714Z sqrt = torch.ops.aten.sqrt.default(add); add = None 2025-09-09T14:15:13.5565598Z div = torch.ops.aten.div.Tensor(bn_weight, sqrt); sqrt = None 2025-09-09T14:15:13.5566232Z reshape = torch.ops.aten.reshape.default(div, [-1, 1, 1, 1]) 2025-09-09T14:15:13.5566792Z mul = torch.ops.aten.mul.Tensor(conv_weight, reshape); conv_weight = reshape = None 2025-09-09T14:15:13.5567404Z activation_post_process_1 = self.activation_post_process_1(mul); mul = None 2025-09-09T14:15:13.5568083Z zeros_like = torch.ops.aten.zeros_like.default(conv_bias, dtype = torch.float32, pin_memory = False) 2025-09-09T14:15:13.5569175Z 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 2025-09-09T14:15:13.5570193Z reshape_1 = torch.ops.aten.reshape.default(div, [1, -1, 1, 1]); div = None 2025-09-09T14:15:13.5570796Z div_1 = torch.ops.aten.div.Tensor(conv2d_1, reshape_1); conv2d_1 = reshape_1 = None 2025-09-09T14:15:13.5571428Z reshape_2 = torch.ops.aten.reshape.default(conv_bias, [1, -1, 1, 1]); conv_bias = None 2025-09-09T14:15:13.5572055Z add_1 = torch.ops.aten.add.Tensor(div_1, reshape_2); div_1 = reshape_2 = None 2025-09-09T14:15:13.5573056Z 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 2025-09-09T14:15:13.5574110Z activation_post_process_2 = self.activation_post_process_2(batch_norm_1); batch_norm_1 = None 2025-09-09T14:15:13.5574766Z return pytree.tree_unflatten((activation_post_process_2,), self._out_spec) 2025-09-09T14:15:13.5575190Z 2025-09-09T14:15:13.5575497Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:15:13.5575885Z model fx: GraphModule( 2025-09-09T14:15:13.5576237Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:15:13.5577343Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0147]), zero_point=tensor([-28], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:15:13.5578593Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.4721859693527222, max_val=2.2869999408721924) 2025-09-09T14:15:13.5579166Z ) 2025-09-09T14:15:13.5579356Z (conv): ConvBn2d( 2025-09-09T14:15:13.5579645Z 3, 3, kernel_size=(3, 3), stride=(2, 2), padding=(4, 4) 2025-09-09T14:15:13.5580122Z (bn): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T14:15:13.5580647Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:15:13.5581709Z 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 2025-09-09T14:15:13.5583408Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.1897, -0.1787, -0.1913]), max_val=tensor([0.1870, 0.1478, 0.1740])) 2025-09-09T14:15:13.5584140Z ) 2025-09-09T14:15:13.5584338Z ) 2025-09-09T14:15:13.5584631Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:15:13.5585670Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0313]), zero_point=tensor([1], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:15:13.5586977Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-4.046965599060059, max_val=3.922553539276123) 2025-09-09T14:15:13.5587543Z ) 2025-09-09T14:15:13.5587724Z ) 2025-09-09T14:15:13.5587841Z 2025-09-09T14:15:13.5587846Z 2025-09-09T14:15:13.5587850Z 2025-09-09T14:15:13.5587939Z def forward(self, x): 2025-09-09T14:15:13.5588325Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:15:13.5588896Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T14:15:13.5589498Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T14:15:13.5589959Z return activation_post_process_1 2025-09-09T14:15:13.5590246Z 2025-09-09T14:15:13.5590536Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:15:13.5590944Z diff: tensor([[[[0., 0., 0., 0., 0., 0.], 2025-09-09T14:15:13.5591242Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:15:13.5591499Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:15:13.5591773Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:15:13.5592052Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:15:13.5592321Z [0., 0., 0., 0., 0., 0.]], 2025-09-09T14:15:13.5592533Z 2025-09-09T14:15:13.5592619Z [[0., 0., 0., 0., 0., 0.], 2025-09-09T14:15:13.5592891Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:15:13.5593162Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:15:13.5593419Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:15:13.5593694Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:15:13.5593954Z [0., 0., 0., 0., 0., 0.]], 2025-09-09T14:15:13.5594135Z 2025-09-09T14:15:13.5594237Z [[0., 0., 0., 0., 0., 0.], 2025-09-09T14:15:13.5594494Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:15:13.5594770Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:15:13.5595026Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:15:13.5595300Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:15:13.5595609Z [0., 0., 0., 0., 0., 0.]]]], grad_fn=) 2025-09-09T14:15:13.5595969Z converted model pt2e: GraphModule( 2025-09-09T14:15:13.5596369Z (conv): Module() 2025-09-09T14:15:13.5596585Z (bn): Module() 2025-09-09T14:15:13.5596802Z ) 2025-09-09T14:15:13.5596904Z 2025-09-09T14:15:13.5596908Z 2025-09-09T14:15:13.5596912Z 2025-09-09T14:15:13.5597002Z def forward(self, x): 2025-09-09T14:15:13.5597312Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:15:13.5597677Z conv_bias = self.conv.bias 2025-09-09T14:15:13.5598400Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.01474190503358841, -28, -128, 127, torch.int8); x = None 2025-09-09T14:15:13.5599778Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.01474190503358841, -28, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T14:15:13.5600712Z _scale_0 = self._scale_0 2025-09-09T14:15:13.5600995Z _zero_point_0 = self._zero_point_0 2025-09-09T14:15:13.5601312Z quantize_per_channel = self._frozen_param0 2025-09-09T14:15:13.5602285Z 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 2025-09-09T14:15:13.5603876Z 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 2025-09-09T14:15:13.5605202Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv2d_2, 0.031253017485141754, 1, -128, 127, torch.int8); conv2d_2 = None 2025-09-09T14:15:13.5606749Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.031253017485141754, 1, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T14:15:13.5607933Z return pytree.tree_unflatten((dequantize_per_tensor_default_1,), self._out_spec) 2025-09-09T14:15:13.5608376Z 2025-09-09T14:15:13.5608680Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:15:13.5609088Z onverted model fx: GraphModule( 2025-09-09T14:15:13.5609561Z (conv): QuantizedConv2d(Reference)(3, 3, kernel_size=(3, 3), stride=(2, 2), padding=(4, 4)) 2025-09-09T14:15:13.5610019Z ) 2025-09-09T14:15:13.5610136Z 2025-09-09T14:15:13.5610143Z 2025-09-09T14:15:13.5610148Z 2025-09-09T14:15:13.5610239Z def forward(self, x): 2025-09-09T14:15:13.5610928Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.01474190503358841, -28, -128, 127, torch.int8); x = None 2025-09-09T14:15:13.5612293Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.01474190503358841, -28, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T14:15:13.5613419Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T14:15:13.5614359Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.031253017485141754, 1, -128, 127, torch.int8); conv = None 2025-09-09T14:15:13.5615757Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.031253017485141754, 1, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T14:15:13.5616734Z return dequantize_per_tensor_default_1 2025-09-09T14:15:13.5617024Z 2025-09-09T14:15:13.5617328Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:15:13.5617735Z diff: tensor([[[[0., 0., 0., 0., 0., 0.], 2025-09-09T14:15:13.5618021Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:15:13.5618291Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:15:13.5618552Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:15:13.5618821Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:15:13.5619080Z [0., 0., 0., 0., 0., 0.]], 2025-09-09T14:15:13.5619271Z 2025-09-09T14:15:13.5619355Z [[0., 0., 0., 0., 0., 0.], 2025-09-09T14:15:13.5619611Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:15:13.5619878Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:15:13.5620132Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:15:13.5620404Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:15:13.5620673Z [0., 0., 0., 0., 0., 0.]], 2025-09-09T14:15:13.5620850Z 2025-09-09T14:15:13.5620940Z [[0., 0., 0., 0., 0., 0.], 2025-09-09T14:15:13.5621205Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:15:13.5621460Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:15:13.5621733Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:15:13.5621986Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:15:13.5622259Z [0., 0., 0., 0., 0., 0.]]]]) 2025-09-09T14:15:13.5622546Z model pt2e: GraphModule( 2025-09-09T14:15:13.5622800Z (conv): Module() 2025-09-09T14:15:13.5623022Z (bn): Module() 2025-09-09T14:15:13.5623337Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:15:13.5624547Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0147]), zero_point=tensor([-28], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:15:13.5625915Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.4721859693527222, max_val=2.2869999408721924) 2025-09-09T14:15:13.5626488Z ) 2025-09-09T14:15:13.5626780Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:15:22.3862748Z 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 2025-09-09T14:15:22.3864471Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.19127479195594788, max_val=0.1870359182357788) 2025-09-09T14:15:22.3865047Z ) 2025-09-09T14:15:22.3865343Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:15:22.3866508Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0312]), zero_point=tensor([2], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:15:22.3867790Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-4.046965599060059, max_val=3.9041571617126465) 2025-09-09T14:15:22.3868359Z ) 2025-09-09T14:15:22.3868555Z ) 2025-09-09T14:15:22.3868655Z 2025-09-09T14:15:22.3868660Z 2025-09-09T14:15:22.3868664Z 2025-09-09T14:15:22.3868756Z def forward(self, x): 2025-09-09T14:15:22.3869068Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:15:22.3869438Z conv_weight = self.conv.weight 2025-09-09T14:15:22.3869745Z conv_bias = self.conv.bias 2025-09-09T14:15:22.3870015Z bn_weight = self.bn.weight 2025-09-09T14:15:22.3870293Z bn_bias = self.bn.bias 2025-09-09T14:15:22.3870578Z bn_running_mean = self.bn.running_mean 2025-09-09T14:15:22.3870898Z bn_running_var = self.bn.running_var 2025-09-09T14:15:22.3871353Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T14:15:22.3871880Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:15:22.3872585Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T14:15:22.3881883Z add = torch.ops.aten.add.Tensor(bn_running_var, 1e-05) 2025-09-09T14:15:22.3882331Z sqrt = torch.ops.aten.sqrt.default(add); add = None 2025-09-09T14:15:22.3882798Z div = torch.ops.aten.div.Tensor(bn_weight, sqrt); sqrt = None 2025-09-09T14:15:22.3883282Z reshape = torch.ops.aten.reshape.default(div, [-1, 1, 1, 1]) 2025-09-09T14:15:22.3883861Z mul = torch.ops.aten.mul.Tensor(conv_weight, reshape); conv_weight = reshape = None 2025-09-09T14:15:22.3884468Z activation_post_process_1 = self.activation_post_process_1(mul); mul = None 2025-09-09T14:15:22.3885148Z zeros_like = torch.ops.aten.zeros_like.default(conv_bias, dtype = torch.float32, pin_memory = False) 2025-09-09T14:15:22.3886254Z 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 2025-09-09T14:15:22.3887245Z reshape_1 = torch.ops.aten.reshape.default(div, [1, -1, 1, 1]); div = None 2025-09-09T14:15:22.3887846Z div_1 = torch.ops.aten.div.Tensor(conv2d_1, reshape_1); conv2d_1 = reshape_1 = None 2025-09-09T14:15:22.3888480Z reshape_2 = torch.ops.aten.reshape.default(conv_bias, [1, -1, 1, 1]); conv_bias = None 2025-09-09T14:15:22.3889111Z add_1 = torch.ops.aten.add.Tensor(div_1, reshape_2); div_1 = reshape_2 = None 2025-09-09T14:15:22.3890080Z 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 2025-09-09T14:15:22.3891090Z activation_post_process_2 = self.activation_post_process_2(batch_norm_1); batch_norm_1 = None 2025-09-09T14:15:22.3891754Z return pytree.tree_unflatten((activation_post_process_2,), self._out_spec) 2025-09-09T14:15:22.3892374Z 2025-09-09T14:15:22.3892694Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:15:22.3893101Z model fx: GraphModule( 2025-09-09T14:15:22.3893443Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:15:22.3894481Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0147]), zero_point=tensor([-28], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:15:22.3895781Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.4721859693527222, max_val=2.2869999408721924) 2025-09-09T14:15:22.3896355Z ) 2025-09-09T14:15:22.3896554Z (conv): ConvBn2d( 2025-09-09T14:15:22.3896833Z 3, 3, kernel_size=(3, 3), stride=(2, 2), padding=(4, 4) 2025-09-09T14:15:22.3897329Z (bn): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T14:15:22.3897834Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:15:22.3898850Z 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 2025-09-09T14:15:22.3900092Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.19127479195594788, max_val=0.1870359182357788) 2025-09-09T14:15:22.3900659Z ) 2025-09-09T14:15:22.3900858Z ) 2025-09-09T14:15:22.3901148Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:15:22.3902185Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0312]), zero_point=tensor([2], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:15:22.3903384Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-4.046965599060059, max_val=3.9041571617126465) 2025-09-09T14:15:22.3903957Z ) 2025-09-09T14:15:22.3904148Z ) 2025-09-09T14:15:22.3904249Z 2025-09-09T14:15:22.3904254Z 2025-09-09T14:15:22.3904258Z 2025-09-09T14:15:22.3904348Z def forward(self, x): 2025-09-09T14:15:22.3904736Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:15:22.3905305Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T14:15:22.3905900Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T14:15:22.3906358Z return activation_post_process_1 2025-09-09T14:15:22.3906644Z 2025-09-09T14:15:22.3906946Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:15:22.3907345Z diff: tensor([[[[0., 0., 0., 0., 0., 0.], 2025-09-09T14:15:22.3907645Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:15:22.3907905Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:15:22.3908175Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:15:22.3908434Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:15:22.3908705Z [0., 0., 0., 0., 0., 0.]], 2025-09-09T14:15:22.3908884Z 2025-09-09T14:15:22.3908970Z [[0., 0., 0., 0., 0., 0.], 2025-09-09T14:15:22.3909238Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:15:22.3909507Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:15:22.3909766Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:15:22.3910036Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:15:22.3910291Z [0., 0., 0., 0., 0., 0.]], 2025-09-09T14:15:22.3910488Z 2025-09-09T14:15:22.3910573Z [[0., 0., 0., 0., 0., 0.], 2025-09-09T14:15:22.3910826Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:15:22.3911094Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:15:22.3911346Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:15:22.3911616Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:15:22.3911927Z [0., 0., 0., 0., 0., 0.]]]], grad_fn=) 2025-09-09T14:15:22.3912268Z converted model pt2e: GraphModule( 2025-09-09T14:15:22.3912635Z (conv): Module() 2025-09-09T14:15:22.3912854Z (bn): Module() 2025-09-09T14:15:22.3913072Z ) 2025-09-09T14:15:22.3913175Z 2025-09-09T14:15:22.3913179Z 2025-09-09T14:15:22.3913183Z 2025-09-09T14:15:22.3913274Z def forward(self, x): 2025-09-09T14:15:22.3913588Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:15:22.3913943Z conv_bias = self.conv.bias 2025-09-09T14:15:22.3914658Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.01474190503358841, -28, -128, 127, torch.int8); x = None 2025-09-09T14:15:22.3916230Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.01474190503358841, -28, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T14:15:22.3917199Z quantize_per_tensor = self._frozen_param0 2025-09-09T14:15:22.3918084Z 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 2025-09-09T14:15:22.3919505Z 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 2025-09-09T14:15:22.3920820Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv2d_2, 0.03118087351322174, 2, -128, 127, torch.int8); conv2d_2 = None 2025-09-09T14:15:22.3922251Z dequantize_per_tensor_default_2 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_2, 0.03118087351322174, 2, -128, 127, torch.int8); quantize_per_tensor_default_2 = None 2025-09-09T14:15:22.3923353Z return pytree.tree_unflatten((dequantize_per_tensor_default_2,), self._out_spec) 2025-09-09T14:15:22.3923812Z 2025-09-09T14:15:22.3924109Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:15:22.3924742Z onverted model fx: GraphModule( 2025-09-09T14:15:22.3925193Z (conv): QuantizedConv2d(Reference)(3, 3, kernel_size=(3, 3), stride=(2, 2), padding=(4, 4)) 2025-09-09T14:15:22.3925659Z ) 2025-09-09T14:15:22.3925763Z 2025-09-09T14:15:22.3925767Z 2025-09-09T14:15:22.3925771Z 2025-09-09T14:15:22.3925875Z def forward(self, x): 2025-09-09T14:15:22.3926548Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.01474190503358841, -28, -128, 127, torch.int8); x = None 2025-09-09T14:15:22.3927925Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.01474190503358841, -28, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T14:15:22.3929027Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T14:15:22.3929958Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.03118087351322174, 2, -128, 127, torch.int8); conv = None 2025-09-09T14:15:22.3931355Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.03118087351322174, 2, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T14:15:22.3932312Z return dequantize_per_tensor_default_1 2025-09-09T14:15:22.3932609Z 2025-09-09T14:15:22.3932898Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:15:22.3933312Z diff: tensor([[[[0., 0., 0., 0., 0., 0.], 2025-09-09T14:15:22.3933597Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:15:22.3933868Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:15:22.3934140Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:15:22.3934397Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:15:34.5370017Z [0., 0., 0., 0., 0., 0.]], 2025-09-09T14:15:34.5370441Z 2025-09-09T14:15:34.5370613Z [[0., 0., 0., 0., 0., 0.], 2025-09-09T14:15:34.5371368Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:15:34.5371698Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:15:34.5372078Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:15:34.5372367Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:15:34.5372703Z [0., 0., 0., 0., 0., 0.]], 2025-09-09T14:15:34.5372926Z 2025-09-09T14:15:34.5373013Z [[0., 0., 0., 0., 0., 0.], 2025-09-09T14:15:34.5373283Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:15:34.5373659Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:15:34.5373930Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:15:34.5374188Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:15:34.5374467Z [0., 0., 0., 0., 0., 0.]]]]) 2025-09-09T14:15:34.5374922Z PASSED 2025-09-09T14:15:34.5375616Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_conv_bn_fusion_no_conv_bias model pt2e: GraphModule( 2025-09-09T14:15:34.5376342Z (conv): Module() 2025-09-09T14:15:34.5376645Z (bn): Module() 2025-09-09T14:15:34.5376973Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:15:34.5378011Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0195]), zero_point=tensor([-13], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:15:34.5379246Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.247849225997925, max_val=2.7226178646087646) 2025-09-09T14:15:34.5379811Z ) 2025-09-09T14:15:34.5380110Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:15:34.5381202Z 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 2025-09-09T14:15:34.5382630Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.1720, -0.1912, -0.1684]), max_val=tensor([0.1914, 0.1792, 0.1824])) 2025-09-09T14:15:34.5383347Z ) 2025-09-09T14:15:34.5383637Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:15:34.5384668Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0200]), zero_point=tensor([1], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:15:34.5385885Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.5782737731933594, max_val=2.5220179557800293) 2025-09-09T14:15:34.5386440Z ) 2025-09-09T14:15:34.5386626Z ) 2025-09-09T14:15:34.5386727Z 2025-09-09T14:15:34.5386732Z 2025-09-09T14:15:34.5386736Z 2025-09-09T14:15:34.5386826Z def forward(self, x): 2025-09-09T14:15:34.5387136Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:15:34.5387506Z conv_weight = self.conv.weight 2025-09-09T14:15:34.5387798Z bn_weight = self.bn.weight 2025-09-09T14:15:34.5388076Z bn_bias = self.bn.bias 2025-09-09T14:15:34.5388345Z bn_running_mean = self.bn.running_mean 2025-09-09T14:15:34.5388673Z bn_running_var = self.bn.running_var 2025-09-09T14:15:34.5389025Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T14:15:34.5389508Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:15:34.5390141Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T14:15:34.5390738Z add = torch.ops.aten.add.Tensor(bn_running_var, 1e-05) 2025-09-09T14:15:34.5391166Z sqrt = torch.ops.aten.sqrt.default(add); add = None 2025-09-09T14:15:34.5391608Z div = torch.ops.aten.div.Tensor(bn_weight, sqrt); sqrt = None 2025-09-09T14:15:34.5392095Z reshape = torch.ops.aten.reshape.default(div, [-1, 1, 1, 1]) 2025-09-09T14:15:34.5392752Z mul = torch.ops.aten.mul.Tensor(conv_weight, reshape); conv_weight = reshape = None 2025-09-09T14:15:34.5393373Z activation_post_process_1 = self.activation_post_process_1(mul); mul = None 2025-09-09T14:15:34.5394284Z 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 2025-09-09T14:15:34.5395200Z reshape_1 = torch.ops.aten.reshape.default(div, [1, -1, 1, 1]); div = None 2025-09-09T14:15:34.5395801Z div_1 = torch.ops.aten.div.Tensor(conv2d_1, reshape_1); conv2d_1 = reshape_1 = None 2025-09-09T14:15:34.5396939Z 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 2025-09-09T14:15:34.5397967Z activation_post_process_2 = self.activation_post_process_2(batch_norm_1); batch_norm_1 = None 2025-09-09T14:15:34.5398628Z return pytree.tree_unflatten((activation_post_process_2,), self._out_spec) 2025-09-09T14:15:34.5399054Z 2025-09-09T14:15:34.5399372Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:15:34.5399766Z model fx: GraphModule( 2025-09-09T14:15:34.5400121Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:15:34.5401153Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0195]), zero_point=tensor([-13], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:15:34.5402376Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.247849225997925, max_val=2.7226178646087646) 2025-09-09T14:15:34.5402950Z ) 2025-09-09T14:15:34.5403136Z (conv): ConvBn2d( 2025-09-09T14:15:34.5403409Z 3, 3, kernel_size=(3, 3), stride=(1, 1), bias=False 2025-09-09T14:15:34.5403876Z (bn): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T14:15:34.5404399Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:15:34.5405450Z 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 2025-09-09T14:15:34.5406883Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.1720, -0.1912, -0.1684]), max_val=tensor([0.1914, 0.1792, 0.1824])) 2025-09-09T14:15:34.5407608Z ) 2025-09-09T14:15:34.5407784Z ) 2025-09-09T14:15:34.5408083Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:15:34.5409109Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0200]), zero_point=tensor([1], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:15:34.5410331Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.5782737731933594, max_val=2.5220179557800293) 2025-09-09T14:15:34.5410901Z ) 2025-09-09T14:15:34.5411071Z ) 2025-09-09T14:15:34.5411171Z 2025-09-09T14:15:34.5411176Z 2025-09-09T14:15:34.5411194Z 2025-09-09T14:15:34.5411282Z def forward(self, x): 2025-09-09T14:15:34.5411653Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:15:34.5412237Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T14:15:34.5412833Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T14:15:34.5413287Z return activation_post_process_1 2025-09-09T14:15:34.5413569Z 2025-09-09T14:15:34.5413857Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:15:34.5414259Z diff: tensor([[[[0., 0., 0.], 2025-09-09T14:15:34.5414510Z [0., 0., 0.], 2025-09-09T14:15:34.5414745Z [0., 0., 0.]], 2025-09-09T14:15:34.5414892Z 2025-09-09T14:15:34.5415057Z [[0., 0., 0.], 2025-09-09T14:15:34.5415289Z [0., 0., 0.], 2025-09-09T14:15:34.5415505Z [0., 0., 0.]], 2025-09-09T14:15:34.5415666Z 2025-09-09T14:15:34.5415746Z [[0., 0., 0.], 2025-09-09T14:15:34.5415973Z [0., 0., 0.], 2025-09-09T14:15:34.5416192Z [0., 0., 0.]]], 2025-09-09T14:15:34.5416341Z 2025-09-09T14:15:34.5416345Z 2025-09-09T14:15:34.5416434Z [[[0., 0., 0.], 2025-09-09T14:15:34.5416678Z [0., 0., 0.], 2025-09-09T14:15:34.5416963Z [0., 0., 0.]], 2025-09-09T14:15:34.5417123Z 2025-09-09T14:15:34.5417205Z [[0., 0., 0.], 2025-09-09T14:15:34.5417421Z [0., 0., 0.], 2025-09-09T14:15:34.5417657Z [0., 0., 0.]], 2025-09-09T14:15:34.5417803Z 2025-09-09T14:15:34.5417886Z [[0., 0., 0.], 2025-09-09T14:15:34.5418119Z [0., 0., 0.], 2025-09-09T14:15:34.5418351Z [0., 0., 0.]]], 2025-09-09T14:15:34.5418502Z 2025-09-09T14:15:34.5418506Z 2025-09-09T14:15:34.5418587Z [[[0., 0., 0.], 2025-09-09T14:15:34.5418822Z [0., 0., 0.], 2025-09-09T14:15:34.5419039Z [0., 0., 0.]], 2025-09-09T14:15:34.5419200Z 2025-09-09T14:15:34.5419281Z [[0., 0., 0.], 2025-09-09T14:15:34.5419499Z [0., 0., 0.], 2025-09-09T14:15:34.5419733Z [0., 0., 0.]], 2025-09-09T14:15:34.5419879Z 2025-09-09T14:15:34.5419960Z [[0., 0., 0.], 2025-09-09T14:15:34.5420188Z [0., 0., 0.], 2025-09-09T14:15:34.5420450Z [0., 0., 0.]]]], grad_fn=) 2025-09-09T14:15:34.5420781Z converted model pt2e: GraphModule( 2025-09-09T14:15:34.5421075Z (conv): Module() 2025-09-09T14:15:34.5421284Z (bn): Module() 2025-09-09T14:15:34.5421496Z ) 2025-09-09T14:15:34.5421596Z 2025-09-09T14:15:34.5421600Z 2025-09-09T14:15:34.5421604Z 2025-09-09T14:15:34.5421693Z def forward(self, x): 2025-09-09T14:15:34.5421998Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:15:34.5422802Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.019492028281092644, -13, -128, 127, torch.int8); x = None 2025-09-09T14:15:34.5424170Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.019492028281092644, -13, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T14:15:34.5425343Z _scale_0 = self._scale_0 2025-09-09T14:15:34.5425612Z _zero_point_0 = self._zero_point_0 2025-09-09T14:15:34.5425950Z quantize_per_channel = self._frozen_param0 2025-09-09T14:15:34.5426922Z 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 2025-09-09T14:15:34.5427879Z conv_weight_bias = self.conv.weight_bias 2025-09-09T14:15:34.5428809Z 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 2025-09-09T14:15:34.5430187Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv2d_2, 0.020001143217086792, 1, -128, 127, torch.int8); conv2d_2 = None 2025-09-09T14:15:34.5431627Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.020001143217086792, 1, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T14:15:34.5432751Z return pytree.tree_unflatten((dequantize_per_tensor_default_1,), self._out_spec) 2025-09-09T14:15:34.5433195Z 2025-09-09T14:15:34.5433501Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:15:34.5433904Z onverted model fx: GraphModule( 2025-09-09T14:15:34.5434325Z (conv): QuantizedConv2d(Reference)(3, 3, kernel_size=(3, 3), stride=(1, 1)) 2025-09-09T14:15:34.5434733Z ) 2025-09-09T14:15:34.5434847Z 2025-09-09T14:15:34.5434851Z 2025-09-09T14:15:34.5435042Z 2025-09-09T14:15:34.5435137Z def forward(self, x): 2025-09-09T14:15:44.9730061Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.019492028281092644, -13, -128, 127, torch.int8); x = None 2025-09-09T14:15:44.9731482Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.019492028281092644, -13, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T14:15:44.9733188Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T14:15:44.9734133Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.020001143217086792, 1, -128, 127, torch.int8); conv = None 2025-09-09T14:15:44.9735536Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.020001143217086792, 1, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T14:15:44.9736513Z return dequantize_per_tensor_default_1 2025-09-09T14:15:44.9736825Z 2025-09-09T14:15:44.9737121Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:15:44.9737532Z diff: tensor([[[[0., 0., 0.], 2025-09-09T14:15:44.9737781Z [0., 0., 0.], 2025-09-09T14:15:44.9738011Z [0., 0., 0.]], 2025-09-09T14:15:44.9738158Z 2025-09-09T14:15:44.9738241Z [[0., 0., 0.], 2025-09-09T14:15:44.9738475Z [0., 0., 0.], 2025-09-09T14:15:44.9738691Z [0., 0., 0.]], 2025-09-09T14:15:44.9738850Z 2025-09-09T14:15:44.9738929Z [[0., 0., 0.], 2025-09-09T14:15:44.9739143Z [0., 0., 0.], 2025-09-09T14:15:44.9739374Z [0., 0., 0.]]], 2025-09-09T14:15:44.9739525Z 2025-09-09T14:15:44.9739530Z 2025-09-09T14:15:44.9739622Z [[[0., 0., 0.], 2025-09-09T14:15:44.9739889Z [0., 0., 0.], 2025-09-09T14:15:44.9740107Z [0., 0., 0.]], 2025-09-09T14:15:44.9740275Z 2025-09-09T14:15:44.9740355Z [[0., 0., 0.], 2025-09-09T14:15:44.9740570Z [0., 0., 0.], 2025-09-09T14:15:44.9740807Z [0., 0., 0.]], 2025-09-09T14:15:44.9740952Z 2025-09-09T14:15:44.9741034Z [[0., 0., 0.], 2025-09-09T14:15:44.9741264Z [0., 0., 0.], 2025-09-09T14:15:44.9741494Z [0., 0., 0.]]], 2025-09-09T14:15:44.9741645Z 2025-09-09T14:15:44.9741649Z 2025-09-09T14:15:44.9741727Z [[[0., 0., 0.], 2025-09-09T14:15:44.9741957Z [0., 0., 0.], 2025-09-09T14:15:44.9742171Z [0., 0., 0.]], 2025-09-09T14:15:44.9742327Z 2025-09-09T14:15:44.9742405Z [[0., 0., 0.], 2025-09-09T14:15:44.9742616Z [0., 0., 0.], 2025-09-09T14:15:44.9742842Z [0., 0., 0.]], 2025-09-09T14:15:44.9742986Z 2025-09-09T14:15:44.9743077Z [[0., 0., 0.], 2025-09-09T14:15:44.9743287Z [0., 0., 0.], 2025-09-09T14:15:44.9743512Z [0., 0., 0.]]]]) 2025-09-09T14:15:44.9743753Z model pt2e: GraphModule( 2025-09-09T14:15:44.9744008Z (conv): Module() 2025-09-09T14:15:44.9744218Z (bn): Module() 2025-09-09T14:15:44.9744544Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:15:44.9745571Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0195]), zero_point=tensor([-13], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:15:44.9746800Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.247849225997925, max_val=2.7226178646087646) 2025-09-09T14:15:44.9747372Z ) 2025-09-09T14:15:44.9747660Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:15:44.9748704Z 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 2025-09-09T14:15:44.9750096Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.19124282896518707, max_val=0.19141820073127747) 2025-09-09T14:15:44.9750681Z ) 2025-09-09T14:15:44.9750986Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:15:44.9752004Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0200]), zero_point=tensor([1], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:15:44.9753287Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.577202320098877, max_val=2.521923303604126) 2025-09-09T14:15:44.9753834Z ) 2025-09-09T14:15:44.9754025Z ) 2025-09-09T14:15:44.9754125Z 2025-09-09T14:15:44.9754129Z 2025-09-09T14:15:44.9754134Z 2025-09-09T14:15:44.9754238Z def forward(self, x): 2025-09-09T14:15:44.9754537Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:15:44.9754909Z conv_weight = self.conv.weight 2025-09-09T14:15:44.9755201Z bn_weight = self.bn.weight 2025-09-09T14:15:44.9755479Z bn_bias = self.bn.bias 2025-09-09T14:15:44.9755750Z bn_running_mean = self.bn.running_mean 2025-09-09T14:15:44.9756081Z bn_running_var = self.bn.running_var 2025-09-09T14:15:44.9756531Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T14:15:44.9757013Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:15:44.9757657Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T14:15:44.9758232Z add = torch.ops.aten.add.Tensor(bn_running_var, 1e-05) 2025-09-09T14:15:44.9758666Z sqrt = torch.ops.aten.sqrt.default(add); add = None 2025-09-09T14:15:44.9759107Z div = torch.ops.aten.div.Tensor(bn_weight, sqrt); sqrt = None 2025-09-09T14:15:44.9759596Z reshape = torch.ops.aten.reshape.default(div, [-1, 1, 1, 1]) 2025-09-09T14:15:44.9760148Z mul = torch.ops.aten.mul.Tensor(conv_weight, reshape); conv_weight = reshape = None 2025-09-09T14:15:44.9760879Z activation_post_process_1 = self.activation_post_process_1(mul); mul = None 2025-09-09T14:15:44.9761803Z 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 2025-09-09T14:15:44.9762708Z reshape_1 = torch.ops.aten.reshape.default(div, [1, -1, 1, 1]); div = None 2025-09-09T14:15:44.9763311Z div_1 = torch.ops.aten.div.Tensor(conv2d_1, reshape_1); conv2d_1 = reshape_1 = None 2025-09-09T14:15:44.9764287Z 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 2025-09-09T14:15:44.9765304Z activation_post_process_2 = self.activation_post_process_2(batch_norm_1); batch_norm_1 = None 2025-09-09T14:15:44.9765954Z return pytree.tree_unflatten((activation_post_process_2,), self._out_spec) 2025-09-09T14:15:44.9766445Z 2025-09-09T14:15:44.9766763Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:15:44.9767151Z model fx: GraphModule( 2025-09-09T14:15:44.9767503Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:15:44.9768539Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0195]), zero_point=tensor([-13], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:15:44.9769755Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.247849225997925, max_val=2.7226178646087646) 2025-09-09T14:15:44.9770315Z ) 2025-09-09T14:15:44.9770497Z (conv): ConvBn2d( 2025-09-09T14:15:44.9770766Z 3, 3, kernel_size=(3, 3), stride=(1, 1), bias=False 2025-09-09T14:15:44.9771230Z (bn): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T14:15:44.9771894Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:15:44.9772917Z 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 2025-09-09T14:15:44.9774147Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.19124282896518707, max_val=0.19141820073127747) 2025-09-09T14:15:44.9774722Z ) 2025-09-09T14:15:44.9774963Z ) 2025-09-09T14:15:44.9775266Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:15:44.9776296Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0200]), zero_point=tensor([1], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:15:44.9777484Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.577202320098877, max_val=2.521923303604126) 2025-09-09T14:15:44.9778049Z ) 2025-09-09T14:15:44.9778223Z ) 2025-09-09T14:15:44.9778335Z 2025-09-09T14:15:44.9778339Z 2025-09-09T14:15:44.9778343Z 2025-09-09T14:15:44.9778434Z def forward(self, x): 2025-09-09T14:15:44.9778811Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:15:44.9779374Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T14:15:44.9779967Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T14:15:44.9780425Z return activation_post_process_1 2025-09-09T14:15:44.9780709Z 2025-09-09T14:15:44.9780996Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:15:44.9781396Z diff: tensor([[[[0., 0., 0.], 2025-09-09T14:15:44.9781650Z [0., 0., 0.], 2025-09-09T14:15:44.9781885Z [0., 0., 0.]], 2025-09-09T14:15:44.9782037Z 2025-09-09T14:15:44.9782131Z [[0., 0., 0.], 2025-09-09T14:15:44.9782350Z [0., 0., 0.], 2025-09-09T14:15:44.9782584Z [0., 0., 0.]], 2025-09-09T14:15:44.9782731Z 2025-09-09T14:15:44.9782809Z [[0., 0., 0.], 2025-09-09T14:15:44.9783036Z [0., 0., 0.], 2025-09-09T14:15:44.9783253Z [0., 0., 0.]]], 2025-09-09T14:15:44.9783412Z 2025-09-09T14:15:44.9783416Z 2025-09-09T14:15:44.9783495Z [[[0., 0., 0.], 2025-09-09T14:15:44.9783708Z [0., 0., 0.], 2025-09-09T14:15:44.9783934Z [0., 0., 0.]], 2025-09-09T14:15:44.9784083Z 2025-09-09T14:15:44.9784174Z [[0., 0., 0.], 2025-09-09T14:15:44.9784386Z [0., 0., 0.], 2025-09-09T14:15:44.9784610Z [0., 0., 0.]], 2025-09-09T14:15:44.9784755Z 2025-09-09T14:15:44.9784833Z [[0., 0., 0.], 2025-09-09T14:15:44.9785059Z [0., 0., 0.], 2025-09-09T14:15:44.9785275Z [0., 0., 0.]]], 2025-09-09T14:15:44.9785436Z 2025-09-09T14:15:44.9785440Z 2025-09-09T14:15:44.9785520Z [[[0., 0., 0.], 2025-09-09T14:15:44.9785734Z [0., 0., 0.], 2025-09-09T14:15:44.9785965Z [0., 0., 0.]], 2025-09-09T14:15:44.9786111Z 2025-09-09T14:15:44.9786202Z [[0., 0., 0.], 2025-09-09T14:15:44.9786416Z [0., 0., 0.], 2025-09-09T14:15:44.9786643Z [0., 0., 0.]], 2025-09-09T14:15:44.9786788Z 2025-09-09T14:15:44.9786867Z [[0., 0., 0.], 2025-09-09T14:15:44.9787091Z [0., 0., 0.], 2025-09-09T14:15:44.9787334Z [0., 0., 0.]]]], grad_fn=) 2025-09-09T14:15:44.9787672Z converted model pt2e: GraphModule( 2025-09-09T14:15:44.9787950Z (conv): Module() 2025-09-09T14:15:44.9788171Z (bn): Module() 2025-09-09T14:15:44.9788381Z ) 2025-09-09T14:15:44.9788483Z 2025-09-09T14:15:44.9788487Z 2025-09-09T14:15:44.9788491Z 2025-09-09T14:15:44.9788579Z def forward(self, x): 2025-09-09T14:15:44.9788885Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:15:44.9789755Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.019492028281092644, -13, -128, 127, torch.int8); x = None 2025-09-09T14:15:44.9791141Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.019492028281092644, -13, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T14:15:44.9792121Z quantize_per_tensor = self._frozen_param0 2025-09-09T14:15:44.9792983Z 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 2025-09-09T14:15:44.9793921Z conv_weight_bias = self.conv.weight_bias 2025-09-09T14:15:46.5223137Z 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 2025-09-09T14:15:46.5225225Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv2d_2, 0.01999657228589058, 1, -128, 127, torch.int8); conv2d_2 = None 2025-09-09T14:15:46.5226979Z dequantize_per_tensor_default_2 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_2, 0.01999657228589058, 1, -128, 127, torch.int8); quantize_per_tensor_default_2 = None 2025-09-09T14:15:46.5228105Z return pytree.tree_unflatten((dequantize_per_tensor_default_2,), self._out_spec) 2025-09-09T14:15:46.5228565Z 2025-09-09T14:15:46.5228873Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:15:46.5229302Z onverted model fx: GraphModule( 2025-09-09T14:15:46.5229730Z (conv): QuantizedConv2d(Reference)(3, 3, kernel_size=(3, 3), stride=(1, 1)) 2025-09-09T14:15:46.5230145Z ) 2025-09-09T14:15:46.5230249Z 2025-09-09T14:15:46.5230267Z 2025-09-09T14:15:46.5230271Z 2025-09-09T14:15:46.5230363Z def forward(self, x): 2025-09-09T14:15:46.5231046Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.019492028281092644, -13, -128, 127, torch.int8); x = None 2025-09-09T14:15:46.5232433Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.019492028281092644, -13, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T14:15:46.5233556Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T14:15:46.5234480Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.01999657228589058, 1, -128, 127, torch.int8); conv = None 2025-09-09T14:15:46.5235891Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.01999657228589058, 1, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T14:15:46.5236935Z return dequantize_per_tensor_default_1 2025-09-09T14:15:46.5237232Z 2025-09-09T14:15:46.5237545Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:15:46.5237943Z diff: tensor([[[[0., 0., 0.], 2025-09-09T14:15:46.5238207Z [0., 0., 0.], 2025-09-09T14:15:46.5238427Z [0., 0., 0.]], 2025-09-09T14:15:46.5238592Z 2025-09-09T14:15:46.5238673Z [[0., 0., 0.], 2025-09-09T14:15:46.5238891Z [0., 0., 0.], 2025-09-09T14:15:46.5239119Z [0., 0., 0.]], 2025-09-09T14:15:46.5239264Z 2025-09-09T14:15:46.5239354Z [[0., 0., 0.], 2025-09-09T14:15:46.5239574Z [0., 0., 0.], 2025-09-09T14:15:46.5239838Z [0., 0., 0.]]], 2025-09-09T14:15:46.5240023Z 2025-09-09T14:15:46.5240027Z 2025-09-09T14:15:46.5240106Z [[[0., 0., 0.], 2025-09-09T14:15:46.5240327Z [0., 0., 0.], 2025-09-09T14:15:46.5240542Z [0., 0., 0.]], 2025-09-09T14:15:46.5240696Z 2025-09-09T14:15:46.5240774Z [[0., 0., 0.], 2025-09-09T14:15:46.5240988Z [0., 0., 0.], 2025-09-09T14:15:46.5241215Z [0., 0., 0.]], 2025-09-09T14:15:46.5241360Z 2025-09-09T14:15:46.5241695Z [[0., 0., 0.], 2025-09-09T14:15:46.5241909Z [0., 0., 0.], 2025-09-09T14:15:46.5242135Z [0., 0., 0.]]], 2025-09-09T14:15:46.5242283Z 2025-09-09T14:15:46.5242287Z 2025-09-09T14:15:46.5242367Z [[[0., 0., 0.], 2025-09-09T14:15:46.5242592Z [0., 0., 0.], 2025-09-09T14:15:46.5242806Z [0., 0., 0.]], 2025-09-09T14:15:46.5242967Z 2025-09-09T14:15:46.5243046Z [[0., 0., 0.], 2025-09-09T14:15:46.5243265Z [0., 0., 0.], 2025-09-09T14:15:46.5243603Z [0., 0., 0.]], 2025-09-09T14:15:46.5243748Z 2025-09-09T14:15:46.5243840Z [[0., 0., 0.], 2025-09-09T14:15:46.5244054Z [0., 0., 0.], 2025-09-09T14:15:46.5244285Z [0., 0., 0.]]]]) 2025-09-09T14:15:46.5244537Z model pt2e: GraphModule( 2025-09-09T14:15:46.5244788Z (conv1): Module() 2025-09-09T14:15:46.5244995Z (bn1): Module() 2025-09-09T14:15:46.5245213Z (conv2): Module() 2025-09-09T14:15:46.5245424Z (bn2): Module() 2025-09-09T14:15:46.5245817Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:15:46.5246866Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0195]), zero_point=tensor([-13], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:15:46.5248080Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.247849225997925, max_val=2.7226178646087646) 2025-09-09T14:15:46.5248654Z ) 2025-09-09T14:15:46.5248987Z (activation_post_process_3): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:15:46.5250081Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0012, 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 2025-09-09T14:15:46.5251520Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.1469, -0.1921, -0.1853]), max_val=tensor([0.1307, 0.1779, 0.1810])) 2025-09-09T14:15:46.5252228Z ) 2025-09-09T14:15:46.5252530Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:15:46.5253611Z 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 2025-09-09T14:15:46.5255034Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.1897, -0.1787, -0.1913]), max_val=tensor([0.1870, 0.1478, 0.1740])) 2025-09-09T14:15:46.5255757Z ) 2025-09-09T14:15:46.5256045Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:15:46.5257079Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0192]), zero_point=tensor([14], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:15:46.5258272Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.725703001022339, max_val=2.165140390396118) 2025-09-09T14:15:46.5258829Z ) 2025-09-09T14:15:46.5259128Z (activation_post_process_4): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:15:46.5260142Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0106]), zero_point=tensor([-2], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:15:46.5261356Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3412710428237915, max_val=1.3707175254821777) 2025-09-09T14:15:46.5261909Z ) 2025-09-09T14:15:46.5262095Z ) 2025-09-09T14:15:46.5262195Z 2025-09-09T14:15:46.5262199Z 2025-09-09T14:15:46.5262203Z 2025-09-09T14:15:46.5262303Z def forward(self, x): 2025-09-09T14:15:46.5262601Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:15:46.5263048Z conv1_weight = self.conv1.weight 2025-09-09T14:15:46.5263353Z bn1_weight = self.bn1.weight 2025-09-09T14:15:46.5263641Z bn1_bias = self.bn1.bias 2025-09-09T14:15:46.5263907Z conv2_weight = self.conv2.weight 2025-09-09T14:15:46.5264211Z conv2_bias = self.conv2.bias 2025-09-09T14:15:46.5264483Z bn2_weight = self.bn2.weight 2025-09-09T14:15:46.5264766Z bn2_bias = self.bn2.bias 2025-09-09T14:15:46.5265058Z bn1_running_mean = self.bn1.running_mean 2025-09-09T14:15:46.5265447Z bn1_running_var = self.bn1.running_var 2025-09-09T14:15:46.5265817Z bn1_num_batches_tracked = self.bn1.num_batches_tracked 2025-09-09T14:15:46.5266186Z bn2_running_mean = self.bn2.running_mean 2025-09-09T14:15:46.5266515Z bn2_running_var = self.bn2.running_var 2025-09-09T14:15:46.5266867Z bn2_num_batches_tracked = self.bn2.num_batches_tracked 2025-09-09T14:15:46.5267353Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:15:46.5268007Z add_ = torch.ops.aten.add_.Tensor(bn1_num_batches_tracked, 1); bn1_num_batches_tracked = add_ = None 2025-09-09T14:15:46.5268731Z add__1 = torch.ops.aten.add_.Tensor(bn2_num_batches_tracked, 1); bn2_num_batches_tracked = add__1 = None 2025-09-09T14:15:46.5269324Z add = torch.ops.aten.add.Tensor(bn2_running_var, 1e-05) 2025-09-09T14:15:46.5269747Z sqrt = torch.ops.aten.sqrt.default(add); add = None 2025-09-09T14:15:46.5270204Z div = torch.ops.aten.div.Tensor(bn2_weight, sqrt); sqrt = None 2025-09-09T14:15:46.5270689Z reshape = torch.ops.aten.reshape.default(div, [-1, 1, 1, 1]) 2025-09-09T14:15:46.5271259Z mul = torch.ops.aten.mul.Tensor(conv2_weight, reshape); conv2_weight = reshape = None 2025-09-09T14:15:46.5271885Z activation_post_process_3 = self.activation_post_process_3(mul); mul = None 2025-09-09T14:15:46.5272551Z zeros_like = torch.ops.aten.zeros_like.default(conv2_bias, dtype = torch.float32, pin_memory = False) 2025-09-09T14:15:46.5273160Z add_2 = torch.ops.aten.add.Tensor(bn1_running_var, 1e-05) 2025-09-09T14:15:46.5273600Z sqrt_1 = torch.ops.aten.sqrt.default(add_2); add_2 = None 2025-09-09T14:15:46.5274086Z div_2 = torch.ops.aten.div.Tensor(bn1_weight, sqrt_1); sqrt_1 = None 2025-09-09T14:15:46.5274586Z reshape_3 = torch.ops.aten.reshape.default(div_2, [-1, 1, 1, 1]) 2025-09-09T14:15:46.5275182Z mul_1 = torch.ops.aten.mul.Tensor(conv1_weight, reshape_3); conv1_weight = reshape_3 = None 2025-09-09T14:15:46.5275832Z activation_post_process_1 = self.activation_post_process_1(mul_1); mul_1 = None 2025-09-09T14:15:46.5276856Z 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 2025-09-09T14:15:46.5277787Z reshape_4 = torch.ops.aten.reshape.default(div_2, [1, -1, 1, 1]); div_2 = None 2025-09-09T14:15:46.5278388Z div_3 = torch.ops.aten.div.Tensor(conv2d_3, reshape_4); conv2d_3 = reshape_4 = None 2025-09-09T14:15:46.5279419Z 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 2025-09-09T14:15:46.5280484Z activation_post_process_2 = self.activation_post_process_2(batch_norm_3); batch_norm_3 = None 2025-09-09T14:15:46.5281537Z 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 2025-09-09T14:15:46.5282526Z reshape_1 = torch.ops.aten.reshape.default(div, [1, -1, 1, 1]); div = None 2025-09-09T14:15:46.5283134Z div_1 = torch.ops.aten.div.Tensor(conv2d_2, reshape_1); conv2d_2 = reshape_1 = None 2025-09-09T14:15:46.5283774Z reshape_2 = torch.ops.aten.reshape.default(conv2_bias, [1, -1, 1, 1]); conv2_bias = None 2025-09-09T14:15:46.5284407Z add_1 = torch.ops.aten.add.Tensor(div_1, reshape_2); div_1 = reshape_2 = None 2025-09-09T14:15:46.5285472Z 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 2025-09-09T14:15:57.0076723Z activation_post_process_4 = self.activation_post_process_4(batch_norm_2); batch_norm_2 = None 2025-09-09T14:15:57.0077659Z return pytree.tree_unflatten((activation_post_process_4,), self._out_spec) 2025-09-09T14:15:57.0078221Z 2025-09-09T14:15:57.0078976Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:15:57.0079509Z model fx: GraphModule( 2025-09-09T14:15:57.0080013Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:15:57.0081403Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0195]), zero_point=tensor([-13], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:15:57.0083076Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.247849225997925, max_val=2.7226178646087646) 2025-09-09T14:15:57.0083824Z ) 2025-09-09T14:15:57.0084087Z (conv1): ConvBn2d( 2025-09-09T14:15:57.0084449Z 3, 3, kernel_size=(3, 3), stride=(1, 1), bias=False 2025-09-09T14:15:57.0085068Z (bn): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T14:15:57.0085748Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:15:57.0087166Z 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 2025-09-09T14:15:57.0089114Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.1897, -0.1787, -0.1913]), max_val=tensor([0.1870, 0.1478, 0.1740])) 2025-09-09T14:15:57.0090039Z ) 2025-09-09T14:15:57.0090217Z ) 2025-09-09T14:15:57.0090529Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:15:57.0091561Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0192]), zero_point=tensor([14], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:15:57.0092779Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.725703001022339, max_val=2.165140390396118) 2025-09-09T14:15:57.0093345Z ) 2025-09-09T14:15:57.0093532Z (conv2): ConvBn2d( 2025-09-09T14:15:57.0093789Z 3, 3, kernel_size=(3, 3), stride=(1, 1) 2025-09-09T14:15:57.0094230Z (bn): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T14:15:57.0094741Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:15:57.0095978Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0012, 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 2025-09-09T14:15:57.0097424Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.1469, -0.1921, -0.1853]), max_val=tensor([0.1307, 0.1779, 0.1810])) 2025-09-09T14:15:57.0098146Z ) 2025-09-09T14:15:57.0098343Z ) 2025-09-09T14:15:57.0098733Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:15:57.0099767Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0106]), zero_point=tensor([-2], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:15:57.0100984Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3412710428237915, max_val=1.3707175254821777) 2025-09-09T14:15:57.0101550Z ) 2025-09-09T14:15:57.0101730Z ) 2025-09-09T14:15:57.0101832Z 2025-09-09T14:15:57.0101837Z 2025-09-09T14:15:57.0102008Z 2025-09-09T14:15:57.0102113Z def forward(self, x): 2025-09-09T14:15:57.0102486Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:15:57.0103077Z conv1 = self.conv1(activation_post_process_0); activation_post_process_0 = None 2025-09-09T14:15:57.0103685Z activation_post_process_1 = self.activation_post_process_1(conv1); conv1 = None 2025-09-09T14:15:57.0104301Z conv2 = self.conv2(activation_post_process_1); activation_post_process_1 = None 2025-09-09T14:15:57.0104980Z activation_post_process_2 = self.activation_post_process_2(conv2); conv2 = None 2025-09-09T14:15:57.0105444Z return activation_post_process_2 2025-09-09T14:15:57.0105730Z 2025-09-09T14:15:57.0106021Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:15:57.0106422Z diff: tensor([[[[0.]], 2025-09-09T14:15:57.0106571Z 2025-09-09T14:15:57.0106649Z [[0.]], 2025-09-09T14:15:57.0106789Z 2025-09-09T14:15:57.0106871Z [[0.]]], 2025-09-09T14:15:57.0107000Z 2025-09-09T14:15:57.0107004Z 2025-09-09T14:15:57.0107095Z [[[0.]], 2025-09-09T14:15:57.0107220Z 2025-09-09T14:15:57.0107300Z [[0.]], 2025-09-09T14:15:57.0107424Z 2025-09-09T14:15:57.0107517Z [[0.]]], 2025-09-09T14:15:57.0107645Z 2025-09-09T14:15:57.0107649Z 2025-09-09T14:15:57.0107727Z [[[0.]], 2025-09-09T14:15:57.0107864Z 2025-09-09T14:15:57.0107944Z [[0.]], 2025-09-09T14:15:57.0108065Z 2025-09-09T14:15:57.0108186Z [[0.]]]], grad_fn=) 2025-09-09T14:15:57.0108497Z converted model pt2e: GraphModule( 2025-09-09T14:15:57.0108790Z (conv1): Module() 2025-09-09T14:15:57.0109000Z (bn1): Module() 2025-09-09T14:15:57.0109219Z (conv2): Module() 2025-09-09T14:15:57.0109428Z (bn2): Module() 2025-09-09T14:15:57.0109639Z ) 2025-09-09T14:15:57.0109740Z 2025-09-09T14:15:57.0109744Z 2025-09-09T14:15:57.0109748Z 2025-09-09T14:15:57.0109837Z def forward(self, x): 2025-09-09T14:15:57.0110146Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:15:57.0110515Z conv2_bias = self.conv2.bias 2025-09-09T14:15:57.0111229Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.019492028281092644, -13, -128, 127, torch.int8); x = None 2025-09-09T14:15:57.0112620Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.019492028281092644, -13, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T14:15:57.0113561Z _scale_0 = self._scale_0 2025-09-09T14:15:57.0113844Z _zero_point_0 = self._zero_point_0 2025-09-09T14:15:57.0114148Z _scale_1 = self._scale_1 2025-09-09T14:15:57.0114415Z _zero_point_1 = self._zero_point_1 2025-09-09T14:15:57.0114750Z quantize_per_channel_1 = self._frozen_param0 2025-09-09T14:15:57.0115741Z 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 2025-09-09T14:15:57.0116822Z conv1_weight_bias = self.conv1.weight_bias 2025-09-09T14:15:57.0117766Z 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 2025-09-09T14:15:57.0119179Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv2d_5, 0.019179778173565865, 14, -128, 127, torch.int8); conv2d_5 = None 2025-09-09T14:15:57.0120634Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.019179778173565865, 14, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T14:15:57.0121606Z quantize_per_channel = self._frozen_param1 2025-09-09T14:15:57.0122666Z 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 2025-09-09T14:15:57.0124191Z 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 2025-09-09T14:15:57.0125819Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv2d_4, 0.010635248385369778, -2, -128, 127, torch.int8); conv2d_4 = None 2025-09-09T14:15:57.0127380Z dequantize_per_tensor_default_2 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_2, 0.010635248385369778, -2, -128, 127, torch.int8); quantize_per_tensor_default_2 = None 2025-09-09T14:15:57.0128506Z return pytree.tree_unflatten((dequantize_per_tensor_default_2,), self._out_spec) 2025-09-09T14:15:57.0128951Z 2025-09-09T14:15:57.0129257Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:15:57.0129691Z onverted model fx: GraphModule( 2025-09-09T14:15:57.0130125Z (conv1): QuantizedConv2d(Reference)(3, 3, kernel_size=(3, 3), stride=(1, 1)) 2025-09-09T14:15:57.0130691Z (conv2): QuantizedConv2d(Reference)(3, 3, kernel_size=(3, 3), stride=(1, 1)) 2025-09-09T14:15:57.0131115Z ) 2025-09-09T14:15:57.0131220Z 2025-09-09T14:15:57.0131224Z 2025-09-09T14:15:57.0131229Z 2025-09-09T14:15:57.0131327Z def forward(self, x): 2025-09-09T14:15:57.0132023Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.019492028281092644, -13, -128, 127, torch.int8); x = None 2025-09-09T14:15:57.0133410Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.019492028281092644, -13, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T14:15:57.0134536Z conv1 = self.conv1(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T14:15:57.0135493Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv1, 0.019179778173565865, 14, -128, 127, torch.int8); conv1 = None 2025-09-09T14:15:57.0136908Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.019179778173565865, 14, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T14:15:57.0138066Z conv2 = self.conv2(dequantize_per_tensor_default_1); dequantize_per_tensor_default_1 = None 2025-09-09T14:15:57.0139036Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv2, 0.010635248385369778, -2, -128, 127, torch.int8); conv2 = None 2025-09-09T14:15:57.0140450Z dequantize_per_tensor_default_2 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_2, 0.010635248385369778, -2, -128, 127, torch.int8); quantize_per_tensor_default_2 = None 2025-09-09T14:15:57.0141429Z return dequantize_per_tensor_default_2 2025-09-09T14:15:57.0141736Z 2025-09-09T14:15:57.0142029Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:15:57.0142429Z diff: tensor([[[[0.]], 2025-09-09T14:15:57.0142577Z 2025-09-09T14:15:57.0142658Z [[0.]], 2025-09-09T14:15:57.0142799Z 2025-09-09T14:15:57.0142877Z [[0.]]], 2025-09-09T14:15:57.0143001Z 2025-09-09T14:15:57.0143009Z 2025-09-09T14:15:57.0143087Z [[[0.]], 2025-09-09T14:15:57.0143220Z 2025-09-09T14:15:57.0143297Z [[0.]], 2025-09-09T14:15:57.0143421Z 2025-09-09T14:15:57.0143512Z [[0.]]], 2025-09-09T14:15:57.0143635Z 2025-09-09T14:15:57.0143639Z 2025-09-09T14:15:57.0143715Z [[[0.]], 2025-09-09T14:15:57.0143848Z 2025-09-09T14:15:57.0143924Z [[0.]], 2025-09-09T14:15:57.0144045Z 2025-09-09T14:15:57.0144124Z [[0.]]]]) 2025-09-09T14:15:57.0144356Z model pt2e: GraphModule( 2025-09-09T14:15:57.0144680Z (conv1): Module() 2025-09-09T14:15:57.0144905Z (bn1): Module() 2025-09-09T14:15:57.0145120Z (conv2): Module() 2025-09-09T14:15:57.0145325Z (bn2): Module() 2025-09-09T14:15:57.0145699Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:15:57.0146739Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0195]), zero_point=tensor([-13], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:15:57.0148014Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.247849225997925, max_val=2.7226178646087646) 2025-09-09T14:15:57.0148580Z ) 2025-09-09T14:15:57.0148873Z (activation_post_process_3): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:15:57.0149922Z 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 2025-09-09T14:15:57.0151155Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.19212442636489868, max_val=0.18097376823425293) 2025-09-09T14:15:57.0151721Z ) 2025-09-09T14:15:57.0152030Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:15:57.0153064Z 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 2025-09-09T14:15:57.0154300Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.19127479195594788, max_val=0.1870359182357788) 2025-09-09T14:15:57.0154865Z ) 2025-09-09T14:15:57.0155156Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:15:57.0156287Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0192]), zero_point=tensor([14], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:15:57.0157483Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.725703001022339, max_val=2.165140390396118) 2025-09-09T14:15:57.0158042Z ) 2025-09-09T14:15:57.0158329Z (activation_post_process_4): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:15:57.0159364Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0107]), zero_point=tensor([-2], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:15:57.0160575Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.349876046180725, max_val=1.373764157295227) 2025-09-09T14:15:57.0161126Z ) 2025-09-09T14:15:57.0161317Z ) 2025-09-09T14:15:57.0161418Z 2025-09-09T14:15:57.0161422Z 2025-09-09T14:15:57.0161426Z 2025-09-09T14:15:57.0161529Z def forward(self, x): 2025-09-09T14:15:57.0161830Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:15:57.0162206Z conv1_weight = self.conv1.weight 2025-09-09T14:15:57.0162500Z bn1_weight = self.bn1.weight 2025-09-09T14:15:57.0162787Z bn1_bias = self.bn1.bias 2025-09-09T14:15:57.0163053Z conv2_weight = self.conv2.weight 2025-09-09T14:15:57.0163356Z conv2_bias = self.conv2.bias 2025-09-09T14:15:57.0163627Z bn2_weight = self.bn2.weight 2025-09-09T14:15:57.0163954Z bn2_bias = self.bn2.bias 2025-09-09T14:15:57.0164284Z bn1_running_mean = self.bn1.running_mean 2025-09-09T14:15:57.0164621Z bn1_running_var = self.bn1.running_var 2025-09-09T14:15:57.0164989Z bn1_num_batches_tracked = self.bn1.num_batches_tracked 2025-09-09T14:15:57.0165358Z bn2_running_mean = self.bn2.running_mean 2025-09-09T14:15:57.0165688Z bn2_running_var = self.bn2.running_var 2025-09-09T14:15:57.0166040Z bn2_num_batches_tracked = self.bn2.num_batches_tracked 2025-09-09T14:15:57.0166648Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:15:57.0167287Z add_ = torch.ops.aten.add_.Tensor(bn1_num_batches_tracked, 1); bn1_num_batches_tracked = add_ = None 2025-09-09T14:15:57.0168025Z add__1 = torch.ops.aten.add_.Tensor(bn2_num_batches_tracked, 1); bn2_num_batches_tracked = add__1 = None 2025-09-09T14:15:57.0168622Z add = torch.ops.aten.add.Tensor(bn2_running_var, 1e-05) 2025-09-09T14:15:57.0169044Z sqrt = torch.ops.aten.sqrt.default(add); add = None 2025-09-09T14:15:57.0169567Z div = torch.ops.aten.div.Tensor(bn2_weight, sqrt); sqrt = None 2025-09-09T14:15:57.0170046Z reshape = torch.ops.aten.reshape.default(div, [-1, 1, 1, 1]) 2025-09-09T14:15:57.0170616Z mul = torch.ops.aten.mul.Tensor(conv2_weight, reshape); conv2_weight = reshape = None 2025-09-09T14:15:57.0171226Z activation_post_process_3 = self.activation_post_process_3(mul); mul = None 2025-09-09T14:15:57.0171909Z zeros_like = torch.ops.aten.zeros_like.default(conv2_bias, dtype = torch.float32, pin_memory = False) 2025-09-09T14:15:57.0172523Z add_2 = torch.ops.aten.add.Tensor(bn1_running_var, 1e-05) 2025-09-09T14:15:57.0172964Z sqrt_1 = torch.ops.aten.sqrt.default(add_2); add_2 = None 2025-09-09T14:15:57.0173447Z div_2 = torch.ops.aten.div.Tensor(bn1_weight, sqrt_1); sqrt_1 = None 2025-09-09T14:15:57.0173948Z reshape_3 = torch.ops.aten.reshape.default(div_2, [-1, 1, 1, 1]) 2025-09-09T14:15:57.0174538Z mul_1 = torch.ops.aten.mul.Tensor(conv1_weight, reshape_3); conv1_weight = reshape_3 = None 2025-09-09T14:15:57.0175250Z activation_post_process_1 = self.activation_post_process_1(mul_1); mul_1 = None 2025-09-09T14:15:57.0176181Z 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 2025-09-09T14:15:57.0177105Z reshape_4 = torch.ops.aten.reshape.default(div_2, [1, -1, 1, 1]); div_2 = None 2025-09-09T14:15:57.0177711Z div_3 = torch.ops.aten.div.Tensor(conv2d_3, reshape_4); conv2d_3 = reshape_4 = None 2025-09-09T14:15:57.0178732Z 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 2025-09-09T14:15:57.0179802Z activation_post_process_2 = self.activation_post_process_2(batch_norm_3); batch_norm_3 = None 2025-09-09T14:15:57.0180846Z 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 2025-09-09T14:15:57.0181836Z reshape_1 = torch.ops.aten.reshape.default(div, [1, -1, 1, 1]); div = None 2025-09-09T14:15:57.0182431Z div_1 = torch.ops.aten.div.Tensor(conv2d_2, reshape_1); conv2d_2 = reshape_1 = None 2025-09-09T14:15:57.0183084Z reshape_2 = torch.ops.aten.reshape.default(conv2_bias, [1, -1, 1, 1]); conv2_bias = None 2025-09-09T14:15:57.0183714Z add_1 = torch.ops.aten.add.Tensor(div_1, reshape_2); div_1 = reshape_2 = None 2025-09-09T14:15:57.0184697Z 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 2025-09-09T14:15:57.0185848Z activation_post_process_4 = self.activation_post_process_4(batch_norm_2); batch_norm_2 = None 2025-09-09T14:15:57.0186490Z return pytree.tree_unflatten((activation_post_process_4,), self._out_spec) 2025-09-09T14:15:57.0186927Z 2025-09-09T14:15:57.0187230Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:15:57.0187616Z model fx: GraphModule( 2025-09-09T14:15:57.0187968Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:15:57.0189076Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0195]), zero_point=tensor([-13], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:15:57.0190306Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.247849225997925, max_val=2.7226178646087646) 2025-09-09T14:15:57.0190882Z ) 2025-09-09T14:15:57.0191076Z (conv1): ConvBn2d( 2025-09-09T14:15:57.0191361Z 3, 3, kernel_size=(3, 3), stride=(1, 1), bias=False 2025-09-09T14:15:57.0191829Z (bn): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T14:15:57.0192408Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:15:57.0193415Z 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 2025-09-09T14:15:57.0194652Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.19127479195594788, max_val=0.1870359182357788) 2025-09-09T14:15:57.0195228Z ) 2025-09-09T14:15:57.0195414Z ) 2025-09-09T14:15:57.0195728Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:15:57.0196848Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0192]), zero_point=tensor([14], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:15:57.0198072Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.725703001022339, max_val=2.165140390396118) 2025-09-09T14:15:57.0198639Z ) 2025-09-09T14:15:57.0198826Z (conv2): ConvBn2d( 2025-09-09T14:15:57.0199087Z 3, 3, kernel_size=(3, 3), stride=(1, 1) 2025-09-09T14:15:57.0199530Z (bn): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T14:15:57.0200040Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:15:57.0201043Z 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 2025-09-09T14:15:57.0202280Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.19212442636489868, max_val=0.18097376823425293) 2025-09-09T14:15:57.0202858Z ) 2025-09-09T14:15:57.0203034Z ) 2025-09-09T14:15:57.0203334Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:15:57.0204359Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0107]), zero_point=tensor([-2], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:15:57.0205572Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.349876046180725, max_val=1.373764157295227) 2025-09-09T14:15:57.0206113Z ) 2025-09-09T14:15:57.0206344Z ) 2025-09-09T14:15:57.0206443Z 2025-09-09T14:15:57.0206448Z 2025-09-09T14:15:57.0206452Z 2025-09-09T14:15:57.0206557Z def forward(self, x): 2025-09-09T14:15:57.0206926Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:16:18.8024980Z conv1 = self.conv1(activation_post_process_0); activation_post_process_0 = None 2025-09-09T14:16:18.8025846Z activation_post_process_1 = self.activation_post_process_1(conv1); conv1 = None 2025-09-09T14:16:18.8026716Z conv2 = self.conv2(activation_post_process_1); activation_post_process_1 = None 2025-09-09T14:16:18.8027839Z activation_post_process_2 = self.activation_post_process_2(conv2); conv2 = None 2025-09-09T14:16:18.8028502Z return activation_post_process_2 2025-09-09T14:16:18.8028936Z 2025-09-09T14:16:18.8029506Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:16:18.8030006Z diff: tensor([[[[0.]], 2025-09-09T14:16:18.8030157Z 2025-09-09T14:16:18.8030249Z [[0.]], 2025-09-09T14:16:18.8030376Z 2025-09-09T14:16:18.8030456Z [[0.]]], 2025-09-09T14:16:18.8030583Z 2025-09-09T14:16:18.8030870Z 2025-09-09T14:16:18.8030953Z [[[0.]], 2025-09-09T14:16:18.8031077Z 2025-09-09T14:16:18.8031157Z [[0.]], 2025-09-09T14:16:18.8031296Z 2025-09-09T14:16:18.8031375Z [[0.]]], 2025-09-09T14:16:18.8031502Z 2025-09-09T14:16:18.8031506Z 2025-09-09T14:16:18.8031600Z [[[0.]], 2025-09-09T14:16:18.8031725Z 2025-09-09T14:16:18.8031837Z [[0.]], 2025-09-09T14:16:18.8031972Z 2025-09-09T14:16:18.8032079Z [[0.]]]], grad_fn=) 2025-09-09T14:16:18.8032512Z converted model pt2e: GraphModule( 2025-09-09T14:16:18.8032794Z (conv1): Module() 2025-09-09T14:16:18.8033023Z (bn1): Module() 2025-09-09T14:16:18.8033236Z (conv2): Module() 2025-09-09T14:16:18.8033465Z (bn2): Module() 2025-09-09T14:16:18.8033669Z ) 2025-09-09T14:16:18.8033783Z 2025-09-09T14:16:18.8033787Z 2025-09-09T14:16:18.8033791Z 2025-09-09T14:16:18.8033879Z def forward(self, x): 2025-09-09T14:16:18.8034175Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:16:18.8034550Z conv2_bias = self.conv2.bias 2025-09-09T14:16:18.8035284Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.019492028281092644, -13, -128, 127, torch.int8); x = None 2025-09-09T14:16:18.8037046Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.019492028281092644, -13, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T14:16:18.8038044Z quantize_per_tensor_1 = self._frozen_param0 2025-09-09T14:16:18.8038956Z dequantize_per_tensor_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_1, 0.0015061007579788566, 0, -127, 127, torch.int8); quantize_per_tensor_1 = None 2025-09-09T14:16:18.8039966Z conv1_weight_bias = self.conv1.weight_bias 2025-09-09T14:16:18.8040913Z 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 2025-09-09T14:16:18.8042300Z quantize_per_tensor_default_3 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv2d_5, 0.019179778173565865, 14, -128, 127, torch.int8); conv2d_5 = None 2025-09-09T14:16:18.8043740Z dequantize_per_tensor_default_3 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_3, 0.019179778173565865, 14, -128, 127, torch.int8); quantize_per_tensor_default_3 = None 2025-09-09T14:16:18.8044725Z quantize_per_tensor = self._frozen_param1 2025-09-09T14:16:18.8045589Z dequantize_per_tensor = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor, 0.001512790797278285, 0, -127, 127, torch.int8); quantize_per_tensor = None 2025-09-09T14:16:18.8046999Z 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 2025-09-09T14:16:18.8048343Z quantize_per_tensor_default_4 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv2d_4, 0.010680941864848137, -2, -128, 127, torch.int8); conv2d_4 = None 2025-09-09T14:16:18.8049765Z dequantize_per_tensor_default_4 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_4, 0.010680941864848137, -2, -128, 127, torch.int8); quantize_per_tensor_default_4 = None 2025-09-09T14:16:18.8050883Z return pytree.tree_unflatten((dequantize_per_tensor_default_4,), self._out_spec) 2025-09-09T14:16:18.8051326Z 2025-09-09T14:16:18.8051631Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:16:18.8052043Z onverted model fx: GraphModule( 2025-09-09T14:16:18.8052456Z (conv1): QuantizedConv2d(Reference)(3, 3, kernel_size=(3, 3), stride=(1, 1)) 2025-09-09T14:16:18.8053024Z (conv2): QuantizedConv2d(Reference)(3, 3, kernel_size=(3, 3), stride=(1, 1)) 2025-09-09T14:16:18.8053433Z ) 2025-09-09T14:16:18.8053653Z 2025-09-09T14:16:18.8053658Z 2025-09-09T14:16:18.8053662Z 2025-09-09T14:16:18.8053753Z def forward(self, x): 2025-09-09T14:16:18.8054428Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.019492028281092644, -13, -128, 127, torch.int8); x = None 2025-09-09T14:16:18.8055808Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.019492028281092644, -13, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T14:16:18.8057008Z conv1 = self.conv1(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T14:16:18.8057952Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv1, 0.019179778173565865, 14, -128, 127, torch.int8); conv1 = None 2025-09-09T14:16:18.8059373Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.019179778173565865, 14, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T14:16:18.8060518Z conv2 = self.conv2(dequantize_per_tensor_default_1); dequantize_per_tensor_default_1 = None 2025-09-09T14:16:18.8061505Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv2, 0.010680941864848137, -2, -128, 127, torch.int8); conv2 = None 2025-09-09T14:16:18.8062907Z dequantize_per_tensor_default_2 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_2, 0.010680941864848137, -2, -128, 127, torch.int8); quantize_per_tensor_default_2 = None 2025-09-09T14:16:18.8063884Z return dequantize_per_tensor_default_2 2025-09-09T14:16:18.8064198Z 2025-09-09T14:16:18.8064494Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:16:18.8064901Z diff: tensor([[[[0.]], 2025-09-09T14:16:18.8065052Z 2025-09-09T14:16:18.8065133Z [[0.]], 2025-09-09T14:16:18.8065274Z 2025-09-09T14:16:18.8065358Z [[0.]]], 2025-09-09T14:16:18.8065487Z 2025-09-09T14:16:18.8065491Z 2025-09-09T14:16:18.8065570Z [[[0.]], 2025-09-09T14:16:18.8065714Z 2025-09-09T14:16:18.8065793Z [[0.]], 2025-09-09T14:16:18.8065917Z 2025-09-09T14:16:18.8066010Z [[0.]]], 2025-09-09T14:16:18.8066140Z 2025-09-09T14:16:18.8066144Z 2025-09-09T14:16:18.8066222Z [[[0.]], 2025-09-09T14:16:18.8066345Z 2025-09-09T14:16:18.8066439Z [[0.]], 2025-09-09T14:16:18.8066566Z 2025-09-09T14:16:18.8066646Z [[0.]]]]) 2025-09-09T14:16:18.8067062Z PASSED 2025-09-09T14:16:18.8067834Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_conv_bn_per_channel_weight_bias PASSED 2025-09-09T14:16:18.8068933Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_conv_bn_relu_fusion model pt2e: GraphModule( 2025-09-09T14:16:18.8069619Z (conv): Module() 2025-09-09T14:16:18.8069836Z (bn): Module() 2025-09-09T14:16:18.8070163Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:16:18.8071189Z 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 2025-09-09T14:16:18.8072417Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.526270866394043, max_val=2.143237352371216) 2025-09-09T14:16:18.8072981Z ) 2025-09-09T14:16:18.8073269Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:16:18.8074357Z 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 2025-09-09T14:16:18.8075840Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.1761, -0.1923, -0.1707]), max_val=tensor([0.1830, 0.1717, 0.1892])) 2025-09-09T14:16:18.8076662Z ) 2025-09-09T14:16:18.8076966Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:16:18.8077994Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0065]), zero_point=tensor([-128], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:16:18.8079953Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=1.6655889749526978) 2025-09-09T14:16:18.8080461Z ) 2025-09-09T14:16:18.8080650Z ) 2025-09-09T14:16:18.8080750Z 2025-09-09T14:16:18.8080754Z 2025-09-09T14:16:18.8080758Z 2025-09-09T14:16:18.8080861Z def forward(self, x): 2025-09-09T14:16:18.8081159Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:16:18.8081532Z conv_weight = self.conv.weight 2025-09-09T14:16:18.8081820Z conv_bias = self.conv.bias 2025-09-09T14:16:18.8082108Z bn_weight = self.bn.weight 2025-09-09T14:16:18.8082371Z bn_bias = self.bn.bias 2025-09-09T14:16:18.8082656Z bn_running_mean = self.bn.running_mean 2025-09-09T14:16:18.8082975Z bn_running_var = self.bn.running_var 2025-09-09T14:16:18.8083336Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T14:16:18.8083814Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:16:18.8084450Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T14:16:18.8085029Z add = torch.ops.aten.add.Tensor(bn_running_var, 1e-05) 2025-09-09T14:16:18.8085447Z sqrt = torch.ops.aten.sqrt.default(add); add = None 2025-09-09T14:16:18.8085897Z div = torch.ops.aten.div.Tensor(bn_weight, sqrt); sqrt = None 2025-09-09T14:16:18.8086371Z reshape = torch.ops.aten.reshape.default(div, [-1, 1, 1, 1]) 2025-09-09T14:16:18.8086934Z mul = torch.ops.aten.mul.Tensor(conv_weight, reshape); conv_weight = reshape = None 2025-09-09T14:16:18.8087548Z activation_post_process_1 = self.activation_post_process_1(mul); mul = None 2025-09-09T14:16:18.8088214Z zeros_like = torch.ops.aten.zeros_like.default(conv_bias, dtype = torch.float32, pin_memory = False) 2025-09-09T14:16:18.8089290Z 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 2025-09-09T14:16:18.8090264Z reshape_1 = torch.ops.aten.reshape.default(div, [1, -1, 1, 1]); div = None 2025-09-09T14:16:18.8090865Z div_1 = torch.ops.aten.div.Tensor(conv2d_1, reshape_1); conv2d_1 = reshape_1 = None 2025-09-09T14:16:18.8091513Z reshape_2 = torch.ops.aten.reshape.default(conv_bias, [1, -1, 1, 1]); conv_bias = None 2025-09-09T14:16:18.8092118Z add_1 = torch.ops.aten.add.Tensor(div_1, reshape_2); div_1 = reshape_2 = None 2025-09-09T14:16:27.9374556Z 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 2025-09-09T14:16:27.9375543Z relu = torch.ops.aten.relu.default(batch_norm_1); batch_norm_1 = None 2025-09-09T14:16:27.9376121Z activation_post_process_2 = self.activation_post_process_2(relu); relu = None 2025-09-09T14:16:27.9376709Z return pytree.tree_unflatten((activation_post_process_2,), self._out_spec) 2025-09-09T14:16:27.9377148Z 2025-09-09T14:16:27.9377448Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:16:27.9377850Z model fx: GraphModule( 2025-09-09T14:16:27.9378190Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:16:27.9379525Z 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 2025-09-09T14:16:27.9380774Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.526270866394043, max_val=2.143237352371216) 2025-09-09T14:16:27.9381331Z ) 2025-09-09T14:16:27.9381546Z (conv): ConvBnReLU2d( 2025-09-09T14:16:27.9381809Z 3, 3, kernel_size=(3, 3), stride=(1, 1) 2025-09-09T14:16:27.9382425Z (bn): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T14:16:27.9383201Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:16:27.9384477Z 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 2025-09-09T14:16:27.9386184Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.1761, -0.1923, -0.1707]), max_val=tensor([0.1830, 0.1717, 0.1892])) 2025-09-09T14:16:27.9386916Z ) 2025-09-09T14:16:27.9387093Z ) 2025-09-09T14:16:27.9387395Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:16:27.9388431Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0065]), zero_point=tensor([-128], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:16:27.9389610Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=1.6655889749526978) 2025-09-09T14:16:27.9390136Z ) 2025-09-09T14:16:27.9390312Z ) 2025-09-09T14:16:27.9390415Z 2025-09-09T14:16:27.9390419Z 2025-09-09T14:16:27.9390423Z 2025-09-09T14:16:27.9390524Z def forward(self, x): 2025-09-09T14:16:27.9390896Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:16:27.9391479Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T14:16:27.9392073Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T14:16:27.9392539Z return activation_post_process_1 2025-09-09T14:16:27.9392828Z 2025-09-09T14:16:27.9393117Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:16:27.9393520Z diff: tensor([[[[0., 0., 0.], 2025-09-09T14:16:27.9393767Z [0., 0., 0.], 2025-09-09T14:16:27.9393997Z [0., 0., 0.]], 2025-09-09T14:16:27.9394143Z 2025-09-09T14:16:27.9394224Z [[0., 0., 0.], 2025-09-09T14:16:27.9405753Z [0., 0., 0.], 2025-09-09T14:16:27.9406146Z [0., 0., 0.]], 2025-09-09T14:16:27.9406369Z 2025-09-09T14:16:27.9406454Z [[0., 0., 0.], 2025-09-09T14:16:27.9406685Z [0., 0., 0.], 2025-09-09T14:16:27.9407045Z [0., 0., 0.]]]], grad_fn=) 2025-09-09T14:16:27.9407548Z converted model pt2e: GraphModule( 2025-09-09T14:16:27.9407943Z (conv): Module() 2025-09-09T14:16:27.9408263Z (bn): Module() 2025-09-09T14:16:27.9408562Z ) 2025-09-09T14:16:27.9408724Z 2025-09-09T14:16:27.9408731Z 2025-09-09T14:16:27.9408737Z 2025-09-09T14:16:27.9408865Z def forward(self, x): 2025-09-09T14:16:27.9409299Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:16:27.9409685Z conv_bias = self.conv.bias 2025-09-09T14:16:27.9410409Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.018311796709895134, 10, -128, 127, torch.int8); x = None 2025-09-09T14:16:27.9411806Z 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 2025-09-09T14:16:27.9412742Z _scale_0 = self._scale_0 2025-09-09T14:16:27.9413026Z _zero_point_0 = self._zero_point_0 2025-09-09T14:16:27.9413348Z quantize_per_channel = self._frozen_param0 2025-09-09T14:16:27.9414475Z 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 2025-09-09T14:16:27.9415978Z 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 2025-09-09T14:16:27.9416910Z relu = torch.ops.aten.relu.default(conv2d_2); conv2d_2 = None 2025-09-09T14:16:27.9417772Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(relu, 0.006531721446663141, -128, -128, 127, torch.int8); relu = None 2025-09-09T14:16:27.9419270Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.006531721446663141, -128, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T14:16:27.9420404Z return pytree.tree_unflatten((dequantize_per_tensor_default_1,), self._out_spec) 2025-09-09T14:16:27.9420862Z 2025-09-09T14:16:27.9421165Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:16:27.9421587Z onverted model fx: GraphModule( 2025-09-09T14:16:27.9421857Z (conv): ConvReLU2d( 2025-09-09T14:16:27.9422231Z (0): QuantizedConv2d(Reference)(3, 3, kernel_size=(3, 3), stride=(1, 1)) 2025-09-09T14:16:27.9422629Z (1): ReLU() 2025-09-09T14:16:27.9422843Z ) 2025-09-09T14:16:27.9423019Z ) 2025-09-09T14:16:27.9423132Z 2025-09-09T14:16:27.9423140Z 2025-09-09T14:16:27.9423144Z 2025-09-09T14:16:27.9423234Z def forward(self, x): 2025-09-09T14:16:27.9423916Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.018311796709895134, 10, -128, 127, torch.int8); x = None 2025-09-09T14:16:27.9425493Z 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 2025-09-09T14:16:27.9426625Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T14:16:27.9427575Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.006531721446663141, -128, -128, 127, torch.int8); conv = None 2025-09-09T14:16:27.9428982Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.006531721446663141, -128, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T14:16:27.9429972Z return dequantize_per_tensor_default_1 2025-09-09T14:16:27.9430264Z 2025-09-09T14:16:27.9430570Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:16:27.9430978Z diff: tensor([[[[0., 0., 0.], 2025-09-09T14:16:27.9431226Z [0., 0., 0.], 2025-09-09T14:16:27.9431463Z [0., 0., 0.]], 2025-09-09T14:16:27.9431610Z 2025-09-09T14:16:27.9431691Z [[0., 0., 0.], 2025-09-09T14:16:27.9431928Z [0., 0., 0.], 2025-09-09T14:16:27.9432150Z [0., 0., 0.]], 2025-09-09T14:16:27.9432311Z 2025-09-09T14:16:27.9432391Z [[0., 0., 0.], 2025-09-09T14:16:27.9432608Z [0., 0., 0.], 2025-09-09T14:16:27.9432842Z [0., 0., 0.]]]]) 2025-09-09T14:16:27.9433091Z model pt2e: GraphModule( 2025-09-09T14:16:27.9433348Z (conv): Module() 2025-09-09T14:16:27.9433571Z (bn): Module() 2025-09-09T14:16:27.9433885Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:16:27.9434928Z 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 2025-09-09T14:16:27.9436188Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.526270866394043, max_val=2.143237352371216) 2025-09-09T14:16:27.9436757Z ) 2025-09-09T14:16:27.9437216Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:16:27.9438268Z 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 2025-09-09T14:16:27.9439506Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.1923346221446991, max_val=0.18921314179897308) 2025-09-09T14:16:27.9440069Z ) 2025-09-09T14:16:27.9440462Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:16:27.9441492Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0065]), zero_point=tensor([-128], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:16:27.9442668Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=1.6606048345565796) 2025-09-09T14:16:27.9443192Z ) 2025-09-09T14:16:27.9443367Z ) 2025-09-09T14:16:27.9443486Z 2025-09-09T14:16:27.9443491Z 2025-09-09T14:16:27.9443495Z 2025-09-09T14:16:27.9443587Z def forward(self, x): 2025-09-09T14:16:27.9443886Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:16:27.9444262Z conv_weight = self.conv.weight 2025-09-09T14:16:27.9444569Z conv_bias = self.conv.bias 2025-09-09T14:16:27.9444838Z bn_weight = self.bn.weight 2025-09-09T14:16:27.9445114Z bn_bias = self.bn.bias 2025-09-09T14:16:27.9445389Z bn_running_mean = self.bn.running_mean 2025-09-09T14:16:27.9445719Z bn_running_var = self.bn.running_var 2025-09-09T14:16:27.9446070Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T14:16:27.9446553Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:16:27.9447192Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T14:16:27.9447776Z add = torch.ops.aten.add.Tensor(bn_running_var, 1e-05) 2025-09-09T14:16:27.9448200Z sqrt = torch.ops.aten.sqrt.default(add); add = None 2025-09-09T14:16:27.9448656Z div = torch.ops.aten.div.Tensor(bn_weight, sqrt); sqrt = None 2025-09-09T14:16:27.9449134Z reshape = torch.ops.aten.reshape.default(div, [-1, 1, 1, 1]) 2025-09-09T14:16:27.9449694Z mul = torch.ops.aten.mul.Tensor(conv_weight, reshape); conv_weight = reshape = None 2025-09-09T14:16:27.9450301Z activation_post_process_1 = self.activation_post_process_1(mul); mul = None 2025-09-09T14:16:27.9450976Z zeros_like = torch.ops.aten.zeros_like.default(conv_bias, dtype = torch.float32, pin_memory = False) 2025-09-09T14:16:41.4754061Z 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 2025-09-09T14:16:41.4755106Z reshape_1 = torch.ops.aten.reshape.default(div, [1, -1, 1, 1]); div = None 2025-09-09T14:16:41.4755726Z div_1 = torch.ops.aten.div.Tensor(conv2d_1, reshape_1); conv2d_1 = reshape_1 = None 2025-09-09T14:16:41.4756494Z reshape_2 = torch.ops.aten.reshape.default(conv_bias, [1, -1, 1, 1]); conv_bias = None 2025-09-09T14:16:41.4757109Z add_1 = torch.ops.aten.add.Tensor(div_1, reshape_2); div_1 = reshape_2 = None 2025-09-09T14:16:41.4758071Z 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 2025-09-09T14:16:41.4759013Z relu = torch.ops.aten.relu.default(batch_norm_1); batch_norm_1 = None 2025-09-09T14:16:41.4759575Z activation_post_process_2 = self.activation_post_process_2(relu); relu = None 2025-09-09T14:16:41.4760172Z return pytree.tree_unflatten((activation_post_process_2,), self._out_spec) 2025-09-09T14:16:41.4760592Z 2025-09-09T14:16:41.4760898Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:16:41.4761289Z model fx: GraphModule( 2025-09-09T14:16:41.4761920Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:16:41.4762977Z 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 2025-09-09T14:16:41.4764202Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.526270866394043, max_val=2.143237352371216) 2025-09-09T14:16:41.4764891Z ) 2025-09-09T14:16:41.4765086Z (conv): ConvBnReLU2d( 2025-09-09T14:16:41.4765359Z 3, 3, kernel_size=(3, 3), stride=(1, 1) 2025-09-09T14:16:41.4765803Z (bn): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T14:16:41.4766419Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:16:41.4767604Z 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 2025-09-09T14:16:41.4768834Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.1923346221446991, max_val=0.18921314179897308) 2025-09-09T14:16:41.4769408Z ) 2025-09-09T14:16:41.4769641Z ) 2025-09-09T14:16:41.4770042Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:16:41.4771088Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0065]), zero_point=tensor([-128], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:16:41.4772251Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=1.6606048345565796) 2025-09-09T14:16:41.4772769Z ) 2025-09-09T14:16:41.4772945Z ) 2025-09-09T14:16:41.4773060Z 2025-09-09T14:16:41.4773064Z 2025-09-09T14:16:41.4773068Z 2025-09-09T14:16:41.4773157Z def forward(self, x): 2025-09-09T14:16:41.4773531Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:16:41.4774112Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T14:16:41.4774704Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T14:16:41.4775156Z return activation_post_process_1 2025-09-09T14:16:41.4775439Z 2025-09-09T14:16:41.4775727Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:16:41.4776137Z diff: tensor([[[[0., 0., 0.], 2025-09-09T14:16:41.4776385Z [0., 0., 0.], 2025-09-09T14:16:41.4776616Z [0., 0., 0.]], 2025-09-09T14:16:41.4776763Z 2025-09-09T14:16:41.4776857Z [[0., 0., 0.], 2025-09-09T14:16:41.4777074Z [0., 0., 0.], 2025-09-09T14:16:41.4777303Z [0., 0., 0.]], 2025-09-09T14:16:41.4777448Z 2025-09-09T14:16:41.4777528Z [[0., 0., 0.], 2025-09-09T14:16:41.4777753Z [0., 0., 0.], 2025-09-09T14:16:41.4778007Z [0., 0., 0.]]]], grad_fn=) 2025-09-09T14:16:41.4778345Z converted model pt2e: GraphModule( 2025-09-09T14:16:41.4778621Z (conv): Module() 2025-09-09T14:16:41.4778842Z (bn): Module() 2025-09-09T14:16:41.4779044Z ) 2025-09-09T14:16:41.4779156Z 2025-09-09T14:16:41.4779160Z 2025-09-09T14:16:41.4779164Z 2025-09-09T14:16:41.4779256Z def forward(self, x): 2025-09-09T14:16:41.4779563Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:16:41.4779926Z conv_bias = self.conv.bias 2025-09-09T14:16:41.4780680Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.018311796709895134, 10, -128, 127, torch.int8); x = None 2025-09-09T14:16:41.4782066Z 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 2025-09-09T14:16:41.4783146Z quantize_per_tensor = self._frozen_param0 2025-09-09T14:16:41.4784027Z dequantize_per_tensor = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor, 0.0015144458739086986, 0, -127, 127, torch.int8); quantize_per_tensor = None 2025-09-09T14:16:41.4785421Z 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 2025-09-09T14:16:41.4786447Z relu = torch.ops.aten.relu.default(conv2d_2); conv2d_2 = None 2025-09-09T14:16:41.4787306Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(relu, 0.006512175779789686, -128, -128, 127, torch.int8); relu = None 2025-09-09T14:16:41.4788718Z dequantize_per_tensor_default_2 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_2, 0.006512175779789686, -128, -128, 127, torch.int8); quantize_per_tensor_default_2 = None 2025-09-09T14:16:41.4789852Z return pytree.tree_unflatten((dequantize_per_tensor_default_2,), self._out_spec) 2025-09-09T14:16:41.4790309Z 2025-09-09T14:16:41.4790606Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:16:41.4791021Z onverted model fx: GraphModule( 2025-09-09T14:16:41.4791291Z (conv): ConvReLU2d( 2025-09-09T14:16:41.4791661Z (0): QuantizedConv2d(Reference)(3, 3, kernel_size=(3, 3), stride=(1, 1)) 2025-09-09T14:16:41.4792063Z (1): ReLU() 2025-09-09T14:16:41.4792282Z ) 2025-09-09T14:16:41.4792467Z ) 2025-09-09T14:16:41.4792567Z 2025-09-09T14:16:41.4792572Z 2025-09-09T14:16:41.4792575Z 2025-09-09T14:16:41.4792663Z def forward(self, x): 2025-09-09T14:16:41.4793342Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.018311796709895134, 10, -128, 127, torch.int8); x = None 2025-09-09T14:16:41.4794710Z 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 2025-09-09T14:16:41.4795830Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T14:16:41.4796875Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.006512175779789686, -128, -128, 127, torch.int8); conv = None 2025-09-09T14:16:41.4798293Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.006512175779789686, -128, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T14:16:41.4799286Z return dequantize_per_tensor_default_1 2025-09-09T14:16:41.4799577Z 2025-09-09T14:16:41.4799881Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:16:41.4800288Z diff: tensor([[[[0., 0., 0.], 2025-09-09T14:16:41.4800536Z [0., 0., 0.], 2025-09-09T14:16:41.4800772Z [0., 0., 0.]], 2025-09-09T14:16:41.4800926Z 2025-09-09T14:16:41.4801006Z [[0., 0., 0.], 2025-09-09T14:16:41.4801234Z [0., 0., 0.], 2025-09-09T14:16:41.4801449Z [0., 0., 0.]], 2025-09-09T14:16:41.4801604Z 2025-09-09T14:16:41.4801685Z [[0., 0., 0.], 2025-09-09T14:16:41.4801898Z [0., 0., 0.], 2025-09-09T14:16:41.4802125Z [0., 0., 0.]]]]) 2025-09-09T14:16:41.4802554Z PASSED 2025-09-09T14:16:41.4803306Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_conv_bn_relu_fusion_cuda SKIPPED 2025-09-09T14:16:41.4804429Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_conv_bn_relu_fusion_no_conv_bias model pt2e: GraphModule( 2025-09-09T14:16:41.4805144Z (conv): Module() 2025-09-09T14:16:41.4805367Z (bn): Module() 2025-09-09T14:16:41.4805684Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:16:41.4806806Z 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 2025-09-09T14:16:41.4808023Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.526270866394043, max_val=2.143237352371216) 2025-09-09T14:16:41.4808574Z ) 2025-09-09T14:16:41.4808876Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:16:41.4809950Z 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 2025-09-09T14:16:41.4811433Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.1720, -0.1912, -0.1684]), max_val=tensor([0.1914, 0.1792, 0.1824])) 2025-09-09T14:16:41.4812169Z ) 2025-09-09T14:16:41.4812479Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:16:41.4813517Z 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 2025-09-09T14:16:41.4814698Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=1.9991776943206787) 2025-09-09T14:16:41.4815211Z ) 2025-09-09T14:16:41.4815405Z ) 2025-09-09T14:16:41.4815506Z 2025-09-09T14:16:41.4815510Z 2025-09-09T14:16:41.4815514Z 2025-09-09T14:16:41.4815620Z def forward(self, x): 2025-09-09T14:16:41.4815919Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:16:41.4816294Z conv_weight = self.conv.weight 2025-09-09T14:16:41.4816586Z bn_weight = self.bn.weight 2025-09-09T14:16:41.4816871Z bn_bias = self.bn.bias 2025-09-09T14:16:41.4817142Z bn_running_mean = self.bn.running_mean 2025-09-09T14:16:41.4817480Z bn_running_var = self.bn.running_var 2025-09-09T14:16:41.4817846Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T14:16:41.4818321Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:16:52.0395245Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T14:16:52.0396002Z add = torch.ops.aten.add.Tensor(bn_running_var, 1e-05) 2025-09-09T14:16:52.0396640Z sqrt = torch.ops.aten.sqrt.default(add); add = None 2025-09-09T14:16:52.0397443Z div = torch.ops.aten.div.Tensor(bn_weight, sqrt); sqrt = None 2025-09-09T14:16:52.0398117Z reshape = torch.ops.aten.reshape.default(div, [-1, 1, 1, 1]) 2025-09-09T14:16:52.0398663Z mul = torch.ops.aten.mul.Tensor(conv_weight, reshape); conv_weight = reshape = None 2025-09-09T14:16:52.0399284Z activation_post_process_1 = self.activation_post_process_1(mul); mul = None 2025-09-09T14:16:52.0400211Z 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 2025-09-09T14:16:52.0401132Z reshape_1 = torch.ops.aten.reshape.default(div, [1, -1, 1, 1]); div = None 2025-09-09T14:16:52.0401730Z div_1 = torch.ops.aten.div.Tensor(conv2d_1, reshape_1); conv2d_1 = reshape_1 = None 2025-09-09T14:16:52.0402701Z 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 2025-09-09T14:16:52.0403643Z relu = torch.ops.aten.relu.default(batch_norm_1); batch_norm_1 = None 2025-09-09T14:16:52.0404488Z activation_post_process_2 = self.activation_post_process_2(relu); relu = None 2025-09-09T14:16:52.0405145Z return pytree.tree_unflatten((activation_post_process_2,), self._out_spec) 2025-09-09T14:16:52.0405572Z 2025-09-09T14:16:52.0405864Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:16:52.0406579Z model fx: GraphModule( 2025-09-09T14:16:52.0406926Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:16:52.0408103Z 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 2025-09-09T14:16:52.0409343Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.526270866394043, max_val=2.143237352371216) 2025-09-09T14:16:52.0410007Z ) 2025-09-09T14:16:52.0410211Z (conv): ConvBnReLU2d( 2025-09-09T14:16:52.0410488Z 3, 3, kernel_size=(3, 3), stride=(1, 1), bias=False 2025-09-09T14:16:52.0410970Z (bn): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T14:16:52.0411488Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:16:52.0412545Z 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 2025-09-09T14:16:52.0413978Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.1720, -0.1912, -0.1684]), max_val=tensor([0.1914, 0.1792, 0.1824])) 2025-09-09T14:16:52.0414687Z ) 2025-09-09T14:16:52.0414877Z ) 2025-09-09T14:16:52.0415182Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:16:52.0416224Z 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 2025-09-09T14:16:52.0417393Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=1.9991776943206787) 2025-09-09T14:16:52.0417905Z ) 2025-09-09T14:16:52.0418096Z ) 2025-09-09T14:16:52.0418198Z 2025-09-09T14:16:52.0418210Z 2025-09-09T14:16:52.0418214Z 2025-09-09T14:16:52.0418315Z def forward(self, x): 2025-09-09T14:16:52.0418687Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:16:52.0419267Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T14:16:52.0419849Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T14:16:52.0420318Z return activation_post_process_1 2025-09-09T14:16:52.0420593Z 2025-09-09T14:16:52.0420896Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:16:52.0421287Z diff: tensor([[[[0., 0., 0.], 2025-09-09T14:16:52.0421550Z [0., 0., 0.], 2025-09-09T14:16:52.0421785Z [0., 0., 0.]], 2025-09-09T14:16:52.0421931Z 2025-09-09T14:16:52.0422012Z [[0., 0., 0.], 2025-09-09T14:16:52.0422239Z [0., 0., 0.], 2025-09-09T14:16:52.0422454Z [0., 0., 0.]], 2025-09-09T14:16:52.0422617Z 2025-09-09T14:16:52.0422696Z [[0., 0., 0.], 2025-09-09T14:16:52.0422909Z [0., 0., 0.], 2025-09-09T14:16:52.0423165Z [0., 0., 0.]]]], grad_fn=) 2025-09-09T14:16:52.0423487Z converted model pt2e: GraphModule( 2025-09-09T14:16:52.0423773Z (conv): Module() 2025-09-09T14:16:52.0423993Z (bn): Module() 2025-09-09T14:16:52.0424192Z ) 2025-09-09T14:16:52.0424522Z 2025-09-09T14:16:52.0424526Z 2025-09-09T14:16:52.0424530Z 2025-09-09T14:16:52.0424644Z def forward(self, x): 2025-09-09T14:16:52.0424940Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:16:52.0425741Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.018311796709895134, 10, -128, 127, torch.int8); x = None 2025-09-09T14:16:52.0427256Z 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 2025-09-09T14:16:52.0428197Z _scale_0 = self._scale_0 2025-09-09T14:16:52.0428485Z _zero_point_0 = self._zero_point_0 2025-09-09T14:16:52.0428805Z quantize_per_channel = self._frozen_param0 2025-09-09T14:16:52.0429785Z 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 2025-09-09T14:16:52.0430831Z conv_weight_bias = self.conv.weight_bias 2025-09-09T14:16:52.0431758Z 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 2025-09-09T14:16:52.0432758Z relu = torch.ops.aten.relu.default(conv2d_2); conv2d_2 = None 2025-09-09T14:16:52.0433608Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(relu, 0.007839912548661232, -128, -128, 127, torch.int8); relu = None 2025-09-09T14:16:52.0435041Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.007839912548661232, -128, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T14:16:52.0436239Z return pytree.tree_unflatten((dequantize_per_tensor_default_1,), self._out_spec) 2025-09-09T14:16:52.0436685Z 2025-09-09T14:16:52.0436999Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:16:52.0437408Z onverted model fx: GraphModule( 2025-09-09T14:16:52.0437695Z (conv): ConvReLU2d( 2025-09-09T14:16:52.0438055Z (0): QuantizedConv2d(Reference)(3, 3, kernel_size=(3, 3), stride=(1, 1)) 2025-09-09T14:16:52.0438472Z (1): ReLU() 2025-09-09T14:16:52.0438675Z ) 2025-09-09T14:16:52.0438867Z ) 2025-09-09T14:16:52.0438968Z 2025-09-09T14:16:52.0438972Z 2025-09-09T14:16:52.0438976Z 2025-09-09T14:16:52.0439079Z def forward(self, x): 2025-09-09T14:16:52.0439750Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.018311796709895134, 10, -128, 127, torch.int8); x = None 2025-09-09T14:16:52.0441131Z 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 2025-09-09T14:16:52.0442256Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T14:16:52.0443199Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.007839912548661232, -128, -128, 127, torch.int8); conv = None 2025-09-09T14:16:52.0444629Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.007839912548661232, -128, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T14:16:52.0445608Z return dequantize_per_tensor_default_1 2025-09-09T14:16:52.0445911Z 2025-09-09T14:16:52.0446207Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:16:52.0446617Z diff: tensor([[[[0., 0., 0.], 2025-09-09T14:16:52.0446877Z [0., 0., 0.], 2025-09-09T14:16:52.0447097Z [0., 0., 0.]], 2025-09-09T14:16:52.0447246Z 2025-09-09T14:16:52.0447340Z [[0., 0., 0.], 2025-09-09T14:16:52.0447558Z [0., 0., 0.], 2025-09-09T14:16:52.0447792Z [0., 0., 0.]], 2025-09-09T14:16:52.0447940Z 2025-09-09T14:16:52.0448019Z [[0., 0., 0.], 2025-09-09T14:16:52.0448248Z [0., 0., 0.], 2025-09-09T14:16:52.0448464Z [0., 0., 0.]]]]) 2025-09-09T14:16:52.0448719Z model pt2e: GraphModule( 2025-09-09T14:16:52.0448970Z (conv): Module() 2025-09-09T14:16:52.0449177Z (bn): Module() 2025-09-09T14:16:52.0449500Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:16:52.0450627Z 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 2025-09-09T14:16:52.0451838Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.526270866394043, max_val=2.143237352371216) 2025-09-09T14:16:52.0452388Z ) 2025-09-09T14:16:52.0452691Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:16:52.0453733Z 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 2025-09-09T14:16:52.0455026Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.19124282896518707, max_val=0.19141820073127747) 2025-09-09T14:16:52.0455604Z ) 2025-09-09T14:16:52.0455892Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:16:52.0456939Z 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 2025-09-09T14:16:52.0458103Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=1.999093770980835) 2025-09-09T14:16:52.0458608Z ) 2025-09-09T14:16:52.0458795Z ) 2025-09-09T14:16:52.0458894Z 2025-09-09T14:16:52.0458898Z 2025-09-09T14:16:52.0458906Z 2025-09-09T14:16:52.0458996Z def forward(self, x): 2025-09-09T14:16:52.0459305Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:16:52.0459672Z conv_weight = self.conv.weight 2025-09-09T14:16:52.0459962Z bn_weight = self.bn.weight 2025-09-09T14:16:52.0460236Z bn_bias = self.bn.bias 2025-09-09T14:16:52.0460503Z bn_running_mean = self.bn.running_mean 2025-09-09T14:16:52.0460832Z bn_running_var = self.bn.running_var 2025-09-09T14:17:00.3890216Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T14:17:00.3891174Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:17:00.3891836Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T14:17:00.3892420Z add = torch.ops.aten.add.Tensor(bn_running_var, 1e-05) 2025-09-09T14:17:00.3892869Z sqrt = torch.ops.aten.sqrt.default(add); add = None 2025-09-09T14:17:00.3893323Z div = torch.ops.aten.div.Tensor(bn_weight, sqrt); sqrt = None 2025-09-09T14:17:00.3893825Z reshape = torch.ops.aten.reshape.default(div, [-1, 1, 1, 1]) 2025-09-09T14:17:00.3894371Z mul = torch.ops.aten.mul.Tensor(conv_weight, reshape); conv_weight = reshape = None 2025-09-09T14:17:00.3894986Z activation_post_process_1 = self.activation_post_process_1(mul); mul = None 2025-09-09T14:17:00.3895903Z 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 2025-09-09T14:17:00.3896826Z reshape_1 = torch.ops.aten.reshape.default(div, [1, -1, 1, 1]); div = None 2025-09-09T14:17:00.3897424Z div_1 = torch.ops.aten.div.Tensor(conv2d_1, reshape_1); conv2d_1 = reshape_1 = None 2025-09-09T14:17:00.3898390Z 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 2025-09-09T14:17:00.3899327Z relu = torch.ops.aten.relu.default(batch_norm_1); batch_norm_1 = None 2025-09-09T14:17:00.3899888Z activation_post_process_2 = self.activation_post_process_2(relu); relu = None 2025-09-09T14:17:00.3900483Z return pytree.tree_unflatten((activation_post_process_2,), self._out_spec) 2025-09-09T14:17:00.3900900Z 2025-09-09T14:17:00.3901206Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:17:00.3901612Z model fx: GraphModule( 2025-09-09T14:17:00.3902232Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:17:00.3903486Z 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 2025-09-09T14:17:00.3904706Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.526270866394043, max_val=2.143237352371216) 2025-09-09T14:17:00.3905436Z ) 2025-09-09T14:17:00.3905732Z (conv): ConvBnReLU2d( 2025-09-09T14:17:00.3906091Z 3, 3, kernel_size=(3, 3), stride=(1, 1), bias=False 2025-09-09T14:17:00.3906574Z (bn): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T14:17:00.3907073Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:17:00.3908099Z 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 2025-09-09T14:17:00.3909324Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.19124282896518707, max_val=0.19141820073127747) 2025-09-09T14:17:00.3909902Z ) 2025-09-09T14:17:00.3910093Z ) 2025-09-09T14:17:00.3910382Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:17:00.3911432Z 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 2025-09-09T14:17:00.3912595Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=1.999093770980835) 2025-09-09T14:17:00.3913112Z ) 2025-09-09T14:17:00.3913306Z ) 2025-09-09T14:17:00.3913408Z 2025-09-09T14:17:00.3913413Z 2025-09-09T14:17:00.3913417Z 2025-09-09T14:17:00.3913505Z def forward(self, x): 2025-09-09T14:17:00.3913895Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:17:00.3914465Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T14:17:00.3915060Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T14:17:00.3915515Z return activation_post_process_1 2025-09-09T14:17:00.3915800Z 2025-09-09T14:17:00.3916191Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:17:00.3916595Z diff: tensor([[[[0., 0., 0.], 2025-09-09T14:17:00.3916859Z [0., 0., 0.], 2025-09-09T14:17:00.3917078Z [0., 0., 0.]], 2025-09-09T14:17:00.3917224Z 2025-09-09T14:17:00.3917315Z [[0., 0., 0.], 2025-09-09T14:17:00.3917529Z [0., 0., 0.], 2025-09-09T14:17:00.3917758Z [0., 0., 0.]], 2025-09-09T14:17:00.3917920Z 2025-09-09T14:17:00.3918000Z [[0., 0., 0.], 2025-09-09T14:17:00.3918230Z [0., 0., 0.], 2025-09-09T14:17:00.3918482Z [0., 0., 0.]]]], grad_fn=) 2025-09-09T14:17:00.3918821Z converted model pt2e: GraphModule( 2025-09-09T14:17:00.3919100Z (conv): Module() 2025-09-09T14:17:00.3919329Z (bn): Module() 2025-09-09T14:17:00.3919534Z ) 2025-09-09T14:17:00.3919651Z 2025-09-09T14:17:00.3919655Z 2025-09-09T14:17:00.3919659Z 2025-09-09T14:17:00.3919750Z def forward(self, x): 2025-09-09T14:17:00.3920060Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:17:00.3920857Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.018311796709895134, 10, -128, 127, torch.int8); x = None 2025-09-09T14:17:00.3922243Z 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 2025-09-09T14:17:00.3923210Z quantize_per_tensor = self._frozen_param0 2025-09-09T14:17:00.3924210Z 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 2025-09-09T14:17:00.3925270Z conv_weight_bias = self.conv.weight_bias 2025-09-09T14:17:00.3926175Z 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 2025-09-09T14:17:00.3927343Z relu = torch.ops.aten.relu.default(conv2d_2); conv2d_2 = None 2025-09-09T14:17:00.3928190Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(relu, 0.007839583791792393, -128, -128, 127, torch.int8); relu = None 2025-09-09T14:17:00.3929615Z dequantize_per_tensor_default_2 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_2, 0.007839583791792393, -128, -128, 127, torch.int8); quantize_per_tensor_default_2 = None 2025-09-09T14:17:00.3930746Z return pytree.tree_unflatten((dequantize_per_tensor_default_2,), self._out_spec) 2025-09-09T14:17:00.3931193Z 2025-09-09T14:17:00.3931500Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:17:00.3931904Z onverted model fx: GraphModule( 2025-09-09T14:17:00.3932190Z (conv): ConvReLU2d( 2025-09-09T14:17:00.3932559Z (0): QuantizedConv2d(Reference)(3, 3, kernel_size=(3, 3), stride=(1, 1)) 2025-09-09T14:17:00.3932956Z (1): ReLU() 2025-09-09T14:17:00.3933163Z ) 2025-09-09T14:17:00.3933335Z ) 2025-09-09T14:17:00.3933434Z 2025-09-09T14:17:00.3933450Z 2025-09-09T14:17:00.3933454Z 2025-09-09T14:17:00.3933542Z def forward(self, x): 2025-09-09T14:17:00.3934214Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.018311796709895134, 10, -128, 127, torch.int8); x = None 2025-09-09T14:17:00.3935600Z 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 2025-09-09T14:17:00.3936718Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T14:17:00.3937651Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.007839583791792393, -128, -128, 127, torch.int8); conv = None 2025-09-09T14:17:00.3939074Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.007839583791792393, -128, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T14:17:00.3940066Z return dequantize_per_tensor_default_1 2025-09-09T14:17:00.3940358Z 2025-09-09T14:17:00.3940661Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:17:00.3941054Z diff: tensor([[[[0., 0., 0.], 2025-09-09T14:17:00.3941323Z [0., 0., 0.], 2025-09-09T14:17:00.3941552Z [0., 0., 0.]], 2025-09-09T14:17:00.3941711Z 2025-09-09T14:17:00.3941792Z [[0., 0., 0.], 2025-09-09T14:17:00.3942006Z [0., 0., 0.], 2025-09-09T14:17:00.3942236Z [0., 0., 0.]], 2025-09-09T14:17:00.3942383Z 2025-09-09T14:17:00.3942473Z [[0., 0., 0.], 2025-09-09T14:17:00.3942688Z [0., 0., 0.], 2025-09-09T14:17:00.3942921Z [0., 0., 0.]]]]) 2025-09-09T14:17:00.3943337Z PASSED 2025-09-09T14:17:00.3943970Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_conv_no_bias model pt2e: GraphModule( 2025-09-09T14:17:00.3944628Z (conv): Module() 2025-09-09T14:17:00.3944960Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:17:00.3946164Z 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 2025-09-09T14:17:00.3947591Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.1782, -0.1825, -0.1912]), max_val=tensor([0.1676, 0.1914, 0.1824])) 2025-09-09T14:17:00.3948320Z ) 2025-09-09T14:17:00.3948616Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:17:00.3949658Z 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 2025-09-09T14:17:00.3950931Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.526270866394043, max_val=2.143237352371216) 2025-09-09T14:17:00.3951485Z ) 2025-09-09T14:17:00.3951789Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:17:00.3952819Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0052]), zero_point=tensor([-128], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:17:00.3953996Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=1.3200514316558838) 2025-09-09T14:17:00.3954518Z ) 2025-09-09T14:17:00.3954691Z ) 2025-09-09T14:17:00.3954790Z 2025-09-09T14:17:00.3954795Z 2025-09-09T14:17:00.3954798Z 2025-09-09T14:17:00.3954899Z def forward(self, x): 2025-09-09T14:17:00.3955197Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:17:00.3955576Z conv_weight = self.conv.weight 2025-09-09T14:17:01.2843263Z activation_post_process_1 = self.activation_post_process_1(conv_weight); conv_weight = None 2025-09-09T14:17:01.2843942Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:17:01.2844998Z conv2d = torch.ops.aten.conv2d.default(activation_post_process_0, activation_post_process_1); activation_post_process_0 = activation_post_process_1 = None 2025-09-09T14:17:01.2845863Z relu = torch.ops.aten.relu.default(conv2d); conv2d = None 2025-09-09T14:17:01.2846383Z activation_post_process_2 = self.activation_post_process_2(relu); relu = None 2025-09-09T14:17:01.2846981Z return pytree.tree_unflatten((activation_post_process_2,), self._out_spec) 2025-09-09T14:17:01.2847394Z 2025-09-09T14:17:01.2847699Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:17:01.2848088Z model fx: GraphModule( 2025-09-09T14:17:01.2848452Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:17:01.2849492Z 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 2025-09-09T14:17:01.2850780Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.526270866394043, max_val=2.143237352371216) 2025-09-09T14:17:01.2851339Z ) 2025-09-09T14:17:01.2851546Z (conv): ConvReLU2d( 2025-09-09T14:17:01.2851817Z 3, 3, kernel_size=(3, 3), stride=(1, 1), bias=False 2025-09-09T14:17:01.2852217Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:17:01.2853269Z 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 2025-09-09T14:17:01.2854717Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.1782, -0.1825, -0.1912]), max_val=tensor([0.1676, 0.1914, 0.1824])) 2025-09-09T14:17:01.2855435Z ) 2025-09-09T14:17:01.2855616Z ) 2025-09-09T14:17:01.2855920Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:17:01.2857201Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0052]), zero_point=tensor([-128], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:17:01.2858389Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=1.3200514316558838) 2025-09-09T14:17:01.2858897Z ) 2025-09-09T14:17:01.2859093Z ) 2025-09-09T14:17:01.2859198Z 2025-09-09T14:17:01.2859203Z 2025-09-09T14:17:01.2859207Z 2025-09-09T14:17:01.2859311Z def forward(self, x): 2025-09-09T14:17:01.2859687Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:17:01.2860372Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T14:17:01.2860958Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T14:17:01.2861424Z return activation_post_process_1 2025-09-09T14:17:01.2861711Z 2025-09-09T14:17:01.2862002Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:17:01.2862410Z diff: tensor([[[[0., 0., 0.], 2025-09-09T14:17:01.2862665Z [0., 0., 0.], 2025-09-09T14:17:01.2862902Z [0., 0., 0.]], 2025-09-09T14:17:01.2863053Z 2025-09-09T14:17:01.2863134Z [[0., 0., 0.], 2025-09-09T14:17:01.2863365Z [0., 0., 0.], 2025-09-09T14:17:01.2863587Z [0., 0., 0.]], 2025-09-09T14:17:01.2863780Z 2025-09-09T14:17:01.2863863Z [[0., 0., 0.], 2025-09-09T14:17:01.2864084Z [0., 0., 0.], 2025-09-09T14:17:01.2864350Z [0., 0., 0.]]]], grad_fn=) 2025-09-09T14:17:01.2864705Z converted model pt2e: GraphModule( 2025-09-09T14:17:01.2865028Z (conv): Module() 2025-09-09T14:17:01.2865329Z ) 2025-09-09T14:17:01.2865530Z 2025-09-09T14:17:01.2865553Z 2025-09-09T14:17:01.2865558Z 2025-09-09T14:17:01.2865710Z def forward(self, x): 2025-09-09T14:17:01.2866212Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:17:01.2866864Z _scale_0 = self._scale_0 2025-09-09T14:17:01.2867152Z _zero_point_0 = self._zero_point_0 2025-09-09T14:17:01.2867521Z quantize_per_channel_default = self._frozen_param0 2025-09-09T14:17:01.2868631Z 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 2025-09-09T14:17:01.2870107Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.018311796709895134, 10, -128, 127, torch.int8); x = None 2025-09-09T14:17:01.2871488Z 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 2025-09-09T14:17:01.2872941Z conv2d = torch.ops.aten.conv2d.default(dequantize_per_tensor_default, dequantize_per_channel_default); dequantize_per_tensor_default = dequantize_per_channel_default = None 2025-09-09T14:17:01.2873860Z relu = torch.ops.aten.relu.default(conv2d); conv2d = None 2025-09-09T14:17:01.2874709Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(relu, 0.005176672246307135, -128, -128, 127, torch.int8); relu = None 2025-09-09T14:17:01.2876233Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.005176672246307135, -128, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T14:17:01.2877374Z return pytree.tree_unflatten((dequantize_per_tensor_default_1,), self._out_spec) 2025-09-09T14:17:01.2877831Z 2025-09-09T14:17:01.2878122Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:17:01.2878534Z onverted model fx: GraphModule( 2025-09-09T14:17:01.2878801Z (conv): ConvReLU2d( 2025-09-09T14:17:01.2879209Z (0): QuantizedConv2d(Reference)(3, 3, kernel_size=(3, 3), stride=(1, 1), bias=False) 2025-09-09T14:17:01.2879650Z (1): ReLU() 2025-09-09T14:17:01.2879861Z ) 2025-09-09T14:17:01.2880165Z ) 2025-09-09T14:17:01.2880282Z 2025-09-09T14:17:01.2880287Z 2025-09-09T14:17:01.2880290Z 2025-09-09T14:17:01.2880385Z def forward(self, x): 2025-09-09T14:17:01.2881069Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.018311796709895134, 10, -128, 127, torch.int8); x = None 2025-09-09T14:17:01.2882432Z 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 2025-09-09T14:17:01.2883625Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T14:17:01.2884574Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.005176672246307135, -128, -128, 127, torch.int8); conv = None 2025-09-09T14:17:01.2885998Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.005176672246307135, -128, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T14:17:01.2886986Z return dequantize_per_tensor_default_1 2025-09-09T14:17:01.2887275Z 2025-09-09T14:17:01.2887583Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:17:01.2887979Z diff: tensor([[[[0., 0., 0.], 2025-09-09T14:17:01.2888239Z [0., 0., 0.], 2025-09-09T14:17:01.2888472Z [0., 0., 0.]], 2025-09-09T14:17:01.2888620Z 2025-09-09T14:17:01.2888703Z [[0., 0., 0.], 2025-09-09T14:17:01.2888928Z [0., 0., 0.], 2025-09-09T14:17:01.2889145Z [0., 0., 0.]], 2025-09-09T14:17:01.2889302Z 2025-09-09T14:17:01.2889382Z [[0., 0., 0.], 2025-09-09T14:17:01.2889596Z [0., 0., 0.], 2025-09-09T14:17:01.2889824Z [0., 0., 0.]]]]) 2025-09-09T14:17:01.2890067Z model pt2e: GraphModule( 2025-09-09T14:17:01.2890318Z (conv): Module() 2025-09-09T14:17:01.2890643Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:17:01.2891692Z 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 2025-09-09T14:17:01.2892935Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.19124378263950348, max_val=0.19141915440559387) 2025-09-09T14:17:01.2893505Z ) 2025-09-09T14:17:01.2893811Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:17:01.2894829Z 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 2025-09-09T14:17:01.2896031Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.526270866394043, max_val=2.143237352371216) 2025-09-09T14:17:01.2896593Z ) 2025-09-09T14:17:01.2896884Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:17:01.2897919Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0052]), zero_point=tensor([-128], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:17:01.2899096Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=1.3200514316558838) 2025-09-09T14:17:01.2899605Z ) 2025-09-09T14:17:01.2899789Z ) 2025-09-09T14:17:01.2899890Z 2025-09-09T14:17:01.2899895Z 2025-09-09T14:17:01.2899899Z 2025-09-09T14:17:01.2899987Z def forward(self, x): 2025-09-09T14:17:01.2900294Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:17:01.2900652Z conv_weight = self.conv.weight 2025-09-09T14:17:01.2901151Z activation_post_process_1 = self.activation_post_process_1(conv_weight); conv_weight = None 2025-09-09T14:17:01.2901857Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:17:01.2902734Z conv2d = torch.ops.aten.conv2d.default(activation_post_process_0, activation_post_process_1); activation_post_process_0 = activation_post_process_1 = None 2025-09-09T14:17:01.2903565Z relu = torch.ops.aten.relu.default(conv2d); conv2d = None 2025-09-09T14:17:01.2904087Z activation_post_process_2 = self.activation_post_process_2(relu); relu = None 2025-09-09T14:17:01.2904741Z return pytree.tree_unflatten((activation_post_process_2,), self._out_spec) 2025-09-09T14:17:01.2905156Z 2025-09-09T14:17:01.2905459Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:17:01.2905860Z model fx: GraphModule( 2025-09-09T14:17:01.2906197Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:17:01.2907239Z 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 2025-09-09T14:17:02.1650921Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.526270866394043, max_val=2.143237352371216) 2025-09-09T14:17:02.1651555Z ) 2025-09-09T14:17:02.1651878Z (conv): ConvReLU2d( 2025-09-09T14:17:02.1652166Z 3, 3, kernel_size=(3, 3), stride=(1, 1), bias=False 2025-09-09T14:17:02.1652614Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:17:02.1653656Z 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 2025-09-09T14:17:02.1655212Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.19124378263950348, max_val=0.19141915440559387) 2025-09-09T14:17:02.1656184Z ) 2025-09-09T14:17:02.1656528Z ) 2025-09-09T14:17:02.1656893Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:17:02.1658376Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0052]), zero_point=tensor([-128], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:17:02.1659568Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=1.3200514316558838) 2025-09-09T14:17:02.1660081Z ) 2025-09-09T14:17:02.1660281Z ) 2025-09-09T14:17:02.1660383Z 2025-09-09T14:17:02.1660388Z 2025-09-09T14:17:02.1660392Z 2025-09-09T14:17:02.1660482Z def forward(self, x): 2025-09-09T14:17:02.1660869Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:17:02.1661441Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T14:17:02.1662046Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T14:17:02.1662520Z return activation_post_process_1 2025-09-09T14:17:02.1662795Z 2025-09-09T14:17:02.1663101Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:17:02.1663497Z diff: tensor([[[[0., 0., 0.], 2025-09-09T14:17:02.1663764Z [0., 0., 0.], 2025-09-09T14:17:02.1663988Z [0., 0., 0.]], 2025-09-09T14:17:02.1664148Z 2025-09-09T14:17:02.1664235Z [[0., 0., 0.], 2025-09-09T14:17:02.1664518Z [0., 0., 0.], 2025-09-09T14:17:02.1664743Z [0., 0., 0.]], 2025-09-09T14:17:02.1664896Z 2025-09-09T14:17:02.1664988Z [[0., 0., 0.], 2025-09-09T14:17:02.1665201Z [0., 0., 0.], 2025-09-09T14:17:02.1665461Z [0., 0., 0.]]]], grad_fn=) 2025-09-09T14:17:02.1665786Z converted model pt2e: GraphModule( 2025-09-09T14:17:02.1666075Z (conv): Module() 2025-09-09T14:17:02.1666278Z ) 2025-09-09T14:17:02.1666395Z 2025-09-09T14:17:02.1666399Z 2025-09-09T14:17:02.1666403Z 2025-09-09T14:17:02.1666494Z def forward(self, x): 2025-09-09T14:17:02.1667079Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:17:02.1667493Z quantize_per_tensor_default = self._frozen_param0 2025-09-09T14:17:02.1668504Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.0015072374371811748, 0, -127, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T14:17:02.1669872Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.018311796709895134, 10, -128, 127, torch.int8); x = None 2025-09-09T14:17:02.1671361Z 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 2025-09-09T14:17:02.1672849Z 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 2025-09-09T14:17:02.1673745Z relu = torch.ops.aten.relu.default(conv2d); conv2d = None 2025-09-09T14:17:02.1674595Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(relu, 0.005176672246307135, -128, -128, 127, torch.int8); relu = None 2025-09-09T14:17:02.1676307Z dequantize_per_tensor_default_2 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_2, 0.005176672246307135, -128, -128, 127, torch.int8); quantize_per_tensor_default_2 = None 2025-09-09T14:17:02.1677447Z return pytree.tree_unflatten((dequantize_per_tensor_default_2,), self._out_spec) 2025-09-09T14:17:02.1677902Z 2025-09-09T14:17:02.1678197Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:17:02.1678615Z onverted model fx: GraphModule( 2025-09-09T14:17:02.1678885Z (conv): ConvReLU2d( 2025-09-09T14:17:02.1679295Z (0): QuantizedConv2d(Reference)(3, 3, kernel_size=(3, 3), stride=(1, 1), bias=False) 2025-09-09T14:17:02.1679733Z (1): ReLU() 2025-09-09T14:17:02.1679945Z ) 2025-09-09T14:17:02.1680131Z ) 2025-09-09T14:17:02.1680231Z 2025-09-09T14:17:02.1680236Z 2025-09-09T14:17:02.1680240Z 2025-09-09T14:17:02.1680328Z def forward(self, x): 2025-09-09T14:17:02.1681010Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.018311796709895134, 10, -128, 127, torch.int8); x = None 2025-09-09T14:17:02.1682372Z 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 2025-09-09T14:17:02.1683495Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T14:17:02.1684440Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.005176672246307135, -128, -128, 127, torch.int8); conv = None 2025-09-09T14:17:02.1685904Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.005176672246307135, -128, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T14:17:02.1686912Z return dequantize_per_tensor_default_1 2025-09-09T14:17:02.1687201Z 2025-09-09T14:17:02.1687504Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:17:02.1687913Z diff: tensor([[[[0., 0., 0.], 2025-09-09T14:17:02.1688169Z [0., 0., 0.], 2025-09-09T14:17:02.1688401Z [0., 0., 0.]], 2025-09-09T14:17:02.1688548Z 2025-09-09T14:17:02.1688630Z [[0., 0., 0.], 2025-09-09T14:17:02.1688856Z [0., 0., 0.], 2025-09-09T14:17:02.1689071Z [0., 0., 0.]], 2025-09-09T14:17:02.1689229Z 2025-09-09T14:17:02.1689307Z [[0., 0., 0.], 2025-09-09T14:17:02.1689521Z [0., 0., 0.], 2025-09-09T14:17:02.1689748Z [0., 0., 0.]]]]) 2025-09-09T14:17:02.1690002Z model pt2e: GraphModule( 2025-09-09T14:17:02.1690344Z (conv): Module() 2025-09-09T14:17:02.1690680Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:17:02.1691761Z 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 2025-09-09T14:17:02.1693200Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.1897, -0.1787, -0.1913]), max_val=tensor([0.1870, 0.1478, 0.1740])) 2025-09-09T14:17:02.1693994Z ) 2025-09-09T14:17:02.1694284Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:17:02.1695317Z 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 2025-09-09T14:17:02.1696579Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.526270866394043, max_val=2.143237352371216) 2025-09-09T14:17:02.1697221Z ) 2025-09-09T14:17:02.1697511Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:17:02.1698542Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0084]), zero_point=tensor([-20], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:17:02.1699764Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.9077497124671936, max_val=1.2348304986953735) 2025-09-09T14:17:02.1700317Z ) 2025-09-09T14:17:02.1700502Z ) 2025-09-09T14:17:02.1700604Z 2025-09-09T14:17:02.1700609Z 2025-09-09T14:17:02.1700613Z 2025-09-09T14:17:02.1700713Z def forward(self, x): 2025-09-09T14:17:02.1701011Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:17:02.1701378Z conv_weight = self.conv.weight 2025-09-09T14:17:02.1701869Z activation_post_process_1 = self.activation_post_process_1(conv_weight); conv_weight = None 2025-09-09T14:17:02.1702505Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:17:02.1703381Z conv2d = torch.ops.aten.conv2d.default(activation_post_process_0, activation_post_process_1); activation_post_process_0 = activation_post_process_1 = None 2025-09-09T14:17:02.1704294Z activation_post_process_2 = self.activation_post_process_2(conv2d); conv2d = None 2025-09-09T14:17:02.1704906Z return pytree.tree_unflatten((activation_post_process_2,), self._out_spec) 2025-09-09T14:17:02.1705323Z 2025-09-09T14:17:02.1705629Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:17:02.1706022Z model fx: GraphModule( 2025-09-09T14:17:02.1706379Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:17:02.1707413Z 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 2025-09-09T14:17:02.1708628Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.526270866394043, max_val=2.143237352371216) 2025-09-09T14:17:02.1709193Z ) 2025-09-09T14:17:02.1709374Z (conv): Conv2d( 2025-09-09T14:17:02.1709641Z 3, 3, kernel_size=(3, 3), stride=(1, 1), bias=False 2025-09-09T14:17:02.1710032Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:17:02.1711090Z 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 2025-09-09T14:17:02.1712518Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.1897, -0.1787, -0.1913]), max_val=tensor([0.1870, 0.1478, 0.1740])) 2025-09-09T14:17:02.1713309Z ) 2025-09-09T14:17:02.1713505Z ) 2025-09-09T14:17:02.1713801Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:17:02.1714852Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0084]), zero_point=tensor([-20], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:17:02.1716165Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.9077497124671936, max_val=1.2348304986953735) 2025-09-09T14:17:02.1716807Z ) 2025-09-09T14:17:02.1716998Z ) 2025-09-09T14:17:02.1717099Z 2025-09-09T14:17:02.1717104Z 2025-09-09T14:17:02.1717121Z 2025-09-09T14:17:02.1717217Z def forward(self, x): 2025-09-09T14:17:03.2343872Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:17:03.2344703Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T14:17:03.2345523Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T14:17:03.2346148Z return activation_post_process_1 2025-09-09T14:17:03.2346512Z 2025-09-09T14:17:03.2346905Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:17:03.2347442Z diff: tensor([[[[0., 0., 0.], 2025-09-09T14:17:03.2347768Z [0., 0., 0.], 2025-09-09T14:17:03.2348073Z [0., 0., 0.]], 2025-09-09T14:17:03.2348269Z 2025-09-09T14:17:03.2348386Z [[0., 0., 0.], 2025-09-09T14:17:03.2348680Z [0., 0., 0.], 2025-09-09T14:17:03.2348961Z [0., 0., 0.]], 2025-09-09T14:17:03.2349165Z 2025-09-09T14:17:03.2349269Z [[0., 0., 0.], 2025-09-09T14:17:03.2349552Z [0., 0., 0.], 2025-09-09T14:17:03.2349887Z [0., 0., 0.]]]], grad_fn=) 2025-09-09T14:17:03.2350316Z converted model pt2e: GraphModule( 2025-09-09T14:17:03.2350692Z (conv): Module() 2025-09-09T14:17:03.2350976Z ) 2025-09-09T14:17:03.2351108Z 2025-09-09T14:17:03.2351122Z 2025-09-09T14:17:03.2351127Z 2025-09-09T14:17:03.2351246Z def forward(self, x): 2025-09-09T14:17:03.2351643Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:17:03.2352101Z _scale_0 = self._scale_0 2025-09-09T14:17:03.2352461Z _zero_point_0 = self._zero_point_0 2025-09-09T14:17:03.2352911Z quantize_per_channel_default = self._frozen_param0 2025-09-09T14:17:03.2354408Z 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 2025-09-09T14:17:03.2356575Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.018311796709895134, 10, -128, 127, torch.int8); x = None 2025-09-09T14:17:03.2358456Z 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 2025-09-09T14:17:03.2360444Z conv2d = torch.ops.aten.conv2d.default(dequantize_per_tensor_default, dequantize_per_channel_default); dequantize_per_tensor_default = dequantize_per_channel_default = None 2025-09-09T14:17:03.2362205Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv2d, 0.00840227585285902, -20, -128, 127, torch.int8); conv2d = None 2025-09-09T14:17:03.2364148Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.00840227585285902, -20, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T14:17:03.2365642Z return pytree.tree_unflatten((dequantize_per_tensor_default_1,), self._out_spec) 2025-09-09T14:17:03.2366249Z 2025-09-09T14:17:03.2366632Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:17:03.2367537Z onverted model fx: GraphModule( 2025-09-09T14:17:03.2368144Z (conv): QuantizedConv2d(Reference)(3, 3, kernel_size=(3, 3), stride=(1, 1), bias=False) 2025-09-09T14:17:03.2368745Z ) 2025-09-09T14:17:03.2368896Z 2025-09-09T14:17:03.2368901Z 2025-09-09T14:17:03.2368906Z 2025-09-09T14:17:03.2369030Z def forward(self, x): 2025-09-09T14:17:03.2369927Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.018311796709895134, 10, -128, 127, torch.int8); x = None 2025-09-09T14:17:03.2371898Z 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 2025-09-09T14:17:03.2373409Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T14:17:03.2374667Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.00840227585285902, -20, -128, 127, torch.int8); conv = None 2025-09-09T14:17:03.2376582Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.00840227585285902, -20, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T14:17:03.2377902Z return dequantize_per_tensor_default_1 2025-09-09T14:17:03.2378288Z 2025-09-09T14:17:03.2378685Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:17:03.2379210Z diff: tensor([[[[0., 0., 0.], 2025-09-09T14:17:03.2379549Z [0., 0., 0.], 2025-09-09T14:17:03.2379832Z [0., 0., 0.]], 2025-09-09T14:17:03.2380038Z 2025-09-09T14:17:03.2380144Z [[0., 0., 0.], 2025-09-09T14:17:03.2380444Z [0., 0., 0.], 2025-09-09T14:17:03.2380727Z [0., 0., 0.]], 2025-09-09T14:17:03.2380919Z 2025-09-09T14:17:03.2381041Z [[0., 0., 0.], 2025-09-09T14:17:03.2381321Z [0., 0., 0.], 2025-09-09T14:17:03.2381618Z [0., 0., 0.]]]]) 2025-09-09T14:17:03.2381943Z model pt2e: GraphModule( 2025-09-09T14:17:03.2382267Z (conv): Module() 2025-09-09T14:17:03.2382676Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:17:03.2383847Z 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 2025-09-09T14:17:03.2385093Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.19127574563026428, max_val=0.18703685700893402) 2025-09-09T14:17:03.2385656Z ) 2025-09-09T14:17:03.2385964Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:17:03.2386986Z 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 2025-09-09T14:17:03.2388199Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.526270866394043, max_val=2.143237352371216) 2025-09-09T14:17:03.2388761Z ) 2025-09-09T14:17:03.2389052Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:17:03.2390083Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0084]), zero_point=tensor([-20], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:17:03.2391294Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.9042347073554993, max_val=1.2348304986953735) 2025-09-09T14:17:03.2391860Z ) 2025-09-09T14:17:03.2392045Z ) 2025-09-09T14:17:03.2392147Z 2025-09-09T14:17:03.2392151Z 2025-09-09T14:17:03.2392156Z 2025-09-09T14:17:03.2392245Z def forward(self, x): 2025-09-09T14:17:03.2392553Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:17:03.2392910Z conv_weight = self.conv.weight 2025-09-09T14:17:03.2393488Z activation_post_process_1 = self.activation_post_process_1(conv_weight); conv_weight = None 2025-09-09T14:17:03.2394120Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:17:03.2395012Z conv2d = torch.ops.aten.conv2d.default(activation_post_process_0, activation_post_process_1); activation_post_process_0 = activation_post_process_1 = None 2025-09-09T14:17:03.2395939Z activation_post_process_2 = self.activation_post_process_2(conv2d); conv2d = None 2025-09-09T14:17:03.2396715Z return pytree.tree_unflatten((activation_post_process_2,), self._out_spec) 2025-09-09T14:17:03.2397154Z 2025-09-09T14:17:03.2397450Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:17:03.2397860Z model fx: GraphModule( 2025-09-09T14:17:03.2398204Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:17:03.2399255Z 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 2025-09-09T14:17:03.2400470Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.526270866394043, max_val=2.143237352371216) 2025-09-09T14:17:03.2401016Z ) 2025-09-09T14:17:03.2401212Z (conv): Conv2d( 2025-09-09T14:17:03.2401467Z 3, 3, kernel_size=(3, 3), stride=(1, 1), bias=False 2025-09-09T14:17:03.2401869Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:17:03.2402881Z 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 2025-09-09T14:17:03.2404105Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.19127574563026428, max_val=0.18703685700893402) 2025-09-09T14:17:03.2404689Z ) 2025-09-09T14:17:03.2404884Z ) 2025-09-09T14:17:03.2405192Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:17:03.2406220Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0084]), zero_point=tensor([-20], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:17:03.2407443Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.9042347073554993, max_val=1.2348304986953735) 2025-09-09T14:17:03.2408005Z ) 2025-09-09T14:17:03.2408199Z ) 2025-09-09T14:17:03.2408301Z 2025-09-09T14:17:03.2408305Z 2025-09-09T14:17:03.2408309Z 2025-09-09T14:17:03.2408414Z def forward(self, x): 2025-09-09T14:17:03.2408790Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:17:03.2409374Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T14:17:03.2409965Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T14:17:03.2410437Z return activation_post_process_1 2025-09-09T14:17:03.2410714Z 2025-09-09T14:17:03.2411021Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:17:03.2411412Z diff: tensor([[[[0., 0., 0.], 2025-09-09T14:17:03.2411677Z [0., 0., 0.], 2025-09-09T14:17:03.2411912Z [0., 0., 0.]], 2025-09-09T14:17:03.2412058Z 2025-09-09T14:17:03.2412140Z [[0., 0., 0.], 2025-09-09T14:17:03.2412370Z [0., 0., 0.], 2025-09-09T14:17:03.2412583Z [0., 0., 0.]], 2025-09-09T14:17:03.2412739Z 2025-09-09T14:17:03.2412819Z [[0., 0., 0.], 2025-09-09T14:17:03.2413033Z [0., 0., 0.], 2025-09-09T14:17:03.2413290Z [0., 0., 0.]]]], grad_fn=) 2025-09-09T14:17:03.2413618Z converted model pt2e: GraphModule( 2025-09-09T14:17:03.2413903Z (conv): Module() 2025-09-09T14:17:03.2414116Z ) 2025-09-09T14:17:03.2414216Z 2025-09-09T14:17:03.2414220Z 2025-09-09T14:17:03.2414224Z 2025-09-09T14:17:03.2414397Z def forward(self, x): 2025-09-09T14:17:03.2414705Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:17:03.2415101Z quantize_per_tensor_default = self._frozen_param0 2025-09-09T14:17:03.2416108Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.0015061082085594535, 0, -127, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T14:17:03.2417586Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.018311796709895134, 10, -128, 127, torch.int8); x = None 2025-09-09T14:17:42.7691841Z 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 2025-09-09T14:17:42.7694467Z 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 2025-09-09T14:17:42.7695901Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv2d, 0.008388491347432137, -20, -128, 127, torch.int8); conv2d = None 2025-09-09T14:17:42.7697338Z dequantize_per_tensor_default_2 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_2, 0.008388491347432137, -20, -128, 127, torch.int8); quantize_per_tensor_default_2 = None 2025-09-09T14:17:42.7698464Z return pytree.tree_unflatten((dequantize_per_tensor_default_2,), self._out_spec) 2025-09-09T14:17:42.7698918Z 2025-09-09T14:17:42.7699215Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:17:42.7699627Z onverted model fx: GraphModule( 2025-09-09T14:17:42.7700088Z (conv): QuantizedConv2d(Reference)(3, 3, kernel_size=(3, 3), stride=(1, 1), bias=False) 2025-09-09T14:17:42.7700537Z ) 2025-09-09T14:17:42.7700640Z 2025-09-09T14:17:42.7700649Z 2025-09-09T14:17:42.7700653Z 2025-09-09T14:17:42.7700754Z def forward(self, x): 2025-09-09T14:17:42.7701427Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.018311796709895134, 10, -128, 127, torch.int8); x = None 2025-09-09T14:17:42.7702811Z 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 2025-09-09T14:17:42.7703940Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T14:17:42.7704873Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.008388491347432137, -20, -128, 127, torch.int8); conv = None 2025-09-09T14:17:42.7706298Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.008388491347432137, -20, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T14:17:42.7707276Z return dequantize_per_tensor_default_1 2025-09-09T14:17:42.7707565Z 2025-09-09T14:17:42.7707868Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:17:42.7708263Z diff: tensor([[[[0., 0., 0.], 2025-09-09T14:17:42.7708523Z [0., 0., 0.], 2025-09-09T14:17:42.7708743Z [0., 0., 0.]], 2025-09-09T14:17:42.7708903Z 2025-09-09T14:17:42.7708987Z [[0., 0., 0.], 2025-09-09T14:17:42.7709202Z [0., 0., 0.], 2025-09-09T14:17:42.7709430Z [0., 0., 0.]], 2025-09-09T14:17:42.7709575Z 2025-09-09T14:17:42.7709665Z [[0., 0., 0.], 2025-09-09T14:17:42.7709878Z [0., 0., 0.], 2025-09-09T14:17:42.7710106Z [0., 0., 0.]]]]) 2025-09-09T14:17:42.7710517Z PASSED 2025-09-09T14:17:42.7711248Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_conv_transpose_bn PASSED 2025-09-09T14:17:42.7712721Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_conv_transpose_bn_relu PASSED 2025-09-09T14:17:42.7713786Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_inplace_add_relu model pt2e: GraphModule( 2025-09-09T14:17:42.7714467Z (conv): Module() 2025-09-09T14:17:42.7714795Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:17:42.7715867Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0002]), zero_point=tensor([127], dtype=torch.int32), dtype=torch.int8, quant_min=-127, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False 2025-09-09T14:17:42.7717379Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.0532]), max_val=tensor([-0.0532])) 2025-09-09T14:17:42.7718014Z ) 2025-09-09T14:17:42.7718309Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:17:42.7719349Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0111]), zero_point=tensor([38], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:17:42.7720575Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.8401848077774048, max_val=0.9828221797943115) 2025-09-09T14:17:42.7721131Z ) 2025-09-09T14:17:42.7721430Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:17:42.7722457Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0021]), zero_point=tensor([-128], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:17:42.7723668Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.3858921527862549, max_val=0.5359839200973511) 2025-09-09T14:17:42.7724235Z ) 2025-09-09T14:17:42.7724712Z (activation_post_process_3): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:17:42.7725753Z 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 2025-09-09T14:17:42.7726915Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=1.372033953666687) 2025-09-09T14:17:42.7727418Z ) 2025-09-09T14:17:42.7727599Z ) 2025-09-09T14:17:42.7735793Z 2025-09-09T14:17:42.7735802Z 2025-09-09T14:17:42.7735806Z 2025-09-09T14:17:42.7735924Z def forward(self, x): 2025-09-09T14:17:42.7736241Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:17:42.7736601Z conv_weight = self.conv.weight 2025-09-09T14:17:42.7737110Z activation_post_process_1 = self.activation_post_process_1(conv_weight); conv_weight = None 2025-09-09T14:17:42.7737622Z conv_bias = self.conv.bias 2025-09-09T14:17:42.7738044Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:17:42.7738896Z conv2d = torch.ops.aten.conv2d.default(activation_post_process_0, activation_post_process_1, conv_bias); activation_post_process_1 = conv_bias = None 2025-09-09T14:17:42.7739787Z activation_post_process_2 = self.activation_post_process_2(conv2d); conv2d = None 2025-09-09T14:17:42.7740669Z add_ = torch.ops.aten.add_.Tensor(activation_post_process_2, activation_post_process_0); activation_post_process_2 = activation_post_process_0 = None 2025-09-09T14:17:42.7741448Z relu_ = torch.ops.aten.relu_.default(add_); add_ = None 2025-09-09T14:17:42.7741973Z activation_post_process_3 = self.activation_post_process_3(relu_); relu_ = None 2025-09-09T14:17:42.7742563Z return pytree.tree_unflatten((activation_post_process_3,), self._out_spec) 2025-09-09T14:17:42.7742993Z 2025-09-09T14:17:42.7743289Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:17:42.7743695Z model fx: GraphModule( 2025-09-09T14:17:42.7744236Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:17:42.7745278Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0111]), zero_point=tensor([38], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:17:42.7746520Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.8401848077774048, max_val=0.9828221797943115) 2025-09-09T14:17:42.7747168Z ) 2025-09-09T14:17:42.7747365Z (conv): Conv2d( 2025-09-09T14:17:42.7747601Z 1, 1, kernel_size=(1, 1), stride=(1, 1) 2025-09-09T14:17:42.7747980Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:17:42.7749004Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0002]), zero_point=tensor([127], dtype=torch.int32), dtype=torch.qint8, quant_min=-127, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False 2025-09-09T14:17:42.7750302Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.0532]), max_val=tensor([-0.0532])) 2025-09-09T14:17:42.7750933Z ) 2025-09-09T14:17:42.7751114Z ) 2025-09-09T14:17:42.7751422Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:17:42.7752460Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0021]), zero_point=tensor([-128], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:17:42.7753676Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.3858921527862549, max_val=0.5359839200973511) 2025-09-09T14:17:42.7754248Z ) 2025-09-09T14:17:42.7754447Z (relu): ReLU(inplace=True) 2025-09-09T14:17:42.7754822Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:17:42.7755871Z 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 2025-09-09T14:17:42.7757128Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=1.372033953666687) 2025-09-09T14:17:42.7757646Z ) 2025-09-09T14:17:42.7757826Z ) 2025-09-09T14:17:42.7757946Z 2025-09-09T14:17:42.7757950Z 2025-09-09T14:17:42.7757954Z 2025-09-09T14:17:42.7758050Z def forward(self, x): 2025-09-09T14:17:42.7758429Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:17:42.7758902Z conv = self.conv(activation_post_process_0) 2025-09-09T14:17:42.7759383Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T14:17:42.7760130Z add = activation_post_process_1 + activation_post_process_0; activation_post_process_1 = activation_post_process_0 = None 2025-09-09T14:17:42.7760749Z relu = self.relu(add); add = None 2025-09-09T14:17:42.7761192Z activation_post_process_2 = self.activation_post_process_2(relu); relu = None 2025-09-09T14:17:42.7761663Z return activation_post_process_2 2025-09-09T14:17:42.7762067Z 2025-09-09T14:17:42.7762512Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:17:42.7763197Z diff: tensor([[[[0., 0., 0.], 2025-09-09T14:17:42.7763547Z [0., 0., 0.], 2025-09-09T14:17:42.7763895Z [0., 0., 0.]]]], grad_fn=) 2025-09-09T14:17:42.7764343Z converted model pt2e: GraphModule( 2025-09-09T14:17:42.7764829Z (conv): Module() 2025-09-09T14:17:42.7765146Z ) 2025-09-09T14:17:42.7765340Z 2025-09-09T14:17:42.7765345Z 2025-09-09T14:17:42.7765350Z 2025-09-09T14:17:42.7765500Z def forward(self, x): 2025-09-09T14:17:42.7766073Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:17:42.7766616Z _scale_0 = self._scale_0 2025-09-09T14:17:42.7767161Z _zero_point_0 = self._zero_point_0 2025-09-09T14:17:42.7767689Z quantize_per_channel_default = self._frozen_param0 2025-09-09T14:17:43.4727931Z 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 2025-09-09T14:17:43.4729022Z conv_bias = self.conv.bias 2025-09-09T14:17:43.4729724Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.011070616543293, 38, -128, 127, torch.int8); x = None 2025-09-09T14:17:43.4731131Z dequantize_per_tensor_default_4 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.011070616543293, 38, -128, 127, torch.int8) 2025-09-09T14:17:43.4732578Z dequantize_per_tensor_default_3 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.011070616543293, 38, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T14:17:43.4734145Z 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 2025-09-09T14:17:43.4735534Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv2d, 0.0021018977276980877, -128, -128, 127, torch.int8); conv2d = None 2025-09-09T14:17:43.4736993Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.0021018977276980877, -128, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T14:17:43.4738460Z 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 2025-09-09T14:17:43.4739317Z relu_ = torch.ops.aten.relu_.default(add_); add_ = None 2025-09-09T14:17:43.4740164Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(relu_, 0.005380525253713131, -128, -128, 127, torch.int8); relu_ = None 2025-09-09T14:17:43.4741597Z dequantize_per_tensor_default_2 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_2, 0.005380525253713131, -128, -128, 127, torch.int8); quantize_per_tensor_default_2 = None 2025-09-09T14:17:43.4742712Z return pytree.tree_unflatten((dequantize_per_tensor_default_2,), self._out_spec) 2025-09-09T14:17:43.4743174Z 2025-09-09T14:17:43.4743470Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:17:43.4743887Z onverted model fx: GraphModule( 2025-09-09T14:17:43.4744309Z (conv): QuantizedConv2d(Reference)(1, 1, kernel_size=(1, 1), stride=(1, 1)) 2025-09-09T14:17:43.4744752Z (relu): ReLU(inplace=True) 2025-09-09T14:17:43.4745004Z ) 2025-09-09T14:17:43.4745107Z 2025-09-09T14:17:43.4745112Z 2025-09-09T14:17:43.4745127Z 2025-09-09T14:17:43.4745217Z def forward(self, x): 2025-09-09T14:17:43.4745874Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.011070616543293, 38, -128, 127, torch.int8); x = None 2025-09-09T14:17:43.4747216Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.011070616543293, 38, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T14:17:43.4748187Z conv = self.conv(dequantize_per_tensor_default) 2025-09-09T14:17:43.4748985Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.0021018977276980877, -128, -128, 127, torch.int8); conv = None 2025-09-09T14:17:43.4750414Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.0021018977276980877, -128, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T14:17:43.4751865Z add = dequantize_per_tensor_default_1 + dequantize_per_tensor_default; dequantize_per_tensor_default_1 = dequantize_per_tensor_default = None 2025-09-09T14:17:43.4752559Z relu = self.relu(add); add = None 2025-09-09T14:17:43.4753328Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(relu, 0.005380525253713131, -128, -128, 127, torch.int8); relu = None 2025-09-09T14:17:43.4754741Z dequantize_per_tensor_default_2 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_2, 0.005380525253713131, -128, -128, 127, torch.int8); quantize_per_tensor_default_2 = None 2025-09-09T14:17:43.4755790Z return dequantize_per_tensor_default_2 2025-09-09T14:17:43.4756164Z 2025-09-09T14:17:43.4756457Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:17:43.4756862Z diff: tensor([[[[0., 0., 0.], 2025-09-09T14:17:43.4757111Z [0., 0., 0.], 2025-09-09T14:17:43.4757347Z [0., 0., 0.]]]]) 2025-09-09T14:17:43.4757589Z model pt2e: GraphModule( 2025-09-09T14:17:43.4757882Z (conv): Module() 2025-09-09T14:17:43.4758215Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:17:43.4759260Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0002]), zero_point=tensor([127], dtype=torch.int32), dtype=torch.int8, quant_min=-127, quant_max=127, qscheme=torch.per_tensor_symmetric, reduce_range=False 2025-09-09T14:17:43.4760512Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.05316734313964844, max_val=-0.05316734313964844) 2025-09-09T14:17:43.4761088Z ) 2025-09-09T14:17:43.4761505Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:17:43.4762546Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0111]), zero_point=tensor([38], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:17:43.4763753Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.8401848077774048, max_val=0.9828221797943115) 2025-09-09T14:17:43.4764323Z ) 2025-09-09T14:17:43.4764613Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:17:43.4765659Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0021]), zero_point=tensor([-128], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:17:43.4766876Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.3858921527862549, max_val=0.5359839200973511) 2025-09-09T14:17:43.4767436Z ) 2025-09-09T14:17:43.4767739Z (activation_post_process_3): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:17:43.4768762Z 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 2025-09-09T14:17:43.4769936Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=1.372033953666687) 2025-09-09T14:17:43.4770457Z ) 2025-09-09T14:17:43.4770631Z ) 2025-09-09T14:17:43.4770736Z 2025-09-09T14:17:43.4770740Z 2025-09-09T14:17:43.4770743Z 2025-09-09T14:17:43.4770848Z def forward(self, x): 2025-09-09T14:17:43.4771147Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:17:43.4771517Z conv_weight = self.conv.weight 2025-09-09T14:17:43.4772003Z activation_post_process_1 = self.activation_post_process_1(conv_weight); conv_weight = None 2025-09-09T14:17:43.4772524Z conv_bias = self.conv.bias 2025-09-09T14:17:43.4772929Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:17:43.4773776Z conv2d = torch.ops.aten.conv2d.default(activation_post_process_0, activation_post_process_1, conv_bias); activation_post_process_1 = conv_bias = None 2025-09-09T14:17:43.4774780Z activation_post_process_2 = self.activation_post_process_2(conv2d); conv2d = None 2025-09-09T14:17:43.4775650Z add_ = torch.ops.aten.add_.Tensor(activation_post_process_2, activation_post_process_0); activation_post_process_2 = activation_post_process_0 = None 2025-09-09T14:17:43.4776427Z relu_ = torch.ops.aten.relu_.default(add_); add_ = None 2025-09-09T14:17:43.4776950Z activation_post_process_3 = self.activation_post_process_3(relu_); relu_ = None 2025-09-09T14:17:43.4777540Z return pytree.tree_unflatten((activation_post_process_3,), self._out_spec) 2025-09-09T14:17:43.4778039Z 2025-09-09T14:17:43.4778331Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:17:43.4778729Z model fx: GraphModule( 2025-09-09T14:17:43.4779069Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:17:43.4780112Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0111]), zero_point=tensor([38], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:17:43.4781333Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.8401848077774048, max_val=0.9828221797943115) 2025-09-09T14:17:43.4781889Z ) 2025-09-09T14:17:43.4782083Z (conv): Conv2d( 2025-09-09T14:17:43.4782316Z 1, 1, kernel_size=(1, 1), stride=(1, 1) 2025-09-09T14:17:43.4782688Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:17:43.4783696Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0002]), zero_point=tensor([127], dtype=torch.int32), dtype=torch.qint8, quant_min=-127, quant_max=127, qscheme=torch.per_tensor_symmetric, reduce_range=False 2025-09-09T14:17:43.4784947Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.05316734313964844, max_val=-0.05316734313964844) 2025-09-09T14:17:43.4785524Z ) 2025-09-09T14:17:43.4785700Z ) 2025-09-09T14:17:43.4786002Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:17:43.4787041Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0021]), zero_point=tensor([-128], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:17:43.4788267Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.3858921527862549, max_val=0.5359839200973511) 2025-09-09T14:17:43.4788831Z ) 2025-09-09T14:17:43.4789028Z (relu): ReLU(inplace=True) 2025-09-09T14:17:43.4789404Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:18:04.2179073Z 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 2025-09-09T14:18:04.2180997Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=1.372033953666687) 2025-09-09T14:18:04.2181507Z ) 2025-09-09T14:18:04.2181701Z ) 2025-09-09T14:18:04.2181825Z 2025-09-09T14:18:04.2181829Z 2025-09-09T14:18:04.2181833Z 2025-09-09T14:18:04.2181922Z def forward(self, x): 2025-09-09T14:18:04.2182306Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:18:04.2182761Z conv = self.conv(activation_post_process_0) 2025-09-09T14:18:04.2183236Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T14:18:04.2183990Z add = activation_post_process_1 + activation_post_process_0; activation_post_process_1 = activation_post_process_0 = None 2025-09-09T14:18:04.2184723Z relu = self.relu(add); add = None 2025-09-09T14:18:04.2185341Z activation_post_process_2 = self.activation_post_process_2(relu); relu = None 2025-09-09T14:18:04.2185794Z return activation_post_process_2 2025-09-09T14:18:04.2186077Z 2025-09-09T14:18:04.2186365Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:18:04.2186770Z diff: tensor([[[[0., 0., 0.], 2025-09-09T14:18:04.2187287Z [0., 0., 0.], 2025-09-09T14:18:04.2187545Z [0., 0., 0.]]]], grad_fn=) 2025-09-09T14:18:04.2187882Z converted model pt2e: GraphModule( 2025-09-09T14:18:04.2188156Z (conv): Module() 2025-09-09T14:18:04.2188375Z ) 2025-09-09T14:18:04.2188479Z 2025-09-09T14:18:04.2188484Z 2025-09-09T14:18:04.2188487Z 2025-09-09T14:18:04.2188576Z def forward(self, x): 2025-09-09T14:18:04.2188886Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:18:04.2189388Z quantize_per_tensor_default = self._frozen_param0 2025-09-09T14:18:04.2190401Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.00041864049853757024, 0, -127, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T14:18:04.2191361Z conv_bias = self.conv.bias 2025-09-09T14:18:04.2192051Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.011070616543293, 38, -128, 127, torch.int8); x = None 2025-09-09T14:18:04.2193632Z dequantize_per_tensor_default_5 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.011070616543293, 38, -128, 127, torch.int8) 2025-09-09T14:18:04.2195192Z dequantize_per_tensor_default_4 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.011070616543293, 38, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T14:18:04.2197094Z 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 2025-09-09T14:18:04.2198493Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv2d, 0.0021018977276980877, -128, -128, 127, torch.int8); conv2d = None 2025-09-09T14:18:04.2199953Z dequantize_per_tensor_default_2 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_2, 0.0021018977276980877, -128, -128, 127, torch.int8); quantize_per_tensor_default_2 = None 2025-09-09T14:18:04.2201399Z 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 2025-09-09T14:18:04.2202264Z relu_ = torch.ops.aten.relu_.default(add_); add_ = None 2025-09-09T14:18:04.2203093Z quantize_per_tensor_default_3 = torch.ops.quantized_decomposed.quantize_per_tensor.default(relu_, 0.005380525253713131, -128, -128, 127, torch.int8); relu_ = None 2025-09-09T14:18:04.2204545Z dequantize_per_tensor_default_3 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_3, 0.005380525253713131, -128, -128, 127, torch.int8); quantize_per_tensor_default_3 = None 2025-09-09T14:18:04.2205667Z return pytree.tree_unflatten((dequantize_per_tensor_default_3,), self._out_spec) 2025-09-09T14:18:04.2206112Z 2025-09-09T14:18:04.2206424Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:18:04.2206829Z onverted model fx: GraphModule( 2025-09-09T14:18:04.2207255Z (conv): QuantizedConv2d(Reference)(1, 1, kernel_size=(1, 1), stride=(1, 1)) 2025-09-09T14:18:04.2207702Z (relu): ReLU(inplace=True) 2025-09-09T14:18:04.2207955Z ) 2025-09-09T14:18:04.2208060Z 2025-09-09T14:18:04.2208064Z 2025-09-09T14:18:04.2208082Z 2025-09-09T14:18:04.2208178Z def forward(self, x): 2025-09-09T14:18:04.2208832Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.011070616543293, 38, -128, 127, torch.int8); x = None 2025-09-09T14:18:04.2210188Z dequantize_per_tensor_default = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.011070616543293, 38, -128, 127, torch.int8); quantize_per_tensor_default = None 2025-09-09T14:18:04.2211161Z conv = self.conv(dequantize_per_tensor_default) 2025-09-09T14:18:04.2212106Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.0021018977276980877, -128, -128, 127, torch.int8); conv = None 2025-09-09T14:18:04.2213544Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.0021018977276980877, -128, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T14:18:04.2214897Z add = dequantize_per_tensor_default_1 + dequantize_per_tensor_default; dequantize_per_tensor_default_1 = dequantize_per_tensor_default = None 2025-09-09T14:18:04.2215655Z relu = self.relu(add); add = None 2025-09-09T14:18:04.2216425Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(relu, 0.005380525253713131, -128, -128, 127, torch.int8); relu = None 2025-09-09T14:18:04.2217858Z dequantize_per_tensor_default_2 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_2, 0.005380525253713131, -128, -128, 127, torch.int8); quantize_per_tensor_default_2 = None 2025-09-09T14:18:04.2218827Z return dequantize_per_tensor_default_2 2025-09-09T14:18:04.2219130Z 2025-09-09T14:18:04.2219424Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:18:04.2219820Z diff: tensor([[[[0., 0., 0.], 2025-09-09T14:18:04.2220068Z [0., 0., 0.], 2025-09-09T14:18:04.2220300Z [0., 0., 0.]]]]) 2025-09-09T14:18:04.2220724Z PASSED 2025-09-09T14:18:04.2221509Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_per_channel_weight_custom_dtype PASSED 2025-09-09T14:18:04.2222697Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_preserve_source_fn_stack PASSED 2025-09-09T14:18:04.2223750Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_update_shared_qspec model pt2e: GraphModule( 2025-09-09T14:18:04.2224626Z (conv): Module() 2025-09-09T14:18:04.2224841Z (bn): Module() 2025-09-09T14:18:04.2225169Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:18:04.2226211Z 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 2025-09-09T14:18:04.2227422Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.526270866394043, max_val=2.143237352371216) 2025-09-09T14:18:04.2227987Z ) 2025-09-09T14:18:04.2228275Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:18:04.2229369Z 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 2025-09-09T14:18:04.2230799Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.1919, -0.1859, -0.1499]), max_val=tensor([0.1902, 0.1880, 0.1882])) 2025-09-09T14:18:04.2231508Z ) 2025-09-09T14:18:04.2231812Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:18:04.2232833Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0145]), zero_point=tensor([-23], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:18:04.2234042Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.5212559700012207, max_val=2.179866313934326) 2025-09-09T14:18:04.2234604Z ) 2025-09-09T14:18:04.2234894Z (activation_post_process_3): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:18:04.2236681Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0145]), zero_point=tensor([-23], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:18:04.2237907Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.5212559700012207, max_val=2.179866313934326) 2025-09-09T14:18:04.2238472Z ) 2025-09-09T14:18:04.2238661Z ) 2025-09-09T14:18:04.2238763Z 2025-09-09T14:18:04.2238767Z 2025-09-09T14:18:04.2238771Z 2025-09-09T14:18:04.2238861Z def forward(self, x): 2025-09-09T14:18:04.2239171Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:18:04.2239615Z conv_weight = self.conv.weight 2025-09-09T14:18:04.2239923Z conv_bias = self.conv.bias 2025-09-09T14:18:04.2240195Z bn_weight = self.bn.weight 2025-09-09T14:18:04.2240475Z bn_bias = self.bn.bias 2025-09-09T14:18:04.2240748Z bn_running_mean = self.bn.running_mean 2025-09-09T14:18:04.2241078Z bn_running_var = self.bn.running_var 2025-09-09T14:18:04.2241437Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T14:18:04.2241909Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:18:04.2242553Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T14:18:04.2243116Z add = torch.ops.aten.add.Tensor(bn_running_var, 1e-05) 2025-09-09T14:18:04.2243542Z sqrt = torch.ops.aten.sqrt.default(add); add = None 2025-09-09T14:18:04.2243978Z div = torch.ops.aten.div.Tensor(bn_weight, sqrt); sqrt = None 2025-09-09T14:18:04.2244461Z reshape = torch.ops.aten.reshape.default(div, [-1, 1, 1, 1]) 2025-09-09T14:18:13.4974341Z mul = torch.ops.aten.mul.Tensor(conv_weight, reshape); conv_weight = reshape = None 2025-09-09T14:18:13.4975216Z activation_post_process_1 = self.activation_post_process_1(mul); mul = None 2025-09-09T14:18:13.4976111Z zeros_like = torch.ops.aten.zeros_like.default(conv_bias, dtype = torch.float32, pin_memory = False) 2025-09-09T14:18:13.4977589Z 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 2025-09-09T14:18:13.4978903Z reshape_1 = torch.ops.aten.reshape.default(div, [1, -1, 1, 1]); div = None 2025-09-09T14:18:13.4979701Z div_1 = torch.ops.aten.div.Tensor(conv2d_1, reshape_1); conv2d_1 = reshape_1 = None 2025-09-09T14:18:13.4980552Z reshape_2 = torch.ops.aten.reshape.default(conv_bias, [1, -1, 1, 1]); conv_bias = None 2025-09-09T14:18:13.4981358Z add_1 = torch.ops.aten.add.Tensor(div_1, reshape_2); div_1 = reshape_2 = None 2025-09-09T14:18:13.4982658Z 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 2025-09-09T14:18:13.4984026Z activation_post_process_2 = self.activation_post_process_2(batch_norm_1); batch_norm_1 = None 2025-09-09T14:18:13.4985111Z hardtanh = torch.ops.aten.hardtanh.default(activation_post_process_2, -1.0, 1.0); activation_post_process_2 = None 2025-09-09T14:18:13.4986167Z activation_post_process_3 = self.activation_post_process_3(hardtanh); hardtanh = None 2025-09-09T14:18:13.4986982Z return pytree.tree_unflatten((activation_post_process_3,), self._out_spec) 2025-09-09T14:18:13.4987557Z 2025-09-09T14:18:13.4987942Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:18:13.4988476Z model fx: GraphModule( 2025-09-09T14:18:13.4988946Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:18:13.4990328Z 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 2025-09-09T14:18:13.4992052Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.526270866394043, max_val=2.143237352371216) 2025-09-09T14:18:13.4992812Z ) 2025-09-09T14:18:13.4993361Z (conv): ConvBn2d( 2025-09-09T14:18:13.4993743Z 3, 3, kernel_size=(3, 3), stride=(1, 1) 2025-09-09T14:18:13.4994326Z (bn): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T14:18:13.4995009Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:18:13.4996515Z 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 2025-09-09T14:18:13.4998570Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.1919, -0.1859, -0.1499]), max_val=tensor([0.1902, 0.1880, 0.1882])) 2025-09-09T14:18:13.4999546Z ) 2025-09-09T14:18:13.4999784Z ) 2025-09-09T14:18:13.5000182Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:18:13.5001585Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0145]), zero_point=tensor([-23], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:18:13.5003214Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.5212559700012207, max_val=2.179866313934326) 2025-09-09T14:18:13.5003980Z ) 2025-09-09T14:18:13.5004286Z (hardtanh): Hardtanh(min_val=-1.0, max_val=1.0) 2025-09-09T14:18:13.5004866Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:18:13.5006274Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0145]), zero_point=tensor([-23], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:18:13.5007896Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.5212559700012207, max_val=2.179866313934326) 2025-09-09T14:18:13.5008538Z ) 2025-09-09T14:18:13.5008725Z ) 2025-09-09T14:18:13.5008844Z 2025-09-09T14:18:13.5008859Z 2025-09-09T14:18:13.5008862Z 2025-09-09T14:18:13.5008956Z def forward(self, x): 2025-09-09T14:18:13.5009342Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:18:13.5009913Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T14:18:13.5010515Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T14:18:13.5011143Z hardtanh = self.hardtanh(activation_post_process_1); activation_post_process_1 = None 2025-09-09T14:18:13.5011816Z activation_post_process_2 = self.activation_post_process_2(hardtanh); hardtanh = None 2025-09-09T14:18:13.5012305Z return activation_post_process_2 2025-09-09T14:18:13.5012586Z 2025-09-09T14:18:13.5012889Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:18:13.5013281Z diff: tensor([[[[0., 0., 0.], 2025-09-09T14:18:13.5013541Z [0., 0., 0.], 2025-09-09T14:18:13.5013765Z [0., 0., 0.]], 2025-09-09T14:18:13.5013924Z 2025-09-09T14:18:13.5014004Z [[0., 0., 0.], 2025-09-09T14:18:13.5014222Z [0., 0., 0.], 2025-09-09T14:18:13.5014451Z [0., 0., 0.]], 2025-09-09T14:18:13.5014597Z 2025-09-09T14:18:13.5014676Z [[0., 0., 0.], 2025-09-09T14:18:13.5014904Z [0., 0., 0.], 2025-09-09T14:18:13.5015165Z [0., 0., 0.]]]], grad_fn=) 2025-09-09T14:18:13.5015492Z converted model pt2e: GraphModule( 2025-09-09T14:18:13.5015791Z (conv): Module() 2025-09-09T14:18:13.5016001Z (bn): Module() 2025-09-09T14:18:13.5016213Z ) 2025-09-09T14:18:13.5016315Z 2025-09-09T14:18:13.5016319Z 2025-09-09T14:18:13.5016323Z 2025-09-09T14:18:13.5016414Z def forward(self, x): 2025-09-09T14:18:13.5016717Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:18:13.5017068Z conv_bias = self.conv.bias 2025-09-09T14:18:13.5017868Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.018311796709895134, 10, -128, 127, torch.int8); x = None 2025-09-09T14:18:13.5019256Z 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 2025-09-09T14:18:13.5020194Z _scale_0 = self._scale_0 2025-09-09T14:18:13.5020485Z _zero_point_0 = self._zero_point_0 2025-09-09T14:18:13.5020806Z quantize_per_channel = self._frozen_param0 2025-09-09T14:18:13.5021845Z 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 2025-09-09T14:18:13.5023335Z 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 2025-09-09T14:18:13.5024870Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv2d_2, 0.014514205045998096, -23, -128, 127, torch.int8); conv2d_2 = None 2025-09-09T14:18:13.5026327Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.014514205045998096, -23, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T14:18:13.5027631Z hardtanh = torch.ops.aten.hardtanh.default(dequantize_per_tensor_default_1, -1.0, 1.0); dequantize_per_tensor_default_1 = None 2025-09-09T14:18:13.5028761Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(hardtanh, 0.014514205045998096, -23, -128, 127, torch.int8); hardtanh = None 2025-09-09T14:18:13.5030213Z dequantize_per_tensor_default_2 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_2, 0.014514205045998096, -23, -128, 127, torch.int8); quantize_per_tensor_default_2 = None 2025-09-09T14:18:13.5031346Z return pytree.tree_unflatten((dequantize_per_tensor_default_2,), self._out_spec) 2025-09-09T14:18:13.5031785Z 2025-09-09T14:18:13.5032090Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:18:13.5032487Z onverted model fx: GraphModule( 2025-09-09T14:18:13.5032902Z (conv): QuantizedConv2d(Reference)(3, 3, kernel_size=(3, 3), stride=(1, 1)) 2025-09-09T14:18:13.5033365Z (hardtanh): Hardtanh(min_val=-1.0, max_val=1.0) 2025-09-09T14:18:13.5033684Z ) 2025-09-09T14:18:13.5033787Z 2025-09-09T14:18:13.5033791Z 2025-09-09T14:18:13.5033795Z 2025-09-09T14:18:13.5033896Z def forward(self, x): 2025-09-09T14:18:13.5034566Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.018311796709895134, 10, -128, 127, torch.int8); x = None 2025-09-09T14:18:13.5035945Z 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 2025-09-09T14:18:13.5037113Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T14:18:13.5038062Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.014514205045998096, -23, -128, 127, torch.int8); conv = None 2025-09-09T14:18:13.5039491Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.014514205045998096, -23, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T14:18:13.5040744Z hardtanh = self.hardtanh(dequantize_per_tensor_default_1); dequantize_per_tensor_default_1 = None 2025-09-09T14:18:13.5041772Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(hardtanh, 0.014514205045998096, -23, -128, 127, torch.int8); hardtanh = None 2025-09-09T14:18:13.5043418Z dequantize_per_tensor_default_2 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_2, 0.014514205045998096, -23, -128, 127, torch.int8); quantize_per_tensor_default_2 = None 2025-09-09T14:18:13.5044393Z return dequantize_per_tensor_default_2 2025-09-09T14:18:13.5044695Z 2025-09-09T14:18:13.5044982Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:18:13.5045388Z diff: tensor([[[[0., 0., 0.], 2025-09-09T14:18:13.5045652Z [0., 0., 0.], 2025-09-09T14:18:13.5045961Z [0., 0., 0.]], 2025-09-09T14:18:13.5046109Z 2025-09-09T14:18:13.5046203Z [[0., 0., 0.], 2025-09-09T14:18:13.5046419Z [0., 0., 0.], 2025-09-09T14:18:22.6604821Z [0., 0., 0.]], 2025-09-09T14:18:22.6605486Z 2025-09-09T14:18:22.6605732Z [[0., 0., 0.], 2025-09-09T14:18:22.6605978Z [0., 0., 0.], 2025-09-09T14:18:22.6606205Z [0., 0., 0.]]]]) 2025-09-09T14:18:22.6606469Z model pt2e: GraphModule( 2025-09-09T14:18:22.6606724Z (conv): Module() 2025-09-09T14:18:22.6606980Z (bn): Module() 2025-09-09T14:18:22.6607310Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:18:22.6608564Z 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 2025-09-09T14:18:22.6609799Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.526270866394043, max_val=2.143237352371216) 2025-09-09T14:18:22.6610376Z ) 2025-09-09T14:18:22.6610665Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:18:22.6611713Z 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 2025-09-09T14:18:22.6612935Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.19193142652511597, max_val=0.1902383267879486) 2025-09-09T14:18:22.6613541Z ) 2025-09-09T14:18:22.6613958Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:18:22.6615118Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0145]), zero_point=tensor([-23], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:18:22.6616330Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.521487832069397, max_val=2.1819007396698) 2025-09-09T14:18:22.6616884Z ) 2025-09-09T14:18:22.6617196Z (activation_post_process_3): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:18:22.6618225Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0145]), zero_point=tensor([-23], dtype=torch.int32), dtype=torch.int8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:18:22.6619434Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.521487832069397, max_val=2.1819007396698) 2025-09-09T14:18:22.6620101Z ) 2025-09-09T14:18:22.6620317Z ) 2025-09-09T14:18:22.6620464Z 2025-09-09T14:18:22.6620470Z 2025-09-09T14:18:22.6620476Z 2025-09-09T14:18:22.6620614Z def forward(self, x): 2025-09-09T14:18:22.6620940Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:18:22.6621302Z conv_weight = self.conv.weight 2025-09-09T14:18:22.6621613Z conv_bias = self.conv.bias 2025-09-09T14:18:22.6621896Z bn_weight = self.bn.weight 2025-09-09T14:18:22.6622161Z bn_bias = self.bn.bias 2025-09-09T14:18:22.6622446Z bn_running_mean = self.bn.running_mean 2025-09-09T14:18:22.6622765Z bn_running_var = self.bn.running_var 2025-09-09T14:18:22.6623130Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T14:18:22.6623599Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:18:22.6624712Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T14:18:22.6625303Z add = torch.ops.aten.add.Tensor(bn_running_var, 1e-05) 2025-09-09T14:18:22.6625738Z sqrt = torch.ops.aten.sqrt.default(add); add = None 2025-09-09T14:18:22.6626193Z div = torch.ops.aten.div.Tensor(bn_weight, sqrt); sqrt = None 2025-09-09T14:18:22.6626666Z reshape = torch.ops.aten.reshape.default(div, [-1, 1, 1, 1]) 2025-09-09T14:18:22.6627226Z mul = torch.ops.aten.mul.Tensor(conv_weight, reshape); conv_weight = reshape = None 2025-09-09T14:18:22.6628136Z activation_post_process_1 = self.activation_post_process_1(mul); mul = None 2025-09-09T14:18:22.6628889Z zeros_like = torch.ops.aten.zeros_like.default(conv_bias, dtype = torch.float32, pin_memory = False) 2025-09-09T14:18:22.6629962Z 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 2025-09-09T14:18:22.6631007Z reshape_1 = torch.ops.aten.reshape.default(div, [1, -1, 1, 1]); div = None 2025-09-09T14:18:22.6631766Z div_1 = torch.ops.aten.div.Tensor(conv2d_1, reshape_1); conv2d_1 = reshape_1 = None 2025-09-09T14:18:22.6632398Z reshape_2 = torch.ops.aten.reshape.default(conv_bias, [1, -1, 1, 1]); conv_bias = None 2025-09-09T14:18:22.6633016Z add_1 = torch.ops.aten.add.Tensor(div_1, reshape_2); div_1 = reshape_2 = None 2025-09-09T14:18:22.6633977Z 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 2025-09-09T14:18:22.6634990Z activation_post_process_2 = self.activation_post_process_2(batch_norm_1); batch_norm_1 = None 2025-09-09T14:18:22.6635804Z hardtanh = torch.ops.aten.hardtanh.default(activation_post_process_2, -1.0, 1.0); activation_post_process_2 = None 2025-09-09T14:18:22.6636725Z activation_post_process_3 = self.activation_post_process_3(hardtanh); hardtanh = None 2025-09-09T14:18:22.6637529Z return pytree.tree_unflatten((activation_post_process_3,), self._out_spec) 2025-09-09T14:18:22.6637959Z 2025-09-09T14:18:22.6638254Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:18:22.6638658Z model fx: GraphModule( 2025-09-09T14:18:22.6639000Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:18:22.6640105Z 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 2025-09-09T14:18:22.6641535Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.526270866394043, max_val=2.143237352371216) 2025-09-09T14:18:22.6642096Z ) 2025-09-09T14:18:22.6642294Z (conv): ConvBn2d( 2025-09-09T14:18:22.6642535Z 3, 3, kernel_size=(3, 3), stride=(1, 1) 2025-09-09T14:18:22.6642999Z (bn): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T14:18:22.6643497Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:18:22.6644515Z 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 2025-09-09T14:18:22.6645757Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.19193142652511597, max_val=0.1902383267879486) 2025-09-09T14:18:22.6646319Z ) 2025-09-09T14:18:22.6646509Z ) 2025-09-09T14:18:22.6646797Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:18:22.6647834Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0145]), zero_point=tensor([-23], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:18:22.6649167Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.521487832069397, max_val=2.1819007396698) 2025-09-09T14:18:22.6649731Z ) 2025-09-09T14:18:22.6649974Z (hardtanh): Hardtanh(min_val=-1.0, max_val=1.0) 2025-09-09T14:18:22.6650395Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:18:22.6651440Z fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([0.0145]), zero_point=tensor([-23], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=False 2025-09-09T14:18:22.6652708Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.521487832069397, max_val=2.1819007396698) 2025-09-09T14:18:22.6653261Z ) 2025-09-09T14:18:22.6653456Z ) 2025-09-09T14:18:22.6653560Z 2025-09-09T14:18:22.6653565Z 2025-09-09T14:18:22.6653569Z 2025-09-09T14:18:22.6653660Z def forward(self, x): 2025-09-09T14:18:22.6654052Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:18:22.6654618Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T14:18:22.6655213Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T14:18:22.6655834Z hardtanh = self.hardtanh(activation_post_process_1); activation_post_process_1 = None 2025-09-09T14:18:22.6656498Z activation_post_process_2 = self.activation_post_process_2(hardtanh); hardtanh = None 2025-09-09T14:18:22.6656995Z return activation_post_process_2 2025-09-09T14:18:22.6657266Z 2025-09-09T14:18:22.6657566Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:18:22.6657957Z diff: tensor([[[[0., 0., 0.], 2025-09-09T14:18:22.6658216Z [0., 0., 0.], 2025-09-09T14:18:22.6658436Z [0., 0., 0.]], 2025-09-09T14:18:22.6658594Z 2025-09-09T14:18:22.6658675Z [[0., 0., 0.], 2025-09-09T14:18:22.6658890Z [0., 0., 0.], 2025-09-09T14:18:22.6659119Z [0., 0., 0.]], 2025-09-09T14:18:22.6659298Z 2025-09-09T14:18:22.6659389Z [[0., 0., 0.], 2025-09-09T14:18:22.6659602Z [0., 0., 0.], 2025-09-09T14:18:22.6659861Z [0., 0., 0.]]]], grad_fn=) 2025-09-09T14:18:22.6660183Z converted model pt2e: GraphModule( 2025-09-09T14:18:22.6660474Z (conv): Module() 2025-09-09T14:18:22.6660684Z (bn): Module() 2025-09-09T14:18:22.6660895Z ) 2025-09-09T14:18:22.6660996Z 2025-09-09T14:18:22.6661005Z 2025-09-09T14:18:22.6661009Z 2025-09-09T14:18:22.6661109Z def forward(self, x): 2025-09-09T14:18:22.6661401Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:18:22.6661770Z conv_bias = self.conv.bias 2025-09-09T14:18:22.6662468Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.018311796709895134, 10, -128, 127, torch.int8); x = None 2025-09-09T14:18:22.6663851Z 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 2025-09-09T14:18:22.6664822Z quantize_per_tensor = self._frozen_param0 2025-09-09T14:18:22.6665696Z dequantize_per_tensor = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor, 0.0015112711116671562, 0, -127, 127, torch.int8); quantize_per_tensor = None 2025-09-09T14:18:22.6667087Z 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 2025-09-09T14:18:22.6668411Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv2d_2, 0.014523092657327652, -23, -128, 127, torch.int8); conv2d_2 = None 2025-09-09T14:18:22.6669936Z dequantize_per_tensor_default_2 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_2, 0.014523092657327652, -23, -128, 127, torch.int8); quantize_per_tensor_default_2 = None 2025-09-09T14:18:59.3192737Z hardtanh = torch.ops.aten.hardtanh.default(dequantize_per_tensor_default_2, -1.0, 1.0); dequantize_per_tensor_default_2 = None 2025-09-09T14:18:59.3194300Z quantize_per_tensor_default_3 = torch.ops.quantized_decomposed.quantize_per_tensor.default(hardtanh, 0.014523092657327652, -23, -128, 127, torch.int8); hardtanh = None 2025-09-09T14:18:59.3196378Z dequantize_per_tensor_default_3 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_3, 0.014523092657327652, -23, -128, 127, torch.int8); quantize_per_tensor_default_3 = None 2025-09-09T14:18:59.3198246Z return pytree.tree_unflatten((dequantize_per_tensor_default_3,), self._out_spec) 2025-09-09T14:18:59.3198855Z 2025-09-09T14:18:59.3199240Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:18:59.3199793Z onverted model fx: GraphModule( 2025-09-09T14:18:59.3200344Z (conv): QuantizedConv2d(Reference)(3, 3, kernel_size=(3, 3), stride=(1, 1)) 2025-09-09T14:18:59.3200969Z (hardtanh): Hardtanh(min_val=-1.0, max_val=1.0) 2025-09-09T14:18:59.3201377Z ) 2025-09-09T14:18:59.3201529Z 2025-09-09T14:18:59.3201535Z 2025-09-09T14:18:59.3201540Z 2025-09-09T14:18:59.3201660Z def forward(self, x): 2025-09-09T14:18:59.3202575Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.018311796709895134, 10, -128, 127, torch.int8); x = None 2025-09-09T14:18:59.3204441Z 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 2025-09-09T14:18:59.3205949Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T14:18:59.3207223Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.014523092657327652, -23, -128, 127, torch.int8); conv = None 2025-09-09T14:18:59.3209131Z dequantize_per_tensor_default_1 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_1, 0.014523092657327652, -23, -128, 127, torch.int8); quantize_per_tensor_default_1 = None 2025-09-09T14:18:59.3210725Z hardtanh = self.hardtanh(dequantize_per_tensor_default_1); dequantize_per_tensor_default_1 = None 2025-09-09T14:18:59.3212097Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(hardtanh, 0.014523092657327652, -23, -128, 127, torch.int8); hardtanh = None 2025-09-09T14:18:59.3214065Z dequantize_per_tensor_default_2 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default_2, 0.014523092657327652, -23, -128, 127, torch.int8); quantize_per_tensor_default_2 = None 2025-09-09T14:18:59.3215392Z return dequantize_per_tensor_default_2 2025-09-09T14:18:59.3215786Z 2025-09-09T14:18:59.3216188Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:18:59.3216717Z diff: tensor([[[[0., 0., 0.], 2025-09-09T14:18:59.3217060Z [0., 0., 0.], 2025-09-09T14:18:59.3217364Z [0., 0., 0.]], 2025-09-09T14:18:59.3217559Z 2025-09-09T14:18:59.3217664Z [[0., 0., 0.], 2025-09-09T14:18:59.3217966Z [0., 0., 0.], 2025-09-09T14:18:59.3218250Z [0., 0., 0.]], 2025-09-09T14:18:59.3218443Z 2025-09-09T14:18:59.3218565Z [[0., 0., 0.], 2025-09-09T14:18:59.3218857Z [0., 0., 0.], 2025-09-09T14:18:59.3219165Z [0., 0., 0.]]]]) 2025-09-09T14:18:59.3219685Z PASSED 2025-09-09T14:18:59.3220614Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQATModels::test_qat_mobilenet_v2 SKIPPED 2025-09-09T14:18:59.3222029Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQATModels::test_qat_resnet18 SKIPPED 2025-09-09T14:18:59.3223395Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizeMixQATAndPTQ::test_mixing_qat_ptq PASSED 2025-09-09T14:18:59.3224513Z test/quantization/pt2e/test_representation.py::TestPT2ERepresentation::test_add PASSED 2025-09-09T14:18:59.3225414Z test/quantization/pt2e/test_representation.py::TestPT2ERepresentation::test_add_relu PASSED 2025-09-09T14:18:59.3226327Z test/quantization/pt2e/test_representation.py::TestPT2ERepresentation::test_conv2d PASSED 2025-09-09T14:18:59.3227282Z test/quantization/pt2e/test_representation.py::TestPT2ERepresentation::test_dynamic_linear PASSED 2025-09-09T14:18:59.3228363Z test/quantization/pt2e/test_representation.py::TestPT2ERepresentation::test_maxpool2d PASSED 2025-09-09T14:18:59.3229278Z test/quantization/pt2e/test_representation.py::TestPT2ERepresentation::test_qdq PASSED 2025-09-09T14:18:59.3230216Z test/quantization/pt2e/test_representation.py::TestPT2ERepresentation::test_qdq_per_channel PASSED 2025-09-09T14:18:59.3231216Z test/quantization/pt2e/test_representation.py::TestPT2ERepresentation::test_static_linear PASSED 2025-09-09T14:18:59.3232567Z 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 frames [('total', 1), ('ok', 1)] 2025-09-09T14:18:59.3233663Z stats [('calls_captured', 4), ('unique_graphs', 1)] 2025-09-09T14:18:59.3234099Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:18:59.3235243Z 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)] 2025-09-09T14:18:59.3236358Z graph_break [] 2025-09-09T14:18:59.3236616Z PASSED 2025-09-09T14:18:59.3237589Z 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 frames [('total', 1), ('ok', 1)] 2025-09-09T14:18:59.3238673Z stats [('calls_captured', 5), ('unique_graphs', 1)] 2025-09-09T14:18:59.3239087Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:18:59.3240044Z 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)] 2025-09-09T14:18:59.3240905Z graph_break [] 2025-09-09T14:18:59.3241139Z PASSED 2025-09-09T14:18:59.3242115Z 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 frames [('total', 1), ('ok', 1)] 2025-09-09T14:18:59.3243188Z stats [('calls_captured', 5), ('unique_graphs', 1)] 2025-09-09T14:18:59.3243623Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:18:59.3245122Z 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)] 2025-09-09T14:18:59.3246879Z graph_break [] 2025-09-09T14:18:59.3247206Z PASSED 2025-09-09T14:18:59.3248517Z 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 frames [('total', 1), ('ok', 1)] 2025-09-09T14:18:59.3249982Z stats [('calls_captured', 5), ('unique_graphs', 1)] 2025-09-09T14:18:59.3250544Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:18:59.3251955Z 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)] 2025-09-09T14:18:59.3253122Z graph_break [] 2025-09-09T14:18:59.3253437Z PASSED 2025-09-09T14:18:59.3254770Z 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 frames [('total', 1), ('ok', 1)] 2025-09-09T14:18:59.3256327Z stats [('calls_captured', 4), ('unique_graphs', 1)] 2025-09-09T14:18:59.3256877Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:18:59.3258164Z 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)] 2025-09-09T14:18:59.3259311Z graph_break [] 2025-09-09T14:18:59.3259643Z PASSED 2025-09-09T14:18:59.3260970Z 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 frames [('total', 1), ('ok', 1)] 2025-09-09T14:18:59.3262436Z stats [('calls_captured', 5), ('unique_graphs', 1)] 2025-09-09T14:18:59.3262991Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:18:59.3264264Z 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)] 2025-09-09T14:18:59.3265413Z graph_break [] 2025-09-09T14:18:59.3265731Z PASSED 2025-09-09T14:18:59.3267050Z 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 frames [('total', 1), ('ok', 1)] 2025-09-09T14:18:59.3268533Z stats [('calls_captured', 4), ('unique_graphs', 1)] 2025-09-09T14:18:59.3269080Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:18:59.3270365Z 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)] 2025-09-09T14:18:59.3271523Z graph_break [] 2025-09-09T14:18:59.3271828Z PASSED 2025-09-09T14:19:08.1903493Z 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 frames [('total', 1), ('ok', 1)] 2025-09-09T14:19:08.1905077Z stats [('calls_captured', 5), ('unique_graphs', 1)] 2025-09-09T14:19:08.1905690Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:19:08.1906990Z 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)] 2025-09-09T14:19:08.1908148Z graph_break [] 2025-09-09T14:19:08.1908652Z PASSED 2025-09-09T14:19:08.1909976Z 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 frames [('total', 1), ('ok', 1)] 2025-09-09T14:19:08.1911474Z stats [('calls_captured', 5), ('unique_graphs', 1)] 2025-09-09T14:19:08.1912024Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:19:08.1913895Z 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)] 2025-09-09T14:19:08.1915338Z graph_break [] 2025-09-09T14:19:08.1915661Z PASSED 2025-09-09T14:19:08.1917042Z 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 frames [('total', 1), ('ok', 1)] 2025-09-09T14:19:08.1918514Z stats [('calls_captured', 6), ('unique_graphs', 1)] 2025-09-09T14:19:08.1919196Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:19:08.1920489Z 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)] 2025-09-09T14:19:08.1921639Z graph_break [] 2025-09-09T14:19:08.1921971Z PASSED 2025-09-09T14:19:08.1923285Z 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 frames [('total', 1), ('ok', 1)] 2025-09-09T14:19:08.1924935Z stats [('calls_captured', 6), ('unique_graphs', 1)] 2025-09-09T14:19:08.1925509Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:19:08.1927394Z 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)] 2025-09-09T14:19:08.1929174Z graph_break [] 2025-09-09T14:19:08.1929507Z PASSED 2025-09-09T14:19:08.1930815Z 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 frames [('total', 1), ('ok', 1)] 2025-09-09T14:19:08.1932276Z stats [('calls_captured', 6), ('unique_graphs', 1)] 2025-09-09T14:19:08.1932824Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:19:08.1933767Z 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)] 2025-09-09T14:19:08.1934620Z graph_break [] 2025-09-09T14:19:08.1934856Z PASSED 2025-09-09T14:19:08.1935835Z 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 frames [('total', 1), ('ok', 1)] 2025-09-09T14:19:08.1936913Z stats [('calls_captured', 5), ('unique_graphs', 1)] 2025-09-09T14:19:08.1937337Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:19:08.1938288Z 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)] 2025-09-09T14:19:08.1939129Z graph_break [] 2025-09-09T14:19:08.1939381Z PASSED 2025-09-09T14:19:08.1940340Z 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 frames [('total', 1), ('ok', 1)] 2025-09-09T14:19:08.1941429Z stats [('calls_captured', 6), ('unique_graphs', 1)] 2025-09-09T14:19:08.1941855Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:19:08.1942797Z 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)] 2025-09-09T14:19:08.1943790Z graph_break [] 2025-09-09T14:19:08.1944031Z PASSED 2025-09-09T14:19:08.1945003Z 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 frames [('total', 1), ('ok', 1)] 2025-09-09T14:19:08.1946091Z stats [('calls_captured', 5), ('unique_graphs', 1)] 2025-09-09T14:19:08.1946506Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:19:08.1948190Z 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)] 2025-09-09T14:19:08.1949027Z graph_break [] 2025-09-09T14:19:08.1949282Z PASSED 2025-09-09T14:19:08.1950245Z 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 frames [('total', 1), ('ok', 1)] 2025-09-09T14:19:08.1951330Z stats [('calls_captured', 6), ('unique_graphs', 1)] 2025-09-09T14:19:08.1951759Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:19:08.1952693Z 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)] 2025-09-09T14:19:08.1953551Z graph_break [] 2025-09-09T14:19:08.1953787Z PASSED 2025-09-09T14:19:08.1954983Z 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 frames [('total', 1), ('ok', 1)] 2025-09-09T14:19:08.1956540Z stats [('calls_captured', 4), ('unique_graphs', 1)] 2025-09-09T14:19:08.1957099Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:19:08.1958668Z 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)] 2025-09-09T14:19:08.1960106Z graph_break [] 2025-09-09T14:19:08.1960422Z PASSED 2025-09-09T14:19:08.1961736Z 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 frames [('total', 1), ('ok', 1)] 2025-09-09T14:19:08.1963194Z stats [('calls_captured', 6), ('unique_graphs', 1)] 2025-09-09T14:19:08.1963763Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:19:08.1965045Z 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)] 2025-09-09T14:19:08.1966205Z graph_break [] 2025-09-09T14:19:08.1966526Z PASSED 2025-09-09T14:19:08.1967828Z 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 frames [('total', 1), ('ok', 1)] 2025-09-09T14:19:08.1969300Z stats [('calls_captured', 4), ('unique_graphs', 1)] 2025-09-09T14:19:08.1969853Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:19:08.1971409Z 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)] 2025-09-09T14:19:08.1972828Z graph_break [] 2025-09-09T14:19:08.1973135Z PASSED 2025-09-09T14:19:08.1974544Z 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 frames [('total', 1), ('ok', 1)] 2025-09-09T14:19:08.1975993Z stats [('calls_captured', 6), ('unique_graphs', 1)] 2025-09-09T14:19:08.1976559Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:19:08.1977845Z 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)] 2025-09-09T14:19:08.1979068Z graph_break [] 2025-09-09T14:19:08.1979384Z PASSED 2025-09-09T14:19:18.3091935Z 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 frames [('total', 1), ('ok', 1)] 2025-09-09T14:19:18.3093502Z stats [('calls_captured', 4), ('unique_graphs', 1)] 2025-09-09T14:19:18.3094077Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:19:18.3095363Z 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)] 2025-09-09T14:19:18.3096526Z graph_break [] 2025-09-09T14:19:18.3097018Z PASSED 2025-09-09T14:19:18.3098349Z 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 frames [('total', 1), ('ok', 1)] 2025-09-09T14:19:18.3099824Z stats [('calls_captured', 6), ('unique_graphs', 1)] 2025-09-09T14:19:18.3100379Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:19:18.3101677Z 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)] 2025-09-09T14:19:18.3102827Z graph_break [] 2025-09-09T14:19:18.3103155Z PASSED 2025-09-09T14:19:18.3104483Z 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 frames [('total', 1), ('ok', 1)] 2025-09-09T14:19:18.3105939Z stats [('calls_captured', 4), ('unique_graphs', 1)] 2025-09-09T14:19:18.3106511Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:19:18.3107789Z 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)] 2025-09-09T14:19:18.3108955Z graph_break [] 2025-09-09T14:19:18.3109289Z PASSED 2025-09-09T14:19:18.3110605Z 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 frames [('total', 1), ('ok', 1)] 2025-09-09T14:19:18.3112076Z stats [('calls_captured', 6), ('unique_graphs', 1)] 2025-09-09T14:19:18.3112623Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:19:18.3113916Z 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)] 2025-09-09T14:19:18.3115069Z graph_break [] 2025-09-09T14:19:18.3115373Z PASSED 2025-09-09T14:19:18.3117005Z 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 frames [('total', 1), ('ok', 1)] 2025-09-09T14:19:18.3118467Z stats [('calls_captured', 5), ('unique_graphs', 1)] 2025-09-09T14:19:18.3119025Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:19:18.3120588Z 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)] 2025-09-09T14:19:18.3121778Z graph_break [] 2025-09-09T14:19:18.3122037Z PASSED 2025-09-09T14:19:18.3122995Z 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 frames [('total', 1), ('ok', 1)] 2025-09-09T14:19:18.3124070Z stats [('calls_captured', 7), ('unique_graphs', 1)] 2025-09-09T14:19:18.3124672Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:19:18.3125612Z 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)] 2025-09-09T14:19:18.3126466Z graph_break [] 2025-09-09T14:19:18.3126704Z PASSED 2025-09-09T14:19:18.3127673Z 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 frames [('total', 1), ('ok', 1)] 2025-09-09T14:19:18.3128751Z stats [('calls_captured', 5), ('unique_graphs', 1)] 2025-09-09T14:19:18.3129168Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:19:18.3130346Z 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)] 2025-09-09T14:19:18.3131397Z graph_break [] 2025-09-09T14:19:18.3131631Z PASSED 2025-09-09T14:19:18.3132593Z 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 frames [('total', 1), ('ok', 1)] 2025-09-09T14:19:18.3133657Z stats [('calls_captured', 7), ('unique_graphs', 1)] 2025-09-09T14:19:18.3134093Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:19:18.3135047Z 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)] 2025-09-09T14:19:18.3135886Z graph_break [] 2025-09-09T14:19:18.3136131Z PASSED 2025-09-09T14:19:18.3137097Z 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 frames [('total', 1), ('ok', 1)] 2025-09-09T14:19:18.3138179Z stats [('calls_captured', 5), ('unique_graphs', 1)] 2025-09-09T14:19:18.3138609Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:19:18.3139545Z 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)] 2025-09-09T14:19:18.3140401Z graph_break [] 2025-09-09T14:19:18.3140635Z PASSED 2025-09-09T14:19:18.3141932Z 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 frames [('total', 1), ('ok', 1)] 2025-09-09T14:19:18.3143544Z stats [('calls_captured', 7), ('unique_graphs', 1)] 2025-09-09T14:19:18.3144101Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:19:18.3145390Z 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)] 2025-09-09T14:19:18.3146535Z graph_break [] 2025-09-09T14:19:18.3146866Z PASSED 2025-09-09T14:19:18.3148176Z 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 frames [('total', 1), ('ok', 1)] 2025-09-09T14:19:18.3149737Z stats [('calls_captured', 5), ('unique_graphs', 1)] 2025-09-09T14:19:18.3150304Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:19:18.3151578Z 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)] 2025-09-09T14:19:18.3152748Z graph_break [] 2025-09-09T14:19:18.3153061Z PASSED 2025-09-09T14:19:18.3154373Z 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 frames [('total', 1), ('ok', 1)] 2025-09-09T14:19:18.3155842Z stats [('calls_captured', 7), ('unique_graphs', 1)] 2025-09-09T14:19:18.3156445Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:19:18.3157728Z 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)] 2025-09-09T14:19:18.3158867Z graph_break [] 2025-09-09T14:19:18.3159191Z PASSED 2025-09-09T14:19:18.3160518Z 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)] 2025-09-09T14:19:18.3161976Z stats [('calls_captured', 4), ('unique_graphs', 1)] 2025-09-09T14:19:18.3162533Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:19:18.3164072Z 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)] 2025-09-09T14:19:18.3165505Z graph_break [] 2025-09-09T14:19:18.3165827Z PASSED 2025-09-09T14:19:27.3419462Z 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)] 2025-09-09T14:19:27.3421080Z stats [('calls_captured', 5), ('unique_graphs', 1)] 2025-09-09T14:19:27.3421641Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:19:27.3422964Z 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)] 2025-09-09T14:19:27.3424142Z graph_break [] 2025-09-09T14:19:27.3424780Z PASSED 2025-09-09T14:19:27.3426114Z 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)] 2025-09-09T14:19:27.3427587Z stats [('calls_captured', 5), ('unique_graphs', 1)] 2025-09-09T14:19:27.3428148Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:19:27.3430310Z 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)] 2025-09-09T14:19:27.3432071Z graph_break [] 2025-09-09T14:19:27.3432406Z PASSED 2025-09-09T14:19:27.3433725Z 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)] 2025-09-09T14:19:27.3435303Z stats [('calls_captured', 5), ('unique_graphs', 1)] 2025-09-09T14:19:27.3435873Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:19:27.3437207Z 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)] 2025-09-09T14:19:27.3438374Z graph_break [] 2025-09-09T14:19:27.3438701Z PASSED 2025-09-09T14:19:27.3440019Z 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)] 2025-09-09T14:19:27.3441506Z stats [('calls_captured', 4), ('unique_graphs', 1)] 2025-09-09T14:19:27.3442055Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:19:27.3443347Z 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)] 2025-09-09T14:19:27.3444506Z graph_break [] 2025-09-09T14:19:27.3444817Z PASSED 2025-09-09T14:19:27.3446164Z 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)] 2025-09-09T14:19:27.3447637Z stats [('calls_captured', 5), ('unique_graphs', 1)] 2025-09-09T14:19:27.3448188Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:19:27.3449451Z 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)] 2025-09-09T14:19:27.3450303Z graph_break [] 2025-09-09T14:19:27.3450556Z PASSED 2025-09-09T14:19:27.3451515Z 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)] 2025-09-09T14:19:27.3452608Z stats [('calls_captured', 4), ('unique_graphs', 1)] 2025-09-09T14:19:27.3453041Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:19:27.3453979Z 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)] 2025-09-09T14:19:27.3454837Z graph_break [] 2025-09-09T14:19:27.3455074Z PASSED 2025-09-09T14:19:27.3456046Z 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)] 2025-09-09T14:19:27.3457128Z stats [('calls_captured', 5), ('unique_graphs', 1)] 2025-09-09T14:19:27.3457540Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:19:27.3458573Z 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)] 2025-09-09T14:19:27.3459417Z graph_break [] 2025-09-09T14:19:27.3459667Z PASSED 2025-09-09T14:19:27.3460639Z 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)] 2025-09-09T14:19:27.3461775Z stats [('calls_captured', 5), ('unique_graphs', 1)] 2025-09-09T14:19:27.3462202Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:19:27.3463341Z 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)] 2025-09-09T14:19:27.3464401Z graph_break [] 2025-09-09T14:19:27.3464651Z PASSED 2025-09-09T14:19:27.3465608Z 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)] 2025-09-09T14:19:27.3466684Z stats [('calls_captured', 6), ('unique_graphs', 1)] 2025-09-09T14:19:27.3467096Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:19:27.3468048Z 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)] 2025-09-09T14:19:27.3469117Z graph_break [] 2025-09-09T14:19:27.3469424Z PASSED 2025-09-09T14:19:27.3470736Z 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)] 2025-09-09T14:19:27.3472183Z stats [('calls_captured', 6), ('unique_graphs', 1)] 2025-09-09T14:19:27.3472744Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:19:27.3474637Z 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)] 2025-09-09T14:19:27.3476475Z graph_break [] 2025-09-09T14:19:27.3476798Z PASSED 2025-09-09T14:19:27.3478087Z 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)] 2025-09-09T14:19:27.3479547Z stats [('calls_captured', 6), ('unique_graphs', 1)] 2025-09-09T14:19:27.3480107Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:19:27.3481373Z 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)] 2025-09-09T14:19:27.3482535Z graph_break [] 2025-09-09T14:19:27.3482838Z PASSED 2025-09-09T14:19:27.3484155Z 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)] 2025-09-09T14:19:27.3485635Z stats [('calls_captured', 5), ('unique_graphs', 1)] 2025-09-09T14:19:27.3486187Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:19:27.3487566Z 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)] 2025-09-09T14:19:27.3488717Z graph_break [] 2025-09-09T14:19:27.3489047Z PASSED 2025-09-09T14:19:27.3490359Z 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)] 2025-09-09T14:19:27.3491874Z stats [('calls_captured', 6), ('unique_graphs', 1)] 2025-09-09T14:19:27.3492433Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:19:27.3493705Z 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)] 2025-09-09T14:19:27.3494863Z graph_break [] 2025-09-09T14:19:27.3495189Z PASSED 2025-09-09T14:19:37.9784121Z 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)] 2025-09-09T14:19:37.9785665Z stats [('calls_captured', 5), ('unique_graphs', 1)] 2025-09-09T14:19:37.9786239Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:19:37.9787517Z 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)] 2025-09-09T14:19:37.9788695Z graph_break [] 2025-09-09T14:19:37.9789247Z PASSED 2025-09-09T14:19:37.9790547Z 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)] 2025-09-09T14:19:37.9792014Z stats [('calls_captured', 6), ('unique_graphs', 1)] 2025-09-09T14:19:37.9792586Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:19:37.9793861Z 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)] 2025-09-09T14:19:37.9795013Z graph_break [] 2025-09-09T14:19:37.9795327Z PASSED 2025-09-09T14:19:37.9796698Z 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)] 2025-09-09T14:19:37.9798161Z stats [('calls_captured', 4), ('unique_graphs', 1)] 2025-09-09T14:19:37.9798711Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:19:37.9800280Z 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)] 2025-09-09T14:19:37.9801703Z graph_break [] 2025-09-09T14:19:37.9802027Z PASSED 2025-09-09T14:19:37.9803340Z 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)] 2025-09-09T14:19:37.9804786Z stats [('calls_captured', 6), ('unique_graphs', 1)] 2025-09-09T14:19:37.9805348Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:19:37.9806624Z 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)] 2025-09-09T14:19:37.9807778Z graph_break [] 2025-09-09T14:19:37.9808376Z PASSED 2025-09-09T14:19:37.9809683Z 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)] 2025-09-09T14:19:37.9811142Z stats [('calls_captured', 4), ('unique_graphs', 1)] 2025-09-09T14:19:37.9811686Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:19:37.9813019Z 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)] 2025-09-09T14:19:37.9814069Z graph_break [] 2025-09-09T14:19:37.9814315Z PASSED 2025-09-09T14:19:37.9815284Z 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)] 2025-09-09T14:19:37.9825249Z stats [('calls_captured', 6), ('unique_graphs', 1)] 2025-09-09T14:19:37.9825695Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:19:37.9826645Z 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)] 2025-09-09T14:19:37.9827527Z graph_break [] 2025-09-09T14:19:37.9827852Z PASSED 2025-09-09T14:19:37.9828852Z 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)] 2025-09-09T14:19:37.9829952Z stats [('calls_captured', 4), ('unique_graphs', 1)] 2025-09-09T14:19:37.9830384Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:19:37.9831345Z 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)] 2025-09-09T14:19:37.9832200Z graph_break [] 2025-09-09T14:19:37.9832441Z PASSED 2025-09-09T14:19:37.9833418Z 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)] 2025-09-09T14:19:37.9834495Z stats [('calls_captured', 6), ('unique_graphs', 1)] 2025-09-09T14:19:37.9834930Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:19:37.9836008Z 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)] 2025-09-09T14:19:37.9837314Z graph_break [] 2025-09-09T14:19:37.9837647Z PASSED 2025-09-09T14:19:37.9838953Z 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)] 2025-09-09T14:19:37.9840419Z stats [('calls_captured', 4), ('unique_graphs', 1)] 2025-09-09T14:19:37.9840973Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:19:37.9842264Z 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)] 2025-09-09T14:19:37.9843420Z graph_break [] 2025-09-09T14:19:37.9843727Z PASSED 2025-09-09T14:19:37.9845247Z 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)] 2025-09-09T14:19:37.9846691Z stats [('calls_captured', 6), ('unique_graphs', 1)] 2025-09-09T14:19:37.9847254Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:19:37.9848546Z 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)] 2025-09-09T14:19:37.9849817Z graph_break [] 2025-09-09T14:19:37.9850142Z PASSED 2025-09-09T14:19:37.9851438Z 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)] 2025-09-09T14:19:37.9852910Z stats [('calls_captured', 5), ('unique_graphs', 1)] 2025-09-09T14:19:37.9853483Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:19:37.9855031Z 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)] 2025-09-09T14:19:37.9856473Z graph_break [] 2025-09-09T14:19:37.9856788Z PASSED 2025-09-09T14:19:37.9858108Z 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)] 2025-09-09T14:19:37.9859569Z stats [('calls_captured', 7), ('unique_graphs', 1)] 2025-09-09T14:19:37.9860119Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:19:37.9861405Z 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)] 2025-09-09T14:19:37.9862546Z graph_break [] 2025-09-09T14:19:37.9862868Z PASSED 2025-09-09T14:19:37.9864174Z 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)] 2025-09-09T14:19:37.9865615Z stats [('calls_captured', 5), ('unique_graphs', 1)] 2025-09-09T14:19:37.9866181Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:19:37.9867730Z 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)] 2025-09-09T14:19:37.9869164Z graph_break [] 2025-09-09T14:19:37.9869492Z PASSED 2025-09-09T14:19:50.0777695Z 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)] 2025-09-09T14:19:50.0779565Z stats [('calls_captured', 7), ('unique_graphs', 1)] 2025-09-09T14:19:50.0780053Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:19:50.0781041Z 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)] 2025-09-09T14:19:50.0781890Z graph_break [] 2025-09-09T14:19:50.0782310Z PASSED 2025-09-09T14:19:50.0783552Z 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)] 2025-09-09T14:19:50.0784651Z stats [('calls_captured', 5), ('unique_graphs', 1)] 2025-09-09T14:19:50.0785086Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:19:50.0786028Z 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)] 2025-09-09T14:19:50.0786984Z graph_break [] 2025-09-09T14:19:50.0787234Z PASSED 2025-09-09T14:19:50.0788204Z 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)] 2025-09-09T14:19:50.0789287Z stats [('calls_captured', 7), ('unique_graphs', 1)] 2025-09-09T14:19:50.0789706Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:19:50.0790665Z 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)] 2025-09-09T14:19:50.0791509Z graph_break [] 2025-09-09T14:19:50.0791763Z PASSED 2025-09-09T14:19:50.0792732Z 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)] 2025-09-09T14:19:50.0793803Z stats [('calls_captured', 5), ('unique_graphs', 1)] 2025-09-09T14:19:50.0794240Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:19:50.0795183Z 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)] 2025-09-09T14:19:50.0796031Z graph_break [] 2025-09-09T14:19:50.0796417Z PASSED 2025-09-09T14:19:50.0797390Z 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)] 2025-09-09T14:19:50.0798471Z stats [('calls_captured', 7), ('unique_graphs', 1)] 2025-09-09T14:19:50.0798886Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:19:50.0799842Z 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)] 2025-09-09T14:19:50.0800680Z graph_break [] 2025-09-09T14:19:50.0800933Z PASSED 2025-09-09T14:19:50.0801917Z 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 frames [('total', 1), ('ok', 1)] 2025-09-09T14:19:50.0802990Z stats [('calls_captured', 5), ('unique_graphs', 1)] 2025-09-09T14:19:50.0803416Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:19:50.0804551Z 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)] 2025-09-09T14:19:50.0805608Z graph_break [] 2025-09-09T14:19:50.0805854Z PASSED 2025-09-09T14:19:50.0806813Z 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 frames [('total', 1), ('ok', 1)] 2025-09-09T14:19:50.0807899Z stats [('calls_captured', 6), ('unique_graphs', 1)] 2025-09-09T14:19:50.0808398Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:19:50.0809353Z 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)] 2025-09-09T14:19:50.0810211Z graph_break [] 2025-09-09T14:19:50.0810448Z PASSED 2025-09-09T14:19:50.0811423Z 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 frames [('total', 1), ('ok', 1)] 2025-09-09T14:19:50.0812564Z stats [('calls_captured', 5), ('unique_graphs', 1)] 2025-09-09T14:19:50.0812992Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:19:50.0814142Z 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)] 2025-09-09T14:19:50.0815184Z graph_break [] 2025-09-09T14:19:50.0815431Z PASSED 2025-09-09T14:19:50.0816391Z 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 frames [('total', 1), ('ok', 1)] 2025-09-09T14:19:50.0817468Z stats [('calls_captured', 6), ('unique_graphs', 1)] 2025-09-09T14:19:50.0817896Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:19:50.0818831Z 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)] 2025-09-09T14:19:50.0819687Z graph_break [] 2025-09-09T14:19:50.0819924Z PASSED 2025-09-09T14:19:50.0820908Z 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 frames [('total', 1), ('ok', 1)] 2025-09-09T14:19:50.0821995Z stats [('calls_captured', 5), ('unique_graphs', 1)] 2025-09-09T14:19:50.0822408Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:19:50.0823359Z 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)] 2025-09-09T14:19:50.0824203Z graph_break [] 2025-09-09T14:19:50.0824741Z PASSED 2025-09-09T14:19:50.0825735Z 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 frames [('total', 1), ('ok', 1)] 2025-09-09T14:19:50.0826824Z stats [('calls_captured', 6), ('unique_graphs', 1)] 2025-09-09T14:19:50.0827257Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:19:50.0828191Z 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)] 2025-09-09T14:19:50.0829052Z graph_break [] 2025-09-09T14:19:50.0829302Z PASSED 2025-09-09T14:19:50.0830261Z 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 frames [('total', 1), ('ok', 1)] 2025-09-09T14:19:50.0831349Z stats [('calls_captured', 5), ('unique_graphs', 1)] 2025-09-09T14:19:50.0831764Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:19:50.0832849Z 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)] 2025-09-09T14:19:50.0833708Z graph_break [] 2025-09-09T14:19:50.0833947Z PASSED 2025-09-09T14:19:50.0834923Z 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 frames [('total', 1), ('ok', 1)] 2025-09-09T14:19:50.0836078Z stats [('calls_captured', 6), ('unique_graphs', 1)] 2025-09-09T14:19:50.0836594Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:19:50.0838131Z 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)] 2025-09-09T14:19:50.0838976Z graph_break [] 2025-09-09T14:19:50.0839246Z PASSED 2025-09-09T14:19:50.0840217Z 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 frames [('total', 1), ('ok', 1)] 2025-09-09T14:20:02.4864990Z stats [('calls_captured', 6), ('unique_graphs', 1)] 2025-09-09T14:20:02.4865607Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:20:02.4867226Z 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)] 2025-09-09T14:20:02.4868712Z graph_break [] 2025-09-09T14:20:02.4869237Z PASSED 2025-09-09T14:20:02.4870600Z 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 frames [('total', 1), ('ok', 1)] 2025-09-09T14:20:02.4872070Z stats [('calls_captured', 7), ('unique_graphs', 1)] 2025-09-09T14:20:02.4872621Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:20:02.4873916Z 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)] 2025-09-09T14:20:02.4875082Z graph_break [] 2025-09-09T14:20:02.4875394Z PASSED 2025-09-09T14:20:02.4876764Z 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 frames [('total', 1), ('ok', 1)] 2025-09-09T14:20:02.4878226Z stats [('calls_captured', 6), ('unique_graphs', 1)] 2025-09-09T14:20:02.4878792Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:20:02.4880358Z 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)] 2025-09-09T14:20:02.4881779Z graph_break [] 2025-09-09T14:20:02.4882107Z PASSED 2025-09-09T14:20:02.4883396Z 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 frames [('total', 1), ('ok', 1)] 2025-09-09T14:20:02.4884858Z stats [('calls_captured', 7), ('unique_graphs', 1)] 2025-09-09T14:20:02.4885412Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:20:02.4886954Z 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)] 2025-09-09T14:20:02.4888105Z graph_break [] 2025-09-09T14:20:02.4888415Z PASSED 2025-09-09T14:20:02.4889737Z 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 frames [('total', 1), ('ok', 1)] 2025-09-09T14:20:02.4891202Z stats [('calls_captured', 6), ('unique_graphs', 1)] 2025-09-09T14:20:02.4891870Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:20:02.4893150Z 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)] 2025-09-09T14:20:02.4894209Z graph_break [] 2025-09-09T14:20:02.4894467Z PASSED 2025-09-09T14:20:02.4895443Z 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 frames [('total', 1), ('ok', 1)] 2025-09-09T14:20:02.4896513Z stats [('calls_captured', 7), ('unique_graphs', 1)] 2025-09-09T14:20:02.4896939Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:20:02.4897879Z 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)] 2025-09-09T14:20:02.4898730Z graph_break [] 2025-09-09T14:20:02.4898974Z PASSED 2025-09-09T14:20:02.4899936Z 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 frames [('total', 1), ('ok', 1)] 2025-09-09T14:20:02.4901011Z stats [('calls_captured', 6), ('unique_graphs', 1)] 2025-09-09T14:20:02.4901426Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:20:02.4902375Z 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)] 2025-09-09T14:20:02.4903227Z graph_break [] 2025-09-09T14:20:02.4903462Z PASSED 2025-09-09T14:20:02.4904427Z 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 frames [('total', 1), ('ok', 1)] 2025-09-09T14:20:02.4905489Z stats [('calls_captured', 7), ('unique_graphs', 1)] 2025-09-09T14:20:02.4905915Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:20:02.4906874Z 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)] 2025-09-09T14:20:02.4907712Z graph_break [] 2025-09-09T14:20:02.4907961Z PASSED 2025-09-09T14:20:02.4908921Z 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 frames [('total', 1), ('ok', 1)] 2025-09-09T14:20:02.4910000Z stats [('calls_captured', 5), ('unique_graphs', 1)] 2025-09-09T14:20:02.4910415Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:20:02.4911581Z 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)] 2025-09-09T14:20:02.4912625Z graph_break [] 2025-09-09T14:20:02.4912878Z PASSED 2025-09-09T14:20:02.4913948Z 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 frames [('total', 1), ('ok', 1)] 2025-09-09T14:20:02.4915033Z stats [('calls_captured', 7), ('unique_graphs', 1)] 2025-09-09T14:20:02.4915466Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:20:02.4916476Z 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)] 2025-09-09T14:20:02.4917415Z graph_break [] 2025-09-09T14:20:02.4917660Z PASSED 2025-09-09T14:20:02.4918634Z 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 frames [('total', 1), ('ok', 1)] 2025-09-09T14:20:02.4919914Z stats [('calls_captured', 5), ('unique_graphs', 1)] 2025-09-09T14:20:02.4920470Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:20:02.4922032Z 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)] 2025-09-09T14:20:02.4923476Z graph_break [] 2025-09-09T14:20:02.4923789Z PASSED 2025-09-09T14:20:02.4925306Z 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 frames [('total', 1), ('ok', 1)] 2025-09-09T14:20:02.4926754Z stats [('calls_captured', 7), ('unique_graphs', 1)] 2025-09-09T14:20:02.4927322Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:20:02.4928612Z 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)] 2025-09-09T14:20:02.4929774Z graph_break [] 2025-09-09T14:20:02.4930108Z PASSED 2025-09-09T14:20:02.4931418Z 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 frames [('total', 1), ('ok', 1)] 2025-09-09T14:20:02.4932902Z stats [('calls_captured', 5), ('unique_graphs', 1)] 2025-09-09T14:20:02.4933452Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:20:02.4934737Z 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)] 2025-09-09T14:20:02.4935901Z graph_break [] 2025-09-09T14:20:02.4936210Z PASSED 2025-09-09T14:20:02.4937522Z 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 frames [('total', 1), ('ok', 1)] 2025-09-09T14:20:02.4938972Z stats [('calls_captured', 7), ('unique_graphs', 1)] 2025-09-09T14:20:02.4939535Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:20:15.1799022Z 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)] 2025-09-09T14:20:15.1800219Z graph_break [] 2025-09-09T14:20:15.1800734Z PASSED 2025-09-09T14:20:15.1802466Z 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 frames [('total', 1), ('ok', 1)] 2025-09-09T14:20:15.1803952Z stats [('calls_captured', 5), ('unique_graphs', 1)] 2025-09-09T14:20:15.1804520Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:20:15.1805812Z 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)] 2025-09-09T14:20:15.1807085Z graph_break [] 2025-09-09T14:20:15.1807426Z PASSED 2025-09-09T14:20:15.1808727Z 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 frames [('total', 1), ('ok', 1)] 2025-09-09T14:20:15.1810182Z stats [('calls_captured', 7), ('unique_graphs', 1)] 2025-09-09T14:20:15.1810749Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:20:15.1812024Z 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)] 2025-09-09T14:20:15.1813176Z graph_break [] 2025-09-09T14:20:15.1813483Z PASSED 2025-09-09T14:20:15.1814787Z 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 frames [('total', 1), ('ok', 1)] 2025-09-09T14:20:15.1816253Z stats [('calls_captured', 6), ('unique_graphs', 1)] 2025-09-09T14:20:15.1816802Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:20:15.1818360Z 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)] 2025-09-09T14:20:15.1819787Z graph_break [] 2025-09-09T14:20:15.1820109Z PASSED 2025-09-09T14:20:15.1821402Z 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 frames [('total', 1), ('ok', 1)] 2025-09-09T14:20:15.1822860Z stats [('calls_captured', 8), ('unique_graphs', 1)] 2025-09-09T14:20:15.1823427Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:20:15.1824881Z 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)] 2025-09-09T14:20:15.1826045Z graph_break [] 2025-09-09T14:20:15.1826357Z PASSED 2025-09-09T14:20:15.1827396Z 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 frames [('total', 1), ('ok', 1)] 2025-09-09T14:20:15.1828465Z stats [('calls_captured', 6), ('unique_graphs', 1)] 2025-09-09T14:20:15.1828879Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:20:15.1830028Z 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)] 2025-09-09T14:20:15.1831086Z graph_break [] 2025-09-09T14:20:15.1831323Z PASSED 2025-09-09T14:20:15.1832282Z 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 frames [('total', 1), ('ok', 1)] 2025-09-09T14:20:15.1833490Z stats [('calls_captured', 8), ('unique_graphs', 1)] 2025-09-09T14:20:15.1833925Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:20:15.1834863Z 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)] 2025-09-09T14:20:15.1835721Z graph_break [] 2025-09-09T14:20:15.1835976Z PASSED 2025-09-09T14:20:15.1837106Z 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 frames [('total', 1), ('ok', 1)] 2025-09-09T14:20:15.1838189Z stats [('calls_captured', 6), ('unique_graphs', 1)] 2025-09-09T14:20:15.1838604Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:20:15.1839562Z 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)] 2025-09-09T14:20:15.1840422Z graph_break [] 2025-09-09T14:20:15.1840665Z PASSED 2025-09-09T14:20:15.1841624Z 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 frames [('total', 1), ('ok', 1)] 2025-09-09T14:20:15.1842685Z stats [('calls_captured', 8), ('unique_graphs', 1)] 2025-09-09T14:20:15.1843107Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:20:15.1844050Z 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)] 2025-09-09T14:20:15.1844891Z graph_break [] 2025-09-09T14:20:15.1845135Z PASSED 2025-09-09T14:20:15.1846085Z 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 frames [('total', 1), ('ok', 1)] 2025-09-09T14:20:15.1847162Z stats [('calls_captured', 6), ('unique_graphs', 1)] 2025-09-09T14:20:15.1847587Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:20:15.1848523Z 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)] 2025-09-09T14:20:15.1849376Z graph_break [] 2025-09-09T14:20:15.1849608Z PASSED 2025-09-09T14:20:15.1850567Z 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 frames [('total', 1), ('ok', 1)] 2025-09-09T14:20:15.1851642Z stats [('calls_captured', 8), ('unique_graphs', 1)] 2025-09-09T14:20:15.1852061Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:20:15.1853377Z 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)] 2025-09-09T14:20:15.1854522Z graph_break [] 2025-09-09T14:20:15.1854850Z PASSED 2025-09-09T14:20:15.1856165Z 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)] 2025-09-09T14:20:15.1857616Z stats [('calls_captured', 5), ('unique_graphs', 1)] 2025-09-09T14:20:15.1858177Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:20:15.1859834Z 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)] 2025-09-09T14:20:15.1861269Z graph_break [] 2025-09-09T14:20:15.1861593Z PASSED 2025-09-09T14:20:15.1862889Z 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)] 2025-09-09T14:20:15.1865178Z stats [('calls_captured', 6), ('unique_graphs', 1)] 2025-09-09T14:20:15.1865730Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:20:15.1867021Z 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)] 2025-09-09T14:20:15.1868192Z graph_break [] 2025-09-09T14:20:15.1868509Z PASSED 2025-09-09T14:20:15.1869832Z 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)] 2025-09-09T14:20:15.1871277Z stats [('calls_captured', 5), ('unique_graphs', 1)] 2025-09-09T14:20:15.1871841Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:20:26.7102733Z 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)] 2025-09-09T14:20:26.7104247Z graph_break [] 2025-09-09T14:20:26.7104733Z PASSED 2025-09-09T14:20:26.7106102Z 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)] 2025-09-09T14:20:26.7107556Z stats [('calls_captured', 6), ('unique_graphs', 1)] 2025-09-09T14:20:26.7108124Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:20:26.7109416Z 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)] 2025-09-09T14:20:26.7110583Z graph_break [] 2025-09-09T14:20:26.7110912Z PASSED 2025-09-09T14:20:26.7112222Z 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)] 2025-09-09T14:20:26.7113691Z stats [('calls_captured', 5), ('unique_graphs', 1)] 2025-09-09T14:20:26.7114259Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:20:26.7115541Z 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)] 2025-09-09T14:20:26.7116755Z graph_break [] 2025-09-09T14:20:26.7117067Z PASSED 2025-09-09T14:20:26.7118394Z 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)] 2025-09-09T14:20:26.7119881Z stats [('calls_captured', 6), ('unique_graphs', 1)] 2025-09-09T14:20:26.7120431Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:20:26.7122000Z 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)] 2025-09-09T14:20:26.7123156Z graph_break [] 2025-09-09T14:20:26.7123485Z PASSED 2025-09-09T14:20:26.7124992Z 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)] 2025-09-09T14:20:26.7126446Z stats [('calls_captured', 5), ('unique_graphs', 1)] 2025-09-09T14:20:26.7127158Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:20:26.7128195Z 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)] 2025-09-09T14:20:26.7129054Z graph_break [] 2025-09-09T14:20:26.7129317Z PASSED 2025-09-09T14:20:26.7130275Z 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)] 2025-09-09T14:20:26.7131350Z stats [('calls_captured', 6), ('unique_graphs', 1)] 2025-09-09T14:20:26.7131764Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:20:26.7132713Z 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)] 2025-09-09T14:20:26.7133570Z graph_break [] 2025-09-09T14:20:26.7133806Z PASSED 2025-09-09T14:20:26.7134778Z 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)] 2025-09-09T14:20:26.7135841Z stats [('calls_captured', 6), ('unique_graphs', 1)] 2025-09-09T14:20:26.7136266Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:20:26.7137421Z 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)] 2025-09-09T14:20:26.7138457Z graph_break [] 2025-09-09T14:20:26.7138705Z PASSED 2025-09-09T14:20:26.7139656Z 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)] 2025-09-09T14:20:26.7140727Z stats [('calls_captured', 7), ('unique_graphs', 1)] 2025-09-09T14:20:26.7141153Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:20:26.7142096Z 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)] 2025-09-09T14:20:26.7142950Z graph_break [] 2025-09-09T14:20:26.7143184Z PASSED 2025-09-09T14:20:26.7144146Z 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)] 2025-09-09T14:20:26.7145220Z stats [('calls_captured', 6), ('unique_graphs', 1)] 2025-09-09T14:20:26.7145633Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:20:26.7146783Z 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)] 2025-09-09T14:20:26.7147956Z graph_break [] 2025-09-09T14:20:26.7148209Z PASSED 2025-09-09T14:20:26.7149166Z 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)] 2025-09-09T14:20:26.7150222Z stats [('calls_captured', 7), ('unique_graphs', 1)] 2025-09-09T14:20:26.7150650Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:20:26.7151661Z 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)] 2025-09-09T14:20:26.7152746Z graph_break [] 2025-09-09T14:20:26.7153073Z PASSED 2025-09-09T14:20:26.7154380Z 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)] 2025-09-09T14:20:26.7155847Z stats [('calls_captured', 6), ('unique_graphs', 1)] 2025-09-09T14:20:26.7156508Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:20:26.7157790Z 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)] 2025-09-09T14:20:26.7158957Z graph_break [] 2025-09-09T14:20:26.7159268Z PASSED 2025-09-09T14:20:26.7160578Z 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)] 2025-09-09T14:20:26.7162022Z stats [('calls_captured', 7), ('unique_graphs', 1)] 2025-09-09T14:20:26.7162591Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:20:26.7163875Z 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)] 2025-09-09T14:20:26.7165018Z graph_break [] 2025-09-09T14:20:26.7165333Z PASSED 2025-09-09T14:20:26.7166620Z 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)] 2025-09-09T14:20:26.7168080Z stats [('calls_captured', 6), ('unique_graphs', 1)] 2025-09-09T14:20:26.7168655Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:20:26.7169927Z 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)] 2025-09-09T14:20:26.7171088Z graph_break [] 2025-09-09T14:20:26.7171397Z PASSED 2025-09-09T14:20:26.7172701Z 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)] 2025-09-09T14:20:26.7174156Z stats [('calls_captured', 7), ('unique_graphs', 1)] 2025-09-09T14:20:26.7174704Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:20:39.0995306Z 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)] 2025-09-09T14:20:39.0996636Z graph_break [] 2025-09-09T14:20:39.0997172Z PASSED 2025-09-09T14:20:39.0998844Z 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)] 2025-09-09T14:20:39.1000456Z stats [('calls_captured', 5), ('unique_graphs', 1)] 2025-09-09T14:20:39.1001014Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:20:39.1002583Z 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)] 2025-09-09T14:20:39.1004138Z graph_break [] 2025-09-09T14:20:39.1004473Z PASSED 2025-09-09T14:20:39.1005779Z 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)] 2025-09-09T14:20:39.1007221Z stats [('calls_captured', 7), ('unique_graphs', 1)] 2025-09-09T14:20:39.1007787Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:20:39.1009066Z 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)] 2025-09-09T14:20:39.1010219Z graph_break [] 2025-09-09T14:20:39.1010538Z PASSED 2025-09-09T14:20:39.1011829Z 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)] 2025-09-09T14:20:39.1013290Z stats [('calls_captured', 5), ('unique_graphs', 1)] 2025-09-09T14:20:39.1013842Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:20:39.1015406Z 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)] 2025-09-09T14:20:39.1016840Z graph_break [] 2025-09-09T14:20:39.1017146Z PASSED 2025-09-09T14:20:39.1018447Z 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)] 2025-09-09T14:20:39.1019883Z stats [('calls_captured', 7), ('unique_graphs', 1)] 2025-09-09T14:20:39.1020447Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:20:39.1021734Z 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)] 2025-09-09T14:20:39.1022650Z graph_break [] 2025-09-09T14:20:39.1022898Z PASSED 2025-09-09T14:20:39.1023860Z 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)] 2025-09-09T14:20:39.1025129Z stats [('calls_captured', 5), ('unique_graphs', 1)] 2025-09-09T14:20:39.1025557Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:20:39.1026502Z 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)] 2025-09-09T14:20:39.1027359Z graph_break [] 2025-09-09T14:20:39.1027602Z PASSED 2025-09-09T14:20:39.1028720Z 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)] 2025-09-09T14:20:39.1029803Z stats [('calls_captured', 7), ('unique_graphs', 1)] 2025-09-09T14:20:39.1030221Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:20:39.1031176Z 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)] 2025-09-09T14:20:39.1032022Z graph_break [] 2025-09-09T14:20:39.1032365Z PASSED 2025-09-09T14:20:39.1033336Z 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)] 2025-09-09T14:20:39.1034405Z stats [('calls_captured', 5), ('unique_graphs', 1)] 2025-09-09T14:20:39.1034837Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:20:39.1035779Z 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)] 2025-09-09T14:20:39.1036729Z graph_break [] 2025-09-09T14:20:39.1036986Z PASSED 2025-09-09T14:20:39.1037936Z 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)] 2025-09-09T14:20:39.1039016Z stats [('calls_captured', 7), ('unique_graphs', 1)] 2025-09-09T14:20:39.1039430Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:20:39.1040386Z 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)] 2025-09-09T14:20:39.1041240Z graph_break [] 2025-09-09T14:20:39.1041481Z PASSED 2025-09-09T14:20:39.1042446Z 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)] 2025-09-09T14:20:39.1043507Z stats [('calls_captured', 6), ('unique_graphs', 1)] 2025-09-09T14:20:39.1043933Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:20:39.1045084Z 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)] 2025-09-09T14:20:39.1046121Z graph_break [] 2025-09-09T14:20:39.1046367Z PASSED 2025-09-09T14:20:39.1047311Z 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)] 2025-09-09T14:20:39.1048378Z stats [('calls_captured', 8), ('unique_graphs', 1)] 2025-09-09T14:20:39.1048802Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:20:39.1049735Z 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)] 2025-09-09T14:20:39.1050803Z graph_break [] 2025-09-09T14:20:39.1051113Z PASSED 2025-09-09T14:20:39.1052401Z 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)] 2025-09-09T14:20:39.1053849Z stats [('calls_captured', 6), ('unique_graphs', 1)] 2025-09-09T14:20:39.1054500Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:20:39.1056057Z 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)] 2025-09-09T14:20:39.1057475Z graph_break [] 2025-09-09T14:20:39.1057842Z PASSED 2025-09-09T14:20:39.1059120Z 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)] 2025-09-09T14:20:39.1060635Z stats [('calls_captured', 8), ('unique_graphs', 1)] 2025-09-09T14:20:39.1061199Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:20:39.1062475Z 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)] 2025-09-09T14:20:39.1063635Z graph_break [] 2025-09-09T14:20:39.1063944Z PASSED 2025-09-09T14:20:39.1065253Z 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)] 2025-09-09T14:20:39.1066712Z stats [('calls_captured', 6), ('unique_graphs', 1)] 2025-09-09T14:20:39.1067267Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:22:00.3208483Z 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)] 2025-09-09T14:22:00.3209782Z graph_break [] 2025-09-09T14:22:00.3210306Z PASSED 2025-09-09T14:22:00.3211690Z 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)] 2025-09-09T14:22:00.3213216Z stats [('calls_captured', 8), ('unique_graphs', 1)] 2025-09-09T14:22:00.3213796Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:22:00.3215141Z 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)] 2025-09-09T14:22:00.3216350Z graph_break [] 2025-09-09T14:22:00.3216680Z PASSED 2025-09-09T14:22:00.3218019Z 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)] 2025-09-09T14:22:00.3219512Z stats [('calls_captured', 6), ('unique_graphs', 1)] 2025-09-09T14:22:00.3220112Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:22:00.3221444Z 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)] 2025-09-09T14:22:00.3222627Z graph_break [] 2025-09-09T14:22:00.3222956Z PASSED 2025-09-09T14:22:00.3224273Z 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)] 2025-09-09T14:22:00.3225918Z stats [('calls_captured', 8), ('unique_graphs', 1)] 2025-09-09T14:22:00.3226509Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:22:00.3228229Z 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)] 2025-09-09T14:22:00.3229431Z graph_break [] 2025-09-09T14:22:00.3229760Z PASSED 2025-09-09T14:22:00.3230701Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_dynamic_qlinear_cpu stats [('calls_captured', 22), ('unique_graphs', 8)] 2025-09-09T14:22:00.3231720Z inline_call [] 2025-09-09T14:22:00.3232032Z frames [('total', 1), ('ok', 1)] 2025-09-09T14:22:00.3232663Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:22:00.3234291Z 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)] 2025-09-09T14:22:00.3235651Z graph_break [] 2025-09-09T14:22:00.3235904Z PASSED 2025-09-09T14:22:00.3236728Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_dynamic_qlinear_input_dim_exceeds_2 stats [('calls_captured', 22), ('unique_graphs', 8)] 2025-09-09T14:22:00.3237530Z inline_call [] 2025-09-09T14:22:00.3237751Z frames [('total', 1), ('ok', 1)] 2025-09-09T14:22:00.3238119Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:22:00.3239274Z 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)] 2025-09-09T14:22:00.3240351Z graph_break [] 2025-09-09T14:22:00.3240595Z PASSED 2025-09-09T14:22:00.3241291Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_dynamic_qlinear_qat_cpu stats [('calls_captured', 22), ('unique_graphs', 8)] 2025-09-09T14:22:00.3242045Z inline_call [] 2025-09-09T14:22:00.3242263Z frames [('total', 1), ('ok', 1)] 2025-09-09T14:22:00.3242639Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:22:00.3243784Z 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)] 2025-09-09T14:22:00.3244843Z graph_break [] 2025-09-09T14:22:00.3245093Z PASSED 2025-09-09T14:22:00.3245760Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_linear_dynamic_fp16 stats [('calls_captured', 20), ('unique_graphs', 16)] 2025-09-09T14:22:00.3246502Z inline_call [] 2025-09-09T14:22:00.3246718Z frames [('total', 1), ('ok', 1)] 2025-09-09T14:22:00.3247081Z aot_autograd [('total', 2), ('autograd_cache_bypass', 2), ('ok', 2)] 2025-09-09T14:22:00.3248025Z 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)] 2025-09-09T14:22:00.3248876Z graph_break [] 2025-09-09T14:22:00.3249119Z PASSED 2025-09-09T14:22:00.3249817Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_linear_relu_dynamic_fp16 stats [('calls_captured', 24), ('unique_graphs', 16)] 2025-09-09T14:22:00.3250573Z inline_call [] 2025-09-09T14:22:00.3250790Z frames [('total', 1), ('ok', 1)] 2025-09-09T14:22:00.3251158Z aot_autograd [('total', 2), ('autograd_cache_bypass', 2), ('ok', 2)] 2025-09-09T14:22:00.3252105Z 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)] 2025-09-09T14:22:00.3252955Z graph_break [] 2025-09-09T14:22:00.3253200Z PASSED 2025-09-09T14:22:00.3253928Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qat_qconv2d stats [('calls_captured', 986), ('unique_graphs', 116)] 2025-09-09T14:22:00.3254651Z inline_call [] 2025-09-09T14:22:00.3254871Z frames [('total', 1), ('ok', 1)] 2025-09-09T14:22:00.3255239Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:22:00.3256542Z 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)] 2025-09-09T14:22:00.3257826Z graph_break [] 2025-09-09T14:22:00.3258075Z PASSED 2025-09-09T14:22:00.3258730Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qat_qconv2d_add stats [('calls_captured', 995), ('unique_graphs', 116)] 2025-09-09T14:22:00.3259459Z inline_call [] 2025-09-09T14:22:00.3259676Z frames [('total', 1), ('ok', 1)] 2025-09-09T14:22:00.3260045Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:22:00.3262068Z 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)] 2025-09-09T14:22:00.3264005Z graph_break [] 2025-09-09T14:22:00.3264249Z PASSED 2025-09-09T14:22:00.3264934Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qat_qconv2d_add_relu stats [('calls_captured', 997), ('unique_graphs', 116)] 2025-09-09T14:22:00.3265672Z inline_call [] 2025-09-09T14:22:00.3265910Z frames [('total', 1), ('ok', 1)] 2025-09-09T14:22:00.3266283Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:22:00.3268315Z 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)] 2025-09-09T14:22:00.3270207Z graph_break [] 2025-09-09T14:22:00.3270460Z PASSED 2025-09-09T14:22:00.3271160Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qat_qconv2d_hardswish stats [('calls_captured', 996), ('unique_graphs', 116)] 2025-09-09T14:22:00.3271910Z inline_call [] 2025-09-09T14:22:00.3272146Z frames [('total', 1), ('ok', 1)] 2025-09-09T14:22:00.3272499Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:22:00.3273833Z 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)] 2025-09-09T14:22:00.3275075Z graph_break [] 2025-09-09T14:22:00.3275311Z PASSED 2025-09-09T14:22:00.3276130Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qat_qconv2d_hardtanh stats [('calls_captured', 996), ('unique_graphs', 116)] 2025-09-09T14:22:00.3276943Z inline_call [] 2025-09-09T14:22:00.3277180Z frames [('total', 1), ('ok', 1)] 2025-09-09T14:22:00.3277535Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:23:52.7750130Z 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)] 2025-09-09T14:23:52.7752288Z graph_break [] 2025-09-09T14:23:52.7752691Z PASSED 2025-09-09T14:23:52.7753406Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qat_qconv2d_relu stats [('calls_captured', 996), ('unique_graphs', 116)] 2025-09-09T14:23:52.7754314Z inline_call [] 2025-09-09T14:23:52.7754553Z frames [('total', 1), ('ok', 1)] 2025-09-09T14:23:52.7754927Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:23:52.7756395Z 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)] 2025-09-09T14:23:52.7757635Z graph_break [] 2025-09-09T14:23:52.7757901Z PASSED 2025-09-09T14:23:52.7758571Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qat_qconv2d_relu6 stats [('calls_captured', 996), ('unique_graphs', 116)] 2025-09-09T14:23:52.7759309Z inline_call [] 2025-09-09T14:23:52.7759530Z frames [('total', 1), ('ok', 1)] 2025-09-09T14:23:52.7759897Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:23:52.7761648Z 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)] 2025-09-09T14:23:52.7763475Z graph_break [] 2025-09-09T14:23:52.7763960Z PASSED 2025-09-09T14:23:52.7765095Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qat_qconv2d_silu stats [('calls_captured', 996), ('unique_graphs', 116)] 2025-09-09T14:23:52.7766097Z inline_call [] 2025-09-09T14:23:52.7766442Z frames [('total', 1), ('ok', 1)] 2025-09-09T14:23:52.7766955Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:23:52.7768936Z 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)] 2025-09-09T14:23:52.7770942Z graph_break [] 2025-09-09T14:23:52.7771399Z PASSED 2025-09-09T14:23:52.7772502Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qcat stats [('calls_captured', 26), ('unique_graphs', 8)] 2025-09-09T14:23:52.7773613Z inline_call [] 2025-09-09T14:23:52.7773961Z frames [('total', 1), ('ok', 1)] 2025-09-09T14:23:52.7774465Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:23:52.7777121Z 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)] 2025-09-09T14:23:52.7779401Z graph_break [] 2025-09-09T14:23:52.7779869Z PASSED 2025-09-09T14:23:52.7781007Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qconv1d_relu_cpu stats [('calls_captured', 20), ('unique_graphs', 8)] 2025-09-09T14:23:52.7782057Z inline_call [] 2025-09-09T14:23:52.7782530Z frames [('total', 1), ('ok', 1)] 2025-09-09T14:23:52.7783108Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:23:52.7785418Z 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)] 2025-09-09T14:23:52.7787762Z graph_break [] 2025-09-09T14:23:52.7788277Z PASSED 2025-09-09T14:23:52.7789507Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qconv2d_add_2 stats [('calls_captured', 13), ('unique_graphs', 8)] 2025-09-09T14:23:52.7790927Z inline_call [] 2025-09-09T14:23:52.7791333Z frames [('total', 1), ('ok', 1)] 2025-09-09T14:23:52.7792030Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:23:52.7794303Z 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)] 2025-09-09T14:23:52.7796474Z graph_break [] 2025-09-09T14:23:52.7796935Z PASSED 2025-09-09T14:23:52.7798211Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qconv2d_add_3 stats [('calls_captured', 29), ('unique_graphs', 8)] 2025-09-09T14:23:52.7799651Z inline_call [] 2025-09-09T14:23:52.7800056Z frames [('total', 1), ('ok', 1)] 2025-09-09T14:23:52.7800754Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:23:52.7805295Z 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)] 2025-09-09T14:23:52.7809638Z graph_break [] 2025-09-09T14:23:52.7810082Z PASSED 2025-09-09T14:23:52.7811543Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qconv2d_add_broadcast_shapes_cpu stats [('calls_captured', 15), ('unique_graphs', 8)] 2025-09-09T14:23:52.7813145Z inline_call [] 2025-09-09T14:23:52.7813552Z frames [('total', 1), ('ok', 1)] 2025-09-09T14:23:52.7814247Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:23:52.7816533Z 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)] 2025-09-09T14:23:52.7818622Z graph_break [] 2025-09-09T14:23:52.7819078Z PASSED 2025-09-09T14:23:52.7820087Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qconv2d_add_cpu inline_call [] 2025-09-09T14:23:52.7821357Z stats [('calls_captured', 24), ('unique_graphs', 8)] 2025-09-09T14:23:52.7822017Z frames [('total', 1), ('ok', 1)] 2025-09-09T14:23:52.7822682Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:23:52.7825281Z 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)] 2025-09-09T14:23:52.7827289Z graph_break [] 2025-09-09T14:23:52.7827733Z PASSED 2025-09-09T14:23:52.7828788Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qconv2d_add_int8_mixed_bf16 inline_call [] 2025-09-09T14:23:52.7829783Z stats [('calls_captured', 24), ('unique_graphs', 8)] 2025-09-09T14:23:52.7830405Z frames [('total', 1), ('ok', 1)] 2025-09-09T14:23:52.7830969Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:23:52.7833752Z inductor [('pattern_matcher_nodes', 20), ('qconv_weight_prepack_matcher_nodes', 12), ('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)] 2025-09-09T14:23:52.7836313Z graph_break [] 2025-09-09T14:23:52.7836805Z PASSED 2025-09-09T14:23:52.7837795Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qconv2d_add_relu_cpu inline_call [] 2025-09-09T14:23:52.7839129Z stats [('calls_captured', 28), ('unique_graphs', 8)] 2025-09-09T14:23:52.7839793Z frames [('total', 1), ('ok', 1)] 2025-09-09T14:23:52.7840477Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:23:52.7843013Z 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)] 2025-09-09T14:23:52.7844312Z graph_break [] 2025-09-09T14:23:52.7844594Z PASSED 2025-09-09T14:23:52.7845175Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qconv2d_add_relu_int8_mixed_bf16 inline_call [] 2025-09-09T14:23:52.7845883Z stats [('calls_captured', 28), ('unique_graphs', 8)] 2025-09-09T14:23:52.7846221Z frames [('total', 1), ('ok', 1)] 2025-09-09T14:23:52.7846595Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:24:11.0457847Z inductor [('pattern_matcher_nodes', 22), ('qconv_weight_prepack_matcher_nodes', 12), ('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)] 2025-09-09T14:24:11.0459238Z graph_break [] 2025-09-09T14:24:11.0459674Z PASSED 2025-09-09T14:24:11.0460348Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qconv2d_cpu stats [('calls_captured', 21), ('unique_graphs', 8)] 2025-09-09T14:24:11.0461053Z inline_call [] 2025-09-09T14:24:11.0461272Z frames [('total', 1), ('ok', 1)] 2025-09-09T14:24:11.0461643Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:24:11.0462970Z 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)] 2025-09-09T14:24:11.0464196Z graph_break [] 2025-09-09T14:24:11.0464445Z PASSED 2025-09-09T14:24:11.0465150Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qconv2d_dequant_promotion_cpu stats [('calls_captured', 24), ('unique_graphs', 8)] 2025-09-09T14:24:11.0465934Z inline_call [] 2025-09-09T14:24:11.0466153Z frames [('total', 1), ('ok', 1)] 2025-09-09T14:24:11.0466521Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:24:11.0468874Z 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)] 2025-09-09T14:24:11.0470793Z graph_break [] 2025-09-09T14:24:11.0471051Z PASSED 2025-09-09T14:24:11.0471728Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qconv2d_hardswish_cpu stats [('calls_captured', 20), ('unique_graphs', 8)] 2025-09-09T14:24:11.0472609Z inline_call [] 2025-09-09T14:24:11.0472846Z frames [('total', 1), ('ok', 1)] 2025-09-09T14:24:11.0473199Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:24:11.0474542Z 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)] 2025-09-09T14:24:11.0475757Z graph_break [] 2025-09-09T14:24:11.0476010Z PASSED 2025-09-09T14:24:11.0476827Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qconv2d_hardswish_int8_mixed_bf16_cpu stats [('calls_captured', 20), ('unique_graphs', 8)] 2025-09-09T14:24:11.0477624Z inline_call [] 2025-09-09T14:24:11.0477857Z frames [('total', 1), ('ok', 1)] 2025-09-09T14:24:11.0478230Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:24:11.0479564Z inductor [('pattern_matcher_nodes', 33), ('qconv_unary_matcher_nodes', 17), ('qconv_weight_prepack_matcher_nodes', 12), ('pattern_matcher_count', 8), ('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)] 2025-09-09T14:24:11.0480797Z graph_break [] 2025-09-09T14:24:11.0481044Z PASSED 2025-09-09T14:24:11.0481727Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qconv2d_hardtanh_cpu stats [('calls_captured', 20), ('unique_graphs', 8)] 2025-09-09T14:24:11.0482611Z inline_call [] 2025-09-09T14:24:11.0482892Z frames [('total', 1), ('ok', 1)] 2025-09-09T14:24:11.0483266Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:24:11.0484579Z 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)] 2025-09-09T14:24:11.0485816Z graph_break [] 2025-09-09T14:24:11.0486073Z PASSED 2025-09-09T14:24:11.0486827Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qconv2d_hardtanh_int8_mixed_bf16_cpu stats [('calls_captured', 20), ('unique_graphs', 8)] 2025-09-09T14:24:11.0487632Z inline_call [] 2025-09-09T14:24:11.0487854Z frames [('total', 1), ('ok', 1)] 2025-09-09T14:24:11.0488233Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:24:11.0489681Z inductor [('pattern_matcher_nodes', 27), ('qconv_weight_prepack_matcher_nodes', 12), ('qconv_unary_matcher_nodes', 11), ('pattern_matcher_count', 8), ('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)] 2025-09-09T14:24:11.0490928Z graph_break [] 2025-09-09T14:24:11.0491187Z PASSED 2025-09-09T14:24:11.0491864Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qconv2d_int8_mixed_bf16 stats [('calls_captured', 21), ('unique_graphs', 8)] 2025-09-09T14:24:11.0492614Z inline_call [] 2025-09-09T14:24:11.0492832Z frames [('total', 1), ('ok', 1)] 2025-09-09T14:24:11.0493316Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:24:11.0494635Z inductor [('pattern_matcher_nodes', 25), ('qconv_weight_prepack_matcher_nodes', 18), ('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)] 2025-09-09T14:24:11.0495854Z graph_break [] 2025-09-09T14:24:11.0496174Z PASSED 2025-09-09T14:24:11.0496825Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qconv2d_relu6_cpu stats [('calls_captured', 20), ('unique_graphs', 8)] 2025-09-09T14:24:11.0497548Z inline_call [] 2025-09-09T14:24:11.0497766Z frames [('total', 1), ('ok', 1)] 2025-09-09T14:24:11.0498129Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:24:11.0499452Z 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)] 2025-09-09T14:24:11.0500661Z graph_break [] 2025-09-09T14:24:11.0500905Z PASSED 2025-09-09T14:24:11.0501551Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qconv2d_relu_cpu stats [('calls_captured', 20), ('unique_graphs', 8)] 2025-09-09T14:24:11.0502279Z inline_call [] 2025-09-09T14:24:11.0502500Z frames [('total', 1), ('ok', 1)] 2025-09-09T14:24:11.0502868Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:24:11.0504198Z 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)] 2025-09-09T14:24:11.0505408Z graph_break [] 2025-09-09T14:24:11.0505654Z PASSED 2025-09-09T14:24:11.0506363Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qconv2d_relu_int8_mixed_bf16_xpu stats [('calls_captured', 20), ('unique_graphs', 8)] 2025-09-09T14:24:11.0507146Z inline_call [] 2025-09-09T14:24:11.0507378Z frames [('total', 1), ('ok', 1)] 2025-09-09T14:24:11.0507732Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:24:11.0509061Z inductor [('pattern_matcher_nodes', 19), ('qconv_weight_prepack_matcher_nodes', 12), ('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)] 2025-09-09T14:24:11.0510281Z graph_break [] 2025-09-09T14:24:11.0510526Z PASSED 2025-09-09T14:24:11.0511190Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qconv2d_silu_cpu stats [('calls_captured', 20), ('unique_graphs', 8)] 2025-09-09T14:24:11.0511903Z inline_call [] 2025-09-09T14:24:11.0512131Z frames [('total', 1), ('ok', 1)] 2025-09-09T14:24:11.0512486Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:24:11.0513808Z 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)] 2025-09-09T14:24:11.0515040Z graph_break [] 2025-09-09T14:24:11.0515274Z PASSED 2025-09-09T14:24:11.0515992Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qconv2d_silu_int8_mixed_bf16_cpu stats [('calls_captured', 20), ('unique_graphs', 8)] 2025-09-09T14:24:11.0516937Z inline_call [] 2025-09-09T14:24:11.0517177Z frames [('total', 1), ('ok', 1)] 2025-09-09T14:24:11.0517532Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:28:04.9723521Z inductor [('pattern_matcher_nodes', 27), ('qconv_weight_prepack_matcher_nodes', 12), ('qconv_unary_matcher_nodes', 11), ('pattern_matcher_count', 8), ('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)] 2025-09-09T14:28:04.9725763Z graph_break [] 2025-09-09T14:28:04.9726184Z PASSED 2025-09-09T14:28:04.9726905Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qconv2d_with_concat_cpu stats [('calls_captured', 32), ('unique_graphs', 8)] 2025-09-09T14:28:04.9727662Z inline_call [] 2025-09-09T14:28:04.9727884Z frames [('total', 1), ('ok', 1)] 2025-09-09T14:28:04.9728257Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:28:04.9730011Z 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)] 2025-09-09T14:28:04.9731654Z graph_break [] 2025-09-09T14:28:04.9731908Z PASSED 2025-09-09T14:28:04.9732534Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qflatten stats [('calls_captured', 27), ('unique_graphs', 8)] 2025-09-09T14:28:04.9733237Z inline_call [] 2025-09-09T14:28:04.9733462Z frames [('total', 1), ('ok', 1)] 2025-09-09T14:28:04.9733851Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:28:04.9735371Z 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)] 2025-09-09T14:28:04.9736763Z graph_break [] 2025-09-09T14:28:04.9737014Z PASSED 2025-09-09T14:28:04.9737708Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_add_cpu_use_relu_False_is_qat_False_is_dynamic_False inline_call [] 2025-09-09T14:28:04.9738513Z stats [('calls_captured', 56), ('unique_graphs', 16)] 2025-09-09T14:28:04.9738869Z frames [('total', 1), ('ok', 1)] 2025-09-09T14:28:04.9739228Z aot_autograd [('total', 2), ('autograd_cache_bypass', 2), ('ok', 2)] 2025-09-09T14:28:04.9741232Z 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)] 2025-09-09T14:28:04.9743126Z graph_break [] 2025-09-09T14:28:04.9743381Z PASSED 2025-09-09T14:28:04.9744056Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_add_cpu_use_relu_False_is_qat_False_is_dynamic_True inline_call [] 2025-09-09T14:28:04.9744859Z stats [('calls_captured', 60), ('unique_graphs', 16)] 2025-09-09T14:28:04.9745199Z frames [('total', 1), ('ok', 1)] 2025-09-09T14:28:04.9745564Z aot_autograd [('total', 2), ('autograd_cache_bypass', 2), ('ok', 2)] 2025-09-09T14:28:04.9747721Z 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)] 2025-09-09T14:28:04.9749690Z graph_break [] 2025-09-09T14:28:04.9749947Z PASSED 2025-09-09T14:28:04.9750620Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_add_cpu_use_relu_False_is_qat_True_is_dynamic_False inline_call [] 2025-09-09T14:28:04.9751422Z stats [('calls_captured', 56), ('unique_graphs', 16)] 2025-09-09T14:28:04.9751780Z frames [('total', 1), ('ok', 1)] 2025-09-09T14:28:04.9752134Z aot_autograd [('total', 2), ('autograd_cache_bypass', 2), ('ok', 2)] 2025-09-09T14:28:04.9754132Z 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)] 2025-09-09T14:28:04.9756121Z graph_break [] 2025-09-09T14:28:04.9756366Z PASSED 2025-09-09T14:28:04.9757048Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_add_cpu_use_relu_False_is_qat_True_is_dynamic_True inline_call [] 2025-09-09T14:28:04.9757834Z stats [('calls_captured', 60), ('unique_graphs', 16)] 2025-09-09T14:28:04.9758187Z frames [('total', 1), ('ok', 1)] 2025-09-09T14:28:04.9758559Z aot_autograd [('total', 2), ('autograd_cache_bypass', 2), ('ok', 2)] 2025-09-09T14:28:04.9760525Z 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)] 2025-09-09T14:28:04.9762406Z graph_break [] 2025-09-09T14:28:04.9762658Z PASSED 2025-09-09T14:28:04.9763327Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_add_cpu_use_relu_True_is_qat_False_is_dynamic_False inline_call [] 2025-09-09T14:28:04.9764125Z stats [('calls_captured', 64), ('unique_graphs', 16)] 2025-09-09T14:28:04.9764463Z frames [('total', 1), ('ok', 1)] 2025-09-09T14:28:04.9764830Z aot_autograd [('total', 2), ('autograd_cache_bypass', 2), ('ok', 2)] 2025-09-09T14:28:04.9766822Z 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)] 2025-09-09T14:28:04.9768685Z graph_break [] 2025-09-09T14:28:04.9768928Z PASSED 2025-09-09T14:28:04.9769590Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_add_cpu_use_relu_True_is_qat_False_is_dynamic_True inline_call [] 2025-09-09T14:28:04.9770393Z stats [('calls_captured', 68), ('unique_graphs', 16)] 2025-09-09T14:28:04.9770818Z frames [('total', 1), ('ok', 1)] 2025-09-09T14:28:04.9771173Z aot_autograd [('total', 2), ('autograd_cache_bypass', 2), ('ok', 2)] 2025-09-09T14:28:04.9773165Z 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)] 2025-09-09T14:28:04.9775105Z graph_break [] 2025-09-09T14:28:04.9775341Z PASSED 2025-09-09T14:28:04.9776018Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_add_cpu_use_relu_True_is_qat_True_is_dynamic_False inline_call [] 2025-09-09T14:28:04.9776809Z stats [('calls_captured', 64), ('unique_graphs', 16)] 2025-09-09T14:28:04.9777161Z frames [('total', 1), ('ok', 1)] 2025-09-09T14:28:04.9777515Z aot_autograd [('total', 2), ('autograd_cache_bypass', 2), ('ok', 2)] 2025-09-09T14:28:04.9779498Z 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)] 2025-09-09T14:28:04.9781385Z graph_break [] 2025-09-09T14:28:04.9781618Z PASSED 2025-09-09T14:28:04.9782298Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_add_cpu_use_relu_True_is_qat_True_is_dynamic_True inline_call [] 2025-09-09T14:36:51.1833793Z stats [('calls_captured', 68), ('unique_graphs', 16)] 2025-09-09T14:36:51.1834191Z frames [('total', 1), ('ok', 1)] 2025-09-09T14:36:51.1834619Z aot_autograd [('total', 2), ('autograd_cache_bypass', 2), ('ok', 2)] 2025-09-09T14:36:51.1836887Z 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)] 2025-09-09T14:36:51.1838959Z graph_break [] 2025-09-09T14:36:51.1839408Z PASSED 2025-09-09T14:36:51.1840227Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_add_int8_mixed_bf16_use_relu_False_is_qat_False_is_dynamic_False inline_call [] 2025-09-09T14:36:51.1841146Z stats [('calls_captured', 72), ('unique_graphs', 16)] 2025-09-09T14:36:51.1841562Z frames [('total', 1), ('ok', 1)] 2025-09-09T14:36:51.1841916Z aot_autograd [('total', 2), ('autograd_cache_bypass', 2), ('ok', 2)] 2025-09-09T14:36:51.1844199Z inductor [('pattern_matcher_nodes', 108), ('qlinear_weight_prepack_matcher_nodes', 56), ('pattern_matcher_count', 44), ('dequant_promotion_matcher_nodes', 10), ('qlinear_binary_matcher_nodes', 10), ('qlinear_weight_prepack_matcher_count', 8), ('qlinear_unary_matcher_nodes', 8), ('extern_calls', 8), ('dequant_promotion_matcher_count', 4), ('qlinear_unary_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)] 2025-09-09T14:36:51.1846375Z graph_break [] 2025-09-09T14:36:51.1847037Z PASSED 2025-09-09T14:36:51.1847845Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_add_int8_mixed_bf16_use_relu_False_is_qat_False_is_dynamic_True inline_call [] 2025-09-09T14:36:51.1848742Z stats [('calls_captured', 76), ('unique_graphs', 16)] 2025-09-09T14:36:51.1849156Z frames [('total', 1), ('ok', 1)] 2025-09-09T14:36:51.1849512Z aot_autograd [('total', 2), ('autograd_cache_bypass', 2), ('ok', 2)] 2025-09-09T14:36:51.1851807Z inductor [('pattern_matcher_nodes', 107), ('qlinear_weight_prepack_matcher_nodes', 56), ('pattern_matcher_count', 44), ('dequant_promotion_matcher_nodes', 10), ('qlinear_binary_matcher_nodes', 9), ('qlinear_weight_prepack_matcher_count', 8), ('qlinear_unary_matcher_nodes', 8), ('extern_calls', 8), ('dequant_promotion_matcher_count', 4), ('qlinear_unary_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)] 2025-09-09T14:36:51.1854105Z graph_break [] 2025-09-09T14:36:51.1854404Z PASSED 2025-09-09T14:36:51.1855202Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_add_int8_mixed_bf16_use_relu_False_is_qat_True_is_dynamic_False inline_call [] 2025-09-09T14:36:51.1856112Z stats [('calls_captured', 72), ('unique_graphs', 16)] 2025-09-09T14:36:51.1856456Z frames [('total', 1), ('ok', 1)] 2025-09-09T14:36:51.1856893Z aot_autograd [('total', 2), ('autograd_cache_bypass', 2), ('ok', 2)] 2025-09-09T14:36:51.1859171Z inductor [('pattern_matcher_nodes', 108), ('qlinear_weight_prepack_matcher_nodes', 56), ('pattern_matcher_count', 44), ('dequant_promotion_matcher_nodes', 10), ('qlinear_binary_matcher_nodes', 10), ('qlinear_weight_prepack_matcher_count', 8), ('qlinear_unary_matcher_nodes', 8), ('extern_calls', 8), ('dequant_promotion_matcher_count', 4), ('qlinear_unary_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)] 2025-09-09T14:36:51.1861350Z graph_break [] 2025-09-09T14:36:51.1861611Z PASSED 2025-09-09T14:36:51.1862381Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_add_int8_mixed_bf16_use_relu_False_is_qat_True_is_dynamic_True inline_call [] 2025-09-09T14:36:51.1863288Z stats [('calls_captured', 76), ('unique_graphs', 16)] 2025-09-09T14:36:51.1863706Z frames [('total', 1), ('ok', 1)] 2025-09-09T14:36:51.1864060Z aot_autograd [('total', 2), ('autograd_cache_bypass', 2), ('ok', 2)] 2025-09-09T14:36:51.1866342Z inductor [('pattern_matcher_nodes', 107), ('qlinear_weight_prepack_matcher_nodes', 56), ('pattern_matcher_count', 44), ('dequant_promotion_matcher_nodes', 10), ('qlinear_binary_matcher_nodes', 9), ('qlinear_weight_prepack_matcher_count', 8), ('qlinear_unary_matcher_nodes', 8), ('extern_calls', 8), ('dequant_promotion_matcher_count', 4), ('qlinear_unary_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)] 2025-09-09T14:36:51.1868511Z graph_break [] 2025-09-09T14:36:51.1868750Z PASSED 2025-09-09T14:36:51.1869528Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_add_int8_mixed_bf16_use_relu_True_is_qat_False_is_dynamic_False inline_call [] 2025-09-09T14:36:51.1870423Z stats [('calls_captured', 80), ('unique_graphs', 16)] 2025-09-09T14:36:51.1870774Z frames [('total', 1), ('ok', 1)] 2025-09-09T14:36:51.1871126Z aot_autograd [('total', 2), ('autograd_cache_bypass', 2), ('ok', 2)] 2025-09-09T14:36:51.1873372Z inductor [('pattern_matcher_nodes', 112), ('qlinear_weight_prepack_matcher_nodes', 56), ('pattern_matcher_count', 44), ('qlinear_binary_matcher_nodes', 14), ('dequant_promotion_matcher_nodes', 10), ('qlinear_weight_prepack_matcher_count', 8), ('qlinear_unary_matcher_nodes', 8), ('extern_calls', 8), ('dequant_promotion_matcher_count', 4), ('qlinear_unary_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)] 2025-09-09T14:36:51.1875379Z graph_break [] 2025-09-09T14:36:51.1875622Z PASSED 2025-09-09T14:36:51.1876483Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_add_int8_mixed_bf16_use_relu_True_is_qat_False_is_dynamic_True inline_call [] 2025-09-09T14:36:51.1877425Z stats [('calls_captured', 84), ('unique_graphs', 16)] 2025-09-09T14:36:51.1877767Z frames [('total', 1), ('ok', 1)] 2025-09-09T14:36:51.1878132Z aot_autograd [('total', 2), ('autograd_cache_bypass', 2), ('ok', 2)] 2025-09-09T14:36:51.1880224Z inductor [('pattern_matcher_nodes', 111), ('qlinear_weight_prepack_matcher_nodes', 56), ('pattern_matcher_count', 44), ('qlinear_binary_matcher_nodes', 13), ('dequant_promotion_matcher_nodes', 10), ('qlinear_weight_prepack_matcher_count', 8), ('qlinear_unary_matcher_nodes', 8), ('extern_calls', 8), ('dequant_promotion_matcher_count', 4), ('qlinear_unary_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)] 2025-09-09T14:36:51.1882215Z graph_break [] 2025-09-09T14:36:51.1882472Z PASSED 2025-09-09T14:36:51.1883193Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_add_int8_mixed_bf16_use_relu_True_is_qat_True_is_dynamic_False inline_call [] 2025-09-09T14:36:51.1884079Z stats [('calls_captured', 80), ('unique_graphs', 16)] 2025-09-09T14:36:51.1884430Z frames [('total', 1), ('ok', 1)] 2025-09-09T14:36:51.1884785Z aot_autograd [('total', 2), ('autograd_cache_bypass', 2), ('ok', 2)] 2025-09-09T14:36:51.1886896Z inductor [('pattern_matcher_nodes', 112), ('qlinear_weight_prepack_matcher_nodes', 56), ('pattern_matcher_count', 44), ('qlinear_binary_matcher_nodes', 14), ('dequant_promotion_matcher_nodes', 10), ('qlinear_weight_prepack_matcher_count', 8), ('qlinear_unary_matcher_nodes', 8), ('extern_calls', 8), ('dequant_promotion_matcher_count', 4), ('qlinear_unary_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)] 2025-09-09T14:36:51.1888898Z graph_break [] 2025-09-09T14:36:51.1889137Z PASSED 2025-09-09T14:36:51.1889854Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_add_int8_mixed_bf16_use_relu_True_is_qat_True_is_dynamic_True inline_call [] 2025-09-09T14:36:51.1890683Z stats [('calls_captured', 84), ('unique_graphs', 16)] 2025-09-09T14:36:51.1891039Z frames [('total', 1), ('ok', 1)] 2025-09-09T14:36:51.1891414Z aot_autograd [('total', 2), ('autograd_cache_bypass', 2), ('ok', 2)] 2025-09-09T14:36:51.1893516Z inductor [('pattern_matcher_nodes', 111), ('qlinear_weight_prepack_matcher_nodes', 56), ('pattern_matcher_count', 44), ('qlinear_binary_matcher_nodes', 13), ('dequant_promotion_matcher_nodes', 10), ('qlinear_weight_prepack_matcher_count', 8), ('qlinear_unary_matcher_nodes', 8), ('extern_calls', 8), ('dequant_promotion_matcher_count', 4), ('qlinear_unary_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)] 2025-09-09T14:36:51.1895515Z graph_break [] 2025-09-09T14:36:51.1895762Z PASSED 2025-09-09T14:37:25.3393132Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_cpu stats [('calls_captured', 16), ('unique_graphs', 8)] 2025-09-09T14:37:25.3393896Z inline_call [] 2025-09-09T14:37:25.3395756Z frames [('total', 1), ('ok', 1)] 2025-09-09T14:37:25.3396261Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:37:25.3397688Z 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)] 2025-09-09T14:37:25.3399112Z graph_break [] 2025-09-09T14:37:25.3399513Z PASSED 2025-09-09T14:37:25.3400244Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_dequant_promotion_cpu stats [('calls_captured', 22), ('unique_graphs', 8)] 2025-09-09T14:37:25.3401026Z inline_call [] 2025-09-09T14:37:25.3401246Z frames [('total', 1), ('ok', 1)] 2025-09-09T14:37:25.3401616Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:37:25.3403697Z 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)] 2025-09-09T14:37:25.3405694Z graph_break [] 2025-09-09T14:37:25.3405950Z PASSED 2025-09-09T14:37:25.3406728Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_dequant_promotion_cpu_input_dim_exceeds_2 stats [('calls_captured', 22), ('unique_graphs', 8)] 2025-09-09T14:37:25.3407579Z inline_call [] 2025-09-09T14:37:25.3407797Z frames [('total', 1), ('ok', 1)] 2025-09-09T14:37:25.3408170Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:37:25.3410263Z 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)] 2025-09-09T14:37:25.3412238Z graph_break [] 2025-09-09T14:37:25.3412486Z PASSED 2025-09-09T14:37:25.3413235Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_dequant_promotion_dynamic_cpu stats [('calls_captured', 27), ('unique_graphs', 8)] 2025-09-09T14:37:25.3414034Z inline_call [] 2025-09-09T14:37:25.3414262Z frames [('total', 1), ('ok', 1)] 2025-09-09T14:37:25.3414620Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:37:25.3416482Z 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)] 2025-09-09T14:37:25.3418245Z graph_break [] 2025-09-09T14:37:25.3418478Z PASSED 2025-09-09T14:37:25.3419233Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_dequant_promotion_int8_mixed_bf16 stats [('calls_captured', 22), ('unique_graphs', 8)] 2025-09-09T14:37:25.3420041Z inline_call [] 2025-09-09T14:37:25.3420338Z frames [('total', 1), ('ok', 1)] 2025-09-09T14:37:25.3420708Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:37:25.3423085Z inductor [('pattern_matcher_nodes', 27), ('qlinear_weight_prepack_matcher_nodes', 18), ('pattern_matcher_count', 9), ('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)] 2025-09-09T14:37:25.3425366Z graph_break [] 2025-09-09T14:37:25.3425651Z PASSED 2025-09-09T14:37:25.3426482Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_dequant_promotion_int8_mixed_bf16_input_dim_exceeds_2 stats [('calls_captured', 22), ('unique_graphs', 8)] 2025-09-09T14:37:25.3427384Z inline_call [] 2025-09-09T14:37:25.3427602Z frames [('total', 1), ('ok', 1)] 2025-09-09T14:37:25.3427966Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:37:25.3430288Z inductor [('pattern_matcher_nodes', 40), ('qlinear_weight_prepack_matcher_nodes', 24), ('pattern_matcher_count', 15), ('dequant_promotion_matcher_nodes', 3), ('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), ('qlinear_unary_matcher_count', 1), ('qlinear_binary_matcher_count', 1), ('fxgraph_cache_bypass', 1), ('qlinear_binary_lower_count', 1), ('qlinear_binary_lower_nodes', 1)] 2025-09-09T14:37:25.3432263Z graph_break [] 2025-09-09T14:37:25.3432524Z PASSED 2025-09-09T14:37:25.3433180Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_gelu_cpu stats [('calls_captured', 20), ('unique_graphs', 8)] 2025-09-09T14:37:25.3433900Z inline_call [] 2025-09-09T14:37:25.3434127Z frames [('total', 1), ('ok', 1)] 2025-09-09T14:37:25.3434481Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:37:25.3435863Z 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)] 2025-09-09T14:37:25.3437219Z graph_break [] 2025-09-09T14:37:25.3437472Z PASSED 2025-09-09T14:37:25.3438177Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_gelu_int8_mixed_bf16 stats [('calls_captured', 20), ('unique_graphs', 8)] 2025-09-09T14:37:25.3438929Z inline_call [] 2025-09-09T14:37:25.3439161Z frames [('total', 1), ('ok', 1)] 2025-09-09T14:37:25.3439524Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:37:25.3440910Z inductor [('pattern_matcher_nodes', 41), ('qlinear_unary_matcher_nodes', 25), ('qlinear_weight_prepack_matcher_nodes', 12), ('pattern_matcher_count', 8), ('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)] 2025-09-09T14:37:25.3442197Z graph_break [] 2025-09-09T14:37:25.3442431Z PASSED 2025-09-09T14:37:25.3443127Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_input_dim_exceeds_2 stats [('calls_captured', 16), ('unique_graphs', 8)] 2025-09-09T14:37:25.3443875Z inline_call [] 2025-09-09T14:37:25.3444102Z frames [('total', 1), ('ok', 1)] 2025-09-09T14:37:25.3444453Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:37:25.3446019Z 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)] 2025-09-09T14:37:25.3447309Z graph_break [] 2025-09-09T14:37:25.3447547Z PASSED 2025-09-09T14:37:25.3448332Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_input_dim_exceeds_2_and_not_contiguous stats [('calls_captured', 20), ('unique_graphs', 8)] 2025-09-09T14:37:25.3449250Z inline_call [] 2025-09-09T14:37:25.3449482Z frames [('total', 1), ('ok', 1)] 2025-09-09T14:37:25.3449852Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:37:25.3451228Z 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)] 2025-09-09T14:37:25.3452514Z graph_break [] 2025-09-09T14:37:25.3452751Z PASSED 2025-09-09T14:37:25.3453441Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_int8_mixed_bf16 stats [('calls_captured', 16), ('unique_graphs', 8)] 2025-09-09T14:37:25.3454191Z inline_call [] 2025-09-09T14:37:25.3454409Z frames [('total', 1), ('ok', 1)] 2025-09-09T14:37:25.3454772Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:38:01.7558390Z inductor [('pattern_matcher_nodes', 16), ('qlinear_weight_prepack_matcher_nodes', 12), ('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)] 2025-09-09T14:38:01.7560224Z graph_break [] 2025-09-09T14:38:01.7560746Z PASSED 2025-09-09T14:38:01.7561790Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_int8_mixed_bf16_input_dim_exceeds_2 stats [('calls_captured', 16), ('unique_graphs', 8)] 2025-09-09T14:38:01.7562907Z inline_call [] 2025-09-09T14:38:01.7563209Z frames [('total', 1), ('ok', 1)] 2025-09-09T14:38:01.7563682Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:38:01.7565606Z inductor [('pattern_matcher_nodes', 24), ('qlinear_weight_prepack_matcher_nodes', 16), ('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)] 2025-09-09T14:38:01.7567358Z graph_break [] 2025-09-09T14:38:01.7567673Z PASSED 2025-09-09T14:38:01.7568806Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_int8_mixed_bf16_input_dim_exceeds_2_and_not_contiguous stats [('calls_captured', 20), ('unique_graphs', 8)] 2025-09-09T14:38:01.7570020Z inline_call [] 2025-09-09T14:38:01.7570307Z frames [('total', 1), ('ok', 1)] 2025-09-09T14:38:01.7570793Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:38:01.7572665Z inductor [('pattern_matcher_nodes', 24), ('qlinear_weight_prepack_matcher_nodes', 16), ('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)] 2025-09-09T14:38:01.7574411Z graph_break [] 2025-09-09T14:38:01.7574725Z PASSED 2025-09-09T14:38:01.7575608Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_mul_cpu stats [('calls_captured', 17), ('unique_graphs', 8)] 2025-09-09T14:38:01.7576856Z inline_call [] 2025-09-09T14:38:01.7577150Z frames [('total', 1), ('ok', 1)] 2025-09-09T14:38:01.7577635Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:38:01.7579498Z 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)] 2025-09-09T14:38:01.7581356Z graph_break [] 2025-09-09T14:38:01.7581686Z PASSED 2025-09-09T14:38:01.7582562Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_relu_cpu stats [('calls_captured', 20), ('unique_graphs', 8)] 2025-09-09T14:38:01.7583542Z inline_call [] 2025-09-09T14:38:01.7583826Z frames [('total', 1), ('ok', 1)] 2025-09-09T14:38:01.7584307Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:38:01.7585895Z 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)] 2025-09-09T14:38:01.7587161Z graph_break [] 2025-09-09T14:38:01.7587409Z PASSED 2025-09-09T14:38:01.7588120Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_relu_input_dim_exceeds_2 stats [('calls_captured', 20), ('unique_graphs', 8)] 2025-09-09T14:38:01.7588904Z inline_call [] 2025-09-09T14:38:01.7589122Z frames [('total', 1), ('ok', 1)] 2025-09-09T14:38:01.7589487Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:38:01.7590888Z 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)] 2025-09-09T14:38:01.7592161Z graph_break [] 2025-09-09T14:38:01.7592410Z PASSED 2025-09-09T14:38:01.7593104Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_relu_int8_mixed_bf16 stats [('calls_captured', 20), ('unique_graphs', 8)] 2025-09-09T14:38:01.7593874Z inline_call [] 2025-09-09T14:38:01.7594105Z frames [('total', 1), ('ok', 1)] 2025-09-09T14:38:01.7594457Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:38:01.7595839Z inductor [('pattern_matcher_nodes', 19), ('qlinear_weight_prepack_matcher_nodes', 12), ('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)] 2025-09-09T14:38:01.7597178Z graph_break [] 2025-09-09T14:38:01.7597430Z PASSED 2025-09-09T14:38:01.7598205Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_relu_int8_mixed_bf16_input_dim_exceeds_2 stats [('calls_captured', 20), ('unique_graphs', 8)] 2025-09-09T14:38:01.7599030Z inline_call [] 2025-09-09T14:38:01.7599266Z frames [('total', 1), ('ok', 1)] 2025-09-09T14:38:01.7599628Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:38:01.7601021Z inductor [('pattern_matcher_nodes', 27), ('qlinear_weight_prepack_matcher_nodes', 16), ('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)] 2025-09-09T14:38:01.7602302Z graph_break [] 2025-09-09T14:38:01.7602628Z PASSED 2025-09-09T14:38:01.7603280Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qmaxpool2d stats [('calls_captured', 19), ('unique_graphs', 8)] 2025-09-09T14:38:01.7603980Z inline_call [] 2025-09-09T14:38:01.7604216Z frames [('total', 1), ('ok', 1)] 2025-09-09T14:38:01.7604573Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:38:01.7606111Z 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)] 2025-09-09T14:38:01.7607594Z graph_break [] 2025-09-09T14:38:01.7607831Z PASSED 2025-09-09T14:38:01.7608679Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_smooth_quant_with_int_mm_has_bias_False_bfloat16_per_channel_quant_False_dynamic_False frames [('total', 1), ('ok', 1)] 2025-09-09T14:38:01.7609631Z stats [('calls_captured', 7), ('unique_graphs', 1)] 2025-09-09T14:38:01.7610074Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:38:01.7611410Z 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)] 2025-09-09T14:38:01.7612559Z graph_break [] 2025-09-09T14:38:01.7612878Z PASSED 2025-09-09T14:38:01.7613991Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_smooth_quant_with_int_mm_has_bias_False_bfloat16_per_channel_quant_False_dynamic_True frames [('total', 1), ('ok', 1)] 2025-09-09T14:38:01.7615280Z stats [('calls_captured', 10), ('unique_graphs', 1)] 2025-09-09T14:38:01.7615845Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:38:01.7617400Z 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)] 2025-09-09T14:38:01.7618830Z graph_break [] 2025-09-09T14:38:01.7619137Z PASSED 2025-09-09T14:38:01.7620264Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_smooth_quant_with_int_mm_has_bias_False_bfloat16_per_channel_quant_True_dynamic_False frames [('total', 1), ('ok', 1)] 2025-09-09T14:38:01.7621545Z stats [('calls_captured', 7), ('unique_graphs', 1)] 2025-09-09T14:38:01.7622091Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:38:01.7623377Z 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)] 2025-09-09T14:38:01.7624978Z graph_break [] 2025-09-09T14:38:01.7625313Z PASSED 2025-09-09T14:38:01.7626439Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_smooth_quant_with_int_mm_has_bias_False_bfloat16_per_channel_quant_True_dynamic_True frames [('total', 1), ('ok', 1)] 2025-09-09T14:38:01.7627703Z stats [('calls_captured', 10), ('unique_graphs', 1)] 2025-09-09T14:38:01.7628274Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:38:01.7629548Z 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)] 2025-09-09T14:38:01.7630699Z graph_break [] 2025-09-09T14:38:01.7631021Z PASSED 2025-09-09T14:38:28.1863682Z 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)] 2025-09-09T14:38:28.1865091Z stats [('calls_captured', 7), ('unique_graphs', 1)] 2025-09-09T14:38:28.1865669Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:38:28.1866957Z 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)] 2025-09-09T14:38:28.1868116Z graph_break [] 2025-09-09T14:38:28.1868768Z PASSED 2025-09-09T14:38:28.1869903Z 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)] 2025-09-09T14:38:28.1871191Z stats [('calls_captured', 10), ('unique_graphs', 1)] 2025-09-09T14:38:28.1871790Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:38:28.1873371Z 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)] 2025-09-09T14:38:28.1874830Z graph_break [] 2025-09-09T14:38:28.1875145Z PASSED 2025-09-09T14:38:28.1876413Z 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)] 2025-09-09T14:38:28.1877696Z stats [('calls_captured', 7), ('unique_graphs', 1)] 2025-09-09T14:38:28.1878250Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:38:28.1879536Z 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)] 2025-09-09T14:38:28.1880687Z graph_break [] 2025-09-09T14:38:28.1881017Z PASSED 2025-09-09T14:38:28.1882120Z 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)] 2025-09-09T14:38:28.1883392Z stats [('calls_captured', 10), ('unique_graphs', 1)] 2025-09-09T14:38:28.1883965Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:38:28.1885231Z 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)] 2025-09-09T14:38:28.1886386Z graph_break [] 2025-09-09T14:38:28.1886693Z PASSED 2025-09-09T14:38:28.1887823Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_smooth_quant_with_int_mm_has_bias_True_bfloat16_per_channel_quant_False_dynamic_False frames [('total', 1), ('ok', 1)] 2025-09-09T14:38:28.1889105Z stats [('calls_captured', 10), ('unique_graphs', 1)] 2025-09-09T14:38:28.1889658Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:38:28.1891097Z 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)] 2025-09-09T14:38:28.1892396Z graph_break [] 2025-09-09T14:38:28.1892718Z PASSED 2025-09-09T14:38:28.1893834Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_smooth_quant_with_int_mm_has_bias_True_bfloat16_per_channel_quant_False_dynamic_True frames [('total', 1), ('ok', 1)] 2025-09-09T14:38:28.1894985Z stats [('calls_captured', 14), ('unique_graphs', 1)] 2025-09-09T14:38:28.1895415Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:38:28.1896759Z 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)] 2025-09-09T14:38:28.1897928Z graph_break [] 2025-09-09T14:38:28.1898186Z PASSED 2025-09-09T14:38:28.1899008Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_smooth_quant_with_int_mm_has_bias_True_bfloat16_per_channel_quant_True_dynamic_False frames [('total', 1), ('ok', 1)] 2025-09-09T14:38:28.1900012Z stats [('calls_captured', 10), ('unique_graphs', 1)] 2025-09-09T14:38:28.1900427Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:38:28.1901487Z 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)] 2025-09-09T14:38:28.1902459Z graph_break [] 2025-09-09T14:38:28.1902701Z PASSED 2025-09-09T14:38:28.1903525Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_smooth_quant_with_int_mm_has_bias_True_bfloat16_per_channel_quant_True_dynamic_True frames [('total', 1), ('ok', 1)] 2025-09-09T14:38:28.1904448Z stats [('calls_captured', 14), ('unique_graphs', 1)] 2025-09-09T14:38:28.1904875Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:38:28.1905938Z 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)] 2025-09-09T14:38:28.1906891Z graph_break [] 2025-09-09T14:38:28.1907139Z PASSED 2025-09-09T14:38:28.1907962Z 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)] 2025-09-09T14:38:28.1908905Z stats [('calls_captured', 10), ('unique_graphs', 1)] 2025-09-09T14:38:28.1909322Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:38:28.1910385Z 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)] 2025-09-09T14:38:28.1911360Z graph_break [] 2025-09-09T14:38:28.1911595Z PASSED 2025-09-09T14:38:28.1912416Z 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)] 2025-09-09T14:38:28.1913338Z stats [('calls_captured', 14), ('unique_graphs', 1)] 2025-09-09T14:38:28.1913766Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:38:28.1915029Z 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)] 2025-09-09T14:38:28.1916293Z graph_break [] 2025-09-09T14:38:28.1916548Z PASSED 2025-09-09T14:38:28.1917364Z 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)] 2025-09-09T14:38:28.1918312Z stats [('calls_captured', 10), ('unique_graphs', 1)] 2025-09-09T14:38:28.1918746Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:38:28.1919866Z 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)] 2025-09-09T14:38:28.1920837Z graph_break [] 2025-09-09T14:38:28.1921075Z PASSED 2025-09-09T14:38:28.1921897Z 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)] 2025-09-09T14:38:28.1922831Z stats [('calls_captured', 14), ('unique_graphs', 1)] 2025-09-09T14:38:28.1923251Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:38:28.1924595Z 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)] 2025-09-09T14:38:28.1925552Z graph_break [] 2025-09-09T14:38:28.1925806Z PASSED 2025-09-09T14:38:28.1926378Z test/quantization/pt2e/test_x86inductor_fusion.py::TestDynamicPatternMatcher::test_q_attention_block inline_call [] 2025-09-09T14:38:28.1927061Z stats [('calls_captured', 49), ('unique_graphs', 8)] 2025-09-09T14:38:28.1927409Z frames [('total', 1), ('ok', 1)] 2025-09-09T14:38:28.1927761Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:38:28.1929400Z 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)] 2025-09-09T14:38:28.1930944Z graph_break [] 2025-09-09T14:38:28.1931256Z aten_mm_info [('aten.bmm_32_384_384_64', 1), ('aten.bmm_32_384_64_384', 1)] 2025-09-09T14:38:28.1931674Z PASSED 2025-09-09T14:38:28.1932366Z test/quantization/pt2e/test_x86inductor_fusion.py::TestDynamicPatternMatcher::test_qat_bn_conv2d stats [('calls_captured', 988), ('unique_graphs', 116)] 2025-09-09T14:38:28.1933128Z inline_call [] 2025-09-09T14:38:28.1933350Z frames [('total', 1), ('ok', 1)] 2025-09-09T14:41:11.5952409Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:41:11.5954765Z 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)] 2025-09-09T14:41:11.5956086Z graph_break [] 2025-09-09T14:41:11.5956588Z PASSED 2025-09-09T14:41:11.5957403Z test/quantization/pt2e/test_x86inductor_fusion.py::TestDynamicPatternMatcher::test_qconv2d_maxpool2d_linear_dynamic_cpu stats [('calls_captured', 30), ('unique_graphs', 8)] 2025-09-09T14:41:11.5958244Z inline_call [] 2025-09-09T14:41:11.5958577Z aot_autograd [('total', 1), ('autograd_cache_bypass', 1), ('ok', 1)] 2025-09-09T14:41:11.5960820Z 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)] 2025-09-09T14:41:11.5962959Z graph_break [] 2025-09-09T14:41:11.5963200Z PASSED 2025-09-09T14:41:11.5963961Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_adaptive_avg_pool2d_recipe PASSED 2025-09-09T14:41:11.5965445Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_annotate_mul_tensor PASSED 2025-09-09T14:41:11.5966599Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_attention_block PASSED 2025-09-09T14:41:11.5967729Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_avg_pool2d_recipe PASSED 2025-09-09T14:41:11.5968821Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_cat_recipe PASSED 2025-09-09T14:41:11.5970067Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_cat_recipe_same_inputs PASSED 2025-09-09T14:41:11.5971240Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_cat_recipe_single_input PASSED 2025-09-09T14:41:11.5972347Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_conv2d PASSED 2025-09-09T14:41:11.5973414Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_conv2d_binary PASSED 2025-09-09T14:41:11.5974501Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_conv2d_binary2 PASSED 2025-09-09T14:41:11.5975637Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_conv2d_binary_unary PASSED 2025-09-09T14:41:11.5976824Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_conv2d_serials_binary_unary PASSED 2025-09-09T14:41:11.5977973Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_conv2d_unary PASSED 2025-09-09T14:41:11.5979098Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_dynamic_quant_linear PASSED 2025-09-09T14:41:11.5980248Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_filter_conv2d_recipe PASSED 2025-09-09T14:41:11.5981410Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_filter_linear_recipe PASSED 2025-09-09T14:41:11.5982578Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_filter_maxpool2d_recipe PASSED 2025-09-09T14:41:11.5983718Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_flatten_recipe PASSED 2025-09-09T14:41:11.5984828Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_flatten_recipe2 PASSED 2025-09-09T14:41:11.5985888Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_linear PASSED 2025-09-09T14:41:11.5986954Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_linear_binary PASSED 2025-09-09T14:41:11.5988054Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_linear_binary2 PASSED 2025-09-09T14:41:11.5989188Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_linear_binary_dynamic PASSED 2025-09-09T14:41:11.5990383Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_linear_binary_dynamic_qat PASSED 2025-09-09T14:41:11.5991541Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_linear_binary_qat PASSED 2025-09-09T14:41:11.5992699Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_linear_binary_unary PASSED 2025-09-09T14:41:11.5993891Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_linear_binary_unary_dynamic PASSED 2025-09-09T14:41:11.5995138Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_linear_binary_unary_dynamic_qat PASSED 2025-09-09T14:41:11.5996532Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_linear_binary_unary_qat PASSED 2025-09-09T14:41:11.5997744Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_linear_binary_unary_serials PASSED 2025-09-09T14:41:11.5998933Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_linear_dynamic_fp16 PASSED 2025-09-09T14:41:11.6000052Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_linear_unary PASSED 2025-09-09T14:41:11.6001228Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_linear_unary_dynamic PASSED 2025-09-09T14:41:11.6002405Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_linear_unary_dynamic_qat PASSED 2025-09-09T14:41:11.6003556Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_linear_unary_qat PASSED 2025-09-09T14:41:11.6004672Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_lowering_to_x86 SKIPPED 2025-09-09T14:41:11.6005798Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_maxpool2d_recipe PASSED 2025-09-09T14:41:11.6006875Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_qat_conv2d PASSED 2025-09-09T14:41:11.6007976Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_qat_conv2d_binary PASSED 2025-09-09T14:41:11.6009115Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_qat_conv2d_binary2 PASSED 2025-09-09T14:41:11.6010259Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_qat_conv2d_binary_unary PASSED 2025-09-09T14:41:11.6011432Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_qat_conv2d_unary PASSED 2025-09-09T14:41:11.6012599Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_qat_dynamic_quant_linear PASSED 2025-09-09T14:41:11.6013838Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_set_module_name_and_module_type_case1 PASSED 2025-09-09T14:41:11.6015131Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_set_module_name_and_module_type_case2 PASSED 2025-09-09T14:41:11.6016475Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_set_module_name_and_module_type_with_mixed_configs PASSED 2025-09-09T14:41:11.6017760Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_set_module_name_qconfig PASSED 2025-09-09T14:41:11.6019025Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_set_module_name_qconfig_for_dynamic_quant PASSED 2025-09-09T14:41:11.6020343Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_set_module_name_qconfig_with_underscores PASSED 2025-09-09T14:41:11.6021644Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_set_module_name_with_mixed_configs PASSED 2025-09-09T14:41:11.6022790Z test/quantization/quantize_/workflows/float8/test_float8_tensor.py::TestFloat8Tensor::test_bmm SKIPPED 2025-09-09T14:41:11.6023915Z test/quantization/quantize_/workflows/float8/test_float8_tensor.py::TestFloat8Tensor::test_cat_granularity0_sizes0 SKIPPED 2025-09-09T14:41:11.6025314Z test/quantization/quantize_/workflows/float8/test_float8_tensor.py::TestFloat8Tensor::test_cat_granularity0_sizes1 SKIPPED 2025-09-09T14:41:11.6026518Z test/quantization/quantize_/workflows/float8/test_float8_tensor.py::TestFloat8Tensor::test_cat_granularity0_sizes2 SKIPPED 2025-09-09T14:41:11.6270796Z test/quantization/quantize_/workflows/float8/test_float8_tensor.py::TestFloat8Tensor::test_cat_granularity1_sizes0 SKIPPED 2025-09-09T14:41:11.6272044Z test/quantization/quantize_/workflows/float8/test_float8_tensor.py::TestFloat8Tensor::test_cat_granularity1_sizes1 SKIPPED 2025-09-09T14:41:11.6273247Z test/quantization/quantize_/workflows/float8/test_float8_tensor.py::TestFloat8Tensor::test_cat_granularity1_sizes2 SKIPPED 2025-09-09T14:41:11.6274552Z test/quantization/quantize_/workflows/float8/test_float8_tensor.py::TestFloat8Tensor::test_expected_gpu_kernel_fbgemm SKIPPED 2025-09-09T14:41:11.6276414Z test/quantization/quantize_/workflows/float8/test_float8_tensor.py::TestFloat8Tensor::test_fp8_linear_variants_bfloat16_mode_dynamic_compile_False_granularity0_kernel_preference_KernelPreference_AUTO_sizes0 SKIPPED 2025-09-09T14:41:11.6278502Z test/quantization/quantize_/workflows/float8/test_float8_tensor.py::TestFloat8Tensor::test_fp8_linear_variants_bfloat16_mode_dynamic_compile_False_granularity0_kernel_preference_KernelPreference_AUTO_sizes1 SKIPPED 2025-09-09T14:41:11.6280530Z test/quantization/quantize_/workflows/float8/test_float8_tensor.py::TestFloat8Tensor::test_fp8_linear_variants_bfloat16_mode_dynamic_compile_False_granularity0_kernel_preference_KernelPreference_FBGEMM_sizes0 SKIPPED 2025-09-09T14:41:11.6282625Z test/quantization/quantize_/workflows/float8/test_float8_tensor.py::TestFloat8Tensor::test_fp8_linear_variants_bfloat16_mode_dynamic_compile_False_granularity0_kernel_preference_KernelPreference_FBGEMM_sizes1 SKIPPED 2025-09-09T14:41:11.6284633Z test/quantization/quantize_/workflows/float8/test_float8_tensor.py::TestFloat8Tensor::test_fp8_linear_variants_bfloat16_mode_dynamic_compile_False_granularity0_kernel_preference_KernelPreference_TORCH_sizes0 SKIPPED 2025-09-09T14:41:11.6286741Z test/quantization/quantize_/workflows/float8/test_float8_tensor.py::TestFloat8Tensor::test_fp8_linear_variants_bfloat16_mode_dynamic_compile_False_granularity0_kernel_preference_KernelPreference_TORCH_sizes1 SKIPPED 2025-09-09T14:41:11.6288742Z 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test/quantization/quantize_/workflows/float8/test_float8_tensor.py::TestFloat8Tensor::test_fp8_linear_variants_float32_mode_weight-only_compile_False_granularity1_kernel_preference_KernelPreference_TORCH_sizes0 SKIPPED 2025-09-09T14:41:11.6960764Z test/quantization/quantize_/workflows/float8/test_float8_tensor.py::TestFloat8Tensor::test_fp8_linear_variants_float32_mode_weight-only_compile_False_granularity1_kernel_preference_KernelPreference_TORCH_sizes1 SKIPPED 2025-09-09T14:41:11.6962809Z test/quantization/quantize_/workflows/float8/test_float8_tensor.py::TestFloat8Tensor::test_fp8_linear_variants_float32_mode_weight-only_compile_True_granularity0_kernel_preference_KernelPreference_AUTO_sizes0 SKIPPED 2025-09-09T14:41:11.6964819Z test/quantization/quantize_/workflows/float8/test_float8_tensor.py::TestFloat8Tensor::test_fp8_linear_variants_float32_mode_weight-only_compile_True_granularity0_kernel_preference_KernelPreference_AUTO_sizes1 SKIPPED 2025-09-09T14:41:11.6966928Z test/quantization/quantize_/workflows/float8/test_float8_tensor.py::TestFloat8Tensor::test_fp8_linear_variants_float32_mode_weight-only_compile_True_granularity0_kernel_preference_KernelPreference_FBGEMM_sizes0 SKIPPED 2025-09-09T14:41:11.6969038Z test/quantization/quantize_/workflows/float8/test_float8_tensor.py::TestFloat8Tensor::test_fp8_linear_variants_float32_mode_weight-only_compile_True_granularity0_kernel_preference_KernelPreference_FBGEMM_sizes1 SKIPPED 2025-09-09T14:41:11.6971075Z test/quantization/quantize_/workflows/float8/test_float8_tensor.py::TestFloat8Tensor::test_fp8_linear_variants_float32_mode_weight-only_compile_True_granularity0_kernel_preference_KernelPreference_TORCH_sizes0 SKIPPED 2025-09-09T14:41:11.6973172Z test/quantization/quantize_/workflows/float8/test_float8_tensor.py::TestFloat8Tensor::test_fp8_linear_variants_float32_mode_weight-only_compile_True_granularity0_kernel_preference_KernelPreference_TORCH_sizes1 SKIPPED 2025-09-09T14:41:11.6975205Z 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test/quantization/quantize_/workflows/int4/test_int4_tensor.py::TestInt4Tensor::test_slice_and_copy_similar_to_vllm SKIPPED 2025-09-09T14:41:29.6259716Z test/quantization/quantize_/workflows/int4/test_int4_tensor.py::TestInt4Tensor::test_slice_preserves_aliasing SKIPPED 2025-09-09T14:41:29.6260880Z test/quantization/quantize_/workflows/int4/test_int4_tensor.py::TestInt4Tensor::test_to_device_sizes0 SKIPPED 2025-09-09T14:41:29.6262137Z test/quantization/quantize_/workflows/int4/test_int4_tensor.py::TestInt4Tensor::test_to_device_sizes1 SKIPPED 2025-09-09T14:41:29.6263217Z test/quantization/quantize_/workflows/int4/test_int4_tensor.py::TestInt4Tensor::test_to_device_sizes2 SKIPPED 2025-09-09T14:41:29.6264619Z test/quantization/quantize_/workflows/int4/test_int4_tile_packed_to_4d_tensor.py::TestInt4TilePackedTo4dTensor::test_cant_initialize_in_cpu SKIPPED 2025-09-09T14:41:29.6266218Z test/quantization/quantize_/workflows/int4/test_int4_tile_packed_to_4d_tensor.py::TestInt4TilePackedTo4dTensor::test_different_group_sizes_group_size_128 SKIPPED 2025-09-09T14:41:29.6267762Z test/quantization/quantize_/workflows/int4/test_int4_tile_packed_to_4d_tensor.py::TestInt4TilePackedTo4dTensor::test_different_group_sizes_group_size_32 SKIPPED 2025-09-09T14:41:29.6269349Z test/quantization/quantize_/workflows/int4/test_int4_tile_packed_to_4d_tensor.py::TestInt4TilePackedTo4dTensor::test_different_group_sizes_group_size_64 SKIPPED 2025-09-09T14:41:29.6270860Z test/quantization/quantize_/workflows/int4/test_int4_tile_packed_to_4d_tensor.py::TestInt4TilePackedTo4dTensor::test_error_conditions SKIPPED 2025-09-09T14:41:29.6272306Z test/quantization/quantize_/workflows/int4/test_int4_tile_packed_to_4d_tensor.py::TestInt4TilePackedTo4dTensor::test_linear_sizes0_config0 SKIPPED 2025-09-09T14:41:29.6273781Z test/quantization/quantize_/workflows/int4/test_int4_tile_packed_to_4d_tensor.py::TestInt4TilePackedTo4dTensor::test_linear_sizes0_config1 SKIPPED 2025-09-09T14:41:29.6275206Z test/quantization/quantize_/workflows/int4/test_int4_tile_packed_to_4d_tensor.py::TestInt4TilePackedTo4dTensor::test_linear_sizes1_config0 SKIPPED 2025-09-09T14:41:29.6276703Z test/quantization/quantize_/workflows/int4/test_int4_tile_packed_to_4d_tensor.py::TestInt4TilePackedTo4dTensor::test_linear_sizes1_config1 SKIPPED 2025-09-09T14:41:29.6278173Z test/quantization/quantize_/workflows/int4/test_int4_tile_packed_to_4d_tensor.py::TestInt4TilePackedTo4dTensor::test_linear_sizes2_config0 SKIPPED 2025-09-09T14:41:29.6279670Z test/quantization/quantize_/workflows/int4/test_int4_tile_packed_to_4d_tensor.py::TestInt4TilePackedTo4dTensor::test_linear_sizes2_config1 SKIPPED 2025-09-09T14:41:29.6281174Z test/quantization/quantize_/workflows/int4/test_int4_tile_packed_to_4d_tensor.py::TestInt4TilePackedTo4dTensor::test_mm_int4wo_device_cuda_bfloat16 SKIPPED 2025-09-09T14:41:29.6282682Z test/quantization/quantize_/workflows/int4/test_int4_tile_packed_to_4d_tensor.py::TestInt4TilePackedTo4dTensor::test_module_path_config0 SKIPPED 2025-09-09T14:41:29.6284081Z test/quantization/quantize_/workflows/int4/test_int4_tile_packed_to_4d_tensor.py::TestInt4TilePackedTo4dTensor::test_module_path_config1 SKIPPED 2025-09-09T14:41:29.6285567Z test/quantization/quantize_/workflows/int4/test_int4_tile_packed_to_4d_tensor.py::TestInt4TilePackedTo4dTensor::test_slice_and_copy_similar_to_vllm_config0 SKIPPED 2025-09-09T14:41:29.6287273Z test/quantization/quantize_/workflows/int4/test_int4_tile_packed_to_4d_tensor.py::TestInt4TilePackedTo4dTensor::test_slice_and_copy_similar_to_vllm_config1 SKIPPED 2025-09-09T14:41:29.6288785Z test/quantization/quantize_/workflows/int4/test_int4_tile_packed_to_4d_tensor.py::TestInt4TilePackedTo4dTensor::test_slice_config0 SKIPPED 2025-09-09T14:41:29.6290181Z test/quantization/quantize_/workflows/int4/test_int4_tile_packed_to_4d_tensor.py::TestInt4TilePackedTo4dTensor::test_slice_config1 SKIPPED 2025-09-09T14:41:29.6291697Z test/quantization/quantize_/workflows/int4/test_int4_tile_packed_to_4d_tensor.py::TestInt4TilePackedTo4dTensor::test_slice_preserves_aliasing_config0 SKIPPED 2025-09-09T14:41:29.6293199Z test/quantization/quantize_/workflows/int4/test_int4_tile_packed_to_4d_tensor.py::TestInt4TilePackedTo4dTensor::test_slice_preserves_aliasing_config1 SKIPPED 2025-09-09T14:41:29.6294670Z test/quantization/quantize_/workflows/int4/test_int4_tile_packed_to_4d_tensor.py::TestInt4TilePackedTo4dTensor::test_to_device SKIPPED 2025-09-09T14:41:29.6297119Z 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 2025-09-09T14:41:29.6300577Z 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 2025-09-09T14:41:29.6303760Z 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 2025-09-09T14:41:29.6415516Z 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 2025-09-09T14:41:29.6418704Z 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 2025-09-09T14:41:29.6421967Z 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 2025-09-09T14:41:29.6425320Z 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 2025-09-09T14:41:29.6428600Z 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 2025-09-09T14:41:29.6431921Z 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 2025-09-09T14:41:29.6435329Z 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 2025-09-09T14:41:29.6438710Z 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 2025-09-09T14:41:29.6441988Z 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 2025-09-09T14:41:29.6445294Z 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 2025-09-09T14:41:29.6448535Z 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 2025-09-09T14:41:29.6451851Z 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 2025-09-09T14:41:29.6455117Z 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 2025-09-09T14:41:29.6458434Z 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 2025-09-09T14:41:29.6461701Z 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 2025-09-09T14:41:29.6464938Z 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 2025-09-09T14:41:29.6468182Z 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 2025-09-09T14:41:29.6471439Z 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 2025-09-09T14:41:29.6474827Z 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 2025-09-09T14:41:29.6478293Z 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 2025-09-09T14:41:29.6481487Z 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 2025-09-09T14:41:29.6601941Z 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 2025-09-09T14:41:29.6605165Z 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 2025-09-09T14:41:29.6608520Z 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 2025-09-09T14:41:29.6611884Z 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 2025-09-09T14:41:29.6615207Z 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 2025-09-09T14:41:29.6618568Z 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 2025-09-09T14:41:29.6622797Z 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 2025-09-09T14:41:29.6626381Z 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 2025-09-09T14:41:29.6629723Z 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 2025-09-09T14:41:29.6633119Z 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 2025-09-09T14:41:29.6636515Z 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 2025-09-09T14:41:29.6639686Z 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 2025-09-09T14:41:29.6643007Z 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 2025-09-09T14:41:29.6646224Z 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 2025-09-09T14:41:29.6649421Z 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 2025-09-09T14:41:29.6652801Z 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 2025-09-09T14:41:29.6656212Z 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 2025-09-09T14:41:29.6659371Z 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 2025-09-09T14:41:29.6662752Z 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 2025-09-09T14:41:29.6665953Z 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 2025-09-09T14:41:29.6669259Z 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 2025-09-09T14:41:29.6788697Z 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 2025-09-09T14:41:29.6791960Z 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 2025-09-09T14:41:29.6795265Z 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 2025-09-09T14:41:29.6798671Z 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 2025-09-09T14:41:29.6802110Z 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 2025-09-09T14:41:29.6805594Z 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 2025-09-09T14:41:29.6808767Z 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 2025-09-09T14:41:29.6812185Z 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 2025-09-09T14:41:29.6815412Z 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 2025-09-09T14:41:29.6818669Z 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 2025-09-09T14:41:29.6821973Z 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 2025-09-09T14:41:29.6825259Z 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 2025-09-09T14:41:29.6828550Z 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 2025-09-09T14:41:29.6831898Z 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 2025-09-09T14:41:29.6835220Z 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 2025-09-09T14:41:29.6838688Z 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 2025-09-09T14:41:29.6841947Z 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 2025-09-09T14:41:29.6845365Z 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 2025-09-09T14:41:29.6848649Z 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 2025-09-09T14:41:29.6851862Z 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 2025-09-09T14:41:29.6855296Z 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 2025-09-09T14:41:29.6981308Z 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 2025-09-09T14:41:29.6984537Z 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 2025-09-09T14:41:29.6987828Z 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 2025-09-09T14:41:29.6991062Z 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 2025-09-09T14:41:29.6994542Z 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 2025-09-09T14:41:29.6998041Z 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 2025-09-09T14:41:29.7001352Z 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 2025-09-09T14:41:29.7004694Z 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 2025-09-09T14:41:29.7007925Z 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 2025-09-09T14:41:29.7011196Z 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 2025-09-09T14:41:29.7014546Z 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 2025-09-09T14:41:29.7017835Z 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 2025-09-09T14:41:29.7021178Z 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 2025-09-09T14:41:29.7024615Z 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 2025-09-09T14:41:29.7027912Z 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 2025-09-09T14:41:29.7031225Z 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 2025-09-09T14:41:29.7034482Z 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 2025-09-09T14:41:29.7038032Z 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 2025-09-09T14:41:29.7041360Z 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 2025-09-09T14:41:29.7044537Z 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 2025-09-09T14:41:29.7047816Z 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 2025-09-09T14:41:29.7164495Z 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 2025-09-09T14:41:29.7167771Z 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 2025-09-09T14:41:29.7171178Z 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 2025-09-09T14:41:29.7174560Z 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 2025-09-09T14:41:29.7177870Z 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 2025-09-09T14:41:29.7181153Z 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 2025-09-09T14:41:29.7184385Z 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 2025-09-09T14:41:29.7187689Z 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 2025-09-09T14:41:29.7191108Z 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 2025-09-09T14:41:29.7194359Z 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 2025-09-09T14:41:29.7197787Z 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 2025-09-09T14:41:29.7201140Z 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 2025-09-09T14:41:29.7204392Z 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 2025-09-09T14:41:29.7207721Z 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 2025-09-09T14:41:29.7211085Z 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 2025-09-09T14:41:29.7214424Z 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 2025-09-09T14:41:29.7217853Z 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 2025-09-09T14:41:29.7221125Z 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 2025-09-09T14:41:29.7224771Z 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 2025-09-09T14:41:29.7228119Z 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 2025-09-09T14:41:29.7231468Z 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 2025-09-09T14:41:29.7349512Z 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 2025-09-09T14:41:29.7352773Z 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 2025-09-09T14:41:29.7356417Z 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 2025-09-09T14:41:29.7359776Z 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 2025-09-09T14:41:29.7363177Z 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 2025-09-09T14:41:29.7366629Z 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 2025-09-09T14:41:29.7369884Z 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 2025-09-09T14:41:29.7373214Z 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 2025-09-09T14:41:29.7376580Z 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 2025-09-09T14:41:29.7380050Z 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 2025-09-09T14:41:29.7383453Z 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 2025-09-09T14:41:29.7386669Z 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 2025-09-09T14:41:29.7390097Z 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 2025-09-09T14:41:29.7393386Z 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 2025-09-09T14:41:29.7396800Z 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 2025-09-09T14:41:29.7400283Z 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 2025-09-09T14:41:29.7403687Z 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 2025-09-09T14:41:29.7406979Z 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 2025-09-09T14:41:29.7410367Z 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 2025-09-09T14:41:29.7413593Z 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 2025-09-09T14:41:29.7417001Z 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 2025-09-09T14:41:29.7534440Z 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 2025-09-09T14:41:29.7537954Z 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 2025-09-09T14:41:29.7541381Z 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 2025-09-09T14:41:29.7544676Z 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 2025-09-09T14:41:29.7548029Z 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 2025-09-09T14:41:29.7551420Z 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 2025-09-09T14:41:29.7554815Z 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 2025-09-09T14:41:29.7558274Z 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 2025-09-09T14:41:29.7561556Z 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 2025-09-09T14:41:29.7564885Z 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 2025-09-09T14:41:29.7568248Z 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 2025-09-09T14:41:29.7571590Z 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 2025-09-09T14:41:29.7575115Z 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 2025-09-09T14:41:29.7578484Z 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 2025-09-09T14:41:29.7581748Z 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 2025-09-09T14:41:29.7585000Z 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 2025-09-09T14:41:29.7588142Z 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 2025-09-09T14:41:29.7591402Z 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 2025-09-09T14:41:29.7594648Z 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 2025-09-09T14:41:29.7597828Z 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 2025-09-09T14:41:29.7601127Z 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 2025-09-09T14:41:29.7719511Z 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 2025-09-09T14:41:29.7722957Z 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 2025-09-09T14:41:29.7726599Z 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 2025-09-09T14:41:29.7729754Z 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 2025-09-09T14:41:29.7733010Z 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 2025-09-09T14:41:29.7736267Z 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 2025-09-09T14:41:29.7739462Z 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 2025-09-09T14:41:29.7742884Z 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 2025-09-09T14:41:29.7746148Z 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 2025-09-09T14:41:29.7749398Z 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 2025-09-09T14:41:29.7752648Z 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 2025-09-09T14:41:29.7755870Z 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 2025-09-09T14:41:29.7759239Z 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 2025-09-09T14:41:29.7762722Z 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 2025-09-09T14:41:29.7766027Z 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 2025-09-09T14:41:29.7769292Z 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 2025-09-09T14:41:29.7772405Z 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 2025-09-09T14:41:29.7775642Z 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 2025-09-09T14:41:29.7778952Z 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 2025-09-09T14:41:29.7782231Z 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 2025-09-09T14:41:29.7785546Z 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 2025-09-09T14:41:29.7901221Z 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 2025-09-09T14:41:29.7904399Z 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 2025-09-09T14:41:29.7907744Z 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 2025-09-09T14:41:29.7911005Z 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 2025-09-09T14:41:29.7914384Z 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 2025-09-09T14:41:29.7917759Z 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 2025-09-09T14:41:29.7920885Z 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 2025-09-09T14:41:29.7924146Z 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 2025-09-09T14:41:29.7927549Z 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 2025-09-09T14:41:29.7930758Z 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 2025-09-09T14:41:29.7934246Z 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 2025-09-09T14:41:29.7937535Z 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 2025-09-09T14:41:29.7940814Z 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 2025-09-09T14:41:29.7944066Z 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 2025-09-09T14:41:29.7947264Z 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 2025-09-09T14:41:29.7950576Z 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 2025-09-09T14:41:29.7953936Z 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 2025-09-09T14:41:29.7957224Z 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 2025-09-09T14:41:29.7960530Z 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 2025-09-09T14:41:29.7963876Z 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 2025-09-09T14:41:29.7967363Z 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 2025-09-09T14:41:29.8086341Z 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 2025-09-09T14:41:29.8090314Z 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 2025-09-09T14:41:29.8093499Z 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 2025-09-09T14:41:29.8096685Z 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 2025-09-09T14:41:29.8099881Z 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 2025-09-09T14:41:29.8103282Z 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 2025-09-09T14:41:29.8106528Z 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 2025-09-09T14:41:29.8109676Z 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 2025-09-09T14:41:29.8112863Z 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 2025-09-09T14:41:29.8116338Z 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 2025-09-09T14:41:29.8119564Z 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 2025-09-09T14:41:29.8122781Z 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 2025-09-09T14:41:29.8126142Z 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 2025-09-09T14:41:29.8129283Z 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 2025-09-09T14:41:29.8132497Z 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 2025-09-09T14:41:29.8135771Z 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 2025-09-09T14:41:29.8138981Z 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 2025-09-09T14:41:29.8142141Z 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 2025-09-09T14:41:29.8145269Z 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 2025-09-09T14:41:29.8148516Z 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 2025-09-09T14:41:29.8151729Z 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 2025-09-09T14:41:29.8276658Z 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 2025-09-09T14:41:29.8280038Z 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 2025-09-09T14:41:29.8283174Z 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 2025-09-09T14:41:29.8286329Z 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 2025-09-09T14:41:29.8289464Z 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 2025-09-09T14:41:29.8292683Z 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 2025-09-09T14:41:29.8295950Z 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 2025-09-09T14:41:29.8299146Z 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 2025-09-09T14:41:29.8302284Z 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 2025-09-09T14:41:29.8305537Z 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 2025-09-09T14:41:29.8308681Z 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 2025-09-09T14:41:29.8311953Z 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 2025-09-09T14:41:29.8315218Z 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 2025-09-09T14:41:29.8318475Z 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 2025-09-09T14:41:29.8321641Z 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 2025-09-09T14:41:29.8324934Z 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 2025-09-09T14:41:29.8328080Z 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 2025-09-09T14:41:29.8331286Z 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 2025-09-09T14:41:29.8334549Z 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 2025-09-09T14:41:29.8337841Z 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 2025-09-09T14:41:29.8340992Z 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 2025-09-09T14:41:29.8461262Z 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 2025-09-09T14:41:29.8464410Z 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 2025-09-09T14:41:29.8467599Z 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 2025-09-09T14:41:29.8470893Z 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 2025-09-09T14:41:29.8474120Z 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 2025-09-09T14:41:29.8477386Z 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 2025-09-09T14:41:29.8480565Z 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 2025-09-09T14:41:29.8483759Z 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 2025-09-09T14:41:29.8487054Z 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 2025-09-09T14:41:29.8490267Z 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 2025-09-09T14:41:29.8493538Z 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 2025-09-09T14:41:29.8496733Z 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 2025-09-09T14:41:29.8499991Z 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 2025-09-09T14:41:29.8503311Z 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 2025-09-09T14:41:29.8506558Z 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 2025-09-09T14:41:29.8509776Z 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 2025-09-09T14:41:29.8512967Z 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 2025-09-09T14:41:29.8516163Z 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 2025-09-09T14:41:29.8519546Z 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 2025-09-09T14:41:29.8522873Z 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 2025-09-09T14:41:29.8526335Z 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 2025-09-09T14:41:29.8643199Z 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 2025-09-09T14:41:29.8646534Z 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 2025-09-09T14:41:29.8649759Z 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 2025-09-09T14:41:29.8653029Z 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 2025-09-09T14:41:29.8656385Z 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 2025-09-09T14:41:29.8659639Z 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 2025-09-09T14:41:29.8662850Z 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 2025-09-09T14:41:29.8666241Z 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 2025-09-09T14:41:29.8669450Z 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 2025-09-09T14:41:29.8672839Z 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 2025-09-09T14:41:29.8676152Z 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 2025-09-09T14:41:29.8679500Z 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 2025-09-09T14:41:29.8682722Z 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 2025-09-09T14:41:29.8685925Z 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 2025-09-09T14:41:29.8689135Z 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 2025-09-09T14:41:29.8692398Z 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 2025-09-09T14:41:29.8695731Z 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 2025-09-09T14:41:29.8699067Z 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 2025-09-09T14:41:29.8702289Z 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 2025-09-09T14:41:29.8705538Z 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 2025-09-09T14:41:29.8708746Z 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 2025-09-09T14:41:30.1027542Z 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 2025-09-09T14:41:30.1032187Z 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 2025-09-09T14:41:30.1036705Z 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 2025-09-09T14:41:30.1041633Z 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 2025-09-09T14:41:30.1046718Z 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 2025-09-09T14:41:30.1051624Z 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 2025-09-09T14:41:30.1057476Z 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 2025-09-09T14:41:30.1063760Z 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 2025-09-09T14:41:30.1067875Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_export_compile_aoti SKIPPED 2025-09-09T14:41:30.1069834Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_moe_quant_intx SKIPPED 2025-09-09T14:41:30.1072454Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_serialization_{'packing_format': , 'compute_target': 'aten'} SKIPPED 2025-09-09T14:41:30.1075123Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_serialization_{'packing_format': , 'compute_target': 'torchao_auto'} SKIPPED 2025-09-09T14:41:30.1077424Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_embedding PASSED 2025-09-09T14:41:30.1079418Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_export_int8_dyn_act_intx_weight_config PASSED 2025-09-09T14:41:30.1081645Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_export_int8_dyn_act_intx_weight_config_with_unwrap PASSED 2025-09-09T14:41:30.1083803Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_export_intx_weight_only_config PASSED 2025-09-09T14:41:30.1087065Z 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 2025-09-09T14:41:30.1091765Z 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 2025-09-09T14:41:30.1096631Z 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 2025-09-09T14:41:30.1101264Z 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 2025-09-09T14:41:30.1106635Z 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 2025-09-09T14:41:30.1112027Z 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 2025-09-09T14:41:30.1117176Z 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 2025-09-09T14:41:30.1122314Z 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 2025-09-09T14:41:30.1127247Z 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 2025-09-09T14:41:30.1131442Z 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 2025-09-09T14:41:30.2170212Z 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 2025-09-09T14:41:30.2174594Z 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 2025-09-09T14:41:30.2178785Z 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 2025-09-09T14:41:30.2183156Z 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 2025-09-09T14:41:30.2188361Z 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 2025-09-09T14:41:30.2193008Z 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 2025-09-09T14:41:30.2197460Z 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 2025-09-09T14:41:30.2201952Z 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 2025-09-09T14:41:30.2206797Z 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 2025-09-09T14:41:30.2211194Z 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 2025-09-09T14:41:30.2215366Z 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 2025-09-09T14:41:30.2219553Z 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 2025-09-09T14:41:30.2223741Z 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 2025-09-09T14:41:30.2228065Z 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 2025-09-09T14:41:30.2232381Z 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 2025-09-09T14:41:30.2237486Z 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 2025-09-09T14:41:30.2242419Z 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 2025-09-09T14:41:30.2246754Z 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 2025-09-09T14:41:30.2251294Z 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 2025-09-09T14:41:30.2256265Z 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 2025-09-09T14:41:30.2261161Z 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 2025-09-09T14:41:30.2265967Z 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 2025-09-09T14:41:30.3306470Z 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 2025-09-09T14:41:30.3310735Z 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 2025-09-09T14:41:30.3315518Z 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 2025-09-09T14:41:30.3321269Z 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 2025-09-09T14:41:30.3326873Z 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 2025-09-09T14:41:30.3331225Z 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 2025-09-09T14:41:30.3337225Z 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 2025-09-09T14:41:30.3343043Z 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 2025-09-09T14:41:30.3348045Z 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 2025-09-09T14:41:30.3354001Z 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 2025-09-09T14:41:30.3358926Z 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 2025-09-09T14:41:30.3363124Z 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 2025-09-09T14:41:30.3367298Z 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 2025-09-09T14:41:30.3371695Z 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 2025-09-09T14:41:30.3376694Z 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 2025-09-09T14:41:30.3381841Z 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 2025-09-09T14:41:30.3386665Z 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 2025-09-09T14:41:30.3391712Z 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 2025-09-09T14:41:30.3396116Z 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 2025-09-09T14:41:30.3401049Z 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 2025-09-09T14:41:30.3406056Z 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 2025-09-09T14:41:30.3410228Z 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 2025-09-09T14:41:30.4458628Z 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 2025-09-09T14:41:30.4463310Z 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 2025-09-09T14:41:30.4467533Z 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 2025-09-09T14:41:30.4472261Z 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 2025-09-09T14:41:30.4477241Z 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 2025-09-09T14:41:30.4481820Z 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 2025-09-09T14:41:30.4486962Z 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 2025-09-09T14:41:30.4492414Z 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 2025-09-09T14:41:30.4497842Z 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 2025-09-09T14:41:30.4502894Z 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 2025-09-09T14:41:30.4507582Z 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 2025-09-09T14:41:30.4511930Z 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 2025-09-09T14:41:30.4516115Z 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 2025-09-09T14:41:30.4520542Z 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 2025-09-09T14:41:30.4525754Z 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 2025-09-09T14:41:30.4530658Z 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 2025-09-09T14:41:30.4535265Z 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 2025-09-09T14:41:30.4540500Z 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 2025-09-09T14:41:30.4545730Z 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 2025-09-09T14:41:30.4550861Z 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 2025-09-09T14:41:30.4555074Z 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 2025-09-09T14:41:30.4559343Z 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 2025-09-09T14:41:30.5606750Z 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 2025-09-09T14:41:30.5611004Z 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 2025-09-09T14:41:30.5615361Z 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 2025-09-09T14:41:30.5619840Z 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 2025-09-09T14:41:30.5624020Z 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 2025-09-09T14:41:30.5628409Z 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 2025-09-09T14:41:30.5632891Z 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 2025-09-09T14:41:30.5637434Z 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 2025-09-09T14:41:30.5641613Z 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 2025-09-09T14:41:30.5645946Z 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 2025-09-09T14:41:30.5651128Z 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 2025-09-09T14:41:30.5655352Z 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 2025-09-09T14:41:30.5659624Z 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 2025-09-09T14:41:30.5664332Z 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 2025-09-09T14:41:30.5669350Z 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 2025-09-09T14:41:30.5674426Z 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 2025-09-09T14:41:30.5679533Z 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 2025-09-09T14:41:30.5684174Z 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 2025-09-09T14:41:30.5690310Z 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 2025-09-09T14:41:30.5695807Z 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 2025-09-09T14:41:30.5701383Z 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 2025-09-09T14:41:30.5706256Z 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 2025-09-09T14:41:30.6742102Z 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 2025-09-09T14:41:30.6746368Z 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 2025-09-09T14:41:30.6751132Z 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 2025-09-09T14:41:30.6756735Z 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 2025-09-09T14:41:30.6761597Z 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 2025-09-09T14:41:30.6766398Z 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 2025-09-09T14:41:30.6770931Z 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 2025-09-09T14:41:30.6776414Z 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 2025-09-09T14:41:30.6781413Z 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 2025-09-09T14:41:30.6787013Z 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 2025-09-09T14:41:30.6791974Z 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 2025-09-09T14:41:30.6796400Z 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 2025-09-09T14:41:30.6800591Z 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 2025-09-09T14:41:30.6804837Z 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 2025-09-09T14:41:30.6809120Z 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 2025-09-09T14:41:30.6814151Z 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 2025-09-09T14:41:30.6818870Z 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 2025-09-09T14:41:30.6823770Z 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 2025-09-09T14:41:30.6829526Z 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 2025-09-09T14:41:30.6834917Z 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 2025-09-09T14:41:30.6840077Z 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 2025-09-09T14:41:30.6844968Z 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 2025-09-09T14:41:30.7895533Z 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 2025-09-09T14:41:30.7899770Z 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 2025-09-09T14:41:30.7904020Z 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 2025-09-09T14:41:30.7908808Z 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 2025-09-09T14:41:30.7913700Z 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 2025-09-09T14:41:30.7918540Z 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 2025-09-09T14:41:30.7923642Z 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 2025-09-09T14:41:30.7928878Z 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 2025-09-09T14:41:30.7933789Z 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 2025-09-09T14:41:30.7938752Z 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 2025-09-09T14:41:30.7942926Z 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 2025-09-09T14:41:30.7947117Z 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 2025-09-09T14:41:30.7951326Z 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 2025-09-09T14:41:30.7955706Z 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 2025-09-09T14:41:30.7960154Z 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 2025-09-09T14:41:30.7964345Z 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 2025-09-09T14:41:30.7968738Z 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 2025-09-09T14:41:30.7973414Z 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 2025-09-09T14:41:30.7977743Z 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 2025-09-09T14:41:30.7982123Z 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 2025-09-09T14:41:30.7986400Z 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 2025-09-09T14:41:30.7990583Z 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 2025-09-09T14:41:30.9059249Z 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 2025-09-09T14:41:30.9063523Z 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 2025-09-09T14:41:30.9067763Z 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 2025-09-09T14:41:30.9072084Z 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 2025-09-09T14:41:30.9077254Z 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 2025-09-09T14:41:30.9081978Z 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 2025-09-09T14:41:30.9087221Z 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 2025-09-09T14:41:30.9092719Z 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 2025-09-09T14:41:30.9097801Z 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 2025-09-09T14:41:30.9102542Z 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 2025-09-09T14:41:30.9106761Z 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 2025-09-09T14:41:30.9110982Z 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 2025-09-09T14:41:30.9115175Z 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 2025-09-09T14:41:30.9119434Z 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 2025-09-09T14:41:30.9124750Z 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 2025-09-09T14:41:30.9129966Z 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 2025-09-09T14:41:30.9134893Z 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 2025-09-09T14:41:30.9139621Z 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 2025-09-09T14:41:30.9144932Z 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 2025-09-09T14:41:30.9149956Z 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 2025-09-09T14:41:30.9154902Z 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 2025-09-09T14:41:30.9159645Z 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 2025-09-09T14:41:31.0222676Z 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 2025-09-09T14:41:31.0227120Z 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 2025-09-09T14:41:31.0231798Z 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 2025-09-09T14:41:31.0237191Z 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 2025-09-09T14:41:31.0243284Z 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 2025-09-09T14:41:31.0247499Z 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 2025-09-09T14:41:31.0253456Z 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 2025-09-09T14:41:31.0259201Z 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 2025-09-09T14:41:31.0264222Z 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 2025-09-09T14:41:31.0269885Z 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 2025-09-09T14:41:31.0275011Z 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 2025-09-09T14:41:31.0279396Z 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 2025-09-09T14:41:31.0283588Z 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 2025-09-09T14:41:31.0287743Z 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 2025-09-09T14:41:31.0291985Z 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 2025-09-09T14:41:31.0297165Z 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 2025-09-09T14:41:31.0301855Z 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 2025-09-09T14:41:31.0306618Z 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 2025-09-09T14:41:31.0311600Z 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 2025-09-09T14:41:31.0316079Z 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 2025-09-09T14:41:31.0321021Z 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 2025-09-09T14:41:31.0326017Z 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 2025-09-09T14:41:31.1389226Z 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 2025-09-09T14:41:31.1395339Z 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 2025-09-09T14:41:31.1401496Z 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 2025-09-09T14:41:31.1408447Z 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 2025-09-09T14:41:31.1416070Z 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 2025-09-09T14:41:31.1422739Z 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 2025-09-09T14:41:31.1430266Z 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 2025-09-09T14:41:31.1436720Z 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 2025-09-09T14:41:31.1443068Z 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 2025-09-09T14:41:31.1450704Z 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 2025-09-09T14:41:31.1456452Z 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 2025-09-09T14:41:31.1462316Z 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 2025-09-09T14:41:31.1468212Z 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 2025-09-09T14:41:31.1474900Z 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 2025-09-09T14:41:31.1481290Z 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 2025-09-09T14:41:31.1487554Z 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 2025-09-09T14:41:31.1493998Z 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 2025-09-09T14:41:31.1500331Z 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 2025-09-09T14:41:31.1507767Z 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 2025-09-09T14:41:31.1513988Z 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 2025-09-09T14:41:31.1520689Z 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 2025-09-09T14:41:31.1526612Z 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 2025-09-09T14:41:31.3689319Z 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 2025-09-09T14:41:31.3695292Z 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 2025-09-09T14:41:31.3701538Z 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 2025-09-09T14:41:31.3707529Z 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 2025-09-09T14:41:31.3713483Z 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 2025-09-09T14:41:31.3721190Z 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 2025-09-09T14:41:31.3727156Z 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 2025-09-09T14:41:31.3733005Z 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 2025-09-09T14:41:31.3736883Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_linear PASSED 2025-09-09T14:41:31.3740728Z 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 2025-09-09T14:41:31.3745703Z 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 2025-09-09T14:41:31.3750574Z 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 2025-09-09T14:41:31.3755854Z 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 2025-09-09T14:41:31.3762157Z 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 2025-09-09T14:41:31.3768445Z 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 2025-09-09T14:41:31.3775442Z 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 2025-09-09T14:41:31.3781735Z 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 2025-09-09T14:41:31.3788314Z 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 2025-09-09T14:41:31.3793822Z 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 2025-09-09T14:41:31.3798849Z 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 2025-09-09T14:41:31.3803738Z 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 2025-09-09T14:41:31.3808708Z 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 2025-09-09T14:41:31.3813645Z 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 2025-09-09T14:41:31.3818632Z 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 2025-09-09T14:41:31.6626324Z 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 2025-09-09T14:41:31.6631888Z 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 2025-09-09T14:41:31.6637110Z 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 2025-09-09T14:41:31.6642282Z 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 2025-09-09T14:41:31.6647538Z 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 2025-09-09T14:41:31.6652975Z 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 2025-09-09T14:41:31.6658925Z 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 2025-09-09T14:41:31.6664037Z 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 2025-09-09T14:41:31.6669082Z 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 2025-09-09T14:41:31.6674260Z 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 2025-09-09T14:41:31.6679285Z 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 2025-09-09T14:41:31.6684753Z 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 2025-09-09T14:41:31.6691194Z 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 2025-09-09T14:41:31.6697463Z 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 2025-09-09T14:41:31.6703457Z 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 2025-09-09T14:41:31.6710418Z 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 2025-09-09T14:41:31.6715617Z 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 2025-09-09T14:41:31.6720782Z 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 2025-09-09T14:41:31.6726168Z 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 2025-09-09T14:41:31.6731083Z 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 2025-09-09T14:41:31.6736569Z 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 2025-09-09T14:41:31.6742918Z 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 2025-09-09T14:41:31.6748776Z 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 2025-09-09T14:41:31.6754999Z 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 2025-09-09T14:41:31.6761223Z 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 2025-09-09T14:41:31.9535802Z 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 2025-09-09T14:41:31.9541475Z 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 2025-09-09T14:41:31.9546657Z 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 2025-09-09T14:41:31.9551730Z 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 2025-09-09T14:41:31.9556953Z 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 2025-09-09T14:41:31.9562254Z 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 2025-09-09T14:41:31.9568487Z 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 2025-09-09T14:41:31.9573861Z 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 2025-09-09T14:41:31.9579068Z 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 2025-09-09T14:41:31.9584384Z 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 2025-09-09T14:41:31.9589744Z 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 2025-09-09T14:41:31.9595261Z 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 2025-09-09T14:41:31.9600485Z 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 2025-09-09T14:41:31.9606516Z 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 2025-09-09T14:41:31.9612877Z 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 2025-09-09T14:41:31.9618332Z 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 2025-09-09T14:41:31.9625055Z 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 2025-09-09T14:41:31.9631492Z 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 2025-09-09T14:41:31.9637087Z 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 2025-09-09T14:41:31.9642247Z 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 2025-09-09T14:41:31.9647667Z 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 2025-09-09T14:41:31.9653022Z 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 2025-09-09T14:41:31.9657833Z 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 2025-09-09T14:41:31.9663371Z 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 2025-09-09T14:41:31.9669672Z 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 2025-09-09T14:41:32.2462539Z 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 2025-09-09T14:41:32.2467809Z 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 2025-09-09T14:41:32.2472963Z 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 2025-09-09T14:41:32.2490393Z 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 2025-09-09T14:41:32.2497012Z 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 2025-09-09T14:41:32.2502004Z 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 2025-09-09T14:41:32.2506933Z 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 2025-09-09T14:41:32.2511922Z 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 2025-09-09T14:41:32.2516994Z 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 2025-09-09T14:41:32.2522262Z 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 2025-09-09T14:41:32.2527605Z 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 2025-09-09T14:41:32.2533841Z 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 2025-09-09T14:41:32.2540167Z 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 2025-09-09T14:41:32.2546586Z 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 2025-09-09T14:41:32.2552066Z 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 2025-09-09T14:41:32.2558557Z 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 2025-09-09T14:41:32.2564292Z 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 2025-09-09T14:41:32.2569273Z 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 2025-09-09T14:41:32.2574245Z 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 2025-09-09T14:41:32.2579233Z 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 2025-09-09T14:41:32.2584206Z 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 2025-09-09T14:41:32.2589370Z 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 2025-09-09T14:41:32.2594781Z 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 2025-09-09T14:41:32.2601404Z 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 2025-09-09T14:41:32.2607223Z 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 2025-09-09T14:41:32.5422115Z 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 2025-09-09T14:41:32.5427644Z 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 2025-09-09T14:41:32.5432870Z 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 2025-09-09T14:41:32.5437975Z 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 2025-09-09T14:41:32.5443142Z 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 2025-09-09T14:41:32.5448496Z 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 2025-09-09T14:41:32.5454596Z 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 2025-09-09T14:41:32.5459750Z 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 2025-09-09T14:41:32.5465088Z 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 2025-09-09T14:41:32.5470282Z 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 2025-09-09T14:41:32.5475427Z 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 2025-09-09T14:41:32.5480968Z 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 2025-09-09T14:41:32.5486861Z 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 2025-09-09T14:41:32.5492785Z 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 2025-09-09T14:41:32.5498160Z 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 2025-09-09T14:41:32.5504200Z 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 2025-09-09T14:41:32.5509328Z 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 2025-09-09T14:41:32.5514320Z 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 2025-09-09T14:41:32.5519621Z 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 2025-09-09T14:41:32.5524918Z 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 2025-09-09T14:41:32.5531216Z 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 2025-09-09T14:41:32.5537278Z 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 2025-09-09T14:41:32.5544084Z 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 2025-09-09T14:41:32.5549609Z 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 2025-09-09T14:41:32.5555046Z 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 2025-09-09T14:41:32.8347692Z 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 2025-09-09T14:41:32.8352956Z 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 2025-09-09T14:41:32.8358285Z 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 2025-09-09T14:41:32.8363437Z 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 2025-09-09T14:41:32.8368605Z 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 2025-09-09T14:41:32.8373975Z 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 2025-09-09T14:41:32.8380709Z 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 2025-09-09T14:41:32.8386074Z 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 2025-09-09T14:41:32.8391170Z 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 2025-09-09T14:41:32.8396902Z 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 2025-09-09T14:41:32.8402266Z 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 2025-09-09T14:41:32.8407281Z 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 2025-09-09T14:41:32.8412477Z 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 2025-09-09T14:41:32.8417965Z 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 2025-09-09T14:41:32.8423591Z 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 2025-09-09T14:41:32.8429340Z 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 2025-09-09T14:41:32.8435131Z 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 2025-09-09T14:41:32.8440608Z 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 2025-09-09T14:41:32.8446196Z 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 2025-09-09T14:41:32.8451331Z 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 2025-09-09T14:41:32.8456896Z 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 2025-09-09T14:41:32.8462210Z 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 2025-09-09T14:41:32.8467039Z 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 2025-09-09T14:41:32.8472972Z 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 2025-09-09T14:41:32.8478981Z 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 2025-09-09T14:41:33.1327743Z 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 2025-09-09T14:41:33.1332989Z 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 2025-09-09T14:41:33.1338212Z 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 2025-09-09T14:41:33.1343326Z 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 2025-09-09T14:41:33.1348530Z 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 2025-09-09T14:41:33.1354587Z 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 2025-09-09T14:41:33.1361212Z 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 2025-09-09T14:41:33.1366317Z 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 2025-09-09T14:41:33.1371217Z 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 2025-09-09T14:41:33.1376156Z 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 2025-09-09T14:41:33.1381107Z 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 2025-09-09T14:41:33.1386034Z 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 2025-09-09T14:41:33.1391082Z 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 2025-09-09T14:41:33.1396682Z 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 2025-09-09T14:41:33.1402664Z 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 2025-09-09T14:41:33.1408811Z 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 2025-09-09T14:41:33.1415474Z 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 2025-09-09T14:41:33.1420833Z 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 2025-09-09T14:41:33.1425941Z 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 2025-09-09T14:41:33.1431037Z 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 2025-09-09T14:41:33.1435998Z 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 2025-09-09T14:41:33.1441030Z 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 2025-09-09T14:41:33.1445943Z 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 2025-09-09T14:41:33.1451433Z 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 2025-09-09T14:41:33.1456932Z 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 2025-09-09T14:41:33.4320810Z 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 2025-09-09T14:41:33.4326101Z 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 2025-09-09T14:41:33.4331340Z 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 2025-09-09T14:41:33.4336881Z 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 2025-09-09T14:41:33.4342090Z 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 2025-09-09T14:41:33.4347247Z 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 2025-09-09T14:41:33.4353476Z 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 2025-09-09T14:41:33.4358700Z 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 2025-09-09T14:41:33.4363889Z 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 2025-09-09T14:41:33.4369098Z 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 2025-09-09T14:41:33.4374146Z 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 2025-09-09T14:41:33.4379438Z 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 2025-09-09T14:41:33.4384840Z 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 2025-09-09T14:41:33.4391216Z 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 2025-09-09T14:41:33.4396978Z 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 2025-09-09T14:41:33.4402940Z 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 2025-09-09T14:41:33.4408991Z 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 2025-09-09T14:41:33.4414399Z 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 2025-09-09T14:41:33.4419454Z 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 2025-09-09T14:41:33.4424793Z 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 2025-09-09T14:41:33.4429866Z 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 2025-09-09T14:41:33.4435777Z 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 2025-09-09T14:41:33.4441597Z 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 2025-09-09T14:41:33.4447265Z 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 2025-09-09T14:41:33.4453748Z 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 2025-09-09T14:41:33.7256369Z 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 2025-09-09T14:41:33.7261715Z 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 2025-09-09T14:41:33.7267322Z 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 2025-09-09T14:41:33.7272705Z 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 2025-09-09T14:41:33.7279822Z 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 2025-09-09T14:41:33.7285169Z 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 2025-09-09T14:41:33.7290431Z 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 2025-09-09T14:41:33.7295574Z 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 2025-09-09T14:41:33.7300281Z 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 2025-09-09T14:41:33.7304918Z 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 2025-09-09T14:41:33.7309787Z 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 2025-09-09T14:41:33.7314947Z 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 2025-09-09T14:41:33.7319926Z 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 2025-09-09T14:41:33.7325568Z 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 2025-09-09T14:41:33.7330941Z 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 2025-09-09T14:41:33.7336228Z 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 2025-09-09T14:41:33.7341261Z 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 2025-09-09T14:41:33.7346018Z 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 2025-09-09T14:41:33.7351211Z 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 2025-09-09T14:41:33.7356487Z 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 2025-09-09T14:41:33.7361647Z 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 2025-09-09T14:41:33.7367109Z 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 2025-09-09T14:41:33.7372415Z 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 2025-09-09T14:41:33.7377665Z 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 2025-09-09T14:41:33.7382520Z 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 2025-09-09T14:41:33.8116436Z 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 2025-09-09T14:41:33.8120713Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_serialization_int8_dyn_act_intx_weight_config PASSED 2025-09-09T14:41:33.8123693Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_serialization_intx_weight_only_config PASSED 2025-09-09T14:41:33.8126819Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_slice PASSED 2025-09-09T14:41:33.8129295Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_slice_and_copy_ PASSED 2025-09-09T14:41:33.8131787Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_to_dtype PASSED 2025-09-09T14:41:33.8133960Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_concat_linear_cpu_x_dim_2_bias_False SKIPPED 2025-09-09T14:41:33.8135816Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_concat_linear_cpu_x_dim_2_bias_True SKIPPED 2025-09-09T14:41:33.8137678Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_concat_linear_cpu_x_dim_3_bias_False SKIPPED 2025-09-09T14:41:33.8139523Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_concat_linear_cpu_x_dim_3_bias_True SKIPPED 2025-09-09T14:41:33.8141506Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_bfloat16_x_dim_2_bias_False_bs_160_sym_quant_a_False SKIPPED 2025-09-09T14:41:33.8143567Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_bfloat16_x_dim_2_bias_False_bs_160_sym_quant_a_True SKIPPED 2025-09-09T14:41:33.8145721Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_bfloat16_x_dim_2_bias_False_bs_1_sym_quant_a_False SKIPPED 2025-09-09T14:41:33.8147780Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_bfloat16_x_dim_2_bias_False_bs_1_sym_quant_a_True SKIPPED 2025-09-09T14:41:33.8149905Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_bfloat16_x_dim_2_bias_True_bs_160_sym_quant_a_False SKIPPED 2025-09-09T14:41:33.8151946Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_bfloat16_x_dim_2_bias_True_bs_160_sym_quant_a_True SKIPPED 2025-09-09T14:41:33.8154018Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_bfloat16_x_dim_2_bias_True_bs_1_sym_quant_a_False SKIPPED 2025-09-09T14:41:33.8156082Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_bfloat16_x_dim_2_bias_True_bs_1_sym_quant_a_True SKIPPED 2025-09-09T14:41:33.8158255Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_bfloat16_x_dim_3_bias_False_bs_160_sym_quant_a_False SKIPPED 2025-09-09T14:41:33.8160415Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_bfloat16_x_dim_3_bias_False_bs_160_sym_quant_a_True SKIPPED 2025-09-09T14:41:33.8162485Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_bfloat16_x_dim_3_bias_False_bs_1_sym_quant_a_False SKIPPED 2025-09-09T14:41:33.8164557Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_bfloat16_x_dim_3_bias_False_bs_1_sym_quant_a_True SKIPPED 2025-09-09T14:41:33.8166671Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_bfloat16_x_dim_3_bias_True_bs_160_sym_quant_a_False SKIPPED 2025-09-09T14:41:33.8168731Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_bfloat16_x_dim_3_bias_True_bs_160_sym_quant_a_True SKIPPED 2025-09-09T14:41:33.8170845Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_bfloat16_x_dim_3_bias_True_bs_1_sym_quant_a_False SKIPPED 2025-09-09T14:41:33.8173167Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_bfloat16_x_dim_3_bias_True_bs_1_sym_quant_a_True SKIPPED 2025-09-09T14:41:33.8175294Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float16_x_dim_2_bias_False_bs_160_sym_quant_a_False SKIPPED 2025-09-09T14:41:33.8177384Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float16_x_dim_2_bias_False_bs_160_sym_quant_a_True SKIPPED 2025-09-09T14:41:33.8179433Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float16_x_dim_2_bias_False_bs_1_sym_quant_a_False SKIPPED 2025-09-09T14:41:33.8181743Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float16_x_dim_2_bias_False_bs_1_sym_quant_a_True SKIPPED 2025-09-09T14:41:33.8183794Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float16_x_dim_2_bias_True_bs_160_sym_quant_a_False SKIPPED 2025-09-09T14:41:33.8185898Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float16_x_dim_2_bias_True_bs_160_sym_quant_a_True SKIPPED 2025-09-09T14:41:33.8187946Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float16_x_dim_2_bias_True_bs_1_sym_quant_a_False SKIPPED 2025-09-09T14:41:33.8189977Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float16_x_dim_2_bias_True_bs_1_sym_quant_a_True SKIPPED 2025-09-09T14:41:33.8192016Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float16_x_dim_3_bias_False_bs_160_sym_quant_a_False SKIPPED 2025-09-09T14:41:33.8194121Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float16_x_dim_3_bias_False_bs_160_sym_quant_a_True SKIPPED 2025-09-09T14:41:33.8196162Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float16_x_dim_3_bias_False_bs_1_sym_quant_a_False SKIPPED 2025-09-09T14:41:33.8198363Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float16_x_dim_3_bias_False_bs_1_sym_quant_a_True SKIPPED 2025-09-09T14:41:33.8200450Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float16_x_dim_3_bias_True_bs_160_sym_quant_a_False SKIPPED 2025-09-09T14:41:33.8202490Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float16_x_dim_3_bias_True_bs_160_sym_quant_a_True SKIPPED 2025-09-09T14:41:33.8204564Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float16_x_dim_3_bias_True_bs_1_sym_quant_a_False SKIPPED 2025-09-09T14:41:33.8206598Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float16_x_dim_3_bias_True_bs_1_sym_quant_a_True SKIPPED 2025-09-09T14:41:33.8208699Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float32_x_dim_2_bias_False_bs_160_sym_quant_a_False SKIPPED 2025-09-09T14:41:33.8210768Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float32_x_dim_2_bias_False_bs_160_sym_quant_a_True SKIPPED 2025-09-09T14:41:33.8212818Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float32_x_dim_2_bias_False_bs_1_sym_quant_a_False SKIPPED 2025-09-09T14:41:33.8214897Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float32_x_dim_2_bias_False_bs_1_sym_quant_a_True SKIPPED 2025-09-09T14:41:33.8216968Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float32_x_dim_2_bias_True_bs_160_sym_quant_a_False SKIPPED 2025-09-09T14:41:33.8219055Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float32_x_dim_2_bias_True_bs_160_sym_quant_a_True SKIPPED 2025-09-09T14:41:33.8221140Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float32_x_dim_2_bias_True_bs_1_sym_quant_a_False SKIPPED 2025-09-09T14:41:33.8223181Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float32_x_dim_2_bias_True_bs_1_sym_quant_a_True SKIPPED 2025-09-09T14:41:33.8225674Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float32_x_dim_3_bias_False_bs_160_sym_quant_a_False SKIPPED 2025-09-09T14:41:33.8227889Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float32_x_dim_3_bias_False_bs_160_sym_quant_a_True SKIPPED 2025-09-09T14:41:33.8229841Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float32_x_dim_3_bias_False_bs_1_sym_quant_a_False SKIPPED 2025-09-09T14:41:33.8231836Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float32_x_dim_3_bias_False_bs_1_sym_quant_a_True SKIPPED 2025-09-09T14:41:33.8234253Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float32_x_dim_3_bias_True_bs_160_sym_quant_a_False SKIPPED 2025-09-09T14:41:33.8236346Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float32_x_dim_3_bias_True_bs_160_sym_quant_a_True SKIPPED 2025-09-09T14:41:33.8238425Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float32_x_dim_3_bias_True_bs_1_sym_quant_a_False SKIPPED 2025-09-09T14:41:33.8240471Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float32_x_dim_3_bias_True_bs_1_sym_quant_a_True SKIPPED 2025-09-09T14:41:33.8242262Z test/quantization/test_gptq.py::TestGPTQ::test_gptq_quantizer_int4_weight_only SKIPPED 2025-09-09T14:41:33.8243844Z test/quantization/test_gptq.py::TestMultiTensorFlow::test_multitensor_add_tensors SKIPPED 2025-09-09T14:41:33.8332604Z test/quantization/test_gptq.py::TestMultiTensorFlow::test_multitensor_inplace_operation SKIPPED 2025-09-09T14:41:33.8334362Z test/quantization/test_gptq.py::TestMultiTensorFlow::test_multitensor_pad_unpad SKIPPED 2025-09-09T14:41:33.8336183Z test/quantization/test_gptq.py::TestMultiTensorInputRecorder::test_gptq_with_input_recorder SKIPPED 2025-09-09T14:41:33.8338034Z test/quantization/test_gptq.py::TestMultiTensorInputRecorder::test_multitensor_input_recorder SKIPPED 2025-09-09T14:41:33.8340303Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_aten SKIPPED 2025-09-09T14:41:33.8343012Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_kleidiai SKIPPED 2025-09-09T14:41:33.8347540Z 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 2025-09-09T14:41:33.8354397Z 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 2025-09-09T14:41:33.8361501Z 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 2025-09-09T14:41:33.8368827Z 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 2025-09-09T14:41:33.8375960Z 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 2025-09-09T14:41:33.8383450Z 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 2025-09-09T14:41:33.8390649Z 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 2025-09-09T14:41:33.8397808Z 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 2025-09-09T14:41:33.8404813Z 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 2025-09-09T14:41:33.8411832Z 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 2025-09-09T14:41:33.8419007Z 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 2025-09-09T14:41:33.8426489Z 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 2025-09-09T14:41:33.8433635Z 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 2025-09-09T14:41:33.8440988Z 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 2025-09-09T14:41:33.8448098Z 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 2025-09-09T14:41:33.8455106Z 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 2025-09-09T14:41:33.8462243Z 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 2025-09-09T14:41:33.8525584Z 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 2025-09-09T14:41:33.8532764Z 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 2025-09-09T14:41:33.8539802Z 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 2025-09-09T14:41:33.8546789Z 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 2025-09-09T14:41:33.8553860Z 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 2025-09-09T14:41:33.8561348Z 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 2025-09-09T14:41:33.8568567Z 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 2025-09-09T14:41:33.8575947Z 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 2025-09-09T14:41:33.8583016Z 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 2025-09-09T14:41:33.8590017Z 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 2025-09-09T14:41:33.8597184Z 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 2025-09-09T14:41:33.8604349Z 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 2025-09-09T14:41:33.8611578Z 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 2025-09-09T14:41:33.8618789Z 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 2025-09-09T14:41:33.8626232Z 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 2025-09-09T14:41:33.8633223Z 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 2025-09-09T14:41:33.8640559Z 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 2025-09-09T14:41:33.8647720Z 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 2025-09-09T14:41:33.8655019Z 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 2025-09-09T14:41:33.8710426Z 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 2025-09-09T14:41:33.8717639Z 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 2025-09-09T14:41:33.8724920Z 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 2025-09-09T14:41:33.8731923Z 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 2025-09-09T14:41:33.8739301Z 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 2025-09-09T14:41:33.8746593Z 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 2025-09-09T14:41:33.8753918Z 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 2025-09-09T14:41:33.8761072Z 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 2025-09-09T14:41:33.8768149Z 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 2025-09-09T14:41:33.8775139Z 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 2025-09-09T14:41:33.8782314Z 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 2025-09-09T14:41:33.8789589Z 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 2025-09-09T14:41:33.8796841Z 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 2025-09-09T14:41:33.8804158Z 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 2025-09-09T14:41:33.8811334Z 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 2025-09-09T14:41:33.8818666Z 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 2025-09-09T14:41:33.8825807Z 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 2025-09-09T14:41:33.8829827Z 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 2025-09-09T14:41:33.8833925Z 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 2025-09-09T14:41:33.8890002Z 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 2025-09-09T14:41:33.8893957Z 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 2025-09-09T14:41:33.8897812Z 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 2025-09-09T14:41:33.8902092Z 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 2025-09-09T14:41:33.8906095Z 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 2025-09-09T14:41:33.8910127Z 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 2025-09-09T14:41:33.8913997Z 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 2025-09-09T14:41:33.8917920Z 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 2025-09-09T14:41:33.8921791Z 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 2025-09-09T14:41:33.8925903Z 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 2025-09-09T14:41:33.8929829Z 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 2025-09-09T14:41:33.8933728Z 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 2025-09-09T14:41:33.8937730Z 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 2025-09-09T14:41:33.8941556Z 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 2025-09-09T14:41:33.8945672Z 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 2025-09-09T14:41:33.8949480Z 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 2025-09-09T14:41:33.8953633Z 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 2025-09-09T14:41:33.8957604Z 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 2025-09-09T14:41:33.8961502Z 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 2025-09-09T14:41:33.9031173Z 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 2025-09-09T14:41:33.9035101Z 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 2025-09-09T14:41:33.9039262Z 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 2025-09-09T14:41:33.9043416Z 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 2025-09-09T14:41:33.9047477Z 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 2025-09-09T14:41:33.9051287Z 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 2025-09-09T14:41:33.9055158Z 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 2025-09-09T14:41:33.9059038Z 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 2025-09-09T14:41:33.9062952Z 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 2025-09-09T14:41:33.9066967Z 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 2025-09-09T14:41:33.9071037Z 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 2025-09-09T14:41:33.9074887Z 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 2025-09-09T14:41:33.9078822Z 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 2025-09-09T14:41:33.9082815Z 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 2025-09-09T14:41:33.9086735Z 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 2025-09-09T14:41:33.9090745Z 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 2025-09-09T14:41:33.9094809Z 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 2025-09-09T14:41:33.9098665Z 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 2025-09-09T14:41:33.9102544Z 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 2025-09-09T14:41:33.9247594Z 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 2025-09-09T14:41:33.9251861Z 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 2025-09-09T14:41:33.9255860Z 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 2025-09-09T14:41:33.9258568Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_export_QDQLayout SKIPPED 2025-09-09T14:41:33.9260250Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_export_compile_aoti_PackedLinearInt8DynamicActivationIntxWeightLayout SKIPPED 2025-09-09T14:41:33.9262170Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_export_dynamic_shape_PackedLinearInt8DynamicActivationIntxWeightLayout SKIPPED 2025-09-09T14:41:33.9264476Z 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 2025-09-09T14:41:33.9266959Z 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 2025-09-09T14:41:33.9269275Z 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 2025-09-09T14:41:33.9271605Z 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 2025-09-09T14:41:33.9273978Z 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 2025-09-09T14:41:33.9276482Z 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 2025-09-09T14:41:33.9278798Z 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 2025-09-09T14:41:33.9281270Z 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 2025-09-09T14:41:33.9283697Z 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 2025-09-09T14:41:33.9286096Z 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 2025-09-09T14:41:33.9288570Z 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 2025-09-09T14:41:33.9291079Z 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 2025-09-09T14:41:33.9293384Z 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 2025-09-09T14:41:33.9295837Z 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 2025-09-09T14:41:33.9298274Z 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 2025-09-09T14:41:33.9300559Z 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 2025-09-09T14:41:33.9303017Z 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 2025-09-09T14:41:33.9305448Z 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 2025-09-09T14:41:33.9307744Z 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 2025-09-09T14:41:33.9310215Z 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 2025-09-09T14:41:33.9312664Z 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 2025-09-09T14:41:33.9314967Z 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 2025-09-09T14:41:33.9317480Z 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 2025-09-09T14:41:33.9320019Z 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 2025-09-09T14:41:33.9409689Z 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 2025-09-09T14:41:33.9412358Z 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 2025-09-09T14:41:33.9414665Z 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 2025-09-09T14:41:33.9417158Z 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 2025-09-09T14:41:33.9419994Z 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 2025-09-09T14:41:33.9423169Z 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 2025-09-09T14:41:33.9426867Z 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 2025-09-09T14:41:33.9430348Z 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 2025-09-09T14:41:33.9433852Z 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 2025-09-09T14:41:33.9437376Z 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 2025-09-09T14:41:33.9441019Z 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 2025-09-09T14:41:33.9444534Z 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 2025-09-09T14:41:33.9448118Z 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 2025-09-09T14:41:33.9451584Z 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 2025-09-09T14:41:33.9455063Z 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 2025-09-09T14:41:33.9458550Z 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 2025-09-09T14:41:33.9461910Z 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 2025-09-09T14:41:33.9465406Z 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 2025-09-09T14:41:33.9468732Z 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 2025-09-09T14:41:33.9472168Z 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 2025-09-09T14:41:33.9475646Z 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 2025-09-09T14:41:33.9479088Z 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 2025-09-09T14:41:33.9564136Z 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 2025-09-09T14:41:33.9567481Z 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 2025-09-09T14:41:33.9571021Z 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 2025-09-09T14:41:33.9574352Z 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 2025-09-09T14:41:33.9577747Z 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 2025-09-09T14:41:33.9581111Z 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 2025-09-09T14:41:33.9584489Z 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 2025-09-09T14:41:33.9587868Z 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 2025-09-09T14:41:33.9591439Z 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 2025-09-09T14:41:33.9594813Z 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 2025-09-09T14:41:33.9598376Z 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 2025-09-09T14:41:33.9601774Z 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 2025-09-09T14:41:33.9605130Z 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 2025-09-09T14:41:33.9608526Z 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 2025-09-09T14:41:33.9611884Z 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 2025-09-09T14:41:33.9615257Z 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 2025-09-09T14:41:33.9618623Z 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 2025-09-09T14:41:33.9621994Z 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 2025-09-09T14:41:33.9625579Z 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 2025-09-09T14:41:33.9628976Z 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 2025-09-09T14:41:33.9632413Z 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 2025-09-09T14:41:33.9725856Z 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 2025-09-09T14:41:33.9729295Z 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 2025-09-09T14:41:33.9732686Z 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 2025-09-09T14:41:33.9736055Z 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 2025-09-09T14:41:33.9739422Z 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 2025-09-09T14:41:33.9742796Z 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 2025-09-09T14:41:33.9746166Z 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 2025-09-09T14:41:33.9749688Z 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 2025-09-09T14:41:33.9753081Z 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 2025-09-09T14:41:33.9756601Z 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 2025-09-09T14:41:33.9760012Z 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 2025-09-09T14:41:33.9763377Z 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 2025-09-09T14:41:33.9766744Z 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 2025-09-09T14:41:33.9770052Z 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 2025-09-09T14:41:33.9773407Z 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 2025-09-09T14:41:33.9776776Z 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 2025-09-09T14:41:33.9780085Z 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 2025-09-09T14:41:33.9783602Z 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 2025-09-09T14:41:33.9786923Z 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 2025-09-09T14:41:33.9790410Z 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 2025-09-09T14:41:33.9793723Z 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 2025-09-09T14:41:33.9882867Z 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 2025-09-09T14:41:33.9886362Z 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 2025-09-09T14:41:33.9889816Z 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 2025-09-09T14:41:33.9893185Z 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 2025-09-09T14:41:33.9896543Z 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 2025-09-09T14:41:33.9899902Z 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 2025-09-09T14:41:33.9903451Z 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 2025-09-09T14:41:33.9906814Z 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 2025-09-09T14:41:33.9910265Z 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 2025-09-09T14:41:33.9913588Z 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 2025-09-09T14:41:33.9917003Z 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 2025-09-09T14:41:33.9920370Z 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 2025-09-09T14:41:33.9923712Z 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 2025-09-09T14:41:33.9927195Z 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 2025-09-09T14:41:33.9930612Z 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 2025-09-09T14:41:33.9933940Z 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 2025-09-09T14:41:33.9937440Z 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 2025-09-09T14:41:33.9940768Z 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 2025-09-09T14:41:33.9944225Z 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 2025-09-09T14:41:33.9947587Z 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 2025-09-09T14:41:33.9950944Z 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 2025-09-09T14:41:34.0037537Z 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 2025-09-09T14:41:34.0040871Z 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 2025-09-09T14:41:34.0044197Z 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 2025-09-09T14:41:34.0047495Z 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 2025-09-09T14:41:34.0050810Z 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 2025-09-09T14:41:34.0054312Z 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 2025-09-09T14:41:34.0057625Z 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 2025-09-09T14:41:34.0061025Z 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 2025-09-09T14:41:34.0064335Z 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 2025-09-09T14:41:34.0067611Z 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 2025-09-09T14:41:34.0070919Z 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 2025-09-09T14:41:34.0074240Z 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 2025-09-09T14:41:34.0077598Z 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 2025-09-09T14:41:34.0080914Z 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 2025-09-09T14:41:34.0084200Z 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 2025-09-09T14:41:34.0087568Z 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 2025-09-09T14:41:34.0090875Z 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 2025-09-09T14:41:34.0094267Z 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 2025-09-09T14:41:34.0097566Z 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 2025-09-09T14:41:34.0100859Z 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 2025-09-09T14:41:34.0104159Z 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 2025-09-09T14:41:34.0195462Z 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 2025-09-09T14:41:34.0198999Z 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 2025-09-09T14:41:34.0202453Z 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 2025-09-09T14:41:34.0205873Z 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 2025-09-09T14:41:34.0209456Z 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 2025-09-09T14:41:34.0212882Z 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 2025-09-09T14:41:34.0216416Z 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 2025-09-09T14:41:34.0219803Z 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 2025-09-09T14:41:34.0223269Z 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 2025-09-09T14:41:34.0226870Z 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 2025-09-09T14:41:34.0230329Z 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 2025-09-09T14:41:34.0233775Z 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 2025-09-09T14:41:34.0237299Z 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 2025-09-09T14:41:34.0240756Z 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 2025-09-09T14:41:34.0244299Z 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 2025-09-09T14:41:34.0247714Z 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 2025-09-09T14:41:34.0251204Z 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 2025-09-09T14:41:34.0254538Z 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 2025-09-09T14:41:34.0257991Z 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 2025-09-09T14:41:34.0261318Z 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 2025-09-09T14:41:34.0264768Z 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 2025-09-09T14:41:34.0349833Z 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 2025-09-09T14:41:34.0353294Z 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 2025-09-09T14:41:34.0356791Z 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 2025-09-09T14:41:34.0360355Z 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 2025-09-09T14:41:34.0363823Z 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 2025-09-09T14:41:34.0367377Z 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 2025-09-09T14:41:34.0370827Z 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 2025-09-09T14:41:34.0374159Z 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 2025-09-09T14:41:34.0377593Z 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 2025-09-09T14:41:34.0381053Z 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 2025-09-09T14:41:34.0384515Z 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 2025-09-09T14:41:34.0388034Z 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 2025-09-09T14:41:34.0391478Z 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 2025-09-09T14:41:34.0395019Z 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 2025-09-09T14:41:34.0398548Z 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 2025-09-09T14:41:34.0402074Z 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 2025-09-09T14:41:34.0405498Z 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 2025-09-09T14:41:34.0408936Z 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 2025-09-09T14:41:34.0412351Z 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 2025-09-09T14:41:34.0415760Z 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 2025-09-09T14:41:34.0419189Z 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 2025-09-09T14:41:34.0506371Z 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 2025-09-09T14:41:34.0509849Z 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 2025-09-09T14:41:34.0513492Z 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 2025-09-09T14:41:34.0516996Z 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 2025-09-09T14:41:34.0520578Z 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 2025-09-09T14:41:34.0524010Z 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 2025-09-09T14:41:34.0527639Z 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 2025-09-09T14:41:34.0531069Z 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 2025-09-09T14:41:34.0534500Z 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 2025-09-09T14:41:34.0537939Z 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 2025-09-09T14:41:34.0541384Z 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 2025-09-09T14:41:34.0544812Z 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 2025-09-09T14:41:34.0548373Z 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 2025-09-09T14:41:34.0551804Z 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 2025-09-09T14:41:34.0555323Z 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 2025-09-09T14:41:34.0558837Z 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 2025-09-09T14:41:34.0562275Z 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 2025-09-09T14:41:34.0565582Z 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 2025-09-09T14:41:34.0569106Z 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 2025-09-09T14:41:34.0572430Z 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 2025-09-09T14:41:34.0575890Z 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 2025-09-09T14:41:34.0660309Z 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 2025-09-09T14:41:34.0663927Z 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 2025-09-09T14:41:34.0667306Z 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 2025-09-09T14:41:34.0670721Z 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 2025-09-09T14:41:34.0674134Z 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 2025-09-09T14:41:34.0677512Z 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 2025-09-09T14:41:34.0680923Z 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 2025-09-09T14:41:34.0684331Z 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 2025-09-09T14:41:34.0687728Z 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 2025-09-09T14:41:34.0691141Z 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 2025-09-09T14:41:34.0694550Z 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 2025-09-09T14:41:34.0698027Z 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 2025-09-09T14:41:34.0701423Z 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 2025-09-09T14:41:34.0704880Z 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 2025-09-09T14:41:34.0708286Z 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 2025-09-09T14:41:34.0711688Z 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 2025-09-09T14:41:34.0715115Z 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 2025-09-09T14:41:34.0718569Z 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 2025-09-09T14:41:34.0721942Z 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 2025-09-09T14:41:34.0725500Z 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 2025-09-09T14:41:34.0728832Z 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 2025-09-09T14:41:34.0816440Z 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 2025-09-09T14:41:34.0819855Z 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 2025-09-09T14:41:34.0823361Z 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 2025-09-09T14:41:34.0826854Z 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 2025-09-09T14:41:34.0830240Z 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 2025-09-09T14:41:34.0833615Z 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 2025-09-09T14:41:34.0837051Z 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 2025-09-09T14:41:34.0840450Z 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 2025-09-09T14:41:34.0843786Z 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 2025-09-09T14:41:34.0847135Z 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 2025-09-09T14:41:34.0850618Z 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 2025-09-09T14:41:34.0853916Z 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 2025-09-09T14:41:34.0857414Z 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 2025-09-09T14:41:34.0860711Z 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 2025-09-09T14:41:34.0864115Z 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 2025-09-09T14:41:34.0867453Z 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 2025-09-09T14:41:34.0870819Z 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 2025-09-09T14:41:34.0874191Z 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 2025-09-09T14:41:34.0877536Z 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 2025-09-09T14:41:34.0880837Z 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 2025-09-09T14:41:34.0884265Z 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 2025-09-09T14:41:34.0975188Z 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 2025-09-09T14:41:34.0978658Z 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 2025-09-09T14:41:34.0982075Z 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 2025-09-09T14:41:34.0985347Z 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 2025-09-09T14:41:34.0988725Z 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 2025-09-09T14:41:34.0992059Z 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 2025-09-09T14:41:34.0995404Z 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 2025-09-09T14:41:34.0998816Z 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 2025-09-09T14:41:34.1002157Z 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 2025-09-09T14:41:34.1005595Z 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 2025-09-09T14:41:34.1008987Z 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 2025-09-09T14:41:34.1012460Z 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 2025-09-09T14:41:34.1015857Z 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 2025-09-09T14:41:34.1019239Z 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 2025-09-09T14:41:34.1022645Z 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 2025-09-09T14:41:34.1026200Z 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 2025-09-09T14:41:34.1029579Z 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 2025-09-09T14:41:34.1032981Z 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 2025-09-09T14:41:34.1036351Z 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 2025-09-09T14:41:34.1039913Z 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 2025-09-09T14:41:34.1043242Z 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 2025-09-09T14:41:34.1132213Z 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 2025-09-09T14:41:34.1135700Z 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 2025-09-09T14:41:34.1139096Z 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 2025-09-09T14:41:34.1142479Z 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 2025-09-09T14:41:34.1145850Z 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 2025-09-09T14:41:34.1149231Z 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 2025-09-09T14:41:34.1152588Z 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 2025-09-09T14:41:34.1155967Z 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 2025-09-09T14:41:34.1159606Z 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 2025-09-09T14:41:34.1163005Z 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 2025-09-09T14:41:34.1166479Z 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 2025-09-09T14:41:34.1169800Z 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 2025-09-09T14:41:34.1173236Z 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 2025-09-09T14:41:34.1176533Z 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 2025-09-09T14:41:34.1179903Z 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 2025-09-09T14:41:34.1183282Z 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 2025-09-09T14:41:34.1186596Z 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 2025-09-09T14:41:34.1190035Z 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 2025-09-09T14:41:34.1193402Z 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 2025-09-09T14:41:34.1196915Z 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 2025-09-09T14:41:34.1200293Z 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 2025-09-09T14:41:34.1288334Z 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 2025-09-09T14:41:34.1291738Z 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 2025-09-09T14:41:34.1295088Z 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 2025-09-09T14:41:34.1298443Z 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 2025-09-09T14:41:34.1301824Z 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 2025-09-09T14:41:34.1305214Z 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 2025-09-09T14:41:34.1308729Z 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 2025-09-09T14:41:34.1312132Z 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 2025-09-09T14:41:34.1315493Z 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 2025-09-09T14:41:34.1319035Z 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 2025-09-09T14:41:34.1322408Z 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 2025-09-09T14:41:34.1325966Z 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 2025-09-09T14:41:34.1329333Z 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 2025-09-09T14:41:34.1332647Z 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 2025-09-09T14:41:34.1336100Z 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 2025-09-09T14:41:34.1339419Z 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 2025-09-09T14:41:34.1342963Z 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 2025-09-09T14:41:34.1346289Z 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 2025-09-09T14:41:34.1349641Z 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 2025-09-09T14:41:34.1353106Z 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 2025-09-09T14:41:34.1356533Z 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 2025-09-09T14:41:34.1442536Z 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 2025-09-09T14:41:34.1446000Z 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 2025-09-09T14:41:34.1449454Z 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 2025-09-09T14:41:34.1452836Z 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 2025-09-09T14:41:34.1456214Z 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 2025-09-09T14:41:34.1459715Z 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 2025-09-09T14:41:34.1463090Z 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 2025-09-09T14:41:34.1466469Z 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 2025-09-09T14:41:34.1469852Z 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 2025-09-09T14:41:34.1473199Z 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 2025-09-09T14:41:34.1476658Z 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 2025-09-09T14:41:34.1480031Z 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 2025-09-09T14:41:34.1483383Z 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 2025-09-09T14:41:34.1486738Z 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 2025-09-09T14:41:34.1490079Z 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 2025-09-09T14:41:34.1493497Z 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 2025-09-09T14:41:34.1496864Z 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 2025-09-09T14:41:34.1500283Z 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 2025-09-09T14:41:34.1503631Z 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 2025-09-09T14:41:34.1507008Z 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 2025-09-09T14:41:34.1510334Z 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 2025-09-09T14:41:34.1598332Z 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 2025-09-09T14:41:34.1601756Z 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 2025-09-09T14:41:34.1605131Z 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 2025-09-09T14:41:34.1608497Z 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 2025-09-09T14:41:34.1612015Z 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 2025-09-09T14:41:34.1615393Z 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 2025-09-09T14:41:34.1618855Z 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 2025-09-09T14:41:34.1622221Z 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 2025-09-09T14:41:34.1625732Z 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 2025-09-09T14:41:34.1629103Z 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 2025-09-09T14:41:34.1632468Z 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 2025-09-09T14:41:34.1635774Z 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 2025-09-09T14:41:34.1639248Z 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 2025-09-09T14:41:34.1642571Z 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 2025-09-09T14:41:34.1646087Z 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 2025-09-09T14:41:34.1649390Z 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 2025-09-09T14:41:34.1652840Z 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 2025-09-09T14:41:34.1656199Z 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 2025-09-09T14:41:34.1659514Z 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 2025-09-09T14:41:34.1662906Z 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 2025-09-09T14:41:34.1666198Z 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 2025-09-09T14:41:34.1753894Z 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 2025-09-09T14:41:34.1757378Z 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 2025-09-09T14:41:34.1760863Z 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 2025-09-09T14:41:34.1764309Z 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 2025-09-09T14:41:34.1767873Z 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 2025-09-09T14:41:34.1771268Z 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 2025-09-09T14:41:34.1774761Z 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 2025-09-09T14:41:34.1778183Z 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 2025-09-09T14:41:34.1781713Z 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 2025-09-09T14:41:34.1785012Z 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 2025-09-09T14:41:34.1788463Z 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 2025-09-09T14:41:34.1791874Z 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 2025-09-09T14:41:34.1795274Z 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 2025-09-09T14:41:34.1798888Z 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 2025-09-09T14:41:34.1802357Z 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 2025-09-09T14:41:34.1805899Z 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 2025-09-09T14:41:34.1809344Z 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 2025-09-09T14:41:34.1812789Z 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 2025-09-09T14:41:34.1816228Z 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 2025-09-09T14:41:34.1819679Z 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 2025-09-09T14:41:34.1823131Z 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 2025-09-09T14:41:34.1909848Z 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 2025-09-09T14:41:34.1913211Z 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 2025-09-09T14:41:34.1916777Z 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 2025-09-09T14:41:34.1920099Z 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 2025-09-09T14:41:34.1923497Z 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 2025-09-09T14:41:34.1926996Z 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 2025-09-09T14:41:34.1930308Z 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 2025-09-09T14:41:34.1933630Z 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 2025-09-09T14:41:34.1936933Z 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 2025-09-09T14:41:34.1940239Z 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 2025-09-09T14:41:34.1943571Z 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 2025-09-09T14:41:34.1946896Z 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 2025-09-09T14:41:34.1950305Z 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 2025-09-09T14:41:34.1953616Z 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 2025-09-09T14:41:34.1957104Z 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 2025-09-09T14:41:34.1960449Z 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 2025-09-09T14:41:34.1963754Z 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 2025-09-09T14:41:34.1967082Z 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 2025-09-09T14:41:34.1970386Z 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 2025-09-09T14:41:34.1973692Z 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 2025-09-09T14:41:34.1977004Z 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 2025-09-09T14:41:34.2072598Z 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 2025-09-09T14:41:34.2076062Z 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 2025-09-09T14:41:34.2079418Z 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 2025-09-09T14:41:34.2082810Z 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 2025-09-09T14:41:34.2086116Z 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 2025-09-09T14:41:34.2089422Z 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 2025-09-09T14:41:34.2092726Z 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 2025-09-09T14:41:34.2096069Z 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 2025-09-09T14:41:34.2099395Z 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 2025-09-09T14:41:34.2102720Z 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 2025-09-09T14:41:34.2106038Z 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 2025-09-09T14:41:34.2109412Z 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 2025-09-09T14:41:34.2112714Z 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 2025-09-09T14:41:34.2116063Z 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 2025-09-09T14:41:34.2119430Z 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 2025-09-09T14:41:34.2122723Z 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 2025-09-09T14:41:34.2126181Z 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 2025-09-09T14:41:34.2129485Z 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 2025-09-09T14:41:34.2132768Z 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 2025-09-09T14:41:34.2136062Z 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 2025-09-09T14:41:34.2139351Z 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 2025-09-09T14:41:34.2227177Z 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 2025-09-09T14:41:34.2230500Z 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 2025-09-09T14:41:34.2233869Z 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 2025-09-09T14:41:34.2237236Z 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 2025-09-09T14:41:34.2240534Z 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 2025-09-09T14:41:34.2243860Z 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 2025-09-09T14:41:34.2247161Z 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 2025-09-09T14:41:34.2250452Z 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 2025-09-09T14:41:34.2253751Z 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 2025-09-09T14:41:34.2257039Z 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 2025-09-09T14:41:34.2260411Z 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 2025-09-09T14:41:34.2263726Z 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 2025-09-09T14:41:34.2267084Z 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 2025-09-09T14:41:34.2270392Z 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 2025-09-09T14:41:34.2273684Z 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 2025-09-09T14:41:34.2277029Z 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 2025-09-09T14:41:34.2280322Z 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 2025-09-09T14:41:34.2283600Z 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 2025-09-09T14:41:34.2286874Z 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 2025-09-09T14:41:34.2290154Z 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 2025-09-09T14:41:34.2293496Z 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 2025-09-09T14:41:34.2383479Z 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 2025-09-09T14:41:34.2386951Z 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 2025-09-09T14:41:34.2390258Z 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 2025-09-09T14:41:34.2393577Z 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 2025-09-09T14:41:34.2396960Z 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 2025-09-09T14:41:34.2400270Z 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 2025-09-09T14:41:34.2403579Z 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 2025-09-09T14:41:34.2406873Z 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 2025-09-09T14:41:34.2410189Z 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 2025-09-09T14:41:34.2413585Z 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 2025-09-09T14:41:34.2416893Z 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 2025-09-09T14:41:34.2420265Z 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 2025-09-09T14:41:34.2423566Z 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 2025-09-09T14:41:34.2427007Z 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 2025-09-09T14:41:34.2430299Z 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 2025-09-09T14:41:34.2433594Z 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 2025-09-09T14:41:34.2436966Z 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 2025-09-09T14:41:34.2440257Z 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 2025-09-09T14:41:34.2443552Z 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 2025-09-09T14:41:34.2446954Z 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 2025-09-09T14:41:34.2450255Z 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 2025-09-09T14:41:34.2538983Z 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 2025-09-09T14:41:34.2542308Z 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 2025-09-09T14:41:34.2545596Z 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 2025-09-09T14:41:34.2548896Z 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 2025-09-09T14:41:34.2552197Z 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 2025-09-09T14:41:34.2555503Z 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 2025-09-09T14:41:34.2558845Z 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 2025-09-09T14:41:34.2562120Z 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 2025-09-09T14:41:34.2565558Z 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 2025-09-09T14:41:34.2568845Z 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 2025-09-09T14:41:34.2572212Z 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 2025-09-09T14:41:34.2575493Z 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 2025-09-09T14:41:34.2578750Z 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 2025-09-09T14:41:34.2582030Z 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 2025-09-09T14:41:34.2585314Z 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 2025-09-09T14:41:34.2600981Z 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 2025-09-09T14:41:34.2604530Z 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 2025-09-09T14:41:34.2607875Z 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 2025-09-09T14:41:34.2611349Z 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 2025-09-09T14:41:34.2614687Z 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 2025-09-09T14:41:34.2618080Z 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 2025-09-09T14:41:34.2694963Z 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 2025-09-09T14:41:34.2698321Z 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 2025-09-09T14:41:34.2701655Z 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 2025-09-09T14:41:34.2704974Z 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 2025-09-09T14:41:34.2708301Z 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 2025-09-09T14:41:34.2711623Z 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 2025-09-09T14:41:34.2714930Z 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 2025-09-09T14:41:34.2718472Z 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 2025-09-09T14:41:34.2721794Z 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 2025-09-09T14:41:34.2725348Z 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 2025-09-09T14:41:34.2728670Z 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 2025-09-09T14:41:34.2731960Z 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 2025-09-09T14:41:34.2735273Z 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 2025-09-09T14:41:34.2738595Z 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 2025-09-09T14:41:34.2741930Z 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 2025-09-09T14:41:34.2745247Z 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 2025-09-09T14:41:34.2748549Z 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 2025-09-09T14:41:34.2751936Z 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 2025-09-09T14:41:34.2755248Z 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 2025-09-09T14:41:34.2758709Z 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 2025-09-09T14:41:34.2762027Z 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 2025-09-09T14:41:34.2851343Z 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 2025-09-09T14:41:34.2854674Z 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 2025-09-09T14:41:34.2857989Z 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 2025-09-09T14:41:34.2861301Z 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 2025-09-09T14:41:34.2864604Z 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 2025-09-09T14:41:34.2867891Z 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 2025-09-09T14:41:34.2871280Z 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 2025-09-09T14:41:34.2874583Z 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 2025-09-09T14:41:34.2878078Z 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 2025-09-09T14:41:34.2881366Z 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 2025-09-09T14:41:34.2884694Z 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 2025-09-09T14:41:34.2888012Z 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 2025-09-09T14:41:34.2891334Z 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 2025-09-09T14:41:34.2894665Z 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 2025-09-09T14:41:34.2897971Z 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 2025-09-09T14:41:34.2901283Z 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 2025-09-09T14:41:34.2904628Z 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 2025-09-09T14:41:34.2907927Z 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 2025-09-09T14:41:34.2911290Z 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 2025-09-09T14:41:34.2914591Z 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 2025-09-09T14:41:34.2917966Z 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 2025-09-09T14:41:34.3005327Z 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 2025-09-09T14:41:34.3008646Z 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 2025-09-09T14:41:34.3012032Z 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 2025-09-09T14:41:34.3015312Z 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 2025-09-09T14:41:34.3018618Z 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 2025-09-09T14:41:34.3022052Z 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 2025-09-09T14:41:34.3025477Z 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 2025-09-09T14:41:34.3028879Z 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 2025-09-09T14:41:34.3032189Z 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 2025-09-09T14:41:34.3037203Z 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 2025-09-09T14:41:34.3043633Z 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 2025-09-09T14:41:34.3050798Z 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 2025-09-09T14:41:34.3056346Z 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 2025-09-09T14:41:34.3059866Z 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 2025-09-09T14:41:34.3064118Z 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 2025-09-09T14:41:34.3067566Z 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 2025-09-09T14:41:34.3070867Z 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 2025-09-09T14:41:34.3077098Z 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 2025-09-09T14:41:34.3082962Z 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 2025-09-09T14:41:34.3087032Z 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 2025-09-09T14:41:34.3090662Z 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 2025-09-09T14:41:34.3161737Z 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 2025-09-09T14:41:34.3165063Z 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 2025-09-09T14:41:34.3168353Z 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 2025-09-09T14:41:34.3171640Z 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 2025-09-09T14:41:34.3175130Z 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 2025-09-09T14:41:34.3178454Z 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 2025-09-09T14:41:34.3181905Z 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 2025-09-09T14:41:34.3185217Z 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 2025-09-09T14:41:34.3188525Z 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 2025-09-09T14:41:34.3191828Z 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 2025-09-09T14:41:34.3195142Z 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 2025-09-09T14:41:34.3198520Z 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 2025-09-09T14:41:34.3201807Z 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 2025-09-09T14:41:34.3205113Z 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 2025-09-09T14:41:34.3208472Z 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 2025-09-09T14:41:34.3211769Z 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 2025-09-09T14:41:34.3215111Z 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 2025-09-09T14:41:34.3218415Z 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 2025-09-09T14:41:34.3221712Z 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 2025-09-09T14:41:34.3225186Z 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 2025-09-09T14:41:34.3228480Z 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 2025-09-09T14:41:34.3320101Z 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 2025-09-09T14:41:34.3323412Z 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 2025-09-09T14:41:34.3326855Z 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 2025-09-09T14:41:34.3330300Z 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 2025-09-09T14:41:34.3333599Z 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 2025-09-09T14:41:34.3336958Z 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 2025-09-09T14:41:34.3340247Z 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 2025-09-09T14:41:34.3343535Z 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 2025-09-09T14:41:34.3350351Z 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 2025-09-09T14:41:34.3353645Z 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 2025-09-09T14:41:34.3357288Z 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 2025-09-09T14:41:34.3360624Z 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 2025-09-09T14:41:34.3363912Z 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 2025-09-09T14:41:34.3368819Z 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 2025-09-09T14:41:34.3372179Z 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 2025-09-09T14:41:34.3375509Z 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 2025-09-09T14:41:34.3378799Z 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 2025-09-09T14:41:34.3382069Z 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 2025-09-09T14:41:34.3385449Z 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 2025-09-09T14:41:34.3388756Z 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 2025-09-09T14:41:34.3392091Z 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 2025-09-09T14:41:34.3474802Z 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 2025-09-09T14:41:34.3478214Z 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 2025-09-09T14:41:34.3481725Z 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 2025-09-09T14:41:34.3485049Z 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 2025-09-09T14:41:34.3488410Z 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 2025-09-09T14:41:34.3491741Z 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 2025-09-09T14:41:34.3495064Z 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 2025-09-09T14:41:34.3498458Z 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 2025-09-09T14:41:34.3501781Z 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 2025-09-09T14:41:34.3505095Z 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 2025-09-09T14:41:34.3508408Z 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 2025-09-09T14:41:34.3511790Z 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 2025-09-09T14:41:34.3515077Z 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 2025-09-09T14:41:34.3518664Z 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 2025-09-09T14:41:34.3522045Z 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 2025-09-09T14:41:34.3525501Z 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 2025-09-09T14:41:34.3528843Z 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 2025-09-09T14:41:34.3532244Z 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 2025-09-09T14:41:34.3535572Z 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 2025-09-09T14:41:34.3538902Z 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 2025-09-09T14:41:34.3542220Z 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 2025-09-09T14:41:34.3631852Z 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 2025-09-09T14:41:34.3635212Z 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 2025-09-09T14:41:34.3638643Z 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 2025-09-09T14:41:34.3641943Z 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 2025-09-09T14:41:34.3645267Z 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 2025-09-09T14:41:34.3648567Z 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 2025-09-09T14:41:34.3651947Z 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 2025-09-09T14:41:34.3655248Z 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 2025-09-09T14:41:34.3658558Z 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 2025-09-09T14:41:34.3661853Z 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 2025-09-09T14:41:34.3665197Z 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 2025-09-09T14:41:34.3668486Z 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 2025-09-09T14:41:34.3671813Z 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 2025-09-09T14:41:34.3675122Z 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 2025-09-09T14:41:34.3678512Z 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 2025-09-09T14:41:34.3681838Z 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 2025-09-09T14:41:34.3685211Z 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 2025-09-09T14:41:34.3688508Z 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 2025-09-09T14:41:34.3691794Z 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 2025-09-09T14:41:34.3695086Z 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 2025-09-09T14:41:34.3698442Z 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 2025-09-09T14:41:34.3785076Z 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 2025-09-09T14:41:34.3788491Z 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 2025-09-09T14:41:34.3791788Z 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 2025-09-09T14:41:34.3795111Z 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 2025-09-09T14:41:34.3798491Z 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 2025-09-09T14:41:34.3801845Z 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 2025-09-09T14:41:34.3805144Z 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 2025-09-09T14:41:34.3808435Z 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 2025-09-09T14:41:34.3811734Z 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 2025-09-09T14:41:34.3815112Z 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 2025-09-09T14:41:34.3818421Z 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 2025-09-09T14:41:34.3821753Z 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 2025-09-09T14:41:34.3825159Z 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 2025-09-09T14:41:34.3828458Z 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 2025-09-09T14:41:34.3831764Z 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 2025-09-09T14:41:34.3835106Z 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 2025-09-09T14:41:34.3838470Z 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 2025-09-09T14:41:34.3841757Z 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 2025-09-09T14:41:34.3845055Z 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 2025-09-09T14:41:34.3848448Z 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 2025-09-09T14:41:34.3851752Z 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 2025-09-09T14:41:34.3940987Z 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 2025-09-09T14:41:34.3944289Z 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 2025-09-09T14:41:34.3947561Z 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 2025-09-09T14:41:34.3950849Z 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 2025-09-09T14:41:34.3954217Z 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 2025-09-09T14:41:34.3957569Z 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 2025-09-09T14:41:34.3960857Z 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 2025-09-09T14:41:34.3964156Z 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 2025-09-09T14:41:34.3967585Z 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 2025-09-09T14:41:34.3970908Z 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 2025-09-09T14:41:34.3974276Z 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 2025-09-09T14:41:34.3977584Z 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 2025-09-09T14:41:34.3980869Z 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 2025-09-09T14:41:34.3984168Z 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 2025-09-09T14:41:34.3987498Z 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 2025-09-09T14:41:34.3990793Z 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 2025-09-09T14:41:34.3994100Z 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 2025-09-09T14:41:34.3997495Z 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 2025-09-09T14:41:34.4000844Z 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 2025-09-09T14:41:34.4004148Z 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 2025-09-09T14:41:34.4007472Z 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 2025-09-09T14:41:34.4096325Z 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 2025-09-09T14:41:34.4099610Z 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 2025-09-09T14:41:34.4102914Z 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 2025-09-09T14:41:34.4106281Z 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 2025-09-09T14:41:34.4109589Z 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 2025-09-09T14:41:34.4112908Z 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 2025-09-09T14:41:34.4116251Z 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 2025-09-09T14:41:34.4119639Z 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 2025-09-09T14:41:34.4122947Z 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 2025-09-09T14:41:34.4126418Z 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 2025-09-09T14:41:34.4129724Z 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 2025-09-09T14:41:34.4133033Z 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 2025-09-09T14:41:34.4136316Z 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 2025-09-09T14:41:34.4139662Z 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 2025-09-09T14:41:34.4142959Z 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 2025-09-09T14:41:34.4146250Z 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 2025-09-09T14:41:34.4149528Z 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 2025-09-09T14:41:34.4152866Z 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 2025-09-09T14:41:34.4156145Z 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 2025-09-09T14:41:34.4159527Z 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 2025-09-09T14:41:34.4162817Z 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 2025-09-09T14:41:34.4250231Z 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 2025-09-09T14:41:34.4253570Z 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 2025-09-09T14:41:34.4257018Z 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 2025-09-09T14:41:34.4260351Z 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 2025-09-09T14:41:34.4263686Z 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 2025-09-09T14:41:34.4267004Z 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 2025-09-09T14:41:34.4270404Z 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 2025-09-09T14:41:34.4273742Z 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 2025-09-09T14:41:34.4277172Z 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 2025-09-09T14:41:34.4280497Z 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 2025-09-09T14:41:34.4283838Z 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 2025-09-09T14:41:34.4287161Z 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 2025-09-09T14:41:34.4290522Z 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 2025-09-09T14:41:34.4293826Z 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 2025-09-09T14:41:34.4297138Z 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 2025-09-09T14:41:34.4300429Z 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 2025-09-09T14:41:34.4303810Z 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 2025-09-09T14:41:34.4307127Z 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 2025-09-09T14:41:34.4310462Z 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 2025-09-09T14:41:34.4313794Z 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 2025-09-09T14:41:34.4317180Z 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 2025-09-09T14:41:34.4407319Z 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 2025-09-09T14:41:34.4410746Z 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 2025-09-09T14:41:34.4414062Z 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 2025-09-09T14:41:34.4417382Z 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 2025-09-09T14:41:34.4420694Z 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 2025-09-09T14:41:34.4424077Z 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 2025-09-09T14:41:34.4427526Z 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 2025-09-09T14:41:34.4430893Z 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 2025-09-09T14:41:34.4434192Z 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 2025-09-09T14:41:34.4437546Z 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 2025-09-09T14:41:34.4440849Z 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 2025-09-09T14:41:34.4444187Z 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 2025-09-09T14:41:34.4447477Z 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 2025-09-09T14:41:34.4450765Z 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 2025-09-09T14:41:34.4454062Z 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 2025-09-09T14:41:34.4457406Z 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 2025-09-09T14:41:34.4460704Z 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 2025-09-09T14:41:34.4466199Z 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 2025-09-09T14:41:34.4469569Z 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 2025-09-09T14:41:34.4472889Z 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 2025-09-09T14:41:34.4476373Z 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 2025-09-09T14:41:34.4560212Z 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 2025-09-09T14:41:34.4563545Z 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 2025-09-09T14:41:34.4566848Z 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 2025-09-09T14:41:34.4570155Z 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 2025-09-09T14:41:34.4573549Z 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 2025-09-09T14:41:34.4576863Z 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 2025-09-09T14:41:34.4580280Z 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 2025-09-09T14:41:34.4583580Z 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 2025-09-09T14:41:34.4586880Z 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 2025-09-09T14:41:34.4590176Z 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 2025-09-09T14:41:34.4593545Z 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 2025-09-09T14:41:34.4596904Z 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 2025-09-09T14:41:34.4600195Z 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 2025-09-09T14:41:34.4603501Z 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 2025-09-09T14:41:34.4606841Z 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 2025-09-09T14:41:34.4610157Z 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 2025-09-09T14:41:34.4613540Z 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 2025-09-09T14:41:34.4616822Z 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 2025-09-09T14:41:34.4620110Z 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 2025-09-09T14:41:34.4623395Z 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 2025-09-09T14:41:34.4626876Z 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 2025-09-09T14:41:34.4714562Z 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 2025-09-09T14:41:34.4717930Z 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 2025-09-09T14:41:34.4721221Z 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 2025-09-09T14:41:34.4724700Z 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 2025-09-09T14:41:34.4727997Z 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 2025-09-09T14:41:34.4731383Z 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 2025-09-09T14:41:34.4734643Z 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 2025-09-09T14:41:34.4737918Z 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 2025-09-09T14:41:34.4741203Z 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 2025-09-09T14:41:34.4744520Z 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 2025-09-09T14:41:34.4747814Z 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 2025-09-09T14:41:34.4751134Z 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 2025-09-09T14:41:34.4754428Z 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 2025-09-09T14:41:34.4757842Z 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 2025-09-09T14:41:34.4761144Z 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 2025-09-09T14:41:34.4764534Z 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 2025-09-09T14:41:34.4767837Z 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 2025-09-09T14:41:34.4771122Z 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 2025-09-09T14:41:34.4774427Z 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 2025-09-09T14:41:34.4777768Z 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 2025-09-09T14:41:34.4781082Z 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 2025-09-09T14:41:34.4870224Z 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 2025-09-09T14:41:34.4873566Z 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 2025-09-09T14:41:34.4877019Z 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 2025-09-09T14:41:34.4880332Z 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 2025-09-09T14:41:34.4883713Z 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 2025-09-09T14:41:34.4887005Z 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 2025-09-09T14:41:34.4890301Z 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 2025-09-09T14:41:34.4893608Z 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 2025-09-09T14:41:34.4896948Z 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 2025-09-09T14:41:34.4900247Z 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 2025-09-09T14:41:34.4903535Z 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 2025-09-09T14:41:34.4906829Z 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 2025-09-09T14:41:34.4910147Z 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 2025-09-09T14:41:34.4913447Z 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 2025-09-09T14:41:34.4916848Z 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 2025-09-09T14:41:34.4920148Z 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 2025-09-09T14:41:34.4923447Z 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 2025-09-09T14:41:34.4926885Z 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 2025-09-09T14:41:34.4930215Z 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 2025-09-09T14:41:34.4933483Z 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 2025-09-09T14:41:34.4936774Z 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 2025-09-09T14:41:34.5023399Z 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 2025-09-09T14:41:34.5026890Z 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 2025-09-09T14:41:34.5030181Z 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 2025-09-09T14:41:34.5033537Z 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 2025-09-09T14:41:34.5036925Z 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 2025-09-09T14:41:34.5040263Z 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 2025-09-09T14:41:34.5043594Z 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 2025-09-09T14:41:34.5046969Z 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 2025-09-09T14:41:34.5050288Z 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 2025-09-09T14:41:34.5053602Z 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 2025-09-09T14:41:34.5056912Z 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 2025-09-09T14:41:34.5060275Z 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 2025-09-09T14:41:34.5063596Z 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 2025-09-09T14:41:34.5066977Z 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 2025-09-09T14:41:34.5070277Z 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 2025-09-09T14:41:34.5073598Z 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 2025-09-09T14:41:34.5076971Z 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 2025-09-09T14:41:34.5080321Z 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 2025-09-09T14:41:34.5083634Z 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 2025-09-09T14:41:34.5086933Z 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 2025-09-09T14:41:34.5090234Z 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 2025-09-09T14:41:34.5179991Z 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 2025-09-09T14:41:34.5183334Z 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 2025-09-09T14:41:34.5186755Z 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 2025-09-09T14:41:34.5190057Z 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 2025-09-09T14:41:34.5193381Z 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 2025-09-09T14:41:34.5196760Z 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 2025-09-09T14:41:34.5200124Z 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 2025-09-09T14:41:34.5203442Z 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 2025-09-09T14:41:34.5206741Z 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 2025-09-09T14:41:34.5210039Z 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 2025-09-09T14:41:34.5213375Z 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 2025-09-09T14:41:34.5216684Z 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 2025-09-09T14:41:34.5220040Z 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 2025-09-09T14:41:34.5223349Z 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 2025-09-09T14:41:34.5226747Z 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 2025-09-09T14:41:34.5230038Z 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 2025-09-09T14:41:34.5233382Z 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 2025-09-09T14:41:34.5236736Z 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 2025-09-09T14:41:34.5240021Z 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 2025-09-09T14:41:34.5243313Z 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 2025-09-09T14:41:34.5246672Z 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 2025-09-09T14:41:34.5335521Z 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 2025-09-09T14:41:34.5338966Z 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 2025-09-09T14:41:34.5342294Z 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 2025-09-09T14:41:34.5345592Z 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 2025-09-09T14:41:34.5348899Z 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 2025-09-09T14:41:34.5352255Z 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 2025-09-09T14:41:34.5355550Z 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 2025-09-09T14:41:34.5358942Z 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 2025-09-09T14:41:34.5362245Z 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 2025-09-09T14:41:34.5365600Z 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 2025-09-09T14:41:34.5368915Z 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 2025-09-09T14:41:34.5372273Z 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 2025-09-09T14:41:34.5375571Z 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 2025-09-09T14:41:34.5378848Z 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 2025-09-09T14:41:34.5382138Z 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 2025-09-09T14:41:34.5385452Z 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 2025-09-09T14:41:34.5388748Z 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 2025-09-09T14:41:34.5392045Z 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 2025-09-09T14:41:34.5395327Z 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 2025-09-09T14:41:34.5398730Z 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 2025-09-09T14:41:34.5402033Z 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 2025-09-09T14:41:34.5488450Z 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 2025-09-09T14:41:34.5491767Z 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 2025-09-09T14:41:34.5495056Z 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 2025-09-09T14:41:34.5498372Z 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 2025-09-09T14:41:34.5501672Z 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 2025-09-09T14:41:34.5504964Z 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 2025-09-09T14:41:34.5508267Z 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 2025-09-09T14:41:34.5511565Z 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 2025-09-09T14:41:34.5514874Z 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 2025-09-09T14:41:34.5518234Z 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 2025-09-09T14:41:34.5521603Z 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 2025-09-09T14:41:34.5525024Z 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 2025-09-09T14:41:34.5528314Z 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 2025-09-09T14:41:34.5531663Z 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 2025-09-09T14:41:34.5534988Z 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 2025-09-09T14:41:34.5538300Z 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 2025-09-09T14:41:34.5541620Z 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 2025-09-09T14:41:34.5544937Z 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 2025-09-09T14:41:34.5548259Z 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 2025-09-09T14:41:34.5551571Z 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 2025-09-09T14:41:34.5554971Z 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 2025-09-09T14:41:34.5645670Z 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 2025-09-09T14:41:34.5648992Z 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 2025-09-09T14:41:34.5652360Z 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 2025-09-09T14:41:34.5655663Z 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 2025-09-09T14:41:34.5658964Z 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 2025-09-09T14:41:34.5662259Z 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 2025-09-09T14:41:34.5665588Z 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 2025-09-09T14:41:34.5668889Z 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 2025-09-09T14:41:34.5672173Z 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 2025-09-09T14:41:34.5675566Z 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 2025-09-09T14:41:34.5678955Z 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 2025-09-09T14:41:34.5682254Z 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 2025-09-09T14:41:34.5685593Z 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 2025-09-09T14:41:34.5688885Z 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 2025-09-09T14:41:34.5692183Z 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 2025-09-09T14:41:34.5695472Z 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 2025-09-09T14:41:34.5698791Z 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 2025-09-09T14:41:34.5702083Z 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 2025-09-09T14:41:34.5705347Z 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 2025-09-09T14:41:34.5708720Z 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 2025-09-09T14:41:34.5712023Z 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 2025-09-09T14:42:12.3166147Z 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 2025-09-09T14:42:12.3170815Z 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 2025-09-09T14:42:12.3174090Z 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 2025-09-09T14:42:12.3177393Z 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 2025-09-09T14:42:12.3180680Z 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 2025-09-09T14:42:12.3184074Z 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 2025-09-09T14:42:12.3187360Z 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 2025-09-09T14:42:12.3189583Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_moe_quant_intx SKIPPED 2025-09-09T14:42:12.3191786Z 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 2025-09-09T14:42:12.3193956Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_serialization_{'layout': QDQLayout()} SKIPPED 2025-09-09T14:42:12.3195182Z test/quantization/test_moe_quant.py::TestMoEQuantCompile::test_fp8dq_base_0_single_token SKIPPED 2025-09-09T14:42:12.3196226Z test/quantization/test_moe_quant.py::TestMoEQuantCompile::test_fp8dq_base_1_multiple_tokens SKIPPED 2025-09-09T14:42:12.3197214Z test/quantization/test_moe_quant.py::TestMoEQuantCompile::test_fp8dq_fake_dim_0_single_token SKIPPED 2025-09-09T14:42:12.3198192Z test/quantization/test_moe_quant.py::TestMoEQuantCompile::test_fp8dq_fake_dim_1_multiple_tokens SKIPPED 2025-09-09T14:42:12.3199156Z test/quantization/test_moe_quant.py::TestMoEQuantCompile::test_fp8wo_base_0_single_token SKIPPED 2025-09-09T14:42:12.3200095Z test/quantization/test_moe_quant.py::TestMoEQuantCompile::test_fp8wo_base_1_multiple_tokens SKIPPED 2025-09-09T14:42:12.3201068Z test/quantization/test_moe_quant.py::TestMoEQuantCompile::test_fp8wo_fake_dim_0_single_token SKIPPED 2025-09-09T14:42:12.3202122Z test/quantization/test_moe_quant.py::TestMoEQuantCompile::test_fp8wo_fake_dim_1_multiple_tokens SKIPPED 2025-09-09T14:42:12.3203079Z test/quantization/test_moe_quant.py::TestMoEQuantCompile::test_int4wo_base_0_single_token SKIPPED 2025-09-09T14:42:12.3204045Z test/quantization/test_moe_quant.py::TestMoEQuantCompile::test_int4wo_base_1_multiple_tokens SKIPPED 2025-09-09T14:42:12.3205016Z test/quantization/test_moe_quant.py::TestMoEQuantCompile::test_int4wo_fake_dim_0_single_token SKIPPED 2025-09-09T14:42:12.3206014Z test/quantization/test_moe_quant.py::TestMoEQuantCompile::test_int4wo_fake_dim_1_multiple_tokens SKIPPED 2025-09-09T14:42:12.3207009Z test/quantization/test_moe_quant.py::TestMoEQuantCompile::test_int8dq_base_0_multiple_tokens SKIPPED 2025-09-09T14:42:12.3207992Z test/quantization/test_moe_quant.py::TestMoEQuantCompile::test_int8dq_fake_dim_0_multiple_tokens SKIPPED 2025-09-09T14:42:12.3208971Z test/quantization/test_moe_quant.py::TestMoEQuantCompile::test_int8wo_base_0_single_token SKIPPED 2025-09-09T14:42:12.3209920Z test/quantization/test_moe_quant.py::TestMoEQuantCompile::test_int8wo_base_1_multiple_tokens SKIPPED 2025-09-09T14:42:12.3210895Z test/quantization/test_moe_quant.py::TestMoEQuantCompile::test_int8wo_base_cpu_0_single_token PASSED 2025-09-09T14:42:12.3212386Z test/quantization/test_moe_quant.py::TestMoEQuantCompile::test_int8wo_base_cpu_1_multiple_tokens W0909 14:41:39.662882 343 site-packages/torch/fx/experimental/symbolic_shapes.py:6823] [1883/0] _maybe_guard_rel() was called on non-relation expression Eq(s61, 1) | Eq(s61, 16) 2025-09-09T14:42:12.3214065Z W0909 14:41:42.939764 343 site-packages/torch/fx/experimental/symbolic_shapes.py:6823] [1883/1] _maybe_guard_rel() was called on non-relation expression Eq(s61, 1) | Eq(s61, 16) 2025-09-09T14:42:12.3215402Z W0909 14:41:46.190889 343 site-packages/torch/fx/experimental/symbolic_shapes.py:6823] [1883/2] _maybe_guard_rel() was called on non-relation expression Eq(s61, 1) | Eq(s61, 16) 2025-09-09T14:42:12.3216702Z W0909 14:41:49.281197 343 site-packages/torch/fx/experimental/symbolic_shapes.py:6823] [1883/3] _maybe_guard_rel() was called on non-relation expression Eq(s61, 1) | Eq(s61, 16) 2025-09-09T14:42:12.3217978Z W0909 14:41:52.168184 343 site-packages/torch/fx/experimental/symbolic_shapes.py:6823] [1883/4] _maybe_guard_rel() was called on non-relation expression Eq(s61, 1) | Eq(s61, 16) 2025-09-09T14:42:12.3219311Z W0909 14:41:55.261192 343 site-packages/torch/fx/experimental/symbolic_shapes.py:6823] [1883/5] _maybe_guard_rel() was called on non-relation expression Eq(s61, 1) | Eq(s61, 16) 2025-09-09T14:42:12.3220598Z W0909 14:41:58.233018 343 site-packages/torch/fx/experimental/symbolic_shapes.py:6823] [1883/6] _maybe_guard_rel() was called on non-relation expression Eq(s61, 1) | Eq(s61, 16) 2025-09-09T14:42:12.3221922Z W0909 14:42:00.981788 343 site-packages/torch/fx/experimental/symbolic_shapes.py:6823] [1883/7] _maybe_guard_rel() was called on non-relation expression Eq(s61, 1) | Eq(s61, 16) 2025-09-09T14:42:12.3223221Z W0909 14:42:03.825709 343 site-packages/torch/fx/experimental/symbolic_shapes.py:6823] [1883/8] _maybe_guard_rel() was called on non-relation expression Eq(s61, 1) | Eq(s61, 16) 2025-09-09T14:42:12.3224720Z W0909 14:42:06.851351 343 site-packages/torch/fx/experimental/symbolic_shapes.py:6823] [1883/9] _maybe_guard_rel() was called on non-relation expression Eq(s61, 1) | Eq(s61, 16) 2025-09-09T14:42:12.3226083Z W0909 14:42:09.782369 343 site-packages/torch/fx/experimental/symbolic_shapes.py:6823] [1883/10] _maybe_guard_rel() was called on non-relation expression Eq(s61, 1) | Eq(s61, 16) 2025-09-09T14:42:12.3226934Z PASSED 2025-09-09T14:42:12.3227558Z test/quantization/test_moe_quant.py::TestMoEQuantCompile::test_int8wo_fake_dim_0_single_token SKIPPED 2025-09-09T14:42:12.3228558Z 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test/sparsity/test_wanda.py::TestWandaSparsifier::test_two_layer_mlp_unstructured PASSED 2025-09-09T14:44:49.9291804Z test/sparsity/test_wanda.py::TestWandaSparsifier::test_two_layer_mlp_unstructured_custom_config PASSED 2025-09-09T14:44:49.9292848Z test/test_ao_models.py::TorchAOBasicTestCase::test_ao_inference_mode_device_cpu_batch_size_1_is_training_False PASSED 2025-09-09T14:44:49.9293991Z test/test_ao_models.py::TorchAOBasicTestCase::test_ao_inference_mode_device_cpu_batch_size_1_is_training_True PASSED 2025-09-09T14:44:49.9295067Z test/test_ao_models.py::TorchAOBasicTestCase::test_ao_inference_mode_device_cpu_batch_size_4_is_training_False PASSED 2025-09-09T14:44:49.9296128Z test/test_ao_models.py::TorchAOBasicTestCase::test_ao_inference_mode_device_cpu_batch_size_4_is_training_True PASSED 2025-09-09T14:44:49.9297202Z test/test_low_bit_optim.py::TestQuantize::test_bf16_stochastic_round_device_cpu_compile_False PASSED 2025-09-09T14:44:49.9298122Z 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test/test_low_bit_optim.py::TestOptim::test_optim_default_dtype_bf16_optim_name_Adam8bit_device_cpu PASSED 2025-09-09T14:44:49.9312568Z test/test_low_bit_optim.py::TestOptim::test_optim_default_dtype_bf16_optim_name_AdamFp8_device_cpu PASSED 2025-09-09T14:44:49.9313496Z test/test_low_bit_optim.py::TestOptim::test_optim_smoke_optim_name_Adam4bit_bfloat16_device_cpu PASSED 2025-09-09T14:44:49.9314396Z test/test_low_bit_optim.py::TestOptim::test_optim_smoke_optim_name_Adam4bit_float32_device_cpu PASSED 2025-09-09T14:44:49.9315307Z test/test_low_bit_optim.py::TestOptim::test_optim_smoke_optim_name_Adam8bit_bfloat16_device_cpu PASSED 2025-09-09T14:44:49.9316314Z test/test_low_bit_optim.py::TestOptim::test_optim_smoke_optim_name_Adam8bit_float32_device_cpu PASSED 2025-09-09T14:44:49.9317263Z test/test_low_bit_optim.py::TestOptim::test_optim_smoke_optim_name_AdamFp8_bfloat16_device_cpu PASSED 2025-09-09T14:44:49.9318174Z test/test_low_bit_optim.py::TestOptim::test_optim_smoke_optim_name_AdamFp8_float32_device_cpu PASSED 2025-09-09T14:44:49.9319082Z test/test_low_bit_optim.py::TestOptim::test_optim_smoke_optim_name_AdamW4bit_bfloat16_device_cpu PASSED 2025-09-09T14:44:49.9319982Z test/test_low_bit_optim.py::TestOptim::test_optim_smoke_optim_name_AdamW4bit_float32_device_cpu PASSED 2025-09-09T14:44:49.9320937Z test/test_low_bit_optim.py::TestOptim::test_optim_smoke_optim_name_AdamW8bit_bfloat16_device_cpu PASSED 2025-09-09T14:44:49.9321840Z test/test_low_bit_optim.py::TestOptim::test_optim_smoke_optim_name_AdamW8bit_float32_device_cpu PASSED 2025-09-09T14:44:49.9322751Z test/test_low_bit_optim.py::TestOptim::test_optim_smoke_optim_name_AdamWFp8_bfloat16_device_cpu PASSED 2025-09-09T14:44:49.9323695Z test/test_low_bit_optim.py::TestOptim::test_optim_smoke_optim_name_AdamWFp8_float32_device_cpu PASSED 2025-09-09T14:44:49.9324731Z test/test_low_bit_optim.py::TestOptim::test_param_groups_optim_name_Adam4bit_device_cpu PASSED 2025-09-09T14:44:49.9325601Z test/test_low_bit_optim.py::TestOptim::test_param_groups_optim_name_Adam8bit_device_cpu PASSED 2025-09-09T14:44:49.9326443Z test/test_low_bit_optim.py::TestOptim::test_param_groups_optim_name_AdamFp8_device_cpu PASSED 2025-09-09T14:44:49.9327298Z test/test_low_bit_optim.py::TestOptim::test_subclass_slice_subclass0_shape0_device_cpu PASSED 2025-09-09T14:44:49.9328149Z test/test_low_bit_optim.py::TestOptim::test_subclass_slice_subclass0_shape1_device_cpu PASSED 2025-09-09T14:44:49.9328989Z test/test_low_bit_optim.py::TestOptim::test_subclass_slice_subclass1_shape0_device_cpu PASSED 2025-09-09T14:44:49.9329843Z test/test_low_bit_optim.py::TestOptim::test_subclass_slice_subclass1_shape1_device_cpu PASSED 2025-09-09T14:44:49.9330681Z test/test_low_bit_optim.py::TestOptim::test_subclass_slice_subclass2_shape0_device_cpu PASSED 2025-09-09T14:44:49.9331597Z test/test_low_bit_optim.py::TestOptim::test_subclass_slice_subclass2_shape1_device_cpu PASSED 2025-09-09T14:44:49.9332637Z test/test_low_bit_optim.py::TestFSDP2::test_fsdp2 I0909 14:44:41.566630 343 site-packages/torch/testing/_internal/common_distributed.py:729] Started process 0 with pid 10698 2025-09-09T14:44:49.9333738Z I0909 14:44:41.607505 343 site-packages/torch/testing/_internal/common_distributed.py:729] Started process 1 with pid 10699 2025-09-09T14:44:49.9334520Z The 8-bit optimizer is not available on your device, only available on CUDA for now. 2025-09-09T14:44:49.9335128Z The 8-bit optimizer is not available on your device, only available on CUDA for now. 2025-09-09T14:44:49.9335591Z dist init r=1, world=2 2025-09-09T14:44:49.9335826Z dist init r=0, world=2 2025-09-09T14:44:49.9336247Z [Gloo] Rank 1 is connected to 1 peer ranks. Expected number of connected peer ranks is : 1 2025-09-09T14:44:49.9336884Z [Gloo] Rank 0 is connected to 1 peer ranks. Expected number of connected peer ranks is : 1 2025-09-09T14:44:49.9337401Z SKIPPED (Need at l...) 2025-09-09T14:44:49.9338214Z test/test_low_bit_optim.py::TestFSDP2::test_uneven_shard I0909 14:44:45.858789 343 site-packages/torch/testing/_internal/common_distributed.py:729] Started process 0 with pid 10746 2025-09-09T14:44:49.9339337Z I0909 14:44:45.899506 343 site-packages/torch/testing/_internal/common_distributed.py:729] Started process 1 with pid 10747 2025-09-09T14:44:49.9340117Z The 8-bit optimizer is not available on your device, only available on CUDA for now. 2025-09-09T14:44:49.9340731Z The 8-bit optimizer is not available on your device, only available on CUDA for now. 2025-09-09T14:44:49.9341174Z dist init r=0, world=2 2025-09-09T14:44:49.9341416Z dist init r=1, world=2 2025-09-09T14:44:49.9341815Z [Gloo] Rank 1 is connected to 1 peer ranks. Expected number of connected peer ranks is : 1 2025-09-09T14:44:49.9342500Z [Gloo] Rank 0 is connected to 1 peer ranks. Expected number of connected peer ranks is : 1 2025-09-09T14:44:49.9342997Z SKIPPED (Ne...) 2025-09-09T14:44:49.9343577Z test/test_model_architecture.py::TestModels::test_ln_linear_activation_model_0_cpu PASSED 2025-09-09T14:44:49.9344374Z test/test_model_architecture.py::TestModels::test_toy_linear_model_0_cpu PASSED 2025-09-09T14:44:49.9345123Z test/test_model_architecture.py::TestModels::test_transformer_block_0_cpu PASSED 2025-09-09T14:44:49.9346104Z test/test_ops.py::TestOps::test_quant_llm_linear_correctness_BS_1_OC_2048_IC_4096_splitK_5_ebits_2_mbits_2_bfloat16 SKIPPED 2025-09-09T14:44:49.9347147Z test/test_ops.py::TestOps::test_quant_llm_linear_correctness_BS_1_OC_2048_IC_4096_splitK_5_ebits_2_mbits_2_float16 SKIPPED 2025-09-09T14:44:49.9348200Z test/test_ops.py::TestOps::test_quant_llm_linear_correctness_BS_1_OC_2048_IC_4096_splitK_5_ebits_3_mbits_2_bfloat16 SKIPPED 2025-09-09T14:44:49.9349300Z test/test_ops.py::TestOps::test_quant_llm_linear_correctness_BS_1_OC_2048_IC_4096_splitK_5_ebits_3_mbits_2_float16 SKIPPED 2025-09-09T14:44:49.9350345Z test/test_ops.py::TestOps::test_quant_llm_linear_correctness_BS_2_OC_8192_IC_8192_splitK_6_ebits_2_mbits_2_bfloat16 SKIPPED 2025-09-09T14:44:49.9351395Z test/test_ops.py::TestOps::test_quant_llm_linear_correctness_BS_2_OC_8192_IC_8192_splitK_6_ebits_2_mbits_2_float16 SKIPPED 2025-09-09T14:44:49.9352437Z test/test_ops.py::TestOps::test_quant_llm_linear_correctness_BS_2_OC_8192_IC_8192_splitK_6_ebits_3_mbits_2_bfloat16 SKIPPED 2025-09-09T14:44:49.9595992Z test/test_ops.py::TestOps::test_quant_llm_linear_correctness_BS_2_OC_8192_IC_8192_splitK_6_ebits_3_mbits_2_float16 SKIPPED 2025-09-09T14:44:49.9597023Z test/test_ops.py::TestOps::test_quant_llm_linear_ebits_2_mbits_2_bfloat16 SKIPPED 2025-09-09T14:44:49.9597845Z test/test_ops.py::TestOps::test_quant_llm_linear_ebits_2_mbits_2_float16 SKIPPED 2025-09-09T14:44:49.9598795Z test/test_ops.py::TestOps::test_quant_llm_linear_ebits_3_mbits_2_bfloat16 SKIPPED 2025-09-09T14:44:49.9599610Z test/test_ops.py::TestOps::test_quant_llm_linear_ebits_3_mbits_2_float16 SKIPPED 2025-09-09T14:44:49.9600680Z test/test_ops.py::TestOps::test_scaled_dot_product_int8_op_batch_size_120_n_head_16_q_seq_len_18_kv_seq_len_100_head_dim_32_bfloat16 SKIPPED 2025-09-09T14:44:49.9601919Z test/test_ops.py::TestOps::test_scaled_dot_product_int8_op_batch_size_120_n_head_16_q_seq_len_18_kv_seq_len_100_head_dim_32_float32 SKIPPED 2025-09-09T14:44:49.9603195Z test/test_ops.py::TestOps::test_scaled_dot_product_int8_op_batch_size_120_n_head_16_q_seq_len_18_kv_seq_len_100_head_dim_32_mask_dtype0 SKIPPED 2025-09-09T14:44:49.9604505Z test/test_ops.py::TestOps::test_scaled_dot_product_int8_op_batch_size_120_n_head_16_q_seq_len_18_kv_seq_len_100_head_dim_64_bfloat16 SKIPPED 2025-09-09T14:44:49.9605725Z test/test_ops.py::TestOps::test_scaled_dot_product_int8_op_batch_size_120_n_head_16_q_seq_len_18_kv_seq_len_100_head_dim_64_float32 SKIPPED 2025-09-09T14:44:49.9607026Z test/test_ops.py::TestOps::test_scaled_dot_product_int8_op_batch_size_120_n_head_16_q_seq_len_18_kv_seq_len_100_head_dim_64_mask_dtype0 SKIPPED 2025-09-09T14:44:49.9608337Z test/test_ops.py::TestOps::test_scaled_dot_product_int8_op_batch_size_120_n_head_16_q_seq_len_18_kv_seq_len_253_head_dim_32_bfloat16 SKIPPED 2025-09-09T14:44:49.9609560Z test/test_ops.py::TestOps::test_scaled_dot_product_int8_op_batch_size_120_n_head_16_q_seq_len_18_kv_seq_len_253_head_dim_32_float32 SKIPPED 2025-09-09T14:44:49.9610856Z test/test_ops.py::TestOps::test_scaled_dot_product_int8_op_batch_size_120_n_head_16_q_seq_len_18_kv_seq_len_253_head_dim_32_mask_dtype0 SKIPPED 2025-09-09T14:44:49.9612117Z test/test_ops.py::TestOps::test_scaled_dot_product_int8_op_batch_size_120_n_head_16_q_seq_len_18_kv_seq_len_253_head_dim_64_bfloat16 SKIPPED 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test/test_ops.py::TestOps::test_scaled_dot_product_int8_op_batch_size_120_n_head_16_q_seq_len_89_kv_seq_len_100_head_dim_64_bfloat16 SKIPPED 2025-09-09T14:44:49.9621167Z test/test_ops.py::TestOps::test_scaled_dot_product_int8_op_batch_size_120_n_head_16_q_seq_len_89_kv_seq_len_100_head_dim_64_float32 SKIPPED 2025-09-09T14:44:49.9622490Z test/test_ops.py::TestOps::test_scaled_dot_product_int8_op_batch_size_120_n_head_16_q_seq_len_89_kv_seq_len_100_head_dim_64_mask_dtype0 SKIPPED 2025-09-09T14:44:49.9623717Z test/test_ops.py::TestOps::test_scaled_dot_product_int8_op_batch_size_120_n_head_16_q_seq_len_89_kv_seq_len_253_head_dim_32_bfloat16 SKIPPED 2025-09-09T14:44:49.9625155Z test/test_ops.py::TestOps::test_scaled_dot_product_int8_op_batch_size_120_n_head_16_q_seq_len_89_kv_seq_len_253_head_dim_32_float32 SKIPPED 2025-09-09T14:44:49.9626463Z test/test_ops.py::TestOps::test_scaled_dot_product_int8_op_batch_size_120_n_head_16_q_seq_len_89_kv_seq_len_253_head_dim_32_mask_dtype0 SKIPPED 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test/test_ops.py::TestOps::test_scaled_dot_product_int8_op_batch_size_120_n_head_2_q_seq_len_18_kv_seq_len_100_head_dim_32_mask_dtype0 SKIPPED 2025-09-09T14:44:49.9634908Z test/test_ops.py::TestOps::test_scaled_dot_product_int8_op_batch_size_120_n_head_2_q_seq_len_18_kv_seq_len_100_head_dim_64_bfloat16 SKIPPED 2025-09-09T14:44:49.9636045Z test/test_ops.py::TestOps::test_scaled_dot_product_int8_op_batch_size_120_n_head_2_q_seq_len_18_kv_seq_len_100_head_dim_64_float32 SKIPPED 2025-09-09T14:44:49.9637332Z test/test_ops.py::TestOps::test_scaled_dot_product_int8_op_batch_size_120_n_head_2_q_seq_len_18_kv_seq_len_100_head_dim_64_mask_dtype0 SKIPPED 2025-09-09T14:44:49.9638476Z test/test_ops.py::TestOps::test_scaled_dot_product_int8_op_batch_size_120_n_head_2_q_seq_len_18_kv_seq_len_253_head_dim_32_bfloat16 SKIPPED 2025-09-09T14:44:49.9639616Z test/test_ops.py::TestOps::test_scaled_dot_product_int8_op_batch_size_120_n_head_2_q_seq_len_18_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_120_n_head_2_q_seq_len_89_kv_seq_len_100_head_dim_32_float32 SKIPPED 2025-09-09T14:44:49.9647868Z 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 2025-09-09T14:44:49.9649031Z 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 2025-09-09T14:44:49.9650160Z 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 2025-09-09T14:44:49.9651317Z 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 2025-09-09T14:44:49.9652471Z 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 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test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(4096, 11008)-2-128] SKIPPED 2025-09-09T14:44:50.1105606Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(4096, 11008)-2-256] SKIPPED 2025-09-09T14:44:50.1106480Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(4096, 11008)-4-32] SKIPPED 2025-09-09T14:44:50.1107346Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(4096, 11008)-4-64] SKIPPED 2025-09-09T14:44:50.1108233Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(4096, 11008)-4-128] SKIPPED 2025-09-09T14:44:50.1109129Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(4096, 11008)-4-256] SKIPPED 2025-09-09T14:44:50.1110048Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(4096, 11008)-8-32] SKIPPED 2025-09-09T14:44:50.1110925Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(4096, 11008)-8-64] SKIPPED 2025-09-09T14:44:50.1111793Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(4096, 11008)-8-128] SKIPPED 2025-09-09T14:44:50.1112671Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(4096, 11008)-8-256] SKIPPED 2025-09-09T14:44:50.1113609Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(11008, 4096)-2-32] SKIPPED 2025-09-09T14:44:50.1114487Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(11008, 4096)-2-64] SKIPPED 2025-09-09T14:44:50.1115419Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(11008, 4096)-2-128] SKIPPED 2025-09-09T14:44:50.1116413Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(11008, 4096)-2-256] SKIPPED 2025-09-09T14:44:50.1117344Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(11008, 4096)-4-32] SKIPPED 2025-09-09T14:44:50.1118216Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(11008, 4096)-4-64] SKIPPED 2025-09-09T14:44:50.1119080Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(11008, 4096)-4-128] SKIPPED 2025-09-09T14:44:50.1119969Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(11008, 4096)-4-256] SKIPPED 2025-09-09T14:44:50.1120891Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(11008, 4096)-8-32] SKIPPED 2025-09-09T14:44:50.1121690Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(11008, 4096)-8-64] SKIPPED 2025-09-09T14:44:50.1122578Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(11008, 4096)-8-128] SKIPPED 2025-09-09T14:44:50.1123373Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(11008, 4096)-8-256] SKIPPED 2025-09-09T14:44:50.1124252Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(4096, 14336)-2-32] SKIPPED 2025-09-09T14:44:50.1125199Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(4096, 14336)-2-64] SKIPPED 2025-09-09T14:44:50.1126014Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(4096, 14336)-2-128] SKIPPED 2025-09-09T14:44:50.1126825Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(4096, 14336)-2-256] SKIPPED 2025-09-09T14:44:50.1127631Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(4096, 14336)-4-32] SKIPPED 2025-09-09T14:44:50.1128586Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(4096, 14336)-4-64] SKIPPED 2025-09-09T14:44:50.1129380Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(4096, 14336)-4-128] SKIPPED 2025-09-09T14:44:50.1130190Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(4096, 14336)-4-256] SKIPPED 2025-09-09T14:44:50.1131002Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(4096, 14336)-8-32] SKIPPED 2025-09-09T14:44:50.1131796Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(4096, 14336)-8-64] SKIPPED 2025-09-09T14:44:50.1132599Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(4096, 14336)-8-128] SKIPPED 2025-09-09T14:44:50.1133392Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(4096, 14336)-8-256] SKIPPED 2025-09-09T14:44:50.1134202Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(14336, 4096)-2-32] SKIPPED 2025-09-09T14:44:50.1135074Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(14336, 4096)-2-64] SKIPPED 2025-09-09T14:44:50.1135874Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(14336, 4096)-2-128] SKIPPED 2025-09-09T14:44:50.1136681Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(14336, 4096)-2-256] SKIPPED 2025-09-09T14:44:50.1137477Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(14336, 4096)-4-32] SKIPPED 2025-09-09T14:44:50.1138280Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(14336, 4096)-4-64] SKIPPED 2025-09-09T14:44:50.1139084Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(14336, 4096)-4-128] SKIPPED 2025-09-09T14:44:50.1139880Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(14336, 4096)-4-256] SKIPPED 2025-09-09T14:44:50.1140744Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(14336, 4096)-8-32] SKIPPED 2025-09-09T14:44:50.1141538Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(14336, 4096)-8-64] SKIPPED 2025-09-09T14:44:50.1142348Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(14336, 4096)-8-128] SKIPPED 2025-09-09T14:44:50.1143156Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(14336, 4096)-8-256] SKIPPED 2025-09-09T14:44:50.1143927Z test/test_ops.py::test_marlin_24[1-128-512-4--1-(1, 1, 1)] SKIPPED (...) 2025-09-09T14:44:50.1144578Z test/test_ops.py::test_marlin_24[1-128-512-4--1-(1, 4, 8)] SKIPPED (...) 2025-09-09T14:44:50.1145207Z test/test_ops.py::test_marlin_24[1-128-512-4--1-(1, 7, 5)] SKIPPED (...) 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2025-09-09T14:44:50.1660528Z test/test_ops.py::test_marlin_24[8-128-512-8--1-(13, 17, 67)] SKIPPED 2025-09-09T14:44:50.1661142Z test/test_ops.py::test_marlin_24[8-128-512-8--1-(26, 37, 13)] SKIPPED 2025-09-09T14:44:50.1661743Z test/test_ops.py::test_marlin_24[8-128-512-8--1-(67, 13, 11)] SKIPPED 2025-09-09T14:44:50.1662354Z test/test_ops.py::test_marlin_24[8-128-512-8-128-(1, 1, 1)] SKIPPED 2025-09-09T14:44:50.1662947Z test/test_ops.py::test_marlin_24[8-128-512-8-128-(1, 4, 8)] SKIPPED 2025-09-09T14:44:50.1663548Z test/test_ops.py::test_marlin_24[8-128-512-8-128-(1, 7, 5)] SKIPPED 2025-09-09T14:44:50.1664169Z test/test_ops.py::test_marlin_24[8-128-512-8-128-(13, 17, 67)] SKIPPED 2025-09-09T14:44:50.1664783Z test/test_ops.py::test_marlin_24[8-128-512-8-128-(26, 37, 13)] SKIPPED 2025-09-09T14:44:50.1665455Z test/test_ops.py::test_marlin_24[8-128-512-8-128-(67, 13, 11)] SKIPPED 2025-09-09T14:44:50.1666059Z test/test_ops.py::test_marlin_24[16-128-512-4--1-(1, 1, 1)] SKIPPED 2025-09-09T14:44:50.1666660Z test/test_ops.py::test_marlin_24[16-128-512-4--1-(1, 4, 8)] SKIPPED 2025-09-09T14:44:50.1667249Z test/test_ops.py::test_marlin_24[16-128-512-4--1-(1, 7, 5)] SKIPPED 2025-09-09T14:44:50.1667865Z test/test_ops.py::test_marlin_24[16-128-512-4--1-(13, 17, 67)] SKIPPED 2025-09-09T14:44:50.1668524Z test/test_ops.py::test_marlin_24[16-128-512-4--1-(26, 37, 13)] SKIPPED 2025-09-09T14:44:50.1669132Z test/test_ops.py::test_marlin_24[16-128-512-4--1-(67, 13, 11)] SKIPPED 2025-09-09T14:44:50.1669748Z test/test_ops.py::test_marlin_24[16-128-512-4-128-(1, 1, 1)] SKIPPED 2025-09-09T14:44:50.1670348Z test/test_ops.py::test_marlin_24[16-128-512-4-128-(1, 4, 8)] SKIPPED 2025-09-09T14:44:50.1670995Z test/test_ops.py::test_marlin_24[16-128-512-4-128-(1, 7, 5)] SKIPPED 2025-09-09T14:44:50.1671624Z test/test_ops.py::test_marlin_24[16-128-512-4-128-(13, 17, 67)] SKIPPED 2025-09-09T14:44:50.1672275Z test/test_ops.py::test_marlin_24[16-128-512-4-128-(26, 37, 13)] SKIPPED 2025-09-09T14:44:50.1672960Z test/test_ops.py::test_marlin_24[16-128-512-4-128-(67, 13, 11)] SKIPPED 2025-09-09T14:44:50.1673562Z test/test_ops.py::test_marlin_24[16-128-512-8--1-(1, 1, 1)] SKIPPED 2025-09-09T14:44:50.1674168Z test/test_ops.py::test_marlin_24[16-128-512-8--1-(1, 4, 8)] SKIPPED 2025-09-09T14:44:50.1674759Z test/test_ops.py::test_marlin_24[16-128-512-8--1-(1, 7, 5)] SKIPPED 2025-09-09T14:44:50.1675370Z test/test_ops.py::test_marlin_24[16-128-512-8--1-(13, 17, 67)] SKIPPED 2025-09-09T14:44:50.1675996Z test/test_ops.py::test_marlin_24[16-128-512-8--1-(26, 37, 13)] SKIPPED 2025-09-09T14:44:50.1676697Z test/test_ops.py::test_marlin_24[16-128-512-8--1-(67, 13, 11)] SKIPPED 2025-09-09T14:44:50.1677365Z test/test_ops.py::test_marlin_24[16-128-512-8-128-(1, 1, 1)] SKIPPED 2025-09-09T14:44:50.1677964Z test/test_ops.py::test_marlin_24[16-128-512-8-128-(1, 4, 8)] SKIPPED 2025-09-09T14:44:50.1678572Z test/test_ops.py::test_marlin_24[16-128-512-8-128-(1, 7, 5)] SKIPPED 2025-09-09T14:44:50.1679181Z test/test_ops.py::test_marlin_24[16-128-512-8-128-(13, 17, 67)] SKIPPED 2025-09-09T14:44:50.1679810Z test/test_ops.py::test_marlin_24[16-128-512-8-128-(26, 37, 13)] SKIPPED 2025-09-09T14:44:50.1680439Z test/test_ops.py::test_marlin_24[16-128-512-8-128-(67, 13, 11)] SKIPPED 2025-09-09T14:44:50.1681043Z test/test_ops.py::test_marlin_24[32-128-512-4--1-(1, 1, 1)] SKIPPED 2025-09-09T14:44:50.1681641Z test/test_ops.py::test_marlin_24[32-128-512-4--1-(1, 4, 8)] SKIPPED 2025-09-09T14:44:50.1682227Z test/test_ops.py::test_marlin_24[32-128-512-4--1-(1, 7, 5)] SKIPPED 2025-09-09T14:44:50.1682844Z test/test_ops.py::test_marlin_24[32-128-512-4--1-(13, 17, 67)] SKIPPED 2025-09-09T14:44:50.1683471Z test/test_ops.py::test_marlin_24[32-128-512-4--1-(26, 37, 13)] SKIPPED 2025-09-09T14:44:50.1684082Z test/test_ops.py::test_marlin_24[32-128-512-4--1-(67, 13, 11)] SKIPPED 2025-09-09T14:44:50.1684696Z test/test_ops.py::test_marlin_24[32-128-512-4-128-(1, 1, 1)] SKIPPED 2025-09-09T14:44:50.1685295Z test/test_ops.py::test_marlin_24[32-128-512-4-128-(1, 4, 8)] SKIPPED 2025-09-09T14:44:50.1685908Z test/test_ops.py::test_marlin_24[32-128-512-4-128-(1, 7, 5)] SKIPPED 2025-09-09T14:44:50.1686521Z test/test_ops.py::test_marlin_24[32-128-512-4-128-(13, 17, 67)] SKIPPED 2025-09-09T14:44:50.1687158Z test/test_ops.py::test_marlin_24[32-128-512-4-128-(26, 37, 13)] SKIPPED 2025-09-09T14:44:50.1687787Z test/test_ops.py::test_marlin_24[32-128-512-4-128-(67, 13, 11)] SKIPPED 2025-09-09T14:44:50.1688389Z test/test_ops.py::test_marlin_24[32-128-512-8--1-(1, 1, 1)] SKIPPED 2025-09-09T14:44:50.1689027Z test/test_ops.py::test_marlin_24[32-128-512-8--1-(1, 4, 8)] SKIPPED 2025-09-09T14:44:50.1689620Z test/test_ops.py::test_marlin_24[32-128-512-8--1-(1, 7, 5)] SKIPPED 2025-09-09T14:44:50.1690233Z test/test_ops.py::test_marlin_24[32-128-512-8--1-(13, 17, 67)] SKIPPED 2025-09-09T14:44:50.1690841Z test/test_ops.py::test_marlin_24[32-128-512-8--1-(26, 37, 13)] SKIPPED 2025-09-09T14:44:50.1691460Z test/test_ops.py::test_marlin_24[32-128-512-8--1-(67, 13, 11)] SKIPPED 2025-09-09T14:44:50.1692110Z test/test_ops.py::test_marlin_24[32-128-512-8-128-(1, 1, 1)] SKIPPED 2025-09-09T14:44:50.1692707Z test/test_ops.py::test_marlin_24[32-128-512-8-128-(1, 4, 8)] SKIPPED 2025-09-09T14:44:50.1693324Z test/test_ops.py::test_marlin_24[32-128-512-8-128-(1, 7, 5)] SKIPPED 2025-09-09T14:44:50.1693932Z test/test_ops.py::test_marlin_24[32-128-512-8-128-(13, 17, 67)] SKIPPED 2025-09-09T14:44:50.1694601Z test/test_ops.py::test_marlin_24[32-128-512-8-128-(26, 37, 13)] SKIPPED 2025-09-09T14:44:50.1695271Z test/test_ops.py::test_marlin_24[32-128-512-8-128-(67, 13, 11)] SKIPPED 2025-09-09T14:44:50.1695874Z test/test_ops.py::test_marlin_24[64-128-512-4--1-(1, 1, 1)] SKIPPED 2025-09-09T14:44:50.1696479Z test/test_ops.py::test_marlin_24[64-128-512-4--1-(1, 4, 8)] SKIPPED 2025-09-09T14:44:50.1697070Z test/test_ops.py::test_marlin_24[64-128-512-4--1-(1, 7, 5)] SKIPPED 2025-09-09T14:44:50.1697689Z test/test_ops.py::test_marlin_24[64-128-512-4--1-(13, 17, 67)] SKIPPED 2025-09-09T14:44:50.2179818Z test/test_ops.py::test_marlin_24[64-128-512-4--1-(26, 37, 13)] SKIPPED 2025-09-09T14:44:50.2180457Z test/test_ops.py::test_marlin_24[64-128-512-4--1-(67, 13, 11)] SKIPPED 2025-09-09T14:44:50.2181084Z test/test_ops.py::test_marlin_24[64-128-512-4-128-(1, 1, 1)] SKIPPED 2025-09-09T14:44:50.2181683Z test/test_ops.py::test_marlin_24[64-128-512-4-128-(1, 4, 8)] SKIPPED 2025-09-09T14:44:50.2182452Z test/test_ops.py::test_marlin_24[64-128-512-4-128-(1, 7, 5)] SKIPPED 2025-09-09T14:44:50.2183176Z test/test_ops.py::test_marlin_24[64-128-512-4-128-(13, 17, 67)] SKIPPED 2025-09-09T14:44:50.2183815Z test/test_ops.py::test_marlin_24[64-128-512-4-128-(26, 37, 13)] SKIPPED 2025-09-09T14:44:50.2184441Z test/test_ops.py::test_marlin_24[64-128-512-4-128-(67, 13, 11)] SKIPPED 2025-09-09T14:44:50.2185044Z test/test_ops.py::test_marlin_24[64-128-512-8--1-(1, 1, 1)] SKIPPED 2025-09-09T14:44:50.2185655Z test/test_ops.py::test_marlin_24[64-128-512-8--1-(1, 4, 8)] SKIPPED 2025-09-09T14:44:50.2186243Z test/test_ops.py::test_marlin_24[64-128-512-8--1-(1, 7, 5)] SKIPPED 2025-09-09T14:44:50.2186852Z test/test_ops.py::test_marlin_24[64-128-512-8--1-(13, 17, 67)] SKIPPED 2025-09-09T14:44:50.2187460Z test/test_ops.py::test_marlin_24[64-128-512-8--1-(26, 37, 13)] SKIPPED 2025-09-09T14:44:50.2188089Z test/test_ops.py::test_marlin_24[64-128-512-8--1-(67, 13, 11)] SKIPPED 2025-09-09T14:44:50.2188704Z test/test_ops.py::test_marlin_24[64-128-512-8-128-(1, 1, 1)] SKIPPED 2025-09-09T14:44:50.2189299Z test/test_ops.py::test_marlin_24[64-128-512-8-128-(1, 4, 8)] SKIPPED 2025-09-09T14:44:50.2189904Z test/test_ops.py::test_marlin_24[64-128-512-8-128-(1, 7, 5)] SKIPPED 2025-09-09T14:44:50.2190512Z test/test_ops.py::test_marlin_24[64-128-512-8-128-(13, 17, 67)] SKIPPED 2025-09-09T14:44:50.2191144Z test/test_ops.py::test_marlin_24[64-128-512-8-128-(26, 37, 13)] SKIPPED 2025-09-09T14:44:50.2191772Z test/test_ops.py::test_marlin_24[64-128-512-8-128-(67, 13, 11)] SKIPPED 2025-09-09T14:44:50.2192401Z test/test_ops.py::test_marlin_qqq[1-128-64-4--1-(1, 1, 1)] SKIPPED (...) 2025-09-09T14:44:50.2193045Z test/test_ops.py::test_marlin_qqq[1-128-64-4--1-(1, 4, 8)] SKIPPED (...) 2025-09-09T14:44:50.2193764Z test/test_ops.py::test_marlin_qqq[1-128-64-4--1-(1, 7, 5)] SKIPPED (...) 2025-09-09T14:44:50.2194403Z test/test_ops.py::test_marlin_qqq[1-128-64-4--1-(13, 17, 67)] SKIPPED 2025-09-09T14:44:50.2195009Z test/test_ops.py::test_marlin_qqq[1-128-64-4--1-(26, 37, 13)] SKIPPED 2025-09-09T14:44:50.2195627Z test/test_ops.py::test_marlin_qqq[1-128-64-4--1-(67, 13, 11)] SKIPPED 2025-09-09T14:44:50.2196322Z test/test_ops.py::test_marlin_qqq[1-128-64-4-128-(1, 1, 1)] SKIPPED 2025-09-09T14:44:50.2196991Z test/test_ops.py::test_marlin_qqq[1-128-64-4-128-(1, 4, 8)] SKIPPED 2025-09-09T14:44:50.2197599Z test/test_ops.py::test_marlin_qqq[1-128-64-4-128-(1, 7, 5)] SKIPPED 2025-09-09T14:44:50.2198210Z test/test_ops.py::test_marlin_qqq[1-128-64-4-128-(13, 17, 67)] SKIPPED 2025-09-09T14:44:50.2198867Z test/test_ops.py::test_marlin_qqq[1-128-64-4-128-(26, 37, 13)] SKIPPED 2025-09-09T14:44:50.2199494Z test/test_ops.py::test_marlin_qqq[1-128-64-4-128-(67, 13, 11)] SKIPPED 2025-09-09T14:44:50.2200171Z test/test_ops.py::test_marlin_qqq[1-128-128-4--1-(1, 1, 1)] SKIPPED 2025-09-09T14:44:50.2200782Z test/test_ops.py::test_marlin_qqq[1-128-128-4--1-(1, 4, 8)] SKIPPED 2025-09-09T14:44:50.2201386Z test/test_ops.py::test_marlin_qqq[1-128-128-4--1-(1, 7, 5)] SKIPPED 2025-09-09T14:44:50.2201990Z test/test_ops.py::test_marlin_qqq[1-128-128-4--1-(13, 17, 67)] SKIPPED 2025-09-09T14:44:50.2202616Z test/test_ops.py::test_marlin_qqq[1-128-128-4--1-(26, 37, 13)] SKIPPED 2025-09-09T14:44:50.2203238Z test/test_ops.py::test_marlin_qqq[1-128-128-4--1-(67, 13, 11)] SKIPPED 2025-09-09T14:44:50.2203855Z test/test_ops.py::test_marlin_qqq[1-128-128-4-128-(1, 1, 1)] SKIPPED 2025-09-09T14:44:50.2204456Z test/test_ops.py::test_marlin_qqq[1-128-128-4-128-(1, 4, 8)] SKIPPED 2025-09-09T14:44:50.2205074Z test/test_ops.py::test_marlin_qqq[1-128-128-4-128-(1, 7, 5)] SKIPPED 2025-09-09T14:44:50.2205706Z test/test_ops.py::test_marlin_qqq[1-128-128-4-128-(13, 17, 67)] SKIPPED 2025-09-09T14:44:50.2206371Z test/test_ops.py::test_marlin_qqq[1-128-128-4-128-(26, 37, 13)] SKIPPED 2025-09-09T14:44:50.2207004Z test/test_ops.py::test_marlin_qqq[1-128-128-4-128-(67, 13, 11)] SKIPPED 2025-09-09T14:44:50.2207610Z test/test_ops.py::test_marlin_qqq[1-128-256-4--1-(1, 1, 1)] SKIPPED 2025-09-09T14:44:50.2208214Z test/test_ops.py::test_marlin_qqq[1-128-256-4--1-(1, 4, 8)] SKIPPED 2025-09-09T14:44:50.2208809Z test/test_ops.py::test_marlin_qqq[1-128-256-4--1-(1, 7, 5)] SKIPPED 2025-09-09T14:44:50.2209431Z test/test_ops.py::test_marlin_qqq[1-128-256-4--1-(13, 17, 67)] SKIPPED 2025-09-09T14:44:50.2210061Z test/test_ops.py::test_marlin_qqq[1-128-256-4--1-(26, 37, 13)] SKIPPED 2025-09-09T14:44:50.2210673Z test/test_ops.py::test_marlin_qqq[1-128-256-4--1-(67, 13, 11)] SKIPPED 2025-09-09T14:44:50.2211292Z test/test_ops.py::test_marlin_qqq[1-128-256-4-128-(1, 1, 1)] SKIPPED 2025-09-09T14:44:50.2211896Z test/test_ops.py::test_marlin_qqq[1-128-256-4-128-(1, 4, 8)] SKIPPED 2025-09-09T14:44:50.2212513Z test/test_ops.py::test_marlin_qqq[1-128-256-4-128-(1, 7, 5)] SKIPPED 2025-09-09T14:44:50.2213137Z test/test_ops.py::test_marlin_qqq[1-128-256-4-128-(13, 17, 67)] SKIPPED 2025-09-09T14:44:50.2213758Z test/test_ops.py::test_marlin_qqq[1-128-256-4-128-(26, 37, 13)] SKIPPED 2025-09-09T14:44:50.2214388Z test/test_ops.py::test_marlin_qqq[1-128-256-4-128-(67, 13, 11)] SKIPPED 2025-09-09T14:44:50.2215014Z test/test_ops.py::test_marlin_qqq[4-128-64-4--1-(1, 1, 1)] SKIPPED (...) 2025-09-09T14:44:50.2215657Z test/test_ops.py::test_marlin_qqq[4-128-64-4--1-(1, 4, 8)] SKIPPED (...) 2025-09-09T14:44:50.2216284Z test/test_ops.py::test_marlin_qqq[4-128-64-4--1-(1, 7, 5)] SKIPPED (...) 2025-09-09T14:44:50.2216919Z test/test_ops.py::test_marlin_qqq[4-128-64-4--1-(13, 17, 67)] SKIPPED 2025-09-09T14:44:50.2217576Z test/test_ops.py::test_marlin_qqq[4-128-64-4--1-(26, 37, 13)] SKIPPED 2025-09-09T14:44:50.2218186Z test/test_ops.py::test_marlin_qqq[4-128-64-4--1-(67, 13, 11)] SKIPPED 2025-09-09T14:44:50.2218796Z test/test_ops.py::test_marlin_qqq[4-128-64-4-128-(1, 1, 1)] SKIPPED 2025-09-09T14:44:50.2219388Z test/test_ops.py::test_marlin_qqq[4-128-64-4-128-(1, 4, 8)] SKIPPED 2025-09-09T14:44:50.2219992Z test/test_ops.py::test_marlin_qqq[4-128-64-4-128-(1, 7, 5)] SKIPPED 2025-09-09T14:44:50.2220645Z test/test_ops.py::test_marlin_qqq[4-128-64-4-128-(13, 17, 67)] SKIPPED 2025-09-09T14:44:50.2221265Z test/test_ops.py::test_marlin_qqq[4-128-64-4-128-(26, 37, 13)] SKIPPED 2025-09-09T14:44:50.2222022Z test/test_ops.py::test_marlin_qqq[4-128-64-4-128-(67, 13, 11)] SKIPPED 2025-09-09T14:44:50.2222707Z test/test_ops.py::test_marlin_qqq[4-128-128-4--1-(1, 1, 1)] SKIPPED 2025-09-09T14:44:50.2223314Z test/test_ops.py::test_marlin_qqq[4-128-128-4--1-(1, 4, 8)] SKIPPED 2025-09-09T14:44:50.2223949Z test/test_ops.py::test_marlin_qqq[4-128-128-4--1-(1, 7, 5)] SKIPPED 2025-09-09T14:44:50.2224805Z test/test_ops.py::test_marlin_qqq[4-128-128-4--1-(13, 17, 67)] SKIPPED 2025-09-09T14:44:50.2225472Z test/test_ops.py::test_marlin_qqq[4-128-128-4--1-(26, 37, 13)] SKIPPED 2025-09-09T14:44:50.2226161Z test/test_ops.py::test_marlin_qqq[4-128-128-4--1-(67, 13, 11)] SKIPPED 2025-09-09T14:44:50.2226856Z test/test_ops.py::test_marlin_qqq[4-128-128-4-128-(1, 1, 1)] SKIPPED 2025-09-09T14:44:50.2227529Z test/test_ops.py::test_marlin_qqq[4-128-128-4-128-(1, 4, 8)] SKIPPED 2025-09-09T14:44:50.2228213Z test/test_ops.py::test_marlin_qqq[4-128-128-4-128-(1, 7, 5)] SKIPPED 2025-09-09T14:44:50.2228903Z test/test_ops.py::test_marlin_qqq[4-128-128-4-128-(13, 17, 67)] SKIPPED 2025-09-09T14:44:50.2229616Z test/test_ops.py::test_marlin_qqq[4-128-128-4-128-(26, 37, 13)] SKIPPED 2025-09-09T14:44:50.2230294Z test/test_ops.py::test_marlin_qqq[4-128-128-4-128-(67, 13, 11)] SKIPPED 2025-09-09T14:44:50.2231002Z test/test_ops.py::test_marlin_qqq[4-128-256-4--1-(1, 1, 1)] SKIPPED 2025-09-09T14:44:50.2231682Z test/test_ops.py::test_marlin_qqq[4-128-256-4--1-(1, 4, 8)] SKIPPED 2025-09-09T14:44:50.2232355Z test/test_ops.py::test_marlin_qqq[4-128-256-4--1-(1, 7, 5)] SKIPPED 2025-09-09T14:44:50.2233044Z test/test_ops.py::test_marlin_qqq[4-128-256-4--1-(13, 17, 67)] SKIPPED 2025-09-09T14:44:50.2233744Z test/test_ops.py::test_marlin_qqq[4-128-256-4--1-(26, 37, 13)] SKIPPED 2025-09-09T14:44:50.2234423Z test/test_ops.py::test_marlin_qqq[4-128-256-4--1-(67, 13, 11)] SKIPPED 2025-09-09T14:44:50.2235111Z test/test_ops.py::test_marlin_qqq[4-128-256-4-128-(1, 1, 1)] SKIPPED 2025-09-09T14:44:50.2235743Z test/test_ops.py::test_marlin_qqq[4-128-256-4-128-(1, 4, 8)] SKIPPED 2025-09-09T14:44:50.2236473Z test/test_ops.py::test_marlin_qqq[4-128-256-4-128-(1, 7, 5)] SKIPPED 2025-09-09T14:44:50.2237169Z test/test_ops.py::test_marlin_qqq[4-128-256-4-128-(13, 17, 67)] SKIPPED 2025-09-09T14:44:50.2237884Z test/test_ops.py::test_marlin_qqq[4-128-256-4-128-(26, 37, 13)] SKIPPED 2025-09-09T14:44:50.2238599Z test/test_ops.py::test_marlin_qqq[4-128-256-4-128-(67, 13, 11)] SKIPPED 2025-09-09T14:44:50.2239298Z test/test_ops.py::test_marlin_qqq[8-128-64-4--1-(1, 1, 1)] SKIPPED (...) 2025-09-09T14:44:50.2240016Z test/test_ops.py::test_marlin_qqq[8-128-64-4--1-(1, 4, 8)] SKIPPED (...) 2025-09-09T14:44:50.2240721Z test/test_ops.py::test_marlin_qqq[8-128-64-4--1-(1, 7, 5)] SKIPPED (...) 2025-09-09T14:44:50.2241427Z test/test_ops.py::test_marlin_qqq[8-128-64-4--1-(13, 17, 67)] SKIPPED 2025-09-09T14:44:50.2242103Z test/test_ops.py::test_marlin_qqq[8-128-64-4--1-(26, 37, 13)] SKIPPED 2025-09-09T14:44:50.2242738Z test/test_ops.py::test_marlin_qqq[8-128-64-4--1-(67, 13, 11)] SKIPPED 2025-09-09T14:44:50.2243494Z test/test_ops.py::test_marlin_qqq[8-128-64-4-128-(1, 1, 1)] SKIPPED 2025-09-09T14:44:50.2244176Z test/test_ops.py::test_marlin_qqq[8-128-64-4-128-(1, 4, 8)] SKIPPED 2025-09-09T14:44:50.2244864Z test/test_ops.py::test_marlin_qqq[8-128-64-4-128-(1, 7, 5)] SKIPPED 2025-09-09T14:44:50.2245539Z test/test_ops.py::test_marlin_qqq[8-128-64-4-128-(13, 17, 67)] SKIPPED 2025-09-09T14:44:50.2246242Z test/test_ops.py::test_marlin_qqq[8-128-64-4-128-(26, 37, 13)] SKIPPED 2025-09-09T14:44:50.2721772Z test/test_ops.py::test_marlin_qqq[8-128-64-4-128-(67, 13, 11)] SKIPPED 2025-09-09T14:44:50.2722493Z test/test_ops.py::test_marlin_qqq[8-128-128-4--1-(1, 1, 1)] SKIPPED 2025-09-09T14:44:50.2723216Z test/test_ops.py::test_marlin_qqq[8-128-128-4--1-(1, 4, 8)] SKIPPED 2025-09-09T14:44:50.2723883Z test/test_ops.py::test_marlin_qqq[8-128-128-4--1-(1, 7, 5)] SKIPPED 2025-09-09T14:44:50.2724688Z test/test_ops.py::test_marlin_qqq[8-128-128-4--1-(13, 17, 67)] SKIPPED 2025-09-09T14:44:50.2725566Z test/test_ops.py::test_marlin_qqq[8-128-128-4--1-(26, 37, 13)] SKIPPED 2025-09-09T14:44:50.2726263Z test/test_ops.py::test_marlin_qqq[8-128-128-4--1-(67, 13, 11)] SKIPPED 2025-09-09T14:44:50.2726955Z test/test_ops.py::test_marlin_qqq[8-128-128-4-128-(1, 1, 1)] SKIPPED 2025-09-09T14:44:50.2727629Z test/test_ops.py::test_marlin_qqq[8-128-128-4-128-(1, 4, 8)] SKIPPED 2025-09-09T14:44:50.2728251Z test/test_ops.py::test_marlin_qqq[8-128-128-4-128-(1, 7, 5)] SKIPPED 2025-09-09T14:44:50.2728942Z test/test_ops.py::test_marlin_qqq[8-128-128-4-128-(13, 17, 67)] SKIPPED 2025-09-09T14:44:50.2729652Z test/test_ops.py::test_marlin_qqq[8-128-128-4-128-(26, 37, 13)] SKIPPED 2025-09-09T14:44:50.2730362Z test/test_ops.py::test_marlin_qqq[8-128-128-4-128-(67, 13, 11)] SKIPPED 2025-09-09T14:44:50.2731034Z test/test_ops.py::test_marlin_qqq[8-128-256-4--1-(1, 1, 1)] SKIPPED 2025-09-09T14:44:50.2731713Z test/test_ops.py::test_marlin_qqq[8-128-256-4--1-(1, 4, 8)] SKIPPED 2025-09-09T14:44:50.2732445Z test/test_ops.py::test_marlin_qqq[8-128-256-4--1-(1, 7, 5)] SKIPPED 2025-09-09T14:44:50.2733134Z test/test_ops.py::test_marlin_qqq[8-128-256-4--1-(13, 17, 67)] SKIPPED 2025-09-09T14:44:50.2733778Z test/test_ops.py::test_marlin_qqq[8-128-256-4--1-(26, 37, 13)] SKIPPED 2025-09-09T14:44:50.2734449Z test/test_ops.py::test_marlin_qqq[8-128-256-4--1-(67, 13, 11)] SKIPPED 2025-09-09T14:44:50.2735147Z test/test_ops.py::test_marlin_qqq[8-128-256-4-128-(1, 1, 1)] SKIPPED 2025-09-09T14:44:50.2735825Z test/test_ops.py::test_marlin_qqq[8-128-256-4-128-(1, 4, 8)] SKIPPED 2025-09-09T14:44:50.2736507Z test/test_ops.py::test_marlin_qqq[8-128-256-4-128-(1, 7, 5)] SKIPPED 2025-09-09T14:44:50.2737188Z test/test_ops.py::test_marlin_qqq[8-128-256-4-128-(13, 17, 67)] SKIPPED 2025-09-09T14:44:50.2737895Z test/test_ops.py::test_marlin_qqq[8-128-256-4-128-(26, 37, 13)] SKIPPED 2025-09-09T14:44:50.2738655Z test/test_ops.py::test_marlin_qqq[8-128-256-4-128-(67, 13, 11)] SKIPPED 2025-09-09T14:44:50.2739304Z test/test_ops.py::test_marlin_qqq[16-128-64-4--1-(1, 1, 1)] SKIPPED 2025-09-09T14:44:50.2739938Z test/test_ops.py::test_marlin_qqq[16-128-64-4--1-(1, 4, 8)] SKIPPED 2025-09-09T14:44:50.2740615Z test/test_ops.py::test_marlin_qqq[16-128-64-4--1-(1, 7, 5)] SKIPPED 2025-09-09T14:44:50.2741295Z test/test_ops.py::test_marlin_qqq[16-128-64-4--1-(13, 17, 67)] SKIPPED 2025-09-09T14:44:50.2741991Z test/test_ops.py::test_marlin_qqq[16-128-64-4--1-(26, 37, 13)] SKIPPED 2025-09-09T14:44:50.2742760Z test/test_ops.py::test_marlin_qqq[16-128-64-4--1-(67, 13, 11)] SKIPPED 2025-09-09T14:44:50.2743445Z test/test_ops.py::test_marlin_qqq[16-128-64-4-128-(1, 1, 1)] SKIPPED 2025-09-09T14:44:50.2744117Z test/test_ops.py::test_marlin_qqq[16-128-64-4-128-(1, 4, 8)] SKIPPED 2025-09-09T14:44:50.2744855Z test/test_ops.py::test_marlin_qqq[16-128-64-4-128-(1, 7, 5)] SKIPPED 2025-09-09T14:44:50.2745514Z test/test_ops.py::test_marlin_qqq[16-128-64-4-128-(13, 17, 67)] SKIPPED 2025-09-09T14:44:50.2746195Z test/test_ops.py::test_marlin_qqq[16-128-64-4-128-(26, 37, 13)] SKIPPED 2025-09-09T14:44:50.2746907Z test/test_ops.py::test_marlin_qqq[16-128-64-4-128-(67, 13, 11)] SKIPPED 2025-09-09T14:44:50.2747594Z test/test_ops.py::test_marlin_qqq[16-128-128-4--1-(1, 1, 1)] SKIPPED 2025-09-09T14:44:50.2748380Z test/test_ops.py::test_marlin_qqq[16-128-128-4--1-(1, 4, 8)] SKIPPED 2025-09-09T14:44:50.2749062Z test/test_ops.py::test_marlin_qqq[16-128-128-4--1-(1, 7, 5)] SKIPPED 2025-09-09T14:44:50.2749742Z test/test_ops.py::test_marlin_qqq[16-128-128-4--1-(13, 17, 67)] SKIPPED 2025-09-09T14:44:50.2750449Z test/test_ops.py::test_marlin_qqq[16-128-128-4--1-(26, 37, 13)] SKIPPED 2025-09-09T14:44:50.2751136Z test/test_ops.py::test_marlin_qqq[16-128-128-4--1-(67, 13, 11)] SKIPPED 2025-09-09T14:44:50.2751906Z test/test_ops.py::test_marlin_qqq[16-128-128-4-128-(1, 1, 1)] SKIPPED 2025-09-09T14:44:50.2752546Z test/test_ops.py::test_marlin_qqq[16-128-128-4-128-(1, 4, 8)] SKIPPED 2025-09-09T14:44:50.2753230Z test/test_ops.py::test_marlin_qqq[16-128-128-4-128-(1, 7, 5)] SKIPPED 2025-09-09T14:44:50.2753943Z test/test_ops.py::test_marlin_qqq[16-128-128-4-128-(13, 17, 67)] SKIPPED 2025-09-09T14:44:50.2754649Z test/test_ops.py::test_marlin_qqq[16-128-128-4-128-(26, 37, 13)] SKIPPED 2025-09-09T14:44:50.2755363Z test/test_ops.py::test_marlin_qqq[16-128-128-4-128-(67, 13, 11)] SKIPPED 2025-09-09T14:44:50.2756039Z test/test_ops.py::test_marlin_qqq[16-128-256-4--1-(1, 1, 1)] SKIPPED 2025-09-09T14:44:50.2756793Z test/test_ops.py::test_marlin_qqq[16-128-256-4--1-(1, 4, 8)] SKIPPED 2025-09-09T14:44:50.2757484Z test/test_ops.py::test_marlin_qqq[16-128-256-4--1-(1, 7, 5)] SKIPPED 2025-09-09T14:44:50.2758168Z test/test_ops.py::test_marlin_qqq[16-128-256-4--1-(13, 17, 67)] SKIPPED 2025-09-09T14:44:50.2758895Z test/test_ops.py::test_marlin_qqq[16-128-256-4--1-(26, 37, 13)] SKIPPED 2025-09-09T14:44:50.2759564Z test/test_ops.py::test_marlin_qqq[16-128-256-4--1-(67, 13, 11)] SKIPPED 2025-09-09T14:44:50.2760261Z test/test_ops.py::test_marlin_qqq[16-128-256-4-128-(1, 1, 1)] SKIPPED 2025-09-09T14:44:50.2760870Z test/test_ops.py::test_marlin_qqq[16-128-256-4-128-(1, 4, 8)] SKIPPED 2025-09-09T14:44:50.2761572Z test/test_ops.py::test_marlin_qqq[16-128-256-4-128-(1, 7, 5)] SKIPPED 2025-09-09T14:44:50.2762232Z test/test_ops.py::test_marlin_qqq[16-128-256-4-128-(13, 17, 67)] SKIPPED 2025-09-09T14:44:50.2762860Z test/test_ops.py::test_marlin_qqq[16-128-256-4-128-(26, 37, 13)] SKIPPED 2025-09-09T14:44:50.2763567Z test/test_ops.py::test_marlin_qqq[16-128-256-4-128-(67, 13, 11)] SKIPPED 2025-09-09T14:44:50.2764231Z test/test_ops.py::test_marlin_qqq[32-128-64-4--1-(1, 1, 1)] SKIPPED 2025-09-09T14:44:50.2764841Z test/test_ops.py::test_marlin_qqq[32-128-64-4--1-(1, 4, 8)] SKIPPED 2025-09-09T14:44:50.2765431Z test/test_ops.py::test_marlin_qqq[32-128-64-4--1-(1, 7, 5)] SKIPPED 2025-09-09T14:44:50.2766048Z test/test_ops.py::test_marlin_qqq[32-128-64-4--1-(13, 17, 67)] SKIPPED 2025-09-09T14:44:50.2766707Z test/test_ops.py::test_marlin_qqq[32-128-64-4--1-(26, 37, 13)] SKIPPED 2025-09-09T14:44:50.2767400Z test/test_ops.py::test_marlin_qqq[32-128-64-4--1-(67, 13, 11)] SKIPPED 2025-09-09T14:44:50.2768021Z test/test_ops.py::test_marlin_qqq[32-128-64-4-128-(1, 1, 1)] SKIPPED 2025-09-09T14:44:50.2768622Z test/test_ops.py::test_marlin_qqq[32-128-64-4-128-(1, 4, 8)] SKIPPED 2025-09-09T14:44:50.2769270Z test/test_ops.py::test_marlin_qqq[32-128-64-4-128-(1, 7, 5)] SKIPPED 2025-09-09T14:44:50.2769903Z test/test_ops.py::test_marlin_qqq[32-128-64-4-128-(13, 17, 67)] SKIPPED 2025-09-09T14:44:50.2770568Z test/test_ops.py::test_marlin_qqq[32-128-64-4-128-(26, 37, 13)] SKIPPED 2025-09-09T14:44:50.2771205Z test/test_ops.py::test_marlin_qqq[32-128-64-4-128-(67, 13, 11)] SKIPPED 2025-09-09T14:44:50.2771854Z test/test_ops.py::test_marlin_qqq[32-128-128-4--1-(1, 1, 1)] SKIPPED 2025-09-09T14:44:50.2772477Z test/test_ops.py::test_marlin_qqq[32-128-128-4--1-(1, 4, 8)] SKIPPED 2025-09-09T14:44:50.2773078Z test/test_ops.py::test_marlin_qqq[32-128-128-4--1-(1, 7, 5)] SKIPPED 2025-09-09T14:44:50.2773761Z test/test_ops.py::test_marlin_qqq[32-128-128-4--1-(13, 17, 67)] SKIPPED 2025-09-09T14:44:50.2774472Z test/test_ops.py::test_marlin_qqq[32-128-128-4--1-(26, 37, 13)] SKIPPED 2025-09-09T14:44:50.2775092Z test/test_ops.py::test_marlin_qqq[32-128-128-4--1-(67, 13, 11)] SKIPPED 2025-09-09T14:44:50.2775720Z test/test_ops.py::test_marlin_qqq[32-128-128-4-128-(1, 1, 1)] SKIPPED 2025-09-09T14:44:50.2776369Z test/test_ops.py::test_marlin_qqq[32-128-128-4-128-(1, 4, 8)] SKIPPED 2025-09-09T14:44:50.2777055Z test/test_ops.py::test_marlin_qqq[32-128-128-4-128-(1, 7, 5)] SKIPPED 2025-09-09T14:44:50.2777688Z test/test_ops.py::test_marlin_qqq[32-128-128-4-128-(13, 17, 67)] SKIPPED 2025-09-09T14:44:50.2778316Z test/test_ops.py::test_marlin_qqq[32-128-128-4-128-(26, 37, 13)] SKIPPED 2025-09-09T14:44:50.2778972Z test/test_ops.py::test_marlin_qqq[32-128-128-4-128-(67, 13, 11)] SKIPPED 2025-09-09T14:44:50.2779647Z test/test_ops.py::test_marlin_qqq[32-128-256-4--1-(1, 1, 1)] SKIPPED 2025-09-09T14:44:50.2780261Z test/test_ops.py::test_marlin_qqq[32-128-256-4--1-(1, 4, 8)] SKIPPED 2025-09-09T14:44:50.2780862Z test/test_ops.py::test_marlin_qqq[32-128-256-4--1-(1, 7, 5)] SKIPPED 2025-09-09T14:44:50.2781486Z test/test_ops.py::test_marlin_qqq[32-128-256-4--1-(13, 17, 67)] SKIPPED 2025-09-09T14:44:50.2782118Z test/test_ops.py::test_marlin_qqq[32-128-256-4--1-(26, 37, 13)] SKIPPED 2025-09-09T14:44:50.2782778Z test/test_ops.py::test_marlin_qqq[32-128-256-4--1-(67, 13, 11)] SKIPPED 2025-09-09T14:44:50.2783405Z test/test_ops.py::test_marlin_qqq[32-128-256-4-128-(1, 1, 1)] SKIPPED 2025-09-09T14:44:50.2784013Z test/test_ops.py::test_marlin_qqq[32-128-256-4-128-(1, 4, 8)] SKIPPED 2025-09-09T14:44:50.2784638Z test/test_ops.py::test_marlin_qqq[32-128-256-4-128-(1, 7, 5)] SKIPPED 2025-09-09T14:44:50.2785260Z test/test_ops.py::test_marlin_qqq[32-128-256-4-128-(13, 17, 67)] SKIPPED 2025-09-09T14:44:50.2785909Z test/test_ops.py::test_marlin_qqq[32-128-256-4-128-(26, 37, 13)] SKIPPED 2025-09-09T14:44:50.2786551Z test/test_ops.py::test_marlin_qqq[32-128-256-4-128-(67, 13, 11)] SKIPPED 2025-09-09T14:44:50.2787160Z test/test_ops.py::test_marlin_qqq[64-128-64-4--1-(1, 1, 1)] SKIPPED 2025-09-09T14:44:50.2787770Z test/test_ops.py::test_marlin_qqq[64-128-64-4--1-(1, 4, 8)] SKIPPED 2025-09-09T14:44:50.2788366Z test/test_ops.py::test_marlin_qqq[64-128-64-4--1-(1, 7, 5)] SKIPPED 2025-09-09T14:44:50.2788987Z test/test_ops.py::test_marlin_qqq[64-128-64-4--1-(13, 17, 67)] SKIPPED 2025-09-09T14:44:50.2789617Z test/test_ops.py::test_marlin_qqq[64-128-64-4--1-(26, 37, 13)] SKIPPED 2025-09-09T14:44:50.2790229Z test/test_ops.py::test_marlin_qqq[64-128-64-4--1-(67, 13, 11)] SKIPPED 2025-09-09T14:44:50.3232189Z test/test_ops.py::test_marlin_qqq[64-128-64-4-128-(1, 1, 1)] SKIPPED 2025-09-09T14:44:50.3232862Z test/test_ops.py::test_marlin_qqq[64-128-64-4-128-(1, 4, 8)] SKIPPED 2025-09-09T14:44:50.3233488Z test/test_ops.py::test_marlin_qqq[64-128-64-4-128-(1, 7, 5)] SKIPPED 2025-09-09T14:44:50.3234118Z test/test_ops.py::test_marlin_qqq[64-128-64-4-128-(13, 17, 67)] SKIPPED 2025-09-09T14:44:50.3234760Z test/test_ops.py::test_marlin_qqq[64-128-64-4-128-(26, 37, 13)] SKIPPED 2025-09-09T14:44:50.3235548Z test/test_ops.py::test_marlin_qqq[64-128-64-4-128-(67, 13, 11)] SKIPPED 2025-09-09T14:44:50.3236235Z test/test_ops.py::test_marlin_qqq[64-128-128-4--1-(1, 1, 1)] SKIPPED 2025-09-09T14:44:50.3236861Z test/test_ops.py::test_marlin_qqq[64-128-128-4--1-(1, 4, 8)] SKIPPED 2025-09-09T14:44:50.3237463Z test/test_ops.py::test_marlin_qqq[64-128-128-4--1-(1, 7, 5)] SKIPPED 2025-09-09T14:44:50.3238092Z test/test_ops.py::test_marlin_qqq[64-128-128-4--1-(13, 17, 67)] SKIPPED 2025-09-09T14:44:50.3238712Z test/test_ops.py::test_marlin_qqq[64-128-128-4--1-(26, 37, 13)] SKIPPED 2025-09-09T14:44:50.3239410Z test/test_ops.py::test_marlin_qqq[64-128-128-4--1-(67, 13, 11)] SKIPPED 2025-09-09T14:44:50.3240039Z test/test_ops.py::test_marlin_qqq[64-128-128-4-128-(1, 1, 1)] SKIPPED 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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 2025-09-09T14:44:50.6258437Z 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 2025-09-09T14:44:50.6259834Z 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 2025-09-09T14:44:50.6261211Z 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 2025-09-09T14:44:50.6262580Z 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 2025-09-09T14:44:50.6263978Z 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 2025-09-09T14:44:50.6265361Z 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 2025-09-09T14:44:50.6266753Z 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 2025-09-09T14:44:50.6268134Z 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 2025-09-09T14:44:50.6269509Z 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 2025-09-09T14:44:50.6270871Z 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 2025-09-09T14:44:50.6272242Z 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 2025-09-09T14:44:50.6273619Z 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 2025-09-09T14:44:50.6275010Z 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 2025-09-09T14:44:50.6276458Z 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 2025-09-09T14:44:50.6277841Z 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 2025-09-09T14:44:50.6279203Z 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 2025-09-09T14:44:50.6280581Z 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 2025-09-09T14:44:50.6281961Z 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 2025-09-09T14:44:50.6282959Z test/test_utils.py::TestTorchVersion::test_torch_version_at_least PASSED 2025-09-09T14:44:50.6283679Z test/test_utils.py::TestTorchVersion::test_torch_version_deprecation PASSED 2025-09-09T14:44:50.6284384Z test/test_utils.py::TestTorchAOBaseTensor::test_default_impls SKIPPED 2025-09-09T14:44:50.6285172Z test/test_utils.py::TestTorchAOBaseTensor::test_default_impls_with_optional_attr SKIPPED 2025-09-09T14:44:50.6286015Z test/test_utils.py::TestTorchAOBaseTensor::test_default_impls_with_optional_data SKIPPED 2025-09-09T14:44:50.6286781Z test/test_utils.py::TestTorchAOBaseTensor::test_print_arg_types PASSED 2025-09-09T14:44:50.6287147Z 2025-09-09T14:44:50.6287403Z =============================== warnings summary =============================== 2025-09-09T14:44:50.6287957Z test/core/test_config.py::test_reconstructable_dict_file_round_trip[config8] 2025-09-09T14:44:50.6289284Z /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 2025-09-09T14:44:50.6290500Z warnings.warn( 2025-09-09T14:44:50.6290655Z 2025-09-09T14:44:50.6290879Z test/dtypes/test_nf4.py::TestNF4Linear::test_to_copy_bfloat16 2025-09-09T14:44:50.6291351Z test/dtypes/test_nf4.py::TestNF4Linear::test_to_copy_float16 2025-09-09T14:44:50.6291827Z test/dtypes/test_nf4.py::TestNF4Linear::test_to_copy_float32 2025-09-09T14:44:50.6293261Z /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. 2025-09-09T14:44:50.6294700Z torch.testing.assert_allclose(input_tensor, nf4_to_dtype, atol=0.13, rtol=0.13) 2025-09-09T14:44:50.6295081Z 2025-09-09T14:44:50.6295308Z test/float8/test_float8_utils.py::test_non_float32_input[invalid_dtype3] 2025-09-09T14:44:50.6295869Z test/float8/test_float8_utils.py::test_non_float32_input[invalid_dtype4] 2025-09-09T14:44:50.6296414Z test/float8/test_float8_utils.py::test_non_float32_input[invalid_dtype5] 2025-09-09T14:44:50.6296975Z test/float8/test_float8_utils.py::test_non_float32_input[invalid_dtype6] 2025-09-09T14:44:50.6297518Z test/float8/test_float8_utils.py::test_non_float32_input[invalid_dtype7] 2025-09-09T14:44:50.6298641Z /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. 2025-09-09T14:44:50.6299701Z non_float32_tensor = torch.tensor([3.0], dtype=invalid_dtype) 2025-09-09T14:44:50.6300032Z 2025-09-09T14:44:50.6300354Z test/integration/test_integration.py::SmoothquantIntegrationTest::test_on_dummy_distilbert 2025-09-09T14:44:50.6301320Z /pytorch/ao/test/integration/test_integration.py:1440: DeprecationWarning: torch.ao.quantization is deprecated and will be removed in 2.10. 2025-09-09T14:44:50.6302063Z For migrations of users: 2025-09-09T14:44:50.6302845Z 1. Eager mode quantization (torch.ao.quantization.quantize, torch.ao.quantization.quantize_dynamic), please migrate to use torchao eager mode quantize_ API instead 2025-09-09T14:44:50.6304344Z 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) 2025-09-09T14:44:50.6305628Z 3. pt2e quantization has been migrated to torchao (https://github.com/pytorch/ao/tree/main/torchao/quantization/pt2e) 2025-09-09T14:44:50.6306338Z see https://github.com/pytorch/ao/issues/2259 for more details 2025-09-09T14:44:50.6306800Z model_copy2 = torch.ao.quantization.quantize_dynamic( 2025-09-09T14:44:50.6307081Z 2025-09-09T14:44:50.6307397Z test/integration/test_integration.py::SmoothquantIntegrationTest::test_on_dummy_distilbert 2025-09-09T14:44:50.6308536Z /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. 2025-09-09T14:44:50.6309437Z For migrations of users: 2025-09-09T14:44:50.6310213Z 1. Eager mode quantization (torch.ao.quantization.quantize, torch.ao.quantization.quantize_dynamic), please migrate to use torchao eager mode quantize_ API instead 2025-09-09T14:44:50.6311682Z 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) 2025-09-09T14:44:50.6313023Z 3. pt2e quantization has been migrated to torchao (https://github.com/pytorch/ao/tree/main/torchao/quantization/pt2e) 2025-09-09T14:44:50.6313734Z see https://github.com/pytorch/ao/issues/2259 for more details 2025-09-09T14:44:50.6314136Z convert(model, mapping, inplace=True) 2025-09-09T14:44:50.6314367Z 2025-09-09T14:44:50.6314591Z test/kernel/test_autotuner.py::TestQuantFlow::test_int_scaled_mm_1_cpu 2025-09-09T14:44:50.6315144Z test/kernel/test_autotuner.py::TestQuantFlow::test_int_scaled_mm_3_cpu 2025-09-09T14:44:50.6316725Z /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. 2025-09-09T14:44:50.6318098Z torch.testing.assert_allclose(out32_1, out32_2) 2025-09-09T14:44:50.6318348Z 2025-09-09T14:44:50.6318713Z test/prototype/test_codebook_quant.py::TestCodebookQuantization::test_choose_qparams_codebook 2025-09-09T14:44:50.6320149Z /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.) 2025-09-09T14:44:50.6321341Z return callable(*args, **kwargs) 2025-09-09T14:44:50.6321540Z 2025-09-09T14:44:50.6321786Z test/prototype/test_parametrization.py::TestFakeSparsity::test_jit_trace 2025-09-09T14:44:50.6323529Z /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! 2025-09-09T14:44:50.6325304Z assert self.mask.shape == x.shape 2025-09-09T14:44:50.6325609Z 2025-09-09T14:44:50.6325842Z test/prototype/test_scheduler.py::TestScheduler::test_lambda_scheduler 2025-09-09T14:44:50.6326396Z test/prototype/test_scheduler.py::TestCubicScheduler::test_step 2025-09-09T14:44:50.6327781Z /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(). 2025-09-09T14:44:50.6329063Z warnings.warn( 2025-09-09T14:44:50.6329203Z 2025-09-09T14:44:50.6329566Z test/prototype/test_structured_sparsifier.py::TestBaseStructuredSparsifier::test_complex_conv2d 2025-09-09T14:44:50.6330858Z /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. 2025-09-09T14:44:50.6332301Z Consider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.) 2025-09-09T14:44:50.6333030Z flattened_pruned_biases = torch.tensor( 2025-09-09T14:44:50.6333251Z 2025-09-09T14:44:50.6333533Z test/quantization/pt2e/test_graph_utils.py::TestGraphUtils::test_conv_bn_conv_relu 2025-09-09T14:44:50.6334951Z /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). 2025-09-09T14:44:50.6336315Z m, guards = torchdynamo.export( # noqa: F841© 2025-09-09T14:44:50.6336571Z 2025-09-09T14:44:50.6336827Z test/quantization/pt2e/test_graph_utils.py::TestGraphUtils::test_conv_bn_relu 2025-09-09T14:44:50.6338271Z /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). 2025-09-09T14:44:50.6339524Z m, guards = torchdynamo.export( # noqa: F841 2025-09-09T14:44:50.6339771Z 2025-09-09T14:44:50.6340104Z test/quantization/pt2e/test_graph_utils.py::TestGraphUtils::test_customized_equivalet_types_dict 2025-09-09T14:44:50.6341571Z /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). 2025-09-09T14:44:50.6342869Z m, guards = torchdynamo.export( # noqa: F841 2025-09-09T14:44:50.6343115Z 2025-09-09T14:44:50.6343291Z test/quantization/pt2e/test_quantize_pt2e.py: 18 warnings 2025-09-09T14:44:50.6343793Z test/quantization/pt2e/test_quantize_pt2e_qat.py: 75 warnings 2025-09-09T14:44:50.6344278Z test/quantization/pt2e/test_representation.py: 8 warnings 2025-09-09T14:44:50.6345125Z /opt/conda/envs/venv/lib/python3.9/site-packages/torchao/testing/pt2e/_xnnpack_quantizer.py:289: UserWarning: XNNPACKQuantizer is deprecated! 2025-09-09T14:44:50.6345954Z warnings.warn(f"{self.__class__.__name__} is deprecated!") 2025-09-09T14:44:50.6346246Z 2025-09-09T14:44:50.6346600Z test/quantization/pt2e/test_quantize_pt2e.py::TestQuantizePT2E::test_allow_exported_model_train_eval 2025-09-09T14:44:50.6347355Z test/quantization/pt2e/test_quantize_pt2e.py::TestQuantizePT2E::test_disallow_eval_train 2025-09-09T14:44:50.6348163Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_annotate_mul_tensor 2025-09-09T14:44:50.6349069Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_annotate_mul_tensor 2025-09-09T14:44:50.6349964Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_annotate_mul_tensor 2025-09-09T14:44:50.6350902Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_filter_conv2d_recipe 2025-09-09T14:44:50.6351798Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_filter_linear_recipe 2025-09-09T14:44:50.6353049Z /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. 2025-09-09T14:44:50.6354001Z For migrations of users: 2025-09-09T14:44:50.6354769Z 1. Eager mode quantization (torch.ao.quantization.quantize, torch.ao.quantization.quantize_dynamic), please migrate to use torchao eager mode quantize_ API instead 2025-09-09T14:44:50.6356334Z 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) 2025-09-09T14:44:50.6357642Z 3. pt2e quantization has been migrated to torchao (https://github.com/pytorch/ao/tree/main/torchao/quantization/pt2e) 2025-09-09T14:44:50.6358340Z see https://github.com/pytorch/ao/issues/2259 for more details 2025-09-09T14:44:50.6358888Z return torch_convert_pt2e(model, use_reference_representation, fold_quantize) 2025-09-09T14:44:50.6359243Z 2025-09-09T14:44:50.6359612Z test/quantization/pt2e/test_quantize_pt2e.py::TestQuantizePT2E::test_embedding_conv_linear_quantization 2025-09-09T14:44:50.6360401Z test/quantization/pt2e/test_quantize_pt2e.py::TestQuantizePT2E::test_embedding_quantizer 2025-09-09T14:44:50.6361504Z /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. 2025-09-09T14:44:50.6362377Z For migrations of users: 2025-09-09T14:44:50.6363193Z 1. Eager mode quantization (torch.ao.quantization.quantize, torch.ao.quantization.quantize_dynamic), please migrate to use torchao eager mode quantize_ API instead 2025-09-09T14:44:50.6364673Z 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) 2025-09-09T14:44:50.6365968Z 3. pt2e quantization has been migrated to torchao (https://github.com/pytorch/ao/tree/main/torchao/quantization/pt2e) 2025-09-09T14:44:50.6366674Z see https://github.com/pytorch/ao/issues/2259 for more details 2025-09-09T14:44:50.6367084Z m_fx = prepare_fx( 2025-09-09T14:44:50.6367282Z 2025-09-09T14:44:50.6367677Z test/quantization/pt2e/test_quantize_pt2e.py::TestQuantizePT2E::test_model_is_exported 2025-09-09T14:44:50.6369163Z /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. 2025-09-09T14:44:50.6370383Z warnings.warn( 2025-09-09T14:44:50.6370522Z 2025-09-09T14:44:50.6370803Z test/quantization/pt2e/test_quantize_pt2e.py::TestQuantizePT2E::test_reentrant 2025-09-09T14:44:50.6371550Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn1d::test_fold_bn_erases_bn_node 2025-09-09T14:44:50.6372424Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn2d::test_fold_bn_erases_bn_node 2025-09-09T14:44:50.6373631Z /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. 2025-09-09T14:44:50.6374530Z warnings.warn( 2025-09-09T14:44:50.6374668Z 2025-09-09T14:44:50.6374943Z test/quantization/pt2e/test_quantize_pt2e.py::TestQuantizePT2E::test_reentrant 2025-09-09T14:44:50.6376143Z /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 2025-09-09T14:44:50.6377213Z warnings.warn( 2025-09-09T14:44:50.6377350Z 2025-09-09T14:44:50.6377778Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn1d::test_qat_conv_bn_bias_derived_qspec 2025-09-09T14:44:50.6378733Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn1d::test_qat_conv_bn_per_channel_weight_bias 2025-09-09T14:44:50.6379717Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn1d::test_qat_per_channel_weight_custom_dtype 2025-09-09T14:44:50.6380669Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_conv_bn_bias_derived_qspec 2025-09-09T14:44:50.6381630Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_conv_bn_per_channel_weight_bias 2025-09-09T14:44:50.6382614Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_per_channel_weight_custom_dtype 2025-09-09T14:44:50.6384091Z /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. 2025-09-09T14:44:50.6385224Z warnings.warn( 2025-09-09T14:44:50.6385361Z 2025-09-09T14:44:50.6385564Z test/quantization/pt2e/test_quantize_pt2e_qat.py: 40 warnings 2025-09-09T14:44:50.6386430Z /pytorch/ao/test/quantization/pt2e/test_quantize_pt2e_qat.py:165: DeprecationWarning: torch.ao.quantization is deprecated and will be removed in 2.10. 2025-09-09T14:44:50.6387211Z For migrations of users: 2025-09-09T14:44:50.6387980Z 1. Eager mode quantization (torch.ao.quantization.quantize, torch.ao.quantization.quantize_dynamic), please migrate to use torchao eager mode quantize_ API instead 2025-09-09T14:44:50.6389512Z 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) 2025-09-09T14:44:50.6390853Z 3. pt2e quantization has been migrated to torchao (https://github.com/pytorch/ao/tree/main/torchao/quantization/pt2e) 2025-09-09T14:44:50.6391566Z see https://github.com/pytorch/ao/issues/2259 for more details 2025-09-09T14:44:50.6391968Z model_fx = prepare_qat_fx( 2025-09-09T14:44:50.6392188Z 2025-09-09T14:44:50.6392381Z test/quantization/pt2e/test_quantize_pt2e_qat.py: 40 warnings 2025-09-09T14:44:50.6393402Z /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. 2025-09-09T14:44:50.6394328Z For migrations of users: 2025-09-09T14:44:50.6395131Z 1. Eager mode quantization (torch.ao.quantization.quantize, torch.ao.quantization.quantize_dynamic), please migrate to use torchao eager mode quantize_ API instead 2025-09-09T14:44:50.6396726Z 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) 2025-09-09T14:44:50.6398007Z 3. pt2e quantization has been migrated to torchao (https://github.com/pytorch/ao/tree/main/torchao/quantization/pt2e) 2025-09-09T14:44:50.6398714Z see https://github.com/pytorch/ao/issues/2259 for more details 2025-09-09T14:44:50.6399262Z convert(root, mapping=module_to_qat_module, inplace=True, remove_qconfig=False) 2025-09-09T14:44:50.6399625Z 2025-09-09T14:44:50.6399939Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qconv2d_add_3 2025-09-09T14:44:50.6400767Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_filter_conv2d_recipe 2025-09-09T14:44:50.6405586Z /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). 2025-09-09T14:44:50.6410325Z warnings.warn( 2025-09-09T14:44:50.6410466Z 2025-09-09T14:44:50.6410845Z test/quantization/pt2e/test_x86inductor_fusion.py::TestDynamicPatternMatcher::test_q_attention_block 2025-09-09T14:44:50.6411678Z test/quantization/pt2e/test_x86inductor_fusion.py::TestDynamicPatternMatcher::test_q_attention_block 2025-09-09T14:44:50.6412605Z test/quantization/pt2e/test_x86inductor_fusion.py::TestDynamicPatternMatcher::test_qconv2d_maxpool2d_linear_dynamic_cpu 2025-09-09T14:44:50.6414234Z /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). 2025-09-09T14:44:50.6415614Z torch.tensor(w_zp_tensor, dtype=torch.int32), name=w_zp.get_name() 2025-09-09T14:44:50.6415921Z 2025-09-09T14:44:50.6416495Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_set_module_name_and_module_type_with_mixed_configs 2025-09-09T14:44:50.6417902Z /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. 2025-09-09T14:44:50.6418879Z warnings.warn( 2025-09-09T14:44:50.6419017Z 2025-09-09T14:45:08.1818968Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_set_module_name_and_module_type_with_mixed_configs 2025-09-09T14:45:08.1820706Z /opt/conda/envs/venv/lib/python3.9/site-packages/torchao/quantization/pt2e/quantizer/x86_inductor_quantizer.py:383: UserWarning: Skip the quantization config for . 2025-09-09T14:45:08.1821756Z warnings.warn( 2025-09-09T14:45:08.1821899Z 2025-09-09T14:45:08.1822156Z test/quantization/test_qat.py::TestQAT::test_legacy_quantize_api_e2e 2025-09-09T14:45:08.1823497Z /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: 2025-09-09T14:45:08.1825093Z 2025-09-09T14:45:08.1825427Z base_config = Int8DynamicActivationInt4WeightConfig(group_size=32) 2025-09-09T14:45:08.1825949Z quantize_(model, QATConfig(base_config, step="prepare")) 2025-09-09T14:45:08.1826319Z # train (not shown) 2025-09-09T14:45:08.1826657Z quantize_(model, QATConfig(base_config, step="convert")) 2025-09-09T14:45:08.1827014Z 2025-09-09T14:45:08.1827321Z Alternatively, if you prefer to pass in fake quantization configs: 2025-09-09T14:45:08.1827714Z 2025-09-09T14:45:08.1828111Z activation_config = IntxFakeQuantizeConfig(torch.int8, "per_token", is_symmetric=False) 2025-09-09T14:45:08.1828734Z weight_config = IntxFakeQuantizeConfig(torch.int4, group_size=32) 2025-09-09T14:45:08.1829149Z qat_config = QATConfig( 2025-09-09T14:45:08.1829533Z activation_config=activation_config, 2025-09-09T14:45:08.1829853Z weight_config=weight_config, 2025-09-09T14:45:08.1830155Z step="prepare", 2025-09-09T14:45:08.1830387Z ) 2025-09-09T14:45:08.1830604Z quantize_(model, qat_config) 2025-09-09T14:45:08.1830878Z 2025-09-09T14:45:08.1831204Z Please see https://github.com/pytorch/ao/issues/2630 for more details. 2025-09-09T14:45:08.1831618Z 2025-09-09T14:45:08.1831817Z warnings.warn( 2025-09-09T14:45:08.1831955Z 2025-09-09T14:45:08.1832193Z test/quantization/test_qat.py::TestQAT::test_legacy_quantize_api_e2e 2025-09-09T14:45:08.1833473Z /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: 2025-09-09T14:45:08.1834623Z 2025-09-09T14:45:08.1834961Z base_config = Int8DynamicActivationInt4WeightConfig(group_size=32) 2025-09-09T14:45:08.1835462Z quantize_(model, QATConfig(base_config, step="prepare")) 2025-09-09T14:45:08.1835841Z # train (not shown) 2025-09-09T14:45:08.1836226Z quantize_(model, QATConfig(base_config, step="convert")) 2025-09-09T14:45:08.1836584Z 2025-09-09T14:45:08.1836888Z Alternatively, if you prefer to pass in fake quantization configs: 2025-09-09T14:45:08.1837286Z 2025-09-09T14:45:08.1837685Z activation_config = IntxFakeQuantizeConfig(torch.int8, "per_token", is_symmetric=False) 2025-09-09T14:45:08.1838320Z weight_config = IntxFakeQuantizeConfig(torch.int4, group_size=32) 2025-09-09T14:45:08.1838747Z qat_config = QATConfig( 2025-09-09T14:45:08.1839038Z activation_config=activation_config, 2025-09-09T14:45:08.1839373Z weight_config=weight_config, 2025-09-09T14:45:08.1839677Z step="prepare", 2025-09-09T14:45:08.1839921Z ) 2025-09-09T14:45:08.1840125Z quantize_(model, qat_config) 2025-09-09T14:45:08.1840489Z 2025-09-09T14:45:08.1840810Z Please see https://github.com/pytorch/ao/issues/2630 for more details. 2025-09-09T14:45:08.1841231Z 2025-09-09T14:45:08.1841430Z warnings.warn( 2025-09-09T14:45:08.1841584Z 2025-09-09T14:45:08.1841801Z test/quantization/test_qat.py::TestQAT::test_qat_fp8a4w_quantizer 2025-09-09T14:45:08.1845305Z /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.) 2025-09-09T14:45:08.1848908Z return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass 2025-09-09T14:45:08.1849363Z 2025-09-09T14:45:08.1849601Z test/sparsity/test_wanda.py::TestWandaSparsifier::test_one_layer_mlp_2x4 2025-09-09T14:45:08.1850647Z /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 2025-09-09T14:45:08.1851573Z warnings.warn( 2025-09-09T14:45:08.1851715Z 2025-09-09T14:45:08.1851963Z test/sparsity/test_wanda.py::TestWandaSparsifier::test_one_layer_mlp_2x4 2025-09-09T14:45:08.1852571Z test/sparsity/test_wanda.py::TestWandaSparsifier::test_one_layer_mlp_unstructured 2025-09-09T14:45:08.1853158Z test/sparsity/test_wanda.py::TestWandaSparsifier::test_prepare 2025-09-09T14:45:08.1853719Z test/sparsity/test_wanda.py::TestWandaSparsifier::test_squash_mask 2025-09-09T14:45:08.1854313Z test/sparsity/test_wanda.py::TestWandaSparsifier::test_two_layer_mlp_unstructured 2025-09-09T14:45:08.1855024Z test/sparsity/test_wanda.py::TestWandaSparsifier::test_two_layer_mlp_unstructured_custom_config 2025-09-09T14:45:08.1856110Z /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. 2025-09-09T14:45:08.1856979Z For migrations of users: 2025-09-09T14:45:08.1857746Z 1. Eager mode quantization (torch.ao.quantization.quantize, torch.ao.quantization.quantize_dynamic), please migrate to use torchao eager mode quantize_ API instead 2025-09-09T14:45:08.1859245Z 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) 2025-09-09T14:45:08.1860540Z 3. pt2e quantization has been migrated to torchao (https://github.com/pytorch/ao/tree/main/torchao/quantization/pt2e) 2025-09-09T14:45:08.1861239Z see https://github.com/pytorch/ao/issues/2259 for more details 2025-09-09T14:45:08.1861699Z torch.ao.quantization.prepare(model, inplace=True) 2025-09-09T14:45:08.1861963Z 2025-09-09T14:45:08.1862187Z -- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html 2025-09-09T14:45:08.1863184Z ======== 1408 passed, 5569 skipped, 239 warnings in 2340.43s (0:39:00) ========= 2025-09-09T14:45:08.1957334Z ##[group]Run pmeier/pytest-results-action@a2c1430e2bddadbad9f49a6f9b879f062c6b19b1 2025-09-09T14:45:08.1957861Z with: 2025-09-09T14:45:08.1958159Z path: /home/ec2-user/actions-runner/_work/_temp/test-results 2025-09-09T14:45:08.1958574Z fail-on-empty: false 2025-09-09T14:45:08.1958803Z env: 2025-09-09T14:45:08.1959051Z DOCKER_IMAGE: pytorch/almalinux-builder:cpu 2025-09-09T14:45:08.1959386Z REPOSITORY: pytorch/ao 2025-09-09T14:45:08.1959646Z PR_NUMBER: 2963 2025-09-09T14:45:08.1961865Z 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/:$PATH python -m pip install --upgrade pip pip install torch==2.8.0 --index-url https://download.pytorch.org/whl/cpu sed -i '' dev-requirements.txt pip install -r dev-requirements.txt pip install . export CONDA=$(dirname $(dirname $(which conda))) export LD_LIBRARY_PATH=$CONDA/lib/:$LD_LIBRARY_PATH pytest test --verbose -s 2025-09-09T14:45:08.1964313Z RUNNER_ARTIFACT_DIR: /home/ec2-user/actions-runner/_work/_temp/artifacts 2025-09-09T14:45:08.1964895Z RUNNER_TEST_RESULTS_DIR: /home/ec2-user/actions-runner/_work/_temp/test-results 2025-09-09T14:45:08.1965423Z RUNNER_DOCS_DIR: /home/ec2-user/actions-runner/_work/_temp/docs 2025-09-09T14:45:08.1965805Z ##[endgroup] 2025-09-09T14:45:08.2590373Z Prepare all required actions 2025-09-09T14:45:08.2629682Z ##[group]Run ./test-infra/.github/actions/chown-directory 2025-09-09T14:45:08.2630035Z with: 2025-09-09T14:45:08.2630311Z directory: /home/ec2-user/actions-runner/_work/ao/ao/ 2025-09-09T14:45:08.2630786Z ALPINE_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/tool/alpine 2025-09-09T14:45:08.2631206Z env: 2025-09-09T14:45:08.2631438Z DOCKER_IMAGE: pytorch/almalinux-builder:cpu 2025-09-09T14:45:08.2631779Z REPOSITORY: pytorch/ao 2025-09-09T14:45:08.2632033Z PR_NUMBER: 2963 2025-09-09T14:45:08.2634246Z SCRIPT: conda create -n venv python=3.9 -y conda activate venv echo "::group::Install newer objcopy that supports --set-section-alignment" dnf install -y gcc-toolset-10-binutils export PATH=/opt/rh/gcc-toolset-10/root/usr/bin/:$PATH python -m pip install --upgrade pip pip install torch==2.8.0 --index-url https://download.pytorch.org/whl/cpu 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/:$LD_LIBRARY_PATH pytest test --verbose -s 2025-09-09T14:45:08.2636806Z RUNNER_ARTIFACT_DIR: /home/ec2-user/actions-runner/_work/_temp/artifacts 2025-09-09T14:45:08.2637377Z RUNNER_TEST_RESULTS_DIR: /home/ec2-user/actions-runner/_work/_temp/test-results 2025-09-09T14:45:08.2637920Z RUNNER_DOCS_DIR: /home/ec2-user/actions-runner/_work/_temp/docs 2025-09-09T14:45:08.2638293Z ##[endgroup] 2025-09-09T14:45:08.2662185Z ##[group]Run docker run --rm -v "${DIRECTORY}":/v -w /v "${ALPINE_IMAGE}" chown -R "$(id -u):$(id -g)" . 2025-09-09T14:45:08.2662879Z docker run --rm -v "${DIRECTORY}":/v -w /v "${ALPINE_IMAGE}" chown -R "$(id -u):$(id -g)" . 2025-09-09T14:45:08.2686719Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-09T14:45:08.2687094Z env: 2025-09-09T14:45:08.2687335Z DOCKER_IMAGE: pytorch/almalinux-builder:cpu 2025-09-09T14:45:08.2687679Z REPOSITORY: pytorch/ao 2025-09-09T14:45:08.2687944Z PR_NUMBER: 2963 2025-09-09T14:45:08.2690120Z SCRIPT: conda create -n venv python=3.9 -y conda activate venv echo "::group::Install newer objcopy that supports --set-section-alignment" dnf install -y gcc-toolset-10-binutils export PATH=/opt/rh/gcc-toolset-10/root/usr/bin/:$PATH python -m pip install --upgrade pip pip install torch==2.8.0 --index-url https://download.pytorch.org/whl/cpu 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/:$LD_LIBRARY_PATH pytest test --verbose -s 2025-09-09T14:45:08.2692491Z RUNNER_ARTIFACT_DIR: /home/ec2-user/actions-runner/_work/_temp/artifacts 2025-09-09T14:45:08.2693071Z RUNNER_TEST_RESULTS_DIR: /home/ec2-user/actions-runner/_work/_temp/test-results 2025-09-09T14:45:08.2693599Z RUNNER_DOCS_DIR: /home/ec2-user/actions-runner/_work/_temp/docs 2025-09-09T14:45:08.2694119Z ALPINE_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/tool/alpine 2025-09-09T14:45:08.2694609Z DIRECTORY: /home/ec2-user/actions-runner/_work/ao/ao/ 2025-09-09T14:45:08.2694980Z ##[endgroup] 2025-09-09T14:45:08.3073643Z Unable to find image '308535385114.dkr.ecr.us-east-1.amazonaws.com/tool/alpine:latest' locally 2025-09-09T14:45:08.5444561Z latest: Pulling from tool/alpine 2025-09-09T14:45:08.5444985Z 540db60ca938: Pulling fs layer 2025-09-09T14:45:08.6332800Z 540db60ca938: Download complete 2025-09-09T14:45:08.7177182Z 540db60ca938: Pull complete 2025-09-09T14:45:08.7304984Z Digest: sha256:def822f9851ca422481ec6fee59a9966f12b351c62ccb9aca841526ffaa9f748 2025-09-09T14:45:08.7359485Z Status: Downloaded newer image for 308535385114.dkr.ecr.us-east-1.amazonaws.com/tool/alpine:latest 2025-09-09T14:45:10.3562119Z Prepare all required actions 2025-09-09T14:45:10.3589088Z ##[group]Run ./test-infra/.github/actions/chown-directory 2025-09-09T14:45:10.3589462Z with: 2025-09-09T14:45:10.3589739Z directory: /home/ec2-user/actions-runner/_work/_temp 2025-09-09T14:45:10.3590239Z ALPINE_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/tool/alpine 2025-09-09T14:45:10.3590679Z env: 2025-09-09T14:45:10.3590919Z DOCKER_IMAGE: pytorch/almalinux-builder:cpu 2025-09-09T14:45:10.3591288Z REPOSITORY: pytorch/ao 2025-09-09T14:45:10.3591532Z PR_NUMBER: 2963 2025-09-09T14:45:10.3593746Z SCRIPT: conda create -n venv python=3.9 -y conda activate venv echo "::group::Install newer objcopy that supports --set-section-alignment" dnf install -y gcc-toolset-10-binutils export PATH=/opt/rh/gcc-toolset-10/root/usr/bin/:$PATH python -m pip install --upgrade pip pip install torch==2.8.0 --index-url https://download.pytorch.org/whl/cpu 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/:$LD_LIBRARY_PATH pytest test --verbose -s 2025-09-09T14:45:10.3596121Z RUNNER_ARTIFACT_DIR: /home/ec2-user/actions-runner/_work/_temp/artifacts 2025-09-09T14:45:10.3596795Z RUNNER_TEST_RESULTS_DIR: /home/ec2-user/actions-runner/_work/_temp/test-results 2025-09-09T14:45:10.3597451Z RUNNER_DOCS_DIR: /home/ec2-user/actions-runner/_work/_temp/docs 2025-09-09T14:45:10.3597821Z ##[endgroup] 2025-09-09T14:45:10.3622613Z ##[group]Run docker run --rm -v "${DIRECTORY}":/v -w /v "${ALPINE_IMAGE}" chown -R "$(id -u):$(id -g)" . 2025-09-09T14:45:10.3623299Z docker run --rm -v "${DIRECTORY}":/v -w /v "${ALPINE_IMAGE}" chown -R "$(id -u):$(id -g)" . 2025-09-09T14:45:10.3633006Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-09T14:45:10.3633385Z env: 2025-09-09T14:45:10.3633632Z DOCKER_IMAGE: pytorch/almalinux-builder:cpu 2025-09-09T14:45:10.3633979Z REPOSITORY: pytorch/ao 2025-09-09T14:45:10.3634240Z PR_NUMBER: 2963 2025-09-09T14:45:10.3636517Z SCRIPT: conda create -n venv python=3.9 -y conda activate venv echo "::group::Install newer objcopy that supports --set-section-alignment" dnf install -y gcc-toolset-10-binutils export PATH=/opt/rh/gcc-toolset-10/root/usr/bin/:$PATH python -m pip install --upgrade pip pip install torch==2.8.0 --index-url https://download.pytorch.org/whl/cpu 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/:$LD_LIBRARY_PATH pytest test --verbose -s 2025-09-09T14:45:10.3638894Z RUNNER_ARTIFACT_DIR: /home/ec2-user/actions-runner/_work/_temp/artifacts 2025-09-09T14:45:10.3639472Z RUNNER_TEST_RESULTS_DIR: /home/ec2-user/actions-runner/_work/_temp/test-results 2025-09-09T14:45:10.3640014Z RUNNER_DOCS_DIR: /home/ec2-user/actions-runner/_work/_temp/docs 2025-09-09T14:45:10.3640529Z ALPINE_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/tool/alpine 2025-09-09T14:45:10.3641006Z DIRECTORY: /home/ec2-user/actions-runner/_work/_temp 2025-09-09T14:45:10.3641354Z ##[endgroup] 2025-09-09T14:45:11.4344791Z ##[group]Run # Only do these steps if we actually want to upload an artifact 2025-09-09T14:45:11.4345422Z # Only do these steps if we actually want to upload an artifact 2025-09-09T14:45:11.4345897Z if [[ -n "${UPLOAD_ARTIFACT_NAME}" ]]; then 2025-09-09T14:45:11.4346439Z  # If the default execution path is followed then we should get a wheel in the dist/ folder 2025-09-09T14:45:11.4347037Z  # attempt to just grab whatever is in there and scoop it all up 2025-09-09T14:45:11.4347528Z  if find "dist/" -name "*.whl" >/dev/null 2>/dev/null; then 2025-09-09T14:45:11.4348039Z  mv -v dist/*.whl "${RUNNER_ARTIFACT_DIR}/" 2025-09-09T14:45:11.4348376Z  fi 2025-09-09T14:45:11.4348643Z  if [[ -d "artifacts-to-be-uploaded" ]]; then 2025-09-09T14:45:11.4349093Z  mv -v artifacts-to-be-uploaded/* "${RUNNER_ARTIFACT_DIR}/" 2025-09-09T14:45:11.4349489Z  fi 2025-09-09T14:45:11.4349733Z fi 2025-09-09T14:45:11.4349928Z  2025-09-09T14:45:11.4350137Z upload_docs=0 2025-09-09T14:45:11.4350520Z # Check if there are files in the documentation folder to upload, note that 2025-09-09T14:45:11.4350985Z # empty folders do not count 2025-09-09T14:45:11.4351438Z if find "${RUNNER_DOCS_DIR}" -mindepth 1 -maxdepth 1 -type f | read -r; then 2025-09-09T14:45:11.4352023Z  # TODO: Add a check here to test if on ec2 because if we're not on ec2 then this 2025-09-09T14:45:11.4352519Z  # upload will probably not work correctly 2025-09-09T14:45:11.4352854Z  upload_docs=1 2025-09-09T14:45:11.4353103Z fi 2025-09-09T14:45:11.4353397Z echo "upload-docs=${upload_docs}" >> "${GITHUB_OUTPUT}" 2025-09-09T14:45:11.4360100Z shell: /usr/bin/bash -e {0} 2025-09-09T14:45:11.4360379Z env: 2025-09-09T14:45:11.4360623Z DOCKER_IMAGE: pytorch/almalinux-builder:cpu 2025-09-09T14:45:11.4360969Z REPOSITORY: pytorch/ao 2025-09-09T14:45:11.4361213Z PR_NUMBER: 2963 2025-09-09T14:45:11.4363400Z SCRIPT: conda create -n venv python=3.9 -y conda activate venv echo "::group::Install newer objcopy that supports --set-section-alignment" dnf install -y gcc-toolset-10-binutils export PATH=/opt/rh/gcc-toolset-10/root/usr/bin/:$PATH python -m pip install --upgrade pip pip install torch==2.8.0 --index-url https://download.pytorch.org/whl/cpu 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/:$LD_LIBRARY_PATH pytest test --verbose -s 2025-09-09T14:45:11.4365872Z RUNNER_ARTIFACT_DIR: /home/ec2-user/actions-runner/_work/_temp/artifacts 2025-09-09T14:45:11.4366451Z RUNNER_TEST_RESULTS_DIR: /home/ec2-user/actions-runner/_work/_temp/test-results 2025-09-09T14:45:11.4366979Z RUNNER_DOCS_DIR: /home/ec2-user/actions-runner/_work/_temp/docs 2025-09-09T14:45:11.4367374Z UPLOAD_ARTIFACT_NAME: 2025-09-09T14:45:11.4367640Z ##[endgroup] 2025-09-09T14:45:11.4471525Z Prepare all required actions 2025-09-09T14:45:11.4508963Z ##[group]Run ./test-infra/.github/actions/teardown-linux 2025-09-09T14:45:11.4509337Z with: 2025-09-09T14:45:11.4509525Z env: 2025-09-09T14:45:11.4509774Z DOCKER_IMAGE: pytorch/almalinux-builder:cpu 2025-09-09T14:45:11.4510117Z REPOSITORY: pytorch/ao 2025-09-09T14:45:11.4510362Z PR_NUMBER: 2963 2025-09-09T14:45:11.4512542Z SCRIPT: conda create -n venv python=3.9 -y conda activate venv echo "::group::Install newer objcopy that supports --set-section-alignment" dnf install -y gcc-toolset-10-binutils export PATH=/opt/rh/gcc-toolset-10/root/usr/bin/:$PATH python -m pip install --upgrade pip pip install torch==2.8.0 --index-url https://download.pytorch.org/whl/cpu 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 2025-09-09T14:45:11.4514948Z RUNNER_ARTIFACT_DIR: /home/ec2-user/actions-runner/_work/_temp/artifacts 2025-09-09T14:45:11.4515515Z RUNNER_TEST_RESULTS_DIR: /home/ec2-user/actions-runner/_work/_temp/test-results 2025-09-09T14:45:11.4516060Z RUNNER_DOCS_DIR: /home/ec2-user/actions-runner/_work/_temp/docs 2025-09-09T14:45:11.4516570Z ##[endgroup] 2025-09-09T14:45:11.4543399Z ##[group]Run set -eou pipefail 2025-09-09T14:45:11.4543736Z set -eou pipefail 2025-09-09T14:45:11.4544015Z  2025-09-09T14:45:11.4544381Z echo "Holding runner for 2 hours until all ssh sessions have logged out" 2025-09-09T14:45:11.4544850Z for _ in $(seq 1440); do 2025-09-09T14:45:11.4545250Z  # Break if no ssh session exists anymore 2025-09-09T14:45:11.4545612Z  if [ "$(who)" = "" ]; then 2025-09-09T14:45:11.4545905Z  break 2025-09-09T14:45:11.4546148Z  fi 2025-09-09T14:45:11.4546383Z  echo "." 2025-09-09T14:45:11.4546620Z  sleep 5 2025-09-09T14:45:11.4546873Z done 2025-09-09T14:45:11.4552513Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-09T14:45:11.4552896Z env: 2025-09-09T14:45:11.4553154Z DOCKER_IMAGE: pytorch/almalinux-builder:cpu 2025-09-09T14:45:11.4553504Z REPOSITORY: pytorch/ao 2025-09-09T14:45:11.4553751Z PR_NUMBER: 2963 2025-09-09T14:45:11.4555940Z 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/:$PATH python -m pip install --upgrade pip pip install torch==2.8.0 --index-url https://download.pytorch.org/whl/cpu sed -i '' dev-requirements.txt pip install -r dev-requirements.txt pip install . export CONDA=$(dirname $(dirname $(which conda))) export LD_LIBRARY_PATH=$CONDA/lib/:$LD_LIBRARY_PATH pytest test --verbose -s 2025-09-09T14:45:11.4558418Z RUNNER_ARTIFACT_DIR: /home/ec2-user/actions-runner/_work/_temp/artifacts 2025-09-09T14:45:11.4559005Z RUNNER_TEST_RESULTS_DIR: /home/ec2-user/actions-runner/_work/_temp/test-results 2025-09-09T14:45:11.4559559Z RUNNER_DOCS_DIR: /home/ec2-user/actions-runner/_work/_temp/docs 2025-09-09T14:45:11.4560033Z ##[endgroup] 2025-09-09T14:45:11.4583846Z Holding runner for 2 hours until all ssh sessions have logged out 2025-09-09T14:45:11.4671144Z ##[group]Run # ignore expansion of "docker ps -q" since it could be empty 2025-09-09T14:45:11.4671714Z # ignore expansion of "docker ps -q" since it could be empty 2025-09-09T14:45:11.4672125Z # shellcheck disable=SC2046 2025-09-09T14:45:11.4672469Z docker stop $(docker ps -q) || true 2025-09-09T14:45:11.4672815Z # Prune all of the docker images 2025-09-09T14:45:11.4673124Z docker system prune -af 2025-09-09T14:45:11.4678788Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-09T14:45:11.4679149Z env: 2025-09-09T14:45:11.4679401Z DOCKER_IMAGE: pytorch/almalinux-builder:cpu 2025-09-09T14:45:11.4679885Z REPOSITORY: pytorch/ao 2025-09-09T14:45:11.4680150Z PR_NUMBER: 2963 2025-09-09T14:45:11.4698104Z SCRIPT: conda create -n venv python=3.9 -y conda activate venv echo "::group::Install newer objcopy that supports --set-section-alignment" dnf install -y gcc-toolset-10-binutils export PATH=/opt/rh/gcc-toolset-10/root/usr/bin/:$PATH python -m pip install --upgrade pip pip install torch==2.8.0 --index-url https://download.pytorch.org/whl/cpu 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/:$LD_LIBRARY_PATH pytest test --verbose -s 2025-09-09T14:45:11.4700544Z RUNNER_ARTIFACT_DIR: /home/ec2-user/actions-runner/_work/_temp/artifacts 2025-09-09T14:45:11.4701116Z RUNNER_TEST_RESULTS_DIR: /home/ec2-user/actions-runner/_work/_temp/test-results 2025-09-09T14:45:11.4701667Z RUNNER_DOCS_DIR: /home/ec2-user/actions-runner/_work/_temp/docs 2025-09-09T14:45:11.4702039Z ##[endgroup] 2025-09-09T14:45:12.6994179Z c20abe3e5295 2025-09-09T14:45:16.0437866Z Deleted Containers: 2025-09-09T14:45:16.0438328Z c20abe3e52959e92a4a1996711464b6b29bb54815bed87faf5f6980eaf0ab30d 2025-09-09T14:45:16.0438680Z 2025-09-09T14:45:20.8184914Z Deleted Images: 2025-09-09T14:45:20.8185264Z untagged: pytorch/almalinux-builder:cpu 2025-09-09T14:45:20.8185953Z untagged: pytorch/almalinux-builder@sha256:10f309602e8cd84e21cb6970f97544761dd12a06b141583ab4d45f0bac4bf651 2025-09-09T14:45:20.8186840Z deleted: sha256:d6a8fef7076378a67f34a587132b48533aeb29b267a5d532b5b9c8df70af258b 2025-09-09T14:45:20.8187497Z deleted: sha256:5ee80ac5eaac1f2e1a07ecf3b3488008351b9350af841eed478e2e8c24e6f42a 2025-09-09T14:45:20.8188324Z deleted: sha256:a65598dc7a77543b8c2087c984c4d399c538c793064f336291e43cd23c0d4bee 2025-09-09T14:45:20.8188973Z deleted: sha256:75bba60f865bdfb654effb55beba5e38d571601662e689a4eb428757bfbd966d 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sha256:6dbb9cc54074106d46d4ccb330f2a40a682d49dda5f4844962b7dce9fe44aaec 2025-09-09T14:45:20.8201750Z deleted: sha256:b2d5eeeaba3a22b9b8aa97261957974a6bd65274ebd43e1d81d0a7b8b752b116 2025-09-09T14:45:20.8202232Z 2025-09-09T14:45:20.8220599Z Total reclaimed space: 7.226GB 2025-09-09T14:45:20.8273785Z ##[group]Run set +e 2025-09-09T14:45:20.8274073Z set +e 2025-09-09T14:45:20.8274324Z if [[ "${NO_SUDO}" == "false" ]]; then 2025-09-09T14:45:20.8274726Z  sudo rm -rf "${GITHUB_WORKSPACE:?}/${REPOSITORY:?}" 2025-09-09T14:45:20.8275070Z else 2025-09-09T14:45:20.8275348Z  rm -rf "${GITHUB_WORKSPACE:?}/${REPOSITORY:?}" 2025-09-09T14:45:20.8275698Z fi 2025-09-09T14:45:20.8275907Z set -e 2025-09-09T14:45:20.8281745Z shell: /usr/bin/bash -e {0} 2025-09-09T14:45:20.8282023Z env: 2025-09-09T14:45:20.8282264Z DOCKER_IMAGE: pytorch/almalinux-builder:cpu 2025-09-09T14:45:20.8282598Z REPOSITORY: pytorch/ao 2025-09-09T14:45:20.8282842Z PR_NUMBER: 2963 2025-09-09T14:45:20.8285066Z 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/:$PATH python -m pip install --upgrade pip pip install torch==2.8.0 --index-url https://download.pytorch.org/whl/cpu sed -i '' dev-requirements.txt pip install -r dev-requirements.txt pip install . export CONDA=$(dirname $(dirname $(which conda))) export LD_LIBRARY_PATH=$CONDA/lib/:$LD_LIBRARY_PATH pytest test --verbose -s 2025-09-09T14:45:20.8287438Z RUNNER_ARTIFACT_DIR: /home/ec2-user/actions-runner/_work/_temp/artifacts 2025-09-09T14:45:20.8288009Z RUNNER_TEST_RESULTS_DIR: /home/ec2-user/actions-runner/_work/_temp/test-results 2025-09-09T14:45:20.8288650Z RUNNER_DOCS_DIR: /home/ec2-user/actions-runner/_work/_temp/docs 2025-09-09T14:45:20.8289065Z NO_SUDO: false 2025-09-09T14:45:20.8289276Z ##[endgroup] 2025-09-09T14:45:21.4367090Z Post job cleanup. 2025-09-09T14:45:21.5400393Z Post job cleanup. 2025-09-09T14:45:21.6355036Z [command]/usr/bin/git version 2025-09-09T14:45:21.6411559Z git version 2.47.1 2025-09-09T14:45:21.6452892Z Temporarily overriding HOME='/home/ec2-user/actions-runner/_work/_temp/fdbd2e47-16aa-497f-87b5-fc1607ab49e1' before making global git config changes 2025-09-09T14:45:21.6453855Z Adding repository directory to the temporary git global config as a safe directory 2025-09-09T14:45:21.6457859Z [command]/usr/bin/git config --global --add safe.directory /home/ec2-user/actions-runner/_work/ao/ao/test-infra 2025-09-09T14:45:21.6488781Z [command]/usr/bin/git config --local --name-only --get-regexp core\.sshCommand 2025-09-09T14:45:21.6524980Z [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' || :" 2025-09-09T14:45:21.6848470Z [command]/usr/bin/git config --local --name-only --get-regexp http\.https\:\/\/github\.com\/\.extraheader 2025-09-09T14:45:21.6868830Z http.https://github.com/.extraheader 2025-09-09T14:45:21.6878754Z [command]/usr/bin/git config --local --unset-all http.https://github.com/.extraheader 2025-09-09T14:45:21.6908058Z [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' || :" 2025-09-09T14:45:21.7261312Z A job completed hook has been configured by the self-hosted runner administrator 2025-09-09T14:45:21.7290553Z ##[group]Run '/home/ec2-user/runner-scripts/after_job.sh' 2025-09-09T14:45:21.7295855Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-09T14:45:21.7296241Z ##[endgroup] 2025-09-09T14:45:21.7478694Z [!ALERT!] Swap in detected! [!ALERT!] 2025-09-09T14:45:32.9851934Z [!ALERT!] Swap out detected [!ALERT!] 2025-09-09T14:45:52.3506173Z Cleaning up orphan processes