2025-09-09T14:02:53.2914075Z Current runner version: '2.328.0' 2025-09-09T14:02:53.2921405Z Runner name: 'i-04cdc10a292600a3b' 2025-09-09T14:02:53.2922247Z Runner group name: 'default' 2025-09-09T14:02:53.2923078Z Machine name: 'ip-10-0-40-205' 2025-09-09T14:02:53.2926139Z ##[group]GITHUB_TOKEN Permissions 2025-09-09T14:02:53.2928635Z Contents: read 2025-09-09T14:02:53.2929238Z Metadata: read 2025-09-09T14:02:53.2929859Z Packages: read 2025-09-09T14:02:53.2930443Z ##[endgroup] 2025-09-09T14:02:53.2932854Z Secret source: Actions 2025-09-09T14:02:53.2933667Z Prepare workflow directory 2025-09-09T14:02:53.3627414Z Prepare all required actions 2025-09-09T14:02:53.3671377Z Getting action download info 2025-09-09T14:02:53.7031839Z Download action repository 'actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683' (SHA:11bd71901bbe5b1630ceea73d27597364c9af683) 2025-09-09T14:02:54.0251642Z Download action repository 'pytorch/pytorch@main' (SHA:4dd73e659a8fd4872e5f49cfd72e420fa7c4e6c9) 2025-09-09T14:03:07.9768636Z Download action repository 'actions/download-artifact@d3f86a106a0bac45b974a628896c90dbdf5c8093' (SHA:d3f86a106a0bac45b974a628896c90dbdf5c8093) 2025-09-09T14:03:08.3267635Z Download action repository 'pmeier/pytest-results-action@a2c1430e2bddadbad9f49a6f9b879f062c6b19b1' (SHA:a2c1430e2bddadbad9f49a6f9b879f062c6b19b1) 2025-09-09T14:03:08.4753552Z Download action repository 'actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02' (SHA:ea165f8d65b6e75b540449e92b4886f43607fa02) 2025-09-09T14:03:08.9925008Z Getting action download info 2025-09-09T14:03:09.2121703Z Uses: pytorch/test-infra/.github/workflows/linux_job_v2.yml@refs/heads/main (e502b6d9079a2a411c68046e8a7694b851c5df33) 2025-09-09T14:03:09.2126150Z ##[group] Inputs 2025-09-09T14:03:09.2128609Z 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/:$PATH python -m pip install --upgrade pip pip install torch==2.7.0 --index-url https://download.pytorch.org/whl/cpu sed -i '' dev-requirements.txt pip install -r dev-requirements.txt pip install . export CONDA=$(dirname $(dirname $(which conda))) export LD_LIBRARY_PATH=$CONDA/lib/:$LD_LIBRARY_PATH pytest test --verbose -s 2025-09-09T14:03:09.2131500Z timeout: 180 2025-09-09T14:03:09.2131739Z runner: linux.4xlarge 2025-09-09T14:03:09.2132017Z upload-artifact: 2025-09-09T14:03:09.2132572Z upload-artifact-to-s3: false 2025-09-09T14:03:09.2132881Z download-artifact: 2025-09-09T14:03:09.2133129Z repository: 2025-09-09T14:03:09.2133388Z fetch-depth: 1 2025-09-09T14:03:09.2133630Z submodules: recursive 2025-09-09T14:03:09.2133871Z ref: 2025-09-09T14:03:09.2134125Z test-infra-repository: pytorch/test-infra 2025-09-09T14:03:09.2134451Z test-infra-ref: 2025-09-09T14:03:09.2134720Z use-custom-docker-registry: true 2025-09-09T14:03:09.2135050Z docker-image: pytorch/almalinux-builder 2025-09-09T14:03:09.2135404Z docker-build-dir: .ci/docker 2025-09-09T14:03:09.2135688Z gpu-arch-type: cpu 2025-09-09T14:03:09.2135939Z gpu-arch-version: 2025-09-09T14:03:09.2136181Z job-name: linux-job 2025-09-09T14:03:09.2136441Z continue-on-error: false 2025-09-09T14:03:09.2136713Z binary-matrix: 2025-09-09T14:03:09.2136940Z run-with-docker: true 2025-09-09T14:03:09.2137201Z secrets-env: 2025-09-09T14:03:09.2137443Z no-sudo: false 2025-09-09T14:03:09.2137681Z ##[endgroup] 2025-09-09T14:03:09.2138256Z Complete job name: test (CPU 2.7, linux.4xlarge, torch==2.7.0 --index-url https://download.pytorch.org/whl/cpu, cpu) / linux-job 2025-09-09T14:03:09.2682814Z A job started hook has been configured by the self-hosted runner administrator 2025-09-09T14:03:09.2798257Z ##[group]Run '/home/ec2-user/runner-scripts/before_job.sh' 2025-09-09T14:03:09.2807965Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-09T14:03:09.2808689Z ##[endgroup] 2025-09-09T14:03:10.5214676Z Runner Type: linux.4xlarge 2025-09-09T14:03:10.5215184Z Instance Type: c5.4xlarge 2025-09-09T14:03:10.5215795Z AMI Name: unknown 2025-09-09T14:03:10.5250143Z AMI ID: ami-05ffe3c48a9991133 2025-09-09T14:03:16.2454959Z ##[group]Run set -euxo pipefail 2025-09-09T14:03:16.2455357Z set -euxo pipefail 2025-09-09T14:03:16.2455676Z if [[ "${NO_SUDO}" == "false" ]]; then 2025-09-09T14:03:16.2456092Z  echo "::group::Cleanup with-sudo debug output" 2025-09-09T14:03:16.2456485Z  sudo rm -rfv "${GITHUB_WORKSPACE}" 2025-09-09T14:03:16.2456810Z else 2025-09-09T14:03:16.2457077Z  echo "::group::Cleanup no-sudo debug output" 2025-09-09T14:03:16.2457456Z  rm -rfv "${GITHUB_WORKSPACE}" 2025-09-09T14:03:16.2457754Z fi 2025-09-09T14:03:16.2457976Z  2025-09-09T14:03:16.2458568Z mkdir -p "${GITHUB_WORKSPACE}" 2025-09-09T14:03:16.2458923Z echo "::endgroup::" 2025-09-09T14:03:16.2469307Z shell: /usr/bin/bash -e {0} 2025-09-09T14:03:16.2469608Z env: 2025-09-09T14:03:16.2469857Z DOCKER_IMAGE: pytorch/almalinux-builder:cpu 2025-09-09T14:03:16.2470241Z REPOSITORY: pytorch/ao 2025-09-09T14:03:16.2470548Z PR_NUMBER: 2963 2025-09-09T14:03:16.2472883Z SCRIPT: conda create -n venv python=3.9 -y conda activate venv echo "::group::Install newer objcopy that supports --set-section-alignment" dnf install -y gcc-toolset-10-binutils export PATH=/opt/rh/gcc-toolset-10/root/usr/bin/:$PATH python -m pip install --upgrade pip pip install torch==2.7.0 --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/:$LD_LIBRARY_PATH pytest test --verbose -s 2025-09-09T14:03:16.2475278Z NO_SUDO: false 2025-09-09T14:03:16.2475502Z ##[endgroup] 2025-09-09T14:03:16.2506673Z + [[ false == \f\a\l\s\e ]] 2025-09-09T14:03:16.2521494Z ##[group]Cleanup with-sudo debug output 2025-09-09T14:03:16.2525333Z + echo '::group::Cleanup with-sudo debug output' 2025-09-09T14:03:16.2525837Z + sudo rm -rfv /home/ec2-user/actions-runner/_work/ao/ao 2025-09-09T14:03:16.3918606Z removed directory '/home/ec2-user/actions-runner/_work/ao/ao' 2025-09-09T14:03:16.3935486Z + mkdir -p /home/ec2-user/actions-runner/_work/ao/ao 2025-09-09T14:03:16.3952475Z + echo ::endgroup:: 2025-09-09T14:03:16.3953318Z ##[endgroup] 2025-09-09T14:03:16.4086588Z ##[group]Run actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 2025-09-09T14:03:16.4087086Z with: 2025-09-09T14:03:16.4087319Z repository: pytorch/test-infra 2025-09-09T14:03:16.4087633Z path: test-infra 2025-09-09T14:03:16.4087880Z submodules: recursive 2025-09-09T14:03:16.4088335Z token: *** 2025-09-09T14:03:16.4088563Z ssh-strict: true 2025-09-09T14:03:16.4088787Z ssh-user: git 2025-09-09T14:03:16.4089039Z persist-credentials: true 2025-09-09T14:03:16.4089304Z clean: true 2025-09-09T14:03:16.4089563Z sparse-checkout-cone-mode: true 2025-09-09T14:03:16.4089856Z fetch-depth: 1 2025-09-09T14:03:16.4090094Z fetch-tags: false 2025-09-09T14:03:16.4090357Z show-progress: true 2025-09-09T14:03:16.4090590Z lfs: false 2025-09-09T14:03:16.4090823Z set-safe-directory: true 2025-09-09T14:03:16.4091124Z env: 2025-09-09T14:03:16.4091380Z DOCKER_IMAGE: pytorch/almalinux-builder:cpu 2025-09-09T14:03:16.4091720Z REPOSITORY: pytorch/ao 2025-09-09T14:03:16.4092043Z PR_NUMBER: 2963 2025-09-09T14:03:16.4094367Z SCRIPT: conda create -n venv python=3.9 -y conda activate venv echo "::group::Install newer objcopy that supports --set-section-alignment" dnf install -y gcc-toolset-10-binutils export PATH=/opt/rh/gcc-toolset-10/root/usr/bin/:$PATH python -m pip install --upgrade pip pip install torch==2.7.0 --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 2025-09-09T14:03:16.4096750Z ##[endgroup] 2025-09-09T14:03:16.5756597Z Syncing repository: pytorch/test-infra 2025-09-09T14:03:16.5757661Z ##[group]Getting Git version info 2025-09-09T14:03:16.5758485Z Working directory is '/home/ec2-user/actions-runner/_work/ao/ao/test-infra' 2025-09-09T14:03:16.5759181Z [command]/usr/bin/git version 2025-09-09T14:03:16.5761877Z git version 2.47.1 2025-09-09T14:03:16.5787256Z ##[endgroup] 2025-09-09T14:03:16.5816209Z Temporarily overriding HOME='/home/ec2-user/actions-runner/_work/_temp/1009e8ee-4bae-48ae-80c6-936f277d3f39' before making global git config changes 2025-09-09T14:03:16.5817233Z Adding repository directory to the temporary git global config as a safe directory 2025-09-09T14:03:16.5820929Z [command]/usr/bin/git config --global --add safe.directory /home/ec2-user/actions-runner/_work/ao/ao/test-infra 2025-09-09T14:03:16.5852023Z ##[group]Initializing the repository 2025-09-09T14:03:16.5856399Z [command]/usr/bin/git init /home/ec2-user/actions-runner/_work/ao/ao/test-infra 2025-09-09T14:03:16.5890686Z hint: Using 'master' as the name for the initial branch. This default branch name 2025-09-09T14:03:16.5891780Z hint: is subject to change. To configure the initial branch name to use in all 2025-09-09T14:03:16.5892870Z hint: of your new repositories, which will suppress this warning, call: 2025-09-09T14:03:16.5893356Z hint: 2025-09-09T14:03:16.5893657Z hint: git config --global init.defaultBranch 2025-09-09T14:03:16.5894016Z hint: 2025-09-09T14:03:16.5894343Z hint: Names commonly chosen instead of 'master' are 'main', 'trunk' and 2025-09-09T14:03:16.5894927Z hint: 'development'. The just-created branch can be renamed via this command: 2025-09-09T14:03:16.5895374Z hint: 2025-09-09T14:03:16.5895618Z hint: git branch -m 2025-09-09T14:03:16.5896444Z Initialized empty Git repository in /home/ec2-user/actions-runner/_work/ao/ao/test-infra/.git/ 2025-09-09T14:03:16.5900703Z [command]/usr/bin/git remote add origin https://github.com/pytorch/test-infra 2025-09-09T14:03:16.5930430Z ##[endgroup] 2025-09-09T14:03:16.5930880Z ##[group]Disabling automatic garbage collection 2025-09-09T14:03:16.5933978Z [command]/usr/bin/git config --local gc.auto 0 2025-09-09T14:03:16.5962816Z ##[endgroup] 2025-09-09T14:03:16.5963244Z ##[group]Setting up auth 2025-09-09T14:03:16.5967896Z [command]/usr/bin/git config --local --name-only --get-regexp core\.sshCommand 2025-09-09T14:03:16.6026000Z [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' || :" 2025-09-09T14:03:16.6321157Z [command]/usr/bin/git config --local --name-only --get-regexp http\.https\:\/\/github\.com\/\.extraheader 2025-09-09T14:03:16.6348485Z [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' || :" 2025-09-09T14:03:16.6780775Z [command]/usr/bin/git config --local http.https://github.com/.extraheader AUTHORIZATION: basic *** 2025-09-09T14:03:16.6836693Z ##[endgroup] 2025-09-09T14:03:16.6837655Z ##[group]Determining the default branch 2025-09-09T14:03:16.6841018Z Retrieving the default branch name 2025-09-09T14:03:16.9197950Z Default branch 'main' 2025-09-09T14:03:16.9199063Z ##[endgroup] 2025-09-09T14:03:16.9199929Z ##[group]Fetching the repository 2025-09-09T14:03:16.9206160Z [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 2025-09-09T14:03:17.3230850Z From https://github.com/pytorch/test-infra 2025-09-09T14:03:17.3231307Z * [new branch] main -> origin/main 2025-09-09T14:03:17.3267197Z ##[endgroup] 2025-09-09T14:03:17.3267895Z ##[group]Determining the checkout info 2025-09-09T14:03:17.3269455Z ##[endgroup] 2025-09-09T14:03:17.3274870Z [command]/usr/bin/git sparse-checkout disable 2025-09-09T14:03:17.3312957Z [command]/usr/bin/git config --local --unset-all extensions.worktreeConfig 2025-09-09T14:03:17.3341686Z ##[group]Checking out the ref 2025-09-09T14:03:17.3346001Z [command]/usr/bin/git checkout --progress --force -B main refs/remotes/origin/main 2025-09-09T14:03:17.4588266Z Switched to a new branch 'main' 2025-09-09T14:03:17.4605765Z branch 'main' set up to track 'origin/main'. 2025-09-09T14:03:17.4613782Z ##[endgroup] 2025-09-09T14:03:17.4614502Z ##[group]Setting up auth for fetching submodules 2025-09-09T14:03:17.4621255Z [command]/usr/bin/git config --global http.https://github.com/.extraheader AUTHORIZATION: basic *** 2025-09-09T14:03:17.4666370Z [command]/usr/bin/git config --global --unset-all url.https://github.com/.insteadOf 2025-09-09T14:03:17.4706451Z [command]/usr/bin/git config --global --add url.https://github.com/.insteadOf git@github.com: 2025-09-09T14:03:17.4737612Z [command]/usr/bin/git config --global --add url.https://github.com/.insteadOf org-21003710@github.com: 2025-09-09T14:03:17.4764239Z ##[endgroup] 2025-09-09T14:03:17.4764642Z ##[group]Fetching submodules 2025-09-09T14:03:17.4768279Z [command]/usr/bin/git submodule sync --recursive 2025-09-09T14:03:17.5093155Z [command]/usr/bin/git -c protocol.version=2 submodule update --init --force --depth=1 --recursive 2025-09-09T14:03:17.5417201Z [command]/usr/bin/git submodule foreach --recursive git config --local gc.auto 0 2025-09-09T14:03:17.5737653Z ##[endgroup] 2025-09-09T14:03:17.5738141Z ##[group]Persisting credentials for submodules 2025-09-09T14:03:17.5743072Z [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' || :" 2025-09-09T14:03:17.6070068Z [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" 2025-09-09T14:03:17.6389344Z [command]/usr/bin/git submodule foreach --recursive git config --local --add 'url.https://github.com/.insteadOf' 'git@github.com:' 2025-09-09T14:03:17.6713269Z [command]/usr/bin/git submodule foreach --recursive git config --local --add 'url.https://github.com/.insteadOf' 'org-21003710@github.com:' 2025-09-09T14:03:17.7034546Z ##[endgroup] 2025-09-09T14:03:17.7073290Z [command]/usr/bin/git log -1 --format=%H 2025-09-09T14:03:17.7098788Z e502b6d9079a2a411c68046e8a7694b851c5df33 2025-09-09T14:03:17.7283124Z Prepare all required actions 2025-09-09T14:03:17.7283653Z Getting action download info 2025-09-09T14:03:17.9008621Z Download action repository 'pytorch/test-infra@main' (SHA:e502b6d9079a2a411c68046e8a7694b851c5df33) 2025-09-09T14:03:19.7581910Z Getting action download info 2025-09-09T14:03:19.8662623Z Download action repository 'nick-fields/retry@3e91a01664abd3c5cd539100d10d33b9c5b68482' (SHA:3e91a01664abd3c5cd539100d10d33b9c5b68482) 2025-09-09T14:03:20.0560478Z ##[group]Run ./test-infra/.github/actions/setup-linux 2025-09-09T14:03:20.0560861Z env: 2025-09-09T14:03:20.0561110Z DOCKER_IMAGE: pytorch/almalinux-builder:cpu 2025-09-09T14:03:20.0561476Z REPOSITORY: pytorch/ao 2025-09-09T14:03:20.0577299Z PR_NUMBER: 2963 2025-09-09T14:03:20.0579675Z 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==2.7.0 --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 2025-09-09T14:03:20.0581992Z ##[endgroup] 2025-09-09T14:03:20.0669882Z ##[group]Run set -euo pipefail 2025-09-09T14:03:20.0670197Z set -euo pipefail 2025-09-09T14:03:20.0670486Z function get_ec2_metadata() { 2025-09-09T14:03:20.0670849Z  # Pulled from instance metadata endpoint for EC2 2025-09-09T14:03:20.0671654Z  # see https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/instancedata-data-retrieval.html 2025-09-09T14:03:20.0672316Z  category=$1 2025-09-09T14:03:20.0673227Z  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}" 2025-09-09T14:03:20.0674143Z } 2025-09-09T14:03:20.0674388Z echo "ami-id: $(get_ec2_metadata ami-id)" 2025-09-09T14:03:20.0674806Z echo "instance-id: $(get_ec2_metadata instance-id)" 2025-09-09T14:03:20.0675267Z echo "instance-type: $(get_ec2_metadata instance-type)" 2025-09-09T14:03:20.0675677Z echo "system info $(uname -a)" 2025-09-09T14:03:20.0682143Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-09T14:03:20.0682512Z env: 2025-09-09T14:03:20.0682760Z DOCKER_IMAGE: pytorch/almalinux-builder:cpu 2025-09-09T14:03:20.0683100Z REPOSITORY: pytorch/ao 2025-09-09T14:03:20.0683340Z PR_NUMBER: 2963 2025-09-09T14:03:20.0685573Z SCRIPT: conda create -n venv python=3.9 -y conda activate venv echo "::group::Install newer objcopy that supports --set-section-alignment" dnf install -y gcc-toolset-10-binutils export PATH=/opt/rh/gcc-toolset-10/root/usr/bin/:$PATH python -m pip install --upgrade pip pip install torch==2.7.0 --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/:$LD_LIBRARY_PATH pytest test --verbose -s 2025-09-09T14:03:20.0687862Z ##[endgroup] 2025-09-09T14:03:20.0834201Z ami-id: ami-05ffe3c48a9991133 2025-09-09T14:03:20.0943352Z instance-id: i-04cdc10a292600a3b 2025-09-09T14:03:20.1052875Z instance-type: c5.4xlarge 2025-09-09T14:03:20.1066190Z system info Linux ip-10-0-40-205.ec2.internal 6.1.141-155.222.amzn2023.x86_64 #1 SMP PREEMPT_DYNAMIC Tue Jun 17 10:29:47 UTC 2025 x86_64 x86_64 x86_64 GNU/Linux 2025-09-09T14:03:20.1109410Z ##[group]Run echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT" 2025-09-09T14:03:20.1110550Z echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT" 2025-09-09T14:03:20.1116915Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-09T14:03:20.1117292Z env: 2025-09-09T14:03:20.1117547Z DOCKER_IMAGE: pytorch/almalinux-builder:cpu 2025-09-09T14:03:20.1117894Z REPOSITORY: pytorch/ao 2025-09-09T14:03:20.1118151Z PR_NUMBER: 2963 2025-09-09T14:03:20.1120523Z SCRIPT: conda create -n venv python=3.9 -y conda activate venv echo "::group::Install newer objcopy that supports --set-section-alignment" dnf install -y gcc-toolset-10-binutils export PATH=/opt/rh/gcc-toolset-10/root/usr/bin/:$PATH python -m pip install --upgrade pip pip install torch==2.7.0 --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/:$LD_LIBRARY_PATH pytest test --verbose -s 2025-09-09T14:03:20.1122867Z ##[endgroup] 2025-09-09T14:03:20.1206005Z ##[group]Run if systemctl is-active --quiet docker; then 2025-09-09T14:03:20.1206483Z if systemctl is-active --quiet docker; then 2025-09-09T14:03:20.1206861Z  echo "Docker daemon is running..."; 2025-09-09T14:03:20.1207194Z else 2025-09-09T14:03:20.1207535Z  echo "Starting docker deamon..." && sudo systemctl start docker; 2025-09-09T14:03:20.1207963Z fi 2025-09-09T14:03:20.1213594Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-09T14:03:20.1213978Z env: 2025-09-09T14:03:20.1214229Z DOCKER_IMAGE: pytorch/almalinux-builder:cpu 2025-09-09T14:03:20.1214559Z REPOSITORY: pytorch/ao 2025-09-09T14:03:20.1214996Z PR_NUMBER: 2963 2025-09-09T14:03:20.1217257Z SCRIPT: conda create -n venv python=3.9 -y conda activate venv echo "::group::Install newer objcopy that supports --set-section-alignment" dnf install -y gcc-toolset-10-binutils export PATH=/opt/rh/gcc-toolset-10/root/usr/bin/:$PATH python -m pip install --upgrade pip pip install torch==2.7.0 --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/:$LD_LIBRARY_PATH pytest test --verbose -s 2025-09-09T14:03:20.1219569Z ##[endgroup] 2025-09-09T14:03:20.1334812Z Docker daemon is running... 2025-09-09T14:03:20.1366410Z ##[group]Run AWS_ACCOUNT_ID=$(aws sts get-caller-identity|grep Account|cut -f4 -d\") 2025-09-09T14:03:20.1367070Z AWS_ACCOUNT_ID=$(aws sts get-caller-identity|grep Account|cut -f4 -d\") 2025-09-09T14:03:20.1367600Z retry () { "$@" || (sleep 1 && "$@") || (sleep 2 && "$@") } 2025-09-09T14:03:20.1368235Z retry aws ecr get-login-password --region "$AWS_DEFAULT_REGION" | docker login --username AWS \ 2025-09-09T14:03:20.1368982Z  --password-stdin "$AWS_ACCOUNT_ID.dkr.ecr.$AWS_DEFAULT_REGION.amazonaws.com" 2025-09-09T14:03:20.1375330Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-09T14:03:20.1375716Z env: 2025-09-09T14:03:20.1375972Z DOCKER_IMAGE: pytorch/almalinux-builder:cpu 2025-09-09T14:03:20.1376318Z REPOSITORY: pytorch/ao 2025-09-09T14:03:20.1376566Z PR_NUMBER: 2963 2025-09-09T14:03:20.1378829Z SCRIPT: conda create -n venv python=3.9 -y conda activate venv echo "::group::Install newer objcopy that supports --set-section-alignment" dnf install -y gcc-toolset-10-binutils export PATH=/opt/rh/gcc-toolset-10/root/usr/bin/:$PATH python -m pip install --upgrade pip pip install torch==2.7.0 --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/:$LD_LIBRARY_PATH pytest test --verbose -s 2025-09-09T14:03:20.1381159Z AWS_RETRY_MODE: standard 2025-09-09T14:03:20.1381467Z AWS_MAX_ATTEMPTS: 5 2025-09-09T14:03:20.1381734Z AWS_DEFAULT_REGION: us-east-1 2025-09-09T14:03:20.1381998Z ##[endgroup] 2025-09-09T14:03:21.2311090Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json. 2025-09-09T14:03:21.2311766Z Configure a credential helper to remove this warning. See 2025-09-09T14:03:21.2312362Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store 2025-09-09T14:03:21.2312774Z 2025-09-09T14:03:21.2317782Z Login Succeeded 2025-09-09T14:03:21.2537040Z ##[group]Run env | grep '^GITHUB' >> "${RUNNER_TEMP}/github_env_${GITHUB_RUN_ID}" 2025-09-09T14:03:21.2537647Z env | grep '^GITHUB' >> "${RUNNER_TEMP}/github_env_${GITHUB_RUN_ID}" 2025-09-09T14:03:21.2538157Z env | grep '^CI' >> "${RUNNER_TEMP}/github_env_${GITHUB_RUN_ID}" 2025-09-09T14:03:21.2545164Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-09T14:03:21.2545540Z env: 2025-09-09T14:03:21.2545798Z DOCKER_IMAGE: pytorch/almalinux-builder:cpu 2025-09-09T14:03:21.2546133Z REPOSITORY: pytorch/ao 2025-09-09T14:03:21.2546397Z PR_NUMBER: 2963 2025-09-09T14:03:21.2548675Z SCRIPT: conda create -n venv python=3.9 -y conda activate venv echo "::group::Install newer objcopy that supports --set-section-alignment" dnf install -y gcc-toolset-10-binutils export PATH=/opt/rh/gcc-toolset-10/root/usr/bin/:$PATH python -m pip install --upgrade pip pip install torch==2.7.0 --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/:$LD_LIBRARY_PATH pytest test --verbose -s 2025-09-09T14:03:21.2550996Z ##[endgroup] 2025-09-09T14:03:21.2643203Z ##[group]Run RUNNER_ARTIFACT_DIR="${RUNNER_TEMP}/artifacts" 2025-09-09T14:03:21.2643847Z RUNNER_ARTIFACT_DIR="${RUNNER_TEMP}/artifacts" 2025-09-09T14:03:21.2644253Z sudo rm -rf "${RUNNER_ARTIFACT_DIR}" 2025-09-09T14:03:21.2644600Z mkdir -p "${RUNNER_ARTIFACT_DIR}" 2025-09-09T14:03:21.2645052Z echo "RUNNER_ARTIFACT_DIR=${RUNNER_ARTIFACT_DIR}" >> "${GITHUB_ENV}" 2025-09-09T14:03:21.2645484Z  2025-09-09T14:03:21.2645786Z RUNNER_TEST_RESULTS_DIR="${RUNNER_TEMP}/test-results" 2025-09-09T14:03:21.2646207Z sudo rm -rf "${RUNNER_TEST_RESULTS_DIR}" 2025-09-09T14:03:21.2646581Z mkdir -p "${RUNNER_TEST_RESULTS_DIR}" 2025-09-09T14:03:21.2647071Z echo "RUNNER_TEST_RESULTS_DIR=${RUNNER_TEST_RESULTS_DIR}" >> "${GITHUB_ENV}" 2025-09-09T14:03:21.2647521Z  2025-09-09T14:03:21.2647762Z RUNNER_DOCS_DIR="${RUNNER_TEMP}/docs" 2025-09-09T14:03:21.2648098Z sudo rm -rf "${RUNNER_DOCS_DIR}" 2025-09-09T14:03:21.2648428Z mkdir -p "${RUNNER_DOCS_DIR}" 2025-09-09T14:03:21.2648832Z echo "RUNNER_DOCS_DIR=${RUNNER_DOCS_DIR}" >> "${GITHUB_ENV}" 2025-09-09T14:03:21.2654526Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-09T14:03:21.2654904Z env: 2025-09-09T14:03:21.2655163Z DOCKER_IMAGE: pytorch/almalinux-builder:cpu 2025-09-09T14:03:21.2655508Z REPOSITORY: pytorch/ao 2025-09-09T14:03:21.2655766Z PR_NUMBER: 2963 2025-09-09T14:03:21.2658030Z SCRIPT: conda create -n venv python=3.9 -y conda activate venv echo "::group::Install newer objcopy that supports --set-section-alignment" dnf install -y gcc-toolset-10-binutils export PATH=/opt/rh/gcc-toolset-10/root/usr/bin/:$PATH python -m pip install --upgrade pip pip install torch==2.7.0 --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/:$LD_LIBRARY_PATH pytest test --verbose -s 2025-09-09T14:03:21.2660789Z ##[endgroup] 2025-09-09T14:03:21.9683325Z ##[group]Run needs=0 2025-09-09T14:03:21.9683609Z needs=0 2025-09-09T14:03:21.9683976Z if lspci -v | grep -e 'controller.*NVIDIA' >/dev/null 2>/dev/null; then 2025-09-09T14:03:21.9684426Z  needs=1 2025-09-09T14:03:21.9684640Z fi 2025-09-09T14:03:21.9684895Z echo "does=${needs}" >> $GITHUB_OUTPUT 2025-09-09T14:03:21.9691194Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-09T14:03:21.9691586Z env: 2025-09-09T14:03:21.9691828Z DOCKER_IMAGE: pytorch/almalinux-builder:cpu 2025-09-09T14:03:21.9692182Z REPOSITORY: pytorch/ao 2025-09-09T14:03:21.9692449Z PR_NUMBER: 2963 2025-09-09T14:03:21.9694713Z SCRIPT: conda create -n venv python=3.9 -y conda activate venv echo "::group::Install newer objcopy that supports --set-section-alignment" dnf install -y gcc-toolset-10-binutils export PATH=/opt/rh/gcc-toolset-10/root/usr/bin/:$PATH python -m pip install --upgrade pip pip install torch==2.7.0 --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 2025-09-09T14:03:21.9697185Z RUNNER_ARTIFACT_DIR: /home/ec2-user/actions-runner/_work/_temp/artifacts 2025-09-09T14:03:21.9697795Z RUNNER_TEST_RESULTS_DIR: /home/ec2-user/actions-runner/_work/_temp/test-results 2025-09-09T14:03:21.9698347Z RUNNER_DOCS_DIR: /home/ec2-user/actions-runner/_work/_temp/docs 2025-09-09T14:03:21.9698742Z ##[endgroup] 2025-09-09T14:03:21.9995712Z ##[group]Run # ignore expansion of "docker ps -q" since it could be empty 2025-09-09T14:03:21.9996290Z # ignore expansion of "docker ps -q" since it could be empty 2025-09-09T14:03:21.9996714Z # shellcheck disable=SC2046 2025-09-09T14:03:21.9997048Z docker stop $(docker ps -q) || true 2025-09-09T14:03:21.9997386Z # Prune all of the docker images 2025-09-09T14:03:21.9997874Z docker system prune -af 2025-09-09T14:03:22.0003919Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-09T14:03:22.0004306Z env: 2025-09-09T14:03:22.0004547Z DOCKER_IMAGE: pytorch/almalinux-builder:cpu 2025-09-09T14:03:22.0004896Z REPOSITORY: pytorch/ao 2025-09-09T14:03:22.0005159Z PR_NUMBER: 2963 2025-09-09T14:03:22.0007431Z SCRIPT: conda create -n venv python=3.9 -y conda activate venv echo "::group::Install newer objcopy that supports --set-section-alignment" dnf install -y gcc-toolset-10-binutils export PATH=/opt/rh/gcc-toolset-10/root/usr/bin/:$PATH python -m pip install --upgrade pip pip install torch==2.7.0 --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 2025-09-09T14:03:22.0009906Z RUNNER_ARTIFACT_DIR: /home/ec2-user/actions-runner/_work/_temp/artifacts 2025-09-09T14:03:22.0010517Z RUNNER_TEST_RESULTS_DIR: /home/ec2-user/actions-runner/_work/_temp/test-results 2025-09-09T14:03:22.0011065Z RUNNER_DOCS_DIR: /home/ec2-user/actions-runner/_work/_temp/docs 2025-09-09T14:03:22.0011467Z ##[endgroup] 2025-09-09T14:03:22.0444626Z "docker stop" requires at least 1 argument. 2025-09-09T14:03:22.0445014Z See 'docker stop --help'. 2025-09-09T14:03:22.0445188Z 2025-09-09T14:03:22.0445364Z Usage: docker stop [OPTIONS] CONTAINER [CONTAINER...] 2025-09-09T14:03:22.0445641Z 2025-09-09T14:03:22.0445752Z Stop one or more running containers 2025-09-09T14:03:22.0643805Z Total reclaimed space: 0B 2025-09-09T14:03:22.0722738Z ##[group]Run ./test-infra/.github/actions/setup-ssh 2025-09-09T14:03:22.0723106Z with: 2025-09-09T14:03:22.0723792Z github-secret: *** 2025-09-09T14:03:22.0724506Z instructions: All testing is done inside the container, to start an interactive session run: docker exec -it $(docker container ps --format '{{.ID}}') bash 2025-09-09T14:03:22.0725286Z activate-with-label: false 2025-09-09T14:03:22.0725562Z label: with-ssh 2025-09-09T14:03:22.0725795Z remove-existing-keys: true 2025-09-09T14:03:22.0726075Z fail-silently: true 2025-09-09T14:03:22.0726299Z env: 2025-09-09T14:03:22.0726541Z DOCKER_IMAGE: pytorch/almalinux-builder:cpu 2025-09-09T14:03:22.0726874Z REPOSITORY: pytorch/ao 2025-09-09T14:03:22.0727133Z PR_NUMBER: 2963 2025-09-09T14:03:22.0729449Z SCRIPT: conda create -n venv python=3.9 -y conda activate venv echo "::group::Install newer objcopy that supports --set-section-alignment" dnf install -y gcc-toolset-10-binutils export PATH=/opt/rh/gcc-toolset-10/root/usr/bin/:$PATH python -m pip install --upgrade pip pip install torch==2.7.0 --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 2025-09-09T14:03:22.0731911Z RUNNER_ARTIFACT_DIR: /home/ec2-user/actions-runner/_work/_temp/artifacts 2025-09-09T14:03:22.0732514Z RUNNER_TEST_RESULTS_DIR: /home/ec2-user/actions-runner/_work/_temp/test-results 2025-09-09T14:03:22.0733076Z RUNNER_DOCS_DIR: /home/ec2-user/actions-runner/_work/_temp/docs 2025-09-09T14:03:22.0733459Z ##[endgroup] 2025-09-09T14:03:22.1847067Z Please see https://github.com/pytorch/pytorch/wiki/Debugging-using-with-ssh-for-Github-Actions for more info. 2025-09-09T14:03:22.7342261Z Grabbing public ssh keys from https://github.com/andrewor14.keys 2025-09-09T14:03:22.8152318Z ~/.ssh/authorized_keys file found on node, removing ~/.ssh and starting fresh 2025-09-09T14:03:22.8168366Z Public keys pulled and installed to /home/ec2-user/.ssh/authorized_keys 2025-09-09T14:03:22.8217662Z Login using: ssh ec2-user@ec2-54-166-141-179.compute-1.amazonaws.com 2025-09-09T14:03:22.8218265Z All testing is done inside the container, to start an interactive session run: 2025-09-09T14:03:22.8218878Z docker exec -it $(docker container ps --format '{{.ID}}') bash 2025-09-09T14:03:22.8338220Z ##[group]Run actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 2025-09-09T14:03:22.8338680Z with: 2025-09-09T14:03:22.8338907Z repository: pytorch/ao 2025-09-09T14:03:22.8339168Z ref: refs/pull/2963/merge 2025-09-09T14:03:22.8339433Z path: pytorch/ao 2025-09-09T14:03:22.8339655Z fetch-depth: 1 2025-09-09T14:03:22.8339891Z submodules: recursive 2025-09-09T14:03:22.8340287Z token: *** 2025-09-09T14:03:22.8340509Z ssh-strict: true 2025-09-09T14:03:22.8340729Z ssh-user: git 2025-09-09T14:03:22.8340973Z persist-credentials: true 2025-09-09T14:03:22.8341242Z clean: true 2025-09-09T14:03:22.8341475Z sparse-checkout-cone-mode: true 2025-09-09T14:03:22.8341778Z fetch-tags: false 2025-09-09T14:03:22.8342001Z show-progress: true 2025-09-09T14:03:22.8342239Z lfs: false 2025-09-09T14:03:22.8342455Z set-safe-directory: true 2025-09-09T14:03:22.8342715Z env: 2025-09-09T14:03:22.8342945Z DOCKER_IMAGE: pytorch/almalinux-builder:cpu 2025-09-09T14:03:22.8343346Z REPOSITORY: pytorch/ao 2025-09-09T14:03:22.8343591Z PR_NUMBER: 2963 2025-09-09T14:03:22.8345856Z SCRIPT: conda create -n venv python=3.9 -y conda activate venv echo "::group::Install newer objcopy that supports --set-section-alignment" dnf install -y gcc-toolset-10-binutils export PATH=/opt/rh/gcc-toolset-10/root/usr/bin/:$PATH python -m pip install --upgrade pip pip install torch==2.7.0 --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 2025-09-09T14:03:22.8348306Z RUNNER_ARTIFACT_DIR: /home/ec2-user/actions-runner/_work/_temp/artifacts 2025-09-09T14:03:22.8348894Z RUNNER_TEST_RESULTS_DIR: /home/ec2-user/actions-runner/_work/_temp/test-results 2025-09-09T14:03:22.8349459Z RUNNER_DOCS_DIR: /home/ec2-user/actions-runner/_work/_temp/docs 2025-09-09T14:03:22.8349862Z ##[endgroup] 2025-09-09T14:03:22.9341688Z Syncing repository: pytorch/ao 2025-09-09T14:03:22.9350553Z ##[group]Getting Git version info 2025-09-09T14:03:22.9351029Z Working directory is '/home/ec2-user/actions-runner/_work/ao/ao/pytorch/ao' 2025-09-09T14:03:22.9378286Z [command]/usr/bin/git version 2025-09-09T14:03:22.9418919Z git version 2.47.1 2025-09-09T14:03:22.9445005Z ##[endgroup] 2025-09-09T14:03:22.9467883Z Temporarily overriding HOME='/home/ec2-user/actions-runner/_work/_temp/525a37f8-31a1-499e-b274-2cda4d61f615' before making global git config changes 2025-09-09T14:03:22.9468867Z Adding repository directory to the temporary git global config as a safe directory 2025-09-09T14:03:22.9472964Z [command]/usr/bin/git config --global --add safe.directory /home/ec2-user/actions-runner/_work/ao/ao/pytorch/ao 2025-09-09T14:03:22.9505915Z ##[group]Initializing the repository 2025-09-09T14:03:22.9510642Z [command]/usr/bin/git init /home/ec2-user/actions-runner/_work/ao/ao/pytorch/ao 2025-09-09T14:03:22.9546808Z hint: Using 'master' as the name for the initial branch. This default branch name 2025-09-09T14:03:22.9547471Z hint: is subject to change. To configure the initial branch name to use in all 2025-09-09T14:03:22.9548066Z hint: of your new repositories, which will suppress this warning, call: 2025-09-09T14:03:22.9548496Z hint: 2025-09-09T14:03:22.9548774Z hint: git config --global init.defaultBranch 2025-09-09T14:03:22.9549133Z hint: 2025-09-09T14:03:22.9549475Z hint: Names commonly chosen instead of 'master' are 'main', 'trunk' and 2025-09-09T14:03:22.9550045Z hint: 'development'. The just-created branch can be renamed via this command: 2025-09-09T14:03:22.9550499Z hint: 2025-09-09T14:03:22.9550715Z hint: git branch -m 2025-09-09T14:03:22.9551227Z Initialized empty Git repository in /home/ec2-user/actions-runner/_work/ao/ao/pytorch/ao/.git/ 2025-09-09T14:03:22.9557040Z [command]/usr/bin/git remote add origin https://github.com/pytorch/ao 2025-09-09T14:03:22.9593712Z ##[endgroup] 2025-09-09T14:03:22.9594665Z ##[group]Disabling automatic garbage collection 2025-09-09T14:03:22.9598176Z [command]/usr/bin/git config --local gc.auto 0 2025-09-09T14:03:22.9626701Z ##[endgroup] 2025-09-09T14:03:22.9627105Z ##[group]Setting up auth 2025-09-09T14:03:22.9632210Z [command]/usr/bin/git config --local --name-only --get-regexp core\.sshCommand 2025-09-09T14:03:22.9659765Z [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' || :" 2025-09-09T14:03:22.9977390Z [command]/usr/bin/git config --local --name-only --get-regexp http\.https\:\/\/github\.com\/\.extraheader 2025-09-09T14:03:23.0007279Z [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' || :" 2025-09-09T14:03:23.0323775Z [command]/usr/bin/git config --local http.https://github.com/.extraheader AUTHORIZATION: basic *** 2025-09-09T14:03:23.0367172Z ##[endgroup] 2025-09-09T14:03:23.0367581Z ##[group]Fetching the repository 2025-09-09T14:03:23.0374776Z [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 2025-09-09T14:03:23.7876715Z From https://github.com/pytorch/ao 2025-09-09T14:03:23.7877184Z * [new ref] refs/pull/2963/merge -> pull/2963/merge 2025-09-09T14:03:23.7900015Z ##[endgroup] 2025-09-09T14:03:23.7900587Z ##[group]Determining the checkout info 2025-09-09T14:03:23.7902312Z ##[endgroup] 2025-09-09T14:03:23.7906660Z [command]/usr/bin/git sparse-checkout disable 2025-09-09T14:03:23.7942197Z [command]/usr/bin/git config --local --unset-all extensions.worktreeConfig 2025-09-09T14:03:23.7968777Z ##[group]Checking out the ref 2025-09-09T14:03:23.7971890Z [command]/usr/bin/git checkout --progress --force refs/remotes/pull/2963/merge 2025-09-09T14:03:23.9096740Z Note: switching to 'refs/remotes/pull/2963/merge'. 2025-09-09T14:03:23.9097132Z 2025-09-09T14:03:23.9097372Z You are in 'detached HEAD' state. You can look around, make experimental 2025-09-09T14:03:23.9097931Z changes and commit them, and you can discard any commits you make in this 2025-09-09T14:03:23.9098499Z state without impacting any branches by switching back to a branch. 2025-09-09T14:03:23.9098826Z 2025-09-09T14:03:23.9099032Z If you want to create a new branch to retain commits you create, you may 2025-09-09T14:03:23.9099546Z do so (now or later) by using -c with the switch command. Example: 2025-09-09T14:03:23.9099841Z 2025-09-09T14:03:23.9099966Z git switch -c 2025-09-09T14:03:23.9100166Z 2025-09-09T14:03:23.9100273Z Or undo this operation with: 2025-09-09T14:03:23.9100452Z 2025-09-09T14:03:23.9100555Z git switch - 2025-09-09T14:03:23.9100685Z 2025-09-09T14:03:23.9100921Z Turn off this advice by setting config variable advice.detachedHead to false 2025-09-09T14:03:23.9101297Z 2025-09-09T14:03:23.9101709Z HEAD is now at 7c05f81 Merge c21284c127b039bc49cc7ffda0e692894ed3b094 into 8b72284fd363b5c096de93fb7ac9cc960a6a601e 2025-09-09T14:03:23.9105751Z ##[endgroup] 2025-09-09T14:03:23.9106175Z ##[group]Setting up auth for fetching submodules 2025-09-09T14:03:23.9111344Z [command]/usr/bin/git config --global http.https://github.com/.extraheader AUTHORIZATION: basic *** 2025-09-09T14:03:23.9152498Z [command]/usr/bin/git config --global --unset-all url.https://github.com/.insteadOf 2025-09-09T14:03:23.9182331Z [command]/usr/bin/git config --global --add url.https://github.com/.insteadOf git@github.com: 2025-09-09T14:03:23.9212438Z [command]/usr/bin/git config --global --add url.https://github.com/.insteadOf org-21003710@github.com: 2025-09-09T14:03:23.9237690Z ##[endgroup] 2025-09-09T14:03:23.9238084Z ##[group]Fetching submodules 2025-09-09T14:03:23.9241099Z [command]/usr/bin/git submodule sync --recursive 2025-09-09T14:03:23.9563833Z [command]/usr/bin/git -c protocol.version=2 submodule update --init --force --depth=1 --recursive 2025-09-09T14:03:23.9874692Z Submodule 'third_party/cutlass' (https://github.com/NVIDIA/cutlass) registered for path 'third_party/cutlass' 2025-09-09T14:03:23.9902770Z Cloning into '/home/ec2-user/actions-runner/_work/ao/ao/pytorch/ao/third_party/cutlass'... 2025-09-09T14:03:25.8854970Z From https://github.com/NVIDIA/cutlass 2025-09-09T14:03:25.8855480Z * branch e51efbfe18fe4f4cbb66ab814c55bf4aa0185491 -> FETCH_HEAD 2025-09-09T14:03:26.5126970Z Submodule path 'third_party/cutlass': checked out 'e51efbfe18fe4f4cbb66ab814c55bf4aa0185491' 2025-09-09T14:03:26.5169559Z [command]/usr/bin/git submodule foreach --recursive git config --local gc.auto 0 2025-09-09T14:03:26.5479921Z Entering 'third_party/cutlass' 2025-09-09T14:03:26.5548776Z ##[endgroup] 2025-09-09T14:03:26.5549346Z ##[group]Persisting credentials for submodules 2025-09-09T14:03:26.5554401Z [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' || :" 2025-09-09T14:03:26.5858747Z Entering 'third_party/cutlass' 2025-09-09T14:03:26.5942949Z [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" 2025-09-09T14:03:26.6247879Z Entering 'third_party/cutlass' 2025-09-09T14:03:26.6308206Z file:/home/ec2-user/actions-runner/_work/ao/ao/pytorch/ao/.git/modules/third_party/cutlass/config remote.origin.url 2025-09-09T14:03:26.6360044Z [command]/usr/bin/git submodule foreach --recursive git config --local --add 'url.https://github.com/.insteadOf' 'git@github.com:' 2025-09-09T14:03:26.6668952Z Entering 'third_party/cutlass' 2025-09-09T14:03:26.6739583Z [command]/usr/bin/git submodule foreach --recursive git config --local --add 'url.https://github.com/.insteadOf' 'org-21003710@github.com:' 2025-09-09T14:03:26.7046872Z Entering 'third_party/cutlass' 2025-09-09T14:03:26.7114403Z ##[endgroup] 2025-09-09T14:03:26.7149933Z [command]/usr/bin/git log -1 --format=%H 2025-09-09T14:03:26.7176422Z 7c05f811b89289f7be3e0e3546626827f2cc1ca4 2025-09-09T14:03:26.7377492Z Prepare all required actions 2025-09-09T14:03:26.7378157Z Getting action download info 2025-09-09T14:03:26.8538866Z Download action repository 'nick-fields/retry@v3.0.0' (SHA:7152eba30c6575329ac0576536151aca5a72780e) 2025-09-09T14:03:27.0131649Z ##[group]Run ./test-infra/.github/actions/calculate-docker-image 2025-09-09T14:03:27.0132050Z with: 2025-09-09T14:03:27.0132291Z use-custom-docker-registry: true 2025-09-09T14:03:27.0132648Z docker-image-name: pytorch/almalinux-builder:cpu 2025-09-09T14:03:27.0133019Z docker-build-dir: .ci/docker 2025-09-09T14:03:27.0133321Z working-directory: pytorch/ao 2025-09-09T14:03:27.0133612Z docker-build-script: ./build.sh 2025-09-09T14:03:27.0134008Z docker-registry: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-09-09T14:03:27.0134420Z force-push: false 2025-09-09T14:03:27.0134646Z env: 2025-09-09T14:03:27.0134876Z DOCKER_IMAGE: pytorch/almalinux-builder:cpu 2025-09-09T14:03:27.0135214Z REPOSITORY: pytorch/ao 2025-09-09T14:03:27.0135507Z PR_NUMBER: 2963 2025-09-09T14:03:27.0137772Z 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==2.7.0 --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/:$LD_LIBRARY_PATH pytest test --verbose -s 2025-09-09T14:03:27.0140212Z RUNNER_ARTIFACT_DIR: /home/ec2-user/actions-runner/_work/_temp/artifacts 2025-09-09T14:03:27.0140813Z RUNNER_TEST_RESULTS_DIR: /home/ec2-user/actions-runner/_work/_temp/test-results 2025-09-09T14:03:27.0141547Z RUNNER_DOCS_DIR: /home/ec2-user/actions-runner/_work/_temp/docs 2025-09-09T14:03:27.0141931Z ##[endgroup] 2025-09-09T14:03:27.0166625Z ##[group]Run set -ex 2025-09-09T14:03:27.0166974Z set -ex 2025-09-09T14:03:27.0167185Z  2025-09-09T14:03:27.0167585Z # If the docker build directory or the build script doesn't exist, the action will 2025-09-09T14:03:27.0168249Z # gracefully return the docker image name as it is. Pulling docker image in Linux 2025-09-09T14:03:27.0168825Z # job could then download the pre-built image as usual 2025-09-09T14:03:27.0169522Z if [[ -d "${DOCKER_BUILD_DIR}" ]] && [[ -f "${DOCKER_BUILD_DIR}/${DOCKER_BUILD_SCRIPT}" ]] && [[ "${USE_CUSTOM_DOCKER_REGISTRY}" == "true" ]]; then 2025-09-09T14:03:27.0170166Z  echo "skip=false" >> "${GITHUB_OUTPUT}" 2025-09-09T14:03:27.0170499Z else 2025-09-09T14:03:27.0170758Z  echo "skip=true" >> "${GITHUB_OUTPUT}" 2025-09-09T14:03:27.0171211Z  echo "docker-image=${DOCKER_IMAGE_NAME}" >> "${GITHUB_OUTPUT}" 2025-09-09T14:03:27.0171625Z  2025-09-09T14:03:27.0172178Z  echo "Not using custom ECR registry. Either it was not requested or there is no Docker build script in the ${REPO_NAME} repo..." 2025-09-09T14:03:27.0172825Z  exit 0 2025-09-09T14:03:27.0173033Z fi 2025-09-09T14:03:27.0173239Z  2025-09-09T14:03:27.0173565Z if [[ "${DOCKER_IMAGE_NAME}" == *"${DOCKER_REGISTRY}/${REPO_NAME}"* ]]; then 2025-09-09T14:03:27.0174179Z  # The docker image name already includes the ECR prefix and tag, so we can just 2025-09-09T14:03:27.0174727Z  # use it as it is, but first let's extract the tag 2025-09-09T14:03:27.0175204Z  DOCKER_TAG=$(echo "${DOCKER_IMAGE_NAME}" | awk -F '[:,]' '{print $2}') 2025-09-09T14:03:27.0175722Z  echo "docker-tag=${DOCKER_TAG}" >> "${GITHUB_OUTPUT}" 2025-09-09T14:03:27.0176211Z  echo "docker-image=${DOCKER_IMAGE_NAME}" >> "${GITHUB_OUTPUT}" 2025-09-09T14:03:27.0176620Z else 2025-09-09T14:03:27.0176872Z  if [[ "${DOCKER_IMAGE_NAME}" == *:* ]]; then 2025-09-09T14:03:27.0177427Z  CUSTOM_TAG_PREFIX=${DOCKER_IMAGE_NAME#*:} 2025-09-09T14:03:27.0177830Z  DOCKER_IMAGE_NAME=${DOCKER_IMAGE_NAME%%:*} 2025-09-09T14:03:27.0178158Z  fi 2025-09-09T14:03:27.0178617Z  DOCKER_TAG=${CUSTOM_TAG_PREFIX:+${CUSTOM_TAG_PREFIX}-}$(git rev-parse HEAD:"${DOCKER_BUILD_DIR}") 2025-09-09T14:03:27.0179221Z  echo "docker-tag=${DOCKER_TAG}" >> "${GITHUB_OUTPUT}" 2025-09-09T14:03:27.0179873Z  echo "docker-image=${DOCKER_REGISTRY}/${REPO_NAME}/${DOCKER_IMAGE_NAME}:${DOCKER_TAG}" >> "${GITHUB_OUTPUT}" 2025-09-09T14:03:27.0180589Z  echo "custom-tag-prefix=${CUSTOM_TAG_PREFIX}" >> "${GITHUB_OUTPUT}" 2025-09-09T14:03:27.0181011Z fi 2025-09-09T14:03:27.0186930Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-09T14:03:27.0187303Z env: 2025-09-09T14:03:27.0187560Z DOCKER_IMAGE: pytorch/almalinux-builder:cpu 2025-09-09T14:03:27.0187895Z REPOSITORY: pytorch/ao 2025-09-09T14:03:27.0188165Z PR_NUMBER: 2963 2025-09-09T14:03:27.0190432Z SCRIPT: conda create -n venv python=3.9 -y conda activate venv echo "::group::Install newer objcopy that supports --set-section-alignment" dnf install -y gcc-toolset-10-binutils export PATH=/opt/rh/gcc-toolset-10/root/usr/bin/:$PATH python -m pip install --upgrade pip pip install torch==2.7.0 --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 2025-09-09T14:03:27.0192881Z RUNNER_ARTIFACT_DIR: /home/ec2-user/actions-runner/_work/_temp/artifacts 2025-09-09T14:03:27.0193476Z RUNNER_TEST_RESULTS_DIR: /home/ec2-user/actions-runner/_work/_temp/test-results 2025-09-09T14:03:27.0194154Z RUNNER_DOCS_DIR: /home/ec2-user/actions-runner/_work/_temp/docs 2025-09-09T14:03:27.0194540Z REPO_NAME: ao 2025-09-09T14:03:27.0194833Z DOCKER_IMAGE_NAME: pytorch/almalinux-builder:cpu 2025-09-09T14:03:27.0195185Z DOCKER_BUILD_DIR: .ci/docker 2025-09-09T14:03:27.0195481Z DOCKER_BUILD_SCRIPT: ./build.sh 2025-09-09T14:03:27.0195856Z DOCKER_REGISTRY: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-09-09T14:03:27.0196270Z USE_CUSTOM_DOCKER_REGISTRY: true 2025-09-09T14:03:27.0196573Z CUSTOM_TAG_PREFIX: 2025-09-09T14:03:27.0196806Z ##[endgroup] 2025-09-09T14:03:27.0224511Z + [[ -d .ci/docker ]] 2025-09-09T14:03:27.0224778Z + echo skip=true 2025-09-09T14:03:27.0225597Z + echo docker-image=pytorch/almalinux-builder:cpu 2025-09-09T14:03:27.0226251Z + echo 'Not using custom ECR registry. Either it was not requested or there is no Docker build script in the ao repo...' 2025-09-09T14:03:27.0227181Z Not using custom ECR registry. Either it was not requested or there is no Docker build script in the ao repo... 2025-09-09T14:03:27.0227915Z + exit 0 2025-09-09T14:03:27.0263241Z ##[group]Run set -eux 2025-09-09T14:03:27.0263532Z set -eux 2025-09-09T14:03:27.0263977Z # It's ok if this steps fails, it would then be an anonymous user like what we used to have 2025-09-09T14:03:27.0265135Z 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 2025-09-09T14:03:27.0272424Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-09T14:03:27.0272800Z env: 2025-09-09T14:03:27.0273059Z DOCKER_IMAGE: pytorch/almalinux-builder:cpu 2025-09-09T14:03:27.0273409Z REPOSITORY: pytorch/ao 2025-09-09T14:03:27.0273663Z PR_NUMBER: 2963 2025-09-09T14:03:27.0276099Z 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==2.7.0 --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/:$LD_LIBRARY_PATH pytest test --verbose -s 2025-09-09T14:03:27.0278569Z RUNNER_ARTIFACT_DIR: /home/ec2-user/actions-runner/_work/_temp/artifacts 2025-09-09T14:03:27.0279159Z RUNNER_TEST_RESULTS_DIR: /home/ec2-user/actions-runner/_work/_temp/test-results 2025-09-09T14:03:27.0279819Z RUNNER_DOCS_DIR: /home/ec2-user/actions-runner/_work/_temp/docs 2025-09-09T14:03:27.0280202Z ##[endgroup] 2025-09-09T14:03:27.0307892Z + aws secretsmanager get-secret-value --secret-id docker_hub_readonly_token 2025-09-09T14:03:27.0308856Z + jq --raw-output .SecretString 2025-09-09T14:03:27.0310155Z + jq -r .docker_hub_readonly_token 2025-09-09T14:03:27.0311371Z + docker login --username pytorchbot --password-stdin 2025-09-09T14:03:27.6281752Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json. 2025-09-09T14:03:27.6282425Z Configure a credential helper to remove this warning. See 2025-09-09T14:03:27.6283020Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store 2025-09-09T14:03:27.6283512Z 2025-09-09T14:03:27.6283652Z Login Succeeded 2025-09-09T14:03:27.6366645Z Prepare all required actions 2025-09-09T14:03:27.6405210Z ##[group]Run ./test-infra/.github/actions/pull-docker-image 2025-09-09T14:03:27.6405587Z with: 2025-09-09T14:03:27.6405849Z docker-image: pytorch/almalinux-builder:cpu 2025-09-09T14:03:27.6406283Z docker-registry: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-09-09T14:03:27.6406681Z env: 2025-09-09T14:03:27.6406929Z DOCKER_IMAGE: pytorch/almalinux-builder:cpu 2025-09-09T14:03:27.6407263Z REPOSITORY: pytorch/ao 2025-09-09T14:03:27.6407695Z PR_NUMBER: 2963 2025-09-09T14:03:27.6410012Z SCRIPT: conda create -n venv python=3.9 -y conda activate venv echo "::group::Install newer objcopy that supports --set-section-alignment" dnf install -y gcc-toolset-10-binutils export PATH=/opt/rh/gcc-toolset-10/root/usr/bin/:$PATH python -m pip install --upgrade pip pip install torch==2.7.0 --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 2025-09-09T14:03:27.6412478Z RUNNER_ARTIFACT_DIR: /home/ec2-user/actions-runner/_work/_temp/artifacts 2025-09-09T14:03:27.6413068Z RUNNER_TEST_RESULTS_DIR: /home/ec2-user/actions-runner/_work/_temp/test-results 2025-09-09T14:03:27.6413635Z RUNNER_DOCS_DIR: /home/ec2-user/actions-runner/_work/_temp/docs 2025-09-09T14:03:27.6414019Z ##[endgroup] 2025-09-09T14:03:27.6567812Z ##[group]Run set -x 2025-09-09T14:03:27.6568126Z set -x 2025-09-09T14:03:27.6568340Z set +e 2025-09-09T14:03:27.6568558Z  2025-09-09T14:03:27.6568756Z login() { 2025-09-09T14:03:27.6569244Z  aws ecr get-login-password --region us-east-1 | docker login -u AWS --password-stdin "$1" 2025-09-09T14:03:27.6569762Z } 2025-09-09T14:03:27.6569970Z  2025-09-09T14:03:27.6570176Z retry () { 2025-09-09T14:03:27.6570443Z  $* || (sleep 1 && $*) || (sleep 2 && $*) 2025-09-09T14:03:27.6570763Z } 2025-09-09T14:03:27.6570956Z  2025-09-09T14:03:27.6571186Z retry login "${DOCKER_REGISTRY}" 2025-09-09T14:03:27.6571481Z  2025-09-09T14:03:27.6572115Z IMAGE_SIZE=$(docker manifest inspect "${DOCKER_IMAGE}" | jq '[.layers[].size, .config.size] | add / 1024 / 1024') 2025-09-09T14:03:27.6572789Z echo "Compressed size of image in MB: ${IMAGE_SIZE}" 2025-09-09T14:03:27.6573155Z  2025-09-09T14:03:27.6573371Z set -e 2025-09-09T14:03:27.6573704Z # ignore output since only exit code is used for conditional 2025-09-09T14:03:27.6574204Z # only pull docker image if it's not available locally 2025-09-09T14:03:27.6574743Z if ! docker inspect --type=image "${DOCKER_IMAGE}" >/dev/null 2>/dev/null; then 2025-09-09T14:03:27.6575259Z  retry docker pull "${DOCKER_IMAGE}" 2025-09-09T14:03:27.6575569Z fi 2025-09-09T14:03:27.6581690Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-09T14:03:27.6582062Z env: 2025-09-09T14:03:27.6582315Z DOCKER_IMAGE: pytorch/almalinux-builder:cpu 2025-09-09T14:03:27.6582663Z REPOSITORY: pytorch/ao 2025-09-09T14:03:27.6582911Z PR_NUMBER: 2963 2025-09-09T14:03:27.6585184Z SCRIPT: conda create -n venv python=3.9 -y conda activate venv echo "::group::Install newer objcopy that supports --set-section-alignment" dnf install -y gcc-toolset-10-binutils export PATH=/opt/rh/gcc-toolset-10/root/usr/bin/:$PATH python -m pip install --upgrade pip pip install torch==2.7.0 --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 2025-09-09T14:03:27.6587649Z RUNNER_ARTIFACT_DIR: /home/ec2-user/actions-runner/_work/_temp/artifacts 2025-09-09T14:03:27.6588411Z RUNNER_TEST_RESULTS_DIR: /home/ec2-user/actions-runner/_work/_temp/test-results 2025-09-09T14:03:27.6588980Z RUNNER_DOCS_DIR: /home/ec2-user/actions-runner/_work/_temp/docs 2025-09-09T14:03:27.6589466Z DOCKER_REGISTRY: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-09-09T14:03:27.6589866Z ##[endgroup] 2025-09-09T14:03:27.6617522Z + set +e 2025-09-09T14:03:27.6618221Z + retry login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-09-09T14:03:27.6618680Z + login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-09-09T14:03:27.6621553Z + aws ecr get-login-password --region us-east-1 2025-09-09T14:03:27.6623093Z + docker login -u AWS --password-stdin 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-09-09T14:03:28.2177103Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json. 2025-09-09T14:03:28.2177733Z Configure a credential helper to remove this warning. See 2025-09-09T14:03:28.2178396Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store 2025-09-09T14:03:28.2178927Z 2025-09-09T14:03:28.2179067Z Login Succeeded 2025-09-09T14:03:28.2199082Z ++ docker manifest inspect pytorch/almalinux-builder:cpu 2025-09-09T14:03:28.2200006Z ++ jq '[.layers[].size, .config.size] | add / 1024 / 1024' 2025-09-09T14:03:28.3624264Z + IMAGE_SIZE=1439.2328958511353 2025-09-09T14:03:28.3624841Z Compressed size of image in MB: 1439.2328958511353 2025-09-09T14:03:28.3625275Z + echo 'Compressed size of image in MB: 1439.2328958511353' 2025-09-09T14:03:28.3625663Z + set -e 2025-09-09T14:03:28.3625988Z + docker inspect --type=image pytorch/almalinux-builder:cpu 2025-09-09T14:03:28.3755273Z + retry docker pull pytorch/almalinux-builder:cpu 2025-09-09T14:03:28.3755903Z + docker pull pytorch/almalinux-builder:cpu 2025-09-09T14:03:28.5256386Z cpu: Pulling from pytorch/almalinux-builder 2025-09-09T14:03:28.5262783Z 19877a9af8e3: Pulling fs layer 2025-09-09T14:03:28.5263118Z fe05152297d3: Pulling fs layer 2025-09-09T14:03:28.5263416Z 9c5a63e97f59: Pulling fs layer 2025-09-09T14:03:28.5263952Z 918715f58173: Pulling fs layer 2025-09-09T14:03:28.5264394Z 692d6799dd80: Pulling fs layer 2025-09-09T14:03:28.5264939Z c6352f35dfa2: Pulling fs layer 2025-09-09T14:03:28.5265538Z 518054e53c81: Pulling fs layer 2025-09-09T14:03:28.5266100Z 4f4fb700ef54: Pulling fs layer 2025-09-09T14:03:28.5266705Z 3b571ac2ab3b: Pulling fs layer 2025-09-09T14:03:28.5267306Z 84008f185523: Pulling fs layer 2025-09-09T14:03:28.5267863Z 9ee5aeef32d7: Pulling fs layer 2025-09-09T14:03:28.5268440Z a80ec369bee3: Pulling fs layer 2025-09-09T14:03:28.5268936Z f1417b667e9d: Pulling fs layer 2025-09-09T14:03:28.5269374Z 0c3cc5825672: Pulling fs layer 2025-09-09T14:03:28.5269708Z 895a870a9edd: Pulling fs layer 2025-09-09T14:03:28.5269977Z 692d6799dd80: Waiting 2025-09-09T14:03:28.5270202Z 918715f58173: Waiting 2025-09-09T14:03:28.5270453Z 84008f185523: Waiting 2025-09-09T14:03:28.5270686Z 9ee5aeef32d7: Waiting 2025-09-09T14:03:28.5270922Z b7eb993f501a: Pulling fs layer 2025-09-09T14:03:28.5271192Z c6352f35dfa2: Waiting 2025-09-09T14:03:28.5271427Z 518054e53c81: Waiting 2025-09-09T14:03:28.5271662Z 3b571ac2ab3b: Waiting 2025-09-09T14:03:28.5271896Z 4d4d94988ad5: Pulling fs layer 2025-09-09T14:03:28.5272166Z 0c3cc5825672: Waiting 2025-09-09T14:03:28.5272387Z b7eb993f501a: Waiting 2025-09-09T14:03:28.5272618Z f1417b667e9d: Waiting 2025-09-09T14:03:28.5272932Z 895a870a9edd: Waiting 2025-09-09T14:03:28.5273202Z 4f4fb700ef54: Waiting 2025-09-09T14:03:28.5273466Z 4d4d94988ad5: Waiting 2025-09-09T14:03:28.5273690Z a80ec369bee3: Waiting 2025-09-09T14:03:28.6015228Z 9c5a63e97f59: Download complete 2025-09-09T14:03:29.0502517Z 918715f58173: Verifying Checksum 2025-09-09T14:03:29.0503076Z 918715f58173: Download complete 2025-09-09T14:03:29.2641655Z 19877a9af8e3: Download complete 2025-09-09T14:03:29.3052426Z c6352f35dfa2: Verifying Checksum 2025-09-09T14:03:29.3052813Z c6352f35dfa2: Download complete 2025-09-09T14:03:29.7850500Z 518054e53c81: Verifying Checksum 2025-09-09T14:03:29.7850876Z 518054e53c81: Download complete 2025-09-09T14:03:29.7926619Z fe05152297d3: Verifying Checksum 2025-09-09T14:03:29.7926965Z fe05152297d3: Download complete 2025-09-09T14:03:29.8145359Z 4f4fb700ef54: Download complete 2025-09-09T14:03:29.8659720Z 84008f185523: Verifying Checksum 2025-09-09T14:03:29.8660208Z 84008f185523: Download complete 2025-09-09T14:03:29.9137810Z 3b571ac2ab3b: Verifying Checksum 2025-09-09T14:03:29.9138314Z 3b571ac2ab3b: Download complete 2025-09-09T14:03:29.9518941Z a80ec369bee3: Download complete 2025-09-09T14:03:29.9938603Z f1417b667e9d: Download complete 2025-09-09T14:03:30.0463177Z 0c3cc5825672: Verifying Checksum 2025-09-09T14:03:30.0463801Z 0c3cc5825672: Download complete 2025-09-09T14:03:30.1784000Z 895a870a9edd: Verifying Checksum 2025-09-09T14:03:30.1784369Z 895a870a9edd: Download complete 2025-09-09T14:03:30.2215693Z b7eb993f501a: Download complete 2025-09-09T14:03:30.9357104Z 692d6799dd80: Verifying Checksum 2025-09-09T14:03:30.9357653Z 692d6799dd80: Download complete 2025-09-09T14:03:31.5686926Z 19877a9af8e3: Pull complete 2025-09-09T14:03:33.7676457Z fe05152297d3: Pull complete 2025-09-09T14:03:33.9387626Z 9c5a63e97f59: Pull complete 2025-09-09T14:03:34.2279245Z 9ee5aeef32d7: Verifying Checksum 2025-09-09T14:03:34.2279821Z 9ee5aeef32d7: Download complete 2025-09-09T14:03:34.2829732Z 918715f58173: Pull complete 2025-09-09T14:03:36.0706739Z 4d4d94988ad5: Verifying Checksum 2025-09-09T14:03:36.0707110Z 4d4d94988ad5: Download complete 2025-09-09T14:03:39.4742175Z 692d6799dd80: Pull complete 2025-09-09T14:03:39.6409223Z c6352f35dfa2: Pull complete 2025-09-09T14:03:40.9431121Z 518054e53c81: Pull complete 2025-09-09T14:03:41.1662852Z 4f4fb700ef54: Pull complete 2025-09-09T14:03:41.5482151Z 3b571ac2ab3b: Pull complete 2025-09-09T14:03:41.7688814Z 84008f185523: Pull complete 2025-09-09T14:03:53.8080305Z 9ee5aeef32d7: Pull complete 2025-09-09T14:03:53.8296035Z a80ec369bee3: Pull complete 2025-09-09T14:03:53.8505757Z f1417b667e9d: Pull complete 2025-09-09T14:03:53.8722262Z 0c3cc5825672: Pull complete 2025-09-09T14:03:54.1996897Z 895a870a9edd: Pull complete 2025-09-09T14:03:54.2422942Z b7eb993f501a: Pull complete 2025-09-09T14:04:09.7905337Z 4d4d94988ad5: Pull complete 2025-09-09T14:04:09.8094722Z Digest: sha256:10f309602e8cd84e21cb6970f97544761dd12a06b141583ab4d45f0bac4bf651 2025-09-09T14:04:09.8168139Z Status: Downloaded newer image for pytorch/almalinux-builder:cpu 2025-09-09T14:04:09.8205683Z docker.io/pytorch/almalinux-builder:cpu 2025-09-09T14:04:09.8253634Z ##[group]Run echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT" 2025-09-09T14:04:09.8254632Z echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT" 2025-09-09T14:04:09.8264820Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-09T14:04:09.8265208Z env: 2025-09-09T14:04:09.8265454Z DOCKER_IMAGE: pytorch/almalinux-builder:cpu 2025-09-09T14:04:09.8265799Z REPOSITORY: pytorch/ao 2025-09-09T14:04:09.8266047Z PR_NUMBER: 2963 2025-09-09T14:04:09.8268365Z SCRIPT: conda create -n venv python=3.9 -y conda activate venv echo "::group::Install newer objcopy that supports --set-section-alignment" dnf install -y gcc-toolset-10-binutils export PATH=/opt/rh/gcc-toolset-10/root/usr/bin/:$PATH python -m pip install --upgrade pip pip install torch==2.7.0 --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/:$LD_LIBRARY_PATH pytest test --verbose -s 2025-09-09T14:04:09.8270928Z RUNNER_ARTIFACT_DIR: /home/ec2-user/actions-runner/_work/_temp/artifacts 2025-09-09T14:04:09.8271518Z RUNNER_TEST_RESULTS_DIR: /home/ec2-user/actions-runner/_work/_temp/test-results 2025-09-09T14:04:09.8272078Z RUNNER_DOCS_DIR: /home/ec2-user/actions-runner/_work/_temp/docs 2025-09-09T14:04:09.8272479Z ##[endgroup] 2025-09-09T14:04:09.8469746Z ##[group]Run set -ex 2025-09-09T14:04:09.8470037Z set -ex 2025-09-09T14:04:09.8470264Z { 2025-09-09T14:04:09.8470486Z  echo "#!/usr/bin/env bash"; 2025-09-09T14:04:09.8470820Z  echo "set -eou pipefail"; 2025-09-09T14:04:09.8471134Z  # shellcheck disable=SC2016 2025-09-09T14:04:09.8471490Z  echo 'eval "$(conda shell.bash hook)"'; 2025-09-09T14:04:09.8471821Z  echo "set -x"; 2025-09-09T14:04:09.8472095Z  echo "${SCRIPT}"; 2025-09-09T14:04:09.8472390Z } > "${RUNNER_TEMP}/exec_script" 2025-09-09T14:04:09.8472887Z chmod +x "${RUNNER_TEMP}/exec_script" 2025-09-09T14:04:09.8473544Z python3 "/home/ec2-user/actions-runner/_work/ao/ao/test-infra/.github/scripts/run_with_env_secrets.py" "" 2025-09-09T14:04:09.8479285Z shell: /usr/bin/bash -e {0} 2025-09-09T14:04:09.8479559Z env: 2025-09-09T14:04:09.8479897Z DOCKER_IMAGE: pytorch/almalinux-builder:cpu 2025-09-09T14:04:09.8480289Z REPOSITORY: pytorch/ao 2025-09-09T14:04:09.8480538Z PR_NUMBER: 2963 2025-09-09T14:04:09.8482809Z SCRIPT: conda create -n venv python=3.9 -y conda activate venv echo "::group::Install newer objcopy that supports --set-section-alignment" dnf install -y gcc-toolset-10-binutils export PATH=/opt/rh/gcc-toolset-10/root/usr/bin/:$PATH python -m pip install --upgrade pip pip install torch==2.7.0 --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/:$LD_LIBRARY_PATH pytest test --verbose -s 2025-09-09T14:04:09.8485263Z RUNNER_ARTIFACT_DIR: /home/ec2-user/actions-runner/_work/_temp/artifacts 2025-09-09T14:04:09.8485850Z RUNNER_TEST_RESULTS_DIR: /home/ec2-user/actions-runner/_work/_temp/test-results 2025-09-09T14:04:09.8486413Z RUNNER_DOCS_DIR: /home/ec2-user/actions-runner/_work/_temp/docs 2025-09-09T14:04:09.8487086Z ALL_SECRETS: { "github_token": "***" } 2025-09-09T14:04:09.8487389Z ##[endgroup] 2025-09-09T14:04:09.8512717Z + echo '#!/usr/bin/env bash' 2025-09-09T14:04:09.8513019Z + echo 'set -eou pipefail' 2025-09-09T14:04:09.8513320Z + echo 'eval "$(conda shell.bash hook)"' 2025-09-09T14:04:09.8513620Z + echo 'set -x' 2025-09-09T14:04:09.8513883Z + echo 'conda create -n venv python=3.9 -y 2025-09-09T14:04:09.8514197Z conda activate venv 2025-09-09T14:04:09.8514606Z echo "::group::Install newer objcopy that supports --set-section-alignment" 2025-09-09T14:04:09.8515085Z dnf install -y gcc-toolset-10-binutils 2025-09-09T14:04:09.8515478Z export PATH=/opt/rh/gcc-toolset-10/root/usr/bin/:$PATH 2025-09-09T14:04:09.8515879Z python -m pip install --upgrade pip 2025-09-09T14:04:09.8516325Z pip install torch==2.7.0 --index-url https://download.pytorch.org/whl/cpu 2025-09-09T14:04:09.8516796Z sed -i '\'''\'' dev-requirements.txt 2025-09-09T14:04:09.8517108Z pip install -r dev-requirements.txt 2025-09-09T14:04:09.8517407Z pip install . 2025-09-09T14:04:09.8517675Z export CONDA=$(dirname $(dirname $(which conda))) 2025-09-09T14:04:09.8518072Z export LD_LIBRARY_PATH=$CONDA/lib/:$LD_LIBRARY_PATH 2025-09-09T14:04:09.8518432Z pytest test --verbose -s 2025-09-09T14:04:09.8518671Z ' 2025-09-09T14:04:09.8518971Z + chmod +x /home/ec2-user/actions-runner/_work/_temp/exec_script 2025-09-09T14:04:09.8526166Z + python3 /home/ec2-user/actions-runner/_work/ao/ao/test-infra/.github/scripts/run_with_env_secrets.py '' 2025-09-09T14:04:36.5821395Z Running command: 2025-09-09T14:04:36.5830047Z docker run -e PR_NUMBER -e RUNNER_ARTIFACT_DIR=/artifacts -e RUNNER_DOCS_DIR=/docs -e RUNNER_TEST_RESULTS_DIR=/test-results --env-file="/home/ec2-user/actions-runner/_work/_temp/github_env_17585175130" `# It is unknown why the container sees a different value for this.` -e GITHUB_STEP_SUMMARY -e SECRET_GITHUB_TOKEN --cap-add=SYS_PTRACE --detach --ipc=host --security-opt seccomp=unconfined --shm-size=2g --tty --ulimit stack=10485760:83886080 --ulimit core=0 -v "/home/ec2-user/actions-runner/_work/ao/ao/pytorch/ao:/pytorch/ao" -v "/home/ec2-user/actions-runner/_work/ao/ao/test-infra:/test-infra" -v "/home/ec2-user/actions-runner/_work/_temp/artifacts:/artifacts" -v "/home/ec2-user/actions-runner/_work/_temp/docs:/docs" -v "/home/ec2-user/actions-runner/_work/_temp/test-results:/test-results" -v "/home/ec2-user/actions-runner/_work/_temp/exec_script:/exec" -v "/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/step_summary_0069af96-40a3-486a-a24f-6605a19586c4":"/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/step_summary_0069af96-40a3-486a-a24f-6605a19586c4" -w /pytorch/ao "pytorch/almalinux-builder:cpu" 2025-09-09T14:04:36.5837124Z 2025-09-09T14:04:36.5837478Z 0e59da47ce8534faee9ce06b47e2f7c81d71b4b2f6765ea7a50a4be21bdffbb0 2025-09-09T14:04:36.5838596Z Running command: docker exec -t 0e59da47ce8534faee9ce06b47e2f7c81d71b4b2f6765ea7a50a4be21bdffbb0 /exec 2025-09-09T14:04:36.5839207Z + conda create -n venv python=3.9 -y 2025-09-09T14:04:36.5839585Z + local cmd=create 2025-09-09T14:04:36.5839841Z + case "$cmd" in 2025-09-09T14:04:36.5840327Z + __conda_exe create -n venv python=3.9 -y 2025-09-09T14:04:36.5840963Z + /opt/conda/bin/conda create -n venv python=3.9 -y 2025-09-09T14:04:36.5842063Z Collecting package metadata (current_repodata.json): - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ done 2025-09-09T14:04:36.5843238Z Solving environment: / done 2025-09-09T14:04:36.5843587Z 2025-09-09T14:04:36.5843595Z 2025-09-09T14:04:36.5843813Z ==> WARNING: A newer version of conda exists. <== 2025-09-09T14:04:36.5844456Z current version: 23.5.2 2025-09-09T14:04:36.5844769Z latest version: 25.7.0 2025-09-09T14:04:36.5845066Z 2025-09-09T14:04:36.5845257Z Please update conda by running 2025-09-09T14:04:36.5845591Z 2025-09-09T14:04:36.5845809Z $ conda update -n base -c defaults conda 2025-09-09T14:04:36.5846260Z 2025-09-09T14:04:36.5846576Z Or to minimize the number of packages updated during conda update use 2025-09-09T14:04:36.5846915Z 2025-09-09T14:04:36.5847016Z conda install conda=25.7.0 2025-09-09T14:04:36.5847195Z 2025-09-09T14:04:36.5847199Z 2025-09-09T14:04:36.5847203Z 2025-09-09T14:04:36.5847332Z ## Package Plan ## 2025-09-09T14:04:36.5847616Z 2025-09-09T14:04:36.5847743Z environment location: /opt/conda/envs/venv 2025-09-09T14:04:36.5848068Z 2025-09-09T14:04:36.5848177Z added / updated specs: 2025-09-09T14:04:36.5848423Z - python=3.9 2025-09-09T14:04:36.5848614Z 2025-09-09T14:04:36.5848619Z 2025-09-09T14:04:36.5848807Z The following packages will be downloaded: 2025-09-09T14:04:36.5849199Z 2025-09-09T14:04:36.5849370Z package | build 2025-09-09T14:04:36.5849853Z ---------------------------|----------------- 2025-09-09T14:04:36.5850413Z bzip2-1.0.8 | h5eee18b_6 262 KB 2025-09-09T14:04:36.5851045Z ld_impl_linux-64-2.40 | h12ee557_0 710 KB 2025-09-09T14:04:36.5851748Z libffi-3.4.4 | h6a678d5_1 141 KB 2025-09-09T14:04:36.5852422Z libxcb-1.17.0 | h9b100fa_0 430 KB 2025-09-09T14:04:36.5852987Z ncurses-6.5 | h7934f7d_0 1.1 MB 2025-09-09T14:04:36.5853420Z pip-25.2 | pyhc872135_0 1.2 MB 2025-09-09T14:04:36.5854021Z pthread-stubs-0.3 | h0ce48e5_1 5 KB 2025-09-09T14:04:36.5854775Z python-3.9.23 | he99959a_0 24.7 MB 2025-09-09T14:04:36.5855467Z readline-8.3 | hc2a1206_0 471 KB 2025-09-09T14:04:36.5856361Z setuptools-78.1.1 | py39h06a4308_0 1.7 MB 2025-09-09T14:04:36.5857125Z sqlite-3.50.2 | hb25bd0a_1 1.1 MB 2025-09-09T14:04:36.5857820Z tk-8.6.15 | h54e0aa7_0 3.4 MB 2025-09-09T14:04:36.5858834Z tzdata-2025b | h04d1e81_0 116 KB 2025-09-09T14:04:36.5859554Z wheel-0.45.1 | py39h06a4308_0 114 KB 2025-09-09T14:04:36.5860302Z xorg-libx11-1.8.12 | h9b100fa_1 895 KB 2025-09-09T14:04:36.5860851Z xorg-libxau-1.0.12 | h9b100fa_0 13 KB 2025-09-09T14:04:36.5861470Z xorg-libxdmcp-1.1.5 | h9b100fa_0 19 KB 2025-09-09T14:04:36.5862265Z xorg-xorgproto-2024.1 | h5eee18b_1 580 KB 2025-09-09T14:04:36.5863012Z xz-5.6.4 | h5eee18b_1 567 KB 2025-09-09T14:04:36.5863664Z zlib-1.2.13 | h5eee18b_1 111 KB 2025-09-09T14:04:36.5864305Z ------------------------------------------------------------ 2025-09-09T14:04:36.5864972Z Total: 37.6 MB 2025-09-09T14:04:36.5865350Z 2025-09-09T14:04:36.5865490Z The following NEW packages will be INSTALLED: 2025-09-09T14:04:36.5865749Z 2025-09-09T14:04:36.5865964Z _libgcc_mutex pkgs/main/linux-64::_libgcc_mutex-0.1-main 2025-09-09T14:04:36.5866572Z _openmp_mutex pkgs/main/linux-64::_openmp_mutex-5.1-1_gnu 2025-09-09T14:04:36.5867428Z bzip2 pkgs/main/linux-64::bzip2-1.0.8-h5eee18b_6 2025-09-09T14:04:36.5868313Z ca-certificates pkgs/main/linux-64::ca-certificates-2025.7.15-h06a4308_0 2025-09-09T14:04:36.5869252Z expat pkgs/main/linux-64::expat-2.7.1-h6a678d5_0 2025-09-09T14:04:36.5870063Z ld_impl_linux-64 pkgs/main/linux-64::ld_impl_linux-64-2.40-h12ee557_0 2025-09-09T14:04:36.5870560Z libffi pkgs/main/linux-64::libffi-3.4.4-h6a678d5_1 2025-09-09T14:04:36.5871181Z libgcc-ng pkgs/main/linux-64::libgcc-ng-11.2.0-h1234567_1 2025-09-09T14:04:36.5872039Z libgomp pkgs/main/linux-64::libgomp-11.2.0-h1234567_1 2025-09-09T14:04:36.5872798Z libstdcxx-ng pkgs/main/linux-64::libstdcxx-ng-11.2.0-h1234567_1 2025-09-09T14:04:36.5873516Z libxcb pkgs/main/linux-64::libxcb-1.17.0-h9b100fa_0 2025-09-09T14:04:36.5874323Z ncurses pkgs/main/linux-64::ncurses-6.5-h7934f7d_0 2025-09-09T14:04:36.5874941Z openssl pkgs/main/linux-64::openssl-3.0.17-h5eee18b_0 2025-09-09T14:04:36.5875372Z pip pkgs/main/noarch::pip-25.2-pyhc872135_0 2025-09-09T14:04:36.5876213Z pthread-stubs pkgs/main/linux-64::pthread-stubs-0.3-h0ce48e5_1 2025-09-09T14:04:36.5877090Z python pkgs/main/linux-64::python-3.9.23-he99959a_0 2025-09-09T14:04:36.5877802Z readline pkgs/main/linux-64::readline-8.3-hc2a1206_0 2025-09-09T14:04:36.5878844Z setuptools pkgs/main/linux-64::setuptools-78.1.1-py39h06a4308_0 2025-09-09T14:04:36.5879836Z sqlite pkgs/main/linux-64::sqlite-3.50.2-hb25bd0a_1 2025-09-09T14:04:36.5880263Z tk pkgs/main/linux-64::tk-8.6.15-h54e0aa7_0 2025-09-09T14:04:36.5880678Z tzdata pkgs/main/noarch::tzdata-2025b-h04d1e81_0 2025-09-09T14:04:36.5881428Z wheel pkgs/main/linux-64::wheel-0.45.1-py39h06a4308_0 2025-09-09T14:04:36.5882226Z xorg-libx11 pkgs/main/linux-64::xorg-libx11-1.8.12-h9b100fa_1 2025-09-09T14:04:36.5882936Z xorg-libxau pkgs/main/linux-64::xorg-libxau-1.0.12-h9b100fa_0 2025-09-09T14:04:36.5884022Z xorg-libxdmcp pkgs/main/linux-64::xorg-libxdmcp-1.1.5-h9b100fa_0 2025-09-09T14:04:36.5885068Z xorg-xorgproto pkgs/main/linux-64::xorg-xorgproto-2024.1-h5eee18b_1 2025-09-09T14:04:36.5885558Z xz pkgs/main/linux-64::xz-5.6.4-h5eee18b_1 2025-09-09T14:04:36.5886262Z zlib pkgs/main/linux-64::zlib-1.2.13-h5eee18b_1 2025-09-09T14:04:36.5886708Z 2025-09-09T14:04:36.5886715Z 2025-09-09T14:04:36.5886933Z 2025-09-09T14:04:36.5887177Z Downloading and Extracting Packages 2025-09-09T14:04:36.5887538Z 2025-09-09T14:04:36.5887877Z ld_impl_linux-64-2.4 | 710 KB | : 0% 0/1 [00:00=4.10.0 (from torch==2.7.0) 2025-09-09T14:04:50.9932120Z Downloading https://download.pytorch.org/whl/typing_extensions-4.12.2-py3-none-any.whl.metadata (3.0 kB) 2025-09-09T14:04:50.9932768Z Collecting sympy>=1.13.3 (from torch==2.7.0) 2025-09-09T14:04:50.9933399Z Downloading https://download.pytorch.org/whl/sympy-1.13.3-py3-none-any.whl.metadata (12 kB) 2025-09-09T14:04:50.9933970Z Collecting networkx (from torch==2.7.0) 2025-09-09T14:04:50.9934554Z Downloading https://download.pytorch.org/whl/networkx-3.2.1-py3-none-any.whl.metadata (5.2 kB) 2025-09-09T14:04:50.9935132Z Collecting jinja2 (from torch==2.7.0) 2025-09-09T14:04:50.9935692Z Downloading 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https://download.pytorch.org/whl/cpu/torch-2.7.0%2Bcpu-cp39-cp39-manylinux_2_28_x86_64.whl (175.9 MB) 2025-09-09T14:04:50.9945381Z [?25l ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0.0/175.9 MB ? eta -:--:-- 2025-09-09T14:04:50.9946165Z  ━━━━━━╺━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 27.5/175.9 MB 143.3 MB/s eta 0:00:02 2025-09-09T14:04:50.9946981Z  ━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━━━━━━━━━━━ 107.0/175.9 MB 266.1 MB/s eta 0:00:01 2025-09-09T14:04:50.9947793Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 175.9/175.9 MB 326.3 MB/s eta 0:00:01 2025-09-09T14:04:50.9948551Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 175.9/175.9 MB 326.3 MB/s eta 0:00:01 2025-09-09T14:04:50.9949311Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 175.9/175.9 MB 180.7 MB/s 0:00:00 2025-09-09T14:04:50.9950048Z [?25hDownloading https://download.pytorch.org/whl/sympy-1.13.3-py3-none-any.whl (6.2 MB) 2025-09-09T14:04:50.9950816Z [?25l ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0.0/6.2 MB ? eta -:--:-- 2025-09-09T14:04:50.9951494Z  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2025-09-09T14:04:58.8896841Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━ 8/9 [torch] 2025-09-09T14:04:58.8897493Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━ 8/9 [torch] 2025-09-09T14:04:58.8898072Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━ 8/9 [torch] 2025-09-09T14:04:58.8898636Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━ 8/9 [torch] 2025-09-09T14:04:58.8899209Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━ 8/9 [torch] 2025-09-09T14:04:58.8899773Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━ 8/9 [torch] 2025-09-09T14:04:58.8900350Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━ 8/9 [torch] 2025-09-09T14:04:58.8900986Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━ 8/9 [torch] 2025-09-09T14:04:58.8901561Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━ 8/9 [torch] 2025-09-09T14:04:58.8902137Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━ 8/9 [torch] 2025-09-09T14:04:58.8902720Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━ 8/9 [torch] 2025-09-09T14:04:58.8903297Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━ 8/9 [torch] 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2025-09-09T14:05:06.1122435Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━ 8/9 [torch] 2025-09-09T14:05:06.1123018Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━ 8/9 [torch] 2025-09-09T14:05:06.1123605Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━ 8/9 [torch] 2025-09-09T14:05:06.1124188Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━ 8/9 [torch] 2025-09-09T14:05:06.1124765Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━ 8/9 [torch] 2025-09-09T14:05:06.1125331Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━ 8/9 [torch] 2025-09-09T14:05:06.1125906Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━ 8/9 [torch] 2025-09-09T14:05:06.1126472Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━ 8/9 [torch] 2025-09-09T14:05:06.1127069Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━ 8/9 [torch] 2025-09-09T14:05:06.1127644Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━ 8/9 [torch] 2025-09-09T14:05:06.1128231Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━ 8/9 [torch] 2025-09-09T14:05:06.1128808Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━ 8/9 [torch] 2025-09-09T14:05:06.1129371Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━ 8/9 [torch] 2025-09-09T14:05:06.1129948Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━ 8/9 [torch] 2025-09-09T14:05:06.1130512Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━ 8/9 [torch] 2025-09-09T14:05:06.1131090Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━ 8/9 [torch] 2025-09-09T14:05:06.1131691Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━ 8/9 [torch] 2025-09-09T14:05:06.1132259Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━ 8/9 [torch] 2025-09-09T14:05:06.1132920Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━ 8/9 [torch] 2025-09-09T14:05:06.1133492Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━ 8/9 [torch] 2025-09-09T14:05:06.1134077Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━ 8/9 [torch] 2025-09-09T14:05:06.1134658Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━ 8/9 [torch] 2025-09-09T14:05:06.1135197Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 9/9 [torch] 2025-09-09T14:05:06.1135576Z [?25h 2025-09-09T14:05:06.1136480Z 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-2.7.0+cpu typing-extensions-4.12.2 2025-09-09T14:05:06.1139049Z 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. 2025-09-09T14:05:06.1140863Z + sed -i '' dev-requirements.txt 2025-09-09T14:05:06.1141203Z + pip install -r dev-requirements.txt 2025-09-09T14:05:06.1141597Z Collecting pytest (from -r dev-requirements.txt (line 2)) 2025-09-09T14:05:06.1142345Z Downloading pytest-8.4.2-py3-none-any.whl.metadata (7.7 kB) 2025-09-09T14:05:06.1142916Z Collecting unittest-xml-reporting (from -r dev-requirements.txt (line 3)) 2025-09-09T14:05:06.1143732Z Downloading unittest_xml_reporting-3.2.0-py2.py3-none-any.whl.metadata (11 kB) 2025-09-09T14:05:06.1144547Z Collecting parameterized (from -r dev-requirements.txt (line 4)) 2025-09-09T14:05:06.1145124Z Downloading parameterized-0.9.0-py2.py3-none-any.whl.metadata (18 kB) 2025-09-09T14:05:06.1145695Z Collecting packaging (from -r dev-requirements.txt (line 5)) 2025-09-09T14:05:06.1146214Z Downloading packaging-25.0-py3-none-any.whl.metadata (3.3 kB) 2025-09-09T14:05:06.1146747Z Collecting transformers (from -r dev-requirements.txt (line 6)) 2025-09-09T14:05:06.1147287Z Downloading transformers-4.56.1-py3-none-any.whl.metadata (42 kB) 2025-09-09T14:05:06.1147833Z Collecting hypothesis (from -r dev-requirements.txt (line 7)) 2025-09-09T14:05:06.1148376Z Downloading hypothesis-6.138.15-py3-none-any.whl.metadata (5.6 kB) 2025-09-09T14:05:06.1148924Z Collecting sentencepiece (from -r dev-requirements.txt (line 8)) 2025-09-09T14:05:06.1149744Z Downloading sentencepiece-0.2.1-cp39-cp39-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.metadata (10 kB) 2025-09-09T14:05:06.1150506Z Collecting expecttest (from -r dev-requirements.txt (line 9)) 2025-09-09T14:05:06.1151314Z Downloading expecttest-0.3.0-py3-none-any.whl.metadata (3.8 kB) 2025-09-09T14:05:06.1151968Z Collecting bitsandbytes (from -r dev-requirements.txt (line 12)) 2025-09-09T14:05:06.1152624Z Downloading bitsandbytes-0.47.0-py3-none-manylinux_2_24_x86_64.whl.metadata (11 kB) 2025-09-09T14:05:06.1153493Z Collecting matplotlib (from -r dev-requirements.txt (line 13)) 2025-09-09T14:05:06.1154230Z Downloading matplotlib-3.9.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (11 kB) 2025-09-09T14:05:06.1154991Z Collecting pandas (from -r dev-requirements.txt (line 14)) 2025-09-09T14:05:06.1155781Z Downloading pandas-2.3.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (91 kB) 2025-09-09T14:05:10.2021289Z Collecting fire (from -r dev-requirements.txt (line 15)) 2025-09-09T14:05:10.2022119Z Downloading fire-0.7.1-py3-none-any.whl.metadata (5.8 kB) 2025-09-09T14:05:10.2023152Z Collecting tabulate (from -r dev-requirements.txt (line 16)) 2025-09-09T14:05:10.2023729Z Downloading tabulate-0.9.0-py3-none-any.whl.metadata (34 kB) 2025-09-09T14:05:10.2024367Z Collecting tiktoken (from -r dev-requirements.txt (line 17)) 2025-09-09T14:05:10.2025161Z Downloading tiktoken-0.11.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (6.7 kB) 2025-09-09T14:05:10.2025902Z Collecting blobfile (from -r dev-requirements.txt (line 18)) 2025-09-09T14:05:10.2026554Z Downloading blobfile-3.1.0-py3-none-any.whl.metadata (15 kB) 2025-09-09T14:05:10.2027144Z Collecting lm_eval (from -r dev-requirements.txt (line 19)) 2025-09-09T14:05:10.2027743Z Downloading lm_eval-0.4.9.1-py3-none-any.whl.metadata (53 kB) 2025-09-09T14:05:10.2028362Z Collecting diskcache (from -r dev-requirements.txt (line 21)) 2025-09-09T14:05:10.2028966Z Downloading diskcache-5.6.3-py3-none-any.whl.metadata (20 kB) 2025-09-09T14:05:10.2029591Z Collecting pycocotools (from -r dev-requirements.txt (line 22)) 2025-09-09T14:05:10.2030328Z Downloading pycocotools-2.0.10-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (1.3 kB) 2025-09-09T14:05:10.2031149Z Collecting tqdm (from -r dev-requirements.txt (line 23)) 2025-09-09T14:05:10.2031676Z Downloading 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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 2025-09-09T14:05:19.8606828Z  Building wheel for rouge-score (setup.py) ... [?25l- done 2025-09-09T14:05:19.8607964Z [?25h Created wheel for rouge-score: filename=rouge_score-0.1.2-py3-none-any.whl size=24988 sha256=b842dfa08bb027ddb19cb37bf2a0f298e0dac7faa94faec6976201ab396cbf6f 2025-09-09T14:05:19.8609083Z Stored in directory: /root/.cache/pip/wheels/9b/3d/39/09558097d3119ca0a4d462df68f22c6f3c1b345ac63a09b86e 2025-09-09T14:05:19.8611735Z  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 2025-09-09T14:05:19.8613864Z  Building wheel for sqlitedict (setup.py) ... [?25l- done 2025-09-09T14:05:19.8614910Z [?25h Created wheel for sqlitedict: filename=sqlitedict-2.1.0-py3-none-any.whl size=16958 sha256=86b5a4e235a4473b74129992afcc2c63c167621cee12b827ae60e53ce750c54b 2025-09-09T14:05:19.8616028Z Stored in directory: /root/.cache/pip/wheels/f6/48/c4/942f7a1d556fddd2348cb9ac262f251873dfd8a39afec5678e 2025-09-09T14:05:19.8618672Z  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 2025-09-09T14:05:19.8620807Z  Building wheel for word2number (setup.py) ... [?25l- done 2025-09-09T14:05:19.8621878Z [?25h Created wheel for word2number: filename=word2number-1.1-py3-none-any.whl size=5658 sha256=7d23f64c93df97ee7f1b77e9edafebc43c85b3438516134ebf0d7bcb73e3f791 2025-09-09T14:05:19.8622997Z Stored in directory: /root/.cache/pip/wheels/a0/4a/5b/d2f2df5c344ddbecb8bea759872c207ea91d93f57fb54e816e 2025-09-09T14:05:19.8623684Z Successfully built rouge-score sqlitedict word2number 2025-09-09T14:05:25.8732555Z 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 2025-09-09T14:05:25.8738251Z [?25l 2025-09-09T14:05:25.8738721Z  ━╺━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━  4/109 [distlib] 2025-09-09T14:05:25.8739366Z  ━━╸━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━  8/109 [urllib3] 2025-09-09T14:05:25.8740020Z  Attempting uninstall: typing-extensions 2025-09-09T14:05:25.8740568Z 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93/109 [pandas] 2025-09-09T14:05:40.9800049Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━  93/109 [pandas] 2025-09-09T14:05:40.9800784Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━  93/109 [pandas] 2025-09-09T14:05:40.9801401Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━  93/109 [pandas] 2025-09-09T14:05:40.9801996Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━  93/109 [pandas] 2025-09-09T14:05:40.9802632Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━  93/109 [pandas] 2025-09-09T14:05:40.9803244Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━  93/109 [pandas] 2025-09-09T14:05:40.9803843Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━  93/109 [pandas] 2025-09-09T14:05:40.9804453Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━  93/109 [pandas] 2025-09-09T14:05:40.9805048Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━  93/109 [pandas] 2025-09-09T14:05:40.9805675Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━  93/109 [pandas] 2025-09-09T14:05:40.9806299Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━  93/109 [pandas] 2025-09-09T14:05:40.9806920Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━  93/109 [pandas] 2025-09-09T14:05:40.9807530Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━  93/109 [pandas] 2025-09-09T14:05:40.9808127Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━  93/109 [pandas] 2025-09-09T14:05:40.9808737Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━  93/109 [pandas] 2025-09-09T14:05:40.9809364Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━  94/109 [matplotlib] 2025-09-09T14:05:40.9809989Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━  94/109 [matplotlib] 2025-09-09T14:05:40.9810698Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━  94/109 [matplotlib] 2025-09-09T14:05:40.9811316Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━  94/109 [matplotlib] 2025-09-09T14:05:40.9812031Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━  94/109 [matplotlib] 2025-09-09T14:05:40.9812674Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━━  94/109 [matplotlib] 2025-09-09T14:05:40.9813297Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━━  95/109 [hypothesis] 2025-09-09T14:05:40.9813952Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━  96/109 [huggingface-hub] 2025-09-09T14:05:40.9814611Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━━  96/109 [huggingface-hub] 2025-09-09T14:05:40.9815288Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━  97/109 [bitsandbytes] 2025-09-09T14:05:40.9815937Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━  97/109 [bitsandbytes] 2025-09-09T14:05:48.4248564Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━  97/109 [bitsandbytes] 2025-09-09T14:05:48.4249319Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━  97/109 [bitsandbytes] 2025-09-09T14:05:48.4249972Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━  97/109 [bitsandbytes] 2025-09-09T14:05:48.4250600Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━━  99/109 [aiohttp] 2025-09-09T14:05:48.4251228Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━ 100/109 [accelerate] 2025-09-09T14:05:48.4251866Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━ 101/109 [transformers] 2025-09-09T14:05:48.4252525Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━ 101/109 [transformers] 2025-09-09T14:05:48.4253160Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━ 101/109 [transformers] 2025-09-09T14:05:48.4254047Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━ 101/109 [transformers] 2025-09-09T14:05:48.4254702Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━ 101/109 [transformers] 2025-09-09T14:05:48.4255343Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━ 101/109 [transformers] 2025-09-09T14:05:48.4255968Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━ 101/109 [transformers] 2025-09-09T14:05:48.4256608Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━ 101/109 [transformers] 2025-09-09T14:05:48.4257266Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━ 101/109 [transformers] 2025-09-09T14:05:48.4258014Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━ 101/109 [transformers] 2025-09-09T14:05:48.4258961Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━ 101/109 [transformers] 2025-09-09T14:05:48.4259606Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━ 101/109 [transformers] 2025-09-09T14:05:48.4260242Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━ 101/109 [transformers] 2025-09-09T14:05:48.4260885Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━ 101/109 [transformers] 2025-09-09T14:05:48.4261509Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━ 101/109 [transformers] 2025-09-09T14:05:48.4262145Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━ 101/109 [transformers] 2025-09-09T14:05:48.4262793Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━ 101/109 [transformers] 2025-09-09T14:05:48.4263432Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━ 101/109 [transformers] 2025-09-09T14:05:48.4264094Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━ 101/109 [transformers] 2025-09-09T14:05:48.4264721Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━ 101/109 [transformers] 2025-09-09T14:05:48.4265365Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━ 101/109 [transformers] 2025-09-09T14:05:48.4265995Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━ 101/109 [transformers] 2025-09-09T14:05:48.4266635Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━ 101/109 [transformers] 2025-09-09T14:05:48.4267272Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━ 101/109 [transformers] 2025-09-09T14:05:48.4268013Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━ 101/109 [transformers] 2025-09-09T14:05:48.4268652Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━ 101/109 [transformers] 2025-09-09T14:05:48.4269376Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━ 101/109 [transformers] 2025-09-09T14:05:48.4270016Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━ 101/109 [transformers] 2025-09-09T14:05:48.4270653Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━ 101/109 [transformers] 2025-09-09T14:05:48.4271280Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━ 101/109 [transformers] 2025-09-09T14:05:48.4271919Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━ 101/109 [transformers] 2025-09-09T14:05:48.4272566Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━━ 101/109 [transformers] 2025-09-09T14:05:48.4273188Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━ 104/109 [peft] 2025-09-09T14:05:48.4273792Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺━ 104/109 [peft] 2025-09-09T14:05:48.4274414Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━ 105/109 [datasets] 2025-09-09T14:05:48.4275051Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━ 106/109 [pytablewriter] 2025-09-09T14:05:48.4275650Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 108/109 [lm_eval] 2025-09-09T14:05:48.4276224Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 108/109 [lm_eval] 2025-09-09T14:05:48.4276783Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 108/109 [lm_eval] 2025-09-09T14:06:14.0566395Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 108/109 [lm_eval] 2025-09-09T14:06:14.0569037Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 108/109 [lm_eval] 2025-09-09T14:06:14.0569673Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 108/109 [lm_eval] 2025-09-09T14:06:14.0570598Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 108/109 [lm_eval] 2025-09-09T14:06:14.0571170Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 108/109 [lm_eval] 2025-09-09T14:06:14.0571746Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 108/109 [lm_eval] 2025-09-09T14:06:14.0572302Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 108/109 [lm_eval] 2025-09-09T14:06:14.0572874Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 108/109 [lm_eval] 2025-09-09T14:06:14.0573574Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 108/109 [lm_eval] 2025-09-09T14:06:14.0574126Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 108/109 [lm_eval] 2025-09-09T14:06:14.0574693Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 108/109 [lm_eval] 2025-09-09T14:06:14.0575267Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 108/109 [lm_eval] 2025-09-09T14:06:14.0575830Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 108/109 [lm_eval] 2025-09-09T14:06:14.0576377Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 108/109 [lm_eval] 2025-09-09T14:06:14.0576928Z  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 109/109 [lm_eval] 2025-09-09T14:06:14.0577657Z [?25h 2025-09-09T14:06:14.0586328Z 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 2025-09-09T14:06:14.0595582Z 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. 2025-09-09T14:06:14.0597873Z + pip install . 2025-09-09T14:06:14.0598126Z Processing /pytorch/ao 2025-09-09T14:06:14.0598482Z Preparing metadata (setup.py) ... [?25l- done 2025-09-09T14:06:14.0598954Z [?25hBuilding wheels for collected packages: torchao 2025-09-09T14:06:14.0601415Z  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 2025-09-09T14:06:14.0603841Z  Building wheel for torchao (setup.py) ... [?25l- \ | / done 2025-09-09T14:06:14.0604997Z [?25h Created wheel for torchao: filename=torchao-0.14.0+git7c05f81-py3-none-any.whl size=1043958 sha256=b3aea36a237ca8f9994fd86c33c6fd03a0ca9579051c7247fa25485cd4fe8ead 2025-09-09T14:06:14.0606507Z Stored in directory: /tmp/pip-ephem-wheel-cache-0t9eskdx/wheels/4d/54/dc/0c70e60a8677bf126f1486798ebe76c8770ada66c7376b401d 2025-09-09T14:06:14.0607222Z Successfully built torchao 2025-09-09T14:06:14.0607505Z Installing collected packages: torchao 2025-09-09T14:06:14.0607874Z Successfully installed torchao-0.14.0+git7c05f81 2025-09-09T14:06:14.0609866Z 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. 2025-09-09T14:06:14.0611512Z ++++ which conda 2025-09-09T14:06:14.0611770Z +++ dirname /opt/conda/condabin/conda 2025-09-09T14:06:14.0612076Z ++ dirname /opt/conda/condabin 2025-09-09T14:06:14.0612367Z + export CONDA=/opt/conda 2025-09-09T14:06:14.0612624Z + CONDA=/opt/conda 2025-09-09T14:06:14.0613156Z + export LD_LIBRARY_PATH=/opt/conda/lib/:/opt/rh/gcc-toolset-11/root/usr/lib64:/opt/rh/gcc-toolset-11/root/usr/lib: 2025-09-09T14:06:14.0614299Z + LD_LIBRARY_PATH=/opt/conda/lib/:/opt/rh/gcc-toolset-11/root/usr/lib64:/opt/rh/gcc-toolset-11/root/usr/lib: 2025-09-09T14:06:14.0614882Z + pytest test --verbose -s 2025-09-09T14:06:14.0615322Z ============================= test session starts ============================== 2025-09-09T14:06:14.0615907Z platform linux -- Python 3.9.23, pytest-8.4.2, pluggy-1.6.0 -- /opt/conda/envs/venv/bin/python3.9 2025-09-09T14:06:14.0616429Z cachedir: .pytest_cache 2025-09-09T14:06:14.0617317Z hypothesis profile 'ci' -> database=None, deadline=None, print_blob=True, derandomize=True, suppress_health_check=(HealthCheck.too_slow,) 2025-09-09T14:06:14.0618000Z rootdir: /pytorch/ao 2025-09-09T14:06:14.0618266Z plugins: hypothesis-6.138.15 2025-09-09T14:06:14.0618599Z collecting ...  2025-09-09T14:06:14.0619053Z collecting 0 items  2025-09-09T14:06:14.0619625Z collecting 26 items  2025-09-09T14:06:14.0620198Z collecting 26 items  2025-09-09T14:06:14.0620884Z collecting 264 items  2025-09-09T14:06:14.0621500Z collecting 1022 items / 3 skipped  2025-09-09T14:06:14.0622975Z collecting 1035 items / 6 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` 2025-09-09T14:06:14.0624065Z  2025-09-09T14:06:14.0624498Z collecting 1365 items / 20 skipped  2025-09-09T14:06:14.0625214Z collecting 1766 items / 20 skipped  2025-09-09T14:06:14.0626137Z collecting 3911 items / 20 skipped  2025-09-09T14:06:14.0626780Z collected 5323 items / 20 skipped  2025-09-09T14:06:14.0627124Z 2025-09-09T14:06:14.1702046Z test/core/test_config.py::test_reconstructable_dict_file_round_trip[config0] PASSED 2025-09-09T14:06:14.1702934Z test/core/test_config.py::test_reconstructable_dict_file_round_trip[config1] PASSED 2025-09-09T14:06:14.1703757Z test/core/test_config.py::test_reconstructable_dict_file_round_trip[config2] PASSED 2025-09-09T14:06:14.1704578Z test/core/test_config.py::test_reconstructable_dict_file_round_trip[config3] PASSED 2025-09-09T14:06:14.1705423Z test/core/test_config.py::test_reconstructable_dict_file_round_trip[config4] PASSED 2025-09-09T14:06:14.1706264Z test/core/test_config.py::test_reconstructable_dict_file_round_trip[config5] PASSED 2025-09-09T14:06:14.1707084Z test/core/test_config.py::test_reconstructable_dict_file_round_trip[config6] PASSED 2025-09-09T14:06:14.1707889Z test/core/test_config.py::test_reconstructable_dict_file_round_trip[config7] PASSED 2025-09-09T14:06:14.1708725Z test/core/test_config.py::test_reconstructable_dict_file_round_trip[config8] PASSED 2025-09-09T14:06:14.1709546Z test/core/test_config.py::test_reconstructable_dict_file_round_trip[config9] PASSED 2025-09-09T14:06:14.1710358Z test/core/test_config.py::test_reconstructable_dict_file_round_trip[config10] PASSED 2025-09-09T14:06:14.1711186Z test/core/test_config.py::test_reconstructable_dict_file_round_trip[config11] PASSED 2025-09-09T14:06:14.1711999Z test/core/test_config.py::test_reconstructable_dict_file_round_trip[config12] PASSED 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test/core/test_config.py::test_reconstructable_dict_file_round_trip[config21] PASSED 2025-09-09T14:06:14.1720300Z test/core/test_config.py::test_reconstructable_dict_file_round_trip[config22] PASSED 2025-09-09T14:06:14.1721116Z test/core/test_config.py::test_disallowed_modules PASSED 2025-09-09T14:06:14.1721701Z test/core/test_config.py::test_version_mismatch PASSED 2025-09-09T14:06:14.1722516Z test/core/test_config.py::test_default_version PASSED 2025-09-09T14:06:14.1723388Z test/dtypes/test_affine_quantized.py::TestAffineQuantized::test_copy__mismatch_metadata_apply_quant0 SKIPPED 2025-09-09T14:06:14.1724469Z test/dtypes/test_affine_quantized.py::TestAffineQuantized::test_copy__mismatch_metadata_apply_quant1 SKIPPED 2025-09-09T14:06:14.1725552Z test/dtypes/test_affine_quantized.py::TestAffineQuantized::test_copy__mismatch_metadata_apply_quant2 SKIPPED 2025-09-09T14:06:14.1726738Z test/dtypes/test_affine_quantized.py::TestAffineQuantized::test_copy__mismatch_metadata_apply_quant3 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test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_fp8_linear_variants_float32_mode_weight-only_compile_True_granularity0_sizes1 SKIPPED 2025-09-09T14:06:14.2581134Z test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_fp8_linear_variants_float32_mode_weight-only_compile_True_granularity1_sizes0 SKIPPED 2025-09-09T14:06:14.2582774Z test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_fp8_linear_variants_float32_mode_weight-only_compile_True_granularity1_sizes1 SKIPPED 2025-09-09T14:06:14.2584204Z test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_fp8_weight_dimension_warning SKIPPED 2025-09-09T14:06:14.2585377Z test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_invalid_granularity SKIPPED 2025-09-09T14:06:14.2586640Z test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_mismatched_granularity SKIPPED 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2025-09-09T14:06:14.2596342Z test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_mm_float8dq_per_row_in_features_1024_out_features_512_leading_shape2_bias_True SKIPPED 2025-09-09T14:06:14.2597969Z test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_mm_float8dq_per_row_in_features_1024_out_features_512_leading_shape3_bias_False SKIPPED 2025-09-09T14:06:14.2599588Z test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_mm_float8dq_per_row_in_features_1024_out_features_512_leading_shape3_bias_True SKIPPED 2025-09-09T14:06:14.2601298Z test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_mm_float8dq_per_row_in_features_1024_out_features_512_leading_shape4_bias_False SKIPPED 2025-09-09T14:06:14.2602932Z test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_mm_float8dq_per_row_in_features_1024_out_features_512_leading_shape4_bias_True SKIPPED 2025-09-09T14:06:14.2604541Z test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_mm_float8dq_per_row_in_features_256_out_features_768_leading_shape0_bias_False SKIPPED 2025-09-09T14:06:14.2606160Z test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_mm_float8dq_per_row_in_features_256_out_features_768_leading_shape0_bias_True SKIPPED 2025-09-09T14:06:14.2607781Z test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_mm_float8dq_per_row_in_features_256_out_features_768_leading_shape1_bias_False SKIPPED 2025-09-09T14:06:14.2609392Z test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_mm_float8dq_per_row_in_features_256_out_features_768_leading_shape1_bias_True SKIPPED 2025-09-09T14:06:14.2611017Z test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_mm_float8dq_per_row_in_features_256_out_features_768_leading_shape2_bias_False SKIPPED 2025-09-09T14:06:14.2612637Z test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_mm_float8dq_per_row_in_features_256_out_features_768_leading_shape2_bias_True SKIPPED 2025-09-09T14:06:14.2614246Z test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_mm_float8dq_per_row_in_features_256_out_features_768_leading_shape3_bias_False SKIPPED 2025-09-09T14:06:14.2615867Z test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_mm_float8dq_per_row_in_features_256_out_features_768_leading_shape3_bias_True SKIPPED 2025-09-09T14:06:14.2617496Z test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_mm_float8dq_per_row_in_features_256_out_features_768_leading_shape4_bias_False SKIPPED 2025-09-09T14:06:14.2619177Z test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_mm_float8dq_per_row_in_features_256_out_features_768_leading_shape4_bias_True SKIPPED 2025-09-09T14:06:14.2620815Z test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_mm_float8dq_per_row_in_features_512_out_features_1024_leading_shape0_bias_False SKIPPED 2025-09-09T14:06:14.2622453Z test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_mm_float8dq_per_row_in_features_512_out_features_1024_leading_shape0_bias_True SKIPPED 2025-09-09T14:06:14.2624138Z test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_mm_float8dq_per_row_in_features_512_out_features_1024_leading_shape1_bias_False SKIPPED 2025-09-09T14:06:14.2625773Z test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_mm_float8dq_per_row_in_features_512_out_features_1024_leading_shape1_bias_True SKIPPED 2025-09-09T14:06:14.2627409Z test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_mm_float8dq_per_row_in_features_512_out_features_1024_leading_shape2_bias_False SKIPPED 2025-09-09T14:06:14.2629029Z test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_mm_float8dq_per_row_in_features_512_out_features_1024_leading_shape2_bias_True SKIPPED 2025-09-09T14:06:14.3473039Z test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_mm_float8dq_per_row_in_features_512_out_features_1024_leading_shape3_bias_False SKIPPED 2025-09-09T14:06:14.3474713Z test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_mm_float8dq_per_row_in_features_512_out_features_1024_leading_shape3_bias_True SKIPPED 2025-09-09T14:06:14.3476385Z test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_mm_float8dq_per_row_in_features_512_out_features_1024_leading_shape4_bias_False SKIPPED 2025-09-09T14:06:14.3478020Z test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_mm_float8dq_per_row_in_features_512_out_features_1024_leading_shape4_bias_True SKIPPED 2025-09-09T14:06:14.3479399Z test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_per_row_with_float32 SKIPPED 2025-09-09T14:06:14.3480661Z test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_preprocess_scale_3d_reshape PASSED 2025-09-09T14:06:14.3481872Z test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_serialization_mode_dynamic SKIPPED 2025-09-09T14:06:14.3483080Z test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_serialization_mode_static SKIPPED 2025-09-09T14:06:14.3484310Z test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_serialization_mode_weight-only SKIPPED 2025-09-09T14:06:14.3485534Z test/dtypes/test_affine_quantized_float.py::TestAffineQuantizedFloat8Compile::test_unsupported_granularity SKIPPED 2025-09-09T14:06:14.3486747Z test/dtypes/test_affine_quantized_tensor_parallel.py::TestInt8woAffineQuantizedTensorParallel::test_tp_bfloat16 SKIPPED 2025-09-09T14:06:14.3487991Z test/dtypes/test_affine_quantized_tensor_parallel.py::TestInt8woAffineQuantizedTensorParallel::test_tp_float16 SKIPPED 2025-09-09T14:06:14.3489227Z test/dtypes/test_affine_quantized_tensor_parallel.py::TestInt8woAffineQuantizedTensorParallel::test_tp_float32 SKIPPED 2025-09-09T14:06:14.3490457Z test/dtypes/test_affine_quantized_tensor_parallel.py::TestInt4woAffineQuantizedTensorParallel::test_tp_bfloat16 SKIPPED 2025-09-09T14:06:14.3491683Z test/dtypes/test_affine_quantized_tensor_parallel.py::TestGemliteLayoutTensorParallel::test_tp_gemlite_float16 SKIPPED 2025-09-09T14:06:14.3493064Z test/dtypes/test_affine_quantized_tensor_parallel.py::TestInt8dqAffineQuantizedTensorParallel::test_tp_bfloat16 SKIPPED 2025-09-09T14:06:14.3493963Z test/dtypes/test_bitpacking.py::test_CPU[0-1] PASSED 2025-09-09T14:06:14.3494532Z test/dtypes/test_bitpacking.py::test_CPU[0-2] PASSED 2025-09-09T14:06:14.3495088Z test/dtypes/test_bitpacking.py::test_CPU[0-3] PASSED 2025-09-09T14:06:14.3495655Z test/dtypes/test_bitpacking.py::test_CPU[0-4] PASSED 2025-09-09T14:06:14.3496203Z test/dtypes/test_bitpacking.py::test_CPU[0-5] PASSED 2025-09-09T14:06:14.3496765Z test/dtypes/test_bitpacking.py::test_CPU[0-6] PASSED 2025-09-09T14:06:14.3497403Z test/dtypes/test_bitpacking.py::test_CPU[0-7] PASSED 2025-09-09T14:06:14.3497975Z test/dtypes/test_bitpacking.py::test_CPU[-1-1] PASSED 2025-09-09T14:06:14.3498546Z test/dtypes/test_bitpacking.py::test_CPU[-1-2] PASSED 2025-09-09T14:06:14.3499104Z test/dtypes/test_bitpacking.py::test_CPU[-1-3] PASSED 2025-09-09T14:06:14.3499680Z test/dtypes/test_bitpacking.py::test_CPU[-1-4] PASSED 2025-09-09T14:06:14.3500232Z test/dtypes/test_bitpacking.py::test_CPU[-1-5] PASSED 2025-09-09T14:06:14.3500794Z test/dtypes/test_bitpacking.py::test_CPU[-1-6] PASSED 2025-09-09T14:06:14.3501346Z test/dtypes/test_bitpacking.py::test_CPU[-1-7] PASSED 2025-09-09T14:06:14.3501908Z test/dtypes/test_bitpacking.py::test_CPU[1-1] PASSED 2025-09-09T14:06:14.3502465Z test/dtypes/test_bitpacking.py::test_CPU[1-2] PASSED 2025-09-09T14:06:14.3503009Z test/dtypes/test_bitpacking.py::test_CPU[1-3] PASSED 2025-09-09T14:06:14.3503565Z test/dtypes/test_bitpacking.py::test_CPU[1-4] PASSED 2025-09-09T14:06:14.3504111Z test/dtypes/test_bitpacking.py::test_CPU[1-5] PASSED 2025-09-09T14:06:14.3504668Z test/dtypes/test_bitpacking.py::test_CPU[1-6] PASSED 2025-09-09T14:06:14.3505206Z test/dtypes/test_bitpacking.py::test_CPU[1-7] PASSED 2025-09-09T14:06:14.3505865Z test/dtypes/test_bitpacking.py::test_GPU[0-1] SKIPPED (CUDA not avai...) 2025-09-09T14:06:14.3506605Z test/dtypes/test_bitpacking.py::test_GPU[0-2] SKIPPED (CUDA not avai...) 2025-09-09T14:06:14.3507320Z test/dtypes/test_bitpacking.py::test_GPU[0-3] SKIPPED (CUDA not avai...) 2025-09-09T14:06:14.3508049Z 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test/dtypes/test_bitpacking.py::test_compile[-1-5] SKIPPED (unsuppor...) 2025-09-09T14:06:14.3529784Z test/dtypes/test_bitpacking.py::test_compile[-1-6] SKIPPED (unsuppor...) 2025-09-09T14:06:14.3530510Z test/dtypes/test_bitpacking.py::test_compile[-1-7] SKIPPED (unsuppor...) 2025-09-09T14:06:14.3531222Z test/dtypes/test_bitpacking.py::test_compile[1-1] SKIPPED (unsupport...) 2025-09-09T14:06:14.3531950Z test/dtypes/test_bitpacking.py::test_compile[1-2] SKIPPED (unsupport...) 2025-09-09T14:06:14.3532666Z test/dtypes/test_bitpacking.py::test_compile[1-3] SKIPPED (unsupport...) 2025-09-09T14:06:14.3533391Z test/dtypes/test_bitpacking.py::test_compile[1-4] SKIPPED (unsupport...) 2025-09-09T14:06:14.3534116Z test/dtypes/test_bitpacking.py::test_compile[1-5] SKIPPED (unsupport...) 2025-09-09T14:06:14.3534830Z test/dtypes/test_bitpacking.py::test_compile[1-6] SKIPPED (unsupport...) 2025-09-09T14:06:14.3535551Z test/dtypes/test_bitpacking.py::test_compile[1-7] SKIPPED (unsupport...) 2025-09-09T14:06:14.3536264Z test/dtypes/test_bitpacking.py::test_pack_example SKIPPED (CUDA not ...) 2025-09-09T14:06:14.3537102Z test/dtypes/test_bitpacking.py::test_pack_example_CPU tensor([ 0, 105, 151, 37], dtype=torch.uint8) tensor([ 39, 146], dtype=torch.uint8) 2025-09-09T14:06:14.3537788Z PASSED 2025-09-09T14:06:14.3538576Z test/dtypes/test_floatx.py::TestFloatxTensorCoreAQTTensorImpl::test_fpx_weight_only_ebits_2_mbits_2_bias_False_bfloat16 SKIPPED 2025-09-09T14:06:14.3539861Z test/dtypes/test_floatx.py::TestFloatxTensorCoreAQTTensorImpl::test_fpx_weight_only_ebits_2_mbits_2_bias_False_float16 SKIPPED 2025-09-09T14:06:14.3541117Z test/dtypes/test_floatx.py::TestFloatxTensorCoreAQTTensorImpl::test_fpx_weight_only_ebits_2_mbits_2_bias_True_bfloat16 SKIPPED 2025-09-09T14:06:14.3542379Z test/dtypes/test_floatx.py::TestFloatxTensorCoreAQTTensorImpl::test_fpx_weight_only_ebits_2_mbits_2_bias_True_float16 SKIPPED 2025-09-09T14:06:14.3543646Z test/dtypes/test_floatx.py::TestFloatxTensorCoreAQTTensorImpl::test_fpx_weight_only_ebits_3_mbits_2_bias_False_bfloat16 SKIPPED 2025-09-09T14:06:14.3544908Z test/dtypes/test_floatx.py::TestFloatxTensorCoreAQTTensorImpl::test_fpx_weight_only_ebits_3_mbits_2_bias_False_float16 SKIPPED 2025-09-09T14:06:14.3546179Z test/dtypes/test_floatx.py::TestFloatxTensorCoreAQTTensorImpl::test_fpx_weight_only_ebits_3_mbits_2_bias_True_bfloat16 SKIPPED 2025-09-09T14:07:12.8414111Z test/dtypes/test_floatx.py::TestFloatxTensorCoreAQTTensorImpl::test_fpx_weight_only_ebits_3_mbits_2_bias_True_float16 SKIPPED 2025-09-09T14:07:12.8415822Z test/dtypes/test_floatx.py::TestFloatxTensorCoreAQTTensorImpl::test_from_scaled_tc_floatx_compile_ebits_2_mbits_2_device_cpu PASSED 2025-09-09T14:07:12.8417499Z test/dtypes/test_floatx.py::TestFloatxTensorCoreAQTTensorImpl::test_from_scaled_tc_floatx_compile_ebits_3_mbits_2_device_cpu PASSED 2025-09-09T14:07:12.8419142Z test/dtypes/test_floatx.py::TestFloatxTensorCoreAQTTensorImpl::test_from_tc_floatx_correctness_ebits_2_mbits_2_device_cpu PASSED 2025-09-09T14:07:12.8420781Z test/dtypes/test_floatx.py::TestFloatxTensorCoreAQTTensorImpl::test_from_tc_floatx_correctness_ebits_3_mbits_2_device_cpu PASSED 2025-09-09T14:07:12.8422431Z test/dtypes/test_floatx.py::TestFloatxTensorCoreAQTTensorImpl::test_pack_tc_fp6_correctness_device_cpu PASSED 2025-09-09T14:07:12.8423836Z test/dtypes/test_floatx.py::TestFloatxTensorCoreAQTTensorImpl::test_to_copy_device_ebits_2_mbits_2 SKIPPED 2025-09-09T14:07:12.8425218Z test/dtypes/test_floatx.py::TestFloatxTensorCoreAQTTensorImpl::test_to_copy_device_ebits_3_mbits_2 SKIPPED 2025-09-09T14:07:12.8426726Z test/dtypes/test_floatx.py::TestFloatxTensorCoreAQTTensorImpl::test_to_scaled_tc_floatx_compile_ebits_2_mbits_2_device_cpu PASSED 2025-09-09T14:07:12.8428356Z test/dtypes/test_floatx.py::TestFloatxTensorCoreAQTTensorImpl::test_to_scaled_tc_floatx_compile_ebits_3_mbits_2_device_cpu PASSED 2025-09-09T14:07:12.8429681Z test/dtypes/test_nf4.py::TestNF4Linear::test_backward_dtype_match_bfloat16 PASSED 2025-09-09T14:07:12.8430673Z test/dtypes/test_nf4.py::TestNF4Linear::test_backward_dtype_match_float16 PASSED 2025-09-09T14:07:12.8431651Z test/dtypes/test_nf4.py::TestNF4Linear::test_backward_dtype_match_float32 PASSED 2025-09-09T14:07:12.8432792Z test/dtypes/test_nf4.py::TestNF4Linear::test_chunk_size_equivalence_bfloat16_shape0_chunk_size_16 SKIPPED 2025-09-09T14:07:12.8434055Z test/dtypes/test_nf4.py::TestNF4Linear::test_chunk_size_equivalence_bfloat16_shape0_chunk_size_32 SKIPPED 2025-09-09T14:07:12.8435315Z test/dtypes/test_nf4.py::TestNF4Linear::test_chunk_size_equivalence_bfloat16_shape0_chunk_size_8 SKIPPED 2025-09-09T14:07:12.8436569Z test/dtypes/test_nf4.py::TestNF4Linear::test_chunk_size_equivalence_bfloat16_shape1_chunk_size_16 SKIPPED 2025-09-09T14:07:12.8438082Z test/dtypes/test_nf4.py::TestNF4Linear::test_chunk_size_equivalence_bfloat16_shape1_chunk_size_32 SKIPPED 2025-09-09T14:07:12.8439346Z test/dtypes/test_nf4.py::TestNF4Linear::test_chunk_size_equivalence_bfloat16_shape1_chunk_size_8 SKIPPED 2025-09-09T14:07:12.8440969Z test/dtypes/test_nf4.py::TestNF4Linear::test_chunk_size_equivalence_float16_shape0_chunk_size_16 SKIPPED 2025-09-09T14:07:12.8442521Z test/dtypes/test_nf4.py::TestNF4Linear::test_chunk_size_equivalence_float16_shape0_chunk_size_32 SKIPPED 2025-09-09T14:07:12.8444317Z test/dtypes/test_nf4.py::TestNF4Linear::test_chunk_size_equivalence_float16_shape0_chunk_size_8 SKIPPED 2025-09-09T14:07:12.8446085Z test/dtypes/test_nf4.py::TestNF4Linear::test_chunk_size_equivalence_float16_shape1_chunk_size_16 SKIPPED 2025-09-09T14:07:12.8447859Z test/dtypes/test_nf4.py::TestNF4Linear::test_chunk_size_equivalence_float16_shape1_chunk_size_32 SKIPPED 2025-09-09T14:07:12.8449631Z test/dtypes/test_nf4.py::TestNF4Linear::test_chunk_size_equivalence_float16_shape1_chunk_size_8 SKIPPED 2025-09-09T14:07:12.8451400Z test/dtypes/test_nf4.py::TestNF4Linear::test_chunk_size_equivalence_float32_shape0_chunk_size_16 SKIPPED 2025-09-09T14:07:12.8453206Z test/dtypes/test_nf4.py::TestNF4Linear::test_chunk_size_equivalence_float32_shape0_chunk_size_32 SKIPPED 2025-09-09T14:07:12.8454999Z test/dtypes/test_nf4.py::TestNF4Linear::test_chunk_size_equivalence_float32_shape0_chunk_size_8 SKIPPED 2025-09-09T14:07:12.8456883Z test/dtypes/test_nf4.py::TestNF4Linear::test_chunk_size_equivalence_float32_shape1_chunk_size_16 SKIPPED 2025-09-09T14:07:12.8458898Z test/dtypes/test_nf4.py::TestNF4Linear::test_chunk_size_equivalence_float32_shape1_chunk_size_32 SKIPPED 2025-09-09T14:07:12.8460650Z test/dtypes/test_nf4.py::TestNF4Linear::test_chunk_size_equivalence_float32_shape1_chunk_size_8 SKIPPED 2025-09-09T14:07:12.8462150Z test/dtypes/test_nf4.py::TestNF4Linear::test_empty_like_input_size0 SKIPPED 2025-09-09T14:07:12.8463409Z test/dtypes/test_nf4.py::TestNF4Linear::test_empty_like_input_size1 SKIPPED 2025-09-09T14:07:12.8465071Z test/dtypes/test_nf4.py::TestNF4Linear::test_load_from_nf4_diff_meta_bfloat16 PASSED 2025-09-09T14:07:12.8466511Z test/dtypes/test_nf4.py::TestNF4Linear::test_load_from_nf4_diff_meta_float16 PASSED 2025-09-09T14:07:12.8467934Z test/dtypes/test_nf4.py::TestNF4Linear::test_load_from_nf4_diff_meta_float32 PASSED 2025-09-09T14:07:12.8469382Z test/dtypes/test_nf4.py::TestNF4Linear::test_load_from_nf4_same_meta_bfloat16 PASSED 2025-09-09T14:07:12.8470793Z test/dtypes/test_nf4.py::TestNF4Linear::test_load_from_nf4_same_meta_float16 PASSED 2025-09-09T14:07:12.8472097Z test/dtypes/test_nf4.py::TestNF4Linear::test_load_from_nf4_same_meta_float32 PASSED 2025-09-09T14:07:12.8473673Z test/dtypes/test_nf4.py::TestNF4Linear::test_load_from_state_dicts_bfloat16 SKIPPED 2025-09-09T14:07:12.8475266Z test/dtypes/test_nf4.py::TestNF4Linear::test_load_from_state_dicts_float16 SKIPPED 2025-09-09T14:07:12.8476845Z test/dtypes/test_nf4.py::TestNF4Linear::test_load_from_state_dicts_float32 SKIPPED 2025-09-09T14:07:12.8478384Z test/dtypes/test_nf4.py::TestNF4Linear::test_nf4_bnb_linear_bfloat16 SKIPPED 2025-09-09T14:07:12.8479944Z test/dtypes/test_nf4.py::TestNF4Linear::test_nf4_bnb_linear_float16 SKIPPED 2025-09-09T14:07:12.8481360Z test/dtypes/test_nf4.py::TestNF4Linear::test_nf4_bnb_linear_float32 SKIPPED 2025-09-09T14:07:12.8482823Z test/dtypes/test_nf4.py::TestNF4Linear::test_output_dtype_match_bfloat16 PASSED 2025-09-09T14:07:12.8484100Z test/dtypes/test_nf4.py::TestNF4Linear::test_output_dtype_match_float16 PASSED 2025-09-09T14:07:12.8485064Z test/dtypes/test_nf4.py::TestNF4Linear::test_output_dtype_match_float32 PASSED 2025-09-09T14:07:12.8486035Z test/dtypes/test_nf4.py::TestNF4Linear::test_quantize_api_compile_False SKIPPED 2025-09-09T14:07:12.8486986Z test/dtypes/test_nf4.py::TestNF4Linear::test_quantize_api_compile_True SKIPPED 2025-09-09T14:07:12.8488033Z test/dtypes/test_nf4.py::TestNF4Linear::test_reconstruction_qlora_vs_bnb_bfloat16 SKIPPED 2025-09-09T14:07:12.8489146Z test/dtypes/test_nf4.py::TestNF4Linear::test_reconstruction_qlora_vs_bnb_float16 SKIPPED 2025-09-09T14:07:12.8490223Z test/dtypes/test_nf4.py::TestNF4Linear::test_reconstruction_qlora_vs_bnb_float32 SKIPPED 2025-09-09T14:07:12.8491279Z test/dtypes/test_nf4.py::TestNF4Linear::test_register_nf4_as_param_bfloat16 PASSED 2025-09-09T14:07:12.8492270Z test/dtypes/test_nf4.py::TestNF4Linear::test_register_nf4_as_param_float16 PASSED 2025-09-09T14:07:12.8493260Z test/dtypes/test_nf4.py::TestNF4Linear::test_register_nf4_as_param_float32 PASSED 2025-09-09T14:07:12.8494223Z test/dtypes/test_nf4.py::TestNF4Linear::test_smoketest_linear_bfloat16 SKIPPED 2025-09-09T14:07:12.8495241Z test/dtypes/test_nf4.py::TestNF4Linear::test_smoketest_linear_compile_bfloat16 SKIPPED 2025-09-09T14:07:12.8496287Z test/dtypes/test_nf4.py::TestNF4Linear::test_smoketest_linear_compile_float16 SKIPPED 2025-09-09T14:07:12.8497324Z test/dtypes/test_nf4.py::TestNF4Linear::test_smoketest_linear_compile_float32 SKIPPED 2025-09-09T14:07:12.8498323Z test/dtypes/test_nf4.py::TestNF4Linear::test_smoketest_linear_float16 SKIPPED 2025-09-09T14:07:12.8499260Z test/dtypes/test_nf4.py::TestNF4Linear::test_smoketest_linear_float32 SKIPPED 2025-09-09T14:07:12.8500382Z test/dtypes/test_nf4.py::TestNF4Linear::test_to_copy_bfloat16 PASSED 2025-09-09T14:07:12.8501241Z test/dtypes/test_nf4.py::TestNF4Linear::test_to_copy_device SKIPPED 2025-09-09T14:07:12.8502075Z test/dtypes/test_nf4.py::TestNF4Linear::test_to_copy_float16 PASSED 2025-09-09T14:07:12.8502907Z test/dtypes/test_nf4.py::TestNF4Linear::test_to_copy_float32 PASSED 2025-09-09T14:07:12.8503744Z test/dtypes/test_nf4.py::TestNF4Linear::test_to_dtype_bfloat16 PASSED 2025-09-09T14:07:12.8504597Z test/dtypes/test_nf4.py::TestNF4Linear::test_to_dtype_float16 PASSED 2025-09-09T14:07:12.8505517Z test/dtypes/test_nf4.py::TestNF4Linear::test_to_dtype_float32 PASSED 2025-09-09T14:07:12.8506385Z test/dtypes/test_nf4.py::TestFSDPOps::test_pin_memory SKIPPED (Need ...) 2025-09-09T14:07:12.8507344Z test/dtypes/test_nf4.py::TestFSDPOps::test_tensor_2d_view_valid_input_size0 PASSED 2025-09-09T14:07:12.8508365Z test/dtypes/test_nf4.py::TestFSDPOps::test_tensor_as_strided_invalid_input_size0 PASSED 2025-09-09T14:07:12.8509428Z test/dtypes/test_nf4.py::TestFSDPOps::test_tensor_as_strided_invalid_input_size1 PASSED 2025-09-09T14:07:12.8510466Z test/dtypes/test_nf4.py::TestFSDPOps::test_tensor_as_strided_valid_input_size1 PASSED 2025-09-09T14:07:12.8511500Z test/dtypes/test_nf4.py::TestFSDPOps::test_tensor_as_strided_valid_input_size2 PASSED 2025-09-09T14:07:12.8512570Z test/dtypes/test_nf4.py::TestFSDPOps::test_tensor_as_strided_valid_input_size_262144 PASSED 2025-09-09T14:07:12.8513584Z test/dtypes/test_nf4.py::TestFSDPOps::test_tensor_deepcopy_input_size1 SKIPPED 2025-09-09T14:07:12.8514547Z test/dtypes/test_nf4.py::TestFSDPOps::test_tensor_deepcopy_input_size2 SKIPPED 2025-09-09T14:07:12.8515529Z test/dtypes/test_nf4.py::TestFSDPOps::test_tensor_deepcopy_input_size_262144 SKIPPED 2025-09-09T14:07:12.8516573Z test/dtypes/test_nf4.py::TestFSDPOps::test_tensor_new_zeros_invalid_input_size1 PASSED 2025-09-09T14:07:12.8517618Z test/dtypes/test_nf4.py::TestFSDPOps::test_tensor_new_zeros_invalid_input_size2 PASSED 2025-09-09T14:07:22.9048192Z test/dtypes/test_nf4.py::TestFSDPOps::test_tensor_new_zeros_invalid_input_size_262144 PASSED 2025-09-09T14:07:22.9049142Z test/dtypes/test_nf4.py::TestFSDPOps::test_tensor_new_zeros_valid_input_size1 PASSED 2025-09-09T14:07:22.9049974Z test/dtypes/test_nf4.py::TestFSDPOps::test_tensor_new_zeros_valid_input_size2 PASSED 2025-09-09T14:07:22.9050815Z test/dtypes/test_nf4.py::TestFSDPOps::test_tensor_new_zeros_valid_input_size_262144 PASSED 2025-09-09T14:07:22.9051656Z test/dtypes/test_nf4.py::TestFSDPOps::test_tensor_slice_1d_invalid PASSED 2025-09-09T14:07:22.9052390Z test/dtypes/test_nf4.py::TestFSDPOps::test_tensor_slice_2d_invalid PASSED 2025-09-09T14:07:22.9053139Z test/dtypes/test_nf4.py::TestFSDPOps::test_tensor_slice_valid_input_size1 PASSED 2025-09-09T14:07:22.9053961Z test/dtypes/test_nf4.py::TestFSDPOps::test_tensor_slice_valid_input_size2 PASSED 2025-09-09T14:07:22.9054761Z test/dtypes/test_nf4.py::TestFSDPOps::test_tensor_slice_valid_input_size_262144 PASSED 2025-09-09T14:07:22.9055576Z test/dtypes/test_nf4.py::TestFSDPOps::test_tensor_view_invalid_input_size0 PASSED 2025-09-09T14:07:22.9056346Z test/dtypes/test_nf4.py::TestFSDPOps::test_tensor_view_valid_input_size0 PASSED 2025-09-09T14:07:22.9057120Z test/dtypes/test_nf4.py::TestFSDPOps::test_tensor_view_valid_input_size1 PASSED 2025-09-09T14:07:22.9057864Z test/dtypes/test_nf4.py::TestFSDPOps::test_to_cpu SKIPPED (Need CUDA...) 2025-09-09T14:07:22.9058790Z test/dtypes/test_nf4.py::TestFSDPOps::test_to_cuda SKIPPED (Need CUD...) 2025-09-09T14:07:22.9059502Z test/dtypes/test_nf4.py::TestFSDPOps::test_to_module SKIPPED (Need C...) 2025-09-09T14:07:22.9060512Z test/dtypes/test_nf4.py::TestFSDPOps::test_torch_chunk_invalid_3d_input_size0 PASSED 2025-09-09T14:07:22.9061402Z test/dtypes/test_nf4.py::TestFSDPOps::test_torch_chunk_invalid_divide_input_size1 PASSED 2025-09-09T14:07:22.9062266Z test/dtypes/test_nf4.py::TestFSDPOps::test_torch_chunk_invalid_divide_input_size2 PASSED 2025-09-09T14:07:22.9063148Z test/dtypes/test_nf4.py::TestFSDPOps::test_torch_chunk_invalid_divide_input_size_261632 PASSED 2025-09-09T14:07:22.9063991Z test/dtypes/test_nf4.py::TestFSDPOps::test_torch_chunk_valid_input_size1 PASSED 2025-09-09T14:07:22.9064752Z test/dtypes/test_nf4.py::TestFSDPOps::test_torch_chunk_valid_input_size2 PASSED 2025-09-09T14:07:22.9065674Z test/dtypes/test_nf4.py::TestFSDPOps::test_torch_chunk_valid_input_size_262144 PASSED 2025-09-09T14:07:22.9066712Z test/dtypes/test_nf4.py::TestQLoRA::test_qlora_fsdp2 WARNING:torchao.kernel.intmm:Warning: Detected no triton, on systems without Triton certain kernels will not work 2025-09-09T14:07:22.9067817Z WARNING:torchao.kernel.intmm:Warning: Detected no triton, on systems without Triton certain kernels will not work 2025-09-09T14:07:22.9068746Z WARNING:bitsandbytes.cextension:The 8-bit optimizer is not available on your device, only available on CUDA for now. 2025-09-09T14:07:22.9069679Z WARNING:bitsandbytes.cextension:The 8-bit optimizer is not available on your device, only available on CUDA for now. 2025-09-09T14:07:22.9070305Z dist init r=1, world=2 2025-09-09T14:07:22.9070558Z dist init r=0, world=2 2025-09-09T14:07:22.9070850Z SKIPPED (Need a...) 2025-09-09T14:07:22.9071606Z test/dtypes/test_nf4.py::TestComm::test_comm WARNING:torchao.kernel.intmm:Warning: Detected no triton, on systems without Triton certain kernels will not work 2025-09-09T14:07:22.9072675Z WARNING:torchao.kernel.intmm:Warning: Detected no triton, on systems without Triton certain kernels will not work 2025-09-09T14:07:22.9073595Z WARNING:bitsandbytes.cextension:The 8-bit optimizer is not available on your device, only available on CUDA for now. 2025-09-09T14:07:22.9074519Z WARNING:bitsandbytes.cextension:The 8-bit optimizer is not available on your device, only available on CUDA for now. 2025-09-09T14:07:22.9075139Z dist init r=0, world=2 2025-09-09T14:07:22.9075382Z dist init r=1, world=2 2025-09-09T14:07:22.9075685Z SKIPPED (Need at least ...) 2025-09-09T14:07:22.9076220Z test/dtypes/test_uint4.py::TestUInt4::test_basic_tensor_ops SKIPPED 2025-09-09T14:07:22.9076914Z test/dtypes/test_uint4.py::TestUInt4::test_gpu_quant SKIPPED (FAILED...) 2025-09-09T14:07:22.9077625Z test/dtypes/test_uint4.py::TestUInt4::test_pt2e_quant SKIPPED (FAILE...) 2025-09-09T14:07:22.9078400Z test/dtypes/test_uintx.py::test_uintx_quant_on_cpu_then_move_to_cuda[32-dtype0] SKIPPED 2025-09-09T14:07:22.9079238Z test/dtypes/test_uintx.py::test_uintx_quant_on_cpu_then_move_to_cuda[32-dtype1] SKIPPED 2025-09-09T14:07:22.9080157Z test/dtypes/test_uintx.py::test_uintx_quant_on_cpu_then_move_to_cuda[32-dtype2] SKIPPED 2025-09-09T14:07:22.9080985Z test/dtypes/test_uintx.py::test_uintx_quant_on_cpu_then_move_to_cuda[32-dtype3] SKIPPED 2025-09-09T14:07:22.9081818Z test/dtypes/test_uintx.py::test_uintx_quant_on_cpu_then_move_to_cuda[32-dtype4] SKIPPED 2025-09-09T14:07:22.9082636Z test/dtypes/test_uintx.py::test_uintx_quant_on_cpu_then_move_to_cuda[32-dtype5] SKIPPED 2025-09-09T14:07:22.9083472Z test/dtypes/test_uintx.py::test_uintx_quant_on_cpu_then_move_to_cuda[32-dtype6] SKIPPED 2025-09-09T14:07:22.9084306Z test/dtypes/test_uintx.py::test_uintx_quant_on_cpu_then_move_to_cuda[64-dtype0] SKIPPED 2025-09-09T14:07:22.9085127Z test/dtypes/test_uintx.py::test_uintx_quant_on_cpu_then_move_to_cuda[64-dtype1] SKIPPED 2025-09-09T14:07:22.9085952Z test/dtypes/test_uintx.py::test_uintx_quant_on_cpu_then_move_to_cuda[64-dtype2] SKIPPED 2025-09-09T14:07:22.9086766Z test/dtypes/test_uintx.py::test_uintx_quant_on_cpu_then_move_to_cuda[64-dtype3] SKIPPED 2025-09-09T14:07:22.9087669Z test/dtypes/test_uintx.py::test_uintx_quant_on_cpu_then_move_to_cuda[64-dtype4] SKIPPED 2025-09-09T14:07:22.9088505Z test/dtypes/test_uintx.py::test_uintx_quant_on_cpu_then_move_to_cuda[64-dtype5] SKIPPED 2025-09-09T14:07:22.9089324Z test/dtypes/test_uintx.py::test_uintx_quant_on_cpu_then_move_to_cuda[64-dtype6] SKIPPED 2025-09-09T14:07:22.9090162Z test/dtypes/test_uintx.py::test_uintx_quant_on_cpu_then_move_to_cuda[128-dtype0] SKIPPED 2025-09-09T14:07:22.9090992Z test/dtypes/test_uintx.py::test_uintx_quant_on_cpu_then_move_to_cuda[128-dtype1] SKIPPED 2025-09-09T14:07:22.9091888Z test/dtypes/test_uintx.py::test_uintx_quant_on_cpu_then_move_to_cuda[128-dtype2] SKIPPED 2025-09-09T14:07:22.9092723Z test/dtypes/test_uintx.py::test_uintx_quant_on_cpu_then_move_to_cuda[128-dtype3] SKIPPED 2025-09-09T14:07:22.9093547Z test/dtypes/test_uintx.py::test_uintx_quant_on_cpu_then_move_to_cuda[128-dtype4] SKIPPED 2025-09-09T14:07:22.9094382Z test/dtypes/test_uintx.py::test_uintx_quant_on_cpu_then_move_to_cuda[128-dtype5] SKIPPED 2025-09-09T14:07:22.9095203Z test/dtypes/test_uintx.py::test_uintx_quant_on_cpu_then_move_to_cuda[128-dtype6] SKIPPED 2025-09-09T14:07:22.9096035Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cpu-32-dtype0] SKIPPED 2025-09-09T14:07:22.9096850Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cpu-32-dtype1] SKIPPED 2025-09-09T14:07:22.9097670Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cpu-32-dtype2] SKIPPED 2025-09-09T14:07:22.9098482Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cpu-32-dtype3] SKIPPED 2025-09-09T14:07:22.9099285Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cpu-32-dtype4] SKIPPED 2025-09-09T14:07:22.9100096Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cpu-32-dtype5] SKIPPED 2025-09-09T14:07:22.9100900Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cpu-32-dtype6] SKIPPED 2025-09-09T14:07:22.9101714Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cpu-64-dtype0] SKIPPED 2025-09-09T14:07:22.9102525Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cpu-64-dtype1] SKIPPED 2025-09-09T14:07:22.9103329Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cpu-64-dtype2] SKIPPED 2025-09-09T14:07:22.9104142Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cpu-64-dtype3] SKIPPED 2025-09-09T14:07:22.9104969Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cpu-64-dtype4] SKIPPED 2025-09-09T14:07:22.9105776Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cpu-64-dtype5] SKIPPED 2025-09-09T14:07:22.9106589Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cpu-64-dtype6] SKIPPED 2025-09-09T14:07:22.9107399Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cpu-128-dtype0] SKIPPED 2025-09-09T14:07:22.9108227Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cpu-128-dtype1] SKIPPED 2025-09-09T14:07:22.9109044Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cpu-128-dtype2] SKIPPED 2025-09-09T14:07:22.9109871Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cpu-128-dtype3] SKIPPED 2025-09-09T14:07:22.9110698Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cpu-128-dtype4] SKIPPED 2025-09-09T14:07:22.9111512Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cpu-128-dtype5] SKIPPED 2025-09-09T14:07:22.9112343Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cpu-128-dtype6] SKIPPED 2025-09-09T14:07:22.9113152Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cuda-32-dtype0] SKIPPED 2025-09-09T14:07:22.9113973Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cuda-32-dtype1] SKIPPED 2025-09-09T14:07:22.9114853Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cuda-32-dtype2] SKIPPED 2025-09-09T14:07:22.9115666Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cuda-32-dtype3] SKIPPED 2025-09-09T14:07:22.9116498Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cuda-32-dtype4] SKIPPED 2025-09-09T14:07:22.9117330Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cuda-32-dtype5] SKIPPED 2025-09-09T14:07:22.9118146Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cuda-32-dtype6] SKIPPED 2025-09-09T14:07:24.1107795Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cuda-64-dtype0] SKIPPED 2025-09-09T14:07:24.1108698Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cuda-64-dtype1] SKIPPED 2025-09-09T14:07:24.1109514Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cuda-64-dtype2] SKIPPED 2025-09-09T14:07:24.1110367Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cuda-64-dtype3] SKIPPED 2025-09-09T14:07:24.1111177Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cuda-64-dtype4] SKIPPED 2025-09-09T14:07:24.1112027Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cuda-64-dtype5] SKIPPED 2025-09-09T14:07:24.1112852Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cuda-64-dtype6] SKIPPED 2025-09-09T14:07:24.1113682Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cuda-128-dtype0] SKIPPED 2025-09-09T14:07:24.1114508Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cuda-128-dtype1] SKIPPED 2025-09-09T14:07:24.1115360Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cuda-128-dtype2] SKIPPED 2025-09-09T14:07:24.1116177Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cuda-128-dtype3] SKIPPED 2025-09-09T14:07:24.1117021Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cuda-128-dtype4] SKIPPED 2025-09-09T14:07:24.1117855Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cuda-128-dtype5] SKIPPED 2025-09-09T14:07:24.1118676Z test/dtypes/test_uintx.py::test_uintx_weight_only_model_quant[cuda-128-dtype6] SKIPPED 2025-09-09T14:07:24.1119477Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cpu-32-dtype0] SKIPPED 2025-09-09T14:07:24.1120317Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cpu-32-dtype1] SKIPPED 2025-09-09T14:07:24.1121077Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cpu-32-dtype2] SKIPPED 2025-09-09T14:07:24.1121836Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cpu-32-dtype3] SKIPPED 2025-09-09T14:07:24.1122597Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cpu-32-dtype4] SKIPPED 2025-09-09T14:07:24.1123351Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cpu-32-dtype5] SKIPPED 2025-09-09T14:07:24.1124103Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cpu-32-dtype6] SKIPPED 2025-09-09T14:07:24.1124861Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cpu-64-dtype0] SKIPPED 2025-09-09T14:07:24.1125605Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cpu-64-dtype1] SKIPPED 2025-09-09T14:07:24.1126363Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cpu-64-dtype2] SKIPPED 2025-09-09T14:07:24.1127114Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cpu-64-dtype3] SKIPPED 2025-09-09T14:07:24.1127860Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cpu-64-dtype4] SKIPPED 2025-09-09T14:07:24.1128621Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cpu-64-dtype5] SKIPPED 2025-09-09T14:07:24.1129365Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cpu-64-dtype6] SKIPPED 2025-09-09T14:07:24.1130130Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cpu-128-dtype0] SKIPPED 2025-09-09T14:07:24.1131140Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cpu-128-dtype1] SKIPPED 2025-09-09T14:07:24.1131916Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cpu-128-dtype2] SKIPPED 2025-09-09T14:07:24.1132688Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cpu-128-dtype3] SKIPPED 2025-09-09T14:07:24.1133443Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cpu-128-dtype4] SKIPPED 2025-09-09T14:07:24.1134214Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cpu-128-dtype5] SKIPPED 2025-09-09T14:07:24.1134968Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cpu-128-dtype6] SKIPPED 2025-09-09T14:07:24.1135856Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cuda-32-dtype0] SKIPPED 2025-09-09T14:07:24.1136624Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cuda-32-dtype1] SKIPPED 2025-09-09T14:07:24.1137385Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cuda-32-dtype2] SKIPPED 2025-09-09T14:07:24.1138153Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cuda-32-dtype3] SKIPPED 2025-09-09T14:07:24.1138903Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cuda-32-dtype4] SKIPPED 2025-09-09T14:07:24.1139665Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cuda-32-dtype5] SKIPPED 2025-09-09T14:07:24.1140430Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cuda-32-dtype6] SKIPPED 2025-09-09T14:07:24.1141180Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cuda-64-dtype0] SKIPPED 2025-09-09T14:07:24.1141946Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cuda-64-dtype1] SKIPPED 2025-09-09T14:07:24.1142700Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cuda-64-dtype2] SKIPPED 2025-09-09T14:07:24.1143465Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cuda-64-dtype3] SKIPPED 2025-09-09T14:07:24.1144221Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cuda-64-dtype4] SKIPPED 2025-09-09T14:07:24.1144989Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cuda-64-dtype5] SKIPPED 2025-09-09T14:07:24.1145753Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cuda-64-dtype6] SKIPPED 2025-09-09T14:07:24.1146512Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cuda-128-dtype0] SKIPPED 2025-09-09T14:07:24.1147290Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cuda-128-dtype1] SKIPPED 2025-09-09T14:07:24.1148053Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cuda-128-dtype2] SKIPPED 2025-09-09T14:07:24.1148838Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cuda-128-dtype3] SKIPPED 2025-09-09T14:07:24.1149611Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cuda-128-dtype4] SKIPPED 2025-09-09T14:07:24.1150370Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cuda-128-dtype5] SKIPPED 2025-09-09T14:07:24.1151148Z test/dtypes/test_uintx.py::test_uintx_weight_only_quant[cuda-128-dtype6] SKIPPED 2025-09-09T14:07:24.1151884Z test/dtypes/test_uintx.py::test_uintx_target_dtype[dtype0] SKIPPED (...) 2025-09-09T14:07:24.1152594Z test/dtypes/test_uintx.py::test_uintx_target_dtype[dtype1] SKIPPED (...) 2025-09-09T14:07:24.1153303Z test/dtypes/test_uintx.py::test_uintx_target_dtype[dtype2] SKIPPED (...) 2025-09-09T14:07:24.1153997Z test/dtypes/test_uintx.py::test_uintx_target_dtype[dtype3] SKIPPED (...) 2025-09-09T14:07:24.1154701Z test/dtypes/test_uintx.py::test_uintx_target_dtype[dtype4] SKIPPED (...) 2025-09-09T14:07:24.1155395Z test/dtypes/test_uintx.py::test_uintx_target_dtype[dtype5] SKIPPED (...) 2025-09-09T14:07:24.1156097Z test/dtypes/test_uintx.py::test_uintx_target_dtype[dtype6] SKIPPED (...) 2025-09-09T14:07:24.1156804Z test/dtypes/test_uintx.py::test_uintx_target_dtype_compile[dtype0] SKIPPED 2025-09-09T14:07:24.1157607Z test/dtypes/test_uintx.py::test_uintx_target_dtype_compile[dtype1] SKIPPED 2025-09-09T14:07:24.1158670Z test/dtypes/test_uintx.py::test_uintx_target_dtype_compile[dtype2] SKIPPED 2025-09-09T14:07:24.1159400Z test/dtypes/test_uintx.py::test_uintx_target_dtype_compile[dtype3] SKIPPED 2025-09-09T14:07:24.1160198Z test/dtypes/test_uintx.py::test_uintx_target_dtype_compile[dtype4] SKIPPED 2025-09-09T14:07:24.1160909Z test/dtypes/test_uintx.py::test_uintx_target_dtype_compile[dtype5] SKIPPED 2025-09-09T14:07:24.1161633Z test/dtypes/test_uintx.py::test_uintx_target_dtype_compile[dtype6] SKIPPED 2025-09-09T14:07:24.1162480Z test/dtypes/test_uintx.py::test_uintx_model_size[dtype0] SKIPPED (Ne...) 2025-09-09T14:07:24.1163171Z test/dtypes/test_uintx.py::test_uintx_model_size[dtype1] SKIPPED (Ne...) 2025-09-09T14:07:24.1163878Z test/dtypes/test_uintx.py::test_uintx_model_size[dtype2] SKIPPED (Ne...) 2025-09-09T14:07:24.1164573Z test/dtypes/test_uintx.py::test_uintx_model_size[dtype3] SKIPPED (Ne...) 2025-09-09T14:07:24.1165276Z test/dtypes/test_uintx.py::test_uintx_model_size[dtype4] SKIPPED (Ne...) 2025-09-09T14:07:24.1165966Z test/dtypes/test_uintx.py::test_uintx_model_size[dtype5] SKIPPED (Ne...) 2025-09-09T14:07:24.1166668Z test/dtypes/test_uintx.py::test_uintx_model_size[dtype6] SKIPPED (Ne...) 2025-09-09T14:07:24.1167631Z test/float8/test_auto_filter.py::test_end_to_end_filtering[tensorwise-module_dims0-valid.layer-filter_fqns0-True] PASSED 2025-09-09T14:07:24.1168825Z test/float8/test_auto_filter.py::test_end_to_end_filtering[tensorwise-module_dims1-skip_layer.linear-filter_fqns1-False] PASSED 2025-09-09T14:07:24.1170029Z test/float8/test_auto_filter.py::test_end_to_end_filtering[tensorwise-module_dims2-valid.layer-filter_fqns2-False] PASSED 2025-09-09T14:07:24.1171221Z test/float8/test_auto_filter.py::test_end_to_end_filtering[rowwise-module_dims3-valid.layer-filter_fqns3-True] PASSED 2025-09-09T14:07:24.1172387Z test/float8/test_auto_filter.py::test_end_to_end_filtering[rowwise-module_dims4-skip_layer.linear-filter_fqns4-False] PASSED 2025-09-09T14:07:24.1173549Z test/float8/test_auto_filter.py::test_end_to_end_filtering[rowwise-module_dims5-valid.layer-filter_fqns5-False] PASSED 2025-09-09T14:07:24.1174476Z test/float8/test_auto_filter.py::test_exact_boundary_dimensions_rowwise PASSED 2025-09-09T14:07:24.1175257Z test/float8/test_auto_filter.py::test_exact_boundary_dimensions_tensorwise PASSED 2025-09-09T14:07:24.1175973Z test/float8/test_auto_filter.py::test_partial_fqn_matching PASSED 2025-09-09T14:07:24.1176722Z test/float8/test_base.py::TestFloat8TrainingTensor::test_preserves_dtype PASSED 2025-09-09T14:07:24.1177567Z test/float8/test_base.py::TestFloat8TrainingTensor::test_differentiable_casts PASSED 2025-09-09T14:07:24.1178367Z test/float8/test_base.py::TestFloat8TrainingTensor::test_split_cat PASSED 2025-09-09T14:07:24.1698114Z test/float8/test_base.py::TestFloat8TrainingTensor::test_index_put PASSED 2025-09-09T14:07:24.1698907Z test/float8/test_base.py::TestFloat8TrainingTensor::test_copy_ PASSED 2025-09-09T14:07:24.1699634Z test/float8/test_base.py::TestFloat8TrainingTensor::test_transpose PASSED 2025-09-09T14:07:24.1700533Z test/float8/test_base.py::TestFloat8TrainingTensor::test_axiswise_dynamic_cast[True-0-shape0] PASSED 2025-09-09T14:07:24.1701531Z test/float8/test_base.py::TestFloat8TrainingTensor::test_axiswise_dynamic_cast[True-0-shape1] PASSED 2025-09-09T14:07:24.1702673Z test/float8/test_base.py::TestFloat8TrainingTensor::test_axiswise_dynamic_cast[True-0-shape2] PASSED 2025-09-09T14:07:24.1703850Z test/float8/test_base.py::TestFloat8TrainingTensor::test_axiswise_dynamic_cast[True--1-shape0] PASSED 2025-09-09T14:07:24.1705109Z test/float8/test_base.py::TestFloat8TrainingTensor::test_axiswise_dynamic_cast[True--1-shape1] PASSED 2025-09-09T14:07:24.1706239Z test/float8/test_base.py::TestFloat8TrainingTensor::test_axiswise_dynamic_cast[True--1-shape2] PASSED 2025-09-09T14:07:24.1707397Z test/float8/test_base.py::TestFloat8TrainingTensor::test_axiswise_dynamic_cast[False-0-shape0] PASSED 2025-09-09T14:07:24.1708412Z test/float8/test_base.py::TestFloat8TrainingTensor::test_axiswise_dynamic_cast[False-0-shape1] PASSED 2025-09-09T14:07:24.1709424Z test/float8/test_base.py::TestFloat8TrainingTensor::test_axiswise_dynamic_cast[False-0-shape2] PASSED 2025-09-09T14:07:24.1710432Z test/float8/test_base.py::TestFloat8TrainingTensor::test_axiswise_dynamic_cast[False--1-shape0] PASSED 2025-09-09T14:07:24.1711674Z test/float8/test_base.py::TestFloat8TrainingTensor::test_axiswise_dynamic_cast[False--1-shape1] PASSED 2025-09-09T14:07:24.1712845Z test/float8/test_base.py::TestFloat8TrainingTensor::test_axiswise_dynamic_cast[False--1-shape2] PASSED 2025-09-09T14:07:24.1713772Z test/float8/test_base.py::TestFloat8TrainingTensor::test_axiswise_reshape PASSED 2025-09-09T14:07:24.1715044Z test/float8/test_base.py::TestFloat8TrainingTensor::test_axiswise_gemm[ScalingGranularity.AXISWISE-ScalingGranularity.AXISWISE-a_shape0] SKIPPED 2025-09-09T14:07:24.1716773Z test/float8/test_base.py::TestFloat8TrainingTensor::test_axiswise_gemm[ScalingGranularity.AXISWISE-ScalingGranularity.AXISWISE-a_shape1] SKIPPED 2025-09-09T14:07:24.1718320Z test/float8/test_base.py::TestFloat8TrainingTensor::test_axiswise_gemm[ScalingGranularity.AXISWISE-ScalingGranularity.AXISWISE-a_shape2] SKIPPED 2025-09-09T14:07:24.1719955Z test/float8/test_base.py::TestFloat8TrainingTensor::test_axiswise_gemm[ScalingGranularity.AXISWISE-ScalingGranularity.TENSORWISE-a_shape0] SKIPPED 2025-09-09T14:07:24.1721640Z test/float8/test_base.py::TestFloat8TrainingTensor::test_axiswise_gemm[ScalingGranularity.AXISWISE-ScalingGranularity.TENSORWISE-a_shape1] SKIPPED 2025-09-09T14:07:24.1723382Z test/float8/test_base.py::TestFloat8TrainingTensor::test_axiswise_gemm[ScalingGranularity.AXISWISE-ScalingGranularity.TENSORWISE-a_shape2] SKIPPED 2025-09-09T14:07:24.1725060Z test/float8/test_base.py::TestFloat8TrainingTensor::test_axiswise_gemm[ScalingGranularity.TENSORWISE-ScalingGranularity.AXISWISE-a_shape0] SKIPPED 2025-09-09T14:07:24.1726800Z test/float8/test_base.py::TestFloat8TrainingTensor::test_axiswise_gemm[ScalingGranularity.TENSORWISE-ScalingGranularity.AXISWISE-a_shape1] SKIPPED 2025-09-09T14:07:24.1728377Z test/float8/test_base.py::TestFloat8TrainingTensor::test_axiswise_gemm[ScalingGranularity.TENSORWISE-ScalingGranularity.AXISWISE-a_shape2] SKIPPED 2025-09-09T14:07:24.1729518Z test/float8/test_base.py::TestFloat8TrainingTensor::test_fp8_dtype SKIPPED 2025-09-09T14:07:24.1731061Z 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 2025-09-09T14:07:24.1733160Z 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 2025-09-09T14:07:24.1735353Z 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 2025-09-09T14:07:24.1737289Z 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 2025-09-09T14:07:24.1739220Z 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 2025-09-09T14:07:24.1741479Z 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 2025-09-09T14:07:24.1743421Z 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 2025-09-09T14:07:24.1745612Z 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 2025-09-09T14:07:24.1747529Z 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 2025-09-09T14:07:24.1749506Z 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 2025-09-09T14:07:24.1751428Z 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 2025-09-09T14:07:24.1753330Z 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 2025-09-09T14:07:24.1755358Z 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 2025-09-09T14:07:24.1757441Z 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 2025-09-09T14:07:24.1759857Z 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 2025-09-09T14:07:24.1761841Z 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 2025-09-09T14:07:24.1763765Z 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 2025-09-09T14:07:24.1765780Z 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 2025-09-09T14:07:24.1767843Z 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 2025-09-09T14:07:24.1770007Z 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 2025-09-09T14:07:24.1771902Z 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 2025-09-09T14:07:24.1773796Z 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 2025-09-09T14:07:24.1775938Z 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 2025-09-09T14:07:24.1777988Z 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 2025-09-09T14:07:24.1779883Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype0-True-x_shape0-Float8LinearRecipeName.ROWWISE] SKIPPED 2025-09-09T14:07:24.1781295Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype0-True-x_shape0-Float8LinearRecipeName.ROWWISE_WITH_GW_HP] SKIPPED 2025-09-09T14:07:24.2296329Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype0-True-x_shape1-Float8LinearRecipeName.ROWWISE] SKIPPED 2025-09-09T14:07:24.2297798Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype0-True-x_shape1-Float8LinearRecipeName.ROWWISE_WITH_GW_HP] SKIPPED 2025-09-09T14:07:24.2299384Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype0-True-x_shape2-Float8LinearRecipeName.ROWWISE] SKIPPED 2025-09-09T14:07:24.2300793Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype0-True-x_shape2-Float8LinearRecipeName.ROWWISE_WITH_GW_HP] SKIPPED 2025-09-09T14:07:24.2302213Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype0-False-x_shape0-Float8LinearRecipeName.ROWWISE] SKIPPED 2025-09-09T14:07:24.2303634Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype0-False-x_shape0-Float8LinearRecipeName.ROWWISE_WITH_GW_HP] SKIPPED 2025-09-09T14:07:24.2305052Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype0-False-x_shape1-Float8LinearRecipeName.ROWWISE] SKIPPED 2025-09-09T14:07:24.2306481Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype0-False-x_shape1-Float8LinearRecipeName.ROWWISE_WITH_GW_HP] SKIPPED 2025-09-09T14:07:24.2307934Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype0-False-x_shape2-Float8LinearRecipeName.ROWWISE] SKIPPED 2025-09-09T14:07:24.2309361Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype0-False-x_shape2-Float8LinearRecipeName.ROWWISE_WITH_GW_HP] SKIPPED 2025-09-09T14:07:24.2310778Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype1-True-x_shape0-Float8LinearRecipeName.ROWWISE] SKIPPED 2025-09-09T14:07:24.2312186Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype1-True-x_shape0-Float8LinearRecipeName.ROWWISE_WITH_GW_HP] SKIPPED 2025-09-09T14:07:24.2313599Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype1-True-x_shape1-Float8LinearRecipeName.ROWWISE] SKIPPED 2025-09-09T14:07:24.2315011Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype1-True-x_shape1-Float8LinearRecipeName.ROWWISE_WITH_GW_HP] SKIPPED 2025-09-09T14:07:24.2316425Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype1-True-x_shape2-Float8LinearRecipeName.ROWWISE] SKIPPED 2025-09-09T14:07:24.2317847Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype1-True-x_shape2-Float8LinearRecipeName.ROWWISE_WITH_GW_HP] SKIPPED 2025-09-09T14:07:24.2319373Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype1-False-x_shape0-Float8LinearRecipeName.ROWWISE] SKIPPED 2025-09-09T14:07:24.2321020Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype1-False-x_shape0-Float8LinearRecipeName.ROWWISE_WITH_GW_HP] SKIPPED 2025-09-09T14:07:24.2322558Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype1-False-x_shape1-Float8LinearRecipeName.ROWWISE] SKIPPED 2025-09-09T14:07:24.2324135Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype1-False-x_shape1-Float8LinearRecipeName.ROWWISE_WITH_GW_HP] SKIPPED 2025-09-09T14:07:24.2325668Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype1-False-x_shape2-Float8LinearRecipeName.ROWWISE] SKIPPED 2025-09-09T14:07:24.2327101Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype1-False-x_shape2-Float8LinearRecipeName.ROWWISE_WITH_GW_HP] SKIPPED 2025-09-09T14:07:24.2328503Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype2-True-x_shape0-Float8LinearRecipeName.ROWWISE] SKIPPED 2025-09-09T14:07:24.2329916Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype2-True-x_shape0-Float8LinearRecipeName.ROWWISE_WITH_GW_HP] SKIPPED 2025-09-09T14:07:24.2331372Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype2-True-x_shape1-Float8LinearRecipeName.ROWWISE] SKIPPED 2025-09-09T14:07:24.2332790Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype2-True-x_shape1-Float8LinearRecipeName.ROWWISE_WITH_GW_HP] SKIPPED 2025-09-09T14:07:24.2334204Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype2-True-x_shape2-Float8LinearRecipeName.ROWWISE] SKIPPED 2025-09-09T14:07:24.2335607Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype2-True-x_shape2-Float8LinearRecipeName.ROWWISE_WITH_GW_HP] SKIPPED 2025-09-09T14:07:24.2337029Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype2-False-x_shape0-Float8LinearRecipeName.ROWWISE] SKIPPED 2025-09-09T14:07:24.2338461Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype2-False-x_shape0-Float8LinearRecipeName.ROWWISE_WITH_GW_HP] SKIPPED 2025-09-09T14:07:24.2339877Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype2-False-x_shape1-Float8LinearRecipeName.ROWWISE] SKIPPED 2025-09-09T14:07:24.2341301Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype2-False-x_shape1-Float8LinearRecipeName.ROWWISE_WITH_GW_HP] SKIPPED 2025-09-09T14:07:24.2342856Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype2-False-x_shape2-Float8LinearRecipeName.ROWWISE] SKIPPED 2025-09-09T14:07:24.2344429Z test/float8/test_base.py::TestFloat8Linear::test_linear_from_recipe[linear_dtype2-False-x_shape2-Float8LinearRecipeName.ROWWISE_WITH_GW_HP] SKIPPED 2025-09-09T14:07:24.2345927Z test/float8/test_base.py::TestFloat8Linear::test_autocast_outputs[Float8LinearRecipeName.TENSORWISE-linear_dtype0-True] SKIPPED 2025-09-09T14:07:24.2347387Z test/float8/test_base.py::TestFloat8Linear::test_autocast_outputs[Float8LinearRecipeName.TENSORWISE-linear_dtype1-True] SKIPPED 2025-09-09T14:07:24.2348709Z test/float8/test_base.py::TestFloat8Linear::test_autocast_outputs[Float8LinearRecipeName.TENSORWISE-linear_dtype2-True] SKIPPED 2025-09-09T14:07:24.2350001Z test/float8/test_base.py::TestFloat8Linear::test_autocast_outputs[Float8LinearRecipeName.ROWWISE-linear_dtype0-True] SKIPPED 2025-09-09T14:07:24.2351264Z test/float8/test_base.py::TestFloat8Linear::test_autocast_outputs[Float8LinearRecipeName.ROWWISE-linear_dtype1-True] SKIPPED 2025-09-09T14:07:24.2352535Z test/float8/test_base.py::TestFloat8Linear::test_autocast_outputs[Float8LinearRecipeName.ROWWISE-linear_dtype2-True] SKIPPED 2025-09-09T14:07:24.2353852Z test/float8/test_base.py::TestFloat8Linear::test_autocast_outputs[Float8LinearRecipeName.ROWWISE_WITH_GW_HP-linear_dtype0-True] SKIPPED 2025-09-09T14:07:24.2355209Z test/float8/test_base.py::TestFloat8Linear::test_autocast_outputs[Float8LinearRecipeName.ROWWISE_WITH_GW_HP-linear_dtype1-True] SKIPPED 2025-09-09T14:07:24.2356678Z test/float8/test_base.py::TestFloat8Linear::test_autocast_outputs[Float8LinearRecipeName.ROWWISE_WITH_GW_HP-linear_dtype2-True] SKIPPED 2025-09-09T14:07:24.2357791Z test/float8/test_base.py::TestFloat8Linear::test_repr PASSED 2025-09-09T14:07:24.2358881Z test/float8/test_base.py::TestFloat8Linear::test_inference_mode SKIPPED 2025-09-09T14:07:24.2359816Z test/float8/test_base.py::TestFloat8Linear::test_quantize SKIPPED (C...) 2025-09-09T14:07:24.2360781Z test/float8/test_base.py::TestScaledMM::test_scaled_mm_vs_emulated[True-base_dtype0] SKIPPED 2025-09-09T14:07:24.2361689Z test/float8/test_base.py::TestScaledMM::test_scaled_mm_vs_emulated[True-base_dtype1] SKIPPED 2025-09-09T14:07:24.2362576Z test/float8/test_base.py::TestScaledMM::test_scaled_mm_vs_emulated[True-base_dtype2] SKIPPED 2025-09-09T14:07:24.2363483Z test/float8/test_base.py::TestScaledMM::test_scaled_mm_vs_emulated[False-base_dtype0] SKIPPED 2025-09-09T14:07:24.2364476Z test/float8/test_base.py::TestScaledMM::test_scaled_mm_vs_emulated[False-base_dtype1] SKIPPED 2025-09-09T14:07:24.2365367Z test/float8/test_base.py::TestScaledMM::test_scaled_mm_vs_emulated[False-base_dtype2] SKIPPED 2025-09-09T14:07:24.2366198Z test/float8/test_base.py::TestScaledMM::test_different_configs_error SKIPPED 2025-09-09T14:07:24.2366997Z test/float8/test_base.py::TestScaledMM::test_pad_inner_dim[True-base_dtype0] SKIPPED 2025-09-09T14:07:24.2367832Z test/float8/test_base.py::TestScaledMM::test_pad_inner_dim[True-base_dtype1] SKIPPED 2025-09-09T14:07:24.2368667Z test/float8/test_base.py::TestScaledMM::test_pad_inner_dim[True-base_dtype2] SKIPPED 2025-09-09T14:07:24.2369491Z test/float8/test_base.py::TestScaledMM::test_pad_inner_dim[False-base_dtype0] SKIPPED 2025-09-09T14:07:24.2370330Z test/float8/test_base.py::TestScaledMM::test_pad_inner_dim[False-base_dtype1] SKIPPED 2025-09-09T14:07:24.2371263Z test/float8/test_base.py::TestScaledMM::test_pad_inner_dim[False-base_dtype2] SKIPPED 2025-09-09T14:07:24.2372259Z test/float8/test_base.py::TestNumerics::test_small_amax_float16[float8_dtype0] SKIPPED 2025-09-09T14:07:24.2373103Z test/float8/test_base.py::TestNumerics::test_small_amax_float16[float8_dtype1] SKIPPED 2025-09-09T14:07:24.2374037Z test/float8/test_base.py::TestNumerics::test_small_amax_float16[float8_dtype2] SKIPPED 2025-09-09T14:07:24.6037609Z test/float8/test_base.py::TestNumerics::test_small_amax_float16[float8_dtype3] 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test/float8/test_compile.py::test_eager_only[dtype1-True-ScalingType.DYNAMIC-ScalingType.DYNAMIC-ScalingType.DYNAMIC-True] SKIPPED 2025-09-09T14:07:24.6058797Z test/float8/test_compile.py::test_aot_eager[dtype0-ScalingType.DYNAMIC-ScalingType.DYNAMIC-ScalingType.DYNAMIC-True-True] SKIPPED 2025-09-09T14:07:24.6060246Z test/float8/test_compile.py::test_aot_eager[dtype1-ScalingType.DYNAMIC-ScalingType.DYNAMIC-ScalingType.DYNAMIC-True-True] SKIPPED 2025-09-09T14:07:24.6061791Z test/float8/test_compile.py::test_inductor_from_config_params[dtype0-ScalingType.DYNAMIC-ScalingType.DYNAMIC-ScalingType.DYNAMIC-False-True] SKIPPED 2025-09-09T14:07:24.6063398Z test/float8/test_compile.py::test_inductor_from_config_params[dtype1-ScalingType.DYNAMIC-ScalingType.DYNAMIC-ScalingType.DYNAMIC-False-True] SKIPPED 2025-09-09T14:07:24.6064881Z test/float8/test_compile.py::test_inductor_from_recipe[Float8LinearRecipeName.ROWWISE] SKIPPED 2025-09-09T14:07:24.6065912Z test/float8/test_compile.py::test_inductor_from_recipe[Float8LinearRecipeName.ROWWISE_WITH_GW_HP] SKIPPED 2025-09-09T14:07:24.6066820Z test/float8/test_compile.py::TestGraphBreaks::test_float8_graph_input SKIPPED 2025-09-09T14:07:24.6067613Z test/float8/test_compile.py::TestGraphBreaks::test_float8_graph_output SKIPPED 2025-09-09T14:07:24.6068489Z test/float8/test_compile.py::TestGraphBreaks::test_float8_with_graph_break_in_the_middle SKIPPED 2025-09-09T14:07:24.6069484Z test/float8/test_compile.py::test_dynamic_scale_numeric_parity[True-dtype0] SKIPPED 2025-09-09T14:07:24.6070310Z test/float8/test_compile.py::test_dynamic_scale_numeric_parity[True-dtype1] SKIPPED 2025-09-09T14:07:24.6071114Z test/float8/test_compile.py::test_dynamic_scale_numeric_parity[True-dtype2] SKIPPED 2025-09-09T14:07:24.6071947Z test/float8/test_compile.py::test_dynamic_scale_numeric_parity[False-dtype0] SKIPPED 2025-09-09T14:07:24.6072767Z test/float8/test_compile.py::test_dynamic_scale_numeric_parity[False-dtype1] SKIPPED 2025-09-09T14:07:24.6073598Z test/float8/test_compile.py::test_dynamic_scale_numeric_parity[False-dtype2] SKIPPED 2025-09-09T14:07:24.6074503Z test/float8/test_float8_utils.py::test_round_scale_down_to_power_of_2_valid_inputs[test_case0] SKIPPED 2025-09-09T14:07:24.6075456Z test/float8/test_float8_utils.py::test_round_scale_down_to_power_of_2_valid_inputs[test_case1] SKIPPED 2025-09-09T14:07:24.6076421Z test/float8/test_float8_utils.py::test_round_scale_down_to_power_of_2_valid_inputs[test_case2] SKIPPED 2025-09-09T14:07:24.6077363Z test/float8/test_float8_utils.py::test_round_scale_down_to_power_of_2_valid_inputs[test_case3] SKIPPED 2025-09-09T14:07:24.6078320Z test/float8/test_float8_utils.py::test_round_scale_down_to_power_of_2_valid_inputs[test_case4] SKIPPED 2025-09-09T14:07:24.6079277Z test/float8/test_float8_utils.py::test_round_scale_down_to_power_of_2_valid_inputs[test_case5] SKIPPED 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test/float8/test_float8_utils.py::test_non_float32_input[invalid_dtype6] PASSED 2025-09-09T14:07:24.6087418Z test/float8/test_float8_utils.py::test_non_float32_input[invalid_dtype7] PASSED 2025-09-09T14:07:24.6088815Z test/float8/test_numerics_integration.py::TestFloat8NumericsIntegrationTest::test_encoder_fw_bw_from_config_params[ScalingType.DYNAMIC-ScalingType.DYNAMIC-ScalingType.DYNAMIC] SKIPPED 2025-09-09T14:07:24.6090528Z test/float8/test_numerics_integration.py::TestFloat8NumericsIntegrationTest::test_encoder_fw_bw_from_recipe[Float8LinearRecipeName.ROWWISE] SKIPPED 2025-09-09T14:07:24.6092090Z test/float8/test_numerics_integration.py::TestFloat8NumericsIntegrationTest::test_encoder_fw_bw_from_recipe[Float8LinearRecipeName.ROWWISE_WITH_GW_HP] SKIPPED 2025-09-09T14:07:24.6093304Z test/hqq/test_hqq_affine.py::TestHQQ::test_hqq_plain_2bit SKIPPED (N...) 2025-09-09T14:07:24.6094019Z test/hqq/test_hqq_affine.py::TestHQQ::test_hqq_plain_3bit SKIPPED (N...) 2025-09-09T14:07:24.6094710Z test/hqq/test_hqq_affine.py::TestHQQ::test_hqq_plain_4bit SKIPPED (N...) 2025-09-09T14:07:24.6095409Z test/hqq/test_hqq_affine.py::TestHQQ::test_hqq_plain_5bit SKIPPED (N...) 2025-09-09T14:07:24.6096095Z test/hqq/test_hqq_affine.py::TestHQQ::test_hqq_plain_6bit SKIPPED (N...) 2025-09-09T14:07:24.6096791Z test/hqq/test_hqq_affine.py::TestHQQ::test_hqq_plain_7bit SKIPPED (N...) 2025-09-09T14:07:24.6097541Z test/hqq/test_hqq_affine.py::TestHQQ::test_hqq_plain_8bit SKIPPED (N...) 2025-09-09T14:07:24.6098333Z test/integration/test_integration.py::SmoothquantUnitTest::test_debug_x_absmax PASSED 2025-09-09T14:07:24.6099192Z test/integration/test_integration.py::SmoothquantUnitTest::test_figure_4 PASSED 2025-09-09T14:07:24.6100108Z test/integration/test_integration.py::SmoothquantUnitTest::test_selective_torch_compile PASSED 2025-09-09T14:07:24.6101622Z test/integration/test_integration.py::SmoothquantUnitTest::test_smooth_linear_cpu [W909 14:07:24.777221153 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()) 2025-09-09T14:07:24.6102897Z PASSED 2025-09-09T14:07:24.6103501Z test/integration/test_integration.py::SmoothquantUnitTest::test_smooth_linear_cuda SKIPPED 2025-09-09T14:07:24.6104455Z test/integration/test_integration.py::SmoothquantUnitTest::test_smooth_linear_edge_cases PASSED 2025-09-09T14:07:24.6105343Z test/integration/test_integration.py::SmoothquantUnitTest::test_swap PASSED 2025-09-09T14:07:24.6106133Z test/integration/test_integration.py::SmoothquantUnitTest::test_tensors PASSED 2025-09-09T14:07:24.6107089Z test/integration/test_integration.py::SmoothquantUnitTest::test_weight_t_and_non_t_numerics_match SKIPPED 2025-09-09T14:07:24.6108069Z test/integration/test_integration.py::PythonQuantUtilOpUnitTest::test__int_mm SKIPPED 2025-09-09T14:07:24.6109118Z test/integration/test_integration.py::PythonQuantUtilOpUnitTest::test__int_mm_eager_and_torch_compile_numerics SKIPPED 2025-09-09T14:07:24.6110311Z test/integration/test_integration.py::PythonQuantUtilOpUnitTest::test_dynamic_quant_per_channel_numerics_cpu PASSED 2025-09-09T14:07:24.6111490Z test/integration/test_integration.py::PythonQuantUtilOpUnitTest::test_dynamic_quant_per_channel_numerics_cuda SKIPPED 2025-09-09T14:07:24.6112600Z test/integration/test_integration.py::PythonQuantUtilOpUnitTest::test_per_token_linear_cpu PASSED 2025-09-09T14:07:24.6113635Z test/integration/test_integration.py::PythonQuantUtilOpUnitTest::test_per_token_linear_cuda SKIPPED 2025-09-09T14:07:24.6114663Z test/integration/test_integration.py::PythonQuantUtilOpUnitTest::test_quantize_per_token_cpu PASSED 2025-09-09T14:07:24.6115714Z test/integration/test_integration.py::PythonQuantUtilOpUnitTest::test_quantize_per_token_cuda SKIPPED 2025-09-09T14:07:24.6116757Z test/integration/test_integration.py::PythonQuantUtilOpUnitTest::test_quantize_per_token_xpu SKIPPED 2025-09-09T14:07:24.6117877Z test/integration/test_integration.py::TestSubclass::test_aq_float8_dynamic_quant_rowwise_scaling_subclass_0_cpu SKIPPED 2025-09-09T14:07:24.6119052Z test/integration/test_integration.py::TestSubclass::test_aq_float8_dynamic_quant_rowwise_scaling_subclass_1_cpu SKIPPED 2025-09-09T14:07:24.6120293Z test/integration/test_integration.py::TestSubclass::test_aq_float8_dynamic_quant_rowwise_scaling_subclass_2_cpu SKIPPED 2025-09-09T14:07:24.6121467Z test/integration/test_integration.py::TestSubclass::test_aq_float8_dynamic_quant_rowwise_scaling_subclass_3_cuda SKIPPED 2025-09-09T14:07:24.6122716Z test/integration/test_integration.py::TestSubclass::test_aq_float8_dynamic_quant_rowwise_scaling_subclass_4_cuda SKIPPED 2025-09-09T14:07:24.6123886Z test/integration/test_integration.py::TestSubclass::test_aq_float8_dynamic_quant_rowwise_scaling_subclass_5_cuda SKIPPED 2025-09-09T14:07:29.0689814Z test/integration/test_integration.py::TestSubclass::test_aq_float8_dynamic_quant_tensorwise_scaling_subclass_0_cpu SKIPPED 2025-09-09T14:07:29.0691383Z test/integration/test_integration.py::TestSubclass::test_aq_float8_dynamic_quant_tensorwise_scaling_subclass_1_cpu SKIPPED 2025-09-09T14:07:29.0692884Z test/integration/test_integration.py::TestSubclass::test_aq_float8_dynamic_quant_tensorwise_scaling_subclass_2_cpu SKIPPED 2025-09-09T14:07:29.0694651Z test/integration/test_integration.py::TestSubclass::test_aq_float8_dynamic_quant_tensorwise_scaling_subclass_3_cuda SKIPPED 2025-09-09T14:07:29.0696188Z test/integration/test_integration.py::TestSubclass::test_aq_float8_dynamic_quant_tensorwise_scaling_subclass_4_cuda SKIPPED 2025-09-09T14:07:29.0697711Z test/integration/test_integration.py::TestSubclass::test_aq_float8_dynamic_quant_tensorwise_scaling_subclass_5_cuda SKIPPED 2025-09-09T14:07:29.0699132Z test/integration/test_integration.py::TestSubclass::test_aq_float8_weight_only_quant_subclass_0_cpu SKIPPED 2025-09-09T14:07:29.0700480Z test/integration/test_integration.py::TestSubclass::test_aq_float8_weight_only_quant_subclass_1_cpu SKIPPED 2025-09-09T14:07:29.0701801Z test/integration/test_integration.py::TestSubclass::test_aq_float8_weight_only_quant_subclass_2_cpu SKIPPED 2025-09-09T14:07:29.0703136Z test/integration/test_integration.py::TestSubclass::test_aq_float8_weight_only_quant_subclass_3_cuda SKIPPED 2025-09-09T14:07:29.0704480Z test/integration/test_integration.py::TestSubclass::test_aq_float8_weight_only_quant_subclass_4_cuda SKIPPED 2025-09-09T14:07:29.0705819Z test/integration/test_integration.py::TestSubclass::test_aq_float8_weight_only_quant_subclass_5_cuda SKIPPED 2025-09-09T14:07:29.0707111Z test/integration/test_integration.py::TestSubclass::test_aq_int8_dynamic_quant_subclass_0_cpu SKIPPED 2025-09-09T14:07:29.0708359Z test/integration/test_integration.py::TestSubclass::test_aq_int8_dynamic_quant_subclass_1_cpu SKIPPED 2025-09-09T14:07:29.0709634Z test/integration/test_integration.py::TestSubclass::test_aq_int8_dynamic_quant_subclass_2_cpu SKIPPED 2025-09-09T14:07:29.0710909Z test/integration/test_integration.py::TestSubclass::test_aq_int8_dynamic_quant_subclass_3_cuda SKIPPED 2025-09-09T14:07:29.0712157Z test/integration/test_integration.py::TestSubclass::test_aq_int8_dynamic_quant_subclass_4_cuda SKIPPED 2025-09-09T14:07:29.0713427Z test/integration/test_integration.py::TestSubclass::test_aq_int8_dynamic_quant_subclass_5_cuda SKIPPED 2025-09-09T14:07:29.0714718Z test/integration/test_integration.py::TestSubclass::test_aq_int8_weight_only_quant_2_subclass_0_cpu SKIPPED 2025-09-09T14:07:29.0716037Z test/integration/test_integration.py::TestSubclass::test_aq_int8_weight_only_quant_2_subclass_1_cpu SKIPPED 2025-09-09T14:07:29.0717357Z test/integration/test_integration.py::TestSubclass::test_aq_int8_weight_only_quant_2_subclass_2_cpu SKIPPED 2025-09-09T14:07:29.0718664Z test/integration/test_integration.py::TestSubclass::test_aq_int8_weight_only_quant_2_subclass_3_cuda SKIPPED 2025-09-09T14:07:29.0720051Z test/integration/test_integration.py::TestSubclass::test_aq_int8_weight_only_quant_2_subclass_4_cuda SKIPPED 2025-09-09T14:07:29.0721384Z test/integration/test_integration.py::TestSubclass::test_aq_int8_weight_only_quant_2_subclass_5_cuda SKIPPED 2025-09-09T14:07:29.0722702Z test/integration/test_integration.py::TestSubclass::test_aq_int8_weight_only_quant_3_subclass_0_cpu SKIPPED 2025-09-09T14:07:29.0724021Z test/integration/test_integration.py::TestSubclass::test_aq_int8_weight_only_quant_3_subclass_1_cpu SKIPPED 2025-09-09T14:07:29.0725468Z test/integration/test_integration.py::TestSubclass::test_aq_int8_weight_only_quant_3_subclass_2_cpu SKIPPED 2025-09-09T14:07:29.0726797Z test/integration/test_integration.py::TestSubclass::test_aq_int8_weight_only_quant_3_subclass_3_cuda SKIPPED 2025-09-09T14:07:29.0728121Z test/integration/test_integration.py::TestSubclass::test_aq_int8_weight_only_quant_3_subclass_4_cuda SKIPPED 2025-09-09T14:07:29.0729435Z test/integration/test_integration.py::TestSubclass::test_aq_int8_weight_only_quant_3_subclass_5_cuda SKIPPED 2025-09-09T14:07:29.0730750Z test/integration/test_integration.py::TestSubclass::test_aq_int8_weight_only_quant_subclass_0_cpu SKIPPED 2025-09-09T14:07:29.0732110Z test/integration/test_integration.py::TestSubclass::test_aq_int8_weight_only_quant_subclass_1_cpu SKIPPED 2025-09-09T14:07:29.0733406Z test/integration/test_integration.py::TestSubclass::test_aq_int8_weight_only_quant_subclass_2_cpu SKIPPED 2025-09-09T14:07:29.0734712Z test/integration/test_integration.py::TestSubclass::test_aq_int8_weight_only_quant_subclass_3_cuda SKIPPED 2025-09-09T14:07:29.0736006Z test/integration/test_integration.py::TestSubclass::test_aq_int8_weight_only_quant_subclass_4_cuda SKIPPED 2025-09-09T14:07:29.0737320Z test/integration/test_integration.py::TestSubclass::test_aq_int8_weight_only_quant_subclass_5_cuda SKIPPED 2025-09-09T14:07:29.0738587Z test/integration/test_integration.py::TestSubclass::test_autoquantizable_flatten_unflatten PASSED 2025-09-09T14:07:29.0739923Z test/integration/test_integration.py::TestSubclass::test_dequantize_int4_weight_only_quant_subclass_0_cpu SKIPPED 2025-09-09T14:07:29.0741338Z test/integration/test_integration.py::TestSubclass::test_dequantize_int4_weight_only_quant_subclass_1_cpu SKIPPED 2025-09-09T14:07:29.0742735Z test/integration/test_integration.py::TestSubclass::test_dequantize_int4_weight_only_quant_subclass_2_cpu SKIPPED 2025-09-09T14:07:29.0744145Z test/integration/test_integration.py::TestSubclass::test_dequantize_int4_weight_only_quant_subclass_3_cuda SKIPPED 2025-09-09T14:07:29.0745554Z 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test/integration/test_integration.py::TestSubclass::test_gemlite_layout_0_cpu SKIPPED 2025-09-09T14:07:29.0775522Z test/integration/test_integration.py::TestSubclass::test_gemlite_layout_1_cpu SKIPPED 2025-09-09T14:07:29.0776612Z test/integration/test_integration.py::TestSubclass::test_gemlite_layout_2_cpu SKIPPED 2025-09-09T14:07:29.0777710Z test/integration/test_integration.py::TestSubclass::test_gemlite_layout_3_cuda SKIPPED 2025-09-09T14:07:29.0778798Z test/integration/test_integration.py::TestSubclass::test_gemlite_layout_4_cuda SKIPPED 2025-09-09T14:07:29.0779901Z test/integration/test_integration.py::TestSubclass::test_gemlite_layout_5_cuda SKIPPED 2025-09-09T14:07:29.0781126Z test/integration/test_integration.py::TestSubclass::test_int4_weight_only_hqq_quant_subclass_api_0_cpu SKIPPED 2025-09-09T14:07:29.0782491Z test/integration/test_integration.py::TestSubclass::test_int4_weight_only_hqq_quant_subclass_api_1_cpu SKIPPED 2025-09-09T14:08:00.9361922Z test/integration/test_integration.py::TestSubclass::test_int4_weight_only_hqq_quant_subclass_api_2_cpu PASSED 2025-09-09T14:08:00.9363339Z test/integration/test_integration.py::TestSubclass::test_int4_weight_only_hqq_quant_subclass_api_3_cuda SKIPPED 2025-09-09T14:08:00.9364728Z test/integration/test_integration.py::TestSubclass::test_int4_weight_only_hqq_quant_subclass_api_4_cuda SKIPPED 2025-09-09T14:08:00.9366127Z test/integration/test_integration.py::TestSubclass::test_int4_weight_only_hqq_quant_subclass_api_5_cuda SKIPPED 2025-09-09T14:08:00.9367466Z test/integration/test_integration.py::TestSubclass::test_int4_weight_only_quant_subclass_0_cpu SKIPPED 2025-09-09T14:08:00.9368749Z test/integration/test_integration.py::TestSubclass::test_int4_weight_only_quant_subclass_1_cpu SKIPPED 2025-09-09T14:08:00.9370040Z test/integration/test_integration.py::TestSubclass::test_int4_weight_only_quant_subclass_2_cpu SKIPPED 2025-09-09T14:08:00.9371311Z 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test/integration/test_integration.py::TestSubclass::test_int8_dynamic_quant_subclass_0_cpu SKIPPED 2025-09-09T14:08:00.9401467Z test/integration/test_integration.py::TestSubclass::test_int8_dynamic_quant_subclass_1_cpu SKIPPED 2025-09-09T14:08:00.9402691Z test/integration/test_integration.py::TestSubclass::test_int8_dynamic_quant_subclass_2_cpu SKIPPED 2025-09-09T14:08:00.9403929Z test/integration/test_integration.py::TestSubclass::test_int8_dynamic_quant_subclass_3_cuda SKIPPED 2025-09-09T14:08:00.9405159Z test/integration/test_integration.py::TestSubclass::test_int8_dynamic_quant_subclass_4_cuda SKIPPED 2025-09-09T14:08:00.9406402Z test/integration/test_integration.py::TestSubclass::test_int8_dynamic_quant_subclass_5_cuda SKIPPED 2025-09-09T14:08:00.9407678Z test/integration/test_integration.py::TestSubclass::test_int8_dynamic_quant_subclass_api_00_cpu SKIPPED 2025-09-09T14:08:00.9408947Z test/integration/test_integration.py::TestSubclass::test_int8_dynamic_quant_subclass_api_01_cpu 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test/integration/test_integration.py::TestSubclass::test_int8_dynamic_quant_subclass_api_09_cuda SKIPPED 2025-09-09T14:08:00.9420596Z test/integration/test_integration.py::TestSubclass::test_int8_dynamic_quant_subclass_api_10_cuda SKIPPED 2025-09-09T14:08:00.9421876Z test/integration/test_integration.py::TestSubclass::test_int8_dynamic_quant_subclass_api_11_cuda SKIPPED 2025-09-09T14:08:00.9423232Z test/integration/test_integration.py::TestSubclass::test_int8_weight_only_quant_subclass_0_cpu SKIPPED 2025-09-09T14:08:00.9424541Z test/integration/test_integration.py::TestSubclass::test_int8_weight_only_quant_subclass_1_cpu SKIPPED 2025-09-09T14:08:00.9425808Z test/integration/test_integration.py::TestSubclass::test_int8_weight_only_quant_subclass_2_cpu SKIPPED 2025-09-09T14:08:00.9427086Z test/integration/test_integration.py::TestSubclass::test_int8_weight_only_quant_subclass_3_cuda SKIPPED 2025-09-09T14:08:00.9428358Z test/integration/test_integration.py::TestSubclass::test_int8_weight_only_quant_subclass_4_cuda SKIPPED 2025-09-09T14:08:00.9429642Z test/integration/test_integration.py::TestSubclass::test_int8_weight_only_quant_subclass_5_cuda SKIPPED 2025-09-09T14:08:00.9430939Z test/integration/test_integration.py::TestSubclass::test_int8_weight_only_quant_subclass_api_0_cpu PASSED 2025-09-09T14:08:00.9432238Z test/integration/test_integration.py::TestSubclass::test_int8_weight_only_quant_subclass_api_1_cpu PASSED 2025-09-09T14:08:00.9433534Z test/integration/test_integration.py::TestSubclass::test_int8_weight_only_quant_subclass_api_2_cpu PASSED 2025-09-09T14:08:00.9434844Z test/integration/test_integration.py::TestSubclass::test_int8_weight_only_quant_subclass_api_3_cuda SKIPPED 2025-09-09T14:08:00.9436167Z test/integration/test_integration.py::TestSubclass::test_int8_weight_only_quant_subclass_api_4_cuda SKIPPED 2025-09-09T14:08:00.9437491Z test/integration/test_integration.py::TestSubclass::test_int8_weight_only_quant_subclass_api_5_cuda SKIPPED 2025-09-09T14:08:00.9438800Z test/integration/test_integration.py::TestSubclass::test_int8_weight_only_quant_with_freeze_0_cpu AUTOTUNE packed_linear(32x64, 1982689x1, 32x64) 2025-09-09T14:08:00.9439854Z cpp_CppMicroGemmFP32Vec_0 0.0031 ms 100.0% 2025-09-09T14:08:00.9440267Z _mkl_linear 0.0163 ms 19.0% 2025-09-09T14:08:00.9440939Z SingleProcess AUTOTUNE benchmarking takes 0.2509 seconds and 2.4531 seconds precompiling for 2 choices 2025-09-09T14:08:00.9441700Z AUTOTUNE packed_linear(32x32, 1982689x1, 32x32) 2025-09-09T14:08:00.9442142Z cpp_CppMicroGemmFP32Vec_1 0.0032 ms 100.0% 2025-09-09T14:08:00.9442562Z _mkl_linear 0.0162 ms 19.6% 2025-09-09T14:08:00.9443186Z SingleProcess AUTOTUNE benchmarking takes 0.2501 seconds and 2.4552 seconds precompiling for 2 choices 2025-09-09T14:08:00.9443781Z PASSED 2025-09-09T14:08:00.9444364Z test/integration/test_integration.py::TestSubclass::test_int8_weight_only_quant_with_freeze_1_cpu AUTOTUNE mm(32x64, 64x32) 2025-09-09T14:08:00.9445040Z cpp_CppMicroGemmFP32Vec_2 0.0037 ms 100.0% 2025-09-09T14:08:00.9445362Z mm 0.0247 ms 15.1% 2025-09-09T14:08:00.9445855Z SingleProcess AUTOTUNE benchmarking takes 0.2493 seconds and 2.5722 seconds precompiling for 2 choices 2025-09-09T14:08:00.9446427Z AUTOTUNE mm(32x32, 32x32) 2025-09-09T14:08:00.9446709Z cpp_CppMicroGemmFP32Vec_3 0.0033 ms 100.0% 2025-09-09T14:08:00.9447030Z mm 0.0238 ms 14.1% 2025-09-09T14:08:00.9447528Z SingleProcess AUTOTUNE benchmarking takes 0.2499 seconds and 2.5640 seconds precompiling for 2 choices 2025-09-09T14:08:00.9448113Z PASSED 2025-09-09T14:08:00.9448865Z test/integration/test_integration.py::TestSubclass::test_int8_weight_only_quant_with_freeze_2_cpu AUTOTUNE _weight_int8pack_mm(32x64, 32x64, 32) 2025-09-09T14:08:00.9449623Z cpp_CppMicroGemmFP32Vec_4 0.0036 ms 100.0% 2025-09-09T14:08:00.9449975Z _weight_int8pack_mm 0.0154 ms 23.2% 2025-09-09T14:08:00.9450531Z SingleProcess AUTOTUNE benchmarking takes 0.2508 seconds and 2.5796 seconds precompiling for 2 choices 2025-09-09T14:08:00.9451134Z AUTOTUNE _weight_int8pack_mm(32x32, 32x32, 32) 2025-09-09T14:08:14.8564110Z cpp_CppMicroGemmFP32Vec_5 0.0034 ms 100.0% 2025-09-09T14:08:14.8564518Z _weight_int8pack_mm 0.0147 ms 23.1% 2025-09-09T14:08:14.8565109Z SingleProcess AUTOTUNE benchmarking takes 0.2503 seconds and 2.5668 seconds precompiling for 2 choices 2025-09-09T14:08:14.8566197Z PASSED 2025-09-09T14:08:14.8566889Z test/integration/test_integration.py::TestSubclass::test_int8_weight_only_quant_with_freeze_3_cuda SKIPPED 2025-09-09T14:08:14.8568173Z test/integration/test_integration.py::TestSubclass::test_int8_weight_only_quant_with_freeze_4_cuda SKIPPED 2025-09-09T14:08:14.8569249Z test/integration/test_integration.py::TestSubclass::test_int8_weight_only_quant_with_freeze_5_cuda SKIPPED 2025-09-09T14:08:14.8570179Z test/integration/test_integration.py::TestDynamicQuant::test_dynamic_quant PASSED 2025-09-09T14:08:14.8571392Z test/integration/test_integration.py::TestWeightOnlyInt8Quant::test_weight_only_groupwise_embedding_quant PASSED 2025-09-09T14:08:14.8572502Z test/integration/test_integration.py::TestWeightOnlyInt8Quant::test_weight_only_groupwise_quant PASSED 2025-09-09T14:08:14.8573524Z test/integration/test_integration.py::TestWeightOnlyInt8Quant::test_weight_only_quant PASSED 2025-09-09T14:08:14.8574613Z test/integration/test_integration.py::TestWeightOnlyInt8Quant::test_weight_only_quant_force_mixed_mm_0_cpu SKIPPED 2025-09-09T14:08:14.8575762Z test/integration/test_integration.py::TestWeightOnlyInt8Quant::test_weight_only_quant_force_mixed_mm_1_cpu SKIPPED 2025-09-09T14:08:14.8577092Z test/integration/test_integration.py::TestWeightOnlyInt8Quant::test_weight_only_quant_force_mixed_mm_2_cpu SKIPPED 2025-09-09T14:08:14.8578241Z test/integration/test_integration.py::TestWeightOnlyInt8Quant::test_weight_only_quant_force_mixed_mm_3_cuda SKIPPED 2025-09-09T14:08:14.8579587Z test/integration/test_integration.py::TestWeightOnlyInt8Quant::test_weight_only_quant_force_mixed_mm_4_cuda SKIPPED 2025-09-09T14:08:14.8580768Z test/integration/test_integration.py::TestWeightOnlyInt8Quant::test_weight_only_quant_force_mixed_mm_5_cuda SKIPPED 2025-09-09T14:08:14.8581917Z test/integration/test_integration.py::TestWeightOnlyInt8Quant::test_weight_only_quant_use_mixed_mm_0_cpu SKIPPED 2025-09-09T14:08:14.8583057Z test/integration/test_integration.py::TestWeightOnlyInt8Quant::test_weight_only_quant_use_mixed_mm_1_cpu SKIPPED 2025-09-09T14:08:14.8584204Z test/integration/test_integration.py::TestWeightOnlyInt8Quant::test_weight_only_quant_use_mixed_mm_2_cpu SKIPPED 2025-09-09T14:08:14.8585494Z test/integration/test_integration.py::TestWeightOnlyInt8Quant::test_weight_only_quant_use_mixed_mm_3_cuda SKIPPED 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test/integration/test_integration.py::TestSaveLoadMeta::test_save_load_dqtensors_5_cuda SKIPPED 2025-09-09T14:08:14.8595207Z test/integration/test_integration.py::TestSaveLoadMeta::test_save_load_int4woqtensors_0_cpu SKIPPED 2025-09-09T14:08:14.8596392Z test/integration/test_integration.py::TestSaveLoadMeta::test_save_load_int4woqtensors_1_cpu SKIPPED 2025-09-09T14:08:14.8597409Z test/integration/test_integration.py::TestSaveLoadMeta::test_save_load_int4woqtensors_2_cpu PASSED 2025-09-09T14:08:14.8598489Z test/integration/test_integration.py::TestSaveLoadMeta::test_save_load_int4woqtensors_3_cuda SKIPPED 2025-09-09T14:08:14.8599505Z test/integration/test_integration.py::TestSaveLoadMeta::test_save_load_int4woqtensors_4_cuda SKIPPED 2025-09-09T14:08:14.8600611Z test/integration/test_integration.py::TestSaveLoadMeta::test_save_load_int4woqtensors_5_cuda SKIPPED 2025-09-09T14:08:14.8601762Z test/integration/test_integration.py::TestSaveLoadMeta::test_save_load_int8woqtensors_0_cpu PASSED 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SKIPPED 2025-09-09T14:08:14.8610802Z test/integration/test_integration.py::SmoothquantIntegrationTest::test_on_dummy_distilbert 2025-09-09T14:08:14.8611391Z tokenizer_config.json: 0% 0.00/48.0 [00:00) 2025-09-09T14:11:43.0273116Z converted model pt2e: GraphModule( 2025-09-09T14:11:43.0273479Z (conv): Module() 2025-09-09T14:11:43.0273739Z (bn): Module() 2025-09-09T14:11:43.0274000Z ) 2025-09-09T14:11:43.0274128Z 2025-09-09T14:11:43.0274133Z 2025-09-09T14:11:43.0274138Z 2025-09-09T14:11:43.0274251Z def forward(self, x): 2025-09-09T14:11:43.0274631Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:11:43.0275080Z conv_bias = self.conv.bias 2025-09-09T14:11:43.0275485Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T14:11:43.0276492Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.010372933000326157, 0, -128, 127, torch.int8); x = None 2025-09-09T14:11:43.0278262Z 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 2025-09-09T14:11:43.0279821Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T14:11:43.0280481Z _scale_0 = self._scale_0 2025-09-09T14:11:43.0280820Z _zero_point_0 = self._zero_point_0 2025-09-09T14:11:43.0281234Z quantize_per_channel = self._frozen_param0 2025-09-09T14:11:43.0282867Z 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 2025-09-09T14:11:43.0284811Z 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 2025-09-09T14:11:43.0286538Z 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 2025-09-09T14:11:43.0288403Z 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 2025-09-09T14:11:43.0290023Z return pytree.tree_unflatten((dequantize_per_tensor_default_1,), self._out_spec) 2025-09-09T14:11:43.0290611Z 2025-09-09T14:11:43.0290981Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:11:43.0291514Z onverted model fx: GraphModule( 2025-09-09T14:11:43.0292017Z (conv): QuantizedConv1d(Reference)(3, 3, kernel_size=(3,), stride=(1,)) 2025-09-09T14:11:43.0292540Z ) 2025-09-09T14:11:43.0292668Z 2025-09-09T14:11:43.0292672Z 2025-09-09T14:11:43.0292677Z 2025-09-09T14:11:43.0292804Z def forward(self, x): 2025-09-09T14:11:43.0293655Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.010372933000326157, 0, -128, 127, torch.int8); x = None 2025-09-09T14:11:43.0295434Z 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 2025-09-09T14:11:43.0296867Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T14:11:43.0298091Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.010256201960146427, -10, -128, 127, torch.int8); conv = None 2025-09-09T14:11:43.0299934Z 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 2025-09-09T14:11:43.0301188Z return dequantize_per_tensor_default_1 2025-09-09T14:11:43.0301566Z 2025-09-09T14:11:43.0301930Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:11:43.0302435Z diff: tensor([[[0., 0., 0.], 2025-09-09T14:11:43.0302757Z [0., 0., 0.], 2025-09-09T14:11:43.0303037Z [0., 0., 0.]]]) 2025-09-09T14:11:43.0303352Z model pt2e: GraphModule( 2025-09-09T14:11:43.0303652Z (conv): Module() 2025-09-09T14:11:43.0303933Z (bn): Module() 2025-09-09T14:11:43.0304330Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:11:43.0305662Z 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 2025-09-09T14:11:43.0307240Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3264806270599365, max_val=1.318617343902588) 2025-09-09T14:11:43.0307954Z ) 2025-09-09T14:11:43.0308329Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:11:43.0309654Z 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 2025-09-09T14:11:43.0311246Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.3201889097690582, max_val=0.3243715763092041) 2025-09-09T14:11:43.0311967Z ) 2025-09-09T14:11:43.0312337Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:11:43.0313754Z 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 2025-09-09T14:11:43.0315306Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.20903742313385, max_val=1.4068148136138916) 2025-09-09T14:11:43.0316025Z ) 2025-09-09T14:11:43.0316239Z ) 2025-09-09T14:11:43.0316376Z 2025-09-09T14:11:43.0316380Z 2025-09-09T14:11:43.0316385Z 2025-09-09T14:11:43.0316495Z def forward(self, x): 2025-09-09T14:11:43.0316877Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:11:43.0317400Z conv_weight = self.conv.weight 2025-09-09T14:11:43.0317776Z conv_bias = self.conv.bias 2025-09-09T14:11:43.0318109Z bn_weight = self.bn.weight 2025-09-09T14:11:43.0318450Z bn_bias = self.bn.bias 2025-09-09T14:11:43.0318787Z bn_running_mean = self.bn.running_mean 2025-09-09T14:11:43.0319197Z bn_running_var = self.bn.running_var 2025-09-09T14:11:43.0319719Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T14:11:43.0320333Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:11:43.0321148Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T14:11:43.0321866Z add = torch.ops.aten.add.Tensor(bn_running_var, 1e-05) 2025-09-09T14:11:43.0322404Z sqrt = torch.ops.aten.sqrt.default(add); add = None 2025-09-09T14:11:43.0322958Z div = torch.ops.aten.div.Tensor(bn_weight, sqrt); sqrt = None 2025-09-09T14:11:43.0323564Z reshape = torch.ops.aten.reshape.default(div, [-1, 1, 1]) 2025-09-09T14:11:43.0324252Z mul = torch.ops.aten.mul.Tensor(conv_weight, reshape); conv_weight = reshape = None 2025-09-09T14:11:43.0325038Z activation_post_process_1 = self.activation_post_process_1(mul); mul = None 2025-09-09T14:11:43.0325908Z zeros_like = torch.ops.aten.zeros_like.default(conv_bias, dtype = torch.float32, pin_memory = False) 2025-09-09T14:11:43.0327277Z 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 2025-09-09T14:11:43.0328534Z reshape_1 = torch.ops.aten.reshape.default(div, [1, -1, 1]); div = None 2025-09-09T14:11:43.0329262Z div_1 = torch.ops.aten.div.Tensor(conv1d_1, reshape_1); conv1d_1 = reshape_1 = None 2025-09-09T14:11:43.0330062Z reshape_2 = torch.ops.aten.reshape.default(conv_bias, [1, -1, 1]); conv_bias = None 2025-09-09T14:11:43.0330829Z add_1 = torch.ops.aten.add.Tensor(div_1, reshape_2); div_1 = reshape_2 = None 2025-09-09T14:11:43.0332052Z 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 2025-09-09T14:11:43.0333375Z activation_post_process_2 = self.activation_post_process_2(batch_norm_1); batch_norm_1 = None 2025-09-09T14:11:43.0334217Z return pytree.tree_unflatten((activation_post_process_2,), self._out_spec) 2025-09-09T14:11:43.0334745Z 2025-09-09T14:11:43.0335127Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:11:43.0335623Z model fx: GraphModule( 2025-09-09T14:11:43.0336063Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:11:43.0337398Z 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 2025-09-09T14:11:43.0338982Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3264806270599365, max_val=1.318617343902588) 2025-09-09T14:11:43.0339706Z ) 2025-09-09T14:11:43.0339938Z (conv): ConvBn1d( 2025-09-09T14:11:43.0340244Z 3, 3, kernel_size=(3,), stride=(1,) 2025-09-09T14:11:43.0340884Z (bn): BatchNorm1d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T14:11:43.0341537Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:11:43.0342841Z 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 2025-09-09T14:11:43.0344456Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.3201889097690582, max_val=0.3243715763092041) 2025-09-09T14:11:43.0345194Z ) 2025-09-09T14:11:43.0345490Z ) 2025-09-09T14:11:55.6267746Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:11:55.6269173Z 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 2025-09-09T14:11:55.6270809Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.20903742313385, max_val=1.4068148136138916) 2025-09-09T14:11:55.6271547Z ) 2025-09-09T14:11:55.6271818Z ) 2025-09-09T14:11:55.6271947Z 2025-09-09T14:11:55.6271954Z 2025-09-09T14:11:55.6271958Z 2025-09-09T14:11:55.6272072Z def forward(self, x): 2025-09-09T14:11:55.6272561Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:11:55.6273306Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T14:11:55.6274055Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T14:11:55.6274660Z return activation_post_process_1 2025-09-09T14:11:55.6275007Z 2025-09-09T14:11:55.6275382Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:11:55.6275882Z diff: tensor([[[0., 0., 0.], 2025-09-09T14:11:55.6276206Z [0., 0., 0.], 2025-09-09T14:11:55.6276515Z [0., 0., 0.]]], grad_fn=) 2025-09-09T14:11:55.6276938Z converted model pt2e: GraphModule( 2025-09-09T14:11:55.6277298Z (conv): Module() 2025-09-09T14:11:55.6277567Z (bn): Module() 2025-09-09T14:11:55.6277833Z ) 2025-09-09T14:11:55.6277960Z 2025-09-09T14:11:55.6277965Z 2025-09-09T14:11:55.6277970Z 2025-09-09T14:11:55.6278084Z def forward(self, x): 2025-09-09T14:11:55.6278469Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:11:55.6278920Z conv_bias = self.conv.bias 2025-09-09T14:11:55.6279330Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T14:11:55.6280395Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.010372933000326157, 0, -128, 127, torch.int8); x = None 2025-09-09T14:11:55.6282178Z 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 2025-09-09T14:11:55.6283678Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T14:11:55.6284355Z quantize_per_tensor = self._frozen_param0 2025-09-09T14:11:55.6285481Z 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 2025-09-09T14:11:55.6287296Z 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 2025-09-09T14:11:55.6289010Z 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 2025-09-09T14:11:55.6291222Z 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 2025-09-09T14:11:55.6292669Z return pytree.tree_unflatten((dequantize_per_tensor_default_2,), self._out_spec) 2025-09-09T14:11:55.6293239Z 2025-09-09T14:11:55.6293619Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:11:55.6294125Z onverted model fx: GraphModule( 2025-09-09T14:11:55.6294647Z (conv): QuantizedConv1d(Reference)(3, 3, kernel_size=(3,), stride=(1,)) 2025-09-09T14:11:55.6295164Z ) 2025-09-09T14:11:55.6295306Z 2025-09-09T14:11:55.6295311Z 2025-09-09T14:11:55.6295316Z 2025-09-09T14:11:55.6295430Z def forward(self, x): 2025-09-09T14:11:55.6296406Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.010372933000326157, 0, -128, 127, torch.int8); x = None 2025-09-09T14:11:55.6298155Z 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 2025-09-09T14:11:55.6299607Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T14:11:55.6300822Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.010258244350552559, -10, -128, 127, torch.int8); conv = None 2025-09-09T14:11:55.6302658Z 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 2025-09-09T14:11:55.6303914Z return dequantize_per_tensor_default_1 2025-09-09T14:11:55.6304289Z 2025-09-09T14:11:55.6304665Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:11:55.6305160Z diff: tensor([[[0., 0., 0.], 2025-09-09T14:11:55.6305478Z [0., 0., 0.], 2025-09-09T14:11:55.6305753Z [0., 0., 0.]]]) 2025-09-09T14:11:55.6306270Z PASSED 2025-09-09T14:11:55.6307211Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn1d::test_qat_conv_bn_fusion_cuda SKIPPED 2025-09-09T14:11:55.6308608Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn1d::test_qat_conv_bn_fusion_literal_args model pt2e: GraphModule( 2025-09-09T14:11:55.6309527Z (conv): Module() 2025-09-09T14:11:55.6309796Z (bn): Module() 2025-09-09T14:11:55.6310203Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:11:55.6311531Z 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 2025-09-09T14:11:55.6313107Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.276310682296753, max_val=1.8198994398117065) 2025-09-09T14:11:55.6313831Z ) 2025-09-09T14:11:55.6314192Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:11:55.6315609Z 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 2025-09-09T14:11:55.6317444Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.3263, -0.3276, -0.3045]), max_val=tensor([0.2760, 0.3011, 0.3298])) 2025-09-09T14:11:55.6318374Z ) 2025-09-09T14:11:55.6318748Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:11:55.6320149Z 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 2025-09-09T14:11:55.6321726Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.7719206809997559, max_val=2.111994981765747) 2025-09-09T14:11:55.6322444Z ) 2025-09-09T14:11:55.6322782Z ) 2025-09-09T14:11:55.6322911Z 2025-09-09T14:11:55.6322916Z 2025-09-09T14:11:55.6322921Z 2025-09-09T14:11:55.6323049Z def forward(self, x): 2025-09-09T14:11:55.6323420Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:11:55.6323888Z conv_weight = self.conv.weight 2025-09-09T14:11:55.6324255Z conv_bias = self.conv.bias 2025-09-09T14:11:55.6324601Z bn_weight = self.bn.weight 2025-09-09T14:11:55.6324929Z bn_bias = self.bn.bias 2025-09-09T14:11:55.6325275Z bn_running_mean = self.bn.running_mean 2025-09-09T14:11:55.6325672Z bn_running_var = self.bn.running_var 2025-09-09T14:11:55.6326201Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T14:11:55.6326810Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:11:55.6327613Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T14:11:55.6328356Z add = torch.ops.aten.add.Tensor(bn_running_var, 1e-05) 2025-09-09T14:11:55.6328884Z sqrt = torch.ops.aten.sqrt.default(add); add = None 2025-09-09T14:11:55.6329456Z div = torch.ops.aten.div.Tensor(bn_weight, sqrt); sqrt = None 2025-09-09T14:11:55.6330052Z reshape = torch.ops.aten.reshape.default(div, [-1, 1, 1]) 2025-09-09T14:11:55.6330751Z mul = torch.ops.aten.mul.Tensor(conv_weight, reshape); conv_weight = reshape = None 2025-09-09T14:11:55.6331537Z activation_post_process_1 = self.activation_post_process_1(mul); mul = None 2025-09-09T14:11:55.6332386Z zeros_like = torch.ops.aten.zeros_like.default(conv_bias, dtype = torch.float32, pin_memory = False) 2025-09-09T14:11:55.6333789Z 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 2025-09-09T14:11:55.6335048Z reshape_1 = torch.ops.aten.reshape.default(div, [1, -1, 1]); div = None 2025-09-09T14:11:55.6335801Z div_1 = torch.ops.aten.div.Tensor(conv1d_1, reshape_1); conv1d_1 = reshape_1 = None 2025-09-09T14:11:55.6336605Z reshape_2 = torch.ops.aten.reshape.default(conv_bias, [1, -1, 1]); conv_bias = None 2025-09-09T14:11:55.6337364Z add_1 = torch.ops.aten.add.Tensor(div_1, reshape_2); div_1 = reshape_2 = None 2025-09-09T14:11:55.6338595Z 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 2025-09-09T14:11:55.6339905Z activation_post_process_2 = self.activation_post_process_2(batch_norm_1); batch_norm_1 = None 2025-09-09T14:11:55.6340744Z return pytree.tree_unflatten((activation_post_process_2,), self._out_spec) 2025-09-09T14:11:55.6341290Z 2025-09-09T14:11:55.6341659Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:11:55.6342165Z model fx: GraphModule( 2025-09-09T14:11:55.6342594Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:11:55.6343928Z 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 2025-09-09T14:11:55.6345505Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.276310682296753, max_val=1.8198994398117065) 2025-09-09T14:11:55.6346234Z ) 2025-09-09T14:11:55.6346484Z (conv): ConvBn1d( 2025-09-09T14:11:55.6346817Z 3, 3, kernel_size=(3,), stride=(2,), padding=(4,) 2025-09-09T14:11:55.6347422Z (bn): BatchNorm1d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T14:11:55.6348061Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:12:05.3907721Z 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 2025-09-09T14:12:05.3911666Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.3263, -0.3276, -0.3045]), max_val=tensor([0.2760, 0.3011, 0.3298])) 2025-09-09T14:12:05.3912609Z ) 2025-09-09T14:12:05.3912849Z ) 2025-09-09T14:12:05.3913213Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:12:05.3914562Z 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 2025-09-09T14:12:05.3916450Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.7719206809997559, max_val=2.111994981765747) 2025-09-09T14:12:05.3917169Z ) 2025-09-09T14:12:05.3917402Z ) 2025-09-09T14:12:05.3917528Z 2025-09-09T14:12:05.3917533Z 2025-09-09T14:12:05.3917538Z 2025-09-09T14:12:05.3917656Z def forward(self, x): 2025-09-09T14:12:05.3918136Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:12:05.3918874Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T14:12:05.3919682Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T14:12:05.3920278Z return activation_post_process_1 2025-09-09T14:12:05.3920625Z 2025-09-09T14:12:05.3920998Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:12:05.3921498Z diff: tensor([[[0., 0., 0., 0., 0., 0.], 2025-09-09T14:12:05.3921876Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:12:05.3922250Z [0., 0., 0., 0., 0., 0.]]], grad_fn=) 2025-09-09T14:12:05.3922687Z converted model pt2e: GraphModule( 2025-09-09T14:12:05.3923045Z (conv): Module() 2025-09-09T14:12:05.3923310Z (bn): Module() 2025-09-09T14:12:05.3923573Z ) 2025-09-09T14:12:05.3923698Z 2025-09-09T14:12:05.3923712Z 2025-09-09T14:12:05.3923717Z 2025-09-09T14:12:05.3923830Z def forward(self, x): 2025-09-09T14:12:05.3924209Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:12:05.3924655Z conv_bias = self.conv.bias 2025-09-09T14:12:05.3925067Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T14:12:05.3926074Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.016063569113612175, 14, -128, 127, torch.int8); x = None 2025-09-09T14:12:05.3927861Z 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 2025-09-09T14:12:05.3929357Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T14:12:05.3929992Z _scale_0 = self._scale_0 2025-09-09T14:12:05.3930341Z _zero_point_0 = self._zero_point_0 2025-09-09T14:12:05.3930758Z quantize_per_channel = self._frozen_param0 2025-09-09T14:12:05.3932009Z 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 2025-09-09T14:12:05.3933969Z 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 2025-09-09T14:12:05.3935694Z 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 2025-09-09T14:12:05.3937570Z 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 2025-09-09T14:12:05.3939093Z return pytree.tree_unflatten((dequantize_per_tensor_default_1,), self._out_spec) 2025-09-09T14:12:05.3939661Z 2025-09-09T14:12:05.3940044Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:12:05.3940551Z onverted model fx: GraphModule( 2025-09-09T14:12:05.3941124Z (conv): QuantizedConv1d(Reference)(3, 3, kernel_size=(3,), stride=(2,), padding=(4,)) 2025-09-09T14:12:05.3941716Z ) 2025-09-09T14:12:05.3941844Z 2025-09-09T14:12:05.3941849Z 2025-09-09T14:12:05.3941854Z 2025-09-09T14:12:05.3941965Z def forward(self, x): 2025-09-09T14:12:05.3942831Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.016063569113612175, 14, -128, 127, torch.int8); x = None 2025-09-09T14:12:05.3944669Z 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 2025-09-09T14:12:05.3946125Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T14:12:05.3947334Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.015231042169034481, -12, -128, 127, torch.int8); conv = None 2025-09-09T14:12:05.3949163Z 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 2025-09-09T14:12:05.3950423Z return dequantize_per_tensor_default_1 2025-09-09T14:12:05.3950804Z 2025-09-09T14:12:05.3951165Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:12:05.3951677Z diff: tensor([[[0., 0., 0., 0., 0., 0.], 2025-09-09T14:12:05.3952033Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:12:05.3952367Z [0., 0., 0., 0., 0., 0.]]]) 2025-09-09T14:12:05.3952709Z model pt2e: GraphModule( 2025-09-09T14:12:05.3953025Z (conv): Module() 2025-09-09T14:12:05.3953299Z (bn): Module() 2025-09-09T14:12:05.3953704Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:12:05.3955012Z 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 2025-09-09T14:12:05.3956586Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.276310682296753, max_val=1.8198994398117065) 2025-09-09T14:12:05.3957316Z ) 2025-09-09T14:12:05.3957685Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:12:05.3959380Z 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 2025-09-09T14:12:05.3961041Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.32764676213264465, max_val=0.3298276662826538) 2025-09-09T14:12:05.3961774Z ) 2025-09-09T14:12:05.3962151Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:12:05.3963467Z 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 2025-09-09T14:12:05.3965038Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.7719206809997559, max_val=2.113234519958496) 2025-09-09T14:12:05.3965765Z ) 2025-09-09T14:12:05.3965982Z ) 2025-09-09T14:12:05.3966108Z 2025-09-09T14:12:05.3966113Z 2025-09-09T14:12:05.3966118Z 2025-09-09T14:12:05.3966240Z def forward(self, x): 2025-09-09T14:12:05.3966610Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:12:05.3967076Z conv_weight = self.conv.weight 2025-09-09T14:12:05.3967439Z conv_bias = self.conv.bias 2025-09-09T14:12:05.3967921Z bn_weight = self.bn.weight 2025-09-09T14:12:05.3968255Z bn_bias = self.bn.bias 2025-09-09T14:12:05.3968608Z bn_running_mean = self.bn.running_mean 2025-09-09T14:12:05.3969024Z bn_running_var = self.bn.running_var 2025-09-09T14:12:05.3969471Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T14:12:05.3970083Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:12:05.3970885Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T14:12:05.3971615Z add = torch.ops.aten.add.Tensor(bn_running_var, 1e-05) 2025-09-09T14:12:05.3972246Z sqrt = torch.ops.aten.sqrt.default(add); add = None 2025-09-09T14:12:05.3972815Z div = torch.ops.aten.div.Tensor(bn_weight, sqrt); sqrt = None 2025-09-09T14:12:05.3973427Z reshape = torch.ops.aten.reshape.default(div, [-1, 1, 1]) 2025-09-09T14:12:05.3974116Z mul = torch.ops.aten.mul.Tensor(conv_weight, reshape); conv_weight = reshape = None 2025-09-09T14:12:05.3974899Z activation_post_process_1 = self.activation_post_process_1(mul); mul = None 2025-09-09T14:12:05.3975745Z zeros_like = torch.ops.aten.zeros_like.default(conv_bias, dtype = torch.float32, pin_memory = False) 2025-09-09T14:12:05.3977149Z 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 2025-09-09T14:12:05.3978405Z reshape_1 = torch.ops.aten.reshape.default(div, [1, -1, 1]); div = None 2025-09-09T14:12:05.3979138Z div_1 = torch.ops.aten.div.Tensor(conv1d_1, reshape_1); conv1d_1 = reshape_1 = None 2025-09-09T14:12:05.3979938Z reshape_2 = torch.ops.aten.reshape.default(conv_bias, [1, -1, 1]); conv_bias = None 2025-09-09T14:12:05.3980695Z add_1 = torch.ops.aten.add.Tensor(div_1, reshape_2); div_1 = reshape_2 = None 2025-09-09T14:12:05.3981931Z 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 2025-09-09T14:12:05.3983246Z activation_post_process_2 = self.activation_post_process_2(batch_norm_1); batch_norm_1 = None 2025-09-09T14:12:05.3984070Z return pytree.tree_unflatten((activation_post_process_2,), self._out_spec) 2025-09-09T14:12:05.3984614Z 2025-09-09T14:12:05.3984983Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:12:05.3985499Z model fx: GraphModule( 2025-09-09T14:12:05.3985932Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:12:05.3987269Z 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 2025-09-09T14:12:17.8809788Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.276310682296753, max_val=1.8198994398117065) 2025-09-09T14:12:17.8810563Z ) 2025-09-09T14:12:17.8810801Z (conv): ConvBn1d( 2025-09-09T14:12:17.8811152Z 3, 3, kernel_size=(3,), stride=(2,), padding=(4,) 2025-09-09T14:12:17.8811746Z (bn): BatchNorm1d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T14:12:17.8812398Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:12:17.8813719Z 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 2025-09-09T14:12:17.8815356Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.32764676213264465, max_val=0.3298276662826538) 2025-09-09T14:12:17.8816104Z ) 2025-09-09T14:12:17.8816329Z ) 2025-09-09T14:12:17.8816705Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:12:17.8818410Z 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 2025-09-09T14:12:17.8819984Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.7719206809997559, max_val=2.113234519958496) 2025-09-09T14:12:17.8820705Z ) 2025-09-09T14:12:17.8820926Z ) 2025-09-09T14:12:17.8821068Z 2025-09-09T14:12:17.8821073Z 2025-09-09T14:12:17.8821078Z 2025-09-09T14:12:17.8821191Z def forward(self, x): 2025-09-09T14:12:17.8821798Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:12:17.8822531Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T14:12:17.8823294Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T14:12:17.8823883Z return activation_post_process_1 2025-09-09T14:12:17.8824247Z 2025-09-09T14:12:17.8824613Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:12:17.8825124Z diff: tensor([[[0., 0., 0., 0., 0., 0.], 2025-09-09T14:12:17.8825498Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:12:17.8825873Z [0., 0., 0., 0., 0., 0.]]], grad_fn=) 2025-09-09T14:12:17.8826316Z converted model pt2e: GraphModule( 2025-09-09T14:12:17.8826663Z (conv): Module() 2025-09-09T14:12:17.8826941Z (bn): Module() 2025-09-09T14:12:17.8827193Z ) 2025-09-09T14:12:17.8827331Z 2025-09-09T14:12:17.8827336Z 2025-09-09T14:12:17.8827341Z 2025-09-09T14:12:17.8827459Z def forward(self, x): 2025-09-09T14:12:17.8827828Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:12:17.8828293Z conv_bias = self.conv.bias 2025-09-09T14:12:17.8828707Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T14:12:17.8829712Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.016063569113612175, 14, -128, 127, torch.int8); x = None 2025-09-09T14:12:17.8831542Z 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 2025-09-09T14:12:17.8833026Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T14:12:17.8833697Z quantize_per_tensor = self._frozen_param0 2025-09-09T14:12:17.8834830Z 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 2025-09-09T14:12:17.8836649Z 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 2025-09-09T14:12:17.8838365Z 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 2025-09-09T14:12:17.8840290Z 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 2025-09-09T14:12:17.8841725Z return pytree.tree_unflatten((dequantize_per_tensor_default_2,), self._out_spec) 2025-09-09T14:12:17.8842293Z 2025-09-09T14:12:17.8842678Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:12:17.8843190Z onverted model fx: GraphModule( 2025-09-09T14:12:17.8843756Z (conv): QuantizedConv1d(Reference)(3, 3, kernel_size=(3,), stride=(2,), padding=(4,)) 2025-09-09T14:12:17.8844321Z ) 2025-09-09T14:12:17.8844461Z 2025-09-09T14:12:17.8844466Z 2025-09-09T14:12:17.8844470Z 2025-09-09T14:12:17.8844582Z def forward(self, x): 2025-09-09T14:12:17.8845547Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.016063569113612175, 14, -128, 127, torch.int8); x = None 2025-09-09T14:12:17.8847317Z 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 2025-09-09T14:12:17.8848752Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T14:12:17.8849943Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.0152359027415514, -12, -128, 127, torch.int8); conv = None 2025-09-09T14:12:17.8851838Z 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 2025-09-09T14:12:17.8853097Z return dequantize_per_tensor_default_1 2025-09-09T14:12:17.8853461Z 2025-09-09T14:12:17.8853834Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:12:17.8854329Z diff: tensor([[[0., 0., 0., 0., 0., 0.], 2025-09-09T14:12:17.8854696Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:12:17.8855019Z [0., 0., 0., 0., 0., 0.]]]) 2025-09-09T14:12:17.8855581Z PASSED 2025-09-09T14:12:17.8856445Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn1d::test_qat_conv_bn_fusion_no_conv_bias model pt2e: GraphModule( 2025-09-09T14:12:17.8857358Z (conv): Module() 2025-09-09T14:12:17.8857639Z (bn): Module() 2025-09-09T14:12:17.8858041Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:12:17.8859625Z 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 2025-09-09T14:12:17.8861335Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.5603605508804321, max_val=3.2356624603271484) 2025-09-09T14:12:17.8862056Z ) 2025-09-09T14:12:17.8862435Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:12:17.8863833Z 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 2025-09-09T14:12:17.8865686Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.2639, -0.2941, -0.2608]), max_val=tensor([0.2795, 0.3227, 0.2891])) 2025-09-09T14:12:17.8866620Z ) 2025-09-09T14:12:17.8866989Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:12:17.8868328Z 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 2025-09-09T14:12:17.8869899Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.9445278644561768, max_val=2.3592891693115234) 2025-09-09T14:12:17.8870636Z ) 2025-09-09T14:12:17.8870852Z ) 2025-09-09T14:12:17.8870993Z 2025-09-09T14:12:17.8870998Z 2025-09-09T14:12:17.8871002Z 2025-09-09T14:12:17.8871113Z def forward(self, x): 2025-09-09T14:12:17.8871507Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:12:17.8871964Z conv_weight = self.conv.weight 2025-09-09T14:12:17.8872341Z bn_weight = self.bn.weight 2025-09-09T14:12:17.8872681Z bn_bias = self.bn.bias 2025-09-09T14:12:17.8873034Z bn_running_mean = self.bn.running_mean 2025-09-09T14:12:17.8873436Z bn_running_var = self.bn.running_var 2025-09-09T14:12:17.8873896Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T14:12:17.8874512Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:12:17.8875494Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T14:12:17.8876239Z add = torch.ops.aten.add.Tensor(bn_running_var, 1e-05) 2025-09-09T14:12:17.8876764Z sqrt = torch.ops.aten.sqrt.default(add); add = None 2025-09-09T14:12:17.8877342Z div = torch.ops.aten.div.Tensor(bn_weight, sqrt); sqrt = None 2025-09-09T14:12:17.8877934Z reshape = torch.ops.aten.reshape.default(div, [-1, 1, 1]) 2025-09-09T14:12:17.8878642Z mul = torch.ops.aten.mul.Tensor(conv_weight, reshape); conv_weight = reshape = None 2025-09-09T14:12:17.8879530Z activation_post_process_1 = self.activation_post_process_1(mul); mul = None 2025-09-09T14:12:17.8880770Z 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 2025-09-09T14:12:17.8881949Z reshape_1 = torch.ops.aten.reshape.default(div, [1, -1, 1]); div = None 2025-09-09T14:12:17.8882689Z div_1 = torch.ops.aten.div.Tensor(conv1d_1, reshape_1); conv1d_1 = reshape_1 = None 2025-09-09T14:12:17.8883960Z 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 2025-09-09T14:12:17.8885280Z activation_post_process_2 = self.activation_post_process_2(batch_norm_1); batch_norm_1 = None 2025-09-09T14:12:17.8886101Z return pytree.tree_unflatten((activation_post_process_2,), self._out_spec) 2025-09-09T14:12:17.8886653Z 2025-09-09T14:12:17.8887025Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:12:17.8887527Z model fx: GraphModule( 2025-09-09T14:12:17.8887949Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:12:17.8889294Z 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 2025-09-09T14:12:26.8412837Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.5603605508804321, max_val=3.2356624603271484) 2025-09-09T14:12:26.8413624Z ) 2025-09-09T14:12:26.8413864Z (conv): ConvBn1d( 2025-09-09T14:12:26.8414197Z 3, 3, kernel_size=(3,), stride=(1,), bias=False 2025-09-09T14:12:26.8414833Z (bn): BatchNorm1d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T14:12:26.8415471Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:12:26.8416876Z 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 2025-09-09T14:12:26.8418788Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.2639, -0.2941, -0.2608]), max_val=tensor([0.2795, 0.3227, 0.2891])) 2025-09-09T14:12:26.8419714Z ) 2025-09-09T14:12:26.8419951Z ) 2025-09-09T14:12:26.8420314Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:12:26.8421658Z 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 2025-09-09T14:12:26.8423233Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.9445278644561768, max_val=2.3592891693115234) 2025-09-09T14:12:26.8423980Z ) 2025-09-09T14:12:26.8424211Z ) 2025-09-09T14:12:26.8424341Z 2025-09-09T14:12:26.8424346Z 2025-09-09T14:12:26.8424351Z 2025-09-09T14:12:26.8424465Z def forward(self, x): 2025-09-09T14:12:26.8424949Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:12:26.8425996Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T14:12:26.8426765Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T14:12:26.8427352Z return activation_post_process_1 2025-09-09T14:12:26.8427716Z 2025-09-09T14:12:26.8428093Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:12:26.8428598Z diff: tensor([[[0., 0., 0.], 2025-09-09T14:12:26.8428929Z [0., 0., 0.], 2025-09-09T14:12:26.8429206Z [0., 0., 0.]], 2025-09-09T14:12:26.8429394Z 2025-09-09T14:12:26.8429496Z [[0., 0., 0.], 2025-09-09T14:12:26.8429888Z [0., 0., 0.], 2025-09-09T14:12:26.8430177Z [0., 0., 0.]], 2025-09-09T14:12:26.8430350Z 2025-09-09T14:12:26.8430451Z [[0., 0., 0.], 2025-09-09T14:12:26.8430739Z [0., 0., 0.], 2025-09-09T14:12:26.8431053Z [0., 0., 0.]]], grad_fn=) 2025-09-09T14:12:26.8431456Z converted model pt2e: GraphModule( 2025-09-09T14:12:26.8431816Z (conv): Module() 2025-09-09T14:12:26.8432085Z (bn): Module() 2025-09-09T14:12:26.8432352Z ) 2025-09-09T14:12:26.8432478Z 2025-09-09T14:12:26.8432483Z 2025-09-09T14:12:26.8432488Z 2025-09-09T14:12:26.8432598Z def forward(self, x): 2025-09-09T14:12:26.8432979Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:12:26.8433486Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T14:12:26.8434498Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.01880793273448944, -45, -128, 127, torch.int8); x = None 2025-09-09T14:12:26.8436294Z 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 2025-09-09T14:12:26.8437779Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T14:12:26.8438426Z _scale_0 = self._scale_0 2025-09-09T14:12:26.8438766Z _zero_point_0 = self._zero_point_0 2025-09-09T14:12:26.8439175Z quantize_per_channel = self._frozen_param0 2025-09-09T14:12:26.8440498Z 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 2025-09-09T14:12:26.8441739Z conv_weight_bias = self.conv.weight_bias 2025-09-09T14:12:26.8442925Z 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 2025-09-09T14:12:26.8444745Z 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 2025-09-09T14:12:26.8446635Z 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 2025-09-09T14:12:26.8448072Z return pytree.tree_unflatten((dequantize_per_tensor_default_1,), self._out_spec) 2025-09-09T14:12:26.8448649Z 2025-09-09T14:12:26.8449029Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:12:26.8449551Z onverted model fx: GraphModule( 2025-09-09T14:12:26.8450056Z (conv): QuantizedConv1d(Reference)(3, 3, kernel_size=(3,), stride=(1,)) 2025-09-09T14:12:26.8450584Z ) 2025-09-09T14:12:26.8450713Z 2025-09-09T14:12:26.8450718Z 2025-09-09T14:12:26.8450732Z 2025-09-09T14:12:26.8450844Z def forward(self, x): 2025-09-09T14:12:26.8451720Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.01880793273448944, -45, -128, 127, torch.int8); x = None 2025-09-09T14:12:26.8453594Z 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 2025-09-09T14:12:26.8455028Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T14:12:26.8456252Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.016877712681889534, -13, -128, 127, torch.int8); conv = None 2025-09-09T14:12:26.8458099Z 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 2025-09-09T14:12:26.8459740Z return dequantize_per_tensor_default_1 2025-09-09T14:12:26.8460126Z 2025-09-09T14:12:26.8460513Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:12:26.8461024Z diff: tensor([[[0., 0., 0.], 2025-09-09T14:12:26.8461334Z [0., 0., 0.], 2025-09-09T14:12:26.8461627Z [0., 0., 0.]], 2025-09-09T14:12:26.8461813Z 2025-09-09T14:12:26.8461915Z [[0., 0., 0.], 2025-09-09T14:12:26.8462201Z [0., 0., 0.], 2025-09-09T14:12:26.8462468Z [0., 0., 0.]], 2025-09-09T14:12:26.8462661Z 2025-09-09T14:12:26.8462763Z [[0., 0., 0.], 2025-09-09T14:12:26.8463044Z [0., 0., 0.], 2025-09-09T14:12:26.8463317Z [0., 0., 0.]]]) 2025-09-09T14:12:26.8463636Z model pt2e: GraphModule( 2025-09-09T14:12:26.8463938Z (conv): Module() 2025-09-09T14:12:26.8464220Z (bn): Module() 2025-09-09T14:12:26.8464619Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:12:26.8465972Z 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 2025-09-09T14:12:26.8467575Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.5603605508804321, max_val=3.2356624603271484) 2025-09-09T14:12:26.8468301Z ) 2025-09-09T14:12:26.8468684Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:12:26.8470018Z 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 2025-09-09T14:12:26.8471618Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.2940981984138489, max_val=0.32268622517585754) 2025-09-09T14:12:26.8472362Z ) 2025-09-09T14:12:26.8472733Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:12:26.8474067Z 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 2025-09-09T14:12:26.8475638Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.9461743831634521, max_val=2.3577661514282227) 2025-09-09T14:12:26.8476376Z ) 2025-09-09T14:12:26.8476597Z ) 2025-09-09T14:12:26.8476731Z 2025-09-09T14:12:26.8476736Z 2025-09-09T14:12:26.8476741Z 2025-09-09T14:12:26.8476852Z def forward(self, x): 2025-09-09T14:12:26.8477232Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:12:26.8477686Z conv_weight = self.conv.weight 2025-09-09T14:12:26.8478057Z bn_weight = self.bn.weight 2025-09-09T14:12:26.8478387Z bn_bias = self.bn.bias 2025-09-09T14:12:26.8490028Z bn_running_mean = self.bn.running_mean 2025-09-09T14:12:26.8490503Z bn_running_var = self.bn.running_var 2025-09-09T14:12:26.8490970Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T14:12:26.8491579Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:12:26.8492404Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T14:12:26.8493381Z add = torch.ops.aten.add.Tensor(bn_running_var, 1e-05) 2025-09-09T14:12:26.8493918Z sqrt = torch.ops.aten.sqrt.default(add); add = None 2025-09-09T14:12:26.8494493Z div = torch.ops.aten.div.Tensor(bn_weight, sqrt); sqrt = None 2025-09-09T14:12:26.8495091Z reshape = torch.ops.aten.reshape.default(div, [-1, 1, 1]) 2025-09-09T14:12:26.8495797Z mul = torch.ops.aten.mul.Tensor(conv_weight, reshape); conv_weight = reshape = None 2025-09-09T14:12:26.8496575Z activation_post_process_1 = self.activation_post_process_1(mul); mul = None 2025-09-09T14:12:26.8497772Z 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 2025-09-09T14:12:26.8499039Z reshape_1 = torch.ops.aten.reshape.default(div, [1, -1, 1]); div = None 2025-09-09T14:12:26.8499774Z div_1 = torch.ops.aten.div.Tensor(conv1d_1, reshape_1); conv1d_1 = reshape_1 = None 2025-09-09T14:12:26.8501040Z 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 2025-09-09T14:12:26.8502371Z activation_post_process_2 = self.activation_post_process_2(batch_norm_1); batch_norm_1 = None 2025-09-09T14:12:26.8503198Z return pytree.tree_unflatten((activation_post_process_2,), self._out_spec) 2025-09-09T14:12:26.8503750Z 2025-09-09T14:12:26.8504126Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:12:26.8504637Z model fx: GraphModule( 2025-09-09T14:12:26.8505074Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:12:36.6244478Z 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 2025-09-09T14:12:36.6246217Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.5603605508804321, max_val=3.2356624603271484) 2025-09-09T14:12:36.6246948Z ) 2025-09-09T14:12:36.6247201Z (conv): ConvBn1d( 2025-09-09T14:12:36.6247520Z 3, 3, kernel_size=(3,), stride=(1,), bias=False 2025-09-09T14:12:36.6248118Z (bn): BatchNorm1d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T14:12:36.6248754Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:12:36.6250061Z 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 2025-09-09T14:12:36.6251682Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.2940981984138489, max_val=0.32268622517585754) 2025-09-09T14:12:36.6252416Z ) 2025-09-09T14:12:36.6252661Z ) 2025-09-09T14:12:36.6253026Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:12:36.6254380Z 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 2025-09-09T14:12:36.6255970Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.9461743831634521, max_val=2.3577661514282227) 2025-09-09T14:12:36.6256693Z ) 2025-09-09T14:12:36.6256930Z ) 2025-09-09T14:12:36.6257062Z 2025-09-09T14:12:36.6257067Z 2025-09-09T14:12:36.6257072Z 2025-09-09T14:12:36.6257185Z def forward(self, x): 2025-09-09T14:12:36.6257678Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:12:36.6258641Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T14:12:36.6259398Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T14:12:36.6259994Z return activation_post_process_1 2025-09-09T14:12:36.6260698Z 2025-09-09T14:12:36.6261071Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:12:36.6261587Z diff: tensor([[[0., 0., 0.], 2025-09-09T14:12:36.6261898Z [0., 0., 0.], 2025-09-09T14:12:36.6262185Z [0., 0., 0.]], 2025-09-09T14:12:36.6262369Z 2025-09-09T14:12:36.6262469Z [[0., 0., 0.], 2025-09-09T14:12:36.6262752Z [0., 0., 0.], 2025-09-09T14:12:36.6263026Z [0., 0., 0.]], 2025-09-09T14:12:36.6263221Z 2025-09-09T14:12:36.6263321Z [[0., 0., 0.], 2025-09-09T14:12:36.6263597Z [0., 0., 0.], 2025-09-09T14:12:36.6264033Z [0., 0., 0.]]], grad_fn=) 2025-09-09T14:12:36.6264454Z converted model pt2e: GraphModule( 2025-09-09T14:12:36.6264804Z (conv): Module() 2025-09-09T14:12:36.6265084Z (bn): Module() 2025-09-09T14:12:36.6265337Z ) 2025-09-09T14:12:36.6265480Z 2025-09-09T14:12:36.6265485Z 2025-09-09T14:12:36.6265489Z 2025-09-09T14:12:36.6265603Z def forward(self, x): 2025-09-09T14:12:36.6265993Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:12:36.6266509Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T14:12:36.6267535Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.01880793273448944, -45, -128, 127, torch.int8); x = None 2025-09-09T14:12:36.6269318Z 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 2025-09-09T14:12:36.6270816Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T14:12:36.6271502Z quantize_per_tensor = self._frozen_param0 2025-09-09T14:12:36.6272626Z 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 2025-09-09T14:12:36.6273772Z conv_weight_bias = self.conv.weight_bias 2025-09-09T14:12:36.6274940Z 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 2025-09-09T14:12:36.6276735Z 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 2025-09-09T14:12:36.6278620Z 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 2025-09-09T14:12:36.6280140Z return pytree.tree_unflatten((dequantize_per_tensor_default_2,), self._out_spec) 2025-09-09T14:12:36.6280728Z 2025-09-09T14:12:36.6281092Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:12:36.6281621Z onverted model fx: GraphModule( 2025-09-09T14:12:36.6282139Z (conv): QuantizedConv1d(Reference)(3, 3, kernel_size=(3,), stride=(1,)) 2025-09-09T14:12:36.6282652Z ) 2025-09-09T14:12:36.6282779Z 2025-09-09T14:12:36.6282784Z 2025-09-09T14:12:36.6282789Z 2025-09-09T14:12:36.6282917Z def forward(self, x): 2025-09-09T14:12:36.6283777Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.01880793273448944, -45, -128, 127, torch.int8); x = None 2025-09-09T14:12:36.6285570Z 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 2025-09-09T14:12:36.6287019Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T14:12:36.6288330Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.016878198832273483, -13, -128, 127, torch.int8); conv = None 2025-09-09T14:12:36.6290180Z 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 2025-09-09T14:12:36.6291461Z return dequantize_per_tensor_default_1 2025-09-09T14:12:36.6291830Z 2025-09-09T14:12:36.6292205Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:12:36.6292704Z diff: tensor([[[0., 0., 0.], 2025-09-09T14:12:36.6293031Z [0., 0., 0.], 2025-09-09T14:12:36.6293387Z [0., 0., 0.]], 2025-09-09T14:12:36.6293581Z 2025-09-09T14:12:36.6293682Z [[0., 0., 0.], 2025-09-09T14:12:36.6293950Z [0., 0., 0.], 2025-09-09T14:12:36.6294231Z [0., 0., 0.]], 2025-09-09T14:12:36.6294407Z 2025-09-09T14:12:36.6294525Z [[0., 0., 0.], 2025-09-09T14:12:36.6294793Z [0., 0., 0.], 2025-09-09T14:12:36.6295074Z [0., 0., 0.]]]) 2025-09-09T14:12:36.6295376Z model pt2e: GraphModule( 2025-09-09T14:12:36.6295689Z (conv1): Module() 2025-09-09T14:12:36.6295951Z (bn1): Module() 2025-09-09T14:12:36.6296224Z (conv2): Module() 2025-09-09T14:12:36.6296482Z (bn2): Module() 2025-09-09T14:12:36.6296894Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:12:36.6298227Z 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 2025-09-09T14:12:36.6299841Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.5603605508804321, max_val=3.2356624603271484) 2025-09-09T14:12:36.6300573Z ) 2025-09-09T14:12:36.6300935Z (activation_post_process_3): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:12:36.6302340Z 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 2025-09-09T14:12:36.6304200Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.3119, -0.2563, -0.2799]), max_val=tensor([0.3101, 0.1970, 0.1855])) 2025-09-09T14:12:36.6305112Z ) 2025-09-09T14:12:36.6305491Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:12:36.6306880Z 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 2025-09-09T14:12:36.6308728Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.3263, -0.3276, -0.3045]), max_val=tensor([0.1376, 0.2760, 0.3298])) 2025-09-09T14:12:36.6309648Z ) 2025-09-09T14:12:36.6310008Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:12:36.6311330Z 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 2025-09-09T14:12:36.6312897Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.6533392667770386, max_val=1.7188055515289307) 2025-09-09T14:12:36.6313623Z ) 2025-09-09T14:12:36.6313984Z (activation_post_process_4): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:12:36.6315317Z 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 2025-09-09T14:12:36.6316884Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.403315544128418, max_val=1.3918161392211914) 2025-09-09T14:12:36.6317593Z ) 2025-09-09T14:12:36.6317821Z ) 2025-09-09T14:12:36.6318047Z 2025-09-09T14:12:36.6318053Z 2025-09-09T14:12:36.6318058Z 2025-09-09T14:12:36.6318192Z def forward(self, x): 2025-09-09T14:12:36.6318563Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:12:36.6319034Z conv1_weight = self.conv1.weight 2025-09-09T14:12:36.6319411Z bn1_weight = self.bn1.weight 2025-09-09T14:12:36.6319869Z bn1_bias = self.bn1.bias 2025-09-09T14:12:36.6320208Z conv2_weight = self.conv2.weight 2025-09-09T14:12:36.6320632Z conv2_bias = self.conv2.bias 2025-09-09T14:12:36.6320980Z bn2_weight = self.bn2.weight 2025-09-09T14:12:36.6321458Z bn2_bias = self.bn2.bias 2025-09-09T14:12:36.6321808Z bn1_running_mean = self.bn1.running_mean 2025-09-09T14:12:36.6322231Z bn1_running_var = self.bn1.running_var 2025-09-09T14:12:36.6322694Z bn1_num_batches_tracked = self.bn1.num_batches_tracked 2025-09-09T14:12:36.6323165Z bn2_running_mean = self.bn2.running_mean 2025-09-09T14:12:36.6323588Z bn2_running_var = self.bn2.running_var 2025-09-09T14:12:36.6324039Z bn2_num_batches_tracked = self.bn2.num_batches_tracked 2025-09-09T14:12:36.6324693Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:12:36.6325510Z add_ = torch.ops.aten.add_.Tensor(bn1_num_batches_tracked, 1); bn1_num_batches_tracked = add_ = None 2025-09-09T14:12:44.8341916Z add__1 = torch.ops.aten.add_.Tensor(bn2_num_batches_tracked, 1); bn2_num_batches_tracked = add__1 = None 2025-09-09T14:12:44.8342703Z add = torch.ops.aten.add.Tensor(bn2_running_var, 1e-05) 2025-09-09T14:12:44.8343146Z sqrt = torch.ops.aten.sqrt.default(add); add = None 2025-09-09T14:12:44.8343651Z div = torch.ops.aten.div.Tensor(bn2_weight, sqrt); sqrt = None 2025-09-09T14:12:44.8344152Z reshape = torch.ops.aten.reshape.default(div, [-1, 1, 1]) 2025-09-09T14:12:44.8344715Z mul = torch.ops.aten.mul.Tensor(conv2_weight, reshape); conv2_weight = reshape = None 2025-09-09T14:12:44.8345379Z activation_post_process_3 = self.activation_post_process_3(mul); mul = None 2025-09-09T14:12:44.8346066Z zeros_like = torch.ops.aten.zeros_like.default(conv2_bias, dtype = torch.float32, pin_memory = False) 2025-09-09T14:12:44.8346691Z add_2 = torch.ops.aten.add.Tensor(bn1_running_var, 1e-05) 2025-09-09T14:12:44.8347141Z sqrt_1 = torch.ops.aten.sqrt.default(add_2); add_2 = None 2025-09-09T14:12:44.8347637Z div_2 = torch.ops.aten.div.Tensor(bn1_weight, sqrt_1); sqrt_1 = None 2025-09-09T14:12:44.8348150Z reshape_3 = torch.ops.aten.reshape.default(div_2, [-1, 1, 1]) 2025-09-09T14:12:44.8348731Z mul_1 = torch.ops.aten.mul.Tensor(conv1_weight, reshape_3); conv1_weight = reshape_3 = None 2025-09-09T14:12:44.8349402Z activation_post_process_1 = self.activation_post_process_1(mul_1); mul_1 = None 2025-09-09T14:12:44.8350365Z 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 2025-09-09T14:12:44.8351329Z reshape_4 = torch.ops.aten.reshape.default(div_2, [1, -1, 1]); div_2 = None 2025-09-09T14:12:44.8351974Z div_3 = torch.ops.aten.div.Tensor(conv1d_3, reshape_4); conv1d_3 = reshape_4 = None 2025-09-09T14:12:44.8353031Z 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 2025-09-09T14:12:44.8354118Z activation_post_process_2 = self.activation_post_process_2(batch_norm_3); batch_norm_3 = None 2025-09-09T14:12:44.8355202Z 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 2025-09-09T14:12:44.8356198Z reshape_1 = torch.ops.aten.reshape.default(div, [1, -1, 1]); div = None 2025-09-09T14:12:44.8356801Z div_1 = torch.ops.aten.div.Tensor(conv1d_2, reshape_1); conv1d_2 = reshape_1 = None 2025-09-09T14:12:44.8357743Z reshape_2 = torch.ops.aten.reshape.default(conv2_bias, [1, -1, 1]); conv2_bias = None 2025-09-09T14:12:44.8358667Z add_1 = torch.ops.aten.add.Tensor(div_1, reshape_2); div_1 = reshape_2 = None 2025-09-09T14:12:44.8359774Z 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 2025-09-09T14:12:44.8360862Z activation_post_process_4 = self.activation_post_process_4(batch_norm_2); batch_norm_2 = None 2025-09-09T14:12:44.8361686Z return pytree.tree_unflatten((activation_post_process_4,), self._out_spec) 2025-09-09T14:12:44.8362134Z 2025-09-09T14:12:44.8362438Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:12:44.8362857Z model fx: GraphModule( 2025-09-09T14:12:44.8363208Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:12:44.8364298Z 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 2025-09-09T14:12:44.8365600Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.5603605508804321, max_val=3.2356624603271484) 2025-09-09T14:12:44.8366183Z ) 2025-09-09T14:12:44.8366384Z (conv1): ConvBn1d( 2025-09-09T14:12:44.8366646Z 3, 3, kernel_size=(3,), stride=(1,), bias=False 2025-09-09T14:12:44.8367132Z (bn): BatchNorm1d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T14:12:44.8367648Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:12:44.8368761Z 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 2025-09-09T14:12:44.8370260Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.3263, -0.3276, -0.3045]), max_val=tensor([0.1376, 0.2760, 0.3298])) 2025-09-09T14:12:44.8370993Z ) 2025-09-09T14:12:44.8371189Z ) 2025-09-09T14:12:44.8371483Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:12:44.8372561Z 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 2025-09-09T14:12:44.8373840Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.6533392667770386, max_val=1.7188055515289307) 2025-09-09T14:12:44.8374417Z ) 2025-09-09T14:12:44.8374616Z (conv2): ConvBn1d( 2025-09-09T14:12:44.8374859Z 3, 3, kernel_size=(3,), stride=(1,) 2025-09-09T14:12:44.8375319Z (bn): BatchNorm1d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T14:12:44.8375832Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:12:44.8377204Z 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 2025-09-09T14:12:44.8378821Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.3119, -0.2563, -0.2799]), max_val=tensor([0.3101, 0.1970, 0.1855])) 2025-09-09T14:12:44.8379561Z ) 2025-09-09T14:12:44.8379766Z ) 2025-09-09T14:12:44.8380058Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:12:44.8381135Z 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 2025-09-09T14:12:44.8382531Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.403315544128418, max_val=1.3918161392211914) 2025-09-09T14:12:44.8383113Z ) 2025-09-09T14:12:44.8383305Z ) 2025-09-09T14:12:44.8383409Z 2025-09-09T14:12:44.8383413Z 2025-09-09T14:12:44.8383417Z 2025-09-09T14:12:44.8383510Z def forward(self, x): 2025-09-09T14:12:44.8383902Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:12:44.8384512Z conv1 = self.conv1(activation_post_process_0); activation_post_process_0 = None 2025-09-09T14:12:44.8385131Z activation_post_process_1 = self.activation_post_process_1(conv1); conv1 = None 2025-09-09T14:12:44.8385824Z conv2 = self.conv2(activation_post_process_1); activation_post_process_1 = None 2025-09-09T14:12:44.8386438Z activation_post_process_2 = self.activation_post_process_2(conv2); conv2 = None 2025-09-09T14:12:44.8386925Z return activation_post_process_2 2025-09-09T14:12:44.8387200Z 2025-09-09T14:12:44.8387517Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:12:44.8387922Z diff: tensor([[[0.], 2025-09-09T14:12:44.8388143Z [0.], 2025-09-09T14:12:44.8388353Z [0.]], 2025-09-09T14:12:44.8388478Z 2025-09-09T14:12:44.8388558Z [[0.], 2025-09-09T14:12:44.8388765Z [0.], 2025-09-09T14:12:44.8388962Z [0.]], 2025-09-09T14:12:44.8389095Z 2025-09-09T14:12:44.8389173Z [[0.], 2025-09-09T14:12:44.8389369Z [0.], 2025-09-09T14:12:44.8389598Z [0.]]], grad_fn=) 2025-09-09T14:12:44.8389914Z converted model pt2e: GraphModule( 2025-09-09T14:12:44.8390214Z (conv1): Module() 2025-09-09T14:12:44.8390442Z (bn1): Module() 2025-09-09T14:12:44.8390653Z (conv2): Module() 2025-09-09T14:12:44.8390879Z (bn2): Module() 2025-09-09T14:12:44.8391084Z ) 2025-09-09T14:12:44.8391189Z 2025-09-09T14:12:44.8391193Z 2025-09-09T14:12:44.8391211Z 2025-09-09T14:12:44.8391303Z def forward(self, x): 2025-09-09T14:12:44.8391614Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:12:44.8391999Z conv2_bias = self.conv2.bias 2025-09-09T14:12:44.8392343Z bn1_num_batches_tracked = self.bn1.num_batches_tracked 2025-09-09T14:12:44.8392775Z bn2_num_batches_tracked = self.bn2.num_batches_tracked 2025-09-09T14:12:44.8393594Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.01880793273448944, -45, -128, 127, torch.int8); x = None 2025-09-09T14:12:44.8395023Z 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 2025-09-09T14:12:44.8396257Z add_ = torch.ops.aten.add_.Tensor(bn1_num_batches_tracked, 1); bn1_num_batches_tracked = add_ = None 2025-09-09T14:12:44.8397001Z add__1 = torch.ops.aten.add_.Tensor(bn2_num_batches_tracked, 1); bn2_num_batches_tracked = add__1 = None 2025-09-09T14:12:44.8397544Z _scale_0 = self._scale_0 2025-09-09T14:12:44.8397831Z _zero_point_0 = self._zero_point_0 2025-09-09T14:12:44.8398127Z _scale_1 = self._scale_1 2025-09-09T14:12:44.8398403Z _zero_point_1 = self._zero_point_1 2025-09-09T14:12:44.8398729Z quantize_per_channel_1 = self._frozen_param0 2025-09-09T14:12:44.8399845Z 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 2025-09-09T14:12:44.8400877Z conv1_weight_bias = self.conv1.weight_bias 2025-09-09T14:12:44.8401868Z 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 2025-09-09T14:12:44.8403423Z 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 2025-09-09T14:12:44.8404915Z 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 2025-09-09T14:12:44.8405934Z quantize_per_channel = self._frozen_param1 2025-09-09T14:12:46.3330404Z 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 2025-09-09T14:12:46.3332712Z 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 2025-09-09T14:12:46.3334453Z 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 2025-09-09T14:12:46.3336331Z 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 2025-09-09T14:12:46.3337761Z return pytree.tree_unflatten((dequantize_per_tensor_default_2,), self._out_spec) 2025-09-09T14:12:46.3338326Z 2025-09-09T14:12:46.3338705Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:12:46.3339213Z onverted model fx: GraphModule( 2025-09-09T14:12:46.3339731Z (conv1): QuantizedConv1d(Reference)(3, 3, kernel_size=(3,), stride=(1,)) 2025-09-09T14:12:46.3340425Z (conv2): QuantizedConv1d(Reference)(3, 3, kernel_size=(3,), stride=(1,)) 2025-09-09T14:12:46.3340953Z ) 2025-09-09T14:12:46.3341086Z 2025-09-09T14:12:46.3341091Z 2025-09-09T14:12:46.3341096Z 2025-09-09T14:12:46.3341221Z def forward(self, x): 2025-09-09T14:12:46.3342083Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.01880793273448944, -45, -128, 127, torch.int8); x = None 2025-09-09T14:12:46.3343870Z 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 2025-09-09T14:12:46.3345307Z conv1 = self.conv1(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T14:12:46.3346546Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv1, 0.013224096968770027, -3, -128, 127, torch.int8); conv1 = None 2025-09-09T14:12:46.3348391Z 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 2025-09-09T14:12:46.3349861Z conv2 = self.conv2(dequantize_per_tensor_default_1); dequantize_per_tensor_default_1 = None 2025-09-09T14:12:46.3351104Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv2, 0.010961300693452358, 0, -128, 127, torch.int8); conv2 = None 2025-09-09T14:12:46.3352942Z 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 2025-09-09T14:12:46.3354185Z return dequantize_per_tensor_default_2 2025-09-09T14:12:46.3354597Z 2025-09-09T14:12:46.3354977Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:12:46.3355467Z diff: tensor([[[0.], 2025-09-09T14:12:46.3355756Z [0.], 2025-09-09T14:12:46.3356000Z [0.]], 2025-09-09T14:12:46.3356168Z 2025-09-09T14:12:46.3356266Z [[0.], 2025-09-09T14:12:46.3356511Z [0.], 2025-09-09T14:12:46.3356766Z [0.]], 2025-09-09T14:12:46.3356918Z 2025-09-09T14:12:46.3357027Z [[0.], 2025-09-09T14:12:46.3357404Z [0.], 2025-09-09T14:12:46.3357666Z [0.]]]) 2025-09-09T14:12:46.3357941Z model pt2e: GraphModule( 2025-09-09T14:12:46.3358592Z (conv1): Module() 2025-09-09T14:12:46.3358861Z (bn1): Module() 2025-09-09T14:12:46.3359144Z (conv2): Module() 2025-09-09T14:12:46.3359405Z (bn2): Module() 2025-09-09T14:12:46.3359866Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:12:46.3361201Z 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 2025-09-09T14:12:46.3362938Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.5603605508804321, max_val=3.2356624603271484) 2025-09-09T14:12:46.3363677Z ) 2025-09-09T14:12:46.3364047Z (activation_post_process_3): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:12:46.3365404Z 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 2025-09-09T14:12:46.3367002Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.31192728877067566, max_val=0.31014329195022583) 2025-09-09T14:12:46.3367728Z ) 2025-09-09T14:12:46.3368110Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:12:46.3369444Z 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 2025-09-09T14:12:46.3371033Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.32764676213264465, max_val=0.3298276662826538) 2025-09-09T14:12:46.3371752Z ) 2025-09-09T14:12:46.3372125Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:12:46.3373452Z 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 2025-09-09T14:12:46.3374998Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.652099370956421, max_val=1.720017671585083) 2025-09-09T14:12:46.3375713Z ) 2025-09-09T14:12:46.3376071Z (activation_post_process_4): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:12:46.3377380Z 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 2025-09-09T14:12:46.3378955Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.4020289182662964, max_val=1.3896838426589966) 2025-09-09T14:12:46.3379671Z ) 2025-09-09T14:12:46.3379900Z ) 2025-09-09T14:12:46.3380025Z 2025-09-09T14:12:46.3380035Z 2025-09-09T14:12:46.3380039Z 2025-09-09T14:12:46.3380151Z def forward(self, x): 2025-09-09T14:12:46.3380533Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:12:46.3381000Z conv1_weight = self.conv1.weight 2025-09-09T14:12:46.3381374Z bn1_weight = self.bn1.weight 2025-09-09T14:12:46.3381733Z bn1_bias = self.bn1.bias 2025-09-09T14:12:46.3382069Z conv2_weight = self.conv2.weight 2025-09-09T14:12:46.3382451Z conv2_bias = self.conv2.bias 2025-09-09T14:12:46.3382795Z bn2_weight = self.bn2.weight 2025-09-09T14:12:46.3383146Z bn2_bias = self.bn2.bias 2025-09-09T14:12:46.3383502Z bn1_running_mean = self.bn1.running_mean 2025-09-09T14:12:46.3383922Z bn1_running_var = self.bn1.running_var 2025-09-09T14:12:46.3384374Z bn1_num_batches_tracked = self.bn1.num_batches_tracked 2025-09-09T14:12:46.3384858Z bn2_running_mean = self.bn2.running_mean 2025-09-09T14:12:46.3385277Z bn2_running_var = self.bn2.running_var 2025-09-09T14:12:46.3385890Z bn2_num_batches_tracked = self.bn2.num_batches_tracked 2025-09-09T14:12:46.3386524Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:12:46.3387343Z add_ = torch.ops.aten.add_.Tensor(bn1_num_batches_tracked, 1); bn1_num_batches_tracked = add_ = None 2025-09-09T14:12:46.3388285Z add__1 = torch.ops.aten.add_.Tensor(bn2_num_batches_tracked, 1); bn2_num_batches_tracked = add__1 = None 2025-09-09T14:12:46.3389044Z add = torch.ops.aten.add.Tensor(bn2_running_var, 1e-05) 2025-09-09T14:12:46.3389575Z sqrt = torch.ops.aten.sqrt.default(add); add = None 2025-09-09T14:12:46.3390219Z div = torch.ops.aten.div.Tensor(bn2_weight, sqrt); sqrt = None 2025-09-09T14:12:46.3390816Z reshape = torch.ops.aten.reshape.default(div, [-1, 1, 1]) 2025-09-09T14:12:46.3391526Z mul = torch.ops.aten.mul.Tensor(conv2_weight, reshape); conv2_weight = reshape = None 2025-09-09T14:12:46.3392310Z activation_post_process_3 = self.activation_post_process_3(mul); mul = None 2025-09-09T14:12:46.3393180Z zeros_like = torch.ops.aten.zeros_like.default(conv2_bias, dtype = torch.float32, pin_memory = False) 2025-09-09T14:12:46.3393952Z add_2 = torch.ops.aten.add.Tensor(bn1_running_var, 1e-05) 2025-09-09T14:12:46.3394504Z sqrt_1 = torch.ops.aten.sqrt.default(add_2); add_2 = None 2025-09-09T14:12:46.3395111Z div_2 = torch.ops.aten.div.Tensor(bn1_weight, sqrt_1); sqrt_1 = None 2025-09-09T14:12:46.3395735Z reshape_3 = torch.ops.aten.reshape.default(div_2, [-1, 1, 1]) 2025-09-09T14:12:46.3396468Z mul_1 = torch.ops.aten.mul.Tensor(conv1_weight, reshape_3); conv1_weight = reshape_3 = None 2025-09-09T14:12:46.3397451Z activation_post_process_1 = self.activation_post_process_1(mul_1); mul_1 = None 2025-09-09T14:12:46.3398720Z 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 2025-09-09T14:12:46.3399993Z reshape_4 = torch.ops.aten.reshape.default(div_2, [1, -1, 1]); div_2 = None 2025-09-09T14:12:46.3400740Z div_3 = torch.ops.aten.div.Tensor(conv1d_3, reshape_4); conv1d_3 = reshape_4 = None 2025-09-09T14:12:46.3402045Z 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 2025-09-09T14:12:46.3403418Z activation_post_process_2 = self.activation_post_process_2(batch_norm_3); batch_norm_3 = None 2025-09-09T14:12:46.3404789Z 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 2025-09-09T14:12:46.3406040Z reshape_1 = torch.ops.aten.reshape.default(div, [1, -1, 1]); div = None 2025-09-09T14:12:46.3406782Z div_1 = torch.ops.aten.div.Tensor(conv1d_2, reshape_1); conv1d_2 = reshape_1 = None 2025-09-09T14:12:46.3407592Z reshape_2 = torch.ops.aten.reshape.default(conv2_bias, [1, -1, 1]); conv2_bias = None 2025-09-09T14:12:46.3408370Z add_1 = torch.ops.aten.add.Tensor(div_1, reshape_2); div_1 = reshape_2 = None 2025-09-09T14:12:53.6844374Z 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 2025-09-09T14:12:53.6845554Z activation_post_process_4 = self.activation_post_process_4(batch_norm_2); batch_norm_2 = None 2025-09-09T14:12:53.6846228Z return pytree.tree_unflatten((activation_post_process_4,), self._out_spec) 2025-09-09T14:12:53.6846703Z 2025-09-09T14:12:53.6847006Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:12:53.6847420Z model fx: GraphModule( 2025-09-09T14:12:53.6847769Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:12:53.6849121Z 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 2025-09-09T14:12:53.6850689Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.5603605508804321, max_val=3.2356624603271484) 2025-09-09T14:12:53.6851386Z ) 2025-09-09T14:12:53.6851592Z (conv1): ConvBn1d( 2025-09-09T14:12:53.6851869Z 3, 3, kernel_size=(3,), stride=(1,), bias=False 2025-09-09T14:12:53.6852347Z (bn): BatchNorm1d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T14:12:53.6853017Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:12:53.6854066Z 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 2025-09-09T14:12:53.6855365Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.32764676213264465, max_val=0.3298276662826538) 2025-09-09T14:12:53.6856077Z ) 2025-09-09T14:12:53.6856260Z ) 2025-09-09T14:12:53.6856570Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:12:53.6857635Z 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 2025-09-09T14:12:53.6859268Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.652099370956421, max_val=1.720017671585083) 2025-09-09T14:12:53.6859863Z ) 2025-09-09T14:12:53.6860056Z (conv2): ConvBn1d( 2025-09-09T14:12:53.6860317Z 3, 3, kernel_size=(3,), stride=(1,) 2025-09-09T14:12:53.6860763Z (bn): BatchNorm1d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T14:12:53.6861290Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:12:53.6862343Z 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 2025-09-09T14:12:53.6863640Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.31192728877067566, max_val=0.31014329195022583) 2025-09-09T14:12:53.6864242Z ) 2025-09-09T14:12:53.6864427Z ) 2025-09-09T14:12:53.6864738Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:12:53.6865803Z 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 2025-09-09T14:12:53.6867079Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.4020289182662964, max_val=1.3896838426589966) 2025-09-09T14:12:53.6867672Z ) 2025-09-09T14:12:53.6867857Z ) 2025-09-09T14:12:53.6867959Z 2025-09-09T14:12:53.6867964Z 2025-09-09T14:12:53.6867969Z 2025-09-09T14:12:53.6868079Z def forward(self, x): 2025-09-09T14:12:53.6868459Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:12:53.6869070Z conv1 = self.conv1(activation_post_process_0); activation_post_process_0 = None 2025-09-09T14:12:53.6869691Z activation_post_process_1 = self.activation_post_process_1(conv1); conv1 = None 2025-09-09T14:12:53.6870317Z conv2 = self.conv2(activation_post_process_1); activation_post_process_1 = None 2025-09-09T14:12:53.6870943Z activation_post_process_2 = self.activation_post_process_2(conv2); conv2 = None 2025-09-09T14:12:53.6871413Z return activation_post_process_2 2025-09-09T14:12:53.6871703Z 2025-09-09T14:12:53.6871996Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:12:53.6872396Z diff: tensor([[[0.], 2025-09-09T14:12:53.6872617Z [0.], 2025-09-09T14:12:53.6872965Z [0.]], 2025-09-09T14:12:53.6873095Z 2025-09-09T14:12:53.6873174Z [[0.], 2025-09-09T14:12:53.6873381Z [0.], 2025-09-09T14:12:53.6873588Z [0.]], 2025-09-09T14:12:53.6873710Z 2025-09-09T14:12:53.6873788Z [[0.], 2025-09-09T14:12:53.6873995Z [0.], 2025-09-09T14:12:53.6874212Z [0.]]], grad_fn=) 2025-09-09T14:12:53.6874538Z converted model pt2e: GraphModule( 2025-09-09T14:12:53.6874823Z (conv1): Module() 2025-09-09T14:12:53.6875047Z (bn1): Module() 2025-09-09T14:12:53.6875256Z (conv2): Module() 2025-09-09T14:12:53.6875573Z (bn2): Module() 2025-09-09T14:12:53.6875775Z ) 2025-09-09T14:12:53.6875892Z 2025-09-09T14:12:53.6875897Z 2025-09-09T14:12:53.6875901Z 2025-09-09T14:12:53.6875992Z def forward(self, x): 2025-09-09T14:12:53.6876312Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:12:53.6876685Z conv2_bias = self.conv2.bias 2025-09-09T14:12:53.6877044Z bn1_num_batches_tracked = self.bn1.num_batches_tracked 2025-09-09T14:12:53.6877456Z bn2_num_batches_tracked = self.bn2.num_batches_tracked 2025-09-09T14:12:53.6878281Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.01880793273448944, -45, -128, 127, torch.int8); x = None 2025-09-09T14:12:53.6879802Z 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 2025-09-09T14:12:53.6881000Z add_ = torch.ops.aten.add_.Tensor(bn1_num_batches_tracked, 1); bn1_num_batches_tracked = add_ = None 2025-09-09T14:12:53.6881768Z add__1 = torch.ops.aten.add_.Tensor(bn2_num_batches_tracked, 1); bn2_num_batches_tracked = add__1 = None 2025-09-09T14:12:53.6882329Z quantize_per_tensor_1 = self._frozen_param0 2025-09-09T14:12:53.6883266Z 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 2025-09-09T14:12:53.6884201Z conv1_weight_bias = self.conv1.weight_bias 2025-09-09T14:12:53.6885166Z 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 2025-09-09T14:12:53.6886612Z 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 2025-09-09T14:12:53.6888105Z 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 2025-09-09T14:12:53.6889108Z quantize_per_tensor = self._frozen_param1 2025-09-09T14:12:53.6890017Z 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 2025-09-09T14:12:53.6891466Z 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 2025-09-09T14:12:53.6892850Z 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 2025-09-09T14:12:53.6894329Z 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 2025-09-09T14:12:53.6895477Z return pytree.tree_unflatten((dequantize_per_tensor_default_4,), self._out_spec) 2025-09-09T14:12:53.6895945Z 2025-09-09T14:12:53.6896309Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:12:53.6896731Z onverted model fx: GraphModule( 2025-09-09T14:12:53.6897156Z (conv1): QuantizedConv1d(Reference)(3, 3, kernel_size=(3,), stride=(1,)) 2025-09-09T14:12:53.6897711Z (conv2): QuantizedConv1d(Reference)(3, 3, kernel_size=(3,), stride=(1,)) 2025-09-09T14:12:53.6898133Z ) 2025-09-09T14:12:53.6898237Z 2025-09-09T14:12:53.6898242Z 2025-09-09T14:12:53.6898246Z 2025-09-09T14:12:53.6898337Z def forward(self, x): 2025-09-09T14:12:53.6899043Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.01880793273448944, -45, -128, 127, torch.int8); x = None 2025-09-09T14:12:53.6900528Z 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 2025-09-09T14:12:53.6901683Z conv1 = self.conv1(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T14:12:53.6902672Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv1, 0.013223988004028797, -3, -128, 127, torch.int8); conv1 = None 2025-09-09T14:12:53.6904145Z 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 2025-09-09T14:12:53.6905319Z conv2 = self.conv2(dequantize_per_tensor_default_1); dequantize_per_tensor_default_1 = None 2025-09-09T14:12:53.6906307Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv2, 0.010947893373668194, 0, -128, 127, torch.int8); conv2 = None 2025-09-09T14:12:53.6907758Z 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 2025-09-09T14:12:53.6908767Z return dequantize_per_tensor_default_2 2025-09-09T14:12:53.6909072Z 2025-09-09T14:12:53.6909400Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:12:53.6909806Z diff: tensor([[[0.], 2025-09-09T14:12:53.6910028Z [0.], 2025-09-09T14:12:53.6910242Z [0.]], 2025-09-09T14:12:53.6910368Z 2025-09-09T14:12:53.6910448Z [[0.], 2025-09-09T14:12:53.6910656Z [0.], 2025-09-09T14:12:53.6910849Z [0.]], 2025-09-09T14:12:53.6910983Z 2025-09-09T14:12:53.6911061Z [[0.], 2025-09-09T14:13:14.8456856Z [0.], 2025-09-09T14:13:14.8457239Z [0.]]]) 2025-09-09T14:13:14.8457730Z PASSED 2025-09-09T14:13:14.8459025Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn1d::test_qat_conv_bn_per_channel_weight_bias PASSED 2025-09-09T14:13:14.8460456Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn1d::test_qat_conv_bn_relu_fusion model pt2e: GraphModule( 2025-09-09T14:13:14.8461361Z (conv): Module() 2025-09-09T14:13:14.8461644Z (bn): Module() 2025-09-09T14:13:14.8462046Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:13:14.8463392Z 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 2025-09-09T14:13:14.8464967Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3264806270599365, max_val=1.318617343902588) 2025-09-09T14:13:14.8465700Z ) 2025-09-09T14:13:14.8466089Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:13:14.8467483Z 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 2025-09-09T14:13:14.8469658Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.2935, -0.3313, -0.3129]), max_val=tensor([0.2532, 0.1628, 0.3013])) 2025-09-09T14:13:14.8470598Z ) 2025-09-09T14:13:14.8470981Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:13:14.8472324Z 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 2025-09-09T14:13:14.8473827Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=1.410499095916748) 2025-09-09T14:13:14.8474620Z ) 2025-09-09T14:13:14.8474894Z ) 2025-09-09T14:13:14.8475023Z 2025-09-09T14:13:14.8475028Z 2025-09-09T14:13:14.8475033Z 2025-09-09T14:13:14.8475150Z def forward(self, x): 2025-09-09T14:13:14.8475540Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:13:14.8476002Z conv_weight = self.conv.weight 2025-09-09T14:13:14.8476380Z conv_bias = self.conv.bias 2025-09-09T14:13:14.8476716Z bn_weight = self.bn.weight 2025-09-09T14:13:14.8477063Z bn_bias = self.bn.bias 2025-09-09T14:13:14.8477446Z bn_running_mean = self.bn.running_mean 2025-09-09T14:13:14.8477845Z bn_running_var = self.bn.running_var 2025-09-09T14:13:14.8478298Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T14:13:14.8478893Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:13:14.8479719Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T14:13:14.8480544Z add = torch.ops.aten.add.Tensor(bn_running_var, 1e-05) 2025-09-09T14:13:14.8481083Z sqrt = torch.ops.aten.sqrt.default(add); add = None 2025-09-09T14:13:14.8481650Z div = torch.ops.aten.div.Tensor(bn_weight, sqrt); sqrt = None 2025-09-09T14:13:14.8482240Z reshape = torch.ops.aten.reshape.default(div, [-1, 1, 1]) 2025-09-09T14:13:14.8482938Z mul = torch.ops.aten.mul.Tensor(conv_weight, reshape); conv_weight = reshape = None 2025-09-09T14:13:14.8483703Z activation_post_process_1 = self.activation_post_process_1(mul); mul = None 2025-09-09T14:13:14.8484559Z zeros_like = torch.ops.aten.zeros_like.default(conv_bias, dtype = torch.float32, pin_memory = False) 2025-09-09T14:13:14.8485925Z 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 2025-09-09T14:13:14.8487183Z reshape_1 = torch.ops.aten.reshape.default(div, [1, -1, 1]); div = None 2025-09-09T14:13:14.8487932Z div_1 = torch.ops.aten.div.Tensor(conv1d_1, reshape_1); conv1d_1 = reshape_1 = None 2025-09-09T14:13:14.8488721Z reshape_2 = torch.ops.aten.reshape.default(conv_bias, [1, -1, 1]); conv_bias = None 2025-09-09T14:13:14.8489489Z add_1 = torch.ops.aten.add.Tensor(div_1, reshape_2); div_1 = reshape_2 = None 2025-09-09T14:13:14.8490705Z 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 2025-09-09T14:13:14.8491891Z relu = torch.ops.aten.relu.default(batch_norm_1); batch_norm_1 = None 2025-09-09T14:13:14.8492617Z activation_post_process_2 = self.activation_post_process_2(relu); relu = None 2025-09-09T14:13:14.8493360Z return pytree.tree_unflatten((activation_post_process_2,), self._out_spec) 2025-09-09T14:13:14.8493901Z 2025-09-09T14:13:14.8494274Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:13:14.8494777Z model fx: GraphModule( 2025-09-09T14:13:14.8495206Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:13:14.8496637Z 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 2025-09-09T14:13:14.8498220Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3264806270599365, max_val=1.318617343902588) 2025-09-09T14:13:14.8498939Z ) 2025-09-09T14:13:14.8499200Z (conv): ConvBnReLU1d( 2025-09-09T14:13:14.8499515Z 3, 3, kernel_size=(3,), stride=(1,) 2025-09-09T14:13:14.8500077Z (bn): BatchNorm1d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T14:13:14.8500732Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:13:14.8502097Z 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 2025-09-09T14:13:14.8504039Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.2935, -0.3313, -0.3129]), max_val=tensor([0.2532, 0.1628, 0.3013])) 2025-09-09T14:13:14.8504957Z ) 2025-09-09T14:13:14.8505199Z ) 2025-09-09T14:13:14.8505580Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:13:14.8506914Z 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 2025-09-09T14:13:14.8508441Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=1.410499095916748) 2025-09-09T14:13:14.8509098Z ) 2025-09-09T14:13:14.8509336Z ) 2025-09-09T14:13:14.8509464Z 2025-09-09T14:13:14.8509469Z 2025-09-09T14:13:14.8509473Z 2025-09-09T14:13:14.8509596Z def forward(self, x): 2025-09-09T14:13:14.8510067Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:13:14.8510813Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T14:13:14.8511571Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T14:13:14.8512166Z return activation_post_process_1 2025-09-09T14:13:14.8512509Z 2025-09-09T14:13:14.8512879Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:13:14.8513372Z diff: tensor([[[0., 0., 0.], 2025-09-09T14:13:14.8513690Z [0., 0., 0.], 2025-09-09T14:13:14.8514007Z [0., 0., 0.]]], grad_fn=) 2025-09-09T14:13:14.8514408Z converted model pt2e: GraphModule( 2025-09-09T14:13:14.8514768Z (conv): Module() 2025-09-09T14:13:14.8515032Z (bn): Module() 2025-09-09T14:13:14.8515299Z ) 2025-09-09T14:13:14.8515423Z 2025-09-09T14:13:14.8515428Z 2025-09-09T14:13:14.8515432Z 2025-09-09T14:13:14.8515545Z def forward(self, x): 2025-09-09T14:13:14.8515920Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:13:14.8516366Z conv_bias = self.conv.bias 2025-09-09T14:13:14.8516771Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T14:13:14.8517766Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.010372933000326157, 0, -128, 127, torch.int8); x = None 2025-09-09T14:13:14.8519529Z 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 2025-09-09T14:13:14.8521083Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T14:13:14.8521741Z _scale_0 = self._scale_0 2025-09-09T14:13:14.8522077Z _zero_point_0 = self._zero_point_0 2025-09-09T14:13:14.8522493Z quantize_per_channel = self._frozen_param0 2025-09-09T14:13:14.8523833Z 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 2025-09-09T14:13:14.8525763Z 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 2025-09-09T14:13:14.8526961Z relu = torch.ops.aten.relu.default(conv1d_2); conv1d_2 = None 2025-09-09T14:13:14.8528056Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(relu, 0.005531368777155876, -128, -128, 127, torch.int8); relu = None 2025-09-09T14:13:14.8529914Z 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 2025-09-09T14:13:14.8531430Z return pytree.tree_unflatten((dequantize_per_tensor_default_1,), self._out_spec) 2025-09-09T14:13:14.8532008Z 2025-09-09T14:13:14.8532387Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:13:14.8532897Z onverted model fx: GraphModule( 2025-09-09T14:13:14.8533252Z (conv): ConvReLU1d( 2025-09-09T14:13:14.8533687Z (0): QuantizedConv1d(Reference)(3, 3, kernel_size=(3,), stride=(1,)) 2025-09-09T14:13:14.8534197Z (1): ReLU() 2025-09-09T14:13:14.8534445Z ) 2025-09-09T14:13:14.8534693Z ) 2025-09-09T14:13:14.8534818Z 2025-09-09T14:13:14.8534823Z 2025-09-09T14:13:14.8534827Z 2025-09-09T14:13:14.8534939Z def forward(self, x): 2025-09-09T14:13:14.8535805Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.010372933000326157, 0, -128, 127, torch.int8); x = None 2025-09-09T14:13:14.8537584Z 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 2025-09-09T14:13:23.6090749Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T14:13:23.6092131Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.005531368777155876, -128, -128, 127, torch.int8); conv = None 2025-09-09T14:13:23.6094017Z 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 2025-09-09T14:13:23.6095301Z return dequantize_per_tensor_default_1 2025-09-09T14:13:23.6095679Z 2025-09-09T14:13:23.6096071Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:13:23.6096589Z diff: tensor([[[0., 0., 0.], 2025-09-09T14:13:23.6096917Z [0., 0., 0.], 2025-09-09T14:13:23.6097193Z [0., 0., 0.]]]) 2025-09-09T14:13:23.6097511Z model pt2e: GraphModule( 2025-09-09T14:13:23.6097812Z (conv): Module() 2025-09-09T14:13:23.6098096Z (bn): Module() 2025-09-09T14:13:23.6098510Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:13:23.6099847Z 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 2025-09-09T14:13:23.6101430Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3264806270599365, max_val=1.318617343902588) 2025-09-09T14:13:23.6102194Z ) 2025-09-09T14:13:23.6102558Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:13:23.6103903Z 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 2025-09-09T14:13:23.6105490Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.3312976360321045, max_val=0.3013271391391754) 2025-09-09T14:13:23.6117173Z ) 2025-09-09T14:13:23.6117975Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:13:23.6119357Z 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 2025-09-09T14:13:23.6120946Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=1.4099854230880737) 2025-09-09T14:13:23.6121625Z ) 2025-09-09T14:13:23.6121852Z ) 2025-09-09T14:13:23.6122002Z 2025-09-09T14:13:23.6122007Z 2025-09-09T14:13:23.6122012Z 2025-09-09T14:13:23.6122276Z def forward(self, x): 2025-09-09T14:13:23.6122671Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:13:23.6123134Z conv_weight = self.conv.weight 2025-09-09T14:13:23.6123519Z conv_bias = self.conv.bias 2025-09-09T14:13:23.6123862Z bn_weight = self.bn.weight 2025-09-09T14:13:23.6124211Z bn_bias = self.bn.bias 2025-09-09T14:13:23.6124554Z bn_running_mean = self.bn.running_mean 2025-09-09T14:13:23.6124966Z bn_running_var = self.bn.running_var 2025-09-09T14:13:23.6125429Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T14:13:23.6126025Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:13:23.6126849Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T14:13:23.6127570Z add = torch.ops.aten.add.Tensor(bn_running_var, 1e-05) 2025-09-09T14:13:23.6128116Z sqrt = torch.ops.aten.sqrt.default(add); add = None 2025-09-09T14:13:23.6128679Z div = torch.ops.aten.div.Tensor(bn_weight, sqrt); sqrt = None 2025-09-09T14:13:23.6129291Z reshape = torch.ops.aten.reshape.default(div, [-1, 1, 1]) 2025-09-09T14:13:23.6129990Z mul = torch.ops.aten.mul.Tensor(conv_weight, reshape); conv_weight = reshape = None 2025-09-09T14:13:23.6130760Z activation_post_process_1 = self.activation_post_process_1(mul); mul = None 2025-09-09T14:13:23.6131624Z zeros_like = torch.ops.aten.zeros_like.default(conv_bias, dtype = torch.float32, pin_memory = False) 2025-09-09T14:13:23.6132994Z 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 2025-09-09T14:13:23.6134242Z reshape_1 = torch.ops.aten.reshape.default(div, [1, -1, 1]); div = None 2025-09-09T14:13:23.6134980Z div_1 = torch.ops.aten.div.Tensor(conv1d_1, reshape_1); conv1d_1 = reshape_1 = None 2025-09-09T14:13:23.6135767Z reshape_2 = torch.ops.aten.reshape.default(conv_bias, [1, -1, 1]); conv_bias = None 2025-09-09T14:13:23.6136542Z add_1 = torch.ops.aten.add.Tensor(div_1, reshape_2); div_1 = reshape_2 = None 2025-09-09T14:13:23.6137752Z 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 2025-09-09T14:13:23.6138952Z relu = torch.ops.aten.relu.default(batch_norm_1); batch_norm_1 = None 2025-09-09T14:13:23.6139675Z activation_post_process_2 = self.activation_post_process_2(relu); relu = None 2025-09-09T14:13:23.6140417Z return pytree.tree_unflatten((activation_post_process_2,), self._out_spec) 2025-09-09T14:13:23.6140959Z 2025-09-09T14:13:23.6141324Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:13:23.6141829Z model fx: GraphModule( 2025-09-09T14:13:23.6142253Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:13:23.6143596Z 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 2025-09-09T14:13:23.6145172Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3264806270599365, max_val=1.318617343902588) 2025-09-09T14:13:23.6145974Z ) 2025-09-09T14:13:23.6146233Z (conv): ConvBnReLU1d( 2025-09-09T14:13:23.6146542Z 3, 3, kernel_size=(3,), stride=(1,) 2025-09-09T14:13:23.6147110Z (bn): BatchNorm1d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T14:13:23.6147745Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:13:23.6149054Z 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 2025-09-09T14:13:23.6150730Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.3312976360321045, max_val=0.3013271391391754) 2025-09-09T14:13:23.6151453Z ) 2025-09-09T14:13:23.6151684Z ) 2025-09-09T14:13:23.6152059Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:13:23.6153403Z 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 2025-09-09T14:13:23.6154926Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=1.4099854230880737) 2025-09-09T14:13:23.6155594Z ) 2025-09-09T14:13:23.6155810Z ) 2025-09-09T14:13:23.6155934Z 2025-09-09T14:13:23.6155939Z 2025-09-09T14:13:23.6155944Z 2025-09-09T14:13:23.6156073Z def forward(self, x): 2025-09-09T14:13:23.6156535Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:13:23.6157281Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T14:13:23.6158028Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T14:13:23.6158903Z return activation_post_process_1 2025-09-09T14:13:23.6159263Z 2025-09-09T14:13:23.6159630Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:13:23.6160203Z diff: tensor([[[0., 0., 0.], 2025-09-09T14:13:23.6160510Z [0., 0., 0.], 2025-09-09T14:13:23.6160828Z [0., 0., 0.]]], grad_fn=) 2025-09-09T14:13:23.6161226Z converted model pt2e: GraphModule( 2025-09-09T14:13:23.6161584Z (conv): Module() 2025-09-09T14:13:23.6161846Z (bn): Module() 2025-09-09T14:13:23.6162107Z ) 2025-09-09T14:13:23.6162232Z 2025-09-09T14:13:23.6162237Z 2025-09-09T14:13:23.6162241Z 2025-09-09T14:13:23.6162364Z def forward(self, x): 2025-09-09T14:13:23.6162727Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:13:23.6163191Z conv_bias = self.conv.bias 2025-09-09T14:13:23.6163580Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T14:13:23.6164577Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.010372933000326157, 0, -128, 127, torch.int8); x = None 2025-09-09T14:13:23.6166354Z 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 2025-09-09T14:13:23.6167839Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T14:13:23.6168521Z quantize_per_tensor = self._frozen_param0 2025-09-09T14:13:23.6169636Z 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 2025-09-09T14:13:23.6171438Z 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 2025-09-09T14:13:23.6172648Z relu = torch.ops.aten.relu.default(conv1d_2); conv1d_2 = None 2025-09-09T14:13:23.6173895Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(relu, 0.00552935479208827, -128, -128, 127, torch.int8); relu = None 2025-09-09T14:13:23.6175736Z 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 2025-09-09T14:13:23.6177191Z return pytree.tree_unflatten((dequantize_per_tensor_default_2,), self._out_spec) 2025-09-09T14:13:23.6177755Z 2025-09-09T14:13:23.6178138Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:13:23.6178643Z onverted model fx: GraphModule( 2025-09-09T14:13:23.6179125Z (conv): ConvReLU1d( 2025-09-09T14:13:23.6179560Z (0): QuantizedConv1d(Reference)(3, 3, kernel_size=(3,), stride=(1,)) 2025-09-09T14:13:23.6180069Z (1): ReLU() 2025-09-09T14:13:23.6180320Z ) 2025-09-09T14:13:23.6180555Z ) 2025-09-09T14:13:23.6180683Z 2025-09-09T14:13:23.6180688Z 2025-09-09T14:13:23.6180693Z 2025-09-09T14:13:23.6180825Z def forward(self, x): 2025-09-09T14:13:23.6181684Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.010372933000326157, 0, -128, 127, torch.int8); x = None 2025-09-09T14:13:36.3488651Z 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 2025-09-09T14:13:36.3490150Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T14:13:36.3491377Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.00552935479208827, -128, -128, 127, torch.int8); conv = None 2025-09-09T14:13:36.3493255Z 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 2025-09-09T14:13:36.3494522Z return dequantize_per_tensor_default_1 2025-09-09T14:13:36.3494905Z 2025-09-09T14:13:36.3495278Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:13:36.3495822Z diff: tensor([[[0., 0., 0.], 2025-09-09T14:13:36.3496141Z [0., 0., 0.], 2025-09-09T14:13:36.3496412Z [0., 0., 0.]]]) 2025-09-09T14:13:36.3496921Z PASSED 2025-09-09T14:13:36.3497900Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn1d::test_qat_conv_bn_relu_fusion_cuda SKIPPED 2025-09-09T14:13:36.3499337Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn1d::test_qat_conv_bn_relu_fusion_no_conv_bias model pt2e: GraphModule( 2025-09-09T14:13:36.3500286Z (conv): Module() 2025-09-09T14:13:36.3500550Z (bn): Module() 2025-09-09T14:13:36.3500956Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:13:36.3502281Z 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 2025-09-09T14:13:36.3503862Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3264806270599365, max_val=1.318617343902588) 2025-09-09T14:13:36.3504588Z ) 2025-09-09T14:13:36.3504951Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:13:36.3506351Z 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 2025-09-09T14:13:36.3508218Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.2639, -0.2941, -0.2608]), max_val=tensor([0.2795, 0.3227, 0.2891])) 2025-09-09T14:13:36.3509137Z ) 2025-09-09T14:13:36.3509512Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:13:36.3511199Z 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 2025-09-09T14:13:36.3512734Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=0.9981018900871277) 2025-09-09T14:13:36.3513402Z ) 2025-09-09T14:13:36.3513625Z ) 2025-09-09T14:13:36.3513754Z 2025-09-09T14:13:36.3513759Z 2025-09-09T14:13:36.3513778Z 2025-09-09T14:13:36.3513892Z def forward(self, x): 2025-09-09T14:13:36.3514263Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:13:36.3514857Z conv_weight = self.conv.weight 2025-09-09T14:13:36.3515222Z bn_weight = self.bn.weight 2025-09-09T14:13:36.3515569Z bn_bias = self.bn.bias 2025-09-09T14:13:36.3515918Z bn_running_mean = self.bn.running_mean 2025-09-09T14:13:36.3516316Z bn_running_var = self.bn.running_var 2025-09-09T14:13:36.3516774Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T14:13:36.3517370Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:13:36.3518186Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T14:13:36.3518905Z add = torch.ops.aten.add.Tensor(bn_running_var, 1e-05) 2025-09-09T14:13:36.3519447Z sqrt = torch.ops.aten.sqrt.default(add); add = None 2025-09-09T14:13:36.3520093Z div = torch.ops.aten.div.Tensor(bn_weight, sqrt); sqrt = None 2025-09-09T14:13:36.3520691Z reshape = torch.ops.aten.reshape.default(div, [-1, 1, 1]) 2025-09-09T14:13:36.3521396Z mul = torch.ops.aten.mul.Tensor(conv_weight, reshape); conv_weight = reshape = None 2025-09-09T14:13:36.3522170Z activation_post_process_1 = self.activation_post_process_1(mul); mul = None 2025-09-09T14:13:36.3523363Z 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 2025-09-09T14:13:36.3524521Z reshape_1 = torch.ops.aten.reshape.default(div, [1, -1, 1]); div = None 2025-09-09T14:13:36.3525274Z div_1 = torch.ops.aten.div.Tensor(conv1d_1, reshape_1); conv1d_1 = reshape_1 = None 2025-09-09T14:13:36.3526535Z 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 2025-09-09T14:13:36.3527723Z relu = torch.ops.aten.relu.default(batch_norm_1); batch_norm_1 = None 2025-09-09T14:13:36.3528461Z activation_post_process_2 = self.activation_post_process_2(relu); relu = None 2025-09-09T14:13:36.3529209Z return pytree.tree_unflatten((activation_post_process_2,), self._out_spec) 2025-09-09T14:13:36.3529757Z 2025-09-09T14:13:36.3530140Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:13:36.3530630Z model fx: GraphModule( 2025-09-09T14:13:36.3531073Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:13:36.3532402Z 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 2025-09-09T14:13:36.3533980Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3264806270599365, max_val=1.318617343902588) 2025-09-09T14:13:36.3534710Z ) 2025-09-09T14:13:36.3534953Z (conv): ConvBnReLU1d( 2025-09-09T14:13:36.3535305Z 3, 3, kernel_size=(3,), stride=(1,), bias=False 2025-09-09T14:13:36.3535891Z (bn): BatchNorm1d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T14:13:36.3536537Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:13:36.3537985Z 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 2025-09-09T14:13:36.3539853Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.2639, -0.2941, -0.2608]), max_val=tensor([0.2795, 0.3227, 0.2891])) 2025-09-09T14:13:36.3540782Z ) 2025-09-09T14:13:36.3541003Z ) 2025-09-09T14:13:36.3541375Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:13:36.3542706Z 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 2025-09-09T14:13:36.3544305Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=0.9981018900871277) 2025-09-09T14:13:36.3544970Z ) 2025-09-09T14:13:36.3545185Z ) 2025-09-09T14:13:36.3545310Z 2025-09-09T14:13:36.3545315Z 2025-09-09T14:13:36.3545320Z 2025-09-09T14:13:36.3545451Z def forward(self, x): 2025-09-09T14:13:36.3545918Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:13:36.3546656Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T14:13:36.3547405Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T14:13:36.3547994Z return activation_post_process_1 2025-09-09T14:13:36.3548355Z 2025-09-09T14:13:36.3548717Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:13:36.3549223Z diff: tensor([[[0., 0., 0.], 2025-09-09T14:13:36.3549536Z [0., 0., 0.], 2025-09-09T14:13:36.3549856Z [0., 0., 0.]]], grad_fn=) 2025-09-09T14:13:36.3550254Z converted model pt2e: GraphModule( 2025-09-09T14:13:36.3550610Z (conv): Module() 2025-09-09T14:13:36.3550874Z (bn): Module() 2025-09-09T14:13:36.3551139Z ) 2025-09-09T14:13:36.3551264Z 2025-09-09T14:13:36.3551269Z 2025-09-09T14:13:36.3551278Z 2025-09-09T14:13:36.3551403Z def forward(self, x): 2025-09-09T14:13:36.3551775Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:13:36.3552296Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T14:13:36.3553283Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.010372933000326157, 0, -128, 127, torch.int8); x = None 2025-09-09T14:13:36.3555063Z 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 2025-09-09T14:13:36.3556559Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T14:13:36.3557198Z _scale_0 = self._scale_0 2025-09-09T14:13:36.3557551Z _zero_point_0 = self._zero_point_0 2025-09-09T14:13:36.3557952Z quantize_per_channel = self._frozen_param0 2025-09-09T14:13:36.3559473Z 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 2025-09-09T14:13:36.3560813Z conv_weight_bias = self.conv.weight_bias 2025-09-09T14:13:36.3561988Z 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 2025-09-09T14:13:36.3563276Z relu = torch.ops.aten.relu.default(conv1d_2); conv1d_2 = None 2025-09-09T14:13:36.3564383Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(relu, 0.0039141252636909485, -128, -128, 127, torch.int8); relu = None 2025-09-09T14:13:36.3566392Z 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 2025-09-09T14:13:36.3567857Z return pytree.tree_unflatten((dequantize_per_tensor_default_1,), self._out_spec) 2025-09-09T14:13:36.3568426Z 2025-09-09T14:13:36.3568806Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:13:36.3569312Z onverted model fx: GraphModule( 2025-09-09T14:13:46.0911050Z (conv): ConvReLU1d( 2025-09-09T14:13:46.0911727Z (0): QuantizedConv1d(Reference)(3, 3, kernel_size=(3,), stride=(1,)) 2025-09-09T14:13:46.0912296Z (1): ReLU() 2025-09-09T14:13:46.0912550Z ) 2025-09-09T14:13:46.0913150Z ) 2025-09-09T14:13:46.0913277Z 2025-09-09T14:13:46.0913282Z 2025-09-09T14:13:46.0913288Z 2025-09-09T14:13:46.0913412Z def forward(self, x): 2025-09-09T14:13:46.0914285Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.010372933000326157, 0, -128, 127, torch.int8); x = None 2025-09-09T14:13:46.0916078Z 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 2025-09-09T14:13:46.0917512Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T14:13:46.0918734Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.0039141252636909485, -128, -128, 127, torch.int8); conv = None 2025-09-09T14:13:46.0920647Z 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 2025-09-09T14:13:46.0921915Z return dequantize_per_tensor_default_1 2025-09-09T14:13:46.0922294Z 2025-09-09T14:13:46.0922664Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:13:46.0923177Z diff: tensor([[[0., 0., 0.], 2025-09-09T14:13:46.0923512Z [0., 0., 0.], 2025-09-09T14:13:46.0923786Z [0., 0., 0.]]]) 2025-09-09T14:13:46.0924098Z model pt2e: GraphModule( 2025-09-09T14:13:46.0924401Z (conv): Module() 2025-09-09T14:13:46.0924677Z (bn): Module() 2025-09-09T14:13:46.0925072Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:13:46.0926407Z 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 2025-09-09T14:13:46.0927983Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3264806270599365, max_val=1.318617343902588) 2025-09-09T14:13:46.0928722Z ) 2025-09-09T14:13:46.0929098Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:13:46.0930437Z 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 2025-09-09T14:13:46.0932034Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.2940981984138489, max_val=0.32268622517585754) 2025-09-09T14:13:46.0932759Z ) 2025-09-09T14:13:46.0933134Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:13:46.0934478Z 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 2025-09-09T14:13:46.0935991Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=1.0074307918548584) 2025-09-09T14:13:46.0936659Z ) 2025-09-09T14:13:46.0936883Z ) 2025-09-09T14:13:46.0937023Z 2025-09-09T14:13:46.0937029Z 2025-09-09T14:13:46.0937033Z 2025-09-09T14:13:46.0937149Z def forward(self, x): 2025-09-09T14:13:46.0937689Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:13:46.0938146Z conv_weight = self.conv.weight 2025-09-09T14:13:46.0938529Z bn_weight = self.bn.weight 2025-09-09T14:13:46.0938862Z bn_bias = self.bn.bias 2025-09-09T14:13:46.0939212Z bn_running_mean = self.bn.running_mean 2025-09-09T14:13:46.0939615Z bn_running_var = self.bn.running_var 2025-09-09T14:13:46.0940072Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T14:13:46.0940669Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:13:46.0941487Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T14:13:46.0942295Z add = torch.ops.aten.add.Tensor(bn_running_var, 1e-05) 2025-09-09T14:13:46.0942816Z sqrt = torch.ops.aten.sqrt.default(add); add = None 2025-09-09T14:13:46.0943383Z div = torch.ops.aten.div.Tensor(bn_weight, sqrt); sqrt = None 2025-09-09T14:13:46.0943974Z reshape = torch.ops.aten.reshape.default(div, [-1, 1, 1]) 2025-09-09T14:13:46.0944670Z mul = torch.ops.aten.mul.Tensor(conv_weight, reshape); conv_weight = reshape = None 2025-09-09T14:13:46.0945440Z activation_post_process_1 = self.activation_post_process_1(mul); mul = None 2025-09-09T14:13:46.0946635Z 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 2025-09-09T14:13:46.0947801Z reshape_1 = torch.ops.aten.reshape.default(div, [1, -1, 1]); div = None 2025-09-09T14:13:46.0948531Z div_1 = torch.ops.aten.div.Tensor(conv1d_1, reshape_1); conv1d_1 = reshape_1 = None 2025-09-09T14:13:46.0949791Z 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 2025-09-09T14:13:46.0950986Z relu = torch.ops.aten.relu.default(batch_norm_1); batch_norm_1 = None 2025-09-09T14:13:46.0951706Z activation_post_process_2 = self.activation_post_process_2(relu); relu = None 2025-09-09T14:13:46.0952462Z return pytree.tree_unflatten((activation_post_process_2,), self._out_spec) 2025-09-09T14:13:46.0952991Z 2025-09-09T14:13:46.0953371Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:13:46.0953864Z model fx: GraphModule( 2025-09-09T14:13:46.0954303Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:13:46.0955646Z 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 2025-09-09T14:13:46.0957220Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3264806270599365, max_val=1.318617343902588) 2025-09-09T14:13:46.0957949Z ) 2025-09-09T14:13:46.0958431Z (conv): ConvBnReLU1d( 2025-09-09T14:13:46.0958796Z 3, 3, kernel_size=(3,), stride=(1,), bias=False 2025-09-09T14:13:46.0959375Z (bn): BatchNorm1d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T14:13:46.0960085Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:13:46.0961393Z 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 2025-09-09T14:13:46.0962987Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.2940981984138489, max_val=0.32268622517585754) 2025-09-09T14:13:46.0963746Z ) 2025-09-09T14:13:46.0963974Z ) 2025-09-09T14:13:46.0964350Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:13:46.0965839Z 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 2025-09-09T14:13:46.0967358Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=1.0074307918548584) 2025-09-09T14:13:46.0968032Z ) 2025-09-09T14:13:46.0968255Z ) 2025-09-09T14:13:46.0968395Z 2025-09-09T14:13:46.0968401Z 2025-09-09T14:13:46.0968406Z 2025-09-09T14:13:46.0968519Z def forward(self, x): 2025-09-09T14:13:46.0968993Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:13:46.0969733Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T14:13:46.0970583Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T14:13:46.0971163Z return activation_post_process_1 2025-09-09T14:13:46.0971524Z 2025-09-09T14:13:46.0971890Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:13:46.0972398Z diff: tensor([[[0., 0., 0.], 2025-09-09T14:13:46.0972706Z [0., 0., 0.], 2025-09-09T14:13:46.0973031Z [0., 0., 0.]]], grad_fn=) 2025-09-09T14:13:46.0973448Z converted model pt2e: GraphModule( 2025-09-09T14:13:46.0973798Z (conv): Module() 2025-09-09T14:13:46.0974076Z (bn): Module() 2025-09-09T14:13:46.0974330Z ) 2025-09-09T14:13:46.0974458Z 2025-09-09T14:13:46.0974463Z 2025-09-09T14:13:46.0974468Z 2025-09-09T14:13:46.0974596Z def forward(self, x): 2025-09-09T14:13:46.0974963Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:13:46.0975483Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T14:13:46.0976465Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.010372933000326157, 0, -128, 127, torch.int8); x = None 2025-09-09T14:13:46.0978247Z 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 2025-09-09T14:13:46.0979741Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T14:13:46.0980414Z quantize_per_tensor = self._frozen_param0 2025-09-09T14:13:46.0981545Z 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 2025-09-09T14:13:46.0982681Z conv_weight_bias = self.conv.weight_bias 2025-09-09T14:13:46.0983849Z 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 2025-09-09T14:13:46.0985117Z relu = torch.ops.aten.relu.default(conv1d_2); conv1d_2 = None 2025-09-09T14:13:46.0986214Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(relu, 0.003950709011405706, -128, -128, 127, torch.int8); relu = None 2025-09-09T14:13:46.0988065Z 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 2025-09-09T14:13:46.0989524Z return pytree.tree_unflatten((dequantize_per_tensor_default_2,), self._out_spec) 2025-09-09T14:13:46.0990091Z 2025-09-09T14:13:46.0990465Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:13:46.0990969Z onverted model fx: GraphModule( 2025-09-09T14:13:46.0991320Z (conv): ConvReLU1d( 2025-09-09T14:13:46.0991752Z (0): QuantizedConv1d(Reference)(3, 3, kernel_size=(3,), stride=(1,)) 2025-09-09T14:13:46.0992260Z (1): ReLU() 2025-09-09T14:13:46.0992524Z ) 2025-09-09T14:13:46.0992743Z ) 2025-09-09T14:13:46.0992867Z 2025-09-09T14:13:46.0992872Z 2025-09-09T14:13:46.0992876Z 2025-09-09T14:13:46.0993001Z def forward(self, x): 2025-09-09T14:13:47.5782479Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.010372933000326157, 0, -128, 127, torch.int8); x = None 2025-09-09T14:13:47.5784323Z 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 2025-09-09T14:13:47.5785767Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T14:13:47.5786991Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.003950709011405706, -128, -128, 127, torch.int8); conv = None 2025-09-09T14:13:47.5789024Z 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 2025-09-09T14:13:47.5790303Z return dequantize_per_tensor_default_1 2025-09-09T14:13:47.5790672Z 2025-09-09T14:13:47.5791064Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:13:47.5791565Z diff: tensor([[[0., 0., 0.], 2025-09-09T14:13:47.5791894Z [0., 0., 0.], 2025-09-09T14:13:47.5792169Z [0., 0., 0.]]]) 2025-09-09T14:13:47.5792676Z PASSED 2025-09-09T14:13:47.5793462Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn1d::test_qat_conv_no_bias model pt2e: GraphModule( 2025-09-09T14:13:47.5794320Z (conv): Module() 2025-09-09T14:13:47.5794724Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:13:47.5796137Z 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 2025-09-09T14:13:47.5798112Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.1720, -0.2918, -0.2941]), max_val=tensor([0.2663, 0.2795, 0.3227])) 2025-09-09T14:13:47.5799047Z ) 2025-09-09T14:13:47.5799413Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:13:47.5800805Z 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 2025-09-09T14:13:47.5802356Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3264806270599365, max_val=1.318617343902588) 2025-09-09T14:13:47.5803092Z ) 2025-09-09T14:13:47.5803478Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:13:47.5804802Z 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 2025-09-09T14:13:47.5806332Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=0.16202233731746674) 2025-09-09T14:13:47.5806991Z ) 2025-09-09T14:13:47.5807225Z ) 2025-09-09T14:13:47.5807357Z 2025-09-09T14:13:47.5807363Z 2025-09-09T14:13:47.5807367Z 2025-09-09T14:13:47.5807494Z def forward(self, x): 2025-09-09T14:13:47.5807861Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:13:47.5808323Z conv_weight = self.conv.weight 2025-09-09T14:13:47.5808938Z activation_post_process_1 = self.activation_post_process_1(conv_weight); conv_weight = None 2025-09-09T14:13:47.5809749Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:13:47.5810885Z conv1d = torch.ops.aten.conv1d.default(activation_post_process_0, activation_post_process_1); activation_post_process_0 = activation_post_process_1 = None 2025-09-09T14:13:47.5811950Z relu = torch.ops.aten.relu.default(conv1d); conv1d = None 2025-09-09T14:13:47.5812753Z activation_post_process_2 = self.activation_post_process_2(relu); relu = None 2025-09-09T14:13:47.5813494Z return pytree.tree_unflatten((activation_post_process_2,), self._out_spec) 2025-09-09T14:13:47.5814033Z 2025-09-09T14:13:47.5814400Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:13:47.5814907Z model fx: GraphModule( 2025-09-09T14:13:47.5815329Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:13:47.5816665Z 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 2025-09-09T14:13:47.5818310Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3264806270599365, max_val=1.318617343902588) 2025-09-09T14:13:47.5819026Z ) 2025-09-09T14:13:47.5819270Z (conv): ConvReLU1d( 2025-09-09T14:13:47.5819596Z 3, 3, kernel_size=(3,), stride=(1,), bias=False 2025-09-09T14:13:47.5820088Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:13:47.5821439Z 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 2025-09-09T14:13:47.5823292Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.1720, -0.2918, -0.2941]), max_val=tensor([0.2663, 0.2795, 0.3227])) 2025-09-09T14:13:47.5824222Z ) 2025-09-09T14:13:47.5824445Z ) 2025-09-09T14:13:47.5824826Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:13:47.5826159Z 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 2025-09-09T14:13:47.5827691Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=0.16202233731746674) 2025-09-09T14:13:47.5828368Z ) 2025-09-09T14:13:47.5828591Z ) 2025-09-09T14:13:47.5828732Z 2025-09-09T14:13:47.5828737Z 2025-09-09T14:13:47.5828742Z 2025-09-09T14:13:47.5828853Z def forward(self, x): 2025-09-09T14:13:47.5829317Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:13:47.5830058Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T14:13:47.5830817Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T14:13:47.5831401Z return activation_post_process_1 2025-09-09T14:13:47.5831771Z 2025-09-09T14:13:47.5832133Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:13:47.5832642Z diff: tensor([[[0., 0., 0.], 2025-09-09T14:13:47.5832948Z [0., 0., 0.], 2025-09-09T14:13:47.5833277Z [0., 0., 0.]]], grad_fn=) 2025-09-09T14:13:47.5833697Z converted model pt2e: GraphModule( 2025-09-09T14:13:47.5834047Z (conv): Module() 2025-09-09T14:13:47.5834315Z ) 2025-09-09T14:13:47.5834444Z 2025-09-09T14:13:47.5834449Z 2025-09-09T14:13:47.5834453Z 2025-09-09T14:13:47.5834565Z def forward(self, x): 2025-09-09T14:13:47.5834947Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:13:47.5835388Z _scale_0 = self._scale_0 2025-09-09T14:13:47.5835736Z _zero_point_0 = self._zero_point_0 2025-09-09T14:13:47.5836171Z quantize_per_channel_default = self._frozen_param0 2025-09-09T14:13:47.5837591Z 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 2025-09-09T14:13:47.5839502Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.010372933000326157, 0, -128, 127, torch.int8); x = None 2025-09-09T14:13:47.5841441Z 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 2025-09-09T14:13:47.5843360Z conv1d = torch.ops.aten.conv1d.default(dequantize_per_tensor_default, dequantize_per_channel_default); dequantize_per_tensor_default = dequantize_per_channel_default = None 2025-09-09T14:13:47.5844531Z relu = torch.ops.aten.relu.default(conv1d); conv1d = None 2025-09-09T14:13:47.5845616Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(relu, 0.0006353817298077047, -128, -128, 127, torch.int8); relu = None 2025-09-09T14:13:47.5847556Z 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 2025-09-09T14:13:47.5849020Z return pytree.tree_unflatten((dequantize_per_tensor_default_1,), self._out_spec) 2025-09-09T14:13:47.5849590Z 2025-09-09T14:13:47.5849976Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:13:47.5850483Z onverted model fx: GraphModule( 2025-09-09T14:13:47.5850833Z (conv): ConvReLU1d( 2025-09-09T14:13:47.5851313Z (0): QuantizedConv1d(Reference)(3, 3, kernel_size=(3,), stride=(1,), bias=False) 2025-09-09T14:13:47.5851866Z (1): ReLU() 2025-09-09T14:13:47.5852112Z ) 2025-09-09T14:13:47.5852346Z ) 2025-09-09T14:13:47.5852471Z 2025-09-09T14:13:47.5852475Z 2025-09-09T14:13:47.5852480Z 2025-09-09T14:13:47.5852607Z def forward(self, x): 2025-09-09T14:13:47.5853458Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.010372933000326157, 0, -128, 127, torch.int8); x = None 2025-09-09T14:13:47.5855242Z 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 2025-09-09T14:13:47.5856670Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T14:13:47.5857892Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.0006353817298077047, -128, -128, 127, torch.int8); conv = None 2025-09-09T14:13:47.5859972Z 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 2025-09-09T14:13:47.5861242Z return dequantize_per_tensor_default_1 2025-09-09T14:13:47.5861620Z 2025-09-09T14:13:47.5861986Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:13:47.5862495Z diff: tensor([[[0., 0., 0.], 2025-09-09T14:13:47.5862818Z [0., 0., 0.], 2025-09-09T14:13:47.5863095Z [0., 0., 0.]]]) 2025-09-09T14:13:47.5863416Z model pt2e: GraphModule( 2025-09-09T14:13:47.5863715Z (conv): Module() 2025-09-09T14:13:47.5864126Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:13:48.4159416Z 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 2025-09-09T14:13:48.4161179Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.2940996587276459, max_val=0.3226878345012665) 2025-09-09T14:13:48.4161912Z ) 2025-09-09T14:13:48.4162334Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:13:48.4163672Z 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 2025-09-09T14:13:48.4165553Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3264806270599365, max_val=1.318617343902588) 2025-09-09T14:13:48.4166293Z ) 2025-09-09T14:13:48.4166667Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:13:48.4168012Z 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 2025-09-09T14:13:48.4169521Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=0.1632835566997528) 2025-09-09T14:13:48.4170297Z ) 2025-09-09T14:13:48.4170532Z ) 2025-09-09T14:13:48.4170664Z 2025-09-09T14:13:48.4170669Z 2025-09-09T14:13:48.4170674Z 2025-09-09T14:13:48.4170787Z def forward(self, x): 2025-09-09T14:13:48.4171175Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:13:48.4171646Z conv_weight = self.conv.weight 2025-09-09T14:13:48.4172277Z activation_post_process_1 = self.activation_post_process_1(conv_weight); conv_weight = None 2025-09-09T14:13:48.4173105Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:13:48.4174242Z conv1d = torch.ops.aten.conv1d.default(activation_post_process_0, activation_post_process_1); activation_post_process_0 = activation_post_process_1 = None 2025-09-09T14:13:48.4175311Z relu = torch.ops.aten.relu.default(conv1d); conv1d = None 2025-09-09T14:13:48.4175966Z activation_post_process_2 = self.activation_post_process_2(relu); relu = None 2025-09-09T14:13:48.4176717Z return pytree.tree_unflatten((activation_post_process_2,), self._out_spec) 2025-09-09T14:13:48.4177263Z 2025-09-09T14:13:48.4177631Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:13:48.4178136Z model fx: GraphModule( 2025-09-09T14:13:48.4178564Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:13:48.4179903Z 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 2025-09-09T14:13:48.4181461Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3264806270599365, max_val=1.318617343902588) 2025-09-09T14:13:48.4182173Z ) 2025-09-09T14:13:48.4182422Z (conv): ConvReLU1d( 2025-09-09T14:13:48.4182739Z 3, 3, kernel_size=(3,), stride=(1,), bias=False 2025-09-09T14:13:48.4183228Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:13:48.4184523Z 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 2025-09-09T14:13:48.4186119Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.2940996587276459, max_val=0.3226878345012665) 2025-09-09T14:13:48.4186890Z ) 2025-09-09T14:13:48.4187119Z ) 2025-09-09T14:13:48.4187492Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:13:48.4188822Z 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 2025-09-09T14:13:48.4190333Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=0.1632835566997528) 2025-09-09T14:13:48.4191004Z ) 2025-09-09T14:13:48.4191222Z ) 2025-09-09T14:13:48.4191349Z 2025-09-09T14:13:48.4191360Z 2025-09-09T14:13:48.4191364Z 2025-09-09T14:13:48.4191490Z def forward(self, x): 2025-09-09T14:13:48.4191956Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:13:48.4192698Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T14:13:48.4193541Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T14:13:48.4194142Z return activation_post_process_1 2025-09-09T14:13:48.4194503Z 2025-09-09T14:13:48.4194868Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:13:48.4195384Z diff: tensor([[[0., 0., 0.], 2025-09-09T14:13:48.4195692Z [0., 0., 0.], 2025-09-09T14:13:48.4196017Z [0., 0., 0.]]], grad_fn=) 2025-09-09T14:13:48.4196421Z converted model pt2e: GraphModule( 2025-09-09T14:13:48.4196787Z (conv): Module() 2025-09-09T14:13:48.4197043Z ) 2025-09-09T14:13:48.4197184Z 2025-09-09T14:13:48.4197264Z 2025-09-09T14:13:48.4197269Z 2025-09-09T14:13:48.4197385Z def forward(self, x): 2025-09-09T14:13:48.4197767Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:13:48.4198270Z quantize_per_tensor_default = self._frozen_param0 2025-09-09T14:13:48.4199563Z 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 2025-09-09T14:13:48.4201406Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.010372933000326157, 0, -128, 127, torch.int8); x = None 2025-09-09T14:13:48.4203215Z 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 2025-09-09T14:13:48.4205154Z 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 2025-09-09T14:13:48.4206343Z relu = torch.ops.aten.relu.default(conv1d); conv1d = None 2025-09-09T14:13:48.4207449Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(relu, 0.0006403276929631829, -128, -128, 127, torch.int8); relu = None 2025-09-09T14:13:48.4209331Z 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 2025-09-09T14:13:48.4210776Z return pytree.tree_unflatten((dequantize_per_tensor_default_2,), self._out_spec) 2025-09-09T14:13:48.4211355Z 2025-09-09T14:13:48.4211725Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:13:48.4212245Z onverted model fx: GraphModule( 2025-09-09T14:13:48.4212592Z (conv): ConvReLU1d( 2025-09-09T14:13:48.4213103Z (0): QuantizedConv1d(Reference)(3, 3, kernel_size=(3,), stride=(1,), bias=False) 2025-09-09T14:13:48.4213664Z (1): ReLU() 2025-09-09T14:13:48.4213914Z ) 2025-09-09T14:13:48.4214152Z ) 2025-09-09T14:13:48.4214280Z 2025-09-09T14:13:48.4214285Z 2025-09-09T14:13:48.4214290Z 2025-09-09T14:13:48.4214403Z def forward(self, x): 2025-09-09T14:13:48.4215273Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.010372933000326157, 0, -128, 127, torch.int8); x = None 2025-09-09T14:13:48.4217025Z 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 2025-09-09T14:13:48.4218475Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T14:13:48.4219702Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.0006403276929631829, -128, -128, 127, torch.int8); conv = None 2025-09-09T14:13:48.4221566Z 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 2025-09-09T14:13:48.4222849Z return dequantize_per_tensor_default_1 2025-09-09T14:13:48.4223322Z 2025-09-09T14:13:48.4223688Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:13:48.4224193Z diff: tensor([[[0., 0., 0.], 2025-09-09T14:13:48.4224500Z [0., 0., 0.], 2025-09-09T14:13:48.4224787Z [0., 0., 0.]]]) 2025-09-09T14:13:48.4225084Z model pt2e: GraphModule( 2025-09-09T14:13:48.4225399Z (conv): Module() 2025-09-09T14:13:48.4225799Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:13:48.4227208Z 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 2025-09-09T14:13:48.4229132Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.3263, -0.3276, -0.3045]), max_val=tensor([0.1376, 0.2760, 0.3298])) 2025-09-09T14:13:48.4230041Z ) 2025-09-09T14:13:48.4230431Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:13:48.4231748Z 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 2025-09-09T14:13:48.4233331Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3264806270599365, max_val=1.318617343902588) 2025-09-09T14:13:48.4234071Z ) 2025-09-09T14:13:48.4234441Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:13:48.4235774Z 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 2025-09-09T14:13:48.4237340Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.284900426864624, max_val=0.7360976338386536) 2025-09-09T14:13:48.4238060Z ) 2025-09-09T14:13:48.4238304Z ) 2025-09-09T14:13:48.4238431Z 2025-09-09T14:13:48.4238436Z 2025-09-09T14:13:48.4238440Z 2025-09-09T14:13:48.4238553Z def forward(self, x): 2025-09-09T14:13:48.4238942Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:13:48.4239401Z conv_weight = self.conv.weight 2025-09-09T14:13:48.4240105Z activation_post_process_1 = self.activation_post_process_1(conv_weight); conv_weight = None 2025-09-09T14:13:49.2644550Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:13:49.2645750Z conv1d = torch.ops.aten.conv1d.default(activation_post_process_0, activation_post_process_1); activation_post_process_0 = activation_post_process_1 = None 2025-09-09T14:13:49.2646961Z activation_post_process_2 = self.activation_post_process_2(conv1d); conv1d = None 2025-09-09T14:13:49.2647728Z return pytree.tree_unflatten((activation_post_process_2,), self._out_spec) 2025-09-09T14:13:49.2648276Z 2025-09-09T14:13:49.2648655Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:13:49.2649161Z model fx: GraphModule( 2025-09-09T14:13:49.2649607Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:13:49.2650939Z 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 2025-09-09T14:13:49.2652531Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3264806270599365, max_val=1.318617343902588) 2025-09-09T14:13:49.2653255Z ) 2025-09-09T14:13:49.2653499Z (conv): Conv1d( 2025-09-09T14:13:49.2653807Z 3, 3, kernel_size=(3,), stride=(1,), bias=False 2025-09-09T14:13:49.2654300Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:13:49.2655910Z 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 2025-09-09T14:13:49.2657757Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.3263, -0.3276, -0.3045]), max_val=tensor([0.1376, 0.2760, 0.3298])) 2025-09-09T14:13:49.2658905Z ) 2025-09-09T14:13:49.2659189Z ) 2025-09-09T14:13:49.2659556Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:13:49.2660895Z 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 2025-09-09T14:13:49.2662623Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.284900426864624, max_val=0.7360976338386536) 2025-09-09T14:13:49.2663340Z ) 2025-09-09T14:13:49.2663577Z ) 2025-09-09T14:13:49.2663702Z 2025-09-09T14:13:49.2663707Z 2025-09-09T14:13:49.2663718Z 2025-09-09T14:13:49.2663829Z def forward(self, x): 2025-09-09T14:13:49.2664306Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:13:49.2665033Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T14:13:49.2665792Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T14:13:49.2666381Z return activation_post_process_1 2025-09-09T14:13:49.2666722Z 2025-09-09T14:13:49.2667097Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:13:49.2667597Z diff: tensor([[[0., 0., 0.], 2025-09-09T14:13:49.2667919Z [0., 0., 0.], 2025-09-09T14:13:49.2668224Z [0., 0., 0.]]], grad_fn=) 2025-09-09T14:13:49.2668644Z converted model pt2e: GraphModule( 2025-09-09T14:13:49.2668987Z (conv): Module() 2025-09-09T14:13:49.2669260Z ) 2025-09-09T14:13:49.2669389Z 2025-09-09T14:13:49.2669394Z 2025-09-09T14:13:49.2669399Z 2025-09-09T14:13:49.2669535Z def forward(self, x): 2025-09-09T14:13:49.2669902Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:13:49.2670361Z _scale_0 = self._scale_0 2025-09-09T14:13:49.2670699Z _zero_point_0 = self._zero_point_0 2025-09-09T14:13:49.2671139Z quantize_per_channel_default = self._frozen_param0 2025-09-09T14:13:49.2672549Z 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 2025-09-09T14:13:49.2674464Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.010372933000326157, 0, -128, 127, torch.int8); x = None 2025-09-09T14:13:49.2676246Z 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 2025-09-09T14:13:49.2678296Z conv1d = torch.ops.aten.conv1d.default(dequantize_per_tensor_default, dequantize_per_channel_default); dequantize_per_tensor_default = dequantize_per_channel_default = None 2025-09-09T14:13:49.2680062Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv1d, 0.007925482466816902, 34, -128, 127, torch.int8); conv1d = None 2025-09-09T14:13:49.2681919Z 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 2025-09-09T14:13:49.2683371Z return pytree.tree_unflatten((dequantize_per_tensor_default_1,), self._out_spec) 2025-09-09T14:13:49.2683959Z 2025-09-09T14:13:49.2684329Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:13:49.2684850Z onverted model fx: GraphModule( 2025-09-09T14:13:49.2685538Z (conv): QuantizedConv1d(Reference)(3, 3, kernel_size=(3,), stride=(1,), bias=False) 2025-09-09T14:13:49.2686108Z ) 2025-09-09T14:13:49.2686239Z 2025-09-09T14:13:49.2686258Z 2025-09-09T14:13:49.2686263Z 2025-09-09T14:13:49.2686379Z def forward(self, x): 2025-09-09T14:13:49.2687233Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.010372933000326157, 0, -128, 127, torch.int8); x = None 2025-09-09T14:13:49.2689007Z 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 2025-09-09T14:13:49.2690526Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T14:13:49.2691728Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.007925482466816902, 34, -128, 127, torch.int8); conv = None 2025-09-09T14:13:49.2693572Z 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 2025-09-09T14:13:49.2694826Z return dequantize_per_tensor_default_1 2025-09-09T14:13:49.2695196Z 2025-09-09T14:13:49.2695584Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:13:49.2696083Z diff: tensor([[[0., 0., 0.], 2025-09-09T14:13:49.2696410Z [0., 0., 0.], 2025-09-09T14:13:49.2696684Z [0., 0., 0.]]]) 2025-09-09T14:13:49.2697001Z model pt2e: GraphModule( 2025-09-09T14:13:49.2697311Z (conv): Module() 2025-09-09T14:13:49.2697727Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:13:49.2699092Z 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 2025-09-09T14:13:49.2700677Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.327648401260376, max_val=0.32982930541038513) 2025-09-09T14:13:49.2701408Z ) 2025-09-09T14:13:49.2701766Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:13:49.2703095Z 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 2025-09-09T14:13:49.2704659Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3264806270599365, max_val=1.318617343902588) 2025-09-09T14:13:49.2717480Z ) 2025-09-09T14:13:49.2717958Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:13:49.2719340Z 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 2025-09-09T14:13:49.2721002Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.284900426864624, max_val=0.7398542761802673) 2025-09-09T14:13:49.2721728Z ) 2025-09-09T14:13:49.2721976Z ) 2025-09-09T14:13:49.2722105Z 2025-09-09T14:13:49.2722110Z 2025-09-09T14:13:49.2722115Z 2025-09-09T14:13:49.2722229Z def forward(self, x): 2025-09-09T14:13:49.2722620Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:13:49.2723092Z conv_weight = self.conv.weight 2025-09-09T14:13:49.2723716Z activation_post_process_1 = self.activation_post_process_1(conv_weight); conv_weight = None 2025-09-09T14:13:49.2724546Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:13:49.2725677Z conv1d = torch.ops.aten.conv1d.default(activation_post_process_0, activation_post_process_1); activation_post_process_0 = activation_post_process_1 = None 2025-09-09T14:13:49.2727000Z activation_post_process_2 = self.activation_post_process_2(conv1d); conv1d = None 2025-09-09T14:13:49.2727788Z return pytree.tree_unflatten((activation_post_process_2,), self._out_spec) 2025-09-09T14:13:49.2728323Z 2025-09-09T14:13:49.2728710Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:13:49.2729207Z model fx: GraphModule( 2025-09-09T14:13:49.2729651Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:13:49.2730981Z 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 2025-09-09T14:13:49.2732642Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3264806270599365, max_val=1.318617343902588) 2025-09-09T14:13:49.2733378Z ) 2025-09-09T14:13:49.2733612Z (conv): Conv1d( 2025-09-09T14:13:49.2733944Z 3, 3, kernel_size=(3,), stride=(1,), bias=False 2025-09-09T14:13:49.2734429Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:13:49.2735743Z 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 2025-09-09T14:13:49.2737330Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.327648401260376, max_val=0.32982930541038513) 2025-09-09T14:13:49.2738064Z ) 2025-09-09T14:13:49.2738304Z ) 2025-09-09T14:13:49.2738664Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:14:27.7527183Z 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 2025-09-09T14:14:27.7528559Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.284900426864624, max_val=0.7398542761802673) 2025-09-09T14:14:27.7529149Z ) 2025-09-09T14:14:27.7529345Z ) 2025-09-09T14:14:27.7529447Z 2025-09-09T14:14:27.7529490Z 2025-09-09T14:14:27.7529494Z 2025-09-09T14:14:27.7529587Z def forward(self, x): 2025-09-09T14:14:27.7529982Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:14:27.7530571Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T14:14:27.7531190Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T14:14:27.7531655Z return activation_post_process_1 2025-09-09T14:14:27.7531963Z 2025-09-09T14:14:27.7532260Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:14:27.7532677Z diff: tensor([[[0., 0., 0.], 2025-09-09T14:14:27.7532931Z [0., 0., 0.], 2025-09-09T14:14:27.7533198Z [0., 0., 0.]]], grad_fn=) 2025-09-09T14:14:27.7533539Z converted model pt2e: GraphModule( 2025-09-09T14:14:27.7533829Z (conv): Module() 2025-09-09T14:14:27.7534052Z ) 2025-09-09T14:14:27.7534155Z 2025-09-09T14:14:27.7534159Z 2025-09-09T14:14:27.7534163Z 2025-09-09T14:14:27.7534255Z def forward(self, x): 2025-09-09T14:14:27.7534565Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:14:27.7534971Z quantize_per_tensor_default = self._frozen_param0 2025-09-09T14:14:27.7536011Z 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 2025-09-09T14:14:27.7537446Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.010372933000326157, 0, -128, 127, torch.int8); x = None 2025-09-09T14:14:27.7539177Z 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 2025-09-09T14:14:27.7540725Z 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 2025-09-09T14:14:27.7542089Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv1d, 0.007940215058624744, 34, -128, 127, torch.int8); conv1d = None 2025-09-09T14:14:27.7543566Z 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 2025-09-09T14:14:27.7544845Z return pytree.tree_unflatten((dequantize_per_tensor_default_2,), self._out_spec) 2025-09-09T14:14:27.7545322Z 2025-09-09T14:14:27.7545625Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:14:27.7546056Z onverted model fx: GraphModule( 2025-09-09T14:14:27.7546511Z (conv): QuantizedConv1d(Reference)(3, 3, kernel_size=(3,), stride=(1,), bias=False) 2025-09-09T14:14:27.7546977Z ) 2025-09-09T14:14:27.7547080Z 2025-09-09T14:14:27.7547084Z 2025-09-09T14:14:27.7547088Z 2025-09-09T14:14:27.7547181Z def forward(self, x): 2025-09-09T14:14:27.7547876Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.010372933000326157, 0, -128, 127, torch.int8); x = None 2025-09-09T14:14:27.7549290Z 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 2025-09-09T14:14:27.7550435Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T14:14:27.7551406Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.007940215058624744, 34, -128, 127, torch.int8); conv = None 2025-09-09T14:14:27.7552871Z 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 2025-09-09T14:14:27.7553864Z return dequantize_per_tensor_default_1 2025-09-09T14:14:27.7554169Z 2025-09-09T14:14:27.7554465Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:14:27.7554882Z diff: tensor([[[0., 0., 0.], 2025-09-09T14:14:27.7555130Z [0., 0., 0.], 2025-09-09T14:14:27.7555361Z [0., 0., 0.]]]) 2025-09-09T14:14:27.7555783Z PASSED 2025-09-09T14:14:27.7556532Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn1d::test_qat_conv_transpose_bn PASSED 2025-09-09T14:14:27.7557725Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn1d::test_qat_conv_transpose_bn_relu PASSED 2025-09-09T14:14:27.7559147Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn1d::test_qat_inplace_add_relu model pt2e: GraphModule( 2025-09-09T14:14:27.7559907Z (conv): Module() 2025-09-09T14:14:27.7560232Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:14:27.7561341Z 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 2025-09-09T14:14:27.7562702Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.5429]), max_val=tensor([-0.5429])) 2025-09-09T14:14:27.7563344Z ) 2025-09-09T14:14:27.7563649Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:14:27.7564911Z 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 2025-09-09T14:14:27.7566197Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.31662631034851074, max_val=-0.1489601731300354) 2025-09-09T14:14:27.7566796Z ) 2025-09-09T14:14:27.7567090Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:14:27.7568171Z 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 2025-09-09T14:14:27.7569432Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.26761165261268616, max_val=0.3586132824420929) 2025-09-09T14:14:27.7570111Z ) 2025-09-09T14:14:27.7570418Z (activation_post_process_3): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:14:27.7571486Z 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 2025-09-09T14:14:27.7572769Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.04198697209358215, max_val=0.11820143461227417) 2025-09-09T14:14:27.7573345Z ) 2025-09-09T14:14:27.7573537Z ) 2025-09-09T14:14:27.7573642Z 2025-09-09T14:14:27.7573647Z 2025-09-09T14:14:27.7573651Z 2025-09-09T14:14:27.7573759Z def forward(self, x): 2025-09-09T14:14:27.7574061Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:14:27.7574441Z conv_weight = self.conv.weight 2025-09-09T14:14:27.7574940Z activation_post_process_1 = self.activation_post_process_1(conv_weight); conv_weight = None 2025-09-09T14:14:27.7575479Z conv_bias = self.conv.bias 2025-09-09T14:14:27.7575881Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:14:27.7576776Z conv1d = torch.ops.aten.conv1d.default(activation_post_process_0, activation_post_process_1, conv_bias); activation_post_process_1 = conv_bias = None 2025-09-09T14:14:27.7577696Z activation_post_process_2 = self.activation_post_process_2(conv1d); conv1d = None 2025-09-09T14:14:27.7578594Z add_ = torch.ops.aten.add_.Tensor(activation_post_process_2, activation_post_process_0); activation_post_process_2 = activation_post_process_0 = None 2025-09-09T14:14:27.7579400Z relu_ = torch.ops.aten.relu_.default(add_); add_ = None 2025-09-09T14:14:27.7579926Z activation_post_process_3 = self.activation_post_process_3(relu_); relu_ = None 2025-09-09T14:14:27.7580545Z return pytree.tree_unflatten((activation_post_process_3,), self._out_spec) 2025-09-09T14:14:27.7580993Z 2025-09-09T14:14:27.7581295Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:14:27.7581708Z model fx: GraphModule( 2025-09-09T14:14:27.7582054Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:14:27.7583142Z 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 2025-09-09T14:14:27.7584411Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.31662631034851074, max_val=-0.1489601731300354) 2025-09-09T14:14:27.7585008Z ) 2025-09-09T14:14:27.7585205Z (conv): Conv1d( 2025-09-09T14:14:27.7585436Z 1, 1, kernel_size=(1,), stride=(1,) 2025-09-09T14:14:27.7585813Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:14:27.7586892Z 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 2025-09-09T14:14:27.7588257Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.5429]), max_val=tensor([-0.5429])) 2025-09-09T14:14:27.7588907Z ) 2025-09-09T14:14:27.7589091Z ) 2025-09-09T14:14:27.7589469Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:14:27.7590542Z 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 2025-09-09T14:14:27.7591828Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.26761165261268616, max_val=0.3586132824420929) 2025-09-09T14:14:27.7592423Z ) 2025-09-09T14:14:27.7592626Z (relu): ReLU(inplace=True) 2025-09-09T14:14:27.7593061Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:14:28.6499203Z 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 2025-09-09T14:14:28.6500536Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.04198697209358215, max_val=0.11820143461227417) 2025-09-09T14:14:28.6501122Z ) 2025-09-09T14:14:28.6501315Z ) 2025-09-09T14:14:28.6501419Z 2025-09-09T14:14:28.6501424Z 2025-09-09T14:14:28.6501428Z 2025-09-09T14:14:28.6501520Z def forward(self, x): 2025-09-09T14:14:28.6501915Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:14:28.6502391Z conv = self.conv(activation_post_process_0) 2025-09-09T14:14:28.6502893Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T14:14:28.6503676Z add = activation_post_process_1 + activation_post_process_0; activation_post_process_1 = activation_post_process_0 = None 2025-09-09T14:14:28.6504322Z relu = self.relu(add); add = None 2025-09-09T14:14:28.6504788Z activation_post_process_2 = self.activation_post_process_2(relu); relu = None 2025-09-09T14:14:28.6505257Z return activation_post_process_2 2025-09-09T14:14:28.6505550Z 2025-09-09T14:14:28.6505858Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:14:28.6506324Z diff: tensor([[[0., 0., 0.]]], grad_fn=) 2025-09-09T14:14:28.6506701Z converted model pt2e: GraphModule( 2025-09-09T14:14:28.6506983Z (conv): Module() 2025-09-09T14:14:28.6507204Z ) 2025-09-09T14:14:28.6507307Z 2025-09-09T14:14:28.6507311Z 2025-09-09T14:14:28.6507315Z 2025-09-09T14:14:28.6507406Z def forward(self, x): 2025-09-09T14:14:28.6507722Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:14:28.6508083Z _scale_0 = self._scale_0 2025-09-09T14:14:28.6508372Z _zero_point_0 = self._zero_point_0 2025-09-09T14:14:28.6508732Z quantize_per_channel_default = self._frozen_param0 2025-09-09T14:14:28.6509868Z 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 2025-09-09T14:14:28.6510974Z conv_bias = self.conv.bias 2025-09-09T14:14:28.6511694Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.0012416718527674675, 127, -128, 127, torch.int8); x = None 2025-09-09T14:14:28.6513000Z dequantize_per_tensor_default_4 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.0012416718527674675, 127, -128, 127, torch.int8) 2025-09-09T14:14:28.6514535Z 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 2025-09-09T14:14:28.6516160Z 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 2025-09-09T14:14:28.6517840Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv1d, 0.0014063265407457948, -128, -128, 127, torch.int8); conv1d = None 2025-09-09T14:14:28.6519361Z 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 2025-09-09T14:14:28.6520982Z 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 2025-09-09T14:14:28.6521908Z relu_ = torch.ops.aten.relu_.default(add_); add_ = None 2025-09-09T14:14:28.6522920Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(relu_, 0.00046353504876606166, -128, -128, 127, torch.int8); relu_ = None 2025-09-09T14:14:28.6524418Z 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 2025-09-09T14:14:28.6525604Z return pytree.tree_unflatten((dequantize_per_tensor_default_2,), self._out_spec) 2025-09-09T14:14:28.6526076Z 2025-09-09T14:14:28.6526379Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:14:28.6526808Z onverted model fx: GraphModule( 2025-09-09T14:14:28.6527215Z (conv): QuantizedConv1d(Reference)(1, 1, kernel_size=(1,), stride=(1,)) 2025-09-09T14:14:28.6527660Z (relu): ReLU(inplace=True) 2025-09-09T14:14:28.6527910Z ) 2025-09-09T14:14:28.6528030Z 2025-09-09T14:14:28.6528035Z 2025-09-09T14:14:28.6528039Z 2025-09-09T14:14:28.6528129Z def forward(self, x): 2025-09-09T14:14:28.6528834Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.0012416718527674675, 127, -128, 127, torch.int8); x = None 2025-09-09T14:14:28.6530268Z 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 2025-09-09T14:14:28.6531295Z conv = self.conv(dequantize_per_tensor_default) 2025-09-09T14:14:28.6532125Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.0014063265407457948, -128, -128, 127, torch.int8); conv = None 2025-09-09T14:14:28.6533600Z 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 2025-09-09T14:14:28.6534997Z add = dequantize_per_tensor_default_1 + dequantize_per_tensor_default; dequantize_per_tensor_default_1 = dequantize_per_tensor_default = None 2025-09-09T14:14:28.6535708Z relu = self.relu(add); add = None 2025-09-09T14:14:28.6536507Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(relu, 0.00046353504876606166, -128, -128, 127, torch.int8); relu = None 2025-09-09T14:14:28.6538000Z 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 2025-09-09T14:14:28.6539013Z return dequantize_per_tensor_default_2 2025-09-09T14:14:28.6539321Z 2025-09-09T14:14:28.6539619Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:14:28.6540038Z diff: tensor([[[0., 0., 0.]]]) 2025-09-09T14:14:28.6540325Z model pt2e: GraphModule( 2025-09-09T14:14:28.6540573Z (conv): Module() 2025-09-09T14:14:28.6540909Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:14:28.6542061Z 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 2025-09-09T14:14:28.6543360Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.5428858995437622, max_val=-0.5428858995437622) 2025-09-09T14:14:28.6543959Z ) 2025-09-09T14:14:28.6544255Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:14:28.6545335Z 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 2025-09-09T14:14:28.6546603Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.31662631034851074, max_val=-0.1489601731300354) 2025-09-09T14:14:28.6547788Z ) 2025-09-09T14:14:28.6548085Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:14:28.6549169Z 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 2025-09-09T14:14:28.6550448Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.26761165261268616, max_val=0.3586132824420929) 2025-09-09T14:14:28.6551023Z ) 2025-09-09T14:14:28.6551332Z (activation_post_process_3): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:14:28.6552393Z 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 2025-09-09T14:14:28.6553679Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.04198697209358215, max_val=0.11820143461227417) 2025-09-09T14:14:28.6554275Z ) 2025-09-09T14:14:28.6554452Z ) 2025-09-09T14:14:28.6554572Z 2025-09-09T14:14:28.6554577Z 2025-09-09T14:14:28.6554581Z 2025-09-09T14:14:28.6554674Z def forward(self, x): 2025-09-09T14:14:28.6554984Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:14:28.6555368Z conv_weight = self.conv.weight 2025-09-09T14:14:28.6555886Z activation_post_process_1 = self.activation_post_process_1(conv_weight); conv_weight = None 2025-09-09T14:14:28.6556406Z conv_bias = self.conv.bias 2025-09-09T14:14:28.6556821Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:14:28.6557697Z conv1d = torch.ops.aten.conv1d.default(activation_post_process_0, activation_post_process_1, conv_bias); activation_post_process_1 = conv_bias = None 2025-09-09T14:14:28.6558997Z activation_post_process_2 = self.activation_post_process_2(conv1d); conv1d = None 2025-09-09T14:14:28.6559979Z add_ = torch.ops.aten.add_.Tensor(activation_post_process_2, activation_post_process_0); activation_post_process_2 = activation_post_process_0 = None 2025-09-09T14:14:28.6560795Z relu_ = torch.ops.aten.relu_.default(add_); add_ = None 2025-09-09T14:14:28.6561328Z activation_post_process_3 = self.activation_post_process_3(relu_); relu_ = None 2025-09-09T14:14:28.6561958Z return pytree.tree_unflatten((activation_post_process_3,), self._out_spec) 2025-09-09T14:14:28.6562387Z 2025-09-09T14:14:28.6562706Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:14:28.6563124Z model fx: GraphModule( 2025-09-09T14:14:28.6563468Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:14:48.2803103Z 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 2025-09-09T14:14:48.2804809Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.31662631034851074, max_val=-0.1489601731300354) 2025-09-09T14:14:48.2805553Z ) 2025-09-09T14:14:48.2805802Z (conv): Conv1d( 2025-09-09T14:14:48.2806133Z 1, 1, kernel_size=(1,), stride=(1,) 2025-09-09T14:14:48.2806890Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:14:48.2808212Z 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 2025-09-09T14:14:48.2809810Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.5428858995437622, max_val=-0.5428858995437622) 2025-09-09T14:14:48.2810548Z ) 2025-09-09T14:14:48.2810785Z ) 2025-09-09T14:14:48.2811145Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:14:48.2812627Z 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 2025-09-09T14:14:48.2814207Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.26761165261268616, max_val=0.3586132824420929) 2025-09-09T14:14:48.2814943Z ) 2025-09-09T14:14:48.2815204Z (relu): ReLU(inplace=True) 2025-09-09T14:14:48.2815656Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:14:48.2817006Z 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 2025-09-09T14:14:48.2818588Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.04198697209358215, max_val=0.11820143461227417) 2025-09-09T14:14:48.2819331Z ) 2025-09-09T14:14:48.2819548Z ) 2025-09-09T14:14:48.2819687Z 2025-09-09T14:14:48.2819692Z 2025-09-09T14:14:48.2819697Z 2025-09-09T14:14:48.2819811Z def forward(self, x): 2025-09-09T14:14:48.2820294Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:14:48.2820877Z conv = self.conv(activation_post_process_0) 2025-09-09T14:14:48.2821488Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T14:14:48.2822448Z add = activation_post_process_1 + activation_post_process_0; activation_post_process_1 = activation_post_process_0 = None 2025-09-09T14:14:48.2823241Z relu = self.relu(add); add = None 2025-09-09T14:14:48.2823808Z activation_post_process_2 = self.activation_post_process_2(relu); relu = None 2025-09-09T14:14:48.2824390Z return activation_post_process_2 2025-09-09T14:14:48.2824752Z 2025-09-09T14:14:48.2825118Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:14:48.2825693Z diff: tensor([[[0., 0., 0.]]], grad_fn=) 2025-09-09T14:14:48.2826136Z converted model pt2e: GraphModule( 2025-09-09T14:14:48.2826501Z (conv): Module() 2025-09-09T14:14:48.2826760Z ) 2025-09-09T14:14:48.2826899Z 2025-09-09T14:14:48.2826903Z 2025-09-09T14:14:48.2826908Z 2025-09-09T14:14:48.2827023Z def forward(self, x): 2025-09-09T14:14:48.2827408Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:14:48.2827915Z quantize_per_tensor_default = self._frozen_param0 2025-09-09T14:14:48.2829207Z 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 2025-09-09T14:14:48.2830427Z conv_bias = self.conv.bias 2025-09-09T14:14:48.2831345Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.0012416718527674675, 127, -128, 127, torch.int8); x = None 2025-09-09T14:14:48.2832990Z 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) 2025-09-09T14:14:48.2835002Z 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 2025-09-09T14:14:48.2837051Z 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 2025-09-09T14:14:48.2838867Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv1d, 0.0014063265407457948, -128, -128, 127, torch.int8); conv1d = None 2025-09-09T14:14:48.2840802Z 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 2025-09-09T14:14:48.2842790Z 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 2025-09-09T14:14:48.2843908Z relu_ = torch.ops.aten.relu_.default(add_); add_ = None 2025-09-09T14:14:48.2844978Z quantize_per_tensor_default_3 = torch.ops.quantized_decomposed.quantize_per_tensor.default(relu_, 0.00046353504876606166, -128, -128, 127, torch.int8); relu_ = None 2025-09-09T14:14:48.2846868Z 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 2025-09-09T14:14:48.2848317Z return pytree.tree_unflatten((dequantize_per_tensor_default_3,), self._out_spec) 2025-09-09T14:14:48.2848908Z 2025-09-09T14:14:48.2849285Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:14:48.2849788Z onverted model fx: GraphModule( 2025-09-09T14:14:48.2850304Z (conv): QuantizedConv1d(Reference)(1, 1, kernel_size=(1,), stride=(1,)) 2025-09-09T14:14:48.2850838Z (relu): ReLU(inplace=True) 2025-09-09T14:14:48.2851152Z ) 2025-09-09T14:14:48.2851279Z 2025-09-09T14:14:48.2851289Z 2025-09-09T14:14:48.2851293Z 2025-09-09T14:14:48.2851403Z def forward(self, x): 2025-09-09T14:14:48.2852280Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.0012416718527674675, 127, -128, 127, torch.int8); x = None 2025-09-09T14:14:48.2854088Z 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 2025-09-09T14:14:48.2855356Z conv = self.conv(dequantize_per_tensor_default) 2025-09-09T14:14:48.2856404Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.0014063265407457948, -128, -128, 127, torch.int8); conv = None 2025-09-09T14:14:48.2858507Z 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 2025-09-09T14:14:48.2860257Z add = dequantize_per_tensor_default_1 + dequantize_per_tensor_default; dequantize_per_tensor_default_1 = dequantize_per_tensor_default = None 2025-09-09T14:14:48.2861158Z relu = self.relu(add); add = None 2025-09-09T14:14:48.2862146Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(relu, 0.00046353504876606166, -128, -128, 127, torch.int8); relu = None 2025-09-09T14:14:48.2864018Z 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 2025-09-09T14:14:48.2865304Z return dequantize_per_tensor_default_2 2025-09-09T14:14:48.2865674Z 2025-09-09T14:14:48.2866054Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:14:48.2866557Z diff: tensor([[[0., 0., 0.]]]) 2025-09-09T14:14:48.2867102Z PASSED 2025-09-09T14:14:48.2868308Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn1d::test_qat_per_channel_weight_custom_dtype PASSED 2025-09-09T14:14:48.2869874Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn1d::test_qat_preserve_source_fn_stack PASSED 2025-09-09T14:14:48.2871256Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn1d::test_qat_update_shared_qspec model pt2e: GraphModule( 2025-09-09T14:14:48.2872124Z (conv): Module() 2025-09-09T14:14:48.2872407Z (bn): Module() 2025-09-09T14:14:48.2872913Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:14:48.2874249Z 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 2025-09-09T14:14:48.2875833Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3264806270599365, max_val=1.318617343902588) 2025-09-09T14:14:48.2876557Z ) 2025-09-09T14:14:48.2876936Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:14:48.2878320Z 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 2025-09-09T14:14:48.2880244Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.3010, -0.2094, -0.2957]), max_val=tensor([0.2519, 0.1882, 0.3171])) 2025-09-09T14:14:48.2881184Z ) 2025-09-09T14:14:48.2881554Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:14:48.2882886Z 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 2025-09-09T14:14:55.6398526Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.407715082168579, max_val=1.379811406135559) 2025-09-09T14:14:55.6399282Z ) 2025-09-09T14:14:55.6399672Z (activation_post_process_3): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:14:55.6401058Z 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 2025-09-09T14:14:55.6402650Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.407715082168579, max_val=1.379811406135559) 2025-09-09T14:14:55.6403411Z ) 2025-09-09T14:14:55.6403635Z ) 2025-09-09T14:14:55.6403763Z 2025-09-09T14:14:55.6403769Z 2025-09-09T14:14:55.6403786Z 2025-09-09T14:14:55.6403897Z def forward(self, x): 2025-09-09T14:14:55.6404264Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:14:55.6404737Z conv_weight = self.conv.weight 2025-09-09T14:14:55.6405099Z conv_bias = self.conv.bias 2025-09-09T14:14:55.6405443Z bn_weight = self.bn.weight 2025-09-09T14:14:55.6405778Z bn_bias = self.bn.bias 2025-09-09T14:14:55.6406109Z bn_running_mean = self.bn.running_mean 2025-09-09T14:14:55.6406516Z bn_running_var = self.bn.running_var 2025-09-09T14:14:55.6406950Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T14:14:55.6407549Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:14:55.6408350Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T14:14:55.6409086Z add = torch.ops.aten.add.Tensor(bn_running_var, 1e-05) 2025-09-09T14:14:55.6409609Z sqrt = torch.ops.aten.sqrt.default(add); add = None 2025-09-09T14:14:55.6410179Z div = torch.ops.aten.div.Tensor(bn_weight, sqrt); sqrt = None 2025-09-09T14:14:55.6411111Z reshape = torch.ops.aten.reshape.default(div, [-1, 1, 1]) 2025-09-09T14:14:55.6411802Z mul = torch.ops.aten.mul.Tensor(conv_weight, reshape); conv_weight = reshape = None 2025-09-09T14:14:55.6412590Z activation_post_process_1 = self.activation_post_process_1(mul); mul = None 2025-09-09T14:14:55.6413439Z zeros_like = torch.ops.aten.zeros_like.default(conv_bias, dtype = torch.float32, pin_memory = False) 2025-09-09T14:14:55.6414832Z 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 2025-09-09T14:14:55.6416208Z reshape_1 = torch.ops.aten.reshape.default(div, [1, -1, 1]); div = None 2025-09-09T14:14:55.6416979Z div_1 = torch.ops.aten.div.Tensor(conv1d_1, reshape_1); conv1d_1 = reshape_1 = None 2025-09-09T14:14:55.6417786Z reshape_2 = torch.ops.aten.reshape.default(conv_bias, [1, -1, 1]); conv_bias = None 2025-09-09T14:14:55.6418564Z add_1 = torch.ops.aten.add.Tensor(div_1, reshape_2); div_1 = reshape_2 = None 2025-09-09T14:14:55.6419781Z 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 2025-09-09T14:14:55.6421098Z activation_post_process_2 = self.activation_post_process_2(batch_norm_1); batch_norm_1 = None 2025-09-09T14:14:55.6422152Z hardtanh = torch.ops.aten.hardtanh.default(activation_post_process_2, -1.0, 1.0); activation_post_process_2 = None 2025-09-09T14:14:55.6423143Z activation_post_process_3 = self.activation_post_process_3(hardtanh); hardtanh = None 2025-09-09T14:14:55.6423943Z return pytree.tree_unflatten((activation_post_process_3,), self._out_spec) 2025-09-09T14:14:55.6424483Z 2025-09-09T14:14:55.6424862Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:14:55.6425356Z model fx: GraphModule( 2025-09-09T14:14:55.6425801Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:14:55.6427151Z 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 2025-09-09T14:14:55.6428718Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3264806270599365, max_val=1.318617343902588) 2025-09-09T14:14:55.6429458Z ) 2025-09-09T14:14:55.6429691Z (conv): ConvBn1d( 2025-09-09T14:14:55.6429995Z 3, 3, kernel_size=(3,), stride=(1,) 2025-09-09T14:14:55.6430545Z (bn): BatchNorm1d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T14:14:55.6431194Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:14:55.6432569Z 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 2025-09-09T14:14:55.6434426Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.3010, -0.2094, -0.2957]), max_val=tensor([0.2519, 0.1882, 0.3171])) 2025-09-09T14:14:55.6435370Z ) 2025-09-09T14:14:55.6435609Z ) 2025-09-09T14:14:55.6435975Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:14:55.6437317Z 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 2025-09-09T14:14:55.6438884Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.407715082168579, max_val=1.379811406135559) 2025-09-09T14:14:55.6439612Z ) 2025-09-09T14:14:55.6439982Z (hardtanh): Hardtanh(min_val=-1.0, max_val=1.0) 2025-09-09T14:14:55.6440535Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:14:55.6441961Z 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 2025-09-09T14:14:55.6443524Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.407715082168579, max_val=1.379811406135559) 2025-09-09T14:14:55.6444246Z ) 2025-09-09T14:14:55.6444470Z ) 2025-09-09T14:14:55.6444614Z 2025-09-09T14:14:55.6444619Z 2025-09-09T14:14:55.6444624Z 2025-09-09T14:14:55.6444738Z def forward(self, x): 2025-09-09T14:14:55.6445302Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:14:55.6446037Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T14:14:55.6446805Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T14:14:55.6447619Z hardtanh = self.hardtanh(activation_post_process_1); activation_post_process_1 = None 2025-09-09T14:14:55.6448475Z activation_post_process_2 = self.activation_post_process_2(hardtanh); hardtanh = None 2025-09-09T14:14:55.6449101Z return activation_post_process_2 2025-09-09T14:14:55.6449461Z 2025-09-09T14:14:55.6449835Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:14:55.6450328Z diff: tensor([[[0., 0., 0.], 2025-09-09T14:14:55.6450647Z [0., 0., 0.], 2025-09-09T14:14:55.6450952Z [0., 0., 0.]]], grad_fn=) 2025-09-09T14:14:55.6451365Z converted model pt2e: GraphModule( 2025-09-09T14:14:55.6451712Z (conv): Module() 2025-09-09T14:14:55.6451992Z (bn): Module() 2025-09-09T14:14:55.6452242Z ) 2025-09-09T14:14:55.6452382Z 2025-09-09T14:14:55.6452387Z 2025-09-09T14:14:55.6452391Z 2025-09-09T14:14:55.6452505Z def forward(self, x): 2025-09-09T14:14:55.6452883Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:14:55.6453329Z conv_bias = self.conv.bias 2025-09-09T14:14:55.6453742Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T14:14:55.6454731Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.010372933000326157, 0, -128, 127, torch.int8); x = None 2025-09-09T14:14:55.6456500Z 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 2025-09-09T14:14:55.6457979Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T14:14:55.6458874Z _scale_0 = self._scale_0 2025-09-09T14:14:55.6459225Z _zero_point_0 = self._zero_point_0 2025-09-09T14:14:55.6459623Z quantize_per_channel = self._frozen_param0 2025-09-09T14:14:55.6460886Z 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 2025-09-09T14:14:55.6462815Z 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 2025-09-09T14:14:55.6464532Z 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 2025-09-09T14:14:55.6466392Z 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 2025-09-09T14:14:55.6468063Z hardtanh = torch.ops.aten.hardtanh.default(dequantize_per_tensor_default_1, -1.0, 1.0); dequantize_per_tensor_default_1 = None 2025-09-09T14:14:55.6469653Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(hardtanh, 0.010931476950645447, 1, -128, 127, torch.int8); hardtanh = None 2025-09-09T14:14:55.6471528Z 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 2025-09-09T14:14:55.6472952Z return pytree.tree_unflatten((dequantize_per_tensor_default_2,), self._out_spec) 2025-09-09T14:14:55.6473537Z 2025-09-09T14:14:55.6473920Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:14:55.6474430Z onverted model fx: GraphModule( 2025-09-09T14:14:55.6475071Z (conv): QuantizedConv1d(Reference)(3, 3, kernel_size=(3,), stride=(1,)) 2025-09-09T14:14:55.6475645Z (hardtanh): Hardtanh(min_val=-1.0, max_val=1.0) 2025-09-09T14:14:55.6476054Z ) 2025-09-09T14:14:55.6476185Z 2025-09-09T14:14:55.6476190Z 2025-09-09T14:14:55.6476195Z 2025-09-09T14:14:55.6476308Z def forward(self, x): 2025-09-09T14:14:55.6477180Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.010372933000326157, 0, -128, 127, torch.int8); x = None 2025-09-09T14:15:05.6290116Z 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 2025-09-09T14:15:05.6291614Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T14:15:05.6292868Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.010931476950645447, 1, -128, 127, torch.int8); conv = None 2025-09-09T14:15:05.6294706Z 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 2025-09-09T14:15:05.6296237Z hardtanh = self.hardtanh(dequantize_per_tensor_default_1); dequantize_per_tensor_default_1 = None 2025-09-09T14:15:05.6297547Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(hardtanh, 0.010931476950645447, 1, -128, 127, torch.int8); hardtanh = None 2025-09-09T14:15:05.6299398Z 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 2025-09-09T14:15:05.6300639Z return dequantize_per_tensor_default_2 2025-09-09T14:15:05.6301019Z 2025-09-09T14:15:05.6301387Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:15:05.6301896Z diff: tensor([[[0., 0., 0.], 2025-09-09T14:15:05.6302203Z [0., 0., 0.], 2025-09-09T14:15:05.6302488Z [0., 0., 0.]]]) 2025-09-09T14:15:05.6302788Z model pt2e: GraphModule( 2025-09-09T14:15:05.6303094Z (conv): Module() 2025-09-09T14:15:05.6303358Z (bn): Module() 2025-09-09T14:15:05.6303770Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:15:05.6305107Z 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 2025-09-09T14:15:05.6306682Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3264806270599365, max_val=1.318617343902588) 2025-09-09T14:15:05.6307407Z ) 2025-09-09T14:15:05.6307767Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:15:05.6309113Z 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 2025-09-09T14:15:05.6310714Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.30097201466560364, max_val=0.3171221613883972) 2025-09-09T14:15:05.6311440Z ) 2025-09-09T14:15:05.6312149Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:15:05.6313472Z 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 2025-09-09T14:15:05.6315041Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.407715082168579, max_val=1.3807092905044556) 2025-09-09T14:15:05.6315768Z ) 2025-09-09T14:15:05.6316131Z (activation_post_process_3): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:15:05.6317566Z 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 2025-09-09T14:15:05.6319115Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.407715082168579, max_val=1.3807092905044556) 2025-09-09T14:15:05.6319920Z ) 2025-09-09T14:15:05.6320145Z ) 2025-09-09T14:15:05.6320285Z 2025-09-09T14:15:05.6320291Z 2025-09-09T14:15:05.6320295Z 2025-09-09T14:15:05.6320410Z def forward(self, x): 2025-09-09T14:15:05.6320801Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:15:05.6321255Z conv_weight = self.conv.weight 2025-09-09T14:15:05.6321638Z conv_bias = self.conv.bias 2025-09-09T14:15:05.6321977Z bn_weight = self.bn.weight 2025-09-09T14:15:05.6322322Z bn_bias = self.bn.bias 2025-09-09T14:15:05.6322659Z bn_running_mean = self.bn.running_mean 2025-09-09T14:15:05.6323078Z bn_running_var = self.bn.running_var 2025-09-09T14:15:05.6323531Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T14:15:05.6324131Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:15:05.6324955Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T14:15:05.6325686Z add = torch.ops.aten.add.Tensor(bn_running_var, 1e-05) 2025-09-09T14:15:05.6326228Z sqrt = torch.ops.aten.sqrt.default(add); add = None 2025-09-09T14:15:05.6326789Z div = torch.ops.aten.div.Tensor(bn_weight, sqrt); sqrt = None 2025-09-09T14:15:05.6327399Z reshape = torch.ops.aten.reshape.default(div, [-1, 1, 1]) 2025-09-09T14:15:05.6328097Z mul = torch.ops.aten.mul.Tensor(conv_weight, reshape); conv_weight = reshape = None 2025-09-09T14:15:05.6328871Z activation_post_process_1 = self.activation_post_process_1(mul); mul = None 2025-09-09T14:15:05.6329733Z zeros_like = torch.ops.aten.zeros_like.default(conv_bias, dtype = torch.float32, pin_memory = False) 2025-09-09T14:15:05.6331121Z 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 2025-09-09T14:15:05.6332377Z reshape_1 = torch.ops.aten.reshape.default(div, [1, -1, 1]); div = None 2025-09-09T14:15:05.6333123Z div_1 = torch.ops.aten.div.Tensor(conv1d_1, reshape_1); conv1d_1 = reshape_1 = None 2025-09-09T14:15:05.6333908Z reshape_2 = torch.ops.aten.reshape.default(conv_bias, [1, -1, 1]); conv_bias = None 2025-09-09T14:15:05.6334685Z add_1 = torch.ops.aten.add.Tensor(div_1, reshape_2); div_1 = reshape_2 = None 2025-09-09T14:15:05.6335902Z 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 2025-09-09T14:15:05.6337227Z activation_post_process_2 = self.activation_post_process_2(batch_norm_1); batch_norm_1 = None 2025-09-09T14:15:05.6338279Z hardtanh = torch.ops.aten.hardtanh.default(activation_post_process_2, -1.0, 1.0); activation_post_process_2 = None 2025-09-09T14:15:05.6339280Z activation_post_process_3 = self.activation_post_process_3(hardtanh); hardtanh = None 2025-09-09T14:15:05.6340159Z return pytree.tree_unflatten((activation_post_process_3,), self._out_spec) 2025-09-09T14:15:05.6340699Z 2025-09-09T14:15:05.6341077Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:15:05.6341567Z model fx: GraphModule( 2025-09-09T14:15:05.6342004Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:15:05.6343338Z 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 2025-09-09T14:15:05.6344964Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3264806270599365, max_val=1.318617343902588) 2025-09-09T14:15:05.6345693Z ) 2025-09-09T14:15:05.6345922Z (conv): ConvBn1d( 2025-09-09T14:15:05.6346225Z 3, 3, kernel_size=(3,), stride=(1,) 2025-09-09T14:15:05.6346770Z (bn): BatchNorm1d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T14:15:05.6347424Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:15:05.6348724Z 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 2025-09-09T14:15:05.6350319Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.30097201466560364, max_val=0.3171221613883972) 2025-09-09T14:15:05.6351065Z ) 2025-09-09T14:15:05.6351290Z ) 2025-09-09T14:15:05.6351665Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:15:05.6353005Z 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 2025-09-09T14:15:05.6354559Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.407715082168579, max_val=1.3807092905044556) 2025-09-09T14:15:05.6355285Z ) 2025-09-09T14:15:05.6355571Z (hardtanh): Hardtanh(min_val=-1.0, max_val=1.0) 2025-09-09T14:15:05.6356116Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:15:05.6357449Z 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 2025-09-09T14:15:05.6359250Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.407715082168579, max_val=1.3807092905044556) 2025-09-09T14:15:05.6360045Z ) 2025-09-09T14:15:05.6360269Z ) 2025-09-09T14:15:05.6360410Z 2025-09-09T14:15:05.6360415Z 2025-09-09T14:15:05.6360420Z 2025-09-09T14:15:05.6360538Z def forward(self, x): 2025-09-09T14:15:05.6361026Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:15:05.6361762Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T14:15:05.6362533Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T14:15:05.6363334Z hardtanh = self.hardtanh(activation_post_process_1); activation_post_process_1 = None 2025-09-09T14:15:05.6364188Z activation_post_process_2 = self.activation_post_process_2(hardtanh); hardtanh = None 2025-09-09T14:15:05.6364809Z return activation_post_process_2 2025-09-09T14:15:05.6365164Z 2025-09-09T14:15:05.6365539Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:15:05.6366044Z diff: tensor([[[0., 0., 0.], 2025-09-09T14:15:05.6366366Z [0., 0., 0.], 2025-09-09T14:15:05.6366674Z [0., 0., 0.]]], grad_fn=) 2025-09-09T14:15:05.6367090Z converted model pt2e: GraphModule( 2025-09-09T14:15:05.6367432Z (conv): Module() 2025-09-09T14:15:05.6367711Z (bn): Module() 2025-09-09T14:15:05.6367967Z ) 2025-09-09T14:15:05.6368103Z 2025-09-09T14:15:05.6368251Z 2025-09-09T14:15:05.6368258Z 2025-09-09T14:15:05.6368373Z def forward(self, x): 2025-09-09T14:15:05.6368754Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:15:05.6369200Z conv_bias = self.conv.bias 2025-09-09T14:15:05.6369609Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T14:15:27.7701036Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.010372933000326157, 0, -128, 127, torch.int8); x = None 2025-09-09T14:15:27.7702874Z 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 2025-09-09T14:15:27.7705428Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T14:15:27.7706114Z quantize_per_tensor = self._frozen_param0 2025-09-09T14:15:27.7707260Z 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 2025-09-09T14:15:27.7709056Z 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 2025-09-09T14:15:27.7710768Z 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 2025-09-09T14:15:27.7712625Z 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 2025-09-09T14:15:27.7714294Z hardtanh = torch.ops.aten.hardtanh.default(dequantize_per_tensor_default_2, -1.0, 1.0); dequantize_per_tensor_default_2 = None 2025-09-09T14:15:27.7715756Z quantize_per_tensor_default_3 = torch.ops.quantized_decomposed.quantize_per_tensor.default(hardtanh, 0.010934998281300068, 1, -128, 127, torch.int8); hardtanh = None 2025-09-09T14:15:27.7717627Z 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 2025-09-09T14:15:27.7719058Z return pytree.tree_unflatten((dequantize_per_tensor_default_3,), self._out_spec) 2025-09-09T14:15:27.7719642Z 2025-09-09T14:15:27.7720095Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:15:27.7720624Z onverted model fx: GraphModule( 2025-09-09T14:15:27.7721151Z (conv): QuantizedConv1d(Reference)(3, 3, kernel_size=(3,), stride=(1,)) 2025-09-09T14:15:27.7721742Z (hardtanh): Hardtanh(min_val=-1.0, max_val=1.0) 2025-09-09T14:15:27.7722152Z ) 2025-09-09T14:15:27.7722283Z 2025-09-09T14:15:27.7722289Z 2025-09-09T14:15:27.7722294Z 2025-09-09T14:15:27.7722415Z def forward(self, x): 2025-09-09T14:15:27.7723282Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.010372933000326157, 0, -128, 127, torch.int8); x = None 2025-09-09T14:15:27.7725049Z 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 2025-09-09T14:15:27.7726498Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T14:15:27.7727709Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.010934998281300068, 1, -128, 127, torch.int8); conv = None 2025-09-09T14:15:27.7729529Z 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 2025-09-09T14:15:27.7731193Z hardtanh = self.hardtanh(dequantize_per_tensor_default_1); dequantize_per_tensor_default_1 = None 2025-09-09T14:15:27.7732509Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(hardtanh, 0.010934998281300068, 1, -128, 127, torch.int8); hardtanh = None 2025-09-09T14:15:27.7734356Z 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 2025-09-09T14:15:27.7735618Z return dequantize_per_tensor_default_2 2025-09-09T14:15:27.7736066Z 2025-09-09T14:15:27.7736451Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:15:27.7736965Z diff: tensor([[[0., 0., 0.], 2025-09-09T14:15:27.7737276Z [0., 0., 0.], 2025-09-09T14:15:27.7737568Z [0., 0., 0.]]]) 2025-09-09T14:15:27.7738096Z PASSED 2025-09-09T14:15:27.7739038Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn2d::test_fold_bn_erases_bn_node PASSED 2025-09-09T14:15:27.7740526Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_conv_bn_bias_derived_qspec PASSED 2025-09-09T14:15:27.7741902Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_conv_bn_fusion model pt2e: GraphModule( 2025-09-09T14:15:27.7742762Z (conv): Module() 2025-09-09T14:15:27.7743029Z (bn): Module() 2025-09-09T14:15:27.7743441Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:15:27.7744771Z 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 2025-09-09T14:15:27.7746423Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.526270866394043, max_val=2.143237352371216) 2025-09-09T14:15:27.7747149Z ) 2025-09-09T14:15:27.7747511Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:15:27.7748921Z 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 2025-09-09T14:15:27.7750786Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.1822, -0.1883, -0.1585]), max_val=tensor([0.1856, 0.1719, 0.1858])) 2025-09-09T14:15:27.7764681Z ) 2025-09-09T14:15:27.7765251Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:15:27.7766615Z 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 2025-09-09T14:15:27.7768232Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.628747820854187, max_val=1.9105255603790283) 2025-09-09T14:15:27.7768968Z ) 2025-09-09T14:15:27.7769196Z ) 2025-09-09T14:15:27.7769329Z 2025-09-09T14:15:27.7769351Z 2025-09-09T14:15:27.7769355Z 2025-09-09T14:15:27.7769470Z def forward(self, x): 2025-09-09T14:15:27.7769849Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:15:27.7770326Z conv_weight = self.conv.weight 2025-09-09T14:15:27.7770692Z conv_bias = self.conv.bias 2025-09-09T14:15:27.7771038Z bn_weight = self.bn.weight 2025-09-09T14:15:27.7771394Z bn_bias = self.bn.bias 2025-09-09T14:15:27.7771733Z bn_running_mean = self.bn.running_mean 2025-09-09T14:15:27.7772154Z bn_running_var = self.bn.running_var 2025-09-09T14:15:27.7772601Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T14:15:27.7773216Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:15:27.7774234Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T14:15:27.7774972Z add = torch.ops.aten.add.Tensor(bn_running_var, 1e-05) 2025-09-09T14:15:27.7775516Z sqrt = torch.ops.aten.sqrt.default(add); add = None 2025-09-09T14:15:27.7776072Z div = torch.ops.aten.div.Tensor(bn_weight, sqrt); sqrt = None 2025-09-09T14:15:27.7776694Z reshape = torch.ops.aten.reshape.default(div, [-1, 1, 1, 1]) 2025-09-09T14:15:27.7777388Z mul = torch.ops.aten.mul.Tensor(conv_weight, reshape); conv_weight = reshape = None 2025-09-09T14:15:27.7778178Z activation_post_process_1 = self.activation_post_process_1(mul); mul = None 2025-09-09T14:15:27.7779135Z zeros_like = torch.ops.aten.zeros_like.default(conv_bias, dtype = torch.float32, pin_memory = False) 2025-09-09T14:15:27.7780527Z 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 2025-09-09T14:15:27.7781795Z reshape_1 = torch.ops.aten.reshape.default(div, [1, -1, 1, 1]); div = None 2025-09-09T14:15:27.7782543Z div_1 = torch.ops.aten.div.Tensor(conv2d_1, reshape_1); conv2d_1 = reshape_1 = None 2025-09-09T14:15:27.7783355Z reshape_2 = torch.ops.aten.reshape.default(conv_bias, [1, -1, 1, 1]); conv_bias = None 2025-09-09T14:15:27.7784128Z add_1 = torch.ops.aten.add.Tensor(div_1, reshape_2); div_1 = reshape_2 = None 2025-09-09T14:15:27.7785354Z 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 2025-09-09T14:15:27.7786672Z activation_post_process_2 = self.activation_post_process_2(batch_norm_1); batch_norm_1 = None 2025-09-09T14:15:27.7787491Z return pytree.tree_unflatten((activation_post_process_2,), self._out_spec) 2025-09-09T14:15:27.7788030Z 2025-09-09T14:15:27.7788405Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:15:27.7788910Z model fx: GraphModule( 2025-09-09T14:15:27.7789340Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:15:27.7790684Z 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 2025-09-09T14:15:27.7792261Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.526270866394043, max_val=2.143237352371216) 2025-09-09T14:15:27.7792979Z ) 2025-09-09T14:15:27.7793228Z (conv): ConvBn2d( 2025-09-09T14:15:27.7793527Z 3, 3, kernel_size=(3, 3), stride=(1, 1) 2025-09-09T14:15:27.7794099Z (bn): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T14:15:27.7794746Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:15:37.9729139Z 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 2025-09-09T14:15:37.9731074Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.1822, -0.1883, -0.1585]), max_val=tensor([0.1856, 0.1719, 0.1858])) 2025-09-09T14:15:37.9731999Z ) 2025-09-09T14:15:37.9732249Z ) 2025-09-09T14:15:37.9732678Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:15:37.9734027Z 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 2025-09-09T14:15:37.9735626Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.628747820854187, max_val=1.9105255603790283) 2025-09-09T14:15:37.9736361Z ) 2025-09-09T14:15:37.9736881Z ) 2025-09-09T14:15:37.9737013Z 2025-09-09T14:15:37.9737019Z 2025-09-09T14:15:37.9737024Z 2025-09-09T14:15:37.9737153Z def forward(self, x): 2025-09-09T14:15:37.9737627Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:15:37.9738362Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T14:15:37.9739113Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T14:15:37.9739704Z return activation_post_process_1 2025-09-09T14:15:37.9740061Z 2025-09-09T14:15:37.9740542Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:15:37.9741050Z diff: tensor([[[[0., 0., 0.], 2025-09-09T14:15:37.9741362Z [0., 0., 0.], 2025-09-09T14:15:37.9741651Z [0., 0., 0.]], 2025-09-09T14:15:37.9741835Z 2025-09-09T14:15:37.9741934Z [[0., 0., 0.], 2025-09-09T14:15:37.9742217Z [0., 0., 0.], 2025-09-09T14:15:37.9742494Z [0., 0., 0.]], 2025-09-09T14:15:37.9742690Z 2025-09-09T14:15:37.9742789Z [[0., 0., 0.], 2025-09-09T14:15:37.9743069Z [0., 0., 0.], 2025-09-09T14:15:37.9743378Z [0., 0., 0.]]]], grad_fn=) 2025-09-09T14:15:37.9743801Z converted model pt2e: GraphModule( 2025-09-09T14:15:37.9744147Z (conv): Module() 2025-09-09T14:15:37.9744425Z (bn): Module() 2025-09-09T14:15:37.9744679Z ) 2025-09-09T14:15:37.9744818Z 2025-09-09T14:15:37.9744823Z 2025-09-09T14:15:37.9744828Z 2025-09-09T14:15:37.9744940Z def forward(self, x): 2025-09-09T14:15:37.9745308Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:15:37.9745776Z conv_bias = self.conv.bias 2025-09-09T14:15:37.9746687Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.018311796709895134, 10, -128, 127, torch.int8); x = None 2025-09-09T14:15:37.9748468Z 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 2025-09-09T14:15:37.9749689Z _scale_0 = self._scale_0 2025-09-09T14:15:37.9750025Z _zero_point_0 = self._zero_point_0 2025-09-09T14:15:37.9750440Z quantize_per_channel = self._frozen_param0 2025-09-09T14:15:37.9751704Z 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 2025-09-09T14:15:37.9753623Z 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 2025-09-09T14:15:37.9755356Z 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 2025-09-09T14:15:37.9757209Z 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 2025-09-09T14:15:37.9758893Z return pytree.tree_unflatten((dequantize_per_tensor_default_1,), self._out_spec) 2025-09-09T14:15:37.9759475Z 2025-09-09T14:15:37.9759912Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:15:37.9760437Z onverted model fx: GraphModule( 2025-09-09T14:15:37.9760956Z (conv): QuantizedConv2d(Reference)(3, 3, kernel_size=(3, 3), stride=(1, 1)) 2025-09-09T14:15:37.9761499Z ) 2025-09-09T14:15:37.9761629Z 2025-09-09T14:15:37.9761634Z 2025-09-09T14:15:37.9761639Z 2025-09-09T14:15:37.9761768Z def forward(self, x): 2025-09-09T14:15:37.9762634Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.018311796709895134, 10, -128, 127, torch.int8); x = None 2025-09-09T14:15:37.9764557Z 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 2025-09-09T14:15:37.9766002Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T14:15:37.9767195Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.0138795031234622, -11, -128, 127, torch.int8); conv = None 2025-09-09T14:15:37.9769011Z 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 2025-09-09T14:15:37.9770352Z return dequantize_per_tensor_default_1 2025-09-09T14:15:37.9770731Z 2025-09-09T14:15:37.9771094Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:15:37.9771604Z diff: tensor([[[[0., 0., 0.], 2025-09-09T14:15:37.9771937Z [0., 0., 0.], 2025-09-09T14:15:37.9772216Z [0., 0., 0.]], 2025-09-09T14:15:37.9772403Z 2025-09-09T14:15:37.9772517Z [[0., 0., 0.], 2025-09-09T14:15:37.9772787Z [0., 0., 0.], 2025-09-09T14:15:37.9773066Z [0., 0., 0.]], 2025-09-09T14:15:37.9773252Z 2025-09-09T14:15:37.9773349Z [[0., 0., 0.], 2025-09-09T14:15:37.9773630Z [0., 0., 0.], 2025-09-09T14:15:37.9773896Z [0., 0., 0.]]]]) 2025-09-09T14:15:37.9774214Z model pt2e: GraphModule( 2025-09-09T14:15:37.9774519Z (conv): Module() 2025-09-09T14:15:37.9774786Z (bn): Module() 2025-09-09T14:15:37.9775187Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:15:37.9776512Z 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 2025-09-09T14:15:37.9778080Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.526270866394043, max_val=2.143237352371216) 2025-09-09T14:15:37.9778791Z ) 2025-09-09T14:15:37.9779167Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:15:37.9780515Z 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 2025-09-09T14:15:37.9782088Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.1882954239845276, max_val=0.18581794202327728) 2025-09-09T14:15:37.9782833Z ) 2025-09-09T14:15:37.9783196Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:15:37.9784532Z 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 2025-09-09T14:15:37.9786109Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.6255242824554443, max_val=1.9112863540649414) 2025-09-09T14:15:37.9786828Z ) 2025-09-09T14:15:37.9787061Z ) 2025-09-09T14:15:37.9787185Z 2025-09-09T14:15:37.9787190Z 2025-09-09T14:15:37.9787194Z 2025-09-09T14:15:37.9787306Z def forward(self, x): 2025-09-09T14:15:37.9787691Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:15:37.9788159Z conv_weight = self.conv.weight 2025-09-09T14:15:37.9788521Z conv_bias = self.conv.bias 2025-09-09T14:15:37.9788871Z bn_weight = self.bn.weight 2025-09-09T14:15:37.9789206Z bn_bias = self.bn.bias 2025-09-09T14:15:37.9789556Z bn_running_mean = self.bn.running_mean 2025-09-09T14:15:37.9789957Z bn_running_var = self.bn.running_var 2025-09-09T14:15:37.9790414Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T14:15:37.9791100Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:15:37.9791928Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T14:15:37.9792668Z add = torch.ops.aten.add.Tensor(bn_running_var, 1e-05) 2025-09-09T14:15:37.9793200Z sqrt = torch.ops.aten.sqrt.default(add); add = None 2025-09-09T14:15:37.9793771Z div = torch.ops.aten.div.Tensor(bn_weight, sqrt); sqrt = None 2025-09-09T14:15:37.9794376Z reshape = torch.ops.aten.reshape.default(div, [-1, 1, 1, 1]) 2025-09-09T14:15:37.9795091Z mul = torch.ops.aten.mul.Tensor(conv_weight, reshape); conv_weight = reshape = None 2025-09-09T14:15:37.9795936Z activation_post_process_1 = self.activation_post_process_1(mul); mul = None 2025-09-09T14:15:37.9796801Z zeros_like = torch.ops.aten.zeros_like.default(conv_bias, dtype = torch.float32, pin_memory = False) 2025-09-09T14:15:37.9798195Z 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 2025-09-09T14:15:37.9799449Z reshape_1 = torch.ops.aten.reshape.default(div, [1, -1, 1, 1]); div = None 2025-09-09T14:15:37.9800275Z div_1 = torch.ops.aten.div.Tensor(conv2d_1, reshape_1); conv2d_1 = reshape_1 = None 2025-09-09T14:15:37.9801080Z reshape_2 = torch.ops.aten.reshape.default(conv_bias, [1, -1, 1, 1]); conv_bias = None 2025-09-09T14:15:37.9801865Z add_1 = torch.ops.aten.add.Tensor(div_1, reshape_2); div_1 = reshape_2 = None 2025-09-09T14:15:37.9803087Z 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 2025-09-09T14:15:37.9804397Z activation_post_process_2 = self.activation_post_process_2(batch_norm_1); batch_norm_1 = None 2025-09-09T14:15:37.9805237Z return pytree.tree_unflatten((activation_post_process_2,), self._out_spec) 2025-09-09T14:15:37.9805771Z 2025-09-09T14:15:37.9806160Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:15:37.9806674Z model fx: GraphModule( 2025-09-09T14:15:37.9807099Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:15:37.9808438Z 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 2025-09-09T14:15:49.8732606Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.526270866394043, max_val=2.143237352371216) 2025-09-09T14:15:49.8733387Z ) 2025-09-09T14:15:49.8733677Z (conv): ConvBn2d( 2025-09-09T14:15:49.8733995Z 3, 3, kernel_size=(3, 3), stride=(1, 1) 2025-09-09T14:15:49.8734559Z (bn): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T14:15:49.8735224Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:15:49.8736541Z 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 2025-09-09T14:15:49.8738153Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.1882954239845276, max_val=0.18581794202327728) 2025-09-09T14:15:49.8738899Z ) 2025-09-09T14:15:49.8739125Z ) 2025-09-09T14:15:49.8739500Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:15:49.8740846Z 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 2025-09-09T14:15:49.8742413Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.6255242824554443, max_val=1.9112863540649414) 2025-09-09T14:15:49.8743155Z ) 2025-09-09T14:15:49.8743694Z ) 2025-09-09T14:15:49.8743843Z 2025-09-09T14:15:49.8743849Z 2025-09-09T14:15:49.8743854Z 2025-09-09T14:15:49.8743967Z def forward(self, x): 2025-09-09T14:15:49.8744455Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:15:49.8745188Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T14:15:49.8745955Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T14:15:49.8746537Z return activation_post_process_1 2025-09-09T14:15:49.8746898Z 2025-09-09T14:15:49.8747262Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:15:49.8747901Z diff: tensor([[[[0., 0., 0.], 2025-09-09T14:15:49.8748220Z [0., 0., 0.], 2025-09-09T14:15:49.8748514Z [0., 0., 0.]], 2025-09-09T14:15:49.8748706Z 2025-09-09T14:15:49.8748825Z [[0., 0., 0.], 2025-09-09T14:15:49.8749099Z [0., 0., 0.], 2025-09-09T14:15:49.8749391Z [0., 0., 0.]], 2025-09-09T14:15:49.8749579Z 2025-09-09T14:15:49.8749683Z [[0., 0., 0.], 2025-09-09T14:15:49.8749964Z [0., 0., 0.], 2025-09-09T14:15:49.8750272Z [0., 0., 0.]]]], grad_fn=) 2025-09-09T14:15:49.8750692Z converted model pt2e: GraphModule( 2025-09-09T14:15:49.8751039Z (conv): Module() 2025-09-09T14:15:49.8751322Z (bn): Module() 2025-09-09T14:15:49.8751574Z ) 2025-09-09T14:15:49.8751715Z 2025-09-09T14:15:49.8751720Z 2025-09-09T14:15:49.8751724Z 2025-09-09T14:15:49.8751836Z def forward(self, x): 2025-09-09T14:15:49.8752215Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:15:49.8752665Z conv_bias = self.conv.bias 2025-09-09T14:15:49.8753574Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.018311796709895134, 10, -128, 127, torch.int8); x = None 2025-09-09T14:15:49.8755358Z 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 2025-09-09T14:15:49.8756619Z quantize_per_tensor = self._frozen_param0 2025-09-09T14:15:49.8757750Z 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 2025-09-09T14:15:49.8759876Z 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 2025-09-09T14:15:49.8761605Z 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 2025-09-09T14:15:49.8763494Z 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 2025-09-09T14:15:49.8764923Z return pytree.tree_unflatten((dequantize_per_tensor_default_2,), self._out_spec) 2025-09-09T14:15:49.8765497Z 2025-09-09T14:15:49.8765866Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:15:49.8766382Z onverted model fx: GraphModule( 2025-09-09T14:15:49.8766913Z (conv): QuantizedConv2d(Reference)(3, 3, kernel_size=(3, 3), stride=(1, 1)) 2025-09-09T14:15:49.8767434Z ) 2025-09-09T14:15:49.8767563Z 2025-09-09T14:15:49.8767568Z 2025-09-09T14:15:49.8767573Z 2025-09-09T14:15:49.8767696Z def forward(self, x): 2025-09-09T14:15:49.8768554Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.018311796709895134, 10, -128, 127, torch.int8); x = None 2025-09-09T14:15:49.8770485Z 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 2025-09-09T14:15:49.8771930Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T14:15:49.8773124Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.013869845308363438, -11, -128, 127, torch.int8); conv = None 2025-09-09T14:15:49.8774964Z 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 2025-09-09T14:15:49.8776329Z return dequantize_per_tensor_default_1 2025-09-09T14:15:49.8776698Z 2025-09-09T14:15:49.8777080Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:15:49.8777575Z diff: tensor([[[[0., 0., 0.], 2025-09-09T14:15:49.8777906Z [0., 0., 0.], 2025-09-09T14:15:49.8778187Z [0., 0., 0.]], 2025-09-09T14:15:49.8778388Z 2025-09-09T14:15:49.8778496Z [[0., 0., 0.], 2025-09-09T14:15:49.8778781Z [0., 0., 0.], 2025-09-09T14:15:49.8779051Z [0., 0., 0.]], 2025-09-09T14:15:49.8779249Z 2025-09-09T14:15:49.8779346Z [[0., 0., 0.], 2025-09-09T14:15:49.8779632Z [0., 0., 0.], 2025-09-09T14:15:49.8779902Z [0., 0., 0.]]]]) 2025-09-09T14:15:49.8780431Z PASSED 2025-09-09T14:15:49.8781364Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_conv_bn_fusion_cuda SKIPPED 2025-09-09T14:15:49.8782774Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_conv_bn_fusion_literal_args model pt2e: GraphModule( 2025-09-09T14:15:49.8783682Z (conv): Module() 2025-09-09T14:15:49.8783957Z (bn): Module() 2025-09-09T14:15:49.8784352Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:15:49.8785706Z 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 2025-09-09T14:15:49.8787291Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.4721859693527222, max_val=2.2869999408721924) 2025-09-09T14:15:49.8788017Z ) 2025-09-09T14:15:49.8788392Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:15:49.8789783Z 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 2025-09-09T14:15:49.8791631Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.1897, -0.1787, -0.1913]), max_val=tensor([0.1870, 0.1478, 0.1740])) 2025-09-09T14:15:49.8792556Z ) 2025-09-09T14:15:49.8792918Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:15:49.8794249Z 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 2025-09-09T14:15:49.8795804Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-4.046965599060059, max_val=3.922553539276123) 2025-09-09T14:15:49.8796507Z ) 2025-09-09T14:15:49.8796736Z ) 2025-09-09T14:15:49.8796861Z 2025-09-09T14:15:49.8796867Z 2025-09-09T14:15:49.8796872Z 2025-09-09T14:15:49.8796981Z def forward(self, x): 2025-09-09T14:15:49.8797358Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:15:49.8797813Z conv_weight = self.conv.weight 2025-09-09T14:15:49.8798183Z conv_bias = self.conv.bias 2025-09-09T14:15:49.8798527Z bn_weight = self.bn.weight 2025-09-09T14:15:49.8798849Z bn_bias = self.bn.bias 2025-09-09T14:15:49.8799194Z bn_running_mean = self.bn.running_mean 2025-09-09T14:15:49.8799766Z bn_running_var = self.bn.running_var 2025-09-09T14:15:49.8800231Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T14:15:49.8800824Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:15:49.8801642Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T14:15:49.8802360Z add = torch.ops.aten.add.Tensor(bn_running_var, 1e-05) 2025-09-09T14:15:49.8802903Z sqrt = torch.ops.aten.sqrt.default(add); add = None 2025-09-09T14:15:49.8803472Z div = torch.ops.aten.div.Tensor(bn_weight, sqrt); sqrt = None 2025-09-09T14:15:49.8804179Z reshape = torch.ops.aten.reshape.default(div, [-1, 1, 1, 1]) 2025-09-09T14:15:49.8804887Z mul = torch.ops.aten.mul.Tensor(conv_weight, reshape); conv_weight = reshape = None 2025-09-09T14:15:49.8805659Z activation_post_process_1 = self.activation_post_process_1(mul); mul = None 2025-09-09T14:15:49.8806681Z zeros_like = torch.ops.aten.zeros_like.default(conv_bias, dtype = torch.float32, pin_memory = False) 2025-09-09T14:15:49.8808099Z 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 2025-09-09T14:15:49.8809375Z reshape_1 = torch.ops.aten.reshape.default(div, [1, -1, 1, 1]); div = None 2025-09-09T14:15:49.8810133Z div_1 = torch.ops.aten.div.Tensor(conv2d_1, reshape_1); conv2d_1 = reshape_1 = None 2025-09-09T14:15:49.8810939Z reshape_2 = torch.ops.aten.reshape.default(conv_bias, [1, -1, 1, 1]); conv_bias = None 2025-09-09T14:15:49.8811739Z add_1 = torch.ops.aten.add.Tensor(div_1, reshape_2); div_1 = reshape_2 = None 2025-09-09T14:15:49.8812962Z 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 2025-09-09T14:15:49.8814270Z activation_post_process_2 = self.activation_post_process_2(batch_norm_1); batch_norm_1 = None 2025-09-09T14:15:59.9315146Z return pytree.tree_unflatten((activation_post_process_2,), self._out_spec) 2025-09-09T14:15:59.9315730Z 2025-09-09T14:15:59.9316128Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:15:59.9316629Z model fx: GraphModule( 2025-09-09T14:15:59.9317078Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:15:59.9318424Z 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 2025-09-09T14:15:59.9320277Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.4721859693527222, max_val=2.2869999408721924) 2025-09-09T14:15:59.9321029Z ) 2025-09-09T14:15:59.9321266Z (conv): ConvBn2d( 2025-09-09T14:15:59.9321762Z 3, 3, kernel_size=(3, 3), stride=(2, 2), padding=(4, 4) 2025-09-09T14:15:59.9322372Z (bn): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T14:15:59.9323020Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:15:59.9324379Z 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 2025-09-09T14:15:59.9326252Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.1897, -0.1787, -0.1913]), max_val=tensor([0.1870, 0.1478, 0.1740])) 2025-09-09T14:15:59.9327190Z ) 2025-09-09T14:15:59.9327418Z ) 2025-09-09T14:15:59.9327788Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:15:59.9329479Z 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 2025-09-09T14:15:59.9331052Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-4.046965599060059, max_val=3.922553539276123) 2025-09-09T14:15:59.9331777Z ) 2025-09-09T14:15:59.9331997Z ) 2025-09-09T14:15:59.9332143Z 2025-09-09T14:15:59.9332189Z 2025-09-09T14:15:59.9332194Z 2025-09-09T14:15:59.9332307Z def forward(self, x): 2025-09-09T14:15:59.9332791Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:15:59.9333518Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T14:15:59.9334405Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T14:15:59.9334985Z return activation_post_process_1 2025-09-09T14:15:59.9335350Z 2025-09-09T14:15:59.9335730Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:15:59.9336239Z diff: tensor([[[[0., 0., 0., 0., 0., 0.], 2025-09-09T14:15:59.9336618Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:15:59.9336946Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:15:59.9337284Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:15:59.9337608Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:15:59.9337944Z [0., 0., 0., 0., 0., 0.]], 2025-09-09T14:15:59.9338171Z 2025-09-09T14:15:59.9338276Z [[0., 0., 0., 0., 0., 0.], 2025-09-09T14:15:59.9338606Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:15:59.9338943Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:15:59.9339265Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:15:59.9339606Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:15:59.9339926Z [0., 0., 0., 0., 0., 0.]], 2025-09-09T14:15:59.9340155Z 2025-09-09T14:15:59.9340276Z [[0., 0., 0., 0., 0., 0.], 2025-09-09T14:15:59.9340601Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:15:59.9340943Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:15:59.9341274Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:15:59.9341611Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:15:59.9342002Z [0., 0., 0., 0., 0., 0.]]]], grad_fn=) 2025-09-09T14:15:59.9342434Z converted model pt2e: GraphModule( 2025-09-09T14:15:59.9342801Z (conv): Module() 2025-09-09T14:15:59.9343073Z (bn): Module() 2025-09-09T14:15:59.9343342Z ) 2025-09-09T14:15:59.9343472Z 2025-09-09T14:15:59.9343477Z 2025-09-09T14:15:59.9343482Z 2025-09-09T14:15:59.9343600Z def forward(self, x): 2025-09-09T14:15:59.9343987Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:15:59.9344445Z conv_bias = self.conv.bias 2025-09-09T14:15:59.9345370Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.01474190503358841, -28, -128, 127, torch.int8); x = None 2025-09-09T14:15:59.9347165Z 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 2025-09-09T14:15:59.9348372Z _scale_0 = self._scale_0 2025-09-09T14:15:59.9348719Z _zero_point_0 = self._zero_point_0 2025-09-09T14:15:59.9349123Z quantize_per_channel = self._frozen_param0 2025-09-09T14:15:59.9350384Z 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 2025-09-09T14:15:59.9352351Z 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 2025-09-09T14:15:59.9354073Z 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 2025-09-09T14:15:59.9356014Z 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 2025-09-09T14:15:59.9357458Z return pytree.tree_unflatten((dequantize_per_tensor_default_1,), self._out_spec) 2025-09-09T14:15:59.9358030Z 2025-09-09T14:15:59.9358626Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:15:59.9359133Z onverted model fx: GraphModule( 2025-09-09T14:15:59.9359774Z (conv): QuantizedConv2d(Reference)(3, 3, kernel_size=(3, 3), stride=(2, 2), padding=(4, 4)) 2025-09-09T14:15:59.9360489Z ) 2025-09-09T14:15:59.9360634Z 2025-09-09T14:15:59.9360639Z 2025-09-09T14:15:59.9360644Z 2025-09-09T14:15:59.9360756Z def forward(self, x): 2025-09-09T14:15:59.9361641Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.01474190503358841, -28, -128, 127, torch.int8); x = None 2025-09-09T14:15:59.9363420Z 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 2025-09-09T14:15:59.9364862Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T14:15:59.9366071Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.031253017485141754, 1, -128, 127, torch.int8); conv = None 2025-09-09T14:15:59.9367887Z 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 2025-09-09T14:15:59.9369156Z return dequantize_per_tensor_default_1 2025-09-09T14:15:59.9369524Z 2025-09-09T14:15:59.9369903Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:15:59.9370421Z diff: tensor([[[[0., 0., 0., 0., 0., 0.], 2025-09-09T14:15:59.9370789Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:15:59.9371129Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:15:59.9371460Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:15:59.9371799Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:15:59.9372122Z [0., 0., 0., 0., 0., 0.]], 2025-09-09T14:15:59.9372361Z 2025-09-09T14:15:59.9372471Z [[0., 0., 0., 0., 0., 0.], 2025-09-09T14:15:59.9372791Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:15:59.9373125Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:15:59.9373446Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:15:59.9373786Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:15:59.9374117Z [0., 0., 0., 0., 0., 0.]], 2025-09-09T14:15:59.9374341Z 2025-09-09T14:15:59.9374448Z [[0., 0., 0., 0., 0., 0.], 2025-09-09T14:15:59.9374777Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:15:59.9375096Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:15:59.9375435Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:15:59.9375755Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:15:59.9376109Z [0., 0., 0., 0., 0., 0.]]]]) 2025-09-09T14:15:59.9376472Z model pt2e: GraphModule( 2025-09-09T14:15:59.9376775Z (conv): Module() 2025-09-09T14:15:59.9377052Z (bn): Module() 2025-09-09T14:15:59.9377449Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:15:59.9378795Z 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 2025-09-09T14:15:59.9380379Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.4721859693527222, max_val=2.2869999408721924) 2025-09-09T14:15:59.9381118Z ) 2025-09-09T14:15:59.9381499Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:15:59.9382967Z 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 2025-09-09T14:15:59.9384571Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.19127479195594788, max_val=0.1870359182357788) 2025-09-09T14:15:59.9385300Z ) 2025-09-09T14:15:59.9385684Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:15:59.9387007Z 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 2025-09-09T14:15:59.9388688Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-4.046965599060059, max_val=3.9041571617126465) 2025-09-09T14:15:59.9389418Z ) 2025-09-09T14:15:59.9389637Z ) 2025-09-09T14:15:59.9389778Z 2025-09-09T14:15:59.9389783Z 2025-09-09T14:15:59.9389788Z 2025-09-09T14:15:59.9389908Z def forward(self, x): 2025-09-09T14:15:59.9390299Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:15:59.9390754Z conv_weight = self.conv.weight 2025-09-09T14:15:59.9391135Z conv_bias = self.conv.bias 2025-09-09T14:15:59.9391474Z bn_weight = self.bn.weight 2025-09-09T14:15:59.9391819Z bn_bias = self.bn.bias 2025-09-09T14:15:59.9392155Z bn_running_mean = self.bn.running_mean 2025-09-09T14:15:59.9392566Z bn_running_var = self.bn.running_var 2025-09-09T14:15:59.9393002Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T14:15:59.9393602Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:15:59.9394427Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T14:15:59.9395145Z add = torch.ops.aten.add.Tensor(bn_running_var, 1e-05) 2025-09-09T14:15:59.9395684Z sqrt = torch.ops.aten.sqrt.default(add); add = None 2025-09-09T14:15:59.9396245Z div = torch.ops.aten.div.Tensor(bn_weight, sqrt); sqrt = None 2025-09-09T14:16:12.8307695Z reshape = torch.ops.aten.reshape.default(div, [-1, 1, 1, 1]) 2025-09-09T14:16:12.8308508Z mul = torch.ops.aten.mul.Tensor(conv_weight, reshape); conv_weight = reshape = None 2025-09-09T14:16:12.8309299Z activation_post_process_1 = self.activation_post_process_1(mul); mul = None 2025-09-09T14:16:12.8310156Z zeros_like = torch.ops.aten.zeros_like.default(conv_bias, dtype = torch.float32, pin_memory = False) 2025-09-09T14:16:12.8311577Z 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 2025-09-09T14:16:12.8312895Z reshape_1 = torch.ops.aten.reshape.default(div, [1, -1, 1, 1]); div = None 2025-09-09T14:16:12.8313641Z div_1 = torch.ops.aten.div.Tensor(conv2d_1, reshape_1); conv2d_1 = reshape_1 = None 2025-09-09T14:16:12.8314460Z reshape_2 = torch.ops.aten.reshape.default(conv_bias, [1, -1, 1, 1]); conv_bias = None 2025-09-09T14:16:12.8315236Z add_1 = torch.ops.aten.add.Tensor(div_1, reshape_2); div_1 = reshape_2 = None 2025-09-09T14:16:12.8316469Z 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 2025-09-09T14:16:12.8317785Z activation_post_process_2 = self.activation_post_process_2(batch_norm_1); batch_norm_1 = None 2025-09-09T14:16:12.8318600Z return pytree.tree_unflatten((activation_post_process_2,), self._out_spec) 2025-09-09T14:16:12.8319148Z 2025-09-09T14:16:12.8319513Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:16:12.8320074Z model fx: GraphModule( 2025-09-09T14:16:12.8320519Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:16:12.8322186Z 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 2025-09-09T14:16:12.8323802Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.4721859693527222, max_val=2.2869999408721924) 2025-09-09T14:16:12.8324530Z ) 2025-09-09T14:16:12.8324777Z (conv): ConvBn2d( 2025-09-09T14:16:12.8325118Z 3, 3, kernel_size=(3, 3), stride=(2, 2), padding=(4, 4) 2025-09-09T14:16:12.8325742Z (bn): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T14:16:12.8326512Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:16:12.8327816Z 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 2025-09-09T14:16:12.8329426Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.19127479195594788, max_val=0.1870359182357788) 2025-09-09T14:16:12.8330160Z ) 2025-09-09T14:16:12.8330402Z ) 2025-09-09T14:16:12.8330770Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:16:12.8332090Z 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 2025-09-09T14:16:12.8333660Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-4.046965599060059, max_val=3.9041571617126465) 2025-09-09T14:16:12.8334375Z ) 2025-09-09T14:16:12.8334607Z ) 2025-09-09T14:16:12.8334733Z 2025-09-09T14:16:12.8334738Z 2025-09-09T14:16:12.8334743Z 2025-09-09T14:16:12.8334870Z def forward(self, x): 2025-09-09T14:16:12.8335335Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:16:12.8336083Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T14:16:12.8336829Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T14:16:12.8337417Z return activation_post_process_1 2025-09-09T14:16:12.8337764Z 2025-09-09T14:16:12.8338136Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:16:12.8338636Z diff: tensor([[[[0., 0., 0., 0., 0., 0.], 2025-09-09T14:16:12.8339011Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:16:12.8339345Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:16:12.8339664Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:16:12.8340003Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:16:12.8340322Z [0., 0., 0., 0., 0., 0.]], 2025-09-09T14:16:12.8340558Z 2025-09-09T14:16:12.8340661Z [[0., 0., 0., 0., 0., 0.], 2025-09-09T14:16:12.8340979Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:16:12.8341312Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:16:12.8341638Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:16:12.8341971Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:16:12.8342303Z [0., 0., 0., 0., 0., 0.]], 2025-09-09T14:16:12.8342529Z 2025-09-09T14:16:12.8342634Z [[0., 0., 0., 0., 0., 0.], 2025-09-09T14:16:12.8342968Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:16:12.8343284Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:16:12.8343614Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:16:12.8343936Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:16:12.8344317Z [0., 0., 0., 0., 0., 0.]]]], grad_fn=) 2025-09-09T14:16:12.8344756Z converted model pt2e: GraphModule( 2025-09-09T14:16:12.8345118Z (conv): Module() 2025-09-09T14:16:12.8345394Z (bn): Module() 2025-09-09T14:16:12.8345650Z ) 2025-09-09T14:16:12.8345777Z 2025-09-09T14:16:12.8345782Z 2025-09-09T14:16:12.8345786Z 2025-09-09T14:16:12.8345910Z def forward(self, x): 2025-09-09T14:16:12.8346381Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:16:12.8346849Z conv_bias = self.conv.bias 2025-09-09T14:16:12.8347747Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.01474190503358841, -28, -128, 127, torch.int8); x = None 2025-09-09T14:16:12.8349552Z 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 2025-09-09T14:16:12.8350800Z quantize_per_tensor = self._frozen_param0 2025-09-09T14:16:12.8351922Z 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 2025-09-09T14:16:12.8353820Z 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 2025-09-09T14:16:12.8355556Z 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 2025-09-09T14:16:12.8357401Z 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 2025-09-09T14:16:12.8359082Z return pytree.tree_unflatten((dequantize_per_tensor_default_2,), self._out_spec) 2025-09-09T14:16:12.8359719Z 2025-09-09T14:16:12.8360113Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:16:12.8360635Z onverted model fx: GraphModule( 2025-09-09T14:16:12.8361209Z (conv): QuantizedConv2d(Reference)(3, 3, kernel_size=(3, 3), stride=(2, 2), padding=(4, 4)) 2025-09-09T14:16:12.8361809Z ) 2025-09-09T14:16:12.8361938Z 2025-09-09T14:16:12.8361943Z 2025-09-09T14:16:12.8361948Z 2025-09-09T14:16:12.8362064Z def forward(self, x): 2025-09-09T14:16:12.8362944Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.01474190503358841, -28, -128, 127, torch.int8); x = None 2025-09-09T14:16:12.8364735Z 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 2025-09-09T14:16:12.8366168Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T14:16:12.8367373Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.03118087351322174, 2, -128, 127, torch.int8); conv = None 2025-09-09T14:16:12.8369180Z 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 2025-09-09T14:16:12.8370435Z return dequantize_per_tensor_default_1 2025-09-09T14:16:12.8370824Z 2025-09-09T14:16:12.8371193Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:16:12.8371712Z diff: tensor([[[[0., 0., 0., 0., 0., 0.], 2025-09-09T14:16:12.8372076Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:16:12.8372413Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:16:12.8372732Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:16:12.8373060Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:16:12.8373391Z [0., 0., 0., 0., 0., 0.]], 2025-09-09T14:16:12.8373616Z 2025-09-09T14:16:12.8373725Z [[0., 0., 0., 0., 0., 0.], 2025-09-09T14:16:12.8374055Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:16:12.8374373Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:16:12.8374700Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:16:12.8375018Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:16:12.8375353Z [0., 0., 0., 0., 0., 0.]], 2025-09-09T14:16:12.8375578Z 2025-09-09T14:16:12.8375861Z [[0., 0., 0., 0., 0., 0.], 2025-09-09T14:16:12.8376196Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:16:12.8376528Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:16:12.8376846Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:16:12.8377178Z [0., 0., 0., 0., 0., 0.], 2025-09-09T14:16:12.8377506Z [0., 0., 0., 0., 0., 0.]]]]) 2025-09-09T14:16:12.8378062Z PASSED 2025-09-09T14:16:12.8378910Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_conv_bn_fusion_no_conv_bias model pt2e: GraphModule( 2025-09-09T14:16:12.8379947Z (conv): Module() 2025-09-09T14:16:12.8380210Z (bn): Module() 2025-09-09T14:16:12.8380628Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:16:12.8382047Z 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 2025-09-09T14:16:12.8383643Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.247849225997925, max_val=2.7226178646087646) 2025-09-09T14:16:12.8384373Z ) 2025-09-09T14:16:12.8384737Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:16:12.8386145Z 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 2025-09-09T14:16:20.3708943Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.1720, -0.1912, -0.1684]), max_val=tensor([0.1914, 0.1792, 0.1824])) 2025-09-09T14:16:20.3709739Z ) 2025-09-09T14:16:20.3710047Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:16:20.3711162Z 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 2025-09-09T14:16:20.3712448Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.5782737731933594, max_val=2.5220179557800293) 2025-09-09T14:16:20.3713030Z ) 2025-09-09T14:16:20.3713225Z ) 2025-09-09T14:16:20.3713328Z 2025-09-09T14:16:20.3713333Z 2025-09-09T14:16:20.3713337Z 2025-09-09T14:16:20.3713432Z def forward(self, x): 2025-09-09T14:16:20.3713754Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:16:20.3714144Z conv_weight = self.conv.weight 2025-09-09T14:16:20.3714459Z bn_weight = self.bn.weight 2025-09-09T14:16:20.3714739Z bn_bias = self.bn.bias 2025-09-09T14:16:20.3715010Z bn_running_mean = self.bn.running_mean 2025-09-09T14:16:20.3715349Z bn_running_var = self.bn.running_var 2025-09-09T14:16:20.3715707Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T14:16:20.3716205Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:16:20.3716856Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T14:16:20.3717450Z add = torch.ops.aten.add.Tensor(bn_running_var, 1e-05) 2025-09-09T14:16:20.3717889Z sqrt = torch.ops.aten.sqrt.default(add); add = None 2025-09-09T14:16:20.3718387Z div = torch.ops.aten.div.Tensor(bn_weight, sqrt); sqrt = None 2025-09-09T14:16:20.3718871Z reshape = torch.ops.aten.reshape.default(div, [-1, 1, 1, 1]) 2025-09-09T14:16:20.3719439Z mul = torch.ops.aten.mul.Tensor(conv_weight, reshape); conv_weight = reshape = None 2025-09-09T14:16:20.3720136Z activation_post_process_1 = self.activation_post_process_1(mul); mul = None 2025-09-09T14:16:20.3721091Z 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 2025-09-09T14:16:20.3722344Z reshape_1 = torch.ops.aten.reshape.default(div, [1, -1, 1, 1]); div = None 2025-09-09T14:16:20.3722947Z div_1 = torch.ops.aten.div.Tensor(conv2d_1, reshape_1); conv2d_1 = reshape_1 = None 2025-09-09T14:16:20.3723966Z 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 2025-09-09T14:16:20.3725030Z activation_post_process_2 = self.activation_post_process_2(batch_norm_1); batch_norm_1 = None 2025-09-09T14:16:20.3725697Z return pytree.tree_unflatten((activation_post_process_2,), self._out_spec) 2025-09-09T14:16:20.3726252Z 2025-09-09T14:16:20.3726552Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:16:20.3726969Z model fx: GraphModule( 2025-09-09T14:16:20.3727315Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:16:20.3728399Z 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 2025-09-09T14:16:20.3729675Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.247849225997925, max_val=2.7226178646087646) 2025-09-09T14:16:20.3730248Z ) 2025-09-09T14:16:20.3730446Z (conv): ConvBn2d( 2025-09-09T14:16:20.3730711Z 3, 3, kernel_size=(3, 3), stride=(1, 1), bias=False 2025-09-09T14:16:20.3731205Z (bn): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T14:16:20.3731720Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:16:20.3732839Z 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 2025-09-09T14:16:20.3734341Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.1720, -0.1912, -0.1684]), max_val=tensor([0.1914, 0.1792, 0.1824])) 2025-09-09T14:16:20.3735080Z ) 2025-09-09T14:16:20.3735278Z ) 2025-09-09T14:16:20.3735574Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:16:20.3736657Z 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 2025-09-09T14:16:20.3737943Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.5782737731933594, max_val=2.5220179557800293) 2025-09-09T14:16:20.3738529Z ) 2025-09-09T14:16:20.3738722Z ) 2025-09-09T14:16:20.3738826Z 2025-09-09T14:16:20.3738830Z 2025-09-09T14:16:20.3738835Z 2025-09-09T14:16:20.3738928Z def forward(self, x): 2025-09-09T14:16:20.3739328Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:16:20.3739930Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T14:16:20.3740533Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T14:16:20.3741019Z return activation_post_process_1 2025-09-09T14:16:20.3741304Z 2025-09-09T14:16:20.3741611Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:16:20.3742017Z diff: tensor([[[[0., 0., 0.], 2025-09-09T14:16:20.3742286Z [0., 0., 0.], 2025-09-09T14:16:20.3742511Z [0., 0., 0.]], 2025-09-09T14:16:20.3742680Z 2025-09-09T14:16:20.3742762Z [[0., 0., 0.], 2025-09-09T14:16:20.3742993Z [0., 0., 0.], 2025-09-09T14:16:20.3743208Z [0., 0., 0.]], 2025-09-09T14:16:20.3743355Z 2025-09-09T14:16:20.3743448Z [[0., 0., 0.], 2025-09-09T14:16:20.3743664Z [0., 0., 0.], 2025-09-09T14:16:20.3743895Z [0., 0., 0.]]], 2025-09-09T14:16:20.3744046Z 2025-09-09T14:16:20.3744123Z 2025-09-09T14:16:20.3744210Z [[[0., 0., 0.], 2025-09-09T14:16:20.3744438Z [0., 0., 0.], 2025-09-09T14:16:20.3744653Z [0., 0., 0.]], 2025-09-09T14:16:20.3744814Z 2025-09-09T14:16:20.3744893Z [[0., 0., 0.], 2025-09-09T14:16:20.3745120Z [0., 0., 0.], 2025-09-09T14:16:20.3755800Z [0., 0., 0.]], 2025-09-09T14:16:20.3756009Z 2025-09-09T14:16:20.3756090Z [[0., 0., 0.], 2025-09-09T14:16:20.3756308Z [0., 0., 0.], 2025-09-09T14:16:20.3756518Z [0., 0., 0.]]], 2025-09-09T14:16:20.3756670Z 2025-09-09T14:16:20.3756821Z 2025-09-09T14:16:20.3756897Z [[[0., 0., 0.], 2025-09-09T14:16:20.3757113Z [0., 0., 0.], 2025-09-09T14:16:20.3757339Z [0., 0., 0.]], 2025-09-09T14:16:20.3757507Z 2025-09-09T14:16:20.3757590Z [[0., 0., 0.], 2025-09-09T14:16:20.3757810Z [0., 0., 0.], 2025-09-09T14:16:20.3758050Z [0., 0., 0.]], 2025-09-09T14:16:20.3758485Z 2025-09-09T14:16:20.3758600Z [[0., 0., 0.], 2025-09-09T14:16:20.3758823Z [0., 0., 0.], 2025-09-09T14:16:20.3759096Z [0., 0., 0.]]]], grad_fn=) 2025-09-09T14:16:20.3759452Z converted model pt2e: GraphModule( 2025-09-09T14:16:20.3759820Z (conv): Module() 2025-09-09T14:16:20.3760040Z (bn): Module() 2025-09-09T14:16:20.3760266Z ) 2025-09-09T14:16:20.3760371Z 2025-09-09T14:16:20.3760376Z 2025-09-09T14:16:20.3760380Z 2025-09-09T14:16:20.3760473Z def forward(self, x): 2025-09-09T14:16:20.3760795Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:16:20.3761639Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.019492028281092644, -13, -128, 127, torch.int8); x = None 2025-09-09T14:16:20.3763065Z 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 2025-09-09T14:16:20.3764056Z _scale_0 = self._scale_0 2025-09-09T14:16:20.3764334Z _zero_point_0 = self._zero_point_0 2025-09-09T14:16:20.3764678Z quantize_per_channel = self._frozen_param0 2025-09-09T14:16:20.3765695Z 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 2025-09-09T14:16:20.3766695Z conv_weight_bias = self.conv.weight_bias 2025-09-09T14:16:20.3767655Z 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 2025-09-09T14:16:20.3769087Z 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 2025-09-09T14:16:20.3770575Z 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 2025-09-09T14:16:20.3771733Z return pytree.tree_unflatten((dequantize_per_tensor_default_1,), self._out_spec) 2025-09-09T14:16:20.3772188Z 2025-09-09T14:16:20.3772502Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:16:20.3772914Z onverted model fx: GraphModule( 2025-09-09T14:16:20.3773344Z (conv): QuantizedConv2d(Reference)(3, 3, kernel_size=(3, 3), stride=(1, 1)) 2025-09-09T14:16:20.3773776Z ) 2025-09-09T14:16:20.3773886Z 2025-09-09T14:16:20.3773891Z 2025-09-09T14:16:20.3773895Z 2025-09-09T14:16:20.3773986Z def forward(self, x): 2025-09-09T14:16:20.3774691Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.019492028281092644, -13, -128, 127, torch.int8); x = None 2025-09-09T14:16:20.3776259Z 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 2025-09-09T14:16:20.3777425Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T14:16:20.3778391Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.020001143217086792, 1, -128, 127, torch.int8); conv = None 2025-09-09T14:16:20.3779829Z 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 2025-09-09T14:16:20.3780916Z return dequantize_per_tensor_default_1 2025-09-09T14:16:20.3781212Z 2025-09-09T14:16:20.3781527Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:16:20.3781946Z diff: tensor([[[[0., 0., 0.], 2025-09-09T14:16:20.3782206Z [0., 0., 0.], 2025-09-09T14:16:20.3782446Z [0., 0., 0.]], 2025-09-09T14:16:20.3782597Z 2025-09-09T14:16:20.3782683Z [[0., 0., 0.], 2025-09-09T14:16:20.3782913Z [0., 0., 0.], 2025-09-09T14:16:20.3783135Z [0., 0., 0.]], 2025-09-09T14:16:20.3783296Z 2025-09-09T14:16:20.3783377Z [[0., 0., 0.], 2025-09-09T14:16:20.3783595Z [0., 0., 0.], 2025-09-09T14:16:20.3783830Z [0., 0., 0.]]], 2025-09-09T14:16:20.3783987Z 2025-09-09T14:16:20.3783991Z 2025-09-09T14:16:20.3784066Z [[[0., 0., 0.], 2025-09-09T14:16:20.3784278Z [0., 0., 0.], 2025-09-09T14:16:20.3784491Z [0., 0., 0.]], 2025-09-09T14:16:20.3784634Z 2025-09-09T14:16:20.3784725Z [[0., 0., 0.], 2025-09-09T14:16:20.3784937Z [0., 0., 0.], 2025-09-09T14:16:20.3785166Z [0., 0., 0.]], 2025-09-09T14:16:20.3785314Z 2025-09-09T14:16:20.3785395Z [[0., 0., 0.], 2025-09-09T14:16:20.3785624Z [0., 0., 0.], 2025-09-09T14:16:20.3785845Z [0., 0., 0.]]], 2025-09-09T14:16:20.3786009Z 2025-09-09T14:16:20.3786013Z 2025-09-09T14:16:20.3786093Z [[[0., 0., 0.], 2025-09-09T14:16:20.3786322Z [0., 0., 0.], 2025-09-09T14:16:20.3786541Z [0., 0., 0.]], 2025-09-09T14:16:20.3786689Z 2025-09-09T14:16:29.0494810Z [[0., 0., 0.], 2025-09-09T14:16:29.0495170Z [0., 0., 0.], 2025-09-09T14:16:29.0495415Z [0., 0., 0.]], 2025-09-09T14:16:29.0495628Z 2025-09-09T14:16:29.0495719Z [[0., 0., 0.], 2025-09-09T14:16:29.0495957Z [0., 0., 0.], 2025-09-09T14:16:29.0496251Z [0., 0., 0.]]]]) 2025-09-09T14:16:29.0496620Z model pt2e: GraphModule( 2025-09-09T14:16:29.0496895Z (conv): Module() 2025-09-09T14:16:29.0497285Z (bn): Module() 2025-09-09T14:16:29.0497626Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:16:29.0499006Z 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 2025-09-09T14:16:29.0500660Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.247849225997925, max_val=2.7226178646087646) 2025-09-09T14:16:29.0501387Z ) 2025-09-09T14:16:29.0501686Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:16:29.0503048Z 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 2025-09-09T14:16:29.0504606Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.19124282896518707, max_val=0.19141820073127747) 2025-09-09T14:16:29.0505208Z ) 2025-09-09T14:16:29.0505660Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:16:29.0507268Z 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 2025-09-09T14:16:29.0508820Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.577202320098877, max_val=2.521923303604126) 2025-09-09T14:16:29.0509416Z ) 2025-09-09T14:16:29.0509731Z ) 2025-09-09T14:16:29.0509835Z 2025-09-09T14:16:29.0509840Z 2025-09-09T14:16:29.0509844Z 2025-09-09T14:16:29.0509952Z def forward(self, x): 2025-09-09T14:16:29.0510391Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:16:29.0510789Z conv_weight = self.conv.weight 2025-09-09T14:16:29.0511352Z bn_weight = self.bn.weight 2025-09-09T14:16:29.0511645Z bn_bias = self.bn.bias 2025-09-09T14:16:29.0512011Z bn_running_mean = self.bn.running_mean 2025-09-09T14:16:29.0512396Z bn_running_var = self.bn.running_var 2025-09-09T14:16:29.0512878Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T14:16:29.0513392Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:16:29.0514196Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T14:16:29.0514922Z add = torch.ops.aten.add.Tensor(bn_running_var, 1e-05) 2025-09-09T14:16:29.0515496Z sqrt = torch.ops.aten.sqrt.default(add); add = None 2025-09-09T14:16:29.0515950Z div = torch.ops.aten.div.Tensor(bn_weight, sqrt); sqrt = None 2025-09-09T14:16:29.0516593Z reshape = torch.ops.aten.reshape.default(div, [-1, 1, 1, 1]) 2025-09-09T14:16:29.0517294Z mul = torch.ops.aten.mul.Tensor(conv_weight, reshape); conv_weight = reshape = None 2025-09-09T14:16:29.0518072Z activation_post_process_1 = self.activation_post_process_1(mul); mul = None 2025-09-09T14:16:29.0519170Z 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 2025-09-09T14:16:29.0520485Z reshape_1 = torch.ops.aten.reshape.default(div, [1, -1, 1, 1]); div = None 2025-09-09T14:16:29.0521256Z div_1 = torch.ops.aten.div.Tensor(conv2d_1, reshape_1); conv2d_1 = reshape_1 = None 2025-09-09T14:16:29.0522433Z 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 2025-09-09T14:16:29.0523789Z activation_post_process_2 = self.activation_post_process_2(batch_norm_1); batch_norm_1 = None 2025-09-09T14:16:29.0524620Z return pytree.tree_unflatten((activation_post_process_2,), self._out_spec) 2025-09-09T14:16:29.0525056Z 2025-09-09T14:16:29.0525516Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:16:29.0525934Z model fx: GraphModule( 2025-09-09T14:16:29.0526425Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:16:29.0527810Z 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 2025-09-09T14:16:29.0529244Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.247849225997925, max_val=2.7226178646087646) 2025-09-09T14:16:29.0529961Z ) 2025-09-09T14:16:29.0530292Z (conv): ConvBn2d( 2025-09-09T14:16:29.0530578Z 3, 3, kernel_size=(3, 3), stride=(1, 1), bias=False 2025-09-09T14:16:29.0531210Z (bn): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T14:16:29.0531872Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:16:29.0533102Z 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 2025-09-09T14:16:29.0534639Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.19124282896518707, max_val=0.19141820073127747) 2025-09-09T14:16:29.0535243Z ) 2025-09-09T14:16:29.0535424Z ) 2025-09-09T14:16:29.0535732Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:16:29.0536958Z 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 2025-09-09T14:16:29.0538205Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.577202320098877, max_val=2.521923303604126) 2025-09-09T14:16:29.0538860Z ) 2025-09-09T14:16:29.0539035Z ) 2025-09-09T14:16:29.0539149Z 2025-09-09T14:16:29.0539153Z 2025-09-09T14:16:29.0539158Z 2025-09-09T14:16:29.0539250Z def forward(self, x): 2025-09-09T14:16:29.0539645Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:16:29.0540331Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T14:16:29.0541008Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T14:16:29.0541476Z return activation_post_process_1 2025-09-09T14:16:29.0541767Z 2025-09-09T14:16:29.0542067Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:16:29.0542484Z diff: tensor([[[[0., 0., 0.], 2025-09-09T14:16:29.0542789Z [0., 0., 0.], 2025-09-09T14:16:29.0543014Z [0., 0., 0.]], 2025-09-09T14:16:29.0543180Z 2025-09-09T14:16:29.0543263Z [[0., 0., 0.], 2025-09-09T14:16:29.0543487Z [0., 0., 0.], 2025-09-09T14:16:29.0543723Z [0., 0., 0.]], 2025-09-09T14:16:29.0543874Z 2025-09-09T14:16:29.0543972Z [[0., 0., 0.], 2025-09-09T14:16:29.0544194Z [0., 0., 0.], 2025-09-09T14:16:29.0544434Z [0., 0., 0.]]], 2025-09-09T14:16:29.0544588Z 2025-09-09T14:16:29.0544592Z 2025-09-09T14:16:29.0544681Z [[[0., 0., 0.], 2025-09-09T14:16:29.0544917Z [0., 0., 0.], 2025-09-09T14:16:29.0545141Z [0., 0., 0.]], 2025-09-09T14:16:29.0545304Z 2025-09-09T14:16:29.0545388Z [[0., 0., 0.], 2025-09-09T14:16:29.0545609Z [0., 0., 0.], 2025-09-09T14:16:29.0545846Z [0., 0., 0.]], 2025-09-09T14:16:29.0545997Z 2025-09-09T14:16:29.0546092Z [[0., 0., 0.], 2025-09-09T14:16:29.0546309Z [0., 0., 0.], 2025-09-09T14:16:29.0546547Z [0., 0., 0.]]], 2025-09-09T14:16:29.0546700Z 2025-09-09T14:16:29.0546704Z 2025-09-09T14:16:29.0546787Z [[[0., 0., 0.], 2025-09-09T14:16:29.0547029Z [0., 0., 0.], 2025-09-09T14:16:29.0547259Z [0., 0., 0.]], 2025-09-09T14:16:29.0547554Z 2025-09-09T14:16:29.0547637Z [[0., 0., 0.], 2025-09-09T14:16:29.0547851Z [0., 0., 0.], 2025-09-09T14:16:29.0548082Z [0., 0., 0.]], 2025-09-09T14:16:29.0548229Z 2025-09-09T14:16:29.0548321Z [[0., 0., 0.], 2025-09-09T14:16:29.0548540Z [0., 0., 0.], 2025-09-09T14:16:29.0548801Z [0., 0., 0.]]]], grad_fn=) 2025-09-09T14:16:29.0549137Z converted model pt2e: GraphModule( 2025-09-09T14:16:29.0549434Z (conv): Module() 2025-09-09T14:16:29.0549646Z (bn): Module() 2025-09-09T14:16:29.0549861Z ) 2025-09-09T14:16:29.0549964Z 2025-09-09T14:16:29.0549968Z 2025-09-09T14:16:29.0549972Z 2025-09-09T14:16:29.0550062Z def forward(self, x): 2025-09-09T14:16:29.0550378Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:16:29.0551208Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.019492028281092644, -13, -128, 127, torch.int8); x = None 2025-09-09T14:16:29.0552638Z 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 2025-09-09T14:16:29.0553651Z quantize_per_tensor = self._frozen_param0 2025-09-09T14:16:29.0554657Z 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 2025-09-09T14:16:29.0555556Z conv_weight_bias = self.conv.weight_bias 2025-09-09T14:16:29.0556507Z 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 2025-09-09T14:16:29.0557918Z 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 2025-09-09T14:16:29.0559768Z 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 2025-09-09T14:16:29.0561023Z return pytree.tree_unflatten((dequantize_per_tensor_default_2,), self._out_spec) 2025-09-09T14:16:29.0561481Z 2025-09-09T14:16:29.0561795Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:16:29.0562207Z onverted model fx: GraphModule( 2025-09-09T14:16:29.0562642Z (conv): QuantizedConv2d(Reference)(3, 3, kernel_size=(3, 3), stride=(1, 1)) 2025-09-09T14:16:29.0563064Z ) 2025-09-09T14:16:29.0563182Z 2025-09-09T14:16:29.0563186Z 2025-09-09T14:16:29.0563191Z 2025-09-09T14:16:29.0563283Z def forward(self, x): 2025-09-09T14:16:29.0563986Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.019492028281092644, -13, -128, 127, torch.int8); x = None 2025-09-09T14:16:29.0565408Z 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 2025-09-09T14:16:29.0566566Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T14:16:29.0567527Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.01999657228589058, 1, -128, 127, torch.int8); conv = None 2025-09-09T14:16:31.7294833Z 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 2025-09-09T14:16:31.7296604Z return dequantize_per_tensor_default_1 2025-09-09T14:16:31.7297101Z 2025-09-09T14:16:31.7297622Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:16:31.7298358Z diff: tensor([[[[0., 0., 0.], 2025-09-09T14:16:31.7298838Z [0., 0., 0.], 2025-09-09T14:16:31.7299231Z [0., 0., 0.]], 2025-09-09T14:16:31.7299511Z 2025-09-09T14:16:31.7299672Z [[0., 0., 0.], 2025-09-09T14:16:31.7300077Z [0., 0., 0.], 2025-09-09T14:16:31.7300482Z [0., 0., 0.]], 2025-09-09T14:16:31.7300756Z 2025-09-09T14:16:31.7300900Z [[0., 0., 0.], 2025-09-09T14:16:31.7301304Z [0., 0., 0.], 2025-09-09T14:16:31.7301687Z [0., 0., 0.]]], 2025-09-09T14:16:31.7301991Z 2025-09-09T14:16:31.7301998Z 2025-09-09T14:16:31.7302145Z [[[0., 0., 0.], 2025-09-09T14:16:31.7302564Z [0., 0., 0.], 2025-09-09T14:16:31.7302973Z [0., 0., 0.]], 2025-09-09T14:16:31.7303241Z 2025-09-09T14:16:31.7303384Z [[0., 0., 0.], 2025-09-09T14:16:31.7303774Z [0., 0., 0.], 2025-09-09T14:16:31.7304178Z [0., 0., 0.]], 2025-09-09T14:16:31.7304460Z 2025-09-09T14:16:31.7304607Z [[0., 0., 0.], 2025-09-09T14:16:31.7305024Z [0., 0., 0.], 2025-09-09T14:16:31.7305432Z [0., 0., 0.]]], 2025-09-09T14:16:31.7305713Z 2025-09-09T14:16:31.7305722Z 2025-09-09T14:16:31.7305860Z [[[0., 0., 0.], 2025-09-09T14:16:31.7306272Z [0., 0., 0.], 2025-09-09T14:16:31.7307061Z [0., 0., 0.]], 2025-09-09T14:16:31.7307361Z 2025-09-09T14:16:31.7307523Z [[0., 0., 0.], 2025-09-09T14:16:31.7307917Z [0., 0., 0.], 2025-09-09T14:16:31.7308337Z [0., 0., 0.]], 2025-09-09T14:16:31.7308621Z 2025-09-09T14:16:31.7308767Z [[0., 0., 0.], 2025-09-09T14:16:31.7309191Z [0., 0., 0.], 2025-09-09T14:16:31.7309601Z [0., 0., 0.]]]]) 2025-09-09T14:16:31.7310086Z model pt2e: GraphModule( 2025-09-09T14:16:31.7310553Z (conv1): Module() 2025-09-09T14:16:31.7310946Z (bn1): Module() 2025-09-09T14:16:31.7311350Z (conv2): Module() 2025-09-09T14:16:31.7311919Z (bn2): Module() 2025-09-09T14:16:31.7312504Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:16:31.7314511Z 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 2025-09-09T14:16:31.7317010Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.247849225997925, max_val=2.7226178646087646) 2025-09-09T14:16:31.7318074Z ) 2025-09-09T14:16:31.7318620Z (activation_post_process_3): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:16:31.7320710Z 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 2025-09-09T14:16:31.7323528Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.1469, -0.1921, -0.1853]), max_val=tensor([0.1307, 0.1779, 0.1810])) 2025-09-09T14:16:31.7324821Z ) 2025-09-09T14:16:31.7325325Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:16:31.7327296Z 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 2025-09-09T14:16:31.7330036Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.1897, -0.1787, -0.1913]), max_val=tensor([0.1870, 0.1478, 0.1740])) 2025-09-09T14:16:31.7331305Z ) 2025-09-09T14:16:31.7331797Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:16:31.7333668Z 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 2025-09-09T14:16:31.7335872Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.725703001022339, max_val=2.165140390396118) 2025-09-09T14:16:31.7336841Z ) 2025-09-09T14:16:31.7337324Z (activation_post_process_4): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:16:31.7339110Z 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 2025-09-09T14:16:31.7341460Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3412710428237915, max_val=1.3707175254821777) 2025-09-09T14:16:31.7342585Z ) 2025-09-09T14:16:31.7342917Z ) 2025-09-09T14:16:31.7343127Z 2025-09-09T14:16:31.7343135Z 2025-09-09T14:16:31.7343142Z 2025-09-09T14:16:31.7343301Z def forward(self, x): 2025-09-09T14:16:31.7343798Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:16:31.7344379Z conv1_weight = self.conv1.weight 2025-09-09T14:16:31.7344869Z bn1_weight = self.bn1.weight 2025-09-09T14:16:31.7345327Z bn1_bias = self.bn1.bias 2025-09-09T14:16:31.7345771Z conv2_weight = self.conv2.weight 2025-09-09T14:16:31.7346233Z conv2_bias = self.conv2.bias 2025-09-09T14:16:31.7346700Z bn2_weight = self.bn2.weight 2025-09-09T14:16:31.7347384Z bn2_bias = self.bn2.bias 2025-09-09T14:16:31.7347874Z bn1_running_mean = self.bn1.running_mean 2025-09-09T14:16:31.7348433Z bn1_running_var = self.bn1.running_var 2025-09-09T14:16:31.7349060Z bn1_num_batches_tracked = self.bn1.num_batches_tracked 2025-09-09T14:16:31.7349738Z bn2_running_mean = self.bn2.running_mean 2025-09-09T14:16:31.7350337Z bn2_running_var = self.bn2.running_var 2025-09-09T14:16:31.7351005Z bn2_num_batches_tracked = self.bn2.num_batches_tracked 2025-09-09T14:16:31.7351879Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:16:31.7353336Z add_ = torch.ops.aten.add_.Tensor(bn1_num_batches_tracked, 1); bn1_num_batches_tracked = add_ = None 2025-09-09T14:16:31.7354609Z add__1 = torch.ops.aten.add_.Tensor(bn2_num_batches_tracked, 1); bn2_num_batches_tracked = add__1 = None 2025-09-09T14:16:31.7355627Z add = torch.ops.aten.add.Tensor(bn2_running_var, 1e-05) 2025-09-09T14:16:31.7356349Z sqrt = torch.ops.aten.sqrt.default(add); add = None 2025-09-09T14:16:31.7357109Z div = torch.ops.aten.div.Tensor(bn2_weight, sqrt); sqrt = None 2025-09-09T14:16:31.7357943Z reshape = torch.ops.aten.reshape.default(div, [-1, 1, 1, 1]) 2025-09-09T14:16:31.7359166Z mul = torch.ops.aten.mul.Tensor(conv2_weight, reshape); conv2_weight = reshape = None 2025-09-09T14:16:31.7360309Z activation_post_process_3 = self.activation_post_process_3(mul); mul = None 2025-09-09T14:16:31.7361539Z zeros_like = torch.ops.aten.zeros_like.default(conv2_bias, dtype = torch.float32, pin_memory = False) 2025-09-09T14:16:31.7362649Z add_2 = torch.ops.aten.add.Tensor(bn1_running_var, 1e-05) 2025-09-09T14:16:31.7363418Z sqrt_1 = torch.ops.aten.sqrt.default(add_2); add_2 = None 2025-09-09T14:16:31.7364211Z div_2 = torch.ops.aten.div.Tensor(bn1_weight, sqrt_1); sqrt_1 = None 2025-09-09T14:16:31.7365085Z reshape_3 = torch.ops.aten.reshape.default(div_2, [-1, 1, 1, 1]) 2025-09-09T14:16:31.7366144Z mul_1 = torch.ops.aten.mul.Tensor(conv1_weight, reshape_3); conv1_weight = reshape_3 = None 2025-09-09T14:16:31.7367247Z activation_post_process_1 = self.activation_post_process_1(mul_1); mul_1 = None 2025-09-09T14:16:31.7368927Z 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 2025-09-09T14:16:31.7370554Z reshape_4 = torch.ops.aten.reshape.default(div_2, [1, -1, 1, 1]); div_2 = None 2025-09-09T14:16:31.7371656Z div_3 = torch.ops.aten.div.Tensor(conv2d_3, reshape_4); conv2d_3 = reshape_4 = None 2025-09-09T14:16:31.7373392Z 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 2025-09-09T14:16:31.7375278Z activation_post_process_2 = self.activation_post_process_2(batch_norm_3); batch_norm_3 = None 2025-09-09T14:16:31.7377258Z 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 2025-09-09T14:16:31.7378977Z reshape_1 = torch.ops.aten.reshape.default(div, [1, -1, 1, 1]); div = None 2025-09-09T14:16:31.7379987Z div_1 = torch.ops.aten.div.Tensor(conv2d_2, reshape_1); conv2d_2 = reshape_1 = None 2025-09-09T14:16:31.7381163Z reshape_2 = torch.ops.aten.reshape.default(conv2_bias, [1, -1, 1, 1]); conv2_bias = None 2025-09-09T14:16:31.7382276Z add_1 = torch.ops.aten.add.Tensor(div_1, reshape_2); div_1 = reshape_2 = None 2025-09-09T14:16:31.7384077Z 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 2025-09-09T14:16:31.7386063Z activation_post_process_4 = self.activation_post_process_4(batch_norm_2); batch_norm_2 = None 2025-09-09T14:16:31.7387550Z return pytree.tree_unflatten((activation_post_process_4,), self._out_spec) 2025-09-09T14:16:31.7388343Z 2025-09-09T14:16:31.7388834Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:16:31.7389556Z model fx: GraphModule( 2025-09-09T14:16:31.7390165Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:16:31.7392151Z 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 2025-09-09T14:16:31.7394617Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.247849225997925, max_val=2.7226178646087646) 2025-09-09T14:16:31.7395626Z ) 2025-09-09T14:16:31.7395969Z (conv1): ConvBn2d( 2025-09-09T14:16:31.7396411Z 3, 3, kernel_size=(3, 3), stride=(1, 1), bias=False 2025-09-09T14:16:31.7397191Z (bn): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T14:16:31.7398003Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:16:31.7399960Z 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 2025-09-09T14:16:31.7402613Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.1897, -0.1787, -0.1913]), max_val=tensor([0.1870, 0.1478, 0.1740])) 2025-09-09T14:16:31.7403901Z ) 2025-09-09T14:16:31.7404222Z ) 2025-09-09T14:16:31.7404768Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:16:40.5938890Z 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 2025-09-09T14:16:40.5941303Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.725703001022339, max_val=2.165140390396118) 2025-09-09T14:16:40.5942289Z ) 2025-09-09T14:16:40.5942622Z (conv2): ConvBn2d( 2025-09-09T14:16:40.5943048Z 3, 3, kernel_size=(3, 3), stride=(1, 1) 2025-09-09T14:16:40.5943833Z (bn): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T14:16:40.5944772Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:16:40.5946759Z 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 2025-09-09T14:16:40.5949418Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.1469, -0.1921, -0.1853]), max_val=tensor([0.1307, 0.1779, 0.1810])) 2025-09-09T14:16:40.5950779Z ) 2025-09-09T14:16:40.5951125Z ) 2025-09-09T14:16:40.5951671Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:16:40.5953523Z 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 2025-09-09T14:16:40.5955802Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.3412710428237915, max_val=1.3707175254821777) 2025-09-09T14:16:40.5956866Z ) 2025-09-09T14:16:40.5957206Z ) 2025-09-09T14:16:40.5957401Z 2025-09-09T14:16:40.5957408Z 2025-09-09T14:16:40.5957414Z 2025-09-09T14:16:40.5957589Z def forward(self, x): 2025-09-09T14:16:40.5958542Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:16:40.5959540Z conv1 = self.conv1(activation_post_process_0); activation_post_process_0 = None 2025-09-09T14:16:40.5961016Z activation_post_process_1 = self.activation_post_process_1(conv1); conv1 = None 2025-09-09T14:16:40.5962136Z conv2 = self.conv2(activation_post_process_1); activation_post_process_1 = None 2025-09-09T14:16:40.5963386Z activation_post_process_2 = self.activation_post_process_2(conv2); conv2 = None 2025-09-09T14:16:40.5964192Z return activation_post_process_2 2025-09-09T14:16:40.5964658Z 2025-09-09T14:16:40.5965180Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:16:40.5965934Z diff: tensor([[[[0.]], 2025-09-09T14:16:40.5966220Z 2025-09-09T14:16:40.5966353Z [[0.]], 2025-09-09T14:16:40.5966928Z 2025-09-09T14:16:40.5967051Z [[0.]]], 2025-09-09T14:16:40.5967253Z 2025-09-09T14:16:40.5967260Z 2025-09-09T14:16:40.5967384Z [[[0.]], 2025-09-09T14:16:40.5967606Z 2025-09-09T14:16:40.5967727Z [[0.]], 2025-09-09T14:16:40.5967926Z 2025-09-09T14:16:40.5968065Z [[0.]]], 2025-09-09T14:16:40.5968272Z 2025-09-09T14:16:40.5968277Z 2025-09-09T14:16:40.5968410Z [[[0.]], 2025-09-09T14:16:40.5968614Z 2025-09-09T14:16:40.5968726Z [[0.]], 2025-09-09T14:16:40.5968903Z 2025-09-09T14:16:40.5969060Z [[0.]]]], grad_fn=) 2025-09-09T14:16:40.5969595Z converted model pt2e: GraphModule( 2025-09-09T14:16:40.5970022Z (conv1): Module() 2025-09-09T14:16:40.5970379Z (bn1): Module() 2025-09-09T14:16:40.5970752Z (conv2): Module() 2025-09-09T14:16:40.5971118Z (bn2): Module() 2025-09-09T14:16:40.5971496Z ) 2025-09-09T14:16:40.5971677Z 2025-09-09T14:16:40.5971684Z 2025-09-09T14:16:40.5971690Z 2025-09-09T14:16:40.5971843Z def forward(self, x): 2025-09-09T14:16:40.5972424Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:16:40.5973102Z conv2_bias = self.conv2.bias 2025-09-09T14:16:40.5974488Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.019492028281092644, -13, -128, 127, torch.int8); x = None 2025-09-09T14:16:40.5977267Z 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 2025-09-09T14:16:40.5979139Z _scale_0 = self._scale_0 2025-09-09T14:16:40.5979666Z _zero_point_0 = self._zero_point_0 2025-09-09T14:16:40.5980221Z _scale_1 = self._scale_1 2025-09-09T14:16:40.5980730Z _zero_point_1 = self._zero_point_1 2025-09-09T14:16:40.5981345Z quantize_per_channel_1 = self._frozen_param0 2025-09-09T14:16:40.5983338Z 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 2025-09-09T14:16:40.5985326Z conv1_weight_bias = self.conv1.weight_bias 2025-09-09T14:16:40.5987211Z 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 2025-09-09T14:16:40.5989926Z 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 2025-09-09T14:16:40.5992608Z 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 2025-09-09T14:16:40.5994524Z quantize_per_channel = self._frozen_param1 2025-09-09T14:16:40.5996413Z 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 2025-09-09T14:16:40.5999269Z 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 2025-09-09T14:16:40.6001920Z 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 2025-09-09T14:16:40.6004545Z 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 2025-09-09T14:16:40.6006638Z return pytree.tree_unflatten((dequantize_per_tensor_default_2,), self._out_spec) 2025-09-09T14:16:40.6007632Z 2025-09-09T14:16:40.6008142Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:16:40.6008800Z onverted model fx: GraphModule( 2025-09-09T14:16:40.6009514Z (conv1): QuantizedConv2d(Reference)(3, 3, kernel_size=(3, 3), stride=(1, 1)) 2025-09-09T14:16:40.6010476Z (conv2): QuantizedConv2d(Reference)(3, 3, kernel_size=(3, 3), stride=(1, 1)) 2025-09-09T14:16:40.6011233Z ) 2025-09-09T14:16:40.6011408Z 2025-09-09T14:16:40.6011414Z 2025-09-09T14:16:40.6011420Z 2025-09-09T14:16:40.6011601Z def forward(self, x): 2025-09-09T14:16:40.6012884Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.019492028281092644, -13, -128, 127, torch.int8); x = None 2025-09-09T14:16:40.6015440Z 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 2025-09-09T14:16:40.6017355Z conv1 = self.conv1(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T14:16:40.6019073Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv1, 0.019179778173565865, 14, -128, 127, torch.int8); conv1 = None 2025-09-09T14:16:40.6021764Z 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 2025-09-09T14:16:40.6023987Z conv2 = self.conv2(dequantize_per_tensor_default_1); dequantize_per_tensor_default_1 = None 2025-09-09T14:16:40.6025733Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv2, 0.010635248385369778, -2, -128, 127, torch.int8); conv2 = None 2025-09-09T14:16:40.6028396Z 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 2025-09-09T14:16:40.6030105Z return dequantize_per_tensor_default_2 2025-09-09T14:16:40.6030643Z 2025-09-09T14:16:40.6031137Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:16:40.6031804Z diff: tensor([[[[0.]], 2025-09-09T14:16:40.6032064Z 2025-09-09T14:16:40.6032198Z [[0.]], 2025-09-09T14:16:40.6032455Z 2025-09-09T14:16:40.6032597Z [[0.]]], 2025-09-09T14:16:40.6032829Z 2025-09-09T14:16:40.6032837Z 2025-09-09T14:16:40.6032989Z [[[0.]], 2025-09-09T14:16:40.6033202Z 2025-09-09T14:16:40.6033332Z [[0.]], 2025-09-09T14:16:40.6033544Z 2025-09-09T14:16:40.6033677Z [[0.]]], 2025-09-09T14:16:40.6033899Z 2025-09-09T14:16:40.6033906Z 2025-09-09T14:16:40.6034047Z [[[0.]], 2025-09-09T14:16:40.6034285Z 2025-09-09T14:16:40.6034416Z [[0.]], 2025-09-09T14:16:40.6034629Z 2025-09-09T14:16:40.6034765Z [[0.]]]]) 2025-09-09T14:16:40.6035146Z model pt2e: GraphModule( 2025-09-09T14:16:40.6035567Z (conv1): Module() 2025-09-09T14:16:40.6035901Z (bn1): Module() 2025-09-09T14:16:40.6036247Z (conv2): Module() 2025-09-09T14:16:40.6036592Z (bn2): Module() 2025-09-09T14:16:40.6037135Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:16:40.6039123Z 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 2025-09-09T14:16:40.6041410Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.247849225997925, max_val=2.7226178646087646) 2025-09-09T14:16:40.6042399Z ) 2025-09-09T14:16:40.6042889Z (activation_post_process_3): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:16:40.6044772Z 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 2025-09-09T14:16:40.6047155Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.19212442636489868, max_val=0.18097376823425293) 2025-09-09T14:16:40.6048175Z ) 2025-09-09T14:16:40.6048718Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:16:40.6050546Z 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 2025-09-09T14:16:40.6052721Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.19127479195594788, max_val=0.1870359182357788) 2025-09-09T14:16:40.6053725Z ) 2025-09-09T14:16:40.6054267Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:16:48.2241303Z 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 2025-09-09T14:16:48.2243531Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.725703001022339, max_val=2.165140390396118) 2025-09-09T14:16:48.2244410Z ) 2025-09-09T14:16:48.2244885Z (activation_post_process_4): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:16:48.2246492Z 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 2025-09-09T14:16:48.2248482Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.349876046180725, max_val=1.373764157295227) 2025-09-09T14:16:48.2249400Z ) 2025-09-09T14:16:48.2249684Z ) 2025-09-09T14:16:48.2249863Z 2025-09-09T14:16:48.2249870Z 2025-09-09T14:16:48.2249876Z 2025-09-09T14:16:48.2250013Z def forward(self, x): 2025-09-09T14:16:48.2250537Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:16:48.2251163Z conv1_weight = self.conv1.weight 2025-09-09T14:16:48.2251651Z bn1_weight = self.bn1.weight 2025-09-09T14:16:48.2252097Z bn1_bias = self.bn1.bias 2025-09-09T14:16:48.2252524Z conv2_weight = self.conv2.weight 2025-09-09T14:16:48.2253017Z conv2_bias = self.conv2.bias 2025-09-09T14:16:48.2253528Z bn2_weight = self.bn2.weight 2025-09-09T14:16:48.2253996Z bn2_bias = self.bn2.bias 2025-09-09T14:16:48.2254467Z bn1_running_mean = self.bn1.running_mean 2025-09-09T14:16:48.2255018Z bn1_running_var = self.bn1.running_var 2025-09-09T14:16:48.2255681Z bn1_num_batches_tracked = self.bn1.num_batches_tracked 2025-09-09T14:16:48.2256377Z bn2_running_mean = self.bn2.running_mean 2025-09-09T14:16:48.2256883Z bn2_running_var = self.bn2.running_var 2025-09-09T14:16:48.2257465Z bn2_num_batches_tracked = self.bn2.num_batches_tracked 2025-09-09T14:16:48.2258564Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:16:48.2259754Z add_ = torch.ops.aten.add_.Tensor(bn1_num_batches_tracked, 1); bn1_num_batches_tracked = add_ = None 2025-09-09T14:16:48.2260978Z add__1 = torch.ops.aten.add_.Tensor(bn2_num_batches_tracked, 1); bn2_num_batches_tracked = add__1 = None 2025-09-09T14:16:48.2262451Z add = torch.ops.aten.add.Tensor(bn2_running_var, 1e-05) 2025-09-09T14:16:48.2263196Z sqrt = torch.ops.aten.sqrt.default(add); add = None 2025-09-09T14:16:48.2263907Z div = torch.ops.aten.div.Tensor(bn2_weight, sqrt); sqrt = None 2025-09-09T14:16:48.2264726Z reshape = torch.ops.aten.reshape.default(div, [-1, 1, 1, 1]) 2025-09-09T14:16:48.2265723Z mul = torch.ops.aten.mul.Tensor(conv2_weight, reshape); conv2_weight = reshape = None 2025-09-09T14:16:48.2266813Z activation_post_process_3 = self.activation_post_process_3(mul); mul = None 2025-09-09T14:16:48.2268032Z zeros_like = torch.ops.aten.zeros_like.default(conv2_bias, dtype = torch.float32, pin_memory = False) 2025-09-09T14:16:48.2269375Z add_2 = torch.ops.aten.add.Tensor(bn1_running_var, 1e-05) 2025-09-09T14:16:48.2270093Z sqrt_1 = torch.ops.aten.sqrt.default(add_2); add_2 = None 2025-09-09T14:16:48.2270936Z div_2 = torch.ops.aten.div.Tensor(bn1_weight, sqrt_1); sqrt_1 = None 2025-09-09T14:16:48.2271813Z reshape_3 = torch.ops.aten.reshape.default(div_2, [-1, 1, 1, 1]) 2025-09-09T14:16:48.2272859Z mul_1 = torch.ops.aten.mul.Tensor(conv1_weight, reshape_3); conv1_weight = reshape_3 = None 2025-09-09T14:16:48.2274037Z activation_post_process_1 = self.activation_post_process_1(mul_1); mul_1 = None 2025-09-09T14:16:48.2275724Z 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 2025-09-09T14:16:48.2277317Z reshape_4 = torch.ops.aten.reshape.default(div_2, [1, -1, 1, 1]); div_2 = None 2025-09-09T14:16:48.2278360Z div_3 = torch.ops.aten.div.Tensor(conv2d_3, reshape_4); conv2d_3 = reshape_4 = None 2025-09-09T14:16:48.2280328Z 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 2025-09-09T14:16:48.2282239Z activation_post_process_2 = self.activation_post_process_2(batch_norm_3); batch_norm_3 = None 2025-09-09T14:16:48.2284039Z 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 2025-09-09T14:16:48.2285655Z reshape_1 = torch.ops.aten.reshape.default(div, [1, -1, 1, 1]); div = None 2025-09-09T14:16:48.2286752Z div_1 = torch.ops.aten.div.Tensor(conv2d_2, reshape_1); conv2d_2 = reshape_1 = None 2025-09-09T14:16:48.2287988Z reshape_2 = torch.ops.aten.reshape.default(conv2_bias, [1, -1, 1, 1]); conv2_bias = None 2025-09-09T14:16:48.2289100Z add_1 = torch.ops.aten.add.Tensor(div_1, reshape_2); div_1 = reshape_2 = None 2025-09-09T14:16:48.2290724Z 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 2025-09-09T14:16:48.2292479Z activation_post_process_4 = self.activation_post_process_4(batch_norm_2); batch_norm_2 = None 2025-09-09T14:16:48.2293588Z return pytree.tree_unflatten((activation_post_process_4,), self._out_spec) 2025-09-09T14:16:48.2294289Z 2025-09-09T14:16:48.2294804Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:16:48.2295473Z model fx: GraphModule( 2025-09-09T14:16:48.2296070Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:16:48.2297977Z 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 2025-09-09T14:16:48.2300294Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.247849225997925, max_val=2.7226178646087646) 2025-09-09T14:16:48.2301209Z ) 2025-09-09T14:16:48.2301554Z (conv1): ConvBn2d( 2025-09-09T14:16:48.2302294Z 3, 3, kernel_size=(3, 3), stride=(1, 1), bias=False 2025-09-09T14:16:48.2303097Z (bn): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T14:16:48.2304029Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:16:48.2305746Z 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 2025-09-09T14:16:48.2307849Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.19127479195594788, max_val=0.1870359182357788) 2025-09-09T14:16:48.2309009Z ) 2025-09-09T14:16:48.2309317Z ) 2025-09-09T14:16:48.2309785Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:16:48.2311831Z 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 2025-09-09T14:16:48.2314001Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.725703001022339, max_val=2.165140390396118) 2025-09-09T14:16:48.2314938Z ) 2025-09-09T14:16:48.2315253Z (conv2): ConvBn2d( 2025-09-09T14:16:48.2315683Z 3, 3, kernel_size=(3, 3), stride=(1, 1) 2025-09-09T14:16:48.2316491Z (bn): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T14:16:48.2317279Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:16:48.2319015Z 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 2025-09-09T14:16:48.2321476Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.19212442636489868, max_val=0.18097376823425293) 2025-09-09T14:16:48.2322542Z ) 2025-09-09T14:16:48.2322834Z ) 2025-09-09T14:16:48.2323309Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:16:48.2325051Z 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 2025-09-09T14:16:48.2327156Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.349876046180725, max_val=1.373764157295227) 2025-09-09T14:16:48.2328050Z ) 2025-09-09T14:16:48.2328349Z ) 2025-09-09T14:16:48.2328509Z 2025-09-09T14:16:48.2328518Z 2025-09-09T14:16:48.2328538Z 2025-09-09T14:16:48.2328692Z def forward(self, x): 2025-09-09T14:16:48.2329339Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:16:48.2330302Z conv1 = self.conv1(activation_post_process_0); activation_post_process_0 = None 2025-09-09T14:16:48.2331370Z activation_post_process_1 = self.activation_post_process_1(conv1); conv1 = None 2025-09-09T14:16:48.2332341Z conv2 = self.conv2(activation_post_process_1); activation_post_process_1 = None 2025-09-09T14:16:48.2333416Z activation_post_process_2 = self.activation_post_process_2(conv2); conv2 = None 2025-09-09T14:16:48.2334248Z return activation_post_process_2 2025-09-09T14:16:48.2334735Z 2025-09-09T14:16:48.2335221Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:16:48.2335880Z diff: tensor([[[[0.]], 2025-09-09T14:16:48.2336143Z 2025-09-09T14:16:48.2336270Z [[0.]], 2025-09-09T14:16:48.2336484Z 2025-09-09T14:16:48.2336633Z [[0.]]], 2025-09-09T14:16:48.2336869Z 2025-09-09T14:16:48.2336877Z 2025-09-09T14:16:48.2336997Z [[[0.]], 2025-09-09T14:16:48.2337202Z 2025-09-09T14:16:48.2337338Z [[0.]], 2025-09-09T14:16:48.2337558Z 2025-09-09T14:16:48.2337688Z [[0.]]], 2025-09-09T14:16:48.2337880Z 2025-09-09T14:16:48.2337900Z 2025-09-09T14:16:48.2338019Z [[[0.]], 2025-09-09T14:16:48.2339271Z 2025-09-09T14:16:48.2339475Z [[0.]], 2025-09-09T14:16:48.2339718Z 2025-09-09T14:16:48.2339897Z [[0.]]]], grad_fn=) 2025-09-09T14:16:48.2340455Z converted model pt2e: GraphModule( 2025-09-09T14:16:48.2340910Z (conv1): Module() 2025-09-09T14:16:48.2341255Z (bn1): Module() 2025-09-09T14:16:48.2341573Z (conv2): Module() 2025-09-09T14:16:48.2341961Z (bn2): Module() 2025-09-09T14:16:48.2342287Z ) 2025-09-09T14:16:48.2342456Z 2025-09-09T14:16:48.2342481Z 2025-09-09T14:16:48.2342488Z 2025-09-09T14:16:48.2342641Z def forward(self, x): 2025-09-09T14:16:48.2343125Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:16:48.2343887Z conv2_bias = self.conv2.bias 2025-09-09T14:16:48.2345105Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.019492028281092644, -13, -128, 127, torch.int8); x = None 2025-09-09T14:16:48.2347493Z 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 2025-09-09T14:16:48.2349286Z quantize_per_tensor_1 = self._frozen_param0 2025-09-09T14:17:02.1394066Z 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 2025-09-09T14:17:02.1395757Z conv1_weight_bias = self.conv1.weight_bias 2025-09-09T14:17:02.1397416Z 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 2025-09-09T14:17:02.1400359Z 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 2025-09-09T14:17:02.1403259Z 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 2025-09-09T14:17:02.1405271Z quantize_per_tensor = self._frozen_param1 2025-09-09T14:17:02.1407025Z 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 2025-09-09T14:17:02.1409811Z 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 2025-09-09T14:17:02.1412385Z 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 2025-09-09T14:17:02.1415269Z 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 2025-09-09T14:17:02.1417220Z return pytree.tree_unflatten((dequantize_per_tensor_default_4,), self._out_spec) 2025-09-09T14:17:02.1418014Z 2025-09-09T14:17:02.1418563Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:17:02.1419236Z onverted model fx: GraphModule( 2025-09-09T14:17:02.1420013Z (conv1): QuantizedConv2d(Reference)(3, 3, kernel_size=(3, 3), stride=(1, 1)) 2025-09-09T14:17:02.1421113Z (conv2): QuantizedConv2d(Reference)(3, 3, kernel_size=(3, 3), stride=(1, 1)) 2025-09-09T14:17:02.1421871Z ) 2025-09-09T14:17:02.1422045Z 2025-09-09T14:17:02.1422051Z 2025-09-09T14:17:02.1422058Z 2025-09-09T14:17:02.1422215Z def forward(self, x): 2025-09-09T14:17:02.1423385Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.019492028281092644, -13, -128, 127, torch.int8); x = None 2025-09-09T14:17:02.1426316Z 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 2025-09-09T14:17:02.1428374Z conv1 = self.conv1(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T14:17:02.1430191Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv1, 0.019179778173565865, 14, -128, 127, torch.int8); conv1 = None 2025-09-09T14:17:02.1432659Z 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 2025-09-09T14:17:02.1435233Z conv2 = self.conv2(dequantize_per_tensor_default_1); dequantize_per_tensor_default_1 = None 2025-09-09T14:17:02.1437064Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv2, 0.010680941864848137, -2, -128, 127, torch.int8); conv2 = None 2025-09-09T14:17:02.1439422Z 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 2025-09-09T14:17:02.1441248Z return dequantize_per_tensor_default_2 2025-09-09T14:17:02.1441749Z 2025-09-09T14:17:02.1442253Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:17:02.1442948Z diff: tensor([[[[0.]], 2025-09-09T14:17:02.1443196Z 2025-09-09T14:17:02.1443354Z [[0.]], 2025-09-09T14:17:02.1443586Z 2025-09-09T14:17:02.1443727Z [[0.]]], 2025-09-09T14:17:02.1443971Z 2025-09-09T14:17:02.1443980Z 2025-09-09T14:17:02.1444139Z [[[0.]], 2025-09-09T14:17:02.1444363Z 2025-09-09T14:17:02.1444508Z [[0.]], 2025-09-09T14:17:02.1444742Z 2025-09-09T14:17:02.1444903Z [[0.]]], 2025-09-09T14:17:02.1445125Z 2025-09-09T14:17:02.1445138Z 2025-09-09T14:17:02.1445252Z [[[0.]], 2025-09-09T14:17:02.1445486Z 2025-09-09T14:17:02.1445621Z [[0.]], 2025-09-09T14:17:02.1445866Z 2025-09-09T14:17:02.1446018Z [[0.]]]]) 2025-09-09T14:17:02.1446664Z PASSED 2025-09-09T14:17:02.1448192Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_conv_bn_per_channel_weight_bias PASSED 2025-09-09T14:17:02.1450062Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_conv_bn_relu_fusion model pt2e: GraphModule( 2025-09-09T14:17:02.1451239Z (conv): Module() 2025-09-09T14:17:02.1451595Z (bn): Module() 2025-09-09T14:17:02.1452157Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:17:02.1454007Z 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 2025-09-09T14:17:02.1456241Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.526270866394043, max_val=2.143237352371216) 2025-09-09T14:17:02.1457306Z ) 2025-09-09T14:17:02.1457798Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:17:02.1459993Z 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 2025-09-09T14:17:02.1462591Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.1761, -0.1923, -0.1707]), max_val=tensor([0.1830, 0.1717, 0.1892])) 2025-09-09T14:17:02.1463841Z ) 2025-09-09T14:17:02.1464362Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:17:02.1466450Z 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 2025-09-09T14:17:02.1468600Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=1.6655889749526978) 2025-09-09T14:17:02.1469508Z ) 2025-09-09T14:17:02.1469769Z ) 2025-09-09T14:17:02.1482990Z 2025-09-09T14:17:02.1483000Z 2025-09-09T14:17:02.1483007Z 2025-09-09T14:17:02.1483221Z def forward(self, x): 2025-09-09T14:17:02.1483809Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:17:02.1484472Z conv_weight = self.conv.weight 2025-09-09T14:17:02.1485265Z conv_bias = self.conv.bias 2025-09-09T14:17:02.1485758Z bn_weight = self.bn.weight 2025-09-09T14:17:02.1486224Z bn_bias = self.bn.bias 2025-09-09T14:17:02.1486697Z bn_running_mean = self.bn.running_mean 2025-09-09T14:17:02.1487273Z bn_running_var = self.bn.running_var 2025-09-09T14:17:02.1487896Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T14:17:02.1488758Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:17:02.1489942Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T14:17:02.1491034Z add = torch.ops.aten.add.Tensor(bn_running_var, 1e-05) 2025-09-09T14:17:02.1491853Z sqrt = torch.ops.aten.sqrt.default(add); add = None 2025-09-09T14:17:02.1492675Z div = torch.ops.aten.div.Tensor(bn_weight, sqrt); sqrt = None 2025-09-09T14:17:02.1493578Z reshape = torch.ops.aten.reshape.default(div, [-1, 1, 1, 1]) 2025-09-09T14:17:02.1494599Z mul = torch.ops.aten.mul.Tensor(conv_weight, reshape); conv_weight = reshape = None 2025-09-09T14:17:02.1495702Z activation_post_process_1 = self.activation_post_process_1(mul); mul = None 2025-09-09T14:17:02.1496986Z zeros_like = torch.ops.aten.zeros_like.default(conv_bias, dtype = torch.float32, pin_memory = False) 2025-09-09T14:17:02.1498976Z 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 2025-09-09T14:17:02.1500626Z reshape_1 = torch.ops.aten.reshape.default(div, [1, -1, 1, 1]); div = None 2025-09-09T14:17:02.1501651Z div_1 = torch.ops.aten.div.Tensor(conv2d_1, reshape_1); conv2d_1 = reshape_1 = None 2025-09-09T14:17:02.1502748Z reshape_2 = torch.ops.aten.reshape.default(conv_bias, [1, -1, 1, 1]); conv_bias = None 2025-09-09T14:17:02.1503800Z add_1 = torch.ops.aten.add.Tensor(div_1, reshape_2); div_1 = reshape_2 = None 2025-09-09T14:17:02.1505539Z 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 2025-09-09T14:17:02.1507221Z relu = torch.ops.aten.relu.default(batch_norm_1); batch_norm_1 = None 2025-09-09T14:17:02.1508302Z activation_post_process_2 = self.activation_post_process_2(relu); relu = None 2025-09-09T14:17:02.1509372Z return pytree.tree_unflatten((activation_post_process_2,), self._out_spec) 2025-09-09T14:17:02.1510130Z 2025-09-09T14:17:02.1510660Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:17:02.1511401Z model fx: GraphModule( 2025-09-09T14:17:02.1512027Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:17:02.1514025Z 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 2025-09-09T14:17:02.1516307Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.526270866394043, max_val=2.143237352371216) 2025-09-09T14:17:02.1517259Z ) 2025-09-09T14:17:02.1517599Z (conv): ConvBnReLU2d( 2025-09-09T14:17:02.1518042Z 3, 3, kernel_size=(3, 3), stride=(1, 1) 2025-09-09T14:17:02.1519047Z (bn): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T14:17:02.1520105Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:17:12.3803481Z 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 2025-09-09T14:17:12.3806126Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.1761, -0.1923, -0.1707]), max_val=tensor([0.1830, 0.1717, 0.1892])) 2025-09-09T14:17:12.3807968Z ) 2025-09-09T14:17:12.3808315Z ) 2025-09-09T14:17:12.3808865Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:17:12.3810973Z 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 2025-09-09T14:17:12.3813391Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=1.6655889749526978) 2025-09-09T14:17:12.3814434Z ) 2025-09-09T14:17:12.3814784Z ) 2025-09-09T14:17:12.3814999Z 2025-09-09T14:17:12.3815007Z 2025-09-09T14:17:12.3815015Z 2025-09-09T14:17:12.3815191Z def forward(self, x): 2025-09-09T14:17:12.3815913Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:17:12.3817020Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T14:17:12.3818213Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T14:17:12.3819059Z return activation_post_process_1 2025-09-09T14:17:12.3819574Z 2025-09-09T14:17:12.3820134Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:17:12.3820854Z diff: tensor([[[[0., 0., 0.], 2025-09-09T14:17:12.3821329Z [0., 0., 0.], 2025-09-09T14:17:12.3821773Z [0., 0., 0.]], 2025-09-09T14:17:12.3822064Z 2025-09-09T14:17:12.3822226Z [[0., 0., 0.], 2025-09-09T14:17:12.3822645Z [0., 0., 0.], 2025-09-09T14:17:12.3823050Z [0., 0., 0.]], 2025-09-09T14:17:12.3823327Z 2025-09-09T14:17:12.3823472Z [[0., 0., 0.], 2025-09-09T14:17:12.3823884Z [0., 0., 0.], 2025-09-09T14:17:12.3824331Z [0., 0., 0.]]]], grad_fn=) 2025-09-09T14:17:12.3824849Z converted model pt2e: GraphModule( 2025-09-09T14:17:12.3825331Z (conv): Module() 2025-09-09T14:17:12.3825684Z (bn): Module() 2025-09-09T14:17:12.3826068Z ) 2025-09-09T14:17:12.3826245Z 2025-09-09T14:17:12.3826252Z 2025-09-09T14:17:12.3826259Z 2025-09-09T14:17:12.3826406Z def forward(self, x): 2025-09-09T14:17:12.3826943Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:17:12.3827571Z conv_bias = self.conv.bias 2025-09-09T14:17:12.3828883Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.018311796709895134, 10, -128, 127, torch.int8); x = None 2025-09-09T14:17:12.3831478Z 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 2025-09-09T14:17:12.3833126Z _scale_0 = self._scale_0 2025-09-09T14:17:12.3833595Z _zero_point_0 = self._zero_point_0 2025-09-09T14:17:12.3834195Z quantize_per_channel = self._frozen_param0 2025-09-09T14:17:12.3836011Z 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 2025-09-09T14:17:12.3838626Z 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 2025-09-09T14:17:12.3840615Z relu = torch.ops.aten.relu.default(conv2d_2); conv2d_2 = None 2025-09-09T14:17:12.3842310Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(relu, 0.006531721446663141, -128, -128, 127, torch.int8); relu = None 2025-09-09T14:17:12.3845189Z 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 2025-09-09T14:17:12.3847085Z return pytree.tree_unflatten((dequantize_per_tensor_default_1,), self._out_spec) 2025-09-09T14:17:12.3848016Z 2025-09-09T14:17:12.3848469Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:17:12.3849151Z onverted model fx: GraphModule( 2025-09-09T14:17:12.3849619Z (conv): ConvReLU2d( 2025-09-09T14:17:12.3850234Z (0): QuantizedConv2d(Reference)(3, 3, kernel_size=(3, 3), stride=(1, 1)) 2025-09-09T14:17:12.3850936Z (1): ReLU() 2025-09-09T14:17:12.3851305Z ) 2025-09-09T14:17:12.3851599Z ) 2025-09-09T14:17:12.3851770Z 2025-09-09T14:17:12.3851777Z 2025-09-09T14:17:12.3851785Z 2025-09-09T14:17:12.3851928Z def forward(self, x): 2025-09-09T14:17:12.3853216Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.018311796709895134, 10, -128, 127, torch.int8); x = None 2025-09-09T14:17:12.3855873Z 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 2025-09-09T14:17:12.3857940Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T14:17:12.3859893Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.006531721446663141, -128, -128, 127, torch.int8); conv = None 2025-09-09T14:17:12.3862454Z 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 2025-09-09T14:17:12.3864275Z return dequantize_per_tensor_default_1 2025-09-09T14:17:12.3864809Z 2025-09-09T14:17:12.3865344Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:17:12.3866106Z diff: tensor([[[[0., 0., 0.], 2025-09-09T14:17:12.3866522Z [0., 0., 0.], 2025-09-09T14:17:12.3866908Z [0., 0., 0.]], 2025-09-09T14:17:12.3867170Z 2025-09-09T14:17:12.3867315Z [[0., 0., 0.], 2025-09-09T14:17:12.3867676Z [0., 0., 0.], 2025-09-09T14:17:12.3868029Z [0., 0., 0.]], 2025-09-09T14:17:12.3868298Z 2025-09-09T14:17:12.3868428Z [[0., 0., 0.], 2025-09-09T14:17:12.3868799Z [0., 0., 0.], 2025-09-09T14:17:12.3869171Z [0., 0., 0.]]]]) 2025-09-09T14:17:12.3869574Z model pt2e: GraphModule( 2025-09-09T14:17:12.3870017Z (conv): Module() 2025-09-09T14:17:12.3870390Z (bn): Module() 2025-09-09T14:17:12.3870939Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:17:12.3872748Z 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 2025-09-09T14:17:12.3874919Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.526270866394043, max_val=2.143237352371216) 2025-09-09T14:17:12.3875910Z ) 2025-09-09T14:17:12.3876453Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:17:12.3878239Z 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 2025-09-09T14:17:12.3880827Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.1923346221446991, max_val=0.18921314179897308) 2025-09-09T14:17:12.3881920Z ) 2025-09-09T14:17:12.3882459Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:17:12.3884308Z 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 2025-09-09T14:17:12.3886376Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=1.6606048345565796) 2025-09-09T14:17:12.3887518Z ) 2025-09-09T14:17:12.3887836Z ) 2025-09-09T14:17:12.3888036Z 2025-09-09T14:17:12.3888043Z 2025-09-09T14:17:12.3888049Z 2025-09-09T14:17:12.3888199Z def forward(self, x): 2025-09-09T14:17:12.3888708Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:17:12.3889364Z conv_weight = self.conv.weight 2025-09-09T14:17:12.3889882Z conv_bias = self.conv.bias 2025-09-09T14:17:12.3890337Z bn_weight = self.bn.weight 2025-09-09T14:17:12.3890835Z bn_bias = self.bn.bias 2025-09-09T14:17:12.3891320Z bn_running_mean = self.bn.running_mean 2025-09-09T14:17:12.3891891Z bn_running_var = self.bn.running_var 2025-09-09T14:17:12.3892545Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T14:17:12.3893419Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:17:12.3894597Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T14:17:12.3895657Z add = torch.ops.aten.add.Tensor(bn_running_var, 1e-05) 2025-09-09T14:17:12.3896455Z sqrt = torch.ops.aten.sqrt.default(add); add = None 2025-09-09T14:17:12.3897278Z div = torch.ops.aten.div.Tensor(bn_weight, sqrt); sqrt = None 2025-09-09T14:17:12.3898213Z reshape = torch.ops.aten.reshape.default(div, [-1, 1, 1, 1]) 2025-09-09T14:17:12.3899284Z mul = torch.ops.aten.mul.Tensor(conv_weight, reshape); conv_weight = reshape = None 2025-09-09T14:17:12.3900437Z activation_post_process_1 = self.activation_post_process_1(mul); mul = None 2025-09-09T14:17:12.3901634Z zeros_like = torch.ops.aten.zeros_like.default(conv_bias, dtype = torch.float32, pin_memory = False) 2025-09-09T14:17:12.3903638Z 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 2025-09-09T14:17:12.3905286Z reshape_1 = torch.ops.aten.reshape.default(div, [1, -1, 1, 1]); div = None 2025-09-09T14:17:12.3906353Z div_1 = torch.ops.aten.div.Tensor(conv2d_1, reshape_1); conv2d_1 = reshape_1 = None 2025-09-09T14:17:12.3907441Z reshape_2 = torch.ops.aten.reshape.default(conv_bias, [1, -1, 1, 1]); conv_bias = None 2025-09-09T14:17:12.3908576Z add_1 = torch.ops.aten.add.Tensor(div_1, reshape_2); div_1 = reshape_2 = None 2025-09-09T14:17:12.3910341Z 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 2025-09-09T14:17:12.3912012Z relu = torch.ops.aten.relu.default(batch_norm_1); batch_norm_1 = None 2025-09-09T14:17:12.3913096Z activation_post_process_2 = self.activation_post_process_2(relu); relu = None 2025-09-09T14:17:12.3914165Z return pytree.tree_unflatten((activation_post_process_2,), self._out_spec) 2025-09-09T14:17:12.3914936Z 2025-09-09T14:17:12.3915520Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:17:12.3916238Z model fx: GraphModule( 2025-09-09T14:17:12.3916892Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:17:24.4952562Z 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 2025-09-09T14:17:24.4954595Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.526270866394043, max_val=2.143237352371216) 2025-09-09T14:17:24.4955331Z ) 2025-09-09T14:17:24.4955579Z (conv): ConvBnReLU2d( 2025-09-09T14:17:24.4955920Z 3, 3, kernel_size=(3, 3), stride=(1, 1) 2025-09-09T14:17:24.4956480Z (bn): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T14:17:24.4957140Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:17:24.4959352Z 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 2025-09-09T14:17:24.4961197Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.1923346221446991, max_val=0.18921314179897308) 2025-09-09T14:17:24.4961938Z ) 2025-09-09T14:17:24.4962161Z ) 2025-09-09T14:17:24.4962544Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:17:24.4963872Z 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 2025-09-09T14:17:24.4965392Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=1.6606048345565796) 2025-09-09T14:17:24.4966060Z ) 2025-09-09T14:17:24.4966276Z ) 2025-09-09T14:17:24.4966403Z 2025-09-09T14:17:24.4966408Z 2025-09-09T14:17:24.4966425Z 2025-09-09T14:17:24.4966542Z def forward(self, x): 2025-09-09T14:17:24.4967008Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:17:24.4967748Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T14:17:24.4968499Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T14:17:24.4969088Z return activation_post_process_1 2025-09-09T14:17:24.4969448Z 2025-09-09T14:17:24.4969813Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:17:24.4970323Z diff: tensor([[[[0., 0., 0.], 2025-09-09T14:17:24.4970637Z [0., 0., 0.], 2025-09-09T14:17:24.4970929Z [0., 0., 0.]], 2025-09-09T14:17:24.4971117Z 2025-09-09T14:17:24.4971264Z [[0., 0., 0.], 2025-09-09T14:17:24.4971533Z [0., 0., 0.], 2025-09-09T14:17:24.4971816Z [0., 0., 0.]], 2025-09-09T14:17:24.4972000Z 2025-09-09T14:17:24.4972099Z [[0., 0., 0.], 2025-09-09T14:17:24.4972386Z [0., 0., 0.], 2025-09-09T14:17:24.4972692Z [0., 0., 0.]]]], grad_fn=) 2025-09-09T14:17:24.4973114Z converted model pt2e: GraphModule( 2025-09-09T14:17:24.4973474Z (conv): Module() 2025-09-09T14:17:24.4973735Z (bn): Module() 2025-09-09T14:17:24.4974001Z ) 2025-09-09T14:17:24.4974128Z 2025-09-09T14:17:24.4974133Z 2025-09-09T14:17:24.4974137Z 2025-09-09T14:17:24.4974255Z def forward(self, x): 2025-09-09T14:17:24.4974632Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:17:24.4975080Z conv_bias = self.conv.bias 2025-09-09T14:17:24.4975989Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.018311796709895134, 10, -128, 127, torch.int8); x = None 2025-09-09T14:17:24.4977771Z 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 2025-09-09T14:17:24.4979018Z quantize_per_tensor = self._frozen_param0 2025-09-09T14:17:24.4980146Z 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 2025-09-09T14:17:24.4982080Z 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 2025-09-09T14:17:24.4983289Z relu = torch.ops.aten.relu.default(conv2d_2); conv2d_2 = None 2025-09-09T14:17:24.4984400Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(relu, 0.006512175779789686, -128, -128, 127, torch.int8); relu = None 2025-09-09T14:17:24.4986235Z 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 2025-09-09T14:17:24.4987756Z return pytree.tree_unflatten((dequantize_per_tensor_default_2,), self._out_spec) 2025-09-09T14:17:24.4988333Z 2025-09-09T14:17:24.4988704Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:17:24.4989227Z onverted model fx: GraphModule( 2025-09-09T14:17:24.4989566Z (conv): ConvReLU2d( 2025-09-09T14:17:24.4990035Z (0): QuantizedConv2d(Reference)(3, 3, kernel_size=(3, 3), stride=(1, 1)) 2025-09-09T14:17:24.4990540Z (1): ReLU() 2025-09-09T14:17:24.4990800Z ) 2025-09-09T14:17:24.4991018Z ) 2025-09-09T14:17:24.4991153Z 2025-09-09T14:17:24.4991157Z 2025-09-09T14:17:24.4991162Z 2025-09-09T14:17:24.4991275Z def forward(self, x): 2025-09-09T14:17:24.4992145Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.018311796709895134, 10, -128, 127, torch.int8); x = None 2025-09-09T14:17:24.4993920Z 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 2025-09-09T14:17:24.4995370Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T14:17:24.4996585Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.006512175779789686, -128, -128, 127, torch.int8); conv = None 2025-09-09T14:17:24.4998429Z 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 2025-09-09T14:17:24.4999767Z return dequantize_per_tensor_default_1 2025-09-09T14:17:24.5000134Z 2025-09-09T14:17:24.5000509Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:17:24.5001004Z diff: tensor([[[[0., 0., 0.], 2025-09-09T14:17:24.5001327Z [0., 0., 0.], 2025-09-09T14:17:24.5001620Z [0., 0., 0.]], 2025-09-09T14:17:24.5001805Z 2025-09-09T14:17:24.5001905Z [[0., 0., 0.], 2025-09-09T14:17:24.5002189Z [0., 0., 0.], 2025-09-09T14:17:24.5002458Z [0., 0., 0.]], 2025-09-09T14:17:24.5002654Z 2025-09-09T14:17:24.5002753Z [[0., 0., 0.], 2025-09-09T14:17:24.5003021Z [0., 0., 0.], 2025-09-09T14:17:24.5003308Z [0., 0., 0.]]]]) 2025-09-09T14:17:24.5003840Z PASSED 2025-09-09T14:17:24.5004816Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_conv_bn_relu_fusion_cuda SKIPPED 2025-09-09T14:17:24.5006265Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_conv_bn_relu_fusion_no_conv_bias model pt2e: GraphModule( 2025-09-09T14:17:24.5007203Z (conv): Module() 2025-09-09T14:17:24.5007464Z (bn): Module() 2025-09-09T14:17:24.5007871Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:17:24.5009200Z 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 2025-09-09T14:17:24.5010780Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.526270866394043, max_val=2.143237352371216) 2025-09-09T14:17:24.5011593Z ) 2025-09-09T14:17:24.5011957Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:17:24.5013369Z 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 2025-09-09T14:17:24.5015204Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.1720, -0.1912, -0.1684]), max_val=tensor([0.1914, 0.1792, 0.1824])) 2025-09-09T14:17:24.5016129Z ) 2025-09-09T14:17:24.5016576Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:17:24.5017909Z 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 2025-09-09T14:17:24.5019425Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=1.9991776943206787) 2025-09-09T14:17:24.5020081Z ) 2025-09-09T14:17:24.5020317Z ) 2025-09-09T14:17:24.5020443Z 2025-09-09T14:17:24.5020448Z 2025-09-09T14:17:24.5020453Z 2025-09-09T14:17:24.5020577Z def forward(self, x): 2025-09-09T14:17:24.5020944Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:17:24.5021408Z conv_weight = self.conv.weight 2025-09-09T14:17:24.5021767Z bn_weight = self.bn.weight 2025-09-09T14:17:24.5022110Z bn_bias = self.bn.bias 2025-09-09T14:17:24.5022440Z bn_running_mean = self.bn.running_mean 2025-09-09T14:17:24.5022848Z bn_running_var = self.bn.running_var 2025-09-09T14:17:24.5023285Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T14:17:24.5023890Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:17:24.5024703Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T14:17:24.5025424Z add = torch.ops.aten.add.Tensor(bn_running_var, 1e-05) 2025-09-09T14:17:24.5025960Z sqrt = torch.ops.aten.sqrt.default(add); add = None 2025-09-09T14:17:24.5026515Z div = torch.ops.aten.div.Tensor(bn_weight, sqrt); sqrt = None 2025-09-09T14:17:24.5027130Z reshape = torch.ops.aten.reshape.default(div, [-1, 1, 1, 1]) 2025-09-09T14:17:24.5027822Z mul = torch.ops.aten.mul.Tensor(conv_weight, reshape); conv_weight = reshape = None 2025-09-09T14:17:24.5028605Z activation_post_process_1 = self.activation_post_process_1(mul); mul = None 2025-09-09T14:17:24.5029800Z 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 2025-09-09T14:17:24.5030970Z reshape_1 = torch.ops.aten.reshape.default(div, [1, -1, 1, 1]); div = None 2025-09-09T14:17:24.5031726Z div_1 = torch.ops.aten.div.Tensor(conv2d_1, reshape_1); conv2d_1 = reshape_1 = None 2025-09-09T14:17:24.5032982Z 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 2025-09-09T14:17:24.5034182Z relu = torch.ops.aten.relu.default(batch_norm_1); batch_norm_1 = None 2025-09-09T14:17:24.5034903Z activation_post_process_2 = self.activation_post_process_2(relu); relu = None 2025-09-09T14:17:34.9676159Z return pytree.tree_unflatten((activation_post_process_2,), self._out_spec) 2025-09-09T14:17:34.9676694Z 2025-09-09T14:17:34.9677186Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:17:34.9677649Z model fx: GraphModule( 2025-09-09T14:17:34.9678008Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:17:34.9679491Z 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 2025-09-09T14:17:34.9680861Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.526270866394043, max_val=2.143237352371216) 2025-09-09T14:17:34.9681434Z ) 2025-09-09T14:17:34.9681648Z (conv): ConvBnReLU2d( 2025-09-09T14:17:34.9681932Z 3, 3, kernel_size=(3, 3), stride=(1, 1), bias=False 2025-09-09T14:17:34.9682431Z (bn): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T14:17:34.9682947Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:17:34.9684057Z 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 2025-09-09T14:17:34.9685704Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.1720, -0.1912, -0.1684]), max_val=tensor([0.1914, 0.1792, 0.1824])) 2025-09-09T14:17:34.9686701Z ) 2025-09-09T14:17:34.9686899Z ) 2025-09-09T14:17:34.9687350Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:17:34.9688668Z 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 2025-09-09T14:17:34.9690209Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=1.9991776943206787) 2025-09-09T14:17:34.9690751Z ) 2025-09-09T14:17:34.9690941Z ) 2025-09-09T14:17:34.9691047Z 2025-09-09T14:17:34.9691052Z 2025-09-09T14:17:34.9691056Z 2025-09-09T14:17:34.9691151Z def forward(self, x): 2025-09-09T14:17:34.9691547Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:17:34.9692146Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T14:17:34.9692753Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T14:17:34.9693232Z return activation_post_process_1 2025-09-09T14:17:34.9693510Z 2025-09-09T14:17:34.9693815Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:17:34.9694218Z diff: tensor([[[[0., 0., 0.], 2025-09-09T14:17:34.9694486Z [0., 0., 0.], 2025-09-09T14:17:34.9694711Z [0., 0., 0.]], 2025-09-09T14:17:34.9694874Z 2025-09-09T14:17:34.9694957Z [[0., 0., 0.], 2025-09-09T14:17:34.9695187Z [0., 0., 0.], 2025-09-09T14:17:34.9695413Z [0., 0., 0.]], 2025-09-09T14:17:34.9695563Z 2025-09-09T14:17:34.9695657Z [[0., 0., 0.], 2025-09-09T14:17:34.9695877Z [0., 0., 0.], 2025-09-09T14:17:34.9696140Z [0., 0., 0.]]]], grad_fn=) 2025-09-09T14:17:34.9696476Z converted model pt2e: GraphModule( 2025-09-09T14:17:34.9696776Z (conv): Module() 2025-09-09T14:17:34.9696990Z (bn): Module() 2025-09-09T14:17:34.9697211Z ) 2025-09-09T14:17:34.9697315Z 2025-09-09T14:17:34.9697320Z 2025-09-09T14:17:34.9697324Z 2025-09-09T14:17:34.9697425Z def forward(self, x): 2025-09-09T14:17:34.9697725Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:17:34.9698545Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.018311796709895134, 10, -128, 127, torch.int8); x = None 2025-09-09T14:17:34.9699961Z 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 2025-09-09T14:17:34.9700948Z _scale_0 = self._scale_0 2025-09-09T14:17:34.9701236Z _zero_point_0 = self._zero_point_0 2025-09-09T14:17:34.9701562Z quantize_per_channel = self._frozen_param0 2025-09-09T14:17:34.9702669Z 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 2025-09-09T14:17:34.9703678Z conv_weight_bias = self.conv.weight_bias 2025-09-09T14:17:34.9704643Z 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 2025-09-09T14:17:34.9705682Z relu = torch.ops.aten.relu.default(conv2d_2); conv2d_2 = None 2025-09-09T14:17:34.9706561Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(relu, 0.007839912548661232, -128, -128, 127, torch.int8); relu = None 2025-09-09T14:17:34.9708102Z 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 2025-09-09T14:17:34.9709273Z return pytree.tree_unflatten((dequantize_per_tensor_default_1,), self._out_spec) 2025-09-09T14:17:34.9709725Z 2025-09-09T14:17:34.9710037Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:17:34.9710449Z onverted model fx: GraphModule( 2025-09-09T14:17:34.9710739Z (conv): ConvReLU2d( 2025-09-09T14:17:34.9711108Z (0): QuantizedConv2d(Reference)(3, 3, kernel_size=(3, 3), stride=(1, 1)) 2025-09-09T14:17:34.9711528Z (1): ReLU() 2025-09-09T14:17:34.9711729Z ) 2025-09-09T14:17:34.9711915Z ) 2025-09-09T14:17:34.9712017Z 2025-09-09T14:17:34.9712021Z 2025-09-09T14:17:34.9712025Z 2025-09-09T14:17:34.9712133Z def forward(self, x): 2025-09-09T14:17:34.9712823Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.018311796709895134, 10, -128, 127, torch.int8); x = None 2025-09-09T14:17:34.9714246Z 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 2025-09-09T14:17:34.9715390Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T14:17:34.9716365Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.007839912548661232, -128, -128, 127, torch.int8); conv = None 2025-09-09T14:17:34.9717852Z 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 2025-09-09T14:17:34.9719055Z return dequantize_per_tensor_default_1 2025-09-09T14:17:34.9719365Z 2025-09-09T14:17:34.9719711Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:17:34.9720137Z diff: tensor([[[[0., 0., 0.], 2025-09-09T14:17:34.9720406Z [0., 0., 0.], 2025-09-09T14:17:34.9720628Z [0., 0., 0.]], 2025-09-09T14:17:34.9720776Z 2025-09-09T14:17:34.9720880Z [[0., 0., 0.], 2025-09-09T14:17:34.9721099Z [0., 0., 0.], 2025-09-09T14:17:34.9721332Z [0., 0., 0.]], 2025-09-09T14:17:34.9721482Z 2025-09-09T14:17:34.9721562Z [[0., 0., 0.], 2025-09-09T14:17:34.9721789Z [0., 0., 0.], 2025-09-09T14:17:34.9722009Z [0., 0., 0.]]]]) 2025-09-09T14:17:34.9722269Z model pt2e: GraphModule( 2025-09-09T14:17:34.9722514Z (conv): Module() 2025-09-09T14:17:34.9722739Z (bn): Module() 2025-09-09T14:17:34.9723072Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:17:34.9724145Z 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 2025-09-09T14:17:34.9725413Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.526270866394043, max_val=2.143237352371216) 2025-09-09T14:17:34.9725980Z ) 2025-09-09T14:17:34.9726375Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:17:34.9727459Z 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 2025-09-09T14:17:34.9728726Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.19124282896518707, max_val=0.19141820073127747) 2025-09-09T14:17:34.9729496Z ) 2025-09-09T14:17:34.9729911Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:17:34.9731065Z 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 2025-09-09T14:17:34.9732288Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=1.999093770980835) 2025-09-09T14:17:34.9732814Z ) 2025-09-09T14:17:34.9733037Z ) 2025-09-09T14:17:34.9733138Z 2025-09-09T14:17:34.9733144Z 2025-09-09T14:17:34.9733147Z 2025-09-09T14:17:34.9733239Z def forward(self, x): 2025-09-09T14:17:34.9733565Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:17:34.9733955Z conv_weight = self.conv.weight 2025-09-09T14:17:34.9734254Z bn_weight = self.bn.weight 2025-09-09T14:17:34.9734543Z bn_bias = self.bn.bias 2025-09-09T14:17:34.9734818Z bn_running_mean = self.bn.running_mean 2025-09-09T14:17:34.9735152Z bn_running_var = self.bn.running_var 2025-09-09T14:17:34.9735512Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T14:17:34.9736017Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:17:34.9736685Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T14:17:34.9737278Z add = torch.ops.aten.add.Tensor(bn_running_var, 1e-05) 2025-09-09T14:17:34.9737719Z sqrt = torch.ops.aten.sqrt.default(add); add = None 2025-09-09T14:17:34.9738172Z div = torch.ops.aten.div.Tensor(bn_weight, sqrt); sqrt = None 2025-09-09T14:17:34.9738679Z reshape = torch.ops.aten.reshape.default(div, [-1, 1, 1, 1]) 2025-09-09T14:17:34.9739240Z mul = torch.ops.aten.mul.Tensor(conv_weight, reshape); conv_weight = reshape = None 2025-09-09T14:17:34.9739878Z activation_post_process_1 = self.activation_post_process_1(mul); mul = None 2025-09-09T14:17:34.9740844Z 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 2025-09-09T14:17:34.9741792Z reshape_1 = torch.ops.aten.reshape.default(div, [1, -1, 1, 1]); div = None 2025-09-09T14:17:34.9742413Z div_1 = torch.ops.aten.div.Tensor(conv2d_1, reshape_1); conv2d_1 = reshape_1 = None 2025-09-09T14:17:34.9743428Z 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 2025-09-09T14:17:43.2944401Z relu = torch.ops.aten.relu.default(batch_norm_1); batch_norm_1 = None 2025-09-09T14:17:43.2945199Z activation_post_process_2 = self.activation_post_process_2(relu); relu = None 2025-09-09T14:17:43.2945953Z return pytree.tree_unflatten((activation_post_process_2,), self._out_spec) 2025-09-09T14:17:43.2946512Z 2025-09-09T14:17:43.2946901Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:17:43.2947429Z model fx: GraphModule( 2025-09-09T14:17:43.2947879Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:17:43.2949228Z 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 2025-09-09T14:17:43.2951155Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.526270866394043, max_val=2.143237352371216) 2025-09-09T14:17:43.2951887Z ) 2025-09-09T14:17:43.2952125Z (conv): ConvBnReLU2d( 2025-09-09T14:17:43.2952483Z 3, 3, kernel_size=(3, 3), stride=(1, 1), bias=False 2025-09-09T14:17:43.2953074Z (bn): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T14:17:43.2953722Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:17:43.2955033Z 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 2025-09-09T14:17:43.2956761Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.19124282896518707, max_val=0.19141820073127747) 2025-09-09T14:17:43.2957511Z ) 2025-09-09T14:17:43.2957733Z ) 2025-09-09T14:17:43.2958121Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:17:43.2959747Z 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 2025-09-09T14:17:43.2961279Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=1.999093770980835) 2025-09-09T14:17:43.2961945Z ) 2025-09-09T14:17:43.2962162Z ) 2025-09-09T14:17:43.2962308Z 2025-09-09T14:17:43.2962314Z 2025-09-09T14:17:43.2962318Z 2025-09-09T14:17:43.2962439Z def forward(self, x): 2025-09-09T14:17:43.2962915Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:17:43.2963658Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T14:17:43.2964418Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T14:17:43.2965009Z return activation_post_process_1 2025-09-09T14:17:43.2965372Z 2025-09-09T14:17:43.2965739Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:17:43.2966252Z diff: tensor([[[[0., 0., 0.], 2025-09-09T14:17:43.2966569Z [0., 0., 0.], 2025-09-09T14:17:43.2966859Z [0., 0., 0.]], 2025-09-09T14:17:43.2967046Z 2025-09-09T14:17:43.2967147Z [[0., 0., 0.], 2025-09-09T14:17:43.2967431Z [0., 0., 0.], 2025-09-09T14:17:43.2967717Z [0., 0., 0.]], 2025-09-09T14:17:43.2967902Z 2025-09-09T14:17:43.2968005Z [[0., 0., 0.], 2025-09-09T14:17:43.2968292Z [0., 0., 0.], 2025-09-09T14:17:43.2968601Z [0., 0., 0.]]]], grad_fn=) 2025-09-09T14:17:43.2969026Z converted model pt2e: GraphModule( 2025-09-09T14:17:43.2969378Z (conv): Module() 2025-09-09T14:17:43.2969659Z (bn): Module() 2025-09-09T14:17:43.2969911Z ) 2025-09-09T14:17:43.2970049Z 2025-09-09T14:17:43.2970054Z 2025-09-09T14:17:43.2970058Z 2025-09-09T14:17:43.2970181Z def forward(self, x): 2025-09-09T14:17:43.2970561Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:17:43.2971577Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.018311796709895134, 10, -128, 127, torch.int8); x = None 2025-09-09T14:17:43.2973393Z 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 2025-09-09T14:17:43.2974657Z quantize_per_tensor = self._frozen_param0 2025-09-09T14:17:43.2975781Z 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 2025-09-09T14:17:43.2976925Z conv_weight_bias = self.conv.weight_bias 2025-09-09T14:17:43.2978293Z 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 2025-09-09T14:17:43.2979565Z relu = torch.ops.aten.relu.default(conv2d_2); conv2d_2 = None 2025-09-09T14:17:43.2980672Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(relu, 0.007839583791792393, -128, -128, 127, torch.int8); relu = None 2025-09-09T14:17:43.2982506Z 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 2025-09-09T14:17:43.2984062Z return pytree.tree_unflatten((dequantize_per_tensor_default_2,), self._out_spec) 2025-09-09T14:17:43.2984641Z 2025-09-09T14:17:43.2985006Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:17:43.2985527Z onverted model fx: GraphModule( 2025-09-09T14:17:43.2985865Z (conv): ConvReLU2d( 2025-09-09T14:17:43.2986333Z (0): QuantizedConv2d(Reference)(3, 3, kernel_size=(3, 3), stride=(1, 1)) 2025-09-09T14:17:43.2986840Z (1): ReLU() 2025-09-09T14:17:43.2987097Z ) 2025-09-09T14:17:43.2987314Z ) 2025-09-09T14:17:43.2987451Z 2025-09-09T14:17:43.2987456Z 2025-09-09T14:17:43.2987461Z 2025-09-09T14:17:43.2987574Z def forward(self, x): 2025-09-09T14:17:43.2988439Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.018311796709895134, 10, -128, 127, torch.int8); x = None 2025-09-09T14:17:43.2990203Z 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 2025-09-09T14:17:43.2991650Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T14:17:43.2992881Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.007839583791792393, -128, -128, 127, torch.int8); conv = None 2025-09-09T14:17:43.2994722Z 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 2025-09-09T14:17:43.2995993Z return dequantize_per_tensor_default_1 2025-09-09T14:17:43.2996363Z 2025-09-09T14:17:43.2996742Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:17:43.2997249Z diff: tensor([[[[0., 0., 0.], 2025-09-09T14:17:43.2997573Z [0., 0., 0.], 2025-09-09T14:17:43.2997861Z [0., 0., 0.]], 2025-09-09T14:17:43.2998047Z 2025-09-09T14:17:43.2998148Z [[0., 0., 0.], 2025-09-09T14:17:43.2998430Z [0., 0., 0.], 2025-09-09T14:17:43.2998707Z [0., 0., 0.]], 2025-09-09T14:17:43.2998905Z 2025-09-09T14:17:43.2999006Z [[0., 0., 0.], 2025-09-09T14:17:43.2999278Z [0., 0., 0.], 2025-09-09T14:17:43.2999572Z [0., 0., 0.]]]]) 2025-09-09T14:17:43.3000186Z PASSED 2025-09-09T14:17:43.3000991Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_conv_no_bias model pt2e: GraphModule( 2025-09-09T14:17:43.3001856Z (conv): Module() 2025-09-09T14:17:43.3002255Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:17:43.3003665Z 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 2025-09-09T14:17:43.3005521Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.1782, -0.1825, -0.1912]), max_val=tensor([0.1676, 0.1914, 0.1824])) 2025-09-09T14:17:43.3006457Z ) 2025-09-09T14:17:43.3006835Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:17:43.3008274Z 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 2025-09-09T14:17:43.3009840Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.526270866394043, max_val=2.143237352371216) 2025-09-09T14:17:43.3010549Z ) 2025-09-09T14:17:43.3010929Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:17:43.3012282Z 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 2025-09-09T14:17:43.3013871Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=1.3200514316558838) 2025-09-09T14:17:43.3014547Z ) 2025-09-09T14:17:43.3014770Z ) 2025-09-09T14:17:43.3014912Z 2025-09-09T14:17:43.3014917Z 2025-09-09T14:17:43.3014927Z 2025-09-09T14:17:43.3015042Z def forward(self, x): 2025-09-09T14:17:43.3015428Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:17:43.3015885Z conv_weight = self.conv.weight 2025-09-09T14:17:43.3016521Z activation_post_process_1 = self.activation_post_process_1(conv_weight); conv_weight = None 2025-09-09T14:17:43.3017324Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:17:43.3018461Z conv2d = torch.ops.aten.conv2d.default(activation_post_process_0, activation_post_process_1); activation_post_process_0 = activation_post_process_1 = None 2025-09-09T14:17:43.3019541Z relu = torch.ops.aten.relu.default(conv2d); conv2d = None 2025-09-09T14:17:43.3020207Z activation_post_process_2 = self.activation_post_process_2(relu); relu = None 2025-09-09T14:17:43.3020970Z return pytree.tree_unflatten((activation_post_process_2,), self._out_spec) 2025-09-09T14:17:43.3021501Z 2025-09-09T14:17:43.3021883Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:17:43.3022372Z model fx: GraphModule( 2025-09-09T14:17:43.3022811Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:17:43.3024142Z 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 2025-09-09T14:17:43.3025712Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.526270866394043, max_val=2.143237352371216) 2025-09-09T14:17:43.3026434Z ) 2025-09-09T14:17:43.3026669Z (conv): ConvReLU2d( 2025-09-09T14:17:43.3027017Z 3, 3, kernel_size=(3, 3), stride=(1, 1), bias=False 2025-09-09T14:17:43.3027504Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:17:44.2028965Z 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 2025-09-09T14:17:44.2030898Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.1782, -0.1825, -0.1912]), max_val=tensor([0.1676, 0.1914, 0.1824])) 2025-09-09T14:17:44.2031834Z ) 2025-09-09T14:17:44.2032074Z ) 2025-09-09T14:17:44.2032442Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:17:44.2033804Z 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 2025-09-09T14:17:44.2035331Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=1.3200514316558838) 2025-09-09T14:17:44.2035991Z ) 2025-09-09T14:17:44.2036223Z ) 2025-09-09T14:17:44.2036352Z 2025-09-09T14:17:44.2036357Z 2025-09-09T14:17:44.2036696Z 2025-09-09T14:17:44.2036816Z def forward(self, x): 2025-09-09T14:17:44.2037301Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:17:44.2038034Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T14:17:44.2038795Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T14:17:44.2039394Z return activation_post_process_1 2025-09-09T14:17:44.2039810Z 2025-09-09T14:17:44.2040198Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:17:44.2040876Z diff: tensor([[[[0., 0., 0.], 2025-09-09T14:17:44.2041208Z [0., 0., 0.], 2025-09-09T14:17:44.2041487Z [0., 0., 0.]], 2025-09-09T14:17:44.2041691Z 2025-09-09T14:17:44.2041796Z [[0., 0., 0.], 2025-09-09T14:17:44.2042068Z [0., 0., 0.], 2025-09-09T14:17:44.2042361Z [0., 0., 0.]], 2025-09-09T14:17:44.2042548Z 2025-09-09T14:17:44.2042666Z [[0., 0., 0.], 2025-09-09T14:17:44.2042939Z [0., 0., 0.], 2025-09-09T14:17:44.2043261Z [0., 0., 0.]]]], grad_fn=) 2025-09-09T14:17:44.2043667Z converted model pt2e: GraphModule( 2025-09-09T14:17:44.2044033Z (conv): Module() 2025-09-09T14:17:44.2044288Z ) 2025-09-09T14:17:44.2044424Z 2025-09-09T14:17:44.2044429Z 2025-09-09T14:17:44.2044433Z 2025-09-09T14:17:44.2044544Z def forward(self, x): 2025-09-09T14:17:44.2044910Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:17:44.2045359Z _scale_0 = self._scale_0 2025-09-09T14:17:44.2045704Z _zero_point_0 = self._zero_point_0 2025-09-09T14:17:44.2046138Z quantize_per_channel_default = self._frozen_param0 2025-09-09T14:17:44.2047552Z 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 2025-09-09T14:17:44.2049462Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.018311796709895134, 10, -128, 127, torch.int8); x = None 2025-09-09T14:17:44.2051252Z 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 2025-09-09T14:17:44.2053138Z conv2d = torch.ops.aten.conv2d.default(dequantize_per_tensor_default, dequantize_per_channel_default); dequantize_per_tensor_default = dequantize_per_channel_default = None 2025-09-09T14:17:44.2054303Z relu = torch.ops.aten.relu.default(conv2d); conv2d = None 2025-09-09T14:17:44.2055387Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(relu, 0.005176672246307135, -128, -128, 127, torch.int8); relu = None 2025-09-09T14:17:44.2057247Z 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 2025-09-09T14:17:44.2058912Z return pytree.tree_unflatten((dequantize_per_tensor_default_1,), self._out_spec) 2025-09-09T14:17:44.2059493Z 2025-09-09T14:17:44.2059854Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:17:44.2060374Z onverted model fx: GraphModule( 2025-09-09T14:17:44.2060706Z (conv): ConvReLU2d( 2025-09-09T14:17:44.2061217Z (0): QuantizedConv2d(Reference)(3, 3, kernel_size=(3, 3), stride=(1, 1), bias=False) 2025-09-09T14:17:44.2061790Z (1): ReLU() 2025-09-09T14:17:44.2062037Z ) 2025-09-09T14:17:44.2062265Z ) 2025-09-09T14:17:44.2062392Z 2025-09-09T14:17:44.2062397Z 2025-09-09T14:17:44.2062402Z 2025-09-09T14:17:44.2062514Z def forward(self, x): 2025-09-09T14:17:44.2063525Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.018311796709895134, 10, -128, 127, torch.int8); x = None 2025-09-09T14:17:44.2065317Z 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 2025-09-09T14:17:44.2066778Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T14:17:44.2067994Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.005176672246307135, -128, -128, 127, torch.int8); conv = None 2025-09-09T14:17:44.2069849Z 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 2025-09-09T14:17:44.2071214Z return dequantize_per_tensor_default_1 2025-09-09T14:17:44.2071594Z 2025-09-09T14:17:44.2071956Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:17:44.2072472Z diff: tensor([[[[0., 0., 0.], 2025-09-09T14:17:44.2072801Z [0., 0., 0.], 2025-09-09T14:17:44.2073078Z [0., 0., 0.]], 2025-09-09T14:17:44.2073264Z 2025-09-09T14:17:44.2073382Z [[0., 0., 0.], 2025-09-09T14:17:44.2073670Z [0., 0., 0.], 2025-09-09T14:17:44.2073955Z [0., 0., 0.]], 2025-09-09T14:17:44.2074140Z 2025-09-09T14:17:44.2074242Z [[0., 0., 0.], 2025-09-09T14:17:44.2074528Z [0., 0., 0.], 2025-09-09T14:17:44.2074801Z [0., 0., 0.]]]]) 2025-09-09T14:17:44.2075118Z model pt2e: GraphModule( 2025-09-09T14:17:44.2075437Z (conv): Module() 2025-09-09T14:17:44.2075837Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:17:44.2077193Z 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 2025-09-09T14:17:44.2078794Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.19124378263950348, max_val=0.19141915440559387) 2025-09-09T14:17:44.2079544Z ) 2025-09-09T14:17:44.2079983Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:17:44.2081313Z 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 2025-09-09T14:17:44.2082887Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.526270866394043, max_val=2.143237352371216) 2025-09-09T14:17:44.2083604Z ) 2025-09-09T14:17:44.2083984Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:17:44.2085323Z 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 2025-09-09T14:17:44.2086840Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=1.3200514316558838) 2025-09-09T14:17:44.2087505Z ) 2025-09-09T14:17:44.2097478Z ) 2025-09-09T14:17:44.2097663Z 2025-09-09T14:17:44.2097668Z 2025-09-09T14:17:44.2097673Z 2025-09-09T14:17:44.2097789Z def forward(self, x): 2025-09-09T14:17:44.2098207Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:17:44.2098676Z conv_weight = self.conv.weight 2025-09-09T14:17:44.2099316Z activation_post_process_1 = self.activation_post_process_1(conv_weight); conv_weight = None 2025-09-09T14:17:44.2100151Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:17:44.2101284Z conv2d = torch.ops.aten.conv2d.default(activation_post_process_0, activation_post_process_1); activation_post_process_0 = activation_post_process_1 = None 2025-09-09T14:17:44.2102356Z relu = torch.ops.aten.relu.default(conv2d); conv2d = None 2025-09-09T14:17:44.2103153Z activation_post_process_2 = self.activation_post_process_2(relu); relu = None 2025-09-09T14:17:44.2103927Z return pytree.tree_unflatten((activation_post_process_2,), self._out_spec) 2025-09-09T14:17:44.2104458Z 2025-09-09T14:17:44.2104851Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:17:44.2105347Z model fx: GraphModule( 2025-09-09T14:17:44.2105793Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:17:44.2107143Z 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 2025-09-09T14:17:44.2108801Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.526270866394043, max_val=2.143237352371216) 2025-09-09T14:17:44.2109533Z ) 2025-09-09T14:17:44.2109773Z (conv): ConvReLU2d( 2025-09-09T14:17:44.2110133Z 3, 3, kernel_size=(3, 3), stride=(1, 1), bias=False 2025-09-09T14:17:44.2110623Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:17:44.2111939Z 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 2025-09-09T14:17:44.2113545Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.19124378263950348, max_val=0.19141915440559387) 2025-09-09T14:17:44.2114278Z ) 2025-09-09T14:17:44.2114530Z ) 2025-09-09T14:17:44.2114894Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:17:44.2116246Z 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 2025-09-09T14:17:44.2117772Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=1.3200514316558838) 2025-09-09T14:17:44.2118433Z ) 2025-09-09T14:17:44.2118671Z ) 2025-09-09T14:17:44.2118803Z 2025-09-09T14:17:44.2118808Z 2025-09-09T14:17:44.2118812Z 2025-09-09T14:17:44.2118927Z def forward(self, x): 2025-09-09T14:17:44.2119413Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:17:44.2120224Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T14:17:44.2120973Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T14:17:45.3047616Z return activation_post_process_1 2025-09-09T14:17:45.3048062Z 2025-09-09T14:17:45.3048644Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:17:45.3049177Z diff: tensor([[[[0., 0., 0.], 2025-09-09T14:17:45.3049497Z [0., 0., 0.], 2025-09-09T14:17:45.3049790Z [0., 0., 0.]], 2025-09-09T14:17:45.3049984Z 2025-09-09T14:17:45.3050120Z [[0., 0., 0.], 2025-09-09T14:17:45.3050419Z [0., 0., 0.], 2025-09-09T14:17:45.3050690Z [0., 0., 0.]], 2025-09-09T14:17:45.3050892Z 2025-09-09T14:17:45.3050995Z [[0., 0., 0.], 2025-09-09T14:17:45.3051280Z [0., 0., 0.], 2025-09-09T14:17:45.3051587Z [0., 0., 0.]]]], grad_fn=) 2025-09-09T14:17:45.3052015Z converted model pt2e: GraphModule( 2025-09-09T14:17:45.3052365Z (conv): Module() 2025-09-09T14:17:45.3052636Z ) 2025-09-09T14:17:45.3052763Z 2025-09-09T14:17:45.3052769Z 2025-09-09T14:17:45.3052774Z 2025-09-09T14:17:45.3052903Z def forward(self, x): 2025-09-09T14:17:45.3053285Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:17:45.3053809Z quantize_per_tensor_default = self._frozen_param0 2025-09-09T14:17:45.3055420Z 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 2025-09-09T14:17:45.3057229Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.018311796709895134, 10, -128, 127, torch.int8); x = None 2025-09-09T14:17:45.3059237Z 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 2025-09-09T14:17:45.3061153Z 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 2025-09-09T14:17:45.3062478Z relu = torch.ops.aten.relu.default(conv2d); conv2d = None 2025-09-09T14:17:45.3063574Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(relu, 0.005176672246307135, -128, -128, 127, torch.int8); relu = None 2025-09-09T14:17:45.3065430Z 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 2025-09-09T14:17:45.3066894Z return pytree.tree_unflatten((dequantize_per_tensor_default_2,), self._out_spec) 2025-09-09T14:17:45.3067473Z 2025-09-09T14:17:45.3067885Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:17:45.3068403Z onverted model fx: GraphModule( 2025-09-09T14:17:45.3068751Z (conv): ConvReLU2d( 2025-09-09T14:17:45.3069250Z (0): QuantizedConv2d(Reference)(3, 3, kernel_size=(3, 3), stride=(1, 1), bias=False) 2025-09-09T14:17:45.3069836Z (1): ReLU() 2025-09-09T14:17:45.3070090Z ) 2025-09-09T14:17:45.3070322Z ) 2025-09-09T14:17:45.3070449Z 2025-09-09T14:17:45.3070454Z 2025-09-09T14:17:45.3070459Z 2025-09-09T14:17:45.3070569Z def forward(self, x): 2025-09-09T14:17:45.3071445Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.018311796709895134, 10, -128, 127, torch.int8); x = None 2025-09-09T14:17:45.3073227Z 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 2025-09-09T14:17:45.3074654Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T14:17:45.3075879Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.005176672246307135, -128, -128, 127, torch.int8); conv = None 2025-09-09T14:17:45.3077713Z 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 2025-09-09T14:17:45.3078991Z return dequantize_per_tensor_default_1 2025-09-09T14:17:45.3079373Z 2025-09-09T14:17:45.3079799Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:17:45.3080310Z diff: tensor([[[[0., 0., 0.], 2025-09-09T14:17:45.3080626Z [0., 0., 0.], 2025-09-09T14:17:45.3080916Z [0., 0., 0.]], 2025-09-09T14:17:45.3081099Z 2025-09-09T14:17:45.3081199Z [[0., 0., 0.], 2025-09-09T14:17:45.3081480Z [0., 0., 0.], 2025-09-09T14:17:45.3081759Z [0., 0., 0.]], 2025-09-09T14:17:45.3081943Z 2025-09-09T14:17:45.3082042Z [[0., 0., 0.], 2025-09-09T14:17:45.3082323Z [0., 0., 0.], 2025-09-09T14:17:45.3082597Z [0., 0., 0.]]]]) 2025-09-09T14:17:45.3082917Z model pt2e: GraphModule( 2025-09-09T14:17:45.3083215Z (conv): Module() 2025-09-09T14:17:45.3083620Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:17:45.3085164Z 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 2025-09-09T14:17:45.3087056Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.1897, -0.1787, -0.1913]), max_val=tensor([0.1870, 0.1478, 0.1740])) 2025-09-09T14:17:45.3087991Z ) 2025-09-09T14:17:45.3088355Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:17:45.3089696Z 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 2025-09-09T14:17:45.3091341Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.526270866394043, max_val=2.143237352371216) 2025-09-09T14:17:45.3092058Z ) 2025-09-09T14:17:45.3092433Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:17:45.3093758Z 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 2025-09-09T14:17:45.3095346Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.9077497124671936, max_val=1.2348304986953735) 2025-09-09T14:17:45.3096070Z ) 2025-09-09T14:17:45.3096302Z ) 2025-09-09T14:17:45.3096431Z 2025-09-09T14:17:45.3096435Z 2025-09-09T14:17:45.3096440Z 2025-09-09T14:17:45.3096565Z def forward(self, x): 2025-09-09T14:17:45.3096934Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:17:45.3097410Z conv_weight = self.conv.weight 2025-09-09T14:17:45.3098032Z activation_post_process_1 = self.activation_post_process_1(conv_weight); conv_weight = None 2025-09-09T14:17:45.3098850Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:17:45.3099988Z conv2d = torch.ops.aten.conv2d.default(activation_post_process_0, activation_post_process_1); activation_post_process_0 = activation_post_process_1 = None 2025-09-09T14:17:45.3101159Z activation_post_process_2 = self.activation_post_process_2(conv2d); conv2d = None 2025-09-09T14:17:45.3101931Z return pytree.tree_unflatten((activation_post_process_2,), self._out_spec) 2025-09-09T14:17:45.3102456Z 2025-09-09T14:17:45.3102837Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:17:45.3103328Z model fx: GraphModule( 2025-09-09T14:17:45.3103765Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:17:45.3105100Z 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 2025-09-09T14:17:45.3106656Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.526270866394043, max_val=2.143237352371216) 2025-09-09T14:17:45.3107378Z ) 2025-09-09T14:17:45.3107616Z (conv): Conv2d( 2025-09-09T14:17:45.3107952Z 3, 3, kernel_size=(3, 3), stride=(1, 1), bias=False 2025-09-09T14:17:45.3108448Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:17:45.3109818Z 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 2025-09-09T14:17:45.3111677Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.1897, -0.1787, -0.1913]), max_val=tensor([0.1870, 0.1478, 0.1740])) 2025-09-09T14:17:45.3112605Z ) 2025-09-09T14:17:45.3112840Z ) 2025-09-09T14:17:45.3113199Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:17:45.3114629Z 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 2025-09-09T14:17:45.3116218Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.9077497124671936, max_val=1.2348304986953735) 2025-09-09T14:17:45.3116934Z ) 2025-09-09T14:17:45.3117166Z ) 2025-09-09T14:17:45.3117291Z 2025-09-09T14:17:45.3117296Z 2025-09-09T14:17:45.3117301Z 2025-09-09T14:17:45.3117412Z def forward(self, x): 2025-09-09T14:17:45.3117896Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:17:45.3118635Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T14:17:45.3119450Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T14:17:45.3120115Z return activation_post_process_1 2025-09-09T14:17:45.3120456Z 2025-09-09T14:17:45.3120831Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:17:45.3121325Z diff: tensor([[[[0., 0., 0.], 2025-09-09T14:17:45.3121654Z [0., 0., 0.], 2025-09-09T14:17:45.3121930Z [0., 0., 0.]], 2025-09-09T14:17:45.3122129Z 2025-09-09T14:17:45.3122229Z [[0., 0., 0.], 2025-09-09T14:17:45.3122512Z [0., 0., 0.], 2025-09-09T14:17:45.3122788Z [0., 0., 0.]], 2025-09-09T14:17:45.3122978Z 2025-09-09T14:17:45.3123091Z [[0., 0., 0.], 2025-09-09T14:17:45.3123360Z [0., 0., 0.], 2025-09-09T14:17:45.3123683Z [0., 0., 0.]]]], grad_fn=) 2025-09-09T14:17:45.3124092Z converted model pt2e: GraphModule( 2025-09-09T14:17:45.3124451Z (conv): Module() 2025-09-09T14:17:45.3124715Z ) 2025-09-09T14:17:45.3124857Z 2025-09-09T14:17:45.3124861Z 2025-09-09T14:17:45.3124866Z 2025-09-09T14:17:45.3124974Z def forward(self, x): 2025-09-09T14:17:45.3125352Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:17:45.3125790Z _scale_0 = self._scale_0 2025-09-09T14:17:45.3126139Z _zero_point_0 = self._zero_point_0 2025-09-09T14:17:45.3126570Z quantize_per_channel_default = self._frozen_param0 2025-09-09T14:17:45.3127995Z 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 2025-09-09T14:17:45.3129910Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.018311796709895134, 10, -128, 127, torch.int8); x = None 2025-09-09T14:17:46.1996682Z 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 2025-09-09T14:17:46.1998717Z conv2d = torch.ops.aten.conv2d.default(dequantize_per_tensor_default, dequantize_per_channel_default); dequantize_per_tensor_default = dequantize_per_channel_default = None 2025-09-09T14:17:46.2000510Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv2d, 0.00840227585285902, -20, -128, 127, torch.int8); conv2d = None 2025-09-09T14:17:46.2002368Z 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 2025-09-09T14:17:46.2003810Z return pytree.tree_unflatten((dequantize_per_tensor_default_1,), self._out_spec) 2025-09-09T14:17:46.2004381Z 2025-09-09T14:17:46.2004768Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:17:46.2005293Z onverted model fx: GraphModule( 2025-09-09T14:17:46.2005870Z (conv): QuantizedConv2d(Reference)(3, 3, kernel_size=(3, 3), stride=(1, 1), bias=False) 2025-09-09T14:17:46.2006442Z ) 2025-09-09T14:17:46.2006591Z 2025-09-09T14:17:46.2006596Z 2025-09-09T14:17:46.2006601Z 2025-09-09T14:17:46.2006714Z def forward(self, x): 2025-09-09T14:17:46.2007919Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.018311796709895134, 10, -128, 127, torch.int8); x = None 2025-09-09T14:17:46.2009694Z 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 2025-09-09T14:17:46.2011150Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T14:17:46.2012347Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.00840227585285902, -20, -128, 127, torch.int8); conv = None 2025-09-09T14:17:46.2014307Z 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 2025-09-09T14:17:46.2015567Z return dequantize_per_tensor_default_1 2025-09-09T14:17:46.2015944Z 2025-09-09T14:17:46.2016326Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:17:46.2016830Z diff: tensor([[[[0., 0., 0.], 2025-09-09T14:17:46.2017159Z [0., 0., 0.], 2025-09-09T14:17:46.2017438Z [0., 0., 0.]], 2025-09-09T14:17:46.2017635Z 2025-09-09T14:17:46.2017734Z [[0., 0., 0.], 2025-09-09T14:17:46.2018013Z [0., 0., 0.], 2025-09-09T14:17:46.2018279Z [0., 0., 0.]], 2025-09-09T14:17:46.2018462Z 2025-09-09T14:17:46.2018578Z [[0., 0., 0.], 2025-09-09T14:17:46.2018844Z [0., 0., 0.], 2025-09-09T14:17:46.2019135Z [0., 0., 0.]]]]) 2025-09-09T14:17:46.2019440Z model pt2e: GraphModule( 2025-09-09T14:17:46.2019750Z (conv): Module() 2025-09-09T14:17:46.2020149Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:17:46.2021508Z 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 2025-09-09T14:17:46.2023122Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.19127574563026428, max_val=0.18703685700893402) 2025-09-09T14:17:46.2023854Z ) 2025-09-09T14:17:46.2024234Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:17:46.2025546Z 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 2025-09-09T14:17:46.2027119Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.526270866394043, max_val=2.143237352371216) 2025-09-09T14:17:46.2027841Z ) 2025-09-09T14:17:46.2028207Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:17:46.2029547Z 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 2025-09-09T14:17:46.2031122Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.9042347073554993, max_val=1.2348304986953735) 2025-09-09T14:17:46.2031838Z ) 2025-09-09T14:17:46.2032070Z ) 2025-09-09T14:17:46.2032203Z 2025-09-09T14:17:46.2032207Z 2025-09-09T14:17:46.2032212Z 2025-09-09T14:17:46.2032325Z def forward(self, x): 2025-09-09T14:17:46.2032705Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:17:46.2033162Z conv_weight = self.conv.weight 2025-09-09T14:17:46.2033799Z activation_post_process_1 = self.activation_post_process_1(conv_weight); conv_weight = None 2025-09-09T14:17:46.2034622Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:17:46.2035837Z conv2d = torch.ops.aten.conv2d.default(activation_post_process_0, activation_post_process_1); activation_post_process_0 = activation_post_process_1 = None 2025-09-09T14:17:46.2037030Z activation_post_process_2 = self.activation_post_process_2(conv2d); conv2d = None 2025-09-09T14:17:46.2037812Z return pytree.tree_unflatten((activation_post_process_2,), self._out_spec) 2025-09-09T14:17:46.2038360Z 2025-09-09T14:17:46.2038724Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:17:46.2039238Z model fx: GraphModule( 2025-09-09T14:17:46.2039743Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:17:46.2041082Z 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 2025-09-09T14:17:46.2042744Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.526270866394043, max_val=2.143237352371216) 2025-09-09T14:17:46.2043450Z ) 2025-09-09T14:17:46.2043694Z (conv): Conv2d( 2025-09-09T14:17:46.2044015Z 3, 3, kernel_size=(3, 3), stride=(1, 1), bias=False 2025-09-09T14:17:46.2044529Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:17:46.2045838Z 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 2025-09-09T14:17:46.2047429Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.19127574563026428, max_val=0.18703685700893402) 2025-09-09T14:17:46.2048181Z ) 2025-09-09T14:17:46.2048406Z ) 2025-09-09T14:17:46.2048783Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:17:46.2050122Z 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 2025-09-09T14:17:46.2051690Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.9042347073554993, max_val=1.2348304986953735) 2025-09-09T14:17:46.2052425Z ) 2025-09-09T14:17:46.2052638Z ) 2025-09-09T14:17:46.2052773Z 2025-09-09T14:17:46.2052778Z 2025-09-09T14:17:46.2052783Z 2025-09-09T14:17:46.2052892Z def forward(self, x): 2025-09-09T14:17:46.2053365Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:17:46.2054087Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T14:17:46.2054847Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T14:17:46.2055433Z return activation_post_process_1 2025-09-09T14:17:46.2055787Z 2025-09-09T14:17:46.2056144Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:17:46.2056645Z diff: tensor([[[[0., 0., 0.], 2025-09-09T14:17:46.2056956Z [0., 0., 0.], 2025-09-09T14:17:46.2057254Z [0., 0., 0.]], 2025-09-09T14:17:46.2057439Z 2025-09-09T14:17:46.2057552Z [[0., 0., 0.], 2025-09-09T14:17:46.2057820Z [0., 0., 0.], 2025-09-09T14:17:46.2058104Z [0., 0., 0.]], 2025-09-09T14:17:46.2058560Z 2025-09-09T14:17:46.2058663Z [[0., 0., 0.], 2025-09-09T14:17:46.2058951Z [0., 0., 0.], 2025-09-09T14:17:46.2059260Z [0., 0., 0.]]]], grad_fn=) 2025-09-09T14:17:46.2059687Z converted model pt2e: GraphModule( 2025-09-09T14:17:46.2060050Z (conv): Module() 2025-09-09T14:17:46.2060314Z ) 2025-09-09T14:17:46.2060455Z 2025-09-09T14:17:46.2060467Z 2025-09-09T14:17:46.2060472Z 2025-09-09T14:17:46.2060591Z def forward(self, x): 2025-09-09T14:17:46.2060971Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:17:46.2061475Z quantize_per_tensor_default = self._frozen_param0 2025-09-09T14:17:46.2063649Z 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 2025-09-09T14:17:46.2065431Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.018311796709895134, 10, -128, 127, torch.int8); x = None 2025-09-09T14:17:46.2067234Z 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 2025-09-09T14:17:46.2069148Z 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 2025-09-09T14:17:46.2070957Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv2d, 0.008388491347432137, -20, -128, 127, torch.int8); conv2d = None 2025-09-09T14:17:46.2072834Z 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 2025-09-09T14:17:46.2074280Z return pytree.tree_unflatten((dequantize_per_tensor_default_2,), self._out_spec) 2025-09-09T14:17:46.2074848Z 2025-09-09T14:17:46.2075231Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:17:46.2075731Z onverted model fx: GraphModule( 2025-09-09T14:17:46.2076296Z (conv): QuantizedConv2d(Reference)(3, 3, kernel_size=(3, 3), stride=(1, 1), bias=False) 2025-09-09T14:17:46.2076874Z ) 2025-09-09T14:17:46.2077020Z 2025-09-09T14:17:46.2077026Z 2025-09-09T14:17:46.2077031Z 2025-09-09T14:17:46.2077141Z def forward(self, x): 2025-09-09T14:17:46.2078005Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.018311796709895134, 10, -128, 127, torch.int8); x = None 2025-09-09T14:18:25.8266367Z 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 2025-09-09T14:18:25.8267590Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T14:18:25.8268616Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.008388491347432137, -20, -128, 127, torch.int8); conv = None 2025-09-09T14:18:25.8270093Z 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 2025-09-09T14:18:25.8271282Z return dequantize_per_tensor_default_1 2025-09-09T14:18:25.8271598Z 2025-09-09T14:18:25.8271905Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:18:25.8272328Z diff: tensor([[[[0., 0., 0.], 2025-09-09T14:18:25.8272586Z [0., 0., 0.], 2025-09-09T14:18:25.8272822Z [0., 0., 0.]], 2025-09-09T14:18:25.8272975Z 2025-09-09T14:18:25.8273069Z [[0., 0., 0.], 2025-09-09T14:18:25.8273285Z [0., 0., 0.], 2025-09-09T14:18:25.8273521Z [0., 0., 0.]], 2025-09-09T14:18:25.8273670Z 2025-09-09T14:18:25.8273756Z [[0., 0., 0.], 2025-09-09T14:18:25.8273994Z [0., 0., 0.], 2025-09-09T14:18:25.8274217Z [0., 0., 0.]]]]) 2025-09-09T14:18:25.8274651Z PASSED 2025-09-09T14:18:25.8275400Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_conv_transpose_bn PASSED 2025-09-09T14:18:25.8276595Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_conv_transpose_bn_relu PASSED 2025-09-09T14:18:25.8277691Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_inplace_add_relu model pt2e: GraphModule( 2025-09-09T14:18:25.8278376Z (conv): Module() 2025-09-09T14:18:25.8279087Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:18:25.8280261Z 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 2025-09-09T14:18:25.8281630Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.0532]), max_val=tensor([-0.0532])) 2025-09-09T14:18:25.8282277Z ) 2025-09-09T14:18:25.8282571Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:18:25.8283772Z 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 2025-09-09T14:18:25.8285032Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.8401848077774048, max_val=0.9828221797943115) 2025-09-09T14:18:25.8285618Z ) 2025-09-09T14:18:25.8285925Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:18:25.8286993Z 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 2025-09-09T14:18:25.8288267Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.3858921527862549, max_val=0.5359839200973511) 2025-09-09T14:18:25.8288838Z ) 2025-09-09T14:18:25.8289150Z (activation_post_process_3): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:18:25.8290228Z 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 2025-09-09T14:18:25.8291437Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=1.372033953666687) 2025-09-09T14:18:25.8291965Z ) 2025-09-09T14:18:25.8292142Z ) 2025-09-09T14:18:25.8292257Z 2025-09-09T14:18:25.8292261Z 2025-09-09T14:18:25.8292265Z 2025-09-09T14:18:25.8292357Z def forward(self, x): 2025-09-09T14:18:25.8292672Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:18:25.8293038Z conv_weight = self.conv.weight 2025-09-09T14:18:25.8293551Z activation_post_process_1 = self.activation_post_process_1(conv_weight); conv_weight = None 2025-09-09T14:18:25.8294070Z conv_bias = self.conv.bias 2025-09-09T14:18:25.8294491Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:18:25.8295533Z conv2d = torch.ops.aten.conv2d.default(activation_post_process_0, activation_post_process_1, conv_bias); activation_post_process_1 = conv_bias = None 2025-09-09T14:18:25.8296471Z activation_post_process_2 = self.activation_post_process_2(conv2d); conv2d = None 2025-09-09T14:18:25.8297390Z add_ = torch.ops.aten.add_.Tensor(activation_post_process_2, activation_post_process_0); activation_post_process_2 = activation_post_process_0 = None 2025-09-09T14:18:25.8298189Z relu_ = torch.ops.aten.relu_.default(add_); add_ = None 2025-09-09T14:18:25.8298729Z activation_post_process_3 = self.activation_post_process_3(relu_); relu_ = None 2025-09-09T14:18:25.8299340Z return pytree.tree_unflatten((activation_post_process_3,), self._out_spec) 2025-09-09T14:18:25.8299779Z 2025-09-09T14:18:25.8300076Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:18:25.8300490Z model fx: GraphModule( 2025-09-09T14:18:25.8300847Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:18:25.8301910Z 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 2025-09-09T14:18:25.8303265Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.8401848077774048, max_val=0.9828221797943115) 2025-09-09T14:18:25.8303845Z ) 2025-09-09T14:18:25.8304037Z (conv): Conv2d( 2025-09-09T14:18:25.8304289Z 1, 1, kernel_size=(1, 1), stride=(1, 1) 2025-09-09T14:18:25.8304664Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:18:25.8305735Z 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 2025-09-09T14:18:25.8307136Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.0532]), max_val=tensor([-0.0532])) 2025-09-09T14:18:25.8307783Z ) 2025-09-09T14:18:25.8307966Z ) 2025-09-09T14:18:25.8308272Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:18:25.8309362Z 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 2025-09-09T14:18:25.8310622Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.3858921527862549, max_val=0.5359839200973511) 2025-09-09T14:18:25.8311207Z ) 2025-09-09T14:18:25.8311404Z (relu): ReLU(inplace=True) 2025-09-09T14:18:25.8311776Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:18:25.8312853Z 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 2025-09-09T14:18:25.8314060Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=1.372033953666687) 2025-09-09T14:18:25.8314588Z ) 2025-09-09T14:18:25.8314762Z ) 2025-09-09T14:18:25.8314875Z 2025-09-09T14:18:25.8314884Z 2025-09-09T14:18:25.8314889Z 2025-09-09T14:18:25.8314981Z def forward(self, x): 2025-09-09T14:18:25.8315370Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:18:25.8315837Z conv = self.conv(activation_post_process_0) 2025-09-09T14:18:25.8316330Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T14:18:25.8317104Z add = activation_post_process_1 + activation_post_process_0; activation_post_process_1 = activation_post_process_0 = None 2025-09-09T14:18:25.8317744Z relu = self.relu(add); add = None 2025-09-09T14:18:25.8318200Z activation_post_process_2 = self.activation_post_process_2(relu); relu = None 2025-09-09T14:18:25.8318680Z return activation_post_process_2 2025-09-09T14:18:25.8318972Z 2025-09-09T14:18:25.8319269Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:18:25.8319763Z diff: tensor([[[[0., 0., 0.], 2025-09-09T14:18:25.8320029Z [0., 0., 0.], 2025-09-09T14:18:25.8320297Z [0., 0., 0.]]]], grad_fn=) 2025-09-09T14:18:25.8320631Z converted model pt2e: GraphModule( 2025-09-09T14:18:25.8320924Z (conv): Module() 2025-09-09T14:18:25.8321132Z ) 2025-09-09T14:18:25.8321249Z 2025-09-09T14:18:25.8321254Z 2025-09-09T14:18:25.8321258Z 2025-09-09T14:18:25.8321349Z def forward(self, x): 2025-09-09T14:18:25.8321658Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:18:25.8322015Z _scale_0 = self._scale_0 2025-09-09T14:18:25.8322299Z _zero_point_0 = self._zero_point_0 2025-09-09T14:18:25.8322650Z quantize_per_channel_default = self._frozen_param0 2025-09-09T14:18:25.8323795Z 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 2025-09-09T14:18:25.8324876Z conv_bias = self.conv.bias 2025-09-09T14:18:25.8325671Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.011070616543293, 38, -128, 127, torch.int8); x = None 2025-09-09T14:18:25.8326944Z dequantize_per_tensor_default_4 = torch.ops.quantized_decomposed.dequantize_per_tensor.default(quantize_per_tensor_default, 0.011070616543293, 38, -128, 127, torch.int8) 2025-09-09T14:18:25.8328428Z 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 2025-09-09T14:18:25.8330106Z 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 2025-09-09T14:18:25.8331565Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv2d, 0.0021018977276980877, -128, -128, 127, torch.int8); conv2d = None 2025-09-09T14:18:26.7426660Z 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 2025-09-09T14:18:26.7428484Z 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 2025-09-09T14:18:26.7429385Z relu_ = torch.ops.aten.relu_.default(add_); add_ = None 2025-09-09T14:18:26.7430260Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(relu_, 0.005380525253713131, -128, -128, 127, torch.int8); relu_ = None 2025-09-09T14:18:26.7431769Z 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 2025-09-09T14:18:26.7432922Z return pytree.tree_unflatten((dequantize_per_tensor_default_2,), self._out_spec) 2025-09-09T14:18:26.7433422Z 2025-09-09T14:18:26.7433744Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:18:26.7434275Z onverted model fx: GraphModule( 2025-09-09T14:18:26.7434847Z (conv): QuantizedConv2d(Reference)(1, 1, kernel_size=(1, 1), stride=(1, 1)) 2025-09-09T14:18:26.7435497Z (relu): ReLU(inplace=True) 2025-09-09T14:18:26.7435765Z ) 2025-09-09T14:18:26.7435874Z 2025-09-09T14:18:26.7435879Z 2025-09-09T14:18:26.7435883Z 2025-09-09T14:18:26.7435985Z def forward(self, x): 2025-09-09T14:18:26.7436682Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.011070616543293, 38, -128, 127, torch.int8); x = None 2025-09-09T14:18:26.7438100Z 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 2025-09-09T14:18:26.7439107Z conv = self.conv(dequantize_per_tensor_default) 2025-09-09T14:18:26.7440186Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.0021018977276980877, -128, -128, 127, torch.int8); conv = None 2025-09-09T14:18:26.7441947Z 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 2025-09-09T14:18:26.7443401Z add = dequantize_per_tensor_default_1 + dequantize_per_tensor_default; dequantize_per_tensor_default_1 = dequantize_per_tensor_default = None 2025-09-09T14:18:26.7444141Z relu = self.relu(add); add = None 2025-09-09T14:18:26.7444941Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(relu, 0.005380525253713131, -128, -128, 127, torch.int8); relu = None 2025-09-09T14:18:26.7446708Z 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 2025-09-09T14:18:26.7447740Z return dequantize_per_tensor_default_2 2025-09-09T14:18:26.7448064Z 2025-09-09T14:18:26.7448368Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:18:26.7448800Z diff: tensor([[[[0., 0., 0.], 2025-09-09T14:18:26.7449059Z [0., 0., 0.], 2025-09-09T14:18:26.7449303Z [0., 0., 0.]]]]) 2025-09-09T14:18:26.7449653Z model pt2e: GraphModule( 2025-09-09T14:18:26.7449913Z (conv): Module() 2025-09-09T14:18:26.7450240Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:18:26.7451346Z 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 2025-09-09T14:18:26.7452764Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.05316734313964844, max_val=-0.05316734313964844) 2025-09-09T14:18:26.7453351Z ) 2025-09-09T14:18:26.7453664Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:18:26.7454742Z 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 2025-09-09T14:18:26.7456002Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.8401848077774048, max_val=0.9828221797943115) 2025-09-09T14:18:26.7456601Z ) 2025-09-09T14:18:26.7456902Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:18:26.7457989Z 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 2025-09-09T14:18:26.7459725Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.3858921527862549, max_val=0.5359839200973511) 2025-09-09T14:18:26.7460302Z ) 2025-09-09T14:18:26.7460611Z (activation_post_process_3): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:18:26.7461678Z 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 2025-09-09T14:18:26.7462903Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=1.372033953666687) 2025-09-09T14:18:26.7463437Z ) 2025-09-09T14:18:26.7463617Z ) 2025-09-09T14:18:26.7463719Z 2025-09-09T14:18:26.7463724Z 2025-09-09T14:18:26.7463729Z 2025-09-09T14:18:26.7463832Z def forward(self, x): 2025-09-09T14:18:26.7464138Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:18:26.7464521Z conv_weight = self.conv.weight 2025-09-09T14:18:26.7465019Z activation_post_process_1 = self.activation_post_process_1(conv_weight); conv_weight = None 2025-09-09T14:18:26.7465557Z conv_bias = self.conv.bias 2025-09-09T14:18:26.7465973Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:18:26.7466850Z conv2d = torch.ops.aten.conv2d.default(activation_post_process_0, activation_post_process_1, conv_bias); activation_post_process_1 = conv_bias = None 2025-09-09T14:18:26.7467771Z activation_post_process_2 = self.activation_post_process_2(conv2d); conv2d = None 2025-09-09T14:18:26.7468673Z add_ = torch.ops.aten.add_.Tensor(activation_post_process_2, activation_post_process_0); activation_post_process_2 = activation_post_process_0 = None 2025-09-09T14:18:26.7469484Z relu_ = torch.ops.aten.relu_.default(add_); add_ = None 2025-09-09T14:18:26.7470743Z activation_post_process_3 = self.activation_post_process_3(relu_); relu_ = None 2025-09-09T14:18:26.7471381Z return pytree.tree_unflatten((activation_post_process_3,), self._out_spec) 2025-09-09T14:18:26.7471830Z 2025-09-09T14:18:26.7472133Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:18:26.7472549Z model fx: GraphModule( 2025-09-09T14:18:26.7472900Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:18:26.7473991Z 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 2025-09-09T14:18:26.7475364Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.8401848077774048, max_val=0.9828221797943115) 2025-09-09T14:18:26.7475947Z ) 2025-09-09T14:18:26.7476151Z (conv): Conv2d( 2025-09-09T14:18:26.7476391Z 1, 1, kernel_size=(1, 1), stride=(1, 1) 2025-09-09T14:18:26.7476783Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:18:26.7477836Z 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 2025-09-09T14:18:26.7479139Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.05316734313964844, max_val=-0.05316734313964844) 2025-09-09T14:18:26.7479828Z ) 2025-09-09T14:18:26.7480014Z ) 2025-09-09T14:18:26.7480332Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:18:26.7481418Z 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 2025-09-09T14:18:26.7482703Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.3858921527862549, max_val=0.5359839200973511) 2025-09-09T14:18:26.7483288Z ) 2025-09-09T14:18:26.7483486Z (relu): ReLU(inplace=True) 2025-09-09T14:18:26.7483864Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:18:26.7484936Z 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 2025-09-09T14:18:26.7486153Z (activation_post_process): MovingAverageMinMaxObserver(min_val=0.0, max_val=1.372033953666687) 2025-09-09T14:18:26.7486689Z ) 2025-09-09T14:18:26.7486864Z ) 2025-09-09T14:18:26.7486969Z 2025-09-09T14:18:26.7486974Z 2025-09-09T14:18:26.7486978Z 2025-09-09T14:18:26.7487079Z def forward(self, x): 2025-09-09T14:18:26.7487456Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:18:26.7487936Z conv = self.conv(activation_post_process_0) 2025-09-09T14:18:26.7488420Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T14:18:26.7489205Z add = activation_post_process_1 + activation_post_process_0; activation_post_process_1 = activation_post_process_0 = None 2025-09-09T14:18:26.7489964Z relu = self.relu(add); add = None 2025-09-09T14:18:26.7490508Z activation_post_process_2 = self.activation_post_process_2(relu); relu = None 2025-09-09T14:18:26.7490994Z return activation_post_process_2 2025-09-09T14:18:26.7491274Z 2025-09-09T14:18:26.7491589Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:18:26.7492005Z diff: tensor([[[[0., 0., 0.], 2025-09-09T14:18:26.7492272Z [0., 0., 0.], 2025-09-09T14:18:26.7492532Z [0., 0., 0.]]]], grad_fn=) 2025-09-09T14:18:26.7492877Z converted model pt2e: GraphModule( 2025-09-09T14:18:26.7493170Z (conv): Module() 2025-09-09T14:18:26.7493377Z ) 2025-09-09T14:18:26.7493481Z 2025-09-09T14:18:26.7493485Z 2025-09-09T14:18:26.7493637Z 2025-09-09T14:18:26.7493731Z def forward(self, x): 2025-09-09T14:18:26.7494030Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:18:26.7494452Z quantize_per_tensor_default = self._frozen_param0 2025-09-09T14:18:46.7572521Z 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 2025-09-09T14:18:46.7573846Z conv_bias = self.conv.bias 2025-09-09T14:18:46.7574742Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.011070616543293, 38, -128, 127, torch.int8); x = None 2025-09-09T14:18:46.7576743Z 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) 2025-09-09T14:18:46.7578645Z 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 2025-09-09T14:18:46.7580646Z 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 2025-09-09T14:18:46.7582461Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv2d, 0.0021018977276980877, -128, -128, 127, torch.int8); conv2d = None 2025-09-09T14:18:46.7584339Z 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 2025-09-09T14:18:46.7586233Z 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 2025-09-09T14:18:46.7587359Z relu_ = torch.ops.aten.relu_.default(add_); add_ = None 2025-09-09T14:18:46.7588437Z quantize_per_tensor_default_3 = torch.ops.quantized_decomposed.quantize_per_tensor.default(relu_, 0.005380525253713131, -128, -128, 127, torch.int8); relu_ = None 2025-09-09T14:18:46.7590282Z 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 2025-09-09T14:18:46.7591739Z return pytree.tree_unflatten((dequantize_per_tensor_default_3,), self._out_spec) 2025-09-09T14:18:46.7592315Z 2025-09-09T14:18:46.7592703Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:18:46.7593227Z onverted model fx: GraphModule( 2025-09-09T14:18:46.7593747Z (conv): QuantizedConv2d(Reference)(1, 1, kernel_size=(1, 1), stride=(1, 1)) 2025-09-09T14:18:46.7594315Z (relu): ReLU(inplace=True) 2025-09-09T14:18:46.7594668Z ) 2025-09-09T14:18:46.7594798Z 2025-09-09T14:18:46.7594804Z 2025-09-09T14:18:46.7594808Z 2025-09-09T14:18:46.7594920Z def forward(self, x): 2025-09-09T14:18:46.7595767Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.011070616543293, 38, -128, 127, torch.int8); x = None 2025-09-09T14:18:46.7597492Z 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 2025-09-09T14:18:46.7598742Z conv = self.conv(dequantize_per_tensor_default) 2025-09-09T14:18:46.7599848Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.0021018977276980877, -128, -128, 127, torch.int8); conv = None 2025-09-09T14:18:46.7601846Z 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 2025-09-09T14:18:46.7603603Z add = dequantize_per_tensor_default_1 + dequantize_per_tensor_default; dequantize_per_tensor_default_1 = dequantize_per_tensor_default = None 2025-09-09T14:18:46.7604500Z relu = self.relu(add); add = None 2025-09-09T14:18:46.7605469Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(relu, 0.005380525253713131, -128, -128, 127, torch.int8); relu = None 2025-09-09T14:18:46.7607314Z 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 2025-09-09T14:18:46.7608637Z return dequantize_per_tensor_default_2 2025-09-09T14:18:46.7609022Z 2025-09-09T14:18:46.7609387Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:18:46.7609901Z diff: tensor([[[[0., 0., 0.], 2025-09-09T14:18:46.7610230Z [0., 0., 0.], 2025-09-09T14:18:46.7610507Z [0., 0., 0.]]]]) 2025-09-09T14:18:46.7611025Z PASSED 2025-09-09T14:18:46.7612031Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_per_channel_weight_custom_dtype PASSED 2025-09-09T14:18:46.7613586Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_preserve_source_fn_stack PASSED 2025-09-09T14:18:46.7614953Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_update_shared_qspec model pt2e: GraphModule( 2025-09-09T14:18:46.7615846Z (conv): Module() 2025-09-09T14:18:46.7616122Z (bn): Module() 2025-09-09T14:18:46.7616559Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:18:46.7617894Z 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 2025-09-09T14:18:46.7619463Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.526270866394043, max_val=2.143237352371216) 2025-09-09T14:18:46.7620183Z ) 2025-09-09T14:18:46.7620561Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:18:46.7621955Z 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 2025-09-09T14:18:46.7623801Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.1919, -0.1859, -0.1499]), max_val=tensor([0.1902, 0.1880, 0.1882])) 2025-09-09T14:18:46.7624717Z ) 2025-09-09T14:18:46.7625103Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:18:46.7626444Z 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 2025-09-09T14:18:46.7627996Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.5212559700012207, max_val=2.179866313934326) 2025-09-09T14:18:46.7628731Z ) 2025-09-09T14:18:46.7629090Z (activation_post_process_3): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:18:46.7630423Z 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 2025-09-09T14:18:46.7632001Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.5212559700012207, max_val=2.179866313934326) 2025-09-09T14:18:46.7632712Z ) 2025-09-09T14:18:46.7632946Z ) 2025-09-09T14:18:46.7633073Z 2025-09-09T14:18:46.7633078Z 2025-09-09T14:18:46.7633083Z 2025-09-09T14:18:46.7633282Z def forward(self, x): 2025-09-09T14:18:46.7633669Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:18:46.7634136Z conv_weight = self.conv.weight 2025-09-09T14:18:46.7634501Z conv_bias = self.conv.bias 2025-09-09T14:18:46.7634853Z bn_weight = self.bn.weight 2025-09-09T14:18:46.7635182Z bn_bias = self.bn.bias 2025-09-09T14:18:46.7635536Z bn_running_mean = self.bn.running_mean 2025-09-09T14:18:46.7635933Z bn_running_var = self.bn.running_var 2025-09-09T14:18:46.7636385Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T14:18:46.7637045Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:18:46.7637859Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T14:18:46.7638586Z add = torch.ops.aten.add.Tensor(bn_running_var, 1e-05) 2025-09-09T14:18:46.7639110Z sqrt = torch.ops.aten.sqrt.default(add); add = None 2025-09-09T14:18:46.7639745Z div = torch.ops.aten.div.Tensor(bn_weight, sqrt); sqrt = None 2025-09-09T14:18:46.7640347Z reshape = torch.ops.aten.reshape.default(div, [-1, 1, 1, 1]) 2025-09-09T14:18:46.7641047Z mul = torch.ops.aten.mul.Tensor(conv_weight, reshape); conv_weight = reshape = None 2025-09-09T14:18:46.7641814Z activation_post_process_1 = self.activation_post_process_1(mul); mul = None 2025-09-09T14:18:46.7642667Z zeros_like = torch.ops.aten.zeros_like.default(conv_bias, dtype = torch.float32, pin_memory = False) 2025-09-09T14:18:46.7644055Z 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 2025-09-09T14:18:46.7645312Z reshape_1 = torch.ops.aten.reshape.default(div, [1, -1, 1, 1]); div = None 2025-09-09T14:18:46.7646069Z div_1 = torch.ops.aten.div.Tensor(conv2d_1, reshape_1); conv2d_1 = reshape_1 = None 2025-09-09T14:18:46.7646876Z reshape_2 = torch.ops.aten.reshape.default(conv_bias, [1, -1, 1, 1]); conv_bias = None 2025-09-09T14:18:46.7647658Z add_1 = torch.ops.aten.add.Tensor(div_1, reshape_2); div_1 = reshape_2 = None 2025-09-09T14:18:46.7648889Z 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 2025-09-09T14:18:46.7650196Z activation_post_process_2 = self.activation_post_process_2(batch_norm_1); batch_norm_1 = None 2025-09-09T14:18:46.7651243Z hardtanh = torch.ops.aten.hardtanh.default(activation_post_process_2, -1.0, 1.0); activation_post_process_2 = None 2025-09-09T14:18:57.3007220Z activation_post_process_3 = self.activation_post_process_3(hardtanh); hardtanh = None 2025-09-09T14:18:57.3008083Z return pytree.tree_unflatten((activation_post_process_3,), self._out_spec) 2025-09-09T14:18:57.3008631Z 2025-09-09T14:18:57.3009034Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:18:57.3009548Z model fx: GraphModule( 2025-09-09T14:18:57.3010037Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:18:57.3011374Z 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 2025-09-09T14:18:57.3013028Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.526270866394043, max_val=2.143237352371216) 2025-09-09T14:18:57.3013775Z ) 2025-09-09T14:18:57.3014011Z (conv): ConvBn2d( 2025-09-09T14:18:57.3014325Z 3, 3, kernel_size=(3, 3), stride=(1, 1) 2025-09-09T14:18:57.3014884Z (bn): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T14:18:57.3015535Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:18:57.3017245Z 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 2025-09-09T14:18:57.3019097Z (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([-0.1919, -0.1859, -0.1499]), max_val=tensor([0.1902, 0.1880, 0.1882])) 2025-09-09T14:18:57.3020023Z ) 2025-09-09T14:18:57.3020250Z ) 2025-09-09T14:18:57.3020635Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:18:57.3021986Z 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 2025-09-09T14:18:57.3032718Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.5212559700012207, max_val=2.179866313934326) 2025-09-09T14:18:57.3033567Z ) 2025-09-09T14:18:57.3033876Z (hardtanh): Hardtanh(min_val=-1.0, max_val=1.0) 2025-09-09T14:18:57.3034439Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:18:57.3035801Z 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 2025-09-09T14:18:57.3037378Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.5212559700012207, max_val=2.179866313934326) 2025-09-09T14:18:57.3038109Z ) 2025-09-09T14:18:57.3038358Z ) 2025-09-09T14:18:57.3038487Z 2025-09-09T14:18:57.3038492Z 2025-09-09T14:18:57.3038496Z 2025-09-09T14:18:57.3038611Z def forward(self, x): 2025-09-09T14:18:57.3039098Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:18:57.3039906Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T14:18:57.3040683Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T14:18:57.3041484Z hardtanh = self.hardtanh(activation_post_process_1); activation_post_process_1 = None 2025-09-09T14:18:57.3042339Z activation_post_process_2 = self.activation_post_process_2(hardtanh); hardtanh = None 2025-09-09T14:18:57.3042974Z return activation_post_process_2 2025-09-09T14:18:57.3043327Z 2025-09-09T14:18:57.3043712Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:18:57.3044213Z diff: tensor([[[[0., 0., 0.], 2025-09-09T14:18:57.3044545Z [0., 0., 0.], 2025-09-09T14:18:57.3044829Z [0., 0., 0.]], 2025-09-09T14:18:57.3045033Z 2025-09-09T14:18:57.3045135Z [[0., 0., 0.], 2025-09-09T14:18:57.3045405Z [0., 0., 0.], 2025-09-09T14:18:57.3045692Z [0., 0., 0.]], 2025-09-09T14:18:57.3045878Z 2025-09-09T14:18:57.3045999Z [[0., 0., 0.], 2025-09-09T14:18:57.3046270Z [0., 0., 0.], 2025-09-09T14:18:57.3046605Z [0., 0., 0.]]]], grad_fn=) 2025-09-09T14:18:57.3047019Z converted model pt2e: GraphModule( 2025-09-09T14:18:57.3047381Z (conv): Module() 2025-09-09T14:18:57.3047648Z (bn): Module() 2025-09-09T14:18:57.3047916Z ) 2025-09-09T14:18:57.3048045Z 2025-09-09T14:18:57.3048050Z 2025-09-09T14:18:57.3048054Z 2025-09-09T14:18:57.3048178Z def forward(self, x): 2025-09-09T14:18:57.3048550Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:18:57.3049007Z conv_bias = self.conv.bias 2025-09-09T14:18:57.3049909Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.018311796709895134, 10, -128, 127, torch.int8); x = None 2025-09-09T14:18:57.3051719Z 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 2025-09-09T14:18:57.3053800Z _scale_0 = self._scale_0 2025-09-09T14:18:57.3054174Z _zero_point_0 = self._zero_point_0 2025-09-09T14:18:57.3054591Z quantize_per_channel = self._frozen_param0 2025-09-09T14:18:57.3055837Z 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 2025-09-09T14:18:57.3057779Z 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 2025-09-09T14:18:57.3059977Z 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 2025-09-09T14:18:57.3061845Z 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 2025-09-09T14:18:57.3063519Z hardtanh = torch.ops.aten.hardtanh.default(dequantize_per_tensor_default_1, -1.0, 1.0); dequantize_per_tensor_default_1 = None 2025-09-09T14:18:57.3064979Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(hardtanh, 0.014514205045998096, -23, -128, 127, torch.int8); hardtanh = None 2025-09-09T14:18:57.3066876Z 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 2025-09-09T14:18:57.3068332Z return pytree.tree_unflatten((dequantize_per_tensor_default_2,), self._out_spec) 2025-09-09T14:18:57.3068904Z 2025-09-09T14:18:57.3069285Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:18:57.3069792Z onverted model fx: GraphModule( 2025-09-09T14:18:57.3070332Z (conv): QuantizedConv2d(Reference)(3, 3, kernel_size=(3, 3), stride=(1, 1)) 2025-09-09T14:18:57.3070935Z (hardtanh): Hardtanh(min_val=-1.0, max_val=1.0) 2025-09-09T14:18:57.3071331Z ) 2025-09-09T14:18:57.3071461Z 2025-09-09T14:18:57.3071466Z 2025-09-09T14:18:57.3071485Z 2025-09-09T14:18:57.3071599Z def forward(self, x): 2025-09-09T14:18:57.3072472Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.018311796709895134, 10, -128, 127, torch.int8); x = None 2025-09-09T14:18:57.3074244Z 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 2025-09-09T14:18:57.3075693Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T14:18:57.3076908Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.014514205045998096, -23, -128, 127, torch.int8); conv = None 2025-09-09T14:18:57.3078730Z 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 2025-09-09T14:18:57.3080322Z hardtanh = self.hardtanh(dequantize_per_tensor_default_1); dequantize_per_tensor_default_1 = None 2025-09-09T14:18:57.3081647Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(hardtanh, 0.014514205045998096, -23, -128, 127, torch.int8); hardtanh = None 2025-09-09T14:18:57.3083522Z 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 2025-09-09T14:18:57.3084780Z return dequantize_per_tensor_default_2 2025-09-09T14:18:57.3085146Z 2025-09-09T14:18:57.3085684Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:18:57.3086200Z diff: tensor([[[[0., 0., 0.], 2025-09-09T14:18:57.3086517Z [0., 0., 0.], 2025-09-09T14:18:57.3086810Z [0., 0., 0.]], 2025-09-09T14:18:57.3086995Z 2025-09-09T14:18:57.3087095Z [[0., 0., 0.], 2025-09-09T14:18:57.3087378Z [0., 0., 0.], 2025-09-09T14:18:57.3087649Z [0., 0., 0.]], 2025-09-09T14:18:57.3087849Z 2025-09-09T14:18:57.3087948Z [[0., 0., 0.], 2025-09-09T14:18:57.3088222Z [0., 0., 0.], 2025-09-09T14:18:57.3088508Z [0., 0., 0.]]]]) 2025-09-09T14:18:57.3088832Z model pt2e: GraphModule( 2025-09-09T14:18:57.3089221Z (conv): Module() 2025-09-09T14:18:57.3089508Z (bn): Module() 2025-09-09T14:18:57.3089903Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:18:57.3091245Z 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 2025-09-09T14:18:57.3092813Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.526270866394043, max_val=2.143237352371216) 2025-09-09T14:18:57.3093538Z ) 2025-09-09T14:18:57.3093916Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:18:57.3095250Z 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 2025-09-09T14:18:57.3096847Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.19193142652511597, max_val=0.1902383267879486) 2025-09-09T14:18:57.3097570Z ) 2025-09-09T14:18:57.3097945Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:19:04.6444438Z 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 2025-09-09T14:19:04.6446122Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.521487832069397, max_val=2.1819007396698) 2025-09-09T14:19:04.6446852Z ) 2025-09-09T14:19:04.6447237Z (activation_post_process_3): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:19:04.6448572Z 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 2025-09-09T14:19:04.6450157Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.521487832069397, max_val=2.1819007396698) 2025-09-09T14:19:04.6450860Z ) 2025-09-09T14:19:04.6451095Z ) 2025-09-09T14:19:04.6451220Z 2025-09-09T14:19:04.6451225Z 2025-09-09T14:19:04.6451230Z 2025-09-09T14:19:04.6451362Z def forward(self, x): 2025-09-09T14:19:04.6451745Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:19:04.6452213Z conv_weight = self.conv.weight 2025-09-09T14:19:04.6452578Z conv_bias = self.conv.bias 2025-09-09T14:19:04.6452927Z bn_weight = self.bn.weight 2025-09-09T14:19:04.6453258Z bn_bias = self.bn.bias 2025-09-09T14:19:04.6453605Z bn_running_mean = self.bn.running_mean 2025-09-09T14:19:04.6454005Z bn_running_var = self.bn.running_var 2025-09-09T14:19:04.6454463Z bn_num_batches_tracked = self.bn.num_batches_tracked 2025-09-09T14:19:04.6455075Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:19:04.6455882Z add_ = torch.ops.aten.add_.Tensor(bn_num_batches_tracked, 1); bn_num_batches_tracked = add_ = None 2025-09-09T14:19:04.6456616Z add = torch.ops.aten.add.Tensor(bn_running_var, 1e-05) 2025-09-09T14:19:04.6457135Z sqrt = torch.ops.aten.sqrt.default(add); add = None 2025-09-09T14:19:04.6457705Z div = torch.ops.aten.div.Tensor(bn_weight, sqrt); sqrt = None 2025-09-09T14:19:04.6458894Z reshape = torch.ops.aten.reshape.default(div, [-1, 1, 1, 1]) 2025-09-09T14:19:04.6459615Z mul = torch.ops.aten.mul.Tensor(conv_weight, reshape); conv_weight = reshape = None 2025-09-09T14:19:04.6460405Z activation_post_process_1 = self.activation_post_process_1(mul); mul = None 2025-09-09T14:19:04.6461253Z zeros_like = torch.ops.aten.zeros_like.default(conv_bias, dtype = torch.float32, pin_memory = False) 2025-09-09T14:19:04.6462635Z 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 2025-09-09T14:19:04.6464013Z reshape_1 = torch.ops.aten.reshape.default(div, [1, -1, 1, 1]); div = None 2025-09-09T14:19:04.6464777Z div_1 = torch.ops.aten.div.Tensor(conv2d_1, reshape_1); conv2d_1 = reshape_1 = None 2025-09-09T14:19:04.6465591Z reshape_2 = torch.ops.aten.reshape.default(conv_bias, [1, -1, 1, 1]); conv_bias = None 2025-09-09T14:19:04.6466367Z add_1 = torch.ops.aten.add.Tensor(div_1, reshape_2); div_1 = reshape_2 = None 2025-09-09T14:19:04.6467596Z 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 2025-09-09T14:19:04.6468888Z activation_post_process_2 = self.activation_post_process_2(batch_norm_1); batch_norm_1 = None 2025-09-09T14:19:04.6469971Z hardtanh = torch.ops.aten.hardtanh.default(activation_post_process_2, -1.0, 1.0); activation_post_process_2 = None 2025-09-09T14:19:04.6470978Z activation_post_process_3 = self.activation_post_process_3(hardtanh); hardtanh = None 2025-09-09T14:19:04.6471769Z return pytree.tree_unflatten((activation_post_process_3,), self._out_spec) 2025-09-09T14:19:04.6472300Z 2025-09-09T14:19:04.6472680Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:19:04.6473177Z model fx: GraphModule( 2025-09-09T14:19:04.6473614Z (activation_post_process_0): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:19:04.6474935Z 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 2025-09-09T14:19:04.6476492Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-2.526270866394043, max_val=2.143237352371216) 2025-09-09T14:19:04.6477213Z ) 2025-09-09T14:19:04.6477442Z (conv): ConvBn2d( 2025-09-09T14:19:04.6477753Z 3, 3, kernel_size=(3, 3), stride=(1, 1) 2025-09-09T14:19:04.6478307Z (bn): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) 2025-09-09T14:19:04.6478954Z (weight_fake_quant): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:19:04.6480317Z 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 2025-09-09T14:19:04.6481916Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-0.19193142652511597, max_val=0.1902383267879486) 2025-09-09T14:19:04.6482660Z ) 2025-09-09T14:19:04.6482885Z ) 2025-09-09T14:19:04.6483262Z (activation_post_process_1): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:19:04.6484591Z 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 2025-09-09T14:19:04.6486149Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.521487832069397, max_val=2.1819007396698) 2025-09-09T14:19:04.6486868Z ) 2025-09-09T14:19:04.6487149Z (hardtanh): Hardtanh(min_val=-1.0, max_val=1.0) 2025-09-09T14:19:04.6487691Z (activation_post_process_2): FusedMovingAvgObsFakeQuantize( 2025-09-09T14:19:04.6489103Z 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 2025-09-09T14:19:04.6490670Z (activation_post_process): MovingAverageMinMaxObserver(min_val=-1.521487832069397, max_val=2.1819007396698) 2025-09-09T14:19:04.6491384Z ) 2025-09-09T14:19:04.6491610Z ) 2025-09-09T14:19:04.6491736Z 2025-09-09T14:19:04.6491741Z 2025-09-09T14:19:04.6491746Z 2025-09-09T14:19:04.6491875Z def forward(self, x): 2025-09-09T14:19:04.6492410Z activation_post_process_0 = self.activation_post_process_0(x); x = None 2025-09-09T14:19:04.6493147Z conv = self.conv(activation_post_process_0); activation_post_process_0 = None 2025-09-09T14:19:04.6493898Z activation_post_process_1 = self.activation_post_process_1(conv); conv = None 2025-09-09T14:19:04.6494709Z hardtanh = self.hardtanh(activation_post_process_1); activation_post_process_1 = None 2025-09-09T14:19:04.6495555Z activation_post_process_2 = self.activation_post_process_2(hardtanh); hardtanh = None 2025-09-09T14:19:04.6496172Z return activation_post_process_2 2025-09-09T14:19:04.6496532Z 2025-09-09T14:19:04.6496900Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:19:04.6497409Z diff: tensor([[[[0., 0., 0.], 2025-09-09T14:19:04.6497726Z [0., 0., 0.], 2025-09-09T14:19:04.6498025Z [0., 0., 0.]], 2025-09-09T14:19:04.6498212Z 2025-09-09T14:19:04.6498333Z [[0., 0., 0.], 2025-09-09T14:19:04.6498609Z [0., 0., 0.], 2025-09-09T14:19:04.6498892Z [0., 0., 0.]], 2025-09-09T14:19:04.6499076Z 2025-09-09T14:19:04.6499178Z [[0., 0., 0.], 2025-09-09T14:19:04.6499459Z [0., 0., 0.], 2025-09-09T14:19:04.6499768Z [0., 0., 0.]]]], grad_fn=) 2025-09-09T14:19:04.6500197Z converted model pt2e: GraphModule( 2025-09-09T14:19:04.6500552Z (conv): Module() 2025-09-09T14:19:04.6500832Z (bn): Module() 2025-09-09T14:19:04.6501085Z ) 2025-09-09T14:19:04.6501229Z 2025-09-09T14:19:04.6501234Z 2025-09-09T14:19:04.6501238Z 2025-09-09T14:19:04.6501353Z def forward(self, x): 2025-09-09T14:19:04.6501732Z x, = fx_pytree.tree_flatten_spec(([x], {}), self._in_spec) 2025-09-09T14:19:04.6502182Z conv_bias = self.conv.bias 2025-09-09T14:19:04.6503092Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.018311796709895134, 10, -128, 127, torch.int8); x = None 2025-09-09T14:19:04.6504859Z 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 2025-09-09T14:19:04.6506126Z quantize_per_tensor = self._frozen_param0 2025-09-09T14:19:04.6507270Z 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 2025-09-09T14:19:04.6509069Z 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 2025-09-09T14:19:04.6510786Z 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 2025-09-09T14:19:04.6512663Z 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 2025-09-09T14:19:04.6514333Z hardtanh = torch.ops.aten.hardtanh.default(dequantize_per_tensor_default_2, -1.0, 1.0); dequantize_per_tensor_default_2 = None 2025-09-09T14:19:04.6515875Z quantize_per_tensor_default_3 = torch.ops.quantized_decomposed.quantize_per_tensor.default(hardtanh, 0.014523092657327652, -23, -128, 127, torch.int8); hardtanh = None 2025-09-09T14:19:04.6517757Z 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 2025-09-09T14:19:04.6519178Z return pytree.tree_unflatten((dequantize_per_tensor_default_3,), self._out_spec) 2025-09-09T14:19:04.6519829Z 2025-09-09T14:19:04.6520198Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:19:04.6520714Z onverted model fx: GraphModule( 2025-09-09T14:19:04.6521297Z (conv): QuantizedConv2d(Reference)(3, 3, kernel_size=(3, 3), stride=(1, 1)) 2025-09-09T14:19:04.6521897Z (hardtanh): Hardtanh(min_val=-1.0, max_val=1.0) 2025-09-09T14:19:04.6522298Z ) 2025-09-09T14:19:04.6522427Z 2025-09-09T14:19:04.6522432Z 2025-09-09T14:19:04.6522436Z 2025-09-09T14:19:04.6522549Z def forward(self, x): 2025-09-09T14:19:04.6523425Z quantize_per_tensor_default = torch.ops.quantized_decomposed.quantize_per_tensor.default(x, 0.018311796709895134, 10, -128, 127, torch.int8); x = None 2025-09-09T14:19:33.9017607Z 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 2025-09-09T14:19:33.9019780Z conv = self.conv(dequantize_per_tensor_default); dequantize_per_tensor_default = None 2025-09-09T14:19:33.9021264Z quantize_per_tensor_default_1 = torch.ops.quantized_decomposed.quantize_per_tensor.default(conv, 0.014523092657327652, -23, -128, 127, torch.int8); conv = None 2025-09-09T14:19:33.9022768Z 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 2025-09-09T14:19:33.9023999Z hardtanh = self.hardtanh(dequantize_per_tensor_default_1); dequantize_per_tensor_default_1 = None 2025-09-09T14:19:33.9025045Z quantize_per_tensor_default_2 = torch.ops.quantized_decomposed.quantize_per_tensor.default(hardtanh, 0.014523092657327652, -23, -128, 127, torch.int8); hardtanh = None 2025-09-09T14:19:33.9026544Z 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 2025-09-09T14:19:33.9027554Z return dequantize_per_tensor_default_2 2025-09-09T14:19:33.9027858Z 2025-09-09T14:19:33.9028171Z # To see more debug info, please use `graph_module.print_readable()` 2025-09-09T14:19:33.9028579Z diff: tensor([[[[0., 0., 0.], 2025-09-09T14:19:33.9028849Z [0., 0., 0.], 2025-09-09T14:19:33.9029077Z [0., 0., 0.]], 2025-09-09T14:19:33.9029243Z 2025-09-09T14:19:33.9029332Z [[0., 0., 0.], 2025-09-09T14:19:33.9029555Z [0., 0., 0.], 2025-09-09T14:19:33.9029793Z [0., 0., 0.]], 2025-09-09T14:19:33.9029943Z 2025-09-09T14:19:33.9030038Z [[0., 0., 0.], 2025-09-09T14:19:33.9030253Z [0., 0., 0.], 2025-09-09T14:19:33.9030483Z [0., 0., 0.]]]]) 2025-09-09T14:19:33.9030906Z PASSED 2025-09-09T14:19:33.9031639Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQATModels::test_qat_mobilenet_v2 SKIPPED 2025-09-09T14:19:33.9032719Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQATModels::test_qat_resnet18 SKIPPED 2025-09-09T14:19:33.9033788Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizeMixQATAndPTQ::test_mixing_qat_ptq PASSED 2025-09-09T14:19:33.9034787Z test/quantization/pt2e/test_representation.py::TestPT2ERepresentation::test_add PASSED 2025-09-09T14:19:33.9035721Z test/quantization/pt2e/test_representation.py::TestPT2ERepresentation::test_add_relu PASSED 2025-09-09T14:19:33.9036982Z test/quantization/pt2e/test_representation.py::TestPT2ERepresentation::test_conv2d PASSED 2025-09-09T14:19:33.9037972Z test/quantization/pt2e/test_representation.py::TestPT2ERepresentation::test_dynamic_linear PASSED 2025-09-09T14:19:33.9038981Z test/quantization/pt2e/test_representation.py::TestPT2ERepresentation::test_maxpool2d PASSED 2025-09-09T14:19:33.9040031Z test/quantization/pt2e/test_representation.py::TestPT2ERepresentation::test_qdq PASSED 2025-09-09T14:19:33.9041020Z test/quantization/pt2e/test_representation.py::TestPT2ERepresentation::test_qdq_per_channel PASSED 2025-09-09T14:19:33.9042193Z test/quantization/pt2e/test_representation.py::TestPT2ERepresentation::test_static_linear PASSED 2025-09-09T14:19:33.9043740Z 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 SKIPPED 2025-09-09T14:19:33.9045703Z 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 SKIPPED 2025-09-09T14:19:33.9047634Z 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 SKIPPED 2025-09-09T14:19:33.9049578Z 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 SKIPPED 2025-09-09T14:19:33.9051531Z 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 SKIPPED 2025-09-09T14:19:33.9053480Z 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 SKIPPED 2025-09-09T14:19:33.9055430Z 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 SKIPPED 2025-09-09T14:19:33.9057372Z 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 SKIPPED 2025-09-09T14:19:33.9059551Z 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 SKIPPED 2025-09-09T14:19:33.9061499Z 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 SKIPPED 2025-09-09T14:19:33.9063434Z 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 SKIPPED 2025-09-09T14:19:33.9065352Z 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 SKIPPED 2025-09-09T14:19:33.9067292Z 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 SKIPPED 2025-09-09T14:19:33.9069337Z 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 SKIPPED 2025-09-09T14:19:33.9071285Z 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 SKIPPED 2025-09-09T14:19:33.9073216Z 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 SKIPPED 2025-09-09T14:19:33.9075236Z 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 SKIPPED 2025-09-09T14:19:33.9077172Z 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 SKIPPED 2025-09-09T14:19:33.9079090Z 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 SKIPPED 2025-09-09T14:19:33.9081094Z 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 SKIPPED 2025-09-09T14:19:33.9083032Z 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 SKIPPED 2025-09-09T14:19:33.9084973Z 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 SKIPPED 2025-09-09T14:19:33.9086911Z 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 SKIPPED 2025-09-09T14:19:33.9088841Z 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 SKIPPED 2025-09-09T14:19:33.9355334Z 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 SKIPPED 2025-09-09T14:19:33.9357286Z 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 SKIPPED 2025-09-09T14:19:33.9359448Z 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 SKIPPED 2025-09-09T14:19:33.9361451Z 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 SKIPPED 2025-09-09T14:19:33.9363369Z 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 SKIPPED 2025-09-09T14:19:33.9365447Z 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 SKIPPED 2025-09-09T14:19:33.9367524Z 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 SKIPPED 2025-09-09T14:19:33.9369437Z 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 SKIPPED 2025-09-09T14:19:33.9371510Z 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 SKIPPED 2025-09-09T14:19:33.9373454Z 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 SKIPPED 2025-09-09T14:19:33.9375367Z 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 SKIPPED 2025-09-09T14:19:33.9377292Z 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 SKIPPED 2025-09-09T14:19:33.9379232Z 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 SKIPPED 2025-09-09T14:19:33.9381175Z 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 SKIPPED 2025-09-09T14:19:33.9383115Z 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 SKIPPED 2025-09-09T14:19:33.9385066Z 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 SKIPPED 2025-09-09T14:19:33.9386995Z 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 SKIPPED 2025-09-09T14:19:33.9388918Z 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 SKIPPED 2025-09-09T14:19:33.9390840Z 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 SKIPPED 2025-09-09T14:19:33.9392753Z 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 SKIPPED 2025-09-09T14:19:33.9394671Z 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 SKIPPED 2025-09-09T14:19:33.9396677Z 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 SKIPPED 2025-09-09T14:19:33.9398617Z 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 SKIPPED 2025-09-09T14:19:33.9400622Z 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 SKIPPED 2025-09-09T14:19:33.9402553Z 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 SKIPPED 2025-09-09T14:19:33.9404554Z 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 SKIPPED 2025-09-09T14:19:33.9406462Z 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 SKIPPED 2025-09-09T14:19:33.9408381Z 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 SKIPPED 2025-09-09T14:19:33.9410317Z 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 SKIPPED 2025-09-09T14:19:33.9412241Z 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 SKIPPED 2025-09-09T14:19:33.9414180Z 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 SKIPPED 2025-09-09T14:19:33.9416104Z 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 SKIPPED 2025-09-09T14:19:33.9418011Z 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 SKIPPED 2025-09-09T14:19:33.9419929Z 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 SKIPPED 2025-09-09T14:19:33.9421848Z 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 SKIPPED 2025-09-09T14:19:33.9423760Z 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 SKIPPED 2025-09-09T14:19:33.9694661Z 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 SKIPPED 2025-09-09T14:19:33.9696638Z 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 SKIPPED 2025-09-09T14:19:33.9698721Z 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 SKIPPED 2025-09-09T14:19:33.9700628Z 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 SKIPPED 2025-09-09T14:19:33.9702563Z 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 SKIPPED 2025-09-09T14:19:33.9704575Z 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 SKIPPED 2025-09-09T14:19:33.9706504Z 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 SKIPPED 2025-09-09T14:19:33.9708417Z 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 SKIPPED 2025-09-09T14:19:33.9710350Z 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 SKIPPED 2025-09-09T14:19:33.9712289Z 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 SKIPPED 2025-09-09T14:19:33.9714220Z 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 SKIPPED 2025-09-09T14:19:33.9716153Z 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 SKIPPED 2025-09-09T14:19:33.9718078Z 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 SKIPPED 2025-09-09T14:19:33.9720062Z 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 SKIPPED 2025-09-09T14:19:33.9721990Z 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 SKIPPED 2025-09-09T14:19:33.9723905Z 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 SKIPPED 2025-09-09T14:19:33.9725815Z 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 SKIPPED 2025-09-09T14:19:33.9727754Z 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 SKIPPED 2025-09-09T14:19:33.9730337Z 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 SKIPPED 2025-09-09T14:19:33.9732278Z 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 SKIPPED 2025-09-09T14:19:33.9734213Z 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 SKIPPED 2025-09-09T14:19:33.9736206Z 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 SKIPPED 2025-09-09T14:19:33.9738117Z 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 SKIPPED 2025-09-09T14:19:33.9740030Z 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 SKIPPED 2025-09-09T14:19:33.9741957Z 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 SKIPPED 2025-09-09T14:19:33.9743887Z 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 SKIPPED 2025-09-09T14:19:33.9745807Z 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 SKIPPED 2025-09-09T14:19:33.9747728Z 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 SKIPPED 2025-09-09T14:19:33.9749647Z 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 SKIPPED 2025-09-09T14:19:33.9751545Z 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 SKIPPED 2025-09-09T14:19:33.9753466Z 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 SKIPPED 2025-09-09T14:19:33.9755371Z 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 SKIPPED 2025-09-09T14:19:33.9757274Z 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 SKIPPED 2025-09-09T14:19:33.9759483Z 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 SKIPPED 2025-09-09T14:19:33.9761484Z 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 SKIPPED 2025-09-09T14:19:33.9763486Z 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 SKIPPED 2025-09-09T14:19:34.0079543Z 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 SKIPPED 2025-09-09T14:19:34.0081581Z 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 SKIPPED 2025-09-09T14:19:34.0083675Z 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 SKIPPED 2025-09-09T14:19:34.0085614Z 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 SKIPPED 2025-09-09T14:19:34.0087544Z 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 SKIPPED 2025-09-09T14:19:34.0089476Z 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 SKIPPED 2025-09-09T14:19:34.0091390Z 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 SKIPPED 2025-09-09T14:19:34.0093313Z 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 SKIPPED 2025-09-09T14:19:34.0095227Z 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 SKIPPED 2025-09-09T14:19:34.0097129Z 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 SKIPPED 2025-09-09T14:19:34.0099042Z 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 SKIPPED 2025-09-09T14:19:34.0100947Z 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 SKIPPED 2025-09-09T14:19:34.0102849Z 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 SKIPPED 2025-09-09T14:19:34.0104771Z 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 SKIPPED 2025-09-09T14:19:34.0106694Z 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 SKIPPED 2025-09-09T14:19:34.0108678Z 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 SKIPPED 2025-09-09T14:19:34.0110598Z 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 SKIPPED 2025-09-09T14:19:34.0112510Z 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 SKIPPED 2025-09-09T14:19:34.0114483Z 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 SKIPPED 2025-09-09T14:19:34.0116379Z 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 SKIPPED 2025-09-09T14:19:34.0118293Z 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 SKIPPED 2025-09-09T14:19:34.0120281Z 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 SKIPPED 2025-09-09T14:19:34.0122193Z 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 SKIPPED 2025-09-09T14:19:34.0124114Z 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 SKIPPED 2025-09-09T14:19:34.0126022Z 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 SKIPPED 2025-09-09T14:19:34.0127915Z 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 SKIPPED 2025-09-09T14:19:34.0129824Z 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 SKIPPED 2025-09-09T14:19:34.0131729Z 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 SKIPPED 2025-09-09T14:19:34.0133623Z 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 SKIPPED 2025-09-09T14:19:34.0135535Z 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 SKIPPED 2025-09-09T14:19:34.0137444Z 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 SKIPPED 2025-09-09T14:19:34.0139400Z 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 SKIPPED 2025-09-09T14:19:34.0140851Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_dynamic_qlinear_cpu SKIPPED 2025-09-09T14:19:34.0142007Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_dynamic_qlinear_input_dim_exceeds_2 SKIPPED 2025-09-09T14:19:34.0143150Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_dynamic_qlinear_qat_cpu SKIPPED 2025-09-09T14:19:34.0144232Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_linear_dynamic_fp16 SKIPPED 2025-09-09T14:19:34.0145425Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_linear_relu_dynamic_fp16 SKIPPED 2025-09-09T14:19:34.0146479Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qat_qconv2d SKIPPED 2025-09-09T14:19:34.0147498Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qat_qconv2d_add SKIPPED 2025-09-09T14:19:34.0148543Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qat_qconv2d_add_relu SKIPPED 2025-09-09T14:19:34.0149627Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qat_qconv2d_hardswish SKIPPED 2025-09-09T14:19:34.0635726Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qat_qconv2d_hardtanh SKIPPED 2025-09-09T14:19:34.0636843Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qat_qconv2d_relu SKIPPED 2025-09-09T14:19:34.0637898Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qat_qconv2d_relu6 SKIPPED 2025-09-09T14:19:34.0638954Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qat_qconv2d_silu SKIPPED 2025-09-09T14:19:34.0639979Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qcat SKIPPED 2025-09-09T14:19:34.0640979Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qconv1d_relu_cpu SKIPPED 2025-09-09T14:19:34.0641988Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qconv2d_add_2 SKIPPED 2025-09-09T14:19:34.0642996Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qconv2d_add_3 SKIPPED 2025-09-09T14:19:34.0644110Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qconv2d_add_broadcast_shapes_cpu SKIPPED 2025-09-09T14:19:34.0645199Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qconv2d_add_cpu SKIPPED 2025-09-09T14:19:34.0646298Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qconv2d_add_int8_mixed_bf16 SKIPPED 2025-09-09T14:19:34.0647393Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qconv2d_add_relu_cpu SKIPPED 2025-09-09T14:19:34.0648536Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qconv2d_add_relu_int8_mixed_bf16 SKIPPED 2025-09-09T14:19:34.0649619Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qconv2d_cpu SKIPPED 2025-09-09T14:19:34.0650689Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qconv2d_dequant_promotion_cpu SKIPPED 2025-09-09T14:19:34.0651814Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qconv2d_hardswish_cpu SKIPPED 2025-09-09T14:19:34.0652973Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qconv2d_hardswish_int8_mixed_bf16_cpu SKIPPED 2025-09-09T14:19:34.0654127Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qconv2d_hardtanh_cpu SKIPPED 2025-09-09T14:19:34.0655283Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qconv2d_hardtanh_int8_mixed_bf16_cpu SKIPPED 2025-09-09T14:19:34.0656579Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qconv2d_int8_mixed_bf16 SKIPPED 2025-09-09T14:19:34.0657665Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qconv2d_relu6_cpu SKIPPED 2025-09-09T14:19:34.0658957Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qconv2d_relu_cpu SKIPPED 2025-09-09T14:19:34.0660077Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qconv2d_relu_int8_mixed_bf16_xpu SKIPPED 2025-09-09T14:19:34.0661180Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qconv2d_silu_cpu SKIPPED 2025-09-09T14:19:34.0662394Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qconv2d_silu_int8_mixed_bf16_cpu SKIPPED 2025-09-09T14:19:34.0663540Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qconv2d_with_concat_cpu SKIPPED 2025-09-09T14:19:34.0664569Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qflatten SKIPPED 2025-09-09T14:19:34.0665762Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_add_cpu_use_relu_False_is_qat_False_is_dynamic_False SKIPPED 2025-09-09T14:19:34.0667164Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_add_cpu_use_relu_False_is_qat_False_is_dynamic_True SKIPPED 2025-09-09T14:19:34.0668559Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_add_cpu_use_relu_False_is_qat_True_is_dynamic_False SKIPPED 2025-09-09T14:19:34.0669939Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_add_cpu_use_relu_False_is_qat_True_is_dynamic_True SKIPPED 2025-09-09T14:19:34.0671326Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_add_cpu_use_relu_True_is_qat_False_is_dynamic_False SKIPPED 2025-09-09T14:19:34.0672703Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_add_cpu_use_relu_True_is_qat_False_is_dynamic_True SKIPPED 2025-09-09T14:19:34.0674080Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_add_cpu_use_relu_True_is_qat_True_is_dynamic_False SKIPPED 2025-09-09T14:19:34.0675457Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_add_cpu_use_relu_True_is_qat_True_is_dynamic_True SKIPPED 2025-09-09T14:19:34.0676899Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_add_int8_mixed_bf16_use_relu_False_is_qat_False_is_dynamic_False SKIPPED 2025-09-09T14:19:34.0678397Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_add_int8_mixed_bf16_use_relu_False_is_qat_False_is_dynamic_True SKIPPED 2025-09-09T14:19:34.0679956Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_add_int8_mixed_bf16_use_relu_False_is_qat_True_is_dynamic_False SKIPPED 2025-09-09T14:19:34.0681440Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_add_int8_mixed_bf16_use_relu_False_is_qat_True_is_dynamic_True SKIPPED 2025-09-09T14:19:34.0682929Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_add_int8_mixed_bf16_use_relu_True_is_qat_False_is_dynamic_False SKIPPED 2025-09-09T14:19:34.0684417Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_add_int8_mixed_bf16_use_relu_True_is_qat_False_is_dynamic_True SKIPPED 2025-09-09T14:19:34.0685887Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_add_int8_mixed_bf16_use_relu_True_is_qat_True_is_dynamic_False SKIPPED 2025-09-09T14:19:34.0687472Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_add_int8_mixed_bf16_use_relu_True_is_qat_True_is_dynamic_True SKIPPED 2025-09-09T14:19:34.0688674Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_cpu SKIPPED 2025-09-09T14:19:34.0689760Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_dequant_promotion_cpu SKIPPED 2025-09-09T14:19:34.0691017Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_dequant_promotion_cpu_input_dim_exceeds_2 SKIPPED 2025-09-09T14:19:34.0692284Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_dequant_promotion_dynamic_cpu SKIPPED 2025-09-09T14:19:34.0693594Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_dequant_promotion_int8_mixed_bf16 SKIPPED 2025-09-09T14:19:34.0694951Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_dequant_promotion_int8_mixed_bf16_input_dim_exceeds_2 SKIPPED 2025-09-09T14:19:34.0696168Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_gelu_cpu SKIPPED 2025-09-09T14:19:34.0697271Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_gelu_int8_mixed_bf16 SKIPPED 2025-09-09T14:19:34.0698401Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_input_dim_exceeds_2 SKIPPED 2025-09-09T14:19:34.0699634Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_input_dim_exceeds_2_and_not_contiguous SKIPPED 2025-09-09T14:19:34.0700828Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_int8_mixed_bf16 SKIPPED 2025-09-09T14:19:34.0702015Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_int8_mixed_bf16_input_dim_exceeds_2 SKIPPED 2025-09-09T14:19:34.0703380Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_int8_mixed_bf16_input_dim_exceeds_2_and_not_contiguous SKIPPED 2025-09-09T14:19:34.0704598Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_mul_cpu SKIPPED 2025-09-09T14:19:34.0705617Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_relu_cpu SKIPPED 2025-09-09T14:19:34.0706729Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_relu_input_dim_exceeds_2 SKIPPED 2025-09-09T14:19:34.0707874Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_relu_int8_mixed_bf16 SKIPPED 2025-09-09T14:19:34.0709117Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qlinear_relu_int8_mixed_bf16_input_dim_exceeds_2 SKIPPED 2025-09-09T14:19:34.0710243Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_qmaxpool2d SKIPPED 2025-09-09T14:20:34.6361389Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_smooth_quant_with_int_mm_has_bias_False_bfloat16_per_channel_quant_False_dynamic_False SKIPPED 2025-09-09T14:20:34.6363519Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_smooth_quant_with_int_mm_has_bias_False_bfloat16_per_channel_quant_False_dynamic_True SKIPPED 2025-09-09T14:20:34.6365526Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_smooth_quant_with_int_mm_has_bias_False_bfloat16_per_channel_quant_True_dynamic_False SKIPPED 2025-09-09T14:20:34.6367547Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_smooth_quant_with_int_mm_has_bias_False_bfloat16_per_channel_quant_True_dynamic_True SKIPPED 2025-09-09T14:20:34.6369584Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_smooth_quant_with_int_mm_has_bias_False_float32_per_channel_quant_False_dynamic_False SKIPPED 2025-09-09T14:20:34.6371955Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_smooth_quant_with_int_mm_has_bias_False_float32_per_channel_quant_False_dynamic_True SKIPPED 2025-09-09T14:20:34.6373992Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_smooth_quant_with_int_mm_has_bias_False_float32_per_channel_quant_True_dynamic_False SKIPPED 2025-09-09T14:20:34.6375994Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_smooth_quant_with_int_mm_has_bias_False_float32_per_channel_quant_True_dynamic_True SKIPPED 2025-09-09T14:20:34.6378005Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_smooth_quant_with_int_mm_has_bias_True_bfloat16_per_channel_quant_False_dynamic_False SKIPPED 2025-09-09T14:20:34.6380165Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_smooth_quant_with_int_mm_has_bias_True_bfloat16_per_channel_quant_False_dynamic_True SKIPPED 2025-09-09T14:20:34.6382182Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_smooth_quant_with_int_mm_has_bias_True_bfloat16_per_channel_quant_True_dynamic_False SKIPPED 2025-09-09T14:20:34.6384168Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_smooth_quant_with_int_mm_has_bias_True_bfloat16_per_channel_quant_True_dynamic_True SKIPPED 2025-09-09T14:20:34.6386173Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_smooth_quant_with_int_mm_has_bias_True_float32_per_channel_quant_False_dynamic_False SKIPPED 2025-09-09T14:20:34.6388168Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_smooth_quant_with_int_mm_has_bias_True_float32_per_channel_quant_False_dynamic_True SKIPPED 2025-09-09T14:20:34.6390155Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_smooth_quant_with_int_mm_has_bias_True_float32_per_channel_quant_True_dynamic_False SKIPPED 2025-09-09T14:20:34.6392151Z test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_smooth_quant_with_int_mm_has_bias_True_float32_per_channel_quant_True_dynamic_True SKIPPED 2025-09-09T14:20:34.6393856Z test/quantization/pt2e/test_x86inductor_fusion.py::TestDynamicPatternMatcher::test_q_attention_block SKIPPED 2025-09-09T14:20:34.6395254Z 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test/quantization/quantize_/workflows/int4/test_int4_tile_packed_to_4d_tensor.py::TestInt4TilePackedTo4dTensor::test_linear_sizes1_config0 SKIPPED 2025-09-09T14:22:37.1797132Z test/quantization/quantize_/workflows/int4/test_int4_tile_packed_to_4d_tensor.py::TestInt4TilePackedTo4dTensor::test_linear_sizes1_config1 SKIPPED 2025-09-09T14:22:37.1798593Z test/quantization/quantize_/workflows/int4/test_int4_tile_packed_to_4d_tensor.py::TestInt4TilePackedTo4dTensor::test_linear_sizes2_config0 SKIPPED 2025-09-09T14:22:37.1800155Z test/quantization/quantize_/workflows/int4/test_int4_tile_packed_to_4d_tensor.py::TestInt4TilePackedTo4dTensor::test_linear_sizes2_config1 SKIPPED 2025-09-09T14:22:37.1801687Z test/quantization/quantize_/workflows/int4/test_int4_tile_packed_to_4d_tensor.py::TestInt4TilePackedTo4dTensor::test_mm_int4wo_device_cuda_bfloat16 SKIPPED 2025-09-09T14:22:37.1803172Z test/quantization/quantize_/workflows/int4/test_int4_tile_packed_to_4d_tensor.py::TestInt4TilePackedTo4dTensor::test_module_path_config0 SKIPPED 2025-09-09T14:22:37.1804633Z test/quantization/quantize_/workflows/int4/test_int4_tile_packed_to_4d_tensor.py::TestInt4TilePackedTo4dTensor::test_module_path_config1 SKIPPED 2025-09-09T14:22:37.1806183Z test/quantization/quantize_/workflows/int4/test_int4_tile_packed_to_4d_tensor.py::TestInt4TilePackedTo4dTensor::test_slice_and_copy_similar_to_vllm_config0 SKIPPED 2025-09-09T14:22:37.1807794Z test/quantization/quantize_/workflows/int4/test_int4_tile_packed_to_4d_tensor.py::TestInt4TilePackedTo4dTensor::test_slice_and_copy_similar_to_vllm_config1 SKIPPED 2025-09-09T14:22:37.1809300Z test/quantization/quantize_/workflows/int4/test_int4_tile_packed_to_4d_tensor.py::TestInt4TilePackedTo4dTensor::test_slice_config0 SKIPPED 2025-09-09T14:22:37.1810689Z test/quantization/quantize_/workflows/int4/test_int4_tile_packed_to_4d_tensor.py::TestInt4TilePackedTo4dTensor::test_slice_config1 SKIPPED 2025-09-09T14:22:37.1812179Z test/quantization/quantize_/workflows/int4/test_int4_tile_packed_to_4d_tensor.py::TestInt4TilePackedTo4dTensor::test_slice_preserves_aliasing_config0 SKIPPED 2025-09-09T14:22:37.1959473Z test/quantization/quantize_/workflows/int4/test_int4_tile_packed_to_4d_tensor.py::TestInt4TilePackedTo4dTensor::test_slice_preserves_aliasing_config1 SKIPPED 2025-09-09T14:22:37.1960982Z test/quantization/quantize_/workflows/int4/test_int4_tile_packed_to_4d_tensor.py::TestInt4TilePackedTo4dTensor::test_to_device SKIPPED 2025-09-09T14:22:37.1963406Z 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 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2025-09-09T14:22:37.1996569Z 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 2025-09-09T14:22:37.1999911Z 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 2025-09-09T14:22:37.2003234Z 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 2025-09-09T14:22:37.2006500Z 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 2025-09-09T14:22:37.2009816Z 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 2025-09-09T14:22:37.2013284Z 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 2025-09-09T14:22:37.2016609Z 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 2025-09-09T14:22:37.2019892Z 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 2025-09-09T14:22:37.2023149Z 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 2025-09-09T14:22:37.2026397Z 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 2025-09-09T14:22:37.2029732Z 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 2025-09-09T14:22:37.2163336Z 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 2025-09-09T14:22:37.2166675Z 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 2025-09-09T14:22:37.2170060Z 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 2025-09-09T14:22:37.2173316Z 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 2025-09-09T14:22:37.2176669Z 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 2025-09-09T14:22:37.2180005Z 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 2025-09-09T14:22:37.2183394Z 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 2025-09-09T14:22:37.2186732Z 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 2025-09-09T14:22:37.2189994Z 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 2025-09-09T14:22:37.2193257Z 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 2025-09-09T14:22:37.2196522Z 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 2025-09-09T14:22:37.2199919Z 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 2025-09-09T14:22:37.2203384Z 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 2025-09-09T14:22:37.2206705Z 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 2025-09-09T14:22:37.2210041Z 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 2025-09-09T14:22:37.2213306Z 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 2025-09-09T14:22:37.2216569Z 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 2025-09-09T14:22:37.2219899Z 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 2025-09-09T14:22:37.2223280Z 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 2025-09-09T14:22:37.2226600Z 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 2025-09-09T14:22:37.2229876Z 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 2025-09-09T14:22:37.2366710Z 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 2025-09-09T14:22:37.2370080Z 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 2025-09-09T14:22:37.2373397Z 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 2025-09-09T14:22:37.2376871Z 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 2025-09-09T14:22:37.2380206Z 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 2025-09-09T14:22:37.2383526Z 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 2025-09-09T14:22:37.2386912Z 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 2025-09-09T14:22:37.2390353Z 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 2025-09-09T14:22:37.2393704Z 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 2025-09-09T14:22:37.2397000Z 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 2025-09-09T14:22:37.2400347Z 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 2025-09-09T14:22:37.2403691Z 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 2025-09-09T14:22:37.2406971Z 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 2025-09-09T14:22:37.2410315Z 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 2025-09-09T14:22:37.2413596Z 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 2025-09-09T14:22:37.2416882Z 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 2025-09-09T14:22:37.2420232Z 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 2025-09-09T14:22:37.2423638Z 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 2025-09-09T14:22:37.2426983Z 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 2025-09-09T14:22:37.2430280Z 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 2025-09-09T14:22:37.2433561Z 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 2025-09-09T14:22:37.2572148Z 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 2025-09-09T14:22:37.2575835Z 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 2025-09-09T14:22:37.2579525Z 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 2025-09-09T14:22:37.2583005Z 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 2025-09-09T14:22:37.2586568Z 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 2025-09-09T14:22:37.2590132Z 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 2025-09-09T14:22:37.2593424Z 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 2025-09-09T14:22:37.2597010Z 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 2025-09-09T14:22:37.2600786Z 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 2025-09-09T14:22:37.2604145Z 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 2025-09-09T14:22:37.2607776Z 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 2025-09-09T14:22:37.2611335Z 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 2025-09-09T14:22:37.2614627Z 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 2025-09-09T14:22:37.2618315Z 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 2025-09-09T14:22:37.2622001Z 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 2025-09-09T14:22:37.2625359Z 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 2025-09-09T14:22:37.2628920Z 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 2025-09-09T14:22:37.2632473Z 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 2025-09-09T14:22:37.2636021Z 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 2025-09-09T14:22:37.2639687Z 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 2025-09-09T14:22:37.2643201Z 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 2025-09-09T14:22:37.2778618Z 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 2025-09-09T14:22:37.2782341Z 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 2025-09-09T14:22:37.2786046Z 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 2025-09-09T14:22:37.2789342Z 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 2025-09-09T14:22:37.2792946Z 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 2025-09-09T14:22:37.2796634Z 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 2025-09-09T14:22:37.2800051Z 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 2025-09-09T14:22:37.2803615Z 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 2025-09-09T14:22:37.2807164Z 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 2025-09-09T14:22:37.2810560Z 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 2025-09-09T14:22:37.2814393Z 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 2025-09-09T14:22:37.2817843Z 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 2025-09-09T14:22:37.2821564Z 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 2025-09-09T14:22:37.2825195Z 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 2025-09-09T14:22:37.2828544Z 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 2025-09-09T14:22:37.2831885Z 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 2025-09-09T14:22:37.2835490Z 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 2025-09-09T14:22:37.2839123Z 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 2025-09-09T14:22:37.2842790Z 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 2025-09-09T14:22:37.2846491Z 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 2025-09-09T14:22:37.2849922Z 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 2025-09-09T14:22:37.2987681Z 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 2025-09-09T14:22:37.2991481Z 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 2025-09-09T14:22:37.2994856Z 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 2025-09-09T14:22:37.2998218Z 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 2025-09-09T14:22:37.3001642Z 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 2025-09-09T14:22:37.3005063Z 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 2025-09-09T14:22:37.3008575Z 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 2025-09-09T14:22:37.3011986Z 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 2025-09-09T14:22:37.3015434Z 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 2025-09-09T14:22:37.3018782Z 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 2025-09-09T14:22:37.3022132Z 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 2025-09-09T14:22:37.3025594Z 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 2025-09-09T14:22:37.3029064Z 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 2025-09-09T14:22:37.3032475Z 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 2025-09-09T14:22:37.3035846Z 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 2025-09-09T14:22:37.3039180Z 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 2025-09-09T14:22:37.3042607Z 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 2025-09-09T14:22:37.3046020Z 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 2025-09-09T14:22:37.3049567Z 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 2025-09-09T14:22:37.3052974Z 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 2025-09-09T14:22:37.3056323Z 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 2025-09-09T14:22:37.3179769Z 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 2025-09-09T14:22:37.3183144Z 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 2025-09-09T14:22:37.3186590Z 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 2025-09-09T14:22:37.3190085Z 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 2025-09-09T14:22:37.3193496Z 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 2025-09-09T14:22:37.3196857Z 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 2025-09-09T14:22:37.3200264Z 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 2025-09-09T14:22:37.3203740Z 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 2025-09-09T14:22:37.3207159Z 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 2025-09-09T14:22:37.3210638Z 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 2025-09-09T14:22:37.3214112Z 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 2025-09-09T14:22:37.3217475Z 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 2025-09-09T14:22:37.3220821Z 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 2025-09-09T14:22:37.3224162Z 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 2025-09-09T14:22:37.3227568Z 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 2025-09-09T14:22:37.3231040Z 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 2025-09-09T14:22:37.3234401Z 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 2025-09-09T14:22:37.3237726Z 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 2025-09-09T14:22:37.3241043Z 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 2025-09-09T14:22:37.3244311Z 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 2025-09-09T14:22:37.3247612Z 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 2025-09-09T14:22:37.3376970Z 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 2025-09-09T14:22:37.3380266Z 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 2025-09-09T14:22:37.3383517Z 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 2025-09-09T14:22:37.3386834Z 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 2025-09-09T14:22:37.3390254Z 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 2025-09-09T14:22:37.3393567Z 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 2025-09-09T14:22:37.3396960Z 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 2025-09-09T14:22:37.3400341Z 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 2025-09-09T14:22:37.3403620Z 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 2025-09-09T14:22:37.3407710Z 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 2025-09-09T14:22:37.3411097Z 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 2025-09-09T14:22:37.3414605Z 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 2025-09-09T14:22:37.3417896Z 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 2025-09-09T14:22:37.3421167Z 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 2025-09-09T14:22:37.3424474Z 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 2025-09-09T14:22:37.3427797Z 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 2025-09-09T14:22:37.3431212Z 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 2025-09-09T14:22:37.3434598Z 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 2025-09-09T14:22:37.3437871Z 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 2025-09-09T14:22:37.3441267Z 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 2025-09-09T14:22:37.3444512Z 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 2025-09-09T14:22:37.3589664Z 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 2025-09-09T14:22:37.3593082Z 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 2025-09-09T14:22:37.3596390Z 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 2025-09-09T14:22:37.3599730Z 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 2025-09-09T14:22:37.3602978Z 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 2025-09-09T14:22:37.3606237Z 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 2025-09-09T14:22:37.3609667Z 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 2025-09-09T14:22:37.3613061Z 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 2025-09-09T14:22:37.3616466Z 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 2025-09-09T14:22:37.3619721Z 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 2025-09-09T14:22:37.3622980Z 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 2025-09-09T14:22:37.3626245Z 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 2025-09-09T14:22:37.3629556Z 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 2025-09-09T14:22:37.3632952Z 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 2025-09-09T14:22:37.3636259Z 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 2025-09-09T14:22:37.3639525Z 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 2025-09-09T14:22:37.3642900Z 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 2025-09-09T14:22:37.3646154Z 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 2025-09-09T14:22:37.3649539Z 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 2025-09-09T14:22:37.3652914Z 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 2025-09-09T14:22:37.3656243Z 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 2025-09-09T14:22:37.3802697Z 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 2025-09-09T14:22:37.3806081Z 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 2025-09-09T14:22:37.3809523Z 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 2025-09-09T14:22:37.3812861Z 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 2025-09-09T14:22:37.3816144Z 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 2025-09-09T14:22:37.3819551Z 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 2025-09-09T14:22:37.3822841Z 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 2025-09-09T14:22:37.3826121Z 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 2025-09-09T14:22:37.3829463Z 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 2025-09-09T14:22:37.3832734Z 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 2025-09-09T14:22:37.3836011Z 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 2025-09-09T14:22:37.3839348Z 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 2025-09-09T14:22:37.3842822Z 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 2025-09-09T14:22:37.3846147Z 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 2025-09-09T14:22:37.3849426Z 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 2025-09-09T14:22:37.3852755Z 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 2025-09-09T14:22:37.3856028Z 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 2025-09-09T14:22:37.3859584Z 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 2025-09-09T14:22:37.3863072Z 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 2025-09-09T14:22:37.3866400Z 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 2025-09-09T14:22:37.3869686Z 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 2025-09-09T14:22:37.4016332Z 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 2025-09-09T14:22:37.4019629Z 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 2025-09-09T14:22:37.4022970Z 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 2025-09-09T14:22:37.4026373Z 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 2025-09-09T14:22:37.4029815Z 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 2025-09-09T14:22:37.4033106Z 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 2025-09-09T14:22:37.4036382Z 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 2025-09-09T14:22:37.4039795Z 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 2025-09-09T14:22:37.4043118Z 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 2025-09-09T14:22:37.4046537Z 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 2025-09-09T14:22:37.4049872Z 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 2025-09-09T14:22:37.4053165Z 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 2025-09-09T14:22:37.4056435Z 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 2025-09-09T14:22:37.4059955Z 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 2025-09-09T14:22:37.4063301Z 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 2025-09-09T14:22:37.4066790Z 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 2025-09-09T14:22:37.4070117Z 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 2025-09-09T14:22:37.4073479Z 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 2025-09-09T14:22:37.4076732Z 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 2025-09-09T14:22:37.4080085Z 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 2025-09-09T14:22:37.4083429Z 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 2025-09-09T14:22:37.4229668Z 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 2025-09-09T14:22:37.4233032Z 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 2025-09-09T14:22:37.4236304Z 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 2025-09-09T14:22:37.4239577Z 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 2025-09-09T14:22:37.4243019Z 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 2025-09-09T14:22:37.4246354Z 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 2025-09-09T14:22:37.4249832Z 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 2025-09-09T14:22:37.4253175Z 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 2025-09-09T14:22:37.4256523Z 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 2025-09-09T14:22:37.4260102Z 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 2025-09-09T14:22:37.4263432Z 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 2025-09-09T14:22:37.4266776Z 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 2025-09-09T14:22:37.4270111Z 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 2025-09-09T14:22:37.4273451Z 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 2025-09-09T14:22:37.4276873Z 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 2025-09-09T14:22:37.4280336Z 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 2025-09-09T14:22:37.4283882Z 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 2025-09-09T14:22:37.4287264Z 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 2025-09-09T14:22:37.4290604Z 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 2025-09-09T14:22:37.4293943Z 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 2025-09-09T14:22:37.4297263Z 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 2025-09-09T14:22:37.4445661Z 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 2025-09-09T14:22:37.4449137Z 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 2025-09-09T14:22:37.4452514Z 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 2025-09-09T14:22:37.4455974Z 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 2025-09-09T14:22:37.4459546Z 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 2025-09-09T14:22:37.4462968Z 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 2025-09-09T14:22:37.4466364Z 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 2025-09-09T14:22:37.4469817Z 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 2025-09-09T14:22:37.4473226Z 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 2025-09-09T14:22:37.4476566Z 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 2025-09-09T14:22:37.4479992Z 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 2025-09-09T14:22:37.4483326Z 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 2025-09-09T14:22:37.4486707Z 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 2025-09-09T14:22:37.4490276Z 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 2025-09-09T14:22:37.4493704Z 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 2025-09-09T14:22:37.4497123Z 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 2025-09-09T14:22:37.4500461Z 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 2025-09-09T14:22:37.4503804Z 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 2025-09-09T14:22:37.4507207Z 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 2025-09-09T14:22:37.4510665Z 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 2025-09-09T14:22:37.4514069Z 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 2025-09-09T14:22:37.7340686Z 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 2025-09-09T14:22:37.7347108Z 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 2025-09-09T14:22:37.7353711Z 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 2025-09-09T14:22:37.7360766Z 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 2025-09-09T14:22:37.7368542Z 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 2025-09-09T14:22:37.7374862Z 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 2025-09-09T14:22:37.7380880Z 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 2025-09-09T14:22:37.7387105Z 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 2025-09-09T14:22:37.7393351Z 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 2025-09-09T14:22:37.7414519Z 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 2025-09-09T14:22:37.7421129Z 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 2025-09-09T14:22:37.7425355Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_export_compile_aoti SKIPPED 2025-09-09T14:22:37.7427754Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_moe_quant_intx SKIPPED 2025-09-09T14:22:37.7431273Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_serialization_{'packing_format': , 'compute_target': 'aten'} SKIPPED 2025-09-09T14:22:37.7434966Z test/quantization/quantize_/workflows/intx/test_intx_opaque_tensor.py::TestIntxOpaqueTensor::test_serialization_{'packing_format': , 'compute_target': 'torchao_auto'} SKIPPED 2025-09-09T14:22:37.7438132Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_embedding PASSED 2025-09-09T14:22:37.7441239Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_export_int8_dyn_act_intx_weight_config PASSED 2025-09-09T14:22:37.7444299Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_export_int8_dyn_act_intx_weight_config_with_unwrap PASSED 2025-09-09T14:22:37.7447462Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_export_intx_weight_only_config PASSED 2025-09-09T14:22:37.7451982Z test/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 2025-09-09T14:22:37.7458800Z test/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 2025-09-09T14:22:37.7465624Z test/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 2025-09-09T14:22:37.7471618Z test/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 2025-09-09T14:22:37.7477607Z test/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 2025-09-09T14:22:37.7483697Z test/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 2025-09-09T14:22:37.7490011Z test/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 2025-09-09T14:22:37.8482410Z test/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 2025-09-09T14:22:37.8486636Z test/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 2025-09-09T14:22:37.8490915Z test/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 2025-09-09T14:22:37.8495141Z test/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 2025-09-09T14:22:37.8499299Z test/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 2025-09-09T14:22:37.8503424Z test/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 2025-09-09T14:22:37.8507569Z test/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 2025-09-09T14:22:37.8511868Z test/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 2025-09-09T14:22:37.8516011Z test/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 2025-09-09T14:22:37.8520289Z test/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 2025-09-09T14:22:37.8524499Z test/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 2025-09-09T14:22:37.8528641Z test/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 2025-09-09T14:22:37.8532883Z test/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 2025-09-09T14:22:37.8537045Z test/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 2025-09-09T14:22:37.8541157Z test/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 2025-09-09T14:22:37.8545445Z test/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 2025-09-09T14:22:37.8549612Z test/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 2025-09-09T14:22:37.8553757Z test/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 2025-09-09T14:22:37.8558122Z test/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 2025-09-09T14:22:37.8563275Z test/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 2025-09-09T14:22:37.8567670Z test/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 2025-09-09T14:22:37.8571924Z test/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 2025-09-09T14:22:37.9592018Z test/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 2025-09-09T14:22:37.9596418Z test/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 2025-09-09T14:22:37.9600551Z test/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 2025-09-09T14:22:37.9604784Z test/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 2025-09-09T14:22:37.9609036Z test/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 2025-09-09T14:22:37.9613298Z test/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 2025-09-09T14:22:37.9617300Z test/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 2025-09-09T14:22:37.9621805Z test/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 2025-09-09T14:22:37.9625976Z test/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 2025-09-09T14:22:37.9630246Z test/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 2025-09-09T14:22:37.9634665Z test/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 2025-09-09T14:22:37.9638863Z test/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 2025-09-09T14:22:37.9643082Z test/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 2025-09-09T14:22:37.9647062Z test/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 2025-09-09T14:22:37.9651082Z test/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 2025-09-09T14:22:37.9655089Z test/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 2025-09-09T14:22:37.9659314Z test/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 2025-09-09T14:22:37.9663562Z test/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 2025-09-09T14:22:37.9667828Z test/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 2025-09-09T14:22:37.9672200Z test/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 2025-09-09T14:22:37.9676328Z test/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 2025-09-09T14:22:37.9680613Z test/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 2025-09-09T14:22:38.0702526Z test/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 2025-09-09T14:22:38.0706610Z test/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 2025-09-09T14:22:38.0710635Z test/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 2025-09-09T14:22:38.0714683Z test/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 2025-09-09T14:22:38.0718686Z test/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 2025-09-09T14:22:38.0722804Z test/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 2025-09-09T14:22:38.0727070Z test/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 2025-09-09T14:22:38.0731608Z test/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 2025-09-09T14:22:38.0735902Z test/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 2025-09-09T14:22:38.0740125Z test/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 2025-09-09T14:22:38.0744393Z test/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 2025-09-09T14:22:38.0748551Z test/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 2025-09-09T14:22:38.0752710Z test/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 2025-09-09T14:22:38.0756835Z test/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 2025-09-09T14:22:38.0761269Z test/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 2025-09-09T14:22:38.0765429Z test/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 2025-09-09T14:22:38.0769544Z test/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 2025-09-09T14:22:38.0774034Z test/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 2025-09-09T14:22:38.0778182Z test/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 2025-09-09T14:22:38.0782300Z test/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 2025-09-09T14:22:38.0786672Z test/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 2025-09-09T14:22:38.0790899Z test/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 2025-09-09T14:22:38.1813508Z test/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 2025-09-09T14:22:38.1817783Z test/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 2025-09-09T14:22:38.1821927Z test/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 2025-09-09T14:22:38.1826291Z test/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 2025-09-09T14:22:38.1830424Z test/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 2025-09-09T14:22:38.1834556Z test/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 2025-09-09T14:22:38.1839013Z test/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 2025-09-09T14:22:38.1843473Z test/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 2025-09-09T14:22:38.1847788Z test/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 2025-09-09T14:22:38.1851910Z test/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 2025-09-09T14:22:38.1856080Z test/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 2025-09-09T14:22:38.1860481Z test/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 2025-09-09T14:22:38.1864623Z test/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 2025-09-09T14:22:38.1868754Z test/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 2025-09-09T14:22:38.1872867Z test/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 2025-09-09T14:22:38.1877640Z test/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 2025-09-09T14:22:38.1882007Z test/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 2025-09-09T14:22:38.1886134Z test/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 2025-09-09T14:22:38.1890288Z test/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 2025-09-09T14:22:38.1895669Z test/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 2025-09-09T14:22:38.1900335Z test/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 2025-09-09T14:22:38.1904566Z test/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 2025-09-09T14:22:38.2921628Z test/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 2025-09-09T14:22:38.2925679Z test/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 2025-09-09T14:22:38.2929708Z test/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 2025-09-09T14:22:38.2934341Z test/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 2025-09-09T14:22:38.2938901Z test/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 2025-09-09T14:22:38.2943156Z test/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 2025-09-09T14:22:38.2947154Z test/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 2025-09-09T14:22:38.2952111Z test/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 2025-09-09T14:22:38.2956250Z test/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 2025-09-09T14:22:38.2960750Z test/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 2025-09-09T14:22:38.2964962Z test/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 2025-09-09T14:22:38.2969173Z test/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 2025-09-09T14:22:38.2973388Z test/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 2025-09-09T14:22:38.2977416Z test/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 2025-09-09T14:22:38.2981443Z test/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 2025-09-09T14:22:38.2985588Z test/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 2025-09-09T14:22:38.2989683Z test/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 2025-09-09T14:22:38.2993983Z test/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 2025-09-09T14:22:38.2998229Z test/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 2025-09-09T14:22:38.3002567Z test/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 2025-09-09T14:22:38.3007424Z test/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 2025-09-09T14:22:38.3012124Z test/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 2025-09-09T14:22:38.4035355Z test/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 2025-09-09T14:22:38.4041576Z test/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 2025-09-09T14:22:38.4047603Z test/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 2025-09-09T14:22:38.4054125Z test/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 2025-09-09T14:22:38.4060405Z test/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 2025-09-09T14:22:38.4066597Z test/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 2025-09-09T14:22:38.4072325Z test/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 2025-09-09T14:22:38.4078338Z test/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 2025-09-09T14:22:38.4085039Z test/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 2025-09-09T14:22:38.4091735Z test/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 2025-09-09T14:22:38.4098150Z test/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 2025-09-09T14:22:38.4104155Z test/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 2025-09-09T14:22:38.4109973Z test/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 2025-09-09T14:22:38.4116167Z test/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 2025-09-09T14:22:38.4122293Z test/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 2025-09-09T14:22:38.4128399Z test/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 2025-09-09T14:22:38.4134408Z test/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 2025-09-09T14:22:38.4140245Z test/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 2025-09-09T14:22:38.4146112Z test/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 2025-09-09T14:22:38.4151999Z test/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 2025-09-09T14:22:38.4159004Z test/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 2025-09-09T14:22:38.4165614Z test/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 2025-09-09T14:22:38.5162865Z test/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 2025-09-09T14:22:38.5168956Z test/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 2025-09-09T14:22:38.5175201Z test/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 2025-09-09T14:22:38.5181333Z test/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 2025-09-09T14:22:38.5187957Z test/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 2025-09-09T14:22:38.5193841Z test/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 2025-09-09T14:22:38.5199849Z test/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 2025-09-09T14:22:38.5205883Z test/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 2025-09-09T14:22:38.5211624Z test/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 2025-09-09T14:22:38.5217891Z test/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 2025-09-09T14:22:38.5224128Z test/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 2025-09-09T14:22:38.5230295Z test/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 2025-09-09T14:22:38.5236495Z test/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 2025-09-09T14:22:38.5243222Z test/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 2025-09-09T14:22:38.5249950Z test/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 2025-09-09T14:22:38.5256439Z test/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 2025-09-09T14:22:38.5263385Z test/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 2025-09-09T14:22:38.5270113Z test/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 2025-09-09T14:22:38.5276831Z test/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 2025-09-09T14:22:38.5283380Z test/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 2025-09-09T14:22:38.5290513Z test/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 2025-09-09T14:22:38.5296662Z test/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 2025-09-09T14:22:38.6305803Z test/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 2025-09-09T14:22:38.6312050Z test/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 2025-09-09T14:22:38.6318044Z test/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 2025-09-09T14:22:38.6325310Z test/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 2025-09-09T14:22:38.6331452Z test/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 2025-09-09T14:22:38.6337444Z test/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 2025-09-09T14:22:38.6344064Z test/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 2025-09-09T14:22:38.6350653Z test/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 2025-09-09T14:22:38.6356717Z test/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 2025-09-09T14:22:38.6364122Z test/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 2025-09-09T14:22:38.6370820Z test/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 2025-09-09T14:22:38.6376750Z test/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 2025-09-09T14:22:38.6382635Z test/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 2025-09-09T14:22:38.6388889Z test/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 2025-09-09T14:22:38.6395070Z test/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 2025-09-09T14:22:38.6401870Z test/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 2025-09-09T14:22:38.6407845Z test/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 2025-09-09T14:22:38.6413542Z test/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 2025-09-09T14:22:38.6420141Z test/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 2025-09-09T14:22:38.6426518Z test/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 2025-09-09T14:22:38.6433395Z test/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 2025-09-09T14:22:38.6439752Z test/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 2025-09-09T14:22:38.7431053Z test/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 2025-09-09T14:22:38.7437556Z test/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 2025-09-09T14:22:38.7443415Z test/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 2025-09-09T14:22:38.7449461Z test/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 2025-09-09T14:22:38.7455516Z test/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 2025-09-09T14:22:38.7461719Z test/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 2025-09-09T14:22:38.7467664Z test/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 2025-09-09T14:22:38.7473573Z test/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 2025-09-09T14:22:38.7479348Z test/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 2025-09-09T14:22:38.7486010Z test/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 2025-09-09T14:22:38.7492320Z test/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 2025-09-09T14:22:38.7498765Z test/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 2025-09-09T14:22:38.7504823Z test/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 2025-09-09T14:22:38.7510779Z test/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 2025-09-09T14:22:38.7516681Z test/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 2025-09-09T14:22:38.7522792Z test/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 2025-09-09T14:22:38.7528725Z test/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 2025-09-09T14:22:38.7534793Z test/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 2025-09-09T14:22:38.7540727Z test/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 2025-09-09T14:22:38.7547140Z test/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 2025-09-09T14:22:38.7553999Z test/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 2025-09-09T14:22:38.7560274Z test/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 2025-09-09T14:22:38.9481346Z test/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 2025-09-09T14:22:38.9487330Z test/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 2025-09-09T14:22:38.9493262Z test/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 2025-09-09T14:22:38.9499196Z test/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 2025-09-09T14:22:38.9505057Z test/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 2025-09-09T14:22:38.9511539Z test/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 2025-09-09T14:22:38.9518176Z test/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 2025-09-09T14:22:38.9524004Z test/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 2025-09-09T14:22:38.9530158Z test/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 2025-09-09T14:22:38.9535951Z test/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 2025-09-09T14:22:38.9541860Z test/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 2025-09-09T14:22:38.9545803Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_linear PASSED 2025-09-09T14:22:38.9549694Z 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 2025-09-09T14:22:38.9555283Z 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 2025-09-09T14:22:38.9561079Z 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 2025-09-09T14:22:38.9567011Z 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 2025-09-09T14:22:38.9572713Z 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 2025-09-09T14:22:38.9578631Z 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 2025-09-09T14:22:38.9584098Z 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 2025-09-09T14:22:38.9589554Z 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 2025-09-09T14:22:38.9594578Z 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 2025-09-09T14:22:38.9599777Z 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 2025-09-09T14:22:38.9605017Z 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 2025-09-09T14:22:38.9610059Z 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 2025-09-09T14:22:39.2394514Z 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 2025-09-09T14:22:39.2400089Z 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 2025-09-09T14:22:39.2405223Z 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 2025-09-09T14:22:39.2410291Z 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 2025-09-09T14:22:39.2415488Z 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 2025-09-09T14:22:39.2420628Z 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 2025-09-09T14:22:39.2426049Z 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 2025-09-09T14:22:39.2431780Z 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 2025-09-09T14:22:39.2436903Z 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 2025-09-09T14:22:39.2442176Z 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 2025-09-09T14:22:39.2447521Z 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 2025-09-09T14:22:39.2452536Z 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 2025-09-09T14:22:39.2457873Z 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 2025-09-09T14:22:39.2463251Z 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 2025-09-09T14:22:39.2468345Z 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 2025-09-09T14:22:39.2473807Z 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 2025-09-09T14:22:39.2479995Z 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 2025-09-09T14:22:39.2485155Z 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 2025-09-09T14:22:39.2490247Z 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 2025-09-09T14:22:39.2495681Z 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 2025-09-09T14:22:39.2500955Z 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 2025-09-09T14:22:39.2506134Z 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 2025-09-09T14:22:39.2512389Z 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 2025-09-09T14:22:39.2518253Z 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 2025-09-09T14:22:39.2523815Z 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 2025-09-09T14:22:39.5285555Z 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 2025-09-09T14:22:39.5290938Z 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 2025-09-09T14:22:39.5296140Z 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 2025-09-09T14:22:39.5301232Z 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 2025-09-09T14:22:39.5306341Z 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 2025-09-09T14:22:39.5311498Z 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 2025-09-09T14:22:39.5317424Z 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 2025-09-09T14:22:39.5323324Z 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 2025-09-09T14:22:39.5329072Z 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 2025-09-09T14:22:39.5334470Z 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 2025-09-09T14:22:39.5340140Z 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 2025-09-09T14:22:39.5345745Z 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 2025-09-09T14:22:39.5351035Z 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 2025-09-09T14:22:39.5356087Z 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 2025-09-09T14:22:39.5361665Z 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 2025-09-09T14:22:39.5366748Z 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 2025-09-09T14:22:39.5371873Z 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 2025-09-09T14:22:39.5377714Z 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 2025-09-09T14:22:39.5383558Z 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 2025-09-09T14:22:39.5389360Z 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 2025-09-09T14:22:39.5395139Z 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 2025-09-09T14:22:39.5401072Z 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 2025-09-09T14:22:39.5406858Z 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 2025-09-09T14:22:39.5411982Z 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 2025-09-09T14:22:39.5417142Z 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 2025-09-09T14:22:39.8227145Z 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 2025-09-09T14:22:39.8232618Z 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 2025-09-09T14:22:39.8237792Z 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 2025-09-09T14:22:39.8243007Z 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 2025-09-09T14:22:39.8248551Z 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 2025-09-09T14:22:39.8253652Z 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 2025-09-09T14:22:39.8260149Z 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 2025-09-09T14:22:39.8265850Z 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 2025-09-09T14:22:39.8271021Z 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 2025-09-09T14:22:39.8276045Z 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 2025-09-09T14:22:39.8281257Z 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 2025-09-09T14:22:39.8286287Z 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 2025-09-09T14:22:39.8291290Z 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 2025-09-09T14:22:39.8296453Z 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 2025-09-09T14:22:39.8301630Z 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 2025-09-09T14:22:39.8307333Z 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 2025-09-09T14:22:39.8313597Z 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 2025-09-09T14:22:39.8319451Z 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 2025-09-09T14:22:39.8325717Z 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 2025-09-09T14:22:39.8331320Z 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 2025-09-09T14:22:39.8336524Z 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 2025-09-09T14:22:39.8341571Z 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 2025-09-09T14:22:39.8346704Z 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 2025-09-09T14:22:39.8351771Z 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 2025-09-09T14:22:39.8356816Z 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 2025-09-09T14:22:40.1167521Z 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 2025-09-09T14:22:40.1172925Z 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 2025-09-09T14:22:40.1178084Z 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 2025-09-09T14:22:40.1183589Z 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 2025-09-09T14:22:40.1188760Z 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 2025-09-09T14:22:40.1194032Z 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 2025-09-09T14:22:40.1199507Z 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 2025-09-09T14:22:40.1204962Z 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 2025-09-09T14:22:40.1210122Z 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 2025-09-09T14:22:40.1215301Z 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 2025-09-09T14:22:40.1220432Z 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 2025-09-09T14:22:40.1225559Z 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 2025-09-09T14:22:40.1230838Z 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 2025-09-09T14:22:40.1236387Z 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 2025-09-09T14:22:40.1242416Z 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 2025-09-09T14:22:40.1248175Z 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 2025-09-09T14:22:40.1254098Z 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 2025-09-09T14:22:40.1259651Z 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 2025-09-09T14:22:40.1264645Z 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 2025-09-09T14:22:40.1269825Z 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 2025-09-09T14:22:40.1274930Z 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 2025-09-09T14:22:40.1280149Z 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 2025-09-09T14:22:40.1285370Z 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 2025-09-09T14:22:40.1291221Z 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 2025-09-09T14:22:40.1296479Z 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 2025-09-09T14:22:40.4065028Z 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 2025-09-09T14:22:40.4070839Z 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 2025-09-09T14:22:40.4076007Z 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 2025-09-09T14:22:40.4081259Z 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 2025-09-09T14:22:40.4086697Z 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 2025-09-09T14:22:40.4091760Z 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 2025-09-09T14:22:40.4097492Z 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 2025-09-09T14:22:40.4103404Z 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 2025-09-09T14:22:40.4108997Z 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 2025-09-09T14:22:40.4114399Z 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 2025-09-09T14:22:40.4120157Z 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 2025-09-09T14:22:40.4125733Z 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 2025-09-09T14:22:40.4131043Z 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 2025-09-09T14:22:40.4136303Z 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 2025-09-09T14:22:40.4141372Z 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 2025-09-09T14:22:40.4146673Z 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 2025-09-09T14:22:40.4152090Z 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 2025-09-09T14:22:40.4159060Z 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 2025-09-09T14:22:40.4164797Z 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 2025-09-09T14:22:40.4170347Z 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 2025-09-09T14:22:40.4175758Z 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 2025-09-09T14:22:40.4181422Z 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 2025-09-09T14:22:40.4187021Z 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 2025-09-09T14:22:40.4192065Z 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 2025-09-09T14:22:40.4197268Z 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 2025-09-09T14:22:40.6966214Z 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 2025-09-09T14:22:40.6971693Z 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 2025-09-09T14:22:40.6977036Z 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 2025-09-09T14:22:40.6982106Z 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 2025-09-09T14:22:40.6987266Z 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 2025-09-09T14:22:40.6992500Z 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 2025-09-09T14:22:40.6998678Z 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 2025-09-09T14:22:40.7004225Z 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 2025-09-09T14:22:40.7009296Z 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 2025-09-09T14:22:40.7014389Z 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 2025-09-09T14:22:40.7019473Z 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 2025-09-09T14:22:40.7024732Z 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 2025-09-09T14:22:40.7029777Z 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 2025-09-09T14:22:40.7034864Z 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 2025-09-09T14:22:40.7040366Z 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 2025-09-09T14:22:40.7045927Z 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 2025-09-09T14:22:40.7051853Z 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 2025-09-09T14:22:40.7057726Z 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 2025-09-09T14:22:40.7064084Z 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 2025-09-09T14:22:40.7069288Z 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 2025-09-09T14:22:40.7074328Z 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 2025-09-09T14:22:40.7079371Z 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 2025-09-09T14:22:40.7084557Z 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 2025-09-09T14:22:40.7089840Z 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 2025-09-09T14:22:40.7094835Z 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 2025-09-09T14:22:40.9900974Z 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 2025-09-09T14:22:40.9906875Z 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 2025-09-09T14:22:40.9912078Z 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 2025-09-09T14:22:40.9917201Z 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 2025-09-09T14:22:40.9922486Z 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 2025-09-09T14:22:40.9927595Z 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 2025-09-09T14:22:40.9933026Z 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 2025-09-09T14:22:40.9938385Z 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 2025-09-09T14:22:40.9943502Z 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 2025-09-09T14:22:40.9948748Z 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 2025-09-09T14:22:40.9954176Z 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 2025-09-09T14:22:40.9959589Z 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 2025-09-09T14:22:40.9965106Z 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 2025-09-09T14:22:40.9970173Z 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 2025-09-09T14:22:40.9975247Z 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 2025-09-09T14:22:40.9981069Z 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 2025-09-09T14:22:40.9986514Z 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 2025-09-09T14:22:40.9991598Z 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 2025-09-09T14:22:40.9996698Z 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 2025-09-09T14:22:41.0002046Z 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 2025-09-09T14:22:41.0007154Z 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 2025-09-09T14:22:41.0012709Z 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 2025-09-09T14:22:41.0018585Z 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 2025-09-09T14:22:41.0023954Z 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 2025-09-09T14:22:41.0029335Z 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 2025-09-09T14:22:41.2798729Z 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 2025-09-09T14:22:41.2804236Z 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 2025-09-09T14:22:41.2809530Z 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 2025-09-09T14:22:41.2814551Z 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 2025-09-09T14:22:41.2819712Z 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 2025-09-09T14:22:41.2824912Z 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 2025-09-09T14:22:41.2830459Z 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 2025-09-09T14:22:41.2836339Z 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 2025-09-09T14:22:41.2842469Z 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 2025-09-09T14:22:41.2847873Z 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 2025-09-09T14:22:41.2853491Z 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 2025-09-09T14:22:41.2859627Z 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 2025-09-09T14:22:41.2865024Z 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 2025-09-09T14:22:41.2870142Z 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 2025-09-09T14:22:41.2875786Z 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 2025-09-09T14:22:41.2881654Z 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 2025-09-09T14:22:41.2886555Z 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 2025-09-09T14:22:41.2892040Z 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 2025-09-09T14:22:41.2897871Z 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 2025-09-09T14:22:41.2903595Z 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 2025-09-09T14:22:41.2909496Z 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 2025-09-09T14:22:41.2915323Z 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 2025-09-09T14:22:41.2921187Z 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 2025-09-09T14:22:41.2926620Z 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 2025-09-09T14:22:41.2931798Z 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 2025-09-09T14:22:41.4033067Z 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 2025-09-09T14:22:41.4038688Z 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 2025-09-09T14:22:41.4044186Z 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 2025-09-09T14:22:41.4049562Z 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 2025-09-09T14:22:41.4053844Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_serialization_int8_dyn_act_intx_weight_config PASSED 2025-09-09T14:22:41.4056991Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_serialization_intx_weight_only_config PASSED 2025-09-09T14:22:41.4060136Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_slice PASSED 2025-09-09T14:22:41.4062745Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_slice_and_copy_ PASSED 2025-09-09T14:22:41.4065471Z test/quantization/quantize_/workflows/intx/test_intx_unpacked_to_int8_tensor.py::TestIntxUnpackedToInt8Tensor::test_to_dtype PASSED 2025-09-09T14:22:41.4067730Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_concat_linear_cpu_x_dim_2_bias_False SKIPPED 2025-09-09T14:22:41.4069935Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_concat_linear_cpu_x_dim_2_bias_True SKIPPED 2025-09-09T14:22:41.4071936Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_concat_linear_cpu_x_dim_3_bias_False SKIPPED 2025-09-09T14:22:41.4073884Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_concat_linear_cpu_x_dim_3_bias_True SKIPPED 2025-09-09T14:22:41.4076030Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_bfloat16_x_dim_2_bias_False_bs_160_sym_quant_a_False SKIPPED 2025-09-09T14:22:41.4078287Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_bfloat16_x_dim_2_bias_False_bs_160_sym_quant_a_True SKIPPED 2025-09-09T14:22:41.4080877Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_bfloat16_x_dim_2_bias_False_bs_1_sym_quant_a_False SKIPPED 2025-09-09T14:22:41.4083044Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_bfloat16_x_dim_2_bias_False_bs_1_sym_quant_a_True SKIPPED 2025-09-09T14:22:41.4085245Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_bfloat16_x_dim_2_bias_True_bs_160_sym_quant_a_False SKIPPED 2025-09-09T14:22:41.4087436Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_bfloat16_x_dim_2_bias_True_bs_160_sym_quant_a_True SKIPPED 2025-09-09T14:22:41.4089690Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_bfloat16_x_dim_2_bias_True_bs_1_sym_quant_a_False SKIPPED 2025-09-09T14:22:41.4091826Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_bfloat16_x_dim_2_bias_True_bs_1_sym_quant_a_True SKIPPED 2025-09-09T14:22:41.4094044Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_bfloat16_x_dim_3_bias_False_bs_160_sym_quant_a_False SKIPPED 2025-09-09T14:22:41.4096253Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_bfloat16_x_dim_3_bias_False_bs_160_sym_quant_a_True SKIPPED 2025-09-09T14:22:41.4098542Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_bfloat16_x_dim_3_bias_False_bs_1_sym_quant_a_False SKIPPED 2025-09-09T14:22:41.4100727Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_bfloat16_x_dim_3_bias_False_bs_1_sym_quant_a_True SKIPPED 2025-09-09T14:22:41.4102904Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_bfloat16_x_dim_3_bias_True_bs_160_sym_quant_a_False SKIPPED 2025-09-09T14:22:41.4105160Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_bfloat16_x_dim_3_bias_True_bs_160_sym_quant_a_True SKIPPED 2025-09-09T14:22:41.4107377Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_bfloat16_x_dim_3_bias_True_bs_1_sym_quant_a_False SKIPPED 2025-09-09T14:22:41.4109542Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_bfloat16_x_dim_3_bias_True_bs_1_sym_quant_a_True SKIPPED 2025-09-09T14:22:41.4111742Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float16_x_dim_2_bias_False_bs_160_sym_quant_a_False SKIPPED 2025-09-09T14:22:41.4113965Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float16_x_dim_2_bias_False_bs_160_sym_quant_a_True SKIPPED 2025-09-09T14:22:41.4116164Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float16_x_dim_2_bias_False_bs_1_sym_quant_a_False SKIPPED 2025-09-09T14:22:41.4118391Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float16_x_dim_2_bias_False_bs_1_sym_quant_a_True SKIPPED 2025-09-09T14:22:41.4120673Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float16_x_dim_2_bias_True_bs_160_sym_quant_a_False SKIPPED 2025-09-09T14:22:41.4122834Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float16_x_dim_2_bias_True_bs_160_sym_quant_a_True SKIPPED 2025-09-09T14:22:41.4125231Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float16_x_dim_2_bias_True_bs_1_sym_quant_a_False SKIPPED 2025-09-09T14:22:41.4127393Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float16_x_dim_2_bias_True_bs_1_sym_quant_a_True SKIPPED 2025-09-09T14:22:41.4129596Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float16_x_dim_3_bias_False_bs_160_sym_quant_a_False SKIPPED 2025-09-09T14:22:41.4131810Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float16_x_dim_3_bias_False_bs_160_sym_quant_a_True SKIPPED 2025-09-09T14:22:41.4133970Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float16_x_dim_3_bias_False_bs_1_sym_quant_a_False SKIPPED 2025-09-09T14:22:41.4136308Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float16_x_dim_3_bias_False_bs_1_sym_quant_a_True SKIPPED 2025-09-09T14:22:41.4138486Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float16_x_dim_3_bias_True_bs_160_sym_quant_a_False SKIPPED 2025-09-09T14:22:41.4140698Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float16_x_dim_3_bias_True_bs_160_sym_quant_a_True SKIPPED 2025-09-09T14:22:41.4142887Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float16_x_dim_3_bias_True_bs_1_sym_quant_a_False SKIPPED 2025-09-09T14:22:41.4145049Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float16_x_dim_3_bias_True_bs_1_sym_quant_a_True SKIPPED 2025-09-09T14:22:41.4147207Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float32_x_dim_2_bias_False_bs_160_sym_quant_a_False SKIPPED 2025-09-09T14:22:41.4149403Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float32_x_dim_2_bias_False_bs_160_sym_quant_a_True SKIPPED 2025-09-09T14:22:41.4151605Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float32_x_dim_2_bias_False_bs_1_sym_quant_a_False SKIPPED 2025-09-09T14:22:41.4153788Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float32_x_dim_2_bias_False_bs_1_sym_quant_a_True SKIPPED 2025-09-09T14:22:41.4156004Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float32_x_dim_2_bias_True_bs_160_sym_quant_a_False SKIPPED 2025-09-09T14:22:41.4158472Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float32_x_dim_2_bias_True_bs_160_sym_quant_a_True SKIPPED 2025-09-09T14:22:41.4160697Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float32_x_dim_2_bias_True_bs_1_sym_quant_a_False SKIPPED 2025-09-09T14:22:41.4162869Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float32_x_dim_2_bias_True_bs_1_sym_quant_a_True SKIPPED 2025-09-09T14:22:41.4165039Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float32_x_dim_3_bias_False_bs_160_sym_quant_a_False SKIPPED 2025-09-09T14:22:41.4180724Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float32_x_dim_3_bias_False_bs_160_sym_quant_a_True SKIPPED 2025-09-09T14:22:41.4326094Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float32_x_dim_3_bias_False_bs_1_sym_quant_a_False SKIPPED 2025-09-09T14:22:41.4328283Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float32_x_dim_3_bias_False_bs_1_sym_quant_a_True SKIPPED 2025-09-09T14:22:41.4330546Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float32_x_dim_3_bias_True_bs_160_sym_quant_a_False SKIPPED 2025-09-09T14:22:41.4332732Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float32_x_dim_3_bias_True_bs_160_sym_quant_a_True SKIPPED 2025-09-09T14:22:41.4334921Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float32_x_dim_3_bias_True_bs_1_sym_quant_a_False SKIPPED 2025-09-09T14:22:41.4337111Z test/quantization/test_da8w4_cpu.py::TestDa8w4Cpu::test_8da4w_cpu_float32_x_dim_3_bias_True_bs_1_sym_quant_a_True SKIPPED 2025-09-09T14:22:41.4339197Z test/quantization/test_gptq.py::TestGPTQ::test_gptq_quantizer_int4_weight_only SKIPPED 2025-09-09T14:22:41.4340944Z test/quantization/test_gptq.py::TestMultiTensorFlow::test_multitensor_add_tensors SKIPPED 2025-09-09T14:22:41.4342804Z test/quantization/test_gptq.py::TestMultiTensorFlow::test_multitensor_inplace_operation SKIPPED 2025-09-09T14:22:41.4344572Z test/quantization/test_gptq.py::TestMultiTensorFlow::test_multitensor_pad_unpad SKIPPED 2025-09-09T14:22:41.4346444Z test/quantization/test_gptq.py::TestMultiTensorInputRecorder::test_gptq_with_input_recorder SKIPPED 2025-09-09T14:22:41.4348739Z test/quantization/test_gptq.py::TestMultiTensorInputRecorder::test_multitensor_input_recorder SKIPPED 2025-09-09T14:22:41.4351072Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_aten SKIPPED 2025-09-09T14:22:41.4353864Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_accuracy_kleidiai SKIPPED 2025-09-09T14:22:41.4359262Z 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 2025-09-09T14:22:41.4366677Z 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 2025-09-09T14:22:41.4374160Z 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 2025-09-09T14:22:41.4381531Z 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 2025-09-09T14:22:41.4389011Z 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 2025-09-09T14:22:41.4396698Z 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 2025-09-09T14:22:41.4404531Z 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 2025-09-09T14:22:41.4411892Z 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 2025-09-09T14:22:41.4419504Z 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 2025-09-09T14:22:41.4426853Z 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 2025-09-09T14:22:41.4434337Z 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 2025-09-09T14:22:41.4442066Z 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 2025-09-09T14:22:41.4449541Z 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 2025-09-09T14:22:41.4456992Z 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 2025-09-09T14:22:41.4464602Z 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 2025-09-09T14:22:41.4527174Z 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 2025-09-09T14:22:41.4534792Z 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 2025-09-09T14:22:41.4542634Z 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 2025-09-09T14:22:41.4550118Z 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 2025-09-09T14:22:41.4557562Z 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 2025-09-09T14:22:41.4565266Z 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 2025-09-09T14:22:41.4572631Z 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 2025-09-09T14:22:41.4580205Z 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 2025-09-09T14:22:41.4587803Z 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 2025-09-09T14:22:41.4595539Z 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 2025-09-09T14:22:41.4603051Z 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 2025-09-09T14:22:41.4610423Z 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 2025-09-09T14:22:41.4618061Z 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 2025-09-09T14:22:41.4625601Z 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 2025-09-09T14:22:41.4633252Z 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 2025-09-09T14:22:41.4640920Z 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 2025-09-09T14:22:41.4648326Z 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 2025-09-09T14:22:41.4655566Z 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 2025-09-09T14:22:41.4663420Z 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 2025-09-09T14:22:41.4724846Z 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 2025-09-09T14:22:41.4732585Z 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 2025-09-09T14:22:41.4740315Z 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 2025-09-09T14:22:41.4747727Z 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 2025-09-09T14:22:41.4755170Z 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 2025-09-09T14:22:41.4762880Z 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 2025-09-09T14:22:41.4770380Z 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 2025-09-09T14:22:41.4778097Z 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 2025-09-09T14:22:41.4785846Z 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 2025-09-09T14:22:41.4793236Z 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 2025-09-09T14:22:41.4800714Z 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 2025-09-09T14:22:41.4808310Z 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 2025-09-09T14:22:41.4815827Z 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 2025-09-09T14:22:41.4823537Z 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 2025-09-09T14:22:41.4831110Z 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 2025-09-09T14:22:41.4838663Z 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 2025-09-09T14:22:41.4846312Z 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 2025-09-09T14:22:41.4853967Z 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 2025-09-09T14:22:41.4861855Z 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 2025-09-09T14:22:41.4920920Z 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 2025-09-09T14:22:41.4928848Z 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 2025-09-09T14:22:41.4936373Z 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 2025-09-09T14:22:41.4943892Z 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 2025-09-09T14:22:41.4951406Z 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 2025-09-09T14:22:41.4959361Z 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 2025-09-09T14:22:41.4967167Z 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 2025-09-09T14:22:41.4975099Z 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 2025-09-09T14:22:41.4982612Z 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 2025-09-09T14:22:41.4990078Z 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 2025-09-09T14:22:41.4997817Z 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 2025-09-09T14:22:41.5005534Z 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 2025-09-09T14:22:41.5013247Z 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 2025-09-09T14:22:41.5020901Z 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 2025-09-09T14:22:41.5028480Z 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 2025-09-09T14:22:41.5035900Z 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 2025-09-09T14:22:41.5043689Z 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 2025-09-09T14:22:41.5051286Z 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 2025-09-09T14:22:41.5059258Z 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 2025-09-09T14:22:41.5119064Z 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 2025-09-09T14:22:41.5126760Z 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 2025-09-09T14:22:41.5134275Z 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 2025-09-09T14:22:41.5141710Z 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 2025-09-09T14:22:41.5149414Z 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 2025-09-09T14:22:41.5157169Z 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 2025-09-09T14:22:41.5165347Z 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 2025-09-09T14:22:41.5172885Z 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 2025-09-09T14:22:41.5180405Z 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 2025-09-09T14:22:41.5188087Z 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 2025-09-09T14:22:41.5195673Z 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 2025-09-09T14:22:41.5203625Z 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 2025-09-09T14:22:41.5211190Z 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 2025-09-09T14:22:41.5218721Z 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 2025-09-09T14:22:41.5226228Z 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 2025-09-09T14:22:41.5233878Z 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 2025-09-09T14:22:41.5241502Z 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 2025-09-09T14:22:41.5249274Z 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 2025-09-09T14:22:41.5257021Z 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 2025-09-09T14:22:41.5387381Z 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 2025-09-09T14:22:41.5391315Z 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 2025-09-09T14:22:41.5395189Z 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 2025-09-09T14:22:41.5399143Z 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 2025-09-09T14:22:41.5403209Z 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 2025-09-09T14:22:41.5405833Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_export_QDQLayout SKIPPED 2025-09-09T14:22:41.5407742Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_export_compile_aoti_PackedLinearInt8DynamicActivationIntxWeightLayout SKIPPED 2025-09-09T14:22:41.5409739Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_export_dynamic_shape_PackedLinearInt8DynamicActivationIntxWeightLayout SKIPPED 2025-09-09T14:22:41.5411966Z 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 2025-09-09T14:22:41.5414379Z 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 2025-09-09T14:22:41.5416864Z 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 2025-09-09T14:22:41.5419247Z 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 2025-09-09T14:22:41.5421641Z 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 2025-09-09T14:22:41.5424031Z 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 2025-09-09T14:22:41.5426428Z 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 2025-09-09T14:22:41.5428808Z 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 2025-09-09T14:22:41.5431189Z 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 2025-09-09T14:22:41.5433596Z 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 2025-09-09T14:22:41.5435982Z 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 2025-09-09T14:22:41.5438374Z 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 2025-09-09T14:22:41.5440843Z 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 2025-09-09T14:22:41.5443285Z 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 2025-09-09T14:22:41.5445656Z 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 2025-09-09T14:22:41.5448042Z 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 2025-09-09T14:22:41.5450487Z 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 2025-09-09T14:22:41.5452849Z 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 2025-09-09T14:22:41.5455237Z 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 2025-09-09T14:22:41.5457635Z 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 2025-09-09T14:22:41.5460333Z 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 2025-09-09T14:22:41.5569209Z 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 2025-09-09T14:22:41.5571621Z 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 2025-09-09T14:22:41.5574011Z 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 2025-09-09T14:22:41.5576388Z 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 2025-09-09T14:22:41.5578771Z 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 2025-09-09T14:22:41.5581150Z 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 2025-09-09T14:22:41.5583868Z 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 2025-09-09T14:22:41.5586603Z 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 2025-09-09T14:22:41.5589757Z 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 2025-09-09T14:22:41.5593321Z 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 2025-09-09T14:22:41.5596783Z 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 2025-09-09T14:22:41.5600317Z 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 2025-09-09T14:22:41.5603756Z 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 2025-09-09T14:22:41.5607200Z 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 2025-09-09T14:22:41.5610656Z 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 2025-09-09T14:22:41.5614105Z 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 2025-09-09T14:22:41.5617603Z 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 2025-09-09T14:22:41.5621042Z 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 2025-09-09T14:22:41.5624490Z 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 2025-09-09T14:22:41.5628002Z 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 2025-09-09T14:22:41.5631447Z 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 2025-09-09T14:22:41.5634886Z 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 2025-09-09T14:22:41.5638304Z 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 2025-09-09T14:22:41.5733764Z 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 2025-09-09T14:22:41.5737233Z 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 2025-09-09T14:22:41.5740670Z 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 2025-09-09T14:22:41.5744217Z 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 2025-09-09T14:22:41.5747658Z 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 2025-09-09T14:22:41.5751097Z 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 2025-09-09T14:22:41.5754618Z 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 2025-09-09T14:22:41.5758056Z 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 2025-09-09T14:22:41.5761819Z 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 2025-09-09T14:22:41.5765264Z 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 2025-09-09T14:22:41.5768690Z 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 2025-09-09T14:22:41.5772135Z 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 2025-09-09T14:22:41.5775569Z 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 2025-09-09T14:22:41.5779127Z 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 2025-09-09T14:22:41.5782557Z 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 2025-09-09T14:22:41.5785966Z 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 2025-09-09T14:22:41.5789473Z 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 2025-09-09T14:22:41.5792893Z 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 2025-09-09T14:22:41.5796326Z 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 2025-09-09T14:22:41.5799836Z 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 2025-09-09T14:22:41.5803246Z 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 2025-09-09T14:22:41.5901008Z 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 2025-09-09T14:22:41.5904485Z 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 2025-09-09T14:22:41.5908024Z 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 2025-09-09T14:22:41.5911475Z 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 2025-09-09T14:22:41.5914903Z 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 2025-09-09T14:22:41.5918416Z 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 2025-09-09T14:22:41.5921916Z 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 2025-09-09T14:22:41.5925364Z 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 2025-09-09T14:22:41.5928793Z 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 2025-09-09T14:22:41.5932213Z 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 2025-09-09T14:22:41.5935656Z 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 2025-09-09T14:22:41.5939084Z 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 2025-09-09T14:22:41.5942570Z 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 2025-09-09T14:22:41.5945989Z 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 2025-09-09T14:22:41.5949458Z 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 2025-09-09T14:22:41.5952882Z 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 2025-09-09T14:22:41.5956305Z 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 2025-09-09T14:22:41.5960051Z 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 2025-09-09T14:22:41.5963492Z 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 2025-09-09T14:22:41.5966922Z 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 2025-09-09T14:22:41.5970357Z 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 2025-09-09T14:22:41.6065442Z 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 2025-09-09T14:22:41.6069003Z 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 2025-09-09T14:22:41.6072428Z 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 2025-09-09T14:22:41.6075926Z 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 2025-09-09T14:22:41.6079343Z 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 2025-09-09T14:22:41.6082835Z 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 2025-09-09T14:22:41.6086260Z 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 2025-09-09T14:22:41.6089682Z 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 2025-09-09T14:22:41.6093105Z 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 2025-09-09T14:22:41.6096511Z 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 2025-09-09T14:22:41.6099921Z 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 2025-09-09T14:22:41.6103383Z 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 2025-09-09T14:22:41.6106794Z 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 2025-09-09T14:22:41.6110253Z 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 2025-09-09T14:22:41.6113664Z 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 2025-09-09T14:22:41.6117086Z 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 2025-09-09T14:22:41.6120596Z 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 2025-09-09T14:22:41.6124040Z 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 2025-09-09T14:22:41.6127480Z 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 2025-09-09T14:22:41.6130918Z 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 2025-09-09T14:22:41.6134341Z 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 2025-09-09T14:22:41.6235020Z 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 2025-09-09T14:22:41.6238457Z 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 2025-09-09T14:22:41.6242032Z 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 2025-09-09T14:22:41.6245477Z 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 2025-09-09T14:22:41.6248911Z 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 2025-09-09T14:22:41.6252348Z 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 2025-09-09T14:22:41.6255758Z 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 2025-09-09T14:22:41.6259463Z 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 2025-09-09T14:22:41.6262891Z 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 2025-09-09T14:22:41.6266325Z 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 2025-09-09T14:22:41.6269849Z 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 2025-09-09T14:22:41.6273252Z 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 2025-09-09T14:22:41.6276768Z 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 2025-09-09T14:22:41.6280266Z 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 2025-09-09T14:22:41.6283702Z 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 2025-09-09T14:22:41.6287133Z 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 2025-09-09T14:22:41.6290540Z 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 2025-09-09T14:22:41.6293962Z 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 2025-09-09T14:22:41.6297376Z 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 2025-09-09T14:22:41.6300796Z 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 2025-09-09T14:22:41.6304281Z 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 2025-09-09T14:22:41.6402767Z 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 2025-09-09T14:22:41.6406566Z 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 2025-09-09T14:22:41.6410005Z 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 2025-09-09T14:22:41.6413451Z 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 2025-09-09T14:22:41.6416874Z 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 2025-09-09T14:22:41.6420297Z 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 2025-09-09T14:22:41.6423754Z 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 2025-09-09T14:22:41.6427184Z 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 2025-09-09T14:22:41.6430614Z 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 2025-09-09T14:22:41.6434209Z 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 2025-09-09T14:22:41.6437727Z 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 2025-09-09T14:22:41.6441418Z 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 2025-09-09T14:22:41.6444893Z 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 2025-09-09T14:22:41.6448380Z 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 2025-09-09T14:22:41.6451850Z 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 2025-09-09T14:22:41.6455318Z 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 2025-09-09T14:22:41.6459066Z 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 2025-09-09T14:22:41.6462540Z 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 2025-09-09T14:22:41.6466004Z 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 2025-09-09T14:22:41.6469577Z 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 2025-09-09T14:22:41.6473029Z 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 2025-09-09T14:22:41.6567843Z 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 2025-09-09T14:22:41.6571308Z 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 2025-09-09T14:22:41.6574746Z 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 2025-09-09T14:22:41.6578199Z 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 2025-09-09T14:22:41.6581626Z 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 2025-09-09T14:22:41.6585071Z 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 2025-09-09T14:22:41.6588514Z 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 2025-09-09T14:22:41.6591964Z 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 2025-09-09T14:22:41.6595544Z 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 2025-09-09T14:22:41.6598986Z 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 2025-09-09T14:22:41.6602591Z 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 2025-09-09T14:22:41.6606032Z 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 2025-09-09T14:22:41.6609469Z 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 2025-09-09T14:22:41.6612911Z 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 2025-09-09T14:22:41.6616329Z 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 2025-09-09T14:22:41.6619770Z 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 2025-09-09T14:22:41.6623202Z 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 2025-09-09T14:22:41.6626636Z 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 2025-09-09T14:22:41.6630144Z 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 2025-09-09T14:22:41.6633562Z 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 2025-09-09T14:22:41.6637046Z 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 2025-09-09T14:22:41.6734247Z 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 2025-09-09T14:22:41.6737700Z 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 2025-09-09T14:22:41.6741140Z 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 2025-09-09T14:22:41.6744586Z 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 2025-09-09T14:22:41.6748036Z 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 2025-09-09T14:22:41.6751476Z 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 2025-09-09T14:22:41.6754920Z 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 2025-09-09T14:22:41.6758660Z 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 2025-09-09T14:22:41.6762188Z 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 2025-09-09T14:22:41.6765700Z 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 2025-09-09T14:22:41.6769134Z 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 2025-09-09T14:22:41.6772567Z 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 2025-09-09T14:22:41.6776027Z 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 2025-09-09T14:22:41.6779466Z 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 2025-09-09T14:22:41.6782898Z 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 2025-09-09T14:22:41.6786333Z 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 2025-09-09T14:22:41.6789759Z 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 2025-09-09T14:22:41.6793232Z 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 2025-09-09T14:22:41.6796659Z 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 2025-09-09T14:22:41.6800214Z 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 2025-09-09T14:22:41.6803646Z 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 2025-09-09T14:22:41.6898838Z 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 2025-09-09T14:22:41.6902308Z 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 2025-09-09T14:22:41.6905737Z 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 2025-09-09T14:22:41.6909171Z 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 2025-09-09T14:22:41.6912576Z 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 2025-09-09T14:22:41.6915990Z 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 2025-09-09T14:22:41.6919527Z 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 2025-09-09T14:22:41.6923025Z 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 2025-09-09T14:22:41.6926534Z 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 2025-09-09T14:22:41.6929946Z 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 2025-09-09T14:22:41.6933363Z 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 2025-09-09T14:22:41.6936785Z 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 2025-09-09T14:22:41.6940201Z 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 2025-09-09T14:22:41.6943614Z 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 2025-09-09T14:22:41.6947010Z 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 2025-09-09T14:22:41.6950425Z 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 2025-09-09T14:22:41.6953882Z 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 2025-09-09T14:22:41.6957291Z 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 2025-09-09T14:22:41.6961586Z 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 2025-09-09T14:22:41.6965021Z 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 2025-09-09T14:22:41.6968464Z 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 2025-09-09T14:22:41.7065232Z 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 2025-09-09T14:22:41.7068684Z 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 2025-09-09T14:22:41.7072125Z 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 2025-09-09T14:22:41.7075554Z 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 2025-09-09T14:22:41.7078989Z 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 2025-09-09T14:22:41.7082609Z 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 2025-09-09T14:22:41.7086058Z 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 2025-09-09T14:22:41.7089589Z 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 2025-09-09T14:22:41.7093013Z 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 2025-09-09T14:22:41.7096449Z 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 2025-09-09T14:22:41.7099885Z 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 2025-09-09T14:22:41.7103305Z 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 2025-09-09T14:22:41.7106734Z 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 2025-09-09T14:22:41.7110147Z 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 2025-09-09T14:22:41.7113564Z 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 2025-09-09T14:22:41.7117055Z 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 2025-09-09T14:22:41.7120552Z 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 2025-09-09T14:22:41.7124061Z 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 2025-09-09T14:22:41.7127477Z 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 2025-09-09T14:22:41.7130902Z 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 2025-09-09T14:22:41.7134323Z 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 2025-09-09T14:22:41.7229494Z 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 2025-09-09T14:22:41.7232947Z 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 2025-09-09T14:22:41.7236365Z 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 2025-09-09T14:22:41.7239872Z 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 2025-09-09T14:22:41.7243392Z 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 2025-09-09T14:22:41.7246821Z 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 2025-09-09T14:22:41.7250321Z 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 2025-09-09T14:22:41.7253742Z 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 2025-09-09T14:22:41.7257161Z 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 2025-09-09T14:22:41.7260774Z 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 2025-09-09T14:22:41.7264194Z 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 2025-09-09T14:22:41.7267615Z 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 2025-09-09T14:22:41.7271063Z 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 2025-09-09T14:22:41.7274542Z 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 2025-09-09T14:22:41.7278061Z 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 2025-09-09T14:22:41.7281599Z 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 2025-09-09T14:22:41.7285144Z 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 2025-09-09T14:22:41.7288589Z 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 2025-09-09T14:22:41.7292057Z 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 2025-09-09T14:22:41.7295495Z 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 2025-09-09T14:22:41.7298943Z 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 2025-09-09T14:22:41.7400139Z 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 2025-09-09T14:22:41.7403610Z 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 2025-09-09T14:22:41.7407058Z 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 2025-09-09T14:22:41.7410608Z 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 2025-09-09T14:22:41.7414045Z 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 2025-09-09T14:22:41.7417555Z 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 2025-09-09T14:22:41.7420987Z 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 2025-09-09T14:22:41.7424430Z 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 2025-09-09T14:22:41.7427846Z 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 2025-09-09T14:22:41.7431286Z 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 2025-09-09T14:22:41.7434736Z 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 2025-09-09T14:22:41.7438180Z 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 2025-09-09T14:22:41.7441807Z 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 2025-09-09T14:22:41.7445249Z 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 2025-09-09T14:22:41.7448673Z 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 2025-09-09T14:22:41.7452173Z 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 2025-09-09T14:22:41.7455612Z 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 2025-09-09T14:22:41.7459303Z 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 2025-09-09T14:22:41.7462752Z 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 2025-09-09T14:22:41.7466174Z 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 2025-09-09T14:22:41.7469613Z 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 2025-09-09T14:22:41.7565179Z 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 2025-09-09T14:22:41.7568748Z 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 2025-09-09T14:22:41.7572178Z 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 2025-09-09T14:22:41.7575588Z 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 2025-09-09T14:22:41.7579092Z 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 2025-09-09T14:22:41.7582516Z 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 2025-09-09T14:22:41.7585959Z 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 2025-09-09T14:22:41.7589410Z 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 2025-09-09T14:22:41.7592839Z 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 2025-09-09T14:22:41.7596286Z 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 2025-09-09T14:22:41.7599798Z 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 2025-09-09T14:22:41.7603294Z 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 2025-09-09T14:22:41.7606727Z 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 2025-09-09T14:22:41.7610154Z 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 2025-09-09T14:22:41.7613650Z 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 2025-09-09T14:22:41.7617092Z 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 2025-09-09T14:22:41.7620542Z 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 2025-09-09T14:22:41.7623986Z 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 2025-09-09T14:22:41.7627423Z 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 2025-09-09T14:22:41.7630847Z 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 2025-09-09T14:22:41.7634279Z 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 2025-09-09T14:22:41.7731783Z 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 2025-09-09T14:22:41.7735231Z 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 2025-09-09T14:22:41.7738644Z 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 2025-09-09T14:22:41.7742158Z 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 2025-09-09T14:22:41.7745588Z 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 2025-09-09T14:22:41.7749021Z 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 2025-09-09T14:22:41.7752450Z 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 2025-09-09T14:22:41.7755875Z 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 2025-09-09T14:22:41.7759535Z 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 2025-09-09T14:22:41.7763051Z 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 2025-09-09T14:22:41.7766572Z 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 2025-09-09T14:22:41.7769995Z 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 2025-09-09T14:22:41.7773416Z 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 2025-09-09T14:22:41.7776933Z 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 2025-09-09T14:22:41.7780357Z 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 2025-09-09T14:22:41.7783775Z 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 2025-09-09T14:22:41.7787190Z 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 2025-09-09T14:22:41.7790593Z 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 2025-09-09T14:22:41.7794005Z 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 2025-09-09T14:22:41.7797410Z 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 2025-09-09T14:22:41.7800929Z 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 2025-09-09T14:22:41.7897635Z 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 2025-09-09T14:22:41.7901102Z 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 2025-09-09T14:22:41.7904667Z 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 2025-09-09T14:22:41.7908098Z 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 2025-09-09T14:22:41.7911542Z 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 2025-09-09T14:22:41.7914973Z 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 2025-09-09T14:22:41.7918406Z 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 2025-09-09T14:22:41.7921920Z 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 2025-09-09T14:22:41.7925341Z 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 2025-09-09T14:22:41.7928869Z 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 2025-09-09T14:22:41.7932310Z 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 2025-09-09T14:22:41.7935746Z 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 2025-09-09T14:22:41.7939234Z 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 2025-09-09T14:22:41.7942644Z 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 2025-09-09T14:22:41.7946067Z 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 2025-09-09T14:22:41.7949502Z 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 2025-09-09T14:22:41.7952934Z 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 2025-09-09T14:22:41.7956358Z 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 2025-09-09T14:22:41.7960084Z 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 2025-09-09T14:22:41.7963612Z 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 2025-09-09T14:22:41.7967046Z 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 2025-09-09T14:22:41.8060677Z 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 2025-09-09T14:22:41.8064231Z 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 2025-09-09T14:22:41.8067651Z 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 2025-09-09T14:22:41.8071254Z 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 2025-09-09T14:22:41.8074690Z 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 2025-09-09T14:22:41.8078108Z 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 2025-09-09T14:22:41.8081603Z 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 2025-09-09T14:22:41.8085076Z 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 2025-09-09T14:22:41.8088578Z 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 2025-09-09T14:22:41.8091998Z 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 2025-09-09T14:22:41.8095403Z 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 2025-09-09T14:22:41.8098871Z 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 2025-09-09T14:22:41.8102287Z 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 2025-09-09T14:22:41.8105689Z 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 2025-09-09T14:22:41.8109100Z 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 2025-09-09T14:22:41.8112542Z 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 2025-09-09T14:22:41.8116005Z 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 2025-09-09T14:22:41.8119454Z 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 2025-09-09T14:22:41.8123031Z 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 2025-09-09T14:22:41.8126482Z 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 2025-09-09T14:22:41.8129917Z 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 2025-09-09T14:22:41.8226678Z 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 2025-09-09T14:22:41.8230150Z 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 2025-09-09T14:22:41.8233609Z 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 2025-09-09T14:22:41.8237042Z 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 2025-09-09T14:22:41.8240557Z 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 2025-09-09T14:22:41.8244016Z 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 2025-09-09T14:22:41.8247450Z 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 2025-09-09T14:22:41.8250992Z 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 2025-09-09T14:22:41.8254420Z 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 2025-09-09T14:22:41.8257867Z 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 2025-09-09T14:22:41.8261630Z 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 2025-09-09T14:22:41.8265062Z 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 2025-09-09T14:22:41.8268515Z 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 2025-09-09T14:22:41.8271954Z 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 2025-09-09T14:22:41.8275394Z 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 2025-09-09T14:22:41.8278847Z 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 2025-09-09T14:22:41.8282355Z 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 2025-09-09T14:22:41.8285877Z 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 2025-09-09T14:22:41.8289309Z 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 2025-09-09T14:22:41.8292816Z 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 2025-09-09T14:22:41.8296256Z 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 2025-09-09T14:22:41.8391530Z 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 2025-09-09T14:22:41.8395281Z 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 2025-09-09T14:22:41.8409798Z 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 2025-09-09T14:22:41.8413887Z 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 2025-09-09T14:22:41.8417356Z 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 2025-09-09T14:22:41.8421139Z 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 2025-09-09T14:22:41.8425514Z 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 2025-09-09T14:22:41.8429169Z 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 2025-09-09T14:22:41.8432724Z 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 2025-09-09T14:22:41.8436518Z 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 2025-09-09T14:22:41.8440052Z 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 2025-09-09T14:22:41.8443873Z 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 2025-09-09T14:22:41.8447315Z 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 2025-09-09T14:22:41.8450955Z 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 2025-09-09T14:22:41.8454573Z 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 2025-09-09T14:22:41.8458452Z 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 2025-09-09T14:22:41.8462201Z 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 2025-09-09T14:22:41.8465800Z 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 2025-09-09T14:22:41.8469516Z 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 2025-09-09T14:22:41.8473124Z 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 2025-09-09T14:22:41.8476726Z 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 2025-09-09T14:22:41.8555698Z 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 2025-09-09T14:22:41.8559543Z 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 2025-09-09T14:22:41.8563243Z 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 2025-09-09T14:22:41.8566843Z 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 2025-09-09T14:22:41.8570435Z 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 2025-09-09T14:22:41.8574166Z 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 2025-09-09T14:22:41.8577763Z 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 2025-09-09T14:22:41.8581474Z 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 2025-09-09T14:22:41.8585065Z 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 2025-09-09T14:22:41.8588669Z 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 2025-09-09T14:22:41.8592288Z 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 2025-09-09T14:22:41.8595873Z 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 2025-09-09T14:22:41.8599295Z 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 2025-09-09T14:22:41.8602785Z 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 2025-09-09T14:22:41.8606382Z 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 2025-09-09T14:22:41.8610281Z 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 2025-09-09T14:22:41.8614023Z 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 2025-09-09T14:22:41.8617802Z 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 2025-09-09T14:22:41.8621382Z 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 2025-09-09T14:22:41.8624936Z 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 2025-09-09T14:22:41.8628504Z 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 2025-09-09T14:22:41.8723475Z 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 2025-09-09T14:22:41.8727059Z 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 2025-09-09T14:22:41.8730645Z 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 2025-09-09T14:22:41.8734211Z 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 2025-09-09T14:22:41.8737936Z 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 2025-09-09T14:22:41.8741527Z 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 2025-09-09T14:22:41.8745214Z 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 2025-09-09T14:22:41.8748824Z 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 2025-09-09T14:22:41.8752391Z 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 2025-09-09T14:22:41.8755844Z 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 2025-09-09T14:22:41.8759878Z 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 2025-09-09T14:22:41.8763335Z 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 2025-09-09T14:22:41.8767056Z 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 2025-09-09T14:22:41.8770483Z 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 2025-09-09T14:22:41.8774171Z 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 2025-09-09T14:22:41.8777750Z 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 2025-09-09T14:22:41.8781419Z 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 2025-09-09T14:22:41.8784977Z 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 2025-09-09T14:22:41.8788532Z 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 2025-09-09T14:22:41.8792120Z 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 2025-09-09T14:22:41.8795682Z 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 2025-09-09T14:22:41.8886546Z 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 2025-09-09T14:22:41.8890151Z 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 2025-09-09T14:22:41.8893735Z 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 2025-09-09T14:22:41.8897425Z 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 2025-09-09T14:22:41.8901000Z 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 2025-09-09T14:22:41.8904664Z 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 2025-09-09T14:22:41.8908100Z 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 2025-09-09T14:22:41.8911657Z 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 2025-09-09T14:22:41.8915206Z 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 2025-09-09T14:22:41.8918769Z 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 2025-09-09T14:22:41.8922402Z 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 2025-09-09T14:22:41.8925971Z 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 2025-09-09T14:22:41.8929536Z 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 2025-09-09T14:22:41.8933160Z 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 2025-09-09T14:22:41.8936722Z 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 2025-09-09T14:22:41.8940362Z 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 2025-09-09T14:22:41.8943776Z 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 2025-09-09T14:22:41.8947474Z 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 2025-09-09T14:22:41.8950919Z 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 2025-09-09T14:22:41.8954678Z 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 2025-09-09T14:22:41.8958381Z 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 2025-09-09T14:22:41.9051484Z 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 2025-09-09T14:22:41.9055089Z 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 2025-09-09T14:22:41.9059028Z 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 2025-09-09T14:22:41.9062670Z 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 2025-09-09T14:22:41.9066366Z 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 2025-09-09T14:22:41.9069968Z 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 2025-09-09T14:22:41.9073570Z 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 2025-09-09T14:22:41.9077327Z 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 2025-09-09T14:22:41.9080966Z 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 2025-09-09T14:22:41.9084574Z 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 2025-09-09T14:22:41.9088187Z 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 2025-09-09T14:22:41.9091643Z 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 2025-09-09T14:22:41.9095450Z 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 2025-09-09T14:22:41.9098902Z 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 2025-09-09T14:22:41.9102693Z 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 2025-09-09T14:22:41.9106151Z 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 2025-09-09T14:22:41.9109745Z 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 2025-09-09T14:22:41.9113340Z 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 2025-09-09T14:22:41.9117004Z 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 2025-09-09T14:22:41.9120824Z 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 2025-09-09T14:22:41.9124401Z 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 2025-09-09T14:22:41.9216907Z 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 2025-09-09T14:22:41.9220699Z 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 2025-09-09T14:22:41.9224301Z 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 2025-09-09T14:22:41.9227969Z 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 2025-09-09T14:22:41.9231559Z 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 2025-09-09T14:22:41.9235131Z 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 2025-09-09T14:22:41.9238713Z 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 2025-09-09T14:22:41.9242350Z 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 2025-09-09T14:22:41.9245913Z 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 2025-09-09T14:22:41.9249637Z 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 2025-09-09T14:22:41.9253345Z 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 2025-09-09T14:22:41.9257167Z 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 2025-09-09T14:22:41.9261182Z 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 2025-09-09T14:22:41.9265146Z 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 2025-09-09T14:22:41.9268890Z 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 2025-09-09T14:22:41.9272480Z 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 2025-09-09T14:22:41.9276078Z 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 2025-09-09T14:22:41.9279530Z 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 2025-09-09T14:22:41.9283319Z 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 2025-09-09T14:22:41.9286765Z 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 2025-09-09T14:22:41.9290465Z 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 2025-09-09T14:22:41.9379941Z 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 2025-09-09T14:22:41.9383557Z 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 2025-09-09T14:22:41.9387251Z 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 2025-09-09T14:22:41.9390819Z 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 2025-09-09T14:22:41.9394389Z 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 2025-09-09T14:22:41.9397965Z 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 2025-09-09T14:22:41.9401588Z 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 2025-09-09T14:22:41.9405152Z 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 2025-09-09T14:22:41.9408859Z 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 2025-09-09T14:22:41.9412569Z 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 2025-09-09T14:22:41.9416100Z 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 2025-09-09T14:22:41.9419693Z 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 2025-09-09T14:22:41.9423336Z 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 2025-09-09T14:22:41.9426898Z 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 2025-09-09T14:22:41.9430463Z 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 2025-09-09T14:22:41.9434053Z 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 2025-09-09T14:22:41.9437614Z 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 2025-09-09T14:22:41.9441266Z 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 2025-09-09T14:22:41.9444956Z 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 2025-09-09T14:22:41.9448684Z 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 2025-09-09T14:22:41.9452259Z 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 2025-09-09T14:22:41.9544894Z 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 2025-09-09T14:22:41.9548793Z 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 2025-09-09T14:22:41.9552482Z 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 2025-09-09T14:22:41.9556060Z 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 2025-09-09T14:22:41.9559947Z 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 2025-09-09T14:22:41.9563517Z 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 2025-09-09T14:22:41.9567104Z 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 2025-09-09T14:22:41.9570692Z 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 2025-09-09T14:22:41.9574300Z 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 2025-09-09T14:22:41.9577990Z 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 2025-09-09T14:22:41.9581586Z 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 2025-09-09T14:22:41.9585260Z 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 2025-09-09T14:22:41.9588841Z 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 2025-09-09T14:22:41.9592269Z 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 2025-09-09T14:22:41.9595974Z 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 2025-09-09T14:22:41.9599419Z 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 2025-09-09T14:22:41.9603089Z 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 2025-09-09T14:22:41.9606652Z 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 2025-09-09T14:22:41.9610209Z 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 2025-09-09T14:22:41.9613839Z 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 2025-09-09T14:22:41.9617413Z 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 2025-09-09T14:22:41.9707753Z 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 2025-09-09T14:22:41.9711359Z 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 2025-09-09T14:22:41.9714925Z 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 2025-09-09T14:22:41.9718507Z 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 2025-09-09T14:22:41.9722026Z 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 2025-09-09T14:22:41.9725620Z 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 2025-09-09T14:22:41.9729196Z 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 2025-09-09T14:22:41.9732897Z 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 2025-09-09T14:22:41.9736484Z 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 2025-09-09T14:22:41.9740065Z 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 2025-09-09T14:22:41.9743738Z 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 2025-09-09T14:22:41.9747306Z 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 2025-09-09T14:22:41.9750884Z 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 2025-09-09T14:22:41.9754468Z 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 2025-09-09T14:22:41.9758029Z 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 2025-09-09T14:22:41.9761918Z 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 2025-09-09T14:22:41.9765341Z 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 2025-09-09T14:22:41.9769103Z 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 2025-09-09T14:22:41.9772532Z 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 2025-09-09T14:22:41.9776099Z 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 2025-09-09T14:22:41.9779729Z 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 2025-09-09T14:22:41.9875254Z 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 2025-09-09T14:22:41.9878911Z 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 2025-09-09T14:22:41.9882552Z 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 2025-09-09T14:22:41.9886156Z 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 2025-09-09T14:22:41.9889762Z 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 2025-09-09T14:22:41.9893345Z 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 2025-09-09T14:22:41.9897063Z 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 2025-09-09T14:22:41.9900657Z 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 2025-09-09T14:22:41.9904255Z 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 2025-09-09T14:22:41.9907964Z 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 2025-09-09T14:22:41.9911576Z 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 2025-09-09T14:22:41.9915184Z 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 2025-09-09T14:22:41.9918777Z 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 2025-09-09T14:22:41.9922301Z 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 2025-09-09T14:22:41.9925915Z 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 2025-09-09T14:22:41.9929503Z 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 2025-09-09T14:22:41.9933157Z 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 2025-09-09T14:22:41.9936755Z 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 2025-09-09T14:22:41.9940338Z 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 2025-09-09T14:22:41.9944015Z 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 2025-09-09T14:22:41.9947459Z 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 2025-09-09T14:22:42.0040279Z 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 2025-09-09T14:22:42.0043902Z 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 2025-09-09T14:22:42.0047479Z 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 2025-09-09T14:22:42.0051076Z 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 2025-09-09T14:22:42.0054662Z 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 2025-09-09T14:22:42.0058600Z 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 2025-09-09T14:22:42.0062201Z 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 2025-09-09T14:22:42.0065782Z 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 2025-09-09T14:22:42.0069459Z 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 2025-09-09T14:22:42.0073035Z 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 2025-09-09T14:22:42.0076623Z 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 2025-09-09T14:22:42.0080112Z 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 2025-09-09T14:22:42.0083799Z 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 2025-09-09T14:22:42.0087234Z 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 2025-09-09T14:22:42.0090938Z 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 2025-09-09T14:22:42.0094469Z 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 2025-09-09T14:22:42.0098070Z 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 2025-09-09T14:22:42.0101639Z 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 2025-09-09T14:22:42.0105290Z 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 2025-09-09T14:22:42.0108867Z 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 2025-09-09T14:22:42.0112444Z 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 2025-09-09T14:22:42.0203488Z 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 2025-09-09T14:22:42.0207239Z 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 2025-09-09T14:22:42.0210961Z 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 2025-09-09T14:22:42.0214552Z 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 2025-09-09T14:22:42.0218262Z 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 2025-09-09T14:22:42.0221868Z 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 2025-09-09T14:22:42.0225456Z 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 2025-09-09T14:22:42.0229113Z 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 2025-09-09T14:22:42.0232549Z 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 2025-09-09T14:22:42.0236136Z 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 2025-09-09T14:22:42.0239772Z 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 2025-09-09T14:22:42.0243358Z 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 2025-09-09T14:22:42.0246939Z 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 2025-09-09T14:22:42.0250524Z 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 2025-09-09T14:22:42.0254313Z 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 2025-09-09T14:22:42.0258047Z 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 2025-09-09T14:22:42.0262000Z 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 2025-09-09T14:22:42.0265792Z 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 2025-09-09T14:22:42.0269482Z 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 2025-09-09T14:22:42.0273195Z 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 2025-09-09T14:22:42.0276769Z 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 2025-09-09T14:22:42.0368628Z 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 2025-09-09T14:22:42.0372227Z 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 2025-09-09T14:22:42.0375800Z 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 2025-09-09T14:22:42.0379503Z 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 2025-09-09T14:22:42.0383091Z 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 2025-09-09T14:22:42.0386657Z 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 2025-09-09T14:22:42.0390302Z 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 2025-09-09T14:22:42.0393862Z 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 2025-09-09T14:22:42.0397441Z 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 2025-09-09T14:22:42.0401213Z 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 2025-09-09T14:22:42.0404947Z 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 2025-09-09T14:22:42.0408420Z 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 2025-09-09T14:22:42.0412141Z 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 2025-09-09T14:22:42.0415671Z 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 2025-09-09T14:22:42.0419380Z 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 2025-09-09T14:22:42.0422823Z 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 2025-09-09T14:22:42.0426670Z 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 2025-09-09T14:22:42.0430307Z 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 2025-09-09T14:22:42.0434036Z 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 2025-09-09T14:22:42.0437626Z 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 2025-09-09T14:22:42.0441133Z 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 2025-09-09T14:22:42.0532017Z 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 2025-09-09T14:22:42.0535625Z 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 2025-09-09T14:22:42.0539321Z 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 2025-09-09T14:22:42.0542765Z 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 2025-09-09T14:22:42.0546493Z 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 2025-09-09T14:22:42.0550018Z 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 2025-09-09T14:22:42.0553609Z 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 2025-09-09T14:22:42.0557199Z 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 2025-09-09T14:22:42.0561133Z 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 2025-09-09T14:22:42.0564727Z 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 2025-09-09T14:22:42.0568306Z 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 2025-09-09T14:22:42.0571873Z 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 2025-09-09T14:22:42.0575534Z 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 2025-09-09T14:22:42.0579122Z 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 2025-09-09T14:22:42.0582686Z 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 2025-09-09T14:22:42.0586358Z 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 2025-09-09T14:22:42.0589920Z 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 2025-09-09T14:22:42.0593469Z 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 2025-09-09T14:22:42.0596891Z 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 2025-09-09T14:22:42.0600672Z 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 2025-09-09T14:22:42.0604104Z 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 2025-09-09T14:22:42.0697531Z 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 2025-09-09T14:22:42.0701223Z 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 2025-09-09T14:22:42.0704799Z 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 2025-09-09T14:22:42.0708390Z 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 2025-09-09T14:22:42.0712142Z 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 2025-09-09T14:22:42.0715748Z 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 2025-09-09T14:22:42.0719348Z 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 2025-09-09T14:22:42.0723013Z 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 2025-09-09T14:22:42.0726621Z 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 2025-09-09T14:22:42.0730217Z 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 2025-09-09T14:22:42.0733820Z 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 2025-09-09T14:22:42.0737320Z 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 2025-09-09T14:22:42.0740932Z 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 2025-09-09T14:22:42.0744605Z 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 2025-09-09T14:22:42.0748201Z 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 2025-09-09T14:22:42.0751803Z 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 2025-09-09T14:22:42.0755398Z 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 2025-09-09T14:22:42.0759180Z 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 2025-09-09T14:22:42.0762779Z 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 2025-09-09T14:22:42.0766409Z 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 2025-09-09T14:22:42.0769855Z 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 2025-09-09T14:22:42.0862766Z 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 2025-09-09T14:22:42.0866382Z 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 2025-09-09T14:22:42.0870086Z 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 2025-09-09T14:22:42.0873664Z 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 2025-09-09T14:22:42.0877245Z 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 2025-09-09T14:22:42.0880887Z 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 2025-09-09T14:22:42.0884475Z 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 2025-09-09T14:22:42.0888069Z 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 2025-09-09T14:22:42.0891648Z 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 2025-09-09T14:22:42.0895229Z 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 2025-09-09T14:22:42.0898880Z 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 2025-09-09T14:22:42.0902314Z 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 2025-09-09T14:22:42.0906090Z 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 2025-09-09T14:22:42.0909519Z 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 2025-09-09T14:22:42.0913214Z 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 2025-09-09T14:22:42.0916658Z 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 2025-09-09T14:22:42.0920312Z 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 2025-09-09T14:22:42.0923906Z 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 2025-09-09T14:22:42.0927495Z 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 2025-09-09T14:22:42.0931098Z 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 2025-09-09T14:22:42.0934759Z 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 2025-09-09T14:22:42.1025141Z 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 2025-09-09T14:22:42.1028873Z 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 2025-09-09T14:22:42.1032469Z 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 2025-09-09T14:22:42.1036050Z 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 2025-09-09T14:22:42.1039718Z 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 2025-09-09T14:22:42.1043325Z 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 2025-09-09T14:22:42.1046790Z 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 2025-09-09T14:22:42.1050363Z 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 2025-09-09T14:22:42.1053950Z 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 2025-09-09T14:22:42.1057630Z 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 2025-09-09T14:22:42.1061315Z 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 2025-09-09T14:22:42.1065114Z 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 2025-09-09T14:22:42.1068533Z 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 2025-09-09T14:22:42.1072243Z 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 2025-09-09T14:22:42.1075671Z 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 2025-09-09T14:22:42.1079240Z 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 2025-09-09T14:22:42.1082876Z 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 2025-09-09T14:22:42.1086438Z 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 2025-09-09T14:22:42.1090020Z 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 2025-09-09T14:22:42.1093680Z 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 2025-09-09T14:22:42.1097250Z 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 2025-09-09T14:22:42.1192143Z 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 2025-09-09T14:22:42.1195629Z 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 2025-09-09T14:22:42.1199109Z 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 2025-09-09T14:22:42.1202693Z 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 2025-09-09T14:22:42.1206183Z 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 2025-09-09T14:22:42.1209668Z 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 2025-09-09T14:22:42.1213121Z 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 2025-09-09T14:22:42.1216524Z 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 2025-09-09T14:22:42.1220165Z 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 2025-09-09T14:22:42.1223598Z 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 2025-09-09T14:22:42.1227139Z 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 2025-09-09T14:22:42.1230581Z 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 2025-09-09T14:22:42.1234059Z 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 2025-09-09T14:22:42.1237559Z 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 2025-09-09T14:22:42.1241134Z 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 2025-09-09T14:22:42.1244637Z 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 2025-09-09T14:22:42.1248110Z 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 2025-09-09T14:22:42.1251542Z 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 2025-09-09T14:22:42.1255116Z 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 2025-09-09T14:22:42.1258966Z 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 2025-09-09T14:22:42.1262592Z 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 2025-09-09T14:22:42.1357126Z 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 2025-09-09T14:22:42.1360976Z 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 2025-09-09T14:22:42.1364492Z 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 2025-09-09T14:22:42.1367966Z 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 2025-09-09T14:22:42.1371450Z 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 2025-09-09T14:22:42.1374919Z 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 2025-09-09T14:22:42.1378375Z 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 2025-09-09T14:22:42.1381966Z 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 2025-09-09T14:22:42.1385495Z 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 2025-09-09T14:22:42.1389031Z 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 2025-09-09T14:22:42.1392529Z 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 2025-09-09T14:22:42.1395997Z 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 2025-09-09T14:22:42.1399483Z 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 2025-09-09T14:22:42.1403033Z 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 2025-09-09T14:22:42.1406510Z 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 2025-09-09T14:22:42.1409986Z 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 2025-09-09T14:22:42.1413453Z 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 2025-09-09T14:22:42.1416984Z 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 2025-09-09T14:22:42.1420431Z 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 2025-09-09T14:22:42.1423985Z 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 2025-09-09T14:22:42.1427397Z 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 2025-09-09T14:22:42.1520665Z 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 2025-09-09T14:22:42.1524165Z 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 2025-09-09T14:22:42.1527632Z 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 2025-09-09T14:22:42.1531118Z 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 2025-09-09T14:22:42.1534590Z 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 2025-09-09T14:22:42.1538030Z 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 2025-09-09T14:22:42.1541677Z 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 2025-09-09T14:22:42.1545166Z 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 2025-09-09T14:22:42.1548817Z 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 2025-09-09T14:22:42.1552294Z 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 2025-09-09T14:22:42.1555793Z 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 2025-09-09T14:22:42.1559482Z 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 2025-09-09T14:22:42.1563012Z 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 2025-09-09T14:22:42.1566572Z 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 2025-09-09T14:22:42.1570024Z 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 2025-09-09T14:22:42.1573519Z 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 2025-09-09T14:22:42.1577125Z 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 2025-09-09T14:22:42.1580633Z 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 2025-09-09T14:22:42.1584219Z 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 2025-09-09T14:22:42.1587706Z 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 2025-09-09T14:22:42.1591189Z 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 2025-09-09T14:22:42.1685956Z 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 2025-09-09T14:22:42.1689470Z 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 2025-09-09T14:22:42.1692977Z 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 2025-09-09T14:22:42.1696477Z 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 2025-09-09T14:22:42.1699975Z 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 2025-09-09T14:22:42.1703605Z 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 2025-09-09T14:22:42.1707108Z 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 2025-09-09T14:22:42.1710695Z 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 2025-09-09T14:22:42.1714233Z 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 2025-09-09T14:22:42.1717698Z 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 2025-09-09T14:22:42.1721274Z 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 2025-09-09T14:22:42.1724736Z 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 2025-09-09T14:22:42.1728236Z 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 2025-09-09T14:22:42.1731726Z 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 2025-09-09T14:22:42.1735207Z 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 2025-09-09T14:22:42.1738762Z 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 2025-09-09T14:22:42.1742228Z 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 2025-09-09T14:22:42.1745768Z 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 2025-09-09T14:22:42.1749250Z 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 2025-09-09T14:22:42.1752727Z 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 2025-09-09T14:22:42.1756154Z 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 2025-09-09T14:22:42.1848755Z 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 2025-09-09T14:22:42.1852473Z 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 2025-09-09T14:22:42.1856141Z 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 2025-09-09T14:22:42.1860041Z 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 2025-09-09T14:22:42.1863679Z 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 2025-09-09T14:22:42.1867344Z 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 2025-09-09T14:22:42.1871155Z 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 2025-09-09T14:22:42.1874767Z 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 2025-09-09T14:22:42.1878415Z 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 2025-09-09T14:22:42.1882135Z 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 2025-09-09T14:22:42.1885762Z 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 2025-09-09T14:22:42.1889435Z 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 2025-09-09T14:22:42.1893094Z 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 2025-09-09T14:22:42.1896845Z 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 2025-09-09T14:22:42.1900669Z 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 2025-09-09T14:22:42.1904454Z 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 2025-09-09T14:22:42.1908317Z 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 2025-09-09T14:22:42.1911986Z 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 2025-09-09T14:22:42.1915659Z 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 2025-09-09T14:22:42.1919331Z 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 2025-09-09T14:22:42.1923207Z 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 2025-09-09T14:22:42.2014020Z 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 2025-09-09T14:22:42.2017736Z 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 2025-09-09T14:22:42.2021554Z 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 2025-09-09T14:22:42.2025238Z 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 2025-09-09T14:22:42.2028881Z 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 2025-09-09T14:22:42.2032610Z 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 2025-09-09T14:22:42.2036282Z 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 2025-09-09T14:22:42.2040157Z 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 2025-09-09T14:22:42.2043934Z 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 2025-09-09T14:22:42.2047720Z 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 2025-09-09T14:22:42.2051520Z 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 2025-09-09T14:22:42.2055288Z 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 2025-09-09T14:22:42.2059459Z 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 2025-09-09T14:22:42.2063305Z 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 2025-09-09T14:22:42.2066736Z 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 2025-09-09T14:22:42.2070600Z 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 2025-09-09T14:22:42.2074454Z 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 2025-09-09T14:22:42.2078212Z 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 2025-09-09T14:22:42.2082061Z 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 2025-09-09T14:22:42.2085740Z 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 2025-09-09T14:22:42.2089370Z 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 2025-09-09T14:22:42.2179238Z 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 2025-09-09T14:22:42.2183342Z 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 2025-09-09T14:22:42.2187294Z 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 2025-09-09T14:22:42.2191081Z 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 2025-09-09T14:22:42.2195121Z 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 2025-09-09T14:22:42.2198915Z 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 2025-09-09T14:22:42.2202762Z 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 2025-09-09T14:22:42.2206559Z 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 2025-09-09T14:22:42.2210339Z 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 2025-09-09T14:22:42.2214311Z 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 2025-09-09T14:22:42.2218049Z 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 2025-09-09T14:22:42.2222151Z 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 2025-09-09T14:22:42.2225857Z 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 2025-09-09T14:22:42.2230133Z 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 2025-09-09T14:22:42.2233671Z 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 2025-09-09T14:22:42.2237938Z 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 2025-09-09T14:22:42.2241451Z 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 2025-09-09T14:22:42.2245600Z 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 2025-09-09T14:22:42.2249148Z 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 2025-09-09T14:22:42.2253305Z 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 2025-09-09T14:22:42.2256846Z 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 2025-09-09T14:23:29.9688566Z 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 2025-09-09T14:23:29.9693030Z 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 2025-09-09T14:23:29.9697380Z 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 2025-09-09T14:23:29.9701824Z 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 2025-09-09T14:23:29.9706129Z 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 2025-09-09T14:23:29.9710440Z 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 2025-09-09T14:23:29.9714761Z 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 2025-09-09T14:23:29.9719044Z 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 2025-09-09T14:23:29.9723420Z 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 2025-09-09T14:23:29.9726354Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_moe_quant_intx SKIPPED 2025-09-09T14:23:29.9729134Z 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 2025-09-09T14:23:29.9731881Z test/quantization/test_int8_dynamic_activation_intx_weight_config_v1.py::TestInt8DynamicActivationIntxWeight::test_serialization_{'layout': QDQLayout()} SKIPPED 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test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(14336, 4096)-4-128] SKIPPED 2025-09-09T14:26:55.1624362Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(14336, 4096)-4-256] SKIPPED 2025-09-09T14:26:55.1625437Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(14336, 4096)-8-32] SKIPPED 2025-09-09T14:26:55.1626257Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(14336, 4096)-8-64] SKIPPED 2025-09-09T14:26:55.1627096Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(14336, 4096)-8-128] SKIPPED 2025-09-09T14:26:55.1628269Z test/test_ops.py::test_dequantize_tensor_core_tiled_layout_op[(14336, 4096)-8-256] SKIPPED 2025-09-09T14:26:55.1629038Z test/test_ops.py::test_marlin_24[1-128-512-4--1-(1, 1, 1)] SKIPPED (...) 2025-09-09T14:26:55.1629715Z test/test_ops.py::test_marlin_24[1-128-512-4--1-(1, 4, 8)] SKIPPED (...) 2025-09-09T14:26:55.1630372Z test/test_ops.py::test_marlin_24[1-128-512-4--1-(1, 7, 5)] SKIPPED (...) 2025-09-09T14:26:55.1631258Z test/test_ops.py::test_marlin_24[1-128-512-4--1-(13, 17, 67)] SKIPPED 2025-09-09T14:26:55.1631901Z test/test_ops.py::test_marlin_24[1-128-512-4--1-(26, 37, 13)] SKIPPED 2025-09-09T14:26:55.1632526Z test/test_ops.py::test_marlin_24[1-128-512-4--1-(67, 13, 11)] SKIPPED 2025-09-09T14:26:55.1633156Z test/test_ops.py::test_marlin_24[1-128-512-4-128-(1, 1, 1)] SKIPPED 2025-09-09T14:26:55.1633769Z test/test_ops.py::test_marlin_24[1-128-512-4-128-(1, 4, 8)] SKIPPED 2025-09-09T14:26:55.1634637Z test/test_ops.py::test_marlin_24[1-128-512-4-128-(1, 7, 5)] SKIPPED 2025-09-09T14:26:55.1635276Z test/test_ops.py::test_marlin_24[1-128-512-4-128-(13, 17, 67)] SKIPPED 2025-09-09T14:26:55.1635935Z test/test_ops.py::test_marlin_24[1-128-512-4-128-(26, 37, 13)] SKIPPED 2025-09-09T14:26:55.1636590Z test/test_ops.py::test_marlin_24[1-128-512-4-128-(67, 13, 11)] SKIPPED 2025-09-09T14:26:55.1637473Z test/test_ops.py::test_marlin_24[1-128-512-8--1-(1, 1, 1)] SKIPPED (...) 2025-09-09T14:26:55.1638133Z test/test_ops.py::test_marlin_24[1-128-512-8--1-(1, 4, 8)] SKIPPED (...) 2025-09-09T14:26:55.1638778Z test/test_ops.py::test_marlin_24[1-128-512-8--1-(1, 7, 5)] SKIPPED (...) 2025-09-09T14:26:55.1639426Z test/test_ops.py::test_marlin_24[1-128-512-8--1-(13, 17, 67)] SKIPPED 2025-09-09T14:26:55.1640152Z test/test_ops.py::test_marlin_24[1-128-512-8--1-(26, 37, 13)] SKIPPED 2025-09-09T14:26:55.1641018Z test/test_ops.py::test_marlin_24[1-128-512-8--1-(67, 13, 11)] SKIPPED 2025-09-09T14:26:55.1641663Z test/test_ops.py::test_marlin_24[1-128-512-8-128-(1, 1, 1)] SKIPPED 2025-09-09T14:26:55.1642277Z test/test_ops.py::test_marlin_24[1-128-512-8-128-(1, 4, 8)] SKIPPED 2025-09-09T14:26:55.1642903Z test/test_ops.py::test_marlin_24[1-128-512-8-128-(1, 7, 5)] SKIPPED 2025-09-09T14:26:55.1643534Z test/test_ops.py::test_marlin_24[1-128-512-8-128-(13, 17, 67)] SKIPPED 2025-09-09T14:26:55.1644181Z test/test_ops.py::test_marlin_24[1-128-512-8-128-(26, 37, 13)] SKIPPED 2025-09-09T14:26:55.1644827Z test/test_ops.py::test_marlin_24[1-128-512-8-128-(67, 13, 11)] SKIPPED 2025-09-09T14:26:55.1645710Z test/test_ops.py::test_marlin_24[4-128-512-4--1-(1, 1, 1)] SKIPPED (...) 2025-09-09T14:26:55.1646371Z test/test_ops.py::test_marlin_24[4-128-512-4--1-(1, 4, 8)] SKIPPED (...) 2025-09-09T14:26:55.1647019Z test/test_ops.py::test_marlin_24[4-128-512-4--1-(1, 7, 5)] SKIPPED (...) 2025-09-09T14:26:55.1647668Z test/test_ops.py::test_marlin_24[4-128-512-4--1-(13, 17, 67)] SKIPPED 2025-09-09T14:26:55.1648542Z test/test_ops.py::test_marlin_24[4-128-512-4--1-(26, 37, 13)] SKIPPED 2025-09-09T14:26:55.1649180Z test/test_ops.py::test_marlin_24[4-128-512-4--1-(67, 13, 11)] SKIPPED 2025-09-09T14:26:55.1649808Z test/test_ops.py::test_marlin_24[4-128-512-4-128-(1, 1, 1)] SKIPPED 2025-09-09T14:26:55.1650539Z test/test_ops.py::test_marlin_24[4-128-512-4-128-(1, 4, 8)] SKIPPED 2025-09-09T14:26:55.1651445Z test/test_ops.py::test_marlin_24[4-128-512-4-128-(1, 7, 5)] SKIPPED 2025-09-09T14:26:55.1652070Z test/test_ops.py::test_marlin_24[4-128-512-4-128-(13, 17, 67)] SKIPPED 2025-09-09T14:26:55.1652724Z test/test_ops.py::test_marlin_24[4-128-512-4-128-(26, 37, 13)] SKIPPED 2025-09-09T14:26:55.1653477Z test/test_ops.py::test_marlin_24[4-128-512-4-128-(67, 13, 11)] SKIPPED 2025-09-09T14:26:55.1654249Z test/test_ops.py::test_marlin_24[4-128-512-8--1-(1, 1, 1)] SKIPPED (...) 2025-09-09T14:26:55.1654988Z test/test_ops.py::test_marlin_24[4-128-512-8--1-(1, 4, 8)] SKIPPED (...) 2025-09-09T14:26:55.1655640Z test/test_ops.py::test_marlin_24[4-128-512-8--1-(1, 7, 5)] SKIPPED (...) 2025-09-09T14:26:55.2189375Z test/test_ops.py::test_marlin_24[4-128-512-8--1-(13, 17, 67)] SKIPPED 2025-09-09T14:26:55.2190084Z test/test_ops.py::test_marlin_24[4-128-512-8--1-(26, 37, 13)] SKIPPED 2025-09-09T14:26:55.2190830Z test/test_ops.py::test_marlin_24[4-128-512-8--1-(67, 13, 11)] SKIPPED 2025-09-09T14:26:55.2191468Z test/test_ops.py::test_marlin_24[4-128-512-8-128-(1, 1, 1)] SKIPPED 2025-09-09T14:26:55.2192094Z test/test_ops.py::test_marlin_24[4-128-512-8-128-(1, 4, 8)] SKIPPED 2025-09-09T14:26:55.2192707Z test/test_ops.py::test_marlin_24[4-128-512-8-128-(1, 7, 5)] SKIPPED 2025-09-09T14:26:55.2193451Z test/test_ops.py::test_marlin_24[4-128-512-8-128-(13, 17, 67)] SKIPPED 2025-09-09T14:26:55.2194099Z test/test_ops.py::test_marlin_24[4-128-512-8-128-(26, 37, 13)] SKIPPED 2025-09-09T14:26:55.2194759Z test/test_ops.py::test_marlin_24[4-128-512-8-128-(67, 13, 11)] SKIPPED 2025-09-09T14:26:55.2195404Z test/test_ops.py::test_marlin_24[8-128-512-4--1-(1, 1, 1)] SKIPPED (...) 2025-09-09T14:26:55.2196068Z test/test_ops.py::test_marlin_24[8-128-512-4--1-(1, 4, 8)] SKIPPED (...) 2025-09-09T14:26:55.2196845Z test/test_ops.py::test_marlin_24[8-128-512-4--1-(1, 7, 5)] SKIPPED (...) 2025-09-09T14:26:55.2197487Z test/test_ops.py::test_marlin_24[8-128-512-4--1-(13, 17, 67)] SKIPPED 2025-09-09T14:26:55.2198123Z test/test_ops.py::test_marlin_24[8-128-512-4--1-(26, 37, 13)] SKIPPED 2025-09-09T14:26:55.2198745Z test/test_ops.py::test_marlin_24[8-128-512-4--1-(67, 13, 11)] SKIPPED 2025-09-09T14:26:55.2199383Z test/test_ops.py::test_marlin_24[8-128-512-4-128-(1, 1, 1)] SKIPPED 2025-09-09T14:26:55.2200154Z test/test_ops.py::test_marlin_24[8-128-512-4-128-(1, 4, 8)] SKIPPED 2025-09-09T14:26:55.2200784Z test/test_ops.py::test_marlin_24[8-128-512-4-128-(1, 7, 5)] SKIPPED 2025-09-09T14:26:55.2201423Z test/test_ops.py::test_marlin_24[8-128-512-4-128-(13, 17, 67)] SKIPPED 2025-09-09T14:26:55.2202065Z test/test_ops.py::test_marlin_24[8-128-512-4-128-(26, 37, 13)] SKIPPED 2025-09-09T14:26:55.2202825Z test/test_ops.py::test_marlin_24[8-128-512-4-128-(67, 13, 11)] SKIPPED 2025-09-09T14:26:55.2203475Z test/test_ops.py::test_marlin_24[8-128-512-8--1-(1, 1, 1)] SKIPPED (...) 2025-09-09T14:26:55.2204134Z test/test_ops.py::test_marlin_24[8-128-512-8--1-(1, 4, 8)] SKIPPED (...) 2025-09-09T14:26:55.2204793Z test/test_ops.py::test_marlin_24[8-128-512-8--1-(1, 7, 5)] SKIPPED (...) 2025-09-09T14:26:55.2205428Z test/test_ops.py::test_marlin_24[8-128-512-8--1-(13, 17, 67)] SKIPPED 2025-09-09T14:26:55.2206175Z test/test_ops.py::test_marlin_24[8-128-512-8--1-(26, 37, 13)] SKIPPED 2025-09-09T14:26:55.2206804Z test/test_ops.py::test_marlin_24[8-128-512-8--1-(67, 13, 11)] SKIPPED 2025-09-09T14:26:55.2207431Z test/test_ops.py::test_marlin_24[8-128-512-8-128-(1, 1, 1)] SKIPPED 2025-09-09T14:26:55.2208042Z test/test_ops.py::test_marlin_24[8-128-512-8-128-(1, 4, 8)] SKIPPED 2025-09-09T14:26:55.2208959Z test/test_ops.py::test_marlin_24[8-128-512-8-128-(1, 7, 5)] SKIPPED 2025-09-09T14:26:55.2209627Z test/test_ops.py::test_marlin_24[8-128-512-8-128-(13, 17, 67)] SKIPPED 2025-09-09T14:26:55.2210267Z test/test_ops.py::test_marlin_24[8-128-512-8-128-(26, 37, 13)] SKIPPED 2025-09-09T14:26:55.2210916Z test/test_ops.py::test_marlin_24[8-128-512-8-128-(67, 13, 11)] SKIPPED 2025-09-09T14:26:55.2211540Z test/test_ops.py::test_marlin_24[16-128-512-4--1-(1, 1, 1)] SKIPPED 2025-09-09T14:26:55.2212266Z test/test_ops.py::test_marlin_24[16-128-512-4--1-(1, 4, 8)] SKIPPED 2025-09-09T14:26:55.2212873Z test/test_ops.py::test_marlin_24[16-128-512-4--1-(1, 7, 5)] SKIPPED 2025-09-09T14:26:55.2213605Z test/test_ops.py::test_marlin_24[16-128-512-4--1-(13, 17, 67)] SKIPPED 2025-09-09T14:26:55.2214250Z test/test_ops.py::test_marlin_24[16-128-512-4--1-(26, 37, 13)] SKIPPED 2025-09-09T14:26:55.2214990Z test/test_ops.py::test_marlin_24[16-128-512-4--1-(67, 13, 11)] SKIPPED 2025-09-09T14:26:55.2215630Z test/test_ops.py::test_marlin_24[16-128-512-4-128-(1, 1, 1)] SKIPPED 2025-09-09T14:26:55.2216246Z test/test_ops.py::test_marlin_24[16-128-512-4-128-(1, 4, 8)] SKIPPED 2025-09-09T14:26:55.2216873Z test/test_ops.py::test_marlin_24[16-128-512-4-128-(1, 7, 5)] SKIPPED 2025-09-09T14:26:55.2217628Z test/test_ops.py::test_marlin_24[16-128-512-4-128-(13, 17, 67)] SKIPPED 2025-09-09T14:26:55.2218270Z test/test_ops.py::test_marlin_24[16-128-512-4-128-(26, 37, 13)] SKIPPED 2025-09-09T14:26:55.2218916Z test/test_ops.py::test_marlin_24[16-128-512-4-128-(67, 13, 11)] SKIPPED 2025-09-09T14:26:55.2219542Z test/test_ops.py::test_marlin_24[16-128-512-8--1-(1, 1, 1)] SKIPPED 2025-09-09T14:26:55.2220265Z test/test_ops.py::test_marlin_24[16-128-512-8--1-(1, 4, 8)] SKIPPED 2025-09-09T14:26:55.2220881Z test/test_ops.py::test_marlin_24[16-128-512-8--1-(1, 7, 5)] SKIPPED 2025-09-09T14:26:55.2221518Z test/test_ops.py::test_marlin_24[16-128-512-8--1-(13, 17, 67)] SKIPPED 2025-09-09T14:26:55.2222158Z test/test_ops.py::test_marlin_24[16-128-512-8--1-(26, 37, 13)] SKIPPED 2025-09-09T14:26:55.2222842Z test/test_ops.py::test_marlin_24[16-128-512-8--1-(67, 13, 11)] SKIPPED 2025-09-09T14:26:55.2223529Z test/test_ops.py::test_marlin_24[16-128-512-8-128-(1, 1, 1)] SKIPPED 2025-09-09T14:26:55.2224147Z test/test_ops.py::test_marlin_24[16-128-512-8-128-(1, 4, 8)] SKIPPED 2025-09-09T14:26:55.2224780Z test/test_ops.py::test_marlin_24[16-128-512-8-128-(1, 7, 5)] SKIPPED 2025-09-09T14:26:55.2225424Z test/test_ops.py::test_marlin_24[16-128-512-8-128-(13, 17, 67)] SKIPPED 2025-09-09T14:26:55.2226178Z test/test_ops.py::test_marlin_24[16-128-512-8-128-(26, 37, 13)] SKIPPED 2025-09-09T14:26:55.2226833Z test/test_ops.py::test_marlin_24[16-128-512-8-128-(67, 13, 11)] SKIPPED 2025-09-09T14:26:55.2227456Z test/test_ops.py::test_marlin_24[32-128-512-4--1-(1, 1, 1)] SKIPPED 2025-09-09T14:26:55.2228083Z test/test_ops.py::test_marlin_24[32-128-512-4--1-(1, 4, 8)] SKIPPED 2025-09-09T14:26:55.2228694Z test/test_ops.py::test_marlin_24[32-128-512-4--1-(1, 7, 5)] SKIPPED 2025-09-09T14:26:55.2229327Z test/test_ops.py::test_marlin_24[32-128-512-4--1-(13, 17, 67)] SKIPPED 2025-09-09T14:26:55.2229971Z test/test_ops.py::test_marlin_24[32-128-512-4--1-(26, 37, 13)] SKIPPED 2025-09-09T14:26:55.2230704Z test/test_ops.py::test_marlin_24[32-128-512-4--1-(67, 13, 11)] SKIPPED 2025-09-09T14:26:55.2231347Z test/test_ops.py::test_marlin_24[32-128-512-4-128-(1, 1, 1)] SKIPPED 2025-09-09T14:26:55.2231967Z test/test_ops.py::test_marlin_24[32-128-512-4-128-(1, 4, 8)] SKIPPED 2025-09-09T14:26:55.2232597Z test/test_ops.py::test_marlin_24[32-128-512-4-128-(1, 7, 5)] SKIPPED 2025-09-09T14:26:55.2233231Z test/test_ops.py::test_marlin_24[32-128-512-4-128-(13, 17, 67)] SKIPPED 2025-09-09T14:26:55.2234083Z test/test_ops.py::test_marlin_24[32-128-512-4-128-(26, 37, 13)] SKIPPED 2025-09-09T14:26:55.2234741Z test/test_ops.py::test_marlin_24[32-128-512-4-128-(67, 13, 11)] SKIPPED 2025-09-09T14:26:55.2235370Z test/test_ops.py::test_marlin_24[32-128-512-8--1-(1, 1, 1)] SKIPPED 2025-09-09T14:26:55.2235999Z test/test_ops.py::test_marlin_24[32-128-512-8--1-(1, 4, 8)] SKIPPED 2025-09-09T14:26:55.2236726Z test/test_ops.py::test_marlin_24[32-128-512-8--1-(1, 7, 5)] SKIPPED 2025-09-09T14:26:55.2237360Z test/test_ops.py::test_marlin_24[32-128-512-8--1-(13, 17, 67)] SKIPPED 2025-09-09T14:26:55.2238002Z test/test_ops.py::test_marlin_24[32-128-512-8--1-(26, 37, 13)] SKIPPED 2025-09-09T14:26:55.2238708Z test/test_ops.py::test_marlin_24[32-128-512-8--1-(67, 13, 11)] SKIPPED 2025-09-09T14:26:55.2239460Z test/test_ops.py::test_marlin_24[32-128-512-8-128-(1, 1, 1)] SKIPPED 2025-09-09T14:26:55.2240162Z test/test_ops.py::test_marlin_24[32-128-512-8-128-(1, 4, 8)] SKIPPED 2025-09-09T14:26:55.2240803Z test/test_ops.py::test_marlin_24[32-128-512-8-128-(1, 7, 5)] SKIPPED 2025-09-09T14:26:55.2241436Z test/test_ops.py::test_marlin_24[32-128-512-8-128-(13, 17, 67)] SKIPPED 2025-09-09T14:26:55.2242206Z test/test_ops.py::test_marlin_24[32-128-512-8-128-(26, 37, 13)] SKIPPED 2025-09-09T14:26:55.2242866Z test/test_ops.py::test_marlin_24[32-128-512-8-128-(67, 13, 11)] SKIPPED 2025-09-09T14:26:55.2243489Z test/test_ops.py::test_marlin_24[64-128-512-4--1-(1, 1, 1)] SKIPPED 2025-09-09T14:26:55.2244109Z test/test_ops.py::test_marlin_24[64-128-512-4--1-(1, 4, 8)] SKIPPED 2025-09-09T14:26:55.2244834Z test/test_ops.py::test_marlin_24[64-128-512-4--1-(1, 7, 5)] SKIPPED 2025-09-09T14:26:55.2245473Z test/test_ops.py::test_marlin_24[64-128-512-4--1-(13, 17, 67)] SKIPPED 2025-09-09T14:26:55.2246102Z test/test_ops.py::test_marlin_24[64-128-512-4--1-(26, 37, 13)] SKIPPED 2025-09-09T14:26:55.2246748Z test/test_ops.py::test_marlin_24[64-128-512-4--1-(67, 13, 11)] SKIPPED 2025-09-09T14:26:55.2247486Z test/test_ops.py::test_marlin_24[64-128-512-4-128-(1, 1, 1)] SKIPPED 2025-09-09T14:26:55.2248109Z test/test_ops.py::test_marlin_24[64-128-512-4-128-(1, 4, 8)] SKIPPED 2025-09-09T14:26:55.2248736Z test/test_ops.py::test_marlin_24[64-128-512-4-128-(1, 7, 5)] SKIPPED 2025-09-09T14:26:55.2249364Z test/test_ops.py::test_marlin_24[64-128-512-4-128-(13, 17, 67)] SKIPPED 2025-09-09T14:26:55.2250017Z test/test_ops.py::test_marlin_24[64-128-512-4-128-(26, 37, 13)] SKIPPED 2025-09-09T14:26:55.2250780Z test/test_ops.py::test_marlin_24[64-128-512-4-128-(67, 13, 11)] SKIPPED 2025-09-09T14:26:55.2251408Z test/test_ops.py::test_marlin_24[64-128-512-8--1-(1, 1, 1)] SKIPPED 2025-09-09T14:26:55.2252025Z test/test_ops.py::test_marlin_24[64-128-512-8--1-(1, 4, 8)] SKIPPED 2025-09-09T14:26:55.2252644Z test/test_ops.py::test_marlin_24[64-128-512-8--1-(1, 7, 5)] SKIPPED 2025-09-09T14:26:55.2253395Z test/test_ops.py::test_marlin_24[64-128-512-8--1-(13, 17, 67)] SKIPPED 2025-09-09T14:26:55.2254024Z test/test_ops.py::test_marlin_24[64-128-512-8--1-(26, 37, 13)] SKIPPED 2025-09-09T14:26:55.2254666Z test/test_ops.py::test_marlin_24[64-128-512-8--1-(67, 13, 11)] SKIPPED 2025-09-09T14:26:55.2255290Z test/test_ops.py::test_marlin_24[64-128-512-8-128-(1, 1, 1)] SKIPPED 2025-09-09T14:26:55.2256030Z test/test_ops.py::test_marlin_24[64-128-512-8-128-(1, 4, 8)] SKIPPED 2025-09-09T14:26:55.2256657Z test/test_ops.py::test_marlin_24[64-128-512-8-128-(1, 7, 5)] SKIPPED 2025-09-09T14:26:55.2257293Z test/test_ops.py::test_marlin_24[64-128-512-8-128-(13, 17, 67)] SKIPPED 2025-09-09T14:26:55.2836108Z test/test_ops.py::test_marlin_24[64-128-512-8-128-(26, 37, 13)] SKIPPED 2025-09-09T14:26:55.2836821Z test/test_ops.py::test_marlin_24[64-128-512-8-128-(67, 13, 11)] SKIPPED 2025-09-09T14:26:55.2837896Z test/test_ops.py::test_marlin_qqq[1-128-64-4--1-(1, 1, 1)] SKIPPED (...) 2025-09-09T14:26:55.2838578Z test/test_ops.py::test_marlin_qqq[1-128-64-4--1-(1, 4, 8)] SKIPPED (...) 2025-09-09T14:26:55.2839231Z test/test_ops.py::test_marlin_qqq[1-128-64-4--1-(1, 7, 5)] SKIPPED (...) 2025-09-09T14:26:55.2839966Z test/test_ops.py::test_marlin_qqq[1-128-64-4--1-(13, 17, 67)] SKIPPED 2025-09-09T14:26:55.2840735Z test/test_ops.py::test_marlin_qqq[1-128-64-4--1-(26, 37, 13)] SKIPPED 2025-09-09T14:26:55.2841364Z test/test_ops.py::test_marlin_qqq[1-128-64-4--1-(67, 13, 11)] SKIPPED 2025-09-09T14:26:55.2841997Z test/test_ops.py::test_marlin_qqq[1-128-64-4-128-(1, 1, 1)] SKIPPED 2025-09-09T14:26:55.2842728Z test/test_ops.py::test_marlin_qqq[1-128-64-4-128-(1, 4, 8)] SKIPPED 2025-09-09T14:26:55.2843472Z test/test_ops.py::test_marlin_qqq[1-128-64-4-128-(1, 7, 5)] SKIPPED 2025-09-09T14:26:55.2844107Z test/test_ops.py::test_marlin_qqq[1-128-64-4-128-(13, 17, 67)] SKIPPED 2025-09-09T14:26:55.2844765Z test/test_ops.py::test_marlin_qqq[1-128-64-4-128-(26, 37, 13)] SKIPPED 2025-09-09T14:26:55.2845412Z test/test_ops.py::test_marlin_qqq[1-128-64-4-128-(67, 13, 11)] SKIPPED 2025-09-09T14:26:55.2846036Z test/test_ops.py::test_marlin_qqq[1-128-128-4--1-(1, 1, 1)] SKIPPED 2025-09-09T14:26:55.2846792Z test/test_ops.py::test_marlin_qqq[1-128-128-4--1-(1, 4, 8)] SKIPPED 2025-09-09T14:26:55.2847407Z test/test_ops.py::test_marlin_qqq[1-128-128-4--1-(1, 7, 5)] SKIPPED 2025-09-09T14:26:55.2848055Z test/test_ops.py::test_marlin_qqq[1-128-128-4--1-(13, 17, 67)] SKIPPED 2025-09-09T14:26:55.2848692Z test/test_ops.py::test_marlin_qqq[1-128-128-4--1-(26, 37, 13)] SKIPPED 2025-09-09T14:26:55.2849459Z test/test_ops.py::test_marlin_qqq[1-128-128-4--1-(67, 13, 11)] SKIPPED 2025-09-09T14:26:55.2850111Z test/test_ops.py::test_marlin_qqq[1-128-128-4-128-(1, 1, 1)] SKIPPED 2025-09-09T14:26:55.2850741Z test/test_ops.py::test_marlin_qqq[1-128-128-4-128-(1, 4, 8)] SKIPPED 2025-09-09T14:26:55.2851377Z test/test_ops.py::test_marlin_qqq[1-128-128-4-128-(1, 7, 5)] SKIPPED 2025-09-09T14:26:55.2852014Z test/test_ops.py::test_marlin_qqq[1-128-128-4-128-(13, 17, 67)] SKIPPED 2025-09-09T14:26:55.2852671Z test/test_ops.py::test_marlin_qqq[1-128-128-4-128-(26, 37, 13)] SKIPPED 2025-09-09T14:26:55.2853329Z test/test_ops.py::test_marlin_qqq[1-128-128-4-128-(67, 13, 11)] SKIPPED 2025-09-09T14:26:55.2853956Z test/test_ops.py::test_marlin_qqq[1-128-256-4--1-(1, 1, 1)] SKIPPED 2025-09-09T14:26:55.2854713Z test/test_ops.py::test_marlin_qqq[1-128-256-4--1-(1, 4, 8)] SKIPPED 2025-09-09T14:26:55.2855330Z test/test_ops.py::test_marlin_qqq[1-128-256-4--1-(1, 7, 5)] SKIPPED 2025-09-09T14:26:55.2855966Z test/test_ops.py::test_marlin_qqq[1-128-256-4--1-(13, 17, 67)] SKIPPED 2025-09-09T14:26:55.2856601Z test/test_ops.py::test_marlin_qqq[1-128-256-4--1-(26, 37, 13)] SKIPPED 2025-09-09T14:26:55.2857367Z test/test_ops.py::test_marlin_qqq[1-128-256-4--1-(67, 13, 11)] SKIPPED 2025-09-09T14:26:55.2858020Z test/test_ops.py::test_marlin_qqq[1-128-256-4-128-(1, 1, 1)] SKIPPED 2025-09-09T14:26:55.2858905Z test/test_ops.py::test_marlin_qqq[1-128-256-4-128-(1, 4, 8)] SKIPPED 2025-09-09T14:26:55.2859547Z test/test_ops.py::test_marlin_qqq[1-128-256-4-128-(1, 7, 5)] SKIPPED 2025-09-09T14:26:55.2860188Z test/test_ops.py::test_marlin_qqq[1-128-256-4-128-(13, 17, 67)] SKIPPED 2025-09-09T14:26:55.2860901Z test/test_ops.py::test_marlin_qqq[1-128-256-4-128-(26, 37, 13)] SKIPPED 2025-09-09T14:26:55.2861676Z test/test_ops.py::test_marlin_qqq[1-128-256-4-128-(67, 13, 11)] SKIPPED 2025-09-09T14:26:55.2862327Z test/test_ops.py::test_marlin_qqq[4-128-64-4--1-(1, 1, 1)] SKIPPED (...) 2025-09-09T14:26:55.2862993Z test/test_ops.py::test_marlin_qqq[4-128-64-4--1-(1, 4, 8)] SKIPPED (...) 2025-09-09T14:26:55.2863833Z test/test_ops.py::test_marlin_qqq[4-128-64-4--1-(1, 7, 5)] SKIPPED (...) 2025-09-09T14:26:55.2864499Z test/test_ops.py::test_marlin_qqq[4-128-64-4--1-(13, 17, 67)] SKIPPED 2025-09-09T14:26:55.2865132Z test/test_ops.py::test_marlin_qqq[4-128-64-4--1-(26, 37, 13)] SKIPPED 2025-09-09T14:26:55.2865779Z test/test_ops.py::test_marlin_qqq[4-128-64-4--1-(67, 13, 11)] SKIPPED 2025-09-09T14:26:55.2866461Z test/test_ops.py::test_marlin_qqq[4-128-64-4-128-(1, 1, 1)] SKIPPED 2025-09-09T14:26:55.2867080Z test/test_ops.py::test_marlin_qqq[4-128-64-4-128-(1, 4, 8)] SKIPPED 2025-09-09T14:26:55.2867803Z test/test_ops.py::test_marlin_qqq[4-128-64-4-128-(1, 7, 5)] SKIPPED 2025-09-09T14:26:55.2868432Z test/test_ops.py::test_marlin_qqq[4-128-64-4-128-(13, 17, 67)] SKIPPED 2025-09-09T14:26:55.2869131Z test/test_ops.py::test_marlin_qqq[4-128-64-4-128-(26, 37, 13)] SKIPPED 2025-09-09T14:26:55.2869774Z test/test_ops.py::test_marlin_qqq[4-128-64-4-128-(67, 13, 11)] SKIPPED 2025-09-09T14:26:55.2870421Z test/test_ops.py::test_marlin_qqq[4-128-128-4--1-(1, 1, 1)] SKIPPED 2025-09-09T14:26:55.2871050Z test/test_ops.py::test_marlin_qqq[4-128-128-4--1-(1, 4, 8)] SKIPPED 2025-09-09T14:26:55.2871666Z test/test_ops.py::test_marlin_qqq[4-128-128-4--1-(1, 7, 5)] SKIPPED 2025-09-09T14:26:55.2872353Z test/test_ops.py::test_marlin_qqq[4-128-128-4--1-(13, 17, 67)] SKIPPED 2025-09-09T14:26:55.2872990Z test/test_ops.py::test_marlin_qqq[4-128-128-4--1-(26, 37, 13)] SKIPPED 2025-09-09T14:26:55.2873640Z test/test_ops.py::test_marlin_qqq[4-128-128-4--1-(67, 13, 11)] SKIPPED 2025-09-09T14:26:55.2874294Z test/test_ops.py::test_marlin_qqq[4-128-128-4-128-(1, 1, 1)] SKIPPED 2025-09-09T14:26:55.2874963Z test/test_ops.py::test_marlin_qqq[4-128-128-4-128-(1, 4, 8)] SKIPPED 2025-09-09T14:26:55.2875600Z test/test_ops.py::test_marlin_qqq[4-128-128-4-128-(1, 7, 5)] SKIPPED 2025-09-09T14:26:55.2876240Z test/test_ops.py::test_marlin_qqq[4-128-128-4-128-(13, 17, 67)] SKIPPED 2025-09-09T14:26:55.2876900Z test/test_ops.py::test_marlin_qqq[4-128-128-4-128-(26, 37, 13)] SKIPPED 2025-09-09T14:26:55.2877588Z test/test_ops.py::test_marlin_qqq[4-128-128-4-128-(67, 13, 11)] SKIPPED 2025-09-09T14:26:55.2878232Z test/test_ops.py::test_marlin_qqq[4-128-256-4--1-(1, 1, 1)] SKIPPED 2025-09-09T14:26:55.2878857Z test/test_ops.py::test_marlin_qqq[4-128-256-4--1-(1, 4, 8)] SKIPPED 2025-09-09T14:26:55.2879472Z test/test_ops.py::test_marlin_qqq[4-128-256-4--1-(1, 7, 5)] SKIPPED 2025-09-09T14:26:55.2880222Z test/test_ops.py::test_marlin_qqq[4-128-256-4--1-(13, 17, 67)] SKIPPED 2025-09-09T14:26:55.2880876Z test/test_ops.py::test_marlin_qqq[4-128-256-4--1-(26, 37, 13)] SKIPPED 2025-09-09T14:26:55.2881526Z test/test_ops.py::test_marlin_qqq[4-128-256-4--1-(67, 13, 11)] SKIPPED 2025-09-09T14:26:55.2882158Z test/test_ops.py::test_marlin_qqq[4-128-256-4-128-(1, 1, 1)] SKIPPED 2025-09-09T14:26:55.2882801Z test/test_ops.py::test_marlin_qqq[4-128-256-4-128-(1, 4, 8)] SKIPPED 2025-09-09T14:26:55.2883484Z test/test_ops.py::test_marlin_qqq[4-128-256-4-128-(1, 7, 5)] SKIPPED 2025-09-09T14:26:55.2884121Z test/test_ops.py::test_marlin_qqq[4-128-256-4-128-(13, 17, 67)] SKIPPED 2025-09-09T14:26:55.2884804Z test/test_ops.py::test_marlin_qqq[4-128-256-4-128-(26, 37, 13)] SKIPPED 2025-09-09T14:26:55.2885445Z test/test_ops.py::test_marlin_qqq[4-128-256-4-128-(67, 13, 11)] SKIPPED 2025-09-09T14:26:55.2886150Z test/test_ops.py::test_marlin_qqq[8-128-64-4--1-(1, 1, 1)] SKIPPED (...) 2025-09-09T14:26:55.2886823Z test/test_ops.py::test_marlin_qqq[8-128-64-4--1-(1, 4, 8)] SKIPPED (...) 2025-09-09T14:26:55.2887475Z test/test_ops.py::test_marlin_qqq[8-128-64-4--1-(1, 7, 5)] SKIPPED (...) 2025-09-09T14:26:55.2888129Z test/test_ops.py::test_marlin_qqq[8-128-64-4--1-(13, 17, 67)] SKIPPED 2025-09-09T14:26:55.2888884Z test/test_ops.py::test_marlin_qqq[8-128-64-4--1-(26, 37, 13)] SKIPPED 2025-09-09T14:26:55.2889539Z test/test_ops.py::test_marlin_qqq[8-128-64-4--1-(67, 13, 11)] SKIPPED 2025-09-09T14:26:55.2890175Z test/test_ops.py::test_marlin_qqq[8-128-64-4-128-(1, 1, 1)] SKIPPED 2025-09-09T14:26:55.2890792Z test/test_ops.py::test_marlin_qqq[8-128-64-4-128-(1, 4, 8)] SKIPPED 2025-09-09T14:26:55.2891441Z test/test_ops.py::test_marlin_qqq[8-128-64-4-128-(1, 7, 5)] SKIPPED 2025-09-09T14:26:55.2892091Z test/test_ops.py::test_marlin_qqq[8-128-64-4-128-(13, 17, 67)] SKIPPED 2025-09-09T14:26:55.2892808Z test/test_ops.py::test_marlin_qqq[8-128-64-4-128-(26, 37, 13)] SKIPPED 2025-09-09T14:26:55.2893444Z test/test_ops.py::test_marlin_qqq[8-128-64-4-128-(67, 13, 11)] SKIPPED 2025-09-09T14:26:55.2894087Z test/test_ops.py::test_marlin_qqq[8-128-128-4--1-(1, 1, 1)] SKIPPED 2025-09-09T14:26:55.2894764Z test/test_ops.py::test_marlin_qqq[8-128-128-4--1-(1, 4, 8)] SKIPPED 2025-09-09T14:26:55.2895383Z test/test_ops.py::test_marlin_qqq[8-128-128-4--1-(1, 7, 5)] SKIPPED 2025-09-09T14:26:55.2896017Z test/test_ops.py::test_marlin_qqq[8-128-128-4--1-(13, 17, 67)] SKIPPED 2025-09-09T14:26:55.2896648Z test/test_ops.py::test_marlin_qqq[8-128-128-4--1-(26, 37, 13)] SKIPPED 2025-09-09T14:26:55.2897337Z test/test_ops.py::test_marlin_qqq[8-128-128-4--1-(67, 13, 11)] SKIPPED 2025-09-09T14:26:55.2897969Z test/test_ops.py::test_marlin_qqq[8-128-128-4-128-(1, 1, 1)] SKIPPED 2025-09-09T14:26:55.2898607Z test/test_ops.py::test_marlin_qqq[8-128-128-4-128-(1, 4, 8)] SKIPPED 2025-09-09T14:26:55.2899246Z test/test_ops.py::test_marlin_qqq[8-128-128-4-128-(1, 7, 5)] SKIPPED 2025-09-09T14:26:55.2899898Z test/test_ops.py::test_marlin_qqq[8-128-128-4-128-(13, 17, 67)] SKIPPED 2025-09-09T14:26:55.2900581Z test/test_ops.py::test_marlin_qqq[8-128-128-4-128-(26, 37, 13)] SKIPPED 2025-09-09T14:26:55.2901227Z test/test_ops.py::test_marlin_qqq[8-128-128-4-128-(67, 13, 11)] SKIPPED 2025-09-09T14:26:55.2901863Z test/test_ops.py::test_marlin_qqq[8-128-256-4--1-(1, 1, 1)] SKIPPED 2025-09-09T14:26:55.2902487Z test/test_ops.py::test_marlin_qqq[8-128-256-4--1-(1, 4, 8)] SKIPPED 2025-09-09T14:26:55.2903145Z test/test_ops.py::test_marlin_qqq[8-128-256-4--1-(1, 7, 5)] SKIPPED 2025-09-09T14:26:55.2903781Z test/test_ops.py::test_marlin_qqq[8-128-256-4--1-(13, 17, 67)] SKIPPED 2025-09-09T14:26:55.2904413Z test/test_ops.py::test_marlin_qqq[8-128-256-4--1-(26, 37, 13)] SKIPPED 2025-09-09T14:26:55.3479334Z test/test_ops.py::test_marlin_qqq[8-128-256-4--1-(67, 13, 11)] SKIPPED 2025-09-09T14:26:55.3480266Z test/test_ops.py::test_marlin_qqq[8-128-256-4-128-(1, 1, 1)] SKIPPED 2025-09-09T14:26:55.3481085Z test/test_ops.py::test_marlin_qqq[8-128-256-4-128-(1, 4, 8)] SKIPPED 2025-09-09T14:26:55.3481918Z test/test_ops.py::test_marlin_qqq[8-128-256-4-128-(1, 7, 5)] SKIPPED 2025-09-09T14:26:55.3482560Z test/test_ops.py::test_marlin_qqq[8-128-256-4-128-(13, 17, 67)] SKIPPED 2025-09-09T14:26:55.3483222Z test/test_ops.py::test_marlin_qqq[8-128-256-4-128-(26, 37, 13)] SKIPPED 2025-09-09T14:26:55.3484172Z test/test_ops.py::test_marlin_qqq[8-128-256-4-128-(67, 13, 11)] SKIPPED 2025-09-09T14:26:55.3484986Z test/test_ops.py::test_marlin_qqq[16-128-64-4--1-(1, 1, 1)] SKIPPED 2025-09-09T14:26:55.3485621Z test/test_ops.py::test_marlin_qqq[16-128-64-4--1-(1, 4, 8)] SKIPPED 2025-09-09T14:26:55.3486400Z test/test_ops.py::test_marlin_qqq[16-128-64-4--1-(1, 7, 5)] SKIPPED 2025-09-09T14:26:55.3487188Z test/test_ops.py::test_marlin_qqq[16-128-64-4--1-(13, 17, 67)] SKIPPED 2025-09-09T14:26:55.3487999Z test/test_ops.py::test_marlin_qqq[16-128-64-4--1-(26, 37, 13)] SKIPPED 2025-09-09T14:26:55.3488645Z test/test_ops.py::test_marlin_qqq[16-128-64-4--1-(67, 13, 11)] SKIPPED 2025-09-09T14:26:55.3489760Z test/test_ops.py::test_marlin_qqq[16-128-64-4-128-(1, 1, 1)] SKIPPED 2025-09-09T14:26:55.3490614Z test/test_ops.py::test_marlin_qqq[16-128-64-4-128-(1, 4, 8)] SKIPPED 2025-09-09T14:26:55.3491247Z test/test_ops.py::test_marlin_qqq[16-128-64-4-128-(1, 7, 5)] SKIPPED 2025-09-09T14:26:55.3492069Z test/test_ops.py::test_marlin_qqq[16-128-64-4-128-(13, 17, 67)] SKIPPED 2025-09-09T14:26:55.3492866Z test/test_ops.py::test_marlin_qqq[16-128-64-4-128-(26, 37, 13)] SKIPPED 2025-09-09T14:26:55.3493686Z test/test_ops.py::test_marlin_qqq[16-128-64-4-128-(67, 13, 11)] SKIPPED 2025-09-09T14:26:55.3494585Z test/test_ops.py::test_marlin_qqq[16-128-128-4--1-(1, 1, 1)] SKIPPED 2025-09-09T14:26:55.3495383Z test/test_ops.py::test_marlin_qqq[16-128-128-4--1-(1, 4, 8)] SKIPPED 2025-09-09T14:26:55.3496191Z test/test_ops.py::test_marlin_qqq[16-128-128-4--1-(1, 7, 5)] SKIPPED 2025-09-09T14:26:55.3496841Z test/test_ops.py::test_marlin_qqq[16-128-128-4--1-(13, 17, 67)] SKIPPED 2025-09-09T14:26:55.3497668Z test/test_ops.py::test_marlin_qqq[16-128-128-4--1-(26, 37, 13)] SKIPPED 2025-09-09T14:26:55.3498459Z test/test_ops.py::test_marlin_qqq[16-128-128-4--1-(67, 13, 11)] SKIPPED 2025-09-09T14:26:55.3499273Z test/test_ops.py::test_marlin_qqq[16-128-128-4-128-(1, 1, 1)] SKIPPED 2025-09-09T14:26:55.3499919Z test/test_ops.py::test_marlin_qqq[16-128-128-4-128-(1, 4, 8)] SKIPPED 2025-09-09T14:26:55.3500847Z test/test_ops.py::test_marlin_qqq[16-128-128-4-128-(1, 7, 5)] SKIPPED 2025-09-09T14:26:55.3501681Z test/test_ops.py::test_marlin_qqq[16-128-128-4-128-(13, 17, 67)] SKIPPED 2025-09-09T14:26:55.3502368Z test/test_ops.py::test_marlin_qqq[16-128-128-4-128-(26, 37, 13)] SKIPPED 2025-09-09T14:26:55.3503263Z test/test_ops.py::test_marlin_qqq[16-128-128-4-128-(67, 13, 11)] SKIPPED 2025-09-09T14:26:55.3503977Z test/test_ops.py::test_marlin_qqq[16-128-256-4--1-(1, 1, 1)] SKIPPED 2025-09-09T14:26:55.3504774Z test/test_ops.py::test_marlin_qqq[16-128-256-4--1-(1, 4, 8)] SKIPPED 2025-09-09T14:26:55.3505413Z test/test_ops.py::test_marlin_qqq[16-128-256-4--1-(1, 7, 5)] SKIPPED 2025-09-09T14:26:55.3506333Z test/test_ops.py::test_marlin_qqq[16-128-256-4--1-(13, 17, 67)] SKIPPED 2025-09-09T14:26:55.3507165Z test/test_ops.py::test_marlin_qqq[16-128-256-4--1-(26, 37, 13)] SKIPPED 2025-09-09T14:26:55.3507825Z test/test_ops.py::test_marlin_qqq[16-128-256-4--1-(67, 13, 11)] SKIPPED 2025-09-09T14:26:55.3508706Z test/test_ops.py::test_marlin_qqq[16-128-256-4-128-(1, 1, 1)] SKIPPED 2025-09-09T14:26:55.3509423Z test/test_ops.py::test_marlin_qqq[16-128-256-4-128-(1, 4, 8)] SKIPPED 2025-09-09T14:26:55.3510221Z test/test_ops.py::test_marlin_qqq[16-128-256-4-128-(1, 7, 5)] SKIPPED 2025-09-09T14:26:55.3510993Z test/test_ops.py::test_marlin_qqq[16-128-256-4-128-(13, 17, 67)] SKIPPED 2025-09-09T14:26:55.3511821Z test/test_ops.py::test_marlin_qqq[16-128-256-4-128-(26, 37, 13)] SKIPPED 2025-09-09T14:26:55.3512651Z test/test_ops.py::test_marlin_qqq[16-128-256-4-128-(67, 13, 11)] SKIPPED 2025-09-09T14:26:55.3513294Z test/test_ops.py::test_marlin_qqq[32-128-64-4--1-(1, 1, 1)] SKIPPED 2025-09-09T14:26:55.3514134Z test/test_ops.py::test_marlin_qqq[32-128-64-4--1-(1, 4, 8)] SKIPPED 2025-09-09T14:26:55.3514856Z test/test_ops.py::test_marlin_qqq[32-128-64-4--1-(1, 7, 5)] SKIPPED 2025-09-09T14:26:55.3515662Z test/test_ops.py::test_marlin_qqq[32-128-64-4--1-(13, 17, 67)] SKIPPED 2025-09-09T14:26:55.3516388Z test/test_ops.py::test_marlin_qqq[32-128-64-4--1-(26, 37, 13)] SKIPPED 2025-09-09T14:26:55.3517312Z test/test_ops.py::test_marlin_qqq[32-128-64-4--1-(67, 13, 11)] SKIPPED 2025-09-09T14:26:55.3518115Z test/test_ops.py::test_marlin_qqq[32-128-64-4-128-(1, 1, 1)] SKIPPED 2025-09-09T14:26:55.3518761Z test/test_ops.py::test_marlin_qqq[32-128-64-4-128-(1, 4, 8)] SKIPPED 2025-09-09T14:26:55.3519886Z test/test_ops.py::test_marlin_qqq[32-128-64-4-128-(1, 7, 5)] SKIPPED 2025-09-09T14:26:55.3520736Z test/test_ops.py::test_marlin_qqq[32-128-64-4-128-(13, 17, 67)] SKIPPED 2025-09-09T14:26:55.3521389Z test/test_ops.py::test_marlin_qqq[32-128-64-4-128-(26, 37, 13)] SKIPPED 2025-09-09T14:26:55.3522223Z test/test_ops.py::test_marlin_qqq[32-128-64-4-128-(67, 13, 11)] SKIPPED 2025-09-09T14:26:55.3523009Z test/test_ops.py::test_marlin_qqq[32-128-128-4--1-(1, 1, 1)] SKIPPED 2025-09-09T14:26:55.3523815Z test/test_ops.py::test_marlin_qqq[32-128-128-4--1-(1, 4, 8)] SKIPPED 2025-09-09T14:26:55.3524529Z test/test_ops.py::test_marlin_qqq[32-128-128-4--1-(1, 7, 5)] SKIPPED 2025-09-09T14:26:55.3525484Z test/test_ops.py::test_marlin_qqq[32-128-128-4--1-(13, 17, 67)] SKIPPED 2025-09-09T14:26:55.3526315Z test/test_ops.py::test_marlin_qqq[32-128-128-4--1-(26, 37, 13)] SKIPPED 2025-09-09T14:26:55.3526973Z test/test_ops.py::test_marlin_qqq[32-128-128-4--1-(67, 13, 11)] SKIPPED 2025-09-09T14:26:55.3527792Z test/test_ops.py::test_marlin_qqq[32-128-128-4-128-(1, 1, 1)] SKIPPED 2025-09-09T14:26:55.3528583Z test/test_ops.py::test_marlin_qqq[32-128-128-4-128-(1, 4, 8)] SKIPPED 2025-09-09T14:26:55.3529393Z test/test_ops.py::test_marlin_qqq[32-128-128-4-128-(1, 7, 5)] SKIPPED 2025-09-09T14:26:55.3530050Z test/test_ops.py::test_marlin_qqq[32-128-128-4-128-(13, 17, 67)] SKIPPED 2025-09-09T14:26:55.3531014Z test/test_ops.py::test_marlin_qqq[32-128-128-4-128-(26, 37, 13)] SKIPPED 2025-09-09T14:26:55.3531864Z test/test_ops.py::test_marlin_qqq[32-128-128-4-128-(67, 13, 11)] SKIPPED 2025-09-09T14:26:55.3532523Z test/test_ops.py::test_marlin_qqq[32-128-256-4--1-(1, 1, 1)] SKIPPED 2025-09-09T14:26:55.3533322Z test/test_ops.py::test_marlin_qqq[32-128-256-4--1-(1, 4, 8)] SKIPPED 2025-09-09T14:26:55.3534101Z test/test_ops.py::test_marlin_qqq[32-128-256-4--1-(1, 7, 5)] SKIPPED 2025-09-09T14:26:55.3534919Z test/test_ops.py::test_marlin_qqq[32-128-256-4--1-(13, 17, 67)] SKIPPED 2025-09-09T14:26:55.3535576Z test/test_ops.py::test_marlin_qqq[32-128-256-4--1-(26, 37, 13)] SKIPPED 2025-09-09T14:26:55.3536388Z test/test_ops.py::test_marlin_qqq[32-128-256-4--1-(67, 13, 11)] SKIPPED 2025-09-09T14:26:55.3537185Z test/test_ops.py::test_marlin_qqq[32-128-256-4-128-(1, 1, 1)] SKIPPED 2025-09-09T14:26:55.3538011Z test/test_ops.py::test_marlin_qqq[32-128-256-4-128-(1, 4, 8)] SKIPPED 2025-09-09T14:26:55.3538643Z test/test_ops.py::test_marlin_qqq[32-128-256-4-128-(1, 7, 5)] SKIPPED 2025-09-09T14:26:55.3539306Z test/test_ops.py::test_marlin_qqq[32-128-256-4-128-(13, 17, 67)] SKIPPED 2025-09-09T14:26:55.3539957Z test/test_ops.py::test_marlin_qqq[32-128-256-4-128-(26, 37, 13)] SKIPPED 2025-09-09T14:26:55.3540619Z test/test_ops.py::test_marlin_qqq[32-128-256-4-128-(67, 13, 11)] SKIPPED 2025-09-09T14:26:55.3541577Z test/test_ops.py::test_marlin_qqq[64-128-64-4--1-(1, 1, 1)] SKIPPED 2025-09-09T14:26:55.3542373Z test/test_ops.py::test_marlin_qqq[64-128-64-4--1-(1, 4, 8)] SKIPPED 2025-09-09T14:26:55.3543004Z test/test_ops.py::test_marlin_qqq[64-128-64-4--1-(1, 7, 5)] SKIPPED 2025-09-09T14:26:55.3543797Z test/test_ops.py::test_marlin_qqq[64-128-64-4--1-(13, 17, 67)] SKIPPED 2025-09-09T14:26:55.3544597Z test/test_ops.py::test_marlin_qqq[64-128-64-4--1-(26, 37, 13)] SKIPPED 2025-09-09T14:26:55.3545408Z test/test_ops.py::test_marlin_qqq[64-128-64-4--1-(67, 13, 11)] SKIPPED 2025-09-09T14:26:55.3546053Z test/test_ops.py::test_marlin_qqq[64-128-64-4-128-(1, 1, 1)] SKIPPED 2025-09-09T14:26:55.3546865Z test/test_ops.py::test_marlin_qqq[64-128-64-4-128-(1, 4, 8)] SKIPPED 2025-09-09T14:26:55.3547629Z test/test_ops.py::test_marlin_qqq[64-128-64-4-128-(1, 7, 5)] SKIPPED 2025-09-09T14:26:55.3548558Z test/test_ops.py::test_marlin_qqq[64-128-64-4-128-(13, 17, 67)] SKIPPED 2025-09-09T14:26:55.3549207Z test/test_ops.py::test_marlin_qqq[64-128-64-4-128-(26, 37, 13)] SKIPPED 2025-09-09T14:26:55.3550040Z test/test_ops.py::test_marlin_qqq[64-128-64-4-128-(67, 13, 11)] SKIPPED 2025-09-09T14:26:55.3550817Z test/test_ops.py::test_marlin_qqq[64-128-128-4--1-(1, 1, 1)] SKIPPED 2025-09-09T14:26:55.3551634Z test/test_ops.py::test_marlin_qqq[64-128-128-4--1-(1, 4, 8)] SKIPPED 2025-09-09T14:26:55.3552268Z test/test_ops.py::test_marlin_qqq[64-128-128-4--1-(1, 7, 5)] SKIPPED 2025-09-09T14:26:55.3553215Z test/test_ops.py::test_marlin_qqq[64-128-128-4--1-(13, 17, 67)] SKIPPED 2025-09-09T14:26:55.3554133Z test/test_ops.py::test_marlin_qqq[64-128-128-4--1-(26, 37, 13)] SKIPPED 2025-09-09T14:26:55.3554778Z test/test_ops.py::test_marlin_qqq[64-128-128-4--1-(67, 13, 11)] SKIPPED 2025-09-09T14:26:55.3555604Z test/test_ops.py::test_marlin_qqq[64-128-128-4-128-(1, 1, 1)] SKIPPED 2025-09-09T14:26:55.3556403Z test/test_ops.py::test_marlin_qqq[64-128-128-4-128-(1, 4, 8)] SKIPPED 2025-09-09T14:26:55.3557214Z test/test_ops.py::test_marlin_qqq[64-128-128-4-128-(1, 7, 5)] SKIPPED 2025-09-09T14:26:55.3557874Z test/test_ops.py::test_marlin_qqq[64-128-128-4-128-(13, 17, 67)] SKIPPED 2025-09-09T14:26:55.3559016Z test/test_ops.py::test_marlin_qqq[64-128-128-4-128-(26, 37, 13)] SKIPPED 2025-09-09T14:26:55.4026950Z test/test_ops.py::test_marlin_qqq[64-128-128-4-128-(67, 13, 11)] SKIPPED 2025-09-09T14:26:55.4027653Z test/test_ops.py::test_marlin_qqq[64-128-256-4--1-(1, 1, 1)] SKIPPED 2025-09-09T14:26:55.4028459Z test/test_ops.py::test_marlin_qqq[64-128-256-4--1-(1, 4, 8)] SKIPPED 2025-09-09T14:26:55.4029151Z test/test_ops.py::test_marlin_qqq[64-128-256-4--1-(1, 7, 5)] SKIPPED 2025-09-09T14:26:55.4029877Z test/test_ops.py::test_marlin_qqq[64-128-256-4--1-(13, 17, 67)] SKIPPED 2025-09-09T14:26:55.4030536Z test/test_ops.py::test_marlin_qqq[64-128-256-4--1-(26, 37, 13)] SKIPPED 2025-09-09T14:26:55.4031290Z test/test_ops.py::test_marlin_qqq[64-128-256-4--1-(67, 13, 11)] SKIPPED 2025-09-09T14:26:55.4032126Z test/test_ops.py::test_marlin_qqq[64-128-256-4-128-(1, 1, 1)] SKIPPED 2025-09-09T14:26:55.4032854Z test/test_ops.py::test_marlin_qqq[64-128-256-4-128-(1, 4, 8)] SKIPPED 2025-09-09T14:26:55.4033486Z test/test_ops.py::test_marlin_qqq[64-128-256-4-128-(1, 7, 5)] SKIPPED 2025-09-09T14:26:55.4034227Z test/test_ops.py::test_marlin_qqq[64-128-256-4-128-(13, 17, 67)] SKIPPED 2025-09-09T14:26:55.4034970Z test/test_ops.py::test_marlin_qqq[64-128-256-4-128-(26, 37, 13)] SKIPPED 2025-09-09T14:26:55.4035734Z test/test_ops.py::test_marlin_qqq[64-128-256-4-128-(67, 13, 11)] SKIPPED 2025-09-09T14:26:55.4036376Z test/test_ops.py::test_swizzle_mm SKIPPED (ROCm not available) 2025-09-09T14:26:55.4037121Z test/test_ops.py::test_scaled_embedding_bag_cpu[1-1-1-torch.int64] SKIPPED 2025-09-09T14:26:55.4037935Z test/test_ops.py::test_scaled_embedding_bag_cpu[1-1-1-torch.int32] 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test/test_ops.py::test_scaled_embedding_bag_cpu[1-2-512-torch.int32] SKIPPED 2025-09-09T14:26:55.4046997Z test/test_ops.py::test_scaled_embedding_bag_cpu[1-128-1-torch.int64] SKIPPED 2025-09-09T14:26:55.4047719Z test/test_ops.py::test_scaled_embedding_bag_cpu[1-128-1-torch.int32] SKIPPED 2025-09-09T14:26:55.4048449Z test/test_ops.py::test_scaled_embedding_bag_cpu[1-128-128-torch.int64] SKIPPED 2025-09-09T14:26:55.4049192Z test/test_ops.py::test_scaled_embedding_bag_cpu[1-128-128-torch.int32] SKIPPED 2025-09-09T14:26:55.4050034Z test/test_ops.py::test_scaled_embedding_bag_cpu[1-128-512-torch.int64] SKIPPED 2025-09-09T14:26:55.4050922Z test/test_ops.py::test_scaled_embedding_bag_cpu[1-128-512-torch.int32] SKIPPED 2025-09-09T14:26:55.4051753Z test/test_ops.py::test_scaled_embedding_bag_cpu[1-1024-1-torch.int64] SKIPPED 2025-09-09T14:26:55.4052472Z test/test_ops.py::test_scaled_embedding_bag_cpu[1-1024-1-torch.int32] SKIPPED 2025-09-09T14:26:55.4053331Z test/test_ops.py::test_scaled_embedding_bag_cpu[1-1024-128-torch.int64] SKIPPED 2025-09-09T14:26:55.4054202Z test/test_ops.py::test_scaled_embedding_bag_cpu[1-1024-128-torch.int32] SKIPPED 2025-09-09T14:26:55.4054961Z test/test_ops.py::test_scaled_embedding_bag_cpu[1-1024-512-torch.int64] SKIPPED 2025-09-09T14:26:55.4055781Z test/test_ops.py::test_scaled_embedding_bag_cpu[1-1024-512-torch.int32] SKIPPED 2025-09-09T14:26:55.4056585Z test/test_ops.py::test_scaled_embedding_bag_cpu[2-1-1-torch.int64] SKIPPED 2025-09-09T14:26:55.4057382Z test/test_ops.py::test_scaled_embedding_bag_cpu[2-1-1-torch.int32] SKIPPED 2025-09-09T14:26:55.4058087Z test/test_ops.py::test_scaled_embedding_bag_cpu[2-1-128-torch.int64] SKIPPED 2025-09-09T14:26:55.4059241Z test/test_ops.py::test_scaled_embedding_bag_cpu[2-1-128-torch.int32] SKIPPED 2025-09-09T14:26:55.4060144Z test/test_ops.py::test_scaled_embedding_bag_cpu[2-1-512-torch.int64] SKIPPED 2025-09-09T14:26:55.4060862Z 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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 2025-09-09T14:26:55.7488123Z 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 2025-09-09T14:26:55.7489539Z 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 2025-09-09T14:26:55.7491150Z 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 2025-09-09T14:26:55.7492199Z test/test_utils.py::TestTorchVersion::test_torch_version_at_least PASSED 2025-09-09T14:26:55.7492938Z test/test_utils.py::TestTorchVersion::test_torch_version_deprecation PASSED 2025-09-09T14:26:55.7493685Z test/test_utils.py::TestTorchAOBaseTensor::test_default_impls SKIPPED 2025-09-09T14:26:55.7494511Z test/test_utils.py::TestTorchAOBaseTensor::test_default_impls_with_optional_attr SKIPPED 2025-09-09T14:26:55.7495389Z test/test_utils.py::TestTorchAOBaseTensor::test_default_impls_with_optional_data SKIPPED 2025-09-09T14:26:55.7496285Z test/test_utils.py::TestTorchAOBaseTensor::test_print_arg_types PASSED 2025-09-09T14:26:55.7496675Z 2025-09-09T14:26:55.7496937Z =============================== warnings summary =============================== 2025-09-09T14:26:55.7497478Z test/core/test_config.py::test_reconstructable_dict_file_round_trip[config8] 2025-09-09T14:26:55.7498815Z /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 2025-09-09T14:26:55.7500159Z warnings.warn( 2025-09-09T14:26:55.7500320Z 2025-09-09T14:26:55.7500521Z test/dtypes/test_nf4.py::TestNF4Linear::test_to_copy_bfloat16 2025-09-09T14:26:55.7501036Z test/dtypes/test_nf4.py::TestNF4Linear::test_to_copy_float16 2025-09-09T14:26:55.7501522Z test/dtypes/test_nf4.py::TestNF4Linear::test_to_copy_float32 2025-09-09T14:26:55.7503004Z /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. 2025-09-09T14:26:55.7504523Z torch.testing.assert_allclose(input_tensor, nf4_to_dtype, atol=0.13, rtol=0.13) 2025-09-09T14:26:55.7504922Z 2025-09-09T14:26:55.7505163Z test/float8/test_float8_utils.py::test_non_float32_input[invalid_dtype3] 2025-09-09T14:26:55.7505756Z test/float8/test_float8_utils.py::test_non_float32_input[invalid_dtype4] 2025-09-09T14:26:55.7506334Z test/float8/test_float8_utils.py::test_non_float32_input[invalid_dtype5] 2025-09-09T14:26:55.7506914Z test/float8/test_float8_utils.py::test_non_float32_input[invalid_dtype6] 2025-09-09T14:26:55.7507484Z test/float8/test_float8_utils.py::test_non_float32_input[invalid_dtype7] 2025-09-09T14:26:55.7508702Z /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. 2025-09-09T14:26:55.7509830Z non_float32_tensor = torch.tensor([3.0], dtype=invalid_dtype) 2025-09-09T14:26:55.7510127Z 2025-09-09T14:26:55.7510359Z test/kernel/test_autotuner.py::TestQuantFlow::test_int_scaled_mm_1_cpu 2025-09-09T14:26:55.7510942Z test/kernel/test_autotuner.py::TestQuantFlow::test_int_scaled_mm_3_cpu 2025-09-09T14:26:55.7512460Z /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. 2025-09-09T14:26:55.7513880Z torch.testing.assert_allclose(out32_1, out32_2) 2025-09-09T14:26:55.7514144Z 2025-09-09T14:26:55.7514483Z test/prototype/test_codebook_quant.py::TestCodebookQuantization::test_choose_qparams_codebook 2025-09-09T14:26:55.7515959Z /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:1531.) 2025-09-09T14:26:55.7517406Z return callable(*args, **kwargs) 2025-09-09T14:26:55.7517624Z 2025-09-09T14:26:55.7517879Z test/prototype/test_parametrization.py::TestFakeSparsity::test_jit_trace 2025-09-09T14:26:55.7519776Z /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! 2025-09-09T14:26:55.7521430Z assert self.mask.shape == x.shape 2025-09-09T14:26:55.7521653Z 2025-09-09T14:26:55.7522038Z test/prototype/test_scheduler.py::TestScheduler::test_lambda_scheduler 2025-09-09T14:26:55.7522603Z test/prototype/test_scheduler.py::TestCubicScheduler::test_step 2025-09-09T14:26:55.7524062Z /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(). 2025-09-09T14:26:55.7525407Z warnings.warn( 2025-09-09T14:26:55.7525550Z 2025-09-09T14:26:55.7525836Z test/quantization/pt2e/test_graph_utils.py::TestGraphUtils::test_conv_bn_conv_relu 2025-09-09T14:26:55.7527387Z /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). 2025-09-09T14:26:55.7528825Z m, guards = torchdynamo.export( # noqa: F841© 2025-09-09T14:26:55.7529074Z 2025-09-09T14:26:55.7529341Z test/quantization/pt2e/test_graph_utils.py::TestGraphUtils::test_conv_bn_relu 2025-09-09T14:26:55.7530802Z /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). 2025-09-09T14:26:55.7532117Z m, guards = torchdynamo.export( # noqa: F841 2025-09-09T14:26:55.7532368Z 2025-09-09T14:26:55.7532715Z test/quantization/pt2e/test_graph_utils.py::TestGraphUtils::test_customized_equivalet_types_dict 2025-09-09T14:26:55.7534260Z /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). 2025-09-09T14:26:55.7535572Z m, guards = torchdynamo.export( # noqa: F841 2025-09-09T14:26:55.7535827Z 2025-09-09T14:26:55.7536007Z test/quantization/pt2e/test_quantize_pt2e.py: 18 warnings 2025-09-09T14:26:55.7536498Z test/quantization/pt2e/test_quantize_pt2e_qat.py: 75 warnings 2025-09-09T14:26:55.7536982Z test/quantization/pt2e/test_representation.py: 8 warnings 2025-09-09T14:26:55.7537857Z /opt/conda/envs/venv/lib/python3.9/site-packages/torchao/testing/pt2e/_xnnpack_quantizer.py:289: UserWarning: XNNPACKQuantizer is deprecated! 2025-09-09T14:26:55.7538722Z warnings.warn(f"{self.__class__.__name__} is deprecated!") 2025-09-09T14:26:55.7539019Z 2025-09-09T14:26:55.7539364Z test/quantization/pt2e/test_quantize_pt2e.py::TestQuantizePT2E::test_fold_all_ops_before_quantize 2025-09-09T14:26:55.7540224Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_flatten_recipe2 2025-09-09T14:26:55.7542152Z /opt/conda/envs/venv/lib/python3.9/site-packages/torch/export/_unlift.py:81: UserWarning: Attempted to insert a get_attr Node with no underlying reference in the owning GraphModule! Call GraphModule.add_submodule to add the necessary submodule, GraphModule.add_parameter to add the necessary Parameter, or nn.Module.register_buffer to add the necessary buffer 2025-09-09T14:26:55.7543809Z getattr_node = gm.graph.get_attr(lifted_node) 2025-09-09T14:26:55.7544054Z 2025-09-09T14:26:55.7544414Z test/quantization/pt2e/test_quantize_pt2e.py::TestQuantizePT2E::test_fold_all_ops_before_quantize 2025-09-09T14:26:55.7545759Z /opt/conda/envs/venv/lib/python3.9/site-packages/torch/fx/graph.py:1772: UserWarning: Node weight target weight weight of does not reference an nn.Module, nn.Parameter, or buffer, which is what 'get_attr' Nodes typically target 2025-09-09T14:26:55.7546845Z warnings.warn( 2025-09-09T14:26:55.7546985Z 2025-09-09T14:26:55.7547378Z test/quantization/pt2e/test_quantize_pt2e.py::TestQuantizePT2E::test_model_is_exported 2025-09-09T14:26:55.7548863Z /opt/conda/envs/venv/lib/python3.9/site-packages/torch/fx/_symbolic_trace.py:913: 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. 2025-09-09T14:26:55.7550125Z warnings.warn( 2025-09-09T14:26:55.7550266Z 2025-09-09T14:26:55.7550544Z test/quantization/pt2e/test_quantize_pt2e.py::TestQuantizePT2E::test_reentrant 2025-09-09T14:26:55.7551335Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn1d::test_fold_bn_erases_bn_node 2025-09-09T14:26:55.7552310Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn2d::test_fold_bn_erases_bn_node 2025-09-09T14:26:55.7553553Z /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. 2025-09-09T14:26:55.7554506Z warnings.warn( 2025-09-09T14:26:55.7554643Z 2025-09-09T14:26:55.7554915Z test/quantization/pt2e/test_quantize_pt2e.py::TestQuantizePT2E::test_reentrant 2025-09-09T14:26:55.7556339Z /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 2025-09-09T14:26:55.7557600Z warnings.warn( 2025-09-09T14:26:55.7557741Z 2025-09-09T14:26:55.7558423Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn1d::test_qat_conv_bn_bias_derived_qspec 2025-09-09T14:26:55.7559452Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn1d::test_qat_conv_bn_per_channel_weight_bias 2025-09-09T14:26:55.7560528Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn1d::test_qat_per_channel_weight_custom_dtype 2025-09-09T14:26:55.7561541Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_conv_bn_bias_derived_qspec 2025-09-09T14:26:55.7562542Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_conv_bn_per_channel_weight_bias 2025-09-09T14:26:55.7563548Z test/quantization/pt2e/test_quantize_pt2e_qat.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_per_channel_weight_custom_dtype 2025-09-09T14:26:55.7565100Z /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. 2025-09-09T14:26:55.7566288Z warnings.warn( 2025-09-09T14:26:55.7566428Z 2025-09-09T14:26:55.7566841Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_filter_conv2d_recipe 2025-09-09T14:26:55.7572240Z /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). 2025-09-09T14:27:14.1298809Z warnings.warn( 2025-09-09T14:27:14.1299034Z 2025-09-09T14:27:14.1299480Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_flatten_recipe2 2025-09-09T14:27:14.1300909Z /opt/conda/envs/venv/lib/python3.9/site-packages/torch/fx/graph.py:1772: UserWarning: Node cls_token target cls_token cls_token of does not reference an nn.Module, nn.Parameter, or buffer, which is what 'get_attr' Nodes typically target 2025-09-09T14:27:14.1302037Z warnings.warn( 2025-09-09T14:27:14.1302178Z 2025-09-09T14:27:14.1302722Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_set_module_name_and_module_type_with_mixed_configs 2025-09-09T14:27:14.1304319Z /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. 2025-09-09T14:27:14.1305336Z warnings.warn( 2025-09-09T14:27:14.1305476Z 2025-09-09T14:27:14.1306024Z test/quantization/pt2e/test_x86inductor_quantizer.py::TestQuantizePT2EX86Inductor::test_set_module_name_and_module_type_with_mixed_configs 2025-09-09T14:27:14.1320177Z /opt/conda/envs/venv/lib/python3.9/site-packages/torchao/quantization/pt2e/quantizer/x86_inductor_quantizer.py:383: UserWarning: Skip the quantization config for . 2025-09-09T14:27:14.1321282Z warnings.warn( 2025-09-09T14:27:14.1321428Z 2025-09-09T14:27:14.1321678Z test/quantization/test_qat.py::TestQAT::test_legacy_quantize_api_e2e 2025-09-09T14:27:14.1323009Z /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: 2025-09-09T14:27:14.1324215Z 2025-09-09T14:27:14.1324553Z base_config = Int8DynamicActivationInt4WeightConfig(group_size=32) 2025-09-09T14:27:14.1325101Z quantize_(model, QATConfig(base_config, step="prepare")) 2025-09-09T14:27:14.1325483Z # train (not shown) 2025-09-09T14:27:14.1325831Z quantize_(model, QATConfig(base_config, step="convert")) 2025-09-09T14:27:14.1326191Z 2025-09-09T14:27:14.1326524Z Alternatively, if you prefer to pass in fake quantization configs: 2025-09-09T14:27:14.1326926Z 2025-09-09T14:27:14.1327375Z activation_config = IntxFakeQuantizeConfig(torch.int8, "per_token", is_symmetric=False) 2025-09-09T14:27:14.1328026Z weight_config = IntxFakeQuantizeConfig(torch.int4, group_size=32) 2025-09-09T14:27:14.1328450Z qat_config = QATConfig( 2025-09-09T14:27:14.1328763Z activation_config=activation_config, 2025-09-09T14:27:14.1329098Z weight_config=weight_config, 2025-09-09T14:27:14.1329414Z step="prepare", 2025-09-09T14:27:14.1329655Z ) 2025-09-09T14:27:14.1329882Z quantize_(model, qat_config) 2025-09-09T14:27:14.1330164Z 2025-09-09T14:27:14.1330501Z Please see https://github.com/pytorch/ao/issues/2630 for more details. 2025-09-09T14:27:14.1330935Z 2025-09-09T14:27:14.1331135Z warnings.warn( 2025-09-09T14:27:14.1331293Z 2025-09-09T14:27:14.1331523Z test/quantization/test_qat.py::TestQAT::test_legacy_quantize_api_e2e 2025-09-09T14:27:14.1332855Z /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: 2025-09-09T14:27:14.1334041Z 2025-09-09T14:27:14.1334382Z base_config = Int8DynamicActivationInt4WeightConfig(group_size=32) 2025-09-09T14:27:14.1334904Z quantize_(model, QATConfig(base_config, step="prepare")) 2025-09-09T14:27:14.1335291Z # train (not shown) 2025-09-09T14:27:14.1335619Z quantize_(model, QATConfig(base_config, step="convert")) 2025-09-09T14:27:14.1335987Z 2025-09-09T14:27:14.1336299Z Alternatively, if you prefer to pass in fake quantization configs: 2025-09-09T14:27:14.1336862Z 2025-09-09T14:27:14.1337269Z activation_config = IntxFakeQuantizeConfig(torch.int8, "per_token", is_symmetric=False) 2025-09-09T14:27:14.1337912Z weight_config = IntxFakeQuantizeConfig(torch.int4, group_size=32) 2025-09-09T14:27:14.1338342Z qat_config = QATConfig( 2025-09-09T14:27:14.1338634Z activation_config=activation_config, 2025-09-09T14:27:14.1338975Z weight_config=weight_config, 2025-09-09T14:27:14.1339272Z step="prepare", 2025-09-09T14:27:14.1339526Z ) 2025-09-09T14:27:14.1339733Z quantize_(model, qat_config) 2025-09-09T14:27:14.1340091Z 2025-09-09T14:27:14.1340412Z Please see https://github.com/pytorch/ao/issues/2630 for more details. 2025-09-09T14:27:14.1340840Z 2025-09-09T14:27:14.1341057Z warnings.warn( 2025-09-09T14:27:14.1341198Z 2025-09-09T14:27:14.1341417Z test/quantization/test_qat.py::TestQAT::test_qat_fp8a4w_quantizer 2025-09-09T14:27:14.1345027Z /opt/conda/envs/venv/lib/python3.9/site-packages/torch/autograd/graph.py:824: 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.) 2025-09-09T14:27:14.1348739Z return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass 2025-09-09T14:27:14.1349197Z 2025-09-09T14:27:14.1349452Z test/sparsity/test_wanda.py::TestWandaSparsifier::test_one_layer_mlp_2x4 2025-09-09T14:27:14.1350548Z /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 2025-09-09T14:27:14.1351495Z warnings.warn( 2025-09-09T14:27:14.1351633Z 2025-09-09T14:27:14.1351865Z -- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html 2025-09-09T14:27:14.1352894Z ======== 1152 passed, 4191 skipped, 141 warnings in 1256.80s (0:20:56) ========= 2025-09-09T14:27:14.1412571Z ##[group]Run pmeier/pytest-results-action@a2c1430e2bddadbad9f49a6f9b879f062c6b19b1 2025-09-09T14:27:14.1413094Z with: 2025-09-09T14:27:14.1413404Z path: /home/ec2-user/actions-runner/_work/_temp/test-results 2025-09-09T14:27:14.1413795Z fail-on-empty: false 2025-09-09T14:27:14.1414039Z env: 2025-09-09T14:27:14.1414271Z DOCKER_IMAGE: pytorch/almalinux-builder:cpu 2025-09-09T14:27:14.1414612Z REPOSITORY: pytorch/ao 2025-09-09T14:27:14.1414859Z PR_NUMBER: 2963 2025-09-09T14:27:14.1417175Z 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/:$PATH python -m pip install --upgrade pip pip install torch==2.7.0 --index-url https://download.pytorch.org/whl/cpu sed -i '' dev-requirements.txt pip install -r dev-requirements.txt pip install . export CONDA=$(dirname $(dirname $(which conda))) export LD_LIBRARY_PATH=$CONDA/lib/:$LD_LIBRARY_PATH pytest test --verbose -s 2025-09-09T14:27:14.1419633Z RUNNER_ARTIFACT_DIR: /home/ec2-user/actions-runner/_work/_temp/artifacts 2025-09-09T14:27:14.1420222Z RUNNER_TEST_RESULTS_DIR: /home/ec2-user/actions-runner/_work/_temp/test-results 2025-09-09T14:27:14.1420785Z RUNNER_DOCS_DIR: /home/ec2-user/actions-runner/_work/_temp/docs 2025-09-09T14:27:14.1421188Z ##[endgroup] 2025-09-09T14:27:14.2040954Z Prepare all required actions 2025-09-09T14:27:14.2083872Z ##[group]Run ./test-infra/.github/actions/chown-directory 2025-09-09T14:27:14.2084257Z with: 2025-09-09T14:27:14.2084531Z directory: /home/ec2-user/actions-runner/_work/ao/ao/ 2025-09-09T14:27:14.2085036Z ALPINE_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/tool/alpine 2025-09-09T14:27:14.2085466Z env: 2025-09-09T14:27:14.2085701Z DOCKER_IMAGE: pytorch/almalinux-builder:cpu 2025-09-09T14:27:14.2086047Z REPOSITORY: pytorch/ao 2025-09-09T14:27:14.2086294Z PR_NUMBER: 2963 2025-09-09T14:27:14.2088615Z SCRIPT: conda create -n venv python=3.9 -y conda activate venv echo "::group::Install newer objcopy that supports --set-section-alignment" dnf install -y gcc-toolset-10-binutils export PATH=/opt/rh/gcc-toolset-10/root/usr/bin/:$PATH python -m pip install --upgrade pip pip install torch==2.7.0 --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/:$LD_LIBRARY_PATH pytest test --verbose -s 2025-09-09T14:27:14.2091241Z RUNNER_ARTIFACT_DIR: /home/ec2-user/actions-runner/_work/_temp/artifacts 2025-09-09T14:27:14.2091829Z RUNNER_TEST_RESULTS_DIR: /home/ec2-user/actions-runner/_work/_temp/test-results 2025-09-09T14:27:14.2092395Z RUNNER_DOCS_DIR: /home/ec2-user/actions-runner/_work/_temp/docs 2025-09-09T14:27:14.2092777Z ##[endgroup] 2025-09-09T14:27:14.2118443Z ##[group]Run docker run --rm -v "${DIRECTORY}":/v -w /v "${ALPINE_IMAGE}" chown -R "$(id -u):$(id -g)" . 2025-09-09T14:27:14.2119169Z docker run --rm -v "${DIRECTORY}":/v -w /v "${ALPINE_IMAGE}" chown -R "$(id -u):$(id -g)" . 2025-09-09T14:27:14.2140354Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-09T14:27:14.2140751Z env: 2025-09-09T14:27:14.2141001Z DOCKER_IMAGE: pytorch/almalinux-builder:cpu 2025-09-09T14:27:14.2141354Z REPOSITORY: pytorch/ao 2025-09-09T14:27:14.2141619Z PR_NUMBER: 2963 2025-09-09T14:27:14.2143895Z SCRIPT: conda create -n venv python=3.9 -y conda activate venv echo "::group::Install newer objcopy that supports --set-section-alignment" dnf install -y gcc-toolset-10-binutils export PATH=/opt/rh/gcc-toolset-10/root/usr/bin/:$PATH python -m pip install --upgrade pip pip install torch==2.7.0 --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 2025-09-09T14:27:14.2146361Z RUNNER_ARTIFACT_DIR: /home/ec2-user/actions-runner/_work/_temp/artifacts 2025-09-09T14:27:14.2146971Z RUNNER_TEST_RESULTS_DIR: /home/ec2-user/actions-runner/_work/_temp/test-results 2025-09-09T14:27:14.2147526Z RUNNER_DOCS_DIR: /home/ec2-user/actions-runner/_work/_temp/docs 2025-09-09T14:27:14.2148060Z ALPINE_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/tool/alpine 2025-09-09T14:27:14.2148560Z DIRECTORY: /home/ec2-user/actions-runner/_work/ao/ao/ 2025-09-09T14:27:14.2148924Z ##[endgroup] 2025-09-09T14:27:14.2523556Z Unable to find image '308535385114.dkr.ecr.us-east-1.amazonaws.com/tool/alpine:latest' locally 2025-09-09T14:27:14.4546427Z latest: Pulling from tool/alpine 2025-09-09T14:27:14.4546823Z 540db60ca938: Pulling fs layer 2025-09-09T14:27:14.5271175Z 540db60ca938: Verifying Checksum 2025-09-09T14:27:14.6179472Z 540db60ca938: Download complete 2025-09-09T14:27:14.6179833Z 540db60ca938: Pull complete 2025-09-09T14:27:14.6308209Z Digest: sha256:def822f9851ca422481ec6fee59a9966f12b351c62ccb9aca841526ffaa9f748 2025-09-09T14:27:14.6360564Z Status: Downloaded newer image for 308535385114.dkr.ecr.us-east-1.amazonaws.com/tool/alpine:latest 2025-09-09T14:27:16.1510501Z Prepare all required actions 2025-09-09T14:27:16.1538419Z ##[group]Run ./test-infra/.github/actions/chown-directory 2025-09-09T14:27:16.1538787Z with: 2025-09-09T14:27:16.1539062Z directory: /home/ec2-user/actions-runner/_work/_temp 2025-09-09T14:27:16.1539548Z ALPINE_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/tool/alpine 2025-09-09T14:27:16.1539981Z env: 2025-09-09T14:27:16.1540221Z DOCKER_IMAGE: pytorch/almalinux-builder:cpu 2025-09-09T14:27:16.1540565Z REPOSITORY: pytorch/ao 2025-09-09T14:27:16.1540828Z PR_NUMBER: 2963 2025-09-09T14:27:16.1543172Z 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==2.7.0 --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/:$LD_LIBRARY_PATH pytest test --verbose -s 2025-09-09T14:27:16.1545820Z RUNNER_ARTIFACT_DIR: /home/ec2-user/actions-runner/_work/_temp/artifacts 2025-09-09T14:27:16.1546406Z RUNNER_TEST_RESULTS_DIR: /home/ec2-user/actions-runner/_work/_temp/test-results 2025-09-09T14:27:16.1546967Z RUNNER_DOCS_DIR: /home/ec2-user/actions-runner/_work/_temp/docs 2025-09-09T14:27:16.1547347Z ##[endgroup] 2025-09-09T14:27:16.1573014Z ##[group]Run docker run --rm -v "${DIRECTORY}":/v -w /v "${ALPINE_IMAGE}" chown -R "$(id -u):$(id -g)" . 2025-09-09T14:27:16.1573743Z docker run --rm -v "${DIRECTORY}":/v -w /v "${ALPINE_IMAGE}" chown -R "$(id -u):$(id -g)" . 2025-09-09T14:27:16.1583586Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-09T14:27:16.1583977Z env: 2025-09-09T14:27:16.1584262Z DOCKER_IMAGE: pytorch/almalinux-builder:cpu 2025-09-09T14:27:16.1584598Z REPOSITORY: pytorch/ao 2025-09-09T14:27:16.1584877Z PR_NUMBER: 2963 2025-09-09T14:27:16.1587152Z SCRIPT: conda create -n venv python=3.9 -y conda activate venv echo "::group::Install newer objcopy that supports --set-section-alignment" dnf install -y gcc-toolset-10-binutils export PATH=/opt/rh/gcc-toolset-10/root/usr/bin/:$PATH python -m pip install --upgrade pip pip install torch==2.7.0 --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/:$LD_LIBRARY_PATH pytest test --verbose -s 2025-09-09T14:27:16.1589614Z RUNNER_ARTIFACT_DIR: /home/ec2-user/actions-runner/_work/_temp/artifacts 2025-09-09T14:27:16.1590208Z RUNNER_TEST_RESULTS_DIR: /home/ec2-user/actions-runner/_work/_temp/test-results 2025-09-09T14:27:16.1590777Z RUNNER_DOCS_DIR: /home/ec2-user/actions-runner/_work/_temp/docs 2025-09-09T14:27:16.1591309Z ALPINE_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/tool/alpine 2025-09-09T14:27:16.1591826Z DIRECTORY: /home/ec2-user/actions-runner/_work/_temp 2025-09-09T14:27:16.1592183Z ##[endgroup] 2025-09-09T14:27:17.1901098Z ##[group]Run # Only do these steps if we actually want to upload an artifact 2025-09-09T14:27:17.1901731Z # Only do these steps if we actually want to upload an artifact 2025-09-09T14:27:17.1902190Z if [[ -n "${UPLOAD_ARTIFACT_NAME}" ]]; then 2025-09-09T14:27:17.1902746Z  # If the default execution path is followed then we should get a wheel in the dist/ folder 2025-09-09T14:27:17.1903373Z  # attempt to just grab whatever is in there and scoop it all up 2025-09-09T14:27:17.1903865Z  if find "dist/" -name "*.whl" >/dev/null 2>/dev/null; then 2025-09-09T14:27:17.1904301Z  mv -v dist/*.whl "${RUNNER_ARTIFACT_DIR}/" 2025-09-09T14:27:17.1904633Z  fi 2025-09-09T14:27:17.1904929Z  if [[ -d "artifacts-to-be-uploaded" ]]; then 2025-09-09T14:27:17.1905392Z  mv -v artifacts-to-be-uploaded/* "${RUNNER_ARTIFACT_DIR}/" 2025-09-09T14:27:17.1905786Z  fi 2025-09-09T14:27:17.1906031Z fi 2025-09-09T14:27:17.1906243Z  2025-09-09T14:27:17.1906445Z upload_docs=0 2025-09-09T14:27:17.1906851Z # Check if there are files in the documentation folder to upload, note that 2025-09-09T14:27:17.1907328Z # empty folders do not count 2025-09-09T14:27:17.1907779Z if find "${RUNNER_DOCS_DIR}" -mindepth 1 -maxdepth 1 -type f | read -r; then 2025-09-09T14:27:17.1908394Z  # TODO: Add a check here to test if on ec2 because if we're not on ec2 then this 2025-09-09T14:27:17.1908887Z  # upload will probably not work correctly 2025-09-09T14:27:17.1909239Z  upload_docs=1 2025-09-09T14:27:17.1909490Z fi 2025-09-09T14:27:17.1909793Z echo "upload-docs=${upload_docs}" >> "${GITHUB_OUTPUT}" 2025-09-09T14:27:17.1915992Z shell: /usr/bin/bash -e {0} 2025-09-09T14:27:17.1916267Z env: 2025-09-09T14:27:17.1916510Z DOCKER_IMAGE: pytorch/almalinux-builder:cpu 2025-09-09T14:27:17.1916869Z REPOSITORY: pytorch/ao 2025-09-09T14:27:17.1917122Z PR_NUMBER: 2963 2025-09-09T14:27:17.1919390Z SCRIPT: conda create -n venv python=3.9 -y conda activate venv echo "::group::Install newer objcopy that supports --set-section-alignment" dnf install -y gcc-toolset-10-binutils export PATH=/opt/rh/gcc-toolset-10/root/usr/bin/:$PATH python -m pip install --upgrade pip pip install torch==2.7.0 --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/:$LD_LIBRARY_PATH pytest test --verbose -s 2025-09-09T14:27:17.1921964Z RUNNER_ARTIFACT_DIR: /home/ec2-user/actions-runner/_work/_temp/artifacts 2025-09-09T14:27:17.1922576Z RUNNER_TEST_RESULTS_DIR: /home/ec2-user/actions-runner/_work/_temp/test-results 2025-09-09T14:27:17.1923126Z RUNNER_DOCS_DIR: /home/ec2-user/actions-runner/_work/_temp/docs 2025-09-09T14:27:17.1923539Z UPLOAD_ARTIFACT_NAME: 2025-09-09T14:27:17.1923792Z ##[endgroup] 2025-09-09T14:27:17.2030712Z Prepare all required actions 2025-09-09T14:27:17.2069645Z ##[group]Run ./test-infra/.github/actions/teardown-linux 2025-09-09T14:27:17.2070015Z with: 2025-09-09T14:27:17.2070201Z env: 2025-09-09T14:27:17.2070450Z DOCKER_IMAGE: pytorch/almalinux-builder:cpu 2025-09-09T14:27:17.2070793Z REPOSITORY: pytorch/ao 2025-09-09T14:27:17.2071043Z PR_NUMBER: 2963 2025-09-09T14:27:17.2073307Z SCRIPT: conda create -n venv python=3.9 -y conda activate venv echo "::group::Install newer objcopy that supports --set-section-alignment" dnf install -y gcc-toolset-10-binutils export PATH=/opt/rh/gcc-toolset-10/root/usr/bin/:$PATH python -m pip install --upgrade pip pip install torch==2.7.0 --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 2025-09-09T14:27:17.2075832Z RUNNER_ARTIFACT_DIR: /home/ec2-user/actions-runner/_work/_temp/artifacts 2025-09-09T14:27:17.2076426Z RUNNER_TEST_RESULTS_DIR: /home/ec2-user/actions-runner/_work/_temp/test-results 2025-09-09T14:27:17.2076985Z RUNNER_DOCS_DIR: /home/ec2-user/actions-runner/_work/_temp/docs 2025-09-09T14:27:17.2077368Z ##[endgroup] 2025-09-09T14:27:17.2103156Z ##[group]Run set -eou pipefail 2025-09-09T14:27:17.2103480Z set -eou pipefail 2025-09-09T14:27:17.2103735Z  2025-09-09T14:27:17.2104107Z echo "Holding runner for 2 hours until all ssh sessions have logged out" 2025-09-09T14:27:17.2104582Z for _ in $(seq 1440); do 2025-09-09T14:27:17.2104908Z  # Break if no ssh session exists anymore 2025-09-09T14:27:17.2105281Z  if [ "$(who)" = "" ]; then 2025-09-09T14:27:17.2105568Z  break 2025-09-09T14:27:17.2105801Z  fi 2025-09-09T14:27:17.2106012Z  echo "." 2025-09-09T14:27:17.2106255Z  sleep 5 2025-09-09T14:27:17.2106488Z done 2025-09-09T14:27:17.2112180Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-09T14:27:17.2112548Z env: 2025-09-09T14:27:17.2112804Z DOCKER_IMAGE: pytorch/almalinux-builder:cpu 2025-09-09T14:27:17.2113139Z REPOSITORY: pytorch/ao 2025-09-09T14:27:17.2113403Z PR_NUMBER: 2963 2025-09-09T14:27:17.2115672Z 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/:$PATH python -m pip install --upgrade pip pip install torch==2.7.0 --index-url https://download.pytorch.org/whl/cpu sed -i '' dev-requirements.txt pip install -r dev-requirements.txt pip install . export CONDA=$(dirname $(dirname $(which conda))) export LD_LIBRARY_PATH=$CONDA/lib/:$LD_LIBRARY_PATH pytest test --verbose -s 2025-09-09T14:27:17.2118289Z RUNNER_ARTIFACT_DIR: /home/ec2-user/actions-runner/_work/_temp/artifacts 2025-09-09T14:27:17.2118980Z RUNNER_TEST_RESULTS_DIR: /home/ec2-user/actions-runner/_work/_temp/test-results 2025-09-09T14:27:17.2119542Z RUNNER_DOCS_DIR: /home/ec2-user/actions-runner/_work/_temp/docs 2025-09-09T14:27:17.2120047Z ##[endgroup] 2025-09-09T14:27:17.2146033Z Holding runner for 2 hours until all ssh sessions have logged out 2025-09-09T14:27:17.2227693Z ##[group]Run # ignore expansion of "docker ps -q" since it could be empty 2025-09-09T14:27:17.2228275Z # ignore expansion of "docker ps -q" since it could be empty 2025-09-09T14:27:17.2228702Z # shellcheck disable=SC2046 2025-09-09T14:27:17.2229046Z docker stop $(docker ps -q) || true 2025-09-09T14:27:17.2229399Z # Prune all of the docker images 2025-09-09T14:27:17.2229735Z docker system prune -af 2025-09-09T14:27:17.2235383Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-09T14:27:17.2235755Z env: 2025-09-09T14:27:17.2236009Z DOCKER_IMAGE: pytorch/almalinux-builder:cpu 2025-09-09T14:27:17.2236495Z REPOSITORY: pytorch/ao 2025-09-09T14:27:17.2236758Z PR_NUMBER: 2963 2025-09-09T14:27:17.2239045Z SCRIPT: conda create -n venv python=3.9 -y conda activate venv echo "::group::Install newer objcopy that supports --set-section-alignment" dnf install -y gcc-toolset-10-binutils export PATH=/opt/rh/gcc-toolset-10/root/usr/bin/:$PATH python -m pip install --upgrade pip pip install torch==2.7.0 --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 2025-09-09T14:27:17.2241611Z RUNNER_ARTIFACT_DIR: /home/ec2-user/actions-runner/_work/_temp/artifacts 2025-09-09T14:27:17.2242223Z RUNNER_TEST_RESULTS_DIR: /home/ec2-user/actions-runner/_work/_temp/test-results 2025-09-09T14:27:17.2242778Z RUNNER_DOCS_DIR: /home/ec2-user/actions-runner/_work/_temp/docs 2025-09-09T14:27:17.2243178Z ##[endgroup] 2025-09-09T14:27:18.2204701Z 0e59da47ce85 2025-09-09T14:27:21.4819971Z Deleted Containers: 2025-09-09T14:27:21.4820430Z 0e59da47ce8534faee9ce06b47e2f7c81d71b4b2f6765ea7a50a4be21bdffbb0 2025-09-09T14:27:21.4820798Z 2025-09-09T14:27:26.4190302Z Deleted Images: 2025-09-09T14:27:26.4190769Z untagged: 308535385114.dkr.ecr.us-east-1.amazonaws.com/tool/alpine:latest 2025-09-09T14:27:26.4191689Z untagged: 308535385114.dkr.ecr.us-east-1.amazonaws.com/tool/alpine@sha256:def822f9851ca422481ec6fee59a9966f12b351c62ccb9aca841526ffaa9f748 2025-09-09T14:27:26.4192628Z deleted: sha256:6dbb9cc54074106d46d4ccb330f2a40a682d49dda5f4844962b7dce9fe44aaec 2025-09-09T14:27:26.4193305Z deleted: sha256:b2d5eeeaba3a22b9b8aa97261957974a6bd65274ebd43e1d81d0a7b8b752b116 2025-09-09T14:27:26.4193864Z untagged: pytorch/almalinux-builder:cpu 2025-09-09T14:27:26.4194485Z untagged: pytorch/almalinux-builder@sha256:10f309602e8cd84e21cb6970f97544761dd12a06b141583ab4d45f0bac4bf651 2025-09-09T14:27:26.4195296Z deleted: sha256:d6a8fef7076378a67f34a587132b48533aeb29b267a5d532b5b9c8df70af258b 2025-09-09T14:27:26.4195956Z deleted: sha256:5ee80ac5eaac1f2e1a07ecf3b3488008351b9350af841eed478e2e8c24e6f42a 2025-09-09T14:27:26.4196637Z deleted: sha256:a65598dc7a77543b8c2087c984c4d399c538c793064f336291e43cd23c0d4bee 2025-09-09T14:27:26.4197298Z deleted: sha256:75bba60f865bdfb654effb55beba5e38d571601662e689a4eb428757bfbd966d 2025-09-09T14:27:26.4197950Z deleted: sha256:b970969a082500ab27d2bf9eac213044fc772525f683f5fc7332989e30c76480 2025-09-09T14:27:26.4198599Z deleted: sha256:bc559781ad080de9f6860d476855c5af704239cf63d44c57086a59b50d27e62d 2025-09-09T14:27:26.4199251Z deleted: sha256:cc6a3c301e1c09d37986dc9f00ef5acee60b16b3beb546ba626465df575ddc6f 2025-09-09T14:27:26.4200000Z deleted: sha256:bfff11b1687c8218c22ee1e3b72bf01d75b62571b1328a8d0fba8d430ad5f2e5 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2025-09-09T14:27:26.4206924Z deleted: sha256:f7827189db61f670718a2b94e71e48195c0baf64ee4f80458bc5fb383510d43f 2025-09-09T14:27:26.4207563Z deleted: sha256:ff4f19608a1944c0c2807cd533515673285a9632dc74bf020e83e18630d1ae35 2025-09-09T14:27:26.4207959Z 2025-09-09T14:27:26.4224690Z Total reclaimed space: 7.073GB 2025-09-09T14:27:26.4274713Z ##[group]Run set +e 2025-09-09T14:27:26.4275015Z set +e 2025-09-09T14:27:26.4275274Z if [[ "${NO_SUDO}" == "false" ]]; then 2025-09-09T14:27:26.4275697Z  sudo rm -rf "${GITHUB_WORKSPACE:?}/${REPOSITORY:?}" 2025-09-09T14:27:26.4276058Z else 2025-09-09T14:27:26.4276341Z  rm -rf "${GITHUB_WORKSPACE:?}/${REPOSITORY:?}" 2025-09-09T14:27:26.4276678Z fi 2025-09-09T14:27:26.4276894Z set -e 2025-09-09T14:27:26.4283586Z shell: /usr/bin/bash -e {0} 2025-09-09T14:27:26.4283868Z env: 2025-09-09T14:27:26.4284113Z DOCKER_IMAGE: pytorch/almalinux-builder:cpu 2025-09-09T14:27:26.4284486Z REPOSITORY: pytorch/ao 2025-09-09T14:27:26.4284736Z PR_NUMBER: 2963 2025-09-09T14:27:26.4287053Z 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/:$PATH python -m pip install --upgrade pip pip install torch==2.7.0 --index-url https://download.pytorch.org/whl/cpu sed -i '' dev-requirements.txt pip install -r dev-requirements.txt pip install . export CONDA=$(dirname $(dirname $(which conda))) export LD_LIBRARY_PATH=$CONDA/lib/:$LD_LIBRARY_PATH pytest test --verbose -s 2025-09-09T14:27:26.4289518Z RUNNER_ARTIFACT_DIR: /home/ec2-user/actions-runner/_work/_temp/artifacts 2025-09-09T14:27:26.4290111Z RUNNER_TEST_RESULTS_DIR: /home/ec2-user/actions-runner/_work/_temp/test-results 2025-09-09T14:27:26.4290681Z RUNNER_DOCS_DIR: /home/ec2-user/actions-runner/_work/_temp/docs 2025-09-09T14:27:26.4291069Z NO_SUDO: false 2025-09-09T14:27:26.4291302Z ##[endgroup] 2025-09-09T14:27:27.0238998Z Post job cleanup. 2025-09-09T14:27:27.1326143Z Post job cleanup. 2025-09-09T14:27:27.2331250Z [command]/usr/bin/git version 2025-09-09T14:27:27.2395273Z git version 2.47.1 2025-09-09T14:27:27.2437743Z Temporarily overriding HOME='/home/ec2-user/actions-runner/_work/_temp/f566a6b5-2be0-4189-accf-33704e068e55' before making global git config changes 2025-09-09T14:27:27.2438733Z Adding repository directory to the temporary git global config as a safe directory 2025-09-09T14:27:27.2443013Z [command]/usr/bin/git config --global --add safe.directory /home/ec2-user/actions-runner/_work/ao/ao/test-infra 2025-09-09T14:27:27.2478787Z [command]/usr/bin/git config --local --name-only --get-regexp core\.sshCommand 2025-09-09T14:27:27.2510202Z [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' || :" 2025-09-09T14:27:27.2870430Z [command]/usr/bin/git config --local --name-only --get-regexp http\.https\:\/\/github\.com\/\.extraheader 2025-09-09T14:27:27.2893824Z http.https://github.com/.extraheader 2025-09-09T14:27:27.2904367Z [command]/usr/bin/git config --local --unset-all http.https://github.com/.extraheader 2025-09-09T14:27:27.2933187Z [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' || :" 2025-09-09T14:27:27.3320503Z A job completed hook has been configured by the self-hosted runner administrator 2025-09-09T14:27:27.3347347Z ##[group]Run '/home/ec2-user/runner-scripts/after_job.sh' 2025-09-09T14:27:27.3352779Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-09-09T14:27:27.3353172Z ##[endgroup] 2025-09-09T14:27:27.3535126Z [!ALERT!] Swap in detected! [!ALERT!] 2025-09-09T14:27:38.9509719Z [!ALERT!] Swap out detected [!ALERT!] 2025-09-09T14:27:58.8501132Z Cleaning up orphan processes