Debug container issue #1
Workflow file for this run
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name: linux-test | ||
on: | ||
workflow_call: | ||
inputs: | ||
build-environment: | ||
required: true | ||
type: string | ||
description: Top-level label for what's being built/tested. | ||
test-matrix: | ||
required: true | ||
type: string | ||
description: JSON description of what test configs to run. | ||
docker-image: | ||
required: true | ||
type: string | ||
description: Docker image to run in. | ||
sync-tag: | ||
required: false | ||
type: string | ||
default: "" | ||
description: | | ||
If this is set, our linter will use this to make sure that every other | ||
job with the same `sync-tag` is identical. | ||
timeout-minutes: | ||
required: false | ||
type: number | ||
default: 240 | ||
description: | | ||
Set the maximum (in minutes) how long the workflow should take to finish | ||
use-gha: | ||
required: false | ||
type: string | ||
default: "" | ||
description: If set to any value, upload to GHA. Otherwise upload to S3. | ||
dashboard-tag: | ||
required: false | ||
type: string | ||
default: "" | ||
s3-bucket: | ||
description: S3 bucket to download artifact | ||
required: false | ||
type: string | ||
default: "gha-artifacts" | ||
aws-role-to-assume: | ||
description: role to assume for downloading artifacts | ||
required: false | ||
type: string | ||
default: "" | ||
secrets: | ||
HUGGING_FACE_HUB_TOKEN: | ||
required: false | ||
description: | | ||
HF Auth token to avoid rate limits when downloading models or datasets from hub | ||
env: | ||
GIT_DEFAULT_BRANCH: ${{ github.event.repository.default_branch }} | ||
jobs: | ||
test: | ||
# Don't run on forked repos or empty test matrix | ||
if: github.repository_owner == 'pytorch' && toJSON(fromJSON(inputs.test-matrix).include) != '[]' | ||
strategy: | ||
matrix: ${{ fromJSON(inputs.test-matrix) }} | ||
fail-fast: false | ||
runs-on: | ||
group: ${{ matrix.runner }} | ||
timeout-minutes: ${{ matrix.mem_leak_check == 'mem_leak_check' && 600 || inputs.timeout-minutes }} | ||
steps: | ||
- name: Setup SSH (Click me for login details) | ||
uses: pytorch/test-infra/.github/actions/setup-ssh@main | ||
if: ${{ !contains(matrix.runner, 'gcp.a100') }} | ||
with: | ||
github-secret: ${{ secrets.GITHUB_TOKEN }} | ||
instructions: | | ||
All testing is done inside the container, to start an interactive session run: | ||
docker exec -it $(docker container ps --format '{{.ID}}') bash | ||
- name: Checkout PyTorch | ||
uses: pytorch/pytorch/.github/actions/checkout-pytorch@main | ||
- name: Setup Linux | ||
uses: ./.github/actions/setup-linux | ||
- name: Configure aws credentials | ||
if: ${{ inputs.aws-role-to-assume != '' }} | ||
uses: aws-actions/configure-aws-credentials@v3 | ||
with: | ||
role-to-assume: ${{ inputs.aws-role-to-assume }} | ||
role-session-name: gha-linux-test | ||
aws-region: us-east-1 | ||
- name: Calculate docker image | ||
id: calculate-docker-image | ||
uses: pytorch/test-infra/.github/actions/calculate-docker-image@main | ||
with: | ||
docker-image-name: ${{ inputs.docker-image }} | ||
- name: Use following to pull public copy of the image | ||
id: print-ghcr-mirror | ||
env: | ||
ECR_DOCKER_IMAGE: ${{ steps.calculate-docker-image.outputs.docker-image }} | ||
shell: bash | ||
run: | | ||
tag=${ECR_DOCKER_IMAGE##*/} | ||
echo "docker pull ghcr.io/pytorch/ci-image:${tag/:/-}" | ||
- name: Pull docker image | ||
uses: pytorch/test-infra/.github/actions/pull-docker-image@main | ||
with: | ||
docker-image: ${{ steps.calculate-docker-image.outputs.docker-image }} | ||
- name: Install nvidia driver, nvidia-docker runtime, set GPU_FLAG | ||
id: install-nvidia-driver | ||
uses: pytorch/test-infra/.github/actions/setup-nvidia@main | ||
if: contains(inputs.build-environment, 'cuda') && !contains(matrix.config, 'nogpu') | ||
- name: Lock NVIDIA A100 40GB Frequency | ||
run: | | ||
sudo nvidia-smi -pm 1 | ||
sudo nvidia-smi -ac 1215,1410 | ||
nvidia-smi | ||
if: contains(matrix.runner, 'a100') | ||
- name: Start monitoring script | ||
id: monitor-script | ||
shell: bash | ||
continue-on-error: true | ||
run: | | ||
python3 -m pip install psutil==5.9.1 nvidia-ml-py==11.525.84 | ||
python3 -m tools.stats.monitor > usage_log.txt 2>&1 & | ||
echo "monitor-script-pid=${!}" >> "${GITHUB_OUTPUT}" | ||
- name: Download build artifacts | ||
uses: ./.github/actions/download-build-artifacts | ||
with: | ||
name: ${{ inputs.build-environment }} | ||
s3-bucket: ${{ inputs.s3-bucket }} | ||
- name: Download TD artifacts | ||
continue-on-error: true | ||
uses: ./.github/actions/download-td-artifacts | ||
- name: Parse ref | ||
id: parse-ref | ||
run: .github/scripts/parse_ref.py | ||
- name: Get workflow job id | ||
id: get-job-id | ||
uses: ./.github/actions/get-workflow-job-id | ||
if: always() | ||
with: | ||
github-token: ${{ secrets.GITHUB_TOKEN }} | ||
- name: Check for keep-going label and re-enabled test issues | ||
# This uses the filter-test-configs action because it conviniently | ||
# checks for labels and re-enabled test issues. It does not actually do | ||
# any filtering. All filtering is done in the build step. | ||
id: keep-going | ||
uses: ./.github/actions/filter-test-configs | ||
with: | ||
github-token: ${{ secrets.GITHUB_TOKEN }} | ||
test-matrix: ${{ inputs.test-matrix }} | ||
job-name: ${{ steps.get-job-id.outputs.job-name }} | ||
- name: Set Test step time | ||
id: test-timeout | ||
shell: bash | ||
env: | ||
JOB_TIMEOUT: ${{ matrix.mem_leak_check == 'mem_leak_check' && 600 || inputs.timeout-minutes }} | ||
run: | | ||
echo "timeout=$((JOB_TIMEOUT-30))" >> "${GITHUB_OUTPUT}" | ||
- name: Test | ||
id: test | ||
timeout-minutes: ${{ fromJson(steps.test-timeout.outputs.timeout) }} | ||
env: | ||
BUILD_ENVIRONMENT: ${{ inputs.build-environment }} | ||
PR_NUMBER: ${{ github.event.pull_request.number }} | ||
GITHUB_REPOSITORY: ${{ github.repository }} | ||
GITHUB_WORKFLOW: ${{ github.workflow }} | ||
GITHUB_JOB: ${{ github.job }} | ||
GITHUB_RUN_ID: ${{ github.run_id }} | ||
GITHUB_RUN_NUMBER: ${{ github.run_number }} | ||
GITHUB_RUN_ATTEMPT: ${{ github.run_attempt }} | ||
JOB_ID: ${{ steps.get-job-id.outputs.job-id }} | ||
JOB_NAME: ${{ steps.get-job-id.outputs.job-name }} | ||
BRANCH: ${{ steps.parse-ref.outputs.branch }} | ||
SHA1: ${{ github.event.pull_request.head.sha || github.sha }} | ||
BASE_SHA: ${{ github.event.pull_request.base.sha || github.sha }} | ||
TEST_CONFIG: ${{ matrix.config }} | ||
SHARD_NUMBER: ${{ matrix.shard }} | ||
NUM_TEST_SHARDS: ${{ matrix.num_shards }} | ||
REENABLED_ISSUES: ${{ steps.keep-going.outputs.reenabled-issues }} | ||
CONTINUE_THROUGH_ERROR: ${{ steps.keep-going.outputs.keep-going }} | ||
VERBOSE_TEST_LOGS: ${{ steps.keep-going.outputs.ci-verbose-test-logs }} | ||
NO_TEST_TIMEOUT: ${{ steps.keep-going.outputs.ci-no-test-timeout }} | ||
NO_TD: ${{ steps.keep-going.outputs.ci-no-td }} | ||
SCCACHE_BUCKET: ossci-compiler-cache-circleci-v2 | ||
SCCACHE_S3_KEY_PREFIX: ${{ github.workflow }} | ||
SHM_SIZE: ${{ contains(inputs.build-environment, 'cuda') && '2g' || '1g' }} | ||
DOCKER_IMAGE: ${{ inputs.docker-image }} | ||
XLA_CUDA: ${{ contains(inputs.build-environment, 'xla') && '0' || '' }} | ||
XLA_CLANG_CACHE_S3_BUCKET_NAME: ossci-compiler-clang-cache-circleci-xla | ||
PYTORCH_TEST_CUDA_MEM_LEAK_CHECK: ${{ matrix.mem_leak_check && '1' || '0' }} | ||
PYTORCH_TEST_RERUN_DISABLED_TESTS: ${{ matrix.rerun_disabled_tests && '1' || '0' }} | ||
DASHBOARD_TAG: ${{ inputs.dashboard-tag }} | ||
HUGGING_FACE_HUB_TOKEN: ${{ secrets.HUGGING_FACE_HUB_TOKEN }} | ||
DOCKER_HOST: unix:///run/docker/docker.sock | ||
run: | | ||
set -x | ||
if [[ $TEST_CONFIG == 'multigpu' ]]; then | ||
TEST_COMMAND=.ci/pytorch/multigpu-test.sh | ||
elif [[ $BUILD_ENVIRONMENT == *onnx* ]]; then | ||
TEST_COMMAND=.ci/onnx/test.sh | ||
else | ||
TEST_COMMAND=.ci/pytorch/test.sh | ||
fi | ||
echo $DOCKER_HOST | ||
docker info | ||
# detached container should get cleaned up by teardown_ec2_linux | ||
# TODO: Stop building test binaries as part of the build phase | ||
# Used for GPU_FLAG since that doesn't play nice | ||
# shellcheck disable=SC2086,SC2090 | ||
container_name=$(docker run \ | ||
${GPU_FLAG:-} \ | ||
-e BUILD_ENVIRONMENT \ | ||
-e PR_NUMBER \ | ||
-e GITHUB_ACTIONS \ | ||
-e GITHUB_REPOSITORY \ | ||
-e GITHUB_WORKFLOW \ | ||
-e GITHUB_JOB \ | ||
-e GITHUB_RUN_ID \ | ||
-e GITHUB_RUN_NUMBER \ | ||
-e GITHUB_RUN_ATTEMPT \ | ||
-e JOB_ID \ | ||
-e JOB_NAME \ | ||
-e BASE_SHA \ | ||
-e BRANCH \ | ||
-e SHA1 \ | ||
-e AWS_DEFAULT_REGION \ | ||
-e IN_WHEEL_TEST \ | ||
-e SHARD_NUMBER \ | ||
-e TEST_CONFIG \ | ||
-e NUM_TEST_SHARDS \ | ||
-e REENABLED_ISSUES \ | ||
-e CONTINUE_THROUGH_ERROR \ | ||
-e VERBOSE_TEST_LOGS \ | ||
-e NO_TEST_TIMEOUT \ | ||
-e NO_TD \ | ||
-e PR_LABELS \ | ||
-e MAX_JOBS="$(nproc --ignore=2)" \ | ||
-e SCCACHE_BUCKET \ | ||
-e SCCACHE_S3_KEY_PREFIX \ | ||
-e XLA_CUDA \ | ||
-e XLA_CLANG_CACHE_S3_BUCKET_NAME \ | ||
-e PYTORCH_TEST_CUDA_MEM_LEAK_CHECK \ | ||
-e PYTORCH_TEST_RERUN_DISABLED_TESTS \ | ||
-e SKIP_SCCACHE_INITIALIZATION=1 \ | ||
-e HUGGING_FACE_HUB_TOKEN \ | ||
-e DASHBOARD_TAG \ | ||
--env-file="/tmp/github_env_${GITHUB_RUN_ID}" \ | ||
--ulimit stack=10485760:83886080 \ | ||
--security-opt seccomp=unconfined \ | ||
--cap-add=SYS_PTRACE \ | ||
--ipc=host \ | ||
--shm-size="${SHM_SIZE}" \ | ||
--tty \ | ||
--detach \ | ||
--name="${container_name}" \ | ||
--user jenkins \ | ||
--privileged \ | ||
-v "${GITHUB_WORKSPACE}:/var/lib/jenkins/workspace" \ | ||
-w /var/lib/jenkins/workspace \ | ||
"${DOCKER_IMAGE}" | ||
) | ||
# echo "${container_name}" | ||
# sleep(10000) | ||
# Propagate download.pytorch.org IP to container | ||
grep download.pytorch.org /etc/hosts | docker exec -i "${container_name}" sudo bash -c "/bin/cat >> /etc/hosts" | ||
echo "DOCKER_CONTAINER_ID=${container_name}" >> "${GITHUB_ENV}" | ||
docker exec -t "${container_name}" sh -c "pip install $(echo dist/*.whl)[opt-einsum] && ${TEST_COMMAND}" | ||
- name: Upload pytest cache if tests failed | ||
uses: ./.github/actions/pytest-cache-upload | ||
continue-on-error: true | ||
if: failure() && steps.test.conclusion && steps.test.conclusion == 'failure' | ||
with: | ||
cache_dir: .pytest_cache | ||
shard: ${{ matrix.shard }} | ||
sha: ${{ github.event.pull_request.head.sha || github.sha }} | ||
test_config: ${{ matrix.config }} | ||
job_identifier: ${{ github.workflow }}_${{ inputs.build-environment }} | ||
- name: Print remaining test logs | ||
shell: bash | ||
if: always() && steps.test.conclusion | ||
run: | | ||
cat test/**/*_toprint.log || true | ||
- name: Stop monitoring script | ||
if: always() && steps.monitor-script.outputs.monitor-script-pid | ||
shell: bash | ||
continue-on-error: true | ||
env: | ||
MONITOR_SCRIPT_PID: ${{ steps.monitor-script.outputs.monitor-script-pid }} | ||
run: | | ||
kill "$MONITOR_SCRIPT_PID" | ||
- name: Upload test artifacts | ||
uses: ./.github/actions/upload-test-artifacts | ||
if: always() && steps.test.conclusion && steps.test.conclusion != 'skipped' | ||
with: | ||
file-suffix: ${{ github.job }}-${{ matrix.config }}-${{ matrix.shard }}-${{ matrix.num_shards }}-${{ matrix.runner }}_${{ steps.get-job-id.outputs.job-id }} | ||
use-gha: ${{ inputs.use-gha }} | ||
- name: Collect backtraces from coredumps (if any) | ||
if: always() | ||
run: | | ||
# shellcheck disable=SC2156 | ||
find . -iname "core.[1-9]*" -exec docker exec "${DOCKER_CONTAINER_ID}" sh -c "gdb python {} -ex 'bt' -ex 'q'" \; | ||
- name: Store Core dumps on S3 | ||
uses: seemethere/upload-artifact-s3@v5 | ||
if: failure() | ||
with: | ||
name: coredumps-${{ matrix.config }}-${{ matrix.shard }}-${{ matrix.num_shards }}-${{ matrix.runner }} | ||
retention-days: 14 | ||
if-no-files-found: ignore | ||
path: ./**/core.[1-9]* | ||
- name: Teardown Linux | ||
uses: pytorch/test-infra/.github/actions/teardown-linux@main | ||
if: always() | ||
# NB: We are currently having an intermittent GPU-related issue on G5 runners with | ||
# A10G GPU. Once this happens, trying to reset the GPU as done in setup-nvidia does | ||
# not seem to help. Here are some symptoms: | ||
# * Calling nvidia-smi timeouts after 60 second | ||
# * Fail to run nvidia-smi with an unable to determine the device handle for GPU | ||
# unknown error | ||
# * Test fails with a missing CUDA GPU error when initializing CUDA in PyTorch | ||
# * Run docker --gpus all fails with error response from daemon | ||
# | ||
# As both the root cause and recovery path are unclear, let's take the runner out of | ||
# service so that it doesn't get any more jobs | ||
- name: Check NVIDIA driver installation step | ||
if: failure() && steps.install-nvidia-driver.outcome && steps.install-nvidia-driver.outcome != 'skipped' | ||
shell: bash | ||
env: | ||
RUNNER_WORKSPACE: ${{ runner.workspace }} | ||
run: | | ||
set +e | ||
set -x | ||
nvidia-smi | ||
# NB: Surprisingly, nvidia-smi command returns successfully with return code 0 even in | ||
# the case where the driver has already crashed as it still can get the driver version | ||
# and some basic information like the bus ID. However, the rest of the information | ||
# would be missing (ERR!), for example: | ||
# | ||
# +-----------------------------------------------------------------------------+ | ||
# | NVIDIA-SMI 525.89.02 Driver Version: 525.89.02 CUDA Version: 12.0 | | ||
# |-------------------------------+----------------------+----------------------+ | ||
# | GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC | | ||
# | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. | | ||
# | | | MIG M. | | ||
# |===============================+======================+======================| | ||
# | 0 ERR! Off | 00000000:00:1E.0 Off | ERR! | | ||
# |ERR! ERR! ERR! ERR! / ERR! | 4184MiB / 23028MiB | ERR! Default | | ||
# | | | ERR! | | ||
# +-------------------------------+----------------------+----------------------+ | ||
# | ||
# +-----------------------------------------------------------------------------+ | ||
# | Processes: | | ||
# | GPU GI CI PID Type Process name GPU Memory | | ||
# | ID ID Usage | | ||
# |=============================================================================| | ||
# +-----------------------------------------------------------------------------+ | ||
# | ||
# This should be reported as a failure instead as it will guarantee to fail when | ||
# Docker tries to run with --gpus all | ||
# | ||
# So, the correct check here is to query one of the missing piece of info like | ||
# GPU name, so that the command can fail accordingly | ||
nvidia-smi --query-gpu=gpu_name --format=csv,noheader --id=0 | ||
NVIDIA_SMI_STATUS=$? | ||
# These are acceptable return code from nvidia-smi as copied from setup-nvidia GitHub action | ||
if [ "$NVIDIA_SMI_STATUS" -ne 0 ] && [ "$NVIDIA_SMI_STATUS" -ne 14 ]; then | ||
echo "NVIDIA driver installation has failed, shutting down the runner..." | ||
.github/scripts/stop_runner_service.sh | ||
fi | ||
# For runner with multiple GPUs, we also want to confirm that the number of GPUs are the | ||
# power of 2, i.e. 1, 2, 4, or 8. This is to avoid flaky test issue when one GPU fails | ||
# https://github.com/pytorch/test-infra/issues/4000 | ||
GPU_COUNT=$(nvidia-smi --list-gpus | wc -l) | ||
NVIDIA_SMI_STATUS=$? | ||
# These are acceptable return code from nvidia-smi as copied from setup-nvidia GitHub action | ||
if [ "$NVIDIA_SMI_STATUS" -ne 0 ] && [ "$NVIDIA_SMI_STATUS" -ne 14 ]; then | ||
echo "NVIDIA driver installation has failed, shutting down the runner..." | ||
.github/scripts/stop_runner_service.sh | ||
fi | ||
# Check the GPU count to be a power of 2 | ||
if [ "$GPU_COUNT" -le 8 ] && [ "$GPU_COUNT" -ne 1 ] && [ "$GPU_COUNT" -ne 2 ] && [ "$GPU_COUNT" -ne 4 ] && [ "$GPU_COUNT" -ne 8 ]; then | ||
echo "NVIDIA driver detects $GPU_COUNT GPUs. The runner has a broken GPU, shutting it down..." | ||
.github/scripts/stop_runner_service.sh | ||
fi |