Add workflow for on-demand benchmarking #19
Workflow file for this run
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| name: android-perf | |
| on: | |
| schedule: | |
| - cron: 0 0 * * * | |
| pull_request: | |
| # Note: GitHub has an upper limit of 10 inputs | |
| workflow_dispatch: | |
| inputs: | |
| models: | |
| description: Models to be benchmarked | |
| required: false | |
| type: string | |
| default: stories110M | |
| devices: | |
| description: Target devices to run benchmark | |
| required: false | |
| type: string | |
| default: false | |
| delegates: | |
| description: Backend delegates | |
| required: false | |
| type: string | |
| default: xnnpack | |
| threadpool: | |
| description: Run with threadpool? | |
| required: false | |
| type: boolean | |
| default: false | |
| benchmark_configs: | |
| description: The list of configs used the benchmark | |
| required: false | |
| type: string | |
| concurrency: | |
| group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref_name }}-${{ github.ref_type == 'branch' && github.sha }}-${{ github.event_name == 'workflow_dispatch' }}-${{ github.event_name == 'schedule' }} | |
| cancel-in-progress: true | |
| permissions: read-all | |
| jobs: | |
| set-models: | |
| runs-on: linux.2xlarge | |
| outputs: | |
| models: ${{ steps.set-models.outputs.models }} | |
| steps: | |
| - name: Set models | |
| id: set-models | |
| shell: bash | |
| run: | | |
| set -ex | |
| MODELS="${{ inputs.models || 'stories110M' }}" | |
| echo "models=$(echo $MODELS | jq -Rc 'split(",")')" >> $GITHUB_OUTPUT | |
| export-models: | |
| name: export-models | |
| uses: pytorch/test-infra/.github/workflows/linux_job.yml@main | |
| needs: set-models | |
| strategy: | |
| matrix: | |
| model: ${{ fromJson(needs.set-models.outputs.models) }} | |
| fail-fast: false | |
| with: | |
| runner: linux.2xlarge | |
| docker-image: executorch-ubuntu-22.04-clang12 | |
| submodules: 'true' | |
| timeout: 60 | |
| upload-artifact: android-models | |
| script: | | |
| # The generic Linux job chooses to use base env, not the one setup by the image | |
| CONDA_ENV=$(conda env list --json | jq -r ".envs | .[-1]") | |
| conda activate "${CONDA_ENV}" | |
| PYTHON_EXECUTABLE=python bash .ci/scripts/setup-linux.sh "cmake" | |
| echo "Exporting model: ${{ matrix.model }}" | |
| export ARTIFACTS_DIR_NAME=artifacts-to-be-uploaded/${{ matrix.model }} | |
| # Install requirements for export_llama | |
| PYTHON_EXECUTABLE=python bash examples/models/llama2/install_requirements.sh | |
| # Test llama2 | |
| PYTHON_EXECUTABLE=python bash .ci/scripts/test_llama.sh "${{ matrix.model }}.pt" "cmake" "fp32" "xnnpack+custom+qe" "${ARTIFACTS_DIR_NAME}"\ | |
| # Upload artifacts to S3. The artifacts are needed not only by the device farm but also TorchChat | |
| upload-models: | |
| needs: export-models | |
| runs-on: linux.2xlarge | |
| steps: | |
| - name: Download the artifacts from GitHub | |
| uses: actions/download-artifact@v3 | |
| with: | |
| # The name here needs to match the name of the upload-artifact parameter | |
| name: android-models | |
| path: ${{ runner.temp }}/artifacts/ | |
| - name: Verify the artifacts | |
| shell: bash | |
| working-directory: ${{ runner.temp }}/artifacts/ | |
| run: | | |
| ls -lah ./ | |
| - name: Upload the artifacts to S3 | |
| uses: seemethere/upload-artifact-s3@v5 | |
| with: | |
| s3-bucket: gha-artifacts | |
| s3-prefix: | | |
| ${{ github.repository }}/${{ github.run_id }}/artifact | |
| retention-days: 1 | |
| if-no-files-found: ignore | |
| path: ${{ runner.temp }}/artifacts/ | |
| build-llm-demo: | |
| name: build-llm-demo | |
| uses: pytorch/test-infra/.github/workflows/linux_job.yml@main | |
| needs: set-models | |
| strategy: | |
| matrix: | |
| tokenizer: [bpe] | |
| with: | |
| runner: linux.2xlarge | |
| docker-image: executorch-ubuntu-22.04-clang12-android | |
| submodules: 'true' | |
| ref: ${{ github.event_name == 'pull_request' && github.event.pull_request.head.sha || github.sha }} | |
| timeout: 90 | |
| upload-artifact: android-apps | |
| script: | | |
| set -eux | |
| # The generic Linux job chooses to use base env, not the one setup by the image | |
| CONDA_ENV=$(conda env list --json | jq -r ".envs | .[-1]") | |
| conda activate "${CONDA_ENV}" | |
| PYTHON_EXECUTABLE=python bash .ci/scripts/setup-linux.sh cmake | |
| export ARTIFACTS_DIR_NAME=artifacts-to-be-uploaded | |
| # TODO: This needs to be replaced with a generic loader .apk | |
| # Build LLM Demo for Android | |
| bash build/build_android_llm_demo.sh ${{ matrix.tokenizer }} ${ARTIFACTS_DIR_NAME} | |
| # Upload artifacts to S3. The artifacts are needed not only by the device farm but also TorchChat | |
| upload-android-apps: | |
| needs: build-llm-demo | |
| runs-on: linux.2xlarge | |
| steps: | |
| - name: Download the artifacts from GitHub | |
| uses: actions/download-artifact@v3 | |
| with: | |
| # The name here needs to match the name of the upload-artifact parameter | |
| name: android-apps | |
| path: ${{ runner.temp }}/artifacts/ | |
| - name: Verify the artifacts | |
| shell: bash | |
| working-directory: ${{ runner.temp }}/artifacts/ | |
| run: | | |
| ls -lah ./ | |
| - name: Upload the artifacts to S3 | |
| uses: seemethere/upload-artifact-s3@v5 | |
| with: | |
| s3-bucket: gha-artifacts | |
| s3-prefix: | | |
| ${{ github.repository }}/${{ github.run_id }}/artifact | |
| retention-days: 14 | |
| if-no-files-found: ignore | |
| path: ${{ runner.temp }}/artifacts/ | |
| # Let's see how expensive this job is, we might want to tone it down by running it periodically | |
| benchmark-on-device: | |
| permissions: | |
| id-token: write | |
| contents: read | |
| uses: pytorch/test-infra/.github/workflows/mobile_job.yml@main | |
| needs: | |
| - set-models | |
| - upload-models | |
| - upload-android-apps | |
| strategy: | |
| matrix: | |
| model: ${{ fromJson(needs.set-models.outputs.models) }} | |
| with: | |
| device-type: android | |
| runner: linux.2xlarge | |
| test-infra-ref: '' | |
| # This is the ARN of ExecuTorch project on AWS | |
| project-arn: arn:aws:devicefarm:us-west-2:308535385114:project:02a2cf0f-6d9b-45ee-ba1a-a086587469e6 | |
| # This is the custom Android device pool that only includes Samsung Galaxy S2x | |
| device-pool-arn: arn:aws:devicefarm:us-west-2:308535385114:devicepool:02a2cf0f-6d9b-45ee-ba1a-a086587469e6/e59f866a-30aa-4aa1-87b7-4510e5820dfa | |
| # Uploaded to S3 from the previous job, the name of the app comes from the project itself. | |
| # Unlike models there are limited numbers of build flavor for apps, and the model controls whether it should build with bpe/tiktoken tokenizer. | |
| # It's okay to build all possible apps with all possible flavors in job "build-llm-demo". However, in this job, once a model is given, there is only | |
| # one app+flavor that could load and run the model. | |
| # TODO: Hard code llm_demo_bpe for now in this job. | |
| android-app-archive: https://gha-artifacts.s3.amazonaws.com/${{ github.repository }}/${{ github.run_id }}/artifact/llm_demo_bpe/app-debug.apk | |
| android-test-archive: https://gha-artifacts.s3.amazonaws.com/${{ github.repository }}/${{ github.run_id }}/artifact/llm_demo_bpe/app-debug-androidTest.apk | |
| # The test spec can be downloaded from https://ossci-assets.s3.amazonaws.com/android-llama2-device-farm-test-spec.yml | |
| test-spec: arn:aws:devicefarm:us-west-2:308535385114:upload:02a2cf0f-6d9b-45ee-ba1a-a086587469e6/abd86868-fa63-467e-a5c7-218194665a77 | |
| # Uploaded to S3 from the previous job | |
| extra-data: https://gha-artifacts.s3.amazonaws.com/${{ github.repository }}/${{ github.run_id }}/artifact/${{ matrix.model }}/model.zip |