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android.yml
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name: Android
on:
push:
branches:
- main
- release/*
pull_request:
paths:
- .ci/docker/**
- .github/workflows/android.yml
- build/*android*.sh
- install_requirements.sh
- examples/demo-apps/android/**
- extension/android/**
- extension/module/**
workflow_dispatch:
concurrency:
group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.sha }}-${{ github.event_name == 'workflow_dispatch' }}-${{ github.event_name == 'schedule' }}
cancel-in-progress: true
jobs:
build-llm-demo:
name: build-llm-demo
uses: pytorch/test-infra/.github/workflows/linux_job.yml@main
strategy:
matrix:
tokenizer: [bpe, tiktoken]
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 buck2
export ARTIFACTS_DIR_NAME=artifacts-to-be-uploaded
# 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-artifacts:
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
# NOTE: Consume stale artifacts won't make sense for benchmarking as the goal is always to
# benchmark models as fresh as possible. I'm okay to keep the 14 retention-days for now
# for TorchChat until we have a periodic job can publish it more often. Ideally I want to
# reduce it to <= 2 day, meaning the benchmark job will run daily.
retention-days: 14
if-no-files-found: ignore
path: ${{ runner.temp }}/artifacts/
# Running Android emulator directly on the runner and not using Docker
run-emulator:
# needs: build-llm-demo
# runs-on: amz2023.linux.4xlarge
runs-on: amz2023.linux.arm64.2xlarge
env:
ANDROID_NDK_VERSION: r26c
API_LEVEL: 34
steps:
- name: Setup SSH (Click me for login details)
uses: pytorch/test-infra/.github/actions/setup-ssh@main
with:
github-secret: ${{ secrets.GITHUB_TOKEN }}
instructions: |
This is used to run Android emulators, ANDROID_HOME is installed at /opt/android/sdk
- uses: actions/checkout@v3
with:
submodules: false
- name: Setup conda
uses: pytorch/test-infra/.github/actions/setup-miniconda@main
with:
python-version: '3.10'
- name: Install Android dependencies
shell: bash
run: |
set -eux
# Reuse the script that install Android on ET Docker image
sudo -E bash .ci/docker/common/install_android.sh
- name: Gradle cache
uses: gradle/actions/setup-gradle@v3
- name: AVD cache
uses: actions/cache@v4
id: avd-cache
with:
path: |
~/.android/avd/*
~/.android/adb*
key: avd-${{ env.API_LEVEL }}
# NB: It takes about 10m to cold boot the emulator here
- name: Run Android emulator
env:
ANDROID_HOME: /opt/android/sdk
uses: reactivecircus/android-emulator-runner@v2
with:
api-level: ${{ env.API_LEVEL }}
# NB: x86_64 emulator is slow because the lack of KVM support on AWS, it
# seems that we can use metal instance for that but it hasn't been tried
# out yet
arch: arm64-v8a
script: ./build/run_android_emulator.sh
# NB: This is to boot the emulator faster following the instructions on
# https://github.com/ReactiveCircus/android-emulator-runner. The max number
# of cores we can set is 6, any higher number will be reduced to 6.
cores: 6
ram-size: 12288M
force-avd-creation: false
disable-animations: true
emulator-options: -no-snapshot-save -no-window -gpu swiftshader_indirect -noaudio -no-boot-anim -camera-back none
# This is to make sure that the job doesn't fail flakily
emulator-boot-timeout: 900
# Let's see how expensive this job is, we might want to tone it down by running it periodically
test-llama-app:
needs: upload-artifacts
permissions:
id-token: write
contents: read
uses: pytorch/test-infra/.github/workflows/mobile_job.yml@main
strategy:
matrix:
# https://github.com/pytorch/executorch/blob/main/examples/demo-apps/android/LlamaDemo/README.md#alternative-2-build-from-local-machine
# mentions that tiktoken is only for Llama3. So, we can export it later in another archive
# like https://ossci-assets.s3.amazonaws.com/executorch-android-llama2-7b-0717.zip when this is
# updated to run Llama3
tokenizer: [bpe]
with:
device-type: android
runner: linux.2xlarge
test-infra-ref: ''
# This is the ARN of ExecuTorch project on AWS
project-arn: arn:aws:devicefarm:us-west-2:308535385114:project:02a2cf0f-6d9b-45ee-ba1a-a086587469e6
# This is the custom Android device pool that only includes Samsung Galaxy S2x
device-pool-arn: arn:aws:devicefarm:us-west-2:308535385114:devicepool:02a2cf0f-6d9b-45ee-ba1a-a086587469e6/e59f866a-30aa-4aa1-87b7-4510e5820dfa
# Uploaded to S3 from the previous job, the name of the app comes from the project itself
android-app-archive: https://gha-artifacts.s3.amazonaws.com/${{ github.repository }}/${{ github.run_id }}/artifact/llm_demo_${{ matrix.tokenizer }}/app-debug.apk
android-test-archive: https://gha-artifacts.s3.amazonaws.com/${{ github.repository }}/${{ github.run_id }}/artifact/llm_demo_${{ matrix.tokenizer }}/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
# Among the input, this is the biggest file, so it is cached on AWS to make the test faster. Note that the file is deleted by AWS after 30
# days and the job will automatically re-upload the file when that happens.
extra-data: https://ossci-assets.s3.amazonaws.com/executorch-android-llama2-7b-0717.zip