From 9ef40d23c8774c794a575e04ed3325239117addc Mon Sep 17 00:00:00 2001 From: priyakasimbeg Date: Wed, 26 Jul 2023 21:23:37 -0700 Subject: [PATCH] Revert "Revert "Docker automation fix"" --- .github/workflows/CI.yml | 90 +- .github/workflows/regression_tests.yml | 136 + CONTRIBUTING.md | 223 +- README.md | 202 +- algorithmic_efficiency/logger_utils.py | 42 +- algorithmic_efficiency/param_utils.py | 106 +- algorithmic_efficiency/spec.py | 26 +- .../workloads/cifar/workload.py | 5 + .../criteo1tb/criteo1tb_pytorch/workload.py | 2 +- .../workloads/criteo1tb/workload.py | 9 +- .../workloads/fastmri/workload.py | 5 + .../workloads/imagenet_resnet/workload.py | 5 + .../imagenet_vit/imagenet_jax/models.py | 4 +- .../imagenet_vit/imagenet_pytorch/models.py | 6 +- .../librispeech_conformer/workload.py | 5 + .../librispeech_jax/models.py | 12 +- .../workloads/mnist/mnist_pytorch/workload.py | 5 +- .../workloads/mnist/workload.py | 9 +- .../workloads/ogbg/ogbg_jax/models.py | 8 +- .../workloads/ogbg/ogbg_pytorch/models.py | 14 +- .../workloads/ogbg/workload.py | 5 + algorithmic_efficiency/workloads/wmt/bleu.py | 4 +- .../workloads/wmt/input_pipeline.py | 5 +- .../workloads/wmt/tokenizer.py | 38 +- .../workloads/wmt/wmt_jax/README.md | 11 - .../workloads/wmt/wmt_jax/decode.py | 12 +- .../workloads/wmt/wmt_jax/models.py | 9 +- .../workloads/wmt/wmt_pytorch/models.py | 416 +- .../workloads/wmt/wmt_pytorch/workload.py | 1 + .../workloads/wmt/workload.py | 5 + algorithmic_efficiency/workloads/workloads.py | 102 + baselines/shampoo/jax/distributed_shampoo.py | 1213 ++--- baselines/shampoo/jax/submission.py | 4 +- datasets/dataset_setup.py | 99 +- datasets/librispeech_preprocess.py | 20 +- datasets/librispeech_tokenizer.py | 2 +- docker/Dockerfile | 37 +- docker/README.md | 20 +- docker/build_docker_images.sh | 34 + docker/scripts/startup.sh | 97 +- getting_started.md | 86 +- .../jax_submission_base.py | 3 +- scoring/score_submission.py | 46 + scoring/scoring.py | 81 +- scoring/scoring_utils.py | 76 +- .../mnist_jax/trial_0/eval_measurements.csv | 4276 +++++++++++++++++ scoring/test_scoring_utils.py | 6 +- setup.cfg | 62 +- submission_runner.py | 197 +- tests/modeldiffs/imagenet_vit/compare.py | 2 +- tests/modeldiffs/wmt/compare.py | 4 +- tests/reference_algorithm_tests.py | 70 +- tests/test_baselines.py | 42 +- tests/test_param_shapes.py | 104 +- tests/test_param_types.py | 252 +- tests/test_traindiffs.py | 16 +- 56 files changed, 6669 insertions(+), 1702 deletions(-) create mode 100644 .github/workflows/regression_tests.yml delete mode 100644 algorithmic_efficiency/workloads/wmt/wmt_jax/README.md create mode 100644 algorithmic_efficiency/workloads/workloads.py create mode 100644 docker/build_docker_images.sh create mode 100644 scoring/score_submission.py create mode 100644 scoring/test_data/experiment_dir/mnist_jax/trial_0/eval_measurements.csv diff --git a/.github/workflows/CI.yml b/.github/workflows/CI.yml index 857871ce7..05d94e896 100644 --- a/.github/workflows/CI.yml +++ b/.github/workflows/CI.yml @@ -6,11 +6,13 @@ jobs: fastmri: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v3 - name: Set up Python 3.9 - uses: actions/setup-python@v2 + uses: actions/setup-python@v4 with: python-version: 3.9 + cache: 'pip' # Cache pip dependencies\. + cache-dependency-path: '**/setup.py' - name: Install Modules and Run run: | pip install .[jax_cpu] @@ -22,11 +24,13 @@ jobs: wmt_jax: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v3 - name: Set up Python 3.9 - uses: actions/setup-python@v2 + uses: actions/setup-python@v4 with: python-version: 3.9 + cache: 'pip' # Cache pip dependencies\. + cache-dependency-path: '**/setup.py' - name: Install Modules and Run run: | pip install .[jax_cpu] @@ -37,11 +41,13 @@ jobs: wmt_pytorch: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v3 - name: Set up Python 3.9 - uses: actions/setup-python@v2 + uses: actions/setup-python@v4 with: python-version: 3.9 + cache: 'pip' # Cache pip dependencies\. + cache-dependency-path: '**/setup.py' - name: Install Modules and Run run: | pip install .[jax_cpu] @@ -52,11 +58,13 @@ jobs: imagenet_jax: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v3 - name: Set up Python 3.9 - uses: actions/setup-python@v2 + uses: actions/setup-python@v4 with: python-version: 3.9 + cache: 'pip' # Cache pip dependencies\. + cache-dependency-path: '**/setup.py' - name: Install Modules and Run run: | pip install .[jax_cpu] @@ -68,11 +76,13 @@ jobs: imagenet_pytorch: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v3 - name: Set up Python 3.9 - uses: actions/setup-python@v2 + uses: actions/setup-python@v4 with: python-version: 3.9 + cache: 'pip' # Cache pip dependencies\. + cache-dependency-path: '**/setup.py' - name: Install Modules and Run run: | pip install .[jax_cpu] @@ -85,11 +95,13 @@ jobs: criteo_jax: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v3 - name: Set up Python 3.9 - uses: actions/setup-python@v2 + uses: actions/setup-python@v4 with: python-version: 3.9 + cache: 'pip' # Cache pip dependencies\. + cache-dependency-path: '**/setup.py' - name: Install Modules and Run run: | pip install .[jax_cpu] @@ -100,11 +112,13 @@ jobs: criteo_pytorch: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v3 - name: Set up Python 3.9 - uses: actions/setup-python@v2 + uses: actions/setup-python@v4 with: python-version: 3.9 + cache: 'pip' # Cache pip dependencies\. + cache-dependency-path: '**/setup.py' - name: Install Modules and Run run: | pip install .[jax_cpu] @@ -115,11 +129,13 @@ jobs: speech_jax: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v3 - name: Set up Python 3.9 - uses: actions/setup-python@v2 + uses: actions/setup-python@v4 with: python-version: 3.9 + cache: 'pip' # Cache pip dependencies\. + cache-dependency-path: '**/setup.py' - name: Install Modules and Run run: | pip install .[jax_cpu] @@ -131,11 +147,13 @@ jobs: speech_pytorch: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v3 - name: Set up Python 3.9 - uses: actions/setup-python@v2 + uses: actions/setup-python@v4 with: python-version: 3.9 + cache: 'pip' # Cache pip dependencies\. + cache-dependency-path: '**/setup.py' - name: Install Modules and Run run: | pip install .[jax_cpu] @@ -147,11 +165,13 @@ jobs: ogbg: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v3 - name: Set up Python 3.9 - uses: actions/setup-python@v2 + uses: actions/setup-python@v4 with: python-version: 3.9 + cache: 'pip' # Cache pip dependencies\. + cache-dependency-path: '**/setup.py' - name: Install Modules and Run run: | pip install .[jax_cpu] @@ -163,11 +183,13 @@ jobs: pytest: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v3 - name: Set up Python 3.9 - uses: actions/setup-python@v2 + uses: actions/setup-python@v4 with: python-version: 3.9 + cache: 'pip' # Cache pip dependencies\. + cache-dependency-path: '**/setup.py' - name: Install pytest run: | python -m pip install --upgrade pip @@ -175,10 +197,30 @@ jobs: pip install .[full] pip install .[jax_cpu] pip install .[pytorch_cpu] - - name: Run pytest + - name: Run pytest tests run: | pytest -vx tests/version_test.py pytest -vx tests/test_num_params.py pytest -vx tests/test_param_shapes.py pytest -vx tests/test_param_types.py - pytest -vx tests/test_ssim.py \ No newline at end of file + pytest -vx tests/test_ssim.py + pytest-baselines: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v3 + - name: Set up Python 3.9 + uses: actions/setup-python@v4 + with: + python-version: 3.9 + cache: 'pip' # Cache pip dependencies\. + cache-dependency-path: '**/setup.py' + - name: Install pytest + run: | + python -m pip install --upgrade pip + pip install pytest + pip install .[full] + pip install .[jax_cpu] + pip install .[pytorch_cpu] + - name: Run baseline tests + run: | + pytest --verbosity=1 tests/test_baselines.py diff --git a/.github/workflows/regression_tests.yml b/.github/workflows/regression_tests.yml new file mode 100644 index 000000000..7e6913c6b --- /dev/null +++ b/.github/workflows/regression_tests.yml @@ -0,0 +1,136 @@ +name: Containerized Regression Tests + +on: + pull_request: + branches: + - 'main' + +jobs: + fastmri_jax: + runs-on: self-hosted + steps: + - uses: actions/checkout@v2 + - name: Run containerized workload + run: | + docker pull us-central1-docker.pkg.dev/training-algorithms-external/mlcommons-docker-repo/algoperf_jax_dev + docker run -v $HOME/data/:/data/ -v $HOME/experiment_runs/:/experiment_runs -v $HOME/experiment_runs/logs:/logs --gpus all --ipc=host us-central1-docker.pkg.dev/training-algorithms-external/mlcommons-docker-repo/algoperf_jax_dev -d fastmri -f jax -s baselines/adamw/jax/submission.py -w fastmri -t baselines/adamw/tuning_search_space.json -e tests/regression_tests/adamw -m 10 -c False -o True -r false + imagenet_resnet_jax: + runs-on: self-hosted + steps: + - uses: actions/checkout@v2 + - name: Run containerized workload + run: | + docker pull us-central1-docker.pkg.dev/training-algorithms-external/mlcommons-docker-repo/algoperf_jax_dev + docker run -v $HOME/data/:/data/ -v $HOME/experiment_runs/:/experiment_runs -v $HOME/experiment_runs/logs:/logs --gpus all --ipc=host us-central1-docker.pkg.dev/training-algorithms-external/mlcommons-docker-repo/algoperf_jax_dev -d imagenet -f jax -s baselines/adamw/jax/submission.py -w imagenet_resnet -t baselines/adamw/tuning_search_space.json -e tests/regression_tests/adamw -m 10 -c False -o True -r false + imagenet_vit_jax: + runs-on: self-hosted + steps: + - uses: actions/checkout@v2 + - name: Run containerized workload + run: | + docker pull us-central1-docker.pkg.dev/training-algorithms-external/mlcommons-docker-repo/algoperf_jax_dev + docker run -v $HOME/data/:/data/ -v $HOME/experiment_runs/:/experiment_runs -v $HOME/experiment_runs/logs:/logs --gpus all --ipc=host us-central1-docker.pkg.dev/training-algorithms-external/mlcommons-docker-repo/algoperf_jax_dev -d imagenet -f jax -s baselines/adamw/jax/submission.py -w imagenet_vit -t baselines/adamw/tuning_search_space.json -e tests/regression_tests/adamw -m 10 -c False -o True -r false + ogbg_jax: + runs-on: self-hosted + steps: + - uses: actions/checkout@v2 + - name: Run containerized workload + run: | + docker pull us-central1-docker.pkg.dev/training-algorithms-external/mlcommons-docker-repo/algoperf_jax_dev + docker run -v $HOME/data/:/data/ -v $HOME/experiment_runs/:/experiment_runs -v $HOME/experiment_runs/logs:/logs --gpus all --ipc=host us-central1-docker.pkg.dev/training-algorithms-external/mlcommons-docker-repo/algoperf_jax_dev -d ogbg -f jax -s baselines/adamw/jax/submission.py -w ogbg -t baselines/adamw/tuning_search_space.json -e tests/regression_tests/adamw -m 10 -c False -o True -r false + criteo_jax: + runs-on: self-hosted + steps: + - uses: actions/checkout@v2 + - name: Run containerized workload + run: | + docker pull us-central1-docker.pkg.dev/training-algorithms-external/mlcommons-docker-repo/algoperf_jax_dev + docker run -v $HOME/data/:/data/ -v $HOME/experiment_runs/:/experiment_runs -v $HOME/experiment_runs/logs:/logs --gpus all --ipc=host us-central1-docker.pkg.dev/training-algorithms-external/mlcommons-docker-repo/algoperf_jax_dev -d criteo1tb -f jax -s baselines/adamw/jax/submission.py -w criteo1tb -t baselines/adamw/tuning_search_space.json -e tests/regression_tests/adamw -m 10 -c False -o True -r false + librispeech_conformer_jax: + runs-on: self-hosted + steps: + - uses: actions/checkout@v2 + - name: Run containerized workload + run: | + docker pull us-central1-docker.pkg.dev/training-algorithms-external/mlcommons-docker-repo/algoperf_jax_dev + docker run -v $HOME/data/:/data/ -v $HOME/experiment_runs/:/experiment_runs -v $HOME/experiment_runs/logs:/logs --gpus all --ipc=host us-central1-docker.pkg.dev/training-algorithms-external/mlcommons-docker-repo/algoperf_jax_dev -d librispeech -f jax -s baselines/adamw/jax/submission.py -w librispeech_conformer -t baselines/adamw/tuning_search_space.json -e tests/regression_tests/adamw -m 10 -c False -o True -r false + librispeech_deepspeech_jax: + runs-on: self-hosted + steps: + - uses: actions/checkout@v2 + - name: Run containerized workload + run: | + docker pull us-central1-docker.pkg.dev/training-algorithms-external/mlcommons-docker-repo/algoperf_jax_dev + docker run -v $HOME/data/:/data/ -v $HOME/experiment_runs/:/experiment_runs -v $HOME/experiment_runs/logs:/logs --gpus all --ipc=host us-central1-docker.pkg.dev/training-algorithms-external/mlcommons-docker-repo/algoperf_jax_dev -d librispeech -f jax -s baselines/adamw/jax/submission.py -w librispeech_deepspeech -t baselines/adamw/tuning_search_space.json -e tests/regression_tests/adamw -m 10 -c False -o True -r false + wmt_jax: + runs-on: self-hosted + steps: + - uses: actions/checkout@v2 + - name: Run containerized workload + run: | + docker pull us-central1-docker.pkg.dev/training-algorithms-external/mlcommons-docker-repo/algoperf_jax_dev + docker run -v $HOME/data/:/data/ -v $HOME/experiment_runs/:/experiment_runs -v $HOME/experiment_runs/logs:/logs --gpus all --ipc=host us-central1-docker.pkg.dev/training-algorithms-external/mlcommons-docker-repo/algoperf_jax_dev -d wmt -f jax -s baselines/adamw/jax/submission.py -w wmt -t baselines/adamw/tuning_search_space.json -e tests/regression_tests/adamw -m 10 -c False -o True -r false + fastmri_pytorch: + runs-on: self-hosted + steps: + - uses: actions/checkout@v2 + - name: Run containerized workload + run: | + docker pull us-central1-docker.pkg.dev/training-algorithms-external/mlcommons-docker-repo/algoperf_pytorch_dev + docker run -v $HOME/data/:/data/ -v $HOME/experiment_runs/:/experiment_runs -v $HOME/experiment_runs/logs:/logs --gpus all --ipc=host us-central1-docker.pkg.dev/training-algorithms-external/mlcommons-docker-repo/algoperf_pytorch_dev -d fastmri -f pytorch -s baselines/adamw/pytorch/submission.py -w fastmri -t baselines/adamw/tuning_search_space.json -e tests/regression_tests/adamw -m 10 -c False -o True -r false + imagenet_resnet_pytorch: + runs-on: self-hosted + steps: + - uses: actions/checkout@v2 + - name: Run containerized workload + run: | + docker pull us-central1-docker.pkg.dev/training-algorithms-external/mlcommons-docker-repo/algoperf_pytorch_dev + docker run -v $HOME/data/:/data/ -v $HOME/experiment_runs/:/experiment_runs -v $HOME/experiment_runs/logs:/logs --gpus all --ipc=host us-central1-docker.pkg.dev/training-algorithms-external/mlcommons-docker-repo/algoperf_pytorch_dev -d imagenet -f pytorch -s baselines/adamw/pytorch/submission.py -w imagenet_resnet -t baselines/adamw/tuning_search_space.json -e tests/regression_tests/adamw -m 10 -c False -o True -r false + imagenet_vit_pytorch: + runs-on: self-hosted + steps: + - uses: actions/checkout@v2 + - name: Run containerized workload + run: | + docker pull us-central1-docker.pkg.dev/training-algorithms-external/mlcommons-docker-repo/algoperf_pytorch_dev + docker run -v $HOME/data/:/data/ -v $HOME/experiment_runs/:/experiment_runs -v $HOME/experiment_runs/logs:/logs --gpus all --ipc=host us-central1-docker.pkg.dev/training-algorithms-external/mlcommons-docker-repo/algoperf_pytorch_dev -d imagenet -f pytorch -s baselines/adamw/pytorch/submission.py -w imagenet_vit -t baselines/adamw/tuning_search_space.json -e tests/regression_tests/adamw -m 10 -c False -o True -r false + ogbg_pytorch: + runs-on: self-hosted + steps: + - uses: actions/checkout@v2 + - name: Run containerized workload + run: | + docker pull us-central1-docker.pkg.dev/training-algorithms-external/mlcommons-docker-repo/algoperf_pytorch_dev + docker run -v $HOME/data/:/data/ -v $HOME/experiment_runs/:/experiment_runs -v $HOME/experiment_runs/logs:/logs --gpus all --ipc=host us-central1-docker.pkg.dev/training-algorithms-external/mlcommons-docker-repo/algoperf_pytorch_dev -d ogbg -f pytorch -s baselines/adamw/pytorch/submission.py -w ogbg -t baselines/adamw/tuning_search_space.json -e tests/regression_tests/adamw -m 10 -c False -o True -r false + criteo_pytorch: + runs-on: self-hosted + steps: + - uses: actions/checkout@v2 + - name: Run containerized workload + run: | + docker pull us-central1-docker.pkg.dev/training-algorithms-external/mlcommons-docker-repo/algoperf_pytorch_dev + docker run -v $HOME/data/:/data/ -v $HOME/experiment_runs/:/experiment_runs -v $HOME/experiment_runs/logs:/logs --gpus all --ipc=host us-central1-docker.pkg.dev/training-algorithms-external/mlcommons-docker-repo/algoperf_pytorch_dev -d criteo1tb -f pytorch -s baselines/adamw/pytorch/submission.py -w criteo1tb -t baselines/adamw/tuning_search_space.json -e tests/regression_tests/adamw -m 10 -c False -o True -r false + librispeech_conformer_pytorch: + runs-on: self-hosted + steps: + - uses: actions/checkout@v2 + - name: Run containerized workload + run: | + docker pull us-central1-docker.pkg.dev/training-algorithms-external/mlcommons-docker-repo/algoperf_pytorch_dev + docker run -v $HOME/data/:/data/ -v $HOME/experiment_runs/:/experiment_runs -v $HOME/experiment_runs/logs:/logs --gpus all --ipc=host us-central1-docker.pkg.dev/training-algorithms-external/mlcommons-docker-repo/algoperf_pytorch_dev -d librispeech -f pytorch -s baselines/adamw/pytorch/submission.py -w librispeech_conformer -t baselines/adamw/tuning_search_space.json -e tests/regression_tests/adamw -m 10 -c False -o True -r false + librispeech_deepspeech_pytorch: + runs-on: self-hosted + steps: + - uses: actions/checkout@v2 + - name: Run containerized workload + run: | + docker pull us-central1-docker.pkg.dev/training-algorithms-external/mlcommons-docker-repo/algoperf_pytorch_dev + docker run -v $HOME/data/:/data/ -v $HOME/experiment_runs/:/experiment_runs -v $HOME/experiment_runs/logs:/logs --gpus all --ipc=host us-central1-docker.pkg.dev/training-algorithms-external/mlcommons-docker-repo/algoperf_pytorch_dev -d librispeech -f pytorch -s baselines/adamw/pytorch/submission.py -w librispeech_deepspeech -t baselines/adamw/tuning_search_space.json -e tests/regression_tests/adamw -m 10 -c False -o True -r false + wmt_pytorch: + runs-on: self-hosted + steps: + - uses: actions/checkout@v2 + - name: Run containerized workload + run: | + docker pull us-central1-docker.pkg.dev/training-algorithms-external/mlcommons-docker-repo/algoperf_pytorch_dev + docker run -v $HOME/data/:/data/ -v $HOME/experiment_runs/:/experiment_runs -v $HOME/experiment_runs/logs:/logs --gpus all --ipc=host us-central1-docker.pkg.dev/training-algorithms-external/mlcommons-docker-repo/algoperf_pytorch_dev -d wmt -f pytorch -s baselines/adamw/pytorch/submission.py -w wmt -t baselines/adamw/tuning_search_space.json -e tests/regression_tests/adamw -m 10 -c False -o True -r false diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 26d33c3dc..b122372a9 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -1,4 +1,4 @@ -## Contributing +# Contributing The best way to contribute to the MLCommons is to get involved with one of our many project communities. You find more information about getting involved with MLCommons [here](https://mlcommons.org/en/get-involved/#getting-started). @@ -7,3 +7,224 @@ Generally we encourage people to become a MLCommons member if they wish to contr To get started contributing code, you or your organization needs to sign the MLCommons CLA found at the [MLC policies page](https://mlcommons.org/en/policies/). Once you or your organization has signed the corporate CLA, please fill out this [CLA sign up form](https://forms.gle/Ew1KkBVpyeJDuRw67) form to get your specific GitHub handle authorized so that you can start contributing code under the proper license. MLCommons project work is tracked with issue trackers and pull requests. Modify the project in your own fork and issue a pull request once you want other developers to take a look at what you have done and discuss the proposed changes. Ensure that cla-bot and other checks pass for your Pull requests. + +# Table of Contents +- [Setup](#setup) +- [Installation](#installation) +- [Docker workflows](#docker-workflows) +- [Submitting PRs](#submitting-prs) +- [Testing](#testing) + + +# Setup +## Setting up a Linux VM on GCP +If you want to run containers on GCP VMs or store and retrieve Docker images from the Google Cloud Container Registry, please read ahead. +If you'd like to use a Linux VM, you will have to install the correct GPU drivers and the NVIDIA Docker toolkit. +We recommmend to use the Deep Learning on Linux image. Further instructions are based on that. + +### Installing GPU Drivers +You can use the `scripts/cloud-startup.sh` as a startup script for the VM. This will automate the installation of the NVIDIA GPU Drivers and NVIDIA Docker toolkit. + +### Authentication for Google Cloud Container Registry +To access the Google Cloud Container Registry, you will have to authenticate to the repository whenever you use Docker. +Use the gcloud credential helper as documented [here](https://cloud.google.com/artifact-registry/docs/docker/pushing-and-pulling#cred-helper). + + +# Installation +If you have not installed the package and dependencies yet see [Installation](./README.md#installation). + +To use the development tools such as `pytest` or `pylint` use the `dev` option: + +```bash +pip3 install -e '.[dev]' +pre-commit install +``` + +To get an installation with the requirements for all workloads and development, use the argument `[full_dev]`. + + + +# Docker workflows +We recommend developing in our Docker image to ensure a consistent environment between developing, testing and scoring submissions. + +To get started see: +- [Installation with Docker](./README.md#docker) +- [Running a submission inside a Docker Container](./getting_started.md#run-your-submission-in-a-docker-container) + +Other resources: +- [Pre-built Images on Google Cloud Container Registry](#pre-built-images-on-google-cloud-container-registry) +- [GCP Data and Experiment Integration](#gcp-integration) + - [Downloading Data from GCP](#downloading-data-from-gcp) + - [Saving Experiments Results to GCP](#saving-experiments-to-gcp) +- [Getting Information from a Container](#getting-information-from-a-container) +- [Mounting local repository](#mounting-local-repository) + + +## Pre-built Images on Google Cloud Container Registry +If you'd like to maintain or use images stored on our Google Cloud Container Registry read this section. +You will have to use an authentication helper to set up permissions to access the repository: +``` +ARTIFACT_REGISTRY_URL=us-central1-docker.pkg.dev +gcloud auth configure-docker $ARTIFACT_REGISTRY_URL +``` + +To pull the latest prebuilt image: + +``` +docker pull us-central1-docker.pkg.dev/training-algorithms-external/mlcommons-docker-repo/ +``` +The naming convention for `image_name` is `algoperf__`. +Currently maintained images on the repository are: +- `algoperf_jax_main` +- `algoperf_pytorch_main` +- `algoperf_both_main` +- `algoperf_jax_dev` +- `algoperf_pytorch_dev` +- `algoperf_both_dev` + +### Trigger rebuild and push of maintained images +To build and push all images (`pytorch`, `jax`, `both`) on maintained branches (`dev`, `main`). +``` +bash docker/build_docker_images.sh -b +``` + +#### Trigger build and push of images on other branch +You can also use the above script to build images from a different branch. +1. Push the branch to `mlcommons/algorithmic-efficiency` repository. +2. Run + ``` + bash docker/build_docker_images.sh -b + ``` + +## GCP Data and Experiment Integration +The Docker entrypoint script can communicate with +our GCP buckets on our internal GCP project. If +you are an approved contributor you can get access to these resources to automatically download the datasets and upload experiment results. +You can use these features by setting the `-i` flag (for internal collaborator) to 'true' for the Docker entrypoint script. + +### Downloading Data from GCP +To run a docker container that will only download data (if not found on host) +``` +docker run -t -d \ +-v $HOME/data/:/data/ \ +-v $HOME/experiment_runs/:/experiment_runs \ +-v $HOME/experiment_runs/logs:/logs \ +--gpus all \ +--ipc=host \ + \ +-d \ +-f \ +-b \ +-i true +``` +If debugging_mode is `true` the main process on the container will persist after finishing the data download. +This run command is useful if you manually want to run a sumbission or look around. + +### Saving Experiments to GCP +If you set the internal collaborator mode to true +experiments will also be automatically uploaded to our GCP bucket under `gs://mlcommons-runs/ \ +-d \ +-f \ +-s \ +-t \ +-e \ +-w \ +-b +-i true \ +``` + +## Getting Information from a Container +To find the container IDs of running containers +``` +docker ps +``` + +To see the logging output +``` +docker logs +``` + +To enter a bash session in the container +``` +docker exec -it /bin/bash +``` + +## Mounting Local Repository +Rebuilding the docker image can become tedious if +you are making frequent changes to the code. +To have changes in your local copy of the algorithmic-efficiency repo be reflected inside the container you can mount the local repository with the `-v` flag. +``` +docker run -t -d \ +-v $HOME/data/:/data/ \ +-v $HOME/experiment_runs/:/experiment_runs \ +-v $HOME/experiment_runs/logs:/logs \ +-v $HOME/algorithmic-efficiency:/algorithmic-efficiency \ +--gpus all \ +--ipc=host \ + \ +-b +``` + +# Submitting PRs +New PRs will be merged on the dev branch by default, given that they pass the presubmits. + +# Testing +We run tests with GitHub Actions, configured in the [.github/workflows](https://github.com/mlcommons/algorithmic-efficiency/tree/main/.github/workflows) folder. + +## Style Testing +We run yapf and linting tests on PRs. You can view and fix offending errors with these instructions. + +To run the below commands, use the versions installed via `pip install -e '.[dev]'`. + +To automatically fix formatting errors, run the following (*WARNING:* this will edit your code, so it is suggested to make a git commit first!): +```bash +yapf -i -r -vv -p algorithmic_efficiency baselines datasets reference_algorithms tests *.py +``` + +To sort all import orderings, run the following: +```bash +isort . +``` + +To just print out all offending import orderings, run the following: +```bash +isort . --check --diff +``` + +To print out all offending pylint issues, run the following: +```bash +pylint algorithmic_efficiency +pylint baselines +pylint datasets +pylint reference_algorithms +pylint submission_runner.py +pylint tests +``` + +## Unit and integration tests +We run unit tests and integration tests as part of the of github actions as well. +You can also use `python tests/reference_algorithm_tests.py` to run a single model update and two model evals for each workload using the reference algorithm in `reference_algorithms/development_algorithms/`. + +## Regression tests +We also have regression tests available in [.github/workflows/regression_tests.yml](https://github.com/mlcommons/algorithmic-efficiency/tree/main/.github/workflows/regression_tests.yml) that can be run semi-automatically. +The regression tests are shorter end-to-end submissions run in a containerized environment across all 8 workloads, in both the jax and pytorch frameworks. +The regression tests run on self-hosted runners and are triggered for pull requests that target the main branch. Typically these PRs will be from the `dev` branch +so the tests will run containers based on images build from the `dev` branch. +To run a regression test: +1. Build and upload latest Docker images from dev branch. + ``` + bash ~/algorithmic-efficiency/docker/build_docker_images.sh -b dev + ``` +2. Turn on the self-hosted runner. +3. Run the self-hosted runner application for the runner to accept jobs. +4. Open a pull request into mian to trigger the workflow. diff --git a/README.md b/README.md index dd21d0732..c60efae60 100644 --- a/README.md +++ b/README.md @@ -22,26 +22,19 @@ [MLCommons Algorithmic Efficiency](https://mlcommons.org/en/groups/research-algorithms/) is a benchmark and competition measuring neural network training speedups due to algorithmic improvements in both training algorithms and models. This repository holds the [competition rules](RULES.md) and the benchmark code to run it. For a detailed description of the benchmark design, see our [paper](https://arxiv.org/abs/2306.07179). -## Installation - -1. Create new environment, e.g. via `conda` or `virtualenv`: - - Python minimum requirement >= 3.7 - - ```bash - sudo apt-get install python3-venv - python3 -m venv env - source env/bin/activate - ``` +# Table of Contents +- [Table of Contents](#table-of-contents) +- [AlgoPerf Benchmark Workloads](#algoperf-benchmark-workloads) +- [Installation](#installation) + - [Docker](#docker) +- [Getting Started](#getting-started) +- [Rules](#rules) +- [Contributing](#contributing) +- [Citing AlgoPerf Benchmark](#citing-algoperf-benchmark) -2. Clone this repository: - ```bash - git clone https://github.com/mlcommons/algorithmic-efficiency.git - cd algorithmic-efficiency - ``` - -3. We use pip to install the `algorithmic_efficiency`. +## Installation +You can install this package and dependences in a [python virtual environment](#virtual-environment) or use a [Docker container](#install-in-docker) (recommended). *TL;DR to install the Jax version for GPU run:* @@ -58,113 +51,124 @@ pip3 install -e '.[pytorch_gpu]' -f 'https://download.pytorch.org/whl/torch_stable.html' pip3 install -e '.[full]' ``` +## Virtual environment +Note: Python minimum requirement >= 3.8 - - #### Additional Details - - You can also install the requirements for individual workloads, e.g. via +To set up a virtual enviornment and install this repository +1. Create new environment, e.g. via `conda` or `virtualenv` ```bash - pip3 install -e '.[librispeech]' + sudo apt-get install python3-venv + python3 -m venv env + source env/bin/activate ``` - or all workloads at once via +2. Clone this repository ```bash - pip3 install -e '.[full]' + git clone https://github.com/mlcommons/algorithmic-efficiency.git + cd algorithmic-efficiency ``` - Depending on the framework you want to use (e.g. `JAX` or `PyTorch`) you need to install them as well. You could either do this manually or by adding the corresponding options: - - **JAX (GPU)** - - ```bash - pip3 install -e '.[jax_gpu]' -f 'https://storage.googleapis.com/jax-releases/jax_cuda_releases.html' - ``` +3. Run pip3 install commands above to install `algorithmic_efficiency`. - **JAX (CPU)** +
+ +Additional Details + +You can also install the requirements for individual workloads, e.g. via - ```bash - pip3 install -e '.[jax_cpu]' - ``` +```bash +pip3 install -e '.[librispeech]' +``` - **PyTorch (GPU)** +or all workloads at once via - ```bash - pip3 install -e '.[pytorch_gpu]' -f 'https://download.pytorch.org/whl/torch_stable.html' - ``` +```bash +pip3 install -e '.[full]' +``` +
- **PyTorch (CPU)** +## Docker +We recommend using a Docker container to ensure a similar environment to our scoring and testing environments. - ```bash - pip3 install -e '.[pytorch_cpu]' - ``` - **Development** +**Prerequisites for NVIDIA GPU set up**: You may have to install the NVIDIA Container Toolkit so that the containers can locate the NVIDIA drivers and GPUs. +See instructions [here](https://github.com/NVIDIA/nvidia-docker). - To use the development tools such as `pytest` or `pylint` use the `dev` option: +### Building Docker Image +1. Clone this repository ```bash - pip3 install -e '.[dev]' - pre-commit install + cd ~ && git clone https://github.com/mlcommons/algorithmic-efficiency.git ``` - To get an installation with the requirements for all workloads and development, use the argument `[full_dev]`. - -#### Setup - -1. Clone this repository: - +2. Build Docker Image ```bash - git clone https://github.com/mlcommons/algorithmic-efficiency.git + cd `algorithmic-efficiency/docker` + docker build -t . --build-args framework= ``` + The `framework` flag can be either `pytorch`, `jax` or `both`. + The `docker_image_name` is arbitrary. -2. Build Docker +### Running Docker Container (Interactive) +1. Run detached Docker Container ```bash - cd algorithmic-efficiency/ && sudo docker build -t algorithmic-efficiency . + docker run -t -d \ + -v $HOME/data/:/data/ \ + -v $HOME/experiment_runs/:/experiment_runs \ + -v $HOME/experiment_runs/logs:/logs \ + -v $HOME/algorithmic-efficiency:/algorithmic-efficiency \ + --gpus all \ + --ipc=host \ + ``` - -3. Run Docker - + This will print out a container id. +2. Open a bash terminal ```bash - sudo docker run --gpus all -it --rm -v $PWD:/home/ubuntu/algorithmic-efficiency --ipc=host algorithmic-efficiency + docker exec -it /bin/bash ``` - Currently docker method installs both PyTorch and JAX - - +### Running Docker Container (End-to-end) +To run a submission end-to-end in a container see [Getting Started Document](./getting_started.md#run-your-submission-in-a-docker-container). +# Getting Started +For instructions on developing and scoring your own algorithm in the benchmark see [Getting Started Document](./getting_started.md). ## Running a workload +To run a submission directly by running a Docker container, see [Getting Started Document](./getting_started.md#run-your-submission-in-a-docker-container). -See the [`reference_algorithms/`](https://github.com/mlcommons/algorithmic-efficiency/tree/main/reference_algorithms) dir for training various algorithm implementations (note that none of these are valid submissions because they have workload-specific logic, so we refer to them as "algorithms" instead of "submissions"). +Alternatively from a your virtual environment or interactively running Docker container `submission_runner.py` run: -### JAX +**JAX** ```bash python3 submission_runner.py \ --framework=jax \ --workload=mnist \ - --experiment_dir=/home/znado \ - --experiment_name=baseline \ + --experiment_dir=$HOME/experiments \ + --experiment_name=my_first_experiment \ --submission_path=reference_algorithms/development_algorithms/mnist/mnist_jax/submission.py \ --tuning_search_space=reference_algorithms/development_algorithms/mnist/tuning_search_space.json ``` -### PyTorch +**Pytorch** ```bash python3 submission_runner.py \ --framework=pytorch \ --workload=mnist \ - --experiment_dir=/home/znado \ - --experiment_name=baseline \ + --experiment_dir=$HOME/experiments \ + --experiment_name=my_first_experiment \ --submission_path=reference_algorithms/development_algorithms/mnist/mnist_pytorch/submission.py \ --tuning_search_space=reference_algorithms/development_algorithms/mnist/tuning_search_space.json ``` +
+ +Using Pytorch DDP (Recommended) + -When using multiple GPUs on a single node it is recommended to use PyTorch's -[distributed data parallel](https://pytorch.org/tutorials/intermediate/ddp_tutorial.html). +When using multiple GPUs on a single node it is recommended to use PyTorch's [distributed data parallel](https://pytorch.org/tutorials/intermediate/ddp_tutorial.html). To do so, simply replace `python3` by ```bash @@ -176,48 +180,28 @@ where `N_GPUS` is the number of available GPUs on the node. To only see output f torchrun --redirects 1:0,2:0,3:0,4:0,5:0,6:0,7:0 --standalone --nnodes=1 --nproc_per_node=8 ``` -## Rules - -The rules for the MLCommons Algorithmic Efficency benchmark can be found in the seperate [rules document](RULES.md). Suggestions, clarifications and questions can be raised via pull requests. - -## Contributing - -If you are interested in contributing to the work of the working group, feel free to [join the weekly meetings](https://mlcommons.org/en/groups/research-algorithms/), open issues, and see the [MLCommons contributing guidelines](CONTRIBUTING.md). - -### Presubmit testing - -We run basic presubmit checks with GitHub Actions, configured in the [.github/workflows](https://github.com/mlcommons/algorithmic-efficiency/tree/main/.github/workflows) folder. - -To run the below commands, use the versions installed via `pip install -e '.[dev]'`. - -To automatically fix formatting errors, run the following (*WARNING:* this will edit your code, so it is suggested to make a git commit first!): -```bash -yapf -i -r -vv -p algorithmic_efficiency baselines datasets reference_algorithms tests *.py +So the complete command is for example: ``` - -To sort all import orderings, run the following: -```bash -isort . +torchrun --redirects 1:0,2:0,3:0,4:0,5:0,6:0,7:0 --standalone --nnodes=1 --nproc_per_node=8 \ +submission_runner.py \ + --framework=pytorch \ + --workload=mnist \ + --experiment_dir=/home/znado \ + --experiment_name=baseline \ + --submission_path=reference_algorithms/development_algorithms/mnist/mnist_pytorch/submission.py \ + --tuning_search_space=reference_algorithms/development_algorithms/mnist/tuning_search_space.json \ ``` +
-To just print out all offending import orderings, run the following: -```bash -isort . --check --diff -``` -To print out all offending pylint issues, run the following: -```bash -pylint algorithmic_efficiency -pylint baselines -pylint datasets -pylint reference_algorithms -pylint submission_runner.py -pylint tests -``` +# Rules +The rules for the MLCommons Algorithmic Efficency benchmark can be found in the seperate [rules document](RULES.md). Suggestions, clarifications and questions can be raised via pull requests. + +# Contributing +If you are interested in contributing to the work of the working group, feel free to [join the weekly meetings](https://mlcommons.org/en/groups/research-algorithms/), open issues. See our [CONTRIBUTING.md](CONTRIBUTING.md) for MLCommons contributing guidelines and setup and workflow instructions. -You can also use `python tests/reference_algorithm_tests.py` to run a single model update and two model evals for each workload using the reference algorithm in `reference_algorithms/development_algorithms/`. -## Note on shared data pipelines between JAX and PyTorch +# Note on shared data pipelines between JAX and PyTorch The JAX and PyTorch versions of the Criteo, FastMRI, Librispeech, OGBG, and WMT workloads are using the same TensorFlow input pipelines. Due to differences in how Jax and PyTorch distribute computations across devices, the PyTorch workloads have an additional overhead for these workloads. diff --git a/algorithmic_efficiency/logger_utils.py b/algorithmic_efficiency/logger_utils.py index 74668828d..af2e61581 100644 --- a/algorithmic_efficiency/logger_utils.py +++ b/algorithmic_efficiency/logger_utils.py @@ -102,6 +102,24 @@ def write_json(name: str, log_dict: dict, indent: int = 2) -> None: f.write(json.dumps(log_dict, indent=indent)) +def write_to_csv( + metrics: dict, + csv_path: str, +) -> None: + try: + with open(csv_path, 'r') as csv_file: + measurements = pd.read_csv(csv_file) + measurements = pd.concat([measurements, pd.DataFrame([metrics])]) + except (pd.errors.EmptyDataError, FileNotFoundError) as e: + measurements = pd.DataFrame([metrics], columns=sorted(metrics.keys())) + if isinstance(e, pd.errors.EmptyDataError): + logging.info('Measurements file is empty. Create a new one, starting ' + 'with metrics from this step.') + with open(csv_path, 'w') as csv_file: + measurements.to_csv(csv_file, index=False) + return + + def _get_utilization() -> dict: util_data = {} @@ -265,12 +283,14 @@ class MetricLogger(object): """ def __init__(self, - csv_path: str = '', + csv_path: str, + eval_csv_path: str, events_dir: Optional[str] = None, configs: Optional[flags.FLAGS] = None, hyperparameters: Optional[spec.Hyperparameters] = None) -> None: self._measurements = {} self._csv_path = csv_path + self._eval_csv_path = eval_csv_path self.use_wandb = configs.use_wandb if events_dir: @@ -284,23 +304,15 @@ def __init__(self, def append_scalar_metrics(self, metrics: dict, global_step: int, - preemption_count: Optional[int] = None) -> None: + preemption_count: Optional[int] = None, + is_eval: bool = False) -> None: metrics['global_step'] = global_step if preemption_count is not None: metrics['preemption_count'] = preemption_count - try: - with open(self._csv_path, 'r') as csv_file: - measurements = pd.read_csv(csv_file) - measurements = measurements.append([metrics]) - except (pd.errors.EmptyDataError, FileNotFoundError) as e: - measurements = pd.DataFrame([metrics], columns=sorted(metrics.keys())) - if isinstance(e, pd.errors.EmptyDataError): - logging.info('Measurements file is empty. Create a new one, starting ' - 'with metrics from this step.') - - with open(self._csv_path, 'w') as csv_file: - measurements.to_csv(csv_file, index=False) + write_to_csv(metrics, self._csv_path) + if is_eval: + write_to_csv(metrics, self._eval_csv_path) if self._tb_metric_writer: self._tb_metric_writer.write_scalars( @@ -319,8 +331,10 @@ def set_up_loggers(train_dir: str, configs: flags.FLAGS, hyperparameters: spec.Hyperparameters) -> MetricLogger: csv_path = os.path.join(train_dir, 'measurements.csv') + eval_csv_path = os.path.join(train_dir, 'eval_measurements.csv') metrics_logger = MetricLogger( csv_path=csv_path, + eval_csv_path=eval_csv_path, events_dir=train_dir, configs=configs, hyperparameters=hyperparameters) diff --git a/algorithmic_efficiency/param_utils.py b/algorithmic_efficiency/param_utils.py index 1c1df2384..00c50ee4f 100644 --- a/algorithmic_efficiency/param_utils.py +++ b/algorithmic_efficiency/param_utils.py @@ -17,14 +17,46 @@ def pytorch_param_types( param_shapes: Dict[str, spec.ShapeTuple]) -> Dict[str, spec.ParameterType]: param_types = {} for name in param_shapes.keys(): - if 'bias' in name: - param_types[name] = spec.ParameterType.BIAS - elif 'bn' in name: - param_types[name] = spec.ParameterType.BATCH_NORM + if 'bn' in name: + if 'weight' in name or 'scale' in name: + param_types[name] = spec.ParameterType.BATCH_NORM_SCALE + elif 'bias' in name: + param_types[name] = spec.ParameterType.BATCH_NORM_BIAS + else: + raise ValueError(f'Unrecognized batch norm parameter: {name}.') + elif 'norm' in name or 'ln' in name: + if 'weight' in name or 'scale' in name: + param_types[name] = spec.ParameterType.LAYER_NORM_SCALE + elif 'bias' in name: + param_types[name] = spec.ParameterType.LAYER_NORM_BIAS + else: + raise ValueError(f'Unrecognized layer norm parameter: {name}.') elif 'conv' in name: - param_types[name] = spec.ParameterType.CONV_WEIGHT - elif 'embedding' in name: + if 'bias' in name: + param_types[name] = spec.ParameterType.BIAS + else: + param_types[name] = spec.ParameterType.CONV_WEIGHT + elif ('embedding' in name or 'embed' in name) and 'weight' in name: param_types[name] = spec.ParameterType.EMBEDDING + elif 'attn' in name or 'attention' in name: + if 'bias' in name: + param_types[name] = spec.ParameterType.ATTENTION_BIAS + elif 'k_proj' in name or 'key' in name: + param_types[name] = spec.ParameterType.ATTENTION_K + elif 'q_proj' in name or 'query' in name: + param_types[name] = spec.ParameterType.ATTENTION_Q + elif 'v_proj' in name or 'value' in name: + param_types[name] = spec.ParameterType.ATTENTION_V + elif 'out' in name and 'weight' in name: + param_types[name] = spec.ParameterType.ATTENTION_OUT + elif 'scale' in name: + param_types[name] = spec.ParameterType.WEIGHT + elif 'in_proj_weight' in name: + param_types[name] = spec.ParameterType.ATTENTION_QKV + else: + raise ValueError(f'Unrecognized attention parameter: {name}.') + elif 'bias' in name: + param_types[name] = spec.ParameterType.BIAS else: param_types[name] = spec.ParameterType.WEIGHT return param_types @@ -37,22 +69,60 @@ def jax_param_shapes( def jax_param_types(param_shapes: spec.ParameterShapeTree, parent_name: str = '') -> Dict[str, spec.ParameterType]: - param_types_dict = {} + param_types = {} for name, value in param_shapes.items(): + name = name.lower() if isinstance(value, dict) or isinstance(value, flax.core.FrozenDict): - param_types_dict[name] = jax_param_types(value, parent_name=name) + param_types[name] = jax_param_types( + value, parent_name=parent_name + '/' + name) else: - if 'bias' in name: - param_types_dict[name] = spec.ParameterType.BIAS - elif 'batchnorm' in parent_name.lower(): - param_types_dict[name] = spec.ParameterType.BATCH_NORM - elif 'conv' in parent_name.lower(): - param_types_dict[name] = spec.ParameterType.CONV_WEIGHT + if 'batchnorm' in parent_name or 'bn' in parent_name: + if name == 'scale': + param_types[name] = spec.ParameterType.BATCH_NORM_SCALE + elif name == 'bias': + param_types[name] = spec.ParameterType.BATCH_NORM_BIAS + else: + raise ValueError( + f'Unrecognized batch norm parameter: {parent_name}/{name}.') + elif 'layernorm' in parent_name or 'ln' in parent_name: + if name == 'scale': + param_types[name] = spec.ParameterType.LAYER_NORM_SCALE + elif name == 'bias': + param_types[name] = spec.ParameterType.LAYER_NORM_BIAS + else: + raise ValueError( + f'Unrecognized layer norm parameter: {parent_name}/{name}.') + elif 'conv' in parent_name: + if 'bias' in name: + param_types[name] = spec.ParameterType.BIAS + else: + param_types[name] = spec.ParameterType.CONV_WEIGHT # Note that this is exact equality, not contained in, because # flax.linen.Embed names the embedding parameter "embedding" # https://github.com/google/flax/blob/main/flax/linen/linear.py#L604. - elif 'embedding' in name: - param_types_dict[name] = spec.ParameterType.EMBEDDING + elif ('embedding' in name or + ('embedding' in parent_name and name == 'kernel')): + param_types[name] = spec.ParameterType.EMBEDDING + elif 'attention' in parent_name: + if name == 'bias': + param_types[name] = spec.ParameterType.ATTENTION_BIAS + elif 'key' in parent_name and name == 'kernel': + param_types[name] = spec.ParameterType.ATTENTION_K + elif 'query' in parent_name and name == 'kernel': + param_types[name] = spec.ParameterType.ATTENTION_Q + elif 'value' in parent_name and name == 'kernel': + param_types[name] = spec.ParameterType.ATTENTION_V + elif 'out' in parent_name and name == 'kernel': + param_types[name] = spec.ParameterType.ATTENTION_OUT + elif 'scale' in name: + param_types[name] = spec.ParameterType.WEIGHT + elif 'in_proj_weight' in name: + param_types[name] = spec.ParameterType.ATTENTION_QKV + else: + raise ValueError( + f'Unrecognized attention parameter: {parent_name}/{name}.') + elif 'bias' in name: + param_types[name] = spec.ParameterType.BIAS else: - param_types_dict[name] = spec.ParameterType.WEIGHT - return param_types_dict + param_types[name] = spec.ParameterType.WEIGHT + return param_types diff --git a/algorithmic_efficiency/spec.py b/algorithmic_efficiency/spec.py index e9737b24e..570b7c55b 100644 --- a/algorithmic_efficiency/spec.py +++ b/algorithmic_efficiency/spec.py @@ -29,8 +29,19 @@ class ParameterType(enum.Enum): WEIGHT = 0 BIAS = 1 CONV_WEIGHT = 2 - BATCH_NORM = 3 - EMBEDDING = 4 + BATCH_NORM_SCALE = 3 + BATCH_NORM_BIAS = 4 + LAYER_NORM_SCALE = 5 + LAYER_NORM_BIAS = 6 + EMBEDDING = 7 + ATTENTION_Q = 8 + ATTENTION_K = 9 + ATTENTION_V = 10 + ATTENTION_OUT = 11 + ATTENTION_QKV = 12 # This is used for implementations that fuse QKV together. + # We need to split this out because otherwise fused QKV models will have a + # different number of biases. + ATTENTION_BIAS = 13 # Of course, Tensor knows its shape and dtype. @@ -45,6 +56,12 @@ class ShapeTuple: def __init__(self, shape_tuple): self.shape_tuple = shape_tuple + def __repr__(self): + return f'ShapeTuple({self.shape_tuple})' + + def __eq__(self, other): + return self.shape_tuple == other.shape_tuple + Shape = Union[Tuple[int], Tuple[int, int], @@ -83,6 +100,11 @@ def __init__(self, *args, **kwargs) -> None: self._eval_iters: Dict[str, Iterator] = {} self.metrics_logger = None + @property + @abc.abstractmethod + def target_metric_name(self) -> str: + """The name of the target metric (useful for scoring/processing code).""" + @abc.abstractmethod def has_reached_validation_target(self, eval_result: Dict[str, float]) -> bool: diff --git a/algorithmic_efficiency/workloads/cifar/workload.py b/algorithmic_efficiency/workloads/cifar/workload.py index fb8e52806..9e36cb291 100644 --- a/algorithmic_efficiency/workloads/cifar/workload.py +++ b/algorithmic_efficiency/workloads/cifar/workload.py @@ -18,6 +18,11 @@ class BaseCifarWorkload(spec.Workload): _num_classes: int = 10 + @property + def target_metric_name(self) -> str: + """The name of the target metric (useful for scoring/processing code).""" + return 'accuracy' + def has_reached_validation_target(self, eval_result: Dict[str, float]) -> bool: return eval_result['validation/accuracy'] > self.validation_target_value diff --git a/algorithmic_efficiency/workloads/criteo1tb/criteo1tb_pytorch/workload.py b/algorithmic_efficiency/workloads/criteo1tb/criteo1tb_pytorch/workload.py index b4540e30b..e8c8df992 100644 --- a/algorithmic_efficiency/workloads/criteo1tb/criteo1tb_pytorch/workload.py +++ b/algorithmic_efficiency/workloads/criteo1tb/criteo1tb_pytorch/workload.py @@ -22,7 +22,7 @@ class Criteo1TbDlrmSmallWorkload(BaseCriteo1TbDlrmSmallWorkload): @property def eval_batch_size(self) -> int: - return 524_288 + return 262_144 def _per_example_sigmoid_binary_cross_entropy( self, logits: spec.Tensor, targets: spec.Tensor) -> spec.Tensor: diff --git a/algorithmic_efficiency/workloads/criteo1tb/workload.py b/algorithmic_efficiency/workloads/criteo1tb/workload.py index aa28b83e7..83ced02ce 100644 --- a/algorithmic_efficiency/workloads/criteo1tb/workload.py +++ b/algorithmic_efficiency/workloads/criteo1tb/workload.py @@ -21,6 +21,11 @@ class BaseCriteo1TbDlrmSmallWorkload(spec.Workload): mlp_top_dims: Tuple[int, int, int] = (1024, 1024, 512, 256, 1) embed_dim: int = 128 + @property + def target_metric_name(self) -> str: + """The name of the target metric (useful for scoring/processing code).""" + return 'loss' + def has_reached_validation_target(self, eval_result: float) -> bool: return eval_result['validation/loss'] < self.validation_target_value @@ -60,10 +65,6 @@ def num_validation_examples(self) -> int: def num_test_examples(self) -> int: return 89_274_637 - @property - def eval_batch_size(self) -> int: - return 524_288 - @property def train_mean(self): raise NotImplementedError diff --git a/algorithmic_efficiency/workloads/fastmri/workload.py b/algorithmic_efficiency/workloads/fastmri/workload.py index f4ced7161..ecfa27547 100644 --- a/algorithmic_efficiency/workloads/fastmri/workload.py +++ b/algorithmic_efficiency/workloads/fastmri/workload.py @@ -9,6 +9,11 @@ class BaseFastMRIWorkload(spec.Workload): + @property + def target_metric_name(self) -> str: + """The name of the target metric (useful for scoring/processing code).""" + return 'ssim' + def has_reached_validation_target(self, eval_result: float) -> bool: return eval_result['validation/ssim'] > self.validation_target_value diff --git a/algorithmic_efficiency/workloads/imagenet_resnet/workload.py b/algorithmic_efficiency/workloads/imagenet_resnet/workload.py index 5334caaf0..fe89e880e 100644 --- a/algorithmic_efficiency/workloads/imagenet_resnet/workload.py +++ b/algorithmic_efficiency/workloads/imagenet_resnet/workload.py @@ -10,6 +10,11 @@ class BaseImagenetResNetWorkload(spec.Workload): _num_classes: int = 1000 + @property + def target_metric_name(self) -> str: + """The name of the target metric (useful for scoring/processing code).""" + return 'accuracy' + def has_reached_validation_target(self, eval_result: Dict[str, float]) -> bool: return eval_result['validation/accuracy'] > self.validation_target_value diff --git a/algorithmic_efficiency/workloads/imagenet_vit/imagenet_jax/models.py b/algorithmic_efficiency/workloads/imagenet_vit/imagenet_jax/models.py index f3d62973c..ab5d1839e 100644 --- a/algorithmic_efficiency/workloads/imagenet_vit/imagenet_jax/models.py +++ b/algorithmic_efficiency/workloads/imagenet_vit/imagenet_jax/models.py @@ -96,7 +96,7 @@ def __call__(self, x: spec.Tensor, train: bool = True) -> spec.Tensor: num_heads=self.num_heads, dropout_rate=self.dropout_rate) x = block(x, train) - return nn.LayerNorm(name='encoder_norm')(x) + return nn.LayerNorm(name='encoder_layernorm')(x) class ViT(nn.Module): @@ -127,7 +127,7 @@ def __call__(self, x: spec.Tensor, *, train: bool = False) -> spec.Tensor: self.patch_size, strides=self.patch_size, padding='VALID', - name='embedding')( + name='conv_patch_extract')( x) n, h, w, c = x.shape diff --git a/algorithmic_efficiency/workloads/imagenet_vit/imagenet_pytorch/models.py b/algorithmic_efficiency/workloads/imagenet_vit/imagenet_pytorch/models.py index 2770d2ae7..55a8e370d 100644 --- a/algorithmic_efficiency/workloads/imagenet_vit/imagenet_pytorch/models.py +++ b/algorithmic_efficiency/workloads/imagenet_vit/imagenet_pytorch/models.py @@ -221,7 +221,7 @@ def __init__( rep_size = self.width if self.rep_size is True else self.rep_size self.pre_logits = nn.Linear(self.width, rep_size) - self.embed = nn.Conv2d( + self.conv_patch_extract = nn.Conv2d( self.channels, self.width, self.patch_size, @@ -241,7 +241,7 @@ def __init__( self.reset_parameters() def reset_parameters(self) -> None: - init_utils.pytorch_default_init(self.embed) + init_utils.pytorch_default_init(self.conv_patch_extract) if self.rep_size: init_utils.pytorch_default_init(self.pre_logits) @@ -258,7 +258,7 @@ def get_posemb(self, x: spec.Tensor) -> spec.Tensor: def forward(self, x: spec.Tensor) -> spec.Tensor: # Patch extraction. - x = self.embed(x) + x = self.conv_patch_extract(x) # Add posemb before adding extra token. n, c, h, w = x.shape diff --git a/algorithmic_efficiency/workloads/librispeech_conformer/workload.py b/algorithmic_efficiency/workloads/librispeech_conformer/workload.py index 0019b80b7..985f4b0eb 100644 --- a/algorithmic_efficiency/workloads/librispeech_conformer/workload.py +++ b/algorithmic_efficiency/workloads/librispeech_conformer/workload.py @@ -8,6 +8,11 @@ class BaseLibrispeechWorkload(spec.Workload): _num_outputs: int = 1024 + @property + def target_metric_name(self) -> str: + """The name of the target metric (useful for scoring/processing code).""" + return 'wer' + def has_reached_validation_target(self, eval_result: Dict[str, float]) -> bool: return eval_result['validation/wer'] < self.validation_target_value diff --git a/algorithmic_efficiency/workloads/librispeech_deepspeech/librispeech_jax/models.py b/algorithmic_efficiency/workloads/librispeech_deepspeech/librispeech_jax/models.py index 34bbc99f2..769e8c496 100644 --- a/algorithmic_efficiency/workloads/librispeech_deepspeech/librispeech_jax/models.py +++ b/algorithmic_efficiency/workloads/librispeech_deepspeech/librispeech_jax/models.py @@ -379,13 +379,13 @@ def __call__( ) else: weight_ih, weight_hh, bias_ih, bias_hh = self.unpack_weights( - weights, input_size) + weights, input_size) y, h, c = rnn.lstm_ref( - x=inputs, h_0=h_0, c_0=c_0, W_ih=weight_ih, W_hh=weight_hh, - b_ih=bias_ih, b_hh=bias_hh, seq_lengths=seq_lengths, - input_size=input_size, hidden_size=self.features, - num_layers=self.num_layers, dropout=dropout, - bidirectional=self.bidirectional, + x=inputs, h_0=h_0, c_0=c_0, W_ih=weight_ih, W_hh=weight_hh, + b_ih=bias_ih, b_hh=bias_hh, seq_lengths=seq_lengths, + input_size=input_size, hidden_size=self.features, + num_layers=self.num_layers, dropout=dropout, + bidirectional=self.bidirectional, ) if return_carry: diff --git a/algorithmic_efficiency/workloads/mnist/mnist_pytorch/workload.py b/algorithmic_efficiency/workloads/mnist/mnist_pytorch/workload.py index 965a6d1c9..b7f33b94b 100644 --- a/algorithmic_efficiency/workloads/mnist/mnist_pytorch/workload.py +++ b/algorithmic_efficiency/workloads/mnist/mnist_pytorch/workload.py @@ -55,7 +55,10 @@ def _build_input_queue( repeat_final_dataset: Optional[bool] = None, num_batches: Optional[int] = None) -> Iterator[Dict[str, spec.Tensor]]: del cache - per_device_batch_size = int(global_batch_size / N_GPUS) + if N_GPUS != 0: + per_device_batch_size = int(global_batch_size / N_GPUS) + else: + per_device_batch_size = int(global_batch_size) # Only create and iterate over tf input pipeline in one Python process to # avoid creating too many threads. diff --git a/algorithmic_efficiency/workloads/mnist/workload.py b/algorithmic_efficiency/workloads/mnist/workload.py index f3867f4f3..dcc195170 100644 --- a/algorithmic_efficiency/workloads/mnist/workload.py +++ b/algorithmic_efficiency/workloads/mnist/workload.py @@ -2,7 +2,6 @@ import abc import functools -import itertools import math from typing import Any, Dict, Iterator, Optional @@ -33,7 +32,7 @@ def _build_mnist_dataset( data_dir: str, global_batch_size: int, cache: bool = False, - repeat_final_dataset: bool = False) -> Iterator[Dict[str, spec.Tensor]]: + repeat_final_dataset: bool = True) -> Iterator[Dict[str, spec.Tensor]]: shuffle = split in ['train', 'eval_train'] assert num_train_examples + num_validation_examples == 60000 if shuffle: @@ -72,6 +71,11 @@ def _build_mnist_dataset( class BaseMnistWorkload(spec.Workload): + @property + def target_metric_name(self) -> str: + """The name of the target metric (useful for scoring/processing code).""" + return 'accuracy' + def has_reached_validation_target(self, eval_result: Dict[str, float]) -> bool: return eval_result['validation/accuracy'] > self.validation_target_value @@ -159,7 +163,6 @@ def _build_input_queue( global_batch_size=global_batch_size, cache=cache, repeat_final_dataset=repeat_final_dataset) - ds = itertools.cycle(ds) return ds @property diff --git a/algorithmic_efficiency/workloads/ogbg/ogbg_jax/models.py b/algorithmic_efficiency/workloads/ogbg/ogbg_jax/models.py index 0d8b7d4ba..358415587 100644 --- a/algorithmic_efficiency/workloads/ogbg/ogbg_jax/models.py +++ b/algorithmic_efficiency/workloads/ogbg/ogbg_jax/models.py @@ -7,10 +7,10 @@ import jraph -def _make_embed(latent_dim): +def _make_embed(latent_dim, name): def make_fn(inputs): - return nn.Dense(features=latent_dim)(inputs) + return nn.Dense(features=latent_dim, name=name)(inputs) return make_fn @@ -55,8 +55,8 @@ def __call__(self, graph, train): globals=jnp.zeros([graph.n_node.shape[0], self.num_outputs])) embedder = jraph.GraphMapFeatures( - embed_node_fn=_make_embed(self.latent_dim), - embed_edge_fn=_make_embed(self.latent_dim)) + embed_node_fn=_make_embed(self.latent_dim, name='node_embedding'), + embed_edge_fn=_make_embed(self.latent_dim, name='edge_embedding')) graph = embedder(graph) for _ in range(self.num_message_passing_steps): diff --git a/algorithmic_efficiency/workloads/ogbg/ogbg_pytorch/models.py b/algorithmic_efficiency/workloads/ogbg/ogbg_pytorch/models.py index 42e132c2c..1b392753b 100644 --- a/algorithmic_efficiency/workloads/ogbg/ogbg_pytorch/models.py +++ b/algorithmic_efficiency/workloads/ogbg/ogbg_pytorch/models.py @@ -12,15 +12,13 @@ def _make_mlp(in_dim, hidden_dims, dropout_rate): """Creates a MLP with specified dimensions.""" - layers = [] + layers = nn.Sequential() for dim in hidden_dims: - layers.extend([ - nn.Linear(in_features=in_dim, out_features=dim), - nn.LayerNorm(dim, eps=1e-6), - nn.ReLU(), - nn.Dropout(dropout_rate), - ]) - return nn.Sequential(*layers) + layers.add_module('dense', nn.Linear(in_features=in_dim, out_features=dim)) + layers.add_module('norm', nn.LayerNorm(dim, eps=1e-6)) + layers.add_module('relu', nn.ReLU()) + layers.add_module('dropout', nn.Dropout(dropout_rate)) + return layers class GNN(nn.Module): diff --git a/algorithmic_efficiency/workloads/ogbg/workload.py b/algorithmic_efficiency/workloads/ogbg/workload.py index 3467efc9e..7ca6ebc1e 100644 --- a/algorithmic_efficiency/workloads/ogbg/workload.py +++ b/algorithmic_efficiency/workloads/ogbg/workload.py @@ -17,6 +17,11 @@ class BaseOgbgWorkload(spec.Workload): _num_outputs: int = 128 + @property + def target_metric_name(self) -> str: + """The name of the target metric (useful for scoring/processing code).""" + return 'mean_average_precision' + def has_reached_validation_target(self, eval_result: float) -> bool: return eval_result[ 'validation/mean_average_precision'] > self.validation_target_value diff --git a/algorithmic_efficiency/workloads/wmt/bleu.py b/algorithmic_efficiency/workloads/wmt/bleu.py index c573be85d..1efc87381 100644 --- a/algorithmic_efficiency/workloads/wmt/bleu.py +++ b/algorithmic_efficiency/workloads/wmt/bleu.py @@ -35,7 +35,7 @@ def corpus_bleu(sys_stream: Sequence[str], :return: A BLEU object containing everything you'd want. """ - # Add some robustness to the input arguments + # Add some robustness to the input arguments. if isinstance(sys_stream, str): sys_stream = [sys_stream] if isinstance(ref_streams, str): @@ -47,7 +47,7 @@ def corpus_bleu(sys_stream: Sequence[str], correct = [0 for _ in range(sacrebleu.NGRAM_ORDER)] total = [0 for _ in range(sacrebleu.NGRAM_ORDER)] - # look for already-tokenized sentences + # Look for already-tokenized sentences. tokenized_count = 0 fhs = [sys_stream] + ref_streams diff --git a/algorithmic_efficiency/workloads/wmt/input_pipeline.py b/algorithmic_efficiency/workloads/wmt/input_pipeline.py index 225a0dd76..af1c54994 100644 --- a/algorithmic_efficiency/workloads/wmt/input_pipeline.py +++ b/algorithmic_efficiency/workloads/wmt/input_pipeline.py @@ -283,9 +283,8 @@ def get_wmt_dataset(data_rng, functools.partial(normalize_feature_names, dataset_builder.info), num_parallel_calls=AUTOTUNE) - # Tokenize data. - sp_tokenizer = tokenizer.load_or_train_tokenizer( - ds, vocab_path=vocab_path, vocab_size=vocab_size, max_corpus_chars=10**7) + # Load tf-text SentencePiece tokenizer. + sp_tokenizer = tokenizer.load_tokenizer(vocab_path=vocab_path) ds = ds.map(tokenizer.TokenizeOp(sp_tokenizer), num_parallel_calls=AUTOTUNE) shuffle = split in ['train', 'eval_train'] diff --git a/algorithmic_efficiency/workloads/wmt/tokenizer.py b/algorithmic_efficiency/workloads/wmt/tokenizer.py index 981d85112..1f001e619 100644 --- a/algorithmic_efficiency/workloads/wmt/tokenizer.py +++ b/algorithmic_efficiency/workloads/wmt/tokenizer.py @@ -98,7 +98,6 @@ def _train_sentencepiece(dataset: tf.data.Dataset, while not tf.io.gfile.exists(abs_model_path): time.sleep(1) time.sleep(1) - return abs_model_path def _load_sentencepiece_tokenizer(model_path: str, @@ -113,24 +112,25 @@ def _load_sentencepiece_tokenizer(model_path: str, return sp_tokenizer -def load_or_train_tokenizer(dataset: tf.data.Dataset, - *, - vocab_path: str, - vocab_size: int, - max_corpus_chars: int, - data_keys: Tuple[str, str] = ('inputs', 'targets')): - """Loads the tokenizer at `vocab_path` or trains a one from `dataset`.""" - try: - return _load_sentencepiece_tokenizer(os.path.expanduser(vocab_path)) - except tf.errors.NotFoundError: - logging.info('SentencePiece vocab not found, building one from data.') - vocab_path = _train_sentencepiece( - dataset, - vocab_size=vocab_size, - maxchars=max_corpus_chars, - model_path=vocab_path, - data_keys=data_keys) - return _load_sentencepiece_tokenizer(vocab_path) +def train_tokenizer(dataset: tf.data.Dataset, + *, + vocab_path: str, + vocab_size: int, + max_corpus_chars: int, + data_keys: Tuple[str, str] = ('inputs', 'targets')): + """Trains a tokenizer from `dataset`.""" + logging.info('Building SentencePiece vocab from data.') + _train_sentencepiece( + dataset, + vocab_size=vocab_size, + maxchars=max_corpus_chars, + model_path=vocab_path, + data_keys=data_keys) + + +def load_tokenizer(vocab_path: str): + """Loads the tokenizer at `vocab_path`.""" + return _load_sentencepiece_tokenizer(os.path.expanduser(vocab_path)) @dataclasses.dataclass diff --git a/algorithmic_efficiency/workloads/wmt/wmt_jax/README.md b/algorithmic_efficiency/workloads/wmt/wmt_jax/README.md deleted file mode 100644 index 22231641f..000000000 --- a/algorithmic_efficiency/workloads/wmt/wmt_jax/README.md +++ /dev/null @@ -1,11 +0,0 @@ -## 1. Overview - -The English-German machine translation workload trains a Tramsfomer model on WMT -2017 Machine Translation en-de dataset. We get bleu value 25.03 (evaluated on -WMT 2014) after 23634 steps or 17652.04 seconds on a 8 V-100 GPU machine (1.34 -step/second). - -## 2. Reference - -https://github.com/google/flax/tree/main/examples/wmt - diff --git a/algorithmic_efficiency/workloads/wmt/wmt_jax/decode.py b/algorithmic_efficiency/workloads/wmt/wmt_jax/decode.py index 93b2eeca7..85d0eaac4 100644 --- a/algorithmic_efficiency/workloads/wmt/wmt_jax/decode.py +++ b/algorithmic_efficiency/workloads/wmt/wmt_jax/decode.py @@ -115,16 +115,16 @@ def gather_topk_beams(nested, score_or_log_prob, batch_size, new_beam_size): class BeamState: """Holds beam search state data.""" # The position of the decoding loop in the length dimension. - cur_index: jnp.DeviceArray # scalar int32: current decoded length index + cur_index: jax.Array # scalar int32: current decoded length index # The active sequence log probabilities and finished sequence scores. - live_logprobs: jnp.DeviceArray # float32: [batch_size, beam_size] - finished_scores: jnp.DeviceArray # float32: [batch_size, beam_size] + live_logprobs: jax.Array # float32: [batch_size, beam_size] + finished_scores: jax.Array # float32: [batch_size, beam_size] # The current active-beam-searching and finished sequences. - live_seqs: jnp.DeviceArray # int32: [batch_size, beam_size, max_decode_len] - finished_seqs: jnp.DeviceArray # int32: [batch_size, beam_size, + live_seqs: jax.Array # int32: [batch_size, beam_size, max_decode_len] + finished_seqs: jax.Array # int32: [batch_size, beam_size, # max_decode_len] # Records which of the 'finished_seqs' is occupied and not a filler slot. - finished_flags: jnp.DeviceArray # bool: [batch_size, beam_size] + finished_flags: jax.Array # bool: [batch_size, beam_size] # The current state of the autoregressive decoding caches. cache: typing.Any # Any pytree of arrays, e.g. flax attention Cache object diff --git a/algorithmic_efficiency/workloads/wmt/wmt_jax/models.py b/algorithmic_efficiency/workloads/wmt/wmt_jax/models.py index 882ad56da..6265e2c75 100644 --- a/algorithmic_efficiency/workloads/wmt/wmt_jax/models.py +++ b/algorithmic_efficiency/workloads/wmt/wmt_jax/models.py @@ -1,4 +1,7 @@ -"""Transformer-based machine translation model.""" +"""Transformer-based machine translation model. + +Reference https://github.com/google/flax/tree/main/examples/wmt. +""" from typing import Any, Callable, Optional @@ -349,7 +352,7 @@ def __call__(self, inputs, inputs_positions=None, encoder_mask=None): x = Encoder1DBlock( config=cfg, name=f'encoderblock_{lyr}')(x, encoder_mask) - encoded = nn.LayerNorm(dtype=cfg.dtype, name='encoder_norm')(x) + encoded = nn.LayerNorm(dtype=cfg.dtype, name='encoder_layernorm')(x) return encoded @@ -420,7 +423,7 @@ def __call__(self, encoded, decoder_mask=decoder_mask, encoder_decoder_mask=encoder_decoder_mask) - y = nn.LayerNorm(dtype=cfg.dtype, name='encoderdecoder_norm')(y) + y = nn.LayerNorm(dtype=cfg.dtype, name='encoderdecoder_layernorm')(y) # Use the transpose of embedding matrix for logit transform. logits = output_embed.attend(y.astype(jnp.float32)) diff --git a/algorithmic_efficiency/workloads/wmt/wmt_pytorch/models.py b/algorithmic_efficiency/workloads/wmt/wmt_pytorch/models.py index cce99740b..9fbc48578 100644 --- a/algorithmic_efficiency/workloads/wmt/wmt_pytorch/models.py +++ b/algorithmic_efficiency/workloads/wmt/wmt_pytorch/models.py @@ -1,3 +1,4 @@ +import copy import math from typing import Any, Callable, Dict, Optional, Tuple, Union import warnings @@ -5,15 +6,13 @@ import torch from torch import nn from torch import Tensor -from torch.nn.functional import _in_projection -from torch.nn.functional import _scaled_dot_product_attention import torch.nn.functional as F from torch.nn.init import normal_ from torch.nn.init import xavier_uniform_ # Mask making utilities ported to PyTorch from -# https://github.com/google/flax/blob/main/flax/linen/attention.py +# https://github.com/google/flax/blob/main/flax/linen/attention.py. def make_attention_mask(query_input: Tensor, key_input: Tensor, pairwise_fn: Callable[..., Any] = torch.mul, @@ -24,8 +23,6 @@ def make_attention_mask(query_input: Tensor, query_input: a batched, flat input of query_length size key_input: a batched, flat input of key_length size pairwise_fn: broadcasting elementwise comparison function - extra_batch_dims: number of extra batch dims to add singleton - axes for, none by default dtype: mask return dtype Returns: @@ -42,6 +39,7 @@ def make_causal_mask(x: Tensor, Args: x: input array of shape `[batch..., len]` + device: device to store the idxs dtype: mask return dtype Returns: @@ -53,7 +51,7 @@ def make_causal_mask(x: Tensor, def make_src_mask(src, inputs_segmentation, nhead): - '''Utility for creating src mask and adjust it for PyTorch Transformer API.''' + """Utility for creating src mask and adjust it for PyTorch Transformer API.""" src_mask = make_attention_mask(src > 0, src > 0) # Add segmentation block-diagonal attention mask if using segmented data. if inputs_segmentation is not None: @@ -74,10 +72,8 @@ def make_tgt_and_memory_mask(tgt, targets_segmentation, decode, nhead): - ''' - Utility for creating target and memory mask and adjust them for PyTorch - Transformer API. - ''' + """ Utility for creating target and memory mask and adjust them for PyTorch + Transformer API.""" if not decode: tgt_mask = torch.logical_and( make_attention_mask(tgt > 0, tgt > 0), @@ -121,9 +117,7 @@ def shift_right(x, axis=1): class Transformer(nn.Module): - ''' - Transformer architecture based on the model from the WMT Jax workload. - ''' + """Transformer architecture based on the model from the WMT Jax workload.""" def __init__(self, ntoken: int = 32000, @@ -209,6 +203,80 @@ def forward(self, return output +class TransformerEncoder(nn.Module): + r"""TransformerEncoder is a stack of N encoder layers. Users can build the + BERT(https://arxiv.org/abs/1810.04805) model with corresponding parameters. + + Args: + encoder_layer: an instance of the TransformerEncoderLayer() class. + num_layers: the number of sub-encoder-layers in the encoder. + norm: the layer normalization component (optional). + enable_nested_tensor: if True, input will automatically convert to + nested tensor (and convert back on output). This will improve + the overall performance of TransformerEncoder when padding + rate is high. + + Examples:: + >>> encoder_layer = nn.TransformerEncoderLayer(12, 8) + >>> transformer_encoder = nn.TransformerEncoder(encoder_layer, 6) + >>> src = torch.rand(10, 32, 512) + >>> out = transformer_encoder(src) + """ + __constants__ = ['norm'] + + def __init__(self, + encoder_layer, + num_layers, + norm=None, + enable_nested_tensor=True, + mask_check=True): + super().__init__() + self.layers = nn.ModuleList( + [copy.deepcopy(encoder_layer) for _ in range(num_layers)]) + self.num_layers = num_layers + self.norm = norm + self.enable_nested_tensor = enable_nested_tensor + self.mask_check = mask_check + + def forward(self, + src: Tensor, + mask: Optional[Tensor] = None, + src_key_padding_mask: Optional[Tensor] = None) -> Tensor: + """Pass the input through the encoder layers in turn. + + Args: + src: the sequence to the encoder (required). + mask: the mask for the src sequence (optional). + src_key_padding_mask: the mask for the src keys per batch (optional). + + Shape: + see the docs in Transformer class. + """ + if src_key_padding_mask is not None: + _skpm_dtype = src_key_padding_mask.dtype # pylint: disable=invalid-name + if _skpm_dtype != torch.bool and not torch.is_floating_point( + src_key_padding_mask): + raise AssertionError( + 'only bool and floating types of key_padding_mask are supported') + output = src + convert_to_nested = False + src_key_padding_mask_for_layers = src_key_padding_mask + + for mod in self.layers: + output = mod( + output, + src_mask=mask, + src_key_padding_mask=src_key_padding_mask_for_layers) + + if convert_to_nested: + output = output.to_padded_tensor(0.) + + if self.norm is not None: + output = self.norm(output) + + return output + + class Encoder(nn.Module): def __init__(self, @@ -231,7 +299,7 @@ def __init__(self, attention_dropout_rate=attention_dropout_rate, layer_norm_eps=layer_norm_eps) encoder_norm = nn.LayerNorm(d_model, eps=layer_norm_eps) - self.encoder = nn.TransformerEncoder(encoder_layer, nlayers, encoder_norm) + self.encoder = TransformerEncoder(encoder_layer, nlayers, encoder_norm) def forward(self, src: Tensor, @@ -277,7 +345,7 @@ def forward( targets_segmentation: Optional[Tensor] = None, decode: bool = False, max_len: Optional[int] = None, - cache: Optional[dict] = None) -> Tensor: + cache: Optional[dict] = None) -> Any: tgt = tgt.to(torch.int) tgt_mask, memory_mask = make_tgt_and_memory_mask( tgt, src, inputs_segmentation, targets_segmentation, @@ -364,7 +432,7 @@ def forward( # https://github.com/pytorch/pytorch/blob/master/torch/nn/modules/transformer.py # Only difference is using custom MultiheadAttention modules without bias and # '_qkv_same_embed_dim' always set to 'False'. -class TransformerEncoderLayer(nn.TransformerEncoderLayer): +class TransformerEncoderLayer(nn.Module): r"""TransformerEncoderLayer is made up of self-attn and feedforward network. This standard encoder layer is based on the paper "Attention Is All You Need". Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, @@ -412,17 +480,7 @@ def __init__(self, device=None, dtype=None) -> None: factory_kwargs = {'device': device, 'dtype': dtype} - super().__init__( - d_model, - nhead, - dim_feedforward=dim_feedforward, - dropout=dropout_rate, - activation=activation, - layer_norm_eps=layer_norm_eps, - batch_first=batch_first, - norm_first=norm_first, - device=device, - dtype=dtype) + super().__init__() self.self_attn = MultiheadAttention( d_model, nhead, @@ -431,23 +489,93 @@ def __init__(self, bias=False, **factory_kwargs) + # Implementation of Feedforward model. + self.linear1 = nn.Linear(d_model, dim_feedforward, **factory_kwargs) + self.dropout = nn.Dropout(dropout_rate) + self.linear2 = nn.Linear(dim_feedforward, d_model, **factory_kwargs) + + self.norm_first = norm_first + self.norm1 = nn.LayerNorm(d_model, eps=layer_norm_eps, **factory_kwargs) + self.norm2 = nn.LayerNorm(d_model, eps=layer_norm_eps, **factory_kwargs) + self.dropout1 = nn.Dropout(dropout_rate) + self.dropout2 = nn.Dropout(dropout_rate) + + # We can't test self.activation in forward() in TorchScript, + # so stash some information about it instead. + if activation is F.relu or isinstance(activation, torch.nn.ReLU): + self.activation_relu_or_gelu = 1 + elif activation is F.gelu or isinstance(activation, torch.nn.GELU): + self.activation_relu_or_gelu = 2 + else: + self.activation_relu_or_gelu = 0 + self.activation = activation + + def forward(self, + src: Tensor, + src_mask: Optional[Tensor] = None, + src_key_padding_mask: Optional[Tensor] = None) -> Tensor: + r"""Pass the input through the encoder layer. + + Args: + src: the sequence to the encoder layer (required). + src_mask: the mask for the src sequence (optional). + src_key_padding_mask: the mask for the src keys per batch (optional). + + Shape: + see the docs in Transformer class. + """ + if src_key_padding_mask is not None: + _skpm_dtype = src_key_padding_mask.dtype # pylint: disable=invalid-name + if _skpm_dtype != torch.bool and not torch.is_floating_point( + src_key_padding_mask): + raise AssertionError( + 'Only bool and floating types of key_padding_mask are supported') + x = src + if self.norm_first: + x = x + self._sa_block(self.norm1(x), src_mask, src_key_padding_mask) + x = x + self._ff_block(self.norm2(x)) + else: + x = self.norm1(x + self._sa_block(x, src_mask, src_key_padding_mask)) + x = self.norm2(x + self._ff_block(x)) + + return x + + # Self-attention block: + def _sa_block(self, + x: Tensor, + attn_mask: Optional[Tensor], + key_padding_mask: Optional[Tensor]) -> Tensor: + x = self.self_attn( + x, + x, + x, + attn_mask=attn_mask, + key_padding_mask=key_padding_mask, + need_weights=False)[0] + return self.dropout1(x) + + # Feed forward block: + def _ff_block(self, x: Tensor) -> Tensor: + x = self.linear2(self.dropout(self.activation(self.linear1(x)))) + return self.dropout2(x) + # Modified to use cache for autoregressive decoding. class TransformerDecoder(nn.Module): r"""TransformerDecoder is a stack of N decoder layers Args: - d_model: the number of expected features in the input (default=1024). - nhead: the number of heads in the multiheadattention models (default=16). + d_model: the number of expected features in the input (default=1024) + nhead: the number of heads in the multiheadattention models (default=16) d_hid: the dimension of the feedforward network model - (default=1024). - dropout_rate: the dropout_rate value (default=0.1). + (default=1024) + dropout_rate: the dropout_rate value (default=0.1) layer_norm_eps: the eps value in layer normalization components (default=1e-6). - decoder_layer: an instance of the TransformerDecoderLayer() class (required) - num_layers: the number of sub-decoder-layers in the decoder (required). + decoder_layer: an instance of the TransformerDecoderLayer() class + num_layers: the number of sub-decoder-layers in the decoder Examples:: - >>> decoder_layer = nn.TransformerDecoderLayer(d_model=512, nhead=8) - >>> transformer_decoder = nn.TransformerDecoder(decoder_layer, num_layers=6) + >>> decoder_layer = nn.TransformerDecoderLayer(12, 8) + >>> transformer_decoder = nn.TransformerDecoder(decoder_layer, 6) >>> memory = torch.rand(10, 32, 512) >>> tgt = torch.rand(20, 32, 512) >>> out = transformer_decoder(tgt, memory) @@ -482,7 +610,7 @@ def forward(self, memory_mask: Optional[Tensor] = None, decode: bool = False, max_len: Optional[int] = None, - cache: Optional[dict] = None) -> Tensor: + cache: Optional[dict] = None) -> Any: r"""Pass the inputs (and mask) through the decoder layer in turn. Args: tgt: the sequence to the decoder (required). @@ -516,7 +644,7 @@ def forward(self, # Modified to use cache for autoregressive decoding. -class TransformerDecoderLayer(nn.TransformerDecoderLayer): +class TransformerDecoderLayer(nn.Module): r"""TransformerDecoderLayer is made up of self-attn, multi-head-attn and feedforward network. This standard decoder layer is based on the paper "Attention Is All You Need". @@ -567,17 +695,7 @@ def __init__(self, device=None, dtype=None) -> None: factory_kwargs = {'device': device, 'dtype': dtype} - super().__init__( - d_model, - nhead, - dim_feedforward=dim_feedforward, - dropout=dropout_rate, - activation=activation, - layer_norm_eps=layer_norm_eps, - batch_first=batch_first, - norm_first=norm_first, - device=device, - dtype=dtype) + super().__init__() self.self_attn = MultiheadAttention( d_model, nhead, @@ -593,6 +711,21 @@ def __init__(self, bias=False, **factory_kwargs) + # Implementation of Feedforward model. + self.linear1 = nn.Linear(d_model, dim_feedforward, **factory_kwargs) + self.dropout = nn.Dropout(dropout_rate) + self.linear2 = nn.Linear(dim_feedforward, d_model, **factory_kwargs) + + self.norm_first = norm_first + self.norm1 = nn.LayerNorm(d_model, eps=layer_norm_eps, **factory_kwargs) + self.norm2 = nn.LayerNorm(d_model, eps=layer_norm_eps, **factory_kwargs) + self.norm3 = nn.LayerNorm(d_model, eps=layer_norm_eps, **factory_kwargs) + self.dropout1 = nn.Dropout(dropout_rate) + self.dropout2 = nn.Dropout(dropout_rate) + self.dropout3 = nn.Dropout(dropout_rate) + + self.activation = activation + def forward( # pylint: disable=arguments-renamed self, tgt: Tensor, @@ -602,7 +735,7 @@ def forward( # pylint: disable=arguments-renamed decode: bool = False, max_len: Optional[int] = None, cache: Optional[dict] = None, - index: Optional[int] = None) -> Tensor: + index: Optional[int] = None) -> Any: r"""Pass the inputs (and mask) through the decoder layer. Args: tgt: the sequence to the decoder layer (required). @@ -642,7 +775,7 @@ def forward( # pylint: disable=arguments-renamed return x, cache - # self-attention block + # Self-attention block: def _sa_block( # pylint: disable=arguments-renamed self, x: Tensor, @@ -650,7 +783,7 @@ def _sa_block( # pylint: disable=arguments-renamed decode: bool = False, max_len: Optional[int] = None, cache: Optional[dict] = None, - index: Optional[int] = None) -> Tensor: + index: Optional[int] = None) -> Any: x, _, cache = self.self_attn( x, x, @@ -663,6 +796,26 @@ def _sa_block( # pylint: disable=arguments-renamed index=index) return self.dropout1(x), cache + # Multihead attention block: + def _mha_block(self, + x: Tensor, + mem: Tensor, + attn_mask: Optional[Tensor], + key_padding_mask: Optional[Tensor]) -> Tensor: + x = self.multihead_attn( + x, + mem, + mem, + attn_mask=attn_mask, + key_padding_mask=key_padding_mask, + need_weights=False)[0] + return self.dropout2(x) + + # Feed forward block. + def _ff_block(self, x: Tensor) -> Tensor: + x = self.linear2(self.dropout(self.activation(self.linear1(x)))) + return self.dropout3(x) + # Only difference to standard PyTorch class is that 'self._qkv_same_embed_dim' # is always set to 'False' and the use of a cache registered as a buffer for @@ -764,7 +917,7 @@ def forward(self, decode: bool = False, max_len: Optional[int] = None, cache: Optional[dict] = None, - index: Optional[int] = None) -> Tuple[Tensor, Optional[Tensor]]: + index: Optional[int] = None) -> Any: r""" Args: query: Query embeddings of shape :math:`(L, E_q)` for unbatched input, @@ -862,29 +1015,62 @@ def forward(self, return attn_output, attn_output_weights, cache +def _in_projection( + q: Tensor, + k: Tensor, + v: Tensor, + w_q: Tensor, + w_k: Tensor, + w_v: Tensor, + b_q: Optional[Tensor] = None, + b_k: Optional[Tensor] = None, + b_v: Optional[Tensor] = None, +) -> Tuple[Tensor, Tensor, Tensor]: + r"""Performs the in-projection step of the attention operation. This is simply + a triple of linear projections, with shape constraints on the weights which + ensure embedding dimension uniformity in the projected outputs. + Output is a triple containing projection tensors for query, key and value. + """ + eq, ek = q.size(-1), k.size(-1) + assert w_q.shape == (eq, eq), \ + f'Expecting query weights shape of {(eq, eq)}, but got {w_q.shape}' + assert w_k.shape == (eq, ek), \ + f'Expecting key weights shape of {(eq, ek)}, but got {w_k.shape}' + assert w_v.shape == (eq, ek), \ + f'Expecting value weights shape of {(eq, ek)}, but got {w_v.shape}' + assert b_q is None or b_q.shape == (eq,), \ + f'Expecting query bias shape of {(eq,)}, but got {b_q.shape}' + assert b_k is None or b_k.shape == (eq,), \ + f'Expecting key bias shape of {(eq,)}, but got {b_k.shape}' + assert b_v is None or b_v.shape == (eq,), \ + f'Expecting value bias shape of {(eq,)}, but got {b_v.shape}' + return torch.nn.functional.linear(q, w_q, b_q), \ + torch.nn.functional.linear(k, w_k, b_k), \ + torch.nn.functional.linear(v, w_v, b_v) + + # Modified to create cache for autoregressive decoding. -def multi_head_attention_forward( - query: Tensor, - key: Tensor, - value: Tensor, - embed_dim_to_check: int, - num_heads: int, - in_proj_bias: Optional[Tensor], - bias_k: Optional[Tensor], - bias_v: Optional[Tensor], - dropout_rate: float, - out_proj_weight: Tensor, - out_proj_bias: Optional[Tensor], - training: bool = True, - need_weights: bool = True, - attn_mask: Optional[Tensor] = None, - q_proj_weight: Optional[Tensor] = None, - k_proj_weight: Optional[Tensor] = None, - v_proj_weight: Optional[Tensor] = None, - average_attn_weights: bool = True, - decode: bool = False, - cache: Optional[dict] = None, - max_len: Optional[int] = None) -> Tuple[Tensor, Optional[Tensor]]: +def multi_head_attention_forward(query: Tensor, + key: Tensor, + value: Tensor, + embed_dim_to_check: int, + num_heads: int, + in_proj_bias: Optional[Tensor], + bias_k: Optional[Tensor], + bias_v: Optional[Tensor], + dropout_rate: float, + out_proj_weight: Tensor, + out_proj_bias: Optional[Tensor], + training: bool = True, + need_weights: bool = True, + attn_mask: Optional[Tensor] = None, + q_proj_weight: Optional[Tensor] = None, + k_proj_weight: Optional[Tensor] = None, + v_proj_weight: Optional[Tensor] = None, + average_attn_weights: bool = True, + decode: bool = False, + cache: Optional[dict] = None, + max_len: Optional[int] = None) -> Any: r""" Args: query, key, value: map a query and a set of key-value pairs to an output. @@ -941,31 +1127,31 @@ def multi_head_attention_forward( per head of shape :math:`(num_heads, L, S)` when input is unbatched or :math:`(N, num_heads, L, S)`. """ - # set up shape vars + # Set up shape variables. tgt_len, bsz, embed_dim = query.shape src_len, _, _ = key.shape assert embed_dim == embed_dim_to_check, \ - f"was expecting dimension of {embed_dim_to_check}, but got {embed_dim}" + f'was expecting dimension of {embed_dim_to_check}, but got {embed_dim}' if isinstance(embed_dim, torch.Tensor): - # embed_dim can be a tensor when JIT tracing + # `embed_dim` can be a tensor when JIT tracing. head_dim = embed_dim.div(num_heads, rounding_mode='trunc') else: head_dim = embed_dim // num_heads assert head_dim * num_heads == embed_dim, \ - f"embed_dim {embed_dim} not divisible by num_heads {num_heads}" - # allow MHA to have different embedding dimensions when separate projection - # weights are used + f'embed_dim {embed_dim} not divisible by num_heads {num_heads}' + # Allow MHA to have different embedding dimensions when separate projection + # weights are used. assert key.shape[:2] == value.shape[:2], \ (f"key's sequence and batch dims {key.shape[:2]} do not match value's " - f"{value.shape[:2]}") + f'{value.shape[:2]}') - # compute in-projection + # Compute in-projection. assert q_proj_weight is not None, \ - "use_separate_proj_weight is True but q_proj_weight is None" + 'use_separate_proj_weight is True but q_proj_weight is None' assert k_proj_weight is not None, \ - "use_separate_proj_weight is True but k_proj_weight is None" + 'use_separate_proj_weight is True but k_proj_weight is None' assert v_proj_weight is not None, \ - "use_separate_proj_weight is True but v_proj_weight is None" + 'use_separate_proj_weight is True but v_proj_weight is None' if in_proj_bias is None: b_q = b_k = b_v = None else: @@ -997,19 +1183,19 @@ def multi_head_attention_forward( assert batch_size == bsz, f'{batch_size} != {bsz}' assert max_length == max_len, f'{max_length} != {max_len}' assert num_features == embed_dim, f'{num_features} != {embed_dim}' - # shape check of cached keys against query input + # Shape check of cached keys against query input. expected_shape = (1, batch_size, num_features) if expected_shape != query.shape: raise ValueError('Autoregressive cache shape error, expected query shape ' f'{expected_shape} instead got {query.shape}.') - # update key, value caches with our new 1d spatial slices + # Update key, value caches with our new 1d spatial slices. cached_key[:, cache_index:cache_index + 1, :] = k.transpose(dim0=0, dim1=1) cached_value[:, cache_index:cache_index + 1, :] = v.transpose( dim0=0, dim1=1) k = cached_key.transpose(dim0=0, dim1=1) v = cached_value.transpose(dim0=0, dim1=1) cache_index += 1 - # causal mask for cached decoder self-attention: + # Causal mask for cached decoder self-attention: # our single query position should only attend to those key # positions that have already been generated and cached, # not the remaining zero elements. @@ -1018,34 +1204,34 @@ def multi_head_attention_forward( attn_mask = (torch.arange(max_length, device=k.device) >= cache_index).reshape(1, max_length) - # prep attention mask + # Prepare attention mask. if not decode and attn_mask is not None: if attn_mask.dtype == torch.uint8: warnings.warn( - "Byte tensor for attn_mask in nn.MultiheadAttention is deprecated." - "Use bool tensor instead.") + 'Byte tensor for attn_mask in nn.MultiheadAttention is deprecated.' + 'Use bool tensor instead.') attn_mask = attn_mask.to(torch.bool) else: assert attn_mask.is_floating_point() or attn_mask.dtype == torch.bool, \ - f"float, byte, and bool types are supported, not {attn_mask.dtype}" + f'float, byte, and bool types are supported, not {attn_mask.dtype}' # ensure attn_mask's dim is 3 if attn_mask.dim() == 2: correct_2d_size = (tgt_len, src_len) if attn_mask.shape != correct_2d_size: raise RuntimeError( - f"The shape of the 2D attn_mask is {attn_mask.shape}, " - f"but should be {correct_2d_size}.") + f'The shape of the 2D attn_mask is {attn_mask.shape}, ' + f'but should be {correct_2d_size}.') attn_mask = attn_mask.unsqueeze(0) elif attn_mask.dim() == 3: correct_3d_size = (bsz * num_heads, tgt_len, src_len) if attn_mask.shape != correct_3d_size: - raise RuntimeError(f"The shape of attn_mask is {attn_mask.shape}, " - f"should be {correct_3d_size}.") + raise RuntimeError(f'The shape of attn_mask is {attn_mask.shape}, ' + f'should be {correct_3d_size}.') else: raise RuntimeError( f"attn_mask's dimension {attn_mask.dim()} is not supported") - # add bias along batch dimension (currently second) + # Add bias along batch dimension (currently second). if bias_k is not None and bias_v is not None: k = torch.cat([k, bias_k.repeat(1, bsz, 1)]) v = torch.cat([v, bias_v.repeat(1, bsz, 1)]) @@ -1055,30 +1241,29 @@ def multi_head_attention_forward( assert bias_k is None assert bias_v is None - # - # reshape q, k, v for multihead attention and make em batch first - # - q = q.contiguous().view(tgt_len, bsz * num_heads, head_dim).transpose(0, 1) - k = k.contiguous().view(k.shape[0], bsz * num_heads, head_dim).transpose(0, 1) - v = v.contiguous().view(v.shape[0], bsz * num_heads, head_dim).transpose(0, 1) + # Reshape q, k, v for multihead attention and make em batch first. + q = \ + q.contiguous().view(tgt_len, bsz * num_heads, head_dim).transpose(0, 1) + k = \ + k.contiguous().view(k.shape[0], bsz * num_heads, head_dim).transpose(0, 1) + v = \ + v.contiguous().view(v.shape[0], bsz * num_heads, head_dim).transpose(0, 1) - # update source sequence length after adjustments + # Update source sequence length after adjustments. src_len = k.size(1) - # convert mask to float + # Convert mask to float. if attn_mask is not None and attn_mask.dtype == torch.bool: new_attn_mask = torch.zeros_like(attn_mask, dtype=q.dtype) new_attn_mask.masked_fill_(attn_mask, -1e10) attn_mask = new_attn_mask - # adjust dropout_rate probability + # Adjust dropout_rate probability. if not training: dropout_rate = 0.0 - # - # (deep breath) calculate attention and out projection - # - attn_output, attn_output_weights = _scaled_dot_product_attention( + # Calculate attention and out projection. + attn_output = torch.nn.functional.scaled_dot_product_attention( q, k, v, attn_mask, dropout_rate) attn_output = attn_output.transpose(0, 1).contiguous().view( tgt_len * bsz, embed_dim) @@ -1086,7 +1271,16 @@ def multi_head_attention_forward( attn_output = attn_output.view(tgt_len, bsz, attn_output.size(1)) if need_weights: - # optionally average attention weights over heads + q_scaled = q / math.sqrt(q.shape[-1]) + + if attn_mask is not None: + attn_output_weights = torch.baddbmm(attn_mask, + q_scaled, + k.transpose(-2, -1)) + else: + attn_output_weights = torch.bmm(q_scaled, k.transpose(-2, -1)) + + # Optionally average attention weights over heads. attn_output_weights = attn_output_weights.view(bsz, num_heads, tgt_len, diff --git a/algorithmic_efficiency/workloads/wmt/wmt_pytorch/workload.py b/algorithmic_efficiency/workloads/wmt/wmt_pytorch/workload.py index 9bbb7031c..f1131fc4e 100644 --- a/algorithmic_efficiency/workloads/wmt/wmt_pytorch/workload.py +++ b/algorithmic_efficiency/workloads/wmt/wmt_pytorch/workload.py @@ -1,4 +1,5 @@ """WMT workload implemented in PyTorch.""" + import contextlib from typing import Any, Dict, Optional, Tuple diff --git a/algorithmic_efficiency/workloads/wmt/workload.py b/algorithmic_efficiency/workloads/wmt/workload.py index 70c0f3038..3003ab097 100644 --- a/algorithmic_efficiency/workloads/wmt/workload.py +++ b/algorithmic_efficiency/workloads/wmt/workload.py @@ -27,6 +27,11 @@ def __init__(self) -> None: super().__init__() self._tokenizer = None + @property + def target_metric_name(self) -> str: + """The name of the target metric (useful for scoring/processing code).""" + return 'bleu' + def has_reached_validation_target(self, eval_result: float) -> bool: return eval_result['validation/bleu'] > self.validation_target_value diff --git a/algorithmic_efficiency/workloads/workloads.py b/algorithmic_efficiency/workloads/workloads.py new file mode 100644 index 000000000..3178e054c --- /dev/null +++ b/algorithmic_efficiency/workloads/workloads.py @@ -0,0 +1,102 @@ +""" Registry of workload info +""" +import importlib +import inspect +import os + +from algorithmic_efficiency import spec + +BASE_WORKLOADS_DIR = 'algorithmic_efficiency/workloads/' + +WORKLOADS = { + 'cifar': { + 'workload_path': 'cifar/cifar', 'workload_class_name': 'CifarWorkload' + }, + 'criteo1tb': { + 'workload_path': 'criteo1tb/criteo1tb', + 'workload_class_name': 'Criteo1TbDlrmSmallWorkload', + }, + 'criteo1tb_test': { + 'workload_path': 'criteo1tb/criteo1tb', + 'workload_class_name': 'Criteo1TbDlrmSmallTestWorkload', + }, + 'fastmri': { + 'workload_path': 'fastmri/fastmri', + 'workload_class_name': 'FastMRIWorkload', + }, + 'imagenet_resnet': { + 'workload_path': 'imagenet_resnet/imagenet', + 'workload_class_name': 'ImagenetResNetWorkload', + }, + 'imagenet_vit': { + 'workload_path': 'imagenet_vit/imagenet', + 'workload_class_name': 'ImagenetVitWorkload', + }, + 'librispeech_conformer': { + 'workload_path': 'librispeech_conformer/librispeech', + 'workload_class_name': 'LibriSpeechConformerWorkload', + }, + 'librispeech_deepspeech': { + 'workload_path': 'librispeech_deepspeech/librispeech', + 'workload_class_name': 'LibriSpeechDeepSpeechWorkload', + }, + 'mnist': { + 'workload_path': 'mnist/mnist', 'workload_class_name': 'MnistWorkload' + }, + 'ogbg': { + 'workload_path': 'ogbg/ogbg', 'workload_class_name': 'OgbgWorkload' + }, + 'wmt': {'workload_path': 'wmt/wmt', 'workload_class_name': 'WmtWorkload'}, +} + + +def convert_filepath_to_module(path: str): + base, extension = os.path.splitext(path) + + if extension != '.py': + raise ValueError(f'Path: {path} must be a python file (*.py)') + + return base.replace('/', '.') + + +def import_workload(workload_path: str, + workload_class_name: str, + return_class=False, + workload_init_kwargs=None) -> spec.Workload: + """Import and add the workload to the registry. + + This importlib loading is nice to have because it allows runners to avoid + installing the dependencies of all the supported frameworks. For example, if + a submitter only wants to write Jax code, the try/except below will catch + the import errors caused if they do not have the PyTorch dependencies + installed on their system. + + Args: + workload_path: the path to the `workload.py` file to load. + workload_class_name: the name of the Workload class that implements the + `Workload` abstract class in `spec.py`. + return_class: if true, then the workload class is returned instead of the + instantiated object. Useful for testing when methods need to be overriden. + workload_init_kwargs: kwargs to pass to the workload constructor. + """ + + # Remove the trailing '.py' and convert the filepath to a Python module. + workload_path = convert_filepath_to_module(workload_path) + + # Import the workload module. + workload_module = importlib.import_module(workload_path) + # Get everything defined in the workload module (including our class). + workload_module_members = inspect.getmembers(workload_module) + workload_class = None + for name, value in workload_module_members: + if name == workload_class_name: + workload_class = value + break + if workload_class is None: + raise ValueError( + f'Could not find member {workload_class_name} in {workload_path}. ' + 'Make sure the Workload class is spelled correctly and defined in ' + 'the top scope of the module.') + if return_class: + return workload_class + return workload_class(**workload_init_kwargs) diff --git a/baselines/shampoo/jax/distributed_shampoo.py b/baselines/shampoo/jax/distributed_shampoo.py index e40576337..725529cae 100644 --- a/baselines/shampoo/jax/distributed_shampoo.py +++ b/baselines/shampoo/jax/distributed_shampoo.py @@ -187,10 +187,10 @@ def wrap_f(x, *args, **kwargs): class InversePthRootDiagnostics: """Diagnostics for inverse p-th root iterative procedure. - Given an inverse pth root B = A^(-1/p), contains the average and - maximum diagonal and off diagonal absolute entrywise errors between - (B^p A) and I. - """ + Given an inverse pth root B = A^(-1/p), contains the average and + maximum diagonal and off diagonal absolute entrywise errors between + (B^p A) and I. + """ max_diag_error: chex.Array = _default_zero_field() avg_diag_error: chex.Array = _default_zero_field() max_off_diag_error: chex.Array = _default_zero_field() @@ -225,11 +225,11 @@ def create(cls, pth_inverse_root, matrix, p): class LOBPCGDiagnostics: """Diagnostics for iterative LOBPCG eigenvalue routine. - Contains consistency error for LOBPCG eigenvalue routine, which - refers to |A v - lambda v| / (lambda + |A v|) for a proposed eigenpair - (v, lambda). This metics dataclass retains consistency error - and other useful LOBPCG values. - """ + Contains consistency error for LOBPCG eigenvalue routine, which + refers to |A v - lambda v| / (lambda + |A v|) for a proposed eigenpair + (v, lambda). This metics dataclass retains consistency error + and other useful LOBPCG values. + """ lobpcg_iters: chex.Array = _default_zero_field() max_consistency_error: chex.Array = _default_zero_field() avg_consistency_error: chex.Array = _default_zero_field() @@ -414,27 +414,27 @@ def power_iteration( ): r"""Power iteration algorithm. - The power iteration algorithm takes a symmetric PSD matrix `A`, and produces - a scalar `\lambda` , which is the greatest (in absolute value) eigenvalue - of `A`, and a vector v, which is the corresponding eigenvector of `A`. - - References: - [Wikipedia, 2021](https://en.wikipedia.org/wiki/Power_iteration) - - Args: - matrix: the symmetric PSD matrix. - num_iters: Number of iterations. - error_tolerance: Iterative exit condition. - precision: precision XLA related flag, the available options are: a) - lax.Precision.DEFAULT (better step time, but not precise) b) - lax.Precision.HIGH (increased precision, slower) c) lax.Precision.HIGHEST - (best possible precision, slowest) - padding_start: if set, assumes rows and columns after padding_start are - zero. - - Returns: - eigen vector, eigen value - """ + The power iteration algorithm takes a symmetric PSD matrix `A`, and produces + a scalar `\lambda` , which is the greatest (in absolute value) eigenvalue + of `A`, and a vector v, which is the corresponding eigenvector of `A`. + + References: + [Wikipedia, 2021](https://en.wikipedia.org/wiki/Power_iteration) + + Args: + matrix: the symmetric PSD matrix. + num_iters: Number of iterations. + error_tolerance: Iterative exit condition. + precision: precision XLA related flag, the available options are: a) + lax.Precision.DEFAULT (better step time, but not precise) b) + lax.Precision.HIGH (increased precision, slower) c) + lax.Precision.HIGHEST (best possible precision, slowest) + padding_start: if set, assumes rows and columns after padding_start are + zero. + + Returns: + eigen vector, eigen value + """ matrix_size = matrix.shape[-1] def _iter_condition(state): @@ -530,46 +530,46 @@ def matrix_inverse_pth_root( ): """Computes `matrix^(-1/p)`, where `p` is a positive integer. - This function uses the Eigh or Coupled newton iterations algorithm for - the computation of a matrix's inverse pth root. - - - References: - [Functions of Matrices, Theory and Computation, - Nicholas J Higham, Pg 184, Eq 7.18]( - https://epubs.siam.org/doi/book/10.1137/1.9780898717778) - - Args: - matrix: the symmetric PSD matrix whose power it to be computed - p: exponent, for p a positive integer. - num_iters: Maximum number of iterations. - ridge_epsilon: Ridge epsilon added to make the matrix positive definite. - error_tolerance: Error indicator, useful for early termination. - precision: precision XLA related flag, the available options are: a) - lax.Precision.DEFAULT (better step time, but not precise) b) - lax.Precision.HIGH (increased precision, slower) c) lax.Precision.HIGHEST - (best possible precision, slowest) - relative_matrix_epsilon: Whether to use relative epsilon to the max eigen - value when computing inverse-pth root. - lobpcg_topk_precondition: If nonzero, specifies the number of top - eigenvectors to subtract out before performing LOBPCG. Note this makes - relative_matrix_epsilon essentially free. - lobpcg_max_iter: Maximum iteration count for LOBPCG, defaults to - `lobpcg_topk_precondition`. - padding_start: If the input matrix was padded, then zeros out columns and - rows at the padding start. - prev: previous iteration's solution, zero-padded (unused) - eigh: If True, uses eigh for inverse-pth root computation. - - Returns: - `(matrix + eps)^(-1/p)` and error metrics. - - Note `eps` is not added to zeroed out padding rows and - columns. `eps` is just `ridge_epsilon` if - `relative_matrix_epsilon` is set to `False`, otherwise, it is the - ridge epsilon value scaled by the derived maximum eigenvalue of - the input matrix. - """ + This function uses the Eigh or Coupled newton iterations algorithm for + the computation of a matrix's inverse pth root. + + + References: + [Functions of Matrices, Theory and Computation, + Nicholas J Higham, Pg 184, Eq 7.18]( + https://epubs.siam.org/doi/book/10.1137/1.9780898717778) + + Args: + matrix: the symmetric PSD matrix whose power it to be computed + p: exponent, for p a positive integer. + num_iters: Maximum number of iterations. + ridge_epsilon: Ridge epsilon added to make the matrix positive definite. + error_tolerance: Error indicator, useful for early termination. + precision: precision XLA related flag, the available options are: a) + lax.Precision.DEFAULT (better step time, but not precise) b) + lax.Precision.HIGH (increased precision, slower) c) + lax.Precision.HIGHEST (best possible precision, slowest) + relative_matrix_epsilon: Whether to use relative epsilon to the max eigen + value when computing inverse-pth root. + lobpcg_topk_precondition: If nonzero, specifies the number of top + eigenvectors to subtract out before performing LOBPCG. Note this makes + relative_matrix_epsilon essentially free. + lobpcg_max_iter: Maximum iteration count for LOBPCG, defaults to + `lobpcg_topk_precondition`. + padding_start: If the input matrix was padded, then zeros out columns and + rows at the padding start. + prev: previous iteration's solution, zero-padded (unused) + eigh: If True, uses eigh for inverse-pth root computation. + + Returns: + `(matrix + eps)^(-1/p)` and error metrics. + + Note `eps` is not added to zeroed out padding rows and + columns. `eps` is just `ridge_epsilon` if + `relative_matrix_epsilon` is set to `False`, otherwise, it is the + ridge epsilon value scaled by the derived maximum eigenvalue of + the input matrix. + """ if eigh: return matrix_inverse_pth_root_eigh(matrix, @@ -696,7 +696,8 @@ def _outer_body_fn(state): _iter_condition, _iter_body, init_state) error = jnp.max(jnp.abs(mat_m - identity)).astype(jnp.float32) is_converged = jnp.asarray(error_ratio < max_error_ratio, old_mat_h.dtype) - resultant_mat_h = is_converged * mat_h + (1 - is_converged) * old_mat_h + resultant_mat_h = is_converged * \ + mat_h + (1 - is_converged) * old_mat_h return (i + 1, resultant_mat_h, error, @@ -732,7 +733,8 @@ def _outer_body_fn(state): total_retries=jnp.array(total_retries, jnp.float32)) if lobpcg_topk_precondition > 0: - damped_matrix = matrix + (ridge_epsilon * (10**total_retries) * identity) + damped_matrix = matrix + \ + (ridge_epsilon * (10**total_retries) * identity) conditioned_diagnostics = InversePthRootDiagnostics.create( conditioned_resultant_mat, damped_matrix, p) unconditioned_damped_matrix = original_matrix + ridge_epsilon * identity @@ -778,33 +780,33 @@ def matrix_inverse_pth_root_eigh( ): """Computes `matrix^(-1/p)`, where `p` is a positive integer. - This function uses eigh for the computation of a matrix's inverse pth - root. - - Args: - matrix: the symmetric PSD matrix whose power it to be computed - p: exponent, for p a positive integer. - ridge_epsilon: Ridge epsilon added to make the matrix positive definite. - error_tolerance: Error indicator, useful for early termination. - precision: precision XLA related flag, the available options are: a) - lax.Precision.DEFAULT (better step time, but not precise) b) - lax.Precision.HIGH (increased precision, slower) c) lax.Precision.HIGHEST - (best possible precision, slowest) - relative_matrix_epsilon: Whether to use relative epsilon to the max eigen - value when computing inverse-pth root. - padding_start: If the input matrix was padded, then zeros out columns and - rows at the padding start. - prev: previous iteration's solution, zero-padded (unused) - - Returns: - `(matrix + eps)^(-1/p)` and error metrics. - - Note `eps` is not added to zeroed out padding rows and - columns. `eps` is just `ridge_epsilon` if - `relative_matrix_epsilon` is set to `False`, otherwise, it is the - ridge epsilon value scaled by the derived maximum eigenvalue of - the input matrix. - """ + This function uses eigh for the computation of a matrix's inverse pth + root. + + Args: + matrix: the symmetric PSD matrix whose power it to be computed + p: exponent, for p a positive integer. + ridge_epsilon: Ridge epsilon added to make the matrix positive definite. + error_tolerance: Error indicator, useful for early termination. + precision: precision XLA related flag, the available options are: a) + lax.Precision.DEFAULT (better step time, but not precise) b) + lax.Precision.HIGH (increased precision, slower) c) + lax.Precision.HIGHEST (best possible precision, slowest) + relative_matrix_epsilon: Whether to use relative epsilon to the max eigen + value when computing inverse-pth root. + padding_start: If the input matrix was padded, then zeros out columns and + rows at the padding start. + prev: previous iteration's solution, zero-padded (unused) + + Returns: + `(matrix + eps)^(-1/p)` and error metrics. + + Note `eps` is not added to zeroed out padding rows and + columns. `eps` is just `ridge_epsilon` if + `relative_matrix_epsilon` is set to `False`, otherwise, it is the + ridge epsilon value scaled by the derived maximum eigenvalue of + the input matrix. + """ del prev assert matrix.shape[0] == matrix.shape[1] matrix_size = matrix.shape[0] @@ -861,17 +863,17 @@ def matrix_inverse_pth_root_eigh( def merge_small_dims(shape_to_merge, max_dim): """Merge small dimensions. - If there are some small dimensions, we collapse them: - e.g. [1, 2, 512, 1, 2048, 1, 3, 4] --> [1024, 2048, 12] if max_dim = 1024 - [1, 2, 768, 1, 2048] --> [2, 768, 2048] + If there are some small dimensions, we collapse them: + e.g. [1, 2, 512, 1, 2048, 1, 3, 4] --> [1024, 2048, 12] if max_dim = 1024 + [1, 2, 768, 1, 2048] --> [2, 768, 2048] - Args: - shape_to_merge: Shape to merge small dimensions. - max_dim: Maximal dimension of output shape used in merging. + Args: + shape_to_merge: Shape to merge small dimensions. + max_dim: Maximal dimension of output shape used in merging. - Returns: - Merged shape. - """ + Returns: + Merged shape. + """ if shape_to_merge and np.all(np.array(shape_to_merge) == 1): return [1] @@ -892,13 +894,13 @@ def merge_small_dims(shape_to_merge, max_dim): def pad_square_matrix(mat, max_size): """Pad a square matrix up to max_size. - Args: - mat: a matrix to pad. - max_size: matrix size requested. + Args: + mat: a matrix to pad. + max_size: matrix size requested. - Returns: - Given M returns [[M, 0], [0, I]] - """ + Returns: + Given M returns [[M, 0], [0, I]] + """ rows, cols = mat.shape if rows != cols: raise ValueError("Must have rows == cols, instead got " @@ -921,13 +923,13 @@ def pad_square_matrix(mat, max_size): def pad_vector(vec, max_size): """Pad a vector to a max_size. - Args: - vec: a vector to pad. - max_size: matrix size requested. + Args: + vec: a vector to pad. + max_size: matrix size requested. - Returns: - Given V returns [V, 0] - """ + Returns: + Given V returns [V, 0] + """ size = vec.shape[0] assert size <= max_size if size == max_size: @@ -1009,25 +1011,25 @@ def merge_partitions(self, partitions): def gram_weighted_update(old_stats, g, axis, w1, w2, precision=None): """Updated statistics via weighted average with new Gram matrix. - Returns w₁ R + w₂ Gᵀ G where R is `old_stats` and G is the matrix whose - columns are the flattened slices of the tensor `g` along the given `axis`. - (So, `old_stats` and the returned matrix have dimensions n x n where - n = `g.shape[axis]`). - - Args: - old_stats: Old statistics. - g: Gradient tensor. - axis: Axis along which to slice `g`. - w1: Scalar weight for old statistics. - w2: Scalar weight for new Gram matrix. - precision: Optional precision XLA related flag, the available options are: - a) lax.Precision.DEFAULT (better step time, but not precise) b) - lax.Precision.HIGH (increased precision, slower) c) lax.Precision.HIGHEST - (best possible precision, slowest) - - Returns: - Weighted average of old and new statistics. - """ + Returns w₁ R + w₂ Gᵀ G where R is `old_stats` and G is the matrix whose + columns are the flattened slices of the tensor `g` along the given `axis`. + (So, `old_stats` and the returned matrix have dimensions n x n where + n = `g.shape[axis]`). + + Args: + old_stats: Old statistics. + g: Gradient tensor. + axis: Axis along which to slice `g`. + w1: Scalar weight for old statistics. + w2: Scalar weight for new Gram matrix. + precision: Optional precision XLA related flag, the available options are: + a) lax.Precision.DEFAULT (better step time, but not precise) b) + lax.Precision.HIGH (increased precision, slower) c) + lax.Precision.HIGHEST (best possible precision, slowest) + + Returns: + Weighted average of old and new statistics. + """ axes = [i for i in range(g.ndim) if i != axis] gram_matrix = jnp.tensordot(g, g, axes=(axes, axes), precision=precision) return w1 * old_stats + w2 * gram_matrix @@ -1046,13 +1048,14 @@ def __init__( ): """Initializes the preconditioner. - Args: - param: parameter to precondition. - block_size: Block size used to split param. - merge_small_dims_block_size: Block size for merging dims. - best_effort_shape_interpretation: Whether to collapse/merge dims together. - preconditioner_type: Type of preconditioner to use. - """ + Args: + param: parameter to precondition. + block_size: Block size used to split param. + merge_small_dims_block_size: Block size for merging dims. + best_effort_shape_interpretation: Whether to + collapse/merge dims together. + preconditioner_type: Type of preconditioner to use. + """ self._original_shape = param.shape self._transformed_shape = param.shape if best_effort_shape_interpretation: @@ -1074,21 +1077,22 @@ def updated_statistics_from_grad( ): """Update statistics from gradients. - Args: - stats: Old statistics or its Cholesky factor if `cholesky` is True. - grad: Gradient to compute statistics from. - w1: Weight for old statistics. - w2: Weight for new statistics. - to_float: Optional function for converting stats to floating point. - from_float: Optional function for converting from floating point. - precision: Optional precision XLA related flag, the available options are: - a) lax.Precision.DEFAULT (better step time, but not precise) b) - lax.Precision.HIGH (increased precision, slower) c) - lax.Precision.HIGHEST (best possible precision, slowest) - - Returns: - A list of updated gradient statistics for each partition. - """ + Args: + stats: Old statistics or its Cholesky factor if `cholesky` is True. + grad: Gradient to compute statistics from. + w1: Weight for old statistics. + w2: Weight for new statistics. + to_float: Optional function for converting stats to floating point. + from_float: Optional function for converting from floating point. + precision: Optional precision XLA related flag, the available options + are: + a) lax.Precision.DEFAULT (better step time, but not precise) b) + lax.Precision.HIGH (increased precision, slower) c) + lax.Precision.HIGHEST (best possible precision, slowest) + + Returns: + A list of updated gradient statistics for each partition. + """ to_float = to_float if to_float is not None else (lambda x: x) from_float = from_float if from_float is not None else (lambda x: x) reshaped_grad = jnp.reshape(grad, self._transformed_shape) @@ -1132,7 +1136,8 @@ def _preconds_for_grad(self, preconditioners, rank, start, end): elif self._preconditioner_type == PreconditionerType.OUTPUT: # When _preconditioner_type is OUTPUT, we append (rank - 1) many None # values to the beginning of the list to handle the False indices. - preconditioners_for_grad = [None] * (rank - 1) + preconditioners_for_grad + preconditioners_for_grad = [None] * \ + (rank - 1) + preconditioners_for_grad assert len(preconditioners_for_grad) == rank return preconditioners_for_grad @@ -1160,13 +1165,13 @@ def exponent_for_preconditioner(self): def preconditioned_grad(self, grad, preconditioners): """Precondition the gradient. - Args: - grad: A gradient tensor to precondition. - preconditioners: A list of preconditioners to apply. + Args: + grad: A gradient tensor to precondition. + preconditioners: A list of preconditioners to apply. - Returns: - A preconditioned gradient. - """ + Returns: + A preconditioned gradient. + """ reshaped_grad = jnp.reshape(grad, self._transformed_shape) partitioned_grads = self._partitioner.partition(reshaped_grad) should_preconditioned_dims = self.should_precondition_dims() @@ -1301,19 +1306,19 @@ def distributed_shampoo( exponent_override=0, # Pass pmap 'batch axis name' in pmap mode. batch_axis_name=None, - ### Only set following 3 params in pjit/spmd mode. - ### WARNING: Experimental + # Only set following 3 params in pjit/spmd mode. + # WARNING: Experimental statistics_partition_spec=None, preconditioner_partition_spec=None, num_devices_for_pjit=None, shard_optimizer_states=False, ### - ### Experimental memory reduction mode + # Experimental memory reduction mode best_effort_memory_usage_reduction=True, ### inverse_failure_threshold=0.1, moving_average_for_momentum=True, - skip_preconditioning_dim_size_gt=2048, + skip_preconditioning_dim_size_gt=0, clip_by_scaled_gradient_norm=None, precision=lax.Precision.HIGHEST, tensordot_precision=None, @@ -1332,98 +1337,101 @@ def distributed_shampoo( ): """Distributed Shampoo optimizer. - Distributed Shampoo is a second-order preconditioned method (concretely, a - variant of full-matrix Adagrad), that provides significant convergence and - wall-clock time improvements compared to conventional first-order methods, - and that has been shown to scale to large state-of-the-art deep learning - models. - - References: - Scalable Second Order Optimization for Deep Learning, - Rohan Anil, Vineet Gupta, Tomer Koren, Kevin Regan, Yoram Singer - - Preprint: https://arxiv.org/abs/2002.09018 - - Args: - learning_rate: the step size used to update the parameters. - block_size: Block size for large layers (if > 0). Preconditioning compute - operation is cubic in the dimension of the tensor. Block size allows us to - chunk the layers into sub-layers of maximal dimension dictated by this - value. Use 128 as default (increase if you have compute budget). - beta1: momentum parameter. - beta2: second moment averaging parameter. - diagonal_epsilon: epsilon for diagonal adagrad (only if layerwise grafting - to AdaGrad is enabled). - matrix_epsilon: epsilon to add to statistics before computing inverse pth - root. If you are running in f32 precision for inverse pth root - (recommended today) this can go upto 1e-6. If you have latest hardware - with native f64 precision, set this upto 1e-12. - weight_decay: Weight decay for regularization. - start_preconditioning_step: When to start Shampoo update before which - diagonal update is used. This is because we dont have enough information - to do stable inverse. - preconditioning_compute_steps: How often to compute preconditioner. - Performance tuning params for controlling memory and compute requirements. - Ideally set this and statistics_compute_steps params to 1. - statistics_compute_steps: How often to compute statistics. - best_effort_shape_interpretation: If there are some small dimensions, - collapse them e.g. [1, 2, 512, 1, 2048, 1, 3, 4] --> [1024, 2048, 12] if - block = 1024, [1, 2, 768, 1, 2048] --> [2, 768, 2048] - graft_type: Grafting is a technique to fix the layerwise scale of Shampoo - optimizer. This allows us to plugin the Shampoo optimizer into settings - where SGD/AdaGrad is already well tuned. - nesterov: Nesterov momentum. - exponent_override: Override the exponent used in matrix inverse. - batch_axis_name: labeled axis over pmap for data-parallel training the - optimizer used for. - statistics_partition_spec: PartitionSpec to be used in sharded mode. - preconditioner_partition_spec: PartitionSpec to be used in sharded mode. - num_devices_for_pjit: Number of devices to parallelize over when using pjit. - shard_optimizer_states: Shard optimizer states to save memory in model - parallel training. - best_effort_memory_usage_reduction: Best effort memory usage reduction. - - diagonal_statistics -> jnp.bfloat16 - momentum buffers (2x) -> jnp.int8 - - statistics, preconditioners -> jnp.int16 + diagonals - inverse_failure_threshold: numerics are hard and inverses fail sometimes; we - determine that using this threshold. - moving_average_for_momentum: Whether to use moving average for momentum - instead of exponential moving average. - skip_preconditioning_dim_size_gt: Skip if preconditioning dim size is - greater than this value. - clip_by_scaled_gradient_norm: Clip by scaled gradient norm (only useful when - using RMSProp Grafting). - precision: precision XLA related flag, the available options are: a) - lax.Precision.DEFAULT (better step time, but not precise) b) - lax.Precision.HIGH (increased precision, slower) c) lax.Precision.HIGHEST - (best possible precision, slowest) - tensordot_precision: Optional precision to use for the tensordot operation - when computing statistics (e.g., G Gᵀ). Same options as `precision` above. - relative_matrix_epsilon: Whether to use relative epsilon to the max eigen - value when computing inverse-pth root. - merge_small_dims_block_size: Used as the maximum block size to merge the - shapes. - lobpcg_topk_precondition: If nonzero, specifies the number of top - eigenvectors to subtract out before performing LOBPCG. Note this makes - relative_matrix_epsilon essentially free. - lobpcg_max_iter: Number of LOBPCG iterations, if zero defaults to - `lobpcg_topk_precondition`. - precondtioner_type: Preconditioner type to select all, left only or right - only preconditioners. - skip_preconditioning_rank_lt: Skips preconditioning for parameters with rank - less than this value. - decoupled_learning_rate: If True, use decoupled learning rate, otherwise - couple it with preconditioned gradient computation. (Default True) - decoupled_weight_decay: If True, use decoupled weight decay, otherwise - couple with weight decay. (Default False) - generate_training_metrics: If True, gather training metrics, otherwise avoid - generating them (to reduce memory usage). - reuse_preconditioner: If True, pass the previous derived preconditioner as a - warm start to the next iteratin's inverse pth root computation. - eigh: If True, and uses eigen decomposition for inverse-pth root. - - Returns: - a GradientTransformation. - """ + Distributed Shampoo is a second-order preconditioned method (concretely, a + variant of full-matrix Adagrad), that provides significant convergence and + wall-clock time improvements compared to conventional first-order methods, + and that has been shown to scale to large state-of-the-art deep learning + models. + + References: + Scalable Second Order Optimization for Deep Learning, + Rohan Anil, Vineet Gupta, Tomer Koren, Kevin Regan, Yoram Singer + + Preprint: https://arxiv.org/abs/2002.09018 + + Args: + learning_rate: the step size used to update the parameters. + block_size: Block size for large layers (if > 0). Preconditioning compute + operation is cubic in the dimension of the tensor. Block size allows us + to chunk the layers into sub-layers of maximal dimension dictated by + this value. Use 128 as default (increase if you have compute budget). + beta1: momentum parameter. + beta2: second moment averaging parameter. + diagonal_epsilon: epsilon for diagonal adagrad (only if layerwise grafting + to AdaGrad is enabled). + matrix_epsilon: epsilon to add to statistics before computing inverse pth + root. If you are running in f32 precision for inverse pth root + (recommended today) this can go upto 1e-6. If you have latest hardware + with native f64 precision, set this upto 1e-12. + weight_decay: Weight decay for regularization. + start_preconditioning_step: When to start Shampoo update before which + diagonal update is used. This is because we dont have enough information + to do stable inverse. + preconditioning_compute_steps: How often to compute preconditioner. + Performance tuning params for controlling memory and compute + requirements. + Ideally set this and statistics_compute_steps params to 1. + statistics_compute_steps: How often to compute statistics. + best_effort_shape_interpretation: If there are some small dimensions, + collapse them e.g. [1, 2, 512, 1, 2048, 1, 3, 4] --> [1024, 2048, 12] if + block = 1024, [1, 2, 768, 1, 2048] --> [2, 768, 2048] + graft_type: Grafting is a technique to fix the layerwise scale of Shampoo + optimizer. This allows us to plugin the Shampoo optimizer into settings + where SGD/AdaGrad is already well tuned. + nesterov: Nesterov momentum. + exponent_override: Override the exponent used in matrix inverse. + batch_axis_name: labeled axis over pmap for data-parallel training the + optimizer used for. + statistics_partition_spec: PartitionSpec to be used in sharded mode. + preconditioner_partition_spec: PartitionSpec to be used in sharded mode. + num_devices_for_pjit: Number of devices to parallelize over when using + pjit. + shard_optimizer_states: Shard optimizer states to save memory in model + parallel training. + best_effort_memory_usage_reduction: Best effort memory usage reduction. - + diagonal_statistics -> jnp.bfloat16 - momentum buffers (2x) + -> jnp.int8 - statistics, preconditioners -> jnp.int16 + diagonals + inverse_failure_threshold: numerics are hard and inverses fail sometimes; + we determine that using this threshold. + moving_average_for_momentum: Whether to use moving average for momentum + instead of exponential moving average. + skip_preconditioning_dim_size_gt: Skip if preconditioning dim size is + greater than this value. + clip_by_scaled_gradient_norm: Clip by scaled gradient norm (only useful + when using RMSProp Grafting). + precision: precision XLA related flag, the available options are: a) + lax.Precision.DEFAULT (better step time, but not precise) b) + lax.Precision.HIGH (increased precision, slower) c) + lax.Precision.HIGHEST (best possible precision, slowest) + tensordot_precision: Optional precision to use for the tensordot operation + when computing statistics (e.g., G Gᵀ). Same options as `precision` + above. + relative_matrix_epsilon: Whether to use relative epsilon to the max eigen + value when computing inverse-pth root. + merge_small_dims_block_size: Used as the maximum block size to merge the + shapes. + lobpcg_topk_precondition: If nonzero, specifies the number of top + eigenvectors to subtract out before performing LOBPCG. Note this makes + relative_matrix_epsilon essentially free. + lobpcg_max_iter: Number of LOBPCG iterations, if zero defaults to + `lobpcg_topk_precondition`. + precondtioner_type: Preconditioner type to select all, left only or right + only preconditioners. + skip_preconditioning_rank_lt: Skips preconditioning for parameters with + rank less than this value. + decoupled_learning_rate: If True, use decoupled learning rate, otherwise + couple it with preconditioned gradient computation. (Default True) + decoupled_weight_decay: If True, use decoupled weight decay, otherwise + couple with weight decay. (Default False) + generate_training_metrics: If True, gather training metrics, otherwise + avoid generating them (to reduce memory usage). + reuse_preconditioner: If True, pass the previous derived preconditioner + as a warm start to the next iteratin's inverse pth root computation. + eigh: If True, and uses eigen decomposition for inverse-pth root. + + Returns: + a GradientTransformation. + """ reset_frequency = None def _graft_type_has_diagonal_statistics(): @@ -1461,44 +1469,6 @@ def _to_float(maybe_quantized): else: return maybe_quantized - def _maybe_quantize_statistics(statistics_list): - return _maybe_quantize_matrices_with_dtype( - statistics_list, quantized_dtype_for_second_moment_statistics_buffers()) - - def _maybe_quantize_preconditioners(statistics_list): - return _maybe_quantize_matrices_with_dtype( - statistics_list, - quantized_dtype_for_second_moment_preconditioner_buffers()) - - def _maybe_quantize_matrices_with_dtype(statistics_list, quantized_dtype): - if quantized_dtype != jnp.float32: - return ([ - QuantizedValue.from_float_value( - s, quantized_dtype, extract_diagonal=True) - for s in statistics_list - ]) - else: - return statistics_list - - def _maybe_dequantize_preconditioners(preconditioner_list): - return _maybe_dequantize_matrices_with_dtype( - preconditioner_list, - quantized_dtype_for_second_moment_preconditioner_buffers()) - - def _maybe_dequantize_matrices_with_dtype(statistics_list, quantized_dtype): - if quantized_dtype != jnp.float32: - return [s.to_float() for s in statistics_list] - else: - return statistics_list - - def _quantize_diagonal_statistics(diagonal_statistics): - return QuantizedValue.from_float_value(diagonal_statistics, jnp.float32) - - def _quantize_momentum(momentum_statistics): - return QuantizedValue.from_float_value( - momentum_statistics, - quantized_dtype_for_momentum_buffers(momentum_statistics)) - def preconditioner_from_params(param): """Returns a Preconditioner object for given param.""" return Preconditioner( @@ -1543,9 +1513,9 @@ def _pad_preconditioner(preconditioner): def sharded_init_fn(params): """Returns optimizer state (for PJIT mode). - Args: - params: the parameters that should be updated. - """ + Args: + params: the parameters that should be updated. + """ params_flat, treedef = jax.tree_util.tree_flatten(params) # Find max size to pad to. max_size = 0 @@ -1589,9 +1559,9 @@ def sharded_init_fn(params): if exponent_override == 0 else exponent_override) exponents.extend([exponent] * len(shapes)) - diagonal_statistics = _quantize_diagonal_statistics(jnp.zeros_like(param)) - diagonal_momentum = _quantize_momentum(jnp.zeros_like(param)) - momentum = _quantize_momentum(jnp.zeros_like(param)) + diagonal_statistics = jnp.zeros_like(param) + diagonal_momentum = jnp.zeros_like(param) + momentum = jnp.zeros_like(param) local_stats_flat.append( LocalShardedParameterStats( @@ -1660,11 +1630,11 @@ def sharded_init_partition_spec_fn(params, """Returns a parallel state tree with PartitionSpec associated with state. - Args: - params: A pytree with params. - params_partition_spec: A pytree with PartitionSpec for params. - partition_spec_for_statistics: PartitionSpec for the statistics. - """ + Args: + params: A pytree with params. + params_partition_spec: A pytree with PartitionSpec for params. + partition_spec_for_statistics: PartitionSpec for the statistics. + """ # Parallel lists of spec, and params. param_pspec_flat, _ = jax.tree_util.tree_flatten( params_partition_spec, is_leaf=lambda x: x is None) @@ -1736,9 +1706,9 @@ def sharded_init_shape_and_dtype_fn(params): """Returns a parallel state tree with shape, dtype associated with state. - Args: - params: A pytree with params. - """ + Args: + params: A pytree with params. + """ # Parallel lists of spec, and params. params_flat, treedef = jax.tree_util.tree_flatten(params) assert params_flat @@ -1818,14 +1788,14 @@ def sharded_init_shape_and_dtype_fn(params): def sharded_update_fn(grads, state, params): """Transform the input gradient and update all statistics in sharded mode. - Args: - grads: the gradient tensors for the parameters. - state: a named tuple containing the state of the optimizer - params: the parameters that should be updated. + Args: + grads: the gradient tensors for the parameters. + state: a named tuple containing the state of the optimizer + params: the parameters that should be updated. - Returns: - A tuple containing the new parameters and the new optimizer state. - """ + Returns: + A tuple containing the new parameters and the new optimizer state. + """ params_flat, treedef = jax.tree_util.tree_flatten(params) grads_flat = treedef.flatten_up_to(grads) @@ -1991,13 +1961,18 @@ def _init(param): if _graft_type_has_diagonal_statistics(): diagonal_statistics = jnp.zeros_like(param) - diagonal_momentum = _quantize_momentum(jnp.zeros_like(param)) - momentum = _quantize_momentum(jnp.zeros_like(param)) + # diagonal_momentum = _quantize_momentum(jnp.zeros_like(param)) + # momentum = _quantize_momentum(jnp.zeros_like(param)) + diagonal_momentum = jnp.zeros_like(param) + momentum = jnp.zeros_like(param) return ParameterStats( - _quantize_diagonal_statistics(diagonal_statistics), - _maybe_quantize_statistics(statistics), - _maybe_quantize_preconditioners(preconditioners), + diagonal_statistics, + statistics, + preconditioners, + # _quantize_diagonal_statistics(diagonal_statistics), + # _maybe_quantize_statistics(statistics), + # _maybe_quantize_preconditioners(preconditioners), diagonal_momentum, momentum, init_training_metrics( @@ -2027,7 +2002,8 @@ def compute_updated_statistics(): w1=w1, w2=w2, to_float=_to_float, - from_float=lambda x: _maybe_quantize_statistics([x])[0], + from_float=lambda x: x, + # from_float=lambda x: _maybe_quantize_statistics([x])[0], precision=tensordot_precision, ) @@ -2060,45 +2036,6 @@ def _matrix_inverse_pth_root_vmap(xs, ps, padding_starts, prev): return jax.vmap(mi_pth_root)( xs, ps, padding_start=padding_starts, prev=prev) - def _quantized_matrix_inverse_pth_root_vmap(qxs, - qds, - qbs, - ps, - padding_starts, - qpxs=None, - qpds=None, - qpbs=None): - assert (qpxs is None) == (qpds is None) == (qpbs is None) - assert (qpxs is None) == (not reuse_preconditioner) - - def _quantized_to_float(qx, qd, qb): - qv = QuantizedValue(qx, qd, qb, qx.dtype, True, list(qx.shape)) - return qv.to_float() - - def matrix_inverse_pth_root_wrapper(qx, - qd, - qb, - p, - padding_start, - qpx, - qpd, - qpb): - v = _quantized_to_float(qx, qd, qb) - prev = _maybe(_quantized_to_float)(qpx, qpd, qpb) - preconditioner, metrics = mi_pth_root( - v, p, padding_start=padding_start, prev=prev) - qp = QuantizedValue.from_float_value(preconditioner, qx.dtype, True) - return qp.quantized, qp.diagonal, qp.bucket_size, metrics - - return jax.vmap(matrix_inverse_pth_root_wrapper)(qxs, - qds, - qbs, - ps, - padding_starts, - qpxs, - qpds, - qpbs) - def _matrix_inverse_pth_root_pjit(xs, ps, padding_starts, @@ -2145,20 +2082,20 @@ def _pmap_compute_preconditioners(states, prev_preconditioners): """Computes preconditioners for given statistics in states in PMAP mode. - Args: - states: A list of optimizer states. - step: Current step number - statistics: A list of statistics for all variables (for every dim) - num_statistics_per_state: Number of statistis per state to reconstruct - output states. - original_shapes: A list of shapes of the statistics. - exponents: Exponent power to use for inverse-pth roots. - max_size: Maximum dim of the statistics to pad. - prev_preconditioners: Previously available preconditioner. - - Returns: - New optimizer states after computing the preconditioner. - """ + Args: + states: A list of optimizer states. + step: Current step number + statistics: A list of statistics for all variables (for every dim) + num_statistics_per_state: Number of statistis per state to reconstruct + output states. + original_shapes: A list of shapes of the statistics. + exponents: Exponent power to use for inverse-pth roots. + max_size: Maximum dim of the statistics to pad. + prev_preconditioners: Previously available preconditioner. + + Returns: + New optimizer states after computing the preconditioner. + """ if batch_axis_name: num_devices = lax.psum(1, batch_axis_name) else: @@ -2253,7 +2190,8 @@ def _select_preconditioner(error, new_p, old_p): new_preconditioners_flat.append( _select_preconditioner(error, p[:shape[0], :shape[1]], prev_p)) - assert len(states) == len(num_statistics_per_state) + assert len(states) == (len(num_statistics_per_state), + f"{len(states)} vs {len(num_statistics_per_state)}") assert len(new_preconditioners_flat) == num_statistics assert len(new_errors_flat) == len(packed_statistics), ( len(new_errors_flat), len(packed_statistics)) @@ -2296,271 +2234,7 @@ def _select_preconditioner(error, new_p, old_p): idx += num_statistics new_states = [] for state, new_preconditioners, new_metrics in zip( - states, preconditioners_for_states, metrics_for_states): - # Note the preconditioner may have been skipped, but we still update the - # metrics with the new error values; whether the preconditioner that's - # actively being used is stale can be derived from the new_metrics - # being greater than the failure threshold. - new_states.append( - ParameterStats(state.diagonal_statistics, - state.statistics, - new_preconditioners, - state.diagonal_momentum, - state.momentum, - new_metrics)) - - return new_states - - def _pmap_quantized_compute_preconditioners(states, - step, - statistics, - num_statistics_per_state, - original_shapes, - exponents, - max_size, - prev_preconditioners): - """Computes preconditioners for given statistics in states in PMAP mode. - - For quantization, each statistic is represented by three values: - quantized matrix, diagonal, and bucket sizes, we run inverse pth-roots - without ever recreating the original matrix in f32. - - Args: - states: A list of optimizer states. - step: Current step number - statistics: A list of statistics for all variables (for every dim) - num_statistics_per_state: Number of statistis per state to reconstruct - output states. - original_shapes: A list of shapes of the statistics. - exponents: Exponent power to use for inverse-pth roots. - max_size: Maximum dim of the statistics to pad. - prev_preconditioners: Previously available preconditioner. - - Returns: - New optimizer states after computing the preconditioner. - """ - num_devices = lax.psum(1, batch_axis_name) - num_statistics = len(statistics) - quantized_dtype = quantized_dtype_for_second_moment_statistics_buffers() - # Complexity here is around: shapes needing be statically shaped, - # our custom quantization type requires a different type of packing. - - # Parallel tensors: - # quantized [dxd] - # diagonals [d] f32 - # bucket_sizes [d] f32 - packed_quantized_statistics = [ - pad_square_matrix(stat.quantized, max_size) for stat in statistics - ] - packed_quantized_diagonals = [ - pad_vector(stat.diagonal, max_size) for stat in statistics - ] - packed_quantized_bucket_sizes = [ - pad_vector(stat.bucket_size, max_size) for stat in statistics - ] - - to_pad = -num_statistics % num_devices - padded_eye = jnp.eye(max_size, dtype=jnp.float32) - quantized_eye = QuantizedValue.from_float_value(padded_eye, - quantized_dtype, - True) - packed_quantized_statistics.extend( - [quantized_eye.quantized for _ in range(to_pad)]) - packed_quantized_diagonals.extend( - [quantized_eye.diagonal for _ in range(to_pad)]) - packed_quantized_bucket_sizes.extend( - [quantized_eye.bucket_size for _ in range(to_pad)]) - exponents.extend([1 for _ in range(to_pad)]) - paddings = [len(stat.quantized) for stat in statistics] + [0] * to_pad - - if not packed_quantized_statistics: - return states - - if reuse_preconditioner: - total = len(packed_quantized_statistics) - packed_quantized_precond_mats = pad_and_maybe_zero_preconditioners( - [p.quantized for p in prev_preconditioners], - total, - max_size, - step, - ) - packed_quantized_precond_diagonals = [ - pad_vector(p.diagonal, max_size) for p in prev_preconditioners - ] + packed_quantized_diagonals[total - to_pad:] - packed_quantized_precond_bucket_sizes = [ - pad_vector(p.bucket_size, max_size) for p in prev_preconditioners - ] + packed_quantized_bucket_sizes[total - to_pad:] - else: - (packed_quantized_precond_mats, - packed_quantized_precond_diagonals, - packed_quantized_precond_bucket_sizes) = (None, None, None) - - all_quantized_statistics = batch(packed_quantized_statistics, num_devices) - all_quantized_diagonals = batch(packed_quantized_diagonals, num_devices) - all_quantized_bucket_sizes = batch(packed_quantized_bucket_sizes, - num_devices) - all_exponents = batch(exponents, num_devices) - all_paddings = batch(paddings, num_devices) - all_quantized_precond_mats = _maybe(batch)(packed_quantized_precond_mats, - num_devices) - all_quantized_precond_diagonals = _maybe(batch)( - packed_quantized_precond_diagonals, num_devices) - all_quantized_precond_bucket_sizes = _maybe(batch)( - packed_quantized_precond_bucket_sizes, num_devices) - - def _internal_inverse_pth_root_all(): - current_replica = lax.axis_index(batch_axis_name) - (quantized_preconditioners, - quantized_diagonals, - quantized_bucket_sizes, - metrics) = _quantized_matrix_inverse_pth_root_vmap( - all_quantized_statistics[current_replica], - all_quantized_diagonals[current_replica], - all_quantized_bucket_sizes[current_replica], - all_exponents[current_replica], - all_paddings[current_replica], - _maybe_ix(all_quantized_precond_mats, current_replica), - _maybe_ix(all_quantized_precond_diagonals, current_replica), - _maybe_ix(all_quantized_precond_bucket_sizes, current_replica), - ) - quantized_preconditioners = jax.lax.all_gather(quantized_preconditioners, - batch_axis_name) - quantized_diagonals = jax.lax.all_gather(quantized_diagonals, - batch_axis_name) - quantized_bucket_sizes = jax.lax.all_gather(quantized_bucket_sizes, - batch_axis_name) - metrics = jax.lax.all_gather(metrics, batch_axis_name) - quantized_preconditioners_flat = unbatch(quantized_preconditioners) - quantized_diagonals_flat = unbatch(quantized_diagonals) - quantized_bucket_sizes_flat = unbatch(quantized_bucket_sizes) - metrics_flat = jax.tree_map(unbatch, metrics) - return (quantized_preconditioners_flat, - quantized_diagonals_flat, - quantized_bucket_sizes_flat, - metrics_flat) - - perform_step = step % preconditioning_compute_steps == 0 - if preconditioning_compute_steps == 1: - (quantized_preconditioners_flat, - quantized_diagonals_flat, - quantized_bucket_sizes_flat, - metrics_flat) = ( - _internal_inverse_pth_root_all()) - else: - # Passing statistics instead of preconditioners as they are similarly - # shaped tensors. Note statistics will be ignored as we are passing in - # a large error value. - pd = precond_dim(max_size) - quantized_preconditioners_init = [ - s[:, :pd] for s in packed_quantized_statistics - ] - quantized_diagonals_init = packed_quantized_diagonals - quantized_bucket_sizes_init = packed_quantized_bucket_sizes - n = len(quantized_preconditioners_init) - metrics_init = jax.tree_map( - lambda x: [x] * n, - default_training_metrics().replace( - inverse_pth_root_errors=inverse_failure_threshold)) - init_state = [ - quantized_preconditioners_init, - quantized_diagonals_init, - quantized_bucket_sizes_init, - metrics_init - ] - (quantized_preconditioners_flat, - quantized_diagonals_flat, - quantized_bucket_sizes_flat, - metrics_flat) = ( - efficient_cond(perform_step, - _internal_inverse_pth_root_all, - init_state)) - - def _skip(error): - condition = jnp.logical_or( - jnp.isnan(error), error >= inverse_failure_threshold) - return condition.astype(error.dtype) - - def _select_preconditioner(error, new_p, old_p): - return lax.cond( - _skip(error), lambda _: old_p, lambda _: new_p, operand=None) - - new_quantized_preconditioners_flat = [] - new_quantized_diagonals_flat = [] - new_quantized_bucket_sizes_flat = [] - new_errors_flat = metrics_flat.inverse_pth_root_errors - for p, d, b, shape, prev_p, error in zip(quantized_preconditioners_flat, - quantized_diagonals_flat, - quantized_bucket_sizes_flat, - original_shapes, - prev_preconditioners, - new_errors_flat): - new_quantized_preconditioners_flat.append( - _select_preconditioner(error, - p[:shape[0], :shape[1]], - prev_p.quantized)) - new_quantized_diagonals_flat.append( - _select_preconditioner(error, d[:shape[0]], prev_p.diagonal)) - new_quantized_bucket_sizes_flat.append( - _select_preconditioner(error, b[:shape[0]], prev_p.bucket_size)) - - assert len(states) == len(num_statistics_per_state) - assert len(new_quantized_preconditioners_flat) == num_statistics - assert len(new_quantized_diagonals_flat) == num_statistics - assert len(new_quantized_bucket_sizes_flat) == num_statistics - - # Add back empty preconditioners so we that we can set the optimizer state. - preconditioners_for_states = [] - metrics_for_states = [] - idx = 0 - for num_statistics, state in zip(num_statistics_per_state, states): - if num_statistics == 0: - preconditioners_for_states.append([]) - metrics_for_states.append( - init_training_metrics(0, generate_training_metrics)) - else: - new_quant_preconditioners_flat = new_quantized_preconditioners_flat - quantized_preconditioners_for_state = new_quant_preconditioners_flat[ - idx:idx + num_statistics] - quantized_diagonals_for_state = new_quantized_diagonals_flat[ - idx:idx + num_statistics] - quantized_bucket_sizes_for_state = new_quantized_bucket_sizes_flat[ - idx:idx + num_statistics] - - if generate_training_metrics: - # pylint:disable=cell-var-from-loop Used immediately. - metrics_for_state = jax.tree_map( - lambda x: jnp.stack(x[idx:idx + num_statistics]), - metrics_flat, - is_leaf=lambda x: isinstance(x, list)) - - assert len( - state.statistics) == len(quantized_preconditioners_for_state) - assert len(state.statistics) == len(quantized_diagonals_for_state) - assert len(state.statistics) == len(quantized_bucket_sizes_for_state) - assert jax.tree_util.tree_all( - jax.tree_map(lambda x: len(state.statistics) == len(x), - metrics_for_state)) - - # If we skipped preconditioner computation, record old metrics. - metrics_for_state = efficient_cond(perform_step, - lambda: [metrics_for_state], - [state.training_metrics])[0] - # pylint:enable=cell-var-from-loop - else: - metrics_for_state = optax.MaskedNode() - - quantized_preconditioners = [] - for qv, qd, qb in zip(quantized_preconditioners_for_state, - quantized_diagonals_for_state, - quantized_bucket_sizes_for_state): - quantized_preconditioners.append( - QuantizedValue(qv, qd, qb, qv.dtype, True, list(qv.shape))) - preconditioners_for_states.append(quantized_preconditioners) - metrics_for_states.append(metrics_for_state) - idx += num_statistics - new_states = [] - for state, new_preconditioners, new_metrics in zip( - states, preconditioners_for_states, metrics_for_states): + states, preconditioners_for_states, metrics_for_states): # Note the preconditioner may have been skipped, but we still update the # metrics with the new error values; whether the preconditioner that's # actively being used is stale can be derived from the new_metrics @@ -2575,158 +2249,17 @@ def _select_preconditioner(error, new_p, old_p): return new_states - def _pjit_compute_preconditioners(states, - step, - statistics, - num_statistics_per_state, - original_shapes, - exponents, - max_size, - prev_preconditioners): - """Computes preconditioners for given statistics in states in PJIT mode. - - Args: - states: A list of optimizer states. - step: Current step number - statistics: A list of statistics for all variables (for every dim) - num_statistics_per_state: Number of statistis per state to reconstruct - output states. - original_shapes: A list of shapes of the statistics. - exponents: Exponent power to use for inverse-pth roots. - max_size: Maximum dim of the statistics to pad. - prev_preconditioners: Previously available preconditioner. - - Returns: - New optimizer states after computing the preconditioner. - """ - num_statistics = len(statistics) - to_pad = -num_statistics % num_devices_for_pjit - padded_statistics = [ - pad_square_matrix(stat, max_size) for stat in statistics - ] - padded_statistics.extend([ - jnp.eye(max_size, dtype=padded_statistics[0].dtype) - for _ in range(to_pad) - ]) - exponents.extend([1 for _ in range(to_pad)]) - paddings = [len(stat) for stat in statistics] + [0] * to_pad - - if reuse_preconditioner: - padded_preconditioners = pad_and_maybe_zero_preconditioners( - prev_preconditioners, len(padded_statistics), max_size, step) - else: - padded_preconditioners = None - - all_statistics = jnp.stack(padded_statistics) - all_exponents = jnp.stack(exponents) - all_paddings = jnp.stack(paddings) - all_preconditioners = _maybe(jnp.stack)(padded_preconditioners) - - def _internal_inverse_pth_root_all(): - preconditioners, metrics = _matrix_inverse_pth_root_pjit( - all_statistics, all_exponents, all_paddings, all_preconditioners) - b1 = preconditioners.shape[0] - - def split(batched_values): - return [ - jnp.squeeze(v) - for v in jnp.split(batched_values, indices_or_sections=b1, axis=0) - ] - - return split(preconditioners), jax.tree_map(split, metrics) - - if preconditioning_compute_steps == 1: - preconditioners_flat, metrics_flat = _internal_inverse_pth_root_all() - else: - # Passing statistics instead of preconditioners as they are similarly - # shaped tensors. Note statistics will be ignored as we are passing in - # a large init value for error. - pd = precond_dim(max_size) - preconditioners_init = [s[:, :pd] for s in padded_statistics] - n = len(padded_statistics) - metrics_init = jax.tree_map( - lambda x: [x] * n, - TrainingMetrics(inverse_pth_root_errors=inverse_failure_threshold)) - init_state = [preconditioners_init, metrics_init] - perform_step = step % preconditioning_compute_steps == 0 - preconditioners_flat, metrics_flat = efficient_cond( - perform_step, _internal_inverse_pth_root_all, init_state) - - def _skip(error): - condition = jnp.logical_or( - jnp.isnan(error), error >= inverse_failure_threshold) - return condition.astype(error.dtype) - - def _select_preconditioner(error, new_p, old_p): - return lax.cond( - _skip(error), lambda _: old_p, lambda _: new_p, operand=None) - - new_preconditioners_flat = [] - new_errors_flat = metrics_flat.inverse_pth_root_errors - for p, shape, prev_p, error in zip(preconditioners_flat, original_shapes, - prev_preconditioners, new_errors_flat): - new_preconditioners_flat.append( - _select_preconditioner(error.inverse_pth_root_errors, - p[:shape[0], :shape[1]], - prev_p)) - - assert len(states) == len(num_statistics_per_state) - assert len(new_preconditioners_flat) == num_statistics - - # Add back empty preconditioners so we that we can set the optimizer state. - preconditioners_for_states = [] - metrics_for_states = [] - idx = 0 - for num_statistics, state in zip(num_statistics_per_state, states): - if num_statistics == 0: - preconditioners_for_states.append([]) - metrics_for_states.append( - init_training_metrics(0, generate_training_metrics)) - else: - preconditioners_for_state = new_preconditioners_flat[idx:idx + - num_statistics] - assert len(state.statistics) == len(preconditioners_for_state) - preconditioners_for_states.append(preconditioners_for_state) - - if generate_training_metrics: - # pylint:disable=cell-var-from-loop Used immediately. - metrics_for_state = jax.tree_map( - lambda x: jnp.stack(x[idx:idx + num_statistics]), - metrics_flat, - is_leaf=functools.partial(isinstance, list)) - assert jax.tree_util.tree_all( - jax.tree_map(lambda x: len(state.statistics) == len(x), - metrics_for_state)) - # pylint:enable=cell-var-from-loop - else: - metrics_for_state = optax.MaskedNode() - metrics_for_states.append(metrics_for_state) - idx += num_statistics - - new_states = [] - for state, new_preconditioners, new_metrics in zip( - states, preconditioners_for_states, metrics_for_states): - new_states.append( - ParameterStats(state.diagonal_statistics, - state.statistics, - new_preconditioners, - state.diagonal_momentum, - state.momentum, - new_metrics)) - - return new_states - def _compute_preconditioners(states, params, step): """Computes preconditioners for given statistics in states. - Args: - states: A list of optimizer states. - params: A list of params. - step: Current step number + Args: + states: A list of optimizer states. + params: A list of params. + step: Current step number - Returns: - New optimizer states after computing the preconditioner. - """ + Returns: + New optimizer states after computing the preconditioner. + """ statistics = [] num_statistics_per_state = [] original_shapes = [] @@ -2750,45 +2283,20 @@ def _compute_preconditioners(states, params, step): prev_preconditioners.extend(state.preconditioners) original_shapes.extend(original_shapes_for_state) - if not shard_optimizer_states: - # Quantization is only enabled if batch_axis_name is not set. - quantized_dtype = quantized_dtype_for_second_moment_statistics_buffers() - - if quantized_dtype == jnp.float32: - return _pmap_compute_preconditioners(states, - step, - statistics, - num_statistics_per_state, - original_shapes, - exponents, - max_size, - prev_preconditioners) - else: - return _pmap_quantized_compute_preconditioners( - states, - step, - statistics, - num_statistics_per_state, - original_shapes, - exponents, - max_size, - prev_preconditioners) - - else: - return _pjit_compute_preconditioners(states, - step, - statistics, - num_statistics_per_state, - original_shapes, - exponents, - max_size, - prev_preconditioners) + return _pmap_compute_preconditioners(states, + step, + statistics, + num_statistics_per_state, + original_shapes, + exponents, + max_size, + prev_preconditioners) def _transform_grad(grad, state, param, step): """Transform per-parameter gradients.""" preconditioner = preconditioner_from_params(param) sgd_update = grad - new_diagonal_statistics = state.diagonal_statistics.to_float() + new_diagonal_statistics = state.diagonal_statistics if (graft_type == GraftingType.ADAGRAD or graft_type == GraftingType.ADAGRAD_NORMALIZED): @@ -2813,8 +2321,7 @@ def _transform_grad(grad, state, param, step): w2 = jnp.where(beta2 == 1.0, beta2, 1.0 - beta2) new_diagonal_statistics = ( - w1 * state.diagonal_statistics.to_float() + - w2 * jnp.square(scaled_grad)) + w1 * state.diagonal_statistics + w2 * jnp.square(scaled_grad)) rmsprop_update = scaled_grad / ( jnp.sqrt(new_diagonal_statistics) + diagonal_epsilon) @@ -2842,9 +2349,8 @@ def _transform_grad(grad, state, param, step): precond_grad = grad if not _skip_preconditioning(param): - precond_grad = preconditioner.preconditioned_grad( - precond_grad, - _maybe_dequantize_preconditioners(state.preconditioners)) + precond_grad = preconditioner.preconditioned_grad(precond_grad, + state.preconditioners) else: if graft_type == GraftingType.NONE: logging.error("skipping preconditioning without grafting for param %s", @@ -2871,11 +2377,10 @@ def _transform_grad(grad, state, param, step): w = (1.0 - beta1) if moving_average_for_momentum else 1.0 shampoo_update_with_wd_momentum = ( - state.momentum.to_float() * beta1 + w * shampoo_update_with_wd) + state.momentum * beta1 + w * shampoo_update_with_wd) grafting_update_with_wd_momentum = ( - state.diagonal_momentum.to_float() * beta1 + - w * grafting_update_with_wd) + state.diagonal_momentum * beta1 + w * grafting_update_with_wd) run_shampoo = (step >= start_preconditioning_step).astype( grafting_update_with_wd_momentum.dtype) @@ -2904,28 +2409,26 @@ def _transform_grad(grad, state, param, step): new_diagonal_momentum = grafting_update_with_wd_momentum new_momentum = shampoo_update_with_wd_momentum - param_stats = ParameterStats( - _quantize_diagonal_statistics(new_diagonal_statistics), - state.statistics, - state.preconditioners, - _quantize_momentum(new_diagonal_momentum), - _quantize_momentum(new_momentum), - state.training_metrics) - + param_stats = ParameterStats(new_diagonal_statistics, + state.statistics, + state.preconditioners, + new_diagonal_momentum, + new_momentum, + state.training_metrics) return transformed_update, param_stats def update_fn(grads, state, params): """Transform the input gradient and update all statistics. - Args: - grads: the gradient tensors for the parameters and any custom gradients - for preconditioners. - state: a named tuple containing the state of the optimizer - params: the parameters that should be updated. + Args: + grads: the gradient tensors for the parameters and any custom + gradients for preconditioners. + state: a named tuple containing the state of the optimizer + params: the parameters that should be updated. - Returns: - A tuple containing the new parameters and the new optimizer state. - """ + Returns: + A tuple containing the new parameters and the new optimizer state. + """ grads_custom = None if custom_preconditioner and isinstance(grads, tuple): grads, grads_custom = grads @@ -2957,10 +2460,8 @@ def update_fn(grads, state, params): new_stats_flat, params_flat) updates_flat, new_stats_flat = list(zip(*outputs)) if outputs else ((), ()) - updates = jax.tree_util.tree_unflatten(treedef, updates_flat) new_stats = jax.tree_util.tree_unflatten(treedef, new_stats_flat) - new_state = ShampooState(count=state.count + 1, stats=new_stats) return updates, new_state diff --git a/baselines/shampoo/jax/submission.py b/baselines/shampoo/jax/submission.py index 422075e9d..cb062faf3 100644 --- a/baselines/shampoo/jax/submission.py +++ b/baselines/shampoo/jax/submission.py @@ -45,7 +45,9 @@ def jax_cosine_warmup(step_hint: int, hyperparameters): learning_rate=lr_schedule_fn, beta1=1.0 - hyperparameters.one_minus_beta1, beta2=hyperparameters.beta2, - weight_decay=hyperparameters.weight_decay) + weight_decay=hyperparameters.weight_decay, + batch_axis_name='batch', + eigh=False) params_zeros_like = jax.tree_map(lambda s: jnp.zeros(s.shape_tuple), workload.param_shapes) optimizer_state = opt_init_fn(params_zeros_like) diff --git a/datasets/dataset_setup.py b/datasets/dataset_setup.py index edcb58a7c..bc4502a24 100644 --- a/datasets/dataset_setup.py +++ b/datasets/dataset_setup.py @@ -65,7 +65,9 @@ # pylint: disable=logging-format-interpolation # pylint: disable=consider-using-with +import functools import os +import resource import shutil import subprocess import tarfile @@ -74,6 +76,7 @@ from absl import flags from absl import logging import requests +import tensorflow as tf import tensorflow_datasets as tfds from torchvision.datasets import CIFAR10 import tqdm @@ -85,6 +88,9 @@ FASTMRI_VAL_TAR_FILENAME = 'knee_singlecoil_val.tar' FASTMRI_TEST_TAR_FILENAME = 'knee_singlecoil_test.tar' +from algorithmic_efficiency.workloads.wmt import tokenizer +from algorithmic_efficiency.workloads.wmt.input_pipeline import \ + normalize_feature_names from datasets import librispeech_preprocess from datasets import librispeech_tokenizer @@ -334,17 +340,20 @@ def setup_fastmri(data_dir): def download_imagenet(data_dir, imagenet_train_url, imagenet_val_url): - data_dir = os.path.join(data_dir, 'imagenet') + imagenet_train_filepath = os.path.join(data_dir, IMAGENET_TRAIN_TAR_FILENAME) + imagenet_val_filepath = os.path.join(data_dir, IMAGENET_VAL_TAR_FILENAME) # Download imagnet train dataset - logging.info( - 'Downloading imagenet train dataset from {}'.format(imagenet_train_url)) - _download_url(url=imagenet_train_url, data_dir=data_dir).download() + if not os.path.exists(imagenet_train_filepath): + logging.info( + 'Downloading imagenet train dataset from {}'.format(imagenet_train_url)) + _download_url(url=imagenet_train_url, data_dir=data_dir).download() # Download imagenet val dataset - logging.info('Donwloading imagenet validation dataset from {}'.format( - imagenet_val_url)) - _download_url(url=imagenet_val_url, data_dir=data_dir).download() + if not os.path.exists(imagenet_val_filepath): + logging.info('Downloading imagenet validation dataset from {}'.format( + imagenet_val_url)) + _download_url(url=imagenet_val_url, data_dir=data_dir).download() # Download imagenet test set download_imagenet_v2(data_dir) @@ -367,15 +376,29 @@ def setup_imagenet_jax(data_dir): # Setup jax dataset dir imagenet_jax_data_dir = os.path.join(data_dir, 'jax') - os.makedirs(imagenet_jax_data_dir) - - # Copy tar file into jax - logging.info('Copying {} to {}'.format(train_tar_file_path, - imagenet_jax_data_dir)) - shutil.copy(train_tar_file_path, imagenet_jax_data_dir) - logging.info('Copying {} to {}'.format(val_tar_file_path, - imagenet_jax_data_dir)) - shutil.copy(val_tar_file_path, imagenet_jax_data_dir) + manual_download_dir = os.path.join(imagenet_jax_data_dir, + 'downloads', + 'manual') + os.makedirs(manual_download_dir, exist_ok=True) + + # Copy tar file into jax/downloads/manual + logging.info('Checking if tar files already exists in jax/downloads/manual.') + if not os.path.exists( + os.path.join(manual_download_dir, IMAGENET_TRAIN_TAR_FILENAME)): + logging.info('Copying {} to {}'.format(train_tar_file_path, + manual_download_dir)) + shutil.move(train_tar_file_path, manual_download_dir) + if not os.path.exists( + os.path.join(manual_download_dir, IMAGENET_VAL_TAR_FILENAME)): + logging.info('Copying {} to {}'.format(val_tar_file_path, + manual_download_dir)) + shutil.move(val_tar_file_path, manual_download_dir) + logging.info('Preparing imagenet data.') + resource.setrlimit(resource.RLIMIT_NOFILE, + (resource.RLIM_INFINITY, resource.RLIM_INFINITY)) + ds_builder = tfds.builder( + 'imagenet2012:5.1.0', data_dir=os.path.join(imagenet_jax_data_dir)) + ds_builder.download_and_prepare() logging.info('Set up imagenet dataset for jax framework complete') @@ -392,10 +415,10 @@ def setup_imagenet_pytorch(data_dir): # Copy tar file into pytorch directory logging.info('Copying {} to {}'.format(train_tar_file_path, imagenet_pytorch_data_dir)) - shutil.copy(train_tar_file_path, imagenet_pytorch_data_dir) + shutil.move(train_tar_file_path, imagenet_pytorch_data_dir) logging.info('Copying {} to {}'.format(val_tar_file_path, imagenet_pytorch_data_dir)) - shutil.copy(val_tar_file_path, imagenet_pytorch_data_dir) + shutil.move(val_tar_file_path, imagenet_pytorch_data_dir) # Extract train data\ logging.info('Extracting imagenet train data') @@ -439,16 +462,13 @@ def download_librispeech(dataset_dir, tmp_dir, train_tokenizer): # After extraction the result is a folder named Librispeech containing audio # files in .flac format along with transcripts containing name of audio file # and corresponding transcription. - tmp_librispeech_dir = os.path.join(tmp_dir, 'librispeech') + tmp_librispeech_dir = os.path.join(tmp_dir, 'LibriSpeech') + _maybe_mkdir(tmp_librispeech_dir) for split in ['dev', 'test']: for version in ['clean', 'other']: - wget_cmd = ( - f'wget --directory-prefix={tmp_librispeech_dir} ' - f'http://www.openslr.org/resources/12/{split}-{version}.tar.gz') - subprocess.Popen(wget_cmd, shell=True).communicate() - subprocess.Popen( - f'tar xzvf {split}-{version}.tar.gz', shell=True).communicate() + wget_cmd = f'wget http://www.openslr.org/resources/12/{split}-{version}.tar.gz -O - | tar xz' # pylint: disable=line-too-long + subprocess.Popen(wget_cmd, shell=True, cwd=tmp_dir).communicate() tars = [ 'raw-metadata.tar.gz', @@ -457,17 +477,14 @@ def download_librispeech(dataset_dir, tmp_dir, train_tokenizer): 'train-other-500.tar.gz', ] for tar_filename in tars: - wget_cmd = (f'wget --directory-prefix={tmp_librispeech_dir} ' - f'http://www.openslr.org/resources/12/{tar_filename}') - subprocess.Popen(wget_cmd, shell=True) - tar_path = os.path.join(tmp_librispeech_dir, tar_filename) - subprocess.Popen(f'tar xzvf {tar_path}', shell=True) + wget_cmd = f'wget http://www.openslr.org/resources/12/{tar_filename} -O - | tar xz ' # pylint: disable=line-too-long + subprocess.Popen(wget_cmd, shell=True, cwd=tmp_dir).communicate() if train_tokenizer: - librispeech_tokenizer.run(train=True, data_dir=tmp_librispeech_dir) + tokenizer_vocab_path = librispeech_tokenizer.run( + train=True, data_dir=tmp_librispeech_dir) # Preprocess data. - tokenizer_vocab_path = os.path.join(tmp_librispeech_dir, 'spm_model.vocab') librispeech_dir = os.path.join(dataset_dir, 'librispeech') librispeech_preprocess.run( input_dir=tmp_librispeech_dir, @@ -486,7 +503,18 @@ def download_ogbg(data_dir): def download_wmt(data_dir): """WMT14 and WMT17 de-en.""" for ds_name in ['wmt14_translate/de-en:1.0.0', 'wmt17_translate/de-en:1.0.0']: - tfds.builder(ds_name, data_dir=data_dir).download_and_prepare() + dataset_builder = tfds.builder(ds_name, data_dir=data_dir) + dataset_builder.download_and_prepare() + + if ds_name == 'wmt17_translate/de-en:1.0.0': + ds = dataset_builder.as_dataset(split='train', shuffle_files=False) + ds = ds.map( + functools.partial(normalize_feature_names, dataset_builder.info), + num_parallel_calls=tf.data.AUTOTUNE) + # Tokenize data. + vocab_path = os.path.join(data_dir, 'wmt_sentencepiece_model') + tokenizer.train_tokenizer( + ds, vocab_path=vocab_path, vocab_size=32000, max_corpus_chars=10**7) def main(_): @@ -543,8 +571,9 @@ def main(_): raise ValueError( 'Please specify either jax or pytorch framework through framework ' 'flag.') - download_imagenet(data_dir, imagenet_train_url, imagenet_val_url) - setup_imagenet(data_dir, framework=FLAGS.framework) + imagenet_data_dir = os.path.join(data_dir, 'imagenet') + download_imagenet(imagenet_data_dir, imagenet_train_url, imagenet_val_url) + setup_imagenet(imagenet_data_dir, framework=FLAGS.framework) if FLAGS.all or FLAGS.librispeech: logging.info('Downloading Librispeech...') diff --git a/datasets/librispeech_preprocess.py b/datasets/librispeech_preprocess.py index 991954ae5..2ce8d79ca 100644 --- a/datasets/librispeech_preprocess.py +++ b/datasets/librispeech_preprocess.py @@ -73,7 +73,7 @@ def report_progress(count, total, start_time): sys.stdout.flush() -def preprocess_data(data_folder, tokenizer, split): +def preprocess_data(in_folder, out_folder, tokenizer, split): finished = Counter() skipped = Counter() start_time = time.time() @@ -111,8 +111,8 @@ def process(index): targets = tokenizer.tokenize(trans).numpy().astype(np.int32) - np.save('data/{}/{}_audio.npy'.format(split, utt), sound) - np.save('data/{}/{}_targets.npy'.format(split, utt), targets) + np.save('{}/{}/{}_audio.npy'.format(out_folder, split, utt), sound) + np.save('{}/{}/{}_targets.npy'.format(out_folder, split, utt), targets) finished.inc() report_progress(finished.val() + skipped.val(), @@ -123,9 +123,9 @@ def process(index): return utterance_ids paths = [] - for _, speaker_folder in enumerate(os.listdir(data_folder)): - for chapter_folder in os.listdir(f'{data_folder}/{speaker_folder}'): - paths.append((data_folder, speaker_folder, chapter_folder)) + for _, speaker_folder in enumerate(os.listdir(in_folder)): + for chapter_folder in os.listdir(f'{in_folder}/{speaker_folder}'): + paths.append((in_folder, speaker_folder, chapter_folder)) sys.stdout.write('\r') pool = multiprocessing.dummy.Pool(32) @@ -167,9 +167,11 @@ def run(input_dir, output_dir, tokenizer_vocab_path): ] for subset in subset_list: logging.info('Processing split = %s...', subset) - subset_dir = os.path.join(output_dir, subset) - os.makedirs(subset_dir, exist_ok=True) - example_ids, num_entries = preprocess_data(subset_dir, tokenizer, subset) + in_dir = os.path.join(input_dir, subset) + out_dir = os.path.join(output_dir, subset) + os.makedirs(out_dir, exist_ok=True) + example_ids, num_entries = preprocess_data( + in_dir, output_dir, tokenizer, subset) if num_entries != librispeech_example_counts[subset]: raise ValueError('Preprocessed dataframe final count not equal to ' diff --git a/datasets/librispeech_tokenizer.py b/datasets/librispeech_tokenizer.py index 7019bf19f..71aa719c2 100644 --- a/datasets/librispeech_tokenizer.py +++ b/datasets/librispeech_tokenizer.py @@ -122,7 +122,7 @@ def run(train, data_dir): if train: logging.info('Training...') splits = ['train-clean-100'] - train_tokenizer(data_dir, splits) + return train_tokenizer(data_dir, splits) else: tokenizer = load_tokenizer(os.path.join(data_dir, 'spm_model.vocab')) test_input = 'OPEN SOURCE ROCKS' diff --git a/docker/Dockerfile b/docker/Dockerfile index 6c9e42566..d2d946851 100644 --- a/docker/Dockerfile +++ b/docker/Dockerfile @@ -1,3 +1,10 @@ +# Dockerfile for AlgoPerf environment. +# To build Docker image with only Jax GPU installed: +# docker build -t --build-arg framework=jax +# To build Docker image with Pytorch GPU installed: +# docker build -t --build-arg framework=pytorch + +# To build Docker image FROM nvidia/cuda:11.8.0-cudnn8-devel-ubuntu20.04 # Installing machine packages @@ -21,12 +28,34 @@ RUN mkdir -p experiment_runs/ # Install Algorithmic efficiency repo RUN echo "Setting up algorithmic_efficiency repo" -ARG GIT_URL=https://github.com/mlcommons/algorithmic-efficiency.git -RUN git clone $GIT_URL && cd algorithmic-efficiency +ARG branch="main" +ARG framework="both" +ARG git_url=https://github.com/mlcommons/algorithmic-efficiency.git +RUN git clone $git_url && cd algorithmic-efficiency +RUN cd algorithmic-efficiency && git checkout $branch RUN cd algorithmic-efficiency && pip install -e '.[full]' -RUN cd algorithmic-efficiency && pip install -e '.[jax_gpu]' -f 'https://storage.googleapis.com/jax-releases/jax_cuda_releases.html' -RUN cd algorithmic-efficiency && pip install -e '.[pytorch_cpu]' -f 'https://download.pytorch.org/whl/torch_stable.html' + +RUN if [ "$framework" = "jax" ] ; then \ + echo "Installing Jax GPU" \ + && cd algorithmic-efficiency \ + && pip install -e '.[jax_gpu]' -f 'https://storage.googleapis.com/jax-releases/jax_cuda_releases.html' \ + && pip install -e '.[pytorch_cpu]' -f 'https://download.pytorch.org/whl/torch_stable.html'; \ + elif [ "$framework" = "pytorch" ] ; then \ + echo "Installing Pytorch GPU" \ + && cd algorithmic-efficiency \ + && pip install -e '.[jax_cpu]' \ + && pip install -e '.[pytorch_gpu]' -f 'https://download.pytorch.org/whl/torch_stable.html'; \ + elif [ "$framework" = "both" ] ; then \ + echo "Installing Jax GPU and Pytorch GPU" \ + && cd algorithmic-efficiency \ + && pip install -e '.[jax_gpu]' -f 'https://storage.googleapis.com/jax-releases/jax_cuda_releases.html' \ + && pip install -e '.[pytorch_gpu]' -f 'https://download.pytorch.org/whl/torch_stable.html'; \ + else \ + echo "Invalid build-arg $framework: framework should be either jax, pytorch or both." >&2 \ + && exit 1 ; \ + fi + RUN cd algorithmic-efficiency && pip install -e '.[wandb]' RUN cd algorithmic-efficiency && git fetch origin diff --git a/docker/README.md b/docker/README.md index 163555e44..7fac6df77 100644 --- a/docker/README.md +++ b/docker/README.md @@ -1,35 +1,35 @@ -# Docker Instructions +## Docker Instructions -## General +### General -### Prerequisites +#### Prerequisites You may have to install the NVIDIA Container Toolkit so that the containers can locate the NVIDIA drivers and GPUs. If you are working with a GCP VM with Container Optimized OS setup, you will have to mount the NVIDIA drivers and devices on `docker run` command (see below). -### Building Image +#### Building Image From `algorithmic-efficiency/docker/` run: ``` docker build -t . ``` -### Container Entry Point Flags +#### Container Entry Point Flags You can run a container that will download data to the host VM (if not already downloaded), run a submission or both. If you only want to download data you can run the container with just the `-d` and `-f` flags (`-f` is only required if `-d` is 'imagenet'). If you want to run a submission the `-d`, `-f`, `-s`, `-t`, `-e`, `-w` flags are all required to locate the data and run the submission script. The container entrypoint script provides the following flags: -- `-d` dataset: can be 'imagenet', 'fastmri', 'librispeech', 'criteo', 'wmt', or 'ogbg'. Setting this flag will download data if `~/data/` does not exist on the host machine. Required for running a submission. +- `-d` dataset: can be 'imagenet', 'fastmri', 'librispeech', 'criteo1tb', 'wmt', or 'ogbg'. Setting this flag will download data if `~/data/` does not exist on the host machine. Required for running a submission. - `-f` framework: can be either 'pytorch' or 'jax'. If you just want to download data, this flag is required for `-d imagenet` since we have two versions of data for imagenet. This flag is also required for running a submission. - `-s` submission_path: path to submission file on container filesystem. If this flag is set, the container will run a submission, so it is required for running a submission. - `-t` tuning_search_space: path to file containing tuning search space on container filesystem. Required for running a submission. - `-e` experiment_name: name of experiment. Required for running a submission. -- `-w` workload: can be 'imagenet_resnet', 'imagenet_jax', 'librispeech_deepspeech', 'librispeech_conformer', 'ogbg', 'wmt', 'fastmri' or 'criteo'. Required for running a submission. +- `-w` workload: can be 'imagenet_resnet', 'imagenet_jax', 'librispeech_deepspeech', 'librispeech_conformer', 'ogbg', 'wmt', 'fastmri' or 'criteo1tb'. Required for running a submission. - `-m` max_steps: maximum number of steps to run the workload for. Optional. - `-b` debugging_mode: can be true or false. If `-b ` (debugging_mode) is `true` the main process on the container will persist. -### Starting container w end-to-end submission runner +#### Starting container w end-to-end submission runner To run the docker container that will download data (if not found host) and run a submisison run: ``` docker run -t -d \ @@ -51,7 +51,7 @@ This will print the container ID to the terminal. If debugging_mode is `true` the main process on the container will persist after finishing the submission runner. -### Starting a container with automated data download +#### Starting a container with automated data download To run a docker container that will only download data (if not found on host): ``` docker run -t -d \ @@ -68,7 +68,7 @@ docker run -t -d \ If debugging_mode is `true` the main process on the container will persist after finishing the data download. This run command is useful if you manually want to run a sumbission or look around. -### Interacting with the container +#### Interacting with the container To find the container IDs of running containers run: ``` docker ps diff --git a/docker/build_docker_images.sh b/docker/build_docker_images.sh new file mode 100644 index 000000000..4a5ae08dc --- /dev/null +++ b/docker/build_docker_images.sh @@ -0,0 +1,34 @@ +# Bash script to build and push dev docker images to artifact repo +# Usage: +# bash build_docker_images.sh -b + +while getopts b: flag +do + case "${flag}" in + b) GIT_BRANCH=${OPTARG};; + esac +done + +if [[ -z ${GIT_BRANCH+x} ]] +then +GIT_BRANCH='main' # Set default argument +fi + +for FRAMEWORK in "jax" "pytorch" "both" +do + IMAGE_NAME="algoperf_${FRAMEWORK}_${GIT_BRANCH}" + DOCKER_BUILD_COMMAND="docker build --no-cache -t $IMAGE_NAME . --build-arg framework=$FRAMEWORK --build-arg branch=$GIT_BRANCH" + DOCKER_TAG_COMMAND="docker tag $IMAGE_NAME us-central1-docker.pkg.dev/training-algorithms-external/mlcommons-docker-repo/$IMAGE_NAME" + DOCKER_PUSH_COMMAND="docker push us-central1-docker.pkg.dev/training-algorithms-external/mlcommons-docker-repo/$IMAGE_NAME" + DOCKER_PULL_COMMAND="docker pull us-central1-docker.pkg.dev/training-algorithms-external/mlcommons-docker-repo/$IMAGE_NAME" + + echo "On branch: ${GIT_BRANCH}" + echo $DOCKER_BUILD_COMMAND + eval $DOCKER_BUILD_COMMAND + echo $DOCKER_TAG_COMMAND + eval $DOCKER_TAG_COMMAND + echo $DOCKER_PUSH_COMMAND + eval $DOCKER_PUSH_COMMAND + echo "To pull container run: " + echo $DOCKER_PULL_COMMAND +done \ No newline at end of file diff --git a/docker/scripts/startup.sh b/docker/scripts/startup.sh index e6b703621..c76340397 100644 --- a/docker/scripts/startup.sh +++ b/docker/scripts/startup.sh @@ -1,9 +1,16 @@ #!/bin/bash +# Bash script to run submission_runner.py. +# The primary purpose of this script is to serve as +# an entrypoint for our Docker images. +# Internal contributors may enable this script +# to download data and upload experiment results to +# our algorithmic-efficiency repo. To do so +# set the -i flag to true. # Defaults DEBUG_MODE="false" -while getopts d:f:s:t:e:w:b:m:o:c:r: flag +while getopts d:f:s:t:e:w:b:m:o:c:r:i: flag do case "${flag}" in d) DATASET=${OPTARG};; @@ -17,51 +24,72 @@ do o) OVERWRITE=${OPTARG};; c) SAVE_CHECKPOINTS=${OPTARG};; r) RSYNC_DATA=${OPTARG};; - + i) INTERNAL_CONTRIBUTOR_MODE=${OPTARG};; esac done +# Check if arguments are valid +VALID_DATASETS=("criteo1tb" "imagenet" "fastmri" "ogbg" "librispeech" \ + "wmt" "mnist") +VALID_WORKLOADS=("criteo1tb" "imagenet_resnet" "imagenet_vit" "fastmri" "ogbg" \ + "wmt" "librispeech_deepspeech" "librispeech_conformer" "mnist") + +if [[ -n ${DATASET+x} ]]; then + if [[ ! " ${VALID_DATASETS[@]} " =~ " $DATASET " ]]; then + echo "Error: invalid argument for dataset (d)." + exit 1 + fi +fi + +if [[ -n ${WORKLOAD+x} ]]; then + if [[ ! " ${VALID_WORKLOADS[@]} " =~ " $WORKLOAD " ]]; then + echo "Error: invalid argument for workload (w)." + exit 1 + fi +fi + +# Set data and experiment paths ROOT_DATA_BUCKET="gs://mlcommons-data" ROOT_DATA_DIR="/data" EXPERIMENT_BUCKET="gs://mlcommons-runs" EXPERIMENT_DIR="/experiment_runs" -if [[ "${FRAMEWORK}" == "jax" ]] -then +# Set run command prefix depending on framework +if [[ "${FRAMEWORK}" == "jax" ]]; then COMMAND_PREFIX="python3" else COMMAND_PREFIX="torchrun --redirects 1:0,2:0,3:0,4:0,5:0,6:0,7:0 --standalone --nnodes=1 --nproc_per_node=8" fi -if [[ "${DATASET}" == "imagenet" ]] -then +# Set data directory and bucket (bucket is only relevant in internal mode) +if [[ "${DATASET}" == "imagenet" ]]; then DATA_DIR="${ROOT_DATA_DIR}/${DATASET}/${FRAMEWORK}" DATA_BUCKET="${ROOT_DATA_BUCKET}/${DATASET}/${FRAMEWORK}" -else +elif [[ ! -z "${DATASET}" ]]; then DATA_DIR="${ROOT_DATA_DIR}/${DATASET}" DATA_BUCKET="${ROOT_DATA_BUCKET}/${DATASET}" fi # Copy data from MLCommons bucket if data has not been downloaded yet -if [[ -z ${RSYNC_DATA+x} ]] -then -RSYNC_DATA='true' # Set default argument +if [[ -z ${INTERNAL_CONTRIBUTOR_MODE+x} ]]; then + INTERNAL_CONTRIBUTOR_MODE='false' # Set default for contributor mode to false fi -if [[ ! -d ${DATA_DIR} ]] && [[${RSYNC_DATA} == 'true']] -then - mkdir -p ${DATA_DIR} +if [[ -z ${RSYNC_DATA+x} ]]; then + RSYNC_DATA='true' # Set default value for rsync to true fi -./google-cloud-sdk/bin/gsutil -m rsync -r ${DATA_BUCKET} ${DATA_DIR} +if [[ ! -z $DATA_DIR ]] && [[ ! -d ${DATA_DIR} ]]; then + mkdir -p ${DATA_DIR} +fi -# Check GPU requirements and run experiment -# python3 scripts/check_gpu.py +if [[ ! -z $DATA_DIR ]] && [[ ${RSYNC_DATA} == 'true' ]] && [[ $INTERNAL_CONTRIBUTOR_MODE == 'true' ]]; then + ./google-cloud-sdk/bin/gsutil -m rsync -r ${DATA_BUCKET} ${DATA_DIR} +fi # Optionally run workload if SUBMISSION_PATH is set -if [[ ! -z ${SUBMISSION_PATH+x} ]] - then +if [[ ! -z ${SUBMISSION_PATH+x} ]]; then NOW=$(date +"%m-%d-%Y-%H-%M-%S") LOG_DIR="/logs" LOG_FILE="$LOG_DIR/${WORKLOAD}_${FRAMEWORK}_${NOW}.log" @@ -69,30 +97,30 @@ if [[ ! -z ${SUBMISSION_PATH+x} ]] cd algorithmic-efficiency # Optionally define max steps flag for submission runner - if [[ ! -z ${MAX_STEPS+x} ]] - then + if [[ ! -z ${MAX_STEPS+x} ]]; then MAX_STEPS_FLAG="--max_global_steps=${MAX_STEPS}" fi # Set overwrite flag to false by default if not set - if [[ -z ${OVERWRITE+x} ]] - then + if [[ -z ${OVERWRITE+x} ]]; then OVERWRITE='False' fi - if [[ -z ${SAVE_CHECKPOINTS+x} ]] - then + if [[ -z ${SAVE_CHECKPOINTS+x} ]]; then SAVE_CHECKPOINTS='True' fi # Define special flags for imagenet and librispeech workloads - if [[ ${DATASET} == 'imagenet' ]] - then + if [[ ${DATASET} == 'imagenet' ]]; then SPECIAL_FLAGS="--imagenet_v2_data_dir=${DATA_DIR}" - elif [[ ${DATASET} == 'librispeech' ]] - then + elif [[ ${DATASET} == 'librispeech' ]]; then SPECIAL_FLAGS="--librispeech_tokenizer_vocab_path=${DATA_DIR}/spm_model.vocab" fi + + # Optionally run torch compile + if [[ ${FRAMEWORK} == "pytorch" ]]; then + TORCH_COMPILE_FLAG="--torch_compile=True" + fi # The TORCH_RUN_COMMAND_PREFIX is only set if FRAMEWORK is "pytorch" COMMAND="${COMMAND_PREFIX} submission_runner.py \ @@ -107,12 +135,17 @@ if [[ ! -z ${SUBMISSION_PATH+x} ]] --overwrite=${OVERWRITE} \ --save_checkpoints=${SAVE_CHECKPOINTS} \ ${MAX_STEPS_FLAG} \ - ${SPECIAL_FLAGS} 2>&1 | tee -a ${LOG_FILE}" + ${SPECIAL_FLAGS} \ + ${TORCH_COMPILE_FLAG} 2>&1 | tee -a ${LOG_FILE}" echo $COMMAND > ${LOG_FILE} + echo $COMMAND eval $COMMAND + RETURN_CODE=$? - /google-cloud-sdk/bin/gsutil -m cp -r ${EXPERIMENT_DIR}/${EXPERIMENT_NAME}/${WORKLOAD}_${FRAMEWORK} ${EXPERIMENT_BUCKET}/${EXPERIMENT_NAME}/ - /google-cloud-sdk/bin/gsutil -m cp ${LOG_FILE} ${EXPERIMENT_BUCKET}/${EXPERIMENT_NAME}/${WORKLOAD}_${FRAMEWORK}/ + if [[ $INTERNAL_CONTRIBUTOR_MODE == 'true' ]]; then + /google-cloud-sdk/bin/gsutil -m cp -r ${EXPERIMENT_DIR}/${EXPERIMENT_NAME}/${WORKLOAD}_${FRAMEWORK} ${EXPERIMENT_BUCKET}/${EXPERIMENT_NAME}/ + /google-cloud-sdk/bin/gsutil -m cp ${LOG_FILE} ${EXPERIMENT_BUCKET}/${EXPERIMENT_NAME}/${WORKLOAD}_${FRAMEWORK}/ + fi fi @@ -124,3 +157,5 @@ then sleep 1000 done fi + +exit $RETURN_CODE diff --git a/getting_started.md b/getting_started.md index e5ed30416..515fbe472 100644 --- a/getting_started.md +++ b/getting_started.md @@ -1,12 +1,22 @@ # Getting Started +Table of Contents: +- [Set up and installation](#workspace-set-up-and-installation) +- [Download the data](#download-the-data) +- [Develop your submission](#develop-your-submission) +- [Run your submission](#run-your-submission) + - [Docker](#run-your-submission-in-a-docker-container) +- [Score your submission](#score-your-submission) + ## Workspace set up and installation To get started you will have to make a few decisions and install the repository along with its dependencies. Specifically: 1. Decide if you would like to develop your submission in either Pytorch or Jax. -2. Set up your workstation or VM. We recommend to use a setup similar to the [benchmarking hardware](https://github.com/mlcommons/algorithmic-efficiency/blob/main/RULES.md#benchmarking-hardware) which consists out of 8 V100 GPUs, 240 GB in RAM and 2 TB in storage for dataset storage. For further recommendations on setting up your own Cloud VM see [here](https://github.com/mlcommons/algorithmic-efficiency/blob/main/docker/README.md#gcp-integration). -4. Clone the algorithmic-efficiency repository and make sure you have installed the dependencies. To install the dependencies we recommend either: - 1. Installing in a python virtual environment as described in the [Installation section in the README](https://github.com/mlcommons/algorithmic-efficiency/blob/main/README.md) instructions. - 2. Use a Docker container with dependencies installed. This option will guarantee that you're code is running on the same CUDA, cudnn, and python dependencies as the competition scoring environment. See the [Docker README](https://github.com/mlcommons/algorithmic-efficiency/blob/main/docker/README.md) for instructions on the Docker workflow. + 2. Set up your workstation or VM. We recommend to use a setup similar to the [benchmarking hardware](https://github.com/mlcommons/algorithmic-efficiency/blob/main/RULES.md#benchmarking-hardware). + The specs on the benchmarking machines are: + - 8 V100 GPUs + - 240 GB in RAM + - 2 TB in storage (for datasets). +3. Install the algorithmic package and dependencies, see [Installation](./README.md#installation). ## Download the data The workloads in this benchmark use 6 different datasets across 8 workloads. You may choose to download some or all of the datasets as you are developing your submission, but your submission will be scored across all 8 workloads. For instructions on obtaining and setting up the datasets see [datasets/README](https://github.com/mlcommons/algorithmic-efficiency/blob/main/datasets/README.md#dataset-setup). @@ -49,10 +59,10 @@ A submission for the external ruleset will consist of a submission module and a ## Run your submission -You can evaluate your submission with the `submission_runner.py` module on one workload at a time. -### JAX submissions -To score your submission on a workload, from the algorithmic-efficency directory run: +From your virtual environment or interactively running Docker container run your submission with `submission_runner.py`: + +**JAX**: to score your submission on a workload, from the algorithmic-efficency directory run: ```bash python3 submission_runner.py \ --framework=jax \ @@ -63,8 +73,7 @@ python3 submission_runner.py \ --tuning_search_space= ``` -### PyTorch submissions -To score your submission on a workload, from the algorithmic-efficency directory run: +**Pytorch**: to score your submission on a workload, from the algorithmic-efficency directory run: ```bash python3 submission_runner.py \ --framework=pytorch \ @@ -97,10 +106,63 @@ torchrun --redirects 1:0,2:0,3:0,4:0,5:0,6:0,7:0 \ --tuning_search_space= ``` -## Run your submission in a Docker container -TODO(kasimbeg) +### Run your submission in a Docker container + +The container entrypoint script provides the following flags: +- `-d` dataset: can be 'imagenet', 'fastmri', 'librispeech', 'criteo1tb', 'wmt', or 'ogbg'. Setting this flag will download data if `~/data/` does not exist on the host machine. Required for running a submission. +- `-f` framework: can be either 'pytorch' or 'jax'. If you just want to download data, this flag is required for `-d imagenet` since we have two versions of data for imagenet. This flag is also required for running a submission. +- `-s` submission_path: path to submission file on container filesystem. If this flag is set, the container will run a submission, so it is required for running a submission. +- `-t` tuning_search_space: path to file containing tuning search space on container filesystem. Required for running a submission. +- `-e` experiment_name: name of experiment. Required for running a submission. +- `-w` workload: can be 'imagenet_resnet', 'imagenet_jax', 'librispeech_deepspeech', 'librispeech_conformer', 'ogbg', 'wmt', 'fastmri' or 'criteo1tb'. Required for running a submission. +- `-m` max_steps: maximum number of steps to run the workload for. Optional. +- `-b` debugging_mode: can be true or false. If `-b ` (debugging_mode) is `true` the main process on the container will persist. + + +To run the docker container that will run the submission runner run: +```bash +docker run -t -d \ +-v $HOME/data/:/data/ \ +-v $HOME/experiment_runs/:/experiment_runs \ +-v $HOME/experiment_runs/logs:/logs \ +--gpus all \ +--ipc=host \ + \ +-d \ +-f \ +-s \ +-t \ +-e \ +-w \ +-b +``` +This will print the container ID to the terminal. +If debugging_mode is `true` the main process on the container will persist after finishing the submission runner. + +#### Docker Tips #### + +To find the container IDs of running containers +``` +docker ps +``` + +To see output of the entrypoint script +``` +docker logs +``` + +To enter a bash session in the container +``` +docker exec -it /bin/bash +``` + ## Score your submission -TODO(kasimbeg) +To produce performance profile and performance table: +```bash +python3 scoring/score_submission.py --experiment_path= --output_dir= +``` + + ## Good Luck! diff --git a/reference_algorithms/target_setting_algorithms/jax_submission_base.py b/reference_algorithms/target_setting_algorithms/jax_submission_base.py index c7bb6d332..2a641b520 100644 --- a/reference_algorithms/target_setting_algorithms/jax_submission_base.py +++ b/reference_algorithms/target_setting_algorithms/jax_submission_base.py @@ -101,7 +101,8 @@ def update_params(workload: spec.Workload, label_smoothing) # Log loss, grad_norm. - if global_step % 100 == 0 and workload.metrics_logger is not None: + if ((global_step <= 100 or global_step % 500 == 0) and + workload.metrics_logger is not None): workload.metrics_logger.append_scalar_metrics( { 'loss': loss[0], diff --git a/scoring/score_submission.py b/scoring/score_submission.py new file mode 100644 index 000000000..42a605dac --- /dev/null +++ b/scoring/score_submission.py @@ -0,0 +1,46 @@ +import os + +from absl import app +from absl import flags +from absl import logging +import scoring_utils + +from algorithmic_efficiency import workloads +import scoring + +flags.DEFINE_string( + 'experiment_path', + None, + 'Path to experiment directory containing workload directories.') +flags.DEFINE_string('submission_tag', 'my.submission', 'Submission tag.') +flags.DEFINE_string('output_dir', + 'scoring_results', + 'Path to save performance profile table and plot.') +FLAGS = flags.FLAGS + + +def main(_): + df = scoring_utils.get_experiment_df(FLAGS.experiment_path) + results = { + FLAGS.submission_tag: df, + } + performance_profile_df = scoring.compute_performance_profiles( + results, + time_col='score', + min_tau=1.0, + max_tau=None, + reference_submission_tag=None, + num_points=100, + scale='linear', + verbosity=0) + if not os.path.exists(FLAGS.output_dir): + os.mkdir(FLAGS.output_dir) + scoring.plot_performance_profiles( + performance_profile_df, 'score', save_dir=FLAGS.output_dir) + + logging.info(performance_profile_df) + + +if __name__ == '__main__': + flags.mark_flag_as_required('experiment_path') + app.run(main) diff --git a/scoring/scoring.py b/scoring/scoring.py index ba19ec026..fff152255 100644 --- a/scoring/scoring.py +++ b/scoring/scoring.py @@ -13,13 +13,13 @@ identifier for a submission and each value is a DataFrame containing one row per trial per workload in that submission. At minimum, this DataFrame should include a column of np.arrays indicating time (e.g., 'global_step'), a column - of np.arrays indicating performance (e.g., 'valid/accuracy') for each + of np.arrays indicating performance (e.g., 'validation/accuracy') for each workload and a column 'workload' that indicates the workload identifier. 2. A dictionary of workload metadata describing each workload in the form: { 'workload_identifier': { 'target': VALUE, - 'metric': 'valid/error_rate', + 'metric': 'validation/error_rate', } } The keys in this dictionary should match the workload identifiers used in @@ -29,11 +29,18 @@ import itertools import operator import os +import re import matplotlib.pyplot as plt import numpy as np import pandas as pd +import algorithmic_efficiency.workloads.workloads as workloads_registry + +WORKLOADS = workloads_registry.WORKLOADS +WORKLOAD_NAME_PATTERN = '(.*)(_jax|_pytorch)' +BASE_WORKLOADS_DIR = 'algorithmic_efficiency/workloads/' + MIN_EVAL_METRICS = [ 'ce_loss', 'error_rate', @@ -41,11 +48,12 @@ 'wer', 'l1_loss', ] -MAX_EVAL_METRICS = ['average_precision', 'ssim', 'bleu_score'] + +MAX_EVAL_METRICS = ['average_precision', 'ssim', 'accuracy', 'bleu_score'] def generate_eval_cols(metrics): - splits = ['train', 'valid', 'test'] + splits = ['train', 'validation', 'test'] return [f'{split}/{col}' for split, col in itertools.product(splits, metrics)] @@ -59,7 +67,6 @@ def check_if_minimized(col_name): """Guess if the eval metric column name should be minimized or not.""" for prefix in ['best_', 'final_']: col_name = col_name.replace(prefix, '') - for col in MINIMIZE_REGISTRY: if col in col_name: return MINIMIZE_REGISTRY[col] @@ -74,7 +81,8 @@ def get_index_that_reaches_best(workload_df, metric_col): Args: workload_df: A subset of a submission's trials DataFrame that includes only the trials in a single workload. - metric_col: Name of array column in workload_df (e.g., `valid/l1_loss`). + metric_col: Name of array column in workload_df + (e.g., `validation/l1_loss`). Returns: Tuple of trial index, time index, and best value where the workload @@ -98,27 +106,39 @@ def get_index_that_reaches_best(workload_df, metric_col): return trial, best_idx[trial], best[trial] -def get_index_that_reaches_target(workload_df, metric_col, target): +def get_index_that_reaches_target(workload_df, + validation_metric, + test_metric, + validation_target, + test_target): """Get the eval index in which a workload reaches the target metric_col. Args: workload_df: A subset of a submission's trials DataFrame that includes only the trials in a single workload. - metric_col: Name of array column in workload_df (e.g., `valid/l1_loss`). + metric_col: Name of array column in workload_df (e.g., `validation/l1_loss`). target: Target value for metric_col. Returns: Tuple of trial index and time index where the workload reached the target metric_col. Return (-1, -1) if not reached. """ - is_minimized = check_if_minimized(metric_col) - series = workload_df[metric_col] + is_minimized = check_if_minimized(validation_metric) + validation_series = workload_df[validation_metric] + test_series = workload_df[test_metric] - series = series[series != np.nan] + validation_series = validation_series[validation_series != np.nan] + validation_series = validation_series[test_series != np.nan] + test_series = test_series[validation_series != np.nan] + test_series = test_series[test_series != np.nan] op = operator.le if is_minimized else operator.ge - target_reached = series.apply(lambda x: op(x, target)) + validation_target_reached = validation_series.apply( + lambda x: op(x, validation_target)) + test_target_reached = test_series.apply(lambda x: op(x, test_target)) + target_reached = pd.Series(validation_target_reached[0] + & test_target_reached[0]) # Remove trials that never reach the target target_reached = target_reached[target_reached.apply(np.any)] @@ -134,7 +154,6 @@ def get_index_that_reaches_target(workload_df, metric_col, target): def get_times_for_submission(submission, submission_tag, - workload_metadata, time_col='global_step', verbosity=1): """Get times to target for each workload in a submission. @@ -143,8 +162,6 @@ def get_times_for_submission(submission, submission: A DataFrame containing one row for each trial in each workload for a given submission. submission_tag: Globally unique identified for a submission. - workload_metadata: Dictionary keyed by workload names with value of - dictionary with `target` and `metric` as keys. time_col: A string indicating which column to use for time. verbosity: Debug level of information; choice of (1, 2, 3). @@ -155,9 +172,28 @@ def get_times_for_submission(submission, submission_name = submission_tag.split('.')[1] for workload, group in submission.groupby('workload'): - metric = workload_metadata[workload]['metric'] - target = workload_metadata[workload]['target'] - trial_idx, time_idx = get_index_that_reaches_target(group, metric, target) + workload_name = re.match(WORKLOAD_NAME_PATTERN, workload).group(1) + framework = re.match(WORKLOAD_NAME_PATTERN, workload).group(2) + workload_metadata = WORKLOADS[workload_name] + + # Extend path according to framework. + workload_metadata['workload_path'] = os.path.join( + BASE_WORKLOADS_DIR, + workload_metadata['workload_path'] + f'{framework}', + 'workload.py') + workload_init_kwargs = {} + workload_obj = workloads_registry.import_workload( + workload_path=workload_metadata['workload_path'], + workload_class_name=workload_metadata['workload_class_name'], + workload_init_kwargs=workload_init_kwargs) + metric_name = workload_obj.target_metric_name + validation_metric = f'validation/{metric_name}' + test_metric = f'test/{metric_name}' + validation_target = workload_obj.validation_target_value + test_target = workload_obj.test_target_value + + trial_idx, time_idx = get_index_that_reaches_target( + group, validation_metric, test_metric, validation_target, test_target) if time_idx > -1: time_val = group[time_col].loc[trial_idx][time_idx] else: @@ -184,7 +220,6 @@ def get_times_for_submission(submission, def compute_performance_profiles(results, - workload_metadata, time_col='global_step', min_tau=1.0, max_tau=None, @@ -199,8 +234,6 @@ def compute_performance_profiles(results, trials where each row is a trial and each column is a field for a given trial. Results should contain keys for each workload's metric, time_col, 'workload'. See file header comment for more details. - workload_metadata: Dictionary keyed by workload names with value of - dictionary with `target` and `metric` as keys. time_col: A string indicating which column to use for time. min_tau: Minimum tau to use for plotting. max_tau: Maximum tau to use for plotting. @@ -224,11 +257,7 @@ def compute_performance_profiles(results, print(f'\nComputing performance profile with respect to `{time_col}` for ' f'{submission_tag}') dfs.append( - get_times_for_submission(result, - submission_tag, - workload_metadata, - time_col, - verbosity)) + get_times_for_submission(result, submission_tag, time_col, verbosity)) df = pd.concat(dfs) if verbosity > 0: diff --git a/scoring/scoring_utils.py b/scoring/scoring_utils.py index bb346acdc..37db73dd4 100644 --- a/scoring/scoring_utils.py +++ b/scoring/scoring_utils.py @@ -2,10 +2,13 @@ import os import re +from absl import logging import pandas as pd -trial_line_regex = '(.*) --- Tuning run (\d+)/(\d+) ---' -metrics_line_regex = '(.*) Metrics: ({.*})' +TRIAL_LINE_REGEX = '(.*) --- Tuning run (\d+)/(\d+) ---' +METRICS_LINE_REGEX = '(.*) Metrics: ({.*})' +TRIAL_DIR_REGEX = 'trial_(\d+)' +MEASUREMENTS_FILENAME = 'eval_measurements.csv' #### File IO helper functions ### @@ -34,7 +37,7 @@ def decode_metrics_line(line): 'step':[100, 200, 300]} """ eval_results = [] - dict_str = re.match(metrics_line_regex, line).group(2) + dict_str = re.match(METRICS_LINE_REGEX, line).group(2) dict_str = dict_str.replace("'", "\"") dict_str = dict_str.replace("(", "") dict_str = dict_str.replace(")", "") @@ -77,12 +80,12 @@ def get_trials_dict(logfile): metrics_lines = {} with open(logfile, 'r') as f: for line in f: - if re.match(trial_line_regex, line): - trial = re.match(trial_line_regex, line).group(2) - if re.match(metrics_line_regex, line): + if re.match(TRIAL_LINE_REGEX, line): + trial = re.match(TRIAL_LINE_REGEX, line).group(2) + if re.match(METRICS_LINE_REGEX, line): metrics_lines[trial] = decode_metrics_line(line) if len(metrics_lines) == 0: - raise ValueError(f"Log file does not have a metrics line {logfile}") + raise ValueError(f'Log file does not have a metrics line {logfile}') return metrics_lines @@ -100,7 +103,7 @@ def get_trials_df_dict(logfile): """ trials_dict = get_trials_dict(logfile) trials_df_dict = {} - for trial in trials_dict: + for trial in trials_dict.keys(): metrics = trials_dict[trial] trials_df_dict[trial] = pd.DataFrame(metrics) return trials_df_dict @@ -108,8 +111,6 @@ def get_trials_df_dict(logfile): def get_trials_df(logfile): """Gets a df of per trial results from a logfile. - The output df can be provided as input to - scoring.compute_performance_profiles. Args: experiment_dir: str @@ -127,3 +128,58 @@ def get_trials_df(logfile): trials_dict = get_trials_dict(logfile) df = pd.DataFrame(trials_dict).transpose() return df + + +## Get scoring code +def get_experiment_df(experiment_dir): + """Gets a df of per trial results from an experiment dir. + The output df can be provided as input to + scoring.compute_performance_profiles. + Args: + experiment_dir: path to experiment directory containing + results for workloads. + The directory structure is assumed to be: + + experiment_dir + + + + + - eval_measurements.csv + + Returns: + df: DataFrame where indices are trials, columns are + metric names and values are lists. + e.g + +----+-----------+---------+--------------------+--------------------+ + | | workload | trial | validation/accuracy| score | + |----+-----------+---------+--------------------+--------------------| + | 0 | mnist_jax | trial_1 | [0.0911, 0.0949] | [10.6396, 10.6464] | + +----+-----------+---------+--------------------+--------------------+ + """ + df = pd.DataFrame() + workload_dirs = os.listdir(experiment_dir) + for workload in workload_dirs: + data = { + 'workload': workload, + } + trial_dirs = [ + t for t in os.listdir(os.path.join(experiment_dir, workload)) + if re.match(TRIAL_DIR_REGEX, t) + ] + for trial in trial_dirs: + eval_measurements_filepath = os.path.join( + experiment_dir, + workload, + trial, + MEASUREMENTS_FILENAME, + ) + try: + trial_df = pd.read_csv(eval_measurements_filepath) + except FileNotFoundError as e: + logging.info(f'Could not read {eval_measurements_filepath}') + continue + data['trial'] = trial + for column in trial_df.columns: + values = trial_df[column].to_numpy() + data[column] = values + trial_df = pd.DataFrame([data]) + df = pd.concat([df, trial_df], ignore_index=True) + return df diff --git a/scoring/test_data/experiment_dir/mnist_jax/trial_0/eval_measurements.csv 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+132.83451008796692,1232.595977306366,37.04212498664856,4275,0,37.04212498664856,0.948900043964386,0.16642215847969055,10000,1403.4556999206543,0.9740000367164612,0.10199849307537079,0.9491000175476074,0.17495805025100708,10000 diff --git a/scoring/test_scoring_utils.py b/scoring/test_scoring_utils.py index 597525386..b766a04d7 100644 --- a/scoring/test_scoring_utils.py +++ b/scoring/test_scoring_utils.py @@ -1,7 +1,8 @@ from absl.testing import absltest import scoring_utils -TEST_LOGFILE = 'test_data/trial_0/adamw_fastmri_jax_04-18-2023-13-10-58.log' +TEST_LOGFILE = 'test_data/adamw_fastmri_jax_04-18-2023-13-10-58.log' +TEST_DIR = 'test_data/experiment_dir' NUM_EVALS = 18 @@ -22,6 +23,9 @@ def test_get_trials_df(self): for column in df.columns: self.assertEqual(len(df.at['1', column]), NUM_EVALS) + def test_get_experiment_df(self): + df = scoring_utils.get_experiment_df(TEST_DIR) + if __name__ == '__main__': absltest.main() diff --git a/setup.cfg b/setup.cfg index 1030b37b0..6f53cd51b 100644 --- a/setup.cfg +++ b/setup.cfg @@ -21,7 +21,6 @@ classifiers = Intended Audience :: Science/Research License :: OSI Approved :: Apache Software License Operating System :: OS Independent - Programming Language :: Python :: 3.7 Programming Language :: Python :: 3.8 Programming Language :: Python :: 3.9 Programming Language :: Python :: 3.10 @@ -35,19 +34,18 @@ setup_requires = setuptools_scm # Dependencies of the project: install_requires = - absl-py==1.0.0 - numpy==1.21.6 - pandas==1.3.5 - protobuf==3.20.* - six==1.16.0 - tensorflow==2.9.0 - tensorflow_datasets==4.8.2 - tensorflow_probability==0.17.0 - tensorflow_addons==0.18.0 + absl-py==1.4.0 + numpy>=1.23 + pandas>=2.0.1 + tensorflow==2.12.0 + tensorflow-datasets==4.9.2 + tensorflow-probability==0.20.0 + tensorflow-addons==0.20.0 gputil==1.4.0 - psutil==5.9.1 + psutil==5.9.5 clu==0.0.7 -python_requires = >=3.7 + matplotlib>=3.7.2 +python_requires = >=3.8 ############################################################################### @@ -78,31 +76,31 @@ full_dev = # Dependencies for developing the package dev = - isort==5.10.1 - pylint==2.16.1 - pytest==7.1.2 - yapf==0.32.0 - pre-commit==2.20.0 + isort==5.12.0 + pylint==2.17.4 + pytest==7.3.1 + yapf==0.33.0 + pre-commit==3.3.1 # Workloads # criteo1tb = - scikit-learn==1.0.1 + scikit-learn==1.2.2 fastmri = - h5py==3.7.0 - scikit_image==0.19.3 + h5py==3.8.0 + scikit-image==0.20.0 ogbg = jraph==0.0.6.dev0 - scikit-learn==1.0.1 + scikit-learn==1.2.2 librispeech_conformer = - sentencepiece==0.1.97 - tensorflow-text==2.9.0 + sentencepiece==0.1.99 + tensorflow-text==2.12.1 wmt = - sentencepiece==0.1.97 - tensorflow-text==2.9.0 + sentencepiece==0.1.99 + tensorflow-text==2.12.1 sacrebleu==1.3.1 # Frameworks # @@ -112,9 +110,9 @@ jax_core_deps = flax==0.6.10 optax==0.1.5 # Fix chex (optax dependency) version. - # Not fixing it can raise dependency issues with our + # Not fixing it can raise dependency issues with our # jax version. - # Todo(kasimbeg): verify if this is necessary after we + # Todo(kasimbeg): verify if this is necessary after we # upgrade jax. chex==0.1.7 @@ -132,17 +130,17 @@ jax_gpu = # PyTorch CPU pytorch_cpu = - torch==1.13.0 - torchvision==0.14.0 + torch==2.0.1 + torchvision==0.15.2 # PyTorch GPU pytorch_gpu = - torch==1.13.0+cu117 - torchvision==0.14.0+cu117 + torch==2.0.1+cu118 + torchvision==0.15.2+cu118 # wandb wandb = - wandb==0.13.4 + wandb==0.15.2 ############################################################################### # Linting Configurations # diff --git a/submission_runner.py b/submission_runner.py index 89218c382..669b776be 100644 --- a/submission_runner.py +++ b/submission_runner.py @@ -11,12 +11,11 @@ --tuning_search_space=reference_algorithms/development_algorithms/mnist/tuning_search_space.json \ --num_tuning_trials=3 \ --experiment_dir=/home/znado/experiment_dir \ - --experiment_name=baseline + --experiment_name=baseline """ import datetime import importlib -import inspect import json import os import struct @@ -41,58 +40,20 @@ from algorithmic_efficiency.pytorch_utils import pytorch_init from algorithmic_efficiency.pytorch_utils import pytorch_setup from algorithmic_efficiency.pytorch_utils import sync_ddp_time +from algorithmic_efficiency.workloads import workloads # Hide any GPUs form TensorFlow. Otherwise TF might reserve memory and make # it unavailable to JAX. tf.config.set_visible_devices([], 'GPU') # disable only for deepspeech if it works fine for other workloads. -os.environ["XLA_FLAGS"] = "--xla_gpu_enable_triton_gemm=false" +os.environ['XLA_FLAGS'] = '--xla_gpu_enable_triton_gemm=false' # TODO(znado): make a nicer registry of workloads that lookup in. -BASE_WORKLOADS_DIR = 'algorithmic_efficiency/workloads/' +BASE_WORKLOADS_DIR = workloads.BASE_WORKLOADS_DIR # Workload_path will be appended by '_pytorch' or '_jax' automatically. -WORKLOADS = { - 'cifar': { - 'workload_path': 'cifar/cifar', 'workload_class_name': 'CifarWorkload' - }, - 'criteo1tb': { - 'workload_path': 'criteo1tb/criteo1tb', - 'workload_class_name': 'Criteo1TbDlrmSmallWorkload', - }, - 'criteo1tb_test': { - 'workload_path': 'criteo1tb/criteo1tb', - 'workload_class_name': 'Criteo1TbDlrmSmallTestWorkload', - }, - 'fastmri': { - 'workload_path': 'fastmri/fastmri', - 'workload_class_name': 'FastMRIWorkload', - }, - 'imagenet_resnet': { - 'workload_path': 'imagenet_resnet/imagenet', - 'workload_class_name': 'ImagenetResNetWorkload', - }, - 'imagenet_vit': { - 'workload_path': 'imagenet_vit/imagenet', - 'workload_class_name': 'ImagenetVitWorkload', - }, - 'librispeech_conformer': { - 'workload_path': 'librispeech_conformer/librispeech', - 'workload_class_name': 'LibriSpeechConformerWorkload', - }, - 'librispeech_deepspeech': { - 'workload_path': 'librispeech_deepspeech/librispeech', - 'workload_class_name': 'LibriSpeechDeepSpeechWorkload', - }, - 'mnist': { - 'workload_path': 'mnist/mnist', 'workload_class_name': 'MnistWorkload' - }, - 'ogbg': { - 'workload_path': 'ogbg/ogbg', 'workload_class_name': 'OgbgWorkload' - }, - 'wmt': {'workload_path': 'wmt/wmt', 'workload_class_name': 'WmtWorkload'}, -} +WORKLOADS = workloads.WORKLOADS flags.DEFINE_string( 'submission_path', @@ -120,15 +81,21 @@ flags.DEFINE_string('imagenet_v2_data_dir', '~/data', 'Dataset location for ImageNet-v2.') +flags.DEFINE_string('librispeech_tokenizer_vocab_path', + '', + 'Location to librispeech tokenizer.') + flags.DEFINE_enum( 'framework', None, enum_values=['jax', 'pytorch'], help='Whether to use Jax or Pytorch for the submission. Controls among ' 'other things if the Jax or Numpy RNG library is used for RNG.') -flags.DEFINE_string('librispeech_tokenizer_vocab_path', - '', - 'Location to librispeech tokenizer.') +flags.DEFINE_boolean( + 'torch_compile', + True, + 'Whether to use `torch.compile` to JIT-compile PyTorch code. ' + 'This will only take effect when `framework`==pytorch.') flags.DEFINE_string( 'experiment_dir', @@ -169,56 +136,22 @@ USE_PYTORCH_DDP, RANK, DEVICE, N_GPUS = pytorch_setup() -def convert_filepath_to_module(path: str): - base, extension = os.path.splitext(path) +def _get_time(): + if torch.cuda.is_available(): + torch.cuda.synchronize() + return time.time() - if extension != '.py': - raise ValueError(f'Path: {path} must be a python file (*.py)') - return base.replace('/', '.') +def _get_time_ddp(): + torch.cuda.synchronize() + t = time.time() + return sync_ddp_time(t, DEVICE) -def import_workload(workload_path: str, - workload_class_name: str, - return_class=False, - workload_init_kwargs=None) -> spec.Workload: - """Import and add the workload to the registry. - - This importlib loading is nice to have because it allows runners to avoid - installing the dependencies of all the supported frameworks. For example, if - a submitter only wants to write Jax code, the try/except below will catch - the import errors caused if they do not have the PyTorch dependencies - installed on their system. - - Args: - workload_path: the path to the `workload.py` file to load. - workload_class_name: the name of the Workload class that implements the - `Workload` abstract class in `spec.py`. - return_class: if true, then the workload class is returned instead of the - instantiated object. Useful for testing when methods need to be overriden. - workload_init_kwargs: kwargs to pass to the workload constructor. - """ - - # Remove the trailing '.py' and convert the filepath to a Python module. - workload_path = convert_filepath_to_module(workload_path) - - # Import the workload module. - workload_module = importlib.import_module(workload_path) - # Get everything defined in the workload module (including our class). - workload_module_members = inspect.getmembers(workload_module) - workload_class = None - for name, value in workload_module_members: - if name == workload_class_name: - workload_class = value - break - if workload_class is None: - raise ValueError( - f'Could not find member {workload_class_name} in {workload_path}. ' - 'Make sure the Workload class is spelled correctly and defined in ' - 'the top scope of the module.') - if return_class: - return workload_class - return workload_class(**workload_init_kwargs) +if USE_PYTORCH_DDP: + get_time = _get_time_ddp +else: + get_time = _get_time def train_once( @@ -257,6 +190,30 @@ def train_once( aux_dropout_rate = hyperparameters.aux_dropout_rate model_params, model_state = workload.init_model_fn( model_init_rng, dropout_rate, aux_dropout_rate) + if FLAGS.framework == 'pytorch' and FLAGS.torch_compile: + compile_error_workloads = [ + 'fastmri', 'ogbg', 'librispeech_deepspeech', 'wmt' + ] + eager_backend_workloads = ['librispeech_conformer'] + aot_eager_backend_workloads = ['criteo1tb'] + if FLAGS.workload in compile_error_workloads: + logging.warning( + 'These workloads cannot be fully compiled under current ' + 'PyTorch version. Proceeding without `torch.compile`.') + elif FLAGS.workload in eager_backend_workloads: + logging.warning( + 'These workloads cannot be fully compiled under current ' + 'PyTorch version. Proceeding with `backend=eager`.') + model_params = torch.compile(model_params, backend='eager') + elif FLAGS.workload in aot_eager_backend_workloads: + logging.warning( + 'These workloads cannot be fully compiled under current ' + 'PyTorch version. Proceeding with `backend=aot_eager`.') + model_params = torch.compile(model_params, backend='aot_eager') + else: + logging.info('Performing `torch.compile`.') + model_params = torch.compile(model_params) + logging.info('Initializing optimizer.') with profiler.profile('Initializing optimizer'): optimizer_state = init_optimizer_state(workload, @@ -275,6 +232,7 @@ def train_once( 'accumulated_submission_time': 0, 'accumulated_eval_time': 0, 'accumulated_logging_time': 0, + 'last_step_end_time': None, } global_step = 0 eval_results = [] @@ -312,10 +270,8 @@ def train_once( hyperparameters) workload.attach_metrics_logger(metrics_logger) - global_start_time = time.time() - if USE_PYTORCH_DDP: - # Make sure all processes start training at the same time. - global_start_time = sync_ddp_time(global_start_time, DEVICE) + global_start_time = get_time() + train_state['last_step_end_time'] = global_start_time logging.info('Starting training loop.') goals_reached = ( @@ -325,13 +281,9 @@ def train_once( not goals_reached and \ not train_state['training_complete']: - train_step_start_time = time.time() - step_rng = prng.fold_in(rng, global_step) data_select_rng, update_rng, eval_rng = prng.split(step_rng, 3) - if USE_PYTORCH_DDP: - train_step_start_time = sync_ddp_time(train_step_start_time, DEVICE) with profiler.profile('Data selection'): batch = data_selection(workload, input_queue, @@ -361,12 +313,10 @@ def train_once( if (max_global_steps is not None) and (global_step == max_global_steps): train_state['training_complete'] = True - train_step_end_time = time.time() - if USE_PYTORCH_DDP: - train_step_end_time = sync_ddp_time(train_step_end_time, DEVICE) + train_step_end_time = get_time() train_state['accumulated_submission_time'] += ( - train_step_end_time - train_step_start_time) + train_step_end_time - train_state['last_step_end_time']) train_state['is_time_remaining'] = ( train_state['accumulated_submission_time'] < workload.max_allowed_runtime_sec) @@ -375,9 +325,7 @@ def train_once( workload.eval_period_time_sec or train_state['training_complete']): with profiler.profile('Evaluation'): try: - eval_start_time = time.time() - if USE_PYTORCH_DDP: - eval_start_time = sync_ddp_time(eval_start_time, DEVICE) + eval_start_time = get_time() latest_eval_result = workload.eval_model(global_eval_batch_size, model_params, model_state, @@ -392,10 +340,10 @@ def train_once( train_state['test_goal_reached'] = ( workload.has_reached_test_target(latest_eval_result) or train_state['test_goal_reached']) + # Save last eval time - eval_end_time = time.time() - if USE_PYTORCH_DDP: - eval_end_time = sync_ddp_time(eval_end_time, DEVICE) + eval_end_time = get_time() + train_state['last_eval_time'] = eval_end_time # Accumulate eval time train_state[ @@ -417,14 +365,15 @@ def train_once( f'\tStep: {global_step}, \t{latest_eval_result}') eval_results.append((global_step, latest_eval_result)) - logging_start_time = time.time() - if USE_PYTORCH_DDP: - logging_start_time = sync_ddp_time(logging_start_time, DEVICE) + logging_start_time = get_time() + if log_dir is not None: metrics_logger.append_scalar_metrics( latest_eval_result, global_step=global_step, - preemption_count=preemption_count) + preemption_count=preemption_count, + is_eval=True, + ) if save_checkpoints: checkpoint_utils.save_checkpoint( framework=FLAGS.framework, @@ -438,11 +387,11 @@ def train_once( checkpoint_dir=log_dir, save_intermediate_checkpoints=FLAGS .save_intermediate_checkpoints) - logging_end_time = time.time() - if USE_PYTORCH_DDP: - logging_end_time = sync_ddp_time(logging_end_time, DEVICE) - train_state['last_eval_time'] = logging_end_time + if FLAGS.framework == 'pytorch' and torch.cuda.is_available(): + torch.cuda.empty_cache() + logging_end_time = get_time() + train_state['accumulated_logging_time'] += ( logging_end_time - logging_start_time) @@ -454,6 +403,8 @@ def train_once( if torch.cuda.is_available(): torch.cuda.empty_cache() + train_state['last_step_end_time'] = get_time() + metrics = {'eval_results': eval_results, 'global_step': global_step} if log_dir is not None: @@ -495,7 +446,7 @@ def score_submission_on_workload(workload: spec.Workload, imagenet_v2_data_dir = os.path.expanduser(imagenet_v2_data_dir) # Remove the trailing '.py' and convert the filepath to a Python module. - submission_module_path = convert_filepath_to_module(submission_path) + submission_module_path = workloads.convert_filepath_to_module(submission_path) submission_module = importlib.import_module(submission_module_path) init_optimizer_state = submission_module.init_optimizer_state @@ -580,6 +531,8 @@ def score_submission_on_workload(workload: spec.Workload, logging.info(f'Hyperparameters: {tuning_search_space[ti]}') logging.info(f'Metrics: {all_metrics[ti]}') logging.info(f'Timing: {all_timings[ti]}') + num_evals = len(all_metrics[ti]['eval_results']) + logging.info(f'Total number of evals: {num_evals}') logging.info('=' * 20) else: rng_seed = struct.unpack('q', os.urandom(8))[0] @@ -609,7 +562,7 @@ def main(_): # Prevent OOM on librispeech conformer. if FLAGS.workload == 'librispeech_conformer': - os.environ["XLA_PYTHON_CLIENT_MEM_FRACTION"] = "0.85" + os.environ['XLA_PYTHON_CLIENT_MEM_FRACTION'] = '0.85' # Extend path according to framework. workload_metadata['workload_path'] = os.path.join( @@ -620,7 +573,7 @@ def main(_): if FLAGS.librispeech_tokenizer_vocab_path: workload_init_kwargs['tokenizer_vocab_path'] = ( FLAGS.librispeech_tokenizer_vocab_path) - workload = import_workload( + workload = workloads.import_workload( workload_path=workload_metadata['workload_path'], workload_class_name=workload_metadata['workload_class_name'], workload_init_kwargs=workload_init_kwargs) diff --git a/tests/modeldiffs/imagenet_vit/compare.py b/tests/modeldiffs/imagenet_vit/compare.py index fa5b41772..1022b5b54 100644 --- a/tests/modeldiffs/imagenet_vit/compare.py +++ b/tests/modeldiffs/imagenet_vit/compare.py @@ -59,7 +59,7 @@ def key_transform(k): elif enc_block and 'MlpBlock' in i: i = 'MlpBlock_3' elif idx == 1 and i == 'LayerNorm_0': - i = 'encoder_norm' + i = 'encoder_layernorm' elif 'weight' in i: if bn or ln: i = i.replace('weight', 'scale') diff --git a/tests/modeldiffs/wmt/compare.py b/tests/modeldiffs/wmt/compare.py index be0b9d340..52c96481c 100644 --- a/tests/modeldiffs/wmt/compare.py +++ b/tests/modeldiffs/wmt/compare.py @@ -66,9 +66,9 @@ def sd_transform(sd): new_key = list(k) if len(k) == 3: if k[0] == 'encoder': - new_key[1] = 'encoder_norm' + new_key[1] = 'encoder_layernorm' else: - new_key[1] = 'encoderdecoder_norm' + new_key[1] = 'encoderdecoder_layernorm' if k[-1] == 'kernel': new_key[-1] = 'scale' new_key = tuple(new_key) diff --git a/tests/reference_algorithm_tests.py b/tests/reference_algorithm_tests.py index 935e33d49..ae834f1f4 100644 --- a/tests/reference_algorithm_tests.py +++ b/tests/reference_algorithm_tests.py @@ -43,6 +43,7 @@ from algorithmic_efficiency import pytorch_utils from algorithmic_efficiency import random_utils as prng from algorithmic_efficiency.profiler import PassThroughProfiler +from algorithmic_efficiency.workloads import workloads from algorithmic_efficiency.workloads.ogbg import \ input_pipeline as ogbg_input_pipeline from algorithmic_efficiency.workloads.ogbg.ogbg_pytorch.workload import \ @@ -67,7 +68,6 @@ 'Run jax and pytorch with identical weights.') FLAGS = flags.FLAGS USE_PYTORCH_DDP, RANK, PYTORCH_DEVICE, N_GPUS = pytorch_utils.pytorch_setup() -N_GPUS = max(N_GPUS, jax.local_device_count()) tf.config.set_visible_devices([], 'GPU') _EXPECTED_METRIC_NAMES = { 'cifar': ['train/loss', 'validation/loss', 'test/accuracy'], @@ -139,9 +139,9 @@ def __init__(self): def append_scalar_metrics(self, scalars, step): if USE_PYTORCH_DDP: for k in sorted(scalars): - scalars[k] = torch.FloatTensor([scalars[k]]).to(PYTORCH_DEVICE) - dist.all_reduce(scalars[k]) - scalars[k] = scalars[k].item() / N_GPUS + scalars[k] = torch.as_tensor([scalars[k]], device=PYTORCH_DEVICE) + dist.all_reduce(scalars[k], op=dist.ReduceOp.AVG) + scalars[k] = scalars[k].item() if RANK == 0: self.scalars.append(scalars) self.save() @@ -169,7 +169,8 @@ def _make_one_batch_workload(workload_class, workload_name, framework, global_batch_size, - use_fake_input_queue): + use_fake_input_queue, + n_gpus): class _OneEvalBatchWorkload(workload_class): @@ -226,7 +227,7 @@ def _build_input_queue(self, *args, **kwargs): np.random.seed(42) if framework == 'jax' or USE_PYTORCH_DDP: - batch_shape = (N_GPUS, global_batch_size // N_GPUS) + batch_shape = (n_gpus, global_batch_size // n_gpus) else: batch_shape = (global_batch_size,) @@ -373,8 +374,12 @@ def to_device(k, v): # the BLEU score. if workload_name == 'wmt': num_batches *= 2 - for _ in range(num_batches * FLAGS.num_train_steps): - yield fake_batch + + def _data_gen(): + for _ in range(num_batches * FLAGS.num_train_steps): + yield fake_batch + + return _data_gen() def eval_model(self, *args, **kwargs): eval_result = super().eval_model(*args, **kwargs) @@ -389,7 +394,8 @@ def _test_submission(workload_name, submission_path, search_space_path, data_dir, - use_fake_input_queue): + use_fake_input_queue, + n_gpus): logging.info(f'========= Testing {workload_name} in {framework}.') FLAGS.framework = framework workload_metadata = copy.deepcopy(submission_runner.WORKLOADS[workload_name]) @@ -397,13 +403,12 @@ def _test_submission(workload_name, submission_runner.BASE_WORKLOADS_DIR, workload_metadata['workload_path'] + '_' + framework, 'workload.py') - workload_class = submission_runner.import_workload( + workload_class = workloads.import_workload( workload_path=workload_metadata['workload_path'], workload_class_name=workload_metadata['workload_class_name'], return_class=True) - submission_module_path = submission_runner.convert_filepath_to_module( - submission_path) + submission_module_path = workloads.convert_filepath_to_module(submission_path) submission_module = importlib.import_module(submission_module_path) init_optimizer_state = submission_module.init_optimizer_state @@ -412,7 +417,7 @@ def _test_submission(workload_name, if FLAGS.all: if FLAGS.global_batch_size > 0: raise ValueError('Cannot set --global_batch_size and --all.') - global_batch_size = 2 * N_GPUS + global_batch_size = 2 * n_gpus else: global_batch_size = FLAGS.global_batch_size if FLAGS.global_batch_size < 0: @@ -421,7 +426,8 @@ def _test_submission(workload_name, workload_name, framework, global_batch_size, - use_fake_input_queue) + use_fake_input_queue, + n_gpus) # Get a sample hyperparameter setting. hyperparameters = {} @@ -441,20 +447,20 @@ def _test_submission(workload_name, hyperparameters, opt_init_rng) - global_step = 0 - data_select_rng, update_rng, eval_rng = prng.split(rng, 3) - batch = data_selection(workload, - input_queue, - optimizer_state, - model_params, - model_state, - hyperparameters, - global_step, - data_select_rng) if USE_PYTORCH_DDP: torch.cuda.empty_cache() dist.barrier() - for _ in range(FLAGS.num_train_steps): + for global_step in range(FLAGS.num_train_steps): + step_rng = prng.fold_in(rng, global_step) + data_select_rng, update_rng, eval_rng = prng.split(step_rng, 3) + batch = data_selection(workload, + input_queue, + optimizer_state, + model_params, + model_state, + hyperparameters, + global_step, + data_select_rng) optimizer_state, model_params, model_state = update_params( workload=workload, current_param_container=model_params, @@ -475,7 +481,7 @@ def _test_submission(workload_name, eval_rng, data_dir, imagenet_v2_data_dir=None, - global_step=0) + global_step=global_step) _ = workload.eval_model( global_batch_size, model_params, @@ -483,7 +489,7 @@ def _test_submission(workload_name, eval_rng, data_dir, imagenet_v2_data_dir=None, - global_step=0) + global_step=global_step) return eval_result @@ -533,6 +539,8 @@ def test_submission(self): for framework in ['jax', 'pytorch']: if framework == 'pytorch': pytorch_utils.pytorch_init(USE_PYTORCH_DDP, RANK, profiler) + # First jax operation has to be called after pytorch_init. + n_gpus = max(N_GPUS, jax.local_device_count()) search_space_path, submission_path = _make_paths( repo_location, framework, workload_name) if search_space_path is None: @@ -543,12 +551,15 @@ def test_submission(self): submission_path, search_space_path, data_dir=FLAGS.data_dir, - use_fake_input_queue=FLAGS.use_fake_input_queue) + use_fake_input_queue=FLAGS.use_fake_input_queue, + n_gpus=n_gpus) self._assert_eval_result(workload_name, eval_result) else: framework = FLAGS.framework if framework == 'pytorch': pytorch_utils.pytorch_init(USE_PYTORCH_DDP, RANK, profiler) + # First jax operation has to be called after pytorch_init. + n_gpus = max(N_GPUS, jax.local_device_count()) workload_name = FLAGS.workload if FLAGS.submission_path and FLAGS.tuning_search_space: search_space_path = FLAGS.tuning_search_space @@ -562,7 +573,8 @@ def test_submission(self): submission_path, search_space_path, data_dir=FLAGS.data_dir, - use_fake_input_queue=FLAGS.use_fake_input_queue) + use_fake_input_queue=FLAGS.use_fake_input_queue, + n_gpus=n_gpus) self._assert_eval_result(workload_name, eval_result) if USE_PYTORCH_DDP: diff --git a/tests/test_baselines.py b/tests/test_baselines.py index 618f99489..0a26aa69d 100644 --- a/tests/test_baselines.py +++ b/tests/test_baselines.py @@ -13,6 +13,7 @@ from absl.testing import parameterized from algorithmic_efficiency.profiler import PassThroughProfiler +from algorithmic_efficiency.workloads import workloads import submission_runner FLAGS = flags.FLAGS @@ -20,26 +21,35 @@ # (see https://github.com/google/model_search/pull/8). FLAGS(sys.argv) -MAX_GLOBAL_STEPS = 500 +MAX_GLOBAL_STEPS = 5 + +baselines = { + 'jax': [ + 'adafactor', + 'adamw', + 'lamb', + 'momentum', + 'nadamw', + 'nesterov', + 'sam', + 'shampoo', + ], + 'pytorch': [ + 'adamw', + 'momentum', + 'nadamw', + 'nesterov', + ], +} -baselines = [ - 'adafactor', - 'adamw', - 'lamb', - 'momentum', - 'nadamw', - 'nesterov', - 'sam', - 'shampoo', -] frameworks = [ - # 'pytorch', # will enable this once all pytorch baselines are ready + 'pytorch', 'jax', ] named_parameters = [] for f in frameworks: - for b in baselines: + for b in baselines[f]: named_parameters.append( dict( testcase_name=f'{b}_{f}', @@ -59,12 +69,12 @@ def test_baseline_submission(self, submission_path, tuning_search_space): FLAGS.framework = framework - workload_metadata = copy.deepcopy(submission_runner.WORKLOADS[workload]) + workload_metadata = copy.deepcopy(workloads.WORKLOADS[workload]) workload_metadata['workload_path'] = os.path.join( - submission_runner.BASE_WORKLOADS_DIR, + workloads.BASE_WORKLOADS_DIR, workload_metadata['workload_path'] + '_' + framework, 'workload.py') - workload_obj = submission_runner.import_workload( + workload_obj = workloads.import_workload( workload_path=workload_metadata['workload_path'], workload_class_name=workload_metadata['workload_class_name'], workload_init_kwargs={}) diff --git a/tests/test_param_shapes.py b/tests/test_param_shapes.py index 367be4dbc..fef9c2978 100644 --- a/tests/test_param_shapes.py +++ b/tests/test_param_shapes.py @@ -2,48 +2,42 @@ import numpy as np import pytest -from algorithmic_efficiency.workloads.cifar.cifar_jax.workload import \ - CifarWorkload as JaxCifarWorkload -from algorithmic_efficiency.workloads.cifar.cifar_pytorch.workload import \ - CifarWorkload as PyTorchCifarWorkload -from algorithmic_efficiency.workloads.criteo1tb.criteo1tb_jax.workload import \ - Criteo1TbDlrmSmallWorkload as JaxCriteoWorkload -from algorithmic_efficiency.workloads.criteo1tb.criteo1tb_pytorch.workload import \ - Criteo1TbDlrmSmallWorkload as PyTorchCriteoWorkload -from algorithmic_efficiency.workloads.fastmri.fastmri_jax.workload import \ - FastMRIWorkload as JaxFastMRIWorkload -from algorithmic_efficiency.workloads.fastmri.fastmri_pytorch.workload import \ - FastMRIWorkload as PyTorchFastMRIWorkload -from algorithmic_efficiency.workloads.imagenet_resnet.imagenet_jax.workload import \ - ImagenetResNetWorkload as JaxImagenetResNetWorkload -from algorithmic_efficiency.workloads.imagenet_resnet.imagenet_pytorch.workload import \ - ImagenetResNetWorkload as PyTorchImagenetResNetWorkload -from algorithmic_efficiency.workloads.imagenet_vit.imagenet_jax.workload import \ - ImagenetVitWorkload as JaxImagenetViTWorkload -from algorithmic_efficiency.workloads.imagenet_vit.imagenet_pytorch.workload import \ - ImagenetVitWorkload as PyTorchImagenetViTWorkload -from algorithmic_efficiency.workloads.mnist.mnist_jax.workload import \ - MnistWorkload as JaxMnistWorkload -from algorithmic_efficiency.workloads.mnist.mnist_pytorch.workload import \ - MnistWorkload as PyTorchMnistWorkload -from algorithmic_efficiency.workloads.ogbg.ogbg_jax.workload import \ - OgbgWorkload as JaxOgbgWorkload -from algorithmic_efficiency.workloads.ogbg.ogbg_pytorch.workload import \ - OgbgWorkload as PyTorchOgbgWorkload -from algorithmic_efficiency.workloads.wmt.wmt_jax.workload import \ - WmtWorkload as JaxWmtWorkload -from algorithmic_efficiency.workloads.wmt.wmt_pytorch.workload import \ - WmtWorkload as PyTorchWmtWorkload +# isort: skip_file +# pylint:disable=line-too-long +from algorithmic_efficiency.workloads.cifar.cifar_jax.workload import CifarWorkload as JaxCifarWorkload +from algorithmic_efficiency.workloads.cifar.cifar_pytorch.workload import CifarWorkload as PyTorchCifarWorkload +from algorithmic_efficiency.workloads.criteo1tb.criteo1tb_jax.workload import Criteo1TbDlrmSmallWorkload as JaxCriteoWorkload +from algorithmic_efficiency.workloads.criteo1tb.criteo1tb_pytorch.workload import Criteo1TbDlrmSmallWorkload as PyTorchCriteoWorkload +from algorithmic_efficiency.workloads.fastmri.fastmri_jax.workload import FastMRIWorkload as JaxFastMRIWorkload +from algorithmic_efficiency.workloads.fastmri.fastmri_pytorch.workload import FastMRIWorkload as PyTorchFastMRIWorkload +from algorithmic_efficiency.workloads.imagenet_resnet.imagenet_jax.workload import ImagenetResNetWorkload as JaxImagenetResNetWorkload +from algorithmic_efficiency.workloads.imagenet_resnet.imagenet_pytorch.workload import ImagenetResNetWorkload as PyTorchImagenetResNetWorkload +from algorithmic_efficiency.workloads.imagenet_vit.imagenet_jax.workload import ImagenetVitWorkload as JaxImagenetViTWorkload +from algorithmic_efficiency.workloads.imagenet_vit.imagenet_pytorch.workload import ImagenetVitWorkload as PyTorchImagenetViTWorkload +from algorithmic_efficiency.workloads.librispeech_conformer.librispeech_jax.workload import LibriSpeechConformerWorkload as JaxLibriSpeechConformerWorkload +from algorithmic_efficiency.workloads.librispeech_conformer.librispeech_pytorch.workload import LibriSpeechConformerWorkload as PytorchLibriSpeechConformerWorkload +from algorithmic_efficiency.workloads.librispeech_deepspeech.librispeech_jax.workload import LibriSpeechDeepSpeechWorkload as JaxLibriSpeechDeepSpeechWorkload +from algorithmic_efficiency.workloads.librispeech_deepspeech.librispeech_pytorch.workload import LibriSpeechDeepSpeechWorkload as PytorchLibriSpeechDeepSpeechWorkload +from algorithmic_efficiency.workloads.mnist.mnist_jax.workload import MnistWorkload as JaxMnistWorkload +from algorithmic_efficiency.workloads.mnist.mnist_pytorch.workload import MnistWorkload as PyTorchMnistWorkload +from algorithmic_efficiency.workloads.ogbg.ogbg_jax.workload import OgbgWorkload as JaxOgbgWorkload +from algorithmic_efficiency.workloads.ogbg.ogbg_pytorch.workload import OgbgWorkload as PyTorchOgbgWorkload +from algorithmic_efficiency.workloads.wmt.wmt_jax.workload import WmtWorkload as JaxWmtWorkload +from algorithmic_efficiency.workloads.wmt.wmt_pytorch.workload import WmtWorkload as PyTorchWmtWorkload +# pylint:enable=line-too-long WORKLOADS = [ - 'mnist', 'cifar', 'criteo1tb', 'fastmri', 'imagenet_resnet', 'imagenet_vit', + # TODO: make tests work for these. + # 'librispeech_conformer', + # 'librispeech_deepspeech', + 'mnist', + 'ogbg', 'wmt', - 'ogbg' ] @@ -70,49 +64,43 @@ def test_param_shapes(workload): def get_workload(workload): - if workload == 'mnist': - # Init Jax workload. - jax_workload = JaxMnistWorkload() - # Init PyTorch workload. - pytorch_workload = PyTorchMnistWorkload() - elif workload == 'cifar': - # Init Jax workload. + if workload == 'cifar': jax_workload = JaxCifarWorkload() - # Init PyTorch workload. pytorch_workload = PyTorchCifarWorkload() elif workload == 'criteo1tb': - # Init Jax workload. jax_workload = JaxCriteoWorkload() - # Init PyTorch workload. pytorch_workload = PyTorchCriteoWorkload() elif workload == 'fastmri': - # Init Jax workload. jax_workload = JaxFastMRIWorkload() - # Init PyTorch workload. pytorch_workload = PyTorchFastMRIWorkload() elif workload == 'imagenet_resnet': - # Init Jax workload. jax_workload = JaxImagenetResNetWorkload() - # Init PyTorch workload. pytorch_workload = PyTorchImagenetResNetWorkload() elif workload == 'imagenet_vit': - # Init Jax workload. jax_workload = JaxImagenetViTWorkload() - # Init PyTorch workload. pytorch_workload = PyTorchImagenetViTWorkload() - elif workload == 'wmt': - # Init Jax workload. - jax_workload = JaxWmtWorkload() - jax_workload._global_batch_size = 128 - # Init PyTorch workload. - pytorch_workload = PyTorchWmtWorkload() + elif workload == 'librispeech_conformer': + jax_workload = JaxLibriSpeechConformerWorkload() + pytorch_workload = PytorchLibriSpeechConformerWorkload() + elif workload == 'librispeech_deepspeech': + jax_workload = JaxLibriSpeechDeepSpeechWorkload() + pytorch_workload = PytorchLibriSpeechDeepSpeechWorkload() + elif workload == 'mnist': + jax_workload = JaxMnistWorkload() + pytorch_workload = PyTorchMnistWorkload() elif workload == 'ogbg': - # Init Jax workload. jax_workload = JaxOgbgWorkload() - # Init PyTorch workload. pytorch_workload = PyTorchOgbgWorkload() + elif workload == 'wmt': + jax_workload = JaxWmtWorkload() + pytorch_workload = PyTorchWmtWorkload() else: raise ValueError(f'Workload {workload} is not available.') _ = jax_workload.init_model_fn(jax.random.PRNGKey(0)) _ = pytorch_workload.init_model_fn([0]) return jax_workload, pytorch_workload + + +if __name__ == '__main__': + for w in WORKLOADS: + test_param_shapes(w) diff --git a/tests/test_param_types.py b/tests/test_param_types.py index 774a3969f..3679289ed 100644 --- a/tests/test_param_types.py +++ b/tests/test_param_types.py @@ -1,121 +1,211 @@ import jax import pytest -from algorithmic_efficiency.workloads.cifar.cifar_jax.workload import \ - CifarWorkload as JaxCifarWorkload -from algorithmic_efficiency.workloads.cifar.cifar_pytorch.workload import \ - CifarWorkload as PyTorchCifarWorkload -from algorithmic_efficiency.workloads.criteo1tb.criteo1tb_jax.workload import \ - Criteo1TbDlrmSmallWorkload as JaxCriteoWorkload -from algorithmic_efficiency.workloads.criteo1tb.criteo1tb_pytorch.workload import \ - Criteo1TbDlrmSmallWorkload as PyTorchCriteoWorkload -from algorithmic_efficiency.workloads.fastmri.fastmri_jax.workload import \ - FastMRIWorkload as JaxFastMRIWorkload -from algorithmic_efficiency.workloads.fastmri.fastmri_pytorch.workload import \ - FastMRIWorkload as PyTorchFastMRIWorkload -from algorithmic_efficiency.workloads.imagenet_resnet.imagenet_jax.workload import \ - ImagenetResNetWorkload as JaxImagenetResNetWorkload -from algorithmic_efficiency.workloads.imagenet_resnet.imagenet_pytorch.workload import \ - ImagenetResNetWorkload as PyTorchImagenetResNetWorkload -from algorithmic_efficiency.workloads.imagenet_vit.imagenet_jax.workload import \ - ImagenetVitWorkload as JaxImagenetViTWorkload -from algorithmic_efficiency.workloads.imagenet_vit.imagenet_pytorch.workload import \ - ImagenetVitWorkload as PyTorchImagenetViTWorkload -from algorithmic_efficiency.workloads.mnist.mnist_jax.workload import \ - MnistWorkload as JaxMnistWorkload -from algorithmic_efficiency.workloads.mnist.mnist_pytorch.workload import \ - MnistWorkload as PyTorchMnistWorkload -from algorithmic_efficiency.workloads.ogbg.ogbg_jax.workload import \ - OgbgWorkload as JaxOgbgWorkload -from algorithmic_efficiency.workloads.ogbg.ogbg_pytorch.workload import \ - OgbgWorkload as PyTorchOgbgWorkload -from algorithmic_efficiency.workloads.wmt.wmt_jax.workload import \ - WmtWorkload as JaxWmtWorkload -from algorithmic_efficiency.workloads.wmt.wmt_pytorch.workload import \ - WmtWorkload as PyTorchWmtWorkload +from absl import logging +from algorithmic_efficiency import spec + +# isort: skip_file +# pylint:disable=line-too-long +from algorithmic_efficiency.workloads.cifar.cifar_jax.workload import CifarWorkload as JaxCifarWorkload +from algorithmic_efficiency.workloads.cifar.cifar_pytorch.workload import CifarWorkload as PyTorchCifarWorkload +from algorithmic_efficiency.workloads.criteo1tb.criteo1tb_jax.workload import Criteo1TbDlrmSmallWorkload as JaxCriteoWorkload +from algorithmic_efficiency.workloads.criteo1tb.criteo1tb_pytorch.workload import Criteo1TbDlrmSmallWorkload as PyTorchCriteoWorkload +from algorithmic_efficiency.workloads.fastmri.fastmri_jax.workload import FastMRIWorkload as JaxFastMRIWorkload +from algorithmic_efficiency.workloads.fastmri.fastmri_pytorch.workload import FastMRIWorkload as PyTorchFastMRIWorkload +from algorithmic_efficiency.workloads.imagenet_resnet.imagenet_jax.workload import ImagenetResNetWorkload as JaxImagenetResNetWorkload +from algorithmic_efficiency.workloads.imagenet_resnet.imagenet_pytorch.workload import ImagenetResNetWorkload as PyTorchImagenetResNetWorkload +from algorithmic_efficiency.workloads.imagenet_vit.imagenet_jax.workload import ImagenetVitWorkload as JaxImagenetViTWorkload +from algorithmic_efficiency.workloads.imagenet_vit.imagenet_pytorch.workload import ImagenetVitWorkload as PyTorchImagenetViTWorkload +from algorithmic_efficiency.workloads.librispeech_conformer.librispeech_jax.workload import LibriSpeechConformerWorkload as JaxLibriSpeechConformerWorkload +from algorithmic_efficiency.workloads.librispeech_conformer.librispeech_pytorch.workload import LibriSpeechConformerWorkload as PytorchLibriSpeechConformerWorkload +from algorithmic_efficiency.workloads.librispeech_deepspeech.librispeech_jax.workload import LibriSpeechDeepSpeechWorkload as JaxLibriSpeechDeepSpeechWorkload +from algorithmic_efficiency.workloads.librispeech_deepspeech.librispeech_pytorch.workload import LibriSpeechDeepSpeechWorkload as PytorchLibriSpeechDeepSpeechWorkload +from algorithmic_efficiency.workloads.mnist.mnist_jax.workload import MnistWorkload as JaxMnistWorkload +from algorithmic_efficiency.workloads.mnist.mnist_pytorch.workload import MnistWorkload as PyTorchMnistWorkload +from algorithmic_efficiency.workloads.ogbg.ogbg_jax.workload import OgbgWorkload as JaxOgbgWorkload +from algorithmic_efficiency.workloads.ogbg.ogbg_pytorch.workload import OgbgWorkload as PyTorchOgbgWorkload +from algorithmic_efficiency.workloads.wmt.wmt_jax.workload import WmtWorkload as JaxWmtWorkload +from algorithmic_efficiency.workloads.wmt.wmt_pytorch.workload import WmtWorkload as PyTorchWmtWorkload +# pylint:enable=line-too-long WORKLOADS = [ - 'mnist', 'cifar', 'criteo1tb', 'fastmri', 'imagenet_resnet', 'imagenet_vit', + 'librispeech_conformer', + 'librispeech_deepspeech', + 'mnist', + 'ogbg', 'wmt', - 'ogbg' ] -@pytest.mark.parametrize('workload', WORKLOADS) -def test_param_types(workload): - jax_workload, pytorch_workload = get_workload(workload) +def count_param_types(param_types): + types_dict = {} + for t in param_types: + if t not in types_dict: + types_dict[t] = 1 + else: + types_dict[t] += 1 + return types_dict + + +def _count_mismatches(jax_param_types_dict, pytorch_param_types_dict, keys): + mismatches = '' + for key in keys: + jax_count = jax_param_types_dict.get(key, 0) + pytorch_count = pytorch_param_types_dict.get(key, 0) + if jax_count != pytorch_count: + mismatches += f'\nKey: {key}, Jax {jax_count} != Pytorch {pytorch_count}.' + return mismatches + + +def _check_attention_qkv_match(jax_param_types_dict, pytorch_param_types_dict): + # Sometimes one framework will implement QKV as a single parameter, so we need + # to make sure there are the same number of QKV params as Q, K, V. + num_qkv = { + 'jax': + jax_param_types_dict.get(spec.ParameterType.ATTENTION_QKV, 0), + 'pytorch': + pytorch_param_types_dict.get(spec.ParameterType.ATTENTION_QKV, 0), + } + num_q = { + 'jax': + jax_param_types_dict.get(spec.ParameterType.ATTENTION_Q, 0), + 'pytorch': + pytorch_param_types_dict.get(spec.ParameterType.ATTENTION_Q, 0), + } + num_k = { + 'jax': + jax_param_types_dict.get(spec.ParameterType.ATTENTION_K, 0), + 'pytorch': + pytorch_param_types_dict.get(spec.ParameterType.ATTENTION_K, 0), + } + num_v = { + 'jax': + jax_param_types_dict.get(spec.ParameterType.ATTENTION_V, 0), + 'pytorch': + pytorch_param_types_dict.get(spec.ParameterType.ATTENTION_V, 0), + } + num_bias = { + 'jax': + jax_param_types_dict.get(spec.ParameterType.ATTENTION_BIAS, 0), + 'pytorch': + pytorch_param_types_dict.get(spec.ParameterType.ATTENTION_BIAS, 0), + } + qkv_match = num_qkv['jax'] == num_qkv['pytorch'] + q_match = num_q['jax'] == num_q['pytorch'] + k_match = num_k['jax'] == num_k['pytorch'] + v_match = num_v['jax'] == num_v['pytorch'] + bias_match = num_bias['jax'] == num_bias['pytorch'] + qkv_match = qkv_match and q_match and k_match and v_match and bias_match + + # We subtract 2 * num_qkv from the number of biases because there are 2 + # missing for each of q, k, v. + jax_qkv_match = ( + num_q['pytorch'] == num_k['pytorch'] == num_v['pytorch'] == num_qkv['jax'] + and (num_qkv['jax'] != 0 and + (num_bias['pytorch'] - 2 * num_qkv['jax']) == num_bias['jax'])) + pytorch_qkv_match = ( + num_q['jax'] == num_k['jax'] == num_v['jax'] == num_qkv['pytorch'] and + (num_qkv['pytorch'] != 0 and + (num_bias['jax'] - 2 * num_qkv['pytorch']) == num_bias['pytorch'])) + qkv_match = qkv_match or jax_qkv_match or pytorch_qkv_match + return qkv_match + + +@pytest.mark.parametrize('workload_name', WORKLOADS) +def test_param_types(workload_name): + logging.info(f'Testing workload {workload_name}...') + jax_workload, pytorch_workload = get_workload(workload_name) + # Compare number of parameter tensors of both models. jax_param_types = jax.tree_util.tree_leaves(jax_workload.model_params_types) pytorch_param_types = jax.tree_util.tree_leaves( pytorch_workload.model_params_types) - assert len(jax_param_types) == len(pytorch_param_types) - - def count_param_types(param_types): - types_dict = {} - for t in param_types: - if t not in types_dict: - types_dict[t] = 1 - else: - types_dict[t] += 1 - return types_dict jax_param_types_dict = count_param_types(jax_param_types) pytorch_param_types_dict = count_param_types(pytorch_param_types) - assert jax_param_types_dict.keys() == pytorch_param_types_dict.keys() + + # Jax fuses LSTM cells together, whereas PyTorch exposes all the weight + # parameters, and there are two per cell, for each of the forward and backward + # directional LSTMs, and there are 6 layers of LSTM in librispeech_deepspeech, + # compared to the 6 Jax LSTM weights. + # + # We also subtract an additional 6 biases because the LSTM biases are + # concatenated to the weights in Jax. + if workload_name == 'librispeech_deepspeech': + pytorch_param_types_dict[spec.ParameterType.WEIGHT] -= 3 * 6 + pytorch_param_types_dict[spec.ParameterType.BIAS] -= 3 * 6 + pytorch_param_types_dict[spec.ParameterType.BIAS] -= 6 + # Check if total number of each type match. - for key in list(jax_param_types_dict.keys()): - assert jax_param_types_dict[key] == pytorch_param_types_dict[key] + attention_keys = { + spec.ParameterType.ATTENTION_QKV, + spec.ParameterType.ATTENTION_Q, + spec.ParameterType.ATTENTION_K, + spec.ParameterType.ATTENTION_V, + spec.ParameterType.ATTENTION_BIAS, + } + non_attention_keys = set(jax_param_types_dict.keys()).union( + set(pytorch_param_types_dict.keys())) + non_attention_keys -= attention_keys + mismatches = '' + mismatches += _count_mismatches(jax_param_types_dict, + pytorch_param_types_dict, + non_attention_keys) + qkv_match = _check_attention_qkv_match(jax_param_types_dict, + pytorch_param_types_dict) + if not qkv_match: + mismatches += _count_mismatches(jax_param_types_dict, + pytorch_param_types_dict, + attention_keys) + if mismatches: + raise ValueError( + f'On workload {workload_name}, count mismatch: {mismatches}') -def get_workload(workload): - if workload == 'mnist': - # Init Jax workload. - jax_workload = JaxMnistWorkload() - # Init PyTorch workload. - pytorch_workload = PyTorchMnistWorkload() - elif workload == 'cifar': - # Init Jax workload. + +def get_workload(workload_name): + if workload_name == 'cifar': jax_workload = JaxCifarWorkload() - # Init PyTorch workload. pytorch_workload = PyTorchCifarWorkload() - elif workload == 'criteo1tb': - # Init Jax workload. + elif workload_name == 'criteo1tb': jax_workload = JaxCriteoWorkload() - # Init PyTorch workload. pytorch_workload = PyTorchCriteoWorkload() - elif workload == 'fastmri': - # Init Jax workload. + elif workload_name == 'fastmri': jax_workload = JaxFastMRIWorkload() - # Init PyTorch workload. pytorch_workload = PyTorchFastMRIWorkload() - elif workload == 'imagenet_resnet': - # Init Jax workload. + elif workload_name == 'imagenet_resnet': jax_workload = JaxImagenetResNetWorkload() - # Init PyTorch workload. pytorch_workload = PyTorchImagenetResNetWorkload() - elif workload == 'imagenet_vit': - # Init Jax workload. + elif workload_name == 'imagenet_vit': jax_workload = JaxImagenetViTWorkload() - # Init PyTorch workload. pytorch_workload = PyTorchImagenetViTWorkload() - elif workload == 'wmt': - # Init Jax workload. - jax_workload = JaxWmtWorkload() - jax_workload._global_batch_size = 128 - # Init PyTorch workload. - pytorch_workload = PyTorchWmtWorkload() - elif workload == 'ogbg': - # Init Jax workload. + elif workload_name == 'librispeech_conformer': + jax_workload = JaxLibriSpeechConformerWorkload() + pytorch_workload = PytorchLibriSpeechConformerWorkload() + elif workload_name == 'librispeech_deepspeech': + jax_workload = JaxLibriSpeechDeepSpeechWorkload() + pytorch_workload = PytorchLibriSpeechDeepSpeechWorkload() + elif workload_name == 'mnist': + jax_workload = JaxMnistWorkload() + pytorch_workload = PyTorchMnistWorkload() + elif workload_name == 'ogbg': jax_workload = JaxOgbgWorkload() - # Init PyTorch workload. pytorch_workload = PyTorchOgbgWorkload() + elif workload_name == 'wmt': + jax_workload = JaxWmtWorkload() + pytorch_workload = PyTorchWmtWorkload() else: - raise ValueError(f'Workload {workload} is not available.') + raise ValueError(f'Workload {workload_name} is not available.') _ = jax_workload.init_model_fn(jax.random.PRNGKey(0)) _ = pytorch_workload.init_model_fn([0]) return jax_workload, pytorch_workload + + +if __name__ == '__main__': + for w in WORKLOADS: + test_param_types(w) diff --git a/tests/test_traindiffs.py b/tests/test_traindiffs.py index 83daef4c9..fec1f9085 100644 --- a/tests/test_traindiffs.py +++ b/tests/test_traindiffs.py @@ -10,9 +10,6 @@ from absl import flags from absl.testing import absltest -import jax - -jax.config.update('jax_platforms', 'cpu') FLAGS = flags.FLAGS @@ -27,6 +24,7 @@ 'criteo1tb' ] GLOBAL_BATCH_SIZE = 16 +NUM_TRAIN_STEPS = 10 class ModelDiffTest(absltest.TestCase): @@ -44,15 +42,15 @@ def test_workload(self): jax_logs = '/tmp/jax_log.pkl' pyt_logs = '/tmp/pyt_log.pkl' run( - f'XLA_PYTHON_CLIENT_ALLOCATOR=platform python3 tests/reference_algorithm_tests.py --workload={workload} --framework=jax --global_batch_size={GLOBAL_BATCH_SIZE} --log_file={jax_logs}' - ' --submission_path=tests/modeldiffs/vanilla_sgd_jax.py --identical=True --tuning_search_space=None --num_train_steps=10', + f'python3 tests/reference_algorithm_tests.py --workload={workload} --framework=jax --global_batch_size={GLOBAL_BATCH_SIZE} --log_file={jax_logs}' + f' --submission_path=tests/modeldiffs/vanilla_sgd_jax.py --identical=True --tuning_search_space=None --num_train_steps={NUM_TRAIN_STEPS}', shell=True, stdout=DEVNULL, stderr=STDOUT, check=True) run( - f'XLA_PYTHON_CLIENT_ALLOCATOR=platform torchrun --standalone --nnodes 1 --nproc_per_node 8 tests/reference_algorithm_tests.py --workload={workload} --framework=pytorch --global_batch_size={GLOBAL_BATCH_SIZE} --log_file={pyt_logs}' - ' --submission_path=tests/modeldiffs/vanilla_sgd_pytorch.py --identical=True --tuning_search_space=None --num_train_steps=10', + f'torchrun --standalone --nnodes 1 --nproc_per_node 8 tests/reference_algorithm_tests.py --workload={workload} --framework=pytorch --global_batch_size={GLOBAL_BATCH_SIZE} --log_file={pyt_logs}' + f' --submission_path=tests/modeldiffs/vanilla_sgd_pytorch.py --identical=True --tuning_search_space=None --num_train_steps={NUM_TRAIN_STEPS}', shell=True, stdout=DEVNULL, stderr=STDOUT, @@ -79,10 +77,10 @@ def test_workload(self): fmt = lambda l: '|' + '|'.join(map(lambda x: f'{x:^20s}', l)) + '|' header = fmt(header) pad = (len(header) - len((name))) // 2 - print("=" * pad, name, '=' * (len(header) - len(name) - pad), sep='') + print('=' * pad, name, '=' * (len(header) - len(name) - pad), sep='') print(header) print('=' * len(header)) - for i in range(10): + for i in range(NUM_TRAIN_STEPS): row = map(lambda x: str(round(x, 5)), [ jax_results['eval_results'][i][k],