Stage Harbor release files 70001-70500

tasks/tasksmith-b1dd32dfa88b/environment/source/.devcontainer/devcontainer.json

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+// File only needed for VSCode users to have proper Docker based interpreters
+{
+    "name": "accelerate_dev_environment",
+    "build": {
+        // ACTION NEEDED: comment/uncomment the relevant line depending on whether you are in a CPU/GPU environment
+         "dockerfile": "../docker/accelerate-cpu/Dockerfile"
+//        "dockerfile": "../docker/accelerate-gpu/Dockerfile"
+    },
+    "runArgs": [
+        // ACTION NEEDED: uncomment the next line if your local machine has GPUs available
+//        "--gpus", "all",
+        // Enable the docker container to access system resources
+        "--ipc", "host"
+    ],
+    "remoteEnv": {
+        "PYTHONPATH": "${containerEnv:PATH}:${containerWorkspaceFolder}"
+    },
+    "customizations": {
+        "vscode": {
+            "extensions": [
+                // Ensure we have IntelliSense in VSCode when running inside container
+                "ms-python.python"
+            ]
+        }
+    },
+    "workspaceFolder": "/workspaces/accelerate",
+    // Need git for VSCode to color code modifications. Only runs when building environment.
+    "onCreateCommand": "apt-get update && apt-get install -y git && pip install -e '.[dev]'"
+}

tasks/tasksmith-b1dd32dfa88b/environment/source/.github/ISSUE_TEMPLATE/bug-report.yml

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+name: "\U0001F41B Bug Report"
+description: Submit a bug report to help us improve Accelerate
+body:
+  - type: markdown
+    attributes: 
+      value: | 
+        Thanks for taking the time to submit a bug report! 🐛 
+        If this is not a bug related to the Accelerate library directly, but instead a general question about your code or the library specifically please use the [forums](https://discuss.huggingface.co/c/accelerate/18).
+
+  - type: textarea
+    id: system-info
+    attributes:
+      label: System Info
+      description: Please share your accelerate configuration with us. You can run the command `accelerate env` and copy-paste its outputs below
+      render: Shell
+      placeholder: accelerate version, OS, python version, numpy version, torch version, and accelerate's configuration
+    validations:
+      required: true
+  
+  - type: checkboxes
+    id: information-scripts-examples
+    attributes:
+      label: Information
+      description: 'The problem arises when using:'
+      options:
+        - label: "The official example scripts"
+        - label: "My own modified scripts"
+  
+  - type: checkboxes
+    id: information-tasks
+    attributes:
+      label: Tasks
+      description: "The tasks I am working on are:"
+      options:
+        - label: "One of the scripts in the examples/ folder of Accelerate or an officially supported `no_trainer` script in the `examples` folder of the `transformers` repo (such as `run_no_trainer_glue.py`)"
+        - label: "My own task or dataset (give details below)"
+  
+  - type: textarea
+    id: reproduction
+    validations:
+      required: true
+    attributes:
+      label: Reproduction
+      description: |
+        Please provide a code sample that reproduces the problem you ran into. It can be a Colab link or just a code snippet.
+        If you have code snippets, error messages, stack traces please provide them here as well.
+        Important! Use code tags to correctly format your code. See https://help.github.com/en/github/writing-on-github/creating-and-highlighting-code-blocks#syntax-highlighting
+        Do not use screenshots, as they are hard to read and (more importantly) don't allow others to copy-and-paste your code.
+
+      placeholder: |
+        Steps to reproduce the behavior:
+          
+          1.
+          2.
+          3.
+
+  - type: textarea
+    id: expected-behavior
+    validations:
+      required: true
+    attributes:
+      label: Expected behavior
+      description: "A clear and concise description of what you would expect to happen."

tasks/tasksmith-b1dd32dfa88b/environment/source/.github/PULL_REQUEST_TEMPLATE.md

@@ -0,0 +1,47 @@
+# What does this PR do?
+
+<!--
+Congratulations! You've made it this far! You're not quite done yet though.
+
+Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution.
+
+Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change.
+
+Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost.
+-->
+
+<!-- Remove if not applicable -->
+
+Fixes # (issue)
+
+
+## Before submitting
+- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
+- [ ] Did you read the [contributor guideline](https://github.com/huggingface/accelerate/blob/main/CONTRIBUTING.md#submitting-a-pull-request-pr),
+      Pull Request section?
+- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
+      to it if that's the case.
+- [ ] Did you make sure to update the documentation with your changes? Here are the
+      [documentation guidelines](https://github.com/huggingface/accelerate/tree/main/docs), and
+      [here are tips on formatting docstrings](https://github.com/huggingface/accelerate/tree/main/docs#writing-documentation---specification).
+- [ ] Did you write any new necessary tests?
+
+
+## Who can review?
+
+Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
+members/contributors who may be interested in your PR.
+
+<!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @
+
+ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
+
+- Big modeling: @SunMarc
+- Fully-Sharded Data Parallism: @SunMarc @zach-huggingface
+- DeepSpeed: @SunMarc @zach-huggingface
+- Command Line Interface: @SunMarc @zach-huggingface
+- Documentation: @SunMarc @zach-huggingface
+- Core parts of the library: @BenjaminBossan @SunMarc @zach-huggingface 
+- Maintained examples: @SunMarc or  @zach-huggingface
+
+ -->

tasks/tasksmith-b1dd32dfa88b/environment/source/.github/workflows/build-docker-images-release.yml

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+name: Build Docker images (releases)
+
+on:
+  workflow_dispatch:
+  release:
+    types: [published]
+
+concurrency:
+  group: docker-image-builds
+  cancel-in-progress: false
+
+jobs:
+  get-version:
+    runs-on: ubuntu-latest
+    outputs:
+      version: ${{ steps.step1.outputs.version }}
+    steps:
+      - uses: actions/checkout@4
+      - id: step1
+        run: echo "version=$(python setup.py --version)" >> $GITHUB_OUTPUT
+
+  version-cpu:
+    name: "Latest Accelerate CPU [version]"
+    runs-on:
+      group: aws-general-8-plus
+    needs: get-version
+    steps:
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v2
+      - name: Login to DockerHub
+        uses: docker/login-action@v2
+        with:
+          username: ${{ secrets.DOCKERHUB_USERNAME }}
+          password: ${{ secrets.DOCKERHUB_PASSWORD }}
+
+      - name: Build and Push CPU
+        uses: docker/build-push-action@v4
+        with:
+          file: docker/accelerate-cpu/Dockerfile
+          push: true
+          tags: huggingface/accelerate:cpu-release-${{ needs.get-version.outputs.version }}
+
+  version-cuda:
+    name: "Latest Accelerate GPU [version]"
+    runs-on:
+      group: aws-g6-4xlarge-plus
+    needs: get-version
+    steps:
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v2
+      - name: Login to DockerHub
+        uses: docker/login-action@v2
+        with:
+          username: ${{ secrets.DOCKERHUB_USERNAME }}
+          password: ${{ secrets.DOCKERHUB_PASSWORD }}
+
+      - name: Build and Push GPU
+        uses: docker/build-push-action@v4
+        with:
+          file: docker/accelerate-gpu/Dockerfile
+          push: true
+          tags: huggingface/accelerate:gpu-release-${{needs.get-version.outputs.version}}
+
+  version-cuda-deepspeed:
+    name: "Latest Accelerate GPU DeepSpeed [version]"
+    runs-on:
+      group: aws-g6-4xlarge-plus
+    needs: get-version
+    steps:
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v2
+      - name: Login to DockerHub
+        uses: docker/login-action@v2
+        with:
+          username: ${{ secrets.DOCKERHUB_USERNAME }}
+          password: ${{ secrets.DOCKERHUB_PASSWORD }}
+
+      - name: Build and Push GPU
+        uses: docker/build-push-action@v4
+        with:
+          file: docker/accelerate-gpu-deepspeed/Dockerfile
+          push: true
+          tags: huggingface/accelerate:gpu-deepspeed-release-${{needs.get-version.outputs.version}}
+
+  version-cuda-fp8-transformerengine:
+    name: "Latest Accelerate GPU FP8 TransformerEngine [version]"
+    runs-on:
+      group: aws-g6-4xlarge-plus
+    needs: get-version
+    steps:
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v2
+      - name: Login to DockerHub
+        uses: docker/login-action@v2
+        with:
+          username: ${{ secrets.DOCKERHUB_USERNAME }}
+          password: ${{ secrets.DOCKERHUB_PASSWORD }}
+
+      - name: Build and Push GPU
+        uses: docker/build-push-action@v4
+        with:
+          file: docker/accelerate-gpu/Dockerfile
+          push: true
+          tags: huggingface/accelerate:gpu-fp8-transformerengine-release-${{needs.get-version.outputs.version}}

tasks/tasksmith-b1dd32dfa88b/environment/source/.github/workflows/build_and_run_tests.yml

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+name: Trigger docker images and run tests
+
+on:
+  push:
+    branches:
+      - main
+  workflow_dispatch:
+
+env:
+  GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+
+jobs:
+  check-for-source:
+    runs-on: ubuntu-latest
+    name: Check if setup was changed
+    outputs:
+      changed: ${{ steps.was_changed.outputs.changed }}
+    steps:
+      - uses: actions/checkout@v4
+        with: 
+          fetch-depth: "2"
+      
+      - name: Get changed files
+        id: changed-files
+        uses: tj-actions/changed-files@3f54ebb830831fc121d3263c1857cfbdc310cdb9 #v42
+      
+      - name: Was setup changed 
+        id: was_changed
+        run: |
+          for file in ${{ steps.changed-files.outputs.all_changed_files }}; do
+            if [ `basename "${file}"` == "setup.py" ]; then
+              echo "changed=1" >> $GITHUB_OUTPUT
+            fi
+          done
+          
+  build-docker-containers:
+    needs: check-for-source
+    if: (github.event_name == 'push') && (needs.check-for-source.outputs.changed == '1')
+    uses: ./.github/workflows/build_docker_images.yml
+    secrets: inherit
+
+  run-merge-tests:
+    needs: build-docker-containers
+    if: always()
+    uses: ./.github/workflows/run_merge_tests.yml
+
+  run-integration-tests:
+    needs: build-docker-containers
+    if: always()
+    uses: ./.github/workflows/self_hosted_integration_tests.yml

tasks/tasksmith-b1dd32dfa88b/environment/source/.github/workflows/build_docker_images.yml

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+name: Build Docker images (scheduled)
+
+on:
+  workflow_dispatch:
+  workflow_call:
+  schedule:
+    - cron: "0 1 * * *"
+
+concurrency:
+  group: docker-image-builds
+  cancel-in-progress: false
+
+jobs:
+  latest-cpu:
+    name: "Latest Accelerate CPU [dev]"
+    runs-on:
+      group: aws-general-8-plus
+    steps:
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v2
+      - name: Login to DockerHub
+        uses: docker/login-action@v2
+        with:
+          username: ${{ secrets.DOCKERHUB_USERNAME }}
+          password: ${{ secrets.DOCKERHUB_PASSWORD }}
+      - name: Get current date
+        id: date
+        run: |
+          echo "date=$(date '+%Y-%m-%d')" >> $GITHUB_ENV
+      - name: Build and Push CPU
+        uses: docker/build-push-action@v4
+        with:
+          file: docker/accelerate-cpu/Dockerfile
+          push: true
+          tags: |
+            huggingface/accelerate:cpu-nightly
+            huggingface/accelerate:cpu-nightly-${{ env.date }}
+
+  latest-cuda:
+    name: "Latest Accelerate GPU [dev]"
+    runs-on:
+      group: aws-g6-4xlarge-plus
+    steps:
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v2
+      - name: Login to DockerHub
+        uses: docker/login-action@v2
+        with:
+          username: ${{ secrets.DOCKERHUB_USERNAME }}
+          password: ${{ secrets.DOCKERHUB_PASSWORD }}
+      - name: Get current date
+        id: date
+        run: |
+          echo "date=$(date '+%Y-%m-%d')" >> $GITHUB_ENV
+      - name: Build and Push GPU
+        uses: docker/build-push-action@v4
+        with:
+          file: docker/accelerate-gpu/Dockerfile
+          push: true
+          tags: |
+            huggingface/accelerate:gpu-nightly
+            huggingface/accelerate:gpu-nightly-${{ env.date }}
+
+  latest-cuda-deepspeed:
+    name: "Latest Accelerate GPU DeepSpeed [dev]"
+    runs-on:
+      group: aws-g6-4xlarge-plus
+    steps:
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v2
+      - name: Login to DockerHub
+        uses: docker/login-action@v2
+        with:
+          username: ${{ secrets.DOCKERHUB_USERNAME }}
+          password: ${{ secrets.DOCKERHUB_PASSWORD }}
+      - name: Get current date
+        id: date
+        run: |
+          echo "date=$(date '+%Y-%m-%d')" >> $GITHUB_ENV
+      - name: Build and Push GPU
+        uses: docker/build-push-action@v4
+        with:
+          file: docker/accelerate-gpu-deepspeed/Dockerfile
+          push: true
+          tags: |
+            huggingface/accelerate:gpu-deepspeed-nightly
+            huggingface/accelerate:gpu-deepspeed-nightly-${{ env.date }}
+
+  latest-cuda-fp8-transformerengine:
+    name: "Latest Accelerate GPU FP8 TransformerEngine [dev]"
+    runs-on:
+      group: aws-g6-4xlarge-plus
+    steps:
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v2
+      - name: Login to DockerHub
+        uses: docker/login-action@v2
+        with:
+          username: ${{ secrets.DOCKERHUB_USERNAME }}
+          password: ${{ secrets.DOCKERHUB_PASSWORD }}
+      - name: Get current date
+        id: date
+        run: |
+          echo "date=$(date '+%Y-%m-%d')" >> $GITHUB_ENV
+          # Get the previous month
+          echo "base_year=$(date -d 'last month' '+%y')" >> $GITHUB_ENV
+          echo "base_month=$(date -d 'last month' '+%m')" >> $GITHUB_ENV
+      - name: Build and Push GPU
+        uses: docker/build-push-action@v4
+        with:
+          file: benchmarks/fp8/transformer_engine/Dockerfile
+          push: true
+          tags: huggingface/accelerate:gpu-fp8-transformerengine-nightly-${{ env.date }}
+          build-args: |
+            BASE_YEAR=${{ env.base_year }}
+            BASE_MONTH=${{ env.base_month }}

tasks/tasksmith-b1dd32dfa88b/environment/source/.github/workflows/build_documentation.yml

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+name: Build documentation
+
+on:
+  push:
+    branches:
+      - main
+      - doc-builder*
+      - v*-release
+
+jobs:
+   build:
+    uses: huggingface/doc-builder/.github/workflows/build_main_documentation.yml@main
+    with:
+      commit_sha: ${{ github.sha }}
+      package: accelerate
+      custom_container: huggingface/transformers-doc-builder
+    secrets:
+      hf_token: ${{ secrets.HF_DOC_BUILD_PUSH }}

tasks/tasksmith-b1dd32dfa88b/environment/source/.github/workflows/build_pr_documentation.yml

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+name: Build PR Documentation
+
+on:
+  pull_request:
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }}
+  cancel-in-progress: true
+
+jobs:
+  build:
+    uses: huggingface/doc-builder/.github/workflows/build_pr_documentation.yml@main
+    with:
+      commit_sha: ${{ github.event.pull_request.head.sha }}
+      pr_number: ${{ github.event.number }}
+      package: accelerate
+      custom_container: huggingface/transformers-doc-builder

tasks/tasksmith-b1dd32dfa88b/environment/source/.github/workflows/fp8_runner.yml

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+name: Test FP8 Runner
+
+on:
+  workflow_dispatch:
+
+env:
+  GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+jobs:
+  set-prev-day:
+    runs-on: ubuntu-latest
+    outputs:
+      prev-day: ${{ steps.set-prev-day.outputs.prev-day }}
+    steps:
+      - name: Set PREV_DAY
+        id: set-prev-day
+        run: |
+          PREV_DAY=$(date -d "yesterday" '+%Y-%m-%d')
+          echo "prev-day=$PREV_DAY" >> $GITHUB_OUTPUT
+  run-fp8-tests:
+    needs: set-prev-day
+    runs-on:
+      group: aws-g6e-12xlarge
+    container:
+      image: huggingface/accelerate:gpu-fp8-transformerengine-nightly-${{ needs.set-prev-day.outputs.prev-day }}
+      options: --gpus all --shm-size "16gb"
+    steps:
+      - uses: actions/checkout@v3
+      - name: Install the library
+        run: |
+            pip install -e .[test_prod,test_fp8]
+      - name: Show installed libraries
+        run: |
+          pip freeze
+      - name: Run TE FP8 tests
+        run: |
+          python -m pytest -s -v ./tests/test_fp8.py
+

tasks/tasksmith-b1dd32dfa88b/environment/source/.github/workflows/gaudi3_scheduled.yml

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+name: Gaudi3 tests (scheduled)
+
+on:
+  workflow_dispatch:
+  schedule: # every day at 6 AM UTC
+    - cron: "0 6 * * *"
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }}
+  cancel-in-progress: true
+
+jobs:
+  run-gaudi3-tests:
+    runs-on:
+      group: itac-bm-emr-gaudi3-dell-2gaudi
+
+    container:
+      image: docker://vault.habana.ai/gaudi-docker/1.21.1/ubuntu22.04/habanalabs/pytorch-installer-2.6.0:latest
+      options: --runtime=habana --shm-size=64G --cap-add=sys_nice --env HABANA_VISIBLE_DEVICES
+      env:
+        OMPI_MCA_btl_vader_single_copy_mechanism: none
+        PT_ENABLE_INT64_SUPPORT: 1
+        PT_HPU_LAZY_MODE: 0
+        RUN_SLOW: 1
+
+    steps:
+      - name: HL-SMI (1)
+        run: |
+          hl-smi
+          echo "HABANA_VISIBLE_DEVICES=${HABANA_VISIBLE_DEVICES}"
+          echo "HABANA_VISIBLE_MODULES=${HABANA_VISIBLE_MODULES}"
+
+      - name: Extract HPU visible modules
+        id: add-modules
+        run: |
+          export HABANA_VISIBLE_MODULES=$(hl-smi -Q module_id -f csv,noheader | tr '\n' ',' | sed 's/,$//')
+          echo "HABANA_VISIBLE_MODULES=${HABANA_VISIBLE_MODULES}" >> $GITHUB_ENV
+
+      - name: HL-SMI (2)
+        run: |
+          hl-smi
+          echo "HABANA_VISIBLE_DEVICES=${HABANA_VISIBLE_DEVICES}"
+          echo "HABANA_VISIBLE_MODULES=${HABANA_VISIBLE_MODULES}"
+
+      - name: Checkout to Accelerate
+        uses: actions/checkout@v4
+
+      - name: Install Accelerate with Transformers & DeepSpeed
+        run: |
+          pip install -e .[testing] \
+            git+https://github.com/HabanaAI/DeepSpeed.git@1.20.0 \
+            git+https://github.com/huggingface/transformers.git
+
+      - name: Run CLI tests
+        if: ${{ !cancelled() && (success() || failure()) }}
+        run: |
+          make test_cli
+
+      - name: Run Core tests
+        if: ${{ !cancelled() && (success() || failure()) }}
+        run: |
+          make test_core
+
+      - name: Run Big Modeling tests
+        if: ${{ !cancelled() && (success() || failure()) }}
+        run: |
+          make test_big_modeling
+
+      - name: Run DeepSpeed integration tests
+        if: ${{ !cancelled() && (success() || failure()) }}
+        run: |
+          make test_deepspeed
+
+      - name: Run FSDP integration tests
+        if: ${{ !cancelled() && (success() || failure()) }}
+        run: |
+          make test_fsdp
+
+      - name: Run TP integration tests
+        if: ${{ !cancelled() && (success() || failure()) }}
+        run: |
+          make test_tp
+
+      - name: Run Examples tests
+        if: ${{ !cancelled() && (success() || failure()) }}
+        run: |
+          make test_examples

tasks/tasksmith-b1dd32dfa88b/environment/source/.github/workflows/integration_tests.yml

@@ -0,0 +1,58 @@
+# CI for specifically ensuring integrations work fine (`transformers` mainly)
+# Useful tips:
+#  - New integrations to test should have its own job, and follow a strategy method where we check both
+#    the pypi and github versions.
+#  - When checking the latest release of the integration, use
+#    git checkout $(git describe --tags `git rev-list --tags --max-count=1`) to get the latest release.
+
+name: Integration Tests
+
+on:
+  pull_request:
+    paths:
+      - "src/**"
+      - "tests/**"
+      - ".github/**"
+      - "examples/**"
+      - "setup.py"
+    types: [opened, synchronize, reopened]
+
+env:
+  HF_HOME: ~/hf_cache
+
+jobs:
+  run-trainer-tests:
+    runs-on: ubuntu-latest
+    strategy:
+      fail-fast: false
+    steps:
+    - uses: actions/checkout@v4
+    - name: Set up python 3.9
+      uses: actions/setup-python@v5
+      with:
+        python-version: 3.9
+        cache: 'pip'
+        cache-dependency-path: 'setup.py'
+
+    - name: Install Accelerate from source
+      run: |
+        pip install --upgrade pip
+        pip install -e .
+    
+    - name: Clone and install transformers
+      run: |
+        cd ..
+        git clone https://github.com/huggingface/transformers
+        cd transformers
+        pip install .[torch,testing]
+
+    - name: Show installed libraries
+      run: |
+        pip freeze
+
+    - name: Run Trainer tests
+      env:
+        WANDB_DISABLED: true
+      run: |
+        cd ../transformers
+        pytest -sv tests/trainer

tasks/tasksmith-b1dd32dfa88b/environment/source/.github/workflows/nightly.yml

@@ -0,0 +1,233 @@
+name: Self-hosted runner with slow tests (scheduled)
+
+on:
+  workflow_dispatch:
+  schedule:
+    - cron: "0 2 * * *"
+
+env:
+  RUN_SLOW: "yes"
+  IS_GITHUB_CI: "1"
+  SLACK_API_TOKEN: ${{ secrets.SLACK_API_TOKEN }}
+
+
+jobs:
+  run_core_tests_single_gpu:
+    runs-on:
+      group: aws-g6-4xlarge-plus
+    env:
+      CUDA_VISIBLE_DEVICES: "0"
+      TEST_TYPE: "single_gpu"
+    container:
+      image: huggingface/accelerate:gpu-nightly
+      options: --gpus all --shm-size "16gb"
+    defaults:
+      run:
+        shell: bash
+    steps:
+      - name: Update clone & pip install
+        run: |
+          source activate accelerate
+          git clone https://github.com/huggingface/accelerate;
+          cd accelerate;
+          git checkout ${{ github.sha }};
+          pip install -e . --no-deps
+          pip install pytest-reportlog tabulate
+
+      - name: Show installed libraries
+        run: |
+          source activate accelerate;
+          pip freeze
+
+      - name: Run test on GPUs
+        working-directory: accelerate
+        run: |
+          source activate accelerate
+          make test
+
+      - name: Run examples on GPUs
+        working-directory: accelerate
+        if: always()
+        run: |
+          source activate accelerate
+          pip uninstall comet_ml -y
+          make test_examples
+
+      - name: Generate Report
+        working-directory: accelerate
+        if: always()
+        run: |
+          pip install slack_sdk tabulate
+          python utils/log_reports.py >> $GITHUB_STEP_SUMMARY
+
+  run_deepspeed_tests_single_gpu:
+    runs-on:
+      group: aws-g6-4xlarge-plus
+    env:
+      CUDA_VISIBLE_DEVICES: "0"
+      TEST_TYPE: "single_gpu_deepspeed"
+    container:
+      image: huggingface/accelerate:gpu-deepspeed-nightly
+      options: --gpus all --shm-size "16gb"
+    defaults:
+      run:
+        shell: bash
+    steps:
+      - name: Update clone & pip install
+        run: |
+          source activate accelerate
+          git clone https://github.com/huggingface/accelerate;
+          cd accelerate;
+          git checkout ${{ github.sha }};
+          pip install -e . --no-deps
+          pip install pytest-reportlog tabulate
+
+      - name: Show installed libraries
+        run: |
+          source activate accelerate;
+          pip freeze
+
+      - name: Run test on GPUs
+        working-directory: accelerate
+        run: |
+          source activate accelerate
+          make test_deepspeed
+
+      - name: Run Integration tests on GPUs
+        working-directory: accelerate
+        if: always()
+        run: |
+          source activate accelerate
+          make test_integrations
+
+      - name: Run examples on GPUs
+        working-directory: accelerate
+        if: always()
+        run: |
+          source activate accelerate
+          pip uninstall comet_ml -y
+          make test_examples
+
+      - name: Generate Report
+        working-directory: accelerate
+        if: always()
+        run: |
+          pip install slack_sdk tabulate
+          python utils/log_reports.py >> $GITHUB_STEP_SUMMARY
+
+  run_core_tests_multi_gpu:
+    runs-on:
+      group: aws-g6-12xlarge-plus
+    env:
+      CUDA_VISIBLE_DEVICES: "0,1"
+      TEST_TYPE: "multi_gpu"
+    container:
+      image: huggingface/accelerate:gpu-nightly
+      options: --gpus all --shm-size "16gb"
+    defaults:
+      run:
+        shell: bash
+    steps:
+      - name: Update clone
+        run: |
+          source activate accelerate
+          git clone https://github.com/huggingface/accelerate;
+          cd accelerate;
+          git checkout ${{ github.sha }};
+          pip install -e . --no-deps
+          pip install pytest-reportlog tabulate
+
+      - name: Show installed libraries
+        run: |
+          source activate accelerate;
+          pip freeze
+
+      - name: Run core and big modeling tests on GPUs
+        working-directory: accelerate
+        run: |
+          source activate accelerate
+          make test_core
+          make test_big_modeling
+          make test_cli
+
+      - name: Run Integration tests on GPUs
+        working-directory: accelerate
+        if: always()
+        run: |
+          source activate accelerate
+          make test_integrations
+
+      - name: Run examples on GPUs
+        working-directory: accelerate
+        if: always()
+        run: |
+          source activate accelerate
+          pip uninstall comet_ml -y
+          make test_examples
+
+      - name: Generate Report
+        working-directory: accelerate
+        if: always()
+        run: |
+          pip install slack_sdk tabulate
+          python utils/log_reports.py >> $GITHUB_STEP_SUMMARY
+
+  run_deepspeed_tests_multi_gpu:
+    runs-on:
+      group: aws-g6-12xlarge-plus
+    env:
+      CUDA_VISIBLE_DEVICES: "0,1"
+      TEST_TYPE: "multi_gpu_deepspeed"
+    container:
+      image: huggingface/accelerate:gpu-deepspeed-nightly
+      options: --gpus all --shm-size "16gb"
+    defaults:
+      run:
+        shell: bash
+    steps:
+      - name: Update clone
+        run: |
+          source activate accelerate
+          git clone https://github.com/huggingface/accelerate;
+          cd accelerate;
+          git checkout ${{ github.sha }};
+          pip install -e . --no-deps
+          pip install pytest-reportlog tabulate
+
+      - name: Show installed libraries
+        run: |
+          source activate accelerate;
+          pip freeze
+
+      - name: Run DeepSpeed tests
+        working-directory: accelerate
+        run: |
+          source activate accelerate
+          make test_deepspeed
+
+      - name: Run Integration tests on GPUs
+        working-directory: accelerate
+        if: always()
+        run: |
+          source activate accelerate
+          make test_integrations
+
+      - name: Run examples on GPUs
+        working-directory: accelerate
+        if: always()
+        run: |
+          source activate accelerate
+          pip uninstall comet_ml -y
+          make test_examples
+
+      - name: Generate Report
+        working-directory: accelerate
+        if: always()
+        run: |
+          pip install slack_sdk tabulate
+          python utils/log_reports.py >> $GITHUB_STEP_SUMMARY
+
+
+  run-integration-tests:
+    if: always()
+    uses: ./.github/workflows/self_hosted_integration_tests.yml

tasks/tasksmith-b1dd32dfa88b/environment/source/.github/workflows/pr_style_bot.yml

@@ -0,0 +1,19 @@
+# To run this bot, comment "@bot /style" on a PR
+name: Style Bot
+
+on:
+  issue_comment:
+    types: [created]
+
+permissions:
+  contents: write
+  pull-requests: write
+
+jobs:
+  style:
+    uses: huggingface/huggingface_hub/.github/workflows/style-bot-action.yml@main
+    with:
+      python_quality_dependencies: "[quality]"
+      style_command_type: "default"
+    secrets:
+      bot_token: ${{ secrets.GITHUB_TOKEN }}

tasks/tasksmith-b1dd32dfa88b/environment/source/.github/workflows/quality.yml

@@ -0,0 +1,24 @@
+name: Quality Check
+
+on: [pull_request]
+
+jobs:
+  quality:
+    runs-on: ubuntu-latest
+    steps:
+    - uses: actions/checkout@v4
+    - name: Set up Python 3.9
+      uses: actions/setup-python@v5
+      with:
+        python-version: 3.9
+        cache: 'pip'
+        cache-dependency-path: 'setup.py'
+    - name: Install Python dependencies
+      run: pip install -e .[quality]
+    - name: Run Quality check
+      run: make quality
+    - name: Check if failure
+      if: ${{ failure() }}
+      run: |
+        echo "Quality check failed. Please ensure the right dependency versions are installed with 'pip install -e .[quality]' and rerun 'make style; make quality;'" >> $GITHUB_STEP_SUMMARY
+

tasks/tasksmith-b1dd32dfa88b/environment/source/.github/workflows/run_merge_tests.yml

@@ -0,0 +1,188 @@
+name: Self-hosted runner tests (push to "main")
+
+on:
+  workflow_call:
+  workflow_dispatch:
+
+env:
+  TESTING_MOCKED_DATALOADERS: "1"
+  IS_GITHUB_CI: "1"
+
+jobs:
+  run_core_tests_single_gpu:
+    runs-on:
+      group: aws-g6-4xlarge-plus
+    env:
+      CUDA_VISIBLE_DEVICES: "0"
+    container:
+      image: huggingface/accelerate:gpu-nightly
+      options: --gpus all --shm-size "16gb"
+    defaults:
+      run:
+        shell: bash
+    steps:
+      - name: Install accelerate
+        run: |
+          source activate accelerate;
+          git clone https://github.com/huggingface/accelerate;
+          cd accelerate;
+          git checkout ${{ github.sha }};
+          pip install -e .[testing,test_trackers] -U;
+          pip install pytest-reportlog tabulate  ;
+
+      - name: Show installed libraries
+        run: |
+          source activate accelerate;
+          pip freeze
+
+      - name: Run CLI tests (use make cli)
+        working-directory: accelerate
+        run: |
+          source activate accelerate;
+          make test_cli
+
+      - name: Run test on GPUs
+        working-directory: accelerate
+        if: always()
+        run: |
+          source activate accelerate;
+          make test
+      - name: Run examples on GPUs
+        working-directory: accelerate
+        if: always()
+        run: |
+          source activate accelerate;
+          pip uninstall comet_ml -y;
+          make test_examples
+
+      - name: Generate Report
+        working-directory: accelerate
+        if: always()
+        run: |
+          pip install tabulate;
+          python utils/log_reports.py >> $GITHUB_STEP_SUMMARY
+
+  run_deepspeed_tests_single_gpu:
+    runs-on:
+      group: aws-g6-4xlarge-plus
+    env:
+      CUDA_VISIBLE_DEVICES: "0"
+    container:
+      image: huggingface/accelerate:gpu-deepspeed-nightly
+      options: --gpus all --shm-size "16gb"
+    defaults:
+      run:
+        shell: bash
+    steps:
+      - name: Install accelerate
+        run: |
+          source activate accelerate;
+          git clone https://github.com/huggingface/accelerate;
+          cd accelerate;
+          git checkout ${{ github.sha }};
+          pip install -e .[testing,test_trackers] -U;
+          pip install pytest-reportlog tabulate  ;
+
+      - name: Show installed libraries
+        run: |
+          source activate accelerate;
+          pip freeze
+
+      - name: Run test on GPUs
+        working-directory: accelerate
+        if: always()
+        run: |
+          source activate accelerate;
+          make test_deepspeed
+
+      - name: Generate Report
+        working-directory: accelerate
+        if: always()
+        run: |
+          pip install tabulate;
+          python utils/log_reports.py >> $GITHUB_STEP_SUMMARY
+
+  run_core_tests_multi_gpu:
+    runs-on:
+      group: aws-g6-12xlarge-plus
+    env:
+      CUDA_VISIBLE_DEVICES: 0,1
+    container:
+      image: huggingface/accelerate:gpu-nightly
+      options: --gpus all --shm-size "16gb"
+    defaults:
+      run:
+        shell: bash
+    steps:
+      - name: Update clone
+        run: |
+          source activate accelerate;
+          git clone https://github.com/huggingface/accelerate;
+          cd accelerate;
+          git checkout ${{ github.sha }};
+          pip install -e .[testing,test_trackers] -U;
+          pip install pytest-reportlog tabulate
+
+      - name: Show installed libraries
+        run: |
+          source activate accelerate;
+          pip freeze
+
+      - name: Run test on GPUs
+        working-directory: accelerate
+        run: |
+          source activate accelerate;
+          make test
+
+      - name: Run examples on GPUs
+        working-directory: accelerate
+        if: always()
+        run: |
+          source activate accelerate;
+          pip uninstall comet_ml -y;
+          make test_examples
+
+      - name: Generate Report
+        working-directory: accelerate
+        if: always()
+        run: |
+          source activate accelerate;
+          python utils/log_reports.py >> $GITHUB_STEP_SUMMARY
+
+  run_deepspeed_tests_multi_gpu:
+    runs-on:
+      group: aws-g6-12xlarge-plus
+    container:
+      image: huggingface/accelerate:gpu-deepspeed-nightly
+      options: --gpus all --shm-size "16gb"
+    defaults:
+      run:
+        shell: bash
+    steps:
+      - name: Install accelerate
+        run: |
+          source activate accelerate;
+          git clone https://github.com/huggingface/accelerate;
+          cd accelerate;
+          git checkout ${{ github.sha }};
+          pip install -e .[testing,test_trackers] -U;
+          pip install pytest-reportlog tabulate  ;
+
+      - name: Show installed libraries
+        run: |
+          source activate accelerate;
+          pip freeze
+
+      - name: Run test on GPUs
+        working-directory: accelerate
+        if: always()
+        run: |
+          source activate accelerate;
+          make test_deepspeed
+
+      - name: Generate Report
+        working-directory: accelerate
+        if: always()
+        run: |
+          pip install tabulate;
+          python utils/log_reports.py >> $GITHUB_STEP_SUMMARY

tasks/tasksmith-b1dd32dfa88b/environment/source/.github/workflows/self_hosted_integration_tests.yml

@@ -0,0 +1,127 @@
+# CI for specifically ensuring integrations work fine (`transformers` mainly) on GPUs
+# Useful tips:
+#  - `working-directory` should be set to the root of the repo, which is cloned on the actual CI runner.
+#    It follows the directory structure of `actions-runner/_work/{repo_name}/{repo_name}/{cloned_repo} on
+#    prem, but in Actions setting `working-directory` looks just in the `{repo_name}` level.
+#  - New integrations to test should have its own job, and follow a strategy method where we check both
+#    the pypi and github versions.
+#  - Workflow call lets this be called from `build_and_run_tests.yml`
+#  - When using a docker container, it's recommended to set `--shm-size`, we use 16gb.
+name: Integration Tests (push to "main")
+
+on:
+  workflow_call:
+  workflow_dispatch:
+
+env:
+  HF_HOME: ~/hf_cache
+
+defaults:
+  run:
+    shell: bash
+
+jobs:
+  run-trainer-tests:
+    container:
+      image: huggingface/accelerate:gpu-deepspeed-nightly
+      options: --gpus all --shm-size "16gb"
+    runs-on:
+      group: aws-g6-12xlarge-plus
+    strategy:
+      fail-fast: false
+      matrix:
+        cuda_visible_devices: [
+          "0",
+          "0,1"
+        ]
+    steps:
+      - name: Install transformers
+        run: |
+          source activate accelerate;
+          git clone https://github.com/huggingface/transformers --depth 1;
+          cd transformers;
+          pip install .[torch,deepspeed-testing];
+          cd ..;
+
+      - name: Install accelerate
+        run: |
+          source activate accelerate;
+          git clone https://github.com/huggingface/accelerate;
+          cd accelerate;
+          git checkout ${{ github.sha }} ;
+          pip install -e .[testing];
+          pip uninstall comet_ml wandb dvclive -y
+          cd ..;
+
+      - name: Show installed libraries
+        run: |
+          source activate accelerate;
+          pip freeze
+
+      - name: Run trainer tests
+        working-directory: transformers/
+        env:
+          CUDA_VISIBLE_DEVICES: ${{ matrix.cuda_visible_devices }}
+          WANDB_DISABLED: true
+        run: |
+          source activate accelerate;
+          pytest -sv tests/trainer
+
+      - name: Run deepspeed tests
+        working-directory: transformers/
+        env:
+          CUDA_VISIBLE_DEVICES: ${{ matrix.cuda_visible_devices }}
+          WANDB_DISABLED: true
+        if: always()
+        run: |
+          source activate accelerate;
+          pytest -sv tests/deepspeed
+
+      - name: Run transformers examples tests
+        working-directory: transformers/
+        env:
+          CUDA_VISIBLE_DEVICES: ${{ matrix.cuda_visible_devices }}
+          WANDB_DISABLED: true
+        run: |
+          source activate accelerate
+          pip install -r examples/pytorch/_tests_requirements.txt
+          pytest -sv examples/pytorch/test_accelerate_examples.py examples/pytorch/test_pytorch_examples.py
+
+  run-skorch-tests:
+    container:
+      image: huggingface/accelerate:gpu-nightly
+      options: --gpus all --shm-size "16gb"
+    runs-on:
+      group: aws-g6-12xlarge-plus
+    strategy:
+      fail-fast: false
+    steps:
+      - name: Install accelerate
+        run:
+          source activate accelerate;
+          git clone https://github.com/huggingface/accelerate;
+          cd accelerate;
+          git checkout ${{ github.sha }};
+          pip install -e .[testing];
+          cd ..
+
+      - name: Install skorch
+        run: |
+          source activate accelerate
+          git clone https://github.com/skorch-dev/skorch;
+          cd skorch;
+          git config --global --add safe.directory '*'
+          git checkout master && git pull
+          pip install .[testing]
+          pip install flaky
+
+      - name: Show installed libraries
+        run: |
+          source activate accelerate;
+          pip freeze
+
+      - name: Run skorch tests
+        working-directory: skorch/
+        run: |
+          source activate accelerate;
+          pytest -sv -k TestAccelerate

tasks/tasksmith-b1dd32dfa88b/environment/source/.github/workflows/stale.yml

@@ -0,0 +1,33 @@
+name: Stale Bot
+
+on:
+  schedule:
+    - cron: "0 15 * * *"
+  workflow_dispatch:
+
+jobs:
+  close_stale_issues:
+    name: Close Stale Issues
+    if: github.repository == 'huggingface/accelerate'
+    runs-on: ubuntu-latest
+    permissions:
+      issues: write
+      pull-requests: write
+    env:
+      GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+    steps:
+    - uses: actions/checkout@v4
+    
+    - name: Setup Python
+      uses: actions/setup-python@v5
+      with:
+        python-version: 3.9
+        cache: 'pip'
+        cache-dependency-path: 'setup.py'
+    
+    - name: Install requirements
+      run: |
+        pip install PyGithub
+    - name: Close stale issues
+      run: |
+        python utils/stale.py

tasks/tasksmith-b1dd32dfa88b/environment/source/.github/workflows/test.yml

@@ -0,0 +1,70 @@
+name: Run Tests
+
+on:
+  pull_request:
+    paths:
+      - "src/**"
+      - "tests/**"
+      - ".github/**"
+      - "examples/**"
+      - "setup.py"
+    types: [opened, synchronize, reopened]
+
+env:
+  HF_HOME: ~/hf_cache
+  TESTING_MOCKED_DATALOADERS: "1"
+  IS_GITHUB_CI: "1"
+
+jobs:
+  run-tests:
+    runs-on: ubuntu-latest
+    strategy:
+      fail-fast: false
+      matrix:
+        pytorch-version: [
+          latest,
+          minimum,
+        ]
+        test-kind: [
+          test_prod,
+          test_core,
+          test_cli,
+          test_big_modeling,
+          test_deepspeed,
+          test_fsdp,
+          test_example_differences,
+          test_checkpoint_step,
+          test_checkpoint_epoch,
+          test_rest
+        ]
+    steps:
+    - uses: actions/checkout@v4
+    - name: Set up python 3.9
+      uses: actions/setup-python@v5
+      with:
+        python-version: 3.9
+        cache: 'pip'
+        cache-dependency-path: 'setup.py'
+    
+    - name: Install the library
+      run: |
+        if [[ ${{ matrix.test-kind }} = test_prod ]]; then pip install -e .[test_prod]; fi
+        if [[ ${{ matrix.test-kind }} != test_prod ]]; then pip install -e .[testing,test_trackers]; fi
+        if [[ ${{ matrix.test-kind }} = test_rest ]]; then pip uninstall comet_ml -y; fi
+        if [[ ${{ matrix.pytorch-version }} = minimum ]]; then pip install torchvision==0.18.1 torch==2.3.1; fi
+        pip install pytest-reportlog tabulate setuptools importlib_metadata
+
+    - name: Show installed libraries
+      run: |
+        pip freeze
+    
+    - name: Run Tests
+      env: 
+        PYTORCH_VERSION: ${{ matrix.pytorch-version }}
+      run: |
+        make ${{ matrix.test-kind }}
+
+    - name: Generate Report
+      if: always()
+      run: |
+        python utils/log_reports.py >> $GITHUB_STEP_SUMMARY

tasks/tasksmith-b1dd32dfa88b/environment/source/.github/workflows/test_imports.yml

@@ -0,0 +1,55 @@
+name: Run Import Tests
+
+on:
+  pull_request:
+    paths:
+      - "src/**"
+      - "tests/**"
+      - ".github/**"
+      - "examples/**"
+      - "setup.py"
+    types: [opened, synchronize, reopened]
+
+env:
+  HF_HOME: ~/hf_cache
+  TESTING_MOCKED_DATALOADERS: "1"
+  IS_GITHUB_CI: "1"
+
+jobs:
+  run-tests:
+    runs-on: ubuntu-latest
+    strategy:
+      fail-fast: false
+      matrix:
+        pytorch-version: [
+          latest,
+          minimum,
+        ]
+    steps:
+    - uses: actions/checkout@v4
+    - name: Set up python 3.9
+      uses: actions/setup-python@v5
+      with:
+        python-version: 3.9
+        cache: 'pip'
+        cache-dependency-path: 'setup.py'
+    
+    - name: Install the library
+      run: |
+        pip install -e .
+        pip install pytest-reportlog tabulate setuptools git+https://github.com/muellerzr/import-timer
+
+    - name: Show installed libraries
+      run: |
+        pip freeze
+    
+    - name: Run Import Tests
+      env: 
+        PYTORCH_VERSION: ${{ matrix.pytorch-version }}
+      run: |
+        pytest -sv tests/test_imports.py
+
+    - name: Generate Report
+      if: always()
+      run: |
+        python utils/log_reports.py >> $GITHUB_STEP_SUMMARY

tasks/tasksmith-b1dd32dfa88b/environment/source/.github/workflows/trufflehog.yml

@@ -0,0 +1,15 @@
+on:
+  push:
+
+name: Secret Leaks
+
+jobs:
+  trufflehog:
+    runs-on: ubuntu-latest
+    steps:
+    - name: Checkout code
+      uses: actions/checkout@v4
+      with:
+        fetch-depth: 0
+    - name: Secret Scanning
+      uses: trufflesecurity/trufflehog@main

tasks/tasksmith-b1dd32dfa88b/environment/source/.github/workflows/upload_pr_documentation.yml

@@ -0,0 +1,16 @@
+name: Upload PR Documentation
+
+on:
+  workflow_run:
+    workflows: ["Build PR Documentation"]
+    types:
+      - completed
+
+jobs:
+  build:
+    uses: huggingface/doc-builder/.github/workflows/upload_pr_documentation.yml@main
+    with:
+      package_name: accelerate
+    secrets:
+      hf_token: ${{ secrets.HF_DOC_BUILD_PUSH }}
+      comment_bot_token: ${{ secrets.COMMENT_BOT_TOKEN }}

tasks/tasksmith-b1dd32dfa88b/environment/source/.gitignore

@@ -0,0 +1,144 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# VSCode
+.vscode
+
+# IntelliJ
+.idea
+
+# Mac .DS_Store
+.DS_Store
+
+# More test things
+wandb
+
+# ruff
+.ruff_cache

tasks/tasksmith-b1dd32dfa88b/environment/source/.pre-commit-config.yaml

@@ -0,0 +1,13 @@
+repos:
+  - repo: https://github.com/astral-sh/ruff-pre-commit
+    rev: v0.2.1
+    hooks:
+      - id: ruff
+        args:
+          - --fix
+      - id: ruff-format
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v4.5.0
+    hooks:
+      - id: check-merge-conflict
+      - id: check-yaml

tasks/tasksmith-b1dd32dfa88b/environment/source/CODE_OF_CONDUCT.md

@@ -0,0 +1,129 @@
+
+# Contributor Covenant Code of Conduct
+
+## Our Pledge
+
+We as members, contributors, and leaders pledge to make participation in our
+community a harassment-free experience for everyone, regardless of age, body
+size, visible or invisible disability, ethnicity, sex characteristics, gender
+identity and expression, level of experience, education, socio-economic status,
+nationality, personal appearance, race, religion, or sexual identity
+and orientation.
+
+We pledge to act and interact in ways that contribute to an open, welcoming,
+diverse, inclusive, and healthy community.
+
+## Our Standards
+
+Examples of behavior that contributes to a positive environment for our
+community include:
+
+* Demonstrating empathy and kindness toward other people
+* Being respectful of differing opinions, viewpoints, and experiences
+* Giving and gracefully accepting constructive feedback
+* Accepting responsibility and apologizing to those affected by our mistakes,
+  and learning from the experience
+* Focusing on what is best not just for us as individuals, but for the
+  overall community
+
+Examples of unacceptable behavior include:
+
+* The use of sexualized language or imagery, and sexual attention or
+  advances of any kind
+* Trolling, insulting or derogatory comments, and personal or political attacks
+* Public or private harassment
+* Publishing others' private information, such as a physical or email
+  address, without their explicit permission
+* Other conduct which could reasonably be considered inappropriate in a
+  professional setting
+
+## Enforcement Responsibilities
+
+Community leaders are responsible for clarifying and enforcing our standards of
+acceptable behavior and will take appropriate and fair corrective action in
+response to any behavior that they deem inappropriate, threatening, offensive,
+or harmful.
+
+Community leaders have the right and responsibility to remove, edit, or reject
+comments, commits, code, wiki edits, issues, and other contributions that are
+not aligned to this Code of Conduct, and will communicate reasons for moderation
+decisions when appropriate.
+
+## Scope
+
+This Code of Conduct applies within all community spaces, and also applies when
+an individual is officially representing the community in public spaces.
+Examples of representing our community include using an official e-mail address,
+posting via an official social media account, or acting as an appointed
+representative at an online or offline event.
+
+## Enforcement
+
+Instances of abusive, harassing, or otherwise unacceptable behavior may be
+reported to the community leaders responsible for enforcement at
+feedback@huggingface.co.
+All complaints will be reviewed and investigated promptly and fairly.
+
+All community leaders are obligated to respect the privacy and security of the
+reporter of any incident.
+
+## Enforcement Guidelines
+
+Community leaders will follow these Community Impact Guidelines in determining
+the consequences for any action they deem in violation of this Code of Conduct:
+
+### 1. Correction
+
+**Community Impact**: Use of inappropriate language or other behavior deemed
+unprofessional or unwelcome in the community.
+
+**Consequence**: A private, written warning from community leaders, providing
+clarity around the nature of the violation and an explanation of why the
+behavior was inappropriate. A public apology may be requested.
+
+### 2. Warning
+
+**Community Impact**: A violation through a single incident or series
+of actions.
+
+**Consequence**: A warning with consequences for continued behavior. No
+interaction with the people involved, including unsolicited interaction with
+those enforcing the Code of Conduct, for a specified period of time. This
+includes avoiding interactions in community spaces as well as external channels
+like social media. Violating these terms may lead to a temporary or
+permanent ban.
+
+### 3. Temporary Ban
+
+**Community Impact**: A serious violation of community standards, including
+sustained inappropriate behavior.
+
+**Consequence**: A temporary ban from any sort of interaction or public
+communication with the community for a specified period of time. No public or
+private interaction with the people involved, including unsolicited interaction
+with those enforcing the Code of Conduct, is allowed during this period.
+Violating these terms may lead to a permanent ban.
+
+### 4. Permanent Ban
+
+**Community Impact**: Demonstrating a pattern of violation of community
+standards, including sustained inappropriate behavior,  harassment of an
+individual, or aggression toward or disparagement of classes of individuals.
+
+**Consequence**: A permanent ban from any sort of public interaction within
+the community.
+
+## Attribution
+
+This Code of Conduct is adapted from the [Contributor Covenant][homepage],
+version 2.0, available at
+https://www.contributor-covenant.org/version/2/0/code_of_conduct.html.
+
+Community Impact Guidelines were inspired by [Mozilla's code of conduct
+enforcement ladder](https://github.com/mozilla/diversity).
+
+[homepage]: https://www.contributor-covenant.org
+
+For answers to common questions about this code of conduct, see the FAQ at
+https://www.contributor-covenant.org/faq. Translations are available at
+https://www.contributor-covenant.org/translations.

tasks/tasksmith-b1dd32dfa88b/environment/source/CONTRIBUTING.md

@@ -0,0 +1,249 @@
+<!---
+Copyright 2022 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+# How to contribute to 🤗 Accelerate?
+
+Everyone is welcome to contribute, and we value everybody's contribution. Code
+is thus not the only way to help the community. Answering questions, helping
+others, reaching out and improving the documentations are immensely valuable to
+the community.
+
+It also helps us if you spread the word: reference the library from blog posts
+on the awesome projects it made possible, shout out on Twitter every time it has
+helped you, or simply star the repo to say "thank you".
+
+Whichever way you choose to contribute, please be mindful to respect our
+[code of conduct](https://github.com/huggingface/accelerate/blob/main/CODE_OF_CONDUCT.md).
+
+## You can contribute in so many ways!
+
+Some of the ways you can contribute to Accelerate:
+* Fixing outstanding issues with the existing code;
+* Contributing to the examples or to the documentation;
+* Submitting issues related to bugs or desired new features.
+
+## Submitting a new issue or feature request
+
+Do your best to follow these guidelines when submitting an issue or a feature
+request. It will make it easier for us to come back to you quickly and with good
+feedback.
+
+### Did you find a bug?
+
+The 🤗 Accelerate library is robust and reliable thanks to the users who notify us of
+the problems they encounter. So thank you for reporting an issue.
+
+First, we would really appreciate it if you could **make sure the bug was not
+already reported** (use the search bar on Github under Issues).
+
+Did not find it? :( So we can act quickly on it, please follow these steps:
+
+* Include your **OS type and version**, the versions of **Python** and **PyTorch**.
+* A short, self-contained, code snippet that allows us to reproduce the bug in
+  less than 30s;
+* Provide the with your Accelerate configuration (located by default in `~/.cache/huggingface/accelerate/default_config.yaml`)
+
+### Do you want a new feature?
+
+A good feature request addresses the following points:
+
+1. Motivation first:
+* Is it related to a problem/frustration with the library? If so, please explain
+  why. Providing a code snippet that demonstrates the problem is best.
+* Is it related to something you would need for a project? We'd love to hear
+  about it!
+* Is it something you worked on and think could benefit the community?
+  Awesome! Tell us what problem it solved for you.
+2. Write a *full paragraph* describing the feature;
+3. Provide a **code snippet** that demonstrates its future use;
+4. In case this is related to a paper, please attach a link;
+5. Attach any additional information (drawings, screenshots, etc.) you think may help.
+
+If your issue is well written we're already 80% of the way there by the time you
+post it.
+
+## Submitting a pull request (PR)
+
+Before writing code, we strongly advise you to search through the existing PRs or
+issues to make sure that nobody is already working on the same thing. If you are
+unsure, it is always a good idea to open an issue to get some feedback.
+
+You will need basic `git` proficiency to be able to contribute to
+🤗 Accelerate. `git` is not the easiest tool to use but it has the greatest
+manual. Type `git --help` in a shell and enjoy. If you prefer books, [Pro
+Git](https://git-scm.com/book/en/v2) is a very good reference.
+
+Follow these steps to start contributing:
+
+1. Fork the [repository](https://github.com/huggingface/accelerate) by
+   clicking on the 'Fork' button on the repository's page. This creates a copy of the code
+   under your GitHub user account.
+
+2. Clone your fork to your local disk, and add the base repository as a remote. The following command
+   assumes you have your public SSH key uploaded to GitHub. See the following guide for more
+   [information](https://docs.github.com/en/repositories/creating-and-managing-repositories/cloning-a-repository).
+
+   ```bash
+   $ git clone git@github.com:<your Github handle>/accelerate.git
+   $ cd accelerate
+   $ git remote add upstream https://github.com/huggingface/accelerate.git
+   ```
+
+3. Create a new branch to hold your development changes, and do this for every new PR you work on.
+
+   Start by synchronizing your `main` branch with the `upstream/main` branch (ore details in the [GitHub Docs](https://docs.github.com/en/github/collaborating-with-issues-and-pull-requests/syncing-a-fork)):
+
+   ```bash
+   $ git checkout main
+   $ git fetch upstream
+   $ git merge upstream/main
+   ```
+
+   Once your `main` branch is synchronized, create a new branch from it:
+
+   ```bash
+   $ git checkout -b a-descriptive-name-for-my-changes
+   ```
+
+   **Do not** work on the `main` branch.
+
+4. Set up a development environment by running the following command in a conda or a virtual environment you've created for working on this library:
+
+   ```bash
+   $ pip install -e ".[dev]"
+   ```
+   
+   This will install all testing and linting/code quality dependencies for the library (see `quality`, `test_dev`, 
+   `test_prod` targets in [`setup.py`](./setup.py)).
+
+   (If accelerate was already installed in the virtual environment, remove
+   it with `pip uninstall accelerate` before reinstalling it in editable
+   mode with the `-e` flag).
+
+   Alternatively, if you are using [Visual Studio Code](https://code.visualstudio.com/Download), the fastest way to get set up is by using
+   the provided Dev Container. Documentation on how to get started with dev containers is available [here](https://code.visualstudio.com/docs/remote/containers).
+
+5. Develop the features on your branch.
+
+   As you work on the features, you should make sure that the test suite
+   passes. You should run the tests impacted by your changes like this (see 
+   below an explanation regarding the environment variable):
+
+   ```bash
+   $ pytest tests/<TEST_TO_RUN>.py
+   ```
+   
+   > For the following commands leveraging the `make` utility, we recommend using the WSL system when running on
+   > Windows. More information [here](https://docs.microsoft.com/en-us/windows/wsl/about).
+
+   You can also run the full suite with the following command.
+
+   ```bash
+   $ make test
+   ```
+
+   `accelerate` relies on `ruff` to format its source code
+   consistently. After you make changes, apply automatic style corrections and code verifications
+   that can't be automated in one go with:
+
+   This target is also optimized to only work with files modified by the PR you're working on.
+
+   If you prefer to run the checks one after the other, the following command apply the
+   style corrections:
+
+   ```bash
+   $ make style
+   ```
+
+   `accelerate` also uses a few custom scripts to check for coding mistakes. Quality
+   control runs in CI, however you can also run the same checks with:
+
+   ```bash
+   $ make quality
+   ```
+
+   You can also set up [`pre-commit`](https://pre-commit.com/) to run these checks
+   automatically as Git commit hooks.
+
+   ```bash
+   $ pip install pre-commit
+   $ pre-commit install
+   ```
+
+   Once you're happy with your changes, add changed files using `git add` and
+   make a commit with `git commit` to record your changes locally:
+
+   ```bash
+   $ git add modified_file.py
+   $ git commit
+   ```
+
+   Please write [good commit messages](https://chris.beams.io/posts/git-commit/).
+
+   It is a good idea to sync your copy of the code with the original
+   repository regularly. This way you can quickly account for changes:
+
+   ```bash
+   $ git fetch upstream
+   $ git rebase upstream/main
+   ```
+
+   Push the changes to your account using:
+
+   ```bash
+   $ git push -u origin a-descriptive-name-for-my-changes
+   ```
+
+6. Once you are satisfied (**and the checklist below is happy too**), go to the
+   webpage of your fork on GitHub. Click on 'Pull request' to send your changes
+   to the project maintainers for review.
+
+7. It's ok if maintainers ask you for changes. It happens to core contributors
+   too! So everyone can see the changes in the Pull request, work in your local
+   branch and push the changes to your fork. They will automatically appear in
+   the pull request.
+
+
+### Checklist
+
+1. The title of your pull request should be a summary of its contribution;
+2. If your pull request addresses an issue, please mention the issue number in
+   the pull request description to make sure they are linked (and people
+   consulting the issue know you are working on it);
+3. To indicate a work in progress please prefix the title with `[WIP]`, or mark
+   the PR as a draft PR. These are useful to avoid duplicated work, and to differentiate
+   it from PRs ready to be merged;
+4. Make sure existing tests pass;
+5. Add high-coverage tests. No quality testing = no merge.
+
+See an example of a good PR here: https://github.com/huggingface/accelerate/pull/255
+
+### Tests
+
+An extensive test suite is included to test the library behavior and several examples. Library tests can be found in
+the [tests folder](https://github.com/huggingface/accelerate/tree/main/tests).
+
+We use `pytest` in order to run the tests. From the root of the
+repository, here's how to run tests with `pytest` for the library:
+
+```bash
+$ python -m pytest -sv ./tests
+```
+
+In fact, that's how `make test` is implemented (sans the `pip install` line)!
+
+You can specify a smaller set of tests in order to test only the feature
+you're working on.

tasks/tasksmith-b1dd32dfa88b/environment/source/Dockerfile

@@ -0,0 +1,7 @@
+FROM python:3.12-slim
+WORKDIR /workspace
+RUN python -m pip install --no-cache-dir --extra-index-url https://download.pytorch.org/whl/cpu torch==2.11.0+cpu numpy==2.5.3 packaging==26.3 psutil==7.2.2 pyyaml==6.0.3 huggingface_hub==1.31.0 safetensors==0.8.0 pytest==8.4.2 setuptools==78.1.0 wheel==0.48.0
+COPY . /workspace
+RUN python -m pip install --no-cache-dir --no-deps --no-build-isolation -e .
+RUN rm -rf /workspace/.git /root/.cache/pip
+ENV PYTHONDONTWRITEBYTECODE=1 PYTEST_DISABLE_PLUGIN_AUTOLOAD=1

tasks/tasksmith-b1dd32dfa88b/environment/source/LICENSE

@@ -0,0 +1,201 @@
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
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+
+      "You" (or "Your") shall mean an individual or Legal Entity
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+      work stoppage, computer failure or malfunction, or any and all
+      other commercial damages or losses), even if such Contributor
+      has been advised of the possibility of such damages.
+
+   9. Accepting Warranty or Additional Liability. While redistributing
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+      of any other Contributor, and only if You agree to indemnify,
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+      of your accepting any such warranty or additional liability.
+
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+
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+
+      To apply the Apache License to your work, attach the following
+      boilerplate notice, with the fields enclosed by brackets "[]"
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+   Copyright [yyyy] [name of copyright owner]
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+   Licensed under the Apache License, Version 2.0 (the "License");
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+
+   Unless required by applicable law or agreed to in writing, software
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+   See the License for the specific language governing permissions and
+   limitations under the License.

tasks/tasksmith-b1dd32dfa88b/environment/source/Makefile

@@ -0,0 +1,101 @@
+.PHONY: quality style test docs utils
+
+check_dirs := .
+
+# Check that source code meets quality standards
+
+extra_quality_checks:
+	python utils/check_copies.py
+	python utils/check_dummies.py
+	python utils/check_repo.py
+	doc-builder style src/accelerate docs/source --max_len 119
+
+# this target runs checks on all files
+quality:
+	ruff check $(check_dirs)
+	ruff format --check $(check_dirs)
+	doc-builder style src/accelerate docs/source --max_len 119 --check_only
+
+# Format source code automatically and check is there are any problems left that need manual fixing
+style:
+	ruff check $(check_dirs) --fix
+	ruff format $(check_dirs)
+	doc-builder style src/accelerate docs/source --max_len 119
+	
+# Run tests for the library
+test_core:
+	python -m pytest -s -v ./tests/ \
+	--ignore=./tests/test_big_modeling.py \
+	--ignore=./tests/test_modeling_utils.py \
+	--ignore=./tests/test_examples.py \
+	--ignore=./tests/test_cli.py \
+	--ignore=./tests/deepspeed \
+	--ignore=./tests/fsdp \
+	--ignore=./tests/tp \
+	$(if $(IS_GITHUB_CI),--report-log "$(PYTORCH_VERSION)_core.log",)
+
+test_cli:
+	python -m pytest -s -v ./tests/test_cli.py $(if $(IS_GITHUB_CI),--report-log "$(PYTORCH_VERSION)_cli.log",)
+
+test_big_modeling:
+	python -m pytest -s -v ./tests/test_big_modeling.py ./tests/test_modeling_utils.py $(if $(IS_GITHUB_CI),--report-log "$(PYTORCH_VERSION)_big_modeling.log",)
+
+test_deepspeed:
+	python -m pytest -s -v ./tests/deepspeed $(if $(IS_GITHUB_CI),--report-log "$(PYTORCH_VERSION)_deepspeed.log",)
+
+test_fsdp:
+	python -m pytest -s -v ./tests/fsdp $(if $(IS_GITHUB_CI),--report-log "$(PYTORCH_VERSION)_fsdp.log",)
+
+test_tp:
+	python -m pytest -s -v ./tests/tp $(if $(IS_GITHUB_CI),--report-log "$(PYTORCH_VERSION)_tp.log",)
+
+# Since the new version of pytest will *change* how things are collected, we need `deepspeed` to 
+# run after test_core and test_cli
+test:
+	$(MAKE) test_core
+	$(MAKE) test_cli
+	$(MAKE) test_big_modeling
+	$(MAKE) test_deepspeed
+	$(MAKE) test_fsdp
+	$(MAKE) test_tp
+
+test_examples:
+	python -m pytest -s -v ./tests/test_examples.py $(if $(IS_GITHUB_CI),--report-log "$(PYTORCH_VERSION)_examples.log",)
+
+# Broken down example tests for the CI runners
+test_integrations:
+	python -m pytest -s -v ./tests/deepspeed ./tests/fsdp ./tests/tp $(if $(IS_GITHUB_CI),--report-log "$(PYTORCH_VERSION)_integrations.log",)
+
+test_example_differences:
+	python -m pytest -s -v ./tests/test_examples.py::ExampleDifferenceTests $(if $(IS_GITHUB_CI),--report-log "$(PYTORCH_VERSION)_example_diff.log",)
+
+test_checkpoint_epoch:
+	python -m pytest -s -v ./tests/test_examples.py::FeatureExamplesTests -k "by_epoch" $(if $(IS_GITHUB_CI),--report-log "$(PYTORCH_VERSION)_checkpoint_epoch.log",)
+
+test_checkpoint_step:
+	python -m pytest -s -v ./tests/test_examples.py::FeatureExamplesTests -k "by_step" $(if $(IS_GITHUB_CI),--report-log "$(PYTORCH_VERSION)_checkpoint_step.log",)
+
+# Same as test but used to install only the base dependencies
+test_prod:
+	$(MAKE) test_core
+
+test_rest:
+	python -m pytest -s -v ./tests/test_examples.py::FeatureExamplesTests -k "not by_step and not by_epoch" $(if $(IS_GITHUB_CI),--report-log "$(PYTORCH_VERSION)_rest.log",)
+
+# For developers to prepare a release
+prepare_release:
+	rm -rf dist build
+	python setup.py bdist_wheel sdist
+
+# Make sure this is ran in a fresh venv of some form
+install_test_release:
+	pip uninstall accelerate -y
+	pip install -i https://testpypi.python.org/pypi --extra-index-url https://pypi.org/simple accelerate$(if $(version),==$(version),)
+
+# Run as `make target=testpypi upload_release`
+upload_release:
+	@if [ "$(target)" != "testpypi" ] && [ "$(target)" != "pypi" ]; then \
+		echo "Error: target must be either 'testpypi' or 'pypi'"; \
+		exit 1; \
+	fi
+	twine upload dist/* -r $(target)

tasks/tasksmith-b1dd32dfa88b/environment/source/README.md

@@ -0,0 +1,277 @@
+<!---
+Copyright 2021 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+<p align="center">
+    <br>
+    <img src="https://raw.githubusercontent.com/huggingface/accelerate/main/docs/source/imgs/accelerate_logo.png" width="400"/>
+    <br>
+<p>
+
+<p align="center">
+    <!-- Uncomment when CircleCI is set up
+    <a href="https://circleci.com/gh/huggingface/accelerate"><img alt="Build" src="https://img.shields.io/circleci/build/github/huggingface/transformers/master"></a>
+    -->
+    <a href="https://github.com/huggingface/accelerate/blob/main/LICENSE"><img alt="License" src="https://img.shields.io/github/license/huggingface/accelerate.svg?color=blue"></a>
+    <a href="https://huggingface.co/docs/accelerate/index.html"><img alt="Documentation" src="https://img.shields.io/website/http/huggingface.co/docs/accelerate/index.html.svg?down_color=red&down_message=offline&up_message=online"></a>
+    <a href="https://github.com/huggingface/accelerate/releases"><img alt="GitHub release" src="https://img.shields.io/github/release/huggingface/accelerate.svg"></a>
+    <a href="https://github.com/huggingface/accelerate/blob/main/CODE_OF_CONDUCT.md"><img alt="Contributor Covenant" src="https://img.shields.io/badge/Contributor%20Covenant-v2.0%20adopted-ff69b4.svg"></a>
+</p>
+
+<h3 align="center">
+<p>Run your *raw* PyTorch training script on any kind of device
+</h3>
+
+<h3 align="center">
+    <a href="https://hf.co/course"><img src="https://raw.githubusercontent.com/huggingface/accelerate/main/docs/source/imgs/course_banner.png"></a>
+</h3>
+
+## Easy to integrate
+
+🤗 Accelerate was created for PyTorch users who like to write the training loop of PyTorch models but are reluctant to write and maintain the boilerplate code needed to use multi-GPUs/TPU/fp16.
+
+🤗 Accelerate abstracts exactly and only the boilerplate code related to multi-GPUs/TPU/fp16 and leaves the rest of your code unchanged.
+
+Here is an example:
+
+```diff
+  import torch
+  import torch.nn.functional as F
+  from datasets import load_dataset
++ from accelerate import Accelerator
+
++ accelerator = Accelerator()
+- device = 'cpu'
++ device = accelerator.device
+
+  model = torch.nn.Transformer().to(device)
+  optimizer = torch.optim.Adam(model.parameters())
+
+  dataset = load_dataset('my_dataset')
+  data = torch.utils.data.DataLoader(dataset, shuffle=True)
+
++ model, optimizer, data = accelerator.prepare(model, optimizer, data)
+
+  model.train()
+  for epoch in range(10):
+      for source, targets in data:
+          source = source.to(device)
+          targets = targets.to(device)
+
+          optimizer.zero_grad()
+
+          output = model(source)
+          loss = F.cross_entropy(output, targets)
+
+-         loss.backward()
++         accelerator.backward(loss)
+
+          optimizer.step()
+```
+
+As you can see in this example, by adding 5-lines to any standard PyTorch training script you can now run on any kind of single or distributed node setting (single CPU, single GPU, multi-GPUs and TPUs) as well as with or without mixed precision (fp8, fp16, bf16).
+
+In particular, the same code can then be run without modification on your local machine for debugging or your training environment.
+
+🤗 Accelerate even handles the device placement for you (which requires a few more changes to your code, but is safer in general), so you can even simplify your training loop further:
+
+```diff
+  import torch
+  import torch.nn.functional as F
+  from datasets import load_dataset
++ from accelerate import Accelerator
+
+- device = 'cpu'
++ accelerator = Accelerator()
+
+- model = torch.nn.Transformer().to(device)
++ model = torch.nn.Transformer()
+  optimizer = torch.optim.Adam(model.parameters())
+
+  dataset = load_dataset('my_dataset')
+  data = torch.utils.data.DataLoader(dataset, shuffle=True)
+
++ model, optimizer, data = accelerator.prepare(model, optimizer, data)
+
+  model.train()
+  for epoch in range(10):
+      for source, targets in data:
+-         source = source.to(device)
+-         targets = targets.to(device)
+
+          optimizer.zero_grad()
+
+          output = model(source)
+          loss = F.cross_entropy(output, targets)
+
+-         loss.backward()
++         accelerator.backward(loss)
+
+          optimizer.step()
+```
+
+Want to learn more? Check out the [documentation](https://huggingface.co/docs/accelerate) or have a look at our [examples](https://github.com/huggingface/accelerate/tree/main/examples).
+
+## Launching script
+
+🤗 Accelerate also provides an optional CLI tool that allows you to quickly configure and test your training environment before launching the scripts. No need to remember how to use `torch.distributed.run` or to write a specific launcher for TPU training!
+On your machine(s) just run:
+
+```bash
+accelerate config
+```
+
+and answer the questions asked. This will generate a config file that will be used automatically to properly set the default options when doing
+
+```bash
+accelerate launch my_script.py --args_to_my_script
+``` 
+
+For instance, here is how you would run the GLUE example on the MRPC task (from the root of the repo):
+
+```bash
+accelerate launch examples/nlp_example.py
+```
+
+This CLI tool is **optional**, and you can still use `python my_script.py` or `python -m torchrun my_script.py` at your convenience.
+
+You can also directly pass in the arguments you would to `torchrun` as arguments to `accelerate launch` if you wish to not run` accelerate config`.
+
+For example, here is how to launch on two GPUs:
+
+```bash
+accelerate launch --multi_gpu --num_processes 2 examples/nlp_example.py
+```
+
+To learn more, check the CLI documentation available [here](https://huggingface.co/docs/accelerate/package_reference/cli).
+
+Or view the configuration zoo [here](https://github.com/huggingface/accelerate/blob/main/examples/config_yaml_templates/)
+
+## Launching multi-CPU run using MPI
+
+🤗 Here is another way to launch multi-CPU run using MPI. You can learn how to install Open MPI on [this page](https://www.open-mpi.org/faq/?category=building#easy-build). You can use Intel MPI or MVAPICH as well.
+Once you have MPI setup on your cluster, just run:
+```bash
+accelerate config
+```
+Answer the questions that are asked, selecting to run using multi-CPU, and answer "yes" when asked if you want accelerate to launch mpirun.
+Then, use `accelerate launch` with your script like:
+```bash
+accelerate launch examples/nlp_example.py
+```
+Alternatively, you can use mpirun directly, without using the CLI like:
+```bash
+mpirun -np 2 python examples/nlp_example.py
+```
+
+## Launching training using DeepSpeed
+
+🤗 Accelerate supports training on single/multiple GPUs using DeepSpeed. To use it, you don't need to change anything in your training code; you can set everything using just `accelerate config`. However, if you desire to tweak your DeepSpeed related args from your Python script, we provide you the `DeepSpeedPlugin`.
+
+```python
+from accelerate import Accelerator, DeepSpeedPlugin
+
+# deepspeed needs to know your gradient accumulation steps beforehand, so don't forget to pass it
+# Remember you still need to do gradient accumulation by yourself, just like you would have done without deepspeed
+deepspeed_plugin = DeepSpeedPlugin(zero_stage=2, gradient_accumulation_steps=2)
+accelerator = Accelerator(mixed_precision='fp16', deepspeed_plugin=deepspeed_plugin)
+
+# How to save your 🤗 Transformer?
+accelerator.wait_for_everyone()
+unwrapped_model = accelerator.unwrap_model(model)
+unwrapped_model.save_pretrained(save_dir, save_function=accelerator.save, state_dict=accelerator.get_state_dict(model))
+```
+
+Note: DeepSpeed support is experimental for now. In case you get into some problem, please open an issue.
+
+## Launching your training from a notebook
+
+🤗 Accelerate also provides a `notebook_launcher` function you can use in a notebook to launch a distributed training. This is especially useful for Colab or Kaggle notebooks with a TPU backend. Just define your training loop in a `training_function` then in your last cell, add:
+
+```python
+from accelerate import notebook_launcher
+
+notebook_launcher(training_function)
+```
+
+An example can be found in [this notebook](https://github.com/huggingface/notebooks/blob/main/examples/accelerate_examples/simple_nlp_example.ipynb). [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/accelerate_examples/simple_nlp_example.ipynb)
+
+## Why should I use 🤗 Accelerate?
+
+You should use 🤗 Accelerate when you want to easily run your training scripts in a distributed environment without having to renounce full control over your training loop. This is not a high-level framework above PyTorch, just a thin wrapper so you don't have to learn a new library. In fact, the whole API of 🤗 Accelerate is in one class, the `Accelerator` object.
+
+## Why shouldn't I use 🤗 Accelerate?
+
+You shouldn't use 🤗 Accelerate if you don't want to write a training loop yourself. There are plenty of high-level libraries above PyTorch that will offer you that, 🤗 Accelerate is not one of them.
+
+## Frameworks using 🤗 Accelerate
+
+If you like the simplicity of 🤗 Accelerate but would prefer a higher-level abstraction around its capabilities, some frameworks and libraries that are built on top of 🤗 Accelerate are listed below:
+
+* [Amphion](https://github.com/open-mmlab/Amphion) is a toolkit for Audio, Music, and Speech Generation. Its purpose is to support reproducible research and help junior researchers and engineers get started in the field of audio, music, and speech generation research and development.
+* [Animus](https://github.com/Scitator/animus) is a minimalistic framework to run machine learning experiments. Animus highlights common "breakpoints" in ML experiments and provides a unified interface for them within [IExperiment](https://github.com/Scitator/animus/blob/main/animus/core.py#L76).
+* [Catalyst](https://github.com/catalyst-team/catalyst#getting-started) is a PyTorch framework for Deep Learning Research and Development. It focuses on reproducibility, rapid experimentation, and codebase reuse so you can create something new rather than write yet another train loop. Catalyst provides a [Runner](https://catalyst-team.github.io/catalyst/api/core.html#runner) to connect all parts of the experiment: hardware backend, data transformations, model training, and inference logic.
+* [fastai](https://github.com/fastai/fastai#installing) is a PyTorch framework for Deep Learning that simplifies training fast and accurate neural nets using modern best practices. fastai provides a [Learner](https://docs.fast.ai/learner.html#Learner) to handle the training, fine-tuning, and inference of deep learning algorithms.
+* [Finetuner](https://github.com/jina-ai/finetuner) is a service that enables models to create higher-quality embeddings for semantic search, visual similarity search, cross-modal text<->image search, recommendation systems, clustering, duplication detection, anomaly detection, or other uses.
+* [InvokeAI](https://github.com/invoke-ai/InvokeAI) is a creative engine for Stable Diffusion models, offering industry-leading WebUI, terminal usage support, and serves as the foundation for many commercial products.
+* [Kornia](https://kornia.readthedocs.io/en/latest/get-started/introduction.html) is a differentiable library that allows classical computer vision to be integrated into deep learning models. Kornia provides a [Trainer](https://kornia.readthedocs.io/en/latest/x.html#kornia.x.Trainer) with the specific purpose to train and fine-tune the supported deep learning algorithms within the library.
+* [Open Assistant](https://projects.laion.ai/Open-Assistant/) is a chat-based assistant that understands tasks, can interact with their party systems, and retrieve information dynamically to do so. 
+* [pytorch-accelerated](https://github.com/Chris-hughes10/pytorch-accelerated) is a lightweight training library, with a streamlined feature set centered around a general-purpose [Trainer](https://pytorch-accelerated.readthedocs.io/en/latest/trainer.html), that places a huge emphasis on simplicity and transparency; enabling users to understand exactly what is going on under the hood, but without having to write and maintain the boilerplate themselves!
+* [Stable Diffusion web UI](https://github.com/AUTOMATIC1111/stable-diffusion-webui) is an open-source browser-based easy-to-use interface based on the Gradio library for Stable Diffusion.
+* [torchkeras](https://github.com/lyhue1991/torchkeras) is a simple tool for training pytorch model just in a keras style, a dynamic and beautiful plot is provided in notebook to monitor your loss or metric.
+* [transformers](https://github.com/huggingface/transformers) as a tool for helping train state-of-the-art machine learning models in PyTorch, Tensorflow, and JAX. (Accelerate is the backend for the PyTorch side).
+
+
+## Installation
+
+This repository is tested on Python 3.8+ and PyTorch 1.10.0+
+
+You should install 🤗 Accelerate in a [virtual environment](https://docs.python.org/3/library/venv.html). If you're unfamiliar with Python virtual environments, check out the [user guide](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/).
+
+First, create a virtual environment with the version of Python you're going to use and activate it.
+
+Then, you will need to install PyTorch: refer to the [official installation page](https://pytorch.org/get-started/locally/#start-locally) regarding the specific install command for your platform. Then 🤗 Accelerate can be installed using pip as follows:
+
+```bash
+pip install accelerate
+```
+
+## Supported integrations
+
+- CPU only
+- multi-CPU on one node (machine)
+- multi-CPU on several nodes (machines)
+- single GPU
+- multi-GPU on one node (machine)
+- multi-GPU on several nodes (machines)
+- TPU
+- FP16/BFloat16 mixed precision
+- FP8 mixed precision with [Transformer Engine](https://github.com/NVIDIA/TransformerEngine) or [MS-AMP](https://github.com/Azure/MS-AMP/)
+- DeepSpeed support (Experimental)
+- PyTorch Fully Sharded Data Parallel (FSDP) support (Experimental)
+- Megatron-LM support (Experimental)
+
+## Citing 🤗 Accelerate
+
+If you use 🤗 Accelerate in your publication, please cite it by using the following BibTeX entry.
+
+```bibtex
+@Misc{accelerate,
+  title =        {Accelerate: Training and inference at scale made simple, efficient and adaptable.},
+  author =       {Sylvain Gugger and Lysandre Debut and Thomas Wolf and Philipp Schmid and Zachary Mueller and Sourab Mangrulkar and Marc Sun and Benjamin Bossan},
+  howpublished = {\url{https://github.com/huggingface/accelerate}},
+  year =         {2022}
+}
+```

tasks/tasksmith-b1dd32dfa88b/environment/source/benchmarks/README.md

@@ -0,0 +1,5 @@
+# Benchmarks
+
+The folders below contain suites to test various functionalities in Accelerate.
+
+See their relevant README.md's for more information.

tasks/tasksmith-b1dd32dfa88b/environment/source/benchmarks/big_model_inference/README.md

@@ -0,0 +1,46 @@
+# Big model inference benchmarks
+
+Running inference with Accelerate on big models.
+
+## Setup
+
+These benchmarks use the `transformers` library:
+
+```bash
+pip install transformers
+```
+
+To reproduce or test a new setup, run
+
+```py
+python big_model_inference.py model_name
+```
+
+This script supports `gpt-j-6b`, `gpt-neox`, `opt` (30B version) and `T0pp` out of the box, but you can specify any valid checkpoint for `model_name`.
+
+To force a different `torch_dtype` than the one in the config: `--torch_dtype xxx`.
+
+If you get an error linked to disk offload, you need to add the option `--disk-offload`
+
+## Results
+
+On a setup with two Titan RTXs (24GB of RAM) and 32GB of RAM, we get the following benchmarks (T0pp does not run in float16, which is why it's not included).
+
+| Model | Model load time | Generation time | dtype | GPU 0 use | GPU 1 use | CPU use | Disk offload |
+|:-----:|:---------------:|:---------------:|:-----:|:---------:|:---------:|:-------:|:------------:|
+| GPT-J-6B | 8.7s | 0.05s per token | float16 | 11.7GB | 0GB | 0GB | no |
+| GPT-J-6B | 12.4s | 0.06s per token | float32 | 21.9GB | 1.5GB | 0GB | no |
+| GPT-Neo-X-20B | 30.9s | 0.08s per token | float16 | 21.5GB | 18GB | 0GB | no |
+| GPT-Neo-X-20B | 78.2s | 10.72s per token | float32 | 20.3GB | 22.7 GB | 24.4GB | yes |
+| T0pp (11B) | 29.4s | 0.05s per token | float32 | 21.1GB | 21.3GB | 0GB | no |
+| OPT-30B | 34.5s | 2.37s per token | float16 | 20.7GB | 22.3GB | 14.1GB | no |
+| OPT-30B | 112.3s | 33.9s per token | float32 | 20.2GB | 21.2GB | 23.5GB | yes |
+
+Note on the results:
+- using two GPUs instead of one does not slow down generation
+- using CPU offload slows down a bit (see OPT-30b)
+- using disk offload slows down a lot (need to implement prefetching)
+
+You will also note that Accelerate does not use anymore GPU and CPU RAM than necessary:
+- peak GPU memory is exactly the size of the model put on a given GPU
+- peak CPU memory is either the size of the biggest checkpoint shard or the part of the model offloaded on CPU, whichever is bigger.

tasks/tasksmith-b1dd32dfa88b/environment/source/benchmarks/big_model_inference/big_model_inference.py

@@ -0,0 +1,143 @@
+# Copyright 2022 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import argparse
+import time
+
+import torch
+import transformers
+from measures_util import end_measure, log_measures, start_measure
+from transformers import AutoConfig, AutoModelForCausalLM, AutoModelForSeq2SeqLM, AutoTokenizer
+
+from accelerate.utils import compute_module_sizes
+
+
+DEFAULT_MODELS = {
+    "gpt-j-6b": {"is_causal": True, "model": "sgugger/sharded-gpt-j-6B", "tokenizer": "EleutherAI/gpt-j-6B"},
+    "gpt-neox": {"is_causal": True, "model": "EleutherAI/gpt-neox-20b"},
+    "opt": {"is_causal": True, "model": "facebook/opt-30b"},
+    "T0pp": {"is_causal": False, "model": "bigscience/T0pp", "model_revision": "sharded"},
+}
+
+PROMPTS = [
+    "Hello, my name is",
+    "Are unicorns real? Unicorns are",
+    "For the first time in several years,",
+    "My name is Julien and I am",
+    "The goal of life is",
+    "Whenever I'm sad, I like to",
+]
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Run and time generations on a big model using Accelerate.")
+    parser.add_argument("model_name", type=str, default=None, help="The name of the model to try.")
+    parser.add_argument(
+        "--tokenizer_name", type=str, default=None, help="The name of the tokenizer (if different from the model."
+    )
+    parser.add_argument("--is_causal", type=bool, default=None, help="Whether or not the model is causal.")
+    parser.add_argument(
+        "--model_revision", type=str, default=None, help="The revision to use for the model checkpoint."
+    )
+    parser.add_argument("--torch_dtype", type=str, default=None, help="The dtype for the model.")
+    parser.add_argument("--disk_offload", action="store_true")
+
+    args = parser.parse_args()
+
+    # Sanitize args
+    if args.model_name in DEFAULT_MODELS:
+        defaults = DEFAULT_MODELS[args.model_name]
+        args.model_name = defaults["model"]
+        if args.tokenizer_name is None:
+            args.tokenizer_name = defaults.get("tokenizer", args.model_name)
+        if args.is_causal is None:
+            args.is_causal = defaults["is_causal"]
+        if args.model_revision is None:
+            args.model_revision = defaults.get("model_revision", "main")
+
+    if args.is_causal is None:
+        raise ValueError("Could not infer the default for `--is_causal`, pass either True or False for it.")
+    if args.tokenizer_name is None:
+        args.tokenizer_name = args.model_name
+    if args.model_revision is None:
+        args.model_revision = "main"
+
+    return args
+
+
+def main():
+    transformers.utils.logging.set_verbosity_error()
+    args = parse_args()
+
+    if args.torch_dtype is None:
+        config = AutoConfig.from_pretrained(args.model_name)
+        torch_dtype = getattr(config, "torch_dtype", torch.float32)
+    else:
+        torch_dtype = getattr(torch, args.torch_dtype)
+    model_cls = AutoModelForCausalLM if args.is_causal else AutoModelForSeq2SeqLM
+    kwargs = {
+        "torch_dtype": torch_dtype,
+        "revision": args.model_revision,
+    }
+    if args.disk_offload:
+        kwargs["offload_folder"] = "tmp_offload"
+        kwargs["offload_state_dict"] = True
+
+    start_measures = start_measure()
+    model = model_cls.from_pretrained(args.model_name, device_map="auto", **kwargs)
+    end_measures = end_measure(start_measures)
+    log_measures(end_measures, "Model loading")
+
+    module_sizes = compute_module_sizes(model)
+    device_size = {v: 0 for v in model.hf_device_map.values()}
+    for module, device in model.hf_device_map.items():
+        device_size[device] += module_sizes[module]
+    message = "\n".join([f"- {device}: {size // 2**20}MiB" for device, size in device_size.items()])
+    print(f"\nTheoretical use:\n{message}")
+
+    tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name)
+
+    start_measures = start_measure()
+    generation_times = []
+    gen_tokens = []
+    texts_outs = []
+    for prompt in PROMPTS:
+        inputs = tokenizer(prompt, return_tensors="pt").to(0)
+        tokens = inputs["input_ids"][0].tolist()
+        before_generate = time.time()
+        outputs = model.generate(inputs["input_ids"])
+        after_generate = time.time()
+        outputs = outputs[0].tolist()
+        num_gen_tokens = len(outputs) if outputs[: len(tokens)] != tokens else len(outputs) - len(tokens)
+        generation_time = after_generate - before_generate
+
+        text_out = tokenizer.decode(outputs, skip_special_tokens=True)
+        texts_outs.append(text_out)
+        generation_times.append(generation_time)
+        gen_tokens.append(num_gen_tokens)
+        print(f"Prompt: {prompt}\nGeneration {text_out}\nIn {generation_time:.2f}s for {num_gen_tokens} tokens\n")
+
+    end_measures = end_measure(start_measures)
+    log_measures(end_measures, "Model generation")
+
+    generation_times_per_token = [gen / tok for gen, tok in zip(generation_times, gen_tokens)]
+    avg_gen = sum(generation_times_per_token) / len(generation_times)
+    print(f"Average time of generation per token: {avg_gen:.2f}s")
+    print(f"First generation (avg time per token): {generation_times_per_token[0]:.2f}s")
+    avg_gen = sum(generation_times_per_token[1:]) / (len(generation_times_per_token) - 1)
+    print(f"Average time of generation per token (excluding the first): {avg_gen:.2f}s")
+
+
+if __name__ == "__main__":
+    main()

tasks/tasksmith-b1dd32dfa88b/environment/source/benchmarks/big_model_inference/measures_util.py

@@ -0,0 +1,104 @@
+# Copyright 2023 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import gc
+import threading
+import time
+
+import psutil
+import torch
+
+from accelerate.test_utils.testing import get_backend
+
+
+torch_device_type, _, _ = get_backend()
+torch_accelerator_module = getattr(torch, torch_device_type, torch.cuda)
+
+
+class PeakCPUMemory:
+    def __init__(self):
+        self.process = psutil.Process()
+        self.peak_monitoring = False
+
+    def peak_monitor(self):
+        self.cpu_memory_peak = -1
+
+        while True:
+            self.cpu_memory_peak = max(self.process.memory_info().rss, self.cpu_memory_peak)
+
+            # can't sleep or will not catch the peak right (this comment is here on purpose)
+            if not self.peak_monitoring:
+                break
+
+    def start(self):
+        self.peak_monitoring = True
+        self.thread = threading.Thread(target=self.peak_monitor)
+        self.thread.daemon = True
+        self.thread.start()
+
+    def stop(self):
+        self.peak_monitoring = False
+        self.thread.join()
+        return self.cpu_memory_peak
+
+
+cpu_peak_tracker = PeakCPUMemory()
+
+
+def start_measure():
+    # Time
+    measures = {"time": time.time()}
+
+    gc.collect()
+    torch_accelerator_module.empty_cache()
+
+    # CPU mem
+    measures["cpu"] = psutil.Process().memory_info().rss
+    cpu_peak_tracker.start()
+
+    # GPU mem
+    for i in range(torch_accelerator_module.device_count()):
+        measures[str(i)] = torch_accelerator_module.memory_allocated(i)
+    torch_accelerator_module.reset_peak_memory_stats()
+
+    return measures
+
+
+def end_measure(start_measures):
+    # Time
+    measures = {"time": time.time() - start_measures["time"]}
+
+    gc.collect()
+    torch_accelerator_module.empty_cache()
+
+    # CPU mem
+    measures["cpu"] = (psutil.Process().memory_info().rss - start_measures["cpu"]) / 2**20
+    measures["cpu-peak"] = (cpu_peak_tracker.stop() - start_measures["cpu"]) / 2**20
+
+    # GPU mem
+    for i in range(torch_accelerator_module.device_count()):
+        measures[str(i)] = (torch_accelerator_module.memory_allocated(i) - start_measures[str(i)]) / 2**20
+        measures[f"{i}-peak"] = (torch_accelerator_module.max_memory_allocated(i) - start_measures[str(i)]) / 2**20
+
+    return measures
+
+
+def log_measures(measures, description):
+    print(f"{description}:")
+    print(f"- Time: {measures['time']:.2f}s")
+    for i in range(torch_accelerator_module.device_count()):
+        print(f"- {torch_device_type} {i} allocated: {measures[str(i)]:.2f}MiB")
+        peak = measures[f"{i}-peak"]
+        print(f"- {torch_device_type} {i} peak: {peak:.2f}MiB")
+    print(f"- CPU RAM allocated: {measures['cpu']:.2f}MiB")
+    print(f"- CPU RAM peak: {measures['cpu-peak']:.2f}MiB")

tasks/tasksmith-b1dd32dfa88b/environment/source/benchmarks/fp8/ms_amp/Dockerfile

@@ -0,0 +1,12 @@
+FROM ghcr.io/azure/msamp
+
+RUN pip install transformers evaluate datasets
+RUN git clone https://github.com/huggingface/accelerate
+
+RUN cd accelerate && \
+    pip install -e . && \
+    cd benchmarks/fp8
+
+CMD ["bash"]
+
+

tasks/tasksmith-b1dd32dfa88b/environment/source/benchmarks/fp8/ms_amp/ddp.py

@@ -0,0 +1,123 @@
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+This script tests to ensure that `accelerate` performs at the same level as raw `MS-AMP`.
+
+This particular script verifies this for DDP training.
+"""
+
+import evaluate
+import msamp
+import torch
+from fp8_utils import evaluate_model, get_training_utilities
+from torch.nn.parallel import DistributedDataParallel as DDP
+
+from accelerate import Accelerator
+from accelerate.state import AcceleratorState
+from accelerate.utils import FP8RecipeKwargs, get_grad_scaler, set_seed
+
+
+MODEL_NAME = "bert-base-cased"
+METRIC = evaluate.load("glue", "mrpc")
+
+
+def train_baseline(opt_level="O2"):
+    set_seed(42)
+    scaler = get_grad_scaler()
+    model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = get_training_utilities(MODEL_NAME)
+    accelerator = Accelerator()
+    device = accelerator.device
+
+    model, optimizer = msamp.initialize(model, optimizer, opt_level=opt_level)
+
+    model.to(device)
+
+    # Convert the model to DDP
+    device_ids, output_device = [accelerator.local_process_index], accelerator.local_process_index
+    model = DDP(model, device_ids=device_ids, output_device=output_device)
+
+    base_model_results = evaluate_model(model, eval_dataloader, METRIC, accelerator=accelerator)
+    model.train()
+
+    for i, batch in enumerate(train_dataloader):
+        with torch.autocast(device_type="cuda", dtype=torch.bfloat16):
+            outputs = model(**batch)
+            loss = outputs.loss
+        scaler.scale(loss).backward()
+        optimizer.step()
+        optimizer.zero_grad()
+        lr_scheduler.step()
+
+    trained_model_results = evaluate_model(model, eval_dataloader, METRIC, accelerator=accelerator)
+
+    assert trained_model_results["accuracy"] > base_model_results["accuracy"], (
+        f"Accuracy should be higher for the trained model: {trained_model_results['accuracy']} > {base_model_results['accuracy']}"
+    )
+    assert trained_model_results["f1"] > base_model_results["f1"], (
+        f"F1 score should be higher for the trained model: {trained_model_results['f1']} > {base_model_results['f1']}"
+    )
+
+    return base_model_results, trained_model_results
+
+
+def train_integration(opt_level="O2"):
+    kwargs_handlers = [FP8RecipeKwargs(backend="msamp", opt_level=opt_level)]
+    AcceleratorState()._reset_state(True)
+    accelerator = Accelerator(mixed_precision="fp8", kwargs_handlers=kwargs_handlers)
+    set_seed(42)
+    model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = get_training_utilities(
+        MODEL_NAME, accelerator=accelerator
+    )
+
+    model, optimizer = accelerator.prepare(model, optimizer)
+    base_model_results = evaluate_model(model, eval_dataloader, METRIC, accelerator=accelerator)
+    model.train()
+    for i, batch in enumerate(train_dataloader):
+        with torch.autocast(device_type="cuda", dtype=torch.bfloat16):
+            outputs = model(**batch)
+            loss = outputs.loss
+        accelerator.backward(loss)
+        optimizer.step()
+        optimizer.zero_grad()
+        lr_scheduler.step()
+
+    trained_model_results = evaluate_model(model, eval_dataloader, METRIC, accelerator=accelerator)
+
+    assert trained_model_results["accuracy"] > base_model_results["accuracy"], (
+        f"Accuracy should be higher for the trained model: {trained_model_results['accuracy']} > {base_model_results['accuracy']}"
+    )
+    assert trained_model_results["f1"] > base_model_results["f1"], (
+        f"F1 score should be higher for the trained model: {trained_model_results['f1']} > {base_model_results['f1']}"
+    )
+
+    return base_model_results, trained_model_results
+
+
+if __name__ == "__main__":
+    for opt_level in ["O1", "O2"]:
+        baseline_not_trained, baseline_trained = train_baseline(opt_level)
+        accelerator_not_trained, accelerator_trained = train_integration(opt_level)
+        assert baseline_not_trained["accuracy"] == accelerator_not_trained["accuracy"], (
+            f"Accuracy not the same for untrained baseline and accelerator using opt_level={opt_level}: {baseline_not_trained['accuracy']} == {accelerator_not_trained['accuracy']}"
+        )
+        assert baseline_not_trained["f1"] == accelerator_not_trained["f1"], (
+            f"F1 not the same for untrained baseline and accelerator using opt_level={opt_level}: {baseline_not_trained['f1']} == {accelerator_not_trained['f1']}"
+        )
+        assert baseline_trained["accuracy"] == accelerator_trained["accuracy"], (
+            f"Accuracy not the same for trained baseline and accelerator using opt_level={opt_level}: {baseline_trained['accuracy']} == {accelerator_trained['accuracy']}"
+        )
+        assert baseline_trained["f1"] == accelerator_trained["f1"], (
+            f"F1 not the same for trained baseline and accelerator using opt_level={opt_level}: {baseline_trained['f1']} == {accelerator_trained['f1']}"
+        )

tasks/tasksmith-b1dd32dfa88b/environment/source/benchmarks/fp8/ms_amp/distrib_deepspeed.py

@@ -0,0 +1,161 @@
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+This script tests to ensure that `accelerate` performs at the same level as raw `MS-AMP`.
+
+This particular script verifies this for DeepSpeed training.
+
+NOTE: MS-AMP does *not* support ZeRO-3.
+"""
+
+# import msamp.deepspeed as msamp_deepspeed
+import evaluate
+import torch
+from fp8_utils import evaluate_model, get_training_utilities
+from msamp import deepspeed as msamp_deepspeed
+
+from accelerate import Accelerator, DeepSpeedPlugin
+from accelerate.state import AcceleratorState
+from accelerate.utils import set_seed
+
+
+MODEL_NAME = "bert-base-cased"
+METRIC = evaluate.load("glue", "mrpc")
+
+
+def train_baseline(zero_stage: int = 1, opt_level: str = "O1"):
+    set_seed(42)
+    accelerator = Accelerator()
+    model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = get_training_utilities(
+        MODEL_NAME, accelerator=accelerator
+    )
+
+    import numpy as np
+
+    config = {
+        "train_batch_size": 32,
+        "train_micro_batch_size_per_gpu": 16,
+        "gradient_accumulation_steps": 1,
+        "zero_optimization": {
+            "stage": zero_stage,
+            "offload_optimizer": {"device": "none", "nvme_path": None},
+            "offload_param": {"device": "none", "nvme_path": None},
+        },
+        "gradient_clipping": 1.0,
+        "steps_per_print": np.inf,
+        "bf16": {"enabled": True},
+        "fp16": {"enabled": False},
+        "zero_allow_untested_optimizer": True,
+        "msamp": {
+            "enabled": True,
+            "opt_level": opt_level,
+        },
+    }
+    (
+        model,
+        optimizer,
+        _,
+        _,
+    ) = msamp_deepspeed.initialize(
+        model=model,
+        optimizer=optimizer,
+        config_params=config,
+    )
+
+    base_model_results = evaluate_model(model, eval_dataloader, METRIC, accelerator=accelerator)
+    model.train()
+
+    for _ in range(2):
+        for batch in train_dataloader:
+            outputs = model(**batch)
+            loss = outputs.loss
+            model.backward(loss)
+            model.step()
+            for _ in range(accelerator.num_processes):
+                lr_scheduler.step()
+
+    trained_model_results = evaluate_model(model, eval_dataloader, METRIC, accelerator=accelerator)
+    model.destroy()
+    torch.cuda.empty_cache()
+    AcceleratorState()._reset_state(True)
+    assert trained_model_results["accuracy"] > base_model_results["accuracy"], (
+        f"Accuracy should be higher for the trained model: {trained_model_results['accuracy']} > {base_model_results['accuracy']}"
+    )
+    assert trained_model_results["f1"] > base_model_results["f1"], (
+        f"F1 score should be higher for the trained model: {trained_model_results['f1']} > {base_model_results['f1']}"
+    )
+
+    return base_model_results, trained_model_results
+
+
+def train_integration(zero_stage: int = 1, opt_level: str = "O1"):
+    set_seed(42)
+    deepspeed_plugin = DeepSpeedPlugin(
+        zero_stage=zero_stage,
+        enable_msamp=True,
+        msamp_opt_level=opt_level,
+    )
+    accelerator = Accelerator(mixed_precision="fp8", deepspeed_plugin=deepspeed_plugin)
+    accelerator.state.deepspeed_plugin.deepspeed_config["train_micro_batch_size_per_gpu"] = 16
+
+    model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = get_training_utilities(
+        MODEL_NAME, accelerator=accelerator
+    )
+
+    model, optimizer, lr_scheduler = accelerator.prepare(model, optimizer, lr_scheduler)
+    base_model_results = evaluate_model(model, eval_dataloader, METRIC, accelerator=accelerator)
+    model.train()
+    for _ in range(2):
+        for batch in train_dataloader:
+            outputs = model(**batch)
+            loss = outputs.loss
+            accelerator.backward(loss)
+            optimizer.step()
+            lr_scheduler.step()
+            optimizer.zero_grad()
+
+    trained_model_results = evaluate_model(model, eval_dataloader, METRIC, accelerator=accelerator)
+    model.destroy()
+    torch.cuda.empty_cache()
+    assert trained_model_results["accuracy"] > base_model_results["accuracy"], (
+        f"Accuracy should be higher for the trained model: {trained_model_results['accuracy']} > {base_model_results['accuracy']}"
+    )
+    assert trained_model_results["f1"] > base_model_results["f1"], (
+        f"F1 score should be higher for the trained model: {trained_model_results['f1']} > {base_model_results['f1']}"
+    )
+
+    AcceleratorState()._reset_state(True)
+    return base_model_results, trained_model_results
+
+
+if __name__ == "__main__":
+    for zero_stage in [1, 2]:
+        for opt_level in ["O1", "O2", "O3"]:
+            baseline_not_trained, baseline_trained = train_baseline(zero_stage, opt_level)
+            accelerator_not_trained, accelerator_trained = train_integration(zero_stage, opt_level)
+            assert baseline_not_trained["accuracy"] == accelerator_not_trained["accuracy"], (
+                f"ZERO stage {zero_stage}, opt_level={opt_level}:\nAccuracy should be the same for the baseline and accelerator: {baseline_not_trained['accuracy']} == {accelerator_not_trained['accuracy']}"
+            )
+            assert baseline_not_trained["f1"] == accelerator_not_trained["f1"], (
+                f"ZERO stage {zero_stage}, opt_level={opt_level}:\nF1 score should be the same for the baseline and accelerator: {baseline_not_trained['f1']} == {accelerator_not_trained['f1']}"
+            )
+            assert baseline_trained["accuracy"] == accelerator_trained["accuracy"], (
+                f"ZERO stage {zero_stage}, opt_level={opt_level}:\nAccuracy should be the same for the baseline and accelerator: {baseline_trained['accuracy']} == {accelerator_trained['accuracy']}"
+            )
+            assert baseline_trained["f1"] == accelerator_trained["f1"], (
+                f"ZERO stage {zero_stage}, opt_level={opt_level}:\nF1 score should be the same for the baseline and accelerator: {baseline_trained['f1']} == {accelerator_trained['f1']}"
+            )
+
+    torch.distributed.destroy_process_group()

tasks/tasksmith-b1dd32dfa88b/environment/source/benchmarks/fp8/ms_amp/fp8_utils.py

@@ -0,0 +1,118 @@
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import torch
+
+
+def get_dataloaders(model_name: str, batch_size: int = 16):
+    from datasets import load_dataset
+    from torch.utils.data import DataLoader
+    from transformers import AutoTokenizer
+
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    datasets = load_dataset("glue", "mrpc")
+
+    def tokenize_function(examples):
+        # max_length=None => use the model max length (it's actually the default)
+        outputs = tokenizer(examples["sentence1"], examples["sentence2"], truncation=True, max_length=None)
+        return outputs
+
+    # Apply the method we just defined to all the examples in all the splits of the dataset
+    # starting with the main process first:
+    tokenized_datasets = datasets.map(
+        tokenize_function,
+        batched=True,
+        remove_columns=["idx", "sentence1", "sentence2"],
+    )
+
+    # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
+    # transformers library
+    tokenized_datasets = tokenized_datasets.rename_column("label", "labels")
+
+    def collate_fn(examples):
+        return tokenizer.pad(
+            examples,
+            padding="longest",
+            pad_to_multiple_of=16,  # Specific for FP8
+            return_tensors="pt",
+        )
+
+    # Instantiate dataloaders.
+    train_dataloader = DataLoader(
+        tokenized_datasets["train"], shuffle=True, collate_fn=collate_fn, batch_size=batch_size, drop_last=True
+    )
+    eval_dataloader = DataLoader(
+        tokenized_datasets["validation"],
+        shuffle=False,
+        collate_fn=collate_fn,
+        batch_size=16,
+        drop_last=True,
+    )
+
+    return train_dataloader, eval_dataloader
+
+
+def get_training_utilities(model_name: str, batch_size: int = 16, accelerator=None):
+    """
+    Returns a tuple of:
+        - Model
+        - Optimizer
+        - Train dataloader (prepared)
+        - Eval dataloader (prepared)
+        - LR Scheduler
+    Suitable for training on the MRPC dataset
+    """
+    from torch.optim import AdamW
+    from transformers import AutoModelForSequenceClassification, get_linear_schedule_with_warmup
+
+    from accelerate import Accelerator
+
+    if accelerator is None:
+        accelerator = Accelerator()
+    model = AutoModelForSequenceClassification.from_pretrained(model_name)
+    train_dataloader, eval_dataloader = get_dataloaders(model_name, batch_size)
+    optimizer = AdamW(model.parameters(), lr=0.0001)
+    lr_scheduler = get_linear_schedule_with_warmup(
+        optimizer=optimizer,
+        num_warmup_steps=100,
+        num_training_steps=len(train_dataloader) * 2,
+    )
+    train_dataloader, eval_dataloader = accelerator.prepare(train_dataloader, eval_dataloader)
+    return model, optimizer, train_dataloader, eval_dataloader, lr_scheduler
+
+
+def get_named_parameters(model):
+    """
+    Same thing as `Accelerator.get_named_parameters` Returns a list of the named parameters of the model (extracted
+    from parallel)
+    """
+    from accelerate.utils import extract_model_from_parallel
+
+    model = extract_model_from_parallel(model)
+    return {n: p for n, p in model.named_parameters()}
+
+
+def evaluate_model(model, dataloader, metric, accelerator=None):
+    "Turns model to .eval(), runs dataloader, calculates metric, then turns eval back on"
+    model.eval()
+    for step, batch in enumerate(dataloader):
+        with torch.no_grad():
+            # W/ MS-AMP, we need to cast while evaluating
+            with torch.autocast(device_type="cuda", dtype=torch.bfloat16):
+                outputs = model(**batch)
+        predictions = outputs.logits.argmax(dim=-1)
+        references = batch["labels"]
+        if accelerator is not None and accelerator.num_processes > 1:
+            predictions, references = accelerator.gather_for_metrics((predictions, references))
+        metric.add_batch(predictions=predictions, references=references)
+    return metric.compute()

tasks/tasksmith-b1dd32dfa88b/environment/source/benchmarks/fp8/ms_amp/non_distributed.py

@@ -0,0 +1,118 @@
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+This script tests to ensure that `accelerate` performs at the same level as raw `MS-AMP`.
+
+This particular script verifies this for single GPU training.
+"""
+
+import evaluate
+import msamp
+import torch
+from fp8_utils import evaluate_model, get_training_utilities
+
+from accelerate import Accelerator
+from accelerate.state import AcceleratorState
+from accelerate.utils import FP8RecipeKwargs, get_grad_scaler, set_seed
+
+
+MODEL_NAME = "bert-base-cased"
+METRIC = evaluate.load("glue", "mrpc")
+
+
+def train_baseline(opt_level="O2"):
+    set_seed(42)
+    model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = get_training_utilities(MODEL_NAME)
+
+    model, optimizer = msamp.initialize(model, optimizer, opt_level=opt_level)
+    model.to("cuda")
+
+    base_model_results = evaluate_model(model, eval_dataloader, METRIC)
+    model.train()
+    scaler = get_grad_scaler()
+
+    for batch in train_dataloader:
+        batch = batch.to("cuda")
+        with torch.autocast(device_type="cuda", dtype=torch.bfloat16):
+            outputs = model(**batch)
+        loss = outputs.loss
+        loss = scaler.scale(loss)
+        loss.backward()
+        optimizer.step()
+        optimizer.zero_grad()
+        lr_scheduler.step()
+
+    trained_model_results = evaluate_model(model, eval_dataloader, METRIC)
+
+    assert trained_model_results["accuracy"] > base_model_results["accuracy"], (
+        f"Accuracy should be higher for the trained model: {trained_model_results['accuracy']} > {base_model_results['accuracy']}"
+    )
+    assert trained_model_results["f1"] > base_model_results["f1"], (
+        f"F1 score should be higher for the trained model: {trained_model_results['f1']} > {base_model_results['f1']}"
+    )
+
+    return base_model_results, trained_model_results
+
+
+def train_integration(opt_level="O2"):
+    kwargs_handlers = [FP8RecipeKwargs(backend="msamp", opt_level=opt_level)]
+    AcceleratorState()._reset_state(True)
+    accelerator = Accelerator(mixed_precision="fp8", kwargs_handlers=kwargs_handlers)
+    set_seed(42)
+    model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = get_training_utilities(
+        MODEL_NAME, accelerator=accelerator
+    )
+
+    model, optimizer, lr_scheduler = accelerator.prepare(model, optimizer, lr_scheduler)
+    base_model_results = evaluate_model(model, eval_dataloader, METRIC)
+    model.train()
+
+    for batch in train_dataloader:
+        outputs = model(**batch)
+        loss = outputs.loss
+        accelerator.backward(loss)
+        optimizer.step()
+        optimizer.zero_grad()
+        lr_scheduler.step()
+
+    trained_model_results = evaluate_model(model, eval_dataloader, METRIC)
+
+    assert trained_model_results["accuracy"] > base_model_results["accuracy"], (
+        f"Accuracy should be higher for the trained model: {trained_model_results['accuracy']} > {base_model_results['accuracy']}"
+    )
+    assert trained_model_results["f1"] > base_model_results["f1"], (
+        f"F1 score should be higher for the trained model: {trained_model_results['f1']} > {base_model_results['f1']}"
+    )
+
+    return base_model_results, trained_model_results
+
+
+if __name__ == "__main__":
+    for opt_level in ["O1", "O2"]:
+        baseline_not_trained, baseline_trained = train_baseline(opt_level)
+        accelerator_not_trained, accelerator_trained = train_integration(opt_level)
+
+        assert baseline_not_trained["accuracy"] == accelerator_not_trained["accuracy"], (
+            f"Accuracy should be the same for the baseline and accelerator: {baseline_not_trained['accuracy']} == {accelerator_not_trained['accuracy']}"
+        )
+        assert baseline_not_trained["f1"] == accelerator_not_trained["f1"], (
+            f"F1 score should be the same for the baseline and accelerator: {baseline_not_trained['f1']} == {accelerator_not_trained['f1']}"
+        )
+        assert baseline_trained["accuracy"] == accelerator_trained["accuracy"], (
+            f"Accuracy should be the same for the baseline and accelerator: {baseline_trained['accuracy']} == {accelerator_trained['accuracy']}"
+        )
+        assert baseline_trained["f1"] == accelerator_trained["f1"], (
+            f"F1 score should be the same for the baseline and accelerator: {baseline_trained['f1']} == {accelerator_trained['f1']}"
+        )

tasks/tasksmith-b1dd32dfa88b/environment/source/benchmarks/fp8/torchao/Dockerfile

@@ -0,0 +1,12 @@
+FROM nvcr.io/nvidia/pytorch:24.07-py3
+
+RUN pip install transformers evaluate datasets
+RUN git clone https://github.com/huggingface/accelerate.git
+
+RUN cd accelerate && \
+    pip install -e . && \
+    cd benchmarks/fp8
+
+RUN /bin/bash
+
+

tasks/tasksmith-b1dd32dfa88b/environment/source/benchmarks/fp8/torchao/README.md

@@ -0,0 +1,32 @@
+# FP8 Benchmarks
+
+Comparing and running [torchao](https://github.com/pytorch/ao/tree/main/torchao/float8) FP8 with accelerate
+
+## Overview
+
+This repo provides scripts which compare native `torchao` model training against `accelerate`'s own integration. Each modeling type is segmented out via a script, supporting the following:
+
+* Single GPU training (`non_distributed.py`)
+* Multi-GPU training via DistributedDataParallelism (`ddp.py`)
+* Fully Sharded Data Parallelism (`fsdp.py`)
+* DeepSpeed ZeRO 1-3 (`deepspeed.py`)
+
+To run them, it's recommended to use a docker image (see the attached `Dockerfile`) and not install `torchao` manually.
+
+## Running:
+
+There are official Docker images located at `huggingface/accelerate:gpu-fp8-torchao-nightly` which can be used.
+
+You can run all scripts using the core `accelerate launch` command without any `accelerate config` being needed.
+
+For single GPU, run it via `python`:
+
+```bash
+python non_distributed.py
+```
+
+For the rest, run it via `accelerate launch`:
+
+```bash
+accelerate launch ddp.py # or distrib_deepspeed.py, ddp.py
+```

tasks/tasksmith-b1dd32dfa88b/environment/source/benchmarks/fp8/torchao/ddp.py

@@ -0,0 +1,158 @@
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+This script tests to ensure that `accelerate` performs at the same level as raw `torchao`.
+
+This particular script verifies this for DDP training.
+"""
+
+from functools import partial
+
+import evaluate
+import torch
+from fp8_utils import get_training_utilities
+from torch.nn.parallel import DistributedDataParallel as DDP
+from torchao.float8 import convert_to_float8_training
+
+from accelerate import Accelerator
+from accelerate.state import AcceleratorState
+from accelerate.utils import AORecipeKwargs, set_seed
+
+
+MODEL_NAME = "bert-base-cased"
+METRIC = evaluate.load("glue", "mrpc")
+
+
+def evaluate_model(model, dataloader, metric, accelerator=None):
+    "Turns model to .eval(), runs dataloader, calculates metric, then turns eval back on"
+    model.eval()
+    for step, batch in enumerate(dataloader):
+        with torch.no_grad():
+            outputs = model(**batch)
+        predictions = outputs.logits.argmax(dim=-1)
+        references = batch["labels"]
+        if accelerator is not None and accelerator.num_processes > 1:
+            predictions, references = accelerator.gather_for_metrics((predictions, references))
+        metric.add_batch(predictions=predictions, references=references)
+    return metric.compute()
+
+
+def filter_linear_layers(module, fqn, first_layer_name=None, last_layer_name=None):
+    if isinstance(module, torch.nn.Linear):
+        if module.in_features % 16 != 0 or module.out_features % 16 != 0:
+            return False
+    # For stability reasons, we skip the first and last linear layers
+    # Otherwise can lead to the model not training or converging properly
+    if fqn in (first_layer_name, last_layer_name):
+        return False
+    return True
+
+
+def train_baseline():
+    set_seed(42)
+    model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = get_training_utilities(MODEL_NAME)
+    first_linear = None
+    last_linear = None
+    for name, module in model.named_modules():
+        if isinstance(module, torch.nn.Linear):
+            if first_linear is None:
+                first_linear = name
+            last_linear = name
+    func = partial(filter_linear_layers, first_layer_name=first_linear, last_layer_name=last_linear)
+    accelerator = Accelerator()
+    device = accelerator.device
+    model.to(device)
+
+    convert_to_float8_training(model, module_filter_fn=func)
+
+    # Convert the model to DDP
+    device_ids, output_device = [accelerator.local_process_index], accelerator.local_process_index
+    model = DDP(model, device_ids=device_ids, output_device=output_device)
+
+    base_model_results = evaluate_model(model, eval_dataloader, METRIC, accelerator=accelerator)
+    model.train()
+
+    for batch in train_dataloader:
+        with torch.autocast(device_type="cuda", dtype=torch.bfloat16):
+            batch = batch.to(device)
+            outputs = model(**batch)
+            loss = outputs.loss
+            loss.backward()
+        optimizer.step()
+        optimizer.zero_grad()
+        lr_scheduler.step()
+
+    trained_model_results = evaluate_model(model, eval_dataloader, METRIC, accelerator=accelerator)
+
+    assert trained_model_results["accuracy"] > base_model_results["accuracy"], (
+        f"Accuracy should be higher for the trained model: {trained_model_results['accuracy']} > {base_model_results['accuracy']}"
+    )
+    assert trained_model_results["f1"] > base_model_results["f1"], (
+        f"F1 score should be higher for the trained model: {trained_model_results['f1']} > {base_model_results['f1']}"
+    )
+
+    return base_model_results, trained_model_results
+
+
+def train_integration():
+    AcceleratorState()._reset_state(True)
+    accelerator = Accelerator(mixed_precision="fp8", kwargs_handlers=[AORecipeKwargs()])
+    set_seed(42)
+    model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = get_training_utilities(
+        MODEL_NAME, accelerator=accelerator
+    )
+
+    model, optimizer = accelerator.prepare(model, optimizer)
+    base_model_results = evaluate_model(model, eval_dataloader, METRIC, accelerator=accelerator)
+    model.train()
+
+    for batch in train_dataloader:
+        outputs = model(**batch)
+        loss = outputs.loss
+        accelerator.backward(loss)
+        optimizer.step()
+        optimizer.zero_grad()
+        lr_scheduler.step()
+
+    trained_model_results = evaluate_model(model, eval_dataloader, METRIC, accelerator=accelerator)
+
+    assert trained_model_results["accuracy"] > base_model_results["accuracy"], (
+        f"Accuracy should be higher for the trained model: {trained_model_results['accuracy']} > {base_model_results['accuracy']}"
+    )
+    assert trained_model_results["f1"] > base_model_results["f1"], (
+        f"F1 score should be higher for the trained model: {trained_model_results['f1']} > {base_model_results['f1']}"
+    )
+
+    return base_model_results, trained_model_results
+
+
+if __name__ == "__main__":
+    baseline_not_trained, baseline_trained = train_baseline()
+    accelerator_not_trained, accelerator_trained = train_integration()
+
+    assert baseline_not_trained["accuracy"] == accelerator_not_trained["accuracy"], (
+        f"Accuracy should be the same for the baseline and accelerator: {baseline_not_trained['accuracy']} == {accelerator_not_trained['accuracy']}"
+    )
+    assert baseline_not_trained["f1"] == accelerator_not_trained["f1"], (
+        f"F1 score should be the same for the baseline and accelerator: {baseline_not_trained['f1']} == {accelerator_not_trained['f1']}"
+    )
+    assert baseline_trained["accuracy"] == accelerator_trained["accuracy"], (
+        f"Accuracy should be the same for the baseline and accelerator: {baseline_trained['accuracy']} == {accelerator_trained['accuracy']}"
+    )
+    assert baseline_trained["f1"] == accelerator_trained["f1"], (
+        f"F1 score should be the same for the baseline and accelerator: {baseline_trained['f1']} == {accelerator_trained['f1']}"
+    )
+
+    torch.distributed.destroy_process_group()

tasks/tasksmith-b1dd32dfa88b/environment/source/benchmarks/fp8/torchao/distrib_deepspeed.py

@@ -0,0 +1,213 @@
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+This script tests to ensure that `accelerate` performs at the same level as raw `torchao`.
+
+This particular script verifies this for deepspeed training.
+"""
+
+from functools import partial
+from unittest.mock import patch
+
+import deepspeed
+import evaluate
+import torch
+from fp8_utils import evaluate_model, get_training_utilities
+from torchao.float8 import convert_to_float8_training
+from transformers.integrations import HfDeepSpeedConfig
+
+from accelerate import Accelerator, DeepSpeedPlugin
+from accelerate.state import AcceleratorState
+from accelerate.utils import AORecipeKwargs, set_seed
+
+
+MODEL_NAME = "bert-base-cased"
+METRIC = evaluate.load("glue", "mrpc")
+
+
+def filter_linear_layers(module, fqn, first_layer_name=None, last_layer_name=None):
+    if isinstance(module, torch.nn.Linear):
+        if module.in_features % 16 != 0 or module.out_features % 16 != 0:
+            return False
+    # For stability reasons, we skip the first and last linear layers
+    # Otherwise can lead to the model not training or converging properly
+    if fqn in (first_layer_name, last_layer_name):
+        return False
+    return True
+
+
+def train_baseline(zero_stage: int = 1):
+    set_seed(42)
+    # This forces transformers to think Zero-3 Init should be used
+    with patch("transformers.integrations.deepspeed.is_deepspeed_zero3_enabled") as mock:
+        mock.return_value = zero_stage == 3
+
+    config = HfDeepSpeedConfig(
+        {
+            "train_micro_batch_size_per_gpu": 16,
+            "gradient_accumulation_steps": 1,
+            "zero_optimization": {"stage": zero_stage},
+        }
+    )
+    plugin = DeepSpeedPlugin(hf_ds_config=config)
+    accelerator = Accelerator(deepspeed_plugin=plugin)
+    model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = get_training_utilities(
+        MODEL_NAME, accelerator=accelerator
+    )
+    first_linear = None
+    last_linear = None
+    for name, module in model.named_modules():
+        if isinstance(module, torch.nn.Linear):
+            if first_linear is None:
+                first_linear = name
+            last_linear = name
+    func = partial(filter_linear_layers, first_layer_name=first_linear, last_layer_name=last_linear)
+
+    convert_to_float8_training(model, module_filter_fn=func)
+
+    import numpy as np
+
+    config = {
+        "train_batch_size": 32,
+        "train_micro_batch_size_per_gpu": 16,
+        "gradient_accumulation_steps": 1,
+        "zero_optimization": {
+            "stage": zero_stage,
+            "offload_optimizer": {"device": "none", "nvme_path": None},
+            "offload_param": {"device": "none", "nvme_path": None},
+            "stage3_gather_16bit_weights_on_model_save": False,
+        },
+        "gradient_clipping": 1.0,
+        "steps_per_print": np.inf,
+        "bf16": {"enabled": True},
+        "fp16": {"enabled": False},
+        "zero_allow_untested_optimizer": True,
+    }
+
+    (
+        model,
+        optimizer,
+        _,
+        lr_scheduler,
+    ) = deepspeed.initialize(
+        model=model,
+        optimizer=optimizer,
+        lr_scheduler=lr_scheduler,
+        config_params=config,
+    )
+
+    base_model_results = evaluate_model(model, eval_dataloader, METRIC, accelerator=accelerator)
+    model.train()
+
+    model_outputs = []
+    data = []
+
+    for batch in train_dataloader:
+        outputs = model(**batch)
+        data.append(batch.to("cpu"))
+        model_outputs.append(outputs.logits.to("cpu"))
+        loss = outputs.loss
+        model.backward(loss)
+        model.step()
+        for _ in range(accelerator.num_processes):
+            lr_scheduler.step()
+
+    trained_model_results = evaluate_model(model, eval_dataloader, METRIC, accelerator=accelerator)
+    model.destroy()
+    assert trained_model_results["accuracy"] > base_model_results["accuracy"], (
+        f"Accuracy should be higher for the trained model: {trained_model_results['accuracy']} > {base_model_results['accuracy']}"
+    )
+    assert trained_model_results["f1"] > base_model_results["f1"], (
+        f"F1 score should be higher for the trained model: {trained_model_results['f1']} > {base_model_results['f1']}"
+    )
+
+    del config
+    return base_model_results, trained_model_results, model_outputs, data
+
+
+def train_integration(zero_stage: int = 1):
+    set_seed(42)
+    AcceleratorState()._reset_state(True)
+    config = HfDeepSpeedConfig(
+        {
+            "train_micro_batch_size_per_gpu": 16,
+            "gradient_accumulation_steps": 1,
+            "zero_optimization": {"stage": zero_stage},
+        }
+    )
+    deepspeed_plugin = DeepSpeedPlugin(
+        hf_ds_config=config,
+    )
+    # This forces transformers to think Zero-3 Init should be used
+    with patch("transformers.integrations.deepspeed.is_deepspeed_zero3_enabled") as mock:
+        mock.return_value = zero_stage == 3
+    accelerator = Accelerator(
+        mixed_precision="fp8", kwargs_handlers=[AORecipeKwargs()], deepspeed_plugin=deepspeed_plugin
+    )
+
+    model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = get_training_utilities(
+        MODEL_NAME, accelerator=accelerator
+    )
+
+    model, optimizer, lr_scheduler, train_dataloader, eval_dataloader = accelerator.prepare(
+        model, optimizer, lr_scheduler, train_dataloader, eval_dataloader
+    )
+    base_model_results = evaluate_model(model, eval_dataloader, METRIC, accelerator=accelerator)
+    model.train()
+    model_outputs = []
+    data = []
+    for batch in train_dataloader:
+        outputs = model(**batch)
+        data.append(batch.to("cpu"))
+        model_outputs.append(outputs.logits.to("cpu"))
+        loss = outputs.loss
+        accelerator.backward(loss)
+        optimizer.step()
+        lr_scheduler.step()
+        optimizer.zero_grad()
+
+    trained_model_results = evaluate_model(model, eval_dataloader, METRIC, accelerator=accelerator)
+    model.destroy()
+    assert trained_model_results["accuracy"] > base_model_results["accuracy"], (
+        f"Accuracy should be higher for the trained model: {trained_model_results['accuracy']} > {base_model_results['accuracy']}"
+    )
+    assert trained_model_results["f1"] > base_model_results["f1"], (
+        f"F1 score should be higher for the trained model: {trained_model_results['f1']} > {base_model_results['f1']}"
+    )
+
+    del config
+    return base_model_results, trained_model_results, model_outputs, data
+
+
+if __name__ == "__main__":
+    for zero_stage in [1, 2, 3]:
+        baseline_not_trained, baseline_trained, baseline_outputs, baseline_data = train_baseline(zero_stage)
+        accelerator_not_trained, accelerator_trained, accelerator_outputs, accelerator_data = train_integration(
+            zero_stage
+        )
+        assert baseline_not_trained["accuracy"] == accelerator_not_trained["accuracy"], (
+            f"ZERO stage {zero_stage}: Accuracy should be the same for the baseline and accelerator: {baseline_not_trained['accuracy']} == {accelerator_not_trained['accuracy']}"
+        )
+        assert baseline_not_trained["f1"] == accelerator_not_trained["f1"], (
+            f"ZERO stage {zero_stage}: F1 score should be the same for the baseline and accelerator: {baseline_not_trained['f1']} == {accelerator_not_trained['f1']}"
+        )
+        assert baseline_trained["accuracy"] == accelerator_trained["accuracy"], (
+            f"ZERO stage {zero_stage}: Accuracy should be the same for the baseline and accelerator: {baseline_trained['accuracy']} == {accelerator_trained['accuracy']}"
+        )
+        assert baseline_trained["f1"] == accelerator_trained["f1"], (
+            f"ZERO stage {zero_stage}: F1 score should be the same for the baseline and accelerator: {baseline_trained['f1']} == {accelerator_trained['f1']}"
+        )
+        AcceleratorState()._reset_state(True)
+    torch.distributed.destroy_process_group()

tasks/tasksmith-b1dd32dfa88b/environment/source/benchmarks/fp8/torchao/fp8_utils.py

@@ -0,0 +1,116 @@
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import torch
+
+
+def get_dataloaders(model_name: str, batch_size: int = 16):
+    from datasets import load_dataset
+    from torch.utils.data import DataLoader
+    from transformers import AutoTokenizer
+
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    datasets = load_dataset("glue", "mrpc")
+
+    def tokenize_function(examples):
+        # max_length=None => use the model max length (it's actually the default)
+        outputs = tokenizer(examples["sentence1"], examples["sentence2"], truncation=True, max_length=None)
+        return outputs
+
+    # Apply the method we just defined to all the examples in all the splits of the dataset
+    # starting with the main process first:
+    tokenized_datasets = datasets.map(
+        tokenize_function,
+        batched=True,
+        remove_columns=["idx", "sentence1", "sentence2"],
+    )
+
+    # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
+    # transformers library
+    tokenized_datasets = tokenized_datasets.rename_column("label", "labels")
+
+    def collate_fn(examples):
+        return tokenizer.pad(
+            examples,
+            padding="longest",
+            pad_to_multiple_of=16,  # Specific for FP8
+            return_tensors="pt",
+        )
+
+    # Instantiate dataloaders.
+    train_dataloader = DataLoader(
+        tokenized_datasets["train"], shuffle=True, collate_fn=collate_fn, batch_size=batch_size, drop_last=True
+    )
+    eval_dataloader = DataLoader(
+        tokenized_datasets["validation"],
+        shuffle=False,
+        collate_fn=collate_fn,
+        batch_size=16,
+        drop_last=True,
+    )
+
+    return train_dataloader, eval_dataloader
+
+
+def get_training_utilities(model_name: str, batch_size: int = 16, accelerator=None, prepare=True):
+    """
+    Returns a tuple of:
+        - Model
+        - Optimizer
+        - Train dataloader (prepared)
+        - Eval dataloader (prepared)
+        - LR Scheduler
+    Suitable for training on the MRPC dataset
+    """
+    from torch.optim import AdamW
+    from transformers import AutoModelForSequenceClassification, get_linear_schedule_with_warmup
+
+    from accelerate import Accelerator
+
+    if accelerator is None:
+        accelerator = Accelerator()
+    model = AutoModelForSequenceClassification.from_pretrained(model_name)
+    train_dataloader, eval_dataloader = get_dataloaders(model_name, batch_size)
+    optimizer = AdamW(model.parameters(), lr=0.0001)
+    lr_scheduler = get_linear_schedule_with_warmup(
+        optimizer=optimizer,
+        num_warmup_steps=100,
+        num_training_steps=len(train_dataloader) * 2,
+    )
+    train_dataloader, eval_dataloader = accelerator.prepare(train_dataloader, eval_dataloader)
+    return model, optimizer, train_dataloader, eval_dataloader, lr_scheduler
+
+
+def get_named_parameters(model):
+    """
+    Same thing as `Accelerator.get_named_parameters` Returns a list of the named parameters of the model (extracted
+    from parallel)
+    """
+    from accelerate.utils import extract_model_from_parallel
+
+    model = extract_model_from_parallel(model)
+    return {n: p for n, p in model.named_parameters()}
+
+
+def evaluate_model(model, dataloader, metric, accelerator=None):
+    "Turns model to .eval(), runs dataloader, calculates metric, then turns eval back on"
+    model.eval()
+    for step, batch in enumerate(dataloader):
+        with torch.no_grad():
+            outputs = model(**batch)
+        predictions = outputs.logits.argmax(dim=-1)
+        references = batch["labels"]
+        if accelerator is not None and accelerator.num_processes > 1:
+            predictions, references = accelerator.gather_for_metrics((predictions, references))
+        metric.add_batch(predictions=predictions, references=references)
+    return metric.compute()

tasks/tasksmith-b1dd32dfa88b/environment/source/benchmarks/fp8/torchao/fsdp.py

@@ -0,0 +1,173 @@
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+This script tests to ensure that `accelerate` performs at the same level as raw `torchao`.
+
+This particular script verifies this for FSDP training.
+"""
+
+from functools import partial
+
+import evaluate
+import torch
+from fp8_utils import get_training_utilities
+from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
+from torch.distributed.fsdp import MixedPrecision
+from torch.distributed.fsdp.wrap import transformer_auto_wrap_policy
+from torchao.float8 import convert_to_float8_training
+from transformers.models.bert import BertLayer
+
+from accelerate import Accelerator
+from accelerate import FullyShardedDataParallelPlugin as FSDPPlugin
+from accelerate.state import AcceleratorState
+from accelerate.utils import AORecipeKwargs, set_seed
+
+
+MODEL_NAME = "bert-base-cased"
+METRIC = evaluate.load("glue", "mrpc")
+
+FSDP_WRAP_POLICY = partial(transformer_auto_wrap_policy, transformer_layer_cls={BertLayer})
+
+
+def filter_linear_layers(module, fqn, first_layer_name=None, last_layer_name=None):
+    if isinstance(module, torch.nn.Linear):
+        if module.in_features % 16 != 0 or module.out_features % 16 != 0:
+            return False
+    # For stability reasons, we skip the first and last linear layers
+    # Otherwise can lead to the model not training or converging properly
+    if fqn in (first_layer_name, last_layer_name):
+        return False
+    return True
+
+
+def evaluate_model(model, dataloader, metric, accelerator=None):
+    "Turns model to .eval(), runs dataloader, calculates metric, then turns eval back on"
+    model.eval()
+    for step, batch in enumerate(dataloader):
+        with torch.no_grad():
+            outputs = model(**batch)
+        predictions = outputs.logits.argmax(dim=-1)
+        references = batch["labels"]
+        if accelerator is not None and accelerator.num_processes > 1:
+            predictions, references = accelerator.gather_for_metrics((predictions, references))
+        metric.add_batch(predictions=predictions, references=references)
+    return metric.compute()
+
+
+def train_baseline():
+    set_seed(42)
+    model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = get_training_utilities(MODEL_NAME)
+    first_linear = None
+    last_linear = None
+    for name, module in model.named_modules():
+        if isinstance(module, torch.nn.Linear):
+            if first_linear is None:
+                first_linear = name
+            last_linear = name
+    func = partial(filter_linear_layers, first_layer_name=first_linear, last_layer_name=last_linear)
+    accelerator = Accelerator()
+    device = accelerator.device
+    model.to(device)
+
+    convert_to_float8_training(model, module_filter_fn=func)
+
+    # Convert the model to FSDP
+    model = FSDP(
+        model,
+        use_orig_params=True,
+        mixed_precision=MixedPrecision(param_dtype=torch.bfloat16, reduce_dtype=torch.float32),
+        auto_wrap_policy=FSDP_WRAP_POLICY,
+    )
+
+    base_model_results = evaluate_model(model, eval_dataloader, METRIC, accelerator=accelerator)
+    model.train()
+
+    for batch in train_dataloader:
+        with torch.autocast(device_type="cuda", dtype=torch.bfloat16):
+            batch = batch.to(device)
+            outputs = model(**batch)
+        loss = outputs.loss
+        loss.backward()
+        optimizer.step()
+        optimizer.zero_grad()
+        lr_scheduler.step()
+
+    trained_model_results = evaluate_model(model, eval_dataloader, METRIC, accelerator=accelerator)
+
+    assert trained_model_results["accuracy"] > base_model_results["accuracy"], (
+        f"Accuracy should be higher for the trained model: {trained_model_results['accuracy']} > {base_model_results['accuracy']}"
+    )
+    assert trained_model_results["f1"] > base_model_results["f1"], (
+        f"F1 score should be higher for the trained model: {trained_model_results['f1']} > {base_model_results['f1']}"
+    )
+
+    return base_model_results, trained_model_results
+
+
+def train_integration():
+    AcceleratorState()._reset_state(True)
+    fsdp_plugin = FSDPPlugin(
+        auto_wrap_policy=FSDP_WRAP_POLICY,
+        use_orig_params=True,
+        mixed_precision_policy=MixedPrecision(param_dtype=torch.bfloat16, reduce_dtype=torch.float32),
+    )
+    accelerator = Accelerator(mixed_precision="fp8", fsdp_plugin=fsdp_plugin, kwargs_handlers=[AORecipeKwargs()])
+    set_seed(42)
+    model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = get_training_utilities(
+        MODEL_NAME, accelerator=accelerator
+    )
+
+    model, optimizer = accelerator.prepare(model, optimizer)
+    base_model_results = evaluate_model(model, eval_dataloader, METRIC, accelerator=accelerator)
+    model.train()
+
+    for batch in train_dataloader:
+        outputs = model(**batch)
+        loss = outputs.loss
+        accelerator.backward(loss)
+        optimizer.step()
+        optimizer.zero_grad()
+        lr_scheduler.step()
+
+    trained_model_results = evaluate_model(model, eval_dataloader, METRIC, accelerator=accelerator)
+
+    assert trained_model_results["accuracy"] > base_model_results["accuracy"], (
+        f"Accuracy should be higher for the trained model: {trained_model_results['accuracy']} > {base_model_results['accuracy']}"
+    )
+    assert trained_model_results["f1"] > base_model_results["f1"], (
+        f"F1 score should be higher for the trained model: {trained_model_results['f1']} > {base_model_results['f1']}"
+    )
+
+    return base_model_results, trained_model_results
+
+
+if __name__ == "__main__":
+    baseline_not_trained, baseline_trained = train_baseline()
+    accelerator_not_trained, accelerator_trained = train_integration()
+
+    assert baseline_not_trained["accuracy"] == accelerator_not_trained["accuracy"], (
+        f"Accuracy should be the same for the baseline and accelerator: {baseline_not_trained['accuracy']} == {accelerator_not_trained['accuracy']}"
+    )
+    assert baseline_not_trained["f1"] == accelerator_not_trained["f1"], (
+        f"F1 score should be the same for the baseline and accelerator: {baseline_not_trained['f1']} == {accelerator_not_trained['f1']}"
+    )
+    assert baseline_trained["accuracy"] == accelerator_trained["accuracy"], (
+        f"Accuracy should be the same for the baseline and accelerator: {baseline_trained['accuracy']} == {accelerator_trained['accuracy']}"
+    )
+    assert baseline_trained["f1"] == accelerator_trained["f1"], (
+        f"F1 score should be the same for the baseline and accelerator: {baseline_trained['f1']} == {accelerator_trained['f1']}"
+    )
+
+    torch.distributed.destroy_process_group()

tasks/tasksmith-b1dd32dfa88b/environment/source/benchmarks/fp8/torchao/non_distributed.py

@@ -0,0 +1,145 @@
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+This script tests to ensure that `accelerate` performs at the same level as raw `torchao`.
+
+This particular script verifies this for single GPU training.
+"""
+
+from functools import partial
+
+import evaluate
+import torch
+from fp8_utils import get_training_utilities
+from torchao.float8 import convert_to_float8_training
+
+from accelerate import Accelerator
+from accelerate.state import AcceleratorState
+from accelerate.utils import AORecipeKwargs, set_seed
+
+
+MODEL_NAME = "bert-base-cased"
+METRIC = evaluate.load("glue", "mrpc")
+
+
+def evaluate_model(model, dataloader, metric, accelerator=None):
+    "Turns model to .eval(), runs dataloader, calculates metric, then turns eval back on"
+    model.eval()
+    for step, batch in enumerate(dataloader):
+        with torch.no_grad():
+            outputs = model(**batch)
+        predictions = outputs.logits.argmax(dim=-1)
+        references = batch["labels"]
+        if accelerator is not None and accelerator.num_processes > 1:
+            predictions, references = accelerator.gather_for_metrics((predictions, references))
+        metric.add_batch(predictions=predictions, references=references)
+    return metric.compute()
+
+
+def filter_linear_layers(module, fqn, first_layer_name=None, last_layer_name=None):
+    if isinstance(module, torch.nn.Linear):
+        if module.in_features % 16 != 0 or module.out_features % 16 != 0:
+            return False
+    # For stability reasons, we skip the first and last linear layers
+    # Otherwise can lead to the model not training or converging properly
+    if fqn in (first_layer_name, last_layer_name):
+        return False
+    return True
+
+
+def train_baseline():
+    set_seed(42)
+    model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = get_training_utilities(MODEL_NAME)
+    first_linear = None
+    last_linear = None
+    for name, module in model.named_modules():
+        if isinstance(module, torch.nn.Linear):
+            if first_linear is None:
+                first_linear = name
+            last_linear = name
+
+    func = partial(filter_linear_layers, first_layer_name=first_linear, last_layer_name=last_linear)
+    model.to("cuda")
+    convert_to_float8_training(model, module_filter_fn=func)
+    base_model_results = evaluate_model(model, eval_dataloader, METRIC)
+    model.train()
+
+    for batch in train_dataloader:
+        with torch.autocast(device_type="cuda", dtype=torch.bfloat16):
+            outputs = model(**batch)
+            loss = outputs.loss
+            loss.backward()
+        optimizer.step()
+        optimizer.zero_grad()
+        lr_scheduler.step()
+
+    trained_model_results = evaluate_model(model, eval_dataloader, METRIC)
+
+    assert trained_model_results["accuracy"] > base_model_results["accuracy"], (
+        f"Accuracy should be higher for the trained model: {trained_model_results['accuracy']} > {base_model_results['accuracy']}"
+    )
+    assert trained_model_results["f1"] > base_model_results["f1"], (
+        f"F1 score should be higher for the trained model: {trained_model_results['f1']} > {base_model_results['f1']}"
+    )
+
+    return base_model_results, trained_model_results
+
+
+def train_integration():
+    set_seed(42)
+    accelerator = Accelerator(mixed_precision="fp8", kwargs_handlers=[AORecipeKwargs()])
+    model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = get_training_utilities(
+        MODEL_NAME, accelerator=accelerator
+    )
+    model = accelerator.prepare(model)
+    base_model_results = evaluate_model(model, eval_dataloader, METRIC)
+    model.train()
+
+    for batch in train_dataloader:
+        outputs = model(**batch)
+        loss = outputs.loss
+        loss.backward()
+        optimizer.step()
+        optimizer.zero_grad()
+        lr_scheduler.step()
+
+    trained_model_results = evaluate_model(model, eval_dataloader, METRIC)
+
+    assert trained_model_results["accuracy"] > base_model_results["accuracy"], (
+        f"Accuracy should be higher for the trained model: {trained_model_results['accuracy']} > {base_model_results['accuracy']}"
+    )
+    assert trained_model_results["f1"] > base_model_results["f1"], (
+        f"F1 score should be higher for the trained model: {trained_model_results['f1']} > {base_model_results['f1']}"
+    )
+
+    return base_model_results, trained_model_results
+
+
+if __name__ == "__main__":
+    baseline_not_trained, baseline_trained = train_baseline()
+    AcceleratorState._reset_state(True)
+    accelerator_not_trained, accelerator_trained = train_integration()
+    assert baseline_not_trained["accuracy"] == accelerator_not_trained["accuracy"], (
+        f"Accuracy should be the same for the baseline and accelerator: {baseline_not_trained['accuracy']} == {accelerator_not_trained['accuracy']}"
+    )
+    assert baseline_not_trained["f1"] == accelerator_not_trained["f1"], (
+        f"F1 score should be the same for the baseline and accelerator: {baseline_not_trained['f1']} == {accelerator_not_trained['f1']}"
+    )
+    assert baseline_trained["accuracy"] == accelerator_trained["accuracy"], (
+        f"Accuracy should be the same for the baseline and accelerator: {baseline_trained['accuracy']} == {accelerator_trained['accuracy']}"
+    )
+    assert baseline_trained["f1"] == accelerator_trained["f1"], (
+        f"F1 score should be the same for the baseline and accelerator: {baseline_trained['f1']} == {accelerator_trained['f1']}"
+    )

tasks/tasksmith-b1dd32dfa88b/environment/source/benchmarks/fp8/transformer_engine/Dockerfile

@@ -0,0 +1,15 @@
+ARG BASE_YEAR=25
+ARG BASE_MONTH=03
+
+FROM nvcr.io/nvidia/pytorch:${BASE_YEAR}.${BASE_MONTH}-py3
+
+RUN pip install transformers evaluate datasets
+RUN git clone https://github.com/huggingface/accelerate.git
+
+RUN cd accelerate && \
+    pip install -e .[deepspeed] && \
+    cd benchmarks/fp8
+
+RUN /bin/bash
+
+

tasks/tasksmith-b1dd32dfa88b/environment/source/benchmarks/fp8/transformer_engine/README.md

@@ -0,0 +1,32 @@
+# FP8 Benchmarks
+
+Comparing and running [TransformerEngine](https://github.com/NVIDIA/TransformerEngine) FP8 with accelerate
+
+## Overview
+
+This repo provides scripts which compare native TransformerEngine model training against `accelerate`'s own integration. Each modeling type is segmented out via a script, supporting the following:
+
+* Single GPU training (`non_distributed.py`)
+* Multi-GPU training via DistributedDataParallelism (`ddp.py`)
+* Fully Sharded Data Parallelism (`fsdp.py`)
+* DeepSpeed ZeRO 1-3 (`deepspeed.py`)
+
+To run them, it's recommended to use a docker image (see the attached `Dockerfile`) and not install `TransformerEngine` manually.
+
+## Running:
+
+There are official Docker images located at `huggingface/accelerate:gpu-fp8-transformerengine-nightly` which can be used.
+
+You can run all scripts using the core `accelerate launch` command without any `accelerate config` being needed.
+
+For single GPU, run it via `python`:
+
+```bash
+python non_distributed.py
+```
+
+For the rest, run it via `accelerate launch`:
+
+```bash
+accelerate launch ddp.py # or distrib_deepspeed.py, ddp.py
+```

tasks/tasksmith-b1dd32dfa88b/environment/source/benchmarks/fp8/transformer_engine/ddp.py

@@ -0,0 +1,144 @@
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+This script tests to ensure that `accelerate` performs at the same level as raw `TransformersEngine`.
+
+This particular script verifies this for DDP training.
+"""
+
+import evaluate
+import torch
+import transformer_engine.common.recipe as te_recipe
+import transformer_engine.pytorch as te
+from fp8_utils import evaluate_model, get_named_parameters, get_training_utilities
+from torch.nn.parallel import DistributedDataParallel as DDP
+from transformer_engine.common.recipe import DelayedScaling
+
+from accelerate import Accelerator
+from accelerate.state import AcceleratorState
+from accelerate.utils import FP8RecipeKwargs, set_seed
+from accelerate.utils.transformer_engine import convert_model
+
+
+MODEL_NAME = "bert-base-cased"
+METRIC = evaluate.load("glue", "mrpc")
+
+
+def train_baseline():
+    set_seed(42)
+    model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = get_training_utilities(MODEL_NAME)
+    accelerator = Accelerator()
+    device = accelerator.device
+    model.to(device)
+
+    # Convert the model to TE
+    old_named_params = get_named_parameters(model)
+
+    with torch.no_grad():
+        convert_model(model)
+
+    FP8_RECIPE_KWARGS = {"fp8_format": te_recipe.Format.HYBRID, "amax_history_len": 32, "amax_compute_algo": "max"}
+    fp8_recipe = DelayedScaling(**FP8_RECIPE_KWARGS)
+
+    new_named_params = get_named_parameters(model)
+
+    # Convert the model to DDP
+    device_ids, output_device = [accelerator.local_process_index], accelerator.local_process_index
+    model = DDP(model, device_ids=device_ids, output_device=output_device)
+
+    mapping = {p: new_named_params[n] for n, p in old_named_params.items()}
+    for param_group in optimizer.param_groups:
+        param_group["params"] = [mapping[p] for p in param_group["params"]]
+
+    base_model_results = evaluate_model(model, eval_dataloader, METRIC, accelerator=accelerator)
+    model.train()
+
+    for _ in range(2):
+        for batch in train_dataloader:
+            with te.fp8_autocast(enabled=True, fp8_recipe=fp8_recipe):
+                with torch.autocast(device_type="cuda", dtype=torch.bfloat16):
+                    batch = batch.to(device)
+                    outputs = model(**batch)
+            loss = outputs.loss
+            loss.backward()
+            optimizer.step()
+            optimizer.zero_grad()
+            lr_scheduler.step()
+
+    trained_model_results = evaluate_model(model, eval_dataloader, METRIC, accelerator=accelerator)
+
+    assert trained_model_results["accuracy"] > base_model_results["accuracy"], (
+        f"Accuracy should be higher for the trained model: {trained_model_results['accuracy']} > {base_model_results['accuracy']}"
+    )
+    assert trained_model_results["f1"] > base_model_results["f1"], (
+        f"F1 score should be higher for the trained model: {trained_model_results['f1']} > {base_model_results['f1']}"
+    )
+
+    return base_model_results, trained_model_results
+
+
+def train_integration():
+    FP8_RECIPE_KWARGS = {"fp8_format": "HYBRID", "amax_history_len": 32, "amax_compute_algo": "max"}
+    kwargs_handlers = [FP8RecipeKwargs(backend="TE", **FP8_RECIPE_KWARGS)]
+    AcceleratorState()._reset_state(True)
+    accelerator = Accelerator(mixed_precision="fp8", kwargs_handlers=kwargs_handlers)
+    set_seed(42)
+    model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = get_training_utilities(
+        MODEL_NAME, accelerator=accelerator
+    )
+
+    model, optimizer = accelerator.prepare(model, optimizer)
+    base_model_results = evaluate_model(model, eval_dataloader, METRIC, accelerator=accelerator)
+    model.train()
+
+    for _ in range(2):
+        for batch in train_dataloader:
+            outputs = model(**batch)
+            loss = outputs.loss
+            accelerator.backward(loss)
+            optimizer.step()
+            optimizer.zero_grad()
+            lr_scheduler.step()
+
+    trained_model_results = evaluate_model(model, eval_dataloader, METRIC, accelerator=accelerator)
+
+    assert trained_model_results["accuracy"] > base_model_results["accuracy"], (
+        f"Accuracy should be higher for the trained model: {trained_model_results['accuracy']} > {base_model_results['accuracy']}"
+    )
+    assert trained_model_results["f1"] > base_model_results["f1"], (
+        f"F1 score should be higher for the trained model: {trained_model_results['f1']} > {base_model_results['f1']}"
+    )
+
+    return base_model_results, trained_model_results
+
+
+if __name__ == "__main__":
+    baseline_not_trained, baseline_trained = train_baseline()
+    accelerator_not_trained, accelerator_trained = train_integration()
+
+    assert baseline_not_trained["accuracy"] == accelerator_not_trained["accuracy"], (
+        f"Accuracy should be the same for the baseline and accelerator: {baseline_not_trained['accuracy']} == {accelerator_not_trained['accuracy']}"
+    )
+    assert baseline_not_trained["f1"] == accelerator_not_trained["f1"], (
+        f"F1 score should be the same for the baseline and accelerator: {baseline_not_trained['f1']} == {accelerator_not_trained['f1']}"
+    )
+    assert baseline_trained["accuracy"] == accelerator_trained["accuracy"], (
+        f"Accuracy should be the same for the baseline and accelerator: {baseline_trained['accuracy']} == {accelerator_trained['accuracy']}"
+    )
+    assert baseline_trained["f1"] == accelerator_trained["f1"], (
+        f"F1 score should be the same for the baseline and accelerator: {baseline_trained['f1']} == {accelerator_trained['f1']}"
+    )
+
+    torch.distributed.destroy_process_group()

tasks/tasksmith-b1dd32dfa88b/environment/source/benchmarks/fp8/transformer_engine/distrib_deepspeed.py

@@ -0,0 +1,191 @@
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+This script tests to ensure that `accelerate` performs at the same level as raw `TransformersEngine`.
+
+This particular script verifies this for DDP training.
+"""
+
+from unittest.mock import patch
+
+import deepspeed
+import evaluate
+import torch
+import transformer_engine.common.recipe as te_recipe
+import transformer_engine.pytorch as te
+from fp8_utils import evaluate_model, get_named_parameters, get_training_utilities
+from transformer_engine.common.recipe import DelayedScaling
+
+from accelerate import Accelerator, DeepSpeedPlugin
+from accelerate.state import AcceleratorState
+from accelerate.utils import FP8RecipeKwargs, set_seed
+from accelerate.utils.transformer_engine import convert_model
+
+
+MODEL_NAME = "bert-base-cased"
+METRIC = evaluate.load("glue", "mrpc")
+
+
+def train_baseline(zero_stage: int = 1):
+    # This forces transformers to think Zero-3 Init should be used
+    with patch("transformers.integrations.deepspeed.is_deepspeed_zero3_enabled") as mock:
+        mock.return_value = zero_stage == 3
+    set_seed(42)
+
+    accelerator = Accelerator()
+    model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = get_training_utilities(
+        MODEL_NAME, accelerator=accelerator
+    )
+
+    # Convert the model to TE
+    old_named_params = get_named_parameters(model)
+
+    with torch.no_grad():
+        convert_model(model)
+    new_named_params = get_named_parameters(model)
+
+    mapping = {p: new_named_params[n] for n, p in old_named_params.items()}
+    for param_group in optimizer.param_groups:
+        param_group["params"] = [mapping[p] for p in param_group["params"]]
+
+    FP8_RECIPE_KWARGS = {"fp8_format": te_recipe.Format.HYBRID, "amax_history_len": 32, "amax_compute_algo": "max"}
+    fp8_recipe = DelayedScaling(**FP8_RECIPE_KWARGS)
+
+    import numpy as np
+
+    config = {
+        "train_batch_size": 16,
+        "train_micro_batch_size_per_gpu": 16,
+        "gradient_accumulation_steps": 1,
+        "zero_optimization": {
+            "stage": zero_stage,
+            "offload_optimizer": {"device": "none", "nvme_path": None},
+            "offload_param": {"device": "none", "nvme_path": None},
+            "stage3_gather_16bit_weights_on_model_save": False,
+        },
+        "gradient_clipping": 1.0,
+        "steps_per_print": np.inf,
+        "bf16": {"enabled": True},
+        "fp16": {"enabled": False},
+        "zero_allow_untested_optimizer": True,
+    }
+
+    (
+        model,
+        optimizer,
+        _,
+        _,
+    ) = deepspeed.initialize(
+        model=model,
+        optimizer=optimizer,
+        config_params=config,
+    )
+
+    base_model_results = evaluate_model(model, eval_dataloader, METRIC, accelerator=accelerator)
+    model.train()
+
+    model_outputs = []
+    data = []
+
+    for _ in range(2):
+        for batch in train_dataloader:
+            with te.fp8_autocast(enabled=True, fp8_recipe=fp8_recipe):
+                outputs = model(**batch)
+                data.append(batch.to("cpu"))
+            model_outputs.append(outputs.logits.to("cpu"))
+            loss = outputs.loss
+            model.backward(loss)
+            model.step()
+            for _ in range(accelerator.num_processes):
+                lr_scheduler.step()
+
+    trained_model_results = evaluate_model(model, eval_dataloader, METRIC, accelerator=accelerator)
+    model.destroy()
+    assert trained_model_results["accuracy"] > base_model_results["accuracy"], (
+        f"Accuracy should be higher for the trained model: {trained_model_results['accuracy']} > {base_model_results['accuracy']}"
+    )
+    assert trained_model_results["f1"] > base_model_results["f1"], (
+        f"F1 score should be higher for the trained model: {trained_model_results['f1']} > {base_model_results['f1']}"
+    )
+
+    return base_model_results, trained_model_results, model_outputs, data
+
+
+def train_integration(zero_stage: int = 1):
+    set_seed(42)
+    FP8_RECIPE_KWARGS = {"fp8_format": "HYBRID", "amax_history_len": 32, "amax_compute_algo": "max"}
+    kwargs_handlers = [FP8RecipeKwargs(backend="TE", **FP8_RECIPE_KWARGS)]
+    AcceleratorState()._reset_state(True)
+    deepspeed_plugin = DeepSpeedPlugin(
+        zero_stage=zero_stage,
+        zero3_init_flag=zero_stage == 3,
+    )
+    accelerator = Accelerator(
+        mixed_precision="fp8", kwargs_handlers=kwargs_handlers, deepspeed_plugin=deepspeed_plugin
+    )
+    accelerator.state.deepspeed_plugin.deepspeed_config["train_micro_batch_size_per_gpu"] = 16
+
+    model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = get_training_utilities(
+        MODEL_NAME, accelerator=accelerator
+    )
+
+    model, optimizer, lr_scheduler = accelerator.prepare(model, optimizer, lr_scheduler)
+    base_model_results = evaluate_model(model, eval_dataloader, METRIC, accelerator=accelerator)
+    model.train()
+    model_outputs = []
+    data = []
+    for _ in range(2):
+        for batch in train_dataloader:
+            outputs = model(**batch)
+            data.append(batch.to("cpu"))
+            model_outputs.append(outputs.logits.to("cpu"))
+            loss = outputs.loss
+            accelerator.backward(loss)
+            optimizer.step()
+            lr_scheduler.step()
+            optimizer.zero_grad()
+
+    trained_model_results = evaluate_model(model, eval_dataloader, METRIC, accelerator=accelerator)
+    model.destroy()
+    assert trained_model_results["accuracy"] > base_model_results["accuracy"], (
+        f"Accuracy should be higher for the trained model: {trained_model_results['accuracy']} > {base_model_results['accuracy']}"
+    )
+    assert trained_model_results["f1"] > base_model_results["f1"], (
+        f"F1 score should be higher for the trained model: {trained_model_results['f1']} > {base_model_results['f1']}"
+    )
+
+    return base_model_results, trained_model_results, model_outputs, data
+
+
+if __name__ == "__main__":
+    for zero_stage in [1, 2, 3]:
+        baseline_not_trained, baseline_trained, baseline_outputs, baseline_data = train_baseline(zero_stage)
+        accelerator_not_trained, accelerator_trained, accelerator_outputs, accelerator_data = train_integration(
+            zero_stage
+        )
+        assert baseline_not_trained["accuracy"] == accelerator_not_trained["accuracy"], (
+            f"ZERO stage {zero_stage}: Accuracy should be the same for the baseline and accelerator: {baseline_not_trained['accuracy']} == {accelerator_not_trained['accuracy']}"
+        )
+        assert baseline_not_trained["f1"] == accelerator_not_trained["f1"], (
+            f"ZERO stage {zero_stage}: F1 score should be the same for the baseline and accelerator: {baseline_not_trained['f1']} == {accelerator_not_trained['f1']}"
+        )
+        assert baseline_trained["accuracy"] == accelerator_trained["accuracy"], (
+            f"ZERO stage {zero_stage}: Accuracy should be the same for the baseline and accelerator: {baseline_trained['accuracy']} == {accelerator_trained['accuracy']}"
+        )
+        assert baseline_trained["f1"] == accelerator_trained["f1"], (
+            f"ZERO stage {zero_stage}: F1 score should be the same for the baseline and accelerator: {baseline_trained['f1']} == {accelerator_trained['f1']}"
+        )
+
+        torch.distributed.destroy_process_group()

tasks/tasksmith-b1dd32dfa88b/environment/source/benchmarks/fp8/transformer_engine/fp8_utils.py

@@ -0,0 +1,116 @@
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import torch
+
+
+def get_dataloaders(model_name: str, batch_size: int = 16):
+    from datasets import load_dataset
+    from torch.utils.data import DataLoader
+    from transformers import AutoTokenizer
+
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    datasets = load_dataset("glue", "mrpc")
+
+    def tokenize_function(examples):
+        # max_length=None => use the model max length (it's actually the default)
+        outputs = tokenizer(examples["sentence1"], examples["sentence2"], truncation=True, max_length=None)
+        return outputs
+
+    # Apply the method we just defined to all the examples in all the splits of the dataset
+    # starting with the main process first:
+    tokenized_datasets = datasets.map(
+        tokenize_function,
+        batched=True,
+        remove_columns=["idx", "sentence1", "sentence2"],
+    )
+
+    # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
+    # transformers library
+    tokenized_datasets = tokenized_datasets.rename_column("label", "labels")
+
+    def collate_fn(examples):
+        return tokenizer.pad(
+            examples,
+            padding="longest",
+            pad_to_multiple_of=16,  # Specific for FP8
+            return_tensors="pt",
+        )
+
+    # Instantiate dataloaders.
+    train_dataloader = DataLoader(
+        tokenized_datasets["train"], shuffle=True, collate_fn=collate_fn, batch_size=batch_size, drop_last=True
+    )
+    eval_dataloader = DataLoader(
+        tokenized_datasets["validation"],
+        shuffle=False,
+        collate_fn=collate_fn,
+        batch_size=16,
+        drop_last=True,
+    )
+
+    return train_dataloader, eval_dataloader
+
+
+def get_training_utilities(model_name: str, batch_size: int = 16, accelerator=None):
+    """
+    Returns a tuple of:
+        - Model
+        - Optimizer
+        - Train dataloader (prepared)
+        - Eval dataloader (prepared)
+        - LR Scheduler
+    Suitable for training on the MRPC dataset
+    """
+    from torch.optim import AdamW
+    from transformers import AutoModelForSequenceClassification, get_linear_schedule_with_warmup
+
+    from accelerate import Accelerator
+
+    if accelerator is None:
+        accelerator = Accelerator()
+    model = AutoModelForSequenceClassification.from_pretrained(model_name)
+    train_dataloader, eval_dataloader = get_dataloaders(model_name, batch_size)
+    optimizer = AdamW(model.parameters(), lr=0.0001)
+    lr_scheduler = get_linear_schedule_with_warmup(
+        optimizer=optimizer,
+        num_warmup_steps=100,
+        num_training_steps=len(train_dataloader) * 2,
+    )
+    train_dataloader, eval_dataloader = accelerator.prepare(train_dataloader, eval_dataloader)
+    return model, optimizer, train_dataloader, eval_dataloader, lr_scheduler
+
+
+def get_named_parameters(model):
+    """
+    Same thing as `Accelerator.get_named_parameters` Returns a list of the named parameters of the model (extracted
+    from parallel)
+    """
+    from accelerate.utils import extract_model_from_parallel
+
+    model = extract_model_from_parallel(model)
+    return {n: p for n, p in model.named_parameters()}
+
+
+def evaluate_model(model, dataloader, metric, accelerator=None):
+    "Turns model to .eval(), runs dataloader, calculates metric, then turns eval back on"
+    model.eval()
+    for step, batch in enumerate(dataloader):
+        with torch.no_grad():
+            outputs = model(**batch)
+        predictions = outputs.logits.argmax(dim=-1)
+        references = batch["labels"]
+        if accelerator is not None and accelerator.num_processes > 1:
+            predictions, references = accelerator.gather_for_metrics((predictions, references))
+        metric.add_batch(predictions=predictions, references=references)
+    return metric.compute()