Release v0.32.0

* added clear_device_cache

* set lambda: 0 for mps and cpu

.devcontainer/devcontainer.json

@@ -0,0 +1,29 @@
+// 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]'"
+}

.github/ISSUE_TEMPLATE/bug-report.yml

@@ -0,0 +1,63 @@
+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."

.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: @muellerzr
+- DeepSpeed: @muellerzr
+- Command Line Interface: @muellerzr
+- Documentation: @muellerzr
+- Core parts of the library: @muellerzr @BenjaminBossan @SunMarc
+- Maintained examples: @muellerzr or @SunMarc
+
+ -->

.github/workflows/build-docker-images-release.yml

@@ -0,0 +1,81 @@
+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: ${{ steps.step1.outputs.version }}
+    steps:
+      - uses: actions/checkout@v3.1.0
+      - id: step1
+        run: echo "version=$(python setup.py --version)" >> $GITHUB_OUTPUT
+
+  version-cpu:
+    name: "Latest Accelerate CPU [version]"
+    runs-on: [self-hosted, intel-cpu, 8-cpu, ci]
+    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: [self-hosted, single-gpu, nvidia-gpu, t4, ci]
+    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: [self-hosted, single-gpu, nvidia-gpu, t4, ci]
+    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}}
+

.github/workflows/build_and_run_tests.yml

@@ -0,0 +1,50 @@
+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@v3.1.0
+        with: 
+          fetch-depth: "2"
+      
+      - name: Get changed files
+        id: changed-files
+        uses: tj-actions/changed-files@v41
+      
+      - 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

.github/workflows/build_docker_images.yml

@@ -0,0 +1,85 @@
+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: [self-hosted, intel-cpu, 8-cpu, ci]
+    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: [self-hosted, nvidia-gpu, t4, ci]
+    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: [self-hosted, nvidia-gpu, t4, ci]
+    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 }}
+

.github/workflows/build_documentation.yml

@@ -13,5 +13,6 @@ jobs:
     with:
       commit_sha: ${{ github.sha }}
       package: accelerate
+      custom_container: huggingface/transformers-doc-builder
     secrets:
-      token: ${{ secrets.HUGGINGFACE_PUSH }}
+      hf_token: ${{ secrets.HF_DOC_BUILD_PUSH }}

.github/workflows/build_pr_documentation.yml

@@ -14,3 +14,4 @@ jobs:
       commit_sha: ${{ github.event.pull_request.head.sha }}
       pr_number: ${{ github.event.number }}
       package: accelerate
+      custom_container: huggingface/transformers-doc-builder

.github/workflows/delete_doc_comment.yml

@@ -1,13 +0,0 @@
-name: Delete dev documentation
-
-on:
-  pull_request:
-    types: [ closed ]
-
-
-jobs:
-  delete:
-    uses: huggingface/doc-builder/.github/workflows/delete_doc_comment.yml@main
-    with:
-      pr_number: ${{ github.event.number }}
-      package: accelerate

.github/workflows/integration_tests.yml

@@ -0,0 +1,56 @@
+# 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@v3.1.0
+    - name: Set up python 3.8
+      uses: actions/setup-python@v3
+      with:
+        python-version: 3.8
+
+    - 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

.github/workflows/nightly.yml

@@ -0,0 +1,229 @@
+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: [self-hosted, single-gpu, nvidia-gpu, t4, ci]
+    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: [self-hosted, single-gpu, nvidia-gpu, t4, ci]
+    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: [self-hosted, multi-gpu, nvidia-gpu, t4, ci]
+    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: [self-hosted, multi-gpu, nvidia-gpu, t4, ci]
+    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

.github/workflows/quality.yml

@@ -6,12 +6,17 @@ jobs:
   quality:
     runs-on: ubuntu-latest
     steps:
-    - uses: actions/checkout@v2
-    - name: Set up Python 3.6
-      uses: actions/setup-python@v2
+    - uses: actions/checkout@v3.1.0
+    - name: Set up Python 3.8
+      uses: actions/setup-python@v3
       with:
-        python-version: 3.6
+        python-version: 3.8
     - name: Install Python dependencies
       run: pip install -e .[quality]
     - name: Run Quality check
-      run: make quality
+      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
+

.github/workflows/run_merge_tests.yml

@@ -0,0 +1,184 @@
+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: [self-hosted, single-gpu, nvidia-gpu, t4, ci]
+    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: [self-hosted, single-gpu, nvidia-gpu, t4, ci]
+    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: [self-hosted, multi-gpu, nvidia-gpu, t4, ci]
+    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: [self-hosted, multi-gpu, nvidia-gpu, t4, ci]
+    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

.github/workflows/self_hosted_integration_tests.yml

@@ -0,0 +1,125 @@
+# 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: [self-hosted, multi-gpu, nvidia-gpu, t4, ci]
+    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: [self-hosted, multi-gpu, nvidia-gpu, t4, ci]
+    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

.github/workflows/stale.yml

@@ -0,0 +1,28 @@
+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
+    env:
+      GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+    steps:
+    - uses: actions/checkout@v3.1.0
+    
+    - name: Setup Python
+      uses: actions/setup-python@v3
+      with:
+        python-version: 3.8
+    
+    - name: Install requirements
+      run: |
+        pip install PyGithub
+    - name: Close stale issues
+      run: |
+        python utils/stale.py

.github/workflows/test.yml

@@ -1,30 +1,68 @@
 name: Run Tests
 
-on: [pull_request]
+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:
-  test:
+  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@v2
-    - name: Set up Python 3.6
-      uses: actions/setup-python@v2
+    - uses: actions/checkout@v3.1.0
+    - name: Set up python 3.8
+      uses: actions/setup-python@v3
       with:
-        python-version: 3.6
-    - name: Install Python dependencies
-      run: pip install setuptools==59.5.0; pip install -e .[test,test_trackers]
+        python-version: 3.8
+    
+    - 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.test-kind }} = minimum ]]; then pip install torch==1.10.0; fi
+        pip install pytest-reportlog tabulate setuptools
+
+    - name: Show installed libraries
+      run: |
+        pip freeze
+    
     - name: Run Tests
-      run: make test
-      
-  test_examples:
-    runs-on: ubuntu-latest
-    steps:
-    - uses: actions/checkout@v2
-    - name: Set up Python 3.6
-      uses: actions/setup-python@v2
-      with:
-        python-version: 3.6
-    - name: Install Python dependencies
-      run: pip install setuptools==59.5.0; pip install -e .[test] tensorboard
-    - name: Run Tests
-      run: make test_examples
+      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

.github/workflows/test_imports.yml

@@ -0,0 +1,53 @@
+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@v3.1.0
+    - name: Set up python 3.8
+      uses: actions/setup-python@v3
+      with:
+        python-version: 3.8
+    
+    - 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

.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

.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 }}

.gitignore

@@ -135,4 +135,10 @@ dmypy.json
 .idea
 
 # Mac .DS_Store
-.DS_Store
+.DS_Store
+
+# More test things
+wandb
+
+# ruff
+.ruff_cache

.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

CONTRIBUTING.md

@@ -130,6 +130,9 @@ Follow these steps to start contributing:
    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
@@ -149,7 +152,7 @@ Follow these steps to start contributing:
    $ make test
    ```
 
-   `accelerate` relies on `black` and `isort` to format its source code
+   `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:
 
@@ -162,13 +165,21 @@ Follow these steps to start contributing:
    $ make style
    ```
 
-   `accelerate` also uses `flake8` and a few custom scripts to check for coding mistakes. Quality
+   `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:
 
@@ -232,4 +243,4 @@ $ 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.
+you're working on.

Makefile

@@ -1,6 +1,6 @@
-.PHONY: quality style test docs
+.PHONY: quality style test docs utils
 
-check_dirs := tests src examples
+check_dirs := .
 
 # Check that source code meets quality standards
 
@@ -8,24 +8,65 @@ extra_quality_checks:
 	python utils/check_copies.py
 	python utils/check_dummies.py
 	python utils/check_repo.py
-	python utils/style_doc.py src/accelerate docs/source --max_len 119
+	doc-builder style src/accelerate docs/source --max_len 119
 
 # this target runs checks on all files
 quality:
-	black --check $(check_dirs)
-	isort --check-only $(check_dirs)
-	flake8 $(check_dirs)
-	python utils/style_doc.py src/accelerate docs/source --max_len 119 --check_only
+	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:
-	black $(check_dirs)
-	isort $(check_dirs)
-	python utils/style_doc.py src/accelerate docs/source --max_len 119
+	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_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_core:
+	python -m pytest -s -v ./tests/ --ignore=./tests/test_examples.py --ignore=./tests/deepspeed --ignore=./tests/test_big_modeling.py \
+	--ignore=./tests/fsdp --ignore=./tests/test_cli.py $(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_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",)
+
+# Since the new version of pytest will *change* how things are collected, we need `deepspeed` to 
+# run after test_core and test_cli
 test:
-	python -m pytest -n auto --dist=loadfile -s -v ./tests/ --ignore=./tests/test_examples.py
+	$(MAKE) test_core
+	$(MAKE) test_cli
+	$(MAKE) test_big_modeling
+	$(MAKE) test_deepspeed
+	$(MAKE) test_fsdp
 
 test_examples:
-	python -m pytest -n auto --dist=loadfile -s -v ./tests/test_examples.py
+	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 $(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",)

README.md

@@ -16,28 +16,18 @@ limitations under the License.
 
 <p align="center">
     <br>
-    <img src="docs/source/imgs/accelerate_logo.png" width="400"/>
+    <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 setup
-    <a href="https://circleci.com/gh/huggingface/accelerate">
-        <img alt="Build" src="https://img.shields.io/circleci/build/github/huggingface/transformers/master">
-    </a>
+    <!-- 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>
+    <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">
@@ -91,7 +81,7 @@ Here is an example:
           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 (fp16).
+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.
 
@@ -132,11 +122,11 @@ In particular, the same code can then be run without modification on your local
           optimizer.step()
 ```
 
-Want to learn more? Check out the [documentation](https://huggingface.co/docs/accelerate) or have look at our [examples](https://github.com/huggingface/accelerate/tree/main/examples).
+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.launch` or to write a specific launcher for TPU training!
+🤗 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
@@ -155,28 +145,46 @@ For instance, here is how you would run the GLUE example on the MRPC task (from
 accelerate launch examples/nlp_example.py
 ```
 
-This CLI tool is **optional**, and you can still use `python my_script.py` or `python -m torch.distributed.launch my_script.py` at your convenance.
+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).
 
 ## 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`.
+🤗 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 accelerator import Accelerator, DeepSpeedPlugin
+from accelerate import Accelerator, DeepSpeedPlugin
 
-# deepspeed needs to know your gradient accumulation steps before hand, so don't forget to pass it
+# 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(fp16=True, deepspeed_plugin=deepspeed_plugin)
+accelerator = Accelerator(mixed_precision='fp16', deepspeed_plugin=deepspeed_plugin)
 
 # How to save your 🤗 Transformer?
 accelerator.wait_for_everyone()
@@ -196,11 +204,11 @@ from accelerate import notebook_launcher
 notebook_launcher(training_function)
 ```
 
-An example can be found in [this notebook](https://github.com/huggingface/notebooks/blob/master/examples/accelerate/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/master/examples/accelerate/simple_nlp_example.ipynb)
+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.
+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?
 
@@ -208,17 +216,25 @@ You shouldn't use 🤗 Accelerate if you don't want to write a training loop you
 
 ## Frameworks using 🤗 Accelerate
 
-If you like the simplicity of 🤗 Accelerate but would prefer a higher-level abstraction around your training loop, some frameworks that are built on top of 🤗 Accelerate are listed below:
+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 train, and inference logic.
+* [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.
-* [pytorch-accelerated](https://github.com/Chris-hughes10/pytorch-accelerated) is a lightweight training library, with a streamlined feature set centred 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!
+* [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 3.6+ and PyTorch 1.4.0+
+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/).
 
@@ -239,5 +255,21 @@ pip install accelerate
 - multi-GPU on one node (machine)
 - multi-GPU on several nodes (machines)
 - TPU
-- FP16 with native AMP (apex on the roadmap)
-- DeepSpeed support (experimental)
+- FP16/BFloat16 mixed precision
+- FP8 mixed precision with [Transformer Engine](https://github.com/NVIDIA/TransformerEngine)
+- 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}
+}
+```

benchmarks/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 inference_acc.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.

benchmarks/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()

benchmarks/measures_util.py

@@ -0,0 +1,98 @@
+# 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
+
+
+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.cuda.empty_cache()
+
+    # CPU mem
+    measures["cpu"] = psutil.Process().memory_info().rss
+    cpu_peak_tracker.start()
+
+    # GPU mem
+    for i in range(torch.cuda.device_count()):
+        measures[str(i)] = torch.cuda.memory_allocated(i)
+    torch.cuda.reset_peak_memory_stats()
+
+    return measures
+
+
+def end_measure(start_measures):
+    # Time
+    measures = {"time": time.time() - start_measures["time"]}
+
+    gc.collect()
+    torch.cuda.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.cuda.device_count()):
+        measures[str(i)] = (torch.cuda.memory_allocated(i) - start_measures[str(i)]) / 2**20
+        measures[f"{i}-peak"] = (torch.cuda.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.cuda.device_count()):
+        print(f"- GPU {i} allocated: {measures[str(i)]:.2f}MiB")
+        peak = measures[f"{i}-peak"]
+        print(f"- GPU {i} peak: {peak:.2f}MiB")
+    print(f"- CPU RAM allocated: {measures['cpu']:.2f}MiB")
+    print(f"- CPU RAM peak: {measures['cpu-peak']:.2f}MiB")

docker/README.md

@@ -0,0 +1,73 @@
+<!---
+Copyright 2024 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.
+-->
+
+# Official Hugging Face Accelerate Docker Images
+
+Accelerate publishes a variety of docker versions as part of our CI that users can also use. These are stable images that Accelerate can run off of which comes with a variety of different setup configurations, all of which are officially hosted on [Docker Hub](https://hub.docker.com/r/huggingface/accelerate).
+
+A breakdown of each are given below
+
+## Naming Conventions
+
+Accelerate docker images follow a tagging convention of:
+
+```bash
+huggingface/accelerate:{accelerator}-{nightly,release}
+```
+
+`accelerator` in this instance is one of many applical pre-configured backend supports:
+* `gpu`: Comes compiled off of the `nvidia/cuda` image and includes core parts like `bitsandbytes`. Runs off python 3.9.
+* `cpu`: Comes compiled off of `python:3.9-slim` and is designed for non-CUDA based workloads.
+* More to come soon
+* `gpu-deepspeed`: Comes compiled off of the `nvidia/cuda` image and includes core parts like `bitsandbytes` as well as the latest `deepspeed` version. Runs off python 3.10.
+
+## Nightlies vs Releases
+
+Each release a new build is pushed with a version number included in the name. For a GPU-supported image of version 0.28.0 for instance, it would look like the following:
+
+```bash
+huggingface/accelerate:gpu-release-0.28.0
+```
+
+Nightlies contain two different image tags. There is a general `nightly` tag which is built each night, and a `nightly-YYYY-MM-DD` which corresponds to a build from a particular date.
+
+For instance, here is an example nightly CPU image from 3/14/2024
+
+```bash
+huggingface/accelerate:cpu-nightly-2024-03-14
+```
+
+## Running the images
+
+Each image comes compiled with `conda` and an `accelerate` environment contains all of the installed dependencies. 
+
+To pull down the latest nightly run:
+
+```bash
+docker pull huggingface/accelerate:gpu-nightly
+```
+
+To then run it in interactive mode with GPU-memory available, run:
+
+```bash
+docker container run --gpus all -it huggingface/accelerate:gpu-nightly
+```
+
+## DEPRECATED IMAGES
+
+CPU and GPU docker images were hosted at `huggingface/accelerate-gpu` and `huggingface/accelerate-cpu`. These builds are now outdated and will not receive updates. 
+
+The builds at the corresponding `huggingface/accelerate:{gpu,cpu}` contain the same `Dockerfile`, so it's as simple as changing the docker image to the desired ones from above. We will not be deleting these images for posterity, but they will not be receiving updates going forward.

docker/accelerate-cpu/Dockerfile

@@ -0,0 +1,35 @@
+# Builds CPU-only Docker image of PyTorch
+# Uses multi-staged approach to reduce size
+# Stage 1
+FROM python:3.8-slim as compile-image
+
+ARG DEBIAN_FRONTEND=noninteractive
+
+RUN apt update
+RUN apt-get install -y --no-install-recommends \
+    build-essential \
+    git \
+    gcc
+
+# Setup virtual environment for Docker
+ENV VIRTUAL_ENV=/opt/venv
+RUN python3 -m venv ${VIRTUAL_ENV}
+# Make sure we use the virtualenv
+ENV PATH="${VIRTUAL_ENV}/bin:$PATH"
+WORKDIR /workspace
+# Install specific CPU torch wheel to save on space
+RUN python3 -m pip install --upgrade --no-cache-dir pip
+RUN python3 -m pip install --no-cache-dir \
+    jupyter \
+    git+https://github.com/huggingface/accelerate#egg=accelerate[testing,test_trackers] \
+    --extra-index-url https://download.pytorch.org/whl/cpu
+    
+# Stage 2
+FROM python:3.8-slim AS build-image
+COPY --from=compile-image /opt/venv /opt/venv
+RUN useradd -ms /bin/bash user
+USER user
+
+# Make sure we use the virtualenv
+ENV PATH="/opt/venv/bin:$PATH"
+CMD ["/bin/bash"]

docker/accelerate-gpu-deepspeed/Dockerfile

@@ -0,0 +1,46 @@
+# Builds GPU docker image of PyTorch specifically
+# Uses multi-staged approach to reduce size
+# Stage 1
+# Use base conda image to reduce time
+FROM continuumio/miniconda3:latest AS compile-image
+# Specify py version
+# Note: DeepSpeed beyond v0.12.6 requires py 3.10
+ENV PYTHON_VERSION=3.10
+# Install apt libs
+RUN apt-get update && \
+    apt-get install -y curl git wget && \
+    apt-get clean && \
+    rm -rf /var/lib/apt/lists*
+
+# Create our conda env
+RUN conda create --name accelerate python=${PYTHON_VERSION} ipython jupyter pip
+# We don't install pytorch here yet since CUDA isn't available
+# instead we use the direct torch wheel
+ENV PATH /opt/conda/envs/accelerate/bin:$PATH
+# Activate our bash shell
+RUN chsh -s /bin/bash
+SHELL ["/bin/bash", "-c"]
+# Activate the conda env, install mpy4pi, and install torch + accelerate
+RUN source activate accelerate && conda install -c conda-forge mpi4py
+RUN source activate accelerate && \
+    python3 -m pip install --no-cache-dir \
+    git+https://github.com/huggingface/accelerate#egg=accelerate[testing,test_trackers,deepspeed] \
+    --extra-index-url https://download.pytorch.org/whl/cu117
+
+RUN python3 -m pip install --no-cache-dir bitsandbytes
+
+# Stage 2
+FROM nvidia/cuda:12.1.0-cudnn8-devel-ubuntu20.04 AS build-image
+COPY --from=compile-image /opt/conda /opt/conda
+ENV PATH /opt/conda/bin:$PATH
+
+# Install apt libs
+RUN apt-get update && \
+    apt-get install -y curl git wget && \
+    apt-get clean && \
+    rm -rf /var/lib/apt/lists*
+
+RUN echo "source activate accelerate" >> ~/.profile
+
+# Activate the virtualenv
+CMD ["/bin/bash"]

docker/accelerate-gpu/Dockerfile

@@ -0,0 +1,45 @@
+# Builds GPU docker image of PyTorch specifically
+# Uses multi-staged approach to reduce size
+# Stage 1
+# Use base conda image to reduce time
+FROM continuumio/miniconda3:latest AS compile-image
+# Specify py version
+ENV PYTHON_VERSION=3.9
+# Install apt libs
+RUN apt-get update && \
+    apt-get install -y curl git wget && \
+    apt-get clean && \
+    rm -rf /var/lib/apt/lists*
+
+# Create our conda env
+RUN conda create --name accelerate python=${PYTHON_VERSION} ipython jupyter pip
+# We don't install pytorch here yet since CUDA isn't available
+# instead we use the direct torch wheel
+ENV PATH /opt/conda/envs/accelerate/bin:$PATH
+# Activate our bash shell
+RUN chsh -s /bin/bash
+SHELL ["/bin/bash", "-c"]
+# Activate the conda env, install mpy4pi, and install torch + accelerate
+RUN source activate accelerate && conda install -c conda-forge mpi4py
+RUN source activate accelerate && \
+    python3 -m pip install --no-cache-dir \
+    git+https://github.com/huggingface/accelerate#egg=accelerate[testing,test_trackers] \
+    --extra-index-url https://download.pytorch.org/whl/cu117
+
+RUN python3 -m pip install --no-cache-dir bitsandbytes
+
+# Stage 2
+FROM nvidia/cuda:12.1.0-cudnn8-devel-ubuntu20.04 AS build-image
+COPY --from=compile-image /opt/conda /opt/conda
+ENV PATH /opt/conda/bin:$PATH
+
+# Install apt libs
+RUN apt-get update && \
+    apt-get install -y curl git wget && \
+    apt-get clean && \
+    rm -rf /var/lib/apt/lists*
+
+RUN echo "source activate accelerate" >> ~/.profile
+
+# Activate the virtualenv
+CMD ["/bin/bash"]

docs/README.md

@@ -0,0 +1,267 @@
+<!---
+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.
+-->
+
+# Generating the documentation
+
+To generate the documentation, you first have to build it. Several packages are necessary to build the doc, 
+you can install them with the following command, at the root of the code repository:
+
+```bash
+pip install -e ".[docs]"
+```
+
+Then you need to install our special tool that builds the documentation:
+
+```bash
+pip install git+https://github.com/huggingface/doc-builder
+```
+
+---
+**NOTE**
+
+You only need to generate the documentation to inspect it locally (if you're planning changes and want to
+check how they look before committing for instance). You don't have to commit the built documentation.
+
+---
+
+## Building the documentation
+
+Once you have setup the `doc-builder` and additional packages, you can generate the documentation by 
+typing the following command:
+
+```bash
+doc-builder build accelerate docs/source/ --build_dir ~/tmp/test-build
+```
+
+You can adapt the `--build_dir` to set any temporary folder that you prefer. This command will create it and generate
+the MDX files that will be rendered as the documentation on the main website. You can inspect them in your favorite
+Markdown editor.
+
+## Previewing the documentation
+
+To preview the docs, first install the `watchdog` module with:
+
+```bash
+pip install watchdog
+```
+
+Then run the following command:
+
+```bash
+doc-builder preview {package_name} {path_to_docs}
+```
+
+For example:
+
+```bash
+doc-builder preview accelerate docs/source/
+```
+
+The docs will be viewable at [http://localhost:3000](http://localhost:3000). You can also preview the docs once you have opened a PR. You will see a bot add a comment to a link where the documentation with your changes lives.
+
+---
+**NOTE**
+
+The `preview` command only works with existing doc files. When you add a completely new file, you need to update `_toctree.yml` & restart `preview` command (`ctrl-c` to stop it & call `doc-builder preview ...` again).
+
+---
+
+## Adding a new element to the navigation bar
+
+Accepted files are Markdown (.md).
+
+Create a file with its extension and put it in the source directory. You can then link it to the toc-tree by putting
+the filename without the extension in the [`_toctree.yml`](https://github.com/huggingface/accelerate/blob/main/docs/source/_toctree.yml) file.
+
+## Renaming section headers and moving sections
+
+It helps to keep the old links working when renaming the section header and/or moving sections from one document to another. This is because the old links are likely to be used in Issues, Forums, and Social media and it'd make for a much more superior user experience if users reading those months later could still easily navigate to the originally intended information.
+
+Therefore, we simply keep a little map of moved sections at the end of the document where the original section was. The key is to preserve the original anchor.
+
+So if you renamed a section from: "Section A" to "Section B", then you can add at the end of the file:
+
+```
+Sections that were moved:
+
+[ <a href="#section-b">Section A</a><a id="section-a"></a> ]
+```
+and of course, if you moved it to another file, then:
+
+```
+Sections that were moved:
+
+[ <a href="../new-file#section-b">Section A</a><a id="section-a"></a> ]
+```
+
+Use the relative style to link to the new file so that the versioned docs continue to work.
+
+
+## Writing Documentation - Specification
+
+The `huggingface/accelerate` documentation follows the
+[Google documentation](https://sphinxcontrib-napoleon.readthedocs.io/en/latest/example_google.html) style for docstrings,
+although we can write them directly in Markdown.
+
+### Adding a new tutorial
+
+Adding a new tutorial or section is done in two steps:
+
+- Add a new file under `./source`. This file can either be ReStructuredText (.rst) or Markdown (.md).
+- Link that file in `./source/_toctree.yml` on the correct toc-tree.
+
+Make sure to put your new file under the proper section. It's unlikely to go in the first section (*Get Started*), so
+depending on the intended targets (beginners, more advanced users, or researchers) it should go in sections two, three, or
+four.
+
+### Writing source documentation
+
+Values that should be put in `code` should either be surrounded by backticks: \`like so\`. Note that argument names
+and objects like True, None, or any strings should usually be put in `code`.
+
+When mentioning a class, function, or method, it is recommended to use our syntax for internal links so that our tool
+adds a link to its documentation with this syntax: \[\`XXXClass\`\] or \[\`function\`\]. This requires the class or 
+function to be in the main package.
+
+If you want to create a link to some internal class or function, you need to
+provide its path. For instance: \[\`utils.gather\`\]. This will be converted into a link with
+`utils.gather` in the description. To get rid of the path and only keep the name of the object you are
+linking to in the description, add a ~: \[\`~utils.gather\`\] will generate a link with `gather` in the description.
+
+The same works for methods so you can either use \[\`XXXClass.method\`\] or \[~\`XXXClass.method\`\].
+
+#### Defining arguments in a method
+
+Arguments should be defined with the `Args:` (or `Arguments:` or `Parameters:`) prefix, followed by a line return and
+an indentation. The argument should be followed by its type, with its shape if it is a tensor, a colon, and its
+description:
+
+```
+    Args:
+        n_layers (`int`): The number of layers of the model.
+```
+
+If the description is too long to fit in one line (more than 119 characters in total), another indentation is necessary 
+before writing the description after the argument.
+
+Finally, to maintain uniformity if any *one* description is too long to fit on one line, the 
+rest of the parameters should follow suit and have an indention before their description.
+
+Here's an example showcasing everything so far:
+
+```
+    Args:
+        gradient_accumulation_steps (`int`, *optional*, default to 1):
+            The number of steps that should pass before gradients are accumulated. A number > 1 should be combined with `Accelerator.accumulate`.
+        cpu (`bool`, *optional*):
+            Whether or not to force the script to execute on CPU. Will ignore GPU available if set to `True` and force the execution on one process only.
+```
+
+For optional arguments or arguments with defaults we follow the following syntax: imagine we have a function with the
+following signature:
+
+```
+def my_function(x: str = None, a: float = 1):
+```
+
+then its documentation should look like this:
+
+```
+    Args:
+        x (`str`, *optional*):
+            This argument controls ... and has a description longer than 119 chars.
+        a (`float`, *optional*, defaults to 1):
+            This argument is used to ... and has a description longer than 119 chars.
+```
+
+Note that we always omit the "defaults to \`None\`" when None is the default for any argument. Also note that even
+if the first line describing your argument type and its default gets long, you can't break it on several lines. You can
+however write as many lines as you want in the indented description (see the example above with `input_ids`).
+
+#### Writing a multi-line code block
+
+Multi-line code blocks can be useful for displaying examples. They are done between two lines of three backticks as usual in Markdown:
+
+
+````
+```python
+# first line of code
+# second line
+# etc
+```
+````
+
+#### Writing a return block
+
+The return block should be introduced with the `Returns:` prefix, followed by a line return and an indentation.
+The first line should be the type of the return, followed by a line return. No need to indent further for the elements
+building the return.
+
+Here's an example of a single value return:
+
+```
+    Returns:
+        `List[int]`: A list of integers in the range [0, 1] --- 1 for a special token, 0 for a sequence token.
+```
+
+Here's an example of a tuple return, comprising several objects:
+
+```
+    Returns:
+        `tuple(torch.FloatTensor)` comprising various elements depending on the configuration ([`BertConfig`]) and inputs:
+        - ** loss** (*optional*, returned when `masked_lm_labels` is provided) `torch.FloatTensor` of shape `(1,)` --
+          Total loss is the sum of the masked language modeling loss and the next sequence prediction (classification) loss.
+        - **prediction_scores** (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`) --
+          Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
+```
+
+## Styling the docstring
+
+We have an automatic script running with the `make style` comment that will make sure that:
+- the docstrings fully take advantage of the line width
+- all code examples are formatted using black, like the code of the Transformers library
+
+This script may have some weird failures if you made a syntax mistake or if you uncover a bug. Therefore, it's
+recommended to commit your changes before running `make style`, so you can revert the changes done by that script
+easily.
+
+## Writing documentation examples
+
+The syntax for Example docstrings can look as follows:
+
+```
+    Example:
+
+    ```python
+    >>> import time
+    >>> from accelerate import Accelerator
+    >>> accelerator = Accelerator()
+    >>> if accelerator.is_main_process:
+    ...     time.sleep(2)
+    >>> else:
+    ...     print("I'm waiting for the main process to finish its sleep...")
+    >>> accelerator.wait_for_everyone()
+    >>> # Should print on every process at the same time
+    >>> print("Everyone is here")
+    ```
+```
+
+The docstring should give a minimal, clear example of how the respective function 
+is to be used in inference and also include the expected (ideally sensible)
+output.
+Often, readers will try out the example before even going through the function 
+or class definitions. Therefore, it is of utmost importance that the example 
+works as expected.

docs/source/_toctree.yml

@@ -1,30 +1,121 @@
-- sections: 
+- sections:
   - local: index
     title: 🤗 Accelerate
-  - local: quicktour
-    title: Quick tour
-  - local: installation
+  - local: basic_tutorials/install
     title: Installation
-  title: Get started
+  - local: quicktour
+    title: Quicktour
+  title: Getting started
 - sections:
-  - local: sagemaker
-    title: Amazon SageMaker
-  title: Guides
+  - local: basic_tutorials/overview
+    title: Overview
+  - local: basic_tutorials/migration
+    title: Add Accelerate to your code
+  - local: basic_tutorials/execution
+    title: Execution process
+  - local: basic_tutorials/tpu
+    title: TPU training
+  - local: basic_tutorials/launch
+    title: Launching distributed code
+  - local: basic_tutorials/notebook
+    title: Launching distributed training from Jupyter Notebooks
+  title: Tutorials
 - sections:
-  - local: accelerator
+  - isExpanded: true
+    sections:
+    - local: usage_guides/explore
+      title: Start Here!
+    - local: usage_guides/model_size_estimator
+      title: Model memory estimator
+    - local: usage_guides/quantization
+      title: Model quantization
+    - local: usage_guides/tracking
+      title: Experiment trackers
+    - local: usage_guides/profiler
+      title: Profiler
+    - local: usage_guides/checkpoint
+      title: Save and load training states
+    - local: basic_tutorials/troubleshooting
+      title: Troubleshoot
+    - local: usage_guides/training_zoo
+      title: Example Zoo
+    title: Accelerate
+  - isExpanded: true
+    sections:
+    - local: usage_guides/gradient_accumulation
+      title: Gradient accumulation
+    - local: usage_guides/local_sgd
+      title: Local SGD
+    - local: usage_guides/low_precision_training
+      title: Low precision (FP8) training
+    - local: usage_guides/deepspeed
+      title: DeepSpeed
+    - local: usage_guides/ddp_comm_hook
+      title: DDP Communication Hooks
+    - local: usage_guides/fsdp
+      title: Fully Sharded Data Parallelism
+    - local: usage_guides/megatron_lm
+      title: Megatron-LM
+    - local: usage_guides/sagemaker
+      title: Amazon SageMaker
+    - local: usage_guides/mps
+      title: Apple M1 GPUs
+    - local: usage_guides/ipex
+      title: IPEX training with CPU
+    title: Training
+  - isExpanded: true
+    sections:
+    - local: usage_guides/big_modeling
+      title: Big Model Inference
+    - local: usage_guides/distributed_inference
+      title: Distributed inference
+    title: Inference
+  title: How to guides
+- sections:
+  - local: concept_guides/internal_mechanism
+    title: 🤗 Accelerate's internal mechanism
+  - local: concept_guides/big_model_inference
+    title: Loading big models into memory
+  - local: concept_guides/performance
+    title: Comparing performance across distributed setups
+  - local: concept_guides/deferring_execution
+    title: Executing and deferring jobs
+  - local: concept_guides/gradient_synchronization
+    title: Gradient synchronization
+  - local: concept_guides/fsdp_and_deepspeed
+    title: FSDP vs DeepSpeed
+  - local: concept_guides/low_precision_training
+    title: How training in low-precision environments is possible (FP8)
+  - local: concept_guides/training_tpu
+    title: TPU best practices
+  title: Concepts and fundamentals
+- sections: 
+  - local: package_reference/accelerator
     title: Accelerator
-  - local: launcher
-    title: Notebook Launcher
-  - local: kwargs
-    title: Kwargs Handlers
-  - local: internal
-    title: Internals
-  - local: checkpoint
-    title: Checkpointing
-  - local: tracking
-    title: Experiment Tracking
-  - local: fsdp
-    title: Fully Sharded Data Parallel
-  - local: memory
-    title: Memory Utilities
-  title: API Reference
+  - local: package_reference/state
+    title: Stateful configuration classes
+  - local: package_reference/cli
+    title: The Command Line
+  - local: package_reference/torch_wrappers
+    title: Torch wrapper classes
+  - local: package_reference/tracking
+    title: Experiment trackers
+  - local: package_reference/launchers
+    title: Distributed launchers
+  - local: package_reference/deepspeed
+    title: DeepSpeed utilities
+  - local: package_reference/logging
+    title: Logging
+  - local: package_reference/big_modeling
+    title: Working with large models
+  - local: package_reference/inference
+    title: Distributed inference with big models
+  - local: package_reference/kwargs
+    title: Kwargs handlers
+  - local: package_reference/utilities
+    title: Utility functions and classes
+  - local: package_reference/megatron_lm
+    title: Megatron-LM Utilities
+  - local: package_reference/fsdp
+    title: Fully Sharded Data Parallelism Utilities
+  title: "Reference"

docs/source/accelerator.mdx

@@ -1,41 +0,0 @@
-<!--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.
--->
-
-# Accelerator
-
-The [`Accelerator`] is the main class provided by 🤗 Accelerate. It serves at the main entrypoint for
-the API. To quickly adapt your script to work on any kind of setup with 🤗 Accelerate juste:
-
-1. Initialize an [`Accelerator`] object (that we will call `accelerator` in the rest of this
-   page) as early as possible in your script.
-2. Pass along your model(s), optimizer(s), dataloader(s) to the [`~Accelerator.prepare`] method.
-3. (Optional but best practice) Remove all the `.cuda()` or `.to(device)` in your code and let the
-   `accelerator` handle device placement for you.
-4. Replace the `loss.backward()` in your code by `accelerator.backward(loss)`.
-5. (Optional, when using distributed evaluation) Gather your predictions and labelsbefore storing them or using them
-   for metric computation using [`~Accelerator.gather`].
-
-This is all what is needed in most cases. For more advanced case or a nicer experience here are the functions you
-should search for and replace by the corresponding methods of your `accelerator`:
-
-- `print` statements should be replaced by [`~Accelerator.print`] to be only printed once per
-  process.
-- Use [`~Accelerator.is_local_main_process`] for statements that should be executed once per server.
-- Use [`~Accelerator.is_main_process`] for statements that should be executed once only.
-- Use [`~Accelerator.wait_for_everyone`] to make sure all processes join that point before continuing
-  (useful before a model save for instance).
-- Use [`~Accelerator.unwrap_model`] to unwrap your model before saving it.
-- Use [`~Accelerator.save`] instead of `torch.save`.
-- Use [`~Accelerator.clip_grad_norm_`] instead of `torch.nn.utils.clip_grad_norm_` and
-  [`~Accelerator.clip_grad_value_`] instead of `torch.nn.utils.clip_grad_value_`.
-
-[[autodoc]] Accelerator

docs/source/basic_tutorials/execution.md

@@ -0,0 +1,128 @@
+<!--Copyright 2024 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.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+-->
+
+# Execution process
+
+When working with distributed training systems, it is important to manage how and when processes are executed across GPUs. Some processes are completed faster than others, and some processes shouldn't begin if others haven't finished yet. Accelerate provides tools for orchestrating when processes are executed to ensure everything remains synchronized across all devices.
+
+This tutorial will teach you how to execute a process on only one machine and how to delay execution until all processes have reached a certain point.
+
+## Execute on one process
+
+Certain code only needs to be run once on a given machine, such as printing a log statement or only displaying one progress bar on the local main process.
+
+<hfoptions id="local-execution">
+<hfoption id="statements">
+
+You should use `accelerator.is_local_main_process` to indicate code that should only be executed once.
+
+```py
+from tqdm.auto import tqdm
+
+progress_bar = tqdm(range(args.max_train_steps), disable=not accelerator.is_local_main_process)
+```
+
+You could also wrap a statement with `accelerator.is_local_main_process`.
+
+> [!TIP]
+> For standalone `print` statements that aren't wrapped in `accelerator.is_local_main_process`, replace `print` with Accelerate's [`~Accelerator.print`] method to only print once per process.
+
+```py
+if accelerator.is_local_main_process:
+    print("Accelerate is the best")
+```
+
+</hfoption>
+<hfoption id="function">
+
+For a function that should only be executed once, use [`~Accelerator.on_local_main_process`].
+
+```py
+@accelerator.on_local_main_process
+def do_my_thing():
+    "Something done once per server"
+    do_thing_once_per_server()
+```
+
+</hfoption>
+</hfoptions>
+
+You could also direct Accelerate to execute code once across *all processes* regardless of the number of machines. This is useful if you're uploading a final model to the Hub.
+
+<hfoptions id="main-execution">
+<hfoption id="statement">
+
+You should use `accelerator.is_main_process` to indicate code that should only be executed once across all processes.
+
+```py
+if accelerator.is_main_process:
+    repo.push_to_hub()
+```
+
+</hfoption>
+<hfoption id="function">
+
+For a function that should only be executed once across all processes, use [`~Accelerator.on_main_process`].
+
+```py
+@accelerator.on_main_process
+def do_my_thing():
+    "Something done once per server"
+    do_thing_once()
+```
+
+</hfoption>
+</hfoptions>
+
+## Execute on a specific process
+
+Accelerate can also help you execute functions that should only be executed on a specific process or a local process index.
+
+<hfoptions id="specific-execution">
+<hfoption id="specific process">
+
+Use the [`~Accelerator.on_process`] method and specify the process index to execute a function on.
+
+```py
+@accelerator.on_process(process_index=0)
+def do_my_thing():
+    "Something done on process index 0"
+    do_thing_on_index_zero()
+```
+
+</hfoption>
+<hfoption id="local process">
+
+Use the [`~Accelerator.on_local_process`] method and specify the local process index to execute a function on.
+
+```py
+@accelerator.on_local_process(local_process_idx=0)
+def do_my_thing():
+    "Something done on process index 0 on each server"
+    do_thing_on_index_zero_on_each_server()
+```
+
+</hfoption>
+</hfoptions>
+
+## Defer execution
+
+When you run your script on several GPUs at the same time, some code may be executed faster than others. You might need to wait for all processes to reach a certain point before executing the next set of instructions. For instance, you shouldn’t save a model before making sure every process is done with training.
+
+To do this, add [`~Accelerator.wait_for_everyone`] in your code. This blocks all processes that have finished first from continuing until all remaining processes have reached the same point (this has no effect if you're running on a single GPU or CPU).
+
+```py
+accelerator.wait_for_everyone()
+```

docs/source/basic_tutorials/install.md

@@ -0,0 +1,102 @@
+<!--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.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+-->
+
+# Installation and Configuration
+
+Before you start, you will need to setup your environment, install the appropriate packages, and configure 🤗 Accelerate. 🤗 Accelerate is tested on **Python 3.8+**.
+
+## Installing 🤗 Accelerate
+
+🤗 Accelerate is available on pypi and conda, as well as on GitHub. Details to install from each are below:
+
+### pip 
+
+To install 🤗 Accelerate from pypi, perform:
+
+```bash
+pip install accelerate
+```
+
+### conda
+
+🤗 Accelerate can also be installed with conda with:
+
+```bash
+conda install -c conda-forge accelerate
+```
+
+### Source
+
+New features are added every day that haven't been released yet. To try them out yourself, install
+from the GitHub repository:
+
+```bash
+pip install git+https://github.com/huggingface/accelerate
+```
+
+If you're working on contributing to the library or wish to play with the source code and see live 
+results as you run the code, an editable version can be installed from a locally-cloned version of the 
+repository:
+
+```bash
+git clone https://github.com/huggingface/accelerate
+cd accelerate
+pip install -e .
+```
+
+## Configuring 🤗 Accelerate
+
+After installing, you need to configure 🤗 Accelerate for how the current system is setup for training. 
+To do so run the following and answer the questions prompted to you:
+
+```bash
+accelerate config
+```
+
+To write a barebones configuration that doesn't include options such as DeepSpeed configuration or running on TPUs, you can quickly run:
+
+```bash
+python -c "from accelerate.utils import write_basic_config; write_basic_config(mixed_precision='fp16')"
+```
+🤗 Accelerate will automatically utilize the maximum number of GPUs available and set the mixed precision mode.
+
+To check that your configuration looks fine, run:
+
+```bash
+accelerate env
+```
+
+An example output is shown below, which describes two GPUs on a single machine with no mixed precision being used:
+
+```bash
+- `Accelerate` version: 0.11.0.dev0
+- Platform: Linux-5.10.0-15-cloud-amd64-x86_64-with-debian-11.3
+- Python version: 3.7.12
+- Numpy version: 1.19.5
+- PyTorch version (GPU?): 1.12.0+cu102 (True)
+- `Accelerate` default config:
+        - compute_environment: LOCAL_MACHINE
+        - distributed_type: MULTI_GPU
+        - mixed_precision: no
+        - use_cpu: False
+        - num_processes: 2
+        - machine_rank: 0
+        - num_machines: 1
+        - main_process_ip: None
+        - main_process_port: None
+        - main_training_function: main
+        - deepspeed_config: {}
+        - fsdp_config: {}
+```

docs/source/basic_tutorials/launch.md

@@ -0,0 +1,232 @@
+<!--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.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+-->
+
+# Launching your 🤗 Accelerate scripts
+
+In the previous tutorial, you were introduced to how to modify your current training script to use 🤗 Accelerate.
+The final version of that code is shown below:
+
+```python
+from accelerate import Accelerator
+
+accelerator = Accelerator()
+
+model, optimizer, training_dataloader, scheduler = accelerator.prepare(
+    model, optimizer, training_dataloader, scheduler
+)
+
+for batch in training_dataloader:
+    optimizer.zero_grad()
+    inputs, targets = batch
+    outputs = model(inputs)
+    loss = loss_function(outputs, targets)
+    accelerator.backward(loss)
+    optimizer.step()
+    scheduler.step()
+```
+
+But how do you run this code and have it utilize the special hardware available to it?
+
+First, you should rewrite the above code into a function, and make it callable as a script. For example:
+
+```diff
+  from accelerate import Accelerator
+  
++ def main():
+      accelerator = Accelerator()
+
+      model, optimizer, training_dataloader, scheduler = accelerator.prepare(
+          model, optimizer, training_dataloader, scheduler
+      )
+
+      for batch in training_dataloader:
+          optimizer.zero_grad()
+          inputs, targets = batch
+          outputs = model(inputs)
+          loss = loss_function(outputs, targets)
+          accelerator.backward(loss)
+          optimizer.step()
+          scheduler.step()
+
++ if __name__ == "__main__":
++     main()
+```
+
+Next, you need to launch it with `accelerate launch`. 
+
+<Tip warning={true}>
+
+  It's recommended you run `accelerate config` before using `accelerate launch` to configure your environment to your liking. 
+  Otherwise 🤗 Accelerate will use very basic defaults depending on your system setup.
+
+</Tip>
+
+
+## Using accelerate launch
+
+🤗 Accelerate has a special CLI command to help you launch your code in your system through `accelerate launch`.
+This command wraps around all of the different commands needed to launch your script on various platforms, without you having to remember what each of them is.
+
+<Tip>
+
+  If you are familiar with launching scripts in PyTorch yourself such as with `torchrun`, you can still do this. It is not required to use `accelerate launch`.
+
+</Tip>
+
+You can launch your script quickly by using:
+
+```bash
+accelerate launch {script_name.py} --arg1 --arg2 ...
+```
+
+Just put `accelerate launch` at the start of your command, and pass in additional arguments and parameters to your script afterward like normal!
+
+Since this runs the various torch spawn methods, all of the expected environment variables can be modified here as well.
+For example, here is how to use `accelerate launch` with a single GPU:
+
+```bash
+CUDA_VISIBLE_DEVICES="0" accelerate launch {script_name.py} --arg1 --arg2 ...
+```
+
+You can also use `accelerate launch` without performing `accelerate config` first, but you may need to manually pass in the right configuration parameters.
+In this case, 🤗 Accelerate will make some hyperparameter decisions for you, e.g., if GPUs are available, it will use all of them by default without the mixed precision.
+Here is how you would use all GPUs and train with mixed precision disabled:
+
+```bash
+accelerate launch --multi_gpu {script_name.py} {--arg1} {--arg2} ...
+```
+
+Or by specifying a number of GPUs to use:
+
+```bash
+accelerate launch --num_processes=2 {script_name.py} {--arg1} {--arg2} ...
+```
+
+To get more specific you should pass in the needed parameters yourself. For instance, here is how you 
+would also launch that same script on two GPUs using mixed precision while avoiding all of the warnings: 
+
+```bash
+accelerate launch --multi_gpu --mixed_precision=fp16 --num_processes=2 {script_name.py} {--arg1} {--arg2} ...
+```
+
+For a complete list of parameters you can pass in, run:
+
+```bash
+accelerate launch -h
+```
+
+<Tip>
+
+  Even if you are not using 🤗 Accelerate in your code, you can still use the launcher for starting your scripts!
+
+</Tip>
+
+For a visualization of this difference, that earlier `accelerate launch` on multi-gpu would look something like so with `torchrun`:
+
+```bash
+MIXED_PRECISION="fp16" torchrun --nproc_per_node=2 --num_machines=1 {script_name.py} {--arg1} {--arg2} ...
+```
+
+You can also launch your script utilizing the launch CLI as a python module itself, enabling the ability to pass in other python-specific
+launching behaviors. To do so, use `accelerate.commands.launch` instead of `accelerate launch`:
+
+```bash
+python -m accelerate.commands.launch --num_processes=2 {script_name.py} {--arg1} {--arg2}
+```
+
+If you want to execute the script with any other python flags, you can pass them in as well similar to `-m`, such as 
+the below example enabling unbuffered stdout and stderr:
+
+```bash
+python -u -m accelerate.commands.launch --num_processes=2 {script_name.py} {--arg1} {--arg2}
+```
+
+<Tip>
+
+  You can run your code on CPU as well! This is helpful for debugging and testing purposes on toy models and datasets. 
+
+```bash
+accelerate launch --cpu {script_name.py} {--arg1} {--arg2}
+```  
+
+</Tip>
+
+## Why you should always use `accelerate config`
+
+Why is it useful to the point you should **always** run `accelerate config`? 
+
+Remember that earlier call to `accelerate launch` as well as `torchrun`?
+Post configuration, to run that script with the needed parts you just need to use `accelerate launch` outright, without passing anything else in:
+
+```bash
+accelerate launch {script_name.py} {--arg1} {--arg2} ...
+```
+
+
+## Custom Configurations
+
+As briefly mentioned earlier, `accelerate launch` should be mostly used through combining set configurations 
+made with the `accelerate config` command. These configs are saved to a `default_config.yaml` file in your cache folder for 🤗 Accelerate. 
+This cache folder is located at (with decreasing order of priority):
+
+- The content of your environment variable `HF_HOME` suffixed with `accelerate`.
+- If it does not exist, the content of your environment variable `XDG_CACHE_HOME` suffixed with
+  `huggingface/accelerate`.
+- If this does not exist either, the folder `~/.cache/huggingface/accelerate`.
+
+To have multiple configurations, the flag `--config_file` can be passed to the `accelerate launch` command paired 
+with the location of the custom yaml. 
+
+An example yaml may look something like the following for two GPUs on a single machine using `fp16` for mixed precision:
+```yaml
+compute_environment: LOCAL_MACHINE
+deepspeed_config: {}
+distributed_type: MULTI_GPU
+fsdp_config: {}
+machine_rank: 0
+main_process_ip: null
+main_process_port: null
+main_training_function: main
+mixed_precision: fp16
+num_machines: 1
+num_processes: 2
+use_cpu: false
+```
+
+Launching a script from the location of that custom yaml file looks like the following:
+```bash
+accelerate launch --config_file {path/to/config/my_config_file.yaml} {script_name.py} {--arg1} {--arg2} ...
+```
+
+## Multi-node training
+Multi-node training with 🤗Accelerate is similar to [multi-node training with torchrun](https://pytorch.org/tutorials/intermediate/ddp_series_multinode.html). The simplest way to launch a multi-node training run is to do the following:
+
+- Copy your codebase and data to all nodes. (or place them on a shared filesystem)
+- Setup your python packages on all nodes.
+- Run `accelerate config` on the main single node first. After specifying the number of nodes, you will be asked to specify the rank of each node (this will be 0 for the main/master node), along with the IP address and port for the main process. This is required for the worker nodes to communicate with the main process. Afterwards, you can copy or send this config file across all of your nodes, changing the `machine_rank` to 1, 2,3, etc. to avoid having to run the command (or just follow their directions directly for launching with `torchrun` as well)
+
+Once you have done this, you can start your multi-node training run by running `accelerate launch` (or `torchrun`) on all nodes.
+
+<Tip>
+    It is required that the command be ran on all nodes for everything to start, not just running it from the main node. You can use something like SLURM or a different process executor to wrap around this requirement and call everything from a single command.
+</Tip>
+
+<Tip>
+
+ It is recommended to use the intranet IP of your main node over the public IP for better latency. This is the `192.168.x.x` or the `172.x.x.x` address you see when you run `hostname -I` on the main node.
+
+</Tip>
+
+To get a better idea about multi-node training, check out our example for [multi-node training with FSDP](https://huggingface.co/blog/ram-efficient-pytorch-fsdp).

docs/source/basic_tutorials/migration.md

@@ -0,0 +1,221 @@
+<!--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.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+-->
+
+# Add Accelerate to your code
+
+Each distributed training framework has their own way of doing things which can require writing a lot of custom code to adapt it to your PyTorch training code and training environment. Accelerate offers a friendly way to interface with these distributed training frameworks without having to learn the specific details of each one. Accelerate takes care of those details for you, so you can focus on the training code and scale it to any distributed training environment.
+
+In this tutorial, you'll learn how to adapt your existing PyTorch code with Accelerate and get you on your way toward training on distributed systems with ease! You'll start with a basic PyTorch training loop (it assumes all the training objects like `model` and `optimizer` have been setup already) and progressively integrate Accelerate into it.
+
+```python
+device = "cuda"
+model.to(device)
+
+for batch in training_dataloader:
+    optimizer.zero_grad()
+    inputs, targets = batch
+    inputs = inputs.to(device)
+    targets = targets.to(device)
+    outputs = model(inputs)
+    loss = loss_function(outputs, targets)
+    loss.backward()
+    optimizer.step()
+    scheduler.step()
+```
+
+## Accelerator
+
+The [`Accelerator`] is the main class for adapting your code to work with Accelerate. It knows about the distributed setup you're using such as the number of different processes and your hardware type. This class also provides access to many of the necessary methods for enabling your PyTorch code to work in any distributed training environment and for managing and executing processes across devices.
+
+That's why you should always start by importing and creating an [`Accelerator`] instance in your script.
+
+```python
+from accelerate import Accelerator
+
+accelerator = Accelerator()
+```
+
+The [`Accelerator`] also knows which device to move your PyTorch objects to, so it is recommended to let Accelerate handle this for you.
+
+```diff
+- device = "cuda"
++ device = accelerator.device
+  model.to(device)
+```
+
+## Prepare PyTorch objects
+
+Next, you need to prepare your PyTorch objects (model, optimizer, scheduler, etc.) for distributed training. The [`~Accelerator.prepare`] method takes care of placing your model in the appropriate container (like single GPU or multi-GPU) for your training setup, adapting the optimizer and scheduler to use Accelerate's [`~optimizer.AcceleratedOptimizer`] and [`~scheduler.AcceleratedScheduler`], and creating a new dataloader that can be sharded across processes.
+
+> [!TIP]
+> Accelerate only prepares objects that inherit from their respective PyTorch classes such as `torch.optim.Optimizer`.
+
+The PyTorch objects are returned in the same order they're sent.
+
+```py
+model, optimizer, training_dataloader, scheduler = accelerator.prepare(
+    model, optimizer, training_dataloader, scheduler
+)
+```
+
+## Training loop
+
+Finally, remove the `to(device)` calls to the inputs and targets in the training loop because Accelerate's DataLoader classes automatically places them on the right device. You should also replace the usual `backward()` pass with Accelerate's [`~Accelerator.backward`] method which scales the gradients for you and uses the appropriate `backward()` method depending on your distributed setup (for example, DeepSpeed or Megatron).
+
+```diff
+-   inputs = inputs.to(device)
+-   targets = targets.to(device)
+    outputs = model(inputs)
+    loss = loss_function(outputs, targets)
+-   loss.backward()
++   accelerator.backward(loss)
+```
+
+Put everything together and your new Accelerate training loop should now look like this!
+
+```python
+from accelerate import Accelerator
+accelerator = Accelerator()
+
+device = accelerator.device
+model, optimizer, training_dataloader, scheduler = accelerator.prepare(
+    model, optimizer, training_dataloader, scheduler
+)
+
+for batch in training_dataloader:
+    optimizer.zero_grad()
+    inputs, targets = batch
+    outputs = model(inputs)
+    loss = loss_function(outputs, targets)
+    accelerator.backward(loss)
+    optimizer.step()
+    scheduler.step()
+```
+
+## Training features
+
+Accelerate offers additional features - like gradient accumulation, gradient clipping, mixed precision training and more - you can add to your script to improve your training run. Let's explore these three features.
+
+### Gradient accumulation
+
+Gradient accumulation enables you to train on larger batch sizes by accumulating the gradients over multiple batches before updating the weights. This can be useful for getting around memory limitations. To enable this feature in Accelerate, specify the `gradient_accumulation_steps` parameter in the [`Accelerator`] class and add the [`~Accelerator.accumulate`] context manager to your script.
+
+```diff
++ accelerator = Accelerator(gradient_accumulation_steps=2)
+  model, optimizer, training_dataloader = accelerator.prepare(model, optimizer, training_dataloader)
+
+  for input, label in training_dataloader:
++     with accelerator.accumulate(model):
+          predictions = model(input)
+          loss = loss_function(predictions, label)
+          accelerator.backward(loss)
+          optimizer.step()
+          scheduler.step()
+          optimizer.zero_grad()
+```
+
+### Gradient clipping
+
+Gradient clipping is a technique to prevent "exploding gradients", and Accelerate offers:
+
+* [`~Accelerator.clip_grad_value_`] to clip gradients to a minimum and maximum value
+* [`~Accelerator.clip_grad_norm_`] for normalizing gradients to a certain value
+
+### Mixed precision
+
+Mixed precision accelerates training by using a lower precision data type like fp16 (half-precision) to calculate the gradients. For the best performance with Accelerate, the loss should be computed inside your model (like in Transformers models) because computations outside of the model are computed in full precision.
+
+Set the mixed precision type to use in the [`Accelerator`], and then use the [`~Accelerator.autocast`] context manager to automatically cast the values to the specified data type.
+
+> [!WARNING]
+> Accelerate enables automatic mixed precision, so [`~Accelerator.autocast`] is only needed if there are other mixed precision operations besides those performed on loss by [`~Accelerator.backward`] which already handles the scaling.
+
+```diff
++ accelerator = Accelerator(mixed_precision="fp16")
++ with accelerator.autocast():
+      loss = complex_loss_function(outputs, target):
+```
+
+## Save and load
+
+Accelerate can also save and load a *model* once training is complete or you can also save the model and optimizer *state* which could be useful for resuming training.
+
+### Model
+
+Once all processes are complete, unwrap the model with the [`~Accelerator.unwrap_model`] method before saving it because the [`~Accelerator.prepare`] method wrapped your model into the proper interface for distributed training. If you don't unwrap the model, saving the model state dictionary also saves any potential extra layers from the larger model and you won't be able to load the weights back into your base model.
+
+You should use the [`~Accelerator.save_model`] method to unwrap and save the model state dictionary. This method can also save a model into sharded checkpoints or into the [safetensors](https://hf.co/docs/safetensors/index) format.
+
+<hfoptions id="save">
+<hfoption id="single checkpoint">
+
+```py
+accelerator.wait_for_everyone()
+accelerator.save_model(model, save_directory)
+```
+
+<Tip>
+
+For models from the [Transformers](https://hf.co/docs/transformers/index) library, save the model with the [`~transformers.PreTrainedModel.save_pretrained`] method so that it can be reloaded with the [`~transformers.PreTrainedModel.from_pretrained`] method.
+
+```py
+from transformers import AutoModel
+
+unwrapped_model = accelerator.unwrap_model(model)
+unwrapped_model.save_pretrained(
+    "path/to/my_model_directory",
+    is_main_process=accelerator.is_main_process,
+    save_function=accelerator.save,
+)
+
+model = AutoModel.from_pretrained("path/to/my_model_directory")
+```
+
+</Tip>
+
+To load your weights, use the [`~Accelerator.unwrap_model`] method to unwrap the model first before loading the weights. All model parameters are references to tensors, so this loads your weights inside `model`.
+
+```py
+unwrapped_model = accelerator.unwrap_model(model)
+path_to_checkpoint = os.path.join(save_directory,"pytorch_model.bin")
+unwrapped_model.load_state_dict(torch.load(path_to_checkpoint))
+```
+
+</hfoption>
+<hfoption id="sharded checkpoint">
+
+Set `safe_serialization=True` to save the model in the safetensor format.
+
+```py
+accelerator.wait_for_everyone()
+accelerator.save_model(model, save_directory, max_shard_size="1GB", safe_serialization=True)
+```
+
+To load a sharded checkpoint or a safetensor formatted checkpoint, use the [`~accelerate.load_checkpoint_in_model`] method. This method allows you to load a checkpoint onto a specific device.
+
+```py
+load_checkpoint_in_model(unwrapped_model, save_directory, device_map={"":device})
+```
+
+</hfoption>
+</hfoptions>
+
+### State
+
+During training, you may want to save the current state of the model, optimizer, random generators, and potentially learning rate schedulers so they can be restored in the *same script*. You should add the [`~Accelerator.save_state`] and [`~Accelerator.load_state`] methods to your script to save and load states.
+
+To further customize where and how states are saved through [`~Accelerator.save_state`], use the [`~utils.ProjectConfiguration`] class. For example, if `automatic_checkpoint_naming` is enabled, each saved checkpoint is stored at `Accelerator.project_dir/checkpoints/checkpoint_{checkpoint_number}`.
+
+Any other stateful items to be stored should be registered with the [`~Accelerator.register_for_checkpointing`] method so they can be saved and loaded. Every object passed to this method to be stored must have a `load_state_dict` and `state_dict` function.

docs/source/basic_tutorials/notebook.md

@@ -0,0 +1,476 @@
+<!--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.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+-->
+
+# Launching Multi-GPU Training from a Jupyter Environment
+
+This tutorial teaches you how to fine tune a computer vision model with 🤗 Accelerate from a Jupyter Notebook on a distributed system.
+You will also learn how to setup a few requirements needed for ensuring your environment is configured properly, your data has been prepared properly, and finally how to launch training.
+
+<Tip>
+
+    This tutorial is also available as a Jupyter Notebook [here](https://github.com/huggingface/notebooks/blob/main/examples/accelerate_examples/simple_cv_example.ipynb)
+
+</Tip>
+
+## Configuring the Environment
+
+Before any training can be performed, a 🤗 Accelerate config file must exist in the system. Usually this can be done by running the following in a terminal and answering the prompts:
+
+```bash
+accelerate config
+```
+
+However, if general defaults are fine and you are *not* running on a TPU, 🤗Accelerate has a utility to quickly write your GPU configuration into a config file via [`utils.write_basic_config`].
+
+The following code will restart Jupyter after writing the configuration, as CUDA code was called to perform this. 
+
+<Tip warning={true}>
+
+    CUDA can't be initialized more than once on a multi-GPU system. It's fine to debug in the notebook and have calls to CUDA, but in order to finally train a full cleanup and restart will need to be performed.
+    
+</Tip>
+
+```python
+import os
+from accelerate.utils import write_basic_config
+
+write_basic_config()  # Write a config file
+os._exit(00)  # Restart the notebook
+```
+
+## Preparing the Dataset and Model
+
+Next you should prepare your dataset. As mentioned at earlier, great care should be taken when preparing the `DataLoaders` and model to make sure that **nothing** is put on *any* GPU. 
+
+If you do, it is recommended to put that specific code into a function and call that from within the notebook launcher interface, which will be shown later. 
+
+Make sure the dataset is downloaded based on the directions [here](https://github.com/huggingface/accelerate/tree/main/examples#simple-vision-example)
+
+```python
+import os, re, torch, PIL
+import numpy as np
+
+from torch.optim.lr_scheduler import OneCycleLR
+from torch.utils.data import DataLoader, Dataset
+from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor
+
+from accelerate import Accelerator
+from accelerate.utils import set_seed
+from timm import create_model
+```
+
+First you need to create a function to extract the class name based on a filename:
+
+```python
+import os
+
+data_dir = "../../images"
+fnames = os.listdir(data_dir)
+fname = fnames[0]
+print(fname)
+```
+
+```python out
+beagle_32.jpg
+```
+
+In the case here, the label is `beagle`. Using regex you can extract the label from the filename:
+
+```python
+import re
+
+
+def extract_label(fname):
+    stem = fname.split(os.path.sep)[-1]
+    return re.search(r"^(.*)_\d+\.jpg$", stem).groups()[0]
+```
+
+```python
+extract_label(fname)
+```
+
+And you can see it properly returned the right name for our file:
+
+```python out
+"beagle"
+```
+
+Next a `Dataset` class should be made to handle grabbing the image and the label:
+
+```python
+class PetsDataset(Dataset):
+    def __init__(self, file_names, image_transform=None, label_to_id=None):
+        self.file_names = file_names
+        self.image_transform = image_transform
+        self.label_to_id = label_to_id
+
+    def __len__(self):
+        return len(self.file_names)
+
+    def __getitem__(self, idx):
+        fname = self.file_names[idx]
+        raw_image = PIL.Image.open(fname)
+        image = raw_image.convert("RGB")
+        if self.image_transform is not None:
+            image = self.image_transform(image)
+        label = extract_label(fname)
+        if self.label_to_id is not None:
+            label = self.label_to_id[label]
+        return {"image": image, "label": label}
+```
+
+Now to build the dataset. Outside the training function you can find and declare all the filenames and labels and use them as references inside the 
+launched function:
+
+```python
+fnames = [os.path.join("../../images", fname) for fname in fnames if fname.endswith(".jpg")]
+```
+
+Next gather all the labels:
+
+```python
+all_labels = [extract_label(fname) for fname in fnames]
+id_to_label = list(set(all_labels))
+id_to_label.sort()
+label_to_id = {lbl: i for i, lbl in enumerate(id_to_label)}
+```
+
+Next, you should make a `get_dataloaders` function that will return your built dataloaders for you. As mentioned earlier, if data is automatically 
+sent to the GPU or a TPU device when building your `DataLoaders`, they must be built using this method. 
+
+```python
+def get_dataloaders(batch_size: int = 64):
+    "Builds a set of dataloaders with a batch_size"
+    random_perm = np.random.permutation(len(fnames))
+    cut = int(0.8 * len(fnames))
+    train_split = random_perm[:cut]
+    eval_split = random_perm[cut:]
+
+    # For training a simple RandomResizedCrop will be used
+    train_tfm = Compose([RandomResizedCrop((224, 224), scale=(0.5, 1.0)), ToTensor()])
+    train_dataset = PetsDataset([fnames[i] for i in train_split], image_transform=train_tfm, label_to_id=label_to_id)
+
+    # For evaluation a deterministic Resize will be used
+    eval_tfm = Compose([Resize((224, 224)), ToTensor()])
+    eval_dataset = PetsDataset([fnames[i] for i in eval_split], image_transform=eval_tfm, label_to_id=label_to_id)
+
+    # Instantiate dataloaders
+    train_dataloader = DataLoader(train_dataset, shuffle=True, batch_size=batch_size, num_workers=4)
+    eval_dataloader = DataLoader(eval_dataset, shuffle=False, batch_size=batch_size * 2, num_workers=4)
+    return train_dataloader, eval_dataloader
+```
+
+Finally, you should import the scheduler to be used later:
+
+```python
+from torch.optim.lr_scheduler import CosineAnnealingLR
+```
+
+## Writing the Training Function
+
+Now you can build the training loop. [`notebook_launcher`] works by passing in a function to call that will be ran across the distributed system.
+
+Here is a basic training loop for the animal classification problem:
+
+<Tip>
+
+    The code has been split up to allow for explanations on each section. A full version that can be copy and pasted will be available at the end
+
+</Tip>
+
+
+```python
+def training_loop(mixed_precision="fp16", seed: int = 42, batch_size: int = 64):
+    set_seed(seed)
+    accelerator = Accelerator(mixed_precision=mixed_precision)
+```
+
+First you should set the seed and create an [`Accelerator`] object as early in the training loop as possible.
+
+<Tip warning={true}>
+
+    If training on the TPU, your training loop should take in the model as a parameter and it should be instantiated 
+    outside of the training loop function. See the [TPU best practices](../concept_guides/training_tpu) 
+    to learn why
+
+</Tip>
+
+Next you should build your dataloaders and create your model:
+
+```python
+    train_dataloader, eval_dataloader = get_dataloaders(batch_size)
+    model = create_model("resnet50d", pretrained=True, num_classes=len(label_to_id))
+```
+
+<Tip>
+
+    You build the model here so that the seed also controls the new weight initialization
+
+</Tip>
+
+As you are performing transfer learning in this example, the encoder of the model starts out frozen so the head of the model can be 
+trained only initially:
+
+```python
+    for param in model.parameters():
+        param.requires_grad = False
+    for param in model.get_classifier().parameters():
+        param.requires_grad = True
+```
+
+Normalizing the batches of images will make training a little faster:
+
+```python
+    mean = torch.tensor(model.default_cfg["mean"])[None, :, None, None]
+    std = torch.tensor(model.default_cfg["std"])[None, :, None, None]
+```
+
+To make these constants available on the active device, you should set it to the Accelerator's device:
+
+```python
+    mean = mean.to(accelerator.device)
+    std = std.to(accelerator.device)
+```
+
+Next instantiate the rest of the PyTorch classes used for training:
+
+```python
+    optimizer = torch.optim.Adam(params=model.parameters(), lr=3e-2 / 25)
+    lr_scheduler = OneCycleLR(optimizer=optimizer, max_lr=3e-2, epochs=5, steps_per_epoch=len(train_dataloader))
+```
+
+Before passing everything to [`~Accelerator.prepare`].
+
+<Tip>
+
+    There is no specific order to remember, you just need to unpack the objects in the same order you gave them to the prepare method.
+
+</Tip>
+
+```python
+    model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = accelerator.prepare(
+        model, optimizer, train_dataloader, eval_dataloader, lr_scheduler
+    )
+```
+
+Now train the model:
+
+```python
+    for epoch in range(5):
+        model.train()
+        for batch in train_dataloader:
+            inputs = (batch["image"] - mean) / std
+            outputs = model(inputs)
+            loss = torch.nn.functional.cross_entropy(outputs, batch["label"])
+            accelerator.backward(loss)
+            optimizer.step()
+            lr_scheduler.step()
+            optimizer.zero_grad()
+```
+
+The evaluation loop will look slightly different compared to the training loop. The number of elements passed as well as the overall 
+total accuracy of each batch will be added to two constants:
+
+```python
+        model.eval()
+        accurate = 0
+        num_elems = 0
+```
+
+Next you have the rest of your standard PyTorch loop:
+
+```python
+        for batch in eval_dataloader:
+            inputs = (batch["image"] - mean) / std
+            with torch.no_grad():
+                outputs = model(inputs)
+            predictions = outputs.argmax(dim=-1)
+```
+
+Before finally the last major difference. 
+
+When performing distributed evaluation, the predictions and labels need to be passed through 
+[`~Accelerator.gather`] so that all of the data is available on the current device and a properly calculated metric can be achieved:
+
+```python
+            accurate_preds = accelerator.gather(predictions) == accelerator.gather(batch["label"])
+            num_elems += accurate_preds.shape[0]
+            accurate += accurate_preds.long().sum()
+```
+
+Now you just need to calculate the actual metric for this problem, and you can print it on the main process using [`~Accelerator.print`]:
+
+```python
+        eval_metric = accurate.item() / num_elems
+        accelerator.print(f"epoch {epoch}: {100 * eval_metric:.2f}")
+```
+
+A full version of this training loop is available below:
+
+```python
+def training_loop(mixed_precision="fp16", seed: int = 42, batch_size: int = 64):
+    set_seed(seed)
+    # Initialize accelerator
+    accelerator = Accelerator(mixed_precision=mixed_precision)
+    # Build dataloaders
+    train_dataloader, eval_dataloader = get_dataloaders(batch_size)
+
+    # Instantiate the model (you build the model here so that the seed also controls new weight initaliziations)
+    model = create_model("resnet50d", pretrained=True, num_classes=len(label_to_id))
+
+    # Freeze the base model
+    for param in model.parameters():
+        param.requires_grad = False
+    for param in model.get_classifier().parameters():
+        param.requires_grad = True
+
+    # You can normalize the batches of images to be a bit faster
+    mean = torch.tensor(model.default_cfg["mean"])[None, :, None, None]
+    std = torch.tensor(model.default_cfg["std"])[None, :, None, None]
+
+    # To make these constants available on the active device, set it to the accelerator device
+    mean = mean.to(accelerator.device)
+    std = std.to(accelerator.device)
+
+    # Instantiate the optimizer
+    optimizer = torch.optim.Adam(params=model.parameters(), lr=3e-2 / 25)
+
+    # Instantiate the learning rate scheduler
+    lr_scheduler = OneCycleLR(optimizer=optimizer, max_lr=3e-2, epochs=5, steps_per_epoch=len(train_dataloader))
+
+    # Prepare everything
+    # There is no specific order to remember, you just need to unpack the objects in the same order you gave them to the
+    # prepare method.
+    model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = accelerator.prepare(
+        model, optimizer, train_dataloader, eval_dataloader, lr_scheduler
+    )
+
+    # Now you train the model
+    for epoch in range(5):
+        model.train()
+        for batch in train_dataloader:
+            inputs = (batch["image"] - mean) / std
+            outputs = model(inputs)
+            loss = torch.nn.functional.cross_entropy(outputs, batch["label"])
+            accelerator.backward(loss)
+            optimizer.step()
+            lr_scheduler.step()
+            optimizer.zero_grad()
+
+        model.eval()
+        accurate = 0
+        num_elems = 0
+        for batch in eval_dataloader:
+            inputs = (batch["image"] - mean) / std
+            with torch.no_grad():
+                outputs = model(inputs)
+            predictions = outputs.argmax(dim=-1)
+            accurate_preds = accelerator.gather(predictions) == accelerator.gather(batch["label"])
+            num_elems += accurate_preds.shape[0]
+            accurate += accurate_preds.long().sum()
+
+        eval_metric = accurate.item() / num_elems
+        # Use accelerator.print to print only on the main process.
+        accelerator.print(f"epoch {epoch}: {100 * eval_metric:.2f}")
+```
+
+## Using the notebook_launcher
+
+All that's left is to use the [`notebook_launcher`].
+
+You pass in the function, the arguments (as a tuple), and the number of processes to train on. (See the [documentation](../package_reference/launchers) for more information)
+
+```python
+from accelerate import notebook_launcher
+```
+
+```python
+args = ("fp16", 42, 64)
+notebook_launcher(training_loop, args, num_processes=2)
+```
+
+In the case of running on multiple nodes, you need to set up a Jupyter session at each node and run the launching cell at the same time.
+
+For an environment containing 2 nodes (computers) with 8 GPUs each and the main computer with an IP address of "172.31.43.8", it would look like so:
+
+```python
+notebook_launcher(training_loop, args, master_addr="172.31.43.8", node_rank=0, num_nodes=2, num_processes=8)
+```
+
+And in the second Jupyter session on the other machine:
+
+<Tip>
+
+    Notice how the `node_rank` has changed
+
+</Tip>
+
+```python
+notebook_launcher(training_loop, args, master_addr="172.31.43.8", node_rank=1, num_nodes=2, num_processes=8)
+```
+
+In the case of running on the TPU, it would look like so:
+
+```python
+model = create_model("resnet50d", pretrained=True, num_classes=len(label_to_id))
+
+args = (model, "fp16", 42, 64)
+notebook_launcher(training_loop, args, num_processes=8)
+```
+
+To launch the training process with elasticity, enabling fault tolerance, you can use the `elastic_launch` feature provided by PyTorch. This requires setting additional parameters such as `rdzv_backend` and `max_restarts`. Here is an example of how to use `notebook_launcher` with elastic capabilities:
+
+```python
+notebook_launcher(
+    training_loop,
+    args,
+    num_processes=2,
+    max_restarts=3
+)
+```
+
+As it's running it will print the progress as well as state how many devices you ran on. This tutorial was ran with two GPUs:
+
+```python out
+Launching training on 2 GPUs.
+epoch 0: 88.12
+epoch 1: 91.73
+epoch 2: 92.58
+epoch 3: 93.90
+epoch 4: 94.71
+```
+
+And that's it!
+
+Please note that [`notebook_launcher`] ignores the 🤗 Accelerate config file, to launch based on the config use:
+
+```bash
+accelerate launch
+```
+
+## Debugging 
+
+A common issue when running the `notebook_launcher` is receiving a CUDA has already been initialized issue. This usually stems
+from an import or prior code in the notebook that makes a call to the PyTorch `torch.cuda` sublibrary. To help narrow down what went wrong,
+you can launch the `notebook_launcher` with `ACCELERATE_DEBUG_MODE=yes` in your environment and an additional check
+will be made when spawning that a regular process can be created and utilize CUDA without issue. (Your CUDA code can still be ran afterwards).
+
+## Conclusion
+
+This notebook showed how to perform distributed training from inside of a Jupyter Notebook. Some key notes to remember:
+
+- Make sure to save any code that use CUDA (or CUDA imports) for the function passed to [`notebook_launcher`]
+- Set the `num_processes` to be the number of devices used for training (such as number of GPUs, CPUs, TPUs, etc)
+- If using the TPU, declare your model outside the training loop function

docs/source/basic_tutorials/overview.md

@@ -0,0 +1,24 @@
+<!--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.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+-->
+
+# Overview
+
+Welcome to the 🤗 Accelerate tutorials! These introductory guides will help catch you up to speed on working with 🤗 Accelerate.
+You'll learn how to modify your code to have it work with the API seamlessly, how to launch your script properly,
+and more!
+
+These tutorials assume some basic knowledge of Python and familiarity with the PyTorch framework.
+
+If you have any questions about 🤗 Accelerate, feel free to join and ask the community on our [forum](https://discuss.huggingface.co/c/accelerate/18).

docs/source/basic_tutorials/tpu.md

@@ -0,0 +1,38 @@
+<!--Copyright 2024 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.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+-->
+
+# TPU training
+
+A [TPU (Tensor Processing Unit)](https://cloud.google.com/tpu/docs/intro-to-tpu) is a type of hardware specifically designed for training models efficiently. Accelerate supports TPU training, but there are a few things you should be aware of, namely graph compilation. This tutorial briefly discusses compilation, and for more details, take a look at the [Training on TPUs with Accelerate](../concept_guides/training_tpu) guide.
+
+## Compilation
+
+A TPU creates a graph of all the operations in the training step such as the forward pass, backward pass and optimizer step. This is why the first training step always takes a while because building and compiling this graph takes time. But once compilation is complete, it is cached and all subsequent steps are much faster.
+
+The key is to avoid compiling your code again or else training is super slow. This means all your operations must be exactly the same:
+
+* all tensors in your batches must have the same length (for example, no dynamic padding for NLP tasks)
+* your code must be static (for example, no layers with for loops that have different lengths depending on the input such as a LSTM)
+
+## Weight tying
+
+A common language model design is to tie the weights of the embedding and softmax layers. However, moving the model to a TPU (either yourself or passing it to the [`~Accelerator.prepare`] method) breaks the weight tying and you'll need to retie the weights.
+
+To add special behavior (like weight tying) in your script for TPUs, set [`~Accelerator.distributed_type`] to `DistributedType.TPU` first. Then you can use the [`~transformers.PreTrainedModel.tie_weights`] method to tie the weights.
+
+```py
+if accelerator.distributed_type == DistributedType.TPU:
+    model.tie_weights()
+```

docs/source/basic_tutorials/troubleshooting.md

@@ -0,0 +1,211 @@
+<!--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.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+-->
+
+# Troubleshoot
+
+This guide provides solutions to some issues you might encounter when using Accelerate. Not all errors are covered because Accelerate is an active library that is continuously evolving and there are many different use cases and distributed training setups. If the solutions described here don't help with your specific error, please take a look at the [Ask for help](#ask-for-help) section to learn where and how to get help.
+
+## Logging
+
+Logging can help you identify where an error is coming from. In a distributed setup with multiple processes, logging can be a challenge, but Accelerate provides the [`~accelerate.logging`] utility to ensure logs are synchronized.
+
+To troubleshoot an issue, use [`~accelerate.logging`] instead of the standard Python [`logging`](https://docs.python.org/3/library/logging.html#module-logging) module. Set the verbosity level (`INFO`, `DEBUG`, `WARNING`, `ERROR`, `CRITICAL`) with the `log_level` parameter, and then you can either:
+
+1. Export the `log_level` as the `ACCELERATE_LOG_LEVEL` environment variable.
+2. Pass the `log_level` directly to `get_logger`.
+
+For example, to set `log_level="INFO"`:
+
+```py
+from accelerate.logging import get_logger
+
+logger = get_logger(__name__, log_level="DEBUG")
+```
+
+By default, the log is called on main processes only. To call it on all processes, pass `main_process_only=False`.
+If a log should be called on all processes and in order, also pass `in_order=True`.
+
+```py
+from accelerate.logging import get_logger
+
+logger = get_logger(__name__, log_level="DEBUG")
+# log all processes
+logger.debug("thing_to_log", main_process_only=False)
+# log all processes in order
+logger.debug("thing_to_log", main_process_only=False, in_order=True)
+```
+
+## Hanging code and timeout errors
+
+There can be many reasons why your code is hanging. Let's take a look at how to solve some of the most common issues that can cause your code to hang.
+
+### Mismatched tensor shapes
+
+Mismatched tensor shapes is a common issue that can cause your code to hang for a significant amount of time on a distributed setup.
+
+When running scripts in a distributed setup, functions such as [`Accelerator.gather`] and [`Accelerator.reduce`] are necessary to grab tensors across devices to collectively perform operations on them. These (and other) functions rely on `torch.distributed` to perform a `gather` operation, which requires tensors to have the **exact same shape** across all processes. When the tensor shapes don't match, your code hangs and you'll eventually hit a timeout exception.
+
+You can use Accelerate's operational debug mode to immediately catch this issue. We recommend enabling this mode during the `accelerate config` setup, but you can also enable it from the CLI, as an environment variable, or by manually editing the `config.yaml` file.
+
+<hfoptions id="mismatch">
+<hfoption id="CLI">
+
+```bash
+accelerate launch --debug {my_script.py} --arg1 --arg2
+```
+
+</hfoption>
+<hfoption id="environment variable">
+
+If enabling debug mode as an environment variable, you don't need to call `accelerate launch`.
+
+```bash
+ACCELERATE_DEBUG_MODE="1" torchrun {my_script.py} --arg1 --arg2
+```
+
+</hfoption>
+<hfoption id="config.yaml">
+
+Add `debug: true` to your `config.yaml` file.
+
+```yaml
+compute_environment: LOCAL_MACHINE
+debug: true
+```
+
+</hfoption>
+</hfoptions>
+
+Once you enable debug mode, you should get a traceback that points to the tensor shape mismatch issue.
+
+```py
+Traceback (most recent call last):
+  File "/home/zach_mueller_huggingface_co/test.py", line 18, in <module>
+    main()
+  File "/home/zach_mueller_huggingface_co/test.py", line 15, in main
+    broadcast_tensor = broadcast(tensor)
+  File "/home/zach_mueller_huggingface_co/accelerate/src/accelerate/utils/operations.py", line 303, in wrapper
+accelerate.utils.operations.DistributedOperationException:
+
+Cannot apply desired operation due to shape mismatches. All shapes across devices must be valid.
+
+Operation: `accelerate.utils.operations.broadcast`
+Input shapes:
+  - Process 0: [1, 5]
+  - Process 1: [1, 2, 5]
+```
+
+### Early stopping
+
+For early stopping in distributed training, if each process has a specific stopping condition (e.g. validation loss), it may not be synchronized across all processes. As a result, a break can happen on process 0 but not on process 1 which will cause your code to hang indefinitely until a timeout occurs.
+
+If you have early stopping conditionals, use the `set_breakpoint` and `check_breakpoint` methods to make sure all the processes
+are ended correctly.
+
+```py
+# Assume `should_do_breakpoint` is a custom defined function that returns a conditional, 
+# and that conditional might be true only on process 1
+if should_do_breakpoint(loss):
+    accelerator.set_breakpoint()
+
+# Later in the training script when we need to check for the breakpoint
+if accelerator.check_breakpoint():
+    break
+```
+
+### Low kernel versions on Linux
+
+On Linux with kernel version < 5.5, hanging processes have been reported. To avoid this problem, upgrade your system to a later kernel version.
+
+### MPI
+
+If your distributed CPU training job using MPI is hanging, ensure that you have
+[passwordless SSH](https://www.open-mpi.org/faq/?category=rsh#ssh-keys) setup (using keys) between the nodes. This means
+that for all nodes in your hostfile, you should to be able to SSH from one node to another without being prompted for a password.
+
+Next, try to run the `mpirun` command as a sanity check. For example, the command below should print out the
+hostnames for each of the nodes.
+
+```bash
+mpirun -f hostfile -n {number of nodes} -ppn 1 hostname
+```
+
+## CUDA Out-of-Memory
+
+One of the most frustrating errors when it comes to running training scripts is hitting "CUDA Out-of-Memory". The entire script needs to be restarted and any progress is lost.
+
+To address this problem, Accelerate provides the [`find_executable_batch_size`] utility that is heavily based on [toma](https://github.com/BlackHC/toma).
+This utility retries code that fails due to OOM (out-of-memory) conditions and automatically lowers batch sizes. For each OOM condition, the algorithm decreases the batch size by half and retries the code until it succeeds.
+
+To use [`find_executable_batch_size`], restructure your training function to include an inner function with `find_executable_batch_size` and build your dataloaders inside it. At a minimum, this only takes 4 new lines of code.
+
+<Tip warning={true}> 
+
+The inner function **must** take batch size as the first parameter, but we do not pass one to it when called. The wrapper will handles this for you. Any object (models, optimizers) that consumes CUDA memory and is passed to the [`Accelerator`] also **must** be declared inside the inner function.
+
+</Tip>
+
+```diff
+def training_function(args):
+    accelerator = Accelerator()
+
++   @find_executable_batch_size(starting_batch_size=args.batch_size)
++   def inner_training_loop(batch_size):
++       nonlocal accelerator # Ensure they can be used in our context
++       accelerator.free_memory() # Free all lingering references
+        model = get_model()
+        model.to(accelerator.device)
+        optimizer = get_optimizer()
+        train_dataloader, eval_dataloader = get_dataloaders(accelerator, batch_size)
+        lr_scheduler = get_scheduler(
+            optimizer, 
+            num_training_steps=len(train_dataloader)*num_epochs
+        )
+        model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = accelerator.prepare(
+            model, optimizer, train_dataloader, eval_dataloader, lr_scheduler
+        )
+        train(model, optimizer, train_dataloader, lr_scheduler)
+        validate(model, eval_dataloader)
++   inner_training_loop()
+```
+
+## Non-reproducible results between device setups
+
+If you changed the device setup and observe different model performance, it is likely you didn't update your script when moving from one setup to another. Even if you're using the same script with the same batch size, the results will still be different on a TPU, multi-GPU, and single GPU.
+
+For example, if you were training on a single GPU with a batch size of 16 and you move to a dual GPU setup, you need to change the batch size to 8 to have the same effective batch size. This is because when training with Accelerate, the batch size passed to the dataloader is the **batch size per GPU**.
+
+To make sure you can reproduce the results between the setups, make sure to use the same seed, adjust the batch size accordingly, and consider scaling the learning rate.
+
+For more details and a quick reference for batch sizes, check out the [Comparing performance between different device setups](../concept_guides/performance) guide.
+
+## Performance issues on different GPUs
+
+If your multi-GPU setup consists of different GPUs, you may encounter some performance issues:
+
+- There may be an imbalance in GPU memory between the GPUs. In this case, the GPU with the smaller memory will limit the batch size or the size of the model that can be loaded onto the GPUs.
+- If you are using GPUs with different performance profiles, the performance will be driven by the slowest GPU you are using because the other GPUs will have to wait for it to complete its workload.
+
+Vastly different GPUs within the same setup can lead to performance bottlenecks.
+
+## Ask for help
+
+If none of the solutions and advice here helped resolve your issue, you can always reach out to the community and Accelerate team for help.
+
+- Ask for help on the Hugging Face forums by posting your question in the [🤗 Accelerate category](https://discuss.huggingface.co/c/accelerate/18). Make sure to write a descriptive post with relevant context about your setup and reproducible code to maximize the likelihood that your problem is solved!
+
+- Post a question on [Discord](http://hf.co/join/discord), and let the team and the community help you.
+
+- Create an Issue on the 🤗 Accelerate [GitHub repository](https://github.com/huggingface/accelerate/issues) if you think you've found a bug related to the library. Include context regarding the bug and details about your distributed setup to help us better figure out what's wrong and how we can fix it.

docs/source/concept_guides/big_model_inference.md

@@ -0,0 +1,341 @@
+<!--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.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+-->
+
+# Handling big models for inference
+
+When loading a pre-trained model in PyTorch, the usual workflow looks like this:
+
+```py
+import torch
+
+my_model = ModelClass(...)
+state_dict = torch.load(checkpoint_file)
+my_model.load_state_dict(state_dict)
+```
+
+In plain English, those steps are:
+1. Create the model with randomly initialized weights
+2. Load the model weights (in a dictionary usually called a state dict) from the disk
+3. Load those weights inside the model
+
+While this works very well for regularly sized models, this workflow has some clear limitations when we deal with a huge model: in step 1, we load a full version of the model in RAM, and spend some time randomly initializing the weights (which will be discarded in step 3). In step 2, we load another full version of the model in RAM, with the pre-trained weights. If you're loading a model with 6 billion parameters, this means you will need 24GB of RAM for each copy of the model, so 48GB in total (half of it to load the model in FP16).
+
+<Tip warning={true}>
+
+This API is quite new and still in its experimental stage. While we strive to provide a stable API, it's possible some small parts of the public API will change in the future.
+
+</Tip>
+
+## How the Process Works: A Quick Overview
+
+<Youtube id="MWCSGj9jEAo" />
+
+## How the Process Works: Working with Code
+
+### Instantiating an empty model
+
+The first tool 🤗 Accelerate introduces to help with big models is a context manager [`init_empty_weights`] that helps you initialize a model without using any RAM so that step 1 can be done on models of any size. Here is how it works:
+
+```py
+from accelerate import init_empty_weights
+
+with init_empty_weights():
+    my_model = ModelClass(...)
+```
+
+For instance:
+
+```py
+with init_empty_weights():
+    model = nn.Sequential(*[nn.Linear(10000, 10000) for _ in range(1000)])
+```
+
+initializes an empty model with a bit more than 100B parameters. Behind the scenes, this relies on the meta device introduced in PyTorch 1.9. During the initialization under the context manager, each time a parameter is created, it is instantly moved to that device.
+
+<Tip warning={true}>
+
+    You can't move a model initialized like this on CPU or another device directly, since it doesn't have any data. It's also very likely that a forward pass with that empty model will fail, as not all operations are supported on the meta device.
+
+</Tip>
+
+### Sharded checkpoints
+
+It's possible your model is so big that even a single copy won't fit in RAM. That doesn't mean it can't be loaded: if you have one or several GPUs, this is more memory available to store your model. In this case, it's better if your checkpoint is split into several smaller files that we call checkpoint shards.
+
+🤗 Accelerate will handle sharded checkpoints as long as you follow the following format: your checkpoint should be in a folder, with several files containing the partial state dicts, and there should be an index in the JSON format that contains a dictionary mapping parameter names to the file containing their weights. You can easily shard your model with [`~Accelerator.save_model`]. For instance, we could have a folder containing:
+
+```bash
+first_state_dict.bin
+index.json
+second_state_dict.bin
+```
+
+with index.json being the following file:
+
+```
+{
+  "linear1.weight": "first_state_dict.bin",
+  "linear1.bias": "first_state_dict.bin",
+  "linear2.weight": "second_state_dict.bin",
+  "linear2.bias": "second_state_dict.bin"
+}
+```
+
+and `first_state_dict.bin` containing the weights for `"linear1.weight"` and `"linear1.bias"`, `second_state_dict.bin` the ones for `"linear2.weight"` and `"linear2.bias"`
+
+### Loading weights
+
+The second tool 🤗 Accelerate introduces is a function [`load_checkpoint_and_dispatch`], that will allow you to load a checkpoint inside your empty model. This supports full checkpoints (a single file containing the whole state dict) as well as sharded checkpoints. It will also automatically dispatch those weights across the devices you have available (GPUs, CPU RAM), so if you are loading a sharded checkpoint, the maximum RAM usage will be the size of the biggest shard.
+
+If you want to use big model inference with 🤗 Transformers models, check out this [documentation](https://huggingface.co/docs/transformers/main/en/main_classes/model#large-model-loading).
+
+Here is how we can use this to load the [GPT2-1.5B](https://huggingface.co/marcsun13/gpt2-xl-linear-sharded) model.
+
+Let's download the sharded version of this model.
+
+```bash
+pip install huggingface_hub
+```
+
+```py
+from huggingface_hub import snapshot_download
+checkpoint = "marcsun13/gpt2-xl-linear-sharded"
+weights_location = snapshot_download(repo_id=checkpoint)
+```
+
+In order to initialize the model, we will use the library minGPT. 
+
+```bash
+git clone https://github.com/karpathy/minGPT.git
+pip install minGPT/
+```
+
+```py
+from accelerate import init_empty_weights
+from mingpt.model import GPT
+
+model_config = GPT.get_default_config()
+model_config.model_type = 'gpt2-xl'
+model_config.vocab_size = 50257
+model_config.block_size = 1024
+
+with init_empty_weights():
+    model = GPT(model_config)
+```
+
+Then, load the checkpoint we just downloaded with:
+
+```py
+from accelerate import load_checkpoint_and_dispatch
+
+model = load_checkpoint_and_dispatch(
+    model, checkpoint=weights_location, device_map="auto", no_split_module_classes=['Block']
+)
+```
+
+By passing `device_map="auto"`, we tell 🤗 Accelerate to determine automatically where to put each layer of the model depending on the available resources:
+- first, we use the maximum space available on the GPU(s)
+- if we still need space, we store the remaining weights on the CPU
+- if there is not enough RAM, we store the remaining weights on the hard drive as memory-mapped tensors
+
+
+#### `no_split_module_classes`
+
+This parameter will indicate that some of the modules with the name `"Block"` should not be split across different devices. You should set here all blocks that 
+include a residual connection of some kind.
+
+
+#### The `device_map`
+
+You can see the `device_map` that 🤗 Accelerate picked by accessing the `hf_device_map` attribute of your model:
+
+```py
+model.hf_device_map
+```
+
+```python out
+{'transformer.wte': 0,
+ 'transformer.wpe': 0,
+ 'transformer.drop': 0,
+ 'transformer.h.0': 0,
+ ...
+ 'transformer.h.21': 0, 
+ 'transformer.h.22': 1, 
+ 'transformer.h.23': 1, 
+ 'transformer.h.24': 1,
+ ...
+ 'transformer.h.47': 1, 
+ 'transformer.ln_f': 1, 
+ 'lm_head': 1}
+ ```
+
+It's fully possible to create your own device map for the layers to use as well, specifying the GPU device to use (a number), `"cpu"`, or `"disk"` and pass this in:
+
+```python
+device_map = {
+    "transformer.wte": "cpu",
+    "transformer.wpe": 0,
+    "transformer.drop": "cpu",
+    "transformer.h.0": "disk"
+}
+
+model = load_checkpoint_and_dispatch(
+    model, checkpoint=weights_location, device_map=device_map
+)
+
+```
+
+### Run the model
+
+Now that we have done this, our model lies across several devices, and maybe the hard drive. But it can still be used as a regular PyTorch model:
+
+```py
+from mingpt.bpe import BPETokenizer
+tokenizer = BPETokenizer()
+inputs = tokenizer("Hello, my name is").to(0)
+
+outputs = model.generate(x1, max_new_tokens=10, do_sample=False)[0]
+tokenizer.decode(outputs.cpu().squeeze())
+```
+
+Behind the scenes, 🤗 Accelerate added hooks to the model, so that:
+- at each layer, the inputs are put on the right device (so even if your model is spread across several GPUs, it works)
+- for the weights offloaded on the CPU, they are put on a GPU just before the forward pass and cleaned up just after
+- for the weights offloaded on the hard drive, they are loaded in RAM then put on a GPU just before the forward pass and cleaned up just after
+
+This way, your model can run for inference even if it doesn't fit on one of the GPUs or the CPU RAM!
+
+<Tip warning={true}>
+
+    This only supports the inference of your model, not training. Most of the computation happens behind `torch.no_grad()` context managers to avoid spending some GPU memory with intermediate activations.
+
+</Tip>
+
+### Designing a device map
+
+You can let 🤗 Accelerate handle the device map computation by setting `device_map` to one of the supported options (`"auto"`, `"balanced"`, `"balanced_low_0"`, `"sequential"`) or create one yourself if you want more control over where each layer should go.
+
+<Tip>
+
+    You can derive all sizes of the model (and thus compute a `device_map`) on a model that is on the meta device.
+
+</Tip>
+
+All the options will produce the same result when you don't have enough GPU memory to accommodate the whole model (which is to fit everything that can on the GPU, then offload weights on the CPU or even on the disk if there is not enough RAM). 
+
+When you have more GPU memory available than the model size, here is the difference between each option:
+- `"auto"` and `"balanced"` evenly split the model on all available GPUs, making it possible for you to use a batch size greater than 1.
+- `"balanced_low_0"` evenly splits the model on all GPUs except the first one, and only puts on GPU 0 what does not fit on the others. This option is great when you need to use GPU 0 for some processing of the outputs, like when using the `generate` function for Transformers models
+- `"sequential"` will fit what it can on GPU 0, then move on GPU 1 and so forth (so won't use the last GPUs if it doesn't need to).
+
+<Tip>
+
+    The options `"auto"` and `"balanced"` produce the same results for now, but the behavior of `"auto"` might change in the future if we find a strategy that makes more sense, while `"balanced"` will stay stable.
+
+</Tip>
+
+First note that you can limit the memory used on each GPU by using the `max_memory` argument (available in [`infer_auto_device_map`] and in all functions using it). When setting `max_memory`, you should pass along a dictionary containing the GPU identifiers (for instance `0`, `1` etc.) and the `"cpu"` key for the maximum RAM you want to use for CPU offload. The values can either be an integer (in bytes) or a string representing a number with its unit, such as `"10GiB"` or `"10GB"`.
+
+Here is an example where we don't want to use more than 10GiB on each of the two GPUs and no more than 30GiB of CPU RAM for the model weights:
+
+```python
+from accelerate import infer_auto_device_map
+
+device_map = infer_auto_device_map(my_model, max_memory={0: "10GiB", 1: "10GiB", "cpu": "30GiB"})
+```
+
+<Tip warning={true}>
+
+    When a first allocation happens in PyTorch, it loads CUDA kernels which take about 1-2GB of memory depending on the GPU. Therefore you always have less usable memory than the actual size of the GPU. To see how much memory is actually used do `torch.ones(1).cuda()` and look at the memory usage.
+
+    Therefore when you create memory maps with `max_memory` make sure to adjust the available memory accordingly to avoid out-of-memory errors.
+
+</Tip>
+
+Additionally, if you do some additional operations with your outputs without placing them back on the CPU (for instance inside the `generate` method of Transformers) and if you placed your inputs on a GPU, that GPU will consume more memory than the others (Accelerate always place the output back to the device of the input). Therefore if you would like to optimize the maximum batch size and you have many GPUs, give the first GPU less memory. For example, with BLOOM-176B on 8x80 A100 setup, the close-to-ideal map is:
+
+```python
+max_memory = {0: "30GIB", 1: "46GIB", 2: "46GIB", 3: "46GIB", 4: "46GIB", 5: "46GIB", 6: "46GIB", 7: "46GIB"}
+```
+as you can see we gave the remaining 7 GPUs ~50% more memory than GPU 0.
+
+If you opt to fully design the `device_map` yourself, it should be a dictionary with keys being module names of your model and values being a valid device identifier (for instance an integer for the GPUs) or `"cpu"` for CPU offload, `"disk"` for disk offload. The keys need to cover the whole model, you can then define your device map as you wish: for instance, if your model has two blocks (let's say `block1` and `block2`) which each contain three linear layers (let's say `linear1`, `linear2` and `linear3`), a valid device map can be:
+
+```python
+device_map = {"block1": 0, "block2": 1}
+```
+
+another one that is valid could be:
+
+```python
+device_map = {"block1": 0, "block2.linear1": 0, "block2.linear2": 1, "block2.linear3": 1}
+```
+
+On the other hand, this one is not valid as it does not cover every parameter of the model:
+
+```python
+device_map = {"block1": 0, "block2.linear1": 1, "block2.linear2": 1}
+```
+
+<Tip>
+
+    To be the most efficient, make sure your device map puts the parameters on the GPUs in a sequential manner (e.g. don't put one of the first weights on GPU 0, then weights on GPU 1 and the last weight back to GPU 0) to avoid making many transfers of data between the GPUs.
+
+</Tip>
+
+## CPU offload only
+
+If you want to offload your model on CPU, you can use [`cpu_offload`]. As a result, all parameters of the model will be offloaded and only one copy of the state dict of the model will be kept. During the forward pass, parameters will be extracted from that state dict and put on the execution device and passed as they are needed, then offloaded again. 
+
+```python
+cpu_offload(model, execution_device)
+```
+
+You can also use [`cpu_offload_with_hook`]. This function will offloads a model on the CPU and puts it back to an execution device when executed. The difference with [`cpu_offload`] is that the model stays on the execution device after the forward and is only offloaded again when the `offload` method of the returned `hook` is called. Furthermore, [`cpu_offload_with_hook`] is more performant but less memory saving. It is useful for pipelines running a model in a loop:
+
+```python
+model_1, hook_1 = cpu_offload_with_hook(model_1, execution_device)
+model_2, hook_2 = cpu_offload_with_hook(model_2, execution_device, prev_module_hook=hook_1)
+model_3, hook_3 = cpu_offload_with_hook(model_3, execution_device, prev_module_hook=hook_2)
+
+hid_1 = model_1(input)
+for i in range(50):
+    # model1 is offloaded on the CPU at the first iteration, model 2 stays on the GPU for this whole loop.
+    hid_2 = model_2(hid_1)
+# model2 is offloaded to the CPU just before this forward.
+hid_3 = model_3(hid_3)
+
+# For model3, you need to manually call the hook offload method.
+hook_3.offload()
+```
+
+## Disk offload only
+
+To perform disk offload, you can use [`disk_offload`]. As a result, all parameters of the model will be offloaded as memory-mapped array in a given folder. During the forward pass, parameters will be accessed from that folder and put on the execution device passed as they are needed, then offloaded again.
+
+```python
+disk_offload(model, offload_dir, execution_device)
+```
+
+## Limits and further development
+
+We are aware of the current limitations in the API:
+
+- [`infer_auto_device_map`] (or `device_map="auto"` in [`load_checkpoint_and_dispatch`]) tries to maximize GPU and CPU RAM it sees available when you execute it. While PyTorch is very good at managing GPU RAM efficiently (and giving it back when not needed), it's not entirely true with Python and CPU RAM. Therefore, an automatically computed device map might be too intense on the CPU. Move a few modules to the disk device if you get crashes due to a lack of RAM.
+- [`infer_auto_device_map`] (or `device_map="auto"` in [`load_checkpoint_and_dispatch`]) attributes devices sequentially (to avoid moving things back and forth) so if your first layer is bigger than the size of the GPU you have, it will end up with everything on the CPU/Disk.
+- [`load_checkpoint_and_dispatch`] and [`load_checkpoint_in_model`] do not perform any check on the correctness of your state dict compared to your model at the moment (this will be fixed in a future version), so you may get some weird errors if trying to load a checkpoint with mismatched or missing keys.
+- The model parallelism used when your model is split on several GPUs is naive and not optimized, meaning that only one GPU works at a given time and the other sits idle.
+- When weights are offloaded on the CPU/hard drive, there is no pre-fetching (yet, we will work on this for future versions) which means the weights are put on the GPU when they are needed and not before.
+- Hard-drive offloading might be very slow if the hardware you run on does not have fast communication between disk and CPU (like NVMes).

docs/source/concept_guides/deferring_execution.md

@@ -0,0 +1,130 @@
+<!--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.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+-->
+
+# Deferring Executions
+
+When you run your usual script, instructions are executed in order. Using 🤗 Accelerate to deploy your script on several
+GPUs at the same time introduces a complication: while each process executes all instructions in order, some may be
+faster than others.
+
+You might need to wait for all processes to have reached a certain point before executing a given instruction. For
+instance, you shouldn't save a model before being sure every process is done with training, and you wouldn't want to 
+continue training before all the model weights have been loaded in. To do this, just write the following line in your code:
+
+```
+accelerator.wait_for_everyone()
+```
+
+This instruction will block all the processes that arrive first until all the other processes have reached that
+point (if you run your script on just one GPU or CPU, this won't do anything).
+
+A few example cases of when to use this utility are listed below:
+
+<Tip>
+
+    Some of these are utilized with the [`~Accelerator.main_process_first`] context manager, which utilizes [`~Accelerator.wait_for_everyone`] to 
+    run a particular set of code on the main process beforehand before triggering and launching the other processes
+
+</Tip>
+
+## Downloading a Dataset 
+
+When downloading a dataset, you should download it first on the main process and then load the cached dataset afterward
+
+<Tip>
+
+    `load_dataset` will perform a lock under the hood to stop multiple downloads from happening at once, but if you are downloading something 
+    not using this library you should use this method.
+    
+</Tip>
+
+```python
+with accelerator.main_process_first():
+    datasets = load_dataset("glue", "mrpc")
+```
+
+Under the hood this is the same as calling: 
+
+```python
+# First do something on the main process
+if accelerator.is_main_process:
+    datasets = load_dataset("glue", "mrpc")
+else:
+    accelerator.wait_for_everyone()
+
+# And then send it to the rest of them
+if not accelerator.is_main_process:
+    datasets = load_dataset("glue", "mrpc")
+else:
+    accelerator.wait_for_everyone()
+```
+
+## Saving the `state_dict`
+
+When saving the `state_dict` of the model, since you would normally save one file on just the main process
+you should specify that:
+
+```python
+if accelerator.is_main_process:
+    model = accelerator.unwrap_model(model)
+    torch.save(model.state_dict(), "weights.pth")
+```
+
+## Loading in the `state_dict`
+
+When loading in the `state_dict` to a model, optimizer, or scheduler, you should wait 
+for all workers to have the weights loaded in before moving on to training
+
+```python
+with accelerator.main_process_first():
+    state = torch.load("weights.pth")
+    model.load_state_dict(state)
+```
+
+## Applying a multi-worker CPU operation 
+
+Applying a `map()` operation on multiple workers, such as tokenizing should be done on the 
+main process first, and then propagated to each one. 
+
+```python
+datasets = load_dataset("glue", "mrpc")
+
+with accelerator.main_process_first():
+    tokenized_datasets = datasets.map(
+        tokenize_function,
+        batched=True,
+        remove_columns=["idx", "sentence1", "sentence2"],
+    )
+```
+
+## Applying checks such as Early Stopping
+
+To have a check that works with a flag set by a particular process, the `set_trigger` and `check_trigger` API should be used. Useful examples
+for doing so can include situations such as using early stopping and monitoring the loss (as each loss slightly differs on each process).
+
+Call [`Accelerator.set_trigger`] when your condition has been met, and [`Accelerator.check_trigger`] when checking if that condition has been met in any process:
+
+```python
+for (x,y) in data_loader:
+    logits = model(x)
+    loss = loss_func(logits, y)
+    # Assume `should_do_early_stopping` is a custom defined function that returns a conditional
+    if should_do_early_stopping(loss):
+        accelerator.set_trigger()
+
+    # Later in the training script when we need to check for the breakpoint
+    if accelerator.check_trigger():
+        break
+```

docs/source/concept_guides/fsdp_and_deepspeed.md

@@ -0,0 +1,192 @@
+<!--Copyright 2024 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.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+-->
+
+# Moving between FSDP And DeepSpeed
+
+🤗 Accelerate offers flexibilty of training frameworks, by integrating two extremely powerful tools for distributed training, namely [Pytorch FSDP](../usage_guides/fsdp) and [Microsoft DeepSpeed](../usage_guides/deepspeed). The aim of this tutorial is to draw parallels, as well as to outline potential differences, to empower the user to switch seamlessly between these two frameworks.
+
+<Tip>
+
+  To switch between the frameworks, we recommend launching code 🤗 `accelerate launch` passing in the correct config file with `--config_file`, or passing in the respective arguments directly for [FSDP and DeepSpeed](../package_reference/cli#accelerate-launch) .
+
+  Example 🤗 Accelerate configurations can be found here for [DeepSpeed](../usage_guides/deepspeed#accelerate-deepspeed-plugin) and [FSDP](../usage_guides/fsdp#how-it-works-out-of-the-box), or in the [example zoo under "Launch Configurations"](../usage_guides/explore)
+ 
+</Tip>
+
+<Tip warning={true}>
+
+This tutorial is for single-node, multi-GPU, scenarios only.
+
+</Tip>
+
+## Configuring Functionalities
+
+Model tensors are split into different GPUs in an attempt to scale up model sizes; this is termed *sharding* in FSDP, and *partitioning* in DeepSpeed. FSDP sharding and DeepSpeed ZeRO (partitioning) stages are configured by `--fsdp_sharding_strategy`, and `--zero_stage`, respectively.  In particular, FSDP `FULL_SHARD` maps to DeepSpeed ZeRO stage `3`; see this [comprehensive mapping between FSDP sharding and DeepSpeed ZeRO settings](../usage_guides/fsdp#mapping-between-fsdp-sharding-strategies-and-deepspeed-zero-stages). The below table summarizes and groups similar settings:
+
+Group | Framework | Configuration | Example | Restrictions (if any)
+--|--|--|--|--
+sharding / partitioning | FSDP<br>DeepSpeed | `--fsdp_sharding_strategy`<br>`--zero_stage` | `1` (`FULL_SHARD`) <br>`3` | 
+offload | FSDP<br>DeepSpeed | `--fsdp_offload_params`<br>`--offload_param_device`<br>`--offload_optimizer_device` | `true`<br>`cpu`<br>`cpu` | all or nothing <br><br> 
+model loading | FSDP<br>DeepSpeed | <span style="white-space:nowrap;">`--fsdp_cpu_ram_efficient_loading`</span><br>`--zero3_init_flag` | `true`<br>`true` | <br>only ZeRO 3
+efficient checkpointing | FSDP<br>DeepSpeed | `--fsdp_state_dict_type`<br>`--zero3_save_16bit_model` |  `SHARDED_STATE_DICT`<br>`true` |  <br>only ZeRO 3
+weights prefetching | FSDP<br><br>DeepSpeed | `--fsdp_forward_prefetch`<br>`--fsdp_backward_prefetch`<br>None | `true`<br>`BACKWARD_PRE` | <br><br>
+model | FSDP<br><br>DeepSpeed |  `--fsdp_auto_wrap_policy`<br><span style="white-space:nowrap;">`--fsdp_transformer_layer_cls_to_wrap`</span><br>None | `TRANSFORMER_BASED_WRAP`<br><Layer Class> |<br>Usually not needed <br>Transparent to user.
+parameters summoning | FSDP<br>DeepSpeed | `--fsdp_use_orig_params`<br>None | `true` | required for `torch.compile`<br>Transparent to user
+parameters syncing | FSDP<br>DeepSpeed | `--fsdp_sync_module_states`<br>None | `true` | 
+training | FSDP<br>DeepSpeed | None<br>`--gradient_accumulation_steps`<br>`--gradient_clipping` | <br>`auto`<br>`auto` | Transparent to user
+
+For detailed descriptions of the above, refer to [🤗 `Accelerate` launch documentation](../package_reference/cli#accelerate-launch).
+
+<Tip>
+
+    To access other DeepSpeed configurations, such as mixed precision settings, 
+    you need to pass in a `--deepspeed_config_file`, see the [documentation](../usage_guides/deepspeed#deepspeed-config-file).  
+
+    DeepSpeed can be also configured via [`DeepSpeedPlugin`], e.g., `DeepSpeedPlugin.zero_stage` is equivalent of `--zero_stage`, and `DeepSpeedPlugin.hf_ds_config` can be used to pass `--deepeed_config_file.`
+
+</Tip>
+
+<Tip>
+
+    FSDP can be also configured via [`FullyShardedDataParallelPlugin`], e.g., `FullyShardedDataParallelPlugin.sharding_strategy` is equivalent of `--fsdp_sharding_strategy`.
+    
+</Tip>
+
+### Checkpointing
+
+Do note that while FSDP can be configured via `--fsdp_state_dict_type` to save either full / sharded checkpoints.
+
+<Tip>
+
+    For DeepSpeed Zero3, one could pass a `--zero3_save_16bit_model true`, which conveniently consolidates the model to a single rank and saves; this is the FSDP equivalent of `fsdp_state_dict_type: FULL_STATE_DICT`. 
+
+</Tip>
+
+<Tip warning={true}>
+
+    For large models, consolidating the model to a single rank can be very slow.
+
+</Tip>
+
+<Tip>
+
+    For quicker checkpointing, for FSDP use `fsdp_state_dict_type: SHARDED_STATE_DICT`, and for DeepSpeed Zero3 [use the `zero_to_fp32.py` script to post-convert sharded checkpoints](https://www.deepspeed.ai/tutorials/zero/#extracting-weights).
+
+
+</Tip>
+
+### Offloading
+
+FSDP only allows *all-or-nothing* offload (i.e., either offload parameters, gradients, and optimizer, or keep them all in GPU), but DeepSpeed can offload parameters and optimizer differently. Furthermore, DeepSpeed also supports [offloading to NVME](https://www.deepspeed.ai/docs/config-json/#parameter-offloading).
+
+### Prefetching
+
+FSDP allows two prefetching configurations `--fsdp_forward_prefetch` and `--fsdp_backward_prefetch` to improve overlap of comms / computation at a cost of extra memory, see [FSDP documentation](https://pytorch.org/docs/stable/fsdp.html). 
+For DeepSpeed, the prefetching will be turned on when needed, and it turns on depending on certain hyper-params like `stage3_param_persistence_threshold`, `stage3_max_reuse_distance`, etc, [that can be configured for Zero3](https://www.deepspeed.ai/docs/config-json/#parameter-offloading); 🤗 `accelerate` may set these hyper-params automatically if you don't set those explicitly in the deepspeed config file.
+
+<Tip>
+
+    For FSDP set `fsdp_backward_prefetch: BACKWARD_PRE` for improved throughputs if memory allows.
+
+</Tip>
+
+### Model Loading
+
+While FSDP require an explicit `--fsdp_cpu_ram_efficient_loading true` to activate efficient model loading, 🤗 `transformers` will activate the similar feature whenever DeepSpeed Zero3 is used.
+
+<Tip>
+
+    For FSDP, whenever setting `--fsdp_cpu_ram_efficient_loading true`, 🤗 `accelerate` will automatically set `sync_module_states` to true. 
+    For RAM efficient loading the weights will be loaded only in a singe rank, and thus requires `sync_module_states` to broadcast weights to other ranks.
+
+</Tip>
+
+### Model
+
+FSDP requires an explicit `--fsdp_auto_wrap_policy` for the algorithm to decide how to schedule the all-gather and reduce-scatter operations. But for DeepSpeed this is transparent to the user.
+
+<Tip>
+
+    For FSDP, simply set `fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP`. With the latest [`transformers`] versions, we try our best to figure out the suitable `fsdp_transformer_layer_cls_to_wrap` for HF transformers models. However, if you get an error regarding it, please specify this.
+
+</Tip>
+
+### Parameters Summoning
+
+FSDP requires an explicit `--fsdp_use_orig_params` flag if using `torch.compile`, see [the pytorch documenation](https://pytorch.org/docs/stable/fsdp.html#module-torch.distributed.fsdp). For DeepSpeed this is transparent to the user.
+
+<Tip>
+
+    For FSDP, when using `torch.compile` please set `fsdp_use_orig_params: True`.
+
+</Tip>
+
+
+## Training
+
+Deepspeed requires explicit `--gradient_accumulation_steps` and `--gradient_clipping` flags. For FSDP this is transparent to the user.
+
+<Tip>
+
+    When using DeepSpeed, set `gradient_accumulation_steps: "auto"` and `gradient_clipping: "auto"` to automatically pick up values set in the [`Accelerator`] or [`TrainingArguments`] (if using `transformers`).
+
+</Tip>
+
+
+## On Differences in Data Precision Handling
+
+To discuss the how data precision is handled in both FSDP and Deepspeed, it is instructive to first give an overview of how model parameters are handled in these frameworks. Before the model / optimizer parameters are distributed across GPUs, parameter preparation is involved to first "flatten" them to  one-dimensional [`torch.Tensor`](https://pytorch.org/docs/stable/tensors.html#torch-tensor). The implementation of FSDP / DeepSpeed varies in the respect of the `dtype` in which these "flattened" parameters are stored, and there are ramifications with regards to how [`torch.Optimizer`](https://pytorch.org/docs/stable/optim.html#module-torch.optim) allocate their `dtype`s. The table below outlines the processes for both frameworks; the "Local" column indicates the process occurring at a per-gpu level, therefore any memory overheads by upcasting should be understood to be amortized by the number of gpus used.
+
+<Tip>
+
+    As a rule of thumb, for stable training with automatic mixed precision, all the trainable parameters have to be in `torch.float32`.
+
+</Tip>
+
+Process | Local | Framework | Details
+--|--|--|--
+Loading, i.e., [`AutoModel.from_pretrained(..., torch_dtype=torch_dtype)`] |  
+Preparation, i.e., creation of "flat params" | ✅ | FSDP<br>DeepSpeed | created in `torch_dtype`.<br> disregards `torch_dtype`, created in `float32`.
+Optimizer initialization | ✅ | FSDP<br>DeepSpeed  | creates parameters in `torch_dtype`<br> creates parameters in `float32`
+Training Step, i.e, forward, backward, reduction | | FSDP<br>DeepSpeed  | follows [`MixedPrecision`](https://pytorch.org/docs/stable/fsdp.html#torch.distributed.fsdp.MixedPrecision)<br> follows `deepspeed_config_file` mixed precision settings.
+Optimizer (Pre-Step) | ✅ | FSDP<br>DeepSpeed | upcasting (if any) to `torch_dtype`<br>upcasted to `float32`
+Optimizer (Actual Step) | ✅ | FSDP<br>DeepSpeed  | occurs in `torch_dtype` <br> occurs in `float32`.
+
+<Tip warning={true}>
+
+    Therefore when using DeepSpeed a small number of GPUs, be aware of potentially significant memory overheads due to the upcasting during preperation.
+
+</Tip>
+
+<Tip>
+
+    With FSDP, in the absence of mixed precision, it is possible to operate the [`torch.Optimizer`](https://pytorch.org/docs/stable/optim.html#module-torch.optim) in low precision `torch_dtype`, which may be helpful when using small number of GPUs. 
+
+</Tip>
+
+<Tip warning={true}>
+
+    With mixed precision, FSDP and DeepSpeed will upcast in the model preparation step (c.f. table above). But do note that FSDP will then save checkpoints in the upcasted precision; Deepspeed may still save low precision checkpoints if `--zero3_save_16bit_model` is specified.
+
+</Tip>
+
+
+To clarify the above table consider the concrete examples below; the optimizer pre- and actual step combined for brevity. With FSDP it is possible to operate in the two modes shown below, but DeepSpeed can only operate in one.
+
+Framework | Model Loading (`torch_dtype`) | Mixed Precision | Preparation (Local) | Training | Optimizer (Local)
+--|--|--|--|--|--
+FSDP | bf16 | default (none) | bf16 | bf16 | bf16
+FSDP | bf16 | bf16 | fp32 | bf16 | fp32
+DeepSpeed   | bf16 | bf16 | fp32 | bf16 | fp32

docs/source/concept_guides/gradient_synchronization.md

@@ -0,0 +1,184 @@
+<!--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.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+-->
+
+# Gradient Synchronization
+
+PyTorch's distributed module operates by communicating back and forth between all of the GPUs in your system.
+This communication takes time, and ensuring all processes know the states of each other happens at particular triggerpoints
+when using the `ddp` module. 
+
+These triggerpoints are added to the PyTorch model, specifically their `forward()` and `backward()` methods. 
+This happens when the model is wrapped with `DistributedDataParallel`:
+```python
+import torch.nn as nn
+from torch.nn.parallel import DistributedDataParallel
+
+model = nn.Linear(10, 10)
+ddp_model = DistributedDataParallel(model)
+```
+In 🤗 Accelerate this conversion happens automatically when calling [`~Accelerator.prepare`] and passing in your model.
+
+```diff
++ from accelerate import Accelerator
++ accelerator = Accelerator()
+  import torch.nn as nn
+- from torch.nn.parallel import DistributedDataParallel
+
+  model = nn.Linear(10,10)
++ model = accelerator.prepare(model)
+```
+
+## The slowdown in gradient accumulation
+
+You now understand that PyTorch adds hooks to the `forward` and `backward` method of your PyTorch model when 
+training in a distributed setup. But how does this risk slowing down your code?
+
+In DDP (distributed data parallel), the specific order in which processes are performed and ran are expected
+at specific points and these must also occur at roughly the same time before moving on.
+
+The most direct example is when you update model parameters through
+`optimizer.step()`.
+Without gradient accumulation, all instances of the model need to have updated
+their gradients computed, collated, and updated before moving on to the next
+batch of data.
+When performing gradient accumulation, you accumulate `n` loss gradients and
+skip `optimizer.step()` until `n` batches have been reached. As all training
+processes only need to synchronize by the time `optimizer.step()` is called,
+without any modification to your training step, this needless inter-process
+communication can cause a significant slowdown.
+
+ How can you avoid this overhead?
+
+## Solving the slowdown problem
+
+Since you are skipping model parameter updates when training on these batches, their gradients do not need to be synchronized until the point where `optimizer.step()` is actually called. 
+PyTorch cannot automagically tell when you need to do this, but they do provide a tool to help through the [`no_sync`](https://pytorch.org/docs/stable/generated/torch.nn.parallel.DistributedDataParallel.html#torch.nn.parallel.DistributedDataParallel.no_sync) context manager
+that is added to your model after converting it to DDP.
+
+Under this context manager, PyTorch will skip synchronizing the gradients when
+`.backward()` is called, and the first call to `.backward()` outside this 
+context manager will trigger the synchronization. See an example below:
+```python
+ddp_model, dataloader, optimizer = accelerator.prepare(model, dataloader, optimizer)
+
+for index, batch in enumerate(dataloader):
+    inputs, targets = batch
+    # Trigger gradient synchronization on the last batch
+    if index != (len(dataloader) - 1):
+        with ddp_model.no_sync():
+            # Gradients only accumulate
+            outputs = ddp_model(inputs)
+            loss = loss_func(outputs)
+            accelerator.backward(loss)
+    else:
+        # Gradients finally sync
+        outputs = ddp_model(inputs)
+        loss = loss_func(outputs)
+        accelerator.backward(loss)
+        optimizer.step()
+```
+
+In 🤗 Accelerate to make this an API that can be called no matter the training device (though it may not do anything if you are not in a distributed system!),
+`ddp_model.no_sync` gets replaced with [`~Accelerator.no_sync`] and operates the same way:
+
+```diff
+  ddp_model, dataloader, optimizer = accelerator.prepare(model, dataloader, optimizer)
+
+  for index, batch in enumerate(dataloader):
+      inputs, targets = batch
+      # Trigger gradient synchronization on the last batch
+      if index != (len(dataloader)-1):
+-         with ddp_model.no_sync():
++         with accelerator.no_sync(model):
+              # Gradients only accumulate
+              outputs = ddp_model(inputs)
+              loss = loss_func(outputs, targets)
+              accelerator.backward(loss)
+      else:
+          # Gradients finally sync
+          outputs = ddp_model(inputs)
+          loss = loss_func(outputs)
+          accelerator.backward(loss)
+          optimizer.step()
+          optimizer.zero_grad()
+```
+
+As you may expect, the [`~Accelerator.accumulate`] function wraps around this conditional check by keeping track of the current batch number, leaving you with the final
+gradient accumulation API:
+
+```python
+ddp_model, dataloader, optimizer = accelerator.prepare(model, dataloader, optimizer)
+
+for batch in dataloader:
+    with accelerator.accumulate(model):
+        optimizer.zero_grad()
+        inputs, targets = batch
+        outputs = model(inputs)
+        loss = loss_function(outputs, targets)
+        accelerator.backward(loss)
+        optimizer.step()
+        optimizer.zero_grad()
+```
+
+As a result, you should either use *`accelerator.accumulate` or `accelerator.no_sync`* when it comes to API choice. 
+
+## Just how much of a slowdown is there, and easy mistakes you can make
+
+To set up a realistic example, consider the following setup:
+
+* Two single-GPU T4 nodes and one node with two GPUs
+* Each GPU is a T4, and are hosted on GCP
+* The script used is a modification of the [NLP Example](https://github.com/muellerzr/timing_experiments/blob/main/baseline.py) script
+* Batch size per GPU is 16, and gradients are accumulated every 4 steps
+
+All scripts are available in [this repository](https://github.com/muellerzr/timing_experiments).
+
+If not careful about gradient synchronization and GPU communication, a *large* amount of time can be wasted 
+from when these GPUs communicate to each other during unnecessary periods.
+
+By how much?
+
+Reference:
+- Baseline: uses no synchronization practices discussed here
+- `no_sync` improperly: `no_sync` only around the `backward` call, not the `forward`
+- `no_sync`: using the `no_sync` pattern properly
+- `accumulate`: using [`~Accelerator.accumulate`] properly
+
+Below are the average seconds per batch iterating over 29 batches of data for each setup on both a single node and on the dual-node setup:
+
+|             | Baseline  | `no_sync` improperly | `no_sync` | `accumulate`| 
+| :---------: | :-------: | :------------------: | :-------: | :---------: |
+| Multi-Node  | 2±0.01s    | 2.13±0.08s | **0.91±0.11s** | **0.91±0.11s** |
+| Single Node | 0.50±0.01s | 0.50±0.01s | **0.41±0.015s** | **0.41±0.015s** |
+
+As you can see, if you are not careful about how you set up your gradient synchronization, you can get upwards of more than a 2x slowdown during training!
+
+If you are worried about making sure everything is done properly, we highly recommend utilizing the [`~Accelerator.accumulate`] function and passing in
+`gradient_accumulation_steps` or `gradient_accumulation_plugin` to the [`Accelerator`] object so Accelerate can handle this for you.
+
+### `no_sync` requires additional GPU memory when using FSDP
+
+Be aware that not syncing gradients can have adverse effects while performing FSDP training. As it has been warned in `torch`, the [`no_sync` context manager for FSDP](https://pytorch.org/docs/stable/fsdp.html#torch.distributed.fsdp.FullyShardedDataParallel.no_sync) will require additional memory.
+
+Therefore in memory intensive situations while using FSDP, we recommend to set `sync_each_batch` to `True` in the [`~utils.GradientAccumulationPlugin`] to disable `no_sync`.
+
+See the example below where we fine-tune Mixtral (47B parameters) on 8 A100-80GB GPUs. We see that even for a modest `gradient_accumulation_steps=2` we quickly go out-of-memory (OOM) if `no_sync` is enabled. Again, this is due to additional memory overheads due to FSDP's `no_sync`. However, if `no_sync` is disabled via `sync_each_batch=True`, then the memory consumption for `gradient_accumulation_steps=16` reverts to that of `gradient_accumulation_steps=1`.
+
+| Model           | `no_sync` (accum=1) | `no_sync` (accum=2) | `no_sync` disabled (accum=16)
+| :-------------: | :-----------------: | :-----------------: | :-----------------: 
+mixtral 8x7B      | 69G                 | OOM                 | 69G
+
+> [!WARNING] 
+> Disabling `no_sync` means there _will be slowdown_ due the extra data syncs, as explained by the earlier sections of this guide.

docs/source/concept_guides/internal_mechanism.md

@@ -0,0 +1,72 @@
+<!--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.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+-->
+
+# 🤗 Accelerate's internal mechanisms
+
+Internally, 🤗 Accelerate works by first analyzing the environment in which the script is launched to determine which
+kind of distributed setup is used, how many different processes there are and which one the current script is in. All
+that information is stored in the [`~AcceleratorState`].
+
+This class is initialized the first time you instantiate an [`~Accelerator`] as well as performing any
+specific initialization your distributed setup needs. Its state is then uniquely shared through all instances of
+[`~state.AcceleratorState`]. (The same can also be done with the [`PartialState`], a more barebones version it inherits)
+
+Then, when calling [`~Accelerator.prepare`], the library:
+
+- wraps your model(s) in the container adapted for the distributed setup,
+- wraps your optimizer(s) in an [`~optimizer.AcceleratedOptimizer`],
+- wraps your scheduler(s) in an [`~scheduler.AcceleratedScheduler`]
+- creates a new version of your dataloader(s) in a [`~data_loader.DataLoaderShard`] or [`~data_loader.DataLoaderDispatcher`]
+
+While the model(s), optimizer(s), and scheduler(s) are just put in simple wrappers, the dataloader(s) are re-created. This is mostly
+because PyTorch does not let the user change the `batch_sampler` of a dataloader once it's been created and the
+library handles the sharding of your data between processes by changing that `batch_sampler` to yield every other
+`num_processes` batches (if enabled).
+
+The [`~data_loader.DataLoaderShard`] subclasses `DataLoader` to add the following functionality:
+
+- it synchronizes the appropriate random number generator of all processes at each new iteration, to ensure any
+  randomization (like shuffling) is done the exact same way across processes.
+- it puts the batches on the proper device before yielding them (unless you have opted out of
+  `device_placement=True`).
+  
+The [`~data_loader.DataLoaderDispatcher`] subclasses differs from the [`~data_loader.DataLoaderShard`] in that when iterating through the `DataLoader`, the data is all starting from process 0 and *then* split and sent off to each process rather than it happening at the dataset level.
+
+The random number generator synchronization will by default synchronize:
+
+- the `generator` attribute of a given sampler (like the PyTorch `RandomSampler`) for PyTorch >= 1.6
+- the main random number generator in PyTorch <=1.5.1
+
+You can choose which random number generator(s) to synchronize with the `rng_types` argument of the main
+[`Accelerator`]. In PyTorch >= 1.6, it is recommended to rely on a local `generator` to avoid
+setting the same seed in the main random number generator in all processes.
+
+<Tip warning={true}>
+
+    Synchronization of the main torch (or CUDA or XLA) random number generator will affect any other potential random
+    artifacts you could have in your dataset (like random data augmentation) in the sense that all processes will get
+    the same random numbers from the torch random modules (so will apply the same random data augmentation if it's
+    controlled by torch).
+
+</Tip>
+
+<Tip>
+
+    The randomization part of your custom sampler, batch sampler or iterable dataset should be done using a local
+    `torch.Generator` object (in PyTorch >= 1.6), see the traditional `RandomSampler`, as an example.
+
+</Tip>
+
+For more details about the internals, see the [Internals page](package_reference/torch_wrappers).

docs/source/concept_guides/low_precision_training.md

@@ -0,0 +1,74 @@
+<!--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.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+-->
+
+# Low Precision Training Methods
+
+The release of new kinds of hardware led to the emergence of new training paradigms that better utilize them. Currently, this is in the form of training
+in 8-bit precision using packages such as [TransformersEngine](https://github.com/NVIDIA/TransformerEngine) (TE) or [MS-AMP](https://github.com/Azure/MS-AMP/tree/main).
+
+For an introduction to the topics discussed today, we recommend reviewing the [low-precision usage guide](../usage_guides/low_precision_training.md) as this documentation will reference it regularly. 
+
+## A Quick Chart
+
+Below is a quick chart from the MS-AMP documentation showing the different bit-precisions for each solution during training:
+
+Optimization Level | Computation(GEMM) | Comm | Weight | Master Weight | Weight Gradient | Optimizer States
+-- | -- | -- | -- | -- | -- | --
+FP16 AMP | FP16 | FP32 | FP32 | N/A | FP32 | FP32+FP32
+Nvidia TE | FP8 | FP32 | FP32 | N/A | FP32 | FP32+FP32
+MS-AMP O1 | FP8 | FP8 | FP16 | N/A | FP8 | FP32+FP32
+MS-AMP O2 | FP8 | FP8 | FP16 | N/A | FP8 | FP8+FP16
+MS-AMP O3 | FP8 | FP8 | FP8 | FP16 | FP8 | FP8+FP16
+
+## `TransformersEngine`
+
+`TransformersEngine` is the first solution to trying to train in 8-bit floating point. It works by using drop-in replacement layers for certain ones in a model that utilizes their FP8-engine to reduce the number of bits (such as 32 to 8) without degrading the final accuracy of the model. 
+
+Specifically, 🤗 Accelerate will find and replace the following layers with `TransformersEngine` versions:
+
+* `nn.LayerNorm` for `te.LayerNorm`
+* `nn.Linear` for `te.Linear`
+
+As a result we wind up with a model that has most of its layers in BF16, while some layers are in FP8 reducing some of the memory. 
+
+Anecdotally, we have noticed that performance gains don't really start showing when using `TransformerEngine` until a large majority of the layers
+in the model are made up of those two layers to replace. As a result, only larger models have shown performance improvements when the number of parameters is around and upwards of a few billion. 
+
+The `TransformerEngine` can receive many different arguments that customize how it performs FP8 calculations and what they do. A full list of the arguments is available below:
+
+* `margin`: The margin to use for the gradient scaling.
+* `interval`: The interval to use for how often the scaling factor is recomputed.
+* `fp8_format``: The format to use for the FP8 recipe. Must be one of `E4M3` or `HYBRID`.
+* `amax_history_len`: The length of the history to use for the scaling factor computation
+* `amax_compute_algo`: The algorithm to use for the scaling factor computation. Must be one of `max` or `most_recent`.
+* `override_linear_precision`: Whether or not to execute `fprop`, `dgrad`, and `wgrad` GEMMS in higher precision.
+
+You can customize each of these as part of [`utils.FP8RecipeKwargs`] to help optimize performance of your models.
+
+If we notice in the chart mentioned earlier, TE simply casts the computation layers into FP8, while everything else is in FP32. As a result this winds up utilizing the most memory but does so with the benefit of guaranteeing the least amount of loss in end accuracy during training. 
+
+## `MS-AMP`
+
+MS-AMP takes a different approach to `TransformersEngine` by providing three different optimization levels to convert more operations in FP8 or FP16.
+
+* The base optimization level (`O1`), passes communications of the weights (such as in DDP) in FP8, stores the weights of the model in FP16, and leaves the optimizer states in FP32. The main benefit of this optimization level is that we can reduce the communication bandwidth by essentially half. Additionally, more GPU memory is saved due to 1/2 of everything being cast in FP8, and the weights being cast to FP16. Notably, both the optimizer states remain in FP32.
+
+* The second optimization level (`O2`) improves upon this by also reducing the precision of the optimizer states. One is in FP8 while the other is in FP16. Generally it's been shown that this will only provide a net-gain of no degraded end accuracy, increased training speed, and reduced memory as now every state is either in FP16 or FP8. 
+
+* Finally, MS-AMP has a third optimization level (`O3`) which helps during DDP scenarios such as DeepSpeed. The weights of the model in memory are fully cast to FP8, and the master weights are now stored in FP16. This fully reduces memory by the highest factor as now not only is almost everything in FP8, only two states are left in FP16. Currently, only DeepSpeed versions up through 0.9.2 are supported, so this capability is not included in the 🤗 Accelerate integration
+
+## Combining the two
+
+More experiments need to be performed but it's been noted that combining both MS-AMP and TransformersEngine can lead to the highest throughput by relying on NVIDIA's optimized FP8 operators and utilizing how MS-AMP reduces the memory overhead.

docs/source/concept_guides/performance.md

@@ -0,0 +1,103 @@
+<!--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.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+-->
+
+# Comparing performance between different device setups
+
+Evaluating and comparing the performance from different setups can be quite tricky if you don't know what to look for.
+For example, you cannot run the same script with the same batch size across TPU, multi-GPU, and single-GPU with Accelerate 
+and expect your results to line up. 
+
+But why?
+
+There are three reasons for this that this tutorial will cover: 
+
+1. **Setting the right seeds**
+2. **Observed Batch Sizes**
+3. **Learning Rates**
+
+## Setting the Seed 
+
+While this issue has not come up as much, make sure to use [`utils.set_seed`] to fully set the seed in all distributed cases so training will be reproducible:
+
+```python
+from accelerate.utils import set_seed
+
+set_seed(42)
+```
+
+Why is this important? Under the hood this will set **5** different seed settings:
+
+```python
+    random.seed(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed_all(seed)
+    # ^^ safe to call this function even if cuda is not available
+    if is_torch_xla_available():
+        xm.set_rng_state(seed)
+```
+
+The random state, numpy's state, torch, torch's cuda state, and if TPUs are available torch_xla's cuda state.
+
+## Observed Batch Sizes 
+
+When training with Accelerate, the batch size passed to the dataloader is the **batch size per GPU**. What this entails is 
+a batch size of 64 on two GPUs is truly a batch size of 128. As a result, when testing on a single GPU this needs to be accounted for,
+as well as similarly for TPUs. 
+
+The below table can be used as a quick reference to try out different batch sizes:
+
+<Tip>
+
+In this example, there are two GPUs for "Multi-GPU" and a TPU pod with 8 workers
+
+</Tip>
+
+| Single GPU Batch Size | Multi-GPU Equivalent Batch Size | TPU Equivalent Batch Size |
+|-----------------------|---------------------------------|---------------------------|
+| 256                   | 128                             | 32                        |
+| 128                   | 64                              | 16                        |
+| 64                    | 32                              | 8                         |
+| 32                    | 16                              | 4                         |
+
+## Learning Rates 
+
+As noted in multiple sources[[1](https://aws.amazon.com/blogs/machine-learning/scalable-multi-node-deep-learning-training-using-gpus-in-the-aws-cloud/)][[2](https://docs.nvidia.com/clara/clara-train-sdk/pt/model.html#classification-models-multi-gpu-training)], the learning rate should be scaled *linearly* based on the number of devices present. The below 
+snippet shows doing so with Accelerate:
+
+<Tip>
+
+Since users can have their own learning rate schedulers defined, we leave this up to the user to decide if they wish to scale their 
+learning rate or not.
+ 
+</Tip>
+
+```python
+learning_rate = 1e-3
+accelerator = Accelerator()
+learning_rate *= accelerator.num_processes
+
+optimizer = AdamW(params=model.parameters(), lr=learning_rate)
+```
+
+You will also find that `accelerate` will step the learning rate based on the number of processes being trained on. This is because 
+of the observed batch size noted earlier. So in the case of 2 GPUs, the learning rate will be stepped twice as often as a single GPU
+to account for the batch size being twice as large (if no changes to the batch size on the single GPU instance are made).
+
+## Gradient Accumulation and Mixed Precision
+
+When using gradient accumulation and mixed precision, due to how gradient averaging works (accumulation) and the precision loss (mixed precision), 
+some degradation in performance is expected. This will be explicitly seen when comparing the batch-wise loss between different compute 
+setups. However, the overall loss, metric, and general performance at the end of training should be _roughly_ the same.

docs/source/concept_guides/training_tpu.md

@@ -0,0 +1,167 @@
+<!--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.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+-->
+
+# Training on TPUs with 🤗 Accelerate
+
+Training on TPUs can be slightly different from training on multi-gpu, even with 🤗 Accelerate. This guide aims to show you 
+where you should be careful and why, as well as the best practices in general.
+
+## Training in a Notebook
+
+The main carepoint when training on TPUs comes from the [`notebook_launcher`]. As mentioned in the [notebook tutorial](../usage_guides/notebook), you need to 
+restructure your training code into a function that can get passed to the [`notebook_launcher`] function and be careful about not declaring any tensors on the GPU.
+
+While on a TPU that last part is not as important, a critical part to understand is that when you launch code from a notebook you do so through a process called **forking**. 
+When launching from the command-line, you perform **spawning**, where a python process is not currently running and you *spawn* a new process in. Since your Jupyter notebook is already 
+utilizing a python process, you need to *fork* a new process from it to launch your code. 
+
+Where this becomes important is in regard to declaring your model. On forked TPU processes, it is recommended that you instantiate your model *once* and pass this into your 
+training function. This is different than training on GPUs where you create `n` models that have their gradients synced and back-propagated at certain moments. Instead, one 
+model instance is shared between all the nodes and it is passed back and forth. This is important especially when training on low-resource TPUs such as those provided in Kaggle kernels or
+on Google Colaboratory. 
+
+Below is an example of a training function passed to the [`notebook_launcher`] if training on CPUs or GPUs:
+
+<Tip>
+
+    This code snippet is based off the one from the `simple_nlp_example` notebook found [here](https://github.com/huggingface/notebooks/blob/main/examples/accelerate_examples/simple_nlp_example.ipynb) with slight 
+    modifications for the sake of simplicity
+
+</Tip>
+
+```python
+def training_function():
+    # Initialize accelerator
+    accelerator = Accelerator()
+    model = AutoModelForSequenceClassification.from_pretrained("bert-base-cased", num_labels=2)
+    train_dataloader, eval_dataloader = create_dataloaders(
+        train_batch_size=hyperparameters["train_batch_size"], eval_batch_size=hyperparameters["eval_batch_size"]
+    )
+
+    # Instantiate optimizer
+    optimizer = AdamW(params=model.parameters(), lr=hyperparameters["learning_rate"])
+
+    # Prepare everything
+    # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
+    # prepare method.
+    model, optimizer, train_dataloader, eval_dataloader = accelerator.prepare(
+        model, optimizer, train_dataloader, eval_dataloader
+    )
+
+    num_epochs = hyperparameters["num_epochs"]
+    # Now we train the model
+    for epoch in range(num_epochs):
+        model.train()
+        for step, batch in enumerate(train_dataloader):
+            outputs = model(**batch)
+            loss = outputs.loss
+            accelerator.backward(loss)
+
+            optimizer.step()
+            optimizer.zero_grad()
+```
+
+```python
+from accelerate import notebook_launcher
+
+notebook_launcher(training_function)
+```
+
+<Tip>
+
+    The `notebook_launcher` will default to 8 processes if 🤗 Accelerate has been configured for a TPU
+
+</Tip>
+
+If you use this example and declare the model *inside* the training loop, then on a low-resource system you will potentially see an error 
+like:
+
+```
+ProcessExitedException: process 0 terminated with signal SIGSEGV
+```
+
+This error is *extremely* cryptic but the basic explanation is you ran out of system RAM. You can avoid this entirely by reconfiguring the training function to 
+accept a single `model` argument, and declare it in an outside cell:
+
+```python
+# In another Jupyter cell
+model = AutoModelForSequenceClassification.from_pretrained("bert-base-cased", num_labels=2)
+```
+
+```diff
++ def training_function(model):
+      # Initialize accelerator
+      accelerator = Accelerator()
+-     model = AutoModelForSequenceClassification.from_pretrained("bert-base-cased", num_labels=2)
+      train_dataloader, eval_dataloader = create_dataloaders(
+          train_batch_size=hyperparameters["train_batch_size"], eval_batch_size=hyperparameters["eval_batch_size"]
+      )
+  ...
+```
+
+And finally calling the training function with:
+
+```diff
+  from accelerate import notebook_launcher
+- notebook_launcher(training_function)
++ notebook_launcher(training_function, (model,))
+```
+
+<Tip>
+
+    The above workaround is only needed when launching a TPU instance from a Jupyter Notebook on a low-resource server such as Google Colaboratory or Kaggle. If 
+    using a script or launching on a much beefier server declaring the model beforehand is not needed.
+
+</Tip>
+
+## Mixed Precision and Global Variables 
+
+As mentioned in the [mixed precision tutorial](../usage_guides/mixed_precision), 🤗 Accelerate supports fp16 and bf16, both of which can be used on TPUs.
+That being said, ideally `bf16` should be utilized as it is extremely efficient to use.
+
+There are two "layers" when using `bf16` and 🤗 Accelerate on TPUs, at the base level and at the operation level. 
+
+At the base level, this is enabled when passing `mixed_precision="bf16"` to `Accelerator`, such as:
+```python
+accelerator = Accelerator(mixed_precision="bf16")
+```
+By default, this will cast `torch.float` and `torch.double` to `bfloat16` on TPUs. 
+The specific configuration being set is an environmental variable of `XLA_USE_BF16` is set to `1`.
+
+There is a further configuration you can perform which is setting the `XLA_DOWNCAST_BF16` environmental variable. If set to `1`, then 
+`torch.float` is `bfloat16` and `torch.double` is `float32`.
+
+This is performed in the `Accelerator` object when passing `downcast_bf16=True`:
+```python
+accelerator = Accelerator(mixed_precision="bf16", downcast_bf16=True)
+```
+
+Using downcasting instead of bf16 everywhere is good for when you are trying to calculate metrics, log values, and more where raw bf16 tensors would be unusable. 
+
+## Training Times on TPUs
+
+As you launch your script, you may notice that training seems exceptionally slow at first. This is because TPUs
+first run through a few batches of data to see how much memory to allocate before finally utilizing this configured 
+memory allocation extremely efficiently. 
+
+If you notice that your evaluation code to calculate the metrics of your model takes longer due to a larger batch size being used, 
+it is recommended to keep the batch size the same as the training data if it is too slow. Otherwise the memory will reallocate to this 
+new batch size after the first few iterations. 
+
+<Tip>
+
+    Just because the memory is allocated does not mean it will be used or that the batch size will increase when going back to your training dataloader.
+
+</Tip>

docs/source/fsdp.mdx

@@ -1,120 +0,0 @@
-<!--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.
--->
-
-# Fully Sharded Data Parallel
-
-To accelerate training huge models on larger batch sizes, we can use a fully sharded data parallel model.
-This type of data parallel paradigm enables fitting more data and larger models by sharding the optimizer states, gradients and parameters.
-To read more about it and the benefits, check out the [Fully Sharded Data Parallel blog](https://pytorch.org/blog/introducing-pytorch-fully-sharded-data-parallel-api/).
-We have integrated the latest PyTorch's Fully Sharded Data Parallel (FSDP) training feature.
-All you need to do is enable it through the config.
-
-## How it works out the box
-
-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 NLP example (from the root of the repo) with FSDP enabled:
-
-```bash
-compute_environment: LOCAL_MACHINE
-deepspeed_config: {}
-distributed_type: FSDP
-fsdp_config:
-  min_num_params: 2000
-  offload_params: false
-  sharding_strategy: 1
-machine_rank: 0
-main_process_ip: null
-main_process_port: null
-main_training_function: main
-mixed_precision: 'no'
-num_machines: 1
-num_processes: 2
-use_cpu: false
-```
-
-```bash
-accelerate launch examples/nlp_example.py
-```
-
-Currently, `Accelerate` supports following config through the CLI:
-
-```bash
-`Sharding Strategy`: [1] FULL_SHARD, [2] SHARD_GRAD_OP
-`Min Num Params`: FSDP\'s minimum number of parameters for Default Auto Wrapping.
-`Offload Params`: Decides Whether to offload parameters and gradients to CPU.
-```
-
-## Few caveats to be aware of
-
-- PyTorch FSDP auto wraps sub-modules, flattens the parameters and shards the parameters in place.
-  Due to this, any optimizer created before model wrapping gets broken and occupies more memory.
-  Hence, it is highly recommended and efficient to prepare model before creating optimizer.
-  `Accelerate` will automatically wrap the model and create an optimizer for you in case of single model with a warning message.
-  > FSDP Warning: When using FSDP, it is efficient and recommended to call prepare for the model before creating the optimizer
-
-However, below is the recommended way to prepare model and optimizer while using FSDP:
-
-```diff
-model = AutoModelForSequenceClassification.from_pretrained("bert-base-cased", return_dict=True)
-+ model = accelerator.prepare(model)
-
-optimizer = torch.optim.AdamW(params=model.parameters(), lr=lr)
-
-- model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = accelerator.prepare(model,
--        optimizer, train_dataloader, eval_dataloader, lr_scheduler
--    )
-
-+ optimizer, train_dataloader, eval_dataloader, lr_scheduler = accelerator.prepare(
-+         optimizer, train_dataloader, eval_dataloader, lr_scheduler
-+        )
-
-```
-
-- In case of a single model, if you have created optimizer with multiple parameter groups and called prepare with them together,
-  then the parameter groups will be lost and the following warning is displayed:
-  > FSDP Warning: When using FSDP, several parameter groups will be conflated into
-  > a single one due to nested module wrapping and parameter flattening.
-  
-  This is because parameter groups created before wrapping will have no meaning post wrapping due parameter flattening of nested FSDP modules into 1D arrays (which can consume many layers).
-  For instance, below are the named parameters of FSDP model on GPU 0 (When using 2 GPUs. Around 55M (110M/2) params in 1D arrays as this will have the 1st shard of the parameters). 
-  Here, if one has applied no weight decay for [bias, LayerNorm.weight] named parameters of unwrapped BERT model, 
-  it can't be applied to the below FSDP wrapped model as there are no named parameters with either of those strings and 
-  the parameters of those layers are concatenated with parameters of various other layers.
-  ```
-  {
-    '_fsdp_wrapped_module.flat_param': torch.Size([494209]), 
-    '_fsdp_wrapped_module._fpw_module.bert.embeddings.word_embeddings._fsdp_wrapped_module.flat_param': torch.Size([11720448]), 
-    '_fsdp_wrapped_module._fpw_module.bert.encoder._fsdp_wrapped_module.flat_param': torch.Size([42527232])
-  }
-  ```
-
-
-- In case of multiple models, it is necessary to prepare the models before creating optimizers else it will throw an error.
-- Mixed precision is currently not supported with FSDP.
-
-For more control, users can leverage the `FullyShardedDataParallelPlugin` wherein they can specify `auto_wrap_policy`, `backward_prefetch` and `ignored_modules`.
-After creating an instance of this class, users can pass it to the Accelerator class instantiation.
-For more information on these options, please refer to the PyTorch [FullyShardedDataParallel](https://github.com/pytorch/pytorch/blob/0df2e863fbd5993a7b9e652910792bd21a516ff3/torch/distributed/fsdp/fully_sharded_data_parallel.py#L236) code.
-
-[[autodoc]] utils.FullyShardedDataParallelPlugin

docs/source/index.md

@@ -0,0 +1,74 @@
+<!--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.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+-->
+
+# Accelerate
+
+🤗 Accelerate is a library that enables the same PyTorch code to be run across any distributed configuration by adding just four lines of code! In short, training and inference at scale made simple, efficient and adaptable.
+
+```diff
++ from accelerate import Accelerator
++ accelerator = Accelerator()
+
++ model, optimizer, training_dataloader, scheduler = accelerator.prepare(
++     model, optimizer, training_dataloader, scheduler
++ )
+
+  for batch in training_dataloader:
+      optimizer.zero_grad()
+      inputs, targets = batch
+      inputs = inputs.to(device)
+      targets = targets.to(device)
+      outputs = model(inputs)
+      loss = loss_function(outputs, targets)
++     accelerator.backward(loss)
+      optimizer.step()
+      scheduler.step()
+```
+
+Built on `torch_xla` and `torch.distributed`, 🤗 Accelerate takes care of the heavy lifting, so you don't have to write any custom code to adapt to these platforms.
+Convert existing codebases to utilize [DeepSpeed](usage_guides/deepspeed), perform [fully sharded data parallelism](usage_guides/fsdp), and have automatic support for mixed-precision training! 
+
+<Tip> 
+
+  To get a better idea of this process, make sure to check out the [Tutorials](basic_tutorials/overview)! 
+
+</Tip>
+
+
+This code can then be launched on any system through Accelerate's CLI interface:
+```bash
+accelerate launch {my_script.py}
+```
+
+<div class="mt-10">
+  <div class="w-full flex flex-col space-y-4 md:space-y-0 md:grid md:grid-cols-2 md:gap-y-4 md:gap-x-5">
+    <a class="!no-underline border dark:border-gray-700 p-5 rounded-lg shadow hover:shadow-lg" href="./basic_tutorials/overview"
+      ><div class="w-full text-center bg-gradient-to-br from-blue-400 to-blue-500 rounded-lg py-1.5 font-semibold mb-5 text-white text-lg leading-relaxed">Tutorials</div>
+      <p class="text-gray-700">Learn the basics and become familiar with using 🤗 Accelerate. Start here if you are using 🤗 Accelerate for the first time!</p>
+    </a>
+    <a class="!no-underline border dark:border-gray-700 p-5 rounded-lg shadow hover:shadow-lg" href="./usage_guides/explore"
+      ><div class="w-full text-center bg-gradient-to-br from-indigo-400 to-indigo-500 rounded-lg py-1.5 font-semibold mb-5 text-white text-lg leading-relaxed">How-to guides</div>
+      <p class="text-gray-700">Practical guides to help you achieve a specific goal. Take a look at these guides to learn how to use 🤗 Accelerate to solve real-world problems.</p>
+    </a>
+    <a class="!no-underline border dark:border-gray-700 p-5 rounded-lg shadow hover:shadow-lg" href="./concept_guides/gradient_synchronization"
+      ><div class="w-full text-center bg-gradient-to-br from-pink-400 to-pink-500 rounded-lg py-1.5 font-semibold mb-5 text-white text-lg leading-relaxed">Conceptual guides</div>
+      <p class="text-gray-700">High-level explanations for building a better understanding of important topics such as avoiding subtle nuances and pitfalls in distributed training and DeepSpeed.</p>
+   </a>
+    <a class="!no-underline border dark:border-gray-700 p-5 rounded-lg shadow hover:shadow-lg" href="./package_reference/accelerator"
+      ><div class="w-full text-center bg-gradient-to-br from-purple-400 to-purple-500 rounded-lg py-1.5 font-semibold mb-5 text-white text-lg leading-relaxed">Reference</div>
+      <p class="text-gray-700">Technical descriptions of how 🤗 Accelerate classes and methods work.</p>
+    </a>
+  </div>
+</div>

docs/source/index.mdx

@@ -1,132 +0,0 @@
-<!--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.
--->
-
-# Accelerate
-
-Run your *raw* PyTorch training script on any kind of device
-
-## Features
-
-- 🤗 Accelerate provides an easy API to make your scripts run with mixed precision and on any kind of distributed
-  setting (multi-GPUs, TPUs etc.) while still letting you write your own training loop. The same code can then runs
-  seamlessly on your local machine for debugging or your training environment.
-
-- 🤗 Accelerate also provides a CLI tool that allows you to quickly configure and test your training environment then
-  launch the scripts.
-
-
-## Easy to integrate
-
-A traditional training loop in PyTorch looks like this:
-
-```python
-my_model.to(device)
-
-for batch in my_training_dataloader:
-    my_optimizer.zero_grad()
-    inputs, targets = batch
-    inputs = inputs.to(device)
-    targets = targets.to(device)
-    outputs = my_model(inputs)
-    loss = my_loss_function(outputs, targets)
-    loss.backward()
-    my_optimizer.step()
-```
-
-Changing it to work with accelerate is really easy and only adds a few lines of code:
-
-```diff
-+ from accelerate import Accelerator
-
-+ accelerator = Accelerator()
-  # Use the device given by the *accelerator* object.
-+ device = accelerator.device
-  my_model.to(device)
-  # Pass every important object (model, optimizer, dataloader) to *accelerator.prepare*
-+ my_model, my_optimizer, my_training_dataloader = accelerate.prepare(
-+     my_model, my_optimizer, my_training_dataloader
-+ )
-
-  for batch in my_training_dataloader:
-      my_optimizer.zero_grad()
-      inputs, targets = batch
-      inputs = inputs.to(device)
-      targets = targets.to(device)
-      outputs = my_model(inputs)
-      loss = my_loss_function(outputs, targets)
-      # Just a small change for the backward instruction
--     loss.backward()
-+     accelerator.backward(loss)
-      my_optimizer.step()
-```
-
-and with this, your script can now run in a distributed environment (multi-GPU, TPU).
-
-You can even simplify your script a bit by letting 🤗 Accelerate handle the device placement for you (which is safer,
-especially for TPU training):
-
-```diff
-+ from accelerate import Accelerator
-
-+ accelerator = Accelerator()
-- my_model.to(device)
-  # Pass every important object (model, optimizer, dataloader) to *accelerator.prepare*
-+ my_model, my_optimizer, my_training_dataloader = accelerate.prepare(
-+     my_model, my_optimizer, my_training_dataloader
-+ )
-
-  for batch in my_training_dataloader:
-      my_optimizer.zero_grad()
-      inputs, targets = batch
--     inputs = inputs.to(device)
--     targets = targets.to(device)
-      outputs = my_model(inputs)
-      loss = my_loss_function(outputs, targets)
-      # Just a small change for the backward instruction
--     loss.backward()
-+     accelerator.backward(loss)
-      my_optimizer.step()
-```
-
-## Script launcher
-
-No need to remember how to use `torch.distributed.launch` or to write a specific launcher for TPU training! 🤗
-Accelerate comes with a CLI tool that will make your life easier when launching distributed scripts.
-
-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 NLP example (from the root of the repo):
-
-```bash
-accelerate launch examples/nlp_example.py
-```
-
-## Supported integrations
-
-- CPU only
-- single GPU
-- multi-GPU on one node (machine)
-- multi-GPU on several nodes (machines)
-- TPU
-- FP16 with native AMP (apex on the roadmap)
-- DeepSpeed (experimental support)

docs/source/installation.mdx

@@ -1,96 +0,0 @@
-<!---
-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.
--->
-
-# Installation
-
-🤗 Accelerate is tested on Python 3.6+, and PyTorch 1.6.0+.
-
-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/). Create a virtual environment with the version of Python you're going
-to use and activate it.
-
-Now, if you want to use 🤗 Accelerate, you can install it with pip.
-
-## Installation with pip
-
-First you need to install PyTorch. Please refer to the
-[PyTorch installation page](https://pytorch.org/get-started/locally/#start-locally) regarding the specific install command for your platform.
-
-When PyTorch has been installed, 🤗 Accelerate can be installed using pip as follows:
-
-```bash
-pip install accelerate
-```
-
-Alternatively, for CPU-support only, you can install 🤗 Accelerate and PyTorch in one line with:
-
-```bash
-pip install accelerate[torch]
-```
-
-To check 🤗 Accelerate is properly installed, run the following command:
-
-```bash
-python -c "TODO write"
-```
-
-## Installing from source
-
-Here is how to quickly install `accelerate` from source:
-
-```bash
-pip install git+https://github.com/huggingface/accelerate
-```
-
-Note that this will install not the latest released version, but the bleeding edge `main` version, which you may want to use in case a bug has been fixed since the last official release and a new release hasn't  been yet rolled out.
-
-While we strive to keep `main` operational at all times, if you notice some issues, they usually get fixed within a few hours or a day and and you're more than welcome to help us detect any problems by opening an [Issue](https://github.com/huggingface/accelerate/issues) and this way, things will get fixed even sooner.
-
-Again, you can run:
-
-```bash
-python -c "TODO write"
-```
-
-to check 🤗 Accelerate is properly installed.
-
-## Editable install
-
-If you want to constantly use the bleeding edge `main` version of the source code, or if you want to contribute to the library and need to test the changes in the code you're making, you will need an editable install. This is done by cloning the repository and installing with the following commands:
-
-``` bash
-git clone https://github.com/huggingface/accelerate.git
-cd accelerate
-pip install -e .
-```
-
-This command performs a magical link between the folder you cloned the repository to and your python library paths, and it'll look inside this folder in addition to the normal library-wide paths. So if normally your python packages get installed into:
-```
-~/anaconda3/envs/main/lib/python3.7/site-packages/
-```
-now this editable install will reside where you clone the folder to, e.g. `~/accelerate/` and python will search it too.
-
-Do note that you have to keep that `accelerate` folder around and not delete it to continue using the 🤗 Accelerate library.
-
-Now, let's get to the real benefit of this installation approach. Say, you saw some new feature has been just committed into `main`. If you have already performed all the steps above, to update your accelerate repo to include all the latest commits, all you need to do is to `cd` into that cloned repository folder and update the clone to the latest version:
-
-```bash
-cd ~/accelerate/
-git pull
-```
-
-There is nothing else to do. Your python environment will find the bleeding edge version of 🤗 Accelerate on the next run.
-

docs/source/launcher.mdx

@@ -1,28 +0,0 @@
-<!--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.
--->
-
-# Notebook Launcher
-
-Launch your training function inside a notebook. Currently supports launching a training with TPUs on [Google
-Colab](https://colab.research.google.com/) and [Kaggle kernels](https://www.kaggle.com/code), as well as training on
-several GPUs (if the machine on which you are running your notebook has them).
-
-An example can be found in [this notebook](https://github.com/huggingface/notebooks/blob/master/examples/accelerate/simple_nlp_example.ipynb).
-
-<Tip warning={true}>
-
-Your `Accelerator` object should only be defined inside the training function. This is because the
-initialization should be done inside the launcher only.
-
-</Tip>
-
-[[autodoc]] notebook_launcher

docs/source/memory.mdx

@@ -1,51 +0,0 @@
-<!--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.
--->
-
-# Memory Utilities
-
-One of the most frustrating errors when it comes to running training scripts is hitting "CUDA Out-of-Memory", 
-as the entire script needs to be restarted, progress is lost, and typically a developer would want to simply
-start their script and let it run.
-
-`Accelerate` provides a utility heavily based on [toma](https://github.com/BlackHC/toma) to give this capability.
-
-## find_executable_batch_size
-
-This algorithm operates with exponential decay, decreasing the batch size in half after each failed run on some 
-training script. To use it, restructure your training function to include an inner function that includes this wrapper, 
-and build your dataloaders inside it. At a minimum, this could look like 4 new lines of code. 
-> Note: The inner function *must* take in the batch size as the first parameter, but we do not pass one to it when called. The wrapper handles this for us
-
-```diff
-def training_function(args):
-    accelerator = Accelerator()
-    model = get_model()
-    model.to(accelerator.device)
-    optimizer = get_optimizer()
-
-+   @find_executable_batch_size(starting_batch_size=args.batch_size)
-+   def inner_training_loop(batch_size):
-+       nonlocal model, optimizer # Ensure they can be used in our context
-        train_dataloader, eval_dataloader = get_dataloaders(accelerator, batch_size)
-        lr_scheduler = get_scheduler(
-            optimizer, 
-            num_training_steps=len(train_dataloader)*num_epochs
-        )
-        model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = accelerator.prepare(
-            model, optimizer, train_dataloader, eval_dataloader, lr_scheduler
-        )
-        train(model, optimizer, train_dataloader, lr_scheduler)
-        validate(model, eval_dataloader)
-+   inner_training_loop()
-```
-
-[[autodoc]] memory_utils.find_executable_batch_size

docs/source/package_reference/accelerator.md

@@ -0,0 +1,26 @@
+<!--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.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+-->
+
+# Accelerator
+
+The [`Accelerator`] is the main class for enabling distributed training on any type of training setup. Read the [Add Accelerator to your code](../basic_tutorials/migration) tutorial to learn more about how to add the [`Accelerator`] to your script.
+
+## Accelerator[[api]]
+
+[[autodoc]] Accelerator
+
+## Utilities
+
+[[autodoc]] accelerate.utils.gather_object

docs/source/package_reference/big_modeling.md

@@ -0,0 +1,47 @@
+<!--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.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+-->
+
+# Working with large models
+
+## Dispatching and Offloading Models
+
+[[autodoc]] big_modeling.init_empty_weights
+[[autodoc]] big_modeling.cpu_offload
+[[autodoc]] big_modeling.cpu_offload_with_hook
+[[autodoc]] big_modeling.disk_offload
+[[autodoc]] big_modeling.dispatch_model
+[[autodoc]] big_modeling.load_checkpoint_and_dispatch
+[[autodoc]] big_modeling.load_checkpoint_in_model
+[[autodoc]] utils.infer_auto_device_map
+
+## Model Hooks
+
+### Hook Classes
+
+[[autodoc]] hooks.ModelHook
+[[autodoc]] hooks.AlignDevicesHook
+[[autodoc]] hooks.SequentialHook
+
+### Adding Hooks
+
+[[autodoc]] hooks.add_hook_to_module
+[[autodoc]] hooks.attach_execution_device_hook
+[[autodoc]] hooks.attach_align_device_hook
+[[autodoc]] hooks.attach_align_device_hook_on_blocks
+
+### Removing Hooks
+
+[[autodoc]] hooks.remove_hook_from_module
+[[autodoc]] hooks.remove_hook_from_submodules

docs/source/package_reference/cli.md

@@ -0,0 +1,312 @@
+<!--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.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+-->
+
+# The Command Line 
+
+Below is a list of all the available commands 🤗 Accelerate with their parameters
+
+## accelerate config
+
+**Command**:
+
+`accelerate config` or `accelerate-config`
+
+Launches a series of prompts to create and save a `default_config.yml` configuration file for your training system. Should 
+always be ran first on your machine.
+
+**Usage**: 
+
+```bash
+accelerate config [arguments]
+```
+
+**Optional Arguments**:
+* `--config_file CONFIG_FILE` (`str`) -- The path to use to store the config file. Will default to a file named default_config.yaml in the cache location, which is the content
+                        of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have such an environment variable, your cache directory
+                        (`~/.cache` or the content of `XDG_CACHE_HOME`) suffixed with `huggingface`.
+* `-h`, `--help` (`bool`) -- Show a help message and exit
+
+## accelerate config default
+
+**Command**:
+
+`accelerate config default` or `accelerate-config default`
+
+Create a default config file for Accelerate with only a few flags set.
+
+**Usage**: 
+
+```bash
+accelerate config default [arguments]
+```
+
+**Optional Arguments**:
+* `--config_file CONFIG_FILE` (`str`) -- The path to use to store the config file. Will default to a file named default_config.yaml in the cache location, which is the content
+                        of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have such an environment variable, your cache directory
+                        (`~/.cache` or the content of `XDG_CACHE_HOME`) suffixed with `huggingface`.
+
+* `-h`, `--help` (`bool`) -- Show a help message and exit
+* `--mixed_precision {no,fp16,bf16}` (`str`) -- Whether or not to use mixed precision training. Choose between FP16 and BF16 (bfloat16) training. BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later.
+
+## accelerate config update
+
+**Command**:
+
+`accelerate config update` or `accelerate-config update`
+
+Update an existing config file with the latest defaults while maintaining the old configuration.
+
+**Usage**: 
+
+```bash
+accelerate config update [arguments]
+```
+
+**Optional Arguments**:
+* `--config_file CONFIG_FILE` (`str`) -- The path to the config file to update. Will default to a file named default_config.yaml in the cache location, which is the content
+                        of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have such an environment variable, your cache directory
+                        (`~/.cache` or the content of `XDG_CACHE_HOME`) suffixed with `huggingface`.
+
+* `-h`, `--help` (`bool`) -- Show a help message and exit
+
+
+## accelerate env
+
+**Command**:
+
+`accelerate env` or `accelerate-env` or `python -m accelerate.commands.env`
+
+Lists the contents of the passed 🤗 Accelerate configuration file. Should always be used when opening an issue on the [GitHub repository](https://github.com/huggingface/accelerate).
+
+**Usage**:
+
+```bash
+accelerate env [arguments]
+```
+
+**Optional Arguments**:
+* `--config_file CONFIG_FILE` (`str`) -- The path to use to store the config file. Will default to a file named default_config.yaml in the cache location, which is the content
+                        of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have such an environment variable, your cache directory
+                        (`~/.cache` or the content of `XDG_CACHE_HOME`) suffixed with `huggingface`.
+* `-h`, `--help` (`bool`) -- Show a help message and exit
+
+## accelerate launch
+
+**Command**:
+
+`accelerate launch` or `accelerate-launch` or `python -m accelerate.commands.launch`
+
+Launches a specified script on a distributed system with the right parameters.
+
+**Usage**: 
+
+```bash
+accelerate launch [arguments] {training_script} --{training_script-argument-1} --{training_script-argument-2} ...
+```
+
+**Positional Arguments**:
+
+- `{training_script}` -- The full path to the script to be launched in parallel
+- `--{training_script-argument-1}` -- Arguments of the training script
+
+**Optional Arguments**:
+
+* `-h`, `--help` (`bool`) -- Show a help message and exit
+* `--config_file CONFIG_FILE` (`str`)-- The config file to use for the default values in the launching script.
+* `-m`, `--module` (`bool`) -- Change each process to interpret the launch script as a Python module, executing with the same behavior as 'python -m'.
+* `--no_python` (`bool`) -- Skip prepending the training script with 'python' - just execute it directly. Useful when the script is not a Python script.
+* `--debug` (`bool`) -- Whether to print out the torch.distributed stack trace when something fails.
+* `-q`, `--quiet` (`bool`) -- Silence subprocess errors from the launch stack trace to only show the relevant tracebacks. (Only applicable to DeepSpeed and single-process configurations).
+
+
+The rest of these arguments are configured through `accelerate config` and are read in from the specified `--config_file` (or default configuration) for their 
+values. They can also be passed in manually.
+
+**Hardware Selection Arguments**:
+
+* `--cpu` (`bool`) -- Whether or not to force the training on the CPU.
+* `--multi_gpu` (`bool`) -- Whether or not this should launch a distributed GPU training.
+* `--tpu` (`bool`) -- Whether or not this should launch a TPU training.
+* `--ipex` (`bool`) -- Whether or not this should launch an Intel Pytorch Extension (IPEX) training.
+
+**Resource Selection Arguments**:
+
+The following arguments are useful for fine-tuning how available hardware should be used
+
+* `--mixed_precision {no,fp16,bf16}` (`str`) -- Whether or not to use mixed precision training. Choose between FP16 and BF16 (bfloat16) training. BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later.
+* `--num_processes NUM_PROCESSES` (`int`) -- The total number of processes to be launched in parallel.
+* `--num_machines NUM_MACHINES` (`int`) -- The total number of machines used in this training.
+* `--num_cpu_threads_per_process NUM_CPU_THREADS_PER_PROCESS` (`int`) -- The number of CPU threads per process. Can be tuned for optimal performance.
+
+**Training Paradigm Arguments**:
+
+The following arguments are useful for selecting which training paradigm to use.
+
+* `--use_deepspeed` (`bool`) -- Whether or not to use DeepSpeed for training.
+* `--use_fsdp` (`bool`) -- Whether or not to use FullyShardedDataParallel for training.
+* `--use_megatron_lm` (`bool`) -- Whether or not to use Megatron-LM for training.
+* `--use_xpu` (`bool`) -- Whether to use IPEX plugin to speed up training on XPU specifically.
+
+**Distributed GPU Arguments**:
+
+The following arguments are only useful when `multi_gpu` is passed or multi-gpu training is configured through `accelerate config`: 
+
+* `--gpu_ids` (`str`) -- What GPUs (by id) should be used for training on this machine as a comma-seperated list
+* `--same_network` (`bool`) -- Whether all machines used for multinode training exist on the same local network.
+* `--machine_rank MACHINE_RANK` (`int`) -- The rank of the machine on which this script is launched.
+* `--main_process_ip MAIN_PROCESS_IP` (`str`) -- The IP address of the machine of rank 0.
+* `--main_process_port MAIN_PROCESS_PORT` (`int`) -- The port to use to communicate with the machine of rank 0.
+* `--rdzv_backend` (`str`) -- The rendezvous method to use, such as "static" or "c10d"
+* `--rdzv_conf` (`str`) -- Additional rendezvous configuration (<key1>=<value1>,<key2>=<value2>,...).
+* `--max_restarts` (`int`) -- Maximum number of worker group restarts before failing.
+* `--monitor_interval` (`float`) -- Interval, in seconds, to monitor the state of workers.
+
+**TPU Arguments**:
+
+The following arguments are only useful when `tpu` is passed or TPU training is configured through `accelerate config`: 
+
+* `--main_training_function MAIN_TRAINING_FUNCTION` (`str`) -- The name of the main function to be executed in your script.
+* `--downcast_bf16` (`bool`) -- Whether when using bf16 precision on TPUs if both float and double tensors are cast to bfloat16 or if double tensors remain as float32.
+
+**DeepSpeed Arguments**:
+
+The following arguments are only useful when `use_deepspeed` is passed or `deepspeed` is configured through `accelerate config`: 
+
+* `--deepspeed_config_file` (`str`) -- DeepSpeed config file.
+* `--zero_stage` (`int`) -- DeepSpeed's ZeRO optimization stage.
+* `--offload_optimizer_device` (`str`) -- Decides where (none|cpu|nvme) to offload optimizer states.
+* `--offload_param_device` (`str`) -- Decides where (none|cpu|nvme) to offload parameters.
+* `--gradient_accumulation_steps` (`int`) -- No of gradient_accumulation_steps used in your training script.
+* `--gradient_clipping` (`float`) -- Gradient clipping value used in your training script.
+* `--zero3_init_flag` (`str`) -- Decides Whether (true|false) to enable `deepspeed.zero.Init` for constructing massive models. Only applicable with DeepSpeed ZeRO Stage-3.
+* `--zero3_save_16bit_model` (`str`) -- Decides Whether (true|false) to save 16-bit model weights when using ZeRO Stage-3. Only applicable with DeepSpeed ZeRO Stage-3.
+* `--deepspeed_hostfile` (`str`) -- DeepSpeed hostfile for configuring multi-node compute resources.
+* `--deepspeed_exclusion_filter` (`str`) -- DeepSpeed exclusion filter string when using mutli-node setup.
+* `--deepspeed_inclusion_filter` (`str`) -- DeepSpeed inclusion filter string when using mutli-node setup.
+* `--deepspeed_multinode_launcher` (`str`) -- DeepSpeed multi-node launcher to use.
+
+**Fully Sharded Data Parallelism Arguments**:
+
+The following arguments are only useful when `use_fsdp` is passed or Fully Sharded Data Parallelism is configured through `accelerate config`:
+
+* `--fsdp_offload_params` (`str`) -- Decides Whether (true|false) to offload parameters and gradients to CPU.
+* `--fsdp_min_num_params` (`int`) -- FSDP's minimum number of parameters for Default Auto Wrapping.
+* `--fsdp_sharding_strategy` (`int`) -- FSDP's Sharding Strategy.
+* `--fsdp_auto_wrap_policy` (`str`) -- FSDP's auto wrap policy.
+* `--fsdp_transformer_layer_cls_to_wrap` (`str`) -- Transformer layer class name (case-sensitive) to wrap, e.g, `BertLayer`, `GPTJBlock`, `T5Block` ...
+* `--fsdp_backward_prefetch_policy` (`str`) -- FSDP's backward prefetch policy.
+* `--fsdp_state_dict_type` (`str`) -- FSDP's state dict type.
+* `--fsdp_forward_prefetch` (`str`) -- FSDP forward prefetch.
+* `--fsdp_use_orig_params` (`str`) -- If True, allows non-uniform `requires_grad` mixed in a FSDP unit.
+* `--fsdp_cpu_ram_efficient_loading` (`str`) - If true, only the first process loads the pretrained model checkoint while all other processes have empty weights. When using this, `--fsdp_sync_module_states` needs to True. 
+* `--fsdp_sync_module_states` (`str`) - If true, each individually wrapped FSDP unit will broadcast module parameters from rank 0.
+
+**Megatron-LM Arguments**:
+
+The following arguments are only useful when `use_megatron_lm` is passed or Megatron-LM is configured through `accelerate config`:
+
+* `--megatron_lm_tp_degree` (``) -- Megatron-LM's Tensor Parallelism (TP) degree.
+* `--megatron_lm_pp_degree` (``) -- Megatron-LM's Pipeline Parallelism (PP) degree.
+* `--megatron_lm_num_micro_batches` (``) -- Megatron-LM's number of micro batches when PP degree > 1.
+* `--megatron_lm_sequence_parallelism` (``) -- Decides Whether (true|false) to enable Sequence Parallelism when TP degree > 1.
+* `--megatron_lm_recompute_activations` (``) -- Decides Whether (true|false) to enable Selective Activation Recomputation.
+* `--megatron_lm_use_distributed_optimizer` (``) -- Decides Whether (true|false) to use distributed optimizer which shards optimizer state and gradients across Data Parallel (DP) ranks.
+* `--megatron_lm_gradient_clipping` (``) -- Megatron-LM's gradient clipping value based on global L2 Norm (0 to disable).
+
+**AWS SageMaker Arguments**:
+
+The following arguments are only useful when training in SageMaker
+
+* `--aws_access_key_id AWS_ACCESS_KEY_ID` (`str`) -- The AWS_ACCESS_KEY_ID used to launch the Amazon SageMaker training job
+* `--aws_secret_access_key AWS_SECRET_ACCESS_KEY` (`str`) -- The AWS_SECRET_ACCESS_KEY used to launch the Amazon SageMaker training job
+
+## accelerate estimate-memory
+
+**Command**:
+
+`accelerate estimate-memory` or `accelerate-estimate-memory` or `python -m accelerate.commands.estimate`
+
+Estimates the total vRAM a particular model hosted on the Hub needs to be loaded in with an estimate for training. Requires that `huggingface_hub` be installed. 
+
+<Tip>
+
+    When performing inference, typically add ≤20% to the result as overall allocation [as referenced here](https://blog.eleuther.ai/transformer-math/). We will have more extensive estimations in the future that will automatically be included in the calculation.
+
+</Tip>
+
+**Usage**: 
+
+```bash
+accelerate estimate-memory {MODEL_NAME} --library_name {LIBRARY_NAME} --dtypes {dtype_1} {dtype_2} ...
+```
+
+**Required Arguments**:
+
+* `MODEL_NAME` (`str`)-- The model name on the Hugging Face Hub
+
+**Optional Arguments**:
+
+* `--library_name {timm,transformers}` (`str`) -- The library the model has an integration with, such as `transformers`, needed only if this information is not stored on the Hub
+* `--dtypes {float32,float16,int8,int4}` (`[{float32,float16,int8,int4} ...]`) -- The dtypes to use for the model, must be one (or many) of `float32`, `float16`, `int8`, and `int4`
+* `--trust_remote_code` (`bool`) -- Whether or not to allow for custom models defined on the Hub in their own modeling files. This option should only be passed for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine.
+
+## accelerate tpu-config
+
+`accelerate tpu-config`
+
+**Usage**:
+
+```bash
+accelerate tpu-config [arguments]
+```
+
+**Optional Arguments**:
+* `-h`, `--help` (`bool`) -- Show a help message and exit
+
+**Config Arguments**:
+
+Arguments that can be configured through `accelerate config`.
+
+* `--config_file` (`str`) -- Path to the config file to use for accelerate.
+* `--tpu_name` (`str`) -- The name of the TPU to use. If not specified, will use the TPU specified in the config file.
+* `--tpu_zone` (`str`) -- The zone of the TPU to use. If not specified, will use the zone specified in the config file.
+
+**TPU Arguments**:
+
+Arguments for options ran inside the TPU.
+
+* `--command_file` (`str`) -- The path to the file containing the commands to run on the pod on startup.
+* `--command` (`str`) -- A command to run on the pod. Can be passed multiple times.
+* `--install_accelerate` (`bool`) -- Whether to install accelerate on the pod. Defaults to False.
+* `--accelerate_version` (`str`) -- The version of accelerate to install on the pod. If not specified, will use the latest pypi version. Specify 'dev' to install from GitHub.
+* `--debug` (`bool`) -- If set, will print the command that would be run instead of running it.
+
+## accelerate test
+
+`accelerate test` or `accelerate-test`
+
+Runs `accelerate/test_utils/test_script.py` to verify that 🤗 Accelerate has been properly configured on your system and runs. 
+
+**Usage**: 
+
+```bash
+accelerate test [arguments]
+```
+
+**Optional Arguments**:
+* `--config_file CONFIG_FILE` (`str`) -- The path to use to store the config file. Will default to a file named default_config.yaml in the cache location, which is the content
+                        of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have such an environment variable, your cache directory
+                        (`~/.cache` or the content of `XDG_CACHE_HOME`) suffixed with `huggingface`.
+* `-h`, `--help` (`bool`) -- Show a help message and exit

docs/source/package_reference/deepspeed.md

@@ -0,0 +1,28 @@
+<!--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.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+-->
+
+# Utilities for DeepSpeed
+
+[[autodoc]] utils.DeepSpeedPlugin
+
+[[autodoc]] utils.deepspeed.DummyOptim
+
+[[autodoc]] utils.deepspeed.DummyScheduler
+
+[[autodoc]] utils.deepspeed.DeepSpeedEngineWrapper
+
+[[autodoc]] utils.deepspeed.DeepSpeedOptimizerWrapper
+
+[[autodoc]] utils.deepspeed.DeepSpeedSchedulerWrapper

docs/source/package_reference/fsdp.md

@@ -0,0 +1,20 @@
+<!--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.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+-->
+
+# Utilities for Fully Sharded Data Parallelism
+
+[[autodoc]] utils.merge_fsdp_weights
+
+[[autodoc]] utils.FullyShardedDataParallelPlugin

docs/source/package_reference/inference.md

@@ -0,0 +1,20 @@
+<!--Copyright 2024 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.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+-->
+
+# The inference API
+
+These docs refer to the [PiPPy](https://github.com/PyTorch/PiPPy) integration.
+
+[[autodoc]] inference.prepare_pippy

docs/source/package_reference/kwargs.md

@@ -8,6 +8,9 @@ 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.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
 -->
 
 # Kwargs Handlers
@@ -15,11 +18,22 @@ specific language governing permissions and limitations under the License.
 The following objects can be passed to the main [`Accelerator`] to customize how some PyTorch objects
 related to distributed training or mixed precision are created.
 
+## AutocastKwargs
+
+[[autodoc]] AutocastKwargs
 
 ## DistributedDataParallelKwargs
 
 [[autodoc]] DistributedDataParallelKwargs
 
+## FP8RecipeKwargs
+
+[[autodoc]] utils.FP8RecipeKwargs
+
+## ProfileKwargs
+
+[[autodoc]] utils.ProfileKwargs
+
 ## GradScalerKwargs
 
 [[autodoc]] GradScalerKwargs
@@ -27,3 +41,7 @@ related to distributed training or mixed precision are created.
 ## InitProcessGroupKwargs
 
 [[autodoc]] InitProcessGroupKwargs
+
+## KwargsHandler
+
+[[autodoc]] utils.KwargsHandler

docs/source/package_reference/launchers.md

@@ -0,0 +1,22 @@
+<!--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.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+-->
+
+# Launchers
+
+Functions for launching training on distributed processes.
+
+
+[[autodoc]] accelerate.notebook_launcher
+[[autodoc]] accelerate.debug_launcher

docs/source/package_reference/logging.md

@@ -0,0 +1,21 @@
+<!--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.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+-->
+
+# Logging with Accelerate
+
+Refer to the [Troubleshooting guide](../usage_guides/troubleshooting#logging) or to the example below to learn 
+how to use 🤗 Accelerate's logger. 
+
+[[autodoc]] logging.get_logger

docs/source/package_reference/megatron_lm.md

@@ -0,0 +1,32 @@
+<!--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.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+-->
+
+# Utilities for Megatron-LM
+
+[[autodoc]] utils.MegatronLMPlugin
+
+[[autodoc]] utils.MegatronLMDummyScheduler
+
+[[autodoc]] utils.MegatronLMDummyDataLoader
+
+[[autodoc]] utils.AbstractTrainStep
+
+[[autodoc]] utils.GPTTrainStep
+
+[[autodoc]] utils.BertTrainStep
+
+[[autodoc]] utils.T5TrainStep
+
+[[autodoc]] utils.avg_losses_across_data_parallel_group

docs/source/package_reference/state.md

@@ -0,0 +1,28 @@
+<!--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.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+-->
+
+# Stateful Classes
+
+Below are variations of a [singleton class](https://en.wikipedia.org/wiki/Singleton_pattern) in the sense that all
+instances share the same state, which is initialized on the first instantiation.
+
+These classes are immutable and store information about certain configurations or 
+states.
+
+[[autodoc]] state.PartialState
+
+[[autodoc]] state.AcceleratorState
+
+[[autodoc]] state.GradientState

docs/source/package_reference/torch_wrappers.md

@@ -8,62 +8,30 @@ 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.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
 -->
 
-# Internals
+# Wrapper classes for torch Dataloaders, Optimizers, and Schedulers
 
-## Optimizer
+The internal classes Accelerate uses to prepare objects for distributed training
+when calling [`~Accelerator.prepare`].
+
+## Datasets and DataLoaders
+
+[[autodoc]] data_loader.prepare_data_loader
+[[autodoc]] data_loader.skip_first_batches
+
+[[autodoc]] data_loader.BatchSamplerShard
+[[autodoc]] data_loader.IterableDatasetShard
+[[autodoc]] data_loader.DataLoaderShard
+[[autodoc]] data_loader.DataLoaderDispatcher
+
+## Optimizers 
 
 [[autodoc]] optimizer.AcceleratedOptimizer
 
-## DataLoader
+## Schedulers 
 
-The main work on your PyTorch `DataLoader` is done by the following function:
-
-[[autodoc]] data_loader.prepare_data_loader
-
-### BatchSamplerShard
-
-[[autodoc]] data_loader.DataLoaderShard
-
-### BatchSamplerShard
-
-[[autodoc]] data_loader.BatchSamplerShard
-
-### IterableDatasetShard
-
-[[autodoc]] data_loader.IterableDatasetShard
-
-## Scheduler
-
-[[autodoc]] scheduler.AcceleratedScheduler
-
-## Distributed Config
-
-### AcceleratorState
-
-[[autodoc]] state.AcceleratorState
-
-### DistributedType
-
-[[autodoc]] state.DistributedType
-
-## Tracking
-
-[[autodoc]] tracking.GeneralTracker
-
-## Utilities
-
-[[autodoc]] utils.extract_model_from_parallel
-
-[[autodoc]] utils.gather
-
-[[autodoc]] utils.send_to_device
-
-[[autodoc]] utils.set_seed
-
-[[autodoc]] utils.synchronize_rng_state
-
-[[autodoc]] utils.synchronize_rng_states
-
-[[autodoc]] utils.wait_for_everyone
+[[autodoc]] scheduler.AcceleratedScheduler

docs/source/package_reference/tracking.md

@@ -0,0 +1,35 @@
+<!--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.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+-->
+
+# Experiment Tracking
+
+## The Base Tracker Class
+
+[[autodoc]] tracking.GeneralTracker
+
+## Integrated Trackers
+
+[[autodoc]] tracking.TensorBoardTracker
+    - __init__
+[[autodoc]] tracking.WandBTracker
+    - __init__
+[[autodoc]] tracking.CometMLTracker
+    - __init__
+[[autodoc]] tracking.AimTracker
+    - __init__
+[[autodoc]] tracking.MLflowTracker
+    - __init__
+[[autodoc]] tracking.ClearMLTracker
+    - __init__

docs/source/package_reference/utilities.md

@@ -0,0 +1,246 @@
+<!--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.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+-->
+
+# Helpful Utilities
+
+Below are a variety of utility functions that 🤗 Accelerate provides, broken down by use-case. 
+
+## Constants
+
+Constants used throughout 🤗 Accelerate for reference
+
+The following are constants used when utilizing [`Accelerator.save_state`]
+
+`utils.MODEL_NAME`: `"pytorch_model"`
+`utils.OPTIMIZER_NAME`: `"optimizer"`
+`utils.RNG_STATE_NAME`: `"random_states"`
+`utils.SCALER_NAME`: `"scaler.pt`
+`utils.SCHEDULER_NAME`: `"scheduler`
+
+The following are constants used when utilizing [`Accelerator.save_model`]
+
+`utils.WEIGHTS_NAME`: `"pytorch_model.bin"`
+`utils.SAFE_WEIGHTS_NAME`: `"model.safetensors"`
+`utils.WEIGHTS_INDEX_NAME`: `"pytorch_model.bin.index.json"`
+`utils.SAFE_WEIGHTS_INDEX_NAME`: `"model.safetensors.index.json"`
+
+## Data Classes
+
+These are basic dataclasses used throughout 🤗 Accelerate and they can be passed in as parameters.
+
+### Standalone
+
+These are standalone dataclasses used for checks, such as the type of distributed system being used
+
+[[autodoc]] utils.ComputeEnvironment
+
+[[autodoc]] utils.DistributedType
+
+[[autodoc]] utils.DynamoBackend
+
+[[autodoc]] utils.LoggerType
+
+[[autodoc]] utils.PrecisionType
+
+[[autodoc]] utils.RNGType
+
+[[autodoc]] utils.SageMakerDistributedType
+
+### Kwargs
+
+These are configurable arguments for specific interactions throughout the PyTorch ecosystem that Accelerate handles under the hood.
+
+[[autodoc]] utils.AutocastKwargs
+
+[[autodoc]] utils.DistributedDataParallelKwargs
+
+[[autodoc]] utils.FP8RecipeKwargs
+
+[[autodoc]] utils.GradScalerKwargs
+
+[[autodoc]] utils.InitProcessGroupKwargs
+
+[[autodoc]] utils.KwargsHandler
+
+## Plugins
+
+These are plugins that can be passed to the [`Accelerator`] object. While they are defined elsewhere in the documentation, 
+for convenience all of them are available to see here:
+
+[[autodoc]] utils.DeepSpeedPlugin
+
+[[autodoc]] utils.FullyShardedDataParallelPlugin
+
+[[autodoc]] utils.GradientAccumulationPlugin
+
+[[autodoc]] utils.MegatronLMPlugin
+
+[[autodoc]] utils.TorchDynamoPlugin
+
+## Configurations
+
+These are classes which can be configured and passed through to the appropriate integration
+
+[[autodoc]] utils.BnbQuantizationConfig
+
+[[autodoc]] utils.DataLoaderConfiguration
+
+[[autodoc]] utils.ProjectConfiguration
+
+## Environmental Variables
+
+These are environmental variables that can be enabled for different use cases
+
+* `ACCELERATE_DEBUG_MODE` (`str`): Whether to run accelerate in debug mode. More info available [here](../usage_guides/debug.md).
+
+
+
+
+## Data Manipulation and Operations
+
+These include data operations that mimic the same `torch` ops but can be used on distributed processes.
+
+[[autodoc]] utils.broadcast
+
+[[autodoc]] utils.broadcast_object_list
+
+[[autodoc]] utils.concatenate
+
+[[autodoc]] utils.convert_outputs_to_fp32
+
+[[autodoc]] utils.convert_to_fp32
+
+[[autodoc]] utils.gather
+
+[[autodoc]] utils.gather_object
+
+[[autodoc]] utils.listify
+
+[[autodoc]] utils.pad_across_processes
+
+[[autodoc]] utils.recursively_apply
+
+[[autodoc]] utils.reduce
+
+[[autodoc]] utils.send_to_device
+
+[[autodoc]] utils.slice_tensors
+
+## Environment Checks
+
+These functionalities check the state of the current working environment including information about the operating system itself, what it can support, and if particular dependencies are installed. 
+
+[[autodoc]] utils.is_bf16_available
+
+[[autodoc]] utils.is_ipex_available
+
+[[autodoc]] utils.is_mps_available
+
+[[autodoc]] utils.is_npu_available
+
+[[autodoc]] utils.is_torch_version
+
+[[autodoc]] utils.is_torch_xla_available
+
+[[autodoc]] utils.is_xpu_available
+
+## Environment Manipulation
+
+[[autodoc]] utils.patch_environment
+
+[[autodoc]] utils.clear_environment
+
+[[autodoc]] utils.write_basic_config
+
+When setting up 🤗 Accelerate for the first time, rather than running `accelerate config` [~utils.write_basic_config] can be used as an alternative for quick configuration.
+
+[[autodoc]] utils.set_numa_affinity
+
+[[autodoc]] utils.environment.override_numa_affinity
+
+## Memory
+
+[[autodoc]] utils.find_executable_batch_size
+
+## Modeling
+
+These utilities relate to interacting with PyTorch models
+
+[[autodoc]] utils.calculate_maximum_sizes
+
+[[autodoc]] utils.compute_module_sizes
+
+[[autodoc]] utils.extract_model_from_parallel
+
+[[autodoc]] utils.get_balanced_memory
+
+[[autodoc]] utils.get_max_layer_size
+
+[[autodoc]] utils.infer_auto_device_map
+
+[[autodoc]] utils.load_checkpoint_in_model
+
+[[autodoc]] utils.load_offloaded_weights
+
+[[autodoc]] utils.load_state_dict
+
+[[autodoc]] utils.offload_state_dict
+
+[[autodoc]] utils.retie_parameters
+
+[[autodoc]] utils.set_module_tensor_to_device
+
+[[autodoc]] utils.shard_checkpoint
+
+
+## Parallel
+
+These include general utilities that should be used when working in parallel.
+
+[[autodoc]] utils.extract_model_from_parallel
+
+[[autodoc]] utils.save
+
+[[autodoc]] utils.wait_for_everyone
+
+
+## Random
+
+These utilities relate to setting and synchronizing of all the random states.
+
+[[autodoc]] utils.set_seed
+
+[[autodoc]] utils.synchronize_rng_state
+
+[[autodoc]] utils.synchronize_rng_states
+
+
+## PyTorch XLA
+
+These include utilities that are useful while using PyTorch with XLA.
+
+[[autodoc]] utils.install_xla
+
+## Loading model weights
+
+These include utilities that are useful to load checkpoints.
+
+[[autodoc]] utils.load_checkpoint_in_model
+
+## Quantization
+
+These include utilities that are useful to quantize model.
+
+[[autodoc]] utils.load_and_quantize_model

docs/source/quicktour.md

@@ -0,0 +1,186 @@
+<!--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.
+
+⚠️ Note that this file is in Markdown but contains specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+-->
+
+# Quicktour
+
+There are many ways to launch and run your code depending on your training environment ([torchrun](https://pytorch.org/docs/stable/elastic/run.html), [DeepSpeed](https://www.deepspeed.ai/), etc.) and available hardware. Accelerate offers a unified interface for launching and training on different distributed setups, allowing you to focus on your PyTorch training code instead of the intricacies of adapting your code to these different setups. This allows you to easily scale your PyTorch code for training and inference on distributed setups with hardware like GPUs and TPUs. Accelerate also provides Big Model Inference to make loading and running inference with really large models that usually don't fit in memory more accessible.
+
+This quicktour introduces the three main features of Accelerate:
+
+* a unified command line launching interface for distributed training scripts
+* a training library for adapting PyTorch training code to run on different distributed setups
+* Big Model Inference
+
+## Unified launch interface
+
+Accelerate automatically selects the appropriate configuration values for any given distributed training framework (DeepSpeed, FSDP, etc.) through a unified configuration file generated from the [`accelerate config`](package_reference/cli#accelerate-config) command. You could also pass the configuration values explicitly to the command line which is helpful in certain situations like if you're using SLURM.
+
+
+But in most cases, you should always run [`accelerate config`](package_reference/cli#accelerate-config) first to help Accelerate learn about your training setup.
+
+```bash
+accelerate config
+```
+
+The [`accelerate config`](package_reference/cli#accelerate-config) command creates and saves a default_config.yaml file in Accelerates cache folder. This file stores the configuration for your training environment, which helps Accelerate correctly launch your training script based on your machine.
+
+After you've configured your environment, you can test your setup with [`accelerate test`](package_reference/cli#accelerate-test), which launches a short script to test the distributed environment.
+
+```bash
+accelerate test
+```
+
+> [!TIP]
+> Add `--config_file` to the `accelerate test` or `accelerate launch` command to specify the location of the configuration file if it is saved in a non-default location like the cache.
+
+Once your environment is setup, launch your training script with [`accelerate launch`](package_reference/cli#accelerate-launch)!
+
+```bash
+accelerate launch path_to_script.py --args_for_the_script
+```
+
+To learn more, check out the [Launch distributed code](basic_tutorials/launch) tutorial for more information about launching your scripts.
+
+## Adapt training code
+
+The next main feature of Accelerate is the [`Accelerator`] class which adapts your PyTorch code to run on different distributed setups.
+
+You only need to add a few lines of code to your training script to enable it to run on multiple GPUs or TPUs.
+
+```diff
++ from accelerate import Accelerator
++ accelerator = Accelerator()
+
++ device = accelerator.device
++ model, optimizer, training_dataloader, scheduler = accelerator.prepare(
++     model, optimizer, training_dataloader, scheduler
++ )
+
+  for batch in training_dataloader:
+      optimizer.zero_grad()
+      inputs, targets = batch
+-     inputs = inputs.to(device)
+-     targets = targets.to(device)
+      outputs = model(inputs)
+      loss = loss_function(outputs, targets)
++     accelerator.backward(loss)
+      optimizer.step()
+      scheduler.step()
+```
+
+1. Import and instantiate the [`Accelerator`] class at the beginning of your training script. The [`Accelerator`] class initializes everything necessary for distributed training, and it automatically detects your training environment (a single machine with a GPU, a machine with several GPUs, several machines with multiple GPUs or a TPU, etc.) based on how the code was launched.
+
+```python
+from accelerate import Accelerator
+
+accelerator = Accelerator()
+```
+
+2. Remove calls like `.cuda()` on your model and input data. The [`Accelerator`] class automatically places these objects on the appropriate device for you.
+
+> [!WARNING]
+> This step is *optional* but it is considered best practice to allow Accelerate to handle device placement. You could also deactivate automatic device placement by passing `device_placement=False` when initializing the [`Accelerator`]. If you want to explicitly place objects on a device with `.to(device)`, make sure you use `accelerator.device` instead. For example, if you create an optimizer before placing a model on `accelerator.device`, training fails on a TPU.
+
+> [!WARNING]
+> Accelerate does not use non-blocking transfers by default for its automatic device placement, which can result in potentially unwanted CUDA synchronizations.  You can enable non-blocking transfers by passing a [`~utils.dataclasses.DataLoaderConfiguration`] with `non_blocking=True` set as the `dataloader_config` when initializing the [`Accelerator`].  As usual, non-blocking transfers will only work if the dataloader also has `pin_memory=True` set.  Be wary that using non-blocking transfers from GPU to CPU may cause incorrect results if it results in CPU operations being performed on non-ready tensors.
+
+```py
+device = accelerator.device
+```
+
+3. Pass all relevant PyTorch objects for training (optimizer, model, dataloader(s), learning rate scheduler) to the [`~Accelerator.prepare`] method as soon as they're created. This method wraps the model in a container optimized for your distributed setup, uses Accelerates version of the optimizer and scheduler, and creates a sharded version of your dataloader for distribution across GPUs or TPUs.
+
+```python
+model, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
+    model, optimizer, train_dataloader, lr_scheduler
+)
+```
+
+4. Replace `loss.backward()` with [`~Accelerator.backward`] to use the correct `backward()` method for your training setup.
+
+```py
+accelerator.backward(loss)
+```
+
+Read [Accelerate’s internal mechanisms](concept_guides/internal_mechanism) guide to learn more details about how Accelerate adapts your code.
+
+### Distributed evaluation
+
+To perform distributed evaluation, pass your validation dataloader to the [`~Accelerator.prepare`] method:
+
+```python
+validation_dataloader = accelerator.prepare(validation_dataloader)
+```
+
+Each device in your distributed setup only receives a part of the evaluation data, which means you should group your predictions together with the [`~Accelerator.gather_for_metrics`] method. This method requires all tensors to be the same size on each process, so if your tensors have different sizes on each process (for instance when dynamically padding to the maximum length in a batch), you should use the [`~Accelerator.pad_across_processes`] method to pad you tensor to the largest size across processes. Note that the tensors needs to be 1D and that we concatenate the tensors along the first dimension. 
+
+```python
+for inputs, targets in validation_dataloader:
+    predictions = model(inputs)
+    # Gather all predictions and targets
+    all_predictions, all_targets = accelerator.gather_for_metrics((predictions, targets))
+    # Example of use with a *Datasets.Metric*
+    metric.add_batch(all_predictions, all_targets)
+```
+
+For more complex cases (e.g. 2D tensors, don't want to concatenate tensors, dict of 3D tensors), you can pass `use_gather_object=True` in `gather_for_metrics`. This will return the list of objects after gathering. Note that using it with GPU tensors is not well supported and inefficient.
+
+> [!TIP]
+> Data at the end of a dataset may be duplicated so the batch can be equally divided among all workers. The [`~Accelerator.gather_for_metrics`] method automatically removes the duplicated data to calculate a more accurate metric.
+
+## Big Model Inference
+
+Accelerate's Big Model Inference has two main features, [`~accelerate.init_empty_weights`] and [`~accelerate.load_checkpoint_and_dispatch`], to load large models for inference that typically don't fit into memory.
+
+> [!TIP]
+> Take a look at the [Handling big models for inference](concept_guides/big_model_inference) guide for a better understanding of how Big Model Inference works under the hood.
+
+### Empty weights initialization
+
+The [`~accelerate.init_empty_weights`] context manager initializes models of any size by creating a *model skeleton* and moving and placing parameters each time they're created to PyTorch's [**meta**](https://pytorch.org/docs/main/meta.html) device. This way, not all weights are immediately loaded and only a small part of the model is loaded into memory at a time.
+
+For example, loading an empty [Mixtral-8x7B](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) model takes significantly less memory than fully loading the models and weights on the CPU.
+
+```py
+from accelerate import init_empty_weights
+from transformers import AutoConfig, AutoModelForCausalLM
+
+config = AutoConfig.from_pretrained("mistralai/Mixtral-8x7B-Instruct-v0.1")
+with init_empty_weights():
+    model = AutoModelForCausalLM.from_config(config)
+```
+
+### Load and dispatch weights
+
+The [`~accelerate.load_checkpoint_and_dispatch`] function loads full or sharded checkpoints into the empty model, and automatically distribute weights across all available devices.
+
+The `device_map` parameter determines where to place each model layer, and specifiying `"auto"` places them on the GPU first, then the CPU, and finally the hard drive as memory-mapped tensors if there's still not enough memory. Use the `no_split_module_classes` parameter to indicate which modules shouldn't be split across devices (typically those with a residual connection).
+
+```py
+from accelerate import load_checkpoint_and_dispatch
+
+model = load_checkpoint_and_dispatch(
+    model, checkpoint="mistralai/Mixtral-8x7B-Instruct-v0.1", device_map="auto", no_split_module_classes=['Block']
+)
+```
+
+## Next steps
+
+Now that you've been introduced to the main Accelerate features, your next steps could include:
+
+* Check out the [tutorials](basic_tutorials/overview) for a gentle walkthrough of Accelerate. This is especially useful if you're new to distributed training and the library.
+* Dive into the [guides](usage_guides/explore) to see how to use Accelerate for specific use-cases.
+* Deepen your conceptual understanding of how Accelerate works internally by reading the [concept guides](concept_guides/internal_mechanism).
+* Look up classes and commands in the [API reference](package_reference/accelerator) to see what parameters and options are available.

docs/source/quicktour.mdx

@@ -1,460 +0,0 @@
-<!--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.
--->
-
-# Quick tour
-
-Let's have a look at a look at 🤗 Accelerate main features and traps to avoid.
-
-## Main use
-
-To use 🤗 Accelerate in your own script, you have to change four things:
-
-1. Import the [`Accelerator`] main class instantiate one in an `accelerator` object:
-
-```python
-from accelerate import Accelerator
-
-accelerator = Accelerator()
-```
-
-This should happen as early as possible in your training script as it will initialize everything necessary for
-distributed training. You don't need to indicate the kind of environment you are in (just one machine with a GPU, one
-match with several GPUs, several machines with multiple GPUs or a TPU), the library will detect this automatically.
-
-2. Remove the call `.to(device)` or `.cuda()` for your model and input data. The `accelerator` object
-will handle this for you and place all those objects on the right device for you. If you know what you're doing, you
-can leave those `.to(device)` calls but you should use the device provided by the `accelerator` object:
-`accelerator.device`.
-
-To fully deactivate the automatic device placement, pass along `device_placement=False` when initializing your
-[`Accelerator`].
-
-<Tip warning={true}>
-
-If you place your objects manually on the proper device, be careful to create your optimizer after putting your
-model on `accelerator.device` or your training will fail on TPU.
-
-</Tip>
-
-3. Pass all objects relevant to training (optimizer, model, training dataloader, learning rate scheduler) to the
-[`~Accelerator.prepare`] method. This will make sure everything is ready for training.
-
-```python
-model, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
-    model, optimizer, train_dataloader, lr_scheduler
-)
-```
-
-In particular, your training dataloader will be sharded accross all GPUs/TPU cores available so that each one sees a
-different portion of the training dataset. Also, the random states of all processes will be synchronized at the
-beginning of each iteration through your dataloader, to make sure the data is shuffled the same way (if you decided to
-use `shuffle=True` or any kind of random sampler).
-
-<Tip>
-
-The actual batch size for your training will be the number of devices used multiplied by the batch size you set in
-your script: for instance training on 4 GPUs with a batch size of 16 set when creating the training dataloader will
-train at an actual batch size of 64.
-
-</Tip>
-
-Alternatively, you can use the option `split_batches=True` when creating initializing your
-[`Accelerator`], in which case the batch size will always stay the same, whether your run your
-script on 1, 2, 4 or 64 GPUs.
-
-You should execute this instruction as soon as all objects for training are created, before starting your actual
-training loop.
-
-<Tip warning={true}>
-
-You should only pass the learning rate scheduler to [`~Accelerator.prepare`] when the scheduler needs to be stepped
-at each optimizer step.
-
-</Tip>
-
-<Tip warning={true}>
-
-Your training dataloader may change length when going through this method: if you run on X GPUs, it will have its
-length divided by X (since your actual batch size will be multiplied by X), unless you set
-`split_batches=True`.
-
-</Tip>
-
-Any instruction using your training dataloader length (for instance if you want to log the number of total training
-steps) should go after the call to [`~Accelerator.prepare`].
-
-You can perfectly send your dataloader to [`~Accelerator.prepare`] on its own, but it's best to send the
-model and optimizer to [`~Accelerator.prepare`] together.
-
-You may or may not want to send your validation dataloader to [`~Accelerator.prepare`], depending on
-whether you want to run distributed evaluation or not (see below).
-
-4. Replace the line `loss.backward()` by `accelerator.backward(loss)`.
-
-And you're all set! With all these changes, your script will run on your local machine as well as on multiple GPUs or a
-TPU! You can either use your favorite tool to launch the distributed training, or you can use the 🤗 Accelerate
-launcher.
-
-
-## Distributed evaluation
-
-You can perform regular evaluation in your training script, if you leave your validation dataloader out of the
-[`~Accelerator.prepare`] method. In this case, you will need to put the input data on the
-`accelerator.device` manually.
-
-To perform distributed evaluation, send along your validation dataloader to the [`~Accelerator.prepare`]
-method:
-
-```python
-validation_dataloader = accelerator.prepare(validation_dataloader)
-```
-
-Like for your training dataloader, it will mean that (should you run your script on multiple devices) each device will
-only see part of the evaluation data. This means you will need to group your predictions together. This is very easy to
-do with the [`~Accelerator.gather`] method.
-
-```python
-for inputs, targets in validation_dataloader:
-    predictions = model(inputs)
-    # Gather all predictions and targets
-    all_predictions = accelerator.gather(predictions)
-    all_targets = accelerator.gather(targets)
-    # Example of use with a *Datasets.Metric*
-    metric.add_batch(all_predictions, all_targets)
-```
-
-<Tip warning={true}>
-
-Like for the training dataloader, passing your validation dataloader through
-[`~Accelerator.prepare`] may change its: if you run on X GPUs, it will have its length divided by X
-(since your actual batch size will be multiplied by X), unless you set `split_batches=True`.
-
-Any instruction using your training dataloader length (for instance if you need the number of total training steps
-to create a learning rate scheduler) should go after the call to [`~Accelerator.prepare`].
-
-</Tip>
-
-<Tip warning={true}>
-
-The [`~Accelerator.gather`] method requires the tensors to be all the same size on each process. If
-you have tensors of different sizes on each process (for instance when dynamically padding to the maximum length in
-a batch), you should use the [`~Accelerator.pad_across_processes`] method to pad you tensor to the
-biggest size across processes.
-
-</Tip>
-
-## Launching your distributed script
-
-You can use the regular commands to launch your distributed training (like `torch.distributed.launch` for
-PyTorch), they are fully compatible with 🤗 Accelerate. The only caveat here is that 🤗 Accelerate uses the environment
-to determine all useful information, so `torch.distributed.launch` should be used with the flag `--use_env`.
-
-🤗 Accelerate also provides a CLI tool that unifies all launcher, so you only have to remember one command. To use it,
-just run
-
-```bash
-accelerate config
-```
-
-on your machine and reply to the questions asked. This will save a *default_config.yaml* file in your cache folder for
-🤗 Accelerate. That cache folder is (with decreasing order of priority):
-
-- The content of your environment variable `HF_HOME` suffixed with *accelerate*.
-- If it does not exist, the content of your environment variable `XDG_CACHE_HOME` suffixed with
-  *huggingface/accelerate*.
-- If this does not exist either, the folder *~/.cache/huggingface/accelerate*
-
-You can also specify with the flag `--config_file` the location of the file you want to save.
-
-Once this is done, you can test everything is going well on your setup by running
-
-```bash
-accelerate test
-```
-
-This will launch a short script that will test the distributed environment. If it runs fine, you are ready for the next
-step!
-
-Note that if you specified a location for the config file in the previous step, you need to pass it here as well:
-
-```bash
-accelerate test --config_file path_to_config.yaml
-```
-
-Now that this is done, you can run your script with the following command:
-
-```bash
-accelerate launch path_to_script.py --args_for_the_script
-```
-
-If you stored the config file in a non-default location, you can indicate it to the launcher like his:
-
-```bash
-accelerate launch --config_file path_to_config.yaml path_to_script.py --args_for_the_script
-```
-
-You can also override any of the arguments determined by your config file, see TODO: insert ref here.
-
-
-## Launching training from a notebook
-
-In Accelerate 0.3.0, a new [`notebook_launcher`] has been introduced to help you launch your training
-function from a notebook. This launcher supports launching a training with TPUs on Colab or Kaggle, as well as training
-on several GPUs (if the machine on which you are running your notebook has them).
-
-Just define a function responsible for your whole training and/or evaluation in a cell of the notebook, then execute a
-cell with the following code:
-
-```python
-from accelerate import notebook_launcher
-
-notebook_launcher(training_function)
-```
-
-<Tip warning={true}>
-
-Your `Accelerator` object should only be defined inside the training function. This is because the
-initialization should be done inside the launcher only.
-
-</Tip>
-
-## Training on TPU
-
-If you want to launch your script on TPUs, there are a few caveats you should be aware of. Behind the scenes, the TPUs
-will create a graph of all the operations happening in your training step (forward pass, backward pass and optimizer
-step). This is why your first step of training will always be very long as building and compiling this graph for
-optimizations takes some time.
-
-The good news is that this compilation will be cached so the second step and all the following will be much faster. The
-bas news is that it only applies if all of your steps do exactly the same operations, which implies:
-
-- having all tensors of the same length in all your lengths
-- having static code (i.e., not a for loop of length that could change from step to step)
-
-Having any of the things above change between two steps will trigger a new compilation which will, once again, take a
-lot of time. In practice, that means you must take special care to have all your tensors in your inputs of the same
-shape (so no dynamic padding for instance if you are in an NLP problem) and should not use layer with for loops that
-have different lengths depending on the inputs (such as an LSTM) or the training will be excruciatingly slow.
-
-To introduce special behavior in your script for TPUs you can check the `distributed_type` of your
-`accelerator`:
-
-```python docstyle-ignore
-from accelerate import DistributedType
-
-if accelerator.distributed_type == DistributedType.TPU:
-    # do something of static shape
-else:
-    # go crazy and be dynamic
-```
-
-The [NLP example](https://github.com/huggingface/accelerate/blob/main/examples/nlp_example.py) shows an example in
-situation with dynamic padding.
-
-One last thing to pay close attnetion to: if your model has tied weights (such as language models which tie the weights
-of the embedding matrix with the weights of the decoder), moving this model to the TPU (either yourself or after you
-passed your model to [`~Accelerator.prepare`]) will break the tying. You will need to retie the weights
-after. You can find an example of this in the [run_clm_no_trainer](https://github.com/huggingface/transformers/blob/master/examples/pytorch/language-modeling/run_clm.py) script in
-the Transformers repository.
-
-
-## Other caveats
-
-We list here all smaller issues you could have in your script conversion and how to resolve them.
-
-### Execute a statement only on one processes
-
-Some of your instructions only need to run for one process on a given server: for instance a data download or a log
-statement. To do this, wrap the statement in a test like this:
-
-```python docstyle-ignore
-if accelerator.is_local_main_process:
-    # Is executed once per server
-```
-
-Another example is progress bars: to avoid having multiple progress bars in your output, you should only display one on
-the local main process:
-
-```python
-from tqdm.auto import tqdm
-
-progress_bar = tqdm(range(args.max_train_steps), disable=not accelerator.is_local_main_process)
-```
-
-The *local* means per machine: if you are running your training on two servers with several GPUs, the instruction will
-be executed once on each of those servers. If you need to execute something only once for all processes (and not per
-machine) for instance, uploading the final model to the 🤗 model hub, wrap it in a test like this:
-
-```python docstyle-ignore
-if accelerator.is_main_process:
-    # Is executed once only
-```
-
-For printing statements you only want executed once per machine, you can just replace the `print` function by
-`accelerator.print`.
-
-
-### Defer execution
-
-When you run your usual script, instructions are executed in order. Using 🤗 Accelerate to deploy your script on several
-GPUs at the same time introduces a complication: while each process executes all instructions in order, some may be
-faster than others.
-
-You might need to wait for all processes to have reached a certain point before executing a given instruction. For
-instance, you shouldn't save a model before being sure every process is done with training. To do this, just write the
-following line in your code:
-
-```
-accelerator.wait_for_everyone()
-```
-
-This instruction will block all the processes that arrive them first until all the other processes have reached that
-point (if you run your script on just one GPU or CPU, this wont' do anything).
-
-
-### Saving/loading a model
-
-Saving the model you trained might need a bit of adjustment: first you should wait for all processes to reach that
-point in the script as shown above, and then, you should unwrap your model before saving it. This is because when going
-through the [`~Accelerator.prepare`] method, your model may have been placed inside a bigger model,
-which deals with the distributed training. This in turn means that saving your model state dictionary without taking
-any precaution will take that potential extra layer into account, and you will end up with weights you can't load back
-in your base model.
-
-This is why it's recommended to *unwrap* your model first. Here is an example:
-
-```
-accelerator.wait_for_everyone()
-unwrapped_model = accelerator.unwrap_model(model)
-accelerator.save(unwrapped_model.state_dict(), filename)
-```
-
-If your script contains a logic to load checkpoint, we also recommend you load your weights in the unwrapped model
-(this is only useful if you use the load function after making your model go through
-[`~Accelerator.prepare`]). Here is an example:
-
-```
-unwrapped_model = accelerator.unwrap_model(model)
-unwrapped_model.load_state_dict(torch.load(filename))
-```
-
-Note that since all the model parameters are references to tensors, this will load your weights inside `model`.
-
-## Saving/loading entire states
-
-When training your model, you may want to save the current state of the model, optimizer, random generators, and potentially LR schedulers to be restored in the _same script_.
-You can use `accelerator.save_state` and `accelerator.load_state` respectively to do so, just by simply passing in a save location. 
-If you have registered any other stateful items to be stored through `accelerator.register_for_checkpointing` they will also be saved and/or loaded.
-<Tip>
-    Every object passed to `register_for_checkpointing` must have a `load_state_dict` and `save_dict` function to be stored
-</Tip>
-
-
-### Gradient clipping
-
-If you are using gradient clipping in your script, you should replace the calls to
-`torch.nn.utils.clip_grad_norm_` or `torch.nn.utils.clip_grad_value_` with `accelerator.clip_grad_norm_`
-and `accelerator.clip_grad_value_` respectively.
-
-
-### Mixed Precision training
-
-If you are running your training in Mixed Precision with Accelerate, you will get the best result with your loss being
-computed inside your model (like in Transformer models for instance). Every computation outside of the model will be
-executed in full precision (which is generally what you want for loss computation, expecially if it involves a
-softmax). However you might want to put your loss computation inside the *accelerator.autocast* context manager:
-
-```
-with accelerator.autocast():
-    loss = complex_loss_function(outputs, target):
-```
-
-Another caveat with Mixed Precision training is that the gradient will skip a few updates at the beginning and
-sometimes during training: because of the dynamic loss scaling strategy, there are points during training where the
-gradients have overflown, and the loss scaling factor is reduced to avoid this happening again at the next step.
-
-This means that you may update your learning rate scheduler when there was no update, which is fine in general, but may
-have an impact when you have very little training data, or if the first learning rate values of your scheduler are very
-important. In this case, you can skip the learning rate scheduler updates when the optimizer step was not done like
-this:
-
-```
-if not accelerator.optimizer_step_was_skipped:
-    lr_scheduler.step()
-```
-
-### DeepSpeed
-
-DeepSpeed support is experimental, so the underlying API will evolve in the near future and may have some slight
-breaking changes. In particular, 🤗 Accelerate does not support DeepSpeed config you have written yourself yet, this
-will be added in a next version.
-
-<Tip warning={true}>
-
-The [`notebook_launcher`] does not support the DeepSpeed integration yet.
-
-</Tip>
-
-## Internal mechanism
-
-Internally, the library works by first analyzing the environment in which the script is launched to determine which
-kind of distributed setup is used, how many different processes there are and which one the current script is in. All
-that information is stored in the [`~AcceleratorState`].
-
-This class is initialized the first time you instantiate a [`Accelerator`] as well as performing any
-specific initialization your distributed setup needs. Its state is then uniquely shared through all instances of
-[`~state.AcceleratorState`].
-
-Then, when calling [`~Accelerator.prepare`], the library:
-
-- wraps your model(s) in the container adapted for the distributed setup,
-- wraps your optimizer(s) in a [`~optimizer.AcceleratedOptimizer`],
-- creates a new version of your dataloader(s) in a [`~data_loader.DataLoaderShard`].
-
-While the model(s) and optimizer(s) are just put in simple wrappers, the dataloader(s) are re-created. This is mostly
-because PyTorch does not let the user change the `batch_sampler` of a dataloader once it's been created and the
-library handles the sharding of your data between processes by changing that `batch_sampler` to yield every other
-`num_processes` batches.
-
-The [`~data_loader.DataLoaderShard`] subclasses `DataLoader` to add the following functionality:
-
-- it synchronizes the appropriate random number generator of all processes at each new iteration, to ensure any
-  randomization (like shuffling) is done the exact same way across processes.
-- it puts the batches on the proper device before yielding them (unless you have opted out of
-  `device_placement=True`).
-
-The random number generator synchronization will by default synchronize:
-
-- the `generator` attribute of a given sampler (like the PyTorch `RandomSampler`) for PyTorch >= 1.6
-- the main random number generator in PyTorch <=1.5.1
-
-You can choose which random number generator(s) to synchronize with the `rng_types` argument of the main
-[`Accelerator`]. In PyTorch >= 1.6, it is recommended to rely on local `generator` to avoid
-setting the same seed in the main random number generator in all processes.
-
-<Tip warning={true}>
-
-Synchronization the main torch (or CUDA or XLA) random number generator will affect any other potential random
-artifacts you could have in your dataset (like random data augmentation) in the sense all processes will get the
-same random numbers from the torch random modules (so will apply the same random data augmentation if it's
-controlled by torch).
-
-</Tip>
-
-<Tip>
-
-The randomization part of your custom sampler, batch sampler or iterable dataset should be done using a local
-`torch.Generator` object (in PyTorch >= 1.6), see the traditional `RandomSampler`, as an example.
-
-</Tip>
-
-See more details about the internal in the [Internals page](internal).

docs/source/tracking.mdx

@@ -1,163 +0,0 @@
-<!--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.
--->
-
-# Tracking
-
-There are a large number of experiment tracking API's available, however getting them all to work with in a multi-processing environment can oftentimes be complex.
-Accelerate provides a general tracking API that can be used to log useful items during your script through [`~Accelerator.log`]
-
-## Integrated Trackers
-
-Currently `Accelerate` supports three trackers out-of-the-box:
-
-
-[[autodoc]] tracking.TensorBoardTracker
-
-[[autodoc]] tracking.WandBTracker
-
-[[autodoc]] tracking.CometMLTracker
-
-To use any of them, pass in the selected type(s) to the `log_with` parameter in [`Accelerate`]:
-```python
-from accelerate import Accelerator
-from accelerate.utils import LoggerType
-
-accelerator = Accelerator(log_with="all")  # For all available trackers in the environment
-accelerator = Accelerator(log_with="wandb")
-accelerator = Accelerator(log_with=["wandb", LoggerType.TENSORBOARD])
-```
-
-At the start of your experiment [`~Accelerator.init_trackers`] should be used to setup your project, and potentially add any experiment hyperparameters to be logged:
-```python
-hps = {"num_iterations": 5, "learning_rate": 1e-2}
-accelerator.init_trackers("my_project", config=hps)
-```
-
-When you are ready to log any data, [`~Accelerator.log`] should be used.
-A `step` can also be passed in to correlate the data with a particular step in the training loop.
-```python
-accelerator.log({"train_loss": 1.12, "valid_loss": 0.8}, step=1)
-```
-
-Once you've finished training, make sure to run [`~Accelerator.end_training`] so that all the trackers can run their finish functionalities if they have any.
-```python
-accelerator.end_training()
-```
-
-
-A full example is below:
-```python
-from accelerate import Accelerator
-
-accelerator = Accelerator(log_with="all")
-config = {
-    "num_iterations": 5,
-    "learning_rate": 1e-2,
-    "loss_function": str(my_loss_function),
-}
-
-accelerator.init_trackers("example_project", config=config)
-
-my_model, my_optimizer, my_training_dataloader = accelerate.prepare(my_model, my_optimizer, my_training_dataloader)
-device = accelerator.device
-my_model.to(device)
-
-for iteration in config["num_iterations"]:
-    for step, batch in my_training_dataloader:
-        my_optimizer.zero_grad()
-        inputs, targets = batch
-        inputs = inputs.to(device)
-        targets = targets.to(device)
-        outputs = my_model(inputs)
-        loss = my_loss_function(outputs, targets)
-        accelerator.backward(loss)
-        my_optimizer.step()
-        accelerator.log({"training_loss": loss}, step=step)
-accelerator.end_training()
-```
-
-
-## Implementing Custom Trackers
-
-To implement a new tracker to be used in `Accelerator`, a new one can be made through implementing the [`~GeneralTracker`] class.
-Every tracker must implement three functions:
-  - `__init__`: 
-    - Should store a `run_name` and initialize the tracker API of the integrated library. 
-    - If a tracker stores their data locally (such as TensorBoard), a `logging_dir` parameter can be added.
-  - `store_init_configuration`: 
-    - Should take in a `values` dictionary and store them as a one-time experiment configuration
-  - `log`: 
-    - Should take in a `values` dictionary and a `step`, and should log them to the run
-
-A brief example can be seen below with an integration with Weights and Biases, containing only the relevent information:
-```python
-from accelerate.tracking import GeneralTracker
-from typing import Optional
-
-import wandb
-
-
-class MyCustomTracker(GeneralTracker):
-    def __init__(self, run_name: str):
-        self.run_name = run_name
-        wandb.init(self.run_name)
-
-    def store_init_configuration(self, values: dict):
-        wandb.config(values)
-
-    def log(self, values: dict, step: Optional[int] = None):
-        wandb.log(values, step=step)
-```
-
-When you are ready to build your `Accelerator` object, pass in an **instance** of your tracker to [`~Accelerator.log_with`] to have it automatically
-be used with the API:
-
-```python
-tracker = MyCustomTracker("some_run_name")
-accelerator = Accelerator(log_with=tracker)
-```
-
-These also can be mixed with existing trackers, including with `"all"`:
-
-```python
-tracker = MyCustomTracker("some_run_name")
-accelerator = Accelerator(log_with=[tracker, "all"])
-```
-
-## When a wrapper cannot work
-
-If a library has an API that does not follow a strict `.log` with an overall dictionary such as Neptune.AI, logging can be done manually under an `if accelerator.is_main_process` statement:
-```diff
-from accelerate import Accelerator
-+ import neptune.new as neptune
-
-accelerator = Accelerator()
-+ run = neptune.init(...)
-
-my_model, my_optimizer, my_training_dataloader = accelerate.prepare(my_model, my_optimizer, my_training_dataloader)
-device = accelerator.device
-my_model.to(device)
-
-for iteration in config["num_iterations"]:
-    for batch in my_training_dataloader:
-        my_optimizer.zero_grad()
-        inputs, targets = batch
-        inputs = inputs.to(device)
-        targets = targets.to(device)
-        outputs = my_model(inputs)
-        loss = my_loss_function(outputs, targets)
-        total_loss += loss
-        accelerator.backward(loss)
-        my_optimizer.step()
-+       if accelerator.is_main_process:
-+           run["logs/training/batch/loss"].log(loss)
-```

docs/source/usage_guides/big_modeling.md

@@ -0,0 +1,150 @@
+<!--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.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+-->
+
+# Handling big models for inference
+
+One of the biggest advancements 🤗 Accelerate provides is the concept of [large model inference](../concept_guides/big_model_inference) wherein you can perform *inference* on models that cannot fully fit on your graphics card. 
+
+This tutorial will be broken down into two parts showcasing how to use both 🤗 Accelerate and 🤗 Transformers (a higher API-level) to make use of this idea.
+
+## Using 🤗 Accelerate
+
+For these tutorials, we'll assume a typical workflow for loading your model in such that:
+
+```py
+import torch
+
+my_model = ModelClass(...)
+state_dict = torch.load(checkpoint_file)
+my_model.load_state_dict(state_dict)
+```
+
+Note that here we assume that `ModelClass` is a model that takes up more video-card memory than what can fit on your device (be it `mps` or `cuda`).
+
+The first step is to init an empty skeleton of the model which won't take up any RAM using the [`init_empty_weights`] context manager:
+
+```py
+from accelerate import init_empty_weights
+with init_empty_weights():
+    my_model = ModelClass(...)
+```
+
+With this `my_model` currently is "parameterless", hence leaving the smaller footprint than what one would normally get loading this onto the CPU directly. 
+
+Next we need to load in the weights to our model so we can perform inference.
+
+For this we will use [`load_checkpoint_and_dispatch`], which as the name implies will load a checkpoint inside your empty model and dispatch the weights for each layer across all the devices you have available (GPU/MPS and CPU RAM). 
+
+To determine how this `dispatch` can be performed, generally specifying `device_map="auto"` will be good enough as 🤗 Accelerate
+will attempt to fill all the space in your GPU(s), then loading them to the CPU, and finally if there is not enough RAM it will be loaded to the disk (the absolute slowest option). 
+
+<Tip>
+
+For more details on designing your own device map, see this section of the [concept guide](../concept_guides/big_model_inference#designing-a-device-map)
+
+</Tip>
+
+See an example below:
+
+```py
+from accelerate import load_checkpoint_and_dispatch
+
+model = load_checkpoint_and_dispatch(
+    model, checkpoint=checkpoint_file, device_map="auto"
+)
+```
+
+<Tip>
+
+    If there are certain "chunks" of layers that shouldn't be split, you can pass them in as `no_split_module_classes`. Read more about it [here](../concept_guides/big_model_inference#loading-weights)
+
+</Tip>
+
+<Tip>
+
+    Also to save on memory (such as if the `state_dict` will not fit in RAM), a model's weights can be divided and split into multiple checkpoint files. Read more about it [here](../concept_guides/big_model_inference#sharded-checkpoints)
+
+</Tip>
+
+Now that the model is dispatched fully, you can perform inference as normal with the model:
+
+```py
+input = torch.randn(2,3)
+input = input.to("cuda")
+output = model(input)
+```
+
+What will happen now is each time the input gets passed through a layer, it will be sent from the CPU to the GPU (or disk to CPU to GPU), the output is calculated, and then the layer is pulled back off the GPU going back down the line. While this adds some overhead to the inference being performed, through this method it is possible to run **any size model** on your system, as long as the largest layer is capable of fitting on your GPU. 
+
+<Tip>
+
+    Multiple GPUs can be utilized, however this is considered "model parallelism" and as a result only one GPU will be active at a given moment, waiting for the prior one to send it the output. You should launch your script normally with `python`
+    and not need `torchrun`, `accelerate launch`, etc.
+
+</Tip>
+
+For a visual representation of this, check out the animation below:
+
+<Youtube id="MWCSGj9jEAo" />
+
+### Complete Example
+
+Below is the full example showcasing what we performed above:
+
+```py
+import torch
+from accelerate import init_empty_weights, load_checkpoint_and_dispatch
+
+with init_empty_weights():
+    model = MyModel(...)
+
+model = load_checkpoint_and_dispatch(
+    model, checkpoint=checkpoint_file, device_map="auto"
+)
+
+input = torch.randn(2,3)
+input = input.to("cuda")
+output = model(input)
+```
+
+## Using 🤗 Transformers, 🤗 Diffusers, and other 🤗 Open Source Libraries
+
+Libraries that support 🤗 Accelerate big model inference include all of the earlier logic in their `from_pretrained` constructors. 
+
+These operate by specifying a string representing the model to download from the [🤗 Hub](https://hf.co/models) and then denoting `device_map="auto"` along with a few extra parameters. 
+
+As a brief example, we will look at using `transformers` and loading in Big Science's T0pp model. 
+
+```py
+from transformers import AutoModelForSeq2SeqLM
+
+model = AutoModelForSeq2SeqLM.from_pretrained("bigscience/T0pp", device_map="auto")
+```
+
+After loading the model in, the initial steps from before to prepare a model have all been done and the model is fully
+ready to make use of all the resources in your machine. Through these constructors, you can also save *more* memory by
+specifying the precision the model is loaded into as well, through the `torch_dtype` parameter, such as:
+
+```py
+from transformers import AutoModelForSeq2SeqLM
+
+model = AutoModelForSeq2SeqLM.from_pretrained("bigscience/T0pp", device_map="auto", torch_dtype=torch.float16)
+```
+
+To learn more about this, check out the 🤗 Transformers documentation available [here](https://huggingface.co/docs/transformers/main/en/main_classes/model#large-model-loading).
+
+## Where to go from here
+
+For a much more detailed look at big model inference, be sure to check out the [Conceptual Guide on it](../concept_guides/big_model_inference)

docs/source/usage_guides/checkpoint.md

@@ -8,36 +8,43 @@ 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.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
 -->
 
 # Checkpointing
 
-When training a PyTorch model with Accelerate, you may often want to save and continue a state of training. Doing so requires
-saving and loading the model, optimizer, RNG generators, and the GradScaler. Inside Accelerate are two convience functions to achieve this quickly:
+When training a PyTorch model with 🤗 Accelerate, you may often want to save and continue a state of training. Doing so requires
+saving and loading the model, optimizer, RNG generators, and the GradScaler. Inside 🤗 Accelerate are two convenience functions to achieve this quickly:
 - Use [`~Accelerator.save_state`] for saving everything mentioned above to a folder location
 - Use [`~Accelerator.load_state`] for loading everything stored from an earlier `save_state`
 
+To further customize where and how states are saved through [`~Accelerator.save_state`] the [`~utils.ProjectConfiguration`] class can be used. For example 
+if `automatic_checkpoint_naming` is enabled each saved checkpoint will be located then at `Accelerator.project_dir/checkpoints/checkpoint_{checkpoint_number}`.
+
 It should be noted that the expectation is that those states come from the same training script, they should not be from two separate scripts.
 
 - By using [`~Accelerator.register_for_checkpointing`], you can register custom objects to be automatically stored or loaded from the two prior functions,
 so long as the object has a `state_dict` **and** a `load_state_dict` functionality. This could include objects such as a learning rate scheduler. 
 
+
 Below is a brief example using checkpointing to save and reload a state during training:
 
 ```python
 from accelerate import Accelerator
 import torch
 
-accelerator = Accelerator()
+accelerator = Accelerator(project_dir="my/save/path")
 
 my_scheduler = torch.optim.lr_scheduler.StepLR(my_optimizer, step_size=1, gamma=0.99)
-my_model, my_optimizer, my_training_dataloader = accelerate.prepare(my_model, my_optimizer, my_training_dataloader)
+my_model, my_optimizer, my_training_dataloader = accelerator.prepare(my_model, my_optimizer, my_training_dataloader)
 
 # Register the LR scheduler
-accelerate.register_for_checkpointing(my_scheduler)
+accelerator.register_for_checkpointing(my_scheduler)
 
 # Save the starting state
-accelerate.save_state("my/save/path")
+accelerator.save_state()
 
 device = accelerator.device
 my_model.to(device)
@@ -55,6 +62,35 @@ for epoch in range(num_epochs):
         my_optimizer.step()
     my_scheduler.step()
 
-# Restore previous state
-accelerate.load_state("my/save/path")
-```
+# Restore the previous state
+accelerator.load_state("my/save/path/checkpointing/checkpoint_0")
+```
+
+## Restoring the state of the DataLoader 
+
+After resuming from a checkpoint, it may also be desirable to resume from a particular point in the active `DataLoader` if 
+the state was saved during the middle of an epoch. You can use [`~Accelerator.skip_first_batches`] to do so. 
+
+```python
+from accelerate import Accelerator
+
+accelerator = Accelerator(project_dir="my/save/path")
+
+train_dataloader = accelerator.prepare(train_dataloader)
+accelerator.load_state("my_state")
+
+# Assume the checkpoint was saved 100 steps into the epoch
+skipped_dataloader = accelerator.skip_first_batches(train_dataloader, 100)
+
+# After the first iteration, go back to `train_dataloader`
+
+# First epoch
+for batch in skipped_dataloader:
+    # Do something
+    pass
+
+# Second epoch
+for batch in train_dataloader:
+    # Do something
+    pass
+```

docs/source/usage_guides/ddp_comm_hook.md

@@ -0,0 +1,325 @@
+<!--
+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.
+
+⚠️ Note that this file is in Markdown but contains specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+-->
+
+# DDP Communication Hooks
+
+Distributed Data Parallel (DDP) communication hooks provide a generic interface to control how gradients are communicated across workers by overriding the vanilla allreduce in `DistributedDataParallel`. A few built-in communication hooks are provided, and users can easily apply any of these hooks to optimize communication.
+
+
+- **FP16 Compression Hook**: Compresses gradients by casting them to half-precision floating-point format (`torch.float16`), reducing communication overhead.
+- **BF16 Compression Hook**: Similar to FP16, but uses the Brain Floating Point format (`torch.bfloat16`), which can be more efficient on certain hardware.
+- **PowerSGD Hook**: An advanced gradient compression algorithm that provides high compression rates and can accelerate bandwidth-bound distributed training.
+
+In this tutorial, you will see how to quickly set up DDP communication hooks and perform training with the utilities provided in 🤗 Accelerate, which can be as simple as adding just one new line of code! This demonstrates how to use DDP communication hooks to optimize gradient communication in distributed training with the 🤗 Accelerate library.
+
+## FP16 Compression Hook
+
+<hfoptions id="fp16">
+<hfoption id="PyTorch">
+
+```python
+import torch
+from torch.nn.parallel import DistributedDataParallel as DDP
+from torch.distributed.algorithms.ddp_comm_hooks import default_hooks
+
+class MyModel(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.layer = torch.nn.Linear(10, 10)
+
+    def forward(self, x):
+        return self.layer(x)
+
+model = MyModel()
+model = DDP(model, device_ids=[torch.cuda.current_device()])
+model.register_comm_hook(state=None, hook=default_hooks.fp16_compress_hook)
+
+# Training loop
+for data, targets in data_loader:
+    outputs = model(data)
+    loss = criterion(outputs, targets)
+    loss.backward()
+    optimizer.step()
+    optimizer.zero_grad()
+```
+
+</hfoption>
+<hfoption id="Accelerate">
+
+```python
+from accelerate import Accelerator, DDPCommunicationHookType, DistributedDataParallelKwargs
+import torch
+
+class MyModel(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.layer = torch.nn.Linear(10, 10)
+
+    def forward(self, x):
+        return self.layer(x)
+
+# DDP Communication Hook setup
+ddp_kwargs = DistributedDataParallelKwargs(comm_hook=DDPCommunicationHookType.FP16)
+accelerator = Accelerator(kwargs_handlers=[ddp_kwargs])
+
+model = MyModel()
+optimizer = torch.optim.Adam(model.parameters())
+data_loader = DataLoader(dataset, batch_size=16)
+
+model, optimizer, data_loader = accelerator.prepare(model, optimizer, data_loader)
+
+# Training loop
+for data, targets in data_loader:
+    outputs = model(data)
+    loss = criterion(outputs, targets)
+    accelerator.backward(loss)
+    optimizer.step()
+    optimizer.zero_grad()
+```
+
+</hfoption>
+</hfoptions>
+
+### BF16 Compression Hook
+
+<Tip warning={true}>
+
+BF16 Compression Hook API is experimental, and it requires NCCL version later than 2.9.6.
+
+</Tip>
+
+<hfoptions id="bf16">
+<hfoption id="PyTorch">
+
+```python
+import torch
+from torch.nn.parallel import DistributedDataParallel as DDP
+from torch.distributed.algorithms.ddp_comm_hooks import default_hooks
+
+class MyModel(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.layer = torch.nn.Linear(10, 10)
+
+    def forward(self, x):
+        return self.layer(x)
+
+model = MyModel()
+model = DDP(model, device_ids=[torch.cuda.current_device()])
+model.register_comm_hook(state=None, hook=default_hooks.bf16_compress_hook)
+
+# Training loop
+for data, targets in data_loader:
+    outputs = model(data)
+    loss = criterion(outputs, targets)
+    loss.backward()
+    optimizer.step()
+    optimizer.zero_grad()
+```
+
+</hfoption>
+<hfoption id="Accelerate">
+
+```python
+from accelerate import Accelerator, DDPCommunicationHookType, DistributedDataParallelKwargs
+import torch
+
+class MyModel(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.layer = torch.nn.Linear(10, 10)
+
+    def forward(self, x):
+        return self.layer(x)
+
+# DDP Communication Hook setup
+ddp_kwargs = DistributedDataParallelKwargs(comm_hook=DDPCommunicationHookType.BF16)
+accelerator = Accelerator(kwargs_handlers=[ddp_kwargs])
+
+model = MyModel()
+optimizer = torch.optim.Adam(model.parameters())
+data_loader = DataLoader(dataset, batch_size=16)
+
+model, optimizer, data_loader = accelerator.prepare(model, optimizer, data_loader)
+
+# Training loop
+for data, targets in data_loader:
+    outputs = model(data)
+    loss = criterion(outputs, targets)
+    accelerator.backward(loss)
+    optimizer.step()
+    optimizer.zero_grad()
+```
+
+</hfoption>
+</hfoptions>
+
+### PowerSGD Hook
+
+<Tip warning={true}>
+
+PowerSGD typically requires extra memory of the same size as the model’s gradients to enable error feedback, which can compensate for biased compressed communication and improve accuracy.
+
+</Tip>
+
+<hfoptions id="powerSGD">
+<hfoption id="PyTorch">
+
+```python
+import torch
+from torch.nn.parallel import DistributedDataParallel as DDP
+from torch.distributed.algorithms.ddp_comm_hooks import powerSGD_hook
+
+class MyModel(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.layer = torch.nn.Linear(10, 10)
+
+    def forward(self, x):
+        return self.layer(x)
+
+model = MyModel()
+model = DDP(model, device_ids=[torch.cuda.current_device()])
+state = powerSGD_hook.PowerSGDState(process_group=None)
+model.register_comm_hook(state=state, hook=powerSGD_hook.powerSGD_hook)
+
+# Training loop
+for data, targets in data_loader:
+    outputs = model(data)
+    loss = criterion(outputs, targets)
+    loss.backward()
+    optimizer.step()
+    optimizer.zero_grad()
+```
+
+</hfoption>
+<hfoption id="Accelerate">
+
+```python
+from accelerate import Accelerator, DDPCommunicationHookType, DistributedDataParallelKwargs
+import torch
+
+class MyModel(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.layer = torch.nn.Linear(10, 10)
+
+    def forward(self, x):
+        return self.layer(x)
+
+# DDP Communication Hook setup
+ddp_kwargs = DistributedDataParallelKwargs(comm_hook=DDPCommunicationHookType.POWER_SGD)
+accelerator = Accelerator(kwargs_handlers=[ddp_kwargs])
+
+model = MyModel()
+optimizer = torch.optim.Adam(model.parameters())
+data_loader = DataLoader(dataset, batch_size=16)
+
+model, optimizer, data_loader = accelerator.prepare(model, optimizer, data_loader)
+
+# Training loop
+for data, targets in data_loader:
+    outputs = model(data)
+    loss = criterion(outputs, targets)
+    accelerator.backward(loss)
+    optimizer.step()
+    optimizer.zero_grad()
+```
+
+</hfoption>
+</hfoptions>
+
+## DDP Communication Hooks utilities
+
+There are two additional utilities for supporting optional functionalities with the communication hooks.
+
+### comm_wrapper
+
+`comm_wrapper` is an option to wrap a communication hook with additional functionality. For example, it can be used to combine FP16 compression with other communication strategies. Currently supported wrappers are `no`, `fp16`, and `bf16`.
+
+```python
+from accelerate import Accelerator, DDPCommunicationHookType, DistributedDataParallelKwargs
+import torch
+
+class MyModel(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.layer = torch.nn.Linear(10, 10)
+
+    def forward(self, x):
+        return self.layer(x)
+
+# DDP Communication Hook setup
+ddp_kwargs = DistributedDataParallelKwargs(
+    comm_hook=DDPCommunicationHookType.POWER_SGD,
+    comm_wrapper=DDPCommunicationHookType.FP16
+)
+accelerator = Accelerator(kwargs_handlers=[ddp_kwargs])
+
+model = MyModel()
+optimizer = torch.optim.Adam(model.parameters())
+data_loader = DataLoader(dataset, batch_size=16)
+
+model, optimizer, data_loader = accelerator.prepare(model, optimizer, data_loader)
+
+# Training loop
+for data, targets in data_loader:
+    outputs = model(data)
+    loss = criterion(outputs, targets)
+    accelerator.backward(loss)
+    optimizer.step()
+    optimizer.zero_grad()
+```
+
+### comm_state_option
+
+`comm_state_option` allows you to pass additional state information required by certain communication hooks. This is particularly useful for stateful hooks like `PowerSGD`, which require maintaining hyperparameters and internal states across training steps. Below is an example showcasing the use of `comm_state_option` with the `PowerSGD` hook.
+
+```python
+from accelerate import Accelerator, DDPCommunicationHookType, DistributedDataParallelKwargs
+import torch
+
+class MyModel(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.layer = torch.nn.Linear(10, 10)
+
+    def forward(self, x):
+        return self.layer(x)
+
+# DDP Communication Hook setup
+ddp_kwargs = DistributedDataParallelKwargs(
+    comm_hook=DDPCommunicationHookType.POWER_SGD,
+    comm_state_option={"matrix_approximation_rank": 2}
+)
+accelerator = Accelerator(kwargs_handlers=[ddp_kwargs])
+
+model = MyModel()
+optimizer = torch.optim.Adam(model.parameters())
+data_loader = DataLoader(dataset, batch_size=16)
+
+model, optimizer, data_loader = accelerator.prepare(model, optimizer, data_loader)
+
+# Training loop
+for data, targets in data_loader:
+    outputs = model(data)
+    loss = criterion(outputs, targets)
+    accelerator.backward(loss)
+    optimizer.step()
+    optimizer.zero_grad()
+```
+
+For more advanced usage and additional hooks, refer to the [PyTorch DDP Communication Hooks documentation](https://pytorch.org/docs/stable/ddp_comm_hooks.html).

docs/source/usage_guides/deepspeed.md

@@ -0,0 +1,738 @@
+<!--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.
+
+⚠️ Note that this file is in Markdown but contains specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+-->
+
+# DeepSpeed
+
+[DeepSpeed](https://github.com/microsoft/DeepSpeed) implements everything described in the [ZeRO paper](https://arxiv.org/abs/1910.02054). Some of the salient optimizations are:
+
+1. Optimizer state partitioning (ZeRO stage 1)
+2. Gradient partitioning (ZeRO stage 2)
+3. Parameter partitioning (ZeRO stage 3)
+4. Custom mixed precision training handling
+5. A range of fast CUDA-extension-based optimizers
+6. ZeRO-Offload to CPU and Disk/NVMe
+7. Hierarchical partitioning of model parameters (ZeRO++)
+
+ZeRO-Offload has its own dedicated paper: [ZeRO-Offload: Democratizing Billion-Scale Model Training](https://arxiv.org/abs/2101.06840). And NVMe-support is described in the paper [ZeRO-Infinity: Breaking the GPU
+Memory Wall for Extreme Scale Deep Learning](https://arxiv.org/abs/2104.07857).
+
+DeepSpeed ZeRO-2 is primarily used only for training, as its features are of no use to inference.
+
+DeepSpeed ZeRO-3 can be used for inference as well since it allows huge models to be loaded on multiple GPUs, which
+won't be possible on a single GPU.
+
+🤗 Accelerate integrates [DeepSpeed](https://github.com/microsoft/DeepSpeed) via 2 options:
+
+1. Integration of the DeepSpeed features via `deepspeed config file` specification in `accelerate config` . You just supply your custom config file or use our template. Most of
+   this document is focused on this feature. This supports all the core features of DeepSpeed and gives user a lot of flexibility.
+   User may have to change a few lines of code depending on the config.
+2. Integration via `deepspeed_plugin`.This supports subset of the DeepSpeed features and uses default options for the rest of the configurations.
+   User need not change any code and is good for those who are fine with most of the default settings of DeepSpeed.
+
+## What is integrated?
+
+Training:
+
+1. 🤗 Accelerate integrates all features of DeepSpeed ZeRO. This includes all the ZeRO stages 1, 2 and 3 as well as ZeRO-Offload, ZeRO-Infinity (which can offload to disk/NVMe) and ZeRO++.
+Below is a short description of Data Parallelism using ZeRO - Zero Redundancy Optimizer along with diagram from this [blog post](https://www.microsoft.com/en-us/research/blog/zero-deepspeed-new-system-optimizations-enable-training-models-with-over-100-billion-parameters/)
+![ZeRO Data Parallelism](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/parallelism-zero.png)
+
+(Source: [link](https://www.microsoft.com/en-us/research/blog/zero-deepspeed-new-system-optimizations-enable-training-models-with-over-100-billion-parameters/))
+
+ a. **Stage 1** : Shards optimizer states across data parallel workers/GPUs
+
+ b. **Stage 2** : Shards optimizer states + gradients across data parallel workers/GPUs
+
+ c. **Stage 3**: Shards optimizer states + gradients + model parameters across data parallel workers/GPUs
+
+ d. **Optimizer Offload**: Offloads the gradients + optimizer states to CPU/Disk building on top of ZERO Stage 2
+
+ e. **Param Offload**: Offloads the model parameters to CPU/Disk building on top of ZERO Stage 3
+
+ f. **Hierarchical Partitioning**: Enables efficient multi-node training with data-parallel training across nodes and ZeRO-3 sharding within a node, built on top of ZeRO Stage 3.
+
+<u>Note</u>: With respect to Disk Offload, the disk should be an NVME for decent speed but it technically works on any Disk
+
+Inference:
+
+1. DeepSpeed ZeRO Inference supports ZeRO stage 3 with ZeRO-Infinity. It uses the same ZeRO protocol as training, but
+   it doesn't use an optimizer and a lr scheduler and only stage 3 is relevant. For more details see:
+   [deepspeed-zero-inference](#deepspeed-zero-inference).
+
+
+## How it works?
+
+**Pre-Requisites**: Install DeepSpeed version >=0.6.5. Please refer to the [DeepSpeed Installation details](https://github.com/microsoft/DeepSpeed#installation)
+for more information.
+
+We will first look at easy to use integration via `accelerate config`.
+Followed by more flexible and feature rich `deepspeed config file` integration.
+
+### Accelerate DeepSpeed Plugin
+On your machine(s) just run:
+
+```bash
+accelerate config
+```
+
+and answer the questions asked. It will ask whether you want to use a config file for DeepSpeed to which you should answer no. Then answer the following questions to generate a basic DeepSpeed config.
+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 NLP example `examples/nlp_example.py` (from the root of the repo) with DeepSpeed Plugin:
+
+**ZeRO Stage-2 DeepSpeed Plugin Example**
+```bash
+compute_environment: LOCAL_MACHINE
+deepspeed_config:
+ gradient_accumulation_steps: 1
+ gradient_clipping: 1.0
+ offload_optimizer_device: none
+ offload_param_device: none
+ zero3_init_flag: true
+ zero_stage: 2
+distributed_type: DEEPSPEED
+fsdp_config: {}
+machine_rank: 0
+main_process_ip: null
+main_process_port: null
+main_training_function: main
+mixed_precision: fp16
+num_machines: 1
+num_processes: 2
+use_cpu: false
+```
+
+```bash
+accelerate launch examples/nlp_example.py --mixed_precision fp16
+```
+
+**ZeRO Stage-3 with CPU Offload DeepSpeed Plugin Example**
+```bash
+compute_environment: LOCAL_MACHINE
+deepspeed_config:
+  gradient_accumulation_steps: 1
+  gradient_clipping: 1.0
+  offload_optimizer_device: cpu
+  offload_param_device: cpu
+  zero3_init_flag: true
+  zero3_save_16bit_model: true
+  zero_stage: 3
+distributed_type: DEEPSPEED
+fsdp_config: {}
+machine_rank: 0
+main_process_ip: null
+main_process_port: null
+main_training_function: main
+mixed_precision: fp16
+num_machines: 1
+num_processes: 2
+use_cpu: false
+```
+
+```bash
+accelerate launch examples/nlp_example.py --mixed_precision fp16
+```
+
+Currently, `Accelerate` supports following config through the CLI:
+
+```bash
+`zero_stage`: [0] Disabled, [1] optimizer state partitioning, [2] optimizer+gradient state partitioning and [3] optimizer+gradient+parameter partitioning
+`gradient_accumulation_steps`: Number of training steps to accumulate gradients before averaging and applying them.
+`gradient_clipping`: Enable gradient clipping with value.
+`offload_optimizer_device`: [none] Disable optimizer offloading, [cpu] offload optimizer to CPU, [nvme] offload optimizer to NVMe SSD. Only applicable with ZeRO >= Stage-2.
+`offload_optimizer_nvme_path`: Decides Nvme Path to offload optimizer states. If unspecified, will default to 'none'.
+`offload_param_device`: [none] Disable parameter offloading, [cpu] offload parameters to CPU, [nvme] offload parameters to NVMe SSD. Only applicable with ZeRO Stage-3.
+`offload_param_nvme_path`: Decides Nvme Path to offload parameters. If unspecified, will default to 'none'.
+`zero3_init_flag`: Decides whether to enable `deepspeed.zero.Init` for constructing massive models. Only applicable with ZeRO Stage-3.
+`zero3_save_16bit_model`: Decides whether to save 16-bit model weights when using ZeRO Stage-3.
+`mixed_precision`: `no` for FP32 training, `fp16` for FP16 mixed-precision training and `bf16` for BF16 mixed-precision training.
+`deepspeed_moe_layer_cls_names`: Comma-separated list of transformer Mixture-of-Experts (MoE) layer class names (case-sensitive) to wrap ,e.g, `MixtralSparseMoeBlock`, `Qwen2MoeSparseMoeBlock`, `JetMoEAttention,JetMoEBlock` ...
+`deepspeed_hostfile`: DeepSpeed hostfile for configuring multi-node compute resources.
+`deepspeed_exclusion_filter`: DeepSpeed exclusion filter string when using mutli-node setup.
+`deepspeed_inclusion_filter`: DeepSpeed inclusion filter string when using mutli-node setup.
+`deepspeed_multinode_launcher`: DeepSpeed multi-node launcher to use. If unspecified, will default to `pdsh`.
+`deepspeed_config_file`: path to the DeepSpeed config file in `json` format. See the next section for more details on this.
+```
+To be able to tweak more options, you will need to use a DeepSpeed config file.
+
+### DeepSpeed Config File
+On your machine(s) just run:
+
+```bash
+accelerate config
+```
+
+and answer the questions asked. It will ask whether you want to use a config file for deepspeed to which you answer yes
+and provide the path to the deepspeed config file.
+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 NLP example `examples/by_feature/deepspeed_with_config_support.py` (from the root of the repo) with DeepSpeed Config File:
+
+**ZeRO Stage-2 DeepSpeed Config File Example**
+```bash
+compute_environment: LOCAL_MACHINE
+deepspeed_config:
+ deepspeed_config_file: /home/ubuntu/accelerate/examples/configs/deepspeed_config_templates/zero_stage2_config.json
+ zero3_init_flag: true
+distributed_type: DEEPSPEED
+fsdp_config: {}
+machine_rank: 0
+main_process_ip: null
+main_process_port: null
+main_training_function: main
+mixed_precision: fp16
+num_machines: 1
+num_processes: 2
+use_cpu: false
+```
+
+with the contents of `zero_stage2_config.json` being:
+```json
+{
+    "fp16": {
+        "enabled": true,
+        "loss_scale": 0,
+        "loss_scale_window": 1000,
+        "initial_scale_power": 16,
+        "hysteresis": 2,
+        "min_loss_scale": 1
+    },
+    "optimizer": {
+        "type": "AdamW",
+        "params": {
+            "lr": "auto",
+            "weight_decay": "auto",
+            "torch_adam": true,
+            "adam_w_mode": true
+        }
+    },
+    "scheduler": {
+        "type": "WarmupDecayLR",
+        "params": {
+            "warmup_min_lr": "auto",
+            "warmup_max_lr": "auto",
+            "warmup_num_steps": "auto",
+            "total_num_steps": "auto"
+        }
+    },
+    "zero_optimization": {
+        "stage": 2,
+        "allgather_partitions": true,
+        "allgather_bucket_size": 2e8,
+        "overlap_comm": true,
+        "reduce_scatter": true,
+        "reduce_bucket_size": "auto",
+        "contiguous_gradients": true
+    },
+    "gradient_accumulation_steps": 1,
+    "gradient_clipping": "auto",
+    "steps_per_print": 2000,
+    "train_batch_size": "auto",
+    "train_micro_batch_size_per_gpu": "auto",
+    "wall_clock_breakdown": false
+}
+```
+
+```bash
+accelerate launch examples/by_feature/deepspeed_with_config_support.py \
+--config_name "gpt2-large" \
+--tokenizer_name "gpt2-large" \
+--dataset_name "wikitext" \
+--dataset_config_name "wikitext-2-raw-v1" \
+--block_size 128 \
+--output_dir "./clm/clm_deepspeed_stage2_accelerate" \
+--learning_rate 5e-4 \
+--per_device_train_batch_size 24 \
+--per_device_eval_batch_size 24 \
+--num_train_epochs 3 \
+--with_tracking \
+--report_to "wandb"\
+```
+
+**ZeRO Stage-3 with CPU offload DeepSpeed Config File Example**
+```bash
+compute_environment: LOCAL_MACHINE
+deepspeed_config:
+ deepspeed_config_file: /home/ubuntu/accelerate/examples/configs/deepspeed_config_templates/zero_stage3_offload_config.json
+ zero3_init_flag: true
+distributed_type: DEEPSPEED
+fsdp_config: {}
+machine_rank: 0
+main_process_ip: null
+main_process_port: null
+main_training_function: main
+mixed_precision: fp16
+num_machines: 1
+num_processes: 2
+use_cpu: false
+```
+with the contents of `zero_stage3_offload_config.json` being:
+```json
+{
+    "fp16": {
+        "enabled": true,
+        "loss_scale": 0,
+        "loss_scale_window": 1000,
+        "initial_scale_power": 16,
+        "hysteresis": 2,
+        "min_loss_scale": 1
+    },
+    "optimizer": {
+        "type": "AdamW",
+        "params": {
+            "lr": "auto",
+            "weight_decay": "auto"
+        }
+    },
+    "scheduler": {
+        "type": "WarmupDecayLR",
+        "params": {
+            "warmup_min_lr": "auto",
+            "warmup_max_lr": "auto",
+            "warmup_num_steps": "auto",
+            "total_num_steps": "auto"
+        }
+    },
+    "zero_optimization": {
+        "stage": 3,
+        "offload_optimizer": {
+            "device": "cpu",
+            "pin_memory": true
+        },
+        "offload_param": {
+            "device": "cpu",
+            "pin_memory": true
+        },
+        "overlap_comm": true,
+        "contiguous_gradients": true,
+        "reduce_bucket_size": "auto",
+        "stage3_prefetch_bucket_size": "auto",
+        "stage3_param_persistence_threshold": "auto",
+        "sub_group_size": 1e9,
+        "stage3_max_live_parameters": 1e9,
+        "stage3_max_reuse_distance": 1e9,
+        "stage3_gather_16bit_weights_on_model_save": "auto"
+    },
+    "gradient_accumulation_steps": 1,
+    "gradient_clipping": "auto",
+    "steps_per_print": 2000,
+    "train_batch_size": "auto",
+    "train_micro_batch_size_per_gpu": "auto",
+    "wall_clock_breakdown": false
+}
+```
+
+```bash
+accelerate launch examples/by_feature/deepspeed_with_config_support.py \
+--config_name "gpt2-large" \
+--tokenizer_name "gpt2-large" \
+--dataset_name "wikitext" \
+--dataset_config_name "wikitext-2-raw-v1" \
+--block_size 128 \
+--output_dir "./clm/clm_deepspeed_stage3_offload_accelerate" \
+--learning_rate 5e-4 \
+--per_device_train_batch_size 32 \
+--per_device_eval_batch_size 32 \
+--num_train_epochs 3 \
+--with_tracking \
+--report_to "wandb"\
+```
+
+**ZeRO++ Config Example**
+You can use the features of ZeRO++ by using the appropriate config parameters. Note that ZeRO++ is an extension for ZeRO Stage 3. Here is how the config file can be modified, from [DeepSpeed's ZeRO++ tutorial](https://www.deepspeed.ai/tutorials/zeropp/):
+
+```json
+{
+    "zero_optimization": {
+        "stage": 3,
+        "reduce_bucket_size": "auto",
+
+        "zero_quantized_weights": true,
+        "zero_hpz_partition_size": 8,
+        "zero_quantized_gradients": true,
+
+        "contiguous_gradients": true,
+        "overlap_comm": true
+    }
+}
+```
+
+For hierarchical partitioning, the partition size `zero_hpz_partition_size` should ideally be set to the number of GPUs per node. (For example, the above config file assumes 8 GPUs per node)
+
+**Important code changes when using DeepSpeed Config File**
+
+1. DeepSpeed Optimizers and Schedulers. For more information on these,
+see the [DeepSpeed Optimizers](https://deepspeed.readthedocs.io/en/latest/optimizers.html) and [DeepSpeed Schedulers](https://deepspeed.readthedocs.io/en/latest/schedulers.html) documentation.
+We will look at the changes needed in the code when using these.
+
+   a. DS Optim + DS Scheduler: The case when both `optimizer` and `scheduler` keys are present in the DeepSpeed config file.
+   In this situation, those will be used and the user has to use `accelerate.utils.DummyOptim` and `accelerate.utils.DummyScheduler` to replace the PyTorch/Custom optimizers and schedulers in their code.
+   Below is the snippet from `examples/by_feature/deepspeed_with_config_support.py` showing this:
+   ```python
+    # Creates Dummy Optimizer if `optimizer` was specified in the config file else creates Adam Optimizer
+    optimizer_cls = (
+        torch.optim.AdamW
+        if accelerator.state.deepspeed_plugin is None
+        or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
+        else DummyOptim
+    )
+    optimizer = optimizer_cls(optimizer_grouped_parameters, lr=args.learning_rate)
+
+    # Creates Dummy Scheduler if `scheduler` was specified in the config file else creates `args.lr_scheduler_type` Scheduler
+    if (
+        accelerator.state.deepspeed_plugin is None
+        or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
+    ):
+        lr_scheduler = get_scheduler(
+            name=args.lr_scheduler_type,
+            optimizer=optimizer,
+            num_warmup_steps=args.num_warmup_steps,
+            num_training_steps=args.max_train_steps,
+        )
+    else:
+        lr_scheduler = DummyScheduler(
+            optimizer, total_num_steps=args.max_train_steps, warmup_num_steps=args.num_warmup_steps
+        )
+   ```
+   b. Custom Optim + Custom Scheduler: The case when both `optimizer` and `scheduler` keys are absent in the DeepSpeed config file.
+   In this situation, no code changes are needed from the user and this is the case when using integration via DeepSpeed Plugin.
+   In the above example we can see that the code remains unchanged if the `optimizer` and `scheduler` keys are absent in the DeepSpeed config file.
+
+   c. Custom Optim + DS Scheduler: The case when only `scheduler` key is present in the DeepSpeed config file.
+   In this situation, the user has to use `accelerate.utils.DummyScheduler` to replace the PyTorch/Custom scheduler in their code.
+
+   d. DS Optim + Custom Scheduler: The case when only `optimizer` key is present in the DeepSpeed config file.
+   This will result in an error because you can only use DS Scheduler when using DS Optim.
+
+2. Notice the `auto` values in the above example DeepSpeed config files. These are automatically handled by `prepare` method
+based on model, dataloaders, dummy optimizer and dummy schedulers provided to `prepare` method.
+Only the `auto` fields specified in above examples are handled by `prepare` method and the rest have to be explicitly specified by the user.
+
+The `auto` values are calculated as:
+
+- `reduce_bucket_size`: `hidden_size * hidden_size`
+- `stage3_prefetch_bucket_size`: `int(0.9 * hidden_size * hidden_size)`
+- `stage3_param_persistence_threshold`: `10 * hidden_size`
+
+For the `auto` feature to work for these 3 config entries - Accelerate will use `model.config.hidden_size` or `max(model.config.hidden_sizes)` as `hidden_size`. If neither of these is available, the launching will fail and you will have to set these 3 config entries manually. Remember the first 2 config entries are the communication buffers - the larger they are the more efficient the comms will be, and the larger they are the more GPU memory they will consume, so it's a tunable performance trade-off.
+
+
+**Things to note when using DeepSpeed Config File**
+
+Below is a sample script using `deepspeed_config_file` in different scenarios.
+
+Code `test.py`:
+
+```python
+from accelerate import Accelerator
+from accelerate.state import AcceleratorState
+
+
+def main():
+    accelerator = Accelerator()
+    accelerator.print(f"{AcceleratorState()}")
+
+
+if __name__ == "__main__":
+    main()
+```
+
+**Scenario 1**: Manually tampered accelerate config file having `deepspeed_config_file` along with other entries.
+
+1. Content of the `accelerate` config:
+
+```yaml
+command_file: null
+commands: null
+compute_environment: LOCAL_MACHINE
+deepspeed_config:
+  gradient_accumulation_steps: 1
+  gradient_clipping: 1.0
+  offload_optimizer_device: 'cpu'
+  offload_param_device: 'cpu'
+  zero3_init_flag: true
+  zero3_save_16bit_model: true
+  zero_stage: 3
+  deepspeed_config_file: 'ds_config.json'
+distributed_type: DEEPSPEED
+downcast_bf16: 'no'
+dynamo_backend: 'NO'
+fsdp_config: {}
+gpu_ids: null
+machine_rank: 0
+main_process_ip: null
+main_process_port: null
+main_training_function: main
+megatron_lm_config: {}
+num_machines: 1
+num_processes: 2
+rdzv_backend: static
+same_network: true
+tpu_name: null
+tpu_zone: null
+use_cpu: false
+```
+
+2. `ds_config.json`:
+
+```json
+{
+    "bf16": {
+        "enabled": true
+    },
+    "zero_optimization": {
+        "stage": 3,
+        "stage3_gather_16bit_weights_on_model_save": false,
+        "offload_optimizer": {
+            "device": "none"
+        },
+        "offload_param": {
+            "device": "none"
+        }
+    },
+    "gradient_clipping": 1.0,
+    "train_batch_size": "auto",
+    "train_micro_batch_size_per_gpu": "auto",
+    "gradient_accumulation_steps": 10,
+    "steps_per_print": 2000000
+}
+```
+
+3. Output of `accelerate launch test.py`:
+
+```bash
+ValueError: When using `deepspeed_config_file`, the following accelerate config variables will be ignored:
+['gradient_accumulation_steps', 'gradient_clipping', 'zero_stage', 'offload_optimizer_device', 'offload_param_device',
+'zero3_save_16bit_model', 'mixed_precision'].
+Please specify them appropriately in the DeepSpeed config file.
+If you are using an accelerate config file, remove other config variables mentioned in the above specified list.
+The easiest method is to create a new config following the questionnaire via `accelerate config`.
+It will only ask for the necessary config variables when using `deepspeed_config_file`.
+```
+
+**Scenario 2**: Use the solution of the error to create new accelerate config and check that no ambiguity error is now thrown.
+
+1. Run `accelerate config`:
+
+```bash
+$ accelerate config
+-------------------------------------------------------------------------------------------------------------------------------
+In which compute environment are you running?
+This machine
+-------------------------------------------------------------------------------------------------------------------------------
+Which type of machine are you using?
+multi-GPU
+How many different machines will you use (use more than 1 for multi-node training)? [1]:
+Do you wish to optimize your script with torch dynamo?[yes/NO]:
+Do you want to use DeepSpeed? [yes/NO]: yes
+Do you want to specify a json file to a DeepSpeed config? [yes/NO]: yes
+Please enter the path to the json DeepSpeed config file: ds_config.json
+Do you want to enable `deepspeed.zero.Init` when using ZeRO Stage-3 for constructing massive models? [yes/NO]: yes
+How many GPU(s) should be used for distributed training? [1]:4
+accelerate configuration saved at ds_config_sample.yaml
+```
+
+2. Content of the `accelerate` config:
+
+```yaml
+compute_environment: LOCAL_MACHINE
+deepspeed_config:
+  deepspeed_config_file: ds_config.json
+  zero3_init_flag: true
+distributed_type: DEEPSPEED
+downcast_bf16: 'no'
+dynamo_backend: 'NO'
+fsdp_config: {}
+machine_rank: 0
+main_training_function: main
+megatron_lm_config: {}
+num_machines: 1
+num_processes: 4
+rdzv_backend: static
+same_network: true
+use_cpu: false
+```
+
+3. Output of `accelerate launch test.py`:
+
+```bash
+Distributed environment: DEEPSPEED  Backend: nccl
+Num processes: 4
+Process index: 0
+Local process index: 0
+Device: cuda:0
+Mixed precision type: bf16
+ds_config: {'bf16': {'enabled': True}, 'zero_optimization': {'stage': 3, 'stage3_gather_16bit_weights_on_model_save': False, 'offload_optimizer': {'device': 'none'}, 'offload_param': {'device': 'none'}}, 'gradient_clipping': 1.0, 'train_batch_size': 'auto', 'train_micro_batch_size_per_gpu': 'auto', 'gradient_accumulation_steps': 10, 'steps_per_print': inf, 'fp16': {'enabled': False}}
+```
+
+**Scenario 3**: Setting the `accelerate launch` command arguments related to DeepSpeed as `"auto"` in the DeepSpeed` configuration file and check that things work as expected.
+
+1. New `ds_config.json` with `"auto"` for the `accelerate launch` DeepSpeed command arguments:
+
+```json
+{
+    "bf16": {
+        "enabled": "auto"
+    },
+    "zero_optimization": {
+        "stage": "auto",
+        "stage3_gather_16bit_weights_on_model_save": "auto",
+        "offload_optimizer": {
+            "device": "auto"
+        },
+        "offload_param": {
+            "device": "auto"
+        }
+    },
+    "gradient_clipping": "auto",
+    "train_batch_size": "auto",
+    "train_micro_batch_size_per_gpu": "auto",
+    "gradient_accumulation_steps": "auto",
+    "steps_per_print": 2000000
+}
+```
+
+2. Output of `accelerate launch --mixed_precision="fp16" --zero_stage=3 --gradient_accumulation_steps=5 --gradient_clipping=1.0 --offload_param_device="cpu" --offload_optimizer_device="nvme" --zero3_save_16bit_model="true" test.py`:
+
+```bash
+Distributed environment: DEEPSPEED  Backend: nccl
+Num processes: 4
+Process index: 0
+Local process index: 0
+Device: cuda:0
+Mixed precision type: fp16
+ds_config: {'bf16': {'enabled': False}, 'zero_optimization': {'stage': 3, 'stage3_gather_16bit_weights_on_model_save': True, 'offload_optimizer': {'device': 'nvme'}, 'offload_param': {'device': 'cpu'}}, 'gradient_clipping': 1.0, 'train_batch_size': 'auto', 'train_micro_batch_size_per_gpu': 'auto', 'gradient_accumulation_steps': 5, 'steps_per_print': inf, 'fp16': {'enabled': True, 'auto_cast': True}}
+```
+
+**Note**:
+1. Remaining `"auto"` values are handled in `accelerator.prepare()` call as explained in point 2 of
+`Important code changes when using DeepSpeed Config File`.
+2. Only when `gradient_accumulation_steps` is `auto`, the value passed while creating `Accelerator` object via `Accelerator(gradient_accumulation_steps=k)` will be used. When using DeepSpeed Plugin, the value from it will be used and it will overwrite the value passed while creating Accelerator object.
+
+## Saving and loading
+
+1. Saving and loading of models is unchanged for ZeRO Stage-1 and Stage-2.
+
+2. under ZeRO Stage-3, `state_dict` contains just the placeholders since the model weights are partitioned across multiple GPUs.
+ZeRO Stage-3 has 2 options:
+
+   a. Saving the entire 16bit model weights to directly load later on using `model.load_state_dict(torch.load(pytorch_model.bin))`.
+   For this, either set `zero_optimization.stage3_gather_16bit_weights_on_model_save` to True in DeepSpeed Config file or set
+   `zero3_save_16bit_model` to True in DeepSpeed Plugin.
+   **Note that this option requires consolidation of the weights on one GPU it can be slow and memory demanding, so only use this feature when needed.**
+   Below is the snippet from `examples/by_feature/deepspeed_with_config_support.py` showing this:
+   ```python
+   unwrapped_model = accelerator.unwrap_model(model)
+
+   # New Code #
+   # Saves the whole/unpartitioned fp16 model when in ZeRO Stage-3 to the output directory if
+   # `stage3_gather_16bit_weights_on_model_save` is True in DeepSpeed Config file or
+   # `zero3_save_16bit_model` is True in DeepSpeed Plugin.
+   # For Zero Stages 1 and 2, models are saved as usual in the output directory.
+   # The model name saved is `pytorch_model.bin`
+   unwrapped_model.save_pretrained(
+       args.output_dir,
+       is_main_process=accelerator.is_main_process,
+       save_function=accelerator.save,
+       state_dict=accelerator.get_state_dict(model),
+   )
+   ```
+
+   b. To get 32bit weights, first save the model using `model.save_checkpoint()`.
+   Below is the snippet from `examples/by_feature/deepspeed_with_config_support.py` showing this:
+   ```python
+   success = model.save_checkpoint(PATH, ckpt_id, checkpoint_state_dict)
+   status_msg = f"checkpointing: PATH={PATH}, ckpt_id={ckpt_id}"
+   if success:
+       logging.info(f"Success {status_msg}")
+   else:
+       logging.warning(f"Failure {status_msg}")
+   ```
+   This will create ZeRO model and optimizer partitions along with `zero_to_fp32.py` script in checkpoint directory.
+   You can use this script to do offline consolidation.
+   It requires no configuration files or GPUs. Here is an example of its usage:
+   ```bash
+   $ cd /path/to/checkpoint_dir
+   $ ./zero_to_fp32.py . pytorch_model.bin
+   Processing zero checkpoint at global_step1
+   Detected checkpoint of type zero stage 3, world_size: 2
+   Saving fp32 state dict to pytorch_model.bin (total_numel=60506624)
+   ```
+   To get 32bit model for saving/inference, you can perform:
+   ```python
+   from deepspeed.utils.zero_to_fp32 import load_state_dict_from_zero_checkpoint
+
+   unwrapped_model = accelerator.unwrap_model(model)
+   fp32_model = load_state_dict_from_zero_checkpoint(unwrapped_model, checkpoint_dir)
+   ```
+   If you are only interested in the `state_dict`, you can do the following:
+   ```python
+   from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint
+
+   state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir)
+   ```
+   Note that all these functions require ~2x memory (general RAM) of the size of the final checkpoint.
+
+## ZeRO Inference
+DeepSpeed ZeRO Inference supports ZeRO stage 3 with ZeRO-Infinity.
+It uses the same ZeRO protocol as training, but it doesn't use an optimizer and a lr scheduler and only stage 3 is relevant.
+With accelerate integration, you just need to prepare the model and dataloader as shown below:
+
+```python
+model, eval_dataloader = accelerator.prepare(model, eval_dataloader)
+```
+
+## Few caveats to be aware of
+
+1. Current integration doesn’t support Pipeline Parallelism of DeepSpeed.
+2. Current integration doesn’t support `mpu`, limiting the tensor parallelism which is supported in Megatron-LM.
+3. Current integration doesn’t support multiple models.
+
+## DeepSpeed Resources
+
+The documentation for the internals related to deepspeed can be found [here](../package_reference/deepspeed).
+
+- [Project's github](https://github.com/microsoft/deepspeed)
+- [Usage docs](https://www.deepspeed.ai/getting-started/)
+- [API docs](https://deepspeed.readthedocs.io/en/latest/index.html)
+- [Blog posts](https://www.microsoft.com/en-us/research/search/?q=deepspeed)
+
+Papers:
+
+- [ZeRO: Memory Optimizations Toward Training Trillion Parameter Models](https://arxiv.org/abs/1910.02054)
+- [ZeRO-Offload: Democratizing Billion-Scale Model Training](https://arxiv.org/abs/2101.06840)
+- [ZeRO-Infinity: Breaking the GPU Memory Wall for Extreme Scale Deep Learning](https://arxiv.org/abs/2104.07857)
+- [ZeRO++: Extremely Efficient Collective Communication for Giant Model Training](https://arxiv.org/abs/2306.10209)
+
+
+Finally, please, remember that 🤗 `Accelerate` only integrates DeepSpeed, therefore if you
+have any problems or questions with regards to DeepSpeed usage, please, file an issue with [DeepSpeed GitHub](https://github.com/microsoft/DeepSpeed/issues).
+
+
+<Tip>
+
+    For those interested in the similarities and differences between FSDP and DeepSpeed, please check out the [concept guide here](../concept_guides/fsdp_and_deepspeed.md)!
+    
+</Tip>

docs/source/usage_guides/distributed_inference.md

@@ -0,0 +1,237 @@
+<!--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.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+-->
+
+# Distributed Inference with 🤗 Accelerate
+
+Distributed inference can fall into three brackets:
+
+1. Loading an entire model onto each GPU and sending chunks of a batch through each GPU's model copy at a time
+2. Loading parts of a model onto each GPU and processing a single input at one time
+3. Loading parts of a model onto each GPU and using what is called scheduled Pipeline Parallelism to combine the two prior techniques. 
+
+We're going to go through the first and the last bracket, showcasing how to do each as they are more realistic scenarios.
+
+
+## Sending chunks of a batch automatically to each loaded model
+
+This is the most memory-intensive solution, as it requires each GPU to keep a full copy of the model in memory at a given time. 
+
+Normally when doing this, users send the model to a specific device to load it from the CPU, and then move each prompt to a different device. 
+
+A basic pipeline using the `diffusers` library might look something like so:
+
+```python
+import torch
+import torch.distributed as dist
+from diffusers import DiffusionPipeline
+
+pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16)
+```
+Followed then by performing inference based on the specific prompt:
+
+```python
+def run_inference(rank, world_size):
+    dist.init_process_group("nccl", rank=rank, world_size=world_size)
+    pipe.to(rank)
+
+    if torch.distributed.get_rank() == 0:
+        prompt = "a dog"
+    elif torch.distributed.get_rank() == 1:
+        prompt = "a cat"
+
+    result = pipe(prompt).images[0]
+    result.save(f"result_{rank}.png")
+```
+One will notice how we have to check the rank to know what prompt to send, which can be a bit tedious.
+
+A user might then also think that with 🤗 Accelerate, using the `Accelerator` to prepare a dataloader for such a task might also be 
+a simple way to manage this. (To learn more, check out the relevant section in the [Quick Tour](../quicktour#distributed-evaluation))
+
+Can it manage it? Yes. Does it add unneeded extra code however: also yes.
+
+
+With 🤗 Accelerate, we can simplify this process by using the [`Accelerator.split_between_processes`] context manager (which also exists in `PartialState` and `AcceleratorState`). 
+This function will automatically split whatever data you pass to it (be it a prompt, a set of tensors, a dictionary of the prior data, etc.) across all the processes (with a potential
+to be padded) for you to use right away.
+
+Let's rewrite the above example using this context manager:
+
+```python
+from accelerate import PartialState  # Can also be Accelerator or AcceleratorState
+from diffusers import DiffusionPipeline
+
+pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16)
+distributed_state = PartialState()
+pipe.to(distributed_state.device)
+
+# Assume two processes
+with distributed_state.split_between_processes(["a dog", "a cat"]) as prompt:
+    result = pipe(prompt).images[0]
+    result.save(f"result_{distributed_state.process_index}.png")
+```
+
+And then to launch the code, we can use the 🤗 Accelerate:
+
+If you have generated a config file to be used using `accelerate config`:
+
+```bash
+accelerate launch distributed_inference.py
+```
+
+If you have a specific config file you want to use:
+
+```bash
+accelerate launch --config_file my_config.json distributed_inference.py
+```
+
+Or if don't want to make any config files and launch on two GPUs:
+
+> Note: You will get some warnings about values being guessed based on your system. To remove these you can do `accelerate config default` or go through `accelerate config` to create a config file.
+
+```bash
+accelerate launch --num_processes 2 distributed_inference.py
+```
+
+We've now reduced the boilerplate code needed to split this data to a few lines of code quite easily.
+
+But what if we have an odd distribution of prompts to GPUs? For example, what if we have 3 prompts, but only 2 GPUs? 
+
+Under the context manager, the first GPU would receive the first two prompts and the second GPU the third, ensuring that 
+all prompts are split and no overhead is needed.
+
+*However*, what if we then wanted to do something with the results of *all the GPUs*? (Say gather them all and perform some kind of post processing)
+You can pass in `apply_padding=True` to ensure that the lists of prompts are padded to the same length, with extra data being taken 
+from the last sample. This way all GPUs will have the same number of prompts, and you can then gather the results.
+
+<Tip>
+
+This is only needed when trying to perform an action such as gathering the results, where the data on each device 
+needs to be the same length. Basic inference does not require this.
+
+</Tip>
+
+For instance:
+
+```python
+from accelerate import PartialState  # Can also be Accelerator or AcceleratorState
+from diffusers import DiffusionPipeline
+
+pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16)
+distributed_state = PartialState()
+pipe.to(distributed_state.device)
+
+# Assume two processes
+with distributed_state.split_between_processes(["a dog", "a cat", "a chicken"], apply_padding=True) as prompt:
+    result = pipe(prompt).images
+```
+
+On the first GPU, the prompts will be `["a dog", "a cat"]`, and on the second GPU it will be `["a chicken", "a chicken"]`.
+Make sure to drop the final sample, as it will be a duplicate of the previous one.
+
+You can find more complex examples [here](https://github.com/huggingface/accelerate/tree/main/examples/inference/distributed) such as how to use it with LLMs.
+
+## Memory-efficient pipeline parallelism (experimental)
+
+This next part will discuss using *pipeline parallelism*. This is an **experimental** API utilizing the [PiPPy library by PyTorch](https://github.com/pytorch/PiPPy/) as a native solution. 
+
+The general idea with pipeline parallelism is: say you have 4 GPUs and a model big enough it can be *split* on four GPUs using `device_map="auto"`. With this method you can send in 4 inputs at a time (for example here, any amount works) and each model chunk will work on an input, then receive the next input once the prior chunk finished, making it *much* more efficient **and faster** than the method described earlier. Here's a visual taken from the PyTorch repository:
+
+![PiPPy example](https://camo.githubusercontent.com/681d7f415d6142face9dd1b837bdb2e340e5e01a58c3a4b119dea6c0d99e2ce0/68747470733a2f2f692e696d6775722e636f6d2f657955633934372e706e67)
+
+To illustrate how you can use this with Accelerate, we have created an [example zoo](https://github.com/huggingface/accelerate/tree/main/examples/inference) showcasing a number of different models and situations. In this tutorial, we'll show this method for GPT2 across two GPUs.
+
+Before you proceed, please make sure you have the latest pippy installed by running the following:
+
+```bash
+pip install torchpippy
+```
+
+We require at least version 0.2.0. To confirm that you have the correct version, run `pip show torchpippy`.
+
+Start by creating the model on the CPU:
+
+```{python}
+from transformers import GPT2ForSequenceClassification, GPT2Config
+
+config = GPT2Config()
+model = GPT2ForSequenceClassification(config)
+model.eval()
+```
+
+Next you'll need to create some example inputs to use. These help PiPPy trace the model.
+
+<Tip warning={true}>
+    However you make this example will determine the relative batch size that will be used/passed
+    through the model at a given time, so make sure to remember how many items there are!
+</Tip>
+
+```{python}
+input = torch.randint(
+    low=0,
+    high=config.vocab_size,
+    size=(2, 1024),  # bs x seq_len
+    device="cpu",
+    dtype=torch.int64,
+    requires_grad=False,
+)
+```
+Next we need to actually perform the tracing and get the model ready. To do so, use the [`inference.prepare_pippy`] function and it will fully wrap the model for pipeline parallelism automatically:
+
+```{python}
+from accelerate.inference import prepare_pippy
+example_inputs = {"input_ids": input}
+model = prepare_pippy(model, example_args=(input,))
+```
+
+<Tip>
+
+    There are a variety of parameters you can pass through to `prepare_pippy`:
+    
+    * `split_points` lets you determine what layers to split the model at. By default we use wherever `device_map="auto" declares, such as `fc` or `conv1`.
+
+    * `num_chunks` determines how the batch will be split and sent to the model itself (so `num_chunks=1` with four split points/four GPUs will have a naive MP where a single input gets passed between the four layer split points)
+
+</Tip>
+
+From here, all that's left is to actually perform the distributed inference!
+
+<Tip warning={true}>
+
+When passing inputs, we highly recommend to pass them in as a tuple of arguments. Using `kwargs` is supported, however, this approach is experimental.
+</Tip>
+
+```{python}
+args = some_more_arguments
+with torch.no_grad():
+    output = model(*args)
+```
+
+When finished all the data will be on the last process only:
+
+```{python}
+from accelerate import PartialState
+if PartialState().is_last_process:
+    print(output)
+```
+
+<Tip>
+
+    If you pass in `gather_output=True` to [`inference.prepare_pippy`], the output will be sent
+    across to all the GPUs afterwards without needing the `is_last_process` check. This is 
+    `False` by default as it incurs a communication call.
+    
+</Tip>
+
+And that's it! To explore more, please check out the inference examples in the [Accelerate repo](https://github.com/huggingface/accelerate/tree/main/examples/inference/pippy) and our [documentation](../package_reference/inference) as we work to improving this integration. 

docs/source/usage_guides/explore.md

@@ -0,0 +1,51 @@
+<!--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.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+-->
+
+# Learning how to incorporate 🤗 Accelerate features quickly!
+
+Please use the interactive tool below to help you get started with learning about a particular 
+feature of 🤗 Accelerate and how to utilize it! It will provide you with a code diff, an explanation
+towards what is going on, as well as provide you with some useful links to explore more within
+the documentation!
+
+Most code examples start from the following python code before integrating 🤗 Accelerate in some way:
+
+```python
+for batch in dataloader:
+    optimizer.zero_grad()
+    inputs, targets = batch
+    inputs = inputs.to(device)
+    targets = targets.to(device)
+    outputs = model(inputs)
+    loss = loss_function(outputs, targets)
+    loss.backward()
+    optimizer.step()
+    scheduler.step()
+```
+
+<div class="block dark:hidden">
+	<iframe 
+        src="https://hf-accelerate-accelerate-examples.hf.space?__theme=light"
+        width="850"
+        height="1600"
+    ></iframe>
+</div>
+<div class="hidden dark:block">
+    <iframe 
+        src="https://hf-accelerate-accelerate-examples.hf.space?__theme=dark"
+        width="850"
+        height="1600"
+    ></iframe>
+</div>

docs/source/usage_guides/fsdp.md

@@ -0,0 +1,200 @@
+<!--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.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+-->
+
+# Fully Sharded Data Parallel
+
+To accelerate training huge models on larger batch sizes, we can use a fully sharded data parallel model.
+This type of data parallel paradigm enables fitting more data and larger models by sharding the optimizer states, gradients and parameters.
+To read more about it and the benefits, check out the [Fully Sharded Data Parallel blog](https://pytorch.org/blog/introducing-pytorch-fully-sharded-data-parallel-api/).
+We have integrated the latest PyTorch's Fully Sharded Data Parallel (FSDP) training feature.
+All you need to do is enable it through the config.
+
+## How it works out of the box
+
+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 `examples/nlp_example.py` (from the root of the repo) with FSDP enabled:
+
+```bash
+compute_environment: LOCAL_MACHINE
+debug: false
+distributed_type: FSDP
+downcast_bf16: 'no'
+fsdp_config:
+  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
+  fsdp_backward_prefetch_policy: BACKWARD_PRE
+  fsdp_forward_prefetch: false
+  fsdp_cpu_ram_efficient_loading: true
+  fsdp_offload_params: false
+  fsdp_sharding_strategy: FULL_SHARD
+  fsdp_state_dict_type: SHARDED_STATE_DICT
+  fsdp_sync_module_states: true
+  fsdp_transformer_layer_cls_to_wrap: BertLayer
+  fsdp_use_orig_params: true
+machine_rank: 0
+main_training_function: main
+mixed_precision: bf16
+num_machines: 1
+num_processes: 2
+rdzv_backend: static
+same_network: true
+tpu_env: []
+tpu_use_cluster: false
+tpu_use_sudo: false
+use_cpu: false
+```
+
+```bash
+accelerate launch examples/nlp_example.py
+```
+
+Currently, `Accelerate` supports the following config through the CLI:
+
+`fsdp_sharding_strategy`: [1] FULL_SHARD (shards optimizer states, gradients and parameters), [2] SHARD_GRAD_OP (shards optimizer states and gradients), [3] NO_SHARD (DDP), [4] HYBRID_SHARD (shards optimizer states, gradients and parameters within each node while each node has full copy), [5] HYBRID_SHARD_ZERO2 (shards optimizer states and gradients within each node while each node has full copy). For more information, please refer the official [PyTorch docs](https://pytorch.org/docs/stable/fsdp.html#torch.distributed.fsdp.ShardingStrategy).
+
+`fsdp_offload_params` : Decides Whether to offload parameters and gradients to CPU
+
+`fsdp_auto_wrap_policy`: [1] TRANSFORMER_BASED_WRAP, [2] SIZE_BASED_WRAP, [3] NO_WRAP
+
+`fsdp_transformer_layer_cls_to_wrap`: Only applicable for 🤗 Transformers. When using `fsdp_auto_wrap_policy=TRANSFORMER_BASED_WRAP`, a user may provide a comma-separated string of transformer layer class names (case-sensitive) to wrap, e.g., `BertLayer`, `GPTJBlock`, `T5Block`, `BertLayer,BertEmbeddings,BertSelfOutput`. This is important because submodules that share weights (e.g., embedding layers) should not end up in different FSDP wrapped units. Using this policy, wrapping happens for each block containing Multi-Head Attention followed by a couple of MLP layers. Remaining layers including the shared embeddings are conveniently wrapped in same outermost FSDP unit. Therefore, use this for transformer-based models. You can use the `model._no_split_modules` for 🤗 Transformer models by answering `yes` to `Do you want to use the model's `_no_split_modules` to wrap. It will try to use `model._no_split_modules` when possible.
+
+`fsdp_min_num_params`: minimum number of parameters when using `fsdp_auto_wrap_policy=SIZE_BASED_WRAP`.
+
+`fsdp_backward_prefetch_policy`: [1] BACKWARD_PRE, [2] BACKWARD_POST, [3] NO_PREFETCH
+
+`fsdp_forward_prefetch`: if True, then FSDP explicitly prefetches the next upcoming all-gather while executing in the forward pass. Should only be used for static-graph models since the prefetching follows the first iteration’s execution order. i.e., if the sub-modules' order changes dynamically during the model's execution do not enable this feature.
+
+`fsdp_state_dict_type`: [1] FULL_STATE_DICT, [2] LOCAL_STATE_DICT, [3] SHARDED_STATE_DICT
+
+`fsdp_use_orig_params`: If True, allows non-uniform `requires_grad` during init, which means support for interspersed frozen and trainable parameters. This setting is useful in cases such as parameter-efficient fine-tuning as discussed in [this post](https://dev-discuss.pytorch.org/t/rethinking-pytorch-fully-sharded-data-parallel-fsdp-from-first-principles/1019). This option also allows one to have multiple optimizer param groups. This should be `True` when creating an optimizer before preparing/wrapping the model with FSDP.
+
+`fsdp_cpu_ram_efficient_loading`: Only applicable for 🤗 Transformers models. If True, only the first process loads the pretrained model checkpoint while all other processes have empty weights. This should be set to False if you experience errors when loading the pretrained 🤗 Transformers model via `from_pretrained` method. When this setting is True `fsdp_sync_module_states` also must to be True, otherwise all the processes except the main process would have random weights leading to unexpected behaviour during training. For this to work, make sure the distributed process group is initialized before calling Transformers `from_pretrained` method. When using 🤗 Trainer API, the distributed process group is initialized when you create an instance of `TrainingArguments` class.
+
+`fsdp_sync_module_states`: If True, each individually wrapped FSDP unit will broadcast module parameters from rank 0.
+
+
+For additional and more nuanced control, you can specify other FSDP parameters via `FullyShardedDataParallelPlugin`.
+When creating `FullyShardedDataParallelPlugin` object, pass it the parameters that weren't part of the accelerate config or if you want to override them.
+The FSDP parameters will be picked based on the accelerate config file or launch command arguments and other parameters that you will pass directly through the `FullyShardedDataParallelPlugin` object will set/override that.
+
+Below is an example:
+
+```py
+from accelerate import FullyShardedDataParallelPlugin
+from torch.distributed.fsdp.fully_sharded_data_parallel import FullOptimStateDictConfig, FullStateDictConfig
+
+fsdp_plugin = FullyShardedDataParallelPlugin(
+    state_dict_config=FullStateDictConfig(offload_to_cpu=False, rank0_only=False),
+    optim_state_dict_config=FullOptimStateDictConfig(offload_to_cpu=False, rank0_only=False),
+)
+
+accelerator = Accelerator(fsdp_plugin=fsdp_plugin)
+```
+
+## Saving and loading
+
+The new recommended way of checkpointing when using FSDP models is to use `SHARDED_STATE_DICT` as `StateDictType` when setting up the accelerate config.
+Below is the code snippet to save using `save_state` utility of accelerate.
+
+```py
+accelerator.save_state("ckpt")
+```
+
+Inspect the checkpoint folder to see model and optimizer as shards per process:
+```
+ls ckpt
+# optimizer_0  pytorch_model_0  random_states_0.pkl  random_states_1.pkl  scheduler.bin
+
+cd ckpt
+
+ls optimizer_0
+# __0_0.distcp  __1_0.distcp
+
+ls pytorch_model_0
+# __0_0.distcp  __1_0.distcp
+```
+
+To load them back for resuming the training, use the `load_state` utility of accelerate
+
+```py
+accelerator.load_state("ckpt")
+```
+
+When using transformers `save_pretrained`, pass `state_dict=accelerator.get_state_dict(model)` to save the model state dict.
+  Below is an example:
+
+```diff
+  unwrapped_model.save_pretrained(
+      args.output_dir,
+      is_main_process=accelerator.is_main_process,
+      save_function=accelerator.save,
++     state_dict=accelerator.get_state_dict(model),
+)
+```
+
+### State Dict
+
+`accelerator.get_state_dict` will call the underlying `model.state_dict` implementation using `FullStateDictConfig(offload_to_cpu=True, rank0_only=True)` context manager to get the state dict only for rank 0 and it will be offloaded to CPU.
+
+You can then pass `state` into the `save_pretrained` method.  There are several modes for `StateDictType` and `FullStateDictConfig` that you can use to control the behavior of `state_dict`.  For more information, see the [PyTorch documentation](https://pytorch.org/docs/stable/fsdp.html).
+
+If you choose to use `StateDictType.SHARDED_STATE_DICT`, the weights of the model during `Accelerator.save_state` will be split into `n` files for each sub-split on the model. To merge them back into
+a single dictionary to load back into the model later after training you can use the `merge_weights` utility:
+
+```py
+from accelerate.utils import merge_fsdp_weights
+
+# Our weights are saved usually in a `pytorch_model_fsdp_{model_number}` folder
+merge_fsdp_weights("pytorch_model_fsdp_0", "output_path", safe_serialization=True)
+```
+The final output will then either be saved to `model.safetensors` or `pytorch_model.bin` (if `safe_serialization=False` is passed). 
+
+This can also be called using the CLI:
+```bash
+accelerate merge-weights pytorch_model_fsdp_0/ output_path
+```
+
+
+## Mapping between FSDP sharding strategies and DeepSpeed ZeRO Stages
+* `FULL_SHARD` maps to the DeepSpeed `ZeRO Stage-3`. Shards optimizer states, gradients and parameters.
+* `SHARD_GRAD_OP` maps to the DeepSpeed `ZeRO Stage-2`. Shards optimizer states and gradients.
+* `NO_SHARD` maps to `ZeRO Stage-0`. No sharding wherein each GPU has full copy of model, optimizer states and gradients.
+* `HYBRID_SHARD` maps to `ZeRO++ Stage-3` wherein `zero_hpz_partition_size=<num_gpus_per_node>`. Here, this will shard optimizer states, gradients and parameters within each node while each node has full copy.
+
+## A few caveats to be aware of
+
+- In case of multiple models, pass the optimizers to the prepare call in the same order as corresponding models else `accelerator.save_state()` and `accelerator.load_state()` will result in wrong/unexpected behaviour.
+- This feature is incompatible with `--predict_with_generate` in the `run_translation.py` script of 🤗 `Transformers` library.
+
+For more control, users can leverage the `FullyShardedDataParallelPlugin`. After creating an instance of this class, users can pass it to the Accelerator class instantiation.
+For more information on these options, please refer to the PyTorch [FullyShardedDataParallel](https://github.com/pytorch/pytorch/blob/0df2e863fbd5993a7b9e652910792bd21a516ff3/torch/distributed/fsdp/fully_sharded_data_parallel.py#L236) code.
+
+
+<Tip>
+
+    For those interested in the similarities and differences between FSDP and DeepSpeed, please check out the [concept guide here](../concept_guides/fsdp_and_deepspeed.md)!
+    
+</Tip>

docs/source/usage_guides/gradient_accumulation.md

@@ -0,0 +1,232 @@
+<!--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.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+-->
+
+# Performing gradient accumulation with 🤗 Accelerate
+
+Gradient accumulation is a technique where you can train on bigger batch sizes than 
+your machine would normally be able to fit into memory. This is done by accumulating gradients over
+several batches, and only stepping the optimizer after a certain number of batches have been performed.
+
+While technically standard gradient accumulation code would work fine in a distributed setup, it is not the most efficient
+method for doing so and you may experience considerable slowdowns!
+
+In this tutorial you will see how to quickly setup gradient accumulation and perform it with the utilities provided in 🤗 Accelerate,
+which can total to adding just one new line of code!
+
+This example will use a very simplistic PyTorch training loop that performs gradient accumulation every two batches:
+
+```python
+device = "cuda"
+model.to(device)
+
+gradient_accumulation_steps = 2
+
+for index, batch in enumerate(training_dataloader):
+    inputs, targets = batch
+    inputs = inputs.to(device)
+    targets = targets.to(device)
+    outputs = model(inputs)
+    loss = loss_function(outputs, targets)
+    loss = loss / gradient_accumulation_steps
+    loss.backward()
+    if (index + 1) % gradient_accumulation_steps == 0:
+        optimizer.step()
+        scheduler.step()
+        optimizer.zero_grad()
+```
+
+## Converting it to 🤗 Accelerate
+
+First the code shown earlier will be converted to utilize 🤗 Accelerate without the special gradient accumulation helper:
+
+```diff
++ from accelerate import Accelerator
++ accelerator = Accelerator()
+
++ model, optimizer, training_dataloader, scheduler = accelerator.prepare(
++     model, optimizer, training_dataloader, scheduler
++ )
+
+  for index, batch in enumerate(training_dataloader):
+      inputs, targets = batch
+-     inputs = inputs.to(device)
+-     targets = targets.to(device)
+      outputs = model(inputs)
+      loss = loss_function(outputs, targets)
+      loss = loss / gradient_accumulation_steps
++     accelerator.backward(loss)
+      if (index+1) % gradient_accumulation_steps == 0:
+          optimizer.step()
+          scheduler.step()
+          optimizer.zero_grad()
+```
+
+<Tip warning={true}>
+
+  In its current state, this code is not going to perform gradient accumulation efficiently due to a process called gradient synchronization. Read more about that in the [Concepts tutorial](../concept_guides/gradient_synchronization)!
+
+</Tip>
+
+## Letting 🤗 Accelerate handle gradient accumulation
+
+All that is left now is to let 🤗 Accelerate handle the gradient accumulation for us. To do so you should pass in a `gradient_accumulation_steps` parameter to [`Accelerator`], dictating the number 
+of steps to perform before each call to `step()` and how to automatically adjust the loss during the call to [`~Accelerator.backward`]:
+
+```diff
+  from accelerate import Accelerator
+- accelerator = Accelerator()
++ accelerator = Accelerator(gradient_accumulation_steps=2)
+```
+
+Alternatively, you can pass in a `gradient_accumulation_plugin` parameter to the [`Accelerator`] object's `__init__`, which will allow you to further customize the gradient accumulation behavior. 
+Read more about that in the [GradientAccumulationPlugin](../package_reference/accelerator#accelerate.utils.GradientAccumulationPlugin) docs.
+
+From here you can use the [`~Accelerator.accumulate`] context manager from inside your training loop to automatically perform the gradient accumulation for you!
+You just wrap it around the entire training part of our code: 
+
+```diff
+- for index, batch in enumerate(training_dataloader):
++ for batch in training_dataloader:
++     with accelerator.accumulate(model):
+          inputs, targets = batch
+          outputs = model(inputs)
+```
+
+You can remove all the special checks for the step number and the loss adjustment:
+
+```diff
+- loss = loss / gradient_accumulation_steps
+  accelerator.backward(loss)
+- if (index+1) % gradient_accumulation_steps == 0:
+  optimizer.step()
+  scheduler.step()
+  optimizer.zero_grad()
+```
+
+As you can see the [`Accelerator`] is able to keep track of the batch number you are on and it will automatically know whether to step through the prepared optimizer and how to adjust the loss. 
+
+<Tip>
+
+Typically with gradient accumulation, you would need to adjust the number of steps to reflect the change in total batches you are 
+training on. 🤗 Accelerate automagically does this for you by default. Behind the scenes we instantiate a [`GradientAccumulationPlugin`] configured to do this.
+
+</Tip>
+
+<Tip warning={true}>
+
+The [`state.GradientState`] is sync'd with the active dataloader being iterated upon. As such it assumes naively that when we have reached the end of the dataloader everything will sync and a step will be performed. To disable this, set `sync_with_dataloader` to be `False` in the [`GradientAccumulationPlugin`]:
+
+```{python}
+from accelerate import Accelerator
+from accelerate.utils import GradientAccumulationPlugin
+
+plugin = GradientAccumulationPlugin(sync_with_dataloader=False)
+accelerator = Accelerator(..., gradient_accumulation_plugin=plugin)
+```
+
+</Tip>
+
+## The finished code
+
+Below is the finished implementation for performing gradient accumulation with 🤗 Accelerate
+
+```python
+from accelerate import Accelerator
+accelerator = Accelerator(gradient_accumulation_steps=2)
+model, optimizer, training_dataloader, scheduler = accelerator.prepare(
+    model, optimizer, training_dataloader, scheduler
+)
+for batch in training_dataloader:
+    with accelerator.accumulate(model):
+        inputs, targets = batch
+        outputs = model(inputs)
+        loss = loss_function(outputs, targets)
+        accelerator.backward(loss)
+        optimizer.step()
+        scheduler.step()
+        optimizer.zero_grad()
+```
+
+<Tip warning={true}>
+
+It's important that **only one forward/backward** should be done inside the context manager `with accelerator.accumulate(model)`.
+
+</Tip>
+
+
+To learn more about what magic this wraps around, read the [Gradient Synchronization concept guide](../concept_guides/gradient_synchronization)
+
+
+## Self-contained example
+
+Here is a self-contained example that you can run to see gradient accumulation in action with 🤗 Accelerate:
+
+```python
+import torch
+import copy
+from accelerate import Accelerator
+from accelerate.utils import set_seed
+from torch.utils.data import TensorDataset, DataLoader
+
+# seed
+set_seed(0)
+
+# define toy inputs and labels
+x = torch.tensor([1., 2., 3., 4., 5., 6., 7., 8.])
+y = torch.tensor([2., 4., 6., 8., 10., 12., 14., 16.])
+gradient_accumulation_steps = 4
+batch_size = len(x) // gradient_accumulation_steps
+
+# define dataset and dataloader
+dataset = TensorDataset(x, y)
+dataloader = DataLoader(dataset, batch_size=batch_size)
+
+# define model, optimizer and loss function
+model = torch.zeros((1, 1), requires_grad=True)
+model_clone = copy.deepcopy(model)
+criterion = torch.nn.MSELoss()
+model_optimizer = torch.optim.SGD([model], lr=0.02)
+accelerator = Accelerator(gradient_accumulation_steps=gradient_accumulation_steps)
+model, model_optimizer, dataloader = accelerator.prepare(model, model_optimizer, dataloader)
+model_clone_optimizer = torch.optim.SGD([model_clone], lr=0.02)
+print(f"initial model weight is {model.mean().item():.5f}")
+print(f"initial model weight is {model_clone.mean().item():.5f}")
+for i, (inputs, labels) in enumerate(dataloader):
+    with accelerator.accumulate(model):
+        inputs = inputs.view(-1, 1)
+        print(i, inputs.flatten())
+        labels = labels.view(-1, 1)
+        outputs = inputs @ model
+        loss = criterion(outputs, labels)
+        accelerator.backward(loss)
+        model_optimizer.step()
+        model_optimizer.zero_grad()
+loss = criterion(x.view(-1, 1) @ model_clone, y.view(-1, 1))
+model_clone_optimizer.zero_grad()
+loss.backward()
+model_clone_optimizer.step()
+print(f"w/ accumulation, the final model weight is {model.mean().item():.5f}")
+print(f"w/o accumulation, the final model weight is {model_clone.mean().item():.5f}")
+```
+```
+initial model weight is 0.00000
+initial model weight is 0.00000
+0 tensor([1., 2.])
+1 tensor([3., 4.])
+2 tensor([5., 6.])
+3 tensor([7., 8.])
+w/ accumulation, the final model weight is 2.04000
+w/o accumulation, the final model weight is 2.04000
+```

docs/source/usage_guides/ipex.md

@@ -0,0 +1,192 @@
+<!--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.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+-->
+
+# Intel® Extension for PyTorch
+
+[IPEX](https://github.com/intel/intel-extension-for-pytorch) is optimized for CPUs with AVX-512 or above, and functionally works for CPUs with only AVX2. So, it is expected to bring performance benefit for Intel CPU generations with AVX-512 or above while CPUs with only AVX2 (e.g., AMD CPUs or older Intel CPUs) might result in a better performance under IPEX, but not guaranteed. IPEX provides performance optimizations for CPU training with both Float32 and BFloat16. The usage of BFloat16 is the main focus of the following sections.
+
+Low precision data type BFloat16 has been natively supported on the 3rd Generation Xeon® Scalable Processors (aka Cooper Lake) with AVX512 instruction set and will be supported on the next generation of Intel® Xeon® Scalable Processors with Intel® Advanced Matrix Extensions (Intel® AMX) instruction set with further boosted performance. The Auto Mixed Precision for CPU backend has been enabled since PyTorch-1.10. At the same time, the support of Auto Mixed Precision with BFloat16 for CPU and BFloat16 optimization of operators has been massively enabled in Intel® Extension for PyTorch, and partially upstreamed to PyTorch master branch. Users can get better performance and user experience with IPEX Auto Mixed Precision.
+
+## IPEX installation:
+
+IPEX release is following PyTorch, to install via pip:
+
+| PyTorch Version   | IPEX version   |
+| :---------------: | :----------:   |
+| 2.0               |  2.0.0         |
+| 1.13              |  1.13.0        |
+| 1.12              |  1.12.300      |
+| 1.11              |  1.11.200      |
+| 1.10              |  1.10.100      |
+
+```
+pip install intel_extension_for_pytorch==<version_name> -f https://developer.intel.com/ipex-whl-stable-cpu
+```
+
+Check more approaches for [IPEX installation](https://intel.github.io/intel-extension-for-pytorch/cpu/latest/tutorials/installation.html).
+
+
+## How It Works For Training optimization in CPU
+
+🤗 Accelerate has integrated [IPEX](https://github.com/intel/intel-extension-for-pytorch), all you need to do is enabling it through the config.
+
+**Scenario 1**: Acceleration of No distributed CPU training
+
+Run <u>accelerate config</u> on your machine:
+
+```bash
+$ accelerate config
+-----------------------------------------------------------------------------------------------------------------------------------------------------------
+In which compute environment are you running?
+This machine
+-----------------------------------------------------------------------------------------------------------------------------------------------------------
+Which type of machine are you using?
+No distributed training
+Do you want to run your training on CPU only (even if a GPU / Apple Silicon device is available)? [yes/NO]:yes
+Do you want to use Intel PyTorch Extension (IPEX) to speed up training on CPU? [yes/NO]:yes
+Do you wish to optimize your script with torch dynamo?[yes/NO]:NO
+Do you want to use DeepSpeed? [yes/NO]: NO
+-----------------------------------------------------------------------------------------------------------------------------------------------------------
+Do you wish to use FP16 or BF16 (mixed precision)?
+bf16
+```
+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 NLP example `examples/nlp_example.py` (from the root of the repo) with IPEX enabled.
+default_config.yaml that is generated after `accelerate config`
+
+```bash
+compute_environment: LOCAL_MACHINE
+distributed_type: 'NO'
+downcast_bf16: 'no'
+ipex_config:
+  ipex: true
+machine_rank: 0
+main_training_function: main
+mixed_precision: bf16
+num_machines: 1
+num_processes: 1
+rdzv_backend: static
+same_network: true
+tpu_env: []
+tpu_use_cluster: false
+tpu_use_sudo: false
+use_cpu: true
+```
+```bash
+accelerate launch examples/nlp_example.py
+```
+
+**Scenario 2**: Acceleration of distributed CPU training
+we use Intel oneCCL for communication, combined with Intel® MPI library to deliver flexible, efficient, scalable cluster messaging on Intel® architecture. you could refer the [here](https://huggingface.co/docs/transformers/perf_train_cpu_many) for the installation guide
+
+Run <u>accelerate config</u> on your machine(node0):
+
+```bash
+$ accelerate config
+-----------------------------------------------------------------------------------------------------------------------------------------------------------
+In which compute environment are you running?
+This machine
+-----------------------------------------------------------------------------------------------------------------------------------------------------------
+Which type of machine are you using?
+multi-CPU
+How many different machines will you use (use more than 1 for multi-node training)? [1]: 4
+-----------------------------------------------------------------------------------------------------------------------------------------------------------
+What is the rank of this machine?
+0
+What is the IP address of the machine that will host the main process? 36.112.23.24
+What is the port you will use to communicate with the main process? 29500
+Are all the machines on the same local network? Answer `no` if nodes are on the cloud and/or on different network hosts [YES/no]: yes
+Do you want to use Intel PyTorch Extension (IPEX) to speed up training on CPU? [yes/NO]:yes
+Do you want accelerate to launch mpirun? [yes/NO]: yes
+Please enter the path to the hostfile to use with mpirun [~/hostfile]: ~/hostfile
+Enter the number of oneCCL worker threads [1]: 1
+Do you wish to optimize your script with torch dynamo?[yes/NO]:NO
+How many processes should be used for distributed training? [1]:16
+-----------------------------------------------------------------------------------------------------------------------------------------------------------
+Do you wish to use FP16 or BF16 (mixed precision)?
+bf16
+```
+For instance, here is how you would run the NLP example `examples/nlp_example.py` (from the root of the repo) with IPEX enabled for distributed CPU training.
+
+default_config.yaml that is generated after `accelerate config`
+```bash
+compute_environment: LOCAL_MACHINE
+distributed_type: MULTI_CPU
+downcast_bf16: 'no'
+ipex_config:
+  ipex: true
+machine_rank: 0
+main_process_ip: 36.112.23.24
+main_process_port: 29500
+main_training_function: main
+mixed_precision: bf16
+mpirun_config:
+  mpirun_ccl: '1'
+  mpirun_hostfile: /home/user/hostfile
+num_machines: 4
+num_processes: 16
+rdzv_backend: static
+same_network: true
+tpu_env: []
+tpu_use_cluster: false
+tpu_use_sudo: false
+use_cpu: true
+```
+
+Set following env and using intel MPI to launch the training
+
+In node0, you need to create a configuration file which contains the IP addresses of each node (for example hostfile) and pass that configuration file path as an argument.
+If you selected to have Accelerate launch `mpirun`, ensure that the location of your hostfile matches the path in the config.
+```bash
+$ cat hostfile
+xxx.xxx.xxx.xxx #node0 ip
+xxx.xxx.xxx.xxx #node1 ip
+xxx.xxx.xxx.xxx #node2 ip
+xxx.xxx.xxx.xxx #node3 ip
+```
+When Accelerate is launching `mpirun`, source the oneCCL bindings setvars.sh to get your Intel MPI environment, and then
+run your script using `accelerate launch`. Note that the python script and environment needs to exist on all of the
+machines being used for multi-CPU training.
+```bash
+oneccl_bindings_for_pytorch_path=$(python -c "from oneccl_bindings_for_pytorch import cwd; print(cwd)")
+source $oneccl_bindings_for_pytorch_path/env/setvars.sh
+
+accelerate launch examples/nlp_example.py
+```
+Otherwise, if you selected not to have Accelerate launch `mpirun`, run the following command in node0 and **16DDP** will
+be enabled in node0,node1,node2,node3 with BF16 mixed precision. When using this method, the python script, python
+environment, and accelerate config file need to be present on all of the machines used for multi-CPU training.
+```bash
+oneccl_bindings_for_pytorch_path=$(python -c "from oneccl_bindings_for_pytorch import cwd; print(cwd)")
+source $oneccl_bindings_for_pytorch_path/env/setvars.sh
+export CCL_WORKER_COUNT=1
+export MASTER_ADDR=xxx.xxx.xxx.xxx #node0 ip
+export CCL_ATL_TRANSPORT=ofi
+mpirun -f hostfile -n 16 -ppn 4 accelerate launch examples/nlp_example.py
+```
+
+## Related Resources
+
+- [Project's github](https://github.com/intel/intel-extension-for-pytorch)
+- [API docs](https://intel.github.io/intel-extension-for-pytorch/cpu/latest/tutorials/api_doc.html)
+- [Tuning guide](https://intel.github.io/intel-extension-for-pytorch/cpu/latest/tutorials/performance_tuning/tuning_guide.html)
+- [Blogs & Publications](https://intel.github.io/intel-extension-for-pytorch/cpu/latest/tutorials/blogs_publications.html)
+

docs/source/usage_guides/local_sgd.md

@@ -0,0 +1,108 @@
+<!--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.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+-->
+
+# Using Local SGD with 🤗 Accelerate
+
+Local SGD is a technique for distributed training where gradients are not synchronized every step. Thus, each process updates its own version of the model weights and after a given number of steps these weights are synchronized by averaging across all processes. This improves communication efficiency and can lead to substantial training speed up especially when a computer lacks a faster interconnect such as NVLink.
+Unlike gradient accumulation (where improving communication efficiency requires increasing the effective batch size), Local SGD does not require changing a batch size or a learning rate / schedule. However, if necessary, Local SGD can be combined with gradient accumulation as well.
+
+In this tutorial you will see how to quickly setup  Local SGD 🤗 Accelerate. Compared to a standard Accelerate setup, this requires only two extra lines of code.
+
+This example will use a very simplistic PyTorch training loop that performs gradient accumulation every two batches:
+
+```python
+device = "cuda"
+model.to(device)
+
+gradient_accumulation_steps = 2
+
+for index, batch in enumerate(training_dataloader):
+    inputs, targets = batch
+    inputs = inputs.to(device)
+    targets = targets.to(device)
+    outputs = model(inputs)
+    loss = loss_function(outputs, targets)
+    loss = loss / gradient_accumulation_steps
+    loss.backward()
+    if (index + 1) % gradient_accumulation_steps == 0:
+        optimizer.step()
+        scheduler.step()
+        optimizer.zero_grad()
+```
+
+## Converting it to 🤗 Accelerate
+
+First the code shown earlier will be converted to use 🤗 Accelerate  with neither a LocalSGD or a gradient accumulation helper:
+
+```diff
++ from accelerate import Accelerator
++ accelerator = Accelerator()
+
++ model, optimizer, training_dataloader, scheduler = accelerator.prepare(
++     model, optimizer, training_dataloader, scheduler
++ )
+
+  for index, batch in enumerate(training_dataloader):
+      inputs, targets = batch
+-     inputs = inputs.to(device)
+-     targets = targets.to(device)
+      outputs = model(inputs)
+      loss = loss_function(outputs, targets)
+      loss = loss / gradient_accumulation_steps
++     accelerator.backward(loss)
+      if (index+1) % gradient_accumulation_steps == 0:
+          optimizer.step()
+          scheduler.step()
+```
+
+## Letting 🤗 Accelerate handle model synchronization 
+
+All that is left now is to let 🤗 Accelerate handle model parameter synchronization **and** the gradient accumulation for us. For simplicity let us assume we need to synchronize every 8 steps. This is
+achieved by adding one `with LocalSGD` statement and one call `local_sgd.step()` after every optimizer step:
+
+```diff
++local_sgd_steps=8
+
++with LocalSGD(accelerator=accelerator, model=model, local_sgd_steps=8, enabled=True) as local_sgd:
+    for batch in training_dataloader:
+        with accelerator.accumulate(model):
+            inputs, targets = batch
+            outputs = model(inputs)
+            loss = loss_function(outputs, targets)
+            accelerator.backward(loss)
+            optimizer.step()
+            scheduler.step()
+            optimizer.zero_grad()
++           local_sgd.step()
+```
+
+Under the hood, the Local SGD code **disables** automatic gradient synchronization (but accumulation still works as expected!). Instead it averages model parameters every `local_sgd_steps` steps (as well as at the end of the training loop).
+
+## Limitations
+
+The current implementation works only with basic multi-GPU (or multi-CPU) training without, e.g., [DeepSpeed.](https://github.com/microsoft/DeepSpeed).
+
+## References
+
+    Although we are not aware of the true origins of this simple approach, the idea of local SGD is quite old and goes
+    back to at least:
+
+    Zhang, J., De Sa, C., Mitliagkas, I., & Ré, C. (2016). [Parallel SGD: When does averaging help?. arXiv preprint
+    arXiv:1606.07365.](https://arxiv.org/abs/1606.07365)
+
+    We credit the term Local SGD to the following paper (but there might be earlier references we are not aware of).
+
+    Stich, Sebastian Urban. ["Local SGD Converges Fast and Communicates Little." ICLR 2019-International Conference on
+    Learning Representations. No. CONF. 2019.](https://arxiv.org/abs/1805.09767)

docs/source/usage_guides/low_precision_training.md

@@ -0,0 +1,92 @@
+<!--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.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+-->
+
+# Low Precision Training Methods
+
+🤗 Accelerate provides integrations to train on lower precision methods using specified supported hardware through the `TransformersEngine` and `MS-AMP` packages. This documentation will help guide you through what hardware is supported, how to configure your [`Accelerator`] to leverage the low precision methods, and what you can expect when training. 
+
+## What training on FP8 means
+
+To explore more of the nitty-gritty in training in FP8 with PyTorch and 🤗 Accelerate, check out the [concept_guide](../concept_guides/low_precision_training.md) on why this can be difficult. But essentially rather than training in BF16, some (or all) aspects of training a model can be performed using 8 bits instead of 16. The challenge is doing so without degrading final performance. 
+
+This is only enabled on specific NVIDIA hardware, namely:
+
+* Anything after the 3000 series consumer graphics cards (such as the 4090)
+* Hopper-based GPU architectures (such as the `H100` and `H200`)
+
+What this will result in is some gain in the memory used (as we've cut the needed memory in half for some parts of training) and an increase in throughput *should* be seen as well for larger models that can replace certain layers with FP8-enabled ones.
+
+## Configuring the Accelerator
+
+Currently two different backends for FP8 are supported (`TransformersEngine` and `MS-AMP`), each with different capabilities and configurations. 
+
+To use either, the same core API is used. Just pass `mixed_precision="fp8"` to either the [`Accelerator`], during `accelerate config` when prompted about mixed precision, or as part of your `config.yaml` file in the `mixed_precision` key:
+
+```{python}
+from accelerate import Accelerator
+accelerator = Accelerator(mixed_precision="fp8")
+```
+
+By default, if `MS-AMP` is available in your environment, 🤗 Accelerate will automatically utilize it as a backend. To specify it yourself (and customize other parts of the FP8 mixed precision setup), you can utilize the [`utils.FP8RecipeKwargs`]:
+
+```{python}
+from accelerate import Accelerator
+from accelerate.utils import FP8RecipeKwargs
+kwargs = [FP8RecipeKwargs(backend="msamp")]
+# Or to specify the backend as `TransformersEngine` even if MS-AMP is installed
+# kwargs = [FP8RecipeKwargs(backend="te")]
+accelerator = Accelerator(mixed_precision="fp8", kwarg_handlers=kwargs)
+```
+
+## Configuring MS-AMP
+
+Of the two, `MS-AMP` is traditionally the easier one to configure as there is only a single argument: the optimization level. 
+
+Currently two levels of optimization are supported in the 🤗 Accelerate integration, `"O1"` and `"O2"` (using the letter 'o', not zero). 
+
+* `"O1"` will cast the weight gradients and `all_reduce` communications to happen in 8-bit, while the rest are done in 16 bit. This reduces the general GPU memory usage and speeds up communication bandwidths.
+* `"O2"` will also cast first-order optimizer states into 8 bit, while the second order states are in FP16. (Currently just the `Adam` optimizer is supported). This tries its best to minimize final accuracy degradation and will save the highest potential memory.
+
+To specify an optimization level, pass it to the `FP8KwargsHandler` by setting the `optimization_level` argument:
+
+```{python}
+from accelerate import Accelerator
+from accelerate.utils import FP8RecipeKwargs
+kwargs = [FP8RecipeKwargs(backend="msamp", optimization_level="O2")]
+accelerator = Accelerator(mixed_precision="fp8", kwarg_handlers=kwargs)
+```
+
+## Configuring TransformersEngine
+
+TransformersEngine has much more available for customizing how and what FP8 calculations are performed. A full list of supported arguments and what they mean are available in [NVIDIA's documentation](https://docs.nvidia.com/deeplearning/transformer-engine/user-guide/api/common.html), however they are restated as part of [`FP8KwargsHandler`]'s docstring for your convenience. 
+
+🤗 Accelerate tries to set sensible defaults, but exploring and tweaking the various parameters yourself can lead to better performance potentially.
+
+To use it, specify `backend="te"` and modify any of the arguments you want as part of your kwarg handler:
+
+```{python}
+from accelerate import Accelerator
+from accelerate.utils import FP8RecipeKwargs
+kwargs = [FP8RecipeKwargs(backend="te", ...)]
+accelerator = Accelerator(mixed_precision="fp8", kwarg_handlers=kwargs)
+```
+
+## Further Reading
+
+To learn more about training in FP8 please check out the following resources:
+
+* [Our concept guide](../concept_guides/low_precision_training.md) detailing into more about both TransformersEngine and MS-AMP
+* [The `transformers-engine` documentation](https://docs.nvidia.com/deeplearning/transformer-engine/user-guide/api/common.html)
+* [The `MS-AMP` documentation](https://azure.github.io/MS-AMP/docs/)

docs/source/usage_guides/megatron_lm.md

@@ -0,0 +1,586 @@
+<!--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.
+
+⚠️ Note that this file is in Markdown but contains specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+-->
+
+
+# Megatron-LM
+
+[Megatron-LM](https://github.com/NVIDIA/Megatron-LM) enables training large transformer language models at scale.
+It provides efficient tensor, pipeline and sequence based model parallelism for pre-training transformer based
+Language Models such as [GPT](https://arxiv.org/abs/2005.14165) (Decoder Only), [BERT](https://arxiv.org/pdf/1810.04805.pdf) (Encoder Only) and [T5](https://arxiv.org/abs/1910.10683) (Encoder-Decoder).
+For detailed information and how things work behind the scene please refer the github [repo](https://github.com/NVIDIA/Megatron-LM).
+
+## What is integrated?
+
+Accelerate integrates following feature of Megatron-LM to enable large scale pre-training/finetuning
+of BERT (Encoder), GPT (Decoder) or T5 models (Encoder and Decoder):
+
+a. **Tensor Parallelism (TP)**: Reduces memory footprint without much additional communication on intra-node ranks.
+Each tensor is split into multiple chunks with each shard residing on separate GPU. At each step, the same mini-batch of data is processed
+independently and in parallel by each shard followed by syncing across all GPUs (`all-reduce` operation). 
+In a simple transformer layer, this leads to 2 `all-reduces` in the forward path and 2 in the backward path.
+For more details, please refer research paper [Megatron-LM: Training Multi-Billion Parameter Language Models Using
+Model Parallelism](https://arxiv.org/pdf/1909.08053.pdf) and 
+this section of 🤗 blogpost [The Technology Behind BLOOM Training](https://huggingface.co/blog/bloom-megatron-deepspeed#tensor-parallelism).
+
+
+b. **Pipeline Parallelism (PP)**: Reduces memory footprint and enables large scale training via inter-node parallelization. 
+Reduces the bubble of naive PP via PipeDream-Flush schedule/1F1B schedule and Interleaved 1F1B schedule. 
+Layers are distributed uniformly across PP stages. For example, if a model has `24` layers and we have `4` GPUs for
+pipeline parallelism, each GPU will have `6` layers (24/4). For more details on schedules to reduce the idle time of PP,
+please refer to the research paper [Efficient Large-Scale Language Model Training on GPU Clusters
+Using Megatron-LM](https://arxiv.org/pdf/2104.04473.pdf) and 
+this section of 🤗 blogpost [The Technology Behind BLOOM Training](https://huggingface.co/blog/bloom-megatron-deepspeed#pipeline-parallelism).
+
+c. **Sequence Parallelism (SP)**: Reduces memory footprint without any additional communication. Only applicable when using TP.
+It reduces activation memory required as it prevents the same copies to be on the tensor parallel ranks 
+post `all-reduce` by replacing then with `reduce-scatter` and `no-op` operation would be replaced by `all-gather`. 
+As `all-reduce = reduce-scatter + all-gather`, this saves a ton of activation memory at no added communication cost. 
+To put it simply, it shards the outputs of each transformer layer along sequence dimension, e.g., 
+if the sequence length is `1024` and the TP size is `4`, each GPU will have `256` tokens (1024/4) for each sample. 
+This increases the batch size that can be supported for training. For more details, please refer to the research paper
+[Reducing Activation Recomputation in Large Transformer Models](https://arxiv.org/pdf/2205.05198.pdf). 
+
+d. **Data Parallelism (DP)** via Distributed Optimizer: Reduces the memory footprint by sharding optimizer states and gradients across DP ranks
+(versus the traditional method of replicating the optimizer state across data parallel ranks). 
+For example, when using Adam optimizer with mixed-precision training, each parameter accounts for 12 bytes of memory.
+This gets distributed equally across the GPUs, i.e., each parameter would account for 3 bytes (12/4) if we have 4 GPUs.
+For more details, please refer the research paper [ZeRO: Memory Optimizations Toward Training Trillion
+Parameter Models](https://arxiv.org/pdf/1910.02054.pdf) and following section of 🤗 blog 
+[The Technology Behind BLOOM Training](https://huggingface.co/blog/bloom-megatron-deepspeed#zero-data-parallelism).
+
+e. **Selective Activation Recomputation**: Reduces the memory footprint of activations significantly via smart activation checkpointing.
+It doesn't store activations occupying large memory while being fast to recompute thereby achieving great tradeoff between memory and recomputation.
+For example, for GPT-3, this leads to 70% reduction in required memory for activations at the expense of
+only 2.7% FLOPs overhead for recomputation of activations. For more details, please refer to the research paper 
+[Reducing Activation Recomputation in Large Transformer Models](https://arxiv.org/pdf/2205.05198.pdf).
+
+f. **Fused Kernels**: Fused Softmax, Mixed Precision Fused Layer Norm and  Fused gradient accumulation to weight gradient computation of linear layer.
+PyTorch JIT compiled Fused GeLU and Fused Bias+Dropout+Residual addition.
+
+g. **Support for Indexed datasets**: Efficient binary format of datasets for large scale training. Support for the `mmap`, `cached` index file and the `lazy` loader format.
+
+h. **Checkpoint reshaping and interoperability**: Utility for reshaping Megatron-LM checkpoints of variable 
+tensor and pipeline parallel sizes to the beloved 🤗 Transformers sharded checkpoints as it has great support with plethora of tools
+such as 🤗 Accelerate Big Model Inference, Megatron-DeepSpeed Inference etc. 
+Support is also available for converting 🤗 Transformers sharded checkpoints to Megatron-LM checkpoint of variable tensor and pipeline parallel sizes
+for large scale training.  
+
+
+## Pre-Requisites 
+
+You will need to install the latest pytorch, cuda, nccl, and NVIDIA [APEX](https://github.com/NVIDIA/apex#quick-start) releases and the nltk library.
+See [documentation](https://github.com/NVIDIA/Megatron-LM#setup) for more details. 
+Another way to setup the environment is to pull an NVIDIA PyTorch Container that comes with all the required installations from NGC.
+
+Below is a step-by-step method to set up the conda environment:
+
+1. Create a virtual environment
+```
+conda create --name ml
+```
+
+2. Assuming that the machine has CUDA 11.3 installed, installing the corresponding PyTorch GPU Version
+```
+conda install pytorch torchvision torchaudio cudatoolkit=11.3 -c pytorch
+```
+
+3. Install Nvidia APEX
+```
+git clone https://github.com/NVIDIA/apex
+cd apex
+pip install -v --disable-pip-version-check --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./
+cd ..
+```
+
+4. Installing Megatron-LM
+
+```
+git clone https://github.com/NVIDIA/Megatron-LM.git
+cd Megatron-LM
+git checkout core_r0.5.0
+pip install --no-use-pep517 -e .
+```
+
+## Accelerate Megatron-LM Plugin
+
+Important features are directly supported via the `accelerate config` command. 
+An example of the corresponding questions for using Megatron-LM features is shown below:
+
+```bash
+:~$ accelerate config --config_file "megatron_gpt_config.yaml"
+In which compute environment are you running? ([0] This machine, [1] AWS (Amazon SageMaker)): 0
+Which type of machine are you using? ([0] No distributed training, [1] multi-CPU, [2] multi-GPU, [3] TPU): 2
+How many different machines will you use (use more than 1 for multi-node training)? [1]: 
+Do you want to use DeepSpeed? [yes/NO]: 
+Do you want to use FullyShardedDataParallel? [yes/NO]: 
+Do you want to use Megatron-LM ? [yes/NO]: yes
+What is the Tensor Parallelism degree/size? [1]:2
+Do you want to enable Sequence Parallelism? [YES/no]: 
+What is the Pipeline Parallelism degree/size? [1]:2
+What is the number of micro-batches? [1]:2
+Do you want to enable selective activation recomputation? [YES/no]: 
+Do you want to use distributed optimizer which shards optimizer state and gradients across data parallel ranks? [YES/no]: 
+What is the gradient clipping value based on global L2 Norm (0 to disable)? [1.0]: 
+How many GPU(s) should be used for distributed training? [1]:4
+Do you wish to use FP16 or BF16 (mixed precision)? [NO/fp16/bf16]: bf16
+```
+
+The resulting config is shown below:
+
+```
+~$ cat megatron_gpt_config.yaml 
+compute_environment: LOCAL_MACHINE
+deepspeed_config: {}
+distributed_type: MEGATRON_LM
+downcast_bf16: 'no'
+fsdp_config: {}
+machine_rank: 0
+main_process_ip: null
+main_process_port: null
+main_training_function: main
+megatron_lm_config:
+  megatron_lm_gradient_clipping: 1.0
+  megatron_lm_num_micro_batches: 2
+  megatron_lm_pp_degree: 2
+  megatron_lm_recompute_activations: true
+  megatron_lm_sequence_parallelism: true
+  megatron_lm_tp_degree: 2
+  megatron_lm_use_distributed_optimizer: true
+mixed_precision: bf16
+num_machines: 1
+num_processes: 4
+rdzv_backend: static
+same_network: true
+use_cpu: false
+```
+
+We will take the example of GPT pre-training. The minimal changes required to the official `run_clm_no_trainer.py` 
+to use Megatron-LM are as follows:
+
+1. As Megatron-LM uses its own implementation of Optimizer, the corresponding scheduler compatible with it needs to be used.
+As such, support for only the Megatron-LM's scheduler is present. User will need to create `accelerate.utils.MegatronLMDummyScheduler`.
+Example is given below:
+
+```python
+from accelerate.utils import MegatronLMDummyScheduler
+
+if accelerator.distributed_type == DistributedType.MEGATRON_LM:
+    lr_scheduler = MegatronLMDummyScheduler(
+        optimizer=optimizer,
+        total_num_steps=args.max_train_steps,
+        warmup_num_steps=args.num_warmup_steps,
+    )
+else:
+    lr_scheduler = get_scheduler(
+        name=args.lr_scheduler_type,
+        optimizer=optimizer,
+        num_warmup_steps=args.num_warmup_steps * args.gradient_accumulation_steps,
+        num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,
+    )
+```
+
+2. Getting the details of the total batch size now needs to be cognization of tensor and pipeline parallel sizes.
+Example of getting the effective total batch size is shown below:
+
+```python
+if accelerator.distributed_type == DistributedType.MEGATRON_LM:
+    total_batch_size = accelerator.state.megatron_lm_plugin.global_batch_size
+else:
+    total_batch_size = args.per_device_train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
+```
+
+3. When using Megatron-LM, the losses are already averaged across the data parallel group
+
+```python
+if accelerator.distributed_type == DistributedType.MEGATRON_LM:
+    losses.append(loss)
+else:
+    losses.append(accelerator.gather_for_metrics(loss.repeat(args.per_device_eval_batch_size)))
+
+if accelerator.distributed_type == DistributedType.MEGATRON_LM:
+    losses = torch.tensor(losses)
+else:
+    losses = torch.cat(losses)
+```
+
+4. For Megatron-LM, we need to save the model using `accelerator.save_state`
+
+```python
+if accelerator.distributed_type == DistributedType.MEGATRON_LM:
+    accelerator.save_state(args.output_dir)
+else:
+    unwrapped_model = accelerator.unwrap_model(model)
+    unwrapped_model.save_pretrained(
+        args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save
+    )
+```
+
+That's it! We are good to go 🚀. Please find the example script in the examples folder at the path `accelerate/examples/by_feature/megatron_lm_gpt_pretraining.py`.
+Let's run it for `gpt-large` model architecture using 4 A100-80GB GPUs.
+
+```bash
+accelerate launch --config_file megatron_gpt_config.yaml \
+examples/by_feature/megatron_lm_gpt_pretraining.py \
+--config_name "gpt2-large" \
+--tokenizer_name "gpt2-large" \
+--dataset_name wikitext \
+--dataset_config_name wikitext-2-raw-v1 \
+--block_size 1024 \
+--learning_rate 5e-5 \
+--per_device_train_batch_size 24 \
+--per_device_eval_batch_size 24 \
+--num_train_epochs 5 \
+--with_tracking \
+--report_to "wandb" \
+--output_dir "awesome_model"
+```
+
+Below are some important excerpts from the output logs:
+
+```bash
+Loading extension module fused_dense_cuda...
+>>> done with compiling and loading fused kernels. Compilation time: 3.569 seconds
+ > padded vocab (size: 50257) with 175 dummy tokens (new size: 50432)
+Building gpt model in the pre-training mode.
+The Megatron LM model weights are initialized at random in `accelerator.prepare`. Please use `accelerator.load_checkpoint` to load a pre-trained checkpoint matching the distributed setup.
+Preparing dataloader
+Preparing dataloader
+Preparing model
+ > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 210753280
+ > number of parameters on (tensor, pipeline) model parallel rank (1, 1): 209445120
+ > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 210753280
+ > number of parameters on (tensor, pipeline) model parallel rank (0, 1): 209445120
+Preparing optimizer
+Preparing scheduler
+> learning rate decay style: linear
+10/10/2022 22:57:22 - INFO - __main__ - ***** Running training *****
+10/10/2022 22:57:22 - INFO - __main__ -   Num examples = 2318
+10/10/2022 22:57:22 - INFO - __main__ -   Num Epochs = 5
+10/10/2022 22:57:22 - INFO - __main__ -   Instantaneous batch size per device = 24
+10/10/2022 22:57:22 - INFO - __main__ -   Total train batch size (w. parallel, distributed & accumulation) = 48
+10/10/2022 22:57:22 - INFO - __main__ -   Gradient Accumulation steps = 1
+10/10/2022 22:57:22 - INFO - __main__ -   Total optimization steps = 245
+ 20%|████████████▍                                                 | 49/245 [01:04<04:09,  1.27s/it]
+ 10/10/2022 22:58:29 - INFO - __main__ - epoch 0: perplexity: 1222.1594275215962 eval_loss: 7.10837459564209
+ 40%|████████████████████████▊                                     | 98/245 [02:10<03:07,  1.28s/it]
+ 10/10/2022 22:59:35 - INFO - __main__ - epoch 1: perplexity: 894.5236583794557 eval_loss: 6.796291351318359
+ 60%|████████████████████████████████████▌                        | 147/245 [03:16<02:05,  1.28s/it]
+ 10/10/2022 23:00:40 - INFO - __main__ - epoch 2: perplexity: 702.8458788508042 eval_loss: 6.555137634277344
+ 80%|████████████████████████████████████████████████▊            | 196/245 [04:22<01:02,  1.28s/it]
+ 10/10/2022 23:01:46 - INFO - __main__ - epoch 3: perplexity: 600.3220028695281 eval_loss: 6.39746618270874
+100%|█████████████████████████████████████████████████████████████| 245/245 [05:27<00:00,  1.28s/it]
+```
+
+There are a large number of other options/features that one can set using `accelerate.utils.MegatronLMPlugin`.
+
+## Advanced features to leverage writing custom train step and Megatron-LM Indexed Datasets
+
+For leveraging more features, please go through below details.
+
+1. Below is an example of changes required to customize the Train Step while using Megatron-LM. 
+You will implement the `accelerate.utils.AbstractTrainStep` or inherit from their corresponding children 
+`accelerate.utils.GPTTrainStep`, `accelerate.utils.BertTrainStep` or `accelerate.utils.T5TrainStep`.
+
+```python
+from accelerate.utils import MegatronLMDummyScheduler, GPTTrainStep, avg_losses_across_data_parallel_group
+
+
+# Custom loss function for the Megatron model
+class GPTTrainStepWithCustomLoss(GPTTrainStep):
+    def __init__(self, megatron_args, **kwargs):
+        super().__init__(megatron_args)
+        self.kwargs = kwargs
+
+    def get_loss_func(self):
+        def loss_func(inputs, loss_mask, output_tensor):
+            batch_size, seq_length = output_tensor.shape
+            losses = output_tensor.float()
+            loss_mask = loss_mask.view(-1).float()
+            loss = losses.view(-1) * loss_mask
+
+            # Resize and average loss per sample
+            loss_per_sample = loss.view(batch_size, seq_length).sum(axis=1)
+            loss_mask_per_sample = loss_mask.view(batch_size, seq_length).sum(axis=1)
+            loss_per_sample = loss_per_sample / loss_mask_per_sample
+
+            # Calculate and scale weighting
+            weights = torch.stack([(inputs == kt).float() for kt in self.kwargs["keytoken_ids"]]).sum(axis=[0, 2])
+            weights = 1.0 + self.kwargs["alpha"] * weights
+            # Calculate weighted average
+            weighted_loss = (loss_per_sample * weights).mean()
+
+            # Reduce loss across data parallel groups
+            averaged_loss = avg_losses_across_data_parallel_group([weighted_loss])
+
+            return weighted_loss, {"lm loss": averaged_loss[0]}
+
+        return loss_func
+
+    def get_forward_step_func(self):
+        def forward_step(data_iterator, model):
+            """Forward step."""
+            # Get the batch.
+            tokens, labels, loss_mask, attention_mask, position_ids = self.get_batch(data_iterator)
+            output_tensor = model(tokens, position_ids, attention_mask, labels=labels)
+
+            return output_tensor, partial(self.loss_func, tokens, loss_mask)
+
+        return forward_step
+
+
+def main():
+    # Custom loss function for the Megatron model
+    keytoken_ids = []
+    keywords = ["plt", "pd", "sk", "fit", "predict", " plt", " pd", " sk", " fit", " predict"]
+    for keyword in keywords:
+        ids = tokenizer([keyword]).input_ids[0]
+        if len(ids) == 1:
+            keytoken_ids.append(ids[0])
+    accelerator.print(f"Keytoken ids: {keytoken_ids}")
+    accelerator.state.megatron_lm_plugin.custom_train_step_class = GPTTrainStepWithCustomLoss
+    accelerator.state.megatron_lm_plugin.custom_train_step_kwargs = {
+        "keytoken_ids": keytoken_ids,
+        "alpha": 0.25,
+    }
+```
+
+2. For using the Megatron-LM datasets, a few more changes are required. Dataloaders for these datasets
+are available only on rank 0 of each tensor parallel group. As such, there are rank where dataloader won't be
+available and this requires tweaks to the training loop. Being able to do all this shows how
+flexible and extensible 🤗 Accelerate is. The changes required are as follows.
+
+a. For Megatron-LM indexed datasets, we need to use `MegatronLMDummyDataLoader` 
+and pass the required dataset args to it such as `data_path`, `seq_length` etc. 
+See [here](https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/arguments.py#L804) for the list of available args. 
+    
+```python
+from accelerate.utils import MegatronLMDummyDataLoader
+
+megatron_dataloader_config = {
+    "data_path": args.data_path,
+    "splits_string": args.splits_string,
+    "seq_length": args.block_size,
+    "micro_batch_size": args.per_device_train_batch_size,
+}
+megatron_dataloader = MegatronLMDummyDataLoader(**megatron_dataloader_config)
+accelerator.state.megatron_lm_plugin.megatron_dataset_flag = True
+```
+
+b. `megatron_dataloader` is repeated 3 times to get training, validation and test dataloaders
+as per the `args.splits_string` proportions
+    
+```python
+model, optimizer, lr_scheduler, train_dataloader, eval_dataloader, _ = accelerator.prepare(
+    model, optimizer, lr_scheduler, megatron_dataloader, megatron_dataloader, megatron_dataloader
+)
+```
+
+c. Changes to training and evaluation loops as dataloader is only available on tensor parallel ranks 0
+So, we need to iterate only if the dataloader isn't `None` else provide empty dict
+As such, we loop using `while` loop and break when `completed_steps` is equal to `args.max_train_steps`
+This is similar to the Megatron-LM setup wherein user has to provide `max_train_steps` when using Megaton-LM indexed datasets.
+This displays how flexible and extensible 🤗 Accelerate is.
+
+```python
+while completed_steps < args.max_train_steps:
+    model.train()
+    batch = next(train_dataloader) if train_dataloader is not None else {}
+    outputs = model(**batch)
+    loss = outputs.loss
+    ...
+
+    if completed_steps % eval_interval == 0:
+        eval_completed_steps = 0
+        losses = []
+        while eval_completed_steps < eval_iters:
+            model.eval()
+            with torch.no_grad():
+                batch = next(eval_dataloader) if eval_dataloader is not None else {}
+                outputs = model(**batch)
+```
+
+    
+## Utility for Checkpoint reshaping and interoperability
+
+1. The scripts for these are present in 🤗 Transformers library under respective models. 
+Currently, it is available for GPT model [checkpoint_reshaping_and_interoperability.py](https://github.com/huggingface/transformers/blob/main/src/transformers/models/megatron_gpt2/checkpoint_reshaping_and_interoperability.py)
+
+2. Below is an example of conversion of checkpoint from Megatron-LM to universal 🤗 Transformers sharded checkpoint.
+```bash
+python checkpoint_reshaping_and_interoperability.py \
+--convert_checkpoint_from_megatron_to_transformers \
+--load_path "gpt/iter_0005000" \
+--save_path "gpt/trfs_checkpoint" \
+--max_shard_size "200MB" \
+--tokenizer_name "gpt2" \
+--print-checkpoint-structure
+```
+
+3. Conversion of checkpoint from transformers to megatron with `tp_size=2`, `pp_size=2` and `dp_size=2`.
+```bash
+python checkpoint_utils/megatgron_gpt2/checkpoint_reshaping_and_interoperability.py \
+--load_path "gpt/trfs_checkpoint" \
+--save_path "gpt/megatron_lm_checkpoint" \
+--target_tensor_model_parallel_size 2 \
+--target_pipeline_model_parallel_size 2 \
+--target_data_parallel_size 2 \
+--target_params_dtype "bf16" \
+--make_vocab_size_divisible_by 128 \
+--use_distributed_optimizer \
+--print-checkpoint-structure
+```
+
+## Megatron-LM GPT models support returning logits and `megatron_generate` function for text generation
+
+1. Returning logits require setting `require_logits=True` in MegatronLMPlugin as shown below. 
+These would be available on the in the last stage of pipeline.
+```python
+megatron_lm_plugin = MegatronLMPlugin(return_logits=True)
+```
+
+2. `megatron_generate` method for Megatron-LM GPT model: This will use Tensor and Pipeline Parallelism to complete 
+generations for a batch of inputs when using greedy with/without top_k/top_p sampling and for individual prompt inputs when using beam search decoding. 
+Only a subset of features of transformers generate is supported. This will help in using large models via tensor and pipeline parallelism 
+for generation (already does key-value caching and uses fused kernels by default).
+This requires data parallel size to be 1, sequence parallelism and activation checkpointing to be disabled.
+It also requires specifying path to tokenizer's vocab file and merges file. 
+Below example shows how to configure and use `megatron_generate` method for Megatron-LM GPT model.
+```python
+# specifying tokenizer's vocab and merges file
+vocab_file = os.path.join(args.resume_from_checkpoint, "vocab.json")
+merge_file = os.path.join(args.resume_from_checkpoint, "merges.txt")
+other_megatron_args = {"vocab_file": vocab_file, "merge_file": merge_file}
+megatron_lm_plugin = MegatronLMPlugin(other_megatron_args=other_megatron_args)
+
+# inference using `megatron_generate` functionality
+tokenizer.pad_token = tokenizer.eos_token
+max_new_tokens = 64
+batch_texts = [
+    "Are you human?",
+    "The purpose of life is",
+    "The arsenal was constructed at the request of",
+    "How are you doing these days?",
+]
+batch_encodings = tokenizer(batch_texts, return_tensors="pt", padding=True)
+
+# top-p sampling
+generated_tokens = model.megatron_generate(
+    batch_encodings["input_ids"],
+    batch_encodings["attention_mask"],
+    max_new_tokens=max_new_tokens,
+    top_p=0.8,
+    top_p_decay=0.5,
+    temperature=0.9,
+)
+decoded_preds = tokenizer.batch_decode(generated_tokens.cpu().numpy())
+accelerator.print(decoded_preds)
+
+# top-k sampling
+generated_tokens = model.megatron_generate(
+    batch_encodings["input_ids"],
+    batch_encodings["attention_mask"],
+    max_new_tokens=max_new_tokens,
+    top_k=50,
+    temperature=0.9,
+)
+decoded_preds = tokenizer.batch_decode(generated_tokens.cpu().numpy())
+accelerator.print(decoded_preds)
+
+# adding `bos` token at the start
+generated_tokens = model.megatron_generate(
+    batch_encodings["input_ids"], batch_encodings["attention_mask"], max_new_tokens=max_new_tokens, add_BOS=True
+)
+decoded_preds = tokenizer.batch_decode(generated_tokens.cpu().numpy())
+accelerator.print(decoded_preds)
+
+# beam search => only takes single prompt
+batch_texts = ["The purpose of life is"]
+batch_encodings = tokenizer(batch_texts, return_tensors="pt", padding=True)
+generated_tokens = model.megatron_generate(
+    batch_encodings["input_ids"],
+    batch_encodings["attention_mask"],
+    max_new_tokens=max_new_tokens,
+    num_beams=20,
+    length_penalty=1.5,
+)
+decoded_preds = tokenizer.batch_decode(generated_tokens.cpu().numpy())
+accelerator.print(decoded_preds)
+```
+
+3. An end-to-end example of using `megatron_generate` method for Megatron-LM GPT model is available at
+[megatron_gpt2_generation.py](https://github.com/pacman100/accelerate-megatron-test/blob/main/src/inference/megatron_gpt2_generation.py) with 
+config file [megatron_lm_gpt_generate_config.yaml](https://github.com/pacman100/accelerate-megatron-test/blob/main/src/Configs/megatron_lm_gpt_generate_config.yaml).
+The bash script with accelerate launch command is available at [megatron_lm_gpt_generate.sh](https://github.com/pacman100/accelerate-megatron-test/blob/main/megatron_lm_gpt_generate.sh).
+The output logs of the script are available at [megatron_lm_gpt_generate.log](https://github.com/pacman100/accelerate-megatron-test/blob/main/output_logs/megatron_lm_gpt_generate.log).
+
+## Support for ROPE and ALiBi Positional embeddings and Multi-Query Attention
+
+1. For ROPE/ALiBi attention, pass `position_embedding_type` with `("absolute" | "rotary" | "alibi")` to `MegatronLMPlugin` as shown below.
+```python
+other_megatron_args = {"position_embedding_type": "alibi"}
+megatron_lm_plugin = MegatronLMPlugin(other_megatron_args=other_megatron_args)
+```
+
+2. For Multi-Query Attention, pass `attention_head_type` with `("multihead" | "multiquery")` to `MegatronLMPlugin` as shown below.
+```python
+other_megatron_args = {"attention_head_type": "multiquery"}
+megatron_lm_plugin = MegatronLMPlugin(other_megatron_args=other_megatron_args)
+```
+
+## Caveats
+
+1. Supports Transformers GPT2, Megatron-BERT and T5 models.
+This covers Decoder only, Encode only and Encoder-Decoder model classes.
+
+2. Only loss is returned from model forward pass as 
+there is quite complex interplay of pipeline, tensor and data parallelism behind the scenes.
+The `model(**batch_data)` call return loss(es) averaged across the data parallel ranks.
+This is fine for most cases wherein pre-training jobs are run using Megatron-LM features and
+you can easily compute the `perplexity` using the loss. 
+For GPT model, returning logits in addition to loss(es) is supported. 
+These logits aren't gathered across data parallel ranks. Use `accelerator.utils.gather_across_data_parallel_groups`
+to gather logits across data parallel ranks. These logits along with labels can be used for computing various 
+performance metrics. 
+
+3. The main process is the last rank as the losses/logits are available in the last stage of pipeline.
+`accelerator.is_main_process` and `accelerator.is_local_main_process` return `True` for last rank when using 
+Megatron-LM integration.
+
+4. In `accelerator.prepare` call, a Megatron-LM model corresponding to a given Transformers model is created
+with random weights. Please use `accelerator.load_state` to load the Megatron-LM checkpoint with matching TP, PP and DP partitions.
+
+5. Currently, checkpoint reshaping and interoperability support is only available for GPT. 
+Soon it will be extended to BERT and T5.
+
+6. `gradient_accumulation_steps` needs to be 1. When using Megatron-LM, micro batches in pipeline parallelism 
+setting is synonymous with gradient accumulation. 
+
+7. When using Megatron-LM, use `accelerator.save_state` and `accelerator.load_state` for saving and loading checkpoints.
+
+8. Below are the mapping from Megatron-LM model architectures to the the equivalent 🤗 transformers model architectures.
+Only these 🤗 transformers model architectures are supported.
+
+a. Megatron-LM [BertModel](https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/model/bert_model.py) : 
+🤗 transformers models with `megatron-bert` in config's model type, e.g., 
+[MegatronBERT](https://huggingface.co/docs/transformers/model_doc/megatron-bert)
+    
+b. Megatron-LM [GPTModel](https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/model/gpt_model.py) : 
+🤗 transformers models with `gpt2` in config's model type, e.g., 
+[OpenAI GPT2](https://huggingface.co/docs/transformers/model_doc/gpt2)
+   
+c. Megatron-LM [T5Model](https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/model/t5_model.py) : 
+🤗 transformers models with `t5` in  config's model type, e.g., 
+[T5](https://huggingface.co/docs/transformers/model_doc/t5) and 
+[MT5](https://huggingface.co/docs/transformers/model_doc/mt5)

docs/source/usage_guides/model_size_estimator.md

@@ -0,0 +1,137 @@
+<!--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.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+-->
+
+# Understanding how big of a model can fit on your machine
+
+One very difficult aspect when exploring potential models to use on your machine is knowing just how big of a model will *fit* into memory with your current graphics card (such as loading the model onto CUDA).
+
+To help alleviate this, 🤗 Accelerate has a CLI interface through `accelerate estimate-memory`. This tutorial will 
+help walk you through using it, what to expect, and at the end link to the interactive demo hosted on the 🤗 Hub which will 
+even let you post those results directly on the model repo!
+
+Currently we support searching for models that can be used in `timm` and `transformers`.
+
+<Tip>
+
+    This API will load the model into memory on the `meta` device, so we are not actually downloading 
+    and loading the full weights of the model into memory, nor do we need to. As a result it's 
+    perfectly fine to measure 8 billion parameter models (or more), without having to worry about 
+    if your CPU can handle it!
+
+</Tip>
+
+## Gradio Demos
+
+Below are a few gradio demos related to what was described above. The first is the official Hugging Face memory estimation space, utilizing Accelerate directly:
+
+<div class="block dark:hidden">
+	<iframe 
+        src="https://hf-accelerate-model-memory-usage.hf.space?__theme=light"
+        width="850"
+        height="1600"
+    ></iframe>
+</div>
+<div class="hidden dark:block">
+    <iframe 
+        src="https://hf-accelerate-model-memory-usage.hf.space?__theme=dark"
+        width="850"
+        height="1600"
+    ></iframe>
+</div>
+
+A community member has taken the idea and expanded it further, allowing you to filter models directly and see if you can run a particular LLM given GPU constraints and LoRA configurations. To play with it, see [here](https://huggingface.co/spaces/Vokturz/can-it-run-llm) for more details.
+
+## The Command
+
+When using `accelerate estimate-memory`, you need to pass in the name of the model you want to use, potentially the framework
+that model utilizing (if it can't be found automatically), and the data types you want the model to be loaded in with.
+
+For example, here is how we can calculate the memory footprint for `bert-base-cased`:
+
+```bash
+accelerate estimate-memory bert-base-cased
+```
+
+This will download the `config.json` for `bert-based-cased`, load the model on the `meta` device, and report back how much space
+it will use:
+
+Memory Usage for loading `bert-base-cased`:
+
+| dtype   | Largest Layer | Total Size | Training using Adam |
+|---------|---------------|------------|---------------------|
+| float32 | 84.95 MB      | 418.18 MB  | 1.61 GB             |
+| float16 | 42.47 MB      | 206.59 MB  | 826.36 MB           |
+| int8    | 21.24 MB      | 103.29 MB  | 413.18 MB           |
+| int4    | 10.62 MB      | 51.65 MB   | 206.59 MB           |
+
+By default it will return all the supported dtypes (`int4` through `float32`), but if you are interested in specific ones these can be filtered.
+
+### Specific libraries
+
+If the source library cannot be determined automatically (like it could in the case of `bert-base-cased`), a library name can
+be passed in. 
+
+```bash
+accelerate estimate-memory HuggingFaceM4/idefics-80b-instruct --library_name transformers
+```
+
+Memory Usage for loading `HuggingFaceM4/idefics-80b-instruct`:
+
+| dtype   | Largest Layer | Total Size | Training using Adam |
+|---------|---------------|------------|---------------------|
+| float32 | 3.02 GB       | 297.12 GB  | 1.16 TB             |
+| float16 | 1.51 GB       | 148.56 GB  | 594.24 GB           |
+| int8    | 772.52 MB     | 74.28 GB   | 297.12 GB           |
+| int4    | 386.26 MB     | 37.14 GB   | 148.56 GB           |
+
+
+```bash
+accelerate estimate-memory timm/resnet50.a1_in1k --library_name timm
+```
+
+Memory Usage for loading `timm/resnet50.a1_in1k`:
+
+| dtype   | Largest Layer | Total Size | Training using Adam |
+|---------|---------------|------------|---------------------|
+| float32 | 9.0 MB        | 97.7 MB    | 390.78 MB           |
+| float16 | 4.5 MB        | 48.85 MB   | 195.39 MB           |
+| int8    | 2.25 MB       | 24.42 MB   | 97.7 MB             |
+| int4    | 1.12 MB       | 12.21 MB   | 48.85 MB            |
+
+### Specific dtypes
+
+As mentioned earlier, while we return `int4` through `float32` by default, any dtype can be used from `float32`, `float16`, `int8`, and `int4`.
+
+To do so, pass them in after specifying `--dtypes`:
+
+```bash
+accelerate estimate-memory bert-base-cased --dtypes float32 float16
+```
+
+Memory Usage for loading `bert-base-cased`:
+
+| dtype   | Largest Layer | Total Size | Training using Adam |
+|---------|---------------|------------|---------------------|
+| float32 | 84.95 MB      | 413.18 MB  | 1.61 GB             |
+| float16 | 42.47 MB      | 206.59 MB  | 826.36 MB           |
+
+## Caveats with this calculator
+
+This calculator will tell you how much memory is needed to purely load the model in, *not* to perform inference.
+
+This calculation is accurate within a few % of the actual value, so it is a very good view of just how much memory it will take. For instance loading `bert-base-cased` actually takes `413.68 MB` when loaded on CUDA in full precision, and the calculator estimates `413.18 MB`.
+
+When performing inference you can expect to add up to an additional 20% as found by [EleutherAI](https://blog.eleuther.ai/transformer-math/). We'll be conducting research into finding a more accurate estimate to these values, and will update 
+this calculator once done.

docs/source/usage_guides/mps.md

@@ -0,0 +1,54 @@
+<!--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.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+-->
+
+# Accelerated PyTorch Training on Mac
+
+With PyTorch v1.12 release, developers and researchers can take advantage of Apple silicon GPUs for significantly faster model training. 
+This unlocks the ability to perform machine learning workflows like prototyping and fine-tuning locally, right on Mac.
+Apple's Metal Performance Shaders (MPS) as a backend for PyTorch enables this and can be used via the new `"mps"` device. 
+This will map computational graphs and primitives on the MPS Graph framework and tuned kernels provided by MPS.
+For more information please refer official documents [Introducing Accelerated PyTorch Training on Mac](https://pytorch.org/blog/introducing-accelerated-pytorch-training-on-mac/)
+and [MPS BACKEND](https://pytorch.org/docs/stable/notes/mps.html).
+
+### Benefits of Training and Inference using Apple Silicon Chips
+
+1. Enables users to train larger networks or batch sizes locally
+2. Reduces data retrieval latency and provides the GPU with direct access to the full memory store due to unified memory architecture. 
+Therefore, improving end-to-end performance.
+3. Reduces costs associated with cloud-based development or the need for additional local GPUs.
+
+**Pre-requisites**: To install torch with mps support, 
+please follow this nice medium article [GPU-Acceleration Comes to PyTorch on M1 Macs](https://medium.com/towards-data-science/gpu-acceleration-comes-to-pytorch-on-m1-macs-195c399efcc1).
+
+
+## How it works out of the box
+It is enabled by default on MacOs machines with MPS enabled Apple Silicon GPUs.
+To disable it, pass `--cpu` flag to `accelerate launch` command or answer the corresponding question when answering the `accelerate config` questionnaire.
+
+You can directly run the following script to test it out on MPS enabled Apple Silicon machines:
+```bash
+accelerate launch /examples/cv_example.py --data_dir images
+```
+
+## A few caveats to be aware of
+
+1. We strongly recommend to install PyTorch >= 1.13 (nightly version at the time of writing) on your MacOS machine. 
+It has major fixes related to model correctness and performance improvements for transformer based models.
+Please refer to https://github.com/pytorch/pytorch/issues/82707 for more details.
+2. Distributed setups `gloo` and `nccl` are not working with `mps` device. 
+This means that currently only single GPU of `mps` device type can be used.
+
+Finally, please, remember that, 🤗 `Accelerate` only integrates MPS backend, therefore if you
+have any problems or questions with regards to MPS backend usage, please, file an issue with [PyTorch GitHub](https://github.com/pytorch/pytorch/issues).

docs/source/usage_guides/profiler.md

@@ -0,0 +1,334 @@
+<!--
+Copyright 2024 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.
+
+⚠️ Note that this file is in Markdown but contains specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+-->
+
+# Profiler
+
+Profiler is a tool that allows the collection of performance metrics during training and inference. Profiler’s context manager API can be used to better understand what model operators are the most expensive, examine their input shapes and stack traces, study device kernel activity, and visualize the execution trace. It provides insights into the performance of your model, allowing you to optimize and improve it.
+
+This guide explains how to use PyTorch Profiler to measure the time and memory consumption of the model’s operators and how to integrate this with 🤗 Accelerate. We will cover various use cases and provide examples for each.
+
+## Using profiler to analyze execution time
+
+Profiler allows one to check which operators were called during the execution of a code range wrapped with a profiler context manager.
+
+Let’s see how we can use profiler to analyze the execution time:
+
+<hfoptions id="cpu execution time">
+<hfoption id="PyTorch">
+
+```python
+import torch
+import torchvision.models as models
+from torch.profiler import profile, record_function, ProfilerActivity
+
+model = models.resnet18()
+inputs = torch.randn(5, 3, 224, 224)
+
+with profile(activities=[ProfilerActivity.CPU], record_shapes=True) as prof:
+    model(inputs)
+
+print(prof.key_averages().table(sort_by="cpu_time_total", row_limit=10))
+```
+
+</hfoption>
+<hfoption id="Accelerate">
+
+```python
+from accelerate import Accelerator, ProfileKwargs
+import torch
+import torchvision.models as models
+
+model = models.resnet18()
+inputs = torch.randn(5, 3, 224, 224)
+
+profile_kwargs = ProfileKwargs(
+    activities=["cpu"],
+    record_shapes=True
+)
+
+accelerator = Accelerator(cpu=True, kwargs_handlers=[profile_kwargs])
+model = accelerator.prepare(model)
+
+with accelerator.profile() as prof:
+    with torch.no_grad():
+        model(inputs)
+
+print(prof.key_averages().table(sort_by="cpu_time_total", row_limit=10))
+```
+
+</hfoption>
+</hfoptions>
+
+The resulting table output (omitting some columns):
+
+```
+---------------------------------  ------------  ------------  ------------  ------------  
+                             Name      Self CPU     CPU total  CPU time avg    # of Calls  
+---------------------------------  ------------  ------------  ------------  ------------  
+                     aten::conv2d     171.000us      52.260ms       2.613ms            20  
+                aten::convolution     227.000us      52.089ms       2.604ms            20  
+               aten::_convolution     270.000us      51.862ms       2.593ms            20  
+         aten::mkldnn_convolution      51.273ms      51.592ms       2.580ms            20  
+                 aten::batch_norm     118.000us       7.059ms     352.950us            20  
+     aten::_batch_norm_impl_index     315.000us       6.941ms     347.050us            20  
+          aten::native_batch_norm       6.305ms       6.599ms     329.950us            20  
+                 aten::max_pool2d      40.000us       4.008ms       4.008ms             1  
+    aten::max_pool2d_with_indices       3.968ms       3.968ms       3.968ms             1  
+                       aten::add_     780.000us     780.000us      27.857us            28  
+---------------------------------  ------------  ------------  ------------  ------------  
+Self CPU time total: 67.016ms
+```
+
+To get a finer granularity of results and include operator input shapes, pass `group_by_input_shape=True` (note: this requires running the profiler with `record_shapes=True`):
+
+```python
+print(prof.key_averages(group_by_input_shape=True).table(sort_by="cpu_time_total", row_limit=10))
+```
+
+## Using profiler to analyze memory consumption
+
+Profiler can also show the amount of memory (used by the model’s tensors) that was allocated (or released) during the execution of the model’s operators. To enable memory profiling functionality pass `profile_memory=True`.
+
+<hfoptions id="memory consumption">
+<hfoption id="PyTorch">
+
+```python
+model = models.resnet18()
+inputs = torch.randn(5, 3, 224, 224)
+
+with profile(activities=[ProfilerActivity.CPU],
+        profile_memory=True, record_shapes=True) as prof:
+    model(inputs)
+
+print(prof.key_averages().table(sort_by="self_cpu_memory_usage", row_limit=10))
+```
+
+</hfoption>
+<hfoption id="Accelerate">
+
+```python
+model = models.resnet18()
+inputs = torch.randn(5, 3, 224, 224)
+
+profile_kwargs = ProfileKwargs(
+    activities=["cpu"],
+    profile_memory=True,
+    record_shapes=True
+)
+
+accelerator = Accelerator(cpu=True, kwargs_handlers=[profile_kwargs])
+model = accelerator.prepare(model)
+
+with accelerator.profile() as prof:
+    model(inputs)
+
+print(prof.key_averages().table(sort_by="self_cpu_memory_usage", row_limit=10))
+```
+
+</hfoption>
+</hfoptions>
+
+The resulting table output (omitting some columns):
+
+```
+---------------------------------  ------------  ------------  ------------  
+                             Name       CPU Mem  Self CPU Mem    # of Calls  
+---------------------------------  ------------  ------------  ------------  
+                      aten::empty      94.85 Mb      94.85 Mb           205  
+    aten::max_pool2d_with_indices      11.48 Mb      11.48 Mb             1  
+                      aten::addmm      19.53 Kb      19.53 Kb             1  
+                       aten::mean      10.00 Kb      10.00 Kb             1  
+              aten::empty_strided         492 b         492 b             5  
+                        aten::cat         240 b         240 b             6  
+                        aten::abs         480 b         240 b             4  
+              aten::masked_select         120 b         112 b             1  
+                         aten::ne          61 b          53 b             3  
+                         aten::eq          30 b          30 b             1  
+---------------------------------  ------------  ------------  ------------  
+Self CPU time total: 69.332ms
+```
+
+
+## Exporting chrome trace
+
+You can examine the sequence of profiled operators and CUDA kernels in Chrome trace viewer (`chrome://tracing`):
+
+![profile_export](https://github.com/huggingface/accelerate/assets/100389977/5acb193f-6d11-4f7b-9873-c600c19e8172)
+
+<hfoptions id="exporting chrome trace">
+<hfoption id="PyTorch">
+
+```python
+model = models.resnet18().cuda()
+inputs = torch.randn(5, 3, 224, 224).cuda()
+
+with profile(activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA]) as prof:
+    model(inputs)
+
+prof.export_chrome_trace("trace.json")
+```
+
+</hfoption>
+<hfoption id="Accelerate">
+
+```python
+profile_kwargs = ProfileKwargs(
+    activities=["cpu", "cuda"],
+    output_trace_dir="trace"
+)
+
+accelerator = Accelerator(kwargs_handlers=[profile_kwargs])
+model = accelerator.prepare(model)
+
+with accelerator.profile() as prof:
+    model(inputs)
+
+# The trace will be saved to the specified directory
+```
+
+</hfoption>
+</hfoptions>
+
+## Using Profiler to Analyze Long-Running Jobs
+
+Profiler offers an additional API to handle long-running jobs (such as training loops). Tracing all of the execution can be slow and result in very large trace files. To avoid this, use optional arguments:
+
+- `schedule_option`: Scheduling options allow you to control when profiling is active. This is useful for long-running jobs to avoid collecting too much data. Available keys are `wait`, `warmup`, `active`, `repeat` and `skip_first`. The profiler will skip the first `skip_first` steps, then wait for `wait` steps, then do the warmup for the next `warmup` steps, then do the active recording for the next `active` steps and then repeat the cycle starting with `wait` steps. The optional number of cycles is specified with the `repeat` parameter, the zero value means that the cycles will continue until the profiling is finished.
+- `on_trace_ready`: specifies a function that takes a reference to the profiler as an input and is called by the profiler each time the new trace is ready.
+
+To illustrate how the API works, consider the following example:
+
+<hfoptions id="custom handler">
+<hfoption id="PyTorch">
+
+```python
+from torch.profiler import schedule
+
+my_schedule = schedule(
+    skip_first=10,
+    wait=5,
+    warmup=1,
+    active=3,
+    repeat=2
+)
+
+def trace_handler(p):
+    output = p.key_averages().table(sort_by="self_cuda_time_total", row_limit=10)
+    print(output)
+    p.export_chrome_trace("/tmp/trace_" + str(p.step_num) + ".json")
+
+with profile(
+    activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA],
+    schedule=my_schedule,
+    on_trace_ready=trace_handler
+) as p:
+    for idx in range(8):
+        model(inputs)
+        p.step()
+```
+
+</hfoption>
+<hfoption id="Accelerate">
+
+```python
+def trace_handler(p):
+    output = p.key_averages().table(sort_by="self_cuda_time_total", row_limit=10)
+    print(output)
+    p.export_chrome_trace("/tmp/trace_" + str(p.step_num) + ".json")
+
+profile_kwargs = ProfileKwargs(
+    activities=["cpu", "cuda"],
+    schedule_option={"wait": 5, "warmup": 1, "active": 3, "repeat": 2, "skip_first": 10},
+    on_trace_ready=trace_handler
+)
+
+accelerator = Accelerator(kwargs_handlers=[profile_kwargs])
+model = accelerator.prepare(model)
+
+with accelerator.profile() as prof:
+    for idx in range(8):
+        model(inputs)
+        prof.step()
+```
+
+</hfoption>
+</hfoptions>
+
+## FLOPS
+
+Use formula to estimate the FLOPs (floating point operations) of specific operators (matrix multiplication and 2D convolution).
+
+To measure floating-point operations (FLOPS):
+
+<hfoptions id="FLOPS">
+<hfoption id="PyTorch">
+
+```python
+with profile(
+    activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA],
+    with_flops=True
+) as prof:
+    model(inputs)
+
+print(prof.key_averages().table(sort_by="flops", row_limit=10))
+```
+
+</hfoption>
+<hfoption id="Accelerate">
+
+```python
+profile_kwargs = ProfileKwargs(
+    with_flops=True
+)
+accelerator = Accelerator(kwargs_handlers=[profile_kwargs])
+
+with accelerator.profile() as prof:
+    model(inputs)
+
+print(prof.key_averages().table(sort_by="flops", row_limit=10))
+```
+
+</hfoption>
+</hfoptions>
+
+The resulting table output (omitting some columns):
+
+```
+-------------------------------------------------------  ------------  ------------  ------------  
+                                                   Name      Self CPU     Self CUDA    Total FLOPs  
+-------------------------------------------------------  ------------  ------------  ------------  
+                                           aten::conv2d     197.000us       0.000us  18135613440.000  
+                                            aten::addmm     103.000us      17.000us     5120000.000  
+                                              aten::mul      29.000us       2.000us          30.000  
+                                      aten::convolution     409.000us       0.000us            --  
+                                     aten::_convolution     253.000us       0.000us            --  
+                                aten::cudnn_convolution       5.465ms       2.970ms            --  
+                                        cudaEventRecord     138.000us       0.000us            --  
+                                  cudaStreamIsCapturing      43.000us       0.000us            --  
+                                  cudaStreamGetPriority      40.000us       0.000us            --  
+                       cudaDeviceGetStreamPriorityRange      10.000us       0.000us            --  
+-------------------------------------------------------  ------------  ------------  ------------  
+Self CPU time total: 21.938ms
+Self CUDA time total: 4.165ms
+```
+
+
+
+## Conclusion and Further Information
+
+PyTorch Profiler is a powerful tool for analyzing the performance of your models. By integrating it with 🤗 Accelerate, you can easily profile your models and gain insights into their performance, helping you to optimize and improve them.
+
+For more detailed information, refer to the [PyTorch Profiler documentation](https://pytorch.org/docs/stable/profiler.html).

docs/source/usage_guides/quantization.md

@@ -0,0 +1,136 @@
+<!--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.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+-->
+
+# Quantization
+
+## `bitsandbytes` Integration
+
+🤗 Accelerate brings `bitsandbytes` quantization to your model. You can now load any pytorch model in 8-bit or 4-bit with a few lines of code.
+
+If you want to use 🤗 Transformers models with `bitsandbytes`, you should follow this [documentation](https://huggingface.co/docs/transformers/main_classes/quantization). 
+
+To learn more about how the `bitsandbytes` quantization works, check out the blog posts on [8-bit quantization](https://huggingface.co/blog/hf-bitsandbytes-integration) and [4-bit quantization](https://huggingface.co/blog/4bit-transformers-bitsandbytes).
+
+### Pre-Requisites
+You will need to install the following requirements:
+
+- Install `bitsandbytes` library
+```bash
+pip install bitsandbytes
+```
+- Install latest `accelerate` from source
+```bash
+pip install git+https://github.com/huggingface/accelerate.git
+```
+- Install `minGPT` and `huggingface_hub` to run examples
+```bash
+git clone https://github.com/karpathy/minGPT.git
+pip install minGPT/
+pip install huggingface_hub
+```
+
+### How it works
+
+First, we need to initialize our model. To save memory, we can initialize an empty model using the context manager [`init_empty_weights`]. 
+
+Let's take the GPT2 model from minGPT library.
+```py
+from accelerate import init_empty_weights
+from mingpt.model import GPT
+
+model_config = GPT.get_default_config()
+model_config.model_type = 'gpt2-xl'
+model_config.vocab_size = 50257
+model_config.block_size = 1024
+
+with init_empty_weights():
+    empty_model = GPT(model_config)
+```
+
+Then, we need to get the path to the weights of your model. The path can be the state_dict file (e.g. "pytorch_model.bin") or a folder containing the sharded checkpoints. 
+
+```py
+from huggingface_hub import snapshot_download
+weights_location = snapshot_download(repo_id="marcsun13/gpt2-xl-linear-sharded")
+```
+
+Finally, you need to set your quantization configuration with [`~utils.BnbQuantizationConfig`].
+
+Here's an example for 8-bit quantization:
+```py
+from accelerate.utils import BnbQuantizationConfig
+bnb_quantization_config = BnbQuantizationConfig(load_in_8bit=True, llm_int8_threshold = 6)
+```
+
+Here's an example for 4-bit quantization:
+```py
+from accelerate.utils import BnbQuantizationConfig
+bnb_quantization_config = BnbQuantizationConfig(load_in_4bit=True, bnb_4bit_compute_dtype=torch.bfloat16, bnb_4bit_use_double_quant=True, bnb_4bit_quant_type="nf4")
+```
+
+To quantize your empty model with the selected configuration, you need to use [`~utils.load_and_quantize_model`]. 
+
+```py
+from accelerate.utils import load_and_quantize_model
+quantized_model = load_and_quantize_model(empty_model, weights_location=weights_location, bnb_quantization_config=bnb_quantization_config, device_map = "auto")
+```
+
+### Saving and loading 8-bit model
+
+You can save your 8-bit model with accelerate using [`~Accelerator.save_model`]. 
+
+```py
+from accelerate import Accelerator
+accelerate = Accelerator()
+new_weights_location = "path/to/save_directory"
+accelerate.save_model(quantized_model, new_weights_location)
+
+quantized_model_from_saved = load_and_quantize_model(empty_model, weights_location=new_weights_location, bnb_quantization_config=bnb_quantization_config, device_map = "auto")
+```
+
+Note that 4-bit model serialization is currently not supported.
+
+### Offload modules to cpu and disk 
+
+You can offload some modules to cpu/disk if you don't have enough space on the GPU to store the entire model on your GPUs.
+This uses big model inference under the hood. Check this [documentation](https://huggingface.co/docs/accelerate/usage_guides/big_modeling) for more details. 
+
+For 8-bit quantization, the selected modules will be converted to 8-bit precision. 
+
+For 4-bit quantization, the selected modules will be kept in `torch_dtype` that the user passed in `BnbQuantizationConfig`.  We will add support to convert these offloaded modules in 4-bit when 4-bit serialization will be possible. 
+
+ You just need to pass a custom `device_map` in order to offload modules on cpu/disk. The offload modules will be dispatched on the GPU when needed. Here's an example :
+
+```py
+device_map = {
+    "transformer.wte": 0,
+    "transformer.wpe": 0,
+    "transformer.drop": 0,
+    "transformer.h": "cpu",
+    "transformer.ln_f": "disk",
+    "lm_head": "disk",
+}
+```
+### Fine-tune a quantized model
+
+It is not possible to perform pure 8bit or 4bit training on these models. However, you can train these models by leveraging parameter efficient fine tuning methods (PEFT) and train for example adapters on top of them. Please have a look at [peft](https://github.com/huggingface/peft) library for more details.
+
+Currently, you can't add adapters on top of any quantized model. However, with the official support of adapters with 🤗 Transformers models, you can fine-tune quantized models. If you want to finetune a 🤗 Transformers model , follow this [documentation](https://huggingface.co/docs/transformers/main_classes/quantization) instead. Check out this [demo](https://colab.research.google.com/drive/1VoYNfYDKcKRQRor98Zbf2-9VQTtGJ24k?usp=sharing) on how to fine-tune a 4-bit 🤗 Transformers model. 
+
+Note that you don’t need to pass `device_map` when loading the model for training. It will automatically load your model on your GPU. Please note that `device_map=auto` should be used for inference only.
+
+### Example demo - running GPT2 1.5b on a Google Colab
+
+Check out the Google Colab [demo](https://colab.research.google.com/drive/1T1pOgewAWVpR9gKpaEWw4orOrzPFb3yM?usp=sharing) for running quantized models on a GTP2 model. The GPT2-1.5B model checkpoint is in FP32 which uses 6GB of memory. After quantization, it uses 1.6GB with 8-bit modules and 1.2GB with 4-bit modules.

docs/source/usage_guides/sagemaker.md

@@ -8,6 +8,9 @@ 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.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
 -->
 
 # Amazon SageMaker
@@ -23,7 +26,7 @@ make it easier than ever to train Hugging Face Transformer models in [Amazon Sag
 Before you can run your 🤗 Accelerate scripts on Amazon SageMaker you need to sign up for an AWS account. If you do not
 have an AWS account yet learn more [here](https://docs.aws.amazon.com/sagemaker/latest/dg/gs-set-up.html).
 
-After you have your AWS Account you need to install the `sagemaker` sdk for 🤗 Accelerate with.
+After you have your AWS Account you need to install the `sagemaker` sdk for 🤗 Accelerate with:
 
 ```bash
 pip install "accelerate[sagemaker]" --upgrade
@@ -31,7 +34,7 @@ pip install "accelerate[sagemaker]" --upgrade
 
 🤗 Accelerate currently uses the 🤗 DLCs, with `transformers`, `datasets` and `tokenizers` pre-installed. 🤗
 Accelerate is not in the DLC yet (will soon be added!) so to use it within Amazon SageMaker you need to create a
-`requirements.txt` in the same directory where your training script is located and add it as dependency.
+`requirements.txt` in the same directory where your training script is located and add it as dependency:
 
 ```
 accelerate
@@ -43,7 +46,7 @@ You should also add any other dependencies you have to this `requirements.txt`.
 ### Configure 🤗 Accelerate
 
 You can configure the launch configuration for Amazon SageMaker the same as you do for non SageMaker training jobs with
-the 🤗 Accelerate CLI.
+the 🤗 Accelerate CLI:
 
 ```bash
 accelerate config
@@ -54,7 +57,7 @@ accelerate config
 
 <Tip>
 
-🤗 Accelerate is not saving any of your credentials.
+    🤗 Accelerate is not saving any of your credentials.
 
 </Tip>
 
@@ -62,7 +65,7 @@ accelerate config
 
 The training script is very similar to a training script you might run outside of SageMaker, but to save your model
 after training you need to specify either `/opt/ml/model` or use `os.environ["SM_MODEL_DIR"]` as your save
-directory. After training, artifacts in this directory are uploaded to S3.
+directory. After training, artifacts in this directory are uploaded to S3:
 
 
 ```diff
@@ -72,14 +75,14 @@ directory. After training, artifacts in this directory are uploaded to S3.
 
 <Tip warning={true}>
 
-SageMaker doesn’t support argparse actions. If you want to use, for example, boolean hyperparameters, you need to
-specify type as bool in your script and provide an explicit True or False value for this hyperparameter. [[REF]](https://sagemaker.readthedocs.io/en/stable/frameworks/pytorch/using_pytorch.html#prepare-a-pytorch-training-script).
+    SageMaker doesn’t support argparse actions. If you want to use, for example, boolean hyperparameters, you need to
+    specify type as bool in your script and provide an explicit True or False value for this hyperparameter. [[REF]](https://sagemaker.readthedocs.io/en/stable/frameworks/pytorch/using_pytorch.html#prepare-a-pytorch-training-script).
 
 </Tip>
 
 ### Launch Training
 
-You can launch your training with 🤗 Accelerate CLI with
+You can launch your training with 🤗 Accelerate CLI with:
 
 ```
 accelerate launch path_to_script.py --args_to_the_script
@@ -92,7 +95,7 @@ arguments needed by your training script as named arguments.
 
 <Tip>
 
-If you run one of the example scripts, don't forget to add `accelerator.save('/opt/ml/model')` to it.
+    If you run one of the example scripts, don't forget to add `accelerator.save('/opt/ml/model')` to it.
 
 </Tip>
 
@@ -129,7 +132,26 @@ You can find your model data at: s3://your-bucket/accelerate-sagemaker-1-2021-04
 
 ### Distributed Training: Data Parallelism
 
-*currently in development, will be supported soon.*
+Set up the accelerate config by running `accelerate config` and answer the SageMaker questions and set it up.
+To use SageMaker DDP, select it when asked 
+`What is the distributed mode? ([0] No distributed training, [1] data parallelism):`.
+Example config below:
+```yaml
+base_job_name: accelerate-sagemaker-1
+compute_environment: AMAZON_SAGEMAKER
+distributed_type: DATA_PARALLEL
+ec2_instance_type: ml.p3.16xlarge
+iam_role_name: xxxxx
+image_uri: null
+mixed_precision: fp16
+num_machines: 1
+profile: xxxxx
+py_version: py38
+pytorch_version: 1.10.2
+region: us-east-1
+transformers_version: 4.17.0
+use_cpu: false
+```
 
 ### Distributed Training: Model Parallelism
 
@@ -141,10 +163,43 @@ You can find your model data at: s3://your-bucket/accelerate-sagemaker-1-2021-04
 want to use different/other Python packages you can do this by adding them to the `requirements.txt`. These packages
 will be installed before your training script is started.
 
-### Remote scripts: Use scripts located on Github
+### Local Training: SageMaker Local mode
 
-*undecided if feature is needed. Contact us if you would like this feature.*
+The local mode in the SageMaker SDK allows you to run your training script locally inside the HuggingFace DLC (Deep Learning container) 
+or using your custom container image. This is useful for debugging and testing your training script inside the final container environment.
+Local mode uses Docker compose (*Note: Docker Compose V2 is not supported yet*). The SDK will handle the authentication against ECR
+to pull the DLC to your local environment. You can emulate CPU (single and multi-instance) and GPU (single instance) SageMaker training jobs.
+
+To use local mode, you need to set your `ec2_instance_type` to `local`.
+
+```yaml
+ec2_instance_type: local
+```
+
+### Advanced configuration
+
+The configuration allows you to override parameters for the [Estimator](https://sagemaker.readthedocs.io/en/stable/api/training/estimators.html).
+These settings have to be applied in the config file and are not part of `accelerate config`. You can control many additional aspects of the training job, e.g. use Spot instances, enable network isolation and many more.
+
+```yaml
+additional_args:
+  # enable network isolation to restrict internet access for containers
+  enable_network_isolation: True
+```
+
+You can find all available configuration [here](https://sagemaker.readthedocs.io/en/stable/api/training/estimators.html).
 
 ### Use Spot Instances
 
-*undecided if feature is needed. Contact us if you would like this feature.*
+You can use Spot Instances e.g. using (see [Advanced configuration](#advanced-configuration)):
+```yaml
+additional_args:
+  use_spot_instances: True
+  max_wait: 86400
+```
+
+*Note: Spot Instances are subject to be terminated and training to be continued from a checkpoint. This is not handled in 🤗 Accelerate out of the box. Contact us if you would like this feature.*
+
+### Remote scripts: Use scripts located on Github
+
+*undecided if feature is needed. Contact us if you would like this feature.*

docs/source/usage_guides/tracking.md

@@ -0,0 +1,233 @@
+<!--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.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+-->
+
+# Tracking
+
+There are a large number of experiment tracking API's available, however getting them all to work with in a multi-processing environment can oftentimes be complex.
+🤗 Accelerate provides a general tracking API that can be used to log useful items during your script through [`Accelerator.log`]
+
+## Integrated Trackers
+
+Currently `Accelerate` supports seven trackers out-of-the-box:
+
+- TensorBoard
+- WandB
+- CometML
+- Aim
+- MLFlow
+- ClearML
+- DVCLive
+
+To use any of them, pass in the selected type(s) to the `log_with` parameter in [`Accelerate`]:
+```python
+from accelerate import Accelerator
+from accelerate.utils import LoggerType
+
+accelerator = Accelerator(log_with="all")  # For all available trackers in the environment
+accelerator = Accelerator(log_with="wandb")
+accelerator = Accelerator(log_with=["wandb", LoggerType.TENSORBOARD])
+```
+
+At the start of your experiment [`Accelerator.init_trackers`] should be used to setup your project, and potentially add any experiment hyperparameters to be logged:
+```python
+hps = {"num_iterations": 5, "learning_rate": 1e-2}
+accelerator.init_trackers("my_project", config=hps)
+```
+
+When you are ready to log any data, [`Accelerator.log`] should be used.
+A `step` can also be passed in to correlate the data with a particular step in the training loop.
+```python
+accelerator.log({"train_loss": 1.12, "valid_loss": 0.8}, step=1)
+```
+
+Once you've finished training, make sure to run [`Accelerator.end_training`] so that all the trackers can run their finish functionalities if they have any.
+```python
+accelerator.end_training()
+```
+
+
+A full example is below:
+```python
+from accelerate import Accelerator
+
+accelerator = Accelerator(log_with="all")
+config = {
+    "num_iterations": 5,
+    "learning_rate": 1e-2,
+    "loss_function": str(my_loss_function),
+}
+
+accelerator.init_trackers("example_project", config=config)
+
+my_model, my_optimizer, my_training_dataloader = accelerate.prepare(my_model, my_optimizer, my_training_dataloader)
+device = accelerator.device
+my_model.to(device)
+
+for iteration in config["num_iterations"]:
+    for step, batch in my_training_dataloader:
+        my_optimizer.zero_grad()
+        inputs, targets = batch
+        inputs = inputs.to(device)
+        targets = targets.to(device)
+        outputs = my_model(inputs)
+        loss = my_loss_function(outputs, targets)
+        accelerator.backward(loss)
+        my_optimizer.step()
+        accelerator.log({"training_loss": loss}, step=step)
+accelerator.end_training()
+```
+
+If a tracker requires a directory to save data to, such as `TensorBoard`, then pass the directory path to `project_dir`. The `project_dir` parameter is useful 
+when there are other configurations to be combined with in the [`~utils.ProjectConfiguration`] data class. For example, you can save the TensorBoard data to `project_dir` and everything else can be logged in the `logging_dir` parameter of [`~utils.ProjectConfiguration`: 
+
+```python
+accelerator = Accelerator(log_with="tensorboard", project_dir=".")
+
+# use with ProjectConfiguration
+config = ProjectConfiguration(project_dir=".", logging_dir="another/directory")
+accelerator = Accelerator(log_with="tensorboard", project_config=config)
+```
+
+## Implementing Custom Trackers
+
+To implement a new tracker to be used in `Accelerator`, a new one can be made through implementing the [`GeneralTracker`] class.
+Every tracker must implement three functions and have three properties:
+  - `__init__`: 
+    - Should store a `run_name` and initialize the tracker API of the integrated library. 
+    - If a tracker stores their data locally (such as TensorBoard), a `logging_dir` parameter can be added.
+  - `store_init_configuration`: 
+    - Should take in a `values` dictionary and store them as a one-time experiment configuration
+  - `log`: 
+    - Should take in a `values` dictionary and a `step`, and should log them to the run
+
+  - `name` (`str`):
+    - A unique string name for the tracker, such as `"wandb"` for the wandb tracker. 
+    - This will be used for interacting with this tracker specifically
+  - `requires_logging_directory` (`bool`):
+    - Whether a `logging_dir` is needed for this particular tracker and if it uses one.
+  - `tracker`: 
+    - This should be implemented as a `@property` function 
+    - Should return the internal tracking mechanism the library uses, such as the `run` object for `wandb`.
+
+Each method should also utilize the [`state.PartialState`] class if the logger should only be executed on the main process for instance.
+
+A brief example can be seen below with an integration with Weights and Biases, containing only the relevant information and logging just on 
+the main process:
+```python
+from accelerate.tracking import GeneralTracker, on_main_process
+from typing import Optional
+
+import wandb
+
+
+class MyCustomTracker(GeneralTracker):
+    name = "wandb"
+    requires_logging_directory = False
+
+    @on_main_process
+    def __init__(self, run_name: str):
+        self.run_name = run_name
+        run = wandb.init(self.run_name)
+
+    @property
+    def tracker(self):
+        return self.run.run
+
+    @on_main_process
+    def store_init_configuration(self, values: dict):
+        wandb.config(values)
+
+    @on_main_process
+    def log(self, values: dict, step: Optional[int] = None):
+        wandb.log(values, step=step)
+```
+
+When you are ready to build your `Accelerator` object, pass in an **instance** of your tracker to [`Accelerator.log_with`] to have it automatically
+be used with the API:
+
+```python
+tracker = MyCustomTracker("some_run_name")
+accelerator = Accelerator(log_with=tracker)
+```
+
+These also can be mixed with existing trackers, including with `"all"`:
+
+```python
+tracker = MyCustomTracker("some_run_name")
+accelerator = Accelerator(log_with=[tracker, "all"])
+```
+
+## Accessing the internal tracker 
+
+If some custom interactions with a tracker might be wanted directly, you can quickly access one using the 
+[`Accelerator.get_tracker`] method. Just pass in the string corresponding to a tracker's `.name` attribute 
+and it will return that tracker on the main process.
+
+This example shows doing so with wandb:
+
+```python
+wandb_tracker = accelerator.get_tracker("wandb")
+```
+
+From there you can interact with `wandb`'s `run` object like normal:
+
+```python
+wandb_run.log_artifact(some_artifact_to_log)
+```
+
+<Tip>
+  Trackers built in Accelerate will automatically execute on the correct process, 
+  so if a tracker is only meant to be ran on the main process it will do so 
+  automatically.
+</Tip>
+
+If you want to truly remove Accelerate's wrapping entirely, you can 
+achieve the same outcome with:
+
+```python
+wandb_tracker = accelerator.get_tracker("wandb", unwrap=True)
+if accelerator.is_main_process:
+    wandb_tracker.log_artifact(some_artifact_to_log)
+```
+
+
+## When a wrapper cannot work
+
+If a library has an API that does not follow a strict `.log` with an overall dictionary such as Neptune.AI, logging can be done manually under an `if accelerator.is_main_process` statement:
+```diff
+  from accelerate import Accelerator
++ import neptune.new as neptune
+
+  accelerator = Accelerator()
++ run = neptune.init(...)
+
+  my_model, my_optimizer, my_training_dataloader = accelerate.prepare(my_model, my_optimizer, my_training_dataloader)
+  device = accelerator.device
+  my_model.to(device)
+
+  for iteration in config["num_iterations"]:
+      for batch in my_training_dataloader:
+          my_optimizer.zero_grad()
+          inputs, targets = batch
+          inputs = inputs.to(device)
+          targets = targets.to(device)
+          outputs = my_model(inputs)
+          loss = my_loss_function(outputs, targets)
+          total_loss += loss
+          accelerator.backward(loss)
+          my_optimizer.step()
++         if accelerator.is_main_process:
++             run["logs/training/batch/loss"].log(loss)
+```

docs/source/usage_guides/training_zoo.md

@@ -0,0 +1,180 @@
+<!--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.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+-->
+
+# Example Zoo
+
+Below contains a non-exhaustive list of tutorials and scripts showcasing 🤗 Accelerate
+
+## Official Accelerate Examples:
+
+### Basic Examples
+
+These examples showcase the base features of Accelerate and are a great starting point
+
+- [Barebones NLP example](https://github.com/huggingface/accelerate/blob/main/examples/nlp_example.py)
+- [Barebones distributed NLP example in a Jupyter Notebook](https://github.com/huggingface/notebooks/blob/main/examples/accelerate_examples/simple_nlp_example.ipynb)
+- [Barebones computer vision example](https://github.com/huggingface/accelerate/blob/main/examples/cv_example.py)
+- [Barebones distributed computer vision example in a Jupyter Notebook](https://github.com/huggingface/notebooks/blob/main/examples/accelerate_examples/simple_cv_example.ipynb)
+- [Using Accelerate in Kaggle](https://www.kaggle.com/code/muellerzr/multi-gpu-and-accelerate)
+
+### Feature Specific Examples
+
+These examples showcase specific features that the Accelerate framework offers
+
+- [Automatic memory-aware gradient accumulation](https://github.com/huggingface/accelerate/blob/main/examples/by_feature/automatic_gradient_accumulation.py)
+- [Checkpointing states](https://github.com/huggingface/accelerate/blob/main/examples/by_feature/checkpointing.py)
+- [Cross validation](https://github.com/huggingface/accelerate/blob/main/examples/by_feature/cross_validation.py)
+- [DeepSpeed](https://github.com/huggingface/accelerate/blob/main/examples/by_feature/deepspeed_with_config_support.py)
+- [Fully Sharded Data Parallelism](https://github.com/huggingface/accelerate/blob/main/examples/by_feature/fsdp_with_peak_mem_tracking.py)
+- [Gradient accumulation](https://github.com/huggingface/accelerate/blob/main/examples/by_feature/gradient_accumulation.py)
+- [Memory-aware batch size finder](https://github.com/huggingface/accelerate/blob/main/examples/by_feature/memory.py)
+- [Metric Computation](https://github.com/huggingface/accelerate/blob/main/examples/by_feature/multi_process_metrics.py)
+- [Using Trackers](https://github.com/huggingface/accelerate/blob/main/examples/by_feature/tracking.py)
+- [Using Megatron-LM](https://github.com/huggingface/accelerate/blob/main/examples/by_feature/megatron_lm_gpt_pretraining.py)
+
+### Full Examples 
+
+These examples showcase every feature in Accelerate at once that was shown in "Feature Specific Examples"
+
+- [Complete NLP example](https://github.com/huggingface/accelerate/blob/main/examples/complete_nlp_example.py)
+- [Complete computer vision example](https://github.com/huggingface/accelerate/blob/main/examples/complete_cv_example.py)
+- [Very complete and extensible vision example showcasing SLURM, hydra, and a very extensible usage of the framework](https://github.com/yuvalkirstain/PickScore)
+- [Causal language model fine-tuning example](https://github.com/huggingface/transformers/blob/main/examples/pytorch/language-modeling/run_clm_no_trainer.py)
+- [Masked language model fine-tuning example](https://github.com/huggingface/transformers/blob/main/examples/pytorch/language-modeling/run_mlm_no_trainer.py)
+- [Speech pretraining example](https://github.com/huggingface/transformers/blob/main/examples/pytorch/speech-pretraining/run_wav2vec2_pretraining_no_trainer.py)
+- [Translation fine-tuning example](https://github.com/huggingface/transformers/blob/main/examples/pytorch/translation/run_translation_no_trainer.py)
+- [Text classification fine-tuning example](https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue_no_trainer.py)
+- [Semantic segmentation fine-tuning example](https://github.com/huggingface/transformers/blob/main/examples/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py)
+- [Question answering fine-tuning example](https://github.com/huggingface/transformers/blob/main/examples/pytorch/question-answering/run_qa_no_trainer.py)
+- [Beam search question answering fine-tuning example](https://github.com/huggingface/transformers/blob/main/examples/pytorch/question-answering/run_qa_beam_search_no_trainer.py)
+- [Multiple choice question answering fine-tuning example](https://github.com/huggingface/transformers/blob/main/examples/pytorch/multiple-choice/run_swag_no_trainer.py)
+- [Named entity recognition fine-tuning example](https://github.com/huggingface/transformers/blob/main/examples/pytorch/token-classification/run_ner_no_trainer.py)
+- [Image classification fine-tuning example](https://github.com/huggingface/transformers/blob/main/examples/pytorch/image-classification/run_image_classification_no_trainer.py)
+- [Summarization fine-tuning example](https://github.com/huggingface/transformers/blob/main/examples/pytorch/summarization/run_summarization_no_trainer.py)
+- [End-to-end examples on how to use AWS SageMaker integration of Accelerate](https://github.com/huggingface/notebooks/blob/main/sagemaker/22_accelerate_sagemaker_examples/README.md)
+- [Megatron-LM examples for various NLp tasks](https://github.com/pacman100/accelerate-megatron-test) 
+
+## Integration Examples 
+
+These are tutorials from libraries that integrate with 🤗 Accelerate: 
+
+> Don't find your integration here? Make a PR to include it!
+
+### Amphion
+- [Training Text-to-Speech Models with Amphion](https://github.com/open-mmlab/Amphion/blob/main/egs/tts/README.md)
+- [Training Singing Voice Conversion Models with Amphion](https://github.com/open-mmlab/Amphion/blob/main/egs/svc/README.md)
+- [Training Vocoders with Amphion](https://github.com/open-mmlab/Amphion/blob/main/egs/vocoder/README.md)
+
+### Catalyst
+
+- [Distributed training tutorial with Catalyst](https://catalyst-team.github.io/catalyst/tutorials/ddp.html)
+
+### DALLE2-pytorch 
+
+- [Fine-tuning DALLE2](https://github.com/lucidrains/DALLE2-pytorch#usage)
+
+### 🤗 diffusers
+
+- [Performing textual inversion with diffusers](https://github.com/huggingface/diffusers/tree/main/examples/textual_inversion)
+- [Training DreamBooth with diffusers](https://github.com/huggingface/diffusers/tree/main/examples/dreambooth)
+
+### fastai 
+
+- [Distributed training from Jupyter Notebooks with fastai](https://docs.fast.ai/tutorial.distributed.html)
+- [Basic distributed training examples with fastai](https://docs.fast.ai/examples/distributed_app_examples.html)
+
+### GradsFlow
+
+- [Auto Image Classification with GradsFlow](https://docs.gradsflow.com/en/latest/examples/nbs/01-ImageClassification/)
+
+### imagen-pytorch 
+
+- [Fine-tuning Imagen](https://github.com/lucidrains/imagen-pytorch#usage)
+
+### Kornia
+
+- [Fine-tuning vision models with Kornia's Trainer](https://kornia.readthedocs.io/en/latest/get-started/training.html)
+
+### PyTorch Accelerated 
+
+- [Quickstart distributed training tutorial with PyTorch Accelerated](https://pytorch-accelerated.readthedocs.io/en/latest/quickstart.html)
+
+### PyTorch3D
+
+- [Perform Deep Learning with 3D data](https://pytorch3d.org/tutorials/)
+
+### Stable-Dreamfusion
+
+- [Training with Stable-Dreamfusion to convert text to a 3D model](https://colab.research.google.com/drive/1MXT3yfOFvO0ooKEfiUUvTKwUkrrlCHpF?usp=sharing)
+
+### Tez 
+
+- [Leaf disease detection with Tez and Accelerate](https://www.kaggle.com/code/abhishek/tez-faster-and-easier-training-for-leaf-detection/notebook)
+
+### trlx 
+
+- [How to implement a sentiment learning task with trlx](https://github.com/CarperAI/trlx#example-how-to-add-a-task)
+
+### Comfy-UI
+
+- [Enabling using large Stable Diffusion Models in low-vram settings using Accelerate](https://github.com/comfyanonymous/ComfyUI/blob/master/comfy/model_management.py#L291-L296)
+
+
+## In Science
+
+Below contains a non-exhaustive list of papers utilizing 🤗 Accelerate. 
+
+> Don't find your paper here? Make a PR to include it!
+
+* Yuval Kirstain, Adam Polyak, Uriel Singer, Shahbuland Matiana, Joe Penna, Omer Levy: “Pick-a-Pic: An Open Dataset of User Preferences for Text-to-Image Generation”, 2023; [arXiv:2305.01569](http://arxiv.org/abs/2305.01569).
+* Lei Wang, Wanyu Xu, Yihuai Lan, Zhiqiang Hu, Yunshi Lan, Roy Ka-Wei Lee, Ee-Peng Lim: “Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models”, 2023; [arXiv:2305.04091](http://arxiv.org/abs/2305.04091).
+* Arthur Câmara, Claudia Hauff: “Moving Stuff Around: A study on efficiency of moving documents into memory for Neural IR models”, 2022; [arXiv:2205.08343](http://arxiv.org/abs/2205.08343).
+* Ying Sheng, Lianmin Zheng, Binhang Yuan, Zhuohan Li, Max Ryabinin, Daniel Y. Fu, Zhiqiang Xie, Beidi Chen, Clark Barrett, Joseph E. Gonzalez, Percy Liang, Christopher Ré, Ion Stoica, Ce Zhang: “High-throughput Generative Inference of Large Language Models with a Single GPU”, 2023; [arXiv:2303.06865](http://arxiv.org/abs/2303.06865).
+* Peter Melchior, Yan Liang, ChangHoon Hahn, Andy Goulding: “Autoencoding Galaxy Spectra I: Architecture”, 2022; [arXiv:2211.07890](http://arxiv.org/abs/2211.07890).
+* Jiaao Chen, Aston Zhang, Mu Li, Alex Smola, Diyi Yang: “A Cheaper and Better Diffusion Language Model with Soft-Masked Noise”, 2023; [arXiv:2304.04746](http://arxiv.org/abs/2304.04746).
+* Ayaan Haque, Matthew Tancik, Alexei A. Efros, Aleksander Holynski, Angjoo Kanazawa: “Instruct-NeRF2NeRF: Editing 3D Scenes with Instructions”, 2023; [arXiv:2303.12789](http://arxiv.org/abs/2303.12789).
+* Luke Melas-Kyriazi, Christian Rupprecht, Iro Laina, Andrea Vedaldi: “RealFusion: 360° Reconstruction of Any Object from a Single Image”, 2023; [arXiv:2302.10663](http://arxiv.org/abs/2302.10663).
+* Xiaoshi Wu, Keqiang Sun, Feng Zhu, Rui Zhao, Hongsheng Li: “Better Aligning Text-to-Image Models with Human Preference”, 2023; [arXiv:2303.14420](http://arxiv.org/abs/2303.14420).
+* Yongliang Shen, Kaitao Song, Xu Tan, Dongsheng Li, Weiming Lu, Yueting Zhuang: “HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in HuggingFace”, 2023; [arXiv:2303.17580](http://arxiv.org/abs/2303.17580).
+* Yue Yang, Wenlin Yao, Hongming Zhang, Xiaoyang Wang, Dong Yu, Jianshu Chen: “Z-LaVI: Zero-Shot Language Solver Fueled by Visual Imagination”, 2022; [arXiv:2210.12261](http://arxiv.org/abs/2210.12261).
+* Sheng-Yen Chou, Pin-Yu Chen, Tsung-Yi Ho: “How to Backdoor Diffusion Models?”, 2022; [arXiv:2212.05400](http://arxiv.org/abs/2212.05400).
+* Junyoung Seo, Wooseok Jang, Min-Seop Kwak, Jaehoon Ko, Hyeonsu Kim, Junho Kim, Jin-Hwa Kim, Jiyoung Lee, Seungryong Kim: “Let 2D Diffusion Model Know 3D-Consistency for Robust Text-to-3D Generation”, 2023; [arXiv:2303.07937](http://arxiv.org/abs/2303.07937).
+* Or Patashnik, Daniel Garibi, Idan Azuri, Hadar Averbuch-Elor, Daniel Cohen-Or: “Localizing Object-level Shape Variations with Text-to-Image Diffusion Models”, 2023; [arXiv:2303.11306](http://arxiv.org/abs/2303.11306).
+* Dídac Surís, Sachit Menon, Carl Vondrick: “ViperGPT: Visual Inference via Python Execution for Reasoning”, 2023; [arXiv:2303.08128](http://arxiv.org/abs/2303.08128).
+* Chenyang Qi, Xiaodong Cun, Yong Zhang, Chenyang Lei, Xintao Wang, Ying Shan, Qifeng Chen: “FateZero: Fusing Attentions for Zero-shot Text-based Video Editing”, 2023; [arXiv:2303.09535](http://arxiv.org/abs/2303.09535).
+* Sean Welleck, Jiacheng Liu, Ximing Lu, Hannaneh Hajishirzi, Yejin Choi: “NaturalProver: Grounded Mathematical Proof Generation with Language Models”, 2022; [arXiv:2205.12910](http://arxiv.org/abs/2205.12910).
+* Elad Richardson, Gal Metzer, Yuval Alaluf, Raja Giryes, Daniel Cohen-Or: “TEXTure: Text-Guided Texturing of 3D Shapes”, 2023; [arXiv:2302.01721](http://arxiv.org/abs/2302.01721).
+* Puijin Cheng, Li Lin, Yijin Huang, Huaqing He, Wenhan Luo, Xiaoying Tang: “Learning Enhancement From Degradation: A Diffusion Model For Fundus Image Enhancement”, 2023; [arXiv:2303.04603](http://arxiv.org/abs/2303.04603).
+* Shun Shao, Yftah Ziser, Shay Cohen: “Erasure of Unaligned Attributes from Neural Representations”, 2023; [arXiv:2302.02997](http://arxiv.org/abs/2302.02997).
+* Seonghyeon Ye, Hyeonbin Hwang, Sohee Yang, Hyeongu Yun, Yireun Kim, Minjoon Seo: “In-Context Instruction Learning”, 2023; [arXiv:2302.14691](http://arxiv.org/abs/2302.14691).
+* Shikun Liu, Linxi Fan, Edward Johns, Zhiding Yu, Chaowei Xiao, Anima Anandkumar: “Prismer: A Vision-Language Model with An Ensemble of Experts”, 2023; [arXiv:2303.02506](http://arxiv.org/abs/2303.02506).
+* Haoyu Chen, Zhihua Wang, Yang Yang, Qilin Sun, Kede Ma: “Learning a Deep Color Difference Metric for Photographic Images”, 2023; [arXiv:2303.14964](http://arxiv.org/abs/2303.14964).
+* Van-Hoang Le, Hongyu Zhang: “Log Parsing with Prompt-based Few-shot Learning”, 2023; [arXiv:2302.07435](http://arxiv.org/abs/2302.07435).
+* Keito Kudo, Yoichi Aoki, Tatsuki Kuribayashi, Ana Brassard, Masashi Yoshikawa, Keisuke Sakaguchi, Kentaro Inui: “Do Deep Neural Networks Capture Compositionality in Arithmetic Reasoning?”, 2023; [arXiv:2302.07866](http://arxiv.org/abs/2302.07866).
+* Ruoyao Wang, Peter Jansen, Marc-Alexandre Côté, Prithviraj Ammanabrolu: “Behavior Cloned Transformers are Neurosymbolic Reasoners”, 2022; [arXiv:2210.07382](http://arxiv.org/abs/2210.07382).
+* Martin Wessel, Tomáš Horych, Terry Ruas, Akiko Aizawa, Bela Gipp, Timo Spinde: “Introducing MBIB -- the first Media Bias Identification Benchmark Task and Dataset Collection”, 2023; [arXiv:2304.13148](http://arxiv.org/abs/2304.13148). DOI: [https://dx.doi.org/10.1145/3539618.3591882 10.1145/3539618.3591882].
+* Hila Chefer, Yuval Alaluf, Yael Vinker, Lior Wolf, Daniel Cohen-Or: “Attend-and-Excite: Attention-Based Semantic Guidance for Text-to-Image Diffusion Models”, 2023; [arXiv:2301.13826](http://arxiv.org/abs/2301.13826).
+* Marcio Fonseca, Yftah Ziser, Shay B. Cohen: “Factorizing Content and Budget Decisions in Abstractive Summarization of Long Documents”, 2022; [arXiv:2205.12486](http://arxiv.org/abs/2205.12486).
+* Elad Richardson, Gal Metzer, Yuval Alaluf, Raja Giryes, Daniel Cohen-Or: “TEXTure: Text-Guided Texturing of 3D Shapes”, 2023; [arXiv:2302.01721](http://arxiv.org/abs/2302.01721).
+* Tianxing He, Jingyu Zhang, Tianle Wang, Sachin Kumar, Kyunghyun Cho, James Glass, Yulia Tsvetkov: “On the Blind Spots of Model-Based Evaluation Metrics for Text Generation”, 2022; [arXiv:2212.10020](http://arxiv.org/abs/2212.10020).
+* Ori Ram, Yoav Levine, Itay Dalmedigos, Dor Muhlgay, Amnon Shashua, Kevin Leyton-Brown, Yoav Shoham: “In-Context Retrieval-Augmented Language Models”, 2023; [arXiv:2302.00083](http://arxiv.org/abs/2302.00083).
+* Dacheng Li, Rulin Shao, Hongyi Wang, Han Guo, Eric P. Xing, Hao Zhang: “MPCFormer: fast, performant and private Transformer inference with MPC”, 2022; [arXiv:2211.01452](http://arxiv.org/abs/2211.01452).
+* Baolin Peng, Michel Galley, Pengcheng He, Chris Brockett, Lars Liden, Elnaz Nouri, Zhou Yu, Bill Dolan, Jianfeng Gao: “GODEL: Large-Scale Pre-Training for Goal-Directed Dialog”, 2022; [arXiv:2206.11309](http://arxiv.org/abs/2206.11309).
+* Egil Rønningstad, Erik Velldal, Lilja Øvrelid: “Entity-Level Sentiment Analysis (ELSA): An exploratory task survey”, 2023, Proceedings of the 29th International Conference on Computational Linguistics, 2022, pages 6773-6783; [arXiv:2304.14241](http://arxiv.org/abs/2304.14241).
+* Charlie Snell, Ilya Kostrikov, Yi Su, Mengjiao Yang, Sergey Levine: “Offline RL for Natural Language Generation with Implicit Language Q Learning”, 2022; [arXiv:2206.11871](http://arxiv.org/abs/2206.11871).
+* Zhiruo Wang, Shuyan Zhou, Daniel Fried, Graham Neubig: “Execution-Based Evaluation for Open-Domain Code Generation”, 2022; [arXiv:2212.10481](http://arxiv.org/abs/2212.10481).
+* Minh-Long Luu, Zeyi Huang, Eric P. Xing, Yong Jae Lee, Haohan Wang: “Expeditious Saliency-guided Mix-up through Random Gradient Thresholding”, 2022; [arXiv:2212.04875](http://arxiv.org/abs/2212.04875).
+* Jun Hao Liew, Hanshu Yan, Daquan Zhou, Jiashi Feng: “MagicMix: Semantic Mixing with Diffusion Models”, 2022; [arXiv:2210.16056](http://arxiv.org/abs/2210.16056).
+* Yaqing Wang, Subhabrata Mukherjee, Xiaodong Liu, Jing Gao, Ahmed Hassan Awadallah, Jianfeng Gao: “LiST: Lite Prompted Self-training Makes Parameter-Efficient Few-shot Learners”, 2021; [arXiv:2110.06274](http://arxiv.org/abs/2110.06274).

examples/README.md

@@ -23,11 +23,12 @@ The [nlp_example.py](./nlp_example.py) script is a simple example to train a Ber
 Prior to running it you should install 🤗 Dataset and 🤗 Transformers:
 
 ```bash
-pip install datasets transformers
+pip install datasets evaluate transformers
 ```
 
 The same script can be run in any of the following configurations:
 - single CPU or single GPU
+- multi CPUs
 - multi GPUs (using PyTorch distributed mode)
 - (multi) TPUs
 - fp16 (mixed-precision) or fp32 (normal precision)
@@ -51,22 +52,34 @@ To run it in each of these various modes, use the following commands:
     python ./nlp_example.py  # from a server with a GPU
     ```
 - with fp16 (mixed-precision)
-    * from any server by passing `fp16=True` to the `Accelerator`.
+    * from any server by passing `mixed_precison=fp16` to the `Accelerator`.
         ```bash
-        python ./nlp_example.py --fp16
+        python ./nlp_example.py --mixed_precision fp16
         ```
     * from any server with Accelerate launcher
         ```bash
-        accelerate launch --fp16 ./nlp_example.py
+        accelerate launch --mixed_precision fp16 ./nlp_example.py
+- multi CPUs (requires Open MPI, Intel MPI, or MVAPICH)
+    * With Accelerate config and launcher, execute the following from node 0:
+        ```bash
+        accelerate config  # Select to have accelerate launch mpirun
+        accelerate launch ./nlp_example.py  # This will run the script on each server
+        ```
+    * With Intel MPI:
+        ```bash
+        export CCL_WORKER_COUNT=1
+        export MASTER_ADDR=xxx.xxx.xxx.xxx #node0 ip
+        mpirun -f hostfile -n 16 -ppn 4 python ./nlp_example.py
+        ```
 - multi GPUs (using PyTorch distributed mode)
     * With Accelerate config and launcher
         ```bash
         accelerate config  # This will create a config file on your server
         accelerate launch ./nlp_example.py  # This will run the script on your server
         ```
-    * With traditional PyTorch launcher
+    * With traditional PyTorch launcher (`python -m torch.distributed.run` can be used instead of `torchrun`)
         ```bash
-        python -m torch.distributed.launch --nproc_per_node 2 --use_env ./nlp_example.py
+        torchrun --nproc_per_node 2 ./nlp_example.py
         ```
 - multi GPUs, multi node (several machines, using PyTorch distributed mode)
     * With Accelerate config and launcher, on each machine:
@@ -74,18 +87,15 @@ To run it in each of these various modes, use the following commands:
         accelerate config  # This will create a config file on each server
         accelerate launch ./nlp_example.py  # This will run the script on each server
         ```
-    * With PyTorch launcher only
+    * With PyTorch launcher only (`python -m torch.distributed.run` can be used instead of `torchrun`). Run this command on each node:
         ```bash
-        python -m torch.distributed.launch --nproc_per_node 2 \
-            --use_env \
-            --node_rank 0 \
-            --master_addr master_node_ip_address \
-            ./nlp_example.py  # On the first server
-        python -m torch.distributed.launch --nproc_per_node 2 \
-            --use_env \
-            --node_rank 1 \
-            --master_addr master_node_ip_address \
-            ./nlp_example.py  # On the second server
+        torchrun \ # python -m torch.distributed.run 
+            --nproc_per_node 2 \
+            --nnodes 2 \
+            --rdzv_id 2299 \ # A unique job id 
+            --rdzv_backend c10d \
+            --rdzv_endpoint master_node_ip_address:29500 \
+            ./nlp_example.py
         ```
 - (multi) TPUs
     * With Accelerate config and launcher
@@ -103,6 +113,7 @@ The [cv_example.py](./cv_example.py) script is a simple example to fine-tune a R
 
 The same script can be run in any of the following configurations:
 - single CPU or single GPU
+- multi CPUs
 - multi GPUs (using PyTorch distributed mode)
 - (multi) TPUs
 - fp16 (mixed-precision) or fp32 (normal precision)
@@ -136,54 +147,110 @@ To run it in each of these various modes, use the following commands:
         ```
 - single GPU:
     ```bash
-    python ./nlp_example.py  # from a server with a GPU
+    python ./cv_example.py  # from a server with a GPU
     ```
 - with fp16 (mixed-precision)
-    * from any server by passing `fp16=True` to the `Accelerator`.
+    * from any server by passing `mixed_precison=fp16` to the `Accelerator`.
         ```bash
-        python ./cv_example.py --data_dir path_to_data --fp16
+        python ./cv_example.py --data_dir path_to_data --mixed_precison fp16
         ```
     * from any server with Accelerate launcher
         ```bash
-        accelerate launch --fp16 ./cv_example.py --data_dir path_to_data
+        accelerate launch --mixed_precison fp16 ./cv_example.py --data_dir path_to_data
+- multi CPUs (requires Open MPI, Intel MPI, or MVAPICH)
+    * With Accelerate config and launcher, run the following from node 0:
+        ```bash
+        accelerate config --config_file config.yaml  # Select to have accelerate launch mpirun
+        accelerate launch ./cv_example.py --data_dir path_to_data # This will run the script on each server
+        ```
+    * With Intel MPI, execute mpirun from node 0:
+        ```bash
+        export CCL_WORKER_COUNT=1
+        export MASTER_ADDR=xxx.xxx.xxx.xxx #node0 ip
+        mpirun -f hostfile -n 16 -ppn 4 python ./cv_example.py --data_dir path_to_data
+        ```
 - multi GPUs (using PyTorch distributed mode)
     * With Accelerate config and launcher
         ```bash
-        accelerate config  # This will create a config file on your server
-        accelerate launch ./cv_example.py --data_dir path_to_data  # This will run the script on your server
+        accelerate config --config_file config.yaml  # This will create a config file on your server to `config.yaml`
+        accelerate launch --config_file config.yaml ./cv_example.py --data_dir path_to_data  # This will run the script on your server
         ```
-    * With traditional PyTorch launcher
+    * With traditional PyTorch launcher (`python -m torch.distributed.run` can be used instead of `torchrun`)
         ```bash
-        python -m torch.distributed.launch --nproc_per_node 2 --use_env ./cv_example.py --data_dir path_to_data
+        torchrun --nproc_per_node 2 ./cv_example.py --data_dir path_to_data
         ```
 - multi GPUs, multi node (several machines, using PyTorch distributed mode)
     * With Accelerate config and launcher, on each machine:
         ```bash
-        accelerate config  # This will create a config file on each server
-        accelerate launch ./cv_example.py --data_dir path_to_data  # This will run the script on each server
+        accelerate config --config_file config.yaml  # This will create a config file on your server to `config.yaml`
+        accelerate launch --config_file config.yaml ./cv_example.py --data_dir path_to_data  # This will run the script on each server
         ```
-    * With PyTorch launcher only
+    * With PyTorch launcher only (`python -m torch.distributed.run` can be used instead of `torchrun`). Run this command on each node:
         ```bash
-        python -m torch.distributed.launch --nproc_per_node 2 \
-            --use_env \
-            --node_rank 0 \
-            --master_addr master_node_ip_address \
-            ./cv_example.py --data_dir path_to_data  # On the first server
-        python -m torch.distributed.launch --nproc_per_node 2 \
-            --use_env \
-            --node_rank 1 \
-            --master_addr master_node_ip_address \
-            ./cv_example.py --data_dir path_to_data  # On the second server
+        torchrun \ # python -m torch.distributed.run
+            --nproc_per_node 2 \
+            --nnodes 2 \
+            --rdzv_id 2299 \ # A unique job id 
+            --rdzv_backend c10d \
+            --rdzv_endpoint master_node_ip_address:29500 \
+            ./cv_example.py --data_dir path_to_data
         ```
 - (multi) TPUs
     * With Accelerate config and launcher
         ```bash
-        accelerate config  # This will create a config file on your TPU server
-        accelerate launch ./cv_example.py --data_dir path_to_data  # This will run the script on each server
+        accelerate config --config_file config.yaml  # This will create a config file on your server to `config.yaml`
+        accelerate launch --config_file config.yaml ./cv_example.py --data_dir path_to_data  # This will run the script on each server
         ```
     * In PyTorch:
         Add an `xmp.spawn` line in your script as you usually do.
 
+### Simple vision example (GANs)
+
+- [huggan project](https://github.com/huggingface/community-events/tree/main/huggan)
+
+### Using AWS SageMaker integration
+- [Examples showcasing AWS SageMaker integration of 🤗 Accelerate.](https://github.com/pacman100/accelerate-aws-sagemaker)
+
+
+## Simple Multi-GPU Hardware Launcher
+
+[multigpu_remote_launcher.py](./multigpu_remote_launcher.py) is a minimal script that demonstrates launching accelerate
+on multiple remote GPUs, and with automatic hardware environment and dependency setup for reproducibility. You can
+easily customize the training function used, training arguments, hyperparameters, and type of compute hardware, and then
+run the script to automatically launch multi GPU training on remote hardware.
+
+This script uses [Runhouse](https://github.com/run-house/runhouse) to launch on self-hosted hardware (e.g. in your own
+cloud account or on-premise cluster) but there are other options for running remotely as well. Runhouse can be installed
+with `pip install runhouse`, and you can refer to
+[hardware setup](https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup)
+for hardware setup instructions, or this
+[Colab tutorial](https://colab.research.google.com/drive/1qVwYyLTCPYPSdz9ZX7BZl9Qm0A3j7RJe) for a more in-depth walkthrough.
+
+## SLURM Scripts 
+In [/slurm/submit_multigpu.sh](./slurm/submit_multigpu.sh) and [/slurm/submit_multinode.sh](./slurm/submit_multinode.sh) we present two scripts for running the examples on a machine with [SLURM](https://slurm.schedmd.com/documentation.html) workload manager. 
+
+In [/slurm/submit_multigpu.sh](./slurm/submit_multigpu.sh) the only parameter in the launcher that needs to be modified is `--num_processes`, which determines the number of GPUs we will use. In this case, using the environment variable `$SLURM_GPUS`, we indicate that we want to utilize all the GPUs available on the node we have requested. 
+
+In [/slurm/submit_multinode.sh](./slurm/submit_multinode.sh) we must specify the number of nodes that will be part of the training (`--num_machines`), how many GPUs we will use in total (`--num_processes`), the [`backend`](https://pytorch.org/docs/stable/elastic/run.html#note-on-rendezvous-backend), `--main_process_ip` which will be the address the master node and the `--main_process_port`.
+
+In [/slurm/submit_multicpu.sh](./slurm/submit_multicpu.sh) we must specify the number of nodes that will be part of the training (`--num_machines`), how many CPU processes we will use in total (`--num_processes`), the [`backend`](https://pytorch.org/docs/stable/elastic/run.html#note-on-rendezvous-backend), `--main_process_ip` which will be the address the master node and the `--main_process_port`. `mpirun_hostfile` specifies to run the job using MPIRun.
+
+In both scripts, we run `activateEnviroment.sh` at the beginning. This script should contain the necessary instructions to initialize the environment for execution. Below, we show an example that loads the necessary libraries ([Environment modules](https://github.com/cea-hpc/modules)), activates the Python environment, and sets up various environment variables, most of them to run the scripts in offline mode in case we don't have internet connection from the cluster.
+
+```bash
+# activateEnvironment.sh 
+module purge
+module load anaconda3/2020.02 cuda/10.2 cudnn/8.0.5 nccl/2.9.9 arrow/7.0.0 openmpi
+source activate /home/nct01/nct01328/pytorch_antoni_local
+
+export HF_HOME=/gpfs/projects/nct01/nct01328/
+export HF_LOCAL_HOME=/gpfs/projects/nct01/nct01328/HF_LOCAL
+export HF_DATASETS_OFFLINE=1
+export TRANSFORMERS_OFFLINE=1
+export PYTHONPATH=/home/nct01/nct01328/transformers-in-supercomputers:$PYTHONPATH 
+export GPUS_PER_NODE=4
+```
+
 ## Finer Examples
 
 While the first two scripts are extremely barebones when it comes to what you can do with accelerate, more advanced features are documented in two other locations.

examples/by_feature/README.md

@@ -19,7 +19,7 @@ Adjustments to each script from the base `nlp_example.py` file can be found quic
 
 All following scripts also accept these arguments in addition to their added ones.
 
-These arguments should be added at the end of any method for starting the python script (such as `python`, `accelerate launch`, `python -m torch.distributed.launch`), such as:
+These arguments should be added at the end of any method for starting the python script (such as `python`, `accelerate launch`, `python -m torch.distributed.run`), such as:
 
 ```bash
 accelerate launch ../nlp_example.py --mixed_precision fp16 --cpu 0
@@ -34,7 +34,7 @@ accelerate launch ../nlp_example.py --mixed_precision fp16 --cpu 0
   - `output_dir`, where saved state folders should be saved to, default is current working directory
   - `resume_from_checkpoint`, what checkpoint folder to resume from. ("epoch_0", "step_22", ...)
 
-These arguments should be added at the end of any method for starting the python script (such as `python`, `accelerate launch`, `python -m torch.distributed.launch`), such as:
+These arguments should be added at the end of any method for starting the python script (such as `python`, `accelerate launch`, `python -m torchrun`), such as:
 
 (Note, `resume_from_checkpoint` assumes that we've ran the script for one epoch with the `--checkpointing_steps epoch` flag)
 
@@ -42,6 +42,18 @@ These arguments should be added at the end of any method for starting the python
 accelerate launch ./checkpointing.py --checkpointing_steps epoch output_dir "checkpointing_tutorial" --resume_from_checkpoint "checkpointing_tutorial/epoch_0"
 ```
 
+### Cross Validation (`cross_validation.py`)
+
+- Shows how to use `Accelerator.free_memory` and run cross validation efficiently with `datasets`.
+- Arguments available:
+  - `num_folds`, the number of folds the training dataset should be split into.
+
+These arguments should be added at the end of any method for starting the python script (such as `python`, `accelerate launch`, `python -m torchrun`), such as:
+
+```bash
+accelerate launch ./cross_validation.py --num_folds 2
+```
+
 ### Experiment Tracking (`tracking.py`)
 
 - Shows how to use `Accelerate.init_trackers` and `Accelerator.log`
@@ -49,20 +61,61 @@ accelerate launch ./checkpointing.py --checkpointing_steps epoch output_dir "che
 - Arguments available:
   - `with_tracking`, whether to load in all available experiment trackers from the environment.
 
-These arguments should be added at the end of any method for starting the python script (such as `python`, `accelerate launch`, `python -m torch.distributed.launch`), such as:
+These arguments should be added at the end of any method for starting the python script (such as `python`, `accelerate launch`, `python -m torchrun`), such as:
 
 ```bash
 accelerate launch ./tracking.py --with_tracking
 ```
 
-### Cross Validation (`cross_validation.py`)
+### Gradient Accumulation (`gradient_accumulation.py`)
 
-- Shows how to use `Accelerator.free_memory` and run cross validation efficiently with `datasets`.
+- Shows how to use `Accelerator.no_sync` to prevent gradient averaging in a distributed setup.
 - Arguments available:
-  - `num_folds`, the number of folds the training dataset should be split into.
+  - `gradient_accumulation_steps`, the number of steps to perform before the gradients are accumulated and the optimizer and scheduler are stepped + zero_grad
 
-These arguments should be added at the end of any method for starting the python script (such as `python`, `accelerate launch`, `python -m torch.distributed.launch`), such as:
+These arguments should be added at the end of any method for starting the python script (such as `python`, `accelerate launch`, `python -m torchrun`), such as:
 
 ```bash
-accelerate launch ./cross_validation.py --num_folds 2
+accelerate launch ./gradient_accumulation.py --gradient_accumulation_steps 5
+```
+
+### LocalSGD (`local_sgd.py`)
+- Shows how to use `Accelerator.no_sync` to prevent gradient averaging in a distributed setup. However, unlike gradient accumulation, this method does not change the effective batch size. Local SGD can be combined with gradient accumulation.
+
+These arguments should be added at the end of any method for starting the python script (such as `python`, `accelerate launch`, `python -m torchrun`), such as:
+
+```bash
+accelerate launch ./local_sgd.py --local_sgd_steps 4
+```
+
+### DDP Communication Hook (`ddp_comm_hook.py`)
+
+- Shows how to use DDP Communication Hooks to control and optimize gradient communication across workers in a DistributedDataParallel setup.
+- Arguments available:
+  - `ddp_comm_hook`, the type of DDP communication hook to use. Choose between `no`, `fp16`, `bf16`, `power_sgd`, and `batched_power_sgd`.
+
+These arguments should be added at the end of any method for starting the python script (such as `accelerate launch`, `python -m torch.distributed.run`), such as:
+
+```bash
+accelerate launch ./ddp_comm_hook.py --mixed_precision fp16 --ddp_comm_hook power_sgd
+```
+
+### Profiler (`profiler.py`)
+
+- Shows how to use the profiling capabilities of `Accelerate` to profile PyTorch models during training.
+- Uses the `ProfileKwargs` handler to customize profiling options, including activities, scheduling, and additional profiling options.
+- Can generate and save profiling traces in JSON format for visualization in Chrome's tracing tool.
+
+Arguments available:
+- `--record_shapes`: If passed, records shapes for profiling.
+- `--profile_memory`: If passed, profiles memory usage.
+- `--with_stack`: If passed, profiles stack traces.
+- `--with_flops`: If passed, profiles floating point operations (FLOPS).
+- `--output_trace_dir`: If specified, saves the profiling trace to the given dir in JSON format.
+- `--cpu`: If passed, trains on the CPU instead of GPU.
+
+These arguments should be added at the end of any method for starting the Python script (such as `python`, `accelerate launch`, `python -m torchrun`), such as:
+
+```bash
+accelerate launch ./profiler.py --record_shapes --profile_memory --with_flops --output_trace_dir "profiler"
 ```

examples/by_feature/automatic_gradient_accumulation.py

@@ -0,0 +1,242 @@
+# 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 os
+
+# New Code #
+import evaluate
+import torch
+from datasets import load_dataset
+from torch.optim import AdamW
+from torch.utils.data import DataLoader
+from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
+
+from accelerate import Accelerator
+from accelerate.utils import find_executable_batch_size
+
+
+########################################################################
+# This is a fully working simple example to use Accelerate,
+# specifically showcasing how to combine both the gradient accumulation
+# and automatic batch size finder utilities of Accelerate to perfrom
+# automatic gradient accumulation
+#
+# This example trains a Bert base model on GLUE MRPC
+# in any of the following settings (with the same script):
+#   - single CPU or single GPU
+#   - multi GPUS (using PyTorch distributed mode)
+#   - (multi) TPUs
+#   - fp16 (mixed-precision) or fp32 (normal precision)
+#
+# New additions from the base script can be found quickly by
+# looking for the # New Code # tags
+#
+# To run it in each of these various modes, follow the instructions
+# in the readme for examples:
+# https://github.com/huggingface/accelerate/tree/main/examples
+#
+########################################################################
+
+EVAL_BATCH_SIZE = 32
+
+
+def get_dataloaders(accelerator: Accelerator, batch_size: int = 16):
+    """
+    Creates a set of `DataLoader`s for the `glue` dataset,
+    using "bert-base-cased" as the tokenizer.
+
+    Args:
+        accelerator (`Accelerator`):
+            An `Accelerator` object
+        batch_size (`int`, *optional*):
+            The batch size for the train and validation DataLoaders.
+    """
+    tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
+    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:
+    with accelerator.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):
+        # When using mixed precision we want round multiples of 8/16
+        if accelerator.mixed_precision == "fp8":
+            pad_to_multiple_of = 16
+        elif accelerator.mixed_precision != "no":
+            pad_to_multiple_of = 8
+        else:
+            pad_to_multiple_of = None
+
+        return tokenizer.pad(
+            examples,
+            padding="longest",
+            pad_to_multiple_of=pad_to_multiple_of,
+            return_tensors="pt",
+        )
+
+    # Instantiate dataloaders.
+    train_dataloader = DataLoader(
+        tokenized_datasets["train"], shuffle=True, collate_fn=collate_fn, batch_size=batch_size
+    )
+    eval_dataloader = DataLoader(
+        tokenized_datasets["validation"], shuffle=False, collate_fn=collate_fn, batch_size=EVAL_BATCH_SIZE
+    )
+
+    return train_dataloader, eval_dataloader
+
+
+# For testing only
+if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
+    from accelerate.test_utils.training import mocked_dataloaders
+
+    get_dataloaders = mocked_dataloaders  # noqa: F811
+
+
+def training_function(config, args):
+    # For testing only
+    if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
+        config["num_epochs"] = 2
+    # Initialize accelerator
+    accelerator = Accelerator(cpu=args.cpu, mixed_precision=args.mixed_precision)
+    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
+    lr = config["lr"]
+    num_epochs = int(config["num_epochs"])
+    seed = int(config["seed"])
+    observed_batch_size = int(config["batch_size"])
+
+    metric = evaluate.load("glue", "mrpc")
+
+    # New Code #
+    # We use the `find_executable_batch_size` decorator, passing in the desired observed batch size
+    # to train on. If a CUDA OOM error occurs, it will retry this loop cutting the batch size in
+    # half each time. From this, we can calculate the number of gradient accumulation steps needed
+    # and modify the Accelerator object as a result
+    @find_executable_batch_size(starting_batch_size=int(observed_batch_size))
+    def inner_training_loop(batch_size):
+        # Since we need to modify the outside accelerator object, we need to bring it
+        # to the local scope
+        nonlocal accelerator
+
+        # We can calculate the number of gradient accumulation steps based on the current
+        # batch size vs the starting batch size
+        num_gradient_accumulation_steps = observed_batch_size // batch_size
+
+        # And then set it in the Accelerator directly:
+        accelerator.gradient_accumulation_steps = num_gradient_accumulation_steps
+
+        # Next we need to free all of the stored model references in the Accelerator each time
+        accelerator.free_memory()
+
+        # And set the seed so our results are reproducable each reset
+        set_seed(seed)
+
+        # Instantiate the model (we build the model here so that the seed also control new weights initialization)
+        model = AutoModelForSequenceClassification.from_pretrained("bert-base-cased", return_dict=True)
+
+        # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
+        # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
+        # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
+        model = model.to(accelerator.device)
+
+        # Instantiate optimizer
+        optimizer = AdamW(params=model.parameters(), lr=lr)
+        train_dataloader, eval_dataloader = get_dataloaders(accelerator, batch_size)
+
+        # Instantiate scheduler
+        lr_scheduler = get_linear_schedule_with_warmup(
+            optimizer=optimizer,
+            num_warmup_steps=100,
+            num_training_steps=(len(train_dataloader) * num_epochs),
+        )
+
+        # Prepare everything
+        # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
+        # prepare method.
+        model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = accelerator.prepare(
+            model, optimizer, train_dataloader, eval_dataloader, lr_scheduler
+        )
+
+        # Now we train the model
+        for epoch in range(num_epochs):
+            model.train()
+            for step, batch in enumerate(train_dataloader):
+                # And perform gradient accumulation
+                with accelerator.accumulate(model):
+                    # We could avoid this line since we set the accelerator with `device_placement=True`.
+                    batch.to(accelerator.device)
+                    outputs = model(**batch)
+                    loss = outputs.loss
+                    accelerator.backward(loss)
+                    optimizer.step()
+                    lr_scheduler.step()
+                    optimizer.zero_grad()
+
+            model.eval()
+            for step, batch in enumerate(eval_dataloader):
+                # We could avoid this line since we set the accelerator with `device_placement=True`.
+                batch.to(accelerator.device)
+                with torch.no_grad():
+                    outputs = model(**batch)
+                predictions = outputs.logits.argmax(dim=-1)
+                predictions, references = accelerator.gather_for_metrics((predictions, batch["labels"]))
+                metric.add_batch(
+                    predictions=predictions,
+                    references=references,
+                )
+
+            eval_metric = metric.compute()
+            # Use accelerator.print to print only on the main process.
+            accelerator.print(f"epoch {epoch}:", eval_metric)
+
+    # New Code #
+    # And call it at the end with no arguments
+    # Note: You could also refactor this outside of your training loop function
+    inner_training_loop()
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Simple example of training script.")
+    parser.add_argument(
+        "--mixed_precision",
+        type=str,
+        default=None,
+        choices=["no", "fp16", "bf16", "fp8"],
+        help="Whether to use mixed precision. Choose"
+        "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
+        "and an Nvidia Ampere GPU.",
+    )
+    parser.add_argument("--cpu", action="store_true", help="If passed, will train on the CPU.")
+    args = parser.parse_args()
+    # New Code #
+    # We modify the starting batch size to be an observed batch size of 256, to guarentee an initial CUDA OOM
+    config = {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 256}
+    training_function(config, args)
+
+
+if __name__ == "__main__":
+    main()

examples/by_feature/checkpointing.py

@@ -1,4 +1,3 @@
-# coding=utf-8
 # Copyright 2021 The HuggingFace Inc. team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -15,18 +14,14 @@
 import argparse
 import os
 
+import evaluate
 import torch
+from datasets import load_dataset
+from torch.optim import AdamW
 from torch.utils.data import DataLoader
+from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
 
 from accelerate import Accelerator, DistributedType
-from datasets import load_dataset, load_metric
-from transformers import (
-    AdamW,
-    AutoModelForSequenceClassification,
-    AutoTokenizer,
-    get_linear_schedule_with_warmup,
-    set_seed,
-)
 
 
 ########################################################################
@@ -76,11 +71,13 @@ def get_dataloaders(accelerator: Accelerator, batch_size: int = 16):
         return outputs
 
     # Apply the method we just defined to all the examples in all the splits of the dataset
-    tokenized_datasets = datasets.map(
-        tokenize_function,
-        batched=True,
-        remove_columns=["idx", "sentence1", "sentence2"],
-    )
+    # starting with the main process first:
+    with accelerator.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
@@ -88,9 +85,22 @@ def get_dataloaders(accelerator: Accelerator, batch_size: int = 16):
 
     def collate_fn(examples):
         # On TPU it's best to pad everything to the same length or training will be very slow.
-        if accelerator.distributed_type == DistributedType.TPU:
-            return tokenizer.pad(examples, padding="max_length", max_length=128, return_tensors="pt")
-        return tokenizer.pad(examples, padding="longest", return_tensors="pt")
+        max_length = 128 if accelerator.distributed_type == DistributedType.XLA else None
+        # When using mixed precision we want round multiples of 8/16
+        if accelerator.mixed_precision == "fp8":
+            pad_to_multiple_of = 16
+        elif accelerator.mixed_precision != "no":
+            pad_to_multiple_of = 8
+        else:
+            pad_to_multiple_of = None
+
+        return tokenizer.pad(
+            examples,
+            padding="longest",
+            max_length=max_length,
+            pad_to_multiple_of=pad_to_multiple_of,
+            return_tensors="pt",
+        )
 
     # Instantiate dataloaders.
     train_dataloader = DataLoader(
@@ -103,13 +113,22 @@ def get_dataloaders(accelerator: Accelerator, batch_size: int = 16):
     return train_dataloader, eval_dataloader
 
 
+# For testing only
+if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
+    from accelerate.test_utils.training import mocked_dataloaders
+
+    get_dataloaders = mocked_dataloaders  # noqa: F811
+
+
 def training_function(config, args):
+    # For testing only
+    if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
+        config["num_epochs"] = 2
     # Initialize accelerator
     accelerator = Accelerator(cpu=args.cpu, mixed_precision=args.mixed_precision)
     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     lr = config["lr"]
     num_epochs = int(config["num_epochs"])
-    correct_bias = config["correct_bias"]
     seed = int(config["seed"])
     batch_size = int(config["batch_size"])
 
@@ -130,11 +149,11 @@ def training_function(config, args):
     set_seed(seed)
 
     train_dataloader, eval_dataloader = get_dataloaders(accelerator, batch_size)
-    metric = load_metric("glue", "mrpc")
+    metric = evaluate.load("glue", "mrpc")
 
     # If the batch size is too big we use gradient accumulation
     gradient_accumulation_steps = 1
-    if batch_size > MAX_GPU_BATCH_SIZE:
+    if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.XLA:
         gradient_accumulation_steps = batch_size // MAX_GPU_BATCH_SIZE
         batch_size = MAX_GPU_BATCH_SIZE
 
@@ -147,7 +166,7 @@ def training_function(config, args):
     model = model.to(accelerator.device)
 
     # Instantiate optimizer
-    optimizer = AdamW(params=model.parameters(), lr=lr, correct_bias=correct_bias)
+    optimizer = AdamW(params=model.parameters(), lr=lr)
 
     # Instantiate scheduler
     lr_scheduler = get_linear_schedule_with_warmup(
@@ -196,13 +215,15 @@ def training_function(config, args):
     # Now we train the model
     for epoch in range(starting_epoch, num_epochs):
         model.train()
-        for step, batch in enumerate(train_dataloader):
-            # New Code #
-            # We need to skip steps until we reach the resumed step during the first epoch
-            if args.resume_from_checkpoint and epoch == starting_epoch:
-                if resume_step is not None and step < resume_step:
-                    overall_step += 1
-                    continue
+        # New Code #
+        if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
+            # We need to skip steps until we reach the resumed step
+            active_dataloader = accelerator.skip_first_batches(train_dataloader, resume_step)
+            overall_step += resume_step
+        else:
+            # After the first iteration though, we need to go back to the original dataloader
+            active_dataloader = train_dataloader
+        for step, batch in enumerate(active_dataloader):
             # We could avoid this line since we set the accelerator with `device_placement=True`.
             batch.to(accelerator.device)
             outputs = model(**batch)
@@ -235,8 +256,7 @@ def training_function(config, args):
             with torch.no_grad():
                 outputs = model(**batch)
             predictions = outputs.logits.argmax(dim=-1)
-            # It is slightly faster to call this once, than multiple times
-            predictions, references = accelerator.gather((predictions, batch["labels"]))
+            predictions, references = accelerator.gather_for_metrics((predictions, batch["labels"]))
             metric.add_batch(
                 predictions=predictions,
                 references=references,
@@ -263,8 +283,8 @@ def main():
     parser.add_argument(
         "--mixed_precision",
         type=str,
-        default="no",
-        choices=["no", "fp16", "bf16"],
+        default=None,
+        choices=["no", "fp16", "bf16", "fp8"],
         help="Whether to use mixed precision. Choose"
         "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
         "and an Nvidia Ampere GPU.",
@@ -289,7 +309,7 @@ def main():
         help="If the training should continue from a checkpoint folder.",
     )
     args = parser.parse_args()
-    config = {"lr": 2e-5, "num_epochs": 3, "correct_bias": True, "seed": 42, "batch_size": 16}
+    config = {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
     training_function(config, args)
 
 

examples/by_feature/cross_validation.py

@@ -1,4 +1,3 @@
-# coding=utf-8
 # Copyright 2022 The HuggingFace Inc. team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -15,23 +14,19 @@
 import argparse
 from typing import List
 
+import evaluate
 import numpy as np
 import torch
-from torch.utils.data import DataLoader
-
-from accelerate import Accelerator, DistributedType
-from datasets import DatasetDict, load_dataset, load_metric
+from datasets import DatasetDict, load_dataset
 
 # New Code #
 # We'll be using StratifiedKFold for this example
 from sklearn.model_selection import StratifiedKFold
-from transformers import (
-    AdamW,
-    AutoModelForSequenceClassification,
-    AutoTokenizer,
-    get_linear_schedule_with_warmup,
-    set_seed,
-)
+from torch.optim import AdamW
+from torch.utils.data import DataLoader
+from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
+
+from accelerate import Accelerator, DistributedType
 
 
 ########################################################################
@@ -62,7 +57,7 @@ MAX_GPU_BATCH_SIZE = 16
 EVAL_BATCH_SIZE = 32
 
 # New Code #
-# We need a different `get_dataloaders` function that will build dataloaders by indexs
+# We need a different `get_dataloaders` function that will build dataloaders by index
 
 
 def get_fold_dataloaders(
@@ -75,9 +70,9 @@ def get_fold_dataloaders(
         accelerator (`Accelerator`):
             The main `Accelerator` object
         train_idxs (list of `int`):
-            The split indicies for the training dataset
+            The split indices for the training dataset
         valid_idxs (list of `int`):
-            The split indicies for the validation dataset
+            The split indices for the validation dataset
         batch_size (`int`):
             The size of the minibatch. Default is 16
     """
@@ -96,11 +91,13 @@ def get_fold_dataloaders(
         return outputs
 
     # Apply the method we just defined to all the examples in all the splits of the dataset
-    tokenized_datasets = datasets.map(
-        tokenize_function,
-        batched=True,
-        remove_columns=["idx", "sentence1", "sentence2"],
-    )
+    # starting with the main process first:
+    with accelerator.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
@@ -108,9 +105,22 @@ def get_fold_dataloaders(
 
     def collate_fn(examples):
         # On TPU it's best to pad everything to the same length or training will be very slow.
-        if accelerator.distributed_type == DistributedType.TPU:
-            return tokenizer.pad(examples, padding="max_length", max_length=128, return_tensors="pt")
-        return tokenizer.pad(examples, padding="longest", return_tensors="pt")
+        max_length = 128 if accelerator.distributed_type == DistributedType.XLA else None
+        # When using mixed precision we want round multiples of 8/16
+        if accelerator.mixed_precision == "fp8":
+            pad_to_multiple_of = 16
+        elif accelerator.mixed_precision != "no":
+            pad_to_multiple_of = 8
+        else:
+            pad_to_multiple_of = None
+
+        return tokenizer.pad(
+            examples,
+            padding="longest",
+            max_length=max_length,
+            pad_to_multiple_of=pad_to_multiple_of,
+            return_tensors="pt",
+        )
 
     # Instantiate dataloaders.
     train_dataloader = DataLoader(
@@ -129,7 +139,6 @@ def get_fold_dataloaders(
 
 def training_function(config, args):
     # New Code #
-    test_labels = None
     test_predictions = []
     # Download the dataset
     datasets = load_dataset("glue", "mrpc")
@@ -140,15 +149,14 @@ def training_function(config, args):
     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     lr = config["lr"]
     num_epochs = int(config["num_epochs"])
-    correct_bias = config["correct_bias"]
     seed = int(config["seed"])
     batch_size = int(config["batch_size"])
 
-    metric = load_metric("glue", "mrpc")
+    metric = evaluate.load("glue", "mrpc")
 
     # If the batch size is too big we use gradient accumulation
     gradient_accumulation_steps = 1
-    if batch_size > MAX_GPU_BATCH_SIZE:
+    if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.XLA:
         gradient_accumulation_steps = batch_size // MAX_GPU_BATCH_SIZE
         batch_size = MAX_GPU_BATCH_SIZE
 
@@ -157,17 +165,15 @@ def training_function(config, args):
     # New Code #
     # Create our folds:
     folds = kfold.split(np.zeros(datasets["train"].num_rows), datasets["train"]["label"])
-
+    test_references = []
     # Iterate over them
-    for train_idxs, valid_idxs in folds:
+    for i, (train_idxs, valid_idxs) in enumerate(folds):
         train_dataloader, eval_dataloader, test_dataloader = get_fold_dataloaders(
             accelerator,
             datasets,
             train_idxs,
             valid_idxs,
         )
-        if test_labels is None:
-            test_labels = datasets["validation"]["label"]
         # Instantiate the model (we build the model here so that the seed also control new weights initialization)
         model = AutoModelForSequenceClassification.from_pretrained("bert-base-cased", return_dict=True)
 
@@ -177,7 +183,7 @@ def training_function(config, args):
         model = model.to(accelerator.device)
 
         # Instantiate optimizer
-        optimizer = AdamW(params=model.parameters(), lr=lr, correct_bias=correct_bias)
+        optimizer = AdamW(params=model.parameters(), lr=lr)
 
         # Instantiate scheduler
         lr_scheduler = get_linear_schedule_with_warmup(
@@ -215,7 +221,7 @@ def training_function(config, args):
                 with torch.no_grad():
                     outputs = model(**batch)
                 predictions = outputs.logits.argmax(dim=-1)
-                predictions, references = accelerator.gather((predictions, batch["labels"]))
+                predictions, references = accelerator.gather_for_metrics((predictions, batch["labels"]))
                 metric.add_batch(
                     predictions=predictions,
                     references=references,
@@ -234,21 +240,20 @@ def training_function(config, args):
             with torch.no_grad():
                 outputs = model(**batch)
             predictions = outputs.logits
-            predictions, references = accelerator.gather((predictions, batch["labels"]))
+            predictions, references = accelerator.gather_for_metrics((predictions, batch["labels"]))
             fold_predictions.append(predictions.cpu())
-            metric.add_batch(
-                predictions=predictions.argmax(dim=-1),
-                references=references,
-            )
-        test_metric = metric.compute()
+            if i == 0:
+                # We need all of the test predictions
+                test_references.append(references.cpu())
         # Use accelerator.print to print only on the main process.
         test_predictions.append(torch.cat(fold_predictions, dim=0))
         # We now need to release all our memory and get rid of the current model, optimizer, etc
-        accelerator.free_memory()
+        model, optimizer = accelerator.free_memory(model, optimizer)
     # New Code #
     # Finally we check the accuracy of our folded results:
-    preds = torch.stack(test_predictions, dim=0).sum(dim=0).div(int(config["n_splits"])).argmax(dim=-1)
-    test_metric = metric.compute(predictions=preds, references=test_labels)
+    test_references = torch.cat(test_references, dim=0)
+    preds = torch.stack(test_predictions, dim=0).sum(dim=0).div(int(args.num_folds)).argmax(dim=-1)
+    test_metric = metric.compute(predictions=preds, references=test_references)
     accelerator.print("Average test metrics from all folds:", test_metric)
 
 
@@ -257,8 +262,8 @@ def main():
     parser.add_argument(
         "--mixed_precision",
         type=str,
-        default="no",
-        choices=["no", "fp16", "bf16"],
+        default=None,
+        choices=["no", "fp16", "bf16", "fp8"],
         help="Whether to use mixed precision. Choose"
         "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
         "and an Nvidia Ampere GPU.",
@@ -267,7 +272,7 @@ def main():
     # New Code #
     parser.add_argument("--num_folds", type=int, default=3, help="The number of splits to perform across the dataset")
     args = parser.parse_args()
-    config = {"lr": 2e-5, "num_epochs": 3, "correct_bias": True, "seed": 42, "batch_size": 16}
+    config = {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
     training_function(config, args)
 
 

examples/by_feature/ddp_comm_hook.py

@@ -0,0 +1,231 @@
+# Copyright 2021 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 argparse
+import os
+
+import evaluate
+import torch
+from datasets import load_dataset
+from torch.optim import AdamW
+from torch.utils.data import DataLoader
+from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
+
+from accelerate import Accelerator, DistributedType
+from accelerate.utils import DDPCommunicationHookType, DistributedDataParallelKwargs
+
+
+########################################################################
+# This is a fully working simple example to use Accelerate
+# and perform ddp communication hook
+#
+# This example trains a Bert base model on GLUE MRPC
+# in any of the following settings (with the same script):
+#   - single CPU or single GPU
+#   - multi GPUS (using PyTorch distributed mode)
+#   - (multi) TPUs
+#   - fp16 (mixed-precision) or fp32 (normal precision)
+#
+# To run it in each of these various modes, follow the instructions
+# in the readme for examples:
+# https://github.com/huggingface/accelerate/tree/main/examples
+#
+########################################################################
+
+
+MAX_GPU_BATCH_SIZE = 16
+EVAL_BATCH_SIZE = 32
+
+
+def get_dataloaders(accelerator: Accelerator, batch_size: int = 16):
+    """
+    Creates a set of `DataLoader`s for the `glue` dataset,
+    using "bert-base-cased" as the tokenizer.
+
+    Args:
+        accelerator (`Accelerator`):
+            An `Accelerator` object
+        batch_size (`int`, *optional*):
+            The batch size for the train and validation DataLoaders.
+    """
+    tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
+    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:
+    with accelerator.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):
+        # On TPU it's best to pad everything to the same length or training will be very slow.
+        max_length = 128 if accelerator.distributed_type == DistributedType.XLA else None
+        # When using mixed precision we want round multiples of 8/16
+        if accelerator.mixed_precision == "fp8":
+            pad_to_multiple_of = 16
+        elif accelerator.mixed_precision != "no":
+            pad_to_multiple_of = 8
+        else:
+            pad_to_multiple_of = None
+
+        return tokenizer.pad(
+            examples,
+            padding="longest",
+            max_length=max_length,
+            pad_to_multiple_of=pad_to_multiple_of,
+            return_tensors="pt",
+        )
+
+    # Instantiate dataloaders.
+    train_dataloader = DataLoader(
+        tokenized_datasets["train"], shuffle=True, collate_fn=collate_fn, batch_size=batch_size
+    )
+    eval_dataloader = DataLoader(
+        tokenized_datasets["validation"], shuffle=False, collate_fn=collate_fn, batch_size=EVAL_BATCH_SIZE
+    )
+
+    return train_dataloader, eval_dataloader
+
+
+# For testing only
+if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
+    from accelerate.test_utils.training import mocked_dataloaders
+
+    get_dataloaders = mocked_dataloaders  # noqa: F811
+
+
+def training_function(config, args):
+    # For testing only
+    if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
+        config["num_epochs"] = 2
+    # New Code #
+    ddp_comm_hook_type = DDPCommunicationHookType(args.ddp_comm_hook)
+    ddp_comm_wrapper = DDPCommunicationHookType(args.ddp_comm_wrapper)
+    ddp_kwargs = DistributedDataParallelKwargs(comm_hook=ddp_comm_hook_type, comm_wrapper=ddp_comm_wrapper)
+    # Initialize accelerator
+    accelerator = Accelerator(cpu=args.cpu, mixed_precision=args.mixed_precision, kwargs_handlers=[ddp_kwargs])
+    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
+    lr = config["lr"]
+    num_epochs = int(config["num_epochs"])
+    seed = int(config["seed"])
+    batch_size = int(config["batch_size"])
+
+    metric = evaluate.load("glue", "mrpc")
+
+    set_seed(seed)
+    train_dataloader, eval_dataloader = get_dataloaders(accelerator, batch_size)
+    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
+    model = AutoModelForSequenceClassification.from_pretrained("bert-base-cased", return_dict=True)
+
+    # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
+    # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
+    # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
+    model = model.to(accelerator.device)
+
+    # Instantiate optimizer
+    optimizer = AdamW(params=model.parameters(), lr=lr)
+
+    # Instantiate scheduler
+    lr_scheduler = get_linear_schedule_with_warmup(
+        optimizer=optimizer,
+        num_warmup_steps=100,
+        num_training_steps=(len(train_dataloader) * num_epochs),
+    )
+
+    # Prepare everything
+    # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
+    # prepare method.
+    model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = accelerator.prepare(
+        model, optimizer, train_dataloader, eval_dataloader, lr_scheduler
+    )
+
+    # Now we train the model
+    for epoch in range(num_epochs):
+        model.train()
+        for step, batch in enumerate(train_dataloader):
+            # We could avoid this line since we set the accelerator with `device_placement=True`.
+            batch.to(accelerator.device)
+            # We use the new `accumulate` context manager to perform gradient accumulation
+            with accelerator.accumulate(model):
+                output = model(**batch)
+                loss = output.loss
+                accelerator.backward(loss)
+                optimizer.step()
+                lr_scheduler.step()
+                optimizer.zero_grad()
+
+        model.eval()
+        for step, batch in enumerate(eval_dataloader):
+            # We could avoid this line since we set the accelerator with `device_placement=True`.
+            batch.to(accelerator.device)
+            with torch.no_grad():
+                outputs = model(**batch)
+            predictions = outputs.logits.argmax(dim=-1)
+            predictions, references = accelerator.gather_for_metrics((predictions, batch["labels"]))
+            metric.add_batch(
+                predictions=predictions,
+                references=references,
+            )
+
+        eval_metric = metric.compute()
+        # Use accelerator.print to print only on the main process.
+        accelerator.print(f"epoch {epoch}:", eval_metric)
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Simple example of training script.")
+    parser.add_argument(
+        "--mixed_precision",
+        type=str,
+        default=None,
+        choices=["no", "fp16", "bf16", "fp8"],
+        help="Whether to use mixed precision. Choose"
+        "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
+        "and an Nvidia Ampere GPU.",
+    )
+    # New Code #
+    parser.add_argument(
+        "--ddp_comm_hook",
+        type=str,
+        default="no",
+        choices=["no", "fp16", "bf16", "power_sgd", "batched_power_sgd"],
+        help="DDP Communication hook to use. Choose between `no`, `fp16`, `bf16`, `power_sgd`, and `batched_power_sgd`.",
+    )
+    # New Code #
+    parser.add_argument(
+        "--ddp_comm_wrapper",
+        type=str,
+        default="no",
+        choices=["no", "fp16", "bf16"],
+        help="DDP Communication wrapper to use. Choose between `no`, `fp16`, and `bf16`.",
+    )
+    parser.add_argument("--cpu", action="store_true", help="If passed, will train on the CPU.")
+    args = parser.parse_args()
+    config = {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
+    training_function(config, args)
+
+
+if __name__ == "__main__":
+    main()

examples/by_feature/deepspeed_with_config_support.py

@@ -0,0 +1,722 @@
+#!/usr/bin/env python
+# Copyright 2022 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.
+"""
+Fine-tuning the library models for causal language modeling (GPT, GPT-2, CTRL, ...)
+on a text file or a dataset without using HuggingFace Trainer.
+
+Here is the full list of checkpoints on the hub that can be fine-tuned by this script:
+https://huggingface.co/models?filter=text-generation
+"""
+# You can also adapt this script on your own causal language modeling task. Pointers for this are left as comments.
+
+import argparse
+import json
+import logging
+import math
+import os
+import random
+from itertools import chain
+from pathlib import Path
+
+import datasets
+import torch
+import transformers
+from datasets import load_dataset
+from huggingface_hub import HfApi
+from torch.utils.data import DataLoader
+from tqdm.auto import tqdm
+from transformers import (
+    CONFIG_MAPPING,
+    MODEL_MAPPING,
+    AutoConfig,
+    AutoModelForCausalLM,
+    AutoTokenizer,
+    SchedulerType,
+    default_data_collator,
+    get_scheduler,
+)
+from transformers.utils.versions import require_version
+
+from accelerate import Accelerator, DistributedType
+from accelerate.logging import get_logger
+from accelerate.utils import DummyOptim, DummyScheduler, set_seed
+
+
+logger = get_logger(__name__)
+
+require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/language-modeling/requirements.txt")
+
+MODEL_CONFIG_CLASSES = list(MODEL_MAPPING.keys())
+MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Finetune a transformers model on a causal language modeling task")
+    parser.add_argument(
+        "--dataset_name",
+        type=str,
+        default=None,
+        help="The name of the dataset to use (via the datasets library).",
+    )
+    parser.add_argument(
+        "--dataset_config_name",
+        type=str,
+        default=None,
+        help="The configuration name of the dataset to use (via the datasets library).",
+    )
+    parser.add_argument(
+        "--train_file", type=str, default=None, help="A csv or a json file containing the training data."
+    )
+    parser.add_argument(
+        "--validation_file", type=str, default=None, help="A csv or a json file containing the validation data."
+    )
+    parser.add_argument(
+        "--validation_split_percentage",
+        default=5,
+        help="The percentage of the train set used as validation set in case there's no validation split",
+    )
+    parser.add_argument(
+        "--model_name_or_path",
+        type=str,
+        help="Path to pretrained model or model identifier from huggingface.co/models.",
+        required=False,
+    )
+    parser.add_argument(
+        "--config_name",
+        type=str,
+        default=None,
+        help="Pretrained config name or path if not the same as model_name",
+    )
+    parser.add_argument(
+        "--tokenizer_name",
+        type=str,
+        default=None,
+        help="Pretrained tokenizer name or path if not the same as model_name",
+    )
+    parser.add_argument(
+        "--use_slow_tokenizer",
+        action="store_true",
+        help="If passed, will use a slow tokenizer (not backed by the 🤗 Tokenizers library).",
+    )
+    parser.add_argument(
+        "--per_device_train_batch_size",
+        type=int,
+        default=8,
+        help="Batch size (per device) for the training dataloader.",
+    )
+    parser.add_argument(
+        "--per_device_eval_batch_size",
+        type=int,
+        default=8,
+        help="Batch size (per device) for the evaluation dataloader.",
+    )
+    parser.add_argument(
+        "--learning_rate",
+        type=float,
+        default=5e-5,
+        help="Initial learning rate (after the potential warmup period) to use.",
+    )
+    parser.add_argument("--weight_decay", type=float, default=0.0, help="Weight decay to use.")
+    parser.add_argument("--num_train_epochs", type=int, default=3, help="Total number of training epochs to perform.")
+    parser.add_argument(
+        "--max_train_steps",
+        type=int,
+        default=None,
+        help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
+    )
+    parser.add_argument(
+        "--gradient_accumulation_steps",
+        type=int,
+        default=1,
+        help="Number of updates steps to accumulate before performing a backward/update pass.",
+    )
+    parser.add_argument(
+        "--lr_scheduler_type",
+        type=SchedulerType,
+        default="linear",
+        help="The scheduler type to use.",
+        choices=["linear", "cosine", "cosine_with_restarts", "polynomial", "constant", "constant_with_warmup"],
+    )
+    parser.add_argument(
+        "--num_warmup_steps", type=int, default=0, help="Number of steps for the warmup in the lr scheduler."
+    )
+    parser.add_argument("--output_dir", type=str, default=None, help="Where to store the final model.")
+    parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
+    parser.add_argument(
+        "--model_type",
+        type=str,
+        default=None,
+        help="Model type to use if training from scratch.",
+        choices=MODEL_TYPES,
+    )
+    parser.add_argument(
+        "--block_size",
+        type=int,
+        default=None,
+        help=(
+            "Optional input sequence length after tokenization. The training dataset will be truncated in block of"
+            " this size for training. Default to the model max input length for single sentence inputs (take into"
+            " account special tokens)."
+        ),
+    )
+    parser.add_argument(
+        "--preprocessing_num_workers",
+        type=int,
+        default=None,
+        help="The number of processes to use for the preprocessing.",
+    )
+    parser.add_argument(
+        "--overwrite_cache", type=bool, default=False, help="Overwrite the cached training and evaluation sets"
+    )
+    parser.add_argument(
+        "--no_keep_linebreaks", action="store_true", help="Do not keep line breaks when using TXT files."
+    )
+    parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
+    parser.add_argument(
+        "--hub_model_id", type=str, help="The name of the repository to keep in sync with the local `output_dir`."
+    )
+    parser.add_argument("--hub_token", type=str, help="The token to use to push to the Model Hub.")
+    parser.add_argument(
+        "--checkpointing_steps",
+        type=str,
+        default=None,
+        help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.",
+    )
+    parser.add_argument(
+        "--resume_from_checkpoint",
+        type=str,
+        default=None,
+        help="If the training should continue from a checkpoint folder.",
+    )
+    # New Code #
+    # Whether to load the best model at the end of training
+    parser.add_argument(
+        "--load_best_model",
+        action="store_true",
+        help="Whether to load the best model at the end of training",
+    )
+    parser.add_argument(
+        "--with_tracking",
+        action="store_true",
+        help="Whether to enable experiment trackers for logging.",
+    )
+    parser.add_argument(
+        "--report_to",
+        type=str,
+        default="all",
+        help=(
+            'The integration to report the results and logs to. Supported platforms are `"tensorboard"`,'
+            ' `"wandb"`, `"comet_ml"`, and `"dvclive"`. Use `"all"` (default) to report to all integrations.'
+            "Only applicable when `--with_tracking` is passed."
+        ),
+    )
+    args = parser.parse_args()
+
+    # Sanity checks
+    if args.dataset_name is None and args.train_file is None and args.validation_file is None:
+        raise ValueError("Need either a dataset name or a training/validation file.")
+    else:
+        if args.train_file is not None:
+            extension = args.train_file.split(".")[-1]
+            assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, json or txt file."
+        if args.validation_file is not None:
+            extension = args.validation_file.split(".")[-1]
+            assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, json or txt file."
+
+    if args.push_to_hub:
+        assert args.output_dir is not None, "Need an `output_dir` to create a repo when `--push_to_hub` is passed."
+
+    return args
+
+
+# New Code #
+def evaluate(args, model, eval_dataloader, accelerator, eval_dataset):
+    model.eval()
+    losses = []
+    for step, batch in enumerate(eval_dataloader):
+        with torch.no_grad():
+            outputs = model(**batch)
+
+        loss = outputs.loss
+        losses.append(accelerator.gather_for_metrics(loss.repeat(args.per_device_eval_batch_size)))
+
+    losses = torch.cat(losses)
+    try:
+        eval_loss = torch.mean(losses)
+        perplexity = math.exp(eval_loss)
+    except OverflowError:
+        perplexity = float("inf")
+    return perplexity, eval_loss
+
+
+def main():
+    args = parse_args()
+
+    # Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
+    # If we're using tracking, we also need to initialize it here and it will by default pick up all supported trackers
+    # in the environment
+
+    # when using DeepSpeed, the `gradient_accumulation_steps` is properly set from the DeepSpeed plugin/config
+    # or from `accelerate launch` via `--gradient_accumulation_steps`  else
+    # defaulting to the passed `args.gradient_accumulation_steps`
+    accelerator = (
+        Accelerator(
+            log_with=args.report_to,
+            project_dir=args.output_dir,
+            gradient_accumulation_steps=args.gradient_accumulation_steps,
+        )
+        if args.with_tracking
+        else Accelerator(gradient_accumulation_steps=args.gradient_accumulation_steps)
+    )
+
+    # Make one log on every process with the configuration for debugging.
+    logging.basicConfig(
+        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
+        datefmt="%m/%d/%Y %H:%M:%S",
+        level=logging.INFO,
+    )
+    logger.info(accelerator.state, main_process_only=False)
+    if accelerator.is_local_main_process:
+        datasets.utils.logging.set_verbosity_warning()
+        transformers.utils.logging.set_verbosity_info()
+    else:
+        datasets.utils.logging.set_verbosity_error()
+        transformers.utils.logging.set_verbosity_error()
+
+    # If passed along, set the training seed now.
+    if args.seed is not None:
+        set_seed(args.seed)
+
+    # Handle the repository creation
+    if accelerator.is_main_process:
+        if args.push_to_hub:
+            api = HfApi(token=args.hub_token)
+
+            # Create repo (repo_name from args or inferred)
+            repo_name = args.hub_model_id
+            if repo_name is None:
+                repo_name = Path(args.output_dir).absolute().name
+            repo_id = api.create_repo(repo_name, exist_ok=True).repo_id
+
+            with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
+                if "step_*" not in gitignore:
+                    gitignore.write("step_*\n")
+                if "epoch_*" not in gitignore:
+                    gitignore.write("epoch_*\n")
+        elif args.output_dir is not None:
+            os.makedirs(args.output_dir, exist_ok=True)
+    accelerator.wait_for_everyone()
+
+    # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
+    # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
+    # (the dataset will be downloaded automatically from the datasets Hub).
+    #
+    # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
+    # 'text' is found. You can easily tweak this behavior (see below).
+    #
+    # In distributed training, the load_dataset function guarantee that only one local process can concurrently
+    # download the dataset.
+    if args.dataset_name is not None:
+        # Downloading and loading a dataset from the hub.
+        raw_datasets = load_dataset(args.dataset_name, args.dataset_config_name)
+        if "validation" not in raw_datasets.keys():
+            raw_datasets["validation"] = load_dataset(
+                args.dataset_name,
+                args.dataset_config_name,
+                split=f"train[:{args.validation_split_percentage}%]",
+            )
+            raw_datasets["train"] = load_dataset(
+                args.dataset_name,
+                args.dataset_config_name,
+                split=f"train[{args.validation_split_percentage}%:]",
+            )
+    else:
+        data_files = {}
+        dataset_args = {}
+        if args.train_file is not None:
+            data_files["train"] = args.train_file
+        if args.validation_file is not None:
+            data_files["validation"] = args.validation_file
+        extension = args.train_file.split(".")[-1]
+        if extension == "txt":
+            extension = "text"
+            dataset_args["keep_linebreaks"] = not args.no_keep_linebreaks
+        raw_datasets = load_dataset(extension, data_files=data_files, **dataset_args)
+        # If no validation data is there, validation_split_percentage will be used to divide the dataset.
+        if "validation" not in raw_datasets.keys():
+            raw_datasets["validation"] = load_dataset(
+                extension,
+                data_files=data_files,
+                split=f"train[:{args.validation_split_percentage}%]",
+                **dataset_args,
+            )
+            raw_datasets["train"] = load_dataset(
+                extension,
+                data_files=data_files,
+                split=f"train[{args.validation_split_percentage}%:]",
+                **dataset_args,
+            )
+
+    # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
+    # https://huggingface.co/docs/datasets/loading_datasets.html.
+
+    # Load pretrained model and tokenizer
+    #
+    # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
+    # download model & vocab.
+    if args.config_name:
+        config = AutoConfig.from_pretrained(args.config_name)
+    elif args.model_name_or_path:
+        config = AutoConfig.from_pretrained(args.model_name_or_path)
+    else:
+        config = CONFIG_MAPPING[args.model_type]()
+        logger.warning("You are instantiating a new config instance from scratch.")
+
+    if args.tokenizer_name:
+        tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name, use_fast=not args.use_slow_tokenizer)
+    elif args.model_name_or_path:
+        tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path, use_fast=not args.use_slow_tokenizer)
+    else:
+        raise ValueError(
+            "You are instantiating a new tokenizer from scratch. This is not supported by this script."
+            "You can do it from another script, save it, and load it from here, using --tokenizer_name."
+        )
+
+    if args.model_name_or_path:
+        model = AutoModelForCausalLM.from_pretrained(
+            args.model_name_or_path,
+            from_tf=bool(".ckpt" in args.model_name_or_path),
+            config=config,
+        )
+    else:
+        logger.info("Training new model from scratch")
+        model = AutoModelForCausalLM.from_config(config)
+
+    model.resize_token_embeddings(len(tokenizer))
+
+    # Preprocessing the datasets.
+    # First we tokenize all the texts.
+    column_names = raw_datasets["train"].column_names
+    text_column_name = "text" if "text" in column_names else column_names[0]
+
+    def tokenize_function(examples):
+        return tokenizer(examples[text_column_name])
+
+    with accelerator.main_process_first():
+        tokenized_datasets = raw_datasets.map(
+            tokenize_function,
+            batched=True,
+            num_proc=args.preprocessing_num_workers,
+            remove_columns=column_names,
+            load_from_cache_file=not args.overwrite_cache,
+            desc="Running tokenizer on dataset",
+        )
+
+    if args.block_size is None:
+        block_size = tokenizer.model_max_length
+        if block_size > 1024:
+            logger.warning(
+                f"The tokenizer picked seems to have a very large `model_max_length` ({tokenizer.model_max_length}). "
+                "Picking 1024 instead. You can change that default value by passing --block_size xxx."
+            )
+        block_size = 1024
+    else:
+        if args.block_size > tokenizer.model_max_length:
+            logger.warning(
+                f"The block_size passed ({args.block_size}) is larger than the maximum length for the model"
+                f"({tokenizer.model_max_length}). Using block_size={tokenizer.model_max_length}."
+            )
+        block_size = min(args.block_size, tokenizer.model_max_length)
+
+    # Main data processing function that will concatenate all texts from our dataset and generate chunks of block_size.
+    def group_texts(examples):
+        # Concatenate all texts.
+        concatenated_examples = {k: list(chain(*examples[k])) for k in examples.keys()}
+        total_length = len(concatenated_examples[list(examples.keys())[0]])
+        # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
+        # customize this part to your needs.
+        if total_length >= block_size:
+            total_length = (total_length // block_size) * block_size
+        # Split by chunks of max_len.
+        result = {
+            k: [t[i : i + block_size] for i in range(0, total_length, block_size)]
+            for k, t in concatenated_examples.items()
+        }
+        result["labels"] = result["input_ids"].copy()
+        return result
+
+    # Note that with `batched=True`, this map processes 1,000 texts together, so group_texts throws away a remainder
+    # for each of those groups of 1,000 texts. You can adjust that batch_size here but a higher value might be slower
+    # to preprocess.
+    #
+    # To speed up this part, we use multiprocessing. See the documentation of the map method for more information:
+    # https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.map
+
+    with accelerator.main_process_first():
+        lm_datasets = tokenized_datasets.map(
+            group_texts,
+            batched=True,
+            num_proc=args.preprocessing_num_workers,
+            load_from_cache_file=not args.overwrite_cache,
+            desc=f"Grouping texts in chunks of {block_size}",
+        )
+
+    train_dataset = lm_datasets["train"]
+    eval_dataset = lm_datasets["validation"]
+
+    # Log a few random samples from the training set:
+    for index in random.sample(range(len(train_dataset)), 3):
+        logger.info(f"Sample {index} of the training set: {train_dataset[index]}.")
+
+    # DataLoaders creation:
+    train_dataloader = DataLoader(
+        train_dataset, shuffle=True, collate_fn=default_data_collator, batch_size=args.per_device_train_batch_size
+    )
+    eval_dataloader = DataLoader(
+        eval_dataset, collate_fn=default_data_collator, batch_size=args.per_device_eval_batch_size
+    )
+
+    # Optimizer
+    # Split weights in two groups, one with weight decay and the other not.
+    no_decay = ["bias", "LayerNorm.weight"]
+    optimizer_grouped_parameters = [
+        {
+            "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],
+            "weight_decay": args.weight_decay,
+        },
+        {
+            "params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)],
+            "weight_decay": 0.0,
+        },
+    ]
+    # New Code #
+    # Creates Dummy Optimizer if `optimizer` was specified in the config file else creates Adam Optimizer
+    optimizer_cls = (
+        torch.optim.AdamW
+        if accelerator.state.deepspeed_plugin is None
+        or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
+        else DummyOptim
+    )
+    optimizer = optimizer_cls(optimizer_grouped_parameters, lr=args.learning_rate)
+
+    # On TPU, the tie weights in our model have been disconnected, so we need to restore the ties.
+    if accelerator.distributed_type == DistributedType.XLA:
+        model.tie_weights()
+
+    # Scheduler and math around the number of training steps.
+    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / accelerator.gradient_accumulation_steps)
+    overrode_max_train_steps = False
+    if args.max_train_steps is None:
+        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
+        overrode_max_train_steps = True
+    else:
+        args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
+
+    # New Code #
+    # Creates Dummy Scheduler if `scheduler` was specified in the config file else creates `args.lr_scheduler_type` Scheduler
+    if (
+        accelerator.state.deepspeed_plugin is None
+        or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
+    ):
+        lr_scheduler = get_scheduler(
+            name=args.lr_scheduler_type,
+            optimizer=optimizer,
+            num_warmup_steps=args.num_warmup_steps,
+            num_training_steps=args.max_train_steps,
+        )
+    else:
+        lr_scheduler = DummyScheduler(
+            optimizer, total_num_steps=args.max_train_steps, warmup_num_steps=args.num_warmup_steps
+        )
+
+    # Prepare everything with our `accelerator`.
+    model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = accelerator.prepare(
+        model, optimizer, train_dataloader, eval_dataloader, lr_scheduler
+    )
+
+    # We need to recalculate our total training steps as the size of the training dataloader may have changed.
+    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / accelerator.gradient_accumulation_steps)
+    if overrode_max_train_steps:
+        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
+    # Afterwards we recalculate our number of training epochs
+    args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
+
+    # Figure out how many steps we should save the Accelerator states
+    checkpointing_steps = args.checkpointing_steps
+    if checkpointing_steps is not None and checkpointing_steps.isdigit():
+        checkpointing_steps = int(checkpointing_steps)
+
+    # We need to initialize the trackers we use, and also store our configuration.
+    # The trackers initializes automatically on the main process.
+    if args.with_tracking:
+        experiment_config = vars(args)
+        # TensorBoard cannot log Enums, need the raw value
+        experiment_config["lr_scheduler_type"] = experiment_config["lr_scheduler_type"].value
+        accelerator.init_trackers("clm_no_trainer", experiment_config)
+
+    # Train!
+    total_batch_size = (
+        args.per_device_train_batch_size * accelerator.num_processes * accelerator.gradient_accumulation_steps
+    )
+
+    logger.info("***** Running training *****")
+    logger.info(f"  Num examples = {len(train_dataset)}")
+    logger.info(f"  Num Epochs = {args.num_train_epochs}")
+    logger.info(f"  Instantaneous batch size per device = {args.per_device_train_batch_size}")
+    logger.info(f"  Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
+    logger.info(f"  Gradient Accumulation steps = {accelerator.gradient_accumulation_steps}")
+    logger.info(f"  Total optimization steps = {args.max_train_steps}")
+    # Only show the progress bar once on each machine.
+    progress_bar = tqdm(range(args.max_train_steps), disable=not accelerator.is_local_main_process)
+    completed_steps = 0
+    starting_epoch = 0
+    best_metric = None
+    best_metric_checkpoint = None
+
+    # Potentially load in the weights and states from a previous save
+    if args.resume_from_checkpoint:
+        accelerator.load_state(args.resume_from_checkpoint)
+        accelerator.print(f"Resumed from checkpoint: {args.resume_from_checkpoint}")
+        path = os.path.basename(args.resume_from_checkpoint)
+        training_difference = os.path.splitext(path)[0]
+
+        if "epoch" in training_difference:
+            starting_epoch = int(training_difference.replace("epoch_", "")) + 1
+            resume_step = None
+            completed_steps = starting_epoch * num_update_steps_per_epoch
+        else:
+            resume_step = int(training_difference.replace("step_", ""))
+            starting_epoch = resume_step // num_update_steps_per_epoch
+            resume_step -= starting_epoch * num_update_steps_per_epoch
+            completed_steps = resume_step
+
+    # update progress bar if resumed from checkpoint
+    progress_bar.update(completed_steps)
+
+    for epoch in range(starting_epoch, args.num_train_epochs):
+        model.train()
+        if args.with_tracking:
+            total_loss = 0
+
+        # skip new `skip_first_batches` to skip the batches when resuming from ckpt
+        if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
+            # We need to skip steps until we reach the resumed step
+            active_dataloader = accelerator.skip_first_batches(train_dataloader, resume_step)
+        else:
+            # After the first iteration though, we need to go back to the original dataloader
+            active_dataloader = train_dataloader
+        for step, batch in enumerate(active_dataloader):
+            # In particular, DeepSpeed handles `gradient_accumulation` via `DeepSpeedEngine`.
+            # Below, we use `accelerator.accumulate` if the user
+            # wants to switch to other approaches such as plain DDP, PyTorch FSDP ...
+            # This avoids having to change any code as things are all handled across different distributed setups.
+            with accelerator.accumulate(model):
+                outputs = model(**batch)
+                loss = outputs.loss
+                accelerator.backward(loss)
+                optimizer.step()
+                lr_scheduler.step()
+                optimizer.zero_grad()
+
+                if accelerator.sync_gradients:
+                    progress_bar.update(1)
+                    completed_steps += 1
+
+            # We keep track of the loss at each epoch
+            if args.with_tracking:
+                step_loss = accelerator.reduce(loss.detach().clone()).item()
+                total_loss += step_loss
+
+            if isinstance(checkpointing_steps, int):
+                if completed_steps % checkpointing_steps == 0:
+                    output_dir = f"step_{completed_steps}"
+                    if args.output_dir is not None:
+                        output_dir = os.path.join(args.output_dir, output_dir)
+                    accelerator.save_state(output_dir)
+            if completed_steps >= args.max_train_steps:
+                break
+
+        perplexity, eval_loss = evaluate(args, model, eval_dataloader, accelerator, eval_dataset)
+        logger.info(f"epoch {epoch}: perplexity: {perplexity} eval_loss: {eval_loss}")
+
+        if args.with_tracking:
+            accelerator.log(
+                {
+                    "perplexity": perplexity,
+                    "eval_loss": eval_loss,
+                    "train_loss": total_loss / len(train_dataloader),
+                    "epoch": epoch,
+                    "step": completed_steps,
+                },
+                step=completed_steps,
+            )
+
+        if isinstance(checkpointing_steps, str) and checkpointing_steps == "epoch":
+            accelerator.save_state(os.path.join(args.output_dir, f"epoch_{epoch}"))
+
+        # New Code #
+        # Tracks the best checkpoint and best metric
+        if best_metric is None or best_metric > perplexity:
+            best_metric = perplexity
+            best_metric_checkpoint = os.path.join(args.output_dir, "best_checkpoint")
+            accelerator.save_state(best_metric_checkpoint)
+            accelerator.print(f"New best metric: {best_metric} at epoch {epoch}")
+            accelerator.print(f"best_metric_checkpoint: {best_metric_checkpoint}")
+
+    # New Code #
+    # Loads the best checkpoint after the training is finished
+    if args.load_best_model:
+        accelerator.load_state(best_metric_checkpoint)
+
+    # New Code #
+    # Evaluates using the best checkpoint
+    perplexity, eval_loss = evaluate(args, model, eval_dataloader, accelerator, eval_dataset)
+    logger.info(f"Best model metrics: perplexity: {perplexity} eval_loss: {eval_loss}")
+    if perplexity != best_metric:
+        raise AssertionError(
+            f"Best metric {best_metric} does not match the metric {perplexity} of the loaded best model."
+        )
+
+    if args.output_dir is not None:
+        accelerator.wait_for_everyone()
+        unwrapped_model = accelerator.unwrap_model(model)
+
+        # New Code #
+        # Saves the whole/unpartitioned fp16 model when in ZeRO Stage-3 to the output directory if
+        # `stage3_gather_16bit_weights_on_model_save` is True in DeepSpeed Config file or
+        # `zero3_save_16bit_model` is True in DeepSpeed Plugin.
+        # For Zero Stages 1 and 2, models are saved as usual in the output directory.
+        # The model name saved is `pytorch_model.bin`
+        unwrapped_model.save_pretrained(
+            args.output_dir,
+            is_main_process=accelerator.is_main_process,
+            save_function=accelerator.save,
+            state_dict=accelerator.get_state_dict(model),
+        )
+        if accelerator.is_main_process:
+            tokenizer.save_pretrained(args.output_dir)
+            if args.push_to_hub:
+                api.upload_folder(
+                    repo_id=repo_id,
+                    folder_path=args.output_dir,
+                    commit_message="End of training",
+                )
+
+        with open(os.path.join(args.output_dir, "all_results.json"), "w") as f:
+            json.dump({"perplexity": perplexity, "eval_loss": eval_loss.item()}, f)
+
+
+if __name__ == "__main__":
+    main()