Release: v0.21.0

.github/ISSUE_TEMPLATE/bug-report.yml

@@ -1,12 +1,6 @@
 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:

.github/PULL_REQUEST_TEMPLATE.md

@@ -1,47 +0,0 @@
-# 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: @pacman100
-- DeepSpeed: @pacman100
-- Command Line Interface: @muellerzr
-- Documentation: @muellerzr
-- Core parts of the library: @muellerzr @BenjaminBossan
-- Maintained examples: @muellerzr or @pacman100
-
- -->

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

@@ -15,46 +15,50 @@ jobs:
     outputs:
       version: ${{ steps.step1.outputs.version }}
     steps:
-      - uses: actions/checkout@v3.1.0
+      - uses: actions/checkout@v3
       - id: step1
         run: echo "version=$(python setup.py --version)" >> $GITHUB_OUTPUT
 
   version-cpu:
     name: "Latest Accelerate CPU [version]"
-    runs-on: [self-hosted, intel-cpu, 8-cpu, ci]
+    runs-on: ubuntu-latest
     needs: get-version
     steps:
       - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v2
+        uses: docker/setup-buildx-action@v1
+      - name: Check out code
+        uses: actions/checkout@v2
       - name: Login to DockerHub
-        uses: docker/login-action@v2
+        uses: docker/login-action@v1
         with:
           username: ${{ secrets.DOCKERHUB_USERNAME }}
           password: ${{ secrets.DOCKERHUB_PASSWORD }}
 
       - name: Build and Push CPU
-        uses: docker/build-push-action@v4
+        uses: docker/build-push-action@v2
         with:
-          file: docker/accelerate-cpu/Dockerfile
+          context: ./docker/accelerate-cpu
           push: true
           tags: huggingface/accelerate-cpu:${{needs.get-version.outputs.version}}
 
   version-cuda:
     name: "Latest Accelerate GPU [version]"
-    runs-on: [self-hosted, single-gpu, nvidia-gpu, t4, ci]
+    runs-on: ubuntu-latest
     needs: get-version
     steps:
       - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v2
+        uses: docker/setup-buildx-action@v1
+      - name: Check out code
+        uses: actions/checkout@v2
       - name: Login to DockerHub
-        uses: docker/login-action@v2
+        uses: docker/login-action@v1
         with:
           username: ${{ secrets.DOCKERHUB_USERNAME }}
           password: ${{ secrets.DOCKERHUB_PASSWORD }}
 
       - name: Build and Push GPU
-        uses: docker/build-push-action@v4
+        uses: docker/build-push-action@v2
         with:
-          file: docker/accelerate-gpu/Dockerfile
+          context: ./docker/accelerate-gpu
           push: true
-          tags: huggingface/accelerate-gpu:${{needs.get-version.outputs.version}}
+          tags: huggingface/accelerate-gpu:${{needs.get-version.outputs.version}}

.github/workflows/build_and_run_tests.yml

@@ -22,7 +22,7 @@ jobs:
       
       - name: Get changed files
         id: changed-files
-        uses: tj-actions/changed-files@v41
+        uses: tj-actions/changed-files@v22.2
       
       - name: Was setup changed 
         id: was_changed
@@ -45,6 +45,6 @@ jobs:
     uses: ./.github/workflows/run_merge_tests.yml
 
   run-integration-tests:
-    needs: build-docker-containers
+    needs: run-merge-tests
     if: always()
     uses: ./.github/workflows/self_hosted_integration_tests.yml

.github/workflows/build_docker_images.yml

@@ -13,37 +13,42 @@ concurrency:
 jobs:
   latest-cpu:
     name: "Latest Accelerate CPU [dev]"
-    runs-on: [self-hosted, intel-cpu, 8-cpu, ci]
+    runs-on: ubuntu-latest
     steps:
       - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v2
+        uses: docker/setup-buildx-action@v1
+      - name: Check out code
+        uses: actions/checkout@v2
       - name: Login to DockerHub
-        uses: docker/login-action@v2
+        uses: docker/login-action@v1
         with:
           username: ${{ secrets.DOCKERHUB_USERNAME }}
           password: ${{ secrets.DOCKERHUB_PASSWORD }}
+
       - name: Build and Push CPU
-        uses: docker/build-push-action@v4
+        uses: docker/build-push-action@v2
         with:
-          file: docker/accelerate-cpu/Dockerfile          
+          context: ./docker/accelerate-cpu
           push: true
           tags: huggingface/accelerate-cpu
 
   latest-cuda:
     name: "Latest Accelerate GPU [dev]"
-    runs-on: [self-hosted, nvidia-gpu, t4, ci]
+    runs-on: ubuntu-latest
     steps:
       - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v2
+        uses: docker/setup-buildx-action@v1
+      - name: Check out code
+        uses: actions/checkout@v2
       - name: Login to DockerHub
-        uses: docker/login-action@v2
+        uses: docker/login-action@v1
         with:
           username: ${{ secrets.DOCKERHUB_USERNAME }}
           password: ${{ secrets.DOCKERHUB_PASSWORD }}
 
       - name: Build and Push GPU
-        uses: docker/build-push-action@v4
+        uses: docker/build-push-action@v2
         with:
-          file: docker/accelerate-gpu/Dockerfile          
+          context: ./docker/accelerate-gpu
           push: true
-          tags: huggingface/accelerate-gpu
+          tags: huggingface/accelerate-gpu

.github/workflows/build_documentation.yml

@@ -14,4 +14,5 @@ jobs:
       commit_sha: ${{ github.sha }}
       package: accelerate
     secrets:
+      token: ${{ secrets.HUGGINGFACE_PUSH }}
       hf_token: ${{ secrets.HF_DOC_BUILD_PUSH }}

.github/workflows/delete_doc_comment.yml

@@ -0,0 +1,14 @@
+name: Delete doc comment
+
+on:
+  workflow_run:
+    workflows: ["Delete doc comment trigger"]
+    types:
+      - completed
+
+
+jobs:
+  delete:
+    uses: huggingface/doc-builder/.github/workflows/delete_doc_comment.yml@main
+    secrets:
+      comment_bot_token: ${{ secrets.COMMENT_BOT_TOKEN }}

.github/workflows/delete_doc_comment_trigger.yml

@@ -0,0 +1,12 @@
+name: Delete doc comment trigger
+
+on:
+  pull_request:
+    types: [ closed ]
+
+
+jobs:
+  delete:
+    uses: huggingface/doc-builder/.github/workflows/delete_doc_comment_trigger.yml@main
+    with:
+      pr_number: ${{ github.event.number }}

.github/workflows/integration_tests.yml

@@ -25,6 +25,11 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       fail-fast: false
+      matrix:
+        transformers-version: [
+          pypi,
+          github
+        ]
     steps:
     - uses: actions/checkout@v3.1.0
     - name: Set up python 3.8
@@ -42,6 +47,9 @@ jobs:
         cd ..
         git clone https://github.com/huggingface/transformers
         cd transformers
+        if [[ ${{ matrix.transformers-version }} = pypi ]]; then 
+          git checkout $(git describe --tags `git rev-list --tags --max-count=1`)
+        fi
         pip install .[torch,testing]
 
     - name: Show installed libraries

.github/workflows/nightly.yml

@@ -13,7 +13,7 @@ env:
 
 jobs:
   run_all_tests_single_gpu:
-    runs-on: [self-hosted, single-gpu, nvidia-gpu, t4, ci]
+    runs-on: [self-hosted, docker-gpu, multi-gpu]
     env:
       CUDA_VISIBLE_DEVICES: "0"
       TEST_TYPE: "single_gpu"
@@ -22,40 +22,36 @@ jobs:
       options: --gpus all --shm-size "16gb"
     defaults:
       run:
+        working-directory: accelerate/
         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 }};
+          git config --global --add safe.directory '*'
+          git fetch && git checkout ${{ github.sha }} 
           pip install -e . --no-deps
           pip install pytest-reportlog tabulate
 
       - 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_all_tests_multi_gpu:
-    runs-on: [self-hosted, multi-gpu, nvidia-gpu, t4, ci]
+    runs-on: [self-hosted, docker-gpu, multi-gpu]
     env:
       CUDA_VISIBLE_DEVICES: "0,1"
       TEST_TYPE: "multi_gpu"
@@ -64,19 +60,18 @@ jobs:
       options: --gpus all --shm-size "16gb"
     defaults:
       run:
+        working-directory: accelerate/
         shell: bash
     steps:
       - name: Update clone
         run: |
           source activate accelerate
-          git clone https://github.com/huggingface/accelerate;
-          cd accelerate;
-          git checkout ${{ github.sha }};
+          git config --global --add safe.directory '*'
+          git fetch && git checkout ${{ github.sha }}
           pip install -e . --no-deps
           pip install pytest-reportlog tabulate
 
       - name: Run core and big modeling tests on GPUs
-        working-directory: accelerate
         run: |
           source activate accelerate
           make test_core
@@ -84,22 +79,17 @@ jobs:
           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
@@ -107,5 +97,6 @@ jobs:
 
   
   run-integration-tests:
+    needs: [run_all_tests_single_gpu, run_all_tests_multi_gpu]
     if: always()
     uses: ./.github/workflows/self_hosted_integration_tests.yml

.github/workflows/quality.yml

@@ -6,7 +6,7 @@ jobs:
   quality:
     runs-on: ubuntu-latest
     steps:
-    - uses: actions/checkout@v3.1.0
+    - uses: actions/checkout@v2
     - name: Set up Python 3.8
       uses: actions/setup-python@v3
       with:

.github/workflows/run_merge_tests.yml

@@ -10,7 +10,7 @@ env:
 
 jobs:
   run_all_tests_single_gpu:
-    runs-on: [self-hosted, single-gpu, nvidia-gpu, t4, ci]
+    runs-on: [self-hosted, docker-gpu, multi-gpu]
     env:
       CUDA_VISIBLE_DEVICES: "0"
     container:
@@ -18,81 +18,69 @@ jobs:
       options: --gpus all --shm-size "16gb"
     defaults:
       run:
+        working-directory: accelerate/
         shell: bash
     steps:
-      - name: Install accelerate
+      - 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 .[testing,test_trackers] -U;
-          pip install pytest-reportlog tabulate  ;
+          source activate accelerate
+          git config --global --add safe.directory '*'
+          git fetch && git checkout ${{ github.sha }}
+          pip install -e .[testing,test_trackers] -U
+          pip install pytest-reportlog tabulate
 
-      - name: Run CLI tests (use make cli)
-        working-directory: accelerate
+      - name: Run CLI tests
         run: |
-          source activate accelerate;
+          source activate accelerate
           make test_cli
           
       - name: Run test on GPUs
-        working-directory: accelerate
-        if: always()
         run: |
-          source activate accelerate;
+          source activate accelerate
           make test
       - name: Run examples on GPUs
-        working-directory: accelerate
-        if: always()
         run: |
-          source activate accelerate;
-          pip uninstall comet_ml -y;
+          source activate accelerate
+          pip uninstall comet_ml -y
           make test_examples
 
       - name: Generate Report
-        working-directory: accelerate
         if: always()
         run: |
-          pip install tabulate;
+          pip install tabulate
           python utils/log_reports.py >> $GITHUB_STEP_SUMMARY
 
   run_all_tests_multi_gpu:
-    runs-on: [self-hosted, multi-gpu, nvidia-gpu, t4, ci]
-    env:
-      CUDA_VISIBLE_DEVICES: 0,1
+    runs-on: [self-hosted, docker-gpu, multi-gpu]
     container:
       image: huggingface/accelerate-gpu:latest
       options: --gpus all --shm-size "16gb"
     defaults:
       run:
+        working-directory: accelerate/
         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;
+          source activate accelerate
+          git config --global --add safe.directory '*'
+          git fetch && git checkout ${{ github.sha }}
+          pip install -e .[testing,test_trackers] -U
           pip install pytest-reportlog tabulate
 
       - name: Run test on GPUs
-        working-directory: accelerate
         run: |
-          source activate accelerate;
+          source activate accelerate
           make test
 
       - name: Run examples on GPUs
-        working-directory: accelerate
-        if: always()
         run: |
-          source activate accelerate;
-          pip uninstall comet_ml -y;
+          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
+          pip install tabulate
+          python utils/log_reports.py >> $GITHUB_STEP_SUMMARY

.github/workflows/self_hosted_integration_tests.yml

@@ -25,32 +25,37 @@ jobs:
     container:
       image: huggingface/accelerate-gpu:latest
       options: --gpus all --shm-size "16gb"
-    runs-on: [self-hosted, multi-gpu, nvidia-gpu, t4, ci]
+    runs-on: [self-hosted, docker-gpu, multi-gpu]
     strategy:
       fail-fast: false
       matrix:
+        transformers-version: [
+          pypi,
+          github
+        ]
         cuda_visible_devices: [
           "0", 
           "0,1"
         ]
     steps:
-      - name: Install transformers
-        run: |
+      - name: Update accelerate clone and pip install
+        working-directory: accelerate/
+        run: 
           source activate accelerate;
-          git clone https://github.com/huggingface/transformers --depth 1;
-          cd transformers;
-          pip install .[torch,deepspeed-testing];
-          cd ..;
+          git config --global --add safe.directory '*';
+          git checkout main && git fetch && git checkout ${{ github.sha }};
+          pip install -e .;
 
-      - name: Install accelerate
+      - name: Update transformers clone & pip install
+        working-directory: transformers/
         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 ..;
+          source activate accelerate
+          git config --global --add safe.directory '*'
+          git checkout main && git pull
+          if [[ ${{ matrix.transformers-version }} = pypi ]]; then 
+            git checkout $(git describe --tags `git rev-list --tags --max-count=1`)
+          fi
+          pip install .[torch,deepspeed-testing]
       
       - name: Show installed libraries
         run: |
@@ -71,45 +76,40 @@ jobs:
         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:latest
       options: --gpus all --shm-size "16gb"
-    runs-on: [self-hosted, multi-gpu, nvidia-gpu, t4, ci]
+    runs-on: [self-hosted, docker-gpu, multi-gpu]
     strategy:
       fail-fast: false
+      matrix:
+        skorch-version: [
+          pypi,
+          github
+        ]
     steps:
-      - name: Install accelerate
+      - name: Update accelerate clone and pip install
+        working-directory: accelerate/
         run: 
           source activate accelerate;
-          git clone https://github.com/huggingface/accelerate;
-          cd accelerate;
-          git checkout ${{ github.sha }};
-          pip install -e .[testing];
-          cd ..
+          git config --global --add safe.directory '*';
+          git checkout main && git fetch && git checkout ${{ github.sha }};
+          pip install -e .;
 
-      - name: Install skorch
+      - name: Update skorch clone & pip install
+        working-directory: 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
+          git checkout main && git pull
+          if [[ ${{ matrix.skorch-version }} = pypi ]]; then 
+            git checkout $(git describe --tags `git rev-list --tags --max-count=1`)
+          fi
           pip install .[testing]
           pip install flaky
 

.github/workflows/stale.yml

@@ -13,10 +13,10 @@ jobs:
     env:
       GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
     steps:
-    - uses: actions/checkout@v3.1.0
+    - uses: actions/checkout@v2
     
     - name: Setup Python
-      uses: actions/setup-python@v3
+      uses: actions/setup-python@v1
       with:
         python-version: 3.8
     

.pre-commit-config.yaml

@@ -1,13 +0,0 @@
-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

@@ -152,7 +152,7 @@ Follow these steps to start contributing:
    $ make test
    ```
 
-   `accelerate` relies on `ruff` to format its source code
+   `accelerate` relies on `black` and `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:
 
@@ -172,14 +172,6 @@ Follow these steps to start contributing:
    $ 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:
 
@@ -243,4 +235,4 @@ $ 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 utils
 
-check_dirs := .
+check_dirs := tests src examples benchmarks utils
 
 # Check that source code meets quality standards
 
@@ -12,17 +12,20 @@ extra_quality_checks:
 
 # this target runs checks on all files
 quality:
-	ruff check $(check_dirs)
-	ruff format --check $(check_dirs)
+	black --required-version 23 --check $(check_dirs)
+	ruff $(check_dirs)
 	doc-builder style src/accelerate docs/source --max_len 119 --check_only
 
 # Format source code automatically and check is there are any problems left that need manual fixing
 style:
-	ruff check $(check_dirs) --fix
-	ruff format $(check_dirs)
+	black --required-version 23 $(check_dirs)
+	ruff $(check_dirs) --fix
 	doc-builder style src/accelerate docs/source --max_len 119
 	
 # Run tests for the library
+test:
+	python -m pytest -s -v ./tests/ --ignore=./tests/test_examples.py $(if $(IS_GITHUB_CI),--report-log "$(PYTORCH_VERSION)_all.log",)
+
 test_big_modeling:
 	python -m pytest -s -v ./tests/test_big_modeling.py ./tests/test_modeling_utils.py $(if $(IS_GITHUB_CI),--report-log "$(PYTORCH_VERSION)_big_modeling.log",)
 
@@ -39,15 +42,6 @@ test_deepspeed:
 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:
-	$(MAKE) test_core
-	$(MAKE) test_cli
-	$(MAKE) test_big_modeling
-	$(MAKE) test_deepspeed
-	$(MAKE) test_fsdp
-
 test_examples:
 	python -m pytest -s -v ./tests/test_examples.py $(if $(IS_GITHUB_CI),--report-log "$(PYTORCH_VERSION)_examples.log",)
 

README.md

@@ -220,7 +220,6 @@ You shouldn't use 🤗 Accelerate if you don't want to write a training loop you
 
 If you like the simplicity of 🤗 Accelerate but would prefer a higher-level abstraction around its capabilities, some frameworks and libraries that are built on top of 🤗 Accelerate are listed below:
 
-* [Amphion](https://github.com/open-mmlab/Amphion) is a toolkit for Audio, Music, and Speech Generation. Its purpose is to support reproducible research and help junior researchers and engineers get started in the field of audio, music, and speech generation research and development.
 * [Animus](https://github.com/Scitator/animus) is a minimalistic framework to run machine learning experiments. Animus highlights common "breakpoints" in ML experiments and provides a unified interface for them within [IExperiment](https://github.com/Scitator/animus/blob/main/animus/core.py#L76).
 * [Catalyst](https://github.com/catalyst-team/catalyst#getting-started) is a PyTorch framework for Deep Learning Research and Development. It focuses on reproducibility, rapid experimentation, and codebase reuse so you can create something new rather than write yet another train loop. Catalyst provides a [Runner](https://catalyst-team.github.io/catalyst/api/core.html#runner) to connect all parts of the experiment: hardware backend, data transformations, model training, and inference logic.
 * [fastai](https://github.com/fastai/fastai#installing) is a PyTorch framework for Deep Learning that simplifies training fast and accurate neural nets using modern best practices. fastai provides a [Learner](https://docs.fast.ai/learner.html#Learner) to handle the training, fine-tuning, and inference of deep learning algorithms.
@@ -270,7 +269,7 @@ If you use 🤗 Accelerate in your publication, please cite it by using the foll
 ```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},
+  author =       {Sylvain Gugger, Lysandre Debut, Thomas Wolf, Philipp Schmid, Zachary Mueller, Sourab Mangrulkar},
   howpublished = {\url{https://github.com/huggingface/accelerate}},
   year =         {2022}
 }

benchmarks/measures_util.py

@@ -1,16 +1,3 @@
-# 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

docker/accelerate-gpu/Dockerfile

@@ -1,10 +1,10 @@
-# Builds GPU docker image of PyTorch specifically
+# Builds GPU docker image of PyTorch
 # 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
+ENV PYTHON_VERSION=3.8
 # Install apt libs
 RUN apt-get update && \
     apt-get install -y curl git wget && \
@@ -19,8 +19,7 @@ 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
+# Activate the conda env and install torch + accelerate
 RUN source activate accelerate && \
     python3 -m pip install --no-cache-dir \
     git+https://github.com/huggingface/accelerate#egg=accelerate[testing,test_trackers] \
@@ -29,7 +28,7 @@ RUN source activate accelerate && \
 RUN python3 -m pip install --no-cache-dir bitsandbytes
 
 # Stage 2
-FROM nvidia/cuda:12.1.0-cudnn8-devel-ubuntu20.04 AS build-image
+FROM nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04 AS build-image
 COPY --from=compile-image /opt/conda /opt/conda
 ENV PATH /opt/conda/bin:$PATH
 

docs/source/_toctree.yml

@@ -10,82 +10,59 @@
   - 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
+    title: Migrating to 🤗 Accelerate
   - local: basic_tutorials/launch
     title: Launching distributed code
   - local: basic_tutorials/notebook
     title: Launching distributed training from Jupyter Notebooks
-  - local: basic_tutorials/troubleshooting
-    title: Troubleshooting guide
   title: Tutorials
 - sections:
-  - 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/checkpoint
-      title: Save and load training states
-    - 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/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
+  - local: usage_guides/explore
+    title: Start Here!
+  - local: usage_guides/training_zoo
+    title: Example Zoo
+  - local: usage_guides/big_modeling
+    title: How to perform inference on large models with small resources
+  - local: usage_guides/quantization
+    title: How to quantize model 
+  - local: usage_guides/distributed_inference
+    title: How to perform distributed inference with normal resources
+  - local: usage_guides/gradient_accumulation
+    title: Performing gradient accumulation
+  - local: usage_guides/local_sgd
+    title: Accelerating training with local SGD
+  - local: usage_guides/checkpoint
+    title: Saving and loading training states
+  - local: usage_guides/tracking
+    title: Using experiment trackers
+  - local: usage_guides/memory
+    title: How to avoid CUDA Out-of-Memory
+  - local: usage_guides/mps
+    title: How to use Apple Silicon M1 GPUs
+  - local: usage_guides/deepspeed
+    title: How to use DeepSpeed
+  - local: usage_guides/fsdp
+    title: How to use Fully Sharded Data Parallelism
+  - local: usage_guides/megatron_lm
+    title: How to use Megatron-LM
+  - local: usage_guides/sagemaker
+    title: How to use 🤗 Accelerate with SageMaker
+  - local: usage_guides/ipex
+    title: How to use 🤗 Accelerate with Intel® Extension for PyTorch for cpu
+  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/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
+    title: Main Accelerator class
   - local: package_reference/state
     title: Stateful configuration classes
   - local: package_reference/cli
@@ -102,14 +79,10 @@
     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/basic_tutorials/execution.md

@@ -1,128 +0,0 @@
-<!--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/launch.md

@@ -153,15 +153,6 @@ the below example enabling unbuffered stdout and stderr:
 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`
 
@@ -209,24 +200,3 @@ Launching a script from the location of that custom yaml file looks like the fol
 ```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

@@ -13,11 +13,21 @@ specific language governing permissions and limitations under the License.
 rendered properly in your Markdown viewer.
 -->
 
-# Add Accelerate to your code
+# Migrating your code to 🤗 Accelerate
 
-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.
+This tutorial will detail how to easily convert existing PyTorch code to use 🤗 Accelerate!
+You'll see that by just changing a few lines of code, 🤗 Accelerate can perform its magic and get you on 
+your way toward running your code on distributed systems with ease!
 
-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.
+## The base training loop
+
+To begin, write out a very basic PyTorch training loop. 
+
+<Tip>
+
+    We are under the presumption that `training_dataloader`, `model`, `optimizer`, `scheduler`, and `loss_function` have been defined beforehand.
+
+</Tip>
 
 ```python
 device = "cuda"
@@ -35,44 +45,50 @@ for batch in training_dataloader:
     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.
+## Add in 🤗 Accelerate
 
+To start using 🤗 Accelerate, first import and create an [`Accelerator`] instance:
 ```python
 from accelerate import Accelerator
 
 accelerator = Accelerator()
 ```
+[`Accelerator`] is the main force behind utilizing all the possible options for distributed training!
 
-The [`Accelerator`] also knows which device to move your PyTorch objects to, so it is recommended to let Accelerate handle this for you.
+### Setting the right device
+
+The [`Accelerator`] class knows the right device to move any PyTorch object to at any time, so you should
+change the definition of `device` to come from [`Accelerator`]:
 
 ```diff
-- device = "cuda"
+- device = 'cuda'
 + device = accelerator.device
   model.to(device)
 ```
 
-## Prepare PyTorch objects
+### Preparing your 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.
+Next, you need to pass all of the important objects related to training into [`~Accelerator.prepare`]. 🤗 Accelerate will
+make sure everything is setup in the current environment for you to start training:
 
-> [!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
 )
 ```
+These objects are returned in the same order they were sent in. By default when using `device_placement=True`, all of the objects that can be sent to the right device will be.
+If you need to work with data that isn't passed to [~Accelerator.prepare] but should be on the active device, you should pass in the `device` you made earlier. 
 
-## Training loop
+<Tip warning={true}>
 
-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).
+    Accelerate will only prepare objects that inherit from their respective PyTorch classes (such as `torch.optim.Optimizer`).
+
+</Tip>
+
+### Modifying the training loop
+
+Finally, three lines of code need to be changed in the training loop. 🤗 Accelerate's DataLoader classes will automatically handle the device placement by default,
+and [`~Accelerator.backward`] should be used for performing the backward pass:
 
 ```diff
 -   inputs = inputs.to(device)
@@ -83,13 +99,17 @@ Finally, remove the `to(device)` calls to the inputs and targets in the training
 +   accelerator.backward(loss)
 ```
 
-Put everything together and your new Accelerate training loop should now look like this!
+With that, your training loop is now ready to use 🤗 Accelerate!
+
+## The finished code
+
+Below is the final version of the converted code: 
 
 ```python
 from accelerate import Accelerator
+
 accelerator = Accelerator()
 
-device = accelerator.device
 model, optimizer, training_dataloader, scheduler = accelerator.prepare(
     model, optimizer, training_dataloader, scheduler
 )
@@ -104,118 +124,6 @@ for batch in training_dataloader:
     scheduler.step()
 ```
 
-## Training features
+## More Resources
 
-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.
+To check out more ways on how to migrate to 🤗 Accelerate, check out our [interactive migration tutorial](https://huggingface.co/docs/accelerate/usage_guides/explore) which showcases other items that need to be watched for when using Accelerate and how to do so quickly.

docs/source/basic_tutorials/notebook.md

@@ -186,7 +186,7 @@ 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
+    The code has been split up to allow for explainations on each section. A full version that can be copy and pasted will be available at the end
 
 </Tip>
 
@@ -344,7 +344,7 @@ def training_loop(mixed_precision="fp16", seed: int = 42, batch_size: int = 64):
     mean = mean.to(accelerator.device)
     std = std.to(accelerator.device)
 
-    # Instantiate the optimizer
+    # Intantiate the optimizer
     optimizer = torch.optim.Adam(params=model.parameters(), lr=3e-2 / 25)
 
     # Instantiate the learning rate scheduler
@@ -401,26 +401,6 @@ 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
@@ -443,13 +423,6 @@ epoch 4: 94.71
 
 And that's it!
 
-## 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:

docs/source/basic_tutorials/tpu.md

@@ -1,38 +0,0 @@
-<!--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

@@ -1,222 +0,0 @@
-<!--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.
--->
-
-# Troubleshooting guide
-
-This guide aims to provide you the tools and knowledge required to navigate some common issues. However, 
-as 🤗 Accelerate continuously evolves and the use cases and setups are diverse, you might encounter an issue not covered in this 
-guide. If the suggestions listed in this guide do not cover your such situation, please refer to the final section of 
-the guide, [Asking for Help](#ask-for-help), to learn where to find help with your specific issue.
-
-## Logging
-
-When facing an error, logging can help narrow down where it is coming from. In a distributed setup with multiple processes, 
-logging can be a challenge, but 🤗 Accelerate provides a utility that streamlines the logging process and ensures that 
-logs are synchronized and managed effectively across the distributed setup. 
-
-To troubleshoot an issue, use `accelerate.logging` instead of the standard Python `logging` module:
-
-```diff
-- import logging
-+ from accelerate.logging import get_logger
-- logger = logging.getLogger(__name__)
-+ logger = get_logger(__name__)
-```
-
-To set the log level (`INFO`, `DEBUG`, `WARNING`, `ERROR`, `CRITICAL`), export it as the `ACCELERATE_LOG_LEVEL` environment,
-or pass as `log_level` to `get_logger`:
-
-```python
-from accelerate.logging import get_logger
-
-logger = get_logger(__name__, log_level="INFO")
-```
-
-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`.
-
-## Hanging code and timeout errors
-
-### Mismatched tensor shapes 
-
-If your code seems to be hanging for a significant amount time on a distributed setup, a common cause is mismatched shapes of tensors on different 
-devices. 
-
-When running scripts in a distributed fashion, functions such as [`Accelerator.gather`] and [`Accelerator.reduce`] are 
-necessary to grab tensors across devices to perform operations on them collectively. These (and other) functions rely on 
-`torch.distributed` performing a `gather` operation, which requires that tensors have the **exact same shape** across all processes.
-When the tensor shapes don't match, you will experience handing code, and eventually hit a timeout exception. 
-
-If you suspect this to be the case, use Accelerate's operational debug mode to immediately catch the issue.
-
-The recommended way to enable Accelerate's operational debug mode is during `accelerate config` setup. 
-Alternative ways to enable debug mode are: 
-
-* From the CLI: 
-
-```bash
-accelerate launch --debug {my_script.py} --arg1 --arg2
-```
-
-* As an environmental variable (which avoids the need for `accelerate launch`):
-
-```bash
-ACCELERATE_DEBUG_MODE="1" torchrun {my_script.py} --arg1 --arg2
-```
-
-* Manually changing the `config.yaml` file:
-
-```diff
- compute_environment: LOCAL_MACHINE
-+debug: true
-```
-
-Once you enable the debug mode, you should get a similar 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 leads to hanging
-
-When doing early stopping in distributed training, if each process has a specific stopping condition (e.g. validation loss), 
-it may not be synchronized across all of them. As a result, a break can happen on process 0 but not on process 1. 
-This will cause the code to hang indefinitely until a timeout occurs.
-
-If you have early stopping conditionals, use `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
-```
-
-### Hanging on low kernel versions on Linux
-
-This is a known issue. On Linux with kernel version < 5.5, hanging processes have been reported. To avoid 
-encountering this problem, we recommend upgrading your system to a later kernel version.
-
-## CUDA out of memory
-
-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.
-
-To address this problem, `Accelerate` offers a utility `find_executable_batch_size` that is heavily based on [toma](https://github.com/BlackHC/toma).
-The utility retries code that fails due to OOM (out-of-memory) conditions and lowers batch sizes automatically.
-
-### 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. 
-
-<Tip warning={true}> 
-
-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.
-
-</Tip>
-
-It should also be noted that anything which will consume CUDA memory and passed to the `accelerator` **must** be declared inside the inner function,
-such as models and optimizers.
-
-```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()
-```
-
-To find out more, check the documentation [here](../package_reference/utilities#accelerate.find_executable_batch_size).
-
-## Non-reproducible results between device setups
-
-If you have changed the device setup and are observing different model performance, this is likely due to the fact that 
-you have not updated your script when moving from one setup to another. The same script with the same batch size across TPU, 
-multi-GPU, and single-GPU with Accelerate will have different results. 
-
-For example, if you were previously training on a single GPU with a batch size of 16, when moving to two 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, 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 hit some limitations:
-
-- There may be an imbalance in GPU memory between the GPUs. In this case, the GPU with 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 that you are using as 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 the above troubleshooting tools and advice did not help you resolve your issue, reach out for help to the community 
-and the team.
-
-### Forums 
-
-Ask for help on the Hugging Face forums - post 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!
-
-### Discord
-
-Post a question on [Discord](http://hf.co/join/discord), and let the team and the community help you.
-
-### GitHub Issues
-
-Create an Issue on the 🤗 Accelerate [GitHub repository](https://github.com/huggingface/accelerate/issues) if you suspect 
-to have 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

@@ -1,341 +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.
-
-⚠️ 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

@@ -108,23 +108,3 @@ with accelerator.main_process_first():
         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/gradient_synchronization.md

@@ -55,8 +55,8 @@ 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
+processes only need to sychronize by the time `optimizer.step()` is called,
+without any modification to your training step, this neededless inter-process
 communication can cause a significant slowdown.
 
  How can you avoid this overhead?

docs/source/concept_guides/internal_mechanism.md

@@ -1,72 +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.
-
-⚠️ 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

@@ -1,74 +0,0 @@
-<!--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

@@ -45,7 +45,7 @@ Why is this important? Under the hood this will set **5** different seed setting
     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():
+    if is_tpu_available():
         xm.set_rng_state(seed)
 ```
 
@@ -74,7 +74,7 @@ In this example, there are two GPUs for "Multi-GPU" and a TPU pod with 8 workers
 
 ## 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 
+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/tlt-mi_archive/clara-train-sdk-v2.0/nvmidl/appendix/training_with_multiple_gpus.html)], the learning rate should be scaled *linearly* based on the number of devices present. The below 
 snippet shows doing so with Accelerate:
 
 <Tip>

docs/source/concept_guides/training_tpu.md

@@ -36,7 +36,7 @@ Below is an example of a training function passed to the [`notebook_launcher`] i
 
 <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 
+    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/simple_nlp_example.ipynb) with slight 
     modifications for the sake of simplicity
 
 </Tip>

docs/source/package_reference/accelerator.md

@@ -15,8 +15,197 @@ 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.
+The [`Accelerator`] is the main class provided by 🤗 Accelerate. 
+It serves at the main entry point for the API. 
 
-## Accelerator[[api]]
+## Quick adaptation of your code
+
+To quickly adapt your script to work on any kind of setup with 🤗 Accelerate just:
+
+1. Initialize an [`Accelerator`] object (that we will call `accelerator` throughout this page) as early as possible in your script.
+2. Pass your dataloader(s), model(s), optimizer(s), and scheduler(s) to the [`~Accelerator.prepare`] method.
+3. Remove all the `.cuda()` or `.to(device)` from your code and let the `accelerator` handle the device placement for you. 
+
+<Tip>
+
+    Step three is optional, but considered a best practice.
+
+</Tip>
+
+4. Replace `loss.backward()` in your code with `accelerator.backward(loss)`
+5. Gather your predictions and labels before storing them or using them for metric computation using [`~Accelerator.gather`]
+
+<Tip warning={true}>
+
+    Step five is mandatory when using distributed evaluation
+    
+</Tip>
+
+In most cases this is all that is needed. The next section lists a few more advanced use cases and nice features
+you should search for and replace by the corresponding methods of your `accelerator`:
+
+## Advanced recommendations
+
+### Printing
+
+`print` statements should be replaced by [`~Accelerator.print`] to be printed once per process:
+
+```diff
+- print("My thing I want to print!")
++ accelerator.print("My thing I want to print!")
+```
+
+### Executing processes
+
+#### Once on a single server
+
+For statements that should be executed once per server, use [`~Accelerator.is_local_main_process`]:
+
+```python
+if accelerator.is_local_main_process:
+    do_thing_once_per_server()
+```
+
+A function can be wrapped using the [`~Accelerator.on_local_main_process`] function to achieve the same 
+behavior on a function's execution:
+
+```python
+@accelerator.on_local_main_process
+def do_my_thing():
+    "Something done once per server"
+    do_thing_once_per_server()
+```
+
+#### Only ever once across all servers
+
+For statements that should only ever be executed once, use [`~Accelerator.is_main_process`]:
+
+```python
+if accelerator.is_main_process:
+    do_thing_once()
+```
+
+A function can be wrapped using the [`~Accelerator.on_main_process`] function to achieve the same 
+behavior on a function's execution:
+
+```python
+@accelerator.on_main_process
+def do_my_thing():
+    "Something done once per server"
+    do_thing_once()
+```
+
+#### On specific processes
+
+If a function should be ran on a specific overall or local process index, there are similar decorators 
+to achieve this:
+
+```python
+@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()
+```
+
+```python
+@accelerator.on_process(process_index=0)
+def do_my_thing():
+    "Something done on process index 0"
+    do_thing_on_index_zero()
+```
+
+### Synchronicity control
+
+Use [`~Accelerator.wait_for_everyone`] to make sure all processes join that point before continuing. (Useful before a model save for instance).
+
+### Saving and loading
+
+```python
+model = MyModel()
+model = accelerator.prepare(model)
+```
+
+Use [`~Accelerator.save_model`] instead of `torch.save` to save a model. It will remove all model wrappers added during the distributed process, get the state_dict of the model and save it.
+
+```diff
+- torch.save(state_dict, "my_state.pkl")
++ accelerator.save_model(model, save_directory)
+```
+
+[`~Accelerator.save_model`] can also save a model into sharded checkpoints or with safetensors format.
+Here is an example: 
+
+```python
+accelerator.save_model(model, save_directory, max_shard_size="1GB", safe_serialization=True)
+```
+
+#### 🤗 Transformers models
+
+If you are using models from the [🤗 Transformers](https://huggingface.co/docs/transformers/) library, you can use the `.save_pretrained()` method.
+
+```python
+from transformers import AutoModel
+
+model = AutoModel.from_pretrained("bert-base-cased")
+model = accelerator.prepare(model)
+
+# ...fine-tune with PyTorch...
+
+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,
+)
+```
+
+This will ensure your model stays compatible with other 🤗 Transformers functionality like the `.from_pretrained()` method.
+
+```python
+from transformers import AutoModel
+
+model = AutoModel.from_pretrained("path/to/my_model_directory")
+```
+
+### Operations
+
+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``
+
+### Gradient Accumulation
+
+To perform gradient accumulation use [`~Accelerator.accumulate`] and specify a gradient_accumulation_steps. 
+This will also automatically ensure the gradients are synced or unsynced when on 
+multi-device training, check if the step should actually be performed, and auto-scale the loss:
+
+```diff
+- accelerator = Accelerator()
++ accelerator = Accelerator(gradient_accumulation_steps=2)
+
+  for (input, label) in training_dataloader:
++     with accelerator.accumulate(model):
+          predictions = model(input)
+          loss = loss_function(predictions, labels)
+          accelerator.backward(loss)
+          optimizer.step()
+          scheduler.step()
+          optimizer.zero_grad()
+```
+#### GradientAccumulationPlugin
+[[autodoc]] utils.GradientAccumulationPlugin
+
+
+Instead of passing `gradient_accumulation_steps` you can instantiate a GradientAccumulationPlugin and pass it to the [`Accelerator`]'s `__init__`
+as `gradient_accumulation_plugin`. You can only pass either one of `gradient_accumulation_plugin` or `gradient_accumulation_steps` passing both will raise an error.
+```diff
+from accelerate.utils import GradientAccumulationPlugin
+
+gradient_accumulation_plugin = GradientAccumulationPlugin(num_steps=2)
+- accelerator = Accelerator()
++ accelerator = Accelerator(gradient_accumulation_plugin=gradient_accumulation_plugin)
+```
+
+In addition to the number of steps, this also lets you configure whether or not you adjust your learning rate scheduler to account for the change in steps due to accumulation.
+
+## Overall API documentation:
 
 [[autodoc]] Accelerator

docs/source/package_reference/big_modeling.md

@@ -19,12 +19,9 @@ rendered properly in your Markdown viewer.
 
 [[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
 

docs/source/package_reference/cli.md

@@ -199,7 +199,7 @@ The following arguments are only useful when `use_deepspeed` is passed or `deeps
 
 **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`:
+The following arguments are only useful when `use_fdsp` 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.
@@ -218,7 +218,7 @@ The following arguments are only useful when `use_megatron_lm` is passed or Mega
 * `--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_use_distributed_optimizer` (``) -- Decides Whether (true|false) to use distributed optimizer which shards optimizer state and gradients across Data Pralellel (DP) ranks.
 * `--megatron_lm_gradient_clipping` (``) -- Megatron-LM's gradient clipping value based on global L2 Norm (0 to disable).
 
 **AWS SageMaker Arguments**:
@@ -228,36 +228,6 @@ 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`

docs/source/package_reference/fsdp.md

@@ -1,18 +0,0 @@
-<!--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.FullyShardedDataParallelPlugin

docs/source/package_reference/inference.md

@@ -1,20 +0,0 @@
-<!--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

@@ -18,18 +18,11 @@ rendered properly in your Markdown viewer.
 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
-
 ## GradScalerKwargs
 
 [[autodoc]] GradScalerKwargs
@@ -37,7 +30,3 @@ related to distributed training or mixed precision are created.
 ## InitProcessGroupKwargs
 
 [[autodoc]] InitProcessGroupKwargs
-
-## KwargsHandler
-
-[[autodoc]] utils.KwargsHandler

docs/source/package_reference/logging.md

@@ -15,7 +15,23 @@ 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. 
+Accelerate has its own logging utility to handle logging while in a distributed system.
+To utilize this replace cases of `logging` with `accelerate.logging`:
+```diff
+- import logging
++ from accelerate.logging import get_logger
+- logger = logging.getLogger(__name__)
++ logger = get_logger(__name__)
+```
+
+## Setting the log level
+
+The log level can be set with the `ACCELERATE_LOG_LEVEL` environment variable or by passing 
+`log_level` to `get_logger`:
+```python
+from accelerate.logging import get_logger
+
+logger = get_logger(__name__, log_level="INFO")
+```
 
 [[autodoc]] logging.get_logger

docs/source/package_reference/torch_wrappers.md

@@ -21,7 +21,6 @@ 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

docs/source/package_reference/tracking.md

@@ -31,5 +31,3 @@ rendered properly in your Markdown viewer.
     - __init__
 [[autodoc]] tracking.MLflowTracker
     - __init__
-[[autodoc]] tracking.ClearMLTracker
-    - __init__

docs/source/package_reference/utilities.md

@@ -17,150 +17,45 @@ rendered properly in your Markdown viewer.
 
 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_tpu_available
 
-[[autodoc]] utils.is_xpu_available
-
-## Environment Manipulation
-
-[[autodoc]] utils.patch_environment
-
-[[autodoc]] utils.clear_environment
+## Environment Configuration
 
 [[autodoc]] utils.write_basic_config
 
@@ -168,38 +63,20 @@ When setting up 🤗 Accelerate for the first time, rather than running `acceler
 
 ## Memory
 
+[[autodoc]] utils.get_max_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
 
@@ -240,3 +117,5 @@ These include utilities that are useful to load checkpoints.
 These include utilities that are useful to quantize model.
 
 [[autodoc]] utils.load_and_quantize_model
+
+[[autodoc]] utils.BnbQuantizationConfig

docs/source/quicktour.md

@@ -9,78 +9,19 @@ Unless required by applicable law or agreed to in writing, software distributed
 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
+⚠️ 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.
 -->
 
-# Quicktour
+# Quick tour
 
-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.
+Let's have a look at the 🤗 Accelerate main features and traps to avoid.
 
-This quicktour introduces the three main features of Accelerate:
+## Main use
 
-* 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
+To use 🤗 Accelerate in your own script, you have to change four things:
 
-## 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`](../../docs/source/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`](../../docs/source/package_reference/cli#accelerate-config) first to help Accelerate learn about your training setup.
-
-```bash
-accelerate config
-```
-
-The [`accelerate config`](../../docs/source/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`](../../docs/source/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`](../../docs/source/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.
+1. Import the [`Accelerator`] main class and instantiate one in an `accelerator` object:
 
 ```python
 from accelerate import Accelerator
@@ -88,16 +29,27 @@ 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.
+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
+machines with several GPUs, several machines with multiple GPUs or a TPU), the library will detect this automatically.
 
-> [!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.
+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`.
 
-```py
-device = accelerator.device
-```
+To fully deactivate the automatic device placement, pass along `device_placement=False` when initializing your
+[`Accelerator`].
 
-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.
+<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(
@@ -105,23 +57,73 @@ model, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
 )
 ```
 
-4. Replace `loss.backward()` with [`~Accelerator.backward`] to use the correct `backward()` method for your training setup.
+In particular, your training dataloader will be sharded across 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).
 
-```py
-accelerator.backward(loss)
-```
+<Tip>
 
-Read [Accelerate’s internal mechanisms](../../docs/source/concept_guides/internal_mechanism) guide to learn more details about how Accelerate adapts your code.
+    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.
 
-### Distributed evaluation
+</Tip>
 
-To perform distributed evaluation, pass your validation dataloader to the [`~Accelerator.prepare`] method:
+Alternatively, you can use the option `split_batches=True` when creating and initializing your
+[`Accelerator`], in which case the batch size will always stay the same, whether you 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)
 ```
 
-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.
+As 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_for_metrics`] method.
 
 ```python
 for inputs, targets in validation_dataloader:
@@ -132,50 +134,387 @@ for inputs, targets in validation_dataloader:
     metric.add_batch(all_predictions, all_targets)
 ```
 
-> [!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.
+<Tip warning={true}>
 
-## Big Model Inference
+    Similar to the training dataloader, passing your validation dataloader through
+    [`~Accelerator.prepare`] may change it: 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`.
 
-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>
 
-> [!TIP]
-> Take a look at the [Handling big models for inference](../../docs/source/concept_guides/big_model_inference) guide for a better understanding of how Big Model Inference works under the hood.
+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`]. 
 
-### Empty weights initialization
+Some data at the end of the dataset may be duplicated so the batch can be divided equally among all workers. As a result, metrics
+should be calculated through the [`~Accelerator.gather_for_metrics`] method to automatically remove the duplicated data while gathering.
 
-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.
+<Tip>
 
-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.
+    If for some reason you don't wish to have this automatically done, [`~Accelerator.gather`] can be used instead to gather 
+    the data across all processes and this can manually be done instead.
 
-```py
-from accelerate import init_empty_weights
-from transformers import AutoConfig, AutoModelForCausalLM
+</Tip>
 
-config = AutoConfig.from_pretrained("mistralai/Mixtral-8x7B-Instruct-v0.1")
-with init_empty_weights():
-    model = AutoModelForCausalLM.from_config(config)
+
+<Tip warning={true}>
+
+    The [`~Accelerator.gather`] and [`~Accelerator.gather_for_metrics`] methods require 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.run` for
+PyTorch), they are fully compatible with 🤗 Accelerate.
+
+🤗 Accelerate also provides a CLI tool that unifies all launchers, so you only have to remember one command. To use it,
+just run:
+
+```bash
+accelerate config
 ```
 
-### Load and dispatch weights
+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 [`~accelerate.load_checkpoint_and_dispatch`] function loads full or sharded checkpoints into the empty model, and automatically distribute weights across all available devices.
+- 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*
 
-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).
+You can also specify with the flag `--config_file` the location of the file you want to save.
 
-```py
-from accelerate import load_checkpoint_and_dispatch
+Once this is done, you can test everything is going well on your setup by running:
 
-model = load_checkpoint_and_dispatch(
-    model, checkpoint="mistralai/Mixtral-8x7B-Instruct-v0.1", device_map="auto", no_split_module_classes=['Block']
-)
+```bash
+accelerate test
 ```
 
-## Next steps
+This will launch a short script that will test the distributed environment. If it runs fine, you are ready for the next
+step!
 
-Now that you've been introduced to the main Accelerate features, your next steps could include:
+Note that if you specified a location for the config file in the previous step, you need to pass it here as well:
 
-* Check out the [tutorials](docs/source/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](docs/source/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](docs/source/concept_guides/internal_mechanism).
-* Look up classes and commands in the [API reference](docs/source/package_reference/accelerator) to see what parameters and options are available.
+```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 this:
+
+```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. 
+To see the complete list of parameters that you can pass in, run `accelerate launch -h`. 
+
+Check out the [Launch tutorial](basic_tutorials/launch) for more information about launching your scripts. 
+
+
+## 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>
+
+Check out the [Notebook Launcher tutorial](basic_tutorials/notebook) for more information about training on TPUs. 
+
+
+## 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
+bad 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 batches
+- 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 layers 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 a 
+situation with dynamic padding.
+
+One last thing to pay close attention 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.
+
+Check out the [TPU tutorial](concept_guides/training_tpu) for more information about training on TPUs. 
+
+
+## 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 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).
+
+
+### 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. The [`~Accelerator.save_model`] method will help you to achieve that. It will unwrap your model and save
+the model state dictionnary.
+
+Here is an example:
+```
+accelerator.wait_for_everyone()
+accelerator.save_model(model, save_directory)
+```
+The [`~Accelerator.save_model`] method can also save a model into sharded checkpoints or with safetensors format.
+Here is an example: 
+
+```python
+accelerator.wait_for_everyone()
+accelerator.save_model(model, save_directory, max_shard_size="1GB", safe_serialization=True)
+```
+
+If your script contains logic to load a 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:
+
+```python
+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))
+```
+
+Note that since all the model parameters are references to tensors, this will load your weights inside `model`.
+
+If you want to load a sharded checkpoint or a checkpoint with safetensors format into the model with a specific `device`, we recommend you to load it with [`~utils.load_checkpoint_in_model`] function. Here's an example:
+
+```python
+load_checkpoint_in_model(unwrapped_model, save_directory, device_map={"":device})
+```
+
+## 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.
+
+To further customize where and how states 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}`.
+
+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 [`~Accelerator.register_for_checkpointing`] must have a `load_state_dict` and `state_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, especially 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()
+```
+
+### Gradient Accumulation 
+
+To perform gradient accumulation use [`~Accelerator.accumulate`] and specify a `gradient_accumulation_steps`. 
+This will also automatically ensure the gradients are synced or unsynced when on multi-device training, check if the step should
+actually be performed, and auto-scale the loss:
+
+```python
+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()
+```
+
+### 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 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`].
+
+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 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/usage_guides/big_modeling.md

@@ -15,13 +15,7 @@ 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:
+When loading a pre-trained model in PyTorch, the usual workflow looks like this:
 
 ```py
 import torch
@@ -31,120 +25,307 @@ 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`).
+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
 
-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:
+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(...)
 ```
 
-With this `my_model` currently is "parameterless", hence leaving the smaller footprint than what one would normally get loading this onto the CPU directly. 
+For instance:
 
-Next we need to load in the weights to our model so we can perform inference.
+```py
+with init_empty_weights():
+    model = nn.Sequential(*[nn.Linear(10000, 10000) for _ in range(1000)])
+```
 
-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). 
+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.
 
-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 warning={true}>
 
-<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)
+    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>
 
-See an example below:
+### 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 minGTP. 
+
+```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=checkpoint_file, device_map="auto"
+    model, checkpoint=weights_location, device_map="auto", no_split_module_classes=['Block']
 )
 ```
 
-<Tip>
+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
 
-    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)
+`no_split_module_classes=["Block"]` indicates that the modules that are `Block` should not be split on different devices. You should set here all blocks that include a residual connection of some kind.
+
+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.1': 0,
+ 'transformer.h.2': 0,
+ 'transformer.h.3': 0,
+ 'transformer.h.4': 0,
+ 'transformer.h.5': 0,
+ 'transformer.h.6': 0,
+ 'transformer.h.7': 0,
+ 'transformer.h.8': 0,
+ 'transformer.h.9': 0,
+ 'transformer.h.10': 0,
+ 'transformer.h.11': 0,
+ 'transformer.h.12': 0,
+ 'transformer.h.13': 0,
+ 'transformer.h.14': 0,
+ 'transformer.h.15': 0,
+ 'transformer.h.16': 0, 
+ 'transformer.h.17': 0, 
+ 'transformer.h.18': 0, 
+ 'transformer.h.19': 0, 
+ 'transformer.h.20': 0, 
+ 'transformer.h.21': 0, 
+ 'transformer.h.22': 1, 
+ 'transformer.h.23': 1, 
+ 'transformer.h.24': 1, 
+ 'transformer.h.25': 1, 
+ 'transformer.h.26': 1, 
+ 'transformer.h.27': 1, 
+ 'transformer.h.28': 1, 
+ 'transformer.h.29': 1, 
+ 'transformer.h.30': 1, 
+ 'transformer.h.31': 1, 
+ 'transformer.h.32': 1, 
+ 'transformer.h.33': 1, 
+ 'transformer.h.34': 1, 
+ 'transformer.h.35': 1, 
+ 'transformer.h.36': 1, 
+ 'transformer.h.37': 1, 
+ 'transformer.h.38': 1, 
+ 'transformer.h.39': 1, 
+ 'transformer.h.40': 1, 
+ 'transformer.h.41': 1, 
+ 'transformer.h.42': 1, 
+ 'transformer.h.43': 1, 
+ 'transformer.h.44': 1, 
+ 'transformer.h.45': 1, 
+ 'transformer.h.46': 1, 
+ 'transformer.h.47': 1, 
+ 'transformer.ln_f': 1, 
+ 'lm_head': 1}
+ ```
+
+You can also design your `device_map` yourself if you prefer to explicitly decide where each layer should be. In this case, the command above becomes:
+
+```py
+model = load_checkpoint_and_dispatch(model, checkpoint=weights_location, device_map=my_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>
 
-<Tip>
+### Designing a device map
 
-    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. 
+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>
 
-    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.
+    You can derive all sizes of the model (and thus compute a `device_map`) on a model that is on the meta device.
 
 </Tip>
 
-For a visual representation of this, check out the animation below:
+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). 
 
-<Youtube id="MWCSGj9jEAo" />
+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).
 
-### Complete Example
+<Tip>
 
-Below is the full example showcasing what we performed above:
+    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.
 
-```py
-import torch
-from accelerate import init_empty_weights, load_checkpoint_and_dispatch
+</Tip>
 
-with init_empty_weights():
-    model = MyModel(...)
+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"`.
 
-model = load_checkpoint_and_dispatch(
-    model, checkpoint=checkpoint_file, device_map="auto"
-)
+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:
 
-input = torch.randn(2,3)
-input = input.to("cuda")
-output = model(input)
+```python
+from accelerate import infer_auto_device_map
+
+device_map = infer_auto_device_map(my_model, max_memory={0: "10GiB", 1: "10GiB", "cpu": "30GiB"})
 ```
 
-## Using 🤗 Transformers, 🤗 Diffusers, and other 🤗 Open Source Libraries
+<Tip warning={true}>
 
-Libraries that support 🤗 Accelerate big model inference include all of the earlier logic in their `from_pretrained` constructors. 
+    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.
 
-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. 
+    Therefore when you create memory maps with `max_memory` make sure to adjust the available memory accordingly to avoid out-of-memory errors.
 
-As a brief example, we will look at using `transformers` and loading in Big Science's T0pp model. 
+</Tip>
 
-```py
-from transformers import AutoModelForSeq2SeqLM
+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:
 
-model = AutoModelForSeq2SeqLM.from_pretrained("bigscience/T0pp", device_map="auto")
+```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}
 ```
 
-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:
+another one that is valid could be:
 
-```py
-from transformers import AutoModelForSeq2SeqLM
-
-model = AutoModelForSeq2SeqLM.from_pretrained("bigscience/T0pp", device_map="auto", torch_dtype=torch.float16)
+```python
+device_map = {"block1": 0, "block2.linear1": 0, "block2.linear2": 1, "block2.linear3": 1}
 ```
 
-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).
+On the other hand, this one is not valid as it does not cover every parameter of the model:
 
-## Where to go from here
+```python
+device_map = {"block1": 0, "block2.linear1": 1, "block2.linear2": 1}
+```
 
-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)
+<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>
+
+## Limits and further development
+
+We are aware of the current limitations in the API:
+
+- While this could theoretically work on just one CPU with potential disk offload, you need at least one GPU to run this API. This will be fixed in further development.
+- [`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/usage_guides/deepspeed.md

@@ -9,13 +9,13 @@ Unless required by applicable law or agreed to in writing, software distributed
 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
+⚠️ 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.
 -->
 
-# DeepSpeed
+# 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:
+[DeepSpeed](https://github.com/microsoft/DeepSpeed) implements everything described in the [ZeRO paper](https://arxiv.org/abs/1910.02054). Currently, it provides full support for:
 
 1. Optimizer state partitioning (ZeRO stage 1)
 2. Gradient partitioning (ZeRO stage 2)
@@ -23,7 +23,6 @@ rendered properly in your Markdown viewer.
 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).
@@ -36,16 +35,16 @@ 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.
+   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.
+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++.
+1. DeepSpeed ZeRO training supports the full ZeRO stages 1, 2 and 3 as well as CPU/Disk offload of optimizer states, gradients and parameters. 
 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)
 
@@ -61,8 +60,6 @@ Below is a short description of Data Parallelism using ZeRO - Zero Redundancy Op
 
  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:
@@ -77,8 +74,8 @@ Inference:
 **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.
+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:
@@ -160,7 +157,7 @@ Currently, `Accelerate` supports following config through the CLI:
 `offload_param_device`: [none] Disable parameter offloading, [cpu] offload parameters to CPU, [nvme] offload parameters to NVMe SSD. Only applicable with ZeRO Stage-3.
 `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.
+`mixed_precision`: `no` for FP32 training, `fp16` for FP16 mixed-precision training and `bf16` for BF16 mixed-precision training. 
 ```
 To be able to tweak more options, you will need to use a DeepSpeed config file.
 
@@ -171,8 +168,8 @@ On your machine(s) just run:
 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.
+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
 
@@ -352,38 +349,17 @@ accelerate launch examples/by_feature/deepspeed_with_config_support.py \
 --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,
+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
+    # Creates Dummy Optimizer if `optimizer` was spcified in the config file else creates Adam Optimizer
     optimizer_cls = (
         torch.optim.AdamW
         if accelerator.state.deepspeed_plugin is None
@@ -392,7 +368,7 @@ We will look at the changes needed in the code when using these.
     )
     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
+    # Creates Dummy Scheduler if `scheduler` was spcified 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
@@ -412,25 +388,16 @@ We will look at the changes needed in the code when using these.
    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.
+   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.
+   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.
+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`: `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.
@@ -515,11 +482,11 @@ use_cpu: false
 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',
+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.
+If you are using an accelerate config file, remove others 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`.
 ```
@@ -532,15 +499,15 @@ It will only ask for the necessary config variables when using `deepspeed_config
 $ accelerate config
 -------------------------------------------------------------------------------------------------------------------------------
 In which compute environment are you running?
-This machine
+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
+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
@@ -618,10 +585,8 @@ 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
+**Note**: 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
 
@@ -632,7 +597,7 @@ 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.
+   `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
@@ -656,15 +621,15 @@ ZeRO Stage-3 has 2 options:
    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}"
+   status_msg = "checkpointing: PATH={}, ckpt_id={}".format(PATH, 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:
+   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
@@ -688,7 +653,7 @@ ZeRO Stage-3 has 2 options:
    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.
+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:
 
@@ -696,11 +661,11 @@ With accelerate integration, you just need to prepare the model and dataloader a
 model, eval_dataloader = accelerator.prepare(model, eval_dataloader)
 ```
 
-## Few caveats to be aware of
+## 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.
+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
 
@@ -716,8 +681,7 @@ 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).
+

docs/source/usage_guides/distributed_inference.md

@@ -15,18 +15,12 @@ rendered properly in your Markdown viewer.
 
 # Distributed Inference with 🤗 Accelerate
 
-Distributed inference can fall into three brackets:
+Distributed inference is a common use case, especially with natural language processing (NLP) models. Users often want to
+send a number of different prompts, each to a different GPU, and then get the results back. This also has other cases
+outside of just NLP, however for this tutorial we will focus on just this idea of each GPU receiving a different prompt,
+and then returning the results.
 
-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. 
+## The Problem
 
 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. 
 
@@ -57,10 +51,11 @@ def run_inference(rank, world_size):
 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))
+a simple way to manage this. (To learn more, check out the relvent section in the [Quick Tour](../quicktour#distributed-evaluation))
 
 Can it manage it? Yes. Does it add unneeded extra code however: also yes.
 
+## The Solution
 
 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
@@ -125,7 +120,7 @@ needs to be the same length. Basic inference does not require this.
 For instance:
 
 ```python
-from accelerate import PartialState  # Can also be Accelerator or AcceleratorState
+from accelerate import PartialState  # Can also be Accelerator or AcceleratorStaet
 from diffusers import DiffusionPipeline
 
 pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16)
@@ -139,97 +134,3 @@ with distributed_state.split_between_processes(["a dog", "a cat", "a chicken"],
 
 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.
-
-## 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) and our [documentation](../package_reference/inference) as we work to improving this integration. 

docs/source/usage_guides/explore.md

@@ -16,7 +16,7 @@ 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
+feature of 🤗 Accelerate and how to utilize it! It will provide you with a code diff, an explaination
 towards what is going on, as well as provide you with some useful links to explore more within
 the documentation!
 
@@ -37,14 +37,14 @@ for batch in dataloader:
 
 <div class="block dark:hidden">
 	<iframe 
-        src="https://hf-accelerate-accelerate-examples.hf.space?__theme=light"
+        src="https://muellerzr-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"
+        src="https://muellerzr-accelerate-examples.hf.space?__theme=dark"
         width="850"
         height="1600"
     ></iframe>

docs/source/usage_guides/fsdp.md

@@ -36,34 +36,27 @@ default options when doing
 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:
+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
-debug: false
+deepspeed_config: {}
 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
+  fsdp_sharding_strategy: 1
+  fsdp_state_dict_type: FULL_STATE_DICT
+  fsdp_transformer_layer_cls_to_wrap: GPT2Block
 machine_rank: 0
+main_process_ip: null
+main_process_port: null
 main_training_function: main
-mixed_precision: bf16
+mixed_precision: 'no'
 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
 ```
 
@@ -73,30 +66,23 @@ 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).
+```bash
+`Sharding Strategy`: [1] FULL_SHARD (shards optimizer states, gradients and parameters), [2] SHARD_GRAD_OP (shards optimizer states and gradients), [3] NO_SHARD
+`Offload Params`: Decides Whether to offload parameters and gradients to CPU
+`Auto Wrap Policy`: [1] TRANSFORMER_BASED_WRAP, [2] SIZE_BASED_WRAP, [3] NO_WRAP [4] "HYBRID_SHARD" [5] "HYBRID_SHARD_ZERO2"
+`Transformer Layer Class to Wrap`: When using `TRANSFORMER_BASED_WRAP`, user specifies comma-separated string of transformer layer class names (case-sensitive) to wrap ,e.g, 
+`BertLayer`, `GPTJBlock`, `T5Block`, `BertLayer,BertEmbeddings,BertSelfOutput`...
+`Min Num Params`: minimum number of parameters when using `SIZE_BASED_WRAP`
+`Backward Prefetch`: [1] BACKWARD_PRE, [2] BACKWARD_POST, [3] NO_PREFETCH
+`State Dict Type`: [1] FULL_STATE_DICT, [2] LOCAL_STATE_DICT, [3] SHARDED_STATE_DICT  
+`Use Orig Params`: If True, allows non-uniform `requires_grad` during init, which means support for interspersed frozen and trainable paramteres. 
+Useful in cases such as parameter-efficient fine-tuning. 
+Please refer this [blog](https://dev-discuss.pytorch.org/t/rethinking-pytorch-fully-sharded-data-parallel-fsdp-from-first-principles/1019)
+`Sync Module States`: If True, each individually wrapped FSDP unit will broadcast module parameters from rank 0
+`Forward Prefetch`: If True, then FSDP explicitly prefetches the next upcoming all-gather while executing in the forward pass
+```
 
-`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`.
+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.
 
@@ -123,9 +109,9 @@ Below is the code snippet to save using `save_state` utility of accelerate.
 accelerator.save_state("ckpt")
 ```
 
-Inspect the checkpoint folder to see model and optimizer as shards per process:
+Inspect the ckeckpoint folder to see model and optimizer as shards per process:
 ```
-ls ckpt
+ls ckpt 
 # optimizer_0  pytorch_model_0  random_states_0.pkl  random_states_1.pkl  scheduler.bin
 
 cd ckpt
@@ -143,7 +129,7 @@ To load them back for resuming the training, use the `load_state` utility of acc
 accelerator.load_state("ckpt")
 ```
 
-When using transformers `save_pretrained`, pass `state_dict=accelerator.get_state_dict(model)` to save the model state dict.
+When using transformers `save_pretrained`, pass `state_dict=accelerator.get_state_dict(model)` to save the model state dict. 
   Below is an example:
 
 ```diff
@@ -157,20 +143,66 @@ When using transformers `save_pretrained`, pass `state_dict=accelerator.get_stat
 
 ### 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.
+`accelerator.get_state_dict` will call the underlying `model.state_dict` implementation.  With a model wrapped by FSDP, the default behavior of `state_dict` is to gather all of the state in the rank 0 device.  This can cause CUDA out of memory errors if the parameters don't fit on a single GPU.
+
+To avoid this, PyTorch provides a context manager that adjusts the behavior of `state_dict`.  To offload some of the state dict onto CPU, you can use the following code:
+
+```
+from torch.distributed.fsdp import FullyShardedDataParallel as FSDP, StateDictType, FullStateDictConfig
+
+full_state_dict_config = FullStateDictConfig(offload_to_cpu=True, rank0_only=True)
+with FSDP.state_dict_type(unwrapped_model, StateDictType.FULL_STATE_DICT, full_state_dict_config):
+    state = accelerator.get_state_dict(unwrapped_model)
+```
 
 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).
 
-
-## 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.
+- 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 the model before creating the 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 the 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 to parameter flattening of nested FSDP modules into 1D arrays (which can consume many layers).
+  For instance, below are the named parameters of an 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] the named parameters of an 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 or else it will throw an error. 
+Then 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.

docs/source/usage_guides/gradient_accumulation.md

@@ -118,24 +118,8 @@ You can remove all the special checks for the step number and the loss adjustmen
 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)
-```
-
+training on. 🤗 Accelerate automagically does this for you by default. Behind the scenes we instantiate a GradientAccumulationPlugin configured to do this.
 </Tip>
 
 ## The finished code
@@ -143,11 +127,6 @@ accelerator = Accelerator(..., gradient_accumulation_plugin=plugin)
 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
@@ -159,74 +138,4 @@ for batch in training_dataloader:
         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/local_sgd.md

@@ -88,7 +88,7 @@ achieved by adding one `with LocalSGD` statement and one call `local_sgd.step()`
 +           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).
+Under the hood, the Local SGD code **disables** automatic gradient synchornization (but accumulation still works as expected!). Instead it averages model parameters every `local_sgd_steps` steps (as well as in the end of the training loop).
 
 ## Limitations
 

docs/source/usage_guides/low_precision_training.md

@@ -1,92 +0,0 @@
-<!--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

@@ -9,7 +9,7 @@ Unless required by applicable law or agreed to in writing, software distributed
 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
+⚠️ 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.
 -->
 
@@ -113,7 +113,7 @@ pip install git+https://github.com/huggingface/Megatron-LM.git
 ## 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:
+An example of thr corresponding questions for using Megatron-LM features is shown below:
 
 ```bash
 :~$ accelerate config --config_file "megatron_gpt_config.yaml"
@@ -128,7 +128,7 @@ 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]: 
+Do you want to use distributed optimizer which shards optimizer state and gradients across data pralellel 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
@@ -355,8 +355,8 @@ def main():
 
 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.
+avaiable and this requires tweaks to the training loop. Being able to do all this shows how
+felixble 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. 
@@ -542,12 +542,12 @@ megatron_lm_plugin = MegatronLMPlugin(other_megatron_args=other_megatron_args)
 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.
+there is quite complex interplay of pipeline, tensor and data parallelsim 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`
+These logits aren't gathered across data prallel 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. 
 
@@ -580,4 +580,4 @@ b. Megatron-LM [GPTModel](https://github.com/NVIDIA/Megatron-LM/blob/main/megatr
 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)
+[MT5](https://huggingface.co/docs/transformers/model_doc/mt5)

docs/source/usage_guides/memory.md

@@ -0,0 +1,58 @@
+<!--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.
+-->
+
+# 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
+
+It should also be noted that anything which will consume CUDA memory and passed to the `accelerator` **must** be declared inside the inner function,
+such as models and optimizers.
+
+```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()
+```
+
+To find out more, check the documentation [here](../package_reference/utilities#accelerate.find_executable_batch_size).

docs/source/usage_guides/model_size_estimator.md

@@ -1,137 +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.
-
-⚠️ 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/quantization.md

@@ -71,20 +71,20 @@ Finally, you need to set your quantization configuration with [`~utils.BnbQuanti
 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)
+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")
+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")
+quantized_model = load_and_quantize_model(empty_model, weights_location=weights_location, quantization_config=quantization_config, device_map = "auto")
 ```
 
 ### Saving and loading 8-bit model
@@ -97,7 +97,7 @@ 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")
+quantized_model_from_saved = load_and_quantize_model(empty_model, weights_location=new_weights_location, quantization_config=quantization_config, device_map = "auto")
 ```
 
 Note that 4-bit model serialization is currently not supported.
@@ -133,4 +133,4 @@ Note that you don’t need to pass `device_map` when loading the model for train
 
 ### 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.
+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/tracking.md

@@ -20,15 +20,12 @@ There are a large number of experiment tracking API's available, however getting
 
 ## Integrated Trackers
 
-Currently `Accelerate` supports seven trackers out-of-the-box:
+Currently `Accelerate` supports four 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

docs/source/usage_guides/training_zoo.md

@@ -15,7 +15,7 @@ rendered properly in your Markdown viewer.
 
 # Example Zoo
 
-Below contains a non-exhaustive list of tutorials and scripts showcasing 🤗 Accelerate
+Below contains a non-exhuastive list of tutorials and scripts showcasing 🤗 Accelerate
 
 ## Official Accelerate Examples:
 
@@ -72,11 +72,6 @@ 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)
@@ -159,12 +154,12 @@ Below contains a non-exhaustive list of papers utilizing 🤗 Accelerate.
 * 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).
+* 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].
+* 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).
@@ -177,4 +172,4 @@ Below contains a non-exhaustive list of papers utilizing 🤗 Accelerate.
 * 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).
+* 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

@@ -64,9 +64,9 @@ To run it in each of these various modes, use the following commands:
         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 (`python -m torch.distributed.run` can be used instead of `torchrun`)
+    * With traditional PyTorch launcher (`torch.distributed.launch` can be used with older versions of PyTorch)
         ```bash
-        torchrun --nproc_per_node 2 ./nlp_example.py
+        python -m torchrun --nproc_per_node 2 --use_env ./nlp_example.py
         ```
 - multi GPUs, multi node (several machines, using PyTorch distributed mode)
     * With Accelerate config and launcher, on each machine:
@@ -74,15 +74,18 @@ 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 (`python -m torch.distributed.run` can be used instead of `torchrun`). Run this command on each node:
+    * With PyTorch launcher only (`torch.distributed.launch` can be used in older versions of PyTorch)
         ```bash
-        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
+        python -m torchrun --nproc_per_node 2 \
+            --use_env \
+            --node_rank 0 \
+            --master_addr master_node_ip_address \
+            ./nlp_example.py  # On the first server
+        python -m torchrun --nproc_per_node 2 \
+            --use_env \
+            --node_rank 1 \
+            --master_addr master_node_ip_address \
+            ./nlp_example.py  # On the second server
         ```
 - (multi) TPUs
     * With Accelerate config and launcher
@@ -146,34 +149,37 @@ To run it in each of these various modes, use the following commands:
 - multi GPUs (using PyTorch distributed mode)
     * With Accelerate config and launcher
         ```bash
-        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
+        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
         ```
-    * With traditional PyTorch launcher (`python -m torch.distributed.run` can be used instead of `torchrun`)
+    * With traditional PyTorch launcher (`torch.distributed.launch` can be used with older versions of PyTorch)
         ```bash
-        torchrun --nproc_per_node 2 ./cv_example.py --data_dir path_to_data
+        python -m torchrun --nproc_per_node 2 --use_env ./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 --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
+        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
         ```
-    * With PyTorch launcher only (`python -m torch.distributed.run` can be used instead of `torchrun`). Run this command on each node:
+    * With PyTorch launcher only (`torch.distributed.launch` can be used with older versions of PyTorch)
         ```bash
-        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
+        python -m torchrun --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 torchrun --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
         ```
 - (multi) TPUs
     * With Accelerate config and launcher
         ```bash
-        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
+        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
         ```
     * In PyTorch:
         Add an `xmp.spawn` line in your script as you usually do.
@@ -200,29 +206,6 @@ with `pip install runhouse`, and you can refer to
 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 `$SLURM_GPUS`, we indicate that we want to utilize all the GPUs available on the node we have requested. 
-
-In [/slurm/submit_multinode.sh](./slurm/submit_multinode.sh) we must specify the number of nodes that will be part of the training (`--num_machines`), how many GPUs we will use in total (`--num_processes`), the [`backend`](https://pytorch.org/docs/stable/elastic/run.html#note-on-rendezvous-backend), `--main_process_ip` which will be the address the master node and the `--main_process_port`.
-
-In both scripts, we run `activateEnviroment.sh` at the beginning. This script should contain the necessary instructions to initialize the environment for execution. Below, we show an example that loads the necessary libraries ([Environment modules](https://github.com/cea-hpc/modules)), activates the Python environment, and sets up various environment variables, most of them to run the scripts in offline mode in case we don't have internet connection from the cluster.
-
-```bash
-# activateEnvironment.sh 
-module purge
-module load anaconda3/2020.02 cuda/10.2 cudnn/8.0.5 nccl/2.9.9 arrow/7.0.0 openmpi
-source activate /home/nct01/nct01328/pytorch_antoni_local
-
-export HF_HOME=/gpfs/projects/nct01/nct01328/
-export HF_LOCAL_HOME=/gpfs/projects/nct01/nct01328/HF_LOCAL
-export HF_DATASETS_OFFLINE=1
-export TRANSFORMERS_OFFLINE=1
-export PYTHONPATH=/home/nct01/nct01328/transformers-in-supercomputers:$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/checkpointing.py

@@ -1,3 +1,4 @@
+# coding=utf-8
 # Copyright 2021 The HuggingFace Inc. team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -85,7 +86,7 @@ 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.
-        max_length = 128 if accelerator.distributed_type == DistributedType.XLA else None
+        max_length = 128 if accelerator.distributed_type == DistributedType.TPU else None
         # When using mixed precision we want round multiples of 8/16
         if accelerator.mixed_precision == "fp8":
             pad_to_multiple_of = 16
@@ -153,7 +154,7 @@ def training_function(config, args):
 
     # If the batch size is too big we use gradient accumulation
     gradient_accumulation_steps = 1
-    if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.XLA:
+    if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
         gradient_accumulation_steps = batch_size // MAX_GPU_BATCH_SIZE
         batch_size = MAX_GPU_BATCH_SIZE
 

examples/by_feature/cross_validation.py

@@ -1,3 +1,4 @@
+# coding=utf-8
 # Copyright 2022 The HuggingFace Inc. team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -105,7 +106,7 @@ 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.
-        max_length = 128 if accelerator.distributed_type == DistributedType.XLA else None
+        max_length = 128 if accelerator.distributed_type == DistributedType.TPU else None
         # When using mixed precision we want round multiples of 8/16
         if accelerator.mixed_precision == "fp8":
             pad_to_multiple_of = 16
@@ -156,7 +157,7 @@ def training_function(config, args):
 
     # If the batch size is too big we use gradient accumulation
     gradient_accumulation_steps = 1
-    if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.XLA:
+    if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
         gradient_accumulation_steps = batch_size // MAX_GPU_BATCH_SIZE
         batch_size = MAX_GPU_BATCH_SIZE
 

examples/by_feature/deepspeed_with_config_support.py

@@ -1,4 +1,5 @@
 #!/usr/bin/env python
+# coding=utf-8
 # Copyright 2022 The HuggingFace Inc. team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -219,7 +220,7 @@ def parse_args():
         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.'
+            ' `"wandb"` and `"comet_ml"`. Use `"all"` (default) to report to all integrations.'
             "Only applicable when `--with_tracking` is passed."
         ),
     )
@@ -242,6 +243,39 @@ def parse_args():
     return args
 
 
+# New Code #
+def checkpoint_model(checkpoint_folder, ckpt_id, model, epoch, last_global_step, **kwargs):
+    """Utility function for checkpointing model + optimizer dictionaries
+    The main purpose for this is to be able to resume training from that instant again
+    """
+    checkpoint_state_dict = {
+        "epoch": epoch,
+        "last_global_step": last_global_step,
+    }
+    # Add extra kwargs too
+    checkpoint_state_dict.update(kwargs)
+
+    success = model.save_checkpoint(checkpoint_folder, ckpt_id, checkpoint_state_dict)
+    status_msg = f"checkpointing: checkpoint_folder={checkpoint_folder}, ckpt_id={ckpt_id}"
+    if success:
+        logging.info(f"Success {status_msg}")
+    else:
+        logging.warning(f"Failure {status_msg}")
+    return
+
+
+# New Code #
+def load_training_checkpoint(model, load_dir, tag=None, **kwargs):
+    """Utility function for checkpointing model + optimizer dictionaries
+    The main purpose for this is to be able to resume training from that instant again
+    """
+    _, checkpoint_state_dict = model.load_checkpoint(load_dir, tag=tag, **kwargs)
+    epoch = checkpoint_state_dict["epoch"]
+    last_global_step = checkpoint_state_dict["last_global_step"]
+    del checkpoint_state_dict
+    return (epoch, last_global_step)
+
+
 # New Code #
 def evaluate(args, model, eval_dataloader, accelerator, eval_dataset):
     model.eval()
@@ -268,20 +302,9 @@ def main():
     # 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)
+        Accelerator(log_with=args.report_to, logging_dir=args.output_dir) if args.with_tracking else Accelerator()
     )
-
     # Make one log on every process with the configuration for debugging.
     logging.basicConfig(
         format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
@@ -511,15 +534,21 @@ def main():
     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:
+    if accelerator.distributed_type == DistributedType.TPU:
         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
+
+    # New Code
+    # Get gradient accumulation steps from deepspeed config if available
+    if accelerator.state.deepspeed_plugin is not None:
+        args.gradient_accumulation_steps = accelerator.state.deepspeed_plugin.deepspeed_config[
+            "gradient_accumulation_steps"
+        ]
+
+    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
     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)
 
@@ -546,16 +575,16 @@ def main():
     )
 
     # 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)
+    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
+    args.max_train_steps = args.num_train_epochs * 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)
+    if hasattr(args.checkpointing_steps, "isdigit"):
+        checkpointing_steps = args.checkpointing_steps
+        if args.checkpointing_steps.isdigit():
+            checkpointing_steps = int(args.checkpointing_steps)
+    else:
+        checkpointing_steps = None
 
     # We need to initialize the trackers we use, and also store our configuration.
     # The trackers initializes automatically on the main process.
@@ -566,16 +595,14 @@ def main():
         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
-    )
+    total_batch_size = args.per_device_train_batch_size * accelerator.num_processes * args.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"  Gradient Accumulation steps = {args.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)
@@ -586,61 +613,45 @@ def main():
 
     # Potentially load in the weights and states from a previous save
     if args.resume_from_checkpoint:
-        accelerator.load_state(args.resume_from_checkpoint)
+        # New Code #
+        # Loads the DeepSpeed checkpoint from the specified path
+        _, last_global_step = load_training_checkpoint(
+            model,
+            args.resume_from_checkpoint,
+            **{"load_optimizer_states": True, "load_lr_scheduler_states": True},
+        )
         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)
+        resume_step = last_global_step
+        starting_epoch = resume_step // len(train_dataloader)
+        resume_step -= starting_epoch * len(train_dataloader)
 
     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:
+        for step, batch in enumerate(train_dataloader):
             # 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)
+            if args.resume_from_checkpoint and epoch == starting_epoch:
+                if resume_step is not None and step < resume_step:
+                    completed_steps += 1
+                    continue
+            outputs = model(**batch)
+            loss = outputs.loss
+            # We keep track of the loss at each epoch
+            if args.with_tracking:
+                total_loss += loss.detach().float()
+            loss = loss / args.gradient_accumulation_steps
+            accelerator.backward(loss)
+            if (step + 1) % args.gradient_accumulation_steps == 0 or step == len(train_dataloader) - 1:
                 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
+                progress_bar.update(1)
+                completed_steps += 1
 
             if isinstance(checkpointing_steps, int):
                 if completed_steps % checkpointing_steps == 0:
-                    output_dir = f"step_{completed_steps}"
+                    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)
@@ -655,29 +666,34 @@ def main():
                 {
                     "perplexity": perplexity,
                     "eval_loss": eval_loss,
-                    "train_loss": total_loss / len(train_dataloader),
+                    "train_loss": total_loss.item() / 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 #
+        # Save the DeepSpeed checkpoint to the specified path
+        checkpoint_model(args.output_dir, epoch, model, epoch, completed_steps)
 
         # 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)
+            best_metric_checkpoint = os.path.join(args.output_dir, str(epoch))
             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)
+        _, last_global_step = load_training_checkpoint(
+            model,
+            "/".join(best_metric_checkpoint.split("/")[:-1]),
+            tag=best_metric_checkpoint.split("/")[-1],
+            **{"load_optimizer_states": True, "load_lr_scheduler_states": True},
+        )
 
     # New Code #
     # Evaluates using the best checkpoint

examples/by_feature/early_stopping.py

@@ -1,245 +0,0 @@
-# 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 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
-
-
-########################################################################
-# This is a fully working simple example to use Accelerate
-# specifically showcasing how to perform early stopping,
-# and builds off the `nlp_example.py` script
-#
-# 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, drop_last=True
-    )
-    eval_dataloader = DataLoader(
-        tokenized_datasets["validation"],
-        shuffle=False,
-        collate_fn=collate_fn,
-        batch_size=EVAL_BATCH_SIZE,
-        drop_last=(accelerator.mixed_precision == "fp8"),
-    )
-
-    return train_dataloader, eval_dataloader
-
-
-# New code
-class EarlyStoppingCallback:
-    "A callback class that helps with early stopping"
-
-    def __init__(self, min_delta=0, patience=5):
-        self.min_delta = min_delta
-        self.patience = patience
-        self.counter = 0
-        self.lowest_loss = float("inf")
-
-    def check_early_stopping(self, eval_loss):
-        delta = self.lowest_loss - eval_loss
-        if delta >= self.min_delta:
-            self.lowest_loss = eval_loss
-            self.counter = 0
-        else:
-            self.counter += 1
-            if self.counter >= self.patience:
-                return True
-        return False
-
-
-callback = EarlyStoppingCallback()
-
-
-def training_function(config, args):
-    # 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"])
-    batch_size = int(config["batch_size"])
-
-    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 and accelerator.distributed_type != DistributedType.XLA:
-        gradient_accumulation_steps = batch_size // MAX_GPU_BATCH_SIZE
-        batch_size = MAX_GPU_BATCH_SIZE
-
-    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) // gradient_accumulation_steps,
-    )
-
-    # 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)
-            outputs = model(**batch)
-            loss = outputs.loss
-            loss = loss / gradient_accumulation_steps
-            accelerator.backward(loss)
-            if step % gradient_accumulation_steps == 0:
-                optimizer.step()
-                lr_scheduler.step()
-                optimizer.zero_grad()
-
-            # New code
-            # Check if we should stop the training on any processes
-            if callback.check_early_stopping(loss.item()):
-                accelerator.set_trigger()
-
-            # If so, we break the loop
-            if accelerator.check_trigger():
-                break
-
-        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.",
-    )
-    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/fsdp_with_peak_mem_tracking.py

@@ -1,3 +1,4 @@
+# coding=utf-8
 # Copyright 2021 The HuggingFace Inc. team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -30,7 +31,6 @@ from transformers import (
 )
 
 from accelerate import Accelerator, DistributedType, FullyShardedDataParallelPlugin
-from accelerate.utils import is_npu_available, is_xpu_available
 
 
 ########################################################################
@@ -68,18 +68,9 @@ def b2mb(x):
 class TorchTracemalloc:
     def __enter__(self):
         gc.collect()
-        if torch.cuda.is_available():
-            torch.cuda.empty_cache()
-            torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
-            self.begin = torch.cuda.memory_allocated()
-        elif is_xpu_available():
-            torch.xpu.empty_cache()
-            torch.xpu.reset_max_memory_allocated()  # reset the peak gauge to zero
-            self.begin = torch.xpu.memory_allocated()
-        elif is_npu_available():
-            torch.npu.empty_cache()
-            torch.npu.reset_max_memory_allocated()  # reset the peak gauge to zero
-            self.begin = torch.npu.memory_allocated()
+        torch.cuda.empty_cache()
+        torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
+        self.begin = torch.cuda.memory_allocated()
         self.process = psutil.Process()
 
         self.cpu_begin = self.cpu_mem_used()
@@ -109,18 +100,9 @@ class TorchTracemalloc:
         self.peak_monitoring = False
 
         gc.collect()
-        if torch.cuda.is_available():
-            torch.cuda.empty_cache()
-            self.end = torch.cuda.memory_allocated()
-            self.peak = torch.cuda.max_memory_allocated()
-        elif is_xpu_available():
-            torch.xpu.empty_cache()
-            self.end = torch.xpu.memory_allocated()
-            self.peak = torch.xpu.max_memory_allocated()
-        elif is_npu_available():
-            torch.npu.empty_cache()
-            self.end = torch.npu.memory_allocated()
-            self.peak = torch.npu.max_memory_allocated()
+        torch.cuda.empty_cache()
+        self.end = torch.cuda.memory_allocated()
+        self.peak = torch.cuda.max_memory_allocated()
         self.used = b2mb(self.end - self.begin)
         self.peaked = b2mb(self.peak - self.begin)
 
@@ -208,13 +190,13 @@ def training_function(config, args):
 
     # If the batch size is too big we use gradient accumulation
     gradient_accumulation_steps = 1
-    if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.XLA:
+    if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
         gradient_accumulation_steps = batch_size // MAX_GPU_BATCH_SIZE
         batch_size = MAX_GPU_BATCH_SIZE
 
     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
+        max_length = 128 if accelerator.distributed_type == DistributedType.TPU else None
         # When using mixed precision we want round multiples of 8/16
         if accelerator.mixed_precision == "fp8":
             pad_to_multiple_of = 16
@@ -246,19 +228,16 @@ def training_function(config, args):
         args.model_name_or_path, return_dict=True, low_cpu_mem_usage=True
     )
 
-    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": 0.003,
-        },
-        {
-            "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 #
+    # For FSDP feature, it is highly recommended and efficient to prepare the model before creating optimizer
+    model = accelerator.prepare(model)
+    accelerator.print(model)
 
-    optimizer = torch.optim.AdamW(params=optimizer_grouped_parameters, lr=lr, weight_decay=2e-4)
+    # Instantiate optimizer
+    # New Code #
+    # For FSDP feature, at present it doesn't support multiple parameter groups,
+    # so we need to create a single parameter group for the whole model
+    optimizer = torch.optim.AdamW(params=model.parameters(), lr=lr, weight_decay=2e-4)
 
     # Instantiate scheduler
     lr_scheduler = get_linear_schedule_with_warmup(
@@ -267,8 +246,13 @@ def training_function(config, args):
         num_training_steps=(len(train_dataloader) * num_epochs) // gradient_accumulation_steps,
     )
 
-    model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = accelerator.prepare(
-        model, optimizer, train_dataloader, eval_dataloader, lr_scheduler
+    # New Code #
+    # For FSDP feature, prepare everything except the model as we have already prepared the model
+    # before creating the optimizer
+    # 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.
+    optimizer, train_dataloader, eval_dataloader, lr_scheduler = accelerator.prepare(
+        optimizer, train_dataloader, eval_dataloader, lr_scheduler
     )
 
     overall_step = 0
@@ -313,6 +297,7 @@ def training_function(config, args):
                 batch.to(accelerator.device)
                 outputs = model(**batch)
                 loss = outputs.loss
+                loss = loss / gradient_accumulation_steps
                 # We keep track of the loss at each epoch
                 if args.with_tracking:
                     total_loss += loss.detach().float()
@@ -333,11 +318,13 @@ def training_function(config, args):
                         accelerator.save_state(output_dir)
         # New Code #
         # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
-        accelerator.print(f"Memory before entering the train : {b2mb(tracemalloc.begin)}")
-        accelerator.print(f"Memory consumed at the end of the train (end-begin): {tracemalloc.used}")
-        accelerator.print(f"Peak Memory consumed during the train (max-begin): {tracemalloc.peaked}")
+        accelerator.print("Memory before entering the train : {}".format(b2mb(tracemalloc.begin)))
+        accelerator.print("Memory consumed at the end of the train (end-begin): {}".format(tracemalloc.used))
+        accelerator.print("Peak Memory consumed during the train (max-begin): {}".format(tracemalloc.peaked))
         accelerator.print(
-            f"Total Peak Memory consumed during the train (max): {tracemalloc.peaked + b2mb(tracemalloc.begin)}"
+            "Total Peak Memory consumed during the train (max): {}".format(
+                tracemalloc.peaked + b2mb(tracemalloc.begin)
+            )
         )
         # Logging the peak memory usage of the GPU to the tracker
         if args.with_tracking:
@@ -384,11 +371,11 @@ def training_function(config, args):
                 accelerator.save_state(output_dir)
         # New Code #
         # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
-        accelerator.print(f"Memory before entering the eval : {b2mb(tracemalloc.begin)}")
-        accelerator.print(f"Memory consumed at the end of the eval (end-begin): {tracemalloc.used}")
-        accelerator.print(f"Peak Memory consumed during the eval (max-begin): {tracemalloc.peaked}")
+        accelerator.print("Memory before entering the eval : {}".format(b2mb(tracemalloc.begin)))
+        accelerator.print("Memory consumed at the end of the eval (end-begin): {}".format(tracemalloc.used))
+        accelerator.print("Peak Memory consumed during the eval (max-begin): {}".format(tracemalloc.peaked))
         accelerator.print(
-            f"Total Peak Memory consumed during the eval (max): {tracemalloc.peaked + b2mb(tracemalloc.begin)}"
+            "Total Peak Memory consumed during the eval (max): {}".format(tracemalloc.peaked + b2mb(tracemalloc.begin))
         )
         # Logging the peak memory usage of the GPU to the tracker
         if args.with_tracking:

examples/by_feature/gradient_accumulation.py

@@ -1,3 +1,4 @@
+# coding=utf-8
 # Copyright 2021 The HuggingFace Inc. team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -80,7 +81,7 @@ 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.
-        max_length = 128 if accelerator.distributed_type == DistributedType.XLA else None
+        max_length = 128 if accelerator.distributed_type == DistributedType.TPU else None
         # When using mixed precision we want round multiples of 8/16
         if accelerator.mixed_precision == "fp8":
             pad_to_multiple_of = 16
@@ -125,7 +126,7 @@ def training_function(config, args):
     accelerator = Accelerator(
         cpu=args.cpu, mixed_precision=args.mixed_precision, gradient_accumulation_steps=gradient_accumulation_steps
     )
-    if accelerator.distributed_type == DistributedType.XLA and gradient_accumulation_steps > 1:
+    if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
         raise NotImplementedError(
             "Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`"
         )

examples/by_feature/local_sgd.py

@@ -1,3 +1,4 @@
+# coding=utf-8
 # Copyright 2023 The HuggingFace Inc. team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -83,7 +84,7 @@ 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.
-        max_length = 128 if accelerator.distributed_type == DistributedType.XLA else None
+        max_length = 128 if accelerator.distributed_type == DistributedType.TPU else None
         # When using mixed precision we want round multiples of 8/16
         if accelerator.mixed_precision == "fp8":
             pad_to_multiple_of = 16
@@ -129,6 +130,8 @@ def training_function(config, args):
     accelerator = Accelerator(
         cpu=args.cpu, mixed_precision=args.mixed_precision, gradient_accumulation_steps=gradient_accumulation_steps
     )
+    if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
+        raise NotImplementedError("LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)")
     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     lr = config["lr"]
     num_epochs = int(config["num_epochs"])

examples/by_feature/megatron_lm_gpt_pretraining.py

@@ -1,4 +1,5 @@
 #!/usr/bin/env python
+# coding=utf-8
 # Copyright 2021 The HuggingFace Inc. team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -215,7 +216,7 @@ def parse_args():
         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.'
+            ' `"wandb"` and `"comet_ml"`. Use `"all"` (default) to report to all integrations.'
             "Only applicable when `--with_tracking` is passed."
         ),
     )
@@ -404,7 +405,7 @@ def main():
                 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
+        block_size = 1024
     else:
         if args.block_size > tokenizer.model_max_length:
             logger.warning(
@@ -505,7 +506,7 @@ def main():
     )
 
     # On TPU, the tie weights in our model have been disconnected, so we need to restore the ties.
-    if accelerator.distributed_type == DistributedType.XLA:
+    if accelerator.distributed_type == DistributedType.TPU:
         model.tie_weights()
 
     # We need to recalculate our total training steps as the size of the training dataloader may have changed.

examples/by_feature/memory.py

@@ -86,7 +86,7 @@ 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.
-        max_length = 128 if accelerator.distributed_type == DistributedType.XLA else None
+        max_length = 128 if accelerator.distributed_type == DistributedType.TPU else None
         # When using mixed precision we want round multiples of 8/16
         if accelerator.mixed_precision == "fp8":
             pad_to_multiple_of = 16

examples/by_feature/multi_process_metrics.py

@@ -1,3 +1,4 @@
+# coding=utf-8
 # Copyright 2022 The HuggingFace Inc. team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -87,7 +88,7 @@ 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.
-        max_length = 128 if accelerator.distributed_type == DistributedType.XLA else None
+        max_length = 128 if accelerator.distributed_type == DistributedType.TPU else None
         # When using mixed precision we want round multiples of 8/16
         if accelerator.mixed_precision == "fp8":
             pad_to_multiple_of = 16
@@ -138,7 +139,7 @@ def training_function(config, args):
 
     # If the batch size is too big we use gradient accumulation
     gradient_accumulation_steps = 1
-    if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.XLA:
+    if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
         gradient_accumulation_steps = batch_size // MAX_GPU_BATCH_SIZE
         batch_size = MAX_GPU_BATCH_SIZE
 

examples/by_feature/tracking.py

@@ -1,3 +1,4 @@
+# coding=utf-8
 # Copyright 2021 The HuggingFace Inc. team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -85,7 +86,7 @@ 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.
-        max_length = 128 if accelerator.distributed_type == DistributedType.XLA else None
+        max_length = 128 if accelerator.distributed_type == DistributedType.TPU else None
         # When using mixed precision we want round multiples of 8/16
         if accelerator.mixed_precision == "fp8":
             pad_to_multiple_of = 16
@@ -148,7 +149,7 @@ def training_function(config, args):
 
     # If the batch size is too big we use gradient accumulation
     gradient_accumulation_steps = 1
-    if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.XLA:
+    if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
         gradient_accumulation_steps = batch_size // MAX_GPU_BATCH_SIZE
         batch_size = MAX_GPU_BATCH_SIZE
 

examples/complete_cv_example.py

@@ -1,3 +1,4 @@
+# coding=utf-8
 # Copyright 2021 The HuggingFace Inc. team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");

examples/complete_nlp_example.py

@@ -1,3 +1,4 @@
+# coding=utf-8
 # Copyright 2021 The HuggingFace Inc. team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -102,13 +103,13 @@ def training_function(config, args):
 
     # If the batch size is too big we use gradient accumulation
     gradient_accumulation_steps = 1
-    if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.XLA:
+    if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
         gradient_accumulation_steps = batch_size // MAX_GPU_BATCH_SIZE
         batch_size = MAX_GPU_BATCH_SIZE
 
     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
+        max_length = 128 if accelerator.distributed_type == DistributedType.TPU else None
         # When using mixed precision we want round multiples of 8/16
         if accelerator.mixed_precision == "fp8":
             pad_to_multiple_of = 16

examples/cv_example.py

@@ -1,3 +1,4 @@
+# coding=utf-8
 # Copyright 2021 The HuggingFace Inc. team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");

examples/inference/README.md

@@ -1,62 +0,0 @@
-# Distributed inference examples with PiPPy
-
-This repo contains a variety of tutorials for using the [PiPPy](https://github.com/PyTorch/PiPPy) pipeline parallelism library with accelerate. You will find examples covering:
-
-1. How to trace the model using `accelerate.prepare_pippy`
-2. How to specify inputs based on what the model expects (when to use `kwargs`, `args`, and such)
-3. How to gather the results at the end.
-
-## Installation
-
-This requires the `main` branch of accelerate (or a version at least 0.27.0),  `pippy` version of 0.2.0 or greater, and at least python 3.9. Please install using `pip install .` to pull from the `setup.py` in this repo, or run manually:
-
-```bash
-pip install 'accelerate>=0.27.0' 'torchpippy>=0.2.0'
-```
-
-## Running code
-
-You can either use `torchrun` or the recommended way of `accelerate launch` (without needing to run `accelerate config`) on each script:
-
-```bash
-accelerate launch bert.py
-```
-
-Or:
-
-```bash
-accelerate launch --num_processes {NUM_GPUS} bert.py
-```
-
-Or:
-
-```bash
-torchrun --nproc-per-node {NUM_GPUS} bert.py
-```
-
-## General speedups
-
-One can expect that PiPPy will outperform native model parallism by a multiplicative factor since all GPUs are running at all times with inputs, rather than one input being passed through a GPU at a time waiting for the prior to finish. 
-
-Below are some benchmarks we have found when using the accelerate-pippy integration for a few models when running on 2x4090's:
-
-### Bert
-
-|  | Accelerate/Sequential | PiPPy + Accelerate |
-|---|---|---|
-| First batch | 0.2137s | 0.3119s |
-| Average of 5 batches | 0.0099s | **0.0062s** |
-
-### GPT2
-
-|  | Accelerate/Sequential | PiPPy + Accelerate |
-|---|---|---|
-| First batch | 0.1959s | 0.4189s |
-| Average of 5 batches | 0.0205s | **0.0126s** |
-
-### T5
-
-|  | Accelerate/Sequential | PiPPy + Accelerate |
-|---|---|---|
-| First batch | 0.2789s | 0.3809s |
-| Average of 5 batches | 0.0198s | **0.0166s** |

examples/inference/bert.py

@@ -1,78 +0,0 @@
-# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-import time
-
-import torch
-from transformers import AutoModelForMaskedLM
-
-from accelerate import PartialState, prepare_pippy
-from accelerate.utils import set_seed
-
-
-# Set the random seed to have reproducable outputs
-set_seed(42)
-
-# Create an example model
-model = AutoModelForMaskedLM.from_pretrained("bert-base-uncased")
-model.eval()
-
-# Input configs
-# Create example inputs for the model
-input = torch.randint(
-    low=0,
-    high=model.config.vocab_size,
-    size=(2, 512),  # bs x seq_len
-    device="cpu",
-    dtype=torch.int64,
-    requires_grad=False,
-)
-
-
-# Create a pipeline stage from the model
-# Using `auto` is equivalent to letting `device_map="auto"` figure
-# out device mapping and will also split the model according to the
-# number of total GPUs available if it fits on one GPU
-model = prepare_pippy(model, split_points="auto", example_args=(input,))
-
-# You can pass `gather_output=True` to have the output from the model
-# available on all GPUs
-# model = prepare_pippy(model, split_points="auto", example_args=(input,), gather_output=True)
-
-# Move the inputs to the first device
-input = input.to("cuda:0")
-
-# Take an average of 5 times
-# Measure first batch
-torch.cuda.synchronize()
-start_time = time.time()
-with torch.no_grad():
-    output = model(input)
-torch.cuda.synchronize()
-end_time = time.time()
-first_batch = end_time - start_time
-
-# Now that CUDA is init, measure after
-torch.cuda.synchronize()
-start_time = time.time()
-for i in range(5):
-    with torch.no_grad():
-        output = model(input)
-torch.cuda.synchronize()
-end_time = time.time()
-
-# The outputs are only on the final process by default
-if PartialState().is_last_process:
-    output = torch.stack(tuple(output[0]))
-    print(f"Time of first pass: {first_batch}")
-    print(f"Average time per batch: {(end_time - start_time) / 5}")

examples/inference/gpt2.py

@@ -1,77 +0,0 @@
-# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-import time
-
-import torch
-from transformers import AutoModelForSequenceClassification
-
-from accelerate import PartialState, prepare_pippy
-from accelerate.utils import set_seed
-
-
-# Set the random seed to have reproducable outputs
-set_seed(42)
-
-# Create an example model
-model = AutoModelForSequenceClassification.from_pretrained("gpt2")
-model.eval()
-
-# Input configs
-# Create example inputs for the model
-input = torch.randint(
-    low=0,
-    high=model.config.vocab_size,
-    size=(2, 1024),  # bs x seq_len
-    device="cpu",
-    dtype=torch.int64,
-    requires_grad=False,
-)
-
-# Create a pipeline stage from the model
-# Using `auto` is equivalent to letting `device_map="auto"` figure
-# out device mapping and will also split the model according to the
-# number of total GPUs available if it fits on one GPU
-model = prepare_pippy(model, split_points="auto", example_args=(input,))
-
-# You can pass `gather_output=True` to have the output from the model
-# available on all GPUs
-# model = prepare_pippy(model, split_points="auto", example_args=(input,), gather_output=True)
-
-# Move the inputs to the first device
-input = input.to("cuda:0")
-
-# Take an average of 5 times
-# Measure first batch
-torch.cuda.synchronize()
-start_time = time.time()
-with torch.no_grad():
-    output = model(input)
-torch.cuda.synchronize()
-end_time = time.time()
-first_batch = end_time - start_time
-
-# Now that CUDA is init, measure after
-torch.cuda.synchronize()
-start_time = time.time()
-for i in range(5):
-    with torch.no_grad():
-        output = model(input)
-torch.cuda.synchronize()
-end_time = time.time()
-
-# The outputs are only on the final process by default
-if PartialState().is_last_process:
-    output = torch.stack(tuple(output[0]))
-    print(f"Time of first pass: {first_batch}")
-    print(f"Average time per batch: {(end_time - start_time) / 5}")

examples/inference/llama.py

@@ -1,54 +0,0 @@
-# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-import torch
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-from accelerate import PartialState, prepare_pippy
-
-
-# sdpa implementation which is the default torch>2.1.2 fails with the tracing + attention mask kwarg
-# with attn_implementation="eager" mode, the forward is very slow for some reason
-model = AutoModelForCausalLM.from_pretrained(
-    "meta-llama/Llama-2-7b-chat-hf", low_cpu_mem_usage=True, attn_implementation="sdpa"
-)
-model.eval()
-
-# Input configs
-# Create example inputs for the model
-tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-chat-hf")
-prompts = ("I would like to", "I really like to", "The weather is")  # bs = 3
-tokenizer.pad_token = tokenizer.eos_token
-inputs = tokenizer(prompts, return_tensors="pt", padding=True)
-
-# Create a pipeline stage from the model
-# Using `auto` is equivalent to letting `device_map="auto"` figure
-# out device mapping and will also split the model according to the
-# number of total GPUs available if it fits on one GPU
-model = prepare_pippy(model, split_points="auto", example_args=inputs)
-
-# You can pass `gather_output=True` to have the output from the model
-# available on all GPUs
-# model = prepare_pippy(model, split_points="auto", example_args=(input,), gather_output=True)
-
-# currently we don't support `model.generate`
-# output = model.generate(**inputs, max_new_tokens=1)
-
-with torch.no_grad():
-    output = model(**inputs)
-
-# The outputs are only on the final process by default
-if PartialState().is_last_process:
-    next_token_logits = output[0][:, -1, :]
-    next_token = torch.argmax(next_token_logits, dim=-1)
-    print(tokenizer.batch_decode(next_token))

examples/inference/requirements.txt

@@ -1,2 +0,0 @@
-accelerate
-pippy>=0.2.0

examples/inference/t5.py

@@ -1,89 +0,0 @@
-# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-import time
-
-import torch
-from transformers import AutoModelForSeq2SeqLM
-
-from accelerate import PartialState, prepare_pippy
-from accelerate.utils import set_seed
-
-
-# Set the random seed to have reproducable outputs
-set_seed(42)
-
-# Create an example model
-model = AutoModelForSeq2SeqLM.from_pretrained("t5-small")
-model.eval()
-
-# Input configs
-# Create example inputs for the model
-input = torch.randint(
-    low=0,
-    high=model.config.vocab_size,
-    size=(2, 1024),  # bs x seq_len
-    device="cpu",
-    dtype=torch.int64,
-    requires_grad=False,
-)
-
-example_inputs = {"input_ids": input, "decoder_input_ids": input}
-
-# Create a pipeline stage from the model
-# Using `auto` is equivalent to letting `device_map="auto"` figure
-# out device mapping and will also split the model according to the
-# number of total GPUs available if it fits on one GPU
-model = prepare_pippy(
-    model,
-    no_split_module_classes=["T5Block"],
-    example_kwargs=example_inputs,
-)
-
-# You can pass `gather_output=True` to have the output from the model
-# available on all GPUs
-# model = prepare_pippy(
-#     model,
-#     no_split_module_classes=["T5Block"],
-#     example_kwargs=example_inputs,
-#     gather_outputs=True
-# )
-
-# The model expects a tuple during real inference
-# with the data on the first device
-args = (example_inputs["input_ids"].to("cuda:0"), example_inputs["decoder_input_ids"].to("cuda:0"))
-
-# Take an average of 5 times
-# Measure first batch
-torch.cuda.synchronize()
-start_time = time.time()
-with torch.no_grad():
-    output = model(*args)
-torch.cuda.synchronize()
-end_time = time.time()
-first_batch = end_time - start_time
-
-# Now that CUDA is init, measure after
-torch.cuda.synchronize()
-start_time = time.time()
-for i in range(5):
-    with torch.no_grad():
-        output = model(*args)
-torch.cuda.synchronize()
-end_time = time.time()
-
-# The outputs are only on the final process by default
-if PartialState().is_last_process:
-    output = torch.stack(tuple(output[0]))
-    print(f"Time of first pass: {first_batch}")
-    print(f"Average time per batch: {(end_time - start_time) / 5}")

examples/multigpu_remote_launcher.py

@@ -1,16 +1,3 @@
-# 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 argparse
 
 import runhouse as rh
@@ -24,7 +11,7 @@ def launch_train(*args):
     num_processes = torch.cuda.device_count()
     print(f"Device count: {num_processes}")
     with patch_environment(
-        world_size=num_processes, master_addr="127.0.0.1", master_port="29500", mixed_precision=args[1].mixed_precision
+        world_size=num_processes, master_addr="127.0.01", master_port="29500", mixed_precision=args[1].mixed_precision
     ):
         launcher = PrepareForLaunch(training_function, distributed_type="MULTI_GPU")
         torch.multiprocessing.start_processes(launcher, args=args, nprocs=num_processes, start_method="spawn")

examples/nlp_example.py

@@ -1,3 +1,4 @@
+# coding=utf-8
 # Copyright 2021 The HuggingFace Inc. team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -77,8 +78,8 @@ def get_dataloaders(accelerator: Accelerator, batch_size: int = 16):
     tokenized_datasets = tokenized_datasets.rename_column("label", "labels")
 
     def collate_fn(examples):
-        # For Torchxla, 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
+        # 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.TPU else None
         # When using mixed precision we want round multiples of 8/16
         if accelerator.mixed_precision == "fp8":
             pad_to_multiple_of = 16
@@ -123,7 +124,7 @@ def training_function(config, args):
 
     # If the batch size is too big we use gradient accumulation
     gradient_accumulation_steps = 1
-    if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.XLA:
+    if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
         gradient_accumulation_steps = batch_size // MAX_GPU_BATCH_SIZE
         batch_size = MAX_GPU_BATCH_SIZE
 

examples/slurm/submit_multigpu.sh

@@ -1,27 +0,0 @@
-#!/bin/bash
-
-#SBATCH --job-name=multigpu
-#SBATCH -D .
-#SBATCH --output=O-%x.%j
-#SBATCH --error=E-%x.%j
-#SBATCH --nodes=1
-#SBATCH --ntasks-per-node=1         # number of MP tasks
-#SBATCH --gres=gpu:4                # number of GPUs per node
-#SBATCH --cpus-per-task=160         # number of cores per tasks
-#SBATCH --time=01:59:00             # maximum execution time (HH:MM:SS)
-
-######################
-### Set enviroment ###
-######################
-source activateEnviroment.sh
-export GPUS_PER_NODE=4
-######################
-
-export SCRIPT=/accelerate/examples/complete_nlp_example.py
-export SCRIPT_ARGS=" \
-    --mixed_precision fp16 \
-    --output_dir /accelerate/examples/output \
-    --with_tracking \
-    "
-
-accelerate launch --num_processes $GPUS_PER_NODE $SCRIPT $SCRIPT_ARGS

examples/slurm/submit_multinode.sh

@@ -1,41 +0,0 @@
-#!/bin/bash
-
-#SBATCH --job-name=multinode
-#SBATCH -D .
-#SBATCH --output=O-%x.%j
-#SBATCH --error=E-%x.%j
-#SBATCH --nodes=4                   # number of nodes
-#SBATCH --ntasks-per-node=1         # number of MP tasks
-#SBATCH --gres=gpu:4                # number of GPUs per node
-#SBATCH --cpus-per-task=160         # number of cores per tasks
-#SBATCH --time=01:59:00             # maximum execution time (HH:MM:SS)
-
-######################
-### Set enviroment ###
-######################
-source activateEnviroment.sh
-export GPUS_PER_NODE=4
-######################
-
-######################
-#### Set network #####
-######################
-head_node_ip=$(scontrol show hostnames $SLURM_JOB_NODELIST | head -n 1)
-######################
-
-export LAUNCHER="accelerate launch \
-    --num_processes $((SLURM_NNODES * GPUS_PER_NODE)) \
-    --num_machines $SLURM_NNODES \
-    --rdzv_backend c10d \
-    --main_process_ip $head_node_ip \
-    --main_process_port 29500 \
-    "
-export SCRIPT="/accelerate/examples/complete_nlp_example.py"
-export SCRIPT_ARGS=" \
-    --mixed_precision fp16 \
-    --output_dir /accelerate/examples/output \
-    "
-    
-# This step is necessary because accelerate launch does not handle multiline arguments properly
-export CMD="$LAUNCHER $PYTHON_FILE $ARGS" 
-srun $CMD

pyproject.toml

@@ -1,44 +1,17 @@
-[tool.ruff]
+[tool.black]
 line-length = 119
-target-version = "py38"
+target-version = ['py37']
 
-[tool.ruff.lint]
-preview = true
-ignore-init-module-imports = true
-extend-select = [
-    "B009", # static getattr
-    "B010", # static setattr
-    "CPY", # Copyright
-    "E", # PEP8 errors
-    "F", # PEP8 formatting
-    "I", # Import sorting
-    "TID251", # Banned API
-    "UP", # Pyupgrade
-    "W", # PEP8 warnings
-]
-ignore = [
-    "E501", # Line length (handled by ruff-format)
-    "E741", # Ambiguous variable name
-    "W605", # Invalid escape sequence
-    "UP007", # X | Y type annotations
-]
+[tool.ruff]
+# Never enforce `E501` (line length violations).
+ignore = ["E501", "E741", "W605"]
+select = ["E", "F", "I", "W"]
+line-length = 119
 
-[tool.ruff.lint.per-file-ignores]
-"__init__.py" = [
-    "F401", # Ignore seemingly unused imports (they're meant for re-export)
-]
-"manim_animations/*" = ["ALL"]
+# Ignore import violations in all `__init__.py` files.
+[tool.ruff.per-file-ignores]
+"__init__.py" = ["E402", "F401", "F403", "F811"]
 
-[tool.ruff.lint.isort]
+[tool.ruff.isort]
 lines-after-imports = 2
 known-first-party = ["accelerate"]
-
-[tool.ruff.format]
-exclude = [
-    "manim_animations/*"
-]
-
-[tool.ruff.lint.flake8-tidy-imports.banned-api]
-"os.getenv".msg = "Use os.environ instead"
-"os.putenv".msg = "Use os.environ instead"
-"os.unsetenv".msg = "Use os.environ instead"

setup.cfg

@@ -0,0 +1,14 @@
+[isort]
+default_section = FIRSTPARTY
+ensure_newline_before_comments = True
+force_grid_wrap = 0
+include_trailing_comma = True
+known_first_party = accelerate
+line_length = 119
+lines_after_imports = 2
+multi_line_output = 3
+use_parentheses = True
+
+[flake8]
+ignore = E203, E722, E501, E741, W503, W605
+max-line-length = 119

setup.py

@@ -12,33 +12,18 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from setuptools import find_packages, setup
-
+from setuptools import setup
+from setuptools import find_packages
 
 extras = {}
-extras["quality"] = [
-    "black ~= 23.1",  # hf-doc-builder has a hidden dependency on `black`
-    "hf-doc-builder >= 0.3.0",
-    "ruff ~= 0.2.1",
-]
+extras["quality"] = ["black ~= 23.1", "ruff >= 0.0.241", "hf-doc-builder >= 0.3.0", "urllib3 < 2.0.0"]
 extras["docs"] = []
-extras["test_prod"] = ["pytest>=7.2.0,<=8.0.0", "pytest-xdist", "pytest-subtests", "parameterized"]
-extras["test_dev"] = [
-    "datasets",
-    "evaluate",
-    "torchpippy>=0.2.0",
-    "transformers",
-    "scipy",
-    "scikit-learn",
-    "deepspeed<0.13.0",
-    "tqdm",
-    "bitsandbytes",
-    "timm",
-]
+extras["test_prod"] = ["pytest", "pytest-xdist", "pytest-subtests", "parameterized"]
+extras["test_dev"] = ["datasets", "evaluate", "transformers", "scipy", "scikit-learn", "deepspeed", "tqdm"]
 extras["testing"] = extras["test_prod"] + extras["test_dev"]
 extras["rich"] = ["rich"]
 
-extras["test_trackers"] = ["wandb", "comet-ml", "tensorboard", "dvclive"]
+extras["test_trackers"] = ["wandb", "comet-ml", "tensorboard"]
 extras["dev"] = extras["quality"] + extras["testing"] + extras["rich"]
 
 extras["sagemaker"] = [
@@ -47,9 +32,9 @@ extras["sagemaker"] = [
 
 setup(
     name="accelerate",
-    version="0.28.0.dev",
+    version="0.21.0",
     description="Accelerate",
-    long_description=open("README.md", encoding="utf-8").read(),
+    long_description=open("README.md", "r", encoding="utf-8").read(),
     long_description_content_type="text/markdown",
     keywords="deep learning",
     license="Apache",
@@ -62,20 +47,11 @@ setup(
         "console_scripts": [
             "accelerate=accelerate.commands.accelerate_cli:main",
             "accelerate-config=accelerate.commands.config:main",
-            "accelerate-estimate-memory=accelerate.commands.estimate:main",
             "accelerate-launch=accelerate.commands.launch:main",
         ]
     },
     python_requires=">=3.8.0",
-    install_requires=[
-        "numpy>=1.17",
-        "packaging>=20.0",
-        "psutil",
-        "pyyaml",
-        "torch>=1.10.0",
-        "huggingface_hub",
-        "safetensors>=0.3.1",
-    ],
+    install_requires=["numpy>=1.17", "packaging>=20.0", "psutil", "pyyaml", "torch>=1.10.0"],
     extras_require=extras,
     classifiers=[
         "Development Status :: 5 - Production/Stable",
@@ -91,29 +67,21 @@ setup(
 )
 
 # Release checklist
-# 1. Checkout the release branch (for a patch the current release branch, for a new minor version, create one):
-#      git checkout -b vXX.xx-release
-#    The -b is only necessary for creation (so remove it when doing a patch)
-# 2. Change the version in __init__.py and setup.py to the proper value.
-# 3. Commit these changes with the message: "Release: v<VERSION>"
-# 4. Add a tag in git to mark the release:
-#      git tag v<VERSION> -m 'Adds tag v<VERSION> for pypi'
-#    Push the tag and release commit to git: git push --tags origin vXX.xx-release
-# 5. Run the following commands in the top-level directory:
-#      rm -rf dist
-#      rm -rf build
+# 1. Change the version in __init__.py and setup.py.
+# 2. Commit these changes with the message: "Release: VERSION"
+# 3. Add a tag in git to mark the release: "git tag VERSION -m 'Adds tag VERSION for pypi' "
+#    Push the tag to git: git push --tags origin main
+# 4. Run the following commands in the top-level directory:
 #      python setup.py bdist_wheel
 #      python setup.py sdist
-# 6. Upload the package to the pypi test server first:
-#      twine upload dist/* -r testpypi
-# 7. Check that you can install it in a virtualenv by running:
-#      pip install accelerate
-#      pip uninstall accelerate
+# 5. Upload the package to the pypi test server first:
+#      twine upload dist/* -r pypitest
+#      twine upload dist/* -r pypitest --repository-url=https://test.pypi.org/legacy/
+# 6. Check that you can install it in a virtualenv by running:
 #      pip install -i https://testpypi.python.org/pypi accelerate
 #      accelerate env
 #      accelerate test
-# 8. Upload the final version to actual pypi:
+# 7. Upload the final version to actual pypi:
 #      twine upload dist/* -r pypi
-# 9. Add release notes to the tag in github once everything is looking hunky-dory.
-# 10. Go back to the main branch and update the version in __init__.py, setup.py to the new version ".dev" and push to
-#     main.
+# 8. Add release notes to the tag in github once everything is looking hunky-dory.
+# 9. Update the version in __init__.py, setup.py to the new version "-dev" and push to master

src/accelerate/__init__.py

@@ -1,17 +1,4 @@
-# Copyright 2020 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.
-__version__ = "0.28.0.dev0"
+__version__ = "0.21.0"
 
 from .accelerator import Accelerator
 from .big_modeling import (
@@ -24,12 +11,9 @@ from .big_modeling import (
     load_checkpoint_and_dispatch,
 )
 from .data_loader import skip_first_batches
-from .inference import prepare_pippy
 from .launchers import debug_launcher, notebook_launcher
 from .state import PartialState
 from .utils import (
-    AutocastKwargs,
-    DataLoaderConfiguration,
     DeepSpeedPlugin,
     DistributedDataParallelKwargs,
     DistributedType,

src/accelerate/big_modeling.py

@@ -12,10 +12,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import logging
 import os
 from contextlib import contextmanager
-from functools import wraps
 from typing import Dict, List, Optional, Union
 
 import torch
@@ -36,28 +34,20 @@ from .utils import (
     find_tied_parameters,
     get_balanced_memory,
     infer_auto_device_map,
-    is_npu_available,
-    is_torch_version,
-    is_xpu_available,
     load_checkpoint_in_model,
     offload_state_dict,
-    parse_flag_from_env,
     retie_parameters,
 )
-from .utils.other import recursive_getattr
-
-
-logger = logging.getLogger(__name__)
 
 
 @contextmanager
-def init_empty_weights(include_buffers: bool = None):
+def init_empty_weights(include_buffers: bool = False):
     """
     A context manager under which models are initialized with all parameters on the meta device, therefore creating an
     empty model. Useful when just initializing the model would blow the available RAM.
 
     Args:
-        include_buffers (`bool`, *optional*):
+        include_buffers (`bool`, *optional*, defaults to `False`):
             Whether or not to also put all buffers on the meta device while initializing.
 
     Example:
@@ -75,26 +65,22 @@ def init_empty_weights(include_buffers: bool = None):
 
     Any model created under this context manager has no weights. As such you can't do something like
     `model.to(some_device)` with it. To load weights inside your empty model, see [`load_checkpoint_and_dispatch`].
-    Make sure to overwrite the default device_map param for [`load_checkpoint_and_dispatch`], otherwise dispatch is not
-    called.
 
     </Tip>
     """
-    if include_buffers is None:
-        include_buffers = parse_flag_from_env("ACCELERATE_INIT_INCLUDE_BUFFERS", False)
     with init_on_device(torch.device("meta"), include_buffers=include_buffers) as f:
         yield f
 
 
 @contextmanager
-def init_on_device(device: torch.device, include_buffers: bool = None):
+def init_on_device(device: torch.device, include_buffers: bool = False):
     """
     A context manager under which models are initialized with all parameters on the specified device.
 
     Args:
         device (`torch.device`):
             Device to initialize all parameters on.
-        include_buffers (`bool`, *optional*):
+        include_buffers (`bool`, *optional*, defaults to `False`):
             Whether or not to also put all buffers on the meta device while initializing.
 
     Example:
@@ -107,15 +93,6 @@ def init_on_device(device: torch.device, include_buffers: bool = None):
         tst = nn.Liner(100, 100)  # on `cuda` device
     ```
     """
-    if include_buffers is None:
-        include_buffers = parse_flag_from_env("ACCELERATE_INIT_INCLUDE_BUFFERS", False)
-
-    # TODO(shingjan): remove the torch version check once older versions are deprecated
-    if is_torch_version(">=", "2.0") and include_buffers:
-        with device:
-            yield
-        return
-
     old_register_parameter = nn.Module.register_parameter
     if include_buffers:
         old_register_buffer = nn.Module.register_buffer
@@ -125,7 +102,6 @@ def init_on_device(device: torch.device, include_buffers: bool = None):
         if param is not None:
             param_cls = type(module._parameters[name])
             kwargs = module._parameters[name].__dict__
-            kwargs["requires_grad"] = param.requires_grad
             module._parameters[name] = param_cls(module._parameters[name].to(device), **kwargs)
 
     def register_empty_buffer(module, name, buffer, persistent=True):
@@ -310,7 +286,6 @@ def dispatch_model(
     offload_buffers: bool = False,
     skip_keys: Optional[Union[str, List[str]]] = None,
     preload_module_classes: Optional[List[str]] = None,
-    force_hooks: bool = False,
 ):
     """
     Dispatches a model according to a given device map. Layers of the model might be spread across GPUs, offloaded on
@@ -341,23 +316,17 @@ def dispatch_model(
             of the forward. This should only be used for classes that have submodules which are registered but not
             called directly during the forward, for instance if a `dense` linear layer is registered, but at forward,
             `dense.weight` and `dense.bias` are used in some operations instead of calling `dense` directly.
-        force_hooks (`bool`, *optional*, defaults to `False`):
-            Whether or not to force device hooks to be attached to the model even if all layers are dispatched to a
-            single device.
     """
     # Error early if the device map is incomplete.
     check_device_map(model, device_map)
 
     # for backward compatibility
-    is_bnb_quantized = (
-        getattr(model, "is_quantized", False) or getattr(model, "is_loaded_in_8bit", False)
-    ) and getattr(model, "quantization_method", "bitsandbytes") == "bitsandbytes"
+    is_quantized = getattr(model, "is_quantized", False) or getattr(model, "is_loaded_in_8bit", False)
 
-    # We attach hooks if the device_map has at least 2 different devices or if
-    # force_hooks is set to `True`. Otherwise, the model in already loaded
+    # We attach hooks if the device_map have at least 2 different devices. Otherwise, the model in already loaded
     # in the unique device and the user can decide where to dispatch the model.
     # If the model is quantized, we always force-dispatch the model
-    if (len(set(device_map.values())) > 1) or is_bnb_quantized or force_hooks:
+    if (len(set(device_map.values())) > 1) or is_quantized:
         if main_device is None:
             if set(device_map.values()) == {"cpu"} or set(device_map.values()) == {"cpu", "disk"}:
                 main_device = "cpu"
@@ -398,22 +367,7 @@ def dispatch_model(
         else:
             weights_map = None
 
-        # When dispatching the model's parameters to the devices specified in device_map, we want to avoid allocating memory several times for the
-        # tied parameters. The dictionary tied_params_map keeps track of the already allocated data for a given tied parameter (represented by its
-        # original pointer) on each devices.
         tied_params = find_tied_parameters(model)
-
-        tied_params_map = {}
-        for group in tied_params:
-            for param_name in group:
-                # data_ptr() is enough here, as `find_tied_parameters` finds tied params simply by comparing `param1 is param2`, so we don't need
-                # to care about views of tensors through storage_offset.
-                data_ptr = recursive_getattr(model, param_name).data_ptr()
-                tied_params_map[data_ptr] = {}
-
-                # Note: To handle the disk offloading case, we can not simply use weights_map[param_name].data_ptr() as the reference pointer,
-                # as we have no guarantee that safetensors' `file.get_tensor()` will always give the same pointer.
-
         attach_align_device_hook_on_blocks(
             model,
             execution_device=execution_device,
@@ -422,62 +376,18 @@ def dispatch_model(
             weights_map=weights_map,
             skip_keys=skip_keys,
             preload_module_classes=preload_module_classes,
-            tied_params_map=tied_params_map,
         )
-
-        # warn if there is any params on the meta device
-        offloaded_devices_str = " and ".join(
-            [device for device in set(device_map.values()) if device in ("cpu", "disk")]
-        )
-        if len(offloaded_devices_str) > 0:
-            logging.warning(
-                f"Some parameters are on the meta device device because they were offloaded to the {offloaded_devices_str}."
-            )
-
         # Attaching the hook may break tied weights, so we retie them
         retie_parameters(model, tied_params)
-
-        # add warning to cuda and to method
-        def add_warning(fn, model):
-            @wraps(fn)
-            def wrapper(*args, **kwargs):
-                warning_msg = "You shouldn't move a model that is dispatched using accelerate hooks."
-                if str(fn.__name__) == "to":
-                    to_device = torch._C._nn._parse_to(*args, **kwargs)[0]
-                    if to_device is not None:
-                        logger.warning(warning_msg)
-                else:
-                    logger.warning(warning_msg)
-                for param in model.parameters():
-                    if param.device == torch.device("meta"):
-                        raise RuntimeError("You can't move a model that has some modules offloaded to cpu or disk.")
-                return fn(*args, **kwargs)
-
-            return wrapper
-
-        model.to = add_warning(model.to, model)
-        if is_npu_available():
-            model.npu = add_warning(model.npu, model)
-        elif is_xpu_available():
-            model.xpu = add_warning(model.xpu, model)
-        else:
-            model.cuda = add_warning(model.cuda, model)
-
     else:
         device = list(device_map.values())[0]
-        # `torch.Tensor.to(<int num>)` is not supported by `torch_npu` (see this [issue](https://github.com/Ascend/pytorch/issues/16)).
-        if is_npu_available() and isinstance(device, int):
-            device = f"npu:{device}"
-        elif is_xpu_available() and isinstance(device, int):
-            device = f"xpu:{device}"
         if device != "disk":
             model.to(device)
         else:
             raise ValueError(
                 "You are trying to offload the whole model to the disk. Please use the `disk_offload` function instead."
             )
-    # Convert OrderedDict back to dict for easier usage
-    model.hf_device_map = dict(device_map)
+    model.hf_device_map = device_map
     return model
 
 
@@ -493,7 +403,6 @@ def load_checkpoint_and_dispatch(
     offload_state_dict: Optional[bool] = None,
     skip_keys: Optional[Union[str, List[str]]] = None,
     preload_module_classes: Optional[List[str]] = None,
-    force_hooks: bool = False,
 ):
     """
     Loads a (potentially sharded) checkpoint inside a model, potentially sending weights to a given device as they are
@@ -511,8 +420,7 @@ def load_checkpoint_and_dispatch(
             name, once a given module name is inside, every submodule of it will be sent to the same device.
 
             To have Accelerate compute the most optimized `device_map` automatically, set `device_map="auto"`. For more
-            information about each option see [here](../concept_guides/big_model_inference#designing-a-device-map).
-            Defaults to None, which means [`dispatch_model`] will not be called.
+            information about each option see [here](big_modeling#designing-a-device-map).
         max_memory (`Dict`, *optional*):
             A dictionary device identifier to maximum memory. Will default to the maximum memory available for each GPU
             and the available CPU RAM if unset.
@@ -537,9 +445,6 @@ def load_checkpoint_and_dispatch(
             of the forward. This should only be used for classes that have submodules which are registered but not
             called directly during the forward, for instance if a `dense` linear layer is registered, but at forward,
             `dense.weight` and `dense.bias` are used in some operations instead of calling `dense` directly.
-        force_hooks (`bool`, *optional*, defaults to `False`):
-            Whether or not to force device hooks to be attached to the model even if all layers are dispatched to a
-            single device.
 
     Example:
 
@@ -600,5 +505,4 @@ def load_checkpoint_and_dispatch(
         offload_buffers=offload_buffers,
         skip_keys=skip_keys,
         preload_module_classes=preload_module_classes,
-        force_hooks=force_hooks,
     )

src/accelerate/checkpointing.py

@@ -12,33 +12,29 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import os
 import random
 from pathlib import Path
 from typing import List
 
 import numpy as np
 import torch
-from safetensors.torch import load_file
 from torch.cuda.amp import GradScaler
 
 from .utils import (
     MODEL_NAME,
     OPTIMIZER_NAME,
     RNG_STATE_NAME,
-    SAFE_MODEL_NAME,
-    SAFE_WEIGHTS_NAME,
-    SAMPLER_NAME,
     SCALER_NAME,
     SCHEDULER_NAME,
-    WEIGHTS_NAME,
     get_pretty_name,
-    is_torch_xla_available,
+    is_tpu_available,
     is_xpu_available,
     save,
 )
 
 
-if is_torch_xla_available():
+if is_tpu_available(check_device=False):
     import torch_xla.core.xla_model as xm
 
 from .logging import get_logger
@@ -53,22 +49,12 @@ def save_accelerator_state(
     model_states: List[dict],
     optimizers: list,
     schedulers: list,
-    dataloaders: list,
     process_index: int,
     scaler: GradScaler = None,
-    save_on_each_node: bool = False,
-    safe_serialization: bool = True,
 ):
     """
     Saves the current states of the models, optimizers, scaler, and RNG generators to a given directory.
 
-    <Tip>
-
-    If `safe_serialization` is `True`, models will be saved with `safetensors` while the rest are saved using native
-    `pickle`.
-
-    </Tip>
-
     Args:
         output_dir (`str` or `os.PathLike`):
             The name of the folder to save all relevant weights and states.
@@ -78,58 +64,35 @@ def save_accelerator_state(
             A list of optimizer instances
         schedulers (`List[torch.optim.lr_scheduler._LRScheduler]`):
             A list of learning rate schedulers
-        dataloaders (`List[torch.utils.data.DataLoader]`):
-            A list of dataloader instances to save their sampler states
         process_index (`int`):
             The current process index in the Accelerator state
         scaler (`torch.cuda.amp.GradScaler`, *optional*):
             An optional gradient scaler instance to save
-        save_on_each_node (`bool`, *optional*):
-            Whether to save on every node, or only the main node.
-        safe_serialization (`bool`, *optional*, defaults to `True`):
-            Whether to save the model using `safetensors` or the traditional PyTorch way (that uses `pickle`).
     """
-    output_dir = Path(output_dir)
     # Model states
     for i, state in enumerate(model_states):
-        weights_name = WEIGHTS_NAME if not safe_serialization else SAFE_WEIGHTS_NAME
-        if i > 0:
-            weights_name = weights_name.replace(".", f"_{i}.")
-        output_model_file = output_dir.joinpath(weights_name)
-        save(state, output_model_file, save_on_each_node=save_on_each_node, safe_serialization=safe_serialization)
+        weights_name = f"{MODEL_NAME}.bin" if i == 0 else f"{MODEL_NAME}_{i}.bin"
+        output_model_file = os.path.join(output_dir, weights_name)
+        save(state, output_model_file)
         logger.info(f"Model weights saved in {output_model_file}")
     # Optimizer states
     for i, opt in enumerate(optimizers):
         state = opt.state_dict()
         optimizer_name = f"{OPTIMIZER_NAME}.bin" if i == 0 else f"{OPTIMIZER_NAME}_{i}.bin"
-        output_optimizer_file = output_dir.joinpath(optimizer_name)
-        save(state, output_optimizer_file, save_on_each_node=save_on_each_node, safe_serialization=False)
+        output_optimizer_file = os.path.join(output_dir, optimizer_name)
+        save(state, output_optimizer_file)
         logger.info(f"Optimizer state saved in {output_optimizer_file}")
     # Scheduler states
     for i, scheduler in enumerate(schedulers):
         state = scheduler.state_dict()
         scheduler_name = f"{SCHEDULER_NAME}.bin" if i == 0 else f"{SCHEDULER_NAME}_{i}.bin"
-        output_scheduler_file = output_dir.joinpath(scheduler_name)
-        save(state, output_scheduler_file, save_on_each_node=save_on_each_node, safe_serialization=False)
+        output_scheduler_file = os.path.join(output_dir, scheduler_name)
+        save(state, output_scheduler_file)
         logger.info(f"Scheduler state saved in {output_scheduler_file}")
-    # DataLoader states
-    for i, dataloader in enumerate(dataloaders):
-        sampler_name = f"{SAMPLER_NAME}.bin" if i == 0 else f"{SAMPLER_NAME}_{i}.bin"
-        output_sampler_file = output_dir.joinpath(sampler_name)
-        # Only save if we have our custom sampler
-        from .data_loader import IterableDatasetShard, SeedableRandomSampler
-
-        if isinstance(dataloader.dataset, IterableDatasetShard):
-            sampler = dataloader.sampler.sampler
-
-            if isinstance(sampler, SeedableRandomSampler):
-                save(sampler, output_sampler_file, save_on_each_node=save_on_each_node, safe_serialization=False)
-        logger.info(f"Sampler state for dataloader {i} saved in {output_sampler_file}")
-
     # GradScaler state
     if scaler is not None:
         state = scaler.state_dict()
-        output_scaler_file = output_dir.joinpath(SCALER_NAME)
+        output_scaler_file = os.path.join(output_dir, SCALER_NAME)
         torch.save(state, output_scaler_file)
         logger.info(f"Gradient scaler state saved in {output_scaler_file}")
     # Random number generator states
@@ -142,9 +105,9 @@ def save_accelerator_state(
         states["torch_xpu_manual_seed"] = torch.xpu.get_rng_state_all()
     else:
         states["torch_cuda_manual_seed"] = torch.cuda.get_rng_state_all()
-    if is_torch_xla_available():
+    if is_tpu_available():
         states["xm_seed"] = xm.get_rng_state()
-    output_states_file = output_dir.joinpath(states_name)
+    output_states_file = os.path.join(output_dir, states_name)
     torch.save(states, output_states_file)
     logger.info(f"Random states saved in {output_states_file}")
     return output_dir
@@ -155,7 +118,6 @@ def load_accelerator_state(
     models,
     optimizers,
     schedulers,
-    dataloaders,
     process_index,
     scaler=None,
     map_location=None,
@@ -190,25 +152,17 @@ def load_accelerator_state(
         map_location = "cpu"
     elif map_location == "on_device":
         map_location = PartialState().device
-
-    input_dir = Path(input_dir)
     # Model states
     for i, model in enumerate(models):
-        ending = f"_{i}" if i > 0 else ""
-        input_model_file = input_dir.joinpath(f"{SAFE_MODEL_NAME}{ending}.safetensors")
-        if input_model_file.exists():
-            state_dict = load_file(input_model_file, device=str(map_location))
-        else:
-            # Load with torch
-            input_model_file = input_dir.joinpath(f"{MODEL_NAME}{ending}.bin")
-            state_dict = torch.load(input_model_file, map_location=map_location)
-        models[i].load_state_dict(state_dict, **load_model_func_kwargs)
+        weights_name = f"{MODEL_NAME}.bin" if i == 0 else f"{MODEL_NAME}_{i}.bin"
+        input_model_file = os.path.join(input_dir, weights_name)
+        models[i].load_state_dict(torch.load(input_model_file, map_location=map_location), **load_model_func_kwargs)
     logger.info("All model weights loaded successfully")
 
     # Optimizer states
     for i, opt in enumerate(optimizers):
         optimizer_name = f"{OPTIMIZER_NAME}.bin" if i == 0 else f"{OPTIMIZER_NAME}_{i}.bin"
-        input_optimizer_file = input_dir.joinpath(optimizer_name)
+        input_optimizer_file = os.path.join(input_dir, optimizer_name)
         optimizer_state = torch.load(input_optimizer_file, map_location=map_location)
         optimizers[i].load_state_dict(optimizer_state)
     logger.info("All optimizer states loaded successfully")
@@ -216,32 +170,19 @@ def load_accelerator_state(
     # Scheduler states
     for i, scheduler in enumerate(schedulers):
         scheduler_name = f"{SCHEDULER_NAME}.bin" if i == 0 else f"{SCHEDULER_NAME}_{i}.bin"
-        input_scheduler_file = input_dir.joinpath(scheduler_name)
+        input_scheduler_file = os.path.join(input_dir, scheduler_name)
         scheduler.load_state_dict(torch.load(input_scheduler_file))
     logger.info("All scheduler states loaded successfully")
 
-    for i, dataloader in enumerate(dataloaders):
-        sampler_name = f"{SAMPLER_NAME}.bin" if i == 0 else f"{SAMPLER_NAME}_{i}.bin"
-        input_sampler_file = input_dir.joinpath(sampler_name)
-        # Only load if we have our custom sampler
-        from .data_loader import IterableDatasetShard, SeedableRandomSampler
-
-        if isinstance(dataloader.dataset, IterableDatasetShard):
-            sampler = dataloader.sampler.sampler
-
-            if isinstance(sampler, SeedableRandomSampler):
-                dataloader.sampler.sampler = torch.load(input_sampler_file)
-    logger.info("All dataloader sampler states loaded successfully")
-
     # GradScaler state
     if scaler is not None:
-        input_scaler_file = input_dir.joinpath(SCALER_NAME)
+        input_scaler_file = os.path.join(input_dir, SCALER_NAME)
         scaler.load_state_dict(torch.load(input_scaler_file))
         logger.info("GradScaler state loaded successfully")
 
     # Random states
     try:
-        states = torch.load(input_dir.joinpath(f"{RNG_STATE_NAME}_{process_index}.pkl"))
+        states = torch.load(os.path.join(input_dir, f"{RNG_STATE_NAME}_{process_index}.pkl"))
         random.setstate(states["random_state"])
         np.random.set_state(states["numpy_random_seed"])
         torch.set_rng_state(states["torch_manual_seed"])
@@ -249,21 +190,21 @@ def load_accelerator_state(
             torch.xpu.set_rng_state_all(states["torch_xpu_manual_seed"])
         else:
             torch.cuda.set_rng_state_all(states["torch_cuda_manual_seed"])
-        if is_torch_xla_available():
+        if is_tpu_available():
             xm.set_rng_state(states["xm_seed"])
         logger.info("All random states loaded successfully")
     except Exception:
         logger.info("Could not load random states")
 
 
-def save_custom_state(obj, path, index: int = 0, save_on_each_node: bool = False):
+def save_custom_state(obj, path, index: int = 0):
     """
     Saves the state of `obj` to `{path}/custom_checkpoint_{index}.pkl`
     """
     # Should this be the right way to get a qual_name type value from `obj`?
     save_location = Path(path) / f"custom_checkpoint_{index}.pkl"
     logger.info(f"Saving the state of {get_pretty_name(obj)} to {save_location}")
-    save(obj.state_dict(), save_location, save_on_each_node=save_on_each_node)
+    torch.save(obj.state_dict(), save_location)
 
 
 def load_custom_state(obj, path, index: int = 0):

src/accelerate/commands/__init__.py

@@ -1,13 +0,0 @@
-# Copyright 2020 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.

src/accelerate/commands/accelerate_cli.py

@@ -12,26 +12,23 @@
 # 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
+# limitations under the License.
+
+from argparse import ArgumentParser
 
 from accelerate.commands.config import get_config_parser
 from accelerate.commands.env import env_command_parser
-from accelerate.commands.estimate import estimate_command_parser
 from accelerate.commands.launch import launch_command_parser
 from accelerate.commands.test import test_command_parser
 from accelerate.commands.tpu import tpu_command_parser
-from accelerate.commands.utils import ArgumentParserWithDashSupport
 
 
 def main():
-    parser = ArgumentParserWithDashSupport(
-        "Accelerate CLI tool", usage="accelerate <command> [<args>]", allow_abbrev=False
-    )
+    parser = ArgumentParser("Accelerate CLI tool", usage="accelerate <command> [<args>]", allow_abbrev=False)
     subparsers = parser.add_subparsers(help="accelerate command helpers")
 
     # Register commands
     get_config_parser(subparsers=subparsers)
-    estimate_command_parser(subparsers=subparsers)
     env_command_parser(subparsers=subparsers)
     launch_command_parser(subparsers=subparsers)
     tpu_command_parser(subparsers=subparsers)

src/accelerate/commands/config/cluster.py

@@ -21,7 +21,6 @@ from ...utils import (
     DistributedType,
     is_deepspeed_available,
     is_mps_available,
-    is_npu_available,
     is_transformers_available,
     is_xpu_available,
 )
@@ -60,11 +59,10 @@ def get_cluster_input():
     main_process_port = None
     rdzv_backend = "static"
     same_network = True
-    debug = False
 
     if distributed_type in [
         DistributedType.MULTI_GPU,
-        DistributedType.MULTI_NPU,
+        DistributedType.MULTI_GPU,
         DistributedType.MULTI_XPU,
         DistributedType.MULTI_CPU,
     ]:
@@ -96,16 +94,10 @@ def get_cluster_input():
                 rdzv_backend = _ask_field(
                     "What rendezvous backend will you use? ('static', 'c10d', ...): ", default="static"
                 )
-        debug = _ask_field(
-            "Should distributed operations be checked while running for errors? This can avoid timeout issues but will be slower. [yes/NO]: ",
-            _convert_yes_no_to_bool,
-            default=False,
-            error_message="Please enter yes or no.",
-        )
 
     if distributed_type == DistributedType.NO:
         use_cpu = _ask_field(
-            "Do you want to run your training on CPU only (even if a GPU / Apple Silicon / Ascend NPU device is available)? [yes/NO]:",
+            "Do you want to run your training on CPU only (even if a GPU / Apple Silicon device is available)? [yes/NO]:",
             _convert_yes_no_to_bool,
             default=False,
             error_message="Please enter yes or no.",
@@ -126,7 +118,7 @@ def get_cluster_input():
     if (
         not use_cpu
         and is_xpu_available()
-        and distributed_type not in [DistributedType.MULTI_GPU, DistributedType.MULTI_NPU, DistributedType.XLA]
+        and distributed_type not in [DistributedType.MULTI_GPU, DistributedType.MULTI_NPU, DistributedType.TPU]
     ):
         ipex_config["use_xpu"] = _ask_field(
             "Do you want to use XPU plugin to speed up training on XPU? [yes/NO]:",
@@ -179,11 +171,7 @@ def get_cluster_input():
 
     use_mps = not use_cpu and is_mps_available()
     deepspeed_config = {}
-    if (
-        distributed_type
-        in [DistributedType.MULTI_GPU, DistributedType.MULTI_XPU, DistributedType.MULTI_NPU, DistributedType.NO]
-        and not use_mps
-    ):
+    if distributed_type in [DistributedType.MULTI_GPU, DistributedType.NO] and not use_mps:
         use_deepspeed = _ask_field(
             "Do you want to use DeepSpeed? [yes/NO]: ",
             _convert_yes_no_to_bool,
@@ -317,7 +305,7 @@ def get_cluster_input():
                         )
 
     fsdp_config = {}
-    if distributed_type in [DistributedType.MULTI_GPU, DistributedType.MULTI_NPU, DistributedType.MULTI_XPU]:
+    if distributed_type in [DistributedType.MULTI_GPU]:
         use_fsdp = _ask_field(
             "Do you want to use FullyShardedDataParallel? [yes/NO]: ",
             _convert_yes_no_to_bool,
@@ -331,7 +319,8 @@ def get_cluster_input():
             fsdp_config["fsdp_sharding_strategy"] = _ask_options(
                 sharding_strategy_query,
                 FSDP_SHARDING_STRATEGY,
-                lambda x: FSDP_SHARDING_STRATEGY[int(x)],
+                lambda x: int(x) + 1,
+                default=1,
             )
             fsdp_config["fsdp_offload_params"] = _ask_field(
                 "Do you want to offload parameters and gradients to CPU? [yes/NO]: ",
@@ -346,18 +335,11 @@ def get_cluster_input():
                 lambda x: FSDP_AUTO_WRAP_POLICY[int(x)],
             )
             if fsdp_config["fsdp_auto_wrap_policy"] == FSDP_AUTO_WRAP_POLICY[0]:
-                use_no_split_modules = _ask_field(
-                    "Do you want to use the model's `_no_split_modules` to wrap. Only applicable for 🤗 Transformers [yes/NO]: ",
-                    _convert_yes_no_to_bool,
-                    default=False,
-                    error_message="Please enter yes or no.",
+                fsdp_config["fsdp_transformer_layer_cls_to_wrap"] = _ask_field(
+                    "Specify the comma-separated list of transformer layer class names (case-sensitive) to wrap ,e.g, :"
+                    "`BertLayer`, `GPTJBlock`, `T5Block`, `BertLayer,BertEmbeddings,BertSelfOutput` ...? : ",
+                    str,
                 )
-                if not use_no_split_modules:
-                    fsdp_config["fsdp_transformer_layer_cls_to_wrap"] = _ask_field(
-                        "Specify the comma-separated list of transformer layer class names (case-sensitive) to wrap ,e.g, :"
-                        "`BertLayer`, `GPTJBlock`, `T5Block`, `BertLayer,BertEmbeddings,BertSelfOutput` ...? : ",
-                        str,
-                    )
             elif fsdp_config["fsdp_auto_wrap_policy"] == FSDP_AUTO_WRAP_POLICY[1]:
                 fsdp_config["fsdp_min_num_params"] = _ask_field(
                     "What should be your FSDP's minimum number of parameters for Default Auto Wrapping Policy? [1e8]: ",
@@ -365,7 +347,7 @@ def get_cluster_input():
                     default=100000000,
                 )
             fsdp_backward_prefetch_query = "What should be your FSDP's backward prefetch policy?"
-            fsdp_config["fsdp_backward_prefetch"] = _ask_options(
+            fsdp_config["fsdp_backward_prefetch_policy"] = _ask_options(
                 fsdp_backward_prefetch_query,
                 FSDP_BACKWARD_PREFETCH,
                 lambda x: FSDP_BACKWARD_PREFETCH[int(x)],
@@ -384,26 +366,17 @@ def get_cluster_input():
                 error_message="Please enter yes or no.",
             )
             fsdp_config["fsdp_use_orig_params"] = _ask_field(
-                "Do you want to enable FSDP's `use_orig_params` feature? [YES/no]: ",
+                "Do you want to enable FSDP's `use_orig_params` feature? [yes/NO]: ",
                 _convert_yes_no_to_bool,
-                default=True,
+                default=False,
                 error_message="Please enter yes or no.",
             )
-            fsdp_config["fsdp_cpu_ram_efficient_loading"] = _ask_field(
-                "Do you want to enable CPU RAM efficient model loading? Only applicable for 🤗 Transformers models. [YES/no]: ",
+            fsdp_config["fsdp_sync_module_states"] = _ask_field(
+                "Do you want each individually wrapped FSDP unit to broadcast module parameters from rank 0 at the start? [yes/NO]: ",
                 _convert_yes_no_to_bool,
-                default=True,
+                default=False,
                 error_message="Please enter yes or no.",
             )
-            if fsdp_config["fsdp_cpu_ram_efficient_loading"]:
-                fsdp_config["fsdp_sync_module_states"] = True
-            else:
-                fsdp_config["fsdp_sync_module_states"] = _ask_field(
-                    "Do you want each individually wrapped FSDP unit to broadcast module parameters from rank 0 at the start? [YES/no]: ",
-                    _convert_yes_no_to_bool,
-                    default=True,
-                    error_message="Please enter yes or no.",
-                )
 
     megatron_lm_config = {}
     if distributed_type in [DistributedType.MULTI_GPU]:
@@ -454,7 +427,7 @@ def get_cluster_input():
 
             megatron_lm_config[prefix + "use_distributed_optimizer"] = _ask_field(
                 "Do you want to use distributed optimizer "
-                "which shards optimizer state and gradients across data parallel ranks? [YES/no]: ",
+                "which shards optimizer state and gradients across data pralellel ranks? [YES/no]: ",
                 _convert_yes_no_to_bool,
                 default=True,
                 error_message="Please enter yes or no.",
@@ -481,7 +454,7 @@ def get_cluster_input():
         DistributedType.MULTI_XPU,
         DistributedType.MULTI_GPU,
         DistributedType.MULTI_NPU,
-        DistributedType.XLA,
+        DistributedType.TPU,
     ]:
         machine_type = str(distributed_type).split(".")[1].replace("MULTI_", "")
         if machine_type == "TPU":
@@ -504,11 +477,6 @@ def get_cluster_input():
     else:
         num_processes = 1
 
-    if (distributed_type == DistributedType.MULTI_GPU) and (num_machines == 1) and (num_processes == 1):
-        raise ValueError(
-            f"Specified distributed type {distributed_type} but only using 1 GPU on a single machine. Please select `No distributed training` for the type of machine you are using."
-        )
-
     if (
         distributed_type
         in [
@@ -520,16 +488,12 @@ def get_cluster_input():
         and not use_cpu
         and not use_mps
     ):
-        if is_npu_available():
-            machine_type = "NPU(s)"
-        else:
-            machine_type = "GPU(s)"
         gpu_ids = _ask_field(
-            f"What {machine_type} (by id) should be used for training on this machine as a comma-seperated list? [all]:",
+            "What GPU(s) (by id) should be used for training on this machine as a comma-seperated list? [all]:",
             default="all",
         )
 
-    if distributed_type == DistributedType.XLA:
+    if distributed_type == DistributedType.TPU:
         mixed_precision = "no"
         main_training_function = _ask_field(
             "What is the name of the function in your script that should be launched in all parallel scripts? [main]: ",
@@ -620,7 +584,7 @@ def get_cluster_input():
             "Torch dynamo used without mixed precision requires TF32 to be efficient. Accelerate will enable it by default when launching your scripts."
         )
 
-    if distributed_type == DistributedType.XLA and mixed_precision == "bf16":
+    if distributed_type == DistributedType.TPU and mixed_precision == "bf16":
         tpu_downcast_bf16 = _ask_field(
             "Should `torch.float` be cast as `bfloat16` and `torch.double` remain `float32` on TPUs?", default="no"
         )
@@ -653,5 +617,4 @@ def get_cluster_input():
         tpu_use_sudo=tpu_use_sudo,
         tpu_use_cluster=tpu_use_cluster,
         dynamo_config=dynamo_config,
-        debug=debug,
     )

src/accelerate/commands/config/config_args.py

@@ -27,7 +27,7 @@ from ...utils.constants import SAGEMAKER_PYTHON_VERSION, SAGEMAKER_PYTORCH_VERSI
 
 
 hf_cache_home = os.path.expanduser(
-    os.environ.get("HF_HOME", os.path.join(os.environ.get("XDG_CACHE_HOME", "~/.cache"), "huggingface"))
+    os.getenv("HF_HOME", os.path.join(os.getenv("XDG_CACHE_HOME", "~/.cache"), "huggingface"))
 )
 cache_dir = os.path.join(hf_cache_home, "accelerate")
 default_json_config_file = os.path.join(cache_dir, "default_config.yaml")
@@ -45,13 +45,13 @@ def load_config_from_file(config_file):
         if not os.path.isfile(config_file):
             raise FileNotFoundError(
                 f"The passed configuration file `{config_file}` does not exist. "
-                "Please pass an existing file to `accelerate launch`, or use the default one "
+                "Please pass an existing file to `accelerate launch`, or use the the default one "
                 "created through `accelerate config` and run `accelerate launch` "
                 "without the `--config_file` argument."
             )
     else:
         config_file = default_config_file
-    with open(config_file, encoding="utf-8") as f:
+    with open(config_file, "r", encoding="utf-8") as f:
         if config_file.endswith(".json"):
             if (
                 json.load(f).get("compute_environment", ComputeEnvironment.LOCAL_MACHINE)
@@ -78,7 +78,6 @@ class BaseConfig:
     distributed_type: Union[DistributedType, SageMakerDistributedType]
     mixed_precision: str
     use_cpu: bool
-    debug: bool
 
     def to_dict(self):
         result = self.__dict__
@@ -94,7 +93,7 @@ class BaseConfig:
     @classmethod
     def from_json_file(cls, json_file=None):
         json_file = default_json_config_file if json_file is None else json_file
-        with open(json_file, encoding="utf-8") as f:
+        with open(json_file, "r", encoding="utf-8") as f:
             config_dict = json.load(f)
         if "compute_environment" not in config_dict:
             config_dict["compute_environment"] = ComputeEnvironment.LOCAL_MACHINE
@@ -107,15 +106,6 @@ class BaseConfig:
             config_dict["dynamo_config"] = {} if dynamo_backend == "NO" else {"dynamo_backend": dynamo_backend}
         if "use_cpu" not in config_dict:
             config_dict["use_cpu"] = False
-        if "debug" not in config_dict:
-            config_dict["debug"] = False
-        extra_keys = sorted(set(config_dict.keys()) - set(cls.__dataclass_fields__.keys()))
-        if len(extra_keys) > 0:
-            raise ValueError(
-                f"The config file at {json_file} had unknown keys ({extra_keys}), please try upgrading your `accelerate`"
-                " version or fix (and potentially remove) these keys from your config file."
-            )
-
         return cls(**config_dict)
 
     def to_json_file(self, json_file):
@@ -126,10 +116,11 @@ class BaseConfig:
     @classmethod
     def from_yaml_file(cls, yaml_file=None):
         yaml_file = default_yaml_config_file if yaml_file is None else yaml_file
-        with open(yaml_file, encoding="utf-8") as f:
+        with open(yaml_file, "r", encoding="utf-8") as f:
             config_dict = yaml.safe_load(f)
         if "compute_environment" not in config_dict:
             config_dict["compute_environment"] = ComputeEnvironment.LOCAL_MACHINE
+
         if "mixed_precision" not in config_dict:
             config_dict["mixed_precision"] = "fp16" if ("fp16" in config_dict and config_dict["fp16"]) else None
         if isinstance(config_dict["mixed_precision"], bool) and not config_dict["mixed_precision"]:
@@ -141,14 +132,6 @@ class BaseConfig:
             config_dict["dynamo_config"] = {} if dynamo_backend == "NO" else {"dynamo_backend": dynamo_backend}
         if "use_cpu" not in config_dict:
             config_dict["use_cpu"] = False
-        if "debug" not in config_dict:
-            config_dict["debug"] = False
-        extra_keys = sorted(set(config_dict.keys()) - set(cls.__dataclass_fields__.keys()))
-        if len(extra_keys) > 0:
-            raise ValueError(
-                f"The config file at {yaml_file} had unknown keys ({extra_keys}), please try upgrading your `accelerate`"
-                " version or fix (and potentially remove) these keys from your config file."
-            )
         return cls(**config_dict)
 
     def to_yaml_file(self, yaml_file):

src/accelerate/commands/config/config_utils.py

@@ -30,15 +30,13 @@ DYNAMO_BACKENDS = [
     "EAGER",
     "AOT_EAGER",
     "INDUCTOR",
-    "AOT_TS_NVFUSER",
-    "NVPRIMS_NVFUSER",
-    "CUDAGRAPHS",
+    "NVFUSER",
+    "AOT_NVFUSER",
+    "AOT_CUDAGRAPHS",
     "OFI",
     "FX2TRT",
     "ONNXRT",
-    "TENSORRT",
     "IPEX",
-    "TVM",
 ]
 
 
@@ -68,7 +66,7 @@ def _convert_compute_environment(value):
 
 def _convert_distributed_mode(value):
     value = int(value)
-    return DistributedType(["NO", "MULTI_CPU", "MULTI_XPU", "MULTI_GPU", "MULTI_NPU", "XLA"][value])
+    return DistributedType(["NO", "MULTI_CPU", "MULTI_XPU", "MULTI_GPU", "MULTI_NPU", "TPU"][value])
 
 
 def _convert_dynamo_backend(value):

src/accelerate/commands/config/default.py

@@ -86,7 +86,6 @@ def write_basic_config(mixed_precision="no", save_location: str = default_json_c
         config["use_cpu"] = True
         config["num_processes"] = 1
         config["distributed_type"] = "NO"
-    config["debug"] = False
     config = ClusterConfig(**config)
     config.to_json_file(path)
     return path

src/accelerate/commands/config/sagemaker.py

@@ -221,15 +221,6 @@ def get_sagemaker_input():
         ec2_instance_query += "? [ml.p3.2xlarge]:"
         ec2_instance_type = _ask_field(ec2_instance_query, lambda x: str(x).lower(), default="ml.p3.2xlarge")
 
-    debug = False
-    if distributed_type != SageMakerDistributedType.NO:
-        debug = _ask_field(
-            "Should distributed operations be checked while running for errors? This can avoid timeout issues but will be slower. [yes/NO]: ",
-            _convert_yes_no_to_bool,
-            default=False,
-            error_message="Please enter yes or no.",
-        )
-
     num_machines = 1
     if distributed_type in (SageMakerDistributedType.DATA_PARALLEL, SageMakerDistributedType.MODEL_PARALLEL):
         num_machines = _ask_field(
@@ -263,5 +254,4 @@ def get_sagemaker_input():
         num_machines=num_machines,
         sagemaker_inputs_file=sagemaker_inputs_file,
         sagemaker_metrics_file=sagemaker_metrics_file,
-        debug=debug,
     )

src/accelerate/commands/estimate.py

@@ -1,272 +0,0 @@
-#!/usr/bin/env python
-
-# 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 argparse
-
-from huggingface_hub import model_info
-from huggingface_hub.utils import GatedRepoError, RepositoryNotFoundError
-
-from accelerate import init_empty_weights
-from accelerate.utils import (
-    calculate_maximum_sizes,
-    convert_bytes,
-    is_timm_available,
-    is_transformers_available,
-)
-
-
-if is_transformers_available():
-    import transformers
-    from transformers import AutoConfig, AutoModel
-
-if is_timm_available():
-    import timm
-
-
-def verify_on_hub(repo: str, token: str = None):
-    "Verifies that the model is on the hub and returns the model info."
-    try:
-        return model_info(repo, token=token)
-    except GatedRepoError:
-        return "gated"
-    except RepositoryNotFoundError:
-        return "repo"
-
-
-def check_has_model(error):
-    """
-    Checks what library spawned `error` when a model is not found
-    """
-    if is_timm_available() and isinstance(error, RuntimeError) and "Unknown model" in error.args[0]:
-        return "timm"
-    elif (
-        is_transformers_available()
-        and isinstance(error, OSError)
-        and "does not appear to have a file named" in error.args[0]
-    ):
-        return "transformers"
-    else:
-        return "unknown"
-
-
-def create_empty_model(model_name: str, library_name: str, trust_remote_code: bool = False, access_token: str = None):
-    """
-    Creates an empty model from its parent library on the `Hub` to calculate the overall memory consumption.
-
-    Args:
-        model_name (`str`):
-            The model name on the Hub
-        library_name (`str`):
-            The library the model has an integration with, such as `transformers`. Will be used if `model_name` has no
-            metadata on the Hub to determine the library.
-        trust_remote_code (`bool`, `optional`, defaults to `False`):
-            Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
-            should only be set to `True` 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.
-        access_token (`str`, `optional`, defaults to `None`):
-            The access token to use to access private or gated models on the Hub. (for use on the Gradio app)
-
-    Returns:
-        `torch.nn.Module`: The torch model that has been initialized on the `meta` device.
-
-    """
-    model_info = verify_on_hub(model_name, access_token)
-    # Simplified errors
-    if model_info == "gated":
-        raise GatedRepoError(
-            f"Repo for model `{model_name}` is gated. You must be authenticated to access it. Please run `huggingface-cli login`."
-        )
-    elif model_info == "repo":
-        raise RepositoryNotFoundError(
-            f"Repo for model `{model_name}` does not exist on the Hub. If you are trying to access a private repo,"
-            " make sure you are authenticated via `huggingface-cli login` and have access."
-        )
-    if library_name is None:
-        library_name = getattr(model_info, "library_name", False)
-        if not library_name:
-            raise ValueError(
-                f"Model `{model_name}` does not have any library metadata on the Hub, please manually pass in a `--library_name` to use (such as `transformers`)"
-            )
-    if library_name == "transformers":
-        if not is_transformers_available():
-            raise ImportError(
-                f"To check `{model_name}`, `transformers` must be installed. Please install it via `pip install transformers`"
-            )
-        print(f"Loading pretrained config for `{model_name}` from `transformers`...")
-        if model_info.config is None:
-            raise RuntimeError(f"Tried to load `{model_name}` with `transformers` but it does not have any metadata.")
-
-        auto_map = model_info.config.get("auto_map", False)
-        config = AutoConfig.from_pretrained(model_name, trust_remote_code=trust_remote_code, token=access_token)
-
-        with init_empty_weights():
-            # remote code could specify a specific `AutoModel` class in the `auto_map`
-            constructor = AutoModel
-            if isinstance(auto_map, dict):
-                value = None
-                for key in auto_map.keys():
-                    if key.startswith("AutoModelFor"):
-                        value = key
-                        break
-                if value is not None:
-                    constructor = getattr(transformers, value)
-            model = constructor.from_config(config, trust_remote_code=trust_remote_code)
-    elif library_name == "timm":
-        if not is_timm_available():
-            raise ImportError(
-                f"To check `{model_name}`, `timm` must be installed. Please install it via `pip install timm`"
-            )
-        print(f"Loading pretrained config for `{model_name}` from `timm`...")
-        with init_empty_weights():
-            model = timm.create_model(model_name, pretrained=False)
-    else:
-        raise ValueError(
-            f"Library `{library_name}` is not supported yet, please open an issue on GitHub for us to add support."
-        )
-    return model
-
-
-def create_ascii_table(headers: list, rows: list, title: str):
-    "Creates a pretty table from a list of rows, minimal version of `tabulate`."
-    sep_char, in_between = "│", "─"
-    column_widths = []
-    for i in range(len(headers)):
-        column_values = [row[i] for row in rows] + [headers[i]]
-        max_column_width = max(len(value) for value in column_values)
-        column_widths.append(max_column_width)
-
-    formats = [f"%{column_widths[i]}s" for i in range(len(rows[0]))]
-
-    pattern = f"{sep_char}{sep_char.join(formats)}{sep_char}"
-    diff = 0
-
-    def make_row(left_char, middle_char, right_char):
-        return f"{left_char}{middle_char.join([in_between * n for n in column_widths])}{in_between * diff}{right_char}"
-
-    separator = make_row("├", "┼", "┤")
-    if len(title) > sum(column_widths):
-        diff = abs(len(title) - len(separator))
-        column_widths[-1] += diff
-
-    # Update with diff
-    separator = make_row("├", "┼", "┤")
-    initial_rows = [
-        make_row("┌", in_between, "┐"),
-        f"{sep_char}{title.center(len(separator) - 2)}{sep_char}",
-        make_row("├", "┬", "┤"),
-    ]
-    table = "\n".join(initial_rows) + "\n"
-    column_widths[-1] += diff
-    centered_line = [text.center(column_widths[i]) for i, text in enumerate(headers)]
-    table += f"{pattern % tuple(centered_line)}\n{separator}\n"
-    for i, line in enumerate(rows):
-        centered_line = [t.center(column_widths[i]) for i, t in enumerate(line)]
-        table += f"{pattern % tuple(centered_line)}\n"
-    table += f'└{"┴".join([in_between * n for n in column_widths])}┘'
-
-    return table
-
-
-def estimate_command_parser(subparsers=None):
-    if subparsers is not None:
-        parser = subparsers.add_parser("estimate-memory")
-    else:
-        parser = argparse.ArgumentParser(description="Model size estimator for fitting a model onto CUDA memory.")
-
-    parser.add_argument("model_name", type=str, help="The model name on the Hugging Face Hub.")
-    parser.add_argument(
-        "--library_name",
-        type=str,
-        help="The library the model has an integration with, such as `transformers`, needed only if this information is not stored on the Hub.",
-        choices=["timm", "transformers"],
-    )
-    parser.add_argument(
-        "--dtypes",
-        type=str,
-        nargs="+",
-        default=["float32", "float16", "int8", "int4"],
-        help="The dtypes to use for the model, must be one (or many) of `float32`, `float16`, `int8`, and `int4`",
-        choices=["float32", "float16", "int8", "int4"],
-    )
-    parser.add_argument(
-        "--trust_remote_code",
-        action="store_true",
-        help="""Whether or not to allow for custom models defined on the Hub in their own modeling files. This flag
-                should only be used 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.""",
-    )
-
-    if subparsers is not None:
-        parser.set_defaults(func=estimate_command)
-    return parser
-
-
-def gather_data(args):
-    "Creates an empty model and gathers the data for the sizes"
-    try:
-        model = create_empty_model(
-            args.model_name, library_name=args.library_name, trust_remote_code=args.trust_remote_code
-        )
-    except (RuntimeError, OSError) as e:
-        library = check_has_model(e)
-        if library != "unknown":
-            raise RuntimeError(
-                f"Tried to load `{args.model_name}` with `{library}` but a possible model to load was not found inside the repo."
-            )
-        raise e
-
-    total_size, largest_layer = calculate_maximum_sizes(model)
-
-    data = []
-
-    for dtype in args.dtypes:
-        dtype_total_size = total_size
-        dtype_largest_layer = largest_layer[0]
-        if dtype == "float16":
-            dtype_total_size /= 2
-            dtype_largest_layer /= 2
-        elif dtype == "int8":
-            dtype_total_size /= 4
-            dtype_largest_layer /= 4
-        elif dtype == "int4":
-            dtype_total_size /= 8
-            dtype_largest_layer /= 8
-        dtype_training_size = dtype_total_size * 4
-        data.append([dtype, dtype_largest_layer, dtype_total_size, dtype_training_size])
-    return data
-
-
-def estimate_command(args):
-    data = gather_data(args)
-    for row in data:
-        for i, item in enumerate(row):
-            if isinstance(item, (int, float)):
-                row[i] = convert_bytes(item)
-
-    headers = ["dtype", "Largest Layer", "Total Size", "Training using Adam"]
-
-    title = f"Memory Usage for loading `{args.model_name}`"
-    table = create_ascii_table(headers, data, title)
-    print(table)
-
-
-def main():
-    parser = estimate_command_parser()
-    args = parser.parse_args()
-    estimate_command(args)
-
-
-if __name__ == "__main__":
-    main()

src/accelerate/commands/launch.py

@@ -28,21 +28,19 @@ import torch
 from accelerate.commands.config import default_config_file, load_config_from_file
 from accelerate.commands.config.config_args import SageMakerConfig
 from accelerate.commands.config.config_utils import DYNAMO_BACKENDS
-from accelerate.commands.utils import ArgumentParserWithDashSupport
 from accelerate.state import get_int_from_env
 from accelerate.utils import (
     ComputeEnvironment,
     DistributedType,
     PrepareForLaunch,
     _filter_args,
-    check_cuda_p2p_ib_support,
     is_bf16_available,
     is_deepspeed_available,
     is_npu_available,
     is_rich_available,
     is_sagemaker_available,
     is_torch_version,
-    is_torch_xla_available,
+    is_tpu_available,
     is_xpu_available,
     patch_environment,
     prepare_deepspeed_cmd_env,
@@ -106,19 +104,19 @@ class _CustomHelpAction(argparse._HelpAction):
             for i, arg in enumerate(opts):
                 # If the argument's container is outside of the used titles, hide it
                 if arg.container.title not in titles + used_titles:
-                    opts[i].help = argparse.SUPPRESS
+                    setattr(opts[i], "help", argparse.SUPPRESS)
                 # If the argument is hardware selection, but not being passed, hide it
                 elif arg.container.title == "Hardware Selection Arguments":
                     if set(arg.option_strings).isdisjoint(set(args)):
-                        opts[i].help = argparse.SUPPRESS
+                        setattr(opts[i], "help", argparse.SUPPRESS)
                     else:
-                        opts[i].help = arg.help + " (currently selected)"
+                        setattr(opts[i], "help", arg.help + " (currently selected)")
                 # If the argument is a training paradigm, but not being passed, hide it
                 elif arg.container.title == "Training Paradigm Arguments":
                     if set(arg.option_strings).isdisjoint(set(used_platforms)):
-                        opts[i].help = argparse.SUPPRESS
+                        setattr(opts[i], "help", argparse.SUPPRESS)
                     else:
-                        opts[i].help = arg.help + " (currently selected)"
+                        setattr(opts[i], "help", arg.help + " (currently selected)")
             for i, group in enumerate(list(parser._action_groups)):
                 # If all arguments in the group are hidden, hide the group
                 if all([arg.help == argparse.SUPPRESS for arg in group._group_actions]):
@@ -128,13 +126,10 @@ class _CustomHelpAction(argparse._HelpAction):
 
 
 def launch_command_parser(subparsers=None):
-    description = "Launch a python script in a distributed scenario. Arguments can be passed in with either hyphens (`a-b`) or underscores (`a_b`)"
     if subparsers is not None:
-        parser = subparsers.add_parser("launch", description=description, add_help=False, allow_abbrev=False)
+        parser = subparsers.add_parser("launch", add_help=False, allow_abbrev=False)
     else:
-        parser = ArgumentParserWithDashSupport(
-            "Accelerate launch command", description=description, add_help=False, allow_abbrev=False
-        )
+        parser = argparse.ArgumentParser("Accelerate launch command", add_help=False, allow_abbrev=False)
 
     parser.register("action", "help", _CustomHelpAction)
     parser.add_argument("-h", "--help", action="help", help="Show this help message and exit.")
@@ -486,8 +481,8 @@ def launch_command_parser(subparsers=None):
     )
     fsdp_args.add_argument(
         "--fsdp_sharding_strategy",
-        type=str,
-        default="FULL_SHARD",
+        type=int,
+        default=1,
         help="FSDP's Sharding Strategy. (useful only when `use_fsdp` flag is passed).",
     )
     fsdp_args.add_argument(
@@ -507,12 +502,6 @@ def launch_command_parser(subparsers=None):
         "--fsdp_backward_prefetch_policy",
         default=None,
         type=str,
-        help="This argument is deprecated and will be removed in version 0.27.0 of 🤗 Accelerate. Use `fsdp_backward_prefetch` instead.",
-    )
-    fsdp_args.add_argument(
-        "--fsdp_backward_prefetch",
-        default=None,
-        type=str,
         help="FSDP's backward prefetch policy. (useful only when `use_fsdp` flag is passed).",
     )
     fsdp_args.add_argument(
@@ -530,22 +519,14 @@ def launch_command_parser(subparsers=None):
     )
     fsdp_args.add_argument(
         "--fsdp_use_orig_params",
-        default="true",
+        default="false",
         type=str,
         help="If True, allows non-uniform `requires_grad` during init, which means support for interspersed frozen and trainable paramteres."
         " (useful only when `use_fsdp` flag is passed).",
     )
-    fsdp_args.add_argument(
-        "--fsdp_cpu_ram_efficient_loading",
-        default="true",
-        type=str,
-        help="If True, only the first process loads the pretrained model checkoint while all other processes have empty weights. "
-        "Only applicable for 🤗 Transformers. When using this, `--fsdp_sync_module_states` needs to True. "
-        "(useful only when `use_fsdp` flag is passed).",
-    )
     fsdp_args.add_argument(
         "--fsdp_sync_module_states",
-        default="true",
+        default="false",
         type=str,
         help="If True, each individually wrapped FSDP unit will broadcast module parameters from rank 0."
         " (useful only when `use_fsdp` flag is passed).",
@@ -653,17 +634,6 @@ def multi_gpu_launcher(args):
     import torch.distributed.run as distrib_run
 
     current_env = prepare_multi_gpu_env(args)
-    if not check_cuda_p2p_ib_support():
-        message = "Using RTX 4000 series which doesn't support faster communication speedups. Ensuring P2P and IB communications are disabled."
-        warn = False
-        if "NCCL_P2P_DISABLE" not in current_env:
-            current_env["NCCL_P2P_DISABLE"] = "1"
-            warn = True
-        if "NCCL_IB_DISABLE" not in current_env:
-            current_env["NCCL_IB_DISABLE"] = "1"
-            warn = True
-        if warn:
-            logger.warning(message)
 
     debug = getattr(args, "debug", False)
     args = _filter_args(
@@ -690,17 +660,6 @@ def deepspeed_launcher(args):
         raise ImportError("DeepSpeed is not installed => run `pip3 install deepspeed` or build it from source.")
 
     cmd, current_env = prepare_deepspeed_cmd_env(args)
-    if not check_cuda_p2p_ib_support():
-        message = "Using RTX 4000 series which doesn't support faster communication speedups. Ensuring P2P and IB communications are disabled."
-        warn = False
-        if "NCCL_P2P_DISABLE" not in current_env:
-            current_env["NCCL_P2P_DISABLE"] = "1"
-            warn = True
-        if "NCCL_IB_DISABLE" not in current_env:
-            current_env["NCCL_IB_DISABLE"] = "1"
-            warn = True
-        if warn:
-            logger.warning(message)
 
     if args.num_machines > 1 and args.deepspeed_multinode_launcher != DEEPSPEED_MULTINODE_LAUNCHERS[1]:
         with open(".deepspeed_env", "a") as f:
@@ -789,7 +748,7 @@ def tpu_pod_launcher(args):
         "--tpu",
         "--no_tpu_cluster",
         "--num_machines",
-        "1",
+        str(1),
         "--mixed_precision",
         "no",
         "--dynamo_backend",
@@ -875,7 +834,7 @@ def _validate_launch_command(args):
                 in (DistributedType.MULTI_GPU, DistributedType.MULTI_NPU, DistributedType.MULTI_XPU)
                 else False
             )
-            args.tpu = defaults.distributed_type == DistributedType.XLA
+            args.tpu = defaults.distributed_type == DistributedType.TPU
             args.use_fsdp = defaults.distributed_type == DistributedType.FSDP
             args.use_megatron_lm = defaults.distributed_type == DistributedType.MEGATRON_LM
             args.tpu_use_cluster = defaults.tpu_use_cluster if args.tpu else False
@@ -918,9 +877,6 @@ def _validate_launch_command(args):
                     and getattr(args, name, None) is None
                 ):
                     setattr(args, name, attr)
-        if not args.debug:
-            args.debug = defaults.debug
-
         if not args.mixed_precision:
             if defaults.mixed_precision is None:
                 args.mixed_precision = "no"
@@ -928,16 +884,14 @@ def _validate_launch_command(args):
                 args.mixed_precision = defaults.mixed_precision
                 mp_from_config_flag = True
         else:
+            native_amp = False
+            err = "{mode} mixed precision requires {requirement}"
             if args.use_cpu or (args.use_xpu and torch.xpu.is_available()):
                 native_amp = is_torch_version(">=", "1.10")
             else:
                 native_amp = is_bf16_available(True)
-            if (
-                args.mixed_precision == "bf16"
-                and not native_amp
-                and not (args.tpu and is_torch_xla_available(check_is_tpu=True))
-            ):
-                raise ValueError("bf16 mixed precision requires PyTorch >= 1.10 and a supported device.")
+            if args.mixed_precision == "bf16" and not native_amp and not (args.tpu and is_tpu_available()):
+                raise ValueError(err.format(mode="bf16", requirement="PyTorch >= 1.10 and a supported device."))
 
         # Silently set the default here
         if args.dynamo_backend is None:
@@ -951,8 +905,6 @@ def _validate_launch_command(args):
             else:
                 args.num_processes = torch.cuda.device_count()
             warned.append(f"\t`--num_processes` was set to a value of `{args.num_processes}`")
-        if args.debug is None:
-            args.debug = False
         if not args.multi_gpu and (
             (args.use_xpu and is_xpu_available() and torch.xpu.device_count() > 1)
             or (is_npu_available() and torch.npu.device_count() > 1)
@@ -974,8 +926,6 @@ def _validate_launch_command(args):
         if args.dynamo_backend is None:
             warned.append("\t`--dynamo_backend` was set to a value of `'no'`")
             args.dynamo_backend = "no"
-    if args.debug:
-        logger.debug("Running script in debug mode, expect distributed operations to be slightly slower.")
 
     is_aws_env_disabled = defaults is None or (
         defaults is not None and defaults.compute_environment != ComputeEnvironment.AMAZON_SAGEMAKER