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

@@ -21,40 +21,44 @@ jobs:
 
   version-cpu:
     name: "Latest Accelerate CPU [version]"
-    runs-on: [self-hosted, docker-gpu, multi-gpu]
+    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, docker-gpu, multi-gpu]
+    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_docker_images.yml

@@ -11,50 +11,44 @@ concurrency:
   cancel-in-progress: false
 
 jobs:
-  clean-storage:
-    name: "Clean docker image storage"
-    runs-on: [self-hosted, docker-gpu, multi-gpu]
-    steps:
-      - name: Clean storage
-        run: |
-          docker image prune --all -f --filter "until=48h"
-          docker system prune --all -f --filter "until=48h"
-
   latest-cpu:
     name: "Latest Accelerate CPU [dev]"
-    runs-on: [self-hosted, docker-gpu, multi-gpu]
-    needs: clean-storage
+    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, docker-gpu, multi-gpu]
-    needs: clean-storage
+    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/nightly.yml

@@ -39,7 +39,6 @@ jobs:
           make test
           
       - name: Run examples on GPUs
-        if: always()
         run: |
           source activate accelerate
           pip uninstall comet_ml -y
@@ -80,13 +79,11 @@ jobs:
           make test_cli
 
       - name: Run Integration tests on GPUs
-        if: always()
         run: |
           source activate accelerate
           make test_integrations
 
       - name: Run examples on GPUs
-        if: always()
         run: |
           source activate accelerate
           pip uninstall comet_ml -y

.github/workflows/run_merge_tests.yml

@@ -35,12 +35,10 @@ jobs:
           make test_cli
           
       - name: Run test on GPUs
-        if: always()
         run: |
           source activate accelerate
           make test
       - name: Run examples on GPUs
-        if: always()
         run: |
           source activate accelerate
           pip uninstall comet_ml -y
@@ -76,7 +74,6 @@ jobs:
           make test
 
       - name: Run examples on GPUs
-        if: always()
         run: |
           source activate accelerate
           pip uninstall comet_ml -y

.github/workflows/self_hosted_integration_tests.yml

@@ -76,7 +76,6 @@ jobs:
         env:
           CUDA_VISIBLE_DEVICES: ${{ matrix.cuda_visible_devices }}
           WANDB_DISABLED: true
-        if: always()
         run: |
           source activate accelerate;
           pytest -sv tests/deepspeed
@@ -107,7 +106,7 @@ jobs:
         run: |
           source activate accelerate
           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

README.md

@@ -269,7 +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, Lysandre Debut, Thomas Wolf, Philipp Schmid, Zachary Mueller, Sourab Mangrulkar, Marc Sun, Benjamin Bossan},
+  author =       {Sylvain Gugger, Lysandre Debut, Thomas Wolf, Philipp Schmid, Zachary Mueller, Sourab Mangrulkar},
   howpublished = {\url{https://github.com/huggingface/accelerate}},
   year =         {2022}
 }

docs/source/_toctree.yml

@@ -23,8 +23,6 @@
     title: Example Zoo
   - local: usage_guides/big_modeling
     title: How to perform inference on large models with small resources
-  - local: usage_guides/model_size_estimator
-    title: Knowing how big of a model you can fit into memory
   - local: usage_guides/quantization
     title: How to quantize model 
   - local: usage_guides/distributed_inference
@@ -37,8 +35,6 @@
     title: Saving and loading training states
   - local: usage_guides/tracking
     title: Using experiment trackers
-  - local: usage_guides/debug
-    title: Debugging timeout errors
   - local: usage_guides/memory
     title: How to avoid CUDA Out-of-Memory
   - local: usage_guides/mps
@@ -55,8 +51,6 @@
     title: How to use 🤗 Accelerate with Intel® Extension for PyTorch for cpu
   title: How-To Guides
 - sections:
-  - 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
@@ -91,6 +85,4 @@
     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/notebook.md

@@ -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/concept_guides/big_model_inference.md

@@ -1,308 +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 residutal 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>
-
-## 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/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/package_reference/accelerator.md

@@ -125,7 +125,7 @@ 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. The state_dict will be in the same precision as the model being trained.
+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")

docs/source/package_reference/big_modeling.md

@@ -22,8 +22,6 @@ rendered properly in your Markdown viewer.
 [[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

@@ -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/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

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/utilities.md

@@ -17,55 +17,18 @@ 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.
 
 [[autodoc]] utils.DistributedType
 
-[[autodoc]] utils.DynamoBackend
-
 [[autodoc]] utils.LoggerType
 
 [[autodoc]] utils.PrecisionType
 
 [[autodoc]] utils.ProjectConfiguration
 
-## Plugins
-
-These are plugins that can be passed to the [`Accelerator`] object. While they are defined elsewhere in the documentation, 
-for convience all of them are available to see here:
-
-[[autodoc]] utils.DeepSpeedPlugin
-
-[[autodoc]] utils.FullyShardedDataParallelPlugin
-
-[[autodoc]] utils.GradientAccumulationPlugin
-
-[[autodoc]] utils.MegatronLMPlugin
-
-[[autodoc]] utils.TorchDynamoPlugin
-
-
 ## Data Manipulation and Operations
 
 These include data operations that mimic the same `torch` ops but can be used on distributed processes.
@@ -88,23 +51,11 @@ These functionalities check the state of the current working environment includi
 
 [[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_tpu_available
 
-[[autodoc]] utils.is_xpu_available
-
-## Environment Manipulation
-
-[[autodoc]] utils.patch_environment
-
-[[autodoc]] utils.clear_environment
+## Environment Configuration
 
 [[autodoc]] utils.write_basic_config
 

docs/source/quicktour.md

@@ -15,20 +15,13 @@ rendered properly in your Markdown viewer.
 
 # Quick tour
 
-This guide aims to help you get started with 🤗 Accelerate quickly. It covers the essential steps you need to take to 
-enable distributed training, as well as the adjustments that you need to make in some common scenarios.
+Let's have a look at the 🤗 Accelerate main features and traps to avoid.
 
-To help you navigate, the guide is split into two sections: 
-* [Getting Started with 🤗 Accelerate](#getting-started-with--accelerate): start here to learn how to modify your script to enable distributed training with 🤗 Accelerate
-* [Common adaptations to the base case](#common-adaptations-to-the-base-case): check out this section for common deviations from the baseline scenario and what adjustments may need to be made to support them.
+## Main use
 
-## Getting started with 🤗 Accelerate
+To use 🤗 Accelerate in your own script, you have to change four things:
 
-### Enable distributed training in your script 
-
-To use 🤗 Accelerate in your own training script, you have to modify four things:
-
-1. Import the [`Accelerator`] main class and instantiate one in an `accelerator` object.
+1. Import the [`Accelerator`] main class and instantiate one in an `accelerator` object:
 
 ```python
 from accelerate import Accelerator
@@ -36,27 +29,27 @@ from accelerate import Accelerator
 accelerator = Accelerator()
 ```
 
-Add this at the beginning of 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 (a single machine with a GPU, a machine with several GPUs, 
-or several machines with multiple GPUs or a TPU), the library will detect this automatically.
+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.
 
-2. Remove the `.to(device)` or `.cuda()` calls for your model and input data.
+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`.
 
-The `accelerator` object will handle placing these objects on the right device for you. 
-If you choose to leave those `.to(device)` calls, make sure to use the device provided by the `accelerator` object: `accelerator.device`.
+To fully deactivate the automatic device placement, pass along `device_placement=False` when initializing your
+[`Accelerator`].
 
 <Tip warning={true}>
 
-    You can fully deactivate the automatic device placement by passing along `device_placement=False` when 
-    initializing [`Accelerator`].
-    However, if you place your objects manually on the proper device, be careful to create your optimizer after putting your
+    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 as soon as these objects are created, before starting your actual
-training loop:
+[`~Accelerator.prepare`] method. This will make sure everything is ready for training.
 
 ```python
 model, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
@@ -64,40 +57,58 @@ model, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
 )
 ```
 
-**Important notes**:
-
-* Only pass the learning rate scheduler to [`~Accelerator.prepare`] when the scheduler needs to be stepped at each optimizer step.
-* While you can send your dataloader to [`~Accelerator.prepare`] on its own, it's best to send it to [`~Accelerator.prepare`] together with the model and optimizer.
-* If you wish to run distributed evaluation, send your validation dataloader to [`~Accelerator.prepare`] as well. There are some nuances to distributed validation, check the [Distributed evaluation](#add-distributed-evaluation) section of the guide.
-* 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`].
-
-Passing these objects to the [`~Accelerator.prepare`] method ensures that 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).
+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).
 
 <Tip>
 
     The actual batch size for your training will be the number of devices used multiplied by the batch size you set in
-    your script. For instance, training on 4 GPUs with a batch size of 16 set when creating the training dataloader will
-    train at an actual batch size of 64. 
-    If you want the batch size remain the same regardless of how many GPUs the script is run on, you can use the 
-    option `split_batches=True` when creating and initializing [`Accelerator`].
+    your script: for instance training on 4 GPUs with a batch size of 16 set when creating the training dataloader will
+    train at an actual batch size of 64.
+
+</Tip>
+
+Alternatively, you can use the option `split_batches=True` when creating 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`].
 
-4. Replace the `loss.backward()` line with `accelerator.backward(loss)`.
+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.
 
-### Add distributed evaluation
+
+## 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
@@ -110,9 +121,9 @@ method:
 validation_dataloader = accelerator.prepare(validation_dataloader)
 ```
 
-Same as with your training dataloader, each device will only see part of the evaluation data should you run your script 
-on multiple devices. This means you will need to group your predictions together which you can do with 
-the [`~Accelerator.gather_for_metrics`] method.
+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:
@@ -131,9 +142,11 @@ for inputs, targets in validation_dataloader:
 
 </Tip>
 
-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.
+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`]. 
+
+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.
 
 <Tip>
 
@@ -152,35 +165,36 @@ data while gathering.
 
 </Tip>
 
-### Launch your distributed script
+## 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.
+PyTorch), they are fully compatible with 🤗 Accelerate.
 
-Alternatively, 🤗 Accelerate provides a CLI tool that unifies all launchers, so you only have to remember one command. \
-To use it, run a quick configuration setup first on your machine and answer the questions:
+🤗 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
 ```
 
-At the end of the setup, a *default_config.yaml* file will be saved in your cache folder for 🤗 Accelerate. That cache 
-folder is (with decreasing order of priority):
+on your machine and reply to the questions asked. This will save a *default_config.yaml* file in your cache folder for
+🤗 Accelerate. That cache folder is (with decreasing order of priority):
 
 - The content of your environment variable `HF_HOME` suffixed with *accelerate*.
 - If it does not exist, the content of your environment variable `XDG_CACHE_HOME` suffixed with
   *huggingface/accelerate*.
-- If this does not exist either, the folder *~/.cache/huggingface/accelerate*.
+- If this does not exist either, the folder *~/.cache/huggingface/accelerate*
 
-By specifying the `--config_file`  flag you can specify an alternative location of the configuration file.
-Once the configuration setup is complete, you can test your setup by running:
+You can also specify with the flag `--config_file` the location of the file you want to save.
+
+Once this is done, you can test everything is going well on your setup by running:
 
 ```bash
 accelerate test
 ```
 
-This will launch a short script that will test the distributed environment. If it runs without issues, you are ready for 
-the next step!
+This will launch a short script that will test the distributed environment. If it runs fine, you are ready for the next
+step!
 
 Note that if you specified a location for the config file in the previous step, you need to pass it here as well:
 
@@ -200,23 +214,19 @@ If you stored the config file in a non-default location, you can indicate it to
 accelerate launch --config_file path_to_config.yaml path_to_script.py --args_for_the_script
 ```
 
-You can 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`. 
+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.
+Check out the [Launch tutorial](basic_tutorials/launch) for more information about launching your scripts. 
 
-## Common modifications of the base case 
 
-The previous section covers the minimal essential steps to move a training script into a distributed setup with 🤗 Accelerate.
-Here we describe common modifications/deviations from the base case scenario and the adjustments you need to make to accommodate for them.
+## Launching training from a notebook
 
-### Launch distributed 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).
 
-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).
-
-Define a function responsible for your whole training and/or evaluation in a cell of the notebook, then execute a
+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
@@ -232,9 +242,10 @@ notebook_launcher(training_function)
 
 </Tip>
 
-Check out the [Notebook Launcher tutorial](basic_tutorials/notebook) for more information about training on TPUs.
+Check out the [Notebook Launcher tutorial](basic_tutorials/notebook) for more information about training on TPUs. 
 
-### Specifics of training on TPU
+
+## 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
@@ -273,7 +284,12 @@ passed your model to [`~Accelerator.prepare`]) will break the tying. You will ne
 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.
+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
 
@@ -307,14 +323,14 @@ For printing statements you only want executed once per machine, you can just re
 `accelerator.print`.
 
 
-### Defer execution on multiple GPUs
+### 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 making sure every process is done with training. To do this, add the
+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:
 
 ```
@@ -325,7 +341,7 @@ This instruction will block all the processes that arrive first until all the ot
 point (if you run your script on just one GPU or CPU, this won't do anything).
 
 
-### Save/load a model in a distributed setup
+### 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
@@ -333,16 +349,15 @@ through the [`~Accelerator.prepare`] method, your model may have been placed ins
 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 dictionary.
+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:
+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()
@@ -361,18 +376,15 @@ 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:
+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
 
-### Save/load entire states
-
-When training your model, you may want to save the current state of the model, optimizer, random generators, and potentially 
-learning rate schedulers to be restored in the _same script_.
+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 
@@ -387,19 +399,19 @@ If you have registered any other stateful items to be stored through [`~Accelera
 </Tip>
 
 
-### Use gradient clipping
+### 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.
 
 
-### Train with mixed precision
+### 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:
+softmax). However you might want to put your loss computation inside the *accelerator.autocast* context manager:
 
 ```
 with accelerator.autocast():
@@ -420,7 +432,7 @@ if not accelerator.optimizer_step_was_skipped:
     lr_scheduler.step()
 ```
 
-### Use gradient accumulation 
+### 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
@@ -439,3 +451,70 @@ for input, label in training_dataloader:
         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 desigining your own device map, see this section of the [concept guide](../concept_guide/big_model_inference#desigining-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 parallism" 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/debug.md

@@ -1,93 +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.
--->
-
-# Debugging Distributed Operations
-
-When running scripts in a distributed fashion, often functions such as [`Accelerator.gather`] and [`Accelerator.reduce`] (and others) are neccessary to grab tensors across devices and perform certain operations on them. However, if the tensors which are being grabbed are not the proper shapes then this will result in your code hanging forever. The only sign that exists of this truly happening is hitting a timeout exception from `torch.distributed`, but this can get quite costly as usually the timeout is 10 minutes.
-
-Accelerate now has a `debug` mode which adds a neglible amount of time to each operation, but allows it to verify that the inputs you are bringing in can *actually* perform the operation you want **without** hitting this timeout problem!
-
-## Visualizing the problem
-
-To have a tangible example of this issue, let's take the following setup (on 2 GPUs):
-
-```python
-from accelerate import PartialState
-
-state = PartialState()
-if state.process_index == 0:
-    tensor = torch.tensor([[0.0, 1, 2, 3, 4]]).to(state.device)
-else:
-    tensor = torch.tensor([[[0.0, 1, 2, 3, 4], [5, 6, 7, 8, 9]]]).to(state.device)
-
-broadcast_tensor = broadcast(tensor)
-print(broadcast_tensor)
-```
-
-We've created a single tensor on each device, with two radically different shapes. With this setup if we want to perform an operation such as [`utils.broadcast`], we would forever hit a timeout because `torch.distributed` requires that these operations have the **exact same shape** across all processes for it to work.
-
-If you run this yourself, you will find that `broadcast_tensor` can be printed on the main process, but its results won't quite be right, and then it will just hang never printing it on any of the other processes:
-
-```
->>> tensor([[0, 1, 2, 3, 4]], device='cuda:0')
-```
-
-## The solution
-
-By enabling Accelerate's operational debug mode, Accelerate will properly find and catch errors such as this and provide a very clear traceback immediatly: 
-
-```
-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
-        main()broadcast_tensor = broadcast(tensor)
-  File "/home/zach_mueller_huggingface_co/accelerate/src/accelerate/utils/operations.py", line 303, in wrapper
-    broadcast_tensor = broadcast(tensor)
-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]
-```
-
-This explains that the shapes across our devices were *not* the same, and that we should ensure that they match properly to be compatible. Typically this means that there is either an extra dimension, or certain dimensions are incompatible with the operation.
-
-To enable this please do one of the following:
-
-Enable it through the questionarre during `accelerate config` (recommended)
-
-From the CLI: 
-
-```
-accelerate launch --debug {my_script.py} --arg1 --arg2
-```
-
-As an environmental variable (which avoids the need for `accelerate launch`):
-
-```
-ACCELERATE_DEBUG_MODE="1" accelerate launch {my_script.py} --arg1 --arg2
-```
-
-Manually changing the `config.yaml` file:
-
-```diff
- compute_environment: LOCAL_MACHINE
-+debug: true
-```
-
-
-

docs/source/usage_guides/deepspeed.md

@@ -585,10 +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
 

docs/source/usage_guides/distributed_inference.md

@@ -120,7 +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)

docs/source/usage_guides/explore.md

@@ -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

@@ -49,7 +49,7 @@ fsdp_config:
   fsdp_offload_params: false
   fsdp_sharding_strategy: 1
   fsdp_state_dict_type: FULL_STATE_DICT
-  fsdp_transformer_layer_cls_to_wrap: BertLayer
+  fsdp_transformer_layer_cls_to_wrap: GPT2Block
 machine_rank: 0
 main_process_ip: null
 main_process_port: null
@@ -67,36 +67,19 @@ accelerate launch examples/nlp_example.py
 Currently, `Accelerate` supports the following config through the CLI:
 
 ```bash
-`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)
-
+`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
-
+`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`...
-This is important because submodules that share weights (e.g., embedding layer) should not end up in different FSDP wrapped units.
-Using this policy, wrapping happens for each block containing Multi-Head Attention followed by 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. Only applicable for 🤗 Transformers`. 
-It will try to use `model._no_split_modules` when available.  
-
 `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 
-
-`Forward Prefetch`: if True, then FSDP explicitly prefetches the next upcoming
-all-gather while executing in the forward pass. only use with Static graphs.
-
+`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
 ```
 
 For additional and more nuanced control, you can specify other FSDP parameters via `FullyShardedDataParallelPlugin`. 

docs/source/usage_guides/gradient_accumulation.md

@@ -127,11 +127,6 @@ training on. 🤗 Accelerate automagically does this for you by default. Behind
 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
@@ -143,74 +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/model_size_estimator.md

@@ -1,121 +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>
-
-## 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.
-
-## Live Gradio Demo
-
-Lastly, we invite you to try the [live Gradio demo](https://huggingface.co/spaces/hf-accelerate/model-memory-usage) of this utility,
-which includes an option to post a discussion thread on a models repository with this data. Doing so will help provide access to these numbers in the community faster and help users know what you've learned!

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.

examples/by_feature/deepspeed_with_config_support.py

@@ -243,6 +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()
@@ -269,20 +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",
@@ -516,11 +538,17 @@ def main():
         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)
 
@@ -547,16 +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.
@@ -567,16 +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)
@@ -587,61 +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)
@@ -656,29 +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

@@ -1,246 +0,0 @@
-# coding=utf-8
-# 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.TPU 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.TPU:
-        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

@@ -31,7 +31,6 @@ from transformers import (
 )
 
 from accelerate import Accelerator, DistributedType, FullyShardedDataParallelPlugin
-from accelerate.utils import is_npu_available, is_xpu_available
 
 
 ########################################################################
@@ -69,18 +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()
@@ -110,18 +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)
 
@@ -316,6 +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()

setup.py

@@ -19,7 +19,7 @@ extras = {}
 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", "pytest-xdist", "pytest-subtests", "parameterized"]
-extras["test_dev"] = ["datasets", "evaluate", "transformers", "scipy", "scikit-learn", "deepspeed", "tqdm", "bitsandbytes", "timm"]
+extras["test_dev"] = ["datasets", "evaluate", "transformers", "scipy", "scikit-learn", "deepspeed", "tqdm"]
 extras["testing"] = extras["test_prod"] + extras["test_dev"]
 extras["rich"] = ["rich"]
 
@@ -32,7 +32,7 @@ extras["sagemaker"] = [
 
 setup(
     name="accelerate",
-    version="0.24.0.dev0",
+    version="0.21.0",
     description="Accelerate",
     long_description=open("README.md", "r", encoding="utf-8").read(),
     long_description_content_type="text/markdown",
@@ -47,12 +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"],
+    install_requires=["numpy>=1.17", "packaging>=20.0", "psutil", "pyyaml", "torch>=1.10.0"],
     extras_require=extras,
     classifiers=[
         "Development Status :: 5 - Production/Stable",
@@ -68,29 +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

@@ -1,4 +1,4 @@
-__version__ = "0.24.0.dev0"
+__version__ = "0.21.0"
 
 from .accelerator import Accelerator
 from .big_modeling import (
@@ -14,7 +14,6 @@ from .data_loader import skip_first_batches
 from .launchers import debug_launcher, notebook_launcher
 from .state import PartialState
 from .utils import (
-    AutocastKwargs,
     DeepSpeedPlugin,
     DistributedDataParallelKwargs,
     DistributedType,

src/accelerate/accelerator.py

@@ -16,7 +16,6 @@ from __future__ import annotations
 
 import collections
 import contextlib
-import functools
 import json
 import math
 import os
@@ -46,7 +45,6 @@ from .utils import (
     SAFE_WEIGHTS_NAME,
     WEIGHTS_INDEX_NAME,
     WEIGHTS_NAME,
-    AutocastKwargs,
     DeepSpeedPlugin,
     DistributedDataParallelKwargs,
     DistributedType,
@@ -68,7 +66,6 @@ from .utils import (
     convert_outputs_to_fp32,
     extract_model_from_parallel,
     gather,
-    gather_object,
     get_mixed_precision_context_manager,
     get_pretty_name,
     has_transformer_engine_layers,
@@ -97,10 +94,11 @@ from .utils import (
     wait_for_everyone,
 )
 from .utils.constants import FSDP_PYTORCH_VERSION
-from .utils.other import is_compiled_module
 
 
 if is_deepspeed_available():
+    import deepspeed
+
     from .utils import (
         DeepSpeedEngineWrapper,
         DeepSpeedOptimizerWrapper,
@@ -136,10 +134,6 @@ if is_tpu_available(check_device=False):
     import torch_xla.distributed.xla_multiprocessing as xmp
 
 
-if is_npu_available(check_device=False):
-    import torch_npu  # noqa: F401
-
-
 try:
     from torch.optim.lr_scheduler import LRScheduler
 except ImportError:
@@ -164,8 +158,9 @@ class Accelerator:
         mixed_precision (`str`, *optional*):
             Whether or not to use mixed precision training. Choose from 'no','fp16','bf16 or 'fp8'. Will default to the
             value in the environment variable `ACCELERATE_MIXED_PRECISION`, which will use the default value in the
-            accelerate config of the current system or the flag passed with the `accelerate.launch` command. 'fp8'
-            requires the installation of transformers-engine.
+            accelerate config of the current system or the flag passed with the `accelerate.launch` command. 'fp16'
+            requires pytorch 1.6 or higher. 'bf16' requires pytorch 1.10 or higher. 'fp8' requires the installation of
+            transformers-engine.
         gradient_accumulation_steps (`int`, *optional*, default to 1):
             The number of steps that should pass before gradients are accumulated. A number > 1 should be combined with
             `Accelerator.accumulate`. If not passed, will default to the value in the environment variable
@@ -333,7 +328,6 @@ class Accelerator:
         self.scaler_handler = None
         self.init_handler = None
         self.fp8_recipe_handler = None
-        self.autocast_handler = None
         if kwargs_handlers is not None:
             for handler in kwargs_handlers:
                 assert isinstance(
@@ -359,11 +353,6 @@ class Accelerator:
                         raise ValueError("You can only pass one `FP8RecipeKwargs` in `kwargs_handler`.")
                     else:
                         self.fp8_recipe_handler = handler
-                elif isinstance(handler, AutocastKwargs):
-                    if self.autocast_handler is not None:
-                        raise ValueError("You can only pass one `AutocastKwargs` in `kwargs_handler`.")
-                    else:
-                        self.autocast_handler = handler
 
         kwargs = self.init_handler.to_kwargs() if self.init_handler is not None else {}
         self.state = AcceleratorState(
@@ -421,10 +410,11 @@ class Accelerator:
         if (
             self.state.mixed_precision == "fp16"
             and self.device.type != "cpu"
+            and self.device.type != "xpu"
             and self.distributed_type not in (DistributedType.DEEPSPEED, DistributedType.MEGATRON_LM)
         ):
             self.native_amp = True
-            if self.device.type not in ("xpu", "cuda", "mps", "npu"):
+            if self.device.type not in ("cuda", "mps", "npu"):
                 raise ValueError(err.format(mode="fp16", requirement="a GPU"))
             kwargs = self.scaler_handler.to_kwargs() if self.scaler_handler is not None else {}
             if self.distributed_type == DistributedType.FSDP:
@@ -466,9 +456,6 @@ class Accelerator:
         if self.rng_types is None:
             self.rng_types = ["generator"]
 
-        # Set a flag tensor for early stopping and other breakpoints
-        self.flag_tensor = None
-
     @property
     def use_distributed(self):
         """
@@ -875,55 +862,6 @@ class Accelerator:
         with context():
             yield
 
-    @staticmethod
-    @contextmanager
-    def trigger_sync_in_backward(model):
-        """Trigger the sync of the gradients in the next backward pass of the model after multiple forward passes under
-        `Accelerator.no_sync` (only applicable in multi-GPU scenarios).
-
-                If the script is not launched in distributed mode, this context manager does nothing.
-
-                Args:
-                    model (`torch.nn.Module`):
-                        The model for which to trigger the gradient synchronization.
-
-                Example:
-
-                ```python
-                >>> from accelerate import Accelerator
-
-                >>> accelerator = Accelerator()
-                >>> dataloader, model, optimizer = accelerator.prepare(dataloader, model, optimizer)
-
-                >>> with accelerator.no_sync():
-                ...     loss_a = loss_func(model(input_a))  # first forward pass
-                ...     loss_b = loss_func(model(input_b))  # second forward pass
-                >>> accelerator.backward(loss_a)  # No synchronization across processes, only accumulate gradients
-                >>> with accelerator.trigger_sync_in_backward(model):
-                ...     accelerator.backward(loss_b)  # Synchronization across all processes
-                >>> optimizer.step()
-                >>> optimizer.zero_grad()
-                ```
-        """
-        if not isinstance(model, torch.nn.parallel.DistributedDataParallel):
-            yield
-            return
-
-        old_require_backward_grad_sync = model.require_backward_grad_sync
-        old_require_forward_param_sync = model.require_forward_param_sync
-
-        # EXPERIMENTAL: This will force grad sync during `backward()`, but it is unknown if it breaks other DDP features.
-        # https://github.com/pytorch/pytorch/blob/e1502c0cdbfd17548c612f25d5a65b1e4b86224d/torch/nn/parallel/distributed.py#L1453-L1466
-        model.require_backward_grad_sync = True
-        model.require_forward_param_sync = True
-        # https://github.com/pytorch/pytorch/blob/e1502c0cdbfd17548c612f25d5a65b1e4b86224d/torch/csrc/distributed/c10d/reducer.cpp#L1371-L1402
-        model.reducer.prepare_for_backward([])
-        try:
-            yield
-        finally:
-            model.require_backward_grad_sync = old_require_backward_grad_sync
-            model.require_forward_param_sync = old_require_forward_param_sync
-
     def _do_sync(self):
         "Sets the right `sync_gradients` context and either resets or increases `self.step`"
         if self.gradient_state.sync_with_dataloader and self.gradient_state.end_of_dataloader:
@@ -950,14 +888,13 @@ class Accelerator:
         self.gradient_state.plugin_kwargs.update({"num_steps": gradient_accumulation_steps})
 
     @contextmanager
-    def accumulate(self, *models):
+    def accumulate(self, model):
         """
         A context manager that will lightly wrap around and perform gradient accumulation automatically
 
         Args:
-            *models (list of `torch.nn.Module`):
-                PyTorch Modules that was prepared with `Accelerator.prepare`. Models passed to `accumulate()` will skip
-                gradient syncing during backward pass in distributed training
+            model (`torch.nn.Module`):
+                PyTorch Module that was prepared with `Accelerator.prepare`
 
         Example:
 
@@ -978,9 +915,12 @@ class Accelerator:
         ```
         """
         self._do_sync()
-        with contextlib.ExitStack() as cm_stack:
-            for m in models:
-                cm_stack.enter_context(contextlib.nullcontext() if self.sync_gradients else self.no_sync(m))
+        if self.sync_gradients:
+            context = contextlib.nullcontext
+        else:
+            context = self.no_sync
+
+        with context(model):
             yield
 
     @contextmanager
@@ -1199,34 +1139,12 @@ class Accelerator:
                 f"`device_placement` should be a list with {len(args)} elements (the number of objects passed)."
             )
 
-        for obj in args:
-            # TODO: Look at enabling native TP training directly with a proper config
-            if (
-                isinstance(obj, torch.nn.Module)
-                and self.verify_device_map(obj)
-                and self.distributed_type != DistributedType.NO
-                and os.environ.get("ACCELERATE_BYPASS_DEVICE_MAP", "false") != "true"
-            ):
-                raise ValueError(
-                    "You can't train a model that has been loaded with `device_map='auto'` in any distributed mode."
-                    " Please rerun your script specifying `--num_processes=1` or by launching with `python {{myscript.py}}`."
-                )
-
         if self.distributed_type == DistributedType.FSDP:
-            from torch.distributed.fsdp.fully_sharded_data_parallel import FullyShardedDataParallel as FSDP
-
             model_count = 0
             optimizer_present = False
-            is_type_fsdp = False
             for obj in args:
                 if isinstance(obj, torch.nn.Module):
                     model_count += 1
-                    # if the model is compiled using PyTorch 2.0,
-                    # check that the wrapped model is FSDP or not;
-                    # else check if it is FSDP or not;
-                    is_type_fsdp = isinstance(obj, FSDP) or (
-                        is_compiled_module(obj) and isinstance(obj._orig_mod, FSDP)
-                    )
                 if isinstance(obj, torch.optim.Optimizer):
                     optimizer_present = True
             if model_count > 1 and optimizer_present:
@@ -1235,7 +1153,7 @@ class Accelerator:
                     "prepare must be called for all the models before optimizers are created. "
                     "Then pass the optimizers to the prepare call in the same order as corresponding models."
                 )
-            elif model_count == 1 and not is_type_fsdp and optimizer_present:
+            elif model_count == 1 and optimizer_present:
                 logger.warning(
                     "FSDP Warning: When using FSDP, "
                     "it is efficient and recommended to call prepare for the model before creating the optimizer"
@@ -1296,12 +1214,7 @@ class Accelerator:
                 if isinstance(obj, torch.optim.Optimizer):
                     obj._switch_parameters(mapping)
 
-        if (
-            self.distributed_type == DistributedType.FSDP
-            and model_count == 1
-            and not is_type_fsdp
-            and optimizer_present
-        ):
+        if self.distributed_type == DistributedType.FSDP and model_count == 1 and optimizer_present:
             result = self._prepare_fsdp(*result)
 
         for item in result:
@@ -1341,17 +1254,13 @@ class Accelerator:
         if device_placement is None:
             device_placement = self.device_placement and self.distributed_type != DistributedType.FSDP
         self._models.append(model)
-
-        # TODO: Look at enabling native TP training directly with a proper config
-        if (
-            self.verify_device_map(model)
-            and self.distributed_type != DistributedType.NO
-            and os.environ.get("ACCELERATE_BYPASS_DEVICE_MAP", "false") != "true"
-        ):
-            raise ValueError(
-                "You can't train a model that has been loaded with `device_map='auto'` in any distributed mode."
-                " Please rerun your script specifying `--num_processes=1` or by launching with `python {{myscript.py}}`."
-            )
+        # We check only for models loaded with `accelerate`
+        # Checks if any of the child module has the attribute `hf_device_map`.
+        has_hf_device_map = False
+        for m in model.modules():
+            if hasattr(m, "hf_device_map"):
+                has_hf_device_map = True
+                break
 
         if (getattr(model, "is_loaded_in_8bit", False) or getattr(model, "is_loaded_in_4bit", False)) and getattr(
             model, "hf_device_map", False
@@ -1379,14 +1288,20 @@ class Accelerator:
                 raise ValueError(
                     "You can't train a model that has been loaded in 8-bit precision with CPU or disk offload."
                 )
-        elif device_placement and not self.verify_device_map(model):
+        elif device_placement and not has_hf_device_map:
             model = model.to(self.device)
 
         if self.native_amp:
             model._original_forward = model.forward
             model_forward_func = model.forward.__func__ if hasattr(model.forward, "__func__") else model.forward
-            autocast_context = get_mixed_precision_context_manager(self.native_amp, self.autocast_handler)
-            new_forward = autocast_context(model_forward_func)
+            if self.mixed_precision == "fp16":
+                if is_npu_available():
+                    new_forward = torch.npu.amp.autocast(dtype=torch.float16)(model_forward_func)
+                else:
+                    new_forward = torch.cuda.amp.autocast(dtype=torch.float16)(model_forward_func)
+            elif self.mixed_precision == "bf16" and self.distributed_type != DistributedType.TPU:
+                new_forward = torch.autocast(device_type=self.device.type, dtype=torch.bfloat16)(model_forward_func)
+
             if hasattr(model.forward, "__func__"):
                 model.forward = MethodType(new_forward, model)
                 model.forward = MethodType(convert_outputs_to_fp32(model.forward.__func__), model)
@@ -1404,7 +1319,9 @@ class Accelerator:
                 kwargs["fp8_format"] = getattr(te_recipe.Format, kwargs["fp8_format"])
             fp8_recipe = te_recipe.DelayedScaling(**kwargs)
             cuda_device_capacity = torch.cuda.get_device_capability()
-            fp8_enabled = cuda_device_capacity >= (8, 9)
+            fp8_enabled = cuda_device_capacity[0] >= 9 or (
+                cuda_device_capacity[0] == 8 and cuda_device_capacity[1] >= 9
+            )
             if not fp8_enabled:
                 logger.warn(
                     f"The current device has compute capability of {cuda_device_capacity} which is "
@@ -1420,27 +1337,15 @@ class Accelerator:
             ):
                 if any(p.requires_grad for p in model.parameters()):
                     kwargs = self.ddp_handler.to_kwargs() if self.ddp_handler is not None else {}
-                    # TODO: Look at enabling native TP training directly with a proper config
-                    if os.environ.get("ACCELERATE_BYPASS_DEVICE_MAP", "false") != "true":
-                        device_ids, output_device = [self.local_process_index], self.local_process_index
-                    else:
-                        device_ids, output_device = None, None
-
                     model = torch.nn.parallel.DistributedDataParallel(
-                        model, device_ids=device_ids, output_device=output_device, **kwargs
+                        model, device_ids=[self.local_process_index], output_device=self.local_process_index, **kwargs
                     )
             elif self.distributed_type == DistributedType.FSDP:
                 from torch.distributed.fsdp.fully_sharded_data_parallel import FullyShardedDataParallel as FSDP
 
                 # Check if the model is already a FSDP model due to `Manual Wrapping` and if so,
                 # don't wrap it again
-                # In case the model is already compiled using PyTorch 2.0 and the wrapped model in it
-                # is a FSDP model, don't wrap it again
-                is_type_fsdp = isinstance(model, FSDP) or (
-                    is_compiled_module(model) and isinstance(model._orig_mod, FSDP)
-                )
-
-                if not is_type_fsdp:
+                if type(model) != FSDP:
                     self.state.fsdp_plugin.set_auto_wrap_policy(model)
                     fsdp_plugin = self.state.fsdp_plugin
                     kwargs = {
@@ -1454,44 +1359,25 @@ class Accelerator:
                         "use_orig_params": fsdp_plugin.use_orig_params,
                         "param_init_fn": fsdp_plugin.param_init_fn,
                         "ignored_modules": fsdp_plugin.ignored_modules,
+                        "ignored_parameters": fsdp_plugin.ignored_parameters,
                         "limit_all_gathers": fsdp_plugin.limit_all_gathers,
                         "device_id": self.device,
                     }
                     model = FSDP(model, **kwargs)
-                    if fsdp_plugin.activation_checkpointing:
-                        from torch.distributed.algorithms._checkpoint.checkpoint_wrapper import (
-                            CheckpointImpl,
-                            apply_activation_checkpointing,
-                            checkpoint_wrapper,
-                        )
-
-                        apply_activation_checkpointing(
-                            model,
-                            checkpoint_wrapper_fn=functools.partial(
-                                checkpoint_wrapper,
-                                checkpoint_impl=CheckpointImpl.NO_REENTRANT,
-                            ),
-                            auto_wrap_policy=fsdp_plugin.auto_wrap_policy,
-                        )
-                # if the previous and current models are same, delete the previous one
-                if len(self._models) > 1 and (self._models[-2] is self._models[-1]):
-                    del self._models[-2]
                 self._models[-1] = model
             elif self.distributed_type == DistributedType.MULTI_CPU:
                 kwargs = self.ddp_handler.to_kwargs() if self.ddp_handler is not None else {}
                 model = torch.nn.parallel.DistributedDataParallel(model, **kwargs)
             elif self.distributed_type == DistributedType.TPU and self.state.fork_launched:
                 model = xmp.MpModelWrapper(model).to(self.device)
-        # torch.compile should be called last and only if the model isn't already compiled.
-        if self.state.dynamo_plugin.backend != DynamoBackend.NO and not is_compiled_module(model):
+        # torch.compile should be called last.
+        if self.state.dynamo_plugin.backend != DynamoBackend.NO:
             if not is_torch_version(">=", "2.0"):
                 raise ValueError("Using `torch.compile` requires PyTorch 2.0 or higher.")
             model = torch.compile(model, **self.state.dynamo_plugin.to_kwargs())
         return model
 
     def _prepare_deepspeed(self, *args):
-        import deepspeed
-
         deepspeed_plugin = self.state.deepspeed_plugin
 
         is_dataloader_present = any(isinstance(obj, torch.utils.data.DataLoader) for obj in args)
@@ -1528,13 +1414,12 @@ class Accelerator:
             batch_size_per_device = deepspeed_plugin.deepspeed_config["train_micro_batch_size_per_gpu"]
             result = [obj for obj in args]
 
-        # handle `gradient_accumulation_steps` when the value is `auto`
-        deepspeed_plugin.fill_match(
-            "gradient_accumulation_steps",
-            must_match=False,
-            gradient_accumulation_steps=self.gradient_accumulation_steps,
-        )
-
+        if self.gradient_accumulation_steps != deepspeed_plugin.deepspeed_config["gradient_accumulation_steps"]:
+            logger.info(
+                f"Updating DeepSpeed's gradient accumulation steps to {self.gradient_accumulation_steps} from "
+                f"{deepspeed_plugin.deepspeed_config['gradient_accumulation_steps']}."
+            )
+            deepspeed_plugin.deepspeed_config["gradient_accumulation_steps"] = self.gradient_accumulation_steps
         config_kwargs = {
             "train_micro_batch_size_per_gpu": batch_size_per_device,
             "train_batch_size": batch_size_per_device
@@ -1579,14 +1464,9 @@ class Accelerator:
                     "Please remove the scheduler from the config file or "
                     "create `accelerate.utils.DummyScheduler` in the code."
                 )
-            elif (
-                "scheduler" not in deepspeed_plugin.deepspeed_config
-                and isinstance(scheduler, (DummyScheduler))
-                and scheduler.lr_scheduler_callable is None
-            ):
+            elif "scheduler" not in deepspeed_plugin.deepspeed_config and isinstance(scheduler, (DummyScheduler)):
                 raise ValueError(
-                    "Either specify a scheduler in the config file or "
-                    "pass in the `lr_scheduler_callable` parameter when using `accelerate.utils.DummyScheduler`."
+                    "You cannot create a `DummyScheduler` without specifying a scheduler in the config file."
                 )
 
         if optimizer is not None and scheduler is not None:
@@ -1616,7 +1496,7 @@ class Accelerator:
                 config_kwargs.update(
                     {"optimizer.params.lr": optimizer.lr, "optimizer.params.weight_decay": optimizer.weight_decay}
                 )
-            if isinstance(scheduler, (DummyScheduler)) and scheduler.lr_scheduler_callable is None:
+            if isinstance(scheduler, (DummyScheduler)):
                 max_lr = (
                     getattr(scheduler.optimizer, "lr", None)
                     if getattr(scheduler.optimizer, "defaults", None) is None
@@ -1641,8 +1521,6 @@ class Accelerator:
             if optimizer is not None:
                 if isinstance(optimizer, (DummyOptim)):
                     kwargs["model_parameters"] = optimizer.params
-                    if isinstance(scheduler, (DummyScheduler)) and scheduler.lr_scheduler_callable is not None:
-                        kwargs["lr_scheduler"] = scheduler.lr_scheduler_callable
                 else:
                     if self.deepspeed_config["zero_optimization"].get("offload_optimizer", {}).get(
                         "device", "none"
@@ -1653,10 +1531,7 @@ class Accelerator:
                         optimizer = DeepSpeedCPUAdam(optimizer.param_groups, **defaults)
                     kwargs["optimizer"] = optimizer
                     if scheduler is not None:
-                        if (
-                            isinstance(scheduler, LRScheduler)
-                            or type(scheduler).__name__ in deepspeed.runtime.lr_schedules.VALID_LR_SCHEDULES
-                        ):
+                        if type(scheduler).__name__ in deepspeed.runtime.lr_schedules.VALID_LR_SCHEDULES:
                             kwargs["lr_scheduler"] = scheduler
 
             engine, optimizer, _, lr_scheduler = deepspeed.initialize(**kwargs)
@@ -1829,9 +1704,7 @@ class Accelerator:
                 result[i] = optimizer
         return tuple(result)
 
-    def prepare_data_loader(
-        self, data_loader: torch.utils.data.DataLoader, device_placement=None, slice_fn_for_dispatch=None
-    ):
+    def prepare_data_loader(self, data_loader: torch.utils.data.DataLoader, device_placement=None):
         """
         Prepares a PyTorch DataLoader for training in any distributed setup. It is recommended to use
         [`Accelerator.prepare`] instead.
@@ -1842,10 +1715,6 @@ class Accelerator:
             device_placement (`bool`, *optional*):
                 Whether or not to place the batches on the proper device in the prepared dataloader. Will default to
                 `self.device_placement`.
-            slice_fn_for_dispatch (`Callable`, *optional*`):
-                If passed, this function will be used to slice tensors across `num_processes`. Will default to
-                [`~utils.slice_tensors`]. This argument is used only when `dispatch_batches` is set to `True` and will
-                be ignored otherwise.
 
         Example:
 
@@ -1875,7 +1744,6 @@ class Accelerator:
             rng_types=self.rng_types.copy(),
             dispatch_batches=self.dispatch_batches,
             even_batches=self.even_batches,
-            slice_fn_for_dispatch=slice_fn_for_dispatch,
         )
         self._dataloaders.append(prepared_data_loader)
         return prepared_data_loader
@@ -1984,65 +1852,6 @@ class Accelerator:
         else:
             loss.backward(**kwargs)
 
-    def set_trigger(self):
-        """
-        Sets the internal trigger tensor to 1 on the current process. A latter check should follow using this which
-        will check across all processes.
-
-        Note:
-            Does not require `wait_for_everyone()`
-
-        Example:
-
-        ```python
-        >>> from accelerate import Accelerator
-
-        >>> accelerator = Accelerator()
-        >>> # Assume later in the training script
-        >>> # `should_do_breakpoint` is a custom function to monitor when to break,
-        >>> # e.g. when the loss is NaN
-        >>> if should_do_breakpoint(loss):
-        ...     accelerator.set_trigger()
-        >>> # Assume later in the training script
-        >>> if accelerator.check_breakpoint():
-        ...     break
-        ```
-        """
-        self.flag_tensor = torch.tensor(1, device=self.device)
-
-    def check_trigger(self):
-        """
-        Checks if the internal trigger tensor has been set to 1 in any of the processes. If so, will return `True` and
-        reset the trigger tensor to 0.
-
-        Note:
-            Does not require `wait_for_everyone()`
-
-        Example:
-
-        ```python
-        >>> from accelerate import Accelerator
-
-        >>> accelerator = Accelerator()
-        >>> # Assume later in the training script
-        >>> # `should_do_breakpoint` is a custom function to monitor when to break,
-        >>> # e.g. when the loss is NaN
-        >>> if should_do_breakpoint(loss):
-        ...     accelerator.set_trigger()
-        >>> # Assume later in the training script
-        >>> if accelerator.check_trigger():
-        ...     break
-        ```
-        """
-        # Now that we are outside `__init__`, we can initialize it if it is `None` on device
-        if self.flag_tensor is None:
-            self.flag_tensor = torch.tensor(0, device=self.device)
-        flag_tensor = self.reduce(self.flag_tensor)
-        if flag_tensor.item() >= 1:
-            self.flag_tensor = torch.tensor(0, device=self.device)
-            return True
-        return False
-
     def unscale_gradients(self, optimizer=None):
         """
         Unscale the gradients in mixed precision training with AMP. This is a noop in all other settings.
@@ -2076,10 +1885,6 @@ class Accelerator:
             for opt in optimizer:
                 while isinstance(opt, AcceleratedOptimizer):
                     opt = opt.optimizer
-                # Reduce gradients first for XLA
-                if self.distributed_type == DistributedType.TPU:
-                    gradients = xm._fetch_gradients(opt)
-                    self.reduce(gradients, scale=1.0 / self.num_processes)
                 self.scaler.unscale_(opt)
 
     def clip_grad_norm_(self, parameters, max_norm, norm_type=2):
@@ -2179,14 +1984,14 @@ class Accelerator:
         """
         return gather(tensor)
 
-    def gather_for_metrics(self, input_data):
+    def gather_for_metrics(self, tensor):
         """
-        Gathers `input_data` and potentially drops duplicates in the last batch if on a distributed system. Should be
-        used for gathering the inputs and targets for metric calculation.
+        Gathers `tensor` and potentially drops duplicates in the last batch if on a distributed system. Should be used
+        for gathering the inputs and targets for metric calculation.
 
         Args:
-            input (`torch.Tensor`, `object`, a nested tuple/list/dictionary of `torch.Tensor`, or a nested tuple/list/dictionary of `object`):
-                The tensors or objects for calculating metrics across all processes
+            tensor (`torch.Tensor`, or a nested tuple/list/dictionary of `torch.Tensor`):
+                The tensors for calculating metrics across all processes.
 
         Example:
 
@@ -2204,44 +2009,28 @@ class Accelerator:
         9
         ```
         """
-
+        tensor = self.gather(tensor)
+        if self.gradient_state.remainder == -1:
+            logger.info(
+                "The used dataset had no length, returning gathered tensors. You should drop the remainder yourself."
+            )
+            return tensor
         try:
-            recursively_apply(lambda x: x, input_data, error_on_other_type=True)
-            all_tensors = True
-        except TypeError:
-            all_tensors = False
+            # Then see if we're on the last batch of our eval dataloader
+            if self.gradient_state.end_of_dataloader and self.gradient_state.remainder > 0:
+                # Last batch needs to be truncated on distributed systems as it contains additional samples
+                def _adjust_samples(tensor):
+                    return tensor[: self.gradient_state.remainder]
 
-        if not all_tensors:
-            data = gather_object(input_data)
-        else:
-            data = self.gather(input_data)
-
-        try:
-            if self.gradient_state.end_of_dataloader:
-                # at the end of a dataloader, `gather_for_metrics` regresses to
-                # `gather` unless the dataset has a remainder so log.
-                if self.gradient_state.remainder == -1:
-                    logger.info(
-                        "The used dataset had no length, returning gathered tensors. You should drop the remainder yourself."
-                    )
-                    return data
-                elif self.gradient_state.remainder > 0:
-                    # Last batch needs to be truncated on distributed systems as it contains additional samples
-                    def _adjust_samples(tensor):
-                        return tensor[: self.gradient_state.remainder]
-
-                    return recursively_apply(_adjust_samples, data)
-                else:  # remainder is 0
-                    # no remainder even though at end of dataloader, so nothing to do.
-                    return data
+                return recursively_apply(_adjust_samples, tensor)
             else:
                 # Not at the end of the dataloader, no need to adjust the tensors
-                return data
+                return tensor
         except Exception:
             # Dataset had no length or raised an error
-            return data
+            return tensor
 
-    def reduce(self, tensor, reduction="sum", scale=1.0):
+    def reduce(self, tensor, reduction="sum"):
         """
         Reduce the values in *tensor* across all processes based on *reduction*.
 
@@ -2253,8 +2042,6 @@ class Accelerator:
                 The tensors to reduce across all processes.
             reduction (`str`, *optional*, defaults to "sum"):
                 A reduction type, can be one of 'sum', 'mean', or 'none'. If 'none', will not perform any operation.
-            scale (`float`, *optional*, defaults to 1.0):
-                A default scaling value to be applied after the reduce, only valied on XLA.
 
         Returns:
             `torch.Tensor`, or a nested tuple/list/dictionary of `torch.Tensor`:
@@ -2275,7 +2062,7 @@ class Accelerator:
         tensor([4, 6])
         ```
         """
-        return reduce(tensor, reduction, scale)
+        return reduce(tensor, reduction)
 
     def pad_across_processes(self, tensor, dim=0, pad_index=0, pad_first=False):
         """
@@ -2499,14 +2286,13 @@ class Accelerator:
         for tracker in self.trackers:
             tracker.finish()
 
-    def save(self, obj, f, safe_serialization=False):
+    def save(self, obj, f):
         """
         Save the object passed to disk once per machine. Use in place of `torch.save`.
 
         Args:
             obj (`object`): The object to save.
             f (`str` or `os.PathLike`): Where to save the content of `obj`.
-            safe_serialization (`bool`, *optional*, defaults to `False`): Whether to save `obj` using `safetensors`
 
         Example:
 
@@ -2518,7 +2304,7 @@ class Accelerator:
         >>> accelerator.save(arr, "array.pkl")
         ```
         """
-        save(obj, f, safe_serialization=safe_serialization)
+        save(obj, f)
 
     def save_model(
         self,
@@ -2598,7 +2384,7 @@ class Accelerator:
                             del state_dict[name]
                             warn_names.add(name)
             if len(warn_names) > 0:
-                logger.warning(
+                logger.warning_once(
                     f"Removed shared tensor {warn_names} while saving. This should be OK, but check by verifying that you don't receive any warning while reloading",
                 )
 
@@ -2629,7 +2415,7 @@ class Accelerator:
 
         # Save the model
         for shard_file, shard in shards.items():
-            self.save(shard, os.path.join(save_directory, shard_file), safe_serialization=safe_serialization)
+            self.save(shard, os.path.join(save_directory, shard_file))
 
         if index is None:
             path_to_weights = os.path.join(save_directory, WEIGHTS_NAME)
@@ -2720,10 +2506,8 @@ class Accelerator:
         os.makedirs(output_dir, exist_ok=True)
         if self.project_configuration.automatic_checkpoint_naming:
             folders = [os.path.join(output_dir, folder) for folder in os.listdir(output_dir)]
-            if (
-                self.project_configuration.total_limit is not None
-                and (len(folders) + 1 > self.project_configuration.total_limit)
-                and self.is_main_process
+            if self.project_configuration.total_limit is not None and (
+                len(folders) + 1 > self.project_configuration.total_limit
             ):
 
                 def _inner(folder):
@@ -2740,7 +2524,6 @@ class Accelerator:
                 raise ValueError(
                     f"Checkpoint directory {output_dir} ({self.save_iteration}) already exists. Please manually override `self.save_iteration` with what iteration to start with."
                 )
-            self.wait_for_everyone()
         os.makedirs(output_dir, exist_ok=True)
         logger.info(f"Saving current state to {output_dir}")
 
@@ -2831,7 +2614,7 @@ class Accelerator:
         self._load_model_state_pre_hook[handle.id] = hook
         return handle
 
-    def load_state(self, input_dir: str = None, **load_model_func_kwargs):
+    def load_state(self, input_dir: str, **load_model_func_kwargs):
         """
         Loads the current states of the model, optimizer, scaler, RNG generators, and registered objects.
 
@@ -2844,8 +2627,7 @@ class Accelerator:
 
         Args:
             input_dir (`str` or `os.PathLike`):
-                The name of the folder all relevant weights and states were saved in. Can be `None` if
-                `automatic_checkpoint_naming` is used, and will pick up from the latest checkpoint.
+                The name of the folder all relevant weights and states were saved in.
             load_model_func_kwargs (`dict`, *optional*):
                 Additional keyword arguments for loading model which can be passed to the underlying load function,
                 such as optional arguments for DeepSpeed's `load_checkpoint` function or a `map_location` to load the
@@ -2862,23 +2644,10 @@ class Accelerator:
         >>> accelerator.load_state("my_checkpoint")
         ```
         """
-        if input_dir is not None:
-            # Check if folder exists
-            input_dir = os.path.expanduser(input_dir)
-            if not os.path.isdir(input_dir):
-                raise ValueError(f"Tried to find {input_dir} but folder does not exist")
-        elif self.project_configuration.automatic_checkpoint_naming:
-            # Pick up from automatic checkpoint naming
-            input_dir = os.path.join(self.project_dir, "checkpoints")
-            folders = [os.path.join(input_dir, folder) for folder in os.listdir(input_dir)]
-
-            def _inner(folder):
-                return list(map(int, re.findall(r"[\/]?([0-9]+)(?=[^\/]*$)", folder)))[0]
-
-            folders.sort(key=_inner)
-            input_dir = folders[-1]
-        else:
-            raise ValueError("No input_dir provided and automatic checkpoint naming is disabled.")
+        # Check if folder exists
+        input_dir = os.path.expanduser(input_dir)
+        if not os.path.isdir(input_dir):
+            raise ValueError(f"Tried to find {input_dir} but folder does not exist")
         logger.info(f"Loading states from {input_dir}")
 
         # Load the models taking care of FSDP and DeepSpeed nuances
@@ -3076,6 +2845,11 @@ class Accelerator:
                 model = self.unwrap_model(model)
             state_dict = model.state_dict()
 
+        if state_dict is not None:
+            for k in state_dict:
+                if getattr(state_dict[k], "dtype", None) == torch.float16:
+                    state_dict[k] = state_dict[k].float()
+
         return state_dict
 
     def register_for_checkpointing(self, *objects):
@@ -3115,14 +2889,11 @@ class Accelerator:
         self._custom_objects.extend(objects)
 
     @contextmanager
-    def autocast(self, cache_enabled: bool = False, autocast_handler: AutocastKwargs = None):
+    def autocast(self, cache_enabled: bool = False):
         """
         Will apply automatic mixed-precision inside the block inside this context manager, if it is enabled. Nothing
         different will happen otherwise.
 
-        A different `autocast_handler` can be passed in to override the one set in the `Accelerator` object. This is
-        useful in blocks under `autocast` where you want to revert to fp32.
-
         Example:
 
         ```python
@@ -3133,19 +2904,7 @@ class Accelerator:
         ...     train()
         ```
         """
-        if cache_enabled:
-            warnings.warn(
-                "Passing `cache_enabled=True` to `accelerator.autocast` is deprecated and will be removed in v0.23.0. "
-                "Please use the `AutocastKwargs` class instead and pass it to the `Accelerator` as a `kwarg_handler`.",
-                FutureWarning,
-            )
-            if self.autocast_handler is not None:
-                self.autocast_handler.cache_enabled = True
-            else:
-                self.autocast_handler = AutocastKwargs(cache_enabled=True)
-        if autocast_handler is None:
-            autocast_handler = self.autocast_handler
-        autocast_context = get_mixed_precision_context_manager(self.native_amp, autocast_handler)
+        autocast_context = get_mixed_precision_context_manager(self.native_amp, cache_enabled=cache_enabled)
         autocast_context.__enter__()
         yield
         autocast_context.__exit__(*sys.exc_info())
@@ -3196,14 +2955,3 @@ class Accelerator:
     def __deepcopy__(self, memo):
         logger.info("Deep copying the `Accelerator` object, note that this will point to the same original object.")
         return self
-
-    def verify_device_map(self, model: torch.nn.Module) -> bool:
-        """
-        Verifies that `model` has not been prepared with big model inference with a device-map resembling `auto`.
-        """
-        # Checks if any of the child modules has the attribute `hf_device_map` and this map has more than one entry.
-        for m in model.modules():
-            if hasattr(m, "hf_device_map") and len(m.hf_device_map) > 1:
-                return True
-
-        return False

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
@@ -36,25 +34,20 @@ from .utils import (
     find_tied_parameters,
     get_balanced_memory,
     infer_auto_device_map,
-    is_torch_version,
     load_checkpoint_in_model,
     offload_state_dict,
-    parse_flag_from_env,
     retie_parameters,
 )
 
 
-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:
@@ -75,21 +68,19 @@ def init_empty_weights(include_buffers: bool = None):
 
     </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:
@@ -102,15 +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
@@ -304,7 +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
@@ -335,23 +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"
@@ -404,22 +379,6 @@ def dispatch_model(
         )
         # 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):
-                logger.warning("You shouldn't move a model when it is dispatched on multiple devices.")
-                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)
-        model.cuda = add_warning(model.cuda, model)
-
     else:
         device = list(device_map.values())[0]
         if device != "disk":
@@ -444,7 +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
@@ -487,9 +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:
 
@@ -550,5 +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/commands/accelerate_cli.py

@@ -18,7 +18,6 @@ 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
@@ -30,7 +29,6 @@ def main():
 
     # 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.",
@@ -313,7 +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,
@@ -343,18 +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]: ",
@@ -387,9 +372,9 @@ def get_cluster_input():
                 error_message="Please enter yes or 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]: ",
+                "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.",
             )
 
@@ -492,11 +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 [
@@ -508,12 +488,8 @@ 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",
         )
 
@@ -641,5 +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

@@ -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__
@@ -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):
@@ -130,6 +120,7 @@ class BaseConfig:
             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/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

@@ -1,270 +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`...")
-
-        auto_map = model_info.config.get("auto_map", False)
-        config = AutoConfig.from_pretrained(model_name, trust_remote_code=trust_remote_code)
-
-        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

@@ -526,7 +526,7 @@ def launch_command_parser(subparsers=None):
     )
     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).",
@@ -877,9 +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"
@@ -908,8 +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)
@@ -931,8 +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

src/accelerate/data_loader.py

@@ -14,7 +14,7 @@
 
 import math
 from contextlib import suppress
-from typing import Callable, List, Optional, Union
+from typing import List, Optional, Union
 
 import torch
 from torch.utils.data import BatchSampler, DataLoader, IterableDataset
@@ -485,9 +485,7 @@ class DataLoaderDispatcher(DataLoader, DataLoaderStateMixin):
         - **total_dataset_length** (`int`) -- Total length of the inner dataset across all processes.
     """
 
-    def __init__(
-        self, dataset, split_batches: bool = False, skip_batches=0, _drop_last: bool = False, slice_fn=None, **kwargs
-    ):
+    def __init__(self, dataset, split_batches: bool = False, skip_batches=0, _drop_last: bool = False, **kwargs):
         shuffle = False
         if is_torch_version(">=", "1.11.0"):
             from torch.utils.data.datapipes.iter.combinatorics import ShufflerIterDataPipe
@@ -505,8 +503,6 @@ class DataLoaderDispatcher(DataLoader, DataLoaderStateMixin):
         self._drop_last = _drop_last
         self.skip_batches = skip_batches
 
-        self.slice_fn = slice_tensors if slice_fn is None else slice_fn
-
     def _fetch_batches(self, iterator):
         batches, batch = None, None
         # On process 0, we gather the batch to dispatch.
@@ -571,12 +567,7 @@ class DataLoaderDispatcher(DataLoader, DataLoaderStateMixin):
 
             if not self._drop_last and first_batch is None:
                 # We keep at least num processes elements of the first batch to be able to complete the last batch
-                first_batch = self.slice_fn(
-                    batch,
-                    slice(0, self.state.num_processes),
-                    process_index=self.state.process_index,
-                    num_processes=self.state.num_processes,
-                )
+                first_batch = slice_tensors(batch, slice(0, self.state.num_processes))
 
             if batch is None:
                 raise ValueError(
@@ -602,12 +593,7 @@ class DataLoaderDispatcher(DataLoader, DataLoaderStateMixin):
                 batch_size += 1
 
             data_slice = slice(self.state.process_index * batch_size, (self.state.process_index + 1) * batch_size)
-            batch = self.slice_fn(
-                batch,
-                data_slice,
-                process_index=self.state.process_index,
-                num_processes=self.state.num_processes,
-            )
+            batch = slice_tensors(batch, data_slice)
 
             if stop_iteration:
                 self.end_of_dataloader = True
@@ -647,7 +633,6 @@ def prepare_data_loader(
     rng_types: Optional[List[Union[str, RNGType]]] = None,
     dispatch_batches: Optional[bool] = None,
     even_batches: bool = True,
-    slice_fn_for_dispatch: Optional[Callable] = None,
 ) -> DataLoader:
     """
     Wraps a PyTorch `DataLoader` to generate batches for one of the processes only.
@@ -697,10 +682,6 @@ def prepare_data_loader(
             If set to `True`, in cases where the total batch size across all processes does not exactly divide the
             dataset, samples at the start of the dataset will be duplicated so the batch can be divided equally among
             all workers.
-        slice_fn_for_dispatch (`Callable`, *optional*`):
-            If passed, this function will be used to slice tensors across `num_processes`. Will default to
-            [`~utils.slice_tensors`]. This argument is used only when `dispatch_batches` is set to `True` and will be
-            ignored otherwise.
 
     Returns:
         `torch.utils.data.dataloader.DataLoader`: A new data loader that will yield the portion of the batches
@@ -805,7 +786,6 @@ def prepare_data_loader(
             split_batches=split_batches,
             batch_sampler=new_batch_sampler,
             _drop_last=dataloader.drop_last,
-            slice_fn=slice_fn_for_dispatch,
             **kwargs,
         )
     elif sampler_is_batch_sampler:

src/accelerate/hooks.py

@@ -155,17 +155,17 @@ def add_hook_to_module(module: nn.Module, hook: ModelHook, append: bool = False)
     module = hook.init_hook(module)
     module._hf_hook = hook
 
-    def new_forward(module, *args, **kwargs):
+    @functools.wraps(old_forward)
+    def new_forward(*args, **kwargs):
         args, kwargs = module._hf_hook.pre_forward(module, *args, **kwargs)
         if module._hf_hook.no_grad:
             with torch.no_grad():
-                output = module._old_forward(*args, **kwargs)
+                output = old_forward(*args, **kwargs)
         else:
-            output = module._old_forward(*args, **kwargs)
+            output = old_forward(*args, **kwargs)
         return module._hf_hook.post_forward(module, output)
 
-    module.forward = functools.update_wrapper(functools.partial(new_forward, module), old_forward)
-
+    module.forward = new_forward
     return module
 
 
@@ -242,7 +242,7 @@ class AlignDevicesHook(ModelHook):
 
     def __repr__(self):
         return (
-            f"AlignDevicesHook(execution_device={self.execution_device}, offload={self.offload}, "
+            f"AlignDeviceHook(execution_device={self.execution_device}, offload={self.offload}, "
             f"io_same_device={self.io_same_device}, offload_buffers={self.offload_buffers}, "
             f"place_submodules={self.place_submodules}, skip_keys={repr(self.skip_keys)})"
         )
@@ -311,7 +311,6 @@ class AlignDevicesHook(ModelHook):
             for name, device in self.original_devices.items():
                 if device != torch.device("meta"):
                     set_module_tensor_to_device(module, name, device, value=self.weights_map.get(name, None))
-        return module
 
 
 def attach_execution_device_hook(

src/accelerate/launchers.py

@@ -18,37 +18,20 @@ import tempfile
 
 import torch
 
-from .state import AcceleratorState, PartialState
+from .state import AcceleratorState
 from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment
 
 
-def test_launch():
-    "Verify a `PartialState` can be initialized."
-    _ = PartialState()
-
-
-def notebook_launcher(
-    function,
-    args=(),
-    num_processes=None,
-    mixed_precision="no",
-    use_port="29500",
-    master_addr="127.0.0.1",
-    node_rank=0,
-    num_nodes=1,
-):
+def notebook_launcher(function, args=(), num_processes=None, mixed_precision="no", use_port="29500"):
     """
-    Launches a training function, using several processes or multiple nodes if it's possible in the current environment
-    (TPU with multiple cores for instance).
+    Launches a training function, using several processes if it's possible in the current environment (TPU with
+    multiple cores for instance).
 
     <Tip warning={true}>
 
     To use this function absolutely zero calls to a CUDA device must be made in the notebook session before calling. If
     any have been made, you will need to restart the notebook and make sure no cells use any CUDA capability.
 
-    Setting `ACCELERATE_DEBUG_MODE="1"` in your environment will run a test before truly launching to ensure that none
-    of those calls have been made.
-
     </Tip>
 
     Args:
@@ -64,12 +47,6 @@ def notebook_launcher(
             If `fp16` or `bf16`, will use mixed precision training on multi-GPU.
         use_port (`str`, *optional*, defaults to `"29500"`):
             The port to use to communicate between processes when launching a multi-GPU training.
-        master_addr (`str`, *optional*, defaults to `"127.0.0.1"`):
-            The address to use for communication between processes.
-        node_rank (`int`, *optional*, defaults to 0):
-            The rank of the current node.
-        num_nodes (`int`, *optional*, defaults to 1):
-            The number of nodes to use for training.
 
     Example:
 
@@ -129,8 +106,7 @@ def notebook_launcher(
             raise ValueError(
                 "You have to specify the number of GPUs you would like to use, add `num_processes=...` to your call."
             )
-        if node_rank >= num_nodes:
-            raise ValueError("The node_rank must be less than the number of nodes.")
+
         if num_processes > 1:
             # Multi-GPU launch
             from torch.multiprocessing import start_processes
@@ -142,33 +118,19 @@ def notebook_launcher(
                     "inside your training function. Restart your notebook and make sure no cells initializes an "
                     "`Accelerator`."
                 )
+
+            if torch.cuda.is_initialized():
+                raise ValueError(
+                    "To launch a multi-GPU training from your notebook, you need to avoid running any instruction "
+                    "using `torch.cuda` in any cell. Restart your notebook and make sure no cells use any CUDA "
+                    "function."
+                )
+
             # torch.distributed will expect a few environment variable to be here. We set the ones common to each
             # process here (the other ones will be set be the launcher).
             with patch_environment(
-                nproc=num_processes,
-                node_rank=node_rank,
-                world_size=num_nodes * num_processes,
-                master_addr=master_addr,
-                master_port=use_port,
-                mixed_precision=mixed_precision,
+                world_size=num_processes, master_addr="127.0.01", master_port=use_port, mixed_precision=mixed_precision
             ):
-                # First dummy launch
-                if os.environ.get("ACCELERATE_DEBUG_MODE", "false").lower() == "true":
-                    launcher = PrepareForLaunch(test_launch, distributed_type="MULTI_GPU")
-                    try:
-                        start_processes(launcher, args=(), nprocs=num_processes, start_method="fork")
-                    except ProcessRaisedException as e:
-                        err = "An issue was found when verifying a stable environment for the notebook launcher."
-                        if "Cannot re-initialize CUDA in forked subprocess" in e.args[0]:
-                            raise RuntimeError(
-                                f"{err}"
-                                "This likely stems from an outside import causing issues once the `notebook_launcher()` is called. "
-                                "Please review your imports and test them when running the `notebook_launcher()` to identify "
-                                "which one is problematic and causing CUDA to be initialized."
-                            ) from e
-                        else:
-                            raise RuntimeError(f"{err} The following error was raised: {e}") from e
-                # Now the actual launch
                 launcher = PrepareForLaunch(function, distributed_type="MULTI_GPU")
                 print(f"Launching training on {num_processes} GPUs.")
                 try:
@@ -179,10 +141,8 @@ def notebook_launcher(
                             "CUDA has been initialized before the `notebook_launcher` could create a forked subprocess. "
                             "This likely stems from an outside import causing issues once the `notebook_launcher()` is called. "
                             "Please review your imports and test them when running the `notebook_launcher()` to identify "
-                            "which one is problematic and causing CUDA to be initialized."
+                            "which one is problematic."
                         ) from e
-                    else:
-                        raise RuntimeError(f"An issue was found when launching the training: {e}") from e
 
         else:
             # No need for a distributed launch otherwise as it's either CPU, GPU or MPS.

src/accelerate/optimizer.py

@@ -59,11 +59,7 @@ class AcceleratedOptimizer(torch.optim.Optimizer):
         self.gradient_state = GradientState()
         self.device_placement = device_placement
         self._is_overflow = False
-
-        if self.scaler is not None:
-            self._accelerate_step_called = False
-            self._optimizer_original_step_method = self.optimizer.step
-            self._optimizer_patched_step_method = patch_optimizer_step(self, self.optimizer.step)
+        self._last_scale = None
 
         # Handle device placement
         if device_placement:
@@ -127,20 +123,21 @@ class AcceleratedOptimizer(torch.optim.Optimizer):
                 optimizer_args = {"closure": closure} if closure is not None else {}
                 xm.optimizer_step(self.optimizer, optimizer_args=optimizer_args)
             elif self.scaler is not None:
-                self.optimizer.step = self._optimizer_patched_step_method
-
+                new_scale = False
+                if self._last_scale is None:
+                    # `get_scale` is an async operation requiring full synchronization
+                    # on CPU and GPUs before finishing. As a result, we store away
+                    # the prior one to reduce the call overhead
+                    self._last_scale = self.scaler.get_scale()
+                    new_scale = True
                 self.scaler.step(self.optimizer, closure)
                 self.scaler.update()
+                scale_after = self.scaler.get_scale()
+                if not new_scale:
+                    # If we reduced the loss scale, it means the optimizer step was skipped because of gradient overflow.
+                    self._is_overflow = scale_after < self._last_scale
+                self._last_scale = scale_after
 
-                if not self._accelerate_step_called:
-                    # If the optimizer step was skipped, gradient overflow was detected.
-                    self._is_overflow = True
-                else:
-                    self._is_overflow = False
-                # Reset the step method to the original one
-                self.optimizer.step = self._optimizer_original_step_method
-                # Reset the indicator
-                self._accelerate_step_called = False
             else:
                 self.optimizer.step(closure)
 
@@ -164,24 +161,7 @@ class AcceleratedOptimizer(torch.optim.Optimizer):
         return self._is_overflow
 
     def __getstate__(self):
-        _ignored_keys = [
-            "_accelerate_step_called",
-            "_optimizer_original_step_method",
-            "_optimizer_patched_step_method",
-        ]
-        return {k: v for k, v in self.__dict__.items() if k not in _ignored_keys}
+        return self.__dict__.copy()
 
     def __setstate__(self, state):
         self.__dict__.update(state)
-        if self.scaler is not None:
-            self._accelerate_step_called = False
-            self._optimizer_original_step_method = self.optimizer.step
-            self._optimizer_patched_step_method = patch_optimizer_step(self, self.optimizer.step)
-
-
-def patch_optimizer_step(accelerated_optimizer: AcceleratedOptimizer, method):
-    def patched_step(*args, **kwargs):
-        accelerated_optimizer._accelerate_step_called = True
-        return method(*args, **kwargs)
-
-    return patched_step

src/accelerate/state.py

@@ -48,10 +48,6 @@ if is_tpu_available(check_device=False):
     import torch_xla.core.xla_model as xm
 
 
-if is_npu_available(check_device=False):
-    import torch_npu  # noqa: F401
-
-
 def is_initialized() -> bool:
     """
     Checks if the `AcceleratorState` has been initialized from `Accelerator`. Same as `AcceleratorState.initialized`,
@@ -110,13 +106,12 @@ class PartialState:
           in use.
         - **local_process_index** (`int`) -- The index of the current process on the current server.
         - **mixed_precision** (`str`) -- Whether or not the current script will use mixed precision, and if so the type
-          of mixed precision being performed. (Choose from 'no','fp16','bf16 or 'fp8').
+          of mixed precision being performed.
         - **num_processes** (`int`) -- The number of processes currently launched in parallel.
         - **process_index** (`int`) -- The index of the current process.
         - **is_last_process** (`bool`) -- Whether or not the current process is the last one.
         - **is_main_process** (`bool`) -- Whether or not the current process is the main one.
         - **is_local_main_process** (`bool`) -- Whether or not the current process is the main one on the local node.
-        - **debug** (`bool`) -- Whether or not the current script is being run in debug mode.
     """
 
     _shared_state = SharedDict()
@@ -128,7 +123,6 @@ class PartialState:
             self.backend = None
             env_device = os.environ.get("ACCELERATE_TORCH_DEVICE", None)
             self.device = torch.device(env_device) if env_device is not None else None
-            self.debug = parse_flag_from_env("ACCELERATE_DEBUG_MODE")
             use_sagemaker_dp = kwargs.pop("_use_sagemaker_dp", None)
             if use_sagemaker_dp is None:
                 use_sagemaker_dp = (
@@ -172,11 +166,7 @@ class PartialState:
 
                     # DeepSpeed always uses nccl
                     kwargs.pop("backend", None)
-                    if is_xpu_available and is_ccl_available():
-                        # Set DeepSpeed backend to ccl for xpu
-                        self.backend = "ccl"
-                    else:
-                        self.backend = "nccl"
+                    self.backend = "nccl"
                     dist.init_distributed(dist_backend=self.backend, auto_mpi_discovery=False, **kwargs)
 
                 self.num_processes = torch.distributed.get_world_size()
@@ -428,24 +418,23 @@ class PartialState:
         if self.num_processes == 1:
             yield inputs
             return
-        length = len(inputs)
         # Nested dictionary of any types
         if isinstance(inputs, dict):
             length = len(inputs[list(inputs.keys())[0]])
             if not all(len(v) == length for v in inputs.values()):
                 raise ValueError("All values in the dictionary must have the same length")
-        num_samples_per_process = math.ceil(length / self.num_processes)
+        num_samples_per_process = math.ceil(len(inputs) / self.num_processes)
         start_index = self.process_index * num_samples_per_process
         end_index = start_index + num_samples_per_process
         if (len(inputs) % self.num_processes != 0) and (self.process_index == self.num_processes - 1):
-            end_index = length
+            if isinstance(inputs, (list, tuple, torch.Tensor)):
+                end_index = len(inputs)
+            elif isinstance(inputs, dict):
+                end_index = len(inputs[list(inputs.keys())[0]])
 
         def _split_values(inputs, start_index, end_index):
             if isinstance(inputs, (list, tuple, torch.Tensor)):
-                if start_index >= len(inputs):
-                    result = inputs[-1:]
-                else:
-                    result = inputs[start_index:end_index]
+                result = inputs[start_index:end_index]
                 if apply_padding:
                     if isinstance(result, torch.Tensor):
                         from accelerate.utils import pad_across_processes, send_to_device
@@ -703,13 +692,12 @@ class AcceleratorState:
         - **initialized** (`bool`) -- Whether or not the `AcceleratorState` has been initialized from `Accelerator`.
         - **local_process_index** (`int`) -- The index of the current process on the current server.
         - **mixed_precision** (`str`) -- Whether or not the current script will use mixed precision, and if so the type
-          of mixed precision being performed. (Choose from 'no','fp16','bf16 or 'fp8').
+          of mixed precision being performed.
         - **num_processes** (`int`) -- The number of processes currently launched in parallel.
         - **process_index** (`int`) -- The index of the current process.
         - **is_last_process** (`bool`) -- Whether or not the current process is the last one.
         - **is_main_process** (`bool`) -- Whether or not the current process is the main one.
         - **is_local_main_process** (`bool`) -- Whether or not the current process is the main one on the local node.
-        - **debug** (`bool`) -- Whether or not the current script is being run in debug mode.
     """
 
     _shared_state = SharedDict()
@@ -734,7 +722,6 @@ class AcceleratorState:
         self._check_initialized(mixed_precision, cpu)
         if not self.initialized:
             self.deepspeed_plugin = None
-            self.use_ipex = None
             mixed_precision = (
                 parse_choice_from_env("ACCELERATE_MIXED_PRECISION", "no")
                 if mixed_precision is None
@@ -772,25 +759,12 @@ class AcceleratorState:
                     self.distributed_type = DistributedType.MEGATRON_LM
                     megatron_lm_plugin.set_mixed_precision(self._mixed_precision)
                     self.megatron_lm_plugin = megatron_lm_plugin
-            elif self.distributed_type == DistributedType.MULTI_NPU:
-                if os.environ.get("ACCELERATE_USE_FSDP", "false") == "true":
-                    self.distributed_type = DistributedType.FSDP
-                    if self._mixed_precision != "no":
-                        fsdp_plugin.set_mixed_precision(self._mixed_precision)
-                    self.fsdp_plugin = fsdp_plugin
             elif self.distributed_type in [DistributedType.MULTI_CPU, DistributedType.MULTI_XPU, DistributedType.NO]:
                 if is_ipex_available():
                     "check if user disables it explicitly"
                     self.use_ipex = parse_flag_from_env("ACCELERATE_USE_IPEX", default=True)
                 else:
                     self.use_ipex = False
-                if self.distributed_type == DistributedType.MULTI_XPU:
-                    if os.environ.get("ACCELERATE_USE_FSDP", "false") == "true":
-                        self.distributed_type = DistributedType.FSDP
-                        if self._mixed_precision != "no":
-                            fsdp_plugin.set_mixed_precision(self._mixed_precision)
-                        self.fsdp_plugin = fsdp_plugin
-
             if (
                 self.dynamo_plugin.backend != DynamoBackend.NO
                 and self._mixed_precision == "no"

src/accelerate/test_utils/__init__.py

@@ -1,6 +1,5 @@
 from .testing import (
     are_the_same_tensors,
-    assert_exception,
     execute_subprocess_async,
     require_bnb,
     require_cpu,

src/accelerate/test_utils/scripts/external_deps/test_metrics.py

@@ -12,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import logging
 import math
 import os
 from copy import deepcopy
@@ -22,11 +21,10 @@ import evaluate
 import torch
 import transformers
 from datasets import load_dataset
-from torch.utils.data import DataLoader, IterableDataset
+from torch.utils.data import DataLoader
 from transformers import AutoModelForSequenceClassification, AutoTokenizer
 
 from accelerate import Accelerator
-from accelerate.data_loader import DataLoaderDispatcher
 from accelerate.test_utils import RegressionDataset, RegressionModel
 from accelerate.utils import is_tpu_available, set_seed
 
@@ -34,15 +32,6 @@ from accelerate.utils import is_tpu_available, set_seed
 os.environ["TRANSFORMERS_NO_ADVISORY_WARNINGS"] = "true"
 
 
-class ListHandler(logging.Handler):
-    def __init__(self, *args, **kwargs):
-        super(ListHandler, self).__init__(*args, **kwargs)
-        self.logs = []
-
-    def emit(self, record):
-        self.logs.append(record)
-
-
 def get_basic_setup(accelerator, num_samples=82, batch_size=16):
     "Returns everything needed to perform basic training"
     set_seed(42)
@@ -149,76 +138,6 @@ def test_mrpc(dispatch_batches: bool = False, split_batches: bool = False):
         ), f"Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n"
 
 
-def test_gather_for_metrics_with_non_tensor_objects_iterable_dataset():
-    class DummyIterableDataset(IterableDataset):
-        def __init__(self, data):
-            self.data = data
-
-        def __len__(self):
-            return len(self.data)
-
-        def __iter__(self):
-            for element in self.data:
-                yield element
-
-    iterable_dataset = DummyIterableDataset([n for n in range(30)])
-    dataloader = DataLoader(iterable_dataset, batch_size=4)
-    accelerator = Accelerator()
-    prepared_dataloader = accelerator.prepare(dataloader)
-
-    if accelerator.is_main_process:
-        logger = logging.root.manager.loggerDict["accelerate.accelerator"]
-        list_handler = ListHandler()
-        logger.addHandler(list_handler)
-
-    batches_for_metrics = []
-    for batch in prepared_dataloader:
-        batches_for_metrics.append(accelerator.gather_for_metrics(batch))
-
-    assert torch.cat(batches_for_metrics).size(0) == 30
-
-    if accelerator.is_main_process:
-        assert len(list_handler.logs) == 0
-        logger.removeHandler(list_handler)
-
-
-def test_gather_for_metrics_with_iterable_dataset():
-    class DummyIterableDataset(IterableDataset):
-        def __init__(self, data):
-            self.data = data
-
-        def __len__(self):
-            return len(self.data)
-
-        def __iter__(self):
-            for element in self.data:
-                yield element
-
-    iterable_dataset = DummyIterableDataset(torch.as_tensor(range(30)))
-    dataloader = DataLoader(iterable_dataset, batch_size=4)
-
-    accelerator = Accelerator()
-    prepared_dataloader = accelerator.prepare(dataloader)
-
-    assert isinstance(prepared_dataloader, DataLoaderDispatcher)
-
-    if accelerator.is_main_process:
-        logger = logging.root.manager.loggerDict["accelerate.accelerator"]
-        list_handler = ListHandler()
-        logger.addHandler(list_handler)
-
-    batches_for_metrics = []
-    for batch in prepared_dataloader:
-        batches_for_metrics.append(accelerator.gather_for_metrics(batch))
-
-    assert torch.cat(batches_for_metrics).size(0) == 30
-
-    if accelerator.is_main_process:
-        assert len(list_handler.logs) == 0
-
-        logger.removeHandler(list_handler)
-
-
 def main():
     accelerator = Accelerator(split_batches=False, dispatch_batches=False)
     if accelerator.is_local_main_process:
@@ -237,10 +156,6 @@ def main():
                     print(f"With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`")
                 test_mrpc(dispatch_batches, split_batches)
                 accelerator.state._reset_state()
-        print("test_gather_for_metrics_with_iterable_dataset")
-        test_gather_for_metrics_with_iterable_dataset()
-        print("test gather_for_metrics_with_non_tensor_objects_iterable_dataset")
-        test_gather_for_metrics_with_non_tensor_objects_iterable_dataset()
     if accelerator.is_local_main_process:
         print("**Test torch metrics**")
     for split_batches in [True, False]:

src/accelerate/test_utils/scripts/external_deps/test_peak_memory_usage.py

@@ -24,7 +24,6 @@ from torch.utils.data import DataLoader
 from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
 
 from accelerate import Accelerator, DistributedType
-from accelerate.utils import is_npu_available, is_xpu_available
 from accelerate.utils.deepspeed import DummyOptim, DummyScheduler
 
 
@@ -41,34 +40,16 @@ 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_npu_available():
-            torch.npu.empty_cache()
-            torch.npu.reset_max_memory_allocated()  # reset the peak gauge to zero
-            self.begin = torch.npu.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()
+        torch.cuda.empty_cache()
+        torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
+        self.begin = torch.cuda.memory_allocated()
         return self
 
     def __exit__(self, *exc):
         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_npu_available():
-            torch.npu.empty_cache()
-            self.end = torch.npu.memory_allocated()
-            self.peak = torch.npu.max_memory_allocated()
-        elif is_xpu_available():
-            torch.xpu.empty_cache()
-            self.end = torch.xpu.memory_allocated()
-            self.peak = torch.xpu.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)
         # print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")

src/accelerate/test_utils/scripts/test_notebook.py

@@ -1,17 +0,0 @@
-# Test file to ensure that in general certain situational setups for notebooks work.
-import argparse
-
-from accelerate import PartialState, notebook_launcher
-
-
-parser = argparse.ArgumentParser()
-parser.add_argument("--num_processes", type=int, default=1)
-args = parser.parse_args()
-
-
-def function():
-    print(f"PartialState:\n{PartialState()}")
-
-
-if __name__ == "__main__":
-    notebook_launcher(function, num_processes=int(args.num_processes))

src/accelerate/test_utils/scripts/test_ops.py

@@ -17,16 +17,7 @@
 import torch
 
 from accelerate import PartialState
-from accelerate.test_utils.testing import assert_exception
-from accelerate.utils.dataclasses import DistributedType
-from accelerate.utils.operations import (
-    DistributedOperationException,
-    broadcast,
-    gather,
-    gather_object,
-    pad_across_processes,
-    reduce,
-)
+from accelerate.utils.operations import broadcast, gather, gather_object, pad_across_processes, reduce
 
 
 def create_tensor(state):
@@ -46,14 +37,6 @@ def test_gather_object(state):
     assert gathered_obj == list(range(state.num_processes)), f"{gathered_obj} != {list(range(state.num_processes))}"
 
 
-def test_gather_non_contigous(state):
-    # Create a non-contiguous tensor
-    tensor = torch.arange(12).view(4, 3).t().to(state.device)
-    assert not tensor.is_contiguous()
-    # Shouldn't error out
-    _ = gather(tensor)
-
-
 def test_broadcast(state):
     tensor = create_tensor(state)
     broadcasted_tensor = broadcast(tensor)
@@ -94,41 +77,6 @@ def test_reduce_mean(state):
     assert torch.allclose(reduced_tensor, truth_tensor), f"{reduced_tensor} != {truth_tensor}"
 
 
-def test_op_checker(state):
-    # Must be in a distributed state
-    if state.distributed_type == DistributedType.NO:
-        return
-    state.debug = True
-    # `pad_across_processes`
-    if state.process_index == 0:
-        data = {"tensor": torch.tensor([[0.0, 1, 2, 3, 4]]).to(state.device)}
-    else:
-        data = {"tensor": torch.tensor([[[0.0, 1, 2, 3, 4, 5]]]).to(state.device)}
-
-    with assert_exception(DistributedOperationException):
-        pad_across_processes(data, dim=0)
-
-    # `reduce`
-    if state.process_index == 0:
-        data = {"tensor": torch.tensor([[0.0, 1, 2, 3, 4]]).to(state.device)}
-    else:
-        data = {"tensor": torch.tensor([[[0.0, 1, 2, 3, 4], [5, 6, 7, 8, 9]]]).to(state.device)}
-
-    with assert_exception(DistributedOperationException):
-        reduce(data)
-
-    # `broadcast`
-    if state.process_index == 0:
-        data = {"tensor": torch.tensor([[0.0, 1, 2, 3, 4]]).to(state.device)}
-    else:
-        data = {"tensor": torch.tensor([[[0.0, 1, 2, 3, 4], [5, 6, 7, 8, 9]]]).to(state.device)}
-
-    with assert_exception(DistributedOperationException):
-        broadcast(data)
-
-    state.debug = False
-
-
 def _mp_fn(index):
     # For xla_spawn (TPUs)
     main()
@@ -141,8 +89,6 @@ def main():
     test_gather(state)
     state.print("testing gather_object")
     test_gather_object(state)
-    state.print("testing gather non-contigous")
-    test_gather_non_contigous(state)
     state.print("testing broadcast")
     test_broadcast(state)
     state.print("testing pad_across_processes")
@@ -151,8 +97,6 @@ def main():
     test_reduce_sum(state)
     state.print("testing reduce_mean")
     test_reduce_mean(state)
-    state.print("testing op_checker")
-    test_op_checker(state)
 
 
 if __name__ == "__main__":

src/accelerate/test_utils/scripts/test_script.py

@@ -480,7 +480,7 @@ def test_split_between_processes_list():
             len(results) == 2
         ), f"Each process did not have two items. Process index: {state.process_index}; Length: {len(results)}"
 
-    data = list(range(0, (3 * state.num_processes) - 1))
+    data = list(range(0, (2 * state.num_processes) + 3))
     with state.split_between_processes(data, apply_padding=True) as results:
         if state.is_last_process:
             # Test that the last process gets the extra item(s)
@@ -493,38 +493,35 @@ def test_split_between_processes_list():
 
 def test_split_between_processes_nested_dict():
     state = AcceleratorState()
-    a = [1, 2, 3, 4, 5, 6, 7, 8]
-    b = ["a", "b", "c", "d", "e", "f", "g", "h"]
-    c = torch.tensor([1, 2, 3, 4, 5, 6, 7, 8])
     if state.num_processes in (1, 2, 4):
-        data = {"a": a, "b": b, "c": c}
+        data = {"a": [1, 2, 3, 4], "b": ["w", "x", "y", "z"], "c": torch.tensor([0, 1, 2, 3])}
         data_copy = deepcopy(data)
         with state.split_between_processes(data) as results:
             if state.process_index == 0:
-                assert results["a"] == data_copy["a"][: 8 // state.num_processes]
+                assert results["a"] == data_copy["a"][: 4 // state.num_processes]
             elif state.num_processes == 2:
-                assert results["a"] == data_copy["a"][4:]
+                assert results["a"] == data_copy["a"][2:]
             elif state.process_index == 3:
                 # We return a list each time
-                assert results["a"] == data_copy["a"][-2:], f'Expected: {data_copy["a"][-2]}, Actual: {results["a"]}'
+                assert results["a"] == data_copy["a"][-1:], f'Expected: {data_copy["a"][-1]}, Actual: {results["a"]}'
             if state.process_index == 0:
-                assert results["b"] == data_copy["b"][: 8 // state.num_processes]
+                assert results["b"] == data_copy["b"][: 4 // state.num_processes]
             elif state.num_processes == 2:
-                assert results["b"] == data_copy["b"][4:]
+                assert results["b"] == data_copy["b"][2:]
             elif state.process_index == 3:
-                assert results["b"] == data_copy["b"][-2:]
+                assert results["b"] == data_copy["b"][-1:]
             if state.process_index == 0:
                 assert torch.allclose(
-                    results["c"], data_copy["c"][: 8 // state.num_processes]
-                ), f"Did not obtain expected values on process 0, expected `{data['c'][:8 // state.num_processes]}`, received: {results['c']}"
+                    results["c"], data_copy["c"][: 4 // state.num_processes]
+                ), f"Did not obtain expected values on process 0, expected `{data['c'][:4//state.num_processes]}`, received: {results['c']}"
             elif state.num_processes == 2:
                 assert torch.allclose(
-                    results["c"], data_copy["c"][4:]
-                ), f"Did not obtain expected values on process 2, expected `{data['c'][4:]}`, received: {results['c']}"
+                    results["c"], data_copy["c"][2:]
+                ), f"Did not obtain expected values on process 2, expected `{data['c'][2:]}`, received: {results['c']}"
             elif state.process_index == 3:
                 assert torch.allclose(
-                    results["c"], data_copy["c"][-2:]
-                ), f"Did not obtain expected values on process 4, expected `{data['c'][-2:]}`, received: {results['c']}"
+                    results["c"], data_copy["c"][3]
+                ), f"Did not obtain expected values on process 4, expected `{data['c'][3]}`, received: {results['c']}"
 
     state.wait_for_everyone()
 
@@ -541,23 +538,6 @@ def test_split_between_processes_tensor():
     state.wait_for_everyone()
 
 
-def test_trigger():
-    accelerator = Accelerator()
-    # should start with being false
-    assert accelerator.check_trigger() is False
-
-    # set a breakpoint on the main process
-    if accelerator.is_main_process:
-        accelerator.set_trigger()
-
-    # check it's been activated across all processes
-    # calls `all_reduce` and triggers a sync
-    assert accelerator.check_trigger() is True
-
-    # check it's been reset after the sync
-    assert accelerator.check_trigger() is False
-
-
 def main():
     accelerator = Accelerator()
     state = accelerator.state
@@ -607,10 +587,6 @@ def main():
         print("\n**Training integration test**")
     training_check()
 
-    if state.local_process_index == 0:
-        print("\n**Breakpoint trigger test**")
-    test_trigger()
-
 
 if __name__ == "__main__":
     main()

src/accelerate/test_utils/scripts/test_sync.py

@@ -150,60 +150,6 @@ def test_distributed_sync(accelerator):
         ddp_input = ddp_input[torch.randperm(len(ddp_input))]
 
 
-def test_distributed_sync_multiple_fwd(accelerator):
-    # Test on distributed setup that context manager behaves properly when used with multiple forwards followed by multiple backwards
-    model, ddp_model, dataloader = get_training_setup(accelerator)
-    # Do multiple forwards
-    losses = []
-    num_iterations = 3
-    for iteration in range(num_iterations):
-        ddp_input, ddp_target = next(iter(dataloader)).values()
-
-        # Gather the distributed inputs and targs for the base model
-        input, target = accelerator.gather((ddp_input, ddp_target))
-        input, target = input.to(accelerator.device), target.to(accelerator.device)
-
-        # Perform our initial ground truth step in non "DDP"
-        step_model(model, input, target, accelerator)
-
-        # Accumulate grads locally
-        with accelerator.no_sync(ddp_model):
-            ddp_output = ddp_model(ddp_input)
-            loss = F.mse_loss(ddp_output, ddp_target.to(ddp_output.device))
-            losses.append(loss)
-
-    # Do multiple backwards and sync only at the last backward
-    for iteration in range(num_iterations):
-        loss = losses[iteration]
-
-        if iteration < num_iterations - 1:
-            # Accumulate grads locally
-            accelerator.backward(loss)
-
-            # DDP model and model should only be in sync after last backward
-            for param, ddp_param in zip(model.parameters(), ddp_model.parameters()):
-                if not param.requires_grad:
-                    continue
-                # Grads should not be in sync
-                assert (
-                    torch.allclose(param.grad, ddp_param.grad) is False
-                ), f"Gradients in sync when they should not be:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})"
-
-        else:
-            # Sync grads if last backward
-            with accelerator.trigger_sync_in_backward(ddp_model):
-                accelerator.backward(loss)
-
-            # DDP model and model should only be in sync after last backward
-            for param, ddp_param in zip(model.parameters(), ddp_model.parameters()):
-                if not param.requires_grad:
-                    continue
-                # Grads should be in sync
-                assert (
-                    torch.allclose(param.grad, ddp_param.grad) is True
-                ), f"Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"
-
-
 def test_gradient_accumulation(split_batches=False, dispatch_batches=False):
     accelerator = Accelerator(
         split_batches=split_batches, dispatch_batches=dispatch_batches, gradient_accumulation_steps=2
@@ -320,14 +266,11 @@ def main():
         if state.local_process_index == 0:
             print("**Test NOOP `no_sync` context manager**")
         test_noop_sync(accelerator)
-    if state.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_NPU, DistributedType.MULTI_CPU):
+    if state.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_CPU):
         if state.local_process_index == 0:
             print("**Test Distributed `no_sync` context manager**")
         test_distributed_sync(accelerator)
-        if state.local_process_index == 0:
-            print("**Test Distributed `no_sync` context manager with multiple forwards**")
-        test_distributed_sync_multiple_fwd(accelerator)
-    if state.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_NPU):
+    if state.distributed_type == DistributedType.MULTI_GPU:
         for split_batch in [True, False]:
             for dispatch_batches in [True, False]:
                 if state.local_process_index == 0:
@@ -345,7 +288,7 @@ def main():
                 "`split_batches=False`, `dispatch_batches=False`**",
             )
         test_gradient_accumulation_with_opt_and_scheduler()
-        if state.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_NPU):
+        if state.distributed_type == DistributedType.MULTI_GPU:
             for split_batch in [True, False]:
                 for dispatch_batches in [True, False]:
                     if not split_batch and not dispatch_batches:

src/accelerate/test_utils/testing.py

@@ -19,7 +19,7 @@ import subprocess
 import sys
 import tempfile
 import unittest
-from contextlib import contextmanager
+from distutils.util import strtobool
 from functools import partial
 from pathlib import Path
 from typing import List, Union
@@ -37,13 +37,11 @@ from ..utils import (
     is_mps_available,
     is_safetensors_available,
     is_tensorboard_available,
-    is_timm_available,
     is_torch_version,
     is_tpu_available,
     is_transformers_available,
     is_wandb_available,
     is_xpu_available,
-    str_to_bool,
 )
 
 
@@ -56,7 +54,7 @@ def parse_flag_from_env(key, default=False):
     else:
         # KEY is set, convert it to True or False.
         try:
-            _value = str_to_bool(value)
+            _value = strtobool(value)
         except ValueError:
             # More values are supported, but let's keep the message simple.
             raise ValueError(f"If set, {key} must be yes or no.")
@@ -117,20 +115,6 @@ def require_huggingface_suite(test_case):
     )(test_case)
 
 
-def require_transformers(test_case):
-    """
-    Decorator marking a test that requires transformers. These tests are skipped when they are not.
-    """
-    return unittest.skipUnless(is_transformers_available(), "test requires the transformers library")(test_case)
-
-
-def require_timm(test_case):
-    """
-    Decorator marking a test that requires transformers. These tests are skipped when they are not.
-    """
-    return unittest.skipUnless(is_timm_available(), "test requires the timm library")(test_case)
-
-
 def require_bnb(test_case):
     """
     Decorator marking a test that requires bitsandbytes. These tests are skipped when they are not.
@@ -431,22 +415,3 @@ def run_command(command: List[str], return_stdout=False):
         raise SubprocessCallException(
             f"Command `{' '.join(command)}` failed with the following error:\n\n{e.output.decode()}"
         ) from e
-
-
-@contextmanager
-def assert_exception(exception_class: Exception, msg: str = None) -> bool:
-    """
-    Context manager to assert that the right `Exception` class was raised.
-
-    If `msg` is provided, will check that the message is contained in the raised exception.
-    """
-    was_ran = False
-    try:
-        yield
-        was_ran = True
-    except Exception as e:
-        assert isinstance(e, exception_class), f"Expected exception of type {exception_class} but got {type(e)}"
-        if msg is not None:
-            assert msg in str(e), f"Expected message '{msg}' to be in exception but got '{str(e)}'"
-    if was_ran:
-        raise AssertionError(f"Expected exception of type {exception_class} but ran without issue.")

src/accelerate/tracking.py

@@ -39,18 +39,31 @@ from .utils import (
 _available_trackers = []
 
 if is_tensorboard_available():
+    try:
+        from torch.utils import tensorboard
+    except ModuleNotFoundError:
+        import tensorboardX as tensorboard
+
     _available_trackers.append(LoggerType.TENSORBOARD)
 
 if is_wandb_available():
+    import wandb
+
     _available_trackers.append(LoggerType.WANDB)
 
 if is_comet_ml_available():
+    from comet_ml import Experiment
+
     _available_trackers.append(LoggerType.COMETML)
 
 if is_aim_available():
+    from aim import Run
+
     _available_trackers.append(LoggerType.AIM)
 
 if is_mlflow_available():
+    import mlflow
+
     _available_trackers.append(LoggerType.MLFLOW)
 
 logger = get_logger(__name__)
@@ -172,10 +185,6 @@ class TensorBoardTracker(GeneralTracker):
 
     @on_main_process
     def __init__(self, run_name: str, logging_dir: Union[str, os.PathLike], **kwargs):
-        try:
-            from torch.utils import tensorboard
-        except ModuleNotFoundError:
-            import tensorboardX as tensorboard
         super().__init__()
         self.run_name = run_name
         self.logging_dir = os.path.join(logging_dir, run_name)
@@ -284,9 +293,6 @@ class WandBTracker(GeneralTracker):
     def __init__(self, run_name: str, **kwargs):
         super().__init__()
         self.run_name = run_name
-
-        import wandb
-
         self.run = wandb.init(project=self.run_name, **kwargs)
         logger.debug(f"Initialized WandB project {self.run_name}")
         logger.debug(
@@ -307,9 +313,7 @@ class WandBTracker(GeneralTracker):
                 Values to be stored as initial hyperparameters as key-value pairs. The values need to have type `bool`,
                 `str`, `float`, `int`, or `None`.
         """
-        import wandb
-
-        wandb.config.update(values, allow_val_change=True)
+        wandb.config.update(values)
         logger.debug("Stored initial configuration hyperparameters to WandB")
 
     @on_main_process
@@ -342,8 +346,6 @@ class WandBTracker(GeneralTracker):
             kwargs:
                 Additional key word arguments passed along to the `wandb.log` method.
         """
-        import wandb
-
         for k, v in values.items():
             self.log({k: [wandb.Image(image) for image in v]}, step=step, **kwargs)
         logger.debug("Successfully logged images to WandB")
@@ -374,7 +376,6 @@ class WandBTracker(GeneralTracker):
             step (`int`, *optional*):
                 The run step. If included, the log will be affiliated with this step.
         """
-        import wandb
 
         values = {table_name: wandb.Table(columns=columns, data=data, dataframe=dataframe)}
         self.log(values, step=step, **kwargs)
@@ -408,9 +409,6 @@ class CometMLTracker(GeneralTracker):
     def __init__(self, run_name: str, **kwargs):
         super().__init__()
         self.run_name = run_name
-
-        from comet_ml import Experiment
-
         self.writer = Experiment(project_name=run_name, **kwargs)
         logger.debug(f"Initialized CometML project {self.run_name}")
         logger.debug(
@@ -486,9 +484,6 @@ class AimTracker(GeneralTracker):
     @on_main_process
     def __init__(self, run_name: str, logging_dir: Optional[Union[str, os.PathLike]] = ".", **kwargs):
         self.run_name = run_name
-
-        from aim import Run
-
         self.writer = Run(repo=logging_dir, **kwargs)
         self.writer.name = self.run_name
         logger.debug(f"Initialized Aim project {self.run_name}")
@@ -586,8 +581,6 @@ class MLflowTracker(GeneralTracker):
 
         nested_run = os.getenv("MLFLOW_NESTED_RUN", nested_run)
 
-        import mlflow
-
         exps = mlflow.search_experiments(filter_string=f"name = '{experiment_name}'")
         if len(exps) > 0:
             if len(exps) > 1:
@@ -627,7 +620,6 @@ class MLflowTracker(GeneralTracker):
             values (`dict`):
                 Values to be stored as initial hyperparameters as key-value pairs.
         """
-        import mlflow
 
         for name, value in list(values.items()):
             # internally, all values are converted to str in MLflow
@@ -666,7 +658,6 @@ class MLflowTracker(GeneralTracker):
                     f'MLflowTracker is attempting to log a value of "{v}" of type {type(v)} for key "{k}" as a metric. '
                     "MLflow's log_metric() only accepts float and int types so we dropped this attribute."
                 )
-        import mlflow
 
         mlflow.log_metrics(metrics, step=step)
         logger.debug("Successfully logged to mlflow")
@@ -676,8 +667,6 @@ class MLflowTracker(GeneralTracker):
         """
         End the active MLflow run.
         """
-        import mlflow
-
         mlflow.end_run()
 
 

src/accelerate/utils/__init__.py

@@ -6,13 +6,11 @@ from .constants import (
     SAFE_WEIGHTS_NAME,
     SCALER_NAME,
     SCHEDULER_NAME,
-    TORCH_DISTRIBUTED_OPERATION_TYPES,
     TORCH_LAUNCH_PARAMS,
     WEIGHTS_INDEX_NAME,
     WEIGHTS_NAME,
 )
 from .dataclasses import (
-    AutocastKwargs,
     BnbQuantizationConfig,
     ComputeEnvironment,
     CustomDtype,
@@ -35,7 +33,7 @@ from .dataclasses import (
     TensorInformation,
     TorchDynamoPlugin,
 )
-from .environment import get_int_from_env, parse_choice_from_env, parse_flag_from_env, str_to_bool
+from .environment import get_int_from_env, parse_choice_from_env, parse_flag_from_env
 from .imports import (
     get_ccl_version,
     is_4bit_bnb_available,
@@ -46,7 +44,6 @@ from .imports import (
     is_boto3_available,
     is_ccl_available,
     is_comet_ml_available,
-    is_cuda_available,
     is_datasets_available,
     is_deepspeed_available,
     is_fp8_available,
@@ -59,14 +56,12 @@ from .imports import (
     is_safetensors_available,
     is_sagemaker_available,
     is_tensorboard_available,
-    is_timm_available,
     is_tpu_available,
     is_transformers_available,
     is_wandb_available,
     is_xpu_available,
 )
 from .modeling import (
-    calculate_maximum_sizes,
     check_device_map,
     check_tied_parameters_in_config,
     check_tied_parameters_on_same_device,
@@ -164,8 +159,6 @@ from .megatron_lm import prepare_optimizer as megatron_lm_prepare_optimizer
 from .megatron_lm import prepare_scheduler as megatron_lm_prepare_scheduler
 from .memory import find_executable_batch_size, release_memory
 from .other import (
-    clear_environment,
-    convert_bytes,
     extract_model_from_parallel,
     get_pretty_name,
     is_port_in_use,

src/accelerate/utils/bnb.py

@@ -15,7 +15,6 @@
 
 import logging
 import os
-from copy import deepcopy
 from typing import Dict, List, Optional, Union
 
 import torch
@@ -24,6 +23,7 @@ import torch.nn as nn
 from accelerate.utils.imports import (
     is_4bit_bnb_available,
     is_8bit_bnb_available,
+    is_bnb_available,
 )
 
 from ..big_modeling import dispatch_model, init_empty_weights
@@ -38,6 +38,12 @@ from .modeling import (
 )
 
 
+if is_bnb_available():
+    import bitsandbytes as bnb
+
+from copy import deepcopy
+
+
 logger = logging.getLogger(__name__)
 
 
@@ -59,7 +65,7 @@ def load_and_quantize_model(
     Args:
         model (`torch.nn.Module`):
             Input model. The model can be already loaded or on the meta device
-        bnb_quantization_config (`BnbQuantizationConfig`):
+        bnb_config (`BnbQuantizationConfig`):
             The bitsandbytes quantization parameters
         weights_location (`str` or `os.PathLike`):
             The folder weights_location to load. It can be:
@@ -314,9 +320,6 @@ def _replace_with_bnb_layers(
 
     Returns the converted model and a boolean that indicates if the conversion has been successfull or not.
     """
-    # bitsandbytes will initialize CUDA on import, so it needs to be imported lazily
-    import bitsandbytes as bnb
-
     has_been_replaced = False
     for name, module in model.named_children():
         if current_key_name is None:
@@ -423,9 +426,6 @@ def get_keys_to_not_convert(model):
 
 def has_4bit_bnb_layers(model):
     """Check if we have `bnb.nn.Linear4bit` or `bnb.nn.Linear8bitLt` layers inside our model"""
-    # bitsandbytes will initialize CUDA on import, so it needs to be imported lazily
-    import bitsandbytes as bnb
-
     for m in model.modules():
         if isinstance(m, bnb.nn.Linear4bit):
             return True

src/accelerate/utils/constants.py

@@ -33,7 +33,7 @@ FSDP_AUTO_WRAP_POLICY = ["TRANSFORMER_BASED_WRAP", "SIZE_BASED_WRAP", "NO_WRAP"]
 FSDP_BACKWARD_PREFETCH = ["BACKWARD_PRE", "BACKWARD_POST", "NO_PREFETCH"]
 FSDP_STATE_DICT_TYPE = ["FULL_STATE_DICT", "LOCAL_STATE_DICT", "SHARDED_STATE_DICT"]
 FSDP_PYTORCH_VERSION = "2.0.1"
-DEEPSPEED_MULTINODE_LAUNCHERS = ["pdsh", "standard", "openmpi", "mvapich", "mpich"]
+DEEPSPEED_MULTINODE_LAUNCHERS = ["pdsh", "standard", "openmpi", "mvapich"]
 TORCH_DYNAMO_MODES = ["default", "reduce-overhead", "max-autotune"]
 
 STR_OPERATION_TO_FUNC = {">": op.gt, ">=": op.ge, "==": op.eq, "!=": op.ne, "<=": op.le, "<": op.lt}
@@ -66,4 +66,4 @@ TORCH_LAUNCH_PARAMS = [
 ]
 
 CUDA_DISTRIBUTED_TYPES = ["DEEPSPEED", "MULTI_GPU", "FSDP", "MEGATRON_LM"]
-TORCH_DISTRIBUTED_OPERATION_TYPES = CUDA_DISTRIBUTED_TYPES + ["MULTI_NPU", "MULTI_XPU", "MULTI_CPU"]
+XPU_DISTRIBUTED_TYPES = ["DEEPSPEED", "MULTI_XPU", "FSDP"]

src/accelerate/utils/dataclasses.py

@@ -26,13 +26,12 @@ import warnings
 from contextlib import contextmanager
 from dataclasses import dataclass, field
 from datetime import timedelta
+from distutils.util import strtobool
 from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple
 
 import torch
 
 from .constants import FSDP_AUTO_WRAP_POLICY, FSDP_BACKWARD_PREFETCH, FSDP_STATE_DICT_TYPE
-from .environment import str_to_bool
-from .versions import compare_versions
 
 
 class KwargsHandler:
@@ -47,37 +46,11 @@ class KwargsHandler:
         """
         Returns a dictionary containing the attributes with values different from the default of this class.
         """
-        # import clear_environment here to avoid circular import problem
-        from .other import clear_environment
-
-        with clear_environment():
-            default_dict = self.__class__().to_dict()
+        default_dict = self.__class__().to_dict()
         this_dict = self.to_dict()
         return {k: v for k, v in this_dict.items() if default_dict[k] != v}
 
 
-@dataclass
-class AutocastKwargs(KwargsHandler):
-    """
-    Use this object in your [`Accelerator`] to customize how `torch.autocast` behaves. Please refer to the
-    documentation of this [context manager](https://pytorch.org/docs/stable/amp.html#torch.autocast) for more
-    information on each argument.
-
-    Example:
-
-    ```python
-    from accelerate import Accelerator
-    from accelerate.utils import AutocastKwargs
-
-    kwargs = AutocastKwargs(cache_enabled=True)
-    accelerator = Accelerator(kwargs_handlers=[kwargs])
-    ```
-    """
-
-    enabled: bool = True
-    cache_enabled: bool = None
-
-
 @dataclass
 class DistributedDataParallelKwargs(KwargsHandler):
     """
@@ -472,9 +445,9 @@ class TorchDynamoPlugin(KwargsHandler):
         if self.mode is None:
             self.mode = os.environ.get(prefix + "MODE", "default")
         if self.fullgraph is None:
-            self.fullgraph = str_to_bool(os.environ.get(prefix + "USE_FULLGRAPH", "False")) == 1
+            self.fullgraph = strtobool(os.environ.get(prefix + "USE_FULLGRAPH", "False")) == 1
         if self.dynamic is None:
-            self.dynamic = str_to_bool(os.environ.get(prefix + "USE_DYNAMIC", "False")) == 1
+            self.dynamic = strtobool(os.environ.get(prefix + "USE_DYNAMIC", "False")) == 1
 
     def to_dict(self):
         dynamo_config = copy.deepcopy(self.__dict__)
@@ -495,10 +468,7 @@ class DeepSpeedPlugin:
         },
     )
     gradient_accumulation_steps: int = field(
-        default=None,
-        metadata={
-            "help": "Number of steps to accumulate gradients before updating optimizer states. If not set, will use the value from the `Accelerator` directly."
-        },
+        default=None, metadata={"help": "Number of steps to accumulate gradients before updating optimizer states"}
     )
     gradient_clipping: float = field(default=None, metadata={"help": "Enable gradient clipping with value"})
     zero_stage: int = field(
@@ -541,8 +511,7 @@ class DeepSpeedPlugin:
         from .deepspeed import HfDeepSpeedConfig
 
         if self.gradient_accumulation_steps is None:
-            gas = os.environ.get("ACCELERATE_GRADIENT_ACCUMULATION_STEPS", "auto")
-            self.gradient_accumulation_steps = int(gas) if gas.isdigit() else gas
+            self.gradient_accumulation_steps = int(os.environ.get("ACCELERATE_GRADIENT_ACCUMULATION_STEPS", 1))
 
         if self.gradient_clipping is None:
             gradient_clipping = os.environ.get("ACCELERATE_GRADIENT_CLIPPING", "none")
@@ -635,7 +604,7 @@ class DeepSpeedPlugin:
         self.deepspeed_config["steps_per_print"] = float("inf")  # this will stop deepspeed from logging @ stdout
         if self.zero3_init_flag is None:
             self.zero3_init_flag = (
-                str_to_bool(os.environ.get("ACCELERATE_DEEPSPEED_ZERO3_INIT", str(self.hf_ds_config.is_zero3()))) == 1
+                strtobool(os.environ.get("ACCELERATE_DEEPSPEED_ZERO3_INIT", str(self.hf_ds_config.is_zero3()))) == 1
             )
         if self.zero3_init_flag and not self.hf_ds_config.is_zero3():
             warnings.warn("DeepSpeed Zero3 Init flag is only applicable for ZeRO Stage 3. Setting it to False.")
@@ -730,10 +699,7 @@ class DeepSpeedPlugin:
             if ds_config["train_batch_size"] == "auto":
                 del ds_config["train_batch_size"]
 
-            if compare_versions("transformers", "<", "4.33"):
-                from transformers.deepspeed import HfDeepSpeedConfig
-            else:
-                from transformers.integrations import HfDeepSpeedConfig
+            from transformers.deepspeed import HfDeepSpeedConfig
 
             self.dschf = HfDeepSpeedConfig(ds_config)  # keep this object alive # noqa
 
@@ -824,6 +790,10 @@ class FullyShardedDataParallelPlugin:
         default=None,
         metadata={"help": "A list of modules to ignore for FSDP."},
     )
+    ignored_parameters: Optional[Iterable[torch.nn.Parameter]] = field(
+        default=None,
+        metadata={"help": "A list of parameters to ignore for FSDP."},
+    )
     state_dict_type: "typing.Any" = field(
         default=None,
         metadata={
@@ -867,7 +837,7 @@ class FullyShardedDataParallelPlugin:
         },
     )
     sync_module_states: bool = field(
-        default=True,
+        default=False,
         metadata={
             "help": "If True, each individually wrapped FSDP unit will broadcast module parameters from rank 0 "
             "to ensure they are the same across all ranks after initialization"
@@ -880,24 +850,23 @@ class FullyShardedDataParallelPlugin:
             "all-gather while executing in the forward pass. only use with Static graphs."
         },
     )
-    activation_checkpointing: bool = field(
-        default=False,
-        metadata={
-            "help": "If True, activation checkpointing is a technique to reduce memory usage by clearing activations of "
-            "certain layers and recomputing them during a backward pass. Effectively, this trades extra computation time "
-            "for reduced memory usage."
-        },
-    )
 
     def __post_init__(self):
-        from torch.distributed.fsdp.fully_sharded_data_parallel import BackwardPrefetch, CPUOffload, ShardingStrategy
+        from torch.distributed.fsdp.fully_sharded_data_parallel import (
+            BackwardPrefetch,
+            CPUOffload,
+            FullOptimStateDictConfig,
+            FullStateDictConfig,
+            ShardingStrategy,
+            StateDictType,
+        )
 
         prefix = "FSDP_"
         if self.sharding_strategy is None:
             self.sharding_strategy = ShardingStrategy(int(os.environ.get(prefix + "SHARDING_STRATEGY", 1)))
 
         if self.cpu_offload is None:
-            if str_to_bool(os.environ.get(prefix + "OFFLOAD_PARAMS", "False")) == 1:
+            if strtobool(os.environ.get(prefix + "OFFLOAD_PARAMS", "False")) == 1:
                 self.cpu_offload = CPUOffload(offload_params=True)
             else:
                 self.cpu_offload = CPUOffload(offload_params=False)
@@ -909,14 +878,17 @@ class FullyShardedDataParallelPlugin:
 
         if self.state_dict_type is None:
             state_dict_type_policy = os.environ.get(prefix + "STATE_DICT_TYPE", "FULL_STATE_DICT")
-            self.set_state_dict_type(state_dict_type_policy)
-        self.use_orig_params = str_to_bool(os.environ.get(prefix + "USE_ORIG_PARAMS", "False")) == 1
-        self.sync_module_states = str_to_bool(os.environ.get(prefix + "SYNC_MODULE_STATES", "True")) == 1
-        self.forward_prefetch = str_to_bool(os.environ.get(prefix + "FORWARD_PREFETCH", "False")) == 1
-        self.activation_checkpointing = str_to_bool(os.environ.get(prefix + "ACTIVATION_CHECKPOINTING", "False")) == 1
+            self.state_dict_type = StateDictType(FSDP_STATE_DICT_TYPE.index(state_dict_type_policy) + 1)
 
-        if self.sync_module_states:
-            self.param_init_fn = lambda x: x.to_empty(device=torch.cuda.current_device(), recurse=False)
+            if self.state_dict_type == StateDictType.FULL_STATE_DICT:
+                if self.state_dict_config is None:
+                    self.state_dict_config = FullStateDictConfig(offload_to_cpu=True, rank0_only=True)
+                if self.optim_state_dict_config is None:
+                    self.optim_state_dict_config = FullOptimStateDictConfig(offload_to_cpu=True, rank0_only=True)
+
+        self.use_orig_params = strtobool(os.environ.get(prefix + "USE_ORIG_PARAMS", "False")) == 1
+        self.sync_module_states = strtobool(os.environ.get(prefix + "SYNC_MODULE_STATES", "False")) == 1
+        self.forward_prefetch = strtobool(os.environ.get(prefix + "FORWARD_PREFETCH", "False")) == 1
 
     @staticmethod
     def get_module_class_from_name(module, name):
@@ -941,15 +913,10 @@ class FullyShardedDataParallelPlugin:
     def set_auto_wrap_policy(self, model):
         from torch.distributed.fsdp.wrap import size_based_auto_wrap_policy, transformer_auto_wrap_policy
 
-        default_transformer_cls_names_to_wrap = (
-            ",".join(model._no_split_modules) if getattr(model, "_no_split_modules", None) is not None else ""
-        )
         if self.auto_wrap_policy is None:
             auto_wrap_policy = os.environ.get("FSDP_AUTO_WRAP_POLICY", "NO_WRAP")
             if auto_wrap_policy == FSDP_AUTO_WRAP_POLICY[0]:
-                transformer_cls_names_to_wrap = os.environ.get(
-                    "FSDP_TRANSFORMER_CLS_TO_WRAP", default_transformer_cls_names_to_wrap
-                ).split(",")
+                transformer_cls_names_to_wrap = os.environ.get("FSDP_TRANSFORMER_CLS_TO_WRAP", "").split(",")
                 transformer_cls_to_wrap = set()
                 for layer_class in transformer_cls_names_to_wrap:
                     transformer_cls = FullyShardedDataParallelPlugin.get_module_class_from_name(model, layer_class)
@@ -982,21 +949,6 @@ class FullyShardedDataParallelPlugin:
         if self.mixed_precision_policy is None:
             self.mixed_precision_policy = MixedPrecision(param_dtype=dtype, reduce_dtype=dtype, buffer_dtype=dtype)
 
-    def set_state_dict_type(self, state_dict_type_policy):
-        from torch.distributed.fsdp.fully_sharded_data_parallel import (
-            FullOptimStateDictConfig,
-            FullStateDictConfig,
-            StateDictType,
-        )
-
-        self.state_dict_type = StateDictType(FSDP_STATE_DICT_TYPE.index(state_dict_type_policy) + 1)
-
-        if self.state_dict_type == StateDictType.FULL_STATE_DICT:
-            if self.state_dict_config is None:
-                self.state_dict_config = FullStateDictConfig(offload_to_cpu=True, rank0_only=True)
-            if self.optim_state_dict_config is None:
-                self.optim_state_dict_config = FullOptimStateDictConfig(offload_to_cpu=True, rank0_only=True)
-
 
 @dataclass
 class MegatronLMPlugin:
@@ -1169,13 +1121,13 @@ class MegatronLMPlugin:
         if self.gradient_clipping is None:
             self.gradient_clipping = float(os.environ.get(prefix + "GRADIENT_CLIPPING", 1.0))
         if self.recompute_activation is None:
-            self.recompute_activation = str_to_bool(os.environ.get(prefix + "RECOMPUTE_ACTIVATION", "False")) == 1
+            self.recompute_activation = strtobool(os.environ.get(prefix + "RECOMPUTE_ACTIVATION", "False")) == 1
         if self.use_distributed_optimizer is None:
             self.use_distributed_optimizer = (
-                str_to_bool(os.environ.get(prefix + "USE_DISTRIBUTED_OPTIMIZER", "False")) == 1
+                strtobool(os.environ.get(prefix + "USE_DISTRIBUTED_OPTIMIZER", "False")) == 1
             )
         if self.sequence_parallelism is None:
-            self.sequence_parallelism = str_to_bool(os.environ.get(prefix + "SEQUENCE_PARALLELISM", "False")) == 1
+            self.sequence_parallelism = strtobool(os.environ.get(prefix + "SEQUENCE_PARALLELISM", "False")) == 1
 
         if self.pp_degree > 1 or self.use_distributed_optimizer:
             self.DDP_impl = "local"

src/accelerate/utils/deepspeed.py

@@ -254,19 +254,16 @@ class DummyScheduler:
     Args:
         optimizer (`torch.optim.optimizer.Optimizer`):
             The optimizer to wrap.
-        total_num_steps (int, *optional*):
+        total_num_steps (int):
             Total number of steps.
-        warmup_num_steps (int, *optional*):
+        warmup_num_steps (int):
             Number of steps for warmup.
-        lr_scheduler_callable (callable, *optional*):
-            A callable function that creates an LR Scheduler. It accepts only one argument `optimizer`.
         **kwargs:
             Other arguments.
     """
 
-    def __init__(self, optimizer, total_num_steps=None, warmup_num_steps=0, lr_scheduler_callable=None, **kwargs):
+    def __init__(self, optimizer, total_num_steps=None, warmup_num_steps=0, **kwargs):
         self.optimizer = optimizer
         self.total_num_steps = total_num_steps
         self.warmup_num_steps = warmup_num_steps
-        self.lr_scheduler_callable = lr_scheduler_callable
         self.kwargs = kwargs

src/accelerate/utils/environment.py

@@ -13,21 +13,7 @@
 # limitations under the License.
 
 import os
-
-
-def str_to_bool(value) -> int:
-    """
-    Converts a string representation of truth to `True` (1) or `False` (0).
-
-    True values are `y`, `yes`, `t`, `true`, `on`, and `1`; False value are `n`, `no`, `f`, `false`, `off`, and `0`;
-    """
-    value = value.lower()
-    if value in ("y", "yes", "t", "true", "on", "1"):
-        return 1
-    elif value in ("n", "no", "f", "false", "off", "0"):
-        return 0
-    else:
-        raise ValueError(f"invalid truth value {value}")
+from distutils.util import strtobool
 
 
 def get_int_from_env(env_keys, default):
@@ -42,7 +28,7 @@ def get_int_from_env(env_keys, default):
 def parse_flag_from_env(key, default=False):
     """Returns truthy value for `key` from the env if available else the default."""
     value = os.environ.get(key, str(default))
-    return str_to_bool(value) == 1  # As its name indicates `str_to_bool` actually returns an int...
+    return strtobool(value) == 1  # As its name indicates `strtobool` actually returns an int...
 
 
 def parse_choice_from_env(key, default="no"):

src/accelerate/utils/fsdp_utils.py

@@ -17,11 +17,10 @@ import torch
 
 from ..logging import get_logger
 from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
-from .imports import is_torch_distributed_available
 from .versions import is_torch_version
 
 
-if is_torch_version(">=", FSDP_PYTORCH_VERSION) and is_torch_distributed_available():
+if is_torch_version(">=", FSDP_PYTORCH_VERSION):
     import torch.distributed.checkpoint as dist_cp
     from torch.distributed.checkpoint.default_planner import DefaultLoadPlanner, DefaultSavePlanner
     from torch.distributed.checkpoint.optimizer import load_sharded_optimizer_state_dict
@@ -34,14 +33,6 @@ logger = get_logger(__name__)
 
 def save_fsdp_model(fsdp_plugin, accelerator, model, output_dir, model_index=0):
     os.makedirs(output_dir, exist_ok=True)
-
-    if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
-        # FSDP raises error when single GPU is used with `offload_to_cpu=True` for FULL_STATE_DICT
-        # so, only enable it when num_processes>1
-        is_multi_process = accelerator.num_processes > 1
-        fsdp_plugin.state_dict_config.offload_to_cpu = is_multi_process
-        fsdp_plugin.state_dict_config.rank0_only = is_multi_process
-
     with FSDP.state_dict_type(
         model, fsdp_plugin.state_dict_type, fsdp_plugin.state_dict_config, fsdp_plugin.optim_state_dict_config
     ):
@@ -79,12 +70,6 @@ def save_fsdp_model(fsdp_plugin, accelerator, model, output_dir, model_index=0):
 
 def load_fsdp_model(fsdp_plugin, accelerator, model, input_dir, model_index=0):
     accelerator.wait_for_everyone()
-    if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
-        # FSDP raises error when single GPU is used with `offload_to_cpu=True` for FULL_STATE_DICT
-        # so, only enable it when num_processes>1
-        is_multi_process = accelerator.num_processes > 1
-        fsdp_plugin.state_dict_config.offload_to_cpu = is_multi_process
-        fsdp_plugin.state_dict_config.rank0_only = is_multi_process
     with FSDP.state_dict_type(
         model, fsdp_plugin.state_dict_type, fsdp_plugin.state_dict_config, fsdp_plugin.optim_state_dict_config
     ):
@@ -126,8 +111,7 @@ def load_fsdp_model(fsdp_plugin, accelerator, model, input_dir, model_index=0):
             )
             state_dict = state_dict["model"]
             logger.info(f"Model loaded from {ckpt_dir}")
-        load_result = model.load_state_dict(state_dict)
-    return load_result
+        model.load_state_dict(state_dict)
 
 
 def save_fsdp_optimizer(fsdp_plugin, accelerator, optimizer, model, output_dir, optimizer_index=0):
@@ -188,5 +172,5 @@ def load_fsdp_optimizer(fsdp_plugin, accelerator, optimizer, model, input_dir, o
             )
             optim_state = optim_state["optimizer"]
             logger.info(f"Optimizer loaded from {ckpt_dir}")
-        flattened_osd = FSDP.optim_state_dict_to_load(model=model, optim=optimizer, optim_state_dict=optim_state)
+        flattened_osd = FSDP.optim_state_dict_to_load(optim_state, model, optimizer)
         optimizer.load_state_dict(flattened_osd)

src/accelerate/utils/imports.py

@@ -16,13 +16,14 @@ import importlib
 import importlib.metadata
 import os
 import warnings
+from distutils.util import strtobool
 from functools import lru_cache
 
 import torch
 from packaging import version
 from packaging.version import parse
 
-from .environment import parse_flag_from_env, str_to_bool
+from .environment import parse_flag_from_env
 from .versions import compare_versions, is_torch_version
 
 
@@ -76,33 +77,19 @@ def is_fp8_available():
     return _is_package_available("transformer_engine")
 
 
-def is_cuda_available():
-    """
-    Checks if `cuda` is available via an `nvml-based` check which won't trigger the drivers and leave cuda
-    uninitialized.
-    """
-    try:
-        os.environ["PYTORCH_NVML_BASED_CUDA_CHECK"] = str(1)
-        available = torch.cuda.is_available()
-    finally:
-        os.environ.pop("PYTORCH_NVML_BASED_CUDA_CHECK", None)
-    return available
-
-
 @lru_cache
 def is_tpu_available(check_device=True):
     "Checks if `torch_xla` is installed and potentially if a TPU is in the environment"
     # Due to bugs on the amp series GPUs, we disable torch-xla on them
-    if is_cuda_available():
+    if torch.cuda.is_available():
         return False
-    if check_device:
-        if _tpu_available:
-            try:
-                # Will raise a RuntimeError if no XLA configuration is found
-                _ = xm.xla_device()
-                return True
-            except RuntimeError:
-                return False
+    if _tpu_available and check_device:
+        try:
+            # Will raise a RuntimeError if no XLA configuration is found
+            _ = xm.xla_device()
+            return True
+        except RuntimeError:
+            return False
     return _tpu_available
 
 
@@ -116,6 +103,8 @@ def is_bf16_available(ignore_tpu=False):
         return not ignore_tpu
     if torch.cuda.is_available():
         return torch.cuda.is_bf16_supported()
+    if is_npu_available():
+        return False
     return True
 
 
@@ -140,7 +129,7 @@ def is_bnb_available():
 
 
 def is_megatron_lm_available():
-    if str_to_bool(os.environ.get("ACCELERATE_USE_MEGATRON_LM", "False")) == 1:
+    if strtobool(os.environ.get("ACCELERATE_USE_MEGATRON_LM", "False")) == 1:
         package_exists = importlib.util.find_spec("megatron") is not None
         if package_exists:
             try:
@@ -163,16 +152,8 @@ def is_datasets_available():
     return _is_package_available("datasets")
 
 
-def is_timm_available():
-    return _is_package_available("timm")
-
-
 def is_aim_available():
-    package_exists = _is_package_available("aim")
-    if package_exists:
-        aim_version = version.parse(importlib.metadata.version("aim"))
-        return compare_versions(aim_version, "<", "4.0.0")
-    return False
+    return _is_package_available("aim")
 
 
 def is_tensorboard_available():
@@ -211,16 +192,7 @@ def is_tqdm_available():
 
 
 def is_mlflow_available():
-    if _is_package_available("mlflow"):
-        return True
-
-    if importlib.util.find_spec("mlflow") is not None:
-        try:
-            _ = importlib.metadata.metadata("mlflow-skinny")
-            return True
-        except importlib.metadata.PackageNotFoundError:
-            return False
-    return False
+    return _is_package_available("mlflow")
 
 
 def is_mps_available():

src/accelerate/utils/launch.py

@@ -61,8 +61,6 @@ def prepare_simple_launcher_cmd_env(args: argparse.Namespace) -> Tuple[List[str]
 
     current_env = os.environ.copy()
     current_env["ACCELERATE_USE_CPU"] = str(args.cpu or args.use_cpu)
-    if args.debug:
-        current_env["ACCELERATE_DEBUG_MODE"] = "true"
     if args.gpu_ids != "all" and args.gpu_ids is not None:
         if is_xpu_available():
             current_env["ZE_AFFINITY_MASK"] = args.gpu_ids
@@ -142,11 +140,9 @@ def prepare_multi_gpu_env(args: argparse.Namespace) -> Dict[str, str]:
         setattr(args, "no_python", True)
 
     current_env = os.environ.copy()
-    if args.debug:
-        current_env["ACCELERATE_DEBUG_MODE"] = "true"
     gpu_ids = getattr(args, "gpu_ids", "all")
     if gpu_ids != "all" and args.gpu_ids is not None:
-        if is_xpu_available():
+        if not is_xpu_available():
             current_env["ZE_AFFINITY_MASK"] = gpu_ids
         elif is_npu_available():
             current_env["ASCEND_RT_VISIBLE_DEVICES"] = gpu_ids
@@ -280,8 +276,6 @@ def prepare_deepspeed_cmd_env(args: argparse.Namespace) -> Tuple[List[str], Dict
         setattr(args, "no_python", True)
 
     current_env = os.environ.copy()
-    if args.debug:
-        current_env["ACCELERATE_DEBUG_MODE"] = "true"
     gpu_ids = getattr(args, "gpu_ids", "all")
     if gpu_ids != "all" and args.gpu_ids is not None:
         if not is_xpu_available():
@@ -329,8 +323,6 @@ def prepare_tpu(
             current_env["XLA_DOWNCAST_BF16"] = "1"
         else:
             current_env["XLA_USE_BF16"] = "1"
-    if args.debug:
-        current_env["ACCELERATE_DEBUG_MODE"] = "true"
     if pod:
         # Take explicit args and set them up for XLA
         args.vm = args.tpu_vm
@@ -537,9 +529,7 @@ class PrepareForLaunch:
         ):
             # Prepare the environment for torch.distributed
             os.environ["LOCAL_RANK"] = str(index)
-            nproc = int(os.environ.get("NPROC", 1))
-            node_rank = int(os.environ.get("NODE_RANK", 0))
-            os.environ["RANK"] = str(nproc * node_rank + index)
+            os.environ["RANK"] = str(index)
 
         os.environ["FORK_LAUNCHED"] = str(1)
         self.launcher(*args)

src/accelerate/utils/modeling.py

@@ -28,17 +28,13 @@ import torch
 import torch.nn as nn
 
 from ..state import AcceleratorState
-from .constants import SAFE_WEIGHTS_NAME, WEIGHTS_NAME
-from .dataclasses import AutocastKwargs, CustomDtype, DistributedType
-from .imports import is_mps_available, is_npu_available, is_safetensors_available, is_xpu_available
+from .constants import WEIGHTS_NAME
+from .dataclasses import CustomDtype, DistributedType
+from .imports import is_mps_available, is_safetensors_available, is_xpu_available
 from .offload import load_offloaded_weight, offload_weight, save_offload_index
 from .tqdm import is_tqdm_available, tqdm
 
 
-if is_npu_available(check_device=False):
-    import torch_npu  # noqa: F401
-
-
 if is_safetensors_available():
     from safetensors import safe_open
     from safetensors.torch import load_file as safe_load_file
@@ -63,34 +59,24 @@ def convert_file_size_to_int(size: Union[int, str]):
     1048576
     ```
     """
-    mem_size = 0
-    err_msg = (
-        f"`size` {size} is not in a valid format. Use an integer for bytes, or a string with an unit (like '5.0GB')."
-    )
-    try:
-        if isinstance(size, int):
-            mem_size = size
-        elif size.upper().endswith("GIB"):
-            mem_size = int(float(size[:-3]) * (2**30))
-        elif size.upper().endswith("MIB"):
-            mem_size = int(float(size[:-3]) * (2**20))
-        elif size.upper().endswith("KIB"):
-            mem_size = int(float(size[:-3]) * (2**10))
-        elif size.upper().endswith("GB"):
-            int_size = int(float(size[:-2]) * (10**9))
-            mem_size = int_size // 8 if size.endswith("b") else int_size
-        elif size.upper().endswith("MB"):
-            int_size = int(float(size[:-2]) * (10**6))
-            mem_size = int_size // 8 if size.endswith("b") else int_size
-        elif size.upper().endswith("KB"):
-            int_size = int(float(size[:-2]) * (10**3))
-            mem_size = int_size // 8 if size.endswith("b") else int_size
-    except ValueError:
-        raise ValueError(err_msg)
-
-    if mem_size <= 0:
-        raise ValueError(err_msg)
-    return mem_size
+    if isinstance(size, int):
+        return size
+    if size.upper().endswith("GIB"):
+        return int(size[:-3]) * (2**30)
+    if size.upper().endswith("MIB"):
+        return int(size[:-3]) * (2**20)
+    if size.upper().endswith("KIB"):
+        return int(size[:-3]) * (2**10)
+    if size.upper().endswith("GB"):
+        int_size = int(size[:-2]) * (10**9)
+        return int_size // 8 if size.endswith("b") else int_size
+    if size.upper().endswith("MB"):
+        int_size = int(size[:-2]) * (10**6)
+        return int_size // 8 if size.endswith("b") else int_size
+    if size.upper().endswith("KB"):
+        int_size = int(size[:-2]) * (10**3)
+        return int_size // 8 if size.endswith("b") else int_size
+    raise ValueError("`size` is not in a valid format. Use an integer followed by the unit, e.g., '5GB'.")
 
 
 def dtype_byte_size(dtype: torch.dtype):
@@ -281,11 +267,6 @@ def set_module_tensor_to_device(
         raise ValueError(f"{tensor_name} is on the meta device, we need a `value` to put in on {device}.")
 
     if value is not None:
-        if old_value.shape != value.shape:
-            raise ValueError(
-                f'Trying to set a tensor of shape {value.shape} in "{tensor_name}" (which has shape {old_value.shape}), this look incorrect.'
-            )
-
         if dtype is None:
             # For compatibility with PyTorch load_state_dict which converts state dict dtype to existing dtype in model
             value = value.to(old_value.dtype)
@@ -656,26 +637,6 @@ def get_max_memory(max_memory: Optional[Dict[Union[int, str], Union[int, str]]]
     for key in max_memory:
         if isinstance(max_memory[key], str):
             max_memory[key] = convert_file_size_to_int(max_memory[key])
-
-    # Need to sort the device by type to make sure that we allocate the gpu first.
-    # As gpu/xpu are represented by int, we need to sort them first.
-    gpu_devices = [k for k in max_memory.keys() if isinstance(k, int)]
-    gpu_devices.sort()
-    # check if gpu/xgpu devices are available and if not, throw a warning
-    num_devices = torch.xpu.device_count() if is_xpu_available() else torch.cuda.device_count()
-    for device in gpu_devices:
-        if device >= num_devices or device < 0:
-            logger.warning(f"Device {device} is not available, available devices are {list(range(num_devices))}")
-    # Add the other devices in the preset order if they are available
-    all_devices = gpu_devices + [k for k in ["mps", "cpu", "disk"] if k in max_memory.keys()]
-    # Raise an error if a device is not recognized
-    for k in max_memory.keys():
-        if k not in all_devices:
-            raise ValueError(
-                f"Device {k} is not recognized, available devices are integers(for GPU/XPU), 'mps', 'cpu' and 'disk'"
-            )
-    max_memory = {k: max_memory[k] for k in all_devices}
-
     return max_memory
 
 
@@ -767,9 +728,11 @@ def get_balanced_memory(
             Transformers generate function).
     """
     # Get default / clean up max_memory
-    user_not_set_max_memory = max_memory is None
     max_memory = get_max_memory(max_memory)
 
+    if not (torch.cuda.is_available() or is_xpu_available()) or is_mps_available():
+        return max_memory
+
     if not is_xpu_available():
         num_devices = len([d for d in max_memory if torch.device(d).type == "cuda" and max_memory[d] > 0])
     else:
@@ -777,30 +740,14 @@ def get_balanced_memory(
             [
                 d
                 for d in max_memory
-                if (
-                    d != "cpu"
-                    and (torch.device(d).type == "xpu" or torch.xpu.get_device_properties(d).dev_type == "gpu")
-                )
+                if (torch.device(d).type == "xpu" or torch.xpu.get_device_properties(d).dev_type == "gpu")
                 and max_memory[d] > 0
             ]
         )
 
-    if num_devices == 0:
-        return max_memory
-
     if num_devices == 1:
-        # We cannot do low_zero on just one GPU, but we will still reserve some memory for the buffer
+        # We cannot do low_zero on just one GPU
         low_zero = False
-        # If user just asked us to handle memory usage, we should avoid OOM
-        if user_not_set_max_memory:
-            for key in max_memory.keys():
-                if isinstance(key, int):
-                    max_memory[key] *= 0.9  # 90% is a good compromise
-                    logger.info(
-                        f"We will use 90% of the memory on device {key} for storing the model, and 10% for the buffer to avoid OOM. "
-                        "You can set `max_memory` in to a higher value to use more memory (at your own risk)."
-                    )
-                    break  # only one device
 
     module_sizes = compute_module_sizes(model, dtype=dtype, special_dtypes=special_dtypes)
     per_gpu = module_sizes[""] // (num_devices - 1 if low_zero else num_devices)
@@ -843,15 +790,11 @@ def get_balanced_memory(
     buffer = int(1.25 * max(buffer, mean_leaves))
     per_gpu += buffer
 
-    # Sorted list of GPUs id (we may have some gpu ids not included in the our max_memory list - let's ignore them)
-    gpus_idx_list = list(
-        sorted(
-            device_id for device_id, device_mem in max_memory.items() if isinstance(device_id, int) and device_mem > 0
-        )
-    )
+    max_memory = get_max_memory(max_memory)
+    last_gpu = max(i for i in max_memory if isinstance(i, int) and max_memory[i] > 0)
     # The last device is left with max_memory just in case the buffer is not enough.
-    for idx in gpus_idx_list[:-1]:
-        max_memory[idx] = min(max_memory[0] if low_zero and idx == 0 else per_gpu, max_memory[idx])
+    for i in range(last_gpu):
+        max_memory[i] = min(max_memory[0] if low_zero and i == 0 else per_gpu, max_memory[i])
 
     if low_zero:
         min_zero = max(0, module_sizes[""] - sum([max_memory[i] for i in range(1, num_devices)]))
@@ -860,24 +803,6 @@ def get_balanced_memory(
     return max_memory
 
 
-def calculate_maximum_sizes(model: torch.nn.Module):
-    "Computes the total size of the model and its largest layer"
-    sizes = compute_module_sizes(model)
-    # `transformers` models store this information for us
-    no_split_modules = getattr(model, "_no_split_modules", None)
-    if no_split_modules is None:
-        no_split_modules = []
-
-    modules_to_treat = (
-        list(model.named_parameters(recurse=False))
-        + list(model.named_children())
-        + list(model.named_buffers(recurse=False))
-    )
-    largest_layer = get_max_layer_size(modules_to_treat, sizes, no_split_modules)
-    total_size = sizes[""]
-    return total_size, largest_layer
-
-
 def infer_auto_device_map(
     model: nn.Module,
     max_memory: Optional[Dict[Union[int, str], Union[int, str]]] = None,
@@ -927,9 +852,9 @@ def infer_auto_device_map(
         no_split_module_classes = [no_split_module_classes]
 
     devices = list(max_memory.keys())
+    gpus = [device for device in devices if device != "cpu"]
     if "disk" not in devices:
         devices.append("disk")
-    gpus = [device for device in devices if device not in ["cpu", "disk"]]
 
     # Devices that need to keep space for a potential offloaded layer.
     if "mps" in gpus:
@@ -1258,7 +1183,7 @@ def load_checkpoint_in_model(
             - a path to a file containing a whole model state dict
             - a path to a `.json` file containing the index to a sharded checkpoint
             - a path to a folder containing a unique `.index.json` file and the shards of a checkpoint.
-            - a path to a folder containing a unique pytorch_model.bin or a model.safetensors file.
+            - a path to a folder containing a unique pytorch_model.bin file.
         device_map (`Dict[str, Union[int, str, torch.device]]`, *optional*):
             A map that specifies where each submodule should go. It doesn't need to be refined to each parameter/buffer
             name, once a given module name is inside, every submodule of it will be sent to the same device.
@@ -1310,18 +1235,15 @@ def load_checkpoint_in_model(
             checkpoint_files = [checkpoint]
     elif os.path.isdir(checkpoint):
         # check if the whole state dict is present
-        potential_state_bin = [f for f in os.listdir(checkpoint) if f == WEIGHTS_NAME]
-        potential_state_safetensor = [f for f in os.listdir(checkpoint) if f == SAFE_WEIGHTS_NAME]
-        if len(potential_state_bin) == 1:
-            checkpoint_files = [os.path.join(checkpoint, potential_state_bin[0])]
-        elif len(potential_state_safetensor) == 1:
-            checkpoint_files = [os.path.join(checkpoint, potential_state_safetensor[0])]
+        potential_state = [f for f in os.listdir(checkpoint) if f == WEIGHTS_NAME]
+        if len(potential_state) == 1:
+            checkpoint_files = [os.path.join(checkpoint, potential_state[0])]
         else:
             # otherwise check for sharded checkpoints
             potential_index = [f for f in os.listdir(checkpoint) if f.endswith(".index.json")]
             if len(potential_index) == 0:
                 raise ValueError(
-                    f"{checkpoint} is not a folder containing a `.index.json` file or a {WEIGHTS_NAME} or a {SAFE_WEIGHTS_NAME} file"
+                    f"{checkpoint} is not a folder containing a `.index.json` file or a {WEIGHTS_NAME} file"
                 )
             elif len(potential_index) == 1:
                 index_filename = os.path.join(checkpoint, potential_index[0])
@@ -1434,7 +1356,7 @@ def load_checkpoint_in_model(
     retie_parameters(model, tied_params)
 
 
-def get_mixed_precision_context_manager(native_amp: bool = False, autocast_kwargs: AutocastKwargs = None):
+def get_mixed_precision_context_manager(native_amp: bool = False, cache_enabled: bool = True):
     """
     Return a context manager for autocasting mixed precision
 
@@ -1445,22 +1367,17 @@ def get_mixed_precision_context_manager(native_amp: bool = False, autocast_kwarg
             Whether the weight cache inside autocast should be enabled.
     """
     state = AcceleratorState()
-    if autocast_kwargs is None:
-        autocast_kwargs = {}
-    else:
-        autocast_kwargs = autocast_kwargs.to_kwargs()
     if native_amp:
         if state.mixed_precision == "fp16":
-            return torch.autocast(device_type=state.device.type, dtype=torch.float16, **autocast_kwargs)
+            return torch.autocast(device_type=state.device.type, dtype=torch.float16, cache_enabled=cache_enabled)
         elif state.mixed_precision == "bf16" and state.distributed_type in [
             DistributedType.NO,
             DistributedType.MULTI_CPU,
             DistributedType.MULTI_GPU,
-            DistributedType.MULTI_NPU,
             DistributedType.MULTI_XPU,
         ]:
-            return torch.autocast(device_type=state.device.type, dtype=torch.bfloat16, **autocast_kwargs)
+            return torch.autocast(device_type=state.device.type, dtype=torch.bfloat16, cache_enabled=cache_enabled)
         else:
-            return torch.autocast(device_type=state.device.type, **autocast_kwargs)
+            return torch.autocast(device_type=state.device.type, cache_enabled=cache_enabled)
     else:
         return contextlib.nullcontext()

src/accelerate/utils/offload.py

@@ -146,8 +146,8 @@ class OffloadedWeightsLoader(Mapping):
         index: Mapping = None,
         device=None,
     ):
-        if state_dict is None and save_folder is None and index is None:
-            raise ValueError("Need either a `state_dict`, a `save_folder` or an `index` containing offloaded weights.")
+        if state_dict is None and save_folder is None:
+            raise ValueError("Need either a `state_dict` or a `save_folder` containing offloaded weights.")
 
         self.state_dict = {} if state_dict is None else state_dict
         self.save_folder = save_folder

src/accelerate/utils/operations.py

@@ -17,13 +17,13 @@ A set of basic tensor ops compatible with tpu, gpu, and multigpu
 """
 
 import pickle
-from functools import update_wrapper, wraps
+from functools import update_wrapper
 from typing import Any, Mapping
 
 import torch
 
 from ..state import PartialState
-from .constants import TORCH_DISTRIBUTED_OPERATION_TYPES
+from .constants import CUDA_DISTRIBUTED_TYPES
 from .dataclasses import DistributedType, TensorInformation
 from .imports import is_torch_distributed_available, is_tpu_available
 
@@ -189,24 +189,6 @@ def get_data_structure(data):
     return recursively_apply(_get_data_structure, data)
 
 
-def get_shape(data):
-    """
-    Recursively gathers the shape of a nested list/tuple/dictionary of tensors as a list.
-
-    Args:
-        data (nested list/tuple/dictionary of `torch.Tensor`):
-            The data to send to analyze.
-
-    Returns:
-        The same data structure as `data` with lists of tensor shapes instead of tensors.
-    """
-
-    def _get_shape(tensor):
-        return list(tensor.shape)
-
-    return recursively_apply(_get_shape, data)
-
-
 def initialize_tensors(data_structure):
     """
     Recursively initializes tensors from a nested list/tuple/dictionary of [`~utils.TensorInformation`].
@@ -269,9 +251,6 @@ def _tpu_gather(tensor):
         if tensor.ndim == 0:
             tensor = tensor.clone()[None]
 
-        # Can only gather contiguous tensors
-        if not tensor.is_contiguous():
-            tensor = tensor.contiguous()
         return xm.all_gather(tensor)
 
     res = recursively_apply(_tpu_gather_one, tensor, error_on_other_type=True)
@@ -283,10 +262,6 @@ def _gpu_gather(tensor):
     def _gpu_gather_one(tensor):
         if tensor.ndim == 0:
             tensor = tensor.clone()[None]
-
-        # Can only gather contiguous tensors
-        if not tensor.is_contiguous():
-            tensor = tensor.contiguous()
         output_tensors = [torch.empty_like(tensor) for _ in range(torch.distributed.get_world_size())]
         torch.distributed.all_gather(output_tensors, tensor)
         return torch.cat(output_tensors, dim=0)
@@ -294,65 +269,9 @@ def _gpu_gather(tensor):
     return recursively_apply(_gpu_gather_one, tensor, error_on_other_type=True)
 
 
-class DistributedOperationException(Exception):
-    """
-    An exception class for distributed operations. Raised if the operation cannot be performed due to the shape of the
-    tensors.
-    """
-
-    pass
+_cpu_gather = _gpu_gather
 
 
-def verify_operation(function):
-    """
-    Verifies that `tensor` is the same shape across all processes. Only ran if `PartialState().debug` is `True`.
-    """
-
-    @wraps(function)
-    def wrapper(*args, **kwargs):
-        if PartialState().distributed_type == DistributedType.NO or not PartialState().debug:
-            return function(*args, **kwargs)
-        operation = f"{function.__module__}.{function.__name__}"
-        if "tensor" in kwargs:
-            tensor = kwargs["tensor"]
-        else:
-            tensor = args[0]
-        shapes = get_shape(tensor)
-        output = gather_object([shapes])
-        if output[0] is not None:
-            are_same = output.count(output[0]) == len(output)
-            if not are_same:
-                process_shape_str = "\n  - ".join([f"Process {i}: {shape}" for i, shape in enumerate(output)])
-                raise DistributedOperationException(
-                    f"Cannot apply desired operation due to shape mismatches. "
-                    "All shapes across devices must be valid."
-                    f"\n\nOperation: `{operation}`\nInput shapes:\n  - {process_shape_str}"
-                )
-        return function(*args, **kwargs)
-
-    return wrapper
-
-
-def chained_operation(function):
-    """
-    Checks that `verify_operation` failed and if so reports a more helpful error chaining the existing
-    `DistributedOperationException`.
-    """
-
-    @wraps(function)
-    def wrapper(*args, **kwargs):
-        try:
-            return function(*args, **kwargs)
-        except DistributedOperationException as e:
-            operation = f"{function.__module__}.{function.__name__}"
-            raise DistributedOperationException(
-                f"Error found while calling `{operation}`. Please see the earlier error for more details."
-            ) from e
-
-    return wrapper
-
-
-@verify_operation
 def gather(tensor):
     """
     Recursively gather tensor in a nested list/tuple/dictionary of tensors from all devices.
@@ -366,8 +285,14 @@ def gather(tensor):
     """
     if PartialState().distributed_type == DistributedType.TPU:
         return _tpu_gather(tensor)
-    elif PartialState().distributed_type in TORCH_DISTRIBUTED_OPERATION_TYPES:
+    elif PartialState().distributed_type in CUDA_DISTRIBUTED_TYPES:
         return _gpu_gather(tensor)
+    elif PartialState().distributed_type in DistributedType.MULTI_NPU:
+        return _gpu_gather(tensor)
+    elif PartialState().distributed_type in DistributedType.MULTI_XPU:
+        return _gpu_gather(tensor)
+    elif PartialState().distributed_type == DistributedType.MULTI_CPU:
+        return _cpu_gather(tensor)
     else:
         return tensor
 
@@ -379,6 +304,9 @@ def _gpu_gather_object(object: Any):
     return [x for y in output_objects for x in y]
 
 
+_cpu_gather_object = _gpu_gather_object
+
+
 def gather_object(object: Any):
     """
     Recursively gather object in a nested list/tuple/dictionary of objects from all devices.
@@ -392,8 +320,14 @@ def gather_object(object: Any):
     """
     if PartialState().distributed_type == DistributedType.TPU:
         raise NotImplementedError("gather objects in TPU is not supported")
-    elif PartialState().distributed_type in TORCH_DISTRIBUTED_OPERATION_TYPES:
+    elif PartialState().distributed_type in CUDA_DISTRIBUTED_TYPES:
         return _gpu_gather_object(object)
+    elif PartialState().distributed_type in DistributedType.MULTI_NPU:
+        return _gpu_gather_object(object)
+    elif PartialState().distributed_type in DistributedType.MULTI_XPU:
+        return _gpu_gather_object(object)
+    elif PartialState().distributed_type == DistributedType.MULTI_CPU:
+        return _cpu_gather_object(object)
     else:
         return object
 
@@ -414,7 +348,6 @@ def _tpu_broadcast(tensor, src=0, name="broadcast tensor"):
     return xm.mesh_reduce(name, tensor, lambda x: x[src])
 
 
-@verify_operation
 def broadcast(tensor, from_process: int = 0):
     """
     Recursively broadcast tensor in a nested list/tuple/dictionary of tensors to all devices.
@@ -430,7 +363,13 @@ def broadcast(tensor, from_process: int = 0):
     """
     if PartialState().distributed_type == DistributedType.TPU:
         return _tpu_broadcast(tensor, src=from_process, name="accelerate.utils.broadcast")
-    elif PartialState().distributed_type in TORCH_DISTRIBUTED_OPERATION_TYPES:
+    elif PartialState().distributed_type in CUDA_DISTRIBUTED_TYPES:
+        return _gpu_broadcast(tensor, src=from_process)
+    elif PartialState().distributed_type in DistributedType.MULTI_NPU:
+        return _gpu_gather_object(object)
+    elif PartialState().distributed_type in DistributedType.MULTI_XPU:
+        return _gpu_broadcast(tensor, src=from_process)
+    elif PartialState().distributed_type == DistributedType.MULTI_CPU:
         return _gpu_broadcast(tensor, src=from_process)
     else:
         return tensor
@@ -452,12 +391,18 @@ def broadcast_object_list(object_list, from_process: int = 0):
     if PartialState().distributed_type == DistributedType.TPU:
         for i, obj in enumerate(object_list):
             object_list[i] = xm.mesh_reduce("accelerate.utils.broadcast_object_list", obj, lambda x: x[from_process])
-    elif PartialState().distributed_type in TORCH_DISTRIBUTED_OPERATION_TYPES:
+    elif PartialState().distributed_type in CUDA_DISTRIBUTED_TYPES:
+        torch.distributed.broadcast_object_list(object_list, src=from_process)
+    elif PartialState().distributed_type in DistributedType.MULTI_NPU:
+        torch.distributed.broadcast_object_list(object_list, src=from_process)
+    elif PartialState().distributed_type in DistributedType.MULTI_XPU:
+        torch.distributed.broadcast_object_list(object_list, src=from_process)
+    elif PartialState().distributed_type == DistributedType.MULTI_CPU:
         torch.distributed.broadcast_object_list(object_list, src=from_process)
     return object_list
 
 
-def slice_tensors(data, tensor_slice, process_index=None, num_processes=None):
+def slice_tensors(data, tensor_slice):
     """
     Recursively takes a slice in a nested list/tuple/dictionary of tensors.
 
@@ -499,7 +444,6 @@ def concatenate(data, dim=0):
     return torch.cat(data, dim=dim)
 
 
-@chained_operation
 def pad_across_processes(tensor, dim=0, pad_index=0, pad_first=False):
     """
     Recursively pad the tensors in a nested list/tuple/dictionary of tensors from all devices to the same size so they
@@ -546,8 +490,7 @@ def pad_across_processes(tensor, dim=0, pad_index=0, pad_first=False):
     )
 
 
-@verify_operation
-def reduce(tensor, reduction="mean", scale=1.0):
+def reduce(tensor, reduction="mean"):
     """
     Recursively reduce the tensors in a nested list/tuple/dictionary of lists of tensors across all processes by the
     mean of a given operation.
@@ -557,29 +500,31 @@ def reduce(tensor, reduction="mean", scale=1.0):
             The data to reduce.
         reduction (`str`, *optional*, defaults to `"mean"`):
             A reduction method. Can be of "mean", "sum", or "none"
-        scale (`float`, *optional*):
-            A default scaling value to be applied after the reduce, only valied on XLA.
 
     Returns:
         The same data structure as `data` with all the tensors reduced.
     """
 
-    def _reduce_across_processes(tensor, reduction="mean", scale=1.0):
+    def _reduce_across_processes(tensor, reduction="mean"):
         state = PartialState()
         cloned_tensor = tensor.clone()
         if state.distributed_type == DistributedType.NO:
             return cloned_tensor
         if state.distributed_type == DistributedType.TPU:
-            xm.all_reduce("sum", cloned_tensor, scale)
-        elif state.distributed_type.value in TORCH_DISTRIBUTED_OPERATION_TYPES:
+            xm.all_reduce("sum", cloned_tensor)
+        elif state.distributed_type.value in CUDA_DISTRIBUTED_TYPES:
+            torch.distributed.all_reduce(cloned_tensor, ReduceOp.SUM)
+        elif state.distributed_type.value in DistributedType.MULTI_NPU:
+            torch.distributed.all_reduce(cloned_tensor, ReduceOp.SUM)
+        elif state.distributed_type.value in DistributedType.MULTI_XPU:
+            torch.distributed.all_reduce(cloned_tensor, ReduceOp.SUM)
+        elif state.distributed_type == DistributedType.MULTI_CPU:
             torch.distributed.all_reduce(cloned_tensor, ReduceOp.SUM)
         if reduction == "mean":
             cloned_tensor /= state.num_processes
         return cloned_tensor
 
-    return recursively_apply(
-        _reduce_across_processes, tensor, error_on_other_type=True, reduction=reduction, scale=scale
-    )
+    return recursively_apply(_reduce_across_processes, tensor, error_on_other_type=True, reduction=reduction)
 
 
 def convert_to_fp32(tensor):

src/accelerate/utils/other.py

@@ -15,25 +15,23 @@
 import os
 import socket
 from contextlib import contextmanager
-from types import MethodType
 
 import torch
 
 from ..commands.config.default import write_basic_config  # noqa: F401
 from ..state import PartialState
-from .constants import FSDP_PYTORCH_VERSION
 from .dataclasses import DistributedType
-from .imports import is_deepspeed_available, is_safetensors_available, is_tpu_available
+from .imports import is_deepspeed_available, is_tpu_available
 from .transformer_engine import convert_model
 from .versions import is_torch_version
 
 
+if is_deepspeed_available():
+    from deepspeed import DeepSpeedEngine
+
 if is_tpu_available(check_device=False):
     import torch_xla.core.xla_model as xm
 
-if is_safetensors_available():
-    from safetensors.torch import save_file as safe_save_file
-
 
 def is_compiled_module(module):
     """
@@ -65,15 +63,8 @@ def extract_model_from_parallel(model, keep_fp32_wrapper: bool = True):
         model = model._orig_mod
 
     if is_deepspeed_available():
-        from deepspeed import DeepSpeedEngine
-
         options += (DeepSpeedEngine,)
 
-    if is_torch_version(">=", FSDP_PYTORCH_VERSION):
-        from torch.distributed.fsdp.fully_sharded_data_parallel import FullyShardedDataParallel as FSDP
-
-        options += (FSDP,)
-
     while isinstance(model, options):
         model = model.module
 
@@ -85,7 +76,7 @@ def extract_model_from_parallel(model, keep_fp32_wrapper: bool = True):
                 forward = forward.__wrapped__
                 if forward == original_forward:
                     break
-            model.forward = MethodType(forward, model)
+            model.forward = forward
         if getattr(model, "_converted_to_transformer_engine", False):
             convert_model(model, to_transformer_engine=False)
 
@@ -109,55 +100,18 @@ def wait_for_everyone():
     PartialState().wait_for_everyone()
 
 
-def save(obj, f, safe_serialization=False):
+def save(obj, f):
     """
     Save the data to disk. Use in place of `torch.save()`.
 
     Args:
         obj: The data to save
         f: The file (or file-like object) to use to save the data
-        safe_serialization (`bool`, *optional*, defaults to `False`): Whether to save `obj` using `safetensors`
     """
     if PartialState().distributed_type == DistributedType.TPU:
         xm.save(obj, f)
     elif PartialState().local_process_index == 0:
-        if safe_serialization:
-            safe_save_file(obj, f, metadata={"format": "pt"})
-        else:
-            torch.save(obj, f)
-
-
-@contextmanager
-def clear_environment():
-    """
-    A context manager that will cache origin `os.environ` and replace it with a empty dictionary in this context.
-
-    When this context exits, the cached `os.environ` will be back.
-
-    Example:
-
-    ```python
-    >>> import os
-    >>> from accelerate.utils import clear_environment
-
-    >>> os.environ["FOO"] = "bar"
-    >>> with clear_environment():
-    ...     print(os.environ)
-    ...     os.environ["FOO"] = "new_bar"
-    ...     print(os.environ["FOO"])
-    {}
-    new_bar
-
-    >>> print(os.environ["FOO"])
-    bar
-    ```
-    """
-    _old_os_environ = os.environ
-    os.environ = dict()
-
-    yield
-
-    os.environ = _old_os_environ
+        torch.save(obj, f)
 
 
 @contextmanager
@@ -178,22 +132,14 @@ def patch_environment(**kwargs):
     >>> print(os.environ["FOO"])  # raises KeyError
     ```
     """
-    existing_vars = {}
     for key, value in kwargs.items():
-        key = key.upper()
-        if key in os.environ:
-            existing_vars[key] = os.environ[key]
-        os.environ[key] = str(value)
+        os.environ[key.upper()] = str(value)
 
     yield
 
     for key in kwargs:
-        key = key.upper()
-        if key in existing_vars:
-            # restore previous value
-            os.environ[key] = existing_vars[key]
-        else:
-            os.environ.pop(key, None)
+        if key.upper() in os.environ:
+            del os.environ[key.upper()]
 
 
 def get_pretty_name(obj):
@@ -236,13 +182,3 @@ def is_port_in_use(port: int = None) -> bool:
         port = 29500
     with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
         return s.connect_ex(("localhost", port)) == 0
-
-
-def convert_bytes(size):
-    "Converts `size` from bytes to the largest possible unit"
-    for x in ["bytes", "KB", "MB", "GB", "TB"]:
-        if size < 1024.0:
-            return f"{round(size, 2)} {x}"
-        size /= 1024.0
-
-    return f"{round(size, 2)} PB"

tests/deepspeed/test_deepspeed.py

@@ -31,6 +31,7 @@ from transformers.utils import is_torch_bf16_available
 
 import accelerate
 from accelerate.accelerator import Accelerator
+from accelerate.scheduler import AcceleratedScheduler
 from accelerate.state import AcceleratorState
 from accelerate.test_utils.testing import (
     AccelerateTestCase,
@@ -331,8 +332,7 @@ class DeepSpeedConfigIntegration(AccelerateTestCase):
                         model, optimizer, train_dataloader, eval_dataloader, dummy_lr_scheduler
                     )
                 self.assertTrue(
-                    "Either specify a scheduler in the config file or "
-                    "pass in the `lr_scheduler_callable` parameter when using `accelerate.utils.DummyScheduler`."
+                    "You cannot create a `DummyScheduler` without specifying a scheduler in the config file."
                     in str(cm.exception)
                 )
 
@@ -352,7 +352,7 @@ class DeepSpeedConfigIntegration(AccelerateTestCase):
                 self.assertTrue(accelerator.deepspeed_config["train_batch_size"], 16)
                 self.assertEqual(type(model), DeepSpeedEngine)
                 self.assertEqual(type(optimizer), DeepSpeedOptimizerWrapper)
-                self.assertEqual(type(lr_scheduler), DeepSpeedSchedulerWrapper)
+                self.assertEqual(type(lr_scheduler), AcceleratedScheduler)
                 self.assertEqual(type(accelerator.deepspeed_engine_wrapped), DeepSpeedEngineWrapper)
 
         elif optim_type == DS_OPTIMIZER and scheduler_type == DS_SCHEDULER:
@@ -483,31 +483,6 @@ class DeepSpeedConfigIntegration(AccelerateTestCase):
                     in str(cm.exception)
                 )
 
-                # passing `DummyScheduler` without `lr_scheduler_callable` should fail
-                with self.assertRaises(ValueError) as cm:
-                    model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = accelerator.prepare(
-                        model, dummy_optimizer, train_dataloader, eval_dataloader, dummy_lr_scheduler
-                    )
-                self.assertTrue(
-                    "Either specify a scheduler in the config file or "
-                    "pass in the `lr_scheduler_callable` parameter when using `accelerate.utils.DummyScheduler`."
-                    in str(cm.exception)
-                )
-
-                # passing `lr_scheduler_callable` to DummyScheduler should enable DS Optim + Custom Scheduler
-                def _lr_scheduler_callable(optimizer):
-                    return get_scheduler(
-                        name="linear",
-                        optimizer=optimizer,
-                        num_warmup_steps=0,
-                        num_training_steps=1000,
-                    )
-
-                dummy_lr_scheduler = DummyScheduler(dummy_optimizer, lr_scheduler_callable=_lr_scheduler_callable)
-                model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = accelerator.prepare(
-                    model, dummy_optimizer, train_dataloader, eval_dataloader, dummy_lr_scheduler
-                )
-
     def test_save_checkpoints(self):
         deepspeed_plugin = DeepSpeedPlugin(
             hf_ds_config=self.ds_config_file[ZERO3],
@@ -624,7 +599,7 @@ class DeepSpeedConfigIntegration(AccelerateTestCase):
         deepspeed_plugin = DeepSpeedPlugin(
             hf_ds_config=ds_config,
             zero3_init_flag=True,
-            gradient_accumulation_steps=2,
+            gradient_accumulation_steps=1,
             gradient_clipping=1.0,
             zero_stage=2,
             offload_optimizer_device="cpu",
@@ -636,7 +611,7 @@ class DeepSpeedConfigIntegration(AccelerateTestCase):
             accelerator = Accelerator(deepspeed_plugin=deepspeed_plugin, mixed_precision=dtype)
             deepspeed_plugin = accelerator.state.deepspeed_plugin
             self.assertEqual(deepspeed_plugin.deepspeed_config["gradient_clipping"], 1.0)
-            self.assertEqual(deepspeed_plugin.deepspeed_config["gradient_accumulation_steps"], 2)
+            self.assertEqual(deepspeed_plugin.deepspeed_config["gradient_accumulation_steps"], 1)
             self.assertEqual(deepspeed_plugin.deepspeed_config["zero_optimization"]["stage"], 2)
             self.assertEqual(
                 deepspeed_plugin.deepspeed_config["zero_optimization"]["offload_optimizer"]["device"], "cpu"
@@ -657,42 +632,6 @@ class DeepSpeedConfigIntegration(AccelerateTestCase):
                 in str(cm.exception)
             )
 
-        # base case of passing in `gradient_accumulation_steps` to `DeepSpeedPlugin`
-        AcceleratorState._reset_state(True)
-        deepspeed_plugin = DeepSpeedPlugin(zero_stage=2, gradient_accumulation_steps=4)
-        with mockenv_context(**self.dist_env):
-            accelerator = Accelerator(deepspeed_plugin=deepspeed_plugin, mixed_precision=dtype)
-            deepspeed_plugin = accelerator.state.deepspeed_plugin
-            self.assertEqual(deepspeed_plugin.deepspeed_config["gradient_accumulation_steps"], 4)
-
-        # filling the `auto` gradient_accumulation_steps via Accelerator's value
-        AcceleratorState._reset_state(True)
-        deepspeed_plugin = DeepSpeedPlugin(
-            hf_ds_config=ds_config,
-            zero3_init_flag=True,
-            gradient_clipping=1.0,
-            zero_stage=2,
-            offload_optimizer_device="cpu",
-            offload_param_device="cpu",
-            zero3_save_16bit_model=True,
-        )
-        with mockenv_context(**self.dist_env):
-            accelerator = Accelerator(
-                deepspeed_plugin=deepspeed_plugin, mixed_precision=dtype, gradient_accumulation_steps=8
-            )
-            train_set = RegressionDataset(length=80)
-            eval_set = RegressionDataset(length=20)
-            train_dataloader = DataLoader(train_set, batch_size=16, shuffle=True)
-            eval_dataloader = DataLoader(eval_set, batch_size=32, shuffle=False)
-            model = AutoModelForCausalLM.from_pretrained("gpt2")
-            dummy_optimizer = DummyOptim(params=model.parameters(), lr=5e-5, weight_decay=1e-4)
-            dummy_lr_scheduler = DummyScheduler(dummy_optimizer, warmup_num_steps=10, total_num_steps=1000)
-            model, _, train_dataloader, eval_dataloader, _ = accelerator.prepare(
-                model, dummy_optimizer, train_dataloader, eval_dataloader, dummy_lr_scheduler
-            )
-            deepspeed_plugin = accelerator.state.deepspeed_plugin
-            self.assertEqual(deepspeed_plugin.deepspeed_config["gradient_accumulation_steps"], 8)
-
     def test_ds_config_assertions(self):
         ambiguous_env = self.dist_env.copy()
         ambiguous_env[

tests/test_accelerator.py

@@ -1,6 +1,5 @@
 import json
 import os
-import pickle
 import tempfile
 from unittest.mock import patch
 
@@ -10,10 +9,9 @@ from torch.utils.data import DataLoader, TensorDataset
 from accelerate import DistributedType, infer_auto_device_map, init_empty_weights
 from accelerate.accelerator import Accelerator
 from accelerate.state import GradientState, PartialState
-from accelerate.test_utils import require_bnb, require_multi_gpu, require_safetensors, slow
+from accelerate.test_utils import require_bnb, require_multi_gpu, slow
 from accelerate.test_utils.testing import AccelerateTestCase, require_cuda
 from accelerate.utils import patch_environment
-from accelerate.utils.modeling import load_checkpoint_in_model
 
 
 def create_components():
@@ -115,30 +113,6 @@ class AcceleratorTester(AccelerateTestCase):
             accelerator.load_state(tmpdirname)
             self.assertTrue(abs(model_signature - get_signature(model)) < 1e-3)
 
-    def test_save_model_pytorch(self):
-        accelerator = Accelerator()
-        model = torch.nn.Linear(10, 10)
-
-        model_signature = get_signature(model)
-        with tempfile.TemporaryDirectory() as tmpdirname:
-            accelerator.save_model(model, tmpdirname, safe_serialization=False)
-            # make sure loaded weights match
-            load_checkpoint_in_model(model, tmpdirname)
-            self.assertTrue(abs(model_signature - get_signature(model)) < 1e-3)
-
-    @require_safetensors
-    def test_save_model_safetensors(self):
-        accelerator = Accelerator()
-        model = torch.nn.Linear(10, 10)
-
-        model_signature = get_signature(model)
-        with tempfile.TemporaryDirectory() as tmpdirname:
-            accelerator.save_model(model, tmpdirname, safe_serialization=True)
-
-            # make sure loaded weights match
-            load_checkpoint_in_model(model, tmpdirname)
-            self.assertTrue(abs(model_signature - get_signature(model)) < 1e-3)
-
     def test_save_load_model_with_hooks(self):
         accelerator = Accelerator()
         model, optimizer, scheduler, train_dl, valid_dl = create_components()
@@ -355,35 +329,3 @@ class AcceleratorTester(AccelerateTestCase):
         sgd = torch.optim.SGD(model.parameters(), lr=0.01)
         accelerator = Accelerator(cpu=True)
         _ = accelerator.prepare(sgd)
-
-    @require_cuda
-    def test_can_unwrap_model_fp16(self):
-        # test for a regression introduced in #872
-        # before the fix, after unwrapping with keep_fp32_wrapper=False, there would be the following error:
-        # Linear.forward() missing 1 required positional argument: 'input'
-        model = create_components()[0]
-        accelerator = Accelerator(mixed_precision="fp16")
-        inputs = torch.randn(10, 2).cuda()
-        model = accelerator.prepare(model)
-        model(inputs)  # sanity check that this works
-
-        model = accelerator.unwrap_model(model, keep_fp32_wrapper=False)
-        model(inputs)  # check that this still works
-
-        # check that pickle roundtrip works
-        model_loaded = pickle.loads(pickle.dumps(model))
-        model_loaded(inputs)
-
-    def test_can_unwrap_model(self):
-        model = create_components()[0]
-        accelerator = Accelerator(mixed_precision="no", cpu=True)
-        inputs = torch.randn(10, 2)
-        model = accelerator.prepare(model)
-        model(inputs)  # sanity check that this works
-
-        model = accelerator.unwrap_model(model, keep_fp32_wrapper=False)
-        model(inputs)  # check that this still works
-
-        # check that pickle roundtrip works
-        model_loaded = pickle.loads(pickle.dumps(model))
-        model_loaded(inputs)

tests/test_big_modeling.py

@@ -11,7 +11,7 @@
 # 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 copy
+
 import os
 import unittest
 from tempfile import TemporaryDirectory
@@ -31,7 +31,7 @@ from accelerate.big_modeling import (
 )
 from accelerate.hooks import remove_hook_from_submodules
 from accelerate.test_utils import require_bnb, require_cuda, require_mps, require_multi_gpu, slow
-from accelerate.utils import is_torch_version, offload_state_dict
+from accelerate.utils import offload_state_dict
 
 
 class ModelForTest(nn.Module):
@@ -45,18 +45,6 @@ class ModelForTest(nn.Module):
         return self.linear2(self.batchnorm(self.linear1(x)))
 
 
-class ModelForTestCopy(nn.Module):
-    def __init__(self, id: int):
-        super().__init__()
-        self.id = id
-        self.linear1 = nn.Linear(3, 4)
-        self.batchnorm = nn.BatchNorm1d(4)
-        self.linear2 = nn.Linear(4, 5)
-
-    def forward(self, x):
-        return self.linear2(self.batchnorm(self.linear1(x))), self.id
-
-
 class ModelForTestTiedWeights(nn.Module):
     def __init__(self):
         super().__init__()
@@ -118,14 +106,8 @@ class BigModelingTester(unittest.TestCase):
         self.assertEqual(module.running_mean.device, torch.device("cpu"))
 
         # Use with include_buffers=True
-        register_parameter_func = nn.Module.register_parameter
-        register_buffer_func = nn.Module.register_buffer
         with init_empty_weights(include_buffers=True):
             module = nn.BatchNorm1d(4)
-            # nn.Module.register_parameter/buffer shouldn't be changed with torch >= 2.0
-            if is_torch_version(">=", "2.0"):
-                self.assertEqual(register_parameter_func, nn.Module.register_parameter)
-                self.assertEqual(register_buffer_func, nn.Module.register_buffer)
         self.assertEqual(module.weight.device, torch.device("meta"))
         self.assertEqual(module.running_mean.device, torch.device("meta"))
 
@@ -337,48 +319,6 @@ class BigModelingTester(unittest.TestCase):
             output = model(x)
             self.assertTrue(torch.allclose(expected, output.cpu(), atol=1e-5))
 
-    @require_cuda
-    def test_dispatch_model_copy(self):
-        original_model = ModelForTestCopy(id=1)
-        device_map = {"linear1": 0, "batchnorm": "cpu", "linear2": 0}
-
-        x = torch.randn(2, 3)
-        expected, original_output_id = original_model(x)
-
-        dispatch_model(original_model, device_map)
-
-        copied_model = copy.deepcopy(original_model)
-        copied_model.id = 2
-        output, copied_output_id = copied_model(x)
-
-        self.assertEqual(original_model.id, original_output_id)
-        self.assertEqual(copied_model.id, copied_output_id)
-        self.assertFalse(copied_model.linear1.forward is original_model.linear1.forward)
-        self.assertTrue(torch.allclose(expected, output.cpu(), atol=1e-5))
-
-    @require_cuda
-    def test_dispatch_model_move_offloaded_model(self):
-        model = ModelForTest()
-        device_map = {"linear1": "disk", "batchnorm": "cpu", "linear2": 0}
-        with TemporaryDirectory() as tmp_dir:
-            dispatch_model(model, device_map, offload_dir=tmp_dir)
-            with self.assertRaises(RuntimeError):
-                model.to(0)
-
-    @require_multi_gpu
-    def test_dispatch_model_move_model_warning(self):
-        model = ModelForTest()
-        device_map = {"linear1": 0, "batchnorm": 0, "linear2": 1}
-        with TemporaryDirectory() as tmp_dir:
-            dispatch_model(model, device_map, offload_dir=tmp_dir)
-            with self.assertLogs("accelerate.big_modeling", level="WARNING"):
-                model.to("cpu")
-            with self.assertLogs("accelerate.big_modeling", level="WARNING"):
-                model.cuda(0)
-            with self.assertRaises(RuntimeError):
-                x = torch.randn(2, 3)
-                model(x)
-
     @slow
     @require_multi_gpu
     def test_dispatch_model_gpt2_on_two_gpus(self):
@@ -475,18 +415,6 @@ class BigModelingTester(unittest.TestCase):
             output = model(x)
             self.assertTrue(torch.allclose(expected, output.cpu(), atol=1e-5))
 
-    @require_cuda
-    def test_dispatch_model_force_hooks(self):
-        model = ModelForTest()
-        device_map = {"": 0}
-
-        x = torch.randn(2, 3)
-        expected = model(x)
-
-        dispatch_model(model, device_map, force_hooks=True)
-        output = model(x)
-        self.assertTrue(torch.allclose(expected, output.cpu(), atol=1e-5))
-
     @require_cuda
     def test_load_checkpoint_and_dispatch(self):
         model = ModelForTest()
@@ -681,16 +609,22 @@ class BigModelingTester(unittest.TestCase):
         with init_empty_weights():
             model = AutoModel.from_config(AutoConfig.from_pretrained("bigscience/bloom-560m"))
 
+        # TODO: @younesbelkada remove the positional arg on the next `transformers` release
         quantization_config = BitsAndBytesConfig(load_in_8bit=True)
         model = replace_with_bnb_linear(
             model, modules_to_not_convert=["lm_head"], quantization_config=quantization_config
         )
 
+        # TODO: @younesbelkada remove this block on the next `transformers` release
+        for p in model.parameters():
+            p.requires_grad = False
+
         model_path = hf_hub_download("bigscience/bloom-560m", "pytorch_model.bin")
 
         model = load_checkpoint_and_dispatch(
             model,
             checkpoint=model_path,
+            # device_map="auto",
             device_map="balanced",
         )
 
@@ -711,11 +645,16 @@ class BigModelingTester(unittest.TestCase):
         with init_empty_weights():
             model = AutoModel.from_config(AutoConfig.from_pretrained("bigscience/bloom-560m"))
 
+        # TODO: @younesbelkada remove the positional arg on the next `transformers` release
         quantization_config = BitsAndBytesConfig(load_in_8bit=True)
         model = replace_with_bnb_linear(
             model, modules_to_not_convert=["lm_head"], quantization_config=quantization_config
         )
 
+        # TODO: @younesbelkada remove this block on the next `transformers` release
+        for p in model.parameters():
+            p.requires_grad = False
+
         model_path = hf_hub_download("bigscience/bloom-560m", "pytorch_model.bin")
 
         # test with auto
@@ -731,10 +670,14 @@ class BigModelingTester(unittest.TestCase):
         with init_empty_weights():
             model = AutoModel.from_config(AutoConfig.from_pretrained("bigscience/bloom-560m"))
 
+        # TODO: @younesbelkada remove the positional arg on the next `transformers` release
         model = replace_with_bnb_linear(
             model, modules_to_not_convert=["lm_head"], quantization_config=quantization_config
         )
 
+        for p in model.parameters():
+            p.requires_grad = False
+
         # test with str device map
         model = load_checkpoint_and_dispatch(
             model,
@@ -748,10 +691,15 @@ class BigModelingTester(unittest.TestCase):
         with init_empty_weights():
             model = AutoModel.from_config(AutoConfig.from_pretrained("bigscience/bloom-560m"))
 
+        # TODO: @younesbelkada remove the positional arg on the next `transformers` release
         model = replace_with_bnb_linear(
             model, modules_to_not_convert=["lm_head"], quantization_config=quantization_config
         )
 
+        # TODO: @younesbelkada remove this block on the next `transformers` release
+        for p in model.parameters():
+            p.requires_grad = False
+
         # test with torch.device device map
         model = load_checkpoint_and_dispatch(
             model,
@@ -764,6 +712,7 @@ class BigModelingTester(unittest.TestCase):
 
     @slow
     @require_bnb
+    @unittest.skip("Un-skip in the next transformers release")
     def test_dipatch_model_fp4_simple(self):
         """Tests that `dispatch_model` quantizes fp4 layers"""
         from huggingface_hub import hf_hub_download

tests/test_cli.py

@@ -18,17 +18,10 @@ import unittest
 from pathlib import Path
 
 import torch
-from huggingface_hub.utils import GatedRepoError, RepositoryNotFoundError
 
 import accelerate
-from accelerate.commands.estimate import estimate_command, estimate_command_parser, gather_data
 from accelerate.test_utils import execute_subprocess_async
-from accelerate.test_utils.testing import (
-    require_timm,
-    require_transformers,
-    run_command,
-)
-from accelerate.utils import patch_environment
+from accelerate.test_utils.testing import run_command
 
 
 class AccelerateLauncherTester(unittest.TestCase):
@@ -67,22 +60,10 @@ class AccelerateLauncherTester(unittest.TestCase):
 
     def test_config_compatibility(self):
         for config in sorted(self.test_config_path.glob("**/*.yaml")):
-            if "invalid" not in str(config):
-                with self.subTest(config_file=config):
-                    execute_subprocess_async(
-                        self.base_cmd + ["--config_file", str(config), self.test_file_path], env=os.environ.copy()
-                    )
-
-    def test_invalid_keys(self):
-        with self.assertRaises(
-            RuntimeError,
-            msg="The config file at 'invalid_keys.yaml' had unknown keys ('another_invalid_key', 'invalid_key')",
-        ):
-            execute_subprocess_async(
-                self.base_cmd
-                + ["--config_file", str(self.test_config_path / "invalid_keys.yaml"), self.test_file_path],
-                env=os.environ.copy(),
-            )
+            with self.subTest(config_file=config):
+                execute_subprocess_async(
+                    self.base_cmd + ["--config_file", str(config), self.test_file_path], env=os.environ.copy()
+                )
 
     def test_accelerate_test(self):
         execute_subprocess_async(["accelerate", "test"], env=os.environ.copy())
@@ -230,137 +211,3 @@ class TpuConfigTester(unittest.TestCase):
             f'{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; pip install accelerate==12.0.0; echo "hello world"; echo "this is a second command" --worker all',
             output,
         )
-
-
-class ModelEstimatorTester(unittest.TestCase):
-    """
-    Test case for checking the output of `accelerate estimate-memory` is correct.
-
-    - Uses `estimate_command` when trying to catch raised errors
-    - Uses `gather_data` when just verifying the calculations are correct
-    """
-
-    parser = estimate_command_parser()
-
-    def test_invalid_model_name(self):
-        with self.assertRaises(
-            RepositoryNotFoundError, msg="Repo for model `somebrokenname` does not exist on the Hub"
-        ):
-            args = self.parser.parse_args(["somebrokenname"])
-            estimate_command(args)
-
-    @require_timm
-    def test_invalid_model_name_timm(self):
-        with self.assertRaises(RuntimeError, msg="Tried to load `muellerzr/dummy` with `timm` but"):
-            args = self.parser.parse_args(["muellerzr/dummy", "--library_name", "timm"])
-            estimate_command(args)
-
-    @require_transformers
-    def test_invalid_model_name_transformers(self):
-        with self.assertRaises(RuntimeError, msg="Tried to load `muellerzr/dummy` with `transformers` but"):
-            args = self.parser.parse_args(["muellerzr/dummy", "--library_name", "transformers"])
-            estimate_command(args)
-
-    def test_no_metadata(self):
-        with self.assertRaises(
-            ValueError, msg="Model `muellerzr/dummy` does not have any library metadata on the Hub"
-        ):
-            args = self.parser.parse_args(["muellerzr/dummy"])
-            estimate_command(args)
-
-    def test_gated(self):
-        with self.assertRaises(GatedRepoError, msg="Repo for model `meta-llama/Llama-2-7b` is gated"):
-            args = self.parser.parse_args(["meta-llama/Llama-2-7b"])
-            with patch_environment(hf_hub_disable_implicit_token="1"):
-                estimate_command(args)
-
-    @require_transformers
-    def test_remote_code(self):
-        # Also tests that custom `Auto` classes work
-        args = self.parser.parse_args(["hf-internal-testing/test_dynamic_model"])
-        with self.assertRaises(ValueError, msg="--trust_remote_code"):
-            gather_data(args)
-
-        # Verify it works with the flag
-        args = self.parser.parse_args(["hf-internal-testing/test_dynamic_model", "--trust_remote_code"])
-        gather_data(args)
-
-    @require_transformers
-    def test_explicit_dtypes(self):
-        args = self.parser.parse_args(["bert-base-cased", "--dtypes", "float32", "float16"])
-        output = gather_data(args)
-        # The largest layer and total size of the model in bytes
-        largest_layer, total_size = 89075712, 433249280
-        # Check that full precision -> int4 is calculating correctly
-        self.assertEqual(len(output), 2, f"Output was missing a precision, expected 2 but received {len(output)}")
-
-        for i, factor in enumerate([1, 2]):
-            precision = 32 // factor
-            precision_str = f"float{precision}"
-            largest_layer_estimate = largest_layer / factor
-            total_size_estimate = total_size / factor
-            total_training_size_estimate = total_size_estimate * 4
-
-            self.assertEqual(precision_str, output[i][0], f"Output is missing precision `{precision_str}`")
-            self.assertEqual(
-                largest_layer_estimate,
-                output[i][1],
-                f"Calculation for largest layer size in `{precision_str}` is incorrect.",
-            )
-
-            self.assertEqual(
-                total_size_estimate,
-                output[i][2],
-                msg=f"Calculation for total size in `{precision_str}` is incorrect.",
-            )
-            self.assertEqual(
-                total_training_size_estimate,
-                output[i][3],
-                msg=f"Calculation for total training size in `{precision_str}` is incorrect.",
-            )
-
-    @require_transformers
-    def test_transformers_model(self):
-        args = self.parser.parse_args(["bert-base-cased", "--dtypes", "float32"])
-        output = gather_data(args)
-        # The largest layer and total size of the model in bytes
-        largest_layer, total_size = 89075712, 433249280
-        self.assertEqual(
-            largest_layer,
-            output[0][1],
-            f"Calculation for largest layer size in `fp32` is incorrect, expected {largest_layer} but received {output[0][1]}",
-        )
-        self.assertEqual(
-            total_size,
-            output[0][2],
-            f"Calculation for total size in `fp32` is incorrect, expected {total_size} but received {output[0][2]}",
-        )
-
-    @require_transformers
-    def test_no_split_modules(self):
-        # idefics-80b-instruct has ["IdeficsDecoderLayer", "IdeficsGatedCrossAttentionLayer"]
-        args = self.parser.parse_args(["HuggingFaceM4/idefics-80b-instruct", "--dtypes", "float32"])
-        output = gather_data(args)
-        # without factoring in `no_split` modules, the largest layer is 721420288 bytes
-        self.assertNotEqual(
-            output[0][1], 721420288, "Largest layer calculation incorrect, did not factor in `no_split` modules."
-        )
-        # the real answer is 3240165632 bytes
-        self.assertEqual(output[0][1], 3240165632)
-
-    @require_timm
-    def test_timm_model(self):
-        args = self.parser.parse_args(["timm/resnet50.a1_in1k", "--library_name", "timm"])
-        output = gather_data(args)
-        # The largest layer and total size of the model in bytes
-        largest_layer, total_size = 9437184, 102441032
-        self.assertEqual(
-            largest_layer,
-            output[0][1],
-            f"Calculation for largest layer size in `fp32` is incorrect, expected {largest_layer} but received {output[0][1]}",
-        )
-        self.assertEqual(
-            total_size,
-            output[0][2],
-            f"Calculation for total size in `fp32` is incorrect, expected {total_size} but received {output[0][2]}",
-        )

tests/test_configs/invalid_keys.yaml

@@ -1,15 +0,0 @@
-compute_environment: LOCAL_MACHINE
-deepspeed_config: {}
-distributed_type: 'NO'
-downcast_bf16: 'no'
-fsdp_config: {}
-machine_rank: 0
-main_process_ip: null
-main_process_port: null
-main_training_function: main
-mixed_precision: 'no'
-num_machines: 1
-num_processes: 1
-use_cpu: false
-invalid_key: "invalid_value"
-another_invalid_key: "another_invalid_value"

tests/test_examples.py

@@ -41,7 +41,6 @@ EXCLUDE_EXAMPLES = [
     "fsdp_with_peak_mem_tracking.py",
     "deepspeed_with_config_support.py",
     "megatron_lm_gpt_pretraining.py",
-    "early_stopping.py",
 ]
 
 
@@ -223,7 +222,3 @@ class FeatureExamplesTests(TempDirTestCase):
     def test_local_sgd(self):
         testargs = ["examples/by_feature/local_sgd.py"]
         run_command(self._launch_args + testargs)
-
-    def test_early_stopping(self):
-        testargs = ["examples/by_feature/early_stopping.py"]
-        run_command(self._launch_args + testargs)

tests/test_kwargs_handlers.py

@@ -22,7 +22,7 @@ import torch
 from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
 from accelerate.state import AcceleratorState
 from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
-from accelerate.utils import AutocastKwargs, KwargsHandler, TorchDynamoPlugin, clear_environment
+from accelerate.utils import KwargsHandler
 
 
 @dataclass
@@ -32,7 +32,7 @@ class MockClass(KwargsHandler):
     c: float = 3.0
 
 
-class KwargsHandlerTester(unittest.TestCase):
+class DataLoaderTester(unittest.TestCase):
     def test_kwargs_handler(self):
         # If no defaults are changed, `to_kwargs` returns an empty dict.
         self.assertDictEqual(MockClass().to_kwargs(), {})
@@ -63,41 +63,6 @@ class KwargsHandlerTester(unittest.TestCase):
         cmd = ["torchrun", f"--nproc_per_node={torch.cuda.device_count()}", inspect.getfile(self.__class__)]
         execute_subprocess_async(cmd, env=os.environ.copy())
 
-    @require_cuda
-    def test_autocast_kwargs(self):
-        kwargs = AutocastKwargs(enabled=False)
-        AcceleratorState._reset_state()
-        accelerator = Accelerator(mixed_precision="fp16")
-
-        a_float32 = torch.rand((8, 8), device=accelerator.device)
-        b_float32 = torch.rand((8, 8), device=accelerator.device)
-        c_float32 = torch.rand((8, 8), device=accelerator.device)
-        d_float32 = torch.rand((8, 8), device=accelerator.device)
-
-        with accelerator.autocast():
-            e_float16 = torch.mm(a_float32, b_float32)
-            assert e_float16.dtype == torch.float16
-
-            with accelerator.autocast(autocast_handler=kwargs):
-                # Convert e_float16 to float32
-                f_float32 = torch.mm(c_float32, e_float16.float())
-                assert f_float32.dtype == torch.float32
-
-            g_float16 = torch.mm(d_float32, f_float32)
-            # We should be back in fp16
-            assert g_float16.dtype == torch.float16
-
-    def test_torch_dynamo_plugin(self):
-        with clear_environment():
-            prefix = "ACCELERATE_DYNAMO_"
-            # nvfuser's dynamo backend name is "nvprims_nvfuser"
-            # use "nvfuser" here to cause exception if this test causes os.environ changed permanently
-            os.environ[prefix + "BACKEND"] = "nvfuser"
-            os.environ[prefix + "MODE"] = "reduce-overhead"
-
-            dynamo_plugin_kwargs = TorchDynamoPlugin().to_kwargs()
-            self.assertEqual(dynamo_plugin_kwargs, {"backend": "nvfuser", "mode": "reduce-overhead"})
-
 
 if __name__ == "__main__":
     ddp_scaler = DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)

tests/test_metrics.py

@@ -58,5 +58,5 @@ class MetricTester(unittest.TestCase):
     def test_metric_gpu_multi(self):
         print(f"Found {torch.cuda.device_count()} devices.")
         cmd = ["torchrun", f"--nproc_per_node={torch.cuda.device_count()}", self.test_file_path]
-        with patch_environment(omp_num_threads=1, ACCELERATE_LOG_LEVEL="INFO"):
+        with patch_environment(omp_num_threads=1):
             execute_subprocess_async(cmd, env=os.environ.copy())

tests/test_modeling_utils.py

@@ -27,7 +27,6 @@ from accelerate.utils.modeling import (
     check_device_map,
     clean_device_map,
     compute_module_sizes,
-    convert_file_size_to_int,
     find_tied_parameters,
     get_balanced_memory,
     infer_auto_device_map,
@@ -139,16 +138,6 @@ class ModelingUtilsTester(unittest.TestCase):
         set_module_tensor_to_device(model, "linear1.weight", "cpu", value=model.linear1.weight, dtype=torch.float16)
         self.assertEqual(model.linear1.weight.dtype, torch.float16)
 
-    def test_set_module_tensor_checks_shape(self):
-        model = ModelForTest()
-        tensor = torch.zeros((2, 2))
-        with self.assertRaises(ValueError) as cm:
-            set_module_tensor_to_device(model, "linear1.weight", "cpu", value=tensor)
-        self.assertEqual(
-            str(cm.exception),
-            'Trying to set a tensor of shape torch.Size([2, 2]) in "weight" (which has shape torch.Size([4, 3])), this look incorrect.',
-        )
-
     def test_named_tensors(self):
         model = nn.BatchNorm1d(4)
         named_tensors = named_module_tensors(model)
@@ -528,10 +517,6 @@ class ModelingUtilsTester(unittest.TestCase):
         max_memory = get_balanced_memory(model, max_memory={0: 200, 1: 200})
         self.assertDictEqual({0: 200, 1: 200}, max_memory)
 
-        # We should be able to set models on a non-contiguous sub-set of
-        max_memory = get_balanced_memory(model, max_memory={0: 200, 2: 200})
-        self.assertDictEqual({0: 200, 2: 200}, max_memory)
-
         max_memory = get_balanced_memory(model, max_memory={0: 300, 1: 300})
         self.assertDictEqual({0: 215, 1: 300}, max_memory)
 
@@ -547,10 +532,6 @@ class ModelingUtilsTester(unittest.TestCase):
         max_memory = get_balanced_memory(model, max_memory={0: 0, 1: 300, 2: 300})
         self.assertDictEqual({0: 0, 1: 215, 2: 300}, max_memory)
 
-        # If we set a device to 0, it's not counted.
-        max_memory = get_balanced_memory(model, max_memory={0: 0, "cpu": 100})
-        self.assertDictEqual({0: 0, "cpu": 100}, max_memory)
-
     @require_cuda
     @require_safetensors
     def test_load_state_dict(self):
@@ -569,31 +550,3 @@ class ModelingUtilsTester(unittest.TestCase):
             for param, device in device_map.items():
                 device = device if device != "disk" else "cpu"
                 self.assertEqual(loaded_state_dict[param].device, torch.device(device))
-
-    def test_convert_file_size(self):
-        result = convert_file_size_to_int("100MB")
-        self.assertEqual(result, 100 * (10**6))
-
-        result = convert_file_size_to_int("2GiB")
-        self.assertEqual(result, 2 * (2**30))
-
-        result = convert_file_size_to_int("512KiB")
-        self.assertEqual(result, 512 * (2**10))
-
-        result = convert_file_size_to_int("1.5GB")
-        self.assertEqual(result, 1.5 * (10**9))
-
-        result = convert_file_size_to_int("100KB")
-        self.assertEqual(result, 100 * (10**3))
-
-        result = convert_file_size_to_int(500)
-        self.assertEqual(result, 500)
-
-        with self.assertRaises(ValueError):
-            convert_file_size_to_int("5MBB")
-
-        with self.assertRaises(ValueError):
-            convert_file_size_to_int("5k0MB")
-
-        with self.assertRaises(ValueError):
-            convert_file_size_to_int("-1GB")

tests/test_multigpu.py

@@ -20,8 +20,7 @@ import torch
 
 import accelerate
 from accelerate import Accelerator
-from accelerate.big_modeling import dispatch_model
-from accelerate.test_utils import assert_exception, execute_subprocess_async, require_multi_gpu, skip
+from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
 from accelerate.utils import patch_environment
 
 
@@ -66,24 +65,6 @@ class MultiGPUTester(unittest.TestCase):
         with patch_environment(omp_num_threads=1, cuda_visible_devices="0,1"):
             execute_subprocess_async(cmd, env=os.environ.copy())
 
-    # Need to see why this test raises forking issues when ran as a suite
-    @skip
-    @require_multi_gpu
-    def test_notebook_launcher(self):
-        """
-        This test checks that the `notebook_launcher` will be able to intialize
-        a `PartialState` without issue
-        """
-        cmd = [
-            "python",
-            "-m",
-            "accelerate.test_utils.scripts.test_notebook",
-            "--num_processes",
-            str(torch.cuda.device_count()),
-        ]
-        with patch_environment(omp_num_threads=1):
-            execute_subprocess_async(cmd, env=os.environ.copy())
-
 
 if __name__ == "__main__":
     accelerator = Accelerator()
@@ -112,22 +93,3 @@ if __name__ == "__main__":
     # Raise error at the end to make sure we don't stop at the first failure.
     if len(error_msg) > 0:
         raise ValueError(error_msg)
-
-    # Check device_map
-    accelerator.print("Test `device_map` cannot be prepared.")
-
-    class ModelForTest(torch.nn.Module):
-        def __init__(self):
-            super().__init__()
-            self.linear1 = torch.nn.Linear(3, 4)
-            self.batchnorm = torch.nn.BatchNorm1d(4)
-            self.linear2 = torch.nn.Linear(4, 5)
-
-        def forward(self, x):
-            return self.linear2(self.batchnorm(self.linear1(x)))
-
-    device_map = {"linear1": 0, "batchnorm": "cpu", "linear2": 1}
-    model = ModelForTest()
-    dispatch_model(model, device_map=device_map)
-    with assert_exception(ValueError, "You can't train a model that has been loaded with"):
-        model = accelerator.prepare_model(model)

tests/test_optimizer.py

@@ -19,7 +19,7 @@ import torch
 
 from accelerate import Accelerator
 from accelerate.state import AcceleratorState
-from accelerate.test_utils import require_cpu, require_cuda
+from accelerate.test_utils import require_cpu
 
 
 @require_cpu
@@ -34,52 +34,3 @@ class OptimizerTester(unittest.TestCase):
         except Exception as e:
             self.fail(f"Accelerated optimizer pickling failed with {e}")
         AcceleratorState._reset_state()
-
-
-@require_cuda
-class CudaOptimizerTester(unittest.TestCase):
-    def test_accelerated_optimizer_step_was_skipped(self):
-        model = torch.nn.Linear(5, 5)
-        optimizer = torch.optim.SGD(model.parameters(), 0.1)
-        accelerator = Accelerator(mixed_precision="fp16")
-        model, optimizer = accelerator.prepare(model, optimizer)
-
-        loss = model(torch.randn(2, 5, device=accelerator.device)).sum()
-        accelerator.backward(loss)
-        for p in model.parameters():
-            # Fake the gradients, as if there's no overflow
-            p.grad.fill_(0.01)
-
-        optimizer.step()
-        self.assertTrue(optimizer.step_was_skipped is False)
-
-        loss = model(torch.randn(2, 5, device=accelerator.device)).sum()
-        accelerator.backward(loss)
-        for p in model.parameters():
-            p.grad.fill_(0.01)
-        # Manually set the gradients to be NaN, as if there's an overflow
-        p.grad[0] = torch.tensor(float("nan"))
-
-        optimizer.step()
-        self.assertTrue(optimizer.step_was_skipped is True)
-
-        loss = model(torch.randn(2, 5, device=accelerator.device)).sum()
-        accelerator.backward(loss)
-        for p in model.parameters():
-            p.grad.fill_(0.01)
-        # Manually set the gradients to be NaN, as if there's an overflow
-        p.grad[0] = torch.tensor(float("nan"))
-
-        optimizer.step()
-        self.assertTrue(optimizer.step_was_skipped is True)
-
-        loss = model(torch.randn(2, 5, device=accelerator.device)).sum()
-        accelerator.backward(loss)
-        for p in model.parameters():
-            # Fake the gradients, as if there's no overflow
-            p.grad.fill_(0.01)
-
-        optimizer.step()
-        self.assertTrue(optimizer.step_was_skipped is False)
-
-        AcceleratorState._reset_state()

tests/test_state_checkpointing.py

@@ -19,8 +19,6 @@ import random
 import shutil
 import tempfile
 import unittest
-import uuid
-from contextlib import contextmanager
 
 import pytest
 import torch
@@ -203,71 +201,6 @@ class CheckpointTest(unittest.TestCase):
             self.assertEqual(opt_state1, opt_state3)
             self.assertEqual(ground_truth_rands, test_rands)
 
-    def test_can_resume_training_checkpoints_relative_path(self):
-        # See #1983
-        # This test is like test_can_resume_training but uses a relative path for the checkpoint and automatically
-        # infers the checkpoint path when loading.
-        @contextmanager
-        def temporary_relative_directory():
-            # This is equivalent to tempfile.TemporaryDirectory() except that it returns a relative path
-            rand_dir = f"test_path_{uuid.uuid4()}"
-            os.mkdir(rand_dir)
-            try:
-                yield rand_dir
-            finally:
-                shutil.rmtree(rand_dir)
-
-        with temporary_relative_directory() as tmpdir:
-            set_seed(42)
-            model = DummyModel()
-            optimizer = torch.optim.Adam(params=model.parameters(), lr=1e-3)
-            train_dataloader, valid_dataloader = dummy_dataloaders()
-            project_config = ProjectConfiguration(automatic_checkpoint_naming=True)
-
-            # Train baseline
-            accelerator = Accelerator(project_dir=tmpdir, project_config=project_config)
-            model, optimizer, train_dataloader, valid_dataloader = accelerator.prepare(
-                model, optimizer, train_dataloader, valid_dataloader
-            )
-            # Save initial
-            accelerator.save_state()
-            (a, b) = model.a.item(), model.b.item()
-            opt_state = optimizer.state_dict()
-            ground_truth_rands = train(3, model, train_dataloader, optimizer, accelerator)
-            (a1, b1) = model.a.item(), model.b.item()
-            opt_state1 = optimizer.state_dict()
-
-            # Train partially
-            set_seed(42)
-            model = DummyModel()
-            optimizer = torch.optim.Adam(params=model.parameters(), lr=1e-3)
-            train_dataloader, valid_dataloader = dummy_dataloaders()
-            project_config = ProjectConfiguration(iteration=1, automatic_checkpoint_naming=True)
-            accelerator = Accelerator(project_dir=tmpdir, project_config=project_config)
-            model, optimizer, train_dataloader, valid_dataloader = accelerator.prepare(
-                model, optimizer, train_dataloader, valid_dataloader
-            )
-            accelerator.load_state()  # <= infer the directory automatically
-            (a2, b2) = model.a.item(), model.b.item()
-            opt_state2 = optimizer.state_dict()
-            self.assertEqual(a, a2)
-            self.assertEqual(b, b2)
-            self.assertEqual(opt_state, opt_state2)
-
-            test_rands = train(2, model, train_dataloader, optimizer, accelerator)
-            # Save everything
-            accelerator.save_state()
-
-            # Load everything back in and make sure all states work
-            accelerator.load_state(os.path.join(tmpdir, "checkpoints", "checkpoint_1"))
-            test_rands += train(1, model, train_dataloader, optimizer, accelerator)
-            (a3, b3) = model.a.item(), model.b.item()
-            opt_state3 = optimizer.state_dict()
-            self.assertEqual(a1, a3)
-            self.assertEqual(b1, b3)
-            self.assertEqual(opt_state1, opt_state3)
-            self.assertEqual(ground_truth_rands, test_rands)
-
     def test_invalid_registration(self):
         t = torch.tensor([1, 2, 3])
         t1 = torch.tensor([2, 3, 4])
@@ -305,35 +238,6 @@ class CheckpointTest(unittest.TestCase):
             accelerator.load_state(os.path.join(tmpdir, "checkpoints", "checkpoint_0"))
             self.assertEqual(scheduler_state, scheduler.state_dict())
 
-    def test_automatic_loading(self):
-        with tempfile.TemporaryDirectory() as tmpdir:
-            set_seed(42)
-            model = DummyModel()
-            optimizer = torch.optim.Adam(params=model.parameters(), lr=1e-3)
-            scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.99)
-            train_dataloader, valid_dataloader = dummy_dataloaders()
-            project_config = ProjectConfiguration(automatic_checkpoint_naming=True)
-            # Train baseline
-            accelerator = Accelerator(project_dir=tmpdir, project_config=project_config)
-            model, optimizer, train_dataloader, valid_dataloader, scheduler = accelerator.prepare(
-                model, optimizer, train_dataloader, valid_dataloader, scheduler
-            )
-            # Save initial
-            accelerator.save_state()
-            train(2, model, train_dataloader, optimizer, accelerator, scheduler)
-            (a2, b2) = model.a.item(), model.b.item()
-            # Save a first time
-            accelerator.save_state()
-            train(1, model, train_dataloader, optimizer, accelerator, scheduler)
-            (a3, b3) = model.a.item(), model.b.item()
-
-            # Load back in the last saved checkpoint, should point to a2, b2
-            accelerator.load_state()
-            self.assertNotEqual(a3, model.a.item())
-            self.assertNotEqual(b3, model.b.item())
-            self.assertEqual(a2, model.a.item())
-            self.assertEqual(b2, model.b.item())
-
     def test_checkpoint_deletion(self):
         with tempfile.TemporaryDirectory() as tmpdir:
             set_seed(42)

tests/test_utils.py

@@ -103,25 +103,6 @@ class UtilsTester(unittest.TestCase):
         self.assertNotIn("AA", os.environ)
         self.assertNotIn("BB", os.environ)
 
-    def test_patch_environment_key_exists(self):
-        # check that patch_environment correctly restores pre-existing env vars
-        with patch_environment(aa=1, BB=2):
-            self.assertEqual(os.environ.get("AA"), "1")
-            self.assertEqual(os.environ.get("BB"), "2")
-
-            with patch_environment(Aa=10, bb="20", cC=30):
-                self.assertEqual(os.environ.get("AA"), "10")
-                self.assertEqual(os.environ.get("BB"), "20")
-                self.assertEqual(os.environ.get("CC"), "30")
-
-            self.assertEqual(os.environ.get("AA"), "1")
-            self.assertEqual(os.environ.get("BB"), "2")
-            self.assertNotIn("CC", os.environ)
-
-        self.assertNotIn("AA", os.environ)
-        self.assertNotIn("BB", os.environ)
-        self.assertNotIn("CC", os.environ)
-
     def test_can_undo_convert_outputs(self):
         model = RegressionModel()
         model._original_forward = model.forward