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Releases: huggingface/accelerate

v1.10.1: Patchfix

25 Aug 13:57
@SunMarc SunMarc
v1.10.1
623cc0b
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Full Changelog: v1.10.0...v1.10.1

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S1ro1 and SunMarc
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v1.10.0: N-D Parallelism

07 Aug 13:39
@SunMarc SunMarc
v1.10.0
5dcd16e
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v1.10.0: N-D Parallelism

N-D Parallelism

Training large models across multiple GPUs can be complex, especially when combining different parallelism strategies (e.g TP, CP, DP). To simplify this process, we've collaborated with Axolotl to introduce an easy-to-use integration that allows you to apply any combination of parallelism strategies directly in your training script. Just pass a ParallelismConfig specifying the size of each parallelism type—it's that simple.
Learn more about how it works in our latest blogpost.

parallelism_config = ParallelismConfig(
    dp_shard_size=2,
    dp_replicate_size=2,
    cp_size=2,
    tp_size=2,
)
accelerator = Accelerator(
    parallelism_config=parallelism_config,
   ...
)
model = AutoModelForCausalLM.from_pretrained("your-model-name", device_mesh=accelerator.torch_device_mesh)
model = accelerator.prepare(model)
  • Parallelism config + TP + HSDP + BYODM (Bring Your Own Device Mesh) by @SalmanMohammadi in #3682
  • Feat: context parallel v2.0 by @S1ro1 in #3700
  • set default submesh_tp_size to prevent unset local variable error by @winglian in #3687
  • Add Parallelism getter property to Accelerator class by @WoosungMyung in #3703
  • Fix: prepare works even if nothing except tp specified (rare) by @S1ro1 in #3707
  • Set parallelism_config in constructor due to Trainer reset of State by @winglian in #3713
  • Fix: tp size wouldn't read from env by @S1ro1 in #3716
  • Remove ParallelismConfig from PartialState by @SunMarc in #3720

FSDP improvements

We've fixed ignored modules attribute. With this, it is now possible to train PEFT model that moe layers that contrains q_proj and v_proj parameters. This is especially important for fine-tuning gpt-oss model.

Minor improvements

  • feature: CpuOffload pre_forward don't attempt to move if already on device by @JoeGaffney in #3695
  • Fix: Ensure environment variable values are case-insensitive in Accelerate by @jp1924 in #3712
  • remove use_ipex by @SunMarc in #3721

New Contributors

Full Changelog: v1.9.0...v1.10.0

Contributors

winglian, BenjaminBossan, and 6 other contributors
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v1.9.0: Trackio support, Model loading speedup, Minor distributed improvements

16 Jul 16:35
@SunMarc SunMarc
v1.9.0
5565412
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v1.9.0: Trackio support, Model loading speedup, Minor distributed improvements

Trackio tracker support

We've added support for a trackio, lightweight, 💯 free experiment tracking Python library built on top of 🤗 Datasets and Spaces.

Screen Recording 2025-06-11 at 5 39 32 PM

Main features are:

  • Local-first design: dashboard runs locally by default. You can also host it on Spaces by specifying a space_id.
  • Persists logs locally (or in a private Hugging Face Dataset)
  • Visualize experiments with a Gradio dashboard locally (or on Hugging Face Spaces)
  • Everything here, including hosting on Hugging Faces, is free!

To use it with accelerate, you need to set log_with and initialize the trackers

accelerator = Accelerator(log_with="trackio")
config={"learning_rate": 0.001, "batch_size": 32}
# init_kwargs in order to host the dashboard on spaces
init_kwargs = {"trackio": {"space_id": "hf_username/space_name"}
accelerator.init_trackers("example_project", config=config, init_kwargs=init_kwargs})

Thanks @pcuenca for the integration !

Model loading speedup when relying set_module_tensor_to_device

Setting tensor while clearing cache is very slow, so we added clear_device option to disable it.
Another small optimization is using non_blocking everywhere and syncing just before returning control to the user. This makes the loading slightly faster.

FDSP, Deepspeed, FP8 minor improvements

  • Add support for e5e2 and default to hybrid when launcher is used by @IlyasMoutawwakil in #3640
  • Fix FP8 tests, enable FP8 to be used without direct Accelerator() configuring by @pstjohn in #3677
  • Bunch of FSDP improvements by @S1ro1 in #3671
  • Fix: properly error when DDP + Dtensor model by @S1ro1 in #3629
  • Fix fsdp2 example typo by @shimizust in #3657
  • Added a check in no_sync() to avoid errors when using deepspeed zero2/3 by @xliu0105 in #3656

🚨🚨🚨 Breaking changes 🚨🚨🚨

find_executable_batch_size() will no longer halves the batch after every OOM. Instead, we will multiply the batch size by 0.9. This should help user not waste gpu capacity.

  • “Stop Halving My Batch!” · Default back-off 0.5 → 0.9 by @SunMarc in #3684

What's Changed

New Contributors

Full Changelog: v1.8.1...v1.9.0

Contributors

pcuenca, pstjohn, and 10 other contributors
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v1.8.1: Patchfix

20 Jun 15:43
@SunMarc SunMarc
v1.8.1
f5513f1
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v1.8.1: Patchfix

Full Changelog: v1.8.0...v1.8.1

Contributors

SunMarc and IlyasMoutawwakil
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v1.8.0: FSDPv2 + FP8, Regional Compilation for DeepSpeed, Faster Distributed Training on Intel CPUs, ipex.optimize deprecation

19 Jun 15:37
@SunMarc SunMarc
v1.8.0
d2ced37
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v1.8.0: FSDPv2 + FP8, Regional Compilation for DeepSpeed, Faster Distributed Training on Intel CPUs, ipex.optimize deprecation

FSDPv2 refactor + FP8 support

We've simplified how to prepare FSDPv2 models, as there were too many ways to compose FSDP2 with other features (e.g., FP8, torch.compile, activation checkpointing, etc.). Although the setup is now more restrictive, it leads to fewer errors and a more performant user experience. We’ve also added support for FP8. You can read about the results here. Thanks to @S1ro1 for this contribution!

Faster Distributed Training on Intel CPUs

We updated the CCL_WORKER_COUNT variable and added KMP parameters for Intel CPU users. This significantly improves distributed training performance (e.g., Tensor Parallelism), with up to a 40% speed-up on Intel 4th Gen Xeon when training transformer TP models.

Regional Compilation for DeepSpeed

We added support for regional compilation with the DeepSpeed engine. DeepSpeed’s .compile() modifies models in-place using torch.nn.Module.compile(...), rather than the out-of-place torch.compile(...), so we had to account for that. Thanks @IlyasMoutawwakil for this feature!

ipex.optimize deprecation

ipex.optimize is being deprecated. Most optimizations have been upstreamed to PyTorch, and future improvements will land there directly. For users without PyTorch 2.8, we’ll continue to rely on IPEX for now.

Better XPU Support

We've greatly expanded and stabilized support for Intel XPUs:

Trackers

We've added support for SwanLab as an experiment tracking backend. Huge thanks to @ShaohonChen for this contribution ! We also deferred all tracker initializations to prevent premature setup of distributed environments.

  • Integrate SwanLab for offline/online experiment tracking for Accelerate by @ShaohonChen in #3605
  • Fix: Defer Tracker Initialization to Prevent Premature Distributed Setup by @yuanjua in #3581

What's Changed

New Contributors

Full Changelog: v1.7.0...v1.8.0

Contributors

kashif, yao-matrix, and 12 other contributors
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v1.7.0 : Regional compilation, Layerwise casting hook, FSDPv2 + QLoRA

15 May 12:33
@SunMarc SunMarc
v1.7.0
9cb1a6b
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v1.7.0 : Regional compilation, Layerwise casting hook, FSDPv2 + QLoRA

Regional compilation

Instead of compiling the entire model at once, regional compilation targets repeated blocks (such as decoder layers) first. This allows the compiler to cache and reuse optimized code for subsequent blocks, significantly reducing the cold start compilation time typically seen during the first inference. Thanks @IlyasMoutawwakil for the feature ! You can view the full benchmark here, and check out our updated compilation guide for more details!

compilation_time-1

To enable this feature, set use_regional_compilation=True in the TorchDynamoPlugin configuration.

# Configure the compilation backend
dynamo_plugin = TorchDynamoPlugin(
    use_regional_compilation=True,
    ... # other parameters
)
# Initialize accelerator with the plugin
accelerator = Accelerator(dynamo_plugin=dynamo_plugin)
# This will apply compile_regions to your model
model = accelerator.prepare(model)

Layerwise casting hook

We've introduced a new hook that enables per-layer upcasting and downcasting (e.g., for Linear layers) during inference. This allows users to run models with separate storage and compute dtypes, resulting in memory savings. The concept was first implemented in diffusers, where downcasting models to FP8 proved effective without major quality degradation. Contributed by @sayakpaul in #3427

model = ....
storage_dtype = torch.float8_e4m3fn
compute_dtype = torch.bfloat16
attach_layerwise_casting_hooks(
            model,
            storage_dtype=storage_dtype,
            compute_dtype=compute_dtype,
        )

Better FSDP2 support

This release includes numerous new features and bug fixes. Notably, we’ve added support for FULL_STATE_DICT, a widely used option in FSDP, now enabling .save_pretrained() in transformers to work with FSDP2 wrapped models. QLoRA training is now supported as well but more testing is needed. We have also resolved a backend issue related to parameter offloading to CPU. Additionally, a significant memory spike that occurred when cpu_ram_efficient_loading=True was enabled has been fixed. Several other minor improvements and fixes are also included—see the What’s Changed section for full details.

  • FULL_STATE_DICT have been enabled by @S1ro1 in #3527
  • QLoRA support by @winglian in #3546
  • set backend correctly for CUDA+FSDP2+cpu-offload in #3574
  • memory spike fixed when using cpu_ram_efficient_loading=True by @S1ro1 in #3482

Better HPU support:

We have added a documentation for Intel Gaudi hardware !
The support is already available since v1.5.0 through this PR.

Torch.compile breaking change for dynamic argument

We've updated the logic for setting self.dynamic to explicitly preserve None rather than defaulting to False when the USE_DYNAMIC environment variable is unset. This change aligns the behavior with the PyTorch documentation for torch.compile. Thanks to @yafshar for contributing this improvement in #3567.

What's Changed

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Contributors

winglian, pstjohn, and 30 other contributors
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v1.6.0: FSDPv2, DeepSpeed TP and XCCL backend support

01 Apr 13:48
@SunMarc SunMarc
v1.6.0
587bc68
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v1.6.0: FSDPv2, DeepSpeed TP and XCCL backend support

FSDPv2 support

This release introduces the support for FSDPv2 thanks to @S1ro1.

If you are using python code, you need to set fsdp_version=2 in FullyShardedDataParallelPlugin:

from accelerate import FullyShardedDataParallelPlugin, Accelerator

fsdp_plugin = FullyShardedDataParallelPlugin(
    fsdp_version=2
    # other options...
)
accelerator = Accelerator(fsdp_plugin=fsdp_plugin)

If want to convert a YAML config that contains the FSDPv1 config to FSDPv2 one , use our conversion tool:

accelerate to-fsdp2 --config_file config.yaml --output_file new_config.yaml`

To learn more about the difference between FSDPv1 and FSDPv2, read the following documentation.

DeepSpeed TP support

We have added initial support for DeepSpeed + TP. Not many changes were required as the DeepSpeed APIs was already compatible. We only needed to make sure that the dataloader was compatible with TP and that we were able to save the TP weights. Thanks @inkcherry for the work ! #3390.

To use TP with deepspeed, you need to update the setting in the deepspeed config file by including tensor_parallel key:

    ....
    "tensor_parallel":{
      "autotp_size": ${autotp_size}
    },
   ...

More details in this deepspeed PR.

Support for XCCL distributed backend

We've added support for XCCL which is an Intel distributed backend which can be used with XPU devices. More details in this torch PR. Thanks @dvrogozh for the integration !

What's Changed

New Contributors

Full Changelog: v1.5.2...v1.6.0

Contributors

booxter, Lothiraldan, and 13 other contributors
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Patch: v1.5.2

14 Mar 14:16
@muellerzr muellerzr
v1.5.2
74e08e5
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Patch: v1.5.2

Bug Fixes:

  • Fixed an issue with torch.get_default_device() requiring a higher version than what we support
  • Fixed a broken pytest import in prod

Full Changelog: v1.5.0...v1.5.2

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v1.5.0: HPU support

12 Mar 14:18
@muellerzr muellerzr
v1.5.0
d5ed283
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v1.5.0: HPU support

HPU Support

  • Adds in HPU accelerator support for 🤗 Accelerate

What's Changed

New Contributors

Full Changelog: v1.4.0...v1.5.0

Contributors

saforem2, muellerzr, and 7 other contributors
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v1.4.0: `torchao` FP8, TP & dataLoader support, fix memory leak

17 Feb 17:18
@muellerzr muellerzr
v1.4.0
b431d1f
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v1.4.0: `torchao` FP8, TP & dataLoader support, fix memory leak

torchao FP8, initial Tensor Parallel support, and memory leak fixes

torchao FP8

This release introduces a new FP8 API and brings in a new backend: torchao. To use, pass in AORecipeKwargs to the Accelerator while setting mixed_precision="fp8". This is initial support, as it matures we will incorporate more into it (such as accelerate config/yaml) in future releases. See our benchmark examples here

TensorParallel

We have intial support for an in-house solution to TP when working with accelerate dataloaders. check out the PR here

Bug fixes

What's Changed

New Contributors

Full Changelog: v1.3.0...v1.4.0

Contributors

muellerzr, stas00, and 7 other contributors
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