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fix(deps): update dependency accelerate to v1 #175

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fix(deps): update dependency accelerate to v1 #175

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@renovate renovate bot commented Nov 6, 2024 •
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This PR contains the following updates:

Package Change Age Adoption Passing Confidence
accelerate ^0.34.0 -> ^1.0.0 age adoption passing confidence

Release Notes

huggingface/accelerate (accelerate)

v1.1.1

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v1.1.0: : Python 3.9 minimum, torch dynamo deepspeed support, and bug fixes

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Internals:

DeepSpeed

Megatron

Big Model Inference

Examples

Full Changelog

New Contributors

Full Changelog: huggingface/accelerate@v1.0.1...v1.1.0

v1.0.1: : Bugfix

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Bugfixes

  • Fixes an issue where the auto values were no longer being parsed when using deepspeed
  • Fixes a broken test in the deepspeed tests related to the auto values

Full Changelog: huggingface/accelerate@v1.0.0...v1.0.1

v1.0.0: Accelerate 1.0.0 is here!

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🚀 Accelerate 1.0 🚀

With accelerate 1.0, we are officially stating that the core parts of the API are now "stable" and ready for the future of what the world of distributed training and PyTorch has to handle. With these release notes, we will focus first on the major breaking changes to get your code fixed, followed by what is new specifically between 0.34.0 and 1.0.

To read more, check out our official blog here

Migration assistance

  • Passing in dispatch_batches, split_batches, even_batches, and use_seedable_sampler to the Accelerator() should now be handled by creating an accelerate.utils.DataLoaderConfiguration() and passing this to the Accelerator() instead (Accelerator(dataloader_config=DataLoaderConfiguration(...)))
  • Accelerator().use_fp16 and AcceleratorState().use_fp16 have been removed; this should be replaced by checking accelerator.mixed_precision == "fp16"
  • Accelerator().autocast() no longer accepts a cache_enabled argument. Instead, an AutocastKwargs() instance should be used which handles this flag (among others) passing it to the Accelerator (Accelerator(kwargs_handlers=[AutocastKwargs(cache_enabled=True)]))
  • accelerate.utils.is_tpu_available should be replaced with accelerate.utils.is_torch_xla_available
  • accelerate.utils.modeling.shard_checkpoint should be replaced with split_torch_state_dict_into_shards from the huggingface_hub library
  • accelerate.tqdm.tqdm() no longer accepts True/False as the first argument, and instead, main_process_only should be passed in as a named argument

Multiple Model DeepSpeed Support

After long request, we finally have multiple model DeepSpeed support in Accelerate! (though it is quite early still). Read the full tutorial here, however essentially:

When using multiple models, a DeepSpeed plugin should be created for each model (and as a result, a separate config). a few examples are below:

Knowledge distillation

(Where we train only one model, zero3, and another is used for inference, zero2)

from accelerate import Accelerator
from accelerate.utils import DeepSpeedPlugin

zero2_plugin = DeepSpeedPlugin(hf_ds_config="zero2_config.json")
zero3_plugin = DeepSpeedPlugin(hf_ds_config="zero3_config.json")

deepspeed_plugins = {"student": zero2_plugin, "teacher": zero3_plugin}

accelerator = Accelerator(deepspeed_plugins=deepspeed_plugins)

To then select which plugin to be used at a certain time (aka when calling prepare), we call `accelerator.state.select_deepspeed_plugin("name"), where the first plugin is active by default:

accelerator.state.select_deepspeed_plugin("student")
student_model, optimizer, scheduler = ...
student_model, optimizer, scheduler, train_dataloader = accelerator.prepare(student_model, optimizer, scheduler, train_dataloader)

accelerator.state.select_deepspeed_plugin("teacher") # This will automatically enable zero init
teacher_model = AutoModel.from_pretrained(...)
teacher_model = accelerator.prepare(teacher_model)
Multiple disjoint models

For disjoint models, separate accelerators should be used for each model, and their own .backward() should be called later:

for batch in dl:
    outputs1 = first_model(**batch)
    first_accelerator.backward(outputs1.loss)
    first_optimizer.step()
    first_scheduler.step()
    first_optimizer.zero_grad()
    
    outputs2 = model2(**batch)
    second_accelerator.backward(outputs2.loss)
    second_optimizer.step()
    second_scheduler.step()
    second_optimizer.zero_grad()

FP8

We've enabled MS-AMP support up to FSDP. At this time we are not going forward with implementing FSDP support with MS-AMP, due to design issues between both libraries that don't make them inter-op easily.

FSDP

  • Fixed FSDP auto_wrap using characters instead of full str for layers
  • Re-enable setting state dict type manually

Big Modeling

  • Removed cpu restriction for bnb training

What's Changed

New Contributors

Full Changelog: huggingface/accelerate@v0.34.2...v1.0.0

Configuration

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🚦 Automerge: Disabled by config. Please merge this manually once you are satisfied.

♻ Rebasing: Whenever PR becomes conflicted, or you tick the rebase/retry checkbox.

🔕 Ignore: Close this PR and you won't be reminded about this update again.

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