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v0.34.0: StatefulDataLoader Support, FP8 Improvements, and PyTorch Updates!
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@muellerzr muellerzr released this 03 Sep 14:58
· 66 commits to main since this release
v0.34.0
159c0dd

Dependency Changes

  • Updated Safetensors Requirement: The library now requires safetensors version 0.4.3.
  • Added support for Numpy 2.0: The library now fully supports numpy 2.0.0

Core

New Script Behavior Changes

  • Process Group Management: PyTorch now requires users to destroy process groups after training. The accelerate library will handle this automatically with accelerator.end_training(), or you can do it manually using PartialState().destroy_process_group().
  • MLU Device Support: Added support for saving and loading RNG states on MLU devices by @huismiling
  • NPU Support: Corrected backend and distributed settings when using transfer_to_npu, ensuring better performance and compatibility.

DataLoader Enhancements

  • Stateful DataDataLoader: We are excited to announce that early support has been added for the StatefulDataLoader from torchdata, allowing better handling of data loading states. Enable by passing use_stateful_dataloader=True to the DataLoaderConfiguration, and when calling load_state() the DataLoader will automatically be resumed from its last step, no more having to iterate through passed batches.
  • Decoupled Data Loader Preparation: The prepare_data_loader() function is now independent of the Accelerator, giving you more flexibility towards which API levels you would like to use.
  • XLA Compatibility: Added support for skipping initial batches when using XLA.
  • Improved State Management: Bug fixes and enhancements for saving/loading DataLoader states, ensuring smoother training sessions.
  • Epoch Setting: Introduced the set_epoch function for MpDeviceLoaderWrapper.

FP8 Training Improvements

  • Enhanced FP8 Training: Fully Sharded Data Parallelism (FSDP) and DeepSpeed support now work seamlessly with TransformerEngine FP8 training, including better defaults for the quantized FP8 weights.
  • Integration baseline: We've added a new suite of examples and benchmarks to ensure that our TransformerEngine integration works exactly as intended. These scripts run one half using 🤗 Accelerate's integration, the other with raw TransformersEngine, providing users with a nice example of what we do under the hood with accelerate, and a good sanity check to make sure nothing breaks down over time. Find them here
  • Import Fixes: Resolved issues with import checks for the Transformers Engine that has downstream issues.
  • FP8 Docker Images: We've added new docker images for TransformerEngine and accelerate as well. Use docker pull huggingface/accelerate@gpu-fp8-transformerengine to quickly get an environment going.

torchpippy no more, long live torch.distributed.pipelining

  • With the latest PyTorch release, torchpippy is now fully integrated into torch core, and as a result we are exclusively supporting the PyTorch implementation from now on
  • There are breaking examples and changes that comes from this shift. Namely:
    • Tracing of inputs is done with a shape each GPU will see, rather than the size of the total batch. So for 2 GPUs, one should pass in an input of [1, n, n] rather than [2, n, n] as before.
    • We no longer support Encoder/Decoder models. PyTorch tracing for pipelining no longer supports encoder/decoder models, so the t5 example has been removed.
    • Computer vision model support currently does not work: There are some tracing issues regarding resnet's we are actively looking into.
  • If either of these changes are too breaking, we recommend pinning your accelerate version. If the encoder/decoder model support is actively blocking your inference using pippy, please open an issue and let us know. We can look towards adding in the old support for torchpippy potentially if needed.

Fully Sharded Data Parallelism (FSDP)

  • Environment Flexibility: Environment variables are now fully optional for FSDP, simplifying configuration. You can now fully create a FullyShardedDataParallelPlugin yourself manually with no need for environment patching:
from accelerate import FullyShardedDataParallelPlugin
fsdp_plugin = FullyShardedDataParallelPlugin(...)
  • FSDP RAM efficient loading: Added a utility to enable RAM-efficient model loading (by setting the proper environmental variable). This is generally needed if not using accelerate launch and need to ensure the env variables are setup properly for model loading:
from accelerate.utils import enable_fsdp_ram_efficient_loading, disable_fsdp_ram_efficient_loading
enable_fsdp_ram_efficient_loading()
  • Model State Dict Management: Enhanced support for unwrapping model state dicts in FSDP, making it easier to manage distributed models.

New Examples

Bug Fixes

New Contributors

Full Changelog:

Detailed Full Changelog:

Contributors

tmm1, siddk, and 16 other contributors
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