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Release history

opacus releases

Training PyTorch models with differential privacy

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v1.6.0 Breaking risk
Breaking changes
  • Minimum torch version raised to >=2.6.0
Notable features
  • Non-wrapping mode (`wrap_model=False`) for better interoperability with Transformers, Accelerate, and other libraries
  • Arithmetic operations support in `DPTensorFastGradientClipping` for custom loss compositions
  • Fully Sharded Data Parallel (FSDP) training support including tutorial and examples
Full changelog

New features

Better interoperability with modern training stacks

  • Add non-wrapping mode for better compatibility with Transformers, Accelerate, and libraries that expect the original module hierarchy (wrap_model=False) (#794)
  • Add arithmetic operations support to DPTensorFastGradientClipping, making it easier to integrate Opacus with custom loss compositions and external trainers (#805)

Distributed and large-model training

  • Add support for Fully Sharded Data Parallel (FSDP) training, including a tutorial and a new example (#761,#772,#781,#782)
  • Add support for mixed and low precision training (#764)
  • Add 1D tensor parallelism support for fast gradient clipping, together with toy and Llama examples; this support is currently beta (#776)

Others

  • Add ability to register custom noise accountants (#784)

Bug fixes

  • Fix epsilon/noise accounting when using adaptive gradient clipping (#807, #779)
  • Fix fast gradient clipping when using ignore_index masking, so ignored tokens do not affect the reduced loss incorrectly (#808)
  • Replace empty-batch handling inside DPDataLoader with a structure-aware approach, fixing failures for custom batch structures under Poisson sampling (#806)
  • Treat IAccountant.mechanism correctly during state_dict handling (#778)

Compatibility

  • Require torch>=2.6.0 (#770)
View release on GitHub

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