v7.4.6: Updates for Python 3.10 and 3.11

✨ New features and improvements

• Updates for Python 3.10 and 3.11 (#791):
  • Update vendored wrapt to v1.14.1.
  • Update dev requirements.
  • Add wheels for Python 3.10 and 3.11.

👥 Contributors

@adrianeboyd, @honnibal, @ines

v8.1.4: Type fixes

🔴 Bug fixes

• Fix issue #785: Revert change to return type for Ops.alloc from #779.

👥 Contributors

@adrianeboyd, @honnibal, @ines, @svlandeg

v8.1.3: Updates for pydantic and mypy

✨ New features and improvements

• Extend pydantic support to v1.10.x (#778).
• Support mypy 0.98x, drop mypy support for Python 3.6 (#776).

🔴 Bug fixes

• Fix issue #775: Fix fix_random_seed entry point in setup.cfg.

👥 Contributors

@adrianeboyd, @honnibal, @ines, @pawamoy, @svlandeg

v8.1.2: Update blis support and CuPy extras

✨ New features and improvements

• Update CuPy extras to add cuda116, cuda117, cuda11x and cuda-autodetect, which uses the new cupy-wheel package (#740).
• Add a pytest-randomly entry point for fix_random_seed (#748).

🔴 Bug fixes

• Fix issue #772: Restrict supported blis versions to ~=0.7.8 to avoid bugs in BLIS 0.9.0.

👥 Contributors

@adrianeboyd, @honnibal, @ines, @rmitsch, @svlandeg, @willfrey

v8.1.1: Use confection, new layers and bugfixes

✨ New features and improvements

• Use confection for configurations (#745).
• Add the Dish activation function and layer (#719).
• Add the with_signpost_interval layer to support layer profiling with macOS Instruments (#711).
• Add remap_ids.v2 layer which allows more types of inputs (#726).
• Extend BLIS support to version 0.9.x (#736).
• Improve performance when gradient scaling is used (#746).
• Improve MaxOut performance by unrolling argmax in maxout (#702).

🔴 Bug fixes

• Fix issue #720: Improve type inference by replacing FloatsType in Ops by a TypeVar.
• Fix issue #739: Fix typing of Ops.asarrayDf methods.
• Fix issue #757: Improve compatibility with supported Tensorflow versions.

👥 Contributors

@adrianeboyd, @cclauss, @danieldk, @honnibal, @ines, @kadarakos, @polm, @rmitsch, @shadeMe

v8.1.0: Updated types and many Ops improvements

✨ New features and improvements

• Added support for mypy 0.950 and pydantic v1.9.0, added bound types throughout layers and ops (#599).
• Made all NumpyOps CPU kernels generic (#627).
• Made all custom CUDA kernels generic (#603).
• Added bounds checks for NumpyOps (#618).
• Fixed out-of-bounds writes in NumpyOps and CupyOps (#664).
• Reduced unnecessary zero-init allocations (#632).
• Fixed reductions when applied to zero-length sequences (#637).
• Added NumpyOps.cblas to get a table of C BLAS functions (#643, #700).
• Improved type-casting in NumpyOps.asarray (#656).
• Simplified CupyOps.asarray (#661).
• Fixed Model.copy() for layers used more than once (#659).
• Fixed potential race in Shim (#677).
• Convert numpy arrays using dlpack in xp2tensorflow and xp2torch when possible (#686).
• Improved speed of HashEmbed by avoiding large temporary arrays (#696).
• Added Ops.reduce_last and Ops.reduce_first (#710).
• Numerous test suite improvements.
• Experimental: Add support for Metal Performance Shaders with PyTorch nightlies (#685).

🔴 Bug fixes

• Fix issue #707: Fix label smoothing threshold for to_categorical.

⚠️ Backwards incompatibilities

• In most cases the typing updates allow many casts and ignores to be removed, but types may also need minor modifications following the updates for mypy and pydantic.

• get_array_module now returns None for non-numpy/cupy array input rather than returning numpy by default.

• The prefer_gpu and require_gpu functions no longer set the default PyTorch torch.Tensor type to torch.cuda.FloatTensor. This means that wrapped PyTorch models cannot assume that Tensors are allocated on a CUDA GPU after calling these functions. For example:

  # Before Thinc v8.1.0, this Tensor would be allocated on the GPU after
  # {prefer,require}_gpu. Now it will be allocated as a CPU tensor by default.
  token_mask = torch.arange(max_seq_len)
  
  # To ensure correct allocation, specify the device where the Tensor should be allocated. 
  # `input` refers to the input of the model. 
  token_mask = torch.arange(max_seq_len, device=input.device) 
  

  This change brings Thinc's behavior in line with how device memory allocation is normally handled in PyTorch.

👥 Contributors

@adrianeboyd, @danieldk, @honnibal, @ines, @kadarakos, @koaning, @richardpaulhudson, @shadeMe, @svlandeg

v8.0.17: Extended requirements, test suite fixes

✨ New features and improvements

• Extend support for typing_extensions up to v4.1.x (for Python 3.7 and earlier).
• Various fixes in the test suite.

👥 Contributors

@adrianeboyd, @danieldk, @honnibal, @ines, @shadeMe

v8.0.16: Bug fixes

✨ New features and improvements

• Make Ops.asarray implementations more robust.

🔴 Bug fixes

• Fix issue #624: Support CPU inference for models trained with gradient scaling.
• Fix issue #633: Fix invalid indexing in Beam when no states have valid transitions.
• Fix issue #639: Improve PyTorch Tensor handling in CupyOps.asarray.
• Fix issue #649: Clamp inputs in Ops.sigmoid to prevent overflow.
• Fix issue #651: Fix type safety issue with model ID assignment.
• Fix issue #653: Correctly handle Tensorflow GPU tensors in tests.
• Fix issue #660: Make is_torch_array work without PyTorch installed.
• Fix issue #664: Fix out of-bounds writes in CupyOps.adam and NumpyOps.adam.

⚠️ Backwards incompatibilities

• The init implementations for layers no longer return Model.

📖 Documentation and examples

• Add notebook demonstrating Bloom embeddings.
• Fix LSTM benchmark example.
• Update installation instructions.

👥 Contributors

@adrianeboyd, @danieldk, @honnibal, @ines, @kadarakos, @koaning, @notplus, @richardpaulhudson, @shadeMe

v8.0.15: Fix compatibility with older PyTorch versions

🔴 Bug fixes

• Fix issue #610: Improve compatibility with PyTorch versions before v1.9.0.

👥 Contributors

@adrianeboyd, @danieldk

v8.0.14: New activation functions, bug fixes and more

✨ New features and improvements

• Add new activation functions: ClippedLinear.v1, Gelu.v1, HardSigmoid.v1, HardSwish.v1, HardSwishMobilenet.v1, HardTanh.v1, ReluK.v1, and Swish.v1.
• Automatically set the GPU allocator to PyTorch when PyTorch models are loaded through PyTorchWrapper on GPU to avoid memory contention between CuPy and PyTorch.
• Support big endian platforms through thinc-bigendian-ops and consistently serialize model data with little endian byte order.
• Add Softmax.v2 with support for softmax with temperature and optional normalization.
• Add CategoricalCrossentropy.v3 and SequenceCategoricalCrossentropy.v3 with support for label smoothing.
• Speed up CupyOps.maxout by exploiting GPU parallelism better.
• Support sequence lengths in the NumpyOps.seq2col and CupyOps.seq2col implementations of Ops.seq2col to determine padding.
• Improve performance of Ragged.
• Support Ragged arrays in expand_window.v1.

🔴 Bug fixes

• Fix issue #552: Do not backpropagate Inf/NaN out of PyTorch layers when using mixed-precision training.
• Fix issue #578: Correctly cast the threshold argument of CupyOps.mish and correct an equation in Ops.backprop_mish.
• Fix issue #587: Correct invariant checks in CategoricalCrossentropy.get_grad.
• Fix issue #592: Update murmurhashrequirement.
• Fix issue #594: Do not sort positional arguments in Config.

⚠️ Backwards incompatibilities

• The out keyword argument of Ops.mish and Ops.backprop_mish is replaced by inplace for consistency with other activations.

📖Documentation and examples

• Update example Jupyter notebooks for the current Thinc version.

👥 Contributors

@adrianeboyd, @andrewsi-z, @danieldk, @honnibal, @ines, @Jette16, @kadarakos, @kianmeng, @polm, @svlandeg, @thatbudakguy