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