v1.2.0

Version 1.2.0.

New features:

• Added support for PyTorch 2.0 and above.
• When automatic block sizing based on available GPU memory is used, if
  it is estimated that no memory is left, instead of raising an
  exception, just try a block size of 1 and hope for the best.

Bug fixes:

• Automatic block sizing now works for tensors of dtype bool.
• Fixed problems with empty argmax tensors in the Viterbi semiring
  (wrong dtype and device).

v1.1.0

New features and performance improvements:

• The block_size argument can now be omitted; by default, an appropriate
  block size will be chosen based on the amount of available memory.
• Sped up log einsum by saving some tensors from the forward pass that
  can be reused in the backward pass, at the cost of more memory usage.
  The old, slower behavior can be restored using the save_max and
  save_sumexpsub options.
• Sped up log einsum by using torch.Tensor._nan_to_num() to clip
  infinite values. Use PyTorch 1.8.0 or later to take advantage of this.
• Sped up log einsum by using torch.amax() to compute maximum values
  over multiple dimensions at once. Use PyTorch 1.7.0 or later to take
  advantage of this.
• Added an option to log einsum to avoid NaNs in the gradient when all terms
  are -inf. By default, the gradient of a summation whose output is -inf
  is still NaN (same as torch.logsumexp()), but the new grad_of_neg_inf
  option can now be used to set it to 0 instead.
• Added support for earlier versions of PyTorch (1.1.0 and later) and
  Python (3.6.1 and later).
• Added a py.typed file for compatibility with type checking tools like
  mypy.

Bug fixes:

• 0-dimensional inputs no longer cause exceptions to be raised.
• Log einsum now handles input values of +inf.
• Log Viterbi einsum no longer raises an exception when there are no
  summed variables or when the output is 0-dimensional.