8000 Remove last_dim_softmax as it's deprecated and scheduled for removal. by schmmd · Pull Request #2207 · allenai/allennlp · GitHub
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Remove last_dim_softmax as it's deprecated and scheduled for removal. #2207

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Dec 18, 2018
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Remove last_dim_softmax as it's deprecated and scheduled for removal. #2207

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31 changes: 0 additions & 31 deletions allennlp/nn/util.py
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Original file line number Diff line number Diff line change
Expand Up @@ -475,37 +475,6 @@ def get_text_field_mask(text_field_tensors: Dict[str, torch.Tensor],
raise ValueError("Expected a tensor with dimension 2 or 3, found {}".format(smallest_dim))


def last_dim_softmax(tensor: torch.Tensor, mask: Optional[torch.Tensor] = None) -> torch.Tensor:
"""
Takes a tensor with 3 or more dimensions and does a masked softmax over the last dimension. We
assume the tensor has shape ``(batch_size, ..., sequence_length)`` and that the mask (if given)
has shape ``(batch_size, sequence_length)``.

.. deprecated:: 0.6.1
``last_dim_softmax`` was deprecated in favor of just using ``masked_softmax`` in version
0.6.1. It will be removed in version 0.8.
"""
warnings.warn("``last_dim_softmax`` was deprecated in favor of just using ``masked_softmax`` "
"in version 0.6.1. It will be removed in version 0.8.", DeprecationWarning)
return masked_softmax(tensor, mask, dim=-1)


def last_dim_log_softmax(tensor: torch.Tensor, mask: Optional[torch.Tensor] = None) -> torch.Tensor:
"""
Takes a tensor with 3 or more dimensions and does a masked log softmax over the last dimension.
We assume the tensor has shape ``(batch_size, ..., sequence_length)`` and that the mask (if given)
has shape ``(batch_size, sequence_length)``.

.. deprecated:: 0.6.1
``last_dim_log_softmax`` was deprecated in favor of just using ``masked_log_softmax`` in
version 0.6.1. It will be removed in version 0.8.
"""
warnings.warn("``last_dim_log_softmax`` was deprecated in favor of just using "
"``masked_log_softmax`` in version 0.6.1. It will be removed in version 0.8.",
DeprecationWarning)
return masked_log_softmax(tensor, mask, dim=-1)


def weighted_sum(matrix: torch.Tensor, attention: torch.Tensor) -> torch.Tensor:
"""
Takes a matrix of vectors and a set of weights over the rows in the matrix (which we call an
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