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Add sparse gradient option to pretrained embedding #7492

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soumith merged 3 commits into from
May 11, 2018
Merged

Add sparse gradient option to pretrained embedding #7492

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1fc523a
Add sparse gradient option to pretrained embedding
borguz May 11, 2018
b54c58e
Add sparse gradient option to pretrained embedding
borguz May 11, 2018
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borguz May 11, 2018
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11 changes: 9 additions & 2 deletions torch/nn/modules/sparse.py
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Original file line number Diff line number Diff line change
Expand Up @@ -122,14 +122,16 @@ def extra_repr(self):
return s.format(**self.__dict__)

@classmethod
def from_pretrained(cls, embeddings, freeze=True):
def from_pretrained(cls, embeddings, freeze=True, sparse=False):

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r"""Creates Embedding instance from given 2-dimensional FloatTensor.

Args:
embeddings (Tensor): FloatTensor containing weights for the Embedding.
First dimension is being passed to Embedding as 'num_embeddings', second as 'embedding_dim'.
freeze (boolean, optional): If ``True``, the tensor does not get updated in the learning process.
Equivalent to ``embedding.weight.requires_grad = False``. Default: ``True``
sparse (bool, optional): if ``True``, gradient w.r.t. weight matrix will be a sparse tensor.
See Notes for more details regarding sparse gradients.

Examples::

Expand All @@ -144,7 +146,12 @@ def from_pretrained(cls, embeddings, freeze=True):
assert embeddings.dim() == 2, \
'Embeddings parameter is expected to be 2-dimensional'
rows, cols = embeddings.shape
embedding = cls(num_embeddings=rows, embedding_dim=cols, _weight=embeddings)
embedding = cls(
num_embeddings=rows,
embedding_dim=cols,
_weight=embeddings,
sparse=sparse,
)
embedding.weight.requires_grad = not freeze
return embedding

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