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Feed forward chunking others #6365
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patrickvonplaten
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Pradhy729:feed_forward_chunking_others
Aug 19, 2020
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Merge pull request #1 from huggingface/master
Pradhy729 b413437
Merge pull request #2 from huggingface/master
Pradhy729 fcaa3aa
Merge pull request #3 from huggingface/master
Pradhy729 623352f
Merge pull request #4 from huggingface/master
Pradhy729 5a26a2d
Merge pull request #5 from huggingface/master
Pradhy729 c96bc33
Merge pull request #6 from huggingface/master
Pradhy729 b85adeb
Merge pull request #7 from huggingface/master
Pradhy729 a258372
Merge pull request #8 from huggingface/master
Pradhy729 b18cb64
Merge pull request #9 from huggingface/master
Pradhy729 04b985f
Merge pull request #10 from huggingface/master
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Merge pull request #11 from huggingface/master
Pradhy729 dce34e5
Feed forward chunking for Distilbert & Albert
Pradhy729 7f370d4
Added ff chunking for many other models
Pradhy729 8d69d6f
Change model signature
Pradhy729 82f79df
Added chunking for XLM
Pradhy729 2ac6418
Cleaned up by removing some variables.
Pradhy729 80c6b27
remove test_chunking flag
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Original file line number | Diff line number | Diff line change |
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@@ -1447,7 +1447,7 @@ def prune_layer( | |
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def apply_chunking_to_forward( | ||
chunk_size: int, chunk_dim: int, forward_fn: Callable[..., torch.Tensor], *input_tensors | ||
forward_fn: Callable[..., torch.Tensor], chunk_size: int, chunk_dim: int, *input_tensors | ||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. Thanks for changing that. @LysandreJik - as you said this is the better order of the arguments and should be fine in terms of breaking backward compatibility |
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) -> torch.Tensor: | ||
""" | ||
This function chunks the :obj:`input_tensors` into smaller input tensor parts of size :obj:`chunk_size` over the | ||
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@@ -1457,12 +1457,12 @@ def apply_chunking_to_forward( | |
directly applying :obj:`forward_fn` to :obj:`input_tensors`. | ||
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Args: | ||
forward_fn (:obj:`Callable[..., torch.Tensor]`): | ||
The forward function of the model. | ||
chunk_size (:obj:`int`): | ||
The chunk size of a chunked tensor: :obj:`num_chunks = len(input_tensors[0]) / chunk_size`. | ||
chunk_dim (:obj:`int`): | ||
The dimension over which the :obj:`input_tensors` should be chunked. | ||
forward_fn (:obj:`Callable[..., torch.Tensor]`): | ||
The forward function of the model. | ||
input_tensors (:obj:`Tuple[torch.Tensor]`): | ||
The input tensors of ``forward_fn`` which will be chunked. | ||
Returns: | ||
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@@ -1478,7 +1478,7 @@ def forward_chunk(self, hidden_states): | |
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# implement a chunked forward function | ||
def forward(self, hidden_states): | ||
return apply_chunking_to_forward(self.chunk_size_lm_head, self.seq_len_dim, self.forward_chunk, hidden_states) | ||
return apply_chunking_to_forward(self.forward_chunk, self.chunk_size_lm_head, self.seq_len_dim, hidden_states) | ||
""" | ||
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assert len(input_tensors) > 0, "{} has to be a tuple/list of tensors".format(input_tensors) | ||
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great, thanks for adding this!
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Can you move the docstring from Reformer to this file and delete the corresponding docstring / config variable from reformer?
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actually it's aleady done - never mind