SUMIN/HuggingFace_transformer
1
0
Fork 0
Code Issues Pull Requests Actions 7 Packages Projects Releases Wiki Activity

WIP reodering arguments for torchscript and TF

Browse Source
This commit is contained in:
thomwolf
2019-09-04 12:43:18 +02:00
parent 38b79b5a63
commit e25cba78cf
10 changed files with 331 additions and 253 deletions
Download Patch File Download Diff File Expand all files Collapse all files

151
pytorch_transformers/modeling_bert.py
View File

@@ -596,18 +596,18 @@ BERT_INPUTS_DOCSTRING = r"""
Indices can be obtained using :class:`pytorch_transformers.BertTokenizer`.
See :func:`pytorch_transformers.PreTrainedTokenizer.encode` and
:func:`pytorch_transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
**position_ids**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
Indices of positions of each input sequence tokens in the position embeddings.
Selected in the range ``[0, config.max_position_embeddings - 1]``.
**attention_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, sequence_length)``:
Mask to avoid performing attention on padding token indices.
Mask values selected in ``[0, 1]``:
``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.
**token_type_ids**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
Segment token indices to indicate first and second portions of the inputs.
Indices are selected in ``[0, 1]``: ``0`` corresponds to a `sentence A` token, ``1``
corresponds to a `sentence B` token
(see `BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding`_ for more details).
**attention_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, sequence_length)``:
Mask to avoid performing attention on padding token indices.
Mask values selected in ``[0, 1]``:
``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.
**position_ids**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
Indices of positions of each input sequence tokens in the position embeddings.
Selected in the range ``[0, config.max_position_embeddings - 1]``.
**head_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(num_heads,)`` or ``(num_layers, num_heads)``:
Mask to nullify selected heads of the self-attention modules.
Mask values selected in ``[0, 1]``:
@@ -668,7 +668,7 @@ class BertModel(BertPreTrainedModel):
for layer, heads in heads_to_prune.items():
self.encoder.layer[layer].attention.prune_heads(heads)
def forward(self, input_ids, token_type_ids=None, attention_mask=None, position_ids=None, head_mask=None):
def forward(self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None):
if attention_mask is None:
attention_mask = torch.ones_like(input_ids)
if token_type_ids is None:
@@ -771,10 +771,14 @@ class BertForPreTraining(BertPreTrainedModel):
self._tie_or_clone_weights(self.cls.predictions.decoder,
self.bert.embeddings.word_embeddings)
def forward(self, input_ids, token_type_ids=None, attention_mask=None, masked_lm_labels=None,
next_sentence_label=None, position_ids=None, head_mask=None):
outputs = self.bert(input_ids, position_ids=position_ids, token_type_ids=token_type_ids,
attention_mask=attention_mask, head_mask=head_mask)
def forward(self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None,
masked_lm_labels=None, next_sentence_label=None):
outputs = self.bert(input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask)
sequence_output, pooled_output = outputs[:2]
prediction_scores, seq_relationship_score = self.cls(sequence_output, pooled_output)
@@ -839,10 +843,14 @@ class BertForMaskedLM(BertPreTrainedModel):
self._tie_or_clone_weights(self.cls.predictions.decoder,
self.bert.embeddings.word_embeddings)
def forward(self, input_ids, token_type_ids=None, attention_mask=None, masked_lm_labels=None,
position_ids=None, head_mask=None):
outputs = self.bert(input_ids, position_ids=position_ids, token_type_ids=token_type_ids,
attention_mask=attention_mask, head_mask=head_mask)
def forward(self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None,
masked_lm_labels=None):
outputs = self.bert(input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask)
sequence_output = outputs[0]
prediction_scores = self.cls(sequence_output)
@@ -896,10 +904,15 @@ class BertForNextSentencePrediction(BertPreTrainedModel):
self.init_weights()
def forward(self, input_ids, token_type_ids=None, attention_mask=None, next_sentence_label=None,
position_ids=None, head_mask=None):
outputs = self.bert(input_ids, position_ids=position_ids, token_type_ids=token_type_ids,
attention_mask=attention_mask, head_mask=head_mask)
def forward(self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None,
next_sentence_label=None):
outputs = self.bert(input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask)
pooled_output = outputs[1]
seq_relationship_score = self.cls(pooled_output)
@@ -957,10 +970,15 @@ class BertForSequenceClassification(BertPreTrainedModel):
self.init_weights()
def forward(self, input_ids, token_type_ids=None, attention_mask=None, labels=None,
position_ids=None, head_mask=None):
outputs = self.bert(input_ids, position_ids=position_ids, token_type_ids=token_type_ids,
attention_mask=attention_mask, head_mask=head_mask)
def forward(self, input_ids, attention_mask=None, token_type_ids=None,
position_ids=None, head_mask=None, labels=None):
outputs = self.bert(input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask)
pooled_output = outputs[1]
pooled_output = self.dropout(pooled_output)
@@ -983,45 +1001,9 @@ class BertForSequenceClassification(BertPreTrainedModel):
@add_start_docstrings("""Bert Model with a multiple choice classification head on top (a linear layer on top of
the pooled output and a softmax) e.g. for RocStories/SWAG tasks. """,
BERT_START_DOCSTRING)
BERT_START_DOCSTRING, BERT_INPUTS_DOCSTRING)
class BertForMultipleChoice(BertPreTrainedModel):
r"""
Inputs:
**input_ids**: ``torch.LongTensor`` of shape ``(batch_size, num_choices, sequence_length)``:
Indices of input sequence tokens in the vocabulary.
The second dimension of the input (`num_choices`) indicates the number of choices to score.
To match pre-training, BERT input sequence should be formatted with [CLS] and [SEP] tokens as follows:
(a) For sequence pairs:
``tokens: [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]``
``token_type_ids: 0 0 0 0 0 0 0 0 1 1 1 1 1 1``
(b) For single sequences:
``tokens: [CLS] the dog is hairy . [SEP]``
``token_type_ids: 0 0 0 0 0 0 0``
Indices can be obtained using :class:`pytorch_transformers.BertTokenizer`.
See :func:`pytorch_transformers.PreTrainedTokenizer.encode` and
:func:`pytorch_transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
**token_type_ids**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, num_choices, sequence_length)``:
Segment token indices to indicate first and second portions of the inputs.
The second dimension of the input (`num_choices`) indicates the number of choices to score.
Indices are selected in ``[0, 1]``: ``0`` corresponds to a `sentence A` token, ``1``
corresponds to a `sentence B` token
(see `BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding`_ for more details).
**attention_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, num_choices, sequence_length)``:
Mask to avoid performing attention on padding token indices.
The second dimension of the input (`num_choices`) indicates the number of choices to score.
Mask values selected in ``[0, 1]``:
``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.
**head_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(num_heads,)`` or ``(num_layers, num_heads)``:
Mask to nullify selected heads of the self-attention modules.
Mask values selected in ``[0, 1]``:
``1`` indicates the head is **not masked**, ``0`` indicates the head is **masked**.
**labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``:
Labels for computing the multiple choice classification loss.
Indices should be in ``[0, ..., num_choices]`` where `num_choices` is the size of the second dimension
@@ -1061,16 +1043,21 @@ class BertForMultipleChoice(BertPreTrainedModel):
self.init_weights()
def forward(self, input_ids, token_type_ids=None, attention_mask=None, labels=None,
position_ids=None, head_mask=None):
def forward(self, input_ids, attention_mask=None, token_type_ids=None,
position_ids=None, head_mask=None, labels=None):
num_choices = input_ids.shape[1]
flat_input_ids = input_ids.view(-1, input_ids.size(-1))
flat_position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None
flat_token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
flat_attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
outputs = self.bert(flat_input_ids, position_ids=flat_position_ids, token_type_ids=flat_token_type_ids,
attention_mask=flat_attention_mask, head_mask=head_mask)
input_ids = input_ids.view(-1, input_ids.size(-1))
attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None
outputs = self.bert(input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask)
pooled_output = outputs[1]
pooled_output = self.dropout(pooled_output)
@@ -1129,10 +1116,15 @@ class BertForTokenClassification(BertPreTrainedModel):
self.init_weights()
def forward(self, input_ids, token_type_ids=None, attention_mask=None, labels=None,
position_ids=None, head_mask=None):
outputs = self.bert(input_ids, position_ids=position_ids, token_type_ids=token_type_ids,
attention_mask=attention_mask, head_mask=head_mask)
def forward(self, input_ids, attention_mask=None, token_type_ids=None,
position_ids=None, head_mask=None, labels=None):
outputs = self.bert(input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask)
sequence_output = outputs[0]
sequence_output = self.dropout(sequence_output)
@@ -1203,10 +1195,15 @@ class BertForQuestionAnswering(BertPreTrainedModel):
self.init_weights()
def forward(self, input_ids, token_type_ids=None, attention_mask=None, start_positions=None,
end_positions=None, position_ids=None, head_mask=None):
outputs = self.bert(input_ids, position_ids=position_ids, token_type_ids=token_type_ids,
attention_mask=attention_mask, head_mask=head_mask)
def forward(self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None,
start_positions=None, end_positions=None):
outputs = self.bert(input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask)
sequence_output = outputs[0]
logits = self.qa_outputs(sequence_output)