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added gpt2 doc

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thomwolf
2019-07-15 09:40:05 +02:00
parent 183fedfed5
commit 5bc3d0cc5b
2 changed files with 210 additions and 211 deletions
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55
pytorch_transformers/modeling_bert.py
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@@ -277,10 +277,11 @@ class BertEmbeddings(nn.Module):
self.LayerNorm = BertLayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, input_ids, token_type_ids=None):
def forward(self, input_ids, position_ids=None, token_type_ids=None):
seq_length = input_ids.size(1)
position_ids = torch.arange(seq_length, dtype=torch.long, device=input_ids.device)
position_ids = position_ids.unsqueeze(0).expand_as(input_ids)
if position_ids is None:
position_ids = torch.arange(seq_length, dtype=torch.long, device=input_ids.device)
position_ids = position_ids.unsqueeze(0).expand_as(input_ids)
if token_type_ids is None:
token_type_ids = torch.zeros_like(input_ids)
@@ -624,6 +625,9 @@ 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[``.
**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``
@@ -687,7 +691,7 @@ 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, head_mask=None):
def forward(self, input_ids, position_ids=None, token_type_ids=None, attention_mask=None, head_mask=None):
if attention_mask is None:
attention_mask = torch.ones_like(input_ids)
if token_type_ids is None:
@@ -723,7 +727,7 @@ class BertModel(BertPreTrainedModel):
else:
head_mask = [None] * self.config.num_hidden_layers
embedding_output = self.embeddings(input_ids, token_type_ids)
embedding_output = self.embeddings(input_ids, position_ids, token_type_ids)
encoder_outputs = self.encoder(embedding_output,
extended_attention_mask,
head_mask=head_mask)
@@ -773,7 +777,7 @@ class BertForPreTraining(BertPreTrainedModel):
>>> model = BertForPreTraining(config)
>>> input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0) # Batch size 1
>>> outputs = model(input_ids)
>>> prediction_scores, seq_relationship_scores = outputs[:1]
>>> prediction_scores, seq_relationship_scores = outputs[:2]
"""
def __init__(self, config):
@@ -792,9 +796,9 @@ 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,
def forward(self, input_ids, position_ids=None, token_type_ids=None, attention_mask=None, masked_lm_labels=None,
next_sentence_label=None, head_mask=None):
outputs = self.bert(input_ids, token_type_ids, attention_mask, head_mask=head_mask)
outputs = self.bert(input_ids, position_ids, token_type_ids, attention_mask, head_mask=head_mask)
sequence_output, pooled_output = outputs[:2]
prediction_scores, seq_relationship_score = self.cls(sequence_output, pooled_output)
@@ -842,7 +846,7 @@ class BertForMaskedLM(BertPreTrainedModel):
>>> model = BertForMaskedLM(config)
>>> input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0) # Batch size 1
>>> outputs = model(input_ids, masked_lm_labels=input_ids)
>>> loss, prediction_scores = outputs[:1]
>>> loss, prediction_scores = outputs[:2]
"""
def __init__(self, config):
@@ -861,8 +865,8 @@ 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, head_mask=None):
outputs = self.bert(input_ids, token_type_ids, attention_mask, head_mask=head_mask)
def forward(self, input_ids, position_ids=None, token_type_ids=None, attention_mask=None, masked_lm_labels=None, head_mask=None):
outputs = self.bert(input_ids, position_ids, token_type_ids, attention_mask, head_mask=head_mask)
sequence_output = outputs[0]
prediction_scores = self.cls(sequence_output)
@@ -918,8 +922,8 @@ class BertForNextSentencePrediction(BertPreTrainedModel):
self.apply(self.init_weights)
def forward(self, input_ids, token_type_ids=None, attention_mask=None, next_sentence_label=None, head_mask=None):
outputs = self.bert(input_ids, token_type_ids, attention_mask, head_mask=head_mask)
def forward(self, input_ids, position_ids=None, token_type_ids=None, attention_mask=None, next_sentence_label=None, head_mask=None):
outputs = self.bert(input_ids, position_ids, token_type_ids, attention_mask, head_mask=head_mask)
pooled_output = outputs[1]
seq_relationship_score = self.cls(pooled_output)
@@ -966,7 +970,7 @@ class BertForSequenceClassification(BertPreTrainedModel):
>>> input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0) # Batch size 1
>>> labels = torch.tensor([1]).unsqueeze(0) # Batch size 1
>>> outputs = model(input_ids, labels=labels)
>>> loss, logits = outputs[:1]
>>> loss, logits = outputs[:2]
"""
def __init__(self, config):
@@ -979,8 +983,8 @@ class BertForSequenceClassification(BertPreTrainedModel):
self.apply(self.init_weights)
def forward(self, input_ids, token_type_ids=None, attention_mask=None, labels=None, head_mask=None):
outputs = self.bert(input_ids, token_type_ids, attention_mask, head_mask=head_mask)
def forward(self, input_ids, position_ids=None, token_type_ids=None, attention_mask=None, labels=None, head_mask=None):
outputs = self.bert(input_ids, position_ids, token_type_ids, attention_mask, head_mask=head_mask)
pooled_output = outputs[1]
pooled_output = self.dropout(pooled_output)
@@ -1071,7 +1075,7 @@ class BertForMultipleChoice(BertPreTrainedModel):
>>> input_ids = torch.tensor([tokenizer.encode(s) for s in choices]).unsqueeze(0) # Batch size 1, 2 choices
>>> labels = torch.tensor(1).unsqueeze(0) # Batch size 1
>>> outputs = model(input_ids, labels=labels)
>>> loss, classification_scores = outputs[:1]
>>> loss, classification_scores = outputs[:2]
"""
def __init__(self, config):
@@ -1083,13 +1087,14 @@ class BertForMultipleChoice(BertPreTrainedModel):
self.apply(self.init_weights)
def forward(self, input_ids, token_type_ids=None, attention_mask=None, labels=None, head_mask=None):
def forward(self, input_ids, position_ids=None, token_type_ids=None, attention_mask=None, labels=None, head_mask=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, flat_token_type_ids, flat_attention_mask, head_mask=head_mask)
outputs = self.bert(flat_input_ids, flat_position_ids, flat_token_type_ids, flat_attention_mask, head_mask=head_mask)
pooled_output = outputs[1]
pooled_output = self.dropout(pooled_output)
@@ -1137,7 +1142,7 @@ class BertForTokenClassification(BertPreTrainedModel):
>>> input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0) # Batch size 1
>>> labels = torch.tensor([1] * input_ids.size(1)).unsqueeze(0) # Batch size 1
>>> outputs = model(input_ids, labels=labels)
>>> loss, scores = outputs[:1]
>>> loss, scores = outputs[:2]
"""
def __init__(self, config):
@@ -1150,8 +1155,8 @@ class BertForTokenClassification(BertPreTrainedModel):
self.apply(self.init_weights)
def forward(self, input_ids, token_type_ids=None, attention_mask=None, labels=None, head_mask=None):
outputs = self.bert(input_ids, token_type_ids, attention_mask, head_mask=head_mask)
def forward(self, input_ids, position_ids=None, token_type_ids=None, attention_mask=None, labels=None, head_mask=None):
outputs = self.bert(input_ids, position_ids, token_type_ids, attention_mask, head_mask=head_mask)
sequence_output = outputs[0]
sequence_output = self.dropout(sequence_output)
@@ -1177,7 +1182,7 @@ class BertForTokenClassification(BertPreTrainedModel):
the hidden-states output to compute `span start logits` and `span end logits`). """,
BERT_START_DOCSTRING, BERT_INPUTS_DOCSTRING)
class BertForQuestionAnswering(BertPreTrainedModel):
r"""
__doc__ = r"""
**start_positions**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``:
Position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`).
@@ -1224,9 +1229,9 @@ class BertForQuestionAnswering(BertPreTrainedModel):
self.apply(self.init_weights)
def forward(self, input_ids, token_type_ids=None, attention_mask=None, start_positions=None,
def forward(self, input_ids, position_ids=None, token_type_ids=None, attention_mask=None, start_positions=None,
end_positions=None, head_mask=None):
outputs = self.bert(input_ids, token_type_ids, attention_mask, head_mask=head_mask)
outputs = self.bert(input_ids, position_ids, token_type_ids, attention_mask, head_mask=head_mask)
sequence_output = outputs[0]
logits = self.qa_outputs(sequence_output)