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merged 3 commits into from
Nov 5, 2019
Merged

model forwards can take an inputs_embeds param #1695

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8e11de0
model forwards can take an inputs_embeds param
julien-c Nov 1, 2019
9eddf44
docstring + check
julien-c Nov 4, 2019
00337e9
[inputs_embeds] All PyTorch models
julien-c Nov 5, 2019
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4 changes: 4 additions & 0 deletions templates/adding_a_new_model/modeling_tf_xxx.py
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Original file line number Diff line number Diff line change
Expand Up @@ -255,6 +255,10 @@ class TFXxxPreTrainedModel(TFPreTrainedModel):
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**.
**inputs_embeds**: (`optional`) ``Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, sequence_length, embedding_dim)``:
Optionally, instead of passing ``input_ids`` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert `input_ids` indices into associated vectors
than the model's internal embedding lookup matrix.
"""

@add_start_docstrings("The bare Xxx Model transformer outputing raw hidden-states without any specific head on top.",
Expand Down
25 changes: 16 additions & 9 deletions templates/adding_a_new_model/modeling_xxx.py
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Original file line number Diff line number Diff line change
Expand Up @@ -238,6 +238,10 @@ def _init_weights(self, module):
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**.
**inputs_embeds**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, embedding_dim)``:
Optionally, instead of passing ``input_ids`` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert `input_ids` indices into associated vectors
than the model's internal embedding lookup matrix.
"""

@add_start_docstrings("The bare Xxx Model transformer outputting raw hidden-states without any specific head on top.",
Expand Down Expand Up @@ -295,7 +299,7 @@ def _prune_heads(self, heads_to_prune):
for layer, heads in heads_to_prune.items():
self.encoder.layer[layer].attention.prune_heads(heads)

def forward(self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None):
def forward(self, input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None):
if attention_mask is None:
attention_mask = torch.ones_like(input_ids)
if token_type_ids is None:
Expand Down Expand Up @@ -449,14 +453,15 @@ def __init__(self, config):

self.init_weights()

def forward(self, input_ids, attention_mask=None, token_type_ids=None,
position_ids=None, head_mask=None, labels=None):
def forward(self, input_ids=None, attention_mask=None, token_type_ids=None,
position_ids=None, head_mask=None, inputs_embeds=None, labels=None):

outputs = self.transformer(input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask)
head_mask=head_mask,
inputs_embeds=inputs_embeds)

pooled_output = outputs[1]

Expand Down Expand Up @@ -520,14 +525,15 @@ def __init__(self, config):

self.init_weights()

def forward(self, input_ids, attention_mask=None, token_type_ids=None,
position_ids=None, head_mask=None, labels=None):
def forward(self, input_ids=None, attention_mask=None, token_type_ids=None,
position_ids=None, head_mask=None, inputs_embeds=None, labels=None):

outputs = self.transformer(input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask)
head_mask=head_mask,
inputs_embeds=inputs_embeds)

sequence_output = outputs[0]

Expand Down Expand Up @@ -603,14 +609,15 @@ def __init__(self, config):

self.init_weights()

def forward(self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None,
def forward(self, input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None,
start_positions=None, end_positions=None):

outputs = self.transformer(input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask)
head_mask=head_mask,
inputs_embeds=inputs_embeds)

sequence_output = outputs[0]

Expand Down
91 changes: 60 additions & 31 deletions transformers/modeling_bert.py
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Original file line number Diff line number Diff line change
Expand Up @@ -158,19 +158,26 @@ def __init__(self, config):
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, position_ids=None):
seq_length = input_ids.size(1)
def forward(self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None):
if input_ids is not None:
input_shape = input_ids.size()
else:
input_shape = inputs_embeds.size()[:-1]

seq_length = input_shape[1]
device = input_ids.device if input_ids is not None else inputs_embeds.device
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)
position_ids = torch.arange(seq_length, dtype=torch.long, device=device)
position_ids = position_ids.unsqueeze(0).expand(input_shape)
if token_type_ids is None:
token_type_ids = torch.zeros_like(input_ids)
token_type_ids = torch.zeros(input_shape, dtype=torch.long)

words_embeddings = self.word_embeddings(input_ids)
if inputs_embeds is None:
inputs_embeds = self.word_embeddings(input_ids)
position_embeddings = self.position_embeddings(position_ids)
token_type_embeddings = self.token_type_embeddings(token_type_ids)

embeddings = words_embeddings + position_embeddings + token_type_embeddings
embeddings = inputs_embeds + position_embeddings + token_type_embeddings
embeddings = self.LayerNorm(embeddings)
embeddings = self.dropout(embeddings)
return embeddings
Expand Down Expand Up @@ -550,6 +557,10 @@ def _init_weights(self, module):
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**.
**inputs_embeds**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, embedding_dim)``:
Optionally, instead of passing ``input_ids`` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert `input_ids` indices into associated vectors
than the model's internal embedding lookup matrix.
**encoder_hidden_states**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, hidden_size)``:
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if the model
is configured as a decoder.
Expand Down Expand Up @@ -615,8 +626,8 @@ def _prune_heads(self, heads_to_prune):
for layer, heads in heads_to_prune.items():
self.encoder.layer[layer].attention.prune_heads(heads)

def forward(self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None,
head_mask=None, encoder_hidden_states=None, encoder_attention_mask=None):
def forward(self, input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None,
head_mask=None, inputs_embeds=None, encoder_hidden_states=None, encoder_attention_mask=None):
""" Forward pass on the Model.

The model can behave as an encoder (with only self-attention) as well
Expand All @@ -632,12 +643,23 @@ def forward(self, input_ids, attention_mask=None, token_type_ids=None, position_
https://arxiv.org/abs/1706.03762

"""
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
input_shape = input_ids.size()
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")

device = input_ids.device if input_ids is not None else inputs_embeds.device

if attention_mask is None:
attention_mask = torch.ones_like(input_ids)
attention_mask = torch.ones(input_shape)
if encoder_attention_mask is None:
encoder_attention_mask = torch.ones_like(input_ids)
encoder_attention_mask = torch.ones(input_shape)
if token_type_ids is None:
token_type_ids = torch.zeros_like(input_ids)
token_type_ids = torch.zeros(input_shape, dtype=torch.long)

# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
# ourselves in which case we just need to make it broadcastable to all heads.
Expand All @@ -649,8 +671,8 @@ def forward(self, input_ids, attention_mask=None, token_type_ids=None, position_
# - if the model is an encoder, make the mask broadcastable to [batch_size, num_heads, seq_length, seq_length]
if attention_mask.dim() == 2:
if self.config.is_decoder:
batch_size, seq_length = input_ids.size()
seq_ids = torch.arange(seq_length, device=input_ids.device)
batch_size, seq_length = input_shape
seq_ids = torch.arange(seq_length, device=device)
causal_mask = seq_ids[None, None, :].repeat(batch_size, seq_length, 1) <= seq_ids[None, :, None]
extended_attention_mask = causal_mask[:, None, :, :] * attention_mask[:, None, None, :]
else:
Expand Down Expand Up @@ -689,7 +711,7 @@ def forward(self, input_ids, attention_mask=None, token_type_ids=None, position_
else:
head_mask = [None] * self.config.num_hidden_layers

embedding_output = self.embeddings(input_ids, position_ids=position_ids, token_type_ids=token_type_ids)
embedding_output = self.embeddings(input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds)
encoder_outputs = self.encoder(embedding_output,
attention_mask=extended_attention_mask,
head_mask=head_mask,
Expand Down Expand Up @@ -754,14 +776,15 @@ def __init__(self, config):
def get_output_embeddings(self):
return self.cls.predictions.decoder

def forward(self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None,
def forward(self, input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=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)
head_mask=head_mask,
inputs_embeds=inputs_embeds)

sequence_output, pooled_output = outputs[:2]
prediction_scores, seq_relationship_score = self.cls(sequence_output, pooled_output)
Expand Down Expand Up @@ -829,14 +852,15 @@ def __init__(self, config):
def get_output_embeddings(self):
return self.cls.predictions.decoder

def forward(self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None,
def forward(self, input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None,
masked_lm_labels=None, encoder_hidden_states=None, encoder_attention_mask=None, 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,
inputs_embeds=inputs_embeds,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask)

Expand Down Expand Up @@ -908,14 +932,15 @@ def __init__(self, config):

self.init_weights()

def forward(self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None,
def forward(self, input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=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)
head_mask=head_mask,
inputs_embeds=inputs_embeds)

pooled_output = outputs[1]

Expand Down Expand Up @@ -975,14 +1000,15 @@ def __init__(self, config):

self.init_weights()

def forward(self, input_ids, attention_mask=None, token_type_ids=None,
position_ids=None, head_mask=None, labels=None):
def forward(self, input_ids=None, attention_mask=None, token_type_ids=None,
position_ids=None, head_mask=None, inputs_embeds=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)
head_mask=head_mask,
inputs_embeds=inputs_embeds)

pooled_output = outputs[1]

Expand Down Expand Up @@ -1049,8 +1075,8 @@ def __init__(self, config):

self.init_weights()

def forward(self, input_ids, attention_mask=None, token_type_ids=None,
position_ids=None, head_mask=None, labels=None):
def forward(self, input_ids=None, attention_mask=None, token_type_ids=None,
position_ids=None, head_mask=None, inputs_embeds=None, labels=None):
num_choices = input_ids.shape[1]

input_ids = input_ids.view(-1, input_ids.size(-1))
Expand All @@ -1062,7 +1088,8 @@ def forward(self, input_ids, attention_mask=None, token_type_ids=None,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask)
head_mask=head_mask,
inputs_embeds=inputs_embeds)

pooled_output = outputs[1]

Expand Down Expand Up @@ -1123,14 +1150,15 @@ def __init__(self, config):

self.init_weights()

def forward(self, input_ids, attention_mask=None, token_type_ids=None,
position_ids=None, head_mask=None, labels=None):
def forward(self, input_ids=None, attention_mask=None, token_type_ids=None,
position_ids=None, head_mask=None, inputs_embeds=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)
head_mask=head_mask,
inputs_embeds=inputs_embeds)

sequence_output = outputs[0]

Expand Down Expand Up @@ -1207,14 +1235,15 @@ def __init__(self, config):

self.init_weights()

def forward(self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None,
def forward(self, input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=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)
head_mask=head_mask,
inputs_embeds=inputs_embeds)

sequence_output = outputs[0]

Expand Down
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