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[inputs_embeds] All PyTorch models

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This commit is contained in:
Julien Chaumond
2019-11-05 00:39:18 +00:00
parent 9eddf44b7a
commit 00337e9687
21 changed files with 361 additions and 147 deletions
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33
transformers/modeling_ctrl.py
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@@ -236,6 +236,10 @@ CTRL_INPUTS_DOCSTRING = r""" Inputs:
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 CTRL Model transformer outputting raw hidden-states without any specific head on top.",
@@ -302,17 +306,26 @@ class CTRLModel(CTRLPreTrainedModel):
for layer, heads in heads_to_prune.items():
self.h[layer].attn.prune_heads(heads)
def forward(self, input_ids, past=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None):
input_shape = input_ids.size()
input_ids = input_ids.view(-1, input_shape[-1])
def forward(self, input_ids=None, past=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None):
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()
input_ids = input_ids.view(-1, input_shape[-1])
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")
if past is None:
past_length = 0
past = [None] * len(self.h)
else:
past_length = past[0][0].size(-2)
if position_ids is None:
position_ids = torch.arange(past_length, input_ids.size(-1) + past_length, dtype=torch.long, device=input_ids.device)
position_ids = position_ids.unsqueeze(0).expand_as(input_ids)
device = input_ids.device if input_ids is not None else inputs_embeds.device
position_ids = torch.arange(past_length, input_shape[-1] + past_length, dtype=torch.long, device=device)
position_ids = position_ids.unsqueeze(0).view(-1, input_shape[-1])
# Attention mask.
if attention_mask is not None:
@@ -354,9 +367,10 @@ class CTRLModel(CTRLPreTrainedModel):
token_type_embeds = 0
position_ids = position_ids.view(-1, input_shape[-1])
inputs_embeds = self.w(input_ids)
if inputs_embeds is None:
inputs_embeds = self.w(input_ids)
# inputs_embeds = embedded.unsqueeze(0) if len(input_ids.shape)<2 else embedded
seq_len = input_ids.shape[-1]
seq_len = input_shape[-1]
mask = torch.triu(torch.ones(seq_len, seq_len), 1).to(inputs_embeds.device)
inputs_embeds *= np.sqrt(self.d_model_size)
@@ -455,14 +469,15 @@ class CTRLLMHeadModel(CTRLPreTrainedModel):
def get_output_embeddings(self):
return self.lm_head
def forward(self, input_ids, past=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None,
def forward(self, input_ids=None, past=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None,
labels=None):
transformer_outputs = self.transformer(input_ids,
past=past,
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)
hidden_states = transformer_outputs[0]