SUMIN/HuggingFace_transformer
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model forwards can take an inputs_embeds param

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Julien Chaumond
2019-11-01 16:28:32 +00:00
parent 68f7064a3e
commit 8e11de0e86
1 changed files with 20 additions and 10 deletions
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30
transformers/modeling_gpt2.py
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@@ -370,9 +370,15 @@ class GPT2Model(GPT2PreTrainedModel):
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:
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 token_type_ids is not None:
token_type_ids = token_type_ids.view(-1, input_shape[-1])
if position_ids is not None:
@@ -384,8 +390,9 @@ class GPT2Model(GPT2PreTrainedModel):
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:
@@ -419,7 +426,8 @@ class GPT2Model(GPT2PreTrainedModel):
else:
head_mask = [None] * self.config.n_layer
inputs_embeds = self.wte(input_ids)
if inputs_embeds is None:
inputs_embeds = self.wte(input_ids)
position_embeds = self.wpe(position_ids)
if token_type_ids is not None:
token_type_embeds = self.wte(token_type_ids)
@@ -520,14 +528,15 @@ class GPT2LMHeadModel(GPT2PreTrainedModel):
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]
lm_logits = self.lm_head(hidden_states)
@@ -623,14 +632,15 @@ class GPT2DoubleHeadsModel(GPT2PreTrainedModel):
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,
mc_token_ids=None, lm_labels=None, mc_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]