model forwards can take an inputs_embeds param

transformers/modeling_gpt2.py

@@ -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):
+    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:
@@ -419,6 +426,7 @@ class GPT2Model(GPT2PreTrainedModel):
         else:
             head_mask = [None] * self.config.n_layer
 
+        if inputs_embeds is None:
             inputs_embeds = self.wte(input_ids)
         position_embeds = self.wpe(position_ids)
         if token_type_ids is not None:
@@ -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]