Different behavior in DistilBERT when using "inputs_embeds" (#21752)

* Different behavior in DistilBERT when using "inputs_embeds"
Fixes #21089

* fix failing test

src/transformers/models/distilbert/modeling_distilbert.py

@@ -105,15 +105,22 @@ def __init__(self, config: PretrainedConfig):
             "position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False
         )
 
-    def forward(self, input_ids: torch.Tensor) -> torch.Tensor:
+    def forward(self, input_ids: torch.Tensor, input_embeds: Optional[torch.Tensor] = None) -> torch.Tensor:
         """
         Parameters:
-            input_ids: torch.tensor(bs, max_seq_length) The token ids to embed.
+            input_ids (torch.Tensor):
+                torch.tensor(bs, max_seq_length) The token ids to embed.
+            input_embeds (*optional*, torch.Tensor):
+                The pre-computed word embeddings. Can only be passed if the input ids are `None`.
+
 
         Returns: torch.tensor(bs, max_seq_length, dim) The embedded tokens (plus position embeddings, no token_type
         embeddings)
         """
-        seq_length = input_ids.size(1)
+        if input_ids is not None:
+            input_embeds = self.word_embeddings(input_ids)  # (bs, max_seq_length, dim)
+
+        seq_length = input_embeds.size(1)
 
         # Setting the position-ids to the registered buffer in constructor, it helps
         # when tracing the model without passing position-ids, solves
@@ -124,10 +131,9 @@ def forward(self, input_ids: torch.Tensor) -> torch.Tensor:
             position_ids = torch.arange(seq_length, dtype=torch.long, device=input_ids.device)  # (max_seq_length)
             position_ids = position_ids.unsqueeze(0).expand_as(input_ids)  # (bs, max_seq_length)
 
-        word_embeddings = self.word_embeddings(input_ids)  # (bs, max_seq_length, dim)
         position_embeddings = self.position_embeddings(position_ids)  # (bs, max_seq_length, dim)
 
-        embeddings = word_embeddings + position_embeddings  # (bs, max_seq_length, dim)
+        embeddings = input_embeds + position_embeddings  # (bs, max_seq_length, dim)
         embeddings = self.LayerNorm(embeddings)  # (bs, max_seq_length, dim)
         embeddings = self.dropout(embeddings)  # (bs, max_seq_length, dim)
         return embeddings
@@ -573,10 +579,10 @@ def forward(
         # Prepare head mask if needed
         head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
 
-        if inputs_embeds is None:
-            inputs_embeds = self.embeddings(input_ids)  # (bs, seq_length, dim)
+        embeddings = self.embeddings(input_ids, inputs_embeds)  # (bs, seq_length, dim)
+
         return self.transformer(
-            x=inputs_embeds,
+            x=embeddings,
             attn_mask=attention_mask,
             head_mask=head_mask,
             output_attentions=output_attentions,