Make `RwkvModel` accept `attention_mask` but discard it internally (#23442)

* fix

---------
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

src/transformers/models/rwkv/modeling_rwkv.py

@@ -565,6 +565,15 @@ RWKV_INPUTS_DOCSTRING = r"""
             [`PreTrainedTokenizer.__call__`] for details.
 
             [What are input IDs?](../glossary#input-ids)
+        attention_mask (`torch.LongTensor` of shape `(batch_size, input_ids_length)`, *optional*):
+            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+
+            This is currently not used by `RwkvModel`, but will be supported in the future.
+
+            [What are attention masks?](../glossary#attention-mask)
         inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
             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
@@ -617,6 +626,7 @@ class RwkvModel(RwkvPreTrainedModel):
     def forward(
         self,
         input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.LongTensor] = None,  # noqa
         inputs_embeds: Optional[torch.FloatTensor] = None,
         state: Optional[List[torch.FloatTensor]] = None,
         use_cache: Optional[bool] = None,
@@ -750,7 +760,7 @@ class RwkvForCausalLM(RwkvPreTrainedModel):
     def forward(
         self,
         input_ids: Optional[torch.LongTensor] = None,
-        attention_mask: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.LongTensor] = None,  # noqa
         inputs_embeds: Optional[torch.FloatTensor] = None,
         state: Optional[List[torch.FloatTensor]] = None,
         labels: Optional[torch.LongTensor] = None,