Mamba & RecurrentGemma: enable strict signature (#31549)

* enable strict signature

* this should not have been deleted

* recurrent_gemma too

src/transformers/generation/utils.py

@@ -2692,13 +2692,12 @@ class GenerationMixin:
             # prepare model inputs
             model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs)
 
+            # prepare variable output controls (note: some models won't accept all output controls)
+            model_inputs.update({"output_attentions": output_attentions} if output_attentions else {})
+            model_inputs.update({"output_hidden_states": output_hidden_states} if output_hidden_states else {})
+
             # forward pass to get next token
-            outputs = self(
-                **model_inputs,
-                return_dict=True,
-                output_attentions=output_attentions,
-                output_hidden_states=output_hidden_states,
-            )
+            outputs = self(**model_inputs, return_dict=True)
 
             if synced_gpus and this_peer_finished:
                 continue  # don't waste resources running the code we don't need
@@ -2919,6 +2918,10 @@ class GenerationMixin:
         while self._has_unfinished_sequences(this_peer_finished, synced_gpus, device=input_ids.device):
             model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs)
 
+            # prepare variable output controls (note: some models won't accept all output controls)
+            model_inputs.update({"output_attentions": output_attentions} if output_attentions else {})
+            model_inputs.update({"output_hidden_states": output_hidden_states} if output_hidden_states else {})
+
             # if sequential is True, split the input to batches of batch_size and run sequentially
             if sequential:
                 if any(
@@ -2944,24 +2947,13 @@ class GenerationMixin:
                     model_inputs, split_size=batch_size, full_batch_size=batch_beam_size
                 )
                 outputs_per_sub_batch = [
-                    self(
-                        **inputs_per_sub_batch,
-                        return_dict=True,
-                        output_attentions=output_attentions,
-                        output_hidden_states=output_hidden_states,
-                    )
-                    for inputs_per_sub_batch in inputs_per_sub_batches
+                    self(**inputs_per_sub_batch, return_dict=True) for inputs_per_sub_batch in inputs_per_sub_batches
                 ]
 
                 outputs = stack_model_outputs(outputs_per_sub_batch)
 
             else:  # Unchanged original behavior
-                outputs = self(
-                    **model_inputs,
-                    return_dict=True,
-                    output_attentions=output_attentions,
-                    output_hidden_states=output_hidden_states,
-                )
+                outputs = self(**model_inputs, return_dict=True)
 
             if synced_gpus and this_peer_finished:
                 cur_len = cur_len + 1
@@ -3241,12 +3233,12 @@ class GenerationMixin:
 
             # do one decoder step on all beams of all sentences in batch
             model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs)
-            outputs = self(
-                **model_inputs,
-                return_dict=True,
-                output_attentions=output_attentions,
-                output_hidden_states=output_hidden_states,
-            )
+
+            # prepare variable output controls (note: some models won't accept all output controls)
+            model_inputs.update({"output_attentions": output_attentions} if output_attentions else {})
+            model_inputs.update({"output_hidden_states": output_hidden_states} if output_hidden_states else {})
+
+            outputs = self(**model_inputs, return_dict=True)
 
             if synced_gpus and this_peer_finished:
                 cur_len = cur_len + 1
@@ -3522,12 +3514,11 @@ class GenerationMixin:
         while self._has_unfinished_sequences(this_peer_finished, synced_gpus, device=input_ids.device):
             model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs)
 
-            outputs = self(
-                **model_inputs,
-                return_dict=True,
-                output_attentions=output_attentions,
-                output_hidden_states=output_hidden_states,
-            )
+            # prepare variable output controls (note: some models won't accept all output controls)
+            model_inputs.update({"output_attentions": output_attentions} if output_attentions else {})
+            model_inputs.update({"output_hidden_states": output_hidden_states} if output_hidden_states else {})
+
+            outputs = self(**model_inputs, return_dict=True)
 
             if synced_gpus and this_peer_finished:
                 cur_len = cur_len + 1
@@ -3793,11 +3784,11 @@ class GenerationMixin:
                 model_inputs["num_logits_to_keep"] = candidate_length + 1
 
             # 2.2. Run a forward pass on the candidate sequence
-            outputs = self(
-                **model_inputs,
-                output_attentions=output_attentions,
-                output_hidden_states=output_hidden_states,
-            )
+            # prepare variable output controls (note: some models won't accept all output controls)
+            model_inputs.update({"output_attentions": output_attentions} if output_attentions else {})
+            model_inputs.update({"output_hidden_states": output_hidden_states} if output_hidden_states else {})
+
+            outputs = self(**model_inputs)
 
             # 2.3. Process the new logits
             new_logits = outputs.logits[:, -candidate_length - 1 :]  # excludes the input prompt if present

src/transformers/models/mamba/modeling_mamba.py

@@ -545,7 +545,6 @@ class MambaModel(MambaPreTrainedModel):
         use_cache: Optional[bool] = None,
         output_hidden_states: Optional[bool] = None,
         return_dict: Optional[bool] = None,
-        **kwargs,  # `attention_mask` is passed by the tokenizer and we don't want it
     ) -> Union[Tuple, MambaOutput]:
         output_hidden_states = (
             output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
@@ -673,7 +672,6 @@ class MambaForCausalLM(MambaPreTrainedModel):
         output_hidden_states: Optional[bool] = None,
         return_dict: Optional[bool] = None,
         use_cache: Optional[bool] = None,
-        **kwargs,  # for now we need this for generation
     ) -> Union[Tuple, MambaCausalLMOutput]:
         r"""
         labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):

src/transformers/models/recurrent_gemma/modeling_recurrent_gemma.py

@@ -684,7 +684,6 @@ class RecurrentGemmaModel(RecurrentGemmaPreTrainedModel):
         use_cache: Optional[bool] = None,
         output_hidden_states: Optional[bool] = None,
         return_dict: Optional[bool] = None,
-        **kwargs,
     ) -> Union[Tuple, BaseModelOutputWithNoAttention]:
         output_hidden_states = (
             output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
@@ -823,7 +822,6 @@ class RecurrentGemmaForCausalLM(RecurrentGemmaPreTrainedModel):
         output_hidden_states: Optional[bool] = None,
         return_dict: Optional[bool] = None,
         use_cache: Optional[bool] = None,
-        **kwargs,  # for now we need this for generation
     ) -> Union[Tuple, CausalLMOutput]:
         r"""
         Args: