Tests: move generate tests to the right mixin and delete redundant tests

src/transformers/generation/utils.py

@@ -378,10 +378,14 @@ def prepare_inputs_for_generation(
         # If we have cache: let's slice `input_ids` through `cache_position`, to keep only the unprocessed tokens
         # Exception 1: when passing input_embeds, input_ids may be missing entries
         # Exception 2: some generation methods do special slicing of input_ids, so we don't need to do it here
-        # Exception 3: with synced GPUs cache_position may go out of bounds, but we only want dummy token in that case
+        # Exception 3: with synced GPUs cache_position may go out of bounds, but we only want dummy token in that case.
+        #              (we can't check exception 3 while compiling)
         if past_key_values is not None:
             model_inputs["past_key_values"] = past_key_values
-            if inputs_embeds is not None or cache_position[-1] >= input_ids.shape[1]:  # Exception 1 or Exception 3
+            if (
+                inputs_embeds is not None  # Exception 1
+                or (is_torchdynamo_compiling() or cache_position[-1] >= input_ids.shape[1])  # Exception 3
+            ):
                 input_ids = input_ids[:, -cache_position.shape[0] :]
             elif input_ids.shape[1] != cache_position.shape[0]:  # Default case (the "else", a no op, is Exception 2)
                 input_ids = input_ids[:, cache_position]
@@ -414,7 +418,7 @@ def prepare_inputs_for_generation(
         for model_input_name in ["position_ids", "token_type_ids"]:
             model_input = kwargs.get(model_input_name)
             if model_input is not None:
-                if past_key_values:
+                if past_key_values is not None:
                     model_input = model_input[:, -input_ids.shape[1] :]
                     model_input = model_input.clone(memory_format=torch.contiguous_format)
                 model_inputs[model_input_name] = model_input
@@ -568,27 +572,34 @@ def _maybe_initialize_input_ids_for_generation(
 
     def _prepare_attention_mask_for_generation(
         self,
-        inputs: torch.Tensor,
-        pad_token_id: Optional[torch.Tensor],
-        eos_token_id: Optional[torch.Tensor],
+        inputs_tensor: torch.Tensor,
+        generation_config: GenerationConfig,
+        model_kwargs: Dict[str, Any],
     ) -> torch.LongTensor:
+        pad_token_id = generation_config._pad_token_tensor
+        eos_token_id = generation_config._eos_token_tensor
+
+        # `input_ids` may be present in the model kwargs, instead of being the main input (e.g. multimodal model)
+        if "input_ids" in model_kwargs and model_kwargs["input_ids"].shape[1] > 0:
+            inputs_tensor = model_kwargs["input_ids"]
+
         # No information for attention mask inference -> return default attention mask
-        default_attention_mask = torch.ones(inputs.shape[:2], dtype=torch.long, device=inputs.device)
+        default_attention_mask = torch.ones(inputs_tensor.shape[:2], dtype=torch.long, device=inputs_tensor.device)
         if pad_token_id is None:
             return default_attention_mask
 
-        is_input_ids = len(inputs.shape) == 2 and inputs.dtype in [torch.int, torch.long]
+        is_input_ids = len(inputs_tensor.shape) == 2 and inputs_tensor.dtype in [torch.int, torch.long]
         if not is_input_ids:
             return default_attention_mask
 
         is_pad_token_in_inputs = (pad_token_id is not None) and (
-            isin_mps_friendly(elements=inputs, test_elements=pad_token_id).any()
+            isin_mps_friendly(elements=inputs_tensor, test_elements=pad_token_id).any()
         )
         is_pad_token_not_equal_to_eos_token_id = (eos_token_id is None) or ~(
             isin_mps_friendly(elements=eos_token_id, test_elements=pad_token_id).any()
         )
         can_infer_attention_mask = is_pad_token_in_inputs * is_pad_token_not_equal_to_eos_token_id
-        attention_mask_from_padding = inputs.ne(pad_token_id).long()
+        attention_mask_from_padding = inputs_tensor.ne(pad_token_id).long()
 
         attention_mask = (
             attention_mask_from_padding * can_infer_attention_mask + default_attention_mask * ~can_infer_attention_mask
@@ -2020,7 +2031,7 @@ def generate(
 
         if not kwargs_has_attention_mask and requires_attention_mask and accepts_attention_mask:
             model_kwargs["attention_mask"] = self._prepare_attention_mask_for_generation(
-                inputs_tensor, generation_config._pad_token_tensor, generation_config._eos_token_tensor
+                inputs_tensor, generation_config, model_kwargs
             )
         elif kwargs_has_attention_mask:
             # TODO (joao): generalize this check with other types of inputs

src/transformers/models/llava_next_video/modeling_llava_next_video.py

@@ -911,7 +911,8 @@ def forward(
 
         if (pixel_values is not None or pixel_values_videos is not None) and inputs_embeds is not None:
             raise ValueError(
-                "You cannot specify both pixel_values and inputs_embeds at the same time, and must specify either one"
+                "You cannot specify both `pixel_values`/`pixel_values_videos` and `inputs_embeds` at the same time, "
+                "and must specify either one"
             )
 
         legacy_processing = False

src/transformers/models/llava_next_video/modular_llava_next_video.py

@@ -424,7 +424,8 @@ def forward(
 
         if (pixel_values is not None or pixel_values_videos is not None) and inputs_embeds is not None:
             raise ValueError(
-                "You cannot specify both pixel_values and inputs_embeds at the same time, and must specify either one"
+                "You cannot specify both `pixel_values`/`pixel_values_videos` and `inputs_embeds` at the same time, "
+                "and must specify either one"
             )
 
         legacy_processing = False

src/transformers/models/llava_onevision/modeling_llava_onevision.py

@@ -657,7 +657,8 @@ def forward(
 
         if (pixel_values is not None or pixel_values_videos is not None) and inputs_embeds is not None:
             raise ValueError(
-                "You cannot specify both pixel_values/pixel_values_videos and inputs_embeds at the same time, and must specify either one"
+                "You cannot specify both `pixel_values`/`pixel_values_videos` and `inputs_embeds` at the same time, "
+                "and must specify either one"
             )
 
         if inputs_embeds is None:

src/transformers/models/musicgen/modeling_musicgen.py

@@ -1562,7 +1562,7 @@ def generate(
 
         if model_kwargs.get("attention_mask", None) is None and requires_attention_mask:
             model_kwargs["attention_mask"] = self._prepare_attention_mask_for_generation(
-                input_ids, generation_config._pad_token_tensor, generation_config._eos_token_tensor
+                input_ids, generation_config, model_kwargs
             )
 
         # 5. Prepare `max_length` depending on other stopping criteria.
@@ -2578,7 +2578,7 @@ def generate(
 
         if model_kwargs.get("attention_mask", None) is None and requires_attention_mask:
             model_kwargs["attention_mask"] = self._prepare_attention_mask_for_generation(
-                inputs_tensor, generation_config._pad_token_tensor, generation_config._eos_token_tensor
+                inputs_tensor, generation_config, model_kwargs
             )
 
         if "encoder_outputs" not in model_kwargs:

src/transformers/models/musicgen_melody/modeling_musicgen_melody.py

@@ -1484,7 +1484,7 @@ def generate(
 
         if model_kwargs.get("attention_mask", None) is None and requires_attention_mask:
             model_kwargs["attention_mask"] = self._prepare_attention_mask_for_generation(
-                input_ids, generation_config._pad_token_tensor, generation_config._eos_token_tensor
+                input_ids, generation_config, model_kwargs
             )
 
         # 5. Prepare `max_length` depending on other stopping criteria.
@@ -2425,7 +2425,7 @@ def generate(
 
         if model_kwargs.get("attention_mask", None) is None and requires_attention_mask:
             model_kwargs["attention_mask"] = self._prepare_attention_mask_for_generation(
-                inputs_tensor, generation_config._pad_token_tensor, generation_config._eos_token_tensor
+                inputs_tensor, generation_config, model_kwargs
             )
 
         if "encoder_hidden_states" not in model_kwargs:

src/transformers/models/video_llava/modeling_video_llava.py

@@ -534,7 +534,8 @@ def forward(
 
         if (pixel_values_images is not None or pixel_values_videos is not None) and inputs_embeds is not None:
             raise ValueError(
-                "You cannot specify both pixel_values and inputs_embeds at the same time, and must specify either one"
+                "You cannot specify both `pixel_values_images`/`pixel_values_videos` and `inputs_embeds` at the same "
+                "time, and must specify either one"
             )
 
         legacy_processing = False