[V1] Add V1 support of Qwen2-VL

docs/source/models/supported_models.md

@@ -754,7 +754,7 @@ See [this page](#generative-models) for more information on how to use generativ
   - `Qwen/QVQ-72B-Preview`, `Qwen/Qwen2-VL-7B-Instruct`, `Qwen/Qwen2-VL-72B-Instruct`, etc.
   - ✅︎
   - ✅︎
-  -
+  - ✅︎
 * - `UltravoxModel`
   - Ultravox
   - T + A<sup>E+</sup>

tests/models/decoder_only/vision_language/test_qwen2_vl.py

@@ -105,7 +105,7 @@ def batch_make_image_embeddings(
     pixel_values = preprocess_result["pixel_values"]
     image_grid_thw = preprocess_result["image_grid_thw"]
 
-    # pixel values to embeddinds & grid_thws
+    # pixel values to embeddings & grid_thws
     with torch.no_grad():
         visual = llm.llm_engine.model_executor.driver_worker. \
             model_runner.model.visual
@@ -124,11 +124,10 @@ def batch_make_image_embeddings(
     for image_batch in image_batches_:
         cur_batch_image_count = len(image_batch)
         merge_size = image_processor.merge_size
-        cur_batch_embed_len = sum([
-            grid_thw.prod() // merge_size // merge_size
+        cur_batch_embed_len = sum(
+            grid_thw.prod(-1) // merge_size // merge_size
             for grid_thw in image_grid_thw[image_counter:image_counter +
-                                           cur_batch_image_count]
-        ])
+                                           cur_batch_image_count])
 
         result.append({
             "image_embeds":
@@ -187,7 +186,7 @@ def batch_make_video_embeddings(
     pixel_values = preprocess_result["pixel_values_videos"]
     video_grid_thw = preprocess_result["video_grid_thw"]
 
-    # pixel values to embeddinds & grid_thws
+    # pixel values to embeddings & grid_thws
     with torch.no_grad():
         visual = llm.llm_engine.model_executor.driver_worker.\
             model_runner.model.visual
@@ -206,11 +205,10 @@ def batch_make_video_embeddings(
     for video_batch in video_batches_:
         cur_batch_video_count = len(video_batch)
         merge_size = image_processor.merge_size
-        cur_batch_embed_len = sum([
-            grid_thw.prod() // merge_size // merge_size
+        cur_batch_embed_len = sum(
+            grid_thw.prod(-1) // merge_size // merge_size
             for grid_thw in video_grid_thw[video_counter:video_counter +
-                                           cur_batch_video_count]
-        ])
+                                           cur_batch_video_count])
 
         result.append({
             "video_embeds":

vllm/compilation/decorators.py

@@ -76,8 +76,8 @@ def forward(self, x: torch.Tensor, y: Optional[torch.Tensor]):
     During runtime, when we actually mark dimensions of tensors,
      it depends on the value of arguments:
 
-    - if it is a single integer, the corresponding dimension of the argument
-        will be marked as dynamic.
+    - if it is a single integer (can be negative), the corresponding dimension 
+        of the argument will be marked as dynamic.
     - if it is `None`, ignored.
     - if it is `IntermediateTensors`, all the tensors in the intermediate
         tensors will be marked as dynamic.
@@ -177,10 +177,20 @@ def __call__(self, *args, **kwargs):
             for k, dims in dynamic_arg_dims.items():
                 arg = bound_args.arguments.get(k)
                 if arg is not None:
+                    dims = [dims] if isinstance(dims, int) else dims
                     if isinstance(arg, torch.Tensor):
+                        # In case dims is specified with negative indexing
+                        dims = [
+                            arg.ndim + dim if dim < 0 else dim for dim in dims
+                        ]
                         torch._dynamo.mark_dynamic(arg, dims)
                     elif isinstance(arg, IntermediateTensors):
                         for tensor in arg.tensors.values():
+                            # In case dims is specified with negative indexing
+                            dims = [
+                                tensor.ndim + dim if dim < 0 else dim
+                                for dim in dims
+                            ]
                             torch._dynamo.mark_dynamic(tensor, dims)
                     else:
                         raise ValueError(

vllm/model_executor/layers/rotary_embedding.py

@@ -841,6 +841,37 @@ def get_input_positions(
     ) -> Tuple[List[List[int]], int]:
         """Get mrope input positions and delta value."""
 
+        llm_positions, mrope_position_delta = \
+            MRotaryEmbedding.get_input_positions_tensor(
+                input_tokens,
+                image_grid_thw,
+                video_grid_thw,
+                image_token_id,
+                video_token_id,
+                vision_start_token_id,
+                vision_end_token_id,
+                spatial_merge_size,
+                context_len,
+                seq_len,
+            )
+
+        return llm_positions.tolist(), mrope_position_delta
+
+    @staticmethod
+    def get_input_positions_tensor(
+        input_tokens: List[int],
+        image_grid_thw: Union[List[List[int]], torch.Tensor],
+        video_grid_thw: Union[List[List[int]], torch.Tensor],
+        image_token_id: int,
+        video_token_id: int,
+        vision_start_token_id: int,
+        vision_end_token_id: int,
+        spatial_merge_size: int,
+        context_len: int = 0,
+        seq_len: Optional[int] = None,
+    ) -> Tuple[torch.Tensor, int]:
+        """Get mrope input positions and delta value."""
+
         if isinstance(image_grid_thw, torch.Tensor):
             image_grid_thw = image_grid_thw.tolist()
         if isinstance(video_grid_thw, torch.Tensor):
@@ -916,7 +947,7 @@ def get_input_positions(
                                 len(input_tokens)).item()
         llm_positions = llm_positions[:, context_len:seq_len]
 
-        return llm_positions.tolist(), mrope_position_delta
+        return llm_positions, mrope_position_delta
 
     @staticmethod
     def get_next_input_positions(
@@ -930,6 +961,17 @@ def get_next_input_positions(
                       seq_len + mrope_position_delta)) for _ in range(3)
         ]
 
+    @staticmethod
+    def get_next_input_positions_tensor(
+        mrope_position_delta: int,
+        context_len: int,
+        seq_len: int,
+    ) -> torch.Tensor:
+        return torch.arange(
+            mrope_position_delta + context_len,
+            mrope_position_delta + seq_len,
+        ).expand(3, -1)
+
 
 _ROPE_DICT: Dict[Tuple, RotaryEmbedding] = {}
 

vllm/model_executor/models/llava_onevision.py

@@ -554,10 +554,12 @@ def _parse_and_validate_multimodal_inputs(self, **kwargs: object) -> dict:
         # Preserve the order of modalities if there are multiple of them
         # from the order of kwargs.
         for input_key in kwargs:
-            if input_key == "pixel_values" and "images" not in modalities:
+            if input_key in ("pixel_values",
+                             "image_embeds") and "images" not in modalities:
                 modalities["images"] = self._parse_and_validate_image_input(
                     **kwargs)
-            if input_key == "pixel_values_videos" and "videos" not in modalities:  # noqa E501
+            if input_key in ("pixel_values_videos",
+                             "video_embeds") and "videos" not in modalities:
                 modalities["videos"] = self._parse_and_validate_video_input(
                     **kwargs)
 

vllm/model_executor/models/qwen2.py

@@ -256,7 +256,15 @@ def forward(
         return hidden_states, residual
 
 
-@support_torch_compile
+@support_torch_compile(
+    dynamic_arg_dims={
+        "input_ids": 0,
+        # positions is of shape (3, seq_len) if mrope is enabled for qwen2-vl,
+        # otherwise (seq_len, ).
+        "positions": -1,
+        "intermediate_tensors": 0,
+        "inputs_embeds": 0,
+    })
 class Qwen2Model(nn.Module):
 
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):