Refactor vlm embedding routine to use precomputed feature (#6543)

Signed-off-by: Xinyuan Tong <justinning0323@outlook.com>

python/sglang/srt/managers/mm_utils.py

@@ -252,40 +252,36 @@ def get_embedding_chunk(
     return embedding_chunk, start_index, end_index
 
 
-def get_embedding_and_mask(
+def _get_precomputed_embedding(
+    items: List[MultimodalDataItem],
+) -> Optional[torch.Tensor]:
+    """
+    If all items have precomputed_features, return their concatenation.
+    If some but not all have precomputed_features, raise NotImplementedError.
+    If none have precomputed_features, return None.
+    """
+    precomputed_features = [item.precomputed_features for item in items]
+    if any(feature is not None for feature in precomputed_features):
+        if not all(feature is not None for feature in precomputed_features):
+            raise NotImplementedError(
+                "MM inputs where only some items are precomputed."
+            )
+        result = torch.concat(precomputed_features)
+        # some models embedding is 3-dim, reshape it to 2-dim (similar to get_embedding_chunk)
+        result = result.reshape(-1, result.shape[-1])
+        return result
+    return None
+
+
+def _get_chunked_prefill_embedding(
     data_embedding_func: Callable[[List[MultimodalDataItem]], torch.Tensor],
     embedding_items: List[MultimodalDataItem],
-    placeholder_tensor: torch.Tensor,
-    input_ids: torch.Tensor,
     items_size: List[int],
     prefix_length: List[int],
     extend_length: List[int],
     items_offset_list: List[List[Tuple[int, int]]],
-) -> Tuple[torch.Tensor, torch.Tensor]:
-    """
-    Generate multimodal embeddings and create a mask for identifying their positions in the input sequence.
-
-    Args:
-        data_embedding_func: Function that generates embeddings for multimodal items
-        embedding_items: List of multimodal items to embed
-        placeholder_tensor: Tensor containing token IDs that serve as placeholders for multimodal content
-        input_ids: The input token IDs tensor
-        items_size: Cumulative sizes of multimodal items per request
-        prefix_length: Prefix lengths for each request
-        extend_length: Sequence lengths for each request
-        items_offset_list: List of offset ranges for multimodal items in each request
-
-    Returns:
-        A tuple containing:
-        - The generated embeddings tensor
-        - A boolean mask tensor indicating where these embeddings should be placed
-
-    Raises:
-        AssertionError: If the number of multimodal tokens in input_ids doesn't match
-                        the number of tokens in the generated embeddings
-    """
-    # 1. Get the embedding
-    #    Calculate embedding for each request, try to get it from cache to avoid repeated calculation
+) -> Optional[torch.Tensor]:
+    # Calculate embedding for each request, try to get it from cache to avoid repeated calculation
     embedding_list = []
     for i in range(len(items_size) - 1):
         if items_size[i] == items_size[i + 1]:
@@ -321,21 +317,28 @@ def get_embedding_and_mask(
             embedding_cache.free(embedding_items_hash)
         embedding_list.append(embedding_per_req_chunk)
     if len(embedding_list) == 0:
-        return None, None
-    embedding = torch.concat(embedding_list, dim=0)
-    # 2. Check the embedding
-    num_mm_tokens_in_embedding = embedding.shape[0]
-    special_multimodal_mask = torch.isin(
-        input_ids,
-        placeholder_tensor,
-    ).unsqueeze(-1)
+        return None
+    return torch.concat(embedding_list, dim=0)
 
-    num_mm_tokens_in_input_ids = special_multimodal_mask.sum().item()
+
+def _get_multimodal_mask(
+    input_ids: torch.Tensor, placeholder_tensor: torch.Tensor
+) -> torch.Tensor:
+    return torch.isin(input_ids, placeholder_tensor).unsqueeze(-1)
+
+
+def _adjust_embedding_length(
+    embedding: torch.Tensor,
+    mask: torch.Tensor,
+    logger,
+) -> torch.Tensor:
+    num_mm_tokens_in_embedding = embedding.shape[0]
+    num_mm_tokens_in_input_ids = mask.sum().item()
     if num_mm_tokens_in_input_ids != num_mm_tokens_in_embedding:
         logger.warning(
             f"Number of tokens in multimodal embedding does not match those in the input text. "
             f"Got {num_mm_tokens_in_input_ids} tokens in the text but {num_mm_tokens_in_embedding} "
-            "tokens from multimodal embeddings."
+            f"tokens from multimodal embeddings."
         )
         if num_mm_tokens_in_input_ids < num_mm_tokens_in_embedding:
             chunked_prefill_size = global_server_args_dict["chunked_prefill_size"]
@@ -353,7 +356,54 @@ def get_embedding_and_mask(
             raise RuntimeError(
                 f"Insufficient multimodal embedding length: {num_mm_tokens_in_input_ids=} vs {num_mm_tokens_in_embedding=}. This is an internal error"
             )
+    return embedding
 
+
+def get_embedding_and_mask(
+    data_embedding_func: Callable[[List[MultimodalDataItem]], torch.Tensor],
+    embedding_items: List[MultimodalDataItem],
+    placeholder_tensor: torch.Tensor,
+    input_ids: torch.Tensor,
+    items_size: List[int],
+    prefix_length: List[int],
+    extend_length: List[int],
+    items_offset_list: List[List[Tuple[int, int]]],
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    """
+    Generate multimodal embeddings and create a mask for identifying their positions in the input sequence.
+
+    Args:
+        data_embedding_func: Function that generates embeddings for multimodal items
+        embedding_items: List of multimodal items to embed
+        placeholder_tensor: Tensor containing token IDs that serve as placeholders for multimodal content
+        input_ids: The input token IDs tensor
+        items_size: Cumulative sizes of multimodal items per request
+        prefix_length: Prefix lengths for each request
+        extend_length: Sequence lengths for each request
+        items_offset_list: List of offset ranges for multimodal items in each request
+
+    Returns:
+        A tuple containing:
+        - The generated embeddings tensor
+        - A boolean mask tensor indicating where these embeddings should be placed
+    """
+    # 1. Get embedding
+    embedding = _get_precomputed_embedding(embedding_items)
+    if embedding is None:
+        embedding = _get_chunked_prefill_embedding(
+            data_embedding_func,
+            embedding_items,
+            items_size,
+            prefix_length,
+            extend_length,
+            items_offset_list,
+        )
+        if embedding is None:
+            return None, None
+    # 2. Get mask
+    special_multimodal_mask = _get_multimodal_mask(input_ids, placeholder_tensor)
+    # 3. Adjust embedding length if needed
+    embedding = _adjust_embedding_length(embedding, special_multimodal_mask, logger)
     return embedding, special_multimodal_mask
 
 

python/sglang/srt/managers/multimodal_processors/qwen_vl.py

@@ -144,12 +144,11 @@ class Qwen2_5VLImageProcessor(SGLangBaseProcessor):
 
         if base_output.images:
             if images_are_preprocessed:
-                image_grid_thw = torch.concat(
-                    [
-                        torch.as_tensor(item.image_grid_thws)
-                        for item in base_output.images
-                    ]
-                )
+                all_image_grid_thws = [
+                    item.image_grid_thws
+                    for item in base_output.images
+                    if item.image_grid_thws is not None
+                ]
                 all_pixel_values = [
                     item.pixel_values
                     for item in base_output.images
@@ -160,6 +159,9 @@ class Qwen2_5VLImageProcessor(SGLangBaseProcessor):
                     for item in base_output.images
                     if item.precomputed_features is not None
                 ]
+                image_grid_thw = (
+                    torch.concat(all_image_grid_thws) if all_image_grid_thws else None
+                )
                 pixel_values = (
                     torch.concat(all_pixel_values) if all_pixel_values else None
                 )

python/sglang/srt/models/gemma3_mm.py

@@ -282,13 +282,6 @@ class Gemma3ForConditionalGeneration(PreTrainedModel):
         Returns:
             image_features (`torch.Tensor`): Image feature tensor of shape `(num_images, image_length, embed_dim)`).
         """
-        if any(item.precomputed_features is not None for item in items):
-            if not all(item.precomputed_features is not None for item in items):
-                raise NotImplementedError(
-                    "MM inputs where only some items are precomputed."
-                )
-            return torch.concat([item.precomputed_features for item in items])
-
         # Process images one by one to handle flatten_batch=True constraint in vision_tower
         all_pixel_values = flatten_nested_list([item.pixel_values for item in items])
         vision_outputs_list = []

python/sglang/srt/models/qwen2_5_vl.py

@@ -499,12 +499,6 @@ class Qwen2_5_VLForConditionalGeneration(nn.Module):
         return pattern.pad_input_tokens(input_ids, mm_inputs)
 
     def get_image_feature(self, items: List[MultimodalDataItem]) -> torch.Tensor:
-        if any(item.precomputed_features is not None for item in items):
-            if not all(item.precomputed_features is not None for item in items):
-                raise NotImplementedError(
-                    "MM inputs where only some items are precomputed."
-                )
-            return torch.concat([item.precomputed_features for item in items])
         # in qwen-vl, last dim is the same
         pixel_values = torch.cat([item.pixel_values for item in items], dim=0).type(
             self.visual.dtype

python/sglang/srt/models/qwen2_vl.py

@@ -486,12 +486,6 @@ class Qwen2VLForConditionalGeneration(nn.Module):
         return pattern.pad_input_tokens(input_ids, mm_inputs)
 
     def get_image_feature(self, items: List[MultimodalDataItem]) -> torch.Tensor:
-        if any(item.precomputed_features is not None for item in items):
-            if not all(item.precomputed_features is not None for item in items):
-                raise NotImplementedError(
-                    "MM inputs where only some items are precomputed."
-                )
-            return torch.concat([item.precomputed_features for item in items])
         # in qwen-vl, last dim is the same
         pixel_values = torch.cat([item.pixel_values for item in items], dim=0).type(
             self.visual.dtype