Fix llama4 vision (#7840)

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

python/sglang/srt/conversation.py

@@ -935,6 +935,19 @@ register_conv_template(
     )
 )
 
+register_conv_template(
+    Conversation(
+        name="llama_4_vision",
+        system_message="You are a helpful language and vision assistant. You are able to understand the visual content that the user provides, and assist the user with a variety of tasks using natural language.",
+        system_template="<|header_start|>system<|header_end|>\n\n{system_message}<|eot|>",
+        roles=("user", "assistant"),
+        sep_style=SeparatorStyle.LLAMA4,
+        sep="",
+        stop_str="<|eot|>",
+        image_token="<|image|>",
+    )
+)
+
 
 @register_conv_template_matching_function
 def match_internvl(model_path: str):
@@ -943,9 +956,11 @@ def match_internvl(model_path: str):
 
 
 @register_conv_template_matching_function
-def match_llama_3_vision(model_path: str):
+def match_llama_vision(model_path: str):
     if re.search(r"llama.*3\.2.*vision", model_path, re.IGNORECASE):
         return "llama_3_vision"
+    if re.search(r"llama.*4.*", model_path, re.IGNORECASE):
+        return "llama_4_vision"
 
 
 @register_conv_template_matching_function

python/sglang/srt/managers/mm_utils.py

@@ -248,7 +248,9 @@ def _get_chunked_prefill_embedding(
 ) -> 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):
+    # FIXME(Xinyuan): temporary workaround for eagle3, which may have len(items_size) > len(prefix_length)
+    max_iterations = min(len(items_size) - 1, len(prefix_length))
+    for i in range(max_iterations):
         if items_size[i] == items_size[i + 1]:
             continue
         embedding_items_per_req = embedding_items[items_size[i] : items_size[i + 1]]
@@ -269,7 +271,7 @@ def _get_chunked_prefill_embedding(
         embedding_per_req_chunk, _, end_index = get_embedding_chunk(
             embedding=embedding_per_req,
             extend_prefix_len=prefix_length[i],
-            extend_seq_len=extend_length[i],
+            extend_seq_len=extend_length[i] if i < len(extend_length) else 0,
             items_offset=items_offset,
         )
         # remove this item from cache if chunk reaches to the end

python/sglang/srt/multimodal/processors/mllama4.py

@@ -60,70 +60,72 @@ class Mllama4ImageProcessor(BaseMultimodalProcessor):
         )
 
         # Handle image resolutions and aspect ratios
-        if "pixel_values" in processor_output:
-            image_processor = processor.image_processor
-            tokenizer = self._processor.tokenizer
+        if "pixel_values" not in processor_output:  # no image processed
+            return None
 
-            # Calculate tile size and find supported resolutions
-            tile_size = self.vision_config.image_size
-            max_num_tiles = getattr(self.vision_config, "max_patches", 1)
+        image_processor = processor.image_processor
+        tokenizer = self._processor.tokenizer
 
-            possible_resolutions = find_supported_resolutions(
-                max_num_chunks=max_num_tiles,
-                patch_size=SizeDict(height=tile_size, width=tile_size),
+        # Calculate tile size and find supported resolutions
+        tile_size = self.vision_config.image_size
+        max_num_tiles = getattr(self.vision_config, "max_patches", 1)
+
+        possible_resolutions = find_supported_resolutions(
+            max_num_chunks=max_num_tiles,
+            patch_size=SizeDict(height=tile_size, width=tile_size),
+        )
+
+        # Find best fit for each image
+        best_fit_sizes = [
+            get_best_fit(
+                (image.size[1], image.size[0]),  # (height, width)
+                torch.tensor(possible_resolutions),
+                resize_to_max_canvas=image_processor.resize_to_max_canvas,
             )
+            for image in processed_data.images
+        ]
+
+        # Calculate aspect ratios and patches per image
+        aspect_ratios = [
+            (image_size[0] // tile_size, image_size[1] // tile_size)
+            for image_size in best_fit_sizes
+        ]
+
+        patches_per_image = [
+            1 if r_h * r_w == 1 else 1 + r_h * r_w for (r_h, r_w) in aspect_ratios
+        ]
+
+        # Add to image_inputs
+        processor_output["aspect_ratios"] = aspect_ratios
+        processor_output["patches_per_image"] = torch.tensor(patches_per_image)
+
+        # Process embed_is_patch
+        vocab = tokenizer.get_vocab()
+        patch_id = vocab.get(processor.img_patch_token, -1)
+        image_end_id = vocab.get(processor.end_of_img_token, -1)
+
+        if patch_id != -1 and image_end_id != -1:
+            input_ids = processor_output["input_ids"].view(-1)
+
+            # Remove BOS token if present
+            if input_ids.size(0) > 0 and input_ids[0] == tokenizer.bos_token_id:
+                input_ids = input_ids[1:]
+
+            # Find image end indices and split input_ids
+            image_end_indices = (input_ids == image_end_id).nonzero().view(-1)
+
+            if image_end_indices.size(0) > 0:
+                # Split at image boundaries
+                split_indices = (image_end_indices + 1)[:-1]
+                split_input_ids = torch.tensor_split(input_ids, split_indices)
+                split_input_ids = [x for x in split_input_ids if x.numel() > 0]
+
+                # Create embed_is_patch for each image
+                embed_is_patch = []
+                for per_image_input_ids in split_input_ids:
+                    embed_is_patch.append(per_image_input_ids == patch_id)
 
-            # Find best fit for each image
-            best_fit_sizes = [
-                get_best_fit(
-                    (image.size[1], image.size[0]),  # (height, width)
-                    torch.tensor(possible_resolutions),
-                    resize_to_max_canvas=image_processor.resize_to_max_canvas,
-                )
-                for image in processed_data.images
-            ]
-
-            # Calculate aspect ratios and patches per image
-            aspect_ratios = [
-                (image_size[0] // tile_size, image_size[1] // tile_size)
-                for image_size in best_fit_sizes
-            ]
-
-            patches_per_image = [
-                1 if r_h * r_w == 1 else 1 + r_h * r_w for (r_h, r_w) in aspect_ratios
-            ]
-
-            # Add to image_inputs
-            processor_output["aspect_ratios"] = aspect_ratios
-            processor_output["patches_per_image"] = torch.tensor(patches_per_image)
-
-            # Process embed_is_patch
-            vocab = tokenizer.get_vocab()
-            patch_id = vocab.get(processor.img_patch_token, -1)
-            image_end_id = vocab.get(processor.end_of_img_token, -1)
-
-            if patch_id != -1 and image_end_id != -1:
-                input_ids = processor_output["input_ids"].view(-1)
-
-                # Remove BOS token if present
-                if input_ids.size(0) > 0 and input_ids[0] == tokenizer.bos_token_id:
-                    input_ids = input_ids[1:]
-
-                # Find image end indices and split input_ids
-                image_end_indices = (input_ids == image_end_id).nonzero().view(-1)
-
-                if image_end_indices.size(0) > 0:
-                    # Split at image boundaries
-                    split_indices = (image_end_indices + 1)[:-1]
-                    split_input_ids = torch.tensor_split(input_ids, split_indices)
-                    split_input_ids = [x for x in split_input_ids if x.numel() > 0]
-
-                    # Create embed_is_patch for each image
-                    embed_is_patch = []
-                    for per_image_input_ids in split_input_ids:
-                        embed_is_patch.append(per_image_input_ids == patch_id)
-
-                    processor_output["embed_is_patch"] = embed_is_patch
+                processor_output["embed_is_patch"] = embed_is_patch
 
         # Convert to the format expected by SGLang
         processor_output["input_ids"] = processor_output["input_ids"].tolist()[0]