Supported precomputed feature for Kimi VL (#6599)

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

@@ -5,7 +5,7 @@ import multiprocessing as mp
 import os
 import re
 from abc import ABC, abstractmethod
-from typing import List, Optional, Tuple, Union
+from typing import Any, List, Optional, Tuple, Union
 
 import numpy as np
 import torch
@@ -382,3 +382,17 @@ class BaseMultimodalProcessor(ABC):
                 "Unsupported: mixture of multimodal inputs where some but not all are preprocessed."
             )
         return ret
+
+    @staticmethod
+    def _extract_processor_features(
+        items: List[Any], attr_name: str
+    ) -> Optional[torch.Tensor]:
+        """
+        Helper function to concat extracted attributes from processor output.
+        """
+        values = [
+            getattr(item, attr_name)
+            for item in items
+            if getattr(item, attr_name) is not None
+        ]
+        return torch.concat(values) if values else None

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

-from typing import List, Union
+import re
+from typing import Any, Dict, List, Optional, Union
+
+import torch
 
 from sglang.srt.managers.multimodal_processors.base_processor import (
     BaseMultimodalProcessor as SGLangBaseProcessor,
@@ -17,20 +20,12 @@ class KimiVLImageProcessor(SGLangBaseProcessor):
     def __init__(self, hf_config, server_args, _processor):
         super().__init__(hf_config, server_args, _processor)
         self.IMAGE_TOKEN = "<|media_pad|>"
+        self.IMAGE_TOKEN_REGEX = re.compile(r"(?:<\|media_pad\|>)+")
         self.im_token_id = _processor.tokenizer.convert_tokens_to_ids(self.IMAGE_TOKEN)
 
-        self.im_start = "<|media_start|>"
-        self.im_start_id = _processor.tokenizer.convert_tokens_to_ids(self.im_start)
-
-        self.im_end = "<|media_end|>"
-        self.im_end_id = _processor.tokenizer.convert_tokens_to_ids(self.im_end)
-
-        self.im_content = "<|media_content|>"
-        self.im_content_id = _processor.tokenizer.convert_tokens_to_ids(self.im_content)
-
     async def process_mm_data_async(
         self,
-        image_data: List[Union[str, bytes]],
+        image_data: List[Union[str, bytes, Dict]],
         input_text,
         request_obj,
         max_req_input_len,
@@ -45,30 +40,54 @@ class KimiVLImageProcessor(SGLangBaseProcessor):
         base_output = self.load_mm_data(
             prompt=input_text,
             image_data=image_data,
-            multimodal_tokens=MultimodalSpecialTokens(image_token=self.IMAGE_TOKEN),
+            multimodal_tokens=MultimodalSpecialTokens(
+                image_token=self.IMAGE_TOKEN, image_token_regex=self.IMAGE_TOKEN_REGEX
+            ),
             max_req_input_len=max_req_input_len,
         )
-        ret = self.process_mm_data(
-            input_text=base_output.input_text,
-            images=base_output.images,
-        )
-        input_ids = ret["input_ids"].flatten()
+
+        images_are_preprocessed = self.mm_inputs_are_preprocessed(base_output.images)
+        if not images_are_preprocessed:
+            ret = self.process_mm_data(
+                input_text=base_output.input_text,
+                images=base_output.images,
+            )
+            input_ids = ret["input_ids"].flatten()
+            image_grid_thws = ret["image_grid_hws"]
+            pixel_values = ret["pixel_values"]
+            precomputed_features = None
+        else:
+            input_ids = self._processor.tokenizer(
+                base_output.input_text,
+                return_tensors="pt",
+                add_special_tokens=True,
+            ).input_ids.flatten()
+
+            image_grid_thws = self._extract_processor_features(
+                base_output.images, "image_grid_thws"
+            )
+            precomputed_features = self._extract_processor_features(
+                base_output.images, "precomputed_features"
+            )
+            pixel_values = self._extract_processor_features(
+                base_output.images, "pixel_values"
+            )
+
         image_offsets = self.get_mm_items_offset(
             input_ids=input_ids,
             mm_token_id=self.im_token_id,
         )
+
         return {
             "input_ids": input_ids.tolist(),
             "mm_items": [
                 MultimodalDataItem(
-                    pixel_values=ret["pixel_values"],
-                    image_grid_thws=ret["image_grid_hws"],
+                    pixel_values=pixel_values,
+                    image_grid_thws=image_grid_thws,
+                    precomputed_features=precomputed_features,
                     modality=Modality.IMAGE,
                     image_offsets=image_offsets,
                 )
             ],
             "im_token_id": self.im_token_id,
-            "im_start_id": self.im_start_id,
-            "im_end_id": self.im_end_id,
-            "im_content_id": self.im_content_id,
         }

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

@@ -42,7 +42,8 @@ class MiniCPMMultimodalProcessor(BaseMultimodalProcessor):
             audio_data=audio_data,
             image_data=image_data,
             multimodal_tokens=MultimodalSpecialTokens(
-                image_token=self.image_token, audio_token=self.audio_token
+                image_token=self.image_token,
+                audio_token=self.audio_token,
             ),
         )
         if base_output is None:

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

@@ -144,31 +144,14 @@ class Qwen2_5VLImageProcessor(SGLangBaseProcessor):
 
         if base_output.images:
             if images_are_preprocessed:
-                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
-                    if item.pixel_values is not None
-                ]
-                all_precomputed_features = [
-                    item.precomputed_features
-                    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
+                image_grid_thw = self._extract_processor_features(
+                    base_output.images, "image_grid_thws"
                 )
-                pixel_values = (
-                    torch.concat(all_pixel_values) if all_pixel_values else None
+                precomputed_features = self._extract_processor_features(
+                    base_output.images, "precomputed_features"
                 )
-                precomputed_features = (
-                    torch.concat(all_precomputed_features)
-                    if all_precomputed_features
-                    else None
+                pixel_values = self._extract_processor_features(
+                    base_output.images, "pixel_values"
                 )
             else:
                 image_grid_thw = ret["image_grid_thw"]

test/srt/test_vlm_input_format.py

@@ -7,6 +7,7 @@ import requests
 import torch
 from PIL import Image
 from transformers import (
+    AutoModel,
     AutoProcessor,
     Gemma3ForConditionalGeneration,
     Qwen2_5_VLForConditionalGeneration,
@@ -51,6 +52,7 @@ class VLMInputTestBase:
             mem_fraction_static=0.8,
             enable_multimodal=True,
             disable_cuda_graph=True,
+            trust_remote_code=True,
         )
 
     def tearDown(self):
@@ -183,5 +185,32 @@ class TestGemmaUnderstandsImage(VLMInputTestBase, unittest.IsolatedAsyncioTestCa
         )
 
 
+class TestKimiVLImageUnderstandsImage(
+    VLMInputTestBase, unittest.IsolatedAsyncioTestCase
+):
+    model_path = "moonshotai/Kimi-VL-A3B-Instruct"
+    chat_template = "kimi-vl"
+
+    @classmethod
+    def _init_visual(cls):
+        model = AutoModel.from_pretrained(cls.model_path, trust_remote_code=True)
+        cls.vision_tower = model.vision_tower.eval().to(cls.device)
+        cls.mm_projector = model.multi_modal_projector.eval().to(cls.device)
+
+        cls.visual = lambda tokenizer_output: cls.mm_projector(
+            cls.vision_tower(
+                pixel_values=tokenizer_output["pixel_values"],
+                grid_hws=tokenizer_output["image_grid_hws"],
+            )
+        )
+
+    def _pixel_values_image_data(self, processor_output):
+        return dict(
+            modality="IMAGE",
+            image_grid_thws=processor_output["image_grid_hws"],
+            pixel_values=processor_output["pixel_values"],
+        )
+
+
 if __name__ == "__main__":
     unittest.main()