fix: fix MMMU loading issue

python/sglang/bench_serving.py

@@ -764,6 +764,7 @@ def get_dataset(args, tokenizer, model_id=None):
             image_content=args.image_content,
             image_format=args.image_format,
             image_resolution=args.image_resolution,
+            backend=args.backend,
         )
     elif args.dataset_name == "generated-shared-prefix":
         assert not tokenize_prompt
@@ -781,6 +782,7 @@ def get_dataset(args, tokenizer, model_id=None):
         input_requests = sample_mmmu_requests(
             num_requests=args.num_prompts,
             processor=processor,
+            backend=args.backend,
             fixed_output_len=args.random_output_len,
             random_sample=True,
         )
@@ -1009,6 +1011,7 @@ async def get_mooncake_request_over_time(
 def sample_mmmu_requests(
     num_requests: int,
     processor: AutoProcessor | AutoTokenizer,
+    backend: str,
     fixed_output_len: Optional[int] = None,
     random_sample: bool = True,
 ) -> List[DatasetRow]:
@@ -1081,7 +1084,7 @@ def sample_mmmu_requests(
                 text_prompt = f"Question: {question}\n\nAnswer: "
                 output_len = fixed_output_len if fixed_output_len is not None else 256
                 data_row = create_mm_data_row(
-                    text_prompt, [image], [image_data], output_len, processor
+                    text_prompt, [image], [image_data], output_len, processor, backend
                 )
                 filtered_dataset.append(data_row)
 
@@ -1316,13 +1319,19 @@ def parse_image_resolution(image_resolution: str) -> Tuple[int, int]:
     )
 
 
-def create_mm_data_row(text_prompt, images: list, images_base64, output_len, processor):
+def create_mm_data_row(
+    text_prompt, images: list, images_base64, output_len, processor, backend
+):
     try:
-        content_items = [
-            {"type": "image", "image": {"url": image_base64}}
-            for image_base64 in images_base64
-        ]
-        content_items.append({"type": "text", "text": text_prompt})
+        if type(processor).__name__ == "Phi4MMProcessor":
+            # <|endoftext10|> is the image token used in the phi-4-multimodal model.
+            content_items = text_prompt.replace("image 1", "|endoftext10|")
+        else:
+            content_items = [
+                {"type": "image", "image": {"url": image_base64}}
+                for image_base64 in images_base64
+            ]
+            content_items.append({"type": "text", "text": text_prompt})
         prompt_str = processor.apply_chat_template(
             [{"role": "user", "content": content_items}],
             add_generation_prompt=True,
@@ -1362,8 +1371,16 @@ def create_mm_data_row(text_prompt, images: list, images_base64, output_len, pro
     # Vision tokens = total tokens - text tokens
     vision_prompt_len = prompt_len - text_prompt_len
 
+    use_raw_prompt = backend in [
+        "sglang-oai",
+        "sglang-oai-chat",
+        "vllm",
+        "vllm-chat",
+        "lmdeploy",
+        "lmdeploy-chat",
+    ]
     return DatasetRow(
-        prompt=text_prompt,
+        prompt=text_prompt if use_raw_prompt else prompt_str,
         prompt_len=prompt_len,
         output_len=output_len,
         text_prompt_len=text_prompt_len,
@@ -1382,6 +1399,7 @@ def sample_image_requests(
     image_content: str,
     image_format: str,
     image_resolution: str,
+    backend: str,
 ) -> List[DatasetRow]:
     """Generate requests with images.
 
@@ -1447,6 +1465,7 @@ def _gen_random_image_data_uri(
             list(images_base64),
             int(output_lens[i]),
             processor,
+            backend,
         )
 
         dataset.append(data_row)