[Refactor] image data process in bench_serving (#6879)

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

python/sglang/bench_serving.py

@@ -388,7 +388,6 @@ async def async_request_sglang_generate(
                         chunk_bytes = chunk_bytes.strip()
                         if not chunk_bytes:
                             continue
-                        # print(chunk_bytes)
 
                         chunk = remove_prefix(chunk_bytes.decode("utf-8"), "data: ")
                         latency = time.perf_counter() - st
@@ -655,6 +654,7 @@ class DatasetRow:
     prompt: str
     prompt_len: int
     output_len: int
+    image_data: Optional[str] = None
 
 
 def sample_mmmu_requests(
@@ -730,42 +730,50 @@ def sample_mmmu_requests(
                     buffered = io.BytesIO()
                     image.save(buffered, format="JPEG")
                     img_str = base64.b64encode(buffered.getvalue()).decode("utf-8")
-                    image_path = f"data:image/jpeg;base64,{img_str}"
+                    image_data = f"data:image/jpeg;base64,{img_str}"
                 else:
                     continue
 
                 # Extract the question
                 question = example.get("question")
 
-                # Create the prompt with image, question
+                # Construct the prompt
                 prompt = f"Question: {question}\n\nAnswer: "
-                prompt = tokenizer.apply_chat_template(
-                    [
-                        {
-                            "role": "user",
-                            "content": [
-                                {"type": "image_url", "image_url": {"url": image_path}},
-                                {"type": "text", "text": prompt},
-                            ],
-                        }
-                    ],
-                    add_generation_prompt=True,
-                    tokenize=False,
-                )
-                prompt = f"<image>{image_path}</image>{prompt}"
 
-                # Calculate token lengths
-                # Note: This is approximate since we're not rendering the actual image tokens
+                try:
+                    prompt = tokenizer.apply_chat_template(
+                        [
+                            {
+                                "role": "user",
+                                "content": [
+                                    {
+                                        "type": "image_url",
+                                        "image_url": {"url": image_data},
+                                    },
+                                    {"type": "text", "text": prompt},
+                                ],
+                            }
+                        ],
+                        add_generation_prompt=True,
+                        tokenize=False,
+                    )
+                except Exception as e:
+                    # Note (Xinyuan): This is a workaround for an issue where some tokenizers do not support content as a list. (e.g. InternVL)
+                    print(f"Error applying chat template: {e}, fallback to <image> tag")
+                    prompt = f"<image>{prompt}"
+
+                # Calculate token lengths for text only (without image data)
                 prompt_token_ids = tokenizer.encode(prompt)
-                prompt_len = (
-                    len(prompt_token_ids) + 512
-                )  # Add estimate for image tokens
+                prompt_len = len(prompt_token_ids)
 
                 output_len = fixed_output_len if fixed_output_len is not None else 256
 
                 filtered_dataset.append(
                     DatasetRow(
-                        prompt=prompt, prompt_len=prompt_len, output_len=output_len
+                        prompt=prompt,
+                        prompt_len=prompt_len,
+                        output_len=output_len,
+                        image_data=image_data,
                     )
                 )
 
@@ -1199,34 +1207,21 @@ async def benchmark(
 
     # Use the first request for all warmup iterations
     test_request = input_requests[0]
-    test_prompt, test_prompt_len, test_output_len = (
-        test_request.prompt,
-        test_request.prompt_len,
-        test_request.output_len,
-    )
+
     if lora_names is not None and len(lora_names) != 0:
         lora_name = lora_names[0]
     else:
         lora_name = None
 
-    if "<image>" in test_prompt:
-        import re
-
-        image_match = re.search(r"<image>(.*?)</image>(.*)", test_prompt, re.DOTALL)
-        image_data = image_match.group(1) if image_match else None
-        test_prompt = image_match.group(2) if image_match else test_prompt
-    else:
-        image_data = None
-
     # Create the test input once
     test_input = RequestFuncInput(
         model=model_id,
-        prompt=test_prompt,
+        prompt=test_request.prompt,
         api_url=api_url,
-        prompt_len=test_prompt_len,
-        output_len=min(test_output_len, 32),
+        prompt_len=test_request.prompt_len,
+        output_len=min(test_request.output_len, 32),
         lora_name=lora_name,
-        image_data=image_data,
+        image_data=test_request.image_data,
         extra_request_body=extra_request_body,
     )
 
@@ -1271,36 +1266,23 @@ async def benchmark(
     benchmark_start_time = time.perf_counter()
     tasks: List[asyncio.Task] = []
     async for request in get_request(input_requests, request_rate):
-        prompt, prompt_len, output_len = (
-            request.prompt,
-            request.prompt_len,
-            request.output_len,
-        )
         if lora_names is not None and len(lora_names) != 0:
             idx = random.randint(0, len(lora_names) - 1)
             lora_name = lora_names[idx]
         else:
             lora_name = None
 
-        if "<image>" in prompt:
-            import re
-
-            image_match = re.search(r"<image>(.*?)</image>(.*)", prompt, re.DOTALL)
-            image_data = image_match.group(1) if image_match else None
-            prompt = image_match.group(2) if image_match else prompt
-        else:
-            image_data = None
-
         request_func_input = RequestFuncInput(
             model=model_id,
-            prompt=prompt,
+            prompt=request.prompt,
             api_url=api_url,
-            prompt_len=prompt_len,
-            output_len=output_len,
+            prompt_len=request.prompt_len,
+            output_len=request.output_len,
             lora_name=lora_name,
-            image_data=image_data,
+            image_data=request.image_data,
             extra_request_body=extra_request_body,
         )
+
         tasks.append(
             asyncio.create_task(
                 limited_request_func(request_func_input=request_func_input, pbar=pbar)

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

@@ -175,6 +175,10 @@ class InternVLImageProcessor(BaseMultimodalProcessor):
         if not image_data:
             return None
 
+        # Ensure image_data is a list
+        if isinstance(image_data, str):
+            image_data = [image_data]
+
         base_output = self.load_mm_data(
             prompt=input_text,
             image_data=image_data,