[Feature] Update benchmark_throughput.py to support image input

benchmarks/README.md

@@ -6,3 +6,14 @@ You can download the dataset by running:
 ```bash
 wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json
 ```
+
+## Downloading the ShareGPT4V dataset
+
+The json file refers to several image datasets (coco, llava, etc.). The benchmark scripts
+will ignore a datapoint if the referred image is missing.
+```bash
+wget https://huggingface.co/datasets/Lin-Chen/ShareGPT4V/resolve/main/sharegpt4v_instruct_gpt4-vision_cap100k.json
+mkdir coco -p
+wget http://images.cocodataset.org/zips/train2017.zip -O coco/train2017.zip
+unzip coco/train2017.zip -d coco/
+```

benchmarks/benchmark_throughput.py

@@ -8,6 +8,7 @@
 
 import torch
 import uvloop
+from PIL import Image
 from tqdm import tqdm
 from transformers import (AutoModelForCausalLM, AutoTokenizer,
                           PreTrainedTokenizerBase)
@@ -38,12 +39,33 @@ class SampleRequest:
     multi_modal_data: Optional[MultiModalDataDict] = None
 
 
-def sample_requests(
-    dataset_path: str,
-    num_requests: int,
-    tokenizer: PreTrainedTokenizerBase,
-    fixed_output_len: Optional[int],
-) -> List[SampleRequest]:
+def _get_prompt_for_image_model(question: str, *, model: str) -> str:
+    """Prepend and append special tokens around the question to form a prompt.
+
+    Args:
+        question: The input question text to wrap with special tokens
+        model: The name of the model being used, to determine which special
+            tokens to add
+
+    Returns:
+        The formatted prompt string with appropriate special tokens for the
+            model
+
+    Raises:
+        ValueError: If an unsupported model name is provided
+    """
+    model = model.lower()
+    if "pixtral" in model:
+        return f"<s>[INST]{question}\n[IMG][/INST]"
+    raise ValueError(f"Unsupported model {model}")
+
+
+def sample_requests(tokenizer: PreTrainedTokenizerBase,
+                    args: argparse.Namespace) -> List[SampleRequest]:
+    dataset_path: str = args.dataset
+    num_requests: int = args.num_prompts
+    fixed_output_len: Optional[int] = args.output_len
+    model: str = args.model
     if fixed_output_len is not None and fixed_output_len < 4:
         raise ValueError("output_len too small")
 
@@ -52,23 +74,36 @@ def sample_requests(
         dataset = json.load(f)
     # Filter out the conversations with less than 2 turns.
     dataset = [data for data in dataset if len(data["conversations"]) >= 2]
-    # Only keep the first two turns of each conversation.
-    dataset = [(data["conversations"][0]["value"],
-                data["conversations"][1]["value"]) for data in dataset]
-
     # Shuffle the dataset.
     random.shuffle(dataset)
 
     # Filter out sequences that are too long or too short
     filtered_dataset: List[SampleRequest] = []
-    for i in range(len(dataset)):
+    for data in dataset:
         if len(filtered_dataset) == num_requests:
             break
 
+        # Only keep the first two turns of each conversation.
+        prompt = data["conversations"][0]["value"]
+        completion = data["conversations"][1]["value"]
+
+        multi_modal_data: Optional[MultiModalDataDict] = None
+        if "image" in data:
+            multi_modal_data = multi_modal_data or {}
+            image_path = data["image"]
+            # TODO(vllm-project/vllm/issues/9778): Support multiple images.
+            assert isinstance(image_path,
+                              str), "Only support single image input"
+            try:
+                multi_modal_data["image"] = Image.open(image_path).convert(
+                    "RGB")
+            except FileNotFoundError:
+                # Ignore datapoint where asset is missing
+                continue
+            prompt = _get_prompt_for_image_model(question=prompt, model=model)
+
         # Tokenize the prompts and completions.
-        prompt = dataset[i][0]
         prompt_token_ids = tokenizer(prompt).input_ids
-        completion = dataset[i][1]
         completion_token_ids = tokenizer(completion).input_ids
         prompt_len = len(prompt_token_ids)
         output_len = len(completion_token_ids
@@ -82,7 +117,8 @@ def sample_requests(
         filtered_dataset.append(
             SampleRequest(prompt=prompt,
                           prompt_len=prompt_len,
-                          expected_output_len=output_len))
+                          expected_output_len=output_len,
+                          multi_modal_data=multi_modal_data))
 
     return filtered_dataset
 
@@ -99,7 +135,9 @@ def run_vllm(
     prompts: List[TextPrompt] = []
     sampling_params: List[SamplingParams] = []
     for request in requests:
-        prompts.append(TextPrompt(prompt=request.prompt))
+        prompts.append(
+            TextPrompt(prompt=request.prompt,
+                       multi_modal_data=request.multi_modal_data))
         sampling_params.append(
             SamplingParams(
                 n=n,
@@ -148,7 +186,9 @@ async def run_vllm_async(
         prompts: List[TextPrompt] = []
         sampling_params: List[SamplingParams] = []
         for request in requests:
-            prompts.append(TextPrompt(prompt=request.prompt))
+            prompts.append(
+                TextPrompt(prompt=request.prompt,
+                           multi_modal_data=request.multi_modal_data))
             sampling_params.append(
                 SamplingParams(
                     n=n,
@@ -272,9 +312,10 @@ def main(args: argparse.Namespace):
             for _ in range(args.num_prompts)
         ]
     else:
-        requests = sample_requests(args.dataset, args.num_prompts, tokenizer,
-                                   args.output_len)
+        requests = sample_requests(tokenizer, args)
 
+    is_multi_modal = any(request.multi_modal_data is not None
+                         for request in requests)
     if args.backend == "vllm":
         if args.async_engine:
             elapsed_time = uvloop.run(
@@ -300,6 +341,11 @@ def main(args: argparse.Namespace):
                            for request in requests)
     total_output_tokens = sum(request.expected_output_len
                               for request in requests)
+    if is_multi_modal:
+        print("\033[91mWARNING\033[0m: Multi-modal request detected. The "
+              "following metrics is not accurate because image tokens are not "
+              "counted. See vllm-project/vllm/issues/9778 for details.")
+        # TODO(vllm-project/vllm/issues/9778): Count molti-modal token length.
     print(f"Throughput: {len(requests) / elapsed_time:.2f} requests/s, "
           f"{total_num_tokens / elapsed_time:.2f} total tokens/s, "
           f"{total_output_tokens / elapsed_time:.2f} output tokens/s")