bench: Add a benchmark for vLM: MMMU

benchmark/mmmu/README.md

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+## Run evaluation
+
+### Evaluate sglang
+
+```
+python benchmark/mmmu/bench_sglang.py --model-path Qwen/Qwen2-VL-7B-Instruct --chat-template qwen2-vl
+```
+
+It's recommended to reduce the memory usage by appending something ike `--mem-fraction-static 0.6` to the command above.
+
+### Evaluate hf
+
+```
+python benchmark/mmmu/bench_hf.py --model-path Qwen/Qwen2-VL-7B-Instruct
+```
+
+Some popular model results:
+
+1. Qwen/Qwen2-VL-2B-Instruct: 0.241
+2. Qwen/Qwen2-VL-7B-Instruct: 0.255
+3. Qwen/Qwen2.5-VL-3B-Instruct: 0.245
+4. Qwen/Qwen2.5-VL-7B-Instruct: 0.242

benchmark/mmmu/bench_hf.py

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+"""
+    Bench the huggingface vLM with benchmark MMMU
+
+    Usage:
+        python benchmark/mmmu/bench_hf.py --model-path Qwen/Qwen2-VL-7B-Instruct
+
+    The eval output will be logged
+"""
+
+import argparse
+import random
+
+import torch
+from bench_sglang import EvalArgs, prepare_samples
+from data_utils import save_json
+from eval_utils import eval_result, get_sampling_params, parse_multi_choice_response
+from tqdm import tqdm
+from transformers import AutoModelForImageTextToText, AutoProcessor
+
+
+@torch.no_grad()
+def eval_mmmu(args):
+    eval_args = EvalArgs.from_cli_args(args)
+
+    model = AutoModelForImageTextToText.from_pretrained(
+        args.model_path,
+        torch_dtype="auto",
+        trust_remote_code=True,
+    )
+    model = model.eval().cuda()
+    model = torch.compile(model)
+
+    processor = AutoProcessor.from_pretrained(
+        args.model_path, torch_dtype="auto", device_map="auto"
+    )
+
+    samples = prepare_samples(eval_args)
+    out_samples = dict()
+
+    sampling_params = get_sampling_params(eval_args)
+
+    answer_dict = {}
+    for sample in tqdm(samples):
+        prompt = sample["final_input_prompt"]
+        image = sample["image"]
+        prefix = prompt.split("<")[0]
+        suffix = prompt.split(">")[1]
+        if image is not None:
+            messages = [
+                {
+                    "role": "user",
+                    "content": [
+                        {"type": "text", "text": prefix},
+                        {
+                            "type": "image",
+                            "image": image,
+                        },
+                        {"type": "text", "text": suffix},
+                    ],
+                }
+            ]
+            text = processor.apply_chat_template(
+                messages, tokenize=False, add_generation_prompt=True
+            )
+
+            inputs = processor(
+                text=[text],
+                images=[image],
+                padding=True,
+                return_tensors="pt",
+            ).to(model.device)
+
+            generated_ids = model.generate(**inputs, **sampling_params)
+
+            response = processor.decode(
+                generated_ids[0],
+                skip_special_tokens=True,
+                clean_up_tokenization_spaces=False,
+            )[len(text) :]
+        else:  # multiple images actually
+            if sample["question_type"] == "multiple-choice":
+                all_choices = sample["all_choices"]
+                response = random.choice(all_choices)
+
+            else:
+                response = "INVALID GENERATION FOR MULTIPLE IMAGE INPUTS"
+
+        if sample["question_type"] == "multiple-choice":
+            pred_ans = parse_multi_choice_response(
+                response, sample["all_choices"], sample["index2ans"]
+            )
+        else:  # open question
+            pred_ans = response
+        out_samples[sample["id"]] = pred_ans
+
+        torch.cuda.empty_cache()
+        # set ground truth answer
+        answer_dict[sample["id"]] = {
+            "question_type": sample["question_type"],
+            "ground_truth": sample["answer"],
+        }
+
+    args.output_path = f"{args.model_path}_val_hf.json"
+    save_json(args.output_path, out_samples)
+    eval_result(output_path=args.output_path, answer_dict=answer_dict)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--model-path",
+        type=str,
+        help="The path of the model weights. This can be a local folder or a Hugging Face repo ID.",
+        required=True,
+    )
+    EvalArgs.add_cli_args(parser)
+    args = parser.parse_args()
+
+    eval_mmmu(args)

benchmark/mmmu/bench_sglang.py

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+"""
+    Bench the sglang-hosted vLM with benchmark MMMU
+
+    Usage:
+        python benchmark/mmmu/bench_sglang.py --model-path Qwen/Qwen2-VL-7B-Instruct --chat-template qwen2-vl
+
+    The eval output will be logged
+"""
+
+import argparse
+import dataclasses
+import random
+import re
+from io import BytesIO
+
+from data_utils import save_json
+from eval_utils import (
+    EvalArgs,
+    eval_result,
+    get_sampling_params,
+    parse_multi_choice_response,
+    prepare_samples,
+)
+from tqdm import tqdm
+
+from sglang import Engine
+from sglang.srt.conversation import chat_templates
+from sglang.srt.server_args import ServerArgs
+
+
+def eval_mmmu(args):
+    server_args = ServerArgs.from_cli_args(args)
+    eval_args = EvalArgs.from_cli_args(args)
+
+    if server_args.chat_template is None:
+        raise ValueError("Chat template must be provided for this benchmark")
+
+    samples = prepare_samples(eval_args)
+
+    backend = Engine(**dataclasses.asdict(server_args))
+
+    out_samples = dict()
+
+    sampling_params = get_sampling_params(eval_args)
+
+    conv = chat_templates[server_args.chat_template].copy()
+    image_token = conv.image_token
+    answer_dict = {}
+    for sample in tqdm(samples):
+        prompt = sample["final_input_prompt"]
+        image = sample["image"]
+        bytes_io = BytesIO()
+        image.save(bytes_io, format="PNG")
+        png_bytes = bytes_io.getvalue()
+
+        prompt = re.sub(r"<[^>]*>", image_token, prompt)
+
+        if image is not None:
+            gen_out = backend.generate(
+                prompt=prompt, image_data=[png_bytes], sampling_params=sampling_params
+            )["text"]
+
+            response = gen_out
+        else:  # multiple images actually
+            if sample["question_type"] == "multiple-choice":
+                all_choices = sample["all_choices"]
+                response = random.choice(all_choices)
+
+            else:
+                response = "INVALID GENERATION FOR MULTIPLE IMAGE INPUTS"
+
+        if sample["question_type"] == "multiple-choice":
+            pred_ans = parse_multi_choice_response(
+                response, sample["all_choices"], sample["index2ans"]
+            )
+        else:  # open question
+            pred_ans = response
+        out_samples[sample["id"]] = pred_ans
+
+        # set ground truth answer
+        answer_dict[sample["id"]] = {
+            "question_type": sample["question_type"],
+            "ground_truth": (
+                sample["correct_choice"]
+                if "correct_choice" in samples
+                else sample["answer"]
+            ),
+        }
+
+    args.output_path = f"{args.model_path}_val_sglang.json"
+    save_json(args.output_path, out_samples)
+    eval_result(output_path=args.output_path, answer_dict=answer_dict)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    ServerArgs.add_cli_args(parser)
+    EvalArgs.add_cli_args(parser)
+    args = parser.parse_args()
+
+    eval_mmmu(args)