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[Misc] Use VisionArena Dataset for VLM Benchmarking #12389

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Jan 24, 2025
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[Misc] Use VisionArena Dataset for VLM Benchmarking #12389

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32 changes: 12 additions & 20 deletions benchmarks/benchmark_serving.py
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Original file line number Diff line number Diff line change
Expand Up @@ -200,7 +200,7 @@ def sample_sonnet_requests(
return sampled_requests


def sample_mmmu_pro_vision_requests(
def sample_vision_arena_requests(
dataset,
num_requests: int,
tokenizer: PreTrainedTokenizerBase,
Expand All @@ -212,13 +212,7 @@ def sample_mmmu_pro_vision_requests(
if len(sampled_requests) == num_requests:
break

# MMMU-Pro vision direct prompt
# Ref: https://github.com/MMMU-Benchmark/MMMU/blob/6ce42f4d8f70c1841c67867152648974415b5cac/mmmu-pro/prompts.yaml#L5
prompt = (
"Answer with the option letter from the given choices directly. "
"The last line of your response should be of the following "
"format: 'Answer: $LETTER' (without quotes) where LETTER is one of "
"options.")
prompt = data["turns"][0][0]['content']

prompt_token_ids = tokenizer(prompt).input_ids
if fixed_output_len is None:
Expand All @@ -230,10 +224,10 @@ def sample_mmmu_pro_vision_requests(
output_len = fixed_output_len

assert isinstance(
data["image"],
data["images"][0],
Image), ("Input image format must be `PIL.Image.Image`, "
f"given {type(data['image'])}.")
image: Image = data["image"]
image: Image = data["images"][0]
image = image.convert("RGB")
image_data = io.BytesIO()
image.save(image_data, format='JPEG')
Expand All @@ -252,27 +246,25 @@ def sample_mmmu_pro_vision_requests(

def sample_hf_requests(
dataset_path: str,
dataset_subset: str,
dataset_subset: Optional[str],
dataset_split: str,
num_requests: int,
tokenizer: PreTrainedTokenizerBase,
random_seed: int,
fixed_output_len: Optional[int] = None,
) -> List[Tuple[str, str, int, Optional[Dict[str, Collection[str]]]]]:

# Special case for MMMU-Pro vision dataset
if dataset_path == 'MMMU/MMMU_Pro' and dataset_subset == 'vision':
assert dataset_split == "test"
# Special case for vision_arena dataset
if dataset_path == 'lmarena-ai/vision-arena-bench-v0.1' \
and dataset_subset is None:
assert dataset_split == "train"
dataset = load_dataset(dataset_path,
name=dataset_subset,
split=dataset_split,
streaming=True)
assert "image" in dataset.features, (
"MMMU/MMMU_Pro vision dataset must have 'image' column.")
filter_func = lambda x: isinstance(x["image"], Image)
dataset = dataset.shuffle(seed=random_seed).filter(filter_func)
return sample_mmmu_pro_vision_requests(dataset, num_requests,
tokenizer, fixed_output_len)
dataset = dataset.shuffle(seed=random_seed)
return sample_vision_arena_requests(dataset, num_requests, tokenizer,
fixed_output_len)

dataset = load_dataset(dataset_path,
name=dataset_subset,
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