[Benchmark][New Dataset]Added benchmark support for Unsloth Vision Datasets

[arunmadhusud]

Essential Elements of an Effective PR Description Checklist

• The purpose of the PR, such as "Fix some issue (link existing issues this PR will resolve)".
• The test plan, such as providing test command.
• The test results, such as pasting the results comparison before and after, or e2e results
• (Optional) The necessary documentation update, such as updating supported_models.md and examples for a new model.

Purpose

This PR adds support for two Hugging Face datasets from Unsloth for vision benchmarking tasks: unsloth/LaTeX_OCR and unsloth/Radiology_mini .

Test Plan

1. Benchmark Serving

# Serve the model
vllm serve unsloth/Qwen2-VL-2B-Instruct \
    --dtype bfloat16 \
    --max-model-len 4096 \
    --max-num-seqs 5 \
    --limit-mm-per-prompt "image=1,video=0" \
    --max-seq-len-to-capture 4096 \
    --mm-processor-kwargs '{"min_pixels": 784, "max_pixels": 1003520}'

# Benchmark
python3 vllm/benchmarks/benchmark_serving.py \
  --backend openai-chat \
  --request-rate 5 \
  --max-concurrency 5 \
  --model unsloth/Qwen2-VL-2B-Instruct \
  --endpoint /v1/chat/completions \
  --dataset-name hf \
  --dataset-path unsloth/LaTeX_OCR \
  --hf-split train \
  --hf-output-len 256 \
  --num-prompts 1000 

2. Benchmark throughput

python3 vllm/benchmarks/benchmark_throughput.py \
  --model unsloth/Qwen2-VL-2B-Instruct \
  --backend vllm-chat \
  --dataset-name hf \
  --dataset-path unsloth/LaTeX_OCR \
  --hf-split train \
  --num-prompts 1000 

Test Result

1. Benchmark serving

============ Serving Benchmark Result ============
Successful requests:                     1000      
Benchmark duration (s):                  201.35    
Total input tokens:                      8000      
Total generated tokens:                  73002     
Request throughput (req/s):              4.97      
Output token throughput (tok/s):         362.57    
Total Token throughput (tok/s):          402.30    
---------------Time to First Token----------------
Mean TTFT (ms):                          44.91     
Median TTFT (ms):                        42.01     
P99 TTFT (ms):                           72.49     
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms):                          5.50      
Median TPOT (ms):                        5.40      
P99 TPOT (ms):                           7.38      
---------------Inter-token Latency----------------
Mean ITL (ms):                           5.43      
Median ITL (ms):                         4.69      
P99 ITL (ms):                            32.08     
==================================================

3. Benchmark throughput

Throughput: 40.87 requests/s, 12228.12 total tokens/s, 10461.48 output tokens/s
Total num prompt tokens:  43231
Total num output tokens:  256000

(Optional) Documentation Update

Updated benchmark README

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[gemini-code-assist]

Summary of Changes

Hello @arunmadhusud, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request significantly enhances the benchmarking capabilities by integrating new multimodal vision datasets from Unsloth. It provides the necessary infrastructure to accurately benchmark models on image-based tasks, complete with updated documentation and specific dataset handling logic.

Highlights

• New Dataset Support: Added support for two new Hugging Face vision datasets from Unsloth: unsloth/LaTeX_OCR and unsloth/Radiology_mini. These datasets are now integrated into the benchmarking suite for both serving and throughput tests.
• Multimodal Benchmarking Capabilities: Introduced a dedicated UnslothVisionDataset class to handle the specific structure of these multimodal datasets, ensuring that image and text prompts are correctly parsed and prepared for benchmarking, including support for multimodal chat transformations.
• Documentation Updates: Updated the benchmarks/README.md to include the newly supported Unsloth vision datasets in the dataset table and provided clear example commands for running serving and throughput benchmarks with them.

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[gemini-code-assist]

Code Review

This pull request introduces support for Unsloth vision datasets in the benchmarking scripts. The changes involve adding a new dataset class and updating the benchmark scripts to include the new datasets. The code appears to be well-structured and follows the existing conventions. However, there are a few areas where additional checks and improvements could be made to enhance robustness and efficiency.

[gemini-code-assist]

Consider adding a check to ensure that the length of self.data is greater than 0 before proceeding with the sampling process. This would prevent a potential IndexError if the dataset is empty.

Suggested change

	if len(sampled_requests) >= num_requests:
	break
	for item in self.data:
	if not self.data:
	break
	if len(sampled_requests) >= num_requests:

[gemini-code-assist]

It would be more efficient to check sampled_requests length before iterating through the data, potentially saving unnecessary iterations.

Suggested change

	break
	if len(sampled_requests) >= num_requests:
	break
	for item in self.data:

[gemini-code-assist]

Consider adding a check to ensure that args.dataset_path is not None before accessing UnslothVisionDataset.SUPPORTED_DATASET_PATHS. This would prevent a potential AttributeError if args.dataset_path is not set.

Suggested change

	elif args.dataset_path in UnslothVisionDataset.SUPPORTED_DATASET_PATHS:
	dataset_cls = UnslothVisionDataset
	common_kwargs['dataset_subset'] = None
	common_kwargs['dataset_split'] = args.hf_split
	sample_kwargs["enable_multimodal_chat"] = True
	elif args.dataset_path and args.dataset_path in UnslothVisionDataset.SUPPORTED_DATASET_PATHS:
	dataset_cls = UnslothVisionDataset
	common_kwargs['dataset_subset'] = None
	common_kwargs['dataset_split'] = args.hf_split
	sample_kwargs["enable_multimodal_chat"] = True

[gemini-code-assist]

Consider adding a check to ensure that args.dataset_path is not None before accessing UnslothVisionDataset.SUPPORTED_DATASET_PATHS.keys(). This would prevent a potential AttributeError if args.dataset_path is not set.

            VisionArenaDataset.SUPPORTED_DATASET_PATHS.keys()
            | ConversationDataset.SUPPORTED_DATASET_PATHS
            | (UnslothVisionDataset.SUPPORTED_DATASET_PATHS.keys() if args.dataset_path else set())

[ywang96]

Thanks for your contribution! This PR looks good to me but I also want @mgoin to take a look in case we want to integrate this into our CLI.