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@mgoin mgoin commented Jul 1, 2025

Adds support for running the mlperf inference datasets for llama-2-70B and llama-3.1-405B through vllm's bench serve script. Datasets: https://huggingface.co/datasets/mgoin/mlperf-inference-llama2-data and https://huggingface.co/datasets/mgoin/mlperf-inference-llama3.1-data

Server:

vllm serve meta-llama/Llama-3.1-8B-Instruct --enforce-eager

Client:

vllm bench serve --model meta-llama/Llama-3.1-8B-Instruct --dataset-name hf --dataset-path mgoin/mlperf-inference-llama2-data --num-prompts 24576

INFO 07-01 21:24:17 [__init__.py:244] Automatically detected platform cuda.
Namespace(subparser='bench', bench_type='serve', dispatch_function=<function BenchmarkServingSubcommand.cmd at 0x7fcfb44493a0>, seed=0, num_prompts=24576, dataset_name='hf', dataset_path='mgoin/mlperf-inference-llama2-data', custom_output_len=256, custom_skip_chat_template=False, sonnet_input_len=550, sonnet_output_len=150, sonnet_prefix_len=200, sharegpt_output_len=None, random_input_len=1024, random_output_len=128, random_range_ratio=0.0, random_prefix_len=0, hf_subset=None, hf_split=None, hf_output_len=None, endpoint_type='openai', label=None, backend='vllm', base_url=None, host='127.0.0.1', port=8000, endpoint='/v1/completions', max_concurrency=None, model='meta-llama/Llama-3.1-8B-Instruct', tokenizer=None, use_beam_search=False, logprobs=None, request_rate=inf, burstiness=1.0, trust_remote_code=False, disable_tqdm=False, profile=False, save_result=False, save_detailed=False, append_result=False, metadata=None, result_dir=None, result_filename=None, ignore_eos=False, percentile_metrics='ttft,tpot,itl', metric_percentiles='99', goodput=None, top_p=None, top_k=None, min_p=None, temperature=None, tokenizer_mode='auto', served_model_name=None, lora_modules=None, ramp_up_strategy=None, ramp_up_start_rps=None, ramp_up_end_rps=None)
README.md: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 541/541 [00:00<00:00, 7.27MB/s]
INFO 07-01 21:24:43 [datasets.py:219] Oversampled requests to reach 24576 total samples.
Starting initial single prompt test run...
Initial test run completed. Starting main benchmark run...
Traffic request rate: inf
Burstiness factor: 1.0 (Poisson process)
Maximum request concurrency: None
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 24576/24576 [06:27<00:00, 63.38it/s]
============ Serving Benchmark Result ============
Successful requests:                     24576     
Benchmark duration (s):                  387.77    
Total input tokens:                      5147391   
Total generated tokens:                  4178056   
Request throughput (req/s):              63.38     
Output token throughput (tok/s):         10774.59  
Total Token throughput (tok/s):          24048.96  
---------------Time to First Token----------------
Mean TTFT (ms):                          206251.00 
Median TTFT (ms):                        186350.21 
P99 TTFT (ms):                           366451.02 
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms):                          67.24     
Median TPOT (ms):                        76.55     
P99 TPOT (ms):                           102.22    
---------------Inter-token Latency----------------
Mean ITL (ms):                           68.51     
Median ITL (ms):                         67.93     
P99 ITL (ms):                            177.94    
==================================================

Signed-off-by: mgoin <mgoin64@gmail.com>
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Summary of Changes

Hello @mgoin, 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 vllm benchmarking capabilities by integrating official MLPerf Llama datasets. It introduces a new dataset class that intelligently processes these datasets, formats prompts using chat templates, and dynamically sets expected output lengths based on reference answers. Furthermore, it adds a useful logging feature to visualize the distribution of prompt and output token lengths, aiding in performance analysis.

Highlights

  • New Dataset Support: Added support for MLPerf Llama2 and Llama3.1 datasets within the vllm benchmarking framework, allowing users to specify these datasets for bench serve runs. This enables more standardized and relevant performance evaluations for Llama models.
  • Dynamic Output Lengths: Implemented logic to dynamically determine the expected output length for each sample based on the tokenized length of the reference answer provided in the MLPerf datasets. This ensures that benchmark scenarios accurately reflect real-world generation lengths.
  • Chat Template Integration: Incorporated the use of the tokenizer's chat template to format system and user prompts from the MLPerf datasets into a conversational style. This aligns the benchmark requests with how these models are typically interacted with in chat-based applications.
  • Benchmarking Insights: Introduced a new logging feature that generates and displays histograms of prompt and output token lengths. This provides valuable insights into the distribution of request sizes during benchmark runs, aiding in performance analysis and bottleneck identification.
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@mergify mergify bot added llama Related to Llama models performance Performance-related issues labels Jul 1, 2025
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Code Review

This pull request successfully integrates the MLPerf inference datasets for Llama models, enhancing the benchmark capabilities. The new MLPerfDataset class correctly handles prompt formatting and output length determination. A useful histogram logging feature has been added to provide insights into token length distributions. The changes are well-structured and follow existing patterns within the codebase.

mgoin added 2 commits July 1, 2025 21:44
Signed-off-by: mgoin <mgoin64@gmail.com>
Signed-off-by: mgoin <mgoin64@gmail.com>
@mgoin mgoin added the ready ONLY add when PR is ready to merge/full CI is needed label Jul 13, 2025
@mgoin mgoin enabled auto-merge (squash) July 13, 2025 20:29
@mgoin mgoin merged commit 8bb43b9 into vllm-project:main Jul 14, 2025
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