[Misc] Modularize CLI Argument Parsing in Benchmark Scripts

[reidliu41]

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• 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

• Modularize CLI Argument Parsing in Benchmark Scripts to clean up entry point

Test Plan

Test Result

(Optional) Documentation Update

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[houseroad]

Better to run the benchmark scripts with the changes to ensure correctness.

[reidliu41]

thanks for your remind, i think it should work.

$ tail benchmark_long_document_qa_throughput.py

    parser = EngineArgs.add_cli_args(parser)

    return parser


if __name__ == "__main__":
    parser = create_argument_parser()
    args = parser.parse_args()
    main(args)


python benchmark_long_document_qa_throughput.py \
  --model meta-llama/Llama-2-7b-chat-hf \
  --document-length 2048 \
  --num-documents 8 \
  --repeat-count 2 \
  --enable-prefix-caching \
  --max-model-len 4096

Capturing CUDA graphs: 100%|████████████████████████████████████████| 67/67 [00:13<00:00,  4.98it/s]
INFO 06-13 12:41:48 [gpu_model_runner.py:2058] Graph capturing finished in 13 secs, took 0.47 GiB
INFO 06-13 12:41:48 [core.py:171] init engine (profile, create kv cache, warmup model) took 20.25 seconds
------warm up------
Adding requests: 100%|███████████████████████████████████████████████| 8/8 [00:00<00:00, 739.17it/s]
Processed prompts: 100%|█| 8/8 [00:02<00:00,  3.38it/s, est. speed input: 6951.96 toks/s, output: 33
Time to execute all requests: 2.3780 secs
------start generating------
Adding requests: 100%|█████████████████████████████████████████████| 16/16 [00:00<00:00, 897.37it/s]
Processed prompts: 100%|█| 16/16 [00:02<00:00,  6.61it/s, est. speed input: 13563.17 toks/s, output:
Time to execute all requests: 2.4378 secs


python3 benchmark_serving.py \
  --backend vllm \
  --model NousResearch/Hermes-3-Llama-3.1-8B \
  --endpoint /v1/completions \
  --dataset-name sharegpt \
  --dataset-path /tmp/ShareGPT_V3_unfiltered_cleaned_split.json \
  --num-prompts 10

Maximum request concurrency: None
100%|███████████████████████████████████████████████████████████████| 10/10 [00:17<00:00,  1.80s/it]
============ Serving Benchmark Result ============
Successful requests:                     10
Benchmark duration (s):                  17.95
Total input tokens:                      1369
Total generated tokens:                  2278
Request throughput (req/s):              0.56
Output token throughput (tok/s):         126.87
Total Token throughput (tok/s):          203.12
---------------Time to First Token----------------
Mean TTFT (ms):                          197.14
Median TTFT (ms):                        215.81
P99 TTFT (ms):                           216.36
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms):                          23.65
Median TPOT (ms):                        23.40
P99 TPOT (ms):                           24.70
---------------Inter-token Latency----------------
Mean ITL (ms):                           23.23
Median ITL (ms):                         23.09
P99 ITL (ms):                            24.39
==================================================


python3 benchmark_throughput.py \
  --model NousResearch/Hermes-3-Llama-3.1-8B \
  --dataset-name sonnet \
  --dataset-path sonnet.txt \
  --num-prompts 10
Adding requests: 100%|████████████████████████████████████████████| 10/10 [00:00<00:00, 1414.65it/s]
Processed prompts: 100%|█| 10/10 [00:04<00:00,  2.41it/s, est. speed input: 1210.23 toks/s, output:
Throughput: 2.41 requests/s, 1569.16 total tokens/s, 361.33 output tokens/s
Total num prompt tokens:  5014
Total num output tokens:  1500


python3 benchmark_latency.py \
  --model NousResearch/Hermes-3-Llama-3.1-8B \
  --batch-size 2
Warmup iterations: 100%|████████████████████████████████████████████| 10/10 [00:29<00:00,  2.94s/it]
Profiling iterations: 100%|█████████████████████████████████████████| 30/30 [01:28<00:00,  2.96s/it]
Avg latency: 2.95804654553552 seconds
10% percentile latency: 2.9449643844825912 seconds
25% percentile latency: 2.952844722523878 seconds
50% percentile latency: 2.955235666508088 seconds
75% percentile latency: 2.969451513257809 seconds
90% percentile latency: 2.9752879458043027 seconds
99% percentile latency: 2.976981793149898 seconds

python benchmark_prefix_caching.py \
    --model meta-llama/Llama-2-7b-chat-hf \
    --enable-prefix-caching \
    --num-prompts 1 \
    --repeat-count 100 \
    --input-length-range 128:256
Testing filtered requests
------start generating------
Adding requests: 100%|██████████████████████████████████████████| 100/100 [00:00<00:00, 3572.20it/s]
Processed prompts: 100%|█| 100/100 [00:00<00:00, 196.37it/s, est. speed input: 43204.25 toks/s, outp
cost time 0.5386934280395508


python benchmark_serving_structured_output.py \
    --backend vllm\
    --model NousResearch/Hermes-3-Llama-3.1-8B \
    --dataset json \
    --structured-output-ratio 1.0 \
    --request-rate 10 \
    --num-prompts 1000
Maximum request concurrency: None
100%|███████████████████████████████████████████████████████████| 1000/1000 [01:43<00:00,  9.70it/s]
============ Serving Benchmark Result ============
Successful requests:                     1000
Benchmark duration (s):                  103.12
Total input tokens:                      123000
Total generated tokens:                  82976
Request throughput (req/s):              9.70
Output token throughput (tok/s):         804.62
Total Token throughput (tok/s):          1997.35
---------------Time to First Token----------------
Mean TTFT (ms):                          57.73
Median TTFT (ms):                        46.48
P99 TTFT (ms):                           516.09
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms):                          26.73
Median TPOT (ms):                        26.59
P99 TPOT (ms):                           32.96
---------------Inter-token Latency----------------
Mean ITL (ms):                           26.41
Median ITL (ms):                         25.86
P99 ITL (ms):                            29.82
==================================================
correct_rate(%) 100.0


python benchmark_prioritization.py \
  --model meta-llama/Llama-2-7b-chat-hf \
  --input-len 128 \
  --output-len 64 \
  --num-prompts 100 \
  --scheduling-policy priority
Capturing CUDA graph shapes: 100%|██████████████████████████████████| 35/35 [00:09<00:00,  3.61it/s]
INFO 06-13 13:08:48 [model_runner.py:1671] Graph capturing finished in 10 secs, took 0.24 GiB
INFO 06-13 13:08:48 [llm_engine.py:428] init engine (profile, create kv cache, warmup model) took 11.47 seconds
Adding requests: 100%|█████████████████████████████████████████| 100/100 [00:00<00:00, 10060.70it/s]
Processed prompts: 100%|█| 100/100 [00:04<00:00, 24.62it/s, est. speed input: 3151.16 toks/s, output
Throughput: 24.56 requests/s, 4714.81 tokens/s
[rank0]:[W613 13:08:52.958746604 ProcessGroupNCCL.cpp:1476] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())

[reidliu41]

hi team, @DarkLight1337 can you help to set ready if no problem? thanks

[ywang96]

hi team, @DarkLight1337 can you help to set ready if no problem? thanks

@reidliu41 Sorry I forgot to put on the ready label - added it just now!

[reidliu41]

seems all passed, is it able to merge?