Benchmark serving structured output

[xuechendi]

Test with

python benchmarks/benchmark_serving_guided.py --port 8080 --model meta-llama/Llama-3.2-3B-Instruct --dataset xgrammar_bench --request-rate 32 --backend openai-chat --endpoint /v1/chat/completions --output-len 512 --num-prompts 128 --guided-decoding-ratio 1.0 --save-results

or

python benchmarks/benchmark_serving_guided.py --port 8080 --model meta-llama/Llama-3.2-3B-Instruct --dataset xgrammar_bench --request-rate 32 --output-len 512 --num-prompts 128 --guided-decoding-ratio 1.0 --save-results

Printed Results

Initial test run completed. Starting main benchmark run...
Traffic request rate: 32.0
Burstiness factor: 1.0 (Poisson process)
Maximum request concurrency: None
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 128/128 [00:09<00:00, 13.97it/s]============ Serving Benchmark Result ============
Successful requests:                     128
Benchmark duration (s):                  9.16
Total input tokens:                      39247
Total generated tokens:                  27716
Request throughput (req/s):              13.97
Output token throughput (tok/s):         3025.13
Total Token throughput (tok/s):          7308.85                                                                                                            
---------------Time to First Token----------------
Mean TTFT (ms):                          50.32
Median TTFT (ms):                        37.43                                                                                                              
P99 TTFT (ms):                           269.63
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms):                          18.31
Median TPOT (ms):                        17.71
P99 TPOT (ms):                           26.15                                                                                                              
---------------Inter-token Latency----------------
Mean ITL (ms):                           16.41
Median ITL (ms):                         12.48
P99 ITL (ms):                            74.89
==================================================

correct_rate(%) 85.15625                                                                                                                                                                                                                                                                                                

saved results:

output = [...],
    "backend": "openai-chat",
    "model_id": "meta-llama/Llama-3.2-3B-Instruct",
    "tokenizer_id": "meta-llama/Llama-3.2-3B-Instruct",
    "num_prompts": 128,
    "request_rate": 32.0,
    "burstiness": 1.0,
    "max_concurrency": null,
    "correct_rate(%)": 68.75,
    "duration": 10.14518375415355,
    "completed": 128,
    "total_input_tokens": 39247,
    "total_output_tokens": 30622,
    "request_throughput": 12.616824209575839,
    "output_throughput": 3018.3780542627446,
    "total_token_throughput": 6886.913208584799,
    "ttft_description": {
        "count": 128.0,
        "mean": 0.08015793653066794,
        "std": 0.08682275402674311,
        "min": 0.01977787702344358,
        "25%": 0.03341050981543958,
        "50%": 0.04755078151356429,
        "75%": 0.07237446604995057,
        "max": 0.40087792300619185
    },
    "tpot_description": {
        "count": 128.0,
        "mean": 0.0217274315153425,
        "std": 0.0010070130538081335,
        "min": 0.0192044503872777,
        "25%": 0.0208703824519354,
        "50%": 0.021955222470058797,
        "75%": 0.02253629300487799,
        "max": 0.023099320525488583
},
...

[github-actions]

👋 Hi! Thank you for contributing to the vLLM project.
Just a reminder: PRs would not trigger full CI run by default. Instead, it would only run fastcheck CI which starts running only a small and essential subset of CI tests to quickly catch errors. You can run other CI tests on top of those by going to your fastcheck build on Buildkite UI (linked in the PR checks section) and unblock them. If you do not have permission to unblock, ping simon-mo or khluu to add you in our Buildkite org.

Once the PR is approved and ready to go, your PR reviewer(s) can run CI to test the changes comprehensively before merging.

To run CI, PR reviewers can do one of these:

• Add ready label to the PR
• Enable auto-merge.

🚀

[mgoin]

I've updated this with a few fixes/additions through using it yesterday and today and I think it is in a good state of usability to land. Thanks for the nice work!