[Frontend][V1] Online serving performance improvements

[njhill]

These help in particular with TTFT, ITL variance, and overall throughput.

• Break up output processing (detokenization) to avoid blocking the event loop for too long
• Freeze the heap after startup to reduce GC overhead/pauses
• Optimize a couple of CPU hotspots seen during profiling

Benchmark on A100:

VLLM_USE_V1=1 vllm serve meta-llama/Llama-3.2-1B-Instruct --disable-log-requests --port 8001 --max-num-batched-tokens 8192 --no-enable-prefix-caching --uvicorn-log-level=error

python benchmarks/benchmark_serving.py \
    --backend vllm \
    --model meta-llama/Llama-3.2-1B-Instruct \
    --dataset-name sharegpt \
    --dataset-path ShareGPT_V3_unfiltered_cleaned_split.json \
    --ignore-eos \
    --port 8001 \
    --save-result \
    --result-dir results \
    --result-filename test.json \
    --num-prompts 6000 \
    --request-rate inf \
    --max-concurrency=400

Before:

============ Serving Benchmark Result ============
Successful requests:                     6000      
Benchmark duration (s):                  94.31     
Total input tokens:                      1350511   
Total generated tokens:                  1211959   
Request throughput (req/s):              63.62     
Output token throughput (tok/s):         12850.45  
Total Token throughput (tok/s):          27169.98  
---------------Time to First Token----------------
Mean TTFT (ms):                          229.23    
Median TTFT (ms):                        158.08    
P99 TTFT (ms):                           1050.70   
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms):                          30.02     
Median TPOT (ms):                        29.64     
P99 TPOT (ms):                           68.90     
---------------Inter-token Latency----------------
Mean ITL (ms):                           28.77     
Median ITL (ms):                         23.19     
P99 ITL (ms):                            386.30    
==================================================

After:

============ Serving Benchmark Result ============
Successful requests:                     6000      
Benchmark duration (s):                  88.60     
Total input tokens:                      1350511   
Total generated tokens:                  1211959   
Request throughput (req/s):              67.72     
Output token throughput (tok/s):         13679.34  
Total Token throughput (tok/s):          28922.50  
---------------Time to First Token----------------
Mean TTFT (ms):                          197.34    
Median TTFT (ms):                        168.03    
P99 TTFT (ms):                           1059.55   
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms):                          28.30     
Median TPOT (ms):                        27.75     
P99 TPOT (ms):                           47.38     
---------------Inter-token Latency----------------
Mean ITL (ms):                           26.64     
Median ITL (ms):                         24.38     
P99 ITL (ms):                            65.19     
==================================================

[github-actions]

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[njhill]

There was noticeable overhead creating this set every time one of these objects is instantiated.

[njhill]

Avoid constructing these objects every time the properties are accessed.

[WoosukKwon]

Nice catch!

[robertgshaw2-redhat]

Wow, the impact on P99 ITL is crazy.

[robertgshaw2-redhat]

Do we need to call unfreeze at some point?

[njhill]

No, this is mostly static stuff that will be around for the lifetime of the process anyhow.

https://www.rippling.com/blog/the-garbage-collector-fights-back