[V1] [7/N] API Server: Multiprocessing Detokenizer [ DO NOT MERGE ]

[robertgshaw2-neuralmagic]

NOTES:

• Adding a 3rd process hurts performance
• Current main API server is within 3% of the LLM performance already
• Performance Benchmarks

So, we will abandon the 3 process architecure and go forward with the 2 process architecture

SUMMARY:

• Splitting up [V1] Improve AsyncLLM and API Server #11237
• Update shutdown logic to use weakref._finalizer
• Split detokenizer and API server into separate processes

PERFORMANCE:

Server

• Launch:

vllm serve $MODEL --no-enable-prefix-caching --gpu-memory-utilization 0.98 --max-num-batched-tokens 8192 --disable-log-requests

• Benchmark:

python3 benchmark_serving.py --model $MODEL --dataset-name sonnet --dataset-path sonnet.txt --sonnet-input-len 250 --sonnet-output-len 200

• Result:
• main: 55.99 requests/sec
• pr: 54.05 requests/sec

AsyncLLM

python3 benchmarks/benchmark_throughput.py --model $MODEL --input-len 230 --output-len 200 --no-enable-prefix-caching --gpu-memory-utilization 0.98 --max-num-batched-tokens 8192 --async-engine

>> Throughput: 55.10 requests/s

• main

python3 benchmarks/benchmark_throughput.py --model $MODEL --input-len 230 --output-len 200 --no-enable-prefix-caching --gpu-memory-utilization 0.98 --max-num-batched-tokens 8192 --async-engine

>> Throughput: 55.26 requests/s

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[robertgshaw2-neuralmagic]

Closing as the implementation did not lead to performance gains