[Core][Bugfix][Perf] Introduce MQLLMEngine to avoid asyncio OH

[alexm-redhat]

SUMMARY:

• Removed almost all the overhead from the OpenAI server, but still saw significant slowdown running in AsyncLLMEngine rather than LLMEngine on H100, including when we ran "headless" (e.g. with no uvicorn server).
• NOTE: performance varies by quality of the CPU. Impact of asyncio on DGX is much lower
• This caused us to believe the asyncio event loop in AsyncLLMEngine was the root cause of the slowdown
• This PR replaces AsyncLLMEngine with MPLLMEngine. MPLLMEngine works similarly to AsyncLLMEngine (i.e. it runs a background loop, accepts new requests, and streams requests back to the clients). We use zeromq as the message passing protocol rather than pulling from queues and pushing to generators
• This PR also fixes the number of sockets in use by the RPCClient, avoiding all issues with Too Many Open Files

Summary Performance vs Offline:

pr	scenario	req/sec
main	multistep	39.3
pr	multistep	42.3
main	single-step	25.2
pr	single-step	33.3

• note: the performance gains are larger on instances with weaker CPUs
• note: the performance gains are smaller on multi-step as we are only streaming a single token

Multistep Performance

1xH100 PERFORMANCE BASELINE:

MODEL="meta-llama/Meta-Llama-3.1-8B-Instruct"
python3 benchmarks/benchmark_throughput.py --model $MODEL --dataset benchmarks/ShareGPT_V3_unfiltered_cleaned_split.json --num-scheduler-steps 8

1xH100 SERVING PERFORMANCE

• Client:

MODEL="meta-llama/Meta-Llama-3.1-8B-Instruct"
python3 benchmarks/benchmark_serving.py --model $MODEL --dataset-path benchmarks/ShareGPT_V3_unfiltered_cleaned_split.json 

• Server:

MODEL="meta-llama/Meta-Llama-3.1-8B-Instruct"
vllm serve $MODEL --disable-log-requests --num-scheduler-steps 8 --max-model-len 8192

Single-Step Performance

1xH100 PERFORMANCE BASELINE:

MODEL="meta-llama/Meta-Llama-3.1-8B-Instruct"
python3 benchmarks/benchmark_throughput.py --model $MODEL --dataset benchmarks/ShareGPT_V3_unfiltered_cleaned_split.json

1xH100 SERVING PERFORMANCE

• Client:

MODEL="meta-llama/Meta-Llama-3.1-8B-Instruct"
python3 benchmarks/benchmark_serving.py --model $MODEL --dataset-path benchmarks/ShareGPT_V3_unfiltered_cleaned_split.json 

• Server:

MODEL="meta-llama/Meta-Llama-3.1-8B-Instruct"
vllm serve $MODEL --disable-log-requests --max-model-len 8192

co-authored by @robertgshaw2-neuralmagic

FIX #7920

BEFORE SUBMITTING, PLEASE READ THE CHECKLIST BELOW AND FILL IN THE DESCRIPTION ABOVE

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[joerunde]

Should this await the task and check that it receives a response with FINISHED_ABORTED?, to check that the abort signal actually made it down to the LLMEngine under the hood?

[joerunde]

Can we avoid the long sleep here by setting the healthcheck interval to be much shorter during these tests?

Or restructure so it passes as soon as the health check fails?

with pytest.raises(RAISED_ERROR):
    for _ in range(15):
        await client.check_health()
        asyncio.sleep(1)

[robertgshaw2-redhat]

This is better, but I dont want to re-run the whole CI so you can post a PR as a follow up if you want

[joerunde]

Would this behavior cause a race condition where the internal LLMEngine could finish a request, but before the response gets to the http server, the connection times out and the http server tries to abort the request, murdering the engine in the process?

[robertgshaw2-redhat]

I dont follow

[joerunde]

nit: Instead of triggering the bad abort task before generation starts and relying on the 2s timing interval, can we instead start the abort task once we get to the first iteration of this body loop? That should ensure that the abort happens after generation has started and make this test a lot faster

[robertgshaw2-redhat]

No, the generation iterator is separate from the engine process

[joerunde]

Is the sleep needed? I thought the await engine.make_client() would only return once the client is connected to a healthy engine

[njhill]

Left over print

[joerunde]

Looking good!

Just left some comments about tests- I think they could be cleaned up to rely on sleeps a bit less and run much faster, but not a huge deal since the whole test suite already takes forever and a day

[jikunshang]

seems this patch break start_profile and 'stop_profile` methods. would you like to take a look? thanks in advance!