[torch.compile] allow candidate compile sizes

[youkaichao]

Usage 1:

vllm serve meta-llama/Meta-Llama-3-8B -O 3 , no compilation for specific sizes

Usage 2:

vllm serve meta-llama/Meta-Llama-3-8B -O "{'level': 3, 'candidate_compile_sizes': [1, 2, 4]}" , compile for size [1, 2, 4]

Usage 3:

vllm serve meta-llama/Meta-Llama-3-8B -O "{'level': 3, 'candidate_compile_sizes': [$(seq -s, 1 5)]}" , compile for size [1, 2, 4]. (the command $(seq -s, 1 5) expands to 1, 2, 3, 4, 5, and 1&3 are removed because they are not cudagraph sizes. this allows users to easily specify compiling sizes no larger than 5) .

[github-actions]

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[youkaichao]

previously this is a json string, but i find json string is too restricted, e.g. we need to use double quotes, we cannot have trailing comma, etc.

therefore here we switch to string form of a python dict.

[youkaichao]

With this PR, we finally get throughput improvement:

$ python benchmarks/benchmark_throughput.py --input-len 256 --output-len 256 --model meta-llama/Meta-Llama-3-8B --num-scheduler-steps 8
Throughput: 42.91 requests/s, 21972.32 total tokens/s, 10986.16 output tokens/s
init engine (profile, create kv cache, warmup model) took 14.49 seconds

$ python benchmarks/benchmark_throughput.py --input-len 256 --output-len 256 --model meta-llama/Meta-Llama-3-8B --num-scheduler-steps 16
Throughput: 43.78 requests/s, 22417.37 total tokens/s, 11208.69 output tokens/s

$ python benchmarks/benchmark_throughput.py --input-len 256 --output-len 256 --model meta-llama/Meta-Llama-3-8B --num-scheduler-steps 32
Throughput: 44.31 requests/s, 22688.84 total tokens/s, 11344.42 output tokens/s

$ python benchmarks/benchmark_throughput.py --input-len 256 --output-len 256 --model meta-llama/Meta-Llama-3-8B --num-scheduler-steps 64
Throughput: 44.74 requests/s, 22905.20 total tokens/s, 11452.60 output tokens/s

$ python benchmarks/benchmark_throughput.py --input-len 256 --output-len 256 --model meta-llama/Meta-Llama-3-8B --num-scheduler-steps 128
Throughput: 44.43 requests/s, 22747.24 total tokens/s, 11373.62 output tokens/s

# the best --num-scheduler-steps is 64 , compare it with using torch.compile :

$ python benchmarks/benchmark_throughput.py --input-len 256 --output-len 256 --model meta-llama/Meta-Llama-3-8B --num-scheduler-steps 64 -O "{'level': 3, 'candidate_compile_sizes': [232, 256]}"
Throughput: 46.13 requests/s, 23617.97 total tokens/s, 11808.98 output tokens/s
init engine (profile, create kv cache, warmup model) took 88.60 seconds

The throughput improvement: 44.74 requests/s --> 46.13 requests/s

If we just look at the decode (output) throughput: 11452.60 output tokens/s --> 11808.98 output tokens/s

Note: the key here, is to use multi-step scheduling, so that the model execution loop is busy, and the benefit of faster model execution can be exposed. If we benchmark it without multi-step scheduling, then the overhead of scheduling just shadows the benefit of faster model execution.

The compilation sizes [232, 256] are derived from the fact that we run 1000 prompts: 3 batches of 256, and 1 batch of 232.

[WoosukKwon]

LGTM!