[Feature]Add async tensor parallelism using compilation pass

[cascade812]

This PR adds torch async tp using compilation pass.
It requires below config to run

config = CompilationConfig(
    level=3,
    compile_sizes=[4, 8, 16],
    splitting_ops=[],
)
config.pass_config.enable_async_tp= True

llm = LLM(model="llama/Llama-3.2-1B-Instruct",
          enforce_eager=False,
          tensor_parallel_size=2,
          dtype=torch.float16,
          compilation_config=config)

If use vllm serve, add -O '{"level":3, "compile_sizes": [4, 8, 16], "pass_config": {"enable_async_tp": true}}'

Some benchmark results on 2 GPUs of A100.

model = unsloth/Meta-Llama-3.1-8B-Instruct
tp_size = 2
batch_size = 1
input_len=2048
output_len=1

Latency is slightly higher when enable async tp with input len is 2048.

python benchmarks/benchmark_latency.py --model unsloth/Meta-Llama-3.1-8B-Instruct --output-len 1 --input-len 2048 --batch-size 1 --tensor-parallel-size 2 --load-format dummy --num_iters_warmup 5 --num_iters 15 -O '{"level":3, "compile_sizes": [2048], "pass_config": {"enable_async_tp": true}}' --no-enable-prefix-caching
Avg latency: 0.19084931214650472 seconds
10% percentile latency: 0.18976202309131623 seconds
25% percentile latency: 0.1900753453373909 seconds
50% percentile latency: 0.1905977502465248 seconds
75% percentile latency: 0.19149887561798096 seconds
90% percentile latency: 0.19235587865114212 seconds
99% percentile latency: 0.19316918954253195 seconds

python benchmarks/benchmark_latency.py --model unsloth/Meta-Llama-3.1-8B-Instruct --output-len 1 --input-len 2048 --batch-size 1 --tensor-parallel-size 2 --load-format dummy --num_iters_warmup 5 --num_iters 15 -O '{"level":3, "compile_sizes": [2048], "pass_config": {"enable_async_tp": false}}' --no-enable-prefix-caching &> benchmark.log

Avg latency: 0.18629603137572606 seconds
10% percentile latency: 0.18520110249519348 seconds
25% percentile latency: 0.18542765080928802 seconds
50% percentile latency: 0.18612460047006607 seconds
75% percentile latency: 0.1871374361217022 seconds
90% percentile latency: 0.18730796426534652 seconds
99% percentile latency: 0.18765324756503104 seconds

Latency is almost the same when enable async tp with input len is 8192.

python benchmarks/benchmark_latency.py --model unsloth/Meta-Llama-3.1-8B-Instruct --output-len 1 --input-len 8192 --batch-size 1 --tensor-parallel-size 2 --load-format dummy --num_iters_warmup 5 --num_iters 15 -O '{"level":3, "compile_sizes": [8192], "pass_config": {"enable_async_tp": true}}' --no-enable-prefix-caching &> benchmark.log
Avg latency: 0.7484328990181287 seconds
10% percentile latency: 0.7470755681395531 seconds
25% percentile latency: 0.7476142570376396 seconds
50% percentile latency: 0.7484151422977448 seconds
75% percentile latency: 0.7493306994438171 seconds
90% percentile latency: 0.749718876183033 seconds
99% percentile latency: 0.7499414524435997 seconds

python benchmarks/benchmark_latency.py --model unsloth/Meta-Llama-3.1-8B-Instruct --output-len 1 --input-len 8192 --batch-size 1 --tensor-parallel-size 2 --load-format dummy --num_iters_warmup 5 --num_iters 15 -O '{"level":3, "compile_sizes": [8192], "pass_config": {"enable_async_tp": false}}' --no-enable-prefix-caching

Avg latency: 0.761994085709254 seconds
10% percentile latency: 0.7605385079979896 seconds
25% percentile latency: 0.7606241554021835 seconds
50% percentile latency: 0.7613658607006073 seconds
75% percentile latency: 0.7634274959564209 seconds
90% percentile latency: 0.7639447942376136 seconds
99% percentile latency: 0.7645426994562149 seconds

I think we can test this feature on a more demanding workload, like a 70B model across 4 GPUs.

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[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @cascade812.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork

[tlrmchlsmth]

This is what I'm seeing on a 4xH200 system:

vLLM main:

python benchmarks/benchmark_latency.py --model meta-llama/Llama-3.1-70B-Instruct --output-len 1 --input-len 8192 --batch-size 1 --tensor-parallel-size 4 --load-format dummy --num_iters_warmup 5 --num_iters 15 -O '{"level":3, "compile_sizes": [8192]}' --no-enable-prefix-caching

Avg latency: 0.5901695430278778 seconds
10% percentile latency: 0.5880581809207797 seconds
25% percentile latency: 0.5890177926048636 seconds
50% percentile latency: 0.5897573744878173 seconds
75% percentile latency: 0.5918027735315263 seconds
90% percentile latency: 0.5927275052294135 seconds
99% percentile latency: 0.5936225369013846 seconds

This PR:

python benchmarks/benchmark_latency.py --model meta-llama/Llama-3.1-70B-Instruct --output-len 1 --input-len 8192 --batch-size 1 --tensor-parallel-size 4 --load-format dummy --num_iters_warmup 5 --num_iters 15 -O '{"level":3, "compile_sizes": [8192], "pass_config": {"enable_async_tp": true, "enable_sequence_parallelism": true}}' --no-enable-prefix-caching

Avg latency: 0.5260226292535662 seconds
10% percentile latency: 0.5204391019418836 seconds
25% percentile latency: 0.5236838199198246 seconds
50% percentile latency: 0.5270518623292446 seconds
75% percentile latency: 0.5288312714546919 seconds
90% percentile latency: 0.5301465425640345 seconds
99% percentile latency: 0.5310012844949961 seconds

[cascade812]

It also shows ~10% latency reduce on 4 X A100 (40GB) for 8B LLM.

python benchmarks/benchmark_latency.py --model unsloth/Meta-Llama-3.1-8B-Instruct --output-len 1 --input-len 8192 --batch-size 1 --tensor-parallel-size 4 --load-format dummy --num_iters_warmup 5 --num_iters 15 -O '{"level":3, "compile_sizes": [8192]' --no-enable-prefix-caching

Avg latency: 0.19637128477916121 seconds
10% percentile latency: 0.19597571501508354 seconds
25% percentile latency: 0.19614936946891248 seconds
50% percentile latency: 0.1963466382585466 seconds
75% percentile latency: 0.19650844135321677 seconds
90% percentile latency: 0.19685424230992793 seconds
99% percentile latency: 0.19704130258411168 seconds

With async tp enabled

python benchmarks/benchmark_latency.py --model unsloth/Meta-Llama-3.1-8B-Instruct --output-len 1 --input-len 8192 --batch-size 1 --tensor-parallel-size 4 --load-format dummy --num_iters_warmup 5 --num_iters 15 -O '{"level":3, "compile_sizes": [8192], "pass_config": {"enable_async_tp": true, "enable_sequence_parallelism": true}}' --no-enable-prefix-caching

Avg latency: 0.17523012173672517 seconds
10% percentile latency: 0.17482020873576404 seconds
25% percentile latency: 0.17494881455786526 seconds
50% percentile latency: 0.1752709816209972 seconds
75% percentile latency: 0.1755139627493918 seconds
90% percentile latency: 0.1756236758083105 seconds
99% percentile latency: 0.17574628297239542 seconds

[JaheimLee]

Hi! Is it necessary to always set sequence_parallelism to true?

[ProExpertProg]

Great PR! Concise and effective. I only had a few cleanup comments.

[cascade812]

Hi! Is it necessary to always set sequence_parallelism to true?

No need. sequence parallelism is enabled by default if enable_async_tp is true.

[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @cascade812.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork

[tlrmchlsmth]

Very nice work