[Benchmark] Change mii to use persistent deployment and support tensor parallel

[IKACE]

Description

When running benchmarks/benchmark/benchmark_throughput.py using deepspeed-mii backend, the original code uses non-persistent pipeline deployment, which does not support tensor parallelism and will generate errors when tp > 1.

Solution

Change deepspeed-mii backend to use persistent deployment.

The code is verified using Llama-2-7b and 4 A100 GPUs. After the change, tensor parallelism (e.g. tp=4) can be correctly supported for deepspeed-mii backend. I also compared performance of running Llama-2-7b on single A100 GPU and the throughput performance is the same as running with non-persistent deployment.

The use of persistent deployment is also encouraged in DeepSpeed's benchmark. (link)

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[simon-mo]

@WoosukKwon i recall originally there were some issue with this approach but I don't remember exactly. Do you think this is okay?

[WoosukKwon]

To my understanding, the persistent deployment will involve the overhead of handling the requests, while the purpose of benchmark_throughput.py is to get the purse performance of the engine without the overhead.

[IKACE]

I think the problem for non-persistent pipeline is that it does not support tuning tensor parallel degree when invoked (to my understanding). It seems that we need to wrap the program with deepspeed launcher according to this post.

[ywang96]

FYI - The way deepspeed-mii works is slightly different from vllm or tensorrt-llm since its inference engine is essentially taken from deepspeed and the batching logic happens on the deepspeed-mii layer instead of on engine itself. That's why unlike LLM from vLLM, their client object is essentially an API server/persistent deployment under the hood.