[Performance]: vllm0.6.5加载GLM4-9B-Chat，动态加载lora，输入长文本时推理性能下降较多

[zh19980310]

Proposal to improve performance

No response

Report of performance regression

A800，单卡处理单条请求

1. vllm0.6.5不加载lora
   （1）启动：
   CUDA_VISIBLE_DEVICES=7 python -m vllm.entrypoints.openai.api_server --model /Work/....../glm-4-9b-chat/ --trust-remote-code
   （2）请求：
   response = client.chat.completions.create(
   model='/Work/....../glm-4-9b-chat/',
   messages=messages,
   n=1,
   temperature=0,
   extra_body={"stop_token_ids": [151329, 151336, 151338]},
   max_tokens=2048,
   stream=True)

2. vllm0.6.5动态加载lora
   【lora模型使用llama_factory框架训练】
   （1）启动：
   CUDA_VISIBLE_DEVICES=7 python -m vllm.entrypoints.openai.api_server --model /Work/....../glm-4-9b-chat/ --enable-lora --max-loras 10 --lora-modules summary=/Work/....../sft_1218/ --trust-remote-code --max-lora-rank 64
   （2）请求：
   response = client.chat.completions.create(
   model='summary',
   messages=messages,
   n=1,
   temperature=0,
   extra_body={"stop_token_ids": [151329, 151336, 151338]},
   max_tokens=2048,
   stream=True)

测试messages中输入不同长度文本时，不同情况下的推理速度：

发现加载lora后，输入文本较长时，推理速度相比于不加载lora下降较多，输入文本较短时下降不多
请问是什么原因造成的，我应该如何解决？谢谢~

Misc discussion on performance

No response

Your current environment (if you think it is necessary)

The output of `python collect_env.py`

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[jeejeelee]

Let me first try to reproduce your results

[jeejeelee]

Please try increasing --max-seq-len-to-capture. The root cause is that when the sequences length exceeds max-seq-len-to-capture, it will fall back to eager mode, which affects LoRA performance.

[zh19980310]

好的，我试一下，感谢~

[jeejeelee]

好的，我试一下，感谢~

If this can address your issue, please let me know, thanks!

[zh19980310]

加上您提供的参数后推理速度问题已经解决，测试如下：

十分感谢~

[noooop]

长知识了

[Candy555]

学习了~

[jeejeelee]

This might be a bug. I will investigate further and try to resolve it.

[noooop]

Are you using the default scheduler or the chunked_prefill scheduler?

default max-seq-len-to-capture = 8102

cuda graph also takes up additional GPU memory. I don’t think there is any need to increase it.

New versions should use chunked_prefill by default，but it's better to check.

Please try the configuration below:

enable_chunked_prefill = True
max_num_seqs=32
max_num_batched_tokens=2048 <- 2048 token can generally make the GPU reach saturation

[jeejeelee]

Are you using the default scheduler or the chunked_prefill scheduler?

default max-seq-len-to-capture = 8102

cuda graph also takes up additional GPU memory. I don’t think there is any need to increase it.

New versions should use chunked_prefill by default，but it's better to check.

Please try the configuration below:

enable_chunked_prefill = True max_num_seqs=32 max_num_batched_tokens=2048 <- 2048 token can generally make the GPU reach saturation

We impl Multi-LoRA based on triton, and triton has significant runtime overhead, so without using cudagraph, it will severely impact decode performance, the above is an example.

Only V1 uses chunked_prefill by default, the current release version still uses the default scheduler

[noooop]

We impl Multi-LoRA based on triton, and triton has significant runtime overhead, so without using cudagraph, it will severely impact decode performance, the above is an example.

So I hope @zh19980310 to test the chunked prefill scheduler.
Might have good performance without increasing max-seq-len-to-capture

Only V1 uses chunked_prefill by default, the current release version still uses the default scheduler

(I seem to have seen a PR similar to "Turn on chunked prefill by default". ok, just for v1.

[zh19980310]

Are you using the default scheduler or the chunked_prefill scheduler?

default max-seq-len-to-capture = 8102

cuda graph also takes up additional GPU memory. I don’t think there is any need to increase it.

New versions should use chunked_prefill by default，but it's better to check.

Please try the configuration below:

enable_chunked_prefill = True max_num_seqs=32 max_num_batched_tokens=2048 <- 2048 token can generally make the GPU reach saturation

按照您提供的参数测试了一下：
CUDA_VISIBLE_DEVICES=7 python -m vllm.entrypoints.openai.api_server --model /Work/....../glm-4-9b-chat/ --enable-lora --lora-modules summary=/Work/....../sft_1218/ --trust-remote-code --max-lora-rank 64 --enable_chunked_prefill --max_num_seqs 32 --max_num_batched_tokens 2048
推理速度与问题描述处的测试结果无差异