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Shanshan Shen 2a678141d4 [Doc] Add vllm-ascend usage doc & fix doc format (#53 )

### What this PR does / why we need it?
1. Add vllm-ascend tutorial doc for Qwen/Qwen2.5-7B-Instruct model
serving doc
2. fix format of files in `docs` dir, e.g. format tables, add underline
for links, add line feed...

### Does this PR introduce _any_ user-facing change?
<!--
Note that it means *any* user-facing change including all aspects such
as API, interface or other behavior changes.
Documentation-only updates are not considered user-facing changes.
-->

no.

### How was this patch tested?
doc CI passed

---------

Signed-off-by: Shanshan Shen <87969357+shen-shanshan@users.noreply.github.com>

2025-02-17 18:37:29 +08:00

6.6 KiB

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Tutorials

Run vllm-ascend on Single NPU

Offline Inference on Single NPU

Run docker container:

docker run \
--name vllm-ascend \
--device /dev/davinci0 \
--device /dev/davinci_manager \
--device /dev/devmm_svm \
--device /dev/hisi_hdc \
-v /usr/local/dcmi:/usr/local/dcmi \
-v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
-v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \
-v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \
-v /etc/ascend_install.info:/etc/ascend_install.info \
-v /root/.cache:/root/.cache \
-p 8000:8000 \
-it quay.io/ascend/vllm-ascend:latest bash

Setup environment variables:

# Use Modelscope mirror to speed up model download
export VLLM_USE_MODELSCOPE=True
export MODELSCOPE_CACHE=/root/.cache/

# To avoid NPU out of memory, set `max_split_size_mb` to any value lower than you need to allocate for Qwen2.5-7B-Instruct
export PYTORCH_NPU_ALLOC_CONF=max_split_size_mb:256

Note

max_split_size_mb prevents the native allocator from splitting blocks larger than this size (in MB). This can reduce fragmentation and may allow some borderline workloads to complete without running out of memory. You can find more details here.

Run the following script to execute offline inference on a single NPU:

from vllm import LLM, SamplingParams

prompts = [
    "Hello, my name is",
    "The future of AI is",
]
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
llm = LLM(model="Qwen/Qwen2.5-7B-Instruct", max_model_len=26240)

outputs = llm.generate(prompts, sampling_params)
for output in outputs:
    prompt = output.prompt
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

If you run this script successfully, you can see the info shown below:

Prompt: 'Hello, my name is', Generated text: ' Daniel and I am an 8th grade student at York Middle School. I'
Prompt: 'The future of AI is', Generated text: ' following you. As the technology advances, a new report from the Institute for the'

Online Serving on Single NPU

Run docker container to start the vLLM server on a single NPU:

docker run \
--name vllm-ascend \
--device /dev/davinci0 \
--device /dev/davinci_manager \
--device /dev/devmm_svm \
--device /dev/hisi_hdc \
-v /usr/local/dcmi:/usr/local/dcmi \
-v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
-v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \
-v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \
-v /etc/ascend_install.info:/etc/ascend_install.info \
-v /root/.cache:/root/.cache \
-p 8000:8000 \
-e VLLM_USE_MODELSCOPE=True \
-e MODELSCOPE_CACHE=/root/.cache/ \
-e PYTORCH_NPU_ALLOC_CONF=max_split_size_mb:256 \
-it quay.io/ascend/vllm-ascend:latest \
vllm serve Qwen/Qwen2.5-7B-Instruct --max_model_len 26240

Note

Add --max_model_len option to avoid ValueError that the Qwen2.5-7B model's max seq len (32768) is larger than the maximum number of tokens that can be stored in KV cache (26240).

Once your server is started, you can query the model with input prompts:

curl http://localhost:8000/v1/completions \
    -H "Content-Type: application/json" \
    -d '{
        "model": "Qwen/Qwen2.5-7B-Instruct",
        "prompt": "The future of AI is",
        "max_tokens": 7,
        "temperature": 0
    }'

If you query the server successfully, you can see the info shown below (client):

{"id":"cmpl-b25a59a2f985459781ce7098aeddfda7","object":"text_completion","created":1739523925,"model":"Qwen/Qwen2.5-7B-Instruct","choices":[{"index":0,"text":" here. It’s not just a","logprobs":null,"finish_reason":"length","stop_reason":null,"prompt_logprobs":null}],"usage":{"prompt_tokens":5,"total_tokens":12,"completion_tokens":7,"prompt_tokens_details":null}}

Logs of the vllm server:

INFO:     172.17.0.1:49518 - "POST /v1/completions HTTP/1.1" 200 OK
INFO 02-13 08:34:35 logger.py:39] Received request cmpl-574f00e342904692a73fb6c1c986c521-0: prompt: 'San Francisco is a', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=7, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None), prompt_token_ids: [23729, 12879, 374, 264], lora_request: None, prompt_adapter_request: None.

Run vllm-ascend on Multi-NPU

Distributed Inference on Multi-NPU