bdfb065b5d
### What this PR does / why we need it?
1. Enable pymarkdown check
2. Enable python `__init__.py` check for vllm and vllm-ascend
3. Make clean code
### How was this patch tested?
- vLLM version: v0.9.2
- vLLM main:
29c6fbe58c
---------
Signed-off-by: wangli <wangli858794774@gmail.com>
243 lines
7.0 KiB
Markdown
243 lines
7.0 KiB
Markdown
# Multi-NPU (Pangu Pro MoE)
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## Run vllm-ascend on Multi-NPU
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Run container:
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```{code-block} bash
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:substitutions:
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# Update the vllm-ascend image
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export IMAGE=quay.io/ascend/vllm-ascend:|vllm_ascend_version|
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docker run --rm \
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--name vllm-ascend \
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--device /dev/davinci0 \
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--device /dev/davinci1 \
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--device /dev/davinci2 \
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--device /dev/davinci3 \
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--device /dev/davinci_manager \
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--device /dev/devmm_svm \
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--device /dev/hisi_hdc \
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-v /usr/local/dcmi:/usr/local/dcmi \
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-v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
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-v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \
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-v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \
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-v /etc/ascend_install.info:/etc/ascend_install.info \
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-v /root/.cache:/root/.cache \
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-p 8000:8000 \
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-it $IMAGE bash
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```
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Setup environment variables:
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```bash
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# Set `max_split_size_mb` to reduce memory fragmentation and avoid out of memory
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export PYTORCH_NPU_ALLOC_CONF=max_split_size_mb:256
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```
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Download the model:
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```bash
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git lfs install
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git clone https://gitcode.com/ascend-tribe/pangu-pro-moe-model.git
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```
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### Online Inference on Multi-NPU
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Run the following script to start the vLLM server on Multi-NPU:
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```bash
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vllm serve /path/to/pangu-pro-moe-model \
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--tensor-parallel-size 4 \
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--enable-expert-parallel \
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--trust-remote-code \
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--enforce-eager
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```
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Once your server is started, you can query the model with input prompts:
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:::::{tab-set}
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::::{tab-item} v1/completions
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```{code-block} bash
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:substitutions:
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export question="你是谁？"
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curl http://localhost:8000/v1/completions \
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-H "Content-Type: application/json" \
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-d '{
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"prompt": "[unused9]系统：[unused10][unused9]用户：'${question}'[unused10][unused9]助手：",
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"max_tokens": 64,
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"top_p": 0.95,
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"top_k": 50,
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"temperature": 0.6
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}'
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```
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::::
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::::{tab-item} v1/chat/completions
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```{code-block} bash
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:substitutions:
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curl http://localhost:8000/v1/chat/completions \
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-H "Content-Type: application/json" \
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-d '{
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"messages": [
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{"role": "system", "content": ""},
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{"role": "user", "content": "你是谁？"}
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],
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"max_tokens": "64",
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"top_p": "0.95",
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"top_k": "50",
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"temperature": "0.6",
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"add_special_tokens" : true
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}'
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```
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::::
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:::::
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If you run this successfully, you can see the info shown below:
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```json
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{"id":"cmpl-2cd4223228ab4be9a91f65b882e65b32","object":"text_completion","created":1751255067,"model":"/root/.cache/pangu-pro-moe-model","choices":[{"index":0,"text":" [unused16] 好的，用户问我是谁，我需要根据之前的设定来回答。用户提到我是华为开发的“盘古Reasoner”，属于盘古大模型系列，作为智能助手帮助解答问题和提供 信息支持。现在用户再次询问，可能是在确认我的身份或者测试我的回答是否一致。\n\n首先，我要确保","logprobs":null,"finish_reason":"length","stop_reason":null,"prompt_logprobs":null}],"usage":{"prompt_tokens":15,"total_tokens":79,"completion_tokens":64,"prompt_tokens_details":null},"kv_transfer_params":null}
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```
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### Offline Inference on Multi-NPU
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Run the following script to execute offline inference on multi-NPU:
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:::::{tab-set}
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::::{tab-item} Graph Mode
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```{code-block} python
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:substitutions:
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import gc
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from transformers import AutoTokenizer
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import torch
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import os
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from vllm import LLM, SamplingParams
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from vllm.distributed.parallel_state import (destroy_distributed_environment,
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destroy_model_parallel)
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os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
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def clean_up():
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destroy_model_parallel()
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destroy_distributed_environment()
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gc.collect()
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torch.npu.empty_cache()
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if __name__ == "__main__":
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tokenizer = AutoTokenizer.from_pretrained("/path/to/pangu-pro-moe-model", trust_remote_code=True)
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tests = [
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"Hello, my name is",
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"The future of AI is",
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]
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prompts = []
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for text in tests:
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messages = [
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{"role": "system", "content": ""}, # Optionally customize system content
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{"role": "user", "content": text}
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]
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prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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prompts.append(prompt)
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sampling_params = SamplingParams(temperature=0.6, top_p=0.95, top_k=40)
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llm = LLM(model="/path/to/pangu-pro-moe-model",
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tensor_parallel_size=4,
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enable_expert_parallel=True,
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distributed_executor_backend="mp",
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max_model_len=1024,
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trust_remote_code=True,
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additional_config={
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'torchair_graph_config': {
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'enabled': True,
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},
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'ascend_scheduler_config':{
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'enabled': True,
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'enable_chunked_prefill' : False,
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'chunked_prefill_enabled': False
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},
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})
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outputs = llm.generate(prompts, sampling_params)
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for output in outputs:
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prompt = output.prompt
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generated_text = output.outputs[0].text
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print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
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del llm
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clean_up()
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```
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::::
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::::{tab-item} Eager Mode
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```{code-block} python
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:substitutions:
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import gc
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from transformers import AutoTokenizer
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import torch
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import os
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from vllm import LLM, SamplingParams
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from vllm.distributed.parallel_state import (destroy_distributed_environment,
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destroy_model_parallel)
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os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
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def clean_up():
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destroy_model_parallel()
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destroy_distributed_environment()
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gc.collect()
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torch.npu.empty_cache()
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if __name__ == "__main__":
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tokenizer = AutoTokenizer.from_pretrained("/path/to/pangu-pro-moe-model", trust_remote_code=True)
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tests = [
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"Hello, my name is",
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"The future of AI is",
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]
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prompts = []
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for text in tests:
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messages = [
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{"role": "system", "content": ""}, # Optionally customize system content
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{"role": "user", "content": text}
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]
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prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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prompts.append(prompt)
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sampling_params = SamplingParams(temperature=0.6, top_p=0.95, top_k=40)
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llm = LLM(model="/path/to/pangu-pro-moe-model",
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tensor_parallel_size=4,
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distributed_executor_backend="mp",
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max_model_len=1024,
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trust_remote_code=True,
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enforce_eager=True)
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outputs = llm.generate(prompts, sampling_params)
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for output in outputs:
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prompt = output.prompt
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generated_text = output.outputs[0].text
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print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
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del llm
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clean_up()
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```
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::::
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:::::
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If you run this script successfully, you can see the info shown below:
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```bash
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Prompt: 'Hello, my name is', Generated text: ' Daniel and I am an 8th grade student at York Middle School. I'
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Prompt: 'The future of AI is', Generated text: ' following you. As the technology advances, a new report from the Institute for the'
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```
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