Atlas 300I

Atlas 300I#

注意

此 Atlas 300I 系列目前处于实验阶段。在未来的版本中，模型覆盖范围和性能改进方面可能会发生行为变化。
目前，310I 系列仅支持 eager 模式和 float16 数据类型。
在 310p 系列上运行 vLLM 存在一些已知问题，您可以参考 vllm-ascend #3316 和 #2795。您可以先使用 v0.10.0rc1 版本。

在 Atlas 300I 系列上运行 vLLM#

运行 docker 容器

# Update the vllm-ascend image
export IMAGE=quay.io/ascend/vllm-ascend:v0.10.0rc1-310p
docker run --rm \
--name vllm-ascend \
--shm-size=1g \
--device /dev/davinci0 \
--device /dev/davinci1 \
--device /dev/davinci2 \
--device /dev/davinci3 \
--device /dev/davinci4 \
--device /dev/davinci5 \
--device /dev/davinci6 \
--device /dev/davinci7 \
--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 $IMAGE bash

设置环境变量

# Load model from ModelScope to speed up download
export VLLM_USE_MODELSCOPE=True

# Set `max_split_size_mb` to reduce memory fragmentation and avoid out of memory
export PYTORCH_NPU_ALLOC_CONF=max_split_size_mb:256

NPU 在线推理#

运行以下脚本以在 NPU 上启动 vLLM 服务器（Qwen3-0.6B：1 卡，Qwen2.5-7B-Instruct：2 卡，Pangu-Pro-MoE-72B：8 卡）

Qwen3-0.6B

运行以下命令启动 vLLM 服务器

vllm serve Qwen/Qwen3-0.6B \
    --tensor-parallel-size 1 \
    --enforce-eager \
    --dtype float16 \
    --compilation-config '{"custom_ops":["none", "+rms_norm", "+rotary_embedding"]}'

服务器启动后，您可以用输入提示查询模型

curl https://:8000/v1/completions \
  -H "Content-Type: application/json" \
  -d '{
    "prompt": "The future of AI is",
    "max_tokens": 64,
    "top_p": 0.95,
    "top_k": 50,
    "temperature": 0.6
  }'

Qwen2.5-7B-Instruct

运行以下命令启动 vLLM 服务器

vllm serve Qwen/Qwen2.5-7B-Instruct \
    --tensor-parallel-size 2 \
    --enforce-eager \
    --dtype float16 \
    --compilation-config '{"custom_ops":["none", "+rms_norm", "+rotary_embedding"]}'

服务器启动后，您可以用输入提示查询模型

curl https://:8000/v1/completions \
  -H "Content-Type: application/json" \
  -d '{
    "prompt": "The future of AI is",
    "max_tokens": 64,
    "top_p": 0.95,
    "top_k": 50,
    "temperature": 0.6
  }'

Qwen2.5-VL-3B-Instruct

运行以下命令启动 vLLM 服务器

vllm serve Qwen/Qwen2.5-VL-3B-Instruct \
    --tensor-parallel-size 1 \
    --enforce-eager \
    --dtype float16 \
    --compilation-config '{"custom_ops":["none", "+rms_norm", "+rotary_embedding"]}'

服务器启动后，您可以用输入提示查询模型

curl https://:8000/v1/completions \
  -H "Content-Type: application/json" \
  -d '{
    "prompt": "The future of AI is",
    "max_tokens": 64,
    "top_p": 0.95,
    "top_k": 50,
    "temperature": 0.6
  }'

Pangu-Pro-MoE-72B

下载模型

git lfs install
git clone https://gitcode.com/ascend-tribe/pangu-pro-moe-model.git

运行以下命令启动 vLLM 服务器

vllm serve /home/pangu-pro-moe-mode/ \
--tensor-parallel-size 4 \
--enable-expert-parallel \
--dtype "float16" \
--trust-remote-code \
--enforce-eager

服务器启动后，您可以使用输入提示查询模型。

export question="你是谁？"
curl https://:8000/v1/completions \
  -H "Content-Type: application/json" \
  -d '{
    "prompt": "[unused9]系统：[unused10][unused9]用户：'${question}'[unused10][unused9]助手：",
    "max_tokens": 64,
    "top_p": 0.95,
    "top_k": 50,
    "temperature": 0.6
  }'

如果成功运行此脚本，您将看到结果。

离线推理#

运行以下脚本（example.py）在 NPU 上执行离线推理

Qwen3-0.6B

from vllm import LLM, SamplingParams
import gc
import torch
from vllm import LLM, SamplingParams
from vllm.distributed.parallel_state import (destroy_distributed_environment,
                                             destroy_model_parallel)

def clean_up():
    destroy_model_parallel()
    destroy_distributed_environment()
    gc.collect()
    torch.npu.empty_cache()
prompts = [
    "Hello, my name is",
    "The future of AI is",
]
# Create a sampling params object.
sampling_params = SamplingParams(max_tokens=100, temperature=0.0)
# Create an LLM.
llm = LLM(
    model="Qwen/Qwen3-0.6B",
    tensor_parallel_size=1,
    enforce_eager=True, # For 300I series, only eager mode is supported.
    dtype="float16", # IMPORTANT cause some ATB ops cannot support bf16 on 300I series
    compilation_config={"custom_ops":["none", "+rms_norm", "+rotary_embedding"]}, # High performance for 300I series
)
# Generate texts from the prompts.
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}")
del llm
clean_up()

Qwen2.5-7B-Instruct

from vllm import LLM, SamplingParams
import gc
import torch
from vllm import LLM, SamplingParams
from vllm.distributed.parallel_state import (destroy_distributed_environment,
                                             destroy_model_parallel)

def clean_up():
    destroy_model_parallel()
    destroy_distributed_environment()
    gc.collect()
    torch.npu.empty_cache()
prompts = [
    "Hello, my name is",
    "The future of AI is",
]
# Create a sampling params object.
sampling_params = SamplingParams(max_tokens=100, temperature=0.0)
# Create an LLM.
llm = LLM(
    model="Qwen/Qwen2.5-7B-Instruct",
    tensor_parallel_size=2,
    enforce_eager=True, # For 300I series, only eager mode is supported.
    dtype="float16", # IMPORTANT cause some ATB ops cannot support bf16 on 300I series
    compilation_config={"custom_ops":["none", "+rms_norm", "+rotary_embedding"]}, # High performance for 300I series
)
# Generate texts from the prompts.
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}")
del llm
clean_up()

Qwen2.5-VL-3B-Instruct

from vllm import LLM, SamplingParams
import gc
import torch
from vllm import LLM, SamplingParams
from vllm.distributed.parallel_state import (destroy_distributed_environment,
                                             destroy_model_parallel)

def clean_up():
    destroy_model_parallel()
    destroy_distributed_environment()
    gc.collect()
    torch.npu.empty_cache()
prompts = [
    "Hello, my name is",
    "The future of AI is",
]
# Create a sampling params object.
sampling_params = SamplingParams(max_tokens=100, top_p=0.95, top_k=50, temperature=0.6)
# Create an LLM.
llm = LLM(
    model="Qwen/Qwen2.5-VL-3B-Instruct",
    tensor_parallel_size=1,
    enforce_eager=True, # For 300I series, only eager mode is supported.
    dtype="float16", # IMPORTANT cause some ATB ops cannot support bf16 on 300I series
    compilation_config={"custom_ops":["none", "+rms_norm", "+rotary_embedding"]}, # High performance for 300I series
)
# Generate texts from the prompts.
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}")
del llm
clean_up()

Pangu-Pro-MoE-72B

下载模型

git lfs install
git clone https://gitcode.com/ascend-tribe/pangu-pro-moe-model.git

import gc
from transformers import AutoTokenizer
import torch

from vllm import LLM, SamplingParams
from vllm.distributed.parallel_state import (destroy_distributed_environment,
                                             destroy_model_parallel)

def clean_up():
    destroy_model_parallel()
    destroy_distributed_environment()
    gc.collect()
    torch.npu.empty_cache()


if __name__ == "__main__":

    tokenizer = AutoTokenizer.from_pretrained("/home/pangu-pro-moe-mode/", trust_remote_code=True)
    tests = [
        "Hello, my name is",
        "The future of AI is",
    ]
    prompts = []
    for text in tests:
        messages = [
        {"role": "system", "content": ""},    # Optionally customize system content
        {"role": "user", "content": text}
    ]
        prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)        # 推荐使用官方的template
        prompts.append(prompt)
    sampling_params = SamplingParams(temperature=0.6, top_p=0.95, top_k=40)

    llm = LLM(model="/home/pangu-pro-moe-mode/",
            tensor_parallel_size=8,
            distributed_executor_backend="mp",
            enable_expert_parallel=True,
            dtype="float16",
            max_model_len=1024,
            trust_remote_code=True,
            enforce_eager=True)

    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}")

    del llm
    clean_up()

运行脚本

python example.py

如果脚本运行成功，您将看到如下所示的信息

Prompt: 'Hello, my name is', Generated text: " Lina. I'm a 22-year-old student from China. I'm interested in studying in the US. I'm looking for a job in the US. I want to know if there are any opportunities in the US for me to work. I'm also interested in the culture and lifestyle in the US. I want to know if there are any opportunities for me to work in the US. I'm also interested in the culture and lifestyle in the US. I'm interested in the culture"
Prompt: 'The future of AI is', Generated text: " not just about the technology itself, but about how we use it to solve real-world problems. As AI continues to evolve, it's important to consider the ethical implications of its use. AI has the potential to bring about significant changes in society, but it also has the power to create new challenges. Therefore, it's crucial to develop a comprehensive approach to AI that takes into account both the benefits and the risks associated with its use. This includes addressing issues such as bias, privacy, and accountability."

Atlas 300I

目录

Atlas 300I#

在 Atlas 300I 系列上运行 vLLM#

NPU 在线推理#

离线推理#