# Tutorials

## Run vllm-ascend on Single NPU

### Offline Inference on Single NPU

Run docker container:

```{code-block} bash
   :substitutions:
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:|vllm_ascend_version| bash
```

Setup environment variables:

```bash
# Use Modelscope mirror to speed up model download
export VLLM_USE_MODELSCOPE=True

# 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 [<u>here</u>](https://www.hiascend.com/document/detail/zh/CANNCommunityEdition/800alpha003/apiref/envref/envref_07_0061.html).
:::

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

```python
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:

```bash
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:

```{code-block} bash
   :substitutions:

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 PYTORCH_NPU_ALLOC_CONF=max_split_size_mb:256 \
-it quay.io/ascend/vllm-ascend:|vllm_ascend_version| \
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). This will differ with different NPU series base on the HBM size. Please modify the value according to a suitable value for your NPU series.
:::

If your service start successfully, you can see the info shown below:

```bash
INFO:     Started server process [6873]
INFO:     Waiting for application startup.
INFO:     Application startup complete.
```

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

```bash
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):

```bash
{"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:

```bash
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

Run docker container:

```{code-block} bash
   :substitutions:

docker run \
--name vllm-ascend \
--device /dev/davinci0 \
--device /dev/davinci1 \
--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:|vllm_ascend_version| bash
```

Setup environment variables:

```bash
# Use Modelscope mirror to speed up model download
export VLLM_USE_MODELSCOPE=True

# 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
```

Run the following script to execute offline inference on multi-NPU:

```python
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",
]
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
llm = LLM(model="Qwen/Qwen2.5-7B-Instruct",
          tensor_parallel_size=2,
          distributed_executor_backend="mp",
          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}")

del llm
clean_up()
```

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

```bash
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 Multi Machine

Run docker container on each machine:

```shell
docker run \
--name vllm-ascend \
--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 quay.io/ascend/vllm-ascend:v0.7.1rc1 bash
```

Choose one machine as head node, the other are worker nodes, then start ray on each machine:

:::{note}
Check out your `nic_name` by command `ip addr`.
:::

```shell
# Head node
export HCCL_IF_IP={local_ip}
export GLOO_SOCKET_IFNAME={nic_name}
export TP_SOCKET_IFNAME={nic_name}
export ASCEND_RT_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
export RAY_EXPERIMENTAL_NOSET_ASCEND_RT_VISIBLE_DEVICES=1
ray start --head --num-gpus=8

# Worker node
export HCCL_IF_IP={local_ip}
export ASCEND_PROCESS_LOG_PATH={plog_save_path}
export GLOO_SOCKET_IFNAME={nic_name}
export TP_SOCKET_IFNAME={nic_name}
export RAY_EXPERIMENTAL_NOSET_ASCEND_RT_VISIBLE_DEVICES=1 
export ASCEND_RT_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
ray start --address='{head_node_ip}:{port_num}' --num-gpus=8 --node-ip-address={local_ip}
```

Start the vLLM server on head node:

```shell
export VLLM_HOST_IP={head_node_ip}
export HCCL_CONNECT_TIMEOUT=120
export ASCEND_PROCESS_LOG_PATH={plog_save_path}
export HCCL_IF_IP={head_node_ip}

if [ -d "{plog_save_path}" ]; then
    rm -rf {plog_save_path}
    echo ">>> remove {plog_save_path}"
fi

LOG_FILE="multinode_$(date +%Y%m%d_%H%M).log"
VLLM_TORCH_PROFILER_DIR=./vllm_profile
python -m vllm.entrypoints.openai.api_server  \
       --model="Deepseek/DeepSeek-V2-Lite-Chat" \
       --trust-remote-code \
       --enforce-eager \
       --max-model-len {max_model_len} \
       --distributed_executor_backend "ray" \
       --tensor-parallel-size 16 \
       --disable-log-requests \
       --disable-log-stats \
       --disable-frontend-multiprocessing \
       --port {port_num} \
```

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

```shell
curl -X POST http://127.0.0.1:{prot_num}/v1/completions  \
     -H "Content-Type: application/json" \
     -d '{
         "model": "Deepseek/DeepSeek-V2-Lite-Chat",
         "prompt": "The future of AI is",
         "max_tokens": 24
     }'
```

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

```
{"id":"cmpl-6dfb5a8d8be54d748f0783285dd52303","object":"text_completion","created":1739957835,"model":"/home/data/DeepSeek-V2-Lite-Chat/","choices":[{"index":0,"text":" heavily influenced by neuroscience and cognitiveGuionistes. The goalochondria is to combine the efforts of researchers, technologists,","logprobs":null,"finish_reason":"length","stop_reason":null,"prompt_logprobs":null}],"usage":{"prompt_tokens":6,"total_tokens":30,"completion_tokens":24,"prompt_tokens_details":null}}
```

Logs of the vllm server:

```
INFO:     127.0.0.1:59384 - "POST /v1/completions HTTP/1.1" 200 OK
INFO 02-19 17:37:35 metrics.py:453] Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 1.9 tokens/s, Running: 0 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.0%, CPU KV cache usage: 0.0%.
```