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docs/get_started/installation/kunlunxin_xpu.md
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docs/get_started/installation/kunlunxin_xpu.md
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# Kunlunxin XPU
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## Requirements
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- OS: Linux
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- Python: 3.10
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- XPU Model: P800
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- XPU Driver Version: ≥ 5.0.21.10
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- XPU Firmware Version: ≥ 1.31
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Verified platform:
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- CPU: INTEL(R) XEON(R) PLATINUM 8563C
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- Memory: 2T
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- Disk: 4T
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- OS: CentOS release 7.6 (Final)
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- Python: 3.10
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- XPU Model: P800 (OAM Edition)
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- XPU Driver Version: 5.0.21.10
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- XPU Firmware Version: 1.31
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**Note:** Currently, only INTEL or Hygon CPU-based P800 (OAM Edition) servers have been verified. Other CPU types and P800 (PCIe Edition) servers have not been tested yet.
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## 1. Set up using Docker (Recommended)
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```bash
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docker pull ccr-2vdh3abv-pub.cnc.bj.baidubce.com/paddlepaddle/fastdeploy-xpu:2.0.0
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```
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## 2. Set up using pre-built wheels
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### Install PaddlePaddle
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```bash
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python -m pip install paddlepaddle-xpu==3.1.0 -i https://www.paddlepaddle.org.cn/packages/stable/xpu-p800/
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```
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Alternatively, you can install the latest version of PaddlePaddle (Not recommended)
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```bash
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python -m pip install --pre paddlepaddle-xpu -i https://www.paddlepaddle.org.cn/packages/nightly/xpu-p800/
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```
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### Install FastDeploy (**Do NOT install via PyPI source**)
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```bash
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python -m pip install fastdeploy-xpu==2.0.0 -i https://www.paddlepaddle.org.cn/packages/stable/xpu-p800/
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```
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Alternatively, you can install the latest version of FastDeploy (Not recommended)
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```bash
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python -m pip install --pre fastdeploy-xpu -i https://www.paddlepaddle.org.cn/packages/nightly/xpu-p800/
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```
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### 3. Build wheel from source
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### Install PaddlePaddle
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```bash
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python -m pip install paddlepaddle-xpu==3.1.0 -i https://www.paddlepaddle.org.cn/packages/stable/xpu-p800/
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```
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Alternatively, you can install the latest version of PaddlePaddle (Not recommended)
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```bash
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python -m pip install --pre paddlepaddle-xpu -i https://www.paddlepaddle.org.cn/packages/nightly/xpu-p800/
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```
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### Download Kunlunxin Toolkit (XTDK) and XVLLM library, then set their paths.
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```bash
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# XTDK
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wget https://klx-sdk-release-public.su.bcebos.com/xtdk_15fusion/dev/3.2.40.1/xtdk-llvm15-ubuntu2004_x86_64.tar.gz
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tar -xvf xtdk-llvm15-ubuntu2004_x86_64.tar.gz && mv xtdk-llvm15-ubuntu2004_x86_64 xtdk
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export CLANG_PATH=$(pwd)/xtdk
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# XVLLM
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wget https://klx-sdk-release-public.su.bcebos.com/xinfer/daily/eb/20250624/output.tar.gz
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tar -xvf output.tar.gz && mv output xvllm
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export XVLLM_PATH=$(pwd)/xvllm
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```
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Alternatively, you can download the latest versions of XTDK and XVLLM (Not recommended)
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```bash
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XTDK: https://klx-sdk-release-public.su.bcebos.com/xtdk_15fusion/dev/latest/xtdk-llvm15-ubuntu2004_x86_64.tar.gz
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XVLLM: https://klx-sdk-release-public.su.bcebos.com/xinfer/daily/eb/latest/output.tar.gz
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```
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### Download FastDeploy source code, checkout the stable branch/TAG, then compile and install.
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```bash
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git clone https://github.com/PaddlePaddle/FastDeploy
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cd FastDeploy
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bash build.sh
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```
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The compiled outputs will be located in the ```FastDeploy/dist``` directory.
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## Installation verification
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```python
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import paddle
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from paddle.jit.marker import unified
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paddle.utils.run_check()
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from fastdeploy.model_executor.ops.xpu import block_attn
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```
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If all the above steps execute successfully, FastDeploy is installed correctly.
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## Quick start
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Currently, P800 has only validated deployment of the following models:
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- ERNIE-4.5-300B-A47B-Paddle 32K WINT4 (8-card)
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- ERNIE-4.5-300B-A47B-Paddle 128K WINT4 (8-card)
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### Offline inference
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After installing FastDeploy, you can perform offline text generation with user-provided prompts using the following code,
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```python
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from fastdeploy import LLM, SamplingParams
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prompts = [
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"Where is the capital of China?",
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]
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sampling_params = SamplingParams(top_p=0.95)
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llm = LLM(model="baidu/ERNIE-4.5-300B-A47B-Paddle", tensor_parallel_size=8, max_model_len=8192, quantization='wint4')
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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.text
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print(f"Prompt: {prompt}")
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print(f"Generated text: {generated_text}")
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```
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Refer to [Parameters](../../parameters.md) for more configuration options.
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## Online serving (OpenAI API-Compatible server)
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Deploy an OpenAI API-compatible server using FastDeploy with the following commands:
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### Start service
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**ERNIE-4.5-300B-A47B-Paddle 32K WINT4 (8-card) (Recommended)**
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```bash
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python -m fastdeploy.entrypoints.openai.api_server \
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--model baidu/ERNIE-4.5-300B-A47B-Paddle \
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--port 8188 \
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--tensor-parallel-size 8 \
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--max-model-len 32768 \
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--max-num-seqs 64 \
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--quantization "wint4" \
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--gpu-memory-utilization 0.9
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```
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**ERNIE-4.5-300B-A47B-Paddle 128K WINT4 (8-card)**
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```bash
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python -m fastdeploy.entrypoints.openai.api_server \
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--model baidu/ERNIE-4.5-300B-A47B-Paddle \
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--port 8188 \
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--tensor-parallel-size 8 \
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--max-model-len 131072 \
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--max-num-seqs 64 \
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--quantization "wint4" \
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--gpu-memory-utilization 0.9
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```
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Refer to [Parameters](../../parameters.md) for more options.
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### Send requests
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Send requests using either curl or Python
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```bash
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curl -X POST "http://0.0.0.0:8188/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": "user", "content": "Where is the capital of China?"}
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]
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}'
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```
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```python
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import openai
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host = "0.0.0.0"
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port = "8188"
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client = openai.Client(base_url=f"http://{host}:{port}/v1", api_key="null")
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response = client.completions.create(
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model="null",
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prompt="Where is the capital of China?",
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stream=True,
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)
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for chunk in response:
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print(chunk.choices[0].text, end='')
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print('\n')
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response = client.chat.completions.create(
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model="null",
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messages=[
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{"role": "system", "content": "I'm a helpful AI assistant."},
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{"role": "user", "content": "Where is the capital of China?"},
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],
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stream=True,
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)
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for chunk in response:
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if chunk.choices[0].delta:
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print(chunk.choices[0].delta.content, end='')
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print('\n')
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```
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For detailed OpenAI protocol specifications, see [OpenAI Chat Compeltion API](https://platform.openai.com/docs/api-reference/chat/create). Differences from the standard OpenAI protocol are documented in [Deployment](../../serving/README.md).
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