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Kunlunxin xpu deployment

Supported Models

Model Name Context Length Quantization XPUs Required Deployment Commands Minimum Version Required
ERNIE-4.5-300B-A47B 32K WINT8 8 export XPU_VISIBLE_DEVICES="0,1,2,3,4,5,6,7"
python -m fastdeploy.entrypoints.openai.api_server \
--model PaddlePaddle/ERNIE-4.5-300B-A47B-Paddle \
--port 8188 \
--tensor-parallel-size 8 \
--max-model-len 32768 \
--max-num-seqs 64 \
--quantization "wint8" \
--gpu-memory-utilization 0.9		 >=2.0.3
ERNIE-4.5-300B-A47B 32K WINT4 4 (recommend) export XPU_VISIBLE_DEVICES="0,1,2,3" or "4,5,6,7"
python -m fastdeploy.entrypoints.openai.api_server \
--model PaddlePaddle/ERNIE-4.5-300B-A47B-Paddle \
--port 8188 \
--tensor-parallel-size 4 \
--max-model-len 32768 \
--max-num-seqs 64 \
--quantization "wint4" \
--gpu-memory-utilization 0.9		 >=2.0.0
ERNIE-4.5-300B-A47B 32K WINT4 8 export XPU_VISIBLE_DEVICES="0,1,2,3,4,5,6,7"
python -m fastdeploy.entrypoints.openai.api_server \
--model PaddlePaddle/ERNIE-4.5-300B-A47B-Paddle \
--port 8188 \
--tensor-parallel-size 8 \
--max-model-len 32768 \
--max-num-seqs 64 \
--quantization "wint4" \
--gpu-memory-utilization 0.9		 >=2.0.0
ERNIE-4.5-300B-A47B 128K WINT4 8 (recommend) export XPU_VISIBLE_DEVICES="0,1,2,3,4,5,6,7"
python -m fastdeploy.entrypoints.openai.api_server \
--model PaddlePaddle/ERNIE-4.5-300B-A47B-Paddle \
--port 8188 \
--tensor-parallel-size 8 \
--max-model-len 131072 \
--max-num-seqs 64 \
--quantization "wint4" \
--gpu-memory-utilization 0.9		 >=2.0.0
ERNIE-4.5-21B-A3B 32K BF16 1 export XPU_VISIBLE_DEVICES="0" # Specify any card
python -m fastdeploy.entrypoints.openai.api_server \
--model PaddlePaddle/ERNIE-4.5-21B-A3B-Paddle \
--port 8188 \
--tensor-parallel-size 1 \
--max-model-len 32768 \
--max-num-seqs 128 \
--gpu-memory-utilization 0.9		 >=2.0.3
ERNIE-4.5-21B-A3B 32K WINT8 1 export XPU_VISIBLE_DEVICES="0" # Specify any card
python -m fastdeploy.entrypoints.openai.api_server \
--model PaddlePaddle/ERNIE-4.5-21B-A3B-Paddle \
--port 8188 \
--tensor-parallel-size 1 \
--max-model-len 32768 \
--max-num-seqs 128 \
--quantization "wint8" \
--gpu-memory-utilization 0.9		 >=2.0.3
ERNIE-4.5-21B-A3B 32K WINT4 1 export XPU_VISIBLE_DEVICES="0" # Specify any card
python -m fastdeploy.entrypoints.openai.api_server \
--model PaddlePaddle/ERNIE-4.5-21B-A3B-Paddle \
--port 8188 \
--tensor-parallel-size 1 \
--max-model-len 32768 \
--max-num-seqs 128 \
--quantization "wint4" \
--gpu-memory-utilization 0.9		 >=2.0.3
ERNIE-4.5-21B-A3B 128K BF16 1 export XPU_VISIBLE_DEVICES="0" # Specify any card
python -m fastdeploy.entrypoints.openai.api_server \
--model PaddlePaddle/ERNIE-4.5-21B-A3B-Paddle \
--port 8188 \
--tensor-parallel-size 1 \
--max-model-len 131072 \
--max-num-seqs 128 \
--gpu-memory-utilization 0.9		 >=2.0.3
ERNIE-4.5-21B-A3B 128K WINT8 1 export XPU_VISIBLE_DEVICES="0" # Specify any card
python -m fastdeploy.entrypoints.openai.api_server \
--model PaddlePaddle/ERNIE-4.5-21B-A3B-Paddle \
--port 8188 \
--tensor-parallel-size 1 \
--max-model-len 131072 \
--max-num-seqs 128 \
--quantization "wint8" \
--gpu-memory-utilization 0.9		 >=2.0.3
ERNIE-4.5-21B-A3B 128K WINT4 1 export XPU_VISIBLE_DEVICES="0" # Specify any card
python -m fastdeploy.entrypoints.openai.api_server \
--model PaddlePaddle/ERNIE-4.5-21B-A3B-Paddle \
--port 8188 \
--tensor-parallel-size 1 \
--max-model-len 131072 \
--max-num-seqs 128 \
--quantization "wint4" \
--gpu-memory-utilization 0.9		 >=2.0.3
ERNIE-4.5-0.3B 32K BF16 1 export XPU_VISIBLE_DEVICES="0" # Specify any card
python -m fastdeploy.entrypoints.openai.api_server \
--model PaddlePaddle/ERNIE-4.5-0.3B-Paddle \
--port 8188 \
--tensor-parallel-size 1 \
--max-model-len 32768 \
--max-num-seqs 128 \
--gpu-memory-utilization 0.9		 >=2.0.3
ERNIE-4.5-0.3B 32K WINT8 1 export XPU_VISIBLE_DEVICES="x" # Specify any card
python -m fastdeploy.entrypoints.openai.api_server \
--model PaddlePaddle/ERNIE-4.5-0.3B-Paddle \
--port 8188 \
--tensor-parallel-size 1 \
--max-model-len 32768 \
--max-num-seqs 128 \
--quantization "wint8" \
--gpu-memory-utilization 0.9		 >=2.0.3
ERNIE-4.5-0.3B 128K BF16 1 export XPU_VISIBLE_DEVICES="0" # Specify any card
python -m fastdeploy.entrypoints.openai.api_server \
--model PaddlePaddle/ERNIE-4.5-0.3B-Paddle \
--port 8188 \
--tensor-parallel-size 1 \
--max-model-len 131072 \
--max-num-seqs 128 \
--gpu-memory-utilization 0.9		 >=2.0.3
ERNIE-4.5-0.3B 128K WINT8 1 export XPU_VISIBLE_DEVICES="0" # Specify any card
python -m fastdeploy.entrypoints.openai.api_server \
--model PaddlePaddle/ERNIE-4.5-0.3B-Paddle \
--port 8188 \
--tensor-parallel-size 1 \
--max-model-len 131072 \
--max-num-seqs 128 \
--quantization "wint8" \
--gpu-memory-utilization 0.9		 >=2.0.3

Quick start

Online serving (OpenAI API-Compatible server)

Deploy an OpenAI API-compatible server using FastDeploy with the following commands:

Start service

Deploy the ERNIE-4.5-300B-A47B-Paddle model with WINT4 precision and 32K context length on 4 XPUs

export XPU_VISIBLE_DEVICES="0,1,2,3" # Specify which cards to be used
python -m fastdeploy.entrypoints.openai.api_server \
    --model baidu/ERNIE-4.5-300B-A47B-Paddle \
    --port 8188 \
    --tensor-parallel-size 4 \
    --max-model-len 32768 \
    --max-num-seqs 64 \
    --quantization "wint4" \
    --gpu-memory-utilization 0.9

Note: When deploying on 4 XPUs, only two configurations are supported which constrained by hardware limitations such as interconnect capabilities. export XPU_VISIBLE_DEVICES="0,1,2,3" or export XPU_VISIBLE_DEVICES="4,5,6,7"

Refer to Parameters for more options.

All supported models can be found in the Supported Models section above.

Send requests

Send requests using either curl or Python

curl -X POST "http://0.0.0.0:8188/v1/chat/completions" \
-H "Content-Type: application/json" \
-d '{
  "messages": [
    {"role": "user", "content": "Where is the capital of China?"}
  ]
}'

import openai
host = "0.0.0.0"
port = "8188"
client = openai.Client(base_url=f"http://{host}:{port}/v1", api_key="null")

response = client.completions.create(
    model="null",
    prompt="Where is the capital of China?",
    stream=True,
)
for chunk in response:
    print(chunk.choices[0].text, end='')
print('\n')

response = client.chat.completions.create(
    model="null",
    messages=[
        {"role": "user", "content": "Where is the capital of China?"},
    ],
    stream=True,
)
for chunk in response:
    if chunk.choices[0].delta:
        print(chunk.choices[0].delta.content, end='')
print('\n')

For detailed OpenAI protocol specifications, see OpenAI Chat Compeltion API. Differences from the standard OpenAI protocol are documented in OpenAI Protocol-Compatible API Server.