如何在天数机器上运行 ERNIE-4.5-300B-A47B-BF16 & ERNIE-4.5-21B-A3B
准备机器
首先运行ERNIE4.5 300B模型需要TP=16, 所以您需要准备以下配置的机器：
| CPU | 内存 | 天数 | 硬盘 |
|---|---|---|---|
| x86 | 1TB | 16xBI150 | 1TB |
目前需要将完整模型 load 到 host memory 中，需要需要大于 600GB 的 host memory，后续版本会优化。
镜像
从官网获取：
docker pull ccr-2vdh3abv-pub.cnc.bj.baidubce.com/device/paddle-ixuca:latest
准备容器
启动容器
docker run -itd --name paddle_infer -v /usr/src:/usr/src -v /lib/modules:/lib/modules -v /dev:/dev -v /home/paddle:/home/paddle --privileged --cap-add=ALL --pid=host ccr-2vdh3abv-pub.cnc.bj.baidubce.com/device/paddle-ixuca:latest
docker exec -it paddle_infer bash
/home/paddle 为模型文件、whl包、脚本所在目录
安装paddle
pip3 install paddlepaddle==3.1.0a0 -i https://www.paddlepaddle.org.cn/packages/stable/cpu/
pip3 install paddle-iluvatar-gpu==3.1.1 -i https://www.paddlepaddle.org.cn/packages/stable/ixuca/
获取Paddle的最新安装版本： PaddlePaddle Installation
安装fastdeploy
pip3 install fastdeploy_iluvatar_gpu==2.1.0.dev0 -i https://www.paddlepaddle.org.cn/packages/stable/ixuca/ --extra-index-url https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simplels
可以按如下步骤编译FastDeploy，，得到最新版本.
git clone https://github.com/PaddlePaddle/FastDeploy
cd FastDeploy
pip install -r requirements_iluvatar.txt
export LD_PRELOAD=/usr/local/corex/lib64/libcuda.so.1
bash build.sh
准备推理demo脚本
推理 demo 路径：/home/paddle/scripts 脚本内容如下
run_demo.sh:
#!/bin/bash
export PADDLE_XCCL_BACKEND=iluvatar_gpu
export INFERENCE_MSG_QUEUE_ID=232132
export LD_PRELOAD=/usr/local/corex/lib64/libcuda.so.1
export FD_DEBUG=1
python3 run_demo.py
run_demo.py
from fastdeploy import LLM, SamplingParams
prompts = [
"Hello, my name is",
"The largest ocean is",
]
# 采样参数
sampling_params = SamplingParams(temperature=0.8, top_p=0.95, max_tokens=256)
# 加载模型
llm = LLM(model="/home/paddle/ernie-4_5-21b-a3b-bf16-paddle", tensor_parallel_size=4, max_model_len=8192, quantization='wint8')
# 批量进行推理（llm内部基于资源情况进行请求排队、动态插入处理）
outputs = llm.generate(prompts, sampling_params)
# 注意将其中`/home/paddle/ernie-4_5-21b-a3b-bf16-paddle`替换为您下载的ERNIE模型的路径。
# 输出结果
for output in outputs:
prompt = output.prompt
generated_text = output.outputs.text
print(prompt, generated_text)
运行demo
执行
./run_demo.sh
会有如下 log 打印；load 模型耗时约74s，demo 运行约240s。
/usr/local/lib/python3.10/site-packages/paddle/utils/cpp_extension/extension_utils.py:715: UserWarning: No ccache found. Please be aware that recompiling all source files may be required. You can download and install ccache from: https://github.com/ccache/ccache/blob/master/doc/INSTALL.md
warnings.warn(warning_message)
/usr/local/lib/python3.10/site-packages/_distutils_hack/__init__.py:31: UserWarning: Setuptools is replacing distutils. Support for replacing an already imported distutils is deprecated. In the future, this condition will fail. Register concerns at https://github.com/pypa/setuptools/issues/new?template=distutils-deprecation.yml
warnings.warn(
[2025-07-02 11:07:42,393] [ INFO] - Loading configuration file /home/paddle/ernie-4_5-21b-a3b-bf16-paddle/generation_config.json
/usr/local/lib/python3.10/site-packages/paddleformers/generation/configuration_utils.py:250: UserWarning: using greedy search strategy. However, `temperature` is set to `0.8` -- this flag is only used in sample-based generation modes. You should set `decode_strategy="greedy_search" ` or unset `temperature`. This was detected when initializing the generation config instance, which means the corresponding file may hold incorrect parameterization and should be fixed.
warnings.warn(
/usr/local/lib/python3.10/site-packages/paddleformers/generation/configuration_utils.py:255: UserWarning: using greedy search strategy. However, `top_p` is set to `0.8` -- this flag is only used in sample-based generation modes. You should set `decode_strategy="greedy_search" ` or unset `top_p`. This was detected when initializing the generation config instance, which means the corresponding file may hold incorrect parameterization and should be fixed.
warnings.warn(
INFO 2025-07-02 11:07:43,589 577964 engine.py[line:207] Waitting worker processes ready...
Loading Weights: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:57<00:00, 1.75it/s]
Loading Layers: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:08<00:00, 11.73it/s]
INFO 2025-07-02 11:08:55,261 577964 engine.py[line:277] Worker processes are launched with 73.76574492454529 seconds.
Processed prompts: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [03:59<00:00, 119.96s/it, est. speed input: 0.00 toks/s, output: 0.00 toks/s]
Hello, my name is Christopher. Today, I'm going to teach you how to draw a cute cartoon ghost. Let's get started!
(1) First, draw a big circle for the ghost's head.
(2) Then, add two small circles for the eyes, making sure they're not too big.
(3) Next, draw a wide, open mouth that looks like a big "U".
(4) After that, create the body by drawing a slightly smaller circle below the head.
(5) Now, let's add some arms. Draw two short, curly lines on each side of the body.
(6) Finally, give the ghost a wavy line at the bottom to represent its floating appearance.
Now, let's break down each step:
**Step 1: Drawing the Head**
- Start with a big circle to form the head of the ghost. This will be the foundation of your drawing.
**Step 2: Adding Eyes**
- On the head, place two small circles for the eyes. They should be centered and not too big, to give the ghost a cute and innocent look.
**Step 3: Drawing the
The largest ocean is the Pacific Ocean, covering an area of approximately ⦠[3], The first scientific expeditions to determine the ocean's depth were the Challenger expedition (1872â1876) and the U.S. Navy Hydrographic Office survey (1877â1879). The oceanic crust is thin and irregular, consisting of upward moving magma from the mantle below, and cooling and solidifying on the surface. The shallowest parts of the ocean are called the continental shelves. Large tides are caused mainly by the alignment of the Sun, Moon, and Earth during new or full moons. The origin of the word "ocean" is not clear. The first global oceanic topography survey was completed by the Challenger expedition (1872â1876). [57] The sound speed in the ocean is primarily a function of water temperature and salinity, and varies with depth. The deep-ocean floor is mostly flat and devoid of life, with the exception of seamounts and various underwater volcanic features, including seamounts and hydrothermal vents. [73] Today, the five ocean
在GSM8K数据集上运行ernie4.5 300B模型
- 下载GSM8K数据集
wget https://raw.githubusercontent.com/openai/grade-school-math/master/grade_school_math/data/test.jsonl
- 准备
bench_gsm8k.py
# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Fastdeploy + ERNIE-4.5-Turbo 的指标评估 """
# adapted from https://github.com/sgl-project/sglang/blob/main/benchmark/gsm8k/bench_other.py
import argparse
import ast
import json
import re
import time
from concurrent.futures import ThreadPoolExecutor
import numpy as np
import requests
from tqdm import tqdm
INVALID = -9999999
def call_generate(prompt, **kwargs):
"""
Generates response based on the input prompt.
Args:
prompt (str): The input prompt text.
**kwargs: Keyword arguments, including server IP address and port number.
Returns:
str: The response generated based on the prompt.
"""
url = f"http://{kwargs['ip']}:{kwargs['port']}/v1/chat/completions"
headers = {"Content-Type": "application/json"}
data = {
"messages": [
{
"role": "user",
"content": prompt,
}
],
"temperature": 0.6,
"max_tokens": 2047,
"top_p": 0.95,
"do_sample": True,
}
response = requests.post(url, headers=headers, data=json.dumps(data))
out = response.json()
return out["choices"][0]["message"]["content"]
def get_one_example(lines, i, include_answer):
"""
Retrieves a question-answer example from the given list of text lines.
Args:
lines (list of dict): A list of question-answer pairs.
i (int): The index of the question-answer pair to retrieve from lines.
include_answer (bool): Whether to include the answer in the returned string.
Returns:
str: A formatted question-answer string in the format "Question: <question>\nAnswer: <answer>".
"""
ret = "Question: " + lines[i]["question"] + "\nAnswer:"
if include_answer:
ret += " " + lines[i]["answer"]
return ret
def get_few_shot_examples(lines, k):
"""
Selects k examples from the given list of text lines and concatenates them into a single string.
Args:
lines (list): A list containing text lines.
k (int): The number of examples to select.
Returns:
str: A string composed of k examples, separated by two newline characters.
"""
ret = ""
for i in range(k):
ret += get_one_example(lines, i, True) + "\n\n"
return ret
def get_answer_value(answer_str):
"""
Extracts numerical values from an answer string and returns them.
Args:
answer_str (str): The string containing the answer.
Returns:
The extracted numerical value; returns "INVALID" if extraction fails.
"""
answer_str = answer_str.replace(",", "")
numbers = re.findall(r"\d+", answer_str)
if len(numbers) < 1:
return INVALID
try:
return ast.literal_eval(numbers[-1])
except SyntaxError:
return INVALID
def read_jsonl(filename: str):
"""
Reads a JSONL file.
Args:
filename (str): Path to the JSONL file.
Yields:
dict: A dictionary object corresponding to each line in the JSONL file.
"""
with open(filename) as fin:
for line in fin:
if line.startswith("#"):
continue
yield json.loads(line)
def main(args):
"""
Process inputs and generate answers by calling the model in parallel using a thread pool.
Args:
args (argparse.Namespace):
- num_questions (int): Number of questions to process.
- num_shots (int): Number of few-shot learning examples.
- ip (str): IP address of the model service.
- port (int): Port number of the model service.
- parallel (int): Number of questions to process in parallel.
- result_file (str): File path to store the results.
Returns:
None
"""
# Read data
filename = "test.jsonl"
lines = list(read_jsonl(filename))
# Construct prompts
num_questions = args.num_questions
num_shots = args.num_shots
few_shot_examples = get_few_shot_examples(lines, num_shots)
questions = []
labels = []
for i in range(len(lines[:num_questions])):
questions.append(get_one_example(lines, i, False))
labels.append(get_answer_value(lines[i]["answer"]))
assert all(l != INVALID for l in labels)
states = [None] * len(labels)
# Use thread pool
def get_one_answer(i):
answer = call_generate(
prompt=few_shot_examples + questions[i],
# stop=["Question", "Assistant:", "<|separator|>"],
ip=args.ip,
port=args.port,
)
states[i] = answer
tic = time.time()
if args.parallel == 1:
for i in tqdm(range(len(questions))):
get_one_answer(i)
else:
with ThreadPoolExecutor(args.parallel) as executor:
list(
tqdm(
executor.map(get_one_answer, list(range(len(questions)))),
total=len(questions),
)
)
latency = time.time() - tic
preds = []
for i in range(len(states)):
preds.append(get_answer_value(states[i]))
# Compute accuracy
acc = np.mean(np.array(preds) == np.array(labels))
invalid = np.mean(np.array(preds) == INVALID)
# Print results
print(f"Accuracy: {acc:.3f}")
print(f"Invalid: {invalid:.3f}")
print(f"Latency: {latency:.3f} s")
with open(args.result_file, "a") as fout:
value = {
"task": "gsm8k",
"backend": "paddlepaddle",
"num_gpus": 1,
"latency": round(latency, 3),
"accuracy": round(acc, 3),
"num_requests": args.num_questions,
"other": {
"num_questions": args.num_questions,
"parallel": args.parallel,
},
}
fout.write(json.dumps(value) + "\n")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--ip", type=str, default="127.0.0.1")
parser.add_argument("--port", type=str, default="8188")
parser.add_argument("--num-shots", type=int, default=10)
parser.add_argument("--data-path", type=str, default="test.jsonl")
parser.add_argument("--num-questions", type=int, default=1319)
parser.add_argument("--result-file", type=str, default="result.jsonl")
parser.add_argument("--parallel", type=int, default=1)
args = parser.parse_args()
main(args)
- 准备
run_bench.sh
#!/bin/bash
export PADDLE_XCCL_BACKEND=iluvatar_gpu
export INFERENCE_MSG_QUEUE_ID=232132
export LD_PRELOAD=/usr/local/corex/lib64/libcuda.so.1
export FD_SAMPLING_CLASS=rejection
python3 -m fastdeploy.entrypoints.openai.api_server --model "/home/paddle/ernie-45t" --port 8188 --tensor-parallel-size 16 --block-size 16 --quantization wint8
- 运行脚本
首先打开一个终端执行服务端命令:
./run_bench.sh
等服务起好后，在打开另一个终端执行客户端命令:
python3 -u bench_gsm8k.py --port 8188 --num-questions 1319 --num-shots 5 --parallel 8
推理整个GSM8K数据集大概需要4.8个小时。
Accuracy: 0.962
Invaild: 0.000
Latency: 17332.728 s