Skip to content

GitLab

Commit 20ffd004 authored by zhuwenwen's avatar zhuwenwen
Browse files

add submodule

parents
Hide whitespace changes
Inline Side-by-side
Showing with 425 additions and 0 deletions
.gitmodules 0 → 100644
View file @ 20ffd004
[submodule "vllm"]
path = vllm
url = http://developer.hpccube.com/codes/OpenDAS/vllm.git
branch = vllm-v0.3.3-dtk24.04
\ No newline at end of file
Dockerfile 0 → 100644
View file @ 20ffd004
FROM image.sourcefind.cn:5000/dcu/admin/base/pytorch:2.1.0-centos7.6-dtk24.04-py310
ENV LANG C.UTF-8
RUN pip install ray==2.9.1 -i http://mirrors.aliyun.com/pypi/simple/ --trusted-host mirrors.aliyun.com
LICENSE 0 → 100644
View file @ 20ffd004
Copyright 2018-2020 Open-MMLab. All rights reserved.
Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/
TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
1. Definitions.
"License" shall mean the terms and conditions for use, reproduction,
and distribution as defined by Sections 1 through 9 of this document.
"Licensor" shall mean the copyright owner or entity authorized by
the copyright owner that is granting the License.
"Legal Entity" shall mean the union of the acting entity and all
other entities that control, are controlled by, or are under common
control with that entity. For the purposes of this definition,
"control" means (i) the power, direct or indirect, to cause the
direction or management of such entity, whether by contract or
otherwise, or (ii) ownership of fifty percent (50%) or more of the
outstanding shares, or (iii) beneficial ownership of such entity.
"You" (or "Your") shall mean an individual or Legal Entity
exercising permissions granted by this License.
"Source" form shall mean the preferred form for making modifications,
including but not limited to software source code, documentation
source, and configuration files.
"Object" form shall mean any form resulting from mechanical
transformation or translation of a Source form, including but
not limited to compiled object code, generated documentation,
and conversions to other media types.
"Work" shall mean the work of authorship, whether in Source or
Object form, made available under the License, as indicated by a
copyright notice that is included in or attached to the work
(an example is provided in the Appendix below).
"Derivative Works" shall mean any work, whether in Source or Object
form, that is based on (or derived from) the Work and for which the
editorial revisions, annotations, elaborations, or other modifications
represent, as a whole, an original work of authorship. For the purposes
of this License, Derivative Works shall not include works that remain
separable from, or merely link (or bind by name) to the interfaces of,
the Work and Derivative Works thereof.
"Contribution" shall mean any work of authorship, including
the original version of the Work and any modifications or additions
to that Work or Derivative Works thereof, that is intentionally
submitted to Licensor for inclusion in the Work by the copyright owner
or by an individual or Legal Entity authorized to submit on behalf of
the copyright owner. For the purposes of this definition, "submitted"
means any form of electronic, verbal, or written communication sent
to the Licensor or its representatives, including but not limited to
communication on electronic mailing lists, source code control systems,
and issue tracking systems that are managed by, or on behalf of, the
Licensor for the purpose of discussing and improving the Work, but
excluding communication that is conspicuously marked or otherwise
designated in writing by the copyright owner as "Not a Contribution."
"Contributor" shall mean Licensor and any individual or Legal Entity
on behalf of whom a Contribution has been received by Licensor and
subsequently incorporated within the Work.
2. Grant of Copyright License. Subject to the terms and conditions of
this License, each Contributor hereby grants to You a perpetual,
worldwide, non-exclusive, no-charge, royalty-free, irrevocable
copyright license to reproduce, prepare Derivative Works of,
publicly display, publicly perform, sublicense, and distribute the
Work and such Derivative Works in Source or Object form.
3. Grant of Patent License. Subject to the terms and conditions of
this License, each Contributor hereby grants to You a perpetual,
worldwide, non-exclusive, no-charge, royalty-free, irrevocable
(except as stated in this section) patent license to make, have made,
use, offer to sell, sell, import, and otherwise transfer the Work,
where such license applies only to those patent claims licensable
by such Contributor that are necessarily infringed by their
Contribution(s) alone or by combination of their Contribution(s)
with the Work to which such Contribution(s) was submitted. If You
institute patent litigation against any entity (including a
cross-claim or counterclaim in a lawsuit) alleging that the Work
or a Contribution incorporated within the Work constitutes direct
or contributory patent infringement, then any patent licenses
granted to You under this License for that Work shall terminate
as of the date such litigation is filed.
4. Redistribution. You may reproduce and distribute copies of the
Work or Derivative Works thereof in any medium, with or without
modifications, and in Source or Object form, provided that You
meet the following conditions:
(a) You must give any other recipients of the Work or
Derivative Works a copy of this License; and
(b) You must cause any modified files to carry prominent notices
stating that You changed the files; and
(c) You must retain, in the Source form of any Derivative Works
that You distribute, all copyright, patent, trademark, and
attribution notices from the Source form of the Work,
excluding those notices that do not pertain to any part of
the Derivative Works; and
(d) If the Work includes a "NOTICE" text file as part of its
distribution, then any Derivative Works that You distribute must
include a readable copy of the attribution notices contained
within such NOTICE file, excluding those notices that do not
pertain to any part of the Derivative Works, in at least one
of the following places: within a NOTICE text file distributed
as part of the Derivative Works; within the Source form or
documentation, if provided along with the Derivative Works; or,
within a display generated by the Derivative Works, if and
wherever such third-party notices normally appear. The contents
of the NOTICE file are for informational purposes only and
do not modify the License. You may add Your own attribution
notices within Derivative Works that You distribute, alongside
or as an addendum to the NOTICE text from the Work, provided
that such additional attribution notices cannot be construed
as modifying the License.
You may add Your own copyright statement to Your modifications and
may provide additional or different license terms and conditions
for use, reproduction, or distribution of Your modifications, or
for any such Derivative Works as a whole, provided Your use,
reproduction, and distribution of the Work otherwise complies with
the conditions stated in this License.
5. Submission of Contributions. Unless You explicitly state otherwise,
any Contribution intentionally submitted for inclusion in the Work
by You to the Licensor shall be under the terms and conditions of
this License, without any additional terms or conditions.
Notwithstanding the above, nothing herein shall supersede or modify
the terms of any separate license agreement you may have executed
with Licensor regarding such Contributions.
6. Trademarks. This License does not grant permission to use the trade
names, trademarks, service marks, or product names of the Licensor,
except as required for reasonable and customary use in describing the
origin of the Work and reproducing the content of the NOTICE file.
7. Disclaimer of Warranty. Unless required by applicable law or
agreed to in writing, Licensor provides the Work (and each
Contributor provides its Contributions) on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
implied, including, without limitation, any warranties or conditions
of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
PARTICULAR PURPOSE. You are solely responsible for determining the
appropriateness of using or redistributing the Work and assume any
risks associated with Your exercise of permissions under this License.
8. Limitation of Liability. In no event and under no legal theory,
whether in tort (including negligence), contract, or otherwise,
unless required by applicable law (such as deliberate and grossly
negligent acts) or agreed to in writing, shall any Contributor be
liable to You for damages, including any direct, indirect, special,
incidental, or consequential damages of any character arising as a
result of this License or out of the use or inability to use the
Work (including but not limited to damages for loss of goodwill,
work stoppage, computer failure or malfunction, or any and all
other commercial damages or losses), even if such Contributor
has been advised of the possibility of such damages.
9. Accepting Warranty or Additional Liability. While redistributing
the Work or Derivative Works thereof, You may choose to offer,
and charge a fee for, acceptance of support, warranty, indemnity,
or other liability obligations and/or rights consistent with this
License. However, in accepting such obligations, You may act only
on Your own behalf and on Your sole responsibility, not on behalf
of any other Contributor, and only if You agree to indemnify,
defend, and hold each Contributor harmless for any liability
incurred by, or claims asserted against, such Contributor by reason
of your accepting any such warranty or additional liability.
END OF TERMS AND CONDITIONS
APPENDIX: How to apply the Apache License to your work.
To apply the Apache License to your work, attach the following
boilerplate notice, with the fields enclosed by brackets "[]"
replaced with your own identifying information. (Don't include
the brackets!) The text should be enclosed in the appropriate
comment syntax for the file format. We also recommend that a
file or class name and description of purpose be included on the
same "printed page" as the copyright notice for easier
identification within third-party archives.
Copyright 2018-2020 Open-MMLab.
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.
README.md 0 → 100644
View file @ 20ffd004
<!--
* @Author: zhuww
* @email: zhuww@sugon.com
* @Date: 2024-05-24 14:15:07
* @LastEditTime: 2024-05-24 15:24:01
-->
# Qwen1.5
## 论文
无
## 模型结构
Qwen1.5是阿里云开源大型语言模型系列，是Qwen2.0的beta版本。相较于以往版本，本次更新着重提升了Chat模型与人类偏好的对齐程度，并且显著增强了模型的多语言处理能力。在序列长度方面，所有规模模型均已实现 32768 个tokens的上下文长度范围支持。同时，预训练 Base 模型的质量也有关键优化，有望在微调过程中带来更佳体验。
<div align=center>
<img src="./doc/qwen1.5.jpg"/>
</div>
## 算法原理
和Qwen一样，Qwen1.5仍然是一个decoder-only的transformer模型，使用SwiGLU激活函数、RoPE、多头注意力机制等。
<div align=center>
<img src="./doc/qwen1.5.png"/>
</div>
## 环境配置
### Docker（方法一）
提供[光源](https://www.sourcefind.cn/#/image/dcu/custom)拉取推理的docker镜像：
```
docker pull image.sourcefind.cn:5000/dcu/admin/base/pytorch:2.1.0-centos7.6-dtk24.04-py310
# <Image ID>用上面拉取docker镜像的ID替换
# <Host Path>主机端路径
# <Container Path>容器映射路径
docker run -it --name qwen1.5_vllm --privileged --shm-size=64G --device=/dev/kfd --device=/dev/dri/ --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --ulimit memlock=-1:-1 --ipc=host --network host --group-add video -v /opt/hyhal:/opt/hyhal -v <Host Path>:<Container Path> <Image ID> /bin/bash
# 更新镜像的ray版本
pip install ray==2.9.1
```
镜像版本依赖：
* DTK驱动：dtk24.04
* Pytorch: 2.1.0
* vllm: 0.3.3
* xformers: 0.0.25
* flash_attn: 2.0.4
* python: python3.10
### Dockerfile（方法二）
```
# <Host Path>主机端路径
# <Container Path>容器映射路径
docker build -t qwen1.5:latest .
docker run -it --name qwen1.5_vllm --privileged --shm-size=64G --device=/dev/kfd --device=/dev/dri/ --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --ulimit memlock=-1:-1 --ipc=host --network host --group-add video -v /opt/hyhal:/opt/hyhal -v <Host Path>:<Container Path> qwen1.5:latest /bin/bash
```
### Anaconda（方法三）
```
conda create -n qwen1.5_vllm python=3.10
```
关于本项目DCU显卡所需的特殊深度学习库可从[光合](https://developer.hpccube.com/tool/)开发者社区下载安装。
* DTK驱动：dtk24.04
* Pytorch: 2.1.0
* triton:2.1.0
* vllm: 0.3.3
* xformers: 0.0.25
* flash_attn: 2.0.4
* python: python3.10
`Tips：以上dtk驱动、python、torch等DCU相关工具版本需要严格一一对应`
## 数据集
无
## 推理
### 源码编译安装
```
# 若使用光源的镜像，可以跳过源码编译安装，镜像中已安装vllm。
git clone http://developer.hpccube.com/codes/modelzoo/qwen1.5_vllm.git
cd qwen1.5_vllm
git submodule init && git submodule update
cd vllm
pip install wheel
python setup.py bdist_wheel
cd dist && pip install vllm*
```
### 模型下载
| 基座模型 | chat模型 | GPTQ模型 |
| ------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------- |
| [Qwen1.5-7B](https://huggingface.co/Qwen/Qwen1.5-7B) | [Qwen1.5-7B-Chat](https://huggingface.co/Qwen/Qwen1.5-7B-Chat) | [Qwen1.5-7B-Chat-GPTQ-Int4](https://huggingface.co/Qwen/Qwen1.5-7B-Chat-GPTQ-Int4) |
| [Qwen1.5-14B](https://huggingface.co/Qwen/Qwen1.5-14B) | [Qwen1.5-14B-Chat](https://huggingface.co/Qwen/Qwen1.5-14B-Chat) | [Qwen1.5-14B-Chat-GPTQ-Int4](https://huggingface.co/Qwen/Qwen1.5-14B-Chat-GPTQ-Int4) |
| [Qwen1.5-32B](https://huggingface.co/Qwen/Qwen1.5-32B) | [Qwen1.5-32B-Chat](https://huggingface.co/Qwen/Qwen1.5-32B-Chat) | [Qwen1.5-32B-Chat-GPTQ-Int4](https://huggingface.co/Qwen/Qwen1.5-32B-Chat-GPTQ-Int4) |
| [Qwen1.5-72B](https://huggingface.co/Qwen/Qwen1.5-72B) | [Qwen1.5-72B-Chat](https://huggingface.co/Qwen/Qwen1.5-72B-Chat) | [Qwen1.5-72B-Chat-GPTQ-Int4](https://huggingface.co/Qwen/Qwen1.5-72B-Chat-GPTQ-Int4) |
| [Qwen1.5-110B](https://huggingface.co/Qwen/Qwen1.5-110B) | [Qwen1.5-110B-Chat](https://huggingface.co/Qwen/Qwen1.5-110B-Chat) | [Qwen1.5-110B-Chat-GPTQ-Int4](https://huggingface.co/Qwen/Qwen1.5-110B-Chat-GPTQ-Int4) |
### 离线批量推理
```bash
python vllm/examples/offline_inference.py
```
其中，`prompts`为提示词；`temperature`为控制采样随机性的值，值越小模型生成越确定，值变高模型生成更随机，0表示贪婪采样，默认为1；`max_tokens=16`为生成长度，默认为1；
`model`为模型路径；`tensor_parallel_size=1`为使用卡数，默认为1；`dtype="float16"`为推理数据类型，如果模型权重是bfloat16,需要修改为float16推理,`quantization="gptq"`为使用gptq量化进行推理,需下载以上GPTQ模型。
### 离线批量推理性能测试
1、指定输入输出
```bash
python vllm/benchmarks/benchmark_throughput.py --num-prompts 1 --input-len 32 --output-len 128 --model Qwen/Qwen1.5-7B-Chat -tp 1 --trust-remote-code --enforce-eager --dtype float16
```
其中`--num-prompts`是batch数，`--input-len`是输入seqlen，`--output-len`是输出token长度，`--model`为模型路径，`-tp`为使用卡数，`dtype="float16"`为推理数据类型，如果模型权重是bfloat16,需要修改为float16推理。若指定`--output-len 1`即为首字延迟。
2、使用数据集
下载数据集：
```bash
wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json
```
```bash
python benchmark_throughput.py --num-prompts 1 --model Qwen/Qwen1.5-7B-Chat --dataset ShareGPT_V3_unfiltered_cleaned_split.json -tp 1 --trust-remote-code --enforce-eager --dtype float16
```
其中`--num-prompts`是batch数，`--model`为模型路径，`--dataset`为使用的数据集，`-tp`为使用卡数，`dtype="float16"`为推理数据类型，如果模型权重是bfloat16,需要修改为float16推理。
### api服务推理性能测试
1、启动服务端：
```bash
python -m vllm.entrypoints.api_server --model Qwen/Qwen1.5-7B-Chat --dtype float16 --enforce-eager -tp 1
```
2、启动客户端：
```bash
python vllm/benchmarks/benchmark_serving.py --model Qwen/Qwen1.5-7B-Chat --dataset ShareGPT_V3_unfiltered_cleaned_split.json --num-prompts 1 --trust-remote-code
```
参数同使用数据集，离线批量推理性能测试，具体参考[vllm/benchmarks/benchmark_serving.py]
### OpenAI兼容服务
启动服务：
```bash
python -m vllm.entrypoints.openai.api_server --model Qwen/Qwen1.5-7B-Chat --enforce-eager --dtype float16 --trust-remote-code
```
这里`--model`为加载模型路径，`--dtype`为数据类型：float16，默认情况使用tokenizer中的预定义聊天模板，`--chat-template`可以添加新模板覆盖默认模板
列出模型型号：
```bash
curl http://localhost:8000/v1/models
```
### OpenAI Completions API和vllm结合使用
```bash
curl http://localhost:8000/v1/completions \
-H "Content-Type: application/json" \
-d '{
"model": "Qwen/Qwen1.5-7B-Chat",
"prompt": "What is deep learning?",
"max_tokens": 7,
"temperature": 0
}'
```
或者使用[vllm/examples/openai_completion_client.py](https://developer.hpccube.com/codes/OpenDAS/vllm/-/tree/3e147e194e5a3b0fc25a61dd91fdc8a682cbba9d/examples/openai_completion_client.py)
### OpenAI Chat API和vllm结合使用
```bash
curl http://localhost:8000/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "Qwen/Qwen1.5-7B-Chat",
"messages": [
{"role": "system", "content": "What is deep learning?"},
{"role": "user", "content": "What is deep learning?"}
]
}'
```
或者使用[vllm/examples/openai_chatcompletion_client.py](https://developer.hpccube.com/codes/OpenDAS/vllm/-/tree/3e147e194e5a3b0fc25a61dd91fdc8a682cbba9d/examples/openai_chatcompletion_client.py)
## result
使用的加速卡:1张 DCU-K100_AI-64G
```
Prompt: 'What is deep learning?', Generated text: ' Deep learning is a subset of machine learning that involves the use of neural networks to model and solve complex problems. Neural networks are a network of interconnected nodes or " neurons" that are designed to recognize patterns in data, learn from examples, and make predictions or decisions.\nThe term "deep" in deep learning refers to the use of multiple layers or hidden layers in these neural networks. Each layer processes the input data in a different way, extracting increasingly abstract features as the data passes through.'
```
### 精度
无
## 应用场景
### 算法类别
对话问答
### 热点应用行业
金融,科研,教育
## 源码仓库及问题反馈
* [https://developer.hpccube.com/codes/modelzoo/qwen1.5_vllm](https://developer.hpccube.com/codes/modelzoo/qwen1.5_vllm)
## 参考资料
* [https://github.com/vllm-project/vllm](https://github.com/vllm-project/vllm)
model.properties 0 → 100644
View file @ 20ffd004
# 模型唯一标识
modelCode=653
# 模型名称
modelName=qwen1.5_vllm
# 模型描述
modelDescription=Qwen1.5是阿里云开源大型语言模型系列，是Qwen2.0的beta版本。
# 应用场景
appScenario=推理,对话问答,科研,教育,政府,金融
# 框架类型
frameType=vllm
vllm @ 675c0abe
Subproject commit 675c0abe47eb9d29c126fbecda86fd5801162eba
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment