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## Contributing to OpenMMLab
Welcome to the MMCV community, we are committed to building a cutting-edge computer vision foundational library and all kinds of contributions are welcomed, including but not limited to
**Fix bug**
You can directly post a Pull Request to fix typo in code or documents
The steps to fix the bug of code implementation are as follows.
1. If the modification involve significant changes, you should create an issue first and describe the error information and how to trigger the bug. Other developers will discuss with you and propose an proper solution.
2. Posting a pull request after fixing the bug and adding corresponding unit test.
**New Feature or Enhancement**
1. If the modification involve significant changes, you should create an issue to discuss with our developers to propose an proper design.
2. Post a Pull Request after implementing the new feature or enhancement and add corresponding unit test.
**Document**
You can directly post a pull request to fix documents. If you want to add a document, you should first create an issue to check if it is reasonable.
### Pull Request Workflow
If you're not familiar with Pull Request, don't worry! The following guidance will tell you how to create a Pull Request step by step. If you want to dive into the develop mode of Pull Request, you can refer to the [official documents](https://docs.github.com/en/github/collaborating-with-issues-and-pull-requests/about-pull-requests)
#### 1. Fork and clone
If you are posting a pull request for the first time, you should fork the OpenMMLab repositories by clicking the **Fork** button in the top right corner of the GitHub page, and the forked repositories will appear under your GitHub profile.
<img src="https://user-images.githubusercontent.com/57566630/167305749-43c7f4e9-449b-4e98-ade5-0c9276d5c9ce.png" width="1200">
Then, you can clone the repositories to local:
```shell
git clone git@github.com:{username}/mmcv.git
```
After that, you should add official repository as the upstream repository
```bash
git remote add upstream git@github.com:open-mmlab/mmcv
```
Check whether remote repository has been added successfully by `git remote -v`
```bash
origin git@github.com:{username}/mmcv.git (fetch)
origin git@github.com:{username}/mmcv.git (push)
upstream git@github.com:open-mmlab/mmcv (fetch)
upstream git@github.com:open-mmlab/mmcv (push)
```
> Here's a brief introduction to origin and upstream. When we use "git clone", we create an "origin" remote by default, which points to the repository cloned from. As for "upstream", we add it ourselves to point to the target repository. Of course, if you don't like the name "upstream", you could name it as you wish. Usually, we'll push the code to "origin". If the pushed code conflicts with the latest code in official("upstream"), we should pull the latest code from upstream to resolve the conflicts, and then push to "origin" again. The posted Pull Request will be updated automatically.
#### 2. Configure pre-commit
You should configure [pre-commit](https://pre-commit.com/#intro) in the local development environment to make sure the code style matches that of OpenMMLab. **Note**: The following code should be executed under the MMCV directory.
```shell
pip install -U pre-commit
pre-commit install
```
Check that pre-commit is configured successfully, and install the hooks defined in `.pre-commit-config.yaml`.
```shell
pre-commit run --all-files
```
<img src="https://user-images.githubusercontent.com/57566630/173660750-3df20a63-cb66-4d33-a986-1f643f1d8aaf.png" width="1200">
<img src="https://user-images.githubusercontent.com/57566630/202368856-0465a90d-8fce-4345-918e-67b8b9c82614.png" width="1200">
If the installation process is interrupted, you can repeatedly run `pre-commit run ... ` to continue the installation.
If the code does not conform to the code style specification, pre-commit will raise a warning and fixes some of the errors automatically.
<img src="https://user-images.githubusercontent.com/57566630/202369176-67642454-0025-4023-a095-263529107aa3.png" width="1200">
If we want to commit our code bypassing the pre-commit hook, we can use the `--no-verify` option(**only for temporarily commit**).
```shell
git commit -m "xxx" --no-verify
```
#### 3. Create a development branch
After configuring the pre-commit, we should create a branch based on the master branch to develop the new feature or fix the bug. The proposed branch name is `username/pr_name`
```shell
git checkout -b yhc/refactor_contributing_doc
```
In subsequent development, if the master branch of the local repository is behind the master branch of "upstream", we need to pull the upstream for synchronization, and then execute the above command:
```shell
git pull upstream master
```
#### 4. Commit the code and pass the unit test
- MMCV introduces mypy to do static type checking to increase the robustness of the code. Therefore, we need to add Type Hints to our code and pass the mypy check. If you are not familiar with Type Hints, you can refer to [this tutorial](https://docs.python.org/3/library/typing.html).
- The committed code should pass through the unit test
```shell
# Pass all unit tests
pytest tests
# Pass the unit test of runner
pytest tests/test_runner/test_runner.py
```
If the unit test fails for lack of dependencies, you can install the dependencies referring to the [guidance](#unit-test)
- If the documents are modified/added, we should check the rendering result referring to [guidance](#document-rendering)
#### 5. Push the code to remote
We could push the local commits to remote after passing through the check of unit test and pre-commit. You can associate the local branch with remote branch by adding `-u` option.
```shell
git push -u origin {branch_name}
```
This will allow you to use the `git push` command to push code directly next time, without having to specify a branch or the remote repository.
#### 6. Create a Pull Request
(1) Create a pull request in GitHub's Pull request interface
<img src="https://user-images.githubusercontent.com/57566630/201533288-516f7ac4-0b14-4dc8-afbd-912475c368b5.png" width="1200">
(2) Modify the PR description according to the guidelines so that other developers can better understand your changes
<img src="https://user-images.githubusercontent.com/57566630/202242953-c91a18ff-e388-4ff9-8591-5fae0ead6c1e.png" width="1200">
Find more details about Pull Request description in [pull request guidelines](#pr-specs).
**note**
(a) The Pull Request description should contain the reason for the change, the content of the change, and the impact of the change, and be associated with the relevant Issue (see [documentation](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue)
(b) If it is your first contribution, please sign the CLA
<img src="https://user-images.githubusercontent.com/57566630/167307569-a794b967-6e28-4eac-a942-00deb657815f.png" width="1200">
(c) Check whether the Pull Request pass through the CI
<img src="https://user-images.githubusercontent.com/57566630/167307490-f9ebf9fa-63c0-4d83-8ba1-081ea169eb3a.png" width="1200">
MMCV will run unit test for the posted Pull Request on different platforms (Linux, Window, Mac), based on different versions of Python, PyTorch, CUDA to make sure the code is correct. We can see the specific test information by clicking `Details` in the above image so that we can modify the code.
(3) If the Pull Request passes the CI, then you can wait for the review from other developers. You'll modify the code based on the reviewer's comments, and repeat the steps [4](#4-commit-the-code-and-pass-the-unit-test)-[5](#5-push-the-code-to-remote) until all reviewers approve it. Then, we will merge it ASAP.
<img src="https://user-images.githubusercontent.com/57566630/202145400-cc2cd8c4-10b0-472f-ba37-07e6f50acc67.png" width="1200">
#### 7. Resolve conflicts
If your local branch conflicts with the latest master branch of "upstream", you'll need to resolove them. There are two ways to do this:
```shell
git fetch --all --prune
git rebase upstream/master
```
or
```shell
git fetch --all --prune
git merge upstream/master
```
If you are very good at handling conflicts, then you can use rebase to resolve conflicts, as this will keep your commit logs tidy. If you are not familiar with `rebase`, then you can use `merge` to resolve conflicts.
### Guidance
#### Unit test
If you cannot run the unit test of some modules for lacking of some dependencies, such as [video](https://github.com/open-mmlab/mmcv/tree/master/mmcv/video) module, you can try to install the following dependencies:
```shell
# Linux
sudo apt-get update -y
sudo apt-get install -y libturbojpeg
sudo apt-get install -y ffmpeg
# Windows
conda install ffmpeg
```
We should also make sure the committed code will not decrease the coverage of unit test, we could run the following command to check the coverage of unit test:
```shell
python -m coverage run -m pytest /path/to/test_file
python -m coverage html
# check file in htmlcov/index.html
```
#### Document rendering
If the documents are modified/added, we should check the rendering result. We could install the dependencies and run the following command to render the documents and check the results:
```shell
pip install -r requirements/docs.txt
cd docs/zh_cn/
# or docs/en
make html
# check file in ./docs/zh_cn/_build/html/index.html
```
### Code style
#### Python
We adopt [PEP8](https://www.python.org/dev/peps/pep-0008/) as the preferred code style.
We use the following tools for linting and formatting:
- [flake8](https://github.com/PyCQA/flake8): A wrapper around some linter tools.
- [isort](https://github.com/timothycrosley/isort): A Python utility to sort imports.
- [yapf](https://github.com/google/yapf): A formatter for Python files.
- [codespell](https://github.com/codespell-project/codespell): A Python utility to fix common misspellings in text files.
- [mdformat](https://github.com/executablebooks/mdformat): Mdformat is an opinionated Markdown formatter that can be used to enforce a consistent style in Markdown files.
- [docformatter](https://github.com/myint/docformatter): A formatter to format docstring.
Style configurations of yapf and isort can be found in [setup.cfg](./setup.cfg).
We use [pre-commit hook](https://pre-commit.com/) that checks and formats for `flake8`, `yapf`, `isort`, `trailing whitespaces`, `markdown files`,
fixes `end-of-files`, `double-quoted-strings`, `python-encoding-pragma`, `mixed-line-ending`, sorts `requirments.txt` automatically on every commit.
The config for a pre-commit hook is stored in [.pre-commit-config](./.pre-commit-config.yaml).
#### C++ and CUDA
We follow the [Google C++ Style Guide](https://google.github.io/styleguide/cppguide.html).
### PR Specs
1. Use [pre-commit](https://pre-commit.com) hook to avoid issues of code style
2. One short-time branch should be matched with only one PR
3. Accomplish a detailed change in one PR. Avoid large PR
- Bad: Support Faster R-CNN
- Acceptable: Add a box head to Faster R-CNN
- Good: Add a parameter to box head to support custom conv-layer number
4. Provide clear and significant commit message
5. Provide clear and meaningful PR description
- Task name should be clarified in title. The general format is: \[Prefix\] Short description of the PR (Suffix)
- Prefix: add new feature \[Feature\], fix bug \[Fix\], related to documents \[Docs\], in developing \[WIP\] (which will not be reviewed temporarily)
- Introduce main changes, results and influences on other modules in short description
- Associate related issues and pull requests with a milestone
README.md
View file @ 720fc533
<div align="center">
<img src="resources/llmdeploy-logo.png" width="450"/>
<div>&nbsp;</div>
<div align="center">
<b><font size="5">OpenMMLab website</font></b>
<sup>
<a href="https://openmmlab.com">
<i><font size="4">HOT</font></i>
</a>
</sup>
&nbsp;&nbsp;&nbsp;&nbsp;
<b><font size="5">OpenMMLab platform</font></b>
<sup>
<a href="https://platform.openmmlab.com">
<i><font size="4">TRY IT OUT</font></i>
</a>
</sup>
</div>
<div>&nbsp;</div>
[![docs](https://img.shields.io/badge/docs-latest-blue)](https://llmdeploy.readthedocs.io/en/latest/)
[![codecov](https://codecov.io/gh/open-mmlab/llmdeploy/branch/main/graph/badge.svg)](https://codecov.io/gh/open-mmlab/llmdeploy)
[![license](https://img.shields.io/github/license/open-mmlab/llmdeploy.svg)](https://github.com/open-mmlab/mmdeploy/tree/main/LICENSE)
[![issue resolution](https://img.shields.io/github/issues-closed-raw/open-mmlab/llmdeploy)](https://github.com/open-mmlab/llmdeploy/issues)
[![open issues](https://img.shields.io/github/issues-raw/open-mmlab/llmdeploy)](https://github.com/open-mmlab/llmdeploy/issues)
English | [简体中文](README_zh-CN.md)
</div>
<div align="center">
<a href="https://openmmlab.medium.com/" style="text-decoration:none;">
<img src="https://user-images.githubusercontent.com/25839884/218352562-cdded397-b0f3-4ca1-b8dd-a60df8dca75b.png" width="3%" alt="" /></a>
<img src="https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png" width="3%" alt="" />
<a href="https://discord.gg/raweFPmdzG" style="text-decoration:none;">
<img src="https://user-images.githubusercontent.com/25839884/218347213-c080267f-cbb6-443e-8532-8e1ed9a58ea9.png" width="3%" alt="" /></a>
<img src="https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png" width="3%" alt="" />
<a href="https://twitter.com/OpenMMLab" style="text-decoration:none;">
<img src="https://user-images.githubusercontent.com/25839884/218346637-d30c8a0f-3eba-4699-8131-512fb06d46db.png" width="3%" alt="" /></a>
<img src="https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png" width="3%" alt="" />
<a href="https://www.youtube.com/openmmlab" style="text-decoration:none;">
<img src="https://user-images.githubusercontent.com/25839884/218346691-ceb2116a-465a-40af-8424-9f30d2348ca9.png" width="3%" alt="" /></a>
</div>
## Introduction
## Installation
Below are quick steps for installation:
```shell
conda create -n open-mmlab python=3.8
conda activate open-mmlab
git clone https://github.com/open-mmlab/llmdeploy.git
cd llmdeploy
pip install -e .
```
## Quick Start
### Serving [LLaMA](https://github.com/facebookresearch/llama)
Weights for the LLaMA models can be obtained from by filling out [this form](https://docs.google.com/forms/d/e/1FAIpQLSfqNECQnMkycAp2jP4Z9TFX0cGR4uf7b_fBxjY_OjhJILlKGA/viewform?usp=send_form)
Run one of the following commands to serve a LLaMA model on NVIDIA GPU server:
<details open>
<summary><b>7B</b></summary>
```shell
python3 llmdeploy/serve/fastertransformer/deploy.py llama-7B /path/to/llama-7b llama \
--tokenizer_path /path/to/tokenizer/model
bash workspace/service_docker_up.sh
```
</details>
<details open>
<summary><b>13B</b></summary>
```shell
python3 llmdeploy/serve/fastertransformer/deploy.py llama-13B /path/to/llama-13b llama \
--tokenizer_path /path/to/tokenizer/model --tp 2
bash workspace/service_docker_up.sh
```
</details>
<details open>
<summary><b>33B</b></summary>
```shell
python3 llmdeploy/serve/fastertransformer/deploy.py llama-33B /path/to/llama-33b llama \
--tokenizer_path /path/to/tokenizer/model --tp 4
bash workspace/service_docker_up.sh
```
</details>
<details open>
<summary><b>65B</b></summary>
```shell
python3 llmdeploy/serve/fastertransformer/deploy.py llama-65B /path/to/llama-65b llama \
--tokenizer_path /path/to/tokenizer/model --tp 8
bash workspace/service_docker_up.sh
```
</details>
### Serving [Vicuna](https://lmsys.org/blog/2023-03-30-vicuna/)
<details open>
<summary><b>7B</b></summary>
```shell
python3 -m pip install fschat
python3 -m fastchat.model.apply_delta \
--base-model-path /path/to/llama-7b \
--target-model-path /path/to/vicuna-7b \
--delta-path lmsys/vicuna-7b-delta-v1.1
python3 llmdeploy/serve/fastertransformer/deploy.py vicuna-7B /path/to/vicuna-7b hf
bash workspace/service_docker_up.sh
```
</details>
<details>
<summary><b>13B</b></summary>
```shell
python3 -m pip install fschat
python3 -m fastchat.model.apply_delta \
--base-model-path /path/to/llama-13b \
--target-model-path /path/to/vicuna-13b \
--delta-path lmsys/vicuna-13b-delta-v1.1
python3 llmdeploy/serve/fastertransformer/deploy.py vicuna-13B /path/to/vicuna-13b hf
bash workspace/service_docker_up.sh
```
</details>
## Inference with Command Line Interface
```shell
python3 llmdeploy/serve/client.py {server_ip_addresss}:33337 1
```
## Contributing
We appreciate all contributions to LLMDeploy. Please refer to [CONTRIBUTING.md](.github/CONTRIBUTING.md) for the contributing guideline.
## Acknowledgement
- [FasterTransformer](https://github.com/NVIDIA/FasterTransformer)
## License
This project is released under the [Apache 2.0 license](LICENSE).
README_zh-CN.md
View file @ 720fc533
<div align="center">
<img src="resources/llmdeploy-logo.png" width="450"/>
<div>&nbsp;</div>
<div align="center">
<b><font size="5">OpenMMLab website</font></b>
<sup>
<a href="https://openmmlab.com">
<i><font size="4">HOT</font></i>
</a>
</sup>
&nbsp;&nbsp;&nbsp;&nbsp;
<b><font size="5">OpenMMLab platform</font></b>
<sup>
<a href="https://platform.openmmlab.com">
<i><font size="4">TRY IT OUT</font></i>
</a>
</sup>
</div>
<div>&nbsp;</div>
[![docs](https://img.shields.io/badge/docs-latest-blue)](https://llmdeploy.readthedocs.io/en/latest/)
[![codecov](https://codecov.io/gh/open-mmlab/llmdeploy/branch/main/graph/badge.svg)](https://codecov.io/gh/open-mmlab/llmdeploy)
[![license](https://img.shields.io/github/license/open-mmlab/llmdeploy.svg)](https://github.com/open-mmlab/mmdeploy/tree/main/LICENSE)
[![issue resolution](https://img.shields.io/github/issues-closed-raw/open-mmlab/llmdeploy)](https://github.com/open-mmlab/llmdeploy/issues)
[![open issues](https://img.shields.io/github/issues-raw/open-mmlab/llmdeploy)](https://github.com/open-mmlab/llmdeploy/issues)
[English](README.md) | 简体中文
</div>
<div align="center">
<a href="https://openmmlab.medium.com/" style="text-decoration:none;">
<img src="https://user-images.githubusercontent.com/25839884/218352562-cdded397-b0f3-4ca1-b8dd-a60df8dca75b.png" width="3%" alt="" /></a>
<img src="https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png" width="3%" alt="" />
<a href="https://discord.gg/raweFPmdzG" style="text-decoration:none;">
<img src="https://user-images.githubusercontent.com/25839884/218347213-c080267f-cbb6-443e-8532-8e1ed9a58ea9.png" width="3%" alt="" /></a>
<img src="https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png" width="3%" alt="" />
<a href="https://twitter.com/OpenMMLab" style="text-decoration:none;">
<img src="https://user-images.githubusercontent.com/25839884/218346637-d30c8a0f-3eba-4699-8131-512fb06d46db.png" width="3%" alt="" /></a>
<img src="https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png" width="3%" alt="" />
<a href="https://www.youtube.com/openmmlab" style="text-decoration:none;">
<img src="https://user-images.githubusercontent.com/25839884/218346691-ceb2116a-465a-40af-8424-9f30d2348ca9.png" width="3%" alt="" /></a>
</div>
## 简介
## 安装
```shell
conda create -n open-mmlab python=3.8
conda activate open-mmlab
git clone https://github.com/open-mmlab/llmdeploy.git
cd llmdeploy
pip install -e .
```
## 快速上手
### 部署 [LLaMA](https://github.com/facebookresearch/llama) 服务
请填写[这张表](<(https://docs.google.com/forms/d/e/1FAIpQLSfqNECQnMkycAp2jP4Z9TFX0cGR4uf7b_fBxjY_OjhJILlKGA/viewform?usp=send_form)>)，获取 LLaMA 模型权重。
执行下面任一命令，可以把 LLaMA 模型部署到 NVIDIA GPU Server：
<details open>
<summary><b>7B</b></summary>
```shell
python3 llmdeploy/serve/fastertransformer/deploy.py llama-7B /path/to/llama-7b llama \
--tokenizer_path /path/to/tokenizer/model
bash workspace/service_docker_up.sh
```
</details>
<details open>
<summary><b>13B</b></summary>
```shell
python3 llmdeploy/serve/fastertransformer/deploy.py llama-13B /path/to/llama-13b llama \
--tokenizer_path /path/to/tokenizer/model --tp 2
bash workspace/service_docker_up.sh
```
</details>
<details open>
<summary><b>33B</b></summary>
```shell
python3 llmdeploy/serve/fastertransformer/deploy.py llama-33B /path/to/llama-33b llama \
--tokenizer_path /path/to/tokenizer/model --tp 4
bash workspace/service_docker_up.sh
```
</details>
<details open>
<summary><b>65B</b></summary>
```shell
python3 llmdeploy/serve/fastertransformer/deploy.py llama-65B /path/to/llama-65b llama \
--tokenizer_path /path/to/tokenizer/model --tp 8
bash workspace/service_docker_up.sh
```
</details>
### 部署 [Vicuna](https://lmsys.org/blog/2023-03-30-vicuna/) 服务
<details open>
<summary><b>7B</b></summary>
```shell
python3 -m pip install fschat
python3 -m fastchat.model.apply_delta \
--base-model-path /path/to/llama-7b \
--target-model-path /path/to/vicuna-7b \
--delta-path lmsys/vicuna-7b-delta-v1.1
python3 llmdeploy/serve/fastertransformer/deploy.py vicuna-7B /path/to/vicuna-7b hf
bash workspace/service_docker_up.sh
```
</details>
<details>
<summary><b>13B</b></summary>
```shell
python3 -m pip install fschat
python3 -m fastchat.model.apply_delta \
--base-model-path /path/to/llama-13b \
--target-model-path /path/to/vicuna-13b \
--delta-path lmsys/vicuna-13b-delta-v1.1
python3 llmdeploy/serve/fastertransformer/deploy.py vicuna-13B /path/to/vicuna-13b hf
bash workspace/service_docker_up.sh
```
</details>
## 通过命令行推理
```shell
python3 llmdeploy/serve/client.py {server_ip_addresss}:33337 1
```
## 贡献指南
我们感谢所有的贡献者为改进和提升 LLMDeploy 所作出的努力。请参考[贡献指南](.github/CONTRIBUTING.md)来了解参与项目贡献的相关指引。
## 致谢
- [FasterTransformer](https://github.com/NVIDIA/FasterTransformer)
## License
该项目采用 [Apache 2.0 开源许可证](LICENSE)。
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