update README and version

README.md

-![FairScale Logo](./docs/source/_static/img/fairscale-logo.png)
 
-[![Support Ukraine](https://img.shields.io/badge/Support-Ukraine-FFD500?style=flat&labelColor=005BBB)](https://opensource.facebook.com/support-ukraine)
-![PyPI](https://img.shields.io/pypi/v/fairscale)
-[![Documentation Status](https://readthedocs.org/projects/fairscale/badge/?version=latest)](https://fairscale.readthedocs.io/en/latest/?badge=latest)
-[![CircleCI](https://circleci.com/gh/facebookresearch/fairscale.svg?style=shield)](https://app.circleci.com/pipelines/github/facebookresearch/fairscale/) ![PyPI - License](https://img.shields.io/pypi/l/fairscale) [![Downloads](https://pepy.tech/badge/fairscale)](https://pepy.tech/project/fairscale) [![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg)](https://github.com/facebookresearch/fairscale/blob/main/CONTRIBUTING.md)
---------------------------------------------------------------------------------
 
-## Description
-FairScale is a PyTorch extension library for high performance and large scale training.
-This library extends basic PyTorch capabilities while adding new SOTA scaling techniques.
-FairScale makes available the latest distributed training techniques in the form of composable
-modules and easy to use APIs. These APIs are a fundamental part of a researcher's toolbox as
-they attempt to scale models with limited resources.
+# fairscale
 
-FairScale was designed with the following values in mind:
+`fairscale`是一个用于高性能和大规模训练的`pytorch`扩展库。 此库扩展了基本的`pytorch`功能，同时添加了新的SOTA扩展技术。 `fairscale`以可组合模块和易于使用的API的形式提供了最新的分布式训练技术。 参考官方文档[README_ORIGIN.md](README_ORIGIN.md)
 
-* **Usability** -  Users should be able to understand and use FairScale APIs with minimum cognitive overload.
 
-* **Modularity** - Users should be able to combine multiple FairScale APIs as part of their training loop seamlessly.
 
-* **Performance** - FairScale APIs provide the best performance in terms of scaling and efficiency.
+# 安装
+组件支持：
 
-## Watch Introductory Video
+* dtk-23.10
+* pytorch-2.1
 
-[![Explain Like I’m 5: FairScale](https://img.youtube.com/vi/oDt7ebOwWIc/0.jpg)](https://www.youtube.com/watch?v=oDt7ebOwWIc)
+## pip安装
 
-## Installation
-
-To install FairScale, please see the following [instructions](https://github.com/facebookresearch/fairscale/blob/main/docs/source/installation_instructions.rst).
-You should be able to install a package with pip or conda, or build directly from source.
-
-## Getting Started
-The full [documentation](https://fairscale.readthedocs.io/) contains instructions for getting started, deep dives and tutorials about the various FairScale APIs.
-
-## FSDP
-
-FullyShardedDataParallel (FSDP) is the recommended method for scaling to large NN models.
-This library has been [upstreamed to PyTorch](https://pytorch.org/blog/introducing-pytorch-fully-sharded-data-parallel-api/).
-The version of FSDP here is for historical references as well as for experimenting with
-new and crazy ideas in research of scaling techniques. Please see the following blog
-for [how to use FairScale FSDP and how does it work](https://engineering.fb.com/2021/07/15/open-source/fsdp/).
-
-## Testing
+在[光合开发者社区](https://developer.hpccube.com/tool/#sdk) AI 生态包中获取对应的fairscale安装包。
+```
+pip install fairscale*
+```
 
-We use circleci to test FairScale with the following PyTorch versions (with CUDA 11.2):
-* the latest stable release (e.g. 1.10.0)
-* the latest LTS release (e.g. 1.8.1)
-* a recent nightly release (e.g. 1.11.0.dev20211101+cu111)
+## 源码安装
+
+```shell
+git clone https://github.com/facebookresearch/fairscale.git # 根据需要切换分支
+cd fairscale
+# 支持GPU需要添加环境变量
+export BUILD_CUDA_EXTENSIONS=1
+export HIPCC_COMPILE_FLAGS_APPEND="--gpu-max-threads-per-block=1024"
+pip install -r requirements.txt
+# -e signified dev mode since e stands for editable
+pip install -e .
+```
 
-Please create an [issue](https://github.com/facebookresearch/fairscale/issues) if you are having trouble with installation.
+如果安装失败，尝试在 `pip install` 命令后，添加 `--no-build-isolation` 选项。
 
-## Contributors
+## 其它
 
-We welcome contributions! Please see the [CONTRIBUTING](CONTRIBUTING.md) instructions for how you can contribute to FairScale.
+torch安装之后，如有库缺失导致的错误，参考以下库的安装
 
-## License
+* 安装 `intel-mkl`库
 
-FairScale is licensed under the [BSD-3-Clause License](LICENSE).
+  ```shell
+  yum-config-manager --add-repo https://yum.repos.intel.com/mkl/setup/intel-mkl.repo
+  yum install intel-mkl-2020.0-088 -y --nogpgchec
+  ```
 
-fairscale.nn.pipe is forked from [torchgpipe](https://github.com/kakaobrain/torchgpipe), Copyright 2019, Kakao Brain, licensed under [Apache License](http://www.apache.org/licenses/LICENSE-2.0).
+  并将库路径添加到环境变量：
 
-fairscale.nn.model_parallel is forked from [Megatron-LM](https://github.com/NVIDIA/Megatron-LM), Copyright 2020, NVIDIA CORPORATION, licensed under [Apache License](http://www.apache.org/licenses/LICENSE-2.0).
+  ```shell
+  export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/opt/intel/mkl/lib/intel64
+  ```
 
-fairscale.optim.adascale is forked from [AdaptDL](https://github.com/petuum/adaptdl), Copyright 2020, Petuum, Inc., licensed under [Apache License](http://www.apache.org/licenses/LICENSE-2.0).
 
-fairscale.nn.misc.flatten_params_wrapper is forked from [PyTorch-Reparam-Module](https://github.com/SsnL/PyTorch-Reparam-Module), Copyright 2018, Tongzhou Wang, licensed under [MIT License](https://github.com/SsnL/PyTorch-Reparam-Module/blob/master/LICENSE).
 
+* 安装 `magma`
 
-## Citing FairScale
+  ```shell
+  # 默认dtk安装路径为 /opt/dtk
+  cd /opt/dtk 
+  wget http://10.6.10.68:8000/debug/pytorch/third_party/magma_v2.7.2-hip_nfs3.2_DTK23.10_intel-2020.1.217_07Oct2023.tar.gz
+  tar -zxf magma_v2.7.2-hip_nfs3.2_DTK23.10_intel-2020.1.217_07Oct2023.tar.gz
+  mv magma_v2.7.2-hip_nfs3.2_DTK23.10_intel-2020.1.217_07Oct2023 magma
+  cd magma/lib/
+  # 添加环境变量
+  export LD_LIBRARY_PATH=${ROCM_PATH}/magma/lib:$LD_LIBRARY_PATH
+  ```
 
-If you use FairScale in your publication, please cite it by using the following BibTeX entry.
+# 验证
+  查询软件版本号，与官方版本同步。
+   ```
+   python -c "import fairscale; print(fairscale.__version__)"
+   ```
 
-```BibTeX
-@Misc{FairScale2021,
-  author =       {FairScale authors},
-  title =        {FairScale:  A general purpose modular PyTorch library for high performance and large scale training},
-  howpublished = {\url{https://github.com/facebookresearch/fairscale}},
-  year =         {2021}
-}
-```

README_ORIGIN.md

+![FairScale Logo](./docs/source/_static/img/fairscale-logo.png)
+
+[![Support Ukraine](https://img.shields.io/badge/Support-Ukraine-FFD500?style=flat&labelColor=005BBB)](https://opensource.facebook.com/support-ukraine)
+![PyPI](https://img.shields.io/pypi/v/fairscale)
+[![Documentation Status](https://readthedocs.org/projects/fairscale/badge/?version=latest)](https://fairscale.readthedocs.io/en/latest/?badge=latest)
+[![CircleCI](https://circleci.com/gh/facebookresearch/fairscale.svg?style=shield)](https://app.circleci.com/pipelines/github/facebookresearch/fairscale/) ![PyPI - License](https://img.shields.io/pypi/l/fairscale) [![Downloads](https://pepy.tech/badge/fairscale)](https://pepy.tech/project/fairscale) [![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg)](https://github.com/facebookresearch/fairscale/blob/main/CONTRIBUTING.md)
+--------------------------------------------------------------------------------
+
+## Description
+FairScale is a PyTorch extension library for high performance and large scale training.
+This library extends basic PyTorch capabilities while adding new SOTA scaling techniques.
+FairScale makes available the latest distributed training techniques in the form of composable
+modules and easy to use APIs. These APIs are a fundamental part of a researcher's toolbox as
+they attempt to scale models with limited resources.
+
+FairScale was designed with the following values in mind:
+
+* **Usability** -  Users should be able to understand and use FairScale APIs with minimum cognitive overload.
+
+* **Modularity** - Users should be able to combine multiple FairScale APIs as part of their training loop seamlessly.
+
+* **Performance** - FairScale APIs provide the best performance in terms of scaling and efficiency.
+
+## Watch Introductory Video
+
+[![Explain Like I’m 5: FairScale](https://img.youtube.com/vi/oDt7ebOwWIc/0.jpg)](https://www.youtube.com/watch?v=oDt7ebOwWIc)
+
+## Installation
+
+To install FairScale, please see the following [instructions](https://github.com/facebookresearch/fairscale/blob/main/docs/source/installation_instructions.rst).
+You should be able to install a package with pip or conda, or build directly from source.
+
+## Getting Started
+The full [documentation](https://fairscale.readthedocs.io/) contains instructions for getting started, deep dives and tutorials about the various FairScale APIs.
+
+## FSDP
+
+FullyShardedDataParallel (FSDP) is the recommended method for scaling to large NN models.
+This library has been [upstreamed to PyTorch](https://pytorch.org/blog/introducing-pytorch-fully-sharded-data-parallel-api/).
+The version of FSDP here is for historical references as well as for experimenting with
+new and crazy ideas in research of scaling techniques. Please see the following blog
+for [how to use FairScale FSDP and how does it work](https://engineering.fb.com/2021/07/15/open-source/fsdp/).
+
+## Testing
+
+We use circleci to test FairScale with the following PyTorch versions (with CUDA 11.2):
+* the latest stable release (e.g. 1.10.0)
+* the latest LTS release (e.g. 1.8.1)
+* a recent nightly release (e.g. 1.11.0.dev20211101+cu111)
+
+Please create an [issue](https://github.com/facebookresearch/fairscale/issues) if you are having trouble with installation.
+
+## Contributors
+
+We welcome contributions! Please see the [CONTRIBUTING](CONTRIBUTING.md) instructions for how you can contribute to FairScale.
+
+## License
+
+FairScale is licensed under the [BSD-3-Clause License](LICENSE).
+
+fairscale.nn.pipe is forked from [torchgpipe](https://github.com/kakaobrain/torchgpipe), Copyright 2019, Kakao Brain, licensed under [Apache License](http://www.apache.org/licenses/LICENSE-2.0).
+
+fairscale.nn.model_parallel is forked from [Megatron-LM](https://github.com/NVIDIA/Megatron-LM), Copyright 2020, NVIDIA CORPORATION, licensed under [Apache License](http://www.apache.org/licenses/LICENSE-2.0).
+
+fairscale.optim.adascale is forked from [AdaptDL](https://github.com/petuum/adaptdl), Copyright 2020, Petuum, Inc., licensed under [Apache License](http://www.apache.org/licenses/LICENSE-2.0).
+
+fairscale.nn.misc.flatten_params_wrapper is forked from [PyTorch-Reparam-Module](https://github.com/SsnL/PyTorch-Reparam-Module), Copyright 2018, Tongzhou Wang, licensed under [MIT License](https://github.com/SsnL/PyTorch-Reparam-Module/blob/master/LICENSE).
+
+
+## Citing FairScale
+
+If you use FairScale in your publication, please cite it by using the following BibTeX entry.
+
+```BibTeX
+@Misc{FairScale2021,
+  author =       {FairScale authors},
+  title =        {FairScale:  A general purpose modular PyTorch library for high performance and large scale training},
+  howpublished = {\url{https://github.com/facebookresearch/fairscale}},
+  year =         {2021}
+}
+```

fairscale/__init__.py

@@ -16,5 +16,10 @@ from typing import List
 from . import nn
 from .version import __version_tuple__
 
+try:
+    from .version import __dcu_version__
+except ImportError:
+    pass
+
 __version__ = ".".join([str(x) for x in __version_tuple__])
 __all__: List[str] = []

setup.py

@@ -29,6 +29,44 @@ def find_version(version_file_path) -> str:
             return ver_str
         raise RuntimeError("Unable to find version tuple.")
 
+def get_abi():
+    try:
+        command = "echo '#include <string>' | gcc -x c++ -E -dM - | fgrep _GLIBCXX_USE_CXX11_ABI"
+        result = subprocess.run(command, shell=True, capture_output=True, text=True)
+        output = result.stdout.strip()
+        abi = "abi" + output.split(" ")[-1]
+        return abi
+    except Exception:
+        return 'abiUnknown'
+tmpversion = find_version("fairscale/version.py")
+dcu_version = tmpversion
+dcu_version +='+das1.1'
+sha = "Unknown"
+cwd = os.path.dirname(os.path.abspath(__file__))
+import subprocess
+try:
+    sha = subprocess.check_output(["git", "rev-parse", "HEAD"], cwd=cwd).decode("ascii").strip()
+except Exception:
+    pass
+
+if sha != 'Unknown':
+    dcu_version += '.git' + sha[:7]
+dcu_version += "." + get_abi()
+if os.getenv("ROCM_PATH"):
+    rocm_path = os.getenv('ROCM_PATH', "")
+    rocm_version_path = os.path.join(rocm_path, '.info', "rocm_version")
+    with open(rocm_version_path, 'r',encoding='utf-8') as file:
+        lines = file.readlines()
+    rocm_version=lines[0][:-2].replace(".", "")
+    dcu_version += ".dtk" + rocm_version
+# torch version
+import torch
+dcu_version += ".torch" + torch.__version__[:]
+def write_version_file():
+    cwd = os.path.dirname(os.path.abspath(__file__))
+    version_path = os.path.join(cwd, "fairscale", "version.py")
+    with open(version_path, "a") as f:
+        f.write(f"\n__dcu_version__ = '{dcu_version}'")
 
 extensions = []
 cmdclass = {}
@@ -54,10 +92,11 @@ if os.getenv("BUILD_CUDA_EXTENSIONS", "0") == "1":
 
 
 if __name__ == "__main__":
+    write_version_file()
     setuptools.setup(
         name="fairscale",
         description="FairScale: A PyTorch library for large-scale and high-performance training.",
-        version=find_version("fairscale/version.py"),
+        version=dcu_version,
         setup_requires=["ninja"],  # ninja is required to build extensions
         install_requires=fetch_requirements(),
         include_package_data=True,