forked from jiuyuan/InfiniTensor
00e6cc2587
* add reduce_mean and gather * fix format * add kunlun allreduce and cmakefile * add kunlun allreduce and cmakefile * deltete cmake opt * fix format * fix makefile * add DIST option in Makefile * add xpu allgather * delete xpu_wait() * add xpu allgather * delete specific compiler * fix format * fix gather * add broadcast * fix format * fix * fix xpu, add where operation, fix element-wise operation * fix softmax * fix softmax * log internal input and output * fix kunlun gather bugs * update CMakeList.txt and Makefile * fix some kunlun kernels * fix Makefile * fix Makefile * set cmake version 3.12 * format * fix where, gather and support gpt2 * "fix format" * fix format * copy onnx.py from master * use KUNLUN_HOME instead of absolute path * fix torchvision models * support torchvison model-zoo * fix format * format fix, CMakeList fix * fix review * fix vecToString return value * fix format * delete empty file --------- Co-authored-by: wanghailu <wanghailu0717@163.com> Co-authored-by: wanghailu <wanghailu@qiyuanlab.com> Co-authored-by: Haojie Wang <haojie0429@gmail.com> |
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.github/workflows | ||
3rd-party | ||
cmake | ||
docs | ||
examples | ||
include | ||
proto | ||
pyinfinitensor | ||
python | ||
scripts | ||
src | ||
test | ||
.clang-format | ||
.cmake-format.json | ||
.gitignore | ||
.gitmodules | ||
CHANGELOG.md | ||
CMakeLists.txt | ||
LICENSE | ||
Makefile | ||
README.md | ||
README_CN.md | ||
env.sh |
README.md
InfiniTensor
InfiniTensor is a high-performance inference engine tailored for GPUs and AI accelerators. Its design focuses on effective deployment and swift academic validation.
Get started
Make Commands
make
/make build
: Builds the project;make install-python
: Builds the project then install the python frontend;make test-cpp
: Builds the project then run cpp unit tests;make test-onnx
: Run python unit tests;
- Sets env:
TEST=OFF
to accelerate compiling.- Sets env:
CUDA=ON
to enable cuda.- Sets env:
BANG=ON
to enable bang.
CMake Options
There are several configurable CMake options, see the CMakeLists.txt file.
- If
USE_BACKTRACE
isON
,libdw-dev
have to be installed. See the README of backward-cpp for details. - If
USE_PROTOBUF
isON
,protobuf
have to be installed. See the README of protobuf for details. - If
USE_CUDA
isON
,cuda
have to be installed.
Roadmap
- RefactorGraph is a newly designed AI framework that is set to replace the current main branch.
- EinNet is going to be merged into the main branch.
- Integration of PET, a tensor program optimizer supporting partially equivalent transformations.
- Supported hardware
- ✔ NVIDIA GPU
- ✔ Cambricon MLU
- ✔ Kunlunxin XPU
- ⬜ Ascend NPU
Contributor Guide
InfiniTensor development is based on the pull request on Github. Before requesting for merging, a PR should satisfy the following requirements
- Pass all tests.
- Now CI on Github will test everything that can be tested in the ci environment, including code format. So, script
test/script/clang_format_inplace.sh
is for formatting all code. - Contributors should run
ctest
manually and copy its output to the PR. Use fenced code blocks (triple backquotes, i.e.,```
) to avoid referencing in Github. Otherwise,#
in the output is interpreted as a Github reference. Do not directly paste the ctest output in commit messages either for the same reason.
- Now CI on Github will test everything that can be tested in the ci environment, including code format. So, script
- Receive at least one approval from reviewers.
- PR title should be concise since it is going to be the commit message in the main branch after merging and squashing.
Reference
Please cite EinNet or PET in your publications if it helps your research:
@article{zheng2023einnet,
title={EINNET: Optimizing Tensor Programs with Derivation-Based Transformations},
author={Zheng, Liyan and Wang, Haojie and Zhai, Jidong and Hu, Muyan and Ma, Zixuan and Wang, Tuowei and Huang, Shuhong and Miao, Xupeng and Tang, Shizhi and Huang, Kezhao and Jia, Zhihao},
booktitle={17th USENIX Symposium on Operating Systems Design and Implementation (OSDI 23)},
pages={739--755},
year={2023}
}
@inproceedings{wang2021pet,
title={PET: Optimizing tensor programs with partially equivalent transformations and automated corrections},
author={Wang, Haojie and Zhai, Jidong and Gao, Mingyu and Ma, Zixuan and Tang, Shizhi and Zheng, Liyan and Li, Yuanzhi and Rong, Kaiyuan and Chen, Yuanyong and Jia, Zhihao},
booktitle={15th USENIX Symposium on Operating Systems Design and Implementation (OSDI 21)},
pages={37--54},
year={2021}
}