NNFusion is a flexible and efficient DNN compiler that can generate high-performance executables from a DNN model description (e.g., TensorFlow frozen models and ONNX format). With the efficient compiler as core, NNFusion aims to:
- facilitate full-stack model optimization
- provide framework-free code generation capability
- support new accelerator devices as target inferencing devices
- Developers who want to speed up the execution performance of their pre-defined or pre-trained DNN model.
- Developers who want to deploy their pre-trained model as framework-free source codes with minimum library dependencies.
- Researchers who want to quickly try new compiler optimization ideas or customize optimizations on some specific models.
- Provide a full-stack optimization mechanism, including:
- Data-flow graph optimizations, e.g., CSE, compile-time constant folding, etc.
- Model-specific kernel selection, kernel co-scheduling, kernel fusion and auto kernel tuner integration.
- Static memory layout and placement optimizations.
- Provide ahead-of-time and source-to-source (model-to-code) compilation to reduce runtime overhead and remove library/framework dependencies.
- Support popular DNN model formats including TensorFlow and ONNX as input models.
- Support customized optimization in an easier and more efficient way, e.g., directly replacing hand-crafted kernels on the generated human-readable code.
- Support commonly used devices like CUDA GPUs, ROCm GPUs and CPU.
- Support parallel training via SuperScaler
For end users, simply use docker to compile your model and generate high-performance executable.
NNFusion supports and is well tested on Ubuntu 16.04 and 18.04 with a CUDA GPU equipped.
You should install nvidia-docker on your device to do the following steps.
We will use a simple TensorFlow LSTM inference model as an example. You can download a frozen version from our model zoo:
wget https://nnfusion.blob.core.windows.net/models/tensorflow/frozen_lstm_l8s8h256_bs1.pb
To use your own model to get started, please refer to Supported Models to see whether it is supported and freeze your model according to Freeze Your Model.
-
Pull docker image
docker pull nnfusion/cuda:10.2-cudnn7-devel-ubuntu18.04 -
Run docker container with the given image
docker run -t --name [YOUR_CONTAINER_NAME] -d nnfusion/cuda:10.2-cudnn7-devel-ubuntu18.04
docker start [YOUR_CONTAINER_NAME]
docker exec -it [YOUR_CONTAINER_NAME] bash
- Put your model in the container
In host, you can use docker cp host_path [YOUR_CONTAINER_NAME]:container_path to copy your model into the container, or use docker run -t -i -v <host_dir>:<container_dir> to map the host dir to the container.
- Compile Model
When model is prepared, we can compile model in the container and run it to see the performance.
cd root
nnfusion path/[YOUR_MODEL_FILE]
Note:
If you are using an ONNX model, the compile command will be nnfusion path/[YOUR_MODEL_FILE] -f onnx
- Build and Run Compiled Model
cd root/nnfusion_rt/cuda_codegen
cmake . && make -j
./main_test
- The output of NNFusion should be Tensors with value and model iteration times. Using the example model
frozen_lstm_l8s8h256_bs1.pb, you will see the output of this model and a summary of performance:
Result_2261_0:
8.921492e-03 1.182088e-02 8.937406e-03 7.932204e-03 1.574194e-02 3.844390e-03 -1.505094e-02 -1.112035e-02 5.026608e-03 -8.032205e-03 .. (size = 256, ends with 1.357487e-02);
Result_2261_0:
8.921492e-03 1.182088e-02 8.937406e-03 7.932204e-03 1.574194e-02 3.844390e-03 -1.505094e-02 -1.112035e-02 5.026608e-03 -8.032205e-03 .. (size = 256, ends with 1.357487e-02);
...
Iteration time 2.735200 ms
Iteration time 2.741376 ms
Iteration time 2.733440 ms
Iteration time 2.726528 ms
Iteration time 2.731616 ms
Iteration time 2.736544 ms
Iteration time 2.728576 ms
Iteration time 2.733440 ms
Iteration time 2.732992 ms
Iteration time 2.729536 ms
Iteration time 2.726656 ms
Iteration time 2.732512 ms
Iteration time 2.732032 ms
Iteration time 2.730208 ms
Iteration time 2.732960 ms
Summary: [min, max, mean] = [2.724704, 2.968352, 2.921987] ms
For more detailed information on NNFusion usage, please refer to NNFusion Usage.
For TensorFlow users, you can refer to Kernel Tuner Tutorial to learn how to compile a TensorFlow model and tune each operator in this model to generate the end-to-end source code.
For detailed example about trainingοΌplease refer to How to use NNFusion Python interface for inference/training.
Researchers or contributors who want to do more research on optimizing model compilation, you can build NNFusion from source code. To build from source code, please read the following documents:
- Read Before Started page to see supported CUDA GPUs and required libs.
- Read Build Guide for more information on how to build and install NNFusion in your native system or in the docker container.
- After building and installing NNFusion, please refer to Compile Guide and Tool Usage to learn how to compile or optimize a DNN model.
To learn how much performance improvement that NNFusion can archive on some typical DNN models, please refer to the README page at our OSDI'20 artifact branch.
This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.
To contribute, please refer to Contribution Guide to see more details.
When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.
This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [email protected] with any additional questions or comments.
Please cite NNFusion or Rammer in your publications if it helps your research:
@inproceedings {rammer-osdi20,
author = {Lingxiao Ma and Zhiqiang Xie and Zhi Yang and Jilong Xue and Youshan Miao and Wei Cui and Wenxiang Hu and Fan Yang and Lintao Zhang and Lidong Zhou},
title = {Rammer: Enabling Holistic Deep Learning Compiler Optimizations with rTasks},
booktitle = {14th {USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 20)},
year = {2020},
isbn = {978-1-939133-19-9},
pages = {881--897},
url = {https://www.usenix.org/conference/osdi20/presentation/ma},
publisher = {{USENIX} Association},
month = nov,
}
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