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### Description In some scenarios, the triton written kernels are more performant than CK or other handwritten kernels, so we implement a framework that onnxruntime can use these triton written kernels. This PR is to integrate triton into ort, so that ort can use kernels that written and compiled by triton. The main change focus on two part: 1. a build part to compile triton written kernel and combine these kernels into libonnxruntime_providers_rocm.so 2. a loader and launcher in c++, for loading and launch triton written kernels. #### Build To compile triton written kernel, add a script `tools/ci_build/compile_triton.py`. This script will dynamic load all kernel files, compile them, and generate `triton_kernel_infos.a` and `triton_kernel_infos.h`. `triton_kernel_infos.a` contains all compiled kernel instructions, this file will be combined into libonnxruntime_providers_rocm.so, using --whole-archive flag. `triton_kernel_infos.h` defines a const array that contains all the metadata for each compiled kernel. These metadata will be used for load and launch. So this header file is included by 'triton_kernel.cu' which defines load and launch functions. Add a build flag in build.py and CMakeList.txt, when building rocm provider, it will call triton_kernel build command, and generate all necessary files. #### C++ Load and Launch On c++ part, we implement load and launch functions in triton_kernel.cu and triton_kernel.h. These two files located in `providers/cuda`, and when compiling rocm, they will be hipified. so this part supports both cuda and rocm. But currently we only call triton kernel in rocm. We also implement a softmax triton op for example. Because there will generate many kernels for different input shape of softmax, we use TunableOp to select the best one. ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> |
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ONNX Runtime is a cross-platform inference and training machine-learning accelerator.
ONNX Runtime inference can enable faster customer experiences and lower costs, supporting models from deep learning frameworks such as PyTorch and TensorFlow/Keras as well as classical machine learning libraries such as scikit-learn, LightGBM, XGBoost, etc. ONNX Runtime is compatible with different hardware, drivers, and operating systems, and provides optimal performance by leveraging hardware accelerators where applicable alongside graph optimizations and transforms. Learn more →
ONNX Runtime training can accelerate the model training time on multi-node NVIDIA GPUs for transformer models with a one-line addition for existing PyTorch training scripts. Learn more →
Get Started & Resources
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General Information: onnxruntime.ai
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Usage documention and tutorials: onnxruntime.ai/docs
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YouTube video tutorials: youtube.com/@ONNXRuntime
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Companion sample repositories:
- ONNX Runtime Inferencing: microsoft/onnxruntime-inference-examples
- ONNX Runtime Training: microsoft/onnxruntime-training-examples
Build Pipeline Status
| System | Inference | Training |
|---|---|---|
| Windows | ||
| Linux | ||
| Mac | ||
| Android | ||
| iOS | ||
| Web | ||
| Other |
Data/Telemetry
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Contributions and Feedback
We welcome contributions! Please see the contribution guidelines.
For feature requests or bug reports, please file a GitHub Issue.
For general discussion or questions, please use GitHub Discussions.
Code of Conduct
This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.
License
This project is licensed under the MIT License.