9e8ad39847
This PR implements distributed reduciton for llama 2. This version doesn't consider any cases requring re-sharding because we haven't seen any use cases. Intutive examples: - [supported] [2,4,6]-tensor with spec=RRS[0] and device_mesh=[0,1] -> Reduce(axes=[0]) -> [1,4,6]-tensor with spec=RRS[0] and device_mesh=[0,1] - [supported] [2,4,6]-tensor with spec=RRS[0] and device_mesh=[0,1] -> Reduce(axes=[1]) -> [2,1,6]-tensor with spec=RRS[0] and device_mesh=[0,1] - [not supported] [2,4,6]-tensor with spec=RRS[0] and device_mesh=[0,1] -> Reduce(axes=[2]) -> [2,4,1]-tensor with spec=RRS[0] and device_mesh=[0,1] Algorithm: When the reduced axes are not sharded, each device can call reduction directly. The output sharding spec will be identical to input sharding spec. We currently throw when input and output sharding specs are different. Review guideline: - Check 97b8d2f for new op's schema and how new op is registered. - Read tests in 2450f93 to get faimilar with the behavior of these ops. - Check the implementation details in 753d9af. |
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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
Builtin Pipeline Status
| System | Inference | Training |
|---|---|---|
| Windows | ||
| Linux | ||
| Mac | ||
| Android | ||
| iOS | ||
| Web | ||
| Other |
Third-party Pipeline Status
| System | Inference | Training |
|---|---|---|
| Linux |  |
Data/Telemetry
Windows distributions of this project may collect usage data and send it to Microsoft to help improve our products and services. See the privacy statement for more details.
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.