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This PR implements DistributedExpand for llama 2. Representative Examples of DistributedExpand: - [shard on non-expanded axis] `input tensor (shape=[8, 1], spec=S[0]R, device_mesh=[0,1]) -> Expand(target_shape=[8, 2] -> output tensor (shape=[8, 2], spec=S[0]R, device_mesh=[0,1])` - [sharding expanded axis is invalid since it must have dim=1 and axis with dim=1 cannot be sharded] `input tensor (shape=[1, 8], spec=S[0]R, device_mesh=[0,1]) -> Expand(target_shape=[2, 8] -> output tensor (shape=[2, 8], spec=S[0]R, device_mesh=[0,1])` From those examples, we observe a few important behaviors. - The output sharding spec is always the same to the input sharding spec. - Expanding always happen on axis with dimension=1. Otherwise, it will violate the broadcasting rule. - No communication is needed since all computation can happen locally. Let's consider the first example again. If you put the first half tensor (shape: [4, 1]) on device 0 and the second half (shape: [4, 1]) on device 1, then `Expand` it with target shape [4, 2] , these two local tensors (shape: [4, 2]) are exactly the same as the one described by output sharding spec. Algorithm: - Compute logical (i.e., unsharded) shapes of input and output. - Compute sharded output shape from logical output. - Call Expand to broadcast local input to sharded output shape. How to review? - Start with [changes in onnxruntime_test_distributed.py]( |
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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 |
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| Windows | ||
| Linux | ||
| Mac | ||
| Android | ||
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| System | Inference | Training |
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| Linux |  |
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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.