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### Description <!-- Describe your changes. --> For InstanceNormalization, it has `y = scale * (x - mean) / sqrt(variance + epsilon) + B` , where mean and variance are computed per instance per channel. Calculating mean and variance per channel is a reduce processing, which is NCHW layout friendly since it makes the adjacent threads can access contiguous data in gpu memory. This PR optimizes both NHWC and NCHW InstanceNormalization. To efficiently calculate the mean and variance, we need to make sure the input is NCHW instead of NHWC. Then use shared memory to do the reduce operation to get `channel_scale` and `channel_shift`. With this PR, getting `channel_scale` and `channel_shift` are same for NHWC and NCHW InstanceNormalization. And the overall performance becomes very close now. Below data comes from SD Turbo profiling results. Before (InstanceNormalization overall time: 140.84 ms) InstanceNormalization\|InstanceNormComputeMean | 129.70 -- | -- InstanceNormalization\|InstanceNormalizationNHWC | 10.55 InstanceNormalization\|InstanceNormComputeChannelScaleShift | 0.59 After (InstanceNormalization overall time: 59.44 ms) InstanceNormalization\|InstanceNormComputeChannelScaleShift | 28.57 -- | -- InstanceNormalization\|TransposeShared | 20.19 InstanceNormalization\|InstanceNormalizationNHWC | 10.68 |
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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 documentation 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.