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### Description Added CUDNN Frontend and used it for NHWC ConvTranspose op including option for bias fusion. Similar to this [Conv PR](https://github.com/microsoft/onnxruntime/pull/19470) ### Backward compatible If ORT is built with cuDNN 8, cuDNN frontend will not be built into binary. Old kernels (using cudnn backend APIs) are used. ### Major Changes For cuDNN 9, we will enable cudnn frontend to fuse data gradient convolution and bias when a provider option fuse_conv_bias=1. ### Potential Issues cuDNN frontend uses TF32 by default. It can be disabled using use_tf32 cuda provider option, but in the case cuDNN frontend encounters issues building an operation graph it will fallback to using TF32. ### Follow ups This is one of the PRs that target to enable NHWC, here the ConvTranspose operation in CUDA EP by default if device supports it. There are other changes will follow up to make it possible. (1) Enable prefer_nhwc by default for device with sm >= 70. (2) Change fuse_conv_bias=1 by default after more testing. (3) Add other NHWC operators (like Resize or UpSample). ### Motivation and Context The new CUDNN Frontend library provides the functionality to fuse operations and provides new heuristics for kernel selection. Here it fuses the convolution data gradient operation (ConvTranspose) with the pointwise bias operation. ### Minor Change In the CUDA convolution operation was a small bug when `GetCudnnConv1dPadToNc1d ` was enabled. |
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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 |
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| Windows | ||
| Linux | ||
| Mac | ||
| Android | ||
| iOS | ||
| Web | ||
| Other |
Third-party Pipeline Status
| System | Inference | Training |
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| 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.