ba22d7879a
### Description Based on https://github.com/microsoft/onnxruntime/pull/9700, and extend it to ArgMin as well. This pull request introduces several enhancements and fixes related to the `ArgMax` and `ArgMin` operators in the CUDA execution provider. The changes ensure proper handling of these operators across different versions and improve kernel registration and fallback mechanisms. Key changes include: #### Enhancements to `ArgMax` and `ArgMin` Operators: * Added new kernel class registrations for `ArgMax` and `ArgMin` for different data types and versions in `onnxruntime/core/providers/cuda/cuda_execution_provider.cc`. [[1]](diffhunk://#diff-57ba769b54dce57acd89df47140ede5f29ea670d61176096076701912d573285R966-R972) [[2]](diffhunk://#diff-57ba769b54dce57acd89df47140ede5f29ea670d61176096076701912d573285R1209-R1215) [[3]](diffhunk://#diff-57ba769b54dce57acd89df47140ede5f29ea670d61176096076701912d573285R1657-R1659) [[4]](diffhunk://#diff-57ba769b54dce57acd89df47140ede5f29ea670d61176096076701912d573285L1825-L1827) [[5]](diffhunk://#diff-57ba769b54dce57acd89df47140ede5f29ea670d61176096076701912d573285R1933-R1939) [[6]](diffhunk://#diff-57ba769b54dce57acd89df47140ede5f29ea670d61176096076701912d573285R2174-R2180) * Introduced `ArgMaxOrArgMinNeedFallbackToCPU` function to handle fallback to CPU when the `select_last_index` attribute is set to 1, as CUDA does not support this attribute. [[1]](diffhunk://#diff-57ba769b54dce57acd89df47140ede5f29ea670d61176096076701912d573285R2597-R2622) [[2]](diffhunk://#diff-57ba769b54dce57acd89df47140ede5f29ea670d61176096076701912d573285R2672-R2674) #### Macro and Kernel Registration Improvements: * Replaced `REGISTER_KERNEL_UNTIL_VERSIONED_TYPED` with `REGISTER_KERNEL_VERSIONED_RANGE_TYPED` and `REGISTER_KERNEL_VERSIONED_SINCE_TYPED` macros for better version handling. [[1]](diffhunk://#diff-ee5316fc3898058f70e942d9a84de36be4c7da09f144633a2504236430d5d033L19-R29) [[2]](diffhunk://#diff-ee5316fc3898058f70e942d9a84de36be4c7da09f144633a2504236430d5d033L40-R46) * Updated kernel registration for `ArgMax` and `ArgMin` to use the new macros, ensuring proper version handling and support for different data types. #### Safety Checks: * Added safety checks in the `ArgMax` and `ArgMin` classes to ensure `select_last_index` is not set to 1, as it is not supported on CUDA. [[1]](diffhunk://#diff-8ab09fef1f4a12cbf3b3432e509f8f1ef561e83c72778a0e047780060aeef6efL91-R99) [[2]](diffhunk://#diff-8ab09fef1f4a12cbf3b3432e509f8f1ef561e83c72778a0e047780060aeef6efL101-R117) #### Testing Enhancements: * Added new tests for `ArgMax` and `ArgMin` operators to verify behavior when `select_last_index` is set to 0, ensuring compatibility with both CPU and CUDA execution providers. [[1]](diffhunk://#diff-77affe1b70d1a9d38c2485f7c6b16ef2b6b541ed94dd727bc9b286f068f1481aR3340-R3360) [[2]](diffhunk://#diff-77affe1b70d1a9d38c2485f7c6b16ef2b6b541ed94dd727bc9b286f068f1481aR3679-R3699) ### Motivation and Context Improve CUDA kernel coverage for stable diffusion model and hence improve its performance on CUDA |
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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 |
This project is tested with BrowserStack.
Third-party Pipeline Status
| System | Inference | Training |
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| Linux |  |
Releases
The current release and past releases can be found here: https://github.com/microsoft/onnxruntime/releases.
For details on the upcoming release, including release dates, announcements, features, and guidance on submitting feature requests, please visit the release roadmap: https://onnxruntime.ai/roadmap.
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.