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### Description A follow up change for https://github.com/microsoft/onnxruntime/pull/13616. SoftmaxCrossEntropyLossInternal/SoftmaxCrossEntropyLossInternalGrad support different type for input and output. Add SCELoss(SCELossGrad) support half(float) input float(half) output ### Test Note #### Add tests for variant input and output types. To add such tests, have to refactor existing testing code for sce loss and scelossinternal gradient. Originally, FP32 input and output, the CPU kernels, runs with CPU kernels the baseline, CUDA/RCOM then runs with same data, user CompareTester to compare with CPU run results. FP16 input and output, the CPU kernels (did not have half kernels), runs with Cast_to_float->CPU kernel->cast_to_half as the baseline, CUDA/RCOM then runs with same data but using Half implementation, user CompareTester to compare with CPU run results. Now, we want the support run different input and output types. The proposed change here is, to run CPU kernels always with float input and output as baseline (because CPU only have float type kernels impl), this step is the very first thing for every test. Then, we run CUDA/RCOM kernels using half_input_half_output, float_input_float_output, half_input_float_output, float_input_half_output if there is corresponding kernel registered. Afterwards, compare the CUDA/ROCM run results with CPU float baselines. Be noted, there is one thing that deserved a special note: CompareOpTester's result compare can be loose than OpTester's. Roughly speaking: the former tolerant diff <= atol + rtol*expected_value, while the later one telerant diff < atol && diff < rtol*expected_value. When the expected value is super small in many cases of our tests cases, the former one can pass but the later one fails. So the refactoring also move the check outside of OpTester, explicitly check the values using the way CompareOPTester did (to align the previous behaviour). ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> |
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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
Build Pipeline Status
| System | Inference | Training |
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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.