37be90c9c8
### Description This PR improves the range calculation for input to Relu/Clip nodes for the symmetric quantization case. ### Motivation and Context Currently, the issue we face is that for the common scenario of conv followed by relu in the symmetric quantization config, different scales could assigned for the tensors corresponding to input & output of relu. The downside is that this may introduce noise due to multiple re-quant, and makes it difficult to fuse conv-relu nodes for hardware accelerators that support fused conv-relu. Instead, it is more efficient to assign the output range of relu as the input range of relu / output range of upstream op wherever possible. This adjustment is currently only being done for the asymmetric quantization case. For the scenario where the upstream op has multiple consumers, this assumption could be incorrect. For this case we do not adjust the ranges. |
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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 | ||
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Third-party Pipeline Status
| 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.