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### Description Whenever a node QuantizeLinear or DequantizeLinear, the type of the weights before being quantize must be known to create the scale with the expected type. Another option would be to add many operator CastLike but that would push the burden to onnxruntime optimizer. The PR tries to avoid changing the signature. To do so, it modified the scale computation to use a numpy array to store the result and not a python float. The numpy array must be of the same type than the weights to quantize. The PR adds many `assert` to check the type of the scale is not a python type or a float64. This was added to make sure all the code follows the same logic. These lines were kept for the first review. DequantizeLinear, QuantizeLinear cannot be tested with onnx==1.15. PR https://github.com/onnx/onnx/pull/5709 is missing to fix shape inference. PR https://github.com/onnx/onnx/pull/5473) is missing to support QLinearMatMul with float 16. That explains why some tests are disabled with float 16. ### Motivation and Context The current quantization tool assumes every weight is float 32. For large models such as LLAMA, it is usually float 16. The quantization needs to quantize such weights. |
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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.