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* `QuantizeBFP` and `DequantizeBFP` schemas - similar to `QuantizeLinear` and `DeQuantizeLinear`. * BFP datatype is represented as a `uint8` tensor with shape and stride metadata. This is preferrable to adding a new datatype for BFP, which is more disruptive and [discouraged by PyTorch](https://discuss.pytorch.org/t/training-with-custom-quantized-datatype/152132/2). Context: The Microsoft Floating Point (BFP) datatype shares an exponent for every n numbers called a “bounding box.” Each number still has its own mantissa and sign bits. BFP has been shown to incur 3-4 less cost (energy and area) than BFloat16 and INT8 counterparts without reductions in accuracy for the ImageNet benchmark as described in [Rouhani 2020](https://proceedings.neurips.cc/paper/2020/file/747e32ab0fea7fbd2ad9ec03daa3f840-Paper.pdf). Requirements: * There are many variants of BFP (number of mantissa bits, number of shared exponent bits, size of bounding box, custom bit fields, etc.) * The size and layout of an BFP variant varies across hardware * bounding box can be over arbitrary dimensions; for example, for the channel "C" dimension in a N x C x H x W tensor for convolution Goals of this PR: * Add initial versions of QuantizeBFP and DequantizeBFP operators to enable QDQ-style quantization with BFP. Once the schemas stabilize, we can consider upstreaming to ONNX. * Add some basic type and shape inferencing tests; tests that run on an EP will be a follow-up. |
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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
General Information: onnxruntime.ai
Usage documention and tutorials: onnxruntime.ai/docs
Companion sample repositories:
- ONNX Runtime Inferencing: microsoft/onnxruntime-inference-examples
- ONNX Runtime Training: microsoft/onnxruntime-training-examples
Build Pipeline Status
| System | CPU | GPU | EPs |
|---|---|---|---|
| Windows | |||
| Linux | |||
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| WebAssembly |
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.