4f6993d567
### Description Fixes scenario in which a bias input quantized to int32 has a scale that is too small. A bias with a scale that is smaller than a certain threshold will overflow the range of an `int32` when quantized, which significantly decreases accuracy. Credit to @yihonglyu for finding out about this issue and the fix. ### Motivation and Context Consider the following Convolution with very small weights and a constant bias input of `[5, -4.5]`.  The QDQ quantizer first computes the following quantization scale for `input_0` and `weight`: - `input_0`: scale=0.5 - `weight`: scale=7.843e-10 **[really small]** The QDQ quantizer then computes the bias input's scale as follows: ``` bias_scale = input_0_scale * weight_0_scale = 0.5 * 7.843e-10 = 3.9215686274509805e-11 ``` This `bias_scale` is too small. Before this PR, the QDQ quantizer would quantize the f32 bias with this `bias_scale`: ``` bias_quant = round(bias_f32 / bias_scale) = round([5.0/bias_scale, -4.5/bias_scale]) = [127500000000, -114750000000] ``` These quantized bias values exceed the range of int32, and so are clipped to [int32.min(), int32.max()], which is very inaccurate. #### New approach This PR increases the `weight_0_scale` by the necessary amount to ensure that `bias_scale` (which equals `weight_0_scale * input_0_scale`) is appropriate for the int32 quantization type. The smallest valid bias scale is given by the normal scale formula: `bias_smallest_valid_scale = (bias_f32_max - bias_f32_min) / (int32_max - int32_min)` Then, we compute the candidate bias scale: `bias_scale_candidate = input_0_scale * weight_0_scale` If the candidate scale is smaller than the smallest valid scale, we increase the `weight_0_scale` by the necessary ratio: ```python if bias_scale_candidate < bias_smallest_valid_scale: ratio = bias_smallest_valid_scale / bias_scale_candidate weight_0_scale = ratio * weight_0_scale ``` Then, we recompute the final bias scale: ```python bias_scale = input_0_scale * weight_0_scale ``` #### Impact on accuracy Here's the above model's quantized output compared to the f32 (ground-truth) output. - Before PR: - f32 model output[0]: **5.0f** - qdq model output[0]: **0.075** - SNR: 0.1369 (higher is better) - After PR: - f32 model output[0]: **5.0f** - qdq model output[0]: **4.992** - SNR: 55.656 (higher is better) |
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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
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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.
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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.