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### Description - Applies a workaround that prevents the histogram-based calibrators (percentile, entropy, distribution) from crashing. The workaround involves copying inference outputs that come directly from model inputs. A description of the bug is here: https://github.com/microsoft/onnxruntime/issues/21922. **This PR does not fix the root bug, but instead provides a workaround to _unblock_ users using histogram-based calibration.** - Adds a unit test that runs all histogram-based calibrators to help catch future regressions. We didn't have unit tests that ran these calibration methods. ### Motivation and Context Trying to quantize a model with the percentile, entropy, or distribution calibration methods raises an exception: ```shell File "/.../site-packages/onnxruntime/quantization/quantize.py", line 691, in quantize quantize_static( File "/.../site-packages/onnxruntime/quantization/quantize.py", line 525, in quantize_static calibrator.collect_data(calibration_data_reader) File "/.../site-packages/onnxruntime/quantization/calibrate.py", line 571, in collect_data self.collector.collect(clean_merged_dict) File "/.../site-packages/onnxruntime/quantization/calibrate.py", line 746, in collect return self.collect_value(name_to_arr) File "/.../site-packages/onnxruntime/quantization/calibrate.py", line 836, in collect_value hist, hist_edges = np.histogram(data_arr, self.num_bins, range=(-threshold, threshold)) File "<__array_function__ internals>", line 180, in histogram File ".../site-packages/numpy/lib/histograms.py", line 793, in histogram bin_edges, uniform_bins = _get_bin_edges(a, bins, range, weights) File "/.../site-packages/numpy/lib/histograms.py", line 426, in _get_bin_edges first_edge, last_edge = _get_outer_edges(a, range) File "/.../site-packages/numpy/lib/histograms.py", line 315, in _get_outer_edges raise ValueError( ValueError: supplied range of [nan, nan] is not finite ``` The calibrators create an augmented model with all tensors (including model inputs) set as model outputs. The data for outputs that are also model inputs is corrupted as described in https://github.com/microsoft/onnxruntime/issues/21922. The corrupted data sometimes contains `NaN` values that cause numpy's histogram utilities to raise an exception. |
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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 | ||
| Android | ||
| iOS | ||
| Web | ||
| Other |
Third-party Pipeline Status
| System | Inference | Training |
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| Linux |  |
Data/Telemetry
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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.