onnxruntime

c7ae9b97 - [Quantization] Apply workaround for crash when using histogram-based calibrators (#21972)

[Quantization] Apply workaround for crash when using histogram-based calibrators (#21972) ### 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.