[Test][Topi] Avoid depending on f32 rounding behavior for crop_and_divide tests #13773
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Failure wasn't reproducible locally with 1k repetitions, each with a different random input. Re-running CI with a fixed seed for random input data. |
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The errors in the i386 test look like they are due to the same type of rounding issue that this PR was intended to avoid. That is, when an input pixel falls exactly on the edge of a bounding box, whether the |
The `crop_and_resize` operator uses floating-point arithmetic to determine whether an index is within a view-box. This can cause the use of `extrapolation_value` to depend on target-dependent rounding differences. For example, this issue was initially noticed on Vulkan during debugging of apache#13530, and was the result of computing `0.2*223.0 + 0.8*223.0 < 223.0`. If all intermediates are cast to float32, the left-hand side evaluates to `223.00002`. If intermediates are kept at a higher precision, the left-hand side evaluates to `223.0`. The floating-point indexing can't be removed, because the operator must match the API defined by TensorFlow's operator implementation. The TensorFlow documentation for [`CropAndResize`](https://www.tensorflow.org/api_docs/cc/class/tensorflow/ops/crop-and-resize) does not specify behavior in these cases, nor do the current TensorFlow unit tests check cases of rounding error. Since the TensorFlow unit tests only use binary fractions for the `boxes` argument, which largely avoids the rounding issue, this commit updates the TVM unit tests to avoid depending on floating-point precision.
This mimics the example usage of `tf.image.crop_and_resize`, whose API this operator is intended to follow. Using any boxes with edges representable as integer fractions has the potential for the in-bounds check to be impacted by rounding error (e.g. `0.2*x + 0.8*x < x`). Unfortunately, there's no way to remove this possibility altogether without changing the API, such as accepting the box location as an integer fraction, rather than a `float32`, but that would break compatibility. To avoid the risk of a flaky unit test based on the specific random boxes used, the PRNG is seeded prior to generation.
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masahi
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Apr 5, 2023
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The
crop_and_resizeoperator uses floating-point arithmetic to determine whether an index is within a view-box. This can cause the use ofextrapolation_valueto depend on target-dependent rounding differences.For example, this issue was initially noticed on Vulkan during debugging of #13530, and was the result of computing
0.2*223.0 + 0.8*223.0 < 223.0. If all intermediates are cast to float32, the left-hand side evaluates to223.00002. If intermediates are kept at a higher precision, the left-hand side evaluates to223.0.The floating-point indexing can't be removed, because the operator must match the API defined by TensorFlow's operator implementation. The TensorFlow documentation for
CropAndResizedoes not specify behavior in these cases, nor do the current TensorFlow unit tests check cases of rounding error. Since the TensorFlow unit tests only use binary fractions for theboxesargument, which largely avoids the rounding issue, this commit updates the TVM unit tests to avoid depending on floating-point precision.