Lunderberg opened a new pull request, #13773: URL: https://github.com/apache/tvm/pull/13773
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 https://github.com/apache/tvm/pull/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 is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: commits-unsubscr...@tvm.apache.org For queries about this service, please contact Infrastructure at: us...@infra.apache.org
