pytorch

937ba248 - Make the Index Rounding Mode Consistent Between the 2D and 3D GridSample Nearest Neighbor Interpolations (#97000)

Make the Index Rounding Mode Consistent Between the 2D and 3D GridSample Nearest Neighbor Interpolations (#97000) ## BC-breaking note: This is technically a bugfix. Prior to this PR, for `torch.nn.functional.grid_sample(mode='nearest')` the 2D kernel used `std::nearbyint` whereas the 3D kernel used `std::round` in order to determine the nearest pixel locations after un-normalization of the grid. This PR fixes the 3D kernel to use `std::nearbyint` which rounds values that are exactly `<>.5` to the nearest even which is consistent with the behavior of `torch.round`. Unnormalized indices that are exactly `<>.5` will now be rounded to the nearest even instead of being rounded away from 0. ## Description In the nearest neighbor interpolation mode, the 2D GridSample rounds index to the nearest even using [std::nearbyint](https://github.com/pytorch/pytorch/blob/v2.0.0/aten/src/ATen/native/cpu/zmath.h#L182) whereas the 3D GridSample rounds index away from zero using std::round. This discrepancy needs to be resolved. We are making both 2D GridSample and 3D GridSample to round to the nearest even. ## Unit Test Goals 1. Make sure the x dimension and y dimension rounding behaviors are the same for 2D GridSample. 2. ~~Make sure the 2D GridSample rounding mode is rounding to the nearest even.~~ 3. Make sure the x dimension, y dimension, and z dimension rounding behaviors are the same for 3D GridSample. 4. ~~Make sure the 3D GridSample rounding mode is rounding to the nearest even.~~ 5. The 2D GridSample and 3D GridSample rounding behaviors are exactly the same. After some experiments, I found 2 and 4 are difficult to achieve. Even though I can compute the normalized coordinates corresponding to the unnormalized coordinates including [0, 0.5, 1.0, 1.5, 2.0, 2.5, ..., 10.0], the unnormalization process in the GridSample implementations always have a small chance of having floating point error. Therefore, it's not possible to unit test the rounding mode from the normalized coordinates. ## Unit Test Methods The unit test is simple. By using the same values along the dimension to be tested in the input tensor and the same normalized indices in the grid tensor. The interpolation on the 2D GridSample x-dimension, 2D GridSample y-dimension, 3D GridSample x-dimension, 3D GridSample y-dimension, 3D GridSample z-dimension. Should produce exactly the same interpolated values. If one CPU/CUDA 2D/3D implementation use a different rounding mode from others, the unit test shall fail. Pull Request resolved: https://github.com/pytorch/pytorch/pull/97000 Approved by: https://github.com/mikaylagawarecki