Fix overflow in torch.remainder when dividend is very large#37758
Closed
xwang233 wants to merge 2 commits intopytorch:masterfrom
Closed
Fix overflow in torch.remainder when dividend is very large#37758xwang233 wants to merge 2 commits intopytorch:masterfrom
xwang233 wants to merge 2 commits intopytorch:masterfrom
Conversation
Collaborator
Author
|
cc @ptrblck |
💊 Build failures summary and remediationsAs of commit d4bbd70 (more details on the Dr. CI page): 💚 💚 Looks good so far! There are no failures yet. 💚 💚 This comment was automatically generated by Dr. CI (expand for details).Follow this link to opt-out of these comments for your Pull Requests.Please report bugs/suggestions on the GitHub issue tracker. This comment has been revised 8 times. |
ngimel
approved these changes
May 4, 2020
Contributor
facebook-github-bot
left a comment
There was a problem hiding this comment.
@ngimel has imported this pull request. If you are a Facebook employee, you can view this diff on Phabricator.
Collaborator
Author
|
@ngimel Is there anything I can help on the internal test failure? |
Collaborator
|
I'll try landing it now. |
Contributor
facebook-github-bot
pushed a commit
that referenced
this pull request
May 14, 2020
Summary: Together with #37758, this fixes #37743 and fixes #24861. This follows the CUDA fix in #37758, vectorised using a `blendv` to replace the if conditionals. Most of the complication is from `remainder` supporting `at::Half` where `fmod` doesn't. I've now got `fmod` working on `Vec256<at::Half>` as well as enabling half dispatch for `fmod` so it matches `remainder`. I also added `fmod` support to `Vec256<at::BFloat16>` before realising that `remainder` doesn't support `BFloat16` anyway. I could also enable `BFloat16` if that's desirable. If not, I don't think `Vec256<BFloat16>` should be missing `fmod` anyway. Pull Request resolved: #38293 Differential Revision: D21539801 Pulled By: ezyang fbshipit-source-id: abac6a3ed2076932adc459174cd3d8d510f3e1d5
laurentdupin
pushed a commit
to laurentdupin/pytorch
that referenced
this pull request
Apr 24, 2026
…37758) Summary: This will fix the GPU implementation in pytorch#37743 and pytorch#24861. Please also check my [comment](pytorch#37743 (comment)). The fixed `remainder_kernel` follows the similar implementation in numpy. See https://github.com/numpy/numpy/blob/79d7bc276afbe89c746e462d28d4bfbb4fc56148/numpy/core/src/npymath/npy_math_internal.h.src#L649-L658 I also slightly update the doc for `torch.remainder`, to make it similar to `torch.fmod`. I'm not sure how to modify the Vec256 code of CPU remainder_kernel, so I just leave it there. Pull Request resolved: pytorch#37758 Differential Revision: D21388417 Pulled By: ngimel fbshipit-source-id: 770ba5801cf34619b2b68b8b0cf95d8cfa52e6f6
laurentdupin
pushed a commit
to laurentdupin/pytorch
that referenced
this pull request
Apr 24, 2026
Summary: Together with pytorch#37758, this fixes pytorch#37743 and fixes pytorch#24861. This follows the CUDA fix in pytorch#37758, vectorised using a `blendv` to replace the if conditionals. Most of the complication is from `remainder` supporting `at::Half` where `fmod` doesn't. I've now got `fmod` working on `Vec256<at::Half>` as well as enabling half dispatch for `fmod` so it matches `remainder`. I also added `fmod` support to `Vec256<at::BFloat16>` before realising that `remainder` doesn't support `BFloat16` anyway. I could also enable `BFloat16` if that's desirable. If not, I don't think `Vec256<BFloat16>` should be missing `fmod` anyway. Pull Request resolved: pytorch#38293 Differential Revision: D21539801 Pulled By: ezyang fbshipit-source-id: abac6a3ed2076932adc459174cd3d8d510f3e1d5
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
5 participants
Add this suggestion to a batch that can be applied as a single commit.This suggestion is invalid because no changes were made to the code.Suggestions cannot be applied while the pull request is closed.Suggestions cannot be applied while viewing a subset of changes.Only one suggestion per line can be applied in a batch.Add this suggestion to a batch that can be applied as a single commit.Applying suggestions on deleted lines is not supported.You must change the existing code in this line in order to create a valid suggestion.Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be applied on multi-line comments.Suggestions cannot be applied while the pull request is queued to merge.Suggestion cannot be applied right now. Please check back later.
This will fix the GPU implementation in #37743 and #24861. Please also check my comment.
The fixed
remainder_kernelfollows the similar implementation in numpy. See https://github.com/numpy/numpy/blob/79d7bc276afbe89c746e462d28d4bfbb4fc56148/numpy/core/src/npymath/npy_math_internal.h.src#L649-L658I also slightly update the doc for
torch.remainder, to make it similar totorch.fmod.I'm not sure how to modify the Vec256 code of CPU remainder_kernel, so I just leave it there.