Closed
Conversation
smessmer
requested changes
Jun 18, 2019
| // Get the appropriate builtin op for this augmented assignment | ||
| // If the RHS is a tensor, return the corresponding ATen in-place op | ||
| // If it's a list of scalars, then return the corresponding list augment op | ||
| Symbol getAugOp(const AugAssign& stmt, bool isTensor) { |
Contributor
There was a problem hiding this comment.
not sure what you're doing here, this seems unrelated. Can we have this in a separate PR?
Collaborator
Author
There was a problem hiding this comment.
This is what's needed to make a += b do the inplace add instead of desugaring it into a = a + b.
smessmer
approved these changes
Jun 25, 2019
Contributor
smessmer
left a comment
There was a problem hiding this comment.
looks good now. Thanks.
Contributor
facebook-github-bot
left a comment
There was a problem hiding this comment.
@Chillee is landing this pull request. If you are a Facebook employee, you can view this diff on Phabricator.
zdevito
pushed a commit
to zdevito/ATen
that referenced
this pull request
Jun 27, 2019
Summary: In talks with smessmer, we decided that it'd be better to put the logic in `list`, as optimal behavior requires knowing `.capacity()` Results on my cpu (for the benchmark here: https://twitter.com/VahidK/status/1138674536679821312) now look like this: ``` Pytorch batch_gather took 0.018311 seconds. Pytorch batch_gather jit took 0.013921 seconds. Pytorch vectorized batch_gather took 0.001384 seconds. ``` Previously, `batch_gather jit` took 3x as long as `batch_gather`. Some logic taken from pytorch/pytorch#21690. Note that these two PR's are somewhat orthogonal. That PR handles this benchmark by looking at the alias analysis, while this PR specializes for `+=`. Note that we can't jit the vectorized version as we think `torch.arange` returns a float tensor. Pull Request resolved: pytorch/pytorch#21896 Differential Revision: D15998628 Pulled By: Chillee fbshipit-source-id: b0085960da4613578b94deb98ac62c0a4532a8c3
Contributor
xzhu1900
pushed a commit
to xzhu1900/pytorch
that referenced
this pull request
Jul 5, 2019
Summary: In talks with smessmer, we decided that it'd be better to put the logic in `list`, as optimal behavior requires knowing `.capacity()` Results on my cpu (for the benchmark here: https://twitter.com/VahidK/status/1138674536679821312) now look like this: ``` Pytorch batch_gather took 0.018311 seconds. Pytorch batch_gather jit took 0.013921 seconds. Pytorch vectorized batch_gather took 0.001384 seconds. ``` Previously, `batch_gather jit` took 3x as long as `batch_gather`. Some logic taken from pytorch#21690. Note that these two PR's are somewhat orthogonal. That PR handles this benchmark by looking at the alias analysis, while this PR specializes for `+=`. Note that we can't jit the vectorized version as we think `torch.arange` returns a float tensor. Pull Request resolved: pytorch#21896 Differential Revision: D15998628 Pulled By: Chillee fbshipit-source-id: b0085960da4613578b94deb98ac62c0a4532a8c3
laurentdupin
pushed a commit
to laurentdupin/pytorch
that referenced
this pull request
Apr 24, 2026
Summary: In talks with smessmer, we decided that it'd be better to put the logic in `list`, as optimal behavior requires knowing `.capacity()` Results on my cpu (for the benchmark here: https://twitter.com/VahidK/status/1138674536679821312) now look like this: ``` Pytorch batch_gather took 0.018311 seconds. Pytorch batch_gather jit took 0.013921 seconds. Pytorch vectorized batch_gather took 0.001384 seconds. ``` Previously, `batch_gather jit` took 3x as long as `batch_gather`. Some logic taken from pytorch#21690. Note that these two PR's are somewhat orthogonal. That PR handles this benchmark by looking at the alias analysis, while this PR specializes for `+=`. Note that we can't jit the vectorized version as we think `torch.arange` returns a float tensor. Pull Request resolved: pytorch#21896 Differential Revision: D15998628 Pulled By: Chillee fbshipit-source-id: b0085960da4613578b94deb98ac62c0a4532a8c3
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.
In talks with @smessmer, we decided that it'd be better to put the logic in
list, as optimal behavior requires knowing.capacity()Results on my cpu (for the benchmark here: https://twitter.com/VahidK/status/1138674536679821312) now look like this:
Previously,
batch_gather jittook 3x as long asbatch_gather.Some logic taken from #21690. Note that these two PR's are somewhat orthogonal. That PR handles this benchmark by looking at the alias analysis, while this PR specializes for
+=.Note that we can't jit the vectorized version as we think
torch.arangereturns a float tensor.