-
Notifications
You must be signed in to change notification settings - Fork 457
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
[Torch] Add support for static uneven divisible AdaptiveAvgPool2d #3566
Conversation
This file contains 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
… kernle/stride size
qingyunqu
reviewed
Jul 30, 2024
qingyunqu
reviewed
Jul 30, 2024
qingyunqu
approved these changes
Aug 1, 2024
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
LGTM. Thanks for the efforts!
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
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Outdated suggestions cannot be applied.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.
The static uneven divisible AdaptiveAvgPool2d means that although the input size is not an integer multiple of ouput size, but the kernel and stride size can also be fixed (not dynamic). The derivation logic of kernel and stride size is consistent with torch/_decomp/decomposations.py:adaptive_avg_pool2d as described in the following:
Stride Size
Firstly , derive the start index in each reduce operation according to the output size (
n
),start_index = ([0, 1, ..., n - 1] * input_size) // output_size
. For each indexk
, ifk * (input_size % output_size) < output_size
, then the current and previous stride keeps the same asinput_size // output_size
. So suppose(n-1) * (input_size % output_size) < output_size
, the stride in the whole AdaptiveAvgPool2d process keeps static, asinput_size // output_size
.Kernel Size
torch/_decomp/decomposations.py:adaptive_avg_pool2d calculates a static kernel size when the input/output sizes satisfy either of the two conditions,
input_size % output_size == 0
oroutput_size % (input_size % output_size) == 0
. Here ifinput_size % output_size == 0
, then the kernel size equalsinput_size // output_size
, otherwiseinput_size // output_size + 1.