[CIR][Lowering] Handling Lowering of multiple dimension array correctly #961
Conversation
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
bcardosolopes
changed the title
|
Code looks good, just need to go over some test failures! |
|
The test failures should have all been fixed by #963, this just needs a rebase. |
ChuanqiXu9
force-pushed
the
handling_multiple_dimension_array
branch
from
e213f76 to
bcd7986
Compare
|
rebased |
smeenai
approved these changes
Oct 14, 2024
There was a problem hiding this comment.
@bcardosolopes had approved the code changes, so we should be good to merge this.
smeenai
pushed a commit
that referenced
this pull request
Oct 14, 2024
This is the following up fix for the previous fix #961 See the attached new test for the reproducer. Sorry for the initial overlook.
keryell
pushed a commit
to keryell/clangir
that referenced
this pull request
Oct 19, 2024
…ly (llvm#961) Close llvm#957 the previous algorithm to convert a multiple dimension array to a tensor is: fill the value one by one and fill the zero values in conditions. And it has some problems handling the multiple dimension array as above issue shows so that the generated values are not in the same shape with the original array. the new algorithm here is, full fill the values ahead of time with the correct element size and full fill the values to different slots and we only need to maintain the index to write. I feel the new version has better performance (avoid allocation) and better readability slightly.
keryell
pushed a commit
to keryell/clangir
that referenced
this pull request
Oct 19, 2024
This is the following up fix for the previous fix llvm#961 See the attached new test for the reproducer. Sorry for the initial overlook.
lanza
pushed a commit
that referenced
this pull request
Nov 5, 2024
…ly (#961) Close #957 the previous algorithm to convert a multiple dimension array to a tensor is: fill the value one by one and fill the zero values in conditions. And it has some problems handling the multiple dimension array as above issue shows so that the generated values are not in the same shape with the original array. the new algorithm here is, full fill the values ahead of time with the correct element size and full fill the values to different slots and we only need to maintain the index to write. I feel the new version has better performance (avoid allocation) and better readability slightly.
lanza
pushed a commit
that referenced
this pull request
Nov 5, 2024
This is the following up fix for the previous fix #961 See the attached new test for the reproducer. Sorry for the initial overlook.
lanza
pushed a commit
that referenced
this pull request
Mar 18, 2025
…ly (#961) Close #957 the previous algorithm to convert a multiple dimension array to a tensor is: fill the value one by one and fill the zero values in conditions. And it has some problems handling the multiple dimension array as above issue shows so that the generated values are not in the same shape with the original array. the new algorithm here is, full fill the values ahead of time with the correct element size and full fill the values to different slots and we only need to maintain the index to write. I feel the new version has better performance (avoid allocation) and better readability slightly.
lanza
pushed a commit
that referenced
this pull request
Mar 18, 2025
This is the following up fix for the previous fix #961 See the attached new test for the reproducer. Sorry for the initial overlook.
lanza
pushed a commit
to lanza/llvm-project
that referenced
this pull request
Aug 11, 2025
This is the following up fix for the previous fix llvm/clangir#961 See the attached new test for the reproducer. Sorry for the initial overlook.
terapines-osc-cir
pushed a commit
to Terapines/clangir
that referenced
this pull request
Sep 2, 2025
…ly (llvm#961) Close llvm#957 the previous algorithm to convert a multiple dimension array to a tensor is: fill the value one by one and fill the zero values in conditions. And it has some problems handling the multiple dimension array as above issue shows so that the generated values are not in the same shape with the original array. the new algorithm here is, full fill the values ahead of time with the correct element size and full fill the values to different slots and we only need to maintain the index to write. I feel the new version has better performance (avoid allocation) and better readability slightly.
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.
Close #957
the previous algorithm to convert a multiple dimension array to a tensor is: fill the value one by one and fill the zero values in conditions. And it has some problems handling the multiple dimension array as above issue shows so that the generated values are not in the same shape with the original array.
the new algorithm here is, full fill the values ahead of time with the correct element size and full fill the values to different slots and we only need to maintain the index to write.
I feel the new version has better performance (avoid allocation) and better readability slightly.