Arm backend: Add 16A8W support and test for slice operation #13798
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
Add 16A8W quantization support and test for the slice operation in ExecutorTorch ARM backend. This follows the pattern established for linear, mul, sigmoid, and tanh operations, extending int16 support to slice operations. Changes: - Add INT16 dtype validation support in op_slice.py - Add test_slice_tensor_16a8w_tosa_INT test function - Enable test_slice.py in test targets configuration The 16A8W configuration uses 16-bit activations with 8-bit weights, enabling higher precision for activations while maintaining weight efficiency. Differential Revision: [D80511095](https://our.internmc.facebook.com/intern/diff/D80511095/) [ghstack-poisoned]
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/13798
Note: Links to docs will display an error until the docs builds have been completed. ❌ 2 New Failures, 7 Unrelated FailuresAs of commit ea012f7 with merge base 1d37845 ( NEW FAILURES - The following jobs have failed:
BROKEN TRUNK - The following jobs failed but were present on the merge base:👉 Rebase onto the `viable/strict` branch to avoid these failures
This comment was automatically generated by Dr. CI and updates every 15 minutes. |
This was referenced Aug 29, 2025
meta-cla
bot
added
the
CLA Signed
|
This pull request was exported from Phabricator. Differential Revision: D80511095 |
zingo
added
partner: arm
zingo
changed the title
digantdesai
reviewed
Aug 29, 2025
digantdesai
approved these changes
Aug 29, 2025
Add 16A8W quantization support and test for the slice operation in ExecutorTorch ARM backend. This follows the pattern established for linear, mul, sigmoid, and tanh operations, extending int16 support to slice operations. Changes: - Add INT16 dtype validation support in op_slice.py - Add test_slice_tensor_16a8w_tosa_INT test function - Enable test_slice.py in test targets configuration The 16A8W configuration uses 16-bit activations with 8-bit weights, enabling higher precision for activations while maintaining weight efficiency. Differential Revision: [D80511095](https://our.internmc.facebook.com/intern/diff/D80511095/) cc digantdesai freddan80 per zingo oscarandersson8218 [ghstack-poisoned]
|
This pull request was exported from Phabricator. Differential Revision: D80511095 |
Add 16A8W quantization support and test for the slice operation in ExecutorTorch ARM backend. This follows the pattern established for linear, mul, sigmoid, and tanh operations, extending int16 support to slice operations. Changes: - Add INT16 dtype validation support in op_slice.py - Add test_slice_tensor_16a8w_tosa_INT test function - Enable test_slice.py in test targets configuration The 16A8W configuration uses 16-bit activations with 8-bit weights, enabling higher precision for activations while maintaining weight efficiency. Differential Revision: [D80511095](https://our.internmc.facebook.com/intern/diff/D80511095/) cc digantdesai freddan80 per zingo oscarandersson8218 [ghstack-poisoned]
|
This pull request was exported from Phabricator. Differential Revision: D80511095 |
Add 16A8W quantization support and test for the slice operation in ExecutorTorch ARM backend. This follows the pattern established for linear, mul, sigmoid, and tanh operations, extending int16 support to slice operations. Changes: - Add INT16 dtype validation support in op_slice.py - Add test_slice_tensor_16a8w_tosa_INT test function - Enable test_slice.py in test targets configuration The 16A8W configuration uses 16-bit activations with 8-bit weights, enabling higher precision for activations while maintaining weight efficiency. Differential Revision: [D80511095](https://our.internmc.facebook.com/intern/diff/D80511095/) cc digantdesai freddan80 per zingo oscarandersson8218 [ghstack-poisoned]
|
This pull request was exported from Phabricator. Differential Revision: D80511095 |
Add 16A8W quantization support and test for the slice operation in ExecutorTorch ARM backend. This follows the pattern established for linear, mul, sigmoid, and tanh operations, extending int16 support to slice operations. Changes: - Add INT16 dtype validation support in op_slice.py - Add test_slice_tensor_16a8w_tosa_INT test function - Enable test_slice.py in test targets configuration The 16A8W configuration uses 16-bit activations with 8-bit weights, enabling higher precision for activations while maintaining weight efficiency. Differential Revision: [D80511095](https://our.internmc.facebook.com/intern/diff/D80511095/) cc digantdesai freddan80 per zingo oscarandersson8218 [ghstack-poisoned]
|
This pull request was exported from Phabricator. Differential Revision: D80511095 |
Add 16A8W quantization support and test for the slice operation in ExecutorTorch ARM backend. This follows the pattern established for linear, mul, sigmoid, and tanh operations, extending int16 support to slice operations. Changes: - Add INT16 dtype validation support in op_slice.py - Add test_slice_tensor_16a8w_tosa_INT test function - Enable test_slice.py in test targets configuration The 16A8W configuration uses 16-bit activations with 8-bit weights, enabling higher precision for activations while maintaining weight efficiency. Differential Revision: [D80511095](https://our.internmc.facebook.com/intern/diff/D80511095/) cc digantdesai freddan80 per zingo oscarandersson8218 [ghstack-poisoned]
|
This pull request was exported from Phabricator. Differential Revision: D80511095 |
Add 16A8W quantization support and test for the slice operation in ExecutorTorch ARM backend. This follows the pattern established for linear, mul, sigmoid, and tanh operations, extending int16 support to slice operations. Changes: - Add INT16 dtype validation support in op_slice.py - Add test_slice_tensor_16a8w_tosa_INT test function - Enable test_slice.py in test targets configuration The 16A8W configuration uses 16-bit activations with 8-bit weights, enabling higher precision for activations while maintaining weight efficiency. Differential Revision: [D80511095](https://our.internmc.facebook.com/intern/diff/D80511095/) cc digantdesai freddan80 per zingo oscarandersson8218 [ghstack-poisoned]
|
This pull request was exported from Phabricator. Differential Revision: D80511095 |
Add 16A8W quantization support and test for the slice operation in ExecutorTorch ARM backend. This follows the pattern established for linear, mul, sigmoid, and tanh operations, extending int16 support to slice operations. Changes: - Add INT16 dtype validation support in op_slice.py - Add test_slice_tensor_16a8w_tosa_INT test function - Enable test_slice.py in test targets configuration The 16A8W configuration uses 16-bit activations with 8-bit weights, enabling higher precision for activations while maintaining weight efficiency. Differential Revision: [D80511095](https://our.internmc.facebook.com/intern/diff/D80511095/) cc digantdesai freddan80 per zingo oscarandersson8218 [ghstack-poisoned]
|
This pull request was exported from Phabricator. Differential Revision: D80511095 |
facebook-github-bot
merged commit 041747e
into
gh/Ninja91/11/base
293 of 303 checks passed
facebook-github-bot
temporarily deployed
to
cherry-pick-bot
GitHub Actions
Inactive
Ninja91
added a commit
that referenced
this pull request
Sep 11, 2025
This PR was created by the merge bot to help merge the original PR into the main branch. ghstack PR number: #13798 by @Ninja91 ^ Please use this as the source of truth for the PR details, comments, and reviews ghstack PR base: https://github.com/pytorch/executorch/tree/gh/Ninja91/11/base ghstack PR head: https://github.com/pytorch/executorch/tree/gh/Ninja91/11/head Merge bot PR base: https://github.com/pytorch/executorch/tree/gh/Ninja91/10/orig Merge bot PR head: https://github.com/pytorch/executorch/tree/gh/Ninja91/11/orig @diff-train-skip-merge --------- Co-authored-by: Nitin Jain <[email protected]>
StrycekSimon
pushed a commit
to nxp-upstream/executorch
that referenced
this pull request
Sep 23, 2025
…14215) This PR was created by the merge bot to help merge the original PR into the main branch. ghstack PR number: pytorch#13798 by @Ninja91 ^ Please use this as the source of truth for the PR details, comments, and reviews ghstack PR base: https://github.com/pytorch/executorch/tree/gh/Ninja91/11/base ghstack PR head: https://github.com/pytorch/executorch/tree/gh/Ninja91/11/head Merge bot PR base: https://github.com/pytorch/executorch/tree/gh/Ninja91/10/orig Merge bot PR head: https://github.com/pytorch/executorch/tree/gh/Ninja91/11/orig @diff-train-skip-merge --------- Co-authored-by: Nitin Jain <[email protected]>
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
4 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.
Stack from ghstack (oldest at bottom):
Add 16A8W quantization support and test for the slice operation in ExecutorTorch ARM backend.
This follows the pattern established for linear, mul, sigmoid, and tanh operations, extending int16 support to slice operations.
Changes:
The 16A8W configuration uses 16-bit activations with 8-bit weights, enabling higher precision for activations while maintaining weight efficiency.
Differential Revision: D80511095
cc @digantdesai @freddan80 @per @zingo @oscarandersson8218