Arm backend: Add 16A8W support for view and transpose operations #13799
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Add 16A8W quantization support for view and transpose operations in ExecutorTorch ARM backend. This follows the pattern established for linear, mul, sigmoid, tanh, and slice operations, extending int16 support to view and transpose operations. Changes: - Add INT16 dtype validation support in op_transpose.py - Add test_view_tensor_16a8w_tosa_INT test function - Enable test_view.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: [D80511313](https://our.internmc.facebook.com/intern/diff/D80511313/) [ghstack-poisoned]
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/13799
Note: Links to docs will display an error until the docs builds have been completed. ❌ 2 New Failures, 7 Unrelated FailuresAs of commit 5e55e7b 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
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…ations" Add 16A8W quantization support for view and transpose operations in ExecutorTorch ARM backend. This follows the pattern established for linear, mul, sigmoid, tanh, and slice operations, extending int16 support to view and transpose operations. Changes: - Add INT16 dtype validation support in op_transpose.py - Add test_view_tensor_16a8w_tosa_INT test function - Enable test_view.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: [D80511313](https://our.internmc.facebook.com/intern/diff/D80511313/) cc digantdesai freddan80 per zingo oscarandersson8218 [ghstack-poisoned]
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This pull request was exported from Phabricator. Differential Revision: D80511313 |
…ations" Add 16A8W quantization support for view and transpose operations in ExecutorTorch ARM backend. This follows the pattern established for linear, mul, sigmoid, tanh, and slice operations, extending int16 support to view and transpose operations. Changes: - Add INT16 dtype validation support in op_transpose.py - Add test_view_tensor_16a8w_tosa_INT test function - Enable test_view.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: [D80511313](https://our.internmc.facebook.com/intern/diff/D80511313/) cc digantdesai freddan80 per zingo oscarandersson8218 [ghstack-poisoned]
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This pull request was exported from Phabricator. Differential Revision: D80511313 |
…ations" Add 16A8W quantization support for view and transpose operations in ExecutorTorch ARM backend. This follows the pattern established for linear, mul, sigmoid, tanh, and slice operations, extending int16 support to view and transpose operations. Changes: - Add INT16 dtype validation support in op_transpose.py - Add test_view_tensor_16a8w_tosa_INT test function - Enable test_view.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: [D80511313](https://our.internmc.facebook.com/intern/diff/D80511313/) cc digantdesai freddan80 per zingo oscarandersson8218 [ghstack-poisoned]
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This pull request was exported from Phabricator. Differential Revision: D80511313 |
…ations" Add 16A8W quantization support for view and transpose operations in ExecutorTorch ARM backend. This follows the pattern established for linear, mul, sigmoid, tanh, and slice operations, extending int16 support to view and transpose operations. Changes: - Add INT16 dtype validation support in op_transpose.py - Add test_view_tensor_16a8w_tosa_INT test function - Enable test_view.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: [D80511313](https://our.internmc.facebook.com/intern/diff/D80511313/) cc digantdesai freddan80 per zingo oscarandersson8218 [ghstack-poisoned]
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This pull request was exported from Phabricator. Differential Revision: D80511313 |
…ations" Add 16A8W quantization support for view and transpose operations in ExecutorTorch ARM backend. This follows the pattern established for linear, mul, sigmoid, tanh, and slice operations, extending int16 support to view and transpose operations. Changes: - Add INT16 dtype validation support in op_transpose.py - Add test_view_tensor_16a8w_tosa_INT test function - Enable test_view.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: [D80511313](https://our.internmc.facebook.com/intern/diff/D80511313/) cc digantdesai freddan80 per zingo oscarandersson8218 [ghstack-poisoned]
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This pull request was exported from Phabricator. Differential Revision: D80511313 |
…ations" Add 16A8W quantization support for view and transpose operations in ExecutorTorch ARM backend. This follows the pattern established for linear, mul, sigmoid, tanh, and slice operations, extending int16 support to view and transpose operations. Changes: - Add INT16 dtype validation support in op_transpose.py - Add test_view_tensor_16a8w_tosa_INT test function - Enable test_view.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: [D80511313](https://our.internmc.facebook.com/intern/diff/D80511313/) cc digantdesai freddan80 per zingo oscarandersson8218 [ghstack-poisoned]
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This pull request was exported from Phabricator. Differential Revision: D80511313 |
…ations" Add 16A8W quantization support for view and transpose operations in ExecutorTorch ARM backend. This follows the pattern established for linear, mul, sigmoid, tanh, and slice operations, extending int16 support to view and transpose operations. Changes: - Add INT16 dtype validation support in op_transpose.py - Add test_view_tensor_16a8w_tosa_INT test function - Enable test_view.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: [D80511313](https://our.internmc.facebook.com/intern/diff/D80511313/) cc digantdesai freddan80 per zingo oscarandersson8218 [ghstack-poisoned]
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This pull request was exported from Phabricator. Differential Revision: D80511313 |
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) This PR was created by the merge bot to help merge the original PR into the main branch. ghstack PR number: #13799 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/Ninja91/12/base ghstack PR head: https://github.com/pytorch/executorch/tree/gh/Ninja91/12/head Merge bot PR base: https://github.com/pytorch/executorch/tree/gh/Ninja91/11/orig Merge bot PR head: https://github.com/pytorch/executorch/tree/gh/Ninja91/12/orig @diff-train-skip-merge --------- Co-authored-by: Nitin Jain <jainnitin@meta.com>
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…orch#14216) This PR was created by the merge bot to help merge the original PR into the main branch. ghstack PR number: pytorch#13799 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/Ninja91/12/base ghstack PR head: https://github.com/pytorch/executorch/tree/gh/Ninja91/12/head Merge bot PR base: https://github.com/pytorch/executorch/tree/gh/Ninja91/11/orig Merge bot PR head: https://github.com/pytorch/executorch/tree/gh/Ninja91/12/orig @diff-train-skip-merge --------- Co-authored-by: Nitin Jain <jainnitin@meta.com>
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Stack from ghstack (oldest at bottom):
Add 16A8W quantization support for view and transpose operations in ExecutorTorch ARM backend.
This follows the pattern established for linear, mul, sigmoid, tanh, and slice operations, extending int16 support to view and transpose operations.
Changes:
The 16A8W configuration uses 16-bit activations with 8-bit weights, enabling higher precision for activations while maintaining weight efficiency.
Differential Revision: D80511313
cc @digantdesai @freddan80 @per @zingo @oscarandersson8218