Arm backend: Add 16A8W support and test for mul operation #13795
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Add 16A8W quantization support and test for the mul operation in ExecutorTorch ARM backend. This follows the pattern established for linear operations, extending int16 support to mul operations. Changes: - Add INT16 dtype validation support in op_mul.py - Add test_mul_tensor_16a8w_tosa_INT test function - Enable test_mul.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: [D80510628](https://our.internmc.facebook.com/intern/diff/D80510628/) [ghstack-poisoned]
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Add 16A8W quantization support and test for the mul operation in ExecutorTorch ARM backend. This follows the pattern established for linear operations, extending int16 support to mul operations. Changes: - Add INT16 dtype validation support in op_mul.py - Add test_mul_tensor_16a8w_tosa_INT test function - Enable test_mul.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: [D80510628](https://our.internmc.facebook.com/intern/diff/D80510628/) cc digantdesai freddan80 per zingo oscarandersson8218 [ghstack-poisoned]
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This pull request was exported from Phabricator. Differential Revision: D80510628 |
Add 16A8W quantization support and test for the mul operation in ExecutorTorch ARM backend. This follows the pattern established for linear operations, extending int16 support to mul operations. Changes: - Add INT16 dtype validation support in op_mul.py - Add test_mul_tensor_16a8w_tosa_INT test function - Enable test_mul.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: [D80510628](https://our.internmc.facebook.com/intern/diff/D80510628/) cc digantdesai freddan80 per zingo oscarandersson8218 [ghstack-poisoned]
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Pull Request resolved: #13795 Add 16A8W quantization support and test for the mul operation in ExecutorTorch ARM backend. This follows the pattern established for linear operations, extending int16 support to mul operations. Changes: - Add INT16 dtype validation support in op_mul.py - Add test_mul_tensor_16a8w_tosa_INT test function - Enable test_mul.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. ghstack-source-id: 307682119 @exported-using-ghexport Differential Revision: [D80510628](https://our.internmc.facebook.com/intern/diff/D80510628/)
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This pull request was exported from Phabricator. Differential Revision: D80510628 |
Add 16A8W quantization support and test for the mul operation in ExecutorTorch ARM backend. This follows the pattern established for linear operations, extending int16 support to mul operations. Changes: - Add INT16 dtype validation support in op_mul.py - Add test_mul_tensor_16a8w_tosa_INT test function - Enable test_mul.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: [D80510628](https://our.internmc.facebook.com/intern/diff/D80510628/) cc digantdesai freddan80 per zingo oscarandersson8218 [ghstack-poisoned]
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This pull request was exported from Phabricator. Differential Revision: D80510628 |
Add 16A8W quantization support and test for the mul operation in ExecutorTorch ARM backend. This follows the pattern established for linear operations, extending int16 support to mul operations. Changes: - Add INT16 dtype validation support in op_mul.py - Add test_mul_tensor_16a8w_tosa_INT test function - Enable test_mul.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: [D80510628](https://our.internmc.facebook.com/intern/diff/D80510628/) cc digantdesai freddan80 per zingo oscarandersson8218 [ghstack-poisoned]
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This pull request was exported from Phabricator. Differential Revision: D80510628 |
Add 16A8W quantization support and test for the mul operation in ExecutorTorch ARM backend. This follows the pattern established for linear operations, extending int16 support to mul operations. Changes: - Add INT16 dtype validation support in op_mul.py - Add test_mul_tensor_16a8w_tosa_INT test function - Enable test_mul.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: [D80510628](https://our.internmc.facebook.com/intern/diff/D80510628/) cc digantdesai freddan80 per zingo oscarandersson8218 [ghstack-poisoned]
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Pull Request resolved: #13795 Add 16A8W quantization support and test for the mul operation in ExecutorTorch ARM backend. This follows the pattern established for linear operations, extending int16 support to mul operations. Changes: - Add INT16 dtype validation support in op_mul.py - Add test_mul_tensor_16a8w_tosa_INT test function - Enable test_mul.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. ghstack-source-id: 308040364 @exported-using-ghexport Differential Revision: [D80510628](https://our.internmc.facebook.com/intern/diff/D80510628/)
Add 16A8W quantization support and test for the mul operation in ExecutorTorch ARM backend. This follows the pattern established for linear operations, extending int16 support to mul operations. Changes: - Add INT16 dtype validation support in op_mul.py - Add test_mul_tensor_16a8w_tosa_INT test function - Enable test_mul.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: [D80510628](https://our.internmc.facebook.com/intern/diff/D80510628/) cc digantdesai freddan80 per zingo oscarandersson8218 [ghstack-poisoned]
Ninja91
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Pull Request resolved: #13795 Add 16A8W quantization support and test for the mul operation in ExecutorTorch ARM backend. This follows the pattern established for linear operations, extending int16 support to mul operations. Changes: - Add INT16 dtype validation support in op_mul.py - Add test_mul_tensor_16a8w_tosa_INT test function - Enable test_mul.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. ghstack-source-id: 308046859 @exported-using-ghexport Differential Revision: [D80510628](https://our.internmc.facebook.com/intern/diff/D80510628/)
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This pull request was exported from Phabricator. Differential Revision: D80510628 |
Add 16A8W quantization support and test for the mul operation in ExecutorTorch ARM backend. This follows the pattern established for linear operations, extending int16 support to mul operations. Changes: - Add INT16 dtype validation support in op_mul.py - Add test_mul_tensor_16a8w_tosa_INT test function - Enable test_mul.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: [D80510628](https://our.internmc.facebook.com/intern/diff/D80510628/) cc digantdesai freddan80 per zingo oscarandersson8218 [ghstack-poisoned]
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Pull Request resolved: #13795 Add 16A8W quantization support and test for the mul operation in ExecutorTorch ARM backend. This follows the pattern established for linear operations, extending int16 support to mul operations. Changes: - Add INT16 dtype validation support in op_mul.py - Add test_mul_tensor_16a8w_tosa_INT test function - Enable test_mul.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. ghstack-source-id: 308058745 @exported-using-ghexport @bypass-github-pytorch-ci-checks @bypass-github-executorch-ci-checks Differential Revision: [D80510628](https://our.internmc.facebook.com/intern/diff/D80510628/)
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This pull request was exported from Phabricator. Differential Revision: D80510628 |
Add 16A8W quantization support and test for the mul operation in ExecutorTorch ARM backend. This follows the pattern established for linear operations, extending int16 support to mul operations. Changes: - Add INT16 dtype validation support in op_mul.py - Add test_mul_tensor_16a8w_tosa_INT test function - Enable test_mul.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: [D80510628](https://our.internmc.facebook.com/intern/diff/D80510628/) cc digantdesai freddan80 per zingo oscarandersson8218 [ghstack-poisoned]
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This pull request was exported from Phabricator. Differential Revision: D80510628 |
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Add 16A8W quantization support and test for the mul operation in ExecutorTorch ARM backend. This follows the pattern established for linear operations, extending int16 support to mul operations. Changes: - Add INT16 dtype validation support in op_mul.py - Add test_mul_tensor_16a8w_tosa_INT test function - Enable test_mul.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: [D80510628](https://our.internmc.facebook.com/intern/diff/D80510628/) cc digantdesai freddan80 per zingo oscarandersson8218 [ghstack-poisoned]
|
This pull request was exported from Phabricator. Differential Revision: D80510628 |
Add 16A8W quantization support and test for the mul operation in ExecutorTorch ARM backend. This follows the pattern established for linear operations, extending int16 support to mul operations. Changes: - Add INT16 dtype validation support in op_mul.py - Add test_mul_tensor_16a8w_tosa_INT test function - Enable test_mul.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: [D80510628](https://our.internmc.facebook.com/intern/diff/D80510628/) cc digantdesai freddan80 per zingo oscarandersson8218 [ghstack-poisoned]
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Pull Request resolved: #13795 Add 16A8W quantization support and test for the mul operation in ExecutorTorch ARM backend. This follows the pattern established for linear operations, extending int16 support to mul operations. Changes: - Add INT16 dtype validation support in op_mul.py - Add test_mul_tensor_16a8w_tosa_INT test function - Enable test_mul.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. ghstack-source-id: 308923221 @exported-using-ghexport @bypass-github-export-checks @bypass-github-pytorch-ci-checks @bypass-github-executorch-ci-checks Differential Revision: [D80510628](https://our.internmc.facebook.com/intern/diff/D80510628/)
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This pull request was exported from Phabricator. Differential Revision: D80510628 |
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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: #13795 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/Ninja91/8/base ghstack PR head: https://github.com/pytorch/executorch/tree/gh/Ninja91/8/head Merge bot PR base: https://github.com/pytorch/executorch/tree/main Merge bot PR head: https://github.com/pytorch/executorch/tree/gh/Ninja91/8/orig @diff-train-skip-merge Co-authored-by: Nitin Jain <jainnitin@meta.com>
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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: pytorch#13795 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/Ninja91/8/base ghstack PR head: https://github.com/pytorch/executorch/tree/gh/Ninja91/8/head Merge bot PR base: https://github.com/pytorch/executorch/tree/main Merge bot PR head: https://github.com/pytorch/executorch/tree/gh/Ninja91/8/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 and test for the mul operation in ExecutorTorch ARM backend.
This follows the pattern established for linear operations, extending int16 support to mul operations.
Changes:
The 16A8W configuration uses 16-bit activations with 8-bit weights, enabling higher precision for activations while maintaining weight efficiency.
Differential Revision: D80510628
cc @digantdesai @freddan80 @per @zingo @oscarandersson8218