Arm backend: Add 16A8W support and test for tanh operation #13797
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Add 16A8W quantization support and test for the tanh operation in ExecutorTorch ARM backend. This follows the pattern established for linear, mul, and sigmoid operations, extending int16 support to tanh operations. Changes: - Add INT16 dtype validation support in op_tanh.py - Add test_tanh_tensor_16a8w_tosa_INT test function - Enable test_tanh.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: [D80510815](https://our.internmc.facebook.com/intern/diff/D80510815/) [ghstack-poisoned]
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/13797
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Add 16A8W quantization support and test for the tanh operation in ExecutorTorch ARM backend. This follows the pattern established for linear, mul, and sigmoid operations, extending int16 support to tanh operations. Changes: - Add INT16 dtype validation support in op_tanh.py - Add test_tanh_tensor_16a8w_tosa_INT test function - Enable test_tanh.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: [D80510815](https://our.internmc.facebook.com/intern/diff/D80510815/) cc digantdesai freddan80 per zingo oscarandersson8218 [ghstack-poisoned]
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This pull request was exported from Phabricator. Differential Revision: D80510815 |
Add 16A8W quantization support and test for the tanh operation in ExecutorTorch ARM backend. This follows the pattern established for linear, mul, and sigmoid operations, extending int16 support to tanh operations. Changes: - Add INT16 dtype validation support in op_tanh.py - Add test_tanh_tensor_16a8w_tosa_INT test function - Enable test_tanh.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: [D80510815](https://our.internmc.facebook.com/intern/diff/D80510815/) cc digantdesai freddan80 per zingo oscarandersson8218 [ghstack-poisoned]
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This pull request was exported from Phabricator. Differential Revision: D80510815 |
Add 16A8W quantization support and test for the tanh operation in ExecutorTorch ARM backend. This follows the pattern established for linear, mul, and sigmoid operations, extending int16 support to tanh operations. Changes: - Add INT16 dtype validation support in op_tanh.py - Add test_tanh_tensor_16a8w_tosa_INT test function - Enable test_tanh.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: [D80510815](https://our.internmc.facebook.com/intern/diff/D80510815/) cc digantdesai freddan80 per zingo oscarandersson8218 [ghstack-poisoned]
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This pull request was exported from Phabricator. Differential Revision: D80510815 |
Add 16A8W quantization support and test for the tanh operation in ExecutorTorch ARM backend. This follows the pattern established for linear, mul, and sigmoid operations, extending int16 support to tanh operations. Changes: - Add INT16 dtype validation support in op_tanh.py - Add test_tanh_tensor_16a8w_tosa_INT test function - Enable test_tanh.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: [D80510815](https://our.internmc.facebook.com/intern/diff/D80510815/) cc digantdesai freddan80 per zingo oscarandersson8218 [ghstack-poisoned]
|
This pull request was exported from Phabricator. Differential Revision: D80510815 |
Add 16A8W quantization support and test for the tanh operation in ExecutorTorch ARM backend. This follows the pattern established for linear, mul, and sigmoid operations, extending int16 support to tanh operations. Changes: - Add INT16 dtype validation support in op_tanh.py - Add test_tanh_tensor_16a8w_tosa_INT test function - Enable test_tanh.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: [D80510815](https://our.internmc.facebook.com/intern/diff/D80510815/) cc digantdesai freddan80 per zingo oscarandersson8218 [ghstack-poisoned]
|
This pull request was exported from Phabricator. Differential Revision: D80510815 |
Add 16A8W quantization support and test for the tanh operation in ExecutorTorch ARM backend. This follows the pattern established for linear, mul, and sigmoid operations, extending int16 support to tanh operations. Changes: - Add INT16 dtype validation support in op_tanh.py - Add test_tanh_tensor_16a8w_tosa_INT test function - Enable test_tanh.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: [D80510815](https://our.internmc.facebook.com/intern/diff/D80510815/) cc digantdesai freddan80 per zingo oscarandersson8218 [ghstack-poisoned]
|
This pull request was exported from Phabricator. Differential Revision: D80510815 |
Add 16A8W quantization support and test for the tanh operation in ExecutorTorch ARM backend. This follows the pattern established for linear, mul, and sigmoid operations, extending int16 support to tanh operations. Changes: - Add INT16 dtype validation support in op_tanh.py - Add test_tanh_tensor_16a8w_tosa_INT test function - Enable test_tanh.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: [D80510815](https://our.internmc.facebook.com/intern/diff/D80510815/) cc digantdesai freddan80 per zingo oscarandersson8218 [ghstack-poisoned]
|
This pull request was exported from Phabricator. Differential Revision: D80510815 |
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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: #13797 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/Ninja91/10/base ghstack PR head: https://github.com/pytorch/executorch/tree/gh/Ninja91/10/head Merge bot PR base: https://github.com/pytorch/executorch/tree/gh/Ninja91/9/orig Merge bot PR head: https://github.com/pytorch/executorch/tree/gh/Ninja91/10/orig @diff-train-skip-merge --------- Co-authored-by: Nitin Jain <jainnitin@meta.com>
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…4214) This PR was created by the merge bot to help merge the original PR into the main branch. ghstack PR number: pytorch#13797 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/Ninja91/10/base ghstack PR head: https://github.com/pytorch/executorch/tree/gh/Ninja91/10/head Merge bot PR base: https://github.com/pytorch/executorch/tree/gh/Ninja91/9/orig Merge bot PR head: https://github.com/pytorch/executorch/tree/gh/Ninja91/10/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 tanh operation in ExecutorTorch ARM backend.
This follows the pattern established for linear, mul, and sigmoid operations, extending int16 support to tanh operations.
Changes:
The 16A8W configuration uses 16-bit activations with 8-bit weights, enabling higher precision for activations while maintaining weight efficiency.
Differential Revision: D80510815
cc @digantdesai @freddan80 @per @zingo @oscarandersson8218