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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
Note: Links to docs will display an error until the docs builds have been completed. ❌ 13 New Failures, 3 Unrelated FailuresAs of commit 1a048c5 with merge base 6208340 ( 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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This pull request was exported from Phabricator. Differential Revision: D80510815 |
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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