Add tanh op to XNNPACK backend #11804
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/11804
Note: Links to docs will display an error until the docs builds have been completed. ✅ You can merge normally! (1 Unrelated Failure)As of commit d94804e with merge base daebcde ( BROKEN TRUNK - The following job failed but were present on the merge base:👉 Rebase onto the `viable/strict` branch to avoid these failures
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### Summary This PR adds support for the tanh operator in ExecuTorch via XNNPACK, enabling optimized execution of torch.tanh on the XNNPACK backend. The implementation includes updates to operator configuration, serialization, and runtime handling. The tanh operator is now properly registered in the XNNPACK partition config and mapped to XNNPACK's xnn_create_tanh_operator API in the compiler. ### Test plan I added a new test class TestTanh that is a simple torch model with a tanh op. It then asserts that the XNNPACK delegate was called while executing the tanh op instead of the torch default tanh op.
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Summary
This PR adds support for the tanh operator in ExecuTorch via XNNPACK, enabling optimized execution of torch.tanh on the XNNPACK backend. The implementation includes updates to operator configuration, serialization, and runtime handling. The tanh operator is now properly registered in the XNNPACK partition config and mapped to XNNPACK's xnn_create_tanh_operator API in the compiler.
Test plan
I added a new test class TestTanh that is a simple torch model with a tanh op. It then asserts that the XNNPACK delegate was called while executing the tanh op instead of the torch default tanh op.