NXP backend: Replace pass to fuse activations functions with joint quantization with activation #14816
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/14816
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Please remove the comments about shared quantization if it is not relevant.
…rch_pipeline.py, fix edge pass manager usage
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+ Move fused activations to separate QDQ cluster
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Summary
This PR replaces optimizations 'fuse_activation_functions.py' by quantization of Conv 2D and Linear ops together with fusable activations - selected activations supported by Neutron (Relu, Relu6, Sigmoid, Tanh). Logic is determined by target specs, now supporting Neutron-C. Tests updated. Relu has now non-shared, standalone quantization.
Test plan
Unit tests provided (test_edge_passes.py, test_quantizer.py).
cc @robert-kalmar @JakeStevens @digantdesai