Cortex-M backend: lower quantized GELU via the activation LUT

[rascani]

Summary

Extend the existing sigmoid/tanh/silu activation-LUT path to GELU: add an exact (erf) _gelu to _ACTIVATION_FNS, register aten.gelu in the quantizer's ACTIVATION_OP_PATTERNS (the generic CortexMActivationCheck needs no change), and add aten.gelu to the quantized_activation dialect substitution. Unlike silu, aten.gelu is a core-aten op that survives to_edge unchanged, so no to_edge preservation is required. build_activation_lut only sees the op target, so the table is always the exact/erf GELU regardless of the approximate kwarg.

Test plan

Verified test-first (RED->GREEN) with five nn.GELU() dialect cases (ramp / saturating / asymmetric-zp / zero) whose numerics are validated against the reference (qtol=1); the full cortex_m op dialect suite still passes (177 passed).

Authored with Claude Code.

[pytorch-bot]

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❌ You can merge normally! (1 Unrelated Failure), 1 Unclassified Failure

As of commit f9d4f41 with merge base 7013c8d ():

UNCLASSIFIED FAILURE - DrCI could not classify the following job because the workflow did not run on the merge base. The failure may be pre-existing on trunk or introduced by this PR:

• Test QNN Windows MSVC build / build-qnn-windows-msvc / windows-job (gh) (this job did not run on the merge base, so DrCI cannot tell whether the failure is pre-existing)
  Process completed with exit code 1.

BROKEN TRUNK - The following job failed but were present on the merge base:

👉 Rebase onto the `viable/strict` branch to avoid these failures

• pull / test-qnn-testsuite-linux / test-backend-linux (qnn, models) / linux-job (gh) (trunk failure)

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