Remove permutes around fused_quant elementwise ops

[mcremon-meta]

Summary:
ConvToChannelsLast wraps every conv in permute(NCHW->NHWC) -> conv -> permute(NHWC->NCHW). When convs are joined by elementwise fused_quant ops
(residual add/mul, activations), permutes end up threaded through the
surrounding region and were never removed: the existing Cadence
RemovePermutesAroundElementwiseOps only recognizes aten/cadence elementwise
ops, not the SAS fused_quant ops -- and it would also wrongly treat their
lifted scale/zero_point operands as constants to be permuted.

This adds a fused_quant-aware permute-removal pass and wires it into the edge
optimization group:

• Extend the shared ExecuTorch RemovePermutesAroundElementwiseOps with a
  small overridable seam (_permute_relevant_inputs) so a subclass can hide
  operands from layout propagation. Behavior-preserving for existing users.
• New RemovePermutesAroundFusedQuantElementwiseOps (SAS) subclasses it,
  adding fused_quant.add/mul and the activation ops as permutable and
  exposing only their tensor operands, so the lifted scale/zero_point
  placeholders are never permuted/compensated (which would break lowering).
  Ops with per-channel qparams are skipped (not permutation-invariant).
• Replace the two Cadence permute passes (which no-op pre-Lower) in the
  optimization group with this single pass.
• Teach the shared subgraph engine about "permutation-sink" flattens: a
  view_copy whose input has <=1 non-unit dim (e.g. the [1, C, 1, 1] -> [1, C]
  after a global pool) is layout-invariant, so a permutation flowing into it
  simply dies. The region can terminate cleanly there with no compensating
  permute -- which lets the residual-block permutes collapse across the
  avgpool -> flatten -> classifier head instead of being stranded by it.

Note: fused_quant is currently SAS-specific, NOT yet a generic cross-backend
dialect, so the fused_quant knowledge deliberately stays in the SAS subclass
rather than the shared ExecuTorch pass. When fused_quant graduates to a shared
dialect, this can fold into the base pass via extra_permutable_ops + the
seam. (The permutation-sink flatten handling is generic and correctly lives in
the shared pass.)

On resnet18 the optimized graph goes from 83 permutes down to a single one (the
model-input boundary); every permute around the residual add/relu blocks and
across the global-pool flatten is removed.

Differential Revision: D113424191

cc @digantdesai @freddan80 @per @zingo @oscarandersson8218 @mansnils @Sebastian-Larsson @robell @rascani

[pytorch-bot]

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