RMSNorm support - Executorch (pytorch#9844)

Summary:

This follows D72014553 which adds support for RMSNorm (cpu backend)
This is a separate diff for Executorch / Github

Reviewed By: Vysarat

Differential Revision: D72258890

Author: ThomasJannaud

backends/cadence/aot/ops_registrations.py

@@ -139,6 +139,7 @@
     "int in_zero_point, bool channel_last=False) -> (Tensor out)"
 )
 lib.define("linalg_vector_norm(Tensor X) -> (Tensor Y)")
+lib.define("rms_norm(Tensor X, float eps, Tensor W) -> (Tensor Y)")
 lib.define(
     "transposed_im2row(Tensor input, int[2] kernel_size, int[2] dilation, int[2] padding, int[2] stride, "
     "int[2] output_padding, Tensor in_zero_point, bool channel_last=False) -> (Tensor out)"
@@ -210,6 +211,7 @@
     "fully_connected.out(Tensor input, Tensor weight, Tensor? bias=None, *, Tensor(a!) out) -> Tensor(a!)"
 )
 lib.define("linalg_vector_norm.out(Tensor X, *, Tensor(a!) out) -> Tensor(a!)")
+lib.define("rms_norm.out(Tensor X, float eps, Tensor W, *, Tensor(a!) out) -> Tensor(a!)")
 lib.define(
     "quantized_fully_connected.out(Tensor src, Tensor weight, Tensor bias, int src_zero_point, "
     "Tensor weight_zero_point, Tensor out_multiplier, Tensor out_shift, int out_zero_point, Tensor? offset, *, Tensor(a!) out) -> Tensor(a!)"
@@ -614,6 +616,13 @@ def linalg_vector_norm_meta(
     # Output of norm is a scalar, so we return a [] tensor
     return X.new_empty([], dtype=X.dtype)
 
+@register_fake("cadence::rms_norm")
+def rms_norm_meta(
+    X: torch.Tensor,
+    eps: float,
+    weight: torch.Tensor,
+) -> torch.Tensor:
+    return X.new_empty(X.shape, dtype=X.dtype)
 
 @register_fake("cadence::requantize")
 def requantize_meta(