Add weighted RMS normalization example#20
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Weighted RMS norm: y = x * rsqrt(mean(x^2) + eps) * w Extends rms_norm by multiplying each normalized element by a learned weight vector. Uses BLOCK_M=2 (2D tiling) with 3 memref arguments (X, W, Y) where W has broadcast indexing. The transform script relies on mlir-air PR #1412 (cross-op buffer sharing in linalg_promote) to share the X subview buffer across the squaring and output generics, keeping DMA count within AIE tile limits. Update mlir-air to 98f2fc3 which includes all necessary fixes: - PR #1407: broadcast operand promotion - PR #1408: dead memref.global cleanup - PR #1411: memory space comparison fix - PR #1412: cross-op promotedValueMap with DominanceInfo Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
reviewdog v0.20.3 (used by action-suggester v1.22) was removed from GitHub releases, causing both clang-format and black check steps to fail with "unable to find 'v0.20.3'". Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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Summary
y = x * rsqrt(mean(x^2) + eps) * warith.divfwhich requires f32 vectors)98f2fc3(includes PR #1412 for cross-op buffer sharing)Test plan
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