onnxruntime

82eb99c0 - Fix SkipLayerNorm fusion incorrectly applied when gamma/beta are not 1D (#27459)

Fix SkipLayerNorm fusion incorrectly applied when gamma/beta are not 1D (#27459) ### Description The `SkipLayerNormFusion` optimizer skips fusion when the `LayerNormalization` gamma or beta inputs are not 1D tensors (e.g. shape `[1, 1, hidden_size]`). The `SkipLayerNormalization` kernel strictly requires 1D gamma/beta, so fusing without this check caused a hard runtime error. - **`skip_layer_norm_fusion.cc`**: After matching the Add+LayerNorm pattern, check that gamma (and beta if present) have exactly 1 dimension before proceeding with fusion. If shape info is unavailable (dynamic), fusion is allowed and runtime validation takes over. - **`graph_transform_test_layernorm.cc`**: Added `SkipLayerNormFusion_3DGamma_NoFusion` test — builds a graph with `Add + LayerNormalization` where gamma/beta are `[1, 1, 4]` and asserts no `SkipLayerNormalization` node is created. ### Motivation and Context Models with residual connections followed by `LayerNormalization` where the scale/bias tensors carry extra batch/sequence dimensions (e.g. exported as `[1, 1, hidden_size]` rather than `[hidden_size]`) would trigger fusion and then fail at runtime: ``` Non-zero status code returned while running SkipLayerNormalization node. Status Message: gamma is expected to have 1 dimension, got 3 ``` The error only appeared with 3D inputs and disappeared at `ORT_ENABLE_BASIC` optimization level (which disables the fusion), confirming the optimizer as the source of the regression. <!-- START COPILOT CODING AGENT TIPS --> --- 💬 We'd love your input! Share your thoughts on Copilot coding agent in our [2 minute survey](https://gh.io/copilot-coding-agent-survey). --------- Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com> Co-authored-by: tianleiwu <30328909+tianleiwu@users.noreply.github.com>