[ET-VK] Synthesize the layer_norm affine parameters that are absent - #22506
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[ET-VK] Synthesize the layer_norm affine parameters that are absent#22506msluszniak wants to merge 1 commit into
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The layer_norm shader reads a weight and a bias binding unconditionally, and add_native_layer_norm_node already synthesized a zero bias for nn.LayerNorm(bias=False). A missing weight was still a hard error, so any model that calls F.layer_norm with no affine parameters at all aborts at prepack with "native_layer_norm requires weight to be non-None". That call is not unusual: kokoro's AdaLayerNorm normalizes with no affine parameters and applies its own style-conditioned scale and shift afterwards. Synthesize a unit weight the same way, sized from normalized_shape when there is no weight tensor to take a shape from. A unit weight and a zero bias reproduce out = (x - mean) * rstd exactly. The two paths now share one helper.
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/22506
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Fixes #22504.
add_native_layer_norm_nodealready synthesizes a zero bias fornn.LayerNorm(bias=False), but still throws on a missing weight.F.layer_normcalled with no affine parameters at all is an ordinary call, used whenever a module applies its own scale and shift afterwards, and it currently makes the model abort at prepack.This synthesizes a unit weight the same way, sizing it from
normalized_shapewhen there is no weight tensor to take a shape from. A unit weight and a zero bias reproduceout = (x - mean) * rstdexactly. The two paths now share one helper.Verified on device: kokoro's duration predictor, whose
AdaLayerNormnormalizes with no affine parameters, aborts onmainand runs to completion with this change on an Adreno 840.cases.pygains threenative_layer_normcases covering weight-only, bias-only and neither.