native_layer_norm (for width dim)#3001
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/3001
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Summary: We implement `native_layer_norm` which has 3 outputs - normalization of the input tensor according to the given `normalized_shape` - mean - 1/sqrt(var + eps) https://www.internalfb.com/code/fbsource/[8db4b5872791bb88a62ecaa60b667ee4c1b189bf]/fbcode/caffe2/aten/src/ATen/native/native_functions.yaml?lines=3252 According to SS-JIA's suggestion, a model specific implementation is more performant and preferred to a generic one. So we implemented the op in the following optimized way - our current use case has `normalized_shape` of len 1, namely we do the normalization through computing the mean and var at the last width dim - we do the computation in just one shader `native_layer_norm.glsl` without invoking the shaders to compute mean and var respectively - we use [Welford's online algorithm](https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Welford's_online_algorithm) to compute mean and variance in one pass Differential Revision: D56005629
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Summary: We implement `native_layer_norm` which has 3 outputs - normalization of the input tensor according to the given `normalized_shape` - mean - 1/sqrt(var + eps) https://www.internalfb.com/code/fbsource/[8db4b5872791bb88a62ecaa60b667ee4c1b189bf]/fbcode/caffe2/aten/src/ATen/native/native_functions.yaml?lines=3252 According to SS-JIA's suggestion, a model specific implementation is more performant and preferred to a generic one. So we implemented the op in the following optimized way - our current use case has `normalized_shape` of len 1, namely we do the normalization through computing the mean and var at the last width dim - we do the computation in just one shader `native_layer_norm.glsl` without invoking the shaders to compute mean and var respectively - we use [Welford's online algorithm](https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Welford's_online_algorithm) to compute mean and variance in one pass Differential Revision: D56005629
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Summary:
We implement
native_layer_normwhich has 3 outputsnormalized_shapehttps://www.internalfb.com/code/fbsource/[8db4b5872791bb88a62ecaa60b667ee4c1b189bf]/fbcode/caffe2/aten/src/ATen/native/native_functions.yaml?lines=3252
According to SS-JIA's suggestion, a model specific implementation is more performant and preferred to a generic one. So we implemented the op in the following optimized way
normalized_shapeof len 1, namely we do the normalization through computing the mean and var at the last width dimnative_layer_norm.glslwithout invoking the shaders to compute mean and var respectivelyDifferential Revision: D56005629