LayerNorm

#include <popnn/LayerNorm.hpp>

Layer normalisation operations.

Layer norm uses group norm with number of groups = 1.

namespace popnn

Functions used in neural networks.

namespace ln

Functions

inline std::pair<poplar::Tensor, poplar::Tensor> layerNormStatistics(poplar::Graph &graph, const poplar::Tensor acts, float eps, poplar::program::Sequence &prog, bool unbiasedVarEstimate, bool stableAlgo = false, const poplar::Type &partialsType = poplar::FLOAT, const poplar::DebugContext &debugContext = {}, const poplar::OptionFlags &options = {})

Estimate mean and inverse of standard deviation of activations.

inline poplar::Tensor layerNormWhiten(poplar::Graph &graph, const poplar::Tensor &acts, const poplar::Tensor &mean, const poplar::Tensor &invStdDev, poplar::program::Sequence &prog, const poplar::DebugContext &debugContext = {}, const poplar::OptionFlags &options = {})

Whiten activations given mean and standard deviation.

inline std::pair<poplar::Tensor, poplar::Tensor> layerNormalise(poplar::Graph &graph, const poplar::Tensor &acts, const poplar::Tensor &gamma, const poplar::Tensor &beta, const poplar::Tensor &mean, const poplar::Tensor &invStdDev, poplar::program::Sequence &prog, const poplar::DebugContext &debugContext = {}, const poplar::OptionFlags &options = {})

Layer normalise activations given mean, standard deviation and norm parameters.

The result is two tensors:

1. normalised activations

2. whitened activations

inline std::pair<poplar::Tensor, poplar::Tensor> layerNormParamGradients(poplar::Graph &graph, const poplar::Tensor &acts, const poplar::Tensor &gradsIn, const poplar::Tensor &mean, const poplar::Tensor &iStdDev, poplar::program::Sequence &prog, const poplar::Type &partialsType = poplar::FLOAT, const poplar::DebugContext &debugContext = {}, const poplar::OptionFlags &options = {})

Compute gradients w.r.t parameters for parameter update.

inline std::pair<poplar::Tensor, poplar::Tensor> layerNormParamGradients(poplar::Graph &graph, const poplar::Tensor &actsWhitened, const poplar::Tensor &gradsIn, poplar::program::Sequence &prog, const poplar::Type &partialsType = poplar::FLOAT, const poplar::DebugContext &debugContext = {}, const poplar::OptionFlags &options = {})

Compute gradients w.r.t parameters for parameter update.

inline poplar::Tensor layerNormGradients(poplar::Graph &graph, const poplar::Tensor &acts, const poplar::Tensor &gradsIn, const poplar::Tensor &mean, const poplar::Tensor &invStdDev, const poplar::Tensor &gamma, poplar::program::Sequence &prog, const poplar::Type &partialsType = poplar::FLOAT, const poplar::DebugContext &debugContext = {}, const poplar::OptionFlags &options = {})

Compute gradients w.r.t input activations for the layer norm layer.

Gradients are propagated through the complete layer including statistics computation.

inline poplar::Tensor layerNormGradients(poplar::Graph &graph, const poplar::Tensor &actsWhitened, const poplar::Tensor &gradsIn, const poplar::Tensor &invStdDev, const poplar::Tensor &gamma, poplar::program::Sequence &prog, const poplar::Type &partialsType = poplar::FLOAT, const poplar::DebugContext &debugContext = {}, const poplar::OptionFlags &options = {})

Compute gradients w.r.t input activations for the layer norm layer.

Gradients are propagated through the complete layer including statistics computation.

inline void layerNormParamUpdate(poplar::Graph &graph, const poplar::Tensor &gammaDelta, const poplar::Tensor &betaDelta, float scale, poplar::Tensor &gamma, poplar::Tensor &beta, poplar::program::Sequence &prog, const poplar::DebugContext &debugContext = {}, const poplar::OptionFlags &options = {})

Update layer norm parameters given the gradients w.r.t. parameters.

inline void layerNormParamUpdate(poplar::Graph &graph, const poplar::Tensor &gammaDelta, const poplar::Tensor &betaDelta, const poplar::Tensor &scale, poplar::Tensor &gamma, poplar::Tensor &beta, poplar::program::Sequence &prog, const poplar::DebugContext &debugContext = {}, const poplar::OptionFlags &options = {})