CTCLoss

#include <popnn/CTCLoss.hpp>

Support for Connectionist Temporal Classification (CTC) Loss.

namespace popnn

Functions used in neural networks.

namespace ctc

Functions

Plan plan(const poplar::Graph &graph, const poplar::Type &inType, const poplar::Type &outType, unsigned batchSize, unsigned maxTime, unsigned maxLabelLength, unsigned numClasses, const poplar::OptionFlags &options = {})

Create a plan for implementing the CTC Loss (gradient) function.

CTC Loss options

• partialsType poplar::Type [=poplar::FLOAT]

  The type to use for partial results.

• availableMemoryProportion Decimal between 0 and 1 (inclusive) [=0.6]

  The maximum proportion of available memory on each tile that this layer should consume temporarily during the course of the operation.

  See also

  Optimising Temporary Memory Usage for Convolutions and Matmuls on the IPU technical note for some practical examples of using availableMemoryProportion

Parameters

• graph – The graph the operation will be added to

• inType – The data type of the probability data input

• outType – The data type of the gradient output

• batchSize – The size of the batch to be processed at once

• maxTime – The maximum time of any data input to be planned for

• maxLabelLength – The maximum length of any label to be planned for

• numClasses – The number of symbols/classes in the “alphabet”, including the blankClass

• options – Any implementation/debug options for the operation

Returns

plan The plan produced, which will specify how the operation is to be implemented

poplar::Tensor createDataInput(poplar::Graph &graph, const poplar::Type &type, const std::size_t batchSize, const std::size_t maxTime, const std::size_t numClasses, const Plan &plan, const poplar::DebugContext &debugContext = {})

Create and map a data input [maxTime, batchSize, numClasses] tensor which the gradient function will use.

Mapping is according to the plan provided.

Parameters

• graph – The graph the data tensor will be added to

• type – The data type of the tensor to be added to the graph

• batchSize – The size of the batch to be processed at once

• maxTime – The time dimension of the tensor to be created

• numClasses – The number of symbols/classes in the “alphabet”, including the blankClass

• plan – The plan which will specify how the tensor is to be mapped

• debugContext – Optional debug information

Returns

The data input [maxTime, batchSize, numClasses] tensor

poplar::Tensor createLabelsInput(poplar::Graph &graph, const poplar::Type &type, const std::size_t batchSize, const std::size_t maxLabelLength, const Plan &plan, const poplar::DebugContext &debugContext = {})

Create and map a labels input [batchSize, maxLabelLength] tensor which the gradient function will use.

Mapping is according to the plan provided.

Parameters

• graph – The graph the labels tensor will be added to

• type – The data type of the tensor to be added to the graph

• batchSize – The size of the batch to be processed at once

• maxLabelLength – The maximum length of any label

• plan – The plan which will specify how the tensor is to be mapped

• debugContext – Optional debug information

Returns

The labels input [batchSize, maxLabelLength] tensor

std::pair<poplar::Tensor, poplar::Tensor> calcLossAndGradientLogProbabilities(poplar::Graph &graph, const poplar::Type &outType, const poplar::Tensor &logProbs, const poplar::Tensor &labels, const poplar::Tensor &dataLengths, const poplar::Tensor &labelLengths, poplar::program::Sequence &prog, const unsigned blankClass, const Plan &plan, const poplar::DebugContext &debugContext = {}, const poplar::OptionFlags &options = {})

Calculate the CTC loss & gradient, creating and mapping the result tensor according to the plan provided.

calcLossAndGradientLogProbabilities options

• includeSoftmaxGradient (true, false) [=true]

  Whether or not to include LogSoftmax in gradient calculation. To avoid numerical issues, it is recommended to be included. But care must be taken to not include gradient of the LogSoftmax (created external to this function call) twice.

• zeroInfinity (true, false) [=false]

  Whether to zero infinite losses and the associated gradients. Infinite losses mainly occur when data batches are too short to be aligned to the labels.

Parameters

• graph – The graph the operation will be added to

• outType – The data type of the gradient output

• logProbs – The data input [maxTime, batchSize, numClasses] tensor

• labels – The labels input [batchSize, maxLabelLength] tensor

• dataLengths – A tensor of shape [batchSize] containing the number of valid timesteps in each data[] batch entry

• labelLengths – A tensor of shape [batchSize] containing the number of valid labels in each labels[] batch entry

• prog – A program sequence to append the operation to

• blankClass – The value associated with the blankClass

• plan – The plan which will specify how the output tensor is to be mapped and how the operation is to be carried out

• debugContext – Optional debug information

• options – Any implementation/debug options for the operation

Returns

The loss[batchSize] (negative log probability), and gradient [maxTime, batchSize, numClasses] tensor

std::pair<poplar::Tensor, poplar::Tensor> calcLossAndGradientLogits(poplar::Graph &graph, const poplar::Type &outType, const poplar::Tensor &logits, const poplar::Tensor &labels, const poplar::Tensor &dataLengths, const poplar::Tensor &labelLengths, poplar::program::Sequence &prog, const unsigned blankClass, const Plan &plan, const poplar::DebugContext &debugContext = {}, const poplar::OptionFlags &options = {})

Calculate the CTC loss & gradient, creating and mapping the result tensor according to the plan provided.

Prior to performing the gradient calculation, applies log softmax to logits input.

Parameters

• graph – The graph the operation will be added to

• outType – The data type of the gradient output

• logits – The data input [maxTime, batchSize, numClasses] tensor

• labels – The labels input [batchSize, maxLabelLength] tensor

• dataLengths – A tensor of shape [batchSize] containing the number of valid timesteps in each data[] batch entry

• labelLengths – A tensor of shape [batchSize] containing the number of valid labels in each labels[] batch entry

• prog – A program sequence to append the operation to

• blankClass – The value associated with the blankClass

• plan – The plan which will specify how the output tensor is to be mapped and how the operation is to be carried out

• debugContext – Optional debug information

• options – Any implementation/debug options for the operation

Returns

The loss[batchSize] (negative log probability), and gradient [maxTime, batchSize, numClasses] tensor

poplar::Tensor calcCTCLossLogProbabilities(poplar::Graph &graph, const poplar::Type &outType, const poplar::Tensor &logProbs, const poplar::Tensor &labels, const poplar::Tensor &dataLengths, const poplar::Tensor &labelLengths, poplar::program::Sequence &prog, const unsigned blankClass, const Plan &plan, const poplar::DebugContext &debugContext = {}, const poplar::OptionFlags &options = {})

Calculate the CTC loss, creating and mapping the result tensor according to the plan provided.

Parameters

• graph – The graph the operation will be added to

• outType – The data type of the output

• logProbs – The data input [maxTime, batchSize, numClasses] tensor

• labels – The labels input [batchSize, maxLabelLength] tensor

• dataLengths – A tensor of shape [batchSize] containing the number of valid timesteps in each data[] batch entry

• labelLengths – A tensor of shape [batchSize] containing the number of valid labels in each labels[] batch entry

• prog – A program sequence to append the operation to

• blankClass – The value associated with the blankClass

• plan – The plan which will specify how the output tensor is to be mapped and how the operation is to be carried out

• debugContext – Optional debug information

• options – Any implementation/debug options for the operation

Returns

The loss[batchSize] (negative log probability)

poplar::Tensor calcCTCLossLogits(poplar::Graph &graph, const poplar::Type &outType, const poplar::Tensor &logits, const poplar::Tensor &labels, const poplar::Tensor &dataLengths, const poplar::Tensor &labelLengths, poplar::program::Sequence &prog, const unsigned blankClass, const Plan &plan, const poplar::DebugContext &debugContext = {}, const poplar::OptionFlags &options = {})

Calculate the CTC loss, creating and mapping the result tensor according to the plan provided.

Applies log softmax to logits input.

Parameters

• graph – The graph the operation will be added to

• outType – The data type of the output

• logits – The data input [maxTime, batchSize, numClasses] tensor

• labels – The labels input [batchSize, maxLabelLength] tensor

• dataLengths – A tensor of shape [batchSize] containing the number of valid timesteps in each data[] batch entry

• labelLengths – A tensor of shape [batchSize] containing the number of valid labels in each labels[] batch entry

• prog – A program sequence to append the operation to

• blankClass – The value associated with the blankClass

• plan – The plan which will specify how the output tensor is to be mapped and how the operation is to be carried out

• debugContext – Optional debug information

• options – Any implementation/debug options for the operation

Returns

The loss[batchSize] (negative log probability)