Support for Connectionist Temporal Classification (CTC) Loss. More...

#include "CTCPlan.hpp"
#include <poplar/Graph.hpp>
#include <poplar/OptionFlags.hpp>
#include <poplar/Program.hpp>

Namespaces
namespace	popnn
	Functions used in neural networks.

Functions
Plan	popnn::ctc::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. More...

poplar::Tensor	popnn::ctc::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. More...

poplar::Tensor	popnn::ctc::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. More...

std::pair< poplar::Tensor, poplar::Tensor >	popnn::ctc::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. More...

std::pair< poplar::Tensor, poplar::Tensor >	popnn::ctc::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. More...

poplar::Tensor	popnn::ctc::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. More...

poplar::Tensor	popnn::ctc::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. More...

Detailed Description

Support for Connectionist Temporal Classification (CTC) Loss.

Function Documentation

◆ calcCTCLossLogits()

poplar::Tensor popnn::ctc::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)

◆ calcCTCLossLogProbabilities()

poplar::Tensor popnn::ctc::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)

◆ calcLossAndGradientLogits()

std::pair< poplar::Tensor, poplar::Tensor > popnn::ctc::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

◆ calcLossAndGradientLogProbabilities()

std::pair< poplar::Tensor, poplar::Tensor > popnn::ctc::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

◆ createDataInput()

poplar::Tensor popnn::ctc::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

◆ createLabelsInput()

poplar::Tensor popnn::ctc::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

◆ plan()

Plan popnn::ctc::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] (https://docs.graphcore.ai/projects/available-memory/) 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

Namespaces

Functions

Detailed Description

Function Documentation

◆ calcCTCLossLogits()

◆ calcCTCLossLogProbabilities()

◆ calcLossAndGradientLogits()

◆ calcLossAndGradientLogProbabilities()

◆ createDataInput()

◆ createLabelsInput()

◆ plan()