Support for Connectionist Temporal Classification (CTC) Beam search decoder. 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
ctc::Plan	popnn::ctc_infer::plan (const poplar::Graph &graph, const poplar::Type &inType, unsigned batchSize, unsigned maxTime, unsigned numClasses, unsigned beamwidth, const poplar::OptionFlags &options={})
	Create a plan for implementing the CTC Beam search inference function. More...

poplar::Tensor	popnn::ctc_infer::createDataInput (poplar::Graph &graph, const poplar::Type &type, const std::size_t batchSize, const std::size_t maxTime, const std::size_t numClasses, const ctc::Plan &plan, const poplar::DebugContext &debugContext={})
	Create and map a data input [maxTime, batchSize, numClasses] tensor which the beam search function will use. More...

std::tuple< poplar::Tensor, poplar::Tensor, poplar::Tensor >	popnn::ctc_infer::beamSearchDecoderLogProbabilities (poplar::Graph &graph, const poplar::Tensor &logProbs, const poplar::Tensor &dataLengths, poplar::program::Sequence &prog, unsigned blankClass, unsigned beamwidth, unsigned topPaths, const ctc::Plan &plan, const poplar::DebugContext &debugContext={}, const poplar::OptionFlags &options={})
	Calculate the most likely `topPaths` labels and their probabilities given the input `logProbs` with lengths `dataLengths`, creating and mapping the result tensors according to the plan provided. More...

std::tuple< poplar::Tensor, poplar::Tensor, poplar::Tensor >	popnn::ctc_infer::beamSearchDecoderLogits (poplar::Graph &graph, const poplar::Tensor &logits, const poplar::Tensor &dataLengths, poplar::program::Sequence &prog, unsigned blankClass, unsigned beamwidth, unsigned topPaths, const ctc::Plan &plan, const poplar::DebugContext &debugContext={}, const poplar::OptionFlags &options={})
	Calculate the most likely `topPaths` labels and their probabilities given the input `logits` with lengths `dataLengths`, creating and mapping the result tensors according to the plan provided. More...

Detailed Description

Support for Connectionist Temporal Classification (CTC) Beam search decoder.

Function Documentation

◆ beamSearchDecoderLogits()

std::tuple< poplar::Tensor, poplar::Tensor, poplar::Tensor > popnn::ctc_infer::beamSearchDecoderLogits	(	poplar::Graph &	graph,
		const poplar::Tensor &	logits,
		const poplar::Tensor &	dataLengths,
		poplar::program::Sequence &	prog,
		unsigned	blankClass,
		unsigned	beamwidth,
		unsigned	topPaths,
		const ctc::Plan &	plan,
		const poplar::DebugContext &	debugContext = `{}`,
		const poplar::OptionFlags &	options = `{}`
	)

Calculate the most likely topPaths labels and their probabilities given the input logits with lengths dataLengths, creating and mapping the result tensors according to the plan provided.

Prior to performing the beam search, applies log softmax to logits input.

Parameters

graph	The graph the operation will be added to
logits	The data input [maxTime, batchSize, numClasses] tensor
dataLengths	A tensor of shape [batchSize] containing the number of valid timesteps in each `logits` batch entry
prog	A program sequence to append the operation to
blankClass	The value associated with the blankClass
beamWidth	The number of beams to use when decoding
topPaths	The number of most likely decoded paths to return, must be less than or equal to `beamWidth`
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 labelProbs[batchSize, topPaths] (negative log probability with the same type as logits), labelLengths[batchSize, topPaths] and decodedLabels [batchSize, topPaths, maxTime] tensors

◆ beamSearchDecoderLogProbabilities()

std::tuple< poplar::Tensor, poplar::Tensor, poplar::Tensor > popnn::ctc_infer::beamSearchDecoderLogProbabilities	(	poplar::Graph &	graph,
		const poplar::Tensor &	logProbs,
		const poplar::Tensor &	dataLengths,
		poplar::program::Sequence &	prog,
		unsigned	blankClass,
		unsigned	beamwidth,
		unsigned	topPaths,
		const ctc::Plan &	plan,
		const poplar::DebugContext &	debugContext = `{}`,
		const poplar::OptionFlags &	options = `{}`
	)

Calculate the most likely topPaths labels and their probabilities given the input logProbs with lengths dataLengths, creating and mapping the result tensors according to the plan provided.

Parameters

graph	The graph the operation will be added to
logProbs	The data input [maxTime, batchSize, numClasses] tensor
dataLengths	A tensor of shape [batchSize] containing the number of valid timesteps in each `logProbs` batch entry
prog	A program sequence to append the operation to
blankClass	The value associated with the blankClass
beamWidth	The number of beams to use when decoding
topPaths	The number of most likely decoded paths to return, must be less than or equal to `beamWidth`
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 labelProbs[batchSize, topPaths] (negative log probability with the same type as logProbs), labelLengths[batchSize, topPaths] and decodedLabels [batchSize, topPaths, maxTime] tensors

◆ createDataInput()

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

Create and map a data input [maxTime, batchSize, numClasses] tensor which the beam search 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

◆ plan()

ctc::Plan popnn::ctc_infer::plan	(	const poplar::Graph &	graph,
		const poplar::Type &	inType,
		unsigned	batchSize,
		unsigned	maxTime,
		unsigned	numClasses,
		unsigned	beamwidth,
		const poplar::OptionFlags &	options = `{}`
	)

Create a plan for implementing the CTC Beam search inference function.

CTC Beam search inference 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.

Parameters

graph	The graph the operation will be added to
inType	The data type of the probability data input
batchSize	The size of the batch to be processed at once
maxTime	The maximum time of any sequence input
numClasses	The number of symbols/classes in the "alphabet", including the blankClass
beamwidth	The number of beams to maintain during beamsearch
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

◆ beamSearchDecoderLogits()

◆ beamSearchDecoderLogProbabilities()

◆ createDataInput()

◆ plan()