Pseudo-random number generator (PRNG) functions. More...

Functions
poplar::Tensor	dropout (poplar::Graph &graph, const poplar::Tensor *seed, const uint32_t seedModifier, const poplar::Tensor &input, const poplar::Tensor &reference, double keepProbability, double scale, poplar::program::Sequence &prog, const poplar::DebugContext &debugContext={})
	Apply dropout to a tensor. More...

poplar::Tensor	dropout (poplar::Graph &graph, const poplar::Tensor *seed, const uint32_t seedModifier, const poplar::Tensor &input, const poplar::Tensor &reference, double keepProbability, double scale, bool outputClonesRef, poplar::program::Sequence &prog, const poplar::DebugContext &debugContext={})
	Apply dropout to a tensor. More...

poplar::Tensor	shapedDropout (poplar::Graph &graph, const poplar::Tensor *seed, const uint32_t seedModifier, const poplar::Tensor &input, const poplar::Tensor &reference, double keepProbability, double scale, poplar::program::Sequence &prog, const poplar::DebugContext &debugContext={})
	Apply shaped dropout to a tensor. More...

poplar::Tensor	uniform (poplar::Graph &graph, const poplar::Tensor *seed, uint32_t seedModifier, const poplar::Tensor &reference, const poplar::Type &outType, double minVal, double maxVal, poplar::program::Sequence &prog, const poplar::DebugContext &debugContext={})
	Uniform distribution in a given interval with `maxVal` > `minVal`. More...

poplar::Tensor	logUniform (poplar::Graph &graph, const poplar::Tensor *seed, uint32_t seedModifier, const poplar::Tensor &reference, const poplar::Type &outType, double minVal, double maxVal, poplar::program::Sequence &prog, double base=M_E, const poplar::DebugContext &debugContext={})
	Log-uniform distribution over a closed interval [`minVal`, `maxVal`]. More...

poplar::Tensor	bernoulli (poplar::Graph &graph, const poplar::Tensor *seed, uint32_t seedModifier, const poplar::Tensor &reference, const poplar::Type &outType, double prob, poplar::program::Sequence &prog, const poplar::DebugContext &debugContext={})
	Bernoulli distribution which has the value 1 with the specified probability. More...

poplar::Tensor	normal (poplar::Graph &graph, const poplar::Tensor *seed, uint32_t seedModifier, const poplar::Tensor &reference, const poplar::Type &outType, double mean, double stdDev, poplar::program::Sequence &prog, const poplar::DebugContext &debugContext={})
	Normal distribution with given mean and standard deviation. More...

poplar::Tensor	truncatedNormal (poplar::Graph &graph, const poplar::Tensor *seed, uint32_t seedModifier, const poplar::Tensor &reference, const poplar::Type &outType, double mean, double stdDev, double alpha, poplar::program::Sequence &prog, const poplar::DebugContext &debugContext={})
	Truncated normal distribution. More...

void	setSeed (poplar::Graph &graph, const poplar::Tensor &masterSeed, uint32_t seedModifier, poplar::program::Sequence &prog, const poplar::DebugContext &debugContext={})
	Sets the random number generator seed on all tiles. More...

Detailed Description

Pseudo-random number generator (PRNG) functions.

Function Documentation

◆ bernoulli()

poplar::Tensor poprand::bernoulli	(	poplar::Graph &	graph,
		const poplar::Tensor *	seed,
		uint32_t	seedModifier,
		const poplar::Tensor &	reference,
		const poplar::Type &	outType,
		double	prob,
		poplar::program::Sequence &	prog,
		const poplar::DebugContext &	debugContext = `{}`
	)

Bernoulli distribution which has the value 1 with the specified probability.

Generates a tensor with random values of 0 and 1, determined by prob.

Parameters

graph	The graph to add this operation to.
seed	If not null, this is a pair of 32-bit integers used to seed the random number generator that generates the distribution.
seedModifier	Provides a further modification of the seed value. Ignored if `seed` is null.
reference	A tensor that specifies the layout of the output tensor.
outType	The type of the output tensor. One of `float`, `half` or `int`.
prob	Probability of an element being 1.
prog	The program to add this operation to.
debugContext	Optional debug information.

Returns: A tensor with elements set to either 0 or 1. The value 1 occurs with probablility prob.

◆ dropout() [1/2]

poplar::Tensor poprand::dropout	(	poplar::Graph &	graph,
		const poplar::Tensor *	seed,
		const uint32_t	seedModifier,
		const poplar::Tensor &	input,
		const poplar::Tensor &	reference,
		double	keepProbability,
		double	scale,
		bool	outputClonesRef,
		poplar::program::Sequence &	prog,
		const poplar::DebugContext &	debugContext = `{}`
	)

Apply dropout to a tensor.

The elements of tensor input are multiplied by a mask consisting of a sequence of randomly generated 1 or 0. The keep probability of the dropout P(1) = keepProbability. The contents of the mask depend on the keep probability, seed, seed modifier and layout of the reference tensor.

Parameters

graph	The graph to add this operation to.
seed	If not null, this is a pair of 32-bit integers used to seed the random number generator that generates the dropout mask.
seedModifier	Provides a further modification of the seed value. Ignored if `seed` is null.
input	The input tensor to be masked.
reference	A tensor that specifies the layout of the output tensor. Must be the same shape as the input.
keepProbability	The probability of keeping an input value.
scale	Scales the output tensor. This is typically the inverse of the dropout probability, (1 / P(1)).
outputClonesRef	When true, the output tensor is a clone of the reference tensors. When false, the output tensor is a clone of the input tensor.
prog	The program to add this operation to.
debugContext	Optional debug information.

Returns: A tensor with elements randomly set to either zero or the scaled input value.

◆ dropout() [2/2]

poplar::Tensor poprand::dropout	(	poplar::Graph &	graph,
		const poplar::Tensor *	seed,
		const uint32_t	seedModifier,
		const poplar::Tensor &	input,
		const poplar::Tensor &	reference,
		double	keepProbability,
		double	scale,
		poplar::program::Sequence &	prog,
		const poplar::DebugContext &	debugContext = `{}`
	)

Apply dropout to a tensor.

The elements of tensor input are multiplied by a mask consisting of a sequence of randomly generated 1 or 0. The keep probability of the dropout P(1) = keepProbability. The contents of the mask depend on the keep probability, seed, seed modifier and layout of the reference tensor.

Parameters

graph	The graph to add this operation to.
seed	If not null, this is a pair of 32-bit integers used to seed the random number generator that generates the dropout mask.
seedModifier	Provides a further modification of the seed value. Ignored if `seed` is null.
input	The input tensor to be masked.
reference	A tensor that specifies the layout of the output tensor. Must be the same shape as the input.
keepProbability	The probability of keeping an input value.
scale	Scales the output tensor. This is typically the inverse of the dropout probability, (1 / P(1)).
prog	The program to add this operation to.
debugContext	Optional debug information.

Returns: A tensor with elements randomly set to either zero or the scaled input value.

◆ logUniform()

poplar::Tensor poprand::logUniform	(	poplar::Graph &	graph,
		const poplar::Tensor *	seed,
		uint32_t	seedModifier,
		const poplar::Tensor &	reference,
		const poplar::Type &	outType,
		double	minVal,
		double	maxVal,
		poplar::program::Sequence &	prog,
		double	base = `M_E`,
		const poplar::DebugContext &	debugContext = `{}`
	)

Log-uniform distribution over a closed interval [minVal, maxVal].

Generates random data log-uniformly distributed in the closed interval [minVal, maxVal]. The output may be of type float, half or int. The base of the log can be specified, but defaults to the natural base.

The actual interval of the samples depends on the representable values of the outType and is a subset of the initial interval; the interval will be squeezed inward to the next representable values of outType. For example, for half, the interval [2049.0, 4098.0] would be squeezed to [2050.0, 4096.0]. Depending on the interval's representability, this may cause spikes in the distribution at the boundaries - careful choice of interval is suggested.

Parameters

graph	The graph to add this operation to.
seed	If not null, this is a pair of 32-bit integers used to seed the random number generator that generates the distribution.
seedModifier	Provides a further modification of the seed value. Ignored if `seed` is null.
reference	A tensor that specifies the layout of the output tensor.
outType	The type of the output tensor. One of `float`, `half` or `int`.
minVal	The minimum value of the distribution.
maxVal	The maximum value of the distribution.
prog	The program to add this operation to.
base	Optional base of the log / exponent of the underlying uniform distribution. Defaults to Euler's number (natural base).
debugContext	Optional debug information.

Returns: A tensor the same size as reference with elements having a log-uniform distribution of random values of type outType.

Exceptions

poputil::poplibs_error	If `minVal` < 1
poputil::poplibs_error	If `maxVal` <= `minVal`
poputil::poplibs_error	If `minVal` and `maxVal` are not suitable for the `outType` (for example the range is too narrow)

◆ normal()

poplar::Tensor poprand::normal	(	poplar::Graph &	graph,
		const poplar::Tensor *	seed,
		uint32_t	seedModifier,
		const poplar::Tensor &	reference,
		const poplar::Type &	outType,
		double	mean,
		double	stdDev,
		poplar::program::Sequence &	prog,
		const poplar::DebugContext &	debugContext = `{}`
	)

Normal distribution with given mean and standard deviation.

Generates random data with a normal (Gaussian) distribution. The mean is given by mean and the standard deviation by stdDev.

Parameters

graph	The graph to add this operation to.
seed	If not null, this is a pair of 32-bit integers used to seed the random number generator that generates the distribution.
seedModifier	Provides a further modification of the seed value. Ignored if `seed` is null.
reference	A tensor that specifies the layout of the output tensor.
outType	The type of the output tensor. One of `float` or `half`.
mean	The mean value of the distribution.
stdDev	The standard deviation of the distribution.
prog	The program to add this operation to.
debugContext	Optional debug information.

Returns: A tensor with elements having a normal (Gaussian) distribution of random values.

◆ setSeed()

void poprand::setSeed	(	poplar::Graph &	graph,
		const poplar::Tensor &	masterSeed,
		uint32_t	seedModifier,
		poplar::program::Sequence &	prog,
		const poplar::DebugContext &	debugContext = `{}`
	)

Sets the random number generator seed on all tiles.

Parameters

graph	The graph to add this operation to.
masterSeed	A 64-bit integer to seed the random number on every tile.
seedModifier	Provides a further modification of the seed value.
prog	The program to add this operation to.
debugContext	Optional debug information.

◆ shapedDropout()

poplar::Tensor poprand::shapedDropout	(	poplar::Graph &	graph,
		const poplar::Tensor *	seed,
		const uint32_t	seedModifier,
		const poplar::Tensor &	input,
		const poplar::Tensor &	reference,
		double	keepProbability,
		double	scale,
		poplar::program::Sequence &	prog,
		const poplar::DebugContext &	debugContext = `{}`
	)

Apply shaped dropout to a tensor.

The elements of tensor input are multiplied by a mask consisting of a sequence of randomly generated 1 or 0. The keep probability of the dropout P(1) = keepProbability.

Shaped dropout allows row, column and dimension wise dropout, versus element-wise standard dropout. The shape of the dropout must be compatible (broadcastable) to input.

The contents of the mask depend on the keep probability, seed, seed modifier and layout of the reference tensor.

Parameters

graph	The graph to add this operation to.
seed	If not null, this is a pair of 32-bit integers used to seed the random number generator that generates the dropout mask.
seedModifier	Provides a further modification of the seed value. Ignored if `seed` is null.
input	The input tensor to be masked.
reference	A tensor that specifies the shape and layout of the dropout. Must be broadcastable to the input.
keepProbability	The probability of keeping an input value.
scale	Scales the output tensor. This is typically the inverse of the dropout probability, (1 / P(1)).
prog	The program to add this operation to.
debugContext	Optional debug information.

Returns: A tensor with elements randomly set to either zero or the scaled input value.

◆ truncatedNormal()

poplar::Tensor poprand::truncatedNormal	(	poplar::Graph &	graph,
		const poplar::Tensor *	seed,
		uint32_t	seedModifier,
		const poplar::Tensor &	reference,
		const poplar::Type &	outType,
		double	mean,
		double	stdDev,
		double	alpha,
		poplar::program::Sequence &	prog,
		const poplar::DebugContext &	debugContext = `{}`
	)

Truncated normal distribution.

Generates random data with a distribution derived from a normal distribution with mean mean and standard deviation stdDev. This normal distribution is truncated symmetrically about the mean at (mean - alpha * stdDev) and (mean + alpha * stdDev)

Parameters

graph	The graph to add this operation to.
seed	If not null, this is a pair of 32-bit integers used to seed the random number generator that generates the distribution.
seedModifier	Provides a further modification of the seed value. Ignored if `seed` is null.
reference	A tensor that specifies the layout of the output tensor.
outType	The type of the output tensor. One of `float` or `half`.
mean	The mean value of the distribution.
stdDev	The standard deviation of the distribution.
alpha	Defines the minimum and maximum values of the distribution.
prog	The program to add this operation to.
debugContext	Optional debug information.

Returns: A tensor with elements having a truncated normal (Gaussian) distribution of random values.

◆ uniform()

poplar::Tensor poprand::uniform	(	poplar::Graph &	graph,
		const poplar::Tensor *	seed,
		uint32_t	seedModifier,
		const poplar::Tensor &	reference,
		const poplar::Type &	outType,
		double	minVal,
		double	maxVal,
		poplar::program::Sequence &	prog,
		const poplar::DebugContext &	debugContext = `{}`
	)

Uniform distribution in a given interval with maxVal > minVal.

Generates random data with uniform distribution in the interval [minVal, maxVal]. The output may be of type float, half or int.

For type int, data is generated in the interval [minVal, maxVal] with uniform probability if (maxVal - minVal) is a power of 2. Otherwise there will be a small bias in the probability generated, with the bias directly proportional to the ratio (maxVal - minVal + 1 ) / 2^32.

Parameters

graph	The graph to add this operation to.
seed	If not null, this is a pair of 32-bit integers used to seed the random number generator that generates the distribution.
seedModifier	Provides a further modification of the seed value. Ignored if `seed` is null.
reference	A tensor that specifies the layout of the output tensor.
outType	The type of the output tensor. One of `float`, `half` or `int`.
minVal	The minimum value of the distribution.
maxVal	The maximum value of the distribution.
prog	The program to add this operation to.
debugContext	Optional debug information.

Returns: A tensor with elements having a uniform distribution of random values.

Functions

Detailed Description

Function Documentation

◆ bernoulli()

◆ dropout() [1/2]

◆ dropout() [2/2]

◆ logUniform()

◆ normal()

◆ setSeed()

◆ shapedDropout()

◆ truncatedNormal()

◆ uniform()