Batchnorm

Batchnorm#

MIOpen: Batchnorm

Enumerations
enum	miopenBatchNormMode_t { miopenBNPerActivation = 0 , miopenBNSpatial = 1 }

Functions
miopenStatus_t	miopenDeriveBNTensorDescriptor (miopenTensorDescriptor_t derivedBnDesc, const miopenTensorDescriptor_t xDesc, miopenBatchNormMode_t bn_mode)
	Derive tensor for gamma and beta from input tensor descriptor. More...

miopenStatus_t	miopenBatchNormalizationForwardTraining (miopenHandle_t handle, miopenBatchNormMode_t bn_mode, void alpha, void beta, const miopenTensorDescriptor_t xDesc, const void x, const miopenTensorDescriptor_t yDesc, void y, const miopenTensorDescriptor_t bnScaleBiasMeanVarDesc, void bnScale, void bnBias, double expAvgFactor, void resultRunningMean, void resultRunningVariance, double epsilon, void resultSaveMean, void resultSaveInvVariance)
	Execute forward training layer for batch normalization. More...

miopenStatus_t	miopenBatchNormalizationForwardInference (miopenHandle_t handle, miopenBatchNormMode_t bn_mode, void alpha, void beta, const miopenTensorDescriptor_t xDesc, const void x, const miopenTensorDescriptor_t yDesc, void y, const miopenTensorDescriptor_t bnScaleBiasMeanVarDesc, void bnScale, void bnBias, void estimatedMean, void estimatedVariance, double epsilon)
	Execute forward inference layer for batch normalization. More...

miopenStatus_t	miopenBatchNormalizationBackward (miopenHandle_t handle, miopenBatchNormMode_t bn_mode, const void alphaDataDiff, const void betaDataDiff, const void alphaParamDiff, const void betaParamDiff, const miopenTensorDescriptor_t xDesc, const void x, const miopenTensorDescriptor_t dyDesc, const void dy, const miopenTensorDescriptor_t dxDesc, void dx, const miopenTensorDescriptor_t bnScaleBiasDiffDesc, const void bnScale, void resultBnScaleDiff, void resultBnBiasDiff, double epsilon, const void savedMean, const void savedInvVariance)
	Execute backwards propagation layer for batch normalization. More...

Detailed Description

Enumeration Type Documentation

◆ miopenBatchNormMode_t

enum miopenBatchNormMode_t

Batch Normalization layer mode

Enumerator
miopenBNPerActivation	Element-wise normalization for fully connected layer
miopenBNSpatial	Mini-batch spatial normalization for convolutional layers

Examples: /home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/latest/include/miopen/miopen.h.

Function Documentation

◆ miopenBatchNormalizationBackward()

miopenStatus_t miopenBatchNormalizationBackward	(	miopenHandle_t	handle,
		miopenBatchNormMode_t	bn_mode,
		const void *	alphaDataDiff,
		const void *	betaDataDiff,
		const void *	alphaParamDiff,
		const void *	betaParamDiff,
		const miopenTensorDescriptor_t	xDesc,
		const void *	x,
		const miopenTensorDescriptor_t	dyDesc,
		const void *	dy,
		const miopenTensorDescriptor_t	dxDesc,
		void *	dx,
		const miopenTensorDescriptor_t	bnScaleBiasDiffDesc,
		const void *	bnScale,
		void *	resultBnScaleDiff,
		void *	resultBnBiasDiff,
		double	epsilon,
		const void *	savedMean,
		const void *	savedInvVariance
	)

Execute backwards propagation layer for batch normalization.

Batch normalization pass for backwards propagation training pass. The method for backwards propagation batch normalization.

Takes in batch normalization mode bn_mode and input tensor data x, input activation tensor dy, output tensor dx, the learned tensors resultBNBiasDiff and resultBNScaleDiff with their descriptor.

If BOTH savedMean, and savedVariance are not null pointers then the method will use the saved mean and variance calculated by the forward training phase.

Parameters

handle	MIOpen handle (input)
bn_mode	Batch normalization mode (input)
alphaDataDiff	Floating point scaling factor, allocated on the host (input)
betaDataDiff	Floating point shift factor, allocated on the host (input)
alphaParamDiff	Floating point scaling factor, allocated on the host (input)
betaParamDiff	Floating point shift factor, allocated on the host (input)
xDesc	Tensor descriptor for data input tensor x (input)
x	Data tensor x (input)
dyDesc	Tensor descriptor for output data tensor y (input)
dy	Data tensor y (input)
dxDesc	Tensor descriptor for output data tensor dx (input)
dx	Data delta tensor dx (output)
bnScaleBiasDiffDesc	Tensor descriptor for BN scaling, shifting, saved variance and mean (input)
bnScale	Batch norm scaling, gamma, tensor (input)
resultBnScaleDiff	Tensor for dscale (output)
resultBnBiasDiff	Tensor for dbias (output)
epsilon	Value to stabilize inverse variance calculation (input)
savedMean	Saved mini-batch mean for backwards pass (input)
savedInvVariance	Saved mini-bathc inverse variance for backwards pass (input)

Returns: miopenStatus_t

Examples: /home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/latest/include/miopen/miopen.h.

◆ miopenBatchNormalizationForwardInference()

miopenStatus_t miopenBatchNormalizationForwardInference	(	miopenHandle_t	handle,
		miopenBatchNormMode_t	bn_mode,
		void *	alpha,
		void *	beta,
		const miopenTensorDescriptor_t	xDesc,
		const void *	x,
		const miopenTensorDescriptor_t	yDesc,
		void *	y,
		const miopenTensorDescriptor_t	bnScaleBiasMeanVarDesc,
		void *	bnScale,
		void *	bnBias,
		void *	estimatedMean,
		void *	estimatedVariance,
		double	epsilon
	)

Execute forward inference layer for batch normalization.

Batch normalization pass for forward inference pass. Takes in batch normalization mode bn_mode and input tensor x, output tensor y, bnBias and bnScale with their descriptor.

If either estimatedMean, or estimatedVariance are null pointers then the values for the mean and variance will be calculated from input data and this calculated mean and variance will be used to update input values. If variance is zero and epsilon is also zero, this function outputs NAN values. Input espilon value should always be non zero positive value.

Parameters

handle	MIOpen handle (input)
bn_mode	Batch normalization mode (input)
alpha	Floating point scaling factor, allocated on the host (input)
beta	Floating point shift factor, allocated on the host (input)
xDesc	Tensor descriptor for data input tensor x (input)
x	Data tensor x (input)
yDesc	Tensor descriptor for output data tensor y (input)
y	Data tensor y (output)
bnScaleBiasMeanVarDesc	Tensor descriptor for BN scaling, shifting, saved variance and mean (input)
bnScale	Batch norm scaling, gamma, tensor (input)
bnBias	Batch norm bias, beta, tensor (input)
estimatedMean	Running average saved during forward training (input)
estimatedVariance	Running variance saved during forward training (input)
epsilon	Value to stabilize inverse variance calculation (input)

Returns: miopenStatus_t

Examples: /home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/latest/include/miopen/miopen.h.

◆ miopenBatchNormalizationForwardTraining()

miopenStatus_t miopenBatchNormalizationForwardTraining	(	miopenHandle_t	handle,
		miopenBatchNormMode_t	bn_mode,
		void *	alpha,
		void *	beta,
		const miopenTensorDescriptor_t	xDesc,
		const void *	x,
		const miopenTensorDescriptor_t	yDesc,
		void *	y,
		const miopenTensorDescriptor_t	bnScaleBiasMeanVarDesc,
		void *	bnScale,
		void *	bnBias,
		double	expAvgFactor,
		void *	resultRunningMean,
		void *	resultRunningVariance,
		double	epsilon,
		void *	resultSaveMean,
		void *	resultSaveInvVariance
	)

Execute forward training layer for batch normalization.

Batch normalization pass for forward training pass. Takes in batch normalization mode bn_mode and input tensor x, output tensor y, bnBias and bnScale with their descriptor.

If either resultSaveMean, or resultSaveInvVariance are null pointers then the values for the mean and inverse variance will not be used.

Likewise, if either resultRunningMean, or resultRunningVariance are null pointers then the values for the running mean and variance will not be saved. Running averages and variances are scaled using an exponential averaging factor:

\[ \mu_{old} = \mu_{new}*factor + \mu_{old}*(1-factor) \]

where

\[ factor=1/(1+iteration) \]

Parameters

handle	MIOpen handle (input)
bn_mode	Batch normalization mode (input)
alpha	Floating point scaling factor, allocated on the host (input)
beta	Floating point shift factor, allocated on the host (input)
xDesc	Tensor descriptor for data input tensor x (input)
x	Data tensor x (input)
yDesc	Tensor descriptor for output data tensor y (input)
y	Data tensor y (output)
bnScaleBiasMeanVarDesc	Tensor descriptor for BN scaling, shifting, saved variance and mean (input)
bnScale	Batch norm scaling, gamma, tensor (input)
bnBias	Batch norm bias, beta, tensor (input)
expAvgFactor	Exponential averaging factor (input)
resultRunningMean	Running average saved for inference (output)
resultRunningVariance	Running variance saved for inference (output)
epsilon	Value to stablize inverse variance calculation (input)
resultSaveMean	Saved mini-batch mean for backwards pass (output)
resultSaveInvVariance	Saved mini-batch inverse variance for backwards pass (output)

Returns: miopenStatus_t

Examples: /home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/latest/include/miopen/miopen.h.

◆ miopenDeriveBNTensorDescriptor()

miopenStatus_t miopenDeriveBNTensorDescriptor	(	miopenTensorDescriptor_t	derivedBnDesc,
		const miopenTensorDescriptor_t	xDesc,
		miopenBatchNormMode_t	bn_mode
	)

Derive tensor for gamma and beta from input tensor descriptor.

This function takes the input tensor descriptor and outputs a derived tensor for the normalization scale (gamma) and shift (beta) tensors.

For an input tensor NCHW and spatial mode, the output derived tensor is 1C11, while for per-activation the derived tensor is 1CHW.

For an input tensor NCDHW and spatial mode, the output derived tensor is 1C111, while for per-activation the derived tensor is 1CDHW.

Parameters

derivedBnDesc	Output derived tensor descriptor (output)
xDesc	Input tensor descriptor (input)
bn_mode	Batch Normalization mode (input)

Returns: miopenStatus_t

Examples: /home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-miopen/checkouts/latest/include/miopen/miopen.h.

Batchnorm

Batchnorm#

Enumerations

Functions

Detailed Description

Enumeration Type Documentation

◆ miopenBatchNormMode_t

Function Documentation

◆ miopenBatchNormalizationBackward()

◆ miopenBatchNormalizationForwardInference()

◆ miopenBatchNormalizationForwardTraining()

◆ miopenDeriveBNTensorDescriptor()