public static extern cudnnStatus cudnnDeriveBNTensorDescriptor(
cudnnTensorDescriptor derivedBnDesc,
cudnnTensorDescriptor xDesc,
cudnnBatchNormMode mode );
public static extern cudnnStatus cudnnBatchNormalizationForwardInference(
cudnnHandle handle,
cudnnBatchNormMode mode,
ref double alpha, // alpha[0] = result blend factor
ref double beta, // beta[0] = dest layer blend factor
cudnnTensorDescriptor xDesc,
CUdeviceptr x, // NxCxHxW
cudnnTensorDescriptor yDesc,
CUdeviceptr y, // NxCxHxW
cudnnTensorDescriptor bnScaleBiasMeanVarDesc,
CUdeviceptr bnScale,
CUdeviceptr bnBias,
CUdeviceptr estimatedMean,
CUdeviceptr estimatedInvVariance,
double epsilon );
public static extern cudnnStatus cudnnBatchNormalizationForwardTraining(
cudnnHandle handle,
cudnnBatchNormMode mode,
ref double alpha, // alpha[0] = result blend factor
ref double beta, // beta[0] = dest layer blend factor
cudnnTensorDescriptor xDesc,
CUdeviceptr x, // NxCxHxW
cudnnTensorDescriptor yDesc,
CUdeviceptr y, // NxCxHxW
/* Shared desc for the next 6 tensors in the argument list.
Data type to be set as follows:
type = (typeOf(x) == double) ? double : float
Dimensions for this descriptor depend on normalization mode
- Spatial Normalization : tensors are expected to have dims 1xCx1x1
(normalization is performed across NxHxW)
- Per-Activation Normalization : tensors are expected to have dims of 1xCxHxW
(normalization is performed across N) */
cudnnTensorDescriptor bnScaleBiasMeanVarDesc,
// 'Gamma' and 'Beta' respectively in Ioffe and Szegedy's paper's notation
CUdeviceptr bnScale,
CUdeviceptr bnBias,
/* MUST use factor=1 in the very first call of a complete training cycle.
Use a factor=1/(1+n) at N-th call to the function to get
Cumulative Moving Average (CMA) behavior
CMA[n] = (x[1]+...+x[n])/n
Since CMA[n+1] = (n*CMA[n]+x[n+1])/(n+1) =
((n+1)*CMA[n]-CMA[n])/(n+1) + x[n+1]/(n+1) =
CMA[n]*(1-1/(n+1)) + x[n+1]*1/(n+1) */
double exponentialAverageFactor,
/* Used in Training phase only.
runningMean = newMean*factor + runningMean*(1-factor) */
CUdeviceptr resultRunningMean,
/* Output in training mode, input in inference. Is the moving average
of 1 / sqrt( epsilon + variance[x] ) */
CUdeviceptr resultRunningInvVariance,
/* Has to be >= CUDNN_BN_MIN_EPSILON. Should be the same in forward and backward functions. */
double epsilon,
/* Optionally save intermediate results from the forward pass here
- can be reused to speed up backward pass. NULL if unused */
CUdeviceptr resultSaveMean,
CUdeviceptr resultSaveInvVariance);
public static extern cudnnStatus cudnnBatchNormalizationBackward(
cudnnHandle handle,
cudnnBatchNormMode mode,
ref double alphaDataDiff,
ref double betaDataDiff,
ref double alphaParamDiff,
ref double betaParamDiff,
cudnnTensorDescriptor xDesc, // same desc for x, dx, dy
CUdeviceptr x,
cudnnTensorDescriptor dyDesc,
CUdeviceptr dy,
cudnnTensorDescriptor dxDesc,
CUdeviceptr dx,
/* Shared tensor desc for the 4 tensors below */
cudnnTensorDescriptor dBnScaleBiasDesc,
CUdeviceptr bnScale, // bnBias doesn't affect backpropagation
/* scale and bias diff are not backpropagated below this layer */
CUdeviceptr dBnScaleResult,
CUdeviceptr dBnBiasResult,
/* Same epsilon as forward pass */
double epsilon,
/* Optionally cached intermediate results from
forward pass */
CUdeviceptr savedMean,
CUdeviceptr savedInvVariance);
