@Override
public INDArray preOutput(
INDArray input, INDArray weights, INDArray bias, int[] kernel, int[] strides, int[] pad) {
int miniBatch = input.size(0);
int inH = input.size(2);
int inW = input.size(3);
int outDepth = weights.size(0);
int inDepth = weights.size(1);
int kH = weights.size(2);
int kW = weights.size(3);
int[] srcStride = input.stride();
checkCudnn(
cudnnSetTensor4dDescriptorEx(
cudnnContext.srcTensorDesc,
dataType,
miniBatch,
inDepth,
inH,
inW,
srcStride[0],
srcStride[1],
srcStride[2],
srcStride[3]));
checkCudnn(
cudnnSetFilter4dDescriptor(
cudnnContext.filterDesc, dataType, tensorFormat, outDepth, inDepth, kH, kW));
checkCudnn(
cudnnSetConvolution2dDescriptor(
cudnnContext.convDesc,
pad[0],
pad[1],
strides[0],
strides[1],
1,
1,
CUDNN_CROSS_CORRELATION));
// find dimension of convolution output
int[] algo = new int[1], n = new int[1], c = new int[1], h = new int[1], w = new int[1];
checkCudnn(
cudnnGetConvolution2dForwardOutputDim(
cudnnContext.convDesc,
cudnnContext.srcTensorDesc,
cudnnContext.filterDesc,
n,
c,
h,
w));
INDArray z = Nd4j.createUninitialized(new int[] {n[0], c[0], h[0], w[0]}, 'c');
int[] dstStride = z.stride();
checkCudnn(
cudnnSetTensor4dDescriptorEx(
cudnnContext.dstTensorDesc,
dataType,
n[0],
c[0],
h[0],
w[0],
dstStride[0],
dstStride[1],
dstStride[2],
dstStride[3]));
checkCudnn(
cudnnGetConvolutionForwardAlgorithm(
cudnnContext,
cudnnContext.srcTensorDesc,
cudnnContext.filterDesc,
cudnnContext.convDesc,
cudnnContext.dstTensorDesc,
CUDNN_CONVOLUTION_FWD_PREFER_FASTEST,
0,
algo));
Allocator allocator = AtomicAllocator.getInstance();
CudaContext context = allocator.getFlowController().prepareAction(input, weights, bias, z);
Pointer srcData = allocator.getPointer(input, context);
Pointer filterData = allocator.getPointer(weights, context);
Pointer biasData = allocator.getPointer(bias, context);
Pointer dstData = allocator.getPointer(z, context);
checkCudnn(cudnnSetStream(cudnnContext, new CUstream_st(context.getOldStream())));
checkCudnn(
cudnnGetConvolutionForwardWorkspaceSize(
cudnnContext,
cudnnContext.srcTensorDesc,
cudnnContext.filterDesc,
cudnnContext.convDesc,
cudnnContext.dstTensorDesc,
algo[0],
sizeInBytes));
if (sizeInBytes.get(0) > workSpace.capacity()) {
workSpace.deallocate();
workSpace = new WorkSpace(sizeInBytes.get(0));
}
checkCudnn(
cudnnConvolutionForward(
cudnnContext,
alpha,
cudnnContext.srcTensorDesc,
srcData,
cudnnContext.filterDesc,
filterData,
cudnnContext.convDesc,
algo[0],
workSpace,
workSpace.capacity(),
beta,
cudnnContext.dstTensorDesc,
dstData));
checkCudnn(
cudnnSetTensor4dDescriptor(
cudnnContext.biasTensorDesc, tensorFormat, dataType, 1, c[0], 1, 1));
checkCudnn(
cudnnAddTensor(
cudnnContext,
alpha,
cudnnContext.biasTensorDesc,
biasData,
alpha,
cudnnContext.dstTensorDesc,
dstData));
allocator.registerAction(context, input, weights, bias, z);
return z;
}
@Override
public INDArray activate(INDArray z, String afn) {
INDArray activation = z;
Allocator allocator = AtomicAllocator.getInstance();
CudaContext context = allocator.getFlowController().prepareAction(z);
Pointer dstData = allocator.getPointer(z, context);
checkCudnn(cudnnSetStream(cudnnContext, new CUstream_st(context.getOldStream())));
switch (afn) {
case "identity":
break;
case "sigmoid":
checkCudnn(
cudnnSetActivationDescriptor(
cudnnContext.activationDesc, CUDNN_ACTIVATION_SIGMOID, CUDNN_PROPAGATE_NAN, 0));
checkCudnn(
cudnnActivationForward(
cudnnContext,
cudnnContext.activationDesc,
alpha,
cudnnContext.dstTensorDesc,
dstData,
beta,
cudnnContext.dstTensorDesc,
dstData));
break;
case "relu":
checkCudnn(
cudnnSetActivationDescriptor(
cudnnContext.activationDesc, CUDNN_ACTIVATION_RELU, CUDNN_PROPAGATE_NAN, 0));
checkCudnn(
cudnnActivationForward(
cudnnContext,
cudnnContext.activationDesc,
alpha,
cudnnContext.dstTensorDesc,
dstData,
beta,
cudnnContext.dstTensorDesc,
dstData));
break;
case "tanh":
checkCudnn(
cudnnSetActivationDescriptor(
cudnnContext.activationDesc, CUDNN_ACTIVATION_TANH, CUDNN_PROPAGATE_NAN, 0));
checkCudnn(
cudnnActivationForward(
cudnnContext,
cudnnContext.activationDesc,
alpha,
cudnnContext.dstTensorDesc,
dstData,
beta,
cudnnContext.dstTensorDesc,
dstData));
break;
case "softmax":
checkCudnn(
cudnnSoftmaxForward(
cudnnContext,
CUDNN_SOFTMAX_ACCURATE,
CUDNN_SOFTMAX_MODE_CHANNEL,
alpha,
cudnnContext.dstTensorDesc,
dstData,
beta,
cudnnContext.dstTensorDesc,
dstData));
break;
case "logsoftmax":
checkCudnn(
cudnnSoftmaxForward(
cudnnContext,
CUDNN_SOFTMAX_LOG,
CUDNN_SOFTMAX_MODE_CHANNEL,
alpha,
cudnnContext.dstTensorDesc,
dstData,
beta,
cudnnContext.dstTensorDesc,
dstData));
break;
default:
activation = null;
}
allocator.registerAction(context, z);
return activation;
}
@Override
public Pair<Gradient, INDArray> backpropGradient(
INDArray input,
INDArray weights,
INDArray delta,
int[] kernel,
int[] strides,
int[] pad,
INDArray biasGradView,
INDArray weightGradView,
String afn) {
int miniBatch = input.size(0);
int inH = input.size(2);
int inW = input.size(3);
int outDepth = weights.size(0);
int inDepth = weights.size(1);
int kH = weights.size(2);
int kW = weights.size(3);
int outH = Convolution.outSize(inH, kernel[0], strides[0], pad[0], false);
int outW = Convolution.outSize(inW, kernel[1], strides[1], pad[1], false);
if (!Shape.strideDescendingCAscendingF(delta)) {
// apparently not supported by cuDNN
delta = delta.dup();
}
int[] srcStride = input.stride();
int[] deltaStride = delta.stride();
int[] algo = new int[1];
checkCudnn(
cudnnSetTensor4dDescriptorEx(
cudnnContext.srcTensorDesc,
dataType,
miniBatch,
inDepth,
inH,
inW,
srcStride[0],
srcStride[1],
srcStride[2],
srcStride[3]));
checkCudnn(
cudnnSetTensor4dDescriptorEx(
cudnnContext.deltaTensorDesc,
dataType,
miniBatch,
outDepth,
outH,
outW,
deltaStride[0],
deltaStride[1],
deltaStride[2],
deltaStride[3]));
checkCudnn(
cudnnSetConvolution2dDescriptor(
cudnnContext.convDesc,
pad[0],
pad[1],
strides[0],
strides[1],
1,
1,
CUDNN_CROSS_CORRELATION));
checkCudnn(
cudnnSetFilter4dDescriptor(
cudnnContext.filterDesc, dataType, tensorFormat, outDepth, inDepth, kH, kW));
checkCudnn(
cudnnGetConvolutionBackwardFilterAlgorithm(
cudnnContext,
cudnnContext.srcTensorDesc,
cudnnContext.deltaTensorDesc,
cudnnContext.convDesc,
cudnnContext.filterDesc,
CUDNN_CONVOLUTION_BWD_FILTER_PREFER_FASTEST,
0,
algo));
INDArray epsNext = Nd4j.create(new int[] {miniBatch, inDepth, inH, inW}, 'c');
int[] dstStride = epsNext.stride();
Allocator allocator = AtomicAllocator.getInstance();
CudaContext context =
allocator
.getFlowController()
.prepareAction(input, weights, weightGradView, biasGradView, delta, epsNext);
Pointer srcData = allocator.getPointer(input, context);
Pointer filterData = allocator.getPointer(weights, context);
Pointer filterGradData = allocator.getPointer(weightGradView, context);
Pointer biasGradData = allocator.getPointer(biasGradView, context);
Pointer deltaData = allocator.getPointer(delta, context);
Pointer dstData = allocator.getPointer(epsNext, context);
checkCudnn(cudnnSetStream(cudnnContext, new CUstream_st(context.getOldStream())));
checkCudnn(
cudnnSetTensor4dDescriptorEx(
cudnnContext.dstTensorDesc,
dataType,
miniBatch,
inDepth,
inH,
inW,
dstStride[0],
dstStride[1],
dstStride[2],
dstStride[3]));
checkCudnn(
cudnnGetConvolutionBackwardFilterWorkspaceSize(
cudnnContext,
cudnnContext.srcTensorDesc,
cudnnContext.deltaTensorDesc,
cudnnContext.convDesc,
cudnnContext.filterDesc,
algo[0],
sizeInBytes));
long sizeInBytes1 = sizeInBytes.get(0);
checkCudnn(
cudnnGetConvolutionBackwardDataWorkspaceSize(
cudnnContext,
cudnnContext.filterDesc,
cudnnContext.deltaTensorDesc,
cudnnContext.convDesc,
cudnnContext.dstTensorDesc,
algo[0],
sizeInBytes));
long sizeInBytes2 = sizeInBytes.get(0);
if (sizeInBytes1 > workSpace.capacity() || sizeInBytes2 > workSpace.capacity()) {
workSpace.deallocate();
workSpace = new WorkSpace(Math.max(sizeInBytes1, sizeInBytes2));
}
checkCudnn(
cudnnSetTensor4dDescriptor(
cudnnContext.biasTensorDesc, tensorFormat, dataType, 1, outDepth, 1, 1));
checkCudnn(
cudnnConvolutionBackwardBias(
cudnnContext,
alpha,
cudnnContext.deltaTensorDesc,
deltaData,
beta,
cudnnContext.biasTensorDesc,
biasGradData));
checkCudnn(
cudnnConvolutionBackwardFilter(
cudnnContext,
alpha,
cudnnContext.srcTensorDesc,
srcData,
cudnnContext.deltaTensorDesc,
deltaData,
cudnnContext.convDesc,
algo[0],
workSpace,
workSpace.capacity(),
beta,
cudnnContext.filterDesc,
filterGradData));
checkCudnn(
cudnnConvolutionBackwardData(
cudnnContext,
alpha,
cudnnContext.filterDesc,
filterData,
cudnnContext.deltaTensorDesc,
deltaData,
cudnnContext.convDesc,
algo[0],
workSpace,
workSpace.capacity(),
beta,
cudnnContext.dstTensorDesc,
dstData));
allocator.registerAction(context, input, weights, weightGradView, biasGradView, delta, epsNext);
Gradient retGradient = new DefaultGradient();
retGradient.setGradientFor(ConvolutionParamInitializer.BIAS_KEY, biasGradView);
retGradient.setGradientFor(ConvolutionParamInitializer.WEIGHT_KEY, weightGradView, 'c');
return new Pair<>(retGradient, epsNext);
}