Examples with GridExecutioner org.nd4j.linalg.api.ops.executioner.GridExecutioner used on opensource projects

Example 11 with GridExecutioner

use of org.nd4j.linalg.api.ops.executioner.GridExecutioner in project deeplearning4j by deeplearning4j.

the class CudnnConvolutionHelper method preOutput.

@Override
public INDArray preOutput(INDArray input, INDArray weights, INDArray bias, int[] kernel, int[] strides, int[] pad, AlgoMode mode, ConvolutionMode convolutionMode) {
    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();
    if (Nd4j.getExecutioner() instanceof GridExecutioner)
        ((GridExecutioner) Nd4j.getExecutioner()).flushQueue();
    int[] outSize;
    if (convolutionMode == ConvolutionMode.Same) {
        //Also performs validation
        outSize = ConvolutionUtils.getOutputSize(input, kernel, strides, null, convolutionMode);
        pad = ConvolutionUtils.getSameModeTopLeftPadding(outSize, new int[] { input.size(2), input.size(3) }, kernel, strides);
    } else {
        //Also performs validation
        outSize = ConvolutionUtils.getOutputSize(input, kernel, strides, pad, convolutionMode);
    }
    INDArray z = Nd4j.createUninitialized(new int[] { miniBatch, outDepth, outSize[0], outSize[1] });
    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
    //        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[] algo = new int[1];
    int[] dstStride = z.stride();
    checkCudnn(cudnnSetTensor4dDescriptorEx(cudnnContext.dstTensorDesc, dataType, miniBatch, outDepth, outSize[0], outSize[1], dstStride[0], dstStride[1], dstStride[2], dstStride[3]));
    checkCudnn(cudnnGetConvolutionForwardAlgorithm(cudnnContext, cudnnContext.srcTensorDesc, cudnnContext.filterDesc, cudnnContext.convDesc, cudnnContext.dstTensorDesc, mode == AlgoMode.NO_WORKSPACE ? CUDNN_CONVOLUTION_FWD_NO_WORKSPACE : CUDNN_CONVOLUTION_FWD_PREFER_FASTEST, 0, algo));
    Allocator allocator = AtomicAllocator.getInstance();
    CudaContext context = allocator.getFlowController().prepareAction(z, input, weights, bias);
    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, outDepth, 1, 1));
    checkCudnn(cudnnAddTensor(cudnnContext, alpha, cudnnContext.biasTensorDesc, biasData, alpha, cudnnContext.dstTensorDesc, dstData));
    allocator.registerAction(context, z, input, weights, bias);
    return z;
}

Example 12 with GridExecutioner

use of org.nd4j.linalg.api.ops.executioner.GridExecutioner in project deeplearning4j by deeplearning4j.

the class CudnnBatchNormalizationHelper method backpropGradient.

@Override
public Pair<Gradient, INDArray> backpropGradient(INDArray input, INDArray epsilon, int[] shape, INDArray gamma, INDArray dGammaView, INDArray dBetaView, double eps) {
    if (eps < CUDNN_BN_MIN_EPSILON) {
        throw new IllegalArgumentException("Error: eps < CUDNN_BN_MIN_EPSILON (" + eps + " < " + CUDNN_BN_MIN_EPSILON + ")");
    }
    int miniBatch = input.size(0);
    int depth = input.size(1);
    int inH = input.size(2);
    int inW = input.size(3);
    Gradient retGradient = new DefaultGradient();
    if (!Shape.strideDescendingCAscendingF(epsilon)) {
        // apparently not supported by cuDNN
        epsilon = epsilon.dup();
    }
    int[] srcStride = input.stride();
    int[] deltaStride = epsilon.stride();
    if (Nd4j.getExecutioner() instanceof GridExecutioner)
        ((GridExecutioner) Nd4j.getExecutioner()).flushQueue();
    checkCudnn(cudnnSetTensor4dDescriptorEx(cudnnContext.srcTensorDesc, dataType, miniBatch, depth, inH, inW, srcStride[0], srcStride[1], srcStride[2], srcStride[3]));
    checkCudnn(cudnnSetTensor4dDescriptorEx(cudnnContext.deltaTensorDesc, dataType, miniBatch, depth, inH, inW, deltaStride[0], deltaStride[1], deltaStride[2], deltaStride[3]));
    INDArray nextEpsilon = Nd4j.createUninitialized(new int[] { miniBatch, depth, inH, inW }, 'c');
    int[] dstStride = nextEpsilon.stride();
    checkCudnn(cudnnSetTensor4dDescriptorEx(cudnnContext.dstTensorDesc, dataType, miniBatch, depth, inH, inW, dstStride[0], dstStride[1], dstStride[2], dstStride[3]));
    int[] gammaStride = gamma.stride();
    checkCudnn(cudnnSetTensor4dDescriptor(cudnnContext.gammaBetaTensorDesc, tensorFormat, dataType, shape[0], shape[1], shape.length > 2 ? shape[2] : 1, shape.length > 3 ? shape[3] : 1));
    Allocator allocator = AtomicAllocator.getInstance();
    CudaContext context = allocator.getFlowController().prepareActionAllWrite(input, epsilon, nextEpsilon, gamma, dGammaView, dBetaView);
    Pointer srcData = allocator.getPointer(input, context);
    Pointer epsData = allocator.getPointer(epsilon, context);
    Pointer dstData = allocator.getPointer(nextEpsilon, context);
    Pointer gammaData = allocator.getPointer(gamma, context);
    Pointer dGammaData = allocator.getPointer(dGammaView, context);
    Pointer dBetaData = allocator.getPointer(dBetaView, context);
    checkCudnn(cudnnSetStream(cudnnContext, new CUstream_st(context.getOldStream())));
    checkCudnn(cudnnBatchNormalizationBackward(cudnnContext, batchNormMode, alpha, beta, alpha, alpha, cudnnContext.srcTensorDesc, srcData, cudnnContext.deltaTensorDesc, epsData, cudnnContext.dstTensorDesc, dstData, cudnnContext.gammaBetaTensorDesc, gammaData, dGammaData, dBetaData, eps, meanCache, varCache));
    allocator.getFlowController().registerActionAllWrite(context, input, epsilon, nextEpsilon, gamma, dGammaView, dBetaView);
    retGradient.setGradientFor(BatchNormalizationParamInitializer.GAMMA, dGammaView);
    retGradient.setGradientFor(BatchNormalizationParamInitializer.BETA, dBetaView);
    return new Pair<>(retGradient, nextEpsilon);
}

Example 13 with GridExecutioner

use of org.nd4j.linalg.api.ops.executioner.GridExecutioner in project deeplearning4j by deeplearning4j.

the class CudnnConvolutionHelper method backpropGradient.

@Override
public Pair<Gradient, INDArray> backpropGradient(INDArray input, INDArray weights, INDArray delta, int[] kernel, int[] strides, int[] pad, INDArray biasGradView, INDArray weightGradView, IActivation afn, AlgoMode mode, ConvolutionMode convolutionMode) {
    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[] outSize;
    if (convolutionMode == ConvolutionMode.Same) {
        //Also performs validation
        outSize = ConvolutionUtils.getOutputSize(input, kernel, strides, null, convolutionMode);
        pad = ConvolutionUtils.getSameModeTopLeftPadding(outSize, new int[] { input.size(2), input.size(3) }, kernel, strides);
    } else {
        //Also performs validation
        outSize = ConvolutionUtils.getOutputSize(input, kernel, strides, pad, convolutionMode);
    }
    int outH = outSize[0];
    int outW = outSize[1];
    if (!Shape.strideDescendingCAscendingF(delta)) {
        // apparently not supported by cuDNN
        delta = delta.dup();
    }
    int[] srcStride = input.stride();
    int[] deltaStride = delta.stride();
    int[] algo1 = new int[1];
    int[] algo2 = new int[1];
    if (Nd4j.getExecutioner() instanceof GridExecutioner)
        ((GridExecutioner) Nd4j.getExecutioner()).flushQueue();
    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, mode == AlgoMode.NO_WORKSPACE ? CUDNN_CONVOLUTION_BWD_FILTER_NO_WORKSPACE : CUDNN_CONVOLUTION_BWD_FILTER_PREFER_FASTEST, 0, algo1));
    checkCudnn(cudnnGetConvolutionBackwardDataAlgorithm(cudnnContext, cudnnContext.filterDesc, cudnnContext.deltaTensorDesc, cudnnContext.convDesc, cudnnContext.srcTensorDesc, mode == AlgoMode.NO_WORKSPACE ? CUDNN_CONVOLUTION_BWD_DATA_NO_WORKSPACE : CUDNN_CONVOLUTION_BWD_DATA_PREFER_FASTEST, 0, algo2));
    INDArray epsNext = Nd4j.create(new int[] { miniBatch, inDepth, inH, inW }, 'c');
    int[] dstStride = epsNext.stride();
    Allocator allocator = AtomicAllocator.getInstance();
    CudaContext context = allocator.getFlowController().prepareActionAllWrite(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, algo1[0], sizeInBytes));
    long sizeInBytes1 = sizeInBytes.get(0);
    checkCudnn(cudnnGetConvolutionBackwardDataWorkspaceSize(cudnnContext, cudnnContext.filterDesc, cudnnContext.deltaTensorDesc, cudnnContext.convDesc, cudnnContext.dstTensorDesc, algo2[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, algo1[0], workSpace, workSpace.capacity(), beta, cudnnContext.filterDesc, filterGradData));
    checkCudnn(cudnnConvolutionBackwardData(cudnnContext, alpha, cudnnContext.filterDesc, filterData, cudnnContext.deltaTensorDesc, deltaData, cudnnContext.convDesc, algo2[0], workSpace, workSpace.capacity(), beta, cudnnContext.dstTensorDesc, dstData));
    allocator.getFlowController().registerActionAllWrite(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);
}

Example 14 with GridExecutioner

use of org.nd4j.linalg.api.ops.executioner.GridExecutioner in project deeplearning4j by deeplearning4j.

the class CudnnConvolutionHelper method activate.

@Override
public INDArray activate(INDArray z, IActivation afn) {
    if (Nd4j.getExecutioner() instanceof GridExecutioner)
        ((GridExecutioner) Nd4j.getExecutioner()).flushQueue();
    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.toString()) {
        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;
}

Example 15 with GridExecutioner

use of org.nd4j.linalg.api.ops.executioner.GridExecutioner in project deeplearning4j by deeplearning4j.

the class CudnnSubsamplingHelper method backpropGradient.

@Override
public Pair<Gradient, INDArray> backpropGradient(INDArray input, INDArray epsilon, int[] kernel, int[] strides, int[] pad, PoolingType poolingType, ConvolutionMode convolutionMode) {
    int miniBatch = input.size(0);
    int depth = input.size(1);
    int inH = input.size(2);
    int inW = input.size(3);
    int[] outSize;
    if (convolutionMode == ConvolutionMode.Same) {
        //Also performs validation
        outSize = ConvolutionUtils.getOutputSize(input, kernel, strides, null, convolutionMode);
        pad = ConvolutionUtils.getSameModeTopLeftPadding(outSize, new int[] { input.size(2), input.size(3) }, kernel, strides);
    } else {
        //Also performs validation
        outSize = ConvolutionUtils.getOutputSize(input, kernel, strides, pad, convolutionMode);
    }
    int outH = outSize[0];
    int outW = outSize[1];
    //subsampling doesn't have weights and thus gradients are not calculated for this layer
    //only scale and reshape epsilon
    Gradient retGradient = new DefaultGradient();
    //Epsilons in shape: [miniBatch, depth, outH, outW]
    //Epsilons out shape: [miniBatch, depth, inH, inW]
    int poolingMode;
    switch(poolingType) {
        case AVG:
            poolingMode = CUDNN_POOLING_AVERAGE_COUNT_INCLUDE_PADDING;
            break;
        case MAX:
            poolingMode = CUDNN_POOLING_MAX;
            break;
        case NONE:
            return new Pair<>(retGradient, epsilon);
        default:
            return null;
    }
    if (!Shape.strideDescendingCAscendingF(epsilon)) {
        // apparently not supported by cuDNN
        epsilon = epsilon.dup();
    }
    int[] srcStride = input.stride();
    int[] deltaStride = epsilon.stride();
    if (Nd4j.getExecutioner() instanceof GridExecutioner)
        ((GridExecutioner) Nd4j.getExecutioner()).flushQueue();
    checkCudnn(cudnnSetTensor4dDescriptorEx(cudnnContext.srcTensorDesc, dataType, miniBatch, depth, inH, inW, srcStride[0], srcStride[1], srcStride[2], srcStride[3]));
    checkCudnn(cudnnSetTensor4dDescriptorEx(cudnnContext.deltaTensorDesc, dataType, miniBatch, depth, outH, outW, deltaStride[0], deltaStride[1], deltaStride[2], deltaStride[3]));
    checkCudnn(cudnnSetPooling2dDescriptor(cudnnContext.poolingDesc, poolingMode, CUDNN_PROPAGATE_NAN, kernel[0], kernel[1], pad[0], pad[1], strides[0], strides[1]));
    INDArray outEpsilon = Nd4j.create(new int[] { miniBatch, depth, inH, inW }, 'c');
    int[] dstStride = outEpsilon.stride();
    checkCudnn(cudnnSetTensor4dDescriptorEx(cudnnContext.dstTensorDesc, dataType, miniBatch, depth, inH, inW, dstStride[0], dstStride[1], dstStride[2], dstStride[3]));
    Allocator allocator = AtomicAllocator.getInstance();
    CudaContext context = allocator.getFlowController().prepareAction(input, epsilon, reduced, outEpsilon);
    Pointer srcData = allocator.getPointer(input, context);
    Pointer epsData = allocator.getPointer(epsilon, context);
    Pointer zData = allocator.getPointer(reduced, context);
    Pointer dstData = allocator.getPointer(outEpsilon, context);
    checkCudnn(cudnnSetStream(cudnnContext, new CUstream_st(context.getOldStream())));
    checkCudnn(cudnnPoolingBackward(cudnnContext, cudnnContext.poolingDesc, alpha, cudnnContext.deltaTensorDesc, zData, cudnnContext.deltaTensorDesc, epsData, cudnnContext.srcTensorDesc, srcData, beta, cudnnContext.dstTensorDesc, dstData));
    allocator.registerAction(context, input, epsilon, reduced, outEpsilon);
    return new Pair<>(retGradient, outEpsilon);
}