Examples with CudaIntDataBuffer org.nd4j.linalg.jcublas.buffer.CudaIntDataBuffer used on opensource projects

Example 1 with CudaIntDataBuffer

use of org.nd4j.linalg.jcublas.buffer.CudaIntDataBuffer in project nd4j by deeplearning4j.

the class BasicTADManager method getTADOnlyShapeInfo.

@Override
public Pair<DataBuffer, DataBuffer> getTADOnlyShapeInfo(INDArray array, int[] dimension) {
    if (dimension != null && dimension.length > 1)
        Arrays.sort(dimension);
    if (dimension == null)
        dimension = new int[] { Integer.MAX_VALUE };
    boolean isScalar = dimension == null || (dimension.length == 1 && dimension[0] == Integer.MAX_VALUE);
    // FIXME: this is fast triage, remove it later
    // dimensionLength <= 1 ? 2 : dimensionLength;
    int targetRank = isScalar ? 2 : array.rank();
    long offsetLength = 0;
    long tadLength = 1;
    if (!isScalar)
        for (int i = 0; i < dimension.length; i++) {
            tadLength *= array.shape()[dimension[i]];
        }
    if (!isScalar)
        offsetLength = array.lengthLong() / tadLength;
    else
        offsetLength = 1;
    // logger.info("Original shape info before TAD: {}", array.shapeInfoDataBuffer());
    // logger.info("dimension: {}, tadLength: {}, offsetLength for TAD: {}", Arrays.toString(dimension),tadLength, offsetLength);
    DataBuffer outputBuffer = new CudaIntDataBuffer(targetRank * 2 + 4);
    DataBuffer offsetsBuffer = new CudaLongDataBuffer(offsetLength);
    AtomicAllocator.getInstance().getAllocationPoint(outputBuffer).tickHostWrite();
    AtomicAllocator.getInstance().getAllocationPoint(offsetsBuffer).tickHostWrite();
    DataBuffer dimensionBuffer = AtomicAllocator.getInstance().getConstantBuffer(dimension);
    Pointer dimensionPointer = AtomicAllocator.getInstance().getHostPointer(dimensionBuffer);
    Pointer xShapeInfo = AddressRetriever.retrieveHostPointer(array.shapeInfoDataBuffer());
    Pointer targetPointer = AddressRetriever.retrieveHostPointer(outputBuffer);
    Pointer offsetsPointer = AddressRetriever.retrieveHostPointer(offsetsBuffer);
    if (!isScalar)
        nativeOps.tadOnlyShapeInfo((IntPointer) xShapeInfo, (IntPointer) dimensionPointer, dimension.length, (IntPointer) targetPointer, new LongPointerWrapper(offsetsPointer));
    else {
        outputBuffer.put(0, 2);
        outputBuffer.put(1, 1);
        outputBuffer.put(2, 1);
        outputBuffer.put(3, 1);
        outputBuffer.put(4, 1);
        outputBuffer.put(5, 0);
        outputBuffer.put(6, 0);
        outputBuffer.put(7, 99);
    }
    AtomicAllocator.getInstance().getAllocationPoint(outputBuffer).tickHostWrite();
    AtomicAllocator.getInstance().getAllocationPoint(offsetsBuffer).tickHostWrite();
    return new Pair<>(outputBuffer, offsetsBuffer);
}

Example 2 with CudaIntDataBuffer

use of org.nd4j.linalg.jcublas.buffer.CudaIntDataBuffer in project nd4j by deeplearning4j.

the class JCublasNDArrayFactory method pullRows.

/**
 * This method produces concatenated array, that consist from tensors, fetched from source array, against some dimension and specified indexes
 *
 * @param source          source tensor
 * @param sourceDimension dimension of source tensor
 * @param indexes         indexes from source array
 * @return
 */
@Override
public INDArray pullRows(INDArray source, int sourceDimension, int[] indexes, char order) {
    if (Nd4j.getExecutioner() instanceof GridExecutioner)
        ((GridExecutioner) Nd4j.getExecutioner()).flushQueue();
    if (indexes == null || indexes.length < 1)
        throw new IllegalStateException("Indexes can't be null or zero-length");
    int[] shape = null;
    if (sourceDimension == 1)
        shape = new int[] { indexes.length, source.shape()[sourceDimension] };
    else if (sourceDimension == 0)
        shape = new int[] { source.shape()[sourceDimension], indexes.length };
    else
        throw new UnsupportedOperationException("2D input is expected");
    INDArray ret = Nd4j.createUninitialized(shape, order);
    AtomicAllocator allocator = AtomicAllocator.getInstance();
    CudaContext context = allocator.getFlowController().prepareAction(ret, source);
    Pointer x = AtomicAllocator.getInstance().getPointer(source, context);
    Pointer xShape = AtomicAllocator.getInstance().getPointer(source.shapeInfoDataBuffer(), context);
    Pointer z = AtomicAllocator.getInstance().getPointer(ret, context);
    Pointer zShape = AtomicAllocator.getInstance().getPointer(ret.shapeInfoDataBuffer(), context);
    PointerPointer extras = new PointerPointer(AddressRetriever.retrieveHostPointer(ret.shapeInfoDataBuffer()), context.getOldStream(), allocator.getDeviceIdPointer());
    CudaIntDataBuffer tempIndexes = new CudaIntDataBuffer(indexes.length);
    AtomicAllocator.getInstance().memcpyBlocking(tempIndexes, new IntPointer(indexes), indexes.length * 4, 0);
    Pointer pIndex = AtomicAllocator.getInstance().getPointer(tempIndexes, context);
    TADManager tadManager = Nd4j.getExecutioner().getTADManager();
    Pair<DataBuffer, DataBuffer> tadBuffers = tadManager.getTADOnlyShapeInfo(source, new int[] { sourceDimension });
    Pair<DataBuffer, DataBuffer> zTadBuffers = tadManager.getTADOnlyShapeInfo(ret, new int[] { sourceDimension });
    Pointer tadShapeInfo = AtomicAllocator.getInstance().getPointer(tadBuffers.getFirst(), context);
    Pointer zTadShapeInfo = AtomicAllocator.getInstance().getPointer(zTadBuffers.getFirst(), context);
    DataBuffer offsets = tadBuffers.getSecond();
    Pointer tadOffsets = AtomicAllocator.getInstance().getPointer(offsets, context);
    Pointer zTadOffsets = AtomicAllocator.getInstance().getPointer(zTadBuffers.getSecond(), context);
    if (ret.data().dataType() == DataBuffer.Type.DOUBLE) {
        nativeOps.pullRowsDouble(extras, (DoublePointer) x, (IntPointer) xShape, (DoublePointer) z, (IntPointer) zShape, indexes.length, (IntPointer) pIndex, (IntPointer) tadShapeInfo, new LongPointerWrapper(tadOffsets), (IntPointer) zTadShapeInfo, new LongPointerWrapper(zTadOffsets));
    } else if (ret.data().dataType() == DataBuffer.Type.FLOAT) {
        nativeOps.pullRowsFloat(extras, (FloatPointer) x, (IntPointer) xShape, (FloatPointer) z, (IntPointer) zShape, indexes.length, (IntPointer) pIndex, (IntPointer) tadShapeInfo, new LongPointerWrapper(tadOffsets), (IntPointer) zTadShapeInfo, new LongPointerWrapper(zTadOffsets));
    } else {
        nativeOps.pullRowsHalf(extras, (ShortPointer) x, (IntPointer) xShape, (ShortPointer) z, (IntPointer) zShape, indexes.length, (IntPointer) pIndex, (IntPointer) tadShapeInfo, new LongPointerWrapper(tadOffsets), (IntPointer) zTadShapeInfo, new LongPointerWrapper(zTadOffsets));
    }
    allocator.registerAction(context, ret, source);
    return ret;
}

Example 3 with CudaIntDataBuffer

use of org.nd4j.linalg.jcublas.buffer.CudaIntDataBuffer in project nd4j by deeplearning4j.

the class JCublasNDArrayFactory method shuffle.

/**
 * Symmetric in place shuffle of an ndarray
 * along a specified set of dimensions. Each array in list should have it's own dimension at the same index of dimensions array
 *
 * @param arrays      the ndarrays to shuffle
 * @param dimensions the dimensions to do the shuffle
 * @return
 */
@Override
public void shuffle(List<INDArray> arrays, Random rnd, List<int[]> dimensions) {
    // no dimension - no shuffle
    if (dimensions == null || dimensions.size() == 0)
        throw new RuntimeException("Dimension can't be null or 0-length");
    if (arrays == null || arrays.size() == 0)
        throw new RuntimeException("No input arrays provided");
    if (dimensions.size() > 1 && arrays.size() != dimensions.size())
        throw new IllegalStateException("Number of dimensions do not match number of arrays to shuffle");
    Nd4j.getExecutioner().push();
    // first we build TAD for input array and dimensions
    AtomicAllocator allocator = AtomicAllocator.getInstance();
    CudaContext context = null;
    for (int x = 0; x < arrays.size(); x++) {
        context = allocator.getFlowController().prepareAction(arrays.get(x));
    }
    int tadLength = 1;
    for (int i = 0; i < dimensions.get(0).length; i++) {
        tadLength *= arrays.get(0).shape()[dimensions.get(0)[i]];
    }
    int numTads = arrays.get(0).length() / tadLength;
    int[] map = ArrayUtil.buildInterleavedVector(rnd, numTads);
    CudaIntDataBuffer shuffle = new CudaIntDataBuffer(map);
    Pointer shuffleMap = allocator.getPointer(shuffle, context);
    PointerPointer extras = new // not used
    PointerPointer(// not used
    null, context.getOldStream(), allocator.getDeviceIdPointer());
    long[] xPointers = new long[arrays.size()];
    long[] xShapes = new long[arrays.size()];
    long[] tadShapes = new long[arrays.size()];
    long[] tadOffsets = new long[arrays.size()];
    for (int i = 0; i < arrays.size(); i++) {
        INDArray array = arrays.get(i);
        Pointer x = AtomicAllocator.getInstance().getPointer(array, context);
        Pointer xShapeInfo = AtomicAllocator.getInstance().getPointer(array.shapeInfoDataBuffer(), context);
        TADManager tadManager = Nd4j.getExecutioner().getTADManager();
        int[] dimension = dimensions.size() > 1 ? dimensions.get(i) : dimensions.get(0);
        Pair<DataBuffer, DataBuffer> tadBuffers = tadManager.getTADOnlyShapeInfo(array, dimension);
        // log.info("Original shape: {}; dimension: {}; TAD shape: {}", array.shapeInfoDataBuffer().asInt(), dimension, tadBuffers.getFirst().asInt());
        Pointer tadShapeInfo = AtomicAllocator.getInstance().getPointer(tadBuffers.getFirst(), context);
        DataBuffer offsets = tadBuffers.getSecond();
        if (offsets.length() != numTads)
            throw new ND4JIllegalStateException("Can't symmetrically shuffle arrays with non-equal number of TADs");
        Pointer tadOffset = AtomicAllocator.getInstance().getPointer(offsets, context);
        xPointers[i] = x.address();
        xShapes[i] = xShapeInfo.address();
        tadShapes[i] = tadShapeInfo.address();
        tadOffsets[i] = tadOffset.address();
    }
    CudaDoubleDataBuffer tempX = new CudaDoubleDataBuffer(arrays.size());
    CudaDoubleDataBuffer tempShapes = new CudaDoubleDataBuffer(arrays.size());
    CudaDoubleDataBuffer tempTAD = new CudaDoubleDataBuffer(arrays.size());
    CudaDoubleDataBuffer tempOffsets = new CudaDoubleDataBuffer(arrays.size());
    AtomicAllocator.getInstance().memcpyBlocking(tempX, new LongPointer(xPointers), xPointers.length * 8, 0);
    AtomicAllocator.getInstance().memcpyBlocking(tempShapes, new LongPointer(xShapes), xPointers.length * 8, 0);
    AtomicAllocator.getInstance().memcpyBlocking(tempTAD, new LongPointer(tadShapes), xPointers.length * 8, 0);
    AtomicAllocator.getInstance().memcpyBlocking(tempOffsets, new LongPointer(tadOffsets), xPointers.length * 8, 0);
    if (Nd4j.dataType() == DataBuffer.Type.DOUBLE) {
        nativeOps.shuffleDouble(extras, new PointerPointer(allocator.getPointer(tempX, context)), new PointerPointer(allocator.getPointer(tempShapes, context)), new PointerPointer(allocator.getPointer(tempX, context)), new PointerPointer(allocator.getPointer(tempShapes, context)), arrays.size(), (IntPointer) shuffleMap, new PointerPointer(allocator.getPointer(tempTAD, context)), new PointerPointer(allocator.getPointer(tempOffsets, context)));
    } else if (Nd4j.dataType() == DataBuffer.Type.FLOAT) {
        nativeOps.shuffleFloat(extras, new PointerPointer(allocator.getPointer(tempX, context)), new PointerPointer(allocator.getPointer(tempShapes, context)), new PointerPointer(allocator.getPointer(tempX, context)), new PointerPointer(allocator.getPointer(tempShapes, context)), arrays.size(), (IntPointer) shuffleMap, new PointerPointer(allocator.getPointer(tempTAD, context)), new PointerPointer(allocator.getPointer(tempOffsets, context)));
    } else {
        // HALFs
        nativeOps.shuffleHalf(extras, new PointerPointer(allocator.getPointer(tempX, context)), new PointerPointer(allocator.getPointer(tempShapes, context)), new PointerPointer(allocator.getPointer(tempX, context)), new PointerPointer(allocator.getPointer(tempShapes, context)), arrays.size(), (IntPointer) shuffleMap, new PointerPointer(allocator.getPointer(tempTAD, context)), new PointerPointer(allocator.getPointer(tempOffsets, context)));
    }
    for (int f = 0; f < arrays.size(); f++) {
        allocator.getFlowController().registerAction(context, arrays.get(f));
    }
    // just to keep reference
    shuffle.address();
    tempX.dataType();
    tempShapes.dataType();
    tempOffsets.dataType();
    tempTAD.dataType();
}