Examples with ND4JIllegalStateException org.nd4j.linalg.exception.ND4JIllegalStateException used on opensource projects

Example 81 with ND4JIllegalStateException

use of org.nd4j.linalg.exception.ND4JIllegalStateException in project nd4j by deeplearning4j.

the class CudaZeroHandler method memcpyDevice.

@Override
public void memcpyDevice(DataBuffer dstBuffer, Pointer srcPointer, long length, long dstOffset, CudaContext context) {
    // log.info("Memcpy device: {} bytes ", length);
    AllocationPoint point = ((BaseCudaDataBuffer) dstBuffer).getAllocationPoint();
    Pointer dP = new CudaPointer((point.getPointers().getDevicePointer().address()) + dstOffset);
    if (nativeOps.memcpyAsync(dP, srcPointer, length, CudaConstants.cudaMemcpyDeviceToDevice, context.getOldStream()) == 0)
        throw new ND4JIllegalStateException("memcpyAsync failed");
    point.tickDeviceWrite();
}

Example 82 with ND4JIllegalStateException

use of org.nd4j.linalg.exception.ND4JIllegalStateException in project nd4j by deeplearning4j.

the class CudaWorkspace method init.

@Override
protected void init() {
    if (workspaceConfiguration.getPolicyLocation() == LocationPolicy.MMAP) {
        throw new ND4JIllegalStateException("CUDA do not support MMAP workspaces yet");
    }
    super.init();
    if (currentSize.get() > 0) {
        // log.info("Allocating {} bytes at DEVICE & HOST space...", currentSize.get());
        isInit.set(true);
        long bytes = currentSize.get();
        if (isDebug.get())
            log.info("Allocating [{}] workspace on device_{}, {} bytes...", id, Nd4j.getAffinityManager().getDeviceForCurrentThread(), bytes);
        if (isDebug.get()) {
            Nd4j.getWorkspaceManager().printAllocationStatisticsForCurrentThread();
        }
        Pointer ptr = memoryManager.allocate((bytes + SAFETY_OFFSET), MemoryKind.HOST, false);
        if (ptr == null)
            throw new ND4JIllegalStateException("Can't allocate memory for workspace");
        workspace.setHostPointer(new PagedPointer(ptr));
        if (workspaceConfiguration.getPolicyMirroring() != MirroringPolicy.HOST_ONLY)
            workspace.setDevicePointer(new PagedPointer(memoryManager.allocate((bytes + SAFETY_OFFSET), MemoryKind.DEVICE, false)));
    // log.info("Workspace [{}] initialized successfully", id);
    }
}

Example 83 with ND4JIllegalStateException

use of org.nd4j.linalg.exception.ND4JIllegalStateException in project nd4j by deeplearning4j.

the class JCublasNDArrayFactory method accumulate.

public INDArray accumulate(INDArray target, INDArray... arrays) {
    if (arrays == null || arrays.length == 0)
        throw new RuntimeException("Input arrays are missing");
    if (arrays.length == 1)
        return target.assign(arrays[0]);
    // we do averaging on GPU only if ALL devices have p2p links
    if (CudaEnvironment.getInstance().getConfiguration().isCrossDeviceAccessAllowed() && nativeOps.isP2PAvailable()) {
        Nd4j.getExecutioner().push();
        long len = target.lengthLong();
        AtomicAllocator allocator = AtomicAllocator.getInstance();
        CudaContext context = allocator.getFlowController().prepareAction(target, arrays);
        PointerPointer extras = new // not used
        PointerPointer(// not used
        null, context.getOldStream(), allocator.getDeviceIdPointer(), new CudaPointer(0));
        Pointer z = AtomicAllocator.getInstance().getPointer(target, context);
        long[] xPointers = new long[arrays.length];
        for (int i = 0; i < arrays.length; i++) {
            if (arrays[i].elementWiseStride() != 1)
                throw new ND4JIllegalStateException("Native averaging is applicable only to continuous INDArrays");
            if (arrays[i].lengthLong() != len)
                throw new ND4JIllegalStateException("All arrays should have equal length for averaging");
            AllocationPoint point = allocator.getAllocationPoint(arrays[i]);
            xPointers[i] = point.getPointers().getDevicePointer().address();
            point.tickDeviceWrite();
        }
        CudaDoubleDataBuffer tempX = new CudaDoubleDataBuffer(arrays.length);
        allocator.memcpyBlocking(tempX, new LongPointer(xPointers), xPointers.length * 8, 0);
        PointerPointer x = new PointerPointer(AtomicAllocator.getInstance().getPointer(tempX, context));
        if (target.data().dataType() == DataBuffer.Type.DOUBLE) {
            nativeOps.accumulateDouble(extras, x, (DoublePointer) z, arrays.length, len);
        } else if (target.data().dataType() == DataBuffer.Type.FLOAT) {
            nativeOps.accumulateFloat(extras, x, (FloatPointer) z, arrays.length, len);
        } else {
            nativeOps.accumulateHalf(extras, x, (ShortPointer) z, arrays.length, len);
        }
        allocator.getFlowController().registerAction(context, target, arrays);
        tempX.address();
        return target;
    } else {
        long len = target.lengthLong();
        Nd4j.getExecutioner().commit();
        CudaContext context = (CudaContext) AtomicAllocator.getInstance().getDeviceContext().getContext();
        PointerPointer dataPointers = new PointerPointer(arrays.length);
        PointerPointer extras = new // not used
        PointerPointer(// not used
        null, context.getOldStream(), AtomicAllocator.getInstance().getDeviceIdPointer(), new CudaPointer(1));
        for (int i = 0; i < arrays.length; i++) {
            Nd4j.getCompressor().autoDecompress(arrays[i]);
            if (arrays[i].elementWiseStride() != 1)
                throw new ND4JIllegalStateException("Native averaging is applicable only to continuous INDArrays");
            if (arrays[i].lengthLong() != len)
                throw new ND4JIllegalStateException("All arrays should have equal length for averaging");
            dataPointers.put(i, AtomicAllocator.getInstance().getHostPointer(arrays[i]));
        }
        if (target.data().dataType() == DataBuffer.Type.DOUBLE) {
            nativeOps.accumulateDouble(extras, dataPointers, (DoublePointer) AtomicAllocator.getInstance().getHostPointer(target), arrays.length, len);
        } else if (target.data().dataType() == DataBuffer.Type.FLOAT) {
            nativeOps.accumulateFloat(extras, dataPointers, (FloatPointer) AtomicAllocator.getInstance().getHostPointer(target), arrays.length, len);
        } else {
            nativeOps.accumulateHalf(extras, dataPointers, (ShortPointer) AtomicAllocator.getInstance().getHostPointer(target), arrays.length, len);
        }
        AtomicAllocator.getInstance().getAllocationPoint(target).tickHostWrite();
        return target;
    }
}

Example 84 with ND4JIllegalStateException

use of org.nd4j.linalg.exception.ND4JIllegalStateException in project nd4j by deeplearning4j.

the class JCublasNDArrayFactory method specialConcat.

@Override
public INDArray specialConcat(int dimension, INDArray... toConcat) {
    if (toConcat.length == 1)
        return toConcat[0];
    if (Nd4j.getExecutioner() instanceof GridExecutioner)
        ((GridExecutioner) Nd4j.getExecutioner()).flushQueue();
    PointerPointer shapeInfoPointers = new PointerPointer(toConcat.length);
    PointerPointer dataPointers = new PointerPointer(toConcat.length);
    AtomicAllocator allocator = AtomicAllocator.getInstance();
    CudaContext context = (CudaContext) allocator.getDeviceContext().getContext();
    int sumAlongDim = 0;
    int[] outputShape = ArrayUtil.copy(toConcat[0].shape());
    for (int i = 0; i < toConcat.length; i++) {
        if (toConcat[i].isCompressed())
            Nd4j.getCompressor().decompressi(toConcat[i]);
        allocator.synchronizeHostData(toConcat[i]);
        shapeInfoPointers.put(i, allocator.getHostPointer(toConcat[i].shapeInfoDataBuffer()));
        dataPointers.put(i, allocator.getHostPointer(toConcat[i].data()));
        sumAlongDim += toConcat[i].size(dimension);
        for (int j = 0; j < toConcat[i].rank(); j++) if (j != dimension && toConcat[i].size(j) != outputShape[j]) {
            throw new IllegalArgumentException("Illegal concatenation at array " + i + " and shape element " + j);
        }
    }
    outputShape[dimension] = sumAlongDim;
    PointerPointer dummy = new PointerPointer(new Pointer[] { null });
    INDArray ret = Nd4j.createUninitialized(outputShape, Nd4j.order());
    if (ret.data().dataType() == DataBuffer.Type.DOUBLE) {
        nativeOps.specialConcatDouble(dummy, dimension, toConcat.length, dataPointers, shapeInfoPointers, (DoublePointer) ret.data().addressPointer(), (IntPointer) ret.shapeInfoDataBuffer().addressPointer(), new PointerPointer(new Pointer[] { null }), new PointerPointer(new Pointer[] { null }));
    } else if (ret.data().dataType() == DataBuffer.Type.FLOAT) {
        nativeOps.specialConcatFloat(dummy, dimension, toConcat.length, dataPointers, shapeInfoPointers, (FloatPointer) ret.data().addressPointer(), (IntPointer) ret.shapeInfoDataBuffer().addressPointer(), new PointerPointer(new Pointer[] { null }), new PointerPointer(new Pointer[] { null }));
    } else if (ret.data().dataType() == DataBuffer.Type.HALF) {
        nativeOps.specialConcatHalf(dummy, dimension, toConcat.length, dataPointers, shapeInfoPointers, (ShortPointer) ret.data().addressPointer(), (IntPointer) ret.shapeInfoDataBuffer().addressPointer(), new PointerPointer(new Pointer[] { null }), new PointerPointer(new Pointer[] { null }));
    } else {
        throw new ND4JIllegalStateException("Unknown dataType: " + ret.data().dataType());
    }
    AllocationPoint point = allocator.getAllocationPoint(ret);
    nativeOps.memcpyAsync(point.getDevicePointer(), point.getHostPointer(), ret.lengthLong() * Nd4j.sizeOfDataType(ret.data().dataType()), CudaConstants.cudaMemcpyHostToDevice, context.getSpecialStream());
    context.getSpecialStream().synchronize();
    point.tickHostRead();
    point.tickDeviceWrite();
    return ret;
}

Example 85 with ND4JIllegalStateException

use of org.nd4j.linalg.exception.ND4JIllegalStateException 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();
}