org.nd4j.linalg.api.ndarray.INDArray#isCompressed

Source File: SmartFancyBlockingQueue.java From deeplearning4j with Apache License 2.0

6 votes

protected INDArray smartDecompress(INDArray encoded, INDArray target) {
    INDArray result = target == null ? Nd4j.create(paramsShape, paramsOrder) : target;

    if (encoded.isCompressed() || encoded.data().dataType() == DataType.INT) {
        int encoding = encoded.data().getInt(3);
        if (encoding == ThresholdCompression.FLEXIBLE_ENCODING) {
            Nd4j.getExecutioner().thresholdDecode(encoded, result);
        } else if (encoding == ThresholdCompression.BITMAP_ENCODING) {
            Nd4j.getExecutioner().bitmapDecode(encoded, result);
        } else
            throw new ND4JIllegalStateException("Unknown encoding mode: [" + encoding + "]");
    } else {
        result.addi(encoded);
    }

    return result;
}

Source File: IndexedTail.java From deeplearning4j with Apache License 2.0

6 votes

protected INDArray smartDecompress(INDArray encoded, @NonNull INDArray target) {
    INDArray result = target;

    if (encoded.isCompressed() || encoded.data().dataType() == DataType.INT) {
        int encoding = encoded.data().getInt(3);
        if (encoding == ThresholdCompression.FLEXIBLE_ENCODING) {
            Nd4j.getExecutioner().thresholdDecode(encoded, result);
        } else if (encoding == ThresholdCompression.BITMAP_ENCODING) {
            Nd4j.getExecutioner().bitmapDecode(encoded, result);
        } else
            throw new ND4JIllegalStateException("Unknown encoding mode: [" + encoding + "]");
    } else {
        result.addi(encoded);
    }

    return result;
}

Source File: AbstractCompressor.java From nd4j with Apache License 2.0

5 votes

@Override
public void decompressi(INDArray array) {
    if (!array.isCompressed())
        return;

    array.markAsCompressed(false);
    array.setData(decompress(array.data()));
}

Source File: CpuNDArrayFactory.java From deeplearning4j with Apache License 2.0

5 votes

@Override
public INDArray[] tear(INDArray tensor, int... dimensions) {
    if (tensor.isCompressed())
        Nd4j.getCompressor().decompressi(tensor);

    Arrays.sort(dimensions);

    Pair<DataBuffer, DataBuffer> tadBuffers = Nd4j.getExecutioner().getTADManager().getTADOnlyShapeInfo(tensor, dimensions);

    long tadLength = 1;
    long[] shape = new long[dimensions.length];
    for (int i = 0; i < dimensions.length; i++) {
        tadLength *= tensor.shape()[dimensions[i]];
        shape[i] = tensor.shape()[dimensions[i]];
    }



    int numTads = (int)(tensor.length() / tadLength);
    INDArray[] result = new INDArray[numTads];

    PointerPointer targets = new PointerPointer(numTads);

    for (int x = 0; x < numTads; x++) {
        result[x] = Nd4j.createUninitialized(shape);

        targets.put(x, result[x].data().pointer());
    }

        nativeOps.tear(null,
                ((BaseCpuDataBuffer) tensor.data()).getOpaqueDataBuffer(), (LongPointer) tensor.shapeInfoDataBuffer().pointer(), null,
                targets, (LongPointer) result[0].shapeInfoDataBuffer().pointer(),
                (LongPointer) tadBuffers.getFirst().pointer(), new LongPointerWrapper(tadBuffers.getSecond().pointer())
        );

    if (nativeOps.lastErrorCode() != 0)
        throw new RuntimeException(nativeOps.lastErrorMessage());

    return result;
}

Source File: AbstractCompressor.java From deeplearning4j with Apache License 2.0

5 votes

@Override
public void decompressi(INDArray array) {
    if (!array.isCompressed())
        return;

    array.markAsCompressed(false);
    array.setData(decompress(array.data(), ((CompressedDataBuffer)array.data()).getCompressionDescriptor().getOriginalDataType()));
}

Source File: AbstractCompressor.java From deeplearning4j with Apache License 2.0

5 votes

@Override
public INDArray decompress(INDArray array) {
    if (!array.isCompressed())
        return array;

    val descriptor = ((CompressedDataBuffer)array.data()).getCompressionDescriptor();
    val buffer = decompress(array.data(), descriptor.getOriginalDataType());
    val shapeInfo = array.shapeInfoDataBuffer();
    INDArray rest = Nd4j.createArrayFromShapeBuffer(buffer, shapeInfo);

    return rest;
}

Source File: BaseScalarOp.java From deeplearning4j with Apache License 2.0

5 votes

public BaseScalarOp(INDArray x, INDArray y, INDArray z, Number num) {
    super(x, y, z);
    if (x.isCompressed())
        Nd4j.getCompressor().decompressi(x);

    try(MemoryWorkspace ws = Nd4j.getMemoryManager().scopeOutOfWorkspaces()) {
        this.scalarValue = Nd4j.scalar(x.dataType(), num);
    }
}

Source File: BaseScalarOp.java From deeplearning4j with Apache License 2.0

5 votes

public BaseScalarOp(INDArray x, Number num) {
    super(x);
    if (x.isCompressed())
        Nd4j.getCompressor().decompressi(x);

    try(MemoryWorkspace ws = Nd4j.getMemoryManager().scopeOutOfWorkspaces()) {
        this.scalarValue = Nd4j.scalar(x.dataType(), num);
    }

}

Source File: BaseScalarOp.java From deeplearning4j with Apache License 2.0

5 votes

public BaseScalarOp(INDArray x, INDArray z, Number set) {
    super(x, null, z);
    if (x.isCompressed())
        Nd4j.getCompressor().decompressi(x);

    try(MemoryWorkspace ws = Nd4j.getMemoryManager().scopeOutOfWorkspaces()) {
        this.scalarValue = Nd4j.scalar(x.dataType(), set);
    }
}

Source File: JCublasNDArrayFactory.java From nd4j with Apache License 2.0

4 votes

public INDArray[] tear(INDArray tensor, int... dimensions) {
    if (tensor.isCompressed())
        Nd4j.getCompressor().decompressi(tensor);

    Arrays.sort(dimensions);

    Pair<DataBuffer, DataBuffer> tadBuffers = Nd4j.getExecutioner().getTADManager().getTADOnlyShapeInfo(tensor, dimensions);

    long tadLength = 1;
    val shape = new long[dimensions.length];
    for (int i = 0; i < dimensions.length; i++) {
        tadLength *= tensor.shape()[dimensions[i]];
        shape[i] = tensor.shape()[dimensions[i]];
    }


    int numTads = (int)(tensor.lengthLong() / tadLength);
    INDArray[] result = new INDArray[numTads];

    long[] xPointers = new long[numTads];

    CudaContext context = AtomicAllocator.getInstance().getFlowController().prepareAction(null, tensor);

    for (int x = 0; x < numTads; x++) {
        result[x] = Nd4j.createUninitialized(shape);

        context = AtomicAllocator.getInstance().getFlowController().prepareAction(result[x]);

        xPointers[x] = AtomicAllocator.getInstance().getPointer(result[x], context).address();
    }

    CudaDoubleDataBuffer tempX = new CudaDoubleDataBuffer(numTads);

    AtomicAllocator.getInstance().memcpyBlocking(tempX, new LongPointer(xPointers), xPointers.length * 8, 0);

    PointerPointer extraz = new PointerPointer(null, // not used
            context.getOldStream(), AtomicAllocator.getInstance().getDeviceIdPointer());

    if (Nd4j.dataType() == DataBuffer.Type.DOUBLE) {
        nativeOps.tearDouble(extraz,
                (DoublePointer) AtomicAllocator.getInstance().getPointer(tensor, context),
                (LongPointer) AtomicAllocator.getInstance().getPointer(tensor.shapeInfoDataBuffer(), context),
                new PointerPointer(AtomicAllocator.getInstance().getPointer(tempX, context)),
                (LongPointer) AtomicAllocator.getInstance().getPointer(result[0].shapeInfoDataBuffer(), context),
                (LongPointer) AtomicAllocator.getInstance().getPointer(tadBuffers.getFirst(), context),
                new LongPointerWrapper(AtomicAllocator.getInstance().getPointer(tadBuffers.getSecond(), context))
        );
    } else if (Nd4j.dataType() == DataBuffer.Type.FLOAT) {
        nativeOps.tearFloat(extraz,
                (FloatPointer) AtomicAllocator.getInstance().getPointer(tensor, context),
                (LongPointer) AtomicAllocator.getInstance().getPointer(tensor.shapeInfoDataBuffer(), context),
                new PointerPointer(AtomicAllocator.getInstance().getPointer(tempX, context)),
                (LongPointer) AtomicAllocator.getInstance().getPointer(result[0].shapeInfoDataBuffer(), context),
                (LongPointer) AtomicAllocator.getInstance().getPointer(tadBuffers.getFirst(), context),
                new LongPointerWrapper(AtomicAllocator.getInstance().getPointer(tadBuffers.getSecond(), context))
        );
    } else if (Nd4j.dataType() == DataBuffer.Type.HALF) {
        nativeOps.tearHalf(extraz,
                (ShortPointer) AtomicAllocator.getInstance().getPointer(tensor, context),
                (LongPointer) AtomicAllocator.getInstance().getPointer(tensor.shapeInfoDataBuffer(), context),
                new PointerPointer(AtomicAllocator.getInstance().getPointer(tempX, context)),
                (LongPointer) AtomicAllocator.getInstance().getPointer(result[0].shapeInfoDataBuffer(), context),
                (LongPointer) AtomicAllocator.getInstance().getPointer(tadBuffers.getFirst(), context),
                new LongPointerWrapper(AtomicAllocator.getInstance().getPointer(tadBuffers.getSecond(), context))
        );
    }

    AtomicAllocator.getInstance().getFlowController().registerActionAllWrite(context, result);
    AtomicAllocator.getInstance().getFlowController().registerAction(context,null, result);

    return result;
}

Source File: CpuNDArrayFactory.java From nd4j with Apache License 2.0

4 votes

public INDArray[] tear(INDArray tensor, int... dimensions) {
    if (tensor.isCompressed())
        Nd4j.getCompressor().decompressi(tensor);

    Arrays.sort(dimensions);

    Pair<DataBuffer, DataBuffer> tadBuffers = Nd4j.getExecutioner().getTADManager().getTADOnlyShapeInfo(tensor, dimensions);

    long tadLength = 1;
    long[] shape = new long[dimensions.length];
    for (int i = 0; i < dimensions.length; i++) {
        tadLength *= tensor.shape()[dimensions[i]];
        shape[i] = tensor.shape()[dimensions[i]];
    }



    int numTads = (int)(tensor.lengthLong() / tadLength);
    INDArray[] result = new INDArray[numTads];

    PointerPointer targets = new PointerPointer(numTads);

    for (int x = 0; x < numTads; x++) {
        result[x] = Nd4j.createUninitialized(shape);

        targets.put(x, result[x].data().pointer());
    }

    if (Nd4j.dataType() == DataBuffer.Type.DOUBLE) {
        nativeOps.tearDouble(null,
                (DoublePointer) tensor.data().pointer(),
                (LongPointer) tensor.shapeInfoDataBuffer().pointer(),
                targets,
                (LongPointer) result[0].shapeInfoDataBuffer().pointer(),
                (LongPointer) tadBuffers.getFirst().pointer(),
                new LongPointerWrapper(tadBuffers.getSecond().pointer())
        );
    } else if (Nd4j.dataType() == DataBuffer.Type.FLOAT) {
        nativeOps.tearFloat(null,
                (FloatPointer) tensor.data().pointer(),
                (LongPointer) tensor.shapeInfoDataBuffer().pointer(),
                targets,
                (LongPointer) result[0].shapeInfoDataBuffer().pointer(),
                (LongPointer) tadBuffers.getFirst().pointer(),
                new LongPointerWrapper(tadBuffers.getSecond().pointer())
                );
    } else if (Nd4j.dataType() == DataBuffer.Type.HALF) {
        throw new UnsupportedOperationException("Half precision isn't supported for CPU backend");
    }

    return result;
}

Source File: BasicNDArrayCompressor.java From nd4j with Apache License 2.0

4 votes

/**
 *
 * @param array
 */
public void autoDecompress(INDArray array) {
    if (array.isCompressed())
        decompressi(array);
}

Source File: BasicNDArrayCompressor.java From deeplearning4j with Apache License 2.0

4 votes

/**
 *
 * @param array
 */
public void autoDecompress(INDArray array) {
    if (array.isCompressed())
        decompressi(array);
}

Java Code Examples for org.nd4j.linalg.api.ndarray.INDArray#isCompressed()