Examples with DataBuffer org.nd4j.linalg.api.buffer.DataBuffer used on opensource projects

Example 71 with DataBuffer

use of org.nd4j.linalg.api.buffer.DataBuffer in project nd4j by deeplearning4j.

the class ConstantBuffersCache method getConstantBuffer.

@Override
public DataBuffer getConstantBuffer(int[] array) {
    ArrayDescriptor descriptor = new ArrayDescriptor(array);
    if (!buffersCache.containsKey(descriptor)) {
        DataBuffer buffer = Nd4j.createBufferDetached(array);
        // we always allow int arrays with length < 3. 99.9% it's just dimension array. we don't want to recreate them over and over
        if (counter.get() < MAX_ENTRIES || array.length < 4) {
            counter.incrementAndGet();
            buffersCache.put(descriptor, buffer);
            bytes.addAndGet(array.length * 4);
        }
        return buffer;
    }
    return buffersCache.get(descriptor);
}

Example 72 with DataBuffer

use of org.nd4j.linalg.api.buffer.DataBuffer in project nd4j by deeplearning4j.

the class ConstantBuffersCache method getConstantBuffer.

@Override
public DataBuffer getConstantBuffer(float[] array) {
    ArrayDescriptor descriptor = new ArrayDescriptor(array);
    if (!buffersCache.containsKey(descriptor)) {
        DataBuffer buffer = Nd4j.createBufferDetached(array);
        if (counter.get() < MAX_ENTRIES) {
            counter.incrementAndGet();
            buffersCache.put(descriptor, buffer);
            bytes.addAndGet(array.length * Nd4j.sizeOfDataType());
        }
        return buffer;
    }
    return buffersCache.get(descriptor);
}

Example 73 with DataBuffer

use of org.nd4j.linalg.api.buffer.DataBuffer in project nd4j by deeplearning4j.

the class DirectSparseInfoProvider method createSparseInformation.

@Override
public DataBuffer createSparseInformation(int[] flags, long[] sparseOffsets, int[] hiddenDimensions, int underlyingRank) {
    SparseDescriptor descriptor = new SparseDescriptor(flags, sparseOffsets, hiddenDimensions, underlyingRank);
    if (!sparseCache.containsKey(descriptor)) {
        if (counter.get() < MAX_ENTRIES) {
            if (!sparseCache.containsKey(descriptor)) {
                counter.incrementAndGet();
                DataBuffer buffer = Shape.createSparseInformation(flags, sparseOffsets, hiddenDimensions, underlyingRank);
                sparseCache.put(descriptor, buffer);
                return buffer;
            }
        } else {
            return Shape.createSparseInformation(flags, sparseOffsets, hiddenDimensions, underlyingRank);
        }
    }
    return sparseCache.get(descriptor);
}

Example 74 with DataBuffer

use of org.nd4j.linalg.api.buffer.DataBuffer in project nd4j by deeplearning4j.

the class JCublasNDArray method leverageTo.

@Override
public INDArray leverageTo(String id) {
    if (!isAttached()) {
        // log.info("Skipping detached");
        return this;
    }
    if (!Nd4j.getWorkspaceManager().checkIfWorkspaceExists(id)) {
        // log.info("Skipping non-existent");
        return this;
    }
    MemoryWorkspace current = Nd4j.getMemoryManager().getCurrentWorkspace();
    MemoryWorkspace target = Nd4j.getWorkspaceManager().getWorkspaceForCurrentThread(id);
    if (current == target) {
        // log.info("Skipping equals A");
        return this;
    }
    if (this.data.getParentWorkspace() == target) {
        // log.info("Skipping equals B");
        return this;
    }
    Nd4j.getMemoryManager().setCurrentWorkspace(target);
    // log.info("Leveraging...");
    INDArray copy = null;
    if (!this.isView()) {
        // if (1 < 0) {
        Nd4j.getExecutioner().commit();
        DataBuffer buffer = Nd4j.createBuffer(this.lengthLong(), false);
        AllocationPoint pointDst = AtomicAllocator.getInstance().getAllocationPoint(buffer);
        AllocationPoint pointSrc = AtomicAllocator.getInstance().getAllocationPoint(this.data);
        CudaContext context = AtomicAllocator.getInstance().getFlowController().prepareAction(pointDst, pointSrc);
        /*
            if (NativeOpsHolder.getInstance().getDeviceNativeOps().memsetAsync(pointDst.getDevicePointer(), 0, 1, 0, context.getOldStream()) == 0)
                throw new ND4JIllegalStateException("memsetAsync 1 failed");

            context.syncOldStream();

            if (NativeOpsHolder.getInstance().getDeviceNativeOps().memsetAsync(pointSrc.getDevicePointer(), 0, 1, 0, context.getOldStream()) == 0)
                throw new ND4JIllegalStateException("memsetAsync 2 failed");

            context.syncOldStream();
*/
        if (pointSrc.isActualOnDeviceSide()) {
            if (NativeOpsHolder.getInstance().getDeviceNativeOps().memcpyAsync(pointDst.getDevicePointer(), pointSrc.getDevicePointer(), this.lengthLong() * Nd4j.sizeOfDataType(buffer.dataType()), CudaConstants.cudaMemcpyDeviceToDevice, context.getOldStream()) == 0)
                throw new ND4JIllegalStateException("memcpyAsync failed");
        } else {
            if (NativeOpsHolder.getInstance().getDeviceNativeOps().memcpyAsync(pointDst.getDevicePointer(), pointSrc.getHostPointer(), this.lengthLong() * Nd4j.sizeOfDataType(buffer.dataType()), CudaConstants.cudaMemcpyHostToDevice, context.getOldStream()) == 0)
                throw new ND4JIllegalStateException("memcpyAsync failed");
        }
        context.syncOldStream();
        copy = Nd4j.createArrayFromShapeBuffer(buffer, this.shapeInfoDataBuffer());
        // tag buffer as valid on device side
        pointDst.tickHostRead();
        pointDst.tickDeviceWrite();
        AtomicAllocator.getInstance().getFlowController().registerAction(context, pointDst, pointSrc);
    } else {
        copy = this.dup(this.ordering());
        Nd4j.getExecutioner().commit();
    }
    Nd4j.getMemoryManager().setCurrentWorkspace(current);
    return copy;
}

Example 75 with DataBuffer

use of org.nd4j.linalg.api.buffer.DataBuffer in project nd4j by deeplearning4j.

the class JCublasNDArray method migrate.

/**
 * This method pulls this INDArray into current Workspace.
 *
 * PLEASE NOTE: If there's no current Workspace - INDArray returned as is
 *
 * @return
 */
@Override
public INDArray migrate() {
    MemoryWorkspace current = Nd4j.getMemoryManager().getCurrentWorkspace();
    if (current == null)
        return this;
    INDArray copy = null;
    if (!this.isView()) {
        Nd4j.getExecutioner().commit();
        DataBuffer buffer = Nd4j.createBuffer(this.lengthLong(), false);
        AllocationPoint pointDst = AtomicAllocator.getInstance().getAllocationPoint(buffer);
        AllocationPoint pointSrc = AtomicAllocator.getInstance().getAllocationPoint(this.data);
        // CudaContext context = (CudaContext) AtomicAllocator.getInstance().getDeviceContext().getContext();
        CudaContext context = AtomicAllocator.getInstance().getFlowController().prepareAction(pointDst, pointSrc);
        if (pointSrc.isActualOnDeviceSide()) {
            if (NativeOpsHolder.getInstance().getDeviceNativeOps().memcpyAsync(pointDst.getDevicePointer(), pointSrc.getDevicePointer(), this.lengthLong() * Nd4j.sizeOfDataType(buffer.dataType()), CudaConstants.cudaMemcpyDeviceToDevice, context.getOldStream()) == 0)
                throw new ND4JIllegalStateException("memcpyAsync failed");
        } else {
            if (NativeOpsHolder.getInstance().getDeviceNativeOps().memcpyAsync(pointDst.getDevicePointer(), pointSrc.getHostPointer(), this.lengthLong() * Nd4j.sizeOfDataType(buffer.dataType()), CudaConstants.cudaMemcpyHostToDevice, context.getOldStream()) == 0)
                throw new ND4JIllegalStateException("memcpyAsync failed");
        }
        context.syncOldStream();
        if (pointDst.getDeviceId() != Nd4j.getMemoryManager().getCurrentWorkspace().getDeviceId()) {
            // log.info("Swapping [{}] -> [{}]", pointDst.getDeviceId(), Nd4j.getMemoryManager().getCurrentWorkspace().getDeviceId());
            pointDst.setDeviceId(Nd4j.getMemoryManager().getCurrentWorkspace().getDeviceId());
        }
        copy = Nd4j.createArrayFromShapeBuffer(buffer, this.shapeInfoDataBuffer());
        // tag buffer as valid on device side
        pointDst.tickHostRead();
        pointDst.tickDeviceWrite();
        AtomicAllocator.getInstance().getFlowController().registerAction(context, pointDst, pointSrc);
    } else {
        copy = this.dup(this.ordering());
    }
    return copy;
}