Examples with Variance org.nd4j.linalg.api.ops.impl.accum.Variance used on opensource projects

Example 1 with Variance

use of org.nd4j.linalg.api.ops.impl.accum.Variance in project nd4j by deeplearning4j.

the class DefaultOpFactory method createAccum.

@Override
public Accumulation createAccum(String name, INDArray x, INDArray y, INDArray z, Object[] extraArgs) {
    Accumulation ret = null;
    switch(name) {
        case "mmul":
        case "std":
            ret = new StandardDeviation(x, y, z, x.length(), (boolean) extraArgs[0]);
            break;
        case "var":
            ret = new Variance(x, y, z, x.length(), (boolean) extraArgs[0]);
            break;
        default:
            try {
                ret = (Accumulation) DifferentialFunctionClassHolder.getInstance().getInstance(name).getClass().getConstructor(INDArray.class, INDArray.class, INDArray.class, long.class).newInstance(x, y, z, x.length());
            } catch (Exception e) {
                throw new RuntimeException(e);
            }
    }
    if (ret == null)
        throw new IllegalArgumentException("Illegal operation opName " + name);
    ret.setExtraArgs(extraArgs);
    return ret;
}

Example 2 with Variance

use of org.nd4j.linalg.api.ops.impl.accum.Variance in project nd4j by deeplearning4j.

the class CudaExecutioner method invoke.

protected CudaContext invoke(Accumulation op, int[] dimension) {
    long st = profilingHookIn(op);
    checkForCompression(op);
    validateDataType(Nd4j.dataType(), op);
    if (extraz.get() == null)
        extraz.set(new PointerPointer(32));
    // dimension is ALWAYS null here.
    if (dimension == null)
        dimension = new int[] { Integer.MAX_VALUE };
    Arrays.sort(dimension);
    for (int i = 0; i < dimension.length; i++) if (dimension[i] >= op.x().rank() && dimension[i] != Integer.MAX_VALUE)
        throw new ND4JIllegalStateException("Op target dimension " + Arrays.toString(dimension) + " contains element that higher then rank of op.X: [" + op.x().rank() + "]");
    CudaContext context = AtomicAllocator.getInstance().getFlowController().prepareAction(op.z(), op.x(), op.y());
    if (CudaEnvironment.getInstance().getConfiguration().isDebug())
        lastOp.set(op.opName());
    Pointer hostYShapeInfo = op.y() == null ? null : AddressRetriever.retrieveHostPointer(op.y().shapeInfoDataBuffer());
    Pointer hostZShapeInfo = op.z() == null ? null : AddressRetriever.retrieveHostPointer(op.z().shapeInfoDataBuffer());
    Pair<DataBuffer, DataBuffer> tadBuffers = tadManager.getTADOnlyShapeInfo(op.x(), dimension);
    Pointer hostTadShapeInfo = AddressRetriever.retrieveHostPointer(tadBuffers.getFirst());
    Pointer devTadShapeInfo = AtomicAllocator.getInstance().getPointer(tadBuffers.getFirst(), context);
    DataBuffer offsets = tadBuffers.getSecond();
    Pointer devTadOffsets = offsets == null ? null : AtomicAllocator.getInstance().getPointer(offsets, context);
    PointerPointer xShapeInfoHostPointer = extraz.get().put(AddressRetriever.retrieveHostPointer(op.x().shapeInfoDataBuffer()), context.getOldStream(), AtomicAllocator.getInstance().getDeviceIdPointer(), context.getBufferAllocation(), context.getBufferReduction(), context.getBufferScalar(), context.getBufferSpecial(), hostYShapeInfo, hostZShapeInfo, hostTadShapeInfo, devTadShapeInfo, devTadOffsets);
    if (op.y() != null) {
        Pair<DataBuffer, DataBuffer> yTadBuffers = tadManager.getTADOnlyShapeInfo(op.y(), dimension);
        Pointer yDevTadShapeInfo = AtomicAllocator.getInstance().getPointer(yTadBuffers.getFirst(), context);
        DataBuffer yOffsets = yTadBuffers.getSecond();
        Pointer yDevTadOffsets = yOffsets == null ? null : AtomicAllocator.getInstance().getPointer(yOffsets, context);
        xShapeInfoHostPointer.put(12, yDevTadShapeInfo);
        xShapeInfoHostPointer.put(13, yDevTadOffsets);
    }
    Pointer x = AtomicAllocator.getInstance().getPointer(op.x(), context);
    Pointer xShapeInfo = AtomicAllocator.getInstance().getPointer(op.x().shapeInfoDataBuffer(), context);
    Pointer extraArgs = op.extraArgs() != null ? AtomicAllocator.getInstance().getPointer(op.extraArgsDataBuff(), context) : null;
    int[] retShape = Shape.wholeArrayDimension(dimension) ? new int[] { 1, 1 } : ArrayUtil.removeIndex(op.x().shape(), dimension);
    // ensure vector is proper shape
    if (retShape.length == 1) {
        if (dimension[0] == 0)
            retShape = new int[] { 1, retShape[0] };
        else
            retShape = new int[] { retShape[0], 1 };
    } else if (retShape.length == 0) {
        retShape = new int[] { 1, 1 };
    }
    if (op.x().isVector() && op.x().length() == ArrayUtil.prod(retShape))
        return null;
    INDArray ret = null;
    if (0.0 + Math.abs(op.zeroDouble()) <= Nd4j.EPS_THRESHOLD) {
        ret = Nd4j.zeros(retShape);
    } else {
        if (op.x().data().dataType() == DataBuffer.Type.DOUBLE)
            ret = Nd4j.valueArrayOf(retShape, op.zeroDouble());
        else if (op.x().data().dataType() == DataBuffer.Type.FLOAT)
            ret = Nd4j.valueArrayOf(retShape, op.zeroFloat());
        else if (op.x().data().dataType() == DataBuffer.Type.HALF)
            ret = Nd4j.valueArrayOf(retShape, op.zeroHalf());
    }
    op.setZ(ret);
    if (op.z().isScalar()) {
        if (op.x().data().dataType() == DataBuffer.Type.DOUBLE) {
            if (op instanceof Variance) {
                double result = nativeOps.execSummaryStatsScalarDouble(xShapeInfoHostPointer, op.opNum(), (DoublePointer) x, (IntPointer) xShapeInfo, (DoublePointer) extraArgs, ((Variance) op).isBiasCorrected());
                op.setFinalResult(result);
            } else if (op.y() != null) {
                Pointer y = AtomicAllocator.getInstance().getPointer(op.y(), context);
                Pointer yShapeInfo = AtomicAllocator.getInstance().getPointer(op.y().shapeInfoDataBuffer(), context);
                double result = nativeOps.execReduce3ScalarDouble(xShapeInfoHostPointer, op.opNum(), (DoublePointer) x, (IntPointer) xShapeInfo, (DoublePointer) extraArgs, (DoublePointer) y, (IntPointer) yShapeInfo);
                op.setFinalResult(result);
            } else {
                double result = nativeOps.execReduceScalarDouble(xShapeInfoHostPointer, op.opNum(), (DoublePointer) x, (IntPointer) xShapeInfo, (DoublePointer) extraArgs);
                op.setFinalResult(result);
            }
        } else if (op.x().data().dataType() == DataBuffer.Type.FLOAT) {
            if (op instanceof Variance) {
                float result = nativeOps.execSummaryStatsScalarFloat(xShapeInfoHostPointer, op.opNum(), (FloatPointer) x, (IntPointer) xShapeInfo, (FloatPointer) extraArgs, ((Variance) op).isBiasCorrected());
                op.setFinalResult(result);
            } else if (op.y() != null) {
                Pointer y = AtomicAllocator.getInstance().getPointer(op.y(), context);
                Pointer yShapeInfo = AtomicAllocator.getInstance().getPointer(op.y().shapeInfoDataBuffer(), context);
                float result = nativeOps.execReduce3ScalarFloat(xShapeInfoHostPointer, op.opNum(), (FloatPointer) x, (IntPointer) xShapeInfo, (FloatPointer) extraArgs, (FloatPointer) y, (IntPointer) yShapeInfo);
                op.setFinalResult(result);
            } else {
                float result = nativeOps.execReduceScalarFloat(xShapeInfoHostPointer, op.opNum(), (FloatPointer) x, (IntPointer) xShapeInfo, (FloatPointer) extraArgs);
                op.setFinalResult(result);
            }
        } else {
            if (op instanceof Variance) {
                float result = nativeOps.execSummaryStatsScalarHalf(xShapeInfoHostPointer, op.opNum(), (ShortPointer) x, (IntPointer) xShapeInfo, (ShortPointer) extraArgs, ((Variance) op).isBiasCorrected());
                op.setFinalResult(result);
            } else if (op.y() != null) {
                Pointer y = AtomicAllocator.getInstance().getPointer(op.y(), context);
                Pointer yShapeInfo = AtomicAllocator.getInstance().getPointer(op.y().shapeInfoDataBuffer(), context);
                float result = nativeOps.execReduce3ScalarHalf(xShapeInfoHostPointer, op.opNum(), (ShortPointer) x, (IntPointer) xShapeInfo, (ShortPointer) extraArgs, (ShortPointer) y, (IntPointer) yShapeInfo);
                op.setFinalResult(result);
            } else {
                float result = nativeOps.execReduceScalarHalf(xShapeInfoHostPointer, op.opNum(), (ShortPointer) x, (IntPointer) xShapeInfo, (ShortPointer) extraArgs);
                op.setFinalResult(result);
            }
        }
    } else {
        Pointer result = AtomicAllocator.getInstance().getPointer(op.z(), context);
        Pointer resultShapeInfo = AtomicAllocator.getInstance().getPointer(op.z().shapeInfoDataBuffer(), context);
        Pointer dimensionPointer = AtomicAllocator.getInstance().getPointer(AtomicAllocator.getInstance().getConstantBuffer(dimension), // AtomicAllocator.getInstance().getPointer(Nd4j.createBuffer(dimension), context);
        context);
        if (op.x().data().dataType() == DataBuffer.Type.DOUBLE) {
            if (op.y() != null) {
                Pointer y = AtomicAllocator.getInstance().getPointer(op.y(), context);
                Pointer yShapeInfo = AtomicAllocator.getInstance().getPointer(op.y().shapeInfoDataBuffer(), context);
                nativeOps.execReduce3Double(xShapeInfoHostPointer, op.opNum(), (DoublePointer) x, (IntPointer) xShapeInfo, (DoublePointer) extraArgs, (DoublePointer) y, (IntPointer) yShapeInfo, (DoublePointer) result, (IntPointer) resultShapeInfo, (IntPointer) dimensionPointer, dimension.length);
            } else {
                if (op instanceof Variance) {
                    nativeOps.execSummaryStatsDouble(xShapeInfoHostPointer, op.opNum(), (DoublePointer) x, (IntPointer) xShapeInfo, (DoublePointer) extraArgs, (DoublePointer) result, (IntPointer) resultShapeInfo, (IntPointer) dimensionPointer, dimension.length, ((Variance) op).isBiasCorrected());
                } else {
                    nativeOps.execReduceDouble(xShapeInfoHostPointer, op.opNum(), (DoublePointer) x, (IntPointer) xShapeInfo, (DoublePointer) extraArgs, (DoublePointer) result, (IntPointer) resultShapeInfo, (IntPointer) dimensionPointer, dimension.length);
                }
            }
        } else // float
        if (op.x().data().dataType() == DataBuffer.Type.FLOAT) {
            if (op.y() != null) {
                Pointer y = AtomicAllocator.getInstance().getPointer(op.y(), context);
                Pointer yShapeInfo = AtomicAllocator.getInstance().getPointer(op.y().shapeInfoDataBuffer(), context);
                nativeOps.execReduce3Float(xShapeInfoHostPointer, op.opNum(), (FloatPointer) x, (IntPointer) xShapeInfo, (FloatPointer) extraArgs, (FloatPointer) y, (IntPointer) yShapeInfo, (FloatPointer) result, (IntPointer) resultShapeInfo, (IntPointer) dimensionPointer, dimension.length);
            } else {
                if (op instanceof Variance) {
                    nativeOps.execSummaryStatsFloat(xShapeInfoHostPointer, op.opNum(), (FloatPointer) x, (IntPointer) xShapeInfo, (FloatPointer) extraArgs, (FloatPointer) result, (IntPointer) resultShapeInfo, (IntPointer) dimensionPointer, dimension.length, ((Variance) op).isBiasCorrected());
                } else {
                    nativeOps.execReduceFloat(xShapeInfoHostPointer, op.opNum(), (FloatPointer) x, (IntPointer) xShapeInfo, (FloatPointer) extraArgs, (FloatPointer) result, (IntPointer) resultShapeInfo, (IntPointer) dimensionPointer, dimension.length);
                }
            }
        } else // Half
        {
            if (op.y() != null) {
                Pointer y = AtomicAllocator.getInstance().getPointer(op.y(), context);
                Pointer yShapeInfo = AtomicAllocator.getInstance().getPointer(op.y().shapeInfoDataBuffer(), context);
                nativeOps.execReduce3Half(xShapeInfoHostPointer, op.opNum(), (ShortPointer) x, (IntPointer) xShapeInfo, (ShortPointer) extraArgs, (ShortPointer) y, (IntPointer) yShapeInfo, (ShortPointer) result, (IntPointer) resultShapeInfo, (IntPointer) dimensionPointer, dimension.length);
            } else {
                if (op instanceof Variance) {
                    nativeOps.execSummaryStatsHalf(xShapeInfoHostPointer, op.opNum(), (ShortPointer) x, (IntPointer) xShapeInfo, (ShortPointer) extraArgs, (ShortPointer) result, (IntPointer) resultShapeInfo, (IntPointer) dimensionPointer, dimension.length, ((Variance) op).isBiasCorrected());
                } else {
                    nativeOps.execReduceHalf(xShapeInfoHostPointer, op.opNum(), (ShortPointer) x, (IntPointer) xShapeInfo, (ShortPointer) extraArgs, (ShortPointer) result, (IntPointer) resultShapeInfo, (IntPointer) dimensionPointer, dimension.length);
                }
            }
        }
    }
    AtomicAllocator.getInstance().registerAction(context, op.z(), op.x(), op.y());
    profilingHookOut(op, st);
    return context;
}

Example 3 with Variance

use of org.nd4j.linalg.api.ops.impl.accum.Variance in project nd4j by deeplearning4j.

the class CudaExecutioner method naiveExec.

/**
 * @param op
 * @param dimension
 * @return
 */
protected INDArray naiveExec(Accumulation op, int... dimension) {
    long st = profilingHookIn(op);
    INDArray ret = op.z();
    validateDataType(Nd4j.dataType(), op);
    for (int i = 0; i < dimension.length; i++) if (dimension[i] >= op.x().rank() && dimension[i] != Integer.MAX_VALUE)
        throw new ND4JIllegalStateException("Op target dimension " + Arrays.toString(dimension) + " contains element that higher then rank of op.X: [" + op.x().rank() + "]");
    CudaContext context = AtomicAllocator.getInstance().getFlowController().prepareAction(op.z(), op.x(), op.y());
    if (CudaEnvironment.getInstance().getConfiguration().isDebug())
        lastOp.set(op.opName());
    Pointer hostYShapeInfo = op.y() == null ? null : AddressRetriever.retrieveHostPointer(op.y().shapeInfoDataBuffer());
    Pointer hostZShapeInfo = op.z() == null ? null : AddressRetriever.retrieveHostPointer(op.z().shapeInfoDataBuffer());
    Pair<DataBuffer, DataBuffer> tadBuffers = tadManager.getTADOnlyShapeInfo(op.x(), dimension);
    /*
        if (op.opNum() == 3) {
            log.info("Max shape: {}", Arrays.toString(op.x().shapeInfoDataBuffer().asInt()));
            log.info("Max TAD: {}", Arrays.toString(tadBuffers.getFirst().asInt()));
            context.syncOldStream();
        }
*/
    Pointer hostTadShapeInfo = AddressRetriever.retrieveHostPointer(tadBuffers.getFirst());
    Pointer devTadShapeInfo = AtomicAllocator.getInstance().getPointer(tadBuffers.getFirst(), context);
    DataBuffer offsets = tadBuffers.getSecond();
    Pointer devTadOffsets = offsets == null ? null : AtomicAllocator.getInstance().getPointer(offsets, context);
    Pointer x = AtomicAllocator.getInstance().getPointer(op.x(), context);
    Pointer xShapeInfo = AtomicAllocator.getInstance().getPointer(op.x().shapeInfoDataBuffer(), context);
    if (extraz.get() == null)
        extraz.set(new PointerPointer(32));
    PointerPointer xShapeInfoHostPointer = extraz.get().put(AddressRetriever.retrieveHostPointer(op.x().shapeInfoDataBuffer()), context.getOldStream(), AtomicAllocator.getInstance().getDeviceIdPointer(), context.getBufferAllocation(), context.getBufferReduction(), context.getBufferScalar(), context.getBufferSpecial(), hostYShapeInfo, hostZShapeInfo, hostTadShapeInfo, devTadShapeInfo, devTadOffsets);
    Pointer yDevTadOffsets = null;
    Pointer yDevTadShapeInfo = null;
    if (op.y() != null) {
        if ((dimension.length == 1 && dimension[0] == Integer.MAX_VALUE) || op.x().tensorAlongDimension(0, dimension).lengthLong() != op.y().lengthLong()) {
            if (!op.isComplexAccumulation() && op.x().lengthLong() != op.y().lengthLong())
                throw new ND4JIllegalStateException("Op.X [" + op.x().lengthLong() + "] and Op.Y [" + op.y().lengthLong() + "] lengths should match");
            Pair<DataBuffer, DataBuffer> yTadBuffers = tadManager.getTADOnlyShapeInfo(op.y(), dimension);
            yDevTadShapeInfo = AtomicAllocator.getInstance().getPointer(yTadBuffers.getFirst(), context);
            DataBuffer yOffsets = yTadBuffers.getSecond();
            yDevTadOffsets = yOffsets == null ? null : AtomicAllocator.getInstance().getPointer(yOffsets, context);
            xShapeInfoHostPointer.put(12, yDevTadShapeInfo);
            xShapeInfoHostPointer.put(13, yDevTadOffsets);
        } else {
            // TAD vs full array code branch
            val fakeOffsets = Nd4j.getConstantHandler().getConstantBuffer(new int[] { 0, 0 });
            yDevTadOffsets = fakeOffsets == null ? null : AtomicAllocator.getInstance().getPointer(fakeOffsets, context);
            yDevTadShapeInfo = AtomicAllocator.getInstance().getPointer(op.y().shapeInfoDataBuffer(), context);
            xShapeInfoHostPointer.put(12, AtomicAllocator.getInstance().getPointer(op.y().shapeInfoDataBuffer(), context));
            xShapeInfoHostPointer.put(13, null);
        }
    }
    Pointer extraArgs = op.extraArgs() != null ? AtomicAllocator.getInstance().getPointer(op.extraArgsDataBuff(), context) : null;
    // Pointer extraArgs = op.extraArgs() != null ? AtomicAllocator.getInstance().getPointer(op.extraArgsDataBuff(), context) : 0;
    // Pointer dimensionPointer = AtomicAllocator.getInstance().getPointer(Nd4j.createBuffer(dimension), context);
    Pointer dimensionPointer = AtomicAllocator.getInstance().getPointer(AtomicAllocator.getInstance().getConstantBuffer(dimension), // AtomicAllocator.getInstance().getPointer(Nd4j.createBuffer(dimension), context);
    context);
    if (op.x().data().dataType() == DataBuffer.Type.DOUBLE) {
        if (op instanceof Variance) {
            if (ret.isScalar()) {
                double res = nativeOps.execSummaryStatsScalarDouble(xShapeInfoHostPointer, op.opNum(), (DoublePointer) x, (IntPointer) xShapeInfo, (DoublePointer) extraArgs, ((Variance) op).isBiasCorrected());
                AtomicAllocator.getInstance().registerAction(context, op.z(), op.x(), op.y());
                ret.assign(res);
                op.setFinalResult(res);
            } else {
                nativeOps.execSummaryStatsDouble(xShapeInfoHostPointer, op.opNum(), (DoublePointer) x, (IntPointer) xShapeInfo, (DoublePointer) extraArgs, (DoublePointer) AtomicAllocator.getInstance().getPointer(op.z(), context), (IntPointer) AtomicAllocator.getInstance().getPointer(op.z().shapeInfoDataBuffer(), context), (IntPointer) dimensionPointer, dimension.length, ((Variance) op).isBiasCorrected());
                AtomicAllocator.getInstance().registerAction(context, op.z(), op.x(), op.y());
            }
        } else if (op.y() != null) {
            if (op.isComplexAccumulation()) {
                val dT = new LongPointerWrapper(devTadOffsets);
                val yT = new LongPointerWrapper(yDevTadOffsets);
                nativeOps.execReduce3AllDouble(xShapeInfoHostPointer, op.opNum(), (DoublePointer) x, (IntPointer) xShapeInfo, (DoublePointer) extraArgs, (DoublePointer) AtomicAllocator.getInstance().getPointer(op.y(), context), (IntPointer) AtomicAllocator.getInstance().getPointer(op.y().shapeInfoDataBuffer(), context), (DoublePointer) AtomicAllocator.getInstance().getPointer(op.z(), context), (IntPointer) AtomicAllocator.getInstance().getPointer(op.z().shapeInfoDataBuffer(), context), (IntPointer) dimensionPointer, dimension.length, (IntPointer) devTadShapeInfo, dT, (IntPointer) yDevTadShapeInfo, yT);
                AtomicAllocator.getInstance().registerAction(context, op.z(), op.x(), op.y());
            } else if (ret.isScalar()) {
                double res = nativeOps.execReduce3ScalarDouble(xShapeInfoHostPointer, op.opNum(), (DoublePointer) x, (IntPointer) xShapeInfo, (DoublePointer) extraArgs, (DoublePointer) AtomicAllocator.getInstance().getPointer(op.y(), context), (IntPointer) AtomicAllocator.getInstance().getPointer(op.y().shapeInfoDataBuffer(), context));
                AtomicAllocator.getInstance().registerAction(context, op.z(), op.x(), op.y());
                ret.assign(res);
                op.setFinalResult(res);
            } else {
                nativeOps.execReduce3Double(xShapeInfoHostPointer, op.opNum(), (DoublePointer) x, (IntPointer) xShapeInfo, (DoublePointer) extraArgs, (DoublePointer) AtomicAllocator.getInstance().getPointer(op.y(), context), (IntPointer) AtomicAllocator.getInstance().getPointer(op.y().shapeInfoDataBuffer(), context), (DoublePointer) AtomicAllocator.getInstance().getPointer(op.z(), context), (IntPointer) AtomicAllocator.getInstance().getPointer(op.z().shapeInfoDataBuffer(), context), (IntPointer) dimensionPointer, dimension.length);
                AtomicAllocator.getInstance().registerAction(context, op.z(), op.x(), op.y());
            }
        } else {
            if (ret.isScalar()) {
                double res = nativeOps.execReduceScalarDouble(xShapeInfoHostPointer, op.opNum(), (DoublePointer) x, (IntPointer) xShapeInfo, (DoublePointer) extraArgs);
                AtomicAllocator.getInstance().registerAction(context, op.z(), op.x(), op.y());
                ret.assign(res);
                op.setFinalResult(res);
            } else {
                nativeOps.execReduceDouble(xShapeInfoHostPointer, op.opNum(), (DoublePointer) x, (IntPointer) xShapeInfo, (DoublePointer) extraArgs, (DoublePointer) AtomicAllocator.getInstance().getPointer(op.z(), context), (IntPointer) AtomicAllocator.getInstance().getPointer(op.z().shapeInfoDataBuffer(), context), (IntPointer) dimensionPointer, dimension.length);
                AtomicAllocator.getInstance().registerAction(context, op.z(), op.x(), op.y());
            }
        }
    } else if (op.x().data().dataType() == DataBuffer.Type.FLOAT) {
        if (op instanceof Variance) {
            if (ret.isScalar()) {
                float res = nativeOps.execSummaryStatsScalarFloat(xShapeInfoHostPointer, op.opNum(), (FloatPointer) x, (IntPointer) xShapeInfo, (FloatPointer) extraArgs, ((Variance) op).isBiasCorrected());
                AtomicAllocator.getInstance().registerAction(context, op.z(), op.x(), op.y());
                ret.assign(res);
                op.setFinalResult(res);
            } else {
                nativeOps.execSummaryStatsFloat(xShapeInfoHostPointer, op.opNum(), (FloatPointer) x, (IntPointer) xShapeInfo, (FloatPointer) extraArgs, (FloatPointer) AtomicAllocator.getInstance().getPointer(op.z(), context), (IntPointer) AtomicAllocator.getInstance().getPointer(op.z().shapeInfoDataBuffer(), context), (IntPointer) dimensionPointer, dimension.length, ((Variance) op).isBiasCorrected());
                AtomicAllocator.getInstance().registerAction(context, op.z(), op.x(), op.y());
            }
        } else if (op.y() != null) {
            if (op.isComplexAccumulation()) {
                nativeOps.execReduce3AllFloat(xShapeInfoHostPointer, op.opNum(), (FloatPointer) x, (IntPointer) xShapeInfo, (FloatPointer) extraArgs, (FloatPointer) AtomicAllocator.getInstance().getPointer(op.y(), context), (IntPointer) AtomicAllocator.getInstance().getPointer(op.y().shapeInfoDataBuffer(), context), (FloatPointer) AtomicAllocator.getInstance().getPointer(op.z(), context), (IntPointer) AtomicAllocator.getInstance().getPointer(op.z().shapeInfoDataBuffer(), context), (IntPointer) dimensionPointer, dimension.length, (IntPointer) devTadShapeInfo, new LongPointerWrapper(devTadOffsets), (IntPointer) yDevTadShapeInfo, new LongPointerWrapper(yDevTadOffsets));
                AtomicAllocator.getInstance().registerAction(context, op.z(), op.x(), op.y());
            } else if (ret.isScalar()) {
                float res = nativeOps.execReduce3ScalarFloat(xShapeInfoHostPointer, op.opNum(), (FloatPointer) x, (IntPointer) xShapeInfo, (FloatPointer) extraArgs, (FloatPointer) AtomicAllocator.getInstance().getPointer(op.y(), context), (IntPointer) AtomicAllocator.getInstance().getPointer(op.y().shapeInfoDataBuffer(), context));
                ret.assign(res);
                op.setFinalResult(res);
                AtomicAllocator.getInstance().registerAction(context, op.z(), op.x(), op.y());
            } else {
                nativeOps.execReduce3Float(xShapeInfoHostPointer, op.opNum(), (FloatPointer) x, (IntPointer) xShapeInfo, (FloatPointer) extraArgs, (FloatPointer) AtomicAllocator.getInstance().getPointer(op.y(), context), (IntPointer) AtomicAllocator.getInstance().getPointer(op.y().shapeInfoDataBuffer(), context), (FloatPointer) AtomicAllocator.getInstance().getPointer(op.z(), context), (IntPointer) AtomicAllocator.getInstance().getPointer(op.z().shapeInfoDataBuffer(), context), (IntPointer) dimensionPointer, dimension.length);
                AtomicAllocator.getInstance().registerAction(context, op.z(), op.x(), op.y());
            }
        } else {
            if (ret.isScalar()) {
                float res = nativeOps.execReduceScalarFloat(xShapeInfoHostPointer, op.opNum(), (FloatPointer) x, (IntPointer) xShapeInfo, (FloatPointer) extraArgs);
                AtomicAllocator.getInstance().registerAction(context, op.z(), op.x(), op.y());
                ret.assign(res);
                op.setFinalResult(res);
            } else {
                nativeOps.execReduceFloat(xShapeInfoHostPointer, op.opNum(), (FloatPointer) x, (IntPointer) xShapeInfo, (FloatPointer) extraArgs, (FloatPointer) AtomicAllocator.getInstance().getPointer(op.z(), context), (IntPointer) AtomicAllocator.getInstance().getPointer(op.z().shapeInfoDataBuffer(), context), (IntPointer) dimensionPointer, dimension.length);
                AtomicAllocator.getInstance().registerAction(context, op.z(), op.x(), op.y());
            }
        }
    } else {
        if (op instanceof Variance) {
            if (ret.isScalar()) {
                float res = nativeOps.execSummaryStatsScalarHalf(xShapeInfoHostPointer, op.opNum(), (ShortPointer) x, (IntPointer) xShapeInfo, (ShortPointer) extraArgs, ((Variance) op).isBiasCorrected());
                AtomicAllocator.getInstance().registerAction(context, op.z(), op.x(), op.y());
                ret.assign(res);
                op.setFinalResult(res);
            } else {
                nativeOps.execSummaryStatsHalf(xShapeInfoHostPointer, op.opNum(), (ShortPointer) x, (IntPointer) xShapeInfo, (ShortPointer) extraArgs, (ShortPointer) AtomicAllocator.getInstance().getPointer(op.z(), context), (IntPointer) AtomicAllocator.getInstance().getPointer(op.z().shapeInfoDataBuffer(), context), (IntPointer) dimensionPointer, dimension.length, ((Variance) op).isBiasCorrected());
                AtomicAllocator.getInstance().registerAction(context, op.z(), op.x(), op.y());
            }
        } else if (op.y() != null) {
            if (op.isComplexAccumulation()) {
                nativeOps.execReduce3AllHalf(xShapeInfoHostPointer, op.opNum(), (ShortPointer) x, (IntPointer) xShapeInfo, (ShortPointer) extraArgs, (ShortPointer) AtomicAllocator.getInstance().getPointer(op.y(), context), (IntPointer) AtomicAllocator.getInstance().getPointer(op.y().shapeInfoDataBuffer(), context), (ShortPointer) AtomicAllocator.getInstance().getPointer(op.z(), context), (IntPointer) AtomicAllocator.getInstance().getPointer(op.z().shapeInfoDataBuffer(), context), (IntPointer) dimensionPointer, dimension.length, (IntPointer) devTadShapeInfo, new LongPointerWrapper(devTadOffsets), (IntPointer) yDevTadShapeInfo, new LongPointerWrapper(yDevTadOffsets));
                AtomicAllocator.getInstance().registerAction(context, op.z(), op.x(), op.y());
            } else if (ret.isScalar()) {
                float res = nativeOps.execReduce3ScalarHalf(xShapeInfoHostPointer, op.opNum(), (ShortPointer) x, (IntPointer) xShapeInfo, (ShortPointer) extraArgs, (ShortPointer) AtomicAllocator.getInstance().getPointer(op.y(), context), (IntPointer) AtomicAllocator.getInstance().getPointer(op.y().shapeInfoDataBuffer(), context));
                AtomicAllocator.getInstance().registerAction(context, op.z(), op.x(), op.y());
                ret.assign(res);
                op.setFinalResult(res);
            } else {
                nativeOps.execReduce3Half(xShapeInfoHostPointer, op.opNum(), (ShortPointer) x, (IntPointer) xShapeInfo, (ShortPointer) extraArgs, (ShortPointer) AtomicAllocator.getInstance().getPointer(op.y(), context), (IntPointer) AtomicAllocator.getInstance().getPointer(op.y().shapeInfoDataBuffer(), context), (ShortPointer) AtomicAllocator.getInstance().getPointer(op.z(), context), (IntPointer) AtomicAllocator.getInstance().getPointer(op.z().shapeInfoDataBuffer(), context), (IntPointer) dimensionPointer, dimension.length);
                AtomicAllocator.getInstance().registerAction(context, op.z(), op.x(), op.y());
            }
        } else {
            if (ret.isScalar()) {
                float res = nativeOps.execReduceScalarHalf(xShapeInfoHostPointer, op.opNum(), (ShortPointer) x, (IntPointer) xShapeInfo, (ShortPointer) extraArgs);
                AtomicAllocator.getInstance().registerAction(context, op.z(), op.x(), op.y());
                ret.assign(res);
                op.setFinalResult(res);
            } else {
                nativeOps.execReduceHalf(xShapeInfoHostPointer, op.opNum(), (ShortPointer) x, (IntPointer) xShapeInfo, (ShortPointer) extraArgs, (ShortPointer) AtomicAllocator.getInstance().getPointer(op.z(), context), (IntPointer) AtomicAllocator.getInstance().getPointer(op.z().shapeInfoDataBuffer(), context), (IntPointer) dimensionPointer, dimension.length);
                AtomicAllocator.getInstance().registerAction(context, op.z(), op.x(), op.y());
            }
        }
    }
    profilingHookOut(op, st);
    return op.z();
}

Example 4 with Variance

use of org.nd4j.linalg.api.ops.impl.accum.Variance in project nd4j by deeplearning4j.

the class OpsMappingTests method getOperations.

protected List<Operation> getOperations(@NonNull Op.Type type) {
    val list = new ArrayList<Operation>();
    Reflections f = new Reflections(new ConfigurationBuilder().filterInputsBy(new FilterBuilder().include(FilterBuilder.prefix("org.nd4j.*")).exclude("^(?!.*\\.class$).*$")).setUrls(ClasspathHelper.forPackage("org.nd4j")).setScanners(new SubTypesScanner()));
    switch(type) {
        case SUMMARYSTATS:
            {
                Set<Class<? extends Variance>> clazzes = f.getSubTypesOf(Variance.class);
                for (Class<? extends DifferentialFunction> clazz : clazzes) addOperation(clazz, list);
            }
            break;
        case RANDOM:
            {
                Set<Class<? extends BaseRandomOp>> clazzes = f.getSubTypesOf(BaseRandomOp.class);
                for (Class<? extends DifferentialFunction> clazz : clazzes) addOperation(clazz, list);
            }
            break;
        case INDEXREDUCE:
            {
                Set<Class<? extends BaseIndexAccumulation>> clazzes = f.getSubTypesOf(BaseIndexAccumulation.class);
                for (Class<? extends DifferentialFunction> clazz : clazzes) addOperation(clazz, list);
            }
            break;
        case REDUCE3:
        case REDUCE:
            {
                Set<Class<? extends BaseAccumulation>> clazzes = f.getSubTypesOf(BaseAccumulation.class);
                for (Class<? extends DifferentialFunction> clazz : clazzes) addOperation(clazz, list);
            }
            break;
        case BROADCAST:
            {
                Set<Class<? extends BaseBroadcastOp>> clazzes = f.getSubTypesOf(BaseBroadcastOp.class);
                for (Class<? extends DifferentialFunction> clazz : clazzes) addOperation(clazz, list);
            }
            break;
        case SCALAR:
            {
                Set<Class<? extends BaseScalarOp>> clazzes = f.getSubTypesOf(BaseScalarOp.class);
                for (Class<? extends DifferentialFunction> clazz : clazzes) addOperation(clazz, list);
            }
            break;
        case PAIRWISE:
        case TRANSFORM:
            {
                Set<Class<? extends BaseTransformOp>> clazzes = f.getSubTypesOf(BaseTransformOp.class);
                for (Class<? extends DifferentialFunction> clazz : clazzes) addOperation(clazz, list);
            }
            break;
        case CUSTOM:
            {
                Set<Class<? extends DynamicCustomOp>> clazzes = f.getSubTypesOf(DynamicCustomOp.class);
                for (Class<? extends DifferentialFunction> clazz : clazzes) {
                    if (clazz.getSimpleName().equalsIgnoreCase("dynamiccustomop"))
                        continue;
                    addOperation(clazz, list);
                }
            }
            break;
    }
    log.info("Group: {}; List size: {}", type, list.size());
    return list;
}

Example 5 with Variance

use of org.nd4j.linalg.api.ops.impl.accum.Variance in project nd4j by deeplearning4j.

the class NativeOpExecutioner method exec.

@Override
public INDArray exec(Accumulation op, int... dimension) {
    dimension = Shape.normalizeAxis(op.x().rank(), dimension);
    validateDataType(Nd4j.dataType(), op);
    if (extraz.get() == null)
        extraz.set(new PointerPointer(32));
    int[] maxShape = Shape.getMaxShape(op.x(), op.y());
    for (int i = 0; i < dimension.length; i++) if (dimension[i] >= maxShape.length && dimension[i] != Integer.MAX_VALUE)
        throw new ND4JIllegalStateException("Op target dimension " + Arrays.toString(dimension) + " contains element that higher then rank of op.X: [" + op.x().rank() + "]");
    for (int i = 0; i < dimension.length; i++) {
        if (dimension[i] < 0)
            dimension[i] += op.x().rank();
    }
    // do op along all dimensions
    if (dimension.length == op.x().rank())
        dimension = new int[] { Integer.MAX_VALUE };
    int[] retShape;
    if (Shape.wholeArrayDimension(dimension))
        retShape = new int[] { 1, 1 };
    else
        retShape = ArrayUtil.removeIndex(maxShape, dimension);
    // ensure vector is proper shape
    if (retShape.length == 1) {
        if (dimension[0] == 0)
            retShape = new int[] { 1, retShape[0] };
        else
            retShape = new int[] { retShape[0], 1 };
    } else if (retShape.length == 0) {
        retShape = new int[] { 1, 1 };
    }
    if (op.x().isVector() && op.x().length() == ArrayUtil.prod(retShape) && ArrayUtil.prodLong(retShape) > 1 && op.y() == null)
        return op.noOp();
    /**
     * This is the result array.
     * We create it only if we hadn't provided it before
     */
    INDArray ret;
    if (op.z() == null || op.z() == op.x()) {
        if (op.isComplexAccumulation()) {
            int xT = op.x().tensorssAlongDimension(dimension);
            int yT = op.y().tensorssAlongDimension(dimension);
            ret = Nd4j.create(xT, yT);
        } else {
            if (op.y() != null) {
                // 2 options here: either pairwise, equal sizes - OR every X TAD vs. entirety of Y
                if (op.x().lengthLong() == op.y().lengthLong()) {
                    // Pairwise
                    if (op.x().tensorssAlongDimension(dimension) != op.y().tensorssAlongDimension(dimension)) {
                        throw new ND4JIllegalStateException("Number of TADs along dimension don't match: (x shape = " + Arrays.toString(op.x().shape()) + ", y shape = " + Arrays.toString(op.y().shape()) + ", dimension = " + Arrays.toString(dimension) + ")");
                    }
                } else {
                    // Every X TAD vs. entirety of Y
                    val xTADSize = op.x().lengthLong() / op.x().tensorssAlongDimension(dimension);
                    if (xTADSize != op.y().length()) {
                        throw new ND4JIllegalStateException("Size of TADs along dimension don't match for pairwise execution:" + " (x TAD size = " + xTADSize + ", y size = " + op.y().lengthLong());
                    }
                }
            }
            if (op.x().data().dataType() == DataBuffer.Type.DOUBLE)
                ret = Nd4j.valueArrayOf(retShape, op.zeroDouble());
            else
                ret = Nd4j.valueArrayOf(retShape, op.zeroFloat());
        }
        op.setZ(ret);
    } else {
        // compare length
        if (!op.isComplexAccumulation() && op.z().lengthLong() != ArrayUtil.prodLong(retShape))
            throw new ND4JIllegalStateException("Shape of target array for reduction [" + Arrays.toString(op.z().shape()) + "] doesn't match expected [" + Arrays.toString(retShape) + "]");
        else if (op.isComplexAccumulation()) {
            int xT = op.x().tensorssAlongDimension(dimension);
            int yT = op.y().tensorssAlongDimension(dimension);
            if (op.z().lengthLong() != xT * yT)
                throw new ND4JIllegalStateException("Shape of target array for reduction [" + Arrays.toString(op.z().shape()) + "] doesn't match expected [" + (xT * yT) + "]");
        }
        if (op.x().data().dataType() == DataBuffer.Type.DOUBLE) {
            op.z().assign(op.zeroDouble());
        } else {
            op.z().assign(op.zeroFloat());
        }
        ret = op.z();
    }
    /**
     * Returns the {@link Shape#createShapeInformation(int[], int[], int, int, char)}
     * and the associated offsets for each {@link INDArray#tensorAlongDimension(int, int...)}
     * The first item is the shape information. The second one is the offsets.
     */
    Pair<DataBuffer, DataBuffer> tadBuffers = tadManager.getTADOnlyShapeInfo(op.x(), dimension);
    Pair<DataBuffer, DataBuffer> yTadBuffers = null;
    /**
     * Note that we use addresses in libnd4j.
     * We use reinterpret cast in c to take the long
     * we pass to JNI. This manages overhead.
     */
    Pointer hostTadShapeInfo = tadBuffers.getFirst().addressPointer();
    DataBuffer offsets = tadBuffers.getSecond();
    Pointer hostTadOffsets = offsets == null ? null : offsets.addressPointer();
    // we're going to check, if that's TAD vs TAD comparison or TAD vs full array. if later - we're going slightly different route
    boolean tvf = false;
    if (op.y() != null) {
        if (op.x().tensorAlongDimension(0, dimension).lengthLong() == op.y().lengthLong()) {
            tvf = true;
        }
    }
    if (op.isComplexAccumulation()) {
        yTadBuffers = tadManager.getTADOnlyShapeInfo(op.y(), dimension);
        if (op.x().tensorAlongDimension(0, dimension).lengthLong() != op.y().tensorAlongDimension(0, dimension).lengthLong())
            throw new ND4JIllegalStateException("Impossible to issue AllDistances operation: TAD lengths mismatch along given dimension");
    }
    /**
     * This is a pointer to a pointer in c.
     */
    // FIXME: we need something better then 3rd element being non-null here...
    PointerPointer dummy = extraz.get().put(hostTadShapeInfo, hostTadOffsets, tvf ? hostTadOffsets : null);
    long st = profilingHookIn(op, tadBuffers.getFirst());
    /**
     * Note because dimension arrays don't change,
     * we use an {@link ConstantHandler} which knows how to reserve memory
     * for immutable buffers for the dimensions.
     * This gives us a pointer which is passed around in libnd4j.
     */
    Pointer dimensionAddress = constantHandler.getConstantBuffer(dimension).addressPointer();
    if (op.x().data().dataType() == DataBuffer.Type.DOUBLE) {
        if (op instanceof Variance) {
            if (ret.isScalar()) {
                ret.putScalar(0, loop.execSummaryStatsScalarDouble(dummy, op.opNum(), (DoublePointer) op.x().data().addressPointer(), (IntPointer) op.x().shapeInfoDataBuffer().addressPointer(), (DoublePointer) getPointerForExtraArgs(op), true));
            } else {
                Variance var = (Variance) op;
                loop.execSummaryStatsDouble(dummy, op.opNum(), (DoublePointer) op.x().data().addressPointer(), (IntPointer) op.x().shapeInfoDataBuffer().addressPointer(), (DoublePointer) getPointerForExtraArgs(op), (DoublePointer) op.z().data().addressPointer(), (IntPointer) op.z().shapeInfoDataBuffer().addressPointer(), (IntPointer) dimensionAddress, dimension.length, var.isBiasCorrected());
            }
        } else // pairwise reduction like similarity of two arrays
        if (op.y() != null && op.getOpType() == Op.Type.REDUCE3) {
            if (op.isComplexAccumulation()) {
                loop.execReduce3AllDouble(dummy, op.opNum(), (DoublePointer) op.x().data().addressPointer(), (IntPointer) op.x().shapeInfoDataBuffer().addressPointer(), (DoublePointer) getPointerForExtraArgs(op), (DoublePointer) op.y().data().addressPointer(), (IntPointer) op.y().shapeInfoDataBuffer().addressPointer(), (DoublePointer) op.z().data().addressPointer(), (IntPointer) op.z().shapeInfoDataBuffer().addressPointer(), (IntPointer) dimensionAddress, dimension.length, (IntPointer) tadBuffers.getFirst().addressPointer(), new LongPointerWrapper(tadBuffers.getSecond().addressPointer()), (IntPointer) yTadBuffers.getFirst().addressPointer(), new LongPointerWrapper(yTadBuffers.getSecond().addressPointer()));
            } else if (ret.isScalar()) {
                ret.putScalar(0, loop.execReduce3ScalarDouble(dummy, op.opNum(), (DoublePointer) op.x().data().addressPointer(), (IntPointer) op.x().shapeInfoDataBuffer().addressPointer(), (DoublePointer) getPointerForExtraArgs(op), (DoublePointer) op.y().data().addressPointer(), (IntPointer) op.y().shapeInfoDataBuffer().addressPointer()));
            } else {
                loop.execReduce3Double(dummy, op.opNum(), (DoublePointer) op.x().data().addressPointer(), (IntPointer) op.x().shapeInfoDataBuffer().addressPointer(), (DoublePointer) getPointerForExtraArgs(op), (DoublePointer) op.y().data().addressPointer(), (IntPointer) op.y().shapeInfoDataBuffer().addressPointer(), (DoublePointer) op.z().data().addressPointer(), (IntPointer) op.z().shapeInfoDataBuffer().addressPointer(), (IntPointer) dimensionAddress, dimension.length);
            }
        } else {
            if (ret.isScalar()) {
                ret.putScalar(0, loop.execReduceScalarDouble(dummy, op.opNum(), (DoublePointer) op.x().data().addressPointer(), (IntPointer) op.x().shapeInfoDataBuffer().addressPointer(), (DoublePointer) getPointerForExtraArgs(op)));
            } else {
                loop.execReduceDouble(dummy, op.opNum(), (DoublePointer) op.x().data().addressPointer(), (IntPointer) op.x().shapeInfoDataBuffer().addressPointer(), (DoublePointer) getPointerForExtraArgs(op), (DoublePointer) op.z().data().addressPointer(), (IntPointer) op.z().shapeInfoDataBuffer().addressPointer(), (IntPointer) dimensionAddress, dimension.length);
            }
        }
    } else {
        if (op instanceof Variance) {
            Variance variance = (Variance) op;
            if (ret.isScalar()) {
                ret.putScalar(0, loop.execSummaryStatsScalarFloat(dummy, op.opNum(), (FloatPointer) op.x().data().addressPointer(), (IntPointer) op.x().shapeInfoDataBuffer().addressPointer(), (FloatPointer) getPointerForExtraArgs(op), variance.isBiasCorrected()));
            } else {
                loop.execSummaryStatsFloat(dummy, op.opNum(), (FloatPointer) op.x().data().addressPointer(), (IntPointer) op.x().shapeInfoDataBuffer().addressPointer(), (FloatPointer) getPointerForExtraArgs(op), (FloatPointer) op.z().data().addressPointer(), (IntPointer) op.z().shapeInfoDataBuffer().addressPointer(), (IntPointer) dimensionAddress, dimension.length, variance.isBiasCorrected());
            }
        } else if (op.y() != null && op.getOpType() == Op.Type.REDUCE3) {
            if (op.isComplexAccumulation()) {
                loop.execReduce3AllFloat(dummy, op.opNum(), (FloatPointer) op.x().data().addressPointer(), (IntPointer) op.x().shapeInfoDataBuffer().addressPointer(), (FloatPointer) getPointerForExtraArgs(op), (FloatPointer) op.y().data().addressPointer(), (IntPointer) op.y().shapeInfoDataBuffer().addressPointer(), (FloatPointer) op.z().data().addressPointer(), (IntPointer) op.z().shapeInfoDataBuffer().addressPointer(), (IntPointer) dimensionAddress, dimension.length, (IntPointer) tadBuffers.getFirst().addressPointer(), new LongPointerWrapper(tadBuffers.getSecond().addressPointer()), (IntPointer) yTadBuffers.getFirst().addressPointer(), new LongPointerWrapper(yTadBuffers.getSecond().addressPointer()));
            } else if (ret.isScalar()) {
                ret.putScalar(0, loop.execReduce3ScalarFloat(dummy, op.opNum(), (FloatPointer) op.x().data().addressPointer(), (IntPointer) op.x().shapeInfoDataBuffer().addressPointer(), (FloatPointer) getPointerForExtraArgs(op), (FloatPointer) op.y().data().addressPointer(), (IntPointer) op.y().shapeInfoDataBuffer().addressPointer()));
            } else {
                loop.execReduce3Float(dummy, op.opNum(), (FloatPointer) op.x().data().addressPointer(), (IntPointer) op.x().shapeInfoDataBuffer().addressPointer(), (FloatPointer) getPointerForExtraArgs(op), (FloatPointer) op.y().data().addressPointer(), (IntPointer) op.y().shapeInfoDataBuffer().addressPointer(), (FloatPointer) op.z().data().addressPointer(), (IntPointer) op.z().shapeInfoDataBuffer().addressPointer(), (IntPointer) dimensionAddress, dimension.length);
            }
        } else {
            if (ret.isScalar()) {
                ret.putScalar(0, loop.execReduceScalarFloat(dummy, op.opNum(), (FloatPointer) op.x().data().addressPointer(), (IntPointer) op.x().shapeInfoDataBuffer().addressPointer(), (FloatPointer) getPointerForExtraArgs(op)));
            } else {
                loop.execReduceFloat(dummy, op.opNum(), (FloatPointer) op.x().data().addressPointer(), (IntPointer) op.x().shapeInfoDataBuffer().addressPointer(), (FloatPointer) getPointerForExtraArgs(op), (FloatPointer) op.z().data().addressPointer(), (IntPointer) op.z().shapeInfoDataBuffer().addressPointer(), (IntPointer) dimensionAddress, dimension.length);
            }
        }
    }
    return ret;
}