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/*
* ******************************************************************************
* *
* *
* * This program and the accompanying materials are made available under the
* * terms of the Apache License, Version 2.0 which is available at
* * https://www.apache.org/licenses/LICENSE-2.0.
* *
* * See the NOTICE file distributed with this work for additional
* * information regarding copyright ownership.
* * Unless required by applicable law or agreed to in writing, software
* * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* * License for the specific language governing permissions and limitations
* * under the License.
* *
* * SPDX-License-Identifier: Apache-2.0
* *****************************************************************************
*/
package org.nd4j.linalg.jcublas;
import lombok.extern.slf4j.Slf4j;
import lombok.val;
import org.nd4j.common.base.Preconditions;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.buffer.DataTypeEx;
import org.nd4j.linalg.api.memory.enums.MemoryKind;
import org.nd4j.linalg.api.ops.custom.Flatten;
import org.nd4j.linalg.api.ops.impl.shape.Concat;
import org.nd4j.linalg.api.ops.performance.PerformanceTracker;
import org.nd4j.linalg.api.shape.options.ArrayOptionsHelper;
import org.nd4j.linalg.api.shape.options.ArrayType;
import org.nd4j.linalg.compression.CompressionUtils;
import org.nd4j.linalg.jcublas.buffer.*;
import org.nd4j.linalg.api.memory.MemcpyDirection;
import org.nd4j.common.primitives.Pair;
import org.bytedeco.javacpp.*;
import org.nd4j.jita.allocator.enums.CudaConstants;
import org.nd4j.jita.allocator.impl.AllocationPoint;
import org.nd4j.jita.allocator.impl.AtomicAllocator;
import org.nd4j.jita.allocator.pointers.CudaPointer;
import org.nd4j.jita.conf.CudaEnvironment;
import org.nd4j.linalg.api.buffer.DataBuffer;
import org.nd4j.linalg.api.memory.MemoryWorkspace;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.executioner.GridExecutioner;
import org.nd4j.linalg.api.shape.Shape;
import org.nd4j.linalg.cache.TADManager;
import org.nd4j.linalg.compression.CompressedDataBuffer;
import org.nd4j.linalg.compression.CompressionDescriptor;
import org.nd4j.linalg.compression.CompressionType;
import org.nd4j.linalg.exception.ND4JIllegalStateException;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.jcublas.blas.*;
import org.nd4j.linalg.jcublas.context.CudaContext;
import org.nd4j.common.util.ArrayUtil;
import org.nd4j.nativeblas.*;
import java.util.*;
/**
* Jcublas ndarray factory. Handles creation of
* jcuda.jcublas ndarrays.
*
* @author mjk
*/
@Slf4j
public class JCublasNDArrayFactory extends BaseNativeNDArrayFactory {
public JCublasNDArrayFactory() { }
public JCublasNDArrayFactory(DataType dtype, Character order) {
super(dtype, order);
}
public JCublasNDArrayFactory(DataType dtype, char order) {
super(dtype, order);
AtomicAllocator.getInstance();
}
@Override
public void createBlas() {
blas = new CudaBlas();
PointerPointer functions = new PointerPointer(13);
functions.put(0, Loader.addressof("cublasSgemv_v2"));
functions.put(1, Loader.addressof("cublasDgemv_v2"));
functions.put(2, Loader.addressof("cublasHgemm"));
functions.put(3, Loader.addressof("cublasSgemm_v2"));
functions.put(4, Loader.addressof("cublasDgemm_v2"));
functions.put(5, Loader.addressof("cublasSgemmEx"));
functions.put(6, Loader.addressof("cublasHgemmBatched"));
functions.put(7, Loader.addressof("cublasSgemmBatched"));
functions.put(8, Loader.addressof("cublasDgemmBatched"));
functions.put(9, Loader.addressof("cusolverDnSgesvd_bufferSize"));
functions.put(10, Loader.addressof("cusolverDnDgesvd_bufferSize"));
functions.put(11, Loader.addressof("cusolverDnSgesvd"));
functions.put(12, Loader.addressof("cusolverDnDgesvd"));
nativeOps.initializeFunctions(functions);
if (nativeOps.lastErrorCode() != 0)
throw new RuntimeException(nativeOps.lastErrorMessage());
/*
val major = new int[1];
val minor = new int[1];
val build = new int[1];
org.bytedeco.cuda.global.cublas.cublasGetProperty(0, major);
org.bytedeco.cuda.global.cublas.cublasGetProperty(1, minor);
org.bytedeco.cuda.global.cublas.cublasGetProperty(2, build);
val pew = new int[100];
org.bytedeco.cuda.global.cudart.cudaDriverGetVersion(pew);
nativeOps.isBlasVersionMatches(major[0], minor[0], build[0]);
if (nativeOps.lastErrorCode() != 0)
throw new RuntimeException(nativeOps.lastErrorMessage());
*/
}
@Override
public void createLevel1() {
level1 = new JcublasLevel1();
}
@Override
public void createLevel2() {
level2 = new JcublasLevel2();
}
@Override
public void createLevel3() {
level3 = new JcublasLevel3();
}
@Override
public void createLapack() {
lapack = new JcublasLapack();
}
@Override
public INDArray create(int[] shape, DataBuffer buffer) {
return new JCublasNDArray(shape, buffer);
}
/**
* Create an ndarray with the given data layout
*
* @param data the data to create the ndarray with
* @return the ndarray with the given data layout
*/
@Override
public INDArray create(double[][] data) {
return new JCublasNDArray(data);
}
@Override
public INDArray create(double[][] data, char ordering) {
return new JCublasNDArray(data, ordering);
}
@Override
public INDArray create(DataBuffer data) {
return new JCublasNDArray(data);
}
@Override
public INDArray create(DataBuffer data, long rows, long columns, int[] stride, long offset) {
// FIXME: int cast
return new JCublasNDArray(data, new long[] {rows, columns}, ArrayUtil.toLongArray(stride), Nd4j.order(), data.dataType());
}
@Override
public INDArray create(int[] shape, char ordering) {
return new JCublasNDArray(shape, ordering);
}
@Override
public INDArray createUninitialized(int[] shape, char ordering) {
return new JCublasNDArray(shape, Nd4j.getStrides(shape, ordering), 0, ordering, false);
}
@Override
public INDArray create(DataBuffer data, int[] newShape, int[] newStride, long offset, char ordering) {
return new JCublasNDArray(data, newShape, newStride, offset, ordering);
}
@Override
public INDArray create(float[] data, int[] shape, long offset, Character order) {
return new JCublasNDArray(data, shape, offset, order);
}
@Override
public INDArray create(float[] data, long rows, long columns, int[] stride, long offset, char ordering) {
return new JCublasNDArray(data, new long[] {rows, columns}, ArrayUtil.toLongArray(stride), offset, ordering);
}
@Override
public INDArray create(double[] data, int[] shape, char ordering) {
return new JCublasNDArray(data, shape, ordering);
}
@Override
public INDArray create(double[] data, long[] shape, char ordering) {
return new JCublasNDArray(data, shape, ordering);
}
@Override
public INDArray create(Collection strings, long[] shape, char order) {
val pairShape = Nd4j.getShapeInfoProvider().createShapeInformation(shape, order, DataType.UTF8);
val buffer = new CudaUtf8Buffer(strings);
val list = new ArrayList(strings);
return Nd4j.createArrayFromShapeBuffer(buffer, pairShape);
}
@Override
public INDArray create(List list, int[] shape, char ordering) {
return new JCublasNDArray(list, shape, ordering);
}
@Override
public INDArray create(double[] data, int[] shape, long offset) {
return new JCublasNDArray(data, shape, (char) offset);
}
@Override
public INDArray create(double[] data, int[] shape, int[] stride, long offset, char ordering) {
return new JCublasNDArray(data, shape, stride, offset, ordering);
}
/**
* Creates an ndarray with the specified shape
*
* @param data
* @param shape the shape of the ndarray
* @param stride the stride for the ndarray
* @param offset the offset of the ndarray
* @return the instance
*/
@Override
public INDArray create(float[] data, int[] shape, int[] stride, long offset) {
return new JCublasNDArray(data, shape, stride, offset);
}
/**
* Creates an ndarray with the specified shape
*
* @param data
* @param shape the shape of the ndarray
* @param stride the stride for the ndarray
* @param offset the offset of the ndarray
* @return the instance
*/
@Override
public INDArray create(double[] data, int[] shape, int[] stride, long offset) {
return new JCublasNDArray(data, shape, stride, offset);
}
@Override
public INDArray create(DataBuffer data, int[] shape) {
return new JCublasNDArray(data, shape);
}
@Override
public INDArray create(DataBuffer data, int[] shape, int[] stride, long offset) {
return new JCublasNDArray(data, ArrayUtil.toLongArray(shape), ArrayUtil.toLongArray(stride), Nd4j.order(), data.dataType());
}
/**
* Creates an ndarray with the specified shape
*
* @param list
* @param shape the shape of the ndarray
* @return the instance
*/
@Override
public INDArray create(List list, int[] shape) {
if (order == FORTRAN)
return new JCublasNDArray(list, shape, ArrayUtil.calcStridesFortran(shape));
else
return new JCublasNDArray(list, shape);
}
@Override
public INDArray create(float[] data, int[] shape, long offset) {
return new JCublasNDArray(data, shape, offset);
}
@Override
public INDArray create(float[] data, long[] shape, long[] stride, char order, DataType dataType, MemoryWorkspace workspace) {
return new JCublasNDArray(Nd4j.createTypedBuffer(data, dataType, workspace), shape, stride, order, dataType);
}
@Override
public INDArray create(long[] data, long[] shape, long[] stride, char order, DataType dataType, MemoryWorkspace workspace) {
return new JCublasNDArray(Nd4j.createTypedBuffer(data, dataType), shape, stride, order, dataType);
}
@Override
public INDArray create(int[] data, long[] shape, long[] stride, char order, DataType dataType, MemoryWorkspace workspace) {
return new JCublasNDArray(Nd4j.createTypedBuffer(data, dataType), shape, stride, order, dataType);
}
@Override
public INDArray create(short[] data, long[] shape, long[] stride, char order, DataType dataType, MemoryWorkspace workspace) {
return new JCublasNDArray(Nd4j.createTypedBuffer(data, dataType), shape, stride, order, dataType);
}
@Override
public INDArray create(byte[] data, long[] shape, long[] stride, char order, DataType dataType, MemoryWorkspace workspace) {
return new JCublasNDArray(Nd4j.createTypedBuffer(data, dataType), shape, stride, order, dataType);
}
@Override
public INDArray create(boolean[] data, long[] shape, long[] stride, char order, DataType dataType, MemoryWorkspace workspace) {
return new JCublasNDArray(Nd4j.createTypedBuffer(data, dataType), shape, stride, order, dataType);
}
@Override
public INDArray create(float[][] floats) {
return new JCublasNDArray(floats);
}
@Override
public INDArray create(float[][] data, char ordering) {
return new JCublasNDArray(data, ordering);
}
@Override
public INDArray create(float[] data, int[] shape, int[] stride, long offset, char ordering) {
return new JCublasNDArray(data, shape, stride, offset, ordering);
}
@Override
public INDArray create(DataBuffer buffer, int[] shape, long offset) {
return new JCublasNDArray(buffer, shape, offset);
}
@Override
public INDArray toFlattened(Collection matrices) {
return this.toFlattened(order(), matrices);
}
@Override
public INDArray toFlattened(char order, Collection matrices) {
if (Nd4j.getExecutioner() instanceof GridExecutioner)
((GridExecutioner) Nd4j.getExecutioner()).flushQueue();
return Nd4j.exec(new Flatten(order, matrices.toArray(new INDArray[0])))[0];
}
@Override
public INDArray concat(int dimension, INDArray... toConcat) {
Nd4j.getExecutioner().push();
return Nd4j.exec(new Concat(dimension, toConcat))[0];
}
@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();
val context = allocator.getDeviceContext();
int sumAlongDim = 0;
val outputShape = ArrayUtil.copy(toConcat[0].shape());
for (int i = 0; i < toConcat.length; i++) {
((BaseCudaDataBuffer) toConcat[i].data()).lazyAllocateHostPointer();
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;
val ret = Nd4j.createUninitialized(toConcat[0].dataType(), outputShape, Nd4j.order());
((BaseCudaDataBuffer) ret.data()).lazyAllocateHostPointer();
nativeOps.specialConcat(null, dimension, toConcat.length, dataPointers, shapeInfoPointers,
ret.data().addressPointer(),
(LongPointer) ret.shapeInfoDataBuffer().addressPointer(),
null, null);
if (nativeOps.lastErrorCode() != 0)
throw new RuntimeException(nativeOps.lastErrorMessage());
AllocationPoint point = allocator.getAllocationPoint(ret);
val perfD = PerformanceTracker.getInstance().helperStartTransaction();
nativeOps.memcpyAsync(point.getDevicePointer(), point.getHostPointer(), ret.length() * Nd4j.sizeOfDataType(ret.data().dataType()), CudaConstants.cudaMemcpyHostToDevice, context.getSpecialStream());
context.getSpecialStream().synchronize();
if (nativeOps.lastErrorCode() != 0)
throw new RuntimeException(nativeOps.lastErrorMessage());
PerformanceTracker.getInstance().helperRegisterTransaction(point.getDeviceId(), perfD, point.getNumberOfBytes(), MemcpyDirection.HOST_TO_DEVICE);
point.tickHostRead();
point.tickDeviceWrite();
return ret;
}
/**
* 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) {
return pullRows(source, sourceDimension, indexes, Nd4j.order());
}
@Override
public INDArray pullRows(INDArray source, int sourceDimension, long[] indexes) {
return pullRows(source, sourceDimension, ArrayUtil.toInts(indexes));
}
/**
* 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 (indexes == null || indexes.length < 1)
throw new IllegalStateException("Indexes can't be null or zero-length");
long[] shape;
if (source.rank() == 1) {
shape = new long[]{indexes.length};
} else if (sourceDimension == 1)
shape = new long[] {indexes.length, source.shape()[sourceDimension]};
else if (sourceDimension == 0)
shape = new long[] {source.shape()[sourceDimension], indexes.length};
else
throw new UnsupportedOperationException("2D input is expected");
return pullRows(source, Nd4j.createUninitialized(source.dataType(), shape, order), sourceDimension, indexes);
}
@Override
public INDArray pullRows(INDArray source, INDArray destination, int sourceDimension, int[] indexes) {
Nd4j.getExecutioner().push();
if (indexes == null || indexes.length < 1)
throw new IllegalStateException("Indexes can't be null or zero-length");
Preconditions.checkArgument(source.dataType() == destination.dataType(), "Source and Destination data types must be the same");
long[] shape = null;
if (source.rank() == 1) {
shape = new long[]{indexes.length};
} else if (sourceDimension == 1)
shape = new long[] {indexes.length, source.shape()[sourceDimension]};
else if (sourceDimension == 0)
shape = new long[] {source.shape()[sourceDimension], indexes.length};
else
throw new UnsupportedOperationException("2D input is expected");
INDArray ret = destination;
if(ret == null){
ret = Nd4j.createUninitialized(source.dataType(), shape, order);
} else {
if(!Arrays.equals(shape, destination.shape())){
throw new IllegalStateException("Cannot pull rows into destination array: expected destination array of" +
" shape " + Arrays.toString(shape) + " but got destination array of shape " + Arrays.toString(destination.shape()));
}
}
AtomicAllocator allocator = AtomicAllocator.getInstance();
CudaContext context = allocator.getFlowController().prepareAction(ret, source);
val x = ((BaseCudaDataBuffer) source.data()).getOpaqueDataBuffer();
val z = ((BaseCudaDataBuffer) ret.data()).getOpaqueDataBuffer();
Pointer xShape = AtomicAllocator.getInstance().getPointer(source.shapeInfoDataBuffer(), context);
Pointer zShape = AtomicAllocator.getInstance().getPointer(ret.shapeInfoDataBuffer(), context);
PointerPointer extras = new PointerPointer(AddressRetriever.retrieveHostPointer(ret.shapeInfoDataBuffer()),
context.getOldStream(), allocator.getDeviceIdPointer());
val tempIndexes = new CudaLongDataBuffer(indexes.length);
AtomicAllocator.getInstance().memcpyBlocking(tempIndexes, new LongPointer(ArrayUtil.toLongArray(indexes)), indexes.length * 8, 0);
Pointer pIndex = AtomicAllocator.getInstance().getPointer(tempIndexes, context);
TADManager tadManager = Nd4j.getExecutioner().getTADManager();
Pair tadBuffers = tadManager.getTADOnlyShapeInfo(source, new int[] {sourceDimension});
Pair 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);
nativeOps.pullRows(extras,
x, (LongPointer) source.shapeInfoDataBuffer().addressPointer(), (LongPointer) xShape,
z, (LongPointer) ret.shapeInfoDataBuffer().addressPointer(), (LongPointer) zShape,
indexes.length,
(LongPointer) pIndex,
(LongPointer) tadShapeInfo,
new LongPointerWrapper(tadOffsets),
(LongPointer) zTadShapeInfo,
new LongPointerWrapper(zTadOffsets));
if (nativeOps.lastErrorCode() != 0)
throw new RuntimeException(nativeOps.lastErrorMessage());
allocator.registerAction(context, ret, source);
return ret;
}
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 (true) {
Nd4j.getExecutioner().push();
long len = target.length();
AtomicAllocator allocator = AtomicAllocator.getInstance();
CudaContext context = allocator.getFlowController().prepareAction(target, arrays);
PointerPointer extras = new PointerPointer(null, // not used
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].length() != len)
throw new ND4JIllegalStateException("All arrays should have equal length for averaging");
AllocationPoint point = allocator.getAllocationPoint(arrays[i]);
xPointers[i] = point.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));
nativeOps.accumulate(extras, null, (LongPointer) arrays[0].shapeInfoDataBuffer().addressPointer(), x, null, null, (LongPointer) allocator.getHostPointer(target.shapeInfoDataBuffer()) , z, (LongPointer) allocator.getPointer(target.shapeInfoDataBuffer()), arrays.length, len);
if (nativeOps.lastErrorCode() != 0)
throw new RuntimeException(nativeOps.lastErrorMessage());
allocator.getFlowController().registerAction(context, target, arrays);
return target;
} else {
long len = target.length();
Nd4j.getExecutioner().commit();
val context = (CudaContext) AtomicAllocator.getInstance().getDeviceContext();
val dataPointers = new PointerPointer(arrays.length);
val extras = new PointerPointer(null, // not used
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].length() != len)
throw new ND4JIllegalStateException("All arrays should have equal length for averaging");
((BaseCudaDataBuffer) arrays[i].data()).lazyAllocateHostPointer();
dataPointers.put(i, AtomicAllocator.getInstance().getHostPointer(arrays[i]));
}
if (target != null)
((BaseCudaDataBuffer) target.data()).lazyAllocateHostPointer();
nativeOps.accumulate(extras,
dataPointers,
(LongPointer) arrays[0].shapeInfoDataBuffer().addressPointer(),
null,
null,
target == null ? null : AtomicAllocator.getInstance().getHostPointer(target),
target == null ? null : (LongPointer) AtomicAllocator.getInstance().getHostPointer(target.shapeInfoDataBuffer()),
null,
null,
arrays.length,
len);
if (nativeOps.lastErrorCode() != 0)
throw new RuntimeException(nativeOps.lastErrorMessage());
AtomicAllocator.getInstance().getAllocationPoint(target).tickHostWrite();
return target;
}
}
@Override
public INDArray average(INDArray target, INDArray[] arrays) {
if (arrays == null || arrays.length == 0)
throw new RuntimeException("Input arrays are missing");
if (arrays.length == 1) {
//Edge case - average 1 array - no op
if(target == null){
return null;
}
return target.assign(arrays[0]);
}
// we do averaging on GPU only if ALL devices have p2p links
if (nativeOps.isP2PAvailable() && CudaEnvironment.getInstance().getConfiguration().isCrossDeviceAccessAllowed()) {
Nd4j.getExecutioner().push();
long len = target != null ? target.length() : arrays[0].length();
AtomicAllocator allocator = AtomicAllocator.getInstance();
CudaContext context = allocator.getFlowController().prepareAction(target, arrays);
PointerPointer extras = new PointerPointer(null, // not used
context.getOldStream(), allocator.getDeviceIdPointer(), new CudaPointer(0));
Pointer z = target == null ? null : 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].length() != len)
throw new ND4JIllegalStateException("All arrays should have equal length for averaging");
AllocationPoint point = allocator.getAllocationPoint(arrays[i]);
xPointers[i] = point.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));
nativeOps.average(extras,
null,
(LongPointer) arrays[0].shapeInfoDataBuffer().addressPointer(),
x,
null,
null,
(LongPointer) (target == null ? null : target.shapeInfoDataBuffer().addressPointer()),
target == null ? null : z,
null,
arrays.length,
len, true);
if (nativeOps.lastErrorCode() != 0)
throw new RuntimeException(nativeOps.lastErrorMessage());
allocator.getFlowController().registerAction(context, target, arrays);
return target;
} else {
// otherwise we do averging on CPU side
/**
* We expect all operations are complete at this point
*/
long len = target == null ? arrays[0].length() : target.length();
val context = (CudaContext) AtomicAllocator.getInstance().getDeviceContext();
val dataPointers = new PointerPointer(arrays.length);
val extras = new PointerPointer(null, // not used
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].length() != len)
throw new ND4JIllegalStateException("All arrays should have equal length for averaging");
((BaseCudaDataBuffer) arrays[i].data()).lazyAllocateHostPointer();
dataPointers.put(i, AtomicAllocator.getInstance().getHostPointer(arrays[i]));
}
if (target != null)
((BaseCudaDataBuffer) target.data()).lazyAllocateHostPointer();
nativeOps.average(extras,
dataPointers,
(LongPointer) arrays[0].shapeInfoDataBuffer().addressPointer(),
null,
null,
target == null ? null : target.data().addressPointer(),
(LongPointer) (target == null ? null : target.shapeInfoDataBuffer().addressPointer()),
null,
null,
arrays.length,
len, true);
if (nativeOps.lastErrorCode() != 0)
throw new RuntimeException(nativeOps.lastErrorMessage());
if (target != null)
AtomicAllocator.getInstance().getAllocationPoint(target).tickHostWrite();
// TODO: make propagation optional maybe?
if (true) {
for (int i = 0; i < arrays.length; i++) {
AtomicAllocator.getInstance().getAllocationPoint(arrays[i]).tickHostWrite();
}
}
return target;
}
}
@Override
public INDArray average(Collection arrays) {
return average(arrays.toArray(new INDArray[0]));
}
/**
* This method averages input arrays, and returns averaged array
*
* @param arrays
* @return
*/
@Override
public INDArray average(INDArray[] arrays) {
if (arrays == null || arrays.length == 0)
throw new RuntimeException("Input arrays are missing");
// we assume all arrays have equal length,
INDArray ret = Nd4j.createUninitialized(arrays[0].dataType(), arrays[0].shape(), arrays[0].ordering());
return average(ret, arrays);
}
/**
* This method averages input arrays, and returns averaged array
*
* @param target
* @param arrays
* @return
*/
@Override
public INDArray average(INDArray target, Collection arrays) {
return average(target, arrays.toArray(new INDArray[0]));
}
/**
* In place shuffle of an ndarray
* along a specified set of dimensions
*
* @param array the ndarray to shuffle
* @param dimension the dimension to do the shuffle
* @return
*/
@Override
public void shuffle(INDArray array, Random rnd, int... dimension) {
shuffle(Collections.singletonList(array), rnd, dimension);
}
/**
* 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 arrays, Random rnd, List 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));
}
val zero = arrays.get(0);
int tadLength = 1;
if (zero.rank() > 1)
for (int i = 0; i < dimensions.get(0).length; i++) {
tadLength *= zero.shape()[dimensions.get(0)[i]];
}
val numTads = zero.length() / tadLength;
val map = ArrayUtil.buildInterleavedVector(rnd, (int) numTads);
val shuffle = new CudaIntDataBuffer(map);
val shuffleMap = allocator.getPointer(shuffle, context);
val extras = new PointerPointer(null, // not used
context.getOldStream(), allocator.getDeviceIdPointer());
long[] hPointers = new long[arrays.size()];
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++) {
val array = arrays.get(i);
//we have to sync manually here as we are calling the method with raw cuda pointers
AllocationPoint point = allocator.getAllocationPoint(array);
if(point.isActualOnHostSide()){
AtomicAllocator.getInstance().getFlowController().synchronizeToDevice(point);
point.tickDeviceWrite();
}
val x = AtomicAllocator.getInstance().getPointer(array, context);
val xShapeInfo = AtomicAllocator.getInstance().getPointer(array.shapeInfoDataBuffer(), context);
val tadManager = Nd4j.getExecutioner().getTADManager();
int[] dimension = dimensions.size() > 1 ? dimensions.get(i) : dimensions.get(0);
val tadBuffers = tadManager.getTADOnlyShapeInfo(array, dimension);
// log.info("Original shape: {}; dimension: {}; TAD shape: {}", array.shapeInfoDataBuffer().asInt(), dimension, tadBuffers.getFirst().asInt());
val tadShapeInfo = AtomicAllocator.getInstance().getPointer(tadBuffers.getFirst(), context);
val offsets = tadBuffers.getSecond();
if (zero.rank() != 1 && offsets.length() != numTads)
throw new ND4JIllegalStateException("Can't symmetrically shuffle arrays with non-equal number of TADs");
val tadOffset = AtomicAllocator.getInstance().getPointer(offsets, context);
hPointers[i] = AtomicAllocator.getInstance().getHostPointer(array.shapeInfoDataBuffer()).address();
xPointers[i] = x.address();
xShapes[i] = xShapeInfo.address();
tadShapes[i] = tadShapeInfo.address();
tadOffsets[i] = tadOffset.address();
}
val hostPointers = new LongPointer(hPointers);
val hosthost = new PointerPointerWrapper(hostPointers);
val tempX = new CudaDoubleDataBuffer(arrays.size());
val tempShapes = new CudaDoubleDataBuffer(arrays.size());
val tempTAD = new CudaDoubleDataBuffer(arrays.size());
val 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);
nativeOps.shuffle(extras,
null,
hosthost,
new PointerPointer(allocator.getPointer(tempX, context)),
new PointerPointer(allocator.getPointer(tempShapes, context)),
null,
null,
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)));
if (nativeOps.lastErrorCode() != 0)
throw new RuntimeException(nativeOps.lastErrorMessage());
for (int f = 0; f < arrays.size(); f++) {
allocator.getFlowController().registerAction(context, arrays.get(f));
}
// just to keep reference
//shuffle.address();
//hostPointers.address();
tempX.dataType();
tempShapes.dataType();
tempOffsets.dataType();
tempTAD.dataType();
}
/**
* Symmetric in place shuffle of an ndarray
* along a specified set of dimensions. All arrays
*
* @param sourceArrays the ndarray to shuffle
* @param dimension the dimension to do the shuffle
* @return
*/
@Override
public void shuffle(Collection sourceArrays, Random rnd, int... dimension) {
shuffle(new ArrayList(sourceArrays), rnd, Collections.singletonList(dimension));
}
/*
public DataBuffer convertToHalfs(DataBuffer buffer) {
DataBuffer halfsBuffer = new CudaHalfDataBuffer(buffer.length());
AtomicAllocator allocator = AtomicAllocator.getInstance();
AllocationPoint pointSrc = allocator.getAllocationPoint(buffer);
AllocationPoint pointDst = allocator.getAllocationPoint(halfsBuffer);
CudaContext context = allocator.getFlowController().prepareAction(pointDst, pointSrc);
PointerPointer extras = new PointerPointer(
null, // not used for conversion
context.getOldStream(),
AtomicAllocator.getInstance().getDeviceIdPointer());
Pointer x = AtomicAllocator.getInstance().getPointer(buffer, context);
Pointer z = AtomicAllocator.getInstance().getPointer(halfsBuffer, context);
if (buffer.dataType() == DataType.FLOAT) {
NativeOpsHolder.getInstance().getDeviceNativeOps().convertFloatsToHalfs(extras, x, (int) buffer.length(), z);
pointDst.tickDeviceWrite();
} else if (buffer.dataType() == DataType.DOUBLE) {
NativeOpsHolder.getInstance().getDeviceNativeOps().convertDoublesToHalfs(extras, x, (int) buffer.length(), z);
pointDst.tickDeviceWrite();
} else if (buffer.dataType() == DataType.HALF) {
log.info("Buffer is already HALF-precision");
return buffer;
} else {
throw new UnsupportedOperationException("Conversion INT->HALF isn't supported yet.");
}
allocator.getFlowController().registerAction(context, pointDst, pointSrc);
return halfsBuffer;
}
public DataBuffer restoreFromHalfs(DataBuffer buffer) {
if (buffer.dataType() != DataType.HALF)
throw new IllegalStateException("Input DataBuffer should contain Halfs");
DataBuffer outputBuffer = null;
if (Nd4j.dataType() == DataType.FLOAT) {
outputBuffer = new CudaFloatDataBuffer(buffer.length());
} else if (Nd4j.dataType() == DataType.DOUBLE) {
outputBuffer = new CudaDoubleDataBuffer(buffer.length());
} else throw new UnsupportedOperationException("DataType ["+Nd4j.dataType()+"] isn't supported yet");
AtomicAllocator allocator = AtomicAllocator.getInstance();
AllocationPoint pointSrc = allocator.getAllocationPoint(buffer);
AllocationPoint pointDst = allocator.getAllocationPoint(outputBuffer);
CudaContext context = allocator.getFlowController().prepareAction(pointDst, pointSrc);
PointerPointer extras = new PointerPointer(
null, // not used for conversion
context.getOldStream(),
AtomicAllocator.getInstance().getDeviceIdPointer());
Pointer x = AtomicAllocator.getInstance().getPointer(buffer, context);
Pointer z = AtomicAllocator.getInstance().getPointer(outputBuffer, context);
if (Nd4j.dataType() == DataType.FLOAT) {
NativeOpsHolder.getInstance().getDeviceNativeOps().convertHalfsToFloats(extras, x, (int) buffer.length(), z);
pointDst.tickDeviceWrite();
} else if (Nd4j.dataType() == DataType.DOUBLE) {
NativeOpsHolder.getInstance().getDeviceNativeOps().convertHalfsToDoubles(extras, x, (int) buffer.length(), z);
pointDst.tickDeviceWrite();
} else if (Nd4j.dataType() == DataType.HALF) {
log.info("Buffer is already HALF-precision");
return buffer;
}
allocator.getFlowController().registerAction(context, pointDst, pointSrc);
return outputBuffer;
}
*/
/**
* This method converts Single/Double precision databuffer to Half-precision databuffer
*
* @param typeSrc
* @param source
* @param typeDst @return
*/
@Override
public INDArray convertDataEx(DataTypeEx typeSrc, INDArray source, DataTypeEx typeDst) {
if (source.isView())
throw new UnsupportedOperationException("Impossible to compress View. Consider using dup() before. ");
DataBuffer buffer = convertDataEx(typeSrc, source.data(), typeDst);
source.setData(buffer);
if (buffer instanceof CompressedDataBuffer)
source.markAsCompressed(true);
else
source.markAsCompressed(false);
return source;
}
@Override
public void convertDataEx(DataTypeEx typeSrc, Pointer source, DataTypeEx typeDst, Pointer target, long length) {
val stream = AtomicAllocator.getInstance().getDeviceContext().getOldStream();
val p = new PointerPointer<>(new Pointer[]{null, stream});
nativeOps.convertTypes(p, typeSrc.ordinal(), source, length, typeDst.ordinal(), target);
if (nativeOps.lastErrorCode() != 0)
throw new RuntimeException(nativeOps.lastErrorMessage());
}
@Override
public void convertDataEx(DataTypeEx typeSrc, Pointer source, DataTypeEx typeDst, DataBuffer buffer) {
Pointer srcPtr = null;
Pointer dstPtr = null;
long size = 0;
long ssize = 0;
val stream = AtomicAllocator.getInstance().getDeviceContext().getOldStream();
if (buffer instanceof CompressedDataBuffer) {
// compressing
size = ((CompressedDataBuffer) buffer).getCompressionDescriptor().getCompressedLength();
ssize = ((CompressedDataBuffer) buffer).getCompressionDescriptor().getOriginalLength();
srcPtr = nativeOps.mallocDevice(ssize, 0, 0);
dstPtr = nativeOps.mallocDevice(size, 0, 0);
if (nativeOps.lastErrorCode() != 0)
throw new RuntimeException(nativeOps.lastErrorMessage());
nativeOps.memcpyAsync(srcPtr, source, ssize, CudaConstants.cudaMemcpyHostToDevice, stream);
if (nativeOps.lastErrorCode() != 0)
throw new RuntimeException(nativeOps.lastErrorMessage());
} else {
// decompressing
throw new UnsupportedOperationException();
}
convertDataEx(typeSrc, srcPtr, typeDst, dstPtr, buffer.length());
nativeOps.memcpyAsync(buffer.addressPointer(), dstPtr, size, CudaConstants.cudaMemcpyHostToHost, stream);
stream.synchronize();
if (nativeOps.lastErrorCode() != 0)
throw new RuntimeException(nativeOps.lastErrorMessage());
if (buffer instanceof CompressedDataBuffer) {
nativeOps.freeDevice(srcPtr, 0);
nativeOps.freeDevice(dstPtr, 0);
if (nativeOps.lastErrorCode() != 0)
throw new RuntimeException(nativeOps.lastErrorMessage());
}
}
@Override
public void convertDataEx(DataTypeEx typeSrc, DataBuffer source, DataTypeEx typeDst, DataBuffer target) {
val stream = AtomicAllocator.getInstance().getDeviceContext().getOldStream();
Pointer srcPtr = null;
Pointer dstPtr = null;
// we have to replace pointer here, temporary
if (Nd4j.getWorkspaceManager().anyWorkspaceActiveForCurrentThread()) {
val ws = Nd4j.getMemoryManager().getCurrentWorkspace();
// if true - we're decompressing from host memory
if (source instanceof CompressedDataBuffer) {
val size = ((CompressedDataBuffer) source).getCompressionDescriptor().getCompressedLength();
srcPtr = ws.alloc(size, MemoryKind.DEVICE, DataType.HALF, false);
nativeOps.memcpyAsync(srcPtr, source.addressPointer(), size, CudaConstants.cudaMemcpyHostToHost, stream);
if (nativeOps.lastErrorCode() != 0)
throw new RuntimeException(nativeOps.lastErrorMessage());
}
// if true - we're compressing into host memory
if (target instanceof CompressedDataBuffer) {
val size = ((CompressedDataBuffer) target).getCompressionDescriptor().getCompressedLength();
dstPtr = ws.alloc(size, MemoryKind.DEVICE, DataType.HALF, false);
}
} else {
// if true - we're decompressing from host memory
if (source instanceof CompressedDataBuffer) {
log.info("Replacing source ptr");
val size = ((CompressedDataBuffer) source).getCompressionDescriptor().getCompressedLength();
srcPtr = nativeOps.mallocDevice(size, 0, 0);
nativeOps.memcpyAsync(srcPtr, source.addressPointer(), size, CudaConstants.cudaMemcpyHostToHost, stream);
stream.synchronize();
if (nativeOps.lastErrorCode() != 0)
throw new RuntimeException(nativeOps.lastErrorMessage());
} else
srcPtr = AtomicAllocator.getInstance().getPointer(source);
// if true - we're compressing into host memory
if (target instanceof CompressedDataBuffer) {
log.info("Replacing target ptr");
val size = ((CompressedDataBuffer) target).getCompressionDescriptor().getCompressedLength();
dstPtr = nativeOps.mallocDevice(size, 0, 0);
if (nativeOps.lastErrorCode() != 0)
throw new RuntimeException(nativeOps.lastErrorMessage());
} else
dstPtr = AtomicAllocator.getInstance().getPointer(target);
}
convertDataEx(typeSrc, srcPtr, typeDst, dstPtr, target.length());
if (nativeOps.lastErrorCode() != 0)
throw new RuntimeException(nativeOps.lastErrorMessage());
Nd4j.getExecutioner().commit();
// we were compressing something into temporary buffer
if (target instanceof CompressedDataBuffer) {
nativeOps.memcpyAsync(target.addressPointer(), dstPtr, target.capacity(), CudaConstants.cudaMemcpyHostToHost, stream);
if (Nd4j.getWorkspaceManager().anyWorkspaceActiveForCurrentThread()) {
// no-op, workspace was used
} else
nativeOps.freeDevice(dstPtr, 0);
}
// we were decompressing something from host memory
if (source instanceof CompressedDataBuffer) {
if (Nd4j.getWorkspaceManager().anyWorkspaceActiveForCurrentThread()) {
// no-op, workspace was used
} else
nativeOps.freeDevice(srcPtr, 0);
}
if (nativeOps.lastErrorCode() != 0)
throw new RuntimeException(nativeOps.lastErrorMessage());
Nd4j.getExecutioner().commit();
}
@Override
public DataBuffer convertDataEx(DataTypeEx typeSrc, DataBuffer source, DataTypeEx typeDst) {
int elementSize = 0;
if (typeDst.ordinal() <= 2)
elementSize = 1;
else if (typeDst.ordinal() <= 5)
elementSize = 2;
else if (typeDst.ordinal() == 6)
elementSize = 4;
else if (typeDst.ordinal() == 7)
elementSize = 8;
else
throw new UnsupportedOperationException("Unknown target TypeEx: " + typeDst.name());
// flushQueue should be blocking here, because typeConversion happens on cpu side
Nd4j.getExecutioner().commit();
DataBuffer buffer = null;
if (!(source instanceof CompressedDataBuffer))
AtomicAllocator.getInstance().synchronizeHostData(source);
if (CompressionUtils.goingToCompress(typeSrc, typeDst)) {
// all types below 8 are compression modes
Pointer pointer = new BytePointer(source.length() * elementSize);
CompressionDescriptor descriptor = new CompressionDescriptor(source, typeDst.name());
descriptor.setCompressionType(CompressionType.LOSSY);
descriptor.setCompressedLength(source.length() * elementSize);
buffer = new CompressedDataBuffer(pointer, descriptor);
} else {
CompressedDataBuffer compressed = (CompressedDataBuffer) source;
CompressionDescriptor descriptor = compressed.getCompressionDescriptor();
// decompression mode
buffer = Nd4j.createBuffer(descriptor.getNumberOfElements(), false);
AllocationPoint point = AtomicAllocator.getInstance().getAllocationPoint(buffer);
point.tickDeviceWrite();
}
convertDataEx(typeSrc, source, typeDst, buffer);
return buffer;
}
@Override
public INDArray[] tear(INDArray tensor, int... dimensions) {
if (tensor.isCompressed())
Nd4j.getCompressor().decompressi(tensor);
Arrays.sort(dimensions);
Pair 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.length() / 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());
val x = ((BaseCudaDataBuffer) tensor.data()).getOpaqueDataBuffer();
nativeOps.tear(extraz,
x, (LongPointer) tensor.shapeInfoDataBuffer().addressPointer(), (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))
);
if (nativeOps.lastErrorCode() != 0)
throw new RuntimeException(nativeOps.lastErrorMessage());
AtomicAllocator.getInstance().getFlowController().registerActionAllWrite(context, result);
AtomicAllocator.getInstance().getFlowController().registerAction(context,null, result);
return result;
}
@Override
public INDArray sort(INDArray x, boolean descending) {
if (x.isScalar())
return x;
Nd4j.getExecutioner().push();
CudaContext context = AtomicAllocator.getInstance().getFlowController().prepareAction(x);
Pointer ptr = AtomicAllocator.getInstance().getHostPointer(x.shapeInfoDataBuffer());
PointerPointer extraz = new PointerPointer(ptr, // 0
context.getOldStream(), // 1
AtomicAllocator.getInstance().getDeviceIdPointer(), // 2
null, // 3
context.getBufferReduction(), // 4
context.getBufferScalar(), // 5
null, // 6
ptr, // 7
AtomicAllocator.getInstance().getHostPointer(x.shapeInfoDataBuffer()), // 8
ptr, // 9
ptr, // 10
ptr, // 11
ptr, // 12
ptr, // 13
ptr, // 14
ptr, // special pointer for IsMax // 15
ptr, // special pointer for IsMax // 16
ptr, // special pointer for IsMax // 17
new CudaPointer(0));
// we're sending > 10m elements to radixSort
boolean isRadix = !x.isView() && (x.length() > 1024 * 1024 * 10);
INDArray tmpX = x;
// we need to guarantee all threads are finished here
if (isRadix)
Nd4j.getExecutioner().commit();
nativeOps.sort(extraz,
null,
(LongPointer) x.shapeInfoDataBuffer().addressPointer(),
AtomicAllocator.getInstance().getPointer(tmpX, context),
(LongPointer) AtomicAllocator.getInstance().getPointer(tmpX.shapeInfoDataBuffer(), context),
descending
);
if (nativeOps.lastErrorCode() != 0)
throw new RuntimeException(nativeOps.lastErrorMessage());
AtomicAllocator.getInstance().getFlowController().registerAction(context, x);
return x;
}
@Override
public INDArray empty(DataType type) {
long extras = ArrayOptionsHelper.setOptionBit(0L, ArrayType.EMPTY);
extras = ArrayOptionsHelper.setOptionBit(extras, type);
val shape = Nd4j.getShapeInfoProvider().createShapeInformation(new long[0], new long[0], 1, 'c', extras);
return new JCublasNDArray(null, (CudaLongDataBuffer) shape.getFirst(), shape.getSecond());
}
@Override
public INDArray sort(INDArray x, boolean descending, int... dimension) {
if (x.isScalar())
return x;
Arrays.sort(dimension);
Nd4j.getExecutioner().push();
val tadBuffers = Nd4j.getExecutioner().getTADManager().getTADOnlyShapeInfo(x, dimension);
val context = AtomicAllocator.getInstance().getFlowController().prepareAction(x);
val extraz = new PointerPointer(AtomicAllocator.getInstance().getHostPointer(x.shapeInfoDataBuffer()), // not used
context.getOldStream(), AtomicAllocator.getInstance().getDeviceIdPointer());
val dimensionPointer = AtomicAllocator.getInstance()
.getHostPointer(AtomicAllocator.getInstance().getConstantBuffer(dimension));
nativeOps.sortTad(extraz,
null,
(LongPointer) x.shapeInfoDataBuffer().addressPointer(),
AtomicAllocator.getInstance().getPointer(x, context),
(LongPointer) AtomicAllocator.getInstance().getPointer(x.shapeInfoDataBuffer(), context),
(IntPointer) dimensionPointer,
dimension.length,
(LongPointer) AtomicAllocator.getInstance().getPointer(tadBuffers.getFirst(), context),
new LongPointerWrapper(AtomicAllocator.getInstance().getPointer(tadBuffers.getSecond(), context)),
descending
);
if (nativeOps.lastErrorCode() != 0)
throw new RuntimeException(nativeOps.lastErrorMessage());
AtomicAllocator.getInstance().getFlowController().registerAction(context, x);
return x;
}
@Override
public INDArray create(float[] data, long[] shape, long[] stride, long offset) {
return new JCublasNDArray(data, shape, stride, offset, Nd4j.order());
}
@Override
public INDArray create(float[] data, long[] shape, long[] stride, char order, DataType dataType) {
return new JCublasNDArray(Nd4j.createTypedBuffer(data, dataType), shape, stride, order, dataType);
}
@Override
public INDArray create(double[] data, long[] shape, long[] stride, long offset) {
return new JCublasNDArray(data, shape, stride, offset, Nd4j.order());
}
@Override
public INDArray create(double[] data, long[] shape, long[] stride, DataType dataType, MemoryWorkspace workspace) {
return new JCublasNDArray(Nd4j.createTypedBuffer(data, dataType, workspace), shape, stride, Nd4j.order(), dataType);
}
@Override
public INDArray create(float[] data, long[] shape, long[] stride, DataType dataType, MemoryWorkspace workspace) {
return new JCublasNDArray(Nd4j.createTypedBuffer(data, dataType, workspace), shape, stride, Nd4j.order(), dataType);
}
@Override
public INDArray create(long[] data, long[] shape, long[] stride, DataType dataType, MemoryWorkspace workspace) {
return new JCublasNDArray(Nd4j.createTypedBuffer(data, dataType), shape, stride, Nd4j.order(), dataType);
}
@Override
public INDArray create(int[] data, long[] shape, long[] stride, DataType dataType, MemoryWorkspace workspace) {
return new JCublasNDArray(Nd4j.createTypedBuffer(data, dataType), shape, stride, Nd4j.order(), dataType);
}
@Override
public INDArray create(short[] data, long[] shape, long[] stride, DataType dataType, MemoryWorkspace workspace) {
return new JCublasNDArray(Nd4j.createTypedBuffer(data, dataType), shape, stride, Nd4j.order(), dataType);
}
@Override
public INDArray create(byte[] data, long[] shape, long[] stride, DataType dataType, MemoryWorkspace workspace) {
return new JCublasNDArray(Nd4j.createTypedBuffer(data, dataType), shape, stride, Nd4j.order(), dataType);
}
@Override
public INDArray create(boolean[] data, long[] shape, long[] stride, DataType dataType, MemoryWorkspace workspace) {
return new JCublasNDArray(Nd4j.createTypedBuffer(data, dataType), shape, stride, Nd4j.order(), dataType);
}
@Override
public INDArray create(double[] data, long[] shape, long[] stride, char order, DataType dataType, MemoryWorkspace workspace) {
return new JCublasNDArray(Nd4j.createTypedBuffer(data, dataType, workspace), shape, stride, order, dataType);
}
@Override
public INDArray create(DataBuffer data, long[] shape) {
return new JCublasNDArray(data, shape);
}
@Override
public INDArray create(DataBuffer data, long[] shape, long[] stride, long offset) {
return new JCublasNDArray(data, shape, stride, offset, Nd4j.order(), data.dataType());
}
@Override
public INDArray create(List list, long[] shape) {
return new JCublasNDArray(list, shape);
}
@Override
public INDArray create(long rows, long columns, long[] stride, long offset) {
return create(new long[] {rows, columns}, stride, offset, Nd4j.order());
}
@Override
public INDArray create(long[] shape, char ordering) {
return new JCublasNDArray(shape, 0, ordering);
}
@Override
public INDArray create(DataType dataType, long[] shape, char ordering, MemoryWorkspace workspace) {
return create(dataType, shape, Nd4j.getStrides(shape, ordering), ordering, workspace);
}
@Override
public INDArray create(DataType dataType, long[] shape, long[] strides, char ordering, MemoryWorkspace workspace) {
return new JCublasNDArray(Nd4j.createBuffer(dataType, Shape.lengthOf(shape), true, workspace), shape, strides, ordering, dataType);
}
@Override
public INDArray createUninitialized(long[] shape, char ordering) {
return new JCublasNDArray(shape, Nd4j.getStrides(shape, ordering), 0, ordering, false);
}
@Override
public INDArray createUninitialized(DataType dataType, long[] shape, char ordering, MemoryWorkspace workspace) {
return new JCublasNDArray(Nd4j.createBuffer(dataType, Shape.lengthOf(shape), false), shape, Nd4j.getStrides(shape, ordering), ordering, dataType);
}
@Override
public INDArray createUninitializedDetached(DataType dataType, char ordering, long... shape) {
return new JCublasNDArray(Nd4j.createBufferDetached(shape, dataType), shape, Nd4j.getStrides(shape, order), order, dataType);
}
@Override
public INDArray create(DataBuffer data, long[] newShape, long[] newStride, long offset, char ordering) {
return new JCublasNDArray(data, newShape, newStride, offset, ordering, data.dataType());
}
@Override
public INDArray create(DataBuffer data, long[] newShape, long[] newStride, long offset, long ews, char ordering) {
return new JCublasNDArray(data, newShape, newStride, offset, ews, ordering, data.dataType());
}
@Override
public INDArray create(DataBuffer data, long[] newShape, long[] newStride, long offset, char ordering, DataType dataType) {
//if (data.dataType() != dataType && data.dataType() != DataType.COMPRESSED)
// throw new ND4JIllegalStateException("Data types mismatch: [" + data.dataType() + "] vs [" + dataType + "]");
return new JCublasNDArray(data, newShape, newStride, offset, ordering, dataType);
}
@Override
public INDArray create(List list, long[] shape, char ordering) {
return new JCublasNDArray(list, shape, ordering);
}
@Override
public INDArray create(float[] data, long[] shape, long[] stride, char order, long offset) {
return new JCublasNDArray(data, shape, stride, offset, order);
}
@Override
public INDArray create(float[] data, long[] shape, long[] stride, long offset, char ordering) {
return new JCublasNDArray(data, shape, stride, offset, ordering);
}
@Override
public INDArray create(double[] data, long[] shape, long[] stride, long offset, char ordering) {
return new JCublasNDArray(data, shape, stride, offset, ordering);
}
@Override
public INDArray create(float[] data, long[] shape, long offset, Character order) {
return new JCublasNDArray(data, shape, Nd4j.getStrides(shape, order), offset, order);
}
@Override
public INDArray create(double[] data, long[] shape, long offset, Character order) {
return new JCublasNDArray(data, shape, Nd4j.getStrides(shape, order), offset, order);
}
@Override
public INDArray create(float[] data, long[] shape, char ordering) {
return new JCublasNDArray(data, shape, Nd4j.getStrides(shape, order), 0, ordering);
}
////////////////////////////////////////////////////////////////////////////////////////////////////////////////
@Override
public INDArray sortCooIndices(INDArray x) {
throw new UnsupportedOperationException();
}
@Override
public INDArray create(DataType dataType, long[] shape, long[] paddings, long[] paddingOffsets, char ordering,
MemoryWorkspace workspace) {
return new JCublasNDArray(dataType, shape, paddings, paddingOffsets, ordering, workspace);
}
}
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