nd4j/nd4j-backends/nd4j-backend-impls/nd4j-cpu-backend-common/src/main/java/org/nd4j/linalg/cpu/nativecpu/CpuNDArrayFactory.java
/*
* ******************************************************************************
* *
* *
* * 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.cpu.nativecpu;
import lombok.extern.slf4j.Slf4j;
import lombok.val;
import org.nd4j.common.base.Preconditions;
import org.nd4j.linalg.api.buffer.*;
import org.nd4j.linalg.api.ops.custom.Flatten;
import org.nd4j.linalg.api.ops.impl.shape.Concat;
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.cpu.nativecpu.buffer.BaseCpuDataBuffer;
import org.nd4j.linalg.cpu.nativecpu.buffer.LongBuffer;
import org.nd4j.linalg.cpu.nativecpu.buffer.Utf8Buffer;
import org.nd4j.common.primitives.Pair;
import org.bytedeco.javacpp.*;
import org.nd4j.linalg.api.memory.MemoryWorkspace;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.compression.CompressedDataBuffer;
import org.nd4j.linalg.compression.CompressionDescriptor;
import org.nd4j.linalg.compression.CompressionType;
import org.nd4j.linalg.cpu.nativecpu.blas.*;
import org.nd4j.linalg.exception.ND4JIllegalStateException;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.common.util.ArrayUtil;
import org.nd4j.nativeblas.BaseNativeNDArrayFactory;
import org.nd4j.nativeblas.LongPointerWrapper;
import org.nd4j.nativeblas.NativeOpsHolder;
import java.util.*;
@Slf4j
public class CpuNDArrayFactory extends BaseNativeNDArrayFactory {
protected ThreadLocal<PointerPointer> extrazA = new ThreadLocal<>();
protected ThreadLocal<PointerPointer> extrazB = new ThreadLocal<>();
protected ThreadLocal<Integer> extrazSize = new ThreadLocal<>();
public CpuNDArrayFactory() {}
static {
//invoke the override
Nd4j.getBlasWrapper();
}
public CpuNDArrayFactory(DataType dtype, Character order) {
super(dtype, order);
}
public CpuNDArrayFactory(DataType dtype, char order) {
super(dtype, order);
}
@Override
public void createBlas() {
// we'll check hardware support first
if (!nativeOps.isMinimalRequirementsMet()) {
// this means cpu binary was built for some arch support, we don't have on this box
val binaryLevel = nativeOps.binaryLevel();
val optimalLevel = nativeOps.optimalLevel();
String binLevel = cpuBinaryLevelToName(binaryLevel);
String optLevel = cpuBinaryLevelToName(optimalLevel);
log.warn("*********************************** CPU Feature Check Failed ***********************************");
log.error("Error initializing ND4J: Attempting to use " + binLevel + " ND4J binary on a CPU with only " + optLevel + " support");
log.error( binLevel + " binaries cannot be run on a CPU without these instructions. See https://deeplearning4j.konduit.ai/multi-project/explanation/configuration/backends/performance-issues#step-11-check-cpu-support-for-hardware-extensions-avx-etc for more details");
log.error("ND4J will now exit.");
log.warn("************************************************************************************************");
System.exit(1);
}
val binaryLevel = nativeOps.binaryLevel();
val optimalLevel = nativeOps.optimalLevel();
String binLevel = cpuBinaryLevelToName(binaryLevel);
String optLevel = cpuBinaryLevelToName(optimalLevel);
log.info("Binary level " + binLevel + " optimization level " + optLevel);
blas = new CpuBlas();
// TODO: add batched gemm here
PointerPointer functions = new PointerPointer(10);
functions.put(0, Loader.addressof("cblas_sgemv"));
functions.put(1, Loader.addressof("cblas_dgemv"));
functions.put(2, Loader.addressof("cblas_sgemm"));
functions.put(3, Loader.addressof("cblas_dgemm"));
functions.put(4, Loader.addressof("cblas_sgemm_batch"));
functions.put(5, Loader.addressof("cblas_dgemm_batch"));
functions.put(6, Loader.addressof("LAPACKE_sgesvd"));
functions.put(7, Loader.addressof("LAPACKE_dgesvd"));
functions.put(8, Loader.addressof("LAPACKE_sgesdd"));
functions.put(9, Loader.addressof("LAPACKE_dgesdd"));
nativeOps.initializeFunctions(functions);
if (nativeOps.lastErrorCode() != 0)
throw new RuntimeException(nativeOps.lastErrorMessage());
}
private static String cpuBinaryLevelToName(int level){
switch (level){
case 3:
return "AVX512";
case 2:
return "AVX/AVX2";
case 1:
case 0:
default:
return "Generic x86";
}
}
@Override
public void createLevel1() {
level1 = new CpuLevel1();
}
@Override
public void createLevel2() {
level2 = new CpuLevel2();
}
@Override
public void createLevel3() {
level3 = new CpuLevel3();
}
@Override
public void createLapack() {
lapack = new CpuLapack();
}
@Override
public INDArray create(int[] shape, DataBuffer buffer) {
return new NDArray(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 NDArray(data);
}
@Override
public INDArray create(double[][] data, char ordering) {
return new NDArray(data, ordering);
}
@Override
public INDArray create(DataBuffer data) {
return new NDArray(data);
}
@Override
public INDArray create(DataBuffer data, long rows, long columns, int[] stride, long offset) {
return create(data, new long[]{rows, columns}, ArrayUtil.toLongArray(stride), offset);
}
@Override
public INDArray create(long rows, long columns, long[] stride, long offset) {
return create(new long[]{rows, columns}, stride, offset);
}
@Override
public INDArray create(int[] shape, char ordering) {
return new NDArray(shape, Nd4j.getStrides(shape, ordering), 0, ordering);
}
@Override
public INDArray create(long[] shape, char ordering) {
return new NDArray(shape, Nd4j.getStrides(shape, ordering), 0, ordering);
}
@Override
public INDArray createUninitialized(int[] shape, char ordering) {
return new NDArray(shape, Nd4j.getStrides(shape, ordering), 0, ordering, false);
}
@Override
public INDArray createUninitialized(long[] shape, char ordering) {
return new NDArray(shape, Nd4j.getStrides(shape, ordering), 0, ordering, false);
}
@Override
public INDArray create(DataType dataType, long[] shape, char ordering, MemoryWorkspace workspace) {
return new NDArray(dataType, shape, Nd4j.getStrides(shape, ordering), 0, ordering, workspace);
}
@Override
public INDArray create(DataType dataType, long[] shape, long[] strides, char ordering, MemoryWorkspace workspace ) {
return new NDArray(dataType, shape, strides, 0, ordering);
}
@Override
public INDArray createUninitialized(DataType dataType, long[] shape, char ordering, MemoryWorkspace workspace) {
return new NDArray(dataType, shape, Nd4j.getStrides(shape, ordering), 0, ordering, false, workspace);
}
@Override
public INDArray createUninitializedDetached(DataType dataType, char ordering, long... shape){
MemoryWorkspace workspace = Nd4j.getMemoryManager().getCurrentWorkspace();
Nd4j.getMemoryManager().setCurrentWorkspace(null);
INDArray ret = new NDArray(dataType, shape, Nd4j.getStrides(shape, ordering), 0, ordering, false);
Nd4j.getMemoryManager().setCurrentWorkspace(workspace);
return ret;
}
@Override
public INDArray create(DataBuffer data, int[] newShape, int[] newStride, long offset, char ordering) {
return new NDArray(data, newShape, newStride, offset, ordering);
}
@Override
public INDArray create(float[] data, int[] shape, long offset, Character order) {
return new NDArray(data, shape, offset, order);
}
@Override
public INDArray create(float[] data, long[] shape, long offset, Character order) {
return new NDArray(data, shape, offset, order);
}
@Override
public INDArray create(float[] data, long rows, long columns, int[] stride, long offset, char ordering) {
return create(data, new long[]{rows, columns}, ArrayUtil.toLongArray(stride), offset, ordering);
}
@Override
public INDArray create(double[] data, int[] shape, char ordering) {
return new NDArray(Nd4j.createBuffer(data), shape, ordering);
}
@Override
public INDArray create(double[] data, long[] shape, char ordering) {
return create(data, shape, (Character) ordering);
}
@Override
public INDArray create(float[] data, long[] shape, char ordering) {
return create(data, shape, (Character) ordering);
}
@Override
public INDArray create(List<INDArray> list, int[] shape, char ordering) {
return new NDArray(list, shape, ordering);
}
@Override
public INDArray create(List<INDArray> list, long[] shape, char ordering) {
return new NDArray(list, shape, ordering);
}
@Override
public INDArray create(double[] data, int[] shape, long offset) {
return new NDArray(Nd4j.createBuffer(data), shape, offset);
}
@Override
public INDArray create(double[] data, long[] shape, long offset, Character order) {
return new NDArray(data, shape, offset, order.charValue());
}
@Override
public INDArray create(double[] data, int[] shape, int[] stride, long offset, char ordering) {
return new NDArray(Nd4j.createTypedBuffer(data, DataType.DOUBLE), shape, stride, offset, ordering);
}
@Override
public INDArray create(double[] data, long[] shape, long[] stride, long offset, char ordering) {
return new NDArray(Nd4j.createTypedBuffer(data, DataType.DOUBLE), shape, stride, offset, ordering);
}
@Override
public INDArray create(float[] data, long[] shape, long[] stride, long offset, char ordering) {
return new NDArray(Nd4j.createTypedBuffer(data, DataType.FLOAT), shape, stride, offset, ordering);
}
@Override
public INDArray create(float[] data, long[] shape, long[] stride, long offset) {
return new NDArray(data, shape, stride, offset, Nd4j.order());
}
@Override
public INDArray create(float[] data, long[] shape, long[] stride, char order, DataType dataType) {
return new NDArray(data, shape, stride, 0, order);
}
@Override
public INDArray create(double[] data, long[] shape, long[] stride, long offset) {
return new NDArray(data, shape, stride, offset, Nd4j.order());
}
@Override
public INDArray create(double[] data, long[] shape, long[] stride, DataType dataType, MemoryWorkspace workspace) {
return new NDArray(Nd4j.createTypedBuffer(data, dataType), shape, stride, Nd4j.order(), dataType);
}
@Override
public INDArray create(float[] data, long[] shape, long[] stride, char order, DataType dataType, MemoryWorkspace workspace) {
return new NDArray(Nd4j.createTypedBuffer(data, dataType), shape, stride, order, dataType, workspace);
}
@Override
public INDArray create(double[] data, long[] shape, long[] stride, char order, DataType dataType, MemoryWorkspace workspace) {
return new NDArray(Nd4j.createTypedBuffer(data, dataType,workspace), shape, stride, order, dataType, workspace);
}
@Override
public INDArray create(long[] data, long[] shape, long[] stride, char order, DataType dataType, MemoryWorkspace workspace) {
return new NDArray(Nd4j.createTypedBuffer(data, dataType,workspace), shape, stride, order, dataType,workspace);
}
@Override
public INDArray create(int[] data, long[] shape, long[] stride, char order, DataType dataType, MemoryWorkspace workspace) {
return new NDArray(Nd4j.createTypedBuffer(data, dataType,workspace), shape, stride, order, dataType,workspace);
}
@Override
public INDArray create(short[] data, long[] shape, long[] stride, char order, DataType dataType, MemoryWorkspace workspace) {
return new NDArray(Nd4j.createTypedBuffer(data, dataType,workspace), shape, stride, order, dataType,workspace);
}
@Override
public INDArray create(byte[] data, long[] shape, long[] stride, char order, DataType dataType, MemoryWorkspace workspace) {
return new NDArray(Nd4j.createTypedBuffer(data, dataType,workspace), shape, stride, order, dataType,workspace);
}
@Override
public INDArray create(boolean[] data, long[] shape, long[] stride, char order, DataType dataType, MemoryWorkspace workspace) {
return new NDArray(Nd4j.createTypedBuffer(data, dataType,workspace), shape, stride, order, dataType,workspace);
}
@Override
public INDArray create(float[] data, long[] shape, long[] stride, DataType dataType, MemoryWorkspace workspace) {
return new NDArray(Nd4j.createTypedBuffer(data, dataType,workspace), shape, stride, Nd4j.order(), dataType,workspace);
}
@Override
public INDArray create(long[] data, long[] shape, long[] stride, DataType dataType, MemoryWorkspace workspace) {
return new NDArray(Nd4j.createTypedBuffer(data, dataType,workspace), shape, stride, Nd4j.order(), dataType,workspace);
}
@Override
public INDArray create(int[] data, long[] shape, long[] stride, DataType dataType, MemoryWorkspace workspace) {
return new NDArray(Nd4j.createTypedBuffer(data, dataType,workspace), shape, stride, Nd4j.order(), dataType,workspace);
}
@Override
public INDArray create(short[] data, long[] shape, long[] stride, DataType dataType, MemoryWorkspace workspace) {
return new NDArray(Nd4j.createTypedBuffer(data, dataType,workspace), shape, stride, Nd4j.order(), dataType,workspace);
}
@Override
public INDArray create(boolean[] data, long[] shape, long[] stride, DataType dataType, MemoryWorkspace workspace) {
return new NDArray(Nd4j.createTypedBuffer(data, dataType,workspace), shape, stride, Nd4j.order(), dataType,workspace);
}
@Override
public INDArray create(byte[] data, long[] shape, long[] stride, DataType dataType, MemoryWorkspace workspace) {
return new NDArray(Nd4j.createTypedBuffer(data, dataType,workspace), shape, stride, Nd4j.order(), dataType,workspace);
}
@Override
public INDArray create(DataBuffer data, long[] shape) {
return new NDArray(data, shape);
}
@Override
public INDArray create(DataBuffer data, long[] shape, long[] stride, long offset) {
return create(data, shape, stride, offset, Nd4j.order());
}
@Override
public INDArray create(DataBuffer data, long[] shape, long[] stride, long offset, char ordering) {
return new NDArray(data, shape, stride, offset, ordering);
}
@Override
public INDArray create(DataBuffer data, long[] shape, long[] stride, long offset, long ews, char ordering) {
return new NDArray(data, shape, stride, offset, ews, ordering);
}
@Override
public INDArray create(DataBuffer data, long[] shape, long[] stride, long offset, char ordering, DataType dataType) {
return new NDArray(data, shape, stride, offset, ordering, dataType);
}
@Override
public INDArray create(float[] data, long[] shape, long[] stride, char order, long offset) {
return new NDArray(data, shape, stride, offset, order);
}
/**
* 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 NDArray(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 NDArray(data, shape, stride, offset);
}
@Override
public INDArray create(DataBuffer data, int[] shape) {
return new NDArray(data, shape);
}
@Override
public INDArray create(DataBuffer data, int[] shape, int[] stride, long offset) {
return new NDArray(data, shape, stride, offset, Nd4j.order());
}
/**
* Creates an ndarray with the specified shape
*
* @param list
* @param shape the shape of the ndarray
* @return the instance
*/
@Override
public INDArray create(List<INDArray> list, int[] shape) {
return new NDArray(list, shape, Nd4j.getStrides(shape));
}
@Override
public INDArray create(List<INDArray> list, long[] shape) {
return new NDArray(list, shape, Nd4j.getStrides(shape));
}
@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 NDArray(null, (LongBuffer) shape.getFirst(), shape.getSecond());
}
@Override
public INDArray create(float[][] floats) {
return new NDArray(floats);
}
@Override
public INDArray create(float[][] data, char ordering) {
return new NDArray(data, ordering);
}
@Override
public INDArray create(float[] data, int[] shape, int[] stride, long offset, char ordering) {
return new NDArray(data, shape, stride, offset, ordering);
}
@Override
public INDArray create(DataBuffer buffer, int[] shape, long offset) {
return new NDArray(buffer, shape, Nd4j.getStrides(shape), offset);
}
@Override
public INDArray create(float[] data, int[] shape, long offset) {
return new NDArray(data, shape, offset);
}
@Override
public INDArray toFlattened(char order, Collection<INDArray> matrices) {
Preconditions.checkArgument(matrices.size() > 0, "toFlattened expects > 0 operands");
return Nd4j.exec(new Flatten(order, matrices.toArray(new INDArray[matrices.size()])))[0];
}
@Override
public INDArray[] tear(INDArray tensor, long... dimensions) {
if (tensor.isCompressed())
Nd4j.getCompressor().decompressi(tensor);
Arrays.sort(dimensions);
Pair<DataBuffer, DataBuffer> tadBuffers = Nd4j.getExecutioner().getTADManager().getTADOnlyShapeInfo(tensor, dimensions);
long tadLength = 1;
long[] shape = new long[dimensions.length];
for (int i = 0; i < dimensions.length; i++) {
tadLength *= tensor.size(dimensions[i]);
shape[i] = tensor.size(dimensions[i]);
}
int numTads = (int)(tensor.length() / tadLength);
INDArray[] result = new INDArray[numTads];
PointerPointer targets = new PointerPointer(numTads);
for (int x = 0; x < numTads; x++) {
result[x] = Nd4j.createUninitialized(shape);
targets.put(x, result[x].data().pointer());
}
nativeOps.tear(null,
((BaseCpuDataBuffer) tensor.data()).getOpaqueDataBuffer(), (LongPointer) tensor.shapeInfoDataBuffer().pointer(), null,
targets, (LongPointer) result[0].shapeInfoDataBuffer().pointer(),
(LongPointer) tadBuffers.getFirst().pointer(), new LongPointerWrapper(tadBuffers.getSecond().pointer())
);
if (nativeOps.lastErrorCode() != 0)
throw new RuntimeException(nativeOps.lastErrorMessage());
return result;
}
/**
* concatenate ndarrays along a dimension
*
* @param dimension the dimension to concatenate along
* @param toConcat the ndarrays to concatenate
* @return the concatenate ndarrays
*/
@Override
public INDArray concat(int dimension, INDArray... toConcat) {
if (toConcat == null || toConcat.length == 0)
throw new ND4JIllegalStateException("Can't concatenate 0 arrays");
if (toConcat.length == 1)
return toConcat[0];
return Nd4j.exec(new Concat(dimension, toConcat))[0];
}
/**
* For CPU backend this method is equal to concat()
*
* @param dimension the dimension to concatneate along
* @param toConcat the ndarrays to concateneate
* @return
*/
@Override
public INDArray specialConcat(int dimension, INDArray... toConcat) {
return concat(dimension, toConcat);
}
/**
* 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, ArrayUtil.toLongArray(indexes));
}
@Override
public INDArray pullRows(INDArray source, int sourceDimension, long[] indexes) {
return pullRows(source, sourceDimension, indexes, Nd4j.order());
}
/**
* 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
*/
public INDArray pullRows(INDArray source, int sourceDimension, long[] indexes, char order) {
if (indexes == null || indexes.length < 1)
throw new IllegalStateException("Indexes can't be null or zero-length");
long[] shape;
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, int sourceDimension, int[] indexes, char order) {
return pullRows(source, sourceDimension, ArrayUtil.toLongArray(indexes), order);
}
@Override
public INDArray pullRows(INDArray source, INDArray destination, int sourceDimension, int[] indexes) {
return pullRows(source, destination, sourceDimension, ArrayUtil.toLongArray(indexes));
}
public INDArray pullRows(INDArray source, INDArray destination, int sourceDimension, long[] indexes) {
if (indexes == null || indexes.length < 1)
throw new IllegalStateException("Indexes can't be null or zero-length");
long[] shape = null;
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()));
}
}
Nd4j.getCompressor().autoDecompress(source);
val dummy = new PointerPointer(new Pointer[] {null});
val tadManager = Nd4j.getExecutioner().getTADManager();
val tadBuffers = tadManager.getTADOnlyShapeInfo(source, new long[] {sourceDimension});
val zTadBuffers = tadManager.getTADOnlyShapeInfo(ret, new long[] {sourceDimension});
val hostTadShapeInfo = tadBuffers.getFirst().addressPointer();
val zTadShapeInfo = zTadBuffers.getFirst().addressPointer();
val pIndex = new LongPointer(indexes);
val offsets = tadBuffers.getSecond();
val hostTadOffsets = offsets == null ? null : offsets.addressPointer();
val zOffsets = zTadBuffers.getSecond();
val zTadOffsets = zOffsets == null ? null : zOffsets.addressPointer();
nativeOps.pullRows(dummy,
((BaseCpuDataBuffer) source.data()).getOpaqueDataBuffer(), (LongPointer) source.shapeInfoDataBuffer().addressPointer(), null,
((BaseCpuDataBuffer) ret.data()).getOpaqueDataBuffer(), (LongPointer) ret.shapeInfoDataBuffer().addressPointer(), null,
indexes.length, pIndex,
(LongPointer) hostTadShapeInfo,
new LongPointerWrapper(hostTadOffsets),
(LongPointer) zTadShapeInfo,
new LongPointerWrapper(zTadOffsets));
if (nativeOps.lastErrorCode() != 0)
throw new RuntimeException(nativeOps.lastErrorMessage());
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.addi(arrays[0]);
long len = target.length();
PointerPointer dataPointers = new PointerPointer(arrays.length);
for (int i = 0; i < arrays.length; i++) {
Nd4j.getCompressor().autoDecompress(arrays[i]);
if (arrays[i].elementWiseStride() != 1)
throw new ND4JIllegalStateException("Native accumulation is applicable only to continuous INDArrays");
if (arrays[i].length() != len)
throw new ND4JIllegalStateException("All arrays should have equal length for accumulation");
dataPointers.put(i, arrays[i].data().addressPointer());
}
nativeOps.accumulate(null,
dataPointers, (LongPointer) arrays[0].shapeInfoDataBuffer().addressPointer(),
null, null,
target.data().addressPointer(), (LongPointer) target.shapeInfoDataBuffer().addressPointer(),
null, null,
arrays.length,
len);
if (nativeOps.lastErrorCode() != 0)
throw new RuntimeException(nativeOps.lastErrorMessage());
return target;
}
/**
* This method averages input arrays, and returns averaged array
*
* @param target
* @param arrays
* @return
*/
@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]);
}
long len = target != null ? target.length() : arrays[0].length();
PointerPointer dataPointers = new PointerPointer(arrays.length);
val firstType = arrays[0].dataType();
for (int i = 0; i < arrays.length; i++) {
Nd4j.getCompressor().autoDecompress(arrays[i]);
Preconditions.checkArgument(arrays[i].dataType() == firstType, "All arrays must have the same data type");
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");
dataPointers.put(i, arrays[i].data().addressPointer());
}
nativeOps.average(null,
dataPointers, (LongPointer) arrays[0].shapeInfoDataBuffer().addressPointer(),
null, null,
target == null ? null : target.data().addressPointer(), target == null ? null : (LongPointer) target.shapeInfoDataBuffer().addressPointer(),
null, null,
arrays.length,
len,
true);
if (nativeOps.lastErrorCode() != 0)
throw new RuntimeException(nativeOps.lastErrorMessage());
return target;
}
/**
* This method averages input arrays, and returns averaged array
*
* @param target
* @param arrays
* @return
*/
@Override
public INDArray average(INDArray target, Collection<INDArray> arrays) {
return average(target, arrays.toArray(new INDArray[0]));
}
@Override
public INDArray average(INDArray[] arrays) {
if (arrays == null || arrays.length == 0)
throw new RuntimeException("Input arrays are missing");
INDArray ret = Nd4j.createUninitialized(arrays[0].dataType(), arrays[0].shape(), arrays[0].ordering());
return average(ret, arrays);
}
@Override
public INDArray average(Collection<INDArray> arrays) {
return average(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, long... dimension) {
shuffle(Collections.singletonList(array), rnd, dimension);
}
/**
* Symmetric in place shuffle of an ndarray
* along a specified set of dimensions. All arrays
*
* @param array the ndarray to shuffle
* @param dimension the dimension to do the shuffle
* @return
*/
@Override
public void shuffle(Collection<INDArray> array, Random rnd, long... dimension) {
shuffle(new ArrayList<>(array), rnd, Collections.singletonList(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<INDArray> arrays, Random rnd, List<long[]> dimensions) {
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");
val zero = arrays.get(0);
int tadLength = 1;
if (zero.rank() > 1)
for (int i = 0; i < dimensions.get(0).length; i++) {
tadLength *= zero.size(dimensions.get(0)[i]);
}
long numTads = zero.length() / tadLength;
val map = ArrayUtil.buildInterleavedVector(rnd, (int) numTads);
val dataPointers = new PointerPointer(arrays.size());
val shapePointers = new PointerPointer(arrays.size());
val tadPointers = new PointerPointer(arrays.size());
val offsetPointers = new PointerPointer(arrays.size());
val dummy = new PointerPointer(new Pointer[] {null});
List<Pair<DataBuffer, DataBuffer>> list = new ArrayList<>();
val tadManager = Nd4j.getExecutioner().getTADManager();
val ptrMap = new IntPointer(map);
long[] ptrs = new long[arrays.size()];
for (int i = 0; i < arrays.size(); i++) {
val array = arrays.get(i);
Nd4j.getCompressor().autoDecompress(array);
val dimension = dimensions.size() > 1 ? dimensions.get(i) : dimensions.get(0);
val tadBuffers = tadManager.getTADOnlyShapeInfo(array, dimension);
list.add(tadBuffers);
val hostTadShapeInfo = tadBuffers.getFirst().addressPointer();
val offsets = tadBuffers.getSecond();
if (array.rank() != 1 && offsets.length() != numTads)
throw new ND4JIllegalStateException("Can't symmetrically shuffle arrays with non-equal number of TADs");
if (offsets == null)
throw new ND4JIllegalStateException("Offsets for shuffle can't be null");
dataPointers.put(i, array.data().addressPointer());
shapePointers.put(i, array.shapeInfoDataBuffer().addressPointer());
offsetPointers.put(i, offsets.addressPointer());
tadPointers.put(i, tadBuffers.getFirst().addressPointer());
}
nativeOps.shuffle(dummy,
dataPointers, shapePointers,
null, null,
dataPointers, shapePointers,
null, null,
arrays.size(),
ptrMap, tadPointers, offsetPointers);
if (nativeOps.lastErrorCode() != 0)
throw new RuntimeException(nativeOps.lastErrorMessage());
dataPointers.address();
shapePointers.address();
tadPointers.address();
offsetPointers.address();
}
/**
* 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);
if (nativeOps.lastErrorCode() != 0)
throw new RuntimeException(nativeOps.lastErrorMessage());
source.setData(buffer);
if (buffer instanceof CompressedDataBuffer)
source.markAsCompressed(true);
else
source.markAsCompressed(false);
return source;
}
@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());
DataBuffer buffer = null;
if (CompressionUtils.goingToCompress(typeSrc, typeDst)) {
// all types below 6 are compression modes
BytePointer 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(), true);
}
convertDataEx(typeSrc, source, typeDst, buffer);
if (nativeOps.lastErrorCode() != 0)
throw new RuntimeException(nativeOps.lastErrorMessage());
return buffer;
}
@Override
public void convertDataEx(DataTypeEx typeSrc, Pointer source, DataTypeEx typeDst, Pointer target,
long length) {
nativeOps.convertTypes(null, 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) {
convertDataEx(typeSrc, source, typeDst, buffer.addressPointer(), buffer.length());
}
@Override
public void convertDataEx(DataTypeEx typeSrc, DataBuffer source, DataTypeEx typeDst,
DataBuffer target) {
convertDataEx(typeSrc, source.addressPointer(), typeDst, target.addressPointer(), target.length());
}
@Override
public INDArray sort(INDArray x, boolean descending) {
if (x.isScalar())
return x;
NativeOpsHolder.getInstance().getDeviceNativeOps().sort(null,
x.data().addressPointer(), (LongPointer) x.shapeInfoDataBuffer().addressPointer(),
null, null,
descending);
return x;
}
@Override
public INDArray sort(INDArray x, boolean descending, long... dimension) {
if (x.isScalar())
return x;
Arrays.sort(dimension);
Pair<DataBuffer, DataBuffer> tadBuffers = Nd4j.getExecutioner().getTADManager().getTADOnlyShapeInfo(x, dimension);
NativeOpsHolder.getInstance().getDeviceNativeOps().sortTad(null,
x.data().addressPointer(), (LongPointer) x.shapeInfoDataBuffer().addressPointer(),
null, null,
(LongPointer) Nd4j.getConstantHandler().getConstantBuffer(dimension, DataType.LONG).addressPointer(),
dimension.length,
(LongPointer) tadBuffers.getFirst().addressPointer(),
new LongPointerWrapper(tadBuffers.getSecond().addressPointer()),
descending);
return x;
}
@Override
public INDArray sortCooIndices(INDArray x) {
throw new UnsupportedOperationException("Not an COO ndarray");
}
@Override
public INDArray create(Collection<String> strings, long[] shape, char order) {
val pairShape = Nd4j.getShapeInfoProvider().createShapeInformation(shape, order, DataType.UTF8);
val buffer = new Utf8Buffer(strings);
return Nd4j.createArrayFromShapeBuffer(buffer, pairShape);
}
@Override
public INDArray create(DataType dataType, long[] shape, long[] paddings, long[] paddingOffsets, char ordering,
MemoryWorkspace workspace) {
return new NDArray(dataType, shape, paddings, paddingOffsets, ordering, workspace);
}
}