org.nd4j.linalg.cpu.nativecpu.ops.NativeOpExecutioner Maven / Gradle / Ivy
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* ******************************************************************************
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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.
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* * SPDX-License-Identifier: Apache-2.0
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package org.nd4j.linalg.cpu.nativecpu.ops;
import lombok.Data;
import lombok.Getter;
import lombok.NonNull;
import lombok.extern.slf4j.Slf4j;
import lombok.val;
import org.bytedeco.javacpp.*;
import org.bytedeco.javacpp.indexer.LongIndexer;
import org.nd4j.autodiff.functions.DifferentialFunction;
import org.nd4j.autodiff.samediff.serde.FlatBuffersMapper;
import org.nd4j.common.base.Preconditions;
import org.nd4j.common.config.ND4JEnvironmentVars;
import org.nd4j.linalg.api.buffer.*;
import org.nd4j.linalg.api.environment.Nd4jEnvironment;
import org.nd4j.linalg.api.memory.pointers.PagedPointer;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ndarray.INDArrayStatistics;
import org.nd4j.linalg.api.ops.*;
import org.nd4j.linalg.api.ops.aggregates.Aggregate;
import org.nd4j.linalg.api.ops.aggregates.Batch;
import org.nd4j.linalg.api.ops.executioner.DefaultOpExecutioner;
import org.nd4j.linalg.api.ops.executioner.OpStatus;
import org.nd4j.linalg.api.ops.impl.scatter.ScatterUpdate;
import org.nd4j.linalg.api.ops.impl.summarystats.Variance;
import org.nd4j.linalg.api.ops.impl.transforms.any.IsMax;
import org.nd4j.linalg.api.ops.performance.PerformanceTracker;
import org.nd4j.linalg.api.ops.random.BaseRandomOp;
import org.nd4j.linalg.api.rng.Random;
import org.nd4j.linalg.api.shape.LongShapeDescriptor;
import org.nd4j.linalg.api.shape.Shape;
import org.nd4j.linalg.api.shape.TadPack;
import org.nd4j.linalg.api.shape.options.ArrayOptionsHelper;
import org.nd4j.linalg.api.shape.options.ArrayType;
import org.nd4j.linalg.cache.ConstantHandler;
import org.nd4j.linalg.cache.TADManager;
import org.nd4j.linalg.cpu.nativecpu.CpuTADManager;
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.linalg.cpu.nativecpu.rng.CpuNativeRandom;
import org.nd4j.linalg.exception.ND4JIllegalArgumentException;
import org.nd4j.linalg.exception.ND4JIllegalStateException;
import org.nd4j.linalg.exception.ND4JOpProfilerException;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.api.memory.MemcpyDirection;
import org.nd4j.common.primitives.AtomicBoolean;
import org.nd4j.common.primitives.Optional;
import org.nd4j.common.primitives.Pair;
import org.nd4j.common.util.ArrayUtil;
import org.nd4j.nativeblas.*;
import java.util.*;
@Slf4j
public class NativeOpExecutioner extends DefaultOpExecutioner {
private NativeOps loop = NativeOpsHolder.getInstance().getDeviceNativeOps();
private ConstantHandler constantHandler = Nd4j.getConstantHandler();
@Getter
private CpuTADManager tadManager = new CpuTADManager();
//thread locals for custom op inputs and outputs to prevent allocations
//every time exec(CustomOp) is called
private ThreadLocal> inputShapes = new ThreadLocal<>();
private ThreadLocal> inputBuffers = new ThreadLocal<>();
private ThreadLocal> outputShapes = new ThreadLocal<>();
private ThreadLocal> outputBuffers = new ThreadLocal<>();
private ThreadLocal> iArgsPointer = new ThreadLocal<>();
private ThreadLocal> tArgsPointer = new ThreadLocal<>();
private ThreadLocal> bArgsPointer = new ThreadLocal<>();
private ThreadLocal> halfArgsPointer = new ThreadLocal<>();
protected Map customOps = null;
protected ThreadLocal extraz = new ThreadLocal<>();
protected AtomicBoolean experimentalMode = new AtomicBoolean(false);
protected Map mklOverrides = new HashMap<>();
/**
* Instead of allocating new memory chunks for each batch invocation, we reuse them on thread/opNum basis
* Since for NativeOpExecutioner all executions are synchronous
*/
private ThreadLocal> batchPointers = new ThreadLocal<>();
private ThreadLocal> memoryBlocks = new ThreadLocal<>();
public NativeOpExecutioner() {
tadManager.init(loop, constantHandler);
experimentalMode.set(loop.isExperimentalEnabled());
// filling vars for possible overrides
val env = System.getenv(ND4JEnvironmentVars.ND4J_MKL_FALLBACK);
if (env != null) {
// in this case we just disable mkl-dnn globally
if (env.equalsIgnoreCase("true")) {
Nd4jCpu.Environment.getInstance().setUseMKLDNN(false);
} else {
val split = env.toLowerCase().split(",");
for (val name:split) {
mklOverrides.put(name, new Boolean(true));
}
}
}
}
@Override
public INDArray exec(Op op) {
return exec(op, null);
}
@Override
public INDArray exec(Op op, OpContext opContext) {
checkForCompression(op);
if (op instanceof ScalarOp) {
ScalarOp s = (ScalarOp) op;
exec(s, opContext);
} else if (op instanceof TransformOp) {
TransformOp t = (TransformOp) op;
exec(t, opContext);
} else if (op instanceof ReduceOp) {
ReduceOp ac = (ReduceOp) op;
exec(ac, opContext);
} else if (op instanceof IndexAccumulation) {
IndexAccumulation iac = (IndexAccumulation) op;
exec(iac, opContext); //Currently using DefaultOpExecutioner
} else if (op instanceof BroadcastOp) {
BroadcastOp broadcastOp = (BroadcastOp) op;
exec(broadcastOp, opContext);
} else if (op instanceof RandomOp) {
RandomOp rngOp = (RandomOp) op;
exec(rngOp, opContext, Nd4j.getRandom());
}
return op.z();
}
@Override
public INDArray exec(IndexAccumulation op) {
return exec(op, null);
}
public INDArray exec(IndexAccumulation op, OpContext oc) {
checkForCompression(op);
INDArray x = getX(op, oc);
INDArray z = getZ(op, oc);
if (extraz.get() == null)
extraz.set(new PointerPointer(32));
val dimension = Shape.normalizeAxis(x.rank(), op.dimensions().toIntVector());
if (x.isEmpty()) {
for (val d:dimension) {
Preconditions.checkArgument(x.shape()[d] != 0, "IndexReduce can't be issued along axis with 0 in shape");
}
}
boolean keepDims = op.isKeepDims();
long[] retShape = Shape.reductionShape(x, dimension, true, keepDims);
if(z == null || x == z) {
val ret = Nd4j.createUninitialized(DataType.LONG, retShape);
setZ(ret, op, oc);
z = ret;
} else if(!Arrays.equals(retShape, z.shape())){
throw new IllegalStateException("Z array shape does not match expected return type for op " + op
+ ": expected shape " + Arrays.toString(retShape) + ", z.shape()=" + Arrays.toString(z.shape()));
}
op.validateDataTypes();
Pointer dimensionAddress = constantHandler.getConstantBuffer(dimension, DataType.INT).addressPointer();
Pair tadBuffers = tadManager.getTADOnlyShapeInfo(x, dimension);
Pointer hostTadShapeInfo = tadBuffers.getFirst().addressPointer();
DataBuffer offsets = tadBuffers.getSecond();
Pointer hostTadOffsets = offsets == null ? null : offsets.addressPointer();
PointerPointer dummy = extraz.get().put(hostTadShapeInfo, hostTadOffsets);
long st = profilingConfigurableHookIn(op, tadBuffers.getFirst());
val xb = ((BaseCpuDataBuffer) x.data()).getOpaqueDataBuffer();
val zb = ((BaseCpuDataBuffer) z.data()).getOpaqueDataBuffer();
if (z.isScalar()) {
loop.execIndexReduceScalar(dummy, op.opNum(),
xb, (LongPointer) x.shapeInfoDataBuffer().addressPointer(), null,
getPointerForExtraArgs(op, x.dataType()),
zb, (LongPointer) z.shapeInfoDataBuffer().addressPointer(), null);
} else {
loop.execIndexReduce(dummy, op.opNum(),
xb, (LongPointer) x.shapeInfoDataBuffer().addressPointer(), null,
getPointerForExtraArgs(op, x.dataType()),
zb, (LongPointer) z.shapeInfoDataBuffer().addressPointer(), null,
((BaseCpuDataBuffer) op.dimensions().data()).getOpaqueDataBuffer(), (LongPointer) op.dimensions().shapeInfoDataBuffer().addressPointer(), null);
}
if (loop.lastErrorCode() != 0)
throw new RuntimeException(loop.lastErrorMessage());
profilingConfigurableHookOut(op, oc, st);
return getZ(op, oc);
}
@Override
public INDArray exec(Variance op) {
return exec((ReduceOp) op);
}
@Override
public INDArray exec(ReduceOp op) {
return exec(op, null);
}
public INDArray exec(ReduceOp op, OpContext oc) {
INDArray x = getX(op, oc);
INDArray y = getY(op, oc);
INDArray z = getZ(op, oc);
Preconditions.checkNotNull(x, "Op.x() cannot be null: Was null for op %s", op);
op.validateDataTypes(oc);
if(op instanceof BaseReduceOp && ((BaseReduceOp)op).isEmptyReduce()){
//Edge case for TF import compatibility: [x,y].reduce(empty) = [x,y]
//Note that "empty" axis is NOT the same as length 0, as in INDArray.sum(new int[0]), which means "all dimensions"
if(z != null){
Preconditions.checkState(x.equalShapes(z), "For empty reductions, result (z) array must have same shape as x shape." +
" Got: x=%ndShape, z=%ndShape", x, z);
z.assign(x);
return z;
} else {
setZ(x.dup(), op, oc);
return z;
}
}
// FIXME: this should be moved down to C++ on per-op basis
val dimension = Shape.normalizeAxis(x.rank(), op.dimensions() != null ? op.dimensions().toIntVector() : null);
// reduce to scalar case, ReduceBool ops require special treatment
if (op instanceof BaseReduceBoolOp && x.isEmpty() && (dimension == null || (dimension.length == 1 && dimension[0] == Integer.MAX_VALUE))) {
if (z == null) {
setZ(Nd4j.scalar(((BaseReduceBoolOp) op).emptyValue()), op, oc);
} else {
z.assign(((BaseReduceBoolOp) op).emptyValue());
}
return z;
}
//validateDataType(Nd4j.dataType(), op);
if (extraz.get() == null)
extraz.set(new PointerPointer(32));
boolean keepDims = op.isKeepDims();
long[] retShape = Shape.reductionShape(x, dimension, true, keepDims);
if (x.isVector() && x.length() == ArrayUtil.prod(retShape) && ArrayUtil.prodLong(retShape) > 1 && y == null)
return op.noOp();
/**
* This is the result array.
* We create it only if we hadn't provided it before
*/
INDArray ret;
if (z == null || z == x) {
if (op.isComplexAccumulation()) {
long xT = x.tensorsAlongDimension(dimension);
long yT = y.tensorsAlongDimension(dimension);
ret = Nd4j.create(op.resultType(), new long[]{xT, yT});
} else {
if (y != null) {
//2 options here: either pairwise, equal sizes - OR every X TAD vs. entirety of Y
if(x.length() == y.length()) {
//Pairwise
if (x.tensorsAlongDimension(dimension) != y.tensorsAlongDimension(dimension)) {
throw new ND4JIllegalStateException("Number of TADs along dimension don't match: (x shape = " +
Arrays.toString(x.shape()) + ", y shape = " + Arrays.toString(y.shape()) +
", dimension = " + Arrays.toString(dimension) + ")");
}
} else {
//Every X TAD vs. entirety of Y
val xTADSize = x.length() / x.tensorsAlongDimension(dimension);
if (xTADSize != y.length()) {
throw new ND4JIllegalStateException("Size of TADs along dimension don't match for pairwise execution:" +
" (x TAD size = " + xTADSize + ", y size = " + y.length());
}
}
}
DataType dt = oc != null ? op.resultType(oc) : op.resultType();
ret = Nd4j.create(dt, retShape);
}
setZ(ret, op, oc);
z = ret;
} else {
// compare length
long shapeProduct = (retShape.length == 0 ? 1 : ArrayUtil.prodLong(retShape));
if (!op.isComplexAccumulation() && z.length() != shapeProduct) {
if(!(x.isEmpty() && op.isKeepDims())){
//Empty reductions are special case: [1,0].sum(0,1,keep=true) -> shape [1,1]
throw new ND4JIllegalStateException("Shape of target array for reduction [" + Arrays.toString(z.shape()) + "] doesn't match expected [" + Arrays.toString(retShape) + "]");
}
}
else if (op.isComplexAccumulation()) {
long xT = x.tensorsAlongDimension(dimension);
long yT = y.tensorsAlongDimension(dimension);
if (z.length() != xT * yT)
throw new ND4JIllegalStateException("Shape of target array for reduction [" + Arrays.toString(z.shape()) + "] doesn't match expected [" + (xT * yT) + "]");
}
ret = z;
}
//log.info("X dtype: {}; Z dtype: {}", x.dataType(), z.dataType());
/**
* 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 tadBuffers = x.isEmpty() ? Pair.makePair(x.data(), null): tadManager.getTADOnlyShapeInfo(x, dimension);
Pair 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 = x.isEmpty() ? x.shapeInfoDataBuffer().addressPointer() : tadBuffers.getFirst().addressPointer();
DataBuffer offsets = x.isEmpty() ? null : 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 (y != null) {
if (x.tensorAlongDimension(0, dimension).length() == y.length()) {
tvf = true;
}
}
if (op.isComplexAccumulation()) {
yTadBuffers = tadManager.getTADOnlyShapeInfo(y, dimension);
if (x.tensorAlongDimension(0, dimension).length() != y.tensorAlongDimension(0, dimension).length())
throw new ND4JIllegalStateException("Impossible to issue AllDistances operation: TAD lengths mismatch along given dimension: " +
"x TAD length = " + x.tensorAlongDimension(0, dimension).length() + ", y TAD length " +
y.tensorAlongDimension(0, dimension).length());
}
/**
* 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 = profilingConfigurableHookIn(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, DataType.INT).addressPointer();
val xb = ((BaseCpuDataBuffer) x.data()).getOpaqueDataBuffer();
val zb = ((BaseCpuDataBuffer) z.data()).getOpaqueDataBuffer();
if (op instanceof Variance) {
if (ret.isScalar()) {
loop.execSummaryStatsScalar(null, op.opNum(),
xb, (LongPointer) x.shapeInfoDataBuffer().addressPointer(), null,
getPointerForExtraArgs(op, z.dataType()),
zb, (LongPointer) z.shapeInfoDataBuffer().addressPointer(), null,
((Variance) op).isBiasCorrected());
} else {
Variance var = (Variance) op;
try {
loop.execSummaryStatsTad(null, op.opNum(),
xb, (LongPointer) x.shapeInfoDataBuffer().addressPointer(), null,
getPointerForExtraArgs(op, z.dataType()),
zb, (LongPointer) z.shapeInfoDataBuffer().addressPointer(), null,
((BaseCpuDataBuffer) op.dimensions().data()).getOpaqueDataBuffer(), (LongPointer) op.dimensions().shapeInfoDataBuffer().addressPointer(), null,
var.isBiasCorrected(), null, null);
} catch (Throwable t){
String str = opInfoString(op, Optional.of(dimension));
throw new RuntimeException("Native AccumulationOp execution (double) failed: " + str, t);
}
}
}
//pairwise reduction like similarity of two arrays
else if (y != null && op.getOpType() == Op.Type.REDUCE3) {
val yb = ((BaseCpuDataBuffer) y.data()).getOpaqueDataBuffer();
if (op.isComplexAccumulation()) {
try {
loop.execReduce3All(null, op.opNum(),
xb, (LongPointer) x.shapeInfoDataBuffer().addressPointer(), null,
getPointerForExtraArgs(op, z.dataType()),
yb, (LongPointer) y.shapeInfoDataBuffer().addressPointer(), null,
zb, (LongPointer) z.shapeInfoDataBuffer().addressPointer(), null,
((BaseCpuDataBuffer) op.dimensions().data()).getOpaqueDataBuffer(), (LongPointer) op.dimensions().shapeInfoDataBuffer().addressPointer(), null,
(LongPointer) tadBuffers.getFirst().addressPointer(), new LongPointerWrapper(tadBuffers.getSecond().addressPointer()),
(LongPointer) yTadBuffers.getFirst().addressPointer(), new LongPointerWrapper(yTadBuffers.getSecond().addressPointer())
);
} catch (Throwable t){
String str = opInfoString(op, Optional.of(dimension));
throw new RuntimeException("Native AccumulationOp execution (double) failed: " + str, t);
}
} else if (ret.isScalar()) {
loop.execReduce3Scalar(null, op.opNum(),
xb, (LongPointer) x.shapeInfoDataBuffer().addressPointer(), null,
getPointerForExtraArgs(op, z.dataType()),
yb, (LongPointer) y.shapeInfoDataBuffer().addressPointer(), null,
zb, (LongPointer) ret.shapeInfoDataBuffer().addressPointer(), null);
} else {
try {
loop.execReduce3Tad(null, op.opNum(),
xb, (LongPointer) x.shapeInfoDataBuffer().addressPointer(), null,
getPointerForExtraArgs(op, z.dataType()),
yb, (LongPointer) y.shapeInfoDataBuffer().addressPointer(), null,
zb, (LongPointer) z.shapeInfoDataBuffer().addressPointer(), null,
((BaseCpuDataBuffer) op.dimensions().data()).getOpaqueDataBuffer(), (LongPointer) op.dimensions().shapeInfoDataBuffer().addressPointer(), null,
null, null, null, null);
} catch (Throwable t){
String str = opInfoString(op, Optional.of(dimension));
throw new RuntimeException("Native AccumulationOp execution (double) failed: " + str, t);
}
}
} else {
if (ret.isScalar()) {
switch (op.getOpType()) {
case REDUCE_FLOAT:
loop.execReduceFloat(null, op.opNum(),
xb, (LongPointer) x.shapeInfoDataBuffer().addressPointer(), null,
getPointerForExtraArgs(op, z.dataType()),
zb, (LongPointer) ret.shapeInfoDataBuffer().addressPointer(), null);
break;
case REDUCE_BOOL:
loop.execReduceBool(null, op.opNum(),
xb, (LongPointer) x.shapeInfoDataBuffer().addressPointer(), null,
getPointerForExtraArgs(op, x.dataType()),
zb, (LongPointer) ret.shapeInfoDataBuffer().addressPointer(), null);
break;
case REDUCE_SAME:
loop.execReduceSame(null, op.opNum(),
xb, (LongPointer) x.shapeInfoDataBuffer().addressPointer(), null,
getPointerForExtraArgs(op, x.dataType()),
zb, (LongPointer) ret.shapeInfoDataBuffer().addressPointer(), null);
break;
case REDUCE_LONG:
loop.execReduceLong(null, op.opNum(),
xb, (LongPointer) x.shapeInfoDataBuffer().addressPointer(), null,
getPointerForExtraArgs(op, x.dataType()),
zb, (LongPointer) ret.shapeInfoDataBuffer().addressPointer(), null);
break;
default:
throw new UnsupportedOperationException("Unsupported op used in reduce: "+ op.getOpType());
}
} else {
switch (op.getOpType()) {
case REDUCE_FLOAT:
loop.execReduceFloat2(null, op.opNum(),
xb, (LongPointer) x.shapeInfoDataBuffer().addressPointer(), null,
getPointerForExtraArgs(op, z.dataType()),
zb, (LongPointer) z.shapeInfoDataBuffer().addressPointer(), null,
((BaseCpuDataBuffer) op.dimensions().data()).getOpaqueDataBuffer(), (LongPointer) op.dimensions().shapeInfoDataBuffer().addressPointer(), null);
break;
case REDUCE_LONG:
loop.execReduceLong2(null, op.opNum(),
xb, (LongPointer) x.shapeInfoDataBuffer().addressPointer(), null,
getPointerForExtraArgs(op, x.dataType()),
zb, (LongPointer) z.shapeInfoDataBuffer().addressPointer(), null,
((BaseCpuDataBuffer) op.dimensions().data()).getOpaqueDataBuffer(),
(LongPointer) op.dimensions().shapeInfoDataBuffer().addressPointer(), null);
break;
case REDUCE_SAME:
loop.execReduceSame2(null, op.opNum(),
xb, (LongPointer) x.shapeInfoDataBuffer().addressPointer(), null,
getPointerForExtraArgs(op, z.dataType()),
zb, (LongPointer) z.shapeInfoDataBuffer().addressPointer(), null,
((BaseCpuDataBuffer) op.dimensions().data()).getOpaqueDataBuffer(),
(LongPointer) op.dimensions().shapeInfoDataBuffer().addressPointer(), null);
break;
case REDUCE_BOOL:
loop.execReduceBool2(null, op.opNum(),
xb, (LongPointer) x.shapeInfoDataBuffer().addressPointer(), null,
getPointerForExtraArgs(op, x.dataType()),
zb, (LongPointer) z.shapeInfoDataBuffer().addressPointer(), null,
((BaseCpuDataBuffer) op.dimensions().data()).getOpaqueDataBuffer(),
(LongPointer) op.dimensions().shapeInfoDataBuffer().addressPointer(), null);
break;
default:
throw new UnsupportedOperationException("Unsupported op used in reduce: "+ op.getOpType());
}
}
}
if (loop.lastErrorCode() != 0)
throw new RuntimeException(loop.lastErrorMessage());
return getZ(op, oc);
}
/**
* ScalarOp execution
* @param op Op to execute
*/
private void invokeScalarAlongDimension(ScalarOp op) {
invokeScalarAlongDimension(op, null);
}
private void invokeScalarAlongDimension(ScalarOp op, OpContext oc) {
INDArray x = getX(op, oc);
INDArray y = getY(op, oc);
INDArray z = getZ(op, oc);
val dimension = op.dimensions().toIntVector();
//dimension = Shape.normalizeAxis(op.x().rank(), dimension);
// do tad magic
/**
* 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 tadBuffers = tadManager.getTADOnlyShapeInfo(op.x(), dimension);
Pointer hostTadShapeInfo = tadBuffers.getFirst().addressPointer();
Pointer hostTadOffsets = tadBuffers.getSecond().addressPointer();
Pointer devTadShapeInfoZ = null;
Pointer devTadOffsetsZ = null;
/**
* 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.
*
* Note that this is the *result* TAD information. An op is always input (x) and output (z)
* for result.
* This is for assigning the result to of the operation along
* the proper dimension.
*/
Pair tadBuffersZ = tadManager.getTADOnlyShapeInfo(op.z(), dimension);
devTadShapeInfoZ = tadBuffersZ.getFirst().addressPointer();
devTadOffsetsZ = tadBuffersZ.getSecond().addressPointer();
if (extraz.get() == null)
extraz.set(new PointerPointer(32));
val xb = ((BaseCpuDataBuffer) x.data()).getOpaqueDataBuffer();
val yb = ((BaseCpuDataBuffer) y.data()).getOpaqueDataBuffer();
val zb = ((BaseCpuDataBuffer) z.data()).getOpaqueDataBuffer();
switch (op.getOpType()) {
case SCALAR:
loop.execScalarTad(null, op.opNum(),
xb, (LongPointer) op.x().shapeInfoDataBuffer().addressPointer(), null,
zb, (LongPointer) op.z().shapeInfoDataBuffer().addressPointer(), null,
yb, (LongPointer) y.shapeInfoDataBuffer().addressPointer(), null,
getPointerForExtraArgs(op, op.z().dataType()),
((BaseCpuDataBuffer) op.dimensions().data()).getOpaqueDataBuffer(), (LongPointer) op.dimensions().shapeInfoDataBuffer().addressPointer(),null,
(LongPointer) hostTadShapeInfo, (LongPointer) hostTadOffsets,
(LongPointer) devTadShapeInfoZ, (LongPointer) devTadOffsetsZ);
break;
case SCALAR_BOOL:
loop.execScalarBoolTad(null, op.opNum(),
xb, (LongPointer) op.x().shapeInfoDataBuffer().addressPointer(), null,
zb, (LongPointer) op.z().shapeInfoDataBuffer().addressPointer(), null,
yb, (LongPointer) op.y().shapeInfoDataBuffer().addressPointer(), null,
getPointerForExtraArgs(op, op.z().dataType()),
((BaseCpuDataBuffer) op.dimensions().data()).getOpaqueDataBuffer(), (LongPointer) op.dimensions().shapeInfoDataBuffer().addressPointer(), null,
(LongPointer) hostTadShapeInfo, (LongPointer) hostTadOffsets,
(LongPointer) devTadShapeInfoZ, (LongPointer) devTadOffsetsZ);
break;
default:
throw new UnsupportedOperationException();
}
if (loop.lastErrorCode() != 0)
throw new RuntimeException(loop.lastErrorMessage());
}
public INDArray exec(ScalarOp op){
return exec(op, null);
}
public INDArray exec(ScalarOp op, OpContext oc) {
long st = profilingConfigurableHookIn(op);
//validateDataType(Nd4j.dataType(), op);
if((oc != null && oc.getOutputArray(0) == null) || getZ(op, oc) == null){
switch (op.getOpType()) {
case SCALAR:
setZ(getX(op, oc).ulike(), op, oc);
// op.setZ(op.x().ulike());
break;
case SCALAR_BOOL:
// op.setZ(Nd4j.createUninitialized(DataType.BOOL, op.x().shape()));
setZ(Nd4j.createUninitialized(DataType.BOOL, getX(op, oc).shape()), op, oc);
break;
default:
throw new ND4JIllegalStateException("Unknown op type: [" + op.getOpType() +"]");
}
}
// if (op.x().length() != op.z().length())
if (getX(op, oc).length() != getZ(op, oc).length())
throw new ND4JIllegalStateException("op.X length should be equal to op.Z length: " +
"x.length()=" + getX(op, oc).length() + ", z.length()=" + getZ(op, oc).length() + " - x shape info = ["
+ Arrays.toString(getX(op, oc).shapeInfoDataBuffer().asInt()) + "], z shape info = ["
+ Arrays.toString(getZ(op, oc).shapeInfoDataBuffer().asInt()) + "]");
if (op.dimensions() != null) {
invokeScalarAlongDimension(op);
return getZ(op, oc);
}
val x = ((BaseCpuDataBuffer) getX(op, oc).data()).getOpaqueDataBuffer();
val scalar = ((BaseCpuDataBuffer) op.scalar().data()).getOpaqueDataBuffer();
val z = ((BaseCpuDataBuffer) getZ(op, oc).data()).getOpaqueDataBuffer();
switch (op.getOpType()) {
case SCALAR:
loop.execScalar(null,
op.opNum(),
x, (LongPointer) getX(op, oc).shapeInfoDataBuffer().addressPointer(), null,
z, (LongPointer) getZ(op, oc).shapeInfoDataBuffer().addressPointer(), null,
scalar, (LongPointer) op.scalar().shapeInfoDataBuffer().addressPointer(), null,
getPointerForExtraArgs(op, getZ(op, oc).dataType()));
break;
case SCALAR_BOOL:
loop.execScalarBool(null,
op.opNum(),
x, (LongPointer) getX(op, oc).shapeInfoDataBuffer().addressPointer(), null,
z, (LongPointer) getZ(op, oc).shapeInfoDataBuffer().addressPointer(), null,
scalar, (LongPointer) op.scalar().shapeInfoDataBuffer().addressPointer(), null,
getPointerForExtraArgs(op, getX(op, oc).dataType()));
break;
default:
throw new ND4JIllegalStateException("Unknown op type: [" + op.getOpType() +"]");
}
if (loop.lastErrorCode() != 0)
throw new RuntimeException(loop.lastErrorMessage());
profilingConfigurableHookOut(op, oc, st);
return getZ(op, oc);
}
private Pointer getPointerForExtraArgs(Op op, DataType type) {
if (op.extraArgs() != null){
val eadb = op.extraArgsDataBuff(type);
if (eadb != null)
return eadb.addressPointer();
else
return null;
}
return null;
}
private void exec(TransformOp op) {
exec(op, null);
}
private void exec(TransformOp op, OpContext oc) {
INDArray x = getX(op, oc);
INDArray y = getY(op, oc);
INDArray z = getZ(op, oc);
long st = 0;
// validateDataType(Nd4j.dataType(), op);
if (extraz.get() == null)
extraz.set(new PointerPointer(32));
PointerPointer dummy = extraz.get();
// Pow operations might be special
if (op.opNum() == 31) {
if (y != null && y.isScalar()) {
// op.setY(Nd4j.valueArrayOf(op.x().shape(), op.y().getDouble(0)));
setY(Nd4j.valueArrayOf(x.shape(), y.getDouble(0)), op, oc);
}
}
/**
* This is the {@link IsMax}
* operation.
*
* @see {@link Op#extraArgs()}
* for what an extra argument is in an op.
*
* The extra argument in the op here is the {@link IsMax#IsMax(INDArray, int...)}
* dimension to do the ismax along
*/
if (op.opName().equalsIgnoreCase("ismax") && op.extraArgs() != null && op.extraArgs().length > 0) {
int[] dimension = new int[(int) op.extraArgs()[0]];
for (int i = 0; i < dimension.length; i++) {
dimension[i] = (int) op.extraArgs()[i + 1];
}
/**
* 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 tadBuffers = tadManager.getTADOnlyShapeInfo(op.z(), dimension);
Pointer tad = tadBuffers.getFirst().addressPointer();
DataBuffer offsets = tadBuffers.getSecond();
Pointer off = offsets == null ? null : offsets.addressPointer();
dummy.put(0, tad);
dummy.put(1, off);
st = profilingConfigurableHookIn(op, tadBuffers.getFirst());
} else
st = profilingConfigurableHookIn(op);
if (y != null) {
if (z == null) {
setZ(Nd4j.create(op.resultType(), x.shape()), op, oc);
z = getZ(op, oc);
}
op.validateDataTypes(oc, experimentalMode.get());
//log.info("X type: {}; Y type: {}; Z type: {}; OpNum: {}", op.x().dataType(), op.y().dataType(), op.z().dataType(), op.opNum());
val xb = ((BaseCpuDataBuffer) x.data()).getOpaqueDataBuffer();
val yb = ((BaseCpuDataBuffer) y.data()).getOpaqueDataBuffer();
val zb = ((BaseCpuDataBuffer) z.data()).getOpaqueDataBuffer();
switch (op.getOpType()) {
case TRANSFORM_ANY:
case TRANSFORM_FLOAT:
case TRANSFORM_STRICT:
case TRANSFORM_SAME:
if (!experimentalMode.get())
Preconditions.checkArgument(x.dataType() == y.dataType() || y.dataType() == DataType.BOOL,
"Op.X and Op.Y must have the same data type, but got %s vs. %s", x.dataType(), y.dataType());
loop.execPairwiseTransform(dummy, op.opNum(),
xb, (LongPointer) x.shapeInfoDataBuffer().addressPointer(), null,
yb, (LongPointer) y.shapeInfoDataBuffer().addressPointer(), null,
zb, (LongPointer) z.shapeInfoDataBuffer().addressPointer(), null,
getPointerForExtraArgs(op, z.dataType()));
break;
case TRANSFORM_BOOL:
case PAIRWISE_BOOL:
loop.execPairwiseTransformBool(dummy, op.opNum(),
xb, (LongPointer) x.shapeInfoDataBuffer().addressPointer(), null,
yb, (LongPointer) y.shapeInfoDataBuffer().addressPointer(), null,
zb, (LongPointer) z.shapeInfoDataBuffer().addressPointer(), null,
getPointerForExtraArgs(op, x.dataType()));
break;
}
} else {
if (z == null) {
setZ(Nd4j.createUninitialized((oc != null ? op.resultType(oc) : op.resultType()), x.shape()), op, oc);
z = getZ(op, oc);
}
op.validateDataTypes(oc, experimentalMode.get());
val xb = ((BaseCpuDataBuffer) x.data()).getOpaqueDataBuffer();
val zb = ((BaseCpuDataBuffer) z.data()).getOpaqueDataBuffer();
switch (op.getOpType()) {
case TRANSFORM_FLOAT: {
val xtraz = getPointerForExtraArgs(op, z.dataType());
loop.execTransformFloat(dummy, op.opNum(),
xb, (LongPointer) x.shapeInfoDataBuffer().addressPointer(), null,
zb, (LongPointer) z.shapeInfoDataBuffer().addressPointer(),
null, xtraz);
break;
}
case TRANSFORM_STRICT: {
val xtraz = getPointerForExtraArgs(op, z.dataType());
loop.execTransformStrict(dummy, op.opNum(),
xb, (LongPointer) x.shapeInfoDataBuffer().addressPointer(), null,
zb, (LongPointer) z.shapeInfoDataBuffer().addressPointer(), null,
xtraz);
break;
}
case TRANSFORM_SAME: {
val xtraz = getPointerForExtraArgs(op, z.dataType());
loop.execTransformSame(dummy, op.opNum(),
xb, (LongPointer) x.shapeInfoDataBuffer().addressPointer(), null,
zb, (LongPointer) z.shapeInfoDataBuffer().addressPointer(), null,
xtraz);
break;
}
case TRANSFORM_ANY: {
val xtraz = getPointerForExtraArgs(op, x.dataType());
val opNum = op.opNum();
loop.execTransformAny(dummy, opNum,
xb, (LongPointer) x.shapeInfoDataBuffer().addressPointer(), null,
zb, (LongPointer) z.shapeInfoDataBuffer().addressPointer(), null,
xtraz);
break;
}
case TRANSFORM_BOOL: {
val xtraz = getPointerForExtraArgs(op, x.dataType());
val opNum = op.opNum();
loop.execTransformBool(dummy, opNum,
xb, (LongPointer) x.shapeInfoDataBuffer().addressPointer(), null,
zb, (LongPointer) z.shapeInfoDataBuffer().addressPointer(), null,
xtraz);
break;
}
default:
throw new UnsupportedOperationException("Unknown transform type: [" + op.getOpType() + "]");
}
}
if (loop.lastErrorCode() != 0)
throw new RuntimeException(loop.lastErrorMessage());
profilingConfigurableHookOut(op, oc, st);
}
public INDArray exec(BroadcastOp op) {
return exec(op, null);
}
public INDArray exec(BroadcastOp op, OpContext oc) {
INDArray x = getX(op, oc);
INDArray y = getY(op, oc);
INDArray z = getZ(op, oc);
long st = profilingConfigurableHookIn(op);
op.validateDataTypes(experimentalMode.get());
val dimension = op.dimensions().toIntVector();
/**
* 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 tadBuffers = tadManager.getTADOnlyShapeInfo(x, dimension);
Pointer hostTadShapeInfo = tadBuffers.getFirst().addressPointer();
Pointer hostTadOffsets = tadBuffers.getSecond().addressPointer();
Pointer devTadShapeInfoZ = null;
Pointer devTadOffsetsZ = null;
// if (!Arrays.equals(x.shape(),z.shape()) || !Arrays.equals(x.stride(),z.stride()) || x.ordering() != z.ordering()) {
// that's the place where we're going to have second TAD in place
Pair tadBuffersZ = tadManager.getTADOnlyShapeInfo(z, dimension);
devTadShapeInfoZ = tadBuffersZ.getFirst().addressPointer();
devTadOffsetsZ = tadBuffersZ.getSecond().addressPointer();
/*
log.info("Broascast dimension: {}", Arrays.toString(dimension));
log.info("x shape: {}; x TAD: {}; comp TAD: {}", Arrays.toString(x.shapeInfoDataBuffer().asInt()), Arrays.toString(tadBuffers.getFirst().asInt()), Arrays.toString(x.tensorAlongDimension(0, dimension).shapeInfoDataBuffer().asInt()));
log.info("z shape: {}; z TAD: {}", Arrays.toString(z.shapeInfoDataBuffer().asInt()), Arrays.toString(tadBuffersZ.getFirst().asInt()));
log.info("y shape: {}", Arrays.toString(y.shapeInfoDataBuffer().asInt()));
log.info("-------------");
*/
if (extraz.get() == null)
extraz.set(new PointerPointer(32));
PointerPointer dummy = extraz.get().put(hostTadShapeInfo, hostTadOffsets, devTadShapeInfoZ, devTadOffsetsZ);
Pointer dimensionAddress = constantHandler.getConstantBuffer(dimension, DataType.INT).addressPointer();
val xb = ((BaseCpuDataBuffer) x.data()).getOpaqueDataBuffer();
val yb = ((BaseCpuDataBuffer) y.data()).getOpaqueDataBuffer();
val zb = ((BaseCpuDataBuffer) z.data()).getOpaqueDataBuffer();
switch (op.getOpType()) {
case BROADCAST:
loop.execBroadcast(dummy, op.opNum(),
xb, (LongPointer) x.shapeInfoDataBuffer().addressPointer(), null,
yb, (LongPointer) y.shapeInfoDataBuffer().addressPointer(), null,
zb, (LongPointer) z.shapeInfoDataBuffer().addressPointer(), null,
((BaseCpuDataBuffer) op.dimensions().data()).getOpaqueDataBuffer(), (LongPointer) op.dimensions().shapeInfoDataBuffer().addressPointer(), null);
break;
case BROADCAST_BOOL:
loop.execBroadcastBool(dummy, op.opNum(),
xb, (LongPointer) x.shapeInfoDataBuffer().addressPointer(), null,
yb, (LongPointer) y.shapeInfoDataBuffer().addressPointer(), null,
zb, (LongPointer) z.shapeInfoDataBuffer().addressPointer(), null,
null,
((BaseCpuDataBuffer) op.dimensions().data()).getOpaqueDataBuffer(), (LongPointer) op.dimensions().shapeInfoDataBuffer().addressPointer(), null);
break;
default:
throw new UnsupportedOperationException("Unknown operation type: [" + op.getOpType() + "]");
}
if (loop.lastErrorCode() != 0)
throw new RuntimeException(loop.lastErrorMessage());
return z;
}
protected Pointer getPointer(Batch batch) {
if (batchPointers.get() == null)
batchPointers.set(new HashMap());
if (!batchPointers.get().containsKey(batch.opNum())) {
val pointer = new IntPointer(batch.getSample().getRequiredBatchMemorySize() / 4 );
batchPointers.get().put(batch.opNum(), pointer);
return pointer;
}
return batchPointers.get().get(batch.opNum());
}
/**
* This method executes previously built batch
*
* @param batch
*/
@Override
public void exec(Batch batch) {
//profilingHookIn(batch);
IntPointer pointer = (IntPointer) getPointer(batch);
int maxTypes = 5;
int maxIntArrays = batch.getSample().maxIntArrays();
int maxArraySize = batch.getSample().maxIntArraySize();
int indexPos = maxTypes * Batch.getBatchLimit();
int intArraysPos = indexPos + (batch.getSample().maxIndexArguments() * Batch.getBatchLimit());
int realPos = (intArraysPos + (maxIntArrays * maxArraySize * Batch.getBatchLimit()))
/ (Nd4j.dataType() == DataType.DOUBLE ? 2 : 1);
int argsPos = (realPos + ((batch.getSample().maxRealArguments() * Batch.getBatchLimit())))
/ (Nd4j.dataType() == DataType.DOUBLE ? 1 : 2);
int shapesPos = argsPos + (batch.getSample().maxArguments() * Batch.getBatchLimit());
DataType dataType = null;
for (int i = 0; i < batch.getNumAggregates(); i++) {
T op = batch.getAggregates().get(i);
if (i == 0)
dataType = op.getArguments().get(0).dataType();
// put num arguments
int idx = i * maxTypes;
pointer.put(idx, op.getArguments().size());
pointer.put(idx + 1, op.getShapes().size());
pointer.put(idx + 2, op.getIndexingArguments().size());
pointer.put(idx + 3, op.getRealArguments().size());
pointer.put(idx + 4, op.getIntArrayArguments().size());
// putting indexing arguments
for (int e = 0; e < op.getIndexingArguments().size(); e++) {
idx = indexPos + i * batch.getSample().maxIndexArguments();
pointer.put(idx + e, op.getIndexingArguments().get(e));
}
// putting intArray values
int bsize = maxIntArrays * maxArraySize;
for (int e = 0; e < op.getIntArrayArguments().size(); e++) {
int step = (i * bsize) + (e * maxArraySize);
if (op.getIntArrayArguments().get(e) != null)
for (int x = 0; x < op.getIntArrayArguments().get(e).length; x++) {
idx = intArraysPos + step + x;
pointer.put(idx, op.getIntArrayArguments().get(e)[x]);
}
}
// TODO: variable datatype should be handled here
// putting real arguments
switch (dataType){
case FLOAT:
FloatPointer fPtr = new FloatPointer(pointer);
for (int e = 0; e < op.getRealArguments().size(); e++) {
idx = realPos + i * op.maxRealArguments();
fPtr.put(idx + e, op.getRealArguments().get(e).floatValue());
}
break;
case DOUBLE:
DoublePointer dPtr = new DoublePointer(pointer);
for (int e = 0; e < op.getRealArguments().size(); e++) {
idx = realPos + (i * op.maxRealArguments());
dPtr.put(idx + e, op.getRealArguments().get(e).doubleValue());
}
break;
default:
throw new ND4JIllegalArgumentException("Only FLOAT and DOUBLE datatypes are supported");
}
if (extraz.get() == null)
extraz.set(new PointerPointer(32));
// putting arguments pointers
PointerPointer ptrPtr = new PointerPointer(pointer);//extraz.get().put(pointer);
for (int e = 0; e < op.getArguments().size(); e++) {
idx = argsPos + i * batch.getSample().maxArguments();
if (op.getArguments().get(e) != null) {
ptrPtr.put(idx + e, op.getArguments().get(e).data().addressPointer());
}
}
// putting shape pointers
for (int e = 0; e < op.getShapes().size(); e++) {
idx = shapesPos + i * batch.getSample().maxShapes();
if (op.getShapes().get(e) != null)
ptrPtr.put(idx + e, op.getShapes().get(e).addressPointer());
}
}
loop.execAggregateBatch(null, batch.getNumAggregates(), batch.opNum(),
batch.getSample().maxArguments(), batch.getSample().maxShapes(),
batch.getSample().maxIntArrays(), batch.getSample().maxIntArraySize(),
batch.getSample().maxIndexArguments(), batch.getSample().maxRealArguments(), pointer, FlatBuffersMapper.getDataTypeAsByte(dataType));
if (loop.lastErrorCode() != 0)
throw new RuntimeException(loop.lastErrorMessage());
}
/**
* This method takes arbitrary
* sized list of {@link Aggregate},
* and packs them into batches
* Note here that this is mainly used for random number generation
* for {@link RandomOp} and things like {@link org.nd4j.linalg.api.rng.distribution.Distribution}
* @param batch the list of {@link Aggregate} to
* execute upon
*/
@Override
public void exec(List batch) {
if (batch.size() == 0)
return;
List> batches = Batch.getBatches(batch);
for (Batch single : batches) {
this.exec(single);
}
}
/**
* This method takes arbitrary
* sized list of {@link Aggregate},
* and packs them into batches
* Note here that this is mainly used for random number generation
* for {@link RandomOp} and things like {@link org.nd4j.linalg.api.rng.distribution.Distribution}
* @param op the list of {@link Aggregate} to
* execute upon
*/
@Override
public void exec(Aggregate op) {
// long st = profilingHookIn(op);
if (memoryBlocks.get() == null)
memoryBlocks.set(new HashMap());
if (memoryBlocks.get().get(op.opNum()) == null)
memoryBlocks.get().put(op.opNum(), new AggregateMemoryBlock(op));
AggregateMemoryBlock block = memoryBlocks.get().get(op.opNum());
int numArguments = op.getArguments().size();
int numIndexArguments = op.getIndexingArguments().size();
int numRealArguments = op.getRealArguments().size();
int numShapes = op.getShapes().size();
int numIntArrays = op.getIntArrayArguments().size();
PointerPointer arguments = block.getArgumentsPointer(); //new PointerPointer(numArguments);
List pointers = new ArrayList<>();
PointerPointer intArrays = block.getArraysPointer(); //new PointerPointer(numIntArrays);
val dataType = op.getArguments().get(0).dataType();
for (int x = 0; x < numArguments; x++) {
arguments.put(x, op.getArguments().get(x) == null ? null
: op.getArguments().get(x).data().addressPointer());
}
PointerPointer shapes = block.getShapesPointer(); //new PointerPointer(numShapes);
for (int x = 0; x < numShapes; x++) {
if (op.getShapes().get(x).dataType() != DataType.LONG)
throw new RuntimeException("ShapeBuffers should have LONG data opType");
shapes.put(x, op.getShapes().get(x) == null ? null : op.getShapes().get(x).addressPointer());
}
//int[] indexes = new int[numIndexArguments];
IntPointer pointer = block.getIndexingPointer();
for (int x = 0; x < numIndexArguments; x++) {
pointer.put(x, op.getIndexingArguments().get(x));
}
//IntPointer pointer = new IntPointer(indexes);
double[] reals = new double[numRealArguments];
for (int x = 0; x < numRealArguments; x++) {
//reals[x] = op.getRealArguments().get(x).doubleValue();
switch (dataType) {
case FLOAT:
((FloatPointer) block.getRealArgumentsPointer()).put(x, op.getRealArguments().get(x).floatValue());
break;
case DOUBLE:
((DoublePointer) block.getRealArgumentsPointer()).put(x, op.getRealArguments().get(x).doubleValue());
break;
default:
throw new ND4JIllegalArgumentException("Only FLOAT and DOUBLE datatypes are supported");
}
}
for (int x = 0; x < numIntArrays; x++) {
IntPointer intPtr = block.getIntArrays().get(x); //new IntPointer(op.getIntArrayArguments().get(x));
intPtr.put(op.getIntArrayArguments().get(x), 0, op.getIntArrayArguments().get(x).length);
intArrays.put(x, intPtr);
pointers.add(intPtr);
}
//INDArray realsBuffer = Nd4j.create(reals);
loop.execAggregate(null, op.opNum(), arguments, numArguments, shapes, numShapes, pointer,
numIndexArguments, intArrays, numIntArrays, block.getRealArgumentsPointer(),
numRealArguments, FlatBuffersMapper.getDataTypeAsByte(dataType));
if (loop.lastErrorCode() != 0)
throw new RuntimeException(loop.lastErrorMessage());
}
/**
* This method return set of key/value and
* key/key/value objects,
* describing current environment
*
* @return
*/
@Override
public Properties getEnvironmentInformation() {
Properties properties = super.getEnvironmentInformation();
properties.put(Nd4jEnvironment.BACKEND_KEY, "CPU");
properties.put(Nd4jEnvironment.OMP_THREADS_KEY, loop.ompGetMaxThreads());
properties.put(Nd4jEnvironment.BLAS_THREADS_KEY, Nd4j.factory().blas().getMaxThreads());
properties.put(Nd4jEnvironment.BLAS_VENDOR_KEY, (Nd4j.factory().blas()).getBlasVendor().toString());
properties.put(Nd4jEnvironment.HOST_FREE_MEMORY_KEY, Pointer.maxBytes() - Pointer.totalBytes());
// fill bandwidth information
/*
Note: Environment information is logged as part of ND4J initialization... but PerformanceTracker required
ND4J init to be completed before it can be initialized. Hence we can get a null PerformanceTracker when
OpExecutioner.printEnvironmentInformation() is called as part of ND4J class initialization - even
though PerformanceTracker.getInstance() refers to a static final field (as it may not yet be initialized)
*/
if(PerformanceTracker.getInstance() != null) {
properties.put(Nd4jEnvironment.MEMORY_BANDWIDTH_KEY, PerformanceTracker.getInstance().getCurrentBandwidth());
}
return properties;
}
/**
* This method executes specified RandomOp using default RNG available via Nd4j.getRandom()
*
* @param op
*/
@Override
public INDArray exec(RandomOp op) {
return exec(op, Nd4j.getRandom());
}
/**
* This method executes specific
* RandomOp against specified RNG
*
* @param op
* @param rng
*/
@Override
public INDArray exec(RandomOp op, Random rng) {
return exec(op, null, rng);
}
public INDArray exec(RandomOp op, OpContext oc, Random rng) {
INDArray x = getX(op, oc);
INDArray y = getY(op, oc);
INDArray z = getZ(op, oc);
if(op instanceof BaseRandomOp && ((BaseRandomOp)op).isTripleArgRngOp() && z != null && x == null && y == null){
//Ugly hack to ensure the triple arg call occurs
//See GaussianDistribution.setZ etc
x = z;
y = z;
}
if (!(rng instanceof CpuNativeRandom))
throw new IllegalStateException(
"You should use one of NativeRandom classes for NativeOperations execution. Op class: " + op.getClass().getName());
long st = profilingConfigurableHookIn(op);
//validateDataType(Nd4j.dataType(), op);
Preconditions.checkArgument(z.isR(), "Op.Z must have one of floating point types");
val xb = x == null ? null : ((BaseCpuDataBuffer) x.data()).getOpaqueDataBuffer();
val yb = y == null ? null : ((BaseCpuDataBuffer) y.data()).getOpaqueDataBuffer();
val zb = z == null ? null : ((BaseCpuDataBuffer) z.data()).getOpaqueDataBuffer();
if (x != null && y != null && z != null) {
DataBuffer dataBuffer = op.extraArgsDataBuff(z.dataType());
// triple arg call
loop.execRandom3(null, op.opNum(), rng.getStatePointer(), // rng state ptr
xb, (LongPointer) x.shapeInfoDataBuffer().addressPointer(), null,
yb, (LongPointer) y.shapeInfoDataBuffer().addressPointer(), null,
zb, (LongPointer) z.shapeInfoDataBuffer().addressPointer(), null,
dataBuffer != null ? dataBuffer.addressPointer() : null);
} else if (x != null && z != null) {
DataBuffer dataBuffer = op.extraArgsDataBuff(z.dataType());
//double arg call
loop.execRandom2(null, op.opNum(), rng.getStatePointer(), // rng state ptr
xb, (LongPointer) x.shapeInfoDataBuffer().addressPointer(), null,
zb, (LongPointer) z.shapeInfoDataBuffer().addressPointer(), null,
dataBuffer != null ? dataBuffer.addressPointer() : null);
} else {
// single arg call
loop.execRandom(null, op.opNum(), rng.getStatePointer(), // rng state ptr
zb, (LongPointer) z.shapeInfoDataBuffer().addressPointer(), null,
op.extraArgsDataBuff(z.dataType()).addressPointer());
}
if (loop.lastErrorCode() != 0)
throw new RuntimeException(loop.lastErrorMessage());
profilingConfigurableHookOut(op, oc, st);
return z;
}
@Override
public TADManager getTADManager() {
return tadManager;
}
/**
* This class holds memory chunks required for single specific Aggregate op.
* Can be used together with ThreadLocal variables
*/
@Data
private static class AggregateMemoryBlock {
private List intArrays = new ArrayList<>();
private IntPointer indexingPointer;
private Pointer realArgumentsPointer;
private PointerPointer shapesPointer;
private PointerPointer argumentsPointer;
private PointerPointer arraysPointer;
private final int opNum;
private AggregateMemoryBlock(@NonNull Aggregate op) {
opNum = op.opNum();
// creating IntArrays
for (int i = 0; i < op.maxIntArrays(); i++) {
intArrays.add(new IntPointer(op.maxIntArraySize()));
}
// allocating chunk for IndexingArguments
indexingPointer = new IntPointer(op.maxIndexArguments());
// allocating chunk for RealArguments
realArgumentsPointer = Nd4j.dataType() == DataType.DOUBLE ? new DoublePointer(op.maxRealArguments())
: new FloatPointer(op.maxRealArguments());
// allocating chunk for shapesPointer
shapesPointer = new PointerPointer(op.maxShapes());
// allocating chunk for argumentsPointer
argumentsPointer = new PointerPointer(op.maxArguments());
// chunk for intArrays
arraysPointer = new PointerPointer(op.maxIntArrays());
}
@Override
public boolean equals(Object o) {
if (this == o)
return true;
if (o == null || getClass() != o.getClass())
return false;
AggregateMemoryBlock that = (AggregateMemoryBlock) o;
return opNum == that.opNum;
}
@Override
public int hashCode() {
return opNum;
}
}
@Override
public synchronized Map getCustomOperations() {
if (customOps == null) {
String list = loop.getAllCustomOps();
if (list == null || list.isEmpty()) {
log.warn("No customs ops available!");
customOps = Collections.emptyMap();
return customOps;
}
val map = new HashMap();
String[] split = list.split(";");
for (String op : split) {
if (op == null || op.isEmpty())
continue;
String[] another = op.split(":");
CustomOpDescriptor descriptor = CustomOpDescriptor.builder()
.hash(Long.valueOf(another[1]))
.numInputs(Integer.valueOf(another[2]))
.numOutputs(Integer.valueOf(another[3]))
.allowsInplace(Integer.valueOf(another[4]) == 1)
.numTArgs(Integer.valueOf(another[5]))
.numIArgs(Integer.valueOf(another[6]))
.build();
map.put(another[0], descriptor);
}
customOps = Collections.unmodifiableMap(map);
}
return customOps;
}
private PointerPointer getPointerPointerFrom(ThreadLocal> map,int numArguments) {
if(map.get() == null) {
Map store = new HashMap<>();
store.put(numArguments,new PointerPointer(numArguments));
map.set(store);
return map.get().get(numArguments);
}
else if (map.get().get(numArguments) == null) {
PointerPointer pointerPointer = new PointerPointer(numArguments);
map.get().put(numArguments,pointerPointer);
return pointerPointer;
}
return map.get().get(numArguments);
}
private ShortPointer getShortPointerFrom(ThreadLocal> map,int numArguments) {
if(map.get() == null) {
Map store = new HashMap<>();
store.put(numArguments,new ShortPointer(numArguments));
map.set(store);
return map.get().get(numArguments);
}
else if (map.get().get(numArguments) == null) {
ShortPointer pointerPointer = new ShortPointer(numArguments);
map.get().put(numArguments,pointerPointer);
return pointerPointer;
}
return map.get().get(numArguments);
}
private LongPointer getLongPointerFrom(ThreadLocal> map,int numArguments) {
if(map.get() == null) {
Map store = new HashMap<>();
store.put(numArguments,new LongPointer(numArguments));
map.set(store);
return map.get().get(numArguments);
}
else if (map.get().get(numArguments) == null) {
val pointerPointer = new LongPointer(numArguments);
map.get().put(numArguments,pointerPointer);
return pointerPointer;
}
return map.get().get(numArguments);
}
private DoublePointer getDoublePointerFrom(ThreadLocal> map,int numArguments) {
if(map.get() == null) {
Map store = new HashMap<>();
store.put(numArguments,new DoublePointer(numArguments));
map.set(store);
return map.get().get(numArguments);
}
else if (map.get().get(numArguments) == null) {
DoublePointer pointerPointer = new DoublePointer(numArguments);
map.get().put(numArguments,pointerPointer);
return pointerPointer;
}
return map.get().get(numArguments);
}
private BooleanPointer getBooleanPointerFrom(ThreadLocal> map,int numArguments) {
if(map.get() == null) {
Map store = new HashMap<>();
store.put(numArguments,new BooleanPointer(numArguments));
map.set(store);
return map.get().get(numArguments);
}
else if (map.get().get(numArguments) == null) {
val pointerPointer = new BooleanPointer(numArguments);
map.get().put(numArguments,pointerPointer);
return pointerPointer;
}
return map.get().get(numArguments);
}
private PointerPointer getInputShapes(int numArguments) {
return getPointerPointerFrom(inputShapes,numArguments);
}
private PointerPointer getInputBuffers(int numArguments) {
return getPointerPointerFrom(inputBuffers,numArguments);
}
private PointerPointer getOutputShapes(int numArguments) {
return getPointerPointerFrom(outputShapes,numArguments);
}
private PointerPointer getOutputBuffers(int numArguments) {
return getPointerPointerFrom(outputBuffers,numArguments);
}
/**
* This method executes given CustomOp
*
* PLEASE NOTE: You're responsible for input/output validation
* @param op Operation to execute
*/
@Override
public INDArray[] exec(@NonNull CustomOp op) {
boolean shapeOverride = false;
if (op.numOutputArguments() == 0 && !op.isInplaceCall()) {
try {
val list = this.calculateOutputShape(op);
if (list.isEmpty())
throw new ND4JIllegalStateException("Op name " + op.opName() + " failed to calculate output datatypes");
for (LongShapeDescriptor shape : list)
op.addOutputArgument(Nd4j.create(shape, false));
shapeOverride = true;
} catch (ND4JIllegalStateException e){
throw e;
} catch (Exception e) {
throw new ND4JIllegalStateException("Op name " + op.opName() + " - no output arrays were provided and calculateOutputShape failed to execute", e);
//throw new RuntimeException(e);
}
}
val name = op.opName();
try (val context = buildContext()) {
// optionally skip shape validation on op execution
if (shapeOverride)
context.shapeFunctionOverride(true);
context.markInplace(op.isInplaceCall());
// transferring rng state
context.setRngStates(Nd4j.getRandom().rootState(), Nd4j.getRandom().nodeState());
//transferring input/output arrays
context.setInputArrays(op.inputArguments());
context.setOutputArrays(op.outputArguments());
// transferring static args
context.setBArguments(op.bArgs());
context.setIArguments(op.iArgs());
context.setTArguments(op.tArgs());
context.setDArguments(op.dArgs());
val result = exec(op, context);
val states = context.getRngStates();
// check if input & output needs update
for (val in:op.inputArguments()) {
if (!in.isEmpty())
((BaseCpuDataBuffer) in.data()).actualizePointerAndIndexer();
}
for (val out:op.outputArguments()) {
if (!out.isEmpty())
((BaseCpuDataBuffer) out.data()).actualizePointerAndIndexer();
}
// pulling states back
Nd4j.getRandom().setStates(states.getFirst(), states.getSecond());
return result;
} catch (ND4JOpProfilerException e){
throw e;
} catch (Exception e) {
throw new RuntimeException("Op [" + name + "] execution failed", e);
}
}
protected LongShapeDescriptor getShapeFromPointer(LongPointer ptr) {
val rank = (int) ptr.get(0);
val shape = new long[rank * 2 + 4];
for (int i = 0; i < shape.length; i++) {
shape[i] = ptr.get(i);
}
//val extras = ptr.get(Shape.shapeInfoLength(rank) - 3);
val t = ArrayOptionsHelper.arrayType(shape);
return LongShapeDescriptor.fromShape(Shape.shape(shape), Shape.stride(shape), Shape.elementWiseStride(shape), Shape.order(shape), ArrayOptionsHelper.dataType(shape), t == ArrayType.EMPTY);
}
@Override
public List calculateOutputShape(@NonNull CustomOp op) {
return calculateOutputShape(op, null);
}
@Override
public List calculateOutputShape(@NonNull CustomOp op, OpContext opContext) {
val lc = op.opName().toLowerCase();
val hash = op.opHash();
val result = new ArrayList();
int nIn = opContext != null ? opContext.numInputArguments() : op.numInputArguments();
if(nIn == 0 && op.getDescriptor().getNumInputs() >= 1) {
if(log.isTraceEnabled()){
log.trace("Could not calculate output shape for op {}: number of input args was 0",
op.getClass().getName());
}
return Collections.emptyList();
}
val inputBuffers = new PointerPointer<>(nIn);
val inputShapes = new PointerPointer<>(nIn);
val inputArgs = opContext != null ? opContext.getInputArrays() : op.inputArguments();
int cnt= 0;
for (val in: inputArgs) {
if (!in.isEmpty())
inputBuffers.put(cnt, in.data().addressPointer());
inputShapes.put(cnt++, in.shapeInfoDataBuffer().addressPointer());
}
int nIArgs = opContext != null ? opContext.numIArguments() : op.numIArguments();
val iArgs = nIArgs > 0 ? new LongPointer(nIArgs) : null;
cnt = 0;
if(opContext != null){
for (val i: opContext.getIArguments())
iArgs.put(cnt++, i);
} else {
for (val i: op.iArgs())
iArgs.put(cnt++, i);
}
int nTArgs = opContext != null ? opContext.numTArguments() : op.numTArguments();
val tArgs = nTArgs > 0 ? new DoublePointer(nTArgs) : null;
int nBArgs = opContext != null ? opContext.numBArguments() : op.numBArguments();
val bArgs = nBArgs > 0 ? new BooleanPointer(nBArgs) : null;
int nDArgs = opContext != null ? opContext.numDArguments() : op.numDArguments();
val dArgs = nDArgs > 0 ? new IntPointer(nDArgs) : null;
cnt = 0;
if(opContext != null) {
for (val b: opContext.getBArguments())
bArgs.put(cnt++, b);
} else {
for (val b: op.bArgs())
bArgs.put(cnt++, b);
}
cnt = 0;
if(opContext != null) {
for (val b: opContext.getTArguments())
tArgs.put(cnt++, b);
} else {
for (val b: op.tArgs())
tArgs.put(cnt++, b);
}
cnt = 0;
if(opContext != null) {
for (val b: opContext.getDArguments())
dArgs.put(cnt++, b.toInt());
} else {
for (val b: op.dArgs())
dArgs.put(cnt++, b.toInt());
}
OpaqueShapeList ptrptr;
try {
ptrptr = loop.calculateOutputShapes2(null,
hash, inputBuffers, inputShapes, nIn, tArgs,
nTArgs, iArgs, nIArgs, bArgs, nBArgs, dArgs, nDArgs);
if (loop.lastErrorCode() != 0) {
DifferentialFunction differentialFunction = (DifferentialFunction) op;
throw new RuntimeException("Op " + op.opName() + " with name " + differentialFunction.getOwnName() + " failed to execute." + opContext.toString() + " Here is the error from c++: " + loop.lastErrorMessage());
}
} catch (Throwable t) {
StringBuilder sb = new StringBuilder();
sb.append("Inputs: [(");
for( int i = 0; i < inputArgs.size(); i++) {
if(i > 0)
sb.append("), (");
sb.append(Shape.shapeToStringShort(inputArgs.get(i)));
}
sb.append(")]");
if(op instanceof DifferentialFunction && ((DifferentialFunction)op).getSameDiff() != null) {
appendSameDiffInfo(sb, (DifferentialFunction) op);
}
int nOut = opContext != null ? opContext.numOutputArguments() : op.numOutputArguments();
log.error("Failed to calculate output shapes for op {}. Attempted to execute with {} inputs, {} outputs, " +
"{} targs, {} iargs, {} bargs and {} dargs. {} - Please see above message (printed out from c++) for a possible cause of error.",
op.opName(), nIn, nOut, nTArgs, nIArgs, nBArgs, nDArgs, sb.toString());
throw t;
}
if (loop.lastErrorCode() != 0)
throw new RuntimeException(loop.lastErrorMessage());
if (ptrptr == null)
throw new RuntimeException();
for (int e = 0; e < loop.getShapeListSize(ptrptr); e++ )
result.add(getShapeFromPointer(new PagedPointer(loop.getShape(ptrptr, e)).asLongPointer()));
loop.deleteShapeList(ptrptr);
if(log.isTraceEnabled()) {/**/
String[] arr = new String[result.size()];
for( int i = 0; i < result.size(); i++) {
arr[i] = result.get(i).toString();
}
DifferentialFunction differentialFunction = (DifferentialFunction) op;
log.trace("Calculated output shapes for op of name {} and type {} - {}",differentialFunction.getOwnName(), op.getClass().getName(), Arrays.toString(arr));
}
return result;
}
@Override
public void enableDebugMode(boolean reallyEnable) {
debug.set(reallyEnable);
loop.enableDebugMode(reallyEnable);
}
@Override
public void enableVerboseMode(boolean reallyEnable) {
verbose.set(reallyEnable);
loop.enableVerboseMode(reallyEnable);
}
@Override
public void registerGraph(long id, Pointer graph) {
loop.registerGraph(null, id, graph);
if (loop.lastErrorCode() != 0)
throw new RuntimeException(loop.lastErrorMessage());
}
@Override
public Map executeGraph(long id, @NonNull Map map, @NonNull Map reverseMap) {
val ptrBuffers = new PointerPointer(map.size());
val ptrShapes = new PointerPointer(map.size());
val ptrIndices = new IntPointer(map.size());
int cnt = 0;
val keySet = new ArrayList(map.keySet());
for (val key: keySet) {
val array = map.get(key);
ptrBuffers.put(cnt, array.data().addressPointer());
ptrShapes.put(cnt, array.shapeInfoDataBuffer().addressPointer());
ptrIndices.put(cnt, reverseMap.get(key));
cnt++;
}
val newMap = new LinkedHashMap();
OpaqueVariablesSet result = loop.executeStoredGraph(null, id, ptrBuffers, ptrShapes, ptrIndices, map.size());
if (loop.lastErrorCode() != 0)
throw new RuntimeException(loop.lastErrorMessage());
OpStatus status = OpStatus.byNumber(loop.getVariablesSetStatus(result));
if (status != OpStatus.ND4J_STATUS_OK)
throw new ND4JIllegalStateException("Op execution failed: " + status);
for (int e = 0; e < loop.getVariablesSetSize(result); e++) {
OpaqueVariable var = loop.getVariable(result, e);
int nodeId = loop.getVariableId(var);
int index = loop.getVariableIndex(var);
LongPointer shapeInfo = loop.getVariableShape(var);
Pointer buffer = loop.getVariableBuffer(var);
val rank = (int) shapeInfo.get(0);
val jshape = new long[rank * 2 + 4];
for (int i = 0; i < jshape.length; i++) {
jshape[i] = shapeInfo.get(i);
}
val shapeOf = Shape.shapeOf(jshape);
val stridesOf = Shape.stridesOf(jshape);
val order = Shape.order(jshape);
val array = Nd4j.create(shapeOf, stridesOf, 0, order);
val perfX = PerformanceTracker.getInstance().helperStartTransaction();
Pointer.memcpy(array.data().addressPointer(), buffer, Shape.lengthOf(shapeOf) * Nd4j.sizeOfDataType(array.dataType()));
PerformanceTracker.getInstance().helperRegisterTransaction(0, perfX, Shape.lengthOf(shapeOf) * Nd4j.sizeOfDataType(array.dataType()), MemcpyDirection.HOST_TO_HOST);
//newMap.put(keySet.get(nodeId), array);
String nodeName = loop.getVariableName(var);
newMap.put(nodeName, array);
}
loop.deleteVariablesSet(result);
return newMap;
}
@Override
public void forgetGraph(long id) {
loop.unregisterGraph(null, id);
if (loop.lastErrorCode() != 0)
throw new RuntimeException(loop.lastErrorMessage());
}
/**
* This method allows to set desired number of elements per thread, for performance optimization purposes.
* I.e. if array contains 2048 elements, and threshold is set to 1024, 2 threads will be used for given op execution.
*
* Default value: 1024
*
* @param threshold
*/
@Override
public void setElementsThreshold(int threshold) {
loop.setElementThreshold(threshold);
}
/**
* This method allows to set desired number of sub-arrays per thread, for performance optimization purposes.
* I.e. if matrix has shape of 64 x 128, and threshold is set to 8, each thread will be processing 8 sub-arrays (sure, if you have 8 core cpu).
* If your cpu has, say, 4, cores, only 4 threads will be spawned, and each will process 16 sub-arrays
*
* Default value: 8
*
* @param threshold
*/
@Override
public void setTadThreshold(int threshold) {
loop.setTADThreshold(threshold);
}
@Override
public String getString(DataBuffer buffer, long index) {
Preconditions.checkArgument(buffer instanceof Utf8Buffer, "Expected Utf8Buffer");
val addr = ((LongIndexer) buffer.indexer()).get(index);
val ptr = new PagedPointer(addr);
val str = new Nd4jCpu.utf8string(ptr);
return str._buffer().capacity(str._length()).getString();
}
@Override
public ExecutionerType type() {
return ExecutionerType.NATIVE_CPU;
}
@Override
public boolean isExperimentalMode() {
return experimentalMode.get();
}
@Override
public void scatterUpdate(ScatterUpdate.UpdateOp op, @NonNull INDArray array, @NonNull INDArray indices, @NonNull INDArray updates, @NonNull int[] axis) {
val tadX = tadManager.getTADOnlyShapeInfo(array, axis);
val tadY = tadManager.getTADOnlyShapeInfo(updates, axis);
if (tadY.getSecond().length() != indices.length())
throw new IllegalStateException("Number of updates doesn't match number of indices. Bad dimensions used?");
loop.scatterUpdate(null, op.ordinal(), (int) indices.length(),
array.data().addressPointer(), (LongPointer) tadX.getFirst().addressPointer(), (LongPointer) tadX.getSecond().addressPointer(), null, null, null,
updates.data().addressPointer(), (LongPointer) tadY.getFirst().addressPointer(), (LongPointer) tadY.getSecond().addressPointer(), null, null, null,
indices.data().addressPointer(), (LongPointer) indices.shapeInfoDataBuffer().addressPointer(), null, null);
if (loop.lastErrorCode() != 0)
throw new RuntimeException(loop.lastErrorMessage());
}
@Override
public OpContext buildContext() {
return new CpuOpContext();
}
@Override
public INDArray[] exec(CustomOp op, @NonNull OpContext context) {
long st = profilingConfigurableHookIn(op, context);
boolean mklOverride = false;
try {
if (Nd4jCpu.Environment.getInstance().isUseMKLDNN()) {
val opName = op.opName();
val state = mklOverrides.get(op);
if (state != null && state == true) {
mklOverride = true;
Nd4jCpu.Environment.getInstance().setUseMKLDNN(true);
}
}
val status = loop.execCustomOp2(null, op.opHash(), context.contextPointer());
if (loop.lastErrorCode() != 0)
throw new RuntimeException(loop.lastErrorMessage());
if (status != 0) {
DifferentialFunction differentialFunction = (DifferentialFunction) op;
throw new RuntimeException("Op with name " + differentialFunction.getOwnName() + " and op type [" + op.opName() + "] execution failed");
}
if (context.getOutputArrays().isEmpty())
return new INDArray[0];
else
return context.getOutputArrays().toArray(new INDArray[context.getOutputArrays().size()]);
} catch (Exception e) {
val sb = new StringBuilder();
sb.append("Inputs: [(");
int nIn = (context.getInputArrays() == null ? 0 : context.getInputArrays().size());
for (int i = 0; i < nIn; i++) {
if (i > 0)
sb.append("), (");
sb.append(Shape.shapeToStringShort(context.getInputArrays().get(i)));
}
sb.append(")]. Outputs: [(");
int nOut = (context.getOutputArrays() == null ? 0 : context.getOutputArrays().size());
for (int i = 0; i < nOut; i++) {
if (i > 0)
sb.append("), (");
sb.append(Shape.shapeToStringShort(context.getOutputArrays().get(i)));
}
sb.append(")]. tArgs: ");
int nT = (context.getTArguments() == null ? 0 : context.getTArguments().size());
if (nT > 0) {
sb.append(context.getTArguments());
} else {
sb.append("-");
}
sb.append(". iArgs: ");
int nI = (context.getIArguments() == null ? 0 : context.getIArguments().size());
if (nI > 0) {
sb.append(context.getIArguments());
} else {
sb.append("-");
}
sb.append(". bArgs: ");
int nB = (context.getBArguments() == null ? 0 : context.getBArguments().size());
if (nB > 0) {
sb.append(context.getBArguments());
} else {
sb.append("-");
}
if (op instanceof DifferentialFunction) {
String n = ((DifferentialFunction) op).getOwnName();
if (n != null && !n.equals(op.opName())) {
sb.append(". Op own name: \"").append(n).append("\"");
}
}
if(op instanceof DifferentialFunction && ((DifferentialFunction)op).getSameDiff() != null){
appendSameDiffInfo(sb, (DifferentialFunction) op);
}
log.error("Failed to execute op " + op.opName() + ". Attempted to execute with " +
nIn + " inputs, " +
nOut + " outputs, " +
nT + " targs," +
nB + " bargs and " +
nI + " iargs. " +
sb.toString() +
" - Please see above message (printed out from c++) for a possible cause of error.");
throw e;
} finally {
if (mklOverride)
Nd4jCpu.Environment.getInstance().setUseMKLDNN(true);
profilingConfigurableHookOut(op, context, st);
}
}
@Override
public INDArrayStatistics inspectArray(INDArray array) {
val debugInfo = new Nd4jCpu.DebugInfo();
loop.inspectArray(null, array.data().addressPointer(), (LongPointer) array.shapeInfoDataBuffer().addressPointer(), null, null, debugInfo);
if (loop.lastErrorCode() != 0)
throw new RuntimeException(loop.lastErrorMessage());
return INDArrayStatistics.builder()
.minValue(debugInfo._minValue())
.maxValue(debugInfo._maxValue())
.meanValue(debugInfo._meanValue())
.stdDevValue(debugInfo._stdDevValue())
.countInf(debugInfo._infCount())
.countNaN(debugInfo._nanCount())
.countNegative(debugInfo._negativeCount())
.countPositive(debugInfo._positiveCount())
.countZero(debugInfo._zeroCount())
.build();
}
@Override
public DataBuffer createShapeInfo(long[] shape, long[] stride, long elementWiseStride, char order, DataType dtype, boolean empty) {
val dbf = loop.shapeBuffer(shape.length, new LongPointer(shape), new LongPointer(stride), dtype.toInt(), order, elementWiseStride, empty);
if (loop.lastErrorCode() != 0)
throw new RuntimeException(loop.lastErrorMessage());
val result = new LongBuffer(loop.getConstantShapeBufferPrimary(dbf), Shape.shapeInfoLength(shape.length));
loop.deleteConstantShapeBuffer(dbf);
return result;
}
@Override
public DataBuffer createShapeInfo(long[] shape, long[] stride, long elementWiseStride, char order, DataType dtype, long extras) {
OpaqueConstantShapeBuffer dbf = loop.shapeBufferEx(shape.length, new LongPointer(shape), new LongPointer(stride), dtype.toInt(), order, elementWiseStride, extras);
if (loop.lastErrorCode() != 0)
throw new RuntimeException(loop.lastErrorMessage());
val result = new LongBuffer(loop.getConstantShapeBufferPrimary(dbf), Shape.shapeInfoLength(shape.length));
loop.deleteConstantShapeBuffer(dbf);
return result;
}
@Override
public TadPack tadShapeInfoAndOffsets(INDArray array, int[] dimension) {
OpaqueTadPack pack = loop.tadOnlyShapeInfo((LongPointer) array.shapeInfoDataBuffer().addressPointer(), new IntPointer(dimension), dimension.length);
if (loop.lastErrorCode() != 0)
throw new RuntimeException(loop.lastErrorMessage());
val tadShape = new LongBuffer(loop.getPrimaryShapeInfo(pack), loop.getShapeInfoLength(pack));
val tadOffsets = new LongBuffer(loop.getPrimaryOffsets(pack), loop.getNumberOfTads(pack));
loop.deleteTadPack(pack);
return new TadPack(tadShape, tadOffsets);
}
protected void appendSameDiffInfo(StringBuilder sb, DifferentialFunction df){
String[] inNames = df.argNames();
String[] outNames = df.outputVariablesNames();
if(inNames != null){
sb.append(". Input var names: ").append(Arrays.toString(inNames));
}
if(outNames != null){
sb.append(". Output var names: ").append(Arrays.toString(outNames));
}
}
}
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