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org.nd4j.linalg.api.ops.BaseBroadcastOp Maven / Gradle / Ivy
/*
* ******************************************************************************
* *
* *
* * 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.api.ops;
import lombok.NoArgsConstructor;
import lombok.extern.slf4j.Slf4j;
import lombok.val;
import onnx.Onnx;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.autodiff.util.SameDiffUtils;
import org.nd4j.common.base.Preconditions;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.shape.LongShapeDescriptor;
import org.nd4j.linalg.api.shape.Shape;
import org.nd4j.linalg.factory.Broadcast;
import org.tensorflow.framework.AttrValue;
import org.tensorflow.framework.GraphDef;
import org.tensorflow.framework.NodeDef;
import java.util.Collections;
import java.util.List;
import java.util.Map;
@NoArgsConstructor
@Slf4j
public abstract class BaseBroadcastOp extends BaseOp implements BroadcastOp {
protected int[] dimension;
public BaseBroadcastOp(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
int[] dimension) {
this(sameDiff, i_v1, i_v2, false, dimension);
}
public BaseBroadcastOp(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
boolean inPlace,
int[] dimension) {
super(sameDiff, inPlace, new Object[]{i_v2});
if (i_v1 != null && i_v2 != null) {
this.sameDiff = sameDiff;
this.inPlace = inPlace;
this.dimension = dimension;
sameDiff.addArgsFor(new SDVariable[]{i_v1,i_v2},this);
} else {
throw new IllegalArgumentException("Input not null variables.");
}
}
public BaseBroadcastOp(SameDiff sameDiff) {
this.sameDiff = sameDiff;
}
public BaseBroadcastOp(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
int[] dimension,
Object[] extraArgs) {
super(sameDiff, extraArgs);
this.dimension = dimension;
if (i_v1 != null && i_v2 != null) {
SameDiffUtils.validateDifferentialFunctionSameDiff(sameDiff, i_v1, this);
SameDiffUtils.validateDifferentialFunctionSameDiff(sameDiff, i_v2, this);
this.sameDiff = sameDiff;
sameDiff.addArgsFor(new SDVariable[]{i_v1,i_v2},this);
} else {
throw new IllegalArgumentException("Input not null variables.");
}
}
public BaseBroadcastOp(SameDiff sameDiff, SDVariable i_v, int[] dimension, boolean inPlace) {
this(sameDiff, i_v, i_v.getShape(), inPlace, dimension, null);
}
public BaseBroadcastOp(SameDiff sameDiff,
SDVariable i_v,
long[] shape,
boolean inPlace,
int[] dimension,
Object[] extraArgs) {
super(sameDiff, inPlace, extraArgs);
this.dimension = dimension;
if (i_v != null) {
SameDiffUtils.validateDifferentialFunctionSameDiff(sameDiff, i_v, this);
sameDiff.addArgsFor(new SDVariable[]{i_v},this);
} else {
throw new IllegalArgumentException("Input not null variable.");
}
}
public BaseBroadcastOp(SameDiff sameDiff,
SDVariable i_v,
int[] dimension,
Object[] extraArgs) {
this(sameDiff, i_v, i_v.getShape(), false, dimension, extraArgs);
}
public BaseBroadcastOp(INDArray x, INDArray y, INDArray z, int... dimension) {
super(x, y, z);
Broadcast.validateBroadcastDims(x,y,z, dimension);
this.dimension = dimension;
defineDimensions(dimension);
}
@Override
public Type opType() {
return Type.BROADCAST;
}
/**
* Calculate the output shape for this op
*
* @return
*/
public List calculateOutputShape() {
if(x == null || y == null)
return Collections.emptyList();
long[] shapeX = x.shape();
long[] shapeY = y.shape();
return Collections.singletonList(LongShapeDescriptor.fromShape(Shape.broadcastOutputShape(shapeX, shapeY),
Shape.pickPairwiseDataType(x.dataType(), y.dataType())));
}
@Override
public int[] getDimension() {
if (dimension == null) {
dimension = Shape.getBroadcastDimensions(larg().getShape(), rarg().getShape());
}
return dimension;
}
@Override
public void setDimension(int... dimension) {
this.dimension = dimension;
}
@Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map attributesForNode, GraphDef graph) {
}
@Override
public void initFromOnnx(Onnx.NodeProto node, SameDiff initWith, Map attributesForNode, Onnx.GraphProto graph) {
}
@Override
public boolean validateDataTypes(boolean experimentalMode) {
val op = opNum();
if (y() != null && z() != null)
Preconditions.checkArgument(y().dataType() == z().dataType() || x().dataType() == z().dataType(),
"Op.Z type must be either Op.X or Op.Y: x.dataType=%s, y.dataType=%s, z.dataType=%s, op=%s",
x.dataType(), y.dataType(), z.dataType(), getClass().getName());
if (!experimentalMode)
Preconditions.checkArgument(x.dataType() == y.dataType() || y.dataType() == DataType.BOOL, "Op.X must have same data type as Op.Y: X.datatype=%s, Y.datatype=%s", x.dataType(), y.dataType());
if (y() != null) {
if (op != 1 && (y().isR() || x().isR()))
Preconditions.checkArgument(z().isR(), "Op.Z must have floating point type, since one of operands is floating point: x.dataType=%s, y.dataType=%s, z.dataType=%s, op=%s",
x.dataType(), y.dataType(), z.dataType(), getClass().getName());
} else if (x().isR())
Preconditions.checkArgument(z().isR(), "Op.Z must have floating point type, since one of operands is floating point: x.dataType=%s, z.dataType=%s, op=%s",
x.dataType(), z.dataType(), getClass().getName());
return true;
}
@Override
public Type getOpType() {
return Type.BROADCAST;
}
}
