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/*
* ******************************************************************************
* *
* *
* * This program and the accompanying materials are made available under the
* * terms of the Apache License, Version 2.0 which is available at
* * https://www.apache.org/licenses/LICENSE-2.0.
* *
* * See the NOTICE file distributed with this work for additional
* * information regarding copyright ownership.
* * Unless required by applicable law or agreed to in writing, software
* * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* * License for the specific language governing permissions and limitations
* * under the License.
* *
* * SPDX-License-Identifier: Apache-2.0
* *****************************************************************************
*/
package org.nd4j.linalg.api.ops.impl.reduce;
import lombok.EqualsAndHashCode;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
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.ops.DynamicCustomOp;
import java.util.List;
@EqualsAndHashCode
public class TensorMmulBp extends DynamicCustomOp {
public TensorMmulBp(){}
public TensorMmulBp(SameDiff samediff, SDVariable x, SDVariable y, SDVariable gradAtOutput, int[][] axes) {
this(samediff, x, y, gradAtOutput, axes[0], axes[1] );
}
public TensorMmulBp(SameDiff samediff, SDVariable x, SDVariable y, SDVariable gradAtOutput, int[] axesX, int[] axesY ) {
super(null, samediff, new SDVariable[]{x,y, gradAtOutput});
int[][] axes = new int[][]{axesX, axesY};
addIArgument(axesX.length);
addIArgument(axesX);
addIArgument(axesY.length);
addIArgument(axesY);
}
public TensorMmulBp(INDArray x, INDArray y, INDArray gradAtOutput, int[][] axes) {
this(x, y, gradAtOutput, axes[0], axes[1] );
}
public TensorMmulBp(INDArray x, INDArray y, INDArray gradAtOutput, int[] axesX, int[] axesY ) {
super(null,new INDArray[]{x, y, gradAtOutput},null);
int[][] axes = new int[][]{axesX, axesY};
addIArgument(axesX.length);
addIArgument(axesX);
addIArgument(axesY.length);
addIArgument(axesY);
}
public TensorMmulBp(INDArray x, INDArray y, INDArray gradAtOutput, INDArray dldx, INDArray dldy, int[][] axes ) {
this(x, y, gradAtOutput, dldx, dldy, axes[0], axes[1] );
}
public TensorMmulBp(INDArray x, INDArray y, INDArray gradAtOutput, INDArray dldx, INDArray dldy, int[] axesX, int[] axesY ) {
super(null, new INDArray[]{x, y, gradAtOutput}, new INDArray[]{dldx, dldy});
int[][] axes = new int[][]{axesX, axesY};
addIArgument(axesX.length);
addIArgument(axesX);
addIArgument(axesY.length);
addIArgument(axesY);
}
@Override
public String opName() {
return "tensormmul_bp";
}
@Override
public List doDiff(List i_v1) {
throw new UnsupportedOperationException("Differentiation of " + getClass().getName() + " not supported");
}
@Override
public List calculateOutputDataTypes(List dataTypes) {
Preconditions.checkState(dataTypes != null && dataTypes.size() == 3, "Expected exactly 3 inputs to tensormmul_bp op, got %s", dataTypes);
Preconditions.checkState(dataTypes.get(0).isFPType() && dataTypes.get(1).isFPType() && dataTypes.get(0).isFPType(), "Inputs to tensormmul_bp op must both be a floating" +
"point type: got %s", dataTypes);
return dataTypes.subList(0, 2);
}
}