org.nd4j.linalg.api.ops.impl.reduce.bp.BaseReductionBp Maven / Gradle / Ivy
The newest version!
/*
* ******************************************************************************
* *
* *
* * 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.bp;
import lombok.NoArgsConstructor;
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.Collections;
import java.util.List;
@NoArgsConstructor
public abstract class BaseReductionBp extends DynamicCustomOp {
protected boolean keepDims;
protected int[] dimensions;
/**
*
* @param origInput Pre-reduced input
* @param gradAtOutput Gradient at the output
* @param keepDims If true: reduction dimensions were kept
* @param dimensions Dimensions to reduce. May be null
*/
public BaseReductionBp(SameDiff sameDiff, SDVariable origInput, SDVariable gradAtOutput, boolean keepDims, int... dimensions) {
super(null, sameDiff, new SDVariable[]{origInput, gradAtOutput}, false);
this.keepDims = keepDims;
this.dimensions = dimensions;
}
/**
*
* @param origInput1 Pre-reduced input 1
* @param origInput2 Pre-reduced input 2
* @param gradAtOutput Gradient at the output
* @param keepDims If true: reduction dimensions were kept
* @param dimensions Dimensions to reduce. May be null
*/
public BaseReductionBp(SameDiff sameDiff, SDVariable origInput1, SDVariable origInput2, SDVariable gradAtOutput, boolean keepDims, int... dimensions) {
super(null, sameDiff, new SDVariable[]{origInput1, origInput2, gradAtOutput}, false);
this.keepDims = keepDims;
this.dimensions = dimensions;
}
/**
*
* @param origInput Pre-reduced input
* @param gradAtOutput Gradient at the output
* @param output Output array - i.e., gradient at the input to the reduction function
* @param keepDims If true: reduction dimensions were kept
* @param dimensions Dimensions to reduce. May be null
*/
public BaseReductionBp(INDArray origInput, INDArray gradAtOutput, INDArray output, boolean keepDims, int... dimensions) {
super(null, new INDArray[]{origInput, gradAtOutput}, (output == null ? null : new INDArray[]{output}));
this.keepDims = keepDims;
this.dimensions = dimensions;
}
/**
*
* @param origInput1 Pre-reduced input1
* @param origInput2 Pre-reduced input2
* @param gradAtOutput Gradient at the output
* @param output Output array - i.e., gradient at the input to the reduction function
* @param keepDims If true: reduction dimensions were kept
* @param dimensions Dimensions to reduce. May be null
*/
public BaseReductionBp(INDArray origInput1, INDArray origInput2, INDArray gradAtOutput, INDArray output, boolean keepDims, int... dimensions){
super(null, new INDArray[]{origInput1, origInput2, gradAtOutput}, (output == null ? null : new INDArray[]{output}));
this.keepDims = keepDims;
this.dimensions = dimensions;
}
public BaseReductionBp(INDArray origInput1, INDArray origInput2, INDArray gradAtOutput, INDArray output1, INDArray output2, boolean keepDims, int... dimensions){
super(null, new INDArray[]{origInput1, origInput2, gradAtOutput}, new INDArray[]{output1, output2});
this.keepDims = keepDims;
this.dimensions = dimensions;
}
public BaseReductionBp(INDArray origInput, INDArray gradAtOutput, INDArray output, boolean keepDims, INDArray dimensions) {
super(null,new INDArray[]{origInput,gradAtOutput,dimensions},new INDArray[]{output});
this.keepDims = keepDims;
}
public BaseReductionBp(SameDiff sameDiff, SDVariable origInput, SDVariable gradAtOutput, boolean keepDims, SDVariable dimensions) {
super(null,sameDiff,new SDVariable[]{origInput,gradAtOutput,dimensions},false);
this.keepDims = keepDims;
}
protected void addArgs() {
addBArgument(keepDims);
if(dimensions != null && dimensions.length > 0) {
if(dimensions.length != 1 || dimensions[0] != Integer.MAX_VALUE) {
//Integer.MAX_VALUE means "full array" but here no dimension args == full array
addIArgument(dimensions);
}
}
}
public abstract String opName();
@Override
public List calculateOutputDataTypes(List dataTypes) {
//Reduction backprop ops: expect 2 inputs... the original input, and the gradient at the outputs
//For example, for y=mean(x), inputs to ReduceMeanBp are x and dL/dy; output is dL/dx
//Now, we expect gradient dL/dx datatype to be same as x - which resticts us to real-valued x input
//i.e., 'gradient' of integer or boolean isn't defined
Preconditions.checkState(dataTypes != null && dataTypes.size() == 2, "Expected exactly 2 input datatype for %s, got input %s", getClass(), dataTypes);
Preconditions.checkState(dataTypes.get(0).isFPType(), "First input must be a floating point type, got %s", dataTypes.get(0));
Preconditions.checkState(dataTypes.get(1).isFPType(), "Second input (gradient at reduction output) must be a floating point type, got %s", dataTypes.get(1));
return Collections.singletonList(dataTypes.get(0));
}
}