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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.
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* * 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
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* * SPDX-License-Identifier: Apache-2.0
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package org.deeplearning4j.nn.graph.vertex.impl;
import lombok.val;
import org.deeplearning4j.nn.api.Layer;
import org.deeplearning4j.nn.api.MaskState;
import org.deeplearning4j.nn.gradient.Gradient;
import org.deeplearning4j.nn.graph.ComputationGraph;
import org.deeplearning4j.nn.graph.vertex.BaseGraphVertex;
import org.deeplearning4j.nn.graph.vertex.VertexIndices;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.memory.MemoryWorkspace;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.impl.transforms.pairwise.bool.Or;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.indexing.INDArrayIndex;
import org.nd4j.linalg.indexing.NDArrayIndex;
import org.nd4j.common.primitives.Pair;
import org.deeplearning4j.nn.workspace.ArrayType;
import org.deeplearning4j.nn.workspace.LayerWorkspaceMgr;
import java.util.Arrays;
public class MergeVertex extends BaseGraphVertex {
private long[][] forwardPassShapes;
private int fwdPassRank;
private int mergeAxis;
public MergeVertex(ComputationGraph graph, String name, int vertexIndex, DataType dataType, int mergeAxis) {
this(graph, name, vertexIndex, null, null, dataType, mergeAxis);
}
public MergeVertex(ComputationGraph graph, String name, int vertexIndex, VertexIndices[] inputVertices,
VertexIndices[] outputVertices, DataType dataType, int mergeAxis) {
super(graph, name, vertexIndex, inputVertices, outputVertices, dataType);
this.mergeAxis = mergeAxis;
}
@Override
public String toString() {
return "MergeVertex(id=" + this.getVertexIndex() + ",name=\"" + this.getVertexName() + "\")";
}
@Override
public boolean hasLayer() {
return false;
}
@Override
public Layer getLayer() {
return null;
}
@Override
public INDArray doForward(boolean training, LayerWorkspaceMgr workspaceMgr) {
if (!canDoForward())
throw new IllegalStateException("Cannot do forward pass: inputs not set");
if (inputs.length == 1) {
//No-op case
val shape = inputs[0].shape();
forwardPassShapes = new long[][] {Arrays.copyOf(shape, shape.length)};
return workspaceMgr.leverageTo(ArrayType.ACTIVATIONS, inputs[0]);
}
INDArray[] in = new INDArray[inputs.length];
for( int i= 0; i < in.length; i++) {
in[i] = inputs[i].castTo(dataType); //No-op if correct type
}
forwardPassShapes = new long[in.length][0];
val nExamples = in[0].size(0);
fwdPassRank = in[0].rank();
for (int i = 0; i < in.length; i++) {
val currShape = in[i].shape();
if (fwdPassRank != currShape.length) {
throw new IllegalStateException(
"Cannot merge activations with different ranks: first activations have rank "
+ fwdPassRank + ", activations[" + i + "] have rank " + currShape.length
+ " (shape=" + Arrays.toString(currShape) + ")");
}
forwardPassShapes[i] = Arrays.copyOf(currShape, currShape.length);
if (currShape[0] != nExamples) {
throw new IllegalStateException(
"Cannot merge activations with different number of examples (activations[0] shape: "
+ Arrays.toString(in[0].shape()) + ", activations[" + i
+ "] shape: " + Arrays.toString(in[i].shape()));
}
}
try(MemoryWorkspace ws = workspaceMgr.notifyScopeBorrowed(ArrayType.ACTIVATIONS)) {
INDArray out = Nd4j.concat(mergeAxis, in);
return out;
}
}
@Override
public Pair doBackward(boolean tbptt, LayerWorkspaceMgr workspaceMgr) {
if (!canDoBackward())
throw new IllegalStateException("Cannot do backward pass: errors not set");
if (forwardPassShapes.length == 1) {
//No op case
return new Pair<>(null, new INDArray[] {workspaceMgr.leverageTo(ArrayType.ACTIVATION_GRAD, epsilon)});
}
//Split the epsilons in the opposite way that the activations were merged
INDArray[] out = new INDArray[forwardPassShapes.length];
for (int i = 0; i < out.length; i++)
out[i] = workspaceMgr.createUninitialized(ArrayType.ACTIVATION_GRAD, epsilon.dataType(), forwardPassShapes[i]);
int cumulative = 0;
switch (fwdPassRank) {
case 2:
//Standard
for (int i = 0; i < forwardPassShapes.length; i++) {
out[i].assign(epsilon.get(NDArrayIndex.all(), //All rows
NDArrayIndex.interval(cumulative, cumulative + forwardPassShapes[i][1]))); //subset of columns
cumulative += forwardPassShapes[i][1];
}
break;
case 3:
for (int i = 0; i < forwardPassShapes.length; i++) {
out[i].assign(epsilon.get(indices(3, mergeAxis, cumulative, cumulative + forwardPassShapes[i][mergeAxis]))); //All time steps
cumulative += forwardPassShapes[i][mergeAxis];
}
break;
case 4:
for (int i = 0; i < forwardPassShapes.length; i++) {
out[i].assign(epsilon.get(indices(4, mergeAxis, cumulative, cumulative + forwardPassShapes[i][mergeAxis]))); //height
cumulative += forwardPassShapes[i][mergeAxis];
}
break;
default:
throw new RuntimeException("Invalid rank during forward pass (not 2, 3, 4)"); //Should never happen
}
return new Pair<>(null, out);
}
private INDArrayIndex[] indices(int num, int axis, long from, long to){
INDArrayIndex[] out = new INDArrayIndex[num];
for( int i=0; i feedForwardMaskArrays(INDArray[] maskArrays, MaskState currentMaskState,
int minibatchSize) {
if (maskArrays == null) {
return new Pair<>(null, currentMaskState);
}
//Most common case: all or none.
//If there's only *some* mask arrays: assume the others (missing) are equivalent to all 1s
//And for handling multiple masks: best strategy seems to be an OR operation
//i.e., output is 1 if any of the input are 1s
//Which means: if any masks are missing, output null (equivalent to no mask)
//Otherwise do an element-wise OR operation
for (INDArray arr : maskArrays) {
if (arr == null) {
return new Pair<>(null, currentMaskState);
}
}
//At this point: all present. Do OR operation
if (maskArrays.length == 1) {
return new Pair<>(maskArrays[0], currentMaskState);
} else {
INDArray ret;
if(maskArrays[0].dataType() == DataType.BOOL){
ret = maskArrays[0].dup(maskArrays[0].ordering());
} else {
ret = maskArrays[0].castTo(DataType.BOOL);
}
Nd4j.getExecutioner().exec(new Or(ret, maskArrays[1].castTo(DataType.BOOL), ret));
for (int i = 2; i < maskArrays.length; i++) {
Nd4j.getExecutioner().exec(new Or(maskArrays[i].castTo(DataType.BOOL), ret, ret));
}
return new Pair<>(ret, currentMaskState);
}
}
}
