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// Copyright 2017 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
package com.yahoo.tensor.functions;
import com.google.common.annotations.Beta;
import com.google.common.collect.ImmutableList;
import com.yahoo.tensor.DimensionSizes;
import com.yahoo.tensor.IndexedTensor;
import com.yahoo.tensor.Tensor;
import com.yahoo.tensor.TensorAddress;
import com.yahoo.tensor.TensorType;
import com.yahoo.tensor.evaluation.EvaluationContext;
import com.yahoo.tensor.evaluation.TypeContext;
import java.util.Arrays;
import java.util.Iterator;
import java.util.List;
import java.util.Objects;
import java.util.Optional;
import java.util.Set;
import java.util.stream.Collectors;
/**
* Concatenation of two tensors along an (indexed) dimension
*
* @author bratseth
*/
@Beta
public class Concat extends PrimitiveTensorFunction {
private final TensorFunction argumentA, argumentB;
private final String dimension;
public Concat(TensorFunction argumentA, TensorFunction argumentB, String dimension) {
Objects.requireNonNull(argumentA, "The first argument tensor cannot be null");
Objects.requireNonNull(argumentB, "The second argument tensor cannot be null");
Objects.requireNonNull(dimension, "The dimension cannot be null");
this.argumentA = argumentA;
this.argumentB = argumentB;
this.dimension = dimension;
}
@Override
public List<TensorFunction> arguments() { return ImmutableList.of(argumentA, argumentB); }
@Override
public TensorFunction withArguments(List<TensorFunction> arguments) {
if (arguments.size() != 2)
throw new IllegalArgumentException("Concat must have 2 arguments, got " + arguments.size());
return new Concat(arguments.get(0), arguments.get(1), dimension);
}
@Override
public PrimitiveTensorFunction toPrimitive() {
return new Concat(argumentA.toPrimitive(), argumentB.toPrimitive(), dimension);
}
@Override
public String toString(ToStringContext context) {
return "concat(" + argumentA.toString(context) + ", " + argumentB.toString(context) + ", " + dimension + ")";
}
@Override
public <NAMETYPE extends TypeContext.Name> TensorType type(TypeContext<NAMETYPE> context) {
return type(argumentA.type(context), argumentB.type(context));
}
/** Returns the type resulting from concatenating a and b */
private TensorType type(TensorType a, TensorType b) {
TensorType.Builder builder = new TensorType.Builder(a, b);
if (builder.getDimension(dimension).get().size().isPresent()) // both types have size: correct to concat size
builder.set(TensorType.Dimension.indexed(dimension, a.dimension(dimension).get().size().get() +
b.dimension(dimension).get().size().get()));
return builder.build();
}
@Override
public <NAMETYPE extends TypeContext.Name> Tensor evaluate(EvaluationContext<NAMETYPE> context) {
Tensor a = argumentA.evaluate(context);
Tensor b = argumentB.evaluate(context);
a = ensureIndexedDimension(dimension, a);
b = ensureIndexedDimension(dimension, b);
IndexedTensor aIndexed = (IndexedTensor) a; // If you get an exception here you have implemented a mixed tensor
IndexedTensor bIndexed = (IndexedTensor) b;
TensorType concatType = type(a.type(), b.type());
DimensionSizes concatSize = concatSize(concatType, aIndexed, bIndexed, dimension);
Tensor.Builder builder = Tensor.Builder.of(concatType, concatSize);
long aDimensionLength = aIndexed.type().indexOfDimension(dimension).map(d -> aIndexed.dimensionSizes().size(d)).orElseThrow(RuntimeException::new);
int[] aToIndexes = mapIndexes(a.type(), concatType);
int[] bToIndexes = mapIndexes(b.type(), concatType);
concatenateTo(aIndexed, bIndexed, aDimensionLength, concatType, aToIndexes, bToIndexes, builder);
concatenateTo(bIndexed, aIndexed, 0, concatType, bToIndexes, aToIndexes, builder);
return builder.build();
}
private void concatenateTo(IndexedTensor a, IndexedTensor b, long offset, TensorType concatType,
int[] aToIndexes, int[] bToIndexes, Tensor.Builder builder) {
Set<String> otherADimensions = a.type().dimensionNames().stream().filter(d -> !d.equals(dimension)).collect(Collectors.toSet());
for (Iterator<IndexedTensor.SubspaceIterator> ia = a.subspaceIterator(otherADimensions); ia.hasNext();) {
IndexedTensor.SubspaceIterator iaSubspace = ia.next();
TensorAddress aAddress = iaSubspace.address();
for (Iterator<IndexedTensor.SubspaceIterator> ib = b.subspaceIterator(otherADimensions); ib.hasNext();) {
IndexedTensor.SubspaceIterator ibSubspace = ib.next();
while (ibSubspace.hasNext()) {
Tensor.Cell bCell = ibSubspace.next();
TensorAddress combinedAddress = combineAddresses(aAddress, aToIndexes, bCell.getKey(), bToIndexes,
concatType, offset, dimension);
if (combinedAddress == null) continue; // incompatible
builder.cell(combinedAddress, bCell.getValue());
}
iaSubspace.reset();
}
}
}
private Tensor ensureIndexedDimension(String dimensionName, Tensor tensor) {
Optional<TensorType.Dimension> dimension = tensor.type().dimension(dimensionName);
if ( dimension.isPresent() ) {
if ( ! dimension.get().isIndexed())
throw new IllegalArgumentException("Concat in dimension '" + dimensionName +
"' requires that dimension to be indexed or absent, " +
"but got a tensor with type " + tensor.type());
return tensor;
}
else { // extend tensor with this dimension
if (tensor.type().dimensions().stream().anyMatch(d -> ! d.isIndexed()))
throw new IllegalArgumentException("Concat requires an indexed tensor, " +
"but got a tensor with type " + tensor.type());
Tensor unitTensor = Tensor.Builder.of(new TensorType.Builder().indexed(dimensionName, 1).build()).cell(1,0).build();
return tensor.multiply(unitTensor);
}
}
/** Returns the concrete (not type) dimension sizes resulting from combining a and b */
private DimensionSizes concatSize(TensorType concatType, IndexedTensor a, IndexedTensor b, String concatDimension) {
DimensionSizes.Builder concatSizes = new DimensionSizes.Builder(concatType.dimensions().size());
for (int i = 0; i < concatSizes.dimensions(); i++) {
String currentDimension = concatType.dimensions().get(i).name();
long aSize = a.type().indexOfDimension(currentDimension).map(d -> a.dimensionSizes().size(d)).orElse(0L);
long bSize = b.type().indexOfDimension(currentDimension).map(d -> b.dimensionSizes().size(d)).orElse(0L);
if (currentDimension.equals(concatDimension))
concatSizes.set(i, aSize + bSize);
else if (aSize != 0 && bSize != 0 && aSize!=bSize )
concatSizes.set(i, Math.min(aSize, bSize));
else
concatSizes.set(i, Math.max(aSize, bSize));
}
return concatSizes.build();
}
/**
* Combine two addresses, adding the offset to the concat dimension
*
* @return the combined address or null if the addresses are incompatible
* (in some other dimension than the concat dimension)
*/
private TensorAddress combineAddresses(TensorAddress a, int[] aToIndexes, TensorAddress b, int[] bToIndexes,
TensorType concatType, long concatOffset, String concatDimension) {
long[] combinedLabels = new long[concatType.dimensions().size()];
Arrays.fill(combinedLabels, -1);
int concatDimensionIndex = concatType.indexOfDimension(concatDimension).get();
mapContent(a, combinedLabels, aToIndexes, concatDimensionIndex, concatOffset); // note: This sets a nonsensical value in the concat dimension
boolean compatible = mapContent(b, combinedLabels, bToIndexes, concatDimensionIndex, concatOffset); // ... which is overwritten by the right value here
if ( ! compatible) return null;
return TensorAddress.of(combinedLabels);
}
/**
* Returns the an array having one entry in order for each dimension of fromType
* containing the index at which toType contains the same dimension name.
* That is, if the returned array contains n at index i then
* fromType.dimensions().get(i).name.equals(toType.dimensions().get(n).name())
* If some dimension in fromType is not present in toType, the corresponding index will be -1
*/
// TODO: Stolen from join
private int[] mapIndexes(TensorType fromType, TensorType toType) {
int[] toIndexes = new int[fromType.dimensions().size()];
for (int i = 0; i < fromType.dimensions().size(); i++)
toIndexes[i] = toType.indexOfDimension(fromType.dimensions().get(i).name()).orElse(-1);
return toIndexes;
}
/**
* Maps the content in the given list to the given array, using the given index map.
*
* @return true if the mapping was successful, false if one of the destination positions was
* occupied by a different value
*/
private boolean mapContent(TensorAddress from, long[] to, int[] indexMap, int concatDimension, long concatOffset) {
for (int i = 0; i < from.size(); i++) {
int toIndex = indexMap[i];
if (concatDimension == toIndex) {
to[toIndex] = from.numericLabel(i) + concatOffset;
}
else {
if (to[toIndex] != -1 && to[toIndex] != from.numericLabel(i)) return false;
to[toIndex] = from.numericLabel(i);
}
}
return true;
}
}
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