1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
|
// Copyright 2020 Oath Inc. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
package ai.vespa.rankingexpression.importer.operations;
import ai.vespa.rankingexpression.importer.OrderedTensorType;
import com.yahoo.searchlib.rankingexpression.Reference;
import com.yahoo.searchlib.rankingexpression.evaluation.DoubleValue;
import com.yahoo.searchlib.rankingexpression.rule.ArithmeticNode;
import com.yahoo.searchlib.rankingexpression.rule.ArithmeticOperator;
import com.yahoo.searchlib.rankingexpression.rule.ConstantNode;
import com.yahoo.searchlib.rankingexpression.rule.EmbracedNode;
import com.yahoo.searchlib.rankingexpression.rule.ExpressionNode;
import com.yahoo.searchlib.rankingexpression.rule.ReferenceNode;
import com.yahoo.searchlib.rankingexpression.rule.TensorFunctionNode;
import com.yahoo.tensor.Tensor;
import com.yahoo.tensor.TensorType;
import com.yahoo.tensor.functions.Generate;
import com.yahoo.tensor.functions.TensorFunction;
import java.util.ArrayList;
import java.util.List;
import java.util.Optional;
import static com.yahoo.searchlib.rankingexpression.rule.TensorFunctionNode.wrapScalar;
/**
* Onnx tile operation.
*/
public class Tile extends IntermediateOperation {
public Tile(String modelName, String nodeName, List<IntermediateOperation> inputs) {
super(modelName, nodeName, inputs);
}
@Override
protected OrderedTensorType lazyGetType() {
if (!allInputTypesPresent(2)) return null;
// required as we use tensor create
inputs.get(0).exportAsRankingFunction = true;
IntermediateOperation repeats = inputs.get(1);
if (repeats.getConstantValue().isEmpty())
throw new IllegalArgumentException("Tile " + name + ": repeats input must be a constant.");
Tensor shape = repeats.getConstantValue().get().asTensor();
if (shape.type().rank() != 1)
throw new IllegalArgumentException("Tile " + name + ": repeats must be a 1-d tensor.");
OrderedTensorType inputType = inputs.get(0).type().get();
if (shape.type().dimensions().get(0).size().get() != inputType.rank())
throw new IllegalArgumentException("Tile " + name + ": repeats must be the same size as input rank.");
List<Integer> dimSizes = new ArrayList<>(inputType.rank());
shape.valueIterator().forEachRemaining(v -> dimSizes.add(v.intValue()));
OrderedTensorType.Builder typeBuilder = new OrderedTensorType.Builder(resultValueType());
for (int i = 0; i < dimSizes.size(); ++i) {
TensorType.Dimension inputDimension = inputType.dimensions().get(i);
typeBuilder.add(TensorType.Dimension.indexed(inputDimension.name(), inputDimension.size().get() * dimSizes.get(i)));
}
return typeBuilder.build();
}
@Override
protected TensorFunction lazyGetFunction() {
if (!allInputFunctionsPresent(2)) return null;
IntermediateOperation input = inputs.get(0);
OrderedTensorType inputType = input.type().get();
String inputFunctionName = input.rankingExpressionFunctionName();
List<com.yahoo.tensor.functions.Slice.DimensionValue<Reference>> dimensionValues = new ArrayList<>();
for (int axis = 0; axis < inputType.rank(); ++axis) {
String inputDimensionName = inputType.dimensions().get(axis).name();
long inputDimensionSize = inputType.dimensions().get(axis).size().get();
ExpressionNode size = new ConstantNode(new DoubleValue(inputDimensionSize));
ExpressionNode reference = new ReferenceNode(inputDimensionName);
ExpressionNode mod = new ArithmeticNode(reference, ArithmeticOperator.MODULO, size);
dimensionValues.add(new com.yahoo.tensor.functions.Slice.DimensionValue<>(Optional.of(inputDimensionName), wrapScalar(new EmbracedNode(mod))));
}
TensorFunction<Reference> inputIndices = new TensorFunctionNode.ExpressionTensorFunction(new ReferenceNode(inputFunctionName));
com.yahoo.tensor.functions.Slice<Reference> sliceIndices = new com.yahoo.tensor.functions.Slice<>(inputIndices, dimensionValues);
ExpressionNode sliceExpression = new TensorFunctionNode(sliceIndices);
TensorFunction generate = Generate.bound(type.type(), wrapScalar(sliceExpression));
return generate;
}
@Override
public Tile withInputs(List<IntermediateOperation> inputs) {
return new Tile(modelName(), name(), inputs);
}
@Override
public String operationName() { return "Tile"; }
}
|
