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
|
// Copyright Verizon Media. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
package com.yahoo.tensor.functions;
import com.yahoo.tensor.Tensor;
import com.yahoo.tensor.TensorType;
import com.yahoo.tensor.evaluation.EvaluationContext;
import com.yahoo.tensor.evaluation.Name;
import com.yahoo.tensor.evaluation.TypeContext;
import java.util.Collections;
import java.util.Iterator;
import java.util.List;
import java.util.Objects;
/**
* The <i>cell_cast</i> tensor function creates a new tensor with the specified cell value type.
*
* @author lesters
*/
public class CellCast<NAMETYPE extends Name> extends PrimitiveTensorFunction<NAMETYPE> {
private final TensorFunction<NAMETYPE> argument;
private final TensorType.Value valueType;
public CellCast(TensorFunction<NAMETYPE> argument, TensorType.Value valueType) {
Objects.requireNonNull(argument, "The argument tensor cannot be null");
Objects.requireNonNull(valueType, "The value type cannot be null");
this.argument = argument;
this.valueType = valueType;
}
@Override
public List<TensorFunction<NAMETYPE>> arguments() { return Collections.singletonList(argument); }
@Override
public TensorFunction<NAMETYPE> withArguments(List<TensorFunction<NAMETYPE>> arguments) {
if ( arguments.size() != 1)
throw new IllegalArgumentException("CellCast must have 1 argument, got " + arguments.size());
return new CellCast<>(arguments.get(0), valueType);
}
@Override
public PrimitiveTensorFunction<NAMETYPE> toPrimitive() {
return new CellCast<>(argument.toPrimitive(), valueType);
}
@Override
public TensorType type(TypeContext<NAMETYPE> context) {
return new TensorType(valueType, argument.type(context).dimensions());
}
@Override
public Tensor evaluate(EvaluationContext<NAMETYPE> context) {
Tensor tensor = argument.evaluate(context);
if (tensor.type().valueType() == valueType) {
return tensor;
}
TensorType type = new TensorType(valueType, tensor.type().dimensions());
return cast(tensor, type);
}
private Tensor cast(Tensor tensor, TensorType type) {
Tensor.Builder builder = Tensor.Builder.of(type);
TensorType.Value fromValueType = tensor.type().valueType();
for (Iterator<Tensor.Cell> i = tensor.cellIterator(); i.hasNext(); ) {
Tensor.Cell cell = i.next();
switch (fromValueType) {
case DOUBLE: builder.cell(cell.getKey(), cell.getDoubleValue()); break;
case FLOAT: builder.cell(cell.getKey(), cell.getFloatValue()); break;
case BFLOAT16: builder.cell(cell.getKey(), cell.getFloatValue()); break;
case INT8: builder.cell(cell.getKey(), cell.getFloatValue()); break;
default:
builder.cell(cell.getKey(), cell.getValue());
}
}
return builder.build();
}
@Override
public String toString(ToStringContext context) {
return "cell_cast(" + argument.toString(context) + ", " + valueType + ")";
}
}
|