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
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
|
// Copyright 2018 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
package ai.vespa.rankingexpression.importer.tensorflow;
import com.yahoo.searchlib.rankingexpression.RankingExpression;
import com.yahoo.searchlib.rankingexpression.evaluation.Context;
import com.yahoo.searchlib.rankingexpression.evaluation.ContextIndex;
import com.yahoo.searchlib.rankingexpression.evaluation.ExpressionOptimizer;
import com.yahoo.searchlib.rankingexpression.evaluation.MapContext;
import com.yahoo.searchlib.rankingexpression.evaluation.TensorValue;
import ai.vespa.rankingexpression.importer.ImportedModel;
import com.yahoo.searchlib.rankingexpression.rule.CompositeNode;
import com.yahoo.searchlib.rankingexpression.rule.ExpressionNode;
import com.yahoo.searchlib.rankingexpression.rule.ReferenceNode;
import com.yahoo.tensor.Tensor;
import com.yahoo.tensor.TensorType;
import org.tensorflow.SavedModelBundle;
import org.tensorflow.Session;
import java.nio.DoubleBuffer;
import java.nio.FloatBuffer;
import java.util.List;
import static org.junit.Assert.assertEquals;
/**
* Helper for TensorFlow import tests: Imports a model and provides asserts on it.
*
* @author bratseth
*/
public class TestableTensorFlowModel {
private SavedModelBundle tensorFlowModel;
private ImportedModel model;
// Spec of the input vector
private final boolean floatInput; // false: double
private final int d0Size;
private final int d1Size;
public TestableTensorFlowModel(String modelName, String modelDir) {
this(modelName, modelDir, 1, 784);
}
public TestableTensorFlowModel(String modelName, String modelDir, int d0Size, int d1Size) {
this(modelName, modelDir, d0Size, d1Size, true);
}
public TestableTensorFlowModel(String modelName, String modelDir, int d0Size, int d1Size, boolean floatInput) {
tensorFlowModel = SavedModelBundle.load(modelDir, "serve");
model = new TensorFlowImporter().importModel(modelName, modelDir, tensorFlowModel);
this.d0Size = d0Size;
this.d1Size = d1Size;
this.floatInput = floatInput;
}
public ImportedModel get() { return model; }
/** Compare that computing the expressions produce the same result to within some tolerance delta */
public void assertEqualResultSum(String inputName, String operationName, double delta) {
Tensor tfResult = tensorFlowExecute(tensorFlowModel, inputName, operationName);
Context context = contextFrom(model);
Tensor placeholder = vespaInputArgument();
context.put(inputName, new TensorValue(placeholder));
model.functions().forEach((k, v) -> evaluateFunction(context, model, k));
RankingExpression expression = model.expressions().get(operationName);
ExpressionOptimizer optimizer = new ExpressionOptimizer();
optimizer.optimize(expression, (ContextIndex)context);
Tensor vespaResult = expression.evaluate(context).asTensor();
assertEquals("Operation '" + operationName + "' produces equal results",
tfResult.sum().asDouble(), vespaResult.sum().asDouble(), delta);
}
/** Compare tensors 100% exactly */
public void assertEqualResult(String inputName, String operationName) {
Tensor tfResult = tensorFlowExecute(tensorFlowModel, inputName, operationName);
Context context = contextFrom(model);
Tensor inputValue = vespaInputArgument();
context.put(inputName, new TensorValue(inputValue));
model.functions().forEach((k, v) -> evaluateFunction(context, model, k));
RankingExpression expression = model.expressions().get(operationName);
ExpressionOptimizer optimizer = new ExpressionOptimizer();
optimizer.optimize(expression, (ContextIndex)context);
Tensor vespaResult = expression.evaluate(context).asTensor();
assertEquals("Operation '" + operationName + "': Actual value from Vespa equals expected value from TensorFlow",
tfResult, vespaResult);
}
private Tensor tensorFlowExecute(SavedModelBundle model, String inputName, String operationName) {
Session.Runner runner = model.session().runner();
org.tensorflow.Tensor<?> input = floatInput ? tensorFlowFloatInputArgument() : tensorFlowDoubleInputArgument();
runner.feed(inputName, input);
List<org.tensorflow.Tensor<?>> results = runner.fetch(operationName).run();
assertEquals(1, results.size());
return TensorConverter.toVespaTensor(results.get(0));
}
static Context contextFrom(ImportedModel result) {
TestableModelContext context = new TestableModelContext();
result.largeConstants().forEach((name, tensor) -> context.put("constant(" + name + ")", new TensorValue(Tensor.from(tensor))));
result.smallConstants().forEach((name, tensor) -> context.put("constant(" + name + ")", new TensorValue(Tensor.from(tensor))));
return context;
}
/** Must be the same as vespaInputArgument() */
private org.tensorflow.Tensor<?> tensorFlowDoubleInputArgument() {
DoubleBuffer fb = DoubleBuffer.allocate(d0Size * d1Size);
int i = 0;
for (int d0 = 0; d0 < d0Size; d0++)
for (int d1 = 0; d1 < d1Size; ++d1)
fb.put(i++, (d1 * 1.0 / d1Size));
return org.tensorflow.Tensor.create(new long[]{ d0Size, d1Size }, fb);
}
/** Must be the same as vespaInputArgument() */
private org.tensorflow.Tensor<?> tensorFlowFloatInputArgument() {
FloatBuffer fb = FloatBuffer.allocate(d0Size * d1Size);
int i = 0;
for (int d0 = 0; d0 < d0Size; d0++)
for (int d1 = 0; d1 < d1Size; ++d1)
fb.put(i++, (float)(d1 * 1.0 / d1Size));
return org.tensorflow.Tensor.create(new long[]{ d0Size, d1Size }, fb);
}
/** Must be the same as tensorFlowFloatInputArgument() */
private Tensor vespaInputArgument() {
Tensor.Builder b = Tensor.Builder.of(new TensorType.Builder().indexed("d0", d0Size).indexed("d1", d1Size).build());
for (int d0 = 0; d0 < d0Size; d0++)
for (int d1 = 0; d1 < d1Size; d1++)
b.cell(d1 * 1.0 / d1Size, d0, d1);
return b.build();
}
private void evaluateFunction(Context context, ImportedModel model, String functionName) {
if (!context.names().contains(functionName)) {
RankingExpression e = RankingExpression.from(model.functions().get(functionName));
evaluateFunctionDependencies(context, model, e.getRoot());
context.put(functionName, new TensorValue(e.evaluate(context).asTensor()));
}
}
private void evaluateFunctionDependencies(Context context, ImportedModel model, ExpressionNode node) {
if (node instanceof ReferenceNode) {
String name = node.toString();
if (model.functions().containsKey(name)) {
evaluateFunction(context, model, name);
}
}
else if (node instanceof CompositeNode) {
for (ExpressionNode child : ((CompositeNode)node).children()) {
evaluateFunctionDependencies(context, model, child);
}
}
}
private static class TestableModelContext extends MapContext implements ContextIndex {
@Override
public int size() {
return bindings().size();
}
@Override
public int getIndex(String name) {
throw new UnsupportedOperationException(this + " does not support index lookup by name");
}
}
}
|
