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
|
// 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 ai.vespa.rankingexpression.importer.ImportedModel;
import ai.vespa.rankingexpression.importer.configmodelview.ImportedMlFunction;
import ai.vespa.rankingexpression.importer.onnx.OnnxImporter;
import com.yahoo.collections.Pair;
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 com.yahoo.searchlib.rankingexpression.rule.CompositeNode;
import com.yahoo.searchlib.rankingexpression.rule.ExpressionNode;
import com.yahoo.searchlib.rankingexpression.rule.ReferenceNode;
import com.yahoo.system.ProcessExecuter;
import com.yahoo.tensor.Tensor;
import com.yahoo.tensor.TensorType;
import org.junit.Assert;
import org.junit.Ignore;
import org.junit.Test;
import org.tensorflow.SavedModelBundle;
import org.tensorflow.Session;
import java.io.IOException;
import java.nio.DoubleBuffer;
import java.nio.FloatBuffer;
import java.util.List;
import java.util.Map;
import static org.junit.Assert.assertEquals;
import static org.junit.Assert.assertNotNull;
import static org.junit.Assert.assertTrue;
public class LesterTensorflowImportTestCase {
@Test
@Ignore
public void testPyTorchExport() {
ImportedModel model = new OnnxImporter().importModel("test", "src/test/models/pytorch/test.onnx");
Tensor onnxResult = evaluateVespa(model, "output", model.inputs());
assertEquals(Tensor.from("tensor(d0[1],d1[2]):[[0.2134095202835272, -0.08556838456161658]]"), onnxResult);
}
@Test
@Ignore
public void testBERT() {
ImportedModel model = new OnnxImporter().importModel("test", "src/test/models/onnx/bert/bertsquad10.onnx");
}
private Tensor evaluateVespa(ImportedModel model, String operationName, Map<String, TensorType> inputs) {
Context context = contextFrom(model);
for (Map.Entry<String, TensorType> entry : inputs.entrySet()) {
Tensor argument = vespaInputArgument(1, entry.getValue().dimensions().get(1).size().get().intValue());
context.put(entry.getKey(), new TensorValue(argument));
}
model.functions().forEach((k, v) -> evaluateFunction(context, model, k));
RankingExpression expression = model.expressions().get(operationName);
ExpressionOptimizer optimizer = new ExpressionOptimizer();
optimizer.optimize(expression, (ContextIndex)context);
return expression.evaluate(context).asTensor();
}
@Test
@Ignore
public void testModelImport() {
// Må endre til tf 2.0 i java!
String modelDir = "src/test/models/tensorflow/tf2/saved_model/";
// output function
String operationName = "out";
// Import TF
SavedModelBundle tensorFlowModel = SavedModelBundle.load(modelDir, "serve");
ImportedModel model = new TensorFlowImporter().importModel("test", modelDir, tensorFlowModel);
ImportedModel.Signature signature = model.signature("serving_default");
assertEquals("Should have no skipped outputs", 0, model.signature("serving_default").skippedOutputs().size());
ImportedMlFunction output = signature.outputFunction("output", operationName);
assertNotNull(output);
// Test TF
Session.Runner runner = tensorFlowModel.session().runner();
runner.feed("x", tensorFlowFloatInputArgument(1, 4));
List<org.tensorflow.Tensor<?>> results = runner.fetch(operationName).run();
assertEquals(1, results.size());
Tensor tfResult = TensorConverter.toVespaTensor(results.get(0));
// Test Vespa
Context context = contextFrom(model);
context.put("x", new TensorValue(vespaInputArgument(1, 4)));
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();
// Equal result?
System.out.println(tfResult);
System.out.println(vespaResult);
assertEquals(tfResult, vespaResult);
}
private org.tensorflow.Tensor<?> tensorFlowFloatInputArgument(int d0Size, int d1Size) {
FloatBuffer fb1 = FloatBuffer.allocate(d0Size * d1Size);
int i = 0;
for (int d0 = 0; d0 < d0Size; d0++)
for (int d1 = 0; d1 < d1Size; ++d1)
fb1.put(i++, (float)(d1 * 1.0 / d1Size));
return org.tensorflow.Tensor.create(new long[]{ d0Size, d1Size }, fb1);
}
private Tensor vespaInputArgument(int d0Size, int d1Size) {
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();
}
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;
}
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");
}
}
}
|
