1 /* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
2
3 Licensed under the Apache License, Version 2.0 (the "License");
4 you may not use this file except in compliance with the License.
5 You may obtain a copy of the License at
6
7 http://www.apache.org/licenses/LICENSE-2.0
8
9 Unless required by applicable law or agreed to in writing, software
10 distributed under the License is distributed on an "AS IS" BASIS,
11 WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12 See the License for the specific language governing permissions and
13 limitations under the License.
14 ==============================================================================*/
15 // Unit test for TFLite sparse lookup op.
16
17 #include <cmath>
18 #include <functional>
19 #include <initializer_list>
20 #include <memory>
21 #include <vector>
22
23 #include <gmock/gmock.h>
24 #include <gtest/gtest.h>
25 #include "flatbuffers/flatbuffers.h" // from @flatbuffers
26 #include "tensorflow/lite/interpreter.h"
27 #include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
28 #include "tensorflow/lite/kernels/test_util.h"
29 #include "tensorflow/lite/schema/schema_generated.h"
30
31 namespace tflite {
32 namespace {
33
34 using ::testing::ElementsAreArray;
35
36 class EmbeddingLookupSparseOpModel : public SingleOpModel {
37 public:
EmbeddingLookupSparseOpModel(CombinerType type,std::initializer_list<int> lookup_shape,std::initializer_list<int> indices_shape,std::initializer_list<int> dense_shape_shape,std::initializer_list<int> value_shape)38 EmbeddingLookupSparseOpModel(CombinerType type,
39 std::initializer_list<int> lookup_shape,
40 std::initializer_list<int> indices_shape,
41 std::initializer_list<int> dense_shape_shape,
42 std::initializer_list<int> value_shape) {
43 lookup_ = AddInput(TensorType_INT32);
44 indices_ = AddInput(TensorType_INT32);
45 dense_shape_ = AddInput(TensorType_INT32);
46 weights_ = AddInput(TensorType_FLOAT32);
47 value_ = AddInput(TensorType_FLOAT32);
48 output_ = AddOutput(TensorType_FLOAT32);
49 SetBuiltinOp(BuiltinOperator_EMBEDDING_LOOKUP_SPARSE,
50 BuiltinOptions_EmbeddingLookupSparseOptions,
51 CreateEmbeddingLookupSparseOptions(builder_, type).Union());
52 BuildInterpreter({lookup_shape, indices_shape, dense_shape_shape,
53 lookup_shape, value_shape});
54 }
55
SetInput(std::initializer_list<int> lookup_data,std::initializer_list<int> indices_data,std::initializer_list<int> dense_shape_data,std::initializer_list<float> weights_data)56 void SetInput(std::initializer_list<int> lookup_data,
57 std::initializer_list<int> indices_data,
58 std::initializer_list<int> dense_shape_data,
59 std::initializer_list<float> weights_data) {
60 PopulateTensor(lookup_, lookup_data);
61 PopulateTensor(indices_, indices_data);
62 PopulateTensor(dense_shape_, dense_shape_data);
63 PopulateTensor(weights_, weights_data);
64 }
65
Set3DWeightMatrix(const std::function<float (int,int,int)> & function)66 void Set3DWeightMatrix(const std::function<float(int, int, int)>& function) {
67 TfLiteTensor* tensor = interpreter_->tensor(value_);
68 int rows = tensor->dims->data[0];
69 int columns = tensor->dims->data[1];
70 int features = tensor->dims->data[2];
71 float* tensor_ptr = GetTensorData<float>(tensor);
72 for (int i = 0; i < rows; i++) {
73 for (int j = 0; j < columns; j++) {
74 for (int k = 0; k < features; k++) {
75 tensor_ptr[(i * columns + j) * features + k] = function(i, j, k);
76 }
77 }
78 }
79 }
80
GetOutput()81 std::vector<float> GetOutput() { return ExtractVector<float>(output_); }
82
83 private:
84 int lookup_;
85 int weights_;
86 int indices_;
87 int dense_shape_;
88 int value_;
89 int output_;
90 };
91
TEST(EmbeddingLookupSparseOpTest,SimpleTest)92 TEST(EmbeddingLookupSparseOpTest, SimpleTest) {
93 EmbeddingLookupSparseOpModel m(CombinerType_SUM, {3}, {3, 2}, {2}, {4, 3, 2});
94 m.SetInput({1, 3, 0}, {0, 0, 2, 0, 2, 1}, {3, 2}, {1.0, 2.0, 4.0});
95 m.Set3DWeightMatrix(
96 [](int i, int j, int k) { return i + j / 10.0f + k / 100.0f; });
97 m.Invoke();
98
99 EXPECT_THAT(m.GetOutput(),
100 ElementsAreArray(ArrayFloatNear({
101 1.00, 1.01, 1.10, 1.11, 1.20, 1.21, // Row 1
102 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, // -
103 6.00, 6.06, 6.60, 6.66, 7.20, 7.26, // 2 * Row 3 + 4 * Row 0
104 })));
105 }
106
TEST(EmbeddingLookupSparseOpTest,SimpleTestMean)107 TEST(EmbeddingLookupSparseOpTest, SimpleTestMean) {
108 EmbeddingLookupSparseOpModel m(CombinerType_MEAN, {3}, {3, 2}, {2},
109 {4, 3, 2});
110 m.SetInput({1, 3, 0}, {0, 0, 2, 0, 2, 1}, {3, 2}, {1.0, 2.0, 4.0});
111 m.Set3DWeightMatrix(
112 [](int i, int j, int k) { return i + j / 10.0f + k / 100.0f; });
113 m.Invoke();
114
115 EXPECT_THAT(m.GetOutput(),
116 ElementsAreArray(ArrayFloatNear({
117 1.00, 1.01, 1.10, 1.11, 1.20, 1.21, // Row 1
118 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, // -
119 1.00, 1.01, 1.10, 1.11, 1.20, 1.21, // 2 * Row 3 + 4 * Row 0
120 })));
121 }
122
TEST(EmbeddingLookupSparseOpTest,SimpleTestSqrtn)123 TEST(EmbeddingLookupSparseOpTest, SimpleTestSqrtn) {
124 EmbeddingLookupSparseOpModel m(CombinerType_SQRTN, {3}, {3, 2}, {2},
125 {4, 3, 2});
126 m.SetInput({1, 3, 0}, {0, 0, 2, 0, 2, 1}, {3, 2}, {1.0, 2.0, 4.0});
127 m.Set3DWeightMatrix(
128 [](int i, int j, int k) { return i + j / 10.0f + k / 100.0f; });
129 m.Invoke();
130
131 EXPECT_THAT(m.GetOutput(),
132 ElementsAreArray(ArrayFloatNear({
133 1.00, 1.01, 1.10, 1.11, 1.20, 1.21, // Row 1
134 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, // -
135 6.00f / std::sqrt(20.0f), 6.06f / std::sqrt(20.0f),
136 6.60f / std::sqrt(20.0f), 6.66f / std::sqrt(20.0f),
137 7.20f / std::sqrt(20.0f),
138 7.26f / std::sqrt(20.0f), // 2 * Row 3 + 4 * Row 0, // 2 *
139 // Row 3 + 4 * Row 0
140 })));
141 }
142
TEST(EmbeddingLookupSparseOpTest,Indices3DTest)143 TEST(EmbeddingLookupSparseOpTest, Indices3DTest) {
144 EmbeddingLookupSparseOpModel m(CombinerType_SUM, {3}, {3, 3}, {3}, {4, 3, 2});
145 m.SetInput({1, 3, 0}, {0, 0, 0, 2, 0, 0, 2, 0, 1}, {3, 2, 2},
146 {1.0, 2.0, 4.0});
147 m.Set3DWeightMatrix(
148 [](int i, int j, int k) { return i + j / 10.0f + k / 100.0f; });
149 m.Invoke();
150
151 EXPECT_THAT(m.GetOutput(),
152 ElementsAreArray(ArrayFloatNear({
153 1.00, 1.01, 1.10, 1.11, 1.20, 1.21, 0.00, 0.00, 0.00,
154 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00,
155 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 6.00, 6.06, 6.60,
156 6.66, 7.20, 7.26, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00,
157 })));
158 }
159
160 } // namespace
161 } // namespace tflite
162