1 //
2 // Copyright © 2021, 2023 Arm Ltd and Contributors. All rights reserved.
3 // SPDX-License-Identifier: MIT
4 //
5
6 #pragma once
7
8 #include "TestUtils.hpp"
9
10 #include <armnn_delegate.hpp>
11 #include <DelegateTestInterpreter.hpp>
12
13 #include <flatbuffers/flatbuffers.h>
14 #include <tensorflow/lite/kernels/register.h>
15 #include <tensorflow/lite/version.h>
16
17 #include <schema_generated.h>
18
19 #include <doctest/doctest.h>
20
21 namespace
22 {
23
24 struct StreamRedirector
25 {
26 public:
StreamRedirector__anon0606af480111::StreamRedirector27 StreamRedirector(std::ostream &stream, std::streambuf *newStreamBuffer)
28 : m_Stream(stream), m_BackupBuffer(m_Stream.rdbuf(newStreamBuffer)) {}
29
~StreamRedirector__anon0606af480111::StreamRedirector30 ~StreamRedirector() { m_Stream.rdbuf(m_BackupBuffer); }
31
32 private:
33 std::ostream &m_Stream;
34 std::streambuf *m_BackupBuffer;
35 };
36
CreateAddDivTfLiteModel(tflite::TensorType tensorType,const std::vector<int32_t> & tensorShape,float quantScale=1.0f,int quantOffset=0)37 std::vector<char> CreateAddDivTfLiteModel(tflite::TensorType tensorType,
38 const std::vector<int32_t>& tensorShape,
39 float quantScale = 1.0f,
40 int quantOffset = 0)
41 {
42 using namespace tflite;
43 flatbuffers::FlatBufferBuilder flatBufferBuilder;
44
45 std::vector<flatbuffers::Offset<tflite::Buffer>> buffers;
46 buffers.push_back(CreateBuffer(flatBufferBuilder));
47 buffers.push_back(CreateBuffer(flatBufferBuilder));
48 buffers.push_back(CreateBuffer(flatBufferBuilder));
49 buffers.push_back(CreateBuffer(flatBufferBuilder));
50 buffers.push_back(CreateBuffer(flatBufferBuilder));
51 buffers.push_back(CreateBuffer(flatBufferBuilder));
52
53 auto quantizationParameters =
54 CreateQuantizationParameters(flatBufferBuilder,
55 0,
56 0,
57 flatBufferBuilder.CreateVector<float>({ quantScale }),
58 flatBufferBuilder.CreateVector<int64_t>({ quantOffset }));
59
60
61 std::array<flatbuffers::Offset<Tensor>, 5> tensors;
62 tensors[0] = CreateTensor(flatBufferBuilder,
63 flatBufferBuilder.CreateVector<int32_t>(tensorShape.data(),
64 tensorShape.size()),
65 tensorType,
66 1,
67 flatBufferBuilder.CreateString("input_0"),
68 quantizationParameters);
69 tensors[1] = CreateTensor(flatBufferBuilder,
70 flatBufferBuilder.CreateVector<int32_t>(tensorShape.data(),
71 tensorShape.size()),
72 tensorType,
73 2,
74 flatBufferBuilder.CreateString("input_1"),
75 quantizationParameters);
76 tensors[2] = CreateTensor(flatBufferBuilder,
77 flatBufferBuilder.CreateVector<int32_t>(tensorShape.data(),
78 tensorShape.size()),
79 tensorType,
80 3,
81 flatBufferBuilder.CreateString("input_2"),
82 quantizationParameters);
83 tensors[3] = CreateTensor(flatBufferBuilder,
84 flatBufferBuilder.CreateVector<int32_t>(tensorShape.data(),
85 tensorShape.size()),
86 tensorType,
87 4,
88 flatBufferBuilder.CreateString("add"),
89 quantizationParameters);
90 tensors[4] = CreateTensor(flatBufferBuilder,
91 flatBufferBuilder.CreateVector<int32_t>(tensorShape.data(),
92 tensorShape.size()),
93 tensorType,
94 5,
95 flatBufferBuilder.CreateString("output"),
96 quantizationParameters);
97
98 // create operator
99 tflite::BuiltinOptions addBuiltinOptionsType = tflite::BuiltinOptions_AddOptions;
100 flatbuffers::Offset<void> addBuiltinOptions =
101 CreateAddOptions(flatBufferBuilder, ActivationFunctionType_NONE).Union();
102
103 tflite::BuiltinOptions divBuiltinOptionsType = tflite::BuiltinOptions_DivOptions;
104 flatbuffers::Offset<void> divBuiltinOptions =
105 CreateAddOptions(flatBufferBuilder, ActivationFunctionType_NONE).Union();
106
107 std::array<flatbuffers::Offset<Operator>, 2> operators;
108 const std::vector<int32_t> addInputs{0, 1};
109 const std::vector<int32_t> addOutputs{3};
110 operators[0] = CreateOperator(flatBufferBuilder,
111 0,
112 flatBufferBuilder.CreateVector<int32_t>(addInputs.data(), addInputs.size()),
113 flatBufferBuilder.CreateVector<int32_t>(addOutputs.data(), addOutputs.size()),
114 addBuiltinOptionsType,
115 addBuiltinOptions);
116 const std::vector<int32_t> divInputs{3, 2};
117 const std::vector<int32_t> divOutputs{4};
118 operators[1] = CreateOperator(flatBufferBuilder,
119 1,
120 flatBufferBuilder.CreateVector<int32_t>(divInputs.data(), divInputs.size()),
121 flatBufferBuilder.CreateVector<int32_t>(divOutputs.data(), divOutputs.size()),
122 divBuiltinOptionsType,
123 divBuiltinOptions);
124
125 const std::vector<int> subgraphInputs{0, 1, 2};
126 const std::vector<int> subgraphOutputs{4};
127 flatbuffers::Offset<SubGraph> subgraph =
128 CreateSubGraph(flatBufferBuilder,
129 flatBufferBuilder.CreateVector(tensors.data(), tensors.size()),
130 flatBufferBuilder.CreateVector<int32_t>(subgraphInputs.data(), subgraphInputs.size()),
131 flatBufferBuilder.CreateVector<int32_t>(subgraphOutputs.data(), subgraphOutputs.size()),
132 flatBufferBuilder.CreateVector(operators.data(), operators.size()));
133
134 flatbuffers::Offset<flatbuffers::String> modelDescription =
135 flatBufferBuilder.CreateString("ArmnnDelegate: Add and Div Operator Model");
136
137 std::array<flatbuffers::Offset<OperatorCode>, 2> codes;
138 codes[0] = CreateOperatorCode(flatBufferBuilder, tflite::BuiltinOperator_ADD);
139 codes[1] = CreateOperatorCode(flatBufferBuilder, tflite::BuiltinOperator_DIV);
140
141 flatbuffers::Offset<Model> flatbufferModel =
142 CreateModel(flatBufferBuilder,
143 TFLITE_SCHEMA_VERSION,
144 flatBufferBuilder.CreateVector(codes.data(), codes.size()),
145 flatBufferBuilder.CreateVector(&subgraph, 1),
146 modelDescription,
147 flatBufferBuilder.CreateVector(buffers.data(), buffers.size()));
148
149 flatBufferBuilder.Finish(flatbufferModel, armnnDelegate::FILE_IDENTIFIER);
150
151 return std::vector<char>(flatBufferBuilder.GetBufferPointer(),
152 flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize());
153 }
154
CreateCosTfLiteModel(tflite::TensorType tensorType,const std::vector<int32_t> & tensorShape,float quantScale=1.0f,int quantOffset=0)155 std::vector<char> CreateCosTfLiteModel(tflite::TensorType tensorType,
156 const std::vector <int32_t>& tensorShape,
157 float quantScale = 1.0f,
158 int quantOffset = 0)
159 {
160 using namespace tflite;
161 flatbuffers::FlatBufferBuilder flatBufferBuilder;
162
163 std::vector<flatbuffers::Offset<tflite::Buffer>> buffers;
164 buffers.push_back(CreateBuffer(flatBufferBuilder));
165
166 auto quantizationParameters =
167 CreateQuantizationParameters(flatBufferBuilder,
168 0,
169 0,
170 flatBufferBuilder.CreateVector<float>({quantScale}),
171 flatBufferBuilder.CreateVector<int64_t>({quantOffset}));
172
173 std::array<flatbuffers::Offset<Tensor>, 2> tensors;
174 tensors[0] = CreateTensor(flatBufferBuilder,
175 flatBufferBuilder.CreateVector<int32_t>(tensorShape.data(),
176 tensorShape.size()),
177 tensorType,
178 0,
179 flatBufferBuilder.CreateString("input"),
180 quantizationParameters);
181 tensors[1] = CreateTensor(flatBufferBuilder,
182 flatBufferBuilder.CreateVector<int32_t>(tensorShape.data(),
183 tensorShape.size()),
184 tensorType,
185 0,
186 flatBufferBuilder.CreateString("output"),
187 quantizationParameters);
188
189 const std::vector<int32_t> operatorInputs({0});
190 const std::vector<int32_t> operatorOutputs({1});
191
192 flatbuffers::Offset<Operator> ceilOperator =
193 CreateOperator(flatBufferBuilder,
194 0,
195 flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()),
196 flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()),
197 BuiltinOptions_NONE);
198
199 flatbuffers::Offset<flatbuffers::String> modelDescription =
200 flatBufferBuilder.CreateString("ArmnnDelegate: CEIL Operator Model");
201 flatbuffers::Offset<OperatorCode> operatorCode =
202 CreateOperatorCode(flatBufferBuilder, tflite::BuiltinOperator_COS);
203
204 const std::vector<int32_t> subgraphInputs({0});
205 const std::vector<int32_t> subgraphOutputs({1});
206 flatbuffers::Offset<SubGraph> subgraph =
207 CreateSubGraph(flatBufferBuilder,
208 flatBufferBuilder.CreateVector(tensors.data(), tensors.size()),
209 flatBufferBuilder.CreateVector<int32_t>(subgraphInputs.data(), subgraphInputs.size()),
210 flatBufferBuilder.CreateVector<int32_t>(subgraphOutputs.data(), subgraphOutputs.size()),
211 flatBufferBuilder.CreateVector(&ceilOperator, 1));
212
213 flatbuffers::Offset<Model> flatbufferModel =
214 CreateModel(flatBufferBuilder,
215 TFLITE_SCHEMA_VERSION,
216 flatBufferBuilder.CreateVector(&operatorCode, 1),
217 flatBufferBuilder.CreateVector(&subgraph, 1),
218 modelDescription,
219 flatBufferBuilder.CreateVector(buffers.data(), buffers.size()));
220
221 flatBufferBuilder.Finish(flatbufferModel, armnnDelegate::FILE_IDENTIFIER);
222 return std::vector<char>(flatBufferBuilder.GetBufferPointer(),
223 flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize());
224 }
225
226 template <typename T>
DelegateOptionTest(tflite::TensorType tensorType,std::vector<int32_t> & tensorShape,std::vector<T> & input0Values,std::vector<T> & input1Values,std::vector<T> & input2Values,std::vector<T> & expectedOutputValues,const armnnDelegate::DelegateOptions & delegateOptions,float quantScale=1.0f,int quantOffset=0)227 void DelegateOptionTest(tflite::TensorType tensorType,
228 std::vector<int32_t>& tensorShape,
229 std::vector<T>& input0Values,
230 std::vector<T>& input1Values,
231 std::vector<T>& input2Values,
232 std::vector<T>& expectedOutputValues,
233 const armnnDelegate::DelegateOptions& delegateOptions,
234 float quantScale = 1.0f,
235 int quantOffset = 0)
236 {
237 using namespace delegateTestInterpreter;
238 std::vector<char> modelBuffer = CreateAddDivTfLiteModel(tensorType,
239 tensorShape,
240 quantScale,
241 quantOffset);
242
243 // Setup interpreter with just TFLite Runtime.
244 auto tfLiteInterpreter = DelegateTestInterpreter(modelBuffer);
245 CHECK(tfLiteInterpreter.AllocateTensors() == kTfLiteOk);
246 CHECK(tfLiteInterpreter.FillInputTensor<T>(input0Values, 0) == kTfLiteOk);
247 CHECK(tfLiteInterpreter.FillInputTensor<T>(input1Values, 1) == kTfLiteOk);
248 CHECK(tfLiteInterpreter.FillInputTensor<T>(input2Values, 2) == kTfLiteOk);
249 CHECK(tfLiteInterpreter.Invoke() == kTfLiteOk);
250 std::vector<T> tfLiteOutputValues = tfLiteInterpreter.GetOutputResult<T>(0);
251 std::vector<int32_t> tfLiteOutputShape = tfLiteInterpreter.GetOutputShape(0);
252
253 // Setup interpreter with Arm NN Delegate applied.
254 auto armnnInterpreter = DelegateTestInterpreter(modelBuffer, delegateOptions);
255 CHECK(armnnInterpreter.AllocateTensors() == kTfLiteOk);
256 CHECK(armnnInterpreter.FillInputTensor<T>(input0Values, 0) == kTfLiteOk);
257 CHECK(armnnInterpreter.FillInputTensor<T>(input1Values, 1) == kTfLiteOk);
258 CHECK(armnnInterpreter.FillInputTensor<T>(input2Values, 2) == kTfLiteOk);
259 CHECK(armnnInterpreter.Invoke() == kTfLiteOk);
260 std::vector<T> armnnOutputValues = armnnInterpreter.GetOutputResult<T>(0);
261 std::vector<int32_t> armnnOutputShape = armnnInterpreter.GetOutputShape(0);
262
263 armnnDelegate::CompareOutputData<T>(tfLiteOutputValues, armnnOutputValues, expectedOutputValues);
264 armnnDelegate::CompareOutputShape(tfLiteOutputShape, armnnOutputShape, tensorShape);
265
266 tfLiteInterpreter.Cleanup();
267 armnnInterpreter.Cleanup();
268 }
269
270 template <typename T>
DelegateOptionNoFallbackTest(tflite::TensorType tensorType,std::vector<int32_t> & tensorShape,std::vector<T> & inputValues,std::vector<T> & expectedOutputValues,const armnnDelegate::DelegateOptions & delegateOptions,float quantScale=1.0f,int quantOffset=0)271 void DelegateOptionNoFallbackTest(tflite::TensorType tensorType,
272 std::vector<int32_t>& tensorShape,
273 std::vector<T>& inputValues,
274 std::vector<T>& expectedOutputValues,
275 const armnnDelegate::DelegateOptions& delegateOptions,
276 float quantScale = 1.0f,
277 int quantOffset = 0)
278 {
279 using namespace delegateTestInterpreter;
280 std::vector<char> modelBuffer = CreateCosTfLiteModel(tensorType,
281 tensorShape,
282 quantScale,
283 quantOffset);
284
285 // Setup interpreter with just TFLite Runtime.
286 auto tfLiteInterpreter = DelegateTestInterpreter(modelBuffer);
287 CHECK(tfLiteInterpreter.AllocateTensors() == kTfLiteOk);
288 CHECK(tfLiteInterpreter.FillInputTensor<T>(inputValues, 0) == kTfLiteOk);
289 CHECK(tfLiteInterpreter.Invoke() == kTfLiteOk);
290 std::vector<T> tfLiteOutputValues = tfLiteInterpreter.GetOutputResult<T>(0);
291 std::vector<int32_t> tfLiteOutputShape = tfLiteInterpreter.GetOutputShape(0);
292 tfLiteInterpreter.Cleanup();
293
294 try
295 {
296 auto armnnInterpreter = DelegateTestInterpreter(modelBuffer, delegateOptions);
297 CHECK(armnnInterpreter.AllocateTensors() == kTfLiteOk);
298 CHECK(armnnInterpreter.FillInputTensor<T>(inputValues, 0) == kTfLiteOk);
299 CHECK(armnnInterpreter.Invoke() == kTfLiteOk);
300 std::vector<T> armnnOutputValues = armnnInterpreter.GetOutputResult<T>(0);
301 std::vector<int32_t> armnnOutputShape = armnnInterpreter.GetOutputShape(0);
302 armnnInterpreter.Cleanup();
303
304 armnnDelegate::CompareOutputData<T>(tfLiteOutputValues, armnnOutputValues, expectedOutputValues);
305 armnnDelegate::CompareOutputShape(tfLiteOutputShape, armnnOutputShape, tensorShape);
306 }
307 catch (const armnn::Exception& e)
308 {
309 // Forward the exception message to std::cout
310 std::cout << e.what() << std::endl;
311 }
312 }
313
314 } // anonymous namespace