Sadik Armagan | 6e36a64 | 2020-11-10 21:18:41 +0000 | [diff] [blame] | 1 | // |
Ryan OShea | 238ecd9 | 2023-03-07 11:44:23 +0000 | [diff] [blame] | 2 | // Copyright © 2020, 2023 Arm Ltd and Contributors. All rights reserved. |
Sadik Armagan | 6e36a64 | 2020-11-10 21:18:41 +0000 | [diff] [blame] | 3 | // SPDX-License-Identifier: MIT |
| 4 | // |
| 5 | |
| 6 | #pragma once |
| 7 | |
Sadik Armagan | f0a6dec | 2021-03-25 07:46:55 +0000 | [diff] [blame] | 8 | #include "TestUtils.hpp" |
| 9 | |
Sadik Armagan | 6e36a64 | 2020-11-10 21:18:41 +0000 | [diff] [blame] | 10 | #include <armnn_delegate.hpp> |
Matthew Sloyan | ebe392d | 2023-03-30 10:12:08 +0100 | [diff] [blame] | 11 | #include <DelegateTestInterpreter.hpp> |
Sadik Armagan | 6e36a64 | 2020-11-10 21:18:41 +0000 | [diff] [blame] | 12 | |
| 13 | #include <flatbuffers/flatbuffers.h> |
Sadik Armagan | 6e36a64 | 2020-11-10 21:18:41 +0000 | [diff] [blame] | 14 | #include <tensorflow/lite/kernels/register.h> |
Sadik Armagan | 6e36a64 | 2020-11-10 21:18:41 +0000 | [diff] [blame] | 15 | #include <tensorflow/lite/version.h> |
| 16 | |
| 17 | #include <doctest/doctest.h> |
| 18 | |
| 19 | namespace |
| 20 | { |
| 21 | |
| 22 | template <typename T> |
| 23 | std::vector<char> CreateFullyConnectedTfLiteModel(tflite::TensorType tensorType, |
| 24 | tflite::ActivationFunctionType activationType, |
| 25 | const std::vector <int32_t>& inputTensorShape, |
| 26 | const std::vector <int32_t>& weightsTensorShape, |
| 27 | const std::vector <int32_t>& biasTensorShape, |
Sadik Armagan | f0a6dec | 2021-03-25 07:46:55 +0000 | [diff] [blame] | 28 | std::vector <int32_t>& outputTensorShape, |
| 29 | std::vector <T>& weightsData, |
| 30 | bool constantWeights = true, |
Sadik Armagan | 6e36a64 | 2020-11-10 21:18:41 +0000 | [diff] [blame] | 31 | float quantScale = 1.0f, |
| 32 | int quantOffset = 0, |
| 33 | float outputQuantScale = 2.0f, |
| 34 | int outputQuantOffset = 0) |
| 35 | { |
| 36 | using namespace tflite; |
| 37 | flatbuffers::FlatBufferBuilder flatBufferBuilder; |
Ryan OShea | 238ecd9 | 2023-03-07 11:44:23 +0000 | [diff] [blame] | 38 | std::array<flatbuffers::Offset<tflite::Buffer>, 5> buffers; |
| 39 | buffers[0] = CreateBuffer(flatBufferBuilder); |
| 40 | buffers[1] = CreateBuffer(flatBufferBuilder); |
Sadik Armagan | 6e36a64 | 2020-11-10 21:18:41 +0000 | [diff] [blame] | 41 | |
| 42 | auto biasTensorType = ::tflite::TensorType_FLOAT32; |
Narumol Prangnawarat | 55518ca | 2020-11-20 14:50:54 +0000 | [diff] [blame] | 43 | if (tensorType == ::tflite::TensorType_INT8) |
Sadik Armagan | 6e36a64 | 2020-11-10 21:18:41 +0000 | [diff] [blame] | 44 | { |
| 45 | biasTensorType = ::tflite::TensorType_INT32; |
Sadik Armagan | f0a6dec | 2021-03-25 07:46:55 +0000 | [diff] [blame] | 46 | } |
| 47 | if (constantWeights) |
| 48 | { |
Ryan OShea | 238ecd9 | 2023-03-07 11:44:23 +0000 | [diff] [blame] | 49 | buffers[2] = CreateBuffer(flatBufferBuilder, |
Sadik Armagan | f0a6dec | 2021-03-25 07:46:55 +0000 | [diff] [blame] | 50 | flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(weightsData.data()), |
| 51 | sizeof(T) * weightsData.size())); |
Sadik Armagan | 6e36a64 | 2020-11-10 21:18:41 +0000 | [diff] [blame] | 52 | |
Sadik Armagan | f0a6dec | 2021-03-25 07:46:55 +0000 | [diff] [blame] | 53 | if (tensorType == ::tflite::TensorType_INT8) |
| 54 | { |
| 55 | std::vector<int32_t> biasData = { 10 }; |
Ryan OShea | 238ecd9 | 2023-03-07 11:44:23 +0000 | [diff] [blame] | 56 | buffers[3] = CreateBuffer(flatBufferBuilder, |
Sadik Armagan | f0a6dec | 2021-03-25 07:46:55 +0000 | [diff] [blame] | 57 | flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(biasData.data()), |
| 58 | sizeof(int32_t) * biasData.size())); |
| 59 | |
| 60 | } |
| 61 | else |
| 62 | { |
| 63 | std::vector<float> biasData = { 10 }; |
Ryan OShea | 238ecd9 | 2023-03-07 11:44:23 +0000 | [diff] [blame] | 64 | buffers[3] = CreateBuffer(flatBufferBuilder, |
Sadik Armagan | f0a6dec | 2021-03-25 07:46:55 +0000 | [diff] [blame] | 65 | flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(biasData.data()), |
| 66 | sizeof(float) * biasData.size())); |
| 67 | } |
Sadik Armagan | 6e36a64 | 2020-11-10 21:18:41 +0000 | [diff] [blame] | 68 | } |
| 69 | else |
| 70 | { |
Ryan OShea | 238ecd9 | 2023-03-07 11:44:23 +0000 | [diff] [blame] | 71 | buffers[2] = CreateBuffer(flatBufferBuilder); |
| 72 | buffers[3] = CreateBuffer(flatBufferBuilder); |
Sadik Armagan | 6e36a64 | 2020-11-10 21:18:41 +0000 | [diff] [blame] | 73 | } |
Ryan OShea | 238ecd9 | 2023-03-07 11:44:23 +0000 | [diff] [blame] | 74 | buffers[4] = CreateBuffer(flatBufferBuilder); |
Sadik Armagan | 6e36a64 | 2020-11-10 21:18:41 +0000 | [diff] [blame] | 75 | |
| 76 | auto quantizationParameters = |
| 77 | CreateQuantizationParameters(flatBufferBuilder, |
| 78 | 0, |
| 79 | 0, |
| 80 | flatBufferBuilder.CreateVector<float>({ quantScale }), |
| 81 | flatBufferBuilder.CreateVector<int64_t>({ quantOffset })); |
| 82 | |
| 83 | auto outputQuantizationParameters = |
| 84 | CreateQuantizationParameters(flatBufferBuilder, |
| 85 | 0, |
| 86 | 0, |
| 87 | flatBufferBuilder.CreateVector<float>({ outputQuantScale }), |
| 88 | flatBufferBuilder.CreateVector<int64_t>({ outputQuantOffset })); |
| 89 | |
| 90 | std::array<flatbuffers::Offset<Tensor>, 4> tensors; |
| 91 | tensors[0] = CreateTensor(flatBufferBuilder, |
| 92 | flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(), |
| 93 | inputTensorShape.size()), |
| 94 | tensorType, |
Ryan OShea | 238ecd9 | 2023-03-07 11:44:23 +0000 | [diff] [blame] | 95 | 1, |
Sadik Armagan | 6e36a64 | 2020-11-10 21:18:41 +0000 | [diff] [blame] | 96 | flatBufferBuilder.CreateString("input_0"), |
| 97 | quantizationParameters); |
| 98 | tensors[1] = CreateTensor(flatBufferBuilder, |
| 99 | flatBufferBuilder.CreateVector<int32_t>(weightsTensorShape.data(), |
| 100 | weightsTensorShape.size()), |
| 101 | tensorType, |
Ryan OShea | 238ecd9 | 2023-03-07 11:44:23 +0000 | [diff] [blame] | 102 | 2, |
Sadik Armagan | 6e36a64 | 2020-11-10 21:18:41 +0000 | [diff] [blame] | 103 | flatBufferBuilder.CreateString("weights"), |
| 104 | quantizationParameters); |
| 105 | tensors[2] = CreateTensor(flatBufferBuilder, |
| 106 | flatBufferBuilder.CreateVector<int32_t>(biasTensorShape.data(), |
| 107 | biasTensorShape.size()), |
| 108 | biasTensorType, |
Ryan OShea | 238ecd9 | 2023-03-07 11:44:23 +0000 | [diff] [blame] | 109 | 3, |
Sadik Armagan | 6e36a64 | 2020-11-10 21:18:41 +0000 | [diff] [blame] | 110 | flatBufferBuilder.CreateString("bias"), |
| 111 | quantizationParameters); |
| 112 | |
| 113 | tensors[3] = CreateTensor(flatBufferBuilder, |
| 114 | flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), |
| 115 | outputTensorShape.size()), |
| 116 | tensorType, |
Ryan OShea | 238ecd9 | 2023-03-07 11:44:23 +0000 | [diff] [blame] | 117 | 4, |
Sadik Armagan | 6e36a64 | 2020-11-10 21:18:41 +0000 | [diff] [blame] | 118 | flatBufferBuilder.CreateString("output"), |
| 119 | outputQuantizationParameters); |
| 120 | |
| 121 | |
| 122 | // create operator |
| 123 | tflite::BuiltinOptions operatorBuiltinOptionsType = BuiltinOptions_FullyConnectedOptions; |
| 124 | flatbuffers::Offset<void> operatorBuiltinOptions = |
| 125 | CreateFullyConnectedOptions(flatBufferBuilder, |
| 126 | activationType, |
| 127 | FullyConnectedOptionsWeightsFormat_DEFAULT, false).Union(); |
| 128 | |
Keith Davis | 892fafe | 2020-11-26 17:40:35 +0000 | [diff] [blame] | 129 | const std::vector<int> operatorInputs{0, 1, 2}; |
| 130 | const std::vector<int> operatorOutputs{3}; |
Sadik Armagan | 6e36a64 | 2020-11-10 21:18:41 +0000 | [diff] [blame] | 131 | flatbuffers::Offset <Operator> fullyConnectedOperator = |
| 132 | CreateOperator(flatBufferBuilder, |
| 133 | 0, |
| 134 | flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()), |
| 135 | flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()), |
| 136 | operatorBuiltinOptionsType, operatorBuiltinOptions); |
| 137 | |
Keith Davis | 892fafe | 2020-11-26 17:40:35 +0000 | [diff] [blame] | 138 | const std::vector<int> subgraphInputs{0, 1, 2}; |
| 139 | const std::vector<int> subgraphOutputs{3}; |
Sadik Armagan | 6e36a64 | 2020-11-10 21:18:41 +0000 | [diff] [blame] | 140 | flatbuffers::Offset <SubGraph> subgraph = |
| 141 | CreateSubGraph(flatBufferBuilder, |
| 142 | flatBufferBuilder.CreateVector(tensors.data(), tensors.size()), |
| 143 | flatBufferBuilder.CreateVector<int32_t>(subgraphInputs.data(), subgraphInputs.size()), |
| 144 | flatBufferBuilder.CreateVector<int32_t>(subgraphOutputs.data(), subgraphOutputs.size()), |
| 145 | flatBufferBuilder.CreateVector(&fullyConnectedOperator, 1)); |
| 146 | |
| 147 | flatbuffers::Offset <flatbuffers::String> modelDescription = |
| 148 | flatBufferBuilder.CreateString("ArmnnDelegate: FullyConnected Operator Model"); |
| 149 | flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, |
| 150 | tflite::BuiltinOperator_FULLY_CONNECTED); |
| 151 | |
| 152 | flatbuffers::Offset <Model> flatbufferModel = |
| 153 | CreateModel(flatBufferBuilder, |
| 154 | TFLITE_SCHEMA_VERSION, |
| 155 | flatBufferBuilder.CreateVector(&operatorCode, 1), |
| 156 | flatBufferBuilder.CreateVector(&subgraph, 1), |
| 157 | modelDescription, |
| 158 | flatBufferBuilder.CreateVector(buffers.data(), buffers.size())); |
| 159 | |
Matthew Sloyan | ebe392d | 2023-03-30 10:12:08 +0100 | [diff] [blame] | 160 | flatBufferBuilder.Finish(flatbufferModel, armnnDelegate::FILE_IDENTIFIER); |
Sadik Armagan | 6e36a64 | 2020-11-10 21:18:41 +0000 | [diff] [blame] | 161 | |
| 162 | return std::vector<char>(flatBufferBuilder.GetBufferPointer(), |
| 163 | flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize()); |
| 164 | } |
| 165 | |
| 166 | template <typename T> |
| 167 | void FullyConnectedTest(std::vector<armnn::BackendId>& backends, |
| 168 | tflite::TensorType tensorType, |
| 169 | tflite::ActivationFunctionType activationType, |
| 170 | const std::vector <int32_t>& inputTensorShape, |
| 171 | const std::vector <int32_t>& weightsTensorShape, |
| 172 | const std::vector <int32_t>& biasTensorShape, |
Sadik Armagan | f0a6dec | 2021-03-25 07:46:55 +0000 | [diff] [blame] | 173 | std::vector <int32_t>& outputTensorShape, |
| 174 | std::vector <T>& inputValues, |
| 175 | std::vector <T>& expectedOutputValues, |
| 176 | std::vector <T>& weightsData, |
| 177 | bool constantWeights = true, |
Sadik Armagan | 6e36a64 | 2020-11-10 21:18:41 +0000 | [diff] [blame] | 178 | float quantScale = 1.0f, |
| 179 | int quantOffset = 0) |
| 180 | { |
Matthew Sloyan | ebe392d | 2023-03-30 10:12:08 +0100 | [diff] [blame] | 181 | using namespace delegateTestInterpreter; |
Sadik Armagan | 6e36a64 | 2020-11-10 21:18:41 +0000 | [diff] [blame] | 182 | |
| 183 | std::vector<char> modelBuffer = CreateFullyConnectedTfLiteModel(tensorType, |
| 184 | activationType, |
| 185 | inputTensorShape, |
| 186 | weightsTensorShape, |
| 187 | biasTensorShape, |
| 188 | outputTensorShape, |
| 189 | weightsData, |
Sadik Armagan | f0a6dec | 2021-03-25 07:46:55 +0000 | [diff] [blame] | 190 | constantWeights, |
Sadik Armagan | 6e36a64 | 2020-11-10 21:18:41 +0000 | [diff] [blame] | 191 | quantScale, |
| 192 | quantOffset); |
Sadik Armagan | f0a6dec | 2021-03-25 07:46:55 +0000 | [diff] [blame] | 193 | |
Matthew Sloyan | ebe392d | 2023-03-30 10:12:08 +0100 | [diff] [blame] | 194 | // Setup interpreter with just TFLite Runtime. |
| 195 | auto tfLiteInterpreter = DelegateTestInterpreter(modelBuffer); |
| 196 | CHECK(tfLiteInterpreter.AllocateTensors() == kTfLiteOk); |
Sadik Armagan | 6e36a64 | 2020-11-10 21:18:41 +0000 | [diff] [blame] | 197 | |
Matthew Sloyan | ebe392d | 2023-03-30 10:12:08 +0100 | [diff] [blame] | 198 | // Setup interpreter with Arm NN Delegate applied. |
| 199 | auto armnnInterpreter = DelegateTestInterpreter(modelBuffer, backends); |
| 200 | CHECK(armnnInterpreter.AllocateTensors() == kTfLiteOk); |
Sadik Armagan | 6e36a64 | 2020-11-10 21:18:41 +0000 | [diff] [blame] | 201 | |
Matthew Sloyan | ebe392d | 2023-03-30 10:12:08 +0100 | [diff] [blame] | 202 | CHECK(tfLiteInterpreter.FillInputTensor<T>(inputValues, 0) == kTfLiteOk); |
| 203 | CHECK(armnnInterpreter.FillInputTensor<T>(inputValues, 0) == kTfLiteOk); |
Sadik Armagan | 6e36a64 | 2020-11-10 21:18:41 +0000 | [diff] [blame] | 204 | |
Sadik Armagan | f0a6dec | 2021-03-25 07:46:55 +0000 | [diff] [blame] | 205 | if (!constantWeights) |
Sadik Armagan | 6e36a64 | 2020-11-10 21:18:41 +0000 | [diff] [blame] | 206 | { |
Matthew Sloyan | ebe392d | 2023-03-30 10:12:08 +0100 | [diff] [blame] | 207 | CHECK(tfLiteInterpreter.FillInputTensor<T>(weightsData, 1) == kTfLiteOk); |
| 208 | CHECK(armnnInterpreter.FillInputTensor<T>(weightsData, 1) == kTfLiteOk); |
Sadik Armagan | f0a6dec | 2021-03-25 07:46:55 +0000 | [diff] [blame] | 209 | |
| 210 | if (tensorType == ::tflite::TensorType_INT8) |
| 211 | { |
| 212 | std::vector <int32_t> biasData = {10}; |
Matthew Sloyan | ebe392d | 2023-03-30 10:12:08 +0100 | [diff] [blame] | 213 | CHECK(tfLiteInterpreter.FillInputTensor<int32_t>(biasData, 2) == kTfLiteOk); |
| 214 | CHECK(armnnInterpreter.FillInputTensor<int32_t>(biasData, 2) == kTfLiteOk); |
Sadik Armagan | f0a6dec | 2021-03-25 07:46:55 +0000 | [diff] [blame] | 215 | } |
| 216 | else |
| 217 | { |
| 218 | std::vector<float> biasData = {10}; |
Matthew Sloyan | ebe392d | 2023-03-30 10:12:08 +0100 | [diff] [blame] | 219 | CHECK(tfLiteInterpreter.FillInputTensor<float>(biasData, 2) == kTfLiteOk); |
| 220 | CHECK(armnnInterpreter.FillInputTensor<float>(biasData, 2) == kTfLiteOk); |
Sadik Armagan | f0a6dec | 2021-03-25 07:46:55 +0000 | [diff] [blame] | 221 | } |
Sadik Armagan | 6e36a64 | 2020-11-10 21:18:41 +0000 | [diff] [blame] | 222 | } |
| 223 | |
Matthew Sloyan | ebe392d | 2023-03-30 10:12:08 +0100 | [diff] [blame] | 224 | CHECK(tfLiteInterpreter.Invoke() == kTfLiteOk); |
| 225 | std::vector<T> tfLiteOutputValues = tfLiteInterpreter.GetOutputResult<T>(0); |
| 226 | std::vector<int32_t> tfLiteOutputShape = tfLiteInterpreter.GetOutputShape(0); |
Sadik Armagan | 6e36a64 | 2020-11-10 21:18:41 +0000 | [diff] [blame] | 227 | |
Matthew Sloyan | ebe392d | 2023-03-30 10:12:08 +0100 | [diff] [blame] | 228 | CHECK(armnnInterpreter.Invoke() == kTfLiteOk); |
| 229 | std::vector<T> armnnOutputValues = armnnInterpreter.GetOutputResult<T>(0); |
| 230 | std::vector<int32_t> armnnOutputShape = armnnInterpreter.GetOutputShape(0); |
| 231 | |
| 232 | armnnDelegate::CompareOutputData<T>(tfLiteOutputValues, armnnOutputValues, expectedOutputValues); |
| 233 | armnnDelegate::CompareOutputShape(tfLiteOutputShape, armnnOutputShape, outputTensorShape); |
| 234 | |
| 235 | tfLiteInterpreter.Cleanup(); |
| 236 | armnnInterpreter.Cleanup(); |
Sadik Armagan | 6e36a64 | 2020-11-10 21:18:41 +0000 | [diff] [blame] | 237 | } |
| 238 | |
| 239 | } // anonymous namespace |