Ryan OShea | 49ed0df | 2022-09-21 16:09:41 +0100 | [diff] [blame] | 1 | // |
Ryan OShea | 238ecd9 | 2023-03-07 11:44:23 +0000 | [diff] [blame] | 2 | // Copyright © 2022-2023 Arm Ltd and Contributors. All rights reserved. |
Ryan OShea | 49ed0df | 2022-09-21 16:09:41 +0100 | [diff] [blame] | 3 | // SPDX-License-Identifier: MIT |
| 4 | // |
| 5 | |
| 6 | #pragma once |
| 7 | |
| 8 | #include "TestUtils.hpp" |
| 9 | |
| 10 | #include <armnn_delegate.hpp> |
Matthew Sloyan | ebe392d | 2023-03-30 10:12:08 +0100 | [diff] [blame] | 11 | #include <DelegateTestInterpreter.hpp> |
Ryan OShea | 49ed0df | 2022-09-21 16:09:41 +0100 | [diff] [blame] | 12 | |
| 13 | #include <flatbuffers/flatbuffers.h> |
Ryan OShea | 49ed0df | 2022-09-21 16:09:41 +0100 | [diff] [blame] | 14 | #include <tensorflow/lite/kernels/register.h> |
Ryan OShea | 49ed0df | 2022-09-21 16:09:41 +0100 | [diff] [blame] | 15 | #include <tensorflow/lite/version.h> |
| 16 | |
| 17 | #include <doctest/doctest.h> |
| 18 | |
| 19 | namespace |
| 20 | { |
Ryan OShea | 238ecd9 | 2023-03-07 11:44:23 +0000 | [diff] [blame] | 21 | std::vector<char> CreateBatchMatMulTfLiteModel( |
| 22 | tflite::BuiltinOperator bmmOperatorCode, |
| 23 | tflite::TensorType tensorType, |
| 24 | const std::vector <int32_t>& LHSInputTensorShape, |
| 25 | const std::vector <int32_t>& RHSInputTensorShape, |
| 26 | const std::vector <int32_t>& outputTensorShape, |
| 27 | bool adjX = false, |
| 28 | bool adjY = false, |
| 29 | float quantScale = 1.0f, |
| 30 | int quantOffset = 0) |
| 31 | { |
| 32 | using namespace tflite; |
| 33 | flatbuffers::FlatBufferBuilder flatBufferBuilder; |
Ryan OShea | 49ed0df | 2022-09-21 16:09:41 +0100 | [diff] [blame] | 34 | |
Ryan OShea | 238ecd9 | 2023-03-07 11:44:23 +0000 | [diff] [blame] | 35 | std::vector<flatbuffers::Offset<tflite::Buffer>> buffers; |
| 36 | buffers.push_back(CreateBuffer(flatBufferBuilder)); |
| 37 | buffers.push_back(CreateBuffer(flatBufferBuilder)); |
| 38 | buffers.push_back(CreateBuffer(flatBufferBuilder)); |
| 39 | buffers.push_back(CreateBuffer(flatBufferBuilder)); |
| 40 | |
| 41 | auto quantizationParameters = |
| 42 | CreateQuantizationParameters(flatBufferBuilder, |
| 43 | 0, |
| 44 | 0, |
| 45 | flatBufferBuilder.CreateVector<float>({ quantScale }), |
| 46 | flatBufferBuilder.CreateVector<int64_t>({ quantOffset })); |
| 47 | |
| 48 | std::array<flatbuffers::Offset<Tensor>, 3> tensors; |
| 49 | tensors[0] = CreateTensor(flatBufferBuilder, |
| 50 | flatBufferBuilder.CreateVector<int32_t>(LHSInputTensorShape.data(), |
| 51 | LHSInputTensorShape.size()), |
| 52 | tensorType, |
| 53 | 1, |
| 54 | flatBufferBuilder.CreateString("LHSInput"), |
| 55 | quantizationParameters); |
| 56 | |
| 57 | tensors[1] = CreateTensor(flatBufferBuilder, |
| 58 | flatBufferBuilder.CreateVector<int32_t>(RHSInputTensorShape.data(), |
| 59 | RHSInputTensorShape.size()), |
| 60 | tensorType, |
| 61 | 2, |
| 62 | flatBufferBuilder.CreateString("RHSInput"), |
| 63 | quantizationParameters); |
| 64 | |
| 65 | tensors[2] = CreateTensor(flatBufferBuilder, |
| 66 | flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), |
| 67 | outputTensorShape.size()), |
| 68 | tensorType, |
| 69 | 3, |
| 70 | flatBufferBuilder.CreateString("output"), |
| 71 | quantizationParameters); |
| 72 | |
| 73 | // create operator |
| 74 | tflite::BuiltinOptions operatorBuiltinOptionsType = BuiltinOptions_BatchMatMulOptions; |
| 75 | flatbuffers::Offset<void> operatorBuiltinOptions = CreateBatchMatMulOptions(flatBufferBuilder, |
| 76 | adjX, |
| 77 | adjY).Union(); |
| 78 | |
| 79 | const std::vector<int32_t> operatorInputs{{0, 1}}; |
| 80 | const std::vector<int32_t> operatorOutputs{2}; |
| 81 | flatbuffers::Offset <Operator> bmmOperator = |
| 82 | CreateOperator(flatBufferBuilder, |
| 83 | 0, |
| 84 | flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()), |
| 85 | flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), |
| 86 | operatorOutputs.size()), |
| 87 | operatorBuiltinOptionsType, |
| 88 | operatorBuiltinOptions); |
| 89 | |
| 90 | const std::vector<int> subgraphInputs{{0, 1}}; |
| 91 | const std::vector<int> subgraphOutputs{2}; |
| 92 | flatbuffers::Offset <SubGraph> subgraph = |
| 93 | CreateSubGraph(flatBufferBuilder, |
| 94 | flatBufferBuilder.CreateVector(tensors.data(), tensors.size()), |
| 95 | flatBufferBuilder.CreateVector<int32_t>(subgraphInputs.data(), subgraphInputs.size()), |
| 96 | flatBufferBuilder.CreateVector<int32_t>(subgraphOutputs.data(), |
| 97 | subgraphOutputs.size()), |
| 98 | flatBufferBuilder.CreateVector(&bmmOperator, 1)); |
| 99 | |
| 100 | flatbuffers::Offset <flatbuffers::String> modelDescription = |
| 101 | flatBufferBuilder.CreateString("ArmnnDelegate: BatchMatMul Operator Model"); |
| 102 | flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, bmmOperatorCode); |
| 103 | |
| 104 | flatbuffers::Offset <Model> flatbufferModel = |
| 105 | CreateModel(flatBufferBuilder, |
| 106 | TFLITE_SCHEMA_VERSION, |
| 107 | flatBufferBuilder.CreateVector(&operatorCode, 1), |
| 108 | flatBufferBuilder.CreateVector(&subgraph, 1), |
| 109 | modelDescription, |
| 110 | flatBufferBuilder.CreateVector(buffers.data(), buffers.size())); |
| 111 | |
Matthew Sloyan | ebe392d | 2023-03-30 10:12:08 +0100 | [diff] [blame] | 112 | flatBufferBuilder.Finish(flatbufferModel, armnnDelegate::FILE_IDENTIFIER); |
Ryan OShea | 238ecd9 | 2023-03-07 11:44:23 +0000 | [diff] [blame] | 113 | |
| 114 | return std::vector<char>(flatBufferBuilder.GetBufferPointer(), |
| 115 | flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize()); |
| 116 | } |
| 117 | |
| 118 | template <typename T> |
| 119 | void BatchMatMulTest(tflite::BuiltinOperator bmmOperatorCode, |
| 120 | tflite::TensorType tensorType, |
Ryan OShea | 238ecd9 | 2023-03-07 11:44:23 +0000 | [diff] [blame] | 121 | std::vector<int32_t>& LHSInputShape, |
| 122 | std::vector<int32_t>& RHSInputShape, |
| 123 | std::vector<int32_t>& outputShape, |
| 124 | std::vector<T>& LHSInputValues, |
| 125 | std::vector<T>& RHSInputValues, |
| 126 | std::vector<T>& expectedOutputValues, |
| 127 | bool adjX = false, |
| 128 | bool adjY = false, |
| 129 | float quantScale = 1.0f, |
Colm Donelan | eff204a | 2023-11-28 15:46:09 +0000 | [diff] [blame^] | 130 | int quantOffset = 0, |
| 131 | const std::vector<armnn::BackendId>& backends = {}) |
Ryan OShea | 238ecd9 | 2023-03-07 11:44:23 +0000 | [diff] [blame] | 132 | { |
Matthew Sloyan | ebe392d | 2023-03-30 10:12:08 +0100 | [diff] [blame] | 133 | using namespace delegateTestInterpreter; |
Ryan OShea | 238ecd9 | 2023-03-07 11:44:23 +0000 | [diff] [blame] | 134 | std::vector<char> modelBuffer = CreateBatchMatMulTfLiteModel(bmmOperatorCode, |
| 135 | tensorType, |
| 136 | LHSInputShape, |
| 137 | RHSInputShape, |
| 138 | outputShape, |
| 139 | adjX, |
| 140 | adjY, |
| 141 | quantScale, |
| 142 | quantOffset); |
| 143 | |
Matthew Sloyan | ebe392d | 2023-03-30 10:12:08 +0100 | [diff] [blame] | 144 | // Setup interpreter with just TFLite Runtime. |
| 145 | auto tfLiteInterpreter = DelegateTestInterpreter(modelBuffer); |
| 146 | CHECK(tfLiteInterpreter.AllocateTensors() == kTfLiteOk); |
| 147 | CHECK(tfLiteInterpreter.FillInputTensor<T>(LHSInputValues, 0) == kTfLiteOk); |
| 148 | CHECK(tfLiteInterpreter.FillInputTensor<T>(RHSInputValues, 1) == kTfLiteOk); |
| 149 | CHECK(tfLiteInterpreter.Invoke() == kTfLiteOk); |
| 150 | std::vector<T> tfLiteOutputValues = tfLiteInterpreter.GetOutputResult<T>(0); |
| 151 | std::vector<int32_t> tfLiteOutputShape = tfLiteInterpreter.GetOutputShape(0); |
Ryan OShea | 238ecd9 | 2023-03-07 11:44:23 +0000 | [diff] [blame] | 152 | |
Matthew Sloyan | ebe392d | 2023-03-30 10:12:08 +0100 | [diff] [blame] | 153 | // Setup interpreter with Arm NN Delegate applied. |
Colm Donelan | eff204a | 2023-11-28 15:46:09 +0000 | [diff] [blame^] | 154 | auto armnnInterpreter = DelegateTestInterpreter(modelBuffer, CaptureAvailableBackends(backends)); |
Matthew Sloyan | ebe392d | 2023-03-30 10:12:08 +0100 | [diff] [blame] | 155 | CHECK(armnnInterpreter.AllocateTensors() == kTfLiteOk); |
| 156 | CHECK(armnnInterpreter.FillInputTensor<T>(LHSInputValues, 0) == kTfLiteOk); |
| 157 | CHECK(armnnInterpreter.FillInputTensor<T>(RHSInputValues, 1) == kTfLiteOk); |
| 158 | CHECK(armnnInterpreter.Invoke() == kTfLiteOk); |
| 159 | std::vector<T> armnnOutputValues = armnnInterpreter.GetOutputResult<T>(0); |
| 160 | std::vector<int32_t> armnnOutputShape = armnnInterpreter.GetOutputShape(0); |
Ryan OShea | 238ecd9 | 2023-03-07 11:44:23 +0000 | [diff] [blame] | 161 | |
Matthew Sloyan | ebe392d | 2023-03-30 10:12:08 +0100 | [diff] [blame] | 162 | armnnDelegate::CompareOutputData<T>(tfLiteOutputValues, armnnOutputValues, expectedOutputValues); |
| 163 | armnnDelegate::CompareOutputShape(tfLiteOutputShape, armnnOutputShape, outputShape); |
Ryan OShea | 238ecd9 | 2023-03-07 11:44:23 +0000 | [diff] [blame] | 164 | |
Matthew Sloyan | ebe392d | 2023-03-30 10:12:08 +0100 | [diff] [blame] | 165 | tfLiteInterpreter.Cleanup(); |
| 166 | armnnInterpreter.Cleanup(); |
Ryan OShea | 238ecd9 | 2023-03-07 11:44:23 +0000 | [diff] [blame] | 167 | } |
Ryan OShea | 49ed0df | 2022-09-21 16:09:41 +0100 | [diff] [blame] | 168 | |
| 169 | } // anonymous namespace |
| 170 | |
| 171 | |
| 172 | |
| 173 | |