/* * Copyright (C) 2017 The Android Open Source Project * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. */ #include #include #include #include "NeuralNetworksWrapper.h" #include "SVDF.h" using ::testing::FloatNear; using ::testing::Matcher; namespace android { namespace nn { namespace wrapper { namespace { std::vector> ArrayFloatNear(const std::vector& values, float max_abs_error = 1.e-6) { std::vector> matchers; matchers.reserve(values.size()); for (const float& v : values) { matchers.emplace_back(FloatNear(v, max_abs_error)); } return matchers; } } // namespace using ::testing::ElementsAreArray; static float svdf_input[] = { 0.12609188, -0.46347019, -0.89598465, 0.12609188, -0.46347019, -0.89598465, 0.14278367, -1.64410412, -0.75222826, 0.14278367, -1.64410412, -0.75222826, 0.49837467, 0.19278903, 0.26584083, 0.49837467, 0.19278903, 0.26584083, -0.11186574, 0.13164264, -0.05349274, -0.11186574, 0.13164264, -0.05349274, -0.68892461, 0.37783599, 0.18263303, -0.68892461, 0.37783599, 0.18263303, -0.81299269, -0.86831826, 1.43940818, -0.81299269, -0.86831826, 1.43940818, -1.45006323, -0.82251364, -1.69082689, -1.45006323, -0.82251364, -1.69082689, 0.03966608, -0.24936394, -0.77526885, 0.03966608, -0.24936394, -0.77526885, 0.11771342, -0.23761693, -0.65898693, 0.11771342, -0.23761693, -0.65898693, -0.89477462, 1.67204106, -0.53235275, -0.89477462, 1.67204106, -0.53235275}; static float svdf_input_rank2[] = { 0.12609188, -0.46347019, -0.89598465, 0.35867718, 0.36897406, 0.73463392, 0.14278367, -1.64410412, -0.75222826, -0.57290924, 0.12729003, 0.7567004, 0.49837467, 0.19278903, 0.26584083, 0.17660543, 0.52949083, -0.77931279, -0.11186574, 0.13164264, -0.05349274, -0.72674477, -0.5683046, 0.55900657, -0.68892461, 0.37783599, 0.18263303, -0.63690937, 0.44483393, -0.71817774, -0.81299269, -0.86831826, 1.43940818, -0.95760226, 1.82078898, 0.71135032, -1.45006323, -0.82251364, -1.69082689, -1.65087092, -1.89238167, 1.54172635, 0.03966608, -0.24936394, -0.77526885, 2.06740379, -1.51439476, 1.43768692, 0.11771342, -0.23761693, -0.65898693, 0.31088525, -1.55601168, -0.87661445, -0.89477462, 1.67204106, -0.53235275, -0.6230064, 0.29819036, 1.06939757, }; static float svdf_golden_output[] = {0.014899, -0.0517661, -0.143725, -0.00271883, 0.014899, -0.0517661, -0.143725, -0.00271883, 0.068281, -0.162217, -0.152268, 0.00323521, 0.068281, -0.162217, -0.152268, 0.00323521, -0.0317821, -0.0333089, 0.0609602, 0.0333759, -0.0317821, -0.0333089, 0.0609602, 0.0333759, -0.00623099, -0.077701, -0.391193, -0.0136691, -0.00623099, -0.077701, -0.391193, -0.0136691, 0.201551, -0.164607, -0.179462, -0.0592739, 0.201551, -0.164607, -0.179462, -0.0592739, 0.0886511, -0.0875401, -0.269283, 0.0281379, 0.0886511, -0.0875401, -0.269283, 0.0281379, -0.201174, -0.586145, -0.628624, -0.0330412, -0.201174, -0.586145, -0.628624, -0.0330412, -0.0839096, -0.299329, 0.108746, 0.109808, -0.0839096, -0.299329, 0.108746, 0.109808, 0.419114, -0.237824, -0.422627, 0.175115, 0.419114, -0.237824, -0.422627, 0.175115, 0.36726, -0.522303, -0.456502, -0.175475, 0.36726, -0.522303, -0.456502, -0.175475}; static float svdf_golden_output_rank_2[] = { -0.09623547, -0.10193135, 0.11083051, -0.0347917, 0.1141196, 0.12965347, -0.12652366, 0.01007236, -0.16396809, -0.21247184, 0.11259045, -0.04156673, 0.10132131, -0.06143532, -0.00924693, 0.10084561, 0.01257364, 0.0506071, -0.19287863, -0.07162561, -0.02033747, 0.22673416, 0.15487903, 0.02525555, -0.1411963, -0.37054959, 0.01774767, 0.05867489, 0.09607603, -0.0141301, -0.08995658, 0.12867066, -0.27142537, -0.16955489, 0.18521598, -0.12528358, 0.00331409, 0.11167502, 0.02218599, -0.07309391, 0.09593632, -0.28361851, -0.0773851, 0.17199151, -0.00075242, 0.33691186, -0.1536046, 0.16572715, -0.27916506, -0.27626723, 0.42615682, 0.3225764, -0.37472126, -0.55655634, -0.05013514, 0.289112, -0.24418658, 0.07540751, -0.1940318, -0.08911639, 0.00732617, 0.46737891, 0.26449674, 0.24888524, -0.17225097, -0.54660404, -0.38795233, 0.08389944, 0.07736043, -0.28260678, 0.15666828, 1.14949894, -0.57454878, -0.64704704, 0.73235172, -0.34616736, 0.21120001, -0.22927976, 0.02455296, -0.35906726, }; #define FOR_ALL_INPUT_AND_WEIGHT_TENSORS(ACTION) \ ACTION(Input) \ ACTION(WeightsFeature) \ ACTION(WeightsTime) \ ACTION(Bias) \ ACTION(StateIn) // For all output and intermediate states #define FOR_ALL_OUTPUT_TENSORS(ACTION) \ ACTION(StateOut) \ ACTION(Output) // Derived class of SingleOpModel, which is used to test SVDF TFLite op. class SVDFOpModel { public: SVDFOpModel(uint32_t batches, uint32_t units, uint32_t input_size, uint32_t memory_size, uint32_t rank) : batches_(batches), units_(units), input_size_(input_size), memory_size_(memory_size), rank_(rank) { std::vector> input_shapes{ {batches_, input_size_}, // Input tensor {units_ * rank_, input_size_}, // weights_feature tensor {units_ * rank_, memory_size_}, // weights_time tensor {units_}, // bias tensor {batches_, memory_size * units_ * rank_}, // state in tensor }; std::vector inputs; auto it = input_shapes.begin(); // Input and weights #define AddInput(X) \ OperandType X##OpndTy(Type::TENSOR_FLOAT32, *it++); \ inputs.push_back(model_.addOperand(&X##OpndTy)); FOR_ALL_INPUT_AND_WEIGHT_TENSORS(AddInput); #undef AddInput // Parameters OperandType RankParamTy(Type::INT32, {}); inputs.push_back(model_.addOperand(&RankParamTy)); OperandType ActivationParamTy(Type::INT32, {}); inputs.push_back(model_.addOperand(&ActivationParamTy)); // Output and other intermediate state std::vector> output_shapes{{batches_, memory_size_ * units_ * rank_}, {batches_, units_}}; std::vector outputs; auto it2 = output_shapes.begin(); #define AddOutput(X) \ OperandType X##OpndTy(Type::TENSOR_FLOAT32, *it2++); \ outputs.push_back(model_.addOperand(&X##OpndTy)); FOR_ALL_OUTPUT_TENSORS(AddOutput); #undef AddOutput Input_.insert(Input_.end(), batches_ * input_size_, 0.f); StateIn_.insert(StateIn_.end(), batches_ * units_ * rank_ * memory_size_, 0.f); auto multiAll = [](const std::vector& dims) -> uint32_t { uint32_t sz = 1; for (uint32_t d : dims) { sz *= d; } return sz; }; it2 = output_shapes.begin(); #define ReserveOutput(X) X##_.insert(X##_.end(), multiAll(*it2++), 0.f); FOR_ALL_OUTPUT_TENSORS(ReserveOutput); model_.addOperation(ANEURALNETWORKS_SVDF, inputs, outputs); model_.identifyInputsAndOutputs(inputs, outputs); model_.finish(); } void Invoke() { ASSERT_TRUE(model_.isValid()); Compilation compilation(&model_); compilation.finish(); Execution execution(&compilation); StateIn_.swap(StateOut_); #define SetInputOrWeight(X) \ ASSERT_EQ(execution.setInput(SVDF::k##X##Tensor, X##_.data(), sizeof(float) * X##_.size()), \ Result::NO_ERROR); FOR_ALL_INPUT_AND_WEIGHT_TENSORS(SetInputOrWeight); #undef SetInputOrWeight #define SetOutput(X) \ EXPECT_TRUE(X##_.data() != nullptr); \ ASSERT_EQ(execution.setOutput(SVDF::k##X##Tensor, X##_.data(), sizeof(float) * X##_.size()), \ Result::NO_ERROR); FOR_ALL_OUTPUT_TENSORS(SetOutput); #undef SetOutput ASSERT_EQ(execution.setInput(SVDF::kRankParam, &rank_, sizeof(rank_)), Result::NO_ERROR); int activation = ActivationFn::kActivationNone; ASSERT_EQ(execution.setInput(SVDF::kActivationParam, &activation, sizeof(activation)), Result::NO_ERROR); ASSERT_EQ(execution.compute(), Result::NO_ERROR); } #define DefineSetter(X) \ void Set##X(const std::vector& f) { X##_.insert(X##_.end(), f.begin(), f.end()); } FOR_ALL_INPUT_AND_WEIGHT_TENSORS(DefineSetter); #undef DefineSetter void SetInput(int offset, float* begin, float* end) { for (; begin != end; begin++, offset++) { Input_[offset] = *begin; } } // Resets the state of SVDF op by filling it with 0's. void ResetState() { std::fill(StateIn_.begin(), StateIn_.end(), 0.f); std::fill(StateOut_.begin(), StateOut_.end(), 0.f); } // Extracts the output tensor from the SVDF op. const std::vector& GetOutput() const { return Output_; } int input_size() const { return input_size_; } int num_units() const { return units_; } int num_batches() const { return batches_; } private: Model model_; const uint32_t batches_; const uint32_t units_; const uint32_t input_size_; const uint32_t memory_size_; const uint32_t rank_; #define DefineTensor(X) std::vector X##_; FOR_ALL_INPUT_AND_WEIGHT_TENSORS(DefineTensor); FOR_ALL_OUTPUT_TENSORS(DefineTensor); #undef DefineTensor }; TEST(SVDFOpTest, BlackBoxTest) { SVDFOpModel svdf(/*batches=*/2, /*units=*/4, /*input_size=*/3, /*memory_size=*/10, /*rank=*/1); svdf.SetWeightsFeature({-0.31930989, -0.36118156, 0.0079667, 0.37613347, 0.22197971, 0.12416199, 0.27901134, 0.27557442, 0.3905206, -0.36137494, -0.06634006, -0.10640851}); svdf.SetWeightsTime({-0.31930989, 0.37613347, 0.27901134, -0.36137494, -0.36118156, 0.22197971, 0.27557442, -0.06634006, 0.0079667, 0.12416199, 0.3905206, -0.10640851, -0.0976817, 0.15294972, 0.39635518, -0.02702999, 0.39296314, 0.15785322, 0.21931258, 0.31053296, -0.36916667, 0.38031587, -0.21580373, 0.27072677, 0.23622236, 0.34936687, 0.18174365, 0.35907319, -0.17493086, 0.324846, -0.10781813, 0.27201805, 0.14324132, -0.23681851, -0.27115166, -0.01580888, -0.14943552, 0.15465137, 0.09784451, -0.0337657}); svdf.SetBias({}); svdf.ResetState(); const int svdf_num_batches = svdf.num_batches(); const int svdf_input_size = svdf.input_size(); const int svdf_num_units = svdf.num_units(); const int input_sequence_size = sizeof(svdf_input) / sizeof(float) / (svdf_input_size * svdf_num_batches); // Going over each input batch, setting the input tensor, invoking the SVDF op // and checking the output with the expected golden values. for (int i = 0; i < input_sequence_size; i++) { float* batch_start = svdf_input + i * svdf_input_size * svdf_num_batches; float* batch_end = batch_start + svdf_input_size * svdf_num_batches; svdf.SetInput(0, batch_start, batch_end); svdf.Invoke(); float* golden_start = svdf_golden_output + i * svdf_num_units * svdf_num_batches; float* golden_end = golden_start + svdf_num_units * svdf_num_batches; std::vector expected; expected.insert(expected.end(), golden_start, golden_end); EXPECT_THAT(svdf.GetOutput(), ElementsAreArray(ArrayFloatNear(expected))); } } TEST(SVDFOpTest, BlackBoxTestRank2) { SVDFOpModel svdf(/*batches=*/2, /*units=*/4, /*input_size=*/3, /*memory_size=*/10, /*rank=*/2); svdf.SetWeightsFeature({-0.31930989, 0.0079667, 0.39296314, 0.37613347, 0.12416199, 0.15785322, 0.27901134, 0.3905206, 0.21931258, -0.36137494, -0.10640851, 0.31053296, -0.36118156, -0.0976817, -0.36916667, 0.22197971, 0.15294972, 0.38031587, 0.27557442, 0.39635518, -0.21580373, -0.06634006, -0.02702999, 0.27072677}); svdf.SetWeightsTime({-0.31930989, 0.37613347, 0.27901134, -0.36137494, -0.36118156, 0.22197971, 0.27557442, -0.06634006, 0.0079667, 0.12416199, 0.3905206, -0.10640851, -0.0976817, 0.15294972, 0.39635518, -0.02702999, 0.39296314, 0.15785322, 0.21931258, 0.31053296, -0.36916667, 0.38031587, -0.21580373, 0.27072677, 0.23622236, 0.34936687, 0.18174365, 0.35907319, -0.17493086, 0.324846, -0.10781813, 0.27201805, 0.14324132, -0.23681851, -0.27115166, -0.01580888, -0.14943552, 0.15465137, 0.09784451, -0.0337657, -0.14884081, 0.19931212, -0.36002168, 0.34663299, -0.11405486, 0.12672701, 0.39463779, -0.07886535, -0.06384811, 0.08249187, -0.26816407, -0.19905911, 0.29211238, 0.31264046, -0.28664589, 0.05698794, 0.11613581, 0.14078894, 0.02187902, -0.21781836, -0.15567942, 0.08693647, -0.38256618, 0.36580828, -0.22922277, -0.0226903, 0.12878349, -0.28122205, -0.10850525, -0.11955214, 0.27179423, -0.04710215, 0.31069002, 0.22672787, 0.09580326, 0.08682203, 0.1258215, 0.1851041, 0.29228821, 0.12366763}); svdf.SetBias({}); svdf.ResetState(); const int svdf_num_batches = svdf.num_batches(); const int svdf_input_size = svdf.input_size(); const int svdf_num_units = svdf.num_units(); const int input_sequence_size = sizeof(svdf_input_rank2) / sizeof(float) / (svdf_input_size * svdf_num_batches); // Going over each input batch, setting the input tensor, invoking the SVDF op // and checking the output with the expected golden values. for (int i = 0; i < input_sequence_size; i++) { float* batch_start = svdf_input_rank2 + i * svdf_input_size * svdf_num_batches; float* batch_end = batch_start + svdf_input_size * svdf_num_batches; svdf.SetInput(0, batch_start, batch_end); svdf.Invoke(); float* golden_start = svdf_golden_output_rank_2 + i * svdf_num_units * svdf_num_batches; float* golden_end = golden_start + svdf_num_units * svdf_num_batches; std::vector expected; expected.insert(expected.end(), golden_start, golden_end); EXPECT_THAT(svdf.GetOutput(), ElementsAreArray(ArrayFloatNear(expected))); } } } // namespace wrapper } // namespace nn } // namespace android