/* * 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 "RNN.h" namespace android { namespace nn { namespace wrapper { using ::testing::Each; using ::testing::FloatNear; using ::testing::Matcher; namespace { std::vector> ArrayFloatNear(const std::vector& values, float max_abs_error = 1.e-5) { std::vector> matchers; matchers.reserve(values.size()); for (const float& v : values) { matchers.emplace_back(FloatNear(v, max_abs_error)); } return matchers; } static float rnn_input[] = { 0.23689353, 0.285385, 0.037029743, -0.19858193, -0.27569133, 0.43773448, 0.60379338, 0.35562468, -0.69424844, -0.93421471, -0.87287879, 0.37144363, -0.62476718, 0.23791671, 0.40060222, 0.1356622, -0.99774903, -0.98858172, -0.38952237, -0.47685933, 0.31073618, 0.71511042, -0.63767755, -0.31729108, 0.33468103, 0.75801885, 0.30660987, -0.37354088, 0.77002847, -0.62747043, -0.68572164, 0.0069220066, 0.65791464, 0.35130811, 0.80834007, -0.61777675, -0.21095741, 0.41213346, 0.73784804, 0.094794154, 0.47791874, 0.86496925, -0.53376222, 0.85315156, 0.10288584, 0.86684, -0.011186242, 0.10513687, 0.87825835, 0.59929144, 0.62827742, 0.18899453, 0.31440187, 0.99059987, 0.87170351, -0.35091716, 0.74861872, 0.17831337, 0.2755419, 0.51864719, 0.55084288, 0.58982027, -0.47443086, 0.20875752, -0.058871567, -0.66609079, 0.59098077, 0.73017097, 0.74604273, 0.32882881, -0.17503482, 0.22396147, 0.19379807, 0.29120302, 0.077113032, -0.70331609, 0.15804303, -0.93407321, 0.40182066, 0.036301374, 0.66521823, 0.0300982, -0.7747041, -0.02038002, 0.020698071, -0.90300065, 0.62870288, -0.23068321, 0.27531278, -0.095755219, -0.712036, -0.17384434, -0.50593495, -0.18646687, -0.96508682, 0.43519354, 0.14744234, 0.62589407, 0.1653645, -0.10651493, -0.045277178, 0.99032974, -0.88255352, -0.85147917, 0.28153265, 0.19455957, -0.55479527, -0.56042433, 0.26048636, 0.84702539, 0.47587705, -0.074295521, -0.12287641, 0.70117295, 0.90532446, 0.89782166, 0.79817224, 0.53402734, -0.33286154, 0.073485017, -0.56172788, -0.044897556, 0.89964068, -0.067662835, 0.76863563, 0.93455386, -0.6324693, -0.083922029}; static float rnn_golden_output[] = { 0.496726, 0, 0.965996, 0, 0.0584254, 0, 0, 0.12315, 0, 0, 0.612266, 0.456601, 0, 0.52286, 1.16099, 0.0291232, 0, 0, 0.524901, 0, 0, 0, 0, 1.02116, 0, 1.35762, 0, 0.356909, 0.436415, 0.0355727, 0, 0, 0, 0, 0, 0.262335, 0, 0, 0, 1.33992, 0, 2.9739, 0, 0, 1.31914, 2.66147, 0, 0, 0.942568, 0, 0, 0, 0.025507, 0, 0, 0, 0.321429, 0.569141, 1.25274, 1.57719, 0.8158, 1.21805, 0.586239, 0.25427, 1.04436, 0, 0.630725, 0, 0.133801, 0.210693, 0.363026, 0, 0.533426, 0, 1.25926, 0.722707, 0, 1.22031, 1.30117, 0.495867, 0.222187, 0, 0.72725, 0, 0.767003, 0, 0, 0.147835, 0, 0, 0, 0.608758, 0.469394, 0.00720298, 0.927537, 0, 0.856974, 0.424257, 0, 0, 0.937329, 0, 0, 0, 0.476425, 0, 0.566017, 0.418462, 0.141911, 0.996214, 1.13063, 0, 0.967899, 0, 0, 0, 0.0831304, 0, 0, 1.00378, 0, 0, 0, 1.44818, 1.01768, 0.943891, 0.502745, 0, 0.940135, 0, 0, 0, 0, 0, 0, 2.13243, 0, 0.71208, 0.123918, 1.53907, 1.30225, 1.59644, 0.70222, 0, 0.804329, 0, 0.430576, 0, 0.505872, 0.509603, 0.343448, 0, 0.107756, 0.614544, 1.44549, 1.52311, 0.0454298, 0.300267, 0.562784, 0.395095, 0.228154, 0, 0.675323, 0, 1.70536, 0.766217, 0, 0, 0, 0.735363, 0.0759267, 1.91017, 0.941888, 0, 0, 0, 0, 0, 1.5909, 0, 0, 0, 0, 0.5755, 0, 0.184687, 0, 1.56296, 0.625285, 0, 0, 0, 0, 0, 0.0857888, 0, 0, 0, 0, 0.488383, 0.252786, 0, 0, 0, 1.02817, 1.85665, 0, 0, 0.00981836, 0, 1.06371, 0, 0, 0, 0, 0, 0, 0.290445, 0.316406, 0, 0.304161, 1.25079, 0.0707152, 0, 0.986264, 0.309201, 0, 0, 0, 0, 0, 1.64896, 0.346248, 0, 0.918175, 0.78884, 0.524981, 1.92076, 2.07013, 0.333244, 0.415153, 0.210318, 0, 0, 0, 0, 0, 2.02616, 0, 0.728256, 0.84183, 0.0907453, 0.628881, 3.58099, 1.49974, 0}; } // anonymous namespace #define FOR_ALL_INPUT_AND_WEIGHT_TENSORS(ACTION) \ ACTION(Input) \ ACTION(Weights) \ ACTION(RecurrentWeights) \ ACTION(Bias) \ ACTION(HiddenStateIn) // For all output and intermediate states #define FOR_ALL_OUTPUT_TENSORS(ACTION) \ ACTION(HiddenStateOut) \ ACTION(Output) class BasicRNNOpModel { public: BasicRNNOpModel(uint32_t batches, uint32_t units, uint32_t size) : batches_(batches), units_(units), input_size_(size), activation_(kActivationRelu) { std::vector inputs; OperandType InputTy(Type::TENSOR_FLOAT32, {batches_, input_size_}); inputs.push_back(model_.addOperand(&InputTy)); OperandType WeightTy(Type::TENSOR_FLOAT32, {units_, input_size_}); inputs.push_back(model_.addOperand(&WeightTy)); OperandType RecurrentWeightTy(Type::TENSOR_FLOAT32, {units_, units_}); inputs.push_back(model_.addOperand(&RecurrentWeightTy)); OperandType BiasTy(Type::TENSOR_FLOAT32, {units_}); inputs.push_back(model_.addOperand(&BiasTy)); OperandType HiddenStateTy(Type::TENSOR_FLOAT32, {batches_, units_}); inputs.push_back(model_.addOperand(&HiddenStateTy)); OperandType ActionParamTy(Type::INT32, {}); inputs.push_back(model_.addOperand(&ActionParamTy)); std::vector outputs; outputs.push_back(model_.addOperand(&HiddenStateTy)); OperandType OutputTy(Type::TENSOR_FLOAT32, {batches_, units_}); outputs.push_back(model_.addOperand(&OutputTy)); Input_.insert(Input_.end(), batches_ * input_size_, 0.f); HiddenStateIn_.insert(HiddenStateIn_.end(), batches_ * units_, 0.f); HiddenStateOut_.insert(HiddenStateOut_.end(), batches_ * units_, 0.f); Output_.insert(Output_.end(), batches_ * units_, 0.f); model_.addOperation(ANEURALNETWORKS_RNN, inputs, outputs); model_.identifyInputsAndOutputs(inputs, outputs); model_.finish(); } #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; } } void ResetHiddenState() { std::fill(HiddenStateIn_.begin(), HiddenStateIn_.end(), 0.f); std::fill(HiddenStateOut_.begin(), HiddenStateOut_.end(), 0.f); } const std::vector& GetOutput() const { return Output_; } uint32_t input_size() const { return input_size_; } uint32_t num_units() const { return units_; } uint32_t num_batches() const { return batches_; } void Invoke() { ASSERT_TRUE(model_.isValid()); HiddenStateIn_.swap(HiddenStateOut_); Compilation compilation(&model_); compilation.finish(); Execution execution(&compilation); #define SetInputOrWeight(X) \ ASSERT_EQ(execution.setInput(RNN::k##X##Tensor, X##_.data(), sizeof(float) * X##_.size()), \ Result::NO_ERROR); FOR_ALL_INPUT_AND_WEIGHT_TENSORS(SetInputOrWeight); #undef SetInputOrWeight #define SetOutput(X) \ ASSERT_EQ(execution.setOutput(RNN::k##X##Tensor, X##_.data(), sizeof(float) * X##_.size()), \ Result::NO_ERROR); FOR_ALL_OUTPUT_TENSORS(SetOutput); #undef SetOutput ASSERT_EQ(execution.setInput(RNN::kActivationParam, &activation_, sizeof(activation_)), Result::NO_ERROR); ASSERT_EQ(execution.compute(), Result::NO_ERROR); } private: Model model_; const uint32_t batches_; const uint32_t units_; const uint32_t input_size_; const int activation_; #define DefineTensor(X) std::vector X##_; FOR_ALL_INPUT_AND_WEIGHT_TENSORS(DefineTensor); FOR_ALL_OUTPUT_TENSORS(DefineTensor); #undef DefineTensor }; TEST(RNNOpTest, BlackBoxTest) { BasicRNNOpModel rnn(2, 16, 8); rnn.SetWeights( {0.461459, 0.153381, 0.529743, -0.00371218, 0.676267, -0.211346, 0.317493, 0.969689, -0.343251, 0.186423, 0.398151, 0.152399, 0.448504, 0.317662, 0.523556, -0.323514, 0.480877, 0.333113, -0.757714, -0.674487, -0.643585, 0.217766, -0.0251462, 0.79512, -0.595574, -0.422444, 0.371572, -0.452178, -0.556069, -0.482188, -0.685456, -0.727851, 0.841829, 0.551535, -0.232336, 0.729158, -0.00294906, -0.69754, 0.766073, -0.178424, 0.369513, -0.423241, 0.548547, -0.0152023, -0.757482, -0.85491, 0.251331, -0.989183, 0.306261, -0.340716, 0.886103, -0.0726757, -0.723523, -0.784303, 0.0354295, 0.566564, -0.485469, -0.620498, 0.832546, 0.697884, -0.279115, 0.294415, -0.584313, 0.548772, 0.0648819, 0.968726, 0.723834, -0.0080452, -0.350386, -0.272803, 0.115121, -0.412644, -0.824713, -0.992843, -0.592904, -0.417893, 0.863791, -0.423461, -0.147601, -0.770664, -0.479006, 0.654782, 0.587314, -0.639158, 0.816969, -0.337228, 0.659878, 0.73107, 0.754768, -0.337042, 0.0960841, 0.368357, 0.244191, -0.817703, -0.211223, 0.442012, 0.37225, -0.623598, -0.405423, 0.455101, 0.673656, -0.145345, -0.511346, -0.901675, -0.81252, -0.127006, 0.809865, -0.721884, 0.636255, 0.868989, -0.347973, -0.10179, -0.777449, 0.917274, 0.819286, 0.206218, -0.00785118, 0.167141, 0.45872, 0.972934, -0.276798, 0.837861, 0.747958, -0.0151566, -0.330057, -0.469077, 0.277308, 0.415818}); rnn.SetBias({0.065691948, -0.69055247, 0.1107955, -0.97084129, -0.23957068, -0.23566568, -0.389184, 0.47481549, -0.4791103, 0.29931796, 0.10463274, 0.83918178, 0.37197268, 0.61957061, 0.3956964, -0.37609905}); rnn.SetRecurrentWeights( {0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1}); rnn.ResetHiddenState(); const int input_sequence_size = sizeof(rnn_input) / sizeof(float) / (rnn.input_size() * rnn.num_batches()); for (int i = 0; i < input_sequence_size; i++) { float* batch_start = rnn_input + i * rnn.input_size(); float* batch_end = batch_start + rnn.input_size(); rnn.SetInput(0, batch_start, batch_end); rnn.SetInput(rnn.input_size(), batch_start, batch_end); rnn.Invoke(); float* golden_start = rnn_golden_output + i * rnn.num_units(); float* golden_end = golden_start + rnn.num_units(); std::vector expected; expected.insert(expected.end(), golden_start, golden_end); expected.insert(expected.end(), golden_start, golden_end); EXPECT_THAT(rnn.GetOutput(), ElementsAreArray(ArrayFloatNear(expected))); } } } // namespace wrapper } // namespace nn } // namespace android