/* * Copyright (c) 2019-2020 Arm Limited. * * SPDX-License-Identifier: MIT * * Permission is hereby granted, free of charge, to any person obtaining a copy * of this software and associated documentation files (the "Software"), to * deal in the Software without restriction, including without limitation the * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or * sell copies of the Software, and to permit persons to whom the Software is * furnished to do so, subject to the following conditions: * * The above copyright notice and this permission notice shall be included in all * copies or substantial portions of the Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE * SOFTWARE. */ #include "arm_compute/runtime/CL/functions/CLLSTMLayerQuantized.h" #include "tests/CL/CLAccessor.h" #include "tests/PaddingCalculator.h" #include "tests/Utils.h" #include "tests/datasets/LSTMLayerDataset.h" #include "tests/framework/Asserts.h" #include "tests/framework/Macros.h" #include "tests/framework/datasets/Datasets.h" #include "tests/validation/Validation.h" #include namespace arm_compute { namespace test { namespace validation { namespace { template inline void fill_tensor(CLTensor &tensor, const std::vector &v) { tensor.map(true); // Import memory accounting for padding TensorShape t_shape = tensor.info()->tensor_shape(); Window window; window.use_tensor_dimensions(t_shape); Iterator out(&tensor, window); execute_window_loop(window, [&](const Coordinates & id) { *reinterpret_cast(out.ptr()) = v[coord2index(t_shape, id)]; }, out); tensor.unmap(); } template inline void fill_tensor(SimpleTensor &tensor, const std::vector &v) { std::memcpy(tensor.data(), v.data(), sizeof(T) * v.size()); } } // namespace TEST_SUITE(CL) TEST_SUITE(LSTMLayerQuantized) // *INDENT-OFF* // clang-format off TEST_SUITE(IntegrationTestCase) TEST_SUITE(MultSmallerEq1) TEST_CASE(RunSmall, framework::DatasetMode::PRECOMMIT) { const int batch_size = 2; const int input_size = 2; const int output_size = 4; QuantizationInfo qasymm(1.f / 128.f, 128); QuantizationInfo qweights(1.f / 128.f, 128); QuantizationInfo qsymm_3(8.f / 32768.f, 0); QuantizationInfo qsymm_4(16.f / 32768.f, 0); TensorShape input_shape{ input_size, batch_size }; TensorShape input_weights_shape{ input_size, output_size }; TensorShape recurrent_weights_shape{ output_size, output_size }; TensorShape output_shape{ output_size, batch_size}; TensorShape bias_shape{ output_size }; auto input_to_input_weights = create_tensor(input_weights_shape, DataType::QASYMM8, 1, qweights); auto input_to_forget_weights = create_tensor(input_weights_shape, DataType::QASYMM8, 1, qweights); auto input_to_cell_weights = create_tensor(input_weights_shape, DataType::QASYMM8, 1, qweights); auto input_to_output_weights = create_tensor(input_weights_shape, DataType::QASYMM8, 1, qweights); auto recurrent_to_input_weights = create_tensor(recurrent_weights_shape, DataType::QASYMM8, 1, qweights); auto recurrent_to_forget_weights = create_tensor(recurrent_weights_shape, DataType::QASYMM8, 1, qweights); auto recurrent_to_cell_weights = create_tensor(recurrent_weights_shape, DataType::QASYMM8, 1, qweights); auto recurrent_to_output_weights = create_tensor(recurrent_weights_shape, DataType::QASYMM8, 1, qweights); auto input_gate_bias = create_tensor(bias_shape, DataType::S32); auto forget_gate_bias = create_tensor(bias_shape, DataType::S32); auto cell_gate_bias = create_tensor(bias_shape, DataType::S32); auto output_gate_bias = create_tensor(bias_shape, DataType::S32); // LSTM input auto input = create_tensor(input_shape, DataType::QASYMM8, 1, qasymm); // LSTM output state auto output_state = create_tensor(output_shape, DataType::QASYMM8, 1, qasymm); // LSTM cell state auto cell_state = create_tensor(output_shape, DataType::QSYMM16, 1, qsymm_4); CLLSTMLayerQuantized lstmq; lstmq.configure(&input, &input_to_input_weights, &input_to_forget_weights, &input_to_cell_weights, &input_to_output_weights, &recurrent_to_input_weights, &recurrent_to_forget_weights, &recurrent_to_cell_weights, &recurrent_to_output_weights, &input_gate_bias, &forget_gate_bias, &cell_gate_bias, &output_gate_bias, &cell_state, &output_state, &cell_state, &output_state); input.allocator()->allocate(); input_to_input_weights.allocator()->allocate(); input_to_forget_weights.allocator()->allocate(); input_to_cell_weights.allocator()->allocate(); input_to_output_weights.allocator()->allocate(); recurrent_to_input_weights.allocator()->allocate(); recurrent_to_forget_weights.allocator()->allocate(); recurrent_to_cell_weights.allocator()->allocate(); recurrent_to_output_weights.allocator()->allocate(); input_gate_bias.allocator()->allocate(); forget_gate_bias.allocator()->allocate(); cell_gate_bias.allocator()->allocate(); output_gate_bias.allocator()->allocate(); cell_state.allocator()->allocate(); output_state.allocator()->allocate(); // Fill weights and biases fill_tensor(input_to_input_weights, std::vector{ 47, 168, 66, 239, 6, 42, 237, 236 }); fill_tensor(input_to_forget_weights, std::vector { 204, 193, 148, 59, 113, 17, 66, 197 }); fill_tensor(input_to_cell_weights, std::vector { 172, 101, 184, 209, 165, 82, 108, 209 }); fill_tensor(input_to_output_weights, std::vector { 203, 244, 219, 114, 130, 16, 163, 222 }); fill_tensor(recurrent_to_input_weights, std::vector { 162, 168, 7, 95, 91, 155, 108, 216, 255, 100, 48, 188, 58, 37, 186, 147 }); fill_tensor(recurrent_to_forget_weights, std::vector { 46, 58, 47, 170, 246, 96, 12, 99, 68, 23, 186, 161, 237, 164, 89, 6 }); fill_tensor(recurrent_to_cell_weights, std::vector { 234, 99, 71, 206, 205, 159, 64, 253, 191, 148, 116, 8, 209, 136, 59, 138 }); fill_tensor(recurrent_to_output_weights, std::vector { 23, 241, 137, 36, 206, 5, 227, 56, 254, 176, 231, 47, 18, 201, 161, 11 }); fill_tensor(input_gate_bias, std::vector {-103038, 30525, 115255, -38154 }); fill_tensor(forget_gate_bias, std::vector { -23428, 126970, 116806, 46307 }); fill_tensor(cell_gate_bias, std::vector { 128006, 69949, -42808, 42568 }); fill_tensor(output_gate_bias, std::vector { -67066, -53607, 47233, 7300 }); SimpleTensor expected_output(output_shape, DataType::QASYMM8, 1, qasymm); // Initialize state fill_tensor(output_state, std::vector { 128, 128, 128, 128, 128, 128, 128, 128 }); fill_tensor(cell_state, std::vector { 0, 0, 0, 0, 0, 0, 0, 0 }); // First input fill_tensor(input, std::vector { 106, 193, 155, 150 }); fill_tensor(expected_output, std::vector { 128, 130, 36, 134, 128, 131, 35, 133 }); lstmq.run(); validate(CLAccessor(output_state), expected_output); // Second input fill_tensor(expected_output, std::vector { 128, 129, 12, 137, 128, 131, 10, 136 }); lstmq.run(); validate(CLAccessor(output_state), expected_output); // Third input fill_tensor(expected_output, std::vector { 128, 129, 8, 140, 128, 130, 6, 138 }); lstmq.run(); validate(CLAccessor(output_state), expected_output); } TEST_CASE(RunLarge, framework::DatasetMode::PRECOMMIT) { const int batch_size = 16; const int input_size = 8; const int output_size = 8; QuantizationInfo qasymm(1.f / 128.f, 128); QuantizationInfo qweights(1.f / 128.f, 128); QuantizationInfo qsymm_3(8.f / 32768.f, 0); QuantizationInfo qsymm_4(16.f / 32768.f, 0); TensorShape input_shape{ input_size, batch_size }; TensorShape input_weights_shape{ input_size, output_size }; TensorShape recurrent_weights_shape{ output_size, output_size }; TensorShape output_shape{ output_size, batch_size}; TensorShape bias_shape{ output_size }; auto input_to_input_weights = create_tensor(input_weights_shape, DataType::QASYMM8, 1, qweights); auto input_to_forget_weights = create_tensor(input_weights_shape, DataType::QASYMM8, 1, qweights); auto input_to_cell_weights = create_tensor(input_weights_shape, DataType::QASYMM8, 1, qweights); auto input_to_output_weights = create_tensor(input_weights_shape, DataType::QASYMM8, 1, qweights); auto recurrent_to_input_weights = create_tensor(recurrent_weights_shape, DataType::QASYMM8, 1, qweights); auto recurrent_to_forget_weights = create_tensor(recurrent_weights_shape, DataType::QASYMM8, 1, qweights); auto recurrent_to_cell_weights = create_tensor(recurrent_weights_shape, DataType::QASYMM8, 1, qweights); auto recurrent_to_output_weights = create_tensor(recurrent_weights_shape, DataType::QASYMM8, 1, qweights); auto input_gate_bias = create_tensor(bias_shape, DataType::S32); auto forget_gate_bias = create_tensor(bias_shape, DataType::S32); auto cell_gate_bias = create_tensor(bias_shape, DataType::S32); auto output_gate_bias = create_tensor(bias_shape, DataType::S32); // LSTM input auto input = create_tensor(input_shape, DataType::QASYMM8, 1, qasymm); // LSTM output state auto output_state = create_tensor(output_shape, DataType::QASYMM8, 1, qasymm); // LSTM cell state auto cell_state = create_tensor(output_shape, DataType::QSYMM16, 1, qsymm_4); CLLSTMLayerQuantized lstmq; lstmq.configure(&input, &input_to_input_weights, &input_to_forget_weights, &input_to_cell_weights, &input_to_output_weights, &recurrent_to_input_weights, &recurrent_to_forget_weights, &recurrent_to_cell_weights, &recurrent_to_output_weights, &input_gate_bias, &forget_gate_bias, &cell_gate_bias, &output_gate_bias, &cell_state, &output_state, &cell_state, &output_state); input.allocator()->allocate(); input_to_input_weights.allocator()->allocate(); input_to_forget_weights.allocator()->allocate(); input_to_cell_weights.allocator()->allocate(); input_to_output_weights.allocator()->allocate(); recurrent_to_input_weights.allocator()->allocate(); recurrent_to_forget_weights.allocator()->allocate(); recurrent_to_cell_weights.allocator()->allocate(); recurrent_to_output_weights.allocator()->allocate(); input_gate_bias.allocator()->allocate(); forget_gate_bias.allocator()->allocate(); cell_gate_bias.allocator()->allocate(); output_gate_bias.allocator()->allocate(); cell_state.allocator()->allocate(); output_state.allocator()->allocate(); // Fill weights and biases fill_tensor(input_to_input_weights, std::vector{ 141, 89, 200, 180, 46, 50, 87, 128, 149, 227, 177, 187, 212, 229, 54, 111, 131, 116, 3, 58, 196, 26, 131, 255, 22, 106, 216, 69, 239, 12, 232, 207, 184, 56, 236, 172, 28, 143, 161, 124, 255, 33, 197, 122, 47, 197, 26, 229, 91, 79, 11, 160, 26, 80, 100, 36, 248, 186, 97, 61, 125, 46, 14, 100, }); fill_tensor(input_to_forget_weights, std::vector { 237, 165, 141, 249, 72, 116, 36 , 115, 234, 213, 85, 84, 59, 62, 150, 246, 182, 102, 158, 214, 182, 183, 94, 11, 158, 192, 92, 189, 160, 219, 206, 249, 88, 213, 193, 244, 151, 72, 129, 49, 239, 83, 106, 9, 169, 187, 125, 171, 32, 141, 126, 92, 13, 36, 224, 150, 187, 250, 178, 169, 89, 214, 91, 173 }); fill_tensor(input_to_cell_weights, std::vector { 93, 103, 226, 139, 185, 252, 129, 171, 159, 32, 25, 175, 224, 183, 165, 35, 207, 69, 238, 228, 149, 214, 79, 6, 5, 66, 102, 14, 19, 111, 36, 143, 22, 85, 13, 78, 236, 121, 122, 77, 249, 39, 88, 12, 205, 143, 93, 240, 167, 89, 188, 50, 73, 69, 201, 251, 59, 32, 203, 184, 139, 191, 199, 74}); fill_tensor(input_to_output_weights, std::vector { 205, 7, 95, 104, 252, 143, 226, 73, 229, 114, 152, 171, 221, 153, 73, 229, 153, 165, 223, 239, 100, 38, 172, 211, 226, 133, 239, 207, 116, 230, 170, 100, 241, 95, 171, 124, 63, 115, 32, 127, 141, 239, 53, 193, 201, 53, 104, 178, 186, 212, 167, 107, 226, 230, 71, 213, 148, 217, 19, 248, 233, 195, 183, 156 }); fill_tensor(recurrent_to_input_weights, std::vector { 147, 112, 140, 103, 3, 255, 17, 49, 84, 112, 144, 213, 138, 142, 112, 66, 117, 30, 101, 35, 25, 132, 211, 229, 183, 208, 102, 16, 38, 85, 101, 152, 226, 83, 132, 22, 161, 110, 157, 129, 184, 63, 168, 42, 220, 126, 209, 157, 5, 88, 243, 83, 249, 19, 226, 209, 173, 96, 185, 77, 146, 227, 238, 136 }); fill_tensor(recurrent_to_forget_weights, std::vector { 52, 132, 92, 200, 213, 32, 213, 37, 116, 142, 116, 180, 4, 172, 158, 143, 110, 40, 99, 28, 221, 153, 133, 2, 247, 144, 198, 100, 20, 15, 221, 196, 159, 178, 188, 151, 171, 15, 25, 217, 178, 109, 110, 118, 128, 39, 232, 234, 184, 214, 177, 13, 56, 6, 28, 252, 89, 187, 242, 59, 146, 111, 132, 129}); fill_tensor(recurrent_to_cell_weights, std::vector { 70, 44, 137, 29, 36, 127, 1, 241, 26, 241, 142, 114, 67, 181, 49, 57, 131, 152, 175, 77, 23, 63, 37, 124, 150, 113, 95, 103, 110, 201, 69, 97, 196, 242, 62, 214, 66, 19, 45, 135, 22, 168, 149, 104, 77, 101, 36, 68, 170, 116, 222, 100, 109, 1, 154, 18, 133, 215, 105, 93, 31, 57, 231, 112 }); fill_tensor(recurrent_to_output_weights, std::vector { 45 , 181 , 220 , 219 , 49 , 63 , 49 , 129, 7 , 166 , 104 , 114 , 83 , 40 , 1 , 195, 245 , 142 , 82 , 232 , 104 , 245 , 82 , 196, 111 , 56 , 156 , 9 , 141 , 240 , 180 , 148, 247 , 198 , 234 , 137 , 13 , 210 , 161 , 192, 196 , 59 , 233 , 184 , 142 , 187 , 140 , 166, 2 , 95 , 152 , 46 , 71 , 46 , 113 , 32, 175 , 229 , 86 , 87 , 62 , 93 , 74 , 130}); fill_tensor(input_gate_bias, std::vector { -40040, -106916, -92315, -79123, 45160, -17954, 50962, -63758 }); fill_tensor(forget_gate_bias, std::vector { -128514, 8463, -57831, 116977, 106547, -28132, -124557, 44941 }); fill_tensor(cell_gate_bias, std::vector { 88388 , 123601, -116148, -13022, 21619, 48926, 57523, 39332 }); fill_tensor(output_gate_bias, std::vector { 59485 , -33070, 21386, -100633, -115959, 125768, -56407, 24897 }); SimpleTensor expected_output(output_shape, DataType::QASYMM8, 1, qasymm); // Initialize state fill_tensor(output_state, std::vector { 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128 }); fill_tensor(cell_state, std::vector { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}); // First input fill_tensor(input, std::vector { 247, 203, 159, 131, 182, 114, 207, 195, 48 , 61 , 154, 16, 80, 101, 116, 255, 50 , 115 , 45, 186, 75, 212, 98, 48, 88 , 146 , 24, 143, 218, 174, 203, 200, 239 , 16 , 66, 136, 234, 54, 94, 51, 101 , 128 , 220, 213, 164, 82, 137, 255, 70 , 165 , 234, 220, 66, 35, 183, 206, 39 , 57 , 180, 202, 23, 172, 224, 109, 102 , 215 , 186, 82, 215, 147, 85, 187, 96 , 249 , 59, 116, 150, 44, 167, 128, 34 , 217 , 148, 193, 243, 38, 250, 208, 112 , 130 , 208, 29, 16, 122, 20, 92, 24 , 72 , 104, 29, 150, 233, 151, 19, 158 , 192 , 254, 70, 73, 142, 106, 152, 3 , 61 , 24, 135, 212, 9, 80, 234, 147 , 246 , 83, 249, 49, 14, 68, 50}); fill_tensor(expected_output, std::vector {131, 128, 128, 128, 128, 180, 129, 133, 136, 128, 126, 128, 128, 173, 135, 130, 160, 128, 128, 128, 128, 138, 132, 129, 131, 128, 127, 128, 128, 169, 129, 131, 133, 128, 128, 128, 128, 182, 130, 129, 131, 128, 128, 128, 128, 163, 129, 130, 131, 128, 128, 128, 128, 149, 132, 129, 143, 128, 127, 128, 128, 150, 134, 131, 134, 128, 128, 128, 128, 167, 130, 130, 131, 128, 128, 128, 128, 152, 132, 129, 128, 128, 128, 128, 128, 169, 130, 130, 173, 128, 128, 128, 128, 148, 139, 130, 152, 128, 128, 128, 128, 168, 139, 132, 147, 128, 128, 128, 128, 161, 131, 132, 130, 128, 128, 128, 128, 159, 134, 128, 140, 128, 128, 128, 128, 133, 132, 128 }); lstmq.run(); validate(CLAccessor(output_state), expected_output); // Second input fill_tensor(expected_output, std::vector { 130, 128, 128, 128, 128, 205, 129, 137, 135, 128, 127, 128, 128, 190, 137, 132, 160, 128, 128, 128, 128, 142, 133, 131, 130, 128, 128, 128, 128, 185, 129, 133, 132, 128, 128, 128, 128, 198, 131, 130, 130, 128, 128, 128, 128, 178, 130, 131, 131, 128, 128, 128, 128, 158, 132, 131, 142, 128, 127, 128, 128, 158, 135, 134, 133, 128, 128, 128, 128, 178, 131, 132, 131, 128, 128, 128, 128, 160, 132, 130, 128, 128, 128, 128, 128, 190, 131, 131, 170, 128, 128, 128, 128, 157, 142, 131, 149, 128, 128, 128, 128, 178, 142, 135, 145, 128, 128, 128, 129, 173, 132, 135, 129, 128, 128, 128, 128, 171, 134, 129, 140, 128, 128, 128, 128, 135, 132, 129}); lstmq.run(); validate(CLAccessor(output_state), expected_output); } TEST_SUITE_END() // MultSmallerEq1 TEST_SUITE(MultGreater1) TEST_CASE(RunSmall, framework::DatasetMode::PRECOMMIT) { //Input sequence length is 1 const int batch_size = 2; const int input_size = 2; const int output_size = 4; QuantizationInfo qasymm(1.f / 128.f, 128); QuantizationInfo qweights(1.f / 16.f, 16); QuantizationInfo qsymm_3(8.f / 32768.f, 0); QuantizationInfo qsymm_4(16.f / 32768.f, 0); TensorShape input_shape{ input_size, batch_size }; TensorShape input_weights_shape{ input_size, output_size }; TensorShape recurrent_weights_shape{ output_size, output_size }; TensorShape output_shape{ output_size, batch_size}; TensorShape bias_shape{ output_size }; auto input_to_input_weights = create_tensor(input_weights_shape, DataType::QASYMM8, 1, qweights); auto input_to_forget_weights = create_tensor(input_weights_shape, DataType::QASYMM8, 1, qweights); auto input_to_cell_weights = create_tensor(input_weights_shape, DataType::QASYMM8, 1, qweights); auto input_to_output_weights = create_tensor(input_weights_shape, DataType::QASYMM8, 1, qweights); auto recurrent_to_input_weights = create_tensor(recurrent_weights_shape, DataType::QASYMM8, 1, qweights); auto recurrent_to_forget_weights = create_tensor(recurrent_weights_shape, DataType::QASYMM8, 1, qweights); auto recurrent_to_cell_weights = create_tensor(recurrent_weights_shape, DataType::QASYMM8, 1, qweights); auto recurrent_to_output_weights = create_tensor(recurrent_weights_shape, DataType::QASYMM8, 1, qweights); auto input_gate_bias = create_tensor(bias_shape, DataType::S32); auto forget_gate_bias = create_tensor(bias_shape, DataType::S32); auto cell_gate_bias = create_tensor(bias_shape, DataType::S32); auto output_gate_bias = create_tensor(bias_shape, DataType::S32); // LSTM input auto input = create_tensor(input_shape, DataType::QASYMM8, 1, qasymm); // LSTM output state auto output_state = create_tensor(output_shape, DataType::QASYMM8, 1, qasymm); // LSTM cell state auto cell_state = create_tensor(output_shape, DataType::QSYMM16, 1, qsymm_4); CLLSTMLayerQuantized lstmq; lstmq.configure(&input, &input_to_input_weights, &input_to_forget_weights, &input_to_cell_weights, &input_to_output_weights, &recurrent_to_input_weights, &recurrent_to_forget_weights, &recurrent_to_cell_weights, &recurrent_to_output_weights, &input_gate_bias, &forget_gate_bias, &cell_gate_bias, &output_gate_bias, &cell_state, &output_state, &cell_state, &output_state); input.allocator()->allocate(); input_to_input_weights.allocator()->allocate(); input_to_forget_weights.allocator()->allocate(); input_to_cell_weights.allocator()->allocate(); input_to_output_weights.allocator()->allocate(); recurrent_to_input_weights.allocator()->allocate(); recurrent_to_forget_weights.allocator()->allocate(); recurrent_to_cell_weights.allocator()->allocate(); recurrent_to_output_weights.allocator()->allocate(); input_gate_bias.allocator()->allocate(); forget_gate_bias.allocator()->allocate(); cell_gate_bias.allocator()->allocate(); output_gate_bias.allocator()->allocate(); cell_state.allocator()->allocate(); output_state.allocator()->allocate(); // Fill weights and biases fill_tensor(input_to_input_weights, std::vector{ 122, 130, 124, 134, 120, 122, 134, 134 }); fill_tensor(input_to_forget_weights, std::vector { 204, 193, 148, 59, 113, 17, 66, 197 }); fill_tensor(input_to_cell_weights, std::vector { 172, 101, 184, 209, 165, 82, 108, 209 }); fill_tensor(input_to_output_weights, std::vector { 203, 244, 219, 114, 130, 16, 163, 222 }); fill_tensor(recurrent_to_input_weights, std::vector { 162, 168, 7, 95, 91, 155, 108, 216, 255, 100, 48, 188, 58, 37, 186, 147 }); fill_tensor(recurrent_to_forget_weights, std::vector { 46, 58, 47, 170, 246, 96, 12, 99, 68, 23, 186, 161, 237, 164, 89, 6 }); fill_tensor(recurrent_to_cell_weights, std::vector { 234, 99, 71, 206, 205, 159, 64, 253, 191, 148, 116, 8, 209, 136, 59, 138 }); fill_tensor(recurrent_to_output_weights, std::vector { 23, 241, 137, 36, 206, 5, 227, 56, 254, 176, 231, 47, 18, 201, 161, 11 }); fill_tensor(input_gate_bias, std::vector {-103038, 30525, 115255, -38154 }); fill_tensor(forget_gate_bias, std::vector { -23428, 126970, 116806, 46307 }); fill_tensor(cell_gate_bias, std::vector { 128006, 69949, -42808, 42568 }); fill_tensor(output_gate_bias, std::vector { -67066, -53607, 47233, 7300 }); SimpleTensor expected_output(output_shape, DataType::QASYMM8, 1, qasymm); // Initialize state fill_tensor(output_state, std::vector { 128, 128, 128, 128, 128, 128, 128, 128 }); fill_tensor(cell_state, std::vector { 0, 0, 0, 0, 0, 0, 0, 0 }); // First input fill_tensor(input, std::vector { 106, 193, 155, 150 }); fill_tensor(expected_output, std::vector { 128, 128, 31, 128, 128, 128, 31, 128 }); lstmq.run(); validate(CLAccessor(output_state), expected_output); // Second input fill_tensor(expected_output, std::vector { 128, 128, 5, 128, 128, 128, 5, 128 }); lstmq.run(); validate(CLAccessor(output_state), expected_output); // Third input fill_tensor(expected_output, std::vector { 128, 128, 1, 128, 128, 128, 1, 128, }); lstmq.run(); validate(CLAccessor(output_state), expected_output); } TEST_SUITE_END() // MultGreater1 TEST_SUITE_END() // IntegrationTestCase // clang-format on // *INDENT-ON* TEST_SUITE_END() // LSTMLayerQuantized TEST_SUITE_END() // CL } // namespace validation } // namespace test } // namespace arm_compute