Michalis Spyrou | ba27e44 | 2019-05-28 10:04:57 +0100 | [diff] [blame] | 1 | /* |
Sheri Zhang | ac6499a | 2021-02-10 15:32:38 +0000 | [diff] [blame] | 2 | * Copyright (c) 2019-2021 Arm Limited. |
Michalis Spyrou | ba27e44 | 2019-05-28 10:04:57 +0100 | [diff] [blame] | 3 | * |
| 4 | * SPDX-License-Identifier: MIT |
| 5 | * |
| 6 | * Permission is hereby granted, free of charge, to any person obtaining a copy |
| 7 | * of this software and associated documentation files (the "Software"), to |
| 8 | * deal in the Software without restriction, including without limitation the |
| 9 | * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or |
| 10 | * sell copies of the Software, and to permit persons to whom the Software is |
| 11 | * furnished to do so, subject to the following conditions: |
| 12 | * |
| 13 | * The above copyright notice and this permission notice shall be included in all |
| 14 | * copies or substantial portions of the Software. |
| 15 | * |
| 16 | * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR |
| 17 | * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, |
| 18 | * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE |
| 19 | * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER |
| 20 | * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, |
| 21 | * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE |
| 22 | * SOFTWARE. |
| 23 | */ |
Michalis Spyrou | ba27e44 | 2019-05-28 10:04:57 +0100 | [diff] [blame] | 24 | #include "arm_compute/runtime/NEON/functions/NELSTMLayerQuantized.h" |
Manuel Bottini | 10c53f1 | 2019-07-17 16:11:53 +0100 | [diff] [blame] | 25 | |
Michalis Spyrou | ba27e44 | 2019-05-28 10:04:57 +0100 | [diff] [blame] | 26 | #include "tests/NEON/Accessor.h" |
| 27 | #include "tests/PaddingCalculator.h" |
| 28 | #include "tests/Utils.h" |
| 29 | #include "tests/datasets/LSTMLayerDataset.h" |
| 30 | #include "tests/framework/Asserts.h" |
| 31 | #include "tests/framework/Macros.h" |
| 32 | #include "tests/framework/datasets/Datasets.h" |
| 33 | #include "tests/validation/Validation.h" |
| 34 | |
| 35 | #include <vector> |
| 36 | |
| 37 | namespace arm_compute |
| 38 | { |
| 39 | namespace test |
| 40 | { |
| 41 | namespace validation |
| 42 | { |
| 43 | namespace |
| 44 | { |
| 45 | template <typename T> |
| 46 | inline void fill_tensor(Tensor &tensor, const std::vector<T> &v) |
| 47 | { |
| 48 | // Import memory accounting for padding |
| 49 | TensorShape t_shape = tensor.info()->tensor_shape(); |
| 50 | Window window; |
| 51 | window.use_tensor_dimensions(t_shape); |
| 52 | Iterator out(&tensor, window); |
| 53 | execute_window_loop(window, [&](const Coordinates & id) |
| 54 | { |
| 55 | *reinterpret_cast<T *>(out.ptr()) = v[coord2index(t_shape, id)]; |
| 56 | }, |
| 57 | out); |
| 58 | } |
| 59 | |
| 60 | template <typename T> |
| 61 | inline void fill_tensor(SimpleTensor<T> &tensor, const std::vector<T> &v) |
| 62 | { |
| 63 | std::memcpy(tensor.data(), v.data(), sizeof(T) * v.size()); |
| 64 | } |
| 65 | |
giuros01 | b5e75db | 2019-07-24 16:29:53 +0100 | [diff] [blame] | 66 | /** Tolerance for quantized asymmetric operations */ |
| 67 | #if defined(__aarch64__) |
| 68 | constexpr AbsoluteTolerance<int16_t> tolerance_qsymm16(0); |
| 69 | #else // defined(__aarch64__) |
| 70 | constexpr AbsoluteTolerance<int16_t> tolerance_qsymm16(1); |
| 71 | #endif // defined(__aarch64__) |
| 72 | |
Michalis Spyrou | ba27e44 | 2019-05-28 10:04:57 +0100 | [diff] [blame] | 73 | } // namespace |
| 74 | |
| 75 | TEST_SUITE(NEON) |
| 76 | TEST_SUITE(LSTMLayerQuantized) |
| 77 | |
| 78 | // *INDENT-OFF* |
| 79 | // clang-format off |
Manuel Bottini | 0726398 | 2019-10-17 18:37:26 +0100 | [diff] [blame] | 80 | TEST_SUITE(IntegrationTestCase) |
| 81 | TEST_SUITE(MultSmallerEq1) |
| 82 | TEST_CASE(RunSmall, framework::DatasetMode::PRECOMMIT) |
Michalis Spyrou | ba27e44 | 2019-05-28 10:04:57 +0100 | [diff] [blame] | 83 | { |
| 84 | const int batch_size = 2; |
| 85 | const int input_size = 2; |
| 86 | const int output_size = 4; |
| 87 | |
| 88 | |
| 89 | QuantizationInfo qasymm(1.f / 128.f, 128); |
| 90 | QuantizationInfo qweights(1.f / 128.f, 128); |
| 91 | QuantizationInfo qsymm_3(8.f / 32768.f, 0); |
| 92 | QuantizationInfo qsymm_4(16.f / 32768.f, 0); |
| 93 | |
| 94 | TensorShape input_shape{ input_size, batch_size }; |
| 95 | TensorShape input_weights_shape{ input_size, output_size }; |
| 96 | TensorShape recurrent_weights_shape{ output_size, output_size }; |
| 97 | TensorShape output_shape{ output_size, batch_size}; |
| 98 | TensorShape bias_shape{ output_size }; |
| 99 | |
| 100 | auto input_to_input_weights = create_tensor<Tensor>(input_weights_shape, DataType::QASYMM8, 1, qweights); |
| 101 | auto input_to_forget_weights = create_tensor<Tensor>(input_weights_shape, DataType::QASYMM8, 1, qweights); |
| 102 | auto input_to_cell_weights = create_tensor<Tensor>(input_weights_shape, DataType::QASYMM8, 1, qweights); |
| 103 | auto input_to_output_weights = create_tensor<Tensor>(input_weights_shape, DataType::QASYMM8, 1, qweights); |
| 104 | auto recurrent_to_input_weights = create_tensor<Tensor>(recurrent_weights_shape, DataType::QASYMM8, 1, qweights); |
| 105 | auto recurrent_to_forget_weights = create_tensor<Tensor>(recurrent_weights_shape, DataType::QASYMM8, 1, qweights); |
| 106 | auto recurrent_to_cell_weights = create_tensor<Tensor>(recurrent_weights_shape, DataType::QASYMM8, 1, qweights); |
| 107 | auto recurrent_to_output_weights = create_tensor<Tensor>(recurrent_weights_shape, DataType::QASYMM8, 1, qweights); |
| 108 | auto input_gate_bias = create_tensor<Tensor>(bias_shape, DataType::S32); |
| 109 | auto forget_gate_bias = create_tensor<Tensor>(bias_shape, DataType::S32); |
| 110 | auto cell_gate_bias = create_tensor<Tensor>(bias_shape, DataType::S32); |
| 111 | auto output_gate_bias = create_tensor<Tensor>(bias_shape, DataType::S32); |
| 112 | |
| 113 | // LSTM input |
| 114 | auto input = create_tensor<Tensor>(input_shape, DataType::QASYMM8, 1, qasymm); |
| 115 | |
| 116 | // LSTM output state |
| 117 | auto output_state = create_tensor<Tensor>(output_shape, DataType::QASYMM8, 1, qasymm); |
| 118 | |
| 119 | // LSTM cell state |
| 120 | auto cell_state = create_tensor<Tensor>(output_shape, DataType::QSYMM16, 1, qsymm_4); |
| 121 | |
| 122 | NELSTMLayerQuantized lstmq; |
| 123 | |
| 124 | lstmq.configure(&input, &input_to_input_weights, &input_to_forget_weights, &input_to_cell_weights, &input_to_output_weights, |
| 125 | &recurrent_to_input_weights, &recurrent_to_forget_weights, &recurrent_to_cell_weights, &recurrent_to_output_weights, |
| 126 | &input_gate_bias, &forget_gate_bias, &cell_gate_bias, &output_gate_bias, &cell_state, &output_state, &cell_state, &output_state); |
| 127 | |
| 128 | input.allocator()->allocate(); |
| 129 | input_to_input_weights.allocator()->allocate(); |
| 130 | input_to_forget_weights.allocator()->allocate(); |
| 131 | input_to_cell_weights.allocator()->allocate(); |
| 132 | input_to_output_weights.allocator()->allocate(); |
| 133 | recurrent_to_input_weights.allocator()->allocate(); |
| 134 | recurrent_to_forget_weights.allocator()->allocate(); |
| 135 | recurrent_to_cell_weights.allocator()->allocate(); |
| 136 | recurrent_to_output_weights.allocator()->allocate(); |
| 137 | input_gate_bias.allocator()->allocate(); |
| 138 | forget_gate_bias.allocator()->allocate(); |
| 139 | cell_gate_bias.allocator()->allocate(); |
| 140 | output_gate_bias.allocator()->allocate(); |
| 141 | cell_state.allocator()->allocate(); |
| 142 | output_state.allocator()->allocate(); |
Michalis Spyrou | ba27e44 | 2019-05-28 10:04:57 +0100 | [diff] [blame] | 143 | |
| 144 | // Fill weights and biases |
| 145 | fill_tensor(input_to_input_weights, std::vector<uint8_t>{ 47, 168, |
| 146 | 66, 239, |
| 147 | 6, 42, |
| 148 | 237, 236 }); |
| 149 | |
| 150 | fill_tensor(input_to_forget_weights, std::vector<uint8_t> { 204, 193, |
| 151 | 148, 59, |
| 152 | 113, 17, |
| 153 | 66, 197 }); |
| 154 | |
| 155 | fill_tensor(input_to_cell_weights, std::vector<uint8_t> { 172, 101, |
| 156 | 184, 209, |
| 157 | 165, 82, |
| 158 | 108, 209 }); |
| 159 | |
| 160 | fill_tensor(input_to_output_weights, std::vector<uint8_t> { 203, 244, |
| 161 | 219, 114, |
| 162 | 130, 16, |
| 163 | 163, 222 }); |
| 164 | |
| 165 | fill_tensor(recurrent_to_input_weights, std::vector<uint8_t> { 162, 168, 7, 95, |
| 166 | 91, 155, 108, 216, |
| 167 | 255, 100, 48, 188, |
| 168 | 58, 37, 186, 147 }); |
| 169 | |
| 170 | fill_tensor(recurrent_to_forget_weights, std::vector<uint8_t> { 46, 58, 47, 170, |
| 171 | 246, 96, 12, 99, |
| 172 | 68, 23, 186, 161, |
| 173 | 237, 164, 89, 6 }); |
| 174 | |
| 175 | fill_tensor(recurrent_to_cell_weights, std::vector<uint8_t> { 234, 99, 71, 206, |
| 176 | 205, 159, 64, 253, |
| 177 | 191, 148, 116, 8, |
| 178 | 209, 136, 59, 138 }); |
| 179 | |
| 180 | fill_tensor(recurrent_to_output_weights, std::vector<uint8_t> { 23, 241, 137, 36, |
| 181 | 206, 5, 227, 56, |
| 182 | 254, 176, 231, 47, |
| 183 | 18, 201, 161, 11 }); |
| 184 | |
| 185 | fill_tensor(input_gate_bias, std::vector<int> {-103038, 30525, 115255, -38154 }); |
| 186 | fill_tensor(forget_gate_bias, std::vector<int> { -23428, 126970, 116806, 46307 }); |
| 187 | fill_tensor(cell_gate_bias, std::vector<int> { 128006, 69949, -42808, 42568 }); |
| 188 | fill_tensor(output_gate_bias, std::vector<int> { -67066, -53607, 47233, 7300 }); |
| 189 | |
| 190 | SimpleTensor<uint8_t> expected_output(output_shape, DataType::QASYMM8, 1, qasymm); |
| 191 | |
| 192 | // Initialize state |
| 193 | fill_tensor(output_state, std::vector<uint8_t> { 128, 128, 128, 128, |
| 194 | 128, 128, 128, 128 }); |
| 195 | fill_tensor(cell_state, std::vector<int16_t> { 0, 0, 0, 0, |
| 196 | 0, 0, 0, 0 }); |
| 197 | |
| 198 | // First input |
| 199 | fill_tensor(input, std::vector<uint8_t> { 106, 193, |
| 200 | 155, 150 }); |
| 201 | |
| 202 | fill_tensor(expected_output, std::vector<uint8_t> { 128, 130, 36, 134, |
| 203 | 128, 131, 35, 133 }); |
| 204 | |
| 205 | lstmq.run(); |
giuros01 | b5e75db | 2019-07-24 16:29:53 +0100 | [diff] [blame] | 206 | validate(Accessor(output_state), expected_output, tolerance_qsymm16); |
Michalis Spyrou | ba27e44 | 2019-05-28 10:04:57 +0100 | [diff] [blame] | 207 | |
| 208 | // Second input |
| 209 | fill_tensor(expected_output, std::vector<uint8_t> { 128, 129, 12, 137, |
| 210 | 128, 131, 10, 136 }); |
| 211 | lstmq.run(); |
giuros01 | b5e75db | 2019-07-24 16:29:53 +0100 | [diff] [blame] | 212 | validate(Accessor(output_state), expected_output, tolerance_qsymm16); |
Michalis Spyrou | ba27e44 | 2019-05-28 10:04:57 +0100 | [diff] [blame] | 213 | |
| 214 | // Third input |
| 215 | fill_tensor(expected_output, std::vector<uint8_t> { 128, 129, 8, 140, |
| 216 | 128, 130, 6, 138 }); |
| 217 | lstmq.run(); |
giuros01 | b5e75db | 2019-07-24 16:29:53 +0100 | [diff] [blame] | 218 | validate(Accessor(output_state), expected_output, tolerance_qsymm16); |
Michalis Spyrou | ba27e44 | 2019-05-28 10:04:57 +0100 | [diff] [blame] | 219 | } |
| 220 | |
Manuel Bottini | 0726398 | 2019-10-17 18:37:26 +0100 | [diff] [blame] | 221 | TEST_CASE(RunLarge, framework::DatasetMode::PRECOMMIT) |
Michalis Spyrou | ba27e44 | 2019-05-28 10:04:57 +0100 | [diff] [blame] | 222 | { |
| 223 | const int batch_size = 16; |
| 224 | const int input_size = 8; |
| 225 | const int output_size = 8; |
| 226 | |
| 227 | |
| 228 | QuantizationInfo qasymm(1.f / 128.f, 128); |
| 229 | QuantizationInfo qweights(1.f / 128.f, 128); |
| 230 | QuantizationInfo qsymm_3(8.f / 32768.f, 0); |
| 231 | QuantizationInfo qsymm_4(16.f / 32768.f, 0); |
| 232 | |
| 233 | TensorShape input_shape{ input_size, batch_size }; |
| 234 | TensorShape input_weights_shape{ input_size, output_size }; |
| 235 | TensorShape recurrent_weights_shape{ output_size, output_size }; |
| 236 | TensorShape output_shape{ output_size, batch_size}; |
| 237 | TensorShape bias_shape{ output_size }; |
| 238 | |
| 239 | auto input_to_input_weights = create_tensor<Tensor>(input_weights_shape, DataType::QASYMM8, 1, qweights); |
| 240 | auto input_to_forget_weights = create_tensor<Tensor>(input_weights_shape, DataType::QASYMM8, 1, qweights); |
| 241 | auto input_to_cell_weights = create_tensor<Tensor>(input_weights_shape, DataType::QASYMM8, 1, qweights); |
| 242 | auto input_to_output_weights = create_tensor<Tensor>(input_weights_shape, DataType::QASYMM8, 1, qweights); |
| 243 | auto recurrent_to_input_weights = create_tensor<Tensor>(recurrent_weights_shape, DataType::QASYMM8, 1, qweights); |
| 244 | auto recurrent_to_forget_weights = create_tensor<Tensor>(recurrent_weights_shape, DataType::QASYMM8, 1, qweights); |
| 245 | auto recurrent_to_cell_weights = create_tensor<Tensor>(recurrent_weights_shape, DataType::QASYMM8, 1, qweights); |
| 246 | auto recurrent_to_output_weights = create_tensor<Tensor>(recurrent_weights_shape, DataType::QASYMM8, 1, qweights); |
| 247 | auto input_gate_bias = create_tensor<Tensor>(bias_shape, DataType::S32); |
| 248 | auto forget_gate_bias = create_tensor<Tensor>(bias_shape, DataType::S32); |
| 249 | auto cell_gate_bias = create_tensor<Tensor>(bias_shape, DataType::S32); |
| 250 | auto output_gate_bias = create_tensor<Tensor>(bias_shape, DataType::S32); |
| 251 | |
| 252 | // LSTM input |
| 253 | auto input = create_tensor<Tensor>(input_shape, DataType::QASYMM8, 1, qasymm); |
| 254 | |
| 255 | // LSTM output state |
| 256 | auto output_state = create_tensor<Tensor>(output_shape, DataType::QASYMM8, 1, qasymm); |
| 257 | |
| 258 | // LSTM cell state |
| 259 | auto cell_state = create_tensor<Tensor>(output_shape, DataType::QSYMM16, 1, qsymm_4); |
| 260 | |
| 261 | NELSTMLayerQuantized lstmq; |
| 262 | |
| 263 | lstmq.configure(&input, &input_to_input_weights, &input_to_forget_weights, &input_to_cell_weights, &input_to_output_weights, |
| 264 | &recurrent_to_input_weights, &recurrent_to_forget_weights, &recurrent_to_cell_weights, &recurrent_to_output_weights, |
| 265 | &input_gate_bias, &forget_gate_bias, &cell_gate_bias, &output_gate_bias, &cell_state, &output_state, &cell_state, &output_state); |
| 266 | |
| 267 | input.allocator()->allocate(); |
| 268 | input_to_input_weights.allocator()->allocate(); |
| 269 | input_to_forget_weights.allocator()->allocate(); |
| 270 | input_to_cell_weights.allocator()->allocate(); |
| 271 | input_to_output_weights.allocator()->allocate(); |
| 272 | recurrent_to_input_weights.allocator()->allocate(); |
| 273 | recurrent_to_forget_weights.allocator()->allocate(); |
| 274 | recurrent_to_cell_weights.allocator()->allocate(); |
| 275 | recurrent_to_output_weights.allocator()->allocate(); |
| 276 | input_gate_bias.allocator()->allocate(); |
| 277 | forget_gate_bias.allocator()->allocate(); |
| 278 | cell_gate_bias.allocator()->allocate(); |
| 279 | output_gate_bias.allocator()->allocate(); |
| 280 | cell_state.allocator()->allocate(); |
| 281 | output_state.allocator()->allocate(); |
| 282 | |
| 283 | // Fill weights and biases |
| 284 | fill_tensor(input_to_input_weights, std::vector<uint8_t>{ 141, 89, 200, 180, 46, 50, 87, 128, |
| 285 | 149, 227, 177, 187, 212, 229, 54, 111, |
| 286 | 131, 116, 3, 58, 196, 26, 131, 255, |
| 287 | 22, 106, 216, 69, 239, 12, 232, 207, |
| 288 | 184, 56, 236, 172, 28, 143, 161, 124, |
| 289 | 255, 33, 197, 122, 47, 197, 26, 229, |
| 290 | 91, 79, 11, 160, 26, 80, 100, 36, |
| 291 | 248, 186, 97, 61, 125, 46, 14, 100, }); |
| 292 | |
| 293 | fill_tensor(input_to_forget_weights, std::vector<uint8_t> { 237, 165, 141, 249, 72, 116, 36 , 115, |
| 294 | 234, 213, 85, 84, 59, 62, 150, 246, |
| 295 | 182, 102, 158, 214, 182, 183, 94, 11, |
| 296 | 158, 192, 92, 189, 160, 219, 206, 249, |
| 297 | 88, 213, 193, 244, 151, 72, 129, 49, |
| 298 | 239, 83, 106, 9, 169, 187, 125, 171, |
| 299 | 32, 141, 126, 92, 13, 36, 224, 150, |
| 300 | 187, 250, 178, 169, 89, 214, 91, 173 }); |
| 301 | |
| 302 | fill_tensor(input_to_cell_weights, std::vector<uint8_t> { 93, 103, 226, 139, 185, 252, 129, 171, |
| 303 | 159, 32, 25, 175, 224, 183, 165, 35, |
| 304 | 207, 69, 238, 228, 149, 214, 79, 6, |
| 305 | 5, 66, 102, 14, 19, 111, 36, 143, |
| 306 | 22, 85, 13, 78, 236, 121, 122, 77, |
| 307 | 249, 39, 88, 12, 205, 143, 93, 240, |
| 308 | 167, 89, 188, 50, 73, 69, 201, 251, |
| 309 | 59, 32, 203, 184, 139, 191, 199, 74}); |
| 310 | |
| 311 | fill_tensor(input_to_output_weights, std::vector<uint8_t> { 205, 7, 95, 104, 252, 143, 226, 73, |
| 312 | 229, 114, 152, 171, 221, 153, 73, 229, |
| 313 | 153, 165, 223, 239, 100, 38, 172, 211, |
| 314 | 226, 133, 239, 207, 116, 230, 170, 100, |
| 315 | 241, 95, 171, 124, 63, 115, 32, 127, |
| 316 | 141, 239, 53, 193, 201, 53, 104, 178, |
| 317 | 186, 212, 167, 107, 226, 230, 71, 213, |
| 318 | 148, 217, 19, 248, 233, 195, 183, 156 }); |
| 319 | |
| 320 | fill_tensor(recurrent_to_input_weights, std::vector<uint8_t> { 147, 112, 140, 103, 3, 255, 17, 49, |
| 321 | 84, 112, 144, 213, 138, 142, 112, 66, |
| 322 | 117, 30, 101, 35, 25, 132, 211, 229, |
| 323 | 183, 208, 102, 16, 38, 85, 101, 152, |
| 324 | 226, 83, 132, 22, 161, 110, 157, 129, |
| 325 | 184, 63, 168, 42, 220, 126, 209, 157, |
| 326 | 5, 88, 243, 83, 249, 19, 226, 209, |
| 327 | 173, 96, 185, 77, 146, 227, 238, 136 }); |
| 328 | |
| 329 | |
| 330 | fill_tensor(recurrent_to_forget_weights, std::vector<uint8_t> { 52, 132, 92, 200, 213, 32, 213, 37, |
| 331 | 116, 142, 116, 180, 4, 172, 158, 143, |
| 332 | 110, 40, 99, 28, 221, 153, 133, 2, |
| 333 | 247, 144, 198, 100, 20, 15, 221, 196, |
| 334 | 159, 178, 188, 151, 171, 15, 25, 217, |
| 335 | 178, 109, 110, 118, 128, 39, 232, 234, |
| 336 | 184, 214, 177, 13, 56, 6, 28, 252, |
| 337 | 89, 187, 242, 59, 146, 111, 132, 129}); |
| 338 | |
| 339 | fill_tensor(recurrent_to_cell_weights, std::vector<uint8_t> { 70, 44, 137, 29, 36, 127, 1, 241, |
| 340 | 26, 241, 142, 114, 67, 181, 49, 57, |
| 341 | 131, 152, 175, 77, 23, 63, 37, 124, |
| 342 | 150, 113, 95, 103, 110, 201, 69, 97, |
| 343 | 196, 242, 62, 214, 66, 19, 45, 135, |
| 344 | 22, 168, 149, 104, 77, 101, 36, 68, |
| 345 | 170, 116, 222, 100, 109, 1, 154, 18, |
| 346 | 133, 215, 105, 93, 31, 57, 231, 112 }); |
| 347 | |
| 348 | |
| 349 | fill_tensor(recurrent_to_output_weights, std::vector<uint8_t> { 45 , 181 , 220 , 219 , 49 , 63 , 49 , 129, |
| 350 | 7 , 166 , 104 , 114 , 83 , 40 , 1 , 195, |
| 351 | 245 , 142 , 82 , 232 , 104 , 245 , 82 , 196, |
| 352 | 111 , 56 , 156 , 9 , 141 , 240 , 180 , 148, |
| 353 | 247 , 198 , 234 , 137 , 13 , 210 , 161 , 192, |
| 354 | 196 , 59 , 233 , 184 , 142 , 187 , 140 , 166, |
| 355 | 2 , 95 , 152 , 46 , 71 , 46 , 113 , 32, |
| 356 | 175 , 229 , 86 , 87 , 62 , 93 , 74 , 130}); |
| 357 | |
| 358 | fill_tensor(input_gate_bias, std::vector<int> { -40040, -106916, -92315, -79123, 45160, -17954, 50962, -63758 }); |
| 359 | fill_tensor(forget_gate_bias, std::vector<int> { -128514, 8463, -57831, 116977, 106547, -28132, -124557, 44941 }); |
| 360 | fill_tensor(cell_gate_bias, std::vector<int> { 88388 , 123601, -116148, -13022, 21619, 48926, 57523, 39332 }); |
| 361 | fill_tensor(output_gate_bias, std::vector<int> { 59485 , -33070, 21386, -100633, -115959, 125768, -56407, 24897 }); |
| 362 | |
| 363 | SimpleTensor<uint8_t> expected_output(output_shape, DataType::QASYMM8, 1, qasymm); |
| 364 | |
| 365 | // Initialize state |
| 366 | fill_tensor(output_state, std::vector<uint8_t> { 128, 128, 128, 128, 128, 128, 128, 128, |
| 367 | 128, 128, 128, 128, 128, 128, 128, 128, |
| 368 | 128, 128, 128, 128, 128, 128, 128, 128, |
| 369 | 128, 128, 128, 128, 128, 128, 128, 128, |
| 370 | 128, 128, 128, 128, 128, 128, 128, 128, |
| 371 | 128, 128, 128, 128, 128, 128, 128, 128, |
| 372 | 128, 128, 128, 128, 128, 128, 128, 128, |
| 373 | 128, 128, 128, 128, 128, 128, 128, 128, |
| 374 | 128, 128, 128, 128, 128, 128, 128, 128, |
| 375 | 128, 128, 128, 128, 128, 128, 128, 128, |
| 376 | 128, 128, 128, 128, 128, 128, 128, 128, |
| 377 | 128, 128, 128, 128, 128, 128, 128, 128, |
| 378 | 128, 128, 128, 128, 128, 128, 128, 128, |
| 379 | 128, 128, 128, 128, 128, 128, 128, 128, |
| 380 | 128, 128, 128, 128, 128, 128, 128, 128, |
| 381 | 128, 128, 128, 128, 128, 128, 128, 128 }); |
| 382 | |
| 383 | fill_tensor(cell_state, std::vector<int16_t> { 0, 0, 0, 0, 0, 0, 0, 0, |
| 384 | 0, 0, 0, 0, 0, 0, 0, 0, |
| 385 | 0, 0, 0, 0, 0, 0, 0, 0, |
| 386 | 0, 0, 0, 0, 0, 0, 0, 0, |
| 387 | 0, 0, 0, 0, 0, 0, 0, 0, |
| 388 | 0, 0, 0, 0, 0, 0, 0, 0, |
| 389 | 0, 0, 0, 0, 0, 0, 0, 0, |
| 390 | 0, 0, 0, 0, 0, 0, 0, 0, |
| 391 | 0, 0, 0, 0, 0, 0, 0, 0, |
| 392 | 0, 0, 0, 0, 0, 0, 0, 0, |
| 393 | 0, 0, 0, 0, 0, 0, 0, 0, |
| 394 | 0, 0, 0, 0, 0, 0, 0, 0, |
| 395 | 0, 0, 0, 0, 0, 0, 0, 0, |
| 396 | 0, 0, 0, 0, 0, 0, 0, 0, |
| 397 | 0, 0, 0, 0, 0, 0, 0, 0, |
| 398 | 0, 0, 0, 0, 0, 0, 0, 0}); |
| 399 | |
| 400 | // First input |
| 401 | fill_tensor(input, std::vector<uint8_t> { 247, 203, 159, 131, 182, 114, 207, 195, |
| 402 | 48 , 61 , 154, 16, 80, 101, 116, 255, |
| 403 | 50 , 115 , 45, 186, 75, 212, 98, 48, |
| 404 | 88 , 146 , 24, 143, 218, 174, 203, 200, |
| 405 | 239 , 16 , 66, 136, 234, 54, 94, 51, |
| 406 | 101 , 128 , 220, 213, 164, 82, 137, 255, |
| 407 | 70 , 165 , 234, 220, 66, 35, 183, 206, |
| 408 | 39 , 57 , 180, 202, 23, 172, 224, 109, |
| 409 | 102 , 215 , 186, 82, 215, 147, 85, 187, |
| 410 | 96 , 249 , 59, 116, 150, 44, 167, 128, |
| 411 | 34 , 217 , 148, 193, 243, 38, 250, 208, |
| 412 | 112 , 130 , 208, 29, 16, 122, 20, 92, |
| 413 | 24 , 72 , 104, 29, 150, 233, 151, 19, |
| 414 | 158 , 192 , 254, 70, 73, 142, 106, 152, |
| 415 | 3 , 61 , 24, 135, 212, 9, 80, 234, |
| 416 | 147 , 246 , 83, 249, 49, 14, 68, 50}); |
| 417 | |
| 418 | fill_tensor(expected_output, std::vector<uint8_t> {131, 128, 128, 128, 128, 180, 129, 133, |
| 419 | 136, 128, 126, 128, 128, 173, 135, 130, |
| 420 | 160, 128, 128, 128, 128, 138, 132, 129, |
| 421 | 131, 128, 127, 128, 128, 169, 129, 131, |
| 422 | 133, 128, 128, 128, 128, 182, 130, 129, |
| 423 | 131, 128, 128, 128, 128, 163, 129, 130, |
| 424 | 131, 128, 128, 128, 128, 149, 132, 129, |
| 425 | 143, 128, 127, 128, 128, 150, 134, 131, |
| 426 | 134, 128, 128, 128, 128, 167, 130, 130, |
| 427 | 131, 128, 128, 128, 128, 152, 132, 129, |
| 428 | 128, 128, 128, 128, 128, 169, 130, 130, |
| 429 | 173, 128, 128, 128, 128, 148, 139, 130, |
| 430 | 152, 128, 128, 128, 128, 168, 139, 132, |
| 431 | 147, 128, 128, 128, 128, 161, 131, 132, |
| 432 | 130, 128, 128, 128, 128, 159, 134, 128, |
| 433 | 140, 128, 128, 128, 128, 133, 132, 128 }); |
| 434 | |
| 435 | lstmq.run(); |
giuros01 | b5e75db | 2019-07-24 16:29:53 +0100 | [diff] [blame] | 436 | validate(Accessor(output_state), expected_output, tolerance_qsymm16); |
Michalis Spyrou | ba27e44 | 2019-05-28 10:04:57 +0100 | [diff] [blame] | 437 | |
| 438 | // Second input |
| 439 | fill_tensor(expected_output, std::vector<uint8_t> { 130, 128, 128, 128, 128, 205, 129, 137, |
| 440 | 135, 128, 127, 128, 128, 190, 137, 132, |
| 441 | 160, 128, 128, 128, 128, 142, 133, 131, |
| 442 | 130, 128, 128, 128, 128, 185, 129, 133, |
| 443 | 132, 128, 128, 128, 128, 198, 131, 130, |
| 444 | 130, 128, 128, 128, 128, 178, 130, 131, |
| 445 | 131, 128, 128, 128, 128, 158, 132, 131, |
| 446 | 142, 128, 127, 128, 128, 158, 135, 134, |
| 447 | 133, 128, 128, 128, 128, 178, 131, 132, |
| 448 | 131, 128, 128, 128, 128, 160, 132, 130, |
| 449 | 128, 128, 128, 128, 128, 190, 131, 131, |
| 450 | 170, 128, 128, 128, 128, 157, 142, 131, |
| 451 | 149, 128, 128, 128, 128, 178, 142, 135, |
| 452 | 145, 128, 128, 128, 129, 173, 132, 135, |
| 453 | 129, 128, 128, 128, 128, 171, 134, 129, |
| 454 | 140, 128, 128, 128, 128, 135, 132, 129}); |
| 455 | lstmq.run(); |
giuros01 | b5e75db | 2019-07-24 16:29:53 +0100 | [diff] [blame] | 456 | validate(Accessor(output_state), expected_output, tolerance_qsymm16); |
Michalis Spyrou | ba27e44 | 2019-05-28 10:04:57 +0100 | [diff] [blame] | 457 | } |
Manuel Bottini | 0726398 | 2019-10-17 18:37:26 +0100 | [diff] [blame] | 458 | TEST_SUITE_END() // MultSmallerEq1 |
| 459 | |
| 460 | TEST_SUITE(MultGreater1) |
| 461 | TEST_CASE(RunSmall, framework::DatasetMode::PRECOMMIT) |
| 462 | { |
| 463 | //Input sequence length is 1 |
| 464 | const int batch_size = 2; |
| 465 | const int input_size = 2; |
| 466 | const int output_size = 4; |
| 467 | |
| 468 | QuantizationInfo qasymm(1.f / 128.f, 128); |
| 469 | QuantizationInfo qweights(1.f / 16.f, 16); |
| 470 | QuantizationInfo qsymm_3(8.f / 32768.f, 0); |
| 471 | QuantizationInfo qsymm_4(16.f / 32768.f, 0); |
| 472 | |
| 473 | TensorShape input_shape{ input_size, batch_size }; |
| 474 | TensorShape input_weights_shape{ input_size, output_size }; |
| 475 | TensorShape recurrent_weights_shape{ output_size, output_size }; |
| 476 | TensorShape output_shape{ output_size, batch_size}; |
| 477 | TensorShape bias_shape{ output_size }; |
| 478 | |
| 479 | auto input_to_input_weights = create_tensor<Tensor>(input_weights_shape, DataType::QASYMM8, 1, qweights); |
| 480 | auto input_to_forget_weights = create_tensor<Tensor>(input_weights_shape, DataType::QASYMM8, 1, qweights); |
| 481 | auto input_to_cell_weights = create_tensor<Tensor>(input_weights_shape, DataType::QASYMM8, 1, qweights); |
| 482 | auto input_to_output_weights = create_tensor<Tensor>(input_weights_shape, DataType::QASYMM8, 1, qweights); |
| 483 | auto recurrent_to_input_weights = create_tensor<Tensor>(recurrent_weights_shape, DataType::QASYMM8, 1, qweights); |
| 484 | auto recurrent_to_forget_weights = create_tensor<Tensor>(recurrent_weights_shape, DataType::QASYMM8, 1, qweights); |
| 485 | auto recurrent_to_cell_weights = create_tensor<Tensor>(recurrent_weights_shape, DataType::QASYMM8, 1, qweights); |
| 486 | auto recurrent_to_output_weights = create_tensor<Tensor>(recurrent_weights_shape, DataType::QASYMM8, 1, qweights); |
| 487 | auto input_gate_bias = create_tensor<Tensor>(bias_shape, DataType::S32); |
| 488 | auto forget_gate_bias = create_tensor<Tensor>(bias_shape, DataType::S32); |
| 489 | auto cell_gate_bias = create_tensor<Tensor>(bias_shape, DataType::S32); |
| 490 | auto output_gate_bias = create_tensor<Tensor>(bias_shape, DataType::S32); |
| 491 | |
| 492 | // LSTM input |
| 493 | auto input = create_tensor<Tensor>(input_shape, DataType::QASYMM8, 1, qasymm); |
| 494 | |
| 495 | // LSTM output state |
| 496 | auto output_state = create_tensor<Tensor>(output_shape, DataType::QASYMM8, 1, qasymm); |
| 497 | |
| 498 | // LSTM cell state |
| 499 | auto cell_state = create_tensor<Tensor>(output_shape, DataType::QSYMM16, 1, qsymm_4); |
| 500 | |
| 501 | NELSTMLayerQuantized lstmq; |
| 502 | |
| 503 | lstmq.configure(&input, &input_to_input_weights, &input_to_forget_weights, &input_to_cell_weights, &input_to_output_weights, |
| 504 | &recurrent_to_input_weights, &recurrent_to_forget_weights, &recurrent_to_cell_weights, &recurrent_to_output_weights, |
| 505 | &input_gate_bias, &forget_gate_bias, &cell_gate_bias, &output_gate_bias, &cell_state, &output_state, &cell_state, &output_state); |
| 506 | |
| 507 | input.allocator()->allocate(); |
| 508 | input_to_input_weights.allocator()->allocate(); |
| 509 | input_to_forget_weights.allocator()->allocate(); |
| 510 | input_to_cell_weights.allocator()->allocate(); |
| 511 | input_to_output_weights.allocator()->allocate(); |
| 512 | recurrent_to_input_weights.allocator()->allocate(); |
| 513 | recurrent_to_forget_weights.allocator()->allocate(); |
| 514 | recurrent_to_cell_weights.allocator()->allocate(); |
| 515 | recurrent_to_output_weights.allocator()->allocate(); |
| 516 | input_gate_bias.allocator()->allocate(); |
| 517 | forget_gate_bias.allocator()->allocate(); |
| 518 | cell_gate_bias.allocator()->allocate(); |
| 519 | output_gate_bias.allocator()->allocate(); |
| 520 | cell_state.allocator()->allocate(); |
| 521 | output_state.allocator()->allocate(); |
| 522 | |
| 523 | // Fill weights and biases |
| 524 | fill_tensor(input_to_input_weights, std::vector<uint8_t>{ 122, 130, |
| 525 | 124, 134, |
| 526 | 120, 122, |
| 527 | 134, 134 }); |
| 528 | |
| 529 | fill_tensor(input_to_forget_weights, std::vector<uint8_t> { 204, 193, |
| 530 | 148, 59, |
| 531 | 113, 17, |
| 532 | 66, 197 }); |
| 533 | |
| 534 | fill_tensor(input_to_cell_weights, std::vector<uint8_t> { 172, 101, |
| 535 | 184, 209, |
| 536 | 165, 82, |
| 537 | 108, 209 }); |
| 538 | |
| 539 | fill_tensor(input_to_output_weights, std::vector<uint8_t> { 203, 244, |
| 540 | 219, 114, |
| 541 | 130, 16, |
| 542 | 163, 222 }); |
| 543 | |
| 544 | fill_tensor(recurrent_to_input_weights, std::vector<uint8_t> { 162, 168, 7, 95, |
| 545 | 91, 155, 108, 216, |
| 546 | 255, 100, 48, 188, |
| 547 | 58, 37, 186, 147 }); |
| 548 | |
| 549 | fill_tensor(recurrent_to_forget_weights, std::vector<uint8_t> { 46, 58, 47, 170, |
| 550 | 246, 96, 12, 99, |
| 551 | 68, 23, 186, 161, |
| 552 | 237, 164, 89, 6 }); |
| 553 | |
| 554 | fill_tensor(recurrent_to_cell_weights, std::vector<uint8_t> { 234, 99, 71, 206, |
| 555 | 205, 159, 64, 253, |
| 556 | 191, 148, 116, 8, |
| 557 | 209, 136, 59, 138 }); |
| 558 | |
| 559 | fill_tensor(recurrent_to_output_weights, std::vector<uint8_t> { 23, 241, 137, 36, |
| 560 | 206, 5, 227, 56, |
| 561 | 254, 176, 231, 47, |
| 562 | 18, 201, 161, 11 }); |
| 563 | |
| 564 | fill_tensor(input_gate_bias, std::vector<int> {-103038, 30525, 115255, -38154 }); |
| 565 | fill_tensor(forget_gate_bias, std::vector<int> { -23428, 126970, 116806, 46307 }); |
| 566 | fill_tensor(cell_gate_bias, std::vector<int> { 128006, 69949, -42808, 42568 }); |
| 567 | fill_tensor(output_gate_bias, std::vector<int> { -67066, -53607, 47233, 7300 }); |
| 568 | |
| 569 | SimpleTensor<uint8_t> expected_output(output_shape, DataType::QASYMM8, 1, qasymm); |
| 570 | |
| 571 | // Initialize state |
| 572 | fill_tensor(output_state, std::vector<uint8_t> { 128, 128, 128, 128, |
| 573 | 128, 128, 128, 128 }); |
| 574 | fill_tensor(cell_state, std::vector<int16_t> { 0, 0, 0, 0, |
| 575 | 0, 0, 0, 0 }); |
| 576 | |
| 577 | // First input |
| 578 | fill_tensor(input, std::vector<uint8_t> { 106, 193, |
| 579 | 155, 150 }); |
| 580 | |
| 581 | fill_tensor(expected_output, std::vector<uint8_t> { 128, 128, 31, 128, |
| 582 | 128, 128, 31, 128 }); |
| 583 | |
| 584 | lstmq.run(); |
| 585 | validate(Accessor(output_state), expected_output); |
| 586 | |
| 587 | // Second input |
| 588 | fill_tensor(expected_output, std::vector<uint8_t> { 128, 128, 5, 128, |
| 589 | 128, 128, 5, 128 }); |
| 590 | lstmq.run(); |
| 591 | validate(Accessor(output_state), expected_output); |
| 592 | |
| 593 | // Third input |
| 594 | fill_tensor(expected_output, std::vector<uint8_t> { 128, 128, 1, 128, |
| 595 | 128, 128, 1, 128, }); |
| 596 | lstmq.run(); |
| 597 | validate(Accessor(output_state), expected_output); |
| 598 | } |
| 599 | TEST_SUITE_END() // MultGreater1 |
| 600 | TEST_SUITE_END() // IntegrationTestCase |
Michalis Spyrou | ba27e44 | 2019-05-28 10:04:57 +0100 | [diff] [blame] | 601 | // clang-format on |
| 602 | // *INDENT-ON* |
| 603 | |
| 604 | TEST_SUITE_END() // LSTMLayerQuantized |
Sheri Zhang | ac6499a | 2021-02-10 15:32:38 +0000 | [diff] [blame] | 605 | TEST_SUITE_END() // Neon |
Michalis Spyrou | ba27e44 | 2019-05-28 10:04:57 +0100 | [diff] [blame] | 606 | } // namespace validation |
| 607 | } // namespace test |
| 608 | } // namespace arm_compute |