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Artem Balyshev
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| /* | ||
| * Copyright (c) 2023 Samsung Electronics Co., Ltd. All Rights Reserved | ||
| * | ||
| * 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. | ||
| */ | ||
|
|
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| #ifndef LUCI_INTERPRETER_PAL_GRU_H | ||
| #define LUCI_INTERPRETER_PAL_GRU_H | ||
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| #include "PALUtils.h" | ||
| #include "ProcessBroadcastShapes.h" | ||
| #include "PALFullyConnected.h" | ||
| #include "PALLogistic.h" | ||
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| namespace luci_interpreter_pal | ||
| { | ||
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| namespace | ||
| { | ||
| void calculateGRU(const float *input_data, const float *weight_input_data, | ||
| const float *weight_hidden_data, const float *bias_input_data, | ||
| const float *bias_hidden_data, float *output_data, | ||
| const int32_t *input_shape, const int32_t *output_shape, const int32_t *weight_input_shape, | ||
| const int32_t *weight_hidden_shape, | ||
| float *output_input_data, | ||
| float *output_hidden_data, const int32_t *output_shape_fc) | ||
| { | ||
| // Calculate FC for hidden (output) | ||
| FullyConnectedParams op_params{}; | ||
| float activation_min{}; | ||
| float activation_max{}; | ||
| luci_interpreter::kernels::calculateActivationRange(luci_interpreter::FusedActFunc::NONE, | ||
| &activation_min, &activation_max); | ||
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| luci_interpreter_pal::FullyConnectedParams params{}; | ||
| op_params.float_activation_min = activation_min; | ||
| op_params.float_activation_max = activation_max; | ||
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| FullyConnected(op_params, output_shape, output_data, weight_hidden_shape, weight_hidden_data, | ||
| bias_hidden_data, output_shape_fc, output_hidden_data); | ||
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| // Calcuate FC for input | ||
| FullyConnected(op_params, input_shape, input_data, weight_input_shape, weight_input_data, | ||
| bias_input_data, output_shape_fc, output_input_data); | ||
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| int num_elements = output_shape_fc[1] / 3; | ||
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| float *second_hidden_part = output_hidden_data + num_elements; | ||
| float *second_input_part = output_input_data + num_elements; | ||
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| float *third_hidden_part = second_hidden_part + num_elements; | ||
| float *third_input_part = second_input_part + num_elements; | ||
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| // Calculate Left part | ||
| for (int i = 0; i < num_elements; ++i) | ||
| { | ||
| output_hidden_data[i] += output_input_data[i]; | ||
| } | ||
| Logistic(num_elements, output_hidden_data, output_hidden_data); | ||
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| // Calculate most left add | ||
| float *most_left_part_final = output_hidden_data; | ||
| float *first_part = output_hidden_data; | ||
| for (int i = 0; i < num_elements; ++i) | ||
| { | ||
| output_data[i] *= most_left_part_final[i]; | ||
| first_part[i] = 1.0f - first_part[i]; | ||
| } | ||
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| // Clalc third part | ||
| float *third_part = third_hidden_part; | ||
| for (int i = 0; i < num_elements; ++i) | ||
| { | ||
| third_part[i] += third_input_part[i]; | ||
| } | ||
| Logistic(num_elements, third_part, third_part); | ||
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| for (int i = 0; i < num_elements; ++i) | ||
| { | ||
| third_part[i] *= second_hidden_part[i]; | ||
| third_part[i] += second_input_part[i]; | ||
| third_part[i] = std::tanh(third_part[i]); | ||
| third_part[i] *= first_part[i]; | ||
| output_data[i] += third_part[i]; | ||
| } | ||
| } | ||
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| } // namespace | ||
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| void GRU(float *input_data, const float *weight_input_data, | ||
| const float *weight_hidden_data, const float *bias_input_data, | ||
| const float *bias_hidden_data, const float *hidden_state_data, float *output_data, | ||
| const int32_t *input_shape, const int32_t *output_shape, const int32_t *weight_input_shape, | ||
| const int32_t *weight_hidden_shape) | ||
| { | ||
| const int32_t time = input_shape[0]; | ||
| input_shape += 1; | ||
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| auto output_input_data = std::make_unique<float []>(weight_hidden_shape[0]); | ||
| auto output_hidden_data = std::make_unique<float []>(weight_hidden_shape[0]); | ||
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| int32_t output_shape_fc[] = {1, 96}; | ||
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| std::memcpy(output_data, hidden_state_data, output_shape[1]); | ||
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| for (int i = 0; i < time; ++i) | ||
| { | ||
| // input_shape should be (1, 6) | ||
| calculateGRU(input_data, weight_input_data, weight_hidden_data, | ||
| bias_input_data, bias_hidden_data, output_data, input_shape, | ||
| output_shape, weight_input_shape, weight_hidden_shape, output_input_data.get(), | ||
| output_hidden_data.get(), output_shape_fc); | ||
| auto tmp = input_shape[1]; | ||
| input_data += input_shape[1]; | ||
| } | ||
| } | ||
|
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| } // namespace luci_interpreter_pal | ||
|
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| #endif // LUCI_INTERPRETER_PAL_GRU_H |
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