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[Draft][onert-micro] Introduce GRU training
This draft introduces GRU training for onert-micro. ONE-DCO-1.0-Signed-off-by: Artem Balyshev <[email protected]
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onert-micro/onert-micro/include/pal/common/PALGRUCommon.h
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| /* | ||
| * Copyright (c) 2024 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 ONERT_MICRO_EXECUTE_PAL_GRU_COMMON_H | ||
| #define ONERT_MICRO_EXECUTE_PAL_GRU_COMMON_H | ||
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| #include "OMStatus.h" | ||
| #include "core/OMRuntimeShape.h" | ||
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| #include "PALUtils.h" | ||
| #include "ProcessBroadcastShapes.h" | ||
| #include "PALFullyConnected.h" | ||
| #include "PALLogistic.h" | ||
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| namespace onert_micro | ||
| { | ||
| namespace execute | ||
| { | ||
| namespace pal | ||
| { | ||
| 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 core::OMRuntimeShape &input_shape, const core::OMRuntimeShape &output_shape, | ||
| const core::OMRuntimeShape &weight_input_shape, | ||
| const core::OMRuntimeShape &weight_hidden_shape, float *output_input_data, | ||
| float *output_hidden_data, const core::OMRuntimeShape &output_shape_fc, | ||
| float *intermediate_buffer) | ||
| { | ||
| core::FullyConnectedParams op_params{}; | ||
| // As FC nodes doesn't have any activations inside GRU, let' use just numeric limits | ||
| op_params.float_activation_min = std::numeric_limits<float>::lowest(); | ||
| op_params.float_activation_max = std::numeric_limits<float>::max(); | ||
| // If intermediate_buffer != nullptr - then it is train mode and we need save intermediate inform | ||
| bool is_train_mode = intermediate_buffer != nullptr; | ||
| if (is_train_mode) | ||
| { | ||
| // Copy input for FC Input to calculate weights gradients | ||
| std::memcpy(intermediate_buffer, output_data, output_shape.flatSize() * sizeof(float)); | ||
| // Move intermediate_buffer pointer | ||
| intermediate_buffer += output_shape.flatSize(); | ||
| } | ||
| // FC Input | ||
| FullyConnected(op_params, output_data, weight_input_shape, weight_input_data, bias_input_data, | ||
| output_shape_fc, output_input_data); | ||
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| // FC Hidden | ||
| // Note: input for this FC node will be saved without intermediate buffer | ||
| FullyConnected(op_params, input_data, weight_hidden_shape, weight_hidden_data, bias_hidden_data, | ||
| output_shape_fc, output_hidden_data); | ||
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| int num_elements = output_shape_fc.dims(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_input_data[i] += output_hidden_data[i]; | ||
| } | ||
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| // If train mode - save logistic input | ||
| if (is_train_mode) | ||
| { | ||
| std::memcpy(intermediate_buffer, output_input_data, output_shape.flatSize() * sizeof(float)); | ||
| // Move intermediate_buffer pointer | ||
| intermediate_buffer += output_shape.flatSize(); | ||
| } | ||
| Logistic(num_elements, output_input_data, output_input_data); | ||
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| // If train mode - save most left mul input (right input) | ||
| if (is_train_mode) | ||
| { | ||
| std::memcpy(intermediate_buffer, output_input_data, output_shape.flatSize() * sizeof(float)); | ||
| // Move intermediate_buffer pointer | ||
| intermediate_buffer += output_shape.flatSize(); | ||
| } | ||
| // Calculate most left mul | ||
| float *most_left_part_final = output_input_data; | ||
| float *first_part = output_input_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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| // Calc second part | ||
| for (int i = 0; i < num_elements; ++i) | ||
| { | ||
| second_hidden_part[i] += second_input_part[i]; | ||
| } | ||
| // If train mode - save logistic input | ||
| if (is_train_mode) | ||
| { | ||
| std::memcpy(intermediate_buffer, second_hidden_part, output_shape.flatSize() * sizeof(float)); | ||
| // Move intermediate_buffer pointer | ||
| intermediate_buffer += output_shape.flatSize(); | ||
| } | ||
| Logistic(num_elements, second_hidden_part, second_hidden_part); | ||
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| // If train mode - save mul input (left and right) | ||
| if (is_train_mode) | ||
| { | ||
| // Left input | ||
| std::memcpy(intermediate_buffer, second_hidden_part, output_shape.flatSize() * sizeof(float)); | ||
| // Move intermediate_buffer pointer | ||
| intermediate_buffer += output_shape.flatSize(); | ||
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| // Right input | ||
| std::memcpy(intermediate_buffer, third_input_part, output_shape.flatSize() * sizeof(float)); | ||
| // Move intermediate_buffer pointer | ||
| intermediate_buffer += output_shape.flatSize(); | ||
| } | ||
| for (int i = 0; i < num_elements; ++i) | ||
| { | ||
| second_hidden_part[i] *= third_input_part[i]; | ||
| second_hidden_part[i] += third_hidden_part[i]; | ||
| } | ||
| // If train mode - save tanh input | ||
| if (is_train_mode) | ||
| { | ||
| std::memcpy(intermediate_buffer, second_hidden_part, output_shape.flatSize() * sizeof(float)); | ||
| // Move intermediate_buffer pointer | ||
| intermediate_buffer += output_shape.flatSize(); | ||
| } | ||
| for (int i = 0; i < num_elements; ++i) | ||
| { | ||
| second_hidden_part[i] = std::tanh(second_hidden_part[i]); | ||
| } | ||
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| // If train mode - save mul input (left and right) | ||
| if (is_train_mode) | ||
| { | ||
| // Left input | ||
| std::memcpy(intermediate_buffer, first_part, output_shape.flatSize() * sizeof(float)); | ||
| // Move intermediate_buffer pointer | ||
| intermediate_buffer += output_shape.flatSize(); | ||
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| // Right input | ||
| std::memcpy(intermediate_buffer, second_hidden_part, output_shape.flatSize() * sizeof(float)); | ||
| // Move intermediate_buffer pointer | ||
| intermediate_buffer += output_shape.flatSize(); | ||
| } | ||
| for (int i = 0; i < num_elements; ++i) | ||
| { | ||
| second_hidden_part[i] *= first_part[i]; | ||
| output_data[i] += second_hidden_part[i]; | ||
| } | ||
| } | ||
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| } // namespace | ||
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| OMStatus GRU(const 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, | ||
| float *output_input_data, float *output_hidden_data, | ||
| const core::OMRuntimeShape &input_shape, const core::OMRuntimeShape &output_shape, | ||
| const core::OMRuntimeShape &weight_input_shape, | ||
| const core::OMRuntimeShape &weight_hidden_shape, const size_t intermediate_buffer_size, | ||
| float *intermediate_buffer) | ||
| { | ||
| const int32_t time = input_shape.dims(0); | ||
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| core::OMRuntimeShape output_shape_fc(2); | ||
| output_shape_fc.setDim(0, 1); | ||
| output_shape_fc.setDim(1, weight_hidden_shape.dims(0)); | ||
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| std::memcpy(output_data, hidden_state_data, output_shape.flatSize() * sizeof(float)); | ||
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| for (int i = 0; i < time; ++i) | ||
| { | ||
| 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, output_hidden_data, output_shape_fc, | ||
| intermediate_buffer); | ||
| input_data += input_shape.dims(2); | ||
| if (intermediate_buffer_size != 0) | ||
| { | ||
| assert(intermediate_buffer != nullptr); | ||
| intermediate_buffer += intermediate_buffer_size; | ||
| } | ||
| } | ||
| return Ok; | ||
| } | ||
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| } // namespace pal | ||
| } // namespace execute | ||
| } // namespace onert_micro | ||
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| #endif // ONERT_MICRO_EXECUTE_PAL_GRU_COMMON_H |
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