[onert-micro] Introduce training configure tool (#13593)

This pr supports training configure tool in onert-micro.

ONE-DCO-1.0-Signed-off-by: Artem Balyshev <a.balyshev@samsung.com>

onert-micro/CMakeLists.txt

@@ -174,7 +174,14 @@ endif ()
 if (DIS_DYN_SHAPES)
     message(STATUS "ONERT-MICRO will not use dynamic shapes")
     add_definitions(-DDIS_DYN_SHAPES)
-endif ()
+    list(APPEND CMAKE_ARM_OPTIONS "-DDIS_DYN_SHAPES=ON")
+endif()
+
+if (OM_MEMORY_ESTIMATE)
+    message(STATUS "ONERT-MICRO will use memory estimation")
+    add_definitions(-DOM_MEMORY_ESTIMATE)
+    list(APPEND CMAKE_ARM_OPTIONS "-DOM_MEMORY_ESTIMATE=ON")
+endif()
 
 set(MICRO_ARM_BUILD_DIR "${CMAKE_CURRENT_BINARY_DIR}/standalone_arm")
 file(MAKE_DIRECTORY "${MICRO_ARM_BUILD_DIR}")
@@ -197,6 +204,7 @@ unset(KERNELS CACHE)
 unset(USE_STATIC_KERNEL CACHE)
 unset(DIS_QUANT CACHE)
 unset(DIS_FLOAT CACHE)
+unset(OM_MEMORY_ESTIMATE CACHE)
 unset(ENABLE_ONERT_MICRO_TEST CACHE)
 unset(NOT_BUILD_EXTERNALS CACHE)
 
@@ -213,6 +221,7 @@ add_custom_command(
 add_custom_target(onert_micro_arm DEPENDS "${MICRO_ARM_BINARY}")
 
 add_subdirectory(eval-driver)
+add_subdirectory(training-configure-tool)
 
 # Should be after add_subdirectory
 unset(ENABLE_ONERT_MICRO_TRAINING CACHE)

onert-micro/training-configure-tool/CMakeLists.txt

@@ -0,0 +1,27 @@
+message(STATUS "START Training Config Tool")
+
+add_definitions(-DOM_MEMORY_ESTIMATE)
+
+set(TRAIN_CONFIG_TOOL_SRC
+        TrainingConfigureTool.cpp
+        src/SparseBackpropagationHandler.cpp
+        src/TensorRankSparseBackpropagationHandler.cpp
+        src/TrainingConfigureFileHandler.cpp
+        src/TrainingDriverHandler.cpp
+        src/SparseBackpropagationHelper.cpp)
+
+add_executable(train_config_tool ${TRAIN_CONFIG_TOOL_SRC})
+
+# This variable is needed to separate standalone interpreter libraries from the libraries used in tool
+set(CUSTOM_OM_SUFFIX "_train_config_tool")
+add_subdirectory(${NNAS_PROJECT_SOURCE_DIR}/onert-micro/onert-micro ${CMAKE_CURRENT_BINARY_DIR}/onert-micro)
+
+target_include_directories(train_config_tool PUBLIC "${CMAKE_CURRENT_SOURCE_DIR}/onert_micro/include")
+target_include_directories(train_config_tool PUBLIC "include")
+target_link_libraries(train_config_tool PUBLIC onert_micro_interpreter)
+target_include_directories(train_config_tool PUBLIC "${CMAKE_CURRENT_SOURCE_DIR}/onert_micro/include")
+target_link_libraries(train_config_tool PUBLIC onert_micro_training_interpreter)
+
+install(TARGETS train_config_tool DESTINATION bin)
+
+message(STATUS "DONE Training Config Tool")

onert-micro/training-configure-tool/TrainingConfigureTool.cpp

@@ -0,0 +1,143 @@
+/*
+ * 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.
+ */
+
+#include "include/SparseBackpropagationHandler.h"
+#include "include/TensorRankSparseBackpropagationHandler.h"
+
+#include "TrainingDriverHandler.h"
+
+#include <iostream>
+
+int entry(int argc, char **argv)
+{
+  if (argc != 9 and argc != 10)
+  {
+    std::cerr << "Two variant of usage with and without wof file: " << argv[0]
+              << " <path/to/circle/model> "
+                 " optional(<path/to/wof/file>) <path/to/save/train/config/result> "
+                 "<path/to/input/train_data> "
+                 "<path/to/input/target_train_data> "
+                 "<path/to/input/test_data> "
+                 "<path/to/input/target_test_data>"
+                 "num_of_train_smpl "
+                 "num_of_test_smpl\n";
+    return EXIT_FAILURE;
+  }
+
+  training_configure_tool::TrainData train_data;
+
+  if (argc == 10)
+  {
+    train_data.circle_model_path = argv[1];
+    train_data.wof_file_path = argv[2];
+    train_data.output_tool_file_path = argv[3];
+    train_data.input_input_train_data_path = argv[4];
+    train_data.input_target_train_data_path = argv[5];
+    train_data.input_input_test_data_path = argv[6];
+    train_data.input_target_test_data_path = argv[7];
+    train_data.num_train_data_samples = atoi(argv[8]);
+    train_data.num_test_data_samples = atoi(argv[9]);
+  }
+  else if (argc == 9)
+  {
+    train_data.circle_model_path = argv[1];
+    train_data.output_tool_file_path = argv[2];
+    train_data.input_input_train_data_path = argv[3];
+    train_data.input_target_train_data_path = argv[4];
+    train_data.input_input_test_data_path = argv[5];
+    train_data.input_target_test_data_path = argv[6];
+    train_data.num_train_data_samples = atoi(argv[7]);
+    train_data.num_test_data_samples = atoi(argv[8]);
+  }
+  else
+  {
+    throw std::runtime_error("Unknown commands number\n");
+  }
+
+  // Configure training mode
+  onert_micro::OMConfig config;
+
+  // Set user defined training settings
+  const uint32_t training_epochs = 25;
+  const float lambda = 0.001f;
+  const uint32_t BATCH_SIZE = 64;
+  const uint32_t num_train_layers = 0;
+  const onert_micro::OMLoss loss = onert_micro::CROSS_ENTROPY;
+  const onert_micro::OMTrainOptimizer train_optimizer = onert_micro::ADAM;
+  const float beta = 0.9;
+  const float beta_squares = 0.999;
+  const float epsilon = 1e-07;
+
+  config.train_mode = true;
+  {
+    onert_micro::OMTrainingContext train_context;
+    train_context.batch_size = BATCH_SIZE;
+    train_context.num_of_train_layers = num_train_layers;
+    train_context.learning_rate = lambda;
+    train_context.loss = loss;
+    train_context.optimizer = train_optimizer;
+    train_context.beta = beta;
+    train_context.beta_squares = beta_squares;
+    train_context.epsilon = epsilon;
+    train_context.epochs = training_epochs;
+
+    config.training_context = train_context;
+  }
+
+  train_data.metrics_to_check_best_config = onert_micro::CROSS_ENTROPY_METRICS;
+  train_data.memory_above_restriction = 300000;
+  train_data.acceptable_diff = 0.02;
+  // Find sparse backpropagation best configure
+  std::unordered_set<uint16_t> best_trainable_op_indexes;
+  training_configure_tool::findBestTrainableOpIndexes(config, train_data,
+                                                      best_trainable_op_indexes);
+
+  // Find the best train tensors ranks
+  training_configure_tool::TrainConfigFileData config_result;
+  auto res = training_configure_tool::findBestSparseBackpropagationTensorsRanks(
+    config, train_data, best_trainable_op_indexes, config_result.trainable_op_indexes_with_ranks);
+
+  // Save result into file
+  assert(!config_result.trainable_op_indexes_with_ranks.empty());
+  training_configure_tool::createResultFile(config_result, train_data.output_tool_file_path);
+
+  return EXIT_SUCCESS;
+}
+
+int entry(int argc, char **argv);
+
+#ifdef NDEBUG
+int main(int argc, char **argv)
+{
+  try
+  {
+    return entry(argc, argv);
+  }
+  catch (const std::exception &e)
+  {
+    std::cerr << "ERROR: " << e.what() << std::endl;
+  }
+
+  return 255;
+}
+#else  // NDEBUG
+int main(int argc, char **argv)
+{
+  // NOTE main does not catch internal exceptions for debug build to make it easy to
+  //      check the stacktrace with a debugger
+  return entry(argc, argv);
+}
+#endif // !NDEBUG

onert-micro/training-configure-tool/include/SparseBackpropagationHandler.h

@@ -0,0 +1,39 @@
+/*
+ * 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.
+ */
+
+#ifndef ONERT_MICRO_TRAINING_CONFIG_TOOL_SPARSE_BACKPROPAGATION_HANDLER
+#define ONERT_MICRO_TRAINING_CONFIG_TOOL_SPARSE_BACKPROPAGATION_HANDLER
+
+#include "OMStatus.h"
+#include "OMConfig.h"
+#include "TrainConfigData.h"
+#include "TrainingConfigureFileHandler.h"
+
+#include <vector>
+
+namespace training_configure_tool
+{
+
+/*
+ * Method to find the most trainable (which gets the best metric result) operators indexes.
+ */
+onert_micro::OMStatus
+findBestTrainableOpIndexes(onert_micro::OMConfig &config, TrainData &train_data,
+                           std::unordered_set<uint16_t> &best_trainable_op_indexes);
+
+} // namespace training_configure_tool
+
+#endif // ONERT_MICRO_TRAINING_CONFIG_TOOL_SPARSE_BACKPROPAGATION_HANDLER

onert-micro/training-configure-tool/include/SparseBackpropagationHelper.h

@@ -0,0 +1,61 @@
+/*
+ * 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.
+ */
+
+#ifndef ONERT_MICRO_TRAINING_CONFIG_TOOL_SPARSE_BACKPROPAGATION_HELPER
+#define ONERT_MICRO_TRAINING_CONFIG_TOOL_SPARSE_BACKPROPAGATION_HELPER
+
+#include "OMStatus.h"
+#include "OMConfig.h"
+#include "TrainConfigData.h"
+
+#include <vector>
+#include <unordered_set>
+
+namespace training_configure_tool
+{
+
+// Find is left train result is better then right in terms of metric result and memory consumptions.
+// acceptable_diff - acceptable difference in metric values in order to select the best result in
+// memory.
+bool cmpTrainResults(const training_configure_tool::TrainResult &left,
+                     const training_configure_tool::TrainResult &right,
+                     const float acceptable_diff);
+
+// To find all trainable ops indexes in the model - initial_train_op_indexes
+std::unordered_set<uint16_t> findAllTrainableOps(const char *circle_model_path);
+
+// To generate all possible sets from initial_train_op_indexes
+std::vector<std::unordered_set<uint16_t>>
+generateAllPossibleOpIndexesSets(const std::unordered_set<uint16_t> &initial_train_op_indexes);
+
+// Remove operations indexes sets with peak memory footprint greater then given restriction:
+//    1 - Run train interpreter with all this sets with single train sample and single test sample
+//    to obtain approximately peak memory footprint for each set.
+//    2 - Cut according to max peak memory.
+std::vector<std::unordered_set<uint16_t>> selectOpIndexesSetsAccordingToMemoryRestriction(
+  const std::vector<std::unordered_set<uint16_t>> &op_indexes_sets, onert_micro::OMConfig config,
+  training_configure_tool::TrainData train_data);
+
+// Find All combinations with ranks for current selected op indexes.
+// Return vector of all possible combinations of train rank for every op.
+std::vector<std::unordered_map<uint16_t, OpTrainableRank>>
+findAllTensorsRanksCombinations(const std::unordered_set<uint16_t> &selected_op_indexes,
+                                onert_micro::OMConfig config,
+                                training_configure_tool::TrainData train_data);
+
+} // namespace training_configure_tool
+
+#endif // ONERT_MICRO_TRAINING_CONFIG_TOOL_SPARSE_BACKPROPAGATION_HELPER

onert-micro/training-configure-tool/include/TensorRankSparseBackpropagationHandler.h

@@ -0,0 +1,44 @@
+/*
+ * 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.
+ */
+
+#ifndef ONERT_MICRO_TRAINING_CONFIG_TOOL_TENSOR_RANK_SPARSE_BACKPROPAGATION_HANDLER
+#define ONERT_MICRO_TRAINING_CONFIG_TOOL_TENSOR_RANK_SPARSE_BACKPROPAGATION_HANDLER
+
+#include "OMStatus.h"
+#include "OMConfig.h"
+#include "TrainConfigData.h"
+#include "TrainingConfigureFileHandler.h"
+
+#include <vector>
+#include <unordered_map>
+
+namespace training_configure_tool
+{
+
+/*
+ * Method to find the most trainable (which gets the best metric result and less peak memory) train
+ * ranks for every operation in selected operators indexes. Note: Train rank - this is an indicator
+ * of how much data of the current operation we will train (for example, the entire operation, only
+ * the bias, only the upper half, and so on)
+ */
+onert_micro::OMStatus findBestSparseBackpropagationTensorsRanks(
+  onert_micro::OMConfig &config, TrainData &train_data,
+  const std::unordered_set<uint16_t> &selected_op_indexes,
+  std::unordered_map<uint16_t, OpTrainableRank> &best_train_ranks);
+
+} // namespace training_configure_tool
+
+#endif // ONERT_MICRO_TRAINING_CONFIG_TOOL_TENSOR_RANK_SPARSE_BACKPROPAGATION_HANDLER