# Copyright (c) 2019 PaddlePaddle Authors. 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. """Defination of trainers.""" import sys from os import path __all__ = ['TrainerDesc', 'MultiTrainer', 'DistMultiTrainer', 'PipelineTrainer'] class TrainerDesc(object): ''' Set proto from python to c++. Can be initialized from train_desc. ''' def __init__(self): ''' self.proto_desc = data_feed_pb2.DataFeedDesc() with open(proto_file, 'r') as f: text_format.Parse(f.read(), self.proto_desc) ''' # Workaround for relative import in protobuf under python3 # TODO: should be fixed cur_path = path.dirname(__file__) sys.path.append(cur_path) sys.path.append(cur_path + "/proto") from proto import trainer_desc_pb2 self.proto_desc = trainer_desc_pb2.TrainerDesc() import multiprocessing as mp # set default thread num == cpu count self.proto_desc.thread_num = mp.cpu_count() self._fleet_desc = None self._device_worker = None self._program = None self._infer = False def _set_fetch_var_and_info(self, fetch_vars, fetch_info, print_period): for i, v in enumerate(fetch_vars): self.proto_desc.fetch_config.fetch_var_names.extend([v.name]) self.proto_desc.fetch_config.fetch_var_str_format.extend( [fetch_info[i]]) self.proto_desc.fetch_config.print_period = print_period def _set_debug(self, debug): self.proto_desc.debug = debug def _set_thread(self, thread_num): self.proto_desc.thread_num = thread_num def _set_device_worker(self, device_worker): self._device_worker = device_worker def _set_infer(self, infer): self._infer = infer def _set_fleet_desc(self, fleet_desc): self._fleet_desc = fleet_desc def _gen_trainer_desc(self): pass def _set_program(self, program): self._program = program def _set_use_cvm(self, use_cvm=False): self.proto_desc.use_cvm = use_cvm def _set_no_cvm(self, no_cvm=False): self.proto_desc.no_cvm = no_cvm def _set_scale_datanorm(self, scale_datanorm=-1): self.proto_desc.scale_datanorm = scale_datanorm def _set_dump_slot(self, dump_slot): self.proto_desc.dump_slot = dump_slot def _set_mpi_rank(self, mpi_rank): self.proto_desc.mpi_rank = mpi_rank def _set_mpi_size(self, mpi_size): self.proto_desc.mpi_size = mpi_size def _set_dump_fields(self, dump_fields): for field in dump_fields: self.proto_desc.dump_fields.append(field) def _set_dump_fields_path(self, path): self.proto_desc.dump_fields_path = path def _set_dump_file_num(self, dump_file_num): self.proto_desc.dump_file_num = dump_file_num def _set_dump_converter(self, converter): self.proto_desc.dump_converter = converter def _set_dump_param(self, dump_param): for param in dump_param: self.proto_desc.dump_param.append(param) def _set_thread_barrier(self, thread_barrier): self.proto_desc.thread_barrier = thread_barrier def _set_check_nan_var_names(self, check_nan_var_names): for var in check_nan_var_names: self.proto_desc.check_nan_var_names.append(var) def _set_loss_names(self, loss_names): for loss in loss_names: self.proto_desc.loss_names.append(loss) def _set_adjust_ins_weight(self, config_dict): self.proto_desc.adjust_ins_weight_config.need_adjust = \ config_dict.get("need_adjust", False) self.proto_desc.adjust_ins_weight_config.nid_slot = \ config_dict.get("nid_slot", "") self.proto_desc.adjust_ins_weight_config.nid_adjw_threshold = \ config_dict.get("nid_adjw_threshold", 0.0) self.proto_desc.adjust_ins_weight_config.nid_adjw_ratio = \ config_dict.get("nid_adjw_ratio", 0.0) self.proto_desc.adjust_ins_weight_config.ins_weight_slot = \ config_dict.get("ins_weight_slot", "") def _set_copy_table_config(self, config_dict): config = self.proto_desc.copy_table_config config.need_copy = config_dict.get("need_copy", False) config.batch_num = config_dict.get("batch_num", 100) src_sparse_tables = config_dict.get("src_sparse_tables", []) if not isinstance(src_sparse_tables, list): src_sparse_tables = [src_sparse_tables] dest_sparse_tables = config_dict.get("dest_sparse_tables", []) if not isinstance(dest_sparse_tables, list): dest_sparse_tables = [dest_sparse_tables] if len(src_sparse_tables) != len(dest_sparse_tables): raise ValueError( "len(src_sparse_tables) != len(dest_sparse_tables)," \ " %s vs %s" % (len(src_sparse_tables), \ len(dest_sparse_tables))) for i in src_sparse_tables: config.src_sparse_tables.append(i) for i in dest_sparse_tables: config.dest_sparse_tables.append(i) src_dense_tables = config_dict.get("src_dense_tables", []) if not isinstance(src_dense_tables, list): src_dense_tables = [src_dense_tables] dest_dense_tables = config_dict.get("dest_dense_tables", []) if not isinstance(dest_dense_tables, list): dest_dense_tables = [dest_dense_tables] if len(src_dense_tables) != len(dest_dense_tables): raise ValueError( "len(src_dense_tables) != len(dest_dense_tables)," \ " %s vs %s" % (len(src_dense_tables), \ len(dest_dense_tables))) for i in src_dense_tables: config.src_dense_tables.append(i) for i in dest_dense_tables: config.dest_dense_tables.append(i) # user can also specify dense variables to copy, # instead of copy dense table src_var_list = config_dict.get("src_var_list", []) if not isinstance(src_var_list, list): src_var_list = [src_var_list] dest_var_list = config_dict.get("dest_var_list", []) if not isinstance(dest_var_list, list): dest_var_list = [dest_var_list] if len(src_var_list) != len(dest_var_list): raise ValueError( "len(src_var_list) != len(dest_var_list), %s vs" \ " %s" % (len(src_var_list), len(dest_var_list))) for i in src_var_list: config.src_var_list.append(i) for i in dest_var_list: config.dest_var_list.append(i) dependency_map = config_dict.get("dependency_map", {}) for key in dependency_map: m = config.table_denpendency_map.add() m.key = key values = dependency_map[key] if not isinstance(values, list): values = [values] if len(values) != 1: raise ValueError("dependency len %s != 1" % len(values)) for value in values: m.values.append(value) config.dense_pull_after_copy = \ config_dict.get("dense_pull_after_copy", True) config.enable_dependency = \ config_dict.get("enable_dependency", False) config.sparse_copy_by_feasign = \ config_dict.get("sparse_copy_by_feasign", True) def _desc(self): from google.protobuf import text_format return self.proto_desc.SerializeToString() def __str__(self): from google.protobuf import text_format return text_format.MessageToString(self.proto_desc) class MultiTrainer(TrainerDesc): ''' Implement of MultiTrainer. Can be init from TrainerDesc. ''' def __init__(self): super(MultiTrainer, self).__init__() pass def _set_program(self, program): super(MultiTrainer, self)._set_program(program) self._program = program def _gen_trainer_desc(self): super(MultiTrainer, self)._gen_trainer_desc() self.proto_desc.class_name = "MultiTrainer" self._device_worker._set_infer(self._infer) self._device_worker._gen_worker_desc(self.proto_desc) class DistMultiTrainer(TrainerDesc): """ Implement of DistMultiTrainer. It's for Distributed training. """ def __init__(self): super(DistMultiTrainer, self).__init__() pass def _set_program(self, program): super(DistMultiTrainer, self)._set_program(program) self._program = program def _gen_trainer_desc(self): super(DistMultiTrainer, self)._gen_trainer_desc() self.proto_desc.class_name = "DistMultiTrainer" if self._program == None: raise RuntimeError("None Program") self._device_worker._set_infer(self._infer) self._device_worker._set_program(self._program) self._device_worker._gen_worker_desc(self.proto_desc) class PipelineTrainer(TrainerDesc): """ Implement of PipelineTrainer. It's for Pipeline. """ def __init__(self): super(PipelineTrainer, self).__init__() pass def _set_program(self, program): super(PipelineTrainer, self)._set_program(program) self._program = program def _gen_trainer_desc(self): super(PipelineTrainer, self)._gen_trainer_desc() self.proto_desc.class_name = "PipelineTrainer" if self._program == None: raise RuntimeError("None Program") self._device_worker._set_infer(self._infer) self._device_worker._set_program(self._program) self._device_worker._gen_worker_desc(self.proto_desc)