# Copyright (c) 2020 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. """ Training use fluid with one node only. """ from __future__ import print_function import time import logging import os import paddle.fluid as fluid from paddlerec.core.trainers.transpiler_trainer import TranspileTrainer from paddlerec.core.utils import envs from paddlerec.core.reader import SlotReader from paddlerec.core.utils import dataloader_instance logging.basicConfig(format="%(asctime)s - %(levelname)s - %(message)s") logger = logging.getLogger("fluid") logger.setLevel(logging.INFO) class SingleInfer(TranspileTrainer): def __init__(self, config=None): super(TranspileTrainer, self).__init__(config) self._env = self._config device = envs.get_global_env("device") if device == 'gpu': self._place = fluid.CUDAPlace(0) elif device == 'cpu': self._place = fluid.CPUPlace() self._exe = fluid.Executor(self._place) self.processor_register() self._model = {} self._dataset = {} envs.set_global_envs(self._config) envs.update_workspace() self._runner_name = envs.get_global_env("mode") def processor_register(self): self.regist_context_processor('uninit', self.instance) self.regist_context_processor('init_pass', self.init) self.regist_context_processor('startup_pass', self.startup) self.regist_context_processor('train_pass', self.executor_train) self.regist_context_processor('terminal_pass', self.terminal) def instance(self, context): context['status'] = 'init_pass' def _get_dataset(self, dataset_name): name = "dataset." + dataset_name + "." sparse_slots = envs.get_global_env(name + "sparse_slots") dense_slots = envs.get_global_env(name + "dense_slots") thread_num = envs.get_global_env(name + "thread_num") batch_size = envs.get_global_env(name + "batch_size") reader_class = envs.get_global_env(name + "data_converter") abs_dir = os.path.dirname(os.path.abspath(__file__)) reader = os.path.join(abs_dir, '../utils', 'dataset_instance.py') if sparse_slots is None and dense_slots is None: pipe_cmd = "python {} {} {} {}".format(reader, reader_class, "TRAIN", self._config_yaml) else: if sparse_slots is None: sparse_slots = "#" if dense_slots is None: dense_slots = "#" padding = envs.get_global_env(name + "padding", 0) pipe_cmd = "python {} {} {} {} {} {} {} {}".format( reader, "slot", "slot", self._config_yaml, "fake", \ sparse_slots.replace(" ", "#"), dense_slots.replace(" ", "#"), str(padding)) dataset = fluid.DatasetFactory().create_dataset() dataset.set_batch_size(envs.get_global_env(name + "batch_size")) dataset.set_pipe_command(pipe_cmd) train_data_path = envs.get_global_env(name + "data_path") file_list = [ os.path.join(train_data_path, x) for x in os.listdir(train_data_path) ] dataset.set_filelist(file_list) for model_dict in self._env["phase"]: if model_dict["dataset_name"] == dataset_name: model = self._model[model_dict["name"]][3] inputs = model._infer_data_var dataset.set_use_var(inputs) break return dataset def _get_dataloader(self, dataset_name, dataloader): name = "dataset." + dataset_name + "." sparse_slots = envs.get_global_env(name + "sparse_slots") dense_slots = envs.get_global_env(name + "dense_slots") thread_num = envs.get_global_env(name + "thread_num") batch_size = envs.get_global_env(name + "batch_size") reader_class = envs.get_global_env(name + "data_converter") abs_dir = os.path.dirname(os.path.abspath(__file__)) if sparse_slots is None and dense_slots is None: reader = dataloader_instance.dataloader_by_name( reader_class, dataset_name, self._config_yaml) reader_class = envs.lazy_instance_by_fliename(reader_class, "TrainReader") reader_ins = reader_class(self._config_yaml) else: reader = dataloader_instance.slotdataloader_by_name( "", dataset_name, self._config_yaml) reader_ins = SlotReader(self._config_yaml) if hasattr(reader_ins, 'generate_batch_from_trainfiles'): dataloader.set_sample_list_generator(reader) else: dataloader.set_sample_generator(reader, batch_size) return dataloader def _create_dataset(self, dataset_name): name = "dataset." + dataset_name + "." sparse_slots = envs.get_global_env(name + "sparse_slots") dense_slots = envs.get_global_env(name + "dense_slots") thread_num = envs.get_global_env(name + "thread_num") batch_size = envs.get_global_env(name + "batch_size") type_name = envs.get_global_env(name + "type") if envs.get_platform() != "LINUX": print("platform ", envs.get_platform(), " change reader to DataLoader") type_name = "DataLoader" padding = 0 if type_name == "DataLoader": return None else: return self._get_dataset(dataset_name) def init(self, context): for model_dict in self._env["phase"]: self._model[model_dict["name"]] = [None] * 5 train_program = fluid.Program() startup_program = fluid.Program() scope = fluid.Scope() dataset_name = model_dict["dataset_name"] opt_name = envs.get_global_env("hyper_parameters.optimizer.class") opt_lr = envs.get_global_env( "hyper_parameters.optimizer.learning_rate") opt_strategy = envs.get_global_env( "hyper_parameters.optimizer.strategy") with fluid.program_guard(train_program, startup_program): with fluid.unique_name.guard(): with fluid.scope_guard(scope): model_path = model_dict["model"].replace( "{workspace}", envs.path_adapter(self._env["workspace"])) model = envs.lazy_instance_by_fliename( model_path, "Model")(self._env) model._infer_data_var = model.input_data( dataset_name=model_dict["dataset_name"]) if envs.get_global_env("dataset." + dataset_name + ".type") == "DataLoader": model._init_dataloader(is_infer=True) self._get_dataloader(dataset_name, model._data_loader) model.net(model._infer_data_var, True) self._model[model_dict["name"]][0] = train_program self._model[model_dict["name"]][1] = startup_program self._model[model_dict["name"]][2] = scope self._model[model_dict["name"]][3] = model self._model[model_dict["name"]][4] = train_program.clone() for dataset in self._env["dataset"]: if dataset["type"] != "DataLoader": self._dataset[dataset["name"]] = self._create_dataset(dataset[ "name"]) context['status'] = 'startup_pass' def startup(self, context): for model_dict in self._env["phase"]: with fluid.scope_guard(self._model[model_dict["name"]][2]): self._exe.run(self._model[model_dict["name"]][1]) context['status'] = 'train_pass' def executor_train(self, context): epochs = int(self._env["epochs"]) for j in range(epochs): for model_dict in self._env["phase"]: if j == 0: with fluid.scope_guard(self._model[model_dict["name"]][2]): train_prog = self._model[model_dict["name"]][0] startup_prog = self._model[model_dict["name"]][1] with fluid.program_guard(train_prog, startup_prog): self.load() reader_name = model_dict["dataset_name"] name = "dataset." + reader_name + "." begin_time = time.time() if envs.get_global_env(name + "type") == "DataLoader": self._executor_dataloader_train(model_dict) else: self._executor_dataset_train(model_dict) with fluid.scope_guard(self._model[model_dict["name"]][2]): train_prog = self._model[model_dict["name"]][4] startup_prog = self._model[model_dict["name"]][1] with fluid.program_guard(train_prog, startup_prog): self.save(j) end_time = time.time() seconds = end_time - begin_time print("epoch {} done, time elasped: {}".format(j, seconds)) context['status'] = "terminal_pass" def _executor_dataset_train(self, model_dict): reader_name = model_dict["dataset_name"] model_name = model_dict["name"] model_class = self._model[model_name][3] fetch_vars = [] fetch_alias = [] fetch_period = 20 metrics = model_class.get_infer_results() if metrics: fetch_vars = metrics.values() fetch_alias = metrics.keys() scope = self._model[model_name][2] program = self._model[model_name][0] reader = self._dataset[reader_name] with fluid.scope_guard(scope): self._exe.infer_from_dataset( program=program, dataset=reader, fetch_list=fetch_vars, fetch_info=fetch_alias, print_period=fetch_period) def _executor_dataloader_train(self, model_dict): reader_name = model_dict["dataset_name"] model_name = model_dict["name"] model_class = self._model[model_name][3] program = self._model[model_name][0].clone() fetch_vars = [] fetch_alias = [] fetch_period = 20 metrics = model_class.get_infer_results() if metrics: fetch_vars = metrics.values() fetch_alias = metrics.keys() metrics_varnames = [] metrics_format = [] fetch_period = 20 metrics_format.append("{}: {{}}".format("batch")) for name, var in metrics.items(): metrics_varnames.append(var.name) metrics_format.append("{}: {{}}".format(name)) metrics_format = ", ".join(metrics_format) reader = self._model[model_name][3]._data_loader reader.start() batch_id = 0 scope = self._model[model_name][2] with fluid.scope_guard(scope): try: while True: metrics_rets = self._exe.run(program=program, fetch_list=metrics_varnames) metrics = [batch_id] metrics.extend(metrics_rets) if batch_id % fetch_period == 0 and batch_id != 0: print(metrics_format.format(*metrics)) batch_id += 1 except fluid.core.EOFException: reader.reset() def terminal(self, context): context['is_exit'] = True def load(self, is_fleet=False): name = "runner." + self._runner_name + "." dirname = envs.get_global_env("epoch.init_model_path", None) if dirname is None: return print("single_infer going to load ", dirname) if is_fleet: fleet.load_persistables(self._exe, dirname) else: fluid.io.load_persistables(self._exe, dirname) def save(self, epoch_id, is_fleet=False): def need_save(epoch_id, epoch_interval, is_last=False): if is_last: return True if epoch_id == -1: return False return epoch_id % epoch_interval == 0 def save_inference_model(): name = "runner." + self._runner_name + "." save_interval = int( envs.get_global_env(name + "save_inference_interval", -1)) if not need_save(epoch_id, save_interval, False): return feed_varnames = envs.get_global_env( name + "save_inference_feed_varnames", None) fetch_varnames = envs.get_global_env( name + "save_inference_fetch_varnames", None) if feed_varnames is None or fetch_varnames is None or feed_varnames == "": return fetch_vars = [ fluid.default_main_program().global_block().vars[varname] for varname in fetch_varnames ] dirname = envs.get_global_env(name + "save_inference_path", None) assert dirname is not None dirname = os.path.join(dirname, str(epoch_id)) if is_fleet: fleet.save_inference_model(self._exe, dirname, feed_varnames, fetch_vars) else: fluid.io.save_inference_model(dirname, feed_varnames, fetch_vars, self._exe) def save_persistables(): name = "runner." + self._runner_name + "." save_interval = int( envs.get_global_env(name + "save_checkpoint_interval", -1)) if not need_save(epoch_id, save_interval, False): return dirname = envs.get_global_env(name + "save_checkpoint_path", None) if dirname is None or dirname == "": return dirname = os.path.join(dirname, str(epoch_id)) if is_fleet: fleet.save_persistables(self._exe, dirname) else: fluid.io.save_persistables(self._exe, dirname) save_persistables() save_inference_model()