# Copyright (c) 2021 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. import sys import time from collections import OrderedDict from contextlib import contextmanager from pathlib import Path import paddle from paddle import distributed as dist from visualdl import LogWriter from paddlespeech.s2t.training.reporter import ObsScope from paddlespeech.s2t.training.reporter import report from paddlespeech.s2t.training.timer import Timer from paddlespeech.s2t.utils import mp_tools from paddlespeech.s2t.utils import profiler from paddlespeech.s2t.utils.checkpoint import Checkpoint from paddlespeech.s2t.utils.log import Log from paddlespeech.s2t.utils.utility import all_version from paddlespeech.s2t.utils.utility import seed_all from paddlespeech.s2t.utils.utility import UpdateConfig __all__ = ["Trainer"] logger = Log(__name__).getlog() class Trainer(): """ An experiment template in order to structure the training code and take care of saving, loading, logging, visualization stuffs. It's intended to be flexible and simple. So it only handles output directory (create directory for the output, create a checkpoint directory, dump the config in use and create visualizer and logger) in a standard way without enforcing any input-output protocols to the model and dataloader. It leaves the main part for the user to implement their own (setup the model, criterion, optimizer, define a training step, define a validation function and customize all the text and visual logs). It does not save too much boilerplate code. The users still have to write the forward/backward/update mannually, but they are free to add non-standard behaviors if needed. We have some conventions to follow. 1. Experiment should have ``model``, ``optimizer``, ``train_loader`` and ``valid_loader``, ``config`` and ``args`` attributes. 2. The config should have a ``training`` field, which has ``valid_interval``, ``save_interval`` and ``max_iteration`` keys. It is used as the trigger to invoke validation, checkpointing and stop of the experiment. 3. There are four methods, namely ``train_batch``, ``valid``, ``setup_model`` and ``setup_dataloader`` that should be implemented. Feel free to add/overwrite other methods and standalone functions if you need. Parameters ---------- config: yacs.config.CfgNode The configuration used for the experiment. args: argparse.Namespace The parsed command line arguments. Examples -------- >>> def main_sp(config, args): >>> exp = Trainer(config, args) >>> exp.setup() >>> exp.run() >>> >>> config = get_cfg_defaults() >>> parser = default_argument_parser() >>> args = parser.parse_args() >>> if args.config: >>> config.merge_from_file(args.config) >>> if args.opts: >>> config.merge_from_list(args.opts) >>> config.freeze() >>> >>> if args.ngpu > 1: >>> dist.spawn(main_sp, args=(config, args), nprocs=args.ngpu) >>> else: >>> main_sp(config, args) """ def __init__(self, config, args): self.config = config self.args = args self.optimizer = None self.visualizer = None self.output_dir = None self.checkpoint_dir = None self.iteration = 0 self.epoch = 0 self.rank = dist.get_rank() self.world_size = dist.get_world_size() self._train = True # print deps version all_version() logger.info(f"Rank: {self.rank}/{self.world_size}") # set device paddle.set_device('gpu' if self.args.ngpu > 0 else 'cpu') if self.parallel: self.init_parallel() self.checkpoint = Checkpoint( kbest_n=self.config.training.checkpoint.kbest_n, latest_n=self.config.training.checkpoint.latest_n) # set random seed if needed if args.seed: seed_all(args.seed) logger.info(f"Set seed {args.seed}") # profiler and benchmark options if hasattr(self.args, "benchmark_batch_size") and self.args.benchmark_batch_size: with UpdateConfig(self.config): self.config.collator.batch_size = self.args.benchmark_batch_size self.config.training.log_interval = 1 logger.info( f"Benchmark reset batch-size: {self.args.benchmark_batch_size}") @property def train(self): return self._train @contextmanager def eval(self): self._train = False yield self._train = True def setup(self): """Setup the experiment. """ self.setup_output_dir() self.dump_config() self.setup_visualizer() self.setup_dataloader() self.setup_model() self.iteration = 0 self.epoch = 0 @property def parallel(self): """A flag indicating whether the experiment should run with multiprocessing. """ return self.args.ngpu > 1 def init_parallel(self): """Init environment for multiprocess training. """ dist.init_parallel_env() @mp_tools.rank_zero_only def save(self, tag=None, infos: dict=None): """Save checkpoint (model parameters and optimizer states). Args: tag (int or str, optional): None for step, else using tag, e.g epoch. Defaults to None. infos (dict, optional): meta data to save. Defaults to None. """ infos = infos if infos else dict() infos.update({ "step": self.iteration, "epoch": self.epoch, "lr": self.optimizer.get_lr() }) self.checkpoint.save_parameters(self.checkpoint_dir, self.iteration if tag is None else tag, self.model, self.optimizer, infos) def resume_or_scratch(self): """Resume from latest checkpoint at checkpoints in the output directory or load a specified checkpoint. If ``args.checkpoint_path`` is not None, load the checkpoint, else resume training. """ scratch = None infos = self.checkpoint.load_latest_parameters( self.model, self.optimizer, checkpoint_dir=self.checkpoint_dir, checkpoint_path=self.args.checkpoint_path) if infos: # just restore ckpt # lr will resotre from optimizer ckpt self.iteration = infos["step"] self.epoch = infos["epoch"] scratch = False logger.info( f"Restore ckpt: epoch {self.epoch }, step {self.iteration}!") else: self.iteration = 0 self.epoch = 0 scratch = True logger.info("Init from scratch!") return scratch def maybe_batch_sampler_step(self): """ batch_sampler seed by epoch """ if hasattr(self.train_loader, "batch_sampler"): batch_sampler = self.train_loader.batch_sampler if isinstance(batch_sampler, paddle.io.DistributedBatchSampler): batch_sampler.set_epoch(self.epoch) def before_train(self): from_scratch = self.resume_or_scratch() if from_scratch: # scratch: save init model, i.e. 0 epoch self.save(tag='init', infos=None) else: # resume: train next_epoch and next_iteration self.epoch += 1 self.iteration += 1 logger.info( f"Resume train: epoch {self.epoch }, step {self.iteration}!") self.maybe_batch_sampler_step() def new_epoch(self): """Reset the train loader seed and increment `epoch`. """ # `iteration` increased by train step self.epoch += 1 self.maybe_batch_sampler_step() def after_train_batch(self): if self.args.benchmark_max_step and self.iteration > self.args.benchmark_max_step: profiler.add_profiler_step(self.args.profiler_options) logger.info( f"Reach benchmark-max-step: {self.args.benchmark_max_step}") sys.exit( f"Reach benchmark-max-step: {self.args.benchmark_max_step}") def do_train(self): """The training process control by epoch.""" self.before_train() logger.info(f"Train Total Examples: {len(self.train_loader.dataset)}") while self.epoch < self.config.training.n_epoch: with Timer("Epoch-Train Time Cost: {}"): self.model.train() try: data_start_time = time.time() for batch_index, batch in enumerate(self.train_loader): dataload_time = time.time() - data_start_time msg = "Train:" observation = OrderedDict() with ObsScope(observation): report("Rank", dist.get_rank()) report("epoch", self.epoch) report('step', self.iteration) report("lr", self.lr_scheduler()) self.train_batch(batch_index, batch, msg) self.after_train_batch() report('iter', batch_index + 1) report('total', len(self.train_loader)) report('reader_cost', dataload_time) observation['batch_cost'] = observation[ 'reader_cost'] + observation['step_cost'] observation['samples'] = observation['batch_size'] observation['ips[sent./sec]'] = observation[ 'batch_size'] / observation['batch_cost'] for k, v in observation.items(): msg += f" {k}: " msg += f"{v:>.8f}" if isinstance(v, float) else f"{v}" msg += "," msg = msg[:-1] # remove the last "," logger.info(msg) data_start_time = time.time() except Exception as e: logger.error(e) raise e with Timer("Eval Time Cost: {}"): total_loss, num_seen_utts = self.valid() if dist.get_world_size() > 1: num_seen_utts = paddle.to_tensor(num_seen_utts) # the default operator in all_reduce function is sum. dist.all_reduce(num_seen_utts) total_loss = paddle.to_tensor(total_loss) dist.all_reduce(total_loss) cv_loss = total_loss / num_seen_utts cv_loss = float(cv_loss) else: cv_loss = total_loss / num_seen_utts logger.info( 'Epoch {} Val info val_loss {}'.format(self.epoch, cv_loss)) if self.visualizer: self.visualizer.add_scalar( tag='eval/cv_loss', value=cv_loss, step=self.epoch) self.visualizer.add_scalar( tag='eval/lr', value=self.lr_scheduler(), step=self.epoch) # after epoch self.save(tag=self.epoch, infos={'val_loss': cv_loss}) # step lr every epoch self.lr_scheduler.step() self.new_epoch() def run(self): """The routine of the experiment after setup. This method is intended to be used by the user. """ try: with Timer("Training Done: {}"): self.do_train() except KeyboardInterrupt: exit(-1) finally: self.destory() def restore(self): """Resume from latest checkpoint at checkpoints in the output directory or load a specified checkpoint. If ``args.checkpoint_path`` is not None, load the checkpoint, else resume training. """ assert self.args.checkpoint_path infos = self.checkpoint.load_latest_parameters( self.model, checkpoint_path=self.args.checkpoint_path) return infos def run_test(self): """Do Test/Decode""" try: with Timer("Test/Decode Done: {}"): with self.eval(): self.restore() self.test() except KeyboardInterrupt: exit(-1) def run_export(self): """Do Model Export""" try: with Timer("Export Done: {}"): with self.eval(): self.restore() self.export() except KeyboardInterrupt: exit(-1) def run_align(self): """Do CTC alignment""" try: with Timer("Align Done: {}"): with self.eval(): self.restore() self.align() except KeyboardInterrupt: sys.exit(-1) def setup_output_dir(self): """Create a directory used for output. """ if self.args.output: output_dir = Path(self.args.output).expanduser() elif self.args.checkpoint_path: output_dir = Path( self.args.checkpoint_path).expanduser().parent.parent elif self.args.export_path: output_dir = Path(self.args.export_path).expanduser().parent.parent self.output_dir = output_dir self.output_dir.mkdir(parents=True, exist_ok=True) self.checkpoint_dir = self.output_dir / "checkpoints" self.checkpoint_dir.mkdir(parents=True, exist_ok=True) self.log_dir = output_dir / "log" self.log_dir.mkdir(parents=True, exist_ok=True) self.test_dir = output_dir / "test" self.test_dir.mkdir(parents=True, exist_ok=True) self.decode_dir = output_dir / "decode" self.decode_dir.mkdir(parents=True, exist_ok=True) self.export_dir = output_dir / "export" self.export_dir.mkdir(parents=True, exist_ok=True) self.visual_dir = output_dir / "visual" self.visual_dir.mkdir(parents=True, exist_ok=True) self.config_dir = output_dir / "conf" self.config_dir.mkdir(parents=True, exist_ok=True) @mp_tools.rank_zero_only def destory(self): """Close visualizer to avoid hanging after training""" # https://github.com/pytorch/fairseq/issues/2357 if self.visualizer: self.visualizer.close() @mp_tools.rank_zero_only def setup_visualizer(self): """Initialize a visualizer to log the experiment. The visual log is saved in the output directory. Notes ------ Only the main process has a visualizer with it. Use multiple visualizers in multiprocess to write to a same log file may cause unexpected behaviors. """ # visualizer visualizer = LogWriter(logdir=str(self.visual_dir)) self.visualizer = visualizer @mp_tools.rank_zero_only def dump_config(self): """Save the configuration used for this experiment. It is saved in to ``config.yaml`` in the output directory at the beginning of the experiment. """ config_file = self.config_dir / "config.yaml" if self.train and config_file.exists(): time_stamp = time.strftime("%Y_%m_%d_%H_%M_%s", time.gmtime()) target_path = self.config_dir / ".".join( [time_stamp, "config.yaml"]) config_file.rename(target_path) with open(config_file, 'wt') as f: print(self.config, file=f) def train_batch(self): """The training loop. A subclass should implement this method. """ raise NotImplementedError("train_batch should be implemented.") @paddle.no_grad() def valid(self): """The validation. A subclass should implement this method. """ raise NotImplementedError("valid should be implemented.") @paddle.no_grad() def test(self): """The test. A subclass should implement this method in Tester. """ raise NotImplementedError("test should be implemented.") @paddle.no_grad() def export(self): """The test. A subclass should implement this method in Tester. """ raise NotImplementedError("export should be implemented.") @paddle.no_grad() def align(self): """The align. A subclass should implement this method in Tester. """ raise NotImplementedError("align should be implemented.") def setup_model(self): """Setup model, criterion and optimizer, etc. A subclass should implement this method. """ raise NotImplementedError("setup_model should be implemented.") def setup_dataloader(self): """Setup training dataloader and validation dataloader. A subclass should implement this method. """ raise NotImplementedError("setup_dataloader should be implemented.")