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325 lines
11 KiB
325 lines
11 KiB
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import time
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from pathlib import Path
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import paddle
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from paddle import distributed as dist
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from tensorboardX import SummaryWriter
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from deepspeech.utils import mp_tools
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from deepspeech.utils.checkpoint import Checkpoint
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from deepspeech.utils.log import Log
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__all__ = ["Trainer"]
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logger = Log(__name__).getlog()
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class Trainer():
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"""
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An experiment template in order to structure the training code and take
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care of saving, loading, logging, visualization stuffs. It's intended to
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be flexible and simple.
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So it only handles output directory (create directory for the output,
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create a checkpoint directory, dump the config in use and create
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visualizer and logger) in a standard way without enforcing any
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input-output protocols to the model and dataloader. It leaves the main
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part for the user to implement their own (setup the model, criterion,
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optimizer, define a training step, define a validation function and
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customize all the text and visual logs).
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It does not save too much boilerplate code. The users still have to write
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the forward/backward/update mannually, but they are free to add
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non-standard behaviors if needed.
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We have some conventions to follow.
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1. Experiment should have ``model``, ``optimizer``, ``train_loader`` and
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``valid_loader``, ``config`` and ``args`` attributes.
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2. The config should have a ``training`` field, which has
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``valid_interval``, ``save_interval`` and ``max_iteration`` keys. It is
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used as the trigger to invoke validation, checkpointing and stop of the
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experiment.
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3. There are four methods, namely ``train_batch``, ``valid``,
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``setup_model`` and ``setup_dataloader`` that should be implemented.
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Feel free to add/overwrite other methods and standalone functions if you
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need.
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Parameters
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----------
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config: yacs.config.CfgNode
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The configuration used for the experiment.
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args: argparse.Namespace
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The parsed command line arguments.
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Examples
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--------
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>>> def main_sp(config, args):
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>>> exp = Trainer(config, args)
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>>> exp.setup()
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>>> exp.run()
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>>>
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>>> config = get_cfg_defaults()
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>>> parser = default_argument_parser()
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>>> args = parser.parse_args()
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>>> if args.config:
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>>> config.merge_from_file(args.config)
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>>> if args.opts:
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>>> config.merge_from_list(args.opts)
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>>> config.freeze()
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>>>
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>>> if args.nprocs > 1 and args.device == "gpu":
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>>> dist.spawn(main_sp, args=(config, args), nprocs=args.nprocs)
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>>> else:
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>>> main_sp(config, args)
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"""
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def __init__(self, config, args):
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self.config = config
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self.args = args
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self.optimizer = None
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self.visualizer = None
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self.output_dir = None
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self.checkpoint_dir = None
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self.iteration = 0
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self.epoch = 0
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def setup(self):
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"""Setup the experiment.
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"""
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paddle.set_device(self.args.device)
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if self.parallel:
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self.init_parallel()
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self.setup_output_dir()
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self.dump_config()
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self.setup_visualizer()
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self.setup_checkpointer()
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self.setup_dataloader()
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self.setup_model()
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self.iteration = 0
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self.epoch = 0
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@property
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def parallel(self):
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"""A flag indicating whether the experiment should run with
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multiprocessing.
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"""
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return self.args.device == "gpu" and self.args.nprocs > 1
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def init_parallel(self):
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"""Init environment for multiprocess training.
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"""
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dist.init_parallel_env()
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@mp_tools.rank_zero_only
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def save(self, tag=None, infos: dict=None):
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"""Save checkpoint (model parameters and optimizer states).
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Args:
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tag (int or str, optional): None for step, else using tag, e.g epoch. Defaults to None.
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infos (dict, optional): meta data to save. Defaults to None.
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"""
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infos = infos if infos else dict()
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infos.update({
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"step": self.iteration,
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"epoch": self.epoch,
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"lr": self.optimizer.get_lr()
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})
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self.checkpoint.add_checkpoint(self.checkpoint_dir, self.iteration
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if tag is None else tag, self.model,
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self.optimizer, infos)
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def resume_or_scratch(self):
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"""Resume from latest checkpoint at checkpoints in the output
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directory or load a specified checkpoint.
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If ``args.checkpoint_path`` is not None, load the checkpoint, else
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resume training.
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"""
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scratch = None
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infos = self.checkpoint.load_last_parameters(
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self.model,
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self.optimizer,
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checkpoint_dir=self.checkpoint_dir,
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checkpoint_path=self.args.checkpoint_path)
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if infos:
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# restore from ckpt
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self.iteration = infos["step"]
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self.epoch = infos["epoch"]
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scratch = False
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else:
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self.iteration = 0
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self.epoch = 0
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scratch = True
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return scratch
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def new_epoch(self):
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"""Reset the train loader seed and increment `epoch`.
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"""
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self.epoch += 1
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if self.parallel:
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self.train_loader.batch_sampler.set_epoch(self.epoch)
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def train(self):
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"""The training process control by epoch."""
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from_scratch = self.resume_or_scratch()
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if from_scratch:
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# save init model, i.e. 0 epoch
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self.save(tag='init', infos=None)
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self.lr_scheduler.step(self.iteration)
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if self.parallel:
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self.train_loader.batch_sampler.set_epoch(self.epoch)
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logger.info(f"Train Total Examples: {len(self.train_loader.dataset)}")
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while self.epoch < self.config.training.n_epoch:
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self.model.train()
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try:
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data_start_time = time.time()
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for batch_index, batch in enumerate(self.train_loader):
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dataload_time = time.time() - data_start_time
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msg = "Train: Rank: {}, ".format(dist.get_rank())
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msg += "epoch: {}, ".format(self.epoch)
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msg += "step: {}, ".format(self.iteration)
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msg += "batch : {}/{}, ".format(batch_index + 1,
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len(self.train_loader))
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msg += "lr: {:>.8f}, ".format(self.lr_scheduler())
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msg += "data time: {:>.3f}s, ".format(dataload_time)
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self.train_batch(batch_index, batch, msg)
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data_start_time = time.time()
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except Exception as e:
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logger.error(e)
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raise e
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total_loss, num_seen_utts = self.valid()
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if dist.get_world_size() > 1:
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num_seen_utts = paddle.to_tensor(num_seen_utts)
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# the default operator in all_reduce function is sum.
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dist.all_reduce(num_seen_utts)
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total_loss = paddle.to_tensor(total_loss)
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dist.all_reduce(total_loss)
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cv_loss = total_loss / num_seen_utts
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cv_loss = float(cv_loss)
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else:
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cv_loss = total_loss / num_seen_utts
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logger.info(
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'Epoch {} Val info val_loss {}'.format(self.epoch, cv_loss))
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if self.visualizer:
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self.visualizer.add_scalars(
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'epoch', {'cv_loss': cv_loss,
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'lr': self.lr_scheduler()}, self.epoch)
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self.save(tag=self.epoch, infos={'val_loss': cv_loss})
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# step lr every epoch
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self.lr_scheduler.step()
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self.new_epoch()
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def run(self):
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"""The routine of the experiment after setup. This method is intended
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to be used by the user.
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"""
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try:
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self.train()
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except KeyboardInterrupt:
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self.save()
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exit(-1)
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finally:
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self.destory()
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logger.info("Training Done.")
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def setup_output_dir(self):
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"""Create a directory used for output.
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"""
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# output dir
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output_dir = Path(self.args.output).expanduser()
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output_dir.mkdir(parents=True, exist_ok=True)
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self.output_dir = output_dir
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def setup_checkpointer(self):
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"""Create a directory used to save checkpoints into.
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It is "checkpoints" inside the output directory.
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"""
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# checkpoint dir
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checkpoint_dir = self.output_dir / "checkpoints"
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checkpoint_dir.mkdir(exist_ok=True)
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self.checkpoint_dir = checkpoint_dir
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self.checkpoint = Checkpoint(
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kbest_n=self.config.training.checkpoint.kbest_n,
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latest_n=self.config.training.checkpoint.latest_n)
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@mp_tools.rank_zero_only
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def destory(self):
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"""Close visualizer to avoid hanging after training"""
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# https://github.com/pytorch/fairseq/issues/2357
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if self.visualizer:
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self.visualizer.close()
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@mp_tools.rank_zero_only
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def setup_visualizer(self):
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"""Initialize a visualizer to log the experiment.
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The visual log is saved in the output directory.
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Notes
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------
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Only the main process has a visualizer with it. Use multiple
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visualizers in multiprocess to write to a same log file may cause
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unexpected behaviors.
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"""
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# visualizer
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visualizer = SummaryWriter(logdir=str(self.output_dir))
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self.visualizer = visualizer
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@mp_tools.rank_zero_only
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def dump_config(self):
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"""Save the configuration used for this experiment.
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It is saved in to ``config.yaml`` in the output directory at the
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beginning of the experiment.
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"""
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with open(self.output_dir / "config.yaml", 'wt') as f:
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print(self.config, file=f)
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def train_batch(self):
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"""The training loop. A subclass should implement this method.
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"""
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raise NotImplementedError("train_batch should be implemented.")
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@paddle.no_grad()
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def valid(self):
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"""The validation. A subclass should implement this method.
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"""
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raise NotImplementedError("valid should be implemented.")
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def setup_model(self):
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"""Setup model, criterion and optimizer, etc. A subclass should
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implement this method.
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"""
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raise NotImplementedError("setup_model should be implemented.")
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def setup_dataloader(self):
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"""Setup training dataloader and validation dataloader. A subclass
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should implement this method.
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"""
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raise NotImplementedError("setup_dataloader should be implemented.")
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