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- #!/usr/bin/env python3 -u
- # Copyright (c) 2017-present, Facebook, Inc.
- # All rights reserved.
- #
- # This source code is licensed under the license found in the LICENSE file in
- # the root directory of this source tree. An additional grant of patent rights
- # can be found in the PATENTS file in the same directory.
- import collections
- import itertools
- import os
- import math
- import torch
- from fairseq import criterions, data, models, options, progress_bar
- from fairseq.meters import AverageMeter, StopwatchMeter
- from fairseq.trainer import Trainer
- def main(args):
- print(args)
- if not torch.cuda.is_available():
- raise NotImplementedError('Training on CPU is not supported')
- torch.cuda.set_device(args.device_id)
- torch.manual_seed(args.seed)
- # Load dataset
- splits = ['train', 'valid']
- if data.has_binary_files(args.data, splits):
- dataset = data.load_dataset(
- args.data, splits, args.source_lang, args.target_lang)
- else:
- dataset = data.load_raw_text_dataset(
- args.data, splits, args.source_lang, args.target_lang)
- if args.source_lang is None or args.target_lang is None:
- # record inferred languages in args, so that it's saved in checkpoints
- args.source_lang, args.target_lang = dataset.src, dataset.dst
- print('| [{}] dictionary: {} types'.format(dataset.src, len(dataset.src_dict)))
- print('| [{}] dictionary: {} types'.format(dataset.dst, len(dataset.dst_dict)))
- for split in splits:
- print('| {} {} {} examples'.format(args.data, split, len(dataset.splits[split])))
- # Build model and criterion
- model = models.build_model(args, dataset.src_dict, dataset.dst_dict)
- criterion = criterions.build_criterion(args, dataset.src_dict, dataset.dst_dict)
- print('| model {}, criterion {}'.format(args.arch, criterion.__class__.__name__))
- print('| num. model params: {}'.format(sum(p.data.numel() for p in model.parameters())))
- # Build trainer
- trainer = Trainer(args, model, criterion)
- print('| training on {} GPUs'.format(args.distributed_world_size))
- print('| max tokens per GPU = {} and max sentences per GPU = {}'.format(
- args.max_tokens,
- args.max_sentences,
- ))
- # Load the latest checkpoint if one is available
- os.makedirs(args.save_dir, exist_ok=True)
- checkpoint_path = os.path.join(args.save_dir, args.restore_file)
- extra_state = trainer.load_checkpoint(checkpoint_path)
- if extra_state is not None:
- epoch = extra_state['epoch']
- batch_offset = extra_state['batch_offset']
- print('| loaded checkpoint {} (epoch {})'.format(checkpoint_path, epoch))
- if batch_offset == 0:
- trainer.lr_step(epoch)
- epoch += 1
- else:
- epoch, batch_offset = 1, 0
- # Train until the learning rate gets too small
- max_epoch = args.max_epoch or math.inf
- lr = trainer.get_lr()
- train_meter = StopwatchMeter()
- train_meter.start()
- while lr > args.min_lr and epoch <= max_epoch:
- # train for one epoch
- train(args, trainer, dataset, epoch, batch_offset)
- # evaluate on validate set
- for k, subset in enumerate(args.valid_subset.split(',')):
- val_loss = validate(args, trainer, dataset, subset, epoch)
- if k == 0:
- # only use first validation loss to update the learning schedule
- lr = trainer.lr_step(epoch, val_loss)
- # save checkpoint
- if not args.no_save:
- save_checkpoint(trainer, args, epoch, 0, val_loss)
- epoch += 1
- batch_offset = 0
- train_meter.stop()
- print('| done training in {:.1f} seconds'.format(train_meter.sum))
- def train(args, trainer, dataset, epoch, batch_offset):
- """Train the model for one epoch."""
- # Set seed based on args.seed and the epoch number so that we get
- # reproducible results when resuming from checkpoints
- seed = args.seed + epoch
- torch.manual_seed(seed)
- # The max number of positions can be different for train and valid
- # e.g., RNNs may support more positions at test time than seen in training
- max_positions_train = (
- min(args.max_source_positions, trainer.get_model().max_encoder_positions()),
- min(args.max_target_positions, trainer.get_model().max_decoder_positions())
- )
- # Initialize dataloader, starting at batch_offset
- itr = dataset.train_dataloader(
- args.train_subset,
- max_tokens=args.max_tokens,
- max_sentences=args.max_sentences,
- max_positions=max_positions_train,
- seed=seed,
- epoch=epoch,
- sample_without_replacement=args.sample_without_replacement,
- sort_by_source_size=(epoch <= args.curriculum),
- shard_id=args.distributed_rank,
- num_shards=args.distributed_world_size,
- )
- progress = progress_bar.build_progress_bar(args, itr, epoch, no_progress_bar='simple')
- itr = itertools.islice(progress, batch_offset, None)
- # reset training meters
- for k in ['train_loss', 'train_nll_loss', 'wps', 'ups', 'wpb', 'bsz', 'clip']:
- meter = trainer.get_meter(k)
- if meter is not None:
- meter.reset()
- extra_meters = collections.defaultdict(lambda: AverageMeter())
- for i, sample in enumerate(itr, start=batch_offset):
- log_output = trainer.train_step(sample)
- # log mid-epoch stats
- stats = get_training_stats(trainer)
- for k, v in log_output.items():
- if k in ['loss', 'nll_loss']:
- continue # these are already logged above
- extra_meters[k].update(v)
- stats[k] = extra_meters[k].avg
- progress.log(stats)
- # save mid-epoch checkpoints
- if i == batch_offset:
- # ignore the first mini-batch in words-per-second calculation
- trainer.get_meter('wps').reset()
- if args.save_interval > 0 and trainer.get_num_updates() % args.save_interval == 0:
- save_checkpoint(trainer, args, epoch, i + 1)
- # log end-of-epoch stats
- stats = get_training_stats(trainer)
- for k, meter in extra_meters.items():
- stats[k] = meter.avg
- progress.print(stats)
- def get_training_stats(trainer):
- stats = collections.OrderedDict()
- stats['loss'] = '{:.3f}'.format(trainer.get_meter('train_loss').avg)
- if trainer.get_meter('train_nll_loss').count > 0:
- nll_loss = trainer.get_meter('train_nll_loss').avg
- stats['nll_loss'] = '{:.3f}'.format(nll_loss)
- else:
- nll_loss = trainer.get_meter('train_loss').avg
- stats['ppl'] = get_perplexity(nll_loss)
- stats['wps'] = round(trainer.get_meter('wps').avg)
- stats['ups'] = '{:.1f}'.format(trainer.get_meter('ups').avg)
- stats['wpb'] = round(trainer.get_meter('wpb').avg)
- stats['bsz'] = round(trainer.get_meter('bsz').avg)
- stats['num_updates'] = trainer.get_num_updates()
- stats['lr'] = trainer.get_lr()
- stats['gnorm'] = '{:.3f}'.format(trainer.get_meter('gnorm').avg)
- stats['clip'] = '{:.0%}'.format(trainer.get_meter('clip').avg)
- stats['oom'] = trainer.get_meter('oom').avg
- return stats
- def validate(args, trainer, dataset, subset, epoch):
- """Evaluate the model on the validation set and return the average loss."""
- # Initialize dataloader
- max_positions_valid = (
- trainer.get_model().max_encoder_positions(),
- trainer.get_model().max_decoder_positions(),
- )
- itr = dataset.eval_dataloader(
- subset,
- max_tokens=args.max_tokens,
- max_sentences=args.max_sentences_valid,
- max_positions=max_positions_valid,
- skip_invalid_size_inputs_valid_test=args.skip_invalid_size_inputs_valid_test,
- descending=True, # largest batch first to warm the caching allocator
- shard_id=args.distributed_rank,
- num_shards=args.distributed_world_size,
- )
- progress = progress_bar.build_progress_bar(
- args, itr, epoch,
- prefix='valid on \'{}\' subset'.format(subset),
- no_progress_bar='simple'
- )
- # reset validation loss meters
- for k in ['valid_loss', 'valid_nll_loss']:
- meter = trainer.get_meter(k)
- if meter is not None:
- meter.reset()
- extra_meters = collections.defaultdict(lambda: AverageMeter())
- for sample in progress:
- log_output = trainer.valid_step(sample)
- # log mid-validation stats
- stats = get_valid_stats(trainer)
- for k, v in log_output.items():
- if k in ['loss', 'nll_loss']:
- continue
- extra_meters[k].update(v)
- stats[k] = extra_meters[k].avg
- progress.log(stats)
- # log validation stats
- stats = get_valid_stats(trainer)
- for k, meter in extra_meters.items():
- stats[k] = meter.avg
- progress.print(stats)
- return stats['valid_loss']
- def get_valid_stats(trainer):
- stats = collections.OrderedDict()
- stats['valid_loss'] = trainer.get_meter('valid_loss').avg
- if trainer.get_meter('valid_nll_loss').count > 0:
- nll_loss = trainer.get_meter('valid_nll_loss').avg
- stats['valid_nll_loss'] = nll_loss
- else:
- nll_loss = trainer.get_meter('valid_loss').avg
- stats['valid_ppl'] = get_perplexity(nll_loss)
- return stats
- def get_perplexity(loss):
- try:
- return '{:.2f}'.format(math.pow(2, loss))
- except OverflowError:
- return float('inf')
- def save_checkpoint(trainer, args, epoch, batch_offset, val_loss=None):
- extra_state = {
- 'epoch': epoch,
- 'batch_offset': batch_offset,
- 'val_loss': val_loss,
- }
- if batch_offset == 0:
- if not args.no_epoch_checkpoints:
- epoch_filename = os.path.join(args.save_dir, 'checkpoint{}.pt'.format(epoch))
- trainer.save_checkpoint(epoch_filename, extra_state)
- assert val_loss is not None
- if not hasattr(save_checkpoint, 'best') or val_loss < save_checkpoint.best:
- save_checkpoint.best = val_loss
- best_filename = os.path.join(args.save_dir, 'checkpoint_best.pt')
- trainer.save_checkpoint(best_filename, extra_state)
- elif not args.no_epoch_checkpoints:
- epoch_filename = os.path.join(
- args.save_dir, 'checkpoint{}_{}.pt'.format(epoch, batch_offset))
- trainer.save_checkpoint(epoch_filename, extra_state)
- last_filename = os.path.join(args.save_dir, 'checkpoint_last.pt')
- trainer.save_checkpoint(last_filename, extra_state)
- if __name__ == '__main__':
- parser = options.get_training_parser()
- args = options.parse_args_and_arch(parser)
- main(args)
|