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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 data, distributed_utils, options, progress_bar, tasks, utils
- from fairseq.fp16_trainer import FP16Trainer
- from fairseq.trainer import Trainer
- from fairseq.meters import AverageMeter, StopwatchMeter
- def main(args):
- if args.max_tokens is None:
- args.max_tokens = 6000
- 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)
- # Setup task, e.g., translation, language modeling, etc.
- task = tasks.setup_task(args)
- # Load dataset splits
- load_dataset_splits(args, task, ['train', 'valid'])
- # Build model and criterion
- model = task.build_model(args)
- criterion = task.build_criterion(args)
- print('| model {}, criterion {}'.format(args.arch, criterion.__class__.__name__))
- print('| num. model params: {}'.format(sum(p.numel() for p in model.parameters())))
- # Build trainer
- if args.fp16:
- trainer = FP16Trainer(args, task, model, criterion)
- else:
- if torch.cuda.get_device_capability(0)[0] >= 7:
- print('| NOTICE: your device may support faster training with --fp16')
- trainer = Trainer(args, task, 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,
- ))
- # Initialize dataloader
- max_positions = trainer.get_model().max_positions()
- epoch_itr = data.EpochBatchIterator(
- dataset=task.dataset(args.train_subset),
- max_tokens=args.max_tokens,
- max_sentences=args.max_sentences_valid,
- max_positions=max_positions,
- ignore_invalid_inputs=True,
- required_batch_size_multiple=8,
- seed=args.seed,
- num_shards=args.distributed_world_size,
- shard_id=args.distributed_rank,
- )
- # Load the latest checkpoint if one is available
- load_checkpoint(args, trainer, epoch_itr)
- # Send a dummy batch to warm the caching allocator
- dummy_batch = task.dataset('train').get_dummy_batch(args.max_tokens, max_positions)
- trainer.dummy_train_step(dummy_batch)
- # Train until the learning rate gets too small
- max_epoch = args.max_epoch or math.inf
- max_update = args.max_update or math.inf
- lr = trainer.get_lr()
- train_meter = StopwatchMeter()
- train_meter.start()
- valid_losses = [None]
- valid_subsets = args.valid_subset.split(',')
- while lr > args.min_lr and epoch_itr.epoch <= max_epoch and trainer.get_num_updates() < max_update:
- # train for one epoch
- train(args, trainer, task, epoch_itr)
- if epoch_itr.epoch % args.validate_interval == 0:
- valid_losses = validate(args, trainer, task, epoch_itr, valid_subsets)
- # only use first validation loss to update the learning rate
- lr = trainer.lr_step(epoch_itr.epoch, valid_losses[0])
- # save checkpoint
- if epoch_itr.epoch % args.save_interval == 0:
- save_checkpoint(args, trainer, epoch_itr, valid_losses[0])
- train_meter.stop()
- print('| done training in {:.1f} seconds'.format(train_meter.sum))
- def train(args, trainer, task, epoch_itr):
- """Train the model for one epoch."""
- # Initialize data iterator
- itr = epoch_itr.next_epoch_itr()
- progress = progress_bar.build_progress_bar(args, itr, epoch_itr.epoch, no_progress_bar='simple')
- # update parameters every N batches
- if epoch_itr.epoch <= len(args.update_freq):
- update_freq = args.update_freq[epoch_itr.epoch - 1]
- else:
- update_freq = args.update_freq[-1]
- extra_meters = collections.defaultdict(lambda: AverageMeter())
- first_valid = args.valid_subset.split(',')[0]
- max_update = args.max_update or math.inf
- num_batches = len(epoch_itr)
- for i, sample in enumerate(progress, start=epoch_itr.iterations_in_epoch):
- if i < num_batches - 1 and (i + 1) % update_freq > 0:
- # buffer updates according to --update-freq
- trainer.train_step(sample, update_params=False)
- continue
- else:
- log_output = trainer.train_step(sample, update_params=True)
- # log mid-epoch stats
- stats = get_training_stats(trainer)
- for k, v in log_output.items():
- if k in ['loss', 'nll_loss', 'sample_size']:
- continue # these are already logged above
- if 'loss' in k:
- extra_meters[k].update(v, log_output['sample_size'])
- else:
- extra_meters[k].update(v)
- stats[k] = extra_meters[k].avg
- progress.log(stats)
- # ignore the first mini-batch in words-per-second calculation
- if i == 0:
- trainer.get_meter('wps').reset()
- num_updates = trainer.get_num_updates()
- if args.save_interval_updates > 0 and num_updates % args.save_interval_updates == 0:
- valid_losses = validate(args, trainer, task, epoch_itr, [first_valid])
- save_checkpoint(args, trainer, epoch_itr, valid_losses[0])
- if num_updates >= max_update:
- break
- # log end-of-epoch stats
- stats = get_training_stats(trainer)
- for k, meter in extra_meters.items():
- stats[k] = meter.avg
- progress.print(stats)
- # 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()
- 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
- if trainer.get_meter('loss_scale') is not None:
- stats['loss_scale'] = '{:.3f}'.format(trainer.get_meter('loss_scale').avg)
- stats['wall'] = round(trainer.get_meter('wall').elapsed_time)
- return stats
- def validate(args, trainer, task, epoch_itr, subsets):
- """Evaluate the model on the validation set(s) and return the losses."""
- valid_losses = []
- for subset in subsets:
- # Initialize data iterator
- itr = data.EpochBatchIterator(
- dataset=task.dataset(subset),
- max_tokens=args.max_tokens,
- max_sentences=args.max_sentences_valid,
- max_positions=trainer.get_model().max_positions(),
- ignore_invalid_inputs=args.skip_invalid_size_inputs_valid_test,
- required_batch_size_multiple=8,
- seed=args.seed,
- num_shards=args.distributed_world_size,
- shard_id=args.distributed_rank,
- ).next_epoch_itr(shuffle=False)
- progress = progress_bar.build_progress_bar(
- args, itr, epoch_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)
- for k, v in log_output.items():
- if k in ['loss', 'nll_loss', 'sample_size']:
- continue
- extra_meters[k].update(v)
- # log validation stats
- stats = get_valid_stats(trainer)
- for k, meter in extra_meters.items():
- stats[k] = meter.avg
- progress.print(stats)
- valid_losses.append(stats['valid_loss'])
- return valid_losses
- 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)
- stats['num_updates'] = trainer.get_num_updates()
- if hasattr(save_checkpoint, 'best'):
- stats['best'] = min(save_checkpoint.best, stats['valid_loss'])
- return stats
- def get_perplexity(loss):
- try:
- return '{:.2f}'.format(math.pow(2, loss))
- except OverflowError:
- return float('inf')
- def save_checkpoint(args, trainer, epoch_itr, val_loss):
- if args.no_save or not distributed_utils.is_master(args):
- return
- epoch = epoch_itr.epoch
- end_of_epoch = epoch_itr.end_of_epoch()
- updates = trainer.get_num_updates()
- checkpoint_conds = collections.OrderedDict()
- checkpoint_conds['checkpoint{}.pt'.format(epoch)] = (
- end_of_epoch and not args.no_epoch_checkpoints and
- epoch % args.save_interval == 0
- )
- checkpoint_conds['checkpoint_{}_{}.pt'.format(epoch, updates)] = (
- not end_of_epoch and args.save_interval_updates > 0 and
- updates % args.save_interval_updates == 0
- )
- checkpoint_conds['checkpoint_best.pt'] = (
- val_loss is not None and
- (not hasattr(save_checkpoint, 'best') or val_loss < save_checkpoint.best)
- )
- checkpoint_conds['checkpoint_last.pt'] = True # keep this last so that it's a symlink
- prev_best = getattr(save_checkpoint, 'best', val_loss)
- if val_loss is not None:
- save_checkpoint.best = min(val_loss, prev_best)
- extra_state = {
- 'best': save_checkpoint.best,
- 'train_iterator': epoch_itr.state_dict(),
- 'val_loss': val_loss,
- }
- checkpoints = [os.path.join(args.save_dir, fn) for fn, cond in checkpoint_conds.items() if cond]
- if len(checkpoints) > 0:
- for fn in checkpoints:
- if os.path.exists(fn):
- os.remove(fn)
- if not end_of_epoch and args.keep_interval_updates > 0:
- for cp in checkpoints:
- trainer.save_checkpoint(cp, extra_state)
- else:
- trainer.save_checkpoint(checkpoints[0], extra_state)
- for fn in checkpoints[1:]:
- os.symlink(os.path.basename(checkpoints[0]), fn)
- if not end_of_epoch and args.keep_interval_updates > 0:
- # remove old checkpoints; checkpoints are sorted in descending order
- checkpoints = utils.checkpoint_paths(args.save_dir, pattern=r'checkpoint_\d+_(\d+)\.pt')
- for old_chk in checkpoints[args.keep_interval_updates:]:
- os.remove(old_chk)
- def load_checkpoint(args, trainer, epoch_itr):
- """Load a checkpoint and replay dataloader to match."""
- os.makedirs(args.save_dir, exist_ok=True)
- checkpoint_path = os.path.join(args.save_dir, args.restore_file)
- if os.path.isfile(checkpoint_path):
- extra_state = trainer.load_checkpoint(checkpoint_path)
- if extra_state is not None:
- # replay train iterator to match checkpoint
- epoch_itr.load_state_dict(extra_state['train_iterator'])
- print('| loaded checkpoint {} (epoch {} @ {} updates)'.format(
- checkpoint_path, epoch_itr.epoch, trainer.get_num_updates()))
- trainer.lr_step(epoch_itr.epoch)
- trainer.lr_step_update(trainer.get_num_updates())
- if 'best' in extra_state:
- save_checkpoint.best = extra_state['best']
- def load_dataset_splits(args, task, splits):
- for split in splits:
- for k in itertools.count():
- split_k = split + (str(k) if k > 0 else '')
- try:
- task.load_dataset(split_k)
- print('| {} {} {} examples'.format(args.data, split_k, len(task.dataset(split_k))))
- except FileNotFoundError as e:
- if k > 0:
- break
- raise e
- if __name__ == '__main__':
- parser = options.get_training_parser()
- args = options.parse_args_and_arch(parser)
- if args.distributed_port > 0 or args.distributed_init_method is not None:
- from distributed_train import main as distributed_main
- distributed_main(args)
- elif args.distributed_world_size > 1:
- from multiprocessing_train import main as multiprocessing_main
- multiprocessing_main(args)
- else:
- main(args)
|