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|
- #!/usr/bin/env python3
- # 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 os
- import torch
- import math
- from fairseq import data, options, utils
- from fairseq.meters import AverageMeter, StopwatchMeter, TimeMeter
- from fairseq.multiprocessing_trainer import MultiprocessingTrainer
- def main():
- parser = options.get_parser('Trainer')
- dataset_args = options.add_dataset_args(parser)
- dataset_args.add_argument('--max-tokens', default=6000, type=int, metavar='N',
- help='maximum number of tokens in a batch')
- dataset_args.add_argument('--max-sentences', type=int, metavar='N',
- help='maximum number of sentences in a batch')
- dataset_args.add_argument('--train-subset', default='train', metavar='SPLIT',
- choices=['train', 'valid', 'test'],
- help='data subset to use for training (train, valid, test)')
- dataset_args.add_argument('--valid-subset', default='valid', metavar='SPLIT',
- help='comma separated list of data subsets '
- ' to use for validation (train, valid, valid1,test, test1)')
- dataset_args.add_argument('--max-sentences-valid', type=int, metavar='N',
- help='maximum number of sentences in a validation batch')
- options.add_optimization_args(parser)
- options.add_checkpoint_args(parser)
- options.add_model_args(parser)
- args = utils.parse_args_and_arch(parser)
- if args.no_progress_bar and args.log_format is None:
- args.log_format = 'simple'
- if args.max_sentences_valid is None:
- args.max_sentences_valid = args.max_sentences
- if not os.path.exists(args.save_dir):
- os.makedirs(args.save_dir)
- 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
- if not torch.cuda.is_available():
- raise NotImplementedError('Training on CPU is not supported')
- args.num_gpus = torch.cuda.device_count()
- print(args)
- 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])))
- print('| using {} GPUs (with max tokens per GPU = {} and max sentences per GPU = {})'.format(
- args.num_gpus, args.max_tokens, args.max_sentences))
- # Build model and criterion
- model = utils.build_model(args, dataset.src_dict, dataset.dst_dict)
- criterion = utils.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())))
- # 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, model.max_encoder_positions()),
- min(args.max_target_positions, model.max_decoder_positions())
- )
- max_positions_valid = (model.max_encoder_positions(), model.max_decoder_positions())
- # Start multiprocessing
- trainer = MultiprocessingTrainer(args, model, criterion)
- # Load the latest checkpoint if one is available
- 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:
- epoch += 1
- else:
- epoch, batch_offset = 1, 0
- # Train until the learning rate gets too small
- val_loss = None
- 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, epoch, batch_offset, trainer, dataset, max_positions_train)
- # evaluate on validate set
- for k, subset in enumerate(args.valid_subset.split(',')):
- val_loss = validate(args, epoch, trainer, dataset, max_positions_valid, subset)
- if k == 0:
- if not args.no_save:
- # save checkpoint
- save_checkpoint(trainer, args, epoch, 0, val_loss)
- # only use first validation loss to update the learning schedule
- lr = trainer.lr_step(val_loss, epoch)
- epoch += 1
- batch_offset = 0
- train_meter.stop()
- print('| done training in {:.1f} seconds'.format(train_meter.sum))
- # Stop multiprocessing
- trainer.stop()
- def get_perplexity(loss):
- try:
- return round(math.pow(2, loss), 2)
- except OverflowError:
- return float('inf')
- def train(args, epoch, batch_offset, trainer, dataset, max_positions):
- """Train the model for one epoch."""
- seed = args.seed + epoch
- torch.manual_seed(seed)
- trainer.set_seed(seed)
- itr = dataset.train_dataloader(
- args.train_subset, num_workers=args.workers,
- max_tokens=args.max_tokens, max_sentences=args.max_sentences,
- max_positions=max_positions, seed=seed, epoch=epoch,
- sample_without_replacement=args.sample_without_replacement,
- sort_by_source_size=(epoch <= args.curriculum))
- loss_meter = AverageMeter()
- nll_loss_meter = AverageMeter()
- bsz_meter = AverageMeter() # sentences per batch
- wpb_meter = AverageMeter() # words per batch
- wps_meter = TimeMeter() # words per second
- clip_meter = AverageMeter() # % of updates clipped
- extra_meters = collections.defaultdict(lambda: AverageMeter())
- lr = trainer.get_lr()
- with utils.build_progress_bar(args, itr, epoch) as t:
- for i, sample in data.skip_group_enumerator(t, args.num_gpus, batch_offset):
- loss_dict = trainer.train_step(sample)
- loss = loss_dict['loss']
- del loss_dict['loss'] # don't include in extra_meters or extra_postfix
- ntokens = sum(s['ntokens'] for s in sample)
- if 'nll_loss' in loss_dict:
- nll_loss = loss_dict['nll_loss']
- nll_loss_meter.update(nll_loss, ntokens)
- nsentences = sum(s['net_input']['src_tokens'].size(0) for s in sample)
- loss_meter.update(loss, nsentences if args.sentence_avg else ntokens)
- bsz_meter.update(nsentences)
- wpb_meter.update(ntokens)
- wps_meter.update(ntokens)
- clip_meter.update(1 if loss_dict['gnorm'] > args.clip_norm else 0)
- extra_postfix = []
- for k, v in loss_dict.items():
- extra_meters[k].update(v)
- extra_postfix.append((k, extra_meters[k].avg))
- t.log(collections.OrderedDict([
- ('loss', loss_meter),
- ('wps', round(wps_meter.avg)),
- ('wpb', round(wpb_meter.avg)),
- ('bsz', round(bsz_meter.avg)),
- ('lr', lr),
- ('clip', '{:.0%}'.format(clip_meter.avg)),
- ] + extra_postfix))
- if i == 0:
- # ignore the first mini-batch in words-per-second calculation
- wps_meter.reset()
- if args.save_interval > 0 and (i + 1) % args.save_interval == 0:
- save_checkpoint(trainer, args, epoch, i + 1)
- t.print(collections.OrderedDict([
- ('train loss', round(loss_meter.avg, 2)),
- ('train ppl', get_perplexity(nll_loss_meter.avg
- if nll_loss_meter.count > 0
- else loss_meter.avg)),
- ('s/checkpoint', round(wps_meter.elapsed_time)),
- ('words/s', round(wps_meter.avg)),
- ('words/batch', round(wpb_meter.avg)),
- ('bsz', round(bsz_meter.avg)),
- ('lr', lr),
- ('clip', '{:3.0f}%'.format(clip_meter.avg * 100)),
- ] + [
- (k, meter.avg)
- for k, meter in extra_meters.items()
- ]))
- def save_checkpoint(trainer, args, epoch, batch_offset, val_loss):
- 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)
- def validate(args, epoch, trainer, dataset, max_positions, subset):
- """Evaluate the model on the validation set and return the average loss."""
- itr = dataset.eval_dataloader(
- subset, max_tokens=args.max_tokens, max_sentences=args.max_sentences_valid,
- max_positions=max_positions,
- skip_invalid_size_inputs_valid_test=args.skip_invalid_size_inputs_valid_test,
- descending=True, # largest batch first to warm the caching allocator
- )
- loss_meter = AverageMeter()
- nll_loss_meter = AverageMeter()
- extra_meters = collections.defaultdict(lambda: AverageMeter())
- prefix = 'valid on \'{}\' subset'.format(subset)
- with utils.build_progress_bar(args, itr, epoch, prefix) as t:
- for _, sample in data.skip_group_enumerator(t, args.num_gpus):
- loss_dict = trainer.valid_step(sample)
- ntokens = sum(s['ntokens'] for s in sample)
- loss = loss_dict['loss']
- del loss_dict['loss'] # don't include in extra_meters or extra_postfix
- if 'nll_loss' in loss_dict:
- nll_loss = loss_dict['nll_loss']
- nll_loss_meter.update(nll_loss, ntokens)
- loss_meter.update(loss, ntokens)
- extra_postfix = []
- for k, v in loss_dict.items():
- extra_meters[k].update(v)
- extra_postfix.append((k, extra_meters[k].avg))
- t.log(collections.OrderedDict([
- ('valid loss', round(loss_meter.avg, 2)),
- ] + extra_postfix))
- t.print(collections.OrderedDict([
- ('valid loss', round(loss_meter.avg, 2)),
- ('valid ppl', get_perplexity(nll_loss_meter.avg
- if nll_loss_meter.count > 0
- else loss_meter.avg)),
- ] + [
- (k, meter.avg)
- for k, meter in extra_meters.items()
- ]))
- # update and return the learning rate
- return loss_meter.avg
- if __name__ == '__main__':
- main()
|