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train.py
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train.py
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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.
"""
Train a new model on one or across multiple GPUs.
"""
import collections
import itertools
import os,time
import math
import random
from collections import OrderedDict
import torch
from fairseq import distributed_utils, options, progress_bar, tasks, utils, bleu
from fairseq.data import iterators
from fairseq.trainer import Trainer
from fairseq.meters import AverageMeter, StopwatchMeter
from fairseq.utils import import_user_module
def main(args, init_distributed=False):
import_user_module(args)
if args.max_tokens is None:
args.max_tokens = 6000
print(args)
if torch.cuda.is_available() and not args.cpu:
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)
assert args.task == 'translation'
# Load dataset splits
load_dataset_splits(task, ['train', 'valid'])
# Initialize distributed training (after data loading)
if init_distributed:
import socket
args.distributed_rank = distributed_utils.distributed_init(args)
print('| initialized host {} as rank {}'.format(socket.gethostname(), args.distributed_rank))
# 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: {} (num. trained: {})'.format(
sum(p.numel() for p in model.parameters()),
sum(p.numel() for p in model.parameters() if p.requires_grad),
))
# Make a dummy batch to (i) warm the caching allocator and (ii) as a
# placeholder DistributedDataParallel when there's an uneven number of
# batches per worker.
max_positions = utils.resolve_max_positions(
task.max_positions(),
model.max_positions(),
)
dummy_batch = task.dataset('train').get_dummy_batch(args.max_tokens, max_positions)
oom_batch = task.dataset('train').get_dummy_batch(1, max_positions)
# Build trainer
trainer = Trainer(args, task, model, criterion, dummy_batch, oom_batch)
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
epoch_itr = task.get_batch_iterator(
dataset=task.dataset(args.train_subset),
max_tokens=args.max_tokens,
max_sentences=args.max_sentences,
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,
num_workers=args.num_workers,
)
# Load the latest checkpoint if one is available
if not load_checkpoint(args, trainer, epoch_itr):
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_bleus = [None]
valid_subsets = args.valid_subset.split(',')
print('start time is :',time.strftime("%Y-%m-%d %X"))
while lr > args.min_lr and epoch_itr.epoch < max_epoch and trainer.get_num_updates() < max_update:
train(args, trainer, task, epoch_itr)
if epoch_itr.epoch % args.validate_interval == 0:
valid_losses, valid_bleus = 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], valid_bleus[0])
train_meter.stop()
print('| done training in {:.1f} seconds'.format(train_meter.sum))
print('finish time is :',time.strftime("%Y-%m-%d %X"))
def train(args, trainer, task, epoch_itr):
"""Train the model for one epoch."""
# Update parameters every N batches
update_freq = args.update_freq[epoch_itr.epoch - 1] \
if epoch_itr.epoch <= len(args.update_freq) else args.update_freq[-1]
# Initialize data iterator
itr = epoch_itr.next_epoch_itr(
fix_batches_to_gpus=args.fix_batches_to_gpus,
shuffle=(epoch_itr.epoch >= args.curriculum),
)
itr = iterators.GroupedIterator(itr, update_freq)
progress = progress_bar.build_progress_bar(
args, itr, epoch_itr.epoch, no_progress_bar='simple',
)
extra_meters = collections.defaultdict(lambda: AverageMeter())
first_valid = args.valid_subset.split(',')[0]
max_update = args.max_update or math.inf
for i, samples in enumerate(progress, start=epoch_itr.iterations_in_epoch):
log_output = trainer.train_step(samples)
if log_output is None:
continue
# log mid-epoch stats
stats = get_training_stats(trainer)
for k, v in log_output.items():
if k in ['loss', 'nll_loss', 'ntokens', 'nsentences', '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, tag='train', step=stats['num_updates'])
# 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 and num_updates > 0:
valid_losses, valid_bleus = validate(args, trainer, task, epoch_itr, [first_valid])
save_checkpoint(args, trainer, epoch_itr, valid_losses[0], valid_bleus[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, tag='train', step=stats['num_updates'])
# reset training meters
for k in [
'train_loss', 'train_nll_loss', 'wps', 'ups', 'wpb', 'bsz', 'gnorm', 'clip',
]:
meter = trainer.get_meter(k)
if meter is not None:
meter.reset()
def get_training_stats(trainer):
stats = collections.OrderedDict()
stats['loss'] = trainer.get_meter('train_loss')
if trainer.get_meter('train_nll_loss').count > 0:
nll_loss = trainer.get_meter('train_nll_loss')
stats['nll_loss'] = nll_loss
else:
nll_loss = trainer.get_meter('train_loss')
stats['ppl'] = get_perplexity(nll_loss.avg)
stats['wps'] = trainer.get_meter('wps')
stats['ups'] = trainer.get_meter('ups')
stats['wpb'] = trainer.get_meter('wpb')
stats['bsz'] = trainer.get_meter('bsz')
stats['num_updates'] = trainer.get_num_updates()
stats['lr'] = trainer.get_lr()
stats['gnorm'] = trainer.get_meter('gnorm')
stats['clip'] = trainer.get_meter('clip')
stats['oom'] = trainer.get_meter('oom')
if trainer.get_meter('loss_scale') is not None:
stats['loss_scale'] = trainer.get_meter('loss_scale')
stats['wall'] = round(trainer.get_meter('wall').elapsed_time)
stats['train_wall'] = trainer.get_meter('train_wall')
return stats
def validate(args, trainer, task, epoch_itr, subsets):
"""Evaluate the model on the validation set(s) and return the losses."""
valid_losses = []
valid_bleus = []
for subset in subsets:
# Initialize data iterator
trainer_scorer = bleu.Scorer(trainer._tgt_dict.pad(), trainer._tgt_dict.eos(), trainer._tgt_dict.unk())
itr = task.get_batch_iterator(
dataset=task.dataset(subset),
max_tokens=args.max_tokens,
max_sentences=args.max_sentences_valid,
max_positions=utils.resolve_max_positions(
task.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,
num_workers=args.num_workers,
).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','valid_bleu']:
meter = trainer.get_meter(k)
meter.reset()
extra_meters = collections.defaultdict(lambda: AverageMeter())
for sample in progress:
log_output = trainer.valid_step(sample, trainer_scorer=trainer_scorer)
for k, v in log_output.items():
if k in ['loss', 'nll_loss', 'ntokens', 'nsentences', 'sample_size']:
continue
extra_meters[k].update(v)
trainer.get_meter('valid_bleu').update(trainer_scorer.score(order=4), 1.0)
# log validation stats
stats = get_valid_stats(trainer)
for k in ['loss', 'nll_loss', 'bleu']:
stats[k] = stats[k].avg
for k, meter in extra_meters.items():
stats[k] = meter.avg
progress.print(stats, tag=subset, step=trainer.get_num_updates())
valid_losses.append(stats['loss'])
valid_bleus.append(stats['bleu'])
trainer_scorer.reset()
return valid_losses, valid_bleus
def get_valid_stats(trainer):
stats = collections.OrderedDict()
stats['loss'] = trainer.get_meter('valid_loss')
stats['bleu'] = trainer.get_meter('valid_bleu')
if trainer.get_meter('valid_nll_loss').count > 0:
nll_loss = trainer.get_meter('valid_nll_loss')
stats['nll_loss'] = nll_loss
else:
nll_loss = stats['loss']
stats['ppl'] = get_perplexity(nll_loss.avg)
stats['num_updates'] = trainer.get_num_updates()
if hasattr(save_checkpoint_bleu, 'best_bleu'):
stats['best_bleu'] = max(save_checkpoint_bleu.best_bleu, stats['bleu'].avg)
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, val_bleu):
save_checkpoint_bleu(args, trainer, epoch_itr, val_loss, val_bleu)
def save_checkpoint_bleu(args, trainer, epoch_itr, val_loss, val_bleu):
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_bleu.pt'] = (
val_bleu is not None and
(not hasattr(save_checkpoint_bleu, 'best_bleu') or val_bleu > save_checkpoint_bleu.best_bleu)
)
checkpoint_conds['checkpoint_last.pt'] = True # keep this last so that it's a symlink
prev_best_bleu = getattr(save_checkpoint_bleu, 'best_bleu', val_bleu)
if val_bleu is not None:
save_checkpoint_bleu.best_bleu = max(val_bleu, prev_best_bleu)
extra_state = {
'train_iterator': epoch_itr.state_dict(),
'val_loss': val_loss,
'val_bleu': val_bleu,
}
if hasattr(save_checkpoint_bleu, 'best_bleu'):
extra_state.update({'best_bleu': save_checkpoint_bleu.best_bleu})
checkpoints = [os.path.join(args.save_dir, fn) for fn, cond in checkpoint_conds.items() if cond]
if len(checkpoints) > 0:
for cp in checkpoints:
trainer.save_checkpoint(cp, extra_state)
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:]:
if os.path.lexists(old_chk):
os.remove(old_chk)
if args.keep_last_epochs > 0:
# remove old epoch checkpoints; checkpoints are sorted in descending order
# checkpoints = utils.checkpoint_paths(args.save_dir, pattern=r'checkpoint\d+\.pt')
checkpoints = utils.checkpoint_paths(args.save_dir, pattern=r'checkpoint(\d+)\.pt')
for old_chk in checkpoints[args.keep_last_epochs:]:
if os.path.lexists(old_chk):
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)
if os.path.isabs(args.restore_file):
checkpoint_path = args.restore_file
else:
checkpoint_path = os.path.join(args.save_dir, args.restore_file)
if os.path.isfile(checkpoint_path):
extra_state = trainer.load_checkpoint(checkpoint_path, args.reset_optimizer, args.reset_lr_scheduler,
eval(args.optimizer_overrides))
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_bleu' in extra_state:
save_checkpoint_bleu.best_bleu = extra_state['best_bleu']
return True
else:
print('| no existing checkpoint found {}'.format(checkpoint_path))
return False
def load_dataset_splits(task, splits):
for split in splits:
if split == 'train':
task.load_dataset(split, combine=True)
else:
for k in itertools.count():
split_k = split + (str(k) if k > 0 else '')
try:
task.load_dataset(split_k, combine=False)
except FileNotFoundError as e:
if k > 0:
break
raise e
def distributed_main(i, args):
args.device_id = i
if args.distributed_rank is None: # torch.multiprocessing.spawn
args.distributed_rank = i
main(args, init_distributed=True)
def cli_main():
parser = options.get_training_parser()
args = options.parse_args_and_arch(parser)
if args.distributed_init_method is None:
distributed_utils.infer_init_method(args)
if args.distributed_init_method is not None:
# distributed training
distributed_main(args.device_id, args)
elif args.distributed_world_size > 1:
# fallback for single node with multiple GPUs
port = random.randint(10000, 20000)
args.distributed_init_method = 'tcp://localhost:{port}'.format(port=port)
args.distributed_rank = None # set based on device id
if max(args.update_freq) > 1 and args.ddp_backend != 'no_c10d':
print('| NOTE: you may get better performance with: --ddp-backend=no_c10d')
torch.multiprocessing.spawn(
fn=distributed_main,
args=(args, ),
nprocs=args.distributed_world_size,
)
else:
# single GPU training
main(args)
if __name__ == '__main__':
cli_main()