def main(args):
"""
Calc loss and perplexity on training and validation set
"""
logging.info('Commencing Validation!')
torch.manual_seed(42)
np.random.seed(42)
utils.init_logging(args)
# Load dictionaries [for each language]
src_dict = Dictionary.load(
os.path.join(args.data, 'dict.{:s}'.format(args.source_lang)))
logging.info('Loaded a source dictionary ({:s}) with {:d} words'.format(
args.source_lang, len(src_dict)))
tgt_dict = Dictionary.load(
os.path.join(args.data, 'dict.{:s}'.format(args.target_lang)))
logging.info('Loaded a target dictionary ({:s}) with {:d} words'.format(
args.target_lang, len(tgt_dict)))
# Load datasets
def load_data(split):
return Seq2SeqDataset(
src_file=os.path.join(args.data,
'{:s}.{:s}'.format(split, args.source_lang)),
tgt_file=os.path.join(args.data,
'{:s}.{:s}'.format(split, args.target_lang)),
src_dict=src_dict,
tgt_dict=tgt_dict)
train_dataset = load_data(
split='train') if not args.train_on_tiny else load_data(
split='tiny_train')
valid_dataset = load_data(split='valid')
# Build model and optimization criterion
model = models.build_model(args, src_dict, tgt_dict)
logging.info('Built a model with {:d} parameters'.format(
sum(p.numel() for p in model.parameters())))
criterion = nn.CrossEntropyLoss(ignore_index=src_dict.pad_idx,
reduction='sum')
if torch.cuda.is_available() and args.cuda:
model = model.cuda()
# Instantiate optimizer and learning rate scheduler
optimizer = torch.optim.Adam(model.parameters(), args.lr)
# Load last checkpoint if one exists
state_dict = utils.load_checkpoint(args, model, optimizer) # lr_scheduler
train_loader = \
torch.utils.data.DataLoader(train_dataset, num_workers = 1, collate_fn = train_dataset.collater,
batch_sampler = BatchSampler(train_dataset, args.max_tokens, args.batch_size, 1,
0, shuffle = True, seed = 42))
# Calculate validation loss
train_perplexity = validate(args, model, criterion, train_dataset, 0)
valid_perplexity = validate(args, model, criterion, valid_dataset, 0)
def main(args):
""" Main training function. Trains the translation model over the course of several epochs, including dynamic
learning rate adjustment and gradient clipping. """
logging.info('Commencing training!')
torch.manual_seed(42)
utils.init_logging(args)
# Load dictionaries
src_dict = Dictionary.load(
os.path.join(args.data, 'dict.{:s}'.format(args.source_lang)))
logging.info('Loaded a source dictionary ({:s}) with {:d} words'.format(
args.source_lang, len(src_dict)))
tgt_dict = Dictionary.load(
os.path.join(args.data, 'dict.{:s}'.format(args.target_lang)))
logging.info('Loaded a target dictionary ({:s}) with {:d} words'.format(
args.target_lang, len(tgt_dict)))
# Load datasets
def load_data(split):
return Seq2SeqDataset(
src_file=os.path.join(args.data,
'{:s}.{:s}'.format(split, args.source_lang)),
tgt_file=os.path.join(args.data,
'{:s}.{:s}'.format(split, args.target_lang)),
src_dict=src_dict,
tgt_dict=tgt_dict)
valid_dataset = load_data(split='valid')
# Build model and optimization criterion
model = models.build_model(args, src_dict, tgt_dict)
logging.info('Built a model with {:d} parameters'.format(
sum(p.numel() for p in model.parameters())))
criterion = nn.CrossEntropyLoss(ignore_index=src_dict.pad_idx,
reduction='sum')
if args.cuda:
model = model.cuda()
criterion = criterion.cuda()
# Instantiate optimizer and learning rate scheduler
optimizer = torch.optim.Adam(model.parameters(), args.lr)
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer,
'min',
factor=0.5,
patience=1)
# Load last checkpoint if one exists
state_dict = utils.load_checkpoint(args, model, optimizer,
scheduler) # lr_scheduler
last_epoch = state_dict['last_epoch'] if state_dict is not None else -1
# Track validation performance for early stopping
bad_epochs = 0
best_validate = float('inf')
for epoch in range(last_epoch + 1, args.max_epoch):
## BPE Dropout
# Set the seed to be equal to the epoch
# (this way we guarantee same seeds over multiple training runs, but not for each training epoch)
seed = epoch
bpe_dropout_if_needed(seed, args.bpe_dropout)
# Load the BPE (dropout-ed) training data
train_dataset = load_data(
split='train') if not args.train_on_tiny else load_data(
split='tiny_train')
train_loader = \
torch.utils.data.DataLoader(train_dataset, num_workers=1, collate_fn=train_dataset.collater,
batch_sampler=BatchSampler(train_dataset, args.max_tokens, args.batch_size, 1,
0, shuffle=True, seed=42))
model.train()
stats = OrderedDict()
stats['loss'] = 0
stats['lr'] = 0
stats['num_tokens'] = 0
stats['batch_size'] = 0
stats['grad_norm'] = 0
stats['clip'] = 0
# Display progress
progress_bar = tqdm(train_loader,
desc='| Epoch {:03d}'.format(epoch),
leave=False,
disable=False)
# Iterate over the training set
for i, sample in enumerate(progress_bar):
if args.cuda:
sample = utils.move_to_cuda(sample)
if len(sample) == 0:
continue
model.train()
output, _ = model(sample['src_tokens'], sample['src_lengths'],
sample['tgt_inputs'])
loss = \
criterion(output.view(-1, output.size(-1)), sample['tgt_tokens'].view(-1)) / len(sample['src_lengths'])
loss.backward()
grad_norm = torch.nn.utils.clip_grad_norm_(model.parameters(),
args.clip_norm)
optimizer.step()
optimizer.zero_grad()
# Update statistics for progress bar
total_loss, num_tokens, batch_size = loss.item(
), sample['num_tokens'], len(sample['src_tokens'])
stats['loss'] += total_loss * len(
sample['src_lengths']) / sample['num_tokens']
stats['lr'] += optimizer.param_groups[0]['lr']
stats['num_tokens'] += num_tokens / len(sample['src_tokens'])
stats['batch_size'] += batch_size
stats['grad_norm'] += grad_norm
stats['clip'] += 1 if grad_norm > args.clip_norm else 0
progress_bar.set_postfix(
{
key: '{:.4g}'.format(value / (i + 1))
for key, value in stats.items()
},
refresh=True)
logging.info('Epoch {:03d}: {}'.format(
epoch, ' | '.join(key + ' {:.4g}'.format(value / len(progress_bar))
for key, value in stats.items())))
# Calculate validation loss
valid_perplexity, valid_loss = validate(args, model, criterion,
valid_dataset, epoch)
model.train()
# Scheduler step
if args.adaptive_lr:
scheduler.step(valid_loss)
# Save checkpoints
if epoch % args.save_interval == 0:
utils.save_checkpoint(args, model, optimizer, scheduler, epoch,
valid_perplexity) # lr_scheduler
# Check whether to terminate training
if valid_perplexity < best_validate:
best_validate = valid_perplexity
bad_epochs = 0
else:
bad_epochs += 1
if bad_epochs >= args.patience:
logging.info(
'No validation set improvements observed for {:d} epochs. Early stop!'
.format(args.patience))
break
def main(args):
if not torch.cuda.is_available():
raise NotImplementedError('Training on CPU is not supported.')
torch.manual_seed(args.seed)
torch.cuda.set_device(args.device_id)
utils.init_logging(args)
if args.distributed_world_size > 1:
torch.distributed.init_process_group(
backend=args.distributed_backend,
init_method=args.distributed_init_method,
world_size=args.distributed_world_size,
rank=args.distributed_rank)
# Load dictionaries
src_dict = Dictionary.load(
os.path.join(args.data, 'dict.{}'.format(args.source_lang)))
logging.info('Loaded a source dictionary ({}) with {} words'.format(
args.source_lang, len(src_dict)))
tgt_dict = Dictionary.load(
os.path.join(args.data, 'dict.{}'.format(args.target_lang)))
logging.info('Loaded a target dictionary ({}) with {} words'.format(
args.target_lang, len(tgt_dict)))
# Load datasets
def load_data(split):
return Seq2SeqDataset(
src_file=os.path.join(args.data,
'{}.{}'.format(split, args.source_lang)),
tgt_file=os.path.join(args.data,
'{}.{}'.format(split, args.target_lang)),
src_dict=src_dict,
tgt_dict=tgt_dict)
train_dataset = load_data(split='train')
valid_dataset = load_data(split='valid')
# Build model and criterion
model = models.build_model(args, src_dict, tgt_dict).cuda()
logging.info('Built a model with {} parameters'.format(
sum(p.numel() for p in model.parameters())))
criterion = nn.CrossEntropyLoss(ignore_index=src_dict.pad_idx,
reduction='sum').cuda()
# Build an optimizer and a learning rate schedule
optimizer = torch.optim.SGD(model.parameters(),
args.lr,
args.momentum,
weight_decay=args.weight_decay,
nesterov=True)
lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
optimizer, patience=0, min_lr=args.min_lr, factor=args.lr_shrink)
# Load last checkpoint if one exists
state_dict = utils.load_checkpoint(args, model, optimizer, lr_scheduler)
last_epoch = state_dict['last_epoch'] if state_dict is not None else -1
for epoch in range(last_epoch + 1, args.max_epoch):
train_loader = torch.utils.data.DataLoader(
train_dataset,
num_workers=args.num_workers,
collate_fn=train_dataset.collater,
batch_sampler=BatchSampler(train_dataset,
args.max_tokens,
args.batch_size,
args.distributed_world_size,
args.distributed_rank,
shuffle=True,
seed=args.seed))
model.train()
stats = {
'loss': 0.,
'lr': 0.,
'num_tokens': 0.,
'batch_size': 0.,
'grad_norm': 0.,
'clip': 0.
}
progress_bar = tqdm(train_loader,
desc='| Epoch {:03d}'.format(epoch),
leave=False,
disable=(args.distributed_rank != 0))
for i, sample in enumerate(progress_bar):
sample = utils.move_to_cuda(sample)
if len(sample) == 0:
continue
# Forward and backward pass
output, _ = model(sample['src_tokens'], sample['src_lengths'],
sample['tgt_inputs'])
loss = criterion(output.view(-1, output.size(-1)),
sample['tgt_tokens'].view(-1))
optimizer.zero_grad()
loss.backward()
# Reduce gradients across all GPUs
if args.distributed_world_size > 1:
utils.reduce_grads(model.parameters())
total_loss, num_tokens, batch_size = list(
map(
sum,
zip(*utils.all_gather_list([
loss.item(), sample['num_tokens'],
len(sample['src_tokens'])
]))))
else:
total_loss, num_tokens, batch_size = loss.item(
), sample['num_tokens'], len(sample['src_tokens'])
# Normalize gradients by number of tokens and perform clipping
for param in model.parameters():
param.grad.data.div_(num_tokens)
grad_norm = torch.nn.utils.clip_grad_norm_(model.parameters(),
args.clip_norm)
optimizer.step()
# Update statistics for progress bar
stats['loss'] += total_loss / num_tokens / math.log(2)
stats['lr'] += optimizer.param_groups[0]['lr']
stats['num_tokens'] += num_tokens / len(sample['src_tokens'])
stats['batch_size'] += batch_size
stats['grad_norm'] += grad_norm
stats['clip'] += 1 if grad_norm > args.clip_norm else 0
progress_bar.set_postfix(
{
key: '{:.4g}'.format(value / (i + 1))
for key, value in stats.items()
},
refresh=True)
logging.info('Epoch {:03d}: {}'.format(
epoch, ' | '.join(key + ' {:.4g}'.format(value / len(progress_bar))
for key, value in stats.items())))
# Adjust learning rate based on validation loss
valid_loss = validate(args, model, criterion, valid_dataset, epoch)
lr_scheduler.step(valid_loss)
# Save checkpoints
if epoch % args.save_interval == 0:
utils.save_checkpoint(args, model, optimizer, lr_scheduler, epoch,
valid_loss)
if optimizer.param_groups[0]['lr'] <= args.min_lr:
logging.info('Done training!')
break
def main(args):
""" Main training function. Trains the translation model over the course of several epochs, including dynamic
learning rate adjustment and gradient clipping. """
logging.info('Commencing training!')
torch.manual_seed(42)
np.random.seed(42)
utils.init_logging(args)
# Load dictionaries
src_dict = Dictionary.load(os.path.join(args.data, 'dict.{:s}'.format(args.source_lang)))
logging.info('Loaded a source dictionary ({:s}) with {:d} words'.format(args.source_lang, len(src_dict)))
tgt_dict = Dictionary.load(os.path.join(args.data, 'dict.{:s}'.format(args.target_lang)))
logging.info('Loaded a target dictionary ({:s}) with {:d} words'.format(args.target_lang, len(tgt_dict)))
# Load datasets
def load_data(split):
return Seq2SeqDataset(
src_file=os.path.join(args.data, '{:s}.{:s}'.format(split, args.source_lang)),
tgt_file=os.path.join(args.data, '{:s}.{:s}'.format(split, args.target_lang)),
src_dict=src_dict, tgt_dict=tgt_dict)
train_dataset = load_data(split='train') if not args.train_on_tiny else load_data(split='tiny_train')
valid_dataset = load_data(split='valid')
# yichao: enable cuda
use_cuda = torch.cuda.is_available() and args.device == 'cuda'
device = torch.device("cuda" if use_cuda else "cpu")
print("===> Using %s" % device)
# Build model and optimization criterion
# yichao: enable cuda, i.e. add .to(device)
model = models.build_model(args, src_dict, tgt_dict).to(device)
logging.info('Built a model with {:d} parameters'.format(sum(p.numel() for p in model.parameters())))
criterion = nn.CrossEntropyLoss(ignore_index=src_dict.pad_idx, reduction='sum').to(device)
# Instantiate optimizer and learning rate scheduler
optimizer = torch.optim.Adam(model.parameters(), args.lr)
# Load last checkpoint if one exists
state_dict = utils.load_checkpoint(args, model, optimizer) # lr_scheduler
last_epoch = state_dict['last_epoch'] if state_dict is not None else -1
# Track validation performance for early stopping
bad_epochs = 0
best_validate = float('inf')
for epoch in range(last_epoch + 1, args.max_epoch):
train_loader = \
torch.utils.data.DataLoader(train_dataset, num_workers=1, collate_fn=train_dataset.collater,
batch_sampler=BatchSampler(train_dataset, args.max_tokens, args.batch_size, 1,
0, shuffle=True, seed=42))
model.train()
stats = OrderedDict()
stats['loss'] = 0
stats['lr'] = 0
stats['num_tokens'] = 0
stats['batch_size'] = 0
stats['grad_norm'] = 0
stats['clip'] = 0
# Display progress
progress_bar = tqdm(train_loader, desc='| Epoch {:03d}'.format(epoch), leave=False, disable=False)
# Iterate over the training set
for i, sample in enumerate(progress_bar):
if len(sample) == 0:
continue
model.train()
'''
___QUESTION-1-DESCRIBE-F-START___
Describe what the following lines of code do.
'''
# yichao: enable cuda
sample['src_tokens'], sample['src_lengths'], sample['tgt_inputs'], sample['tgt_tokens'] = \
sample['src_tokens'].to(device), sample['src_lengths'].to(device), \
sample['tgt_inputs'].to(device), sample['tgt_tokens'].to(device)
output, _ = model(sample['src_tokens'], sample['src_lengths'], sample['tgt_inputs'])
loss = \
criterion(output.view(-1, output.size(-1)), sample['tgt_tokens'].view(-1)) / len(sample['src_lengths'])
loss.backward()
grad_norm = torch.nn.utils.clip_grad_norm_(model.parameters(), args.clip_norm)
optimizer.step()
optimizer.zero_grad()
'''___QUESTION-1-DESCRIBE-F-END___'''
# Update statistics for progress bar
total_loss, num_tokens, batch_size = loss.item(), sample['num_tokens'], len(sample['src_tokens'])
stats['loss'] += total_loss * len(sample['src_lengths']) / sample['num_tokens']
stats['lr'] += optimizer.param_groups[0]['lr']
stats['num_tokens'] += num_tokens / len(sample['src_tokens'])
stats['batch_size'] += batch_size
stats['grad_norm'] += grad_norm
stats['clip'] += 1 if grad_norm > args.clip_norm else 0
progress_bar.set_postfix({key: '{:.4g}'.format(value / (i + 1)) for key, value in stats.items()},
refresh=True)
logging.info('Epoch {:03d}: {}'.format(epoch, ' | '.join(key + ' {:.4g}'.format(
value / len(progress_bar)) for key, value in stats.items())))
# Calculate validation loss
valid_perplexity = validate(args, model, criterion, valid_dataset, epoch)
model.train()
# Save checkpoints
if epoch % args.save_interval == 0:
utils.save_checkpoint(args, model, optimizer, epoch, valid_perplexity) # lr_scheduler
# Check whether to terminate training
if valid_perplexity < best_validate:
best_validate = valid_perplexity
bad_epochs = 0
else:
bad_epochs += 1
if bad_epochs >= args.patience:
logging.info('No validation set improvements observed for {:d} epochs. Early stop!'.format(args.patience))
break
def main(args):
""" Main training function. Trains the translation model over the course of several epochs, including dynamic
learning rate adjustment and gradient clipping. """
logging.info('Commencing training!')
torch.manual_seed(42)
utils.init_logging(args)
# Load dictionaries
src_dict = Dictionary.load(
os.path.join(args.data, 'dict.{:s}'.format(args.source_lang)))
logging.info('Loaded a source dictionary ({:s}) with {:d} words'.format(
args.source_lang, len(src_dict)))
tgt_dict = Dictionary.load(
os.path.join(args.data, 'dict.{:s}'.format(args.target_lang)))
logging.info('Loaded a target dictionary ({:s}) with {:d} words'.format(
args.target_lang, len(tgt_dict)))
# Load datasets
def load_data(split):
return Seq2SeqDataset(
src_file=os.path.join(args.data,
'{:s}.{:s}'.format(split, args.source_lang)),
tgt_file=os.path.join(args.data,
'{:s}.{:s}'.format(split, args.target_lang)),
src_dict=src_dict,
tgt_dict=tgt_dict)
train_dataset = load_data(
split='train') if not args.train_on_tiny else load_data(
split='tiny_train')
valid_dataset = load_data(split='valid')
# Build model and optimization criterion
model = models.build_model(args, src_dict, tgt_dict)
model_rev = models.build_model(args, tgt_dict, src_dict)
logging.info('Built a model with {:d} parameters'.format(
sum(p.numel() for p in model.parameters())))
criterion = nn.CrossEntropyLoss(ignore_index=src_dict.pad_idx,
reduction='sum')
criterion2 = nn.MSELoss(reduction='sum')
if args.cuda:
model = model.cuda()
model_rev = model_rev.cuda()
criterion = criterion.cuda()
# Instantiate optimizer and learning rate scheduler
optimizer = torch.optim.Adam(model.parameters(), args.lr)
# Load last checkpoint if one exists
state_dict = utils.load_checkpoint(args, model, optimizer) # lr_scheduler
utils.load_checkpoint_rev(args, model_rev, optimizer) # lr_scheduler
last_epoch = state_dict['last_epoch'] if state_dict is not None else -1
# Track validation performance for early stopping
bad_epochs = 0
best_validate = float('inf')
for epoch in range(last_epoch + 1, args.max_epoch):
train_loader = \
torch.utils.data.DataLoader(train_dataset, num_workers=1, collate_fn=train_dataset.collater,
batch_sampler=BatchSampler(train_dataset, args.max_tokens, args.batch_size, 1,
0, shuffle=True, seed=42))
model.train()
model_rev.train()
stats = OrderedDict()
stats['loss'] = 0
stats['lr'] = 0
stats['num_tokens'] = 0
stats['batch_size'] = 0
stats['grad_norm'] = 0
stats['clip'] = 0
# Display progress
progress_bar = tqdm(train_loader,
desc='| Epoch {:03d}'.format(epoch),
leave=False,
disable=False)
# Iterate over the training set
for i, sample in enumerate(progress_bar):
if args.cuda:
sample = utils.move_to_cuda(sample)
if len(sample) == 0:
continue
model.train()
(output, att), src_out = model(sample['src_tokens'],
sample['src_lengths'],
sample['tgt_inputs'])
# print(sample['src_lengths'])
# print(sample['tgt_inputs'].size())
# print(sample['src_tokens'].size())
src_inputs = sample['src_tokens'].clone()
src_inputs[0, 1:src_inputs.size(1)] = sample['src_tokens'][0, 0:(
src_inputs.size(1) - 1)]
src_inputs[0, 0] = sample['src_tokens'][0, src_inputs.size(1) - 1]
tgt_lengths = sample['src_lengths'].clone(
) #torch.tensor([sample['tgt_tokens'].size(1)])
tgt_lengths += sample['tgt_inputs'].size(
1) - sample['src_tokens'].size(1)
# print(tgt_lengths)
# print(sample['num_tokens'])
# if args.cuda:
# tgt_lengths = tgt_lengths.cuda()
(output_rev,
att_rev), src_out_rev = model_rev(sample['tgt_tokens'],
tgt_lengths, src_inputs)
# notice that those are without masks already
# print(sample['tgt_tokens'].view(-1))
d, d_rev = get_diff(att, src_out, att_rev, src_out_rev)
# print(sample['src_tokens'].size())
# print(sample['tgt_inputs'].size())
# print(att.size())
# print(src_out.size())
# print(acontext.size())
# print(src_out_rev.size())
# # print(sample['tgt_inputs'].dtype)
# # print(sample['src_lengths'])
# # print(sample['src_tokens'])
# # print('output %s' % str(output.size()))
# # print(att)
# # print(len(sample['src_lengths']))
# print(d)
# print(d_rev)
# print(criterion(output.view(-1, output.size(-1)), sample['tgt_tokens'].view(-1)) / len(sample['src_lengths']))
# print(att2)
# output=output.cpu().detach().numpy()
# output=torch.from_numpy(output).cuda()
# output_rev=output_rev.cpu().detach().numpy()
# output_rev=torch.from_numpy(output_rev).cuda()
loss = \
criterion(output.view(-1, output.size(-1)), sample['tgt_tokens'].view(-1)) / len(sample['src_lengths']) + d +\
criterion(output_rev.view(-1, output_rev.size(-1)), sample['src_tokens'].view(-1)) / len(tgt_lengths) +d_rev
loss.backward()
grad_norm = torch.nn.utils.clip_grad_norm_(model.parameters(),
args.clip_norm)
# loss_rev = \
# criterion(output_rev.view(-1, output_rev.size(-1)), sample['src_tokens'].view(-1)) / len(tgt_lengths)
# loss_rev.backward()
# grad_norm_rev = torch.nn.utils.clip_grad_norm_(model_rev.parameters(), args.clip_norm)
optimizer.step()
optimizer.zero_grad()
# Update statistics for progress bar
total_loss, num_tokens, batch_size = (
loss - d - d_rev).item(), sample['num_tokens'], len(
sample['src_tokens'])
stats['loss'] += total_loss * len(
sample['src_lengths']) / sample['num_tokens']
# stats['loss_rev'] += loss_rev.item() * len(sample['src_lengths']) / sample['src_tokens'].size(0) / sample['src_tokens'].size(1)
stats['lr'] += optimizer.param_groups[0]['lr']
stats['num_tokens'] += num_tokens / len(sample['src_tokens'])
stats['batch_size'] += batch_size
stats['grad_norm'] += grad_norm
stats['clip'] += 1 if grad_norm > args.clip_norm else 0
progress_bar.set_postfix(
{
key: '{:.4g}'.format(value / (i + 1))
for key, value in stats.items()
},
refresh=True)
logging.info('Epoch {:03d}: {}'.format(
epoch, ' | '.join(key + ' {:.4g}'.format(value / len(progress_bar))
for key, value in stats.items())))
# Calculate validation loss
valid_perplexity = validate(args, model, model_rev, criterion,
valid_dataset, epoch)
model.train()
model_rev.train()
# Save checkpoints
if epoch % args.save_interval == 0:
utils.save_checkpoint(args, model, model_rev, optimizer, epoch,
valid_perplexity) # lr_scheduler
# Check whether to terminate training
if valid_perplexity < best_validate:
best_validate = valid_perplexity
bad_epochs = 0
else:
bad_epochs += 1
if bad_epochs >= args.patience:
logging.info(
'No validation set improvements observed for {:d} epochs. Early stop!'
.format(args.patience))
break
