def get_parser_with_args():
parser = options.get_parser("Trainer", default_task="pytorch_translate")
pytorch_translate_options.add_verbosity_args(parser, train=True)
pytorch_translate_options.add_dataset_args(parser, train=True, gen=True)
options.add_distributed_training_args(parser)
# Adds args related to training (validation and stopping criterions).
optimization_group = options.add_optimization_args(parser)
pytorch_translate_options.expand_optimization_args(optimization_group)
# Adds args related to checkpointing.
checkpointing_group = options.add_checkpoint_args(parser)
pytorch_translate_options.expand_checkpointing_args(checkpointing_group)
# Add model related args
options.add_model_args(parser)
# Adds args for generating intermediate BLEU eval while training.
generation_group = options.add_generation_args(parser)
pytorch_translate_options.expand_generation_args(generation_group,
train=True)
# Adds args related to input data files (preprocessing, numberizing, and
# binarizing text files; creating vocab files)
pytorch_translate_options.add_preprocessing_args(parser)
return parser
def get_parser_with_args():
parser = options.get_parser("Trainer")
parser.add_argument(
"--log-verbose",
action="store_true",
help="Whether to output more verbose logs for debugging/profiling.",
)
pytorch_translate_options.add_dataset_args(parser, train=True, gen=True)
options.add_distributed_training_args(parser)
# Adds args related to training (validation and stopping criterions).
optimization_group = options.add_optimization_args(parser)
pytorch_translate_options.expand_optimization_args(optimization_group)
# Adds args related to checkpointing.
checkointing_group = options.add_checkpoint_args(parser)
pytorch_translate_options.expand_checkpointing_args(checkointing_group)
# Add model related args
options.add_model_args(parser)
# Adds args for generating intermediate BLEU eval while training.
generation_group = options.add_generation_args(parser)
pytorch_translate_options.expand_generation_args(generation_group, train=True)
# Adds args related to input data files (preprocessing, numberizing, and
# binarizing text files; creating vocab files)
pytorch_translate_options.add_preprocessing_args(parser)
return parser
def main():
parser = options.get_parser('Trainer')
dataset_args = options.add_dataset_args(parser)
dataset_args.add_argument('--max-tokens',
default=0,
type=int,
metavar='N',
help='maximum number of tokens in a batch')
dataset_args.add_argument('--batch-size',
default=32,
type=int,
metavar='N',
help='batch size')
dataset_args.add_argument('--test-batch-size',
default=32,
type=int,
metavar='N',
help='batch size for test set')
dataset_args.add_argument('--valid-batch-size',
default=32,
type=int,
metavar='N',
help='batch size for validation set')
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 ofdata subsets '
' to use for validation (train, valid, valid1,test, test1)')
dataset_args.add_argument('--test-subset',
default='test',
metavar='SPLIT',
help='comma separated list ofdata subset '
'to use for testing (train, valid, test)')
dataset_args.add_argument(
'--valid-script',
nargs='+',
metavar='PATH',
help='path to external validation script (optional).')
options.add_optimization_args(parser)
options.add_checkpoint_args(parser)
options.add_model_args(parser)
args = utils.parse_args_and_arch(parser)
print(args)
if args.no_progress_bar:
progress_bar.enabled = False
progress_bar.print_interval = args.log_interval
if not os.path.exists(args.save_dir):
os.makedirs(args.save_dir)
torch.manual_seed(args.seed)
# Setting args.max_tokens to infinity(same as setting to None)
if args.max_tokens == 0:
args.max_tokens = None
# Load dataset
dataset = data.load_with_check(args.data, 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 dataset.splits:
print('| {} {} {} examples'.format(args.data, split,
len(dataset.splits[split])))
if not torch.cuda.is_available():
raise NotImplementedError('Training on CPU is not supported')
num_gpus = torch.cuda.device_count()
print('| using {} GPUs (with max tokens per GPU = {})'.format(
num_gpus, args.max_tokens))
# Build model
print('| model {}'.format(args.arch))
model = utils.build_model(args, dataset)
criterion = utils.build_criterion(args, dataset)
# Start multiprocessing
trainer = MultiprocessingTrainer(args, model)
# Load the latest checkpoint if one is available
epoch, batch_offset = trainer.load_checkpoint(
os.path.join(args.save_dir, args.restore_file))
# 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, criterion, dataset, num_gpus)
# evaluate on validate set
for k, subset in enumerate(args.valid_subset.split(',')):
val_loss = validate(args, epoch, trainer, criterion, dataset,
subset, num_gpus)
if k == 0:
if not args.no_save:
# save checkpoint
trainer.save_checkpoint(
args,
epoch,
0,
val_loss,
validation_script=args.valid_script)
# 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))
# Generate on test set and compute BLEU score
for beam in [1, 5, 10, 20]:
for subset in args.test_subset.split(','):
scorer = score_test(args,
trainer.get_model(),
dataset,
subset,
beam,
cuda_device=(0 if num_gpus > 0 else None))
print('| Test on {} with beam={}: {}'.format(
subset, beam, scorer.result_string()))
# Stop multiprocessing
trainer.stop()
def get_parser_with_args():
parser = options.get_parser("Trainer")
options.add_dataset_args(parser, train=True, gen=True)
options.add_distributed_training_args(parser)
options.add_optimization_args(parser)
options.add_checkpoint_args(parser)
options.add_model_args(parser)
options.add_generation_args(parser)
parser.add_argument(
"--log-verbose",
action="store_true",
help="Whether to output more verbose logs for debugging/profiling.",
)
# Adds args related to training (validation and stopping criterions).
group = parser.add_argument_group("Optimization")
group.add_argument(
"--subepoch-validate-interval",
default=0,
type=int,
metavar="N",
help="Calculates loss over the validation set every N batch updates. "
"Note that validation is done at the end of every epoch regardless. "
"A value of <= 0 disables this.",
)
group.add_argument(
"--stop-time-hr",
default=-1,
type=int,
metavar="N",
help="Stops training after N hours have elapsed. "
"A value of < 0 disables this.",
)
group.add_argument(
"--stop-no-best-validate-loss",
default=-1,
type=int,
metavar="N",
help="Stops training after N validations have been run without "
"achieving a better loss than before. Note that this is affected by "
"--validation-interval in how frequently we run validation in the "
"first place. A value of < 0 disables this.",
)
group.add_argument(
"--stop-no-best-bleu-eval",
default=-1,
type=int,
metavar="N",
help="Stops training after N evals have been run without "
"achieving a better BLEU score than before. Note that this is affected "
"by --generate-bleu-eval-interval in how frequently we run BLEU eval "
"in the first place. A value of < 0 disables this.",
)
# Adds args related to input data files (preprocessing, numberizing, and
# binarizing text files; creating vocab files)
preprocess.add_args(parser)
# Adds args related to checkpointing.
group = parser.add_argument_group("Checkpointing")
group.add_argument(
"--no-end-of-epoch-checkpoints",
action="store_true",
help="Disables saving checkpoints at the end of the epoch. "
"This differs from --no-save and --no-epoch-checkpoints in that it "
"still allows for intra-epoch checkpoints if --save-interval is set.",
)
group.add_argument(
"--max-checkpoints-kept",
default=-1,
type=int,
metavar="N",
help="Keep at most the last N checkpoints file around. "
"A value < -1 keeps all. "
"When --generate-bleu-eval-avg-checkpoints is used and is > N, the "
"number of checkpoints kept around is automatically adjusted "
"to allow BLEU to work properly.",
)
# Adds args for generating intermediate BLEU eval while training.
# generate.add_args() adds args used by both train.py and the standalone
# generate binary, while the flags defined here are used only by train.py.
generate.add_args(parser)
group = parser.add_argument_group("Generation")
group.add_argument(
"--generate-bleu-eval-per-epoch",
action="store_true",
help="Whether to generate BLEU score eval after each epoch.",
)
group.add_argument(
"--generate-bleu-eval-interval",
default=0,
type=int,
metavar="N",
help="Does BLEU eval every N batch updates. Note that "
"--save-interval also affects this - we can only eval as "
"frequently as a checkpoint is written. A value of <= 0 "
"disables this.",
)
group.add_argument(
"--generate-bleu-eval-avg-checkpoints",
default=1,
type=int,
metavar="N",
help="Maximum number of last N checkpoints to average over when "
"doing BLEU eval. Must be >= 1.",
)
group.add_argument(
"--continuous-averaging-after-epochs",
type=int,
default=-1,
help=("Average parameter values after each step since previous "
"checkpoint, beginning after the specified number of epochs. "),
)
return parser
def add_cmdline_args(argparser):
"""Add command-line arguments specifically for this agent."""
# first we need to add the general torch agent operations
TorchAgent.add_cmdline_args(argparser)
agent = argparser.add_argument_group('Fairseq Arguments')
agent.add_argument(
'--seed',
default=1,
type=int,
metavar='N',
help='pseudo random number generator seed'
)
agent.add_argument(
'--skip-generation',
default=False,
type=bool,
metavar='BOOL',
help='Skips test time beam search. Much faster if you only need PPL',
)
# Dictionary construction stuff. Using the subclass in case we end up
# needing any fairseq specific things
_FairseqDictionary.add_cmdline_args(argparser)
# Optimization and learning rate schedule specific arguments
options.add_optimization_args(argparser)
known_args = argparser.parse_known_args(nohelp=True)[0]
if hasattr(known_args, "optimizer"):
optimizer = known_args.optimizer
opt_group = argparser.add_argument_group(
'{} optimizer arguments'.format(optimizer)
)
optim.OPTIMIZER_REGISTRY[optimizer].add_args(opt_group)
if hasattr(known_args, "lr_scheduler"):
lr_scheduler = known_args.lr_scheduler
lr_group = argparser.add_argument_group(
'{} scheduler arguments'.format(lr_scheduler)
)
optim.lr_scheduler.LR_SCHEDULER_REGISTRY[lr_scheduler].add_args(lr_group)
# Generation arguments
options.add_generation_args(argparser)
# We need to find out the fairseq model-specific options, so grab the
# architecture stuff and look up its options
arch_group = options.add_model_args(argparser)
# Fairseq marks the arch flag as required, but it may be specified
# by a saved model cache, so we do some weird stuff to undo that
for a in arch_group._actions:
if a.dest == "arch":
a.required = False
a.default = None
break
known_args = argparser.parse_known_args(nohelp=True)[0]
if hasattr(known_args, "arch") and known_args.arch is not None:
arch = known_args.arch
arch_group = argparser.add_argument_group(
"{} architecture arguments".format(arch)
)
models.ARCH_MODEL_REGISTRY[arch].add_args(arch_group)
# Override a few defaults from within fairseq to more sensible defaults
argparser.set_defaults(
clip_norm=0.1,
adam_betas="(0.9,0.98)"
)
def get_parser_with_args():
parser = options.get_parser('Trainer')
options.add_dataset_args(parser, train=True, gen=True)
options.add_distributed_training_args(parser)
options.add_optimization_args(parser)
options.add_checkpoint_args(parser)
options.add_model_args(parser)
options.add_generation_args(parser)
parser.add_argument(
'--log-verbose',
action='store_true',
help='Whether to output more verbose logs for debugging/profiling.',
)
# Adds args related to training (validation and stopping criterions).
group = parser.add_argument_group('Optimization')
group.add_argument(
'--subepoch-validate-interval',
default=0,
type=int,
metavar='N',
help='Calculates loss over the validation set every N batch updates. '
'Note that validation is done at the end of every epoch regardless. '
'A value of <= 0 disables this.',
)
group.add_argument(
'--stop-time-hr',
default=-1,
type=int,
metavar='N',
help='Stops training after N hours have elapsed. '
'A value of < 0 disables this.',
)
group.add_argument(
'--stop-no-best-validate-loss',
default=-1,
type=int,
metavar='N',
help='Stops training after N validations have been run without '
'achieving a better loss than before. Note that this is affected by '
'--validation-interval in how frequently we run validation in the '
'first place. A value of < 0 disables this.',
)
group.add_argument(
'--stop-no-best-bleu-eval',
default=-1,
type=int,
metavar='N',
help='Stops training after N evals have been run without '
'achieving a better BLEU score than before. Note that this is affected '
'by --generate-bleu-eval-interval in how frequently we run BLEU eval '
'in the first place. A value of < 0 disables this.',
)
# Args related to dataset.
group = parser.add_argument_group('Dataset and data loading')
group.add_argument(
'--source-vocab-file',
default='',
metavar='FILE',
help='Path to text file representing the fairseq Dictionary to use. '
'If left empty, the dict is auto-generated from source training data.',
)
group.add_argument(
'--source-max-vocab-size',
default=-1,
type=int,
metavar='N',
help='If a new vocab file needs to be generated, restrict it to the '
'top N most common words. If we re-use an existing vocab file, this '
'flag will have no effect. A value of < 0 means no max size.',
)
group.add_argument(
'--target-vocab-file',
default='',
metavar='FILE',
help='Path to text file representing the fairseq Dictionary to use. '
'If left empty, the dict is auto-generated from target training data.',
)
group.add_argument(
'--target-max-vocab-size',
default=-1,
type=int,
metavar='N',
help='If a new vocab file needs to be generated, restrict it to the '
'top N most common words. If we re-use an existing vocab file, this '
'flag will have no effect. A value of < 0 means no max size.',
)
group.add_argument(
'--train-source-text-file',
default='',
metavar='FILE',
help='Path to raw text file containing source training examples. '
'This overrides what would be loaded from the data dir.',
)
group.add_argument(
'--train-target-text-file',
default='',
metavar='FILE',
help='Path to raw text file containing target training examples. '
'This overrides what would be loaded from the data dir.',
)
group.add_argument(
'--eval-source-text-file',
default='',
metavar='FILE',
help='Path to raw text file containing source eval examples for '
'calculating validation loss and BLEU eval scores. '
'This overrides what would be loaded from the data dir.',
)
group.add_argument(
'--eval-target-text-file',
default='',
metavar='FILE',
help='Path to raw text file containing target eval examples for '
'calculating validation loss and BLEU eval scores. '
'This overrides what would be loaded from the data dir.',
)
# Adds args related to checkpointing.
group = parser.add_argument_group('Checkpointing')
group.add_argument(
'--no-end-of-epoch-checkpoints',
action='store_true',
help='Disables saving checkpoints at the end of the epoch. '
'This differs from --no-save and --no-epoch-checkpoints in that it '
'still allows for intra-epoch checkpoints if --save-interval is set.')
# Adds args for generating intermediate BLEU eval while training.
# generate.add_args() adds args used by both train.py and the standalone
# generate binary, while the flags defined here are used only by train.py.
generate.add_args(parser)
group = parser.add_argument_group('Generation')
group.add_argument(
'--generate-bleu-eval-per-epoch',
action='store_true',
help='Whether to generate BLEU score eval after each epoch.',
)
group.add_argument(
'--generate-bleu-eval-interval',
default=0,
type=int,
metavar='N',
help='Does BLEU eval every N batch updates. Note that '
'--save-interval also affects this - we can only eval as '
'frequently as a checkpoint is written. A value of <= 0 '
'disables this.',
)
group.add_argument(
'--generate-bleu-eval-avg-checkpoints',
default=1,
type=int,
metavar='N',
help='Maximum number of last N checkpoints to average over when '
'doing BLEU eval. Must be >= 1.',
)
group.add_argument(
'--continuous-averaging-after-epochs',
type=int,
default=-1,
help=('Average parameter values after each step since previous '
'checkpoint, beginning after the specified number of epochs. '),
)
return parser
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(
'--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 ofdata subsets '
' to use for validation (train, valid, valid1,test, test1)')
options.add_optimization_args(parser)
options.add_checkpoint_args(parser)
options.add_model_args(parser)
args = utils.parse_args_and_arch(parser)
print(args)
if args.no_progress_bar:
progress_bar.enabled = False
progress_bar.print_interval = args.log_interval
if not os.path.exists(args.save_dir):
os.makedirs(args.save_dir)
torch.manual_seed(args.seed)
# Load dataset
dataset = data.load_with_check(args.data, ['train', 'valid'],
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 ['train', 'valid']:
print('| {} {} {} examples'.format(args.data, split,
len(dataset.splits[split])))
if not torch.cuda.is_available():
raise NotImplementedError('Training on CPU is not supported')
num_gpus = torch.cuda.device_count()
print('| using {} GPUs (with max tokens per GPU = {})'.format(
num_gpus, args.max_tokens))
# 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__))
# 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, num_gpus)
# evaluate on validate set
for k, subset in enumerate(args.valid_subset.split(',')):
val_loss = validate(args, epoch, trainer, dataset, subset,
num_gpus)
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 add_cmdline_args(cls, argparser):
"""Add command-line arguments specifically for this agent."""
# first we need to add the general torch agent operations
TorchAgent.add_cmdline_args(argparser)
agent = argparser.add_argument_group('Fairseq Arguments')
agent.add_argument('--fp16',
default=False,
type=bool,
help='Use fp16 training')
agent.add_argument('--seed',
default=1,
type=int,
metavar='N',
help='pseudo random number generator seed')
agent.add_argument(
'--skip-generation',
default=False,
type=bool,
metavar='BOOL',
help=
'Skips test time beam search. Much faster if you only need PPL',
)
# Dictionary construction stuff. Using the subclass in case we end up
# needing any fairseq specific things
cls.dictionary_class().add_cmdline_args(argparser)
# Check subargs for generation, optimizers, criterions, archs, etc
options.add_generation_args(argparser)
options.add_optimization_args(argparser)
# make sure we set defaults according to the model before parsing
argparser.set_defaults(**cls.DEFAULT_OPTIONS)
known_args = argparser.parse_known_args(nohelp=True)[0]
if hasattr(known_args, "optimizer"):
optimizer = known_args.optimizer
opt_group = argparser.add_argument_group(
'{} optimizer arguments'.format(optimizer))
optim.OPTIMIZER_REGISTRY[optimizer].add_args(opt_group)
if hasattr(known_args, "lr_scheduler"):
lr_scheduler = known_args.lr_scheduler
lr_group = argparser.add_argument_group(
'{} scheduler arguments'.format(lr_scheduler))
optim.lr_scheduler.LR_SCHEDULER_REGISTRY[lr_scheduler].add_args(
lr_group)
# We need to find out the fairseq model-specific options, so grab the
# architecture stuff and look up its options
arch_group = options.add_model_args(argparser)
# Fairseq marks the arch flag as required, but it may be specified
# by a saved model cache, so we do some weird stuff to undo that
for a in arch_group._actions:
if a.dest == "arch":
a.required = False
a.default = None
break
# make sure we set defaults according to parlai model before parsing
argparser.set_defaults(**cls.DEFAULT_OPTIONS)
known_args = argparser.parse_known_args(nohelp=True)[0]
if hasattr(known_args, "arch") and known_args.arch is not None:
arch = known_args.arch
arch_group = argparser.add_argument_group(
"{} architecture arguments".format(arch))
models.ARCH_MODEL_REGISTRY[arch].add_args(arch_group)
if hasattr(known_args, "criterion"):
crit_group = argparser.add_argument_group(
'{} criterion arguments'.format(known_args.criterion))
criterions.CRITERION_REGISTRY[known_args.criterion].add_args(
crit_group)
# As one final check, let's make sure we set defaults correctly
argparser.set_defaults(**cls.DEFAULT_OPTIONS)
def add_cmdline_args(cls, argparser):
"""Add command-line arguments specifically for this agent."""
# first we need to add the general torch agent operations
super(FairseqAgent, cls).add_cmdline_args(argparser)
# let's store any defaults that were overridden
old_defaults = argparser._defaults
if 'clip_norm' not in old_defaults:
# fairseq has a few awful defaults
old_defaults['clip_norm'] = 1.0
if 'optimizer' not in old_defaults:
old_defaults['optimizer'] = 'adam'
old_defaults['adam_betas'] = '(0.9,0.98)'
agent = argparser.add_argument_group('Fairseq Arguments')
agent.add_argument('--fp16',
default=False,
type='bool',
help='Use fp16 training')
agent.add_argument(
'--fp16-init-scale',
default=2**7,
type=int,
help='default FP16 loss scale',
)
agent.add_argument(
'--seed',
default=1,
type=int,
metavar='N',
help='pseudo random number generator seed',
)
agent.add_argument(
'--skip-generation',
default=False,
type='bool',
metavar='BOOL',
help=
'Skips test time beam search. Much faster if you only need PPL',
)
# Check subargs for generation, optimizers, criterions, archs, etc
options.add_generation_args(argparser)
options.add_optimization_args(argparser)
options.add_checkpoint_args(argparser)
# restore any user set defaults that fairseq possibly overrode
argparser.set_defaults(**old_defaults)
known_args = argparser.parse_known_args(nohelp=True)[0]
if hasattr(known_args, "optimizer"):
optimizer = known_args.optimizer
opt_group = argparser.add_argument_group(
'{} optimizer arguments'.format(optimizer))
optim.OPTIMIZER_REGISTRY[optimizer].add_args(opt_group)
if hasattr(known_args, "lr_scheduler"):
lr_scheduler = known_args.lr_scheduler
lr_group = argparser.add_argument_group(
'{} scheduler arguments'.format(lr_scheduler))
optim.lr_scheduler.LR_SCHEDULER_REGISTRY[lr_scheduler].add_args(
lr_group)
# We need to find out the fairseq model-specific options, so grab the
# architecture stuff and look up its options
arch_group = options.add_model_args(argparser)
# Fairseq marks the arch flag as required, but it may be specified
# by a saved model cache, so we do some weird stuff to undo that
for a in arch_group._actions:
if a.dest == "arch":
a.required = False
a.default = None
break
# once again restore any user-set defaults
argparser.set_defaults(**old_defaults)
known_args = argparser.parse_known_args(nohelp=True)[0]
if hasattr(known_args, "arch") and known_args.arch is not None:
arch = known_args.arch
arch_group = argparser.add_argument_group(
"{} architecture arguments".format(arch))
models.ARCH_MODEL_REGISTRY[arch].add_args(arch_group)
if hasattr(known_args, "criterion"):
crit_group = argparser.add_argument_group(
'{} criterion arguments'.format(known_args.criterion))
criterions.CRITERION_REGISTRY[known_args.criterion].add_args(
crit_group)
# one last time, restore any user set defaults
argparser.set_defaults(**old_defaults)
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)
# Create files to save losses
traincsv_path = os.path.join(args.save_dir, 'train_losses.csv')
validcsv_path = os.path.join(args.save_dir, 'valid_losses.csv')
output_path = [traincsv_path, validcsv_path]
for path in output_path:
with open(path, 'w+') as csvfile:
csvwriter = csv.writer(csvfile, delimiter=',')
csvwriter.writerow(['Epoch', 'Perplexity', 'Loss'])
csvfile.close()
# 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,
traincsv_path)
# evaluate on validate set
for k, subset in enumerate(args.valid_subset.split(',')):
val_loss = validate(args, epoch, trainer, dataset,
max_positions_valid, subset, validcsv_path)
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 predict_length_beam(gold_target_len, predicted_lengths, length_beam_size):
if gold_target_len is not None:
beam_starts = gold_target_len - (length_beam_size - 1) // 2
beam_ends = gold_target_len + length_beam_size // 2 + 1
beam = torch.stack([torch.arange(beam_starts[batch], beam_ends[batch], device=beam_starts.device) for batch in range(gold_target_len.size(0))], dim=0)
else:
beam = predicted_lengths.topk(length_beam_size, dim=1)[1]
beam[beam < 2] = 2
return beam
if __name__ == '__main__':
parser = options.get_generation_parser()
options.add_model_args(parser)
parser.add_argument("--all", action="store_true")
parser.add_argument("--stepwise", action="store_true")
parser.add_argument("--semiat", action="store_true")
parser.add_argument("--scan-checkpoints", action="store_true")
parser.add_argument("--checkpoint-name", type=str, default="best")
parser.add_argument("--ensemble", action="store_true")
parser.add_argument("--end-iteration", default=-1, type=int)
args = options.parse_args_and_arch(parser)
if args.all:
for i in [0, 2, 4, 8, 10]:
print("testing with iterations", i)
args.decoding_iterations = i
main(args)
elif args.stepwise:
for i in range(args.decoding_iterations):
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)')
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 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
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])))
if not torch.cuda.is_available():
raise NotImplementedError('Training on CPU is not supported')
num_gpus = torch.cuda.device_count()
print('| using {} GPUs (with max tokens per GPU = {} and max sentences per GPU = {})'.format(
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__))
# 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 = (args.max_source_positions, args.max_target_positions)
max_positions_valid = (
min(args.max_source_positions, model.max_encoder_positions()),
min(args.max_target_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, num_gpus)
# evaluate on validate set
for k, subset in enumerate(args.valid_subset.split(',')):
val_loss = validate(args, epoch, trainer, dataset, max_positions_valid, subset, num_gpus)
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_parser_with_args():
parser = options.get_parser("Trainer")
options.add_dataset_args(parser, train=True, gen=True)
options.add_distributed_training_args(parser)
options.add_optimization_args(parser)
options.add_checkpoint_args(parser)
options.add_model_args(parser)
options.add_generation_args(parser)
parser.add_argument(
"--log-verbose",
action="store_true",
help="Whether to output more verbose logs for debugging/profiling.",
)
# Adds args related to training (validation and stopping criterions).
group = parser.add_argument_group("Optimization")
group.add_argument(
"--subepoch-validate-interval",
default=0,
type=int,
metavar="N",
help="Calculates loss over the validation set every N batch updates. "
"Note that validation is done at the end of every epoch regardless. "
"A value of <= 0 disables this.",
)
group.add_argument(
"--stop-time-hr",
default=-1,
type=int,
metavar="N",
help="Stops training after N hours have elapsed. "
"A value of < 0 disables this.",
)
group.add_argument(
"--stop-no-best-validate-loss",
default=-1,
type=int,
metavar="N",
help="Stops training after N validations have been run without "
"achieving a better loss than before. Note that this is affected by "
"--validation-interval in how frequently we run validation in the "
"first place. A value of < 0 disables this.",
)
group.add_argument(
"--stop-no-best-bleu-eval",
default=-1,
type=int,
metavar="N",
help="Stops training after N evals have been run without "
"achieving a better BLEU score than before. Note that this is affected "
"by --generate-bleu-eval-interval in how frequently we run BLEU eval "
"in the first place. A value of < 0 disables this.",
)
# Args related to dataset.
group = parser.add_argument_group("Dataset and data loading")
group.add_argument(
"--source-vocab-file",
default="",
metavar="FILE",
help="Path to text file representing the fairseq Dictionary to use. "
"If left empty, the dict is auto-generated from source training data.",
)
group.add_argument(
"--source-max-vocab-size",
default=-1,
type=int,
metavar="N",
help="If a new vocab file needs to be generated, restrict it to the "
"top N most common words. If we re-use an existing vocab file, this "
"flag will have no effect. A value of < 0 means no max size.",
)
group.add_argument(
"--target-vocab-file",
default="",
metavar="FILE",
help="Path to text file representing the fairseq Dictionary to use. "
"If left empty, the dict is auto-generated from target training data.",
)
group.add_argument(
"--target-max-vocab-size",
default=-1,
type=int,
metavar="N",
help="If a new vocab file needs to be generated, restrict it to the "
"top N most common words. If we re-use an existing vocab file, this "
"flag will have no effect. A value of < 0 means no max size.",
)
group.add_argument(
"--train-source-text-file",
default="",
metavar="FILE",
help="Path to raw text file containing source training examples. "
"This overrides what would be loaded from the data dir.",
)
group.add_argument(
"--train-target-text-file",
default="",
metavar="FILE",
help="Path to raw text file containing target training examples. "
"This overrides what would be loaded from the data dir.",
)
group.add_argument(
"--eval-source-text-file",
default="",
metavar="FILE",
help="Path to raw text file containing source eval examples for "
"calculating validation loss and BLEU eval scores. "
"This overrides what would be loaded from the data dir.",
)
group.add_argument(
"--eval-target-text-file",
default="",
metavar="FILE",
help="Path to raw text file containing target eval examples for "
"calculating validation loss and BLEU eval scores. "
"This overrides what would be loaded from the data dir.",
)
group.add_argument(
"--penalized-target-tokens-file",
default="",
metavar="FILE",
help="Path to text file of tokens to receive a penalty in decoding."
"If left empty, no penalty will be applied",
)
# Adds args related to checkpointing.
group = parser.add_argument_group("Checkpointing")
group.add_argument(
"--no-end-of-epoch-checkpoints",
action="store_true",
help="Disables saving checkpoints at the end of the epoch. "
"This differs from --no-save and --no-epoch-checkpoints in that it "
"still allows for intra-epoch checkpoints if --save-interval is set.",
)
group.add_argument(
"--max-checkpoints-kept",
default=-1,
type=int,
metavar="N",
help="Keep at most the last N checkpoints file around. "
"A value < -1 keeps all. "
"When --generate-bleu-eval-avg-checkpoints is used and is > N, the "
"number of checkpoints kept around is automatically adjusted "
"to allow BLEU to work properly.",
)
# Adds args for generating intermediate BLEU eval while training.
# generate.add_args() adds args used by both train.py and the standalone
# generate binary, while the flags defined here are used only by train.py.
generate.add_args(parser)
group = parser.add_argument_group("Generation")
group.add_argument(
"--generate-bleu-eval-per-epoch",
action="store_true",
help="Whether to generate BLEU score eval after each epoch.",
)
group.add_argument(
"--generate-bleu-eval-interval",
default=0,
type=int,
metavar="N",
help="Does BLEU eval every N batch updates. Note that "
"--save-interval also affects this - we can only eval as "
"frequently as a checkpoint is written. A value of <= 0 "
"disables this.",
)
group.add_argument(
"--generate-bleu-eval-avg-checkpoints",
default=1,
type=int,
metavar="N",
help="Maximum number of last N checkpoints to average over when "
"doing BLEU eval. Must be >= 1.",
)
group.add_argument(
"--continuous-averaging-after-epochs",
type=int,
default=-1,
help=("Average parameter values after each step since previous "
"checkpoint, beginning after the specified number of epochs. "),
)
return parser
def cli_main():
parser = options.get_generation_parser(interactive=True)
options.add_model_args(parser)
args = options.parse_args_and_arch(parser)
main(args)