def add_cmdline_args(argparser):
"""Add command-line arguments specifically for this agent."""
DictionaryAgent.add_cmdline_args(argparser)
agent = argparser.add_argument_group('Fairseq Arguments')
agent.add_argument(
'-tr', '--truncate',
type=int, default=-1,
help='truncate input & output lengths to speed up training (may '
'reduce accuracy). This fixes all input and output to have a '
'maximum length. This reduces the total amount of padding in '
'the batches.')
agent.add_argument(
'--max-positions',
default=1024,
type=int,
metavar='N',
help='max number of tokens in the sequence')
agent.add_argument(
'--seed',
default=1,
type=int,
metavar='N',
help='pseudo random number generator seed')
options.add_optimization_args(argparser)
options.add_generation_args(argparser)
options.add_model_args(argparser)
def cli_main():
parser = options.get_training_parser()
parser.add_argument('--train-subtransformer', action='store_true', default=False, help='whether train SuperTransformer or SubTransformer')
parser.add_argument('--sub-configs', required=False, is_config_file=True, help='when training SubTransformer, use --configs to specify architecture and --sub-configs to specify other settings')
# for profiling
parser.add_argument('--profile-flops', action='store_true', help='measure the FLOPs of a SubTransformer')
parser.add_argument('--latgpu', action='store_true', help='measure SubTransformer latency on GPU')
parser.add_argument('--latcpu', action='store_true', help='measure SubTransformer latency on CPU')
parser.add_argument('--latiter', type=int, default=300, help='how many iterations to run when measure the latency')
parser.add_argument('--latsilent', action='store_true', help='keep silent when measure latency')
parser.add_argument('--validate-subtransformer', action='store_true', help='evaluate the SubTransformer on the validation set')
options.add_generation_args(parser)
args = options.parse_args_and_arch(parser)
if args.latcpu:
args.cpu = True
args.fp16 = False
if args.latgpu or args.latcpu or args.profile_flops:
args.distributed_world_size = 1
if args.pdb:
pdb.set_trace()
if args.distributed_init_method is None:
distributed_utils.infer_init_method(args)
if args.distributed_init_method is not None:
# distributed training
if torch.cuda.device_count() > 1 and not args.distributed_no_spawn:
start_rank = args.distributed_rank
args.distributed_rank = None # assign automatically
torch.multiprocessing.spawn(
fn=distributed_main,
args=(args, start_rank),
nprocs=torch.cuda.device_count(),
)
else:
distributed_main(args.device_id, args)
elif args.distributed_world_size > 1:
# fallback for single node with multiple GPUs
assert args.distributed_world_size <= torch.cuda.device_count()
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)
def get_parser_with_args():
parser = options.get_parser('Generation')
options.add_dataset_args(parser, gen=True)
options.add_generation_args(parser)
add_args(parser)
group = parser.add_argument_group('Generation')
group.add_argument(
'--source-vocab-file',
default='',
metavar='FILE',
help='Path to text file representing the Dictionary to use.')
group.add_argument(
'--target-vocab-file',
default='',
metavar='FILE',
help='Path to text file representing the Dictionary to use.')
group.add_argument(
'--source-text-file',
default='',
metavar='FILE',
help='Path to raw text file containing examples in source dialect. '
'This overrides what would be loaded from the data dir.',
)
group.add_argument(
'--target-text-file',
default='',
metavar='FILE',
help='Path to raw text file containing examples in target dialect. '
'This overrides what would be loaded from the data dir.',
)
return parser
def get_parser_with_args():
parser = options.get_parser("Generation")
options.add_dataset_args(parser, gen=True)
options.add_generation_args(parser)
add_args(parser)
group = parser.add_argument_group("Generation")
group.add_argument(
"--source-vocab-file",
default="",
metavar="FILE",
help="Path to text file representing the Dictionary to use.",
)
group.add_argument(
"--target-vocab-file",
default="",
metavar="FILE",
help="Path to text file representing the Dictionary to use.",
)
group.add_argument(
"--source-text-file",
default="",
metavar="FILE",
help="Path to raw text file containing examples in source dialect. "
"This overrides what would be loaded from the data dir.",
)
group.add_argument(
"--target-text-file",
default="",
metavar="FILE",
help="Path to raw text file containing examples in target dialect. "
"This overrides what would be loaded from the data dir.",
)
return parser
def main():
parser = options.get_parser('Generation')
parser.add_argument('--path', metavar='FILE', required=True, action='append',
help='path(s) to model file(s)')
options.add_dataset_args(parser)
options.add_generation_args(parser)
args = parser.parse_args()
print(args)
use_cuda = torch.cuda.is_available() and not args.cpu
# Load ensemble
print('| loading model(s) from {}'.format(', '.join(args.path)))
models, model_args = utils.load_ensemble_for_inference(args.path, data_dir=args.data)
src_dict, dst_dict = models[0].src_dict, models[0].dst_dict
print('| [{}] dictionary: {} types'.format(model_args.source_lang, len(src_dict)))
print('| [{}] dictionary: {} types'.format(model_args.target_lang, len(dst_dict)))
# Optimize ensemble for generation
for model in models:
model.make_generation_fast_(
beamable_mm_beam_size=None if args.no_beamable_mm else args.beam)
# Initialize generator
translator = SequenceGenerator(
models, beam_size=args.beam, stop_early=(not args.no_early_stop),
normalize_scores=(not args.unnormalized), len_penalty=args.lenpen,
unk_penalty=args.unkpen)
if use_cuda:
translator.cuda()
# Load alignment dictionary for unknown word replacement
# (None if no unknown word replacement, empty if no path to align dictionary)
align_dict = utils.load_align_dict(args.replace_unk)
print('| Type the input sentence and press return:')
for src_str in sys.stdin:
src_str = src_str.strip()
src_tokens = tokenizer.Tokenizer.tokenize(src_str, src_dict, add_if_not_exist=False).long()
if use_cuda:
src_tokens = src_tokens.cuda()
translations = translator.generate(Variable(src_tokens.view(1, -1)))
hypos = translations[0]
print('O\t{}'.format(src_str))
# Process top predictions
for hypo in hypos[:min(len(hypos), args.nbest)]:
hypo_tokens, hypo_str, alignment = utils.post_process_prediction(
hypo_tokens=hypo['tokens'].int().cpu(),
src_str=src_str,
alignment=hypo['alignment'].int().cpu(),
align_dict=align_dict,
dst_dict=dst_dict,
remove_bpe=args.remove_bpe)
print('H\t{}\t{}'.format(hypo['score'], hypo_str))
print('A\t{}'.format(' '.join(map(str, alignment))))
def get_training_and_generation_parser(default_task='translation'):
parser = options.get_parser('Trainer', default_task)
options.add_dataset_args(parser, train=True, gen=True)
options.add_generation_args(parser)
options.add_distributed_training_args(parser)
options.add_model_args(parser)
options.add_optimization_args(parser)
options.add_checkpoint_args(parser)
return parser
def cli_main():
parser = options.get_training_parser()
parser.add_argument('--evo-configs', required=True, is_config_file=True)
parser.add_argument('--evo-iter', type=int, default=30)
parser.add_argument('--population-size', type=int, default=125)
parser.add_argument('--parent-size', type=int, default=25)
parser.add_argument('--mutation-size', type=int, default=50)
parser.add_argument('--crossover-size', type=int, default=50)
parser.add_argument('--mutation-prob', type=float, default=0.3)
parser.add_argument('--feature-norm',
type=float,
nargs='+',
help='normalizing factor for each feature')
parser.add_argument('--lat-norm',
type=float,
help='normalizing factor for latency')
parser.add_argument('--ckpt-path',
type=str,
help='path to load latency predictor weights')
parser.add_argument('--latency-constraint',
type=float,
default=-1,
help='latency constraint')
parser.add_argument(
'--valid-cnt-max',
type=int,
default=1e9,
help='max number of sentences to use in validation set')
parser.add_argument(
'--write-config-path',
type=str,
help='path to write out the searched best SubTransformer')
options.add_generation_args(parser)
args = options.parse_args_and_arch(parser)
if args.pdb:
pdb.set_trace()
# one GPU is fast enough to do the search
args.distributed_world_size = 1
# if search on CPU, use fp32 as default
if args.cpu:
args.fp16 = False
main(args)
def get_parser_with_args():
parser = options.get_parser("Collect Top-K Probs",
default_task="pytorch_translate")
pytorch_translate_options.add_verbosity_args(parser)
pytorch_translate_options.add_dataset_args(parser, gen=True)
generation_group = options.add_generation_args(parser)
generation_group.add_argument(
"--source-binary-file",
default="",
help="Path for the binary file containing source eval examples. "
"(Overrides --source-text-file. Must be used in conjunction with "
"--target-binary-file).",
)
generation_group.add_argument(
"--target-binary-file",
default="",
help="Path for the binary file containing target eval examples. "
"(Overrides --target-text-file. Must be used in conjunction with "
"--source-binary-file).",
)
generation_group.add_argument(
"--k-probs-to-collect",
type=int,
default=8,
help="Number of probabilities to collect for each output step.",
)
generation_group.add_argument(
"--top-k-probs-binary-file",
type=str,
default="",
help="File into which to save top-K probabilities for each token.",
)
return parser
def cli_main(
modify_parser: Optional[Callable[[argparse.ArgumentParser], None]] = None
) -> None:
parser = options.get_training_parser()
# options.add_pruning_args(parser)
options.add_generation_args(parser)
args = options.parse_args_and_arch(parser, modify_parser=modify_parser)
cfg = convert_namespace_to_omegaconf(args)
if args.profile:
with torch.cuda.profiler.profile():
with torch.autograd.profiler.emit_nvtx():
distributed_utils.call_main(cfg, main)
else:
distributed_utils.call_main(cfg, main)
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 add_cmdline_args(argparser):
"""Add command-line arguments specifically for this agent."""
DictionaryAgent.add_cmdline_args(argparser)
agent = argparser.add_argument_group('Fairseq Arguments')
agent.add_argument('--max-positions',
default=1024,
type=int,
metavar='N',
help='max number of tokens in the sequence')
agent.add_argument('--seed',
default=1,
type=int,
metavar='N',
help='pseudo random number generator seed')
options.add_optimization_args(argparser)
options.add_generation_args(argparser)
options.add_model_args(argparser)
def get_parser_with_args():
parser = options.get_parser("Generation")
options.add_dataset_args(parser, gen=True)
options.add_generation_args(parser)
pytorch_translate_generate.add_args(parser)
group = parser.add_argument_group("Generation")
group.add_argument(
"--source-vocab-file",
default="",
metavar="FILE",
help="Path to text file representing the Dictionary to use.",
)
group.add_argument(
"--target-vocab-file",
default="",
metavar="FILE",
help="Path to text file representing the Dictionary to use.",
)
# Add args related to benchmarking.
group = parser.add_argument_group("Benchmarking")
group.add_argument(
"--increment",
default=5,
type=int,
help="Difference in lengths between synthesized sentences. "
"Must be integer >=1.",
)
group.add_argument(
"--max-length",
default=100,
type=int,
help="Maximum allowed length for synthesized sentences. "
"Should be greater than --increment.",
)
group.add_argument(
"--samples-per-length",
default=1,
type=int,
help="Number of sentences to be synthesized at each length. ",
)
return parser
def cli_main():
parser = options.get_training_parser()
parser.add_argument('--latgpu',
action='store_true',
help='measure SubTransformer cache misses on GPU')
parser.add_argument('--latcpu',
action='store_true',
help='measure SubTransformer cache misses on CPU')
parser.add_argument(
'--latiter',
type=int,
default=300,
help='how many iterations to run when measure the latency'
) #TODO remove it later
parser.add_argument('--latsilent',
action='store_true',
help='keep silent when measure cache misses')
parser.add_argument('--lat-dataset-path',
type=str,
default='./device_dataset/lat.tmp',
help='the path to write device dataset')
parser.add_argument('--lat-dataset-size',
type=int,
default=200,
help='number of data points for the dataset')
options.add_generation_args(parser)
args = options.parse_args_and_arch(parser)
if args.latcpu:
args.cpu = True
args.fp16 = False
else:
print('GPU mode not supported yet')
return
if args.pdb:
pdb.set_trace()
main(args)
def cli_main():
parser = options.get_training_parser()
parser.add_argument('--latgpu',
action='store_true',
help='measure SubTransformer latency on GPU')
parser.add_argument('--latcpu',
action='store_true',
help='measure SubTransformer latency on CPU')
parser.add_argument(
'--latiter',
type=int,
default=300,
help='how many iterations to run when measure the latency')
parser.add_argument('--latsilent',
action='store_true',
help='keep silent when measure latency')
parser.add_argument('--lat-dataset-path',
type=str,
default='./latency_dataset/lat.tmp',
help='the path to write latency dataset')
parser.add_argument('--lat-dataset-size',
type=int,
default=200,
help='number of data points for the dataset')
options.add_generation_args(parser)
args = options.parse_args_and_arch(parser)
if args.latcpu:
args.cpu = True
args.fp16 = False
if args.pdb:
pdb.set_trace()
main(args)
def make_parser():
"""
Additional args:
1. Provide the dataset dir path using --data.
2. Loading the dataset doesn't require config, provide --config-yaml to apply additional feature transforms
"""
parser = options.get_speech_generation_parser()
parser.add_argument(
"--subset",
default=None,
type=str,
required=True,
help="Subset to use for dataset generation",
)
parser.add_argument(
"--dataset-save-dir",
default=None,
type=str,
required=False,
help="Dir path in which the datasets are to be saved",
)
parser.add_argument(
"--ref-dataset",
default=None,
type=str,
required=False,
help=
"If provided, the ids in the reference dataset will be used to filter the new dataset generated.",
)
parser.add_argument("--dataset-save-token",
default="",
type=str,
required=False)
options.add_generation_args(parser)
return parser
def get_parser_with_args(default_task="pytorch_translate"):
parser = options.get_parser("Trainer", default_task=default_task)
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 main():
parser = options.get_parser('Generation')
parser.add_argument('--path',
metavar='FILE',
required=True,
action='append',
help='path(s) to model file(s)')
options.add_dataset_args(parser)
options.add_generation_args(parser)
args = parser.parse_args()
print(args)
use_cuda = torch.cuda.is_available() and not args.cpu
# Load ensemble
print('| loading model(s) from {}'.format(', '.join(args.path)))
models, model_args = utils.load_ensemble_for_inference(args.path,
data_dir=args.data)
src_dict, dst_dict = models[0].src_dict, models[0].dst_dict
print('| [{}] dictionary: {} types'.format(model_args.source_lang,
len(src_dict)))
print('| [{}] dictionary: {} types'.format(model_args.target_lang,
len(dst_dict)))
# Optimize ensemble for generation
for model in models:
model.make_generation_fast_(
beamable_mm_beam_size=None if args.no_beamable_mm else args.beam)
# Initialize generator
translator = SequenceGenerator(models,
beam_size=args.beam,
stop_early=(not args.no_early_stop),
normalize_scores=(not args.unnormalized),
len_penalty=args.lenpen,
unk_penalty=args.unkpen)
if use_cuda:
translator.cuda()
# Load alignment dictionary for unknown word replacement
# (None if no unknown word replacement, empty if no path to align dictionary)
align_dict = utils.load_align_dict(args.replace_unk)
print('| Type the input sentence and press return:')
for src_str in sys.stdin:
src_str = src_str.strip()
src_tokens = tokenizer.Tokenizer.tokenize(
src_str, src_dict, add_if_not_exist=False).long()
if use_cuda:
src_tokens = src_tokens.cuda()
translations = translator.generate(Variable(src_tokens.view(1, -1)))
hypos = translations[0]
print('O\t{}'.format(src_str))
# Process top predictions
for hypo in hypos[:min(len(hypos), args.nbest)]:
hypo_tokens, hypo_str, alignment = utils.post_process_prediction(
hypo_tokens=hypo['tokens'].int().cpu(),
src_str=src_str,
alignment=hypo['alignment'].int().cpu(),
align_dict=align_dict,
dst_dict=dst_dict,
remove_bpe=args.remove_bpe)
print('H\t{}\t{}'.format(hypo['score'], hypo_str))
print('A\t{}'.format(' '.join(map(str, alignment))))
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 get_parser_with_args():
parser = options.get_parser("Generation")
pytorch_translate_options.add_verbosity_args(parser)
pytorch_translate_options.add_dataset_args(parser, gen=True)
generation_group = options.add_generation_args(parser)
pytorch_translate_options.expand_generation_args(generation_group)
# Adds args used by the standalone generate binary.
generation_group.add_argument(
"--source-vocab-file",
default="",
metavar="FILE",
help="Path to text file representing the Dictionary to use.",
)
generation_group.add_argument(
"--char-source-vocab-file",
default="",
metavar="FILE",
help=(
"Same as --source-vocab-file except using characters. "
"(For use with char_source models only.)"
),
)
generation_group.add_argument(
"--target-vocab-file",
default="",
metavar="FILE",
help="Path to text file representing the Dictionary to use.",
)
generation_group.add_argument(
"--source-text-file",
default="",
nargs="+",
metavar="FILE",
help="Path to raw text file containing examples in source dialect. "
"This overrides what would be loaded from the data dir. "
"You can specify multiple source files (eg. for use in combination "
"with --source-ensembling). By default this will only translate the "
"first source file",
)
generation_group.add_argument(
"--target-text-file",
default="",
metavar="FILE",
help="Path to raw text file containing examples in target dialect. "
"This overrides what would be loaded from the data dir.",
)
generation_group.add_argument(
"--source-binary-file",
default="",
help="Path for the binary file containing source eval examples. "
"(Overrides --source-text-file. Must be used in conjunction with "
"--target-binary-file).",
)
generation_group.add_argument(
"--target-binary-file",
default="",
help="Path for the binary file containing target eval examples. "
"(Overrides --target-text-file. Must be used in conjunction with "
"--source-binary-file).",
)
generation_group.add_argument(
"--translation-output-file",
default="",
type=str,
metavar="FILE",
help="Path to text file to store the output of the model. ",
)
generation_group.add_argument(
"--translation-probs-file",
default="",
type=str,
metavar="FILE",
help="Path to text file to store the probs of translation output. ",
)
generation_group.add_argument(
"--multiling-source-lang-id",
type=int,
default=None,
help=(
"Must be set for decoding with multilingual models. Set to i if "
"the source language is the i-th language in the training parameter "
"--multiling-encoder-lang (0-indexed)"
),
)
generation_group.add_argument(
"--multiling-target-lang-id",
type=int,
default=None,
help=(
"Must be set for decoding with multilingual models. Set to i if "
"the target language is the i-th language in the training parameter "
"--multiling-decoder-lang (0-indexed)"
),
)
generation_group.add_argument(
"--source-ensembling",
action="store_true",
help="If this flag is present, the model will ensemble the predictions "
"conditioned on multiple source sentences (one per source-text-file)",
)
return parser
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 get_parser_with_args():
parser = options.get_parser("Generation", default_task="pytorch_translate")
pytorch_translate_options.add_verbosity_args(parser)
pytorch_translate_options.add_dataset_args(parser, gen=True)
generation_group = options.add_generation_args(parser)
pytorch_translate_options.expand_generation_args(generation_group)
generation_group.add_argument(
"--source-vocab-file",
default="",
metavar="FILE",
help="Path to text file representing the Dictionary to use.",
)
generation_group.add_argument(
"--char-source-vocab-file",
default="",
metavar="FILE",
help=(
"Same as --source-vocab-file except using characters. "
"(For use with char_source models only.)"
),
)
generation_group.add_argument(
"--target-vocab-file",
default="",
metavar="FILE",
help="Path to text file representing the Dictionary to use.",
)
generation_group.add_argument(
"--multiling-source-lang",
action="append",
metavar="SRC",
help=(
"Must be set for decoding with multilingual models. "
"Must match an entry from --multiling-encoder-lang from training."
),
)
generation_group.add_argument(
"--multiling-target-lang",
action="append",
metavar="TARGET",
help=(
"Must be set for decoding with multilingual models. "
"Must match an entry from --multiling-decoder-lang from training."
),
)
# Add args related to benchmarking.
group = parser.add_argument_group("Benchmarking")
group.add_argument(
"--runs-per-length",
default=10,
type=int,
help="Number of times to run generation on each length.",
)
group.add_argument(
"--examples-per-length",
default=1,
type=int,
help="Sentences of each length to include in each eval (batched if >1).",
)
return parser
def main():
parser = options.get_parser('Generation')
parser.add_argument('--path',
metavar='FILE',
required=True,
action='append',
help='path(s) to model file(s)')
dataset_args = options.add_dataset_args(parser)
dataset_args.add_argument('--batch-size',
default=32,
type=int,
metavar='N',
help='batch size')
dataset_args.add_argument(
'--gen-subset',
default='test',
metavar='SPLIT',
help='data subset to generate (train, valid, test)')
dataset_args.add_argument('--num-shards',
default=1,
type=int,
metavar='N',
help='shard generation over N shards')
dataset_args.add_argument(
'--shard-id',
default=0,
type=int,
metavar='ID',
help='id of the shard to generate (id < num_shards)')
options.add_generation_args(parser)
args = parser.parse_args()
if args.no_progress_bar and args.log_format is None:
args.log_format = 'none'
# print(args)
use_cuda = torch.cuda.is_available() and not args.cpu
if hasattr(torch, 'set_grad_enabled'):
torch.set_grad_enabled(False)
# Load dataset
if args.replace_unk is None:
dataset = data.load_dataset(args.data, [args.gen_subset],
args.source_lang, args.target_lang)
else:
dataset = data.load_raw_text_dataset(args.data, [args.gen_subset],
args.source_lang,
args.target_lang)
if args.source_lang is None or args.target_lang is None:
# record inferred languages in args
args.source_lang, args.target_lang = dataset.src, dataset.dst
# Load ensemble
# print('| loading model(s) from {}'.format(', '.join(args.path)))
models, _ = utils.load_ensemble_for_inference(args.path, dataset.src_dict,
dataset.dst_dict)
# print('| [{}] dictionary: {} types'.format(dataset.src, len(dataset.src_dict)))
# print('| [{}] dictionary: {} types'.format(dataset.dst, len(dataset.dst_dict)))
# print('| {} {} {} examples'.format(args.data, args.gen_subset, len(dataset.splits[args.gen_subset])))
# Optimize ensemble for generation
for model in models:
model.make_generation_fast_(
beamable_mm_beam_size=None if args.no_beamable_mm else args.beam)
# Initialize generator
translator = SequenceGenerator(models,
beam_size=args.beam,
stop_early=(not args.no_early_stop),
normalize_scores=(not args.unnormalized),
len_penalty=args.lenpen,
unk_penalty=args.unkpen)
if use_cuda:
translator.cuda()
# Load alignment dictionary for unknown word replacement
# (None if no unknown word replacement, empty if no path to align dictionary)
align_dict = utils.load_align_dict(args.replace_unk)
# Generate and compute BLEU score
#scorer = bleu.Scorer(dataset.dst_dict.pad(), dataset.dst_dict.eos(), dataset.dst_dict.unk())
max_positions = min(model.max_encoder_positions() for model in models)
itr = dataset.eval_dataloader(args.gen_subset,
max_sentences=args.batch_size,
max_positions=max_positions,
skip_invalid_size_inputs_valid_test=args.
skip_invalid_size_inputs_valid_test)
if args.num_shards > 1:
if args.shard_id < 0 or args.shard_id >= args.num_shards:
raise ValueError('--shard-id must be between 0 and num_shards')
itr = data.sharded_iterator(itr, args.num_shards, args.shard_id)
num_sentences = 0
with utils.build_progress_bar(args, itr) as t:
wps_meter = TimeMeter()
gen_timer = StopwatchMeter()
translations = translator.generate_batched_itr(
t,
maxlen_a=args.max_len_a,
maxlen_b=args.max_len_b,
cuda_device=0 if use_cuda else None,
timer=gen_timer)
correct = 0
total = 0
for sample_id, src_tokens, target_tokens, hypos in translations:
# Process input and ground truth
target_tokens = target_tokens.int().cpu()
# Either retrieve the original sentences or regenerate them from tokens.
if align_dict is not None:
src_str = dataset.splits[
args.gen_subset].src.get_original_text(sample_id)
target_str = dataset.splits[
args.gen_subset].dst.get_original_text(sample_id)
else:
src_str = dataset.src_dict.string(src_tokens, args.remove_bpe)
target_str = dataset.dst_dict.string(target_tokens,
args.remove_bpe,
escape_unk=True)
# if not args.quiet:
# print('S-{}\t{}'.format(sample_id, src_str))
# print('T-{}\t{}'.format(sample_id, target_str))
total += 1
# Process top predictions
for i, hypo in enumerate(hypos[:min(len(hypos), args.nbest)]):
hypo_tokens, hypo_str, alignment = utils.post_process_prediction(
hypo_tokens=hypo['tokens'].int().cpu(),
src_str=src_str,
alignment=hypo['alignment'].int().cpu(),
align_dict=align_dict,
dst_dict=dataset.dst_dict,
remove_bpe=args.remove_bpe)
#if src_str == 'walk around right thrice after jump opposite left twice':
# import pdb; pdb.set_trace()
# if not args.quiet:
# print('H-{}\t{}\t{}'.format(sample_id, hypo['score'], hypo_str))
# print('A-{}\t{}'.format(sample_id, ' '.join(map(str, alignment))))
# Score only the top hypothesis
if i == 0:
if align_dict is not None or args.remove_bpe is not None:
# Convert back to tokens for evaluation with unk replacement and/or without BPE
target_tokens = tokenizer.Tokenizer.tokenize(
target_str,
dataset.dst_dict,
add_if_not_exist=True)
#scorer.add(target_tokens, hypo_tokens)
mat = ''
for row in hypo['attention']:
for column in row:
mat += str(column) + '\t'
mat += '\n'
tar = '/' + target_str
tra = '=' + str(target_str == hypo_str)
to_write.write(mat)
to_write.write(src_str)
to_write.write('\n')
to_write.write(hypo_str)
to_write.write('\n')
to_write.write(tar)
to_write.write('\n')
to_write.write(tra)
to_write.write('\n')
to_write.write('-----------')
to_write.write('\n')
if hypo_str == target_str:
correct += 1
wps_meter.update(src_tokens.size(0))
t.log({'wps': round(wps_meter.avg)})
num_sentences += 1
print('| Correct : {} - Total: {}. Accuracy: {:.5f}'.format(
correct, total, correct / total))
def main():
parser = options.get_parser('Generation')
parser.add_argument('--path', metavar='FILE', required=True, action='append',
help='path(s) to model file(s)')
dataset_args = options.add_dataset_args(parser)
dataset_args.add_argument('-i', '--interactive', action='store_true',
help='generate translations in interactive mode')
dataset_args.add_argument('--batch-size', default=32, type=int, metavar='N',
help='batch size')
dataset_args.add_argument('--gen-subset', default='test', metavar='SPLIT',
help='data subset to generate (train, valid, test)')
options.add_generation_args(parser)
args = parser.parse_args()
print(args)
if args.no_progress_bar:
progress_bar.enabled = False
use_cuda = torch.cuda.is_available() and not args.cpu
# Load model and dataset
print('| loading model(s) from {}'.format(', '.join(args.path)))
models, dataset = utils.load_ensemble_for_inference(args.path, args.data)
print('| [{}] dictionary: {} types'.format(dataset.src, len(dataset.src_dict)))
print('| [{}] dictionary: {} types'.format(dataset.dst, len(dataset.dst_dict)))
if not args.interactive:
print('| {} {} {} examples'.format(args.data, args.gen_subset, len(dataset.splits[args.gen_subset])))
# Optimize model for generation
for model in models:
model.make_generation_fast_(not args.no_beamable_mm)
# Initialize generator
translator = SequenceGenerator(models, dataset.dst_dict, beam_size=args.beam,
stop_early=(not args.no_early_stop),
normalize_scores=(not args.unnormalized),
len_penalty=args.lenpen)
align_dict = {}
if args.unk_replace_dict != '':
assert args.interactive, "Unkown words replacing requires access to original source and is only" \
"supported in interactive mode"
with open(args.unk_replace_dict, 'r') as f:
for line in f:
l = line.split()
align_dict[l[0]] = l[1]
def replace_unk(hypo_str, align_str, src, unk):
hypo_tokens = hypo_str.split()
src_tokens = tokenizer.tokenize_line(src)
align_idx = [int(i) for i in align_str.split()]
for i, ht in enumerate(hypo_tokens):
if ht == unk:
src_token = src_tokens[align_idx[i]]
if src_token in align_dict:
hypo_tokens[i] = align_dict[src_token]
else:
hypo_tokens[i] = src_token
return ' '.join(hypo_tokens)
if use_cuda:
translator.cuda()
bpe_symbol = '@@ ' if args.remove_bpe else None
def display_hypotheses(id, src, orig, ref, hypos):
id_str = '' if id is None else '-{}'.format(id)
src_str = to_sentence(dataset.src_dict, src, bpe_symbol)
print('S{}\t{}'.format(id_str, src_str))
if orig is not None:
print('O{}\t{}'.format(id_str, orig.strip()))
if ref is not None:
print('T{}\t{}'.format(id_str, to_sentence(dataset.dst_dict, ref, bpe_symbol, ref_unk=True)))
for hypo in hypos:
hypo_str = to_sentence(dataset.dst_dict, hypo['tokens'], bpe_symbol)
align_str = ' '.join(map(str, hypo['alignment']))
if args.unk_replace_dict != '':
hypo_str = replace_unk(hypo_str, align_str, orig, unk_symbol(dataset.dst_dict))
print('H{}\t{}\t{}'.format(
id_str, hypo['score'], hypo_str))
print('A{}\t{}'.format(id_str, align_str))
if args.interactive:
for line in sys.stdin:
tokens = tokenizer.Tokenizer.tokenize(line, dataset.src_dict, add_if_not_exist=False).long()
start = dataset.src_dict.pad() + 1
positions = torch.arange(start, start + len(tokens)).type_as(tokens)
if use_cuda:
positions = positions.cuda()
tokens = tokens.cuda()
translations = translator.generate(Variable(tokens.view(1, -1)), Variable(positions.view(1, -1)))
hypos = translations[0]
display_hypotheses(None, tokens, line, None, hypos[:min(len(hypos), args.nbest)])
else:
def maybe_remove_bpe(tokens):
"""Helper for removing BPE symbols from a hypothesis."""
if not args.remove_bpe:
return tokens
assert (tokens == dataset.dst_dict.pad()).sum() == 0
hypo_minus_bpe = to_sentence(dataset.dst_dict, tokens, bpe_symbol)
return tokenizer.Tokenizer.tokenize(hypo_minus_bpe, dataset.dst_dict, add_if_not_exist=True)
# Generate and compute BLEU score
scorer = bleu.Scorer(dataset.dst_dict.pad(), dataset.dst_dict.eos(), dataset.dst_dict.unk())
itr = dataset.dataloader(args.gen_subset, batch_size=args.batch_size, max_positions=args.max_positions)
num_sentences = 0
with progress_bar(itr, smoothing=0, leave=False) as t:
wps_meter = TimeMeter()
gen_timer = StopwatchMeter()
translations = translator.generate_batched_itr(
t, maxlen_a=args.max_len_a, maxlen_b=args.max_len_b,
cuda_device=0 if use_cuda else None, timer=gen_timer)
for id, src, ref, hypos in translations:
ref = ref.int().cpu()
top_hypo = hypos[0]['tokens'].int().cpu()
scorer.add(maybe_remove_bpe(ref), maybe_remove_bpe(top_hypo))
display_hypotheses(id, src, None, ref, hypos[:min(len(hypos), args.nbest)])
wps_meter.update(src.size(0))
t.set_postfix(wps='{:5d}'.format(round(wps_meter.avg)))
num_sentences += 1
print('| Translated {} sentences ({} tokens) in {:.1f}s ({:.2f} tokens/s)'.format(
num_sentences, gen_timer.n, gen_timer.sum, 1. / gen_timer.avg))
print('| Generate {} with beam={}: {}'.format(args.gen_subset, args.beam, scorer.result_string()))
def add_constrainted_generation_args(parser):
group = add_generation_args(parser)
group.add_argument('--order-constr',
action='store_true',
help='activate order constraint')
return group
def add_args(parser):
from fairseq.options import add_generation_args
"""Add criterion-specific arguments to the parser."""
add_generation_args(parser)
group.add_argument("--one-minus", action="store_true")
group.add_argument("--one-head", action="store_true")
group.add_argument("--encoder-self-only",
action="store_true",
help="Only prune from the encoder self attention")
group.add_argument("--encoder-decoder-only",
action="store_true",
help="Only prune from the encoder decoder attention")
group.add_argument("--decoder-self-only",
action="store_true",
help="Only prune from the decoder self attention")
if __name__ == '__main__':
parser = options.get_training_parser()
add_pruning_args(parser)
options.add_pruning_args(parser)
options.add_generation_args(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)
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 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 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(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 get_parser_with_args():
parser = options.get_parser("Generation", default_task="pytorch_translate")
pytorch_translate_options.add_verbosity_args(parser)
pytorch_translate_options.add_dataset_args(parser, gen=True)
generation_group = options.add_generation_args(parser)
pytorch_translate_options.expand_generation_args(generation_group)
# Adds args used by the standalone generate binary.
generation_group.add_argument(
"--source-vocab-file",
default="",
metavar="FILE",
help="Path to text file representing the Dictionary to use.",
)
generation_group.add_argument(
"--char-source-vocab-file",
default="",
metavar="FILE",
help=(
"Same as --source-vocab-file except using characters. "
"(For use with char_source models only.)"
),
)
generation_group.add_argument(
"--target-vocab-file",
default="",
metavar="FILE",
help="Path to text file representing the Dictionary to use.",
)
generation_group.add_argument(
"--source-text-file",
default="",
nargs="+",
metavar="FILE",
help="Path to raw text file containing examples in source dialect. "
"This overrides what would be loaded from the data dir. "
"You can specify multiple source files (eg. for use in combination "
"with --source-ensembling). By default this will only translate the "
"first source file",
)
generation_group.add_argument(
"--target-text-file",
default="",
metavar="FILE",
help="Path to raw text file containing examples in target dialect. "
"This overrides what would be loaded from the data dir.",
)
generation_group.add_argument(
"--source-binary-file",
default="",
help="Path for the binary file containing source eval examples. "
"(Overrides --source-text-file. Must be used in conjunction with "
"--target-binary-file).",
)
generation_group.add_argument(
"--target-binary-file",
default="",
help="Path for the binary file containing target eval examples. "
"(Overrides --target-text-file. Must be used in conjunction with "
"--source-binary-file).",
)
generation_group.add_argument(
"--translation-output-file",
default="",
type=str,
metavar="FILE",
help="Path to text file to store the output of the model. ",
)
generation_group.add_argument(
"--translation-probs-file",
default="",
type=str,
metavar="FILE",
help="Path to text file to store the probs of translation output. ",
)
generation_group.add_argument(
"--multiling-source-lang",
action="append",
metavar="SRC",
help=(
"Must be set for decoding with multilingual models. "
"Must match an entry from --multiling-encoder-lang from training."
),
)
generation_group.add_argument(
"--multiling-target-lang",
action="append",
metavar="TARGET",
help=(
"Must be set for decoding with multilingual models. "
"Must match an entry from --multiling-decoder-lang from training."
),
)
generation_group.add_argument(
"--source-ensembling",
action="store_true",
help="If this flag is present, the model will ensemble the predictions "
"conditioned on multiple source sentences (one per source-text-file)",
)
generation_group.add_argument(
"--competing-completed-beam-search",
action="store_true",
help="If this flag is present, use the alternative beam search "
"implementation in research/beam_search. This beam search keeps completed "
"hypos in the beam and let them compete against hypo expansions in the "
"next time step.",
)
return parser
def main():
parser = options.get_parser('Generation')
parser.add_argument('--path', metavar='FILE', required=True, action='append',
help='path(s) to model file(s)')
dataset_args = options.add_dataset_args(parser)
dataset_args.add_argument('--batch-size', default=32, type=int, metavar='N',
help='batch size')
dataset_args.add_argument('--gen-subset', default='test', metavar='SPLIT',
help='data subset to generate (train, valid, test)')
options.add_generation_args(parser)
args = parser.parse_args()
if args.no_progress_bar and args.log_format is None:
args.log_format = 'none'
print(args)
use_cuda = torch.cuda.is_available() and not args.cpu
# Load dataset
if args.replace_unk is None:
dataset = data.load_dataset(args.data, [args.gen_subset], args.source_lang, args.target_lang)
else:
dataset = data.load_raw_text_dataset(args.data, [args.gen_subset], args.source_lang, args.target_lang)
if args.source_lang is None or args.target_lang is None:
# record inferred languages in args
args.source_lang, args.target_lang = dataset.src, dataset.dst
# Load ensemble
print('| loading model(s) from {}'.format(', '.join(args.path)))
models, _ = utils.load_ensemble_for_inference(args.path, dataset.src_dict, dataset.dst_dict)
print('| [{}] dictionary: {} types'.format(dataset.src, len(dataset.src_dict)))
print('| [{}] dictionary: {} types'.format(dataset.dst, len(dataset.dst_dict)))
print('| {} {} {} examples'.format(args.data, args.gen_subset, len(dataset.splits[args.gen_subset])))
# Optimize ensemble for generation
for model in models:
model.make_generation_fast_(
beamable_mm_beam_size=None if args.no_beamable_mm else args.beam)
# Initialize generator
translator = SequenceGenerator(
models, beam_size=args.beam, stop_early=(not args.no_early_stop),
normalize_scores=(not args.unnormalized), len_penalty=args.lenpen,
unk_penalty=args.unkpen)
if use_cuda:
translator.cuda()
# Load alignment dictionary for unknown word replacement
# (None if no unknown word replacement, empty if no path to align dictionary)
align_dict = utils.load_align_dict(args.replace_unk)
# Generate and compute BLEU score
scorer = bleu.Scorer(dataset.dst_dict.pad(), dataset.dst_dict.eos(), dataset.dst_dict.unk())
max_positions = min(model.max_encoder_positions() for model in models)
itr = dataset.eval_dataloader(
args.gen_subset, max_sentences=args.batch_size, max_positions=max_positions,
skip_invalid_size_inputs_valid_test=args.skip_invalid_size_inputs_valid_test)
num_sentences = 0
with utils.build_progress_bar(args, itr) as t:
wps_meter = TimeMeter()
gen_timer = StopwatchMeter()
translations = translator.generate_batched_itr(
t, maxlen_a=args.max_len_a, maxlen_b=args.max_len_b,
cuda_device=0 if use_cuda else None, timer=gen_timer)
for sample_id, src_tokens, target_tokens, hypos in translations:
# Process input and ground truth
target_tokens = target_tokens.int().cpu()
# Either retrieve the original sentences or regenerate them from tokens.
if align_dict is not None:
src_str = dataset.splits[args.gen_subset].src.get_original_text(sample_id)
target_str = dataset.splits[args.gen_subset].dst.get_original_text(sample_id)
else:
src_str = dataset.src_dict.string(src_tokens, args.remove_bpe)
target_str = dataset.dst_dict.string(target_tokens, args.remove_bpe, escape_unk=True)
if not args.quiet:
print('S-{}\t{}'.format(sample_id, src_str))
print('T-{}\t{}'.format(sample_id, target_str))
# Process top predictions
for i, hypo in enumerate(hypos[:min(len(hypos), args.nbest)]):
hypo_tokens, hypo_str, alignment = utils.post_process_prediction(
hypo_tokens=hypo['tokens'].int().cpu(),
src_str=src_str,
alignment=hypo['alignment'].int().cpu(),
align_dict=align_dict,
dst_dict=dataset.dst_dict,
remove_bpe=args.remove_bpe)
if not args.quiet:
print('H-{}\t{}\t{}'.format(sample_id, hypo['score'], hypo_str))
print('A-{}\t{}'.format(sample_id, ' '.join(map(str, alignment))))
# Score only the top hypothesis
if i == 0:
if align_dict is not None or args.remove_bpe is not None:
# Convert back to tokens for evaluation with unk replacement and/or without BPE
target_tokens = tokenizer.Tokenizer.tokenize(target_str,
dataset.dst_dict,
add_if_not_exist=True)
scorer.add(target_tokens, hypo_tokens)
wps_meter.update(src_tokens.size(0))
t.log({'wps': round(wps_meter.avg)})
num_sentences += 1
print('| Translated {} sentences ({} tokens) in {:.1f}s ({:.2f} tokens/s)'.format(
num_sentences, gen_timer.n, gen_timer.sum, 1. / gen_timer.avg))
print('| Generate {} with beam={}: {}'.format(args.gen_subset, args.beam, scorer.result_string()))
