def get_parser(desc, default_task='translation'):
# Before creating the true parser, we need to import optional user module
# in order to eagerly import custom tasks, optimizers, architectures, etc.
usr_parser = argparse.ArgumentParser(add_help=False, allow_abbrev=False)
usr_parser.add_argument('--user-dir', default=None)
usr_args, _ = usr_parser.parse_known_args()
utils.import_user_module(usr_args)
parser = argparse.ArgumentParser(allow_abbrev=False)
# fmt: off
parser.add_argument('--no-progress-bar', action='store_true', help='disable progress bar')
parser.add_argument('--log-interval', type=int, default=1000, metavar='N',
help='log progress every N batches (when progress bar is disabled)')
parser.add_argument('--log-format', default=None, help='log format to use',
choices=['json', 'none', 'simple', 'tqdm'])
parser.add_argument('--tensorboard-logdir', metavar='DIR', default='',
help='path to save logs for tensorboard, should match --logdir '
'of running tensorboard (default: no tensorboard logging)')
parser.add_argument("--tbmf-wrapper", action="store_true",
help="[FB only] ")
parser.add_argument('--seed', default=1, type=int, metavar='N',
help='pseudo random number generator seed')
parser.add_argument('--cpu', action='store_true', help='use CPU instead of CUDA')
parser.add_argument('--fp16', action='store_true', help='use FP16')
parser.add_argument('--memory-efficient-fp16', action='store_true',
help='use a memory-efficient version of FP16 training; implies --fp16')
parser.add_argument('--fp16-init-scale', default=2 ** 7, type=int,
help='default FP16 loss scale')
parser.add_argument('--fp16-scale-window', type=int,
help='number of updates before increasing loss scale')
parser.add_argument('--fp16-scale-tolerance', default=0.0, type=float,
help='pct of updates that can overflow before decreasing the loss scale')
parser.add_argument('--min-loss-scale', default=1e-4, type=float, metavar='D',
help='minimum FP16 loss scale, after which training is stopped')
parser.add_argument('--threshold-loss-scale', type=float,
help='threshold FP16 loss scale from below')
parser.add_argument('--user-dir', default=None,
help='path to a python module containing custom extensions (tasks and/or architectures)')
parser.add_argument('--empty-cache-freq', default=0, type=int,
help='how often to clear the PyTorch CUDA cache (0 to disable)')
from fairseq.registry import REGISTRIES
for registry_name, REGISTRY in REGISTRIES.items():
parser.add_argument(
'--' + registry_name.replace('_', '-'),
default=REGISTRY['default'],
choices=REGISTRY['registry'].keys(),
)
# Task definitions can be found under fairseq/tasks/
from fairseq.tasks import TASK_REGISTRY
parser.add_argument('--task', metavar='TASK', default=default_task,
choices=TASK_REGISTRY.keys(),
help='task')
# fmt: on
return parser
def get_parser(desc, default_task='translation'):
parser = argparse.ArgumentParser(
description='Facebook AI Research Sequence-to-Sequence Toolkit -- ' + desc)
parser.add_argument('--no-progress-bar', action='store_true', help='disable progress bar')
parser.add_argument('--log-interval', type=int, default=1000, metavar='N',
help='log progress every N batches (when progress bar is disabled)')
parser.add_argument('--log-format', default=None, help='log format to use',
choices=['json', 'none', 'simple', 'tqdm'])
parser.add_argument('--seed', default=1, type=int, metavar='N',
help='pseudo random number generator seed')
parser.add_argument('--fp16', action='store_true', help='use FP16')
# Task definitions can be found under fairseq/tasks/
parser.add_argument(
'--task', metavar='TASK', default=default_task, choices=TASK_REGISTRY.keys(),
help='task: {} (default: {})'.format(', '.join(TASK_REGISTRY.keys()), default_task)
)
return parser
def add_ac_args(parser):
from fairseq.tasks import TASK_REGISTRY
from fairseq.criterions import CRITERION_REGISTRY
parser.add_argument('--actor-restore-file',
default='checkpoints/actor/model.pt',
metavar='DIR',
help='path to restore actor')
parser.add_argument('--actor-task',
default='translation',
metavar='TASK',
choices=TASK_REGISTRY.keys(),
help='task for actor')
parser.add_argument('--actor-criterion',
default='ac-loss-actor',
metavar='CRITERION',
choices=CRITERION_REGISTRY.keys(),
help='criterion for actor')
parser.add_argument('--actor-save-update',
'--asu',
default=0,
type=int,
metavar='N',
help='force stop training actor at specified update')
parser.add_argument('--critic-restore-file',
default='checkpoints/critic/model.pt',
metavar='DIR',
help='path to restore critic')
parser.add_argument('--critic-task',
default='translation',
metavar='TASK',
choices=TASK_REGISTRY.keys(),
help='task for critic')
parser.add_argument('--critic-criterion',
default='ac-loss-critic',
metavar='CRITERION',
choices=CRITERION_REGISTRY.keys(),
help='criterion for critic')
parser.add_argument('--critic-save-update',
'--csu',
default=0,
type=int,
metavar='N',
help='force stop training critic at specified update')
def get_parser(desc, default_task='translation'):
parser = argparse.ArgumentParser()
# fmt: off
parser.add_argument(
'--no-progress-bar', action='store_true', help='disable progress bar')
parser.add_argument(
'--log-interval',
type=int,
default=1000,
metavar='N',
help='log progress every N batches (when progress bar is disabled)')
parser.add_argument(
'--log-format',
default=None,
help='log format to use',
choices=['json', 'none', 'simple', 'tqdm'])
parser.add_argument(
'--seed',
default=1,
type=int,
metavar='N',
help='pseudo random number generator seed')
parser.add_argument(
'--cpu', action='store_true', help='use CPU instead of CUDA')
parser.add_argument('--fp16', action='store_true', help='use FP16')
parser.add_argument(
'--memory-efficient-fp16',
action='store_true',
help='use a memory-efficient version of FP16 training; implies --fp16')
parser.add_argument(
'--fp16-init-scale',
default=2**7,
type=int,
help='default FP16 loss scale')
parser.add_argument(
'--fp16-scale-window',
type=int,
help='number of updates before increasing loss scale')
parser.add_argument(
'--fp16-scale-tolerance',
default=0.0,
type=float,
help='pct of updates that can overflow before decreasing the loss scale'
)
# Task definitions can be found under fairseq/tasks/
parser.add_argument(
'--task',
metavar='TASK',
default=default_task,
choices=TASK_REGISTRY.keys(),
help='task')
# fmt: on
return parser
def get_parser(desc, default_task='translation'):
parser = argparse.ArgumentParser()
parser.add_argument('--no-progress-bar', action='store_true', help='disable progress bar')
parser.add_argument('--log-interval', type=int, default=1000, metavar='N',
help='log progress every N batches (when progress bar is disabled)')
parser.add_argument('--log-format', default=None, help='log format to use',
choices=['json', 'none', 'simple', 'tqdm'])
parser.add_argument('--seed', default=1, type=int, metavar='N',
help='pseudo random number generator seed')
parser.add_argument('--fp16', action='store_true', help='use FP16')
# Task definitions can be found under fairseq/tasks/
parser.add_argument(
'--task', metavar='TASK', default=default_task,
choices=TASK_REGISTRY.keys(),
help='task',
)
return parser
def get_parser(desc, default_task='translation'):
parser = argparse.ArgumentParser()
parser.add_argument('--no-progress-bar',
action='store_true',
help='disable progress bar')
parser.add_argument(
'--log-interval',
type=int,
default=1000,
metavar='N',
help='log progress every N batches (when progress bar is disabled)')
parser.add_argument('--log-format',
default=None,
help='log format to use',
choices=['json', 'none', 'simple', 'tqdm'])
parser.add_argument('--seed',
default=1,
type=int,
metavar='N',
help='pseudo random number generator seed')
parser.add_argument('--fp16', action='store_true', help='use FP16')
parser.add_argument('--fp16-init-scale',
default=2**7,
type=int,
help='default FP16 loss scale')
parser.add_argument('--fp16-scale-window',
type=int,
help='number of updates before increasing loss scale')
# Task definitions can be found under fairseq/tasks/
parser.add_argument(
'--task',
metavar='TASK',
default=default_task,
choices=TASK_REGISTRY.keys(),
help='task',
)
return parser
def get_parser(desc, default_task='translation'):
# Before creating the true parser, we need to import optional user module
# in order to eagerly import custom tasks, optimizers, architectures, etc.
usr_parser = argparse.ArgumentParser(add_help=False)
usr_parser.add_argument('--user-dir', default=None)
usr_args, _ = usr_parser.parse_known_args()
if usr_args.user_dir is not None:
import_user_module(usr_args.user_dir)
parser = argparse.ArgumentParser()
# fmt: off
parser.add_argument('--no-progress-bar', action='store_true', help='disable progress bar')
parser.add_argument('--log-interval', type=int, default=1000, metavar='N',
help='log progress every N batches (when progress bar is disabled)')
parser.add_argument('--log-format', default=None, help='log format to use',
choices=['json', 'none', 'simple', 'tqdm'])
parser.add_argument('--seed', default=1, type=int, metavar='N',
help='pseudo random number generator seed')
parser.add_argument('--cpu', action='store_true', help='use CPU instead of CUDA')
parser.add_argument('--fp16', action='store_true', help='use FP16')
parser.add_argument('--memory-efficient-fp16', action='store_true',
help='use a memory-efficient version of FP16 training; implies --fp16')
parser.add_argument('--fp16-init-scale', default=2**7, type=int,
help='default FP16 loss scale')
parser.add_argument('--fp16-scale-window', type=int,
help='number of updates before increasing loss scale')
parser.add_argument('--fp16-scale-tolerance', default=0.0, type=float,
help='pct of updates that can overflow before decreasing the loss scale')
parser.add_argument('--user-dir', default=None,
help='path to a python module containing custom extensions (tasks and/or architectures)')
# Task definitions can be found under fairseq/tasks/
parser.add_argument('--task', metavar='TASK', default=default_task,
choices=TASK_REGISTRY.keys(),
help='task')
# fmt: on
return parser
def get_parser(desc, default_task="translation"):
# Before creating the true parser, we need to import optional user module
# in order to eagerly import custom tasks, optimizers, architectures, etc.
usr_parser = argparse.ArgumentParser(add_help=False, allow_abbrev=False)
usr_parser.add_argument("--user-dir", default=None)
usr_args, _ = usr_parser.parse_known_args()
utils.import_user_module(usr_args)
parser = argparse.ArgumentParser(allow_abbrev=False)
# fmt: off
parser.add_argument('--no-progress-bar',
action='store_true',
help='disable progress bar')
parser.add_argument(
'--log-interval',
type=int,
default=100,
metavar='N',
help='log progress every N batches (when progress bar is disabled)')
parser.add_argument('--log-format',
default=None,
help='log format to use',
choices=['json', 'none', 'simple', 'tqdm'])
parser.add_argument(
'--tensorboard-logdir',
metavar='DIR',
default='',
help='path to save logs for tensorboard, should match --logdir '
'of running tensorboard (default: no tensorboard logging)')
parser.add_argument('--seed',
default=None,
type=int,
metavar='N',
help='pseudo random number generator seed')
parser.add_argument('--cpu',
action='store_true',
help='use CPU instead of CUDA')
parser.add_argument('--tpu',
action='store_true',
help='use TPU instead of CUDA')
parser.add_argument('--ort', action='store_true', help='use ORT')
parser.add_argument('--ort_cuda_mem_limit_in_gbs',
default=32,
type=int,
help='GPU memory')
parser.add_argument('--bf16',
action='store_true',
help='use bfloat16; implies --tpu')
parser.add_argument('--fp16', action='store_true', help='use FP16')
parser.add_argument(
'--memory-efficient-bf16',
action='store_true',
help='use a memory-efficient version of BF16 training; implies --bf16')
parser.add_argument(
'--memory-efficient-fp16',
action='store_true',
help='use a memory-efficient version of FP16 training; implies --fp16')
parser.add_argument('--fp16-no-flatten-grads',
action='store_true',
help='don\'t flatten FP16 grads tensor')
parser.add_argument('--fp16-init-scale',
default=2**7,
type=int,
help='default FP16 loss scale')
parser.add_argument('--fp16-scale-window',
type=int,
help='number of updates before increasing loss scale')
parser.add_argument(
'--fp16-scale-tolerance',
default=0.0,
type=float,
help='pct of updates that can overflow before decreasing the loss scale'
)
parser.add_argument(
'--min-loss-scale',
default=1e-4,
type=float,
metavar='D',
help='minimum FP16 loss scale, after which training is stopped')
parser.add_argument('--threshold-loss-scale',
type=float,
help='threshold FP16 loss scale from below')
parser.add_argument(
'--user-dir',
default=None,
help=
'path to a python module containing custom extensions (tasks and/or architectures)'
)
parser.add_argument(
'--empty-cache-freq',
default=0,
type=int,
help='how often to clear the PyTorch CUDA cache (0 to disable)')
parser.add_argument(
'--all-gather-list-size',
default=16384,
type=int,
help='number of bytes reserved for gathering stats from workers')
parser.add_argument('--model-parallel-size',
type=int,
metavar='N',
default=1,
help='total number of GPUs to parallelize model over')
parser.add_argument('--checkpoint-suffix',
default='',
help='suffix to add to the checkpoint file name')
parser.add_argument('--quantization-config-path',
default=None,
help='path to quantization config file')
parser.add_argument('--profile',
action='store_true',
help='enable autograd profiler emit_nvtx')
from fairseq.registry import REGISTRIES
for registry_name, REGISTRY in REGISTRIES.items():
parser.add_argument(
'--' + registry_name.replace('_', '-'),
default=REGISTRY['default'],
choices=REGISTRY['registry'].keys(),
)
# Task definitions can be found under fairseq/tasks/
from fairseq.tasks import TASK_REGISTRY
parser.add_argument('--task',
metavar='TASK',
default=default_task,
choices=TASK_REGISTRY.keys(),
help='task')
# fmt: on
return parser
help='data subset to generate (train, valid, test)')
parser.add_argument('--num-shards',
default=1,
type=int,
metavar='N',
help='shard generation over N shards')
parser.add_argument('--shard-id',
default=0,
type=int,
metavar='ID',
help='id of the shard to generate (id < num_shards)')
parser.add_argument('--task',
metavar='TASK',
default='translation',
choices=TASK_REGISTRY.keys(),
help='task: {} (default: {})'.format(
', '.join(TASK_REGISTRY.keys()), 'translation'))
parser.add_argument('--source-lang',
default=None,
metavar='SRC',
help='source language')
parser.add_argument('--target-lang',
default=None,
metavar='TARGET',
help='target language')
parser.add_argument('--raw-text',
action='store_true',
help='load raw text dataset')
parser.add_argument('--left-pad-source',
default='True',
def get_parser(desc, default_task='translation'):
# Before creating the true parser, we need to import optional user module
# in order to eagerly import custom tasks, optimizers, architectures, etc.
usr_parser = argparse.ArgumentParser(add_help=False, allow_abbrev=False)
usr_parser.add_argument('--user-dir', default=None)
usr_args, _ = usr_parser.parse_known_args()
utils.import_user_module(usr_args)
parser = argparse.ArgumentParser(allow_abbrev=False)
# fmt: off
parser.add_argument('--no-progress-bar',
action='store_true',
help='disable progress bar')
parser.add_argument(
'--log-interval',
type=int,
default=1000,
metavar='N',
help='log progress every N batches (when progress bar is disabled)')
parser.add_argument('--log-format',
default=None,
help='log format to use',
choices=['json', 'none', 'simple', 'tqdm'])
parser.add_argument(
'--tensorboard-logdir',
metavar='DIR',
default='',
help='path to save logs for tensorboard, should match --logdir '
'of running tensorboard (default: no tensorboard logging)')
parser.add_argument("--tbmf-wrapper",
action="store_true",
help="[FB only] ")
parser.add_argument('--seed',
default=1,
type=int,
metavar='N',
help='pseudo random number generator seed')
parser.add_argument('--cpu',
action='store_true',
help='use CPU instead of CUDA')
parser.add_argument('--fp16', action='store_true', help='use FP16')
parser.add_argument(
'--memory-efficient-fp16',
action='store_true',
help='use a memory-efficient version of FP16 training; implies --fp16')
parser.add_argument('--fp16-init-scale',
default=2**7,
type=int,
help='default FP16 loss scale')
parser.add_argument('--fp16-scale-window',
type=int,
help='number of updates before increasing loss scale')
parser.add_argument(
'--fp16-scale-tolerance',
default=0.0,
type=float,
help='pct of updates that can overflow before decreasing the loss scale'
)
parser.add_argument(
'--min-loss-scale',
default=1e-4,
type=float,
metavar='D',
help='minimum FP16 loss scale, after which training is stopped')
parser.add_argument('--threshold-loss-scale',
type=float,
help='threshold FP16 loss scale from below')
parser.add_argument(
'--user-dir',
default=None,
help=
'path to a python module containing custom extensions (tasks and/or architectures)'
)
parser.add_argument('--sde',
action='store_true',
help='whether to use sde')
parser.add_argument('--update-language-sampling',
type=int,
default=-1,
help='update language sampling every N step')
parser.add_argument('--extra-update-language-sampling',
type=int,
default=-1,
help='update language sampling every N step')
parser.add_argument('--scale-norm',
action='store_true',
help='whether to use scaled norm')
parser.add_argument('--fix-norm',
type=float,
default=None,
help='whether to use fixed norm at output embedding')
parser.add_argument(
'--data-actor',
type=str,
default=None,
help='type of data actor [base|ave_emb|only_grad|interpolate_grad]')
parser.add_argument('--data-actor-embed-dropout',
type=float,
default=0.,
help='')
parser.add_argument('--data-actor-proj-dropout',
type=float,
default=0.,
help='')
parser.add_argument('--data-actor-proj-linear-bias',
type=float,
default=None,
help='the bias term to data actor linear projection')
parser.add_argument(
'--data-actor-proj-post-bias',
type=float,
default=0,
help='the bias term to add after data actor project activation')
parser.add_argument(
'--data-actor-sigmoid-scale',
type=float,
default=1.,
help='the bias term to add after data actor project activation')
parser.add_argument('--extra-data-actor',
type=str,
default=None,
help='type of data actor [ave_emb]')
parser.add_argument('--combine-probs',
type=str,
default=None,
help=' [weight_by_size]')
parser.add_argument('--data-actor-lr',
type=eval_str_list,
default=0.01,
help='lr for optimizing data actor')
parser.add_argument('--data-actor-optim-step',
type=int,
default=1,
help='number of steps to optimize data actor')
parser.add_argument('--data-actor-lr-scheduler', type=str, default=None)
parser.add_argument('--data-actor-embed-dim',
type=int,
default=32,
help='dimension of word embedding for data actor')
parser.add_argument('--lan-embed-dim',
type=int,
default=None,
help='dimension of word embedding for data actor')
parser.add_argument('--data-actor-model-embed',
type=int,
default=0,
help='[0|1] whether to use model embedding')
parser.add_argument('--data-actor-embed-grad',
type=int,
default=1,
help='[0|1] whether to optimize model embedding')
parser.add_argument('--data-loss-lambda',
type=float,
default=0,
help='the percentage of using actual data loss')
parser.add_argument('--data-loss-lambda-warmup-steps',
type=int,
default=-1)
parser.add_argument('--data-loss-lambda-init', type=float, default=-1)
parser.add_argument('--data-loss-lambda-final', type=float, default=-1)
parser.add_argument('--out-score-type',
type=str,
default='sigmoid',
help='[sigmoid|exp]')
parser.add_argument(
'--data-actor-share-model',
action='store_true',
help=
'whether to allow data actor and main model to share the same parameter'
)
parser.add_argument('--tanh-constant',
type=float,
default=10,
help='the constant multiplier for tanh output')
parser.add_argument('--exp-constant',
type=float,
default=0.1,
help='the constant multiplier for tanh output')
parser.add_argument('--eval-bleu',
action='store_true',
help='whether to valid on bleu score')
parser.add_argument('--only-load-data-actor',
action='store_true',
help='whether to valid on bleu score')
parser.add_argument('--data-actor-proj-grad-only', action='store_true')
parser.add_argument('--load-model-as-data-actor',
action='store_true',
help='use the model as data actor')
parser.add_argument('--grad-sim',
type=str,
default='cosine',
help='[cosine|dot_prod]')
parser.add_argument('--dev-grad-eta', type=float, default=0.0001)
parser.add_argument('--proj-grad-sim',
type=str,
default='cosine',
help='[cosine|dot_prod]')
parser.add_argument('--loss-steps',
type=int,
default=1,
help='number of steps to calculate loss for grad sim')
parser.add_argument('--scale-reward',
action='store_true',
help='whether to scale reward by current p')
parser.add_argument('--baseline',
action='store_true',
help='whether to scale reward by current p')
parser.add_argument('--relu-reward',
action='store_true',
help='whether to relu the reward')
parser.add_argument('--discount-reward',
type=float,
default=-1,
help='discount factor for reward')
parser.add_argument('--reward-scale',
type=float,
default=0.0001,
help='scale factor of the reward')
parser.add_argument('--language-weight',
type=str,
default=None,
help='dev language weights separated by comma')
parser.add_argument('--data-actor-step-update',
action='store_true',
help='whether to update at training step')
parser.add_argument(
'--exact-update',
action='store_true',
help='whether to do exact update in the approximate setting')
parser.add_argument('--loss-weight', type=str, default=None, help='[low|]')
parser.add_argument('--discount-grad',
action='store_true',
help='whether to use the default discount grad')
parser.add_argument('--a0', type=float, default=0.05, help='[low|]')
parser.add_argument('--a1', type=float, default=0.95, help='[low|]')
parser.add_argument('--switch-obj-epoch',
type=int,
default=1,
help='the epoch to update val loss to trainer')
parser.add_argument('--embedding-file',
type=str,
default=None,
help='the file path to init data actor embedding')
parser.add_argument('--data-actor-feature-postprocess',
type=str,
default='last',
help='[tanh|average]')
# TCS options
parser.add_argument(
'--lan-dists',
default=None,
type=str,
help='comman separated numbers that indicate language distance')
parser.add_argument(
'--data-condition',
default="target",
type=str,
help='[source|target] whether to condition on source or target')
parser.add_argument(
'--sample-instance',
action='store_true',
help=
'whether to sample for each instance in a batch for mulitlingual_data')
parser.add_argument(
'--sample-tag-prob',
default=-1,
type=float,
help='probability of using tags other than the language')
parser.add_argument('--data-actor-multilin',
action='store_true',
help='whether to multiling version of the actor')
parser.add_argument('--utility-type',
type=str,
default='ave',
help='type of utility function [ave|min-half|median]')
parser.add_argument('--eval-lang-pairs',
type=str,
default=None,
help='dev data keys for multilin actor')
parser.add_argument('--no-dev',
action='store_true',
help='not use dev set gradient')
parser.add_argument('--pretrain-data-actor',
action='store_true',
help='pretrain the data actor')
parser.add_argument('--pretrain-type',
type=str,
default='lan_dist',
help='[lan_dist|datasize]')
parser.add_argument('--feature-type',
type=str,
default='ones',
help='[ones|valid_loss|train_loss]')
parser.add_argument('--layerwise-dds',
action='store_true',
help='use layerwise DDS')
parser.add_argument('--tensorwise-dds', action='store_true')
parser.add_argument('--dds-no-neg-reward',
action='store_true',
help='set the negative reward for DDS to 0')
parser.add_argument('--proj-grad', action='store_true')
parser.add_argument('--train-on-proj', action='store_true')
parser.add_argument('--train-proj-grad',
action='store_true',
help="use the training grad to project")
parser.add_argument('--train-proj-grad-sum',
action='store_true',
help="use the training grad to project")
parser.add_argument('--save-proj-train',
action='store_true',
help="whether to use saved moving avg grad to project")
parser.add_argument(
'--remove-sample-id',
action='store_true',
help="do not project on current language being trained")
parser.add_argument('--proj-lan-id', type=str, default=None)
parser.add_argument('--paramwise-proj-grad', action='store_true')
parser.add_argument('--sample-proj-count',
type=int,
default=1,
help='number of tasks to sample for projection')
parser.add_argument(
'--optim-weight-softmax-tau',
type=float,
default=-1,
help='a float between (0, 1], smaller value makes weight more peaky')
parser.add_argument('--optim-weight-above-one', action='store_true')
parser.add_argument('--datasize-t',
type=int,
default=None,
help='temperature for controlling datasize sampling')
parser.add_argument('--alpha-p',
type=float,
default=0,
help='[0-1] amount of interpolation for p')
parser.add_argument(
'--num-dev-samples',
type=int,
default=8,
help=
"number of samples to select for dev batch for gradient; max token is set to 1200"
)
parser.add_argument('--reward-level', type=str, default="sent")
parser.add_argument('--reward-constant', type=float, default=0.01)
parser.add_argument('--only-optim-model-key', type=str, default=None)
parser.add_argument('--upsample-factor', type=int, default=0)
parser.add_argument('--data-score-label-smooth',
type=str,
default="none",
help="[no_smooth|weigted_smooth]")
from fairseq.registry import REGISTRIES
for registry_name, REGISTRY in REGISTRIES.items():
parser.add_argument(
'--' + registry_name.replace('_', '-'),
default=REGISTRY['default'],
choices=REGISTRY['registry'].keys(),
)
# Task definitions can be found under fairseq/tasks/
from fairseq.tasks import TASK_REGISTRY
parser.add_argument('--task',
metavar='TASK',
default=default_task,
choices=TASK_REGISTRY.keys(),
help='task')
# fmt: on
return parser
def get_parser(desc, default_task='translation'):
# Before creating the true parser, we need to import optional user module
# in order to eagerly import custom tasks, optimizers, architectures, etc.
usr_parser = argparse.ArgumentParser(add_help=False)
usr_parser.add_argument('--user-dir', default=None)
usr_args, _ = usr_parser.parse_known_args()
import_user_module(usr_args)
parser = argparse.ArgumentParser()
# fmt: off
parser.add_argument('--no-progress-bar',
action='store_true',
help='disable progress bar')
parser.add_argument(
'--log-interval',
type=int,
default=1000,
metavar='N',
help='log progress every N batches (when progress bar is disabled)')
parser.add_argument('--log-format',
default=None,
help='log format to use',
choices=['json', 'none', 'simple', 'tqdm'])
parser.add_argument(
'--tensorboard-logdir',
metavar='DIR',
default='',
help='path to save logs for tensorboard, should match --logdir '
'of running tensorboard (default: no tensorboard logging)')
parser.add_argument('--seed',
default=1,
type=int,
metavar='N',
help='pseudo random number generator seed')
parser.add_argument('--cpu',
action='store_true',
help='use CPU instead of CUDA')
parser.add_argument('--fp16', action='store_true', help='use FP16')
parser.add_argument(
'--memory-efficient-fp16',
action='store_true',
help='use a memory-efficient version of FP16 training; implies --fp16')
parser.add_argument('--fp16-init-scale',
default=2**7,
type=int,
help='default FP16 loss scale')
parser.add_argument('--fp16-scale-window',
type=int,
help='number of updates before increasing loss scale')
parser.add_argument(
'--fp16-scale-tolerance',
default=0.0,
type=float,
help='pct of updates that can overflow before decreasing the loss scale'
)
parser.add_argument(
'--min-loss-scale',
default=1e-4,
type=float,
metavar='D',
help='minimum FP16 loss scale, after which training is stopped')
parser.add_argument('--threshold-loss-scale',
type=float,
help='threshold FP16 loss scale from below')
parser.add_argument(
'--user-dir',
default=None,
help=
'path to a python module containing custom extensions (tasks and/or architectures)'
)
# Task definitions can be found under fairseq/tasks/
parser.add_argument('--task',
metavar='TASK',
default=default_task,
choices=TASK_REGISTRY.keys(),
help='task')
# fmt: on
parser.add_argument(
'--domains',
default=None,
nargs='+',
type=str,
metavar='DOMAINSPLIT',
help=
'comma separated list of data domains to use for training, valid and test'
'[europarl, medical]')
parser.add_argument(
'--train-domains',
default=None,
nargs='+',
type=str,
metavar='TRAINSPLIT',
help='comma separated list of data domains to use for training'
'[europarl, medical]')
parser.add_argument(
'--valid-domains',
default=None,
nargs='+',
type=str,
metavar='VALIDSPLIT',
help='comma separated list of data domains to use for valid'
'[europarl, medical]')
parser.add_argument(
'--test-domains',
default=None,
nargs='+',
type=str,
metavar='TESTSPLIT',
help='comma separated list of data domains to use for test'
'[europarl, medical]')
parser.add_argument(
'--valid-select',
default=None,
nargs='+',
type=str,
metavar='SELECTSPLIT',
help='comma separated list of data domains to use for valid select'
'[europarl, medical]')
parser.add_argument(
'--num-ref',
default=None,
nargs='+',
action=StoreDictKeyPair,
metavar='NUMREFSPLIT',
help='comma separated list of number of references for valid and test'
'[1, 1]')
return parser
def get_parser(desc, default_task='translation'):
parser = argparse.ArgumentParser()
parser.add_argument('--no-progress-bar',
action='store_true',
help='disable progress bar')
parser.add_argument(
'--log-interval',
type=int,
default=1000,
metavar='N',
help='log progress every N batches (when progress bar is disabled)')
parser.add_argument('--log-format',
default=None,
help='log format to use',
choices=['json', 'none', 'simple', 'tqdm'])
parser.add_argument('--seed',
default=1,
type=int,
metavar='N',
help='pseudo random number generator seed')
parser.add_argument('--fp16', action='store_true', help='use FP16')
parser.add_argument('--fp16-init-scale',
default=2**7,
type=int,
help='default FP16 loss scale')
parser.add_argument(
'--encoder-seq',
type=int,
nargs='+',
default=[1, 2, 1, 1, 2, 1, 1, 2, 1, 1, 2, 1, 1, 2, 1, 1, 2, 1],
help='the sequence of encoder nodes that make up the Transformer model'
)
parser.add_argument(
'--encoder-para',
type=int,
nargs='+',
default=[1, 4, 1, 1, 4, 1, 1, 4, 1, 1, 4, 1, 1, 4, 1, 1, 4, 1],
help='parameter information for each encoder node')
parser.add_argument(
'--decoder-seq',
type=int,
nargs='+',
default=[
1, 2, 4, 1, 1, 2, 4, 1, 1, 2, 4, 1, 1, 2, 4, 1, 1, 2, 4, 1, 1, 2,
4, 1
],
help='the sequence of decoder nodes that make up the Transformer model'
)
parser.add_argument('--decoder-para',
type=int,
nargs='+',
default=[
1, 4, 4, 1, 1, 4, 4, 1, 1, 4, 4, 1, 1, 4, 4, 1, 1,
4, 4, 1, 1, 4, 4, 1
],
help='parameter information for each decoder node')
parser.add_argument(
'--para',
type=int,
default=1,
help='The max size of parameters of an evolved transformer')
# Task definitions can be found under fairseq/tasks/
parser.add_argument(
'--task',
metavar='TASK',
default=default_task,
choices=TASK_REGISTRY.keys(),
help='task',
)
return parser
