code stringlengths 66 870k | docstring stringlengths 19 26.7k | func_name stringlengths 1 138 | language stringclasses 1
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def load_state_dict(self, state_dict, optimizer_overrides=None):
"""Load an LR scheduler state dict."""
for k, state in state_dict.items():
if k not in self.optimizers:
# skip extra keys like "loss_scale" added by fp16 optimizer
continue
overrides... | Load an LR scheduler state dict. | load_state_dict | python | OFA-Sys/OFA | fairseq/fairseq/optim/composite.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/fairseq/optim/composite.py | Apache-2.0 |
def optimizer(self):
"""Return a torch.optim.optimizer.Optimizer instance."""
if not hasattr(self, "_optimizer"):
raise NotImplementedError
if not isinstance(self._optimizer, torch.optim.Optimizer):
raise ValueError("_optimizer must be an instance of torch.optim.Optimizer... | Return a torch.optim.optimizer.Optimizer instance. | optimizer | python | OFA-Sys/OFA | fairseq/fairseq/optim/fairseq_optimizer.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/fairseq/optim/fairseq_optimizer.py | Apache-2.0 |
def params(self):
"""Return an iterable of the parameters held by the optimizer."""
for param_group in self.param_groups:
for p in param_group["params"]:
yield p | Return an iterable of the parameters held by the optimizer. | params | python | OFA-Sys/OFA | fairseq/fairseq/optim/fairseq_optimizer.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/fairseq/optim/fairseq_optimizer.py | Apache-2.0 |
def load_state_dict(self, state_dict, optimizer_overrides=None):
"""Load an optimizer state dict.
In general we should prefer the configuration of the existing optimizer
instance (e.g., learning rate) over that found in the state_dict. This
allows us to resume training from a checkpoint... | Load an optimizer state dict.
In general we should prefer the configuration of the existing optimizer
instance (e.g., learning rate) over that found in the state_dict. This
allows us to resume training from a checkpoint using a new set of
optimizer args.
| load_state_dict | python | OFA-Sys/OFA | fairseq/fairseq/optim/fairseq_optimizer.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/fairseq/optim/fairseq_optimizer.py | Apache-2.0 |
def zero_grad(self):
"""Clears the gradients of all optimized parameters."""
for p in self.params:
p.grad = None
self.optimizer.zero_grad() | Clears the gradients of all optimized parameters. | zero_grad | python | OFA-Sys/OFA | fairseq/fairseq/optim/fairseq_optimizer.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/fairseq/optim/fairseq_optimizer.py | Apache-2.0 |
def supports_flat_params(self):
"""
Whether the optimizer supports collapsing of the model
parameters/gradients into a single contiguous Tensor.
"""
if hasattr(self.optimizer, "supports_flat_params"):
return self.optimizer.supports_flat_params
return False |
Whether the optimizer supports collapsing of the model
parameters/gradients into a single contiguous Tensor.
| supports_flat_params | python | OFA-Sys/OFA | fairseq/fairseq/optim/fairseq_optimizer.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/fairseq/optim/fairseq_optimizer.py | Apache-2.0 |
def broadcast_global_state_dict(self, state_dict):
"""
Broadcasts a global state dict to all ranks.
Useful for optimizers that shard state between ranks.
"""
if hasattr(self.optimizer, "broadcast_global_state_dict"):
return self.optimizer.broadcast_global_state_dict(s... |
Broadcasts a global state dict to all ranks.
Useful for optimizers that shard state between ranks.
| broadcast_global_state_dict | python | OFA-Sys/OFA | fairseq/fairseq/optim/fairseq_optimizer.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/fairseq/optim/fairseq_optimizer.py | Apache-2.0 |
def backward(self, loss):
"""Computes the sum of gradients of the given tensor w.r.t. graph leaves.
Compared to :func:`fairseq.optim.FairseqOptimizer.backward`, this
function additionally dynamically scales the loss to avoid gradient
underflow.
"""
if self.scaler is not ... | Computes the sum of gradients of the given tensor w.r.t. graph leaves.
Compared to :func:`fairseq.optim.FairseqOptimizer.backward`, this
function additionally dynamically scales the loss to avoid gradient
underflow.
| backward | python | OFA-Sys/OFA | fairseq/fairseq/optim/fp16_optimizer.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/fairseq/optim/fp16_optimizer.py | Apache-2.0 |
def clip_grad_norm(self, max_norm, aggregate_norm_fn=None):
"""Clips gradient norm and updates dynamic loss scaler."""
self._sync_fp16_grads_to_fp32()
grad_norm = self._multiply_factor * self.fp32_optimizer.clip_grad_norm(
0, aggregate_norm_fn
)
if self.scaler is no... | Clips gradient norm and updates dynamic loss scaler. | clip_grad_norm | python | OFA-Sys/OFA | fairseq/fairseq/optim/fp16_optimizer.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/fairseq/optim/fp16_optimizer.py | Apache-2.0 |
def build_optimizer(cls, cfg: DictConfig, params, **kwargs):
"""
Args:
args (argparse.Namespace): fairseq args
params (iterable): iterable of parameters to optimize
"""
fp16_optimizer = optim.build_optimizer(cfg.optimizer, params)
return cls(cfg, params, f... |
Args:
args (argparse.Namespace): fairseq args
params (iterable): iterable of parameters to optimize
| build_optimizer | python | OFA-Sys/OFA | fairseq/fairseq/optim/fp16_optimizer.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/fairseq/optim/fp16_optimizer.py | Apache-2.0 |
def get_fused_adam_class():
"""
Look for the FusedAdam optimizer from apex. We first try to load the
"contrib" interface, which is a bit faster than the main interface,
but is technically deprecated.
"""
try:
# The "deprecated" interface in recent versions of apex is a bit
# fast... |
Look for the FusedAdam optimizer from apex. We first try to load the
"contrib" interface, which is a bit faster than the main interface,
but is technically deprecated.
| get_fused_adam_class | python | OFA-Sys/OFA | fairseq/fairseq/optim/fused_adam.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/fairseq/optim/fused_adam.py | Apache-2.0 |
def step(self, closure=None, grads=None, scale=1.0, grad_norms=None):
"""Performs a single optimization step.
Args:
closure (callable, optional): A closure that reevaluates the model
and returns the loss.
grads (list of tensors, optional): weight gradient to use f... | Performs a single optimization step.
Args:
closure (callable, optional): A closure that reevaluates the model
and returns the loss.
grads (list of tensors, optional): weight gradient to use for the
optimizer update. If gradients have type torch.half, param... | step | python | OFA-Sys/OFA | fairseq/fairseq/optim/fused_adam.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/fairseq/optim/fused_adam.py | Apache-2.0 |
def broadcast_global_state_dict(
self, state_dict: Dict[str, Any]
) -> Dict[str, Any]:
"""
Broadcasts the entire state_dict to all other ranks
each rank is responsible to load their own partition of data
"""
return utils.broadcast_object(
... |
Broadcasts the entire state_dict to all other ranks
each rank is responsible to load their own partition of data
| broadcast_global_state_dict | python | OFA-Sys/OFA | fairseq/fairseq/optim/shard.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/fairseq/optim/shard.py | Apache-2.0 |
def step(self, epoch, val_loss=None):
"""Update the learning rate at the end of the given epoch."""
super().step(epoch, val_loss)
# we don't change the learning rate at epoch boundaries
return self.optimizer.get_lr() | Update the learning rate at the end of the given epoch. | step | python | OFA-Sys/OFA | fairseq/fairseq/optim/lr_scheduler/cosine_lr_scheduler.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/fairseq/optim/lr_scheduler/cosine_lr_scheduler.py | Apache-2.0 |
def add_args(cls, parser):
"""Add arguments to the parser for this LR scheduler."""
dc = getattr(cls, "__dataclass", None)
if dc is not None:
gen_parser_from_dataclass(parser, dc()) | Add arguments to the parser for this LR scheduler. | add_args | python | OFA-Sys/OFA | fairseq/fairseq/optim/lr_scheduler/fairseq_lr_scheduler.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/fairseq/optim/lr_scheduler/fairseq_lr_scheduler.py | Apache-2.0 |
def step_begin_epoch(self, epoch):
"""Update the learning rate at the beginning of the given epoch."""
self.lr = self.get_next_lr(epoch)
self.optimizer.set_lr(self.warmup_factor * self.lr)
return self.optimizer.get_lr() | Update the learning rate at the beginning of the given epoch. | step_begin_epoch | python | OFA-Sys/OFA | fairseq/fairseq/optim/lr_scheduler/fixed_schedule.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/fairseq/optim/lr_scheduler/fixed_schedule.py | Apache-2.0 |
def state_dict(self):
"""Return the LR scheduler state dict."""
return {
"best": self.lr_scheduler.best,
"last_epoch": self.lr_scheduler.last_epoch,
} | Return the LR scheduler state dict. | state_dict | python | OFA-Sys/OFA | fairseq/fairseq/optim/lr_scheduler/reduce_lr_on_plateau.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/fairseq/optim/lr_scheduler/reduce_lr_on_plateau.py | Apache-2.0 |
def step(self, epoch, val_loss=None):
"""
Update the learning rate at the end of the given epoch if warmup
finishes otherwise no update of lr on epoch boundaries
"""
if val_loss is not None and self.warmup_end is True:
self.lr_scheduler.step(val_loss)
else:
... |
Update the learning rate at the end of the given epoch if warmup
finishes otherwise no update of lr on epoch boundaries
| step | python | OFA-Sys/OFA | fairseq/fairseq/optim/lr_scheduler/reduce_lr_on_plateau.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/fairseq/optim/lr_scheduler/reduce_lr_on_plateau.py | Apache-2.0 |
def step_update(self, num_updates):
"""
Update the learning rate after each update."""
# if there is warmup
if self.cfg.warmup_updates > 0:
if num_updates <= self.cfg.warmup_updates:
self.lr = self.cfg.warmup_init_lr + num_updates * self.lr_step
... |
Update the learning rate after each update. | step_update | python | OFA-Sys/OFA | fairseq/fairseq/optim/lr_scheduler/reduce_lr_on_plateau.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/fairseq/optim/lr_scheduler/reduce_lr_on_plateau.py | Apache-2.0 |
def _decide_stage(self, update_step):
"""
return stage, and the corresponding steps within the current stage
"""
if update_step < self.warmup_steps:
# warmup state
return 0, update_step
offset = self.warmup_steps
if update_step < offset + self.ho... |
return stage, and the corresponding steps within the current stage
| _decide_stage | python | OFA-Sys/OFA | fairseq/fairseq/optim/lr_scheduler/tri_stage_lr_scheduler.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/fairseq/optim/lr_scheduler/tri_stage_lr_scheduler.py | Apache-2.0 |
def remove_punctuation(cls, sent: str):
"""Remove punctuation based on Unicode category."""
return cls.SPACE.join(
t
for t in sent.split(cls.SPACE)
if not all(unicodedata.category(c)[0] == "P" for c in t)
) | Remove punctuation based on Unicode category. | remove_punctuation | python | OFA-Sys/OFA | fairseq/fairseq/scoring/tokenizer.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/fairseq/scoring/tokenizer.py | Apache-2.0 |
def _lang_to_id(self, languages: str):
"""
Build a map from languages to ids. These ids are used as segment labels
for cross-lingual LM training.
"""
lang2id = {}
langs = [l.strip() for l in languages.split(",")]
for id, lang in enumerate(langs):
lang2... |
Build a map from languages to ids. These ids are used as segment labels
for cross-lingual LM training.
| _lang_to_id | python | OFA-Sys/OFA | fairseq/fairseq/tasks/cross_lingual_lm.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/fairseq/tasks/cross_lingual_lm.py | Apache-2.0 |
def build_dataset_for_inference(self, src_tokens, src_lengths, **kwargs):
"""
Generate batches for inference. We assume that the input begins with a
bos symbol (`<s>`) and ends with an eos symbol (`</s>`).
"""
pad = self.source_dictionary.pad()
eos = self.source_dictionar... |
Generate batches for inference. We assume that the input begins with a
bos symbol (`<s>`) and ends with an eos symbol (`</s>`).
| build_dataset_for_inference | python | OFA-Sys/OFA | fairseq/fairseq/tasks/denoising.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/fairseq/tasks/denoising.py | Apache-2.0 |
def build_dictionary(
cls, filenames, workers=1, threshold=-1, nwords=-1, padding_factor=8
):
"""Build the dictionary
Args:
filenames (list): list of filenames
workers (int): number of concurrent workers
threshold (int): defines the minimum word count
... | Build the dictionary
Args:
filenames (list): list of filenames
workers (int): number of concurrent workers
threshold (int): defines the minimum word count
nwords (int): defines the total number of words in the final dictionary,
including special s... | build_dictionary | python | OFA-Sys/OFA | fairseq/fairseq/tasks/fairseq_task.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/fairseq/tasks/fairseq_task.py | Apache-2.0 |
def load_dataset(
self,
split: str,
combine: bool = False,
task_cfg: FairseqDataclass = None,
**kwargs
):
"""Load a given dataset split.
Args:
split (str): name of the split (e.g., train, valid, test)
combine (bool): combines a split s... | Load a given dataset split.
Args:
split (str): name of the split (e.g., train, valid, test)
combine (bool): combines a split segmented into pieces into one dataset
task_cfg (FairseqDataclass): optional task configuration stored in the checkpoint that can be used
... | load_dataset | python | OFA-Sys/OFA | fairseq/fairseq/tasks/fairseq_task.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/fairseq/tasks/fairseq_task.py | Apache-2.0 |
def dataset(self, split):
"""
Return a loaded dataset split.
Args:
split (str): name of the split (e.g., train, valid, test)
Returns:
a :class:`~fairseq.data.FairseqDataset` corresponding to *split*
"""
from fairseq.data import FairseqDataset
... |
Return a loaded dataset split.
Args:
split (str): name of the split (e.g., train, valid, test)
Returns:
a :class:`~fairseq.data.FairseqDataset` corresponding to *split*
| dataset | python | OFA-Sys/OFA | fairseq/fairseq/tasks/fairseq_task.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/fairseq/tasks/fairseq_task.py | Apache-2.0 |
def filter_indices_by_size(
self, indices, dataset, max_positions=None, ignore_invalid_inputs=False
):
"""
Filter examples that are too large
Args:
indices (np.array): original array of sample indices
dataset (~fairseq.data.FairseqDataset): dataset to batch
... |
Filter examples that are too large
Args:
indices (np.array): original array of sample indices
dataset (~fairseq.data.FairseqDataset): dataset to batch
max_positions (optional): max sentence length supported by the
model (default: None).
i... | filter_indices_by_size | python | OFA-Sys/OFA | fairseq/fairseq/tasks/fairseq_task.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/fairseq/tasks/fairseq_task.py | Apache-2.0 |
def get_batch_iterator(
self,
dataset,
max_tokens=None,
max_sentences=None,
max_positions=None,
ignore_invalid_inputs=False,
required_batch_size_multiple=1,
seed=1,
num_shards=1,
shard_id=0,
num_workers=0,
epoch=1,
d... |
Get an iterator that yields batches of data from the given dataset.
Args:
dataset (~fairseq.data.FairseqDataset): dataset to batch
max_tokens (int, optional): max number of tokens in each batch
(default: None).
max_sentences (int, optional): max numb... | get_batch_iterator | python | OFA-Sys/OFA | fairseq/fairseq/tasks/fairseq_task.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/fairseq/tasks/fairseq_task.py | Apache-2.0 |
def build_model(self, cfg: FairseqDataclass):
"""
Build the :class:`~fairseq.models.BaseFairseqModel` instance for this
task.
Args:
cfg (FairseqDataclass): configuration object
Returns:
a :class:`~fairseq.models.BaseFairseqModel` instance
"""
... |
Build the :class:`~fairseq.models.BaseFairseqModel` instance for this
task.
Args:
cfg (FairseqDataclass): configuration object
Returns:
a :class:`~fairseq.models.BaseFairseqModel` instance
| build_model | python | OFA-Sys/OFA | fairseq/fairseq/tasks/fairseq_task.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/fairseq/tasks/fairseq_task.py | Apache-2.0 |
def build_criterion(self, cfg: DictConfig):
"""
Build the :class:`~fairseq.criterions.FairseqCriterion` instance for
this task.
Args:
cfg (omegaconf.DictConfig): configration object
Returns:
a :class:`~fairseq.criterions.FairseqCriterion` instance
... |
Build the :class:`~fairseq.criterions.FairseqCriterion` instance for
this task.
Args:
cfg (omegaconf.DictConfig): configration object
Returns:
a :class:`~fairseq.criterions.FairseqCriterion` instance
| build_criterion | python | OFA-Sys/OFA | fairseq/fairseq/tasks/fairseq_task.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/fairseq/tasks/fairseq_task.py | Apache-2.0 |
def build_generator(
self, models, args, seq_gen_cls=None, extra_gen_cls_kwargs=None, prefix_allowed_tokens_fn=None,
):
"""
Build a :class:`~fairseq.SequenceGenerator` instance for this
task.
Args:
models (List[~fairseq.models.FairseqModel]): ensemble of models
... |
Build a :class:`~fairseq.SequenceGenerator` instance for this
task.
Args:
models (List[~fairseq.models.FairseqModel]): ensemble of models
args (fairseq.dataclass.configs.GenerationConfig):
configuration object (dataclass) for generation
extra... | build_generator | python | OFA-Sys/OFA | fairseq/fairseq/tasks/fairseq_task.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/fairseq/tasks/fairseq_task.py | Apache-2.0 |
def train_step(
self, sample, model, criterion, optimizer, update_num, ignore_grad=False, **extra_kwargs
):
"""
Do forward and backward, and return the loss as computed by *criterion*
for the given *model* and *sample*.
Args:
sample (dict): the mini-batch. The fo... |
Do forward and backward, and return the loss as computed by *criterion*
for the given *model* and *sample*.
Args:
sample (dict): the mini-batch. The format is defined by the
:class:`~fairseq.data.FairseqDataset`.
model (~fairseq.models.BaseFairseqModel):... | train_step | python | OFA-Sys/OFA | fairseq/fairseq/tasks/fairseq_task.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/fairseq/tasks/fairseq_task.py | Apache-2.0 |
def aggregate_logging_outputs(self, logging_outputs, criterion):
"""[deprecated] Aggregate logging outputs from data parallel training."""
utils.deprecation_warning(
"The aggregate_logging_outputs API is deprecated. "
"Please use the reduce_metrics API instead."
)
... | [deprecated] Aggregate logging outputs from data parallel training. | aggregate_logging_outputs | python | OFA-Sys/OFA | fairseq/fairseq/tasks/fairseq_task.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/fairseq/tasks/fairseq_task.py | Apache-2.0 |
def build_model(self, args: Namespace):
"""
Build the :class:`~fairseq.models.BaseFairseqModel` instance for this
task.
Args:
args (argparse.Namespace): parsed command-line arguments
Returns:
a :class:`~fairseq.models.BaseFairseqModel` instance
"... |
Build the :class:`~fairseq.models.BaseFairseqModel` instance for this
task.
Args:
args (argparse.Namespace): parsed command-line arguments
Returns:
a :class:`~fairseq.models.BaseFairseqModel` instance
| build_model | python | OFA-Sys/OFA | fairseq/fairseq/tasks/fairseq_task.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/fairseq/tasks/fairseq_task.py | Apache-2.0 |
def build_criterion(self, args: Namespace):
"""
Build the :class:`~fairseq.criterions.FairseqCriterion` instance for
this task.
Args:
args (argparse.Namespace): parsed command-line arguments
Returns:
a :class:`~fairseq.criterions.FairseqCriterion` instan... |
Build the :class:`~fairseq.criterions.FairseqCriterion` instance for
this task.
Args:
args (argparse.Namespace): parsed command-line arguments
Returns:
a :class:`~fairseq.criterions.FairseqCriterion` instance
| build_criterion | python | OFA-Sys/OFA | fairseq/fairseq/tasks/fairseq_task.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/fairseq/tasks/fairseq_task.py | Apache-2.0 |
def setup_task(cls, args, **kwargs):
"""Setup the task (e.g., load dictionaries).
Args:
args (argparse.Namespace): parsed command-line arguments
"""
dictionary, output_dictionary = cls.setup_dictionary(args, **kwargs)
# upgrade old checkpoints
if getattr(arg... | Setup the task (e.g., load dictionaries).
Args:
args (argparse.Namespace): parsed command-line arguments
| setup_task | python | OFA-Sys/OFA | fairseq/fairseq/tasks/language_modeling.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/fairseq/tasks/language_modeling.py | Apache-2.0 |
def load_dataset(
self, split: str, epoch=1, combine=False, **kwargs
) -> MonolingualDataset:
"""Load a given dataset split.
Args:
split (str): name of the split (e.g., train, valid, valid1, test)
"""
paths = utils.split_paths(self.args.data)
assert len(p... | Load a given dataset split.
Args:
split (str): name of the split (e.g., train, valid, valid1, test)
| load_dataset | python | OFA-Sys/OFA | fairseq/fairseq/tasks/language_modeling.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/fairseq/tasks/language_modeling.py | Apache-2.0 |
def build_dataset_for_inference(self, src_tokens, src_lengths, **kwargs):
"""
Generate batches for inference. We prepend an eos token to src_tokens
(or bos if `--add-bos-token` is set) and we append a <pad> to target.
This is convenient both for generation with a prefix and LM scoring.
... |
Generate batches for inference. We prepend an eos token to src_tokens
(or bos if `--add-bos-token` is set) and we append a <pad> to target.
This is convenient both for generation with a prefix and LM scoring.
| build_dataset_for_inference | python | OFA-Sys/OFA | fairseq/fairseq/tasks/language_modeling.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/fairseq/tasks/language_modeling.py | Apache-2.0 |
def _get_sample_prob(self, dataset_lens):
"""
Get smoothed sampling porbability by languages. This helps low resource
languages by upsampling them.
"""
prob = dataset_lens / dataset_lens.sum()
smoothed_prob = prob ** self.args.multilang_sampling_alpha
smoothed_pro... |
Get smoothed sampling porbability by languages. This helps low resource
languages by upsampling them.
| _get_sample_prob | python | OFA-Sys/OFA | fairseq/fairseq/tasks/multilingual_denoising.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/fairseq/tasks/multilingual_denoising.py | Apache-2.0 |
def max_positions(self):
"""Return the max sentence length allowed by the task."""
if len(self.datasets.values()) == 0:
return {
"%s-%s"
% (self.args.source_lang, self.args.target_lang): (
self.args.max_source_positions,
... | Return the max sentence length allowed by the task. | max_positions | python | OFA-Sys/OFA | fairseq/fairseq/tasks/multilingual_translation.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/fairseq/tasks/multilingual_translation.py | Apache-2.0 |
def from_string(configuration: str) -> "PiecewiseLinearFn":
"""
Parse the configuration of lambda coefficient (for scheduling).
x = "3" # lambda will be a constant equal to x
x = "0:1,1000:0" # lambda will start from 1 and linearly decrease
... |
Parse the configuration of lambda coefficient (for scheduling).
x = "3" # lambda will be a constant equal to x
x = "0:1,1000:0" # lambda will start from 1 and linearly decrease
# to 0 during the first 1000 iterations
x = "0:0,100... | from_string | python | OFA-Sys/OFA | fairseq/fairseq/tasks/online_backtranslation.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/fairseq/tasks/online_backtranslation.py | Apache-2.0 |
def load_train_dataset(self, data_path: str) -> FairseqDataset:
"""The training dataset is made of backtranslation dataset and denoising dataset."""
data = []
for lang in self.mono_langs:
train_path = os.path.join(data_path, lang, "train")
# TODO: could we do the BT using... | The training dataset is made of backtranslation dataset and denoising dataset. | load_train_dataset | python | OFA-Sys/OFA | fairseq/fairseq/tasks/online_backtranslation.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/fairseq/tasks/online_backtranslation.py | Apache-2.0 |
def load_bt_dataset(self, data_path: str, lang: str) -> FairseqDataset:
"""The BT dataset is generated with (tgt, tgt) pairs.
The actual translation to a (generated_src, tgt) pair
is done on the fly during training.
"""
mono_dataset = data_utils.load_indexed_dataset(
... | The BT dataset is generated with (tgt, tgt) pairs.
The actual translation to a (generated_src, tgt) pair
is done on the fly during training.
| load_bt_dataset | python | OFA-Sys/OFA | fairseq/fairseq/tasks/online_backtranslation.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/fairseq/tasks/online_backtranslation.py | Apache-2.0 |
def backtranslate_sample(self, smp, orig_lang, other_lang) -> None:
"""
* WARNING: smp is modified in place.
* At the start of this function, `smp` has the same input and target:
|--------------------------------------------------------|
| smp['net_input']['src_tokens'] | sm... |
* WARNING: smp is modified in place.
* At the start of this function, `smp` has the same input and target:
|--------------------------------------------------------|
| smp['net_input']['src_tokens'] | smp['target'] |
| (from data) __en__ hello world | __en__ hello... | backtranslate_sample | python | OFA-Sys/OFA | fairseq/fairseq/tasks/online_backtranslation.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/fairseq/tasks/online_backtranslation.py | Apache-2.0 |
def parse_lambda_config(x):
"""
Parse the configuration of lambda coefficient (for scheduling).
x = "3" # lambda will be a constant equal to x
x = "0:1,1000:0" # lambda will start from 1 and linearly decrease
# to 0 during the first 1000 iterations
... |
Parse the configuration of lambda coefficient (for scheduling).
x = "3" # lambda will be a constant equal to x
x = "0:1,1000:0" # lambda will start from 1 and linearly decrease
# to 0 during the first 1000 iterations
x = "0:0,1000:0,2000:1" # lambd... | parse_lambda_config | python | OFA-Sys/OFA | fairseq/fairseq/tasks/semisupervised_translation.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/fairseq/tasks/semisupervised_translation.py | Apache-2.0 |
def lambda_step_func(config, n_iter):
"""
Update a lambda value according to its schedule configuration.
"""
ranges = [
i
for i in range(len(config) - 1)
if config[i][0] <= n_iter < config[i + 1][0]
]
... |
Update a lambda value according to its schedule configuration.
| lambda_step_func | python | OFA-Sys/OFA | fairseq/fairseq/tasks/semisupervised_translation.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/fairseq/tasks/semisupervised_translation.py | Apache-2.0 |
def antidiag_indices(offset, min_i=0, max_i=None, min_j=0, max_j=None):
"""
for a (3, 4) matrix with min_i=1, max_i=3, min_j=1, max_j=4, outputs
offset=2 (1, 1),
offset=3 (2, 1), (1, 2)
offset=4 (2, 2), (1, 3)
offset=5 (2, 3)
constraints:
i + j = offset
min_j <= j < max_j
... |
for a (3, 4) matrix with min_i=1, max_i=3, min_j=1, max_j=4, outputs
offset=2 (1, 1),
offset=3 (2, 1), (1, 2)
offset=4 (2, 2), (1, 3)
offset=5 (2, 3)
constraints:
i + j = offset
min_j <= j < max_j
min_i <= offset - j < max_i
| antidiag_indices | python | OFA-Sys/OFA | fairseq/fairseq/tasks/text_to_speech.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/fairseq/tasks/text_to_speech.py | Apache-2.0 |
def batch_dynamic_time_warping(distance, shapes=None):
"""full batched DTW without any constraints
distance: (batchsize, max_M, max_N) matrix
shapes: (batchsize,) vector specifying (M, N) for each entry
"""
# ptr: 0=left, 1=up-left, 2=up
ptr2dij = {0: (0, -1), 1: (-1, -1), 2: (-1, 0)}
bsz... | full batched DTW without any constraints
distance: (batchsize, max_M, max_N) matrix
shapes: (batchsize,) vector specifying (M, N) for each entry
| batch_dynamic_time_warping | python | OFA-Sys/OFA | fairseq/fairseq/tasks/text_to_speech.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/fairseq/tasks/text_to_speech.py | Apache-2.0 |
def batch_mel_cepstral_distortion(
y1, y2, sr, normalize_type="path", mfcc_fn=None
):
"""
https://arxiv.org/pdf/2011.03568.pdf
The root mean squared error computed on 13-dimensional MFCC using DTW for
alignment. MFCC features are computed from an 80-channel log-mel
spectrogram using a 50ms ... | ERROR: type should be string, got "\n https://arxiv.org/pdf/2011.03568.pdf\n\n The root mean squared error computed on 13-dimensional MFCC using DTW for\n alignment. MFCC features are computed from an 80-channel log-mel\n spectrogram using a 50ms Hann window and hop of 12.5ms.\n\n y1: list of waveforms\n y2: list of waveforms\n sr: sampling rate\n " | batch_mel_cepstral_distortion | python | OFA-Sys/OFA | fairseq/fairseq/tasks/text_to_speech.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/fairseq/tasks/text_to_speech.py | Apache-2.0 |
def get_batch_iterator(
self,
dataset,
max_tokens=None,
max_sentences=None,
max_positions=None,
ignore_invalid_inputs=False,
required_batch_size_multiple=1,
seed=1,
num_shards=1,
shard_id=0,
num_workers=0,
epoch=1,
d... |
Get an iterator that yields batches of data from the given dataset.
Args:
dataset (~fairseq.data.FairseqDataset): dataset to batch
max_tokens (int, optional): max number of tokens in each batch
(default: None).
max_sentences (int, optional): max numb... | get_batch_iterator | python | OFA-Sys/OFA | fairseq/fairseq/tasks/translation_multi_simple_epoch.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/fairseq/tasks/translation_multi_simple_epoch.py | Apache-2.0 |
def register_task(name, dataclass=None):
"""
New tasks can be added to fairseq with the
:func:`~fairseq.tasks.register_task` function decorator.
For example::
@register_task('classification')
class ClassificationTask(FairseqTask):
(...)
.. note::
All Tasks mus... |
New tasks can be added to fairseq with the
:func:`~fairseq.tasks.register_task` function decorator.
For example::
@register_task('classification')
class ClassificationTask(FairseqTask):
(...)
.. note::
All Tasks must implement the :class:`~fairseq.tasks.FairseqTa... | register_task | python | OFA-Sys/OFA | fairseq/fairseq/tasks/__init__.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/fairseq/tasks/__init__.py | Apache-2.0 |
def eval_lm(
models: List[fairseq.models.FairseqModel],
source_dictionary: fairseq.data.Dictionary,
batch_iterator: Iterable,
post_process: Optional[str] = None,
output_word_probs: bool = False,
output_word_stats: bool = False,
target_dictionary: Optional[fairseq.data.Dictionary] = None,
... |
Args:
models (List[~fairseq.models.FairseqModel]): list of models to
evaluate. Models are essentially `nn.Module` instances, but
must be compatible with fairseq's `SequenceScorer`.
source_dictionary (~fairseq.data.Dictionary): dictionary for
applying any relevant... | eval_lm | python | OFA-Sys/OFA | fairseq/fairseq_cli/eval_lm.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/fairseq_cli/eval_lm.py | Apache-2.0 |
def add(self, log_prob, next_word_prob):
"""increments counters for the sum of log probs of current word and next
word (given context ending at current word). Since the next word might be at the end of the example,
or it might be not counted because it is not an ending subword unit,
also... | increments counters for the sum of log probs of current word and next
word (given context ending at current word). Since the next word might be at the end of the example,
or it might be not counted because it is not an ending subword unit,
also keeps track of how many of those we have seen | add | python | OFA-Sys/OFA | fairseq/fairseq_cli/eval_lm.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/fairseq_cli/eval_lm.py | Apache-2.0 |
def average_checkpoints(inputs):
"""Loads checkpoints from inputs and returns a model with averaged weights.
Args:
inputs: An iterable of string paths of checkpoints to load from.
Returns:
A dict of string keys mapping to various values. The 'model' key
from the returned dict should corr... | Loads checkpoints from inputs and returns a model with averaged weights.
Args:
inputs: An iterable of string paths of checkpoints to load from.
Returns:
A dict of string keys mapping to various values. The 'model' key
from the returned dict should correspond to an OrderedDict mapping
s... | average_checkpoints | python | OFA-Sys/OFA | fairseq/scripts/average_checkpoints.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/scripts/average_checkpoints.py | Apache-2.0 |
def test_activation_offloading_does_not_change_metrics(self):
"""Neither ----checkpoint-activations nor --offload-activations should change loss"""
with tempfile.TemporaryDirectory("test_transformer_with_act_cpt") as data_dir:
with self.assertLogs():
create_dummy_data(data_d... | Neither ----checkpoint-activations nor --offload-activations should change loss | test_activation_offloading_does_not_change_metrics | python | OFA-Sys/OFA | fairseq/tests/test_binaries.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/tests/test_binaries.py | Apache-2.0 |
def test_activation_checkpointing_does_not_change_metrics(self):
"""--checkpoint-activations should not change loss"""
with tempfile.TemporaryDirectory("test_transformer_with_act_cpt") as data_dir:
with self.assertLogs():
create_dummy_data(data_dir, num_examples=20)
... | --checkpoint-activations should not change loss | test_activation_checkpointing_does_not_change_metrics | python | OFA-Sys/OFA | fairseq/tests/test_binaries.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/tests/test_binaries.py | Apache-2.0 |
def test_packing(self):
"""Ensures the list of lists of tensors gets packed correctly."""
for batch_constraints, expected_tensor in self.examples:
packed = pack_constraints(batch_constraints)
assert torch.equal(packed, expected_tensor) | Ensures the list of lists of tensors gets packed correctly. | test_packing | python | OFA-Sys/OFA | fairseq/tests/test_constraints.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/tests/test_constraints.py | Apache-2.0 |
def test_graphs(self):
"""
Test whether unordered graph systems are created correctly.
"""
for example in self.examples:
constraints, expected, gold_counts = example
c = ConstraintNode.create(constraints)
assert (
ConstraintNode.print_g... |
Test whether unordered graph systems are created correctly.
| test_graphs | python | OFA-Sys/OFA | fairseq/tests/test_constraints.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/tests/test_constraints.py | Apache-2.0 |
def test_next_tokens(self):
"""
Tests that the set of next tokens is correct.
"""
for example in self.examples:
constraints, expected, gold_counts = example
root = ConstraintNode.create(constraints)
root_tokens = set(root.children.keys())
... |
Tests that the set of next tokens is correct.
| test_next_tokens | python | OFA-Sys/OFA | fairseq/tests/test_constraints.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/tests/test_constraints.py | Apache-2.0 |
def batch_by_size_baseline(
cls,
indices,
num_tokens_vec,
max_tokens,
max_sentences,
bsz_mult,
):
"""Simple, reliable and slow implementation of batch by size """
batches = []
start = 0
while start < len(indices):
for end in... | Simple, reliable and slow implementation of batch by size | batch_by_size_baseline | python | OFA-Sys/OFA | fairseq/tests/test_data_utils.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/tests/test_data_utils.py | Apache-2.0 |
def _run_compare_with_baseline_sweep(self, batch_by_size_impl):
"""Compare reference batch_by_size implementation with batch_by_size_baseline
across a dense grid of hyperparam values"""
MAX_MAX_TOKENS = 10
NUM_TOKENS_VECS_COUNT = 5
for indices_len in [10, 11]: # try odd and even... | Compare reference batch_by_size implementation with batch_by_size_baseline
across a dense grid of hyperparam values | _run_compare_with_baseline_sweep | python | OFA-Sys/OFA | fairseq/tests/test_data_utils.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/tests/test_data_utils.py | Apache-2.0 |
def __init__(self) -> None:
"""LightningModule for testing purposes
Args:
epoch_min_loss_override (int, optional): Pass in an epoch that will be set to the minimum
validation loss for testing purposes (zero based). If None this is ignored. Defaults to None.
"""
... | LightningModule for testing purposes
Args:
epoch_min_loss_override (int, optional): Pass in an epoch that will be set to the minimum
validation loss for testing purposes (zero based). If None this is ignored. Defaults to None.
| __init__ | python | OFA-Sys/OFA | fairseq/tests/test_ema.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/tests/test_ema.py | Apache-2.0 |
def get_dummy_task_and_parser():
"""
Return a dummy task and argument parser, which can be used to
create a model/criterion.
"""
parser = argparse.ArgumentParser(
description="test_dummy_s2s_task", argument_default=argparse.SUPPRESS
)
DummyTask.add_args(parser)
args = parser.pars... |
Return a dummy task and argument parser, which can be used to
create a model/criterion.
| get_dummy_task_and_parser | python | OFA-Sys/OFA | fairseq/tests/test_export.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/tests/test_export.py | Apache-2.0 |
def get_dummy_task_and_parser():
"""
to build a fariseq model, we need some dummy parse and task. This function
is used to create dummy task and parser to faciliate model/criterion test
Note: we use FbSpeechRecognitionTask as the dummy task. You may want
to use other task by providing another funct... |
to build a fariseq model, we need some dummy parse and task. This function
is used to create dummy task and parser to faciliate model/criterion test
Note: we use FbSpeechRecognitionTask as the dummy task. You may want
to use other task by providing another function
| get_dummy_task_and_parser | python | OFA-Sys/OFA | fairseq/tests/test_lstm_jitable.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/tests/test_lstm_jitable.py | Apache-2.0 |
def _get_test_data_with_bpe_cont_marker(self, append_eos=True):
"""
Args:
append_eos: if True, each input sentence in the source tokens tensor
will have an EOS appended to the end.
Returns:
vocabs: BPE vocab with continuation markers as suffixes to denote... |
Args:
append_eos: if True, each input sentence in the source tokens tensor
will have an EOS appended to the end.
Returns:
vocabs: BPE vocab with continuation markers as suffixes to denote
non-end of word tokens. This is the standard BPE format us... | _get_test_data_with_bpe_cont_marker | python | OFA-Sys/OFA | fairseq/tests/test_noising.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/tests/test_noising.py | Apache-2.0 |
def _get_test_data_with_bpe_end_marker(self, append_eos=True):
"""
Args:
append_eos: if True, each input sentence in the source tokens tensor
will have an EOS appended to the end.
Returns:
vocabs: BPE vocab with end-of-word markers as suffixes to denote
... |
Args:
append_eos: if True, each input sentence in the source tokens tensor
will have an EOS appended to the end.
Returns:
vocabs: BPE vocab with end-of-word markers as suffixes to denote
tokens at the end of a word. This is an alternative to fair... | _get_test_data_with_bpe_end_marker | python | OFA-Sys/OFA | fairseq/tests/test_noising.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/tests/test_noising.py | Apache-2.0 |
def _get_test_data_with_word_vocab(self, append_eos=True):
"""
Args:
append_eos: if True, each input sentence in the source tokens tensor
will have an EOS appended to the end.
Returns:
vocabs: word vocab
x: input tensor containing numberized s... |
Args:
append_eos: if True, each input sentence in the source tokens tensor
will have an EOS appended to the end.
Returns:
vocabs: word vocab
x: input tensor containing numberized source tokens, with EOS at the
end if append_eos is tru... | _get_test_data_with_word_vocab | python | OFA-Sys/OFA | fairseq/tests/test_noising.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/tests/test_noising.py | Apache-2.0 |
def assert_eos_at_end(self, x, x_len, eos):
"""Asserts last token of every sentence in x is EOS """
for i in range(len(x_len)):
self.assertEqual(
x[x_len[i] - 1][i],
eos,
(
"Expected eos (token id {eos}) at the end of senten... | Asserts last token of every sentence in x is EOS | assert_eos_at_end | python | OFA-Sys/OFA | fairseq/tests/test_noising.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/tests/test_noising.py | Apache-2.0 |
def assert_word_shuffle_matches_expected(
self,
x,
x_len,
max_shuffle_distance: int,
vocab: Dictionary,
expected_shufle_maps: List[Dict[int, int]],
expect_eos_at_end: bool,
bpe_end_marker=None,
):
"""
This verifies that with a given x, ... |
This verifies that with a given x, x_len, max_shuffle_distance, and
vocab, we get the expected shuffle result.
Args:
x: Tensor of shape (T x B) = (sequence_length, batch_size)
x_len: Tensor of length B = batch_size
max_shuffle_distance: arg to pass to noisin... | assert_word_shuffle_matches_expected | python | OFA-Sys/OFA | fairseq/tests/test_noising.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/tests/test_noising.py | Apache-2.0 |
def test_word_shuffle_with_eos_nonbpe(self):
"""The purpose of this is to test shuffling logic with word vocabs"""
vocab, x, x_len = self._get_test_data_with_word_vocab(append_eos=True)
# Assert word shuffle with max shuffle distance 0 causes input to be
# unchanged
self.assert_... | The purpose of this is to test shuffling logic with word vocabs | test_word_shuffle_with_eos_nonbpe | python | OFA-Sys/OFA | fairseq/tests/test_noising.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/tests/test_noising.py | Apache-2.0 |
def test_word_shuffle_without_eos(self):
"""Same result as word shuffle with eos except no EOS at end"""
vocab, x, x_len = self._get_test_data_with_bpe_cont_marker(append_eos=False)
# Assert word shuffle with max shuffle distance 0 causes input to be
# unchanged
self.assert_word... | Same result as word shuffle with eos except no EOS at end | test_word_shuffle_without_eos | python | OFA-Sys/OFA | fairseq/tests/test_noising.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/tests/test_noising.py | Apache-2.0 |
def test_word_shuffle_without_eos_with_bpe_end_marker(self):
"""Same result as word shuffle without eos except using BPE end token"""
vocab, x, x_len = self._get_test_data_with_bpe_end_marker(append_eos=False)
# Assert word shuffle with max shuffle distance 0 causes input to be
# unchan... | Same result as word shuffle without eos except using BPE end token | test_word_shuffle_without_eos_with_bpe_end_marker | python | OFA-Sys/OFA | fairseq/tests/test_noising.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/tests/test_noising.py | Apache-2.0 |
def assert_no_eos_at_end(self, x, x_len, eos):
"""Asserts that the last token of each sentence in x is not EOS """
for i in range(len(x_len)):
self.assertNotEqual(
x[x_len[i] - 1][i],
eos,
"Expected no eos (token id {eos}) at the end of sentenc... | Asserts that the last token of each sentence in x is not EOS | assert_no_eos_at_end | python | OFA-Sys/OFA | fairseq/tests/test_noising.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/tests/test_noising.py | Apache-2.0 |
def test_word_dropout_without_eos(self):
"""Same result as word dropout with eos except no EOS at end"""
vocab, x, x_len = self._get_test_data_with_bpe_cont_marker(append_eos=False)
with data_utils.numpy_seed(1234):
noising_gen = noising.WordDropout(vocab)
x_noised, l_no... | Same result as word dropout with eos except no EOS at end | test_word_dropout_without_eos | python | OFA-Sys/OFA | fairseq/tests/test_noising.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/tests/test_noising.py | Apache-2.0 |
def test_word_blank_without_eos(self):
"""Same result as word blank with eos except no EOS at end"""
vocab, x, x_len = self._get_test_data_with_bpe_cont_marker(append_eos=False)
with data_utils.numpy_seed(1234):
noising_gen = noising.WordDropout(vocab)
x_noised, l_noised... | Same result as word blank with eos except no EOS at end | test_word_blank_without_eos | python | OFA-Sys/OFA | fairseq/tests/test_noising.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/tests/test_noising.py | Apache-2.0 |
def _get_noising_dataset_batch(
self,
src_tokens_no_pad,
src_dict,
append_eos_to_tgt=False,
):
"""
Constructs a NoisingDataset and the corresponding
``LanguagePairDataset(NoisingDataset(src), src)``. If
*append_eos_to_tgt* is True, wrap the source data... |
Constructs a NoisingDataset and the corresponding
``LanguagePairDataset(NoisingDataset(src), src)``. If
*append_eos_to_tgt* is True, wrap the source dataset in
:class:`TransformEosDataset` to append EOS to the clean source when
using it as the target.
| _get_noising_dataset_batch | python | OFA-Sys/OFA | fairseq/tests/test_noising.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/tests/test_noising.py | Apache-2.0 |
def test_noising_dataset_without_eos(self):
"""
Similar to test noising dataset with eos except that we have to set
*append_eos_to_tgt* to ``True``.
"""
src_dict, src_tokens, _ = self._get_test_data_with_bpe_cont_marker(
append_eos=False
)
# Format d... |
Similar to test noising dataset with eos except that we have to set
*append_eos_to_tgt* to ``True``.
| test_noising_dataset_without_eos | python | OFA-Sys/OFA | fairseq/tests/test_noising.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/tests/test_noising.py | Apache-2.0 |
def test_roberta_batching(self, device: str):
"""
Checks that the batch of size 2 give twice the same results than the batch of size 1.
"""
_, model = get_toy_model(device)
sample = mk_sample("en", device, batch_size=1)
slen = sample["net_input"]["src_lengths"][0]
... |
Checks that the batch of size 2 give twice the same results than the batch of size 1.
| test_roberta_batching | python | OFA-Sys/OFA | fairseq/tests/test_roberta.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/tests/test_roberta.py | Apache-2.0 |
def test_roberta_incremental_decoder(self, device: str):
"""
Checks that incremental decoding yields the same result than non incremental one.
"""
task, model = get_toy_model(device)
en_sample = mk_sample("en", device)
en_tokens = en_sample["net_input"]["src_tokens"]
... |
Checks that incremental decoding yields the same result than non incremental one.
| test_roberta_incremental_decoder | python | OFA-Sys/OFA | fairseq/tests/test_roberta.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/tests/test_roberta.py | Apache-2.0 |
def test_ngram_blocking_same_as_default_implem(self):
"""Test that cuda extension returns same things as default impl in many settings."""
vocab_size = 4
step = 6
for _ in range(2):
block_param = np.random.choice([1, 2, 3, 4])
batch_size = np.random.randint(1, 8)
... | Test that cuda extension returns same things as default impl in many settings. | test_ngram_blocking_same_as_default_implem | python | OFA-Sys/OFA | fairseq/tests/test_sequence_generator.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/tests/test_sequence_generator.py | Apache-2.0 |
def _compare_cuda_ext_to_default_implem(
self, bsz, beam_size, generated_tok, lprobs, step, block_param
):
"""Assert that cuda extension and default implem return the same thing."""
blocker = NGramRepeatBlock(block_param)
assert blocker.use_extension, "Extension not compiled"
... | Assert that cuda extension and default implem return the same thing. | _compare_cuda_ext_to_default_implem | python | OFA-Sys/OFA | fairseq/tests/test_sequence_generator.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/tests/test_sequence_generator.py | Apache-2.0 |
def test_4billion_tokens(self):
"""Regression test for numpy type promotion issue https://github.com/numpy/numpy/issues/5745"""
data = [torch.tensor(list(range(10000)), dtype=torch.long)] * 430000
ds = self._build_dataset(
data, block_size=6, pad=0, eos=1, break_mode="complete"
... | Regression test for numpy type promotion issue https://github.com/numpy/numpy/issues/5745 | test_4billion_tokens | python | OFA-Sys/OFA | fairseq/tests/test_token_block_dataset.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/tests/test_token_block_dataset.py | Apache-2.0 |
def setup_model_loss_criterion(cfg, args, rank, is_cuda):
"""
setup model, criterion and optimizer based on input args
"""
args.distributed_rank = rank
cfg.distributed_training.distributed_rank = args.distributed_rank
if cfg.distributed_training.distributed_world_size > 1:
distributed_ut... |
setup model, criterion and optimizer based on input args
| setup_model_loss_criterion | python | OFA-Sys/OFA | fairseq/tests/distributed/test_bmuf.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/tests/distributed/test_bmuf.py | Apache-2.0 |
def get_dummy_encoder_output(encoder_out_shape=(100, 80, 5)):
"""
This only provides an example to generate dummy encoder output
"""
(T, B, D) = encoder_out_shape
encoder_out = {}
encoder_out["encoder_out"] = torch.from_numpy(
np.random.randn(*encoder_out_shape).astype(np.float32)
)... |
This only provides an example to generate dummy encoder output
| get_dummy_encoder_output | python | OFA-Sys/OFA | fairseq/tests/speech_recognition/asr_test_base.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/tests/speech_recognition/asr_test_base.py | Apache-2.0 |
def check_encoder_output(encoder_output, batch_size=None):
"""we expect encoder_output to be a dict with the following
key/value pairs:
- encoder_out: a Torch.Tensor
- encoder_padding_mask: a binary Torch.Tensor
"""
if not isinstance(encoder_output, dict):
msg = (
"FairseqEnc... | we expect encoder_output to be a dict with the following
key/value pairs:
- encoder_out: a Torch.Tensor
- encoder_padding_mask: a binary Torch.Tensor
| check_encoder_output | python | OFA-Sys/OFA | fairseq/tests/speech_recognition/asr_test_base.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/tests/speech_recognition/asr_test_base.py | Apache-2.0 |
def check_decoder_output(decoder_output):
"""we expect output from a decoder is a tuple with the following constraint:
- the first element is a torch.Tensor
- the second element can be anything (reserved for future use)
"""
if not isinstance(decoder_output, tuple):
msg = "FariseqDecoder outp... | we expect output from a decoder is a tuple with the following constraint:
- the first element is a torch.Tensor
- the second element can be anything (reserved for future use)
| check_decoder_output | python | OFA-Sys/OFA | fairseq/tests/speech_recognition/asr_test_base.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/tests/speech_recognition/asr_test_base.py | Apache-2.0 |
def get_src_tokens(self, correct_prediction, aggregate):
"""
correct_prediction: True if the net_output (src_tokens) should
predict the correct target
aggregate: True if the criterion expects net_output (src_tokens)
aggregated across time axis
"""
predicted_idx = ... |
correct_prediction: True if the net_output (src_tokens) should
predict the correct target
aggregate: True if the criterion expects net_output (src_tokens)
aggregated across time axis
| get_src_tokens | python | OFA-Sys/OFA | fairseq/tests/speech_recognition/asr_test_base.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/tests/speech_recognition/asr_test_base.py | Apache-2.0 |
def override_config(args):
"""
vggtrasformer_1 use 14 layers of transformer,
for testing purpose, it is too expensive. For fast turn-around
test, reduce the number of layers to 3.
"""
args.transformer_enc_config = (
"((1024, 16, 409... |
vggtrasformer_1 use 14 layers of transformer,
for testing purpose, it is too expensive. For fast turn-around
test, reduce the number of layers to 3.
| override_config | python | OFA-Sys/OFA | fairseq/tests/speech_recognition/test_vggtransformer.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/tests/speech_recognition/test_vggtransformer.py | Apache-2.0 |
def override_config(args):
"""
vggtrasformer_2 use 16 layers of transformer,
for testing purpose, it is too expensive. For fast turn-around
test, reduce the number of layers to 3.
"""
args.transformer_enc_config = (
"((1024, 16, 409... |
vggtrasformer_2 use 16 layers of transformer,
for testing purpose, it is too expensive. For fast turn-around
test, reduce the number of layers to 3.
| override_config | python | OFA-Sys/OFA | fairseq/tests/speech_recognition/test_vggtransformer.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/tests/speech_recognition/test_vggtransformer.py | Apache-2.0 |
def override_config(args):
"""
vggtrasformer_base use 12 layers of transformer,
for testing purpose, it is too expensive. For fast turn-around
test, reduce the number of layers to 3.
"""
args.transformer_enc_config = (
"((512, 8, 20... |
vggtrasformer_base use 12 layers of transformer,
for testing purpose, it is too expensive. For fast turn-around
test, reduce the number of layers to 3.
| override_config | python | OFA-Sys/OFA | fairseq/tests/speech_recognition/test_vggtransformer.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/tests/speech_recognition/test_vggtransformer.py | Apache-2.0 |
def load(name: str, device: Union[str, torch.device] = "cuda" if torch.cuda.is_available() else "cpu", jit: bool = False, download_root: str = None):
"""Load a CLIP model
Parameters
----------
name : str
A model name listed by `clip.available_models()`, or the path to a model checkpoint contain... | Load a CLIP model
Parameters
----------
name : str
A model name listed by `clip.available_models()`, or the path to a model checkpoint containing the state_dict
device : Union[str, torch.device]
The device to put the loaded model
jit : bool
Whether to load the optimized JI... | load | python | OFA-Sys/OFA | models/clip/clip.py | https://github.com/OFA-Sys/OFA/blob/master/models/clip/clip.py | Apache-2.0 |
def tokenize(texts: Union[str, List[str]], context_length: int = 77, truncate: bool = False) -> torch.LongTensor:
"""
Returns the tokenized representation of given input string(s)
Parameters
----------
texts : Union[str, List[str]]
An input string or a list of input strings to tokenize
... |
Returns the tokenized representation of given input string(s)
Parameters
----------
texts : Union[str, List[str]]
An input string or a list of input strings to tokenize
context_length : int
The context length to use; all CLIP models use 77 as the context length
truncate: bool... | tokenize | python | OFA-Sys/OFA | models/clip/clip.py | https://github.com/OFA-Sys/OFA/blob/master/models/clip/clip.py | Apache-2.0 |
def forward(
self,
x: torch.Tensor,
x_length: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor]:
"""Subsample x.
Args:
x (torch.Tensor): Input tensor (#batch, time, idim).
x_mask (torch.Tensor): Input mask (#batch, 1, time).
Ret... | Subsample x.
Args:
x (torch.Tensor): Input tensor (#batch, time, idim).
x_mask (torch.Tensor): Input mask (#batch, 1, time).
Returns:
torch.Tensor: Subsampled tensor (#batch, time', odim),
where time' = time // 4.
torch.Tensor: Subsampled... | forward | python | OFA-Sys/OFA | models/ofa/ofa_speech.py | https://github.com/OFA-Sys/OFA/blob/master/models/ofa/ofa_speech.py | Apache-2.0 |
def drop_path(x, drop_prob: float = 0., training: bool = False):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
This is the same as the DropConnect impl I created for EfficientNet, etc networks, however,
the original name is misleading as 'Drop Connect' is a.sh d... | Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
This is the same as the DropConnect impl I created for EfficientNet, etc networks, however,
the original name is misleading as 'Drop Connect' is a.sh different form of dropout in a.sh separate paper...
See discussion: h... | drop_path | python | OFA-Sys/OFA | models/ofa/resnet.py | https://github.com/OFA-Sys/OFA/blob/master/models/ofa/resnet.py | Apache-2.0 |
def drop_path(x, drop_prob: float = 0.0, training: bool = False):
"""
Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
Comment by Ross Wightman: This is the same as the DropConnect impl I created for EfficientNet, etc networks,
however, the original name is mislea... |
Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
Comment by Ross Wightman: This is the same as the DropConnect impl I created for EfficientNet, etc networks,
however, the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper... | drop_path | python | OFA-Sys/OFA | models/ofa/unify_transformer_layer.py | https://github.com/OFA-Sys/OFA/blob/master/models/ofa/unify_transformer_layer.py | Apache-2.0 |
def forward(self, z):
"""
Inputs the output of the encoder network z and maps it to a discrete
one-hot vector that is the index of the closest embedding vector e_j
z (continuous) -> z_q (discrete)
z.shape = (batch, channel, height, width)
quantization pipeline:
... |
Inputs the output of the encoder network z and maps it to a discrete
one-hot vector that is the index of the closest embedding vector e_j
z (continuous) -> z_q (discrete)
z.shape = (batch, channel, height, width)
quantization pipeline:
1. get encoder input (B,C,H,W)
... | forward | python | OFA-Sys/OFA | models/taming/modules/vqvae/quantize.py | https://github.com/OFA-Sys/OFA/blob/master/models/taming/modules/vqvae/quantize.py | Apache-2.0 |
def evaluate_on_coco_caption(res_file, label_file, outfile=None):
"""
res_file: txt file, each row is [image_key, json format list of captions].
Each caption is a dict, with fields "caption", "conf".
label_file: JSON file of ground truth captions in COCO format.
"""
coco = COCO(label_fi... |
res_file: txt file, each row is [image_key, json format list of captions].
Each caption is a dict, with fields "caption", "conf".
label_file: JSON file of ground truth captions in COCO format.
| evaluate_on_coco_caption | python | OFA-Sys/OFA | run_scripts/caption/coco_eval.py | https://github.com/OFA-Sys/OFA/blob/master/run_scripts/caption/coco_eval.py | Apache-2.0 |
def get_activations(images, model, batch_size=64, dims=2048, cuda=False, verbose=True):
"""Calculates the activations of the pool_3 layer for all images.
Params:
-- images : Numpy array of dimension (n_images, 3, hi, wi). The values
must lie between 0 and 1.
-- model : I... | Calculates the activations of the pool_3 layer for all images.
Params:
-- images : Numpy array of dimension (n_images, 3, hi, wi). The values
must lie between 0 and 1.
-- model : Instance of inception model
-- batch_size : the images numpy array is split into batches wi... | get_activations | python | OFA-Sys/OFA | run_scripts/image_gen/fid_score.py | https://github.com/OFA-Sys/OFA/blob/master/run_scripts/image_gen/fid_score.py | Apache-2.0 |
def calculate_frechet_distance(mu1, sigma1, mu2, sigma2, eps=1e-6):
"""Numpy implementation of the Frechet Distance.
The Frechet distance between two multivariate Gaussians X_1 ~ N(mu_1, C_1)
and X_2 ~ N(mu_2, C_2) is
d^2 = ||mu_1 - mu_2||^2 + Tr(C_1 + C_2 - 2*sqrt(C_1*C_2)).
Stable version... | Numpy implementation of the Frechet Distance.
The Frechet distance between two multivariate Gaussians X_1 ~ N(mu_1, C_1)
and X_2 ~ N(mu_2, C_2) is
d^2 = ||mu_1 - mu_2||^2 + Tr(C_1 + C_2 - 2*sqrt(C_1*C_2)).
Stable version by Dougal J. Sutherland.
Params:
-- mu1 : Numpy array containin... | calculate_frechet_distance | python | OFA-Sys/OFA | run_scripts/image_gen/fid_score.py | https://github.com/OFA-Sys/OFA/blob/master/run_scripts/image_gen/fid_score.py | Apache-2.0 |
def calculate_activation_statistics(images, model, batch_size=64,
dims=2048, cuda=False, verbose=True):
"""Calculation of the statistics used by the FID.
Params:
-- images : Numpy array of dimension (n_images, 3, hi, wi). The values
must lie betw... | Calculation of the statistics used by the FID.
Params:
-- images : Numpy array of dimension (n_images, 3, hi, wi). The values
must lie between 0 and 1.
-- model : Instance of inception model
-- batch_size : The images numpy array is split into batches with
... | calculate_activation_statistics | python | OFA-Sys/OFA | run_scripts/image_gen/fid_score.py | https://github.com/OFA-Sys/OFA/blob/master/run_scripts/image_gen/fid_score.py | Apache-2.0 |
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