code stringlengths 66 870k | docstring stringlengths 19 26.7k | func_name stringlengths 1 138 | language stringclasses 1
value | repo stringlengths 7 68 | path stringlengths 5 324 | url stringlengths 46 389 | license stringclasses 7
values |
|---|---|---|---|---|---|---|---|
def on_thread_stop(self) -> None:
"""Override this method instead of :meth:`stop()`.
:meth:`stop()` calls this method.
This method is called immediately after the thread is signaled to stop.
""" | Override this method instead of :meth:`stop()`.
:meth:`stop()` calls this method.
This method is called immediately after the thread is signaled to stop.
| on_thread_stop | python | gorakhargosh/watchdog | src/watchdog/utils/__init__.py | https://github.com/gorakhargosh/watchdog/blob/master/src/watchdog/utils/__init__.py | Apache-2.0 |
def on_thread_start(self) -> None:
"""Override this method instead of :meth:`start()`. :meth:`start()`
calls this method.
This method is called right before this thread is started and this
object's run() method is invoked.
""" | Override this method instead of :meth:`start()`. :meth:`start()`
calls this method.
This method is called right before this thread is started and this
object's run() method is invoked.
| on_thread_start | python | gorakhargosh/watchdog | src/watchdog/utils/__init__.py | https://github.com/gorakhargosh/watchdog/blob/master/src/watchdog/utils/__init__.py | Apache-2.0 |
def load_module(module_name: str) -> ModuleType:
"""Imports a module given its name and returns a handle to it."""
try:
__import__(module_name)
except ImportError as e:
error = f"No module named {module_name}"
raise ImportError(error) from e
return sys.modules[module_name] | Imports a module given its name and returns a handle to it. | load_module | python | gorakhargosh/watchdog | src/watchdog/utils/__init__.py | https://github.com/gorakhargosh/watchdog/blob/master/src/watchdog/utils/__init__.py | Apache-2.0 |
def load_class(dotted_path: str) -> type[Trick]:
"""Loads and returns a class definition provided a dotted path
specification the last part of the dotted path is the class name
and there is at least one module name preceding the class name.
Notes
-----
You will need to ensure that the module yo... | Loads and returns a class definition provided a dotted path
specification the last part of the dotted path is the class name
and there is at least one module name preceding the class name.
Notes
-----
You will need to ensure that the module you are trying to load
exists in the Python path.
... | load_class | python | gorakhargosh/watchdog | src/watchdog/utils/__init__.py | https://github.com/gorakhargosh/watchdog/blob/master/src/watchdog/utils/__init__.py | Apache-2.0 |
def _no_thread_leaks():
"""
Fail on thread leak.
We do not use pytest-threadleak because it is not reliable.
"""
old_thread_count = threading.active_count()
yield
gc.collect() # Clear the stuff from other function-level fixtures
assert threading.active_count() == old_thread_count # Onl... |
Fail on thread leak.
We do not use pytest-threadleak because it is not reliable.
| _no_thread_leaks | python | gorakhargosh/watchdog | tests/conftest.py | https://github.com/gorakhargosh/watchdog/blob/master/tests/conftest.py | Apache-2.0 |
def mkdir(path, *, parents=False):
"""Creates a directory (optionally also creates all the parent directories
in the path)."""
if parents:
try:
os.makedirs(path)
except OSError as e:
if e.errno != errno.EEXIST:
raise
else:
os.mkdir(path) | Creates a directory (optionally also creates all the parent directories
in the path). | mkdir | python | gorakhargosh/watchdog | tests/shell.py | https://github.com/gorakhargosh/watchdog/blob/master/tests/shell.py | Apache-2.0 |
def touch(path, times=None):
"""Updates the modified timestamp of a file or directory."""
if os.path.isdir(path):
os.utime(path, times)
else:
with open(path, "ab"):
os.utime(path, times) | Updates the modified timestamp of a file or directory. | touch | python | gorakhargosh/watchdog | tests/shell.py | https://github.com/gorakhargosh/watchdog/blob/master/tests/shell.py | Apache-2.0 |
def msize(path):
"""Modify the file size without updating the modified time."""
with open(path, "w") as w:
w.write("")
os.utime(path, (0, 0))
time.sleep(0.4)
with open(path, "w") as w:
w.write("0")
os.utime(path, (0, 0)) | Modify the file size without updating the modified time. | msize | python | gorakhargosh/watchdog | tests/shell.py | https://github.com/gorakhargosh/watchdog/blob/master/tests/shell.py | Apache-2.0 |
def test_load_config_valid(tmpdir):
"""Verifies the load of a valid yaml file"""
yaml_file = os.path.join(tmpdir, "config_file.yaml")
with open(yaml_file, "w") as f:
f.write("one: value\ntwo:\n- value1\n- value2\n")
config = watchmedo.load_config(yaml_file)
assert isinstance(config, dict)
... | Verifies the load of a valid yaml file | test_load_config_valid | python | gorakhargosh/watchdog | tests/test_0_watchmedo.py | https://github.com/gorakhargosh/watchdog/blob/master/tests/test_0_watchmedo.py | Apache-2.0 |
def test_load_config_invalid(tmpdir):
"""Verifies if safe load avoid the execution
of untrusted code inside yaml files"""
critical_dir = os.path.join(tmpdir, "critical")
yaml_file = os.path.join(tmpdir, "tricks_file.yaml")
with open(yaml_file, "w") as f:
content = f'one: value\nrun: !!pytho... | Verifies if safe load avoid the execution
of untrusted code inside yaml files | test_load_config_invalid | python | gorakhargosh/watchdog | tests/test_0_watchmedo.py | https://github.com/gorakhargosh/watchdog/blob/master/tests/test_0_watchmedo.py | Apache-2.0 |
def test_auto_restart_on_file_change_debounce(tmpdir, capfd):
"""Simulate changing 3 files quickly and then another change later.
Expect 2 restarts due to debouncing.
"""
script = make_dummy_script(tmpdir, n=2)
trick = AutoRestartTrick([sys.executable, script], debounce_interval_seconds=0.5)
tr... | Simulate changing 3 files quickly and then another change later.
Expect 2 restarts due to debouncing.
| test_auto_restart_on_file_change_debounce | python | gorakhargosh/watchdog | tests/test_0_watchmedo.py | https://github.com/gorakhargosh/watchdog/blob/master/tests/test_0_watchmedo.py | Apache-2.0 |
def test_auto_restart_subprocess_termination(tmpdir, capfd, restart_on_command_exit):
"""Run auto-restart with a script that terminates in about 2 seconds.
After 5 seconds, expect it to have been restarted at least once.
"""
script = make_dummy_script(tmpdir, n=2)
trick = AutoRestartTrick([sys.exec... | Run auto-restart with a script that terminates in about 2 seconds.
After 5 seconds, expect it to have been restarted at least once.
| test_auto_restart_subprocess_termination | python | gorakhargosh/watchdog | tests/test_0_watchmedo.py | https://github.com/gorakhargosh/watchdog/blob/master/tests/test_0_watchmedo.py | Apache-2.0 |
def test_add_watch_twice(observer: BaseObserver, p: P) -> None:
"""Adding the same watch twice used to result in a null pointer return without an exception.
See https://github.com/gorakhargosh/watchdog/issues/765
"""
a = p("a")
mkdir(a)
h = FileSystemEventHandler()
w = ObservedWatch(a, rec... | Adding the same watch twice used to result in a null pointer return without an exception.
See https://github.com/gorakhargosh/watchdog/issues/765
| test_add_watch_twice | python | gorakhargosh/watchdog | tests/test_fsevents.py | https://github.com/gorakhargosh/watchdog/blob/master/tests/test_fsevents.py | Apache-2.0 |
def test_watcher_deletion_while_receiving_events_1(
caplog: pytest.LogCaptureFixture,
p: P,
start_watching: StartWatching,
) -> None:
"""
When the watcher is stopped while there are events, such exception could happen:
Traceback (most recent call last):
File "observers/fsevents.... |
When the watcher is stopped while there are events, such exception could happen:
Traceback (most recent call last):
File "observers/fsevents.py", line 327, in events_callback
self.queue_events(self.timeout, events)
File "observers/fsevents.py", line 187, in queue_events... | test_watcher_deletion_while_receiving_events_1 | python | gorakhargosh/watchdog | tests/test_fsevents.py | https://github.com/gorakhargosh/watchdog/blob/master/tests/test_fsevents.py | Apache-2.0 |
def test_watcher_deletion_while_receiving_events_2(
caplog: pytest.LogCaptureFixture,
p: P,
start_watching: StartWatching,
) -> None:
"""Note: that test takes about 20 seconds to complete.
Quite similar test to prevent another issue
when the watcher is stopped while there are events, such excep... | Note: that test takes about 20 seconds to complete.
Quite similar test to prevent another issue
when the watcher is stopped while there are events, such exception could happen:
Traceback (most recent call last):
File "observers/fsevents.py", line 327, in events_callback
self.... | test_watcher_deletion_while_receiving_events_2 | python | gorakhargosh/watchdog | tests/test_fsevents.py | https://github.com/gorakhargosh/watchdog/blob/master/tests/test_fsevents.py | Apache-2.0 |
def test_remove_watch_twice(start_watching: StartWatching) -> None:
"""
ValueError: PyCapsule_GetPointer called with invalid PyCapsule object
The above exception was the direct cause of the following exception:
src/watchdog/utils/__init__.py:92: in stop
self.on_thread_stop()
src/watchdog/o... |
ValueError: PyCapsule_GetPointer called with invalid PyCapsule object
The above exception was the direct cause of the following exception:
src/watchdog/utils/__init__.py:92: in stop
self.on_thread_stop()
src/watchdog/observers/fsevents.py:73: SystemError
def on_thread_stop(self):
... | test_remove_watch_twice | python | gorakhargosh/watchdog | tests/test_fsevents.py | https://github.com/gorakhargosh/watchdog/blob/master/tests/test_fsevents.py | Apache-2.0 |
def test_unschedule_removed_folder(observer: BaseObserver, p: P) -> None:
"""
TypeError: PyCObject_AsVoidPtr called with null pointer
The above exception was the direct cause of the following exception:
def on_thread_stop(self):
if self.watch:
_fsevents.remove_watch(self.watch)
... |
TypeError: PyCObject_AsVoidPtr called with null pointer
The above exception was the direct cause of the following exception:
def on_thread_stop(self):
if self.watch:
_fsevents.remove_watch(self.watch)
E SystemError: <built-in function stop> returned a result with an error set... | test_unschedule_removed_folder | python | gorakhargosh/watchdog | tests/test_fsevents.py | https://github.com/gorakhargosh/watchdog/blob/master/tests/test_fsevents.py | Apache-2.0 |
def test_recursive_check_accepts_relative_paths(p: P) -> None:
"""See https://github.com/gorakhargosh/watchdog/issues/797
The test code provided in the defect observes the current working directory
using ".". Since the watch path wasn't normalized then that failed.
This test emulates the scenario.
... | See https://github.com/gorakhargosh/watchdog/issues/797
The test code provided in the defect observes the current working directory
using ".". Since the watch path wasn't normalized then that failed.
This test emulates the scenario.
| test_recursive_check_accepts_relative_paths | python | gorakhargosh/watchdog | tests/test_fsevents.py | https://github.com/gorakhargosh/watchdog/blob/master/tests/test_fsevents.py | Apache-2.0 |
def test_watchdog_recursive(p: P) -> None:
"""See https://github.com/gorakhargosh/watchdog/issues/706"""
import os.path
from watchdog.events import FileSystemEventHandler
from watchdog.observers import Observer
class Handler(FileSystemEventHandler):
def __init__(self):
super().... | See https://github.com/gorakhargosh/watchdog/issues/706 | test_watchdog_recursive | python | gorakhargosh/watchdog | tests/test_fsevents.py | https://github.com/gorakhargosh/watchdog/blob/master/tests/test_fsevents.py | Apache-2.0 |
def test_non_ascii_path(p: P, event_queue: TestEventQueue, start_watching: StartWatching) -> None:
"""
Inotify can construct an event for a path containing non-ASCII.
"""
path = p("\N{SNOWMAN}")
start_watching(path=p(""))
os.mkdir(path)
event, _ = event_queue.get(timeout=5)
assert isinst... |
Inotify can construct an event for a path containing non-ASCII.
| test_non_ascii_path | python | gorakhargosh/watchdog | tests/test_inotify_c.py | https://github.com/gorakhargosh/watchdog/blob/master/tests/test_inotify_c.py | Apache-2.0 |
def test_root_deleted(event_queue, emitter):
r"""Test the event got when removing the watched folder.
The regression to prevent is:
Exception in thread Thread-1:
Traceback (most recent call last):
File "watchdog\observers\winapi.py", line 333, in read_directory_changes
ctype... | Test the event got when removing the watched folder.
The regression to prevent is:
Exception in thread Thread-1:
Traceback (most recent call last):
File "watchdog\observers\winapi.py", line 333, in read_directory_changes
ctypes.byref(nbytes), None, None)
File "watchdog\o... | test_root_deleted | python | gorakhargosh/watchdog | tests/test_observers_winapi.py | https://github.com/gorakhargosh/watchdog/blob/master/tests/test_observers_winapi.py | Apache-2.0 |
def wait():
"""
Wait long enough for file/folder mtime to change. This is needed
to be able to detected modifications.
"""
if platform.is_darwin() or platform.is_windows():
# on macOS resolution of stat.mtime is only 1 second
time.sleep(1.5)
else:
time.sleep(0.5) |
Wait long enough for file/folder mtime to change. This is needed
to be able to detected modifications.
| wait | python | gorakhargosh/watchdog | tests/test_snapshot_diff.py | https://github.com/gorakhargosh/watchdog/blob/master/tests/test_snapshot_diff.py | Apache-2.0 |
def test_pickle(p):
"""It should be possible to pickle a snapshot."""
mkdir(p("dir1"))
snasphot = DirectorySnapshot(p("dir1"))
pickle.dumps(snasphot) | It should be possible to pickle a snapshot. | test_pickle | python | gorakhargosh/watchdog | tests/test_snapshot_diff.py | https://github.com/gorakhargosh/watchdog/blob/master/tests/test_snapshot_diff.py | Apache-2.0 |
def train(
cfg: DictConfig, trainer: Trainer, task: tasks.FairseqTask, epoch_itr
) -> Tuple[List[Optional[float]], bool]:
"""Train the model for one epoch and return validation losses."""
# Initialize data iterator
itr = epoch_itr.next_epoch_itr(
fix_batches_to_gpus=cfg.distributed_training.fix_... | Train the model for one epoch and return validation losses. | train | python | OFA-Sys/OFA | train.py | https://github.com/OFA-Sys/OFA/blob/master/train.py | Apache-2.0 |
def validate(
cfg: DictConfig,
trainer: Trainer,
task: tasks.FairseqTask,
epoch_itr,
subsets: List[str],
) -> List[Optional[float]]:
"""Evaluate the model on the validation set(s) and return the losses."""
if cfg.dataset.fixed_validation_seed is not None:
# set fixed seed for every ... | Evaluate the model on the validation set(s) and return the losses. | validate | python | OFA-Sys/OFA | train.py | https://github.com/OFA-Sys/OFA/blob/master/train.py | Apache-2.0 |
def reinitialize(self):
"""Reinitialize the Trainer, typically after model params change."""
self._lr_scheduler = None
self._optimizer = None
self._wrapped_criterion = None
self._wrapped_model = None | Reinitialize the Trainer, typically after model params change. | reinitialize | python | OFA-Sys/OFA | trainer.py | https://github.com/OFA-Sys/OFA/blob/master/trainer.py | Apache-2.0 |
def should_save_checkpoint_on_current_rank(self) -> bool:
"""Indicates whether to save checkpoints on the current DDP rank."""
if (
self.is_fsdp and self.cfg.distributed_training.use_sharded_state
) or getattr(self.cfg.model, "base_layers", 0) > 0:
return True
els... | Indicates whether to save checkpoints on the current DDP rank. | should_save_checkpoint_on_current_rank | python | OFA-Sys/OFA | trainer.py | https://github.com/OFA-Sys/OFA/blob/master/trainer.py | Apache-2.0 |
def checkpoint_suffix(self) -> str:
"""Suffix to add to the checkpoint file name."""
if self.is_fsdp and self.cfg.distributed_training.use_sharded_state:
return self.cfg.checkpoint.checkpoint_suffix + "-shard{0}".format(
self.data_parallel_rank
)
else:
... | Suffix to add to the checkpoint file name. | checkpoint_suffix | python | OFA-Sys/OFA | trainer.py | https://github.com/OFA-Sys/OFA/blob/master/trainer.py | Apache-2.0 |
def consolidate_optimizer(self):
"""For OSS, we need to consolidate the state dict."""
if self.cfg.checkpoint.no_save_optimizer_state:
return
self._gathered_optim_state = None
if hasattr(self.optimizer.optimizer, "consolidate_state_dict"):
self.optimizer.optimizer... | For OSS, we need to consolidate the state dict. | consolidate_optimizer | python | OFA-Sys/OFA | trainer.py | https://github.com/OFA-Sys/OFA/blob/master/trainer.py | Apache-2.0 |
def save_checkpoint(self, filename, extra_state):
"""Save all training state in a checkpoint file."""
logger.info(f"Saving checkpoint to {filename}")
# call state_dict on all ranks in case it needs internal communication
state_dict = utils.move_to_cpu(self.state_dict())
state_dic... | Save all training state in a checkpoint file. | save_checkpoint | python | OFA-Sys/OFA | trainer.py | https://github.com/OFA-Sys/OFA/blob/master/trainer.py | Apache-2.0 |
def load_checkpoint(
self,
filename,
reset_optimizer=False,
reset_lr_scheduler=False,
optimizer_overrides=None,
reset_meters=False,
):
"""
Load all training state from a checkpoint file.
rank = 0 will load the checkpoint, and then broadcast it ... |
Load all training state from a checkpoint file.
rank = 0 will load the checkpoint, and then broadcast it to all
other ranks.
| load_checkpoint | python | OFA-Sys/OFA | trainer.py | https://github.com/OFA-Sys/OFA/blob/master/trainer.py | Apache-2.0 |
def get_train_iterator(
self,
epoch,
combine=True,
load_dataset=True,
data_selector=None,
shard_batch_itr=True,
disable_iterator_cache=False,
):
"""Return an EpochBatchIterator over the training set for a given epoch."""
if load_dataset:
... | Return an EpochBatchIterator over the training set for a given epoch. | get_train_iterator | python | OFA-Sys/OFA | trainer.py | https://github.com/OFA-Sys/OFA/blob/master/trainer.py | Apache-2.0 |
def get_valid_iterator(
self,
subset,
disable_iterator_cache=False,
):
"""Return an EpochBatchIterator over given validation subset for a given epoch."""
self.task.dataset(subset).dataset._seek()
batch_iterator = self.task.get_batch_iterator(
dataset=self.... | Return an EpochBatchIterator over given validation subset for a given epoch. | get_valid_iterator | python | OFA-Sys/OFA | trainer.py | https://github.com/OFA-Sys/OFA/blob/master/trainer.py | Apache-2.0 |
def begin_epoch(self, epoch):
"""Called at the beginning of each epoch."""
logger.info("begin training epoch {}".format(epoch))
self.lr_step_begin_epoch(epoch)
if self.quantizer is not None:
self.quantizer.begin_epoch(epoch)
# task specific setup per epoch
... | Called at the beginning of each epoch. | begin_epoch | python | OFA-Sys/OFA | trainer.py | https://github.com/OFA-Sys/OFA/blob/master/trainer.py | Apache-2.0 |
def train_step(self, samples, raise_oom=False):
"""Do forward, backward and parameter update."""
self._set_seed()
self.model.train()
self.criterion.train()
self.zero_grad()
metrics.log_start_time("train_wall", priority=800, round=0)
# If EMA is enabled through s... | Do forward, backward and parameter update. | train_step | python | OFA-Sys/OFA | trainer.py | https://github.com/OFA-Sys/OFA/blob/master/trainer.py | Apache-2.0 |
def maybe_no_sync():
"""
Whenever *samples* contains more than one mini-batch, we
want to accumulate gradients locally and only call
all-reduce in the last backwards pass.
"""
if (
self.data_parallel_worl... |
Whenever *samples* contains more than one mini-batch, we
want to accumulate gradients locally and only call
all-reduce in the last backwards pass.
| maybe_no_sync | python | OFA-Sys/OFA | trainer.py | https://github.com/OFA-Sys/OFA/blob/master/trainer.py | Apache-2.0 |
def valid_step(self, sample, raise_oom=False):
"""Do forward pass in evaluation mode."""
if self.tpu:
import torch_xla.core.xla_model as xm
xm.rendezvous("valid_step") # wait for all workers
# If EMA is enabled through store_ema=True
# and task.uses_ema is True... | Do forward pass in evaluation mode. | valid_step | python | OFA-Sys/OFA | trainer.py | https://github.com/OFA-Sys/OFA/blob/master/trainer.py | Apache-2.0 |
def lr_step_begin_epoch(self, epoch):
"""Adjust the learning rate at the beginning of the epoch."""
self.lr_scheduler.step_begin_epoch(epoch)
# prefer updating the LR based on the number of steps
return self.lr_step_update() | Adjust the learning rate at the beginning of the epoch. | lr_step_begin_epoch | python | OFA-Sys/OFA | trainer.py | https://github.com/OFA-Sys/OFA/blob/master/trainer.py | Apache-2.0 |
def lr_step(self, epoch, val_loss=None):
"""Adjust the learning rate at the end of the epoch."""
self.lr_scheduler.step(epoch, val_loss)
# prefer updating the LR based on the number of steps
return self.lr_step_update() | Adjust the learning rate at the end of the epoch. | lr_step | python | OFA-Sys/OFA | trainer.py | https://github.com/OFA-Sys/OFA/blob/master/trainer.py | Apache-2.0 |
def lr_step_update(self):
"""Update the learning rate after each update."""
new_lr = self.lr_scheduler.step_update(self.get_num_updates())
if isinstance(new_lr, dict):
for k, v in new_lr.items():
metrics.log_scalar(f"lr_{k}", v, weight=0, priority=300)
new... | Update the learning rate after each update. | lr_step_update | python | OFA-Sys/OFA | trainer.py | https://github.com/OFA-Sys/OFA/blob/master/trainer.py | Apache-2.0 |
def get_meter(self, name):
"""[deprecated] Get a specific meter by name."""
from fairseq import meters
if "get_meter" not in self._warn_once:
self._warn_once.add("get_meter")
utils.deprecation_warning(
"Trainer.get_meter is deprecated. Please use fairseq.... | [deprecated] Get a specific meter by name. | get_meter | python | OFA-Sys/OFA | trainer.py | https://github.com/OFA-Sys/OFA/blob/master/trainer.py | Apache-2.0 |
def set_num_updates(self, num_updates):
"""Set the number of parameters updates."""
self._num_updates = num_updates
self.lr_step_update()
if self.quantizer:
self.quantizer.step_update(self._num_updates)
metrics.log_scalar("num_updates", self._num_updates, weight=0, pr... | Set the number of parameters updates. | set_num_updates | python | OFA-Sys/OFA | trainer.py | https://github.com/OFA-Sys/OFA/blob/master/trainer.py | Apache-2.0 |
def _all_gather_list_sync(
self,
logging_outputs: List[Dict[str, Any]],
*extra_stats_to_sum,
ignore=False,
):
"""
Sync logging outputs across workers. all_gather_list_sync is
suitable when logging outputs are complex types.
"""
if self.tpu:
... |
Sync logging outputs across workers. all_gather_list_sync is
suitable when logging outputs are complex types.
| _all_gather_list_sync | python | OFA-Sys/OFA | trainer.py | https://github.com/OFA-Sys/OFA/blob/master/trainer.py | Apache-2.0 |
def _fast_stat_sync_sum(
self,
logging_outputs: List[Dict[str, Any]],
*extra_stats_to_sum,
ignore=False,
):
"""
Sync logging outputs across workers. fast_stat_sync_sum is
faster than all_gather_list_sync, but is only suitable when
logging outputs are s... |
Sync logging outputs across workers. fast_stat_sync_sum is
faster than all_gather_list_sync, but is only suitable when
logging outputs are scalars and can be summed. Note that
*logging_outputs* cannot contain any nested dicts/lists.
| _fast_stat_sync_sum | python | OFA-Sys/OFA | trainer.py | https://github.com/OFA-Sys/OFA/blob/master/trainer.py | Apache-2.0 |
def _check_grad_norms(self, grad_norm):
"""Check that grad norms are consistent across workers."""
if self._grad_norm_buf is not None:
self._grad_norm_buf.zero_()
self._grad_norm_buf[self.data_parallel_rank] = grad_norm
distributed_utils.all_reduce(
se... | Check that grad norms are consistent across workers. | _check_grad_norms | python | OFA-Sys/OFA | trainer.py | https://github.com/OFA-Sys/OFA/blob/master/trainer.py | Apache-2.0 |
def forward(self, model, sample, update_num=0, reduce=True):
"""Compute the loss for the given sample.
Returns a tuple with three elements:
1) the loss
2) the sample size, which is used as the denominator for the gradient
3) logging outputs to display while training
"""
... | Compute the loss for the given sample.
Returns a tuple with three elements:
1) the loss
2) the sample size, which is used as the denominator for the gradient
3) logging outputs to display while training
| forward | python | OFA-Sys/OFA | criterions/clip_scst_loss.py | https://github.com/OFA-Sys/OFA/blob/master/criterions/clip_scst_loss.py | Apache-2.0 |
def _calculate_clip_scores(self, gen_res, gt_text, device):
'''
gen_res: generated images, list of Image
gt_text: input captions.
device: device for clip model
'''
batch_size = len(gt_text)
gen_res_size = len(gen_res)
img_per_seq = gen_res_size // batch_si... |
gen_res: generated images, list of Image
gt_text: input captions.
device: device for clip model
| _calculate_clip_scores | python | OFA-Sys/OFA | criterions/clip_scst_loss.py | https://github.com/OFA-Sys/OFA/blob/master/criterions/clip_scst_loss.py | Apache-2.0 |
def reduce_metrics(cls, logging_outputs) -> None:
"""Aggregate logging outputs from data parallel training."""
loss_sum = sum(log.get("loss", 0) for log in logging_outputs)
score_sum = sum(log.get("score", 0) for log in logging_outputs)
ntokens = sum(log.get("ntokens", 0) for log in logg... | Aggregate logging outputs from data parallel training. | reduce_metrics | python | OFA-Sys/OFA | criterions/clip_scst_loss.py | https://github.com/OFA-Sys/OFA/blob/master/criterions/clip_scst_loss.py | Apache-2.0 |
def _calculate_eval_scores(self, gen_res, gt_idx, gt_res):
'''
gen_res: generated captions, list of str
gt_idx: list of int, of the same length as gen_res
gt_res: ground truth captions, list of list of str.
gen_res[i] corresponds to gt_res[gt_idx[i]]
Each image ca... |
gen_res: generated captions, list of str
gt_idx: list of int, of the same length as gen_res
gt_res: ground truth captions, list of list of str.
gen_res[i] corresponds to gt_res[gt_idx[i]]
Each image can have multiple ground truth captions
| _calculate_eval_scores | python | OFA-Sys/OFA | criterions/scst_loss.py | https://github.com/OFA-Sys/OFA/blob/master/criterions/scst_loss.py | Apache-2.0 |
def infer_language_pair(path):
"""Infer language pair from filename: <split>.<lang1>-<lang2>.(...).idx"""
src, dst = None, None
for filename in PathManager.ls(path):
parts = filename.split(".")
if len(parts) >= 3 and len(parts[1].split("-")) == 2:
return parts[1].split("-")
r... | Infer language pair from filename: <split>.<lang1>-<lang2>.(...).idx | infer_language_pair | python | OFA-Sys/OFA | data/data_utils.py | https://github.com/OFA-Sys/OFA/blob/master/data/data_utils.py | Apache-2.0 |
def collate_tokens(
values,
pad_idx,
eos_idx=None,
left_pad=False,
move_eos_to_beginning=False,
pad_to_length=None,
pad_to_multiple=1,
pad_to_bsz=None,
):
"""Convert a list of 1d tensors into a padded 2d tensor."""
size = max(v.size(0) for v in values)
size = size if pad_to_l... | Convert a list of 1d tensors into a padded 2d tensor. | collate_tokens | python | OFA-Sys/OFA | data/data_utils.py | https://github.com/OFA-Sys/OFA/blob/master/data/data_utils.py | Apache-2.0 |
def load_indexed_dataset(
path, dictionary=None, dataset_impl=None, combine=False, default="cached"
):
"""A helper function for loading indexed datasets.
Args:
path (str): path to indexed dataset (e.g., 'data-bin/train')
dictionary (~fairseq.data.Dictionary): data dictionary
dataset... | A helper function for loading indexed datasets.
Args:
path (str): path to indexed dataset (e.g., 'data-bin/train')
dictionary (~fairseq.data.Dictionary): data dictionary
dataset_impl (str, optional): which dataset implementation to use. If
not provided, it will be inferred autom... | load_indexed_dataset | python | OFA-Sys/OFA | data/data_utils.py | https://github.com/OFA-Sys/OFA/blob/master/data/data_utils.py | Apache-2.0 |
def numpy_seed(seed, *addl_seeds):
"""Context manager which seeds the NumPy PRNG with the specified seed and
restores the state afterward"""
if seed is None:
yield
return
if len(addl_seeds) > 0:
seed = int(hash((seed, *addl_seeds)) % 1e6)
state = np.random.get_state()
np.... | Context manager which seeds the NumPy PRNG with the specified seed and
restores the state afterward | numpy_seed | python | OFA-Sys/OFA | data/data_utils.py | https://github.com/OFA-Sys/OFA/blob/master/data/data_utils.py | Apache-2.0 |
def collect_filtered(function, iterable, filtered):
"""
Similar to :func:`filter` but collects filtered elements in ``filtered``.
Args:
function (callable): function that returns ``False`` for elements that
should be filtered
iterable (iterable): iterable to filter
filte... |
Similar to :func:`filter` but collects filtered elements in ``filtered``.
Args:
function (callable): function that returns ``False`` for elements that
should be filtered
iterable (iterable): iterable to filter
filtered (list): list to store filtered elements
| collect_filtered | python | OFA-Sys/OFA | data/data_utils.py | https://github.com/OFA-Sys/OFA/blob/master/data/data_utils.py | Apache-2.0 |
def filter_by_size(indices, dataset, max_positions, raise_exception=False):
"""
[deprecated] Filter indices based on their size.
Use `FairseqDataset::filter_indices_by_size` instead.
Args:
indices (List[int]): ordered list of dataset indices
dataset (FairseqDataset): fairseq dataset ins... |
[deprecated] Filter indices based on their size.
Use `FairseqDataset::filter_indices_by_size` instead.
Args:
indices (List[int]): ordered list of dataset indices
dataset (FairseqDataset): fairseq dataset instance
max_positions (tuple): filter elements larger than this size.
... | filter_by_size | python | OFA-Sys/OFA | data/data_utils.py | https://github.com/OFA-Sys/OFA/blob/master/data/data_utils.py | Apache-2.0 |
def filter_paired_dataset_indices_by_size(src_sizes, tgt_sizes, indices, max_sizes):
"""Filter a list of sample indices. Remove those that are longer
than specified in max_sizes.
Args:
indices (np.array): original array of sample indices
max_sizes (int or list[int] or tuple[int]): max s... | Filter a list of sample indices. Remove those that are longer
than specified in max_sizes.
Args:
indices (np.array): original array of sample indices
max_sizes (int or list[int] or tuple[int]): max sample size,
can be defined separately for src and tgt (then list or tuple)
... | filter_paired_dataset_indices_by_size | python | OFA-Sys/OFA | data/data_utils.py | https://github.com/OFA-Sys/OFA/blob/master/data/data_utils.py | Apache-2.0 |
def batch_by_size(
indices,
num_tokens_fn,
num_tokens_vec=None,
max_tokens=None,
max_sentences=None,
required_batch_size_multiple=1,
fixed_shapes=None,
):
"""
Yield mini-batches of indices bucketed by size. Batches may contain
sequences of different lengths.
Args:
in... |
Yield mini-batches of indices bucketed by size. Batches may contain
sequences of different lengths.
Args:
indices (List[int]): ordered list of dataset indices
num_tokens_fn (callable): function that returns the number of tokens at
a given index
num_tokens_vec (List[int]... | batch_by_size | python | OFA-Sys/OFA | data/data_utils.py | https://github.com/OFA-Sys/OFA/blob/master/data/data_utils.py | Apache-2.0 |
def compute_mask_indices(
shape: Tuple[int, int],
padding_mask: Optional[torch.Tensor],
mask_prob: float,
mask_length: int,
mask_type: str = "static",
mask_other: float = 0.0,
min_masks: int = 0,
no_overlap: bool = False,
min_space: int = 0,
) -> np.ndarray:
"""
Computes rand... |
Computes random mask spans for a given shape
Args:
shape: the the shape for which to compute masks.
should be of size 2 where first element is batch size and 2nd is timesteps
padding_mask: optional padding mask of the same size as shape, which will prevent masking padded elements
... | compute_mask_indices | python | OFA-Sys/OFA | data/data_utils.py | https://github.com/OFA-Sys/OFA/blob/master/data/data_utils.py | Apache-2.0 |
def raise_if_valid_subsets_unintentionally_ignored(train_cfg) -> None:
"""Raises if there are paths matching 'valid*[0-9].*' which are not combined or ignored."""
if (
train_cfg.dataset.ignore_unused_valid_subsets
or train_cfg.dataset.combine_valid_subsets
or train_cfg.dataset.disable_va... | Raises if there are paths matching 'valid*[0-9].*' which are not combined or ignored. | raise_if_valid_subsets_unintentionally_ignored | python | OFA-Sys/OFA | data/data_utils.py | https://github.com/OFA-Sys/OFA/blob/master/data/data_utils.py | Apache-2.0 |
def collater(self, samples, pad_to_length=None):
"""Merge samples of different tasks to form two mini-batches.
Args:
samples (List[Tuple]): samples to collate
Returns:
Tuple[dict]: two mini-batch containing the data of different tasks
"""
samples_v1 = [] ... | Merge samples of different tasks to form two mini-batches.
Args:
samples (List[Tuple]): samples to collate
Returns:
Tuple[dict]: two mini-batch containing the data of different tasks
| collater | python | OFA-Sys/OFA | data/pretrain_data/unify_dataset.py | https://github.com/OFA-Sys/OFA/blob/master/data/pretrain_data/unify_dataset.py | Apache-2.0 |
def _collate_frames(
frames: List[torch.Tensor]
):
"""
Convert a list of 2D frames into a padded 3D tensor
Args:
frames (list): list of 2D frames of size L[i]*f_dim. Where L[i] is
length of i-th frame and f_dim is static dimension of features
Retur... |
Convert a list of 2D frames into a padded 3D tensor
Args:
frames (list): list of 2D frames of size L[i]*f_dim. Where L[i] is
length of i-th frame and f_dim is static dimension of features
Returns:
3D tensor of size len(frames)*len_max*f_dim where len_max ... | _collate_frames | python | OFA-Sys/OFA | data/s2t_data/unify_dataset.py | https://github.com/OFA-Sys/OFA/blob/master/data/s2t_data/unify_dataset.py | Apache-2.0 |
def add_args(parser):
"""Add optimizer-specific arguments to the parser."""
# fmt: off
parser.add_argument('--weight-decay', '--wd', default=0.0, type=float, metavar='WD',
help='weight decay')
parser.add_argument('--adagrad-clip', default=0.0, type=float, meta... | Add optimizer-specific arguments to the parser. | add_args | python | OFA-Sys/OFA | fairseq/examples/adaptive_span/adagrad_with_grad_clip.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/adaptive_span/adagrad_with_grad_clip.py | Apache-2.0 |
def optimizer_config(self):
"""
Return a kwarg dictionary that will be used to override optimizer
args stored in checkpoints. This allows us to load a checkpoint and
resume training using a different set of optimizer args, e.g., with a
different learning rate.
"""
... |
Return a kwarg dictionary that will be used to override optimizer
args stored in checkpoints. This allows us to load a checkpoint and
resume training using a different set of optimizer args, e.g., with a
different learning rate.
| optimizer_config | python | OFA-Sys/OFA | fairseq/examples/adaptive_span/adagrad_with_grad_clip.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/adaptive_span/adagrad_with_grad_clip.py | Apache-2.0 |
def forward(self, attn, normalize=True):
"""mask attention with the right span"""
# batch and head dimensions are merged together, so separate them first
self.clamp_param()
if self._adapt_span_layer:
attn = self._mask(attn)
else:
B = attn.size(0) # batch ... | mask attention with the right span | forward | python | OFA-Sys/OFA | fairseq/examples/adaptive_span/adaptive_span_attention.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/adaptive_span/adaptive_span_attention.py | Apache-2.0 |
def get_trim_len(self):
"""how much of memory can be trimmed to reduce computation"""
L = self._max_span
trim_len = min(L - 1, L - self._mask.get_current_max_size())
# too fine granularity might be bad for the memory management
trim_len = math.floor(trim_len / 64) * 64
re... | how much of memory can be trimmed to reduce computation | get_trim_len | python | OFA-Sys/OFA | fairseq/examples/adaptive_span/adaptive_span_attention.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/adaptive_span/adaptive_span_attention.py | Apache-2.0 |
def trim_memory(self, query, key, value, key_pe):
"""trim out unnecessary memory beforehand to reduce computation"""
trim_len = self.get_trim_len()
cache_size = key.size(1) - query.size(1)
trim_len_cache = trim_len - (self._max_span - cache_size)
if trim_len_cache > 0:
... | trim out unnecessary memory beforehand to reduce computation | trim_memory | python | OFA-Sys/OFA | fairseq/examples/adaptive_span/adaptive_span_attention.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/adaptive_span/adaptive_span_attention.py | Apache-2.0 |
def get_cache_size(self):
"""determine how long the cache should be"""
trim_len = self.get_trim_len()
# give a buffer of 64 steps since a span might increase
# in future updates
return min(self._max_span, self._max_span - trim_len + 64) | determine how long the cache should be | get_cache_size | python | OFA-Sys/OFA | fairseq/examples/adaptive_span/adaptive_span_attention.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/adaptive_span/adaptive_span_attention.py | Apache-2.0 |
def forward(self, model, sample, reduce=True):
"""Compute the loss for the given sample.
Returns a tuple with three elements:
1) the loss here is summed, different from the adaptive span code
2) the sample size, which is used as the denominator for the gradient
3) logging output... | Compute the loss for the given sample.
Returns a tuple with three elements:
1) the loss here is summed, different from the adaptive span code
2) the sample size, which is used as the denominator for the gradient
3) logging outputs to display while training
| forward | python | OFA-Sys/OFA | fairseq/examples/adaptive_span/adaptive_span_loss.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/adaptive_span/adaptive_span_loss.py | Apache-2.0 |
def _skew(X, pad_value):
"""shift every row 1 step to right"""
# X = B x M x L
B, M, L = X.size()
X = F.pad(X, (0, M + 1), value=pad_value) # B x M x (L+M+1)
X = X.view(B, -1) # B x ML+MM+M
X = X[:, :-M] # B x ML+MM
X = X.view(B, M, M + L) # B x M x L+M
return X | shift every row 1 step to right | _skew | python | OFA-Sys/OFA | fairseq/examples/adaptive_span/adaptive_span_model.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/adaptive_span/adaptive_span_model.py | Apache-2.0 |
def reorder_incremental_state(
self,
incremental_state: Dict[str, Dict[str, Optional[torch.Tensor]]],
new_order: torch.Tensor,
):
"""Reorder incremental state.
This will be called when the order of the input has changed from the
previous time step. A typical use case... | Reorder incremental state.
This will be called when the order of the input has changed from the
previous time step. A typical use case is beam search, where the input
order changes between time steps based on the selection of beams.
| reorder_incremental_state | python | OFA-Sys/OFA | fairseq/examples/adaptive_span/adaptive_span_model_wrapper.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/adaptive_span/adaptive_span_model_wrapper.py | Apache-2.0 |
def load_dataset(self, split, epoch=1, combine=False, **kwargs):
"""Load a given dataset split (e.g., train, valid, test)"""
# support sharded datasets
paths = utils.split_paths(self.cfg.data)
assert len(paths) > 0
data_path = paths[(epoch - 1) % len(paths)]
split_path =... | Load a given dataset split (e.g., train, valid, test) | load_dataset | python | OFA-Sys/OFA | fairseq/examples/adaptive_span/truncated_bptt_lm_task.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/adaptive_span/truncated_bptt_lm_task.py | Apache-2.0 |
def main():
"""
Usage::
python examples/bart/summarize.py \
--model-dir $HOME/bart.large.cnn \
--model-file model.pt \
--src $HOME/data-bin/cnn_dm/test.source
"""
parser = argparse.ArgumentParser()
parser.add_argument(
"--model-dir",
requ... |
Usage::
python examples/bart/summarize.py --model-dir $HOME/bart.large.cnn --model-file model.pt --src $HOME/data-bin/cnn_dm/test.source
| main | python | OFA-Sys/OFA | fairseq/examples/bart/summarize.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/bart/summarize.py | Apache-2.0 |
def update_init_roberta_model_state(state):
"""
update the state_dict of a Roberta model for initializing
weights of the BertRanker
"""
for k in list(state.keys()):
if ".lm_head." in k or "version" in k:
del state[k]
continue
# remove 'encoder/decoder.sentence_en... |
update the state_dict of a Roberta model for initializing
weights of the BertRanker
| update_init_roberta_model_state | python | OFA-Sys/OFA | fairseq/examples/discriminative_reranking_nmt/models/discriminative_reranking_model.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/discriminative_reranking_nmt/models/discriminative_reranking_model.py | Apache-2.0 |
def load_dictionary(cls, cfg, filename):
"""Load the dictionary from the filename"""
dictionary = Dictionary.load(filename)
dictionary.add_symbol("<mask>") # for loading pretrained XLMR model
return dictionary | Load the dictionary from the filename | load_dictionary | python | OFA-Sys/OFA | fairseq/examples/discriminative_reranking_nmt/tasks/discriminative_reranking_task.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/discriminative_reranking_nmt/tasks/discriminative_reranking_task.py | Apache-2.0 |
def load_dataset(self, split, epoch=0, combine=False, **kwargs):
"""Load a given dataset split (e.g., train, valid, test)."""
if self.cfg.data.endswith("1"):
data_shard = (epoch - 1) % self.cfg.num_data_splits + 1
data_path = self.cfg.data[:-1] + str(data_shard)
else:
... | Load a given dataset split (e.g., train, valid, test). | load_dataset | python | OFA-Sys/OFA | fairseq/examples/discriminative_reranking_nmt/tasks/discriminative_reranking_task.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/discriminative_reranking_nmt/tasks/discriminative_reranking_task.py | Apache-2.0 |
def __init__(
self,
combine_method,
tgt_dict,
src_dict=None,
beam_size=1,
max_len_a=0,
max_len_b=200,
min_len=1,
len_penalty=1.0,
unk_penalty=0.0,
retain_dropout=False,
temperature=1.0,
match_source_len=False,
... | Generates translations of a given source sentence,
using beam search with noisy channel decoding.
Args:
combine_method (string, optional): Method to combine direct, LM and
channel model scores (default: None)
tgt_dict (~fairseq.data.Dictionary): target diction... | __init__ | python | OFA-Sys/OFA | fairseq/examples/fast_noisy_channel/noisy_channel_sequence_generator.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/fast_noisy_channel/noisy_channel_sequence_generator.py | Apache-2.0 |
def generate(
self,
models,
sample,
prefix_tokens=None,
bos_token=None,
**kwargs
):
"""Generate a batch of translations.
Args:
models (List[~fairseq.models.FairseqModel]): ensemble of models
sample (dict): batch
pref... | Generate a batch of translations.
Args:
models (List[~fairseq.models.FairseqModel]): ensemble of models
sample (dict): batch
prefix_tokens (torch.LongTensor, optional): force decoder to begin
with these tokens
| generate | python | OFA-Sys/OFA | fairseq/examples/fast_noisy_channel/noisy_channel_sequence_generator.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/fast_noisy_channel/noisy_channel_sequence_generator.py | Apache-2.0 |
def is_finished(sent, step, unfin_idx):
"""
Check whether we've finished generation for a given sentence, by
comparing the worst score among finalized hypotheses to the best
possible score among unfinalized hypotheses.
"""
assert len(finalized[sent... |
Check whether we've finished generation for a given sentence, by
comparing the worst score among finalized hypotheses to the best
possible score among unfinalized hypotheses.
| is_finished | python | OFA-Sys/OFA | fairseq/examples/fast_noisy_channel/noisy_channel_sequence_generator.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/fast_noisy_channel/noisy_channel_sequence_generator.py | Apache-2.0 |
def finalize_hypos(step, bbsz_idx, eos_scores, combined_noisy_channel_eos_scores):
"""
Finalize the given hypotheses at this step, while keeping the total
number of finalized hypotheses per sentence <= beam_size.
Note: the input must be in the desired finalization order,... |
Finalize the given hypotheses at this step, while keeping the total
number of finalized hypotheses per sentence <= beam_size.
Note: the input must be in the desired finalization order, so that
hypotheses that appear earlier in the input are preferred to those
... | finalize_hypos | python | OFA-Sys/OFA | fairseq/examples/fast_noisy_channel/noisy_channel_sequence_generator.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/fast_noisy_channel/noisy_channel_sequence_generator.py | Apache-2.0 |
def noisy_channel_rescoring(lprobs, beam_size, bsz, src_tokens, tokens, k):
"""Rescore the top k hypothesis from each beam using noisy channel modeling
Returns:
new_fw_lprobs: the direct model probabilities after pruning the top k
new_ch_lm_lprobs: the combined c... | Rescore the top k hypothesis from each beam using noisy channel modeling
Returns:
new_fw_lprobs: the direct model probabilities after pruning the top k
new_ch_lm_lprobs: the combined channel and language model probabilities
new_lm_lprobs: the language model p... | noisy_channel_rescoring | python | OFA-Sys/OFA | fairseq/examples/fast_noisy_channel/noisy_channel_sequence_generator.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/fast_noisy_channel/noisy_channel_sequence_generator.py | Apache-2.0 |
def normalized_scores_with_batch_vocab(
model_decoder, features, target_ids, k, bsz, beam_size,
pad_idx, top_k=0, vocab_size_meter=None, start_idx=None,
end_idx=None, **kwargs):
"""
Get normalized probabilities (or log probs) from a net's output
w.r.t. vocab consisting of tar... |
Get normalized probabilities (or log probs) from a net's output
w.r.t. vocab consisting of target IDs in the batch
| normalized_scores_with_batch_vocab | python | OFA-Sys/OFA | fairseq/examples/fast_noisy_channel/noisy_channel_sequence_generator.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/fast_noisy_channel/noisy_channel_sequence_generator.py | Apache-2.0 |
def add_args(parser):
"""Add task-specific arguments to the parser."""
TranslationTask.add_args(parser)
# fmt: off
parser.add_argument('--channel-model', metavar='FILE',
help='path to P(S|T) model. P(S|T) and P(T|S) must share source and target dictionaries.')... | Add task-specific arguments to the parser. | add_args | python | OFA-Sys/OFA | fairseq/examples/fast_noisy_channel/noisy_channel_translation.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/fast_noisy_channel/noisy_channel_translation.py | Apache-2.0 |
def comp_joint_prob(uid2refs, uid2hyps):
"""
Args:
pad: padding for spliced-feature derived labels
"""
cnts = Counter()
skipped = []
abs_frmdiff = 0
for uid in uid2refs:
if uid not in uid2hyps:
skipped.append(uid)
continue
refs = uid2refs[uid]
... |
Args:
pad: padding for spliced-feature derived labels
| comp_joint_prob | python | OFA-Sys/OFA | fairseq/examples/hubert/measure_teacher_quality.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/hubert/measure_teacher_quality.py | Apache-2.0 |
def read_lab(tsv_path, lab_path, pad_len=0, upsample=1):
"""
tsv is needed to retrieve the uids for the labels
"""
with open(tsv_path) as f:
f.readline()
uids = [op.splitext(op.basename(line.rstrip().split()[0]))[0] for line in f]
with open(lab_path) as f:
labs_list = [pad(li... |
tsv is needed to retrieve the uids for the labels
| read_lab | python | OFA-Sys/OFA | fairseq/examples/hubert/measure_teacher_quality.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/hubert/measure_teacher_quality.py | Apache-2.0 |
def add_args(parser):
"""Add model-specific arguments to the parser."""
parser.add_argument(
"--dropout",
default=0.1,
type=float,
metavar="D",
help="dropout probability",
)
parser.add_argument(
"--encoder-embed-dim"... | Add model-specific arguments to the parser. | add_args | python | OFA-Sys/OFA | fairseq/examples/laser/laser_src/laser_lstm.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/laser/laser_src/laser_lstm.py | Apache-2.0 |
def reorder_encoder_out(self, encoder_out: Dict[str, List[Tensor]], new_order):
"""
Same as the one in transformer.py, with new_sentemb
"""
if len(encoder_out["sentemb"]) == 0:
new_sentemb = []
else:
new_sentemb = [encoder_out["sentemb"][0].index_select(0,... |
Same as the one in transformer.py, with new_sentemb
| reorder_encoder_out | python | OFA-Sys/OFA | fairseq/examples/laser/laser_src/laser_transformer.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/laser/laser_src/laser_transformer.py | Apache-2.0 |
def extract_features(
self,
prev_output_tokens,
encoder_out: Optional[Dict[str, List[Tensor]]],
incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None,
full_context_alignment: bool = False,
alignment_layer: Optional[int] = None,
alignment_heads... |
Similar to *forward* but only return features.
Includes several features from "Jointly Learning to Align and
Translate with Transformer Models" (Garg et al., EMNLP 2019).
Args:
full_context_alignment (bool, optional): don't apply
auto-regressive mask to sel... | extract_features | python | OFA-Sys/OFA | fairseq/examples/laser/laser_src/laser_transformer.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/laser/laser_src/laser_transformer.py | Apache-2.0 |
def forward(
self,
prev_output_tokens,
encoder_out: Optional[Dict[str, List[Tensor]]] = None,
incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None,
features_only: bool = False,
alignment_layer: Optional[int] = None,
alignment_heads: Optional[... |
Args:
prev_output_tokens (LongTensor): previous decoder outputs of shape
`(batch, tgt_len)`, for teacher forcing
encoder_out (optional): output from the encoder, used for
encoder-side attention
incremental_state (dict): dictionary used for sto... | forward | python | OFA-Sys/OFA | fairseq/examples/laser/laser_src/laser_transformer.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/laser/laser_src/laser_transformer.py | Apache-2.0 |
def next_epoch_idx(self):
"""Return the epoch index after *next_epoch_itr* is called."""
epochs = [itr.next_epoch_idx for itr in self.iterators]
self.epoch = epochs[0]
assert all(epoch == self.epoch for epoch in epochs)
return self.epoch | Return the epoch index after *next_epoch_itr* is called. | next_epoch_idx | python | OFA-Sys/OFA | fairseq/examples/laser/laser_src/multitask_data_utils.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/laser/laser_src/multitask_data_utils.py | Apache-2.0 |
def sample(self, logit_idx):
"""To leverage the efficiency of distributed training, samples for all
layers are computed at once for each logit_idx. Logits are parameters
learnt independent of each other.
Args:
logit_idx: The index of logit parameters used for sampling.
... | To leverage the efficiency of distributed training, samples for all
layers are computed at once for each logit_idx. Logits are parameters
learnt independent of each other.
Args:
logit_idx: The index of logit parameters used for sampling.
| sample | python | OFA-Sys/OFA | fairseq/examples/latent_depth/latent_depth_src/modules/latent_layers.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/latent_depth/latent_depth_src/modules/latent_layers.py | Apache-2.0 |
def forward(
self,
query,
key: Optional[Tensor],
value: Optional[Tensor],
key_padding_mask: Optional[Tensor] = None,
incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None,
need_weights: bool = True,
static_kv: bool = False,
att... | Input shape: Time x Batch x Channel
Args:
key_padding_mask (ByteTensor, optional): mask to exclude
keys that are pads, of shape `(batch, src_len)`, where
padding elements are indicated by 1s.
need_weights (bool, optional): return the attention weights,
... | forward | python | OFA-Sys/OFA | fairseq/examples/linformer/linformer_src/modules/multihead_linear_attention.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/linformer/linformer_src/modules/multihead_linear_attention.py | Apache-2.0 |
def reorder_incremental_state(
self,
incremental_state: Dict[str, Dict[str, Optional[Tensor]]],
new_order: Tensor,
):
"""Reorder buffered internal state (for incremental generation)."""
input_buffer = self._get_input_buffer(incremental_state)
if input_buffer is not No... | Reorder buffered internal state (for incremental generation). | reorder_incremental_state | python | OFA-Sys/OFA | fairseq/examples/linformer/linformer_src/modules/multihead_linear_attention.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/linformer/linformer_src/modules/multihead_linear_attention.py | Apache-2.0 |
def match_target_hypo(args, target_outfile, hypo_outfile):
"""combine scores from the LM and bitext models, and write the top scoring hypothesis to a file"""
if len(args.weight1) == 1:
res = score_target_hypo(
args,
args.weight1[0],
args.weight2[0],
args.w... | combine scores from the LM and bitext models, and write the top scoring hypothesis to a file | match_target_hypo | python | OFA-Sys/OFA | fairseq/examples/noisychannel/rerank.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/noisychannel/rerank.py | Apache-2.0 |
def get_prefix(sentence, prefix_len):
"""assuming no bpe, gets the prefix of the sentence with prefix_len words"""
tokens = sentence.strip("\n").split()
if prefix_len >= len(tokens):
return sentence.strip("\n")
else:
return " ".join(tokens[:prefix_len]) | assuming no bpe, gets the prefix of the sentence with prefix_len words | get_prefix | python | OFA-Sys/OFA | fairseq/examples/noisychannel/rerank_utils.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/noisychannel/rerank_utils.py | Apache-2.0 |
def get_prefix_from_len(sentence, bpe_symbol, prefix_len):
"""get the prefix of sentence with bpe, with prefix len in terms of words, not bpe tokens"""
bpe_count = sum([bpe_symbol.strip(" ") in t for t in sentence[:prefix_len]])
if bpe_count == 0:
return sentence[:prefix_len]
else:
retur... | get the prefix of sentence with bpe, with prefix len in terms of words, not bpe tokens | get_prefix_from_len | python | OFA-Sys/OFA | fairseq/examples/noisychannel/rerank_utils.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/noisychannel/rerank_utils.py | Apache-2.0 |
def get_num_bpe_tokens_from_len(sentence, bpe_symbol, prefix_len):
"""given a prefix length in terms of words, return the number of bpe tokens"""
prefix = get_prefix_no_bpe(sentence, bpe_symbol, prefix_len)
assert len(remove_bpe(prefix, bpe_symbol).split()) <= prefix_len
return len(prefix.split(" ")) | given a prefix length in terms of words, return the number of bpe tokens | get_num_bpe_tokens_from_len | python | OFA-Sys/OFA | fairseq/examples/noisychannel/rerank_utils.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/noisychannel/rerank_utils.py | Apache-2.0 |
def get_full_from_prefix(hypo_prefix, hypos):
"""given a hypo prefix, recover the first hypo from the list of complete hypos beginning with that prefix"""
for hypo in hypos:
hypo_prefix = hypo_prefix.strip("\n")
len_prefix = len(hypo_prefix)
if hypo[:len_prefix] == hypo_prefix:
... | given a hypo prefix, recover the first hypo from the list of complete hypos beginning with that prefix | get_full_from_prefix | python | OFA-Sys/OFA | fairseq/examples/noisychannel/rerank_utils.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/noisychannel/rerank_utils.py | Apache-2.0 |
def replace_oovs(source_in, target_in, target_out):
"""Replaces <unk-N> tokens in the target text with the corresponding word in
the source text.
"""
oov_re = re.compile("^<unk-([0-9]+)>$")
for source_seq, target_seq in zip(source_in, target_in):
target_seq_out = []
pos_to_word = ... | Replaces <unk-N> tokens in the target text with the corresponding word in
the source text.
| replace_oovs | python | OFA-Sys/OFA | fairseq/examples/pointer_generator/postprocess.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/pointer_generator/postprocess.py | Apache-2.0 |
def replace_oovs(source_in, target_in, vocabulary, source_out, target_out):
"""Replaces out-of-vocabulary words in source and target text with <unk-N>,
where N in is the position of the word in the source sequence.
"""
def format_unk(pos):
return "<unk-{}>".format(pos)
if target_in is None... | Replaces out-of-vocabulary words in source and target text with <unk-N>,
where N in is the position of the word in the source sequence.
| replace_oovs | python | OFA-Sys/OFA | fairseq/examples/pointer_generator/preprocess.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/pointer_generator/preprocess.py | Apache-2.0 |
def forward(
self,
src_tokens,
src_lengths: Optional[Tensor] = None,
return_all_hiddens: bool = False,
token_embeddings: Optional[Tensor] = None
):
"""
Runs the `forward()` method of the parent Transformer class. Then adds
the source tokens into the en... |
Runs the `forward()` method of the parent Transformer class. Then adds
the source tokens into the encoder output tuple.
While it might be more elegant that the model would pass the source
tokens to the `forward()` method of the decoder too, this would require
changes to `Sequen... | forward | python | OFA-Sys/OFA | fairseq/examples/pointer_generator/pointer_generator_src/transformer_pg.py | https://github.com/OFA-Sys/OFA/blob/master/fairseq/examples/pointer_generator/pointer_generator_src/transformer_pg.py | Apache-2.0 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.