Search is not available for this dataset
identifier stringlengths 1 155 | parameters stringlengths 2 6.09k | docstring stringlengths 11 63.4k | docstring_summary stringlengths 0 63.4k | function stringlengths 29 99.8k | function_tokens list | start_point list | end_point list | language stringclasses 1
value | docstring_language stringlengths 2 7 | docstring_language_predictions stringlengths 18 23 | is_langid_reliable stringclasses 2
values |
|---|---|---|---|---|---|---|---|---|---|---|---|
test_gradient_clipping | (tmpdir) |
Test gradient clipping
|
Test gradient clipping
| def test_gradient_clipping(tmpdir):
"""
Test gradient clipping
"""
tutils.reset_seed()
model = EvalModelTemplate()
trainer = Trainer(
max_steps=1,
max_epochs=1,
gradient_clip_val=1.0,
default_root_dir=tmpdir,
)
trainer.train_loop.old_training_step_and_b... | [
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958,
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991,
22
] | python | en | ['en', 'error', 'th'] | False |
test_gradient_clipping_fp16 | (tmpdir) |
Test gradient clipping with fp16
|
Test gradient clipping with fp16
| def test_gradient_clipping_fp16(tmpdir):
"""
Test gradient clipping with fp16
"""
tutils.reset_seed()
model = EvalModelTemplate()
trainer = Trainer(
max_steps=1,
max_epochs=1,
precision=16,
gpus=1,
gradient_clip_val=1.0,
default_root_dir=tmpdir,
... | [
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996,
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1030,
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test_num_sanity_val_steps | (tmpdir, limit_val_batches) |
Test that the number of sanity check batches is clipped to `limit_val_batches`.
|
Test that the number of sanity check batches is clipped to `limit_val_batches`.
| def test_num_sanity_val_steps(tmpdir, limit_val_batches):
"""
Test that the number of sanity check batches is clipped to `limit_val_batches`.
"""
model = EvalModelTemplate()
model.validation_step = model.validation_step__multiple_dataloaders
model.validation_epoch_end = model.validation_epoch_en... | [
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1056,
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] | [
1081,
9
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test_num_sanity_val_steps_neg_one | (tmpdir, limit_val_batches) |
Test that `num_sanity_val_steps=-1` runs through all validation data once, and as many batches as
limited by `limit_val_batches` Trainer argument.
|
Test that `num_sanity_val_steps=-1` runs through all validation data once, and as many batches as
limited by `limit_val_batches` Trainer argument.
| def test_num_sanity_val_steps_neg_one(tmpdir, limit_val_batches):
"""
Test that `num_sanity_val_steps=-1` runs through all validation data once, and as many batches as
limited by `limit_val_batches` Trainer argument.
"""
model = EvalModelTemplate()
model.validation_step = model.validation_step__... | [
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1093,
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] | [
1115,
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] | python | en | ['en', 'error', 'th'] | False |
test_trainer_setup_call | (tmpdir) | Test setup call with fit and test call. | Test setup call with fit and test call. | def test_trainer_setup_call(tmpdir):
"""Test setup call with fit and test call."""
class CurrentModel(EvalModelTemplate):
def setup(self, stage):
self.stage = stage
class TrainerSubclass(Trainer):
def setup(self, model, stage):
assert model is not None
... | [
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1402,
0
] | [
1427,
51
] | python | en | ['en', 'en', 'en'] | True |
test_pytorch_profiler_describe | (pytorch_profiler) | Ensure the profiler won't fail when reporting the summary. | Ensure the profiler won't fail when reporting the summary. | def test_pytorch_profiler_describe(pytorch_profiler):
"""Ensure the profiler won't fail when reporting the summary."""
with pytorch_profiler.profile("test_step"):
pass
# log to stdout and print to file
pytorch_profiler.describe()
data = Path(pytorch_profiler.output_fname).read_text()
as... | [
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1587,
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test_pytorch_profiler_value_errors | (pytorch_profiler) | Ensure errors are raised where expected. | Ensure errors are raised where expected. | def test_pytorch_profiler_value_errors(pytorch_profiler):
"""Ensure errors are raised where expected."""
action = "test_step"
with pytest.raises(ValueError):
pytorch_profiler.stop(action)
pytorch_profiler.start(action)
pytorch_profiler.stop(action) | [
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test_pytorch_profiler_trainer_ddp | (tmpdir, use_output_filename) | Ensure that the profiler can be given to the training and default step are properly recorded. | Ensure that the profiler can be given to the training and default step are properly recorded. | def test_pytorch_profiler_trainer_ddp(tmpdir, use_output_filename):
"""Ensure that the profiler can be given to the training and default step are properly recorded. """
if use_output_filename:
output_filename = os.path.join(tmpdir, "profiler.txt")
else:
output_filename = None
profiler ... | [
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1606,
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] | [
1637,
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test_pytorch_profiler_nested | (tmpdir) | Ensure that the profiler handles nested context | Ensure that the profiler handles nested context | def test_pytorch_profiler_nested(tmpdir):
"""Ensure that the profiler handles nested context"""
pytorch_profiler = PyTorchProfiler(
profiled_functions=["a", "b", "c"], use_cuda=False, output_filename=os.path.join(tmpdir, "profiler.txt")
)
with pytorch_profiler.profile("a"):
a = torch.o... | [
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1640,
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test_disabled_training_for_insufficient_limit_train_batches | (
tmpdir, limit_train_batches, global_step, num_training_batches, current_epoch, should_train
) |
Verify when `limit_train_batches` is float & between [0.0, 1.0] and
`int(self.num_training_batches * self.limit_train_batches) == 0`, the training loop is disabled.
|
Verify when `limit_train_batches` is float & between [0.0, 1.0] and
`int(self.num_training_batches * self.limit_train_batches) == 0`, the training loop is disabled.
| def test_disabled_training_for_insufficient_limit_train_batches(
tmpdir, limit_train_batches, global_step, num_training_batches, current_epoch, should_train
):
"""
Verify when `limit_train_batches` is float & between [0.0, 1.0] and
`int(self.num_training_batches * self.limit_train_batches) == 0`, the tr... | [
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1738,
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test_repeated_fit_calls_with_max_epochs_and_steps | (tmpdir, max_steps, max_epochs, global_step) |
Ensure that the training loop is bound by `max_steps` and
`max_epochs` for repeated calls of `trainer.fit`, and
disabled if the limit is reached
|
Ensure that the training loop is bound by `max_steps` and
`max_epochs` for repeated calls of `trainer.fit`, and
disabled if the limit is reached
| def test_repeated_fit_calls_with_max_epochs_and_steps(tmpdir, max_steps, max_epochs, global_step):
"""
Ensure that the training loop is bound by `max_steps` and
`max_epochs` for repeated calls of `trainer.fit`, and
disabled if the limit is reached
"""
dataset_len = 200
batch_size = 10
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1742,
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] | [
1764,
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] | python | en | ['en', 'error', 'th'] | False |
test_trainer_access_in_configure_optimizers | (tmpdir) |
Verify that the configure optimizer function can reference the trainer.
|
Verify that the configure optimizer function can reference the trainer.
| def test_trainer_access_in_configure_optimizers(tmpdir):
"""
Verify that the configure optimizer function can reference the trainer.
"""
class TestModel(BoringModel):
def configure_optimizers(self):
assert self.trainer is not None, "Expect to have access to the trainer within `conf... | [
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test_setup_hook_move_to_device_correctly | (tmpdir) |
Verify that if a user defines a layer in the setup hook function, this is moved to the correct device.
|
Verify that if a user defines a layer in the setup hook function, this is moved to the correct device.
| def test_setup_hook_move_to_device_correctly(tmpdir):
"""
Verify that if a user defines a layer in the setup hook function, this is moved to the correct device.
"""
class TestModel(BoringModel):
def setup(self, stage: str) -> None:
self.new_layer = torch.nn.Linear(2, 2)
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"nn"... | [
1785,
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] | [
1808,
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binaryToDecimal | (binary) |
>>> binaryToDecimal(111110000)
496
>>> binaryToDecimal(10100)
20
>>> binaryToDecimal(101011)
43
|
>>> binaryToDecimal(111110000)
496
>>> binaryToDecimal(10100)
20
>>> binaryToDecimal(101011)
43
| def binaryToDecimal(binary):
"""
>>> binaryToDecimal(111110000)
496
>>> binaryToDecimal(10100)
20
>>> binaryToDecimal(101011)
43
"""
decimal, i, n = 0, 0, 0
while(binary != 0):
dec = binary % 10
decimal = decimal + dec * pow(2, i)
binary = binary//10
i += 1
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PrecisionPlugin.master_params | (self, optimizer: Optimizer) | The master params of the model. Returns the plain model params here.
Maybe different in other precision plugins.
| The master params of the model. Returns the plain model params here.
Maybe different in other precision plugins. | def master_params(self, optimizer: Optimizer) -> Generator[torch.Tensor, None, None]:
"""The master params of the model. Returns the plain model params here.
Maybe different in other precision plugins.
"""
for group in optimizer.param_groups:
for p in group["params"]:
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PrecisionPlugin.connect | (self, model: Module, optimizers: Sequence,
lr_schedulers: Sequence) | Connects this plugin to the accelerator and the training process | Connects this plugin to the accelerator and the training process | def connect(self, model: Module, optimizers: Sequence,
lr_schedulers: Sequence) -> Tuple[Module, Sequence, Sequence]:
"""Connects this plugin to the accelerator and the training process"""
return model, optimizers, lr_schedulers | [
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PrecisionPlugin.backward | (
self,
model: LightningModule,
closure_loss: torch.Tensor,
optimizer: Optimizer,
opt_idx: int,
should_accumulate: bool,
*args: Any,
**kwargs: Any,
) | performs the actual backpropagation
Args:
model: the model to be optimized
closure_loss: the loss value obtained from the closure
optimizer: the optimizer to perform the step lateron
opt_idx: the optimizer's index
should_accumulate: whether to accumul... | performs the actual backpropagation | def backward(
self,
model: LightningModule,
closure_loss: torch.Tensor,
optimizer: Optimizer,
opt_idx: int,
should_accumulate: bool,
*args: Any,
**kwargs: Any,
) -> torch.Tensor:
"""performs the actual backpropagation
Args:
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PrecisionPlugin.pre_optimizer_step | (
self, pl_module: LightningModule, optimizer: Optimizer, optimizer_idx: int, closure: Callable, **kwargs
) | Hook to do something before each optimizer step. | Hook to do something before each optimizer step. | def pre_optimizer_step(
self, pl_module: LightningModule, optimizer: Optimizer, optimizer_idx: int, closure: Callable, **kwargs
) -> bool:
"""Hook to do something before each optimizer step."""
return True | [
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PrecisionPlugin.post_optimizer_step | (self, optimizer: Optimizer, optimizer_idx: int) | Hook to do something after each optimizer step. | Hook to do something after each optimizer step. | def post_optimizer_step(self, optimizer: Optimizer, optimizer_idx: int) -> None:
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PrecisionPlugin.clip_gradients | (self, optimizer: Optimizer, clip_val: Union[int, float], norm_type: float = float(2.0)) | Clips the gradients to a specific value | Clips the gradients to a specific value | def clip_gradients(self, optimizer: Optimizer, clip_val: Union[int, float], norm_type: float = float(2.0)) -> None:
"""Clips the gradients to a specific value"""
# TODO: separate TPU case from here
if clip_val is None:
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ImageNetLightningModel.__accuracy | (output, target, topk=(1, )) | Computes the accuracy over the k top predictions for the specified values of k | Computes the accuracy over the k top predictions for the specified values of k | def __accuracy(output, target, topk=(1, )):
"""Computes the accuracy over the k top predictions for the specified values of k"""
with torch.no_grad():
maxk = max(topk)
batch_size = target.size(0)
_, pred = output.topk(maxk, 1, True, True)
pred = pred.t()
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term_all_children | () | Iterate through all known child processes, send a TERM signal to each of
them, and then wait up to TERM_WAIT_SECONDS for them to exit gracefully,
exiting early if all children go away. If one or more children have not
exited after TERM_WAIT_SECONDS, they will be forcibly killed | Iterate through all known child processes, send a TERM signal to each of
them, and then wait up to TERM_WAIT_SECONDS for them to exit gracefully,
exiting early if all children go away. If one or more children have not
exited after TERM_WAIT_SECONDS, they will be forcibly killed | def term_all_children():
""" Iterate through all known child processes, send a TERM signal to each of
them, and then wait up to TERM_WAIT_SECONDS for them to exit gracefully,
exiting early if all children go away. If one or more children have not
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":"... | [
19,
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61,
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] | python | en | ['en', 'en', 'en'] | True |
force_kill_all_children | () | Iterate through all known child processes and force kill them. Typically
term_all_children() should be attempted first to give child processes an
opportunity to clean up state before exiting | Iterate through all known child processes and force kill them. Typically
term_all_children() should be attempted first to give child processes an
opportunity to clean up state before exiting | def force_kill_all_children():
""" Iterate through all known child processes and force kill them. Typically
term_all_children() should be attempted first to give child processes an
opportunity to clean up state before exiting """
global pid_list
for pid in pid_list:
print("force killing PID={}"... | [
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shutdown | () | Attempt to gracefully shutdown all child Envoy processes and then exit.
See term_all_children() for further discussion. | Attempt to gracefully shutdown all child Envoy processes and then exit.
See term_all_children() for further discussion. | def shutdown():
""" Attempt to gracefully shutdown all child Envoy processes and then exit.
See term_all_children() for further discussion. """
term_all_children()
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sigterm_handler | (signum, frame) | Handler for SIGTERM. | Handler for SIGTERM. | def sigterm_handler(signum, frame):
""" Handler for SIGTERM. """
print("got SIGTERM")
shutdown() | [
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sigint_handler | (signum, frame) | Handler for SIGINT (ctrl-c). The same as the SIGTERM handler. | Handler for SIGINT (ctrl-c). The same as the SIGTERM handler. | def sigint_handler(signum, frame):
""" Handler for SIGINT (ctrl-c). The same as the SIGTERM handler. """
print("got SIGINT")
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sighup_handler | (signum, frame) | Handler for SIGHUP. This signal is used to cause the restarter to fork and exec a new
child. | Handler for SIGHUP. This signal is used to cause the restarter to fork and exec a new
child. | def sighup_handler(signum, frame):
""" Handler for SIGHUP. This signal is used to cause the restarter to fork and exec a new
child. """
print("got SIGHUP")
fork_and_exec() | [
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sigusr1_handler | (signum, frame) | Handler for SIGUSR1. Propagate SIGUSR1 to all of the child processes | Handler for SIGUSR1. Propagate SIGUSR1 to all of the child processes | def sigusr1_handler(signum, frame):
""" Handler for SIGUSR1. Propagate SIGUSR1 to all of the child processes """
global pid_list
for pid in pid_list:
print("sending SIGUSR1 to PID={}".format(pid))
try:
os.kill(pid, signal.SIGUSR1)
except OSError:
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sigchld_handler | (signum, frame) | Handler for SIGCHLD. Iterates through all of our known child processes and figures out whether
the signal/exit was expected or not. Python doesn't have any of the native signal handlers
ability to get the child process info directly from the signal handler so we need to iterate
through all child proc... | Handler for SIGCHLD. Iterates through all of our known child processes and figures out whether
the signal/exit was expected or not. Python doesn't have any of the native signal handlers
ability to get the child process info directly from the signal handler so we need to iterate
through all child proc... | def sigchld_handler(signum, frame):
""" Handler for SIGCHLD. Iterates through all of our known child processes and figures out whether
the signal/exit was expected or not. Python doesn't have any of the native signal handlers
ability to get the child process info directly from the signal handler so we n... | [
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fork_and_exec | () | This routine forks and execs a new child process and keeps track of its PID. Before we fork,
set the current restart epoch in an env variable that processes can read if they care. | This routine forks and execs a new child process and keeps track of its PID. Before we fork,
set the current restart epoch in an env variable that processes can read if they care. | def fork_and_exec():
""" This routine forks and execs a new child process and keeps track of its PID. Before we fork,
set the current restart epoch in an env variable that processes can read if they care. """
global restart_epoch
os.environ['RESTART_EPOCH'] = str(restart_epoch)
print("forking and... | [
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main | () | Script main. This script is designed so that a process watcher like runit or monit can watch
this process and take corrective action if it ever goes away. | Script main. This script is designed so that a process watcher like runit or monit can watch
this process and take corrective action if it ever goes away. | def main():
""" Script main. This script is designed so that a process watcher like runit or monit can watch
this process and take corrective action if it ever goes away. """
print("starting hot-restarter with target: {}".format(sys.argv[1]))
signal.signal(signal.SIGTERM, sigterm_handler)
signal... | [
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test_optimizer_with_scheduling | (tmpdir) | Verify that learning rate scheduling is working | Verify that learning rate scheduling is working | def test_optimizer_with_scheduling(tmpdir):
""" Verify that learning rate scheduling is working """
hparams = EvalModelTemplate.get_default_hparams()
model = EvalModelTemplate(**hparams)
model.configure_optimizers = model.configure_optimizers__single_scheduler
# fit model
trainer = Trainer(
... | [
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test_multi_optimizer_with_scheduling | (tmpdir) | Verify that learning rate scheduling is working | Verify that learning rate scheduling is working | def test_multi_optimizer_with_scheduling(tmpdir):
""" Verify that learning rate scheduling is working """
hparams = EvalModelTemplate.get_default_hparams()
model = EvalModelTemplate(**hparams)
model.configure_optimizers = model.configure_optimizers__multiple_schedulers
# fit model
trainer = Tr... | [
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test_reducelronplateau_with_no_monitor_raises | (tmpdir) |
Test exception when a ReduceLROnPlateau is used with no monitor
|
Test exception when a ReduceLROnPlateau is used with no monitor
| def test_reducelronplateau_with_no_monitor_raises(tmpdir):
"""
Test exception when a ReduceLROnPlateau is used with no monitor
"""
model = EvalModelTemplate()
optimizer = torch.optim.Adam(model.parameters())
model.configure_optimizers = lambda: ([optimizer], [torch.optim.lr_scheduler.ReduceLROnP... | [
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test_reducelronplateau_with_no_monitor_in_lr_scheduler_dict_raises | (tmpdir) |
Test exception when lr_scheduler dict has a ReduceLROnPlateau with no monitor
|
Test exception when lr_scheduler dict has a ReduceLROnPlateau with no monitor
| def test_reducelronplateau_with_no_monitor_in_lr_scheduler_dict_raises(tmpdir):
"""
Test exception when lr_scheduler dict has a ReduceLROnPlateau with no monitor
"""
model = EvalModelTemplate()
optimizer = torch.optim.Adam(model.parameters())
model.configure_optimizers = lambda: {
'optim... | [
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test_configure_optimizer_from_dict | (tmpdir) | Tests if `configure_optimizer` method could return a dictionary with `optimizer` field only. | Tests if `configure_optimizer` method could return a dictionary with `optimizer` field only. | def test_configure_optimizer_from_dict(tmpdir):
"""Tests if `configure_optimizer` method could return a dictionary with `optimizer` field only."""
class CurrentModel(EvalModelTemplate):
def configure_optimizers(self):
config = {'optimizer': torch.optim.SGD(params=self.parameters(), lr=1e-0... | [
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test_configure_optimizers_with_frequency | (tmpdir) |
Test that multiple optimizers work when corresponding frequency is set.
|
Test that multiple optimizers work when corresponding frequency is set.
| def test_configure_optimizers_with_frequency(tmpdir):
"""
Test that multiple optimizers work when corresponding frequency is set.
"""
model = EvalModelTemplate()
model.configure_optimizers = model.configure_optimizers__multiple_optimizers_frequency
trainer = Trainer(default_root_dir=tmpdir, max... | [
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test_init_optimizers_during_testing | (tmpdir) |
Test that optimizers is an empty list during testing.
|
Test that optimizers is an empty list during testing.
| def test_init_optimizers_during_testing(tmpdir):
"""
Test that optimizers is an empty list during testing.
"""
model = EvalModelTemplate()
model.configure_optimizers = model.configure_optimizers__multiple_schedulers
trainer = Trainer(default_root_dir=tmpdir, limit_test_batches=10)
trainer.t... | [
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test_multiple_optimizers_callbacks | (tmpdir) |
Tests that multiple optimizers can be used with callbacks
|
Tests that multiple optimizers can be used with callbacks
| def test_multiple_optimizers_callbacks(tmpdir):
"""
Tests that multiple optimizers can be used with callbacks
"""
class CB(Callback):
def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx):
pass
def on_train_epoch_start(self, trainer, p... | [
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test_lr_scheduler_strict | (tmpdir) |
Test "strict" support in lr_scheduler dict
|
Test "strict" support in lr_scheduler dict
| def test_lr_scheduler_strict(tmpdir):
"""
Test "strict" support in lr_scheduler dict
"""
model = EvalModelTemplate()
optimizer = torch.optim.Adam(model.parameters())
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer)
trainer = Trainer(default_root_dir=tmpdir, max_epochs=1)
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test_unknown_configure_optimizers_raises | (tmpdir) |
Test exception with an unsupported configure_optimizers return
|
Test exception with an unsupported configure_optimizers return
| def test_unknown_configure_optimizers_raises(tmpdir):
"""
Test exception with an unsupported configure_optimizers return
"""
model = EvalModelTemplate()
model.configure_optimizers = lambda: 1
trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=True)
with pytest.raises(MisconfigurationExc... | [
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test_lr_scheduler_with_unknown_interval_raises | (tmpdir) |
Test exception when lr_scheduler dict has unknown interval param value
|
Test exception when lr_scheduler dict has unknown interval param value
| def test_lr_scheduler_with_unknown_interval_raises(tmpdir):
"""
Test exception when lr_scheduler dict has unknown interval param value
"""
model = BoringModel()
optimizer = torch.optim.Adam(model.parameters())
model.configure_optimizers = lambda: {
'optimizer': optimizer,
'lr_sch... | [
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test_lr_scheduler_with_extra_keys_warns | (tmpdir) |
Test warning when lr_scheduler dict has extra keys
|
Test warning when lr_scheduler dict has extra keys
| def test_lr_scheduler_with_extra_keys_warns(tmpdir):
"""
Test warning when lr_scheduler dict has extra keys
"""
model = EvalModelTemplate()
optimizer = torch.optim.Adam(model.parameters())
model.configure_optimizers = lambda: {
'optimizer': optimizer,
'lr_scheduler': {
... | [
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test_lr_scheduler_with_no_actual_scheduler_raises | (tmpdir) |
Test exception when lr_scheduler dict has no scheduler
|
Test exception when lr_scheduler dict has no scheduler
| def test_lr_scheduler_with_no_actual_scheduler_raises(tmpdir):
"""
Test exception when lr_scheduler dict has no scheduler
"""
model = EvalModelTemplate()
model.configure_optimizers = lambda: {
'optimizer': torch.optim.Adam(model.parameters()),
'lr_scheduler': {},
}
trainer = ... | [
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test_invalid_optimizer_in_scheduler | (tmpdir) |
Test exception when optimizer attatched to lr_schedulers wasn't returned
|
Test exception when optimizer attatched to lr_schedulers wasn't returned
| def test_invalid_optimizer_in_scheduler(tmpdir):
"""
Test exception when optimizer attatched to lr_schedulers wasn't returned
"""
class InvalidOptimizerModel(BoringModel):
def configure_optimizers(self):
opt1 = torch.optim.SGD(self.layer.parameters(), lr=0.1)
opt2 = tor... | [
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uses_accelerate_framework | (info) | Returns True if Accelerate framework is used for BLAS/LAPACK | Returns True if Accelerate framework is used for BLAS/LAPACK | def uses_accelerate_framework(info):
""" Returns True if Accelerate framework is used for BLAS/LAPACK """
# If we're not building on Darwin (macOS), don't use Accelerate
if sys.platform != "darwin":
return False
# If we're building on macOS, but targeting a different platform,
# don't use Ac... | [
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get_sgemv_fix | () | Returns source file needed to correct SGEMV | Returns source file needed to correct SGEMV | def get_sgemv_fix():
""" Returns source file needed to correct SGEMV """
path = os.path.abspath(os.path.dirname(__file__))
return [os.path.join(path, 'src', 'apple_sgemv_fix.c')] | [
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load_all_fcompiler_classes | () | Cache all the FCompiler classes found in modules in the
numpy.distutils.fcompiler package.
| Cache all the FCompiler classes found in modules in the
numpy.distutils.fcompiler package.
| def load_all_fcompiler_classes():
"""Cache all the FCompiler classes found in modules in the
numpy.distutils.fcompiler package.
"""
from glob import glob
global fcompiler_class, fcompiler_aliases
if fcompiler_class is not None:
return
pys = os.path.join(os.path.dirname(__file__), '*.... | [
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get_default_fcompiler | (osname=None, platform=None, requiref90=False,
c_compiler=None) | Determine the default Fortran compiler to use for the given
platform. | Determine the default Fortran compiler to use for the given
platform. | def get_default_fcompiler(osname=None, platform=None, requiref90=False,
c_compiler=None):
"""Determine the default Fortran compiler to use for the given
platform."""
matching_compiler_types = available_fcompilers_for_platform(osname,
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new_fcompiler | (plat=None,
compiler=None,
verbose=0,
dry_run=0,
force=0,
requiref90=False,
c_compiler = None) | Generate an instance of some FCompiler subclass for the supplied
platform/compiler combination.
| Generate an instance of some FCompiler subclass for the supplied
platform/compiler combination.
| def new_fcompiler(plat=None,
compiler=None,
verbose=0,
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show_fcompilers | (dist=None) | Print list of available compilers (used by the "--help-fcompiler"
option to "config_fc").
| Print list of available compilers (used by the "--help-fcompiler"
option to "config_fc").
| def show_fcompilers(dist=None):
"""Print list of available compilers (used by the "--help-fcompiler"
option to "config_fc").
"""
if dist is None:
from distutils.dist import Distribution
from numpy.distutils.command.config_compiler import config_fc
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di... | [
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is_free_format | (file) | Check if file is in free format Fortran. | Check if file is in free format Fortran. | def is_free_format(file):
"""Check if file is in free format Fortran."""
# f90 allows both fixed and free format, assuming fixed unless
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get_f77flags | (src) |
Search the first 20 lines of fortran 77 code for line pattern
`CF77FLAGS(<fcompiler type>)=<f77 flags>`
Return a dictionary {<fcompiler type>:<f77 flags>}.
|
Search the first 20 lines of fortran 77 code for line pattern
`CF77FLAGS(<fcompiler type>)=<f77 flags>`
Return a dictionary {<fcompiler type>:<f77 flags>}.
| def get_f77flags(src):
"""
Search the first 20 lines of fortran 77 code for line pattern
`CF77FLAGS(<fcompiler type>)=<f77 flags>`
Return a dictionary {<fcompiler type>:<f77 flags>}.
"""
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FCompiler.find_executables | (self) | Go through the self.executables dictionary, and attempt to
find and assign appropriate executables.
Executable names are looked for in the environment (environment
variables, the distutils.cfg, and command line), the 0th-element of
the command list, and the self.possible_executables lis... | Go through the self.executables dictionary, and attempt to
find and assign appropriate executables. | def find_executables(self):
"""Go through the self.executables dictionary, and attempt to
find and assign appropriate executables.
Executable names are looked for in the environment (environment
variables, the distutils.cfg, and command line), the 0th-element of
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FCompiler.update_executables | (self) | Called at the beginning of customisation. Subclasses should
override this if they need to set up the executables dictionary.
Note that self.find_executables() is run afterwards, so the
self.executables dictionary values can contain <F77> or <F90> as
the command, which will be replaced b... | Called at the beginning of customisation. Subclasses should
override this if they need to set up the executables dictionary. | def update_executables(self):
"""Called at the beginning of customisation. Subclasses should
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FCompiler.get_flags | (self) | List of flags common to all compiler types. | List of flags common to all compiler types. | def get_flags(self):
"""List of flags common to all compiler types."""
return [] + self.pic_flags | [
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FCompiler.get_flags_f77 | (self) | List of Fortran 77 specific flags. | List of Fortran 77 specific flags. | def get_flags_f77(self):
"""List of Fortran 77 specific flags."""
return self._get_command_flags('compiler_f77') | [
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FCompiler.get_flags_f90 | (self) | List of Fortran 90 specific flags. | List of Fortran 90 specific flags. | def get_flags_f90(self):
"""List of Fortran 90 specific flags."""
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FCompiler.get_flags_free | (self) | List of Fortran 90 free format specific flags. | List of Fortran 90 free format specific flags. | def get_flags_free(self):
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FCompiler.get_flags_fix | (self) | List of Fortran 90 fixed format specific flags. | List of Fortran 90 fixed format specific flags. | def get_flags_fix(self):
"""List of Fortran 90 fixed format specific flags."""
return self._get_command_flags('compiler_fix') | [
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FCompiler.get_flags_linker_so | (self) | List of linker flags to build a shared library. | List of linker flags to build a shared library. | def get_flags_linker_so(self):
"""List of linker flags to build a shared library."""
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FCompiler.get_flags_linker_exe | (self) | List of linker flags to build an executable. | List of linker flags to build an executable. | def get_flags_linker_exe(self):
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FCompiler.get_flags_ar | (self) | List of archiver flags. | List of archiver flags. | def get_flags_ar(self):
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FCompiler.get_flags_opt | (self) | List of architecture independent compiler flags. | List of architecture independent compiler flags. | def get_flags_opt(self):
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FCompiler.get_flags_arch | (self) | List of architecture dependent compiler flags. | List of architecture dependent compiler flags. | def get_flags_arch(self):
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FCompiler.get_flags_debug | (self) | List of compiler flags to compile with debugging information. | List of compiler flags to compile with debugging information. | def get_flags_debug(self):
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FCompiler.get_libraries | (self) | List of compiler libraries. | List of compiler libraries. | def get_libraries(self):
"""List of compiler libraries."""
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FCompiler.get_library_dirs | (self) | List of compiler library directories. | List of compiler library directories. | def get_library_dirs(self):
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FCompiler.customize | (self, dist = None) | Customize Fortran compiler.
This method gets Fortran compiler specific information from
(i) class definition, (ii) environment, (iii) distutils config
files, and (iv) command line (later overrides earlier).
This method should be always called after constructing a
compiler insta... | Customize Fortran compiler. | def customize(self, dist = None):
"""Customize Fortran compiler.
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FCompiler.dump_properties | (self) | Print out the attributes of a compiler instance. | Print out the attributes of a compiler instance. | def dump_properties(self):
"""Print out the attributes of a compiler instance."""
props = []
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FCompiler._compile | (self, obj, src, ext, cc_args, extra_postargs, pp_opts) | Compile 'src' to product 'obj'. | Compile 'src' to product 'obj'. | def _compile(self, obj, src, ext, cc_args, extra_postargs, pp_opts):
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src_flags = {}
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FCompiler.can_ccompiler_link | (self, ccompiler) |
Check if the given C compiler can link objects produced by
this compiler.
|
Check if the given C compiler can link objects produced by
this compiler.
| def can_ccompiler_link(self, ccompiler):
"""
Check if the given C compiler can link objects produced by
this compiler.
"""
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FCompiler.wrap_unlinkable_objects | (self, objects, output_dir, extra_dll_dir) |
Convert a set of object files that are not compatible with the default
linker, to a file that is compatible.
Parameters
----------
objects : list
List of object files to include.
output_dir : str
Output directory to place generated object files.
... |
Convert a set of object files that are not compatible with the default
linker, to a file that is compatible. | def wrap_unlinkable_objects(self, objects, output_dir, extra_dll_dir):
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Convert a set of object files that are not compatible with the default
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test_trainer_callback_system | (torch_save, tmpdir) | Test the callback system. | Test the callback system. | def test_trainer_callback_system(torch_save, tmpdir):
"""Test the callback system."""
model = BoringModel()
callback_mock = MagicMock()
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test_callbacks_configured_in_model | (tmpdir) | Test the callback system with callbacks added through the model hook. | Test the callback system with callbacks added through the model hook. | def test_callbacks_configured_in_model(tmpdir):
""" Test the callback system with callbacks added through the model hook. """
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test_configure_callbacks_hook_multiple_calls | (tmpdir) | Test that subsequent calls to `configure_callbacks` do not change the callbacks list. | Test that subsequent calls to `configure_callbacks` do not change the callbacks list. | def test_configure_callbacks_hook_multiple_calls(tmpdir):
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PytestRunner.cov_collect | (self) | The path to a file to collect coverage in | The path to a file to collect coverage in | def cov_collect(self) -> str:
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] | python | en | ['en', 'en', 'en'] | True |
PytestRunner.add_arguments | (self, parser: argparse.ArgumentParser) | Add arguments to the arg parser | Add arguments to the arg parser | def add_arguments(self, parser: argparse.ArgumentParser) -> None:
"""Add arguments to the arg parser"""
parser.add_argument("--cov-collect", default=None, help="Collect coverage data to path") | [
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Log.good | (self, msg, *args) |
If we log WARN messages, log this message as a 'nice' anti-warn
message.
|
If we log WARN messages, log this message as a 'nice' anti-warn
message. | def good(self, msg, *args):
"""
If we log WARN messages, log this message as a 'nice' anti-warn
message.
"""
if WARN >= self.threshold:
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Interrupt.wait_for | (self, timeout: int) | Returns True if the interrupt happened and false
if timeout milliseconds passed without an interrupt | Returns True if the interrupt happened and false
if timeout milliseconds passed without an interrupt | def wait_for(self, timeout: int) -> bool:
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if timeout milliseconds passed without an interrupt"""
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Interrupt.close | (self) | Releases any resources held by the interrupt | Releases any resources held by the interrupt | def close(self):
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EarlyStopping._run_early_stopping_check | (self, trainer, pl_module) |
Checks whether the early stopping condition is met
and if so tells the trainer to stop the training.
|
Checks whether the early stopping condition is met
and if so tells the trainer to stop the training.
| def _run_early_stopping_check(self, trainer, pl_module):
"""
Checks whether the early stopping condition is met
and if so tells the trainer to stop the training.
"""
logs = trainer.callback_metrics
if (
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RPCSequentialPlugin.__init__ | (
self,
balance: Optional[List[int]] = None,
microbatches: int = 8,
checkpoint: str = 'except_last',
balance_mode: str = "balance_by_size",
pipelined_backward: Optional[bool] = True,
rpc_timeout_sec: float = DEFAULT_RPC_TIMEOUT_SEC,
**kwargs
) |
Provides sequential model parallelism for :class:`nn.Sequential <torch.nn.Sequential>` module.
If the module requires lots of memory, Pipe can be used to reduce this by leveraging multiple GPUs.
.. _RPCSequentialPlugin: https://arxiv.org/abs/1811.06965
Pipeline parallelism comes with ... |
Provides sequential model parallelism for :class:`nn.Sequential <torch.nn.Sequential>` module.
If the module requires lots of memory, Pipe can be used to reduce this by leveraging multiple GPUs. | def __init__(
self,
balance: Optional[List[int]] = None,
microbatches: int = 8,
checkpoint: str = 'except_last',
balance_mode: str = "balance_by_size",
pipelined_backward: Optional[bool] = True,
rpc_timeout_sec: float = DEFAULT_RPC_TIMEOUT_SEC,
**kwargs
... | [
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RPCSequentialPlugin._sync_balance_to_all_parallel_groups | (self, main_rank=0) |
Ensures that we sync the balance to all main processes, so that the balance is the same per replica.
Args:
main_rank: The rank with the balance we'd like to replicate.
|
Ensures that we sync the balance to all main processes, so that the balance is the same per replica.
Args:
main_rank: The rank with the balance we'd like to replicate.
| def _sync_balance_to_all_parallel_groups(self, main_rank=0):
"""
Ensures that we sync the balance to all main processes, so that the balance is the same per replica.
Args:
main_rank: The rank with the balance we'd like to replicate.
"""
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RPCSequentialPlugin._skip_init_connections | (self) |
Skip initialization if torch is already initialized and we're in testing.
Returns: Whether to skip initialization
|
Skip initialization if torch is already initialized and we're in testing.
Returns: Whether to skip initialization | def _skip_init_connections(self):
"""
Skip initialization if torch is already initialized and we're in testing.
Returns: Whether to skip initialization
"""
return torch_distrib.is_initialized() and self.lightning_module.running_stage == RunningStage.TESTING | [
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RPCSequentialPlugin._infer_check_num_gpus | (self) |
Infer the number of GPUs per model.
Returns: The appropriate balance for the model
|
Infer the number of GPUs per model. | def _infer_check_num_gpus(self):
"""
Infer the number of GPUs per model.
Returns: The appropriate balance for the model
"""
if isinstance(self.balance, list):
if len(self.balance) != (self.world_size / self.num_nodes):
raise MisconfigurationException(... | [
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RPCSequentialPlugin.pre_backward | (self, closure_loss: torch.Tensor, should_accumulate: bool, optimizer: Optimizer, opt_idx: int) | Run before precision plugin executes backward | Run before precision plugin executes backward | def pre_backward(self, closure_loss: torch.Tensor, should_accumulate: bool, optimizer: Optimizer, opt_idx: int):
"""Run before precision plugin executes backward""" | [
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RPCSequentialPlugin.post_optimizer_step | (self, optimizer: Optimizer, optimizer_idx: int, **kwargs) | Hook to do something after each optimizer step. | Hook to do something after each optimizer step. | def post_optimizer_step(self, optimizer: Optimizer, optimizer_idx: int, **kwargs) -> None:
"""Hook to do something after each optimizer step."""
if self.rpc_enabled and self.main_rpc_process:
# Initialize optimizer step on main process
self.worker_optimizer_step(model=self.lightn... | [
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extract_clang_proto_style | (clang_format_text) | Extract a key:value dictionary for proto formatting.
Args:
clang_format_text: text from a .clang-format file.
Returns:
key:value dictionary suitable for passing to clang-format --style.
| Extract a key:value dictionary for proto formatting. | def extract_clang_proto_style(clang_format_text):
"""Extract a key:value dictionary for proto formatting.
Args:
clang_format_text: text from a .clang-format file.
Returns:
key:value dictionary suitable for passing to clang-format --style.
"""
lang = None
format_dict = {}
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clang_format | (contents) | Run proto-style oriented clang-format over given string.
Args:
contents: a string with proto contents.
Returns:
clang-formatted string
| Run proto-style oriented clang-format over given string. | def clang_format(contents):
"""Run proto-style oriented clang-format over given string.
Args:
contents: a string with proto contents.
Returns:
clang-formatted string
"""
return subprocess.run(
['clang-format',
'--style=%s' % CLANG_FORMAT_STYLE, '--assume-filename=.... | [
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format_block | (block) | Append \n to a .proto section (e.g.
comment, message definition, etc.) if non-empty.
Args:
block: a string representing the section.
Returns:
A string with appropriate whitespace.
| Append \n to a .proto section (e.g. | def format_block(block):
"""Append \n to a .proto section (e.g.
comment, message definition, etc.) if non-empty.
Args:
block: a string representing the section.
Returns:
A string with appropriate whitespace.
"""
if block.strip():
return block + '\n'
return '' | [
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104,
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] | python | en | ['en', 'en', 'sw'] | True |
format_comments | (comments) | Format a list of comment blocks from SourceCodeInfo.
Prefixes // to each line, separates blocks by spaces.
Args:
comments: a list of blocks, each block is a list of strings representing
lines in each block.
Returns:
A string reprenting the formatted comment blocks.
| Format a list of comment blocks from SourceCodeInfo. | def format_comments(comments):
"""Format a list of comment blocks from SourceCodeInfo.
Prefixes // to each line, separates blocks by spaces.
Args:
comments: a list of blocks, each block is a list of strings representing
lines in each block.
Returns:
A string reprenting the ... | [
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129,
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] | python | en | ['en', 'en', 'en'] | True |
create_next_free_field_xform | (msg_proto) | Return the next free field number annotation transformer of a message.
Args:
msg_proto: DescriptorProto for message.
Returns:
the next free field number annotation transformer.
| Return the next free field number annotation transformer of a message. | def create_next_free_field_xform(msg_proto):
"""Return the next free field number annotation transformer of a message.
Args:
msg_proto: DescriptorProto for message.
Returns:
the next free field number annotation transformer.
"""
next_free = max(
sum([
[f.number ... | [
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format_type_context_comments | (type_context, annotation_xforms=None) | Format the leading/trailing comments in a given TypeContext.
Args:
type_context: contextual information for message/enum/field.
annotation_xforms: a dict of transformers for annotations in leading
comment.
Returns:
Tuple of formatted leading and trailing comment blocks.
| Format the leading/trailing comments in a given TypeContext. | def format_type_context_comments(type_context, annotation_xforms=None):
"""Format the leading/trailing comments in a given TypeContext.
Args:
type_context: contextual information for message/enum/field.
annotation_xforms: a dict of transformers for annotations in leading
comment.
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"get_comment_with_t... | [
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166,
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format_header_from_file | (
source_code_info, file_proto, empty_file, requires_deprecation_annotation) | Format proto header.
Args:
source_code_info: SourceCodeInfo object.
file_proto: FileDescriptorProto for file.
empty_file: are there no message/enum/service defs in file?
requires_deprecation_annotation: does the proto have the deprecated version annotation or
... | Format proto header. | def format_header_from_file(
source_code_info, file_proto, empty_file, requires_deprecation_annotation):
"""Format proto header.
Args:
source_code_info: SourceCodeInfo object.
file_proto: FileDescriptorProto for file.
empty_file: are there no message/enum/service defs in file?
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normalize_field_type_name | (type_context, field_fqn) | Normalize a fully qualified field type name, e.g.
.envoy.foo.bar is normalized to foo.bar.
Considers type context to minimize type prefix.
Args:
field_fqn: a fully qualified type name from FieldDescriptorProto.type_name.
type_context: contextual information for message/enum/field.
Re... | Normalize a fully qualified field type name, e.g. | def normalize_field_type_name(type_context, field_fqn):
"""Normalize a fully qualified field type name, e.g.
.envoy.foo.bar is normalized to foo.bar.
Considers type context to minimize type prefix.
Args:
field_fqn: a fully qualified type name from FieldDescriptorProto.type_name.
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format_field_type | (type_context, field) | Format a FieldDescriptorProto type description.
Args:
type_context: contextual information for message/enum/field.
field: FieldDescriptor proto.
Returns:
Formatted proto field type as string.
| Format a FieldDescriptorProto type description. | def format_field_type(type_context, field):
"""Format a FieldDescriptorProto type description.
Args:
type_context: contextual information for message/enum/field.
field: FieldDescriptor proto.
Returns:
Formatted proto field type as string.
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format_service_method | (type_context, method) | Format a service MethodDescriptorProto.
Args:
type_context: contextual information for method.
method: MethodDescriptorProto proto.
Returns:
Formatted service method as string.
| Format a service MethodDescriptorProto. | def format_service_method(type_context, method):
"""Format a service MethodDescriptorProto.
Args:
type_context: contextual information for method.
method: MethodDescriptorProto proto.
Returns:
Formatted service method as string.
"""
def format_streaming(s):
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format_field | (type_context, field) | Format FieldDescriptorProto as a proto field.
Args:
type_context: contextual information for message/enum/field.
field: FieldDescriptor proto.
Returns:
Formatted proto field as a string.
| Format FieldDescriptorProto as a proto field. | def format_field(type_context, field):
"""Format FieldDescriptorProto as a proto field.
Args:
type_context: contextual information for message/enum/field.
field: FieldDescriptor proto.
Returns:
Formatted proto field as a string.
"""
if protoxform_options.has_hide_option(fie... | [
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format_enum_value | (type_context, value) | Format a EnumValueDescriptorProto as a proto enum value.
Args:
type_context: contextual information for message/enum/field.
value: EnumValueDescriptorProto.
Returns:
Formatted proto enum value as a string.
| Format a EnumValueDescriptorProto as a proto enum value. | def format_enum_value(type_context, value):
"""Format a EnumValueDescriptorProto as a proto enum value.
Args:
type_context: contextual information for message/enum/field.
value: EnumValueDescriptorProto.
Returns:
Formatted proto enum value as a string.
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