def __init__(
self,
parallel_devices: Optional[List[torch.device]] = None,
num_nodes: Optional[int] = None,
cluster_environment: ClusterEnvironment = None,
sync_batchnorm: Optional[bool] = None,
ddp_comm_state: Optional[object] = None,
ddp_comm_hook: Optional[callable] = None,
ddp_comm_wrapper: Optional[callable] = None,
**kwargs: Any,
):
super().__init__(parallel_devices=parallel_devices, cluster_environment=cluster_environment)
if num_nodes is not None:
rank_zero_deprecation(
"Argument `num_nodes` in `DDPPlugin` is deprecated in v1.4, and will be removed in v1.6. "
"Notice that it will be overriden by the trainer setting."
)
self._num_nodes = num_nodes or 1
if sync_batchnorm is not None:
rank_zero_deprecation(
"Argument `sync_batchnorm` in `DDPPlugin` is deprecated in v1.4, and will be removed in v1.6. "
"Notice that it will be overriden by the trainer setting."
)
self._sync_batchnorm = sync_batchnorm or False
self._ddp_kwargs = kwargs
self.dist = LightningDistributed()
self.num_processes = len(parallel_devices) if parallel_devices is not None else 0
self.mp_queue = None
self._ddp_comm_state = ddp_comm_state
self._ddp_comm_hook = ddp_comm_hook
self._ddp_comm_wrapper = ddp_comm_wrapper
self._local_rank = 0
self.set_world_ranks()
def metrics_to_scalars(self, metrics: dict) -> dict:
rank_zero_deprecation(
"Internal: TrainerLoggingMixin.metrics_to_scalars is deprecated in v1.3"
" and will be removed in v1.5."
" Use `pytorch_lightning.utilities.metrics.metrics_to_scalars` instead."
)
return new_metrics_to_scalars(metrics)
def wrapped_fn(*args: str, **kwargs: Optional[str]) -> Any:
name = fn.__name__
has_run = False
# If calling setup, we check the stage and assign stage-specific bool args
if name in ("setup", "teardown"):
# Get stage either by grabbing from args or checking kwargs.
# If not provided, set call status of 'fit', 'validate', and 'test' to True.
# We do this so __attach_datamodule in trainer.py doesn't mistakenly call
# setup('test') on trainer.test()
stage = args[0] if len(args) else kwargs.get("stage", None)
if stage is None:
has_run = True
for s in ("fit", "validate", "test"):
attr = f"_has_{name}_{s}"
has_run &= getattr(obj, attr)
setattr(obj, attr, True)
else:
attr = f"_has_{name}_{stage}"
has_run = getattr(obj, attr)
setattr(obj, attr, True)
elif name == "prepare_data":
has_run = obj._has_prepared_data
obj._has_prepared_data = True
if has_run:
rank_zero_deprecation(
f"DataModule.{name} has already been called, so it will not be called again. "
f"In v1.6 this behavior will change to always call DataModule.{name}."
)
else:
fn(*args, **kwargs)
def grad_norm(self, norm_type: Union[float, int, str]) -> Dict[str, float]:
"""Compute each parameter's gradient's norm and their overall norm.
.. deprecated:: v1.3
Will be removed in v1.5.0. Use :func:`pytorch_lightning.utilities.grads.grad_norm` instead.
"""
rank_zero_deprecation(
"LightningModule.grad_norm is deprecated in v1.3 and will be removed in v1.5."
" Use grad_norm from pytorch_lightning.utilities.grads instead.")
return new_grad_norm(self, norm_type)
def has_setup_fit(self) -> bool:
"""Return bool letting you know if ``datamodule.setup(stage='fit')`` has been called or not.
Returns:
bool: True ``if datamodule.setup(stage='fit')`` has been called. False by default.
.. deprecated:: v1.4
Will be removed in v1.6.0.
"""
rank_zero_deprecation('DataModule property `has_setup_fit` was deprecated in v1.4 and will be removed in v1.6.')
return self._has_setup_fit
def has_arg(self, f_name: str, arg_name: str) -> bool:
rank_zero_deprecation(
"Internal: TrainerModelHooksMixin.is_function_implemented is deprecated in v1.4"
" and will be removed in v1.6."
" Use `pytorch_lightning.utilities.signature_utils.is_param_in_hook_signature` instead."
)
model = self.lightning_module
f_op = getattr(model, f_name, None)
if not f_op:
return False
return is_param_in_hook_signature(f_op, arg_name)
def is_using_torchelastic(self) -> bool:
"""
.. deprecated:: v1.3
Will be removed in v1.5.0.
Returns:
``True`` if the current process was launched using the torchelastic command.
"""
rank_zero_deprecation(
"The property `AcceleratorConnector.is_using_torchelastic` was deprecated in v1.3"
" and will be removed in 1.5. Use `TorchElasticEnvironment.is_using_torchelastic()` instead.",
)
return TorchElasticEnvironment.is_using_torchelastic()
def is_function_implemented(
self,
f_name: str,
model: Optional[LightningModule] = None) -> bool:
rank_zero_deprecation(
"Internal: TrainerModelHooksMixin.is_function_implemented is deprecated in v1.4"
" and will be removed in v1.6.")
# note: currently unused - kept as it is public
if model is None:
model = self.lightning_module
f_op = getattr(model, f_name, None)
return callable(f_op)
def has_teardown_validate(self) -> bool:
"""Return bool letting you know if ``datamodule.teardown(stage='validate')`` has been called or not.
Returns:
bool: True if ``datamodule.teardown(stage='validate')`` has been called. False by default.
.. deprecated:: v1.4
Will be removed in v1.6.0.
"""
rank_zero_deprecation(
'DataModule property `has_teardown_validate` was deprecated in v1.4 and will be removed in v1.6.'
)
return self._has_teardown_validate
def has_prepared_data(self) -> bool:
"""Return bool letting you know if ``datamodule.prepare_data()`` has been called or not.
Returns:
bool: True if ``datamodule.prepare_data()`` has been called. False by default.
.. deprecated:: v1.4
Will be removed in v1.6.0.
"""
rank_zero_deprecation(
'DataModule property `has_prepared_data` was deprecated in v1.4 and will be removed in v1.6.'
)
return self._has_prepared_data
def __deprecation_check(
self,
profiled_functions: Optional[List[str]],
record_functions: Optional[Set[str]],
) -> Set[str]:
if record_functions is None:
record_functions = set()
if profiled_functions is not None:
rank_zero_deprecation(
"`PyTorchProfiler.profiled_functions` has been renamed to"
" `record_functions` in v1.3 and will be removed in v1.5", )
if not record_functions:
record_functions |= set(profiled_functions)
else:
raise MisconfigurationException(
"You set `PytorchProfiler.profiled_functions` and `PyTorchProfiler.record_functions`."
" Please use only the later.")
return record_functions
def on_trainer_init(
self,
gradient_clip_val: float,
gradient_clip_algorithm: str,
track_grad_norm: Union[int, float, str],
accumulate_grad_batches: Union[int, Dict[int, int], List[list]],
truncated_bptt_steps: Optional[int],
terminate_on_nan: bool,
):
self.trainer.terminate_on_nan = terminate_on_nan
# gradient clipping
if gradient_clip_algorithm not in list(GradClipAlgorithmType):
raise MisconfigurationException(
f"gradient_clip_algorithm should be in {list(GradClipAlgorithmType)}"
)
self.trainer.gradient_clip_val = gradient_clip_val
self.trainer.gradient_clip_algorithm = GradClipAlgorithmType(
gradient_clip_algorithm)
# gradient norm tracking
if not isinstance(track_grad_norm,
(int, float)) and track_grad_norm != 'inf':
raise MisconfigurationException(
"track_grad_norm can be an int, a float or 'inf' (infinity norm)."
)
self.trainer.track_grad_norm = float(track_grad_norm)
# accumulated grads
self.trainer.accumulate_grad_batches = accumulate_grad_batches
self.configure_accumulated_gradients(accumulate_grad_batches)
if truncated_bptt_steps is not None and truncated_bptt_steps > 0:
rank_zero_deprecation(
"Trainer.truncated_bptt_steps is deprecated in v1.3 and will be removed in v1.5."
" Set truncated_bptt_steps directly on the LightningModule instead."
)
self.trainer.truncated_bptt_steps = truncated_bptt_steps
def test_v1_6_0_rank_zero_warnings_moved():
with pytest.deprecated_call(match='in v1.3.7 and will be removed in v1.6'):
rank_zero_warn('test')
with pytest.deprecated_call(match='in v1.3.7 and will be removed in v1.6'):
rank_zero_deprecation('test')
from pytorch_lightning.utilities.distributed import rank_zero_deprecation
rank_zero_deprecation(
"Using ``import pytorch_lightning.profiler.profilers`` is depreceated in v1.4, and will be removed in v1.6. "
"HINT: Use ``import pytorch_lightning.profiler`` directly.")
from pytorch_lightning.profiler.advanced import AdvancedProfiler # noqa E402
from pytorch_lightning.profiler.base import AbstractProfiler, BaseProfiler, PassThroughProfiler # noqa E402
from pytorch_lightning.profiler.pytorch import PyTorchProfiler # noqa E402
from pytorch_lightning.profiler.simple import SimpleProfiler # noqa E402
__all__ = [
'AbstractProfiler',
'BaseProfiler',
'AdvancedProfiler',
'PassThroughProfiler',
'PyTorchProfiler',
'SimpleProfiler',
]
def deprecation(self, m, *args, **kwargs):
if m not in self:
self.add(m)
rank_zero_deprecation(m, *args, **kwargs)
