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Remove deprecated accelerator pass through functions in Accelerator (L…
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…ightning-AI#10403)

Co-authored-by: Rohit Gupta <[email protected]>
Co-authored-by: Adrian Wälchli <[email protected]>
Co-authored-by: Carlos Mocholí <[email protected]>
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@puhuk @rohitgr7
@awaelchli @carmocca @Raalsky
4 people authored and Raalsky committed Nov 23, 2021
1 parent cc62c7a commit e7a4a12
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3 changes: 3 additions & 0 deletions CHANGELOG.md
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Original file line number Diff line number Diff line change
Expand Up @@ -90,6 +90,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
- Removed deprecated method `master_params` from PrecisionPlugin ([#10372](https://github.com/PyTorchLightning/pytorch-lightning/pull/10372))


- Removed deprecated passthrough methods and properties from `Accelerator` base class ([#10403](https://github.com/PyTorchLightning/pytorch-lightning/pull/10403))


### Fixed

- Fixed `apply_to_collection(defaultdict)` ([#10316](https://github.com/PyTorchLightning/pytorch-lightning/issues/10316))
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334 changes: 1 addition & 333 deletions pytorch_lightning/accelerators/accelerator.py
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Original file line number Diff line number Diff line change
Expand Up @@ -13,14 +13,13 @@
# limitations under the License.
import contextlib
from abc import abstractmethod
from typing import Any, Callable, Dict, Generator, Iterable, List, Optional, Union
from typing import Any, Callable, Dict, Generator, List, Optional, Union

import torch
from torch import Tensor
from torch.cuda.amp import GradScaler
from torch.nn import Module
from torch.optim import Optimizer
from torch.utils.data import DataLoader

import pytorch_lightning as pl
from pytorch_lightning.plugins.precision import ApexMixedPrecisionPlugin, NativeMixedPrecisionPlugin, PrecisionPlugin
Expand Down Expand Up @@ -59,21 +58,6 @@ def __init__(self, precision_plugin: PrecisionPlugin, training_type_plugin: Trai
self.lr_schedulers: List = []
self.optimizer_frequencies: List = []

def connect(self, model: "pl.LightningModule") -> None:
"""Transfers ownership of the model to this plugin.
See deprecation warning below.
.. deprecated:: v1.5
This method is deprecated in v1.5 and will be removed in v1.6.
Please call `training_type_plugin.on_train_batch_start` directly.
"""
rank_zero_deprecation(
"`Accelerator.connect` is deprecated in v1.5 and will be removed in v1.6. "
"`connect` logic is implemented directly in the `TrainingTypePlugin` implementations."
)
self.training_type_plugin.connect(model)

def setup_environment(self) -> None:
"""Setup any processes or distributed connections.
Expand Down Expand Up @@ -215,18 +199,6 @@ def training_step(self, step_kwargs: Dict[str, Union[Any, int]]) -> STEP_OUTPUT:
with self.precision_plugin.train_step_context():
return self.training_type_plugin.training_step(*step_kwargs.values())

def post_training_step(self) -> None:
"""
.. deprecated:: v1.5
This method is deprecated in v1.5 and will be removed in v1.6.
Please call `training_type_plugin.post_training_step` directly.
"""
rank_zero_deprecation(
"`Accelerator.post_training_step` is deprecated in v1.5 and will be removed in v1.6. "
"`post_training_step` logic is implemented directly in the `TrainingTypePlugin` implementations."
)
self.training_type_plugin.post_training_step()

def validation_step(self, step_kwargs: Dict[str, Union[Any, int]]) -> Optional[STEP_OUTPUT]:
"""The actual validation step.
Expand All @@ -251,54 +223,6 @@ def predict_step(self, step_kwargs: Dict[str, Union[Any, int]]) -> STEP_OUTPUT:
with self.precision_plugin.predict_step_context():
return self.training_type_plugin.predict_step(*step_kwargs.values())

def training_step_end(self, output: STEP_OUTPUT) -> STEP_OUTPUT:
"""A hook to do something at the end of the training step.
.. deprecated:: v1.5
This method is deprecated in v1.5 and will be removed in v1.6.
Please call `training_type_plugin.training_step_end` directly.
Args:
output: the output of the training step
"""
rank_zero_deprecation(
"`Accelerator.training_step_end` is deprecated in v1.5 and will be removed in v1.6. "
"`training_step_end` logic is implemented directly in the `TrainingTypePlugin` implementations."
)
return self.training_type_plugin.training_step_end(output)

def test_step_end(self, output: Optional[STEP_OUTPUT]) -> Optional[STEP_OUTPUT]:
"""A hook to do something at the end of the test step.
.. deprecated:: v1.5
This method is deprecated in v1.5 and will be removed in v1.6.
Please call `training_type_plugin.test_step_end` directly.
Args:
output: the output of the test step
"""
rank_zero_deprecation(
"`Accelerator.test_step_end` is deprecated in v1.5 and will be removed in v1.6. "
"`test_step_end` logic is implemented directly in the `TrainingTypePlugin` implementations."
)
return self.training_type_plugin.test_step_end(output)

def validation_step_end(self, output: Optional[STEP_OUTPUT]) -> Optional[STEP_OUTPUT]:
"""A hook to do something at the end of the validation step.
.. deprecated:: v1.5
This method is deprecated in v1.5 and will be removed in v1.6.
Please call `training_type_plugin.validation_step_end` directly.
Args:
output: the output of the validation step
"""
rank_zero_deprecation(
"`Accelerator.validation_step_end` is deprecated in v1.5 and will be removed in v1.6. "
"`validation_step_end` logic is implemented directly in the `TrainingTypePlugin` implementations."
)
return self.training_type_plugin.validation_step_end(output)

def backward(self, closure_loss: Tensor, *args: Any, **kwargs: Any) -> Tensor:
"""Forwards backward-calls to the precision plugin.
Expand Down Expand Up @@ -389,104 +313,6 @@ def optimizer_state(self, optimizer: Optimizer) -> Dict[str, Tensor]:
"""
return getattr(self.training_type_plugin, "optimizer_state", lambda x: x.state_dict())(optimizer)

def lightning_module_state_dict(self) -> Dict[str, Union[Any, Tensor]]:
"""Returns state of model.
.. deprecated:: v1.5
This method is deprecated in v1.5 and will be removed in v1.6.
Please call `training_type_plugin.lightning_module_state_dict` directly.
Allows for syncing/collating model state from processes in custom plugins.
"""
rank_zero_deprecation(
"`Accelerator.lightning_module_state_dict` is deprecated in v1.5 and will be removed in v1.6. "
"`lightning_module_state_dict` logic is implemented directly in the `TrainingTypePlugin` implementations."
)
return self.training_type_plugin.lightning_module_state_dict()

def barrier(self, name: Optional[str] = None) -> None:
"""
.. deprecated:: v1.5
This method is deprecated in v1.5 and will be removed in v1.6.
Please call `training_type_plugin.barrier` directly.
"""
rank_zero_deprecation(
"`Accelerator.barrier` is deprecated in v1.5 and will be removed in v1.6. "
"`Barrier` logic is implemented directly in the `TrainingTypePlugin` implementations."
)
self.training_type_plugin.barrier(name=name)

def broadcast(self, obj: object, src: int = 0) -> object:
"""Broadcasts an object to all processes, such that the src object is broadcast to all other ranks if
needed.
.. deprecated:: v1.5
This method is deprecated in v1.5 and will be removed in v1.6.
Please call `training_type_plugin.broadcast` directly.
Args:
obj: Object to broadcast to all process, usually a tensor or collection of tensors.
src: The source rank of which the object will be broadcast from
"""
rank_zero_deprecation(
"`Accelerator.broadcast` is deprecated in v1.5 and will be removed in v1.6. "
"`Broadcast` logic is implemented directly in the `TrainingTypePlugin` implementations."
)
return self.training_type_plugin.broadcast(obj, src)

def all_gather(self, tensor: Tensor, group: Optional[Any] = None, sync_grads: bool = False) -> Tensor:
"""Function to gather a tensor from several distributed processes.
.. deprecated:: v1.5
This method is deprecated in v1.5 and will be removed in v1.6.
Please call `training_type_plugin.all_gather` directly.
Args:
tensor: tensor of shape (batch, ...)
group: the process group to gather results from. Defaults to all processes (world)
sync_grads: flag that allows users to synchronize gradients for all_gather op
Return:
A tensor of shape (world_size, batch, ...)
"""
rank_zero_deprecation(
"`Accelerator.all_gather` is deprecated in v1.5 and will be removed in v1.6. "
"`all_gather` logic is implemented directly in the `TrainingTypePlugin` implementations."
)
return self.training_type_plugin.all_gather(tensor, group=group, sync_grads=sync_grads)

def process_dataloader(self, dataloader: Union[Iterable, DataLoader]) -> Union[Iterable, DataLoader]:
"""Wraps the dataloader if necessary.
.. deprecated:: v1.5
This method is deprecated in v1.5 and will be removed in v1.6.
Please call `training_type_plugin.process_dataloader` directly.
Args:
dataloader: iterable. Ideally of type: :class:`torch.utils.data.DataLoader`
"""
rank_zero_deprecation(
"`Accelerator.process_dataloader` is deprecated in v1.5 and will be removed in v1.6. "
"`process_dataloader` logic is implemented directly in the `TrainingTypePlugin` implementations."
)
return self.training_type_plugin.process_dataloader(dataloader)

@property
def results(self) -> Any:
"""The results of the last run will be cached within the training type plugin.
.. deprecated:: v1.5
This property is deprecated in v1.5 and will be removed in v1.6.
Please call `training_type_plugin.results` directly.
In distributed training, we make sure to transfer the results to the appropriate main process.
"""
rank_zero_deprecation(
"`Accelerator.results` is deprecated in v1.5 and will be removed in v1.6. "
"Accesse results directly from the `TrainingTypePlugin`."
)
return self.training_type_plugin.results

@contextlib.contextmanager
def model_sharded_context(self) -> Generator[None, None, None]:
"""Provide hook to create modules in a distributed aware context. This is useful for when we'd like to.
Expand Down Expand Up @@ -517,43 +343,6 @@ def save_checkpoint(self, checkpoint: Dict[str, Any], filepath: _PATH) -> None:
)
self.training_type_plugin.save_checkpoint(checkpoint, filepath)

@property
def setup_optimizers_in_pre_dispatch(self) -> bool:
"""Override to delay setting optimizers and schedulers till after dispatch. This is useful when the
`TrainingTypePlugin` requires operating on the wrapped accelerator model. However this may break certain
precision plugins such as APEX which require optimizers to be set.
.. deprecated:: v1.5
This property is deprecated in v1.5 and will be removed in v1.6.
Please call `training_type_plugin.setup_optimizers_in_pre_dispatch` directly.
Returns:
If True, delay setup optimizers until `pre_dispatch`, else call within `setup`.
"""
rank_zero_deprecation(
"`Accelerator.setup_optimizers_in_pre_dispatch` is deprecated in v1.5 and will be removed in v1.6. "
"Accesse `setup_optimizers_in_pre_dispatch directly` from the `TrainingTypePlugin`."
)
return self.training_type_plugin.setup_optimizers_in_pre_dispatch

@property
def restore_checkpoint_after_pre_dispatch(self) -> bool:
"""Override to delay restoring from checkpoint till after pre-dispatch. This is useful when the plugin
requires all the setup hooks to run before loading checkpoint.
.. deprecated:: v1.5
This property is deprecated in v1.5 and will be removed in v1.6.
Please call `training_type_plugin.restore_checkpoint_after_pre_dispatch` directly.
Returns:
If true, restore checkpoint after pre_dispatch.
"""
rank_zero_deprecation(
"`Accelerator.restore_checkpoint_after_pre_dispatch` is deprecated in v1.5 and will be removed in v1.6."
" Access `restore_checkpoint_after_pre_dispatch` directly from the `TrainingTypePlugin`."
)
return self.training_type_plugin.restore_checkpoint_after_pre_dispatch

def get_device_stats(self, device: Union[str, torch.device]) -> Dict[str, Any]:
"""Gets stats for a given device.
Expand All @@ -569,127 +358,6 @@ def on_train_start(self) -> None:
"""Called when train begins."""
return self.training_type_plugin.on_train_start()

def on_validation_start(self) -> None:
"""Called when validation begins.
See deprecation warning below.
.. deprecated:: v1.5
This method is deprecated in v1.5 and will be removed in v1.6.
Please call `training_type_plugin.on_validation_start` directly.
"""
rank_zero_deprecation(
"`Accelerator.on_validation_start` is deprecated in v1.5 and will be removed in v1.6. "
"`on_validation_start` logic is implemented directly in the `TrainingTypePlugin` implementations."
)
return self.training_type_plugin.on_validation_start()

def on_test_start(self) -> None:
"""Called when test begins.
See deprecation warning below.
.. deprecated:: v1.5
This method is deprecated in v1.5 and will be removed in v1.6.
Please call `training_type_plugin.on_test_start` directly.
"""
rank_zero_deprecation(
"`Accelerator.on_test_start` is deprecated in v1.5 and will be removed in v1.6. "
"`on_test_start` logic is implemented directly in the `TrainingTypePlugin` implementations."
)
return self.training_type_plugin.on_test_start()

def on_predict_start(self) -> None:
"""Called when predict begins.
See deprecation warning below.
.. deprecated:: v1.5
This method is deprecated in v1.5 and will be removed in v1.6.
Please call `training_type_plugin.on_predict_start` directly.
"""
rank_zero_deprecation(
"`Accelerator.on_predict_start` is deprecated in v1.5 and will be removed in v1.6. "
"`on_predict_start` logic is implemented directly in the `TrainingTypePlugin` implementations."
)
return self.training_type_plugin.on_predict_start()

def on_validation_end(self) -> None:
"""Called when validation ends.
See deprecation warning below.
.. deprecated:: v1.5
This method is deprecated in v1.5 and will be removed in v1.6.
Please call `training_type_plugin.on_validation_end` directly.
"""
rank_zero_deprecation(
"`Accelerator.on_validation_end` is deprecated in v1.5 and will be removed in v1.6. "
"`on_validation_end` logic is implemented directly in the `TrainingTypePlugin` implementations."
)
return self.training_type_plugin.on_validation_end()

def on_test_end(self) -> None:
"""Called when test end.
See deprecation warning below.
.. deprecated:: v1.5
This method is deprecated in v1.5 and will be removed in v1.6.
Please call `training_type_plugin.on_test_end` directly.
"""
rank_zero_deprecation(
"`Accelerator.on_test_end` is deprecated in v1.5 and will be removed in v1.6. "
"`on_test_end` logic is implemented directly in the `TrainingTypePlugin` implementations."
)
return self.training_type_plugin.on_test_end()

def on_predict_end(self) -> None:
"""Called when predict ends.
See deprecation warning below.
.. deprecated:: v1.5
This method is deprecated in v1.5 and will be removed in v1.6.
Please call `training_type_plugin.on_predict_end` directly.
"""
rank_zero_deprecation(
"`Accelerator.on_predict_end` is deprecated in v1.5 and will be removed in v1.6. "
"`on_predict_end` logic is implemented directly in the `TrainingTypePlugin` implementations."
)
return self.training_type_plugin.on_predict_end()

def on_train_end(self) -> None:
"""Called when train ends.
See deprecation warning below.
.. deprecated:: v1.5
This method is deprecated in v1.5 and will be removed in v1.6.
Please call `training_type_plugin.on_train_end` directly.
"""
rank_zero_deprecation(
"`Accelerator.on_train_end` is deprecated in v1.5 and will be removed in v1.6. "
"`on_train_end` logic is implemented directly in the `TrainingTypePlugin` implementations."
)
return self.training_type_plugin.on_train_end()

# TODO: Update this in v1.7 (deprecation: #9816)
def on_train_batch_start(self, batch: Any, batch_idx: int, dataloader_idx: int = 0) -> None:
"""Called in the training loop before anything happens for that batch.
See deprecation warning below.
.. deprecated:: v1.5
This method is deprecated in v1.5 and will be removed in v1.6.
Please call `training_type_plugin.on_train_batch_start` directly.
"""
rank_zero_deprecation(
"`Accelerator.on_train_batch_start` is deprecated in v1.5 and will be removed in v1.6. "
"`on_train_batch_start` logic is implemented directly in the `TrainingTypePlugin` implementations."
)
return self.training_type_plugin.on_train_batch_start(batch, batch_idx)

@staticmethod
@abstractmethod
def auto_device_count() -> int:
Expand Down
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