Update optimizer_step methods in accelerator and plugins (#10023)

Co-authored-by: Carlos Mocholi <[email protected]>

CHANGELOG.md

@@ -213,6 +213,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
     * Added `TrainingTypePlugin.{_setup_model, _setup_optimizer}` methods ([#9994](https://github.com/PyTorchLightning/pytorch-lightning/pull/9994))
     * Implemented `DataParallelPlugin._setup_model` ([#10010](https://github.com/PyTorchLightning/pytorch-lightning/pull/10010))
     * Implemented `DeepSpeedPlugin._setup_models_and_optimizers` ([#10009](https://github.com/PyTorchLightning/pytorch-lightning/pull/10009))
+    * Added optional `model` argument to the `optimizer_step` methods in accelerators and plugins ([#10023](https://github.com/PyTorchLightning/pytorch-lightning/pull/10023))
+
 
 ### Changed
 

pytorch_lightning/accelerators/accelerator.py

@@ -314,16 +314,25 @@ def backward(self, closure_loss: Tensor, *args: Any, **kwargs: Any) -> Tensor:
 
         return closure_loss
 
-    def optimizer_step(self, optimizer: Optimizer, opt_idx: int, lambda_closure: Callable, **kwargs: Any) -> None:
+    def optimizer_step(
+        self,
+        optimizer: Optimizer,
+        opt_idx: int,
+        lambda_closure: Callable[[], Any],
+        model: Optional[Union["pl.LightningModule", Module]] = None,
+        **kwargs: Any
+    ) -> None:
         """performs the actual optimizer step.
 
         Args:
             optimizer: the optimizer performing the step
             opt_idx: index of the current optimizer
             lambda_closure: closure calculating the loss value
+            model: reference to the model, optionally defining optimizer step related hooks
         """
+        model = model or self.lightning_module
         make_optimizer_step = self.precision_plugin.pre_optimizer_step(
-            self.lightning_module, optimizer, opt_idx, lambda_closure, **kwargs
+            model, optimizer, opt_idx, lambda_closure, **kwargs
         )
         if make_optimizer_step:
             self.training_type_plugin.optimizer_step(optimizer, lambda_closure=lambda_closure, **kwargs)

pytorch_lightning/plugins/precision/apex_amp.py

@@ -11,10 +11,11 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-from typing import Any, Callable, Dict, Optional, Sequence
+from typing import Any, Callable, Dict, Optional, Sequence, Union
 
 import torch
 from torch import Tensor
+from torch.nn import Module
 from torch.optim import LBFGS, Optimizer
 
 import pytorch_lightning as pl
@@ -97,7 +98,7 @@ def reinit_scheduler_properties(optimizers: Sequence[Optimizer], schedulers: Seq
 
     def pre_optimizer_step(
         self,
-        model: "pl.LightningModule",
+        model: Union["pl.LightningModule", Module],
         optimizer: Optimizer,
         optimizer_idx: int,
         lambda_closure: Callable,
@@ -112,7 +113,7 @@ def pre_optimizer_step(
         super().pre_optimizer_step(model, optimizer, optimizer_idx, lambda_closure, **kwargs)
         skipped_backward = result is None
         # in manual optimization, the closure does not return a value
-        if not model.automatic_optimization or not skipped_backward:
+        if not isinstance(model, pl.LightningModule) or not model.automatic_optimization or not skipped_backward:
             # the following should be in a `optimizer_step` hook but we don't have one in the precision plugin.
             optimizer.step(**kwargs)
         return False

pytorch_lightning/plugins/precision/deepspeed_precision.py

@@ -48,7 +48,7 @@ def _run_backward(self, tensor: Tensor, model: Module, *args: Any, **kwargs: Any
 
     def pre_optimizer_step(
         self,
-        model: "pl.LightningModule",
+        model: Union["pl.LightningModule", Module],
         optimizer: Optimizer,
         optimizer_idx: int,
         lambda_closure: Callable,
@@ -63,12 +63,12 @@ def pre_optimizer_step(
         super().pre_optimizer_step(model, optimizer, optimizer_idx, lambda_closure, **kwargs)
         skipped_backward = result is None
         # in manual optimization, the closure does not return a value
-        if model.automatic_optimization and skipped_backward:
+        if isinstance(model, pl.LightningModule) and model.automatic_optimization and skipped_backward:
             raise MisconfigurationException(
                 "Skipping backward by returning `None` from your `training_step` is not supported by `DeepSpeed`"
             )
         # DeepSpeed handles the optimizer step internally
-        deepspeed_engine = model.trainer.model
+        deepspeed_engine = model.trainer.model if isinstance(model, pl.LightningModule) else model
         deepspeed_engine.step()
         return False
 

pytorch_lightning/plugins/precision/ipu_precision.py

@@ -40,7 +40,7 @@ def backward(self, model: "pl.LightningModule", *args: Any, **kwargs: Any) -> No
 
     def pre_optimizer_step(
         self,
-        model: "pl.LightningModule",
+        model: Union["pl.LightningModule", Module],
         optimizer: Optimizer,
         optimizer_idx: int,
         lambda_closure: Callable[[], Any],
@@ -55,7 +55,7 @@ def pre_optimizer_step(
         closure_result = lambda_closure()
         skipped_backward = closure_result is None
         # in manual optimization, the closure does not return a value
-        if model.automatic_optimization and skipped_backward:
+        if isinstance(model, pl.LightningModule) and model.automatic_optimization and skipped_backward:
             # we lack coverage here and IPUs are (currently) limited - something to explore if there's demand
             raise MisconfigurationException(
                 "Skipping backward by returning `None` from your `training_step` is not implemented for IPUs."

pytorch_lightning/plugins/precision/native_amp.py

@@ -66,7 +66,7 @@ def _run_backward(self, tensor: Tensor, model: Module, *args: Any, **kwargs: Any
 
     def pre_optimizer_step(
         self,
-        model: "pl.LightningModule",
+        model: Union["pl.LightningModule", Module],
         optimizer: Optimizer,
         optimizer_idx: int,
         lambda_closure: Callable,
@@ -84,7 +84,7 @@ def pre_optimizer_step(
         super().pre_optimizer_step(model, optimizer, optimizer_idx, lambda_closure, **kwargs)
         skipped_backward = result is None
         # in manual optimization, the closure does not return a value
-        if not model.automatic_optimization or not skipped_backward:
+        if not isinstance(model, pl.LightningModule) or not model.automatic_optimization or not skipped_backward:
             # note: the scaler will skip the `optimizer.step` if nonfinite gradients are found
             self.scaler.step(optimizer)
             self.scaler.update()

pytorch_lightning/plugins/precision/precision_plugin.py

@@ -99,14 +99,15 @@ def _run_backward(self, tensor: Tensor, model: Module, *args: Any, **kwargs: Any
 
     def pre_optimizer_step(
         self,
-        model: "pl.LightningModule",
+        model: Union["pl.LightningModule", Module],
         optimizer: Optimizer,
         optimizer_idx: int,
         lambda_closure: Callable,
         **kwargs: Any,
     ) -> bool:
         """Hook to do something before each optimizer step."""
-        model.trainer.call_hook("on_before_optimizer_step", optimizer, optimizer_idx)
+        if isinstance(model, pl.LightningModule):
+            model.trainer.call_hook("on_before_optimizer_step", optimizer, optimizer_idx)
         return True
 
     def clip_gradients(

pytorch_lightning/plugins/precision/tpu.py

@@ -11,8 +11,9 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-from typing import Any, Callable
+from typing import Any, Callable, Union
 
+from torch.nn import Module
 from torch.optim import Optimizer
 
 import pytorch_lightning as pl
@@ -27,7 +28,7 @@
 class TPUPrecisionPlugin(PrecisionPlugin):
     def pre_optimizer_step(
         self,
-        model: "pl.LightningModule",
+        model: Union["pl.LightningModule", Module],
         optimizer: Optimizer,
         optimizer_idx: int,
         lambda_closure: Callable[[], Any],
@@ -37,7 +38,7 @@ def pre_optimizer_step(
         closure_result = xm.optimizer_step(optimizer, optimizer_args={"closure": lambda_closure, **kwargs})
         skipped_backward = closure_result is None
         # in manual optimization, the closure does not return a value
-        if model.automatic_optimization and skipped_backward:
+        if isinstance(model, pl.LightningModule) and model.automatic_optimization and skipped_backward:
             # we lack coverage here so disable this - something to explore if there's demand
             raise MisconfigurationException(
                 "Skipping backward by returning `None` from your `training_step` is not implemented for TPUs."

pytorch_lightning/plugins/training_type/training_type_plugin.py

@@ -250,7 +250,7 @@ def process_dataloader(self, dataloader: Union[Iterable, DataLoader]) -> Union[I
     def init_optimizers(self, trainer: "pl.Trainer", model: "pl.LightningModule"):
         return trainer.init_optimizers(model)
 
-    def optimizer_step(self, optimizer: torch.optim.Optimizer, lambda_closure: Callable, **kwargs):
+    def optimizer_step(self, optimizer: torch.optim.Optimizer, lambda_closure: Callable, **kwargs: Any) -> None:
         optimizer.step(closure=lambda_closure, **kwargs)
 
     @property