Add the on_before_optimizer_step hook

CHANGELOG.md

@@ -81,6 +81,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Added trainer stage hooks for Training Plugins and Accelerators ([#7864](https://github.com/PyTorchLightning/pytorch-lightning/pull/7864))
 
 
+- Added the `on_before_optimizer_step` hook ([#8048](https://github.com/PyTorchLightning/pytorch-lightning/pull/8048))
+
+
 - Added IPU Accelerator ([#7867](https://github.com/PyTorchLightning/pytorch-lightning/pull/7867))
 
 
@@ -244,10 +247,10 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Moved profilers to their own file ([#7822](https://github.com/PyTorchLightning/pytorch-lightning/pull/7822))
 
 
-- The `on_after_backward` hook is now called on accumulating iterations ([#8328](https://github.com/PyTorchLightning/pytorch-lightning/pull/8328))
+- The `on_after_backward` hook is now called on accumulating iterations. Use the `on_before_optimizer_step` hook to mimic the old behaviour ([#8328](https://github.com/PyTorchLightning/pytorch-lightning/pull/8328))
 
 
-- The mixed precision loss is no longer unscaled before the `on_after_backward` hook ([#8328](https://github.com/PyTorchLightning/pytorch-lightning/pull/8328))
+- The mixed precision loss is no longer unscaled before the `on_after_backward` hook. Use the `on_before_optimizer_step` hook to mimic the old behaviour  ([#8328](https://github.com/PyTorchLightning/pytorch-lightning/pull/8328))
 
 
 - The `TrainingTypePlugin.{pre,post}_backward` hooks no longer take the `optimizer, opt_idx, should_accumulate` arguments ([#8328](https://github.com/PyTorchLightning/pytorch-lightning/pull/8328))

docs/source/common/lightning_module.rst

@@ -1195,6 +1195,7 @@ for more information.
             backward()
             on_after_backward()
 
+            on_before_optimizer_step()
             optimizer_step()
 
             on_train_batch_end()
@@ -1451,6 +1452,12 @@ on_test_model_train
 .. automethod:: pytorch_lightning.core.hooks.ModelHooks.on_test_model_train
     :noindex:
 
+on_before_optimizer_step
+~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. automethod:: pytorch_lightning.core.hooks.ModelHooks.on_before_optimizer_step
+    :noindex:
+
 optimizer_step
 ~~~~~~~~~~~~~~
 

docs/source/extensions/callbacks.rst

@@ -363,6 +363,12 @@ on_after_backward
 .. automethod:: pytorch_lightning.callbacks.Callback.on_after_backward
     :noindex:
 
+on_before_optimizer_step
+^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. automethod:: pytorch_lightning.callbacks.Callback.on_before_optimizer_step
+    :noindex:
+
 on_before_zero_grad
 ^^^^^^^^^^^^^^^^^^^
 

pytorch_lightning/callbacks/base.py

@@ -302,7 +302,13 @@ def on_before_backward(self, trainer: 'pl.Trainer', pl_module: 'pl.LightningModu
         pass
 
     def on_after_backward(self, trainer: 'pl.Trainer', pl_module: 'pl.LightningModule') -> None:
-        """Called after ``loss.backward()`` and before optimizers do anything."""
+        """Called after ``loss.backward()`` and before optimizers are stepped."""
+        pass
+
+    def on_before_optimizer_step(
+        self, trainer: 'pl.Trainer', pl_module: 'pl.LightningModule', optimizer: Optimizer, opt_idx: int
+    ) -> None:
+        """Called before ``optimizer.step()``."""
         pass
 
     def on_before_zero_grad(self, trainer: 'pl.Trainer', pl_module: 'pl.LightningModule', optimizer: Optimizer) -> None:

pytorch_lightning/callbacks/lambda_function.py

@@ -79,6 +79,7 @@ def __init__(
         on_load_checkpoint: Optional[Callable] = None,
         on_before_backward: Optional[Callable] = None,
         on_after_backward: Optional[Callable] = None,
+        on_before_optimizer_step: Optional[Callable] = None,
         on_before_zero_grad: Optional[Callable] = None,
         on_predict_start: Optional[Callable] = None,
         on_predict_end: Optional[Callable] = None,

pytorch_lightning/core/hooks.py

@@ -306,19 +306,36 @@ def on_before_backward(self, loss: torch.Tensor) -> None:
 
     def on_after_backward(self) -> None:
         """
-        Called in the training loop after loss.backward() and before optimizers do anything.
-        This is the ideal place to inspect or log gradient information.
+        Called after ``loss.backward()`` and before optimizers are stepped.
+
+        Note:
+            If using native AMP, the gradients will not be unscaled at this point.
+            Use the ``on_before_optimizer_step`` if you need the unscaled gradients.
+        """
+
+    def on_before_optimizer_step(self, optimizer: Optimizer, optimizer_idx: int) -> None:
+        """
+        Called before ``optimizer.step()``.
+
+        The hook is only called if gradients do not need to be accumulated.
+        See: :paramref:`~pytorch_lightning.trainer.Trainer.accumulate_grad_batches`.
+        If using native AMP, the loss will be unscaled before calling this hook.
+        See these `docs <https://pytorch.org/docs/stable/notes/amp_examples.html#working-with-unscaled-gradients>`__
+        for more information on the scaling of gradients.
+
+        Args:
+            optimizer: Current optimizer being used.
+            optimizer_idx: Index of the current optimizer being used.
 
         Example::
 
-            def on_after_backward(self):
+            def on_before_optimizer_step(self, optimizer, optimizer_idx):
                 # example to inspect gradient information in tensorboard
                 if self.trainer.global_step % 25 == 0:  # don't make the tf file huge
                     for k, v in self.named_parameters():
                         self.logger.experiment.add_histogram(
                             tag=k, values=v.grad, global_step=self.trainer.global_step
                         )
-
         """
 
     def on_post_move_to_device(self) -> None:

pytorch_lightning/plugins/precision/apex_amp.py

@@ -90,17 +90,16 @@ def reinit_scheduler_properties(optimizers: Sequence[Optimizer], schedulers: Seq
 
     def pre_optimizer_step(
         self,
-        pl_module: 'pl.LightningModule',
+        model: 'pl.LightningModule',
         optimizer: Optimizer,
         optimizer_idx: int,
         lambda_closure: Callable,
         **kwargs: Any,
     ) -> bool:
-        """
-        always called before the optimizer step.
-        """
-        # apex amp does not support closures.
-        lambda_closure()
+        """Hook to do something before each optimizer step."""
+        super().pre_optimizer_step(model, optimizer, optimizer_idx, lambda_closure, **kwargs)
+        # the following should be in a `optimizer_step` hook but we don't have one in the precision plugin.
+        lambda_closure()  # APEX amp does not support closures
         optimizer.step(**kwargs)
         return False
 

pytorch_lightning/plugins/precision/deepspeed_precision.py

@@ -35,15 +35,17 @@ def __init__(self, precision: int) -> None:
 
     def pre_optimizer_step(
         self,
-        pl_module: 'pl.LightningModule',
+        model: 'pl.LightningModule',
         optimizer: Optimizer,
         optimizer_idx: int,
         lambda_closure: Callable,
         **kwargs: Any,
     ) -> bool:
-        # DeepSpeed not support closures.
-        lambda_closure()
-        deepspeed_engine = pl_module.trainer.model
+        """Hook to do something before each optimizer step."""
+        super().pre_optimizer_step(model, optimizer, optimizer_idx, lambda_closure, **kwargs)
+        # the following should be in a `optimizer_step` hook but we don't have one in the precision plugin.
+        lambda_closure()  # DeepSpeed does not support closures
+        deepspeed_engine = model.trainer.model
         deepspeed_engine.step()
         return False
 

pytorch_lightning/plugins/precision/native_amp.py

@@ -47,7 +47,7 @@ def pre_backward(
 
     def pre_optimizer_step(
         self,
-        pl_module: 'pl.LightningModule',
+        model: 'pl.LightningModule',
         optimizer: Optimizer,
         optimizer_idx: int,
         lambda_closure: Callable,
@@ -58,16 +58,15 @@ def pre_optimizer_step(
                 f"native PyTorch amp and lbfgs are not compatible (optimizer {optimizer_idx})."
                 " To request, please file a Github issue in PyTorch and tag @mcarilli"
             )
-        # TODO: Add `on_before_optimizer_step`
-        # self.scaler.unscale_(optimizer)
-        # pl_module.trainer.call_hook("on_before_optimizer_step")
-        if pl_module.automatic_optimization:
+        result = True
+        if model.automatic_optimization:
             result = lambda_closure()
-            if result is None:
-                # lambda_closure returning None indicates that backward has been skipped
-                return False
-        self.scaler.step(optimizer)
-        self.scaler.update()
+        self.scaler.unscale_(optimizer)
+        super().pre_optimizer_step(model, optimizer, optimizer_idx, lambda_closure, **kwargs)
+        # lambda_closure returning None indicates that backward has been skipped
+        if result is not None:
+            self.scaler.step(optimizer)
+            self.scaler.update()
         return False
 
     @contextmanager

pytorch_lightning/plugins/precision/precision_plugin.py

@@ -104,13 +104,14 @@ def post_backward(
 
     def pre_optimizer_step(
         self,
-        pl_module: 'pl.LightningModule',
+        model: 'pl.LightningModule',
         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)
         return True
 
     def post_optimizer_step(self, optimizer: Optimizer, optimizer_idx: int) -> None:

pytorch_lightning/trainer/callback_hook.py

@@ -327,6 +327,13 @@ def on_after_backward(self):
         for callback in self.callbacks:
             callback.on_after_backward(self, self.lightning_module)
 
+    def on_before_optimizer_step(self, optimizer, optimizer_idx):
+        """
+        Called after on_after_backward() once the gradient is accumulated and before optimizer.step().
+        """
+        for callback in self.callbacks:
+            callback.on_before_optimizer_step(self, self.lightning_module, optimizer, optimizer_idx)
+
     def on_before_zero_grad(self, optimizer):
         """
         Called after optimizer.step() and before optimizer.zero_grad().

pytorch_lightning/trainer/connectors/logger_connector/fx_validator.py

@@ -23,6 +23,7 @@ class FxValidator:
         on_configure_sharded_model=None,
         on_before_backward=dict(on_step=(False, True), on_epoch=(False, True)),
         on_after_backward=dict(on_step=(False, True), on_epoch=(False, True)),
+        on_before_optimizer_step=dict(on_step=(False, True), on_epoch=(False, True)),
         on_before_zero_grad=dict(on_step=(False, True), on_epoch=(False, True)),
         on_init_start=None,
         on_init_end=None,

tests/models/test_hooks.py

@@ -299,6 +299,10 @@ def _auto_train_batch(trainer, model, batches, device=torch.device('cpu'), curre
         using_native_amp = kwargs.get('amp_backend') == 'native'
         using_deepspeed = kwargs.get('plugins') == 'deepspeed'
         out = []
+        on_before_optimizer_step = [
+            dict(name='Callback.on_before_optimizer_step', args=(trainer, model, ANY, 0)),
+            dict(name='on_before_optimizer_step', args=(ANY, 0)),
+        ]
         for i in range(batches):
             out.extend([
                 dict(name='on_before_batch_transfer', args=(ANY, 0)),
@@ -308,7 +312,10 @@ def _auto_train_batch(trainer, model, batches, device=torch.device('cpu'), curre
                 dict(name='Callback.on_batch_start', args=(trainer, model)),
                 dict(name='Callback.on_train_batch_start', args=(trainer, model, ANY, i, 0)),
                 dict(name='on_train_batch_start', args=(ANY, i, 0)),
-                # TODO: `on_before_optimizer_step`
+                # these are before the training step because
+                # they are not part of the `training_step_and_backward` closure, however,
+                # with native amp, the closure is run first and then the optimizer step.
+                *(on_before_optimizer_step if not using_native_amp else []),
                 dict(name='forward', args=(ANY, )),
                 dict(name='training_step', args=(ANY, i)),
                 dict(name='training_step_end', args=(dict(loss=ANY), )),
@@ -321,6 +328,7 @@ def _auto_train_batch(trainer, model, batches, device=torch.device('cpu'), curre
                 *([dict(name='backward', args=(ANY, ANY, 0))] if not using_deepspeed else []),
                 dict(name='Callback.on_after_backward', args=(trainer, model)),
                 dict(name='on_after_backward'),
+                *(on_before_optimizer_step if using_native_amp else []),
                 dict(
                     name='optimizer_step',
                     args=(current_epoch, i, ANY, 0, ANY),
@@ -354,7 +362,8 @@ def _manual_train_batch(trainer, model, batches, device=torch.device('cpu'), **k
                 dict(name='on_after_backward'),
                 # `manual_backward` calls the previous 3
                 dict(name='manual_backward', args=(ANY, )),
-                # TODO: `on_before_optimizer_step`
+                dict(name='Callback.on_before_optimizer_step', args=(trainer, model, ANY, 0)),
+                dict(name='on_before_optimizer_step', args=(ANY, 0)),
                 dict(name='training_step', args=(ANY, i)),
                 dict(name='training_step_end', args=(dict(loss=ANY), )),
                 dict(name='Callback.on_train_batch_end', args=(trainer, model, dict(loss=ANY), ANY, i, 0)),

tests/plugins/test_amp_plugins.py

@@ -71,12 +71,7 @@ def test_amp_apex_ddp(
 
 class GradientUnscaleBoringModel(BoringModel):
 
-    def on_after_backward(self):
-        # TODO: replace with `on_before_optimizer_step` so we don't need to check accumulate and unscale manually
-        if self.trainer.fit_loop.should_accumulate():
-            return
-        opt = self.optimizers()
-        self.trainer.precision_plugin.scaler.unscale_(opt)
+    def on_before_optimizer_step(self, *_):
         norm = torch.nn.utils.clip_grad_norm_(self.parameters(), 2)
         if not (torch.isinf(norm) or torch.isnan(norm)):
             assert norm.item() < 15.

tests/trainer/logging_/test_logger_connector.py

@@ -34,6 +34,7 @@ def test_fx_validator(tmpdir):
     callbacks_func = [
         'on_before_backward',
         'on_after_backward',
+        'on_before_optimizer_step',
         'on_batch_end',
         'on_batch_start',
         'on_before_accelerator_backend_setup',
@@ -124,6 +125,7 @@ def test_fx_validator(tmpdir):
         # creating allowed condition
         allowed = (
             is_stage or "batch" in func_name or "epoch" in func_name or "grad" in func_name or "backward" in func_name
+            or "optimizer_step" in func_name
         )
         allowed = (
             allowed and "pretrain" not in func_name and "predict" not in func_name