Fix manual optimization on AMP and skipping backward

pytorch_lightning/core/lightning.py

@@ -632,7 +632,7 @@ def training_step(self, *args, **kwargs) -> STEP_OUTPUT:
             - ``None`` - Training will skip to the next batch
 
         Note:
-            Returning ``None`` is currently not supported for multi-GPU or TPU, or with 16-bit precision enabled.
+            Returning ``None`` is currently not supported for multi-GPU or TPU.
 
         In this step you'd normally do the forward pass and calculate the loss for a batch.
         You can also do fancier things like multiple forward passes or something model specific.

pytorch_lightning/plugins/precision/apex_amp.py

@@ -97,10 +97,13 @@ def pre_optimizer_step(
         **kwargs: Any,
     ) -> bool:
         """Hook to do something before each optimizer step."""
-        lambda_closure()  # APEX amp does not support closures
+        result = lambda_closure()  # APEX amp does not support closures
         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.
-        optimizer.step(**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:
+            # the following should be in a `optimizer_step` hook but we don't have one in the precision plugin.
+            optimizer.step(**kwargs)
         return False
 
     def on_load_checkpoint(self, checkpoint: Dict[str, Any]) -> None:

pytorch_lightning/plugins/precision/deepspeed_precision.py

@@ -42,11 +42,14 @@ def pre_optimizer_step(
         **kwargs: Any,
     ) -> bool:
         """Hook to do something before each optimizer step."""
-        lambda_closure()  # DeepSpeed does not support closures
+        result = lambda_closure()  # DeepSpeed does not support closures
         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.
-        deepspeed_engine = model.trainer.model
-        deepspeed_engine.step()
+        skipped_backward = result is None
+        # in manual optimization, the closure does not return a value
+        if 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.
+            deepspeed_engine = model.trainer.model
+            deepspeed_engine.step()
         return False
 
     def backward(self, model: "pl.LightningModule", closure_loss: Tensor, *args: Any, **kwargs: Any) -> None:

pytorch_lightning/plugins/precision/native_amp.py

@@ -96,14 +96,13 @@ 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"
             )
-        result = True
-        # FIXME: is this correct for manual?
-        if model.automatic_optimization:
-            result = lambda_closure()
+        result = lambda_closure()
         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:
+        skipped_backward = result is None
+        # in manual optimization, the closure does not return a value
+        if 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()
         return False