Sharded Plugin 3/n: Expose step input to DDP plugin

pytorch_lightning/accelerators/ddp2_accelerator.py

@@ -61,21 +61,23 @@ def train(self):
         return self.ddp_train(process_idx=self.task_idx, mp_queue=None, model=model)
 
     def training_step(self, args):
+        return self._step(args)
+
+    def validation_step(self, args):
+        return self._step(args)
+
+    def test_step(self, args):
+        return self._step(args)
+
+    def _step(self, args):
+        args = self.ddp_plugin.on_before_forward(self.trainer.get_model(), *args)
         if self.trainer.amp_backend == AMPType.NATIVE:
             with torch.cuda.amp.autocast():
                 output = self.trainer.model(*args)
         else:
             output = self.trainer.model(*args)
         return output
 
-    def validation_step(self, args):
-        output = self.training_step(args)
-        return output
-
-    def test_step(self, args):
-        output = self.training_step(args)
-        return output
-
     def barrier(self, name: Optional[str] = None):
         if torch_distrib.is_initialized():
             torch_distrib.barrier()

pytorch_lightning/accelerators/ddp_accelerator.py

@@ -151,21 +151,23 @@ def train(self):
         return results
 
     def training_step(self, args):
+        return self._step(args)
+
+    def validation_step(self, args):
+        return self._step(args)
+
+    def test_step(self, args):
+        return self._step(args)
+
+    def _step(self, args):
+        args = self.ddp_plugin.on_before_forward(self.trainer.get_model(), *args)
         if self.trainer.amp_backend == AMPType.NATIVE:
             with torch.cuda.amp.autocast():
                 output = self.trainer.model(*args)
         else:
             output = self.trainer.model(*args)
         return output
 
-    def validation_step(self, args):
-        output = self.training_step(args)
-        return output
-
-    def test_step(self, args):
-        output = self.training_step(args)
-        return output
-
     def barrier(self, name: Optional[str] = None):
         if torch_distrib.is_initialized():
             torch_distrib.barrier()

pytorch_lightning/accelerators/ddp_cpu_spawn_accelerator.py

@@ -156,21 +156,23 @@ def ddp_train(self, process_idx, mp_queue, model):
         torch.cuda.empty_cache()
 
     def training_step(self, args):
+        return self._step(args)
+
+    def validation_step(self, args):
+        return self._step(args)
+
+    def test_step(self, args):
+        return self._step(args)
+
+    def _step(self, args):
+        args = self.ddp_plugin.on_before_forward(self.trainer.get_model(), *args)
         if self.trainer.amp_backend == AMPType.NATIVE:
             with torch.cuda.amp.autocast():
                 output = self.trainer.model(*args)
         else:
             output = self.trainer.model(*args)
         return output
 
-    def validation_step(self, args):
-        output = self.training_step(args)
-        return output
-
-    def test_step(self, args):
-        output = self.training_step(args)
-        return output
-
     def barrier(self, name: Optional[str] = None):
         if torch_distrib.is_initialized():
             torch_distrib.barrier()

pytorch_lightning/accelerators/ddp_hpc_accelerator.py

@@ -77,21 +77,23 @@ def get_device_ids(self):
         return device_ids
 
     def training_step(self, args):
+        return self._step(args)
+
+    def validation_step(self, args):
+        return self._step(args)
+
+    def test_step(self, args):
+        return self._step(args)
+
+    def _step(self, args):
+        args = self.ddp_plugin.on_before_forward(self.trainer.get_model(), *args)
         if self.trainer.amp_backend == AMPType.NATIVE:
             with torch.cuda.amp.autocast():
                 output = self.trainer.model(*args)
         else:
             output = self.trainer.model(*args)
         return output
 
-    def validation_step(self, args):
-        output = self.training_step(args)
-        return output
-
-    def test_step(self, args):
-        output = self.training_step(args)
-        return output
-
     def barrier(self, name: Optional[str] = None):
         if torch_distrib.is_initialized():
             torch_distrib.barrier()

pytorch_lightning/accelerators/ddp_spawn_accelerator.py

@@ -182,21 +182,23 @@ def get_device_ids(self):
         return device_ids
 
     def training_step(self, args):
+        return self._step(args)
+
+    def validation_step(self, args):
+        return self._step(args)
+
+    def test_step(self, args):
+        return self._step(args)
+
+    def _step(self, args):
+        args = self.ddp_plugin.on_before_forward(self.trainer.get_model(), *args)
         if self.trainer.amp_backend == AMPType.NATIVE:
             with torch.cuda.amp.autocast():
                 output = self.trainer.model(*args)
         else:
             output = self.trainer.model(*args)
         return output
 
-    def validation_step(self, args):
-        output = self.training_step(args)
-        return output
-
-    def test_step(self, args):
-        output = self.training_step(args)
-        return output
-
     def barrier(self, name: Optional[str] = None):
         if torch_distrib.is_initialized():
             torch_distrib.barrier()

pytorch_lightning/plugins/ddp_plugin.py

@@ -62,3 +62,21 @@ def configure_ddp(self, model, device_ids):
             **self._ddp_kwargs,
         )
         return model
+
+    def on_before_forward(self, model: LightningModule, *args):
+        """
+        Override to handle custom input to device logic. For DDP, no logic is required as this is handled internally
+        within the DDP wrapper.
+
+        Example::
+
+            def on_before_forward(self, model, *args):
+                batch, batch_idx = args
+                return batch.to(model.device)
+
+        Args:
+            args: Inputs to the model.
+            model: Model to train.
+        Returns: args moved to correct device if needed.
+        """
+        return args