ref: decouple apex second attemp part 10/n

pytorch_lightning/accelerators/base_accelerator.py

@@ -71,9 +71,11 @@ def validation_step_end(self, output):
     def process_dataloader(self, dataloader):
         return dataloader
 
-    def backward(self, closure_loss, optimizer, *args, **kwargs):
+    def backward(self, closure_loss, optimizer, opt_idx, *args, **kwargs):
         if self.trainer.precision == 16:
-            closure_loss = self.trainer.precision_connector.backend.backward(closure_loss, optimizer, *args, **kwargs)
+            closure_loss = self.trainer.precision_connector.backend.backward(
+                closure_loss, optimizer, opt_idx, *args, **kwargs
+            )
         else:
             # do backward pass
             closure_loss.backward(*args, **kwargs)

pytorch_lightning/accelerators/horovod_backend.py

@@ -150,8 +150,8 @@ def test_step(self, args):
             output = self.trainer.model.test_step(*args)
         return output
 
-    def backward(self, closure_loss, optimizer, *args, **kwargs):
-        super().backward(closure_loss, optimizer, *args, **kwargs)
+    def backward(self, closure_loss, optimizer, opt_idx, *args, **kwargs):
+        super().backward(closure_loss, optimizer, opt_idx,  *args, **kwargs)
         optimizer.synchronize()
 
     def on_train_epoch_end(self, outputs):

pytorch_lightning/accelerators/tpu_backend.py

@@ -222,7 +222,7 @@ def __setup_tpu_training(self, model: LightningModule, trainer):
                  f' global rank: {trainer.tpu_global_core_rank}'
                  f' with XLA_USE_BF16={os.environ.get("XLA_USE_BF16")}')
 
-    def backward(self, closure_loss, optimizer, *args, **kwargs):
+    def backward(self, closure_loss, optimizer, opt_idx, *args, **kwargs):
         # do backward pass
         closure_loss.backward(*args, **kwargs)
 

pytorch_lightning/core/lightning.py

@@ -1053,7 +1053,7 @@ def training_step(...):
                 # automatically applies scaling, etc...
                 self.manual_backward(loss, opt_a)
         """
-        self.trainer.train_loop.backward(loss, optimizer, *args, **kwargs)
+        self.trainer.train_loop.backward(loss, optimizer, -1, *args, **kwargs)
 
     def backward(self, loss: Tensor, optimizer: Optimizer, optimizer_idx: int) -> None:
         """

pytorch_lightning/plugins/apex.py

@@ -36,7 +36,7 @@ def training_step(self, fx, args):
         output = fx(args)
         return output
 
-    def backward(self, closure_loss, optimizer, *args, **kwargs):
+    def backward(self, closure_loss, optimizer, opt_idx, *args, **kwargs):
         closure_loss = amp.scale_loss(closure_loss, optimizer)
 
         # enter apex context

pytorch_lightning/plugins/native_amp.py

@@ -23,7 +23,7 @@ def __init__(self, trainer):
     def connect(self, model, optimizers):
         return model, optimizers
 
-    def backward(self, closure_loss, optimizer, *args, **kwargs):
+    def backward(self, closure_loss, optimizer, opt_idx, *args, **kwargs):
         closure_loss = self.trainer.scaler.scale(closure_loss)
 
         # do backward pass

pytorch_lightning/trainer/training_loop.py

@@ -750,23 +750,24 @@ def training_step_and_backward(self, split_batch, batch_idx, opt_idx, optimizer,
 
         # backward pass
         with self.trainer.profiler.profile('model_backward'):
-            self.backward(result, optimizer)
+            self.backward(result, optimizer, opt_idx)
 
         # hook
         self.on_after_backward(result.training_step_output, batch_idx, result.loss)
 
         return result
 
-    def backward(self, result, optimizer, *args, **kwargs):
+    def backward(self, result, optimizer, opt_idx, *args, **kwargs):
         self.trainer.dev_debugger.track_event('backward_call')
 
-        # backward can be called manually in the training loop.
+        # backward can be called manually in the training loop
         if isinstance(result, torch.Tensor):
-            self.trainer.accelerator_backend.backward(result, optimizer, *args, **kwargs)
+            self.trainer.accelerator_backend.backward(result, optimizer, opt_idx, *args, **kwargs)
         else:
             result.closure_loss = self.trainer.accelerator_backend.backward(
                 result.closure_loss,
                 optimizer,
+                opt_idx,
                 *args,
                 **kwargs
             )