Revert "Auxiliary commit to revert individual files from 7bd873b"

This reverts commit 232d2f0967853204e85d6fb02d7a5463acab2c0e.

monai/engines/trainer.py

@@ -129,8 +129,7 @@ class SupervisedTrainer(Trainer):
             `device`, `non_blocking`.
         amp_kwargs: dict of the args for `torch.cuda.amp.autocast()` API, for more details:
             https://pytorch.org/docs/stable/amp.html#torch.cuda.amp.autocast.
-        gradient_accumulation_steps: Number of steps the gradients should be accumulated across.
-            (default: 1 (means no gradient accumulation))
+
     """
 
     def __init__(
@@ -158,7 +157,6 @@ def __init__(
         optim_set_to_none: bool = False,
         to_kwargs: dict | None = None,
         amp_kwargs: dict | None = None,
-        gradient_accumulation_steps: int = 1,
     ) -> None:
         super().__init__(
             device=device,
@@ -187,12 +185,7 @@ def __init__(
         self.inferer = SimpleInferer() if inferer is None else inferer
         self.optim_set_to_none = optim_set_to_none
 
-        if gradient_accumulation_steps <= 0:
-            raise ValueError("Gradient_accumulation_steps must be strictly positive. "
-                            "No gradient accumulation if the value set to one (default).")
-        self.gradient_accumulation_steps = gradient_accumulation_steps
-
-    def _iteration(self, engine: SupervisedTrainer, batchdata: dict[str, torch.Tensor]):
+    def _iteration(self, engine: SupervisedTrainer, batchdata: dict[str, torch.Tensor]) -> dict:
         """
         Callback function for the Supervised Training processing logic of 1 iteration in Ignite Engine.
         Return below items in a dictionary:
@@ -228,23 +221,20 @@ def _compute_pred_loss():
             engine.fire_event(IterationEvents.LOSS_COMPLETED)
 
         engine.network.train()
-        if (engine.state.iteration - 1) % self.gradient_accumulation_steps == 0:
-            engine.optimizer.zero_grad(set_to_none=engine.optim_set_to_none)
+        engine.optimizer.zero_grad(set_to_none=engine.optim_set_to_none)
 
         if engine.amp and engine.scaler is not None:
             with torch.cuda.amp.autocast(**engine.amp_kwargs):
                 _compute_pred_loss()
-            engine.scaler.scale(engine.state.output[Keys.LOSS] / self.gradient_accumulation_steps).backward()
+            engine.scaler.scale(engine.state.output[Keys.LOSS]).backward()
             engine.fire_event(IterationEvents.BACKWARD_COMPLETED)
-            if engine.state.iteration % self.gradient_accumulation_steps == 0:
-                engine.scaler.step(engine.optimizer)
-                engine.scaler.update()
+            engine.scaler.step(engine.optimizer)
+            engine.scaler.update()
         else:
             _compute_pred_loss()
-            (engine.state.output[Keys.LOSS] / self.gradient_accumulation_steps).backward()
+            engine.state.output[Keys.LOSS].backward()
             engine.fire_event(IterationEvents.BACKWARD_COMPLETED)
-            if engine.state.iteration % self.gradient_accumulation_steps == 0:
-                engine.optimizer.step()
+            engine.optimizer.step()
         engine.fire_event(IterationEvents.MODEL_COMPLETED)
 
         return engine.state.output