Fixing the corner case when the optimizer has no trainable parameters (…

…pytorch#619)

Summary:
Pull Request resolved: pytorch#619

We made the following changes:
1. We has fixed the corner case when the optimizer has no trainable parameters. This might happen when there are more than one optimizers while some of them are frozen during the fine-tuning.
2. We have changed the "closure" logic in the "step" function in "ddpoptimizer.py", to make it consistent with "optimizer.py".

Differential Revision: D53055273

fbshipit-source-id: 4e8e1e6184f1c9d380da862f585bdad2d6c2bf55

opacus/optimizers/ddpoptimizer.py

@@ -70,8 +70,12 @@ def reduce_gradients(self):
     def step(
         self, closure: Optional[Callable[[], float]] = None
     ) -> Optional[torch.Tensor]:
+        if closure is not None:
+            with torch.enable_grad():
+                closure()
+
         if self.pre_step():
             self.reduce_gradients()
-            return self.original_optimizer.step(closure)
+            return self.original_optimizer.step()
         else:
             return None

opacus/optimizers/optimizer.py

@@ -491,6 +491,11 @@ def pre_step(
             closure: A closure that reevaluates the model and
                 returns the loss. Optional for most optimizers.
         """
+        # The corner case when the optimizer has no trainable parameters.
+        # Essentially the DPOptimizer act as a normal optimizer
+        if self.grad_samples is None or len(self.grad_samples) == 0:
+            return True
+
         self.clip_and_accumulate()
         if self._check_skip_next_step():
             self._is_last_step_skipped = True