Separate function for preparing criterion in PrivacyEngine (#703)

Summary:
Pull Request resolved: #703

Having a separate function for preparing the criterion makes it easy to build custom extensions of PrivacyEnginge for methods that require a different DPLoss class, e.g., adaptive clipping.

Reviewed By: EnayatUllah

Differential Revision: D67458234

fbshipit-source-id: 9fca64fcde7714708ac1cb9a35a991099606f449

opacus/privacy_engine.py

@@ -212,6 +212,26 @@ def _prepare_model(
                     loss_reduction=loss_reduction,
                 )
 
+    def _prepare_criterion(
+        self,
+        *,
+        module: GradSampleModule,
+        optimizer: DPOptimizer,
+        criterion=nn.CrossEntropyLoss(),
+        loss_reduction: str = "mean",
+        **kwargs,
+    ) -> DPLossFastGradientClipping:
+        """
+        Args:
+            module: GradSampleModule used for training,
+            optimizer: DPOptimizer used for training,
+            criterion: Loss function used for training,
+            loss_reduction: "mean" or "sum", indicates if the loss reduction (for aggregating the gradients)
+
+        Prepare the DP loss class, which packages the two backward passes for fast gradient clipping.
+        """
+        return DPLossFastGradientClipping(module, optimizer, criterion, loss_reduction)
+
     def is_compatible(
         self,
         *,
@@ -403,9 +423,14 @@ def make_private(
             self.accountant.get_optimizer_hook_fn(sample_rate=sample_rate)
         )
         if grad_sample_mode == "ghost":
-            criterion = DPLossFastGradientClipping(
-                module, optimizer, criterion, loss_reduction
+            criterion = self._prepare_criterion(
+                module=module,
+                optimizer=optimizer,
+                criterion=criterion,
+                loss_reduction=loss_reduction,
+                **kwargs,
             )
+
             return module, optimizer, criterion, data_loader
 
         return module, optimizer, data_loader