diff --git a/opacus/grad_sample/grad_sample_module.py b/opacus/grad_sample/grad_sample_module.py
index 19b5ffa6..12111531 100644
--- a/opacus/grad_sample/grad_sample_module.py
+++ b/opacus/grad_sample/grad_sample_module.py
@@ -145,6 +145,20 @@ def __init__(
             force_functorch=force_functorch,
         )
 
+    def requires_grad_(self, requires_grad: bool = True) -> nn.Module:
+        "Rewrite requires_grad_ to add/remove hooks based on requires_grad value"
+        if requires_grad:
+            # Attack hook to the module
+            self.add_hooks(
+                loss_reduction=self.loss_reduction,
+                batch_first=self.batch_first,
+                force_functorch=self.force_functorch,
+            )
+        else:
+            # Remove hooks
+            self.remove_hooks()
+        return super().requires_grad_(requires_grad)
+
     def forward(self, *args, **kwargs):
         return self._module(*args, **kwargs)
 
diff --git a/opacus/grad_sample/grad_sample_module_fast_gradient_clipping.py b/opacus/grad_sample/grad_sample_module_fast_gradient_clipping.py
index 5a9adbb9..3214843f 100644
--- a/opacus/grad_sample/grad_sample_module_fast_gradient_clipping.py
+++ b/opacus/grad_sample/grad_sample_module_fast_gradient_clipping.py
@@ -117,7 +117,7 @@ def __init__(
             strict=strict,
             force_functorch=force_functorch,
         )
-        self.trainable_parameters = [p for _, p in trainable_parameters(self._module)]
+        self.all_parameters = [p for p in self.parameters()]
         self.max_grad_norm = max_grad_norm
         self.use_ghost_clipping = use_ghost_clipping
         self._per_sample_gradient_norms = None
@@ -130,7 +130,12 @@ def get_clipping_coef(self) -> torch.Tensor:
     def get_norm_sample(self) -> torch.Tensor:
         """Get per-example gradient norms."""
         norm_sample = torch.stack(
-            [param._norm_sample for param in self.trainable_parameters], dim=0
+            [
+                param._norm_sample
+                for param in self.all_parameters
+                if param.requires_grad
+            ],
+            dim=0,
         ).norm(2, dim=0)
         self.per_sample_gradient_norms = norm_sample
         return norm_sample