diff --git a/transformer_engine/pytorch/graph.py b/transformer_engine/pytorch/graph.py
index 37fff943d6..f4b1fb23ae 100644
--- a/transformer_engine/pytorch/graph.py
+++ b/transformer_engine/pytorch/graph.py
@@ -451,11 +451,12 @@ def hook_fn(
                 if is_training:
                     inputs = tuple(i for i in static_input_surface if i.requires_grad)
                     with _none_grad_context_wrapper(inputs):
+                        outputs_requiring_grad = tuple(
+                            o for o in outputs if o is not None and o.requires_grad
+                        )
                         torch.autograd.backward(
-                            tuple(o for o in outputs if o.requires_grad),
-                            grad_tensors=tuple(
-                                torch.empty_like(o) for o in outputs if o.requires_grad
-                            ),
+                            outputs_requiring_grad,
+                            grad_tensors=tuple(torch.empty_like(o) for o in outputs_requiring_grad),
                         )
                         grad_inputs = tuple(input.grad for input in inputs)
 
@@ -616,19 +617,22 @@ def hook_fn(
                         # Note for _reuse_graph_input_output_buffers: grad output is only used
                         # within backward, so we can reuse the same static buffers every time.
                         static_grad_outputs_keys = tuple(
-                            (o.shape, o.dtype, o.layout) for o in static_outputs if o.requires_grad
+                            (o.shape, o.dtype, o.layout)
+                            for o in static_outputs
+                            if o is not None and o.requires_grad
                         )
                         if static_grad_outputs_keys in static_grad_outputs_dict:
                             static_grad_outputs = static_grad_outputs_dict[static_grad_outputs_keys]
                         else:
                             static_grad_outputs = tuple(
-                                torch.empty_like(o) if o.requires_grad else None
+                                torch.empty_like(o) if o is not None and o.requires_grad else None
                                 for o in static_outputs
                             )
                             static_grad_outputs_dict[static_grad_outputs_keys] = static_grad_outputs
                     else:
                         static_grad_outputs = tuple(
-                            torch.empty_like(o) if o.requires_grad else None for o in static_outputs
+                            torch.empty_like(o) if o is not None and o.requires_grad else None
+                            for o in static_outputs
                         )
                     if is_training:
                         inputs = tuple(i for i in static_input_surface if i.requires_grad)
@@ -636,7 +640,9 @@ def hook_fn(
                             bwd_graph, pool=mempool
                         ):
                             torch.autograd.backward(
-                                tuple(o for o in static_outputs if o.requires_grad),
+                                tuple(
+                                    o for o in static_outputs if o is not None and o.requires_grad
+                                ),
                                 grad_tensors=tuple(o for o in static_grad_outputs if o is not None),
                                 retain_graph=retain_graph_in_backward,
                             )
@@ -719,7 +725,8 @@ def hook_fn(
         ):
             # For now, assumes all static_outputs require grad
             static_grad_outputs = tuple(
-                torch.empty_like(o) if o.requires_grad else None for o in static_outputs
+                torch.empty_like(o) if o is not None and o.requires_grad else None
+                for o in static_outputs
             )
             if is_training:
                 inputs = tuple(i for i in static_input_surface if i.requires_grad)
@@ -727,7 +734,7 @@ def hook_fn(
                     bwd_graph, pool=mempool
                 ):
                     torch.autograd.backward(
-                        tuple(o for o in static_outputs if o.requires_grad),
+                        tuple(o for o in static_outputs if o is not None and o.requires_grad),
                         grad_tensors=tuple(o for o in static_grad_outputs if o is not None),
                         retain_graph=retain_graph_in_backward,
                     )
@@ -794,7 +801,7 @@ def forward(ctx, skip_fp8_weight_update, *inputs):
                 # Replay forward graph
                 fwd_graph.replay()
                 assert isinstance(static_outputs, tuple)
-                return tuple(o.detach() for o in static_outputs)
+                return tuple(o.detach() if o is not None else o for o in static_outputs)
 
             @staticmethod
             @torch.autograd.function.once_differentiable