diff --git a/colossalai/pipeline/schedule/zero_bubble_pp.py b/colossalai/pipeline/schedule/zero_bubble_pp.py
index 9771277e2..ae35bc967 100644
--- a/colossalai/pipeline/schedule/zero_bubble_pp.py
+++ b/colossalai/pipeline/schedule/zero_bubble_pp.py
@@ -614,14 +614,24 @@ class ZeroBubbleVPipeScheduler(PipelineSchedule):
             outputs=outputs,
         )
 
-        # Step3: release_tensor_data output for bwd b & w; (do not detach output)
-        deallocate_output_obj = tree_map(clone, output_obj)
+        # Step3:
+        # 3-1:detach output; detach output for send fwd;
+        if model_chunk_id == 1 and self.stage_manager.is_first_stage(ignore_chunk=True):
+            # We should not detach bwd LOSS
+            pass
+        else:
+            # detach output
+            detached_output_obj = tree_map(detach, output_obj)
+        # 3-2 clone output
+        output_obj = tree_map(clone, output_obj)
+        # 3-3 release cloned output.data; release_tensor_data output for bwd b & w; (do not detach output)
+        output_obj = tree_map(clone, output_obj)
         if model_chunk_id == 1 and self.stage_manager.is_first_stage(ignore_chunk=True):
             # We should not release_tensor_data bwd LOSS
             pass
         else:
             # release_tensor_data output
-            tree_map(release_tensor_data, deallocate_output_obj)
+            tree_map(release_tensor_data, output_obj)
 
         # add input and output object for backward b
         self.input_tensors[model_chunk_id].append((micro_batch, input_obj))
@@ -629,33 +639,25 @@ class ZeroBubbleVPipeScheduler(PipelineSchedule):
         # for bwd b&w, we only need the graph(grad_fn) of output_obj
         # Do not release_tensor_data loss, release_tensor_data other output_obj;
         if model_chunk_id == 1 and self.stage_manager.is_first_stage(ignore_chunk=True):
-            self.output_tensors[model_chunk_id].append(deallocate_output_obj)
-            self.output_tensors_dw[model_chunk_id].append(deallocate_output_obj)
+            self.output_tensors[model_chunk_id].append(output_obj)
+            self.output_tensors_dw[model_chunk_id].append(output_obj)
         else:
-            self.output_tensors[model_chunk_id].append(deallocate_output_obj)
-            self.output_tensors_dw[model_chunk_id].append(deallocate_output_obj)
-
-        # Step4: detach output for send fwd;
-        if model_chunk_id == 1 and self.stage_manager.is_first_stage(ignore_chunk=True):
-            # We should not detach bwd LOSS
-            pass
-        else:
-            # detach output
-            output_obj = tree_map(detach, output_obj)
+            self.output_tensors[model_chunk_id].append(output_obj)
+            self.output_tensors_dw[model_chunk_id].append(output_obj)
 
         # add output to send_fwd_buffer
         if model_chunk_id == 0:  # chunk 0
             # is last stage; send to local_send_forward_buffer
             if self.stage_manager.is_last_stage(ignore_chunk=True):
-                self.local_send_forward_buffer.append(output_obj)
+                self.local_send_forward_buffer.append(detached_output_obj)
             else:
-                self.send_forward_buffer[model_chunk_id].append(output_obj)
+                self.send_forward_buffer[model_chunk_id].append(detached_output_obj)
         else:  # chunk 1
             # is first stage; end of fwd; do nothing
             if self.stage_manager.is_first_stage(ignore_chunk=True):
                 pass
             else:
-                self.send_forward_buffer[model_chunk_id].append(output_obj)
+                self.send_forward_buffer[model_chunk_id].append(detached_output_obj)
 
     def schedule_b(
         self,