dont wait for send op under dygraph pp (#46209)

python/paddle/distributed/fleet/meta_parallel/pp_utils/p2p_communication.py

@@ -329,23 +329,21 @@ def _p2p_helper(tensor_send_next, tensor_send_prev, recv_prev, recv_next):
             for d in tensor_send_prev:
                 if _in_legacy_dygraph():
                     paddle.distributed.wait(d, use_calc_stream=True)
-                tasks.append(
-                    send_partial(d,
-                                 dst=0,
-                                 nranks=mp_degree,
-                                 rank_id=mp_rank,
-                                 group=_hcg.send_prev_group,
-                                 use_calc_stream=False))
-        else:
-            if _in_legacy_dygraph():
-                paddle.distributed.wait(tensor_send_prev, use_calc_stream=True)
-            tasks.append(
-                send_partial(tensor_send_prev,
+                send_partial(d,
                              dst=0,
                              nranks=mp_degree,
                              rank_id=mp_rank,
                              group=_hcg.send_prev_group,
-                             use_calc_stream=False))
+                             use_calc_stream=False)
+        else:
+            if _in_legacy_dygraph():
+                paddle.distributed.wait(tensor_send_prev, use_calc_stream=True)
+            send_partial(tensor_send_prev,
+                         dst=0,
+                         nranks=mp_degree,
+                         rank_id=mp_rank,
+                         group=_hcg.send_prev_group,
+                         use_calc_stream=False)
 
     if tensor_recv_prev is not None:
         if isinstance(tensor_recv_prev, tuple):
@@ -371,23 +369,21 @@ def _p2p_helper(tensor_send_next, tensor_send_prev, recv_prev, recv_next):
             for d in tensor_send_next:
                 if _in_legacy_dygraph():
                     paddle.distributed.wait(d, use_calc_stream=True)
-                tasks.append(
-                    send_partial(d,
-                                 dst=1,
-                                 nranks=mp_degree,
-                                 rank_id=mp_rank,
-                                 group=_hcg.send_next_group,
-                                 use_calc_stream=False))
-        else:
-            if _in_legacy_dygraph():
-                paddle.distributed.wait(tensor_send_next, use_calc_stream=True)
-            tasks.append(
-                send_partial(tensor_send_next,
+                send_partial(d,
                              dst=1,
                              nranks=mp_degree,
                              rank_id=mp_rank,
                              group=_hcg.send_next_group,
-                             use_calc_stream=False))
+                             use_calc_stream=False)
+        else:
+            if _in_legacy_dygraph():
+                paddle.distributed.wait(tensor_send_next, use_calc_stream=True)
+            send_partial(tensor_send_next,
+                         dst=1,
+                         nranks=mp_degree,
+                         rank_id=mp_rank,
+                         group=_hcg.send_next_group,
+                         use_calc_stream=False)
 
     if tensor_recv_next is not None:
         if isinstance(tensor_recv_next, tuple):
@@ -438,10 +434,11 @@ def _p2p_helper(tensor_send_next, tensor_send_prev, recv_prev, recv_next):
                               group=mp_group,
                               use_calc_stream=True))
 
-    for task in tasks:
-        # wait partial all gather tasks
-        if task is not None:
-            task.wait()
+    if in_dygraph_mode():
+        for task in tasks:
+            # wait partial all gather tasks
+            if task is not None:
+                task.wait()
 
     return tensor_recv_prev, tensor_recv_next