diff --git a/python/paddle/distributed/fleet/meta_parallel/pp_utils/p2p_communication.py b/python/paddle/distributed/fleet/meta_parallel/pp_utils/p2p_communication.py
index 17c7f5a9bbc4af1656fc4005721cd22b490ed3c1..6f917d9f89d6a8e26fa49f802dd68749704ced9d 100644
--- a/python/paddle/distributed/fleet/meta_parallel/pp_utils/p2p_communication.py
+++ b/python/paddle/distributed/fleet/meta_parallel/pp_utils/p2p_communication.py
@@ -54,25 +54,29 @@ class SendRecvMeta:
     def _recv_shape_dtype(self, group):
         # recv len(shape)
         dims = paddle.to_tensor([0])
-        paddle.distributed.recv(dims, src=0, group=group)
+        src_rank = group.ranks[0]
+
+        paddle.distributed.recv(dims, src=src_rank, group=group)
         dims = dims.item()
 
         # recv shape
         shape = paddle.to_tensor([0] * dims)
-        paddle.distributed.recv(shape, src=0, group=group)
+        paddle.distributed.recv(shape, src=src_rank, group=group)
 
         # recv dtype
         dtype = paddle.to_tensor([0])
-        paddle.distributed.recv(dtype, src=0, group=group)
+        paddle.distributed.recv(dtype, src=src_rank, group=group)
 
         # recv stop_gradient
         stop_grad = paddle.to_tensor([0])
-        paddle.distributed.recv(stop_grad, src=0, group=group)
+        paddle.distributed.recv(stop_grad, src=src_rank, group=group)
         return shape.numpy().tolist(), dtype.item(), stop_grad.item()
 
     def recv_meta(self, group):
         tensor_type = paddle.to_tensor([0])
-        paddle.distributed.recv(tensor_type, src=0, group=group)
+        src_rank = group.ranks[0]
+
+        paddle.distributed.recv(tensor_type, src=src_rank, group=group)
         tensor_type = tensor_type.item()
 
         if tensor_type == 0:
@@ -83,7 +87,7 @@ class SendRecvMeta:
 
         elif tensor_type == 1:
             num = paddle.to_tensor([0])
-            paddle.distributed.recv(num, src=0, group=group)
+            paddle.distributed.recv(num, src=src_rank, group=group)
             num = num.item()
             shapes = []
             dtypes = []
@@ -101,34 +105,38 @@ class SendRecvMeta:
     def _send_dims_shape_dtype(self, tensor, group):
         # send len(shape)
         dims = paddle.to_tensor(len(tensor.shape))
-        paddle.distributed.send(dims, dst=1, group=group)
+        dst_rank = group.ranks[1]
+
+        paddle.distributed.send(dims, dst=dst_rank, group=group)
 
         # send shape
         shape = paddle.to_tensor(tensor.shape)
-        paddle.distributed.send(shape, dst=1, group=group)
+        paddle.distributed.send(shape, dst=dst_rank, group=group)
 
         # send dtype
         dtype = paddle.to_tensor(paddle_2_number(tensor.dtype))
-        paddle.distributed.send(dtype, dst=1, group=group)
+        paddle.distributed.send(dtype, dst=dst_rank, group=group)
 
         # send trainable
         stop_grad = paddle.to_tensor(int(tensor.stop_gradient))
-        paddle.distributed.send(stop_grad, dst=1, group=group)
+        paddle.distributed.send(stop_grad, dst=dst_rank, group=group)
 
     def send_meta(self, tensor, group):
+        dst_rank = group.ranks[1]
+
         if isinstance(tensor, (paddle.Tensor, core.eager.Tensor)):
             tensor_type = paddle.to_tensor([0])
             # send tensor type
-            paddle.distributed.send(tensor_type, dst=1, group=group)
+            paddle.distributed.send(tensor_type, dst=dst_rank, group=group)
 
             self._send_dims_shape_dtype(tensor, group)
         elif isinstance(tensor, tuple):
             tensor_type = paddle.to_tensor([1])
             # send tensor type
-            paddle.distributed.send(tensor_type, dst=1, group=group)
+            paddle.distributed.send(tensor_type, dst=dst_rank, group=group)
 
             nums = paddle.to_tensor(len(tensor))
-            paddle.distributed.send(nums, dst=1, group=group)
+            paddle.distributed.send(nums, dst=dst_rank, group=group)
 
             for d in tensor:
                 assert isinstance(d, (paddle.Tensor, core.eager.Tensor))
@@ -166,6 +174,7 @@ def send_partial(tensor,
                  rank_id=0,
                  group=None,
                  use_calc_stream=True):
+    # dst: local rank in group
     if group is not None and not group.is_member():
         return
     ring_id = 0 if group is None else group.id
@@ -176,7 +185,7 @@ def send_partial(tensor,
                                    dst, 'num', nranks, 'id', rank_id)
     else:
         return paddle.distributed.send(tensor.detach(),
-                                       dst=dst,
+                                       dst=group.ranks[dst],
                                        group=group,
                                        use_calc_stream=use_calc_stream)
 
@@ -187,6 +196,7 @@ def recv_partial(tensor,
                  rank_id=0,
                  group=None,
                  use_calc_stream=True):
+    # src: local rank in group
     if group is not None and not group.is_member():
         return
     ring_id = 0 if group is None else group.id
@@ -198,7 +208,7 @@ def recv_partial(tensor,
                             tensor.shape)
     else:
         paddle.distributed.recv(tensor.detach(),
-                                src=src,
+                                src=group.ranks[src],
                                 group=group,
                                 use_calc_stream=use_calc_stream)