Fix virtualpp with mp/recompute bugs (#47242)

python/paddle/distributed/fleet/meta_parallel/parallel_layers/pp_layers.py

@@ -598,11 +598,12 @@ class PipelineLayer(Layer):
         return run_function
 
     def forward_function(self, start, end):
+        run_function = self.run_function
 
         def execute_func(*x):
             if len(x) == 1:
                 x = x[0]
-            for idx, layer in enumerate(self.run_function[start:end]):
+            for idx, layer in enumerate(run_function[start:end]):
                 x = layer(x)
             return x
 

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

@@ -168,17 +168,18 @@ def _is_valid_send_recv_partial(tensor, mp_degree):
 
 def _partial_send_op(tensor, group, use_calc_stream, ring_id, dst, nranks,
                      rank_id):
+    dst_rank_in_group = dst if group is None else group.get_group_rank(dst)
     if _in_legacy_dygraph():
         return _legacy_C_ops.partial_send(tensor.detach(), 'use_calc_stream',
                                           use_calc_stream, 'ring_id', ring_id,
-                                          'peer', dst, 'num', nranks, 'id',
-                                          rank_id)
+                                          'peer', dst_rank_in_group, 'num',
+                                          nranks, 'id', rank_id)
     elif in_dygraph_mode():
         group = paddle.distributed.collective._get_default_group(
         ) if group is None else group
         comm_op = group.process_group.send_partial_on_calc_stream \
             if use_calc_stream else group.process_group.send_partial
-        return comm_op(tensor, dst, nranks, rank_id)
+        return comm_op(tensor, dst_rank_in_group, nranks, rank_id)
 
 
 def send_partial(tensor,
@@ -192,12 +193,13 @@ def send_partial(tensor,
         return
     ring_id = 0 if group is None else group.id
 
+    dst_rank = _hcg._get_p2p_next_rank(
+    ) if dst == 1 else _hcg._get_p2p_prev_rank()
+
     if _is_valid_send_recv_partial(tensor, nranks):
-        return _partial_send_op(tensor, group, use_calc_stream, ring_id, dst,
-                                nranks, rank_id)
+        return _partial_send_op(tensor, group, use_calc_stream, ring_id,
+                                dst_rank, nranks, rank_id)
     else:
-        dst_rank = _hcg._get_p2p_next_rank(
-        ) if dst == 1 else _hcg._get_p2p_prev_rank()
         if _in_legacy_dygraph():
             send_op = lambda x, dst, group: \
                     paddle.distributed.send(x, dst, group, use_calc_stream)
@@ -208,19 +210,21 @@ def send_partial(tensor,
 
 def _partial_recv_op(tensor, group, use_calc_stream, ring_id, src, nranks,
                      rank_id):
+    src_rank_in_group = src if group is None else group.get_group_rank(src)
     if _in_legacy_dygraph():
         assert use_calc_stream
         return _legacy_C_ops.partial_recv(tensor.detach(), 'use_calc_stream',
                                           use_calc_stream, 'ring_id', ring_id,
-                                          'peer', src, 'num', nranks, 'id',
-                                          rank_id, 'dtype', tensor.dtype,
-                                          'out_shape', tensor.shape)
+                                          'peer', src_rank_in_group, 'num',
+                                          nranks, 'id', rank_id, 'dtype',
+                                          tensor.dtype, 'out_shape',
+                                          tensor.shape)
     elif in_dygraph_mode():
         group = paddle.distributed.collective._get_default_group(
         ) if group is None else group
         comm_op = group.process_group.recv_partial_on_calc_stream \
             if use_calc_stream else group.process_group.recv_partial
-        return comm_op(tensor, src, nranks, rank_id)
+        return comm_op(tensor, src_rank_in_group, nranks, rank_id)
 
 
 def recv_partial(tensor,
@@ -234,12 +238,13 @@ def recv_partial(tensor,
         return
     ring_id = 0 if group is None else group.id
 
+    src_rank = _hcg._get_p2p_prev_rank(
+    ) if src == 0 else _hcg._get_p2p_next_rank()
+
     if _is_valid_send_recv_partial(tensor, nranks):
-        return _partial_recv_op(tensor, group, use_calc_stream, ring_id, src,
-                                nranks, rank_id)
+        return _partial_recv_op(tensor, group, use_calc_stream, ring_id,
+                                src_rank, nranks, rank_id)
     else:
-        src_rank = _hcg._get_p2p_prev_rank(
-        ) if src == 0 else _hcg._get_p2p_next_rank()
         if _in_legacy_dygraph() or use_calc_stream:
             recv_op = paddle.distributed.recv
         elif in_dygraph_mode():