Fix hang of hybrid parallel in new_group

python/paddle/distributed/collective.py

@@ -239,31 +239,37 @@ def new_group(ranks=None, backend=None):
     if global_rank not in ranks:
         gp = Group(-1, -1, ring_id, ranks)
         _group_map[ring_id] = gp
-        return gp
-
-    ranks = sorted(ranks)
-    group_rank = ranks.index(global_rank)
-    group_size = len(ranks)
-    gp = Group(group_rank, group_size, ring_id, ranks)
-    _group_map[ring_id] = gp
-
-    if group_size < 2:
-        return gp
-
-    strategy = core.ParallelStrategy()
-    strategy.nranks = group_size
-    strategy.local_rank = group_rank
-    strategy.trainer_endpoints = [genv.trainer_endpoints[i] for i in ranks]
-    strategy.current_endpoint = genv.current_endpoint
-    strategy.nrings = 1
-
-    if core.is_compiled_with_cuda():
-        place = core.CUDAPlace(genv.device_id)
-        core.NCCLParallelContext(strategy, place).init_with_ring_id(ring_id)
     else:
-        assert False, ("no cuda device found")
-    # need to barrier to construct group
-    barrier(gp)
+        ranks = sorted(ranks)
+        group_rank = ranks.index(global_rank)
+        group_size = len(ranks)
+        gp = Group(group_rank, group_size, ring_id, ranks)
+        _group_map[ring_id] = gp
+
+        if group_size >= 2:
+            strategy = core.ParallelStrategy()
+            strategy.nranks = group_size
+            strategy.local_rank = group_rank
+            strategy.trainer_endpoints = [
+                genv.trainer_endpoints[i] for i in ranks
+            ]
+            strategy.current_endpoint = genv.current_endpoint
+            strategy.nrings = 1
+
+            if core.is_compiled_with_cuda():
+                place = core.CUDAPlace(genv.device_id)
+                core.NCCLParallelContext(strategy,
+                                         place).init_with_ring_id(ring_id)
+            else:
+                assert False, ("no cuda device found")
+        else:
+            return gp
+
+    # TODO(shenliang03): This is a temporary solution to solve the problem of 
+    # hang caused by cross-creation of new_group
+    tmp = fill_constant([0], dtype="int32", value="1")
+    paddle.distributed.all_reduce(tmp, use_calc_stream=True)
+    paddle.distributed.wait(tmp)
     return gp