Fix VirtualDiv Int32 error

mindspore/ops/_grad/grad_comm_ops.py

@@ -225,7 +225,8 @@ def get_bprop_virtual_div_operator(self):
 
     def bprop(x, out, dout):
         if F.issubclass_(F.typeof(dout), mstype.tensor):
-            if F.issubclass_(F.dtype(dout), mstype.bool_):
+            if F.issubclass_(F.dtype(dout), mstype.bool_) or F.issubclass_(F.dtype(dout), mstype.int32) \
+                                     or F.issubclass_(F.dtype(dout), mstype.int16):
                 return (dout,)
             dx = op(dout, cast(F.scalar_to_array(divisor), dtype(dout)))
             return (dx,)

tests/ut/python/parallel/test_unsortedsegmentsum.py

@@ -21,7 +21,6 @@ from mindspore import context
 from mindspore.common.api import _executor
 from mindspore.ops import composite as C
 from mindspore.ops import operations as P
-from mindspore.ops.operations.comm_ops import _VirtualDataset
 from tests.ut.python.ops.test_math_ops import VirtualLoss
 
 context.set_context(mode=context.GRAPH_MODE)
@@ -33,7 +32,6 @@ grad_all = C.GradOperation(get_all=True)
 class Net(nn.Cell):
     def __init__(self, strategy1, strategy2, num_segments):
         super(Net, self).__init__()
-        self.virtual_dataset = _VirtualDataset()
         self.merge_op = P.UnsortedSegmentSum().shard((strategy1, strategy2))
         self.num_segments = num_segments
 
@@ -54,8 +52,8 @@ class GradWrap(nn.Cell):
 class NetWithLoss(nn.Cell):
     def __init__(self, network):
         super(NetWithLoss, self).__init__()
-        self.loss = VirtualLoss()
         self.network = network
+        self.loss = VirtualLoss()
 
     def construct(self, x, y):
         predict = self.network(x, y)
@@ -63,13 +61,13 @@ class NetWithLoss(nn.Cell):
 
 
 def compile_graph(x, y, segments, strategy1, strategy2, auto=False):
-    net = GradWrap(NetWithLoss(Net(strategy1, strategy2, segments)))
-    net.set_auto_parallel()
-    net.set_train()
     if auto:
         context.set_auto_parallel_context(parallel_mode="auto_parallel")
     else:
         context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
+    net = GradWrap(NetWithLoss(Net(strategy1, strategy2, segments)))
+    net.set_auto_parallel()
+    net.set_train()
     _executor.compile(net, x, y)
 
 
@@ -151,3 +149,13 @@ def test_unsortedsegmentsum_model_parallel_index_vector_slice_3d():
     strategy1 = (2, 1, 2)
     strategy2 = (2, 1)
     compile_graph(x, y, num_segments, strategy1, strategy2)
+
+
+def test_unsortedsegmentsum_model_parallel_repeat_caculate():
+    context.set_auto_parallel_context(device_num=4, global_rank=0)
+    x = Tensor(np.ones((4, 4, 8)), ms.float32)
+    y = Tensor(np.ones((4, 4)), ms.int32)
+    num_segments = 16
+    strategy1 = (1, 1, 1)
+    strategy2 = (1, 1)
+    compile_graph(x, y, num_segments, strategy1, strategy2)