change dimension of input for FusedBatchNormEx from 2D to 4D in test_two_matmul_batchnorm_ex.

tests/ut/python/parallel/test_batchnorm_ex_batch_parallel.py

@@ -51,13 +51,13 @@ def test_two_matmul_batchnorm_ex():
     class Net(nn.Cell):
         def __init__(self, strategy1, strategy2):
             super().__init__()
-            self.matmul1 = P.MatMul().shard(strategy1)
+            self.matmul1 = P.BatchMatMul().shard(strategy1)
             self.norm = P.FusedBatchNormEx()
             self.gamma = Parameter(Tensor(np.ones([64]), dtype=ms.float32), name="gamma")
             self.beta = Parameter(Tensor(np.ones([64]), dtype=ms.float32), name="beta")
             self.mean = Parameter(Tensor(np.ones([64]), dtype=ms.float32), name="mean")
             self.var = Parameter(Tensor(np.ones([64]), dtype=ms.float32), name="var")
-            self.matmul2 = P.MatMul().shard(strategy2)
+            self.matmul2 = P.BatchMatMul().shard(strategy2)
 
         def construct(self, x, y, b):
             out = self.matmul1(x, y)
@@ -66,12 +66,12 @@ def test_two_matmul_batchnorm_ex():
             return out
 
     context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=8)
-    strategy1 = ((4, 2), (2, 1))
-    strategy2 = ((1, 8), (8, 1))
+    strategy1 = ((1, 1, 4, 2), (1, 1, 2, 1))
+    strategy2 = ((1, 1, 1, 8), (1, 1, 8, 1))
     net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
     net.set_auto_parallel()
-    x = Tensor(np.ones([128, 32]), dtype=ms.float32)
-    y = Tensor(np.ones([32, 64]), dtype=ms.float32)
-    b = Tensor(np.ones([64, 64]), dtype=ms.float32)
+    x = Tensor(np.ones([64, 64, 128, 32]), dtype=ms.float32)
+    y = Tensor(np.ones([64, 64, 32, 64]), dtype=ms.float32)
+    b = Tensor(np.ones([64, 64, 64, 64]), dtype=ms.float32)
     net.set_train()
     _executor.compile(net, x, y, b)