Fix the test case bug for matmul, test=develop (#23674)

Fix the bug of matmul test_case

python/paddle/fluid/tests/unittests/test_matmul_op.py

@@ -246,26 +246,26 @@ for dim in [4]:
 class API_TestMm(unittest.TestCase):
     def test_out(self):
         with fluid.program_guard(fluid.Program()):
-            x = fluid.data(name="x", shape=[3, 2], dtype="float32")
-            y = fluid.data(name='y', shape=[2, 3], dtype='float32')
-            res = fluid.data(name="output", shape=[3, 3], dtype="float32")
+            x = fluid.data(name="x", shape=[3, 2], dtype="float64")
+            y = fluid.data(name='y', shape=[2, 3], dtype='float64')
+            res = fluid.data(name="output", shape=[3, 3], dtype="float64")
             y_1 = paddle.mm(x, y, out=res)
             exe = fluid.Executor(fluid.CPUPlace())
-            data1 = np.random.rand(3, 2).astype('float32')
-            data2 = np.random.rand(2, 3).astype('float32')
+            data1 = np.random.rand(3, 2)
+            data2 = np.random.rand(2, 3)
             np_res, np_y_1 = exe.run(feed={'x': data1,
                                            'y': data2},
                                      fetch_list=[res, y_1])
         self.assertEqual((np_res == np_y_1).all(), True)
 
         with fluid.program_guard(fluid.Program()):
-            x = fluid.data(name="x", shape=[2], dtype="float32")
-            y = fluid.data(name='y', shape=[2], dtype='float32')
-            res = fluid.data(name="output", shape=[1], dtype="float32")
+            x = fluid.data(name="x", shape=[2], dtype="float64")
+            y = fluid.data(name='y', shape=[2], dtype='float64')
+            res = fluid.data(name="output", shape=[1], dtype="float64")
             result = paddle.mm(x, y)
             exe = fluid.Executor(fluid.CPUPlace())
-            data1 = np.random.rand(2).astype('float32')
-            data2 = np.random.rand(2).astype('float32')
+            data1 = np.random.rand(2)
+            data2 = np.random.rand(2)
             np_res = exe.run(feed={'x': data1, 'y': data2}, fetch_list=[result])
             expected_result = np.matmul(
                 data1.reshape(1, 2), data2.reshape(2, 1))