change uniform_random to uniform and optimize function names in unittest for random ops (#26951)

* fix unittest format and extract common function

* change function name

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

@@ -239,24 +239,24 @@ class TestGaussianRandomAPI(unittest.TestCase):
     def test_default_dtype(self):
         paddle.disable_static()
 
-        def test_default_fp_16():
+        def test_default_fp16():
             paddle.framework.set_default_dtype('float16')
             paddle.tensor.random.gaussian([2, 3])
 
-        self.assertRaises(TypeError, test_default_fp_16)
+        self.assertRaises(TypeError, test_default_fp16)
 
-        def test_default_fp_32():
+        def test_default_fp32():
             paddle.framework.set_default_dtype('float32')
             out = paddle.tensor.random.gaussian([2, 3])
             self.assertEqual(out.dtype, fluid.core.VarDesc.VarType.FP32)
 
-        def test_default_fp_64():
+        def test_default_fp64():
             paddle.framework.set_default_dtype('float64')
             out = paddle.tensor.random.gaussian([2, 3])
             self.assertEqual(out.dtype, fluid.core.VarDesc.VarType.FP64)
 
-        test_default_fp_64()
+        test_default_fp64()
-        test_default_fp_32()
+        test_default_fp32()
 
         paddle.enable_static()
 
@@ -265,24 +265,24 @@ class TestStandardNormalDtype(unittest.TestCase):
     def test_default_dtype(self):
         paddle.disable_static()
 
-        def test_default_fp_16():
+        def test_default_fp16():
             paddle.framework.set_default_dtype('float16')
             paddle.tensor.random.standard_normal([2, 3])
 
-        self.assertRaises(TypeError, test_default_fp_16)
+        self.assertRaises(TypeError, test_default_fp16)
 
-        def test_default_fp_32():
+        def test_default_fp32():
             paddle.framework.set_default_dtype('float32')
             out = paddle.tensor.random.standard_normal([2, 3])
             self.assertEqual(out.dtype, fluid.core.VarDesc.VarType.FP32)
 
-        def test_default_fp_64():
+        def test_default_fp64():
             paddle.framework.set_default_dtype('float64')
             out = paddle.tensor.random.standard_normal([2, 3])
             self.assertEqual(out.dtype, fluid.core.VarDesc.VarType.FP64)
 
-        test_default_fp_64()
+        test_default_fp64()
-        test_default_fp_32()
+        test_default_fp32()
 
         paddle.enable_static()
 

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

@@ -120,24 +120,24 @@ class TestRandDtype(unittest.TestCase):
     def test_default_dtype(self):
         paddle.disable_static()
 
-        def test_default_fp_16():
+        def test_default_fp16():
             paddle.framework.set_default_dtype('float16')
             paddle.tensor.random.rand([2, 3])
 
-        self.assertRaises(TypeError, test_default_fp_16)
+        self.assertRaises(TypeError, test_default_fp16)
 
-        def test_default_fp_32():
+        def test_default_fp32():
             paddle.framework.set_default_dtype('float32')
             out = paddle.tensor.random.rand([2, 3])
             self.assertEqual(out.dtype, fluid.core.VarDesc.VarType.FP32)
 
-        def test_default_fp_64():
+        def test_default_fp64():
             paddle.framework.set_default_dtype('float64')
             out = paddle.tensor.random.rand([2, 3])
             self.assertEqual(out.dtype, fluid.core.VarDesc.VarType.FP64)
 
-        test_default_fp_64()
+        test_default_fp64()
-        test_default_fp_32()
+        test_default_fp32()
 
         paddle.enable_static()
 

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

@@ -540,24 +540,24 @@ class TestUniformDtype(unittest.TestCase):
     def test_default_dtype(self):
         paddle.disable_static()
 
-        def test_default_fp_16():
+        def test_default_fp16():
             paddle.framework.set_default_dtype('float16')
             paddle.tensor.random.uniform([2, 3])
 
-        self.assertRaises(TypeError, test_default_fp_16)
+        self.assertRaises(TypeError, test_default_fp16)
 
-        def test_default_fp_32():
+        def test_default_fp32():
             paddle.framework.set_default_dtype('float32')
             out = paddle.tensor.random.uniform([2, 3])
             self.assertEqual(out.dtype, fluid.core.VarDesc.VarType.FP32)
 
-        def test_default_fp_64():
+        def test_default_fp64():
             paddle.framework.set_default_dtype('float64')
             out = paddle.tensor.random.uniform([2, 3])
             self.assertEqual(out.dtype, fluid.core.VarDesc.VarType.FP64)
 
-        test_default_fp_64()
+        test_default_fp64()
-        test_default_fp_32()
+        test_default_fp32()
 
         paddle.enable_static()
 

python/paddle/tensor/random.py

@@ -393,8 +393,8 @@ def uniform(shape, dtype=None, min=-1.0, max=1.0, seed=0, name=None):
         dtype = paddle.framework.get_default_dtype()
         if dtype not in ['float32', 'float64']:
             raise TypeError(
-                "uniform only supports [float32, float64], but the default dtype is %s"
+                "uniform/rand only supports [float32, float64], but the default dtype is {}".
-                % dtype)
+                format(dtype))
 
     if not isinstance(dtype, core.VarDesc.VarType):
         dtype = convert_np_dtype_to_dtype_(dtype)
@@ -405,15 +405,15 @@ def uniform(shape, dtype=None, min=-1.0, max=1.0, seed=0, name=None):
                                        float(min), 'max',
                                        float(max), 'seed', seed, 'dtype', dtype)
 
-    check_type(shape, 'shape', (list, tuple, Variable), 'uniform_random/rand')
+    check_type(shape, 'shape', (list, tuple, Variable), 'uniform/rand')
-    check_dtype(dtype, 'dtype', ('float32', 'float64'), 'uniform_random/rand')
+    check_dtype(dtype, 'dtype', ('float32', 'float64'), 'uniform/rand')
 
     inputs = dict()
     attrs = {'seed': seed, 'min': min, 'max': max, 'dtype': dtype}
     utils.get_shape_tensor_inputs(
-        inputs=inputs, attrs=attrs, shape=shape, op_type='uniform_random/rand')
+        inputs=inputs, attrs=attrs, shape=shape, op_type='uniform/rand')
 
-    helper = LayerHelper("uniform_random", **locals())
+    helper = LayerHelper("uniform", **locals())
     out = helper.create_variable_for_type_inference(dtype)
     helper.append_op(
         type="uniform_random", inputs=inputs, attrs=attrs,