erase Raises and refine doce of random functions (#26901) (#26971)

python/paddle/fluid/data_feeder.py

@@ -132,6 +132,28 @@ def check_dtype(input_dtype,
              extra_message))
 
 
+def check_shape(shape,
+                op_name,
+                expected_shape_type=(list, tuple, Variable),
+                expected_element_type=(int, Variable),
+                expected_tensor_dtype=('int32', 'int64')):
+    # See NOTE [ Why skip dynamic graph check ]
+    if in_dygraph_mode():
+        return
+    check_type(shape, 'shape', expected_shape_type, op_name)
+    if expected_element_type is not None and not isinstance(shape, Variable):
+        for item in shape:
+            check_type(item, 'element of shape', expected_element_type, op_name)
+            if expected_tensor_dtype is not None and isinstance(item, Variable):
+                check_dtype(
+                    item.dtype, 'element of shape', expected_tensor_dtype,
+                    op_name,
+                    'If element of shape is Tensor, its data type should be {}'.
+                    format(', '.join(expected_tensor_dtype)))
+    if expected_tensor_dtype is not None and isinstance(shape, Variable):
+        check_dtype(shape.dtype, 'shape', expected_tensor_dtype, op_name)
+
+
 class DataToLoDTensorConverter(object):
     def __init__(self, place, lod_level, shape, dtype):
         self.place = place

python/paddle/fluid/layers/nn.py

@@ -10600,7 +10600,7 @@ def gaussian_random(shape,
         dtype = convert_np_dtype_to_dtype_(dtype)
 
     if in_dygraph_mode():
-        shape = utils._convert_shape_to_list(shape)
+        shape = utils.convert_shape_to_list(shape)
         return core.ops.gaussian_random('shape', shape, 'mean',
                                         float(mean), 'std',
                                         float(std), 'seed', seed, 'dtype',
@@ -10617,7 +10617,7 @@ def gaussian_random(shape,
         'dtype': dtype,
         'use_mkldnn': False
     }
-    utils._get_shape_tensor_inputs(
+    utils.get_shape_tensor_inputs(
         inputs=inputs,
         attrs=attrs,
         shape=shape,
@@ -15094,7 +15094,7 @@ def uniform_random(shape, dtype='float32', min=-1.0, max=1.0, seed=0,
         dtype = convert_np_dtype_to_dtype_(dtype)
 
     if in_dygraph_mode():
-        shape = utils._convert_shape_to_list(shape)
+        shape = utils.convert_shape_to_list(shape)
         return core.ops.uniform_random('shape', shape, 'min',
                                        float(min), 'max',
                                        float(max), 'seed', seed, 'dtype', dtype)
@@ -15104,7 +15104,7 @@ def uniform_random(shape, dtype='float32', min=-1.0, max=1.0, seed=0,
 
     inputs = dict()
     attrs = {'seed': seed, 'min': min, 'max': max, 'dtype': dtype}
-    utils._get_shape_tensor_inputs(
+    utils.get_shape_tensor_inputs(
         inputs=inputs, attrs=attrs, shape=shape, op_type='uniform_random/rand')
 
     helper = LayerHelper("uniform_random", **locals())

python/paddle/fluid/layers/tensor.py

@@ -694,7 +694,7 @@ def fill_constant(shape, dtype, value, force_cpu=False, out=None, name=None):
             attrs['str_value'] = str(float(value))
 
     if in_dygraph_mode():
-        shape = utils._convert_shape_to_list(shape)
+        shape = utils.convert_shape_to_list(shape)
         if out is None:
             out = _varbase_creator(dtype=dtype)
 
@@ -731,7 +731,7 @@ def fill_constant(shape, dtype, value, force_cpu=False, out=None, name=None):
                                  'fill_constant')
 
     helper = LayerHelper("fill_constant", **locals())
-    utils._get_shape_tensor_inputs(
+    utils.get_shape_tensor_inputs(
         inputs=inputs, attrs=attrs, shape=shape, op_type='fill_constant')
 
     if out is None:

python/paddle/fluid/layers/utils.py

@@ -282,7 +282,7 @@ def _contain_var(list_or_tuple):
     return False
 
 
-def _get_shape_tensor_inputs(inputs, attrs, shape, op_type):
+def get_shape_tensor_inputs(inputs, attrs, shape, op_type):
     from .tensor import fill_constant, cast
 
     def _get_attr_shape(list_shape):
@@ -347,7 +347,7 @@ def _convert_to_tensor_list(old_list, dtype="int32"):
     return new_list_tensor
 
 
-def _convert_shape_to_list(shape):
+def convert_shape_to_list(shape):
     """
     Convert shape(list, tuple, variable) to list in imperative mode
     """

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

@@ -241,18 +241,18 @@ class TestGaussianRandomAPI(unittest.TestCase):
 
         def test_default_fp_16():
             paddle.framework.set_default_dtype('float16')
-            paddle.tensor.random.gaussian_random([2, 3])
+            paddle.tensor.random.gaussian([2, 3])
 
         self.assertRaises(TypeError, test_default_fp_16)
 
         def test_default_fp_32():
             paddle.framework.set_default_dtype('float32')
-            out = paddle.tensor.random.gaussian_random([2, 3])
+            out = paddle.tensor.random.gaussian([2, 3])
             self.assertEqual(out.dtype, fluid.core.VarDesc.VarType.FP32)
 
         def test_default_fp_64():
             paddle.framework.set_default_dtype('float64')
-            out = paddle.tensor.random.gaussian_random([2, 3])
+            out = paddle.tensor.random.gaussian([2, 3])
             self.assertEqual(out.dtype, fluid.core.VarDesc.VarType.FP64)
 
         test_default_fp_64()

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

@@ -58,6 +58,11 @@ class TestRandintOpError(unittest.TestCase):
             self.assertRaises(TypeError, paddle.randint, 5, dtype='float32')
             self.assertRaises(ValueError, paddle.randint, 5, 5)
             self.assertRaises(ValueError, paddle.randint, -5)
+            self.assertRaises(TypeError, paddle.randint, 5, shape=['2'])
+            shape_tensor = paddle.static.data('X', [1])
+            self.assertRaises(TypeError, paddle.randint, 5, shape=shape_tensor)
+            self.assertRaises(
+                TypeError, paddle.randint, 5, shape=[shape_tensor])
 
 
 class TestRandintOp_attr_tensorlist(OpTest):