Rename uniform_random API (#26347)

* Rename uniform and gaussian APIs

* rename uniform_random API as uniform

* Fixed unittest

* fixed unittest AttributeError

* Fixed unittest

* Add uniform function rather than alias

* remove templatedoc

* solve conflict and fix doc code

* fix doc code

python/paddle/__init__.py

@@ -190,7 +190,7 @@ from .tensor.math import trace  #DEFINE_ALIAS
 from .tensor.math import kron  #DEFINE_ALIAS
 from .tensor.math import prod  #DEFINE_ALIAS
 # from .tensor.random import gaussin        #DEFINE_ALIAS
-# from .tensor.random import uniform        #DEFINE_ALIAS
+from .tensor.random import uniform  #DEFINE_ALIAS
 from .tensor.random import shuffle  #DEFINE_ALIAS
 from .tensor.random import randn  #DEFINE_ALIAS
 from .tensor.random import rand  #DEFINE_ALIAS

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

@@ -26,8 +26,8 @@ import paddle
 class TestDirectory(unittest.TestCase):
     def get_import_command(self, module):
         paths = module.split('.')
-        if len(paths) <= 1:
-            return module
+        if len(paths) == 1:
+            return 'import {}'.format(module)
         package = '.'.join(paths[:-1])
         func = paths[-1]
         cmd = 'from {} import {}'.format(package, func)

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

@@ -14,9 +14,12 @@
 
 from __future__ import print_function
 
+import sys
+import subprocess
 import unittest
 import numpy as np
 from op_test import OpTest
+import paddle
 import paddle.fluid.core as core
 from paddle.fluid.op import Operator
 import paddle.fluid as fluid
@@ -472,5 +475,61 @@ class TestUniformRandomBatchSizeLikeOpError(unittest.TestCase):
             self.assertRaises(TypeError, test_dtype)
 
 
+class TestUniformAlias(unittest.TestCase):
+    def test_alias(self):
+        paddle.uniform([2, 3], min=-5.0, max=5.0)
+        paddle.tensor.uniform([2, 3], min=-5.0, max=5.0)
+        paddle.tensor.random.uniform([2, 3], min=-5.0, max=5.0)
+
+        def test_uniform_random():
+            paddle.tensor.random.uniform_random([2, 3], min=-5.0, max=5.0)
+
+        self.assertRaises(AttributeError, test_uniform_random)
+
+
+class TestUniformOpError(unittest.TestCase):
+    def test_errors(self):
+        main_prog = Program()
+        start_prog = Program()
+        with program_guard(main_prog, start_prog):
+
+            def test_Variable():
+                x1 = fluid.create_lod_tensor(
+                    np.zeros((4, 784)), [[1, 1, 1, 1]], fluid.CPUPlace())
+                paddle.tensor.random.uniform(x1)
+
+            self.assertRaises(TypeError, test_Variable)
+
+            def test_Variable2():
+                x1 = np.zeros((4, 784))
+                paddle.tensor.random.uniform(x1)
+
+            self.assertRaises(TypeError, test_Variable2)
+
+            def test_dtype():
+                x2 = fluid.layers.data(
+                    name='x2', shape=[4, 784], dtype='float32')
+                paddle.tensor.random.uniform(x2, 'int32')
+
+            self.assertRaises(TypeError, test_dtype)
+
+            def test_out_dtype():
+                out = paddle.tensor.random.uniform(
+                    shape=[3, 4], dtype='float64')
+                self.assertEqual(out.dtype, fluid.core.VarDesc.VarType.FP64)
+
+            test_out_dtype()
+
+
+class TestUniformDygraphMode(unittest.TestCase):
+    def test_check_output(self):
+        with fluid.dygraph.guard():
+            x = paddle.tensor.random.uniform(
+                [10], dtype="float32", min=0.0, max=1.0)
+            x_np = x.numpy()
+            for i in range(10):
+                self.assertTrue((x_np[i] > 0 and x_np[i] < 1.0))
+
+
 if __name__ == "__main__":
     unittest.main()

python/paddle/tensor/__init__.py

@@ -159,7 +159,7 @@ from .math import trace  #DEFINE_ALIAS
 from .math import kron  #DEFINE_ALIAS
 from .math import prod  #DEFINE_ALIAS
 # from .random import gaussin        #DEFINE_ALIAS
-# from .random import uniform        #DEFINE_ALIAS
+from .random import uniform  #DEFINE_ALIAS
 from .random import shuffle  #DEFINE_ALIAS
 from .random import randn  #DEFINE_ALIAS
 from .random import rand  #DEFINE_ALIAS

python/paddle/tensor/random.py

@@ -21,14 +21,14 @@ from ..fluid.framework import device_guard, in_dygraph_mode, _varbase_creator, V
 from ..fluid.layers.layer_function_generator import templatedoc
 from ..fluid.layer_helper import LayerHelper
 from ..fluid.data_feeder import convert_dtype, check_variable_and_dtype, check_type, check_dtype
-from ..fluid.layers import utils, uniform_random, gaussian_random
+from ..fluid.layers import utils, gaussian_random
 from ..fluid.layers.tensor import fill_constant
 
 from ..fluid.io import shuffle  #DEFINE_ALIAS
 
 __all__ = [
     #       'gaussin',
-    #       'uniform',
+    'uniform',
     'shuffle',
     'randn',
     'rand',
@@ -37,6 +37,111 @@ __all__ = [
 ]
 
 
+def uniform(shape, dtype='float32', min=-1.0, max=1.0, seed=0, name=None):
+    """
+    This OP returns a Tensor filled with random values sampled from a uniform
+    distribution in the range [``min``, ``max``), with ``shape`` and ``dtype``.
+
+    Examples:
+    ::
+
+        Input:
+          shape = [1, 2]
+
+        Output:
+          result=[[0.8505902, 0.8397286]]
+
+    Args:
+        shape(list|tuple|Tensor): The shape of the output Tensor. If ``shape``
+            is a list or tuple, the elements of it should be integers or Tensors
+            (with the shape [1], and the data type int32 or int64). If ``shape``
+            is a Tensor, it should be a 1-D Tensor(with the data type int32 or
+            int64).
+        dtype(str|np.dtype|core.VarDesc.VarType, optional): The data type of
+            the output Tensor. Supported data types: float32, float64.
+            Default is float32.
+        min(float|int, optional): The lower bound on the range of random values
+            to generate, ``min`` is included in the range. Default is -1.0.
+        max(float|int, optional): The upper bound on the range of random values
+            to generate, ``max`` is excluded in the range. Default is 1.0.
+        seed(int, optional): Random seed used for generating samples. 0 means
+            use a seed generated by the system. Note that if seed is not 0,
+            this operator will always generate the same random numbers every
+            time. Default is 0.
+        name(str, optional): The default value is None. Normally there is no
+            need for user to set this property. For more information, please
+            refer to :ref:`api_guide_Name`.
+
+    Returns:
+        Tensor: A Tensor filled with random values sampled from a uniform
+        distribution in the range [``min``, ``max``), with ``shape`` and ``dtype``.
+
+    Raises:
+        TypeError: If ``shape`` is not list, tuple, Tensor.
+        TypeError: If ``dtype`` is not float32, float64.
+
+    Examples:
+        .. code-block:: python
+            
+            import numpy as np
+            import paddle
+
+            paddle.disable_static()
+
+            # example 1:
+            # attr shape is a list which doesn't contain Tensor.
+            result_1 = paddle.tensor.random.uniform(shape=[3, 4])
+            # [[ 0.84524226,  0.6921872,   0.56528175,  0.71690357],
+            #  [-0.34646994, -0.45116323, -0.09902662, -0.11397249],
+            #  [ 0.433519,    0.39483607, -0.8660099,   0.83664286]]
+
+            # example 2:
+            # attr shape is a list which contains Tensor.
+            dim_1 = paddle.fill_constant([1], "int64", 2)
+            dim_2 = paddle.fill_constant([1], "int32", 3)
+            result_2 = paddle.tensor.random.uniform(shape=[dim_1, dim_2])
+            # [[-0.9951253,   0.30757582, 0.9899647 ],
+            #  [ 0.5864527,   0.6607096,  -0.8886161 ]]
+
+            # example 3:
+            # attr shape is a Tensor, the data type must be int64 or int32.
+            shape = np.array([2, 3])
+            shape_tensor = paddle.to_tensor(shape)
+
+            result_3 = paddle.tensor.random.uniform(shape_tensor)
+            # if shape_tensor's value is [2, 3]
+            # result_3 is:
+            # [[-0.8517412,  -0.4006908,   0.2551912 ],
+            #  [ 0.3364414,   0.36278176, -0.16085452]]
+
+            paddle.enable_static()
+
+    """
+    if not isinstance(dtype, core.VarDesc.VarType):
+        dtype = convert_np_dtype_to_dtype_(dtype)
+
+    if in_dygraph_mode():
+        shape = utils._convert_shape_to_list(shape)
+        return core.ops.uniform_random('shape', shape, 'min',
+                                       float(min), 'max',
+                                       float(max), 'seed', seed, 'dtype', dtype)
+
+    check_type(shape, 'shape', (list, tuple, Variable), 'uniform_random/rand')
+    check_dtype(dtype, 'dtype', ('float32', 'float64'), 'uniform_random/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')
+
+    helper = LayerHelper("uniform_random", **locals())
+    out = helper.create_variable_for_type_inference(dtype)
+    helper.append_op(
+        type="uniform_random", inputs=inputs, attrs=attrs,
+        outputs={"Out": out})
+    return out
+
+
 def randint(low=0, high=None, shape=[1], dtype=None, name=None):
     """
 	:alias_main: paddle.randint
@@ -352,6 +457,6 @@ def rand(shape, dtype=None, name=None):
     if dtype is None:
         dtype = 'float32'
 
-    out = uniform_random(shape, dtype, min=0.0, max=1.0, name=name)
+    out = uniform(shape, dtype, min=0.0, max=1.0, name=name)
     out.stop_gradient = True
     return out