randn API: remove out, devive, stop_gradient; add name (#25409)

python/paddle/fluid/layers/nn.py

@@ -10416,20 +10416,28 @@ def uniform_random_batch_size_like(input,
 
 
 @templatedoc()
-def gaussian_random(shape, mean=0.0, std=1.0, seed=0, dtype='float32'):
+def gaussian_random(shape,
+                    mean=0.0,
+                    std=1.0,
+                    seed=0,
+                    dtype='float32',
+                    name=None):
     """
     Generate a random tensor whose data is drawn from a Gaussian distribution.
 
     Args:
-        shape (tuple[int] | list[int] | Variable | list[Variable]): Shape of the generated random tensor.
-        
-        mean (float): Mean of the random tensor, defaults to 0.0.
-            
-        std (float): Standard deviation of the random tensor, defaults to 1.0.
-        
-        seed (int): ${seed_comment}
-        
-        dtype(np.dtype | core.VarDesc.VarType | str): Output data type, float32 or float64.
+        shape(list|tuple|Variable): Shape of the Tensor to be created. The data
+            type is ``int32`` or ``int64`` . If ``shape`` is a list or tuple,
+            the elements of it should be integers or Tensors with shape [1]. If
+            ``shape`` is a Variable, it should be an 1-D Tensor .
+        mean(float): Mean of the random tensor, defaults to 0.0.
+        std(float): Standard deviation of the random tensor, defaults to 1.0.
+        seed(int): ${seed_comment}
+        dtype(np.dtype|core.VarDesc.VarType|str, optional): Data type of the output
+            tensor, which can be float32, float64. Default is float32.
+        name(str, optional): Normally there is no need for user to set this property.
+            For more information, please refer to :ref:`api_guide_Name` .
+            Default is None.
 
     Returns:
         Variable: Random tensor whose data is drawn from a Gaussian distribution, dtype: flaot32 or float64 as specified.
@@ -10492,11 +10500,16 @@ def gaussian_random(shape, mean=0.0, std=1.0, seed=0, dtype='float32'):
            # array([[2.3060477 , 2.676496  , 3.9911983 , 0.9990833 ],
            #        [2.8675377 , 2.2279181 , 0.79029655, 2.8447366 ]], dtype=float32)
     """
-
-    check_type(shape, 'shape', (list, tuple, Variable), 'gaussian_random')
     if not isinstance(dtype, core.VarDesc.VarType):
         dtype = convert_np_dtype_to_dtype_(dtype)
-    check_dtype(dtype, 'dtype', ['float32', 'float64'], 'gaussian_random')
+
+    if in_dygraph_mode():
+        shape = utils._convert_shape_to_list(shape)
+        return core.ops.gaussian_random('shape', shape, 'mean', mean, 'std',
+                                        std, 'seed', seed, 'dtype', dtype)
+
+    check_type(shape, 'shape', (list, tuple, Variable), 'gaussian_random/randn')
+    check_dtype(dtype, 'dtype', ['float32', 'float64'], 'gaussian_random/randn')
 
     inputs = {}
     attrs = {
@@ -10507,7 +10520,10 @@ def gaussian_random(shape, mean=0.0, std=1.0, seed=0, dtype='float32'):
         'use_mkldnn': False
     }
     utils._get_shape_tensor_inputs(
-        inputs=inputs, attrs=attrs, shape=shape, op_type='gaussian_random')
+        inputs=inputs,
+        attrs=attrs,
+        shape=shape,
+        op_type='gaussian_random/randn')
 
     helper = LayerHelper('gaussian_random', **locals())
     out = helper.create_variable_for_type_inference(dtype)
@@ -15011,13 +15027,13 @@ def uniform_random(shape, dtype='float32', min=-1.0, max=1.0, seed=0,
                                        float(min), 'max',
                                        float(max), 'seed', seed, 'dtype', dtype)
 
-    check_type(shape, 'shape', (list, tuple, Variable), 'uniform_random')
-    check_dtype(dtype, 'dtype', ('float32', 'float64'), 'uniform_random')
+    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')
+        inputs=inputs, attrs=attrs, shape=shape, op_type='uniform_random/rand')
 
     helper = LayerHelper("uniform_random", **locals())
     out = helper.create_variable_for_type_inference(dtype)

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

@@ -17,92 +17,71 @@ from __future__ import print_function
 import unittest
 import numpy as np
 import paddle
-import paddle.fluid as fluid
 import paddle.fluid.core as core
-from paddle.fluid import Program, program_guard
+from paddle import Program, program_guard
 
 
 class TestRandnOp(unittest.TestCase):
     def test_api(self):
-        x1 = paddle.randn(shape=[1000, 784], dtype='float32')
-        x2 = paddle.randn(shape=[1000, 784], dtype='float64')
-        x3 = fluid.layers.fill_constant(
-            shape=[1000, 784], dtype='float32', value=0)
-        paddle.randn(shape=[1000, 784], out=x3, dtype='float32')
-        x4 = paddle.randn(shape=[1000, 784], dtype='float32', device='cpu')
-        x5 = paddle.randn(shape=[1000, 784], dtype='float32', device='gpu')
-        x6 = paddle.randn(
-            shape=[1000, 784],
-            dtype='float32',
-            device='gpu',
-            stop_gradient=False)
-
-        place = fluid.CUDAPlace(0) if core.is_compiled_with_cuda(
-        ) else fluid.CPUPlace()
-        exe = fluid.Executor(place)
-        res = exe.run(fluid.default_main_program(),
-                      feed={},
-                      fetch_list=[x1, x2, x3, x4, x5, x6])
-
-        self.assertAlmostEqual(np.mean(res[0]), .0, delta=0.1)
-        self.assertAlmostEqual(np.std(res[0]), 1., delta=0.1)
-        self.assertAlmostEqual(np.mean(res[1]), .0, delta=0.1)
-        self.assertAlmostEqual(np.std(res[1]), 1., delta=0.1)
-        self.assertAlmostEqual(np.mean(res[2]), .0, delta=0.1)
-        self.assertAlmostEqual(np.std(res[2]), 1., delta=0.1)
-        self.assertAlmostEqual(np.mean(res[3]), .0, delta=0.1)
-        self.assertAlmostEqual(np.std(res[3]), 1., delta=0.1)
-        self.assertAlmostEqual(np.mean(res[4]), .0, delta=0.1)
-        self.assertAlmostEqual(np.std(res[4]), 1., delta=0.1)
-        self.assertAlmostEqual(np.mean(res[5]), .0, delta=0.1)
-        self.assertAlmostEqual(np.std(res[5]), 1., delta=0.1)
+        shape = [1000, 784]
+        train_program = Program()
+        startup_program = Program()
+        with program_guard(train_program, startup_program):
+            x1 = paddle.randn(shape, 'float32')
+            x2 = paddle.randn(shape, 'float64')
+
+            dim_1 = paddle.fill_constant([1], "int64", 20)
+            dim_2 = paddle.fill_constant([1], "int32", 50)
+            x3 = paddle.randn([dim_1, dim_2, 784])
+
+            var_shape = paddle.nn.data('X', [2], 'int32')
+            x4 = paddle.randn(var_shape)
+
+        place = paddle.CUDAPlace(0) if core.is_compiled_with_cuda(
+        ) else paddle.CPUPlace()
+        exe = paddle.Executor(place)
+        res = exe.run(train_program,
+                      feed={'X': np.array(
+                          shape, dtype='int32')},
+                      fetch_list=[x1, x2, x3, x4])
+
+        for out in res:
+            self.assertAlmostEqual(np.mean(out), .0, delta=0.1)
+            self.assertAlmostEqual(np.std(out), 1., delta=0.1)
+
+
+class TestRandnOpForDygraph(unittest.TestCase):
+    def test_api(self):
+        shape = [1000, 784]
+        place = paddle.CUDAPlace(0) if core.is_compiled_with_cuda(
+        ) else paddle.CPUPlace()
+        with paddle.imperative.guard(place):
+            x1 = paddle.randn(shape, 'float32')
+            x2 = paddle.randn(shape, 'float64')
+
+            dim_1 = paddle.fill_constant([1], "int64", 20)
+            dim_2 = paddle.fill_constant([1], "int32", 50)
+            x3 = paddle.randn(shape=[dim_1, dim_2, 784])
+
+            var_shape = paddle.imperative.to_variable(np.array(shape))
+            x4 = paddle.randn(var_shape)
+
+            for out in [x1, x2, x3, x4]:
+                self.assertAlmostEqual(np.mean(out.numpy()), .0, delta=0.1)
+                self.assertAlmostEqual(np.std(out.numpy()), 1., delta=0.1)
 
 
 class TestRandnOpError(unittest.TestCase):
     def test_error(self):
         with program_guard(Program(), Program()):
-
             # The argument shape's size of randn_op should not be 0.
-            def test_shape_size():
-                out = paddle.randn(shape=[])
-
-            self.assertRaises(AssertionError, test_shape_size)
+            self.assertRaises(AssertionError, paddle.randn, [])
 
             # The argument shape's type of randn_op should be list or tuple.
-            def test_shape_type():
-                out = paddle.randn(shape=1)
-
-            self.assertRaises(TypeError, test_shape_type)
-
-            # The argument dtype of randn_op should be float32 or float64.
-            def test_dtype_float16():
-                out = paddle.randn(shape=[1, 2], dtype='float16')
-
-            self.assertRaises(TypeError, test_dtype_float16)
+            self.assertRaises(TypeError, paddle.randn, 1)
 
             # The argument dtype of randn_op should be float32 or float64.
-            def test_dtype_int32():
-                out = paddle.randn(shape=[1, 2], dtype='int32')
-
-            self.assertRaises(TypeError, test_dtype_int32)
-
-            # The argument dtype of randn_op should be float32 or float64.
-            def test_dtype_int64():
-                out = paddle.randn(shape=[1, 2], dtype='int64')
-
-            self.assertRaises(TypeError, test_dtype_int64)
-
-            # The argument dtype of randn_op should be float32 or float64.
-            def test_dtype_uint8():
-                out = paddle.randn(shape=[1, 2], dtype='uint8')
-
-            self.assertRaises(TypeError, test_dtype_uint8)
-
-            # The argument dtype of randn_op should be float32 or float64.
-            def test_dtype_bool():
-                out = paddle.randn(shape=[1, 2], dtype='bool')
-
-            self.assertRaises(TypeError, test_dtype_bool)
+            self.assertRaises(TypeError, paddle.randn, [1, 2], 'int32')
 
 
 if __name__ == "__main__":

python/paddle/tensor/random.py

@@ -21,7 +21,7 @@ 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 uniform_random, utils
+from ..fluid.layers import utils, uniform_random, gaussian_random
 from ..fluid.layers.tensor import fill_constant
 
 from ..fluid.io import shuffle  #DEFINE_ALIAS
@@ -206,36 +206,23 @@ def randint(low,
     return out
 
 
-def randn(shape,
-          out=None,
-          dtype=None,
-          device=None,
-          stop_gradient=True,
-          name=None):
+def randn(shape, dtype=None, name=None):
     """
 	:alias_main: paddle.randn
 	:alias: paddle.randn,paddle.tensor.randn,paddle.tensor.random.randn
 
     This function returns a tensor filled with random numbers from a normal 
-    distribution with mean 0 and variance 1 (also called the standard normal
+    distribution with mean 0 and standard deviation 1 (also called the standard normal
     distribution).
 
     Args:
-        shape(list|tuple): Shape of the generated random tensor.
-        out(Variable, optional): Optional output which can be any created Variable 
-            that meets the requirements to store the result of operation. If the 
-            out is `None`, a new Variable will be returned to store the result.
-            Default is None.
-        dtype(np.dtype|core.VarDesc.VarType|str, optional): Data type of the output 
-            tensor, which can be float32, float64. if dtype is `None` , the data 
-            type of output tensor is `float32` .
-            Default is None.
-        device(str, optional): Specific the output variable to be saved in cpu
-            or gpu memory. Supported None, 'cpu', 'gpu'. If it is None, the output
-            variable will be automatically assigned devices. 
-            Default: None.
-        stop_gradient(bool, optional): Indicating if we stop gradient from current(out) 
-            Variable. Default is True.
+        shape(list|tuple|Variable): Shape of the Tensor to be created. The data
+            type is ``int32`` or ``int64`` . If ``shape`` is a list or tuple,
+            the elements of it should be integers or Tensors with shape [1]. If
+            ``shape`` is a Variable, it should be an 1-D Tensor .
+        dtype(np.dtype|core.VarDesc.VarType|str, optional): Data type of the output
+            tensor, which can be float32, float64. If dtype is `None` , the data
+            type of output tensor is `float32` . Default is None.
         name(str, optional): Normally there is no need for user to set this property.
             For more information, please refer to :ref:`api_guide_Name` .
             Default is None.
@@ -244,75 +231,50 @@ def randn(shape,
         Random tensor whose data is drawn from a standard normal distribution,
         dtype: flaot32 or float64 as specified.
 
-    Return type:
-        Variable
+    Return type: Variable
 
     Raises:
-        TypeError: If the type of `shape` is not list or tuple.
+        TypeError: If the type of `shape` is not Variable, list or tuple.
         TypeError: If the data type of `dtype` is not float32 or float64.
         ValueError: If the length of `shape` is not bigger than 0.
 
     Examples:
         .. code-block:: python
 
-            # declarative mode
-            import paddle
-            import paddle.fluid as fluid
+        import paddle
+        import numpy as np
 
-            data = paddle.randn([2, 4])
-            place = fluid.CPUPlace()
-            exe = fluid.Executor(place)
-            res, = exe.run(fluid.default_main_program(), feed={}, fetch_list=[data])
-            print(res)
-            # [[-1.4187592   0.7368311  -0.53748125 -0.0146909 ]
-            #  [-0.66294265 -1.3090698   0.1898754  -0.14065823]]
+        paddle.enable_imperative()
 
-        .. code-block:: python
+        # example 1: attr shape is a list which doesn't contain tensor Variable.
+        result_1 = paddle.randn(shape=[2, 3])
+        # [[-2.923464    0.11934398 -0.51249987]
+        #  [ 0.39632758  0.08177969  0.2692008 ]]
 
-            # imperative mode
-            import paddle
-            import paddle.fluid as fluid
-            import paddle.fluid.dygraph as dg
-
-            place = fluid.CPUPlace()
-            with dg.guard(place) as g:
-                x = paddle.randn([2, 4])
-                x_np = x.numpy()
-                print(x_np)
-                # [[ 1.5149173  -0.26234224 -0.592486    1.4523455 ]
-                #  [ 0.04581212 -0.85345626  1.1687907  -0.02512913]]
-    """
-    helper = LayerHelper("randn", **locals())
-    check_type(shape, 'shape', (list, tuple), 'randn')
-    assert len(shape) > 0, ("The size of argument(shape) can't be zero.")
+        # example 2: attr shape is a list which contains tensor Variable.
+        dim_1 = paddle.fill_constant([1], "int64", 2)
+        dim_2 = paddle.fill_constant([1], "int32", 3)
+        result_2 = paddle.randn(shape=[dim_1, dim_2, 2])
+        # [[[-2.8852394  -0.25898588]
+        #   [-0.47420555  0.17683524]
+        #   [-0.7989969   0.00754541]]
+        #  [[ 0.85201347  0.32320443]
+        #   [ 1.1399018   0.48336947]
+        #   [ 0.8086993   0.6868893 ]]]
+
+        # example 3: attr shape is a Variable, the data type must be int64 or int32.
+        var_shape = paddle.imperative.to_variable(np.array([2, 3]))
+        result_3 = paddle.randn(var_shape)
+        # [[-2.878077    0.17099959  0.05111201]
+        #  [-0.3761474  -1.044801    1.1870178 ]]
 
+    """
     if dtype is None:
         dtype = 'float32'
 
-    check_dtype(dtype, 'create data type', ['float32', 'float64'], 'randn')
-
-    if out is None:
-        out = helper.create_variable_for_type_inference(dtype=dtype)
-    else:
-        check_variable_and_dtype(out, 'out', [dtype], 'randn')
-
-    out.stop_gradient = stop_gradient
-
-    dtype = convert_np_dtype_to_dtype_(dtype)
-    seed = np.random.randint(0, 100)
-
-    with device_guard(device):
-        helper.append_op(
-            type='gaussian_random',
-            outputs={'Out': out},
-            attrs={
-                'shape': shape,
-                'mean': 0.0,
-                'std': 1.0,
-                'seed': seed,
-                'dtype': dtype,
-                'use_mkldnn': False
-            })
+    out = gaussian_random(
+        shape=shape, mean=0.0, std=1.0, seed=0, dtype=dtype, name=name)
+    out.stop_gradient = True
     return out
 
 
@@ -369,6 +331,7 @@ def randperm(n, dtype="int64", name=None):
     attrs = {'n': n, 'dtype': dtype, 'seed': 0}
     helper.append_op(
         type='randperm', inputs={}, outputs={'Out': out}, attrs=attrs)
+    out.stop_gradient = True
     return out
 
 
@@ -439,4 +402,7 @@ def rand(shape, dtype=None, name=None):
     """
     if dtype is None:
         dtype = 'float32'
-    return uniform_random(shape, dtype, min=0.0, max=1.0, name=name)
+
+    out = uniform_random(shape, dtype, min=0.0, max=1.0, name=name)
+    out.stop_gradient = True
+    return out