Add new API : randn (#23211)

* Add new API : randn

test=develop

* Add new API : randn

test=develop

* Add new API : randn

test=develop

* Add new API : randn

test=develop

* aAdd new API : randn

test=develop

* Add new API : randn

test=develop

* Add new API : randn

test=develop

* Add new API : randn

test=develop

* Add new API : randn

test=develop

* Add new API : randn

test=develop

* Add new API : randn

test=develop

* Add new API : randn

test=develop

python/paddle/__init__.py

@@ -87,7 +87,7 @@ from .tensor.logic import elementwise_equal  #DEFINE_ALIAS
 # from .tensor.random import gaussin   #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 randn  #DEFINE_ALIAS
 from .tensor.random import randperm
 # from .tensor.random import rand   #DEFINE_ALIAS
 from .tensor.random import randint  #DEFINE_ALIAS

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

@@ -0,0 +1,109 @@
+#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+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
+
+
+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)
+
+
+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)
+
+            # 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)
+
+            # 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)
+
+
+if __name__ == "__main__":
+    unittest.main()

python/paddle/tensor/__init__.py

@@ -62,7 +62,7 @@ from .logic import elementwise_equal  #DEFINE_ALIAS
 # from .random import gaussin   #DEFINE_ALIAS
 # from .random import uniform   #DEFINE_ALIAS
 # from .random import shuffle   #DEFINE_ALIAS
-# from .random import randn   #DEFINE_ALIAS
+from .random import randn  #DEFINE_ALIAS
 # from .random import rand   #DEFINE_ALIAS
 from .random import randint  #DEFINE_ALIAS
 from .random import randperm

python/paddle/tensor/random.py

@@ -21,8 +21,10 @@
 #            'rand',
 #            'randint']
 
+import numpy as np
+
 from ..fluid import core
-from ..fluid.framework import device_guard, in_dygraph_mode, _varbase_creator, Variable
+from ..fluid.framework import device_guard, in_dygraph_mode, _varbase_creator, Variable, convert_np_dtype_to_dtype_
 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
@@ -191,6 +193,113 @@ def randint(low,
     return out
 
 
+def randn(shape,
+          out=None,
+          dtype=None,
+          device=None,
+          stop_gradient=True,
+          name=None):
+    """
+    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).
+
+    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 wiil 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.
+        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:
+        Random tensor whose data is drawn from a Gaussian distribution, 
+        dtype: flaot32 or float64 as specified.
+
+    Return type:
+        Variable
+
+    Raises:
+        TypeError: If the type of `shape` is not 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
+
+            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]]
+
+        .. code-block:: python
+
+            # 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.")
+
+    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
+            })
+    return out
+
+
 @templatedoc()
 def randperm(n,
              out=None,

python/setup.py.in

@@ -113,6 +113,7 @@ packages=['paddle',
           'paddle.fluid',
           'paddle.tensor',
           'paddle.fluid.dygraph',
+          'paddle.tensor',
           'paddle.fluid.dygraph.dygraph_to_static',
           'paddle.fluid.proto',
           'paddle.fluid.proto.profiler',