var, std: input->x, adjust attr order, remove out, add docs (#26446)

python/paddle/fluid/layers/nn.py

@@ -12301,13 +12301,10 @@ def clip_by_norm(x, max_norm, name=None):
     return out
 
 
+@deprecated(since="2.0.0", update_to="paddle.mean")
 @templatedoc()
 def mean(x, name=None):
     """
-    :alias_main: paddle.mean
-	:alias: paddle.mean,paddle.tensor.mean,paddle.tensor.stat.mean
-	:old_api: paddle.fluid.layers.mean
-
     ${comment}
 
     Args:

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

@@ -15,65 +15,104 @@
 import unittest
 import numpy as np
 import paddle
-import paddle.fluid as fluid
 
 
-class TestStdLayer(unittest.TestCase):
+def ref_std(x, axis=None, unbiased=True, keepdim=False):
+    ddof = 1 if unbiased else 0
+    if isinstance(axis, int):
+        axis = (axis, )
+    if axis is not None:
+        axis = tuple(axis)
+    return np.std(x, axis=axis, ddof=ddof, keepdims=keepdim)
+
+
+class TestStdAPI(unittest.TestCase):
     def setUp(self):
-        self._dtype = "float64"
-        self._input = np.random.random([2, 3, 4, 5]).astype(self._dtype)
-
-    def static(self, axis=None, keepdim=False, unbiased=True):
-        prog = fluid.Program()
-        with fluid.program_guard(prog):
-            data = fluid.data(
-                name="data", dtype=self._dtype, shape=[None, 3, 4, 5])
-            out = prog.current_block().create_var(
-                dtype=self._dtype, shape=[2, 3, 4, 5])
-            paddle.std(input=data,
-                       axis=axis,
-                       keepdim=keepdim,
-                       unbiased=unbiased,
-                       out=out)
-
-        exe = fluid.Executor(self._place)
-        return exe.run(feed={"data": self._input},
-                       program=prog,
-                       fetch_list=[out])[0]
-
-    def dynamic(self, axis=None, keepdim=False, unbiased=True):
-        with fluid.dygraph.guard(self._place):
-            data = fluid.dygraph.to_variable(self._input)
-            out = paddle.std(input=data,
-                             axis=axis,
-                             keepdim=keepdim,
-                             unbiased=unbiased)
-            return out.numpy()
-
-    def numpy(self, axis=None, keepdim=False, unbiased=True):
-        ddof = 1 if unbiased else 0
-        axis = tuple(axis) if isinstance(axis, list) else axis
-        return np.std(self._input, axis=axis, keepdims=keepdim, ddof=ddof)
-
-    def test_equal(self):
-        places = []
-        if fluid.core.is_compiled_with_cuda():
-            places.append(fluid.CUDAPlace(0))
-        for place in places:
-            self._place = place
-            self.assertTrue(np.allclose(self.numpy(), self.static()))
-            self.assertTrue(
-                np.allclose(
-                    self.numpy(axis=[0, 2]), self.dynamic(axis=[0, 2])))
-            self.assertTrue(
-                np.allclose(
-                    self.numpy(
-                        axis=[1, 3], keepdim=True),
-                    self.dynamic(
-                        axis=[1, 3], keepdim=True)))
-            self.assertTrue(
-                np.allclose(
-                    self.numpy(unbiased=False), self.dynamic(unbiased=False)))
+        self.dtype = 'float64'
+        self.shape = [1, 3, 4, 10]
+        self.axis = [1, 3]
+        self.keepdim = False
+        self.unbiased = True
+        self.set_attrs()
+        self.x = np.random.uniform(-1, 1, self.shape).astype(self.dtype)
+        self.place=paddle.CUDAPlace(0) \
+            if paddle.fluid.core.is_compiled_with_cuda() \
+            else paddle.CPUPlace()
+
+    def set_attrs(self):
+        pass
+
+    def static(self):
+        with paddle.static.program_guard(paddle.static.Program()):
+            x = paddle.data('X', self.shape, self.dtype)
+            out = paddle.std(x, self.axis, self.unbiased, self.keepdim)
+            exe = paddle.static.Executor(self.place)
+            res = exe.run(feed={'X': self.x}, fetch_list=[out])
+        return res[0]
+
+    def dygraph(self):
+        paddle.disable_static()
+        x = paddle.to_tensor(self.x)
+        out = paddle.std(x, self.axis, self.unbiased, self.keepdim)
+        paddle.enable_static()
+        return out.numpy()
+
+    def test_api(self):
+        out_ref = ref_std(self.x, self.axis, self.unbiased, self.keepdim)
+        out_dygraph = self.dygraph()
+        out_static = self.static()
+        for out in [out_dygraph, out_static]:
+            self.assertTrue(np.allclose(out_ref, out))
+            self.assertTrue(np.equal(out_ref.shape, out.shape).all())
+
+
+class TestStdAPI_dtype(TestStdAPI):
+    def set_attrs(self):
+        self.dtype = 'float32'
+
+
+class TestStdAPI_axis_int(TestStdAPI):
+    def set_attrs(self):
+        self.axis = 2
+
+
+class TestStdAPI_axis_list(TestStdAPI):
+    def set_attrs(self):
+        self.axis = [1, 2]
+
+
+class TestStdAPI_axis_tuple(TestStdAPI):
+    def set_attrs(self):
+        self.axis = (1, 3)
+
+
+class TestStdAPI_keepdim(TestStdAPI):
+    def set_attrs(self):
+        self.keepdim = False
+
+
+class TestStdAPI_unbiased(TestStdAPI):
+    def set_attrs(self):
+        self.unbiased = False
+
+
+class TestStdAPI_alias(unittest.TestCase):
+    def test_alias(self):
+        paddle.disable_static()
+        x = paddle.to_tensor(np.array([10, 12], 'float32'))
+        out1 = paddle.std(x).numpy()
+        out2 = paddle.tensor.std(x).numpy()
+        out3 = paddle.tensor.stat.std(x).numpy()
+        self.assertTrue(np.allclose(out1, out2))
+        self.assertTrue(np.allclose(out1, out3))
+        paddle.enable_static()
+
+
+class TestStdError(unittest.TestCase):
+    def test_error(self):
+        with paddle.static.program_guard(paddle.static.Program()):
+            x = paddle.data('X', [2, 3, 4], 'int32')
+            self.assertRaises(TypeError, paddle.std, x)
 
 
 if __name__ == '__main__':

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

@@ -15,65 +15,104 @@
 import unittest
 import numpy as np
 import paddle
-import paddle.fluid as fluid
 
 
-class TestVarianceLayer(unittest.TestCase):
+def ref_var(x, axis=None, unbiased=True, keepdim=False):
+    ddof = 1 if unbiased else 0
+    if isinstance(axis, int):
+        axis = (axis, )
+    if axis is not None:
+        axis = tuple(axis)
+    return np.var(x, axis=axis, ddof=ddof, keepdims=keepdim)
+
+
+class TestVarAPI(unittest.TestCase):
     def setUp(self):
-        self._dtype = "float64"
-        self._input = np.random.random([2, 3, 4, 5]).astype(self._dtype)
-
-    def static(self, axis=None, keepdim=False, unbiased=True):
-        prog = fluid.Program()
-        with fluid.program_guard(prog):
-            data = fluid.data(
-                name="data", dtype=self._dtype, shape=[None, 3, 4, 5])
-            out = prog.current_block().create_var(
-                dtype=self._dtype, shape=[2, 3, 4, 5])
-            paddle.var(input=data,
-                       axis=axis,
-                       keepdim=keepdim,
-                       unbiased=unbiased,
-                       out=out)
-
-        exe = fluid.Executor(self._place)
-        return exe.run(feed={"data": self._input},
-                       program=prog,
-                       fetch_list=[out])[0]
-
-    def dynamic(self, axis=None, keepdim=False, unbiased=True):
-        with fluid.dygraph.guard(self._place):
-            data = fluid.dygraph.to_variable(self._input)
-            out = paddle.var(input=data,
-                             axis=axis,
-                             keepdim=keepdim,
-                             unbiased=unbiased)
-            return out.numpy()
-
-    def numpy(self, axis=None, keepdim=False, unbiased=True):
-        ddof = 1 if unbiased else 0
-        axis = tuple(axis) if isinstance(axis, list) else axis
-        return np.var(self._input, axis=axis, keepdims=keepdim, ddof=ddof)
-
-    def test_equal(self):
-        places = [fluid.CPUPlace()]
-        if fluid.core.is_compiled_with_cuda():
-            places.append(fluid.CUDAPlace(0))
-        for place in places:
-            self._place = place
-            self.assertTrue(np.allclose(self.numpy(), self.static()))
-            self.assertTrue(
-                np.allclose(
-                    self.numpy(axis=[0, 2]), self.dynamic(axis=[0, 2])))
-            self.assertTrue(
-                np.allclose(
-                    self.numpy(
-                        axis=[1, 3], keepdim=True),
-                    self.dynamic(
-                        axis=[1, 3], keepdim=True)))
-            self.assertTrue(
-                np.allclose(
-                    self.numpy(unbiased=False), self.dynamic(unbiased=False)))
+        self.dtype = 'float64'
+        self.shape = [1, 3, 4, 10]
+        self.axis = [1, 3]
+        self.keepdim = False
+        self.unbiased = True
+        self.set_attrs()
+        self.x = np.random.uniform(-1, 1, self.shape).astype(self.dtype)
+        self.place=paddle.CUDAPlace(0) \
+            if paddle.fluid.core.is_compiled_with_cuda() \
+            else paddle.CPUPlace()
+
+    def set_attrs(self):
+        pass
+
+    def static(self):
+        with paddle.static.program_guard(paddle.static.Program()):
+            x = paddle.data('X', self.shape, self.dtype)
+            out = paddle.var(x, self.axis, self.unbiased, self.keepdim)
+            exe = paddle.static.Executor(self.place)
+            res = exe.run(feed={'X': self.x}, fetch_list=[out])
+        return res[0]
+
+    def dygraph(self):
+        paddle.disable_static()
+        x = paddle.to_tensor(self.x)
+        out = paddle.var(x, self.axis, self.unbiased, self.keepdim)
+        paddle.enable_static()
+        return out.numpy()
+
+    def test_api(self):
+        out_ref = ref_var(self.x, self.axis, self.unbiased, self.keepdim)
+        out_dygraph = self.dygraph()
+        out_static = self.static()
+        for out in [out_dygraph, out_static]:
+            self.assertTrue(np.allclose(out_ref, out))
+            self.assertTrue(np.equal(out_ref.shape, out.shape).all())
+
+
+class TestVarAPI_dtype(TestVarAPI):
+    def set_attrs(self):
+        self.dtype = 'float32'
+
+
+class TestVarAPI_axis_int(TestVarAPI):
+    def set_attrs(self):
+        self.axis = 2
+
+
+class TestVarAPI_axis_list(TestVarAPI):
+    def set_attrs(self):
+        self.axis = [1, 2]
+
+
+class TestVarAPI_axis_tuple(TestVarAPI):
+    def set_attrs(self):
+        self.axis = (1, 3)
+
+
+class TestVarAPI_keepdim(TestVarAPI):
+    def set_attrs(self):
+        self.keepdim = False
+
+
+class TestVarAPI_unbiased(TestVarAPI):
+    def set_attrs(self):
+        self.unbiased = False
+
+
+class TestVarAPI_alias(unittest.TestCase):
+    def test_alias(self):
+        paddle.disable_static()
+        x = paddle.to_tensor(np.array([10, 12], 'float32'))
+        out1 = paddle.var(x).numpy()
+        out2 = paddle.tensor.var(x).numpy()
+        out3 = paddle.tensor.stat.var(x).numpy()
+        self.assertTrue(np.allclose(out1, out2))
+        self.assertTrue(np.allclose(out1, out3))
+        paddle.enable_static()
+
+
+class TestVarError(unittest.TestCase):
+    def test_error(self):
+        with paddle.static.program_guard(paddle.static.Program()):
+            x = paddle.data('X', [2, 3, 4], 'int32')
+            self.assertRaises(TypeError, paddle.var, x)
 
 
 if __name__ == '__main__':

python/paddle/tensor/creation.py

@@ -361,14 +361,14 @@ def ones_like(x, dtype=None, name=None):
     Examples:
         .. code-block:: python
 
-        import paddle
-        import numpy as np
+            import paddle
+            import numpy as np
 
-        paddle.disable_static()
+            paddle.disable_static()
 
-        x = paddle.to_tensor(np.array([1,2,3], dtype='float32'))
-        out1 = paddle.zeros_like(x) # [1., 1., 1.]
-        out2 = paddle.zeros_like(x, dtype='int32') # [1, 1, 1]
+            x = paddle.to_tensor(np.array([1,2,3], dtype='float32'))
+            out1 = paddle.zeros_like(x) # [1., 1., 1.]
+            out2 = paddle.zeros_like(x, dtype='int32') # [1, 1, 1]
 
     """
     return full_like(x=x, fill_value=1, dtype=dtype, name=name)
@@ -451,14 +451,14 @@ def zeros_like(x, dtype=None, name=None):
     Examples:
         .. code-block:: python
 
-        import paddle
-        import numpy as np
+            import paddle
+            import numpy as np
 
-        paddle.disable_static()
+            paddle.disable_static()
 
-        x = paddle.to_tensor(np.array([1,2,3], dtype='float32'))
-        out1 = paddle.zeros_like(x) # [0., 0., 0.]
-        out2 = paddle.zeros_like(x, dtype='int32') # [0, 0, 0]
+            x = paddle.to_tensor(np.array([1,2,3], dtype='float32'))
+            out1 = paddle.zeros_like(x) # [0., 0., 0.]
+            out2 = paddle.zeros_like(x, dtype='int32') # [0, 0, 0]
 
     """
     return full_like(x=x, fill_value=0, dtype=dtype, name=name)

python/paddle/tensor/stat.py

@@ -40,9 +40,9 @@ def mean(x, axis=None, keepdim=False, name=None):
             should be in range [-D, D), where D is the dimensions of ``x`` . If
             ``axis`` or element(s) of ``axis`` is less than 0, it works the
             same way as :math:`axis + D` . If ``axis`` is None, mean is
-            calculated along all elements of ``x``. Default is None.
+            calculated over all elements of ``x``. Default is None.
         keepdim (bool, optional): Whether to reserve the reduced dimension(s)
-            in the output Tensor. If ``keep_dim`` is True, the dimensions of
+            in the output Tensor. If ``keepdim`` is True, the dimensions of
             the output Tensor is the same as ``x`` except in the reduced
             dimensions(it is of size 1 in this case). Otherwise, the shape of
             the output Tensor is squeezed in ``axis`` . Default is False.
@@ -67,7 +67,7 @@ def mean(x, axis=None, keepdim=False, name=None):
                           [[13, 14, 15, 16],
                            [17, 18, 19, 20],
                            [21, 22, 23, 24]]], 'float32')
-            x = paddle.to_variable(x)
+            x = paddle.to_tensor(x)
             out1 = paddle.mean(x)
             # [12.5]
             out2 = paddle.mean(x, axis=-1)
@@ -111,142 +111,120 @@ def mean(x, axis=None, keepdim=False, name=None):
     return out
 
 
-def var(input, axis=None, keepdim=False, unbiased=True, out=None, name=None):
+def var(x, axis=None, unbiased=True, keepdim=False, name=None):
     """
-	:alias_main: paddle.var
-	:alias: paddle.var,paddle.tensor.var,paddle.tensor.stat.var
-
-    Computes the variance of the input Variable's elements along the specified 
-    axis.
+    Computes the variance of ``x`` along ``axis`` .
 
     Args:
-        input (Variable): The input Variable to be computed variance, with data 
-            type float32 and float64 supported.
-        axis (list|int, optional): The axis along which the variance is computed. 
-            If `None`, compute the variance over all elements of :attr:`input`
-            and return a Variable with a single element, otherwise it must be in 
-            the range :math:`[-rank(input), rank(input))`. If :math:`axis[i] < 0`, 
-            the axis to compute is :math:`rank(input) + axis[i]`.
-        keepdim (bool, optional): Whether to reserve the reduced dimensions in 
-            the output Variable. The dimensions in :attr:`axis` will be squeezed 
-            and the result Variable will have :attr:`len(axis)` fewer dimensions 
-            than the :attr:`input` unless :attr:`keepdim` is true, default False.
-        unbiased (bool, optional): Whether to compute variance via the unbiased 
-            estimator, in which the divisor used in the computation is 
-            :math:`N - 1`, where :math:`N` represents the number of elements 
-            along :attr:`axis`, otherwise the divisor is :math:`N`. Default True.
-        out (Variable, optional): Alternate output Variable to store the result
-            variance. Default None.
-        name (str, optional): The name for this layer. Normally there is no 
-            need for user to set this property.  For more information, please 
-            refer to :ref:`api_guide_Name`. Default None.
+        x (Tensor): The input Tensor with data type float32, float64.
+        axis (int|list|tuple, optional): The axis along which to perform
+            variance calculations. ``axis`` should be int, list(int) or
+            tuple(int). If ``axis`` is a list/tuple of dimension(s), variance
+            is calculated along all element(s) of ``axis`` . ``axis`` or
+            element(s) of ``axis`` should be in range [-D, D), where D is the
+            dimensions of ``x`` . If ``axis`` or element(s) of ``axis`` is less
+            than 0, it works the same way as :math:`axis + D` . If ``axis`` is
+            None, variance is calculated over all elements of ``x``. Default
+            is None.
+        unbiased (bool, optional): Whether to use the unbiased estimation. If
+            ``unbiased`` is True, the divisor used in the computation is
+            :math:`N - 1`, where :math:`N` represents the number of elements
+            along ``axis`` , otherwise the divisor is :math:`N`. Default is True.
+        keepdim (bool, optional): Whether to reserve the reduced dimension(s)
+            in the output Tensor. If ``keepdim`` is True, the dimensions of
+            the output Tensor is the same as ``x`` except in the reduced
+            dimensions(it is of size 1 in this case). Otherwise, the shape of
+            the output Tensor is squeezed in ``axis`` . Default is False.
+        name (str, optional): Name for the operation (optional, default is None).
+            For more information, please refer to :ref:`api_guide_Name`.
 
     Returns:
-        Variable: The result variance with the same dtype as :attr:`input`. 
-            If :attr:`out = None`, returns a new Variable containing the 
-            variance, otherwise returns a reference to the output Variable.
+        Tensor, results of variance along ``axis`` of ``x``, with the same data
+        type as ``x``.
 
     Examples:
         .. code-block:: python
 
-            import numpy as np
             import paddle
-            import paddle.fluid.dygraph as dg
-
-            a = np.array([[1.0, 2.0], [3.0, 4.0]]).astype("float32")
-            with dg.guard():
-                data = dg.to_variable(a)
-                variance = paddle.var(data, axis=[1])
-                print(variance.numpy())   
-                # [0.5 0.5]
+            import numpy as np
+            
+            paddle.disable_static()
+
+            x = np.array([[1.0, 2.0, 3.0], [1.0, 4.0, 5.0]])
+            x = paddle.to_tensor(x)
+            out1 = paddle.var(x)
+            # [2.66666667]
+            out2 = paddle.var(x, axis=1)
+            # [1.         4.33333333]
     """
-    dtype = convert_dtype(input.dtype)
-    if dtype not in ["float32", "float64"]:
-        raise ValueError("Layer tensor.var() only supports floating-point "
-                         "dtypes, but received {}.".format(dtype))
-    rank = len(input.shape)
-    axes = axis if axis != None and axis != [] else range(rank)
-    axes = [e if e >= 0 else e + rank for e in axes]
-    inp_shape = input.shape if in_dygraph_mode() else layers.shape(input)
-    mean = layers.reduce_mean(input, dim=axis, keep_dim=True, name=name)
-    tmp = layers.reduce_mean(
-        (input - mean)**2, dim=axis, keep_dim=keepdim, name=name)
+    if not in_dygraph_mode():
+        check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'var')
 
+    u = mean(x, axis, True, name)
+    out = paddle.sum((x - u)**2, axis, keepdim=keepdim, name=name)
+
+    n = paddle.cast(paddle.numel(x), x.dtype) \
+        / paddle.cast(paddle.numel(out), x.dtype)
     if unbiased:
-        n = 1
-        for i in axes:
-            n *= inp_shape[i]
-        if not in_dygraph_mode():
-            n = layers.cast(n, dtype)
-            zero_const = layers.fill_constant(shape=[1], dtype=dtype, value=0.0)
-            factor = where(n > 1.0, n / (n - 1.0), zero_const)
-        else:
-            factor = n / (n - 1.0) if n > 1.0 else 0.0
-        tmp *= factor
-    if out:
-        layers.assign(input=tmp, output=out)
-        return out
-    else:
-        return tmp
-
-
-def std(input, axis=None, keepdim=False, unbiased=True, out=None, name=None):
-    """
-	:alias_main: paddle.std
-	:alias: paddle.std,paddle.tensor.std,paddle.tensor.stat.std
+        one_const = paddle.ones([1], x.dtype)
+        n = where(n > one_const, n - 1., one_const)
+    out /= n
+    return out
+
 
-    Computes the standard-deviation  of the input Variable's elements along the specified 
-    axis.
+def std(x, axis=None, unbiased=True, keepdim=False, name=None):
+    """
+    Computes the standard-deviation of ``x`` along ``axis`` .
 
     Args:
-        input (Variable): The input Variable to be computed standard-deviation, with data 
-            type float32 and float64 supported.
-        axis (list|int, optional): The axis along which the standard-deviation is computed. 
-            If `None`, compute the standard-deviation over all elements of :attr:`input`
-            and return a Variable with a single element, otherwise it must be in 
-            the range :math:`[-rank(input), rank(input))`. If :math:`axis[i] < 0`, 
-            the axis to compute is :math:`rank(input) + axis[i]`.
-        keepdim (bool, optional): Whether to reserve the reduced dimensions in 
-            the output Variable. The dimensions in :attr:`axis` will be squeezed 
-            and the result Variable will have :attr:`len(axis)` fewer dimensions 
-            than the :attr:`input` unless :attr:`keepdim` is true, default False.
-        unbiased (bool, optional): Whether to compute standard-deviation via the unbiased 
-            estimator, in which the divisor used in the computation is 
-            :math:`N - 1`, where :math:`N` represents the number of elements 
-            along :attr:`axis`, otherwise the divisor is :math:`N`. Default True.
-        out (Variable, optional): Alternate output Variable to store the result
-            standard-deviation . Default None.
-        name (str, optional): The name for this layer. Normally there is no 
-            need for user to set this property.  For more information, please 
-            refer to :ref:`api_guide_Name`. Default None.
+        x (Tensor): The input Tensor with data type float32, float64.
+        axis (int|list|tuple, optional): The axis along which to perform
+            standard-deviation calculations. ``axis`` should be int, list(int)
+            or tuple(int). If ``axis`` is a list/tuple of dimension(s),
+            standard-deviation is calculated along all element(s) of ``axis`` .
+            ``axis`` or element(s) of ``axis`` should be in range [-D, D),
+            where D is the dimensions of ``x`` . If ``axis`` or element(s) of
+            ``axis`` is less than 0, it works the same way as :math:`axis + D` .
+            If ``axis`` is None, standard-deviation is calculated over all
+            elements of ``x``. Default is None.
+        unbiased (bool, optional): Whether to use the unbiased estimation. If
+            ``unbiased`` is True, the standard-deviation is calculated via the
+            unbiased estimator. If ``unbiased`` is True,  the divisor used in
+            the computation is :math:`N - 1`, where :math:`N` represents the
+            number of elements along ``axis`` , otherwise the divisor is
+            :math:`N`. Default is True.
+        keepdim (bool, optional): Whether to reserve the reduced dimension(s)
+            in the output Tensor. If ``keepdim`` is True, the dimensions of
+            the output Tensor is the same as ``x`` except in the reduced
+            dimensions(it is of size 1 in this case). Otherwise, the shape of
+            the output Tensor is squeezed in ``axis`` . Default is False.
+        name (str, optional): Name for the operation (optional, default is None).
+            For more information, please refer to :ref:`api_guide_Name`.
 
     Returns:
-        Variable: The result standard-deviation  with the same dtype as :attr:`input`. 
-            If :attr:`out = None`, returns a new Variable containing the 
-            standard-deviation , otherwise returns a reference to the output Variable.
+        Tensor, results of standard-deviation along ``axis`` of ``x``, with the
+        same data type as ``x``.
+
     Examples:
         .. code-block:: python
 
             import paddle
-            import paddle.fluid as fluid
-            # x is a Tensor variable with following elements:
-            #    [[0.2, 0.3, 0.5, 0.9]
-            #     [0.1, 0.2, 0.6, 0.7]]
-            # Each example is followed by the corresponding output tensor.
-            x = fluid.data(name='x', shape=[2, 4], dtype='float32')
-            paddle.std(x)  # [0.28252685] 
-            paddle.std(x, axis=[0])  # [0.0707107, 0.07071075, 0.07071064, 0.1414217]
-            paddle.std(x, axis=[-1])  # [0.30956957, 0.29439208] 
+            import numpy as np
+            
+            paddle.disable_static()
+
+            x = np.array([[1.0, 2.0, 3.0], [1.0, 4.0, 5.0]])
+            x = paddle.to_tensor(x)
+            out1 = paddle.std(x)
+            # [1.63299316]
+            out2 = paddle.std(x, axis=1)
+            # [1.       2.081666]
     """
-    check_variable_and_dtype(input, 'input', ['float32', 'float64'], 'std')
-
-    tmp = var(input, axis=axis, keepdim=keepdim, unbiased=unbiased, name=name)
-    tmp = layers.sqrt(tmp)
-    if out is not None:
-        layers.assign(input=tmp, output=out)
-        return out
-    else:
-        return tmp
+    if not in_dygraph_mode():
+        check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'std')
+
+    out = var(**locals())
+    return paddle.sqrt(out)
 
 
 def numel(x, name=None):