fix the reduce api en doc (#20223)

* fix the reduce api en doc test=document_fix test=develop

* fix the fluid.data test=develop test=document_fix

* fix the API.spec test=develop test=document_fix

* fix according the review test=develop test=document_fix

* fix the confilict test=develop test=document_fix

paddle/fluid/API.spec

@@ -157,11 +157,11 @@ paddle.fluid.layers.sequence_expand_as (ArgSpec(args=['x', 'y', 'name'], varargs
 paddle.fluid.layers.sequence_pad (ArgSpec(args=['x', 'pad_value', 'maxlen', 'name'], varargs=None, keywords=None, defaults=(None, None)), ('document', 'df08b9c499ab3a90f95d08ab5b6c6c62'))
 paddle.fluid.layers.sequence_unpad (ArgSpec(args=['x', 'length', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'e478180d5bc010a84f35af958cafa62c'))
 paddle.fluid.layers.lstm_unit (ArgSpec(args=['x_t', 'hidden_t_prev', 'cell_t_prev', 'forget_bias', 'param_attr', 'bias_attr', 'name'], varargs=None, keywords=None, defaults=(0.0, None, None, None)), ('document', 'fe126c58e4339410e875ab1eba246d21'))
-paddle.fluid.layers.reduce_sum (ArgSpec(args=['input', 'dim', 'keep_dim', 'name'], varargs=None, keywords=None, defaults=(None, False, None)), ('document', 'dd5f06fb7cf39ca06cbab4abd03e6893'))
-paddle.fluid.layers.reduce_mean (ArgSpec(args=['input', 'dim', 'keep_dim', 'name'], varargs=None, keywords=None, defaults=(None, False, None)), ('document', 'a3024789eba11a70c2ef27c358173400'))
-paddle.fluid.layers.reduce_max (ArgSpec(args=['input', 'dim', 'keep_dim', 'name'], varargs=None, keywords=None, defaults=(None, False, None)), ('document', '10023caec4d7f78c3b901f023a1feaa7'))
-paddle.fluid.layers.reduce_min (ArgSpec(args=['input', 'dim', 'keep_dim', 'name'], varargs=None, keywords=None, defaults=(None, False, None)), ('document', '1a1c91625ce3c32646f69ca10d4d1da7'))
-paddle.fluid.layers.reduce_prod (ArgSpec(args=['input', 'dim', 'keep_dim', 'name'], varargs=None, keywords=None, defaults=(None, False, None)), ('document', 'b386471f0476c80c61d8c8672278063d'))
+paddle.fluid.layers.reduce_sum (ArgSpec(args=['input', 'dim', 'keep_dim', 'name'], varargs=None, keywords=None, defaults=(None, False, None)), ('document', 'ecb55075fdf89a866bcede85e60aebad'))
+paddle.fluid.layers.reduce_mean (ArgSpec(args=['input', 'dim', 'keep_dim', 'name'], varargs=None, keywords=None, defaults=(None, False, None)), ('document', '968c9b17affaf714e5021c3dc8d68c73'))
+paddle.fluid.layers.reduce_max (ArgSpec(args=['input', 'dim', 'keep_dim', 'name'], varargs=None, keywords=None, defaults=(None, False, None)), ('document', 'd37e3a9a05c00e032d4b7876c4f6b414'))
+paddle.fluid.layers.reduce_min (ArgSpec(args=['input', 'dim', 'keep_dim', 'name'], varargs=None, keywords=None, defaults=(None, False, None)), ('document', 'fad9e8057b48a075502b4d845f776b01'))
+paddle.fluid.layers.reduce_prod (ArgSpec(args=['input', 'dim', 'keep_dim', 'name'], varargs=None, keywords=None, defaults=(None, False, None)), ('document', 'e165c6e99b1f5b773897c5910ccc7f4f'))
 paddle.fluid.layers.reduce_all (ArgSpec(args=['input', 'dim', 'keep_dim', 'name'], varargs=None, keywords=None, defaults=(None, False, None)), ('document', 'e365c540ec448a73e2b2e75796ed029e'))
 paddle.fluid.layers.reduce_any (ArgSpec(args=['input', 'dim', 'keep_dim', 'name'], varargs=None, keywords=None, defaults=(None, False, None)), ('document', 'fbc9e73da7a2964ba5693864aed36abb'))
 paddle.fluid.layers.sequence_first_step (ArgSpec(args=['input'], varargs=None, keywords=None, defaults=None), ('document', '227a75392ae194de0504f5c6812dade9'))

python/paddle/fluid/layers/nn.py

@@ -5889,20 +5889,26 @@ def reduce_sum(input, dim=None, keep_dim=False, name=None):
     Computes the sum of tensor elements over the given dimension.
 
     Args:
-        input (Variable): The input variable which is a Tensor or LoDTensor.
-        dim (list|int|None): The dimensions along which the sum is performed. If
+        input (Variable): The input variable which is a Tensor, the data type is float32,
+            float64, int32, int64.
+        dim (list|int, optional): The dimensions along which the sum is performed. If
             :attr:`None`, sum all elements of :attr:`input` and return a
             Tensor variable with a single element, otherwise must be in the
             range :math:`[-rank(input), rank(input))`. If :math:`dim[i] < 0`,
             the dimension to reduce is :math:`rank + dim[i]`.
-        keep_dim (bool|False): Whether to reserve the reduced dimension in the
+        keep_dim (bool, optional): Whether to reserve the reduced dimension in the
             output Tensor. The result tensor will have one fewer dimension
-            than the :attr:`input` unless :attr:`keep_dim` is true.
-        name(str|None): A name for this layer(optional). If set None, the layer
-                       will be named automatically.
+            than the :attr:`input` unless :attr:`keep_dim` is true, default
+            value is False.
+        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:
-        Variable: The reduced Tensor variable.
+        Variable: Tensor, results of summation operation on the specified dim of input tensor,
+        it's data type is the same as input's Tensor.
+
+    Raises:
+        TypeError, if out data type is different with the input data type.
     
     Examples:
         .. code-block:: python
@@ -5912,7 +5918,7 @@ def reduce_sum(input, dim=None, keep_dim=False, name=None):
             #    [[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.layers.data(name='x', shape=[4, 2], dtype='float32')
+            x = fluid.data(name='x', shape=[2, 4], dtype='float32')
             fluid.layers.reduce_sum(x)  # [3.5]
             fluid.layers.reduce_sum(x, dim=0)  # [0.3, 0.5, 1.1, 1.6]
             fluid.layers.reduce_sum(x, dim=-1)  # [1.9, 1.6]
@@ -5922,7 +5928,7 @@ def reduce_sum(input, dim=None, keep_dim=False, name=None):
             #      [[[1, 2], [3, 4]],
             #      [[5, 6], [7, 8]]]
             # Each example is followed by the corresponding output tensor.
-            y = fluid.layers.data(name='y', shape=[2, 2, 2], dtype='float32')
+            y = fluid.data(name='y', shape=[2, 2, 2], dtype='float32')
             fluid.layers.reduce_sum(y, dim=[1, 2]) # [10, 26]
             fluid.layers.reduce_sum(y, dim=[0, 1]) # [16, 20]
 
@@ -5957,21 +5963,27 @@ def reduce_mean(input, dim=None, keep_dim=False, name=None):
     Computes the mean of the input tensor's elements along the given dimension.
 
     Args:
-        input (Variable): The input variable which is a Tensor or LoDTensor.
-        dim (list|int|None): The dimension along which the mean is computed. If
+        input (Variable): The input variable which is a Tensor, the data type is float32,
+            float64, int32, int64.
+        dim (list|int, optional): The dimension along which the mean is computed. If
             `None`, compute the mean 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:`dim[i] < 0`, the dimension to reduce is
             :math:`rank(input) + dim[i]`.
-        keep_dim (bool): Whether to reserve the reduced dimension in the
+        keep_dim (bool, optional): Whether to reserve the reduced dimension in the
             output Tensor. The result tensor will have one fewer dimension
-            than the :attr:`input` unless :attr:`keep_dim` is true.
-        name(str|None): A name for this layer(optional). If set `None`, the layer
-                       will be named automatically.
+            than the :attr:`input` unless :attr:`keep_dim` is true, default 
+            value is False.
+        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:
-        Variable: The reduced mean Variable.
+        Variable: Tensor, results of average on the specified dim of input tensor,
+        it's data type is the same as input's Tensor.
+    
+    Raises:
+        TypeError, if out data type is different with the input data type.
     
     Examples:
         .. code-block:: python
@@ -5981,7 +5993,7 @@ def reduce_mean(input, dim=None, keep_dim=False, name=None):
             #    [[0.2, 0.3, 0.5, 0.9]
             #     [0.1, 0.2, 0.6, 0.7]]
             # Each example is followed by the correspending output tensor.
-            x = fluid.layers.data(name='x', shape=[4, 2], dtype='float32')
+            x = fluid.data(name='x', shape=[2, 4], dtype='float32')
             fluid.layers.reduce_mean(x)  # [0.4375]
             fluid.layers.reduce_mean(x, dim=0)  # [0.15, 0.25, 0.55, 0.8]
             fluid.layers.reduce_mean(x, dim=-1)  # [0.475, 0.4]
@@ -5991,7 +6003,7 @@ def reduce_mean(input, dim=None, keep_dim=False, name=None):
             #      [[[1.0, 2.0], [3.0, 4.0]],
             #      [[5.0, 6.0], [7.0, 8.0]]]
             # Each example is followed by the correspending output tensor.
-            y = fluid.layers.data(name='y', shape=[2, 2, 2], dtype='float32')
+            y = fluid.data(name='y', shape=[2, 2, 2], dtype='float32')
             fluid.layers.reduce_mean(y, dim=[1, 2]) # [2.5, 6.5]
             fluid.layers.reduce_mean(y, dim=[0, 1]) # [4.0, 5.0]
     """
@@ -6025,20 +6037,23 @@ def reduce_max(input, dim=None, keep_dim=False, name=None):
     Computes the maximum of tensor elements over the given dimension.
 
     Args:
-        input (Variable): The input variable which is a Tensor or LoDTensor.
-        dim (list|int|None): The dimension along which the maximum is computed.
+        input (Variable): The input variable which is a Tensor, the data type is float32,
+            float64, int32, int64.
+        dim (list|int, optional): The dimension along which the maximum is computed.
             If :attr:`None`, compute the maximum over all elements of
             :attr:`input` and return a Tensor variable with a single element,
             otherwise must be in the range :math:`[-rank(input), rank(input))`.
             If :math:`dim[i] < 0`, the dimension to reduce is :math:`rank + dim[i]`.
-        keep_dim (bool): Whether to reserve the reduced dimension in the
+        keep_dim (bool, optional): Whether to reserve the reduced dimension in the
             output Tensor. The result tensor will have one fewer dimension
-            than the :attr:`input` unless :attr:`keep_dim` is true.
-        name(str|None): A name for this layer(optional). If set None, the layer
-                       will be named automatically.
+            than the :attr:`input` unless :attr:`keep_dim` is true, default
+            value is False.
+        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:
-        Variable: The reduced Tensor variable.
+        Variable: Tensor, results of maximum on the specified dim of input tensor,
+        it's data type is the same as input's Tensor.
 
     Examples:
         .. code-block:: python
@@ -6048,7 +6063,7 @@ def reduce_max(input, dim=None, keep_dim=False, name=None):
             #    [[0.2, 0.3, 0.5, 0.9]
             #     [0.1, 0.2, 0.6, 0.7]]
             # Each example is followed by the correspending output tensor.
-            x = fluid.layers.data(name='x', shape=[4, 2], dtype='float32')
+            x = fluid.data(name='x', shape=[2, 4], dtype='float32')
             fluid.layers.reduce_max(x)  # [0.9]
             fluid.layers.reduce_max(x, dim=0)  # [0.2, 0.3, 0.6, 0.9]
             fluid.layers.reduce_max(x, dim=-1)  # [0.9, 0.7]
@@ -6058,7 +6073,7 @@ def reduce_max(input, dim=None, keep_dim=False, name=None):
             #      [[[1.0, 2.0], [3.0, 4.0]],
             #      [[5.0, 6.0], [7.0, 8.0]]]
             # Each example is followed by the correspending output tensor.
-            y = fluid.layers.data(name='y', shape=[2, 2, 2], dtype='float32')
+            y = fluid.data(name='y', shape=[2, 2, 2], dtype='float32')
             fluid.layers.reduce_max(y, dim=[1, 2]) # [4.0, 8.0]
             fluid.layers.reduce_max(y, dim=[0, 1]) # [7.0, 8.0]
     """
@@ -6083,20 +6098,23 @@ def reduce_min(input, dim=None, keep_dim=False, name=None):
     Computes the minimum of tensor elements over the given dimension.
 
     Args:
-        input (Variable): The input variable which is a Tensor or LoDTensor.
-        dim (list|int|None): The dimensions along which the minimum is computed.
+        input (Variable): The input variable which is a Tensor, the data type is float32,
+            float64, int32, int64.
+        dim (list|int, optional): The dimensions along which the minimum is computed.
             If :attr:`None`, compute the minimum over all elements of
             :attr:`input` and return a Tensor variable with a single element,
             otherwise must be in the range :math:`[-rank(input), rank(input))`.
             If :math:`dim[i] < 0`, the dimension to reduce is :math:`rank + dim[i]`.
-        keep_dim (bool): Whether to reserve the reduced dimension in the
+        keep_dim (bool, optional): Whether to reserve the reduced dimension in the
             output Tensor. The result tensor will have one fewer dimension
-            than the :attr:`input` unless :attr:`keep_dim` is true.
-        name(str|None): A name for this layer(optional). If set None, the layer
-                       will be named automatically.
+            than the :attr:`input` unless :attr:`keep_dim` is true, default
+            value is False.
+        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:
-        Variable: The reduced Tensor variable.
+        Variable: Tensor, result of minimum on the specified dim of input tensor,
+        it's data type is the same as input's Tensor.
 
     Examples:
         .. code-block:: python
@@ -6106,7 +6124,7 @@ def reduce_min(input, dim=None, keep_dim=False, name=None):
             #    [[0.2, 0.3, 0.5, 0.9]
             #     [0.1, 0.2, 0.6, 0.7]]
             # Each example is followed by the correspending output tensor.
-            x = fluid.layers.data(name='x', shape=[4, 2], dtype='float32')
+            x = fluid.data(name='x', shape=[2, 4], dtype='float32')
             fluid.layers.reduce_min(x)  # [0.1]
             fluid.layers.reduce_min(x, dim=0)  # [0.1, 0.2, 0.5, 0.7]
             fluid.layers.reduce_min(x, dim=-1)  # [0.2, 0.1]
@@ -6116,7 +6134,7 @@ def reduce_min(input, dim=None, keep_dim=False, name=None):
             #      [[[1.0, 2.0], [3.0, 4.0]],
             #      [[5.0, 6.0], [7.0, 8.0]]]
             # Each example is followed by the correspending output tensor.
-            y = fluid.layers.data(name='y', shape=[2, 2, 2], dtype='float32')
+            y = fluid.data(name='y', shape=[2, 2, 2], dtype='float32')
             fluid.layers.reduce_min(y, dim=[1, 2]) # [1.0, 5.0]
             fluid.layers.reduce_min(y, dim=[0, 1]) # [1.0, 2.0]
     """
@@ -6141,20 +6159,23 @@ def reduce_prod(input, dim=None, keep_dim=False, name=None):
     Computes the product of tensor elements over the given dimension.
 
     Args:
-        input (Variable): The input variable which is a Tensor or LoDTensor.
-        dim (list|int|None): The dimensions along which the product is performed. If
+        input (Variable): The input variable which is a Tensor, the data type is float32,
+            float64, int32, int64.
+        dim (list|int, optional): The dimensions along which the product is performed. If
             :attr:`None`, multipy all elements of :attr:`input` and return a
             Tensor variable with a single element, otherwise must be in the
             range :math:`[-rank(input), rank(input))`. If :math:`dim[i] < 0`,
             the dimension to reduce is :math:`rank + dim[i]`.
-        keep_dim (bool|False): Whether to reserve the reduced dimension in the
+        keep_dim (bool, optional): Whether to reserve the reduced dimension in the
             output Tensor. The result tensor will have one fewer dimension
-            than the :attr:`input` unless :attr:`keep_dim` is true.
-        name(str|None): A name for this layer(optional). If set None, the
-            layer will be named automatically.
+            than the :attr:`input` unless :attr:`keep_dim` is true, default
+            value is False.
+        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:
-        Variable: The reduced Tensor variable.
+        Variable: Tensor, result of product on the specified dim of input tensor,
+        it's data type is the same as input's Tensor.
     
     Examples:
         .. code-block:: python
@@ -6164,7 +6185,7 @@ def reduce_prod(input, dim=None, keep_dim=False, name=None):
             #    [[0.2, 0.3, 0.5, 0.9]
             #     [0.1, 0.2, 0.6, 0.7]]
             # Each example is followed by the correspending output tensor.
-            x = fluid.layers.data(name='x', shape=[4, 2], dtype='float32')
+            x = fluid.data(name='x', shape=[2, 4], dtype='float32')
             fluid.layers.reduce_prod(x)  # [0.0002268]
             fluid.layers.reduce_prod(x, dim=0)  # [0.02, 0.06, 0.3, 0.63]
             fluid.layers.reduce_prod(x, dim=-1)  # [0.027, 0.0084]
@@ -6175,7 +6196,7 @@ def reduce_prod(input, dim=None, keep_dim=False, name=None):
             #      [[[1.0, 2.0], [3.0, 4.0]],
             #      [[5.0, 6.0], [7.0, 8.0]]]
             # Each example is followed by the correspending output tensor.
-            y = fluid.layers.data(name='y', shape=[2, 2, 2], dtype='float32')
+            y = fluid.data(name='y', shape=[2, 2, 2], dtype='float32')
             fluid.layers.reduce_prod(y, dim=[1, 2]) # [24.0, 1680.0]
             fluid.layers.reduce_prod(y, dim=[0, 1]) # [105.0, 384.0]
     """