fix APIs, to_variable、NCE、PRelu、softmax、rankloss for dygraph, test=document_fix, test=develop (#20142)

paddle/fluid/API.spec

@@ -141,7 +141,7 @@ paddle.fluid.layers.conv2d (ArgSpec(args=['input', 'num_filters', 'filter_size',
 paddle.fluid.layers.conv3d (ArgSpec(args=['input', 'num_filters', 'filter_size', 'stride', 'padding', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act', 'name', 'data_format'], varargs=None, keywords=None, defaults=(1, 0, 1, None, None, None, True, None, None, 'NCDHW')), ('document', 'feff9c8ebb4d4d0be5345f9042f57c8e'))
 paddle.fluid.layers.sequence_pool (ArgSpec(args=['input', 'pool_type', 'is_test', 'pad_value'], varargs=None, keywords=None, defaults=(False, 0.0)), ('document', '5a709f7ef3fdb8fc819d09dc4fbada9a'))
 paddle.fluid.layers.sequence_softmax (ArgSpec(args=['input', 'use_cudnn', 'name'], varargs=None, keywords=None, defaults=(False, None)), ('document', 'eaa9d0bbd3d4e017c8bc4ecdac483711'))
-paddle.fluid.layers.softmax (ArgSpec(args=['input', 'use_cudnn', 'name', 'axis'], varargs=None, keywords=None, defaults=(False, None, -1)), ('document', '7ccaea1b93fe4f7387a6036692986c6b'))
+paddle.fluid.layers.softmax (ArgSpec(args=['input', 'use_cudnn', 'name', 'axis'], varargs=None, keywords=None, defaults=(False, None, -1)), ('document', 'f7d6a5173c92c23f9a25cbc58a0eb577'))
 paddle.fluid.layers.pool2d (ArgSpec(args=['input', 'pool_size', 'pool_type', 'pool_stride', 'pool_padding', 'global_pooling', 'use_cudnn', 'ceil_mode', 'name', 'exclusive', 'data_format'], varargs=None, keywords=None, defaults=(-1, 'max', 1, 0, False, True, False, None, True, 'NCHW')), ('document', 'daf9ae55b2d54bd5f35acb397fd1e1b5'))
 paddle.fluid.layers.pool3d (ArgSpec(args=['input', 'pool_size', 'pool_type', 'pool_stride', 'pool_padding', 'global_pooling', 'use_cudnn', 'ceil_mode', 'name', 'exclusive', 'data_format'], varargs=None, keywords=None, defaults=(-1, 'max', 1, 0, False, True, False, None, True, 'NCDHW')), ('document', 'df8edcb8dd020fdddf778c9f613dc650'))
 paddle.fluid.layers.adaptive_pool2d (ArgSpec(args=['input', 'pool_size', 'pool_type', 'require_index', 'name'], varargs=None, keywords=None, defaults=('max', False, None)), ('document', 'd873fdd73bcd74f9203d347cfb90de75'))
@@ -187,7 +187,7 @@ paddle.fluid.layers.multiplex (ArgSpec(args=['inputs', 'index'], varargs=None, k
 paddle.fluid.layers.layer_norm (ArgSpec(args=['input', 'scale', 'shift', 'begin_norm_axis', 'epsilon', 'param_attr', 'bias_attr', 'act', 'name'], varargs=None, keywords=None, defaults=(True, True, 1, 1e-05, None, None, None, None)), ('document', '678de6d6d0c93da74189990b039daae8'))
 paddle.fluid.layers.group_norm (ArgSpec(args=['input', 'groups', 'epsilon', 'param_attr', 'bias_attr', 'act', 'data_layout', 'name'], varargs=None, keywords=None, defaults=(1e-05, None, None, None, 'NCHW', None)), ('document', '87dd4b818f102bc1a780e1804c28bd38'))
 paddle.fluid.layers.spectral_norm (ArgSpec(args=['weight', 'dim', 'power_iters', 'eps', 'name'], varargs=None, keywords=None, defaults=(0, 1, 1e-12, None)), ('document', '7b3d14d6707d878923847ec617d7d521'))
-paddle.fluid.layers.softmax_with_cross_entropy (ArgSpec(args=['logits', 'label', 'soft_label', 'ignore_index', 'numeric_stable_mode', 'return_softmax', 'axis'], varargs=None, keywords=None, defaults=(False, -100, True, False, -1)), ('document', '54e1675aa0364f4a78fa72804ec0f413'))
+paddle.fluid.layers.softmax_with_cross_entropy (ArgSpec(args=['logits', 'label', 'soft_label', 'ignore_index', 'numeric_stable_mode', 'return_softmax', 'axis'], varargs=None, keywords=None, defaults=(False, -100, True, False, -1)), ('document', '6992e4140d667fdf816d0617648b5c00'))
 paddle.fluid.layers.smooth_l1 (ArgSpec(args=['x', 'y', 'inside_weight', 'outside_weight', 'sigma'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', 'cbe8940643ac80ef75e1abdfbdb09e88'))
 paddle.fluid.layers.one_hot (ArgSpec(args=['input', 'depth', 'allow_out_of_range'], varargs=None, keywords=None, defaults=(False,)), ('document', 'cdf5dc2078f1e20dc61dd0bec7e28a29'))
 paddle.fluid.layers.autoincreased_step_counter (ArgSpec(args=['counter_name', 'begin', 'step'], varargs=None, keywords=None, defaults=(None, 1, 1)), ('document', 'd016c137beb9a4528b7378b437d00151'))
@@ -221,7 +221,7 @@ paddle.fluid.layers.selu (ArgSpec(args=['x', 'scale', 'alpha', 'name'], varargs=
 paddle.fluid.layers.log (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '02f668664e3bfc4df6c00d7363467140'))
 paddle.fluid.layers.crop (ArgSpec(args=['x', 'shape', 'offsets', 'name'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '32196a194f757b4da114a595a5bc6414'))
 paddle.fluid.layers.crop_tensor (ArgSpec(args=['x', 'shape', 'offsets', 'name'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', 'd460aaf35afbbeb9beea4789aa6e4343'))
-paddle.fluid.layers.rank_loss (ArgSpec(args=['label', 'left', 'right', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '8eb36596bb43d7a907d3397c7aedbdb3'))
+paddle.fluid.layers.rank_loss (ArgSpec(args=['label', 'left', 'right', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '6d49ba251e23f32cb09df54a851bb960'))
 paddle.fluid.layers.margin_rank_loss (ArgSpec(args=['label', 'left', 'right', 'margin', 'name'], varargs=None, keywords=None, defaults=(0.1, None)), ('document', '1a177f30e5013fae7ee6c45860cf4946'))
 paddle.fluid.layers.elu (ArgSpec(args=['x', 'alpha', 'name'], varargs=None, keywords=None, defaults=(1.0, None)), ('document', '9af1926c06711eacef9e82d7a9e4d308'))
 paddle.fluid.layers.relu6 (ArgSpec(args=['x', 'threshold', 'name'], varargs=None, keywords=None, defaults=(6.0, None)), ('document', '538fc860b2a1734e118b94e4a1a3ee67'))
@@ -586,7 +586,7 @@ paddle.fluid.dygraph.Layer.sublayers (ArgSpec(args=['self', 'include_sublayers']
 paddle.fluid.dygraph.Layer.train (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
 paddle.fluid.dygraph.__impl__ (ArgSpec(args=['func'], varargs=None, keywords=None, defaults=()), ('document', '75d1d3afccc8b39cdebf05cb1f5969f9'))
 paddle.fluid.dygraph.guard (ArgSpec(args=['place'], varargs=None, keywords=None, defaults=(None,)), ('document', '7071320ffe2eec9aacdae574951278c6'))
-paddle.fluid.dygraph.to_variable (ArgSpec(args=['value', 'block', 'name'], varargs=None, keywords=None, defaults=(None, None)), ('document', '0e69fa3666f15dd01b6e3e270b9371cd'))
+paddle.fluid.dygraph.to_variable (ArgSpec(args=['value', 'block', 'name'], varargs=None, keywords=None, defaults=(None, None)), ('document', '7df6297d66295bdc933e3982caa6f1a8'))
 paddle.fluid.dygraph.Conv2D ('paddle.fluid.dygraph.nn.Conv2D', ('document', '10915f3c643e232d9c6789ce20a96869'))
 paddle.fluid.dygraph.Conv2D.__init__ (ArgSpec(args=['self', 'name_scope', 'num_filters', 'filter_size', 'stride', 'padding', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act', 'dtype'], varargs=None, keywords=None, defaults=(1, 0, 1, None, None, None, True, None, 'float32')), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
 paddle.fluid.dygraph.Conv2D.add_parameter (ArgSpec(args=['self', 'name', 'parameter'], varargs=None, keywords=None, defaults=None), ('document', 'f35ab374c7d5165c3daf3bd64a5a2ec1'))
@@ -723,7 +723,7 @@ paddle.fluid.dygraph.LayerNorm.set_dict (ArgSpec(args=['self', 'stat_dict', 'inc
 paddle.fluid.dygraph.LayerNorm.state_dict (ArgSpec(args=['self', 'destination', 'include_sublayers'], varargs=None, keywords=None, defaults=(None, True)), ('document', '9d689f44592cd22812c7ec06a9654eac'))
 paddle.fluid.dygraph.LayerNorm.sublayers (ArgSpec(args=['self', 'include_sublayers'], varargs=None, keywords=None, defaults=(True,)), ('document', '00a881005ecbc96578faf94513bf0d62'))
 paddle.fluid.dygraph.LayerNorm.train (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
-paddle.fluid.dygraph.NCE ('paddle.fluid.dygraph.nn.NCE', ('document', '993aeea9be436e9c709a758795cb23e9'))
+paddle.fluid.dygraph.NCE ('paddle.fluid.dygraph.nn.NCE', ('document', '148e58ba1698e0cd60a3490fd4188d04'))
 paddle.fluid.dygraph.NCE.__init__ (ArgSpec(args=['self', 'name_scope', 'num_total_classes', 'sample_weight', 'param_attr', 'bias_attr', 'num_neg_samples', 'sampler', 'custom_dist', 'seed', 'is_sparse'], varargs=None, keywords=None, defaults=(None, None, None, None, 'uniform', None, 0, False)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
 paddle.fluid.dygraph.NCE.add_parameter (ArgSpec(args=['self', 'name', 'parameter'], varargs=None, keywords=None, defaults=None), ('document', 'f35ab374c7d5165c3daf3bd64a5a2ec1'))
 paddle.fluid.dygraph.NCE.add_sublayer (ArgSpec(args=['self', 'name', 'sublayer'], varargs=None, keywords=None, defaults=None), ('document', '839ff3c0534677ba6ad8735c3fd4e995'))
@@ -740,7 +740,7 @@ paddle.fluid.dygraph.NCE.set_dict (ArgSpec(args=['self', 'stat_dict', 'include_s
 paddle.fluid.dygraph.NCE.state_dict (ArgSpec(args=['self', 'destination', 'include_sublayers'], varargs=None, keywords=None, defaults=(None, True)), ('document', '9d689f44592cd22812c7ec06a9654eac'))
 paddle.fluid.dygraph.NCE.sublayers (ArgSpec(args=['self', 'include_sublayers'], varargs=None, keywords=None, defaults=(True,)), ('document', '00a881005ecbc96578faf94513bf0d62'))
 paddle.fluid.dygraph.NCE.train (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
-paddle.fluid.dygraph.PRelu ('paddle.fluid.dygraph.nn.PRelu', ('document', 'da956af1676b08bf15553751a3643b55'))
+paddle.fluid.dygraph.PRelu ('paddle.fluid.dygraph.nn.PRelu', ('document', '58141577833fedf619f2f324eea57e00'))
 paddle.fluid.dygraph.PRelu.__init__ (ArgSpec(args=['self', 'name_scope', 'mode', 'param_attr'], varargs=None, keywords=None, defaults=(None,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
 paddle.fluid.dygraph.PRelu.add_parameter (ArgSpec(args=['self', 'name', 'parameter'], varargs=None, keywords=None, defaults=None), ('document', 'f35ab374c7d5165c3daf3bd64a5a2ec1'))
 paddle.fluid.dygraph.PRelu.add_sublayer (ArgSpec(args=['self', 'name', 'sublayer'], varargs=None, keywords=None, defaults=None), ('document', '839ff3c0534677ba6ad8735c3fd4e995'))

python/paddle/fluid/dygraph/base.py

@@ -150,15 +150,15 @@ def _print_debug_msg(limit=5, is_test=False):
 @framework.dygraph_only
 def to_variable(value, block=None, name=None):
     """
-    This function will create a variable from ndarray
+    The API will create a ``Variable`` object from numpy\.ndarray or Variable object.
 
-    Args:
+    Parameters:
-        value(ndarray): the numpy value need to be convert
+        value(ndarray): The numpy\.ndarray object that needs to be converted, it can be multi-dimension, and the data type is one of numpy\.{float16, float32, float64, int16, int32, int64, uint8, uint16}.
-        block(fluid.Block|None): which block this variable will be in
+        block(fluid.Block, optional): Which block this variable will be in. Default: None.
-        name(str|None): Name of Variable
+        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`
 
-    return:
+    Returns:
-        Variable: The variable created from given numpy
+        Variable: ``Tensor`` created from the specified numpy\.ndarray object, data type and shape is the same as ``value`` .
 
     Examples:
 

python/paddle/fluid/dygraph/nn.py

@@ -1838,39 +1838,43 @@ class GRUUnit(layers.Layer):
 
 class NCE(layers.Layer):
     """
-    Compute and return the noise-contrastive estimation training loss. See
+    This interface is used to construct a callable object of the ``NCE`` class.
+    For more details, refer to code examples.
+    It implements the function of the ``NCE`` loss function.
+    By default this function uses a uniform distribution for sampling, and it
+    compute and return the noise-contrastive estimation training loss. See
     `Noise-contrastive estimation: A new estimation principle for unnormalized statistical models <http://www.jmlr.org/proceedings/papers/v9/gutmann10a/gutmann10a.pdf>`_ .
-    By default this operator uses a uniform distribution for sampling.
 
     Parameters:
         name_scope(str): The name of this class.
         num_total_classes (int): Total number of classes in all samples
-        param_attr (ParamAttr|None): The parameter attribute for learnable parameters/weights
+        param_attr (ParamAttr, optional): The parameter attribute for learnable weights(Parameter)
              of nce. If it is set to None or one attribute of ParamAttr, nce
              will create ParamAttr as param_attr. If the Initializer of the param_attr
              is not set, the parameter is initialized with Xavier. Default: None.
-        bias_attr (ParamAttr|bool|None): The parameter attribute for the bias of nce.
+        bias_attr (ParamAttr or bool, optional): The attribute for the bias of nce.
              If it is set to False, no bias will be added to the output units.
              If it is set to None or one attribute of ParamAttr, nce
              will create ParamAttr as bias_attr. If the Initializer of the bias_attr
              is not set, the bias is initialized zero. Default: None.
-        num_neg_samples (int): The number of negative classes. The default value is 10.
+        num_neg_samples (int, optional): The number of negative classes. The default value is 10.
-        sampler (str): The sampler used to sample class from negtive classes.
+        sampler (str, optional): The sampler used to sample class from negtive classes.
                        It can be 'uniform', 'log_uniform' or 'custom_dist'.
                        default: 'uniform'.
-        custom_dist (float[]|None): A float[] with size=num_total_classes.
+        custom_dist (float[], optional): A float[] with size=num_total_classes.
                        It is used when sampler is set to 'custom_dist'.
                        custom_dist[i] is the probability of i-th class to be sampled.
                        Default: None.
-        seed (int): The seed used in sampler. Default: 0.
+        seed (int, optional): The seed used in sampler. Default: 0.
-        is_sparse(bool): The flag indicating whether to use sparse update, the weight@GRAD and bias@GRAD will be changed to SelectedRows. Default: False.
+        is_sparse(bool, optional): The flag indicating whether to use sparse update. If is_sparse is True, the weight@GRAD and bias@GRAD will be changed to SelectedRows. Default: False.
 
-    Attributes:
+    Attribute:
-        weight (Parameter): the learnable weights of this layer.
+        **weight** (Parameter): the learnable weights of this layer.
-        bias (Parameter|None): the learnable bias of this layer.
 
+        **bias** (Parameter or None): the learnable bias of this layer.
+    
     Returns:
-        Variable: The output nce loss.
+        None
 
     Examples:
         .. code-block:: python
@@ -2087,6 +2091,10 @@ class NCE(layers.Layer):
 
 class PRelu(layers.Layer):
     """
+    This interface is used to construct a callable object of the ``PRelu`` class.
+    For more details, refer to code examples.
+    It implements three activation methods of the ``PRelu`` activation function.
+
     Equation:
 
     .. math::
@@ -2098,30 +2106,32 @@ class PRelu(layers.Layer):
           and element. all: all elements share same weight
           channel:elements in a channel share same weight
           element:each element has a weight
-        param_attr(ParamAttr|None): The parameter attribute for the learnable
+        param_attr(ParamAttr, optional): The parameter attribute for the learnable
-          weight (alpha).
+          weight (alpha). Default: None.
-
-    Attributes:
-        weight (Parameter): the learnable weights of this layer.
 
+    Attribute:
+        **weight** (Parameter): the learnable weights of this layer.
+    
     Returns:
-        Variable: The output tensor with the same shape as input.
+        None
 
     Examples:
 
         .. code-block:: python
 
           import paddle.fluid as fluid
+          from paddle.fluid.dygraph.base import to_variable
           import numpy as np
 
           inp_np = np.ones([5, 200, 100, 100]).astype('float32')
           with fluid.dygraph.guard():
+              inp_np = to_variable(inp_np)
               mode = 'channel'
               prelu = fluid.PRelu(
                  'prelu',
                  mode=mode,
                  param_attr=fluid.ParamAttr(initializer=fluid.initializer.Constant(1.0)))
-              dy_rlt = prelu(fluid.dygraph.base.to_variable(inp_np))
+              dy_rlt = prelu(inp_np)
 
     """