fix con2d transpose bias by create and init it in build_once

paddle/fluid/API.spec

@@ -676,7 +676,7 @@ paddle.fluid.dygraph.LayerNorm.state_dict (ArgSpec(args=['self', 'destination',
 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', '47eb439a5568468fad70235f1e61ead9'))
-paddle.fluid.dygraph.NCE.__init__ (ArgSpec(args=['self', 'name_scope', 'num_total_classes', 'param_attr', 'bias_attr', 'num_neg_samples', 'sampler', 'custom_dist', 'seed', 'is_sparse'], varargs=None, keywords=None, defaults=(None, None, None, 'uniform', None, 0, False)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
+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'))
 paddle.fluid.dygraph.NCE.backward (ArgSpec(args=['self'], varargs='inputs', keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))

python/paddle/fluid/dygraph/nn.py

@@ -23,6 +23,7 @@
 from ..param_attr import ParamAttr
 from ..initializer import Normal, Constant, NumpyArrayInitializer
 import numpy as np
+import logging
 
 __all__ = [
     'Conv2D', 'Conv3D', 'Pool2D', 'FC', 'BatchNorm', 'Embedding', 'GRUUnit',
@@ -1374,13 +1375,20 @@ def _build_once(self, input):
                 shape=param_shape,
                 dtype=self._dtype,
                 default_initializer=Constant(1.0))
+        else:
+            if self._param_attr:
+                logging.warn("param_attr are only avaliable with scale is True")
+
         if self._shift:
             assert self._bias_attr is not False
             self._bias_w = self.create_parameter(
                 attr=self._bias_attr,
                 shape=param_shape,
                 dtype=self._dtype,
                 is_bias=True)
+        else:
+            if self._bias_attr:
+                logging.warn("bias_attr are only avaliable with shift is True")
 
     def forward(self, input):
         inputs = dict()
@@ -1410,7 +1418,7 @@ def forward(self, input):
                 "begin_norm_axis": self._begin_norm_axis
             })
 
-        return self._helper.append_activation(layer_norm_out)
+        return self._helper.append_activation(layer_norm_out, act=self._act)
 
 
 class GRUUnit(layers.Layer):
@@ -1648,6 +1656,7 @@ class NCE(layers.Layer):
     def __init__(self,
                  name_scope,
                  num_total_classes,
+                 sample_weight=None,
                  param_attr=None,
                  bias_attr=None,
                  num_neg_samples=None,
@@ -1661,7 +1670,7 @@ def __init__(self,
         self._num_total_classes = num_total_classes
 
         self._inputs = dict()
-
+        self._inputs['SampleWeight'] = sample_weight if sample_weight is not None else []
         if sampler == "uniform":
             sampler = 0
         elif sampler == "log_uniform":
@@ -1939,17 +1948,17 @@ def _build_once(self, x, y):
             dtype=self._dtype,
             is_bias=False)
 
-        if self._bias_attr:
-            bias_size = [1, self._size]
-            bias = self.create_parameter(
-                attr=self._bias_attr,
-                shape=bias_size,
-                dtype=self._dtype,
-                is_bias=True)
-            self._inputs["Bias"] = bias
+        bias_size = [1, self._size]
+        self._bias_param = self.create_parameter(
+            attr=self._bias_attr,
+            shape=bias_size,
+            dtype=self._dtype,
+            is_bias=True)
 
     def forward(self, x, y):
         self._inputs = {"X": x, "Y": y, "Weight": self._w}
+        if self._bias_param:
+            self._inputs["Bias"] = self._bias_param
         if self._name is not None:
             out = self._helper.create_variable(
                 name=".".join([self.full_name(), self._name]),
@@ -1964,7 +1973,7 @@ def forward(self, x, y):
             outputs={"Out": out})
 
         # add activation
-        return self._helper.append_activation(out)
+        return self._helper.append_activation(out, act=self._act)
 
 
 class Conv2DTranspose(layers.Layer):
@@ -2099,6 +2108,7 @@ def __init__(self,
         assert param_attr is not False, "param_attr should not be False in conv2d_transpose."
         self._param_attr = param_attr
         self._bias_attr = bias_attr
+        self._act = act
         self._groups = groups
         self._num_filters = num_filters
         self._use_cudnn = use_cudnn
@@ -2162,6 +2172,12 @@ def _build_once(self, input):
         self._img_filter = self.create_parameter(
             dtype=input.dtype, shape=filter_shape, attr=self._param_attr)
 
+        self._bias_param = self.create_parameter(
+            attr=self._bias_attr,
+            shape=[self._num_filters],
+            dtype=self._dtype,
+            is_bias=True)
+
     def forward(self, input):
         pre_bias = self._helper.create_variable_for_type_inference(
             dtype=input.dtype)
@@ -2179,8 +2195,19 @@ def forward(self, input):
                 'use_cudnn': self._use_cudnn
             })
 
-        pre_act = self._helper.append_bias_op(pre_bias, dim_start=1, dim_end=2)
-        out = self._helper.append_activation(pre_act)
+        if self._bias_param is not None:
+            pre_act = self._helper.create_variable_for_type_inference(
+                dtype=self._dtype)
+            self._helper.append_op(
+                type='elementwise_add',
+                inputs={'X': [pre_bias],
+                        'Y': [self._bias_param]},
+                outputs={'Out': [pre_act]},
+                attrs={'axis': 1})
+        else:
+            pre_act = pre_bias
+
+        out = self._helper.append_activation(pre_act, act=self._act)
         return out
 
 
@@ -2230,13 +2257,20 @@ def __init__(self,
         self._padding = padding
         self._bias_attr = bias_attr
         self._param_attr = param_attr
+        self._act = act
 
     def _build_once(self, input):
         self._dtype = self._helper.input_dtype(input)
         filter_shape = [self._filter_size * input.shape[1], self._num_filters]
         self._filter_param = self.create_parameter(
             attr=self._param_attr, shape=filter_shape, dtype=self._dtype)
 
+        self._bias_param = self.create_parameter(
+            attr=self._bias_attr,
+            shape=[self._num_filters],
+            dtype=self._dtype,
+            is_bias=True)
+
     def forward(self, input):
         pre_bias = self._helper.create_variable_for_type_inference(self._dtype)
         self._helper.append_op(
@@ -2251,8 +2285,20 @@ def forward(self, input):
                 'contextStart': -int(self._filter_size // 2),
                 'contextLength': self._filter_size
             })
-        pre_act = self._helper.append_bias_op(pre_bias)
-        return self._helper.append_activation(pre_act)
+
+        if self._bias_param is not None:
+            pre_act = self._helper.create_variable_for_type_inference(
+                dtype=self._dtype)
+            self._helper.append_op(
+                type='elementwise_add',
+                inputs={'X': [pre_bias],
+                        'Y': [self._bias_param]},
+                outputs={'Out': [pre_act]},
+                attrs={'axis': 1})
+        else:
+            pre_act = pre_bias
+
+        return self._helper.append_activation(pre_act, act=self._act)
 
 
 class RowConv(layers.Layer):
@@ -2614,6 +2660,7 @@ def forward(self, nodes_vector, edge_set):
             out = self.create_variable(
                 name=self._name, dtype=self._dtype, persistable=False)
         else:
+
             out = self._helper.create_variable_for_type_inference(
                 dtype=self._dtype)