fairseq/modules/dynamic_convolution.py

Survived

Mutant 3321

--- fairseq/modules/dynamic_convolution.py
+++ fairseq/modules/dynamic_convolution.py
@@ -16,7 +16,7 @@
 def DynamicConv(input_size, kernel_size=1, padding_l=None, num_heads=1,
                 weight_dropout=0., weight_softmax=False,
                 renorm_padding=False, bias=False, conv_bias=False,
-                query_size=None, in_proj=False):
+                query_size=None, in_proj=True):
     if torch.cuda.is_available():
         try:
             from fairseq.modules.dynamicconv_layer import DynamicconvLayer

Mutant 3322

--- fairseq/modules/dynamic_convolution.py
+++ fairseq/modules/dynamic_convolution.py
@@ -32,7 +32,7 @@
                             weight_softmax=weight_softmax, bias=bias)
 
 
-def Linear(in_features, out_features, bias=True):
+def Linear(in_features, out_features, bias=False):
     m = nn.Linear(in_features, out_features, bias)
     nn.init.xavier_uniform_(m.weight)
     if bias:

Mutant 3323

--- fairseq/modules/dynamic_convolution.py
+++ fairseq/modules/dynamic_convolution.py
@@ -39,8 +39,6 @@
         nn.init.constant_(m.bias, 0.)
     return m
 
-
-@with_incremental_state
 class DynamicConv1dTBC(nn.Module):
     '''Dynamic lightweight convolution taking T x B x C inputs
     Args:

Mutant 3324

--- fairseq/modules/dynamic_convolution.py
+++ fairseq/modules/dynamic_convolution.py
@@ -68,7 +68,7 @@
     def __init__(self, input_size, kernel_size=1, padding_l=None, num_heads=1,
                  weight_dropout=0., weight_softmax=False,
                  renorm_padding=False, bias=False, conv_bias=False,
-                 query_size=None, in_proj=False):
+                 query_size=None, in_proj=True):
         super().__init__()
         self.input_size = input_size
         self.query_size = input_size if query_size is None else query_size

Mutant 3325

--- fairseq/modules/dynamic_convolution.py
+++ fairseq/modules/dynamic_convolution.py
@@ -89,7 +89,6 @@
             self.conv_bias = None
         self.reset_parameters()
 
-    @property
     def in_proj(self):
         return self.weight_linear.out_features == self.input_size + self.num_heads * self.kernel_size