Fix broadcastable pattern of gradient in abstract conv

theano/tensor/nnet/abstract_conv.py

@@ -5,7 +5,7 @@ Define abstract conv2d interface
 import logging
 import theano
 
-from theano.tensor import as_tensor_variable
+from theano.tensor import as_tensor_variable, patternbroadcast
 from theano.gof import Apply, Op
 
 
@@ -314,6 +314,12 @@ class AbstractConv2d(BaseAbstractConv2d):
                                                self.filter_flip)(
 
             bottom, top, weights.shape[-2:])
+
+        # Make sure that the broadcastable pattern of the inputs is used
+        # for the gradients, even if the grad opts are not able to infer
+        # that the dimensions are broadcastable.
+        d_bottom = patternbroadcast(d_bottom, bottom.broadcastable)
+        d_weights = patternbroadcast(d_weights, weights.broadcastable)
         return d_bottom, d_weights
 
 
@@ -369,6 +375,12 @@ class AbstractConv2d_gradWeights(BaseAbstractConv2d):
                                self.border_mode,
                                self.subsample,
                                self.filter_flip)(bottom, weights)
+        # Make sure that the broadcastable pattern of the inputs is used
+        # for the gradients, even if the grad opts are not able to infer
+        # that the dimensions are broadcastable.
+        d_bottom = patternbroadcast(d_bottom, bottom.broadcastable)
+        d_top = patternbroadcast(d_top, top.broadcastable)
+
         d_height_width = (theano.gradient.DisconnectedType()(),)
         return (d_bottom, d_top) + d_height_width
 
@@ -425,6 +437,12 @@ class AbstractConv2d_gradInputs(BaseAbstractConv2d):
         d_top = AbstractConv2d(self.imshp, self.kshp,
                                self.border_mode, self.subsample)(
                                    bottom, weights)
+        # Make sure that the broadcastable pattern of the inputs is used
+        # for the gradients, even if the grad opts are not able to infer
+        # that the dimensions are broadcastable.
+        d_weights = patternbroadcast(d_weights, weights.broadcastable)
+        d_top = patternbroadcast(d_top, top.broadcastable)
+
         d_height_width = (theano.gradient.DisconnectedType()(),)
         return (d_weights, d_top) + d_height_width