Following a change in pygpu to raise a ValueError instead of a GpuArrayException…

theano/sandbox/gpuarray/basic_ops.py

@@ -734,53 +734,10 @@ class GpuJoin(HideC, Join):
                      [GpuArrayType(broadcastable=node.outputs[0].broadcastable,
                                    dtype=node.outputs[0].dtype)()])
 
-    def _need_broadcast(self, node):
-        p_broadcastable = node.inputs[1].broadcastable
-        need_b = [any(t.broadcastable[i] != p_broadcastable[i]
-                      for t in node.inputs[2:])
-                  for i in range(node.inputs[1].ndim)]
-        try:
-            # We never need to broadcast on the join axis
-            axis_v = int(tensor.basic.get_scalar_constant_value(node.inputs[0]))
-            need_b[axis_v] = False
-        except tensor.basic.NotScalarConstantError:
-            pass
-        return any(need_b)
-
     def perform(self, node, axis_and_tensors, out_):
         out, = out_
         axis = int(axis_and_tensors[0])
         tensors = axis_and_tensors[1:]
-        if not hasattr(node, '_need_broadcast'):
-            node._need_broadcast = self._need_broadcast(node)
-        if node._need_broadcast:
-            width_sum = 0
-            template_shape = list(tensors[0].shape)
-            for t in tensors:
-                width_sum += t.shape[axis]
-                tmp_shape = list(t.shape)
-                tmp_shape[axis] = template_shape[axis]
-                if tmp_shape != template_shape:
-                    raise ValueError("Shape of input GpuArrays must"
-                                     " agree except for the 'axis' dimension")
-            template_shape[axis] = width_sum
-            rval = pygpu.zeros(template_shape, dtype=node.outputs[0].dtype)
-
-            curpos = 0
-
-            def construct_slices(curlen):
-                slices = [slice(None, None, None) for i in \
-                              range(len(template_shape))]
-                slices[axis] = slice(curpos, curpos + curlen, None)
-                return tuple(slices)
-
-            for t in tensors:
-                curlen = t.shape[axis]
-                rval.__setitem__(construct_slices(curlen), t)
-                curpos += curlen
-
-            out[0] = rval
-        else:
         out[0] = pygpu.concatenate(tensors, axis=axis).astype(
             node.outputs[0].dtype)
 
@@ -788,9 +745,6 @@ class GpuJoin(HideC, Join):
         return (1,)
 
     def c_code(self, node, name, inputs, out_, sub):
-        if self._need_broadcast(node):
-            node._need_broadcast = True
-            raise MethodNotDefined, 'broadcast not supported'
         copy_to_list = []
         restype=pygpu.gpuarray.dtype_to_typecode(node.outputs[0].dtype)
         for i, inp in enumerate(inputs[1:]):