Remove useless optimizer now that mixed-type abstractconv can't happen.

theano/sandbox/gpuarray/opt.py

@@ -14,10 +14,8 @@ from theano.gof.optdb import LocalGroupDB
 from theano.scalar.basic import Scalar, Pow, Cast
 from theano.scan_module import scan_utils, scan_op, scan_opt
 
-from theano.tensor import as_tensor_variable
 from theano.tensor.nnet.conv import ConvOp
-from theano.tensor.nnet.abstract_conv import (BaseAbstractConv2d,
-                                              AbstractConv2d,
+from theano.tensor.nnet.abstract_conv import (AbstractConv2d,
                                               AbstractConv2d_gradWeights,
                                               AbstractConv2d_gradInputs)
 
@@ -819,26 +817,6 @@ def local_lift_abstractconv2d(node, context_name):
                                    context_name=context_name)
     return [node.op(*inps)]
 
-
-# This will deal with ops that don't have an explicit transfer but
-# have one of their inputs on the GPU already and the other not on the
-# GPU (to avoid endlessly replacing things).
-@register_opt('fast_compile')
-@local_optimizer([AbstractConv2d,
-                  AbstractConv2d_gradWeights,
-                  AbstractConv2d_gradInputs])
-def local_gpu_abstractconv2d(node):
-    if isinstance(node.op, BaseAbstractConv2d):
-        if ((isinstance(node.inputs[0].type, GpuArrayType) or
-             isinstance(node.inputs[1].type, GpuArrayType)) and
-            not (isinstance(node.inputs[0].type, GpuArrayType) or
-                 isinstance(node.inputs[1].type, GpuArrayType))):
-            inps = list(node.inputs)
-            ctx_name = infer_context_name(inps[0], inps[1])
-            inps[0] = as_gpuarray_variable(inps[0], context_name=ctx_name)
-            inps[1] = as_gpuarray_variable(inps[1], context_name=ctx_name)
-            return as_tensor_variable(node.op(*inps))
-
 # Register this here so that it goes after the abstract lifting
 register_opt()(conv_groupopt)