Fix tests for GpuCAReduce and a type error in some nan cases.

theano/sandbox/gpuarray/elemwise.py

@@ -319,13 +319,14 @@ class GpuCAReduce(HideC, CAReduceDtype):
 
         acc_dtype = getattr(self, 'acc_dtype', None)
         if acc_dtype is None:
-            acc_dtype = node.output[0].type.dtype
+            acc_dtype = node.outputs[0].type.dtype
 
         if any(redux):
             if not hasattr(node, '_cache_reduction_k'):
                 node._cache_reduction_k = self.generate_kernel(node, acc_dtype,
                                                                redux)
-            output[0] = node._cache_reduction_k(input)
+            output[0] = node._cache_reduction_k(input).astype(copy=False,
+                                             dtype=node.outputs[0].type.dtype)
         else:
             output[0] = pygpu.gpuarray.array(input, copy=True,
                                              dtype=node.outputs[0].type.dtype)

theano/sandbox/gpuarray/tests/test_elemwise.py

@@ -46,7 +46,6 @@ class test_GpuCAReduce(test_CAReduce):
                 self.with_linker(gof.PerformLinker(), op, dtype=dtype)
 
     def test_perform_nan(self):
-        raise SkipTest("for now")
         for dtype in self.dtypes:
             for op in self.reds:
                 self.with_linker(gof.PerformLinker(), op, dtype=dtype,

theano/tensor/tests/test_elemwise.py

@@ -11,7 +11,7 @@ from theano.gof.python25 import all, any
 from theano import gof, scalar, config
 
 from theano import tensor
-from theano.tensor import TensorType
+from theano.tensor import TensorType, as_tensor_variable
 from theano.compile.mode import get_default_mode
 from theano.tensor.elemwise import (CAReduce, Elemwise, DimShuffle,
                                     Prod, ProdWithoutZeros)
@@ -306,9 +306,9 @@ class test_CAReduce(unittest_tools.InferShapeTester):
                 dtype = theano.config.floatX
             x = TensorType(dtype, [(entry == 1) for entry in xsh])('x')
             if tensor_op is None:
-                e = self.op(scalar_op, axis=tosum)(x)
+                e = as_tensor_variable(self.op(scalar_op, axis=tosum)(x))
             else:
-                e = tensor_op(x, axis=tosum)
+                e = as_tensor_variable(tensor_op(x, axis=tosum))
 
             if tosum is None:
                 tosum = range(len(xsh))
@@ -413,7 +413,7 @@ class test_CAReduce(unittest_tools.InferShapeTester):
             if isinstance(linker, gof.PerformLinker):
                 x = TensorType(dtype, [(entry == 1) for entry in xsh])('x')
                 if tensor_op is None:
-                    e = CAReduce(scalar_op, axis=tosum)(x)
+                    e = self.op(scalar_op, axis=tosum)(x)
                 else:
                     e = tensor_op(x, axis=tosum)
                 if tosum is None:
@@ -509,8 +509,8 @@ class test_CAReduce(unittest_tools.InferShapeTester):
                 tosum = range(len(xsh))
             xv = numpy.asarray(numpy.random.rand(*xsh), dtype=dtype)
             self._compile_and_check([x],
-                            [CAReduce(scalar.add, axis=tosum)(x)],
-                            [xv], CAReduce, ["local_cut_useless_reduce"])
+                            [self.op(scalar.add, axis=tosum)(x)],
+                            [xv], self.op, ["local_cut_useless_reduce"])
 
 
 class test_Prod(unittest.TestCase):