Make sure to run the new test on the GPU and skip it if we can't use the new backend.

.jenkins/jenkins_test2.sh

@@ -74,6 +74,7 @@ export PYTHONPATH=${PYTHONPATH}:$LIBDIR/lib/python
 THEANO_GPUARRAY_TESTS="theano/gpuarray/tests \
                        theano/sandbox/tests/test_rng_mrg.py:test_consistency_GPUA_serial \
                        theano/sandbox/tests/test_rng_mrg.py:test_consistency_GPUA_parallel \
+                       theano/sandbox/tests/test_rng_mrg.py:test_GPUA_full_fill \
                        theano/scan_module/tests/test_scan.py:T_Scan_Gpuarray"
 FLAGS="init_gpu_device=$DEVICE,gpuarray.preallocate=1000,mode=FAST_RUN"
 THEANO_FLAGS=${FLAGS} time nosetests -v --with-xunit --xunit-file=theanogpuarray_tests.xml ${THEANO_GPUARRAY_TESTS}

theano/sandbox/tests/test_rng_mrg.py

@@ -363,7 +363,7 @@ def test_GPU_nstreams_limit():
 def test_consistency_GPUA_serial():
     # Verify that the random numbers generated by GPUA_mrg_uniform, serially,
     # are the same as the reference (Java) implementation by L'Ecuyer et al.
-    from theano.gpuarray.tests.test_basic_ops import mode_with_gpu as mode
+    from theano.gpuarray.tests.config import mode_with_gpu as mode
     from theano.gpuarray.type import gpuarray_shared_constructor
 
     seed = 12345
@@ -414,7 +414,7 @@ def test_consistency_GPUA_parallel():
     # Verify that the random numbers generated by GPUA_mrg_uniform, in
     # parallel, are the same as the reference (Java) implementation by
     # L'Ecuyer et al.
-    from theano.gpuarray.tests.test_basic_ops import mode_with_gpu as mode
+    from theano.gpuarray.tests.config import mode_with_gpu as mode
     from theano.gpuarray.type import gpuarray_shared_constructor
 
     seed = 12345
@@ -463,6 +463,7 @@ def test_consistency_GPUA_parallel():
 def test_GPUA_full_fill():
     # Make sure the whole sample buffer is filled.  Also make sure
     # large samples are consistent with CPU results.
+    import theano.gpuarray.tests.config
     from theano.gpuarray.type import gpuarray_shared_constructor
 
     # This needs to be large to trigger the problem on GPU