Merge pull request #631 from nouiz/fix_tests

theano/sandbox/cuda/blas.py

@@ -770,6 +770,11 @@ class GpuConv(GpuOp):
         verbose = self.verbose
         sub = sub.copy()
         max_threads_dim0 = self.max_threads_dim0
+        if max_threads_dim0 is None:
+            raise NotImplementedError("GpuConv.c_code should not be called "
+                                      "directly. It should be called by "
+                                      "make_thunk() that add some information "
+                                      "related to the selected GPU.")
         sub.update(locals())
         return """
     //Mandatory args

theano/sparse/tests/test_basic.py

@@ -244,7 +244,7 @@ class SparseInferShapeTester(utt.InferShapeTester):
                 [sp.csr_matrix(random_lil((10, 40),
                                config.floatX, 3)),
                  numpy.random.randn(10, 40).astype(config.floatX)],
-                MulSD)
+                MulSD, excluding=["local_mul_s_d"])
 
     def test_remove0(self):
         x = SparseType('csr', dtype=config.floatX)()

theano/tests/unittest_tools.py

@@ -170,10 +170,15 @@ class InferShapeTester(unittest.TestCase):
         # optimizations, if we don't want to enumerate them explicitly.
         self.mode = theano.compile.get_default_mode().including("canonicalize")
 
-    def _compile_and_check(self, inputs, outputs, numeric_inputs, cls):
-        outputs_function = theano.function(inputs, outputs, mode=self.mode)
+    def _compile_and_check(self, inputs, outputs, numeric_inputs, cls,
+                           excluding=None):
+        mode = self.mode
+        if excluding:
+            mode = mode.excluding(*excluding)
+
+        outputs_function = theano.function(inputs, outputs, mode=mode)
         shapes_function = theano.function(inputs, [o.shape for o in outputs],
-                mode=self.mode)
+                                          mode=mode)
         #theano.printing.debugprint(shapes_function)
         # Check that the Op is removed from the compiled function.
         topo_shape = shapes_function.maker.env.toposort()