fixed a bug in old backend and cleaned some tests

theano/sandbox/cuda/dnn_fwd.c

@@ -224,6 +224,18 @@ APPLY_SPECIFIC(conv_fwd)(CudaNdarray *input, CudaNdarray *kerns,
                                                   APPLY_SPECIFIC(output),
                                                   chosen_algo,
                                                   &worksize);
+    if (err == CUDNN_STATUS_NOT_SUPPORTED) {
+      // Fallback to none algo if not supported
+      chosen_algo = CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_GEMM;
+
+      err = cudnnGetConvolutionForwardWorkspaceSize(_handle,
+                                                    APPLY_SPECIFIC(input),
+                                                    APPLY_SPECIFIC(kerns),
+                                                    desc,
+                                                    APPLY_SPECIFIC(output),
+                                                    chosen_algo,
+                                                    &worksize);
+    }
     if (err != CUDNN_STATUS_SUCCESS) {
       PyErr_Format(PyExc_RuntimeError,
                    "GpuDnnConv: error getting worksize: %s",

theano/sandbox/cuda/tests/test_dnn.py

@@ -393,8 +393,9 @@ def test_pooling_with_tensor_vars():
 
 
 def test_old_pool_interface():
-    if not cuda.dnn.dnn_available():
+    if not cuda.dnn.dnn_available() or cuda.dnn.version() > (5000, 5000):
         raise SkipTest(cuda.dnn.dnn_available.msg)
+
     testfile_dir = os.path.dirname(os.path.realpath(__file__))
     fname = 'old_pool_interface.pkl'
     with open(os.path.join(testfile_dir, fname), 'rb') as fp:

theano/sandbox/gpuarray/tests/test_dnn.py

@@ -464,6 +464,9 @@ class TestDnnInferShapes(utt.InferShapeTester):
                                         [conv_modes[0]])),
                           testcase_func_name=utt.custom_name_func)
     def test_conv(self, algo, border_mode, conv_mode):
+        if algo == 'winograd' and dnn.version() < 5000:
+            raise SkipTest(dnn.dnn_available.msg)
+
         self._test_conv(T.ftensor4('img'),
                         T.ftensor4('kerns'),
                         T.ftensor4('out'),