check num_groups is 1 before trying alternative passes

theano/gpuarray/dnn.py

@@ -978,7 +978,7 @@ def dnn_conv(img, kerns, border_mode='valid', subsample=(1, 1), dilation=(1, 1),
     fgraph = getattr(img, 'fgraph', None) or getattr(kerns, 'fgraph', None)
     ctx_name = infer_context_name(img, kerns)
     if (border_mode == 'valid' and subsample == (1, 1) and dilation == (1, 1) and
-            direction_hint == 'bprop weights'):
+            direction_hint == 'bprop weights' and num_groups == 1):
         # Special case: We are asked to use GpuDnnConvGradW. We need to set
         # up a suitable 'fake' convolution to compute the gradient for.
         img = gpu_contiguous(img.dimshuffle(1, 0, 2, 3))
@@ -999,7 +999,7 @@ def dnn_conv(img, kerns, border_mode='valid', subsample=(1, 1), dilation=(1, 1),
         return as_gpuarray_variable(conv.dimshuffle(1, 0, 2, 3), ctx_name)
 
     elif (border_mode == 'full' and subsample == (1, 1) and dilation == (1, 1) and
-          direction_hint != 'forward!'):
+          direction_hint != 'forward!' and num_groups == 1):
         # Special case: We can be faster by using GpuDnnConvGradI to compute
         # the full convolution as the backward pass of a valid convolution.
         # We just need to set up a suitable 'fake' valid convolution.

theano/gpuarray/opt.py

@@ -1568,7 +1568,8 @@ def local_abstractconv_gemm(node):
                 (None not in node.op.imshp[-2:]) and
                 (node.op.kshp is not None) and
                 (None not in node.op.kshp) and
-                border_mode != "half"):
+                border_mode != "half" and
+                node.op.num_groups == 1):
             # we know the kernel and output size
             prod1 = node.op.kshp[0] * node.op.kshp[1]
             prod2 = ((node.op.imshp[-2] - node.op.kshp[0] + 1) *