add cudnn gradweights alternative

theano/gpuarray/dnn.py

@@ -3012,6 +3012,7 @@ def local_abstractconv3d_cudnn_alternative(node):
     border_mode = node.op.border_mode
     subsample = node.op.subsample
     filter_dilation = node.op.filter_dilation
+    precision = get_precision(None, [inp1, inp2])
 
     if node.op.filter_flip:
         conv_mode = 'conv'
@@ -3034,7 +3035,41 @@ def local_abstractconv3d_cudnn_alternative(node):
                           dilation=filter_dilation,
                           direction_hint=direction_hint,
                           conv_mode=conv_mode)
-    return rval
+
+    if isinstance(op, AbstractConv3d_gradWeights):
+        if(border_mode == 'valid' and subsample == (1, 1, 1) and
+           filter_dilation == (1, 1, 1)):
+            img = gpu_contiguous(inp1)
+            topgrad = gpu_contiguous(inp2)
+            ctx_name = infer_context_name(img, topgrad)
+            img = gpu_contiguous(img.dimshuffle(1, 0, 2, 3, 4))
+            topgrad = gpu_contiguous(topgrad.dimshuffle(1, 0, 2, 3, 4))
+            ishape = [shape_i_op(i)(img) for i in range(img.ndim)]
+            tshape = [shape_i_op(i)(topgrad) for i in range(topgrad.ndim)]
+            out_shp = get_conv_output_shape(ishape,
+                                            tshape,
+                                            border_mode=border_mode,
+                                            subsample=subsample,
+                                            filter_dilation=filter_dilation)
+
+            out_shp = assert_conv_shape(out_shp)
+            out = GpuAllocEmpty(dtype=img.dtype, context_name=ctx_name)(*out_shp)
+            desc = GpuDnnConvDesc(border_mode=border_mode,
+                                  subsample=subsample,
+                                  dilation=filter_dilation,
+                                  conv_mode='cross',
+                                  precision=precision)(out.shape)
+
+            conv = GpuDnnConv(algo=None)(img, topgrad, out, desc)
+            if conv_mode == 'conv':
+                conv = conv[:, :, ::-1, ::-1, ::-1]
+
+            rval = as_gpuarray_variable(conv.dimshuffle(1, 0, 2, 3, 4), ctx_name)
+        else:
+            return None
+
+
+    return [rval]
 
 
 @local_optimizer([AbstractConv2d_gradWeights, AbstractConv3d_gradWeights])