added gradinputs

theano/gpuarray/dnn.py

@@ -2888,39 +2888,71 @@ def local_abstractconv_cudnn(node):
         return local_abstractconv3d_cudnn_graph(node.op, ctx, node.inputs, node.outputs)
 
 
-@local_optimizer([AbstractConv2d])
+@local_optimizer([AbstractConv2d, AbstractConv2d_gradWeights, AbstractConv2d_gradInputs])
 def local_abstractconv_cudnn_alternative(node):
-    if not isinstance(node.op, AbstractConv2d):
+    if(not isinstance(node.op, (AbstractConv2d, AbstractConv2d_gradWeights,
+       AbstractConv2d_gradInputs))):
         return
 
     if version(raises=False) < 6000 and node.op.filter_dilation != (1, 1):
         return None
-
     inp1 = node.inputs[0]
     inp2 = node.inputs[1]
 
     if not dnn_available(inp1.type.context_name):
         return
 
+    op = node.op
+    border_mode = node.op.border_mode
+    subsample = node.op.subsample
+    filter_dilation = node.op.filter_dilation
+    num_groups = node.op.num_groups
+    precision = get_precision(None, [inp1, inp2])
+
     if node.op.filter_flip:
         conv_mode = 'conv'
     else:
         conv_mode = 'cross'
 
-    if node.op.border_mode == 'full':
+    if isinstance(op, AbstractConv2d):
+        if border_mode == 'full':
             direction_hint = 'bprop inputs'
-    elif node.op.border_mode == 'valid':
+        elif border_mode == 'valid':
             direction_hint = 'bprop weights'
         else:
             return None
 
         rval = dnn_conv(inp1, inp2,
-                    border_mode=node.op.border_mode,
-                    subsample=node.op.subsample,
-                    dilation=node.op.filter_dilation,
+                        border_mode=border_mode,
+                        subsample=subsample,
+                        dilation=filter_dilation,
                         direction_hint=direction_hint,
                         conv_mode=conv_mode,
-                    num_groups=node.op.num_groups)
+                        num_groups=num_groups)
+
+    if isinstance(op, AbstractConv2d_gradInputs):
+        if border_mode == 'valid' and subsample == (1, 1) and num_groups == 1:
+            kerns = gpu_contiguous(inp1.dimshuffle(1, 0, 2, 3))
+            topgrad = gpu_contiguous(inp2)
+            ctx_name = infer_context_name(kerns, topgrad)
+            conv_mode = 'cross' if conv_mode == 'conv' else 'conv'
+            desc = GpuDnnConvDesc(border_mode='full',
+                                  subsample=subsample,
+                                  dilation=filter_dilation,
+                                  conv_mode=conv_mode,
+                                  precision=precision)(kerns.shape)
+
+            tshape = [shape_i_op(i)(topgrad) for i in range(topgrad.ndim)]
+            kshape = [shape_i_op(i)(kerns) for i in range(kerns.ndim)]
+            shape = get_conv_output_shape(tshape,
+                                          kshape,
+                                          border_mode='full',
+                                          subsample=subsample,
+                                          filter_dilation=filter_dilation)
+
+            shape = assert_conv_shape(shape)
+            out = GpuAllocEmpty(dtype=topgrad.dtype, context_name=ctx_name)(*shape)
+            rval = GpuDnnConv(algo=None, num_groups=num_groups)(topgrad, kerns, out, desc)
 
     return [rval]