change positioning of num_groups parameter and add algo param to GpuconvGradW and GpuconvGradI

theano/gpuarray/dnn.py

@@ -868,8 +868,8 @@ class GpuDnnConvGradI(DnnBase):
 
 
 def dnn_conv(img, kerns, border_mode='valid', subsample=(1, 1), dilation=(1, 1),
-             num_groups=1, conv_mode='conv', direction_hint=None, workmem=None,
-             algo=None, precision=None):
+             conv_mode='conv', direction_hint=None, workmem=None,
+             algo=None, precision=None, num_groups=1):
     """
     GPU convolution using cuDNN from NVIDIA.
 
@@ -1111,7 +1111,8 @@ def dnn_conv3d(img, kerns, border_mode='valid', subsample=(1, 1, 1), dilation=(1
 
 
 def dnn_gradweight(img, topgrad, kerns_shp, border_mode='valid',
-                   subsample=(1, 1), dilation=(1, 1), num_groups=1, conv_mode='conv', precision=None):
+                   subsample=(1, 1), dilation=(1, 1), conv_mode='conv',
+                   precision=None, algo=None, num_groups=1):
     """
     TODO: document this
     """
@@ -1126,7 +1127,7 @@ def dnn_gradweight(img, topgrad, kerns_shp, border_mode='valid',
     desc = GpuDnnConvDesc(border_mode=border_mode, subsample=subsample, dilation=dilation,
                           conv_mode=conv_mode, precision=precision)(kerns_shp)
     out = GpuAllocEmpty(dtype=img.dtype, context_name=ctx_name)(*kerns_shp)
-    return GpuDnnConvGradW(num_groups=num_groups)(img, topgrad, out, desc)
+    return GpuDnnConvGradW(algo=algo, num_groups=num_groups)(img, topgrad, out, desc)
 
 
 def dnn_gradweight3d(img, topgrad, kerns_shp, border_mode='valid',
@@ -1139,7 +1140,8 @@ def dnn_gradweight3d(img, topgrad, kerns_shp, border_mode='valid',
 
 
 def dnn_gradinput(kerns, topgrad, img_shp, border_mode='valid',
-                  subsample=(1, 1), dilation=(1, 1), num_groups=1, conv_mode='conv', precision=None):
+                  subsample=(1, 1), dilation=(1, 1), conv_mode='conv',
+                  precision=None, algo=None, num_groups=1):
     """
     TODO: document this
     """
@@ -1154,7 +1156,7 @@ def dnn_gradinput(kerns, topgrad, img_shp, border_mode='valid',
     desc = GpuDnnConvDesc(border_mode=border_mode, subsample=subsample, dilation=dilation,
                           conv_mode=conv_mode, precision=precision)(kerns.shape)
     out = GpuAllocEmpty(dtype=kerns.dtype, context_name=ctx_name)(*img_shp)
-    return GpuDnnConvGradI(num_groups=num_groups)(kerns, topgrad, out, desc)
+    return GpuDnnConvGradI(algo=algo, num_groups=num_groups)(kerns, topgrad, out, desc)
 
 
 def dnn_gradinput3d(kerns, topgrad, img_shp, border_mode='valid',
@@ -2679,9 +2681,9 @@ def local_abstractconv_cudnn_graph(op, context_name, inputs, outputs):
                         border_mode=op.border_mode,
                         subsample=op.subsample,
                         dilation=op.filter_dilation,
-                        num_groups=op.num_groups,
                         direction_hint='forward!',
-                        conv_mode=conv_mode)
+                        conv_mode=conv_mode,
+                        num_groups=op.num_groups)
     elif isinstance(op, AbstractConv2d_gradWeights):
         shape = (inp2.shape[1], inp1.shape[1],
                  inputs[2][0], inputs[2][1])
@@ -2689,8 +2691,8 @@ def local_abstractconv_cudnn_graph(op, context_name, inputs, outputs):
                               border_mode=op.border_mode,
                               subsample=op.subsample,
                               dilation=op.filter_dilation,
-                              num_groups=op.num_groups,
-                              conv_mode=conv_mode)
+                              conv_mode=conv_mode,
+                              num_groups=op.num_groups)
     elif isinstance(op, AbstractConv2d_gradInputs):
         shape = (inp2.shape[0], inp1.shape[1],
                  inputs[2][0], inputs[2][1])
@@ -2698,8 +2700,8 @@ def local_abstractconv_cudnn_graph(op, context_name, inputs, outputs):
                              border_mode=op.border_mode,
                              subsample=op.subsample,
                              dilation=op.filter_dilation,
-                             num_groups=op.num_groups,
-                             conv_mode=conv_mode)
+                             conv_mode=conv_mode,
+                             num_groups=op.num_groups)
     return [rval]