Make sure to always pass num_groups

theano/gpuarray/dnn.py

@@ -1108,8 +1108,9 @@ def dnn_conv(img, kerns, border_mode='valid', subsample=(1, 1), dilation=(1, 1),
         out = GpuAllocEmpty(dtype=img.dtype, context_name=ctx_name)(*out_shp)
         precision, _ = get_precision(precision, [img, kerns], for_grad=True)
         desc = GpuDnnConvDesc(border_mode='valid', subsample=(1, 1), dilation=(1, 1),
+                              num_groups=num_groups,
                               conv_mode='cross', precision=precision)(out.shape)
-        conv = GpuDnnConvGradW()(img, kerns, out, desc)
+        conv = GpuDnnConvGradW(num_groups=num_groups)(img, kerns, out, desc)
         return as_gpuarray_variable(conv.dimshuffle(1, 0, 2, 3), ctx_name)
 
     elif (border_mode == 'full' and subsample == (1, 1) and
@@ -1128,8 +1129,9 @@ def dnn_conv(img, kerns, border_mode='valid', subsample=(1, 1), dilation=(1, 1),
         out = GpuAllocEmpty(dtype=img.dtype, context_name=ctx_name)(*out_shp)
         precision, _ = get_precision(precision, [img, kerns], for_grad=True)
         desc = GpuDnnConvDesc(border_mode='valid', subsample=(1, 1), dilation=dilation,
+                              num_groups=num_groups,
                               conv_mode=conv_mode, precision=precision)(kerns.shape)
-        return GpuDnnConvGradI()(kerns, img, out, desc)
+        return GpuDnnConvGradI(num_groups=num_groups)(kerns, img, out, desc)
 
     # Standard case: We use GpuDnnConv with suitable padding.
     return _dnn_conv(img, kerns, algo=algo, border_mode=border_mode, subsample=subsample, dilation=dilation,
@@ -1213,8 +1215,9 @@ def dnn_conv3d(img, kerns, border_mode='valid', subsample=(1, 1, 1), dilation=(1
         out = GpuAllocEmpty(dtype=img.dtype, context_name=ctx_name)(*out_shp)
         precision, _ = get_precision(precision, [img, kerns], for_grad=True)
         desc = GpuDnnConvDesc(border_mode='valid', subsample=(1, 1, 1), dilation=(1, 1, 1),
+                              num_groups=num_groups,
                               conv_mode='cross', precision=precision)(out.shape)
-        conv = GpuDnnConvGradW()(img, kerns, out, desc)
+        conv = GpuDnnConvGradW(num_groups=num_groups)(img, kerns, out, desc)
         return as_gpuarray_variable(conv.dimshuffle(1, 0, 2, 3, 4), ctx_name)
 
     elif (border_mode == 'full' and subsample == (1, 1, 1) and
@@ -1234,8 +1237,9 @@ def dnn_conv3d(img, kerns, border_mode='valid', subsample=(1, 1, 1), dilation=(1
         out = GpuAllocEmpty(dtype=img.dtype, context_name=ctx_name)(*out_shp)
         precision, _ = get_precision(precision, [img, kerns], for_grad=True)
         desc = GpuDnnConvDesc(border_mode='valid', subsample=(1, 1, 1), dilation=dilation,
+                              num_groups=num_groups,
                               conv_mode=conv_mode, precision=precision)(kerns.shape)
-        return GpuDnnConvGradI()(kerns, img, out, desc)
+        return GpuDnnConvGradI(num_groups=num_groups)(kerns, img, out, desc)
 
     # Standard case: We use GpuDnnConv with suitable padding.
     return _dnn_conv(img, kerns, algo=algo, border_mode=border_mode, subsample=subsample, dilation=dilation,
@@ -3435,9 +3439,11 @@ def local_abstractconv3d_cudnn_alt(node):
                                   subsample=subsample,
                                   dilation=filter_dilation,
                                   conv_mode='cross',
+                                  num_groups=num_groups,
                                   precision=precision)(out.shape)
 
-            conv = GpuDnnConv(algo=None)(img, topgrad, out, desc)
+            conv = GpuDnnConv(algo=None, num_groups=num_groups)(
+                img, topgrad, out, desc)
             if conv_mode == 'conv':
                 conv = conv[:, :, ::-1, ::-1, ::-1]
 
@@ -3455,6 +3461,7 @@ def local_abstractconv3d_cudnn_alt(node):
                                   subsample=subsample,
                                   dilation=filter_dilation,
                                   conv_mode=conv_mode,
+                                  num_groups=num_groups,
                                   precision=precision)(kerns.shape)
 
             tshape = [shape_i_op(i)(topgrad) for i in range(topgrad.ndim)]
@@ -3467,7 +3474,8 @@ def local_abstractconv3d_cudnn_alt(node):
 
             shape = assert_conv_shape(shape)
             out = GpuAllocEmpty(dtype=topgrad.dtype, context_name=ctx_name)(*shape)
-            rval = GpuDnnConv(algo=None)(topgrad, kerns, out, desc)
+            rval = GpuDnnConv(algo=None, num_groups=num_groups)(
+                topgrad, kerns, out, desc)
         else:
             return None
 
@@ -3554,21 +3562,21 @@ def local_dnn_convi_alpha_merge(node, *inputs):
 @output_merge(GpuDnnConv, alpha_in=4, beta_in=5, out_in=2)
 def local_dnn_conv_output_merge(node, *inputs):
     inputs = inputs[0:2] + (gpu_contiguous(inputs[2]),) + inputs[3:]
-    return [GpuDnnConv(algo=node.op.algo)(*inputs)]
+    return [GpuDnnConv(algo=node.op.algo, num_groups=node.op.num_groups)(*inputs)]
 
 
 @register_opt('cudnn')
 @output_merge(GpuDnnConvGradW, alpha_in=4, beta_in=5, out_in=2)
 def local_dnn_convw_output_merge(node, *inputs):
     inputs = inputs[0:2] + (gpu_contiguous(inputs[2]),) + inputs[3:]
-    return [GpuDnnConvGradW(algo=node.op.algo)(*inputs)]
+    return [GpuDnnConvGradW(algo=node.op.algo, num_groups=node.op.num_groups)(*inputs)]
 
 
 @register_opt('cudnn')
 @output_merge(GpuDnnConvGradI, alpha_in=4, beta_in=5, out_in=2)
 def local_dnn_convi_output_merge(node, *inputs):
     inputs = inputs[0:2] + (gpu_contiguous(inputs[2]),) + inputs[3:]
-    return [GpuDnnConvGradI(algo=node.op.algo)(*inputs)]
+    return [GpuDnnConvGradI(algo=node.op.algo, num_groups=node.op.num_groups)(*inputs)]
 
 
 def local_gpua_pool_dnn_alternative(op, ctx_name, inputs, outputs):