Update dnn_conv to use new config flags

theano/sandbox/cuda/dnn.py

@@ -1053,7 +1053,7 @@ class GpuDnnConv3dGradI(GpuDnnConvGradI):
 
 
 def dnn_conv(img, kerns, border_mode='valid', subsample=(1, 1),
-             conv_mode='conv', direction_hint=None, workmem=None):
+             conv_mode='conv', direction_hint=None, workmem=None, algo=None):
     """
     GPU convolution using cuDNN from NVIDIA.
 
@@ -1075,17 +1075,25 @@ def dnn_conv(img, kerns, border_mode='valid', subsample=(1, 1),
         *not* 'forward!', it will use GpuDnnConvGradI.
         This parameter is used internally by graph optimizers and may be
         removed at any time without a deprecation period. You have been warned.
-    :param workmem: Specify the amount of working memory allowed.
-      More memory is usually faster.  One of 'none', 'small' or
-      'large'.  (default is None which takes its value from
-      :attr:`config.dnn.conv.workmem`)
-
+    :param workmem: *deprecated*, use param algo instead
+    :param algo: convolution implementation to use. One of 'none', 'small',
+        'large', 'fft', 'guess_once', 'guess_on_shape_change', 'time_once' or
+        'time_on_shape_change'. Some of these values may require certain
+        versions of CuDNN to be installed. Default is the value of
+        :attr:`config.dnn.conv.algo_fwd.
 
     :warning: The cuDNN library only works with GPU that have a compute
       capability of 3.0 or higer.  This means that older GPU will not
       work with this Op.
     """
 
+    # Check if deprecated param 'workmem' is used
+    if workmem is not None:
+        warnings.warn(("dnn_conv: parameter 'workmem' is deprecated. Use "
+                       "'algo' instead."), stacklevel=3)
+        assert algo == None
+        algo = workmem
+
     # Ensure the value of direction_hint is supported
     assert direction_hint in [None, 'bprop weights', 'forward']
 
@@ -1138,7 +1146,7 @@ def dnn_conv(img, kerns, border_mode='valid', subsample=(1, 1),
                                        desc_op.border_mode,
                                        desc_op.subsample)
     out = gpu_alloc_empty(*out_shp)
-    return GpuDnnConv(workmem=workmem)(img, kerns, out, desc)
+    return GpuDnnConv(algo=algo)(img, kerns, out, desc)
 
 
 def dnn_conv3d(img, kerns, border_mode='valid', subsample=(1, 1, 1),