add alternative implementation for gradweights

theano/gpuarray/opt.py

@@ -1759,6 +1759,37 @@ def local_abstractconv_gradweights_gemm(node):
     return [rval]
 
 
+@local_optimizer([AbstractConv2d_gradWeights])
+def local_abstractconv_gemm_gradweights_alt(node):
+    if not isinstance(node.op, AbstractConv2d_gradWeights):
+        return None
+    img, topgrad, shape = node.inputs
+    if not isinstance(img.type, GpuArrayType) or \
+            not isinstance(topgrad.type, GpuArrayType):
+        return None
+    ctx = infer_context_name(img, topgrad)
+    border_mode = node.op.border_mode
+    subsample = node.op.subsample
+    filter_dilation = node.op.filter_dilation
+
+    if border_mode == 'valid' and subsample == (1, 1) and filter_dilation == (1, 1):
+        rval = GpuCorrMM(border_mode,
+                         subsample,
+                         filter_dilation)(
+            gpu_contiguous(img.dimshuffle(1, 0, 2, 3)),
+            gpu_contiguous(topgrad.dimshuffle(1, 0, 2, 3)))
+
+        if node.op.filter_flip:
+            rval = rval[:, :, ::-1, ::-1]
+
+        rval = rval.dimshuffle(1, 0, 2, 3)
+        rval = tensor.patternbroadcast(rval, node.outputs[0].broadcastable)
+        rval = as_gpuarray_variable(rval, context_name=ctx)
+        return [rval]
+    else:
+        return None
+
+
 @local_optimizer([AbstractConv3d_gradWeights])
 def local_abstractconv3d_gradweights_gemm(node):
     if not isinstance(node.op, AbstractConv3d_gradWeights):
@@ -2511,6 +2542,7 @@ if config.optimizer_excluding:
 
 conv_metaopt.register(abstractconv_groupopt.query(*running_list).opts)
 conv_metaopt.register([local_abstractconv_gemm_alternative])
+conv_metaopt.register([local_abstractconv_gemm_gradweights_alt])
 abstractconv_groupopt.register('conv_metaopt', conv_metaopt, 'conv_meta', position=0)
 
 # Register cuDNN batch normalization implementation