replacing transposed dot patternsub with local opt

theano/tensor/opt.py

@@ -334,26 +334,31 @@ def local_dimshuffle_lift(node):
         else:
             return DimShuffle(iinput.type.broadcastable, new_order, inplace).make_node(iinput).outputs
 
-## dot(x,y).T -> dot(y.T, x.T)
-# These optimizations "lift" (propagate towards the inputs) DimShuffle
-# through dot product.  It allows to put the graph in a more standard shape,
-# and to later merge consecutive DimShuffles.
-inplace_matrix_transpose = T.DimShuffle([False,False], [1,0], inplace=True)
-matrix_transpose = T.DimShuffle([False,False], [1,0], inplace=False)
-# The transformation should be apply whether or not the transpose is inplace.
-# The newly-introduced transpositions are not inplace, this will be taken care
-# of in a later optimization phase.
-# First optimization: inplace
-local_transposed_dot_inplace = gof.PatternSub(
-        (inplace_matrix_transpose, (T.dot, 'x', 'y')),
-        (T.dot, (matrix_transpose, 'y'), (matrix_transpose, 'x')))
-# Second optimization: not inplace
-local_transposed_dot = gof.PatternSub(
-        (matrix_transpose, (T.dot, 'x', 'y')),
-        (T.dot, (matrix_transpose, 'y'), (matrix_transpose, 'x')))
-# Register in the canonization phase only
-register_canonicalize(local_transposed_dot_inplace, name='local_transposed_dot_inplace')
-register_canonicalize(local_transposed_dot, name='local_transposed_dot')
+
+@register_canonicalize
+@gof.local_optimizer([])
+def local_lift_transpose_through_dot(node):
+    """
+    dot(x,y).T -> dot(y.T, x.T)
+
+    These optimizations "lift" (propagate towards the inputs) DimShuffle
+    through dot product.  It allows to put the graph in a more standard shape,
+    and to later merge consecutive DimShuffles.
+
+    The transformation should be apply whether or not the transpose is
+    inplace.  The newly-introduced transpositions are not inplace, this will
+    be taken care of in a later optimization phase.
+    """
+    if not (isinstance(node.op, T.DimShuffle)
+            and node.op.new_order == (1, 0)):
+        return False
+    if not (node.inputs[0].owner and node.inputs[0].owner.op == T.dot):
+        return False
+    x, y = node.inputs[0].owner.inputs
+
+    if x.ndim == y.ndim == 2:
+        return [broadcast_like(T.dot(y.T, x.T), node.outputs[0], node.env)]
+
 
 @gof.local_optimizer([])
 def dimshuffle_as_view(node):