Move and better comment the transpose-dot lifting optimization.

theano/tensor/opt.py

@@ -283,6 +283,27 @@ 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')
+
 @gof.local_optimizer([])
 def dimshuffle_as_view(node):
     op = node.op
@@ -2824,23 +2845,6 @@ register_canonicalize(constant_folding, 'fast_compile')
 register_stabilize(constant_folding) # because
 register_specialize(constant_folding)
 
-## dot(x,y).T -> dot(y.T, x.T)
-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')))
-register_canonicalize(local_transposed_dot_inplace, name='local_transposed_dot_inplace')
-# Second optimization: not inplace
-local_transposed_dot = gof.PatternSub(
-        (matrix_transpose, (T.dot, 'x', 'y')),
-        (T.dot, (matrix_transpose, 'y'), (matrix_transpose, 'x')))
-register_canonicalize(local_transposed_dot, name='local_transposed_dot')
-
 def _is_1(expr):
     """rtype bool. True iff expr is a constant close to 1
     """