move sparse opt to the right file.

theano/sparse/basic.py

@@ -731,23 +731,6 @@ class CSMGrad(gof.op.Op):
 csm_grad = CSMGrad
 
 
-@gof.local_optimizer([csm_properties])
-def skip_pack_csc01(node):
-    """if we find csm_properties(CSM(*args)), then we can replace that with the
-    *args directly"""
-    if node.op == csm_properties:
-        csm, = node.inputs
-        if csm.owner and (csm.owner.op == CSC or csm.owner.op == CSR):
-            # csm.owner.inputs could be broadcastable. In that case, we have
-            # to adjust the broadcasting flag here.
-            ret_var = [tensor.patternbroadcast(i, o.broadcastable)
-                    for i, o in izip(csm.owner.inputs, node.outputs)]
-            return ret_var
-
-    return False
-register_specialize(skip_pack_csc01)
-
-
 #
 # Conversion
 #

theano/sparse/opt.py

+from itertools import izip
+
 import theano
 from theano import gof
-from theano.sparse import Remove0
+from theano.sparse import (CSC, CSR, csm_properties, Remove0,
+                           register_specialize)
 
 
 @gof.local_optimizer([None])
@@ -17,3 +20,20 @@ theano.compile.optdb.register('local_inplace_remove0',
                               gof.TopoOptimizer(local_inplace_remove0,
     failure_callback=gof.TopoOptimizer.warn_inplace),
                               60, 'fast_run', 'inplace')
+
+
+@gof.local_optimizer([csm_properties])
+def skip_pack_csc01(node):
+    """if we find csm_properties(CSM(*args)), then we can replace that with the
+    *args directly"""
+    if node.op == csm_properties:
+        csm, = node.inputs
+        if csm.owner and (csm.owner.op == CSC or csm.owner.op == CSR):
+            # csm.owner.inputs could be broadcastable. In that case, we have
+            # to adjust the broadcasting flag here.
+            ret_var = [theano.tensor.patternbroadcast(i, o.broadcastable)
+                    for i, o in izip(csm.owner.inputs, node.outputs)]
+            return ret_var
+
+    return False
+register_specialize(skip_pack_csc01)