Add test to local_csm_properties_csm optimization

theano/sparse/opt.py

@@ -22,7 +22,7 @@ local_usmm = gof.opt.PatternSub(
     (usmm, (theano.tensor.neg, 'alpha'), 'x', 'y', 'z'))
 register_specialize(local_usmm, name="local_usmm")
 
-
+# This is tested in tests/test_opt.py:test_local_csm_grad_c
 @gof.local_optimizer([csm_grad(None)])
 def local_csm_grad_c(node):
     """ csm_grad(None) -> csm_grad_c """

theano/sparse/sandbox/sp2.py

@@ -13,7 +13,14 @@ from theano.sparse.basic import (
     _is_sparse)
 from theano.sparse.sandbox.sp import sp_sum
 
+
 class Cast(gof.op.Op):
+    """Cast sparse variable to the desired dtype.
+
+    This wrap the method astype from scipy.
+    """
+    # It returns a new matrix, not a view.
+
     def __init__(self, out_type):
         self.out_type = out_type
 

theano/sparse/tests/test_opt.py

@@ -12,7 +12,8 @@ from theano.gof.python25 import any
 if not enable_sparse:
     raise SkipTest('Optional package sparse disabled')
 
-from theano.sparse import CSM, CSMProperties, csm_properties, CSC, CSR
+from theano.sparse import (CSM, CSMProperties, csm_properties, CSC, CSR,
+                           DenseFromSparse, CSMGrad)
 from theano.sparse.tests.test_basic import random_lil
 
 
@@ -32,3 +33,22 @@ def test_local_csm_properties_csm():
         v = cast(random_lil((10, 40),
                             config.floatX, 3))
         f(v.data, v.indices, v.indptr, v.shape)
+
+
+def test_local_csm_grad_c():
+    data = tensor.vector()
+    indices, indptr, shape = (tensor.ivector(), tensor.ivector(),
+                              tensor.ivector())
+    mode = theano.compile.mode.get_default_mode()
+    mode = mode.including("specialize", "local_csm_grad_c")
+    for CS, cast in [(CSC, sp.csc_matrix), (CSR, sp.csr_matrix)]:
+        cost = tensor.sum(DenseFromSparse()(CS(data, indices, indptr, shape)))
+        f = theano.function(
+            [data, indices, indptr, shape],
+            cost,
+            mode=mode)
+        assert not any(isinstance(node.op, CSMGrad) for node
+                       in f.maker.env.toposort())
+        v = cast(random_lil((10, 40),
+                            config.floatX, 3))
+        f(v.data, v.indices, v.indptr, v.shape)