made some sparse.basic ops work with DisconnectedType

theano/sparse/basic.py

@@ -7,7 +7,6 @@ http://www-users.cs.umn.edu/~saad/software/SPARSKIT/paper.ps
 # TODO
 # Automatic methods for determining best sparse format?
 
-from itertools import izip
 import sys
 
 import numpy
@@ -16,7 +15,7 @@ import scipy.sparse
 
 from theano import gof, tensor, compile, scalar, config
 from theano.gof.python25 import all
-from theano.tensor import blas
+from theano.gradient import DisconnectedType
 from theano.sparse.utils import hash_from_sparse
 import theano.tests.unittest_tools as utt
 
@@ -626,7 +625,18 @@ class CSMProperties(gof.Op):
         out[3][0] = theano._asarray(csm.shape, dtype='int32')
 
     def grad(self, (csm,), g):
-        assert [gg is None for gg in g[1:]]
+
+        #g[1:] is all integers, so their Jacobian in this op
+        #is 0. We thus don't need to worry about what their values
+        #are.
+
+        #if g[0] is disconnected, then this op doesn't contribute
+        #any gradient anywhere. but we know that at least one of
+        #g[1:] is connected, or this grad method wouldn't have been
+        #called, so we should report zeros
+        if isinstance(g[0].type, DisconnectedType):
+            return [csm.zeros_like()]
+
         data, indices, indptr, shape = csm_properties(csm)
         return [CSM(csm.format)(g[0], indices, indptr, shape)]
 # don't make this a function or it breaks some optimizations below
@@ -662,10 +672,10 @@ class CSM(gof.Op):
 
     :param data: One dimensionnal tensor representing
                  the data of the sparse to construct.
-    :param indices: One dimensionnal tensor of integers
+    :param indices: One dimensional tensor of integers
                     representing the indices of the sparse
                     matrix to construct.
-    :param indptr: One dimensionnal tensor of integers
+    :param indptr: One dimensional tensor of integers
                    representing the indice pointer for
                    the sparse matrix to construct.
     :param shape: One dimensionnal tensor of integers
@@ -673,9 +683,9 @@ class CSM(gof.Op):
                   matrix to construct.
 
     :return: A sparse matrix having the properties
-             speficied by the inputs.
+             specified by the inputs.
 
-    :note: The grad method returns a dense vector, so it provide
+    :note: The grad method returns a dense vector, so it provides
            a regular grad.
     """
 
@@ -777,7 +787,7 @@ class CSM(gof.Op):
         #unpack the data vector and wrap it as a 1d TensorType
         g_data = csm_grad(self.kmap)(x_data, x_indices, x_indptr, x_shape,
             g_data, g_indices, g_indptr, g_shape)
-        return [g_data, None, None, None]
+        return [g_data, DisconnectedType()(), DisconnectedType()(), DisconnectedType()]
 
     def infer_shape(self, node, shapes):
         if self.kmap is None: