[CRASH] in theano.grad when mulsd do not return a dense grad for dense input.

theano/gradient.py

@@ -1089,6 +1089,14 @@ def _populate_grad_dict(var_to_app_to_idx,
                     raise ValueError(("%s returned the wrong number of" +
                                       " gradient terms.") % str(node.op))
 
+                for ig, i in zip(input_grads, inputs):
+                    if (not isinstance(ig.type, DisconnectedType) and
+                        type(ig.type) != type(i.type)):
+                        raise ValueError(
+                            "%s returned the wrong type for gradient terms."
+                            " Sparse inputs must have sparse grads and dense"
+                            " inputs must have dense grad" % str(node.op))
+
             # must convert to list in case the op returns a tuple
             # we won't be able to post-process out the Nones if it does that
             input_grads = list(input_grads)

theano/sparse/basic.py

@@ -2244,7 +2244,7 @@ class MulSD(gof.op.Op):
     def grad(self, (x, y), (gz,)):
         assert _is_sparse_variable(x) and _is_dense_variable(y)
         assert _is_sparse_variable(gz)
-        return y * gz, x * gz
+        return y * gz, dense_from_sparse(x * gz)
 
     def infer_shape(self, node, shapes):
         return [shapes[0]]

theano/sparse/tests/test_basic.py

@@ -561,6 +561,8 @@ class T_AddMul(unittest.TestCase):
                       theano.shared(array1)]:
                 for dtype1, dtype2 in [('float64', 'int8'),
                                        ('int8', 'float64'),
+                                       # Needed to test the grad
+                                       ('float32', 'float64'),
                                    ]:
                     a = a.astype(dtype1)
                     b = mtype(array2).astype(dtype2)
@@ -580,6 +582,8 @@ class T_AddMul(unittest.TestCase):
                         self.assertTrue(numpy.all(val == ans))
                         if isinstance(a, theano.Constant):
                             a = a.data
+                        if getattr(a, 'owner', None):
+                            continue
                         if dtype1.startswith('float') and dtype2.startswith('float'):
                             verify_grad_sparse(op, [a, b], structured=True)
                     elif op is mul:
@@ -589,6 +593,8 @@ class T_AddMul(unittest.TestCase):
                             [[1, 0], [9, 0], [0, 36]])))
                         if isinstance(a, theano.Constant):
                             a = a.data
+                        if getattr(a, 'owner', None):
+                            continue
                         if dtype1.startswith('float') and dtype2.startswith('float'):
                             verify_grad_sparse(op, [a, b], structured=False)