various sparse grads

theano/sparse/basic.py

@@ -18,6 +18,7 @@ from theano.gof.python25 import all
 from theano.gradient import DisconnectedType
 from theano.sparse.utils import hash_from_sparse
 import theano.tests.unittest_tools as utt
+from theano.gradient import grad_not_implemented
 
 sparse_formats = ['csc', 'csr']
 
@@ -2149,7 +2150,9 @@ class MulSV(gof.op.Op):
         assert y.type.ndim == 1
 
         if x.type.dtype != y.type.dtype:
-            raise NotImplementedError()
+            raise NotImplementedError(
+                    "MulSV not implemented for differing dtypes."
+                    "Got %s and %s." % (str(x.type.dtype),str(y.type.dtype)))
         return gof.Apply(self,
                          [x, y],
                          [SparseType(dtype=x.type.dtype,
@@ -2163,6 +2166,15 @@ class MulSV(gof.op.Op):
     def grad(self, (x, y), (gz,)):
         assert _is_sparse_variable(x) and _is_dense_variable(y)
         assert _is_sparse_variable(gz)
+
+        # mul_s_v is not implemented if the types vary
+
+        if gz.dtype == 'float64' and y.dtype == 'float32':
+            y = y.astype('float64')
+
+        if gz.dtype == 'float32' and y.dtype == 'float64':
+            gz = gz.astype('float64')
+
         return mul_s_v(gz, y), sp_sum(x * gz, axis=0, sparse_grad=True)
 
     def infer_shape(self, node, ins_shapes):
@@ -2197,6 +2209,11 @@ def mul(x, y):
 
     assert x_is_sparse_variable or y_is_sparse_variable
     if x_is_sparse_variable and y_is_sparse_variable:
+
+        # mul_s_s is not implemented if the types differ
+        if y.dtype == 'float64' and x.dtype == 'float32':
+            x = x.astype('float64')
+
         return mul_s_s(x, y)
     elif x_is_sparse_variable and not y_is_sparse_variable:
 
@@ -3286,7 +3303,7 @@ class SamplingDot(gof.op.Op):
         rval = [
             dot(p * gz, y),
             dot((p * gz).T, x),
-            None
+            grad_not_implemented(self, 2, p)
         ]
 
         return rval