fixes to various gradients including samplingdot

theano/sparse/sandbox/sp2.py

@@ -363,7 +363,7 @@ class Sum(gof.op.Op):
         out[0] = numpy.asarray(x.sum(a), dtype=x.dtype).flatten()
     
     def grad(self, (x, a, ), (gz, )):
-        return None, None
+        return sp_ones_like(x) * gz, None
 sum = Sum()
 
 
@@ -424,7 +424,7 @@ def structured_monoid(tensor_op):
 
             data = tensor_op(data, *xs)
 
-            return CSR(data, ind, ptr, shape)
+            return CSM(x.format)(data, ind, ptr, shape)
         return wrapper
     return decorator
 
@@ -461,6 +461,19 @@ def structured_minimum(x, y):
     """
     # see decorator for function body
 
+@structured_monoid(tensor.maximum)
+def structured_maximum(x, y):
+    """structured elemwise maximum of sparse matrix
+    x by scalar y.
+    """
+    # see decorator for function body
+
+@structured_monoid(tensor.add)
+def structured_add(x):
+    """structured addition of sparse matrix
+    x and scalar y.
+    """
+    # see decorator for function body
 
 class StructuredAddSV(gof.op.Op):
     '''Structured addition of a sparse matrix and a dense vector.
@@ -676,8 +689,8 @@ class SamplingDot(gof.op.Op):
 
     def grad(self, (x, y, p), (gz,)):
         rval = [
-            dot(gz, y),
-            dot(gz.T, x),
+            dot(p * gz, y),
+            dot(p.T * gz.T, x),
             None
         ]