Implement theano.sparse.mul(sparse1, sparse2) when both input don't have the same sparsity pattern.

NEWS.txt

@@ -35,6 +35,11 @@ New Features
  * MRG random now raises an error with a clear message when the passed shape
    contains dimensions with bad value like 0. (Frédéric B. reported by Ian G.)
 
+Sparse
+
+ * Implement theano.sparse.mul(sparse1, sparse2) when both input don't
+   have the same sparsity pattern. (Frederic B.)
+
 Sparse Sandbox graduate
  * Remove0 op: it removes stored elements with value 0. (Frederic B.)
 

theano/sparse/basic.py

@@ -1180,12 +1180,9 @@ class MulSS(gof.op.Op):
         assert _is_sparse(x) and _is_sparse(y)
         assert len(x.shape) == 2
         assert y.shape == x.shape
-        if (numpy.all(y.indptr == x.indptr) and
-            numpy.all(y.indices == x.indices)):
-            out[0] = y.copy()
-            out[0].data *= x.data
-        else:
-            raise NotImplementedError()  # RowScale / ColScale
+        # This call the element-wise multiple
+        # x * y call dot...
+        out[0] = x.multiply(y)
 
     def grad(self, (x, y), (gz,)):
         return y * gz, x * gz

theano/sparse/tests/test_basic.py

@@ -323,17 +323,17 @@ class T_AddMul(unittest.TestCase):
     def testMulSS(self):
         self._testSS(mul,
                      numpy.array([[1., 0], [3, 0], [0, 6]]),
-                     numpy.array([[1., 0], [3, 0], [0, 6]]))
+                     numpy.array([[1., 2], [3, 0], [0, 6]]))
 
     def testMulSD(self):
         self._testSD(mul,
                      numpy.array([[1., 0], [3, 0], [0, 6]]),
-                     numpy.array([[1., 0], [3, 0], [0, 6]]))
+                     numpy.array([[1., 2], [3, 0], [0, 6]]))
 
     def testMulDS(self):
         self._testDS(mul,
                      numpy.array([[1., 0], [3, 0], [0, 6]]),
-                     numpy.array([[1., 0], [3, 0], [0, 6]]))
+                     numpy.array([[1., 2], [3, 0], [0, 6]]))
 
     def _testSS(self, op, array1=numpy.array([[1., 0], [3, 0], [0, 6]]),
                 array2=numpy.asarray([[0, 2.], [0, 4], [5, 0]])):
@@ -361,15 +361,12 @@ class T_AddMul(unittest.TestCase):
             val = eval_outputs([apb])
             self.assertTrue(val.shape == (3, 2))
             if op is add:
-                self.assertTrue(numpy.all(val.todense() == (a + b).todense()))
-                ans = numpy.array([[1., 2], [3, 4], [5, 6]])
-                self.assertTrue(numpy.all(val.todense() == ans))
+                self.assertTrue(numpy.all(val.todense() == (array1 + array2)))
                 verify_grad_sparse(op, [a, b], structured=False)
             elif op is mul:
                 self.assertTrue(numpy.all(val.todense()
-                                          == (a.multiply(b)).todense()))
-                ans = numpy.array([[1, 0], [9, 0], [0, 36]])
-                self.assertTrue(numpy.all(val.todense() == ans))
+                                          == (array1 * array2)))
+                verify_grad_sparse(op, [a, b], structured=False)
 
     def _testSD(self, op, array1=numpy.array([[1., 0], [3, 0], [0, 6]]),
                 array2=numpy.asarray([[0, 2.], [0, 4], [5, 0]])):