make tensor.stack generate MakeVector when he receive python/numpy scalar.

theano/tensor/basic.py

@@ -3269,12 +3269,15 @@ def stack(*tensors):
         raise Exception('theano.tensor.stack(*tensors) must have at least one parameter')
     # If all tensors are scalars of the same type, call make_vector.
     # It makes the graph simpler, by not adding DimShuffles and Rebroadcasts
-    if numpy.all([isinstance(t, Variable) and\
-                  isinstance(t.type, TensorType) and\
-                  t.ndim==0 and \
-                  t.type.__class__==tensors[0].type.__class__\
+    if numpy.all([(isinstance(t, Variable) and
+                  isinstance(t.type, TensorType) and
+                  t.ndim==0 and
+                  t.type.__class__==tensors[0].type.__class__)
+                  or isinstance(t, (numpy.number, float, int, complex))#in case their is direct int
                   for t in tensors]):
-        return theano.tensor.opt.MakeVector(scal.upcast(*[i.dtype for i in tensors]))(*tensors)
+        tensors = map(as_tensor_variable,tensors)#in case their is direct int
+        dtype = scal.upcast(*[i.dtype for i in tensors])
+        return theano.tensor.opt.MakeVector(dtype)(*tensors)
     return join(0, *[shape_padleft(t, 1) for t in tensors])
 
 @constructor

theano/tensor/tests/test_basic.py

@@ -1566,6 +1566,20 @@ class T_Join_and_Split(unittest.TestCase):
         assert len([n for n in e if isinstance(n, Join)]) == 0
         assert f.maker.env.outputs[0].dtype == 'int64'
 
+    def test_stack_scalar_make_vector_constant(self):
+        '''Test that calling stack() on scalars instantiates MakeVector,
+        event when the scalar are simple int type.'''
+        a = tensor.iscalar('a')
+        b = tensor.lscalar('b')
+        s = stack(a,b,10, numpy.int8(3))
+        f = function([a,b], s)
+        val = f(1,2)
+        self.failUnless(numpy.all(val == [1,2,10,3]))
+        e = f.maker.env.toposort()
+        assert len([n for n in e if isinstance(n.op,opt.MakeVector)]) > 0
+        assert len([n for n in e if isinstance(n, Join)]) == 0
+        assert f.maker.env.outputs[0].dtype == 'int64'
+
     def test_join_vector(self):
         a = as_tensor_variable(numpy.array([1, 2, 3]))
         b = as_tensor_variable(numpy.array([7, 8, 9]))