better handling for NullType and DisconnectedType

major changes: - The self._grad_op now only returns zeros_like() for special types like NullType() or DisconnectedType() - call to grad() will furthur replace returned zero tensors with special types - proposed gradient override interface : (single or list of below) Ellipsis -> <no_override> (-) since python 2 does not support `[...]` syntax, this may result in uglier code in python 2 None -> NullType() int(0) -> DisconnectedType() OpFromGraph instance or callable -> <override> minor changes: - various typo/bug fix notes: - This commit breaks OpFromGraph.R_op, which is expected to be fixed in upcoming commits.

better handling for NullType and DisconnectedType
8e8758a3 · khaotik · a6e5cd74 · 8e8758a3 · 8e8758a3
--- a/theano/compile/builders.py
+++ b/theano/compile/builders.py
--- a/theano/compile/tests/test_builders.py
+++ b/theano/compile/tests/test_builders.py
@@ -160,7 +160,7 @@ class T_OpFromGraph(unittest_tools.InferShapeTester):
        w, b = T.vectors('wb')
        # we make the 3rd gradient default (no override)
-        op_linear = cls_ofg([x, w, b], [x * w + b], grad_overrides=[go1, go2])
+        op_linear = cls_ofg([x, w, b], [x * w + b], grad_overrides=[go1, go2, Ellipsis])
        xx, ww, bb = T.vector('xx'), T.vector('yy'), T.vector('bb')
        zz = T.sum(op_linear(xx, ww, bb))
        dx, dw, db = T.grad(zz, [xx, ww, bb])
@@ -281,21 +281,19 @@ class T_OpFromGraph(unittest_tools.InferShapeTester):
                         [True, False, True]]
        assert results == expect_result
-    @test_params
+    def test_infer_shape(self):
-    def test_infer_shape(self, cls_ofg):
+        # test infer shape does not need to against inline case
+        # since the Op is remove during optimization phase
        x = T.matrix('x')
        y = T.matrix('y')
        o1 = x + y
        o2 = x * y
-        op_graph = cls_ofg([x, y], [o1, o2])
+        op_graph = OpFromGraph([x, y], [o1, o2])
        q = T.matrix('q')
        p = T.matrix('p')
-        # we don't want check_topo for inline ops
-        # since the inline op is replaced during optimization
        self._compile_and_check([q, p],
                                op_graph(q, p),
                                [np.ones([3, 4], dtype=config.floatX),
                                 np.ones([3, 4], dtype=config.floatX)],
-                                cls_ofg,
+                                OpFromGraph)
-                                check_topo=not op_graph.is_inline)