Merge pull request #6618 from abergeron/opfromgraph_disconnected

theano/compile/builders.py

@@ -105,6 +105,11 @@ class OpFromGraph(gof.Op):
         :class:`Variable <theano.gof.Variable>`. Each list element corresponds
         to a specific output of R_op, length of list must be equal to number of outputs.
 
+    connection_pattern : list of list
+
+        If not ``None``, this will be used as the connection_pattern
+        for this op.
+
     name : string, optional
         A name for debugging purposes
 
@@ -248,6 +253,7 @@ class OpFromGraph(gof.Op):
         lop_overrides='default',
         grad_overrides='default',
         rop_overrides='default',
+        connection_pattern=None,
         name=None, **kwargs
     ):
         if not isinstance(outputs, list):
@@ -298,6 +304,8 @@ class OpFromGraph(gof.Op):
             self._lop_type = 'lop'
         self.set_rop_overrides(rop_overrides)
 
+        self._connection_pattern = connection_pattern
+
         if name is not None:
             assert isinstance(name, str), 'name must be None or string object'
         self.name = name
@@ -637,6 +645,9 @@ class OpFromGraph(gof.Op):
         Return connection pattern of subfgraph defined by inputs and outputs.
 
         """
+        if self._connection_pattern is not None:
+            return self._connection_pattern
+
         inp_len = len(self.local_inputs)
         out_len = len(self.local_outputs)
         cpmat_self = io_connection_pattern(

theano/compile/tests/test_builders.py

@@ -266,6 +266,37 @@ class T_OpFromGraph(unittest_tools.InferShapeTester):
 
         # TODO list override case
 
+    @test_params
+    def test_connection_pattern_override(self, cls_ofg):
+        x, y = T.vectors('xy')
+
+        def f1(x, y):
+            del x
+            # but we know how to backpropagate for x for some reasons
+            # and we don't care about the gradient wrt y.
+            return y + T.round(y)
+
+        def f1_back(inputs, output_gradients):
+            return [
+                output_gradients[0],
+                theano.gradient.disconnected_type()]
+
+        op = cls_ofg(
+            inputs=[x, y],
+            outputs=[f1(x, y)],
+            grad_overrides=f1_back,
+            connection_pattern=[[True], [False]],  # This is new
+            on_unused_input='ignore')  # This is new
+
+        c = op(x, y)
+
+        g1 = theano.grad(c.sum(), x)
+
+        out = g1.eval({
+            x: np.ones((5,), dtype=np.float32),
+            y: np.ones((5,), dtype=np.float32)})
+        assert np.allclose(out, [1.] * 5)
+
     @test_params
     def test_nested(self, cls_ofg):
         x, y = T.vectors('xy')