Draft of OpFromGraph.connection_pattern and testcase. Test pass.

theano/compile/builders.py

+import numpy
 import theano
 from theano import gof
 from theano.compat import izip
 from theano.compile.function_module import orig_function
 from theano.compile import SharedVariable, rebuild_collect_shared
 from theano.gof import ops_with_inner_function
+from theano.gof import FunctionGraph
 
 
 class OpFromGraph(gof.Op):
@@ -134,6 +136,59 @@ class OpFromGraph(gof.Op):
             # we wont need this copy anymore
             output[0] = variable.copy()
 
+    def connection_pattern(self, node):
+        """
+        Connection_pattern is hard to calculate. In the function, we calculate
+        the transpose of connection_pattern, where M[output_index,input_index]
+        indicates whether input with index i affects output with index i.
+        At last we return the transpose of final result
+        """
+        # or ori_inputs because user do not customize sharejvariable
+        fgraph = FunctionGraph(self.new_inputs, self.new_outputs)
+
+        # c for connection, stores the connection pattern of each variable
+        c_map = {}
+        num_of_input = len(fgraph.inputs)
+        # Initialize input connection pattern, each input affects itself
+        for index in range(num_of_input):
+            vec = [False] * num_of_input
+            vec[index] = True
+            # Make use of numpy.array to simplify codes
+            c_map.setdefault(fgraph.inputs[index], numpy.array(vec))
+
+        # Toposort the fgraph and get connection pattern for each variable
+        for node in fgraph.toposort():
+            # connection pattern of node's inputs.
+            in_vecs = []
+            for var in node.inputs:
+                if not isinstance(var, theano.Constant):
+                    in_vecs.append(c_map[var])
+
+            if not hasattr(node.op, 'connection_pattern'):
+                # By default, nodes inputs affect all outputs
+                result = in_vecs[0]
+                for vec in in_vecs[1:]:
+                    result |= vec
+                results = result * len(node.outputs)
+            else:
+                # If node's output connect to node's input, and that input
+                # connect to fgraph.input, that output connect to fgraph.input
+                # Therefore we use OR operation here.
+                results = []
+                out_vecs = numpy.array(node.op.connection_pattern(node))
+                for out_vec in out_vecs.T:
+                    result = [False] * num_of_input
+                    for in_vec, val in zip(in_vecs, out_vec):
+                        result |= (in_vec & val)
+                    results.append(result)
+
+            for var, result in zip(node.outputs, results):
+                c_map.setdefault(var, result)
+
+        # Transpose final result and convert pattern into python list
+        pattern = numpy.array([c_map[var] for var in fgraph.outputs]).T
+        return [list(vec) for vec in pattern]
+
     def grad(self, inputs, output_grads):
         # OpFromGraph doesn't implement a connection_pattern, so for
         # now we regard all inputs and outputs as connected. This will

theano/compile/tests/test_builders.py

@@ -107,6 +107,19 @@ class T_OpFromGraph(unittest.TestCase):
         fn = function([x, y, z], f)
         assert numpy.allclose(15.0 + s.get_value(),
                               fn(xv, yv, zv))
+    
+    def test_connection_pattern(self):
+        import numpy
+        x, y, z = T.matrices('xyz')
+        out1 = x * y
+        out2 = y * z
+
+        op = OpFromGraph([x ,y, z], [out1, out2], moe='FAST_RUN')
+        results = op.connection_pattern(None)
+        expect_result = [[True, False],
+                         [True, True],
+                         [False, True]]
+        assert results == expect_result
 
 
 if __name__ == '__main__':