change op-level seed during copy; fix #501

edward/util/random_variables.py

@@ -93,11 +93,13 @@ def copy(org_instance, dict_swap=None, scope="copied",
   where any of its ancestors existing in `dict_swap` are
   replaced with `dict_swap`'s corresponding value.
 
-  The copying is done recursively, so any `Operation` whose output
-  is required to evaluate `org_instance` is also copied (if it isn't
-  already copied within the new scope). This is with the exception of
-  `tf.Variable`s, `tf.placeholder`s, and nodes of type `Queue`, which
-  are reused and not newly copied.
+  Copying is done recursively. Any `Operation` whose output is
+  required to copy `org_instance` is also copied (if it isn't already
+  copied within the new scope).
+
+  `tf.Variable`s, `tf.placeholder`s, and nodes of type `Queue` are
+  always reused and not copied. In addition, `tf.Operation`s with
+  operation-level seeds are copied with a new operation-level seed.
 
   Parameters
   ----------
@@ -259,10 +261,12 @@ def copy(org_instance, dict_swap=None, scope="copied",
       return op
 
     # Copy the node def.
-    # It stores string-based info such as name, device, and type of
-    # the op. It is unique to every Operation instance.
+    # It is unique to every Operation instance. Replace the name and
+    # its operation-level seed if it has one.
     node_def = deepcopy(op.node_def)
     node_def.name = new_name
+    if 'seed2' in node_def.attr:
+      node_def.attr['seed2'].i = tf.get_seed(None)[1]
 
     # Copy other arguments needed for initialization.
     output_types = op._output_types[:]

tests/test-util/test_copy.py

@@ -54,6 +54,15 @@ def test_list(self):
       z_new = ed.copy(z, {x: y.value()})
       self.assertGreater(z_new.value().eval(), 5.0)
 
+  def test_random(self):
+    with self.test_session() as sess:
+      ed.set_seed(3742)
+      x = tf.random_normal([])
+      x_copy = ed.copy(x)
+
+      result_copy, result = sess.run([x_copy, x])
+      self.assertNotAlmostEquals(result_copy, result)
+
   def test_scan(self):
     with self.test_session() as sess:
       ed.set_seed(42)
@@ -64,18 +73,6 @@ def test_scan(self):
       self.assertAllClose(result_copy, [2.0, 5.0, 6.0])
       self.assertAllClose(result, [2.0, 5.0, 6.0])
 
-  def test_scan_random(self):
-    with self.test_session() as sess:
-      ed.set_seed(1234)
-      op = tf.scan(lambda a, x: a + x, tf.random_normal([3]))
-      copy_op = ed.copy(op)
-
-      # check that the random inputs are different
-      # currently ed.set_seed prevents variate generation to work
-      # result_copy, result = sess.run([copy_op, op])
-      # for elem_copy, elem in zip(result_copy, result):
-      #   self.assertNotAlmostEquals(elem_copy, elem)
-
   def test_swap_tensor_tensor(self):
     with self.test_session():
       x = tf.constant(2.0)