Make tf.metrics work with TPU Strategy.

PiperOrigin-RevId: 209064406

1 files changed, 9 insertions, 0 deletions

diff --git a/tensorflow/contrib/distribute/python/mirrored_strategy.py b/tensorflow/contrib/distribute/python/mirrored_strategy.py
index 72d1c6b7dd..edd5c6d17a 100644
--- a/tensorflow/contrib/distribute/python/mirrored_strategy.py
+++ b/tensorflow/contrib/distribute/python/mirrored_strategy.py
@@ -383,12 +383,21 @@ class MirroredStrategy(distribute_lib.DistributionStrategy):
       with ops.control_dependencies([fn_result]):
         return [i + 1] + flat_last_step_outputs
 
+    # We capture the control_flow_context at this point, before we run `fn`
+    # inside a while_loop. This is useful in cases where we might need to exit
+    # these contexts and get back to the outer context to do some things, for
+    # e.g. create an op which should be evaluated only once at the end of the
+    # loop on the host. One such usage is in creating metrics' value op.
+    self._outer_control_flow_context = (
+        ops.get_default_graph()._get_control_flow_context())  # pylint: disable=protected-access
+
     cond = lambda i, *args: i < iterations
     i = constant_op.constant(0)
     loop_result = control_flow_ops.while_loop(
         cond, body, [i] + initial_loop_values, name="",
         parallel_iterations=1, back_prop=False, swap_memory=False,
         return_same_structure=True)
+    del self._outer_control_flow_context
 
     ctx.run_op = control_flow_ops.group(loop_result)