Use strategy default device (if not None) as the destination for strategy.reduce.

This fixes a bug where strategy.reduce is not called within a strategy.scope in MultiworkerMirroredStrategy, and the strategy incorrectly tries to reduce values to "/job:localhost/replica:0/task:0/device:CPU:0", which is not a valid device in MultiworkerMirroredStrategy.

PiperOrigin-RevId: 309076162
Change-Id: Ifd6746ecb8bdbe9549807bcc32d9a4b45eefc6c0

tensorflow/python/distribute/BUILD

@@ -1523,35 +1523,22 @@ cuda_py_test(
         ":combinations",
         ":cross_device_utils",
         ":multi_worker_test_base",
-        ":multi_worker_util",
-        ":reduce_util",
         ":strategy_combinations",
         ":strategy_test_lib",
-        ":values",
         "//tensorflow/core:protos_all_py",
         "//tensorflow/python:array_ops",
         "//tensorflow/python:client_testlib",
         "//tensorflow/python:constant_op",
         "//tensorflow/python:dtypes",
-        "//tensorflow/python:errors",
         "//tensorflow/python:framework_ops",
-        "//tensorflow/python:framework_test_lib",
         "//tensorflow/python:gradients",
         "//tensorflow/python:init_ops",
-        "//tensorflow/python:nn",
-        "//tensorflow/python:random_ops",
-        "//tensorflow/python:training_lib",
-        "//tensorflow/python:training_util",
         "//tensorflow/python:variable_scope",
         "//tensorflow/python:variables",
-        "//tensorflow/python/data/ops:dataset_ops",
-        "//tensorflow/python/distribute/cluster_resolver:cluster_resolver_lib",
         "//tensorflow/python/eager:context",
-        "//tensorflow/python/keras:testing_utils",
-        "//tensorflow/python/keras/layers:core",
-        "//tensorflow/python/keras/mixed_precision/experimental:policy",
+        "//tensorflow/python/estimator:estimator_py",
+        "//tensorflow/python/keras/layers",
         "//tensorflow/python/keras/mixed_precision/experimental:test_util",
-        "//tensorflow/python/ops/losses",
         "//third_party/py/numpy",
         "@absl_py//absl/testing:parameterized",
     ],

tensorflow/python/distribute/collective_all_reduce_strategy_test.py

@@ -31,7 +31,6 @@ from tensorflow.python.distribute import cross_device_utils
 from tensorflow.python.distribute import multi_worker_test_base
 from tensorflow.python.distribute import multi_worker_util
 from tensorflow.python.distribute import reduce_util
-from tensorflow.python.distribute import strategy_combinations
 from tensorflow.python.distribute import strategy_test_lib
 from tensorflow.python.distribute import values
 from tensorflow.python.distribute.cluster_resolver import SimpleClusterResolver
@@ -565,33 +564,6 @@ class DistributedCollectiveAllReduceStrategyTestWithChief(
           num_gpus=required_gpus)
 
 
-class MultiworkerMirroredStrategyTest(test.TestCase, parameterized.TestCase):
-
-  @combinations.generate(
-      combinations.combine(
-          strategy=strategy_combinations.multi_worker_mirrored_two_workers,
-          mode=['eager']))
-  def testReduce(self, strategy):
-
-    def fn():
-
-      def replica_fn():
-        return array_ops.ones((), dtypes.int32)
-
-      per_replica_value = strategy.run(replica_fn)
-      return strategy.reduce(
-          reduce_util.ReduceOp.SUM, value=per_replica_value, axis=None)
-
-    # Run reduce under the strategy scope to explicitly enter
-    # strategy default_device scope.
-    with strategy.scope():
-      self.assertEqual(fn().numpy(), 2)
-
-    # Run reduce without a strategy scope to implicitly enter
-    # strategy default_device scope.
-    self.assertEqual(fn().numpy(), 2)
-
-
 class LocalCollectiveAllReduceStrategy(
     CollectiveAllReduceStrategyTestBase,
     strategy_test_lib.DistributionTestBase,
@@ -701,4 +673,4 @@ class LocalCollectiveAllReduceStrategy(
 
 
 if __name__ == '__main__':
-  combinations.main()
+  test.main()

tensorflow/python/distribute/distribute_lib.py

@@ -1912,8 +1912,9 @@ class StrategyExtendedV2(object):
 
   def _reduce(self, reduce_op, value):
     # Default implementation until we have an implementation for each strategy.
-    dst = device_util.current() or self._default_device or "/device:CPU:0"
-    return self._local_results(self.reduce_to(reduce_op, value, dst))[0]
+    return self._local_results(
+        self.reduce_to(reduce_op, value,
+                       device_util.current() or "/device:CPU:0"))[0]
 
   def reduce_to(self, reduce_op, value, destinations, experimental_hints=None):
     """Combine (via e.g. sum or mean) values across replicas.