Turn off MirroredStrategy Dataset prefetching in tests when using the

combinations library. It adds some small non-determinism to the input
batches which can make tests flaky.

Also add a default DistributionStrategy combination.

PiperOrigin-RevId: 196866569

diff --git a/tensorflow/contrib/distribute/python/BUILD b/tensorflow/contrib/distribute/python/BUILD
index 340ffae..6c5c49d 100644 (file)
--- a/tensorflow/contrib/distribute/python/BUILD
+++ b/tensorflow/contrib/distribute/python/BUILD
@@ -151,6 +151,7 @@ py_library(
         ":one_device_strategy",
         ":tpu_strategy",
         "//tensorflow/contrib/optimizer_v2:training",
+        "//tensorflow/python:distribute",
         "//tensorflow/python:framework_ops",
         "//tensorflow/python:training",
         "//tensorflow/python:util",

diff --git a/tensorflow/contrib/distribute/python/combinations.py b/tensorflow/contrib/distribute/python/combinations.py
index 45d1911..d719234 100644 (file)
--- a/tensorflow/contrib/distribute/python/combinations.py
+++ b/tensorflow/contrib/distribute/python/combinations.py
@@ -51,6 +51,7 @@ from tensorflow.contrib.optimizer_v2 import gradient_descent as gradient_descent
 from tensorflow.python.eager import context
 from tensorflow.python.framework import ops
 from tensorflow.python.training import adam
+from tensorflow.python.training import distribute as distribute_lib
 from tensorflow.python.training import gradient_descent
 from tensorflow.python.util import tf_inspect
 
@@ -262,25 +263,31 @@ class NamedDistribution(object):
     return self._required_tpu
 
 
+default_strategy = NamedDistribution(
+    "Default",
+    distribute_lib._default_distribution_strategy,  # pylint: disable=protected-access
+    required_gpus=None)
 one_device_strategy = NamedDistribution(
     "OneDeviceCPU", one_device_strategy.OneDeviceStrategy("/cpu:0"),
-    None)
+    required_gpus=None)
 tpu_strategy_single_iteration = NamedDistribution(
     "TPUSingleIteration",
     tpu_strategy.TPUStrategy(iterations_per_step=1),
     required_tpu=True)
 tpu_strategy = NamedDistribution(
     "TPU", tpu_strategy.TPUStrategy(), required_tpu=True)
+# Note that we disable prefetching for testing since prefetching makes
+# the input non-deterministic.
 mirrored_strategy_with_gpu_and_cpu = NamedDistribution(
     "MirroredCPUAndGPU",
-    mirrored_strategy.MirroredStrategy(["/gpu:0", "/cpu:0"]), 1)
-mirrored_strategy_without_prefetch = NamedDistribution(
-    "MirroredCPUAndGPUNoPrefetch",
     mirrored_strategy.MirroredStrategy(
-        ["/gpu:0", "/cpu:0"], prefetch_on_device=False), 1)
+        ["/gpu:0", "/cpu:0"], prefetch_on_device=False),
+    required_gpus=1)
 mirrored_strategy_with_two_gpus = NamedDistribution(
     "Mirrored2GPUs",
-    mirrored_strategy.MirroredStrategy(["/gpu:0", "/gpu:1"]), 2)
+    mirrored_strategy.MirroredStrategy(
+        ["/gpu:0", "/gpu:1"], prefetch_on_device=False),
+    required_gpus=2)
 
 adam_optimizer_v1_fn = NamedObject(
     "AdamV1", lambda: adam.AdamOptimizer(0.2, epsilon=1))

diff --git a/tensorflow/contrib/distribute/python/minimize_loss_test.py b/tensorflow/contrib/distribute/python/minimize_loss_test.py
index d205471..5c056a7 100644 (file)
--- a/tensorflow/contrib/distribute/python/minimize_loss_test.py
+++ b/tensorflow/contrib/distribute/python/minimize_loss_test.py
@@ -207,11 +207,11 @@ class MinimizeLossStepTest(test.TestCase, parameterized.TestCase):
           renorm=renorm,
           update_ops_in_tower_mode=not update_ops_in_cross_tower_mode)
 
-      # Disable prefetching since that makes the specific input on each device
-      # to be non deterministic, and this test relies on specific input being
-      # on each device.
+      # Make sure prefetching is disabled since that makes the
+      # specific input on each device to be non deterministic, and
+      # this test relies on specific input being on each device.
       if isinstance(distribution, mirrored_strategy.MirroredStrategy):
-        distribution._prefetch_on_device = False
+        self.assertFalse(distribution._prefetch_on_device)
       iterator = distribution.distribute_dataset(
           dataset_fn).make_one_shot_iterator()