Enable TF Hub's tests with KerasLayer.

PiperOrigin-RevId: 253730171

tensorflow/examples/saved_model/integration_tests/BUILD

@@ -23,6 +23,7 @@ py_library(
     ],
     visibility = ["//tensorflow:internal"],
     deps = [
+        ":distribution_strategy_utils",
         ":mnist_util",
         "//tensorflow:tensorflow_py",
         "@absl_py//absl/testing:parameterized",
@@ -38,14 +39,26 @@ py_library(
     ],
 )
 
+py_library(
+    name = "distribution_strategy_utils",
+    srcs = ["distribution_strategy_utils.py"],
+    visibility = ["//tensorflow:internal"],
+    deps = [
+        "//tensorflow/python/distribute:strategy_combinations",
+    ],
+)
+
 cuda_py_test(
     name = "saved_model_test",
     srcs = [
         "saved_model_test.py",
     ],
     additional_deps = [
+        ":distribution_strategy_utils",
         ":integration_scripts",
+        "@absl_py//absl/testing:parameterized",
         "//tensorflow:tensorflow_py",
+        "//tensorflow/python/distribute:combinations",
     ],
     shard_count = 4,
     tags = [

tensorflow/examples/saved_model/integration_tests/distribution_strategy_utils.py

@@ -0,0 +1,38 @@
+# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Utils related to tf.distribute.strategy."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import collections
+
+from tensorflow.python.distribute import strategy_combinations
+
+_strategies = [
+    strategy_combinations.one_device_strategy,
+    strategy_combinations.mirrored_strategy_with_one_cpu,
+    strategy_combinations.mirrored_strategy_with_one_gpu,
+    strategy_combinations.mirrored_strategy_with_gpu_and_cpu,
+    strategy_combinations.mirrored_strategy_with_two_gpus,
+]
+
+named_strategies = collections.OrderedDict()
+named_strategies[None] = None
+
+
+for strategy in _strategies:
+  named_strategies[str(strategy)] = strategy

tensorflow/examples/saved_model/integration_tests/mnist_util.py

@@ -33,7 +33,7 @@ def _load_random_data(num_train_and_test):
 
 def load_reshaped_data(use_fashion_mnist=False, fake_tiny_data=False):
   """Returns MNIST or Fashion MNIST or fake train and test data."""
-  load = ((lambda: _load_random_data([10, 50])) if fake_tiny_data else
+  load = ((lambda: _load_random_data([16, 128])) if fake_tiny_data else
           tf.keras.datasets.fashion_mnist.load_data if use_fashion_mnist else
           tf.keras.datasets.mnist.load_data)
   (x_train, y_train), (x_test, y_test) = load()

tensorflow/examples/saved_model/integration_tests/saved_model_test.py

@@ -23,10 +23,12 @@ import os
 from absl.testing import parameterized
 import tensorflow.compat.v2 as tf
 
-from tensorflow.examples.saved_model.integration_tests import integration_scripts
+from tensorflow.examples.saved_model.integration_tests import distribution_strategy_utils as ds_utils
+from tensorflow.examples.saved_model.integration_tests import integration_scripts as scripts
+from tensorflow.python.distribute import combinations
 
 
-class SavedModelTest(integration_scripts.TestCase, parameterized.TestCase):
+class SavedModelTest(scripts.TestCase, parameterized.TestCase):
 
   def __init__(self, method_name="runTest", has_extra_deps=False):
     super(SavedModelTest, self).__init__(method_name)
@@ -71,40 +73,48 @@ class SavedModelTest(integration_scripts.TestCase, parameterized.TestCase):
     self.assertCommandSucceeded(
         "use_text_embedding_in_dataset", model_dir=export_dir)
 
-  NAMED_PARAMETERS_FOR_TEST_MNIST_CNN = (
-      ("", dict()),
-      ("_with_retraining", dict(
-          retrain=True,
-          regularization_loss_multiplier=2,  # Test impact of b/134528831.
-      )),
-      ("_with_mirrored_strategy", dict(
-          retrain=True,  # That's the relevant case for distribution.
-          use_mirrored_strategy=True,
-      )),
-  )
+  @combinations.generate(
+      combinations.combine(
+          named_strategy=list(ds_utils.named_strategies.values()),
+          retrain_flag_value=["true", "false"],
+          regularization_loss_multiplier=[0, 2]),
+      test_combinations=[combinations.NamedGPUCombination()])
+  def test_mnist_cnn(self, named_strategy, retrain_flag_value,
+                     regularization_loss_multiplier):
 
-  @parameterized.named_parameters(*NAMED_PARAMETERS_FOR_TEST_MNIST_CNN)
-  def test_mnist_cnn(self, use_kwargs):
     self.skipIfMissingExtraDeps()
-    if use_kwargs.get("use_mirrored_strategy", None):
-      self.skipTest(
-          "b/129134185 - saved model and distribution strategy integration")
+
     fast_test_mode = True
     temp_dir = self.get_temp_dir()
     feature_extrator_dir = os.path.join(temp_dir, "mnist_feature_extractor")
-    full_model_dir = os.path.join(temp_dir, "full_model")
+
+    # TODO(b/135043074): remove this if-else.
+    if named_strategy is None:
+      full_model_dir = os.path.join(temp_dir, "full_model")
+    else:
+      full_model_dir = None
+
     self.assertCommandSucceeded(
-        "export_mnist_cnn", fast_test_mode=fast_test_mode,
+        "export_mnist_cnn",
+        fast_test_mode=fast_test_mode,
         export_dir=feature_extrator_dir)
+
     self.assertCommandSucceeded(
-        "use_mnist_cnn", fast_test_mode=fast_test_mode,
+        "use_mnist_cnn",
+        fast_test_mode=fast_test_mode,
         input_saved_model_dir=feature_extrator_dir,
-        output_saved_model_dir=full_model_dir, **use_kwargs)
-    self.assertCommandSucceeded(
-        "deploy_mnist_cnn", fast_test_mode=fast_test_mode,
-        saved_model_dir=full_model_dir)
+        output_saved_model_dir=full_model_dir,
+        strategy=str(named_strategy),
+        retrain=retrain_flag_value,
+        regularization_loss_multiplier=regularization_loss_multiplier)
+
+    if full_model_dir is not None:
+      self.assertCommandSucceeded(
+          "deploy_mnist_cnn",
+          fast_test_mode=fast_test_mode,
+          saved_model_dir=full_model_dir)
 
 
 if __name__ == "__main__":
-  integration_scripts.MaybeRunScriptInstead()
+  scripts.MaybeRunScriptInstead()
   tf.test.main()

tensorflow/examples/saved_model/integration_tests/use_mnist_cnn.py

@@ -31,6 +31,7 @@ from absl import flags
 import tensorflow.compat.v2 as tf
 import tensorflow_hub as hub
 
+from tensorflow.examples.saved_model.integration_tests import distribution_strategy_utils as ds_utils
 from tensorflow.examples.saved_model.integration_tests import mnist_util
 
 FLAGS = flags.FLAGS
@@ -57,12 +58,29 @@ flags.DEFINE_bool(
 flags.DEFINE_bool(
     'fast_test_mode', False,
     'Shortcut training for running in unit tests.')
-flags.DEFINE_bool(
-    'use_mirrored_strategy', False,
-    'Whether to use mirrored distribution strategy.')
 flags.DEFINE_string(
     'output_saved_model_dir', None,
     'Directory of the SavedModel that was exported for reuse.')
+flags.DEFINE_string('strategy', None,
+                    'Name of the distribution strategy to use.')
+
+
+class MaybeDistributionScope(object):
+  """Provides a context allowing no distribution strategy."""
+
+  def __init__(self, distribution):
+    self._distribution = distribution
+    self._scope = None
+
+  def __enter__(self):
+    if self._distribution:
+      self._scope = self._distribution.scope()
+      self._scope.__enter__()
+
+  def __exit__(self, exc_type, value, traceback):
+    if self._distribution:
+      self._scope.__exit__(exc_type, value, traceback)
+      self._scope = None
 
 
 def make_feature_extractor(saved_model_path, trainable,
@@ -108,12 +126,11 @@ def make_classifier(feature_extractor, l2_strength=0.01, dropout_rate=0.5):
 def main(argv):
   del argv
 
-  if FLAGS.use_mirrored_strategy:
-    strategy = tf.distribute.MirroredStrategy()
-  else:
-    strategy = tf.distribute.get_strategy()
+  named_strategy = (
+      ds_utils.named_strategies.get(FLAGS.strategy) if FLAGS.strategy else None)
+  strategy = named_strategy.strategy if named_strategy else None
 
-  with strategy.scope():
+  with MaybeDistributionScope(strategy):
     feature_extractor = make_feature_extractor(
         FLAGS.input_saved_model_dir,
         FLAGS.retrain,
@@ -137,7 +154,7 @@ def main(argv):
             verbose=1,
             validation_data=(x_test, y_test))
 
-  if FLAGS.output_saved_model_dir:
+  if FLAGS.output_saved_model_dir and FLAGS.output_saved_model_dir != 'None':
     tf.saved_model.save(model, FLAGS.output_saved_model_dir)
 
 

tensorflow/python/eager/function.py

@@ -72,8 +72,9 @@ BACKWARD_FUNCTION_ATTRIBUTE_NAME = "backward_function_name"
 
 
 CacheKey = collections.namedtuple("CacheKey", [
-    "input_signature", "parent_graph", "device_functions",
-    "colocation_stack"])
+    "input_signature", "parent_graph", "device_functions", "colocation_stack",
+    "in_cross_replica_context"
+])
 
 CacheKey.replace = CacheKey._replace  # pylint: disable=protected-access
 
@@ -1562,9 +1563,15 @@ class Function(object):
         device_functions = tuple(default_graph._device_functions_outer_to_inner)
       else:
         device_functions = ()
-    # pylint: enable=protected-access
+
+    in_cross_replica_context = False
+    try:
+      in_cross_replica_context = (strategy_stack[-1].replica_context is None)  # pylint: disable=protected-access
+    except (AttributeError, IndexError):
+      pass
+
     return CacheKey(input_signature, parent_graph, device_functions,
-                    colocation_stack)
+                    colocation_stack, in_cross_replica_context)
 
   def _create_graph_function(self, args, kwargs, override_flat_arg_shapes=None):
     """Create a `ConcreteFunction` from `args` and `kwargs`."""
@@ -1664,6 +1671,7 @@ class Function(object):
     if self.input_signature is None or args is not None or kwargs is not None:
       args, kwargs = self._function_spec.canonicalize_function_inputs(
           *args, **kwargs)
+
     cache_key = self._cache_key(args, kwargs)
 
     try: