[Clean up] Remove enable_eager in the session config: Model garden is TF2 only now.

Remove is_v2_0

PiperOrigin-RevId: 312336907

official/benchmark/models/resnet_cifar_main.py

@@ -119,7 +119,6 @@ def run(flags_obj):
     Dictionary of training and eval stats.
   """
   keras_utils.set_session_config(
-      enable_eager=flags_obj.enable_eager,
       enable_xla=flags_obj.enable_xla)
 
   # Execute flag override logic for better model performance

official/benchmark/models/resnet_cifar_test.py

@@ -26,7 +26,6 @@ from tensorflow.python.eager import context
 from tensorflow.python.platform import googletest
 from official.benchmark.models import cifar_preprocessing
 from official.benchmark.models import resnet_cifar_main
-from official.utils.misc import keras_utils
 from official.utils.testing import integration
 
 
@@ -60,8 +59,6 @@ class KerasCifarTest(googletest.TestCase):
 
   def test_end_to_end_no_dist_strat(self):
     """Test Keras model with 1 GPU, no distribution strategy."""
-    config = keras_utils.get_config_proto_v1()
-    tf.compat.v1.enable_eager_execution(config=config)
 
     extra_flags = [
         "-distribution_strategy", "off",
@@ -94,8 +91,6 @@ class KerasCifarTest(googletest.TestCase):
 
   def test_end_to_end_1_gpu(self):
     """Test Keras model with 1 GPU."""
-    config = keras_utils.get_config_proto_v1()
-    tf.compat.v1.enable_eager_execution(config=config)
 
     if context.num_gpus() < 1:
       self.skipTest(
@@ -140,8 +135,6 @@ class KerasCifarTest(googletest.TestCase):
 
   def test_end_to_end_2_gpu(self):
     """Test Keras model with 2 GPUs."""
-    config = keras_utils.get_config_proto_v1()
-    tf.compat.v1.enable_eager_execution(config=config)
 
     if context.num_gpus() < 2:
       self.skipTest(

official/benchmark/models/resnet_imagenet_main.py

@@ -51,7 +51,6 @@ def run(flags_obj):
     Dictionary of training and eval stats.
   """
   keras_utils.set_session_config(
-      enable_eager=flags_obj.enable_eager,
       enable_xla=flags_obj.enable_xla)
 
   # Execute flag override logic for better model performance

official/benchmark/models/resnet_imagenet_test.py

@@ -23,7 +23,6 @@ import tensorflow as tf
 
 from tensorflow.python.eager import context
 from official.benchmark.models import resnet_imagenet_main
-from official.utils.misc import keras_utils
 from official.utils.testing import integration
 from official.vision.image_classification.resnet import imagenet_preprocessing
 
@@ -85,8 +84,6 @@ class KerasImagenetTest(tf.test.TestCase):
 
   def test_end_to_end_no_dist_strat(self, flags_key):
     """Test Keras model with 1 GPU, no distribution strategy."""
-    config = keras_utils.get_config_proto_v1()
-    tf.compat.v1.enable_eager_execution(config=config)
 
     extra_flags = [
         "-distribution_strategy", "off",
@@ -115,8 +112,6 @@ class KerasImagenetTest(tf.test.TestCase):
 
   def test_end_to_end_1_gpu(self, flags_key):
     """Test Keras model with 1 GPU."""
-    config = keras_utils.get_config_proto_v1()
-    tf.compat.v1.enable_eager_execution(config=config)
 
     if context.num_gpus() < 1:
       self.skipTest(
@@ -138,8 +133,6 @@ class KerasImagenetTest(tf.test.TestCase):
 
   def test_end_to_end_1_gpu_fp16(self, flags_key):
     """Test Keras model with 1 GPU and fp16."""
-    config = keras_utils.get_config_proto_v1()
-    tf.compat.v1.enable_eager_execution(config=config)
 
     if context.num_gpus() < 1:
       self.skipTest(
@@ -164,8 +157,6 @@ class KerasImagenetTest(tf.test.TestCase):
 
   def test_end_to_end_2_gpu(self, flags_key):
     """Test Keras model with 2 GPUs."""
-    config = keras_utils.get_config_proto_v1()
-    tf.compat.v1.enable_eager_execution(config=config)
 
     if context.num_gpus() < 2:
       self.skipTest(
@@ -186,8 +177,6 @@ class KerasImagenetTest(tf.test.TestCase):
 
   def test_end_to_end_xla_2_gpu(self, flags_key):
     """Test Keras model with XLA and 2 GPUs."""
-    config = keras_utils.get_config_proto_v1()
-    tf.compat.v1.enable_eager_execution(config=config)
 
     if context.num_gpus() < 2:
       self.skipTest(
@@ -209,8 +198,6 @@ class KerasImagenetTest(tf.test.TestCase):
 
   def test_end_to_end_2_gpu_fp16(self, flags_key):
     """Test Keras model with 2 GPUs and fp16."""
-    config = keras_utils.get_config_proto_v1()
-    tf.compat.v1.enable_eager_execution(config=config)
 
     if context.num_gpus() < 2:
       self.skipTest(
@@ -235,9 +222,6 @@ class KerasImagenetTest(tf.test.TestCase):
 
   def test_end_to_end_xla_2_gpu_fp16(self, flags_key):
     """Test Keras model with XLA, 2 GPUs and fp16."""
-    config = keras_utils.get_config_proto_v1()
-    tf.compat.v1.enable_eager_execution(config=config)
-
     if context.num_gpus() < 2:
       self.skipTest(
           "{} GPUs are not available for this test. {} GPUs are available".

official/benchmark/models/resnet_imagenet_test_tpu.py

@@ -21,7 +21,6 @@ from __future__ import print_function
 from absl.testing import parameterized
 import tensorflow as tf
 from official.benchmark.models import resnet_imagenet_main
-from official.utils.misc import keras_utils
 from official.utils.testing import integration
 from official.vision.image_classification.resnet import imagenet_preprocessing
 
@@ -70,8 +69,6 @@ class KerasImagenetTest(tf.test.TestCase, parameterized.TestCase):
   ])
   def test_end_to_end_tpu(self, flags_key):
     """Test Keras model with TPU distribution strategy."""
-    config = keras_utils.get_config_proto_v1()
-    tf.compat.v1.enable_eager_execution(config=config)
 
     extra_flags = [
         "-distribution_strategy", "tpu",
@@ -89,8 +86,6 @@ class KerasImagenetTest(tf.test.TestCase, parameterized.TestCase):
   @parameterized.parameters(["resnet"])
   def test_end_to_end_tpu_bf16(self, flags_key):
     """Test Keras model with TPU and bfloat16 activation."""
-    config = keras_utils.get_config_proto_v1()
-    tf.compat.v1.enable_eager_execution(config=config)
 
     extra_flags = [
         "-distribution_strategy", "tpu",

official/benchmark/models/shakespeare/shakespeare_main.py

@@ -139,7 +139,6 @@ def build_model(vocab_size,
   Returns:
     A Keras Model.
   """
-  assert keras_utils.is_v2_0()
   LSTM = functools.partial(tf.keras.layers.LSTM, implementation=2)
 
   # By indirecting the activation through a lambda layer, the logic to dispatch
@@ -275,7 +274,6 @@ def run(flags_obj):
     tf.keras.mixed_precision.experimental.set_policy(policy)
 
   keras_utils.set_session_config(
-      enable_eager=flags_obj.enable_eager,
       enable_xla=flags_obj.enable_xla)
 
   strategy = distribution_utils.get_distribution_strategy(

official/benchmark/shakespeare_benchmark.py

@@ -273,7 +273,6 @@ class ShakespeareKerasBenchmarkReal(ShakespeareBenchmarkBase):
     FLAGS.num_gpus = 1
     FLAGS.batch_size = 64
     FLAGS.cudnn = False
-    FLAGS.enable_eager = keras_utils.is_v2_0()
     self._run_and_report_benchmark()
 
   def benchmark_1_gpu_no_ds(self):
@@ -307,7 +306,6 @@ class ShakespeareKerasBenchmarkReal(ShakespeareBenchmarkBase):
     FLAGS.num_gpus = 1
     FLAGS.batch_size = 64
     FLAGS.cudnn = False
-    FLAGS.enable_eager = keras_utils.is_v2_0()
     FLAGS.enable_xla = True
     self._run_and_report_benchmark()
 
@@ -326,7 +324,6 @@ class ShakespeareKerasBenchmarkReal(ShakespeareBenchmarkBase):
     FLAGS.batch_size = 64 * 8
     FLAGS.log_steps = 10
     FLAGS.cudnn = False
-    FLAGS.enable_eager = keras_utils.is_v2_0()
     self._run_and_report_benchmark()
 
   def benchmark_xla_8_gpu(self):
@@ -345,7 +342,6 @@ class ShakespeareKerasBenchmarkReal(ShakespeareBenchmarkBase):
     FLAGS.batch_size = 64 * 8
     FLAGS.log_steps = 10
     FLAGS.cudnn = False
-    FLAGS.enable_eager = keras_utils.is_v2_0()
     FLAGS.enable_xla = True
     self._run_and_report_benchmark()
 

official/nlp/bert/run_classifier.py

@@ -347,7 +347,7 @@ def run_bert(strategy,
   if FLAGS.mode != 'train_and_eval':
     raise ValueError('Unsupported mode is specified: %s' % FLAGS.mode)
   # Enables XLA in Session Config. Should not be set for TPU.
-  keras_utils.set_config_v2(FLAGS.enable_xla)
+  keras_utils.set_session_config(FLAGS.enable_xla)
   performance.set_mixed_precision_policy(common_flags.dtype())
 
   epochs = FLAGS.num_train_epochs

official/nlp/bert/run_squad_helper.py

@@ -227,7 +227,7 @@ def train_squad(strategy,
     logging.info('Training using customized training loop with distribution'
                  ' strategy.')
   # Enables XLA in Session Config. Should not be set for TPU.
-  keras_utils.set_config_v2(FLAGS.enable_xla)
+  keras_utils.set_session_config(FLAGS.enable_xla)
   performance.set_mixed_precision_policy(common_flags.dtype())
 
   epochs = FLAGS.num_train_epochs

official/r1/boosted_trees/train_higgs.py

@@ -48,7 +48,7 @@ import os
 from absl import app as absl_app
 from absl import flags
 import numpy as np
-import tensorflow as tf
+import tensorflow.compat.v1 as tf
 
 from official.r1.utils.logs import logger
 from official.utils.flags import core as flags_core

official/r1/mnist/mnist.py

@@ -21,7 +21,7 @@ from absl import app as absl_app
 from absl import flags
 from absl import logging
 from six.moves import range
-import tensorflow as tf
+import tensorflow.compat.v1 as tf
 
 from official.r1.mnist import dataset
 from official.r1.utils.logs import hooks_helper

official/r1/mnist/mnist_eager_test.py

-# Copyright 2018 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.
-# ==============================================================================
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import unittest
-
-import tensorflow as tf  # pylint: disable=g-bad-import-order
-from tensorflow.python import eager as tfe  # pylint: disable=g-bad-import-order
-
-from official.r1.mnist import mnist
-from official.r1.mnist import mnist_eager
-from official.utils.misc import keras_utils
-
-
-def device():
-  return '/device:GPU:0' if tfe.context.num_gpus() else '/device:CPU:0'
-
-
-def data_format():
-  return 'channels_first' if tfe.context.num_gpus() else 'channels_last'
-
-
-def random_dataset():
-  batch_size = 64
-  images = tf.random_normal([batch_size, 784])
-  labels = tf.random_uniform([batch_size], minval=0, maxval=10, dtype=tf.int32)
-  return tf.data.Dataset.from_tensors((images, labels))
-
-
-def train(defun=False):
-  model = mnist.create_model(data_format())
-  if defun:
-    model.call = tf.function(model.call)
-  optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.01)
-  dataset = random_dataset()
-  with tf.device(device()):
-    mnist_eager.train(model, optimizer, dataset,
-                      step_counter=tf.train.get_or_create_global_step())
-
-
-def evaluate(defun=False):
-  model = mnist.create_model(data_format())
-  dataset = random_dataset()
-  if defun:
-    model.call = tf.function(model.call)
-  with tf.device(device()):
-    mnist_eager.test(model, dataset)
-
-
-class MNISTTest(tf.test.TestCase):
-  """Run tests for MNIST eager loop.
-
-  MNIST eager uses contrib and will not work with TF 2.0.  All tests are
-  disabled if using TF 2.0.
-  """
-
-  def setUp(self):
-    if not keras_utils.is_v2_0():
-      tf.compat.v1.enable_v2_behavior()
-    super(MNISTTest, self).setUp()
-
-  @unittest.skipIf(keras_utils.is_v2_0(), 'TF 1.0 only test.')
-  def test_train(self):
-    train(defun=False)
-
-  @unittest.skipIf(keras_utils.is_v2_0(), 'TF 1.0 only test.')
-  def test_evaluate(self):
-    evaluate(defun=False)
-
-  @unittest.skipIf(keras_utils.is_v2_0(), 'TF 1.0 only test.')
-  def test_train_with_defun(self):
-    train(defun=True)
-
-  @unittest.skipIf(keras_utils.is_v2_0(), 'TF 1.0 only test.')
-  def test_evaluate_with_defun(self):
-    evaluate(defun=True)
-
-
-if __name__ == '__main__':
-  tf.test.main()

official/r1/mnist/mnist_test.py

@@ -18,12 +18,10 @@ from __future__ import division
 from __future__ import print_function
 
 import time
-import unittest
 
-import tensorflow as tf  # pylint: disable=g-bad-import-order
+import tensorflow.compat.v1 as tf  # pylint: disable=g-bad-import-order
 from absl import logging
 from official.r1.mnist import mnist
-from official.utils.misc import keras_utils
 
 BATCH_SIZE = 100
 
@@ -51,7 +49,6 @@ class Tests(tf.test.TestCase):
   using TF 2.0.
   """
 
-  @unittest.skipIf(keras_utils.is_v2_0(), 'TF 1.0 only test.')
   def test_mnist(self):
     classifier = make_estimator()
     classifier.train(input_fn=dummy_input_fn, steps=2)
@@ -71,7 +68,6 @@ class Tests(tf.test.TestCase):
       self.assertEqual(predictions['probabilities'].shape, (10,))
       self.assertEqual(predictions['classes'].shape, ())
 
-  @unittest.skipIf(keras_utils.is_v2_0(), 'TF 1.0 only test.')
   def mnist_model_fn_helper(self, mode, multi_gpu=False):
     features, labels = dummy_input_fn()
     image_count = features.shape[0]
@@ -99,19 +95,15 @@ class Tests(tf.test.TestCase):
       self.assertEqual(eval_metric_ops['accuracy'][0].dtype, tf.float32)
       self.assertEqual(eval_metric_ops['accuracy'][1].dtype, tf.float32)
 
-  @unittest.skipIf(keras_utils.is_v2_0(), 'TF 1.0 only test.')
   def test_mnist_model_fn_train_mode(self):
     self.mnist_model_fn_helper(tf.estimator.ModeKeys.TRAIN)
 
-  @unittest.skipIf(keras_utils.is_v2_0(), 'TF 1.0 only test.')
   def test_mnist_model_fn_train_mode_multi_gpu(self):
     self.mnist_model_fn_helper(tf.estimator.ModeKeys.TRAIN, multi_gpu=True)
 
-  @unittest.skipIf(keras_utils.is_v2_0(), 'TF 1.0 only test.')
   def test_mnist_model_fn_eval_mode(self):
     self.mnist_model_fn_helper(tf.estimator.ModeKeys.EVAL)
 
-  @unittest.skipIf(keras_utils.is_v2_0(), 'TF 1.0 only test.')
   def test_mnist_model_fn_predict_mode(self):
     self.mnist_model_fn_helper(tf.estimator.ModeKeys.PREDICT)
 
@@ -144,4 +136,5 @@ class Benchmarks(tf.test.Benchmark):
 
 if __name__ == '__main__':
   logging.set_verbosity(logging.ERROR)
+  tf.disable_v2_behavior()
   tf.test.main()

official/r1/resnet/cifar10_test.py

@@ -24,7 +24,6 @@ import numpy as np
 import tensorflow as tf
 
 from official.r1.resnet import cifar10_main
-from official.utils.misc import keras_utils
 from official.utils.testing import integration
 
 logging.set_verbosity(logging.ERROR)
@@ -44,8 +43,7 @@ class BaseTest(tf.test.TestCase):
   @classmethod
   def setUpClass(cls):  # pylint: disable=invalid-name
     super(BaseTest, cls).setUpClass()
-    if keras_utils.is_v2_0:
-      tf.compat.v1.disable_eager_execution()
+    tf.compat.v1.disable_eager_execution()
     cifar10_main.define_cifar_flags()
 
   def setUp(self):

official/r1/resnet/imagenet_test.py

@@ -23,7 +23,6 @@ import tensorflow as tf  # pylint: disable=g-bad-import-order
 from absl import logging
 
 from official.r1.resnet import imagenet_main
-from official.utils.misc import keras_utils
 from official.utils.testing import integration
 
 logging.set_verbosity(logging.ERROR)
@@ -43,8 +42,7 @@ class BaseTest(tf.test.TestCase):
 
   def setUp(self):
     super(BaseTest, self).setUp()
-    if keras_utils.is_v2_0:
-      tf.compat.v1.disable_eager_execution()
+    tf.compat.v1.disable_eager_execution()
     self._num_validation_images = imagenet_main.NUM_IMAGES['validation']
     imagenet_main.NUM_IMAGES['validation'] = 4
 

official/r1/utils/data/file_io_test.py

@@ -28,7 +28,6 @@ import tensorflow as tf
 # pylint: enable=wrong-import-order
 
 from official.r1.utils.data import file_io
-from official.utils.misc import keras_utils
 
 
 _RAW_ROW = "raw_row"
@@ -108,8 +107,7 @@ class BaseTest(tf.test.TestCase):
 
   def setUp(self):
     super(BaseTest, self).setUp()
-    if keras_utils.is_v2_0:
-      tf.compat.v1.disable_eager_execution()
+    tf.compat.v1.disable_eager_execution()
 
   def _test_sharding(self, row_count, cpu_count, expected):
     df = pd.DataFrame({_DUMMY_COL: list(range(row_count))})

official/r1/wide_deep/census_dataset.py

@@ -26,7 +26,7 @@ from absl import app as absl_app
 from absl import flags
 from six.moves import urllib
 from six.moves import zip
-import tensorflow as tf
+import tensorflow.compat.v1 as tf
 # pylint: enable=wrong-import-order
 
 from official.utils.flags import core as flags_core

official/r1/wide_deep/census_main.py

@@ -18,7 +18,7 @@ import os
 
 from absl import app as absl_app
 from absl import flags
-import tensorflow as tf
+import tensorflow.compat.v1 as tf
 from official.r1.utils.logs import logger
 from official.r1.wide_deep import census_dataset
 from official.r1.wide_deep import wide_deep_run_loop

official/r1/wide_deep/census_test.py

@@ -18,15 +18,13 @@ from __future__ import division
 from __future__ import print_function
 
 import os
-import unittest
 
-import tensorflow as tf  # pylint: disable=g-bad-import-order
 from absl import logging
+import tensorflow.compat.v1 as tf
 
-from official.utils.misc import keras_utils
-from official.utils.testing import integration
 from official.r1.wide_deep import census_dataset
 from official.r1.wide_deep import census_main
+from official.utils.testing import integration
 
 logging.set_verbosity(logging.ERROR)
 
@@ -73,7 +71,6 @@ class BaseTest(tf.test.TestCase):
           os.path.join(self.temp_dir, fname), 'w') as test_csv:
         test_csv.write(test_csv_contents)
 
-  @unittest.skipIf(keras_utils.is_v2_0(), 'TF 1.0 only test.')
   def test_input_fn(self):
     dataset = census_dataset.input_fn(self.input_csv, 1, False, 1)
     features, labels = dataset.make_one_shot_iterator().get_next()
@@ -127,11 +124,9 @@ class BaseTest(tf.test.TestCase):
                        initial_results['auc_precision_recall'])
     self.assertGreater(final_results['accuracy'], initial_results['accuracy'])
 
-  @unittest.skipIf(keras_utils.is_v2_0(), 'TF 1.0 only test.')
   def test_wide_deep_estimator_training(self):
     self.build_and_test_estimator('wide_deep')
 
-  @unittest.skipIf(keras_utils.is_v2_0(), 'TF 1.0 only test.')
   def test_end_to_end_wide(self):
     integration.run_synthetic(
         main=census_main.main, tmp_root=self.get_temp_dir(),
@@ -142,7 +137,6 @@ class BaseTest(tf.test.TestCase):
         ],
         synth=False)
 
-  @unittest.skipIf(keras_utils.is_v2_0(), 'TF 1.0 only test.')
   def test_end_to_end_deep(self):
     integration.run_synthetic(
         main=census_main.main, tmp_root=self.get_temp_dir(),
@@ -153,7 +147,6 @@ class BaseTest(tf.test.TestCase):
         ],
         synth=False)
 
-  @unittest.skipIf(keras_utils.is_v2_0(), 'TF 1.0 only test.')
   def test_end_to_end_wide_deep(self):
     integration.run_synthetic(
         main=census_main.main, tmp_root=self.get_temp_dir(),
@@ -166,4 +159,5 @@ class BaseTest(tf.test.TestCase):
 
 
 if __name__ == '__main__':
+  tf.disable_eager_execution()
   tf.test.main()

official/r1/wide_deep/movielens_dataset.py

@@ -25,7 +25,7 @@ import os
 from absl import app as absl_app
 from absl import flags
 import numpy as np
-import tensorflow as tf
+import tensorflow.compat.v1 as tf
 # pylint: enable=wrong-import-order
 
 from official.recommendation import movielens