Merge pull request #6 from tensorflow/master

Updated

official/nlp/transformer/translate.py

@@ -18,6 +18,7 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+from absl import logging
 import numpy as np
 import tensorflow as tf
 
@@ -117,8 +118,7 @@ def translate_file(model,
           maxlen=params["decode_max_length"],
           dtype="int32",
           padding="post")
-      tf.compat.v1.logging.info("Decoding batch %d out of %d.", i,
-                                num_decode_batches)
+      logging.info("Decoding batch %d out of %d.", i, num_decode_batches)
       yield batch
 
   @tf.function
@@ -172,16 +172,15 @@ def translate_file(model,
         translation = _trim_and_decode(val_outputs[j], subtokenizer)
         translations.append(translation)
         if print_all_translations:
-          tf.compat.v1.logging.info(
-              "Translating:\n\tInput: %s\n\tOutput: %s" %
-              (sorted_inputs[j + i * batch_size], translation))
+          logging.info("Translating:\n\tInput: %s\n\tOutput: %s",
+                       sorted_inputs[j + i * batch_size], translation)
 
   # Write translations in the order they appeared in the original file.
   if output_file is not None:
     if tf.io.gfile.isdir(output_file):
       raise ValueError("File output is a directory, will not save outputs to "
                        "file.")
-    tf.compat.v1.logging.info("Writing to file %s" % output_file)
+    logging.info("Writing to file %s", output_file)
     with tf.compat.v1.gfile.Open(output_file, "w") as f:
       for i in sorted_keys:
         f.write("%s\n" % translations[i])
@@ -191,10 +190,10 @@ def translate_from_text(model, subtokenizer, txt):
   encoded_txt = _encode_and_add_eos(txt, subtokenizer)
   result = model.predict(encoded_txt)
   outputs = result["outputs"]
-  tf.compat.v1.logging.info("Original: \"%s\"" % txt)
+  logging.info("Original: \"%s\"", txt)
   translate_from_input(outputs, subtokenizer)
 
 
 def translate_from_input(outputs, subtokenizer):
   translation = _trim_and_decode(outputs, subtokenizer)
-  tf.compat.v1.logging.info("Translation: \"%s\"" % translation)
+  logging.info("Translation: \"%s\"", translation)

official/nlp/transformer/utils/tokenizer.py

@@ -22,6 +22,7 @@ import collections
 import re
 import sys
 import unicodedata
+from absl import logging
 
 import numpy as np
 import six
@@ -71,8 +72,7 @@ class Subtokenizer(object):
 
   def __init__(self, vocab_file, reserved_tokens=None, master_char_set=None):
     """Initializes class, creating a vocab file if data_files is provided."""
-    tf.compat.v1.logging.info("Initializing Subtokenizer from file %s." %
-                              vocab_file)
+    logging.info("Initializing Subtokenizer from file %s.", vocab_file)
 
     if master_char_set is None:
       master_char_set = _ALPHANUMERIC_CHAR_SET
@@ -130,17 +130,17 @@ class Subtokenizer(object):
       reserved_tokens = RESERVED_TOKENS
 
     if tf.io.gfile.exists(vocab_file):
-      tf.compat.v1.logging.info("Vocab file already exists (%s)" % vocab_file)
+      logging.info("Vocab file already exists (%s)", vocab_file)
     else:
-      tf.compat.v1.logging.info("Begin steps to create subtoken vocabulary...")
+      logging.info("Begin steps to create subtoken vocabulary...")
       token_counts = _count_tokens(files, file_byte_limit, correct_strip,
                                    master_char_set)
       alphabet = _generate_alphabet_dict(token_counts)
       subtoken_list = _generate_subtokens_with_target_vocab_size(
           token_counts, alphabet, target_vocab_size, threshold, min_count,
           reserved_tokens)
-      tf.compat.v1.logging.info("Generated vocabulary with %d subtokens." %
-                                len(subtoken_list))
+      logging.info("Generated vocabulary with %d subtokens.",
+                   len(subtoken_list))
       _save_vocab_file(vocab_file, subtoken_list)
     return Subtokenizer(vocab_file, master_char_set=master_char_set)
 
@@ -439,23 +439,22 @@ def _generate_subtokens_with_target_vocab_size(token_counts,
     reserved_tokens = RESERVED_TOKENS
 
   if min_count is not None:
-    tf.compat.v1.logging.info(
-        "Using min_count=%d to generate vocab with target size %d" %
-        (min_count, target_size))
+    logging.info("Using min_count=%d to generate vocab with target size %d",
+                 min_count, target_size)
     return _generate_subtokens(
         token_counts, alphabet, min_count, reserved_tokens=reserved_tokens)
 
   def bisect(min_val, max_val):
     """Recursive function to binary search for subtoken vocabulary."""
     cur_count = (min_val + max_val) // 2
-    tf.compat.v1.logging.info("Binary search: trying min_count=%d (%d %d)" %
-                              (cur_count, min_val, max_val))
+    logging.info("Binary search: trying min_count=%d (%d %d)", cur_count,
+                 min_val, max_val)
     subtoken_list = _generate_subtokens(
         token_counts, alphabet, cur_count, reserved_tokens=reserved_tokens)
 
     val = len(subtoken_list)
-    tf.compat.v1.logging.info(
-        "Binary search: min_count=%d resulted in %d tokens" % (cur_count, val))
+    logging.info("Binary search: min_count=%d resulted in %d tokens", cur_count,
+                 val)
 
     within_threshold = abs(val - target_size) < threshold
     if within_threshold or min_val >= max_val or cur_count < 2:
@@ -471,8 +470,7 @@ def _generate_subtokens_with_target_vocab_size(token_counts,
       return other_subtoken_list
     return subtoken_list
 
-  tf.compat.v1.logging.info("Finding best min_count to get target size of %d" %
-                            target_size)
+  logging.info("Finding best min_count to get target size of %d", target_size)
   return bisect(_MIN_MIN_COUNT, _MAX_MIN_COUNT)
 
 
@@ -644,7 +642,7 @@ def _generate_subtokens(token_counts,
   # subtoken_dict, count how often the resulting subtokens appear, and update
   # the dictionary with subtokens w/ high enough counts.
   for i in xrange(num_iterations):
-    tf.compat.v1.logging.info("\tGenerating subtokens: iteration %d" % i)
+    logging.info("\tGenerating subtokens: iteration %d", i)
     # Generate new subtoken->id dictionary using the new subtoken list.
     subtoken_dict = _list_to_index_dict(subtoken_list)
 
@@ -658,5 +656,5 @@ def _generate_subtokens(token_counts,
     subtoken_list, max_subtoken_length = _gen_new_subtoken_list(
         subtoken_counts, min_count, alphabet, reserved_tokens)
 
-    tf.compat.v1.logging.info("\tVocab size: %d" % len(subtoken_list))
+    logging.info("\tVocab size: %d", len(subtoken_list))
   return subtoken_list

official/nlp/xlnet/training_utils.py

@@ -28,7 +28,7 @@ from absl import logging
 import tensorflow as tf
 from typing import Any, Callable, Dict, Text, Optional
 
-from official.modeling import model_training_utils
+from official.nlp.bert import model_training_utils
 from official.nlp.xlnet import data_utils
 from official.nlp.xlnet import xlnet_modeling as modeling
 

official/r1/mnist/mnist.py

@@ -19,8 +19,9 @@ from __future__ import print_function
 
 from absl import app as absl_app
 from absl import flags
+from absl import logging
 from six.moves import range
-import tensorflow as tf  # pylint: disable=g-bad-import-order
+import tensorflow as tf
 
 from official.r1.mnist import dataset
 from official.utils.flags import core as flags_core
@@ -182,8 +183,8 @@ def run_mnist(flags_obj):
 
   data_format = flags_obj.data_format
   if data_format is None:
-    data_format = ('channels_first'
-                   if tf.test.is_built_with_cuda() else 'channels_last')
+    data_format = ('channels_first' if tf.config.list_physical_devices('GPU')
+                   else 'channels_last')
   mnist_classifier = tf.estimator.Estimator(
       model_fn=model_function,
       model_dir=flags_obj.model_dir,
@@ -241,6 +242,6 @@ def main(_):
 
 
 if __name__ == '__main__':
-  tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.INFO)
+  logging.set_verbosity(logging.INFO)
   define_mnist_flags()
   absl_app.run(main)

official/r1/mnist/mnist_test.py

@@ -21,7 +21,7 @@ import time
 import unittest
 
 import tensorflow 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
 
@@ -143,5 +143,5 @@ class Benchmarks(tf.test.Benchmark):
 
 
 if __name__ == '__main__':
-  tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
+  logging.set_verbosity(logging.ERROR)
   tf.test.main()

official/r1/resnet/cifar10_main.py

@@ -22,8 +22,9 @@ import os
 
 from absl import app as absl_app
 from absl import flags
+from absl import logging
 from six.moves import range
-import tensorflow as tf  # pylint: disable=g-bad-import-order
+import tensorflow as tf
 
 from official.r1.resnet import resnet_model
 from official.r1.resnet import resnet_run_loop
@@ -139,9 +140,9 @@ def input_fn(is_training,
   dataset = tf.data.FixedLengthRecordDataset(filenames, _RECORD_BYTES)
 
   if input_context:
-    tf.compat.v1.logging.info(
-        'Sharding the dataset: input_pipeline_id=%d num_input_pipelines=%d' % (
-            input_context.input_pipeline_id, input_context.num_input_pipelines))
+    logging.info(
+        'Sharding the dataset: input_pipeline_id=%d num_input_pipelines=%d',
+        input_context.input_pipeline_id, input_context.num_input_pipelines)
     dataset = dataset.shard(input_context.num_input_pipelines,
                             input_context.input_pipeline_id)
 
@@ -270,7 +271,7 @@ def run_cifar(flags_obj):
     Dictionary of results. Including final accuracy.
   """
   if flags_obj.image_bytes_as_serving_input:
-    tf.compat.v1.logging.fatal(
+    logging.fatal(
         '--image_bytes_as_serving_input cannot be set to True for CIFAR. '
         'This flag is only applicable to ImageNet.')
     return
@@ -291,6 +292,6 @@ def main(_):
 
 
 if __name__ == '__main__':
-  tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.INFO)
+  logging.set_verbosity(logging.INFO)
   define_cifar_flags()
   absl_app.run(main)

official/r1/resnet/cifar10_test.py

@@ -19,14 +19,15 @@ from __future__ import print_function
 
 from tempfile import mkstemp
 
+from absl import logging
 import numpy as np
-import tensorflow as tf  # pylint: disable=g-bad-import-order
+import tensorflow as tf
 
 from official.r1.resnet import cifar10_main
 from official.utils.misc import keras_utils
 from official.utils.testing import integration
 
-tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
+logging.set_verbosity(logging.ERROR)
 
 _BATCH_SIZE = 128
 _HEIGHT = 32

official/r1/resnet/estimator_benchmark.py

@@ -21,6 +21,7 @@ import os
 import time
 
 from absl import flags
+from absl import logging
 from absl.testing import flagsaver
 import tensorflow as tf  # pylint: disable=g-bad-import-order
 
@@ -56,7 +57,7 @@ class EstimatorBenchmark(tf.test.Benchmark):
 
   def _setup(self):
     """Sets up and resets flags before each test."""
-    tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.INFO)
+    logging.set_verbosity(logging.INFO)
     if EstimatorBenchmark.local_flags is None:
       for flag_method in self.flag_methods:
         flag_method()

official/r1/resnet/imagenet_main.py

@@ -22,6 +22,7 @@ import os
 
 from absl import app as absl_app
 from absl import flags
+from absl import logging
 from six.moves import range
 import tensorflow as tf
 
@@ -194,9 +195,9 @@ def input_fn(is_training,
   dataset = tf.data.Dataset.from_tensor_slices(filenames)
 
   if input_context:
-    tf.compat.v1.logging.info(
-        'Sharding the dataset: input_pipeline_id=%d num_input_pipelines=%d' % (
-            input_context.input_pipeline_id, input_context.num_input_pipelines))
+    logging.info(
+        'Sharding the dataset: input_pipeline_id=%d num_input_pipelines=%d',
+        input_context.input_pipeline_id, input_context.num_input_pipelines)
     dataset = dataset.shard(input_context.num_input_pipelines,
                             input_context.input_pipeline_id)
 
@@ -387,6 +388,6 @@ def main(_):
 
 
 if __name__ == '__main__':
-  tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.INFO)
+  logging.set_verbosity(logging.INFO)
   define_imagenet_flags()
   absl_app.run(main)

official/r1/resnet/imagenet_test.py

@@ -20,12 +20,13 @@ from __future__ import print_function
 import unittest
 
 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
 
-tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
+logging.set_verbosity(logging.ERROR)
 
 _BATCH_SIZE = 32
 _LABEL_CLASSES = 1001

official/r1/resnet/resnet_model.py

@@ -391,8 +391,8 @@ class Model(object):
     self.resnet_size = resnet_size
 
     if not data_format:
-      data_format = (
-          'channels_first' if tf.test.is_built_with_cuda() else 'channels_last')
+      data_format = ('channels_first' if tf.config.list_physical_devices('GPU')
+                     else 'channels_last')
 
     self.resnet_version = resnet_version
     if resnet_version not in (1, 2):

official/r1/resnet/resnet_run_loop.py

@@ -29,6 +29,7 @@ import multiprocessing
 import os
 
 from absl import flags
+from absl import logging
 import tensorflow as tf
 
 from official.r1.resnet import imagenet_preprocessing
@@ -83,8 +84,8 @@ def process_record_dataset(dataset,
     options.experimental_threading.private_threadpool_size = (
         datasets_num_private_threads)
     dataset = dataset.with_options(options)
-    tf.compat.v1.logging.info('datasets_num_private_threads: %s',
-                              datasets_num_private_threads)
+    logging.info('datasets_num_private_threads: %s',
+                 datasets_num_private_threads)
 
   # Disable intra-op parallelism to optimize for throughput instead of latency.
   options = tf.data.Options()
@@ -205,17 +206,15 @@ def override_flags_and_set_envars_for_gpu_thread_pool(flags_obj):
     what has been set by the user on the command-line.
   """
   cpu_count = multiprocessing.cpu_count()
-  tf.compat.v1.logging.info('Logical CPU cores: %s', cpu_count)
+  logging.info('Logical CPU cores: %s', cpu_count)
 
   # Sets up thread pool for each GPU for op scheduling.
   per_gpu_thread_count = 1
   total_gpu_thread_count = per_gpu_thread_count * flags_obj.num_gpus
   os.environ['TF_GPU_THREAD_MODE'] = flags_obj.tf_gpu_thread_mode
   os.environ['TF_GPU_THREAD_COUNT'] = str(per_gpu_thread_count)
-  tf.compat.v1.logging.info('TF_GPU_THREAD_COUNT: %s',
-                            os.environ['TF_GPU_THREAD_COUNT'])
-  tf.compat.v1.logging.info('TF_GPU_THREAD_MODE: %s',
-                            os.environ['TF_GPU_THREAD_MODE'])
+  logging.info('TF_GPU_THREAD_COUNT: %s', os.environ['TF_GPU_THREAD_COUNT'])
+  logging.info('TF_GPU_THREAD_MODE: %s', os.environ['TF_GPU_THREAD_MODE'])
 
   # Reduces general thread pool by number of threads used for GPU pool.
   main_thread_count = cpu_count - total_gpu_thread_count
@@ -648,7 +647,7 @@ def resnet_main(
         hooks=train_hooks,
         max_steps=flags_obj.max_train_steps)
     eval_spec = tf.estimator.EvalSpec(input_fn=input_fn_eval)
-    tf.compat.v1.logging.info('Starting to train and evaluate.')
+    logging.info('Starting to train and evaluate.')
     tf.estimator.train_and_evaluate(classifier, train_spec, eval_spec)
     # tf.estimator.train_and_evalute doesn't return anything in multi-worker
     # case.
@@ -671,8 +670,7 @@ def resnet_main(
       schedule[-1] = train_epochs - sum(schedule[:-1])  # over counting.
 
     for cycle_index, num_train_epochs in enumerate(schedule):
-      tf.compat.v1.logging.info('Starting cycle: %d/%d', cycle_index,
-                                int(n_loops))
+      logging.info('Starting cycle: %d/%d', cycle_index, int(n_loops))
 
       if num_train_epochs:
         # Since we are calling classifier.train immediately in each loop, the
@@ -691,7 +689,7 @@ def resnet_main(
       # allows the eval (which is generally unimportant in those circumstances)
       # to terminate.  Note that eval will run for max_train_steps each loop,
       # regardless of the global_step count.
-      tf.compat.v1.logging.info('Starting to evaluate.')
+      logging.info('Starting to evaluate.')
       eval_results = classifier.evaluate(input_fn=input_fn_eval,
                                          steps=flags_obj.max_train_steps)
 

official/r1/utils/data/file_io.py

@@ -25,10 +25,11 @@ import os
 import tempfile
 import uuid
 
+from absl import logging
 import numpy as np
 import six
-
 import tensorflow as tf
+# pylint:disable=logging-format-interpolation
 
 
 class _GarbageCollector(object):
@@ -50,9 +51,9 @@ class _GarbageCollector(object):
       for i in self.temp_buffers:
         if tf.io.gfile.exists(i):
           tf.io.gfile.remove(i)
-          tf.compat.v1.logging.info("Buffer file {} removed".format(i))
+          logging.info("Buffer file {} removed".format(i))
     except Exception as e:
-      tf.compat.v1.logging.error("Failed to cleanup buffer files: {}".format(e))
+      logging.error("Failed to cleanup buffer files: {}".format(e))
 
 
 _GARBAGE_COLLECTOR = _GarbageCollector()
@@ -176,7 +177,7 @@ def write_to_buffer(dataframe, buffer_path, columns, expected_size=None):
     actual_size = tf.io.gfile.stat(buffer_path).length
     if expected_size == actual_size:
       return buffer_path
-    tf.compat.v1.logging.warning(
+    logging.warning(
         "Existing buffer {} has size {}. Expected size {}. Deleting and "
         "rebuilding buffer.".format(buffer_path, actual_size, expected_size))
     tf.io.gfile.remove(buffer_path)
@@ -187,8 +188,7 @@ def write_to_buffer(dataframe, buffer_path, columns, expected_size=None):
 
   tf.io.gfile.makedirs(os.path.split(buffer_path)[0])
 
-  tf.compat.v1.logging.info("Constructing TFRecordDataset buffer: {}"
-                            .format(buffer_path))
+  logging.info("Constructing TFRecordDataset buffer: {}".format(buffer_path))
 
   count = 0
   pool = multiprocessing.dummy.Pool(multiprocessing.cpu_count())
@@ -198,10 +198,10 @@ def write_to_buffer(dataframe, buffer_path, columns, expected_size=None):
                                             rows_per_core=_ROWS_PER_CORE):
         _serialize_shards(df_shards, columns, pool, writer)
         count += sum([len(s) for s in df_shards])
-        tf.compat.v1.logging.info("{}/{} examples written."
-                                  .format(str(count).ljust(8), len(dataframe)))
+        logging.info("{}/{} examples written.".format(
+            str(count).ljust(8), len(dataframe)))
   finally:
     pool.terminate()
 
-  tf.compat.v1.logging.info("Buffer write complete.")
+  logging.info("Buffer write complete.")
   return buffer_path

official/r1/wide_deep/census_test.py

@@ -21,13 +21,14 @@ import os
 import unittest
 
 import tensorflow as tf  # pylint: disable=g-bad-import-order
+from absl import logging
 
 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
 
-tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
+logging.set_verbosity(logging.ERROR)
 
 TEST_INPUT = ('18,Self-emp-not-inc,987,Bachelors,12,Married-civ-spouse,abc,'
               'Husband,zyx,wvu,34,56,78,tsr,<=50K')

official/r1/wide_deep/movielens_test.py

@@ -28,8 +28,9 @@ from official.utils.misc import keras_utils
 from official.utils.testing import integration
 from official.r1.wide_deep import movielens_dataset
 from official.r1.wide_deep import movielens_main
+from absl import logging
 
-tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
+logging.set_verbosity(logging.ERROR)
 
 
 TEST_INPUT_VALUES = {

official/requirements.txt

@@ -11,7 +11,8 @@ py-cpuinfo>=3.3.0
 scipy>=0.19.1
 tensorflow-hub>=0.6.0
 tensorflow-model-optimization>=0.2.1
-tensorflow_datasets
+tensorflow-datasets
+tensorflow-addons
 dataclasses
 gin-config
 typing

official/staging/training/controller.py

@@ -117,11 +117,18 @@ class Controller(object):
     if self.train_fn is not None:
       self.train_steps = train_steps
       self.steps_per_loop = steps_per_loop
-      self.summary_dir = summary_dir or checkpoint_manager.directory
+      if summary_dir:
+        self.summary_dir = summary_dir
+      elif checkpoint_manager:
+        self.summary_dir = checkpoint_manager.directory
+      else:
+        self.summary_dir = None
 
       self.summary_interval = summary_interval
-      summary_writer = tf.summary.create_file_writer(
-          self.summary_dir) if self.summary_interval else None
+      if self.summary_dir and self.summary_interval:
+        summary_writer = tf.summary.create_file_writer(self.summary_dir)
+      else:
+        summary_writer = None
       # TODO(rxsang): Consider pass SummaryManager directly into Controller for
       # maximum customizability.
       self.summary_manager = utils.SummaryManager(
@@ -140,14 +147,14 @@ class Controller(object):
       self.eval_steps = eval_steps
       self.eval_interval = eval_interval
 
-      # Create and initialize the interval triggers.
+      # Creates and initializes the interval triggers.
       self.eval_trigger = utils.IntervalTrigger(self.eval_interval,
-                                                self.global_step.numpy())
+                                                self.global_step.numpy())  # pytype: disable=attribute-error
 
     if self.global_step:
       tf.summary.experimental.set_step(self.global_step)
 
-    # Restore Model if needed.
+    # Restores the model if needed.
     if self.checkpoint_manager is not None:
       model_restored = self._restore_model()
       if not model_restored and self.checkpoint_manager.checkpoint_interval:
@@ -192,7 +199,7 @@ class Controller(object):
     self.eval_summary_manager.flush()
 
   def _maybe_save_checkpoints(self, current_step, force_trigger=False):
-    if self.checkpoint_manager.checkpoint_interval:
+    if self.checkpoint_manager and self.checkpoint_manager.checkpoint_interval:
       ckpt_path = self.checkpoint_manager.save(
           checkpoint_number=current_step, check_interval=not force_trigger)
       if ckpt_path is not None:

official/staging/training/controller_test.py

@@ -143,6 +143,52 @@ class ControllerTest(tf.test.TestCase, parameterized.TestCase):
     super(ControllerTest, self).setUp()
     self.model_dir = self.get_temp_dir()
 
+  def test_no_checkpoint(self):
+    test_runnable = TestRunnable()
+    # No checkpoint manager and no strategy.
+    test_controller = controller.Controller(
+        train_fn=test_runnable.train,
+        eval_fn=test_runnable.evaluate,
+        global_step=test_runnable.global_step,
+        train_steps=10,
+        steps_per_loop=2,
+        summary_dir=os.path.join(self.model_dir, "summaries/train"),
+        summary_interval=2,
+        eval_summary_dir=os.path.join(self.model_dir, "summaries/eval"),
+        eval_steps=2,
+        eval_interval=5)
+    test_controller.train(evaluate=True)
+    self.assertEqual(test_runnable.global_step.numpy(), 10)
+    # Loss and accuracy values should be written into summaries.
+    self.assertNotEmpty(
+        tf.io.gfile.listdir(os.path.join(self.model_dir, "summaries/train")))
+    self.assertTrue(
+        check_eventfile_for_keyword(
+            "loss", os.path.join(self.model_dir, "summaries/train")))
+    self.assertNotEmpty(
+        tf.io.gfile.listdir(os.path.join(self.model_dir, "summaries/eval")))
+    self.assertTrue(
+        check_eventfile_for_keyword(
+            "eval_loss", os.path.join(self.model_dir, "summaries/eval")))
+    # No checkpoint, so global step starts from 0.
+    test_runnable.global_step.assign(0)
+    test_controller.train(evaluate=True)
+    self.assertEqual(test_runnable.global_step.numpy(), 10)
+
+  def test_no_checkpoint_and_summaries(self):
+    test_runnable = TestRunnable()
+    # No checkpoint + summary directories.
+    test_controller = controller.Controller(
+        train_fn=test_runnable.train,
+        eval_fn=test_runnable.evaluate,
+        global_step=test_runnable.global_step,
+        train_steps=10,
+        steps_per_loop=2,
+        eval_steps=2,
+        eval_interval=5)
+    test_controller.train(evaluate=True)
+    self.assertEqual(test_runnable.global_step.numpy(), 10)
+
   @combinations.generate(all_strategy_combinations())
   def test_train_and_evaluate(self, strategy):
     with strategy.scope():

official/staging/training/grad_utils.py

@@ -54,7 +54,7 @@ def _filter_and_allreduce_gradients(grads_and_vars,
   This utils function is used when users intent to explicitly allreduce
   gradients and customize gradients operations before and after allreduce.
   The allreduced gradients are then passed to optimizer.apply_gradients(
-  all_reduce_sum_gradients=False).
+  experimental_aggregate_gradients=False).
 
   Arguments:
       grads_and_vars: gradients and variables pairs.
@@ -139,4 +139,5 @@ def minimize_using_explicit_allreduce(tape,
     grads_and_vars = zip(allreduced_grads, filtered_training_vars)
   if post_allreduce_callbacks:
     grads_and_vars = _run_callbacks(post_allreduce_callbacks, grads_and_vars)
-  optimizer.apply_gradients(grads_and_vars, all_reduce_sum_gradients=False)
+  optimizer.apply_gradients(
+      grads_and_vars, experimental_aggregate_gradients=False)

official/utils/flags/_device.py

@@ -19,7 +19,7 @@ from __future__ import division
 from __future__ import print_function
 
 from absl import flags
-import tensorflow as tf
+from absl import logging
 
 from official.utils.flags._conventions import help_wrap
 
@@ -39,7 +39,7 @@ def require_cloud_storage(flag_names):
     valid_flags = True
     for key in flag_names:
       if not flag_values[key].startswith("gs://"):
-        tf.compat.v1.logging.error("{} must be a GCS path.".format(key))
+        logging.error("%s must be a GCS path.", key)
         valid_flags = False
 
     return valid_flags