V2 contrib tweaks (#6184)

* Remove contrib thread pool.

* Remove commented out contrib import.

* Fix lint issues.

* move tf.data.options higher. Tweak line breaks.

* do not monkey patch on or off if dist_strat is off

* Do not monkey patch if no_dist_strat.

* Fix file permissions.

* fix file permissions.

* Revert change to main.  Add hasattr(tf, 'contrib') to utils

* compat.v1.logging

* tf.compat.v1.get_local_variables.

official/utils/misc/distribution_utils.py

@@ -48,18 +48,18 @@ def get_distribution_strategy(num_gpus,
     if turn_off_distribution_strategy:
       return None
     else:
-      return tf.contrib.distribute.OneDeviceStrategy("device:CPU:0")
+      return tf.contrib.distribute.OneDeviceStrategy('device:CPU:0')
   elif num_gpus == 1:
     if turn_off_distribution_strategy:
       return None
     else:
-      return tf.contrib.distribute.OneDeviceStrategy("device:GPU:0")
+      return tf.contrib.distribute.OneDeviceStrategy('device:GPU:0')
   elif turn_off_distribution_strategy:
-    raise ValueError("When {} GPUs are specified, "
-                     "turn_off_distribution_strategy flag cannot be set to"
-                     "True.".format(num_gpus))
+    raise ValueError('When {} GPUs are specified, '
+                     'turn_off_distribution_strategy flag cannot be set to'
+                     'True.'.format(num_gpus))
   else:  # num_gpus > 1 and not turn_off_distribution_strategy
-    devices = ["device:GPU:%d" % i for i in range(num_gpus)]
+    devices = ['device:GPU:%d' % i for i in range(num_gpus)]
     if all_reduce_alg:
       return tf.distribute.MirroredStrategy(
           devices=devices,
@@ -92,13 +92,14 @@ def per_device_batch_size(batch_size, num_gpus):
 
   remainder = batch_size % num_gpus
   if remainder:
-    err = ("When running with multiple GPUs, batch size "
-           "must be a multiple of the number of available GPUs. Found {} "
-           "GPUs with a batch size of {}; try --batch_size={} instead."
+    err = ('When running with multiple GPUs, batch size '
+           'must be a multiple of the number of available GPUs. Found {} '
+           'GPUs with a batch size of {}; try --batch_size={} instead.'
           ).format(num_gpus, batch_size, batch_size - remainder)
     raise ValueError(err)
   return int(batch_size / num_gpus)
 
+
 # The `SyntheticDataset` is a temporary solution for generating synthetic data
 # directly on devices. It is only useful for Keras with Distribution
 # Strategies. We will have better support in `tf.data` or Distribution Strategy
@@ -109,7 +110,7 @@ class SyntheticDataset(object):
   def __init__(self, dataset, split_by=1):
     self._input_data = {}
     # dataset.take(1) doesn't have GPU kernel.
-    with tf.device("device:CPU:0"):
+    with tf.device('device:CPU:0'):
       tensor = tf.data.experimental.get_single_element(dataset.take(1))
     flat_tensor = tf.nest.flatten(tensor)
     variable_data = []
@@ -117,7 +118,8 @@ class SyntheticDataset(object):
     for t in flat_tensor:
       rebatched_t = tf.split(t, num_or_size_splits=split_by, axis=0)[0]
       assert rebatched_t.shape.is_fully_defined(), rebatched_t.shape
-      v = tf.get_local_variable(self.random_name(), initializer=rebatched_t)  # pylint: disable=cell-var-from-loop
+      v = tf.compat.v1.get_local_variable(self.random_name(),
+                                          initializer=rebatched_t)
       variable_data.append(v)
       self._initializers.append(v.initializer)
     self._input_data = tf.nest.pack_sequence_as(tensor, variable_data)
@@ -132,13 +134,13 @@ class SyntheticDataset(object):
       return self._initializers
 
   def random_name(self, size=10, chars=string.ascii_uppercase + string.digits):
-    return "".join(random.choice(chars) for _ in range(size))
+    return ''.join(random.choice(chars) for _ in range(size))
 
 
 def _monkey_patch_dataset_method(strategy):
   """Monkey-patch `strategy`'s `make_dataset_iterator` method."""
   def make_dataset_iterator(self, dataset):
-    tf.logging.info("Using pure synthetic data.")
+    tf.compat.v1.logging.info('Using pure synthetic data.')
     with self.scope():
       if self.extended._global_batch_size:  # pylint: disable=protected-access
         return SyntheticDataset(dataset, self.num_replicas_in_sync)
@@ -150,17 +152,25 @@ def _monkey_patch_dataset_method(strategy):
 
 
 def _undo_monkey_patch_dataset_method(strategy):
-  if hasattr(strategy, "org_make_dataset_iterator"):
+  if hasattr(strategy, 'org_make_dataset_iterator'):
     strategy.make_dataset_iterator = strategy.org_make_dataset_iterator
 
 
 def set_up_synthetic_data():
   _monkey_patch_dataset_method(tf.distribute.MirroredStrategy)
+  # TODO(tobyboyd): Remove when contrib.distribute is all in core.
+  if hasattr(tf, 'contrib'):
     _monkey_patch_dataset_method(tf.contrib.distribute.MirroredStrategy)
     _monkey_patch_dataset_method(tf.contrib.distribute.OneDeviceStrategy)
+  else:
+    print('Contrib missing: Skip monkey patch tf.contrib.distribute.*')
 
 
 def undo_set_up_synthetic_data():
   _undo_monkey_patch_dataset_method(tf.distribute.MirroredStrategy)
+  # TODO(tobyboyd): Remove when contrib.distribute is all in core.
+  if hasattr(tf, 'contrib'):
     _undo_monkey_patch_dataset_method(tf.contrib.distribute.MirroredStrategy)
     _undo_monkey_patch_dataset_method(tf.contrib.distribute.OneDeviceStrategy)
+  else:
+    print('Contrib missing: Skip remove monkey patch tf.contrib.distribute.*')