[Clean up]: remove is_v2() check inside transformer.

PiperOrigin-RevId: 312988874

official/nlp/transformer/beam_search.py

@@ -17,7 +17,6 @@
 import tensorflow as tf
 
 from official.nlp.transformer import beam_search_v1 as v1
-from official.nlp.transformer import misc
 
 _StateKeys = v1._StateKeys  # pylint: disable=protected-access
 
@@ -52,8 +51,8 @@ class SequenceBeamSearchV2(v1.SequenceBeamSearch):
     # Account for corner case where there are no finished sequences for a
     # particular batch item. In that case, return alive sequences for that batch
     # item.
-    finished_seq = tf.compat.v2.where(seq_cond, finished_seq, alive_seq)
-    finished_scores = tf.compat.v2.where(
+    finished_seq = tf.where(seq_cond, finished_seq, alive_seq)
+    finished_scores = tf.where(
         score_cond, finished_scores, alive_log_probs)
     return finished_seq, finished_scores
 
@@ -102,14 +101,9 @@ def sequence_beam_search(symbols_to_logits_fn,
   batch_size = (
       initial_ids.shape.as_list()[0] if padded_decode else
       tf.shape(initial_ids)[0])
-  if misc.is_v2():
-    sbs = SequenceBeamSearchV2(symbols_to_logits_fn, vocab_size, batch_size,
-                               beam_size, alpha, max_decode_length, eos_id,
-                               padded_decode, dtype)
-  else:
-    sbs = v1.SequenceBeamSearch(symbols_to_logits_fn, vocab_size, batch_size,
-                                beam_size, alpha, max_decode_length, eos_id,
-                                padded_decode, dtype)
+  sbs = SequenceBeamSearchV2(symbols_to_logits_fn, vocab_size, batch_size,
+                             beam_size, alpha, max_decode_length, eos_id,
+                             padded_decode, dtype)
   return sbs.search(initial_ids, initial_cache)
 
 

official/nlp/transformer/data_pipeline.py

@@ -56,7 +56,6 @@ import os
 from absl import logging
 import tensorflow as tf
 
-from official.nlp.transformer import misc
 from official.utils.misc import model_helpers
 
 # Buffer size for reading records from a TFRecord file. Each training file is
@@ -313,9 +312,5 @@ def eval_input_fn(params, ctx=None):
 def map_data_for_transformer_fn(x, y):
   """Maps data for training, and handles weried behaviors for different vers."""
   # Will transform input x and targets y into tuple(x, y) as new model inputs.
-  if misc.is_v2():
-    # For TF v2, the 2nd parameter is omitted to make Keras training work.
-    return ((x, y),)
-  else:
-    # For TF v1, Keras requires a dummy placeholder as the 2nd parameter.
-    return ((x, y), tf.constant(0.0))
+  # For TF v2, the 2nd parameter is omitted to make Keras training work.
+  return ((x, y),)

official/nlp/transformer/misc.py

@@ -22,10 +22,6 @@ from __future__ import print_function
 from absl import flags
 import tensorflow as tf
 
-# TODO(tianlin) Import internal library. Remove this when some functions for
-# different TF versions are fixed.
-from tensorflow.python import tf2 as tf2_internal
-
 from official.nlp.transformer import model_params
 from official.utils.flags import core as flags_core
 from official.utils.misc import keras_utils
@@ -39,11 +35,6 @@ PARAMS_MAP = {
 }
 
 
-def is_v2():
-  """Returns whether it is v2."""
-  return tf2_internal.enabled()
-
-
 def get_model_params(param_set, num_gpus):
   """Gets predefined model params."""
   if num_gpus > 1:
@@ -78,17 +69,6 @@ def define_transformer_flags():
       fp16_implementation=True
   )
 
-  # Additional performance flags
-  # TODO(b/76028325): Remove when generic layout optimizer is ready.
-  flags.DEFINE_boolean(
-      name='enable_grappler_layout_optimizer',
-      default=True,
-      help='Enable Grappler layout optimizer. Currently Grappler can '
-           'de-optimize fp16 graphs by forcing NCHW layout for all '
-           'convolutions and batch normalizations, and this flag allows to '
-           'disable it.'
-  )
-
   flags_core.define_benchmark()
   flags_core.define_device(tpu=True)