def setUp(self):
temp_dir = self.get_temp_dir()
if TransformerTaskTest.local_flags is None:
misc.define_transformer_flags()
# Loads flags, array cannot be blank.
flags.FLAGS(['foo'])
TransformerTaskTest.local_flags = flagsaver.save_flag_values()
else:
flagsaver.restore_flag_values(TransformerTaskTest.local_flags)
FLAGS.model_dir = os.path.join(temp_dir, FIXED_TIMESTAMP)
FLAGS.param_set = 'tiny'
FLAGS.use_synthetic_data = True
FLAGS.steps_between_evals = 1
FLAGS.train_steps = 2
FLAGS.validation_steps = 1
FLAGS.batch_size = 8
FLAGS.num_gpus = 1
FLAGS.distribution_strategy = 'off'
FLAGS.dtype = 'fp32'
self.model_dir = FLAGS.model_dir
self.temp_dir = temp_dir
self.vocab_file = os.path.join(temp_dir, 'vocab')
self.vocab_size = misc.get_model_params(FLAGS.param_set,
0)['vocab_size']
self.bleu_source = os.path.join(temp_dir, 'bleu_source')
self.bleu_ref = os.path.join(temp_dir, 'bleu_ref')
self.orig_policy = tf.keras.mixed_precision.experimental.global_policy(
)
return opt
def _ensure_dir(log_dir):
"""Makes log dir if not existed."""
if not tf.io.gfile.exists(log_dir):
tf.io.gfile.makedirs(log_dir)
def main(_):
flags_obj = flags.FLAGS
with logger.benchmark_context(flags_obj):
task = TransformerTask(flags_obj)
if flags_obj.mode == "train":
task.train()
elif flags_obj.mode == "predict":
task.predict()
elif flags_obj.mode == "eval":
task.eval()
else:
raise ValueError("Invalid mode {}".format(flags_obj.mode))
if __name__ == "__main__":
tf.compat.v1.enable_v2_behavior()
logging.set_verbosity(logging.INFO)
misc.define_transformer_flags()
app.run(main)
