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# Lint as: python3
# Copyright 2020 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.
# ==============================================================================
"""Tests for official.nlp.nhnet.trainer."""
import os
from absl import flags
from absl.testing import parameterized
import tensorflow as tf
# pylint: disable=g-direct-tensorflow-import
from tensorflow.python.distribute import combinations
from tensorflow.python.distribute import strategy_combinations
# pylint: enable=g-direct-tensorflow-import
from official.nlp.nhnet import trainer
from official.nlp.nhnet import utils
FLAGS = flags.FLAGS
trainer.define_flags()
def all_strategy_combinations():
return combinations.combine(
distribution=[
strategy_combinations.one_device_strategy,
strategy_combinations.one_device_strategy_gpu,
strategy_combinations.mirrored_strategy_with_gpu_and_cpu,
strategy_combinations.tpu_strategy,
],
mode="eager",
)
def get_trivial_data(config) -> tf.data.Dataset:
"""Gets trivial data in the ImageNet size."""
batch_size, num_docs = 2, len(config.passage_list),
len_passage = config.len_passage
len_title = config.len_title
def generate_data(_) -> tf.data.Dataset:
fake_ids = tf.zeros((num_docs, len_passage), dtype=tf.int32)
title = tf.zeros((len_title), dtype=tf.int32)
return dict(
input_ids=fake_ids,
input_mask=fake_ids,
segment_ids=fake_ids,
target_ids=title)
dataset = tf.data.Dataset.range(1)
dataset = dataset.repeat()
dataset = dataset.map(
generate_data, num_parallel_calls=tf.data.experimental.AUTOTUNE)
dataset = dataset.prefetch(buffer_size=1).batch(batch_size)
return dataset
class TrainerTest(tf.test.TestCase, parameterized.TestCase):
def setUp(self):
super(TrainerTest, self).setUp()
self._config = utils.get_test_params()
self._config.override(
{
"vocab_size": 49911,
"max_position_embeddings": 200,
"len_title": 15,
"len_passage": 20,
"beam_size": 5,
"alpha": 0.6,
"learning_rate": 0.0,
"learning_rate_warmup_steps": 0,
"multi_channel_cross_attention": True,
"passage_list": ["a", "b"],
},
is_strict=False)
@combinations.generate(all_strategy_combinations())
def test_train(self, distribution):
FLAGS.train_steps = 10
FLAGS.checkpoint_interval = 5
FLAGS.model_dir = self.get_temp_dir()
FLAGS.model_type = "nhnet"
stats = trainer.train(self._config, distribution,
get_trivial_data(self._config))
self.assertIn("training_loss", stats)
self.assertLen(
tf.io.gfile.glob(os.path.join(FLAGS.model_dir, "ckpt*.index")), 2)
if __name__ == "__main__":
tf.test.main()
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