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# Copyright 2023 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 that masked LM models are deterministic when determinism is enabled."""
import tensorflow as tf, tf_keras
from official.nlp.configs import bert
from official.nlp.configs import encoders
from official.nlp.data import pretrain_dataloader
from official.nlp.tasks import masked_lm
class MLMTaskTest(tf.test.TestCase):
def _build_dataset(self, params, vocab_size):
def dummy_data(_):
dummy_ids = tf.random.uniform((1, params.seq_length), maxval=vocab_size,
dtype=tf.int32)
dummy_mask = tf.ones((1, params.seq_length), dtype=tf.int32)
dummy_type_ids = tf.zeros((1, params.seq_length), dtype=tf.int32)
dummy_lm = tf.zeros((1, params.max_predictions_per_seq), dtype=tf.int32)
return dict(
input_word_ids=dummy_ids,
input_mask=dummy_mask,
input_type_ids=dummy_type_ids,
masked_lm_positions=dummy_lm,
masked_lm_ids=dummy_lm,
masked_lm_weights=tf.cast(dummy_lm, dtype=tf.float32),
next_sentence_labels=tf.zeros((1, 1), dtype=tf.int32))
dataset = tf.data.Dataset.range(1)
dataset = dataset.repeat()
dataset = dataset.map(
dummy_data, num_parallel_calls=tf.data.experimental.AUTOTUNE)
return dataset
def _build_and_run_model(self, config, num_steps=5):
task = masked_lm.MaskedLMTask(config)
model = task.build_model()
metrics = task.build_metrics()
dataset = self._build_dataset(config.train_data,
config.model.encoder.get().vocab_size)
iterator = iter(dataset)
optimizer = tf_keras.optimizers.SGD(lr=0.1)
# Run training
for _ in range(num_steps):
logs = task.train_step(next(iterator), model, optimizer, metrics=metrics)
for metric in metrics:
logs[metric.name] = metric.result()
# Run validation
validation_logs = task.validation_step(next(iterator), model,
metrics=metrics)
for metric in metrics:
validation_logs[metric.name] = metric.result()
return logs, validation_logs, model.weights
def test_task_determinism(self):
config = masked_lm.MaskedLMConfig(
init_checkpoint=self.get_temp_dir(),
scale_loss=True,
model=bert.PretrainerConfig(
encoder=encoders.EncoderConfig(
bert=encoders.BertEncoderConfig(vocab_size=30522,
num_layers=1)),
cls_heads=[
bert.ClsHeadConfig(
inner_dim=10, num_classes=2, name="next_sentence")
]),
train_data=pretrain_dataloader.BertPretrainDataConfig(
max_predictions_per_seq=20,
seq_length=128,
global_batch_size=1))
tf_keras.utils.set_random_seed(1)
logs1, validation_logs1, weights1 = self._build_and_run_model(config)
tf_keras.utils.set_random_seed(1)
logs2, validation_logs2, weights2 = self._build_and_run_model(config)
self.assertEqual(logs1["loss"], logs2["loss"])
self.assertEqual(validation_logs1["loss"], validation_logs2["loss"])
for weight1, weight2 in zip(weights1, weights2):
self.assertAllEqual(weight1, weight2)
if __name__ == "__main__":
tf.config.experimental.enable_op_determinism()
tf.test.main()
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