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# Copyright 2023 The Orbit 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 orbit.standard_runner."""
from absl.testing import parameterized
from orbit import standard_runner
from orbit import utils
import tensorflow as tf, tf_keras
def dataset_fn(input_context=None):
del input_context
def dummy_data(_):
return tf.zeros((1, 1), dtype=tf.float32)
dataset = tf.data.Dataset.range(1)
dataset = dataset.repeat()
dataset = dataset.map(
dummy_data, num_parallel_calls=tf.data.experimental.AUTOTUNE)
return dataset
class TestTrainer(standard_runner.StandardTrainer):
"""A StandardTrainer subclass for tests."""
def __init__(self, options=None):
self.strategy = tf.distribute.get_strategy()
self.global_step = utils.create_global_step()
dataset = self.strategy.distribute_datasets_from_function(dataset_fn)
super().__init__(train_dataset=dataset, options=options)
def train_loop_begin(self):
self.global_step.assign(0)
def train_step(self, iterator):
def replica_step(_):
self.global_step.assign_add(1)
self.strategy.run(replica_step, args=(next(iterator),))
def train_loop_end(self):
return self.global_step.numpy()
class TestEvaluator(standard_runner.StandardEvaluator):
"""A StandardEvaluator subclass for tests."""
def __init__(self, options=None):
self.strategy = tf.distribute.get_strategy()
self.global_step = utils.create_global_step()
dataset = self.strategy.distribute_datasets_from_function(dataset_fn)
super().__init__(eval_dataset=dataset, options=options)
def eval_begin(self):
self.global_step.assign(0)
def eval_step(self, iterator):
def replica_step(_):
self.global_step.assign_add(1)
self.strategy.run(replica_step, args=(next(iterator),))
def eval_end(self):
return self.global_step.numpy()
class TestEvaluatorWithOutputsAggregation(standard_runner.StandardEvaluator):
"""A StandardEvaluator subclass for tests."""
def __init__(self, options=None):
self.strategy = tf.distribute.get_strategy()
dataset = self.strategy.distribute_datasets_from_function(
lambda _: tf.data.Dataset.range(10))
super().__init__(eval_dataset=dataset, options=options)
def eval_begin(self):
return {"logits": tf.constant((0.0,))}
def eval_reduce(self, state, step_outputs):
state["logits"] = tf.concat([state["logits"], step_outputs], 0)
return state
def eval_step(self, iterator):
def replica_step(x):
x = tf.cast(x, tf.float32)
return tf.reduce_sum(x)
return self.strategy.experimental_local_results(
self.strategy.run(replica_step, args=(next(iterator),)))
def eval_end(self, outputs):
return tf.reduce_sum(outputs["logits"])
class StandardRunnerTest(parameterized.TestCase):
def test_default_trainer(self):
trainer = TestTrainer()
self.assertEqual(trainer.train(tf.constant(10)), 10)
def test_trainer_with_tpu_summary_optimization(self):
options = standard_runner.StandardTrainerOptions(
use_tpu_summary_optimization=True)
trainer = TestTrainer(options)
self.assertEqual(trainer.train(tf.constant(10)), 10)
@parameterized.named_parameters(("use_tf_while_loop", True), ("", False))
def test_default_evaluator(self, use_tf_while_loop):
options = standard_runner.StandardEvaluatorOptions(
use_tf_while_loop=use_tf_while_loop)
evaluator = TestEvaluator(options)
self.assertEqual(evaluator.evaluate(tf.constant(10)), 10)
@parameterized.named_parameters(("use_tf_while_loop", True), ("", False))
def test_evaluator_with_outputs_aggregation(self, use_tf_while_loop):
options = standard_runner.StandardEvaluatorOptions(
use_tf_while_loop=use_tf_while_loop)
evaluator = TestEvaluatorWithOutputsAggregation(options)
self.assertEqual(evaluator.evaluate(tf.constant(10)), 45)
@parameterized.named_parameters(
("recreate_iterator_for_each_eval", True, 10, 10),
("not_recreate_iterator_for_each_eval", False, 10, 35))
def test_evaluator_with_repeat_dataset(self, recreate_iterator_for_each_eval,
sum_for_1st_time, sum_for_2nd_time):
options = standard_runner.StandardEvaluatorOptions(
recreate_iterator_for_each_eval=recreate_iterator_for_each_eval)
evaluator = TestEvaluatorWithOutputsAggregation(options)
self.assertEqual(evaluator.evaluate(tf.constant(5)), sum_for_1st_time)
self.assertEqual(evaluator.evaluate(tf.constant(5)), sum_for_2nd_time)
if __name__ == "__main__":
tf.test.main()
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