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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.utils.tpu_summaries.""" | |
| import functools | |
| import os | |
| from orbit.utils import common | |
| from orbit.utils import tpu_summaries | |
| import tensorflow as tf, tf_keras | |
| class TrainFunctionWithSummaries(tpu_summaries.OptionalSummariesFunction): | |
| """Implements a two-program approach for summaries on TPU.""" | |
| def __call__(self, num_steps): | |
| if tf.summary.should_record_summaries(): | |
| output = self.with_summaries(tf.constant(1)) | |
| num_steps -= 1 | |
| if num_steps >= 1: | |
| output = self.without_summaries(num_steps) | |
| return output | |
| def train_function_with_summaries(function=None, **kwargs): | |
| if function is not None: | |
| return TrainFunctionWithSummaries(function, **kwargs) | |
| return functools.partial(TrainFunctionWithSummaries, **kwargs) | |
| class DummyTrainer(tf.Module): | |
| def __init__(self): | |
| self.step_counter = common.create_global_step() | |
| def train_with_tpu_summary_optimization(self, num_steps): | |
| for _ in tf.range(num_steps): | |
| tf.summary.scalar("step", self.step_counter, step=self.step_counter) | |
| self.step_counter.assign_add(1) | |
| return self.step_counter | |
| def train_with_tpu_summary_optimization_and_input_signature(self, num_steps): | |
| for _ in tf.range(num_steps): | |
| tf.summary.scalar("step", self.step_counter, step=self.step_counter) | |
| self.step_counter.assign_add(1) | |
| return self.step_counter | |
| def train_with_tpu_summary_optimization_no_decorator(self, num_steps): | |
| for _ in tf.range(num_steps): | |
| tf.summary.scalar("step", self.step_counter, step=self.step_counter) | |
| self.step_counter.assign_add(1) | |
| return self.step_counter | |
| class TpuSummariesTest(tf.test.TestCase): | |
| def setUp(self): | |
| super().setUp() | |
| self.trainer = DummyTrainer() | |
| def _get_events_from_logdir(self, logdir): | |
| event_files = tf.io.gfile.listdir(logdir) | |
| self.assertLen(event_files, 1) | |
| path = os.path.join(logdir, event_files[0]) | |
| events = list(tf.compat.v1.train.summary_iterator(path)) | |
| return [event for event in events if event.WhichOneof("what") == "summary"] | |
| def _validate_tpu_summary_optimization(self, function, *args, **kwargs): | |
| logdir = self.get_temp_dir() | |
| with tf.summary.create_file_writer(logdir).as_default(): | |
| with tf.summary.record_if(lambda: self.trainer.step_counter % 20 == 0): | |
| for _ in range(4): | |
| output = function(tf.constant(10), *args, **kwargs) | |
| events = self._get_events_from_logdir(logdir) | |
| self.assertLen(events, 2) | |
| self.assertEqual(events[0].step, 0) | |
| self.assertEqual(events[1].step, 20) | |
| return output | |
| def test_train_with_tpu_summary_optimization(self): | |
| output = self._validate_tpu_summary_optimization( | |
| self.trainer.train_with_tpu_summary_optimization) | |
| self.assertEqual(output, self.trainer.step_counter.numpy()) | |
| def test_train_with_tpu_summary_optimization_no_decorator(self): | |
| optimized = train_function_with_summaries( | |
| self.trainer.train_with_tpu_summary_optimization_no_decorator) | |
| output = self._validate_tpu_summary_optimization(optimized) | |
| self.assertEqual(output, self.trainer.step_counter.numpy()) | |
| def test_train_with_tpu_summary_optimization_and_input_signature(self): | |
| output = self._validate_tpu_summary_optimization( | |
| self.trainer.train_with_tpu_summary_optimization_and_input_signature) | |
| self.assertEqual(output, self.trainer.step_counter.numpy()) | |
| function = self.trainer.train_with_tpu_summary_optimization_and_input_signature | |
| expected = (tf.TensorSpec((), dtype=tf.int32),) | |
| input_signature = function.with_summaries.input_signature | |
| self.assertEqual(input_signature, expected) | |
| input_signature = function.without_summaries.input_signature | |
| self.assertEqual(input_signature, expected) | |
| if __name__ == "__main__": | |
| tf.test.main() | |