official/recommendation/ranking/train_test.py

# 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.

"""Unit tests for ranking model and associated functionality."""

import json
import os
from absl import flags
from absl.testing import parameterized
import tensorflow as tf, tf_keras

from official.recommendation.ranking import common
from official.recommendation.ranking import train

FLAGS = flags.FLAGS


def _get_params_override(vocab_sizes,
                         interaction='dot',
                         use_orbit=True,
                         strategy='mirrored'):
  # Update `data_dir` if `synthetic_data=False`.
  data_dir = ''

  return json.dumps({
      'runtime': {
          'distribution_strategy': strategy,
      },
      'task': {
          'model': {
              'vocab_sizes': vocab_sizes,
              'embedding_dim': [8] * len(vocab_sizes),
              'bottom_mlp': [64, 32, 8],
              'interaction': interaction,
          },
          'train_data': {
              'input_path': os.path.join(data_dir, 'train/*'),
              'global_batch_size': 16,
          },
          'validation_data': {
              'input_path': os.path.join(data_dir, 'eval/*'),
              'global_batch_size': 16,
          },
          'use_synthetic_data': True,
      },
      'trainer': {
          'use_orbit': use_orbit,
          'validation_interval': 20,
          'validation_steps': 20,
          'train_steps': 40,
      },
  })


class TrainTest(parameterized.TestCase, tf.test.TestCase):

  def setUp(self):
    super().setUp()
    self._temp_dir = self.get_temp_dir()
    self._model_dir = os.path.join(self._temp_dir, 'model_dir')
    tf.io.gfile.makedirs(self._model_dir)
    FLAGS.model_dir = self._model_dir

    FLAGS.tpu = ''

  def tearDown(self):
    tf.io.gfile.rmtree(self._model_dir)
    super().tearDown()

  @parameterized.named_parameters(
      ('DlrmOneDeviceCTL', 'one_device', 'dot', True),
      ('DlrmOneDevice', 'one_device', 'dot', False),
      ('DcnOneDeviceCTL', 'one_device', 'cross', True),
      ('DcnOneDevice', 'one_device', 'cross', False),
      ('DlrmTPUCTL', 'tpu', 'dot', True),
      ('DlrmTPU', 'tpu', 'dot', False),
      ('DcnTPUCTL', 'tpu', 'cross', True),
      ('DcnTPU', 'tpu', 'cross', False),
      ('DlrmMirroredCTL', 'Mirrored', 'dot', True),
      ('DlrmMirrored', 'Mirrored', 'dot', False),
      ('DcnMirroredCTL', 'Mirrored', 'cross', True),
      ('DcnMirrored', 'Mirrored', 'cross', False),
  )
  def testTrainEval(self, strategy, interaction, use_orbit=True):
    # Set up simple trainer with synthetic data.
    # By default the mode must be `train_and_eval`.
    self.assertEqual(FLAGS.mode, 'train_and_eval')

    vocab_sizes = [40, 12, 11, 13]

    FLAGS.params_override = _get_params_override(vocab_sizes=vocab_sizes,
                                                 interaction=interaction,
                                                 use_orbit=use_orbit,
                                                 strategy=strategy)
    train.main('unused_args')
    self.assertNotEmpty(
        tf.io.gfile.glob(os.path.join(self._model_dir, 'params.yaml')))

  @parameterized.named_parameters(
      ('DlrmTPUCTL', 'tpu', 'dot', True),
      ('DlrmTPU', 'tpu', 'dot', False),
      ('DcnTPUCTL', 'tpu', 'cross', True),
      ('DcnTPU', 'tpu', 'cross', False),
      ('DlrmMirroredCTL', 'Mirrored', 'dot', True),
      ('DlrmMirrored', 'Mirrored', 'dot', False),
      ('DcnMirroredCTL', 'Mirrored', 'cross', True),
      ('DcnMirrored', 'Mirrored', 'cross', False),
  )
  def testTrainThenEval(self, strategy, interaction, use_orbit=True):
    # Set up simple trainer with synthetic data.
    vocab_sizes = [40, 12, 11, 13]

    FLAGS.params_override = _get_params_override(vocab_sizes=vocab_sizes,
                                                 interaction=interaction,
                                                 use_orbit=use_orbit,
                                                 strategy=strategy)

    default_mode = FLAGS.mode
    # Training.
    FLAGS.mode = 'train'
    train.main('unused_args')
    self.assertNotEmpty(
        tf.io.gfile.glob(os.path.join(self._model_dir, 'params.yaml')))

    # Evaluation.
    FLAGS.mode = 'eval'
    train.main('unused_args')
    FLAGS.mode = default_mode


if __name__ == '__main__':
  common.define_flags()
  tf.test.main()