Spaces:
Runtime error
Runtime error
File size: 4,894 Bytes
5672777 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 |
# 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()
|