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optimizer_factory_test.py
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optimizer_factory_test.py
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# Copyright 2024 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 for optimizer_factory.py."""
from absl.testing import parameterized
import numpy as np
import tensorflow as tf, tf_keras
from official.modeling.optimization import optimizer_factory
from official.modeling.optimization.configs import optimization_config
class OptimizerFactoryTest(tf.test.TestCase, parameterized.TestCase):
@parameterized.parameters(('sgd'), ('rmsprop'), ('adam'), ('adamw'), ('lamb'),
('lars'), ('adagrad'))
def test_optimizers(self, optimizer_type):
params = {
'optimizer': {
'type': optimizer_type
},
'learning_rate': {
'type': 'constant',
'constant': {
'learning_rate': 0.1
}
}
}
optimizer_cls = optimizer_factory.LEGACY_OPTIMIZERS_CLS[optimizer_type]
expected_optimizer_config = optimizer_cls().get_config()
expected_optimizer_config['learning_rate'] = 0.1
opt_config = optimization_config.OptimizationConfig(params)
opt_factory = optimizer_factory.OptimizerFactory(opt_config)
lr = opt_factory.build_learning_rate()
optimizer = opt_factory.build_optimizer(lr, postprocessor=lambda x: x)
self.assertIsInstance(optimizer, optimizer_cls)
self.assertEqual(expected_optimizer_config, optimizer.get_config())
@parameterized.parameters(('sgd'), ('rmsprop'), ('adam'), ('adamw'), ('lamb'),
('lars'), ('adagrad'))
def test_new_optimizers(self, optimizer_type):
params = {
'optimizer': {
'type': optimizer_type
},
'learning_rate': {
'type': 'constant',
'constant': {
'learning_rate': 0.1
}
}
}
optimizer_cls = optimizer_factory.NEW_OPTIMIZERS_CLS[optimizer_type]
expected_optimizer_config = optimizer_cls().get_config()
expected_optimizer_config['learning_rate'] = 0.1
opt_config = optimization_config.OptimizationConfig(params)
if optimizer_type == 'sgd':
# Delete unsupported arg `decay` from SGDConfig.
delattr(opt_config.optimizer.sgd, 'decay')
opt_factory = optimizer_factory.OptimizerFactory(opt_config)
lr = opt_factory.build_learning_rate()
optimizer = opt_factory.build_optimizer(
lr, postprocessor=lambda x: x, use_legacy_optimizer=False)
self.assertIsInstance(optimizer, optimizer_cls)
self.assertEqual(expected_optimizer_config, optimizer.get_config())
def test_gradient_aggregator(self):
params = {
'optimizer': {
'type': 'adam',
},
'learning_rate': {
'type': 'constant',
'constant': {
'learning_rate': 1.0
}
}
}
opt_config = optimization_config.OptimizationConfig(params)
opt_factory = optimizer_factory.OptimizerFactory(opt_config)
lr = opt_factory.build_learning_rate()
# Dummy function to zero out gradients.
zero_grads = lambda gv: [(tf.zeros_like(g), v) for g, v in gv]
optimizer = opt_factory.build_optimizer(lr, gradient_aggregator=zero_grads)
if isinstance(optimizer, tf_keras.optimizers.experimental.Optimizer):
self.skipTest('New Keras optimizer does not support '
'`gradient_aggregator` arg.')
var0 = tf.Variable([1.0, 2.0])
var1 = tf.Variable([3.0, 4.0])
grads0 = tf.constant([1.0, 1.0])
grads1 = tf.constant([1.0, 1.0])
grads_and_vars = list(zip([grads0, grads1], [var0, var1]))
optimizer.apply_gradients(grads_and_vars)
self.assertAllClose(np.array([1.0, 2.0]), var0.numpy())
self.assertAllClose(np.array([3.0, 4.0]), var1.numpy())
@parameterized.parameters((None, None), (1.0, None), (None, 1.0))
def test_gradient_clipping(self, clipnorm, clipvalue):
params = {
'optimizer': {
'type': 'sgd',
'sgd': {
'clipnorm': clipnorm,
'clipvalue': clipvalue
}
},
'learning_rate': {
'type': 'constant',
'constant': {
'learning_rate': 1.0
}
}
}
opt_config = optimization_config.OptimizationConfig(params)
opt_factory = optimizer_factory.OptimizerFactory(opt_config)
lr = opt_factory.build_learning_rate()
optimizer = opt_factory.build_optimizer(lr)
var0 = tf.Variable([1.0, 2.0])
var1 = tf.Variable([3.0, 4.0])
grads0 = tf.constant([0.1, 0.1])
grads1 = tf.constant([2.0, 3.0])
grads_and_vars = list(zip([grads0, grads1], [var0, var1]))
optimizer.apply_gradients(grads_and_vars)
self.assertAllClose(np.array([0.9, 1.9]), var0.numpy())
if clipvalue is not None:
self.assertAllClose(np.array([2.0, 3.0]), var1.numpy())
elif clipnorm is not None:
self.assertAllClose(np.array([2.4452999, 3.1679497]), var1.numpy())
else:
self.assertAllClose(np.array([1.0, 1.0]), var1.numpy())
def test_missing_types(self):
params = {'optimizer': {'type': 'sgd', 'sgd': {'momentum': 0.9}}}
with self.assertRaises(ValueError):
optimizer_factory.OptimizerFactory(
optimization_config.OptimizationConfig(params))
params = {
'learning_rate': {
'type': 'stepwise',
'stepwise': {
'boundaries': [10000, 20000],
'values': [0.1, 0.01, 0.001]
}
}
}
with self.assertRaises(ValueError):
optimizer_factory.OptimizerFactory(
optimization_config.OptimizationConfig(params))
def test_wrong_return_type(self):
optimizer_type = 'sgd'
params = {
'optimizer': {
'type': optimizer_type
},
'learning_rate': {
'type': 'constant',
'constant': {
'learning_rate': 0.1
}
}
}
opt_config = optimization_config.OptimizationConfig(params)
opt_factory = optimizer_factory.OptimizerFactory(opt_config)
with self.assertRaises(TypeError):
_ = opt_factory.build_optimizer(0.1, postprocessor=lambda x: None)
# TODO(b/187559334) refactor lr_schedule tests into `lr_schedule_test.py`.
def test_stepwise_lr_schedule(self):
params = {
'optimizer': {
'type': 'sgd',
'sgd': {
'momentum': 0.9
}
},
'learning_rate': {
'type': 'stepwise',
'stepwise': {
'boundaries': [10000, 20000],
'values': [0.1, 0.01, 0.001]
}
}
}
expected_lr_step_values = [[0, 0.1], [5000, 0.1], [10000, 0.1],
[10001, 0.01], [20000, 0.01], [20001, 0.001]]
opt_config = optimization_config.OptimizationConfig(params)
opt_factory = optimizer_factory.OptimizerFactory(opt_config)
lr = opt_factory.build_learning_rate()
for step, value in expected_lr_step_values:
self.assertAlmostEqual(lr(step).numpy(), value)
def test_stepwise_lr_with_warmup_schedule(self):
params = {
'optimizer': {
'type': 'sgd',
'sgd': {
'momentum': 0.9
}
},
'learning_rate': {
'type': 'stepwise',
'stepwise': {
'boundaries': [10000, 20000],
'values': [0.1, 0.01, 0.001]
}
},
'warmup': {
'type': 'linear',
'linear': {
'warmup_steps': 500,
'warmup_learning_rate': 0.01
}
}
}
expected_lr_step_values = [[0, 0.01], [250, 0.055], [500, 0.1], [5500, 0.1],
[10000, 0.1], [10001, 0.01], [20000, 0.01],
[20001, 0.001]]
opt_config = optimization_config.OptimizationConfig(params)
opt_factory = optimizer_factory.OptimizerFactory(opt_config)
lr = opt_factory.build_learning_rate()
for step, value in expected_lr_step_values:
self.assertAlmostEqual(lr(step).numpy(), value)
def test_exponential_lr_schedule(self):
params = {
'optimizer': {
'type': 'sgd',
'sgd': {
'momentum': 0.9
}
},
'learning_rate': {
'type': 'exponential',
'exponential': {
'initial_learning_rate': 0.1,
'decay_steps': 1000,
'decay_rate': 0.96,
'staircase': True
}
}
}
expected_lr_step_values = [
[0, 0.1],
[999, 0.1],
[1000, 0.096],
[1999, 0.096],
[2000, 0.09216],
]
opt_config = optimization_config.OptimizationConfig(params)
opt_factory = optimizer_factory.OptimizerFactory(opt_config)
lr = opt_factory.build_learning_rate()
for step, value in expected_lr_step_values:
self.assertAlmostEqual(lr(step).numpy(), value)
def test_polynomial_lr_schedule(self):
params = {
'optimizer': {
'type': 'sgd',
'sgd': {
'momentum': 0.9
}
},
'learning_rate': {
'type': 'polynomial',
'polynomial': {
'initial_learning_rate': 0.1,
'decay_steps': 1000,
'end_learning_rate': 0.001
}
}
}
expected_lr_step_values = [[0, 0.1], [500, 0.0505], [1000, 0.001]]
opt_config = optimization_config.OptimizationConfig(params)
opt_factory = optimizer_factory.OptimizerFactory(opt_config)
lr = opt_factory.build_learning_rate()
for step, value in expected_lr_step_values:
self.assertAlmostEqual(lr(step).numpy(), value)
def test_cosine_lr_schedule(self):
params = {
'optimizer': {
'type': 'sgd',
'sgd': {
'momentum': 0.9
}
},
'learning_rate': {
'type': 'cosine',
'cosine': {
'initial_learning_rate': 0.1,
'decay_steps': 1000
}
}
}
expected_lr_step_values = [[0, 0.1], [250, 0.08535534], [500, 0.04999999],
[750, 0.01464466], [1000, 0]]
opt_config = optimization_config.OptimizationConfig(params)
opt_factory = optimizer_factory.OptimizerFactory(opt_config)
lr = opt_factory.build_learning_rate()
for step, value in expected_lr_step_values:
self.assertAlmostEqual(lr(step).numpy(), value)
def test_constant_lr_with_warmup_schedule(self):
params = {
'optimizer': {
'type': 'sgd',
'sgd': {
'momentum': 0.9
}
},
'learning_rate': {
'type': 'constant',
'constant': {
'learning_rate': 0.1
}
},
'warmup': {
'type': 'linear',
'linear': {
'warmup_steps': 500,
'warmup_learning_rate': 0.01
}
}
}
expected_lr_step_values = [[0, 0.01], [250, 0.055], [500, 0.1], [5000, 0.1],
[10000, 0.1], [20000, 0.1]]
opt_config = optimization_config.OptimizationConfig(params)
opt_factory = optimizer_factory.OptimizerFactory(opt_config)
lr = opt_factory.build_learning_rate()
for step, value in expected_lr_step_values:
self.assertAlmostEqual(lr(step).numpy(), value)
def test_stepwise_lr_with_polynomial_warmup_schedule(self):
params = {
'optimizer': {
'type': 'sgd',
'sgd': {
'momentum': 0.9
}
},
'learning_rate': {
'type': 'stepwise',
'stepwise': {
'boundaries': [10000, 20000],
'values': [0.1, 0.01, 0.001]
}
},
'warmup': {
'type': 'polynomial',
'polynomial': {
'warmup_steps': 500,
'power': 2.
}
}
}
expected_lr_step_values = [[0, 0.0], [250, 0.025], [500, 0.1], [5500, 0.1],
[10000, 0.1], [10001, 0.01], [20000, 0.01],
[20001, 0.001]]
opt_config = optimization_config.OptimizationConfig(params)
opt_factory = optimizer_factory.OptimizerFactory(opt_config)
lr = opt_factory.build_learning_rate()
for step, value in expected_lr_step_values:
self.assertAlmostEqual(lr(step).numpy(), value, places=6)
def test_power_lr_schedule(self):
params = {
'optimizer': {
'type': 'sgd',
'sgd': {
'momentum': 0.9
}
},
'learning_rate': {
'type': 'power',
'power': {
'initial_learning_rate': 1.0,
'power': -1.0
}
}
}
expected_lr_step_values = [[0, 1.0], [1, 1.0], [250, 1. / 250.]]
opt_config = optimization_config.OptimizationConfig(params)
opt_factory = optimizer_factory.OptimizerFactory(opt_config)
lr = opt_factory.build_learning_rate()
for step, value in expected_lr_step_values:
self.assertAlmostEqual(lr(step).numpy(), value)
def test_power_linear_lr_schedule(self):
params = {
'optimizer': {
'type': 'sgd',
'sgd': {
'momentum': 0.9
}
},
'learning_rate': {
'type': 'power_linear',
'power_linear': {
'initial_learning_rate': 1.0,
'power': -1.0,
'linear_decay_fraction': 0.5,
'total_decay_steps': 100,
'offset': 0,
}
}
}
expected_lr_step_values = [[0, 1.0], [1, 1.0], [40, 1. / 40.],
[60, 1. / 60. * 0.8]]
opt_config = optimization_config.OptimizationConfig(params)
opt_factory = optimizer_factory.OptimizerFactory(opt_config)
lr = opt_factory.build_learning_rate()
for step, value in expected_lr_step_values:
self.assertAlmostEqual(lr(step).numpy(), value)
def test_power_with_offset_lr_schedule(self):
params = {
'optimizer': {
'type': 'sgd',
'sgd': {
'momentum': 0.9
}
},
'learning_rate': {
'type': 'power_with_offset',
'power_with_offset': {
'initial_learning_rate': 1.0,
'power': -1.0,
'offset': 10,
'pre_offset_learning_rate': 3.0,
}
}
}
expected_lr_step_values = [[1, 3.0], [10, 3.0], [20, 1. / 10.]]
opt_config = optimization_config.OptimizationConfig(params)
opt_factory = optimizer_factory.OptimizerFactory(opt_config)
lr = opt_factory.build_learning_rate()
for step, value in expected_lr_step_values:
self.assertAlmostEqual(lr(step).numpy(), value)
def test_step_cosine_lr_schedule_with_warmup(self):
params = {
'optimizer': {
'type': 'sgd',
'sgd': {
'momentum': 0.9
}
},
'learning_rate': {
'type': 'step_cosine_with_offset',
'step_cosine_with_offset': {
'values': (0.0001, 0.00005),
'boundaries': (0, 500000),
'offset': 10000,
}
},
'warmup': {
'type': 'linear',
'linear': {
'warmup_steps': 10000,
'warmup_learning_rate': 0.0
}
}
}
expected_lr_step_values = [[0, 0.0], [5000, 1e-4 / 2.0], [10000, 1e-4],
[20000, 9.994863e-05], [499999, 5e-05]]
opt_config = optimization_config.OptimizationConfig(params)
opt_factory = optimizer_factory.OptimizerFactory(opt_config)
lr = opt_factory.build_learning_rate()
for step, value in expected_lr_step_values:
self.assertAlmostEqual(lr(step).numpy(), value)
class OptimizerFactoryRegistryTest(tf.test.TestCase):
def test_registry(self):
class MyClass():
pass
optimizer_factory.register_optimizer_cls('test', MyClass)
self.assertIn('test', optimizer_factory.LEGACY_OPTIMIZERS_CLS)
with self.assertRaisesRegex(ValueError, 'test already registered.*'):
optimizer_factory.register_optimizer_cls('test', MyClass)
if __name__ == '__main__':
tf.test.main()