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25e57c6 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 | # 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.
"""Mega experiments."""
# pylint: disable=g-doc-return-or-yield,line-too-long
from official.core import config_definitions as cfg
from official.core import exp_factory
from official.modeling import optimization
from official.nlp.configs import encoders
from official.nlp.data import sentence_prediction_dataloader
from official.nlp.tasks import sentence_prediction
from official.projects.lra import lra_dual_encoder_dataloader
from official.projects.lra import lra_dual_encoder_task
from official.projects.lra.mega import MegaEncoderConfig
AdamWeightDecay = optimization.AdamWeightDecayConfig
PolynomialLr = optimization.PolynomialLrConfig
PolynomialWarmupConfig = optimization.PolynomialWarmupConfig
_TRAINER = cfg.TrainerConfig(
optimizer_config=optimization.OptimizationConfig({
'optimizer': {
'type': 'adamw',
'adamw': {
'weight_decay_rate': 0.01,
'exclude_from_weight_decay': [
'LayerNorm',
'layer_norm',
'bias',
],
},
},
'learning_rate': {
'type': 'polynomial',
'polynomial': {
'initial_learning_rate': 1e-7,
'end_learning_rate': 0.0,
},
},
'warmup': {'type': 'polynomial'},
})
)
@exp_factory.register_config_factory('mega/lra_listops')
def mega_listops() -> cfg.ExperimentConfig:
"""Mega lra fine-tuning."""
config = cfg.ExperimentConfig(
task=sentence_prediction.SentencePredictionConfig(
model=sentence_prediction.ModelConfig(
encoder=encoders.EncoderConfig(
type='any', any=MegaEncoderConfig()
)
),
train_data=sentence_prediction_dataloader.SentencePredictionDataConfig(),
validation_data=sentence_prediction_dataloader.SentencePredictionDataConfig(
is_training=False, drop_remainder=False
),
),
trainer=_TRAINER,
)
return config
@exp_factory.register_config_factory('mega/lra_imdb')
def mega_imdb() -> cfg.ExperimentConfig:
"""Mega lra fine-tuning."""
config = cfg.ExperimentConfig(
task=sentence_prediction.SentencePredictionConfig(
model=sentence_prediction.ModelConfig(
encoder=encoders.EncoderConfig(
type='any', any=MegaEncoderConfig()
)
),
train_data=sentence_prediction_dataloader.SentencePredictionDataConfig(),
validation_data=sentence_prediction_dataloader.SentencePredictionDataConfig(
is_training=False, drop_remainder=False
),
),
trainer=_TRAINER,
)
return config
@exp_factory.register_config_factory('mega/lra_cifar')
def mega_cifar() -> cfg.ExperimentConfig:
"""Mega lra fine-tuning."""
config = cfg.ExperimentConfig(
task=sentence_prediction.SentencePredictionConfig(
model=sentence_prediction.ModelConfig(
encoder=encoders.EncoderConfig(
type='any', any=MegaEncoderConfig()
)
),
train_data=sentence_prediction_dataloader.SentencePredictionDataConfig(),
validation_data=sentence_prediction_dataloader.SentencePredictionDataConfig(
is_training=False, drop_remainder=False
),
),
trainer=_TRAINER,
)
return config
@exp_factory.register_config_factory('mega/lra_pathfinder')
def mega_pathfinder() -> cfg.ExperimentConfig:
"""Mega lra fine-tuning."""
config = cfg.ExperimentConfig(
task=sentence_prediction.SentencePredictionConfig(
model=sentence_prediction.ModelConfig(
encoder=encoders.EncoderConfig(
type='any', any=MegaEncoderConfig()
)
),
train_data=sentence_prediction_dataloader.SentencePredictionDataConfig(),
validation_data=sentence_prediction_dataloader.SentencePredictionDataConfig(
is_training=False, drop_remainder=False
),
),
trainer=_TRAINER,
)
return config
@exp_factory.register_config_factory('mega/lra_aan')
def mega_aan() -> cfg.ExperimentConfig:
"""Mega LRA task."""
config = cfg.ExperimentConfig(
task=lra_dual_encoder_task.DualEncoderConfig(
model=lra_dual_encoder_task.ModelConfig(
encoder=encoders.EncoderConfig(
type='any', any=MegaEncoderConfig()
)
),
train_data=lra_dual_encoder_dataloader.DualEncoderDataConfig(),
validation_data=lra_dual_encoder_dataloader.DualEncoderDataConfig(
is_training=False, drop_remainder=False
),
),
trainer=_TRAINER,
)
return config
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