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# 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.
"""Configuration definitions for multi-task training."""
import dataclasses
from typing import Optional, Tuple
from official.core import config_definitions as cfg
from official.modeling import hyperparams
from official.modeling.privacy import configs as dp_configs
@dataclasses.dataclass
class TaskRoutine(hyperparams.Config):
# TODO(hongkuny): deprecate the task_name once we migrated client code.
task_name: str = ""
task_config: cfg.TaskConfig = None
eval_steps: Optional[int] = None
task_weight: Optional[float] = 1.0
@dataclasses.dataclass
class MultiTaskConfig(hyperparams.Config):
init_checkpoint: str = ""
model: hyperparams.Config = None
task_routines: Tuple[TaskRoutine, ...] = ()
# Configs for differential privacy
# These configs are only effective if you use create_optimizer in
# tensorflow_models/official/core/base_task.py
# DEPRECATED b/264611883
differential_privacy_config: Optional[
dp_configs.DifferentialPrivacyConfig] = None
@dataclasses.dataclass
class ProportionalSampleConfig(hyperparams.Config):
alpha: float = 1.0
@dataclasses.dataclass
class AnnealingSampleConfig(hyperparams.Config):
steps_per_epoch: int = 5
total_steps: int = 20
@dataclasses.dataclass
class TaskSamplingConfig(hyperparams.OneOfConfig):
type: str = ""
uniform: hyperparams.Config = dataclasses.field(
default_factory=hyperparams.Config
)
proportional: ProportionalSampleConfig = dataclasses.field(
default_factory=ProportionalSampleConfig
)
annealing: AnnealingSampleConfig = dataclasses.field(
default_factory=AnnealingSampleConfig
)
@dataclasses.dataclass
class MultiTaskTrainerConfig(cfg.TrainerConfig):
trainer_type: str = "interleaving"
task_sampler: TaskSamplingConfig = dataclasses.field(
default_factory=lambda: TaskSamplingConfig(type="proportional")
)
@dataclasses.dataclass
class MultiTaskExperimentConfig(hyperparams.Config):
"""An experiment config for multi-task training and multi-task evaluation."""
task: MultiTaskConfig = dataclasses.field(default_factory=MultiTaskConfig)
trainer: MultiTaskTrainerConfig = dataclasses.field(
default_factory=MultiTaskTrainerConfig
)
runtime: cfg.RuntimeConfig = dataclasses.field(
default_factory=cfg.RuntimeConfig
)
@dataclasses.dataclass
class MultiEvalExperimentConfig(cfg.ExperimentConfig):
"""An experiment config for single-task training and multi-task evaluation.
Attributes:
eval_tasks: individual evaluation tasks.
"""
eval_tasks: Tuple[TaskRoutine, ...] = ()
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