mirror of
https://github.com/twitter/the-algorithm-ml.git
synced 2024-11-17 13:39:22 +01:00
109 lines
3.7 KiB
Python
109 lines
3.7 KiB
Python
"""Optimization configurations for models."""
|
|
|
|
import typing
|
|
|
|
import tml.core.config as base_config
|
|
import tml.optimizers.config as optimizers_config_mod
|
|
|
|
import pydantic
|
|
|
|
|
|
class RecapAdamConfig(base_config.BaseConfig):
|
|
"""
|
|
Configuration settings for the Adam optimizer used in Recap.
|
|
|
|
Args:
|
|
beta_1 (float): Momentum term (default: 0.9).
|
|
beta_2 (float): Exponential weighted decay factor (default: 0.999).
|
|
epsilon (float): Numerical stability in the denominator (default: 1e-7).
|
|
|
|
Example:
|
|
To define an Adam optimizer configuration for Recap, use:
|
|
|
|
```python
|
|
adam_config = RecapAdamConfig(beta_1=0.9, beta_2=0.999, epsilon=1e-7)
|
|
```
|
|
|
|
Note:
|
|
This class configures the parameters of the Adam optimizer, which is commonly used for optimizing neural networks.
|
|
|
|
Warning:
|
|
This class is intended for internal use within Recap and should not be directly accessed or modified by external code.
|
|
"""
|
|
|
|
beta_1: float = 0.9 # Momentum term.
|
|
beta_2: float = 0.999 # Exponential weighted decay factor.
|
|
epsilon: float = 1e-7 # Numerical stability in denominator.
|
|
|
|
|
|
class MultiTaskLearningRates(base_config.BaseConfig):
|
|
"""
|
|
Configuration settings for multiple learning rates in Recap.
|
|
|
|
Args:
|
|
tower_learning_rates (Dict[str, optimizers_config_mod.LearningRate]): Learning rates for different towers of the model.
|
|
backbone_learning_rate (optimizers_config_mod.LearningRate): Learning rate for the model's backbone (default: None).
|
|
|
|
Example:
|
|
To define multiple learning rates for different towers in Recap, use:
|
|
|
|
```python
|
|
multi_task_lr = MultiTaskLearningRates(
|
|
tower_learning_rates={
|
|
'task1': learning_rate1,
|
|
'task2': learning_rate2,
|
|
},
|
|
backbone_learning_rate=backbone_lr,
|
|
)
|
|
```
|
|
|
|
Note:
|
|
This class allows specifying different learning rates for different parts of the model, including task-specific towers and the backbone.
|
|
|
|
Warning:
|
|
This class is intended for internal use within Recap and should not be directly accessed or modified by external code.
|
|
"""
|
|
tower_learning_rates: typing.Dict[str, optimizers_config_mod.LearningRate] = pydantic.Field(
|
|
description="Learning rates for different towers of the model."
|
|
)
|
|
|
|
backbone_learning_rate: optimizers_config_mod.LearningRate = pydantic.Field(
|
|
None, description="Learning rate for backbone of the model."
|
|
)
|
|
|
|
|
|
class RecapOptimizerConfig(base_config.BaseConfig):
|
|
"""
|
|
Configuration settings for the Recap optimizer.
|
|
|
|
Args:
|
|
multi_task_learning_rates (MultiTaskLearningRates): Multiple learning rates for different tasks (optional).
|
|
single_task_learning_rate (optimizers_config_mod.LearningRate): Learning rate for a single task (optional).
|
|
adam (RecapAdamConfig): Configuration settings for the Adam optimizer.
|
|
|
|
Example:
|
|
To define an optimizer configuration for training with Recap, use:
|
|
|
|
```python
|
|
optimizer_config = RecapOptimizerConfig(
|
|
multi_task_learning_rates=multi_task_lr,
|
|
single_task_learning_rate=single_task_lr,
|
|
adam=adam_config,
|
|
)
|
|
```
|
|
|
|
Warning:
|
|
This class is intended for internal use to configure the optimizer settings within Recap and should not be
|
|
directly accessed by external code.
|
|
"""
|
|
|
|
multi_task_learning_rates: MultiTaskLearningRates = pydantic.Field(
|
|
None, description="Multiple learning rates for different tasks.", one_of="lr"
|
|
)
|
|
|
|
single_task_learning_rate: optimizers_config_mod.LearningRate = pydantic.Field(
|
|
None, description="Single task learning rates", one_of="lr"
|
|
)
|
|
|
|
adam: RecapAdamConfig = pydantic.Field(one_of="optimizer")
|