mirror of
https://github.com/twitter/the-algorithm-ml.git
synced 2024-12-23 14:51:49 +01:00
83 lines
2.4 KiB
Python
83 lines
2.4 KiB
Python
"""Optimization configurations for models."""
|
|
|
|
import typing
|
|
|
|
import tml.core.config as base_config
|
|
|
|
import pydantic
|
|
|
|
|
|
class PiecewiseConstant(base_config.BaseConfig):
|
|
learning_rate_boundaries: typing.List[int] = pydantic.Field(None)
|
|
learning_rate_values: typing.List[float] = pydantic.Field(None)
|
|
|
|
|
|
class LinearRampToConstant(base_config.BaseConfig):
|
|
learning_rate: float
|
|
num_ramp_steps: pydantic.PositiveInt = pydantic.Field(
|
|
description="Number of steps to ramp this up from zero."
|
|
)
|
|
|
|
|
|
class LinearRampToCosine(base_config.BaseConfig):
|
|
learning_rate: float
|
|
final_learning_rate: float
|
|
num_ramp_steps: pydantic.PositiveInt = pydantic.Field(
|
|
description="Number of steps to ramp this up from zero."
|
|
)
|
|
final_num_steps: pydantic.PositiveInt = pydantic.Field(
|
|
description="Final number of steps where decay stops."
|
|
)
|
|
|
|
|
|
class LearningRate(base_config.BaseConfig):
|
|
constant: float = pydantic.Field(None, one_of="lr")
|
|
linear_ramp_to_cosine: LinearRampToCosine = pydantic.Field(None, one_of="lr")
|
|
linear_ramp_to_constant: LinearRampToConstant = pydantic.Field(None, one_of="lr")
|
|
piecewise_constant: PiecewiseConstant = pydantic.Field(None, one_of="lr")
|
|
|
|
|
|
class OptimizerAlgorithmConfig(base_config.BaseConfig):
|
|
"""Base class for optimizer configurations."""
|
|
|
|
lr: float
|
|
...
|
|
|
|
|
|
class AdamConfig(OptimizerAlgorithmConfig):
|
|
# see https://pytorch.org/docs/stable/generated/torch.optim.Adam.html#torch.optim.Adam
|
|
lr: float
|
|
betas: typing.Tuple[float, float] = [0.9, 0.999]
|
|
eps: float = 1e-7 # Numerical stability in denominator.
|
|
|
|
|
|
class SgdConfig(OptimizerAlgorithmConfig):
|
|
lr: float
|
|
momentum: float = 0.0
|
|
|
|
|
|
class AdagradConfig(OptimizerAlgorithmConfig):
|
|
lr: float
|
|
eps: float = 0
|
|
|
|
|
|
class OptimizerConfig(base_config.BaseConfig):
|
|
learning_rate: LearningRate = pydantic.Field(
|
|
None,
|
|
description="Constant learning rates",
|
|
)
|
|
adam: AdamConfig = pydantic.Field(None, one_of="optimizer")
|
|
sgd: SgdConfig = pydantic.Field(None, one_of="optimizer")
|
|
adagrad: AdagradConfig = pydantic.Field(None, one_of="optimizer")
|
|
|
|
|
|
def get_optimizer_algorithm_config(optimizer_config: OptimizerConfig):
|
|
if optimizer_config.adam is not None:
|
|
return optimizer_config.adam
|
|
elif optimizer_config.sgd is not None:
|
|
return optimizer_config.sgd
|
|
elif optimizer_config.adagrad is not None:
|
|
return optimizer_config.adagrad
|
|
else:
|
|
raise ValueError(f"No optimizer selected in optimizer_config, passed {optimizer_config}")
|