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