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37 lines
1.4 KiB
37 lines
1.4 KiB
import torch
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from torch import optim
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from typing import Any, Dict, Iterable, Optional, Tuple
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from pydantic import BaseModel, ConfigDict
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from .base import BaseOptimizer
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class AdamParams(BaseModel):
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"""Configuration for `torch.optim.Adam` optimizer."""
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model_config = ConfigDict(frozen=True)
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lr: float = 1e-3 # Learning rate
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betas: Tuple[float, float] = (0.9, 0.999) # Coefficients for computing running averages
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eps: float = 1e-8 # Term added to denominator for numerical stability
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weight_decay: float = 0.0 # L2 regularization
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amsgrad: bool = False # Whether to use the AMSGrad variant
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def asdict(self) -> Dict[str, Any]:
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"""Returns a dictionary of valid parameters for `torch.optim.Adam`."""
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return self.model_dump()
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class AdamOptimizer(BaseOptimizer):
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"""
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Wrapper around torch.optim.Adam.
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"""
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def __init__(self, model_params: Iterable[torch.nn.Parameter], optim_params: AdamParams):
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"""
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Initializes the Adam optimizer with given parameters.
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Args:
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model_params (Iterable[Parameter]): Parameters to optimize.
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optim_params (AdamParams): Optimizer parameters.
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"""
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super().__init__(model_params, optim_params)
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self.optim = optim.Adam(model_params, **optim_params.asdict()) |