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https://github.com/labmlai/annotated_deep_learning_paper_implementations.git
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146 lines
4.6 KiB
Python
146 lines
4.6 KiB
Python
"""
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---
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title: Configurable optimizer module
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summary: This implements a configurable module for optimizers.
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---
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# Configurable Optimizer
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"""
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from typing import Tuple
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import torch
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from labml.configs import BaseConfigs, option, meta_config
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from labml_nn.optimizers import WeightDecay
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class OptimizerConfigs(BaseConfigs):
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"""
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<a id="OptimizerConfigs">
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## Optimizer Configurations
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</a>
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"""
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# Optimizer
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optimizer: torch.optim.Adam
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# Weight decay
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weight_decay_obj: WeightDecay
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# Whether weight decay is decoupled;
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# i.e. weight decay is not added to gradients
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weight_decouple: bool = True
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# Weight decay
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weight_decay: float = 0.0
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# Whether weight decay is absolute or should be multiplied by learning rate
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weight_decay_absolute: bool = False
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# Whether the adam update is optimized (different epsilon)
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optimized_adam_update: bool = True
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# Parameters to be optimized
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parameters: any
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# Learning rate $\alpha$
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learning_rate: float = 0.01
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# Beta values $(\beta_1, \beta_2)$ for Adam
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betas: Tuple[float, float] = (0.9, 0.999)
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# Epsilon $\epsilon$ for adam
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eps: float = 1e-08
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# Momentum for SGD
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momentum: float = 0.5
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# Whether to use AMSGrad
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amsgrad: bool = False
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# Number of warmup optimizer steps
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warmup: int = 2_000
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# Total number of optimizer steps (for cosine decay)
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total_steps: int = int(1e10)
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# Whether to degenerate to SGD in AdaBelief
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degenerate_to_sgd: bool = True
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# Whether to use Rectified Adam in AdaBelief
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rectify: bool = True
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# Model embedding size for Noam optimizer
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d_model: int
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def __init__(self):
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super().__init__(_primary='optimizer')
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meta_config(OptimizerConfigs.parameters)
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@option(OptimizerConfigs.weight_decay_obj, 'L2')
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def _weight_decay(c: OptimizerConfigs):
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return WeightDecay(c.weight_decay, c.weight_decouple, c.weight_decay_absolute)
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@option(OptimizerConfigs.optimizer, 'SGD')
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def _sgd_optimizer(c: OptimizerConfigs):
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return torch.optim.SGD(c.parameters, c.learning_rate, c.momentum)
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@option(OptimizerConfigs.optimizer, 'Adam')
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def _adam_optimizer(c: OptimizerConfigs):
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if c.amsgrad:
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from labml_nn.optimizers.amsgrad import AMSGrad
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return AMSGrad(c.parameters,
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lr=c.learning_rate, betas=c.betas, eps=c.eps,
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optimized_update=c.optimized_adam_update,
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weight_decay=c.weight_decay_obj, amsgrad=c.amsgrad)
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else:
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from labml_nn.optimizers.adam import Adam
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return Adam(c.parameters,
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lr=c.learning_rate, betas=c.betas, eps=c.eps,
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optimized_update=c.optimized_adam_update,
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weight_decay=c.weight_decay_obj)
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@option(OptimizerConfigs.optimizer, 'AdamW')
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def _adam_warmup_optimizer(c: OptimizerConfigs):
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from labml_nn.optimizers.adam_warmup import AdamWarmup
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return AdamWarmup(c.parameters,
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lr=c.learning_rate, betas=c.betas, eps=c.eps,
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weight_decay=c.weight_decay_obj, amsgrad=c.amsgrad, warmup=c.warmup)
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@option(OptimizerConfigs.optimizer, 'RAdam')
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def _radam_optimizer(c: OptimizerConfigs):
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from labml_nn.optimizers.radam import RAdam
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return RAdam(c.parameters,
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lr=c.learning_rate, betas=c.betas, eps=c.eps,
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weight_decay=c.weight_decay_obj, amsgrad=c.amsgrad,
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degenerated_to_sgd=c.degenerate_to_sgd)
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@option(OptimizerConfigs.optimizer, 'AdaBelief')
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def _ada_belief_optimizer(c: OptimizerConfigs):
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from labml_nn.optimizers.ada_belief import AdaBelief
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return AdaBelief(c.parameters,
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lr=c.learning_rate, betas=c.betas, eps=c.eps,
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weight_decay=c.weight_decay_obj, amsgrad=c.amsgrad,
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degenerate_to_sgd=c.degenerate_to_sgd,
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rectify=c.rectify)
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@option(OptimizerConfigs.optimizer, 'Noam')
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def _noam_optimizer(c: OptimizerConfigs):
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from labml_nn.optimizers.noam import Noam
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return Noam(c.parameters,
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lr=c.learning_rate, betas=c.betas, eps=c.eps,
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weight_decay=c.weight_decay_obj, amsgrad=c.amsgrad, warmup=c.warmup,
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d_model=c.d_model)
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@option(OptimizerConfigs.optimizer, 'AdamWarmupCosineDecay')
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def _noam_optimizer(c: OptimizerConfigs):
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from labml_nn.optimizers.adam_warmup_cosine_decay import AdamWarmupCosineDecay
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return AdamWarmupCosineDecay(c.parameters,
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lr=c.learning_rate, betas=c.betas, eps=c.eps,
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weight_decay=c.weight_decay_obj, amsgrad=c.amsgrad,
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warmup=c.warmup, total_steps=c.total_steps)
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