mirror of
https://github.com/SillyTavern/SillyTavern-Extras.git
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77 lines
2.6 KiB
Python
77 lines
2.6 KiB
Python
# Copyright (c) Facebook, Inc. and its affiliates.
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#
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# This source code is licensed under the MIT license found in the
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# LICENSE file in the root directory of this source tree.
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from dataclasses import dataclass, field
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from typing import Optional, List
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from omegaconf import II
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from fairseq.dataclass import FairseqDataclass
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from fairseq.optim.lr_scheduler import FairseqLRScheduler, register_lr_scheduler
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@dataclass
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class FixedLRScheduleConfig(FairseqDataclass):
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force_anneal: Optional[int] = field(
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default=None,
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metadata={"help": "force annealing at specified epoch"},
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)
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lr_shrink: float = field(
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default=0.1,
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metadata={"help": "shrink factor for annealing, lr_new = (lr * lr_shrink)"},
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)
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warmup_updates: int = field(
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default=0,
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metadata={"help": "warmup the learning rate linearly for the first N updates"},
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)
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lr: List[float] = II("optimization.lr")
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@register_lr_scheduler("fixed", dataclass=FixedLRScheduleConfig)
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class FixedLRSchedule(FairseqLRScheduler):
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"""Decay the LR on a fixed schedule."""
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def __init__(self, cfg: FixedLRScheduleConfig, optimizer):
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super().__init__(cfg, optimizer)
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self.lr = cfg.lr[0]
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if cfg.warmup_updates > 0:
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self.warmup_factor = 1.0 / cfg.warmup_updates
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else:
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self.warmup_factor = 1
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def state_dict(self):
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return {"lr": self.lr}
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def load_state_dict(self, state_dict):
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if "lr" in state_dict:
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self.lr = state_dict["lr"]
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def get_next_lr(self, epoch):
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lrs = self.cfg.lr
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if self.cfg.force_anneal is None or epoch < self.cfg.force_anneal:
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# use fixed LR schedule
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next_lr = lrs[min(epoch - 1, len(lrs) - 1)]
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else:
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# annneal based on lr_shrink
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next_lr = lrs[-1] * self.cfg.lr_shrink ** (
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epoch + 1 - self.cfg.force_anneal
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)
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return next_lr
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def step_begin_epoch(self, epoch):
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"""Update the learning rate at the beginning of the given epoch."""
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self.lr = self.get_next_lr(epoch)
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self.optimizer.set_lr(self.warmup_factor * self.lr)
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return self.optimizer.get_lr()
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def step_update(self, num_updates):
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"""Update the learning rate after each update."""
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if self.cfg.warmup_updates > 0 and num_updates < self.cfg.warmup_updates:
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self.warmup_factor = (num_updates + 1) / float(self.cfg.warmup_updates)
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self.optimizer.set_lr(self.warmup_factor * self.lr)
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else:
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self.optimizer.set_lr(self.lr)
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return self.optimizer.get_lr()
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