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from dataclasses import dataclass |
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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 PassThroughScheduleConfig(FairseqDataclass): |
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pass |
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@register_lr_scheduler("pass_through", dataclass=PassThroughScheduleConfig) |
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class PassThroughScheduleSchedule(FairseqLRScheduler): |
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"""Delegate lr scheduling to the optimizer.""" |
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def __init__(self, cfg: PassThroughScheduleConfig, optimizer): |
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super().__init__(cfg, optimizer) |
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assert ( |
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hasattr(optimizer, "lr_scheduler") and optimizer.lr_scheduler is not None |
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), "Pass-through schedule can only be used with optimizers with their own schedulers" |
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def state_dict(self): |
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return self.optimizer.lr_scheduler.state_dict() |
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def load_state_dict(self, state_dict): |
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self.optimizer.lr_scheduler.load_state_dict(state_dict) |
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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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return self.optimizer.lr_scheduler.step_begin_epoch(epoch) |
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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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return self.optimizer.lr_scheduler.step_update(num_updates) |
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