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31 lines
974 B
31 lines
974 B
from dataclasses import dataclass, field, InitVar
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from typing import Dict, Any
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import torch
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from .updater import Updater
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@dataclass
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class UpdaterCudaWithScaler(Updater):
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scaler_kwargs: InitVar[Dict[str, Any]] = None
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def __post_init__(self, scaler_kwargs: Dict[str, Any]):
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super().__post_init__()
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scaler_kwargs = scaler_kwargs or {}
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self.grad_scaler = torch.cuda.amp.GradScaler(**scaler_kwargs)
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def apply(self, loss: torch.Tensor, accumulate=False):
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self.grad_scaler.scale(loss).backward(create_graph=self.create_graph)
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if accumulate:
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# unscale first?
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return
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if self.clip_fn is not None:
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# unscale the gradients of optimizer's assigned params in-place
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self.grad_scaler.unscale_(self.optimizer)
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self.clip_fn(self.clip_params_fn(), self.clip_value)
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self.grad_scaler.step(self.optimizer)
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self.grad_scaler.update()
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self.reset()
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