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53 lines
1.7 KiB
53 lines
1.7 KiB
4 years ago
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from typing import Callable, Optional, Union, Any
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import torch
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try:
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import torch_xla.core.xla_model as xm
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import torch_xla.amp as xa
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_HAS_XLA = True
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except ImportError as e:
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xm = None
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_HAS_XLA = False
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from .updater import Updater
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class UpdaterXla(Updater):
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def __init__(
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self,
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optimizer: torch.optim.Optimizer,
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clip_value: Optional[Union[Callable, float]] = None,
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clip_mode: str = 'norm',
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use_scaler: bool = False,
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scaler_kwargs: Any = None,
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):
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super().__init__(optimizer=optimizer, clip_value=clip_value, clip_mode=clip_mode)
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self.after_step_closure = True
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if use_scaler:
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self.scaler = xa.GradScaler(**scaler_kwargs)
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def apply(self, loss: torch.Tensor, accumulate: bool = False):
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if self.scaler is None:
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loss.backward(create_graph=self.create_graph)
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gradients = xm._fetch_gradients(self.optimizer)
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xm.all_reduce('sum', gradients, scale=1.0 / xm.xrt_world_size())
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if self.clipper is not None:
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self.clipper()
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if not accumulate:
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xm.optimizer_step(self.optimizer)
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else:
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self.scaler.scale(loss).backward(create_graph=self.create_graph)
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if self.clipper is not None:
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self.scaler.unscale_(self.optimizer) # unscale the gradients of optimizer's assigned params in-place
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self.clipper()
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if not accumulate:
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self.scaler.step(self.optimizer)
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self.reset()
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self.scaler.update()
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def after_step(self, after_step_fn, *args):
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xm.add_step_closure(after_step_fn, *args)
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