62 lines
2.0 KiB
Python
62 lines
2.0 KiB
Python
from typing import Callable, Union
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import torch
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from torch.optim import Optimizer
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from pytorch_lightning.core.lightning import LightningModule
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from pytorch_lightning.plugins.precision.precision_plugin import PrecisionPlugin
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from pytorch_lightning.utilities.model_helpers import is_overridden
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from pytorch_lightning.utilities.warnings import WarningCache
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warning_cache = WarningCache()
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class DeepSpeedPrecisionPlugin(PrecisionPlugin):
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def __init__(self, precision):
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super().__init__()
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self.precision = precision
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def pre_optimizer_step(
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self, pl_module: LightningModule, optimizer: Optimizer, optimizer_idx: int, lambda_closure: Callable, **kwargs
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) -> bool:
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deepspeed_engine = pl_module.trainer.model
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# DeepSpeed not support closures.
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lambda_closure()
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if not pl_module.automatic_optimization:
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pl_module.trainer.call_hook("on_after_backward")
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deepspeed_engine.step()
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return False
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def backward(
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self,
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lightning_module: LightningModule,
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closure_loss: torch.Tensor,
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optimizer: torch.optim.Optimizer,
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opt_idx: int,
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should_accumulate: bool,
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*args,
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**kwargs,
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):
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if is_overridden('backward', lightning_module):
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warning_cache.warn(
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"Overridden backward hook in the LightningModule will be ignored since DeepSpeed handles"
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"backward logic outside of the LightningModule"
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)
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# todo: hack around for deepspeed engine to call backward
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deepspeed_engine = lightning_module.trainer.model
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deepspeed_engine.backward(closure_loss, **kwargs)
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# once backward has been applied, release graph
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closure_loss = closure_loss.detach()
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return closure_loss
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def clip_gradients(self, optimizer: Optimizer, clip_val: Union[int, float], norm_type: float = float(2.0)):
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"""
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DeepSpeed handles clipping gradients via the training type plugin.
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"""
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pass
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