98 lines
3.7 KiB
Python
98 lines
3.7 KiB
Python
# Copyright The PyTorch Lightning team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from contextlib import contextmanager
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from typing import Any, Callable, Dict, Generator
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import torch
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from torch.optim import LBFGS, Optimizer
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import pytorch_lightning as pl
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from pytorch_lightning.plugins.precision.mixed import MixedPrecisionPlugin
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from pytorch_lightning.utilities import _NATIVE_AMP_AVAILABLE, AMPType
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from pytorch_lightning.utilities.exceptions import MisconfigurationException
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class NativeMixedPrecisionPlugin(MixedPrecisionPlugin):
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"""Plugin for native mixed precision training with :mod:`torch.cuda.amp`."""
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def __init__(self) -> None:
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super().__init__()
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if not _NATIVE_AMP_AVAILABLE:
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raise MisconfigurationException(
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"You have asked for native AMP but your PyTorch version does not support it."
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" Consider upgrading with `pip install torch>=1.6`."
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)
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self.backend = AMPType.NATIVE
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self.scaler = torch.cuda.amp.GradScaler()
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def pre_backward(self, model: "pl.LightningModule", closure_loss: torch.Tensor) -> torch.Tensor:
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closure_loss = self.scaler.scale(closure_loss)
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return super().pre_backward(model, closure_loss)
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def pre_optimizer_step(
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self,
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model: "pl.LightningModule",
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optimizer: Optimizer,
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optimizer_idx: int,
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lambda_closure: Callable,
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**kwargs: Any,
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) -> bool:
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if isinstance(optimizer, LBFGS):
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raise MisconfigurationException(
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f"native PyTorch amp and lbfgs are not compatible (optimizer {optimizer_idx})."
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" To request, please file a Github issue in PyTorch and tag @mcarilli"
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)
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result = True
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if model.automatic_optimization:
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result = lambda_closure()
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self.scaler.unscale_(optimizer)
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super().pre_optimizer_step(model, optimizer, optimizer_idx, lambda_closure, **kwargs)
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# lambda_closure returning None indicates that backward has been skipped
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if result is not None:
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self.scaler.step(optimizer)
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self.scaler.update()
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return False
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@contextmanager
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def train_step_context(self) -> Generator[None, None, None]:
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"""Enable autocast context"""
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with torch.cuda.amp.autocast():
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yield
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@contextmanager
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def val_step_context(self) -> Generator[None, None, None]:
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"""Enable autocast context"""
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with torch.cuda.amp.autocast():
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yield
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@contextmanager
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def test_step_context(self) -> Generator[None, None, None]:
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"""Enable autocast context"""
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with torch.cuda.amp.autocast():
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yield
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@contextmanager
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def predict_step_context(self) -> Generator[None, None, None]:
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"""Enable autocast context"""
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with torch.cuda.amp.autocast():
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yield
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def on_load_checkpoint(self, checkpoint: Dict[str, Any]) -> None:
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if "native_amp_scaling_state" in checkpoint:
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self.scaler.load_state_dict(checkpoint["native_amp_scaling_state"])
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def on_save_checkpoint(self, checkpoint: Dict[str, Any]) -> None:
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checkpoint["native_amp_scaling_state"] = self.scaler.state_dict()
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