116 lines
5.0 KiB
Python
116 lines
5.0 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, Optional, Union
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import torch
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from torch import Tensor
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from torch.nn import Module
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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 _TORCH_GREATER_EQUAL_1_10, AMPType
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from pytorch_lightning.utilities.exceptions import MisconfigurationException
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if _TORCH_GREATER_EQUAL_1_10:
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from torch import autocast
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else:
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from torch.cuda.amp import autocast
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class NativeMixedPrecisionPlugin(MixedPrecisionPlugin):
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"""Plugin for Native Mixed Precision (AMP) training with ``torch.autocast``.
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Args:
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precision: Whether to use ``torch.float16`` (``16``) or ``torch.bfloat16`` (``'bf16'``).
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device: The device for ``torch.autocast``.
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scaler: An optional :class:`torch.cuda.amp.GradScaler` to use.
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"""
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backend = AMPType.NATIVE
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def __init__(
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self, precision: Union[str, int], device: str, scaler: Optional[torch.cuda.amp.GradScaler] = None
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) -> None:
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super().__init__()
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if precision == "bf16" and not _TORCH_GREATER_EQUAL_1_10:
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raise MisconfigurationException(
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"To use bfloat16 with native amp you must install torch greater or equal to 1.10."
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)
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if scaler is None and precision == 16:
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scaler = torch.cuda.amp.GradScaler()
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if scaler is not None and precision == "bf16":
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raise MisconfigurationException(f"`precision='bf16'` does not use a scaler, found {scaler}.")
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self.precision = precision
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self.device = device
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self.scaler = scaler
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def pre_backward(self, model: "pl.LightningModule", closure_loss: torch.Tensor) -> torch.Tensor:
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if self.scaler is not None:
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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 _run_backward(self, tensor: Tensor, model: Module, *args: Any, **kwargs: Any) -> None:
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if self.scaler is not None:
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tensor = self.scaler.scale(tensor)
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super()._run_backward(tensor, model, *args, **kwargs)
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def optimizer_step(
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self,
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model: Union["pl.LightningModule", Module],
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optimizer: Optimizer,
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optimizer_idx: int,
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closure: Callable[[], Any],
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**kwargs: Any,
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) -> None:
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if self.scaler is None:
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# skip scaler logic, as bfloat16 does not require scaler
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return super().optimizer_step(model, optimizer, optimizer_idx, closure, **kwargs)
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if isinstance(optimizer, LBFGS):
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raise MisconfigurationException(
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f"Native AMP and the LBFGS optimizer are not compatible (optimizer {optimizer_idx})."
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)
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closure_result = closure()
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# `unscale` after the closure is executed but before the `on_before_optimizer_step` hook.
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self.scaler.unscale_(optimizer)
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self._after_closure(model, optimizer, optimizer_idx)
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skipped_backward = closure_result is None
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# in manual optimization, the closure does not return a value
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if not isinstance(model, pl.LightningModule) or not model.automatic_optimization or not skipped_backward:
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# note: the scaler will skip the `optimizer.step` if nonfinite gradients are found
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self.scaler.step(optimizer, **kwargs)
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self.scaler.update()
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def autocast_context_manager(self) -> autocast:
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if _TORCH_GREATER_EQUAL_1_10:
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# the dtype could be automatically inferred but we need to manually set it due to a bug upstream
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# https://github.com/pytorch/pytorch/issues/67233
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return autocast(self.device, dtype=torch.bfloat16 if self.precision == "bf16" else torch.half)
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return autocast()
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@contextmanager
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def forward_context(self) -> Generator[None, None, None]:
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"""Enable autocast context."""
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with self.autocast_context_manager():
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yield
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def on_load_checkpoint(self, checkpoint: Dict[str, Any]) -> None:
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if self.scaler is not None and "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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if self.scaler is not None:
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checkpoint["native_amp_scaling_state"] = self.scaler.state_dict()
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