124 lines
5.2 KiB
Python
124 lines
5.2 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, 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_BFLOAT_AVAILABLE, _TORCH_CPU_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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Args:
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precision: Whether to use torch.float16 (16) or torch.bfloat16 (bf16).
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"""
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def __init__(self, precision: Union[int, str] = 16, use_cpu: bool = False) -> None:
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super().__init__()
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if use_cpu and not _TORCH_CPU_AMP_AVAILABLE:
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raise MisconfigurationException(
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"You have asked for native AMP on CPU, but AMP is only available on GPU for PyTorch 1.9 "
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"and lower. To use native AMP on CPU, install PyTorch 1.10 or later."
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)
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self.use_cpu = use_cpu
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self._dtype = self._select_precision_dtype(precision)
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self.backend = AMPType.NATIVE
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if not self.is_bfloat16:
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self.scaler = torch.cuda.amp.GradScaler()
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def _select_precision_dtype(self, precision: Union[int, str] = 16) -> torch.dtype:
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if precision == "bf16":
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if not _TORCH_BFLOAT_AVAILABLE:
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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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return torch.bfloat16
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elif self.use_cpu:
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raise MisconfigurationException(
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"CPU native amp only supports bfloat16. Please pass precision='bf16' to the Trainer."
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)
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return torch.float16
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@property
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def is_bfloat16(self) -> bool:
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return self._dtype == torch.bfloat16
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def pre_backward(self, model: "pl.LightningModule", closure_loss: torch.Tensor) -> torch.Tensor:
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if self.is_bfloat16:
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return super().pre_backward(model, closure_loss)
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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 not self.is_bfloat16:
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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 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 self.is_bfloat16:
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# skip scaler logic, as bfloat16 does not require scaler
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return super().pre_optimizer_step(model, optimizer, optimizer_idx, lambda_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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result = lambda_closure() # native amp does not support closures
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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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skipped_backward = result is None
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# in manual optimization, the closure does not return a value
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if 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)
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self.scaler.update()
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return False
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def autocast_context_manager(self) -> torch.cuda.amp.autocast:
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if self.use_cpu:
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return torch.cpu.amp.autocast(dtype=self._dtype) # Only reached in pytorch==1.10 where this is ok. skipcq
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if self.is_bfloat16:
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return torch.cuda.amp.autocast(dtype=self._dtype) # Only reached in pytorch==1.10 where this is ok. skipcq
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return torch.cuda.amp.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 "native_amp_scaling_state" in checkpoint and not self.is_bfloat16:
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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 not self.is_bfloat16:
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checkpoint["native_amp_scaling_state"] = self.scaler.state_dict()
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