Source code for pytorch_lightning.plugins.precision.native_amp

# Copyright The PyTorch Lightning team.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.
from contextlib import contextmanager
from typing import Any, Callable, cast, Dict, Generator, Optional, Union

import torch
from torch import Tensor
from torch.optim import LBFGS, Optimizer
from typing_extensions import Literal

import pytorch_lightning as pl
from lightning_fabric.accelerators.cuda import _patch_cuda_is_available
from lightning_fabric.utilities.types import Optimizable
from pytorch_lightning.plugins.precision.precision_plugin import PrecisionPlugin
from pytorch_lightning.utilities import GradClipAlgorithmType
from pytorch_lightning.utilities.exceptions import MisconfigurationException
from pytorch_lightning.utilities.rank_zero import rank_zero_deprecation

[docs]class MixedPrecisionPlugin(PrecisionPlugin): """Plugin for Automatic Mixed Precision (AMP) training with ``torch.autocast``. Args: precision: Whether to use ``torch.float16`` (``16``) or ``torch.bfloat16`` (``'bf16'``). device: The device for ``torch.autocast``. scaler: An optional :class:`torch.cuda.amp.GradScaler` to use. """ def __init__( self, precision: Literal["16", 16, "bf16"], device: str, scaler: Optional[torch.cuda.amp.GradScaler] = None ) -> None: self.precision = cast(Literal["16", "bf16"], str(precision)) if scaler is None and self.precision == "16": with _patch_cuda_is_available(): # if possible, we defer CUDA initialization to support strategies that will attempt forks scaler = torch.cuda.amp.GradScaler() if scaler is not None and self.precision == "bf16": raise MisconfigurationException(f"`precision='bf16'` does not use a scaler, found {scaler}.") self.device = device self.scaler = scaler
[docs] def pre_backward(self, tensor: Tensor, module: "pl.LightningModule") -> Tensor: # type: ignore[override] if self.scaler is not None: tensor = self.scaler.scale(tensor) return super().pre_backward(tensor, module)
[docs] def optimizer_step( # type: ignore[override] self, optimizer: Optimizable, model: "pl.LightningModule", optimizer_idx: int, closure: Callable[[], Any], **kwargs: Any, ) -> Any: if self.scaler is None: # skip scaler logic, as bfloat16 does not require scaler return super().optimizer_step( optimizer, model=model, optimizer_idx=optimizer_idx, closure=closure, **kwargs ) if isinstance(optimizer, LBFGS): raise MisconfigurationException( f"Native AMP and the LBFGS optimizer are not compatible (optimizer {optimizer_idx})." ) closure_result = closure() if not _optimizer_handles_unscaling(optimizer): # Unscaling needs to be performed here in case we are going to apply gradient clipping. # Optimizers that perform unscaling in their `.step()` method are not supported (e.g., fused Adam). # Note: `unscale` happens after the closure is executed, but before the `on_before_optimizer_step` hook. self.scaler.unscale_(optimizer) self._after_closure(model, optimizer, optimizer_idx) skipped_backward = closure_result is None # in manual optimization, the closure does not return a value if not model.automatic_optimization or not skipped_backward: # note: the scaler will skip the `optimizer.step` if nonfinite gradients are found step_output = self.scaler.step(optimizer, **kwargs) self.scaler.update() return step_output return closure_result
[docs] def clip_gradients( self, optimizer: Optimizer, clip_val: Union[int, float] = 0.0, gradient_clip_algorithm: GradClipAlgorithmType = GradClipAlgorithmType.NORM, ) -> None: if clip_val > 0 and _optimizer_handles_unscaling(optimizer): raise RuntimeError( f"The current optimizer, {type(optimizer).__qualname__}, does not allow for gradient clipping" " because it performs unscaling of gradients internally. HINT: Are you using a 'fused' optimizer?" ) super().clip_gradients(optimizer=optimizer, clip_val=clip_val, gradient_clip_algorithm=gradient_clip_algorithm)
def autocast_context_manager(self) -> torch.autocast: # the dtype could be automatically inferred but we need to manually set it due to a bug upstream # return torch.autocast(self.device, dtype=torch.bfloat16 if self.precision == "bf16" else torch.half)
[docs] @contextmanager def forward_context(self) -> Generator[None, None, None]: """Enable autocast context.""" with self.autocast_context_manager(): yield
[docs] def state_dict(self) -> Dict[str, Any]: if self.scaler is not None: return self.scaler.state_dict() return {}
[docs] def load_state_dict(self, state_dict: Dict[str, Any]) -> None: if self.scaler is not None: self.scaler.load_state_dict(state_dict)
class NativeMixedPrecisionPlugin(MixedPrecisionPlugin): backend = "native" def __init__(self, *args: Any, **kwargs: Any) -> None: rank_zero_deprecation( "The `NativeMixedPrecisionPlugin` class has been renamed in v1.9.0 and will be removed in" " v2.0.0. Please use `pytorch_lightning.plugins.MixedPrecisionPlugin` instead." ) super().__init__(*args, **kwargs) def _optimizer_handles_unscaling(optimizer: Any) -> bool: """Determines whether a PyTorch optimizer handles unscaling gradients in the step method rather than through the :class:`torch.cuda.amp.GradScaler`. Since, the current implementation of this function checks a PyTorch internal variable on the optimizer, the return value will only be reliable for built-in PyTorch optimizers. """ return getattr(optimizer, "_step_supports_amp_scaling", False)

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