Source code for composer.algorithms.low_precision_groupnorm.low_precision_groupnorm

# Copyright 2022 MosaicML Composer authors
# SPDX-License-Identifier: Apache-2.0

"""Low Precision GroupNorm."""

from __future__ import annotations

import logging
import warnings
from typing import Optional, Sequence, Type, Union

import torch
import torch.nn.functional as F
from torch.optim import Optimizer

from composer.algorithms.warnings import NoEffectWarning
from composer.core import Algorithm, Event, Precision, State
from composer.loggers import Logger
from composer.utils import module_surgery

log = logging.getLogger(__name__)

[docs]def apply_low_precision_groupnorm( model, precision: Optional[Precision] = None, optimizers: Optional[Union[Optimizer, Sequence[Optimizer]]] = None, ): if (precision != Precision.AMP_FP16 and precision != Precision.AMP_BF16): warnings.warn(NoEffectWarning('Low Precision GroupNorm only applies to AMP_FP16 and AMP_BF16 precisions.')) return model policy: dict[Type[torch.nn.Module], module_surgery.ReplacementFunction] = {torch.nn.GroupNorm: _to_LPGroupNorm} replaced_instances = module_surgery.replace_module_classes(module=model, optimizers=optimizers, policies=policy) if len(replaced_instances) == 0: warnings.warn(NoEffectWarning('No instances of torch.nn.GroupNorm found.'))'Successfully replaced {len(replaced_instances)} instances of GroupNorm with LowPrecisionGroupNorm')
[docs]class LowPrecisionGroupNorm(Algorithm): """ Replaces all instances of :class:`torch.nn.GroupNorm` with class:`.LPGroupNorm`. LPGroupNorm is a thin wrapper around :class:`torch.nn.GroupNorm` which forces the layer to run in lower precision (torch.float16 or torch.bfloat16) if autocast is enabled. This algorithm has no effect in FP32 or DeepSpeed FP16 mode, where autocast is disabled. This algorithm is intended to be used instead of Fused GroupNorm. They have similar behavior and performance. Args: apply_at (Event): Event where algorithm is applied. """ def __init__(self, apply_at: Event = Event.INIT): self.apply_at = apply_at if self.apply_at not in {Event.INIT, Event.BEFORE_LOAD, Event.AFTER_LOAD}: raise ValueError( 'LowPrecisionGroupNorm only supports application on Event.INIT, Event.BEFORE_LOAD, and Event.AFTER_LOAD.', ) def __repr__(self) -> str: return f'{self.__class__.__name__}(apply_at={self.apply_at})' @staticmethod def required_on_load() -> bool: return True def match(self, event: Event, state: State) -> bool: del state # unused return event == self.apply_at def apply(self, event: Event, state: State, logger: Logger) -> Optional[int]: del event, logger # unused apply_low_precision_groupnorm(model=state.model, optimizers=state.optimizers, precision=state._precision)
class LPGroupNorm(torch.nn.GroupNorm): def __init__(self, num_groups, num_channels, eps=1e-05, affine=True, device=None, dtype=None): super().__init__( num_groups=num_groups, num_channels=num_channels, eps=eps, affine=affine, device=device, dtype=dtype, ) def forward(self, x): module_device = x.device downcast_x = _cast_if_autocast_enabled(x) downcast_weight = _cast_if_autocast_enabled( self.weight, ) if self.weight is not None else self.weight # pyright: ignore[reportUnnecessaryComparison] downcast_bias = _cast_if_autocast_enabled( self.bias, ) if self.bias is not None else self.bias # pyright: ignore[reportUnnecessaryComparison] with torch.autocast(enabled=False, device_type=module_device.type): return F.group_norm(downcast_x, self.num_groups, downcast_weight, downcast_bias, self.eps) def _cast_if_autocast_enabled(tensor): if torch.is_autocast_enabled(): if tensor.device.type == 'cuda': dtype = torch.get_autocast_gpu_dtype() elif tensor.device.type == 'cpu': dtype = torch.get_autocast_cpu_dtype() else: raise NotImplementedError() return return tensor def _to_LPGroupNorm(layer: torch.nn.Module, module_index: int) -> LPGroupNorm: """Defines a replacement policy from a `torch.nn.GroupNorm` to a `LPGroupNorm`""" if not isinstance(layer, torch.nn.GroupNorm): raise TypeError(f'Expected torch.nn.GroupNorm, got {type(layer)}') lp_groupnorm = LPGroupNorm(layer.num_groups, layer.num_channels, layer.eps, layer.affine) with torch.no_grad(): if layer.weight is None: # pyright: ignore[reportUnnecessaryComparison] lp_groupnorm.register_parameter('weight', None) else: lp_groupnorm.weight.copy_(layer.weight) # type: ignore if layer.bias is None: # pyright: ignore[reportUnnecessaryComparison] lp_groupnorm.register_parameter('bias', None) else: lp_groupnorm.bias.copy_(layer.bias) # type: ignore return lp_groupnorm