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ColossalAI/colossalai/amp/naive_amp/__init__.py

50 lines
1.8 KiB

import inspect
import torch.nn as nn
from torch.optim import Optimizer
from colossalai.utils import is_no_pp_or_last_stage
from .naive_amp import NaiveAMPOptimizer, NaiveAMPModel
from .grad_scaler import DynamicGradScaler, ConstantGradScaler
def convert_to_naive_amp(model: nn.Module, optimizer: Optimizer, amp_config):
"""A helper function to wrap training components with naive AMP modules
:param model: your model object
:type model: :class:`torch.nn.Module`
:param optimizer: your optimizer object
:type optimizer: :class:`torch.optim.Optimzer`
:param amp_config: configuration for naive mode amp
:type amp_config: :class:`colossalai.context.Config` or dict
:return: (model, optimizer)
:rtype: Tuple
"""
if isinstance(model, nn.ModuleList):
# interleaved pipeline
module_list = []
for chunk, m in enumerate(model):
output_to_fp32 = is_no_pp_or_last_stage() and chunk == len(model) - 1
module_list.append(NaiveAMPModel(m, output_to_fp32=output_to_fp32))
model = nn.ModuleList(module_list)
else:
output_to_fp32 = is_no_pp_or_last_stage()
model = NaiveAMPModel(model, output_to_fp32=output_to_fp32)
use_dynamic_grad_scaler = amp_config.pop('dynamic_grad_scale', True)
if use_dynamic_grad_scaler:
scaler_class = DynamicGradScaler
else:
scaler_class = ConstantGradScaler
sig = inspect.signature(scaler_class.__init__)
kwargs = dict()
for param in sig.parameters.values():
if param.name in amp_config:
kwargs[param.name] = amp_config.pop(param.name)
grad_scaler = scaler_class(**kwargs)
optimizer = NaiveAMPOptimizer(optimizer, grad_scaler, **amp_config)
return model, optimizer
__all__ = ['convert_to_naive_amp', 'NaiveAMPOptimizer']