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ColossalAI/colossalai/nn/layer/colossalai_layer/_utils.py

39 lines
1.3 KiB

import torch.nn as nn
from torch import Tensor
from ..parallel_2d._operation import split_batch_2d
from ..parallel_2p5d._operation import split_batch_2p5d
from ..parallel_3d._operation import split_batch_3d
from ..utils import get_tensor_parallel_mode
_parallel_split_batch = {'2d': split_batch_2d, '2.5d': split_batch_2p5d, '3d': split_batch_3d}
def partition_batch(input_) -> Tensor:
tensor_parallel_mode = get_tensor_parallel_mode()
if tensor_parallel_mode in _parallel_split_batch:
if isinstance(input_, dict):
return {k: _parallel_split_batch[tensor_parallel_mode](v) for k, v in input_.items()}
else:
return _parallel_split_batch[tensor_parallel_mode](input_)
else:
return input_
class ColossalaiModule(nn.Module):
def __init__(self, module: nn.Module, **kwargs):
super().__init__()
# copy values
self.__dict__ = module.__dict__.copy()
# copy methods
for name, attr in module.__class__.__dict__.items():
if name not in ['__init__', 'forward'] and callable(attr):
setattr(self, name, getattr(module, name))
self._forward_func = module.forward
for k, v in kwargs.items():
setattr(self, k, v)
def forward(self, *args):
return self._forward_func(*args)