mirror of https://github.com/hpcaitech/ColossalAI
36 lines
1.1 KiB
Python
36 lines
1.1 KiB
Python
from typing import Optional
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from colossalai.utils import get_current_device
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from torch import nn
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from ... import init as init
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from ..parallel_1d import *
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from ..parallel_2d import *
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from ..parallel_2p5d import *
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from ..parallel_3d import *
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from ..utils import get_tensor_parallel_mode
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from ..vanilla import *
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_parallel_layernorm = {'2d': LayerNorm2D, '2.5d': LayerNorm2p5D, '3d': LayerNorm3D}
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class LayerNorm(nn.Module):
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def __init__(self, normalized_shape: int, eps=1e-05, dtype=None) -> None:
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super().__init__()
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tensor_parallel = get_tensor_parallel_mode()
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if tensor_parallel in ['None', '1d']:
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self.norm = nn.LayerNorm(normalized_shape, eps=eps, device=get_current_device(), dtype=dtype)
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else:
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self.norm = _parallel_layernorm[tensor_parallel](normalized_shape, eps=eps, dtype=dtype)
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@property
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def weight(self):
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return self.norm.weight
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@property
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def bias(self):
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return self.norm.bias
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def forward(self, *args):
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return self.norm(*args)
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