mirror of https://github.com/hpcaitech/ColossalAI
98 lines
2.9 KiB
Python
98 lines
2.9 KiB
Python
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import math
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from typing import Callable, Optional
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from colossalai.nn.layer.parallel_1d.layers import Classifier1D
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from colossalai.utils import get_current_device
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from torch import dtype, 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_linear = {'1d': Linear1D, '2d': Linear2D, '2.5d': Linear2p5D, '3d': Linear3D}
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_parallel_classifier = {
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'None': VanillaClassifier,
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'1d': Classifier1D,
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'2d': Classifier2D,
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'2.5d': Classifier2p5D,
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'3d': Classifier3D
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}
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class Linear(nn.Module):
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def __init__(self,
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in_features: int,
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out_features: int,
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bias: bool = True,
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dtype: dtype = None,
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weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)),
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bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1),
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**kwargs) -> None:
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super().__init__()
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tensor_parallel = get_tensor_parallel_mode()
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if tensor_parallel == 'None':
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self.layer = nn.Linear(in_features, out_features, bias=bias, device=get_current_device(), dtype=dtype)
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weight_initializer(self.layer.weight, fan_in=in_features, fan_out=out_features)
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if bias:
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bias_initializer(self.layer.bias, fan_in=in_features)
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else:
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self.layer = _parallel_linear[tensor_parallel](
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in_features,
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out_features,
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bias=bias,
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dtype=dtype,
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weight_initializer=weight_initializer,
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bias_initializer=bias_initializer,
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**kwargs,
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)
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@property
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def weight(self):
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return self.layer.weight
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@property
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def bias(self):
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return self.layer.bias
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def forward(self, *args):
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return self.layer(*args)
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class Classifier(nn.Module):
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def __init__(
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self,
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in_features: int,
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num_classes: int,
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weight: nn.Parameter = None,
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bias: bool = True,
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dtype: dtype = None,
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weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)),
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bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1)
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) -> None:
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super().__init__()
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self.layer = _parallel_classifier[get_tensor_parallel_mode()](
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in_features,
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num_classes,
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weight=weight,
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bias=bias,
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dtype=dtype,
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weight_initializer=weight_initializer,
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bias_initializer=bias_initializer,
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)
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@property
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def weight(self):
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return self.layer.weight
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@property
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def bias(self):
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return self.layer.bias
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def forward(self, *args):
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return self.layer(*args)
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