mirror of https://github.com/hpcaitech/ColossalAI
65 lines
2.2 KiB
Python
65 lines
2.2 KiB
Python
import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import torch.nn.init as init
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from torch.nn import Parameter
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class Linear(nn.Module):
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"""Linear layer with column parallelism.
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The linear layer is defined as Y = XA + b. A is parallelized along
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its second dimension as A = [A_1, ..., A_p].
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Arguments:
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input_size: first dimension of matrix A.
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output_size: second dimension of matrix A.
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bias: If true, add bias
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init_method: method to initialize weights. Note that bias is always set
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to zero.
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stride: For the strided linear layers.
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keep_master_weight_for_test: This was added for testing and should be
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set to False. It returns the master weights
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used for initialization.
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skip_bias_add: This was added to enable performance optimations where bias
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can be fused with other elementwise operations. we skip
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adding bias but instead return it.
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"""
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def __init__(self, input_size, output_size, bias=True, skip_bias_add=False):
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super(Linear, self).__init__()
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# Keep input parameters
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self.input_size = input_size
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self.output_size = output_size
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self.skip_bias_add = skip_bias_add
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self.weight = Parameter(
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torch.empty(
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self.output_size,
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self.input_size,
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)
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)
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init.normal_(self.weight)
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if bias:
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self.bias = Parameter(torch.empty(self.output_size))
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# Always initialize bias to zero.
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with torch.no_grad():
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self.bias.zero_()
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else:
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self.register_parameter("bias", None)
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def forward(self, input_):
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# Matrix multiply.
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bias = self.bias if not self.skip_bias_add else None
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output = F.linear(input_, self.weight, bias)
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if self.skip_bias_add:
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return output, self.bias
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else:
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return output
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def __repr__(self):
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return (
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f"Linear(in_features={self.input_size}, out_features={self.output_size}, "
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+ f"bias={self.bias is not None}, skip_bias_add={self.skip_bias_add})"
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)
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