forked from p83651209/CPM-9G-8B
45 lines
1.3 KiB
Python
45 lines
1.3 KiB
Python
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import math
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import torch
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import torch.nn.functional as F
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class Linear(torch.nn.Module):
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def __init__(
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self,
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dim_in: int,
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dim_out: int,
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dtype: torch.dtype = torch.half,
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init_mean: float = 0.0,
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init_std: float = 1,
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scale: bool = True,
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scale_before: bool = False,
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):
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super().__init__()
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self.dim_in = self.in_features = dim_in
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self.dim_out = self.out_features = dim_out
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self.scale = scale
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self.scale_before = scale_before
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self.weight = torch.nn.parameter.Parameter(torch.empty((dim_out, dim_in), dtype=dtype))
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torch.nn.init.normal_(self.weight, mean=init_mean, std=init_std)
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def forward(self, x: torch.Tensor):
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"""
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Args:
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x (:obj:`torch.Tensor` of shape ``(batch, seq_len, dim_in)``): The input of linear layer
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Returns:
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:obj:`torch.Tensor` of shape ``(batch, seq_len, dim_out)``: The output of the linear transform y.
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""" # noqa: E501
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if self.scale:
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if self.scale_before:
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x = x / math.sqrt(self.dim_in)
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x = F.linear(x, self.weight)
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else:
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x = F.linear(x, self.weight)
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x = x / math.sqrt(self.dim_in)
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else:
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x = F.linear(x, self.weight)
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return x
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