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