File size: 1,427 Bytes
64c2cbc | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 | import torch
from torch import nn
import torch.nn.functional as F
class CustomLinear(nn.Linear):
def __init__(self, *args, init_eye_val=0.0, is_diagonal=False, **kwargs):
super().__init__(*args, **kwargs)
self.init_eye_val = init_eye_val
class CustomLinearInitialized(nn.Linear):
def __init__(self, in_features: int, out_features: int, bias: bool = True,
device=None, dtype=None, init_fun=None) -> None:
super().__init__(in_features, out_features, bias, device, dtype)
self.init_fun = init_fun
class CustomDiagonalLinear(nn.Module):
def __init__(self, d_model, bias=True, init_eye_val=0.0):
super().__init__()
self.init_eye_val = init_eye_val
self.weight = nn.Parameter(torch.full((d_model,), init_eye_val))
self.bias = nn.Parameter(torch.zeros(d_model)) if bias else None
def forward(self, input):
out = input * self.weight
if self.bias is not None:
out += self.bias
return out
class Gate(nn.Module):
def __init__(self, items, init_val=0.0):
super().__init__()
self.init_val = init_val
self.gate = nn.Parameter(torch.full((items,), init_val))
def forward(self, input, dim):
if input.ndim != 4:
raise ValueError('input must be a 4D tensor')
shape = [1] * 4
shape[dim] = -1
return input * self.gate.view(*shape)
|