I have the following ‘Linear’ function:

import math

import torch

from torch.nn.parameter import Parameter

from … import functional as F

from … import init

from .module import Module

from …_jit_internal import weak_module, weak_script_method

@weak_module

class Linear(Module):

r"""Applies a linear transformation to the incoming data: :math:`y = xA^T + b`

```
Args:
in_features: size of each input sample
out_features: size of each output sample
bias: If set to False, the layer will not learn an additive bias.
Default: ``True``
Shape:
- Input: :math:`(N, *, \text{in\_features})` where :math:`*` means any number of
additional dimensions
- Output: :math:`(N, *, \text{out\_features})` where all but the last dimension
are the same shape as the input.
Attributes:
weight: the learnable weights of the module of shape
:math:`(\text{out\_features}, \text{in\_features})`. The values are
initialized from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})`, where
:math:`k = \frac{1}{\text{in\_features}}`
bias: the learnable bias of the module of shape :math:`(\text{out\_features})`.
If :attr:`bias` is ``True``, the values are initialized from
:math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})` where
:math:`k = \frac{1}{\text{in\_features}}`
Examples::
>>> m = nn.Linear(20, 30)
>>> input = torch.randn(128, 20)
>>> output = m(input)
>>> print(output.size())
torch.Size([128, 30])
"""
__constants__ = ['bias']
def __init__(self, in_features, out_features, bias=True):
super(Linear, self).__init__()
self.in_features = in_features
self.out_features = out_features
self.weight = Parameter(torch.Tensor(out_features, in_features))
if bias:
self.bias = Parameter(torch.Tensor(out_features))
else:
self.register_parameter('bias', None)
self.reset_parameters()
def reset_parameters(self):
init.kaiming_uniform_(self.weight, a=math.sqrt(5))
if self.bias is not None:
fan_in, _ = init._calculate_fan_in_and_fan_out(self.weight)
bound = 1 / math.sqrt(fan_in)
init.uniform_(self.bias, -bound, bound)
@weak_script_method
def forward(self, input):
return F.linear(input, self.weight, self.bias)
def extra_repr(self):
return 'in_features={}, out_features={}, bias={}'.format(
self.in_features, self.out_features, self.bias is not None
)
```

@weak_module

class Bilinear(Module):

r"""Applies a bilinear transformation to the incoming data:

:math:`y = x_1 A x_2 + b`

```
Args:
in1_features: size of each first input sample
in2_features: size of each second input sample
out_features: size of each output sample
bias: If set to False, the layer will not learn an additive bias.
Default: ``True``
Shape:
- Input: :math:`(N, *, \text{in1\_features})`, :math:`(N, *, \text{in2\_features})`
where :math:`*` means any number of additional dimensions. All but the last
dimension of the inputs should be the same.
- Output: :math:`(N, *, \text{out\_features})` where all but the last dimension
are the same shape as the input.
Attributes:
weight: the learnable weights of the module of shape
:math:`(\text{out\_features} x \text{in1\_features} x \text{in2\_features})`.
The values are initialized from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})`, where
:math:`k = \frac{1}{\text{in1\_features}}`
bias: the learnable bias of the module of shape :math:`(\text{out\_features})`
If :attr:`bias` is ``True``, the values are initialized from
:math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})`, where
:math:`k = \frac{1}{\text{in1\_features}}`
Examples::
>>> m = nn.Bilinear(20, 30, 40)
>>> input1 = torch.randn(128, 20)
>>> input2 = torch.randn(128, 30)
>>> output = m(input1, input2)
>>> print(output.size())
torch.Size([128, 40])
"""
__constants__ = ['in1_features', 'in2_features', 'out_features', 'bias']
def __init__(self, in1_features, in2_features, out_features, bias=True):
super(Bilinear, self).__init__()
self.in1_features = in1_features
self.in2_features = in2_features
self.out_features = out_features
self.weight = Parameter(torch.Tensor(out_features, in1_features, in2_features))
if bias:
self.bias = Parameter(torch.Tensor(out_features))
else:
self.register_parameter('bias', None)
self.reset_parameters()
def reset_parameters(self):
bound = 1 / math.sqrt(self.weight.size(1))
init.uniform_(self.weight, -bound, bound)
if self.bias is not None:
init.uniform_(self.bias, -bound, bound)
@weak_script_method
def forward(self, input1, input2):
return F.bilinear(input1, input2, self.weight, self.bias)
def extra_repr(self):
return 'in1_features={}, in2_features={}, out_features={}, bias={}'.format(
self.in1_features, self.in2_features, self.out_features, self.bias is not None
)
```

# TODO: PartialLinear - maybe in sparse?

But the link (as mentioned in the previous post) discussed about the ‘LinearFunction’ which is not present in the torch.nn.