Source code for physicsnemo.nn.module.weight_fact
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import torch
import torch.nn as nn
import torch.nn.functional as F
import physicsnemo # noqa: F401 for docs
from physicsnemo.nn.functional import weight_fact
Tensor = torch.Tensor
[docs]
class WeightFactLinear(nn.Module):
"""Weight Factorization Layer for 2D Tensors, more details in https://arxiv.org/abs/2210.01274
Parameters
----------
in_features : int
Size of the input features
out_features : int
Size of the output features
bias : bool, optional
Apply the bias to the output of linear layer, by default True
reparam : dict, optional
Dictionary with the mean and standard deviation to reparametrize the weight matrix,
by default {'mean': 1.0, 'stddev': 0.1}
Example
-------
>>> wfact = physicsnemo.nn.WeightFactLinear(2,4)
>>> input = torch.rand(2,2)
>>> output = wfact(input)
>>> output.size()
torch.Size([2, 4])
"""
def __init__(
self,
in_features: int,
out_features: int,
bias: bool = True,
mean: float = 1.0,
stddev=0.1,
) -> None:
super().__init__()
self.in_features = in_features
self.out_features = out_features
self.mean = mean
self.stddev = stddev
self.weight = nn.Parameter(torch.empty((out_features, in_features)))
if bias:
self.bias = nn.Parameter(torch.empty(out_features))
else:
self.register_parameter("bias", None)
self.reset_parameters()
[docs]
def reset_parameters(self) -> None:
"""Factorize weights and reset bias"""
nn.init.xavier_uniform_(self.weight)
g, v = weight_fact(self.weight.detach(), mean=self.mean, stddev=self.stddev)
self.g = nn.Parameter(g)
self.v = nn.Parameter(v)
self.weight = None # remove the weight parameter
if self.bias is not None:
nn.init.constant_(self.bias, 0.0)
[docs]
def forward(self, input: Tensor) -> Tensor:
weight = self.g * self.v
return F.linear(input, weight, self.bias)
