mirror of
https://github.com/rasbt/LLMs-from-scratch.git
synced 2025-10-30 17:29:59 +00:00
43 lines
1.5 KiB
Python
43 lines
1.5 KiB
Python
# Copyright (c) Sebastian Raschka under Apache License 2.0 (see LICENSE.txt).
|
|
# Source for "Build a Large Language Model From Scratch"
|
|
# - https://www.manning.com/books/build-a-large-language-model-from-scratch
|
|
# Code: https://github.com/rasbt/LLMs-from-scratch
|
|
|
|
import torch
|
|
import math
|
|
|
|
|
|
class LoRALayer(torch.nn.Module):
|
|
def __init__(self, in_dim, out_dim, rank, alpha):
|
|
super().__init__()
|
|
self.A = torch.nn.Parameter(torch.empty(in_dim, rank))
|
|
torch.nn.init.kaiming_uniform_(self.A, a=math.sqrt(5)) # similar to standard weight initialization
|
|
self.B = torch.nn.Parameter(torch.zeros(rank, out_dim))
|
|
self.alpha = alpha
|
|
|
|
def forward(self, x):
|
|
x = self.alpha * (x @ self.A @ self.B)
|
|
return x
|
|
|
|
|
|
class LinearWithLoRA(torch.nn.Module):
|
|
def __init__(self, linear, rank, alpha):
|
|
super().__init__()
|
|
self.linear = linear
|
|
self.lora = LoRALayer(
|
|
linear.in_features, linear.out_features, rank, alpha
|
|
)
|
|
|
|
def forward(self, x):
|
|
return self.linear(x) + self.lora(x)
|
|
|
|
|
|
def replace_linear_with_lora(model, rank, alpha):
|
|
for name, module in model.named_children():
|
|
if isinstance(module, torch.nn.Linear):
|
|
# Replace the Linear layer with LinearWithLoRA
|
|
setattr(model, name, LinearWithLoRA(module, rank, alpha))
|
|
else:
|
|
# Recursively apply the same function to child modules
|
|
replace_linear_with_lora(module, rank, alpha)
|