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import torch
from torch import nn
from torch.nn import functional as F
from einops import rearrange
"""
This Version of SALT uses:
- W = U (\Sigma . A + B) + XY
- we uses srLoRA
"""
class SALTLinear(nn.Linear):
def __init__(
self,
in_features: int,
out_features: int,
rank: int, # rank for truncated SVD
lora_rank: int, # rank for rsLoRA
alpha: float = 32.0, # scaling factor for rsLoRA
bias: bool = True,
device=None,
dtype=None
) -> None:
super().__init__(in_features, out_features, bias, device, dtype)
# Perform full SVD initially
self.U, self.S, self.Vt = torch.linalg.svd(self.weight, full_matrices=False)
self.weight.requires_grad = False
self.done_svd = False
max_possible_rank = min(self.U.shape[1], self.S.shape[0], self.Vt.shape[0])
print("\nThe max possible rank is", max_possible_rank)
# Initialize A and B for singular value transformation
self.A = nn.Parameter(torch.ones(rank))
self.B = nn.Parameter(torch.zeros(rank))
self.A_frozen = torch.ones(max_possible_rank - self.A.shape[0])
self.B_frozen = torch.ones(max_possible_rank - self.B.shape[0])
# Initialize rsLoRA parameters with the new scaling
rs_lora_scaling = alpha / (lora_rank ** 0.5)
self.lora_X = nn.Parameter(torch.randn(out_features, lora_rank) * rs_lora_scaling)
self.lora_Y = nn.Parameter(torch.randn(lora_rank, in_features) * rs_lora_scaling)
self.reset_parameters()
def reset_parameters(self) -> None:
nn.Linear.reset_parameters(self)
if hasattr(self, 'A'):
nn.init.ones_(self.A)
if hasattr(self, 'B'):
nn.init.zeros_(self.B)
if hasattr(self, 'lora_X'):
nn.init.normal_(self.lora_X, std=0.01)
if hasattr(self, 'lora_Y'):
nn.init.normal_(self.lora_Y, std=0.01)
def perform_svd(self):
self.U, self.S, self.Vt = torch.linalg.svd(self.weight, full_matrices=False)
self.done_svd = True
def forward(self, input: torch.Tensor) -> torch.Tensor:
if not self.done_svd:
self.perform_svd()
# Transform singular values: A路危_r + B
A_total = torch.cat([self.A, self.A_frozen.to(input.device)])
B_total = torch.cat([self.B, self.B_frozen.to(input.device)])
transformed_S = A_total * self.S + B_total
# Compute truncated SVD part: U_r(A路危_r + B)V_r^T
weight_svd = self.U @ torch.diag(F.relu(transformed_S)) @ self.Vt
# Add rsLoRA part: X路Y
weight_rslora = self.lora_X @ self.lora_Y
# Combine both parts
weight_updated = weight_svd + weight_rslora
# Compute regularization loss
reg_loss = (
torch.norm(1 - self.A) +
torch.norm(self.B) +
torch.norm(self.lora_X) * torch.norm(self.lora_Y)
)
return F.linear(input, weight_updated, self.bias), reg_loss
class SALTConv2d(nn.Conv2d):
def __init__(
self,
in_channels: int,
out_channels: int,
kernel_size: int,
rank: int, # rank for truncated SVD
lora_rank: int, # rank for rsLoRA
alpha: float = 1.0, # scaling factor for rsLoRA
**kwargs
):
super().__init__(in_channels, out_channels, kernel_size, **kwargs)
assert isinstance(kernel_size, int)
# Reshape weight and perform SVD
weight_reshaped = rearrange(self.weight, 'co cin h w -> co (cin h w)')
self.U, self.S, self.Vt = torch.linalg.svd(weight_reshaped, full_matrices=False)
self.done_svd = False
max_possible_rank = min(self.U.shape[1], self.S.shape[0], self.Vt.shape[0])
print("\nThe max possible rank is", max_possible_rank)
self.actual_rank = min(rank, max_possible_rank)
# Initialize A and B for singular value transformation
self.A = nn.Parameter(torch.ones(self.actual_rank))
self.B = nn.Parameter(torch.zeros(self.actual_rank))
self.A_frozen = torch.ones(max_possible_rank - self.actual_rank)
self.B_frozen = torch.ones(max_possible_rank - self.actual_rank)
# Initialize rsLoRA parameters with scaling
total_kernel_size = in_channels * kernel_size * kernel_size
rs_lora_scaling = alpha / (lora_rank ** 0.5)
self.lora_X = nn.Parameter(torch.randn(out_channels, lora_rank) * rs_lora_scaling)
self.lora_Y = nn.Parameter(torch.randn(lora_rank, total_kernel_size) * rs_lora_scaling)
# Freeze original weights
self.weight.requires_grad = False
# Save shapes for reshaping
self.weight_shape = self.weight.shape
self.reset_parameters()
def perform_svd(self):
weight_reshaped = rearrange(self.weight, 'co cin h w -> co (cin h w)')
self.U, self.S, self.Vt = torch.linalg.svd(weight_reshaped, full_matrices=False)
self.done_svd = True
def reset_parameters(self) -> None:
nn.Conv2d.reset_parameters(self)
if hasattr(self, 'A'):
nn.init.ones_(self.A)
if hasattr(self, 'B'):
nn.init.zeros_(self.B)
if hasattr(self, 'lora_X'):
nn.init.normal_(self.lora_X, std=0.01)
if hasattr(self, 'lora_Y'):
nn.init.normal_(self.lora_Y, std=0.01)
def forward(self, x: torch.Tensor) -> torch.Tensor:
if not self.done_svd:
self.perform_svd()
A_total = torch.cat([self.A, self.A_frozen.to(x.device)])
B_total = torch.cat([self.B, self.B_frozen.to(x.device)])
transformed_S = A_total * self.S + B_total
# Compute truncated SVD part: U_r(A路危_r + B)V_r^T
weight_svd = self.U @ torch.diag(F.relu(transformed_S)) @ self.Vt
# Add rsLoRA part: X路Y
weight_rslora = self.lora_X @ self.lora_Y
# Combine both parts
weight_updated = weight_svd + weight_rslora
# Reshape back to conv2d weight shape
weight_updated = rearrange(
weight_updated,
'co (cin h w) -> co cin h w',
cin=self.weight_shape[1],
h=self.weight_shape[2],
w=self.weight_shape[3]
)
# Compute regularization loss
reg_loss = (
torch.norm(1 - self.A) +
torch.norm(self.B) +
torch.norm(self.lora_X) * torch.norm(self.lora_Y)
)
return F.conv2d(
x, weight_updated, self.bias,
self.stride, self.padding,
self.dilation, self.groups
), reg_loss
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