218 lines
6.8 KiB
Python
218 lines
6.8 KiB
Python
from functools import cache
|
|
|
|
import torch
|
|
import torch.nn as nn
|
|
import torch.nn.functional as F
|
|
|
|
from .base import LycorisBaseModule
|
|
from ..functional import factorization
|
|
from ..logging import logger
|
|
|
|
|
|
@cache
|
|
def log_oft_factorize(dim, factor, num, bdim):
|
|
logger.info(
|
|
f"Use OFT(block num: {num}, block dim: {bdim})"
|
|
f" (equivalent to lora_dim={num}) "
|
|
f"for {dim=} and lora_dim={factor=}"
|
|
)
|
|
|
|
|
|
class DiagOFTModule(LycorisBaseModule):
|
|
name = "diag-oft"
|
|
support_module = {
|
|
"linear",
|
|
"conv1d",
|
|
"conv2d",
|
|
"conv3d",
|
|
}
|
|
weight_list = [
|
|
"oft_blocks",
|
|
"rescale",
|
|
"alpha",
|
|
]
|
|
weight_list_det = ["oft_blocks"]
|
|
|
|
def __init__(
|
|
self,
|
|
lora_name,
|
|
org_module: nn.Module,
|
|
multiplier=1.0,
|
|
lora_dim=4,
|
|
alpha=1,
|
|
dropout=0.0,
|
|
rank_dropout=0.0,
|
|
module_dropout=0.0,
|
|
use_tucker=False,
|
|
use_scalar=False,
|
|
rank_dropout_scale=False,
|
|
constraint=0,
|
|
rescaled=False,
|
|
bypass_mode=None,
|
|
**kwargs,
|
|
):
|
|
super().__init__(
|
|
lora_name,
|
|
org_module,
|
|
multiplier,
|
|
dropout,
|
|
rank_dropout,
|
|
module_dropout,
|
|
rank_dropout_scale,
|
|
bypass_mode,
|
|
)
|
|
if self.module_type not in self.support_module:
|
|
raise ValueError(f"{self.module_type} is not supported in Diag-OFT algo.")
|
|
|
|
out_dim = self.dim
|
|
self.block_size, self.block_num = factorization(out_dim, lora_dim)
|
|
# block_num > block_size
|
|
self.rescaled = rescaled
|
|
self.constraint = constraint * out_dim
|
|
self.register_buffer("alpha", torch.tensor(constraint))
|
|
self.oft_blocks = nn.Parameter(
|
|
torch.zeros(self.block_num, self.block_size, self.block_size)
|
|
)
|
|
if rescaled:
|
|
self.rescale = nn.Parameter(
|
|
torch.ones(out_dim, *(1 for _ in range(org_module.weight.dim() - 1)))
|
|
)
|
|
|
|
log_oft_factorize(
|
|
dim=out_dim,
|
|
factor=lora_dim,
|
|
num=self.block_num,
|
|
bdim=self.block_size,
|
|
)
|
|
|
|
@classmethod
|
|
def algo_check(cls, state_dict, lora_name):
|
|
if f"{lora_name}.oft_blocks" in state_dict:
|
|
oft_blocks = state_dict[f"{lora_name}.oft_blocks"]
|
|
if oft_blocks.ndim == 3:
|
|
return True
|
|
return False
|
|
|
|
@classmethod
|
|
def make_module_from_state_dict(
|
|
cls, lora_name, orig_module, oft_blocks, rescale, alpha
|
|
):
|
|
n, s, _ = oft_blocks.shape
|
|
module = cls(
|
|
lora_name,
|
|
orig_module,
|
|
1,
|
|
lora_dim=s,
|
|
constraint=float(alpha),
|
|
rescaled=rescale is not None,
|
|
)
|
|
module.oft_blocks.copy_(oft_blocks)
|
|
if rescale is not None:
|
|
module.rescale.copy_(rescale)
|
|
return module
|
|
|
|
@property
|
|
def I(self):
|
|
return torch.eye(self.block_size, device=self.device)
|
|
|
|
def get_r(self):
|
|
I = self.I
|
|
# for Q = -Q^T
|
|
q = self.oft_blocks - self.oft_blocks.transpose(1, 2)
|
|
normed_q = q
|
|
if self.constraint > 0:
|
|
q_norm = torch.norm(q) + 1e-8
|
|
if q_norm > self.constraint:
|
|
normed_q = q * self.constraint / q_norm
|
|
# use float() to prevent unsupported type
|
|
r = (I + normed_q) @ (I - normed_q).float().inverse()
|
|
return r
|
|
|
|
def make_weight(self, scale=1, device=None, diff=False):
|
|
r = self.get_r()
|
|
_, *shape = self.org_weight.shape
|
|
org_weight = self.org_weight.to(device, dtype=r.dtype)
|
|
org_weight = org_weight.view(self.block_num, self.block_size, *shape)
|
|
# Init R=0, so add I on it to ensure the output of step0 is original model output
|
|
weight = torch.einsum(
|
|
"k n m, k n ... -> k m ...",
|
|
self.rank_drop(r * scale) - scale * self.I + (0 if diff else self.I),
|
|
org_weight,
|
|
).view(-1, *shape)
|
|
if self.rescaled:
|
|
weight = self.rescale * weight
|
|
if diff:
|
|
weight = weight + (self.rescale - 1) * org_weight
|
|
return weight.to(self.oft_blocks.dtype)
|
|
|
|
def get_diff_weight(self, multiplier=1, shape=None, device=None):
|
|
diff = self.make_weight(scale=multiplier, device=device, diff=True)
|
|
if shape is not None:
|
|
diff = diff.view(shape)
|
|
return diff, None
|
|
|
|
def get_merged_weight(self, multiplier=1, shape=None, device=None):
|
|
diff = self.make_weight(scale=multiplier, device=device)
|
|
if shape is not None:
|
|
diff = diff.view(shape)
|
|
return diff, None
|
|
|
|
@torch.no_grad()
|
|
def apply_max_norm(self, max_norm, device=None):
|
|
orig_norm = self.oft_blocks.to(device).norm()
|
|
norm = torch.clamp(orig_norm, max_norm / 2)
|
|
desired = torch.clamp(norm, max=max_norm)
|
|
ratio = desired / norm
|
|
|
|
scaled = norm != desired
|
|
if scaled:
|
|
self.oft_blocks *= ratio
|
|
|
|
return scaled, orig_norm * ratio
|
|
|
|
def _bypass_forward(self, x, scale=1, diff=False):
|
|
r = self.get_r()
|
|
org_out = self.org_forward(x)
|
|
if self.op in {F.conv2d, F.conv1d, F.conv3d}:
|
|
org_out = org_out.transpose(1, -1)
|
|
*shape, _ = org_out.shape
|
|
org_out = org_out.view(*shape, self.block_num, self.block_size)
|
|
mask = neg_mask = 1
|
|
if self.dropout != 0 and self.training:
|
|
mask = torch.ones_like(org_out)
|
|
mask = self.drop(mask)
|
|
neg_mask = torch.max(mask) - mask
|
|
oft_out = torch.einsum(
|
|
"k n m, ... k n -> ... k m",
|
|
r * scale * mask + (1 - scale) * self.I * neg_mask,
|
|
org_out,
|
|
)
|
|
if diff:
|
|
out = out - org_out
|
|
out = oft_out.view(*shape, -1)
|
|
if self.rescaled:
|
|
out = self.rescale.transpose(-1, 0) * out
|
|
out = out + (self.rescale.transpose(-1, 0) - 1) * org_out
|
|
if self.op in {F.conv2d, F.conv1d, F.conv3d}:
|
|
out = out.transpose(1, -1)
|
|
return out
|
|
|
|
def bypass_forward_diff(self, x, scale=1):
|
|
return self._bypass_forward(x, scale, diff=True)
|
|
|
|
def bypass_forward(self, x, scale=1):
|
|
return self._bypass_forward(x, scale, diff=False)
|
|
|
|
def forward(self, x: torch.Tensor, *args, **kwargs):
|
|
if self.module_dropout and self.training:
|
|
if torch.rand(1) < self.module_dropout:
|
|
return self.org_forward(x)
|
|
scale = self.multiplier
|
|
|
|
if self.bypass_mode:
|
|
return self.bypass_forward(x, scale)
|
|
else:
|
|
w = self.make_weight(scale, x.device)
|
|
kw_dict = self.kw_dict | {"weight": w, "bias": self.org_module[0].bias}
|
|
return self.op(x, **kw_dict)
|