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import torch
import torch.nn as nn
import torch.nn.functional as F
from .base import LycorisBaseModule
from ..functional import tucker_weight_from_conv
class GLoRAModule(LycorisBaseModule):
name = "glora"
support_module = {
"linear",
"conv1d",
"conv2d",
"conv3d",
}
weight_list = [
"a1.weight",
"a2.weight",
"b1.weight",
"b2.weight",
"bm.weight",
"alpha",
]
weight_list_det = ["a1.weight"]
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,
weight_decompose=False,
bypass_mode=None,
rs_lora=False,
**kwargs,
):
"""
f(x) = WX + WAX + BX, where A and B are low-rank matrices
bypass_forward(x) = W(X+A(X)) + B(X)
bypass_forward_diff(x) = W(A(X)) + B(X)
get_merged_weight() = W + WA + B
get_diff_weight() = WA + B
"""
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 GLoRA algo.")
self.lora_dim = lora_dim
self.tucker = False
self.rs_lora = rs_lora
if self.module_type.startswith("conv"):
self.isconv = True
# For general LoCon
in_dim = org_module.in_channels
k_size = org_module.kernel_size
stride = org_module.stride
padding = org_module.padding
out_dim = org_module.out_channels
use_tucker = use_tucker and all(i == 1 for i in k_size)
self.down_op = self.op
self.up_op = self.op
# A
self.a2 = self.module(in_dim, lora_dim, 1, bias=False)
self.a1 = self.module(lora_dim, in_dim, 1, bias=False)
# B
if use_tucker and any(i != 1 for i in k_size):
self.b2 = self.module(in_dim, lora_dim, 1, bias=False)
self.bm = self.module(
lora_dim, lora_dim, k_size, stride, padding, bias=False
)
self.tucker = True
else:
self.b2 = self.module(
in_dim, lora_dim, k_size, stride, padding, bias=False
)
self.b1 = self.module(lora_dim, out_dim, 1, bias=False)
else:
self.isconv = False
self.down_op = F.linear
self.up_op = F.linear
in_dim = org_module.in_features
out_dim = org_module.out_features
self.a2 = nn.Linear(in_dim, lora_dim, bias=False)
self.a1 = nn.Linear(lora_dim, in_dim, bias=False)
self.b2 = nn.Linear(in_dim, lora_dim, bias=False)
self.b1 = nn.Linear(lora_dim, out_dim, bias=False)
if type(alpha) == torch.Tensor:
alpha = alpha.detach().float().numpy() # without casting, bf16 causes error
alpha = lora_dim if alpha is None or alpha == 0 else alpha
r_factor = lora_dim
if self.rs_lora:
r_factor = math.sqrt(r_factor)
self.scale = alpha / r_factor
self.register_buffer("alpha", torch.tensor(alpha)) # 定数として扱える
if use_scalar:
self.scalar = nn.Parameter(torch.tensor(0.0))
else:
self.register_buffer("scalar", torch.tensor(1.0), persistent=False)
# same as microsoft's
torch.nn.init.kaiming_uniform_(self.a1.weight, a=math.sqrt(5))
torch.nn.init.kaiming_uniform_(self.b1.weight, a=math.sqrt(5))
if use_scalar:
torch.nn.init.kaiming_uniform_(self.a2.weight, a=math.sqrt(5))
torch.nn.init.kaiming_uniform_(self.b2.weight, a=math.sqrt(5))
else:
torch.nn.init.zeros_(self.a2.weight)
torch.nn.init.zeros_(self.b2.weight)
@classmethod
def make_module_from_state_dict(
cls, lora_name, orig_module, a1, a2, b1, b2, bm, alpha
):
module = cls(
lora_name,
orig_module,
1,
a2.size(0),
float(alpha),
use_tucker=bm is not None,
)
module.a1.weight.data.copy_(a1)
module.a2.weight.data.copy_(a2)
module.b1.weight.data.copy_(b1)
module.b2.weight.data.copy_(b2)
if bm is not None:
module.bm.weight.data.copy_(bm)
return module
def custom_state_dict(self):
destination = {}
destination["alpha"] = self.alpha
destination["a1.weight"] = self.a1.weight
destination["a2.weight"] = self.a2.weight * self.scalar
destination["b1.weight"] = self.b1.weight
destination["b2.weight"] = self.b2.weight * self.scalar
if self.tucker:
destination["bm.weight"] = self.bm.weight
return destination
def load_weight_hook(self, module: nn.Module, incompatible_keys):
missing_keys = incompatible_keys.missing_keys
for key in missing_keys:
if "scalar" in key:
del missing_keys[missing_keys.index(key)]
if isinstance(self.scalar, nn.Parameter):
self.scalar.data.copy_(torch.ones_like(self.scalar))
elif getattr(self, "scalar", None) is not None:
self.scalar.copy_(torch.ones_like(self.scalar))
else:
self.register_buffer(
"scalar", torch.ones_like(self.scalar), persistent=False
)
def make_weight(self, device=None):
wa1 = self.a1.weight.view(self.a1.weight.size(0), -1)
wa2 = self.a2.weight.view(self.a2.weight.size(0), -1)
orig = self.org_weight
if self.tucker:
wb = tucker_weight_from_conv(self.b1.weight, self.b2.weight, self.bm.weight)
else:
wb1 = self.b1.weight.view(self.b1.weight.size(0), -1)
wb2 = self.b2.weight.view(self.b2.weight.size(0), -1)
wb = wb1 @ wb2
wb = wb.view(*orig.shape)
if orig.dim() > 2:
w_wa1 = torch.einsum("o i ..., i j -> o j ...", orig, wa1)
w_wa2 = torch.einsum("o i ..., i j -> o j ...", w_wa1, wa2)
else:
w_wa2 = (orig @ wa1) @ wa2
return (wb + w_wa2) * self.scale * self.scalar
def get_diff_weight(self, multiplier=1.0, shape=None, device=None):
weight = self.make_weight(device) * multiplier
if shape is not None:
weight = weight.view(shape)
return weight, None
def get_merged_weight(self, multiplier=1, shape=None, device=None):
diff_w, _ = self.get_diff_weight(multiplier, shape, device)
return self.org_weight + diff_w, None
def _bypass_forward(self, x, scale=1, diff=False):
scale = self.scale * scale
ax_mid = self.a2(x) * scale
bx_mid = self.b2(x) * scale
if self.rank_dropout and self.training:
drop_a = (
torch.rand(self.lora_dim, device=ax_mid.device) < self.rank_dropout
).to(ax_mid.dtype)
drop_b = (
torch.rand(self.lora_dim, device=bx_mid.device) < self.rank_dropout
).to(bx_mid.dtype)
if self.rank_dropout_scale:
drop_a /= drop_a.mean()
drop_b /= drop_b.mean()
if (dims := len(x.shape)) == 4:
drop_a = drop_a.view(1, -1, 1, 1)
drop_b = drop_b.view(1, -1, 1, 1)
else:
drop_a = drop_a.view(*[1] * (dims - 1), -1)
drop_b = drop_b.view(*[1] * (dims - 1), -1)
ax_mid = ax_mid * drop_a
bx_mid = bx_mid * drop_b
return (
self.org_forward(
(0 if diff else x) + self.drop(self.a1(ax_mid)) * self.scale
)
+ self.drop(self.b1(bx_mid)) * self.scale
)
def bypass_forward_diff(self, x, scale=1):
return self._bypass_forward(x, scale=scale, diff=True)
def bypass_forward(self, x, scale=1):
return self._bypass_forward(x, scale=scale, diff=False)
def forward(self, x, *args, **kwargs):
if self.module_dropout and self.training:
if torch.rand(1) < self.module_dropout:
return self.org_forward(x)
if self.bypass_mode:
return self.bypass_forward(x, self.multiplier)
else:
weight = (
self.org_module[0].weight.data.to(self.dtype)
+ self.get_diff_weight(multiplier=self.multiplier)[0]
)
bias = (
None
if self.org_module[0].bias is None
else self.org_module[0].bias.data
)
return self.op(x, weight, bias, **self.kw_dict)
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