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# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Callable, Dict, List, Optional, Set, Tuple, Type, Union
import torch
import torch.nn.functional as F
from torch import nn
class LoRALinearLayer(nn.Module):
def __init__(self, in_features, out_features, rank=4, network_alpha=None, device=None, dtype=None):
super().__init__()
self.down = nn.Linear(in_features, rank, bias=False, device=device, dtype=dtype)
self.up = nn.Linear(rank, out_features, bias=False, device=device, dtype=dtype)
# This value has the same meaning as the `--network_alpha` option in the kohya-ss trainer script.
# See https://github.com/darkstorm2150/sd-scripts/blob/main/docs/train_network_README-en.md#execute-learning
self.network_alpha = network_alpha
self.rank = rank
self.out_features = out_features
self.in_features = in_features
nn.init.normal_(self.down.weight, std=1 / rank)
nn.init.zeros_(self.up.weight)
def forward(self, hidden_states):
orig_dtype = hidden_states.dtype
dtype = self.down.weight.dtype
down_hidden_states = self.down(hidden_states.to(dtype))
up_hidden_states = self.up(down_hidden_states)
if self.network_alpha is not None:
up_hidden_states *= self.network_alpha / self.rank
return up_hidden_states.to(orig_dtype)
class LoRAConv2dLayer(nn.Module):
def __init__(
self, in_features, out_features, rank=4, kernel_size=(1, 1), stride=(1, 1), padding=0, network_alpha=None
):
super().__init__()
self.down = nn.Conv2d(in_features, rank, kernel_size=kernel_size, stride=stride, padding=padding, bias=False)
# according to the official kohya_ss trainer kernel_size are always fixed for the up layer
# # see: https://github.com/bmaltais/kohya_ss/blob/2accb1305979ba62f5077a23aabac23b4c37e935/networks/lora_diffusers.py#L129
self.up = nn.Conv2d(rank, out_features, kernel_size=(1, 1), stride=(1, 1), bias=False)
# This value has the same meaning as the `--network_alpha` option in the kohya-ss trainer script.
# See https://github.com/darkstorm2150/sd-scripts/blob/main/docs/train_network_README-en.md#execute-learning
self.network_alpha = network_alpha
self.rank = rank
nn.init.normal_(self.down.weight, std=1 / rank)
nn.init.zeros_(self.up.weight)
def forward(self, hidden_states):
orig_dtype = hidden_states.dtype
dtype = self.down.weight.dtype
down_hidden_states = self.down(hidden_states.to(dtype))
up_hidden_states = self.up(down_hidden_states)
if self.network_alpha is not None:
up_hidden_states *= self.network_alpha / self.rank
return up_hidden_states.to(orig_dtype)
class LoRACompatibleConv(nn.Conv2d):
"""
A convolutional layer that can be used with LoRA.
"""
def __init__(self, *args, lora_layer: Optional[LoRAConv2dLayer] = None, scale: float = 1.0, **kwargs):
super().__init__(*args, **kwargs)
self.lora_layer = lora_layer
self.scale = scale
def set_lora_layer(self, lora_layer: Optional[LoRAConv2dLayer]):
self.lora_layer = lora_layer
def _fuse_lora(self, lora_scale=1.0):
if self.lora_layer is None:
return
dtype, device = self.weight.data.dtype, self.weight.data.device
w_orig = self.weight.data.float()
w_up = self.lora_layer.up.weight.data.float()
w_down = self.lora_layer.down.weight.data.float()
if self.lora_layer.network_alpha is not None:
w_up = w_up * self.lora_layer.network_alpha / self.lora_layer.rank
fusion = torch.mm(w_up.flatten(start_dim=1), w_down.flatten(start_dim=1))
fusion = fusion.reshape((w_orig.shape))
fused_weight = w_orig + (lora_scale * fusion)
self.weight.data = fused_weight.to(device=device, dtype=dtype)
# we can drop the lora layer now
self.lora_layer = None
# offload the up and down matrices to CPU to not blow the memory
self.w_up = w_up.cpu()
self.w_down = w_down.cpu()
self._lora_scale = lora_scale
def _unfuse_lora(self):
if not (hasattr(self, "w_up") and hasattr(self, "w_down")):
return
fused_weight = self.weight.data
dtype, device = fused_weight.data.dtype, fused_weight.data.device
self.w_up = self.w_up.to(device=device).float()
self.w_down = self.w_down.to(device).float()
fusion = torch.mm(self.w_up.flatten(start_dim=1), self.w_down.flatten(start_dim=1))
fusion = fusion.reshape((fused_weight.shape))
unfused_weight = fused_weight.float() - (self._lora_scale * fusion)
self.weight.data = unfused_weight.to(device=device, dtype=dtype)
self.w_up = None
self.w_down = None
def forward(self, hidden_states, scale: float = None):
if scale is None:
scale = self.scale
if self.lora_layer is None:
# make sure to the functional Conv2D function as otherwise torch.compile's graph will break
# see: https://github.com/huggingface/diffusers/pull/4315
return F.conv2d(
hidden_states, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups
)
else:
return super().forward(hidden_states) + (scale * self.lora_layer(hidden_states))
class LoRACompatibleLinear(nn.Linear):
"""
A Linear layer that can be used with LoRA.
"""
def __init__(self, *args, lora_layer: Optional[LoRALinearLayer] = None, scale: float = 1.0, **kwargs):
super().__init__(*args, **kwargs)
self.lora_layer = lora_layer
self.scale = scale
def set_lora_layer(self, lora_layer: Optional[LoRALinearLayer]):
self.lora_layer = lora_layer
def _fuse_lora(self, lora_scale=1.0):
if self.lora_layer is None:
return
dtype, device = self.weight.data.dtype, self.weight.data.device
w_orig = self.weight.data.float()
w_up = self.lora_layer.up.weight.data.float()
w_down = self.lora_layer.down.weight.data.float()
if self.lora_layer.network_alpha is not None:
w_up = w_up * self.lora_layer.network_alpha / self.lora_layer.rank
fused_weight = w_orig + (lora_scale * torch.bmm(w_up[None, :], w_down[None, :])[0])
self.weight.data = fused_weight.to(device=device, dtype=dtype)
# we can drop the lora layer now
self.lora_layer = None
# offload the up and down matrices to CPU to not blow the memory
self.w_up = w_up.cpu()
self.w_down = w_down.cpu()
self._lora_scale = lora_scale
def _unfuse_lora(self):
if not (hasattr(self, "w_up") and hasattr(self, "w_down")):
return
fused_weight = self.weight.data
dtype, device = fused_weight.dtype, fused_weight.device
w_up = self.w_up.to(device=device).float()
w_down = self.w_down.to(device).float()
unfused_weight = fused_weight.float() - (self._lora_scale * torch.bmm(w_up[None, :], w_down[None, :])[0])
self.weight.data = unfused_weight.to(device=device, dtype=dtype)
self.w_up = None
self.w_down = None
def forward(self, hidden_states, scale: float = None):
if scale is None:
scale = self.scale
if self.lora_layer is None:
out = super().forward(hidden_states)
return out
else:
out = super().forward(hidden_states) + (scale * self.lora_layer(hidden_states))
return out
def _find_children(
model,
search_class: List[Type[nn.Module]] = [nn.Linear],
):
"""
Find all modules of a certain class (or union of classes).
Returns all matching modules, along with the parent of those moduless and the
names they are referenced by.
"""
# For each target find every linear_class module that isn't a child of a LoraInjectedLinear
for parent in model.modules():
for name, module in parent.named_children():
if any([isinstance(module, _class) for _class in search_class]):
yield parent, name, module
def _find_modules_v2(
model,
ancestor_class: Optional[Set[str]] = None,
search_class: List[Type[nn.Module]] = [nn.Linear],
exclude_children_of: Optional[List[Type[nn.Module]]] = [
LoRACompatibleLinear,
LoRACompatibleConv,
LoRALinearLayer,
LoRAConv2dLayer,
],
):
"""
Find all modules of a certain class (or union of classes) that are direct or
indirect descendants of other modules of a certain class (or union of classes).
Returns all matching modules, along with the parent of those moduless and the
names they are referenced by.
"""
# Get the targets we should replace all linears under
if ancestor_class is not None:
ancestors = (module for module in model.modules() if module.__class__.__name__ in ancestor_class)
else:
# this, incase you want to naively iterate over all modules.
ancestors = [module for module in model.modules()]
# For each target find every linear_class module that isn't a child of a LoraInjectedLinear
for ancestor in ancestors:
for fullname, module in ancestor.named_modules():
if any([isinstance(module, _class) for _class in search_class]):
# Find the direct parent if this is a descendant, not a child, of target
*path, name = fullname.split(".")
parent = ancestor
flag = False
while path:
try:
parent = parent.get_submodule(path.pop(0))
except:
flag = True
break
if flag:
continue
# Skip this linear if it's a child of a LoraInjectedLinear
if exclude_children_of and any([isinstance(parent, _class) for _class in exclude_children_of]):
continue
# Otherwise, yield it
yield parent, name, module
_find_modules = _find_modules_v2
def inject_trainable_lora_extended(
model: nn.Module,
target_replace_module: Set[str] = None,
rank: int = 4,
scale: float = 1.0,
):
for _module, name, _child_module in _find_modules(
model, target_replace_module, search_class=[nn.Linear, nn.Conv2d]
):
if _child_module.__class__ == nn.Linear:
weight = _child_module.weight
bias = _child_module.bias
lora_layer = LoRALinearLayer(
in_features=_child_module.in_features,
out_features=_child_module.out_features,
rank=rank,
)
_tmp = (
LoRACompatibleLinear(
_child_module.in_features,
_child_module.out_features,
lora_layer=lora_layer,
scale=scale,
)
.to(weight.dtype)
.to(weight.device)
)
_tmp.weight = weight
if bias is not None:
_tmp.bias = bias
elif _child_module.__class__ == nn.Conv2d:
weight = _child_module.weight
bias = _child_module.bias
lora_layer = LoRAConv2dLayer(
in_features=_child_module.in_channels,
out_features=_child_module.out_channels,
rank=rank,
kernel_size=_child_module.kernel_size,
stride=_child_module.stride,
padding=_child_module.padding,
)
_tmp = (
LoRACompatibleConv(
_child_module.in_channels,
_child_module.out_channels,
kernel_size=_child_module.kernel_size,
stride=_child_module.stride,
padding=_child_module.padding,
lora_layer=lora_layer,
scale=scale,
)
.to(weight.dtype)
.to(weight.device)
)
_tmp.weight = weight
if bias is not None:
_tmp.bias = bias
else:
continue
_module._modules[name] = _tmp
# print('injecting lora layer to', _module, name)
return
def update_lora_scale(
model: nn.Module,
target_module: Set[str] = None,
scale: float = 1.0,
):
for _module, name, _child_module in _find_modules(
model, target_module, search_class=[LoRACompatibleLinear, LoRACompatibleConv]
):
_child_module.scale = scale
return
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