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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 Optional
import torch
import torch.nn.functional as F
from torch import nn
from ..utils import logging
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
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, **kwargs):
super().__init__(*args, **kwargs)
self.lora_layer = lora_layer
def set_lora_layer(self, lora_layer: Optional[LoRAConv2dLayer]):
self.lora_layer = lora_layer
def _fuse_lora(self):
if self.lora_layer is None:
return
dtype, device = self.weight.data.dtype, self.weight.data.device
logger.info(f"Fusing LoRA weights for {self.__class__}")
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 + 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()
def _unfuse_lora(self):
if not (hasattr(self, "w_up") and hasattr(self, "w_down")):
return
logger.info(f"Unfusing LoRA weights for {self.__class__}")
fused_weight = self.weight.data
dtype, device = fused_weight.data.dtype, fused_weight.data.device
self.w_up = self.w_up.to(device=device, dtype=dtype)
self.w_down = self.w_down.to(device, dtype=dtype)
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 - fusion
self.weight.data = unfused_weight.to(device=device, dtype=dtype)
self.w_up = None
self.w_down = None
def forward(self, x):
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(x, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups)
else:
return super().forward(x) + self.lora_layer(x)
class LoRACompatibleLinear(nn.Linear):
"""
A Linear layer that can be used with LoRA.
"""
def __init__(self, *args, lora_layer: Optional[LoRALinearLayer] = None, **kwargs):
super().__init__(*args, **kwargs)
self.lora_layer = lora_layer
def set_lora_layer(self, lora_layer: Optional[LoRALinearLayer]):
self.lora_layer = lora_layer
def _fuse_lora(self):
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 + 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()
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() - 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, lora_scale: int = 1):
if self.lora_layer is None:
return super().forward(hidden_states)
else:
return super().forward(hidden_states) + lora_scale * self.lora_layer(hidden_states)