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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. | |
# IMPORTANT: # | |
################################################################### | |
# ----------------------------------------------------------------# | |
# This file is deprecated and will be removed soon # | |
# (as soon as PEFT will become a required dependency for LoRA) # | |
# ----------------------------------------------------------------# | |
################################################################### | |
from typing import Optional, Tuple, Union | |
import torch | |
import torch.nn.functional as F | |
from torch import nn | |
from ..utils import logging | |
from ..utils.import_utils import is_transformers_available | |
if is_transformers_available(): | |
from transformers import CLIPTextModel, CLIPTextModelWithProjection | |
logger = logging.get_logger(__name__) # pylint: disable=invalid-name | |
def text_encoder_attn_modules(text_encoder): | |
attn_modules = [] | |
if isinstance(text_encoder, (CLIPTextModel, CLIPTextModelWithProjection)): | |
for i, layer in enumerate(text_encoder.text_model.encoder.layers): | |
name = f"text_model.encoder.layers.{i}.self_attn" | |
mod = layer.self_attn | |
attn_modules.append((name, mod)) | |
else: | |
raise ValueError(f"do not know how to get attention modules for: {text_encoder.__class__.__name__}") | |
return attn_modules | |
def text_encoder_mlp_modules(text_encoder): | |
mlp_modules = [] | |
if isinstance(text_encoder, (CLIPTextModel, CLIPTextModelWithProjection)): | |
for i, layer in enumerate(text_encoder.text_model.encoder.layers): | |
mlp_mod = layer.mlp | |
name = f"text_model.encoder.layers.{i}.mlp" | |
mlp_modules.append((name, mlp_mod)) | |
else: | |
raise ValueError(f"do not know how to get mlp modules for: {text_encoder.__class__.__name__}") | |
return mlp_modules | |
def adjust_lora_scale_text_encoder(text_encoder, lora_scale: float = 1.0): | |
for _, attn_module in text_encoder_attn_modules(text_encoder): | |
if isinstance(attn_module.q_proj, PatchedLoraProjection): | |
attn_module.q_proj.lora_scale = lora_scale | |
attn_module.k_proj.lora_scale = lora_scale | |
attn_module.v_proj.lora_scale = lora_scale | |
attn_module.out_proj.lora_scale = lora_scale | |
for _, mlp_module in text_encoder_mlp_modules(text_encoder): | |
if isinstance(mlp_module.fc1, PatchedLoraProjection): | |
mlp_module.fc1.lora_scale = lora_scale | |
mlp_module.fc2.lora_scale = lora_scale | |
class PatchedLoraProjection(torch.nn.Module): | |
def __init__(self, regular_linear_layer, lora_scale=1, network_alpha=None, rank=4, dtype=None): | |
super().__init__() | |
from ..models.lora import LoRALinearLayer | |
self.regular_linear_layer = regular_linear_layer | |
device = self.regular_linear_layer.weight.device | |
if dtype is None: | |
dtype = self.regular_linear_layer.weight.dtype | |
self.lora_linear_layer = LoRALinearLayer( | |
self.regular_linear_layer.in_features, | |
self.regular_linear_layer.out_features, | |
network_alpha=network_alpha, | |
device=device, | |
dtype=dtype, | |
rank=rank, | |
) | |
self.lora_scale = lora_scale | |
# overwrite PyTorch's `state_dict` to be sure that only the 'regular_linear_layer' weights are saved | |
# when saving the whole text encoder model and when LoRA is unloaded or fused | |
def state_dict(self, *args, destination=None, prefix="", keep_vars=False): | |
if self.lora_linear_layer is None: | |
return self.regular_linear_layer.state_dict( | |
*args, destination=destination, prefix=prefix, keep_vars=keep_vars | |
) | |
return super().state_dict(*args, destination=destination, prefix=prefix, keep_vars=keep_vars) | |
def _fuse_lora(self, lora_scale=1.0, safe_fusing=False): | |
if self.lora_linear_layer is None: | |
return | |
dtype, device = self.regular_linear_layer.weight.data.dtype, self.regular_linear_layer.weight.data.device | |
w_orig = self.regular_linear_layer.weight.data.float() | |
w_up = self.lora_linear_layer.up.weight.data.float() | |
w_down = self.lora_linear_layer.down.weight.data.float() | |
if self.lora_linear_layer.network_alpha is not None: | |
w_up = w_up * self.lora_linear_layer.network_alpha / self.lora_linear_layer.rank | |
fused_weight = w_orig + (lora_scale * torch.bmm(w_up[None, :], w_down[None, :])[0]) | |
if safe_fusing and torch.isnan(fused_weight).any().item(): | |
raise ValueError( | |
"This LoRA weight seems to be broken. " | |
f"Encountered NaN values when trying to fuse LoRA weights for {self}." | |
"LoRA weights will not be fused." | |
) | |
self.regular_linear_layer.weight.data = fused_weight.to(device=device, dtype=dtype) | |
# we can drop the lora layer now | |
self.lora_linear_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 (getattr(self, "w_up", None) is not None and getattr(self, "w_down", None) is not None): | |
return | |
fused_weight = self.regular_linear_layer.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.regular_linear_layer.weight.data = unfused_weight.to(device=device, dtype=dtype) | |
self.w_up = None | |
self.w_down = None | |
def forward(self, input): | |
if self.lora_scale is None: | |
self.lora_scale = 1.0 | |
if self.lora_linear_layer is None: | |
return self.regular_linear_layer(input) | |
return self.regular_linear_layer(input) + (self.lora_scale * self.lora_linear_layer(input)) | |
class LoRALinearLayer(nn.Module): | |
r""" | |
A linear layer that is used with LoRA. | |
Parameters: | |
in_features (`int`): | |
Number of input features. | |
out_features (`int`): | |
Number of output features. | |
rank (`int`, `optional`, defaults to 4): | |
The rank of the LoRA layer. | |
network_alpha (`float`, `optional`, defaults to `None`): | |
The value of the network alpha used for stable learning and preventing underflow. 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 | |
device (`torch.device`, `optional`, defaults to `None`): | |
The device to use for the layer's weights. | |
dtype (`torch.dtype`, `optional`, defaults to `None`): | |
The dtype to use for the layer's weights. | |
""" | |
def __init__( | |
self, | |
in_features: int, | |
out_features: int, | |
rank: int = 4, | |
network_alpha: Optional[float] = None, | |
device: Optional[Union[torch.device, str]] = None, | |
dtype: Optional[torch.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: torch.Tensor) -> torch.Tensor: | |
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): | |
r""" | |
A convolutional layer that is used with LoRA. | |
Parameters: | |
in_features (`int`): | |
Number of input features. | |
out_features (`int`): | |
Number of output features. | |
rank (`int`, `optional`, defaults to 4): | |
The rank of the LoRA layer. | |
kernel_size (`int` or `tuple` of two `int`, `optional`, defaults to 1): | |
The kernel size of the convolution. | |
stride (`int` or `tuple` of two `int`, `optional`, defaults to 1): | |
The stride of the convolution. | |
padding (`int` or `tuple` of two `int` or `str`, `optional`, defaults to 0): | |
The padding of the convolution. | |
network_alpha (`float`, `optional`, defaults to `None`): | |
The value of the network alpha used for stable learning and preventing underflow. 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 | |
""" | |
def __init__( | |
self, | |
in_features: int, | |
out_features: int, | |
rank: int = 4, | |
kernel_size: Union[int, Tuple[int, int]] = (1, 1), | |
stride: Union[int, Tuple[int, int]] = (1, 1), | |
padding: Union[int, Tuple[int, int], str] = 0, | |
network_alpha: Optional[float] = 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: torch.Tensor) -> torch.Tensor: | |
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, lora_scale: float = 1.0, safe_fusing: bool = False): | |
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) | |
if safe_fusing and torch.isnan(fused_weight).any().item(): | |
raise ValueError( | |
"This LoRA weight seems to be broken. " | |
f"Encountered NaN values when trying to fuse LoRA weights for {self}." | |
"LoRA weights will not be fused." | |
) | |
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 (getattr(self, "w_up", None) is not None and getattr(self, "w_down", None) is not None): | |
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: torch.Tensor, scale: float = 1.0) -> torch.Tensor: | |
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: | |
original_outputs = F.conv2d( | |
hidden_states, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups | |
) | |
return original_outputs + (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, **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, lora_scale: float = 1.0, safe_fusing: bool = False): | |
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]) | |
if safe_fusing and torch.isnan(fused_weight).any().item(): | |
raise ValueError( | |
"This LoRA weight seems to be broken. " | |
f"Encountered NaN values when trying to fuse LoRA weights for {self}." | |
"LoRA weights will not be fused." | |
) | |
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 (getattr(self, "w_up", None) is not None and getattr(self, "w_down", None) is not None): | |
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: torch.Tensor, scale: float = 1.0) -> torch.Tensor: | |
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 | |