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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 ..loaders import PatchedLoraProjection, text_encoder_attn_modules, text_encoder_mlp_modules | |
| from ..utils import logging | |
| logger = logging.get_logger(__name__) # pylint: disable=invalid-name | |
| def adjust_lora_scale_text_encoder(text_encoder, lora_scale: float = 1.0, use_peft_backend: bool = False): | |
| if use_peft_backend: | |
| from peft.tuners.lora import LoraLayer | |
| for module in text_encoder.modules(): | |
| if isinstance(module, LoraLayer): | |
| module.scaling[module.active_adapter] = lora_scale | |
| else: | |
| 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 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, 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 (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, scale: float = 1.0): | |
| 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, **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=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 (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, scale: float = 1.0): | |
| 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 | |