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# Copyright 2024-present the HuggingFace Inc. team.
#
# 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 copy import deepcopy
import torch
import torch.nn.functional as F
from torch import nn
from peft.utils.integrations import dequantize_module_weight, gather_params_ctx
from peft.utils.other import transpose
class DoraLinearLayer(nn.Module):
def __init__(self, fan_in_fan_out):
super().__init__()
self.fan_in_fan_out = fan_in_fan_out
def get_weight_norm(self, weight, lora_weight, scaling) -> torch.Tensor:
# calculate L2 norm of weight matrix, column-wise
weight = transpose(weight, self.fan_in_fan_out)
weight = weight + scaling * lora_weight
weight_norm = torch.linalg.norm(weight, dim=1).to(weight.dtype)
return weight_norm
def update_layer(self, *, base_layer, lora_A, lora_B, scaling, place_on_cpu=False) -> None:
# temporarily convert fp16 to fp32, as fp16 can cause trouble on CPU with PyTorch < 2.2
dtype_is_fp16 = lora_A.dtype == torch.float16
if dtype_is_fp16:
lora_A = lora_A.float()
lora_B = lora_B.float()
with gather_params_ctx(base_layer.parameters()):
if base_layer.__class__.__name__ == "Linear4bit":
# We have to create a copy of the base layer, otherwise, FSDP will throw an error. 8bit does not work
# yet because Int8Params cannot be correctly deep-copied (attributes vanish)
base_layer = deepcopy(base_layer)
weight = dequantize_module_weight(base_layer)
if weight.data.ndim >= 4: # For handling LoRAs applied to Conv layers.
lora_weight = torch.mm(lora_B.flatten(start_dim=1), lora_A.flatten(start_dim=1))
lora_weight = lora_weight.reshape(weight.shape)
else:
lora_weight = lora_B @ lora_A
if dtype_is_fp16:
lora_weight = lora_weight.half()
weight_norm = self.get_weight_norm(weight.to(lora_A.device), lora_weight, scaling)
if place_on_cpu:
weight_norm = weight_norm.to("cpu")
self.weight = nn.Parameter(weight_norm, requires_grad=True)
def forward(self, x, *, lora_A, lora_B, scaling, base_layer, base_result=None):
"""
For DoRA, calculate the extra output from LoRA with DoRA applied. This should be added on top of the base layer
output.
"""
# Don't use `lora_weight = lora_B.weight @ lora_A.weight` because this causes errors with FSDP. Instead,
# calculate the same but using forward.
x_eye = torch.eye(lora_A.weight.shape[1], device=lora_A.weight.device, dtype=x.dtype)
lora_weight = lora_B(lora_A(x_eye)).T
magnitude = self.weight
weight = dequantize_module_weight(base_layer)
weight = weight.to(x.dtype)
weight_norm = self.get_weight_norm(weight, lora_weight.detach(), scaling)
# see section 4.3 of DoRA (https://arxiv.org/abs/2402.09353)
# "[...] we suggest treating ||V +∆V ||_c in
# Eq. (5) as a constant, thereby detaching it from the gradient
# graph. This means that while ||V + ∆V ||_c dynamically
# reflects the updates of ∆V , it won’t receive any gradient
# during backpropagation"
weight_norm = weight_norm.detach()
mag_norm_scale = (magnitude / weight_norm).view(1, -1)
lora_result = lora_B(lora_A(x))
bias = None
if base_result is not None:
bias = base_layer.bias
if bias is not None:
base_result = base_result - bias
else:
base_result = F.linear(x, transpose(weight, self.fan_in_fan_out))
result_dora = (mag_norm_scale - 1) * base_result + mag_norm_scale * lora_result * scaling
return result_dora
def __repr__(self) -> str:
rep = super().__repr__()
return "lora.dora." + rep
class DoraEmbeddingLayer(DoraLinearLayer):
def forward(self, x, *, lora_A, lora_B, scaling, base_layer, embed_fn):
"""
For DoRA, calculate the extra output from LoRA with DoRA applied. This should be added on top of the base layer
output.
"""
lora_weight = (lora_A @ lora_B).T
magnitude = self.weight
weight = base_layer.weight
weight_norm = self.get_weight_norm(weight, lora_weight.detach(), scaling)
# see section 4.3 of DoRA (https://arxiv.org/abs/2402.09353)
# "[...] we suggest treating ||V +∆V ||_c in
# Eq. (5) as a constant, thereby detaching it from the gradient
# graph. This means that while ||V + ∆V ||_c dynamically
# reflects the updates of ∆V , it won’t receive any gradient
# during backpropagation"
weight_norm = weight_norm.detach()
mag_norm_scale = magnitude / weight_norm
result_dora = mag_norm_scale * (embed_fn(x, lora_A) @ lora_B) * scaling
return mag_norm_scale, result_dora
def __repr__(self) -> str:
rep = super().__repr__()
return "lora.dora." + rep
class _DoraConvNdLayer(DoraLinearLayer):
def get_weight_norm(self, weight, lora_weight, scaling) -> torch.Tensor:
# calculate L2 norm of weight matrix, column-wise
weight = weight + scaling * lora_weight
# the following is needed to have compatibility with the 4/5D weight tensors of Conv2D/3D
dim = tuple(range(1, weight.dim()))
weight_norm = weight.norm(p=2, dim=dim, keepdim=True).transpose(1, 0)
return weight_norm
def forward(self, x, *, lora_A, lora_B, scaling, base_layer):
"""
For DoRA, calculate the extra output from LoRA with DoRA applied. This should be added on top of the base layer
output.
"""
weight = base_layer.weight
lora_weight = torch.mm(lora_B.weight.flatten(start_dim=1), lora_A.weight.flatten(start_dim=1))
lora_weight = lora_weight.reshape(weight.shape)
magnitude = self.weight
weight_norm = self.get_weight_norm(weight, lora_weight.detach(), scaling)
# see section 4.3 of DoRA (https://arxiv.org/abs/2402.09353)
# "[...] we suggest treating ||V +∆V ||_c in
# Eq. (5) as a constant, thereby detaching it from the gradient
# graph. This means that while ||V + ∆V ||_c dynamically
# reflects the updates of ∆V , it won’t receive any gradient
# during backpropagation"
weight_norm = weight_norm.detach()
mag_norm_scale = magnitude / weight_norm
result_dora = (mag_norm_scale - 1) * (
self.conv_fn(
x,
weight,
bias=None,
stride=base_layer.stride,
padding=base_layer.padding,
dilation=base_layer.dilation,
groups=base_layer.groups,
)
) + mag_norm_scale * lora_B(lora_A(x)) * scaling
return result_dora
def __repr__(self) -> str:
rep = super().__repr__()
return "lora.dora." + rep
class DoraConv2dLayer(_DoraConvNdLayer):
def __init__(self, fan_in_fan_out):
super().__init__(fan_in_fan_out)
self.conv_fn = F.conv2d
class DoraConv3dLayer(_DoraConvNdLayer):
def __init__(self, fan_in_fan_out):
super().__init__(fan_in_fan_out)
self.conv_fn = F.conv3d
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