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# Copyright (c) 2021 microsoft
# 2023 Alan ([email protected])
# -----------------------------------------------------------------------------
# Licensed under the MIT License (MIT). See LICENSE in the repo root for
# license information.
# -----------------------------------------------------------------------------
import torch
import torch.nn as nn
import torch.nn.functional as F
import math
from typing import List
class LoRALayer():
def __init__(
self,
r: int,
lora_alpha: int,
lora_dropout: float,
merge_weights: bool,
):
self.r = r
self.lora_alpha = lora_alpha
# Optional dropout
if lora_dropout > 0.:
self.lora_dropout = nn.Dropout(p=lora_dropout)
else:
self.lora_dropout = self.identity
# Mark the weight as unmerged
self.merged = False
self.merge_weights = merge_weights
def identity(self, x):
return x
class Embedding(nn.Embedding, LoRALayer):
# LoRA implemented in a dense layer
def __init__(self,
num_embeddings: int,
embedding_dim: int,
r: int = 0,
lora_alpha: int = 1,
merge_weights: bool = True,
**kwargs):
nn.Embedding.__init__(self, num_embeddings, embedding_dim, **kwargs)
LoRALayer.__init__(self,
r=r,
lora_alpha=lora_alpha,
lora_dropout=0,
merge_weights=merge_weights)
# Actual trainable parameters
if r > 0:
self.lora_A = nn.Parameter(
self.weight.new_zeros((r, num_embeddings)))
self.lora_B = nn.Parameter(
self.weight.new_zeros((embedding_dim, r)))
self.scaling = self.lora_alpha / self.r
# Freezing the pre-trained weight matrix
self.weight.requires_grad = False
self.reset_parameters()
def reset_parameters(self):
nn.Embedding.reset_parameters(self)
if hasattr(self, 'lora_A'):
# initialize A the same way as the default for nn.Linear and B to zero
nn.init.zeros_(self.lora_A)
nn.init.normal_(self.lora_B)
def train(self, mode: bool = True):
nn.Embedding.train(self, mode)
if mode:
if self.merge_weights and self.merged:
# Make sure that the weights are not merged
if self.r > 0:
temp = (self.lora_B @ self.lora_A).transpose(0, 1)
self.weight.data -= temp * self.scaling
self.merged = False
else:
if self.merge_weights and not self.merged:
# Merge the weights and mark it
if self.r > 0:
temp = (self.lora_B @ self.lora_A).transpose(0, 1)
self.weight.data += temp * self.scaling
self.merged = True
def forward(self, x: torch.Tensor):
if self.r > 0 and not self.merged:
result = nn.Embedding.forward(self, x)
after_A = F.embedding(x, self.lora_A.transpose(0, 1),
self.padding_idx, self.max_norm,
self.norm_type, self.scale_grad_by_freq,
self.sparse)
result += (after_A @ self.lora_B.transpose(0, 1)) * self.scaling
return result
else:
return nn.Embedding.forward(self, x)
class Linear(nn.Linear, LoRALayer):
# LoRA implemented in a dense layer
def __init__(
self,
in_features: int,
out_features: int,
r: int = 0,
lora_alpha: int = 1,
lora_dropout: float = 0.,
fan_in_fan_out: bool = False,
# Set this to True if the layer to replace stores weight like (fan_in,
# fan_out)
merge_weights: bool = True,
**kwargs):
nn.Linear.__init__(self, in_features, out_features, **kwargs)
LoRALayer.__init__(self,
r=r,
lora_alpha=lora_alpha,
lora_dropout=lora_dropout,
merge_weights=merge_weights)
self.fan_in_fan_out = fan_in_fan_out
# Actual trainable parameters
if r > 0:
self.lora_A = nn.Parameter(self.weight.new_zeros((r, in_features)))
self.lora_B = nn.Parameter(self.weight.new_zeros(
(out_features, r)))
self.scaling = self.lora_alpha / self.r
# Freezing the pre-trained weight matrix
self.weight.requires_grad = False
self.reset_parameters()
if fan_in_fan_out:
self.weight.data = self.weight.data.transpose(0, 1)
def reset_parameters(self):
nn.Linear.reset_parameters(self)
if hasattr(self, 'lora_A'):
# initialize A the same way as the default for nn.Linear and B to zero
nn.init.kaiming_uniform_(self.lora_A, a=math.sqrt(5))
nn.init.zeros_(self.lora_B)
def T(self, w):
return w.transpose(0, 1) if self.fan_in_fan_out else w
def train(self, mode: bool = True):
nn.Linear.train(self, mode)
if mode:
if self.merge_weights and self.merged:
# Make sure that the weights are not merged
if self.r > 0:
temp = self.T(self.lora_B @ self.lora_A)
self.weight.data -= temp * self.scaling
self.merged = False
else:
if self.merge_weights and not self.merged:
# Merge the weights and mark it
if self.r > 0:
temp = self.T(self.lora_B @ self.lora_A)
self.weight.data += temp * self.scaling
self.merged = True
def forward(self, x: torch.Tensor):
if self.r > 0 and not self.merged:
result = F.linear(x, self.T(self.weight), bias=self.bias)
result += (self.lora_dropout(x) @ self.lora_A.transpose(0, 1)
@ self.lora_B.transpose(0, 1)) * self.scaling
return result
else:
return F.linear(x, self.T(self.weight), bias=self.bias)
class MergedLinear(nn.Linear, LoRALayer):
# LoRA implemented in a dense layer
def __init__(self,
in_features: int,
out_features: int,
r: int = 0,
lora_alpha: int = 1,
lora_dropout: float = 0.,
enable_lora: List[bool] = None,
fan_in_fan_out: bool = False,
merge_weights: bool = True,
**kwargs):
if enable_lora is None:
enable_lora = [False]
nn.Linear.__init__(self, in_features, out_features, **kwargs)
LoRALayer.__init__(self,
r=r,
lora_alpha=lora_alpha,
lora_dropout=lora_dropout,
merge_weights=merge_weights)
assert out_features % len(enable_lora) == 0, \
'The length of enable_lora must divide out_features'
self.enable_lora = enable_lora
self.fan_in_fan_out = fan_in_fan_out
# Actual trainable parameters
if r > 0 and any(enable_lora):
self.lora_A = nn.Parameter(
self.weight.new_zeros((r * sum(enable_lora), in_features)))
self.lora_B = nn.Parameter(
self.weight.new_zeros(
(out_features // len(enable_lora) * sum(enable_lora), r)))
# weights for Conv1D with groups=sum(enable_lora)
self.scaling = self.lora_alpha / self.r
# Freezing the pre-trained weight matrix
self.weight.requires_grad = False
# Compute the indices
self.lora_ind = self.weight.new_zeros(
(out_features, ), dtype=torch.bool).view(len(enable_lora), -1)
self.lora_ind[enable_lora, :] = True
self.lora_ind = self.lora_ind.view(-1)
self.reset_parameters()
if fan_in_fan_out:
self.weight.data = self.weight.data.transpose(0, 1)
def reset_parameters(self):
nn.Linear.reset_parameters(self)
if hasattr(self, 'lora_A'):
# initialize A the same way as the default for nn.Linear and B to zero
nn.init.kaiming_uniform_(self.lora_A, a=math.sqrt(5))
nn.init.zeros_(self.lora_B)
def zero_pad(self, x):
result = x.new_zeros((len(self.lora_ind), *x.size()[1:]))
result[self.lora_ind] = x
return result
def T(self, w):
return w.transpose(0, 1) if self.fan_in_fan_out else w
def merge_AB(self):
delta_w = F.conv1d(self.lora_A.unsqueeze(0),
self.lora_B.unsqueeze(-1),
groups=sum(self.enable_lora)).squeeze(0)
return self.T(delta_w)
def train(self, mode: bool = True):
nn.Linear.train(self, mode)
if mode:
if self.merge_weights and self.merged:
# Make sure that the weights are not merged
if self.r > 0 and any(self.enable_lora):
self.weight.data -= self.merge_AB() * self.scaling
self.merged = False
else:
if self.merge_weights and not self.merged:
# Merge the weights and mark it
if self.r > 0 and any(self.enable_lora):
self.weight.data += self.merge_AB() * self.scaling
self.merged = True
def forward(self, x: torch.Tensor):
if self.merged:
return F.linear(x, self.T(self.weight), bias=self.bias)
else:
result = F.linear(x, self.T(self.weight), bias=self.bias)
if self.r > 0:
temp = self.T(self.merge_AB().T)
result += self.lora_dropout(x) @ temp * self.scaling
return result
class ConvLoRA(nn.Module, LoRALayer):
def __init__(self,
conv_module,
in_channels,
out_channels,
kernel_size,
r=0,
lora_alpha=1,
lora_dropout=0.,
merge_weights=True,
**kwargs):
super(ConvLoRA, self).__init__()
self.conv = conv_module(in_channels, out_channels, kernel_size,
**kwargs)
LoRALayer.__init__(self,
r=r,
lora_alpha=lora_alpha,
lora_dropout=lora_dropout,
merge_weights=merge_weights)
assert isinstance(kernel_size, int)
# Actual trainable parameters
if r > 0:
self.lora_A = nn.Parameter(
self.conv.weight.new_zeros(
(r * kernel_size, in_channels * kernel_size)))
self.lora_B = nn.Parameter(
self.conv.weight.new_zeros(
(out_channels // self.conv.groups * kernel_size,
r * kernel_size)))
self.scaling = self.lora_alpha / self.r
# Freezing the pre-trained weight matrix
self.conv.weight.requires_grad = False
self.reset_parameters()
self.merged = False
def reset_parameters(self):
self.conv.reset_parameters()
if hasattr(self, 'lora_A'):
# initialize A the same way as the default for nn.Linear and B to zero
nn.init.kaiming_uniform_(self.lora_A, a=math.sqrt(5))
nn.init.zeros_(self.lora_B)
def train(self, mode=True):
super(ConvLoRA, self).train(mode)
if mode:
if self.merge_weights and self.merged:
if self.r > 0:
# Make sure that the weights are not merged
self.conv.weight.data -= (self.lora_B @ self.lora_A).view(
self.conv.weight.shape) * self.scaling
self.merged = False
else:
if self.merge_weights and not self.merged:
if self.r > 0:
# Merge the weights and mark it
self.conv.weight.data += (self.lora_B @ self.lora_A).view(
self.conv.weight.shape) * self.scaling
self.merged = True
def forward(self, x):
if self.r > 0 and not self.merged:
return self.conv._conv_forward(
x, self.conv.weight +
(self.lora_B @ self.lora_A).view(self.conv.weight.shape) *
self.scaling, self.conv.bias)
return self.conv(x)
class Conv2d(ConvLoRA):
def __init__(self, *args, **kwargs):
super(Conv2d, self).__init__(nn.Conv2d, *args, **kwargs)
class Conv1d(ConvLoRA):
def __init__(self, *args, **kwargs):
super(Conv1d, self).__init__(nn.Conv1d, *args, **kwargs)
# Can Extend to other ones like this
class Conv3d(ConvLoRA):
def __init__(self, *args, **kwargs):
super(Conv3d, self).__init__(nn.Conv3d, *args, **kwargs)
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