# ------------------------------------------------------------------------------------------
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# Copyright (c) Microsoft Corporation. All rights reserved.
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# Licensed under the MIT License (MIT). See LICENSE in the repo root for license information.
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# ------------------------------------------------------------------------------------------
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import math
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from typing import Optional, List
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class LoRALayer():
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def __init__(
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self,
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r: int,
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lora_alpha: int,
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lora_dropout: float,
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merge_weights: bool,
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):
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self.r = r
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self.lora_alpha = lora_alpha
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# Optional dropout
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if lora_dropout > 0.:
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self.lora_dropout = nn.Dropout(p=lora_dropout)
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else:
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self.lora_dropout = lambda x: x
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# Mark the weight as unmerged
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self.merged = False
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self.merge_weights = merge_weights
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class Embedding(nn.Embedding, LoRALayer):
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# LoRA implemented in a dense layer
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def __init__(
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self,
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num_embeddings: int,
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embedding_dim: int,
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r: int = 0,
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lora_alpha: int = 1,
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merge_weights: bool = True,
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**kwargs
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):
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nn.Embedding.__init__(self, num_embeddings, embedding_dim, **kwargs)
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LoRALayer.__init__(self, r=r, lora_alpha=lora_alpha, lora_dropout=0,
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merge_weights=merge_weights)
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# Actual trainable parameters
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if r > 0:
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self.lora_A = nn.Parameter(self.weight.new_zeros((r, num_embeddings)))
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self.lora_B = nn.Parameter(self.weight.new_zeros((embedding_dim, r)))
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self.scaling = self.lora_alpha / self.r
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# Freezing the pre-trained weight matrix
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self.weight.requires_grad = False
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self.reset_parameters()
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def reset_parameters(self):
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nn.Embedding.reset_parameters(self)
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if hasattr(self, 'lora_A'):
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# initialize A the same way as the default for nn.Linear and B to zero
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nn.init.zeros_(self.lora_A)
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nn.init.normal_(self.lora_B)
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def train(self, mode: bool = True):
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nn.Embedding.train(self, mode)
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if mode:
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if self.merge_weights and self.merged:
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# Make sure that the weights are not merged
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if self.r > 0:
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self.weight.data -= (self.lora_B @ self.lora_A).transpose(0, 1) * self.scaling
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self.merged = False
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else:
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if self.merge_weights and not self.merged:
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# Merge the weights and mark it
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if self.r > 0:
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self.weight.data += (self.lora_B @ self.lora_A).transpose(0, 1) * self.scaling
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self.merged = True
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def forward(self, x: torch.Tensor):
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if self.r > 0 and not self.merged:
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result = nn.Embedding.forward(self, x)
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after_A = F.embedding(
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x, self.lora_A.transpose(0, 1), self.padding_idx, self.max_norm,
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self.norm_type, self.scale_grad_by_freq, self.sparse
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)
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result += (after_A @ self.lora_B.transpose(0, 1)) * self.scaling
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return result
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else:
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return nn.Embedding.forward(self, x)
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class Linear(nn.Linear, LoRALayer):
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# LoRA implemented in a dense layer
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def __init__(
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self,
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in_features: int,
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out_features: int,
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r: int = 0,
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lora_alpha: int = 1,
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lora_dropout: float = 0.,
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fan_in_fan_out: bool = False, # Set this to True if the layer to replace stores weight like (fan_in, fan_out)
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merge_weights: bool = True,
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**kwargs
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):
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nn.Linear.__init__(self, in_features, out_features, **kwargs)
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LoRALayer.__init__(self, r=r, lora_alpha=lora_alpha, lora_dropout=lora_dropout,
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merge_weights=merge_weights)
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self.fan_in_fan_out = fan_in_fan_out
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# Actual trainable parameters
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if r > 0:
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self.lora_A = nn.Parameter(self.weight.new_zeros((r, in_features)))
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self.lora_B = nn.Parameter(self.weight.new_zeros((out_features, r)))
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self.scaling = self.lora_alpha / self.r
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# Freezing the pre-trained weight matrix
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self.weight.requires_grad = False
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self.reset_parameters()
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if fan_in_fan_out:
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self.weight.data = self.weight.data.transpose(0, 1)
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def reset_parameters(self):
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nn.Linear.reset_parameters(self)
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if hasattr(self, 'lora_A'):
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# initialize A the same way as the default for nn.Linear and B to zero
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nn.init.kaiming_uniform_(self.lora_A, a=math.sqrt(5))
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nn.init.zeros_(self.lora_B)
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def train(self, mode: bool = True):
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def T(w):
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return w.transpose(0, 1) if self.fan_in_fan_out else w
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nn.Linear.train(self, mode)
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if mode:
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if self.merge_weights and self.merged:
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# Make sure that the weights are not merged
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if self.r > 0:
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self.weight.data -= T(self.lora_B @ self.lora_A) * self.scaling
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self.merged = False
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else:
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if self.merge_weights and not self.merged:
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# Merge the weights and mark it
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if self.r > 0:
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self.weight.data += T(self.lora_B @ self.lora_A) * self.scaling
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self.merged = True
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def forward(self, x: torch.Tensor):
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def T(w):
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return w.transpose(0, 1) if self.fan_in_fan_out else w
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if self.r > 0 and not self.merged:
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result = F.linear(x, T(self.weight), bias=self.bias)
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result += (self.lora_dropout(x) @ self.lora_A.transpose(0, 1) @ self.lora_B.transpose(0, 1)) * self.scaling
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return result
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else:
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return F.linear(x, T(self.weight), bias=self.bias)
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class MergedLinear(nn.Linear, LoRALayer):
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# LoRA implemented in a dense layer
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def __init__(
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self,
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in_features: int,
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out_features: int,
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r: int = 0,
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lora_alpha: int = 1,
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lora_dropout: float = 0.,
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enable_lora: List[bool] = [False],
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fan_in_fan_out: bool = False,
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merge_weights: bool = True,
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**kwargs
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):
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nn.Linear.__init__(self, in_features, out_features, **kwargs)
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LoRALayer.__init__(self, r=r, lora_alpha=lora_alpha, lora_dropout=lora_dropout,
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merge_weights=merge_weights)
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assert out_features % len(enable_lora) == 0, \
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'The length of enable_lora must divide out_features'
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self.enable_lora = enable_lora
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self.fan_in_fan_out = fan_in_fan_out
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# Actual trainable parameters
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if r > 0 and any(enable_lora):
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self.lora_A = nn.Parameter(
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self.weight.new_zeros((r * sum(enable_lora), in_features)))
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self.lora_B = nn.Parameter(
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self.weight.new_zeros((out_features // len(enable_lora) * sum(enable_lora), r))
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) # weights for Conv1D with groups=sum(enable_lora)
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self.scaling = self.lora_alpha / self.r
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# Freezing the pre-trained weight matrix
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self.weight.requires_grad = False
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# Compute the indices
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self.lora_ind = self.weight.new_zeros(
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(out_features, ), dtype=torch.bool
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).view(len(enable_lora), -1)
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self.lora_ind[enable_lora, :] = True
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self.lora_ind = self.lora_ind.view(-1)
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self.reset_parameters()
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if fan_in_fan_out:
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self.weight.data = self.weight.data.transpose(0, 1)
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def reset_parameters(self):
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nn.Linear.reset_parameters(self)
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if hasattr(self, 'lora_A'):
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# initialize A the same way as the default for nn.Linear and B to zero
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nn.init.kaiming_uniform_(self.lora_A, a=math.sqrt(5))
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nn.init.zeros_(self.lora_B)
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def zero_pad(self, x):
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result = x.new_zeros((*x.shape[:-1], self.out_features))
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result = result.view(-1, self.out_features)
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result[:, self.lora_ind] = x.reshape(
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-1, self.out_features // len(self.enable_lora) * sum(self.enable_lora)
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)
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return result.view((*x.shape[:-1], self.out_features))
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def train(self, mode: bool = True):
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def T(w):
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return w.transpose(0, 1) if self.fan_in_fan_out else w
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nn.Linear.train(self, mode)
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if mode:
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if self.merge_weights and self.merged:
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# Make sure that the weights are not merged
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if self.r > 0 and any(self.enable_lora):
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delta_w = F.conv1d(
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self.lora_A.data.unsqueeze(0),
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self.lora_B.data.unsqueeze(-1),
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groups=sum(self.enable_lora)
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).squeeze(0)
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self.weight.data -= self.zero_pad(T(delta_w * self.scaling))
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self.merged = False
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else:
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if self.merge_weights and not self.merged:
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# Merge the weights and mark it
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if self.r > 0 and any(self.enable_lora):
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delta_w = F.conv1d(
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self.lora_A.data.unsqueeze(0),
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self.lora_B.data.unsqueeze(-1),
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groups=sum(self.enable_lora)
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).squeeze(0)
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self.weight.data += self.zero_pad(T(delta_w * self.scaling))
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self.merged = True
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def forward(self, x: torch.Tensor):
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def T(w):
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return w.transpose(0, 1) if self.fan_in_fan_out else w
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if self.merged:
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return F.linear(x, T(self.weight), bias=self.bias)
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else:
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result = F.linear(x, T(self.weight), bias=self.bias)
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if self.r > 0:
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after_A = F.linear(self.lora_dropout(x), self.lora_A)
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after_B = F.conv1d(
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after_A.transpose(-2, -1),
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self.lora_B.unsqueeze(-1),
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groups=sum(self.enable_lora)
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).transpose(-2, -1)
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result += self.zero_pad(after_B) * self.scaling
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return result
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class ConvLoRA(nn.Module, LoRALayer):
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def __init__(self, conv_module, in_channels, out_channels, kernel_size, r=0, lora_alpha=1, lora_dropout=0., merge_weights=True, **kwargs):
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super(ConvLoRA, self).__init__()
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self.conv = conv_module(in_channels, out_channels, kernel_size, **kwargs)
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LoRALayer.__init__(self, r=r, lora_alpha=lora_alpha, lora_dropout=lora_dropout, merge_weights=merge_weights)
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assert isinstance(kernel_size, int)
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# Actual trainable parameters
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if r > 0:
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self.lora_A = nn.Parameter(
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self.conv.weight.new_zeros((r * kernel_size, in_channels * kernel_size))
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)
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self.lora_B = nn.Parameter(
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self.conv.weight.new_zeros((out_channels//self.conv.groups*kernel_size, r*kernel_size))
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)
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self.scaling = self.lora_alpha / self.r
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# Freezing the pre-trained weight matrix
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self.conv.weight.requires_grad = False
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self.reset_parameters()
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self.merged = False
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def reset_parameters(self):
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self.conv.reset_parameters()
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if hasattr(self, 'lora_A'):
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# initialize A the same way as the default for nn.Linear and B to zero
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nn.init.kaiming_uniform_(self.lora_A, a=math.sqrt(5))
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nn.init.zeros_(self.lora_B)
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def train(self, mode=True):
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super(ConvLoRA, self).train(mode)
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if mode:
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if self.merge_weights and self.merged:
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if self.r > 0:
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# Make sure that the weights are not merged
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self.conv.weight.data -= (self.lora_B @ self.lora_A).view(self.conv.weight.shape) * self.scaling
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self.merged = False
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else:
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if self.merge_weights and not self.merged:
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if self.r > 0:
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# Merge the weights and mark it
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self.conv.weight.data += (self.lora_B @ self.lora_A).view(self.conv.weight.shape) * self.scaling
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self.merged = True
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def forward(self, x):
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if self.r > 0 and not self.merged:
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return self.conv._conv_forward(
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x,
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self.conv.weight + (self.lora_B @ self.lora_A).view(self.conv.weight.shape) * self.scaling,
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self.conv.bias
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)
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return self.conv(x)
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class Conv2d(ConvLoRA):
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def __init__(self, *args, **kwargs):
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super(Conv2d, self).__init__(nn.Conv2d, *args, **kwargs)
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class Conv1d(ConvLoRA):
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def __init__(self, *args, **kwargs):
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super(Conv1d, self).__init__(nn.Conv1d, *args, **kwargs)
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# Can Extend to other ones like this
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class Conv3d(ConvLoRA):
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def __init__(self, *args, **kwargs):
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super(Conv3d, self).__init__(nn.Conv3d, *args, **kwargs)
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