import math
import os
from functools import partial
from typing import Iterator, Optional, Tuple, Union
import torch
import torch.nn.utils.parametrize as parametrize
from torch import nn
from torch.nn import Parameter
from transformers import PretrainedConfig
from .modeling_bert import BertModel, BertPreTrainedModel, JinaBertConfig
def initialized_weights(
shape: Tuple[int], num_adaptions: int, init: str = "kaiming"
) -> torch.Tensor:
weight_data = []
for _ in range(num_adaptions):
new_adaption = torch.zeros(shape)
if init == "kaiming":
nn.init.kaiming_uniform_(new_adaption, a=math.sqrt(5))
elif init == "normal":
nn.init.normal_(new_adaption)
else:
raise NotImplementedError
weight_data.append(new_adaption)
return torch.stack(weight_data, dim=0)
class LoRAParametrization(nn.Module):
"""
This LoRA implementation was inspired by https://github.com/cccntu/minLoRA
The MIT License (MIT) Copyright (c) 2020 Andrej Karpathy
Permission is hereby granted, free of charge, to any person obtaining a copy of this software
and associated documentation files (the "Software"), to deal in the Software without restriction,
including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense,
and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so,
subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial
portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT
LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
"""
def __init__(
self,
fan_in: int,
fan_out: int,
layer_type: str = "linear",
num_adaptions: int = 1,
rank: int = 4,
lora_dropout_p: float = 0.0,
lora_alpha: float = 1,
):
super().__init__()
# if weight is stored as (fan_out, fan_in), the memory layout of A & B follows (W + BA)x
# otherwise, it's x(W + AB). This allows us to tie the weights between linear layers and embeddings
fan_in_fan_out = layer_type == "embedding"
self.swap = (lambda x: (x[1], x[0])) if fan_in_fan_out else (lambda x: x)
if layer_type == "linear":
self.lora_A = nn.Parameter(
initialized_weights((rank, fan_in), num_adaptions, init="kaiming")
)
self.lora_B = nn.Parameter(torch.zeros((num_adaptions, fan_out, rank)))
elif layer_type == "embedding":
self.lora_A = nn.Parameter(torch.zeros((num_adaptions, fan_in, rank)))
self.lora_B = nn.Parameter(
initialized_weights(
(rank, fan_out), num_adaptions=num_adaptions, init="normal"
)
)
else:
raise NotImplementedError
self.lora_alpha, self.rank = lora_alpha, rank
self.scaling = lora_alpha / rank
self.lora_dropout = (
nn.Dropout(p=lora_dropout_p) if lora_dropout_p > 0 else lambda x: x
)
self.dropout_fn = self._dropout if lora_dropout_p > 0 else lambda x: x
self.register_buffer(
"lora_dropout_mask",
torch.ones(self.swap((1, fan_in)), dtype=self.lora_A.dtype),
persistent=False,
)
self.forward_fn = lambda x: x
self.current_task = None
def _dropout(self, A):
# to mimic the original implementation: A @ dropout(x), we do (A * dropout(ones)) @ x
return A * self.lora_dropout(self.lora_dropout_mask)
def lora_forward(self, X):
assert self.current_task is not None
return (
X
+ torch.matmul(
*self.swap(
(
self.lora_B[self.current_task],
self.dropout_fn(self.lora_A[self.current_task]),
)
)
).view(X.shape)
* self.scaling
)
def forward(self, X):
return self.forward_fn(X)
@property
def current_task(self):
return self._current_task
@current_task.setter
def current_task(self, task: Union[None, int]):
self._current_task = task
if task is None:
self.forward_fn = lambda x: x
else:
self.forward_fn = self.lora_forward
@classmethod
def from_linear(
cls,
layer: nn.Module,
num_adaptions: int = 1,
rank: int = 4,
lora_dropout_p: float = 0.0,
lora_alpha: int = 1,
):
assert isinstance(layer, nn.Linear)
fan_out, fan_in = layer.weight.shape
return cls(
fan_in,
fan_out,
num_adaptions=num_adaptions,
layer_type="linear",
rank=rank,
lora_dropout_p=lora_dropout_p,
lora_alpha=lora_alpha,
)
@classmethod
def from_embedding(
cls, layer, num_adaptions=1, rank=4, lora_dropout_p=0.0, lora_alpha=1
):
assert isinstance(layer, nn.Embedding)
fan_in, fan_out = layer.weight.shape
return cls(
fan_in,
fan_out,
num_adaptions=num_adaptions,
layer_type="embedding",
rank=rank,
lora_dropout_p=lora_dropout_p,
lora_alpha=lora_alpha,
)
@classmethod
def add_to_layer(
cls, layer, num_adaptions=1, rank=4, lora_dropout_p=0.0, lora_alpha=1
):
if isinstance(layer, nn.Linear):
parametrize.register_parametrization(
layer,
"weight",
cls.from_linear(
layer,
num_adaptions=num_adaptions,
rank=rank,
lora_dropout_p=lora_dropout_p,
lora_alpha=lora_alpha,
),
)
elif isinstance(layer, nn.Embedding):
parametrize.register_parametrization(
layer,
"weight",
cls.from_embedding(
layer,
num_adaptions=num_adaptions,
rank=rank,
lora_dropout_p=lora_dropout_p,
lora_alpha=lora_alpha,
),
)
@classmethod
def select_task_for_layer(cls, layer: nn.Module, task_idx: Optional[int] = None):
if isinstance(layer, LoRAParametrization):
layer.current_task = task_idx
class BertLoRA(BertPreTrainedModel):
def __init__(self, config: JinaBertConfig, bert: Optional[BertModel] = None, add_pooling_layer=True, num_adaptions=1):
super().__init__(config)
if bert is None:
self.bert = BertModel(config, add_pooling_layer=add_pooling_layer)
else:
self.bert = bert
self._num_adaptions = num_adaptions
self._register_lora(num_adaptions)
self.main_params_trainable = False
self.current_task = 0
@property
def main_params_trainable(self):
return self._main_params_trainable
@main_params_trainable.setter
def main_params_trainable(self, val):
self._main_params_trainable = val
for name, param in super().named_parameters():
if "lora" not in name:
param.requires_grad_(val)
@classmethod
def from_bert(cls, *args, num_adaptions=1, **kwargs):
bert = BertModel.from_pretrained(*args, **kwargs)
config = JinaBertConfig.from_pretrained(*args, **kwargs)
return cls(config, bert=bert, num_adaptions=num_adaptions)
@classmethod
def from_pretrained(
cls,
pretrained_model_name_or_path: Optional[Union[str, os.PathLike]],
*model_args,
config: Optional[Union[PretrainedConfig, str, os.PathLike]] = None,
cache_dir: Optional[Union[str, os.PathLike]] = None,
ignore_mismatched_sizes: bool = False,
force_download: bool = False,
local_files_only: bool = False,
token: Optional[Union[str, bool]] = None,
revision: str = "main",
use_safetensors: bool = None,
**kwargs,
):
# TODO: choose between from_bert and super().from_pretrained
return cls.from_bert(pretrained_model_name_or_path)
def _register_lora(self, num_adaptions=1, rank=4, lora_dropout_p=0.0, lora_alpha=1):
self.apply(
partial(
LoRAParametrization.add_to_layer,
num_adaptions=num_adaptions,
rank=rank,
lora_dropout_p=lora_dropout_p,
lora_alpha=lora_alpha,
)
)
@property
def current_task(self):
return self._task_idx
@current_task.setter
def current_task(self, task_idx: Union[None, int]):
assert task_idx is None or 0 <= task_idx < self._num_adaptions
self._task_idx = task_idx
self.apply(
partial(LoRAParametrization.select_task_for_layer, task_idx=task_idx)
)
def forward(self, *args, current_task: Union[None, int] = -1, **kwargs):
if current_task is None or current_task >= 0:
self.current_task = current_task
return self.bert(*args, **kwargs)
def parameters(self, recurse: bool = True) -> Iterator[Parameter]:
for _, param in self.named_parameters(recurse=recurse):
yield param
def named_parameters(
self, prefix: str = "", recurse: bool = True, remove_duplicate: bool = True
) -> Iterator[Tuple[str, Parameter]]:
for name, param in super().named_parameters(
prefix=prefix, recurse=recurse, remove_duplicate=remove_duplicate
):
if "lora" in name or self.main_params_trainable:
yield name, param