import math
import os
import warnings
from functools import partial
from typing import Iterator, List, Optional, Tuple, Union
import numpy as np
import torch
import torch.nn.utils.parametrize as parametrize
from torch import nn
from torch.nn import Parameter
from torch.nn import functional as F
from transformers import PretrainedConfig
from .modeling_xlm_roberta import XLMRobertaFlashConfig, XLMRobertaModel, XLMRobertaPreTrainedModel
LORA_NO_UPDATE = '__lora_no_update__'
def initialized_weights(
shape: Tuple[int], num_adaptations: int, init: str = "kaiming"
) -> torch.Tensor:
weight_data = []
for _ in range(num_adaptations):
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_adaptations: int = 1,
rank: int = 4,
dropout_p: float = 0.0,
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_adaptations, init="kaiming")
)
self.lora_B = nn.Parameter(torch.zeros((num_adaptations, fan_out, rank)))
elif layer_type == "embedding":
self.lora_A = nn.Parameter(torch.zeros((num_adaptations, fan_in, rank)))
self.lora_B = nn.Parameter(
initialized_weights(
(rank, fan_out), num_adaptations=num_adaptations, init="normal"
)
)
else:
raise NotImplementedError
self.lora_alpha, self.rank = alpha, rank
self.scaling = alpha / rank
self.lora_dropout = nn.Dropout(p=dropout_p) if dropout_p > 0 else lambda x: x
self.dropout_fn = self._dropout if 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,
)
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, current_task):
return (
X
+ torch.matmul(
*self.swap(
(
self.lora_B[current_task],
self.dropout_fn(self.lora_A[current_task]),
)
)
).view(X.shape)
* self.scaling
)
def forward(self, X):
return X
@classmethod
def from_linear(
cls,
layer: nn.Module,
num_adaptations: int,
rank: int,
dropout_p: float,
alpha: float,
):
assert isinstance(layer, nn.Linear)
fan_out, fan_in = layer.weight.shape
return cls(
fan_in,
fan_out,
num_adaptations=num_adaptations,
layer_type="linear",
rank=rank,
dropout_p=dropout_p,
alpha=alpha,
)
@classmethod
def from_embedding(
cls,
layer: nn.Module,
num_adaptations: int,
rank: int,
dropout_p: float,
alpha: float,
):
assert isinstance(layer, nn.Embedding)
fan_in, fan_out = layer.weight.shape
return cls(
fan_in,
fan_out,
num_adaptations=num_adaptations,
layer_type="embedding",
rank=rank,
dropout_p=dropout_p,
alpha=alpha,
)
@classmethod
def add_to_layer(
cls,
layer: nn.Module,
num_adaptations: int,
rank: int,
dropout_p: float,
alpha: float,
adaptation_map: dict,
):
if isinstance(layer, nn.Linear):
parametrize.register_parametrization(
layer,
"weight",
cls.from_linear(
layer,
num_adaptations=num_adaptations,
rank=rank,
dropout_p=dropout_p,
alpha=alpha,
),
)
def new_forward(self, input, task, residual=False):
task_idx = adaptation_map[task] if task else None
if task_idx is not None:
weights = self.parametrizations.weight[0].lora_forward(self.weight, current_task=task_idx)
else:
weights = self.weight
out = F.linear(input, weights, self.bias)
if residual:
return out, input
return out
layer.forward = new_forward.__get__(layer, layer.__class__)
elif isinstance(layer, nn.Embedding):
parametrize.register_parametrization(
layer,
"weight",
cls.from_embedding(
layer,
num_adaptations=num_adaptations,
rank=rank,
dropout_p=dropout_p,
alpha=alpha,
),
)
def new_forward(self, input, task):
task_idx = adaptation_map[task] if task else None
if task_idx is not None:
weights = self.parametrizations.weight[0].lora_forward(self.weight, current_task=task_idx)
else:
weights = self.weight
out = F.embedding(
input, weights, self.padding_idx, self.max_norm,
self.norm_type, self.scale_grad_by_freq, self.sparse)
return out
layer.forward = new_forward.__get__(layer, layer.__class__)
class XLMRobertaLoRA(XLMRobertaPreTrainedModel):
def __init__(
self,
config: XLMRobertaFlashConfig,
roberta: Optional[XLMRobertaModel] = None
):
super().__init__(config)
if roberta is None:
self.roberta = XLMRobertaModel(config)
else:
self.roberta = roberta
self._lora_adaptations = config.lora_adaptations
if (
not isinstance(self._lora_adaptations, list)
or len(self._lora_adaptations) < 1
):
raise ValueError(
f'`lora_adaptations` must be a list and contain at least one element'
)
self._adaptation_map = {
name: idx for idx, name in enumerate(self._lora_adaptations)
}
self._rank = config.lora_rank
self._dropout_p = config.lora_dropout_p
self._alpha = config.lora_alpha
self._register_lora(
num_adaptations=len(self._lora_adaptations),
rank=self._rank,
dropout_p=self._dropout_p,
alpha=self._alpha,
)
self.main_params_trainable = config.lora_main_params_trainable
@property
def main_params_trainable(self):
return self._main_params_trainable
@main_params_trainable.setter
def main_params_trainable(self, val: bool):
"""Whether the main parameters (i.e. those that are not LoRA) should be trainable.
This method sets the `requires_grad_` attribute of the main weights
and controls which parameters are returned in `self.parameters()`.
:param val: Whether or not to make the parameters trainable.
:return: None
"""
self._main_params_trainable = val
for name, param in super().named_parameters():
if "lora" not in name:
param.requires_grad_(val)
@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,
):
config = XLMRobertaFlashConfig.from_pretrained(
pretrained_model_name_or_path, *model_args, **kwargs
)
if config.load_trained_adapters:
return super().from_pretrained(
pretrained_model_name_or_path, *model_args, **kwargs
)
else:
roberta = XLMRobertaModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
return cls(config, roberta=roberta)
def _register_lora(self, num_adaptations, rank, dropout_p, alpha):
self.apply(
partial(
LoRAParametrization.add_to_layer,
num_adaptations=num_adaptations,
rank=rank,
dropout_p=dropout_p,
alpha=alpha,
adaptation_map=self._adaptation_map,
)
)
def forward(self, *args, **kwargs):
return self.roberta(*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
@torch.inference_mode()
def encode(
self,
*args,
task: Optional[str] = None,
**kwargs,
) -> Union[List[torch.Tensor], np.ndarray, torch.Tensor]:
"""
Computes sentence embeddings
task(`str`, *optional*, defaults to `LORA_NO_UPDATE`):
Specifies the task for which the encoding is intended. This parameter controls the
use of specialized LoRA adapters that are tuned for specific tasks. If `task` is set
to `LORA_NO_UPDATE`, there will be no update to the current task, retaining the
existing adapter configuration. If `task` is explicitly set to `None`, all LoRA
adapters are disabled, and the model reverts to its original, general-purpose weights.
If `task` is set to a specific LoRA adaptation, that adaptation is activated.
"""
if task and task not in self._lora_adaptations:
raise ValueError(
f"Unsupported task '{task}'. "
f"Supported tasks are: {', '.join(self.config.lora_adaptations)}."
f"Alternatively, don't pass the `task` argument to disable LoRA."
)
return self.roberta.encode(*args, **kwargs)