jina-bert-flash-implementation / modeling_lora.py
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feat: return from_bert for from_pretrained
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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):
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)
def select_task(self, task=None):
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.select_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._register_lora(num_adaptions)
for name, param in super().named_parameters():
if "lora" not in name:
param.requires_grad_(False)
self.select_task(0)
@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,
)
)
def select_task(self, task_idx: Union[None, int]):
self.apply(
partial(LoRAParametrization.select_task_for_layer, task_idx=task_idx)
)
def forward(self, *args, **kwargs):
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:
yield name, param