modeling_lora.py

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._register_lora(num_adaptions)
        for name, param in super().named_parameters():
            if "lora" not in name:
                param.requires_grad_(False)
        self.current_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,
            )
        )

    @property
    def current_task(self):
        return self._task_idx

    @current_task.setter
    def current_task(self, task_idx: Union[None, int]):
        self._task_idx = task_idx
        self.apply(
            partial(LoRAParametrization.select_task_for_layer, task_idx=task_idx)
        )

    def forward(self, *args, **kwargs, current_task: Union[None, int] = -1):
        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:
                yield name, param