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[论文阅读] Knowledge Fusion of Large Language Models

article 2025/2/9 3:08:55

Knowledge Fusion of Large Language Models (FuseLLM)

Methodology

整体Pipeline如下图所示
在这里插入图片描述

不同的动物代表不同的LLM。左边第一，第二分别是Ensemble以及Weight Merging方法。最右侧为本文提出的FuseLLM。

Ensemble: 融合多个models的预测结果，比如求加权平均等。
Weight Merging：在权重/参数层面融合，但通常仅限于相同架构的模型。
FuseLLM 主要思想为：融合多个LLMs（可以是不同架构的）的probabilistic matrices，得到Fused Matrix后，喂给Target Model，起到知识蒸馏的作用。

这里面会涉及到一个关键：

不同LLM，使用的Tokenizer可能不同，设置也可能不一样（如 model_max_length ），分词结果可能不一样（比如对同一个句子分词，tokens总数不同），使用的Vocabulary也可能不一样，因此生成的probabilistic matrix在维度上可能有所不同，如何解决对齐问题？这个实际上就是 token alignment 问题，本文中着重描述了解决方案。

Definition of Problem

假设我们有一个语料库 $\mathcal{C}$ ， $K$ 个source LLMs, 对于文本 $\in \mathcal{C}$ ，经过 $K$ 个LLM处理，可以得到对应的概率分布矩阵 probabilistic distribution matrix： $\{\mathbf{P}^{\theta_j}_t\}^K_{j=1}$ ，其中 $\theta_j$ 表示第 $j$ 个LLM的参数。我们要做的就是将这 $K$ 个概率分布矩阵融合，然后送入Target LLM中辅助训练：
$\begin{align} \mathbf{P}_t=\mathbb{F}\mathrm{usion}(\mathbf{P}_t^{\theta_1},\mathbf{P}_t^{\theta_2},\ldots,\mathbf{P}_t^{\theta_K}), \end{align}$
$\mathbf{P}_t$ 即得到的融合概率分布矩阵（Fused Representation Matrix）。

为了将 $\mathbf{P}_t$ 迁移至target model中，我们假设 $\mathbf{Q}_t$ 为其输出的representation matrix，则Knowledge Fusion的训练目标为：
$\begin{align} \mathcal{L}_{\mathrm{Fusion}}=-\mathbb{E}_{t\sim\mathcal{C}}\left[\mathbb{D}(\mathbf{Q}_t,\mathbf{P}_t)\right]. \end{align}$
其中 $\mathbb{D}(\cdot, \cdot)$ 表示差异性函数，具体实现可以是KL散度。
整体的模型损失如下：
$\begin{align}\mathcal{L}=\lambda\mathcal{L}_{\mathrm{CLM}}+(1-\lambda)\mathcal{L}_{\mathrm{Fusion}}.\end{align}$
其中 $\mathcal{L}_{\mathrm{CLM}}$ 表示最原始的ground-truth之间的损失， $\lambda$ 为系数。

实现细节

Token Alignment

我们假设有两个LLM，使用不同的tokenizer。对同一段文本分词，得到的token序列不同，长度也不同：
在这里插入图片描述
如上图，用DeepSeek和TinyLlama各自的分词器分词，得到的结果完全不一样。最终预测的概率分布矩阵也不一样。

Token-Level Alignment

为了解决这个问题，FuseLLM采用基于最小编辑距离Minimal Edit Distance(MinED)的动态规划策略，在token-level实现对齐，以下图为例：
在这里插入图片描述
具体实现的源代码other.py如下：


def dtw(series_1, series_2, norm_func=np.linalg.norm):
    """Use dynamic time wrapping to align to tokenizers, modified from:
    https://github.com/talcs/simpledtw/blob/master/simpledtw.py"""

    """

    Parameters
    ----------
    series_1: List[str]
        blending_input_tokens
    series_2: List[str]
        base_input_tokens
    norm_func: function
        edit distance evaluation between 2 tokens


    Return Values
    ----------
    matches: List[Tuple]
        matched pairs between a base token and a blending token
    matrix[-1, -1]: int 
        the total cost for mapping the two series of tokens
    mappings_series_1: List[List]
        mapping from blending tokens to base tokens
        eg: [0], [1, 2], [3, 4, 5], [6], ...
    mappings_series_2: List[List]
        mapping from base tokens to blending tokens
    matrix: List[int]
        the dtw matrix

    """

    matrix = np.zeros((len(series_1) + 1, len(series_2) + 1))
    matrix[0, :] = np.inf
    matrix[:, 0] = np.inf
    matrix[0, 0] = 0
    for i, vec1 in enumerate(series_1):
        for j, vec2 in enumerate(series_2):
            cost = norm_func(vec1, vec2)
            matrix[i + 1, j + 1] = cost + min(
                matrix[i, j + 1], matrix[i + 1, j], matrix[i, j]
            )
    matrix = matrix[1:, 1:]
    i = matrix.shape[0] - 1
    j = matrix.shape[1] - 1
    matches = []
    mappings_series_1 = [list() for v in range(matrix.shape[0])]
    mappings_series_2 = [list() for v in range(matrix.shape[1])]
    while i > 0 or j > 0:
        matches.append((i, j))
        mappings_series_1[i].append(j)
        mappings_series_2[j].append(i)
        option_diag = matrix[i - 1, j - 1] if i > 0 and j > 0 else np.inf
        option_up = matrix[i - 1, j] if i > 0 else np.inf
        option_left = matrix[i, j - 1] if j > 0 else np.inf
        move = np.argmin([option_diag, option_up, option_left])
        if move == 0:
            i -= 1
            j -= 1
        elif move == 1:
            i -= 1
        else:
            j -= 1
    matches.append((0, 0))
    mappings_series_1[0].append(0)
    mappings_series_2[0].append(0)
    matches.reverse()
    for mp in mappings_series_1:
        mp.reverse()
    for mp in mappings_series_2:
        mp.reverse()

    return matches, matrix[-1, -1], mappings_series_1, mappings_series_2, matrix

Logit-Level Alignment

利用该对齐结果，将不同LLMs得到的representation matrix对齐。关键代码other.py如下：


def transform_step_logits(
    base_model_tokenizer: transformers.tokenization_utils_base.PreTrainedTokenizerBase,
    blending_model_tokenizer: transformers.tokenization_utils_base.PreTrainedTokenizerBase,
    base_model_vocab: Dict[str, int],
    base_model_input_ids: List[int],
    blending_model_input_ids: List[int],
    blending_model_per_step_logits: List[List[float]],
    blending_model_per_step_indices: List[List[int]],
    vocab_align_type: str = "hard",
    blending_to_base_mapping: Dict[str, str] = None,
):
    """Align blending model per step logits & indices with base model."""


    """

    Parameters
    ----------
    base_model_tokenizer: transformers.tokenization_utils_base.PreTrainedTokenizerBase
    blending_model_tokenizer: transformers.tokenization_utils_base.PreTrainedTokenizerBase
    base_model_vocab: Dict[str, int]
        mapping token to id using vocabulary of base model
    base_model_input_ids: List[int]
        ids of base_model_input_tokens
    blending_model_input_ids: List[int]
        ids of blending_model_input_tokens
    blending_model_per_step_logits: List[List[float]]
        logits for each token in blending_model_input_tokens 
    blending_model_per_step_indices: List[List[int]]
        indices corresponding to logits for each token in blending_model_input_tokens 
    vocab_align_type: str = "hard"
    blending_to_base_mapping: Dict[str, str] = None
        mapping each blending token to its corresponding base token 


    Return Values
    ----------
    aligned_blending_model_per_step_logits: List[List[float]]
        aligned logits for each token in base_model_input_tokens for the FuseLLM training
    aligned_blending_model_per_step_indices: List[List[int]]
        aligned indices corresponding aligned logits for each token in base_model_input_tokens for the FuseLLM training. 
        Use the base model vocabulary to look up the token.
    """



    base_model_tokens = base_model_tokenizer.convert_ids_to_tokens(base_model_input_ids)
    blending_model_tokens = blending_model_tokenizer.convert_ids_to_tokens(
        blending_model_input_ids
    )
    base_model_special_token = TOKENIZER_TO_SPECIAL_TOKEN[
        base_model_tokenizer.__class__
    ]
    blending_model_special_token = TOKENIZER_TO_SPECIAL_TOKEN[
        blending_model_tokenizer.__class__
    ]


    def dist_fn(a, b):
        """Calculate editdistance between two tokens, a is from blending model, b is from base model."""
        aa = a.replace(blending_model_special_token, "")
        bb = b.replace(base_model_special_token, "")
        dist = editdistance.eval(aa, bb)
        return dist

    _, _, _, base_to_blending, _ = dtw(
        blending_model_tokens, base_model_tokens, norm_func=dist_fn
    )
    aligned_blending_model_per_step_logits, aligned_blending_model_per_step_indices = (
        [],
        [],
    )
    for i, blending_idx in enumerate(base_to_blending):
        aligned_blending_model_per_step_logit = []
        aligned_blending_model_per_step_index = []
        if len(blending_idx) == 1:  # one base token map to one blending token
            j = blending_idx[0]
            base_token = base_model_tokens[i]
            blending_token = blending_model_tokens[j].replace(
                blending_model_special_token, base_model_special_token
            )
            if (
                (
                    blending_model_tokenizer.__class__
                    == transformers.GPTNeoXTokenizerFast
                    or blending_model_tokenizer.__class__
                    == transformers.GPT2TokenizerFast
                )
                and i == 0
                and base_token.startswith(base_model_special_token)
                and not blending_token.startswith(base_model_special_token)
            ):
                blending_token = (
                    base_model_special_token + blending_token
                )  # special case for mpt
            if vocab_align_type == "hard":
                if (
                    base_token == blending_token
                ):  # find the aligned mapping, use the corresponding logits
                    # the logits and indices at this step
                    for blending_logit, blending_index in zip(
                        blending_model_per_step_logits[j],
                        blending_model_per_step_indices[j],
                    ):
                        # the token corresponds to the logit and indices
                        blending_t = blending_model_tokenizer.convert_ids_to_tokens(
                            [blending_index]
                        )[0].replace(
                            blending_model_special_token, base_model_special_token
                        )
                        if blending_t in base_model_vocab:
                            aligned_index = base_model_vocab[
                                blending_t
                            ]  # the index of the token in base model vocab
                            if (
                                aligned_index
                                not in aligned_blending_model_per_step_index
                            ):
                                aligned_blending_model_per_step_index.append(
                                    aligned_index
                                )
                                aligned_blending_model_per_step_logit.append(
                                    blending_logit
                                )
                else:  # find error aligned mapping, use the one-hot logits
                    aligned_blending_model_per_step_index.append(
                        base_model_vocab[base_token]
                    )
                    aligned_blending_model_per_step_logit.append(1.0)
            elif vocab_align_type == "soft":
                if (base_token == blending_token) or (
                    blending_token in blending_to_base_mapping
                    and base_token == blending_to_base_mapping[blending_token]
                ):  # find the aligned mapping, use the corresponding logits
                    # the logits and indices at this step
                    for blending_logit, blending_index in zip(
                        blending_model_per_step_logits[j],
                        blending_model_per_step_indices[j],
                    ):
                        # the token corresponds to the logit and indices
                        blending_t = blending_model_tokenizer.convert_ids_to_tokens(
                            [blending_index]
                        )[0].replace(
                            blending_model_special_token, base_model_special_token
                        )
                        blending_t = blending_to_base_mapping[blending_t]
                        if blending_t in base_model_vocab:
                            aligned_index = base_model_vocab[
                                blending_t
                            ]  # the index of the token in base model vocab
                            if (
                                aligned_index
                                not in aligned_blending_model_per_step_index
                            ):
                                aligned_blending_model_per_step_index.append(
                                    aligned_index
                                )
                                aligned_blending_model_per_step_logit.append(
                                    blending_logit
                                )
                        else:
                            logger.warning(
                                f"blending_t: {blending_t} not in base_model_vocab!"
                            )
                else:  # find error aligned mapping, use the one-hot logits
                    aligned_blending_model_per_step_index.append(
                        base_model_vocab[base_token]
                    )
                    aligned_blending_model_per_step_logit.append(1.0)
            else:
                logger.warning(
                    f"The vocab_align_type: '{vocab_align_type}' is not support!"
                )
                raise NotImplementedError
        else:  # one base token map to multiple blending token, in this case only fit base token. use the one-hot logits
            base_token = base_model_tokens[i]
            aligned_blending_model_per_step_index.append(base_model_vocab[base_token])
            aligned_blending_model_per_step_logit.append(1.0)
        aligned_blending_model_per_step_indices.append(
            aligned_blending_model_per_step_index
        )
        aligned_blending_model_per_step_logits.append(
            aligned_blending_model_per_step_logit
        )
    return (
        aligned_blending_model_per_step_logits,
        aligned_blending_model_per_step_indices,
    )

Fusion Strategies:

得到对齐的representation matrix以后，由于不同的LLM具有不同的性能，可以使用概率分布矩阵与ground-truth之间的交叉熵损失(CE loss)评估LLM的优劣，再根据此判断选择哪些LLM参与知识融合。CE loss越低，证明模型效果更好。具体而言，作者提出了两种Fusion Strategy:

MinCE: 仅选择CE loss最小的representation matrix用于知识融合。
AvgCE: 基于各个模型的CE loss，采用多个representation matrices的加权平均，用于知识融合。

整体的算法流程如下：
在这里插入图片描述

注：这里Eq.5实际是本文中上述的Eq.3

一些思考

本文的思路是将多个LLMs输出的概率分布矩阵视为知识，将知识融合后，送入target LLM进行训练，以达到融合多种模型知识，提升目标模型性能的目的。但在实际的实现当中我们会发现，logit-level的alignment，要么是直接采用blending_model_per_step_logits/indices，要么直接用ground-truth one-hot作为融合后的知识，而没有充分评估logit-level中，blending/base_model_per_step_logits之间的差异性。为此，Probabilistic Token Alignment for Large Language Model Fusion提出采用Probabilistic Token Alignment方法，在logit-level实现alignment。

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