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动手学习RAG: 迟交互模型colbert微调实践 bge-m3

article 2024/9/20 2:15:16

动手学习RAG: 向量模型
动手学习RAG: BGE向量模型微调实践]()
动手学习RAG: BCEmbedding 向量模型微调实践]()
动手学习RAG: moka-ai/m3e 模型微调deepspeed与对比学习
动手学习RAG: 大模型向量模型微调 intfloat/e5-mistral-7b-instruct
动手学习RAG：大模型重排模型 bge-reranker-v2-gemma微调
BCE ranking 微调实践]()
GTE向量与排序模型微调实践]()
模型微调中的模型序列长度]()
相似度与温度系数

本文我们来进行ColBERT模型的实践，按惯例，还是以open-retrievals中的代码为蓝本。在RAG兴起之后，ColBERT也获得了更多的关注。ColBERT整体结构和双塔特别相似，但迟交互式也就意味着比起一般ranking模型，交互来的更晚一些。
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1. 准备环境

pip install transformers
pip install open-retrievals

注意安装时是 pip install open-retrievals，但调用时只需要 import retrievals
欢迎关注最新的更新 https://github.com/LongxingTan/open-retrievals

2. 准备数据

还是采用C-MTEB/T2Reranking数据。

每个样本有query, positive, negative。其中query和positive构成正样本对，query和negative构成负样本对

3. 使用m3-ColBERT模型

由于ColBERT作为迟交互式模型，既可以像向量模型一样生成向量，也可以计算相似度。BAAI/bge-m3中的colbert模型是基于XLMRoberta训练而来，因此使用ColBERT可以直接从bge-m3中加载预训练权重。

import transformers
from retrievals import ColBERT
model_name_or_path: str =  'BAAI/bge-m3' 
model = ColBERT.from_pretrained(
    model_name_or_path,
    colbert_dim=1024,    
    use_fp16=True,
    loss_fn=ColbertLoss(use_inbatch_negative=True),
)

model

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生成向量的方法

sentences_1 = ["In 1974, I won the championship in Southeast Asia in my first kickboxing match", "In 1982, I defeated the heavy hitter Ryu Long."]
sentences_2 = ['A dog is chasing car.', 'A man is playing a guitar.']

output_1 = model.encode(sentences_1, normalize_embeddings=True)
print(output_1.shape, output_1)

output_2 = model.encode(sentences_2, normalize_embeddings=True)
print(output_2.shape, output_2)

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计算句子对相似度的方法

sentences = [
    ["In 1974, I won the championship in Southeast Asia in my first kickboxing match", "In 1982, I defeated the heavy hitter Ryu Long."],
    ["In 1974, I won the championship in Southeast Asia in my first kickboxing match", 'A man is playing a guitar.'],
]

scores_list = model.compute_score(sentences)
print(scores_list)

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4. 微调

尝试了两种方法来做，一种是调包自己写代码，一种是采用open-retrievals中的代码写shell脚本。这里我们采用第一种，另外一种方法可参考文章最后番外中的微调

import transformers
from transformers import AutoTokenizer, TrainingArguments, get_cosine_schedule_with_warmup, AdamW
from retrievals import AutoModelForRanking, RerankCollator, RerankTrainDataset, RerankTrainer, ColBERT, RetrievalTrainDataset, ColBertCollator
from retrievals.losses import ColbertLoss
transformers.logging.set_verbosity_error()


model_name_or_path: str = 'BAAI/bge-m3'

learning_rate: float = 1e-5
batch_size: int = 2
epochs: int = 1
output_dir: str = './checkpoints'

train_dataset = RetrievalTrainDataset(
    'C-MTEB/T2Reranking', positive_key='positive', negative_key='negative', dataset_split='dev'
)


tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=False)

data_collator = ColBertCollator(
    tokenizer,
    query_max_length=64,
    document_max_length=128,
    positive_key='positive',
    negative_key='negative',
)
model = ColBERT.from_pretrained(
    model_name_or_path,
    colbert_dim=1024,
    loss_fn=ColbertLoss(use_inbatch_negative=False),
)

optimizer = AdamW(model.parameters(), lr=learning_rate)
num_train_steps = int(len(train_dataset) / batch_size * epochs)
scheduler = get_cosine_schedule_with_warmup(optimizer, num_warmup_steps=0.05 * num_train_steps, num_training_steps=num_train_steps)

training_args = TrainingArguments(
    learning_rate=learning_rate,
    per_device_train_batch_size=batch_size,
    num_train_epochs=epochs,
    output_dir = './checkpoints',
    remove_unused_columns=False,
    gradient_accumulation_steps=8,
    logging_steps=100,

)
trainer = RerankTrainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
    data_collator=data_collator,
)
trainer.optimizer = optimizer
trainer.scheduler = scheduler
trainer.train()

model.save_pretrained(output_dir)

训练过程中会加载BAAI/bge-m3模型权重
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损失函数下降

{'loss': 7.4858, 'grad_norm': 30.484981536865234, 'learning_rate': 4.076305220883534e-06, 'epoch': 0.6024096385542169}
{'loss': 1.18, 'grad_norm': 28.68316650390625, 'learning_rate': 3.072289156626506e-06, 'epoch': 1.2048192771084336}
{'loss': 1.1399, 'grad_norm': 14.203865051269531, 'learning_rate': 2.068273092369478e-06, 'epoch': 1.8072289156626506}
{'loss': 1.1261, 'grad_norm': 24.30337905883789, 'learning_rate': 1.0642570281124499e-06, 'epoch': 2.4096385542168672}
{'train_runtime': 471.8191, 'train_samples_per_second': 33.827, 'train_steps_per_second': 1.055, 'train_loss': 2.4146631079984, 'epoch': 3.0}

5. 评测

在C-MTEB中进行评测。微调前保留10%的数据集作为测试集验证

from datasets import load_dataset

dataset = load_dataset("C-MTEB/T2Reranking", split="dev")
ds = dataset.train_test_split(test_size=0.1, seed=42)

ds_train = ds["train"].filter(
    lambda x: len(x["positive"]) > 0 and len(x["negative"]) > 0
)

ds_train.to_json("t2_ranking.jsonl", force_ascii=False)

微调前的指标：
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微调后的指标：

{
  "dataset_revision": null,
  "mteb_dataset_name": "CustomReranking",
  "mteb_version": "1.1.1",
  "test": {
    "evaluation_time": 221.45,
    "map": 0.6950128151840831,
    "mrr": 0.8193114944390455
  }
}

番外：从语言模型直接训练ColBERT

之前的例子里是从BAAI/bge-m3继续微调，这里再跑一个从hfl/chinese-roberta-wwm-ext训练一个ColBERT模型

注意，从头跑需要设置更大的学习率与更多的epochs

MODEL_NAME='hfl/chinese-roberta-wwm-ext'
TRAIN_DATA="/root/kaggle101/src/open-retrievals/t2/t2_ranking.jsonl"
OUTPUT_DIR="/root/kaggle101/src/open-retrievals/t2/ft_out"

cd /root/open-retrievals/src

torchrun --nproc_per_node 1 \
  --module retrievals.pipelines.rerank \
  --output_dir $OUTPUT_DIR \
  --overwrite_output_dir \
  --model_name_or_path $MODEL_NAME \
  --tokenizer_name $MODEL_NAME \
  --model_type colbert \
  --do_train \
  --data_name_or_path $TRAIN_DATA \
  --positive_key positive \
  --negative_key negative \
  --learning_rate 5e-5 \
  --bf16 \
  --num_train_epochs 5 \
  --per_device_train_batch_size 32 \
  --dataloader_drop_last True \
  --query_max_length 128 \
  --max_length 256 \
  --train_group_size 4 \
  --unfold_each_positive false \
  --save_total_limit 1 \
  --logging_steps 100 \
  --use_inbatch_negative False

微调后指标

{
  "dataset_revision": null,
  "mteb_dataset_name": "CustomReranking",
  "mteb_version": "1.1.1",
  "test": {
    "evaluation_time": 75.38,
    "map": 0.6865308507184888,
    "mrr": 0.8039965986394558
  }
}

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