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动手学习RAG: moka-ai/m3e 模型微调deepspeed与对比学习

article 2024/11/17 4:26:24

动手学习RAG: 向量模型
动手学习RAG：迟交互模型colbert微调实践 bge-m3

1. 环境准备

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. 使用M3E模型

from retrievals import AutoModelForEmbedding

embedder = AutoModelForEmbedding.from_pretrained('moka-ai/m3e-base', pooling_method='mean')
embedder

请添加图片描述

sentences = [
    '* Moka 此文本嵌入模型由 MokaAI 训练并开源，训练脚本使用 uniem',
    '* Massive 此文本嵌入模型通过**千万级**的中文句对数据集进行训练',
    '* Mixed 此文本嵌入模型支持中英双语的同质文本相似度计算，异质文本检索等功能，未来还会支持代码检索，ALL in one'
]

embeddings = embedder.encode(sentences)

for sentence, embedding in zip(sentences, embeddings):
    print("Sentence:", sentence)
    print("Embedding:", embedding)
    print("")

请添加图片描述

3. deepspeed 微调m3e模型

数据仍然采用之前介绍的t2-ranking数据集

deepspeed配置保存为 ds_zero2_no_offload.json. 不过虽然设置了zero2，这里我只用了一张卡. 但deepspeed也很容易扩展到多卡，或多机多卡
- 关于deepspeed的分布式设置，可参考Tranformer分布式特辑

{
    "fp16": {
        "enabled": "auto",
        "loss_scale": 0,
        "loss_scale_window": 100,
        "initial_scale_power": 16,
        "hysteresis": 2,
        "min_loss_scale": 1e-10
    },

    "zero_optimization": {
        "stage": 2,
        "allgather_partitions": true,
        "allgather_bucket_size": 1e8,
        "overlap_comm": true,
        "reduce_scatter": true,
        "reduce_bucket_size": 1e8,
        "contiguous_gradients": true
    },

    "gradient_accumulation_steps": "auto",
    "gradient_clipping": "auto",
    "steps_per_print": 2000,
    "train_batch_size": "auto",
    "train_micro_batch_size_per_gpu": "auto",
    "wall_clock_breakdown": false
}

这里稍微修改了open-retrievals这里的代码，主要是修改了导入为包的导入，而不是相对引用。保存文件为embed.py

"""Embedding fine tune pipeline"""

import logging
import os
import pickle
from dataclasses import dataclass, field
from pathlib import Path
from typing import List, Optional

import torch
from torch.utils.data import DataLoader
from transformers import AutoTokenizer, HfArgumentParser, TrainingArguments, set_seed

from retrievals import (
    EncodeCollator,
    EncodeDataset,
    PairCollator,
    RetrievalTrainDataset,
    TripletCollator,
)
from retrievals.losses import AutoLoss, InfoNCE, SimCSE, TripletLoss
from retrievals.models.embedding_auto import AutoModelForEmbedding
from retrievals.trainer import RetrievalTrainer

# os.environ["WANDB_LOG_MODEL"] = "false"
logger = logging.getLogger(__name__)


@dataclass
class ModelArguments:
    model_name_or_path: str = field(
        metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
    )
    config_name: Optional[str] = field(
        default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
    )
    tokenizer_name: Optional[str] = field(
        default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
    )
    cache_dir: Optional[str] = field(
        default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}
    )
    causal_lm: bool = field(default=False, metadata={'help': "Whether the model is a causal lm or not"})
    lora_path: Optional[str] = field(default=None, metadata={'help': "Lora adapter save path"})


@dataclass
class DataArguments:
    data_name_or_path: str = field(default=None, metadata={"help": "Path to train data"})
    train_group_size: int = field(default=2)
    unfold_each_positive: bool = field(default=False)
    query_max_length: int = field(
        default=32,
        metadata={
            "help": "The maximum total input sequence length after tokenization for passage. Sequences longer "
            "than this will be truncated, sequences shorter will be padded."
        },
    )
    document_max_length: int = field(
        default=128,
        metadata={
            "help": "The maximum total input sequence length after tokenization for passage. Sequences longer "
            "than this will be truncated, sequences shorter will be padded."
        },
    )
    query_instruction: str = field(default=None, metadata={"help": "instruction for query"})
    document_instruction: str = field(default=None, metadata={"help": "instruction for document"})
    query_key: str = field(default=None)
    positive_key: str = field(default='positive')
    negative_key: str = field(default='negative')
    is_query: bool = field(default=False)
    encoding_save_file: str = field(default='embed.pkl')

    def __post_init__(self):
        # self.data_name_or_path = 'json'
        self.dataset_split = 'train'
        self.dataset_language = 'default'

        if self.data_name_or_path is not None:
            if not os.path.isfile(self.data_name_or_path) and not os.path.isdir(self.data_name_or_path):
                info = self.data_name_or_path.split('/')
                self.dataset_split = info[-1] if len(info) == 3 else 'train'
                self.data_name_or_path = "/".join(info[:-1]) if len(info) == 3 else '/'.join(info)
                self.dataset_language = 'default'
                if ':' in self.data_name_or_path:
                    self.data_name_or_path, self.dataset_language = self.data_name_or_path.split(':')


@dataclass
class RetrieverTrainingArguments(TrainingArguments):
    train_type: str = field(default='pairwise', metadata={'help': "train type of point, pair, or list"})
    negatives_cross_device: bool = field(default=False, metadata={"help": "share negatives across devices"})
    temperature: Optional[float] = field(default=0.02)
    fix_position_embedding: bool = field(
        default=False, metadata={"help": "Freeze the parameters of position embeddings"}
    )
    pooling_method: str = field(default='cls', metadata={"help": "the pooling method, should be cls or mean"})
    normalized: bool = field(default=True)
    loss_fn: str = field(default='infonce')
    use_inbatch_negative: bool = field(default=True, metadata={"help": "use documents in the same batch as negatives"})
    remove_unused_columns: bool = field(default=False)
    use_lora: bool = field(default=False)
    use_bnb_config: bool = field(default=False)
    do_encode: bool = field(default=False, metadata={"help": "run the encoding loop"})
    report_to: Optional[List[str]] = field(
        default="none", metadata={"help": "The list of integrations to report the results and logs to."}
    )


def main():
    parser = HfArgumentParser((ModelArguments, DataArguments, RetrieverTrainingArguments))
    model_args, data_args, training_args = parser.parse_args_into_dataclasses()
    model_args: ModelArguments
    data_args: DataArguments
    training_args: TrainingArguments

    if (
        os.path.exists(training_args.output_dir)
        and os.listdir(training_args.output_dir)
        and training_args.do_train
        and not training_args.overwrite_output_dir
    ):
        raise ValueError(
            f"Output directory ({training_args.output_dir}) already exists and is not empty. "
            "Use --overwrite_output_dir to overcome."
        )

    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
        datefmt="%m/%d/%Y %H:%M:%S",
        level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN,
    )
    logger.warning(
        "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",
        training_args.local_rank,
        training_args.device,
        training_args.n_gpu,
        bool(training_args.local_rank != -1),
        training_args.fp16,
    )
    logger.info("Training/evaluation parameters %s", training_args)
    logger.info("Model parameters %s", model_args)
    logger.info("Data parameters %s", data_args)

    set_seed(training_args.seed)

    tokenizer = AutoTokenizer.from_pretrained(
        model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
        cache_dir=model_args.cache_dir,
        use_fast=False,
    )
    if training_args.use_bnb_config:
        from transformers import BitsAndBytesConfig

        logger.info('Use quantization bnb config')
        quantization_config = BitsAndBytesConfig(
            load_in_4bit=True,
            bnb_4bit_use_double_quant=True,
            bnb_4bit_quant_type="nf4",
            bnb_4bit_compute_dtype=torch.bfloat16,
        )
    else:
        quantization_config = None

    if training_args.do_train:
        model = AutoModelForEmbedding.from_pretrained(
            model_name_or_path=model_args.model_name_or_path,
            pooling_method=training_args.pooling_method,
            use_lora=training_args.use_lora,
            quantization_config=quantization_config,
        )

        loss_fn = AutoLoss(
            loss_name=training_args.loss_fn,
            loss_kwargs={
                'use_inbatch_negative': training_args.use_inbatch_negative,
                'temperature': training_args.temperature,
            },
        )

        model = model.set_train_type(
            "pairwise",
            loss_fn=loss_fn,
        )

        train_dataset = RetrievalTrainDataset(
            args=data_args,
            tokenizer=tokenizer,
            positive_key=data_args.positive_key,
            negative_key=data_args.negative_key,
        )
        logger.info(f"Total training examples: {len(train_dataset)}")

        trainer = RetrievalTrainer(
            model=model,
            args=training_args,
            train_dataset=train_dataset,
            data_collator=TripletCollator(
                tokenizer,
                query_max_length=data_args.query_max_length,
                document_max_length=data_args.document_max_length,
                positive_key=data_args.positive_key,
                negative_key=data_args.negative_key,
            ),
        )

        Path(training_args.output_dir).mkdir(parents=True, exist_ok=True)

        trainer.train()
        # trainer.save_model(training_args.output_dir)
        model.save_pretrained(training_args.output_dir)

        if trainer.is_world_process_zero():
            tokenizer.save_pretrained(training_args.output_dir)

    if training_args.do_encode:
        model = AutoModelForEmbedding.from_pretrained(
            model_name_or_path=model_args.model_name_or_path,
            pooling_method=training_args.pooling_method,
            use_lora=training_args.use_lora,
            quantization_config=quantization_config,
            lora_path=model_args.lora_path,
        )

        max_length = data_args.query_max_length if data_args.is_query else data_args.document_max_length
        logger.info(f'Encoding will be saved in {training_args.output_dir}')

        encode_dataset = EncodeDataset(args=data_args, tokenizer=tokenizer, max_length=max_length, text_key='text')
        logger.info(f"Number of train samples: {len(encode_dataset)}, max_length: {max_length}")

        encode_loader = DataLoader(
            encode_dataset,
            batch_size=training_args.per_device_eval_batch_size,
            collate_fn=EncodeCollator(tokenizer, max_length=max_length, padding='max_length'),
            shuffle=False,
            drop_last=False,
            num_workers=training_args.dataloader_num_workers,
        )

        embeddings = model.encode(encode_loader, show_progress_bar=True, convert_to_numpy=True)
        lookup_indices = list(range(len(encode_dataset)))

        with open(os.path.join(training_args.output_dir, data_args.encoding_save_file), 'wb') as f:
            pickle.dump((embeddings, lookup_indices), f)


if __name__ == "__main__":
    main()

最终调用文件 shell run.sh

MODEL_NAME="moka-ai/m3e-base"

TRAIN_DATA="/root/kag101/src/open-retrievals/t2/t2_ranking.jsonl"
OUTPUT_DIR="/root/kag101/src/open-retrievals/t2/ft_out"


# loss_fn: infonce, simcse

deepspeed -m --include localhost:0 embed.py \
  --deepspeed ds_zero2_no_offload.json \
  --output_dir $OUTPUT_DIR \
  --overwrite_output_dir \
  --model_name_or_path $MODEL_NAME \
  --do_train \
  --data_name_or_path $TRAIN_DATA \
  --positive_key positive \
  --negative_key negative \
  --pooling_method mean \
  --loss_fn infonce \
  --use_lora False \
  --query_instruction "" \
  --document_instruction "" \
  --learning_rate 3e-5 \
  --fp16 \
  --num_train_epochs 5 \
  --per_device_train_batch_size 32 \
  --dataloader_drop_last True \
  --query_max_length 64 \
  --document_max_length 256 \
  --train_group_size 4 \
  --logging_steps 100 \
  --temperature 0.02 \
  --save_total_limit 1 \
  --use_inbatch_negative false