finetune.py

# This code is based on the revised code from fastchat based on tatsu-lab/stanford_alpaca.


from dataclasses import dataclass, field
import json
import math
import logging
import os
from typing import Dict, Optional, List
import torch
from torch.utils.data import Dataset
# 导入deepspeed库中的zero模块。DeepSpeed是一个深度学习优化库，zero是它的一个特性，可以减少模型的内存占用。
from deepspeed import zero

# 从deepspeed的zero模块中导入ZeroParamStatus。它与模型参数的分区有关，通常与zero优化有关。
from deepspeed.runtime.zero.partition_parameters import ZeroParamStatus

# 导入transformers库。transformers是一个提供大量预训练模型的库，例如BERT、GPT-2等。
import transformers

# 从transformers库中导入Trainer、GPTQConfig和deepspeed。Trainer是用于训练模型的类，GPTQConfig可能是针对某种GPT模型的配置，而deepspeed是与DeepSpeed集成相关的模块。
from transformers import Trainer, GPTQConfig, deepspeed

# 从transformers的trainer_pt_utils模块中导入LabelSmoother。LabelSmoother是一个用于标签平滑的工具，可以提高模型的泛化能力。
from transformers.trainer_pt_utils import LabelSmoother

# 从peft库中导入LoraConfig、get_peft_model和prepare_model_for_kbit_training。不过，到目前为止（2022年1月）我不熟悉peft这个库，所以具体的功能需要查阅相关文档。
from peft import LoraConfig, get_peft_model, prepare_model_for_kbit_training

# 定义一个常量`IGNORE_TOKEN_ID`，其值等于LabelSmoother的ignore_index属性。这通常用于指定在计算损失时应该忽略的token ID。
IGNORE_TOKEN_ID = LabelSmoother.ignore_index

# 使用Python的dataclass定义一个名为ModelArguments的类，它有一个属性model_name_or_path，默认值为"Qwen/Qwen-7B"。
@dataclass
class ModelArguments:
    model_name_or_path: Optional[str] = field(default="Qwen/Qwen-7B")

# 使用dataclass定义一个名为DataArguments的类，用于指定数据相关的参数，如训练数据路径、评估数据路径和是否使用懒加载预处理。
@dataclass
class DataArguments:
    data_path: str = field(
        default=None, metadata={"help": "Path to the training data."}
    )
    eval_data_path: str = field(
        default=None, metadata={"help": "Path to the evaluation data."}
    )
    lazy_preprocess: bool = False

# 使用dataclass定义一个名为TrainingArguments的类，它继承了transformers的TrainingArguments类。这个类定义了训练相关的参数，如缓存目录、优化器、模型的最大序列长度和是否使用Lora。
@dataclass
class TrainingArguments(transformers.TrainingArguments):
    cache_dir: Optional[str] = field(default=None)
    optim: str = field(default="adamw_torch")
    model_max_length: int = field(
        default=8192,
        metadata={
            "help": "Maximum sequence length. Sequences will be right padded (and possibly truncated)."
        },
    )
    use_lora: bool = False



@dataclass
class LoraArguments:
    lora_r: int = 64
    lora_alpha: int = 16
    lora_dropout: float = 0.05
    lora_target_modules: List[str] = field(
        default_factory=lambda: ["c_attn", "c_proj", "w1", "w2"]
    )
    lora_weight_path: str = ""
    lora_bias: str = "none"
    q_lora: bool = False


def maybe_zero_3(param):
    if hasattr(param, "ds_id"):
        assert param.ds_status == ZeroParamStatus.NOT_AVAILABLE
        with zero.GatheredParameters([param]):
            param = param.data.detach().cpu().clone()
    else:
        param = param.detach().cpu().clone()
    return param


# Borrowed from peft.utils.get_peft_model_state_dict
def get_peft_state_maybe_zero_3(named_params, bias):
    if bias == "none":
        to_return = {k: t for k, t in named_params if "lora_" in k}
    elif bias == "all":
        to_return = {k: t for k, t in named_params if "lora_" in k or "bias" in k}
    elif bias == "lora_only":
        to_return = {}
        maybe_lora_bias = {}
        lora_bias_names = set()
        for k, t in named_params:
            if "lora_" in k:
                to_return[k] = t
                bias_name = k.split("lora_")[0] + "bias"
                lora_bias_names.add(bias_name)
            elif "bias" in k:
                maybe_lora_bias[k] = t
        for k, t in maybe_lora_bias:
            if bias_name in lora_bias_names:
                to_return[bias_name] = t
    else:
        raise NotImplementedError
    to_return = {k: maybe_zero_3(v) for k, v in to_return.items()}
    return to_return

local_rank = None

def rank0_print(*args):
    if local_rank == 0:
        print(*args)


def safe_save_model_for_hf_trainer(trainer: transformers.Trainer, output_dir: str, bias="none"):
    """Collects the state dict and dump to disk."""
    # check if zero3 mode enabled
    if deepspeed.is_deepspeed_zero3_enabled():
        state_dict = trainer.model_wrapped._zero3_consolidated_16bit_state_dict()
    else:
        if trainer.args.use_lora:
            state_dict = get_peft_state_maybe_zero_3(
                trainer.model.named_parameters(), bias
            )
        else:
            state_dict = trainer.model.state_dict()
    if trainer.args.should_save and trainer.args.local_rank == 0:
        trainer._save(output_dir, state_dict=state_dict)


def preprocess(
    sources,
    tokenizer: transformers.PreTrainedTokenizer,
    max_len: int,
    system_message: str = "You are a helpful assistant."
) -> Dict:
    roles = {"user": "<|im_start|>user", "assistant": "<|im_start|>assistant"}

    im_start = tokenizer.im_start_id
    im_end = tokenizer.im_end_id
    nl_tokens = tokenizer('\n').input_ids
    _system = tokenizer('system').input_ids + nl_tokens
    _user = tokenizer('user').input_ids + nl_tokens
    _assistant = tokenizer('assistant').input_ids + nl_tokens

    # Apply prompt templates
    input_ids, targets = [], []
    for i, source in enumerate(sources):
        if roles[source[0]["from"]] != roles["user"]:
            source = source[1:]

        input_id, target = [], []
        system = [im_start] + _system + tokenizer(system_message).input_ids + [im_end] + nl_tokens
        input_id += system
        target += [im_start] + [IGNORE_TOKEN_ID] * (len(system)-3) + [im_end] + nl_tokens
        assert len(input_id) == len(target)
        for j, sentence in enumerate(source):
            role = roles[sentence["from"]]
            _input_id = tokenizer(role).input_ids + nl_tokens + \
                tokenizer(sentence["value"]).input_ids + [im_end] + nl_tokens
            input_id += _input_id
            if role == '<|im_start|>user':
                _target = [im_start] + [IGNORE_TOKEN_ID] * (len(_input_id)-3) + [im_end] + nl_tokens
            elif role == '<|im_start|>assistant':
                _target = [im_start] + [IGNORE_TOKEN_ID] * len(tokenizer(role).input_ids) + \
                    _input_id[len(tokenizer(role).input_ids)+1:-2] + [im_end] + nl_tokens
            else:
                raise NotImplementedError
            target += _target
        assert len(input_id) == len(target)
        input_id += [tokenizer.pad_token_id] * (max_len - len(input_id))
        target += [IGNORE_TOKEN_ID] * (max_len - len(target))
        input_ids.append(input_id[:max_len])
        targets.append(target[:max_len])
    input_ids = torch.tensor(input_ids, dtype=torch.int)
    targets = torch.tensor(targets, dtype=torch.int)

    return dict(
        input_ids=input_ids,
        labels=targets,
        attention_mask=input_ids.ne(tokenizer.pad_token_id),
    )


class SupervisedDataset(Dataset):
    """Dataset for supervised fine-tuning."""

    def __init__(self, raw_data, tokenizer: transformers.PreTrainedTokenizer, max_len: int):
        super(SupervisedDataset, self).__init__()

        rank0_print("Formatting inputs...")
        sources = [example["conversations"] for example in raw_data]
        data_dict = preprocess(sources, tokenizer, max_len)

        self.input_ids = data_dict["input_ids"]
        self.labels = data_dict["labels"]
        self.attention_mask = data_dict["attention_mask"]

    def __len__(self):
        return len(self.input_ids)

    def __getitem__(self, i) -> Dict[str, torch.Tensor]:
        return dict(
            input_ids=self.input_ids[i],
            labels=self.labels[i],
            attention_mask=self.attention_mask[i],
        )


class LazySupervisedDataset(Dataset):
    """Dataset for supervised fine-tuning."""

    def __init__(self, raw_data, tokenizer: transformers.PreTrainedTokenizer, max_len: int):
        super(LazySupervisedDataset, self).__init__()
        self.tokenizer = tokenizer
        self.max_len = max_len

        rank0_print("Formatting inputs...Skip in lazy mode")
        self.tokenizer = tokenizer
        self.raw_data = raw_data
        self.cached_data_dict = {}

    def __len__(self):
        return len(self.raw_data)

    def __getitem__(self, i) -> Dict[str, torch.Tensor]:
        if i in self.cached_data_dict:
            return self.cached_data_dict[i]

        ret = preprocess([self.raw_data[i]["conversations"]], self.tokenizer, self.max_len)
        ret = dict(
            input_ids=ret["input_ids"][0],
            labels=ret["labels"][0],
            attention_mask=ret["attention_mask"][0],
        )
        self.cached_data_dict[i] = ret

        return ret


def make_supervised_data_module(
    tokenizer: transformers.PreTrainedTokenizer, data_args, max_len,
) -> Dict:
    """Make dataset and collator for supervised fine-tuning."""
    dataset_cls = (
        LazySupervisedDataset if data_args.lazy_preprocess else SupervisedDataset
    )
    rank0_print("Loading data...")

    train_json = json.load(open(data_args.data_path, "r"))
    train_dataset = dataset_cls(train_json, tokenizer=tokenizer, max_len=max_len)

    if data_args.eval_data_path:
        eval_json = json.load(open(data_args.eval_data_path, "r"))
        eval_dataset = dataset_cls(eval_json, tokenizer=tokenizer, max_len=max_len)
    else:
        eval_dataset = None

    return dict(train_dataset=train_dataset, eval_dataset=eval_dataset)


def train():
    global local_rank

    parser = transformers.HfArgumentParser(
        (ModelArguments, DataArguments, TrainingArguments, LoraArguments)
    )
    (
        model_args,
        data_args,
        training_args,
        lora_args,
    ) = parser.parse_args_into_dataclasses()

    compute_dtype = (
        torch.float16
        if training_args.fp16
        else (torch.bfloat16 if training_args.bf16 else torch.float32)
    )

    local_rank = training_args.local_rank

    device_map = None
    world_size = int(os.environ.get("WORLD_SIZE", 1))
    ddp = world_size != 1
    if lora_args.q_lora:
        device_map = {"": int(os.environ.get("LOCAL_RANK") or 0)} if ddp else None
        if len(training_args.fsdp) > 0 or deepspeed.is_deepspeed_zero3_enabled():
            logging.warning(
                "FSDP or ZeRO3 are not incompatible with QLoRA."
            )

    # Set RoPE scaling factor
    config = transformers.AutoConfig.from_pretrained(
        model_args.model_name_or_path,
        cache_dir=training_args.cache_dir,
        trust_remote_code=True,
    )
    config.use_cache = False

    # Load model and tokenizer
    model = transformers.AutoModelForCausalLM.from_pretrained(
        model_args.model_name_or_path,
        config=config,
        cache_dir=training_args.cache_dir,
        device_map=device_map,
        trust_remote_code=True,
        quantization_config=GPTQConfig(
            bits=4, disable_exllama=True
        )
        if training_args.use_lora and lora_args.q_lora
        else None,
    )
    tokenizer = transformers.AutoTokenizer.from_pretrained(
        model_args.model_name_or_path,
        cache_dir=training_args.cache_dir,
        model_max_length=training_args.model_max_length,
        padding_side="right",
        use_fast=False,
        trust_remote_code=True,
    )
    tokenizer.pad_token_id = tokenizer.eod_id

    if training_args.use_lora:
        lora_config = LoraConfig(
            r=lora_args.lora_r,
            lora_alpha=lora_args.lora_alpha,
            target_modules=lora_args.lora_target_modules,
            lora_dropout=lora_args.lora_dropout,
            bias=lora_args.lora_bias,
            task_type="CAUSAL_LM",
            modules_to_save=["wte", "lm_head"]  # This argument serves for adding new tokens.
        )
        if lora_args.q_lora:
            model = prepare_model_for_kbit_training(
                model, use_gradient_checkpointing=training_args.gradient_checkpointing
            )

        model = get_peft_model(model, lora_config)

        if training_args.gradient_checkpointing:
            model.enable_input_require_grads()

    # Load data
    data_module = make_supervised_data_module(
        tokenizer=tokenizer, data_args=data_args, max_len=training_args.model_max_length
    )

    # Start trainner
    trainer = Trainer(
        model=model, tokenizer=tokenizer, args=training_args, **data_module
    )

    trainer.train()
    trainer.save_state()

    safe_save_model_for_hf_trainer(trainer=trainer, output_dir=training_args.output_dir, bias=lora_args.lora_bias)


if __name__ == "__main__":
    train()