instruct_train.py

"""
Refer: https://huggingface.co/docs/trl/sft_trainer#add-special-tokens-for-chat-format for more advance tools
"""

import os
import argparse
from typing import Optional, Union, List
from dataclasses import dataclass, field

import datasets
from transformers import AutoTokenizer, TrainerCallback
from transformers.trainer_utils import get_last_checkpoint
from trl import (
    ModelConfig,
    SFTConfig,
    SFTTrainer,
    TrlParser,
    get_kbit_device_map,
    get_peft_config,
    get_quantization_config,
)
import swanlab


################
# Model kwargs
################
@dataclass
class ChatGLM4ModelConfig(ModelConfig):
    model_name_or_path: Optional[str] = field(
        default="./weights/glm-4-9b-hf",
        metadata={
            "help": "Model checkpoint for weights initialization. default used glm4"
        },
    )
    torch_dtype: Optional[str] = field(
        default="bfloat16",
        metadata={
            "help": "Override the default `torch.dtype` and load the model under this dtype.",
            "choices": ["auto", "bfloat16", "float16", "float32"],
        },
    )
    use_peft: bool = field(
        default=True,
        metadata={"help": "Whether to use PEFT for training. Default true"},
    )
    lora_r: int = field(
        default=8,
        metadata={"help": "LoRA R value."},
    )
    lora_alpha: int = field(
        default=32,
        metadata={"help": "LoRA alpha."},
    )
    lora_dropout: float = field(
        default=0.1,
        metadata={"help": "LoRA dropout."},
    )
    lora_target_modules: Optional[list[str]] = field(
        default_factory=lambda: ["q_proj", "k_proj", "v_proj"],
        metadata={"help": "LoRA target modules."},
    )


################
# Datasets kwargs
################
@dataclass
class DataTrainingArguments:
    data_files: Optional[str] = field(
        default="./data/alpaca_gpt4_data_zh.json",
        metadata={"help": "The name of the dataset to use (via the datasets library)."},
    )


################
# Train kwargs
################
@dataclass
class MySFTConfig(SFTConfig):
    output_dir: Optional[str] = field(
        default="./output/lora-glm4-9b-alpaca",
        metadata={
            "help": "The output directory where the model predictions and checkpoints will be written. Defaults to 'lora-glm4-9b-toolcall' if not provided."
        },
    )
    num_train_epochs: float = field(
        default=3.0, metadata={"help": "Total number of training epochs to perform."}
    )
    per_device_train_batch_size: int = field(
        default=2,
        metadata={"help": "Batch size per GPU/TPU/MPS/NPU core/CPU for training."},
    )
    per_device_eval_batch_size: int = field(
        default=4,
        metadata={"help": "Batch size per GPU/TPU/MPS/NPU core/CPU for evaluation."},
    )
    gradient_accumulation_steps: int = field(
        default=1,
        metadata={
            "help": "Number of updates steps to accumulate before performing a backward/update pass."
        },
    )
    learning_rate: float = field(
        default=5e-4, metadata={"help": "The initial learning rate for AdamW."}
    )
    bf16: bool = field(
        default=True,
        metadata={
            "help": (
                "Whether to use bf16 (mixed) precision instead of 32-bit. Requires Ampere or higher NVIDIA"
                " architecture or using CPU (use_cpu) or Ascend NPU. This is an experimental API and it may change."
            )
        },
    )
    bf16_full_eval: bool = field(
        default=True,
        metadata={
            "help": (
                "Whether to use full bfloat16 evaluation instead of 32-bit. This is an experimental API and it may"
                " change."
            )
        },
    )
    max_seq_length: Optional[int] = field(
        default=512,
        metadata={
            "help": "Maximum length of the tokenized sequence. Sequences longer than `max_seq_length` are truncated "
            "from the right. If `None`, no truncation is applied. When packing is enabled, this value sets the "
            "sequence length."
        },
    )
    eval_strategy: Union[str] = field(
        default="steps",
        metadata={"help": "The evaluation strategy to use."},
    )
    eval_steps: Optional[float] = field(
        default=0.1,
        metadata={
            "help": (
                "Run an evaluation every X steps. Should be an integer or a float in range `[0,1)`. "
                "If smaller than 1, will be interpreted as ratio of total training steps."
            )
        },
    )
    logging_steps: float = field(
        default=10,
        metadata={
            "help": (
                "Log every X updates steps. Should be an integer or a float in range `[0,1)`. "
                "If smaller than 1, will be interpreted as ratio of total training steps."
            )
        },
    )
    save_steps: float = field(
        default=0.1,
        metadata={
            "help": (
                "Save checkpoint every X updates steps. Should be an integer or a float in range `[0,1)`. "
                "If smaller than 1, will be interpreted as ratio of total training steps."
            )
        },
    )


################
# Print prediction text callback
################
class SavePredictCallback(TrainerCallback):
    def __init__(self, num_steps=10):
        self.num_steps = num_steps

    def on_save(self, args, state, control, model, processing_class, **kwargs):
        if state.is_world_process_zero:
            tokenizer = processing_class
            batch_test_message = [
                [{"role": "user", "content": "你好，告诉我你的名字。"}],
                [{"role": "user", "content": "告诉我1+2等于多少？"}],
            ]
            batch_inputs_text = tokenizer.apply_chat_template(
                batch_test_message,
                return_tensors="pt",
                return_dict=True,
                padding=True,
                padding_side="left",
                add_generation_prompt=True,
            ).to(model.device)

            # print(batch_inputs_text)
            outputs = model.generate(**batch_inputs_text, max_new_tokens=512)
            batch_reponse = tokenizer.batch_decode(outputs, skip_special_tokens=False)
            log_text_list = [swanlab.Text(response) for response in batch_reponse]
            swanlab.log({"Prediction": log_text_list}, step=state.global_step)


def main(model_args, data_args, training_args):
    ################
    # Model init kwargs & Tokenizer
    ################
    quantization_config = get_quantization_config(model_args)
    model_kwargs = dict(
        trust_remote_code=model_args.trust_remote_code,
        attn_implementation=model_args.attn_implementation,
        torch_dtype=model_args.torch_dtype,
        use_cache=False if training_args.gradient_checkpointing else True,
        device_map=get_kbit_device_map() if quantization_config is not None else None,
        quantization_config=quantization_config,
    )
    training_args.model_init_kwargs = model_kwargs
    tokenizer = AutoTokenizer.from_pretrained(
        model_args.model_name_or_path,
        trust_remote_code=model_args.trust_remote_code,
        use_fast=True,
    )
    if tokenizer.pad_token is None:
        tokenizer.pad_token = tokenizer.eos_token
    if tokenizer.chat_template is None:
        tokenizer.chat_template = "[gMASK]<sop>{% for item in messages %}{% if item['tools'] is defined %}<|system|>\n你是一个名为 ChatGLM 的人工智能助手。你是基于智谱AI训练的语言模型 GLM-4 模型开发的，你的任务是针对用户的问题和要求提供适当的答复和支持。\n\n# 可用工具{% set tools = item['tools'] %}{% for tool in tools %}{% if tool['type'] == 'function' %}\n\n## {{ tool['function']['name'] }}\n\n{{ tool['function'] | tojson(indent=4) }}\n在调用上述函数时，请使用 Json 格式表示调用的参数。{% elif tool['type'] == 'python' %}\n\n## python\n\n当你向 `python` 发送包含 Python 代码的消息时，该代码将会在一个有状态的 Jupyter notebook 环境中执行。\n`python` 返回代码执行的输出，或在执行 60 秒后返回超时。\n`/mnt/data` 将会持久化存储你的文件。在此会话中，`python` 无法访问互联网。不要使用 `python` 进行任何网络请求或者在线 API 调用，这些在线内容的访问将不会成功。{% elif tool['type'] == 'simple_browser' %}\n\n## simple_browser\n\n你可以使用 `simple_browser` 工具。该工具支持以下函数：\n`search(query: str, recency_days: int)`：使用搜索引擎进行查询并显示结果，可以使用 `recency_days` 参数控制搜索内容的时效性。\n`mclick(ids: list[int])`：获取一系列指定 id 的页面内容。每次调用时，须选择3-10个页面。选择多个角度的页面，同时尽可能选择可信任的信息来源。考虑到部分页面是无法加载的，你也可以多打开一些可能有用的页面而不用担心内容过多。\n`open_url(url: str)`：打开指定的 URL。\n\n使用 `【{引用 id}†{引用文本}】` 来引用内容。\n\n操作步骤：1. 使用 `search` 来获得信息列表; 2. 使用 `mclick` 来获取指定 ID 页面的内容; 3. 根据获得的内容进行回复。在回复中应当引用信息来源。\n 如果用户提供了 URL，也可以用 `open_url` 直接打开页面。\n如果初次搜索结果没有找到合适的信息，也可以再次使用 `search` 进行搜索。{% elif tool['type'] == 'cogview' %}\n\n## cogview\n\n如果用户的请求中包含了对图像的描述，你可以使用 `cogview` 来生成图像并展示给用户。你需要向 `cogview` 发送图像描述，规则：\n- 发送给 `cogview` 的消息必须使用英语。用户的中文描述必须完全翻译为英语。\n- 应当尽可能详细地描述图像生成的需求，需求描述约 100 英文单词。\n- 保持用户原始描述的意图。不要虚构内容或者没见过的人物。\n- 如无特殊说明，所在地为中国，持有中国立场并遵循中国社会主义价值观。{% endif %}{% endfor %}{% endif %}{% if item['content'] %}<|{{ item['role'] }}|>{{ item['metadata'] }}\n{{ item['content'] }}{% endif %}{% endfor %}{% if add_generation_prompt %}<|assistant|>{% endif %}"
    ################
    # Dataset
    ################
    raw_datasets = datasets.load_dataset("json", data_files=data_args.data_files)
    raw_datasets = raw_datasets["train"].train_test_split(0.05)  # split train test data

    def formatting_func(example):
        """
        process data format
        """
        prompt = example["instruction"]
        if len(example["input"]) != 0:
            prompt += "\n\n" + example["input"]
        conversations = [
            {"role": "user", "content": prompt},
            {"role": "assistant", "content": example["output"]},
        ]
        output_text = tokenizer.apply_chat_template(
            conversation=conversations, tokenize=False
        )
        return output_text

    ################
    # Training
    ################
    last_checkpoint = None
    if (
        os.path.isdir(training_args.output_dir)
        and not training_args.overwrite_output_dir
    ):
        last_checkpoint = get_last_checkpoint(training_args.output_dir)
        if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
            raise ValueError(
                f"Output directory ({training_args.output_dir}) already exists and is not empty. "
                "Use --overwrite_output_dir to overcome."
            )
        print(
            f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
            "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
        )
    trainer = SFTTrainer(
        model=model_args.model_name_or_path,
        args=training_args,
        data_collator=None,
        train_dataset=raw_datasets["train"],
        eval_dataset=(
            raw_datasets["test"] if training_args.eval_strategy != "no" else None
        ),
        processing_class=tokenizer,
        peft_config=get_peft_config(model_args),
        formatting_func=formatting_func,
        callbacks=[SavePredictCallback()],
    )
    trainer.train(resume_from_checkpoint=last_checkpoint)

    # Save
    trainer.save_model(training_args.output_dir)


def make_parser(subparsers: argparse._SubParsersAction = None):
    dataclass_types = (ChatGLM4ModelConfig, DataTrainingArguments, MySFTConfig)
    if subparsers is not None:
        parser = subparsers.add_parser(
            "sft", help="Run the SFT training script", dataclass_types=dataclass_types
        )
    else:
        parser = TrlParser(dataclass_types)
    return parser


if __name__ == "__main__":
    parser = make_parser()
    model_args, data_args, training_args = parser.parse_args_and_config()
    main(model_args, data_args, training_args)