kopa/finetune_kopa.py

import os
import sys
from typing import List

import fire
import torch
import transformers
from datasets import load_dataset

from kopa import KoPAWithAdapter

"""
Unused imports:
import torch.nn as nn
import bitsandbytes as bnb
"""

from peft import PrefixTuningConfig, get_peft_model
from transformers import LlamaForCausalLM, AutoTokenizer

from utils.prompter import Prompter


def custom_collate_fn(batch):
    input_ids_list = []
    attention_mask_list = []
    static_prefix_list = []
    sensor_data_list = []

    for b in batch:
        # 确保输入是张量
        if isinstance(b["input_ids"], list):
            input_ids = torch.tensor(b["input_ids"], dtype=torch.long)
        else:
            input_ids = b["input_ids"]
        input_ids_list.append(input_ids)

        if isinstance(b["attention_mask"], list):
            attention_mask = torch.tensor(b["attention_mask"], dtype=torch.long)
        else:
            attention_mask = b["attention_mask"]
        attention_mask_list.append(attention_mask)

        if "static_prefix" in b:
            if isinstance(b["static_prefix"], list):
                static_prefix = torch.tensor(b["static_prefix"], dtype=torch.long)
            else:
                static_prefix = b["static_prefix"]
            static_prefix_list.append(static_prefix)

        if "sensor_data" in b:
            if isinstance(b["sensor_data"], list):
                sensor_data = torch.tensor(b["sensor_data"], dtype=torch.float)
            else:
                sensor_data = b["sensor_data"]
            sensor_data_list.append(sensor_data)

    # 堆叠数据
    result = {
        "input_ids": torch.stack(input_ids_list),
        "attention_mask": torch.stack(attention_mask_list),
    }

    if static_prefix_list:
        result["static_prefix"] = torch.stack(static_prefix_list)

    if sensor_data_list:
        result["sensor_data"] = torch.stack(sensor_data_list)

    if "labels" in batch[0]:
        labels_list = []
        for b in batch:
            if isinstance(b["labels"], list):
                labels = torch.tensor(b["labels"], dtype=torch.long)
            else:
                labels = b["labels"]
            labels_list.append(labels)

        result["labels"] = torch.stack(labels_list)

    return result


def train(
        # model/data params
        base_model="models/Llama-3.2-3B-Instruct",
        data_path: str = "data/CoDeX-S-train.json",
        output_dir: str = "output",
        # training hyperparams
        batch_size: int = 16,
        micro_batch_size: int = 16,
        num_epochs: int = 2,
        learning_rate: float = 3e-4,
        cutoff_len: int = 512,
        val_set_size: int = 0,
        num_prefix: int = 1,
        # llm hyperparams
        train_on_inputs: bool = True,  # if False, masks out inputs in loss
        add_eos_token: bool = False,
        group_by_length: bool = False,  # faster, but produces an odd training loss curve
        # wandb params
        wandb_project: str = "",
        wandb_run_name: str = "",
        wandb_watch: str = "",  # options: false | gradients | all
        wandb_log_model: str = "",  # options: false | true
        resume_from_checkpoint: str = None,  # either training checkpoint or final adapter
        prompt_template_name: str = "alpaca",  # The prompt template to use, will default to alpaca.
):
    if int(os.environ.get("LOCAL_RANK", 0)) == 0:
        print(
            f"Training Alpaca model with params:\n"
            f"base_model: {base_model}\n"
            f"data_path: {data_path}\n"
            f"output_dir: {output_dir}\n"
            f"batch_size: {batch_size}\n"
            f"micro_batch_size: {micro_batch_size}\n"
            f"num_epochs: {num_epochs}\n"
            f"learning_rate: {learning_rate}\n"
            f"cutoff_len: {cutoff_len}\n"
            f"val_set_size: {val_set_size}\n"
            f"train_on_inputs: {train_on_inputs}\n"
            f"add_eos_token: {add_eos_token}\n"
            f"group_by_length: {group_by_length}\n"
            f"wandb_project: {wandb_project}\n"
            f"wandb_run_name: {wandb_run_name}\n"
            f"wandb_watch: {wandb_watch}\n"
            f"wandb_log_model: {wandb_log_model}\n"
            f"resume_from_checkpoint: {resume_from_checkpoint or False}\n"
            f"prompt template: {prompt_template_name}\n"
        )
    assert (
        base_model
    ), "Please specify a --base_model, e.g. --base_model='huggyllama/llama-7b'"
    gradient_accumulation_steps = batch_size // micro_batch_size

    prompter = Prompter(prompt_template_name)

    device_map = "auto"
    world_size = int(os.environ.get("WORLD_SIZE", 1))
    ddp = world_size != 1
    if ddp:
        device_map = {"": int(os.environ.get("LOCAL_RANK") or 0)}
        gradient_accumulation_steps = gradient_accumulation_steps // world_size

    model = LlamaForCausalLM.from_pretrained(
        base_model,
        # load_in_8bit=True,
        torch_dtype=torch.float16,
        device_map=device_map,
    )

    tokenizer = AutoTokenizer.from_pretrained(base_model)





    # tokenizer.pad_token_id = (
    #     0  # unk. we want this to be different from the eos token
    # )
    tokenizer.padding_side = "left"  # Allow batched inference

    tokenizer.pad_token = tokenizer.eos_token
    model.config.pad_token_id = model.config.eos_token_id
    model.generation_config.pad_token_id = model.generation_config.eos_token_id

    def ensure_consistent_keys(dataset):
        all_keys = set()
        for example in dataset:
            all_keys.update(example.keys())

        for example in dataset:
            for key in all_keys:
                if key not in example:
                    if key == "static_prefix":
                        example[key] = ""
                    elif key == "sensor_data":
                        example[key] = [0, 0, 0]

        return dataset

    # def tokenize(prompt, add_eos_token=True):
    #     # there's probably a way to do this with the tokenizer settings
    #     # but again, gotta move fast
    #     result = tokenizer(
    #         prompt,
    #         truncation=True,
    #         max_length=cutoff_len,
    #         padding=False,
    #         return_tensors=None,
    #     )
    #     if (
    #             result["input_ids"][-1] != tokenizer.eos_token_id
    #             and len(result["input_ids"]) < cutoff_len
    #             and add_eos_token
    #     ):
    #         result["input_ids"].append(tokenizer.eos_token_id)
    #         result["attention_mask"].append(1)
    #
    #     result["labels"] = result["input_ids"].copy()
    #
    #     return result

    def generate_and_tokenize_prompt(data_point):
        full_prompt = prompter.generate_prompt(
            data_point["instruction"],
            data_point["input"],
            data_point["output"],
        )

        # Tokenizer 处理文本
        tokenized_full_prompt = tokenizer(
            full_prompt,
            truncation=True,
            max_length=128,
            padding="max_length",
            return_tensors="pt",
        )

        tokenized_full_prompt = {k: v.squeeze(0) for k, v in tokenized_full_prompt.items()}

        # 处理静态前缀
        static_prefix = tokenizer(
            data_point["instruction"],
            truncation=True,
            max_length=10,
            padding="max_length",
            return_tensors="pt"
        )["input_ids"].squeeze(0)

        # 限制索引范围，确保 `static_prefix` 不会超出 `vocab_size`
        static_prefix = torch.clamp(static_prefix, min=0, max=tokenizer.vocab_size - 1)

        tokenized_full_prompt["static_prefix"] = static_prefix
        # print(f"[DEBUG] static_prefix (after clamp): {static_prefix}")
        print(f"[DEBUG] tokenizer vocab_size: {tokenizer.vocab_size}")

        # **处理动态数据**
        sensor_values = torch.zeros(3, dtype=torch.float)  # **默认值为 Tensor，而不是 list**

        if data_point["type"] == "dynamic" and "sensor_data" in data_point:
            raw_sensor_values = data_point["sensor_data"]

            try:
                sensor_values = torch.tensor([
                    float(raw_sensor_values.get("temperature", 0.0)),
                    float(raw_sensor_values.get("humidity", 0.0)),
                    float(raw_sensor_values.get("conductivity", 0.0))
                ], dtype=torch.float)
            except Exception as e:
                # print(f"[ERROR] sensor_data 解析错误: {raw_sensor_values}, {e}")
                if torch.isnan(sensor_values).any() or torch.isinf(sensor_values).any():
                    # print(f"[ERROR] NaN/Inf detected in sensor_values: {sensor_values}")
                    sensor_values = torch.zeros(3, dtype=torch.float)

        # ✅ 确保 sensor_values 是 `Tensor`
        if torch.isnan(sensor_values).any() or torch.isinf(sensor_values).any():
            print(f"[ERROR] NaN/Inf detected in sensor_values")
            if torch.isnan(sensor_values).any() or torch.isinf(sensor_values).any():
                print(f"[ERROR] NaN/Inf detected in sensor_values")
                sensor_values = torch.zeros(3, dtype=torch.float)

        # 限制范围，防止异常值
        sensor_values = torch.clamp(sensor_values, min=-100, max=100)

        print(f"[DEBUG] sensor_values (AFTER FIX): {sensor_values}")  # 🔥 打印调试信息
        if not isinstance(sensor_values, torch.Tensor):
            sensor_values = torch.tensor(sensor_values, dtype=torch.float)

        tokenized_full_prompt["sensor_data"] = sensor_values  # **确保始终是 Tensor**

        # 最后增加类型检查和转换
        for key in tokenized_full_prompt:
            if key in ["input_ids", "attention_mask"] and isinstance(tokenized_full_prompt[key], list):
                tokenized_full_prompt[key] = torch.tensor(tokenized_full_prompt[key], dtype=torch.long)

        if isinstance(tokenized_full_prompt["static_prefix"], list):
            tokenized_full_prompt["static_prefix"] = torch.tensor(tokenized_full_prompt["static_prefix"],
                                                                  dtype=torch.long)

        # 确保sensor_data是tensor
        if not isinstance(tokenized_full_prompt["sensor_data"], torch.Tensor):
            tokenized_full_prompt["sensor_data"] = torch.tensor(tokenized_full_prompt["sensor_data"], dtype=torch.float)

        tokenized_full_prompt["labels"] = tokenized_full_prompt["input_ids"].clone()

        # 如果不想对输入部分计算损失，可以将输入部分的标签设为-100
        if not train_on_inputs:
            # 找到用户输入和助手输出的分界点
            sep = tokenizer.encode(prompter.separator)
            instruction_tokens = tokenizer.encode(data_point["instruction"])

            # 将用户输入部分的标签设为-100
            sep_pos = tokenized_full_prompt["input_ids"].tolist().index(sep[0])
            tokenized_full_prompt["labels"][:sep_pos] = -100

        return tokenized_full_prompt

        # 创建PrefixTuning配置

    prefix_config = PrefixTuningConfig(
        num_virtual_tokens=num_prefix,
        task_type="CAUSAL_LM"
    )

    # 创建PEFT模型
    peft_model = get_peft_model(model, prefix_config)


    # 创建最终的KoPAWithAdapter模型
    final_model = KoPAWithAdapter(peft_model, num_prefix, tokenizer)
    device = next(model.parameters()).device
    print(f"[INFO] 使用设备: {device}")

    # 确保final_model及其组件都在相同设备上
    final_model = final_model.to(device)


    if data_path.endswith(".json") or data_path.endswith(".jsonl"):
        data = load_dataset("json", data_files=data_path)
    else:
        data = load_dataset(data_path)

    if resume_from_checkpoint:
        # Check the available weights and load them
        checkpoint_name = os.path.join(
            resume_from_checkpoint, "pytorch_model.bin"
        )  # Full checkpoint
        if not os.path.exists(checkpoint_name):
            checkpoint_name = os.path.join(
                resume_from_checkpoint, "adapter_model.bin"
            )  # only LoRA model - LoRA config above has to fit
            resume_from_checkpoint = (
                False  # So the trainer won't try loading its state
            )
        # The two files above have a different name depending on how they were saved, but are actually the same.
        if os.path.exists(checkpoint_name):
            print(f"Restarting from {checkpoint_name}")
            adapters_weights = torch.load(checkpoint_name)
        else:
            print(f"Checkpoint {checkpoint_name} not found")

    # model.print_trainable_parameters()  # Be more transparent about the % of trainable params.

    if val_set_size > 0:
        train_val = data["train"].train_test_split(
            test_size=val_set_size, shuffle=True, seed=42
        )
        train_data = (
            train_val["train"].shuffle().map(generate_and_tokenize_prompt)

        )
        train_data = ensure_consistent_keys(train_data)
        val_data = (
            train_val["test"].shuffle().map(generate_and_tokenize_prompt)
        )
    else:
        train_data = data["train"].shuffle().map(generate_and_tokenize_prompt)
        train_data = ensure_consistent_keys(train_data)
        val_data = None

    if not ddp and torch.cuda.device_count() > 1:
        # keeps Trainer from trying its own DataParallelism when more than 1 gpu is available
        model.is_parallelizable = True
        model.model_parallel = True

    trainer = transformers.Trainer(
        model=final_model,
        data_collator=custom_collate_fn,
        train_dataset=train_data,
        eval_dataset=val_data,
        args=transformers.TrainingArguments(
            per_device_train_batch_size=micro_batch_size,
            gradient_accumulation_steps=gradient_accumulation_steps,
            warmup_steps=100,
            num_train_epochs=num_epochs,
            learning_rate=learning_rate,
            fp16=True,
            logging_steps=10,
            optim="adamw_hf",
            evaluation_strategy="steps" if val_set_size > 0 else "no",
            save_strategy="steps",
            eval_steps=None,
            save_steps=5000,
            output_dir=output_dir,
            save_total_limit=2,
            load_best_model_at_end=True if val_set_size > 0 else False,
            ddp_find_unused_parameters=False if ddp else None,
            group_by_length=group_by_length,
            report_to=None,
            run_name=None,
        ),
    )
    # final_model.config.use_cache = False

    if torch.__version__ >= "2" and sys.platform != "win32":
        final_model = torch.compile(model)

    trainer.train(resume_from_checkpoint=resume_from_checkpoint)

    final_model.save_pretrained(output_dir)

    # ⭐ 确保embeddings存在再保存
    if hasattr(final_model, "embeddings"):
        torch.save(final_model.embeddings, os.path.join(output_dir, "embeddings.pth"))
    else:
        print("[WARNING] final_model没有embeddings属性，跳过保存。")

    try:
        final_model.model.save_pretrained(os.path.join(output_dir, "peft_model"))
        print(f"[INFO] PEFT模型保存到 {os.path.join(output_dir, 'peft_model')}")
    except Exception as e:
        print(f"[WARNING] 保存PEFT模型时出错: {e}")


if __name__ == "__main__":
    fire.Fire(train)