lora.py

from datasets import Dataset
import pandas as pd
from transformers import AutoTokenizer, AutoModelForCausalLM, DataCollatorForSeq2Seq, TrainingArguments, Trainer, GenerationConfig

df = pd.read_json('dataset/huanhuan.json')
ds = Dataset.from_pandas(df)

print(ds[:3])

tokenizer = AutoTokenizer.from_pretrained('/workspace/checkpoints/Meta-Llama-3___1-8B-Instruct', use_fast=False, trust_remote_code=True)
tokenizer.pad_token = tokenizer.eos_token

messages = [
        {"role": "system", "content": "现在你要扮演皇帝身边的女人--甄嬛"},
        {"role": "user", "content": '你好呀'},
        {"role": "assistant", "content": "你好，我是甄嬛，你有什么事情要问我吗？"},    
    ]
print(tokenizer.apply_chat_template(messages, tokenize=False))

def process_func(example):
    MAX_LENGTH = 384    # Llama分词器会将一个中文字切分为多个token，因此需要放开一些最大长度，保证数据的完整性
    input_ids, attention_mask, labels = [], [], []
    instruction = tokenizer(f"<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\nCutting Knowledge Date: December 2023\nToday Date: 26 Jul 2024\n\n现在你要扮演皇帝身边的女人--甄嬛<|eot_id|><|start_header_id|>user<|end_header_id|>\n\n{example['instruction'] + example['input']}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n", add_special_tokens=False)  # add_special_tokens 不在开头加 special_tokens
    response = tokenizer(f"{example['output']}<|eot_id|>", add_special_tokens=False)
    input_ids = instruction["input_ids"] + response["input_ids"] + [tokenizer.pad_token_id]
    attention_mask = instruction["attention_mask"] + response["attention_mask"] + [1]  # 因为eos token咱们也是要关注的所以 补充为1
    labels = [-100] * len(instruction["input_ids"]) + response["input_ids"] + [tokenizer.pad_token_id]  
    if len(input_ids) > MAX_LENGTH:  # 做一个截断
        input_ids = input_ids[:MAX_LENGTH]
        attention_mask = attention_mask[:MAX_LENGTH]
        labels = labels[:MAX_LENGTH]
    return {
        "input_ids": input_ids,
        "attention_mask": attention_mask,
        "labels": labels
    }

tokenized_id = ds.map(process_func, remove_columns=ds.column_names)
tokenized_id

tokenizer.decode(tokenized_id[0]['input_ids'])

tokenizer.decode(list(filter(lambda x: x != -100, tokenized_id[1]["labels"])))

import torch

model = AutoModelForCausalLM.from_pretrained('/workspace/models/Meta-Llama-3___1-8B-Instruct', device_map="auto",torch_dtype=torch.bfloat16)

model

model.enable_input_require_grads() # 开启梯度检查点时，要执行该方法

model.dtype

from peft import LoraConfig, TaskType, get_peft_model

config = LoraConfig(
    task_type=TaskType.CAUSAL_LM, 
    target_modules=["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"],
    inference_mode=False, # 训练模式
    r=8, # Lora 秩
    lora_alpha=32, # Lora alaph，具体作用参见 Lora 原理
    lora_dropout=0.1# Dropout 比例
)

config

model = get_peft_model(model, config)

config

model.print_trainable_parameters()