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import numpy as np
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from torch.utils.data import DataLoader
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from functools import partial
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from tqdm import tqdm
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import datetime
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import wandb
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from datasets import Dataset
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from typing import Dict, List, Any
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# from datasets import load_dataset
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# from data.aligner import convert_tldr
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# from data.preprocess import preprocess_rl_dataset_v1
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# from constants import IGNORE_INDEX
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os.environ["NCCL_P2P_DISABLE"] = "1"
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os.environ["NCCL_IB_DISABLE"] = "1"
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def preprocess_rl_dataset_v1(
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examples: Dict[str, List[Any]],
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tokenizer: PreTrainedTokenizer,
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) -> Dict[str, List[List[int]]]:
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model_inputs = {"prompt": [], "response":[]}
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for i in range(len(examples["prompt"])):
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prompt = examples["prompt"][i]
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response = examples["response"][i]
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input_str = tokenizer.apply_chat_template(prompt, template=tokenizer.chat_template, tokenize=False, add_generation_prompt=True)
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model_inputs["prompt"].append(input_str)
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model_inputs["response"].append(response[0]['content'])
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return model_inputs
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def get_demo_data():
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data = {
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"prompt": [
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[
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{'role': 'system', 'content': "你是一个夸夸机器人"},
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{'role': 'user', 'content': "尝试用尽量浮夸的语气夸我"}
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]
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],
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"response": [
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[
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{'role': 'assistant', 'content': ""}
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]
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]
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}
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dataset = Dataset.from_dict(data)
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dataset.set_format(type="torch", columns=["prompt", "response"])
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return dataset
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def main():
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def save_manager(current_epoch, current_steps, current_avg_reward, max_save=None, prefix=None, push_to_hub=False, repo_id=None):
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checkpoints_dir = f"checkpoints/{wandb_run_name}"
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if not os.path.exists(checkpoints_dir):
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os.makedirs(checkpoints_dir)
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if max_save is not None:
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step_dirs = [d for d in os.listdir(checkpoints_dir)
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if os.path.isdir(os.path.join(checkpoints_dir, d)) and d.startswith(prefix)]
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step_dirs.sort(key=lambda x: os.path.getctime(os.path.join(checkpoints_dir, x)))
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while len(step_dirs) >= max_save:
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oldest_dir = os.path.join(checkpoints_dir, step_dirs[0])
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shutil.rmtree(oldest_dir)
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step_dirs.pop(0)
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save_dir = os.path.join(checkpoints_dir, f"{prefix}_{current_steps}_{datetime.datetime.now().strftime('%Y%m%d_%H%M%S')}")
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os.makedirs(save_dir, exist_ok=True)
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unwrapped_model = accelerator.unwrap_model(model)
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unwrapped_model.save_pretrained(save_dir)
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tokenizer.save_pretrained(save_dir)
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training_state = {
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'step': current_steps,
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'optimizer_state_dict': optimizer.state_dict(),
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'scheduler_state_dict': lr_scheduler.state_dict(),
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'reward': current_avg_reward,
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'epoch': current_epoch,
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}
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torch.save(training_state, os.path.join(save_dir, "training_state.pt"))
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if push_to_hub and repo_id:
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try:
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unwrapped_model.push_to_hub(repo_id, commit_message=f"Step {current_steps} with reward {current_avg_reward:.4f}")
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tokenizer.push_to_hub(repo_id, commit_message=f"Step {current_steps} with reward {current_avg_reward:.4f}")
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logger.info(f"Successfully pushed model to hub: {repo_id}")
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except Exception as e:
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logger.error(f"Failed to push to hub: {e}")
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model_name_or_path = "lm_models/Qwen2.5-0.5B-Instruct" # 使用Qwen2.5-0.5B-Instruct作为基础模型
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dataset_dir = "dataset/tldr"
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learning_rate = 1e-6
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group_num = 8
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mini_batch_size = 1
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batch_size = 4 # 每个global_steps更新 batch_size / mini_batch_size 次
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gradient_accumulation_steps = 1
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