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+import os
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+import torch
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+from unsloth import FastLanguageModel
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+from unsloth import is_bfloat16_supported
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+from trl import SFTTrainer, GRPOConfig, GRPOTrainer
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+from datasets import load_dataset
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+from transformers import TrainingArguments
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+
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+import re
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+from datasets import load_dataset, Dataset
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+from modelscope.msdatasets import MsDataset
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+
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+# Load and prep dataset
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+SYSTEM_PROMPT = """
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+Respond in the following format:
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+<reasoning>
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+...
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+</reasoning>
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+<answer>
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+...
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+</answer>
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+"""
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+
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+XML_COT_FORMAT = """\
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+<reasoning>
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+{reasoning}
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+</reasoning>
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+<answer>
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+{answer}
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+</answer>
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+"""
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+
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+def extract_xml_answer(text: str) -> str:
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+ answer = text.split("<answer>")[-1]
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+ answer = answer.split("</answer>")[0]
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+ return answer.strip()
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+
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+def extract_hash_answer(text: str) -> str | None:
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+ if "####" not in text:
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+ return None
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+ return text.split("####")[1].strip()
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+
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+# uncomment middle messages for 1-shot prompting
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+def get_gsm8k_questions(split = "train") -> Dataset:
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+ # data = load_dataset('https://huggingface.co/datasets/openai/gsm8k', 'main')[split] # type: ignore
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+
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+ data = MsDataset.load('openai-mirror/gsm8k', subset_name='main', split=split)
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+ data = data.map(lambda x: { # type: ignore
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+ 'prompt': [
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+ {'role': 'system', 'content': SYSTEM_PROMPT},
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+ {'role': 'user', 'content': x['question']}
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+ ],
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+ 'answer': extract_hash_answer(x['answer'])
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+ }) # type: ignore
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+ return data # type: ignore
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+
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+dataset = get_gsm8k_questions()
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+
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+# Reward functions
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+def correctness_reward_func(prompts, completions, answer, **kwargs) -> list[float]:
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+ responses = [completion[0]['content'] for completion in completions]
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+ q = prompts[0][-1]['content']
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+ extracted_responses = [extract_xml_answer(r) for r in responses]
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+ print('-'*20, f"Question:\n{q}", f"\nAnswer:\n{answer[0]}", f"\nResponse:\n{responses[0]}", f"\nExtracted:\n{extracted_responses[0]}")
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+ return [2.0 if r == a else 0.0 for r, a in zip(extracted_responses, answer)]
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+
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+def int_reward_func(completions, **kwargs) -> list[float]:
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+ responses = [completion[0]['content'] for completion in completions]
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+ extracted_responses = [extract_xml_answer(r) for r in responses]
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+ return [0.5 if r.isdigit() else 0.0 for r in extracted_responses]
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+
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+def strict_format_reward_func(completions, **kwargs) -> list[float]:
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+ """Reward function that checks if the completion has a specific format."""
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+ pattern = r"^<reasoning>\n.*?\n</reasoning>\n<answer>\n.*?\n</answer>\n$"
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+ responses = [completion[0]["content"] for completion in completions]
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+ matches = [re.match(pattern, r) for r in responses]
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+ return [0.5 if match else 0.0 for match in matches]
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+
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+def soft_format_reward_func(completions, **kwargs) -> list[float]:
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+ """Reward function that checks if the completion has a specific format."""
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+ pattern = r"<reasoning>.*?</reasoning>\s*<answer>.*?</answer>"
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+ responses = [completion[0]["content"] for completion in completions]
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+ matches = [re.match(pattern, r) for r in responses]
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+ return [0.5 if match else 0.0 for match in matches]
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+
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+def count_xml(text) -> float:
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+ count = 0.0
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+ if text.count("<reasoning>\n") == 1:
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+ count += 0.125
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+ if text.count("\n</reasoning>\n") == 1:
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+ count += 0.125
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+ if text.count("\n<answer>\n") == 1:
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+ count += 0.125
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+ count -= len(text.split("\n</answer>\n")[-1])*0.001
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+ if text.count("\n</answer>") == 1:
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+ count += 0.125
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+ count -= (len(text.split("\n</answer>")[-1]) - 1)*0.001
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+ return count
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+
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+def xmlcount_reward_func(completions, **kwargs) -> list[float]:
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+ contents = [completion[0]["content"] for completion in completions]
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+ return [count_xml(c) for c in contents]
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+
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+class ModelTrainer:
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+ def __init__(self, model_name, max_seq_length, dtype, load_in_4bit,lora_rank=32):
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+ # 初始化 ModelTrainer 类，设置模型名称、最大序列长度、数据类型和是否以4位加载
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+ self.model_name = model_name
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+ self.max_seq_length = max_seq_length
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+ self.dtype = dtype # dtype: 数据类型，如 torch.float16 或 torch.bfloat16
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+ self.load_in_4bit = load_in_4bit # load_in_4bit: 是否以4位精度加载模型，用于节省显存
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+ self.lora_rank=lora_rank #Larger rank = smarter, but slower
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+
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+ def load_model(self):
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+ # 加载预训练模型和分词器
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+ model, tokenizer = FastLanguageModel.from_pretrained(
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+ model_name=self.model_name,
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+ max_seq_length=self.max_seq_length,
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+ load_in_4bit=self.load_in_4bit, # 值为True 以 4 bit量化进行微调，为False LoRA 16bit。这将内存使用量减少了 4 倍，使我们能够在免费的 16GB 内存 GPU 中实际进行微调。4 位量化本质上将权重转换为一组有限的数字以减少内存使用量。这样做的缺点是准确度会下降 1-2%。如果您想要这种微小的额外准确度，请在较大的 GPU（如 H100）上将其设置为 False。
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+ dtype=self.dtype,
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+ fast_inference = False, # Enable vLLM fast inference
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+ max_lora_rank = lora_rank,
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+ gpu_memory_utilization=0.6,# Reduce if out of memory
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+ )
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+
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+ # 添加 LoRA 适配器
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+ model = FastLanguageModel.get_peft_model(
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+ model,
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+ max_seq_length=self.max_seq_length, # 最大上下文（序列）长度
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+ r=16, # LoRA 的秩，控制适配器的复杂度
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+ target_modules=["q_proj", "k_proj", "v_proj", "o_proj",
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+ "gate_proj", "up_proj", "down_proj"], # 应用 LoRA 的目标模块
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+ lora_alpha=16, # LoRA 的 alpha 参数，控制适配器的缩放
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+ lora_dropout=0, # LoRA 的 dropout 率，设置为0以优化性能
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+ bias="none", # 是否在 LoRA 中添加偏置，设置为 "none" 以优化性能
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+ use_gradient_checkpointing="unsloth", # 使用梯度检查点以节省显存,对于非常长的上下文，使用 True 或 "unsloth"
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+ random_state=3407, # 随机种子，确保实验可复现
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+ use_rslora=False, # 是否使用 rank stabilized LoRA，当前不支持
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+ loftq_config=None, # LoftQ 配置，当前不支持
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+ )
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+
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+ return model, tokenizer
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+
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+ def load_data(self, train_data_path):
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+ # 加载训练集和测试集
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+ train_dataset = load_dataset("json", data_files={"train": train_data_path}, split="train")
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+ # train_data_path: 训练数据路径，格式为 JSONL
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+ return train_dataset
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+
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+ def train(self, model, tokenizer, train_dataset):
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+ print("is_bfloat16_supported()=",is_bfloat16_supported())
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+ training_args = GRPOConfig(
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+ use_vllm = True, # use vLLM for fast inference!
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+ learning_rate = 5e-6,
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+ adam_beta1 = 0.9,
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+ adam_beta2 = 0.99,
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+ weight_decay = 0.1,
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+ warmup_ratio = 0.1,
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+ lr_scheduler_type = "cosine",
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+ optim = "adamw_8bit",
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+ logging_steps = 1,
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+ bf16 = is_bfloat16_supported(),
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+ fp16 = not is_bfloat16_supported(),
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+ per_device_train_batch_size = 1,
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+ gradient_accumulation_steps = 1, # Increase to 4 for smoother training
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+ num_generations = 8, # Decrease if out of memory
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+ max_prompt_length = 256,
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+ max_completion_length = 200,
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+ # num_train_epochs = 1, # Set to 1 for a full training run
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+ max_steps = 250,
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+ save_steps = 250,
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+ max_grad_norm = 0.1,
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+ report_to = "none", # Can use Weights & Biases
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+ output_dir = "outputs",
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+ )
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+
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+ # 初始化 SFTTrainer
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+ trainer = GRPOTrainer(
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+ model = model,
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+ processing_class = tokenizer,
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+ reward_funcs = [
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+ xmlcount_reward_func,
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+ soft_format_reward_func,
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+ strict_format_reward_func,
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+ int_reward_func,
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+ correctness_reward_func,
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+ ],
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+ args = training_args,
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+ train_dataset = dataset,
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+ )
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+
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+ # 训练模型
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+ trainer.train()
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+
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+ return model
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+
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+ def save_model(self, model, tokenizer, save_path):
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+ # 保存模型和分词器
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+ model.save_pretrained(save_path)
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+ tokenizer.save_pretrained(save_path)
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+ print(f"Model saved to {save_path}")
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+
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+if __name__ == "__main__":
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+ # 配置参数
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+ model_name = os.path.join('..', 'models', 'pretrained', 'DeepSeek-R1-Distill-Qwen-1.5B')
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+ # model_name: 预训练模型的路径
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+ max_seq_length = 2048 # 最大序列长度
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+ dtype = torch.float16 # 数据类型
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+ load_in_4bit = True # 是否以4位精度加载模型
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+ lora_rank=32
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+
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+ # 定义训练集和测试集路径
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+ train_data_path = os.path.join('..', 'data', 'processed', 'train.jsonl')
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+ # train_data_path: 训练数据路径
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+
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+ # 初始化 ModelTrainer
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+ trainer = ModelTrainer(model_name, max_seq_length, dtype, load_in_4bit)
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+
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+ # 加载模型和分词器
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+ model, tokenizer = trainer.load_model()
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+
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+ # 加载数据集
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+ train_dataset = trainer.load_data(train_data_path)
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+
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+ # 训练模型
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+ model = trainer.train(model, tokenizer, train_dataset)
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+
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+ # 保存模型
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+ save_path = os.path.join('..', 'models', 'trained', 'DeepSeek-R1-Distill-Qwen-1.5B')
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+ trainer.save_model(model, tokenizer, save_path)
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