通过LLM多轮对话生成单元测试用例

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通过LLM多轮对话生成单元测试用例

代码

在采用随机生成pytorch算子测试序列且保证算子参数合法这种方法之前,曾通过本文的方法生成算子组合测试用例。目前所测LLM生成的代码均会出现BUG,且多次交互后仍不能解决.也许随着LLM的更新,这个问题会得到解决.记录备用。

代码

import re
import os
import logging
import random
import numpy as np
import os
import re
import traceback
import subprocess
import tempfile
import copy
import requests
import jsonimport os
os.environ['MKL_THREADING_LAYER'] = 'GNU'
os.environ['MKL_SERVICE_FORCE_INTEL'] = '1'os.environ["QIANFAN_AK"] = ""
os.environ["QIANFAN_SK"] = ""
os.environ['DASHSCOPE_API_KEY'] = 'sk-'
os.environ['MOONSHOT_API_KEY']="sk-"
os.environ['SPARKAI_APP_ID'] = ''
os.environ['SPARKAI_API_SECRET'] = ''
os.environ['SPARKAI_API_KEY'] = ''
os.environ['SPARKAI_DOMAIN'] = 'generalv3.5'
os.environ['ZhipuAI_API_KEY'] = ''
os.environ['YI_API_KEY']=""logger = logging.getLogger('llm_logger')
logger.setLevel(logging.DEBUG)  # 设置日志级别# 创建一个handler，用于写入日志文件
log_file = 'llm_opt.log'
file_handler = logging.FileHandler(log_file)
file_handler.setLevel(logging.DEBUG)# 创建一个handler，用于将日志输出到控制台
console_handler = logging.StreamHandler()
console_handler.setLevel(logging.DEBUG)# 设置日志格式
formatter = logging.Formatter('%(message)s')
file_handler.setFormatter(formatter)
console_handler.setFormatter(formatter)# 将handlers添加到logger
logger.addHandler(file_handler)
logger.addHandler(console_handler)system_prompt="你是一位pytorch专家,现在需要编写各种测试程序,挖掘算子的潜在BUG"question =f'''
背景描述:
1.为了测试pytorch不同算子组合时的精度是否正常,需要构建module级别的测试用例
2.尤其需要关注unsqueeze,repeat,permute,transpose,reshape,expand,view等维度变换算子的各种组合
3.以及在这些组合之后添加其它io或计算类的算子如(contiguous,matmul,mul,concat等)需求:
1.你一次生成一个测试用例(pytorch module及测例),只包含cpu计算
2.之后,我会从的回复中提取出python代码,执行并将结果反馈给你
3.你根据我的反馈,预测性地生成下一个测试用例
4.我们通过多次交互,最大程度地挖掘出潜在的BUG约束:
1.所有测试用例的代码放在一个```python ```中,方便提取
2.为了防止shape不匹配,建议在forward中计算shape,并根据当前的shape合理地设置下一个算子的参数
3.你每次提供的代码都必须是完整的,不要添加任何注释
4.测试代码只输出成功、失败或抛异常,不需要输出任何多余信息
5.特别需要注意矩阵乘维度是否匹配如果你明白我的意思,请直接输出第一个测试用例
'''def extract_and_run_python_code(markdown_text):pattern = re.compile(r'```python\n([^```].*?)\n```', re.DOTALL)code_blocks = pattern.findall(markdown_text)if len(code_blocks)==0:return "没有找到Python代码块。"results = []for code in code_blocks:try:with tempfile.NamedTemporaryFile(delete=False, suffix=".py") as temp_file:temp_file.write(code.encode())temp_filename = temp_file.nameresult = subprocess.run(['python3', temp_filename], capture_output=True, text=True)    output=f"{result.stderr}{result.stdout}"results.append(output)except Exception as e:error_message = f"error:{traceback.format_exc()}"results.append(error_message)        finally:os.remove(temp_filename)return "".join(results)class LLMInfer(object):def __init__(self, system_prompt,question,history_len=5):self.system_prompt=system_promptself.question=question    self.history_len=history_len   def infer(self,user_input=None):pass    def reset(self):passclass dashscope_llm(LLMInfer):def __init__(self, system_prompt, question):super().__init__(system_prompt, question)import dashscopedashscope.api_key=os.environ['DASHSCOPE_API_KEY'] self.history=[]self.history.append({'role': 'system', 'content': self.system_prompt})self.history.append({'role': 'user', 'content': self.question})		def reset(self):if len(self.history)>self.history_len:self.history=self.history[:2] + self.history[-3:]def infer(self,user_input=None):from dashscope import Generationfrom http import HTTPStatus          if user_input:self.history.append({'role': 'user', 'content': user_input})response = Generation.call(model="qwen-plus", messages=self.history,result_format='message')if response.status_code == HTTPStatus.OK:role=response.output.choices[0]['message']['role']content=response.output.choices[0]['message']['content']self.history.append({'role': role,'content': content})return contentelse:return Noneclass moonshot_llm(LLMInfer):def __init__(self, system_prompt, question):super().__init__(system_prompt, question)'''pip install --upgrade 'openai>=1.0''''from openai import OpenAIself.client = OpenAI(api_key = os.environ['MOONSHOT_API_KEY'],base_url = "https://api.moonshot.cn/v1",)self.history=[]self.history.append({'role': 'system', 'content': self.system_prompt})self.history.append({'role': 'user', 'content': self.question})		def reset(self):if len(self.history)>self.history_len:self.history=self.history[:2] + self.history[-3:]def infer(self,user_input=None):      if user_input:self.history.append({'role': 'user', 'content': user_input})completion = self.client.chat.completions.create(model="moonshot-v1-128k",messages=self.history,temperature=0.3,top_p=0.1)role="assistant"content=completion.choices[0].message.contentself.history.append({'role': role,'content': content})return contentclass qianfan_llm(LLMInfer):def __init__(self, system_prompt, question):super().__init__(system_prompt, question)'''pip3 install qianfan'''self.history=[]#self.history.append({'role': 'system', 'content': self.system_prompt})self.history.append({'role': 'user', 'content': self.question})		def reset(self):if len(self.history)>self.history_len:self.history=self.history[:1] + self.history[-2:]def infer(self,user_input=None):    import qianfan  if user_input:self.history.append({'role': 'user', 'content': user_input})response = qianfan.ChatCompletion().do(endpoint="completions_pro", messages=self.history,temperature=0.7, top_p=0.8, penalty_score=1,                                             disable_search=False, enable_citation=False)role="assistant"content=response.body["result"]self.history.append({'role': role,'content': content})return contentclass sparkai_llm(LLMInfer):def __init__(self, system_prompt, question):super().__init__(system_prompt, question)'''pip3 install --upgrade spark_ai_python'''from sparkai.llm.llm import ChatSparkLLMfrom sparkai.core.messages import ChatMessageself.spark = ChatSparkLLM(spark_api_url='wss://spark-api.xf-yun.com/v3.5/chat',spark_app_id=os.environ['SPARKAI_APP_ID'],spark_api_key=os.environ['SPARKAI_API_KEY'],spark_api_secret=os.environ['SPARKAI_API_SECRET'],spark_llm_domain=os.environ['SPARKAI_DOMAIN'],streaming=False,        temperature=0.1)self.history=[]self.history.append(ChatMessage(role="system",content=self.system_prompt))self.history.append(ChatMessage(role="user",content=self.question))def reset(self):if len(self.history)>self.history_len:self.history=self.history[:2] + self.history[-3:]def infer(self,user_input=None):    from sparkai.core.messages import ChatMessagefrom sparkai.llm.llm import ChunkPrintHandlerif user_input:self.history.append(ChatMessage(role="user",content=user_input))        handler = ChunkPrintHandler()response = self.spark.generate([self.history], callbacks=[handler])self.history.append(response.generations[0][0].message)return response.generations[0][0].textclass zhipuai_llm(LLMInfer):def __init__(self, system_prompt, question):super().__init__(system_prompt, question)'''pip install zhipuai'''from zhipuai import ZhipuAIself.client = ZhipuAI(api_key=os.environ['ZhipuAI_API_KEY'])self.history=[]self.history.append({'role': 'system', 'content': self.system_prompt})self.history.append({'role': 'user', 'content': self.question})		def reset(self):if len(self.history)>self.history_len:self.history=self.history[:2] + self.history[-3:]def infer(self,user_input=None):      if user_input:self.history.append({'role': 'user', 'content': user_input})completion = self.client.chat.completions.create(model="glm-4",messages=self.history,temperature=0.3,top_p=0.1)role="assistant"content=completion.choices[0].message.contentself.history.append({'role': role,'content': content})return contentclass yi_llm(LLMInfer):def __init__(self, system_prompt, question):super().__init__(system_prompt, question)'''pip install --upgrade 'openai>=1.0''''from openai import OpenAIself.client = OpenAI(api_key = os.environ['YI_API_KEY'],base_url = "https://api.lingyiwanwu.com/v1",)self.history=[]self.history.append({'role': 'system', 'content': self.system_prompt})self.history.append({'role': 'user', 'content': self.question})		def reset(self):if len(self.history)>self.history_len:self.history=self.history[:2] + self.history[-3:]def infer(self,user_input=None):      if user_input:self.history.append({'role': 'user', 'content': user_input})completion = self.client.chat.completions.create(model="yi-large",messages=self.history,temperature=0.3,top_p=0.1)role="assistant"content=completion.choices[0].message.contentself.history.append({'role': role,'content': content})return contentllms=[dashscope_llm,moonshot_llm,qianfan_llm,sparkai_llm,zhipuai_llm,yi_llm]
for llm in llms:logger.info(f" ---------------------------------- {llm.__name__} ---------------------------------- ")llm=llm(system_prompt,question)response = llm.infer()for i in range(15):llm.reset()logger.info(f" ---------------------------------- 第{i}轮 ---------------------------------- ")result=Nonelogger.info("####### bot #######")logger.info(f"{response}")if response:result=f"{extract_and_run_python_code(response)}"     logger.info("####### user #######")logger.info(f"{result}")response=llm.infer(result)