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提交toollearning 评测代码与示例
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‎README_zh.md‎

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Original file line numberDiff line numberDiff line change
@@ -253,9 +253,9 @@ answer: D
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explanation: 根据分析，题目中的时间序列在12点出的值265要明显大于周围数据，存在着突增现象，因此选择D是正确的。
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```
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#### 🔧 ToolLearning样本示例
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工具学习样本的数据格式与OpenAI的函数调用格式兼容。<br>
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详情请参阅[tool_learning_info_zh.md](resources/tool_learning_info_zh.md)。<br>
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工具学习评测过程，详情请参阅见 [tool_learning_evalution.md](resources/tool_learning_evalution.md)。<br>
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工具学习样本的数据格式与OpenAI的函数调用格式兼容。
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详情请参阅[tool_learning_info_zh.md](resources/tool_learning_info_zh.md)。
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工具学习评测过程，详情请参阅见 [tool_learning_evalution.md](resources/tool_learning_evalution.md)。
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<br>
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## 🚀 如何进行测试
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##tool learning 数据集评测教程
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###chatml接入方式
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如果需要在自己的 huggingface 格式的模型上进行测试的话，总的步骤分为如下几步:
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1. 编写~/evals/FuncCallEvalution 的 create_prompts 函数
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2. 编写~/models/base_model 的 相关函数
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3. 注册模型和评估函数
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4. 执行测试脚本
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如果模型在加载进来后不需要特殊的处理，而且输入也不需要转换为特定的格式（e.g. chatml 格式或者其他的 human-bot 格式），请直接跳转到第四步直接发起测试。
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####1. 编写 loader 函数
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如果模型在加载进来还需要做一些额外的处理（e.g. tokenizer 调整），需要去`src.context_builder.context_builder_family.py` 中继承`ModelAndTokenizerLoader` 类来覆写对应的`load_model` 和`load_tokenizer` 函数，具体可以参照以下示例：
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```python
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classFuncCallEvalution(ToolEvalution):
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defcreate_prompts(self,func_call_datas):
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'''
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datas: [
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{
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"instruction": history[his_idx],
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"input": "",
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"output": output,
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"history": [(human_content, ai_content), (), ()],
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"functions": tools
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}
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]
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'''
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system_content='''CodeFuse是一个面向研发领域的智能助手，旨在中立的、无害的帮助用户解决开发相关的问题，所有的回答均使用Markdown格式返回。
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你能利用许多工具和功能来完成给定的任务，在每一步中，你需要分析当前状态，并通过执行函数调用来确定下一步的行动方向。你可以进行多次尝试。如果你计划连续尝试不同的条件，请每次尝试一种条件。若给定了Finish函数,则以Finish调用结束，若没提供Finish函数，则以不带function_call的对话结束。'''
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function_format='''You are ToolGPT, you have access to the following APIs:\n{tools}'''
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func_call_train_datas= []
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history_error_cnt=0
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funccall_error_cnt=0
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for datain func_call_datas:
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tools= data["functions"]
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chatrounds= data["chatrounds"]
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function_content=""
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iflen(tools)>0:
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function_content= function_format.format(tools=json.dumps(tools,ensure_ascii=False,sort_keys=True))
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history= []
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for iin chatrounds:
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if i["role"]=="system":
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continue
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if i["role"]=="user":
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history.append(("user", i["content"]))
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if i["role"]=="assistant":
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if"function_call"in i:
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ifnotisinstance(i["function_call"],dict):
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funccall_error_cnt+=1
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continue
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content="#function"+ json.dumps({**{"content": i["content"]},**i["function_call"]},ensure_ascii=False)
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else:
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content= i["content"]
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history.append(("assistant", content))
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if i["role"]=="function":
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content= json.dumps({**{"content": i["content"]},**{"name": i["name"]}},ensure_ascii=False)
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history.append(("user", content))
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history= [i[1]for iin history]
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history[0]="\n".join([system_content,function_content, history[0]])
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for his_idxinrange(0,len(history),2):
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output= history[his_idx+1]
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if"#function"in output:
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output= output.split("#function")[-1]
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try:
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output= json.loads(output)
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except:
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output= {"content": output}
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func_call_train_datas.append(
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{
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"instruction": history[his_idx],
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"input":"",
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"output": output,
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"history": [history[:his_idx+2][i:i+2]for iinrange(0,len(history[:his_idx]),2)],
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"functions": tools
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},
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)
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return func_call_train_datas
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```
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####2. 编写 Model 的 context_builder 函数
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如果输入需要转换为特定的格式（e.g. chatml 格式或者其他的 human-bot 格式），则需要去`src.context_builder.context_builder_family` 中继承 ContextBuilder 类来覆写 make_context 函数，这个函数是用来将输入转换格式为对应需要的输出的，一个示例如下：
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```python
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classToolModel:
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def__init__(self,model_path:str,template:str,trust_remote_code=True,tensor_parallel_size=1,gpu_memory_utilization=0.25):
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self.model_path= model_path
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self.trust_remote_code= trust_remote_code
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self.tensor_parallel_size= tensor_parallel_size
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self.gpu_memory_utilization= gpu_memory_utilization
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self.load_model(self.model_path,self.trust_remote_code,self.tensor_parallel_size,self.gpu_memory_utilization)
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defgenerate(self,prompts:str,template:str=None,generate_configs: GenerateConfigs=None) ->list:
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'''产出对应结果'''
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pass
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defgenerate_params(
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self,generate_configs: GenerateConfigs,
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):
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'''generate param'''
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kargs= generate_configs.dict()
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return kargs
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defload_model(self,model_path,trust_remote_code=True,tensor_parallel_size=1,gpu_memory_utilization=0.25):
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'''加载模型'''
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self.tokenizer= AutoTokenizer.from_pretrained(self.model_path,trust_remote_code=trust_remote_code)
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self.model= AutoModelForCausalLM.from_pretrained(self.model_path,device_map="auto",trust_remote_code=trust_remote_code).eval()
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# self.model = LLM(model=model_path, trust_remote_code=trust_remote_code, tensor_parallel_size=tensor_parallel_size, gpu_memory_utilization=gpu_memory_utilization)
123+
```
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####3. 注册模型和eval函数即可
126+
在~/models/__init__.py 中注册即可
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```python
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from .base_modelimport ToolModel
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__all__= [
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"ToolModel",
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]
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```
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在~/evasl/__init__.py 中注册即可
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```python
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from .base_evalutionimport ToolEvalution
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from .toolfill_evalutionimport ToolFillEvalution
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from .toolparser_evalutionimport ToolParserEvalution
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from .toolsummary_evalutionimport ToolSummaryEvalution
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from .func_call_evalutionimport FuncCallEvalution
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__all__= [
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"ToolEvalution","ToolFillEvalution","ToolParserEvalution","ToolSummaryEvalution","FuncCallEvalution"
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]
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```
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####4. 执行测试脚本
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修改~/src/qwen_eval_main.py# datainfos和model_infos
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```python
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model_infos= [
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{"model_name":"","template":"chatml","model_path":"",
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"peft_path":"","model_class": QwenModel}]
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datainfos= [
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{"dataset_path":"~/fcdata_luban_zh_test.jsonl","dataset_name":"fcdata_luban_zh","tool_task":"func_call"},
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{"dataset_path":"~/test_datas/fcdata_zh_test_v1.jsonl","dataset_name":"fcdata_zh","tool_task":"func_call"},
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]
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```
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运行下述命令即可
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```Bash
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python qwen_eval_main.py
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```
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<br>
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###非chatml接入
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如果需要在自己的 huggingface 格式的模型上进行测试的话，总的步骤分为如下几步:
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1. 编写~/getAssistantAns.py 相关代码
172+
2. 执行测试脚本
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####1、编写 getAssistantAns 示例
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```
177+
class GetAssistantAns():
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# 按照自己推理需求自己修改代码
179+
180+
def __init__(self, gpu_num=1):
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model = AutoModelForCausalLM.from_pretrained(model_name)
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device_list = []
183+
for gpu_idx in range(gpu_num):
184+
device_list.append(torch.device("cuda:0"))
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# 将模型移动到指定的GPU设备
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model.to(device)
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189+
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def gen_answer(self, chat_dict, gpu_index):
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# 这里实际根据自己推理逻辑 然后转为标准格式返回
192+
# 以下仅仅是样例
193+
import time
194+
print(os.environ["CUDA_VISIBLE_DEVICES"])
195+
time.sleep(1)
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rtn_dict1 = {
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"role": "assistant",
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"content": None,
199+
"function_call":
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{
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"name": "get_fudan_university_scoreline",
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"arguments": "{\n \"year\": \"2020\"\n}"
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}
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}
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rtn_dict2 = {
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"role": "assistant",
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"content": "2020年复旦大学的分数线如下：\n\n- 文科一批：630分\n- 文科二批：610分\n- 理科一批：650分\n- 理科二批：630分"
209+
}
210+
211+
return random.choice([rtn_dict1, rtn_dict2])
212+
```
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####2、执行测试脚本
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修改~/src/opensource_functioncall_evalution.py # test_ans_file_list
215+
```python
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test_ans_file_list= [
217+
"fcdata_zh_test.jsonl"
218+
]
219+
```
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运行下述命令即可
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```Bash
223+
python opensource_functioncall_evalution.py
224+
```

‎src/getAssistantAns.py‎

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importos
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importjson
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importrandom
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importtorch
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fromtransformersimportAutoModelForCausalLM
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classGetAssistantAns():
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# 按照自己推理需求自己修改代码
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10+
def__init__(self,gpu_num=1):
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model=AutoModelForCausalLM.from_pretrained(model_name)
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device_list= []
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forgpu_idxinrange(gpu_num):
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device_list.append(torch.device("cuda:0"))
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# 将模型移动到指定的GPU设备
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model.to(device)
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19+
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defgen_answer(self,chat_dict,gpu_index):
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# 这里实际根据自己推理逻辑 然后转为标准格式返回
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# 以下仅仅是样例
23+
importtime
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print(os.environ["CUDA_VISIBLE_DEVICES"])
25+
time.sleep(1)
26+
rtn_dict1= {
27+
"role":"assistant",
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"content":None,
29+
"function_call":
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{
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"name":"get_fudan_university_scoreline",
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"arguments":"{\n\"year\":\"2020\"\n}"
33+
}
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}
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36+
rtn_dict2= {
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"role":"assistant",
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"content":"2020年复旦大学的分数线如下：\n\n- 文科一批：630分\n- 文科二批：610分\n- 理科一批：650分\n- 理科二批：630分"
39+
}
40+
41+
returnrandom.choice([rtn_dict1,rtn_dict2])
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# ======================================================================
44+
# 下面注释的部分是一个huggingface推理的多卡的demo
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# 备注 线程数量==2，也就是2卡效率最高 多个卡并不会提升效率，存在资源抢占的情况
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# 可以采用多卡部署服务，然后调用服务的方式提升效率
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# import os
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# import re
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# import json
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# import random
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# import torch
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# import copy
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# import transformers
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# from transformers import AutoModelForCausalLM, CodeLlamaTokenizer, TextStreamer
56+
# end_token_id = 2
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# checkpoint = "<your_huggingface_model_dir>"
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# tokenizer = CodeLlamaTokenizer.from_pretrained(checkpoint)
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# torch.manual_seed(random.randint(0, 100000))
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# temperature = 0.2
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# top_p = 0.95
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# top_k = 40
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# repetition_penalty = 1.1
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# output_len = 2048
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# role_start = "[START]"
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# role_end = "[END]"
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68+
69+
# def change2traindata(fc_dict):
70+
# chatrounds_list = []
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# # insert your code
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# # eg
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# # chatrounds_list = [
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# # {
75+
# # "role": "system",
76+
# # "content":"你好，我是小助手，我能帮你做什么呢？"
77+
# # },
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# # {
79+
# # "role": "functionapis"
80+
# # "content": "You are tool gpt, you can using following apis []"
81+
# # },
82+
# # {
83+
# # "role": "user",
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# # "content": "我想知道复旦大学的分数线"
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# # },
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# # {
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# # "role": "bot",
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# # "content": "#function{*****}"
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# # },
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# # {
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# # "role": "function",
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# # "content": ******
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# # },
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# # {
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# # "role": "bot",
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# # "content": "复旦大学分数线640"
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# # }
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# # ]
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# return chatrounds_list
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101+
102+
# def get_chatrounds_ids(chatrounds_list):
103+
# input_ids = []
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# for chatround in chatrounds_list:
105+
# input_ids += tokenizer.encode(role_start + chatround["role"]+ role_end) + tokenizer.encode(chatround["content"], add_special_tokens=False) + [tokenizer.eos_token_id]
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# input_ids += tokenizer.encode(role_start + "bot" + role_end)
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# return input_ids
108+
109+
110+
# class GetAssistantAns():
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# # 按照自己推理需求自己修改代码
112+
113+
# def __init__(self, gpu_num=1):
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# print(checkpoint)
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# print("Loading model")
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# model = AutoModelForCausalLM.from_pretrained(checkpoint).half().eval()
117+
# device_list = [torch.device(f"cuda:%d"%(i)) for i in range(gpu_num)]
118+
# self.model_list = [copy.deepcopy(model.to(device)) for device in device_list]
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# print("Loading finish")
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# def gen_answer(self, chat_dict, gpu_index=0):
122+
# chatrounds_list = change2traindata(chat_dict)
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# input_ids = get_chatrounds_ids(chatrounds_list)
124+
# output_ids = self.model_list[gpu_index].generate(torch.tensor([input_ids]).to(self.model_list[gpu_index].device), max_new_tokens=output_len, num_beams=1, num_return_sequences=1, do_sample=True, temperature=temperature, top_p=top_p, eos_token_id=end_token_id, top_k=top_k, streamer=None, repetition_penalty=repetition_penalty, pad_token_id=10000)[0]
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# res = tokenizer.decode(output_ids[len(input_ids):-1])
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# save_dict = {"role": "assistant"}
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# if res.startswith("#function"):
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# try:
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# res_dict = json.loads(re.sub("^#function", "", res))
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# save_dict["content"] = res_dict["content"]
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# save_dict["function_call"] = {}
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# save_dict["function_call"]["name"] = res_dict["name"]
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# save_dict["function_call"]["arguments"] = res_dict["arguments"]
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# except Exception as e:
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# print(e)
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# save_dict = {"role": "assistant"}
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# save_dict["content"] = res
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# else:
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# save_dict["content"] = res
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# # print(save_dict)
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# return save_dict

‎src/qwen_eval_main.py‎

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fromsrc.modelsimportQwenModel,ToolModel,InternlmModel
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fromsrc.models.generate_configsimportGenerateConfigs
77
fromsrc.prompts.base_prompts_configimportTOOL_FILL_BASE_PROMPT,TOOL_PARSER_BASE_PROMPT,TOOL_SUMMARY_BASE_PROMPT
8+
fromsrc.utils.jsonl_utilsimportsave_to_jsonl_file
89

910
importwarnings
1011
importre

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