修改模型生成的调用方式，兼容Chain调用

修改模型切换的bug

api.py

@@ -384,8 +384,10 @@ async def chat(
             ],
         ),
 ):
-    for answer_result in local_doc_qa.llm.generatorAnswer(prompt=question, history=history,
-                                                          streaming=True):
+    answer_result_stream_result = local_doc_qa.llm_model_chain(
+        {"prompt": question, "history": history, "streaming": True})
+
+    for answer_result in answer_result_stream_result['answer_result_stream']:
         resp = answer_result.llm_output["answer"]
         history = answer_result.history
         pass
@@ -486,7 +488,6 @@ def api_start(host, port, **kwargs):
     global local_doc_qa
 
     llm_model_ins = shared.loaderLLM()
-    llm_model_ins.set_history_len(LLM_HISTORY_LEN)
 
     app = FastAPI()
     # Add CORS middleware to allow all origins

chains/local_doc_qa.py

@@ -18,6 +18,7 @@ from agent import bing_search
 from langchain.docstore.document import Document
 from functools import lru_cache
 from textsplitter.zh_title_enhance import zh_title_enhance
+from langchain.chains.base import Chain
 
 
 # patch HuggingFaceEmbeddings to make it hashable
@@ -119,7 +120,7 @@ def search_result2docs(search_results):
 
 
 class LocalDocQA:
-    llm: BaseAnswer = None
+    llm_model_chain: Chain = None
     embeddings: object = None
     top_k: int = VECTOR_SEARCH_TOP_K
     chunk_size: int = CHUNK_SIZE
@@ -129,10 +130,10 @@ class LocalDocQA:
     def init_cfg(self,
                  embedding_model: str = EMBEDDING_MODEL,
                  embedding_device=EMBEDDING_DEVICE,
-                 llm_model: BaseAnswer = None,
+                 llm_model: Chain = None,
                  top_k=VECTOR_SEARCH_TOP_K,
                  ):
-        self.llm = llm_model
+        self.llm_model_chain = llm_model
         self.embeddings = HuggingFaceEmbeddings(model_name=embedding_model_dict[embedding_model],
                                                 model_kwargs={'device': embedding_device})
         self.top_k = top_k
@@ -236,8 +237,10 @@ class LocalDocQA:
         else:
             prompt = query
 
-        for answer_result in self.llm.generatorAnswer(prompt=prompt, history=chat_history,
-                                                      streaming=streaming):
+        answer_result_stream_result = self.llm_model_chain(
+            {"prompt": prompt, "history": chat_history, "streaming": streaming})
+
+        for answer_result in answer_result_stream_result['answer_result_stream']:
             resp = answer_result.llm_output["answer"]
             history = answer_result.history
             history[-1][0] = query
@@ -276,8 +279,10 @@ class LocalDocQA:
         result_docs = search_result2docs(results)
         prompt = generate_prompt(result_docs, query)
 
-        for answer_result in self.llm.generatorAnswer(prompt=prompt, history=chat_history,
-                                                      streaming=streaming):
+        answer_result_stream_result = self.llm_model_chain(
+            {"prompt": prompt, "history": chat_history, "streaming": streaming})
+
+        for answer_result in answer_result_stream_result['answer_result_stream']:
             resp = answer_result.llm_output["answer"]
             history = answer_result.history
             history[-1][0] = query
@@ -296,7 +301,7 @@ class LocalDocQA:
     def update_file_from_vector_store(self,
                                       filepath: str or List[str],
                                       vs_path,
-                                      docs: List[Document],):
+                                      docs: List[Document], ):
         vector_store = load_vector_store(vs_path, self.embeddings)
         status = vector_store.update_doc(filepath, docs)
         return status
@@ -320,7 +325,6 @@ if __name__ == "__main__":
     args_dict = vars(args)
     shared.loaderCheckPoint = LoaderCheckPoint(args_dict)
     llm_model_ins = shared.loaderLLM()
-    llm_model_ins.set_history_len(LLM_HISTORY_LEN)
 
     local_doc_qa = LocalDocQA()
     local_doc_qa.init_cfg(llm_model=llm_model_ins)

configs/model_config.py

@@ -37,61 +37,67 @@ llm_model_dict = {
         "name": "chatglm-6b-int4-qe",
         "pretrained_model_name": "THUDM/chatglm-6b-int4-qe",
         "local_model_path": None,
-        "provides": "ChatGLM"
+        "provides": "ChatGLMLLMChain"
     },
     "chatglm-6b-int4": {
         "name": "chatglm-6b-int4",
         "pretrained_model_name": "THUDM/chatglm-6b-int4",
         "local_model_path": None,
-        "provides": "ChatGLM"
+        "provides": "ChatGLMLLMChain"
     },
     "chatglm-6b-int8": {
         "name": "chatglm-6b-int8",
         "pretrained_model_name": "THUDM/chatglm-6b-int8",
         "local_model_path": None,
-        "provides": "ChatGLM"
+        "provides": "ChatGLMLLMChain"
     },
     "chatglm-6b": {
         "name": "chatglm-6b",
         "pretrained_model_name": "THUDM/chatglm-6b",
         "local_model_path": None,
-        "provides": "ChatGLM"
+        "provides": "ChatGLMLLMChain"
     },
     "chatglm2-6b": {
         "name": "chatglm2-6b",
         "pretrained_model_name": "THUDM/chatglm2-6b",
         "local_model_path": None,
-        "provides": "ChatGLM"
+        "provides": "ChatGLMLLMChain"
     },
     "chatglm2-6b-int4": {
         "name": "chatglm2-6b-int4",
         "pretrained_model_name": "THUDM/chatglm2-6b-int4",
         "local_model_path": None,
-        "provides": "ChatGLM"
+        "provides": "ChatGLMLLMChain"
     },
     "chatglm2-6b-int8": {
         "name": "chatglm2-6b-int8",
         "pretrained_model_name": "THUDM/chatglm2-6b-int8",
         "local_model_path": None,
-        "provides": "ChatGLM"
+        "provides": "ChatGLMLLMChain"
     },
     "chatyuan": {
         "name": "chatyuan",
         "pretrained_model_name": "ClueAI/ChatYuan-large-v2",
         "local_model_path": None,
-        "provides": "MOSSLLM"
+        "provides": "MOSSLLMChain"
     },
     "moss": {
         "name": "moss",
         "pretrained_model_name": "fnlp/moss-moon-003-sft",
         "local_model_path": None,
-        "provides": "MOSSLLM"
+        "provides": "MOSSLLMChain"
     },
     "vicuna-13b-hf": {
         "name": "vicuna-13b-hf",
         "pretrained_model_name": "vicuna-13b-hf",
         "local_model_path": None,
-        "provides": "LLamaLLM"
+        "provides": "LLamaLLMChain"
+    },
+    "vicuna-7b-hf": {
+        "name": "vicuna-13b-hf",
+        "pretrained_model_name": "vicuna-13b-hf",
+        "local_model_path": None,
+        "provides": "LLamaLLMChain"
     },
     # 直接调用返回requests.exceptions.ConnectionError错误，需要通过huggingface_hub包里的snapshot_download函数
     # 下载模型，如果snapshot_download还是返回网络错误，多试几次，一般是可以的，
@@ -101,7 +107,7 @@ llm_model_dict = {
         "name": "bloomz-7b1",
         "pretrained_model_name": "bigscience/bloomz-7b1",
         "local_model_path": None,
-        "provides": "MOSSLLM"
+        "provides": "MOSSLLMChain"
 
     },
     # 实测加载bigscience/bloom-3b需要170秒左右，暂不清楚为什么这么慢
@@ -110,14 +116,14 @@ llm_model_dict = {
         "name": "bloom-3b",
         "pretrained_model_name": "bigscience/bloom-3b",
         "local_model_path": None,
-        "provides": "MOSSLLM"
+        "provides": "MOSSLLMChain"
 
     },
     "baichuan-7b": {
         "name": "baichuan-7b",
         "pretrained_model_name": "baichuan-inc/baichuan-7B",
         "local_model_path": None,
-        "provides": "MOSSLLM"
+        "provides": "MOSSLLMChain"
     },
     # llama-cpp模型的兼容性问题参考https://github.com/abetlen/llama-cpp-python/issues/204
     "ggml-vicuna-13b-1.1-q5": {
@@ -131,7 +137,7 @@ llm_model_dict = {
         # 需要手动从https://github.com/abetlen/llama-cpp-python/releases/tag/下载对应的wheel安装
         # 实测v0.1.63与本模型的vicuna/ggml-vicuna-13b-1.1/ggml-vic13b-q5_1.bin可以兼容
         "local_model_path": f'''{"/".join(os.path.abspath(__file__).split("/")[:3])}/.cache/huggingface/hub/models--vicuna--ggml-vicuna-13b-1.1/blobs/''',
-        "provides": "LLamaLLM"
+        "provides": "LLamaLLMChain"
     },
 
     # 通过 fastchat 调用的模型请参考如下格式
@@ -139,7 +145,7 @@ llm_model_dict = {
         "name": "chatglm-6b",  # "name"修改为fastchat服务中的"model_name"
         "pretrained_model_name": "chatglm-6b",
         "local_model_path": None,
-        "provides": "FastChatOpenAILLM",  # 使用fastchat api时，需保证"provides"为"FastChatOpenAILLM"
+        "provides": "FastChatOpenAILLMChain",  # 使用fastchat api时，需保证"provides"为"FastChatOpenAILLMChain"
         "api_base_url": "http://localhost:8000/v1",  # "name"修改为fastchat服务中的"api_base_url"
         "api_key": "EMPTY"
     },
@@ -147,7 +153,7 @@ llm_model_dict = {
         "name": "chatglm2-6b",  # "name"修改为fastchat服务中的"model_name"
         "pretrained_model_name": "chatglm2-6b",
         "local_model_path": None,
-        "provides": "FastChatOpenAILLM",  # 使用fastchat api时，需保证"provides"为"FastChatOpenAILLM"
+        "provides": "FastChatOpenAILLMChain",  # 使用fastchat api时，需保证"provides"为"FastChatOpenAILLMChain"
         "api_base_url": "http://localhost:8000/v1"  # "name"修改为fastchat服务中的"api_base_url"
     },
 
@@ -156,7 +162,7 @@ llm_model_dict = {
         "name": "vicuna-13b-hf",  # "name"修改为fastchat服务中的"model_name"
         "pretrained_model_name": "vicuna-13b-hf",
         "local_model_path": None,
-        "provides": "FastChatOpenAILLM",  # 使用fastchat api时，需保证"provides"为"FastChatOpenAILLM"
+        "provides": "FastChatOpenAILLMChain",  # 使用fastchat api时，需保证"provides"为"FastChatOpenAILLMChain"
         "api_base_url": "http://localhost:8000/v1",  # "name"修改为fastchat服务中的"api_base_url"
         "api_key": "EMPTY"
     },
@@ -171,7 +177,7 @@ llm_model_dict = {
     "openai-chatgpt-3.5": {
         "name": "gpt-3.5-turbo",
         "pretrained_model_name": "gpt-3.5-turbo",
-        "provides": "FastChatOpenAILLM",
+        "provides": "FastChatOpenAILLMChain",
         "local_model_path": None,
         "api_base_url": "https://api.openapi.com/v1",
         "api_key": ""
@@ -226,7 +232,7 @@ LLM_HISTORY_LEN = 3
 VECTOR_SEARCH_TOP_K = 5
 
 # 知识检索内容相关度 Score, 数值范围约为0-1100，如果为0，则不生效，经测试设置为小于500时，匹配结果更精准
-VECTOR_SEARCH_SCORE_THRESHOLD = 0
+VECTOR_SEARCH_SCORE_THRESHOLD = 390
 
 NLTK_DATA_PATH = os.path.join(os.path.dirname(os.path.dirname(__file__)), "nltk_data")
 

models/__init__.py

-from .chatglm_llm import ChatGLM
-from .llama_llm import LLamaLLM
-from .moss_llm import MOSSLLM
-from .fastchat_openai_llm import FastChatOpenAILLM
+from .chatglm_llm import ChatGLMLLMChain
+from .llama_llm import LLamaLLMChain
+from .fastchat_openai_llm import FastChatOpenAILLMChain
+from .moss_llm import MOSSLLMChain

models/base/__init__.py

 from models.base.base import (
     AnswerResult,
-    BaseAnswer
-)
+    BaseAnswer,
+    AnswerResultStream,
+    AnswerResultQueueSentinelTokenListenerQueue)
 from models.base.remote_rpc_model import (
     RemoteRpcModel
 )
-
 __all__ = [
     "AnswerResult",
     "BaseAnswer",
     "RemoteRpcModel",
+    "AnswerResultStream",
+    "AnswerResultQueueSentinelTokenListenerQueue"
 ]

models/base/base.py

 from abc import ABC, abstractmethod
-from typing import Optional, List
+from typing import Any, Dict, List, Optional, Generator
 import traceback
 from collections import deque
 from queue import Queue
 from threading import Thread
-
+from langchain.callbacks.manager import CallbackManagerForChainRun
+from models.loader import LoaderCheckPoint
 import torch
 import transformers
-from models.loader import LoaderCheckPoint
+
+
+class ListenerToken:
+    """
+    观测结果
+    """
+
+    input_ids: torch.LongTensor
+    _scores: torch.FloatTensor
+
+    def __init__(self, input_ids: torch.LongTensor, _scores: torch.FloatTensor):
+        self.input_ids = input_ids
+        self._scores = _scores
 
 
 class AnswerResult:
@@ -16,6 +29,123 @@ class AnswerResult:
     """
     history: List[List[str]] = []
     llm_output: Optional[dict] = None
+    listenerToken: ListenerToken = None
+
+
+class AnswerResultStream:
+    def __init__(self, callback_func=None):
+        self.callback_func = callback_func
+
+    def __call__(self, answerResult: AnswerResult):
+        if self.callback_func is not None:
+            self.callback_func(answerResult)
+
+
+class AnswerResultQueueSentinelTokenListenerQueue(transformers.StoppingCriteria):
+    """
+     定义模型stopping_criteria 监听者，在每次响应时将队列数据同步到AnswerResult
+     实现此监听器的目的是，不同模型的预测输出可能不是矢量信息，hf框架可以自定义transformers.StoppingCriteria入参来接收每次预测的Tensor和损失函数，
+     通过给 StoppingCriteriaList指定模型生成答案时停止的条件。每个 StoppingCriteria 对象表示一个停止条件
+     当每轮预测任务开始时，StoppingCriteria都会收到相同的预测结果，最终由下层实现类确认是否结束
+     输出值可用于 generatorAnswer generate_with_streaming的自定义参数观测，以实现更加精细的控制
+    """
+
+    listenerQueue: deque = deque(maxlen=1)
+
+    def __init__(self):
+        transformers.StoppingCriteria.__init__(self)
+
+    def __call__(self, input_ids: torch.LongTensor, _scores: torch.FloatTensor, **kwargs) -> bool:
+        """
+        每次响应时将数据添加到响应队列
+        :param input_ids:
+        :param _scores:
+        :param kwargs:
+        :return:
+        """
+        self.listenerQueue.append(ListenerToken(input_ids=input_ids, _scores=_scores))
+        return False
+
+
+class Iteratorize:
+    """
+    Transforms a function that takes a callback
+    into a lazy iterator (generator).
+    """
+
+    def __init__(self, func, kwargs={}):
+        self.mfunc = func
+        self.q = Queue()
+        self.sentinel = object()
+        self.kwargs = kwargs
+        self.stop_now = False
+
+        def _callback(val):
+            """
+            模型输出预测结果收集
+            通过定义generate_with_callback收集器AnswerResultStream，收集模型预测的AnswerResult响应结果，最终由下层实现类确认是否结束
+            结束条件包含如下
+                1、模型预测结束、收集器self.q队列收到 self.sentinel标识
+                2、在处理迭代器队列消息时返回了break跳出迭代器，触发了StopIteration事件
+                3、模型预测出错
+            因为当前类是迭代器，所以在for in 中执行了break后 __exit__ 方法会被调用，最终stop_now属性会被更新，然后抛出异常结束预测行为
+            迭代器收集的行为如下
+                创建Iteratorize迭代对象，
+                定义generate_with_callback收集器AnswerResultStream
+                启动一个线程异步预测结果来调用上游checkpoint的实现方法_generate_answer
+                _generate_answer通过generate_with_callback定义的收集器，收集上游checkpoint包装的AnswerResult消息体
+                由于self.q是阻塞模式，每次预测后会被消费后才会执行下次预测
+                这时generate_with_callback会被阻塞
+                主线程Iteratorize对象的__next__方法调用获取阻塞消息并消费
+                    1、消息为上游checkpoint包装的AnswerResult消息体，返回下游处理
+                    2、消息为self.sentinel标识，抛出StopIteration异常
+                主线程Iteratorize对象__exit__收到消息，最终stop_now属性会被更新
+                异步线程检测stop_now属性被更新，抛出异常结束预测行为
+            迭代行为结束
+            :param val:
+            :return:
+            """
+            if self.stop_now:
+                raise ValueError
+            self.q.put(val)
+
+        def gen():
+            try:
+                ret = self.mfunc(callback=_callback, **self.kwargs)
+            except ValueError:
+                pass
+            except:
+                traceback.print_exc()
+                pass
+
+            self.q.put(self.sentinel)
+
+        self.thread = Thread(target=gen)
+        self.thread.start()
+
+    def __iter__(self):
+        return self
+
+    def __next__(self):
+        obj = self.q.get(True, None)
+        if obj is self.sentinel:
+            raise StopIteration
+        else:
+            return obj
+
+    def __del__(self):
+        """
+        暂无实现
+        :return:
+        """
+        pass
+
+    def __enter__(self):
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        """ break 后会执行 """
+        self.stop_now = True
 
 
 class BaseAnswer(ABC):
@@ -25,17 +155,25 @@ class BaseAnswer(ABC):
     @abstractmethod
     def _check_point(self) -> LoaderCheckPoint:
         """Return _check_point of llm."""
+    def generatorAnswer(self,
+                        inputs: Dict[str, Any],
+                        run_manager: Optional[CallbackManagerForChainRun] = None,) -> Generator[Any, str, bool]:
+        def generate_with_callback(callback=None, **kwargs):
+            kwargs['generate_with_callback'] = AnswerResultStream(callback_func=callback)
+            self._generate_answer(**kwargs)
 
-    @property
-    @abstractmethod
-    def _history_len(self) -> int:
-        """Return _history_len of llm."""
+        def generate_with_streaming(**kwargs):
+            return Iteratorize(generate_with_callback, kwargs)
 
-    @abstractmethod
-    def set_history_len(self, history_len: int) -> None:
-        """Return _history_len of llm."""
+        with generate_with_streaming(inputs=inputs, run_manager=run_manager) as generator:
+            for answerResult in generator:
+                if answerResult.listenerToken:
+                    output = answerResult.listenerToken.input_ids
+                yield answerResult
 
-    def generatorAnswer(self, prompt: str,
-                        history: List[List[str]] = [],
-                        streaming: bool = False):
+    @abstractmethod
+    def _generate_answer(self,
+                         inputs: Dict[str, Any],
+                         run_manager: Optional[CallbackManagerForChainRun] = None,
+                         generate_with_callback: AnswerResultStream = None) -> None:
         pass

models/chatglm_llm.py

 from abc import ABC
-from langchain.llms.base import LLM
-from typing import Optional, List
+from langchain.chains.base import Chain
+from typing import Any, Dict, List, Optional, Generator
+from langchain.callbacks.manager import CallbackManagerForChainRun
+from transformers.generation.logits_process import LogitsProcessor
+from transformers.generation.utils import LogitsProcessorList, StoppingCriteriaList
 from models.loader import LoaderCheckPoint
 from models.base import (BaseAnswer,
-                         AnswerResult)
+                         AnswerResult,
+                         AnswerResultStream,
+                         AnswerResultQueueSentinelTokenListenerQueue)
+import torch
+import transformers
 
 
-class ChatGLM(BaseAnswer, LLM, ABC):
+class ChatGLMLLMChain(BaseAnswer, Chain, ABC):
     max_token: int = 10000
     temperature: float = 0.01
-    top_p = 0.9
+    # 相关度
+    top_p = 0.4
+    # 候选词数量
+    top_k = 10
     checkPoint: LoaderCheckPoint = None
     # history = []
     history_len: int = 10
+    streaming_key: str = "streaming"  #: :meta private:
+    history_key: str = "history"  #: :meta private:
+    prompt_key: str = "prompt"  #: :meta private:
+    output_key: str = "answer_result_stream"  #: :meta private:
 
     def __init__(self, checkPoint: LoaderCheckPoint = None):
         super().__init__()
         self.checkPoint = checkPoint
 
     @property
-    def _llm_type(self) -> str:
-        return "ChatGLM"
+    def _chain_type(self) -> str:
+        return "ChatGLMLLMChain"
 
     @property
     def _check_point(self) -> LoaderCheckPoint:
         return self.checkPoint
 
     @property
-    def _history_len(self) -> int:
-        return self.history_len
+    def input_keys(self) -> List[str]:
+        """Will be whatever keys the prompt expects.
 
-    def set_history_len(self, history_len: int = 10) -> None:
-        self.history_len = history_len
+        :meta private:
+        """
+        return [self.prompt_key]
 
-    def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
-        print(f"__call:{prompt}")
-        response, _ = self.checkPoint.model.chat(
-            self.checkPoint.tokenizer,
-            prompt,
-            history=[],
-            max_length=self.max_token,
-            temperature=self.temperature
-        )
-        print(f"response:{response}")
-        print(f"+++++++++++++++++++++++++++++++++++")
-        return response
+    @property
+    def output_keys(self) -> List[str]:
+        """Will always return text key.
 
-    def generatorAnswer(self, prompt: str,
-                         history: List[List[str]] = [],
-                         streaming: bool = False):
+        :meta private:
+        """
+        return [self.output_key]
 
+    def _call(
+            self,
+            inputs: Dict[str, Any],
+            run_manager: Optional[CallbackManagerForChainRun] = None,
+    ) -> Dict[str, Generator]:
+        generator = self.generatorAnswer(inputs=inputs, run_manager=run_manager)
+        return {self.output_key: generator}
+
+    def _generate_answer(self,
+                         inputs: Dict[str, Any],
+                         run_manager: Optional[CallbackManagerForChainRun] = None,
+                         generate_with_callback: AnswerResultStream = None) -> None:
+        history = inputs[self.history_key]
+        streaming = inputs[self.streaming_key]
+        prompt = inputs[self.prompt_key]
+        print(f"__call:{prompt}")
+        # Create the StoppingCriteriaList with the stopping strings
+        stopping_criteria_list = transformers.StoppingCriteriaList()
+        # 定义模型stopping_criteria 队列，在每次响应时将 torch.LongTensor, torch.FloatTensor同步到AnswerResult
+        listenerQueue = AnswerResultQueueSentinelTokenListenerQueue()
+        stopping_criteria_list.append(listenerQueue)
         if streaming:
             history += [[]]
             for inum, (stream_resp, _) in enumerate(self.checkPoint.model.stream_chat(
                     self.checkPoint.tokenizer,
                     prompt,
-                    history=history[-self.history_len:-1] if self.history_len > 1 else [],
+                    history=history[-self.history_len:-1] if self.history_len > 0 else [],
                     max_length=self.max_token,
-                    temperature=self.temperature
+                    temperature=self.temperature,
+                    top_p=self.top_p,
+                    top_k=self.top_k,
+                    stopping_criteria=stopping_criteria_list
             )):
                 # self.checkPoint.clear_torch_cache()
                 history[-1] = [prompt, stream_resp]
                 answer_result = AnswerResult()
                 answer_result.history = history
                 answer_result.llm_output = {"answer": stream_resp}
-                yield answer_result
+                if listenerQueue.listenerQueue.__len__() > 0:
+                    answer_result.listenerToken = listenerQueue.listenerQueue.pop()
+                generate_with_callback(answer_result)
             self.checkPoint.clear_torch_cache()
         else:
             response, _ = self.checkPoint.model.chat(
@@ -72,13 +104,18 @@ class ChatGLM(BaseAnswer, LLM, ABC):
                 prompt,
                 history=history[-self.history_len:] if self.history_len > 0 else [],
                 max_length=self.max_token,
-                temperature=self.temperature
+                temperature=self.temperature,
+                top_p=self.top_p,
+                top_k=self.top_k,
+                stopping_criteria=stopping_criteria_list
             )
             self.checkPoint.clear_torch_cache()
             history += [[prompt, response]]
             answer_result = AnswerResult()
             answer_result.history = history
             answer_result.llm_output = {"answer": response}
-            yield answer_result
+            if listenerQueue.listenerQueue.__len__() > 0:
+                answer_result.listenerToken = listenerQueue.listenerQueue.pop()
 
+            generate_with_callback(answer_result)
 

models/fastchat_openai_llm.py

 from abc import ABC
-import requests
-from typing import Optional, List
-from langchain.llms.base import LLM
-
+from langchain.chains.base import Chain
+from typing import Any, Dict, List, Optional, Generator, Collection
 from models.loader import LoaderCheckPoint
-from models.base import (RemoteRpcModel,
-                         AnswerResult)
-from typing import (
-    Collection,
-    Dict
-)
+from langchain.callbacks.manager import CallbackManagerForChainRun
+from models.base import (BaseAnswer,
+                         RemoteRpcModel,
+                         AnswerResult,
+                         AnswerResultStream,
+                         AnswerResultQueueSentinelTokenListenerQueue)
+import torch
+import transformers
 
 
 def _build_message_template() -> Dict[str, str]:
@@ -22,18 +22,42 @@ def _build_message_template() -> Dict[str, str]:
     }
 
 
-class FastChatOpenAILLM(RemoteRpcModel, LLM, ABC):
+# 将历史对话数组转换为文本格式
+def build_message_list(query, history: List[List[str]]) -> Collection[Dict[str, str]]:
+    build_messages: Collection[Dict[str, str]] = []
+    for i, (old_query, response) in enumerate(history):
+        user_build_message = _build_message_template()
+        user_build_message['role'] = 'user'
+        user_build_message['content'] = old_query
+        system_build_message = _build_message_template()
+        system_build_message['role'] = 'system'
+        system_build_message['content'] = response
+        build_messages.append(user_build_message)
+        build_messages.append(system_build_message)
+
+    user_build_message = _build_message_template()
+    user_build_message['role'] = 'user'
+    user_build_message['content'] = query
+    build_messages.append(user_build_message)
+    return build_messages
+
 
+class FastChatOpenAILLMChain(RemoteRpcModel, Chain, ABC):
     api_base_url: str = "http://localhost:8000/v1"
     model_name: str = "chatglm-6b"
     max_token: int = 10000
     temperature: float = 0.01
     top_p = 0.9
     checkPoint: LoaderCheckPoint = None
-    history = []
+    # history = []
     history_len: int = 10
     api_key: str = ""
 
+    streaming_key: str = "streaming"  #: :meta private:
+    history_key: str = "history"  #: :meta private:
+    prompt_key: str = "prompt"  #: :meta private:
+    output_key: str = "answer_result_stream"  #: :meta private:
+
     def __init__(self,
                  checkPoint: LoaderCheckPoint = None,
                  #  api_base_url:str="http://localhost:8000/v1",
@@ -44,19 +68,28 @@ class FastChatOpenAILLM(RemoteRpcModel, LLM, ABC):
         self.checkPoint = checkPoint
 
     @property
-    def _llm_type(self) -> str:
-        return "FastChat"
+    def _chain_type(self) -> str:
+        return "LLamaLLMChain"
 
     @property
     def _check_point(self) -> LoaderCheckPoint:
         return self.checkPoint
 
     @property
-    def _history_len(self) -> int:
-        return self.history_len
+    def input_keys(self) -> List[str]:
+        """Will be whatever keys the prompt expects.
 
-    def set_history_len(self, history_len: int = 10) -> None:
-        self.history_len = history_len
+        :meta private:
+        """
+        return [self.prompt_key]
+
+    @property
+    def output_keys(self) -> List[str]:
+        """Will always return text key.
+
+        :meta private:
+        """
+        return [self.output_key]
 
     @property
     def _api_key(self) -> str:
@@ -75,53 +108,25 @@ class FastChatOpenAILLM(RemoteRpcModel, LLM, ABC):
     def call_model_name(self, model_name):
         self.model_name = model_name
 
-    def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
+    def _call(
+            self,
+            inputs: Dict[str, Any],
+            run_manager: Optional[CallbackManagerForChainRun] = None,
+    ) -> Dict[str, Generator]:
+        generator = self.generatorAnswer(inputs=inputs, run_manager=run_manager)
+        return {self.output_key: generator}
+
+    def _generate_answer(self,
+                         inputs: Dict[str, Any],
+                         run_manager: Optional[CallbackManagerForChainRun] = None,
+                         generate_with_callback: AnswerResultStream = None) -> None:
+
+        history = inputs[self.history_key]
+        streaming = inputs[self.streaming_key]
+        prompt = inputs[self.prompt_key]
         print(f"__call:{prompt}")
         try:
-            import openai
-            # Not support yet
-            # openai.api_key = "EMPTY"
-            openai.key = self.api_key
-            openai.api_base = self.api_base_url
-        except ImportError:
-            raise ValueError(
-                "Could not import openai python package. "
-                "Please install it with `pip install openai`."
-            )
-        # create a chat completion
-        completion = openai.ChatCompletion.create(
-            model=self.model_name,
-            messages=self.build_message_list(prompt)
-        )
-        print(f"response:{completion.choices[0].message.content}")
-        print(f"+++++++++++++++++++++++++++++++++++")
-        return completion.choices[0].message.content
 
-    # 将历史对话数组转换为文本格式
-    def build_message_list(self, query) -> Collection[Dict[str, str]]:
-        build_message_list: Collection[Dict[str, str]] = []
-        history = self.history[-self.history_len:] if self.history_len > 0 else []
-        for i, (old_query, response) in enumerate(history):
-            user_build_message = _build_message_template()
-            user_build_message['role'] = 'user'
-            user_build_message['content'] = old_query
-            system_build_message = _build_message_template()
-            system_build_message['role'] = 'system'
-            system_build_message['content'] = response
-            build_message_list.append(user_build_message)
-            build_message_list.append(system_build_message)
-
-        user_build_message = _build_message_template()
-        user_build_message['role'] = 'user'
-        user_build_message['content'] = query
-        build_message_list.append(user_build_message)
-        return build_message_list
-
-    def generatorAnswer(self, prompt: str,
-                        history: List[List[str]] = [],
-                        streaming: bool = False):
-
-        try:
             import openai
             # Not support yet
             # openai.api_key = "EMPTY"
@@ -135,12 +140,13 @@ class FastChatOpenAILLM(RemoteRpcModel, LLM, ABC):
         # create a chat completion
         completion = openai.ChatCompletion.create(
             model=self.model_name,
-            messages=self.build_message_list(prompt)
+            messages=build_message_list(prompt)
         )
+        print(f"response:{completion.choices[0].message.content}")
+        print(f"+++++++++++++++++++++++++++++++++++")
 
         history += [[prompt, completion.choices[0].message.content]]
         answer_result = AnswerResult()
         answer_result.history = history
         answer_result.llm_output = {"answer": completion.choices[0].message.content}
-
-        yield answer_result
+        generate_with_callback(answer_result)

models/llama_llm.py

-from abc import ABC
 
-from langchain.llms.base import LLM
-import random
-import torch
-import transformers
+from abc import ABC
+from langchain.chains.base import Chain
+from typing import Any, Dict, List, Optional, Generator, Union
+from langchain.callbacks.manager import CallbackManagerForChainRun
 from transformers.generation.logits_process import LogitsProcessor
 from transformers.generation.utils import LogitsProcessorList, StoppingCriteriaList
-from typing import Optional, List, Dict, Any,Union
 from models.loader import LoaderCheckPoint
 from models.base import (BaseAnswer,
-                         AnswerResult)
+                         AnswerResult,
+                         AnswerResultStream,
+                         AnswerResultQueueSentinelTokenListenerQueue)
+import torch
+import transformers
 
 
 class InvalidScoreLogitsProcessor(LogitsProcessor):
-    def __call__(self, input_ids: Union[torch.LongTensor,list], scores: Union[torch.FloatTensor,list]) -> torch.FloatTensor:
+    def __call__(self, input_ids: Union[torch.LongTensor, list],
+                 scores: Union[torch.FloatTensor, list]) -> torch.FloatTensor:
         # llama-cpp模型返回的是list,为兼容性考虑，需要判断input_ids和scores的类型，将list转换为torch.Tensor
-        input_ids = torch.tensor(input_ids) if isinstance(input_ids,list) else input_ids
-        scores = torch.tensor(scores) if isinstance(scores,list) else scores
+        input_ids = torch.tensor(input_ids) if isinstance(input_ids, list) else input_ids
+        scores = torch.tensor(scores) if isinstance(scores, list) else scores
         if torch.isnan(scores).any() or torch.isinf(scores).any():
             scores.zero_()
             scores[..., 5] = 5e4
         return scores
 
 
-class LLamaLLM(BaseAnswer, LLM, ABC):
+class LLamaLLMChain(BaseAnswer, Chain, ABC):
     checkPoint: LoaderCheckPoint = None
     # history = []
     history_len: int = 3
@@ -37,32 +40,34 @@ class LLamaLLM(BaseAnswer, LLM, ABC):
     min_length: int = 0
     logits_processor: LogitsProcessorList = None
     stopping_criteria: Optional[StoppingCriteriaList] = None
-    eos_token_id: Optional[int] = [2]
-
-    state: object = {'max_new_tokens': 50,
-                     'seed': 1,
-                     'temperature': 0, 'top_p': 0.1,
-                     'top_k': 40, 'typical_p': 1,
-                     'repetition_penalty': 1.2,
-                     'encoder_repetition_penalty': 1,
-                     'no_repeat_ngram_size': 0,
-                     'min_length': 0,
-                     'penalty_alpha': 0,
-                     'num_beams': 1,
-                     'length_penalty': 1,
-                     'early_stopping': False, 'add_bos_token': True, 'ban_eos_token': False,
-                     'truncation_length': 2048, 'custom_stopping_strings': '',
-                     'cpu_memory': 0, 'auto_devices': False, 'disk': False, 'cpu': False, 'bf16': False,
-                     'load_in_8bit': False, 'wbits': 'None', 'groupsize': 'None', 'model_type': 'None',
-                     'pre_layer': 0, 'gpu_memory_0': 0}
+    streaming_key: str = "streaming"  #: :meta private:
+    history_key: str = "history"  #: :meta private:
+    prompt_key: str = "prompt"  #: :meta private:
+    output_key: str = "answer_result_stream"  #: :meta private:
 
     def __init__(self, checkPoint: LoaderCheckPoint = None):
         super().__init__()
         self.checkPoint = checkPoint
 
     @property
-    def _llm_type(self) -> str:
-        return "LLamaLLM"
+    def _chain_type(self) -> str:
+        return "LLamaLLMChain"
+
+    @property
+    def input_keys(self) -> List[str]:
+        """Will be whatever keys the prompt expects.
+
+        :meta private:
+        """
+        return [self.prompt_key]
+
+    @property
+    def output_keys(self) -> List[str]:
+        """Will always return text key.
+
+        :meta private:
+        """
+        return [self.output_key]
 
     @property
     def _check_point(self) -> LoaderCheckPoint:
@@ -107,35 +112,31 @@ class LLamaLLM(BaseAnswer, LLM, ABC):
         formatted_history += "### Human：{}\n### Assistant：".format(query)
         return formatted_history
 
-    def prepare_inputs_for_generation(self,
-                                      input_ids: torch.LongTensor):
-        """
-        预生成注意力掩码和 输入序列中每个位置的索引的张量
-        # TODO 没有思路
-        :return:
-        """
-
-        mask_positions = torch.zeros((1, input_ids.shape[1]), dtype=input_ids.dtype).to(self.checkPoint.model.device)
-
-        attention_mask = self.get_masks(input_ids, input_ids.device)
-
-        position_ids = self.get_position_ids(
-            input_ids,
-            device=input_ids.device,
-            mask_positions=mask_positions
-        )
-
-        return input_ids, position_ids, attention_mask
-
-    @property
-    def _history_len(self) -> int:
-        return self.history_len
-
-    def set_history_len(self, history_len: int = 10) -> None:
-        self.history_len = history_len
-
-    def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
+    def _call(
+            self,
+            inputs: Dict[str, Any],
+            run_manager: Optional[CallbackManagerForChainRun] = None,
+    ) -> Dict[str, Generator]:
+        generator = self.generatorAnswer(inputs=inputs, run_manager=run_manager)
+        return {self.output_key: generator}
+
+    def _generate_answer(self,
+                         inputs: Dict[str, Any],
+                         run_manager: Optional[CallbackManagerForChainRun] = None,
+                         generate_with_callback: AnswerResultStream = None) -> None:
+
+        history = inputs[self.history_key]
+        streaming = inputs[self.streaming_key]
+        prompt = inputs[self.prompt_key]
         print(f"__call:{prompt}")
+
+        # Create the StoppingCriteriaList with the stopping strings
+        self.stopping_criteria = transformers.StoppingCriteriaList()
+        # 定义模型stopping_criteria 队列，在每次响应时将 torch.LongTensor, torch.FloatTensor同步到AnswerResult
+        listenerQueue = AnswerResultQueueSentinelTokenListenerQueue()
+        self.stopping_criteria.append(listenerQueue)
+        # TODO 需要实现chat对话模块和注意力模型，目前_call为langchain的LLM拓展的api，默认为无提示词模式，如果需要操作注意力模型，可以参考chat_glm的实现
+        soft_prompt = self.history_to_text(query=prompt, history=history)
         if self.logits_processor is None:
             self.logits_processor = LogitsProcessorList()
         self.logits_processor.append(InvalidScoreLogitsProcessor())
@@ -154,16 +155,10 @@ class LLamaLLM(BaseAnswer, LLM, ABC):
             "logits_processor": self.logits_processor}
 
         #  向量转换
-        input_ids = self.encode(prompt, add_bos_token=self.state['add_bos_token'], truncation_length=self.max_new_tokens)
-        # input_ids, position_ids, attention_mask = self.prepare_inputs_for_generation(input_ids=filler_input_ids)
-
+        input_ids = self.encode(soft_prompt, add_bos_token=self.checkPoint.tokenizer.add_bos_token,
+                                truncation_length=self.max_new_tokens)
 
         gen_kwargs.update({'inputs': input_ids})
-        # 注意力掩码
-        # gen_kwargs.update({'attention_mask': attention_mask})
-        # gen_kwargs.update({'position_ids': position_ids})
-        if self.stopping_criteria is None:
-            self.stopping_criteria = transformers.StoppingCriteriaList()
         # 观测输出
         gen_kwargs.update({'stopping_criteria': self.stopping_criteria})
         # llama-cpp模型的参数与transformers的参数字段有较大差异，直接调用会返回不支持的字段错误
@@ -173,11 +168,13 @@ class LLamaLLM(BaseAnswer, LLM, ABC):
         if "llama_cpp" in self.checkPoint.model.__str__():
             import inspect
 
-            common_kwargs_keys = set(inspect.getfullargspec(self.checkPoint.model.generate).args)&set(gen_kwargs.keys())
-            common_kwargs = {key:gen_kwargs[key] for key in common_kwargs_keys}
-            #? llama-cpp模型的generate方法似乎只接受.cpu类型的输入，响应很慢，慢到哭泣
-            #?为什么会不支持GPU呢，不应该啊？
-            output_ids = torch.tensor([list(self.checkPoint.model.generate(input_id_i.cpu(),**common_kwargs)) for input_id_i in input_ids])
+            common_kwargs_keys = set(inspect.getfullargspec(self.checkPoint.model.generate).args) & set(
+                gen_kwargs.keys())
+            common_kwargs = {key: gen_kwargs[key] for key in common_kwargs_keys}
+            # ? llama-cpp模型的generate方法似乎只接受.cpu类型的输入，响应很慢，慢到哭泣
+            # ?为什么会不支持GPU呢，不应该啊？
+            output_ids = torch.tensor(
+                [list(self.checkPoint.model.generate(input_id_i.cpu(), **common_kwargs)) for input_id_i in input_ids])
 
         else:
             output_ids = self.checkPoint.model.generate(**gen_kwargs)
@@ -185,17 +182,11 @@ class LLamaLLM(BaseAnswer, LLM, ABC):
         reply = self.decode(output_ids[0][-new_tokens:])
         print(f"response:{reply}")
         print(f"+++++++++++++++++++++++++++++++++++")
-        return reply
-
-    def generatorAnswer(self, prompt: str,
-                         history: List[List[str]] = [],
-                         streaming: bool = False):
-
-        # TODO 需要实现chat对话模块和注意力模型，目前_call为langchain的LLM拓展的api，默认为无提示词模式，如果需要操作注意力模型，可以参考chat_glm的实现
-        softprompt = self.history_to_text(prompt,history=history)
-        response = self._call(prompt=softprompt, stop=['\n###'])
 
         answer_result = AnswerResult()
-        answer_result.history = history + [[prompt, response]]
-        answer_result.llm_output = {"answer": response}
-        yield answer_result
+        history += [[prompt, reply]]
+        answer_result.history = history
+        if listenerQueue.listenerQueue.__len__() > 0:
+            answer_result.listenerToken = listenerQueue.listenerQueue.pop()
+        answer_result.llm_output = {"answer": reply}
+        generate_with_callback(answer_result)

models/loader/loader.py

@@ -20,6 +20,7 @@ class LoaderCheckPoint:
     no_remote_model: bool = False
     # 模型名称
     model_name: str = None
+    pretrained_model_name: str = None
     tokenizer: object = None
     # 模型全路径
     model_path: str = None
@@ -67,48 +68,49 @@ class LoaderCheckPoint:
         self.load_in_8bit = params.get('load_in_8bit', False)
         self.bf16 = params.get('bf16', False)
 
-
-    def _load_model_config(self, model_name):
+    def _load_model_config(self):
 
         if self.model_path:
-            self.model_path = re.sub("\s","",self.model_path)
+            self.model_path = re.sub("\s", "", self.model_path)
             checkpoint = Path(f'{self.model_path}')
         else:
-            if not self.no_remote_model:
-                checkpoint = model_name
-            else:
+            if self.no_remote_model:
                 raise ValueError(
                     "本地模型local_model_path未配置路径"
                 )
+            else:
+                checkpoint = self.pretrained_model_name
+
+        print(f"load_model_config {checkpoint}...")
         try:
+
             model_config = AutoConfig.from_pretrained(checkpoint, trust_remote_code=True)
             return model_config
         except Exception as e:
             print(e)
             return checkpoint
 
-    def _load_model(self, model_name):
+    def _load_model(self):
         """
         加载自定义位置的model
-        :param model_name:
         :return:
         """
-        print(f"Loading {model_name}...")
         t0 = time.time()
 
         if self.model_path:
-            self.model_path = re.sub("\s","",self.model_path)
+            self.model_path = re.sub("\s", "", self.model_path)
             checkpoint = Path(f'{self.model_path}')
         else:
-            if not self.no_remote_model:
-                checkpoint = model_name
-            else:
+            if self.no_remote_model:
                 raise ValueError(
                     "本地模型local_model_path未配置路径"
                 )
+            else:
+                checkpoint = self.pretrained_model_name
 
+        print(f"Loading {checkpoint}...")
         self.is_llamacpp = len(list(Path(f'{checkpoint}').glob('ggml*.bin'))) > 0
-        if 'chatglm' in model_name.lower() or "chatyuan" in model_name.lower():
+        if 'chatglm' in self.model_name.lower() or "chatyuan" in self.model_name.lower():
             LoaderClass = AutoModel
         else:
             LoaderClass = AutoModelForCausalLM
@@ -138,7 +140,7 @@ class LoaderCheckPoint:
                                                         torch_dtype=torch.bfloat16 if self.bf16 else torch.float16,
                                                         trust_remote_code=True).half().to(self.llm_device)
                 else:
-                    from accelerate import dispatch_model,infer_auto_device_map
+                    from accelerate import dispatch_model, infer_auto_device_map
 
                     model = LoaderClass.from_pretrained(checkpoint,
                                                         config=self.model_config,
@@ -146,10 +148,10 @@ class LoaderCheckPoint:
                                                         trust_remote_code=True).half()
                     # 可传入device_map自定义每张卡的部署情况
                     if self.device_map is None:
-                        if 'chatglm' in model_name.lower():
+                        if 'chatglm' in self.model_name.lower():
                             self.device_map = self.chatglm_auto_configure_device_map(num_gpus)
-                        elif 'moss' in model_name.lower():
-                            self.device_map = self.moss_auto_configure_device_map(num_gpus, model_name)
+                        elif 'moss' in self.model_name.lower():
+                            self.device_map = self.moss_auto_configure_device_map(num_gpus, checkpoint)
                         else:
                             # 基于如下方式作为默认的多卡加载方案针对新模型基本不会失败
                             # 在chatglm2-6b,bloom-3b,blooz-7b1上进行了测试，GPU负载也相对均衡
@@ -166,9 +168,9 @@ class LoaderCheckPoint:
                             # 其他模型定义的层类几乎不可能与chatglm和moss一致，使用chatglm_auto_configure_device_map
                             # 百分百会报错，使用infer_auto_device_map虽然可能导致负载不均衡，但至少不会报错
                             # 实测在bloom模型上如此
-#                             self.device_map = infer_auto_device_map(model,
-#                                                                     dtype=torch.int8,
-#                                                                     no_split_module_classes=model._no_split_modules)
+                    #                             self.device_map = infer_auto_device_map(model,
+                    #                                                                     dtype=torch.int8,
+                    #                                                                     no_split_module_classes=model._no_split_modules)
 
                     model = dispatch_model(model, device_map=self.device_map)
             else:
@@ -202,7 +204,7 @@ class LoaderCheckPoint:
 
             # tokenizer = model.tokenizer
             # todo 此处调用AutoTokenizer的tokenizer，但后续可以测试自带tokenizer是不是兼容
-            #* -> 自带的tokenizer不与transoformers的tokenizer兼容,无法使用
+            # * -> 自带的tokenizer不与transoformers的tokenizer兼容,无法使用
 
             tokenizer = AutoTokenizer.from_pretrained(self.model_name)
             return model, tokenizer
@@ -231,7 +233,7 @@ class LoaderCheckPoint:
                                                                    llm_int8_enable_fp32_cpu_offload=False)
 
             with init_empty_weights():
-                model = LoaderClass.from_config(self.model_config,trust_remote_code = True)
+                model = LoaderClass.from_config(self.model_config, trust_remote_code=True)
             model.tie_weights()
             if self.device_map is not None:
                 params['device_map'] = self.device_map
@@ -321,7 +323,7 @@ class LoaderCheckPoint:
 
         return device_map
 
-    def moss_auto_configure_device_map(self, num_gpus: int, model_name) -> Dict[str, int]:
+    def moss_auto_configure_device_map(self, num_gpus: int, checkpoint) -> Dict[str, int]:
         try:
 
             from accelerate import init_empty_weights
@@ -336,16 +338,6 @@ class LoaderCheckPoint:
                 "`pip install bitsandbytes``pip install accelerate`."
             ) from exc
 
-        if self.model_path:
-            checkpoint = Path(f'{self.model_path}')
-        else:
-            if not self.no_remote_model:
-                checkpoint = model_name
-            else:
-                raise ValueError(
-                    "本地模型local_model_path未配置路径"
-                )
-
         cls = get_class_from_dynamic_module(class_reference="fnlp/moss-moon-003-sft--modeling_moss.MossForCausalLM",
                                             pretrained_model_name_or_path=checkpoint)
 
@@ -452,7 +444,7 @@ class LoaderCheckPoint:
 
     def reload_model(self):
         self.unload_model()
-        self.model_config = self._load_model_config(self.model_name)
+        self.model_config = self._load_model_config()
 
         if self.use_ptuning_v2:
             try:
@@ -464,7 +456,7 @@ class LoaderCheckPoint:
             except Exception as e:
                 print("加载PrefixEncoder config.json失败")
 
-        self.model, self.tokenizer = self._load_model(self.model_name)
+        self.model, self.tokenizer = self._load_model()
 
         if self.lora:
             self._add_lora_to_model([self.lora])

models/moss_llm.py

 from abc import ABC
-from langchain.llms.base import LLM
-from typing import Optional, List
+from langchain.chains.base import Chain
+from typing import Any, Dict, List, Optional, Generator, Union
+from langchain.callbacks.manager import CallbackManagerForChainRun
+from transformers.generation.logits_process import LogitsProcessor
+from transformers.generation.utils import LogitsProcessorList, StoppingCriteriaList
 from models.loader import LoaderCheckPoint
 from models.base import (BaseAnswer,
-                         AnswerResult)
+                         AnswerResult,
+                         AnswerResultStream,
+                         AnswerResultQueueSentinelTokenListenerQueue)
+import torch
+import transformers
 
 import torch
+
 # todo 建议重写instruction,在该instruction下，各模型的表现比较差
 META_INSTRUCTION = \
     """You are an AI assistant whose name is MOSS.
@@ -20,41 +28,65 @@ META_INSTRUCTION = \
     Capabilities and tools that MOSS can possess.
     """
 
+
 # todo 在MOSSLLM类下，各模型的响应速度很慢，后续要检查一下原因
-class MOSSLLM(BaseAnswer, LLM, ABC):
+class MOSSLLMChain(BaseAnswer, Chain, ABC):
     max_token: int = 2048
     temperature: float = 0.7
     top_p = 0.8
     # history = []
     checkPoint: LoaderCheckPoint = None
     history_len: int = 10
+    streaming_key: str = "streaming"  #: :meta private:
+    history_key: str = "history"  #: :meta private:
+    prompt_key: str = "prompt"  #: :meta private:
+    output_key: str = "answer_result_stream"  #: :meta private:
 
     def __init__(self, checkPoint: LoaderCheckPoint = None):
         super().__init__()
         self.checkPoint = checkPoint
 
     @property
-    def _llm_type(self) -> str:
-        return "MOSS"
+    def _chain_type(self) -> str:
+        return "MOSSLLMChain"
 
     @property
-    def _check_point(self) -> LoaderCheckPoint:
-        return self.checkPoint
+    def input_keys(self) -> List[str]:
+        """Will be whatever keys the prompt expects.
+
+        :meta private:
+        """
+        return [self.prompt_key]
 
     @property
-    def _history_len(self) -> int:
+    def output_keys(self) -> List[str]:
+        """Will always return text key.
 
-        return self.history_len
+        :meta private:
+        """
+        return [self.output_key]
 
-    def set_history_len(self, history_len: int) -> None:
-        self.history_len = history_len
+    @property
+    def _check_point(self) -> LoaderCheckPoint:
+        return self.checkPoint
 
-    def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
-        pass
+    def _call(
+            self,
+            inputs: Dict[str, Any],
+            run_manager: Optional[CallbackManagerForChainRun] = None,
+    ) -> Dict[str, Generator]:
+        generator = self.generatorAnswer(inputs=inputs, run_manager=run_manager)
+        return {self.output_key: generator}
 
-    def generatorAnswer(self, prompt: str,
-                         history: List[List[str]] = [],
-                         streaming: bool = False):
+    def _generate_answer(self,
+                         inputs: Dict[str, Any],
+                         run_manager: Optional[CallbackManagerForChainRun] = None,
+                         generate_with_callback: AnswerResultStream = None) -> None:
+
+        history = inputs[self.history_key]
+        streaming = inputs[self.streaming_key]
+        prompt = inputs[self.prompt_key]
+        print(f"__call:{prompt}")
         if len(history) > 0:
             history = history[-self.history_len:] if self.history_len > 0 else []
             prompt_w_history = str(history)
@@ -79,13 +111,12 @@ class MOSSLLM(BaseAnswer, LLM, ABC):
                 num_return_sequences=1,
                 eos_token_id=106068,
                 pad_token_id=self.checkPoint.tokenizer.pad_token_id)
-            response = self.checkPoint.tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
+            response = self.checkPoint.tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:],
+                                                        skip_special_tokens=True)
             self.checkPoint.clear_torch_cache()
             history += [[prompt, response]]
             answer_result = AnswerResult()
             answer_result.history = history
             answer_result.llm_output = {"answer": response}
 
-            yield answer_result
-
-
+            generate_with_callback(answer_result)

models/shared.py

@@ -24,13 +24,12 @@ def loaderLLM(llm_model: str = None, no_remote_model: bool = False, use_ptuning_
     if use_ptuning_v2:
         loaderCheckPoint.use_ptuning_v2 = use_ptuning_v2
 
+    # 如果指定了参数，则使用参数的配置
     if llm_model:
         llm_model_info = llm_model_dict[llm_model]
 
-    if loaderCheckPoint.no_remote_model:
     loaderCheckPoint.model_name = llm_model_info['name']
-    else:
-        loaderCheckPoint.model_name = llm_model_info['pretrained_model_name']
+    loaderCheckPoint.pretrained_model_name = llm_model_info['pretrained_model_name']
 
     loaderCheckPoint.model_path = llm_model_info["local_model_path"]
 

test/models/test_fastchat_openai_llm.py

-import sys
-import os
-
-sys.path.append(os.path.dirname(os.path.abspath(__file__)) + '/../../')
-import asyncio
-from argparse import Namespace
-from models.loader.args import parser
-from models.loader import LoaderCheckPoint
-
-
-import models.shared as shared
-
-
-
-async def dispatch(args: Namespace):
-    args_dict = vars(args)
-
-    shared.loaderCheckPoint = LoaderCheckPoint(args_dict)
-
-    llm_model_ins = shared.loaderLLM()
-
-    history = [
-        ("which city is this?", "tokyo"),
-        ("why?", "she's japanese"),
-
-    ]
-    for answer_result in llm_model_ins.generatorAnswer(prompt="你好? ", history=history,
-                                                       streaming=False):
-        resp = answer_result.llm_output["answer"]
-
-        print(resp)
-
-if __name__ == '__main__':
-    args = None
-    args = parser.parse_args(args=['--model-dir', '/media/checkpoint/',  '--model', 'fastchat-chatglm-6b', '--no-remote-model'])
-
-    loop = asyncio.new_event_loop()
-    asyncio.set_event_loop(loop)
-    loop.run_until_complete(dispatch(args))

webui.py

@@ -85,8 +85,11 @@ def get_answer(query, vs_path, history, mode, score_threshold=VECTOR_SEARCH_SCOR
             yield history + [[query,
                               "请选择知识库后进行测试，当前未选择知识库。"]], ""
     else:
-        for answer_result in local_doc_qa.llm.generatorAnswer(prompt=query, history=history,
-                                                              streaming=streaming):
+
+        answer_result_stream_result = local_doc_qa.llm_model_chain(
+            {"prompt": query, "history": history, "streaming": streaming})
+
+        for answer_result in answer_result_stream_result['answer_result_stream']:
             resp = answer_result.llm_output["answer"]
             history = answer_result.history
             history[-1][-1] = resp
@@ -101,11 +104,12 @@ def init_model():
     args_dict = vars(args)
     shared.loaderCheckPoint = LoaderCheckPoint(args_dict)
     llm_model_ins = shared.loaderLLM()
-    llm_model_ins.set_history_len(LLM_HISTORY_LEN)
     try:
         local_doc_qa.init_cfg(llm_model=llm_model_ins)
-        generator = local_doc_qa.llm.generatorAnswer("你好")
-        for answer_result in generator:
+        answer_result_stream_result = local_doc_qa.llm_model_chain(
+            {"prompt": "你好", "history": [], "streaming": False})
+
+        for answer_result in answer_result_stream_result['answer_result_stream']:
             print(answer_result.llm_output)
         reply = """模型已成功加载，可以开始对话，或从右侧选择模式后开始对话"""
         logger.info(reply)
@@ -141,7 +145,7 @@ def reinit_model(llm_model, embedding_model, llm_history_len, no_remote_model, u
 def get_vector_store(vs_id, files, sentence_size, history, one_conent, one_content_segmentation):
     vs_path = os.path.join(KB_ROOT_PATH, vs_id, "vector_store")
     filelist = []
-    if local_doc_qa.llm and local_doc_qa.embeddings:
+    if local_doc_qa.llm_model_chain and local_doc_qa.embeddings:
         if isinstance(files, list):
             for file in files:
                 filename = os.path.split(file.name)[-1]
@@ -165,7 +169,7 @@ def get_vector_store(vs_id, files, sentence_size, history, one_conent, one_conte
 
 def change_vs_name_input(vs_id, history):
     if vs_id == "新建知识库":
-        return gr.update(visible=True), gr.update(visible=True), gr.update(visible=False), None, history,\
+        return gr.update(visible=True), gr.update(visible=True), gr.update(visible=False), None, history, \
                gr.update(choices=[]), gr.update(visible=False)
     else:
         vs_path = os.path.join(KB_ROOT_PATH, vs_id, "vector_store")
@@ -218,7 +222,7 @@ def change_chunk_conent(mode, label_conent, history):
 
 
 def add_vs_name(vs_name, chatbot):
-    if vs_name is None or vs_name.strip() == "" :
+    if vs_name is None or vs_name.strip() == "":
         vs_status = "知识库名称不能为空，请重新填写知识库名称"
         chatbot = chatbot + [[None, vs_status]]
         return gr.update(visible=True), gr.update(visible=True), gr.update(visible=True), gr.update(
@@ -262,6 +266,7 @@ def reinit_vector_store(vs_id, history):
 def refresh_vs_list():
     return gr.update(choices=get_vs_list()), gr.update(choices=get_vs_list())
 
+
 def delete_file(vs_id, files_to_delete, chatbot):
     vs_path = os.path.join(KB_ROOT_PATH, vs_id, "vector_store")
     content_path = os.path.join(KB_ROOT_PATH, vs_id, "content")
@@ -275,11 +280,11 @@ def delete_file(vs_id, files_to_delete, chatbot):
     rested_files = local_doc_qa.list_file_from_vector_store(vs_path)
     if "fail" in status:
         vs_status = "文件删除失败。"
-    elif len(rested_files)>0:
+    elif len(rested_files) > 0:
         vs_status = "文件删除成功。"
     else:
         vs_status = f"文件删除成功，知识库{vs_id}中无已上传文件，请先上传文件后，再开始提问。"
-    logger.info(",".join(files_to_delete)+vs_status)
+    logger.info(",".join(files_to_delete) + vs_status)
     chatbot = chatbot + [[None, vs_status]]
     return gr.update(choices=local_doc_qa.list_file_from_vector_store(vs_path), value=[]), chatbot
 
@@ -290,7 +295,8 @@ def delete_vs(vs_id, chatbot):
         status = f"成功删除知识库{vs_id}"
         logger.info(status)
         chatbot = chatbot + [[None, status]]
-        return gr.update(choices=get_vs_list(), value=get_vs_list()[0]), gr.update(visible=True), gr.update(visible=True), \
+        return gr.update(choices=get_vs_list(), value=get_vs_list()[0]), gr.update(visible=True), gr.update(
+            visible=True), \
                gr.update(visible=False), chatbot, gr.update(visible=False)
     except Exception as e:
         logger.error(e)
@@ -333,7 +339,8 @@ default_theme_args = dict(
 
 with gr.Blocks(css=block_css, theme=gr.themes.Default(**default_theme_args)) as demo:
     vs_path, file_status, model_status = gr.State(
-        os.path.join(KB_ROOT_PATH, get_vs_list()[0], "vector_store") if len(get_vs_list()) > 1 else ""), gr.State(""), gr.State(
+        os.path.join(KB_ROOT_PATH, get_vs_list()[0], "vector_store") if len(get_vs_list()) > 1 else ""), gr.State(
+        ""), gr.State(
         model_status)
     gr.Markdown(webui_title)
     with gr.Tab("对话"):

webui_st.py

@@ -85,9 +85,10 @@ def get_answer(query, vs_path, history, mode, score_threshold=VECTOR_SEARCH_SCOR
             yield history + [[query,
                               "请选择知识库后进行测试，当前未选择知识库。"]], ""
     else:
-        for answer_result in local_doc_qa.llm.generatorAnswer(prompt=query, history=history,
-                                                              streaming=streaming):
+        answer_result_stream_result = local_doc_qa.llm_model_chain(
+            {"prompt": query, "history": history, "streaming": streaming})
 
+        for answer_result in answer_result_stream_result['answer_result_stream']:
             resp = answer_result.llm_output["answer"]
             history = answer_result.history
             history[-1][-1] = resp + (
@@ -105,13 +106,14 @@ def init_model(llm_model: str = 'chat-glm-6b', embedding_model: str = 'text2vec'
     args_dict.update(model=llm_model)
     shared.loaderCheckPoint = LoaderCheckPoint(args_dict)
     llm_model_ins = shared.loaderLLM()
-    llm_model_ins.set_history_len(LLM_HISTORY_LEN)
 
     try:
         local_doc_qa.init_cfg(llm_model=llm_model_ins,
                               embedding_model=embedding_model)
-        generator = local_doc_qa.llm.generatorAnswer("你好")
-        for answer_result in generator:
+        answer_result_stream_result = local_doc_qa.llm_model_chain(
+            {"prompt": "你好", "history": [], "streaming": False})
+
+        for answer_result in answer_result_stream_result['answer_result_stream']:
             print(answer_result.llm_output)
         reply = """模型已成功加载，可以开始对话，或从右侧选择模式后开始对话"""
         logger.info(reply)
@@ -468,7 +470,7 @@ with st.sidebar:
             top_k = st.slider('向量匹配数量', 1, 20, VECTOR_SEARCH_TOP_K)
             history_len = st.slider(
                 'LLM对话轮数', 1, 50, LLM_HISTORY_LEN)  # 也许要跟知识库分开设置
-            local_doc_qa.llm.set_history_len(history_len)
+            # local_doc_qa.llm.set_history_len(history_len)
             chunk_conent = st.checkbox('启用上下文关联', False)
             st.text('')
             # chunk_conent = st.checkbox('分割文本', True) # 知识库文本分割入库