llm_model_dict 处理了loader的一些预设行为，如加载位置，模型名称，模型处理器实例, 定义checkpoint名称和远程路径

loader.py: 模型重载
定义 generatorAnswer 增加 AnswerResultStream
   定义generate_with_callback收集器，在每次响应时将队列数据同步到AnswerResult
requirements.txt 变更项目依赖

chains/local_doc_qa.py

@@ -12,10 +12,14 @@ from tqdm import tqdm
 from pypinyin import lazy_pinyin
 from loader import UnstructuredPaddleImageLoader
 from loader import UnstructuredPaddlePDFLoader
+from models.base import (BaseAnswer,
+                         AnswerResult,
+                         AnswerResultStream,
+                         AnswerResultQueueSentinelTokenListenerQueue)
+from models.loader.args import parser
+from models.loader import LoaderCheckPoint
+import models.shared as shared
 
-DEVICE_ = EMBEDDING_DEVICE
-DEVICE_ID = "0" if torch.cuda.is_available() else None
-DEVICE = f"{DEVICE_}:{DEVICE_ID}" if DEVICE_ID else DEVICE_
 
 
 def load_file(filepath, sentence_size=SENTENCE_SIZE):
@@ -132,7 +136,7 @@ def similarity_search_with_score_by_vector(
 
 
 class LocalDocQA:
-    llm: object = None
+    llm: BaseAnswer = None
     embeddings: object = None
     top_k: int = VECTOR_SEARCH_TOP_K
     chunk_size: int = CHUNK_SIZE
@@ -142,23 +146,10 @@ class LocalDocQA:
     def init_cfg(self,
                  embedding_model: str = EMBEDDING_MODEL,
                  embedding_device=EMBEDDING_DEVICE,
-                 llm_history_len: int = LLM_HISTORY_LEN,
-                 llm_model: str = LLM_MODEL,
-                 llm_device=LLM_DEVICE,
+                 llm_model: BaseAnswer = None,
                  top_k=VECTOR_SEARCH_TOP_K,
-                 use_ptuning_v2: bool = USE_PTUNING_V2,
-                 use_lora: bool = USE_LORA,
                  ):
-        if llm_model.startswith('moss'):
-            from models.moss_llm import MOSS
-            self.llm = MOSS()
-        else:
-            from models.chatglm_llm import ChatGLM
-            self.llm = ChatGLM()
-        self.llm.load_model(model_name_or_path=llm_model_dict[llm_model],
-                            llm_device=llm_device, use_ptuning_v2=use_ptuning_v2, use_lora=use_lora)
-        self.llm.history_len = llm_history_len
-
+        self.llm = llm_model
         self.embeddings = HuggingFaceEmbeddings(model_name=embedding_model_dict[embedding_model],
                                                 model_kwargs={'device': embedding_device})
         self.top_k = top_k
@@ -259,16 +250,16 @@ class LocalDocQA:
         torch_gc()
         prompt = generate_prompt(related_docs_with_score, query)
 
-        for result, history in self.llm._call(prompt=prompt,
-                                              history=chat_history,
+        for answer_result in self.llm.generatorAnswer(prompt=prompt, history=chat_history,
                                                       streaming=streaming):
-            torch_gc()
+            resp = answer_result.llm_output["answer"]
+            history = answer_result.history
             history[-1][0] = query
             response = {"query": query,
-                        "result": result,
+                        "result": resp,
                         "source_documents": related_docs_with_score}
             yield response, history
-            torch_gc()
+
 
     # query      查询内容
     # vs_path    知识库路径
@@ -297,10 +288,19 @@ class LocalDocQA:
 
 
 if __name__ == "__main__":
+    # 初始化消息
+    args = None
+    args = parser.parse_args(args=['--model-dir', '/media/checkpoint/',  '--model', 'chatglm-6b', '--no-remote-model'])
+
+    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()
+    local_doc_qa.init_cfg(llm_model=llm_model_ins)
     query = "本项目使用的embedding模型是什么，消耗多少显存"
-    vs_path = "/Users/liuqian/Downloads/glm-dev/vector_store/aaa"
+    vs_path = "/media/gpt4-pdf-chatbot-langchain/dev-langchain-ChatGLM/vector_store/test"
     last_print_len = 0
     for resp, history in local_doc_qa.get_knowledge_based_answer(query=query,
                                                                  vs_path=vs_path,

configs/model_config.py

@@ -22,14 +22,54 @@ EMBEDDING_MODEL = "text2vec"
 # Embedding running device
 EMBEDDING_DEVICE = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"
 
+
 # supported LLM models
+"""
+llm_model_dict 处理了loader的一些预设行为，如加载位置，模型名称，模型处理器实例
+"""
 llm_model_dict = {
-    "chatyuan": "ClueAI/ChatYuan-large-v2",
-    "chatglm-6b-int4-qe": "THUDM/chatglm-6b-int4-qe",
-    "chatglm-6b-int4": "THUDM/chatglm-6b-int4",
-    "chatglm-6b-int8": "THUDM/chatglm-6b-int8",
-    "chatglm-6b": "THUDM/chatglm-6b",
-    "moss": "fnlp/moss-moon-003-sft",
+    "chatglm-6b-int4-qe": {
+        "name": "chatglm-6b-int4-qe",
+        "remote-checkpoint": "THUDM/chatglm-6b-int4-qe",
+        "path": None,
+        "provides": "ChatGLM"
+    },
+    "chatglm-6b-int4": {
+        "name": "chatglm-6b-int4",
+        "remote-checkpoint": "THUDM/chatglm-6b-int4",
+        "path": None,
+        "provides": "ChatGLM"
+    },
+    "chatglm-6b": {
+        "name": "chatglm-6b",
+        "remote-checkpoint": "THUDM/chatglm-6b-int4",
+        "path": None,
+        "provides": "ChatGLM"
+    },
+    "llama-7b-hf": {
+        "name": "llama-7b-hf",
+        "remote-checkpoint": "llama-7b-hf",
+        "path": None,
+        "provides": "LLamaLLM"
+    },
+    "vicuna-13b-hf": {
+        "name": "vicuna-13b-hf",
+        "remote-checkpoint": "vicuna-13b-hf",
+        "path": None,
+        "provides": "LLamaLLM"
+    },
+    "chatyuan": {
+        "name": "chatyuan",
+        "remote-checkpoint": "ClueAI/ChatYuan-large-v2",
+        "path": None,
+        "provides": None
+    },
+    "chatglm-6b-int8":{
+        "name": "chatglm-6b-int8",
+        "remote-checkpoint":  "THUDM/chatglm-6b-int8",
+        "path": None,
+        "provides": "ChatGLM"
+    },
 }
 
 # LLM model name

fastchat/api/__init__.py

+from .fastchat_api import *
\ No newline at end of file

fastchat/api/conversation.py

+"""
+Conversation prompt template.
+
+Now we support
+- Vicuna
+- Koala
+- OpenAssistant/oasst-sft-1-pythia-12b
+- StabilityAI/stablelm-tuned-alpha-7b
+- databricks/dolly-v2-12b
+- THUDM/chatglm-6b
+- Alpaca/LLaMa
+"""
+
+import dataclasses
+from enum import auto, Enum
+from typing import List, Tuple, Any
+
+
+class SeparatorStyle(Enum):
+    """Different separator style."""
+
+    SINGLE = auto()
+    TWO = auto()
+    DOLLY = auto()
+    OASST_PYTHIA = auto()
+
+
+@dataclasses.dataclass
+class Conversation:
+    """A class that keeps all conversation history."""
+
+    system: str
+    roles: List[str]
+    messages: List[List[str]]
+    offset: int
+    sep_style: SeparatorStyle = SeparatorStyle.SINGLE
+    sep: str = "###"
+    sep2: str = None
+
+    # Used for gradio server
+    skip_next: bool = False
+    conv_id: Any = None
+
+    def get_prompt(self):
+        if self.sep_style == SeparatorStyle.SINGLE:
+            ret = self.system
+            for role, message in self.messages:
+                if message:
+                    ret += self.sep + " " + role + ": " + message
+                else:
+                    ret += self.sep + " " + role + ":"
+            return ret
+        elif self.sep_style == SeparatorStyle.TWO:
+            seps = [self.sep, self.sep2]
+            ret = self.system + seps[0]
+            for i, (role, message) in enumerate(self.messages):
+                if message:
+                    ret += role + ": " + message + seps[i % 2]
+                else:
+                    ret += role + ":"
+            return ret
+        elif self.sep_style == SeparatorStyle.DOLLY:
+            seps = [self.sep, self.sep2]
+            ret = self.system
+            for i, (role, message) in enumerate(self.messages):
+                if message:
+                    ret += role + ":\n" + message + seps[i % 2]
+                    if i % 2 == 1:
+                        ret += "\n\n"
+                else:
+                    ret += role + ":\n"
+            return ret
+        elif self.sep_style == SeparatorStyle.OASST_PYTHIA:
+            ret = self.system
+            for role, message in self.messages:
+                if message:
+                    ret += role + message + self.sep
+                else:
+                    ret += role
+            return ret
+        else:
+            raise ValueError(f"Invalid style: {self.sep_style}")
+
+    def append_message(self, role, message):
+        self.messages.append([role, message])
+
+    def to_gradio_chatbot(self):
+        ret = []
+        for i, (role, msg) in enumerate(self.messages[self.offset :]):
+            if i % 2 == 0:
+                ret.append([msg, None])
+            else:
+                ret[-1][-1] = msg
+        return ret
+
+    def copy(self):
+        return Conversation(
+            system=self.system,
+            roles=self.roles,
+            messages=[[x, y] for x, y in self.messages],
+            offset=self.offset,
+            sep_style=self.sep_style,
+            sep=self.sep,
+            sep2=self.sep2,
+            conv_id=self.conv_id,
+        )
+
+    def dict(self):
+        return {
+            "system": self.system,
+            "roles": self.roles,
+            "messages": self.messages,
+            "offset": self.offset,
+            "sep": self.sep,
+            "sep2": self.sep2,
+            "conv_id": self.conv_id,
+        }
+
+
+conv_one_shot = Conversation(
+    system="A chat between a curious human and an artificial intelligence assistant. "
+    "The assistant gives helpful, detailed, and polite answers to the human's questions.",
+    roles=("Human", "Assistant"),
+    messages=(
+        (
+            "Human",
+            "What are the key differences between renewable and non-renewable energy sources?",
+        ),
+        (
+            "Assistant",
+            "Renewable energy sources are those that can be replenished naturally in a relatively "
+            "short amount of time, such as solar, wind, hydro, geothermal, and biomass. "
+            "Non-renewable energy sources, on the other hand, are finite and will eventually be "
+            "depleted, such as coal, oil, and natural gas. Here are some key differences between "
+            "renewable and non-renewable energy sources:\n"
+            "1. Availability: Renewable energy sources are virtually inexhaustible, while non-renewable "
+            "energy sources are finite and will eventually run out.\n"
+            "2. Environmental impact: Renewable energy sources have a much lower environmental impact "
+            "than non-renewable sources, which can lead to air and water pollution, greenhouse gas emissions, "
+            "and other negative effects.\n"
+            "3. Cost: Renewable energy sources can be more expensive to initially set up, but they typically "
+            "have lower operational costs than non-renewable sources.\n"
+            "4. Reliability: Renewable energy sources are often more reliable and can be used in more remote "
+            "locations than non-renewable sources.\n"
+            "5. Flexibility: Renewable energy sources are often more flexible and can be adapted to different "
+            "situations and needs, while non-renewable sources are more rigid and inflexible.\n"
+            "6. Sustainability: Renewable energy sources are more sustainable over the long term, while "
+            "non-renewable sources are not, and their depletion can lead to economic and social instability.",
+        ),
+    ),
+    offset=2,
+    sep_style=SeparatorStyle.SINGLE,
+    sep="###",
+)
+
+
+conv_vicuna_v1_1 = Conversation(
+    system="A chat between a curious user and an artificial intelligence assistant. "
+    "The assistant gives helpful, detailed, and polite answers to the user's questions.",
+    roles=("USER", "ASSISTANT"),
+    messages=(),
+    offset=0,
+    sep_style=SeparatorStyle.TWO,
+    sep=" ",
+    sep2="</s>",
+)
+
+
+conv_koala_v1 = Conversation(
+    system="BEGINNING OF CONVERSATION:",
+    roles=("USER", "GPT"),
+    messages=(),
+    offset=0,
+    sep_style=SeparatorStyle.TWO,
+    sep=" ",
+    sep2="</s>",
+)
+
+conv_dolly = Conversation(
+    system="Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n",
+    roles=("### Instruction", "### Response"),
+    messages=(),
+    offset=0,
+    sep_style=SeparatorStyle.DOLLY,
+    sep="\n\n",
+    sep2="### End",
+)
+
+conv_oasst = Conversation(
+    system="",
+    roles=("<|prompter|>", "<|assistant|>"),
+    messages=(),
+    offset=0,
+    sep_style=SeparatorStyle.OASST_PYTHIA,
+    sep="<|endoftext|>",
+)
+
+conv_stablelm = Conversation(
+    system="""<|SYSTEM|># StableLM Tuned (Alpha version)
+- StableLM is a helpful and harmless open-source AI language model developed by StabilityAI.
+- StableLM is excited to be able to help the user, but will refuse to do anything that could be considered harmful to the user.
+- StableLM is more than just an information source, StableLM is also able to write poetry, short stories, and make jokes.
+- StableLM will refuse to participate in anything that could harm a human.
+""",
+    roles=("<|USER|>", "<|ASSISTANT|>"),
+    messages=(),
+    offset=0,
+    sep_style=SeparatorStyle.OASST_PYTHIA,
+    sep="",
+)
+
+conv_templates = {
+    "conv_one_shot": conv_one_shot,
+    "vicuna_v1.1": conv_vicuna_v1_1,
+    "koala_v1": conv_koala_v1,
+    "dolly": conv_dolly,
+    "oasst": conv_oasst,
+}
+
+
+def get_default_conv_template(model_name):
+    model_name = model_name.lower()
+    if "vicuna" in model_name or "output" in model_name:
+        return conv_vicuna_v1_1
+    elif "koala" in model_name:
+        return conv_koala_v1
+    elif "dolly-v2" in model_name:
+        return conv_dolly
+    elif "oasst" in model_name and "pythia" in model_name:
+        return conv_oasst
+    elif "stablelm" in model_name:
+        return conv_stablelm
+    return conv_one_shot
+
+
+def compute_skip_echo_len(model_name, conv, prompt):
+    model_name = model_name.lower()
+    if "chatglm" in model_name:
+        skip_echo_len = len(conv.messages[-2][1]) + 1
+    elif "dolly-v2" in model_name:
+        special_toks = ["### Instruction:", "### Response:", "### End"]
+        skip_echo_len = len(prompt)
+        for tok in special_toks:
+            skip_echo_len -= prompt.count(tok) * len(tok)
+    elif "oasst" in model_name and "pythia" in model_name:
+        special_toks = ["<|prompter|>", "<|assistant|>", "<|endoftext|>"]
+        skip_echo_len = len(prompt)
+        for tok in special_toks:
+            skip_echo_len -= prompt.count(tok) * len(tok)
+    elif "stablelm" in model_name:
+        special_toks = ["<|SYSTEM|>", "<|USER|>", "<|ASSISTANT|>"]
+        skip_echo_len = len(prompt)
+        for tok in special_toks:
+            skip_echo_len -= prompt.count(tok) * len(tok)
+    else:
+        skip_echo_len = len(prompt) + 1 - prompt.count("</s>") * 3
+    return skip_echo_len
+
+
+if __name__ == "__main__":
+    print(default_conversation.get_prompt())

fastchat/api/fastchat_api.py

+"""Wrapper around FastChat APIs."""
+from __future__ import annotations
+
+import logging
+import sys
+import warnings
+from typing import (
+    AbstractSet,
+    Any,
+    Callable,
+    Collection,
+    Dict,
+    Generator,
+    List,
+    Literal,
+    Mapping,
+    Optional,
+    Set,
+    Tuple,
+    Union,
+)
+
+from pydantic import Extra, Field, root_validator
+from tenacity import (
+    before_sleep_log,
+    retry,
+    retry_if_exception_type,
+    stop_after_attempt,
+    wait_exponential,
+)
+
+from langchain.llms.base import BaseLLM
+from langchain.schema import Generation, LLMResult
+from langchain.utils import get_from_dict_or_env
+
+import requests
+import json
+
+
+logger = logging.getLogger(__name__)
+FAST_CHAT_API = "http://localhost:21002/worker_generate_stream"
+
+
+def _streaming_response_template() -> Dict[str, Any]:
+    """
+    :return: 响应结构
+    """
+    return {
+        "text": "",
+        "error_code": 0,
+    }
+
+
+def _update_response(response: Dict[str, Any], stream_response: Dict[str, Any]) -> None:
+    """Update response from the stream response."""
+    response["text"] += stream_response["text"]
+    response["error_code"] += stream_response["error_code"]
+
+
+class BaseFastChat(BaseLLM):
+    """Wrapper around FastChat large language models."""
+
+    model_name: str = "text-davinci-003"
+    """Model name to use."""
+    temperature: float = 0.7
+    """What sampling temperature to use."""
+    max_new_tokens: int = 200
+    stop: int = 20
+    batch_size: int = 20
+    """Maximum number of retries to make when generating."""
+    streaming: bool = False
+    """Penalizes repeated tokens."""
+    n: int = 1
+    """Whether to stream the results or not."""
+    allowed_special: Union[Literal["all"], AbstractSet[str]] = set()
+    """Set of special tokens that are allowed。"""
+    disallowed_special: Union[Literal["all"], Collection[str]] = "all"
+    """Set of special tokens that are not allowed。"""
+
+    class Config:
+        """Configuration for this pydantic object."""
+
+        extra = Extra.ignore
+
+    @root_validator(pre=True)
+    def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
+        """Build extra kwargs from additional params that were passed in."""
+        all_required_field_names = {field.alias for field in cls.__fields__.values()}
+
+        extra = values.get("model_kwargs", {})
+        for field_name in list(values):
+            if field_name not in all_required_field_names:
+                if field_name in extra:
+                    raise ValueError(f"Found {field_name} supplied twice.")
+                logger.warning(
+                    f"""WARNING! {field_name} is not default parameter.
+                    {field_name} was transfered to model_kwargs.
+                    Please confirm that {field_name} is what you intended."""
+                )
+                extra[field_name] = values.pop(field_name)
+        values["model_kwargs"] = extra
+        return values
+
+    @property
+    def _default_params(self) -> Dict[str, Any]:
+        """Get the default parameters for calling FastChat API."""
+        normal_params = {
+            "model": self.model_name,
+            "prompt": '',
+            "max_new_tokens": self.max_new_tokens,
+            "temperature": self.temperature,
+        }
+
+        return {**normal_params}
+
+    def _generate(
+            self, prompts: List[str], stop: Optional[List[str]] = None
+    ) -> LLMResult:
+        """Call out to FastChat's endpoint with k unique prompts.
+
+        Args:
+            prompts: The prompts to pass into the model.
+            stop: Optional list of stop words to use when generating.
+
+        Returns:
+            The full LLM output.
+
+        Example:
+            .. code-block:: python
+
+                response = fastchat.generate(["Tell me a joke."])
+        """
+        # TODO: write a unit test for this
+        params = self._invocation_params
+        sub_prompts = self.get_sub_prompts(params, prompts)
+        choices = []
+        token_usage: Dict[str, int] = {}
+        headers = {"User-Agent": "fastchat Client"}
+        for _prompts in sub_prompts:
+
+            params["prompt"] = _prompts[0]
+
+            if stop is not None:
+                if "stop" in params:
+                    raise ValueError("`stop` found in both the input and default params.")
+                params["stop"] = stop
+
+            if self.streaming:
+                if len(_prompts) > 1:
+                    raise ValueError("Cannot stream results with multiple prompts.")
+
+                response_template = _streaming_response_template()
+                response = requests.post(
+                    FAST_CHAT_API,
+                    headers=headers,
+                    json=params,
+                    stream=True,
+                    )
+                for stream_resp in response.iter_lines(
+                        chunk_size=8192, decode_unicode=False, delimiter=b"\0"
+                ):
+                    if stream_resp:
+                        data = json.loads(stream_resp.decode("utf-8"))
+                        skip_echo_len = len(_prompts[0])
+                        output = data["text"][skip_echo_len:].strip()
+                        data["text"] = output
+                        self.callback_manager.on_llm_new_token(
+                            output,
+                            verbose=self.verbose,
+                            logprobs=data["error_code"],
+                        )
+                        _update_response(response_template, data)
+                choices.append(response_template)
+            else:
+                response_template = _streaming_response_template()
+                response = requests.post(
+                    FAST_CHAT_API,
+                    headers=headers,
+                    json=params,
+                    stream=True,
+                    )
+                for stream_resp in response.iter_lines(
+                        chunk_size=8192, decode_unicode=False, delimiter=b"\0"
+                ):
+                    if stream_resp:
+                        data = json.loads(stream_resp.decode("utf-8"))
+                        skip_echo_len = len(_prompts[0])
+                        output = data["text"][skip_echo_len:].strip()
+                        data["text"] = output
+                        _update_response(response_template, data)
+
+                choices.append(response_template)
+
+        return self.create_llm_result(choices, prompts, token_usage)
+
+    async def _agenerate(
+            self, prompts: List[str], stop: Optional[List[str]] = None
+    ) -> LLMResult:
+        """Call out to FastChat's endpoint async with k unique prompts."""
+        params = self._invocation_params
+        sub_prompts = self.get_sub_prompts(params, prompts)
+        choices = []
+        token_usage: Dict[str, int] = {}
+
+        headers = {"User-Agent": "fastchat Client"}
+        for _prompts in sub_prompts:
+
+            params["prompt"] = _prompts[0]
+            if stop is not None:
+                if "stop" in params:
+                    raise ValueError("`stop` found in both the input and default params.")
+                params["stop"] = stop
+
+            if self.streaming:
+                if len(_prompts) > 1:
+                    raise ValueError("Cannot stream results with multiple prompts.")
+
+                response_template = _streaming_response_template()
+                response = requests.post(
+                    FAST_CHAT_API,
+                    headers=headers,
+                    json=params,
+                    stream=True,
+                    )
+                for stream_resp in response.iter_lines(
+                        chunk_size=8192, decode_unicode=False, delimiter=b"\0"
+                ):
+                    if stream_resp:
+                        data = json.loads(stream_resp.decode("utf-8"))
+                        skip_echo_len = len(_prompts[0])
+                        output = data["text"][skip_echo_len:].strip()
+                        data["text"] = output
+                        self.callback_manager.on_llm_new_token(
+                            output,
+                            verbose=self.verbose,
+                            logprobs=data["error_code"],
+                        )
+                        _update_response(response_template, data)
+                choices.append(response_template)
+            else:
+                response_template = _streaming_response_template()
+                response = requests.post(
+                    FAST_CHAT_API,
+                    headers=headers,
+                    json=params,
+                    stream=True,
+                    )
+                for stream_resp in response.iter_lines(
+                        chunk_size=8192, decode_unicode=False, delimiter=b"\0"
+                ):
+                    if stream_resp:
+                        data = json.loads(stream_resp.decode("utf-8"))
+                        skip_echo_len = len(_prompts[0])
+                        output = data["text"][skip_echo_len:].strip()
+                        data["text"] = output
+                        _update_response(response_template, data)
+
+                choices.append(response_template)
+
+        return self.create_llm_result(choices, prompts, token_usage)
+
+    def get_sub_prompts(
+            self,
+            params: Dict[str, Any],
+            prompts: List[str],
+    ) -> List[List[str]]:
+        """Get the sub prompts for llm call."""
+        if params["max_new_tokens"] == -1:
+            if len(prompts) != 1:
+                raise ValueError(
+                    "max_new_tokens set to -1 not supported for multiple inputs."
+                )
+            params["max_new_tokens"] = self.max_new_tokens_for_prompt(prompts[0])
+        # append pload
+        sub_prompts = [
+            prompts[i: i + self.batch_size]
+            for i in range(0, len(prompts), self.batch_size)
+        ]
+
+        return sub_prompts
+
+    def create_llm_result(
+            self, choices: Any, prompts: List[str], token_usage: Dict[str, int]
+    ) -> LLMResult:
+        """Create the LLMResult from the choices and prompts."""
+        generations = []
+        for i, _ in enumerate(prompts):
+            sub_choices = choices[i * self.n: (i + 1) * self.n]
+            generations.append(
+                [
+                    Generation(
+                        text=choice["text"],
+                        generation_info=dict(
+                            finish_reason='over',
+                            logprobs=choice["text"],
+                        ),
+                    )
+                    for choice in sub_choices
+                ]
+            )
+        llm_output = {"token_usage": token_usage, "model_name": self.model_name}
+        return LLMResult(generations=generations, llm_output=llm_output)
+
+    def stream(self, prompt: str, stop: Optional[List[str]] = None) -> Generator:
+        """Call FastChat with streaming flag and return the resulting generator.
+
+        BETA: this is a beta feature while we figure out the right abstraction.
+        Once that happens, this interface could change.
+
+        Args:
+            prompt: The prompts to pass into the model.
+            stop: Optional list of stop words to use when generating.
+
+        Returns:
+            A generator representing the stream of tokens from OpenAI.
+
+        Example:
+            .. code-block:: python
+
+                generator = fastChat.stream("Tell me a joke.")
+                for token in generator:
+                    yield token
+        """
+        params = self._invocation_params
+        params["prompt"] = prompt
+        if stop is not None:
+            if "stop" in params:
+                raise ValueError("`stop` found in both the input and default params.")
+            params["stop"] = stop
+
+        headers = {"User-Agent": "fastchat Client"}
+        response = requests.post(
+            FAST_CHAT_API,
+            headers=headers,
+            json=params,
+            stream=True,
+        )
+        for stream_resp in response.iter_lines(
+                chunk_size=8192, decode_unicode=False, delimiter=b"\0"
+        ):
+            if stream_resp:
+                data = json.loads(stream_resp.decode("utf-8"))
+                skip_echo_len = len(_prompts[0])
+                output = data["text"][skip_echo_len:].strip()
+                data["text"] = output
+                yield data
+
+    @property
+    def _invocation_params(self) -> Dict[str, Any]:
+        """Get the parameters used to invoke the model."""
+        return self._default_params
+
+    @property
+    def _identifying_params(self) -> Mapping[str, Any]:
+        """Get the identifying parameters."""
+        return {**{"model_name": self.model_name}, **self._default_params}
+
+    @property
+    def _llm_type(self) -> str:
+        """Return type of llm."""
+        return "fastChat"
+
+    def get_num_tokens(self, text: str) -> int:
+        """Calculate num tokens with tiktoken package."""
+        # tiktoken NOT supported for Python < 3.8
+        if sys.version_info[1] < 8:
+            return super().get_num_tokens(text)
+        try:
+            import tiktoken
+        except ImportError:
+            raise ValueError(
+                "Could not import tiktoken python package. "
+                "This is needed in order to calculate get_num_tokens. "
+                "Please install it with `pip install tiktoken`."
+            )
+
+        enc = tiktoken.encoding_for_model(self.model_name)
+
+        tokenized_text = enc.encode(
+            text,
+            allowed_special=self.allowed_special,
+            disallowed_special=self.disallowed_special,
+        )
+
+        # calculate the number of tokens in the encoded text
+        return len(tokenized_text)
+
+    def modelname_to_contextsize(self, modelname: str) -> int:
+        """Calculate the maximum number of tokens possible to generate for a model.
+
+        Args:
+            modelname: The modelname we want to know the context size for.
+
+        Returns:
+            The maximum context size
+
+        Example:
+            .. code-block:: python
+
+                max_new_tokens = openai.modelname_to_contextsize("text-davinci-003")
+        """
+        model_token_mapping = {
+            "vicuna-13b": 2049,
+            "koala": 2049,
+            "dolly-v2": 2049,
+            "oasst": 2049,
+            "stablelm": 2049,
+        }
+
+        context_size = model_token_mapping.get(modelname, None)
+
+        if context_size is None:
+            raise ValueError(
+                f"Unknown model: {modelname}. Please provide a valid OpenAI model name."
+                "Known models are: " + ", ".join(model_token_mapping.keys())
+            )
+
+        return context_size
+
+    def max_new_tokens_for_prompt(self, prompt: str) -> int:
+        """Calculate the maximum number of tokens possible to generate for a prompt.
+
+        Args:
+            prompt: The prompt to pass into the model.
+
+        Returns:
+            The maximum number of tokens to generate for a prompt.
+
+        Example:
+            .. code-block:: python
+
+                max_new_tokens = openai.max_token_for_prompt("Tell me a joke.")
+        """
+        num_tokens = self.get_num_tokens(prompt)
+
+        # get max context size for model by name
+        max_size = self.modelname_to_contextsize(self.model_name)
+        return max_size - num_tokens
+
+
+class FastChat(BaseFastChat):
+    """Wrapper around OpenAI large language models.
+
+    To use, you should have the ``openai`` python package installed, and the
+    environment variable ``OPENAI_API_KEY`` set with your API key.
+
+    Any parameters that are valid to be passed to the openai.create call can be passed
+    in, even if not explicitly saved on this class.
+
+    Example:
+        .. code-block:: python
+
+            from langchain.llms import OpenAI
+            openai = FastChat(model_name="vicuna")
+    """
+
+    @property
+    def _invocation_params(self) -> Dict[str, Any]:
+        return {**{"model": self.model_name}, **super()._invocation_params}

models/__init__.py

 
-from .chatglm_llm import *
+from .chatglm_llm import ChatGLM
\ No newline at end of file
+from .llama_llm import LLamaLLM
+from .moss_llm import MOSSLLM

models/__main__.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
+
+from langchain.agents import initialize_agent, Tool
+from langchain.agents import AgentType
+
+import models.shared as shared
+
+from langchain.chains import LLMChain
+from langchain.memory import ConversationBufferMemory, ReadOnlySharedMemory
+from langchain.prompts import PromptTemplate
+from langchain.agents import ZeroShotAgent, Tool, AgentExecutor
+from typing import List, Set
+
+
+
+class CustomLLMSingleActionAgent(ZeroShotAgent):
+    allowed_tools: List[str]
+
+    def __init__(self, *args, **kwargs):
+        super(CustomLLMSingleActionAgent, self).__init__(*args, **kwargs)
+        self.allowed_tools = kwargs['allowed_tools']
+
+    def get_allowed_tools(self) -> Set[str]:
+        return set(self.allowed_tools)
+
+
+async def dispatch(args: Namespace):
+    args_dict = vars(args)
+
+    shared.loaderCheckPoint = LoaderCheckPoint(args_dict)
+    llm_model_ins = shared.loaderLLM()
+
+    template = """This is a conversation between a human and a bot:
+    
+{chat_history}
+
+Write a summary of the conversation for {input}:
+"""
+
+    prompt = PromptTemplate(
+        input_variables=["input", "chat_history"],
+        template=template
+    )
+    memory = ConversationBufferMemory(memory_key="chat_history")
+    readonlymemory = ReadOnlySharedMemory(memory=memory)
+    summry_chain = LLMChain(
+        llm=llm_model_ins,
+        prompt=prompt,
+        verbose=True,
+        memory=readonlymemory,  # use the read-only memory to prevent the tool from modifying the memory
+    )
+
+
+    tools = [
+        Tool(
+            name="Summary",
+            func=summry_chain.run,
+            description="useful for when you summarize a conversation. The input to this tool should be a string, representing who will read this summary."
+        )
+    ]
+
+    prefix = """Have a conversation with a human, answering the following questions as best you can. You have access to the following tools:"""
+    suffix = """Begin!
+     
+Question: {input}
+{agent_scratchpad}"""
+
+
+    prompt = CustomLLMSingleActionAgent.create_prompt(
+        tools,
+        prefix=prefix,
+        suffix=suffix,
+        input_variables=["input",   "agent_scratchpad"]
+    )
+    tool_names = [tool.name for tool in tools]
+    llm_chain = LLMChain(llm=llm_model_ins, prompt=prompt)
+    agent = CustomLLMSingleActionAgent(llm_chain=llm_chain, tools=tools, allowed_tools=tool_names)
+    agent_chain = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools)
+
+    agent_chain.run(input="你好")
+    agent_chain.run(input="你是谁?")
+    agent_chain.run(input="我们之前聊了什么?")
+
+if __name__ == '__main__':
+    args = None
+    args = parser.parse_args(args=['--model-dir', '/media/checkpoint/',  '--model', 'vicuna-13b-hf', '--no-remote-model', '--load-in-8bit'])
+
+    loop = asyncio.new_event_loop()
+    asyncio.set_event_loop(loop)
+    loop.run_until_complete(dispatch(args))

models/base.py

+from abc import ABC, abstractmethod
+from typing import Optional, List
+import traceback
+from collections import deque
+from queue import Queue
+from threading import Thread
+
+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:
+    """
+    消息实体
+    """
+    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):
+    """上层业务包装器.用于结果生成统一api调用"""
+
+    @property
+    @abstractmethod
+    def _check_point(self) -> LoaderCheckPoint:
+        """Return _check_point of llm."""
+
+    @property
+    @abstractmethod
+    def _history_len(self) -> int:
+        """Return _history_len of llm."""
+
+    @abstractmethod
+    def set_history_len(self, history_len: int) -> None:
+        """Return _history_len of llm."""
+
+    def generatorAnswer(self, prompt: str,
+                        history: List[List[str]] = [],
+                        streaming: bool = False):
+        def generate_with_callback(callback=None, **kwargs):
+            kwargs['generate_with_callback'] = AnswerResultStream(callback_func=callback)
+            self._generate_answer(**kwargs)
+
+        def generate_with_streaming(**kwargs):
+            return Iteratorize(generate_with_callback, kwargs)
+
+        """
+        eos_token_id是指定token（例如，"</s>"），
+        用于表示序列的结束。在生成文本任务中，生成器在生成序列时，将不断地生成token，直到生成此特殊的eos_token_id，表示序列生成已经完成。
+        在Hugging Face Transformer模型中，eos_token_id是由tokenizer自动添加到输入中的。
+        在模型生成输出时，如果模型生成了eos_token_id，则生成过程将停止并返回生成的序列。
+        """
+        eos_token_ids = [
+            self._check_point.tokenizer.eos_token_id] if self._check_point.tokenizer.eos_token_id is not None else []
+
+        with generate_with_streaming(prompt=prompt, history=history, streaming=streaming) as generator:
+            for answerResult in generator:
+                if answerResult.listenerToken:
+                    output = answerResult.listenerToken.input_ids
+                yield answerResult
+
+    @abstractmethod
+    def _generate_answer(self, prompt: str,
+                         history: List[List[str]] = [],
+                         streaming: bool = False,
+                         generate_with_callback: AnswerResultStream = None) -> None:
+        pass

models/chatglm_llm.py

-import json
+
+from abc import ABC
+
 from langchain.llms.base import LLM
-from typing import List, Dict, Optional
-from transformers import AutoTokenizer, AutoModel, AutoConfig
-import torch
-from configs.model_config import *
-from utils import torch_gc
-
-DEVICE_ = LLM_DEVICE
-DEVICE_ID = "0" if torch.cuda.is_available() else None
-DEVICE = f"{DEVICE_}:{DEVICE_ID}" if DEVICE_ID else DEVICE_
-
-
-def auto_configure_device_map(num_gpus: int, use_lora: bool) -> Dict[str, int]:
-    # transformer.word_embeddings 占用1层
-    # transformer.final_layernorm 和 lm_head 占用1层
-    # transformer.layers 占用 28 层
-    # 总共30层分配到num_gpus张卡上
-    num_trans_layers = 28
-    per_gpu_layers = 30 / num_gpus
-
-    # bugfix: PEFT加载lora模型出现的层命名不同
-    if LLM_LORA_PATH and use_lora:
-        layer_prefix = 'base_model.model.transformer'
-    else:
-        layer_prefix = 'transformer'
-
-    # bugfix: 在linux中调用torch.embedding传入的weight,input不在同一device上,导致RuntimeError
-    # windows下 model.device 会被设置成 transformer.word_embeddings.device
-    # linux下 model.device 会被设置成 lm_head.device
-    # 在调用chat或者stream_chat时,input_ids会被放到model.device上
-    # 如果transformer.word_embeddings.device和model.device不同,则会导致RuntimeError
-    # 因此这里将transformer.word_embeddings,transformer.final_layernorm,lm_head都放到第一张卡上
-    device_map = {f'{layer_prefix}.word_embeddings': 0,
-                  f'{layer_prefix}.final_layernorm': 0, 'lm_head': 0,
-                  f'base_model.model.lm_head': 0, }
-
-    used = 2
-    gpu_target = 0
-    for i in range(num_trans_layers):
-        if used >= per_gpu_layers:
-            gpu_target += 1
-            used = 0
-        assert gpu_target < num_gpus
-        device_map[f'{layer_prefix}.layers.{i}'] = gpu_target
-        used += 1
-
-    return device_map
-
-
-class ChatGLM(LLM):
+from typing import Optional, List
+from models.loader import LoaderCheckPoint
+from models.base import (BaseAnswer,
+                         AnswerResult,
+                         AnswerResultStream,
+                         AnswerResultQueueSentinelTokenListenerQueue)
+
+
+import transformers
+
+
+class ChatGLM(BaseAnswer, LLM, ABC):
     max_token: int = 10000
-    temperature: float = 0.8
+    temperature: float = 0.01
     top_p = 0.9
+    checkPoint: LoaderCheckPoint = None
     # history = []
-    tokenizer: object = None
-    model: object = None
     history_len: int = 10
 
-    def __init__(self):
+    def __init__(self, checkPoint: LoaderCheckPoint = None):
         super().__init__()
+        self.checkPoint = checkPoint
 
     @property
     def _llm_type(self) -> str:
         return "ChatGLM"
 
-    def _call(self,
-              prompt: str,
+    @property
+    def _check_point(self) -> LoaderCheckPoint:
+        return self.checkPoint
+
+    @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:
+        pass
+
+    def _generate_answer(self, prompt: str,
                          history: List[List[str]] = [],
-              streaming: bool = STREAMING):  # -> Tuple[str, List[List[str]]]:
+                         streaming: bool = False,
+                         generate_with_callback: AnswerResultStream = None) -> None:
+        # 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:
-            for inum, (stream_resp, _) in enumerate(self.model.stream_chat(
-                    self.tokenizer,
+
+            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 > 0 else [],
                     max_length=self.max_token,
                     temperature=self.temperature,
-                    top_p=self.top_p,
+                    stopping_criteria=stopping_criteria_list
             )):
-                torch_gc()
+                self.checkPoint.clear_torch_cache()
                 if inum == 0:
                     history += [[prompt, stream_resp]]
                 else:
                     history[-1] = [prompt, stream_resp]
-                yield stream_resp, history
-                torch_gc()
+                answer_result = AnswerResult()
+                answer_result.history = history
+                answer_result.llm_output = {"answer": stream_resp}
+                if listenerQueue.listenerQueue.__len__() > 0:
+                    answer_result.listenerToken = listenerQueue.listenerQueue.pop()
+                generate_with_callback(answer_result)
         else:
-            response, _ = self.model.chat(
-                self.tokenizer,
+            response, _ = self.checkPoint.model.chat(
+                self.checkPoint.tokenizer,
                 prompt,
                 history=history[-self.history_len:] if self.history_len > 0 else [],
                 max_length=self.max_token,
                 temperature=self.temperature,
-                top_p=self.top_p,
+                stopping_criteria=stopping_criteria_list
             )
-            torch_gc()
+            self.checkPoint.clear_torch_cache()
             history += [[prompt, response]]
-            yield response, history
-            torch_gc()
-
-    # def chat(self,
-    #          prompt: str) -> str:
-    #     response, _ = self.model.chat(
-    #         self.tokenizer,
-    #         prompt,
-    #         history=self.history[-self.history_len:] if self.history_len > 0 else [],
-    #         max_length=self.max_token,
-    #         temperature=self.temperature,
-    #     )
-    #     torch_gc()
-    #     self.history = self.history + [[None, response]]
-    #     return response
-
-    def load_model(self,
-                   model_name_or_path: str = "THUDM/chatglm-6b",
-                   llm_device=LLM_DEVICE,
-                   use_ptuning_v2=False,
-                   use_lora=False,
-                   device_map: Optional[Dict[str, int]] = None,
-                   **kwargs):
-        self.tokenizer = AutoTokenizer.from_pretrained(
-            model_name_or_path,
-            trust_remote_code=True
-        )
+            answer_result = AnswerResult()
+            answer_result.history = history
+            answer_result.llm_output = {"answer": response}
+            if listenerQueue.listenerQueue.__len__() > 0:
+                answer_result.listenerToken = listenerQueue.listenerQueue.pop()
+
+            generate_with_callback(answer_result)
+
 
-        model_config = AutoConfig.from_pretrained(model_name_or_path, trust_remote_code=True)
-
-        if use_ptuning_v2:
-            try:
-                prefix_encoder_file = open('ptuning-v2/config.json', 'r')
-                prefix_encoder_config = json.loads(prefix_encoder_file.read())
-                prefix_encoder_file.close()
-                model_config.pre_seq_len = prefix_encoder_config['pre_seq_len']
-                model_config.prefix_projection = prefix_encoder_config['prefix_projection']
-            except Exception as e:
-                logger.error(f"加载PrefixEncoder config.json失败: {e}")
-        self.model = AutoModel.from_pretrained(model_name_or_path, config=model_config, trust_remote_code=True,
-                                               **kwargs)
-        if LLM_LORA_PATH and use_lora:
-            from peft import PeftModel
-            self.model = PeftModel.from_pretrained(self.model, LLM_LORA_PATH)
-
-        if torch.cuda.is_available() and llm_device.lower().startswith("cuda"):
-            # 根据当前设备GPU数量决定是否进行多卡部署
-            num_gpus = torch.cuda.device_count()
-            if num_gpus < 2 and device_map is None:
-                self.model = self.model.half().cuda()
-            else:
-                from accelerate import dispatch_model
-
-                # model = AutoModel.from_pretrained(model_name_or_path, trust_remote_code=True,
-                #         config=model_config, **kwargs)
-                if LLM_LORA_PATH and use_lora:
-                    from peft import PeftModel
-                    model = PeftModel.from_pretrained(self.model, LLM_LORA_PATH)
-                # 可传入device_map自定义每张卡的部署情况
-                if device_map is None:
-                    device_map = auto_configure_device_map(num_gpus, use_lora)
-
-                self.model = dispatch_model(self.model.half(), device_map=device_map)
-        else:
-            self.model = self.model.float().to(llm_device)
-
-        if use_ptuning_v2:
-            try:
-                prefix_state_dict = torch.load('ptuning-v2/pytorch_model.bin')
-                new_prefix_state_dict = {}
-                for k, v in prefix_state_dict.items():
-                    if k.startswith("transformer.prefix_encoder."):
-                        new_prefix_state_dict[k[len("transformer.prefix_encoder."):]] = v
-                self.model.transformer.prefix_encoder.load_state_dict(new_prefix_state_dict)
-                self.model.transformer.prefix_encoder.float()
-            except Exception as e:
-                logger.error(f"加载PrefixEncoder模型参数失败:{e}")
-
-        self.model = self.model.eval()
-
-
-if __name__ == "__main__":
-    llm = ChatGLM()
-    llm.load_model(model_name_or_path=llm_model_dict[LLM_MODEL],
-                   llm_device=LLM_DEVICE, )
-    last_print_len = 0
-    for resp, history in llm._call("你好", streaming=True):
-        logger.info(resp[last_print_len:], end="", flush=True)
-        last_print_len = len(resp)
-    for resp, history in llm._call("你好", streaming=False):
-        logger.info(resp)
-    pass

models/extensions/callback.py

+import gc
+import traceback
+from queue import Queue
+from threading import Thread
+import threading
+from typing import Optional, List, Dict, Any
+from collections import deque
+import torch
+import transformers
+
+from models.extensions.thread_with_exception import ThreadWithException
+import models.shared as shared
+
+
+class LimitedLengthDict(dict):
+    def __init__(self, maxlen=None, *args, **kwargs):
+        self.maxlen = maxlen
+        self._keys = deque()
+        super().__init__(*args, **kwargs)
+
+    def __setitem__(self, key, value):
+        if key not in self:
+            if self.maxlen is not None and len(self) >= self.maxlen:
+                oldest_key = self._keys.popleft()
+                if oldest_key in self:
+                    del self[oldest_key]
+        self._keys.append(key)
+        super().__setitem__(key, value)
+
+
+class FixedLengthQueue:
+
+    # 停止符号列表
+    stop_sequence: Optional[str] = []
+    # 缓冲区
+    max_length: int = 0
+    # 缓冲区容器
+    queue: deque = None
+    # 输入区容器
+    queue_in: LimitedLengthDict[int, str] = {}
+    # 输出区容器
+    queue_out: Dict[int, str] = {}
+
+    def __new__(cls, *args, **kwargs):
+        # 创建新的实例
+        instance = super().__new__(cls)
+        # 在这里可以对实例进行额外的设置
+        return instance
+
+    def __init__(self, stop_sequence):
+        if stop_sequence is None:
+            self.stop_sequence = []
+            self.max_length = 0
+        elif isinstance(stop_sequence, str):
+            self.stop_sequence = [stop_sequence]
+            self.max_length = 1
+        else:
+            self.stop_sequence = stop_sequence
+            self.max_length = len(''.join(stop_sequence))
+
+        self.queue = deque(maxlen=self.max_length)
+        self.queue.clear()
+        self.queue_in.clear()
+        self.queue_out.clear()
+
+    def add(self, index, item):
+        self.queue_in[index] = item
+
+    def _add_out(self, index, item):
+        self.queue_out[index] = item
+
+    def put_replace_out(self, index):
+        return self.queue_out[index]
+
+    def contains_replace_sequence(self):
+        """
+        替换字符
+        :return:
+        """
+
+        for key, value in self.queue_in.items():
+
+            word_index = value.rfind("：")
+            if word_index != -1:
+                value = value.replace("：", ":")
+
+            word_index = value.rfind("[")
+            if word_index != -1:
+                value = value.replace("[", "")
+
+            word_index = value.rfind("]")
+            if word_index != -1:
+                value = value.replace("]", "")
+
+            self._add_out(key, value)
+
+    def contains_stop_sequence(self):
+        # 截取固定大小的数据判断
+        self.queue.clear()
+        last_three_keys = list(self.queue_out.keys())[-self.max_length:]
+        joined_queue = ''.join([self.queue_out[key] for key in last_three_keys])
+        for char in joined_queue:
+            self.queue.append(char)
+
+        joined_queue = ''.join(self.queue)
+        # Initialize a variable to store the index of the last found stop string
+        last_stop_str_index = -1
+
+        # Iterate through the stop string list
+        for stop_word in self.stop_sequence:
+            # Find the last occurrence of the stop string in the output
+            stop_word_index = joined_queue.rfind(stop_word)
+
+            # If the stop string is found, compare the index with the previously found index
+            if stop_word_index != -1 and stop_word_index > last_stop_str_index:
+                last_stop_str_index = stop_word_index
+
+        # Handle the last found stop string index here
+        return last_stop_str_index
+
+    def __repr__(self):
+        return str(self.queue)
+
+
+# Copied from https://github.com/PygmalionAI/gradio-ui/
+class _SentinelTokenStoppingCriteria(transformers.StoppingCriteria):
+
+    def __init__(self, sentinel_token_ids: list, starting_idx: int):
+        transformers.StoppingCriteria.__init__(self)
+        self.sentinel_token_ids = sentinel_token_ids
+        self.starting_idx = starting_idx
+
+    def __call__(self, input_ids: torch.LongTensor, _scores: torch.FloatTensor) -> bool:
+        for sample in input_ids:
+            trimmed_sample = sample[self.starting_idx:]
+
+            for i in range(len(self.sentinel_token_ids)):
+                # Can't unfold, output is still too tiny. Skip.
+                if trimmed_sample.shape[-1] < self.sentinel_token_ids[i].shape[-1]:
+                    continue
+                for window in trimmed_sample.unfold(0, self.sentinel_token_ids[i].shape[-1], 1):
+                    if torch.all(torch.eq(self.sentinel_token_ids[i][0], window)):
+                        return True
+        return False
+
+
+class Stream(transformers.StoppingCriteria):
+    def __init__(self, callback_func=None):
+        self.callback_func = callback_func
+
+    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
+        if shared.stop_everything:
+            raise ValueError
+        if self.callback_func is not None:
+            self.callback_func(input_ids[0])
+        return False
+
+
+class Iteratorize:
+    """
+    Transforms a function that takes a callback
+    into a lazy iterator (generator).
+    """
+
+    thread: ThreadWithException = None
+
+    def __new__(cls, *args, **kwargs):
+        # 创建新的实例
+        instance = super().__new__(cls)
+        # 在这里可以对实例进行额外的设置
+        return instance
+
+
+    def __init__(self, func, kwargs={}, callback=None):
+        self.mfunc = func
+        self.c_callback = callback
+        self.q = Queue()
+        self.sentinel = object()
+        self.kwargs = kwargs
+
+        def _callback(val):
+            if shared.stop_everything:
+                raise ValueError
+            self.q.put(val)
+
+        def gen():
+            try:
+                ret = self.mfunc(callback=_callback, **self.kwargs)
+            except ValueError:
+                print("print(ValueError)")
+            except:
+                traceback.print_exc()
+                print("traceback.print_exc()")
+            self.q.put(self.sentinel)
+
+        self.thread = ThreadWithException(target=gen)
+        self.thread.start()
+
+    def __iter__(self):
+        shared.stop_everything = False
+        return self
+
+    def __next__(self):
+        obj = self.q.get(True, None)
+        if obj is self.sentinel:
+            raise StopIteration
+        else:
+            return obj
+
+    def __del__(self):
+        shared.stop_everything = False
+        self.q.empty()
+        shared.loaderCheckPoint.clear_torch_cache()
+
+    def __enter__(self):
+        shared.stop_everything = False
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        shared.stop_everything = True
+        shared.loaderCheckPoint.clear_torch_cache()
+        self.thread.raise_exception()

models/extensions/extensions.py

+import gc
+import traceback
+import torch
+
+# This iterator returns the extensions in the order specified in the command-line
+def iterator():
+    state_extensions = {}
+    for name in sorted(state_extensions, key=lambda x: state_extensions[x][1]):
+        if state_extensions[name][0]:
+            yield getattr(extensions, name).script, name
\ No newline at end of file

models/extensions/llamacpp_model_alternative.py

+'''
+Based on
+https://github.com/abetlen/llama-cpp-python
+
+Documentation:
+https://abetlen.github.io/llama-cpp-python/
+'''
+
+from llama_cpp import Llama, LlamaCache
+
+from modules import shared
+from modules.callbacks import Iteratorize
+
+
+class LlamaCppModel:
+    def __init__(self):
+        self.initialized = False
+
+    @classmethod
+    def from_pretrained(self, path):
+        result = self()
+
+        params = {
+            'model_path': str(path),
+            'n_ctx': 2048,
+            'seed': 0,
+            'n_threads': shared.args.threads or None
+        }
+        self.model = Llama(**params)
+        self.model.set_cache(LlamaCache)
+
+        # This is ugly, but the model and the tokenizer are the same object in this library.
+        return result, result
+
+    def encode(self, string):
+        if type(string) is str:
+            string = string.encode()
+        return self.model.tokenize(string)
+
+    def generate(self, context="", token_count=20, temperature=1, top_p=1, top_k=50, repetition_penalty=1, callback=None):
+        if type(context) is str:
+            context = context.encode()
+        tokens = self.model.tokenize(context)
+
+        output = b""
+        count = 0
+        for token in self.model.generate(tokens, top_k=top_k, top_p=top_p, temp=temperature, repeat_penalty=repetition_penalty):
+            text = self.model.detokenize([token])
+            output += text
+            if callback:
+                callback(text.decode())
+
+            count += 1
+            if count >= token_count or (token == self.model.token_eos()):
+                break
+
+        return output.decode()
+
+    def generate_with_streaming(self, **kwargs):
+        with Iteratorize(self.generate, kwargs, callback=None) as generator:
+            reply = ''
+            for token in generator:
+                reply += token
+                yield reply

models/extensions/thread_with_exception.py

+# Python program raising
+# exceptions in a python
+# thread
+
+import threading
+import ctypes
+import time
+
+
+class ThreadWithException(threading.Thread):
+
+    def get_id(self):
+        return self.ident
+
+    def raise_exception(self):
+        """raises the exception, performs cleanup if needed"""
+        try:
+            thread_id = self.get_id()
+            tid = ctypes.c_long(thread_id)
+            res = ctypes.pythonapi.PyThreadState_SetAsyncExc(tid, ctypes.py_object(SystemExit))
+            if res == 0:
+                # pass
+                raise ValueError("invalid thread id")
+            elif res != 1:
+                # """if it returns a number greater than one, you're in trouble,
+                # and you should call it again with exc=NULL to revert the effect"""
+                ctypes.pythonapi.PyThreadState_SetAsyncExc(tid, None)
+                raise SystemError("PyThreadState_SetAsyncExc failed")
+        except Exception as err:
+            print(err)

models/llama_llm.py

+from abc import ABC
+
+from langchain.llms.base import LLM
+import random
+import torch
+import transformers
+from transformers.generation.logits_process import LogitsProcessor
+from transformers.generation.utils import LogitsProcessorList, StoppingCriteriaList
+from typing import Optional, List, Dict, Any
+from models.loader import LoaderCheckPoint
+from models.extensions.callback import (Iteratorize, Stream, FixedLengthQueue)
+import models.shared as shared
+from models.base import (BaseAnswer,
+                         AnswerResult,
+                         AnswerResultStream,
+                         AnswerResultQueueSentinelTokenListenerQueue)
+
+
+def _streaming_response_template() -> Dict[str, Any]:
+    """
+    :return: 响应结构
+    """
+    return {
+        "text": ""
+    }
+
+
+def _update_response(response: Dict[str, Any], stream_response: str) -> None:
+    """Update response from the stream response."""
+    response["text"] += stream_response
+
+
+class InvalidScoreLogitsProcessor(LogitsProcessor):
+    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
+        if torch.isnan(scores).any() or torch.isinf(scores).any():
+            scores.zero_()
+            scores[..., 5] = 5e4
+        return scores
+
+
+class LLamaLLM(BaseAnswer, LLM, ABC):
+    checkPoint: LoaderCheckPoint = None
+    history = []
+    history_len: int = 3
+    max_new_tokens: int = 500
+    num_beams: int = 1
+    temperature: float = 0.5
+    top_p: float = 0.4
+    top_k: int = 10
+    repetition_penalty: float = 1.2
+    encoder_repetition_penalty: int = 1
+    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}
+
+    def __init__(self, checkPoint: LoaderCheckPoint = None):
+        super().__init__()
+        self.checkPoint = checkPoint
+
+    @property
+    def _llm_type(self) -> str:
+        return "LLamaLLM"
+
+    @property
+    def _check_point(self) -> LoaderCheckPoint:
+        return self.checkPoint
+
+    def encode(self, prompt, add_special_tokens=True, add_bos_token=True, truncation_length=None):
+        input_ids = self.checkPoint.tokenizer.encode(str(prompt), return_tensors='pt',
+                                                     add_special_tokens=add_special_tokens)
+        # This is a hack for making replies more creative.
+        if not add_bos_token and input_ids[0][0] == self.checkPoint.tokenizer.bos_token_id:
+            input_ids = input_ids[:, 1:]
+
+        # Llama adds this extra token when the first character is '\n', and this
+        # compromises the stopping criteria, so we just remove it
+        if type(self.checkPoint.tokenizer) is transformers.LlamaTokenizer and input_ids[0][0] == 29871:
+            input_ids = input_ids[:, 1:]
+
+        # Handling truncation
+        if truncation_length is not None:
+            input_ids = input_ids[:, -truncation_length:]
+
+        return input_ids.cuda()
+
+    def decode(self, output_ids):
+        reply = self.checkPoint.tokenizer.decode(output_ids, skip_special_tokens=True)
+        return reply
+
+    def generate_with_callback(self, callback=None, **kwargs):
+        self.checkPoint.clear_torch_cache()
+        kwargs['stopping_criteria'].append(Stream(callback_func=callback))
+        with torch.no_grad():
+            self.checkPoint.model.generate(**kwargs)
+            print("方法结束")
+
+    def generate_with_streaming(self, **kwargs):
+        return Iteratorize(self.generate_with_callback, kwargs)
+
+    # 将历史对话数组转换为文本格式
+    def history_to_text(self, query):
+        formatted_history = ''
+        history = self.history[-self.history_len:] if self.history_len > 0 else []
+        for i, (old_query, response) in enumerate(history):
+            formatted_history += "[Round {}]\n问：{}\n答：{}\n".format(i, old_query, response)
+        formatted_history += "[Round {}]\n问：{}\n答：".format(len(history), 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
+
+    def get_position_ids(self, input_ids: torch.LongTensor, mask_positions, device):
+        """
+        注意力偏移量
+        :param input_ids:
+        :param mask_positions:
+        :param device:
+        :param use_gmasks:
+        :return:
+        """
+        batch_size, seq_length = input_ids.shape
+        context_lengths = [seq.tolist().index(self.checkPoint.model_config.bos_token_id) for seq in input_ids]
+        position_ids = torch.arange(seq_length, dtype=torch.long, device=device).unsqueeze(0).repeat(batch_size, 1)
+        for i, context_length in enumerate(context_lengths):
+            position_ids[i, context_length:] = mask_positions[i]
+        block_position_ids = [torch.cat((
+            torch.zeros(context_length, dtype=torch.long, device=device),
+            torch.arange(seq_length - context_length, dtype=torch.long, device=device) + 1
+        )) for context_length in context_lengths]
+        block_position_ids = torch.stack(block_position_ids, dim=0)
+        position_ids = torch.stack((position_ids, block_position_ids), dim=1)
+        return position_ids
+
+    def get_masks(self, input_ids, device):
+        """
+        获取注意力掩码
+        :param input_ids:
+        :param device:
+        :return:
+        """
+        batch_size, seq_length = input_ids.shape
+        context_lengths = [seq.tolist().index(self.checkPoint.model_config.bos_token_id) for seq in input_ids]
+        attention_mask = torch.ones((batch_size, seq_length, seq_length), device=device)
+        attention_mask.tril_()
+        for i, context_length in enumerate(context_lengths):
+            attention_mask[i, :, :context_length] = 1
+        attention_mask.unsqueeze_(1)
+        attention_mask = (attention_mask < 0.5).bool()
+        return attention_mask
+
+    def generate_softprompt_history_tensors(self, query):
+        """
+        历史对话软提示
+            这段代码首先定义了一个名为 history_to_text 的函数，用于将 self.history
+            数组转换为所需的文本格式。然后，我们将格式化后的历史文本
+            再用 self.encode 将其转换为向量表示。最后，将历史对话向量与当前输入的对话向量拼接在一起。
+        :return:
+        """
+
+        # 对话内容
+        # 处理历史对话
+        formatted_history = self.history_to_text(query)
+        return formatted_history
+
+    @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:
+        print(f"__call:{prompt}")
+        if self.logits_processor is None:
+            self.logits_processor = LogitsProcessorList()
+        self.logits_processor.append(InvalidScoreLogitsProcessor())
+
+        gen_kwargs = {
+            "max_new_tokens": self.max_new_tokens,
+            "num_beams": self.num_beams,
+            "top_p": self.top_p,
+            "top_k": self.top_k,
+            "repetition_penalty": self.repetition_penalty,
+            "encoder_repetition_penalty": self.encoder_repetition_penalty,
+            "min_length": self.min_length,
+            "temperature": self.temperature,
+            "eos_token_id": self.eos_token_id,
+            "logits_processor": self.logits_processor}
+
+        #  向量拼接
+        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)
+
+        # 对话模型prompt
+        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})
+        shared.stop_everything = False
+        stopped = False
+        response_template = _streaming_response_template()
+
+        # TODO 此流输出方法需要重写！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！！
+        # stopping_criteria方法不可控制 迭代器的变量无法共享
+        with self.generate_with_streaming(**gen_kwargs) as generator:
+            last_reply_len = 0
+            reply_index = 0
+            # Create a FixedLengthQueue with the desired stop sequence and a maximum length.
+            queue = FixedLengthQueue(stop)
+            for output in generator:
+                new_tokens = len(output) - len(input_ids[0])
+                reply = self.decode(output[-new_tokens:])
+
+                new_reply = len(reply) - last_reply_len
+                output_reply = reply[-new_reply:]
+                queue.add(reply_index, output_reply)
+                queue.contains_replace_sequence()
+                if stop:
+                    pos = queue.contains_stop_sequence()
+                    if pos != -1:
+                        shared.stop_everything = True
+                        stopped = True
+
+                #print(f"{reply_index}：reply  {output_reply}")
+                english_reply = queue.put_replace_out(reply_index)
+                #print(f"{reply_index}：english_reply  {english_reply}")
+                _update_response(response_template, english_reply)
+                last_reply_len = len(reply)
+
+                reply_index += 1
+                if new_tokens == self.max_new_tokens - 1 or stopped:
+                    break
+
+        response = response_template['text']
+        print(f"response:{response}")
+        self.history = self.history + [[None, response]]
+        return response
+
+    def _generate_answer(self, prompt: str,
+                         history: List[List[str]] = [],
+                         streaming: bool = False,
+                         generate_with_callback: AnswerResultStream = None) -> None:
+        if history:
+            self.history = history
+        # 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
+        softprompt = self.generate_softprompt_history_tensors(prompt)
+        response = self._call(prompt=softprompt, stop=['\n###'])
+        answer_result = AnswerResult()
+        answer_result.history = self.history
+        if listenerQueue.listenerQueue.__len__() > 0:
+            answer_result.listenerToken = listenerQueue.listenerQueue.pop()
+        answer_result.llm_output = {"answer": response}
+        generate_with_callback(answer_result)

models/loader/__init__.py

+
+from .loader import *

models/loader/args.py

+import argparse
+import os
+
+
+
+# Additional argparse types
+def path(string):
+    if not string:
+        return ''
+    s = os.path.expanduser(string)
+    if not os.path.exists(s):
+        raise argparse.ArgumentTypeError(f'No such file or directory: "{string}"')
+    return s
+
+
+def file_path(string):
+    if not string:
+        return ''
+    s = os.path.expanduser(string)
+    if not os.path.isfile(s):
+        raise argparse.ArgumentTypeError(f'No such file: "{string}"')
+    return s
+
+
+def dir_path(string):
+    if not string:
+        return ''
+    s = os.path.expanduser(string)
+    if not os.path.isdir(s):
+        raise argparse.ArgumentTypeError(f'No such directory: "{string}"')
+    return s
+
+
+parser = argparse.ArgumentParser(prog='langchina-ChatGLM',
+                                 description='基于langchain和chatGML的LLM文档阅读器')
+
+
+
+parser.add_argument('--no-remote-model',  action='store_true', default=False,  help='remote in the model on loader checkpoint, if your load local model to add the ` --no-remote-model`')
+parser.add_argument('--model', type=str, default='chatglm-6b', help='Name of the model to load by default.')
+parser.add_argument('--lora', type=str, help='Name of the LoRA to apply to the model by default.')
+parser.add_argument("--model-dir", type=str, default='model/', help="Path to directory with all the models")
+parser.add_argument("--lora-dir", type=str, default='loras/', help="Path to directory with all the loras")
+
+# Accelerate/transformers
+parser.add_argument('--cpu', action='store_true', help='Use the CPU to generate text. Warning: Training on CPU is extremely slow.')
+parser.add_argument('--auto-devices', action='store_true', help='Automatically split the model across the available GPU(s) and CPU.')
+parser.add_argument('--gpu-memory', type=str, nargs="+", help='Maxmimum GPU memory in GiB to be allocated per GPU. Example: --gpu-memory 10 for a single GPU, --gpu-memory 10 5 for two GPUs. You can also set values in MiB like --gpu-memory 3500MiB.')
+parser.add_argument('--cpu-memory', type=str, help='Maximum CPU memory in GiB to allocate for offloaded weights. Same as above.')
+parser.add_argument('--load-in-8bit', action='store_true', help='Load the model with 8-bit precision.')
+parser.add_argument('--bf16', action='store_true', help='Load the model with bfloat16 precision. Requires NVIDIA Ampere GPU.')
+
+
+args = parser.parse_args([])
+# Generares dict with a default value for each argument
+DEFAULT_ARGS = vars(args)
+
+
+

models/loader/loader.py

+import gc
+import json
+import os
+import re
+import time
+from pathlib import Path
+from peft import PeftModel
+from typing import Optional, List, Dict, Tuple, Union
+import torch
+import transformers
+
+from transformers import (AutoConfig, AutoModel, AutoModelForCausalLM,
+                          AutoTokenizer, BitsAndBytesConfig, LlamaTokenizer)
+from transformers.dynamic_module_utils import get_class_from_dynamic_module
+from transformers.modeling_utils import no_init_weights
+from transformers.utils import ContextManagers
+from accelerate import init_empty_weights
+from accelerate.utils import get_balanced_memory, infer_auto_device_map
+
+
+class LoaderCheckPoint:
+    """
+    加载自定义 model CheckPoint
+    """
+    # remote in the model on loader checkpoint
+    no_remote_model: bool = False
+    # 模型名称
+    model_name: str = None
+    tokenizer: object = None
+    # 模型全路径
+    model_path: str = None
+    model: object = None
+    model_config: object = None
+    lora_names: set = []
+    model_dir: str = None
+    lora_dir: str = None
+    ptuning_dir: str = None
+    use_ptuning_v2: bool = False
+    cpu: bool = False
+    gpu_memory: object = None
+    cpu_memory: object = None
+    auto_devices: object = True
+    # 如果开启了8bit量化加载,项目无法启动，参考此位置，选择合适的cuda版本，https://github.com/TimDettmers/bitsandbytes/issues/156
+    load_in_8bit: bool = False
+    is_llamacpp: bool = False
+    bf16: bool = False
+    params: object = None
+    # 自定义设备网络
+    device_map: Optional[Dict[str, int]] = None
+    # 默认 cuda ，如果不支持cuda使用多卡， 如果不支持多卡 使用cpu
+    llm_device = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"
+
+    def __init__(self, params: dict = None):
+        """
+        模型初始化
+        :param params:
+        """
+        self.model_path = None
+        self.params = params or {}
+        self.no_remote_model = params.get('no_remote_model', False)
+        self.model_name = params.get('model', '')
+        self.lora = params.get('lora', '')
+        self.use_ptuning_v2 = params.get('use_ptuning_v2', False)
+        self.model = None
+        self.tokenizer = None
+        self.model_dir = params.get('model_dir', '')
+        self.lora_dir = params.get('lora_dir', '')
+        self.ptuning_dir = params.get('ptuning_dir', '')
+        self.cpu = params.get('cpu', False)
+        self.gpu_memory = params.get('gpu_memory', None)
+        self.cpu_memory = params.get('cpu_memory', None)
+        self.auto_devices = params.get('auto_devices', True)
+        self.load_in_8bit = params.get('load_in_8bit', False)
+        self.bf16 = params.get('bf16', False)
+
+    def _load_model_config(self, model_name):
+        checkpoint = Path(f'{self.model_dir}/{model_name}')
+
+        if self.model_path:
+            checkpoint = Path(f'{self.model_path}')
+        else:
+            if not self.no_remote_model:
+                checkpoint = model_name
+
+        model_config = AutoConfig.from_pretrained(checkpoint, trust_remote_code=True)
+
+        return model_config
+
+    def _load_model(self, model_name):
+        """
+        加载自定义位置的model
+        :param model_name:
+        :return:
+        """
+        print(f"Loading {model_name}...")
+        t0 = time.time()
+
+        checkpoint = Path(f'{self.model_dir}/{model_name}')
+
+        self.is_llamacpp = len(list(checkpoint.glob('ggml*.bin'))) > 0
+
+        if self.model_path:
+            checkpoint = Path(f'{self.model_path}')
+        else:
+            if not self.no_remote_model:
+                checkpoint = model_name
+
+        if 'chatglm' in model_name.lower():
+            LoaderClass = AutoModel
+        else:
+            LoaderClass = AutoModelForCausalLM
+
+        # Load the model in simple 16-bit mode by default
+        if not any([self.cpu, self.load_in_8bit, self.auto_devices, self.gpu_memory is not None,
+                    self.cpu_memory is not None, self.is_llamacpp]):
+
+            if torch.cuda.is_available() and self.llm_device.lower().startswith("cuda"):
+                # 根据当前设备GPU数量决定是否进行多卡部署
+                num_gpus = torch.cuda.device_count()
+                if num_gpus < 2 and self.device_map is None:
+                    model = (
+                        LoaderClass.from_pretrained(checkpoint,
+                                                    low_cpu_mem_usage=True,
+                                                    config=self.model_config,
+                                                    torch_dtype=torch.bfloat16 if self.bf16 else torch.float16,
+                                                    trust_remote_code=True)
+                        .half()
+                        .cuda()
+                    )
+                else:
+                    from accelerate import dispatch_model
+
+                    model = LoaderClass.from_pretrained(checkpoint,
+                                                        low_cpu_mem_usage=True,
+                                                        config=self.model_config,
+                                                        torch_dtype=torch.bfloat16 if self.bf16 else torch.float16,
+                                                        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():
+                            device_map = self.chatglm_auto_configure_device_map(num_gpus)
+                        elif 'moss' in model_name.lower():
+                            device_map = self.moss_auto_configure_device_map(num_gpus,model_name)
+                        else:
+                            device_map = self.chatglm_auto_configure_device_map(num_gpus)
+
+                    model = dispatch_model(model, device_map=device_map)
+            else:
+                print(
+                    "Warning: torch.cuda.is_available() returned False.\nThis means that no GPU has been detected.\nFalling back to CPU mode.\n")
+                model = (
+                    AutoModel.from_pretrained(
+                        checkpoint,
+                        config=self.model_config,
+                        trust_remote_code=True)
+                    .float()
+                    .to(self.llm_device)
+                )
+
+        elif self.is_llamacpp:
+            from models.extensions.llamacpp_model_alternative import LlamaCppModel
+
+            model_file = list(checkpoint.glob('ggml*.bin'))[0]
+            print(f"llama.cpp weights detected: {model_file}\n")
+
+            model, tokenizer = LlamaCppModel.from_pretrained(model_file)
+            return model, tokenizer
+
+        # Custom
+        else:
+            params = {"low_cpu_mem_usage": True}
+            if not any((self.cpu, torch.cuda.is_available(), torch.has_mps)):
+                print(
+                    "Warning: torch.cuda.is_available() returned False.\nThis means that no GPU has been detected.\nFalling back to CPU mode.\n")
+                self.cpu = True
+
+            if self.cpu:
+                params["torch_dtype"] = torch.float32
+            else:
+                params["device_map"] = 'auto'
+                params["trust_remote_code"] = True
+                if self.load_in_8bit and any((self.auto_devices, self.gpu_memory)):
+                    params['quantization_config'] = BitsAndBytesConfig(load_in_8bit=True,
+                                                                       llm_int8_enable_fp32_cpu_offload=True)
+                elif self.load_in_8bit:
+                    params['quantization_config'] = BitsAndBytesConfig(load_in_8bit=True)
+                elif self.bf16:
+                    params["torch_dtype"] = torch.bfloat16
+                else:
+                    params["torch_dtype"] = torch.float16
+
+                if self.gpu_memory:
+                    memory_map = list(map(lambda x: x.strip(), self.gpu_memory))
+                    max_cpu_memory = self.cpu_memory.strip() if self.cpu_memory is not None else '99GiB'
+                    max_memory = {}
+                    for i in range(len(memory_map)):
+                        max_memory[i] = f'{memory_map[i]}GiB' if not re.match('.*ib$', memory_map[i].lower()) else \
+                        memory_map[i]
+                    max_memory['cpu'] = max_cpu_memory
+                    params['max_memory'] = max_memory
+                elif self.auto_devices:
+                    total_mem = (torch.cuda.get_device_properties(0).total_memory / (1024 * 1024))
+                    suggestion = round((total_mem - 1000) / 1000) * 1000
+                    if total_mem - suggestion < 800:
+                        suggestion -= 1000
+                    suggestion = int(round(suggestion / 1000))
+                    print(
+                        f"\033[1;32;1mAuto-assiging --gpu-memory {suggestion} for your GPU to try to prevent out-of-memory errors.\nYou can manually set other values.\033[0;37;0m")
+
+                    max_memory = {0: f'{suggestion}GiB', 'cpu': f'{self.cpu_memory or 99}GiB'}
+                    params['max_memory'] = max_memory
+
+            if self.load_in_8bit and params.get('max_memory', None) is not None and params['device_map'] == 'auto':
+                config = AutoConfig.from_pretrained(checkpoint)
+                with init_empty_weights():
+                    model = LoaderClass.from_config(config)
+                model.tie_weights()
+                if self.device_map is not None:
+                    params['device_map'] = self.device_map
+                else:
+                    params['device_map'] = infer_auto_device_map(
+                        model,
+                        dtype=torch.int8,
+                        max_memory=params['max_memory'],
+                        no_split_module_classes=model._no_split_modules
+                    )
+
+            model = LoaderClass.from_pretrained(checkpoint, **params)
+
+        # Loading the tokenizer
+        if type(model) is transformers.LlamaForCausalLM:
+            tokenizer = LlamaTokenizer.from_pretrained(checkpoint, clean_up_tokenization_spaces=True)
+            # Leaving this here until the LLaMA tokenizer gets figured out.
+            # For some people this fixes things, for others it causes an error.
+            try:
+                tokenizer.eos_token_id = 2
+                tokenizer.bos_token_id = 1
+                tokenizer.pad_token_id = 0
+            except:
+                pass
+        else:
+            tokenizer = AutoTokenizer.from_pretrained(checkpoint, trust_remote_code=True)
+
+        print(f"Loaded the model in {(time.time() - t0):.2f} seconds.")
+        return model, tokenizer
+
+    def chatglm_auto_configure_device_map(self, num_gpus: int) -> Dict[str, int]:
+        # transformer.word_embeddings 占用1层
+        # transformer.final_layernorm 和 lm_head 占用1层
+        # transformer.layers 占用 28 层
+        # 总共30层分配到num_gpus张卡上
+        num_trans_layers = 28
+        per_gpu_layers = 30 / num_gpus
+
+        # bugfix: PEFT加载lora模型出现的层命名不同
+        if self.lora:
+            layer_prefix = 'base_model.model.transformer'
+        else:
+            layer_prefix = 'transformer'
+
+        # bugfix: 在linux中调用torch.embedding传入的weight,input不在同一device上,导致RuntimeError
+        # windows下 model.device 会被设置成 transformer.word_embeddings.device
+        # linux下 model.device 会被设置成 lm_head.device
+        # 在调用chat或者stream_chat时,input_ids会被放到model.device上
+        # 如果transformer.word_embeddings.device和model.device不同,则会导致RuntimeError
+        # 因此这里将transformer.word_embeddings,transformer.final_layernorm,lm_head都放到第一张卡上
+        device_map = {f'{layer_prefix}.word_embeddings': 0,
+                      f'{layer_prefix}.final_layernorm': 0, 'lm_head': 0,
+                      f'base_model.model.lm_head': 0, }
+
+        used = 2
+        gpu_target = 0
+        for i in range(num_trans_layers):
+            if used >= per_gpu_layers:
+                gpu_target += 1
+                used = 0
+            assert gpu_target < num_gpus
+            device_map[f'{layer_prefix}.layers.{i}'] = gpu_target
+            used += 1
+
+        return device_map
+
+    def moss_auto_configure_device_map(self, num_gpus: int, model_name) -> Dict[str, int]:
+        checkpoint = Path(f'{self.model_dir}/{model_name}')
+
+        if self.model_path:
+            checkpoint = Path(f'{self.model_path}')
+        else:
+            if not self.no_remote_model:
+                checkpoint = model_name
+        cls = get_class_from_dynamic_module(class_reference="fnlp/moss-moon-003-sft--modeling_moss.MossForCausalLM",
+                                            pretrained_model_name_or_path=checkpoint)
+
+        with ContextManagers([no_init_weights(_enable=True), init_empty_weights()]):
+            model = cls(self.model_config)
+            max_memory = get_balanced_memory(model, dtype=torch.int8 if self.load_in_8bit else None,
+                                             low_zero=False, no_split_module_classes=model._no_split_modules)
+            device_map = infer_auto_device_map(
+                model, dtype=torch.float16 if not self.load_in_8bit else torch.int8, max_memory=max_memory,
+                no_split_module_classes=model._no_split_modules)
+            device_map["transformer.wte"] = 0
+            device_map["transformer.drop"] = 0
+            device_map["transformer.ln_f"] = 0
+            device_map["lm_head"] = 0
+            return device_map
+
+    def _add_lora_to_model(self, lora_names):
+        # 目前加载的lora
+        prior_set = set(self.lora_names)
+        # 需要加载的
+        added_set = set(lora_names) - prior_set
+        # 删除的lora
+        removed_set = prior_set - set(lora_names)
+        self.lora_names = list(lora_names)
+
+        # Nothing to do = skip.
+        if len(added_set) == 0 and len(removed_set) == 0:
+            return
+
+        # Only adding, and already peft? Do it the easy way.
+        if len(removed_set) == 0 and len(prior_set) > 0:
+            print(f"Adding the LoRA(s) named {added_set} to the model...")
+            for lora in added_set:
+                self.model.load_adapter(Path(f"{self.lora_dir}/{lora}"), lora)
+            return
+
+        # If removing anything, disable all and re-add.
+        if len(removed_set) > 0:
+            self.model.disable_adapter()
+
+        if len(lora_names) > 0:
+            print("Applying the following LoRAs to {}: {}".format(self.model_name, ', '.join(lora_names)))
+            params = {}
+            if not self.cpu:
+                params['dtype'] = self.model.dtype
+                if hasattr(self.model, "hf_device_map"):
+                    params['device_map'] = {"base_model.model." + k: v for k, v in self.model.hf_device_map.items()}
+                elif self.load_in_8bit:
+                    params['device_map'] = {'': 0}
+            self.model.resize_token_embeddings(len(self.tokenizer))
+
+            self.model = PeftModel.from_pretrained(self.model, Path(f"{self.lora_dir}/{lora_names[0]}"), **params)
+
+            for lora in lora_names[1:]:
+                self.model.load_adapter(Path(f"{self.lora_dir}/{lora}"), lora)
+
+            if not self.load_in_8bit and not self.cpu:
+
+                if not hasattr(self.model, "hf_device_map"):
+                    if torch.has_mps:
+                        device = torch.device('mps')
+                        self.model = self.model.to(device)
+                    else:
+                        self.model = self.model.cuda()
+
+    def clear_torch_cache(self):
+        gc.collect()
+        if not self.cpu:
+            device_id = "0" if torch.cuda.is_available() else None
+            CUDA_DEVICE = f"{self.llm_device}:{device_id}" if device_id else self.llm_device
+            with torch.cuda.device(CUDA_DEVICE):
+                torch.cuda.empty_cache()
+                torch.cuda.ipc_collect()
+
+    def unload_model(self):
+        del self.model
+        del self.tokenizer
+        self.model = self.tokenizer = None
+        self.clear_torch_cache()
+
+    def set_model_path(self, model_path):
+        self.model_path = model_path
+
+    def reload_model(self):
+        self.unload_model()
+        self.model_config = self._load_model_config(self.model_name)
+
+        if self.use_ptuning_v2:
+            try:
+                prefix_encoder_file = open(Path(f'{self.ptuning_dir}/config.json'), 'r')
+                prefix_encoder_config = json.loads(prefix_encoder_file.read())
+                prefix_encoder_file.close()
+                self.model_config.pre_seq_len = prefix_encoder_config['pre_seq_len']
+                self.model_config.prefix_projection = prefix_encoder_config['prefix_projection']
+            except Exception:
+                print("加载PrefixEncoder config.json失败")
+
+        self.model, self.tokenizer = self._load_model(self.model_name)
+
+        if self.lora:
+            self._add_lora_to_model([self.lora])
+
+        if self.use_ptuning_v2:
+            try:
+                prefix_state_dict = torch.load(Path(f'{self.ptuning_dir}/pytorch_model.bin'))
+                new_prefix_state_dict = {}
+                for k, v in prefix_state_dict.items():
+                    if k.startswith("transformer.prefix_encoder."):
+                        new_prefix_state_dict[k[len("transformer.prefix_encoder."):]] = v
+                self.model.transformer.prefix_encoder.load_state_dict(new_prefix_state_dict)
+                self.model.transformer.prefix_encoder.float()
+            except Exception:
+                print("加载PrefixEncoder模型参数失败")
+
+        self.model = self.model.eval()

models/moss_llm.py

-import json
-from langchain.llms.base import LLM
-from typing import List, Dict, Optional
-from transformers import AutoTokenizer, AutoModelForCausalLM, AutoConfig
-from transformers.dynamic_module_utils import get_class_from_dynamic_module
-from transformers.modeling_utils import no_init_weights
-from transformers.utils import ContextManagers
-import torch
-from configs.model_config import *
-from utils import torch_gc
+from abc import ABC
 
-from accelerate import init_empty_weights
-from accelerate.utils import get_balanced_memory, infer_auto_device_map
+from langchain.llms.base import LLM
+from typing import Optional, List
+from models.loader import LoaderCheckPoint
+from models.base import (BaseAnswer,
+                         AnswerResult,
+                         AnswerResultStream,
+                         AnswerResultQueueSentinelTokenListenerQueue)
 
-DEVICE_ = LLM_DEVICE
-DEVICE_ID = "0" if torch.cuda.is_available() else None
-DEVICE = f"{DEVICE_}:{DEVICE_ID}" if DEVICE_ID else DEVICE_
+import torch
 
 META_INSTRUCTION = \
     """You are an AI assistant whose name is MOSS.
@@ -30,45 +24,40 @@ META_INSTRUCTION = \
     """
 
 
-def auto_configure_device_map() -> Dict[str, int]:
-    cls = get_class_from_dynamic_module(class_reference="fnlp/moss-moon-003-sft--modeling_moss.MossForCausalLM",
-                                        pretrained_model_name_or_path=llm_model_dict['moss'])
-
-    with ContextManagers([no_init_weights(_enable=True), init_empty_weights()]):
-        model_config = AutoConfig.from_pretrained(llm_model_dict['moss'], trust_remote_code=True)
-        model = cls(model_config)
-        max_memory = get_balanced_memory(model, dtype=torch.int8 if LOAD_IN_8BIT else None,
-                                         low_zero=False, no_split_module_classes=model._no_split_modules)
-        device_map = infer_auto_device_map(
-            model, dtype=torch.float16 if not LOAD_IN_8BIT else torch.int8, max_memory=max_memory,
-            no_split_module_classes=model._no_split_modules)
-        device_map["transformer.wte"] = 0
-        device_map["transformer.drop"] = 0
-        device_map["transformer.ln_f"] = 0
-        device_map["lm_head"] = 0
-        return device_map
-
-
-class MOSS(LLM):
+class MOSSLLM(BaseAnswer, LLM, ABC):
     max_token: int = 2048
     temperature: float = 0.7
     top_p = 0.8
     # history = []
-    tokenizer: object = None
-    model: object = None
+    checkPoint: LoaderCheckPoint = None
     history_len: int = 10
 
-    def __init__(self):
+    def __init__(self, checkPoint: LoaderCheckPoint = None):
         super().__init__()
+        self.checkPoint = checkPoint
 
     @property
     def _llm_type(self) -> str:
         return "MOSS"
 
-    def _call(self,
-              prompt: str,
+    @property
+    def _check_point(self) -> LoaderCheckPoint:
+        return self.checkPoint
+
+    @property
+    def set_history_len(self) -> int:
+        return self.history_len
+
+    def _set_history_len(self, history_len: int) -> None:
+        self.history_len = history_len
+
+    def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
+        pass
+
+    def _generate_answer(self, prompt: str,
                          history: List[List[str]] = [],
-              streaming: bool = STREAMING):  # -> Tuple[str, List[List[str]]]:
+                         streaming: bool = False,
+                         generate_with_callback: AnswerResultStream = None) -> None:
         if len(history) > 0:
             history = history[-self.history_len:-1] if self.history_len > 0 else []
             prompt_w_history = str(history)
@@ -77,9 +66,9 @@ class MOSS(LLM):
             prompt_w_history = META_INSTRUCTION
             prompt_w_history += '<|Human|>: ' + prompt + '<eoh>'
 
-        inputs = self.tokenizer(prompt_w_history, return_tensors="pt")
+        inputs = self.checkPoint.tokenizer(prompt_w_history, return_tensors="pt")
         with torch.no_grad():
-            outputs = self.model.generate(
+            outputs = self.checkPoint.model.generate(
                 inputs.input_ids.cuda(),
                 attention_mask=inputs.attention_mask.cuda(),
                 max_length=self.max_token,
@@ -92,78 +81,8 @@ class MOSS(LLM):
                 eos_token_id=106068,
                 pad_token_id=self.tokenizer.pad_token_id)
             response = self.tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
-            torch_gc()
+            self.checkPoint.clear_torch_cache()
             history += [[prompt, response]]
             yield response, history
-            torch_gc()
-
-    def load_model(self,
-                   model_name_or_path: str = "fnlp/moss-moon-003-sft",
-                   llm_device=LLM_DEVICE,
-                   use_ptuning_v2=False,
-                   use_lora=False,
-                   device_map: Optional[Dict[str, int]] = None,
-                   **kwargs):
-        self.tokenizer = AutoTokenizer.from_pretrained(
-            model_name_or_path,
-            trust_remote_code=True
-        )
-
-        model_config = AutoConfig.from_pretrained(model_name_or_path, trust_remote_code=True)
-
-        if use_ptuning_v2:
-            try:
-                prefix_encoder_file = open('ptuning-v2/config.json', 'r')
-                prefix_encoder_config = json.loads(prefix_encoder_file.read())
-                prefix_encoder_file.close()
-                model_config.pre_seq_len = prefix_encoder_config['pre_seq_len']
-                model_config.prefix_projection = prefix_encoder_config['prefix_projection']
-            except Exception as e:
-                print(e)
-                print("加载PrefixEncoder config.json失败")
-
-        if torch.cuda.is_available() and llm_device.lower().startswith("cuda"):
-            # accelerate自动多卡部署
-            self.model = AutoModelForCausalLM.from_pretrained(model_name_or_path, config=model_config,
-                                                              load_in_8bit=LOAD_IN_8BIT, trust_remote_code=True,
-                                                              device_map=auto_configure_device_map(), **kwargs)
 
-            if LLM_LORA_PATH and use_lora:
-                from peft import PeftModel
-                self.model = PeftModel.from_pretrained(self.model, LLM_LORA_PATH)
 
-        else:
-            self.model = self.model.float().to(llm_device)
-            if LLM_LORA_PATH and use_lora:
-                from peft import PeftModel
-                self.model = PeftModel.from_pretrained(self.model, LLM_LORA_PATH)
-
-        if use_ptuning_v2:
-            try:
-                prefix_state_dict = torch.load('ptuning-v2/pytorch_model.bin')
-                new_prefix_state_dict = {}
-                for k, v in prefix_state_dict.items():
-                    if k.startswith("transformer.prefix_encoder."):
-                        new_prefix_state_dict[k[len("transformer.prefix_encoder."):]] = v
-                self.model.transformer.prefix_encoder.load_state_dict(new_prefix_state_dict)
-                self.model.transformer.prefix_encoder.float()
-            except Exception as e:
-                print(e)
-                print("加载PrefixEncoder模型参数失败")
-
-        self.model = self.model.eval()
-
-
-if __name__ == "__main__":
-    llm = MOSS()
-    llm.load_model(model_name_or_path=llm_model_dict['moss'],
-                   llm_device=LLM_DEVICE, )
-    last_print_len = 0
-    # for resp, history in llm._call("你好", streaming=True):
-    #     print(resp[last_print_len:], end="", flush=True)
-    #     last_print_len = len(resp)
-    for resp, history in llm._call("你好", streaming=False):
-        print(resp)
-    import time
-    time.sleep(10)
-    pass

models/shared.py

+import sys
+
+from models.loader.args import parser
+from models.loader import LoaderCheckPoint
+from configs.model_config import (llm_model_dict, LLM_MODEL)
+from models.base import BaseAnswer
+"""迭代器是否停止状态"""
+stop_everything = False
+
+loaderCheckPoint: LoaderCheckPoint = None
+
+
+def loaderLLM(llm_model: str = None, no_remote_model: bool = False, use_ptuning_v2: bool = False) -> BaseAnswer:
+    """
+    init llm_model_ins LLM
+    :param llm_model: model_name
+    :param no_remote_model:  remote in the model on loader checkpoint, if your load local model to add the ` --no-remote-model
+    :param use_ptuning_v2: Use p-tuning-v2 PrefixEncoder
+    :return:
+    """
+    pre_model_name = loaderCheckPoint.model_name
+    llm_model_info = llm_model_dict[pre_model_name]
+
+    if no_remote_model:
+        loaderCheckPoint.no_remote_model = no_remote_model
+    if use_ptuning_v2:
+        loaderCheckPoint.use_ptuning_v2 = use_ptuning_v2
+
+    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['remote-checkpoint']
+
+    loaderCheckPoint.model_path = llm_model_info['path']
+
+    loaderCheckPoint.reload_model()
+
+    provides_class = getattr(sys.modules['models'], llm_model_info['provides'])
+    modelInsLLM = provides_class(checkPoint=loaderCheckPoint)
+    return modelInsLLM

requirements.txt

@@ -17,6 +17,8 @@ fastapi
 uvicorn
 peft
 pypinyin
-bitsandbytes
 click~=8.1.3
 tabulate
+bitsandbytes; platform_system != "Windows"
+llama-cpp-python==0.1.34; platform_system != "Windows"
+https://github.com/abetlen/llama-cpp-python/releases/download/v0.1.34/llama_cpp_python-0.1.34-cp310-cp310-win_amd64.whl; platform_system == "Windows"

webui.py

 import gradio as gr
 import os
 import shutil
+
 from chains.local_doc_qa import LocalDocQA
 from configs.model_config import *
 import nltk
+from models.base import (BaseAnswer,
+                         AnswerResult,
+                         AnswerResultStream,
+                         AnswerResultQueueSentinelTokenListenerQueue)
+import models.shared as shared
+from models.loader.args import parser
+from models.loader import LoaderCheckPoint
 
 nltk.data.path = [NLTK_DATA_PATH] + nltk.data.path
 
@@ -69,7 +77,11 @@ 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 resp, history in local_doc_qa.llm._call(query, history, streaming=streaming):
+        for answer_result in local_doc_qa.llm.generatorAnswer(prompt=query, history=history,
+                                                              streaming=streaming):
+
+            resp = answer_result.llm_output["answer"]
+            history = answer_result.history
             history[-1][-1] = resp + (
                 "\n\n当前知识库为空，如需基于知识库进行问答，请先加载知识库后，再进行提问。" if mode == "知识库问答" else "")
             yield history, ""
@@ -77,10 +89,12 @@ def get_answer(query, vs_path, history, mode, score_threshold=VECTOR_SEARCH_SCOR
     flag_csv_logger.flag([query, vs_path, history, mode], username=FLAG_USER_NAME)
 
 
-def init_model():
+def init_model(llm_model: BaseAnswer = None):
     try:
-        local_doc_qa.init_cfg()
-        local_doc_qa.llm._call("你好")
+        local_doc_qa.init_cfg(llm_model=llm_model)
+        generator = local_doc_qa.llm.generatorAnswer("你好")
+        for answer_result in generator:
+            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)
         return reply
@@ -95,14 +109,13 @@ def init_model():
         return reply
 
 
-def reinit_model(llm_model, embedding_model, llm_history_len, use_ptuning_v2, use_lora, top_k, history):
+def reinit_model(llm_model, embedding_model, llm_history_len, no_remote_model, use_ptuning_v2, use_lora, top_k, history):
     try:
-        local_doc_qa.init_cfg(llm_model=llm_model,
+        llm_model_ins = shared.loaderLLM(llm_model, no_remote_model, use_ptuning_v2)
+        llm_model_ins.history_len = llm_history_len
+        local_doc_qa.init_cfg(llm_model=llm_model_ins,
                               embedding_model=embedding_model,
-                              llm_history_len=llm_history_len,
-                              use_ptuning_v2=use_ptuning_v2,
-                              use_lora=use_lora,
-                              top_k=top_k, )
+                              top_k=top_k)
         model_status = """模型已成功重新加载，可以开始对话，或从右侧选择模式后开始对话"""
         logger.info(model_status)
     except Exception as e:
@@ -219,7 +232,17 @@ init_message = f"""欢迎使用 langchain-ChatGLM Web UI！
 知识库暂不支持文件删除，该功能将在后续版本中推出。
 """
 
-model_status = init_model()
+# 初始化消息
+args = None
+args = parser.parse_args()
+
+args_dict = vars(args)
+shared.loaderCheckPoint = LoaderCheckPoint(args_dict)
+llm_model_ins = shared.loaderLLM()
+llm_model_ins.set_history_len(LLM_HISTORY_LEN)
+
+model_status = init_model(llm_model=llm_model_ins)
+
 
 default_theme_args = dict(
     font=["Source Sans Pro", 'ui-sans-serif', 'system-ui', 'sans-serif'],
@@ -399,6 +422,10 @@ with gr.Blocks(css=block_css, theme=gr.themes.Default(**default_theme_args)) as
                              label="LLM 模型",
                              value=LLM_MODEL,
                              interactive=True)
+        no_remote_model = gr.Checkbox(shared.LoaderCheckPoint.no_remote_model,
+                                      label="加载本地模型",
+                                      interactive=True)
+
         llm_history_len = gr.Slider(0, 10,
                                     value=LLM_HISTORY_LEN,
                                     step=1,
@@ -418,7 +445,7 @@ with gr.Blocks(css=block_css, theme=gr.themes.Default(**default_theme_args)) as
                           label="向量匹配 top k", interactive=True)
         load_model_button = gr.Button("重新加载模型")
         load_model_button.click(reinit_model, show_progress=True,
-                                inputs=[llm_model, embedding_model, llm_history_len, use_ptuning_v2, use_lora,
+                                inputs=[llm_model, embedding_model, llm_history_len, no_remote_model, use_ptuning_v2, use_lora,
                                         top_k, chatbot], outputs=chatbot)
 
 (demo