File size: 13,351 Bytes
17d0a32
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
15f14f5
17d0a32
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
import time
import threading
from toolbox import update_ui, Singleton
from multiprocessing import Process, Pipe
from contextlib import redirect_stdout
from request_llms.queued_pipe import create_queue_pipe

class ThreadLock(object):
    def __init__(self):
        self._lock = threading.Lock()

    def acquire(self):
        # print("acquiring", self)
        #traceback.print_tb
        self._lock.acquire()
        # print("acquired", self)

    def release(self):
        # print("released", self)
        #traceback.print_tb
        self._lock.release()

    def __enter__(self):
        self.acquire()

    def __exit__(self, type, value, traceback):
        self.release()

@Singleton
class GetSingletonHandle():
    def __init__(self):
        self.llm_model_already_running = {}

    def get_llm_model_instance(self, cls, *args, **kargs):
        if cls not in self.llm_model_already_running:
            self.llm_model_already_running[cls] = cls(*args, **kargs)
            return self.llm_model_already_running[cls]
        elif self.llm_model_already_running[cls].corrupted:
            self.llm_model_already_running[cls] = cls(*args, **kargs)
            return self.llm_model_already_running[cls]
        else:
            return self.llm_model_already_running[cls]

def reset_tqdm_output():
    import sys, tqdm
    def status_printer(self, file):
        fp = file
        if fp in (sys.stderr, sys.stdout):
            getattr(sys.stderr, 'flush', lambda: None)()
            getattr(sys.stdout, 'flush', lambda: None)()

        def fp_write(s):
            print(s)
        last_len = [0]

        def print_status(s):
            from tqdm.utils import disp_len
            len_s = disp_len(s)
            fp_write('\r' + s + (' ' * max(last_len[0] - len_s, 0)))
            last_len[0] = len_s
        return print_status
    tqdm.tqdm.status_printer = status_printer


class LocalLLMHandle(Process):
    def __init__(self):
        # ⭐run in main process
        super().__init__(daemon=True)
        self.is_main_process = True # init
        self.corrupted = False
        self.load_model_info()
        self.parent, self.child = create_queue_pipe()
        self.parent_state, self.child_state = create_queue_pipe()
        # allow redirect_stdout
        self.std_tag = "[Subprocess Message] "
        self.running = True
        self._model = None
        self._tokenizer = None
        self.state = ""
        self.check_dependency()
        self.is_main_process = False    # state wrap for child process
        self.start()
        self.is_main_process = True     # state wrap for child process
        self.threadLock = ThreadLock()

    def get_state(self):
        # ⭐run in main process
        while self.parent_state.poll():
            self.state = self.parent_state.recv()
        return self.state

    def set_state(self, new_state):
        # ⭐run in main process or 🏃‍♂️🏃‍♂️🏃‍♂️ run in child process 
        if self.is_main_process:
            self.state = new_state
        else:
            self.child_state.send(new_state)

    def load_model_info(self):
        # 🏃‍♂️🏃‍♂️🏃‍♂️ run in child process
        raise NotImplementedError("Method not implemented yet")
        self.model_name = ""
        self.cmd_to_install = ""

    def load_model_and_tokenizer(self):
        """
        This function should return the model and the tokenizer
        """
        # 🏃‍♂️🏃‍♂️🏃‍♂️ run in child process
        raise NotImplementedError("Method not implemented yet")

    def llm_stream_generator(self, **kwargs):
        # 🏃‍♂️🏃‍♂️🏃‍♂️ run in child process
        raise NotImplementedError("Method not implemented yet")

    def try_to_import_special_deps(self, **kwargs):
        """
        import something that will raise error if the user does not install requirement_*.txt
        """
        # ⭐run in main process
        raise NotImplementedError("Method not implemented yet")

    def check_dependency(self):
        # ⭐run in main process
        try:
            self.try_to_import_special_deps()
            self.set_state("`依赖检测通过`")
            self.running = True
        except:
            self.set_state(f"缺少{self.model_name}的依赖,如果要使用{self.model_name},除了基础的pip依赖以外,您还需要运行{self.cmd_to_install}安装{self.model_name}的依赖。")
            self.running = False

    def run(self):
        # 🏃‍♂️🏃‍♂️🏃‍♂️ run in child process
        # 第一次运行,加载参数
        self.child.flush = lambda *args: None
        self.child.write = lambda x: self.child.send(self.std_tag + x)
        reset_tqdm_output()
        self.set_state("`尝试加载模型`")
        try:
            with redirect_stdout(self.child):
                self._model, self._tokenizer = self.load_model_and_tokenizer()
        except:
            self.set_state("`加载模型失败`")
            self.running = False
            from toolbox import trimmed_format_exc
            self.child.send(
                f'[Local Message] 不能正常加载{self.model_name}的参数.' + '\n```\n' + trimmed_format_exc() + '\n```\n')
            self.child.send('[FinishBad]')
            raise RuntimeError(f"不能正常加载{self.model_name}的参数!")

        self.set_state("`准备就绪`")
        while True:
            # 进入任务等待状态
            kwargs = self.child.recv()
            # 收到消息,开始请求
            try:
                for response_full in self.llm_stream_generator(**kwargs):
                    self.child.send(response_full)
                    # print('debug' + response_full)
                self.child.send('[Finish]')
                # 请求处理结束,开始下一个循环
            except:
                from toolbox import trimmed_format_exc
                self.child.send(
                    f'[Local Message] 调用{self.model_name}失败.' + '\n```\n' + trimmed_format_exc() + '\n```\n')
                self.child.send('[Finish]')

    def clear_pending_messages(self):
        # ⭐run in main process
        while True:
            if  self.parent.poll():
                self.parent.recv()
                continue
            for _ in range(5):
                time.sleep(0.5)
                if  self.parent.poll():
                    r = self.parent.recv()
                    continue
            break
        return 
    
    def stream_chat(self, **kwargs):
        # ⭐run in main process
        if self.get_state() == "`准备就绪`":
            yield "`正在等待线程锁,排队中请稍后 ...`"

        with self.threadLock:
            if self.parent.poll():
                yield "`排队中请稍后 ...`"
                self.clear_pending_messages()
            self.parent.send(kwargs)
            std_out = ""
            std_out_clip_len = 4096
            while True:
                res = self.parent.recv()
                # pipe_watch_dog.feed()
                if res.startswith(self.std_tag):
                    new_output = res[len(self.std_tag):]
                    std_out = std_out[:std_out_clip_len]
                    print(new_output, end='')
                    std_out = new_output + std_out
                    yield self.std_tag + '\n```\n' + std_out + '\n```\n'
                elif res == '[Finish]':
                    break
                elif res == '[FinishBad]':
                    self.running = False
                    self.corrupted = True
                    break
                else:
                    std_out = ""
                    yield res

def get_local_llm_predict_fns(LLMSingletonClass, model_name, history_format='classic'):
    load_message = f"{model_name}尚未加载,加载需要一段时间。注意,取决于`config.py`的配置,{model_name}消耗大量的内存(CPU)或显存(GPU),也许会导致低配计算机卡死 ……"

    def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
        """
            refer to request_llms/bridge_all.py
        """
        _llm_handle = GetSingletonHandle().get_llm_model_instance(LLMSingletonClass)
        if len(observe_window) >= 1:
            observe_window[0] = load_message + "\n\n" + _llm_handle.get_state()
        if not _llm_handle.running:
            raise RuntimeError(_llm_handle.get_state())

        if history_format == 'classic':
            # 没有 sys_prompt 接口,因此把prompt加入 history
            history_feedin = []
            history_feedin.append([sys_prompt, "Certainly!"])
            for i in range(len(history)//2):
                history_feedin.append([history[2*i], history[2*i+1]])
        elif history_format == 'chatglm3':
            # 有 sys_prompt 接口
            conversation_cnt = len(history) // 2
            history_feedin = [{"role": "system", "content": sys_prompt}]
            if conversation_cnt:
                for index in range(0, 2*conversation_cnt, 2):
                    what_i_have_asked = {}
                    what_i_have_asked["role"] = "user"
                    what_i_have_asked["content"] = history[index]
                    what_gpt_answer = {}
                    what_gpt_answer["role"] = "assistant"
                    what_gpt_answer["content"] = history[index+1]
                    if what_i_have_asked["content"] != "":
                        if what_gpt_answer["content"] == "":
                            continue
                        history_feedin.append(what_i_have_asked)
                        history_feedin.append(what_gpt_answer)
                    else:
                        history_feedin[-1]['content'] = what_gpt_answer['content']

        watch_dog_patience = 5  # 看门狗 (watchdog) 的耐心, 设置5秒即可
        response = ""
        for response in _llm_handle.stream_chat(query=inputs, history=history_feedin, max_length=llm_kwargs['max_length'], top_p=llm_kwargs['top_p'], temperature=llm_kwargs['temperature']):
            if len(observe_window) >= 1:
                observe_window[0] = response
            if len(observe_window) >= 2:
                if (time.time()-observe_window[1]) > watch_dog_patience:
                    raise RuntimeError("程序终止。")
        return response

    def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream=True, additional_fn=None):
        """
            refer to request_llms/bridge_all.py
        """
        chatbot.append((inputs, ""))

        _llm_handle = GetSingletonHandle().get_llm_model_instance(LLMSingletonClass)
        chatbot[-1] = (inputs, load_message + "\n\n" + _llm_handle.get_state())
        yield from update_ui(chatbot=chatbot, history=[])
        if not _llm_handle.running:
            raise RuntimeError(_llm_handle.get_state())

        if additional_fn is not None:
            from core_functional import handle_core_functionality
            inputs, history = handle_core_functionality(
                additional_fn, inputs, history, chatbot)

        # 处理历史信息
        if history_format == 'classic':
            # 没有 sys_prompt 接口,因此把prompt加入 history
            history_feedin = []
            history_feedin.append([system_prompt, "Certainly!"])
            for i in range(len(history)//2):
                history_feedin.append([history[2*i], history[2*i+1]])
        elif history_format == 'chatglm3':
            # 有 sys_prompt 接口
            conversation_cnt = len(history) // 2
            history_feedin = [{"role": "system", "content": system_prompt}]
            if conversation_cnt:
                for index in range(0, 2*conversation_cnt, 2):
                    what_i_have_asked = {}
                    what_i_have_asked["role"] = "user"
                    what_i_have_asked["content"] = history[index]
                    what_gpt_answer = {}
                    what_gpt_answer["role"] = "assistant"
                    what_gpt_answer["content"] = history[index+1]
                    if what_i_have_asked["content"] != "":
                        if what_gpt_answer["content"] == "":
                            continue
                        history_feedin.append(what_i_have_asked)
                        history_feedin.append(what_gpt_answer)
                    else:
                        history_feedin[-1]['content'] = what_gpt_answer['content']

        # 开始接收回复
        response = f"[Local Message] 等待{model_name}响应中 ..."
        for response in _llm_handle.stream_chat(query=inputs, history=history_feedin, max_length=llm_kwargs['max_length'], top_p=llm_kwargs['top_p'], temperature=llm_kwargs['temperature']):
            chatbot[-1] = (inputs, response)
            yield from update_ui(chatbot=chatbot, history=history)

        # 总结输出
        if response == f"[Local Message] 等待{model_name}响应中 ...":
            response = f"[Local Message] {model_name}响应异常 ..."
        history.extend([inputs, response])
        yield from update_ui(chatbot=chatbot, history=history)

    return predict_no_ui_long_connection, predict