File size: 7,386 Bytes
377a7af
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
from flask import Flask, request, Response
import logging
from llama_cpp import Llama
import threading
from huggingface_hub import snapshot_download

SYSTEM_PROMPT = "Ты — русскоязычный автоматический ассистент. Ты максимально точно и отвечаешь на запросы пользователя."
SYSTEM_TOKEN = 1788
USER_TOKEN = 1404
BOT_TOKEN = 9225
LINEBREAK_TOKEN = 13

ROLE_TOKENS = {
    "user": USER_TOKEN,
    "bot": BOT_TOKEN,
    "system": SYSTEM_TOKEN
}

# Create a lock object
lock = threading.Lock()

app = Flask(__name__)
# Configure Flask logging
app.logger.setLevel(logging.DEBUG)  # Set the desired logging level

# Initialize the model when the application starts
#model_path = "../models/model-q4_K.gguf"  # Replace with the actual model path
#model_name = "model/ggml-model-q4_K.gguf"

repo_name = "IlyaGusev/saiga2_13b_gguf"
model_name = "model-q4_K.gguf"

snapshot_download(repo_id=repo_name, local_dir=".", allow_patterns=model_name)

model = Llama(
    model_path=model_name,
    n_ctx=2000,
    n_parts=1,
    #n_batch=100,
    logits_all=True,
    #n_threads=12,
    verbose=True
)


def get_message_tokens(model, role, content):
    message_tokens = model.tokenize(content.encode("utf-8"))
    message_tokens.insert(1, ROLE_TOKENS[role])
    message_tokens.insert(2, LINEBREAK_TOKEN)
    message_tokens.append(model.token_eos())
    return message_tokens

def get_system_tokens(model):
    system_message = {
        "role": "system",
        "content": SYSTEM_PROMPT
    }
    return get_message_tokens(model, **system_message)

def get_system_tokens_for_preprompt(model, preprompt):
    system_message = {
        "role": "system",
        "content": preprompt
    }
    return get_message_tokens(model, **system_message)

app.logger.info('Evaluating system tokens start')
#system_tokens = get_system_tokens(model)
#model.eval(system_tokens)
app.logger.info('Evaluating system tokens end')

stop_generation = False

def generate_tokens(model, generator):
    global stop_generation
    app.logger.info('generate_tokens started')
    #with lock:
    for token in generator:            
        if token == model.token_eos() or stop_generation:
            stop_generation = False
            yield b''  # End of chunk
            break
            
        token_str = model.detokenize([token])#.decode("utf-8", errors="ignore")
        yield token_str 

@app.route('/stop_generation', methods=['GET'])
def handler_stop_generation():
    global stop_generation
    stop_generation = True
    return Response('Stopped', content_type='text/plain')        
                
@app.route('/', methods=['GET', 'PUT', 'DELETE', 'PATCH'])
def generate_unknown_response():
    app.logger.info('unknown method: '+request.method)
    try:
        request_payload = request.get_json()
        app.logger.info('payload: '+request.get_json())
    except Exception as e:
        app.logger.info('payload empty')

    return Response('What do you want?', content_type='text/plain')
    
@app.route('/search_request', methods=['POST'])
def generate_search_request():
    global stop_generation
    stop_generation = False
    data = request.get_json()
    app.logger.info(data)
    user_query = data.get("query", "")
    preprompt = data.get("preprompt", "Ты — русскоязычный автоматический ассистент для написании запросов для поисковых систем. Отвечай на сообщения пользователя только текстом поискового запроса, релевантным запросу пользователя. Если запрос пользователя уже хорош, используй его в качестве результата.")
    parameters = data.get("parameters", {})
    
    # Extract parameters from the request
    temperature = 0.01
    truncate = parameters.get("truncate", 1000)
    max_new_tokens = parameters.get("max_new_tokens", 1024)
    top_p = 0.8
    repetition_penalty = parameters.get("repetition_penalty", 1.2)
    top_k = 20
    return_full_text = parameters.get("return_full_text", False)

    tokens = get_system_tokens_for_preprompt(model, preprompt)
    tokens.append(LINEBREAK_TOKEN)        
    
    tokens = get_message_tokens(model=model, role="user", content=user_query[:200]) + [model.token_bos(), BOT_TOKEN, LINEBREAK_TOKEN]

    generator = model.generate(
        tokens,
        top_k=top_k,
        top_p=top_p,
        temp=temperature,
        repeat_penalty=repetition_penalty
    )

    # Use Response to stream tokens
    return Response(generate_tokens(model, generator), content_type='text/plain', status=200, direct_passthrough=True)
    
@app.route('/', methods=['POST'])
def generate_response():
    global stop_generation
    stop_generation = False
    
    data = request.get_json()
    app.logger.info(data)
    messages = data.get("messages", [])
    preprompt = data.get("preprompt", "")
    parameters = data.get("parameters", {})
    
    # Extract parameters from the request
    temperature = 0.02#parameters.get("temperature", 0.01)
    truncate = parameters.get("truncate", 1000)
    max_new_tokens = parameters.get("max_new_tokens", 1024)
    top_p = 80#parameters.get("top_p", 0.85)
    repetition_penalty = parameters.get("repetition_penalty", 1.2)
    top_k = 25#parameters.get("top_k", 30)
    return_full_text = parameters.get("return_full_text", False)

    
    
    # Generate the response
    #system_tokens = get_system_tokens(model)
    #tokens = system_tokens

    #if preprompt != "":
    #    tokens = get_system_tokens_for_preprompt(model, preprompt)
    #else:
    tokens = get_system_tokens(model)
    tokens.append(LINEBREAK_TOKEN)
    #model.eval(tokens)
        
    
    tokens = []
    
    for message in messages:#[:-1]:
        if message.get("from") == "assistant":
            message_tokens = get_message_tokens(model=model, role="bot", content=message.get("content", ""))
        else:
            message_tokens = get_message_tokens(model=model, role="user", content=message.get("content", ""))
    
        tokens.extend(message_tokens)
        #LINEBREAK_TOKEN)
        
    #app.logger.info('model.eval start')
    #model.eval(tokens)
    #app.logger.info('model.eval end')
    
    #last_message = messages[-1]
    #if last_message.get("from") == "assistant":
    #    last_message_tokens = get_message_tokens(model=model, role="bot", content=last_message.get("content", ""))
    #else:
    #    last_message_tokens = get_message_tokens(model=model, role="user", content=last_message.get("content", ""))
            
    tokens.extend([model.token_bos(), BOT_TOKEN, LINEBREAK_TOKEN])

    app.logger.info('Prompt:')
    app.logger.info(model.detokenize(tokens).decode("utf-8", errors="ignore"))

    app.logger.info('Generate started')
    generator = model.generate(
        tokens,
        top_k=top_k,
        top_p=top_p,
        temp=temperature,
        repeat_penalty=repetition_penalty
    )
    app.logger.info('Generator created')

    # Use Response to stream tokens
    return Response(generate_tokens(model, generator), content_type='text/plain', status=200, direct_passthrough=True)

if __name__ == "__main__":
    app.run(host="0.0.0.0", port=7860, debug=False)#, threaded=False)