File size: 7,670 Bytes
461166a
 
 
 
 
 
 
 
 
 
 
 
 
 
0fc9653
9125999
 
461166a
 
 
 
 
 
 
 
 
833b3d3
dc1cc98
461166a
 
 
 
 
0fc9653
9125999
 
461166a
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
779b0c8
461166a
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
import json
import re
import requests
from tiktoken import get_encoding as tiktoken_get_encoding
from messagers.message_outputer import OpenaiStreamOutputer
from utils.logger import logger
from utils.enver import enver


class MessageStreamer:
    MODEL_MAP = {
        "mixtral-8x7b": "mistralai/Mixtral-8x7B-Instruct-v0.1",  # 72.62, fast [Recommended]
        "mistral-7b": "mistralai/Mistral-7B-Instruct-v0.2",  # 65.71, fast
        "nous-mixtral-8x7b": "NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO",
        "zephyr-7b-beta": "HuggingFaceH4/zephyr-7b-beta",  # ❌ Too Slow
        "starchat2-15b-v0.1": "HuggingFaceH4/starchat2-15b-v0.1",  # ❌ Too Slow

        # "llama-70b": "meta-llama/Llama-2-70b-chat-hf",  # ❌ Require Pro User
        # "codellama-34b": "codellama/CodeLlama-34b-Instruct-hf",  # ❌ Low Score
        # "falcon-180b": "tiiuae/falcon-180B-chat",  # ❌ Require Pro User
        "default": "mistralai/Mixtral-8x7B-Instruct-v0.1",
    }
    STOP_SEQUENCES_MAP = {
        "mixtral-8x7b": "</s>",
        "mistral-7b": "</s>",
        "nous-mixtral-8x7b": "<|im_end|>",
        "zephyr-7b-beta": "</s>",
        "starchat2-15b-v0.1": "<|im_end|>",
    }
    TOKEN_LIMIT_MAP = {
        "mixtral-8x7b": 32768,
        "mistral-7b": 32768,
        "nous-mixtral-8x7b": 32768,
        "zephyr-7b-beta": 4096,
        "starchat2-15b-v0.1": 8192,

    }
    TOKEN_RESERVED = 100

    def __init__(self, model: str):
        if model in self.MODEL_MAP.keys():
            self.model = model
        else:
            self.model = "default"
        self.model_fullname = self.MODEL_MAP[self.model]
        self.message_outputer = OpenaiStreamOutputer()
        self.tokenizer = tiktoken_get_encoding("cl100k_base")

    def parse_line(self, line):
        line = line.decode("utf-8")
        line = re.sub(r"data:\s*", "", line)
        data = json.loads(line)
        try:
            content = data["token"]["text"]
        except:
            logger.err(data)
        return content

    def count_tokens(self, text):
        tokens = self.tokenizer.encode(text)
        token_count = len(tokens)
        logger.note(f"Prompt Token Count: {token_count}")
        return token_count

    def chat_response(
        self,
        prompt: str = None,
        temperature: float = 0.5,
        top_p: float = 0.95,
        max_new_tokens: int = None,
        api_key: str = None,
        use_cache: bool = False,
    ):
        # https://huggingface.co/docs/api-inference/detailed_parameters?code=curl
        # curl --proxy http://<server>:<port> https://api-inference.huggingface.co/models/<org>/<model_name> -X POST -d '{"inputs":"who are you?","parameters":{"max_new_token":64}}' -H 'Content-Type: application/json' -H 'Authorization: Bearer <HF_TOKEN>'
        self.request_url = (
            f"https://api-inference.huggingface.co/models/{self.model_fullname}"
        )
        self.request_headers = {
            "Content-Type": "application/json",
        }

        if api_key:
            logger.note(
                f"Using API Key: {api_key[:3]}{(len(api_key)-7)*'*'}{api_key[-4:]}"
            )
            self.request_headers["Authorization"] = f"Bearer {api_key}"

        if temperature is None or temperature < 0:
            temperature = 0.0
        # temperature must  0 < and < 1 for HF LLM models
        temperature = max(temperature, 0.01)
        temperature = min(temperature, 0.99)
        top_p = max(top_p, 0.01)
        top_p = min(top_p, 0.99)

        token_limit = int(
            self.TOKEN_LIMIT_MAP[self.model]
            - self.TOKEN_RESERVED
            - self.count_tokens(prompt) * 1.35
        )
        if token_limit <= 0:
            raise ValueError("Prompt exceeded token limit!")

        if max_new_tokens is None or max_new_tokens <= 0:
            max_new_tokens = token_limit
        else:
            max_new_tokens = min(max_new_tokens, token_limit)

        # References:
        #   huggingface_hub/inference/_client.py:
        #     class InferenceClient > def text_generation()
        #   huggingface_hub/inference/_text_generation.py:
        #     class TextGenerationRequest > param `stream`
        # https://huggingface.co/docs/text-generation-inference/conceptual/streaming#streaming-with-curl
        # https://huggingface.co/docs/api-inference/detailed_parameters#text-generation-task
        self.request_body = {
            "inputs": prompt,
            "parameters": {
                "temperature": temperature,
                "top_p": top_p,
                "max_new_tokens": max_new_tokens,
                "return_full_text": False,
            },
            "options": {
                "use_cache": use_cache,
            },
            "stream": True,
        }

        if self.model in self.STOP_SEQUENCES_MAP.keys():
            self.stop_sequences = self.STOP_SEQUENCES_MAP[self.model]
        #     self.request_body["parameters"]["stop_sequences"] = [
        #         self.STOP_SEQUENCES[self.model]
        #     ]

        logger.back(self.request_url)
        enver.set_envs(proxies=True)
        stream_response = requests.post(
            self.request_url,
            headers=self.request_headers,
            json=self.request_body,
            proxies=enver.requests_proxies,
            stream=True,
        )
        status_code = stream_response.status_code
        if status_code == 200:
            logger.success(status_code)
        else:
            logger.err(status_code)

        return stream_response

    def chat_return_dict(self, stream_response):
        # https://platform.openai.com/docs/guides/text-generation/chat-completions-response-format
        final_output = self.message_outputer.default_data.copy()
        final_output["choices"] = [
            {
                "index": 0,
                "finish_reason": "stop",
                "message": {
                    "role": "assistant",
                    "content": "",
                },
            }
        ]
        logger.back(final_output)

        final_content = ""
        for line in stream_response.iter_lines():
            if not line:
                continue

            content = self.parse_line(line)

            if content.strip() == self.stop_sequences:
                logger.success("\n[Finished]")
                break
            else:
                logger.back(content, end="")
                final_content += content

        if self.model in self.STOP_SEQUENCES_MAP.keys():
            final_content = final_content.replace(self.stop_sequences, "")

        final_content = final_content.strip()
        final_output["choices"][0]["message"]["content"] = final_content
        return final_output

    def chat_return_generator(self, stream_response):
        is_finished = False
        line_count = 0
        for line in stream_response.iter_lines():
            if line:
                line_count += 1
            else:
                continue

            content = self.parse_line(line)

            if content.strip() == self.stop_sequences:
                content_type = "Finished"
                logger.success("\n[Finished]")
                is_finished = True
            else:
                content_type = "Completions"
                if line_count == 1:
                    content = content.lstrip()
                logger.back(content, end="")

            output = self.message_outputer.output(
                content=content, content_type=content_type
            )
            yield output

        if not is_finished:
            yield self.message_outputer.output(content="", content_type="Finished")