File size: 6,815 Bytes
b5f45b3
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
import json
import re
import requests


from tclogger import logger
from transformers import AutoTokenizer

from constants.models import (
    MODEL_MAP,
    STOP_SEQUENCES_MAP,
    TOKEN_LIMIT_MAP,
    TOKEN_RESERVED,
)
from constants.envs import PROXIES
from messagers.message_outputer import OpenaiStreamOutputer


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

        if self.model == "gemma-7b":
            # this is not wrong, as repo `google/gemma-7b-it` is gated and must authenticate to access it
            # so I use mistral-7b as a fallback
            self.tokenizer = AutoTokenizer.from_pretrained(MODEL_MAP["mistral-7b"])
        else:
            self.tokenizer = AutoTokenizer.from_pretrained(self.model_fullname)

    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(
            TOKEN_LIMIT_MAP[self.model] - TOKEN_RESERVED - self.count_tokens(prompt)
        )
        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 STOP_SEQUENCES_MAP.keys():
            self.stop_sequences = STOP_SEQUENCES_MAP[self.model]
        #     self.request_body["parameters"]["stop_sequences"] = [
        #         self.STOP_SEQUENCES[self.model]
        #     ]

        logger.back(self.request_url)
        stream_response = requests.post(
            self.request_url,
            headers=self.request_headers,
            json=self.request_body,
            proxies=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 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")