File size: 7,798 Bytes
8a5e8bc
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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







# ------------------------------------------------------------------------------------------------------------------------
# 🔌💻 Source Code From https://huggingface.co/K024/ChatGLM-6b-onnx-u8s8/blob/main/model.py
# ------------------------------------------------------------------------------------------------------------------------
import re
import numpy as np
# import torch
from onnxruntime import InferenceSession, SessionOptions


# Currently `MatMulInteger` and `DynamicQuantizeLinear` are only supported on CPU,
# although they are documented as supported on CUDA.
providers = ["CPUExecutionProvider"]

# if torch.cuda.is_available():
#     providers = ["CUDAExecutionProvider"] + providers


# Default paths
tokenizer_path = "chatglm-6b-int8-onnx-merged/sentencepiece.model"
onnx_model_path = "chatglm-6b-int8-onnx-merged/chatglm-6b-int8.onnx"


# input & output names
past_names = [f"past_{name}_{i}" for i in range(28) for name in ["key", "value"]]
present_names = [f"present_{name}_{i}" for i in range(28) for name in ["key", "value"]]
output_names = ["logits"] + present_names


# default kv_cache for first inference
default_past_key_values = {
    k: np.zeros((1, 0, 32, 128), dtype=np.float32) for k in past_names
}


def chat_template(history: list[tuple[str, str]], current: str):
    prompt = ""
    chat_round = 0
    for question, answer in history:
        prompt += f"[Round {chat_round}]\n问:{question}\n答:{answer}\n"
        chat_round += 1
    prompt += f"[Round {chat_round}]\n问:{current}\n答:"
    return prompt


def process_response(response: str):
    response = response.strip()
    response = response.replace("[[训练时间]]", "2023年")
    punkts = [
        [",", ","],
        ["!", "!"],
        [":", ":"],
        [";", ";"],
        ["\?", "?"],
    ]
    for item in punkts:
        response = re.sub(r"([\u4e00-\u9fff])%s" % item[0], r"\1%s" % item[1], response)
        response = re.sub(r"%s([\u4e00-\u9fff])" % item[0], r"%s\1" % item[1], response)
    return response


class ChatGLMModel():

    def __init__(self, onnx_model_path=onnx_model_path, tokenizer_path=tokenizer_path, profile=False) -> None:
        self.tokenizer = ChatGLMTokenizer(tokenizer_path)
        options = SessionOptions()
        options.enable_profiling = profile
        self.session = InferenceSession(onnx_model_path, options, providers=providers)
        self.eop_token_id = self.tokenizer["<eop>"]


    def prepare_input(self, prompt: str):
        input_ids, prefix_mask = self.tokenizer.encode(prompt)

        input_ids = np.array([input_ids], dtype=np.longlong)
        prefix_mask = np.array([prefix_mask], dtype=np.longlong)

        return input_ids, prefix_mask, default_past_key_values


    def sample_next_token(self, logits: np.ndarray, top_k=50, top_p=0.7, temperature=1):
        # softmax with temperature
        exp_logits = np.exp(logits / temperature)
        probs = exp_logits / np.sum(exp_logits)

        # top k
        top_k_idx = np.argsort(-probs)[:top_k]
        top_k_probs = probs[top_k_idx]

        # top p
        cumsum_probs = np.cumsum(top_k_probs)
        top_k_probs[(cumsum_probs - top_k_probs) > top_p] = 0.0
        top_k_probs = top_k_probs / np.sum(top_k_probs)

        # sample
        next_token = np.random.choice(top_k_idx, size=1, p=top_k_probs)
        return next_token[0].item()


    def generate_iterate(self, prompt: str, max_generated_tokens=100, top_k=50, top_p=0.7, temperature=1):
        input_ids, prefix_mask, past_key_values = self.prepare_input(prompt)
        output_tokens = []

        while True:
            inputs = {
                "input_ids": input_ids,
                "prefix_mask": prefix_mask,
                "use_past": np.array(len(output_tokens) > 0),
            }
            inputs.update(past_key_values)

            logits, *past_key_values = self.session.run(output_names, inputs)
            past_key_values = { k: v for k, v in zip(past_names, past_key_values) }

            next_token = self.sample_next_token(logits[0, -1], top_k=top_k, top_p=top_p, temperature=temperature)
            
            output_tokens += [next_token]

            if next_token == self.eop_token_id or len(output_tokens) > max_generated_tokens:
                break

            input_ids = np.array([[next_token]], dtype=np.longlong)
            prefix_mask = np.concatenate([prefix_mask, np.array([[0]], dtype=np.longlong)], axis=1)

            yield process_response(self.tokenizer.decode(output_tokens))

        return process_response(self.tokenizer.decode(output_tokens))














# ------------------------------------------------------------------------------------------------------------------------
# 🔌💻 Source Code From https://huggingface.co/K024/ChatGLM-6b-onnx-u8s8/blob/main/tokenizer.py
# ------------------------------------------------------------------------------------------------------------------------

import re
from sentencepiece import SentencePieceProcessor


def replace_spaces_with_blank(match: re.Match[str]):
    return f"<|blank_{len(match.group())}|>"


def replace_blank_with_spaces(match: re.Match[str]):
    return " " * int(match.group(1))


class ChatGLMTokenizer:
    def __init__(self, vocab_file):
        assert vocab_file is not None
        self.vocab_file = vocab_file
        self.special_tokens = ["[MASK]", "[gMASK]", "[sMASK]", "<unused_0>", "<sop>", "<eop>", "<ENC>", "<dBLOCK>"]
        self.text_tokenizer = SentencePieceProcessor(str(vocab_file))

    def __len__(self):
        return len(self.text_tokenizer)

    def __getitem__(self, key: str):
        return self.text_tokenizer[key]


    def preprocess(self, text: str, linebreak=True, whitespaces=True):
        if linebreak:
            text = text.replace("\n", "<n>")
        if whitespaces:
            text = text.replace("\t", "<|tab|>")
            text = re.sub(r" {2,80}", replace_spaces_with_blank, text)
        return text


    def encode(
        self, text: str, text_pair: str = None,
        linebreak=True, whitespaces=True,
        add_dummy_prefix=True, special_tokens=True,
    ) -> tuple[list[int], list[int]]:
        """
        text: Text to encode. Bidirectional part with a [gMASK] and an <sop> for causal LM.
        text_pair: causal LM part.
        linebreak: Whether to encode newline (\n) in text.
        whitespaces: Whether to encode multiple whitespaces or tab in text, useful for source code encoding.
        special_tokens: Whether to encode special token ([MASK], [gMASK], etc.) in text.
        add_dummy_prefix: Whether to add dummy blank space in the beginning.
        """
        text = self.preprocess(text, linebreak, whitespaces)
        if not add_dummy_prefix:
            text = "<n>" + text

        tokens = self.text_tokenizer.encode(text)
        prefix_mask = [1] * len(tokens)
        if special_tokens:
            tokens += [self.text_tokenizer["[gMASK]"], self.text_tokenizer["<sop>"]]
            prefix_mask += [1, 0]

        if text_pair is not None:
            text_pair = self.preprocess(text_pair, linebreak, whitespaces)
            pair_tokens = self.text_tokenizer.encode(text_pair)
            tokens += pair_tokens
            prefix_mask += [0] * len(pair_tokens)
            if special_tokens:
                tokens += [self.text_tokenizer["<eop>"]]
                prefix_mask += [0]

        return (tokens if add_dummy_prefix else tokens[2:]), prefix_mask


    def decode(self, text_ids: list[int]) -> str:
        text = self.text_tokenizer.decode(text_ids)
        text = text.replace("<n>", "\n")
        text = text.replace("<|tab|>", "\t")
        text = re.sub(r"<\|blank_(\d\d?)\|>", replace_blank_with_spaces, text)
        return text