# ------------------------------------------------------------------------------------------------------------------------
# 🔌💻 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