230 lines
7.6 KiB
Python
230 lines
7.6 KiB
Python
|
||
|
||
|
||
|
||
|
||
|
||
|
||
# ------------------------------------------------------------------------------------------------------------------------
|
||
# 🔌💻 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
|
||
|
||
|