# ------------------------------------------------------------------------------------------------------------------------ # 🔌💻 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[""] 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]", "", "", "", "", ""] 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", "") 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 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 = "" + text tokens = self.text_tokenizer.encode(text) prefix_mask = [1] * len(tokens) if special_tokens: tokens += [self.text_tokenizer["[gMASK]"], self.text_tokenizer[""]] 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[""]] 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") text = text.replace("<|tab|>", "\t") text = re.sub(r"<\|blank_(\d\d?)\|>", replace_blank_with_spaces, text) return text