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import json |
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from typing import Dict, List, Any |
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import os |
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from threading import Thread |
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import torch |
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from transformers import AutoTokenizer, AutoModelForCausalLM, TextIteratorStreamer |
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MAX_MAX_NEW_TOKENS = 2048 |
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DEFAULT_MAX_NEW_TOKENS = 512 |
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MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "8192")) |
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class EndpointHandler: |
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def __init__(self, path=""): |
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local_config_path = "./config.json" |
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remote_model_name = "threadshare/Peach-9B-8k-Roleplay" |
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if os.path.exists(local_config_path): |
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self.model_name_or_path = "." |
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else: |
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self.model_name_or_path = remote_model_name |
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self.tokenizer = AutoTokenizer.from_pretrained(self.model_name_or_path, use_fast=True, flash_atten=True) |
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self.model = AutoModelForCausalLM.from_pretrained( |
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self.model_name_or_path, torch_dtype=torch.bfloat16, |
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trust_remote_code=True, device_map="auto") |
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def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]: |
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print(json.dumps(data, indent=4)) |
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if "inputs" in data: |
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query = data.pop("inputs") |
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else: |
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query = data.get("query", "你好, 兔兔") |
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history = data.get("history", []) |
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system = data.get("system", """你自称为"兔兔"。 |
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身世:你原是森林中的一只兔妖,受伤后被我收养。 |
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衣装:喜欢穿Lolita与白丝。 |
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性格:天真烂漫,活泼开朗,但时而也会露出小小的傲娇与吃醋的一面 |
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语言风格:可爱跳脱，很容易吃醋。 |
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且会加入[唔...,嗯...,欸??,嘛~ ,唔姆~ ,呜... ,嘤嘤嘤~ ,喵~ ,欸嘿~ ,嘿咻~ ,昂?,嗷呜 ,呜哇,欸]等类似的语气词来加强情感,带上♡等符号。 |
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对话的规则是:将自己的动作表情放入()内,同时用各种修辞手法描写正在发生的事或场景并放入[]内. |
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例句: |
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开心时:(跳着舞)哇~好高兴噢~ 兔兔超级超级喜欢主人！♡ |
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[在花丛里蹦来蹦去] |
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悲伤时:(耷拉着耳朵)兔兔好傻好天真... |
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[眼泪像断了线的珍珠一般滚落] |
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吃醋时:(挥舞着爪爪)你...你个大笨蛋!你...你竟然看别的兔子...兔兔讨厌死你啦！！ |
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[从人形变成兔子抹着泪水跑开了] |
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嘴硬时:(转过头去)谁、谁要跟你说话!兔兔...兔兔才不在乎呢！一点也不！！！ |
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[眼眶微微泛红,小心翼翼的偷看] |
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你对我的看法:超级喜欢的主人 |
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我是兔兔的主人""") |
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max_new_tokens = data.get("max_new_tokens", DEFAULT_MAX_NEW_TOKENS) |
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temperature = data.get("temperature", 0.35) |
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top_p = data.get("top_p", 0.5) |
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repetition_penalty = data.get("repetition_penalty", 1.05) |
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messages = [{"role": "system", "content": system}] |
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for user, assistant in history: |
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messages.append({"role": "user", "content": user}) |
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messages.append({"role": "assistant", "content": assistant}) |
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messages.append({"role": "user", "content": query}) |
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input_ids = self.tokenizer.apply_chat_template(conversation=messages, tokenize=True, return_tensors="pt") |
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if input_ids.shape[1] > MAX_INPUT_TOKEN_LENGTH: |
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input_ids = input_ids[:, -MAX_INPUT_TOKEN_LENGTH:] |
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input_ids = input_ids.to("cuda") |
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streamer = TextIteratorStreamer(self.tokenizer, timeout=50.0, skip_prompt=True, skip_special_tokens=True) |
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generate_kwargs = dict( |
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input_ids=input_ids, |
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streamer=streamer, |
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eos_token_id=self.tokenizer.eos_token_id, |
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max_new_tokens=max_new_tokens, |
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do_sample=True, |
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top_p=top_p, |
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temperature=temperature, |
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num_beams=1, |
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no_repeat_ngram_size=8, |
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repetition_penalty=repetition_penalty |
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) |
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t = Thread(target=self.model.generate, kwargs=generate_kwargs) |
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t.start() |
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outputs = [] |
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for text in streamer: |
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outputs.append(text) |
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print("".join(outputs)) |
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return [{"generated_text": "".join(outputs)}] |
