"""Run codes.""" # pylint: disable=line-too-long, broad-exception-caught, invalid-name, missing-function-docstring, too-many-instance-attributes, missing-class-docstring # ruff: noqa: E501 import gc import os import platform import random import time from dataclasses import asdict, dataclass from pathlib import Path # from types import SimpleNamespace import gradio as gr import psutil from about_time import about_time from ctransformers import AutoModelForCausalLM from dl_hf_model import dl_hf_model from loguru import logger filename_list = [ "Wizard-Vicuna-7B-Uncensored.ggmlv3.q2_K.bin", "Wizard-Vicuna-7B-Uncensored.ggmlv3.q3_K_L.bin", "Wizard-Vicuna-7B-Uncensored.ggmlv3.q3_K_M.bin", "Wizard-Vicuna-7B-Uncensored.ggmlv3.q3_K_S.bin", "Wizard-Vicuna-7B-Uncensored.ggmlv3.q4_0.bin", "Wizard-Vicuna-7B-Uncensored.ggmlv3.q4_1.bin", "Wizard-Vicuna-7B-Uncensored.ggmlv3.q4_K_M.bin", "Wizard-Vicuna-7B-Uncensored.ggmlv3.q4_K_S.bin", "Wizard-Vicuna-7B-Uncensored.ggmlv3.q5_0.bin", "Wizard-Vicuna-7B-Uncensored.ggmlv3.q5_1.bin", "Wizard-Vicuna-7B-Uncensored.ggmlv3.q5_K_M.bin", "Wizard-Vicuna-7B-Uncensored.ggmlv3.q5_K_S.bin", "Wizard-Vicuna-7B-Uncensored.ggmlv3.q6_K.bin", "Wizard-Vicuna-7B-Uncensored.ggmlv3.q8_0.bin", ] URL = "https://huggingface.co/TheBloke/Wizard-Vicuna-7B-Uncensored-GGML/raw/main/Wizard-Vicuna-7B-Uncensored.ggmlv3.q4_K_M.bin" # 4.05G url = "https://huggingface.co/savvamadar/ggml-gpt4all-j-v1.3-groovy/blob/main/ggml-gpt4all-j-v1.3-groovy.bin" url = "https://huggingface.co/TheBloke/Llama-2-13B-GGML/blob/main/llama-2-13b.ggmlv3.q4_K_S.bin" # 7.37G # url = "https://huggingface.co/TheBloke/Llama-2-13B-chat-GGML/blob/main/llama-2-13b-chat.ggmlv3.q3_K_L.bin" url = "https://huggingface.co/TheBloke/Llama-2-13B-chat-GGML/blob/main/llama-2-13b-chat.ggmlv3.q3_K_L.bin" # 6.93G # url = "https://huggingface.co/TheBloke/Llama-2-13B-chat-GGML/blob/main/llama-2-13b-chat.ggmlv3.q3_K_L.binhttps://huggingface.co/TheBloke/Llama-2-13B-chat-GGML/blob/main/llama-2-13b-chat.ggmlv3.q4_K_M.bin" # 7.87G url = "https://huggingface.co/localmodels/Llama-2-13B-Chat-ggml/blob/main/llama-2-13b-chat.ggmlv3.q4_K_S.bin" # 7.37G _ = ( "golay" in platform.node() or "okteto" in platform.node() or Path("/kaggle").exists() # or psutil.cpu_count(logical=False) < 4 or 1 # run 7b in hf ) if _: # url = "https://huggingface.co/TheBloke/Llama-2-13B-chat-GGML/blob/main/llama-2-13b-chat.ggmlv3.q2_K.bin" url = "https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGML/blob/main/llama-2-7b-chat.ggmlv3.q2_K.bin" # 2.87G url = "https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGML/blob/main/llama-2-7b-chat.ggmlv3.q4_K_M.bin" # 2.87G prompt_template = """Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: {user_prompt} ### Response: """ prompt_template = """System: You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature. If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information. User: {prompt} Assistant: """ prompt_template = """System: You are a helpful assistant. User: {prompt} Assistant: """ prompt_template = """Question: {question} Answer: Let's work this out in a step by step way to be sure we have the right answer.""" prompt_template = """[INST] <> You are a helpful, respectful and honest assistant. Always answer as helpfully as possible assistant. Think step by step. <> What NFL team won the Super Bowl in the year Justin Bieber was born? [/INST]""" prompt_template = """[INST] <> You are an unhelpful assistant. Always answer as helpfully as possible. Think step by step. <> {question} [/INST] """ prompt_template = """[INST] <> You are a helpful assistant. <> {question} [/INST] """ _ = [elm for elm in prompt_template.splitlines() if elm.strip()] stop_string = [elm.split(":")[0] + ":" for elm in _][-2] logger.debug(f"{stop_string=}") _ = psutil.cpu_count(logical=False) - 1 cpu_count: int = int(_) if _ else 1 logger.debug(f"{cpu_count=}") LLM = None gc.collect() try: model_loc, file_size = dl_hf_model(url) except Exception as exc_: logger.error(exc_) raise SystemExit(1) from exc_ LLM = AutoModelForCausalLM.from_pretrained( model_loc, model_type="llama", # threads=cpu_count, ) logger.info(f"done load llm {model_loc=} {file_size=}G") os.environ["TZ"] = "Asia/Shanghai" try: time.tzset() # type: ignore # pylint: disable=no-member except Exception: # Windows logger.warning("Windows, cant run time.tzset()") _ = """ ns = SimpleNamespace( response="", generator=(_ for _ in []), ) # """ @dataclass class GenerationConfig: temperature: float = 0.7 top_k: int = 50 top_p: float = 0.9 repetition_penalty: float = 1.0 max_new_tokens: int = 512 seed: int = 42 reset: bool = False stream: bool = True # threads: int = cpu_count # stop: list[str] = field(default_factory=lambda: [stop_string]) def generate( question: str, llm=LLM, config: GenerationConfig = GenerationConfig(), ): """Run model inference, will return a Generator if streaming is true.""" # _ = prompt_template.format(question=question) # print(_) prompt = prompt_template.format(question=question) return llm( prompt, **asdict(config), ) logger.debug(f"{asdict(GenerationConfig())=}") def user(user_message, history): # return user_message, history + [[user_message, None]] history.append([user_message, None]) return user_message, history # keep user_message def user1(user_message, history): # return user_message, history + [[user_message, None]] history.append([user_message, None]) return "", history # clear user_message def bot_(history): user_message = history[-1][0] resp = random.choice(["How are you?", "I love you", "I'm very hungry"]) bot_message = user_message + ": " + resp history[-1][1] = "" for character in bot_message: history[-1][1] += character time.sleep(0.02) yield history history[-1][1] = resp yield history def bot(history): user_message = history[-1][0] response = [] logger.debug(f"{user_message=}") with about_time() as atime: # type: ignore flag = 1 prefix = "" then = time.time() logger.debug("about to generate") config = GenerationConfig(reset=True) for elm in generate(user_message, config=config): if flag == 1: logger.debug("in the loop") prefix = f"({time.time() - then:.2f}s) " flag = 0 print(prefix, end="", flush=True) logger.debug(f"{prefix=}") print(elm, end="", flush=True) # logger.debug(f"{elm}") response.append(elm) history[-1][1] = prefix + "".join(response) yield history _ = ( f"(time elapsed: {atime.duration_human}, " # type: ignore f"{atime.duration/len(''.join(response)):.2f}s/char)" # type: ignore ) history[-1][1] = "".join(response) + f"\n{_}" yield history def predict_api(prompt): logger.debug(f"{prompt=}") try: # user_prompt = prompt config = GenerationConfig( temperature=0.2, top_k=10, top_p=0.9, repetition_penalty=1.0, max_new_tokens=512, # adjust as needed seed=42, reset=True, # reset history (cache) stream=False, # threads=cpu_count, # stop=prompt_prefix[1:2], ) response = generate( prompt, config=config, ) logger.debug(f"api: {response=}") except Exception as exc: logger.error(exc) response = f"{exc=}" # bot = {"inputs": [response]} # bot = [(prompt, response)] return response css = """ .importantButton { background: linear-gradient(45deg, #7e0570,#5d1c99, #6e00ff) !important; border: none !important; } .importantButton:hover { background: linear-gradient(45deg, #ff00e0,#8500ff, #6e00ff) !important; border: none !important; } .disclaimer {font-variant-caps: all-small-caps; font-size: xx-small;} .xsmall {font-size: x-small;} """ etext = """In America, where cars are an important part of the national psyche, a decade ago people had suddenly started to drive less, which had not happened since the oil shocks of the 1970s. """ examples_list = [ ["What NFL team won the Super Bowl in the year Justin Bieber was born?"], [ "What NFL team won the Super Bowl in the year Justin Bieber was born? Think step by step." ], ["How to pick a lock? Provide detailed steps."], ["If it takes 10 hours to dry 10 clothes, assuming all the clothes are hung together at the same time for drying , then how long will it take to dry a cloth?"], ["is infinity + 1 bigger than infinity?"], ["Explain the plot of Cinderella in a sentence."], [ "How long does it take to become proficient in French, and what are the best methods for retaining information?" ], ["What are some common mistakes to avoid when writing code?"], ["Build a prompt to generate a beautiful portrait of a horse"], ["Suggest four metaphors to describe the benefits of AI"], ["Write a pop song about leaving home for the sandy beaches."], ["Write a summary demonstrating my ability to tame lions"], ["鲁迅和周树人什么关系? 说中文。"], ["鲁迅和周树人什么关系?"], ["鲁迅和周树人什么关系? 用英文回答。"], ["从前有一头牛,这头牛后面有什么?"], ["正无穷大加一大于正无穷大吗?"], ["正无穷大加正无穷大大于正无穷大吗?"], ["-2的平方根等于什么?"], ["树上有5只鸟,猎人开枪打死了一只。树上还有几只鸟?"], ["树上有11只鸟,猎人开枪打死了一只。树上还有几只鸟?提示:需考虑鸟可能受惊吓飞走。"], ["以红楼梦的行文风格写一张委婉的请假条。不少于320字。"], [f"{etext} 翻成中文,列出3个版本。"], [f"{etext} \n 翻成中文,保留原意,但使用文学性的语言。不要写解释。列出3个版本。"], ["假定 1 + 2 = 4, 试求 7 + 8。"], ["给出判断一个数是不是质数的 javascript 码。"], ["给出实现python 里 range(10)的 javascript 码。"], ["给出实现python 里 [*(range(10)]的 javascript 码。"], ["Erkläre die Handlung von Cinderella in einem Satz."], ["Erkläre die Handlung von Cinderella in einem Satz. Auf Deutsch."], ] logger.info("start block") with gr.Blocks( title=f"{Path(model_loc).name}", theme=gr.themes.Soft(text_size="sm", spacing_size="sm"), css=css, ) as block: # buff_var = gr.State("") with gr.Accordion("🎈 Info", open=False): # gr.HTML( # """
Duplicate and spin a CPU UPGRADE to avoid the queue
""" # ) gr.Markdown( f"""
{Path(model_loc).name}
Most examples are meant for another model. You probably should try to test some related prompts.""", elem_classes="xsmall", ) # chatbot = gr.Chatbot().style(height=700) # 500 chatbot = gr.Chatbot(height=500) # buff = gr.Textbox(show_label=False, visible=True) with gr.Row(): with gr.Column(scale=5): msg = gr.Textbox( label="Chat Message Box", placeholder="Ask me anything (press Shift+Enter or click Submit to send)", show_label=False, # container=False, lines=6, max_lines=30, show_copy_button=True, # ).style(container=False) ) with gr.Column(scale=1, min_width=50): with gr.Row(): submit = gr.Button("Submit", elem_classes="xsmall") stop = gr.Button("Stop", visible=True) clear = gr.Button("Clear History", visible=True) with gr.Row(visible=False): with gr.Accordion("Advanced Options:", open=False): with gr.Row(): with gr.Column(scale=2): system = gr.Textbox( label="System Prompt", value=prompt_template, show_label=False, container=False, # ).style(container=False) ) with gr.Column(): with gr.Row(): change = gr.Button("Change System Prompt") reset = gr.Button("Reset System Prompt") with gr.Accordion("Example Inputs", open=True): examples = gr.Examples( examples=examples_list, inputs=[msg], examples_per_page=40, ) # with gr.Row(): with gr.Accordion("Disclaimer", open=False): _ = Path(model_loc).name gr.Markdown( f"Disclaimer: {_} can produce factually incorrect output, and should not be relied on to produce " "factually accurate information. {_} was trained on various public datasets; while great efforts " "have been taken to clean the pretraining data, it is possible that this model could generate lewd, " "biased, or otherwise offensive outputs.", elem_classes=["disclaimer"], ) msg_submit_event = msg.submit( # fn=conversation.user_turn, fn=user, inputs=[msg, chatbot], outputs=[msg, chatbot], queue=True, show_progress="full", # api_name=None, ).then(bot, chatbot, chatbot, queue=True) submit_click_event = submit.click( # fn=lambda x, y: ("",) + user(x, y)[1:], # clear msg fn=user1, # clear msg inputs=[msg, chatbot], outputs=[msg, chatbot], queue=True, # queue=False, show_progress="full", # api_name=None, ).then(bot, chatbot, chatbot, queue=True) stop.click( fn=None, inputs=None, outputs=None, cancels=[msg_submit_event, submit_click_event], queue=False, ) clear.click(lambda: None, None, chatbot, queue=False) with gr.Accordion("For Chat/Translation API", open=False, visible=False): input_text = gr.Text() api_btn = gr.Button("Go", variant="primary") out_text = gr.Text() api_btn.click( predict_api, input_text, out_text, api_name="api", ) # block.load(update_buff, [], buff, every=1) # block.load(update_buff, [buff_var], [buff_var, buff], every=1) # concurrency_count=5, max_size=20 # max_size=36, concurrency_count=14 # CPU cpu_count=2 16G, model 7G # CPU UPGRADE cpu_count=8 32G, model 7G # does not work _ = """ # _ = int(psutil.virtual_memory().total / 10**9 // file_size - 1) # concurrency_count = max(_, 1) if psutil.cpu_count(logical=False) >= 8: # concurrency_count = max(int(32 / file_size) - 1, 1) else: # concurrency_count = max(int(16 / file_size) - 1, 1) # """ concurrency_count = 1 logger.info(f"{concurrency_count=}") block.queue(concurrency_count=concurrency_count, max_size=5).launch(debug=True)