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Running
on
L4
import os | |
from threading import Thread | |
from typing import Iterator | |
import gradio as gr | |
import spaces | |
import torch | |
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer | |
import time | |
MAX_MAX_NEW_TOKENS = 2048 | |
DEFAULT_MAX_NEW_TOKENS = 1024 | |
MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096")) | |
DESCRIPTION = """\ | |
# Dorna-Llama3-8B-Instruct Chat | |
""" | |
PLACEHOLDER = """ | |
<div style="padding: 30px; text-align: center; display: flex; flex-direction: column; align-items: center;"> | |
<img src="https://avatars.githubusercontent.com/u/39557177?v=4" style="width: 80%; max-width: 550px; height: auto; opacity: 0.80; "> | |
<h1 style="font-size: 28px; margin-bottom: 2px; opacity: 0.55;">Dorna-Llama3-8B-Instruct</h1> | |
</div> | |
""" | |
custom_css = """ | |
@import url('https://fonts.googleapis.com/css2?family=Vazirmatn&display=swap'); | |
body, .gradio-container, .gr-button, .gr-input, .gr-slider, .gr-dropdown, .gr-markdown { | |
font-family: 'Vazirmatn', sans-serif !important; | |
} | |
._button { | |
font-size: 20px; | |
} | |
pre, code { | |
direction: ltr !important; | |
unicode-bidi: plaintext !important; | |
} | |
""" | |
system_prompt = str(os.getenv("SYSTEM_PROMPT")) | |
def execution_time_calculator(start_time, log=True): | |
delta = time.time() - start_time | |
if log: | |
print("--- %s seconds ---" % (delta)) | |
return delta | |
def token_per_second_calculator(tokens_count, time_delta): | |
return tokens_count/time_delta | |
if not torch.cuda.is_available(): | |
DESCRIPTION = "\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>" | |
if torch.cuda.is_available(): | |
model_id = "PartAI/Dorna-Llama3-8B-Instruct" | |
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto") | |
tokenizer = AutoTokenizer.from_pretrained(model_id) | |
generation_speed = 0 | |
def get_generation_speed(): | |
global generation_speed | |
return generation_speed | |
def generate( | |
message: str, | |
chat_history: list[tuple[str, str]], | |
max_new_tokens: int = 1024, | |
temperature: float = 0.6, | |
top_p: float = 0.9, | |
top_k: int = 50, | |
repetition_penalty: float = 1.2, | |
do_sample: bool =True, | |
) -> Iterator[str]: | |
global generation_speed | |
global system_prompt | |
conversation = [] | |
if system_prompt: | |
conversation.append({"role": "system", "content": system_prompt}) | |
for user, assistant in chat_history: | |
conversation.extend([{"role": "user", "content": user}, {"role": "assistant", "content": assistant}]) | |
conversation.append({"role": "user", "content": message}) | |
input_ids = tokenizer.apply_chat_template(conversation, return_tensors="pt") | |
if input_ids.shape[1] > MAX_INPUT_TOKEN_LENGTH: | |
input_ids = input_ids[:, -MAX_INPUT_TOKEN_LENGTH:] | |
gr.Warning(f"Trimmed input from conversation as it was longer than {MAX_INPUT_TOKEN_LENGTH} tokens.") | |
input_ids = input_ids.to(model.device) | |
streamer = TextIteratorStreamer(tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True) | |
generate_kwargs = dict( | |
{"input_ids": input_ids}, | |
streamer=streamer, | |
max_new_tokens=max_new_tokens, | |
do_sample=do_sample, | |
top_p=top_p, | |
top_k=top_k, | |
temperature=temperature, | |
num_beams=1, | |
repetition_penalty=repetition_penalty, | |
) | |
start_time = time.time() | |
t = Thread(target=model.generate, kwargs=generate_kwargs) | |
t.start() | |
outputs = [] | |
sum_tokens = 0 | |
for text in streamer: | |
num_tokens = len(tokenizer.tokenize(text)) | |
sum_tokens += num_tokens | |
outputs.append(text) | |
yield "".join(outputs) | |
time_delta = execution_time_calculator(start_time, log=False) | |
generation_speed = token_per_second_calculator(sum_tokens, time_delta) | |
print(f"generation_speed: {generation_speed}") | |
chatbot = gr.Chatbot(placeholder=PLACEHOLDER, scale=1, show_copy_button=True, height="68%", rtl=True) #, elem_classes=["chatbot"]) | |
chat_input = gr.Textbox(show_label=False, lines=2, rtl=True, placeholder="ورودی", show_copy_button=True, scale=4) | |
submit_btn = gr.Button(variant="primary", value="ارسال", size="sm", scale=1, elem_classes=["_button"]) | |
chat_interface = gr.ChatInterface( | |
fn=generate, | |
additional_inputs_accordion=gr.Accordion(label="ورودیهای اضافی", open=False), | |
additional_inputs=[ | |
gr.Slider( | |
label="حداکثر تعداد توکن ها", | |
minimum=1, | |
maximum=MAX_MAX_NEW_TOKENS, | |
step=1, | |
value=DEFAULT_MAX_NEW_TOKENS, | |
), | |
gr.Slider( | |
label="Temperature", | |
minimum=0.01, | |
maximum=4.0, | |
step=0.01, | |
value=0.5, | |
), | |
gr.Slider( | |
label="Top-p", | |
minimum=0.05, | |
maximum=1.0, | |
step=0.01, | |
value=0.9, | |
), | |
gr.Slider( | |
label="Top-k", | |
minimum=1, | |
maximum=1000, | |
step=1, | |
value=20, | |
), | |
gr.Slider( | |
label="جریمه تکرار", | |
minimum=1.0, | |
maximum=2.0, | |
step=0.05, | |
value=1.2, | |
), | |
gr.Dropdown( | |
label="نمونهگیری", | |
choices=[False, True], | |
value=True) | |
], | |
stop_btn="توقف", | |
chatbot=chatbot, | |
textbox=chat_input, | |
submit_btn=submit_btn, | |
retry_btn="🔄 تلاش مجدد", | |
undo_btn="↩️ بازگشت", | |
clear_btn="🗑️ پاک کردن", | |
title="درنا، محصول مرکز تحقیقات هوش مصنوعی پارت" | |
) | |
with gr.Blocks(css=custom_css, fill_height=False) as demo: | |
gr.Markdown(DESCRIPTION) | |
chat_interface.render() | |
if __name__ == "__main__": | |
demo.queue(max_size=20).launch() | |