app.py

import os
import json
import subprocess
from threading import Thread

import torch
import spaces
import gradio as gr
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
    BitsAndBytesConfig,
    TextIteratorStreamer,
)

subprocess.run(
    "pip install flash-attn --no-build-isolation",
    env={"FLASH_ATTENTION_SKIP_CUDA_BUILD": "TRUE"},
    shell=True,
)

MODEL_ID = os.environ.get("MODEL_ID")
CHAT_TEMPLATE = os.environ.get("CHAT_TEMPLATE")
MODEL_NAME = MODEL_ID.split("/")[-1]
CONTEXT_LENGTH = int(os.environ.get("CONTEXT_LENGTH"))
COLOR = os.environ.get("COLOR")
EMOJI = os.environ.get("EMOJI")
DESCRIPTION = os.environ.get("DESCRIPTION")


@spaces.GPU()
def predict(
    message,
    history,
    system_prompt,
    temperature,
    max_new_tokens,
    top_k,
    repetition_penalty,
    top_p,
):
    # Format history with a given chat template
    if CHAT_TEMPLATE == "ChatML":
        stop_tokens = ["<|endoftext|>", "<|im_end|>"]
        instruction = "<|im_start|>system\n" + system_prompt + "\n<|im_end|>\n"
        for human, assistant in history:
            instruction += (
                "<|im_start|>user\n"
                + human
                + "\n<|im_end|>\n<|im_start|>assistant\n"
                + assistant
            )
        instruction += (
            "\n<|im_start|>user\n" + message + "\n<|im_end|>\n<|im_start|>assistant\n"
        )
    elif CHAT_TEMPLATE == "Mistral Instruct":
        stop_tokens = ["</s>", "[INST]", "[INST] ", "<s>", "[/INST]", "[/INST] "]
        instruction = "<s>[INST] " + system_prompt
        for human, assistant in history:
            instruction += human + " [/INST] " + assistant + "</s>[INST]"
        instruction += " " + message + " [/INST]"
    else:
        raise Exception(
            "Incorrect chat template, select 'ChatML' or 'Mistral Instruct'"
        )
    print(instruction)

    streamer = TextIteratorStreamer(
        tokenizer, skip_prompt=True, skip_special_tokens=True
    )
    enc = tokenizer([instruction], return_tensors="pt", padding=True, truncation=True)
    input_ids, attention_mask = enc.input_ids, enc.attention_mask

    if input_ids.shape[1] > CONTEXT_LENGTH:
        input_ids = input_ids[:, -CONTEXT_LENGTH:]

    generate_kwargs = dict(
        {
            "input_ids": input_ids.to(device),
            "attention_mask": attention_mask.to(device),
        },
        streamer=streamer,
        do_sample=True,
        temperature=temperature,
        max_new_tokens=max_new_tokens,
        top_k=top_k,
        repetition_penalty=repetition_penalty,
        top_p=top_p,
    )
    t = Thread(target=model.generate, kwargs=generate_kwargs)
    t.start()
    outputs = []
    for new_token in streamer:
        outputs.append(new_token)
        if new_token in stop_tokens:
            break
        yield "".join(outputs)


# Load model
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
quantization_config = BitsAndBytesConfig(
    load_in_8bit=False, bnb_4bit_compute_dtype=torch.bfloat16
)
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
model = AutoModelForCausalLM.from_pretrained(
    MODEL_ID,
    device_map="auto",
    quantization_config=quantization_config,
    attn_implementation="flash_attention_2",
)

# Create Gradio interface
gr.ChatInterface(
    predict,
    title=EMOJI + " " + MODEL_NAME,
    description=DESCRIPTION,
    examples=[
        ["Can you solve the equation 2x + 3 = 11 for x?"],
        ["Write an epic poem about Ancient Rome."],
        ["Who was the first person to walk on the Moon?"],
        [
            "Use a list comprehension to create a list of squares for numbers from 1 to 10."
        ],
        ["Recommend some popular science fiction books."],
        ["Can you write a short story about a time-traveling detective?"],
    ],
    additional_inputs_accordion=gr.Accordion(label="⚙️ Parameters", open=False),
    additional_inputs=[
        gr.Textbox(
            "Perform the task to the best of your ability.", label="System prompt"
        ),
        gr.Slider(0, 1, 0.8, label="Temperature"),
        gr.Slider(128, 4096, 1024, label="Max new tokens"),
        gr.Slider(1, 80, 40, label="Top K sampling"),
        gr.Slider(0, 2, 1.1, label="Repetition penalty"),
        gr.Slider(0, 1, 0.95, label="Top P sampling"),
    ],
    theme=gr.themes.Soft(primary_hue=COLOR),
).queue().launch()