app.py

import datetime
import json
import os
import shutil
from typing import Optional
from typing import Tuple
from typing import Union

import gradio as gr
import requests
import torch
from fastchat.conversation import Conversation
from fastchat.conversation import SeparatorStyle
from fastchat.conversation import get_conv_template
from fastchat.conversation import register_conv_template
from fastchat.model.model_adapter import BaseAdapter
from fastchat.model.model_adapter import load_model
from fastchat.model.model_adapter import model_adapters
from fastchat.serve.cli import SimpleChatIO
from fastchat.serve.inference import generate_stream
from huggingface_hub import Repository
from huggingface_hub import snapshot_download
from peft import LoraConfig
from peft import PeftModel
from peft import get_peft_model
from peft import set_peft_model_state_dict
import transformers
from transformers import PreTrainedModel
from transformers import PreTrainedTokenizerBase

transformers.AutoTokenizer = transformers.LlamaTokenizer
transformers.AutoModelForCausalLM = transformers.LlamaForCausalLM

def load_lora_model(
    model_path: str,
    lora_weight: str,
    device: str,
    num_gpus: int,
    max_gpu_memory: Optional[str] = None,
    load_8bit: bool = False,
    cpu_offloading: bool = False,
    debug: bool = False,
) -> Tuple[Union[PreTrainedModel, PeftModel], PreTrainedTokenizerBase]:
    model: Union[PreTrainedModel, PeftModel]
    tokenizer: PreTrainedTokenizerBase
    model, tokenizer = load_model(
        model_path=model_path,
        device=device,
        num_gpus=num_gpus,
        max_gpu_memory=max_gpu_memory,
        load_8bit=load_8bit,
        cpu_offloading=cpu_offloading,
        debug=debug,
    )
    if lora_weight is not None:
        # model = PeftModelForCausalLM.from_pretrained(model, model_path, **kwargs)
        config = LoraConfig.from_pretrained(lora_weight)
        model = get_peft_model(model, config)

        # Check the available weights and load them
        checkpoint_name = os.path.join(
            lora_weight, "pytorch_model.bin"
        )  # Full checkpoint
        if not os.path.exists(checkpoint_name):
            checkpoint_name = os.path.join(
                lora_weight, "adapter_model.bin"
            )  # only LoRA model - LoRA config above has to fit
        # The two files above have a different name depending on how they were saved,
        # but are actually the same.
        if os.path.exists(checkpoint_name):
            adapters_weights = torch.load(checkpoint_name)
            set_peft_model_state_dict(model, adapters_weights)
        else:
            raise IOError(f"Checkpoint {checkpoint_name} not found")

    if debug:
        print(model)

    return model, tokenizer


print(datetime.datetime.now())

NUM_THREADS = 1

print(NUM_THREADS)

print("starting server ...")

BASE_MODEL = "decapoda-research/llama-13b-hf"
LORA_WEIGHTS_HF = "izumi-lab/llama-13b-japanese-lora-v0-1ep"
HF_TOKEN = os.environ.get("HF_TOKEN", None)
DATASET_REPOSITORY = os.environ.get("DATASET_REPOSITORY", None)
SLACK_WEBHOOK = os.environ.get("SLACK_WEBHOOK", None)

LORA_WEIGHTS = snapshot_download(LORA_WEIGHTS_HF)

repo = None
LOCAL_DIR = "/home/user/data/"

if HF_TOKEN and DATASET_REPOSITORY:
    try:
        shutil.rmtree(LOCAL_DIR)
    except Exception:
        pass

    repo = Repository(
        local_dir=LOCAL_DIR,
        clone_from=DATASET_REPOSITORY,
        use_auth_token=HF_TOKEN,
        repo_type="dataset",
    )
    repo.git_pull()

if torch.cuda.is_available():
    device = "cuda"
else:
    device = "cpu"

model, tokenizer = load_lora_model(
    model_path=BASE_MODEL,
    lora_weight=LORA_WEIGHTS,
    device=device,
    num_gpus=1,
    max_gpu_memory="16GiB",
    load_8bit=True,
    cpu_offloading=False,
    debug=False,
)

Conversation._get_prompt = Conversation.get_prompt
Conversation._append_message = Conversation.append_message


def conversation_append_message(cls, role: str, message: str):
    cls.offset = -2
    return cls._append_message(role, message)


def conversation_get_prompt_overrider(cls: Conversation) -> str:
    cls.messages = cls.messages[-2:]
    return cls._get_prompt()


def save_inputs_and_outputs(now, inputs, outputs, generate_kwargs):
    current_hour = now.strftime("%Y-%m-%d_%H")
    file_name = f"prompts_{LORA_WEIGHTS.split('/')[-1]}_{current_hour}.jsonl"

    if repo is not None:
        repo.git_pull(rebase=True)
        with open(os.path.join(LOCAL_DIR, file_name), "a", encoding="utf-8") as f:
            json.dump(
                {
                    "inputs": inputs,
                    "outputs": outputs,
                    "generate_kwargs": generate_kwargs,
                },
                f,
                ensure_ascii=False,
            )
            f.write("\n")
        repo.push_to_hub()


# we cant add typing now
# https://github.com/gradio-app/gradio/issues/3514
def evaluate(
    instruction,
    temperature=0.7,
    max_tokens=256,
    repetition_penalty=1.0,
):
    try:
        inputs = tokenizer(instruction, return_tensors="pt")
        if len(inputs["input_ids"][0]) > max_tokens - 40:
            if HF_TOKEN and DATASET_REPOSITORY:
                try:
                    now = datetime.datetime.now()
                    current_time = now.strftime("%Y-%m-%d %H:%M:%S")
                    print(f"[{current_time}] Pushing prompt and completion to the Hub")
                    save_inputs_and_outputs(
                        now,
                        instruction,
                        "",
                        {
                            "temperature": temperature,
                            "max_tokens": max_tokens,
                            "repetition_penalty": repetition_penalty,
                        },
                    )
                except Exception as e:
                    print(e)
            return (
                f"please reduce the input length. Currently, {len(inputs['input_ids'][0])} ( > {max_tokens - 40}) tokens are used.",
                gr.update(interactive=True),
                gr.update(interactive=True),
            )

        conv = get_conv_template()

        conv.append_message(conv.roles[0], instruction)
        conv.append_message(conv.roles[1], None)

        generate_stream_func = generate_stream
        prompt = conv.get_prompt()

        gen_params = {
            "model": BASE_MODEL,
            "prompt": prompt,
            "temperature": temperature,
            "max_new_tokens": max_tokens - len(inputs["input_ids"][0]) - 30,
            "stop": conv.stop_str,
            "stop_token_ids": conv.stop_token_ids,
            "echo": False,
            "repetition_penalty": repetition_penalty,
        }
        chatio = SimpleChatIO()
        chatio.prompt_for_output(conv.roles[1])
        output_stream = generate_stream_func(model, tokenizer, gen_params, device)
        output = chatio.stream_output(output_stream)

        if HF_TOKEN and DATASET_REPOSITORY:
            try:
                now = datetime.datetime.now()
                current_time = now.strftime("%Y-%m-%d %H:%M:%S")
                print(f"[{current_time}] Pushing prompt and completion to the Hub")
                save_inputs_and_outputs(
                    now,
                    prompt,
                    output,
                    {
                        "temperature": temperature,
                        "max_tokens": max_tokens,
                        "repetition_penalty": repetition_penalty,
                    },
                )
            except Exception as e:
                print(e)
        return output, gr.update(interactive=True), gr.update(interactive=True)
    except Exception as e:
        print(e)
        import traceback

        if SLACK_WEBHOOK:
            payload_dic = {
                "text": f"BASE_MODEL: {BASE_MODEL}\n LORA_WEIGHTS: {LORA_WEIGHTS}\n"
                + f"instruction: {instruction}\ninput: {input}\ntemperature: {temperature}\n"
                + f"max_tokens: {max_tokens}\nrepetition_penalty: {repetition_penalty}\n\n"
                + str(traceback.format_exc()),
                "username": "Hugging Face Space",
                "channel": "#monitor",
            }

            try:
                requests.post(SLACK_WEBHOOK, data=json.dumps(payload_dic))
            except Exception:
                pass
        return (
            "Error happend. Please return later.",
            gr.update(interactive=True),
            gr.update(interactive=True),
        )


def reset_textbox():
    return gr.update(value=""), gr.update(value=""), gr.update(value="")


def no_interactive() -> Tuple[gr.Request, gr.Request]:
    return gr.update(interactive=False), gr.update(interactive=False)


title = """<h1 align="center">LLaMA-13B Japanese LoRA</h1>"""

theme = gr.themes.Default(primary_hue="green")
description = (
    "The official demo for **[izumi-lab/llama-13b-japanese-lora-v0-1ep](https://huggingface.co/izumi-lab/llama-13b-japanese-lora-v0-1ep)**.  "
    "It is a 13B-parameter LLaMA model finetuned to follow instructions.  "
    "It is trained on the [izumi-lab/llm-japanese-dataset](https://huggingface.co/datasets/izumi-lab/llm-japanese-dataset) dataset.  "
    "For more information, please visit [the project's website](https://llm.msuzuki.me).  "
    "This model can output up to 256 tokens, but the maximum number of tokens is 200 due to the GPU memory limit of HuggingFace Space.  "
    "It takes about **1 minute** to output. When access is concentrated, the operation may become slow."
)
with gr.Blocks(
    css="""#col_container { margin-left: auto; margin-right: auto;}""",
    theme=theme,
) as demo:
    gr.HTML(title)
    gr.Markdown(description)
    with gr.Column(elem_id="col_container", visible=False) as main_block:
        with gr.Row():
            with gr.Column():
                instruction = gr.Textbox(
                    lines=2, label="Instruction", placeholder="こんにちは"
                )
                inputs = gr.Textbox(lines=1, label="Input", placeholder="none")
                with gr.Row():
                    with gr.Column(scale=3):
                        clear_button = gr.Button("Clear").style(full_width=True)
                    with gr.Column(scale=5):
                        submit_button = gr.Button("Submit").style(full_width=True)
            outputs = gr.Textbox(lines=4, label="Output")

        # inputs, top_p, temperature, top_k, repetition_penalty
        with gr.Accordion("Parameters", open=True):
            temperature = gr.Slider(
                minimum=0,
                maximum=1.0,
                value=0.7,
                step=0.05,
                interactive=False,
                label="Temperature",
            )
            max_tokens = gr.Slider(
                minimum=20,
                maximum=200,
                value=100,
                step=1,
                interactive=True,
                label="Max length (Pre-prompt + instruction + input + output)",
            )
            repetition_penalty = gr.Slider(
                minimum=1.0,
                maximum=5.0,
                value=1.2,
                step=0.1,
                interactive=True,
                label="Repetition penalty",
            )

    with gr.Column(elem_id="user_consent_container") as user_consent_block:
        # Get user consent
        gr.Markdown(
            """
            ## User Consent for Data Collection, Use, and Sharing:
            By using our app, you acknowledge and agree to the following terms regarding the data you provide:

            - **Collection**: We may collect inputs you type into our app.
            - **Use**: We may use the collected data for research purposes, to improve our services, and to develop new products or services, including commercial applications.
            - **Sharing and Publication**: Your input data may be published, shared with third parties, or used for analysis and reporting purposes.
            - **Data Retention**: We may retain your input data for as long as necessary.

            By continuing to use our app, you provide your explicit consent to the collection, use, and potential sharing of your data as described above. If you do not agree with our data collection, use, and sharing practices, please do not use our app.

            Please note that this space utilizes [decapoda-research/llama-13b-hf](https://huggingface.co/decapoda-research/llama-13b-hf) and its special license is applied.

            ## データ収集、利用、共有に関するユーザーの同意：
            本アプリを使用することにより、提供するデータに関する以下の条件に同意するものとします：

            - **収集**: 本アプリに入力されるテキストデータは収集される場合があります。
            - **利用**: 収集されたデータは研究や、商用アプリケーションを含むサービスの開発に使用される場合があります。
            - **共有および公開**: 入力データは第三者と共有されたり、分析や公開の目的で使用される場合があります。
            - **データ保持**: 入力データは必要な限り保持されます。

            本アプリを引き続き使用することにより、上記のようにデータの収集・利用・共有について同意します。データの利用方法に同意しない場合は、本アプリを使用しないでください。

            なお、このスペースは [decapoda-research/llama-13b-hf](https://huggingface.co/decapoda-research/llama-13b-hf) を利用しており、そのライセンスが適用されます。
            """
        )
        accept_button = gr.Button("I Agree")

        def enable_inputs():
            return user_consent_block.update(visible=False), main_block.update(
                visible=True
            )

        accept_button.click(
            fn=enable_inputs,
            inputs=[],
            outputs=[user_consent_block, main_block],
            queue=False,
        )
    submit_button.click(no_interactive, [], [submit_button, clear_button])
    submit_button.click(
        evaluate,
        [instruction, temperature, max_tokens, repetition_penalty],
        [outputs, submit_button, clear_button],
    )
    clear_button.click(reset_textbox, [], [instruction, outputs], queue=False)

    demo.queue(max_size=20, concurrency_count=NUM_THREADS, api_open=False).launch(
        server_name="0.0.0.0", server_port=7860
    )