app.py

import gradio as gr
import os, gc, copy, torch, re
from datetime import datetime
from huggingface_hub import hf_hub_download
from pynvml import *
nvmlInit()
gpu_h = nvmlDeviceGetHandleByIndex(0)
ctx_limit = 1536
title = "RWKV-4-World-7B-v1-20230626-ctx4096"

os.environ["RWKV_JIT_ON"] = '1'
os.environ["RWKV_CUDA_ON"] = '1' # if '1' then use CUDA kernel for seq mode (much faster)

from rwkv.model import RWKV
model_path = hf_hub_download(repo_id="BlinkDL/rwkv-4-world", filename=f"{title}.pth")
model = RWKV(model=model_path, strategy='cuda fp16i8 *8 -> cuda fp16')
from rwkv.utils import PIPELINE, PIPELINE_ARGS
pipeline = PIPELINE(model, "rwkv_vocab_v20230424")

def generate_prompt(instruction, input=None):
    instruction = instruction.strip().replace('\r\n','\n').replace('\n\n','\n').replace('\n\n','\n')
    input = input.strip().replace('\r\n','\n').replace('\n\n','\n').replace('\n\n','\n')
    if input:
        return f"""Instruction: {instruction}

Input: {input}

Response:"""
    else:
        return f"""Question: {instruction}

Answer:"""

def evaluate(
    instruction,
    input=None,
    token_count=200,
    temperature=1.0,
    top_p=0.7,
    presencePenalty = 0.1,
    countPenalty = 0.1,
):
    args = PIPELINE_ARGS(temperature = max(0.2, float(temperature)), top_p = float(top_p),
                     alpha_frequency = countPenalty,
                     alpha_presence = presencePenalty,
                     token_ban = [], # ban the generation of some tokens
                     token_stop = [0]) # stop generation whenever you see any token here

    instruction = re.sub(r'\n{2,}', '\n', instruction).strip().replace('\r\n','\n')
    input = re.sub(r'\n{2,}', '\n', input).strip().replace('\r\n','\n')
    ctx = generate_prompt(instruction, input)
    
    all_tokens = []
    out_last = 0
    out_str = ''
    occurrence = {}
    state = None
    for i in range(int(token_count)):
        out, state = model.forward(pipeline.encode(ctx)[-ctx_limit:] if i == 0 else [token], state)
        for n in occurrence:
            out[n] -= (args.alpha_presence + occurrence[n] * args.alpha_frequency)

        token = pipeline.sample_logits(out, temperature=args.temperature, top_p=args.top_p)
        if token in args.token_stop:
            break
        all_tokens += [token]
        for xxx in occurrence:
            occurrence[xxx] *= 0.996
        if token not in occurrence:
            occurrence[token] = 1
        else:
            occurrence[token] += 1
        
        tmp = pipeline.decode(all_tokens[out_last:])
        if '\ufffd' not in tmp:
            out_str += tmp
            yield out_str.strip()
            out_last = i + 1
        if '\n\n' in out_str:
            break

    gpu_info = nvmlDeviceGetMemoryInfo(gpu_h)
    print(f'vram {gpu_info.total} used {gpu_info.used} free {gpu_info.free}')  
    del out
    del state
    gc.collect()
    torch.cuda.empty_cache()
    yield out_str.strip()

examples = [
    ["東京で訪れるべき素晴らしい場所とその紹介をいくつか挙げてください。", "", 300, 1.2, 0.5, 0.4, 0.4],
    ["Écrivez un programme Python pour miner 1 Bitcoin, avec des commentaires.", "", 300, 1.2, 0.5, 0.4, 0.4],
    ["Write a song about ravens.", "", 300, 1.2, 0.5, 0.4, 0.4],
    ["Explain the following metaphor: Life is like cats.", "", 300, 1.2, 0.5, 0.4, 0.4],
    ["Write a story using the following information", "A man named Alex chops a tree down", 300, 1.2, 0.5, 0.4, 0.4],
    ["Generate a list of adjectives that describe a person as brave.", "", 300, 1.2, 0.5, 0.4, 0.4],
    ["You have $100, and your goal is to turn that into as much money as possible with AI and Machine Learning. Please respond with detailed plan.", "", 300, 1.2, 0.5, 0.4, 0.4],
]

##########################################################################

chat_intro = '''The following is a coherent verbose detailed conversation between <|user|> and an AI girl named <|bot|>.

<|user|>: Hi <|bot|>, Would you like to chat with me for a while?

<|bot|>: Hi <|user|>. Sure. What would you like to talk about? I'm listening.
'''

def user(message, chatbot):
    chatbot = chatbot or []
    # print(f"User: {message}")
    return "", chatbot + [[message, None]]

def alternative(chatbot, history):
    if not chatbot or not history:
        return chatbot, history
    
    chatbot[-1][1] = None
    history[0] = copy.deepcopy(history[1])

    return chatbot, history

def chat(
        prompt,
        user,
        bot,
        chatbot,
        history,
        temperature=1.0,
        top_p=0.8,
        presence_penalty=0.1,
        count_penalty=0.1,
):
    args = PIPELINE_ARGS(temperature=max(0.2, float(temperature)), top_p=float(top_p),
                         alpha_frequency=float(count_penalty),
                         alpha_presence=float(presence_penalty),
                         token_ban=[],  # ban the generation of some tokens
                         token_stop=[])  # stop generation whenever you see any token here
    
    if not chatbot:
        return chatbot, history

    message = chatbot[-1][0]
    message = message.strip().replace('\r\n','\n').replace('\n\n','\n')
    ctx = f"{user}: {message}\n\n{bot}:"

    if not history:
        prompt = prompt.replace("<|user|>", user.strip())
        prompt = prompt.replace("<|bot|>", bot.strip())
        prompt = prompt.strip()
        prompt = f"\n{prompt}\n\n"

        out, state = model.forward(pipeline.encode(prompt), None)
        history = [state, None, []]  # [state, state_pre, tokens]
        # print("History reloaded.")

    [state, _, all_tokens] = history
    state_pre_0 = copy.deepcopy(state)

    out, state = model.forward(pipeline.encode(ctx)[-ctx_limit:], state)
    state_pre_1 = copy.deepcopy(state)    # For recovery

    # print("Bot:", end='')

    begin = len(all_tokens)
    out_last = begin
    out_str: str = ''
    occurrence = {}
    for i in range(300):
        if i <= 0:
            nl_bias = -float('inf')
        elif i <= 30:
            nl_bias = (i - 30) * 0.1
        elif i <= 130:
            nl_bias = 0
        else:
            nl_bias = (i - 130) * 0.25
        out[11] += nl_bias
        for n in occurrence:
            out[n] -= (args.alpha_presence + occurrence[n] * args.alpha_frequency)

        token = pipeline.sample_logits(out, temperature=args.temperature, top_p=args.top_p)
        next_tokens = [token]
        if token == 0:
            next_tokens = pipeline.encode('\n\n')
        all_tokens += next_tokens
        for xxx in occurrence:
            occurrence[xxx] *= 0.996
        if token not in occurrence:
            occurrence[token] = 1
        else:
            occurrence[token] += 1

        out, state = model.forward(next_tokens, state)

        tmp = pipeline.decode(all_tokens[out_last:])
        if '\ufffd' not in tmp:
            # print(tmp, end='', flush=True)
            out_last = begin + i + 1
            out_str += tmp

            chatbot[-1][1] = out_str.strip()
            history = [state, all_tokens]
            yield chatbot, history

        out_str = pipeline.decode(all_tokens[begin:])
        out_str = out_str.replace("\r\n", '\n')

        if '\n\n' in out_str:
            break

        # State recovery
        if f'{user}:' in out_str or f'{bot}:' in out_str:
            idx_user = out_str.find(f'{user}:')
            idx_user = len(out_str) if idx_user == -1 else idx_user
            idx_bot = out_str.find(f'{bot}:')
            idx_bot = len(out_str) if idx_bot == -1 else idx_bot
            idx = min(idx_user, idx_bot)

            if idx < len(out_str):
                out_str = f" {out_str[:idx].strip()}\n\n"
                tokens = pipeline.encode(out_str)

                all_tokens = all_tokens[:begin] + tokens
                out, state = model.forward(tokens, state_pre_1)
                break

    gpu_info = nvmlDeviceGetMemoryInfo(gpu_h)
    print(f'vram {gpu_info.total} used {gpu_info.used} free {gpu_info.free}')  

    gc.collect()
    torch.cuda.empty_cache()

    chatbot[-1][1] = out_str.strip()
    history = [state, state_pre_0, all_tokens]
    yield chatbot, history

##########################################################################

with gr.Blocks(title=title) as demo:
    gr.HTML(f"<div style=\"text-align: center;\">\n<h1>🌍World - {title}</h1>\n</div>")
    with gr.Tab("Instruct mode"):
        gr.Markdown(f"World is [RWKV 7B](https://github.com/BlinkDL/ChatRWKV) 100% RNN [RWKV-LM](https://github.com/BlinkDL/RWKV-LM) ***trained on 100+ world languages***. *** Please try examples first (bottom of page) *** (edit them to use your question). Demo limited to ctxlen {ctx_limit}. Finetuned on alpaca, gpt4all, codealpaca and more. For best results, *** keep you prompt short and clear ***.</b>.") # <b>UPDATE: now with Chat (see above, as a tab) ==> turn off as of now due to VRAM leak caused by buggy code.
        with gr.Row():
            with gr.Column():
                instruction = gr.Textbox(lines=2, label="Instruction", value='東京で訪れるべき素晴らしい場所とその紹介をいくつか挙げてください。')
                input = gr.Textbox(lines=2, label="Input", placeholder="none")
                token_count = gr.Slider(10, 300, label="Max Tokens", step=10, value=300)
                temperature = gr.Slider(0.2, 2.0, label="Temperature", step=0.1, value=1.2)
                top_p = gr.Slider(0.0, 1.0, label="Top P", step=0.05, value=0.5)
                presence_penalty = gr.Slider(0.0, 1.0, label="Presence Penalty", step=0.1, value=0.4)
                count_penalty = gr.Slider(0.0, 1.0, label="Count Penalty", step=0.1, value=0.4)
            with gr.Column():
                with gr.Row():
                    submit = gr.Button("Submit", variant="primary")
                    clear = gr.Button("Clear", variant="secondary")
                output = gr.Textbox(label="Output", lines=5)
        data = gr.Dataset(components=[instruction, input, token_count, temperature, top_p, presence_penalty, count_penalty], samples=examples, label="Example Instructions", headers=["Instruction", "Input", "Max Tokens", "Temperature", "Top P", "Presence Penalty", "Count Penalty"])
        submit.click(evaluate, [instruction, input, token_count, temperature, top_p, presence_penalty, count_penalty], [output])
        clear.click(lambda: None, [], [output])
        data.click(lambda x: x, [data], [instruction, input, token_count, temperature, top_p, presence_penalty, count_penalty])
    
    # with gr.Tab("Chat (Experimental - Might be buggy - use ChatRWKV for reference)"):
    #     gr.Markdown(f'''<b>*** The length of response is restricted in this demo. Use ChatRWKV for longer generations. ***</b> Say "go on" or "continue" can sometimes continue the response. If you'd like to edit the scenario, make sure to follow the exact same format: empty lines between (and only between) different speakers. Changes only take effect after you press [Clear]. <b>The default "Bob" & "Alice" names work the best.</b>''', label="Description")
    #     with gr.Row():
    #         with gr.Column():
    #             chatbot = gr.Chatbot()
    #             state = gr.State()
    #             message = gr.Textbox(label="Message", value="Write me a python code to land on moon.")
    #             with gr.Row():
    #                 send = gr.Button("Send", variant="primary")
    #                 alt = gr.Button("Alternative", variant="secondary")
    #                 clear = gr.Button("Clear", variant="secondary")
    #         with gr.Column():
    #             with gr.Row():
    #                 user_name = gr.Textbox(lines=1, max_lines=1, label="User Name", value="Bob")
    #                 bot_name = gr.Textbox(lines=1, max_lines=1, label="Bot Name", value="Alice")
    #             prompt = gr.Textbox(lines=10, max_lines=50, label="Scenario", value=chat_intro)
    #             temperature = gr.Slider(0.2, 2.0, label="Temperature", step=0.1, value=1.2)
    #             top_p = gr.Slider(0.0, 1.0, label="Top P", step=0.05, value=0.5)
    #             presence_penalty = gr.Slider(0.0, 1.0, label="Presence Penalty", step=0.1, value=0.4)
    #             count_penalty = gr.Slider(0.0, 1.0, label="Count Penalty", step=0.1, value=0.4)
    #     chat_inputs = [
    #         prompt,
    #         user_name,
    #         bot_name,
    #         chatbot,
    #         state,
    #         temperature,
    #         top_p,
    #         presence_penalty,
    #         count_penalty
    #     ]
    #     chat_outputs = [chatbot, state]
    #     message.submit(user, [message, chatbot], [message, chatbot], queue=False).then(chat, chat_inputs, chat_outputs)
    #     send.click(user, [message, chatbot], [message, chatbot], queue=False).then(chat, chat_inputs, chat_outputs)
    #     alt.click(alternative, [chatbot, state], [chatbot, state], queue=False).then(chat, chat_inputs, chat_outputs)
    #     clear.click(lambda: ([], None, ""), [], [chatbot, state, message], queue=False)

demo.queue(concurrency_count=1, max_size=10)
demo.launch(share=False)