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| import gradio as gr | |
| import os, gc, copy, torch | |
| from datetime import datetime | |
| from huggingface_hub import hf_hub_download | |
| from pynvml import * | |
| nvmlInit() | |
| gpu_h = nvmlDeviceGetHandleByIndex(0) | |
| ctx_limit = 1024 | |
| title = "RWKV-4-Raven-14B-v10-Eng99%-Other1%-20230427-ctx8192" | |
| 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-raven", filename=f"{title}.pth") | |
| model = RWKV(model=model_path, strategy='cuda fp16i8 *24 -> cuda fp16') | |
| from rwkv.utils import PIPELINE, PIPELINE_ARGS | |
| pipeline = PIPELINE(model, "20B_tokenizer.json") | |
| def generate_prompt(instruction, input=None): | |
| instruction = instruction.strip().replace('\r\n','\n').replace('\n\n','\n') | |
| input = input.strip().replace('\r\n','\n').replace('\n\n','\n') | |
| if input: | |
| return f"""Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request. | |
| # Instruction: | |
| {instruction} | |
| # Input: | |
| {input} | |
| # Response: | |
| """ | |
| else: | |
| return f"""Below is an instruction that describes a task. Write a response that appropriately completes the request. | |
| # Instruction: | |
| {instruction} | |
| # Response: | |
| """ | |
| 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 = instruction.strip().replace('\r\n','\n').replace('\n\n','\n') | |
| input = input.strip().replace('\r\n','\n').replace('\n\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] | |
| 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 | |
| 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() | |
| yield out_str.strip() | |
| examples = [ | |
| ["Tell me about ravens.", "", 150, 1.2, 0.5, 0.4, 0.4], | |
| ["Write a python function to mine 1 BTC, with details and comments.", "", 150, 1.2, 0.5, 0.4, 0.4], | |
| ["Write a song about ravens.", "", 150, 1.2, 0.5, 0.4, 0.4], | |
| ["Explain the following metaphor: Life is like cats.", "", 150, 1.2, 0.5, 0.4, 0.4], | |
| ["Write a story using the following information", "A man named Alex chops a tree down", 150, 1.2, 0.5, 0.4, 0.4], | |
| ["Generate a list of adjectives that describe a person as brave.", "", 150, 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.", "", 150, 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[187] += 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 | |
| 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').replace('\\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>🐦Raven - {title}</h1>\n</div>") | |
| with gr.Tab("Instruct mode"): | |
| gr.Markdown(f"Raven is [RWKV 14B](https://github.com/BlinkDL/ChatRWKV) 100% RNN [RWKV-LM](https://github.com/BlinkDL/RWKV-LM) finetuned to follow instructions. *** 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>UPDATE: now with Chat (see above, as a tab)</b>.") | |
| with gr.Row(): | |
| with gr.Column(): | |
| instruction = gr.Textbox(lines=2, label="Instruction", value="Tell me about ravens.") | |
| input = gr.Textbox(lines=2, label="Input", placeholder="none") | |
| token_count = gr.Slider(10, 200, label="Max Tokens", step=10, value=150) | |
| 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) | |