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 = 1536 title = "RWKV-4-Raven-14B-v11x-Eng99%-Other1%-20230501-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}') del out del state 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"
\n

🐦Raven - {title}

\n
") 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 ***. 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="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'''*** The length of response is restricted in this demo. Use ChatRWKV for longer generations. *** 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]. The default "Bob" & "Alice" names work the best.''', 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)