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 = 2000 title = "RWKV-5-World-1.5B-v2-OnlyForTest_56%_trained-20231013-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/temp", filename=f"{title}.pth") model = RWKV(model=model_path, strategy='cuda fp16') from rwkv.utils import PIPELINE, PIPELINE_ARGS pipeline = PIPELINE(model, "rwkv_vocab_v20230424") def generate_prompt(instruction, input=""): 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"""Instruction: {instruction} Input: {input} Response:""" else: return f"""User: hi Assistant: Hi. I am your assistant and I will provide expert full response in full details. Please feel free to ask any question and I will always answer it. User: {instruction} Assistant:""" def evaluate( ctx, 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 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 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 = [ [generate_prompt("Tell me about ravens."), 500, 1, 0.5, 0.4, 0.4], [generate_prompt("Write a python function to mine 1 BTC."), 500, 1, 0.5, 0.4, 0.4], [generate_prompt("Write a song about ravens."), 500, 1, 0.5, 0.4, 0.4], [generate_prompt("Write a story using the following information", "A man named Alex chops a tree down"), 500, 1, 0.5, 0.4, 0.4], [generate_prompt("Generate a list of adjectives that describe a person as brave."), 500, 1, 0.5, 0.4, 0.4], [generate_prompt("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."), 500, 1, 0.5, 0.4, 0.4], ] ########################################################################## with gr.Blocks(title=title) as demo: gr.HTML(f"
\n

RWKV-5 World v2 - {title}

\n
") with gr.Tab("Free Generation"): gr.Markdown(f"This is a *** 1.5B *** [RWKV-5 World v2](https://huggingface.co/BlinkDL/rwkv-5-world), which is 100% RNN [RWKV-LM](https://github.com/BlinkDL/RWKV-LM) and attention-free. *** Please try examples first (bottom of page) *** (edit them to use your question). Demo limited to ctxlen {ctx_limit}. For best results, *** keep you prompt short and clear ***.") with gr.Row(): with gr.Column(): prompt = gr.Textbox(lines=2, label="Prompt", value=generate_prompt("Tell me about ravens.")) token_count = gr.Slider(10, 500, label="Max Tokens", step=10, value=500) temperature = gr.Slider(0.2, 2.0, label="Temperature", step=0.1, value=1.0) 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=20) data = gr.Dataset(components=[prompt, token_count, temperature, top_p, presence_penalty, count_penalty], samples=examples, label="Example Instructions", headers=["Prompt", "Max Tokens", "Temperature", "Top P", "Presence Penalty", "Count Penalty"]) submit.click(evaluate, [prompt, token_count, temperature, top_p, presence_penalty, count_penalty], [output]) clear.click(lambda: None, [], [output]) data.click(lambda x: x, [data], [prompt, token_count, temperature, top_p, presence_penalty, count_penalty]) demo.queue(concurrency_count=1, max_size=10) demo.launch(share=False)