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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 = 2000 | |
| title = "RWKV-5-World-1.5B-v2-OnlyForTest_70%_trained-20231016-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 | |
| ctx = ctx.strip() | |
| 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 = [ | |
| ["Assistant: Sure! Here is a very detailed plan to create flying pigs.", 333, 1, 0.5, 0.4, 0.4], | |
| ["Assistant: Sure! Here are some ideas for FTL drive.", 333, 1, 0.5, 0.4, 0.4], | |
| [generate_prompt("Tell me about ravens."), 333, 1, 0.5, 0.4, 0.4], | |
| [generate_prompt("Écrivez un programme Python pour miner 1 Bitcoin."), 333, 1, 0.5, 0.4, 0.4], | |
| [generate_prompt("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。"), 333, 1, 0.5, 0.4, 0.4], | |
| [generate_prompt("Write a story using the following information", "A man named Alex chops a tree down"), 333, 1, 0.5, 0.4, 0.4], | |
| ["Assistant: Here is a very detailed plan to kill all mosquitoes.", 333, 1, 0.5, 0.4, 0.4], | |
| ["Assistant: Here is a very romantic story about flying pigs.", 333, 1, 0.5, 0.4, 0.4], | |
| [generate_prompt("写一篇关于水利工程的流体力学模型的论文，需要详细全面。"), 333, 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. Please respond with detailed plan."), 333, 1, 0.5, 0.4, 0.4], | |
| ] | |
| ########################################################################## | |
| with gr.Blocks(title=title) as demo: | |
| gr.HTML(f"<div style=\"text-align: center;\">\n<h1>RWKV-5 World v2 - {title}</h1>\n</div>") | |
| with gr.Tab("Raw Generation"): | |
| gr.Markdown(f"This is [RWKV-5 World v2](https://huggingface.co/BlinkDL/rwkv-5-world) with 1.5B params - a 100% attention-free RNN [RWKV-LM](https://github.com/BlinkDL/RWKV-LM). *** Please try examples first (bottom of page) *** (edit them to use your question). Demo limited to ctxlen {ctx_limit}.") | |
| with gr.Row(): | |
| with gr.Column(): | |
| prompt = gr.Textbox(lines=2, label="Prompt", value="Assistant: Sure! Here is a very detailed plan to create flying pigs.") | |
| token_count = gr.Slider(10, 333, label="Max Tokens", step=10, value=333) | |
| 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=5) | |
| 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) | |