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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 = 3000
title = "RWKV-4-World-0.4B-v1-20230529-ctx4096.pth-LoRA-rwkv-19"
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="hunthinn/movie_title", 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=""):
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)
timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
print(f'{timestamp} - 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 = []
##########################################################################
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 7B params - a 100% attention-free RNN [RWKV-LM](https://github.com/BlinkDL/RWKV-LM). Supports all 100+ world languages and code. And we have [200+ Github RWKV projects](https://github.com/search?o=desc&p=1&q=rwkv&s=updated&type=Repositories). *** 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=0.3)
presence_penalty = gr.Slider(0.0, 1.0, label="Presence Penalty", step=0.1, value=0)
count_penalty = gr.Slider(0.0, 1.0, label="Count Penalty", step=0.1, value=1)
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(max_size=10)
demo.launch(share=True)
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