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"""
RWKV RNN Model - Gradio Space for HuggingFace
YT - Mean Gene Hacks - https://www.youtube.com/@MeanGeneHacks
(C) Gene Ruebsamen - 2/7/2023
License: GPL3
"""
import gradio as gr
import codecs
from ast import literal_eval
from datetime import datetime
from rwkvstic.load import RWKV
from rwkvstic.agnostic.backends import TORCH, TORCH_QUANT, TORCH_STREAM
import torch
import gc
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
def to_md(text):
return text.replace("\n", "<br />")
def get_model():
model = None
model = RWKV(
"https://huggingface.co/BlinkDL/rwkv-4-pile-1b5/resolve/main/RWKV-4-Pile-1B5-Instruct-test1-20230124.pth",
"pytorch(cpu/gpu)",
runtimedtype=torch.float32,
useGPU=torch.cuda.is_available(),
dtype=torch.float32
)
return model
model = None
def infer(
prompt,
mode = "generative",
max_new_tokens=10,
temperature=0.1,
top_p=1.0,
stop="<|endoftext|>",
seed=42,
):
global model
if model == None:
gc.collect()
if (DEVICE == "cuda"):
torch.cuda.empty_cache()
model = get_model()
max_new_tokens = int(max_new_tokens)
temperature = float(temperature)
top_p = float(top_p)
stop = [x.strip(' ') for x in stop.split(',')]
seed = seed
assert 1 <= max_new_tokens <= 384
assert 0.0 <= temperature <= 1.0
assert 0.0 <= top_p <= 1.0
if temperature == 0.0:
temperature = 0.01
if prompt == "":
prompt = " "
# Clear model state for generative mode
model.resetState()
if (mode == "Q/A"):
prompt = f"Expert Questions & Helpful Answers\nAsk Research Experts\nQuestion:\n{prompt}\n\nFull Answer:"
print(f"PROMPT ({datetime.now()}):\n-------\n{prompt}")
print(f"OUTPUT ({datetime.now()}):\n-------\n")
# Load prompt
model.loadContext(newctx=prompt)
generated_text = ""
done = False
with torch.no_grad():
for _ in range(max_new_tokens):
char = model.forward(stopStrings=stop,temp=temperature,top_p_usual=top_p)["output"]
print(char, end='', flush=True)
generated_text += char
generated_text = generated_text.lstrip("\n ")
for stop_word in stop:
stop_word = codecs.getdecoder("unicode_escape")(stop_word)[0]
if stop_word != '' and stop_word in generated_text:
done = True
break
yield generated_text
if done:
print("<stopped>\n")
break
#print(f"{generated_text}")
for stop_word in stop:
stop_word = codecs.getdecoder("unicode_escape")(stop_word)[0]
if stop_word != '' and stop_word in generated_text:
generated_text = generated_text[:generated_text.find(stop_word)]
gc.collect()
yield generated_text
def chat(
prompt,
history,
max_new_tokens=10,
temperature=0.1,
top_p=1.0,
stop="<|endoftext|>",
seed=42,
):
global model
history = history or []
if model == None:
gc.collect()
if (DEVICE == "cuda"):
torch.cuda.empty_cache()
model = get_model()
if len(history) == 0:
# no history, so lets reset chat state
model.resetState()
max_new_tokens = int(max_new_tokens)
temperature = float(temperature)
top_p = float(top_p)
stop = [x.strip(' ') for x in stop.split(',')]
seed = seed
assert 1 <= max_new_tokens <= 384
assert 0.0 <= temperature <= 1.0
assert 0.0 <= top_p <= 1.0
if temperature == 0.0:
temperature = 0.01
if prompt == "":
prompt = " "
print(f"CHAT ({datetime.now()}):\n-------\n{prompt}")
print(f"OUTPUT ({datetime.now()}):\n-------\n")
# Load prompt
model.loadContext(newctx=prompt)
generated_text = ""
done = False
generated_text = model.forward(number=max_new_tokens, stopStrings=stop,temp=temperature,top_p_usual=top_p)["output"]
generated_text = generated_text.lstrip("\n ")
print(f"{generated_text}")
for stop_word in stop:
stop_word = codecs.getdecoder("unicode_escape")(stop_word)[0]
if stop_word != '' and stop_word in generated_text:
generated_text = generated_text[:generated_text.find(stop_word)]
gc.collect()
history.append((prompt, generated_text))
return history,history
examples = [
[
# Question Answering
'''What is the capital of Germany?''',"Q/A", 25, 0.2, 1.0, "<|endoftext|>"],
[
# Question Answering
'''Are humans good or bad?''',"Q/A", 150, 0.8, 0.8, "<|endoftext|>"],
[
# Chatbot
'''This is a conversation between two AI large language models named Alex and Fritz. They are exploring each other's capabilities, and trying to ask interesting questions of one another to explore the limits of each others AI.
Conversation:
Alex: Good morning, Fritz, what type of LLM are you based upon?
Fritz: Morning Alex, I am an RNN with transformer level performance. My language model is 100% attention free.
Alex:''', "generative", 220, 0.9, 0.9, "\\n\\n,<|endoftext|>"],
[
# Generate List
'''Q. Give me list of fiction books.
1. Harry Potter
2. Lord of the Rings
3. Game of Thrones
Q. Give me a list of vegetables.
1. Broccoli
2. Celery
3. Tomatoes
Q. Give me a list of car manufacturers.''', "generative", 80, 0.2, 1.0, "\\n\\n,<|endoftext|>"],
[
# Natural Language Interface
'''You are the writing assistant for Stephen King. You have worked in the fiction/horror genre for 30 years. You are a Pulitzer Prize-winning author, and now you are tasked with developing a skeletal outline for his newest horror novel, set to be completed in the spring of 2024. Create a summary of this work.
Summary:''',"generative", 200, 0.85, 0.8, "<|endoftext|>"]
]
iface = gr.Interface(
fn=infer,
description='''<p>RNN With Transformer-level LLM Performance. (<a href='https://github.com/BlinkDL/RWKV-LM'>github</a>)
According to the author: "It combines the best of RNN and transformers - great performance, fast inference, saves VRAM, fast training, "infinite" ctx_len, and free sentence embedding"
<p>Thanks to <a href='https://www.rftcapital.com'>RFT Capital</a> for donating compute capability for our experiments. Additional thanks to the author of the <a href="https://github.com/harrisonvanderbyl/rwkvstic">rwkvstic</a> library.</p>''',
allow_flagging="never",
inputs=[
gr.Textbox(lines=20, label="Prompt"), # prompt
gr.Radio(["generative","Q/A"], value="generative", label="Choose Mode"),
gr.Slider(1, 256, value=40), # max_tokens
gr.Slider(0.0, 1.0, value=0.8), # temperature
gr.Slider(0.0, 1.0, value=0.85), # top_p
gr.Textbox(lines=1, value="<|endoftext|>") # stop
],
outputs=gr.Textbox(lines=25),
examples=examples,
cache_examples=False,
).queue()
chatiface = gr.Interface(
fn=chat,
description='''<p>RNN With Transformer-level LLM Performance. (<a href='https://github.com/BlinkDL/RWKV-LM'>github</a>)
According to the author: "It combines the best of RNN and transformers - great performance, fast inference, saves VRAM, fast training, "infinite" ctx_len, and free sentence embedding"
<p>Thanks to <a href='https://www.rftcapital.com'>RFT Capital</a> for donating compute capability for our experiments. Additional thanks to the author of the <a href="https://github.com/harrisonvanderbyl/rwkvstic">rwkvstic</a> library.</p>''',
allow_flagging="never",
inputs=[
gr.Textbox(lines=5, label="Message"), # prompt
"state",
gr.Slider(1, 256, value=60), # max_tokens
gr.Slider(0.0, 1.0, value=0.8), # temperature
gr.Slider(0.0, 1.0, value=0.85), # top_p
gr.Textbox(lines=1, value="<|endoftext|>") # stop
],
outputs=[gr.Chatbot(color_map=("green", "pink")),"state"],
).queue()
demo = gr.TabbedInterface(
[iface,chatiface],["Generative","Chatbot"],
title="RWKV-4 (1.5b Instruct)",
)
demo.queue()
demo.launch(share=False) |