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import gradio as gr
import os, gc, torch
from datetime import datetime
from huggingface_hub import hf_hub_download
from pynvml import *
nvmlInit()
gpu_h = nvmlDeviceGetHandleByIndex(0)
ctx_limit = 1024
title = "RWKV-4-Pile-7B-Instruct-test4-20230326"
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-pile-7b", filename=f"{title}.pth")
model = RWKV(model=model_path, strategy='cuda fp16i8 *20 -> cuda fp16')
from rwkv.utils import PIPELINE, PIPELINE_ARGS
pipeline = PIPELINE(model, "20B_tokenizer.json")
def generate_prompt(instruction, input=None):
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()
input = input.strip()
ctx = generate_prompt(instruction, input)
gpu_info = nvmlDeviceGetMemoryInfo(gpu_h)
print(f'vram {gpu_info.total} used {gpu_info.used} free {gpu_info.free}')
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
gc.collect()
torch.cuda.empty_cache()
yield out_str.strip()
g = gr.Interface(
fn=evaluate,
inputs=[
gr.components.Textbox(lines=2, label="Instruction", value="Tell me about alpacas."),
gr.components.Textbox(lines=2, label="Input", placeholder="none"),
gr.components.Slider(minimum=10, maximum=250, step=10, value=200), # token_count
gr.components.Slider(minimum=0.2, maximum=2.0, step=0.1, value=1.0), # temperature
gr.components.Slider(minimum=0, maximum=1, step=0.05, value=0.7), # top_p
gr.components.Slider(0.0, 1.0, step=0.1, value=0.2), # presencePenalty
gr.components.Slider(0.0, 1.0, step=0.1, value=0.2), # countPenalty
],
outputs=[
gr.inputs.Textbox(
lines=5,
label="Output",
)
],
title=f"🐦Raven {title}",
description="Raven is [RWKV 7B](https://github.com/BlinkDL/ChatRWKV) finetuned to follow instructions. It is trained on the [Stanford Alpaca](https://github.com/tatsu-lab/stanford_alpaca) dataset and more.",
)
g.queue(concurrency_count=1, max_size=10)
g.launch(share=False)
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