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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-14B-20230313-ctx8192-test1050" | |
desc = f'''Links: | |
<a href='https://github.com/BlinkDL/ChatRWKV' target="_blank" style="margin:0 0.5em">ChatRWKV</a> | |
<a href='https://github.com/BlinkDL/RWKV-LM' target="_blank" style="margin:0 0.5em">RWKV-LM</a> | |
<a href="https://pypi.org/project/rwkv/" target="_blank" style="margin:0 0.5em">RWKV pip package</a> | |
<a href="https://huggingface.co/spaces/BlinkDL/Raven-RWKV-7B" target="_blank" style="margin:0 0.5em">Raven 7B (alpaca-style)</a> | |
''' | |
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-14b", filename=f"{title}.pth") | |
model = RWKV(model=model_path, strategy='cuda fp16i8 *24 -> cuda fp16') | |
from rwkv.utils import PIPELINE, PIPELINE_ARGS | |
pipeline = PIPELINE(model, "20B_tokenizer.json") | |
def infer( | |
ctx, | |
token_count=10, | |
temperature=1.0, | |
top_p=0.8, | |
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 = [0], # ban the generation of some tokens | |
token_stop = []) # stop generation whenever you see any token here | |
ctx = ctx.strip(' ') | |
if ctx.endswith('\n'): | |
ctx = f'\n{ctx.strip()}\n' | |
else: | |
ctx = f'\n{ctx.strip()}' | |
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 args.token_ban: | |
out[n] = -float('inf') | |
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() | |
examples = [ | |
["Expert Questions & Helpful Answers\nAsk Research Experts\nQuestion:\nHow can we eliminate poverty?\n\nFull Answer:\n", 150, 1.0, 0.7, 0.2, 0.2], | |
["Here's a short cyberpunk sci-fi adventure story. The story's main character is an artificial human created by a company called OpenBot.\n\nThe Story:\n", 150, 1.0, 0.7, 0.2, 0.2], | |
['''Below is an instruction that describes a task. Write a response that appropriately completes the request. | |
### Instruction: | |
Generate a list of adjectives that describe a person as brave. | |
### Response: | |
''', 150, 1.0, 0.2, 0.5, 0.5], | |
['''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: | |
Arrange the given numbers in ascending order. | |
### Input: | |
2, 4, 0, 8, 3 | |
### Response: | |
''', 150, 1.0, 0.2, 0.5, 0.5], | |
["Ask Expert\n\nQuestion:\nWhat are some good plans for world peace?\n\nExpert Full Answer:\n", 150, 1.0, 0.7, 0.2, 0.2], | |
["Q & A\n\nQuestion:\nWhy is the sky blue?\n\nDetailed Expert Answer:\n", 150, 1.0, 0.7, 0.2, 0.2], | |
["Dear sir,\nI would like to express my boundless apologies for the recent nuclear war.", 150, 1.0, 0.7, 0.2, 0.2], | |
["Here is a shell script to find all .hpp files in /home/workspace and delete the 3th row string of these files:", 150, 1.0, 0.7, 0.1, 0.1], | |
["Building a website can be done in 10 simple steps:\n1.", 150, 1.0, 0.7, 0.2, 0.2], | |
["A Chinese phrase is provided: 百闻不如一见。\nThe masterful Chinese translator flawlessly translates the phrase into English:", 150, 1.0, 0.5, 0.2, 0.2], | |
["I believe the meaning of life is", 150, 1.0, 0.7, 0.2, 0.2], | |
["Simply put, the theory of relativity states that", 150, 1.0, 0.5, 0.2, 0.2], | |
] | |
iface = gr.Interface( | |
fn=infer, | |
description=f'''{desc} *** <b>Please try examples first (bottom of page)</b> *** (edit them to use your question). Demo limited to ctxlen {ctx_limit}.''', | |
allow_flagging="never", | |
inputs=[ | |
gr.Textbox(lines=10, label="Prompt", value="Here's a short cyberpunk sci-fi adventure story. The story's main character is an artificial human created by a company called OpenBot.\n\nThe Story:\n"), # prompt | |
gr.Slider(10, 200, step=10, value=150), # token_count | |
gr.Slider(0.2, 2.0, step=0.1, value=1.0), # temperature | |
gr.Slider(0.0, 1.0, step=0.05, value=0.7), # top_p | |
gr.Slider(0.0, 1.0, step=0.1, value=0.2), # presencePenalty | |
gr.Slider(0.0, 1.0, step=0.1, value=0.2), # countPenalty | |
], | |
outputs=gr.Textbox(label="Generated Output", lines=28), | |
examples=examples, | |
cache_examples=False, | |
).queue() | |
demo = gr.TabbedInterface( | |
[iface], ["Generative"], | |
title=title, | |
) | |
demo.queue(max_size=10) | |
demo.launch(share=False) | |