File size: 4,355 Bytes
ed6ea08 febd133 ed6ea08 febd133 ed6ea08 febd133 ed6ea08 febd133 ed6ea08 71af355 ed6ea08 febd133 ed6ea08 febd133 d2b43fb ed6ea08 d214241 ed6ea08 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 |
#!/usr/bin/python3
# -*- coding: utf-8 -*-
import argparse
from collections import defaultdict
import os
import platform
import gradio as gr
from threading import Thread
from transformers.models.gpt2.modeling_gpt2 import GPT2LMHeadModel
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.generation.streamers import TextIteratorStreamer
import torch
from project_settings import project_path
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument("--max_new_tokens", default=512, type=int)
parser.add_argument("--top_p", default=0.9, type=float)
parser.add_argument("--temperature", default=0.35, type=float)
parser.add_argument("--repetition_penalty", default=1.0, type=float)
parser.add_argument('--device', default="cuda" if torch.cuda.is_available() else "cpu", type=str)
args = parser.parse_args()
return args
description = """
## GPT2 Chat
"""
examples = [
]
def main():
args = get_args()
if args.device == 'auto':
device = 'cuda' if torch.cuda.is_available() else 'cpu'
else:
device = args.device
input_text_box = gr.Text(label="text")
output_text_box = gr.Text(lines=4, label="generated_content")
def fn_stream(text: str,
max_new_tokens: int = 200,
top_p: float = 0.85,
temperature: float = 0.35,
repetition_penalty: float = 1.2,
model_name: str = "qgyd2021/lib_service_4chan",
is_chat: bool = True,
):
tokenizer = BertTokenizer.from_pretrained(model_name)
model = GPT2LMHeadModel.from_pretrained(model_name)
model = model.eval()
text_encoded = tokenizer.__call__(text, add_special_tokens=False)
input_ids_ = text_encoded["input_ids"]
input_ids = [tokenizer.cls_token_id]
input_ids.extend(input_ids_)
if is_chat:
input_ids.append(tokenizer.sep_token_id)
input_ids = torch.tensor([input_ids], dtype=torch.long)
input_ids = input_ids.to(device)
streamer = TextIteratorStreamer(tokenizer=tokenizer)
generation_kwargs = dict(
inputs=input_ids,
max_new_tokens=max_new_tokens,
do_sample=True,
top_p=top_p,
temperature=temperature,
repetition_penalty=repetition_penalty,
eos_token_id=tokenizer.sep_token_id if is_chat else None,
pad_token_id=tokenizer.pad_token_id,
streamer=streamer,
)
thread = Thread(target=model.generate, kwargs=generation_kwargs)
thread.start()
output: str = ""
first_answer = True
for output_ in streamer:
if first_answer:
first_answer = False
continue
# output_ = output_.replace(text, "")
# output_ = output_.replace("[CLS]", "")
output_ = output_.replace("[SEP]", "\n")
output_ = output_.replace("[UNK]", "")
output_ = output_.replace(" ", "")
output += output_.strip()
output_text_box.value += output
yield output
model_name_choices = ["trained_models/lib_service_4chan"] \
if platform.system() == "Windows" else ["qgyd2021/lib_service_4chan"]
demo = gr.Interface(
fn=fn_stream,
inputs=[
input_text_box,
gr.Slider(minimum=0, maximum=512, value=512, step=1, label="max_new_tokens"),
gr.Slider(minimum=0, maximum=1, value=0.85, step=0.01, label="top_p"),
gr.Slider(minimum=0, maximum=1, value=0.35, step=0.01, label="temperature"),
gr.Slider(minimum=0, maximum=2, value=1.2, step=0.01, label="repetition_penalty"),
gr.Dropdown(choices=model_name_choices, value=model_name_choices[0], label="model_name"),
gr.Checkbox(value=True, label="is_chat")
],
outputs=[output_text_box],
examples=[
["怎样擦屁股才能擦的干净", 512, 0.75, 0.35, 1.2, "qgyd2021/lib_service_4chan", True],
],
cache_examples=False,
examples_per_page=50,
title="GPT2 Chat",
description=description,
)
demo.queue().launch()
return
if __name__ == '__main__':
main()
|