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Update examples
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"""Credit to https://github.com/THUDM/ChatGLM2-6B/blob/main/web_demo.py while mistakes are mine."""
# pylint: disable=broad-exception-caught, redefined-outer-name, missing-function-docstring, missing-module-docstring, too-many-arguments, line-too-long, invalid-name, redefined-builtin, redefined-argument-from-local
# import gradio as gr
# model_name = "models/THUDM/chatglm2-6b-int4"
# gr.load(model_name).lauch()
# %%writefile demo-4bit.py
import os
import time
from textwrap import dedent
import gradio as gr
import mdtex2html
import torch
from loguru import logger
from transformers import AutoModel, AutoTokenizer
# fix timezone in Linux
os.environ["TZ"] = "Asia/Shanghai"
try:
time.tzset() # type: ignore # pylint: disable=no-member
except Exception:
# Windows
logger.warning("Windows, cant run time.tzset()")
# model_name = "THUDM/chatglm2-6b" # 7x?G
model_name = "THUDM/chatglm2-6b-int4" # 3.9G
RETRY_FLAG = False
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
# model = AutoModel.from_pretrained(model_name, trust_remote_code=True).cuda()
# 4/8 bit
# model = AutoModel.from_pretrained("THUDM/chatglm2-6b", trust_remote_code=True).quantize(4).cuda()
has_cuda = torch.cuda.is_available()
# has_cuda = False # force cpu
if has_cuda:
if model_name.endswith("int4"):
model = AutoModel.from_pretrained(model_name, trust_remote_code=True).cuda()
else:
model = (
AutoModel.from_pretrained(model_name, trust_remote_code=True).cuda().half()
)
else:
model = AutoModel.from_pretrained(
model_name, trust_remote_code=True
).float() # .half().float(), .float() required for CPU
model = model.eval()
_ = """Override Chatbot.postprocess"""
def postprocess(self, y):
if y is None:
return []
for i, (message, response) in enumerate(y):
y[i] = (
None if message is None else mdtex2html.convert((message)),
None if response is None else mdtex2html.convert(response),
)
return y
gr.Chatbot.postprocess = postprocess
def parse_text(text):
"""Copy from https://github.com/GaiZhenbiao/ChuanhuChatGPT/."""
lines = text.split("\n")
lines = [line for line in lines if line != ""]
count = 0
for i, line in enumerate(lines):
if "```" in line:
count += 1
items = line.split("`")
if count % 2 == 1:
lines[i] = f'<pre><code class="language-{items[-1]}">'
else:
lines[i] = "<br></code></pre>"
else:
if i > 0:
if count % 2 == 1:
line = line.replace("`", r"\`")
line = line.replace("<", "&lt;")
line = line.replace(">", "&gt;")
line = line.replace(" ", "&nbsp;")
line = line.replace("*", "&ast;")
line = line.replace("_", "&lowbar;")
line = line.replace("-", "&#45;")
line = line.replace(".", "&#46;")
line = line.replace("!", "&#33;")
line = line.replace("(", "&#40;")
line = line.replace(")", "&#41;")
line = line.replace("$", "&#36;")
lines[i] = "<br>" + line
text = "".join(lines)
return text
def predict(
RETRY_FLAG, input, chatbot, max_length, top_p, temperature, history, past_key_values
):
try:
chatbot.append((parse_text(input), ""))
except Exception as exc:
logger.error(exc)
logger.debug(f"{chatbot=}")
_ = """
if chatbot:
chatbot[-1] = (parse_text(input), str(exc))
yield chatbot, history, past_key_values
# """
yield chatbot, history, past_key_values
for response, history, past_key_values in model.stream_chat(
tokenizer,
input,
history,
past_key_values=past_key_values,
return_past_key_values=True,
max_length=max_length,
top_p=top_p,
temperature=temperature,
):
chatbot[-1] = (parse_text(input), parse_text(response))
yield chatbot, history, past_key_values
def trans_api(input, max_length=4096, top_p=0.8, temperature=0.2):
if max_length < 10:
max_length = 4096
if top_p < 0.1 or top_p > 1:
top_p = 0.85
if temperature <= 0 or temperature > 1:
temperature = 0.01
try:
res, _ = model.chat(
tokenizer,
input,
history=[],
past_key_values=None,
max_length=max_length,
top_p=top_p,
temperature=temperature,
)
# logger.debug(f"{res=} \n{_=}")
except Exception as exc:
logger.error(f"{exc=}")
res = str(exc)
return res
def reset_user_input():
return gr.update(value="")
def reset_state():
return [], [], None
# Delete last turn
def delete_last_turn(chat, history):
if chat and history:
chat.pop(-1)
history.pop(-1)
return chat, history
# Regenerate response
def retry_last_answer(
user_input, chatbot, max_length, top_p, temperature, history, past_key_values
):
if chatbot and history:
# Removing the previous conversation from chat
chatbot.pop(-1)
# Setting up a flag to capture a retry
RETRY_FLAG = True
# Getting last message from user
user_input = history[-1][0]
# Removing bot response from the history
history.pop(-1)
yield from predict(
RETRY_FLAG, # type: ignore
user_input,
chatbot,
max_length,
top_p,
temperature,
history,
past_key_values,
)
with gr.Blocks(title="ChatGLM2-6B-int4", theme=gr.themes.Soft(text_size="sm")) as demo:
# gr.HTML("""<h1 align="center">ChatGLM2-6B-int4</h1>""")
gr.HTML(
"""<center><a href="https://huggingface.co/spaces/mikeee/chatglm2-6b-4bit?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>To avoid the queue and for faster inference Duplicate this Space and upgrade to GPU</center>"""
)
with gr.Accordion("🎈 Info", open=False):
_ = f"""
## {model_name}
Try to refresh the browser and try again when occasionally an error occurs.
With a GPU, a query takes from a few seconds to a few tens of seconds, dependent on the number of words/characters
the question and responses contain. The quality of the responses varies quite a bit it seems. Even the same
question with the same parameters, asked at different times, can result in quite different responses.
* Low temperature: responses will be more deterministic and focused; High temperature: responses more creative.
* Suggested temperatures -- translation: up to 0.3; chatting: > 0.4
* Top P controls dynamic vocabulary selection based on context.
For a table of example values for different scenarios, refer to [this](https://community.openai.com/t/cheat-sheet-mastering-temperature-and-top-p-in-chatgpt-api-a-few-tips-and-tricks-on-controlling-the-creativity-deterministic-output-of-prompt-responses/172683)
If the instance is not on a GPU (T4), it will be very slow. You can try to run the colab notebook [chatglm2-6b-4bit colab notebook](https://colab.research.google.com/drive/1WkF7kOjVCcBBatDHjaGkuJHnPdMWNtbW?usp=sharing) for a spin.
The T4 GPU is sponsored by a community GPU grant from Huggingface. Thanks a lot!
"""
gr.Markdown(dedent(_))
chatbot = gr.Chatbot()
with gr.Row():
with gr.Column(scale=4):
with gr.Column(scale=12):
user_input = gr.Textbox(
show_label=False,
placeholder="Input...",
).style(container=False)
RETRY_FLAG = gr.Checkbox(value=False, visible=False)
with gr.Column(min_width=32, scale=1):
with gr.Row():
submitBtn = gr.Button("Submit", variant="primary")
deleteBtn = gr.Button("Delete last turn", variant="secondary")
retryBtn = gr.Button("Regenerate", variant="secondary")
with gr.Column(scale=1):
emptyBtn = gr.Button("Clear History")
max_length = gr.Slider(
0,
32768,
value=8192,
step=1.0,
label="Maximum length",
interactive=True,
)
top_p = gr.Slider(
0, 1, value=0.85, step=0.01, label="Top P", interactive=True
)
temperature = gr.Slider(
0.01, 1, value=0.95, step=0.01, label="Temperature", interactive=True
)
history = gr.State([])
past_key_values = gr.State(None)
user_input.submit(
predict,
[
RETRY_FLAG,
user_input,
chatbot,
max_length,
top_p,
temperature,
history,
past_key_values,
],
[chatbot, history, past_key_values],
show_progress="full",
)
submitBtn.click(
predict,
[
RETRY_FLAG,
user_input,
chatbot,
max_length,
top_p,
temperature,
history,
past_key_values,
],
[chatbot, history, past_key_values],
show_progress="full",
api_name="predict",
)
submitBtn.click(reset_user_input, [], [user_input])
emptyBtn.click(
reset_state, outputs=[chatbot, history, past_key_values], show_progress="full"
)
retryBtn.click(
retry_last_answer,
inputs=[
user_input,
chatbot,
max_length,
top_p,
temperature,
history,
past_key_values,
],
# outputs = [chatbot, history, last_user_message, user_message]
outputs=[chatbot, history, past_key_values],
)
deleteBtn.click(delete_last_turn, [chatbot, history], [chatbot, history])
with gr.Accordion("Example inputs", open=True):
etext = """In America, where cars are an important part of the national psyche, a decade ago people had suddenly started to drive less, which had not happened since the oil shocks of the 1970s. """
examples = gr.Examples(
examples=[
["What NFL team won the Super Bowl in the year Justin Bieber was born? "],
["What NFL team won the Super Bowl in the year Justin Bieber was born? Think step by step."],
["Explain the plot of Cinderella in a sentence."],
[
"How long does it take to become proficient in French, and what are the best methods for retaining information?"
],
["What are some common mistakes to avoid when writing code?"],
["Build a prompt to generate a beautiful portrait of a horse"],
["Suggest four metaphors to describe the benefits of AI"],
["Write a pop song about leaving home for the sandy beaches."],
["Write a summary demonstrating my ability to tame lions"],
["鲁迅和周树人什么关系"],
["从前有一头牛,这头牛后面有什么?"],
["正无穷大加一大于正无穷大吗?"],
["正无穷大加正无穷大大于正无穷大吗?"],
["-2的平方根等于什么"],
["树上有5只鸟,猎人开枪打死了一只。树上还有几只鸟?"],
["树上有11只鸟,猎人开枪打死了一只。树上还有几只鸟?提示:需考虑鸟可能受惊吓飞走。"],
["鲁迅和周树人什么关系 用英文回答"],
["以红楼梦的行文风格写一张委婉的请假条。不少于320字。"],
[f"{etext} 翻成中文,列出3个版本"],
[f"{etext} \n 翻成中文,保留原意,但使用文学性的语言。不要写解释。列出3个版本"],
["js 判断一个数是不是质数"],
["js 实现python 的 range(10)"],
["js 实现python 的 [*(range(10)]"],
["假定 1 + 2 = 4, 试求 7 + 8"],
["Erkläre die Handlung von Cinderella in einem Satz."],
["Erkläre die Handlung von Cinderella in einem Satz. Auf Deutsch"],
],
inputs=[user_input],
examples_per_page=30,
)
with gr.Accordion("For Chat/Translation API", open=False, visible=False):
input_text = gr.Text()
tr_btn = gr.Button("Go", variant="primary")
out_text = gr.Text()
tr_btn.click(
trans_api,
[input_text, max_length, top_p, temperature],
out_text,
# show_progress="full",
api_name="tr",
)
_ = """
input_text.submit(
trans_api,
[input_text, max_length, top_p, temperature],
out_text,
show_progress="full",
api_name="tr1",
)
# """
# demo.queue().launch(share=False, inbrowser=True)
# demo.queue().launch(share=True, inbrowser=True, debug=True)
# concurrency_count > 1 requires more memory, max_size: queue size
# T4 medium: 30GB, model size: ~4G concurrency_count = 6
# leave one for api access
# reduce to 5 if OOM occurs to often
demo.queue(concurrency_count=6, max_size=30).launch(debug=True)