gogpt-3b-bloom / app.py
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# -*-coding:utf-8-*-
from typing import Optional
import datetime
import os
from threading import Event, Thread
from uuid import uuid4
import gradio as gr
import requests
import torch
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
StoppingCriteria,
StoppingCriteriaList,
TextIteratorStreamer,
)
model_name = "golaxy/chinese-bloom-3b"
max_new_tokens = 2048
print(f"Starting to load the model {model_name} into memory")
tok = AutoTokenizer.from_pretrained(model_name)
#m = AutoModelForCausalLM.from_pretrained(model_name).eval()
m = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto")
print("m=====>device",m.device)
# tok.convert_tokens_to_ids(["<|im_end|>", "<|endoftext|>"])
stop_token_ids = [tok.eos_token_id]
print(f"Successfully loaded the model {model_name} into memory")
class StopOnTokens(StoppingCriteria):
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
for stop_id in stop_token_ids:
if input_ids[0][-1] == stop_id:
return True
return False
PROMPT_DICT = {
"prompt_input": (
"Below is an instruction that describes a task, paired with an input that provides further context. "
"Write a response that appropriately completes the request.\n\n"
"### Instruction:\n{instruction}\n\n### Input:\n{input}\n\n### Response:"
),
"prompt_no_input": (
"Below is an instruction that describes a task. "
"Write a response that appropriately completes the request.\n\n"
"### Instruction:\n{instruction}\n\n### Response:"
),
}
def generate_input(instruction: Optional[str] = None, input_str: Optional[str] = None) -> str:
if input_str is None:
return PROMPT_DICT['prompt_no_input'].format_map({'instruction': instruction})
else:
return PROMPT_DICT['prompt_input'].format_map({'instruction': instruction, 'input': input_str})
def convert_history_to_text(history):
user_input = history[-1][0]
text = generate_input(user_input)
return text
def log_conversation(conversation_id, history, messages, generate_kwargs):
logging_url = os.getenv("LOGGING_URL", None)
if logging_url is None:
return
timestamp = datetime.datetime.now().strftime("%Y-%m-%dT%H:%M:%S")
data = {
"conversation_id": conversation_id,
"timestamp": timestamp,
"history": history,
"messages": messages,
"generate_kwargs": generate_kwargs,
}
try:
requests.post(logging_url, json=data)
except requests.exceptions.RequestException as e:
print(f"Error logging conversation: {e}")
def user(message, history):
# Append the user's message to the conversation history
return "", history + [[message, ""]]
def bot(history, temperature, top_p, top_k, repetition_penalty, conversation_id):
print(f"history: {history}")
# Initialize a StopOnTokens object
stop = StopOnTokens()
# Construct the input message string for the model by concatenating the current system message and conversation history
messages = convert_history_to_text(history)
# Tokenize the messages string
input_ids = tok(messages, return_tensors="pt").input_ids
input_ids = input_ids.to(m.device)
streamer = TextIteratorStreamer(
tok, timeout=10.0, skip_prompt=True, skip_special_tokens=True)
generate_kwargs = dict(
input_ids=input_ids,
max_new_tokens=max_new_tokens,
temperature=temperature,
do_sample=temperature > 0.0,
top_p=top_p,
top_k=top_k,
repetition_penalty=repetition_penalty,
streamer=streamer,
stopping_criteria=StoppingCriteriaList([stop]),
)
print(generate_kwargs)
stream_complete = Event()
def generate_and_signal_complete():
m.generate(**generate_kwargs)
stream_complete.set()
def log_after_stream_complete():
stream_complete.wait()
log_conversation(
conversation_id,
history,
messages,
{
"top_k": top_k,
"top_p": top_p,
"temperature": temperature,
"repetition_penalty": repetition_penalty,
},
)
t1 = Thread(target=generate_and_signal_complete)
t1.start()
t2 = Thread(target=log_after_stream_complete)
t2.start()
# Initialize an empty string to store the generated text
partial_text = ""
for new_text in streamer:
partial_text += new_text
history[-1][1] = partial_text
yield history
def get_uuid():
return str(uuid4())
with gr.Blocks(
theme=gr.themes.Soft(),
css=".disclaimer {font-variant-caps: all-small-caps;}",
) as demo:
conversation_id = gr.State(get_uuid)
chatbot = gr.Chatbot().style(height=500)
with gr.Row():
with gr.Column():
msg = gr.Textbox(
label="Chat Message Box",
placeholder="Chat Message Box",
show_label=False,
).style(container=False)
with gr.Column():
with gr.Row():
submit = gr.Button("Submit")
stop = gr.Button("Stop")
clear = gr.Button("Clear")
with gr.Row():
with gr.Accordion("Advanced Options:", open=False):
with gr.Row():
with gr.Column():
with gr.Row():
temperature = gr.Slider(
label="Temperature",
value=0.1,
minimum=0.0,
maximum=1.0,
step=0.1,
interactive=True,
info="Higher values produce more diverse outputs",
)
with gr.Column():
with gr.Row():
top_p = gr.Slider(
label="Top-p (nucleus sampling)",
value=1.0,
minimum=0.0,
maximum=1,
step=0.01,
interactive=True,
info=(
"Sample from the smallest possible set of tokens whose cumulative probability "
"exceeds top_p. Set to 1 to disable and sample from all tokens."
),
)
with gr.Column():
with gr.Row():
top_k = gr.Slider(
label="Top-k",
value=0,
minimum=0.0,
maximum=200,
step=1,
interactive=True,
info="Sample from a shortlist of top-k tokens — 0 to disable and sample from all tokens.",
)
with gr.Column():
with gr.Row():
repetition_penalty = gr.Slider(
label="Repetition Penalty",
value=1.1,
minimum=1.0,
maximum=2.0,
step=0.1,
interactive=True,
info="Penalize repetition — 1.0 to disable.",
)
# with gr.Row():
# gr.Markdown(
# "demo 2",
# elem_classes=["disclaimer"],
# )
submit_event = msg.submit(
fn=user,
inputs=[msg, chatbot],
outputs=[msg, chatbot],
queue=False,
).then(
fn=bot,
inputs=[
chatbot,
temperature,
top_p,
top_k,
repetition_penalty,
conversation_id,
],
outputs=chatbot,
queue=True,
)
submit_click_event = submit.click(
fn=user,
inputs=[msg, chatbot],
outputs=[msg, chatbot],
queue=False,
).then(
fn=bot,
inputs=[
chatbot,
temperature,
top_p,
top_k,
repetition_penalty,
conversation_id,
],
outputs=chatbot,
queue=True,
)
stop.click(
fn=None,
inputs=None,
outputs=None,
cancels=[submit_event, submit_click_event],
queue=False,
)
clear.click(lambda: None, None, chatbot, queue=False)
demo.queue(max_size=128, concurrency_count=2)
demo.launch(server_name="0.0.0.0",server_port=7777)