File size: 7,695 Bytes
4237375 6d3c376 4237375 af3e7fb 4237375 6d3c376 4237375 6d3c376 4237375 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 |
from typing import List, Union
from pathlib import Path
import gradio as gr
import torch
import argparse
from threading import Thread
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
TextIteratorStreamer,
)
import warnings
import spaces
import os
warnings.filterwarnings('ignore', category=UserWarning, message='TypedStorage is deprecated')
MODEL_PATH = os.environ.get('MODEL_PATH', 'IndexTeam/Index-1.9B-Character')
TOKENIZER_PATH = os.environ.get("TOKENIZER_PATH", MODEL_PATH)
tokenizer = AutoTokenizer.from_pretrained(TOKENIZER_PATH, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(MODEL_PATH,
torch_dtype=torch.bfloat16,
device_map="auto",
trust_remote_code=True)
def _resolve_path(path: Union[str, Path]) -> Path:
return Path(path).expanduser().resolve()
@spaces.GPU
def hf_gen(dialog: List, top_k, top_p, temperature, repetition_penalty, max_dec_len):
"""
Generate model output with Huggingface API
Args:
dialog (List): List of dialog messages.
top_k (int): The number of highest probability vocabulary tokens to keep for top-k-filtering.
top_p (float): Only the smallest set of most probable tokens with probabilities that add up to top_p or higher are kept for generation.
temperature (float): Strictly positive float value used to modulate the logits distribution.
repetition_penalty (float): The parameter for repetition penalty.
max_dec_len (int): The maximum numbers of tokens to generate.
Yields:
str: Real-time generation results of HF model.
"""
inputs = tokenizer.apply_chat_template(dialog, tokenize=False, add_generation_prompt=False)
enc = tokenizer(inputs, return_tensors="pt").to("cuda")
streamer = TextIteratorStreamer(tokenizer, **tokenizer.init_kwargs)
generation_kwargs = dict(
enc,
do_sample=True,
top_k=int(top_k),
top_p=float(top_p),
temperature=float(temperature),
repetition_penalty=float(repetition_penalty),
max_new_tokens=int(max_dec_len),
pad_token_id=tokenizer.eos_token_id,
streamer=streamer,
)
thread = Thread(target=model.generate, kwargs=generation_kwargs)
thread.start()
answer = ""
for new_text in streamer:
answer += new_text
yield answer[len(inputs):]
@spaces.GPU
def generate(chat_history: List, query, top_k, top_p, temperature, repetition_penalty, max_dec_len, system_message):
"""
Generate after hitting "submit" button
Args:
chat_history (List): List that stores all QA records.
query (str): Query of current round.
top_p (float): Only the smallest set of most probable tokens with probabilities that add up to top_p or higher are kept for generation.
temperature (float): Strictly positive float value used to modulate the logits distribution.
max_dec_len (int): The maximum numbers of tokens to generate.
Yields:
List: Updated chat_history with the current round QA.
"""
assert query != "", "Input must not be empty!!!"
# apply chat template
model_input = []
if system_message:
model_input.append({
"role": "system",
"content": system_message
})
for q, a in chat_history:
model_input.append({"role": "user", "content": q})
model_input.append({"role": "assistant", "content": a})
model_input.append({"role": "user", "content": query})
# yield model generation
chat_history.append([query, ""])
for answer in hf_gen(model_input, top_k, top_p, temperature, repetition_penalty, max_dec_len):
chat_history[-1][1] = answer.strip(tokenizer.eos_token)
yield gr.update(value=""), chat_history
@spaces.GPU
def regenerate(chat_history: List, top_k, top_p, temperature, repetition_penalty, max_dec_len, system_message):
"""
Re-generate the answer of last round's query
Args:
chat_history (List): List that stores all QA records.
top_p (float): Only the smallest set of most probable tokens with probabilities that add up to top_p or higher are kept for generation.
temperature (float): Strictly positive float value used to modulate the logits distribution.
max_dec_len (int): The maximum numbers of tokens to generate.
Yields:
List: Updated chat_history.
"""
assert len(chat_history) >= 1, "History is empty. Nothing to regenerate!!"
# apply chat template
model_input = []
if system_message:
model_input.append({
"role": "system",
"content": system_message
})
for q, a in chat_history[:-1]:
model_input.append({"role": "user", "content": q})
model_input.append({"role": "assistant", "content": a})
model_input.append({"role": "user", "content": chat_history[-1][0]})
# yield model generation
for answer in hf_gen(model_input, top_k, top_p, temperature, repetition_penalty, max_dec_len):
chat_history[-1][1] = answer.strip(tokenizer.eos_token)
yield gr.update(value=""), chat_history
def clear_history():
"""
Clear all chat history
Returns:
List: Empty chat history
"""
torch.cuda.empty_cache()
return []
def reverse_last_round(chat_history):
"""
Reverse last round QA and keep the chat history before
Args:
chat_history (List): List that stores all QA records.
Returns:
List: Updated chat_history without the last round.
"""
assert len(chat_history) >= 1, "History is empty. Nothing to reverse!!"
return chat_history[:-1]
# launch gradio demo
with gr.Blocks(theme="soft") as demo:
gr.Markdown("""# Index-1.9B-Character Gradio Demo""")
with gr.Row():
with gr.Column(scale=1):
top_k = gr.Slider(1, 10, value=5, step=1, label="top_k")
top_p = gr.Slider(0, 1, value=0.8, step=0.1, label="top_p")
temperature = gr.Slider(0.1, 2.0, value=0.3, step=0.1, label="temperature")
repetition_penalty = gr.Slider(0.1, 2.0, value=1.1, step=0.1, label="repetition_penalty")
max_dec_len = gr.Slider(1, 4096, value=1024, step=1, label="max_dec_len")
with gr.Row():
system_message = gr.Textbox(label="System Message", placeholder="Input your system message", value="你是由哔哩哔哩自主研发的大语言模型，名为“Index-1.9B-Character”。你能够根据用户传入的信息，帮助用户完成指定的任务，并生成恰当的、符合要求的回复。")
with gr.Column(scale=10):
chatbot = gr.Chatbot(bubble_full_width=False, height=500, label='Index-1.9B-Character')
user_input = gr.Textbox(label="User", placeholder="Input your query here!", lines=8)
with gr.Row():
submit = gr.Button("🚀 Submit")
clear = gr.Button("🧹 Clear")
regen = gr.Button("🔄 Regenerate")
reverse = gr.Button("⬅️ Reverse")
submit.click(generate, inputs=[chatbot, user_input, top_k, top_p, temperature, repetition_penalty, max_dec_len, system_message],
outputs=[user_input, chatbot])
regen.click(regenerate, inputs=[chatbot, top_k, top_p, temperature, repetition_penalty, max_dec_len, system_message],
outputs=[user_input, chatbot])
clear.click(clear_history, inputs=[], outputs=[chatbot])
reverse.click(reverse_last_round, inputs=[chatbot], outputs=[chatbot])
demo.queue().launch()
|