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from threading import Thread
import logging
import time
logging.basicConfig(
level=logging.INFO,
format="%(asctime)s [%(levelname)s] %(name)s - %(message)s",
)
import torch
import gradio as gr
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, TextIteratorStreamer
model_id = "pszemraj/tFINE-850m-24x24-v0.5-instruct-L1"
torch_device = "cuda" if torch.cuda.is_available() else "cpu"
logging.info(f"Running on device:\t {torch_device}")
logging.info(f"CPU threads:\t {torch.get_num_threads()}")
if torch_device == "cuda":
model = AutoModelForSeq2SeqLM.from_pretrained(
model_id, load_in_8bit=True, device_map="auto"
)
else:
model = AutoModelForSeq2SeqLM.from_pretrained(model_id, torch_dtype=torch.bfloat16)
try:
model = torch.compile(model)
except Exception as e:
logging.error(f"Unable to compile model:\t{e}")
tokenizer = AutoTokenizer.from_pretrained(model_id)
def run_generation(
user_text,
top_p,
temperature,
top_k,
max_new_tokens,
repetition_penalty=1.1,
length_penalty=1.0,
no_repeat_ngram_size=4,
use_generation_config=False,
):
st = time.perf_counter()
# Get the model and tokenizer, and tokenize the user text.
model_inputs = tokenizer([user_text], return_tensors="pt").to(torch_device)
# Start generation on a separate thread, so that we don't block the UI. The text is pulled from the streamer
# in the main thread. Adds timeout to the streamer to handle exceptions in the generation thread.
streamer = TextIteratorStreamer(
tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True
)
generate_kwargs = dict(
model_inputs,
streamer=streamer,
max_new_tokens=max_new_tokens,
do_sample=True,
num_beams=1,
top_p=top_p,
temperature=float(temperature),
top_k=top_k,
repetition_penalty=repetition_penalty,
length_penalty=length_penalty,
no_repeat_ngram_size=no_repeat_ngram_size,
renormalize_logits=True,
)
t = Thread(target=model.generate, kwargs=generate_kwargs)
t.start()
# Pull the generated text from the streamer, and update the model output.
model_output = ""
for new_text in streamer:
model_output += new_text
yield model_output
logging.info("Total rt:\t{rt} sec".format(rt=round(time.perf_counter() - st, 3)))
return model_output
def reset_textbox():
return gr.update(value="")
with gr.Blocks() as demo:
duplicate_link = (
"https://huggingface.co/spaces/joaogante/transformers_streaming?duplicate=true"
)
gr.Markdown(
"# 🤗 Transformers 🔥Streaming🔥 on Gradio\n"
"This demo showcases the use of the "
"[streaming feature](https://huggingface.co/docs/transformers/main/en/generation_strategies#streaming) "
"of 🤗 Transformers with Gradio to generate text in real-time. It uses "
f"[{model_id}](https://huggingface.co/{model_id}) and the Spaces free compute tier.\n\n"
f"Feel free to [duplicate this Space]({duplicate_link}) to try your own models or use this space as a "
"template! 💛"
)
gr.Markdown("---")
with gr.Row():
with gr.Column(scale=4):
user_text = gr.Textbox(
value="How to become a polar bear tamer?",
label="User input",
)
model_output = gr.Textbox(label="Model output", lines=10, interactive=False)
button_submit = gr.Button(value="Submit", variant="primary")
with gr.Column(scale=1):
max_new_tokens = gr.Slider(
minimum=32,
maximum=1024,
value=256,
step=32,
interactive=True,
label="Max New Tokens",
)
top_p = gr.Slider(
minimum=0.05,
maximum=1.0,
value=0.95,
step=0.05,
interactive=True,
label="Top-p (nucleus sampling)",
)
top_k = gr.Slider(
minimum=1,
maximum=50,
value=50,
step=1,
interactive=True,
label="Top-k",
)
temperature = gr.Slider(
minimum=0.1,
maximum=1.4,
value=0.3,
step=0.05,
interactive=True,
label="Temperature",
)
repetition_penalty = gr.Slider(
minimum=0.9,
maximum=2.5,
value=1.1,
step=0.1,
interactive=True,
label="Repetition Penalty",
)
length_penalty = gr.Slider(
minimum=0.8,
maximum=1.5,
value=1.0,
step=0.1,
interactive=True,
label="Length Penalty",
)
user_text.submit(
run_generation,
[user_text, top_p, temperature, top_k, max_new_tokens, repetition_penalty, length_penalty],
model_output,
)
button_submit.click(
run_generation,
[user_text, top_p, temperature, top_k, max_new_tokens, repetition_penalty, length_penalty],
model_output,
)
demo.queue(max_size=10).launch() |