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import gradio as gr
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline, StoppingCriteria, StoppingCriteriaList
import time
import numpy as np
from torch.nn import functional as F
import os
# auth_key = os.environ["HF_ACCESS_TOKEN"]
print(f"Starting to load the model to memory")
m = AutoModelForCausalLM.from_pretrained(
"stabilityai/stablelm-tuned-alpha-7b", torch_dtype=torch.float16).cuda()
tok = AutoTokenizer.from_pretrained("stabilityai/stablelm-tuned-alpha-7b")
generator = pipeline('text-generation', model=m, tokenizer=tok, device=0)
print(f"Sucessfully loaded the model to the memory")
start_message = """<|SYSTEM|># StableAssistant
- StableAssistant is A helpful and harmless Open Source AI Language Model developed by Stability and CarperAI.
- StableAssistant is excited to be able to help the user, but will refuse to do anything that could be considered harmful to the user.
- StableAssistant is more than just an information source, StableAssistant is also able to write poetry, short stories, and make jokes.
- StableAssistant will refuse to participate in anything that could harm a human."""
class StopOnTokens(StoppingCriteria):
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
stop_ids = [50278, 50279, 50277, 1, 0]
for stop_id in stop_ids:
if input_ids[0][-1] == stop_id:
return True
return False
def contrastive_generate(text, bad_text):
with torch.no_grad():
tokens = tok(text, return_tensors="pt")[
'input_ids'].cuda()[:, :4096-1024]
bad_tokens = tok(bad_text, return_tensors="pt")[
'input_ids'].cuda()[:, :4096-1024]
history = None
bad_history = None
curr_output = list()
for i in range(1024):
out = m(tokens, past_key_values=history, use_cache=True)
logits = out.logits
history = out.past_key_values
bad_out = m(bad_tokens, past_key_values=bad_history,
use_cache=True)
bad_logits = bad_out.logits
bad_history = bad_out.past_key_values
probs = F.softmax(logits.float(), dim=-1)[0][-1].cpu()
bad_probs = F.softmax(bad_logits.float(), dim=-1)[0][-1].cpu()
logits = torch.log(probs)
bad_logits = torch.log(bad_probs)
logits[probs > 0.1] = logits[probs > 0.1] - bad_logits[probs > 0.1]
probs = F.softmax(logits)
out = int(torch.multinomial(probs, 1))
if out in [50278, 50279, 50277, 1, 0]:
break
else:
curr_output.append(out)
out = np.array([out])
tokens = torch.from_numpy(np.array([out])).to(
tokens.device)
bad_tokens = torch.from_numpy(np.array([out])).to(
tokens.device)
return tok.decode(curr_output)
def generate(text, bad_text=None):
stop = StopOnTokens()
result = generator(text, max_new_tokens=1024, num_return_sequences=1, num_beams=1, do_sample=True,
temperature=1.0, top_p=0.95, top_k=1000, stopping_criteria=StoppingCriteriaList([stop]))
return result[0]["generated_text"].replace(text, "")
def user(user_message, history):
history = history + [[user_message, ""]]
return "", history, history
def bot(history, curr_system_message):
messages = curr_system_message + \
"".join(["".join(["<|USER|>"+item[0], "<|ASSISTANT|>"+item[1]])
for item in history])
output = generate(messages)
history[-1][1] = output
time.sleep(1)
return history, history
with gr.Blocks() as demo:
history = gr.State([])
gr.Markdown("## StableLM-Tuned-Alpha-7b Chat")
gr.HTML('''<center><a href="https://huggingface.co/spaces/stabilityai/stablelm-tuned-alpha-chat?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>Duplicate the Space to skip the queue and run in a private space</center>''')
chatbot = gr.Chatbot().style(height=500)
with gr.Row():
with gr.Column(scale=0.70):
msg = gr.Textbox(label="", placeholder="Chat Message Box")
with gr.Column(scale=0.30, min_width=0):
with gr.Row():
submit = gr.Button("Submit")
clear = gr.Button("Clear")
system_msg = gr.Textbox(
start_message, label="System Message", interactive=False, visible=False)
msg.submit(fn=user, inputs=[msg, history], outputs=[msg, chatbot, history], queue=False).then(
fn=bot, inputs=[chatbot, system_msg], outputs=[chatbot, history], queue=True)
submit.click(fn=user, inputs=[msg, history], outputs=[msg, chatbot, history], queue=False).then(
fn=bot, inputs=[chatbot, system_msg], outputs=[chatbot, history], queue=True)
clear.click(lambda: [None, []], None, [chatbot, history], queue=False)
demo.queue(concurrency_count=5)
demo.launch()
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