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import os
import torch
import gradio as gr
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer
# Environment variables
os.environ["TOKENIZERS_PARALLELISM"] = "0"
os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True"
# Global variables for model and tokenizer
model = None
tokenizer = None
def get_gpu_memory():
return torch.cuda.memory_allocated() / 1024 / 1024 # Convert to MiB
class TorchTracemalloc:
def __init__(self):
self.begin = 0
self.peak = 0
def __enter__(self):
torch.cuda.empty_cache()
torch.cuda.reset_peak_memory_stats()
torch.cuda.synchronize()
self.begin = get_gpu_memory()
return self
def __exit__(self, *exc):
torch.cuda.synchronize()
self.peak = (
torch.cuda.max_memory_allocated() / 1024 / 1024
) # Convert to MiB
def consumed(self):
return self.peak - self.begin
def load_model_and_tokenizer(model_name, dtype, kv_bits):
global model, tokenizer
if model is None or tokenizer is None:
tokenizer = AutoTokenizer.from_pretrained(model_name)
special_tokens = {"pad_token": "<PAD>"}
tokenizer.add_special_tokens(special_tokens)
config = AutoConfig.from_pretrained(model_name)
if kv_bits != "unquantized":
quantizer_path = f"codebooks/{model_name.split('/')[-1]}_{kv_bits}bit.xmad"
setattr(config, "quantizer_path", quantizer_path)
dtype = torch.__dict__.get(dtype, torch.float32)
model = AutoModelForCausalLM.from_pretrained(model_name, config=config, torch_dtype=dtype, device_map="auto")
if len(tokenizer) > model.get_input_embeddings().weight.shape[0]:
model.resize_token_embeddings(len(tokenizer))
tokenizer.padding_side = "left"
model.config.pad_token_id = tokenizer.pad_token_id
return model, tokenizer
# Initialize model and tokenizer
model, tokenizer = load_model_and_tokenizer("NousResearch/Hermes-2-Theta-Llama-3-8B", "fp16", "1")
def process_dialog(dialog, model, tokenizer, max_tokens, temperature):
prompt = tokenizer.apply_chat_template(
dialog, tokenize=False, add_generation_prompt=True
)
tokenized_input_prompt_ids = tokenizer(
prompt, return_tensors="pt"
).input_ids.to(model.device)
torch.cuda.empty_cache()
torch.cuda.reset_peak_memory_stats()
with TorchTracemalloc() as tt:
start_time = time.time()
with torch.no_grad():
token_ids_for_each_answer = model.generate(
tokenized_input_prompt_ids,
max_new_tokens=max_tokens,
temperature=temperature,
do_sample=True,
eos_token_id=tokenizer.eos_token_id,
pad_token_id=tokenizer.pad_token_id,
)
torch.cuda.synchronize()
end_time = time.time()
response = token_ids_for_each_answer[0][
tokenized_input_prompt_ids.shape[-1] :
]
cleaned_response = tokenizer.decode(
response,
skip_special_tokens=True,
clean_up_tokenization_spaces=True,
)
return cleaned_response
def respond(message, history, system_message, max_tokens, temperature):
dialog = [{"role": "system", "content": system_message}]
for val in history:
if val[0]:
dialog.append({"role": "user", "content": val[0]})
if val[1]:
dialog.append({"role": "assistant", "content": val[1]})
dialog.append({"role": "user", "content": message})
response = process_dialog(dialog, model, tokenizer, max_tokens, temperature)
history.append((message, response))
return history, history
# Initialize Gradio ChatInterface
demo = gr.ChatInterface(
fn=respond,
additional_inputs=[
gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
],
theme="default",
title="1bit llama3 by xMAD.ai",
description="The first industrial level 1 bit quantization Llama3, we can achieve 800 tokens per second on NVIDIA V100 and 1200 on NVIDIA A100, 90% cost down of your cloud hosting cost",
css=".scrollable { height: 400px; overflow-y: auto; padding: 10px; border: 1px solid #ccc; }"
)
if __name__ == "__main__":
demo.launch()
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