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import json
import os
import time
import gradio as gr
import torch
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer
os.environ["TOKENIZERS_PARALLELISM"] = "0"
os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True"
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 = "NousResearch/Hermes-2-Theta-Llama-3-8B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
special_tokens = {"pad_token": "<PAD>"}
tokenizer.add_special_tokens(special_tokens)
config = AutoConfig.from_pretrained(model_name)
setattr(
config,
"quantizer_path",
f"codebooks/Hermes-2-Theta-Llama-3-8B_1bit.xmad",
)
setattr(config, "window_length", 32)
model = AutoModelForCausalLM.from_pretrained(
model_name, config=config, torch_dtype=torch.float16, device_map="cuda:2"
)
if len(tokenizer) > model.get_input_embeddings().weight.shape[0]:
print(
"WARNING: Resizing the embedding matrix to match the tokenizer vocab size."
)
model.resize_token_embeddings(len(tokenizer))
model.config.pad_token_id = tokenizer.pad_token_id
return model, tokenizer
def process_dialog(dialog, model, tokenizer):
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=512,
temperature=0.7,
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
model, tokenizer = load_model_and_tokenizer()
def chatbot_interface(user_input, chat_history):
dialog = [{"role": "user", "content": user_input}]
response = process_dialog(dialog, model, tokenizer)
chat_history.append((user_input, response))
return chat_history, chat_history
def main():
with gr.Blocks() as demo:
chatbot = gr.Chatbot()
user_input = gr.Textbox(placeholder="Type your message here...")
clear = gr.Button("Clear")
user_input.submit(chatbot_interface, [user_input, chatbot], [chatbot, chatbot])
clear.click(lambda: None, None, chatbot)
demo.launch()
if __name__ == "__main__":
main()
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