import os import time import spaces import gradio as gr import argparse try: from .model.ea_model import EaModel except: from model.ea_model import EaModel import torch from fastchat.model import get_conversation_template import re def truncate_list(lst, num): if num not in lst: return lst first_index = lst.index(num) return lst[:first_index + 1] def find_list_markers(text): pattern = re.compile(r'(?m)(^\d+\.\s|\n)') matches = pattern.finditer(text) return [(match.start(), match.end()) for match in matches] def checkin(pointer,start,marker): for b,e in marker: if b<=pointer{text[pointer:start]}" result += sub_text pointer = end if pointer < len(text): result += f"{text[pointer:]}" return result @spaces.GPU(duration=60) def warmup(model): model.cuda() conv = get_conversation_template(args.model_type) if args.model_type == "llama-2-chat": sys_p = "You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information." conv.system_message = sys_p elif args.model_type == "mixtral": conv = get_conversation_template("llama-2-chat") conv.system_message = '' conv.sep2 = "" conv.append_message(conv.roles[0], "Hello") conv.append_message(conv.roles[1], None) prompt = conv.get_prompt() if args.model_type == "llama-2-chat": prompt += " " input_ids = model.tokenizer([prompt]).input_ids input_ids = torch.as_tensor(input_ids).to(model.base_model.device) for output_ids in model.ea_generate(input_ids): ol=output_ids.shape[1] @spaces.GPU(duration=60) def bot(history, temperature, top_p, use_EaInfer, highlight_EaInfer,session_state,): model.cuda() warmup_id = torch.tensor([[0,1]]).cuda() warmup_hidden= torch.randn(1,2,model.base_model.config.hidden_size).half().cuda() out=model.base_model(warmup_id) out0=model.ea_layer(warmup_hidden,warmup_id) torch.cuda.synchronize() del out,out0,warmup_id,warmup_hidden if not history: return history, "0.00 tokens/s", "0.00", session_state pure_history = session_state.get("pure_history", []) assert args.model_type == "llama-2-chat" or "vicuna" conv = get_conversation_template(args.model_type) if args.model_type == "llama-2-chat": sys_p = "You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information." conv.system_message = sys_p elif args.model_type == "mixtral": conv = get_conversation_template("llama-2-chat") conv.system_message = '' conv.sep2 = "" elif args.model_type == "llama-3-instruct": messages = [ {"role": "system", "content": "You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information."}, ] for query, response in pure_history: if args.model_type == "llama-3-instruct": messages.append({ "role": "user", "content": query }) if response!=None: messages.append({ "role": "assistant", "content": response }) else: conv.append_message(conv.roles[0], query) if args.model_type == "llama-2-chat" and response: response = " " + response conv.append_message(conv.roles[1], response) if args.model_type == "llama-3-instruct": prompt = model.tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, ) else: prompt = conv.get_prompt() if args.model_type == "llama-2-chat": prompt += " " input_ids = model.tokenizer([prompt]).input_ids input_ids = torch.as_tensor(input_ids).to(model.base_model.device) input_len = input_ids.shape[1] naive_text = [] cu_len = input_len totaltime=0 start_time=time.time() total_ids=0 if use_EaInfer: for output_ids in model.ea_generate(input_ids, temperature=temperature, top_p=top_p, max_new_tokens=args.max_new_token,is_llama3=args.model_type=="llama-3-instruct"): totaltime+=(time.time()-start_time) total_ids+=1 decode_ids = output_ids[0, input_len:].tolist() decode_ids = truncate_list(decode_ids, model.tokenizer.eos_token_id) if args.model_type == "llama-3-instruct": decode_ids = truncate_list(decode_ids, model.tokenizer.convert_tokens_to_ids("<|eot_id|>")) text = model.tokenizer.decode(decode_ids, skip_special_tokens=True, spaces_between_special_tokens=False, clean_up_tokenization_spaces=True, ) naive_text.append(model.tokenizer.decode(output_ids[0, cu_len], skip_special_tokens=True, spaces_between_special_tokens=False, clean_up_tokenization_spaces=True, )) cu_len = output_ids.shape[1] colored_text = highlight_text(text, naive_text, "orange") if highlight_EaInfer: history[-1][1] = colored_text else: history[-1][1] = text pure_history[-1][1] = text session_state["pure_history"] = pure_history new_tokens = cu_len-input_len yield history,f"{new_tokens/totaltime:.2f} tokens/s",f"{new_tokens/total_ids:.2f}",session_state start_time = time.time() else: for output_ids in model.naive_generate(input_ids, temperature=temperature, top_p=top_p, max_new_tokens=args.max_new_token,is_llama3=args.model_type=="llama-3-instruct"): totaltime += (time.time() - start_time) total_ids+=1 decode_ids = output_ids[0, input_len:].tolist() decode_ids = truncate_list(decode_ids, model.tokenizer.eos_token_id) text = model.tokenizer.decode(decode_ids, skip_special_tokens=True, spaces_between_special_tokens=False, clean_up_tokenization_spaces=True, ) naive_text.append(model.tokenizer.decode(output_ids[0, cu_len], skip_special_tokens=True, spaces_between_special_tokens=False, clean_up_tokenization_spaces=True, )) cu_len = output_ids.shape[1] colored_text = highlight_text(text, naive_text, "orange") if highlight_EaInfer and use_EaInfer: history[-1][1] = colored_text else: history[-1][1] = text history[-1][1] = text pure_history[-1][1] = text new_tokens = cu_len - input_len yield history,f"{new_tokens/totaltime:.2f} tokens/s",f"{new_tokens/total_ids:.2f}",session_state start_time = time.time() def user(user_message, history,session_state): if history==None: history=[] pure_history = session_state.get("pure_history", []) pure_history += [[user_message, None]] session_state["pure_history"] = pure_history return "", history + [[user_message, None]],session_state def regenerate(history,session_state): if not history: return history, None,"0.00 tokens/s","0.00",session_state pure_history = session_state.get("pure_history", []) pure_history[-1][-1] = None session_state["pure_history"]=pure_history if len(history) > 1: # Check if there's more than one entry in history (i.e., at least one bot response) new_history = history[:-1] # Remove the last bot response last_user_message = history[-1][0] # Get the last user message return new_history + [[last_user_message, None]], None,"0.00 tokens/s","0.00",session_state history[-1][1] = None return history, None,"0.00 tokens/s","0.00",session_state def clear(history,session_state): pure_history = session_state.get("pure_history", []) pure_history = [] session_state["pure_history"] = pure_history return [],"0.00 tokens/s","0.00",session_state parser = argparse.ArgumentParser() parser.add_argument( "--ea-model-path", type=str, default="yuhuili/EAGLE-LLaMA3-Instruct-8B", help="The path to the weights. This can be a local folder or a Hugging Face repo ID.", ) parser.add_argument("--base-model-path", type=str, default="8B", help="path of basemodel, huggingface project or local path") parser.add_argument( "--load-in-8bit", action="store_true", help="Use 8-bit quantization" ) parser.add_argument( "--load-in-4bit", action="store_true", help="Use 4-bit quantization" ) parser.add_argument("--model-type", type=str, default="llama-3-instruct",choices=["llama-2-chat","vicuna","mixtral","llama-3-instruct"]) parser.add_argument( "--total-token", type=int, default=64, help="The maximum number of new generated tokens.", ) parser.add_argument( "--max-new-token", type=int, default=512, help="The maximum number of new generated tokens.", ) args = parser.parse_args() a=torch.tensor(1).cuda() print(a) model = EaModel.from_pretrained( base_model_path=args.base_model_path, ea_model_path=args.ea_model_path, total_token=args.total_token, torch_dtype=torch.float16, low_cpu_mem_usage=True, load_in_4bit=args.load_in_4bit, load_in_8bit=args.load_in_8bit, device_map="auto", ) model.eval() warmup(model) custom_css = """ #speed textarea { color: red; font-size: 30px; }""" examples = [ ["Introduce artificial intelligence to me."], ["What are the benefits of renewable energy?"], ["How does a neural network work?"] ] with gr.Blocks(css=custom_css) as demo: gs = gr.State({"pure_history": []}) gr.Markdown('''## EAGLE-2 Chatbot''') with gr.Row(): speed_box = gr.Textbox(label="Speed", elem_id="speed", interactive=False, value="0.00 tokens/s") compression_box = gr.Textbox(label="Compression Ratio", elem_id="speed", interactive=False, value="0.00") with gr.Row(): with gr.Column(): use_EaInfer = gr.Checkbox(label="Use EAGLE-2", value=True) highlight_EaInfer = gr.Checkbox(label="Highlight the tokens generated by EAGLE-2", value=True) temperature = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label="temperature", value=0.5) top_p = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label="top_p", value=0.9) note=gr.Markdown(show_label=False,value='''The original LLM is LLaMA3-Instruct 8B, running on a single RTX 3090. The Compression Ratio is defined as the number of generated tokens divided by the number of forward passes in the original LLM. If "Highlight the tokens generated by EAGLE-2" is checked, the tokens correctly guessed by EAGLE-2 will be displayed in orange. Note: Checking this option may cause special formatting rendering issues in a few cases, especially when generating code''') chatbot = gr.Chatbot(height=600,show_label=False) msg = gr.Textbox(label="Your input") gr.Examples(examples=examples, inputs=msg) with gr.Row(): send_button = gr.Button("Send") stop_button = gr.Button("Stop") regenerate_button = gr.Button("Regenerate") clear_button = gr.Button("Clear") enter_event=msg.submit(user, [msg, chatbot,gs], [msg, chatbot,gs], queue=True).then( bot, [chatbot, temperature, top_p, use_EaInfer, highlight_EaInfer,gs], [chatbot,speed_box,compression_box,gs] ) clear_button.click(clear, [chatbot,gs], [chatbot,speed_box,compression_box,gs], queue=True) send_event=send_button.click(user, [msg, chatbot,gs], [msg, chatbot,gs],queue=True).then( bot, [chatbot, temperature, top_p, use_EaInfer, highlight_EaInfer,gs], [chatbot,speed_box,compression_box,gs] ) regenerate_event=regenerate_button.click(regenerate, [chatbot,gs], [chatbot, msg,speed_box,compression_box,gs],queue=True).then( bot, [chatbot, temperature, top_p, use_EaInfer, highlight_EaInfer,gs], [chatbot,speed_box,compression_box,gs] ) stop_button.click(fn=None, inputs=None, outputs=None, cancels=[send_event,regenerate_event,enter_event]) demo.queue() demo.launch()