app.py

import os
import time

#os.environ["CUDA_VISIBLE_DEVICES"] = "0,1"
import gradio as gr
import argparse
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<e:
            return True
        if b<=start<e:
            return True
    return False

def highlight_text(text, text_list,color="black"):
    pointer = 0
    result = ""
    markers=find_list_markers(text)

    for sub_text in text_list:

        start = text.find(sub_text, pointer)
        if start==-1:
            continue
        end = start + len(sub_text)

        if checkin(pointer,start,markers):
            result += text[pointer:start]
        else:
            result += f"<span style='color: {color};'>{text[pointer:start]}</span>"


        result += sub_text


        pointer = end

    if pointer < len(text):
        result += f"<span style='color: {color};'>{text[pointer:]}</span>"

    return result


def warmup(model):
    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
    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).cuda()
    for output_ids in model.ea_generate(input_ids):
        ol=output_ids.shape[1]

def bot(history, session_state):
    temperature = 0.5
    top_p = 0.9
    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

    for query, response in pure_history:
        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)

    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).cuda()
    input_len = input_ids.shape[1]
    naive_text = []
    cu_len = input_len
    totaltime=0
    start_time=time.time()
    total_ids=0


    for output_ids in model.ea_generate(input_ids, temperature=temperature, top_p=top_p,
                                        max_steps=args.max_new_token):
        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_ExInfer:
        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()





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):

    try:

        if not history:
            return history, None,"0.00 tokens/s","0.00",session_state
        pure_history = session_state.get("pure_history", [])
        try:
            pure_history[-1][-1] = None
        except:
            print(1)
        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

    except:
        print(2)
        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=".",
    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="lmsys/vicuna-7b-v1.3",
                    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="vicuna", help="llama-2-chat or vicuna, for chat template")
parser.add_argument(
    "--max-new-token",
    type=int,
    default=512,
    help="The maximum number of new generated tokens.",
)
args = parser.parse_args()

model = EaModel.from_pretrained(
    base_model_path=args.base_model_path,
    ea_model_path=args.ea_model_path,
    torch_dtype=torch.float16,
    low_cpu_mem_usage=True,
    load_in_4bit=args.load_in_4bit,
    load_in_8bit=True,
    device_map="auto"
)
model.eval()
warmup(model)

custom_css = """
#speed textarea {
    color: red;   
    font-size: 30px; 
}"""


with gr.Blocks(css=custom_css) as demo:
    gs=gr.State({"pure_history":[]})
    gr.Markdown('''## EAGLE 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")
    note1 = gr.Markdown(show_label=False,
                       value='''The Compression Ratio is defined as the number of generated tokens divided by the number of forward passes in the original LLM. The original LLM is Vicuna 7B, with inference conducted on a T4 GPU and at a precision of int8.''')
    note=gr.Markdown(show_label=False,value='''The tokens that EAGLE correctly guesses will be highlighted in orange. Note: This highlighting may lead to special formatting rendering issues in some instances, particularly when generating code.''')


    chatbot = gr.Chatbot(height=600,show_label=False)


    msg = gr.Textbox(label="Your input")
    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,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,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,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()