WangRongsheng/Aurora

import gradio as gr
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, StoppingCriteria, StoppingCriteriaList, TextIteratorStreamer
from threading import Thread
from peft import PeftModel
import time

# download base model weights
# https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1
# or
# https://modelscope.cn/models/AI-ModelScope/Mixtral-8x7B-Instruct-v0.1
model_name_or_path = "mistralai/Mixtral-8x7B-Instruct-v0.1"

# download lora model weights
# https://huggingface.co/wangrongsheng/Aurora
# or
# https://modelscope.cn/models/wangrongsheng/Aurora-Mixtral-8x7B
lora_weights = "wangrongsheng/Aurora"

tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
model0 = AutoModelForCausalLM.from_pretrained(model_name_or_path, load_in_4bit=True, device_map="auto", torch_dtype=torch.bfloat16)
model = PeftModel.from_pretrained(
    model0,
    lora_weights,
)

class StopOnTokens(StoppingCriteria):
    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
        stop_ids = [0,]
        for stop_id in stop_ids:
            if input_ids[0][-1] == stop_id:
                return True
        return False

def convert_history_to_text(history):
    text = ""
    if len(history) > 1:
        text = "<s> " + "".join(
                [
                    "".join(
                        [
                            f"[INST]{item[0]}[/INST] {item[1]} ",
                        ]
                    )
                    for item in history[:-1]
                ]
            ) + "</s> "
    text += "".join(
        [
            "".join(
                [
                    f"[INST]{history[-1][0]}[/INST]",
                ]
            )
        ]
    )
    return text

def predict(message, history):
    history_transformer_format = history + [[message, ""]]
    stop = StopOnTokens()

    messages = convert_history_to_text(history_transformer_format)

    model_inputs = tokenizer([messages], return_tensors="pt").to("cuda")
    streamer = TextIteratorStreamer(tokenizer, timeout=10., skip_prompt=True, skip_special_tokens=True)
    generate_kwargs = dict(
        model_inputs,
        streamer=streamer,
        max_new_tokens=4096,
        do_sample=True,
        top_p=0.95,
        top_k=1000,
        temperature=1.0,
        num_beams=1,
        pad_token_id=tokenizer.eos_token_id,
        stopping_criteria=StoppingCriteriaList([stop])
        )
    t = Thread(target=model.generate, kwargs=generate_kwargs)
    t.start()

    partial_message  = ""
    t1 = time.time()
    count = 0
    for new_token in streamer:
        if new_token != '<':
            partial_message += new_token
            count += 1
            yield partial_message
    t2 = time.time()
    speed = count/(t2-t1)
    print("inference speed: %f tok/s" % speed)

gr.ChatInterface(predict,chatbot=gr.Chatbot(height=600,),title="MoE").queue().launch()

CUDA_VISIBLE_DEVICES=5 python   src/train_bash.py \
    --stage sft \
    --model_name_or_path ./Mixtral-8x7B-Instruct-v0.1 \
    --do_train \
    --dataset alpaca_zh,alpaca_gpt4_zh,sharegpt \
    --finetuning_type lora \
    --quantization_bit 4 \
    --overwrite_cache \
    --output_dir output/ \
    --per_device_train_batch_size 2 \
    --gradient_accumulation_steps 4 \
    --lr_scheduler_type cosine \
    --logging_steps 100 \
    --save_steps 1000 \
    --learning_rate 5e-5 \
    --num_train_epochs 3.0 \
    --plot_loss \
    --fp16 \
    --template mistral \
    --lora_target q_proj,v_proj

CUDA_VISIBLE_DEVICES=0 python src/evaluate.py \
    --model_name_or_path ./Mixtral-8x7B-Instruct-v0.1 \
    --checkpoint_dir Aurora/checkpoint-5000 \
    --finetuning_type lora \
    --quantization_bit 4 \
    --template mistral \
    --task cmmlu \ # cmmlu, mmlu, ceval
    --split test \
    --lang en \ # zh, en
    --n_shot 5 \
    --batch_size 8