InternLM2 Demo初體驗-書生浦語大模型實戰營學習筆記2

香草阿草發表於2024-03-30

本文包括第二期實戰營的第2課內容。本來是想給官方教程做做補充的，沒想到官方教程的質量還是相當高的，跟著一步一步做基本上沒啥坑。所以這篇筆記主要是拆解一下InternStudio封裝的一些東西，防止在本地復現時出現各種問題。

搭建環境

首先是搭建環境這裡，官方教程說：

進入開發機後，在 `terminal` 中輸入環境配置命令 (配置環境時間較長，需耐心等待)：

studio-conda -o internlm-base -t demo
# 與 studio-conda 等效的配置方案
# conda create -n demo python==3.10 -y
# conda activate demo
# conda install pytorch==2.0.1 torchvision==0.15.2 torchaudio==2.0.2 pytorch-cuda=11.7 -c pytorch -c nvidia

`studio-conda` 探秘

那麼，這句studio-conda -o internlm-base -t demo究竟是什麼呢？我們直接檢視一下/root/.bashrc，發現裡面就一句：

source /share/.aide/config/bashrc

繼續檢視/share/.aide/config/bashrc，這個可長了，這裡給出最後兩句：

export HF_ENDPOINT='https://hf-mirror.com'
alias studio-conda="/share/install_conda_env.sh"
alias studio-smi="/share/studio-smi"

點選檢視/share/.aide/config/bashrc的全部程式碼

#! /bin/bash

# ~/.bashrc: executed by bash(1) for non-login shells.
# see /usr/share/doc/bash/examples/startup-files (in the package bash-doc)
# for examples

# If not running interactively, don't do anything
case $- in
    *i*) ;;
      *) return;;
esac

# don't put duplicate lines or lines starting with space in the history.
# See bash(1) for more options
HISTCONTROL=ignoreboth

# append to the history file, don't overwrite it
shopt -s histappend

# for setting history length see HISTSIZE and HISTFILESIZE in bash(1)
HISTSIZE=1000
HISTFILESIZE=2000

# check the window size after each command and, if necessary,
# update the values of LINES and COLUMNS.
shopt -s checkwinsize

# If set, the pattern "**" used in a pathname expansion context will
# match all files and zero or more directories and subdirectories.
#shopt -s globstar

# make less more friendly for non-text input files, see lesspipe(1)
[ -x /usr/bin/lesspipe ] && eval "$(SHELL=/bin/sh lesspipe)"

# set variable identifying the chroot you work in (used in the prompt below)
if [ -z "${debian_chroot:-}" ] && [ -r /etc/debian_chroot ]; then
    debian_chroot=$(cat /etc/debian_chroot)
fi

# set a fancy prompt (non-color, unless we know we "want" color)
case "$TERM" in
    xterm-color|*-256color) color_prompt=yes;;
esac

# uncomment for a colored prompt, if the terminal has the capability; turned
# off by default to not distract the user: the focus in a terminal window
# should be on the output of commands, not on the prompt
#force_color_prompt=yes

if [ -n "$force_color_prompt" ]; then
    if [ -x /usr/bin/tput ] && tput setaf 1 >&/dev/null; then
	# We have color support; assume it's compliant with Ecma-48
	# (ISO/IEC-6429). (Lack of such support is extremely rare, and such
	# a case would tend to support setf rather than setaf.)
	color_prompt=yes
    else
	color_prompt=
    fi
fi

if [ "$color_prompt" = yes ]; then
    PS1='${debian_chroot:+($debian_chroot)}\[\033[01;32m\]\u@\h\[\033[00m\]:\[\033[01;34m\]\w\[\033[00m\]\$ '
else
    PS1='${debian_chroot:+($debian_chroot)}\u@\h:\w\$ '
fi
unset color_prompt force_color_prompt

# If this is an xterm set the title to user@host:dir
case "$TERM" in
xterm*|rxvt*)
    PS1="\[\e]0;${debian_chroot:+($debian_chroot)}\u@\h: \w\a\]$PS1"
    ;;
*)
    ;;
esac

# enable color support of ls and also add handy aliases
if [ -x /usr/bin/dircolors ]; then
    test -r ~/.dircolors && eval "$(dircolors -b ~/.dircolors)" || eval "$(dircolors -b)"
    alias ls='ls --color=auto'
    #alias dir='dir --color=auto'
    #alias vdir='vdir --color=auto'

    alias grep='grep --color=auto'
    alias fgrep='fgrep --color=auto'
    alias egrep='egrep --color=auto'
fi

# colored GCC warnings and errors
#export GCC_COLORS='error=01;31:warning=01;35:note=01;36:caret=01;32:locus=01:quote=01'

# some more ls aliases
alias ll='ls -alF'
alias la='ls -A'
alias l='ls -CF'

# Add an "alert" alias for long running commands.  Use like so:
#   sleep 10; alert
alias alert='notify-send --urgency=low -i "$([ $? = 0 ] && echo terminal || echo error)" "$(history|tail -n1|sed -e '\''s/^\s*[0-9]\+\s*//;s/[;&|]\s*alert$//'\'')"'

# Alias definitions.
# You may want to put all your additions into a separate file like
# ~/.bash_aliases, instead of adding them here directly.
# See /usr/share/doc/bash-doc/examples in the bash-doc package.

if [ -f ~/.bash_aliases ]; then
    . ~/.bash_aliases
fi

# enable programmable completion features (you don't need to enable
# this, if it's already enabled in /etc/bash.bashrc and /etc/profile
# sources /etc/bash.bashrc).
if ! shopt -oq posix; then
  if [ -f /usr/share/bash-completion/bash_completion ]; then
    . /usr/share/bash-completion/bash_completion
  elif [ -f /etc/bash_completion ]; then
    . /etc/bash_completion
  fi
fi

# >>> conda initialize >>>
# !! Contents within this block are managed by 'conda init' !!
__conda_setup="$('/root/.conda/condabin/conda' 'shell.bash' 'hook' 2> /dev/null)"
if [ $? -eq 0 ]; then
    eval "$__conda_setup"
else
    if [ -f "/root/.conda/etc/profile.d/conda.sh" ]; then
        . "/root/.conda/etc/profile.d/conda.sh"
    else
        export PATH="/root/.conda/condabin:$PATH"
    fi
fi
unset __conda_setup
# <<< conda initialize <<<

if [ -d "/root/.conda/envs/xtuner" ]; then
  CONDA_ENV=xtuner
else
  CONDA_ENV=base
fi

source activate $CONDA_ENV

cat /share/.aide/config/welcome_vgpu

#if [ $CONDA_ENV != "xtuner" ]; then
#  echo -e """
#  \033[31m 檢測到您尚未初始化xtuner環境, 建議執行> source init_xtuner_env.sh \033[0m
#  """
#fi
export https_proxy=http://proxy.intern-ai.org.cn:50000
export http_proxy=http://proxy.intern-ai.org.cn:50000
export no_proxy='localhost,127.0.0.1,0.0.0.0,172.18.47.140'
export PATH=/root/.local/bin:$PATH
export HF_ENDPOINT='https://hf-mirror.com'
alias studio-conda="/share/install_conda_env.sh"
alias studio-smi="/share/studio-smi"

注意到倒數第二行：alias studio-conda="/share/install_conda_env.sh"，也就是說studio-conda是/share/install_conda_env.sh的別名。我們在執行studio-conda -o internlm-base -t demo的時候，實際上呼叫的是/share/install_conda_env.sh這個指令碼。我們進一步檢視/share/install_conda_env.sh：

HOME_DIR=/root
CONDA_HOME=$HOME_DIR/.conda
SHARE_CONDA_HOME=/share/conda_envs
SHARE_HOME=/share

    echo -e "\033[34m [1/2] 開始安裝conda環境: <$target>. \033[0m"
    sleep 3
    tar --skip-old-files -xzvf /share/pkgs.tar.gz -C ${CONDA_HOME}
    wait_echo&
    wait_pid=$!
    conda create -n $target --clone ${SHARE_CONDA_HOME}/${source}
    if [ $? -ne 0 ]; then
        echo -e "\033[31m 初始化conda環境: ${target}失敗 \033[0m"
        exit 10
    fi

    kill $wait_pid

    # for xtuner, re-install dependencies
    case "$source" in
    xtuner)
        source_install_xtuner $target
        ;;
    esac

    echo -e "\033[34m [2/2] 同步當前conda環境至jupyterlab kernel \033[0m"
    lab add $target
    source $CONDA_HOME/bin/activate $target
    cd $HOME_DIR

點選檢視/share/install_conda_env.sh的全部程式碼

#!/bin/bash
# clone internlm-base conda env to user's conda env
# created by xj on 01.07.2024
# modifed by xj on 01.19.2024 to fix bug of conda env clone
# modified by ljy on 01.26.2024 to extend

XTUNER_UPDATE_DATE=`cat /share/repos/UPDATE | grep xtuner |awk -F= '{print $2}'`
HOME_DIR=/root
CONDA_HOME=$HOME_DIR/.conda
SHARE_CONDA_HOME=/share/conda_envs
SHARE_HOME=/share

list() {
    cat <<-EOF
  預設環境          描述

  internlm-base    pytorch:2.0.1, pytorch-cuda:11.7
  xtuner           Xtuner(原始碼安裝: main $(echo -e "\033[4mhttps://github.com/InternLM/xtuner/tree/main\033[0m"), 更新日期：$XTUNER_UPDATE_DATE)
  pytorch-2.1.2    pytorch:2.1.2, pytorch-cuda:11.8
EOF
}


help() {
    cat <<-EOF
  說明: 用於快速clone預設的conda環境

  使用: 
    1. studio-conda env -l/list 列印預設的conda環境列表
    2. studio-conda <target-conda-name> 快速clone: 預設複製internlm-base conda環境
    3. studio-conda -t <target-conda-name> -o <origin-conda-name> 將預設的conda環境複製到指定的conda環境
        
EOF
}

clone() {
    source=$1
    target=$2

    if [[ -z "$source" || -z "$target" ]]; then
        echo -e "\033[31m 輸入不符合規範 \033[0m"
        help
        exit 1
    fi

    if [ ! -d "${SHARE_CONDA_HOME}/$source" ]; then
        echo -e "\033[34m 指定的預設環境: $source不存在\033[0m"
        list
        exit 1
    fi

    if [ -d "${CONDA_HOME}/envs/$target" ]; then
        echo -e "\033[34m 指定conda環境的目錄: ${CONDA_HOME}/envs/$target已存在, 將清空原目錄安裝 \033[0m"
        wait_echo&
        wait_pid=$!
        rm -rf "${CONDA_HOME}/envs/$target"
        kill $wait_pid
    fi

    echo -e "\033[34m [1/2] 開始安裝conda環境: <$target>. \033[0m"
    sleep 3
    tar --skip-old-files -xzvf /share/pkgs.tar.gz -C ${CONDA_HOME}
    wait_echo&
    wait_pid=$!
    conda create -n $target --clone ${SHARE_CONDA_HOME}/${source}
    if [ $? -ne 0 ]; then
        echo -e "\033[31m 初始化conda環境: ${target}失敗 \033[0m"
        exit 10
    fi

    kill $wait_pid

    # for xtuner, re-install dependencies
    case "$source" in
    xtuner)
        source_install_xtuner $target
        ;;
    esac

    echo -e "\033[34m [2/2] 同步當前conda環境至jupyterlab kernel \033[0m"
    lab add $target
    source $CONDA_HOME/bin/activate $target
    cd $HOME_DIR

    echo -e "\033[32m conda環境: $target安裝成功! \033[0m"

    echo """
    ============================================
                    ALL DONE!
    ============================================
    """
}

source_install_xtuner() {
    conda_env=$1
    echo -e "\033[34m 原始碼安裝xtuner... \033[0m"
    sleep 2
    pip config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple

    install=0
    if [ -d "${HOME_DIR}/xtuner" ]; then
        read -r -p "$HOME_DIR中已存在目錄xtuner: 是否清空目錄? [Y/N][yes/no]" input
	case $input in
          [yY][eE][sS]|[yY])
            echo -e "\033[34m 清空目錄: $HOME_DIR/xtuner, 並同步原始碼至該目錄進行原始碼安裝... \033[0m"
	    install=1
	    ;;
	  *)
	    echo -e "\033[34m 嘗試使用: $HOME_DIR/xtuner目錄進行原始碼安裝... \033[0m" 
	    ;;
        esac
    else
        install=1
    fi
    
    if [ $install -eq 1 ]; then
        rm -rf $HOME_DIR/xtuner
	mkdir -p $HOME_DIR/xtuner
	cp -rf $SHARE_HOME/repos/xtuner/* $HOME_DIR/xtuner/
    fi

    cd $HOME_DIR/xtuner

    $CONDA_HOME/envs/$conda_env/bin/pip install -e '.[all]'
    if [ $? -ne 0 ]; then
        echo -e "\033[31m 原始碼安裝xtuner失敗 \033[0m"
	exit 10
    fi
    $CONDA_HOME/envs/$conda_env/bin/pip install cchardet
    $CONDA_HOME/envs/$conda_env/bin/pip install -U datasets
}


wait_echo() {
    local i=0
    local sp='/-\|'
    local n=${#sp}
    printf ' '
    while sleep 0.1; do
        printf '\b%s' "${sp:i++%n:1}"
    done
}

dispatch() {

    if [ $# -lt 1 ]; then
        help
        exit -2
    fi

    if [ $1 == "env" ]; then
        list
        exit 0
    fi

    if [[ $1 == "-h" || $1 == "help" ]]; then
        help
        exit 0
    fi

    origin_env=
    target_env=
    if [ $# -eq 1 ]; then
        origin_env=internlm-base
        target_env=$1
    else
        while getopts t:o: flag; do
            case "${flag}" in
            t) target_env=${OPTARG} ;;
            o) origin_env=${OPTARG} ;;
            esac
        done
    fi

    echo -e "\033[32m 預設環境: $origin_env \033[0m"
    echo -e "\033[32m 目標conda環境名稱: $target_env \033[0m"
    sleep 3
    clone $origin_env $target_env
}

dispatch $@

這個檔案就是它設定程式碼環境的了。指令碼里面定義了幾個變數和函式，之後就直接呼叫dispatch函式了。之後的流程如下：

因為我們給的引數是-o internlm-base -t demo，所以會直接從dispatch這裡執行指令碼中的clone函式，引數是 internlm-base demo。
CONDA_HOME會透過HOME_DIR=/root; CONDA_HOME=$HOME_DIR/.conda指定為/root/.conda，即工作區下的資料夾。
然後，將/share/pkgs.tar.gz解壓至目錄，再透過conda create clone的方式克隆環境完成環境的搭建。

所以這個命令實際上是將預配置好的環境打包解壓克隆了一遍，和教程中的等效程式碼還是有較大不同的。

然後需要我們執行以下程式碼配置環境。輕輕吐槽一下既然都是直接解壓並conda clone了，為什麼不直接做一個裝好這些庫的conda環境壓縮包。

conda activate demo
pip install huggingface-hub==0.17.3
pip install transformers==4.34 
pip install psutil==5.9.8
pip install accelerate==0.24.1
pip install streamlit==1.32.2 
pip install matplotlib==3.8.3 
pip install modelscope==1.9.5
pip install sentencepiece==0.1.99

下載模型

再透過呼叫modelscope.hub.snapshot_download從modelscope下載模型：

import os
from modelscope.hub.snapshot_download import snapshot_download

os.system("mkdir /root/models")
save_dir="/root/models"

snapshot_download("Shanghai_AI_Laboratory/internlm2-chat-1_8b", 
                  cache_dir=save_dir, revision='v1.1.0')

有一說一，官方教程新建資料夾這裡不呼叫os.mkdir而是直接os.system("mkdir /root/models")真是個bad practice，別學。

模型推理

使用以下程式碼完成模型推理：

# 匯入相關的庫
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name_or_path = "/root/models/Shanghai_AI_Laboratory/internlm2-chat-1_8b"

# Hugging Face 的 AutoTokenizer 和 AutoModelForCausalLM 類熟悉大模型的不會陌生，用於自動載入預訓練模型和相應的tokenizer。
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, trust_remote_code=True, device_map='cuda:0')
# 相信遠端程式碼以便從HuggingFace拉取確實模型權重，使用bf16量化節省記憶體，指定使用第一張顯示卡
model = AutoModelForCausalLM.from_pretrained(model_name_or_path, trust_remote_code=True, torch_dtype=torch.bfloat16, device_map='cuda:0')
model = model.eval()

system_prompt = """You are an AI assistant whose name is InternLM (書生·浦語).
- InternLM (書生·浦語) is a conversational language model that is developed by Shanghai AI Laboratory (上海人工智慧實驗室). It is designed to be helpful, honest, and harmless.
- InternLM (書生·浦語) can understand and communicate fluently in the language chosen by the user such as English and 中文.
"""

messages = [(system_prompt, '')]

print("=============Welcome to InternLM chatbot, type 'exit' to exit.=============")

while True:
    input_text = input("\nUser  >>> ")
    input_text = input_text.replace(' ', '')  # 移除使用者輸入文字中的空格
    if input_text == "exit":  # 如果要退出，輸入exit即可
        break

    length = 0
	# 對模型的 stream_chat 方法進行迭代，該方法會生成一個對話的生成器。迭代過程中，每次生成一個回覆訊息 response 和一個佔位符 _。
    for response, _ in model.stream_chat(tokenizer, input_text, messages):
		# 如果回覆訊息不為空，則列印回覆訊息中從上次列印位置 length 開始到結尾的部分，並重新整理輸出緩衝區。
        if response is not None:
            print(response[length:], flush=True, end="")
			# 更新上次列印的位置，以便下一次列印時從正確位置開始。
            length = len(response)

基礎作業執行結果

輸入命令，執行 Demo 程式：

conda activate demo
python /root/demo/cli_demo.py

基礎作業

基礎作業還是輕輕又鬆鬆啊哈哈哈哈。。。不過其實之前模型輸出崩壞過一次：

崩壞的模型輸出

對的，模型直接給了30個故事的名字。我直接掐斷了模型的輸出。

伺服器顯示卡資訊

出於好奇看了看顯示卡資訊：

顯示卡資訊

原來真的是A100啊，不過很好奇他們是怎麼控制單個開發機的視訊記憶體開銷為10%、30%、50%的了。哈哈哈哈哈哈哈哈。

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