本文将带大家手把手使用mlc-llm将 InternLM2.5 部署到安卓手机上
首先我们来看一下最终的效果～
参考 https://forge.rust-lang.org/infra/other-installation-methods.html#which
使用了国内的镜像，出现选项直接Enter
export RUSTUP_DIST_SERVER=https://mirrors.ustc.edu.cn/rust-static
export RUSTUP_UPDATE_ROOT=https://mirrors.ustc.edu.cn/rust-static/rustup
curl --proto '=https' --tlsv1.2 -sSf https://mirrors.ustc.edu.cn/misc/rustup-install.sh | sh
参考 https://developer.android.com/studio
mkdir -p /root/android && cd /root/android
wget https://redirector.gvt1.com/edgedl/android/studio/ide-zips/2024.1.1.12/android-studio-2024.1.1.12-linux.tar.gz
tar -xvzf android-studio-2024.1.1.12-linux.tar.gz
cd android-studio
wget https://dl.google.com/android/repository/commandlinetools-linux-11076708_latest.zip?hl=zh-cn
unzip commandlinetools-linux-11076708_latest.zip\?hl\=zh-cn
export JAVA_HOME=/root/android/android-studio/jbr
cmdline-tools/bin/sdkmanager "ndk;27.0.12077973" "cmake;3.22.1" "platforms;android-34" "build-tools;33.0.1" --sdk_root='sdk'
. "$HOME/.cargo/env"
export ANDROID_NDK=/root/android/android-studio/sdk/ndk/27.0.12077973
export TVM_NDK_CC=$ANDROID_NDK/toolchains/llvm/prebuilt/linux-x86_64/bin/aarch64-linux-android24-clang
export JAVA_HOME=/root/android/android-studio/jbr
export ANDROID_HOME=/root/android/android-studio/sdk
export PATH=/usr/local/cuda-12/bin:$PATH
export PATH=/root/android/android-studio/sdk/cmake/3.22.1/bin:$PATH
参考https://llm.mlc.ai/docs/install/mlc_llm.html，安装mlc-llm可能需要代理
安装pytorch部分也可以使用其他包含torch的conda环境
conda create --name mlc-prebuilt python=3.11
conda activate mlc-prebuilt
conda install -c conda-forge git-lfs
conda install pytorch torchvision torchaudio pytorch-cuda=12.1 -c pytorch -c nvidia
python -m pip install --pre -U -f https://mlc.ai/wheels mlc-llm-nightly-cu122 mlc-ai-nightly-cu122
测试如下输出说明安装正确
python -c "import mlc_llm; print(mlc_llm)"
克隆项目
git clone https://github.com/mlc-ai/mlc-llm.git
cd mlc-llm
git submodule update --init --recursive
（如果不想上传到huggingface可以跳过这一步，有公开上传的）
使用 mlc_llm 的 convert_weight 对模型参数进行转换和量化，转换后的参数可以跨平台使用
mkdir -p /root/models/
ln -s /share/new_models/Shanghai_AI_Laboratory/internlm2_5-1_8b-chat /root/models/internlm2_5-1_8b-chat
cd android/MLCChat
export TVM_SOURCE_DIR=/root/android/mlc-llm/3rdparty/tvm
export MLC_LLM_SOURCE_DIR=/root/android/mlc-llm
mlc_llm convert_weight /root/models/internlm2_5-1_8b-chat/ \
--quantization q4f16_1 \
-o dist/internlm2_5-1_8b-chat-q4f16_1-MLC
（如果不想上传到huggingface可以跳过这一步，有公开上传的）
使用 mlc_llm 的 gen_config 生成 mlc-chat-config.json 并处理 tokenizer
出现提示时输入y
mlc_llm gen_config /root/models/internlm2_5-1_8b-chat/ \
--quantization q4f16_1 --conv-template chatml \
-o dist/internlm2_5-1_8b-chat-q4f16_1-MLC
Do you wish to run the custom code? [y/N] y
上传这一步需要能访问huggingface，可能需要部署代理并耗费一定流量
具体方法可以参考网上的大量教程
如果不想上传到huggingface可以跳过这一步，直接在接下来的配置中使用如下链接的模型（和文档中的转换方法一样） https://huggingface.co/timws/internlm2_5-1_8b-chat-q4f16_1-MLC
在打包之前可以测试模型效果，需要编译成二进制文件
在个人电脑上运行测试代码正常，InternStudio上暂未成功
mlc_llm compile ./dist/internlm2_5-1_8b-chat-q4f16_1-MLC/mlc-chat-config.json \
--device cuda -o dist/libs/internlm2_5-1_8b-chat-q4f16_1-MLC-cuda.so
测试编译的模型是否符合预期，手机端运行的效果和测试效果接近
from mlc_llm import MLCEngine
# Create engine
engine = MLCEngine(model="./dist/internlm2_5-1_8b-chat-q4f16_1-MLC", model_lib="./dist/libs/internlm2_5-1_8b-chat-q4f16_1-MLC-cuda.so")
# Run chat completion in OpenAI API.
print(engine)
for response in engine.chat.completions.create(
messages=[{"role": "user", "content": "你是谁？"}],
stream=True
):
for choice in response.choices:
print(choice.delta.content, end="", flush=True)
print("\n")
engine.terminate()修改mlc-package-config.json
参考如下
{
"device": "android",
"model_list": [
{
"model": "HF://timws/internlm2_5-1_8b-chat-q4f16_1-MLC",
"estimated_vram_bytes": 3980990464,
"model_id": "internlm2_5-1_8b-chat-q4f16_1-MLC"
},
{
"model": "HF://mlc-ai/gemma-2b-it-q4f16_1-MLC",
"model_id": "gemma-2b-q4f16_1-MLC",
"estimated_vram_bytes": 3980990464
}
]
}
这一步需要能访问huggingface，可能需要部署代理
mlc_llm package
cd /root/android/mlc-llm/android/MLCChat
/root/android/android-studio/jbr/bin/keytool -genkey -v -keystore my-release-key.jks -keyalg RSA -keysize 2048 -validity 10000
Enter keystore password:
Re-enter new password:
What is your first and last name?
[Unknown]: Any
What is the name of your organizational unit?
[Unknown]: Any
What is the name of your organization?
[Unknown]: Any
What is the name of your City or Locality?
[Unknown]: Any
What is the name of your State or Province?
[Unknown]: Any
What is the two-letter country code for this unit?
[Unknown]: CN
Is CN=Any, OU=Any, O=Any, L=Any, ST=Any, C=CN correct?
[no]: yes
Generating 2,048 bit RSA key pair and self-signed certificate (SHA256withRSA) with a validity of 10,000 days
for: CN=Any, OU=Any, O=Any, L=Any, ST=Any, C=CN
[Storing my-release-key.jks]
如果是本地可以WIFI或USB调试不用签名，在服务器构建需要签名
修改app/build.gradle为如下内容，主要是增加了签名部分，注意确认签名文件的位置
plugins {
id 'com.android.application'
id 'org.jetbrains.kotlin.android'
}
android {
namespace 'ai.mlc.mlcchat'
compileSdk 34
defaultConfig {
applicationId "ai.mlc.mlcchat"
minSdk 26
targetSdk 33
versionCode 1
versionName "1.0"
testInstrumentationRunner "androidx.test.runner.AndroidJUnitRunner"
vectorDrawables {
useSupportLibrary true
}
}
compileOptions {
sourceCompatibility JavaVersion.VERSION_1_8
targetCompatibility JavaVersion.VERSION_1_8
}
kotlinOptions {
jvmTarget = '1.8'
}
buildFeatures {
compose true
}
composeOptions {
kotlinCompilerExtensionVersion '1.4.3'
}
packagingOptions {
resources {
excludes += '/META-INF/{AL2.0,LGPL2.1}'
}
}
signingConfigs {
release {
storeFile file("/root/android/mlc-llm/android/MLCChat/my-release-key.jks")
storePassword "123456"
keyAlias "mykey"
keyPassword "123456"
}
}
buildTypes {
release {
minifyEnabled false
proguardFiles getDefaultProguardFile('proguard-android-optimize.txt'), 'proguard-rules.pro'
signingConfig signingConfigs.release
}
}
}
dependencies {
implementation project(":mlc4j")
implementation 'androidx.core:core-ktx:1.10.1'
implementation 'androidx.lifecycle:lifecycle-runtime-ktx:2.6.1'
implementation 'androidx.activity:activity-compose:1.7.1'
implementation platform('androidx.compose:compose-bom:2022.10.00')
implementation 'androidx.lifecycle:lifecycle-viewmodel-compose:2.6.1'
implementation 'androidx.compose.ui:ui'
implementation 'androidx.compose.ui:ui-graphics'
implementation 'androidx.compose.ui:ui-tooling-preview'
implementation 'androidx.compose.material3:material3:1.1.0'
implementation 'androidx.compose.material:material-icons-extended'
implementation 'androidx.appcompat:appcompat:1.6.1'
implementation 'androidx.navigation:navigation-compose:2.5.3'
implementation 'com.google.code.gson:gson:2.10.1'
implementation fileTree(dir: 'src/main/libs', include: ['*.aar', '*.jar'], exclude: [])
testImplementation 'junit:junit:4.13.2'
androidTestImplementation 'androidx.test.ext:junit:1.1.5'
androidTestImplementation 'androidx.test.espresso:espresso-core:3.5.1'
androidTestImplementation platform('androidx.compose:compose-bom:2022.10.00')
androidTestImplementation 'androidx.compose.ui:ui-test-junit4'
debugImplementation 'androidx.compose.ui:ui-tooling'
debugImplementation 'androidx.compose.ui:ui-test-manifest'
}
运行编译命令，完成后在app/build/outputs/apk/release生成app-release.apk安装包，下载到手机上运行
运行App需要能访问huggingface下载模型(参考文档中的bundle方法需要ADB刷入模型数据)
./gradlew assembleRelease
- 运行App需要能访问huggingface下载模型
- 需要大概4G运行内存
- 如果运行闪退，和可能是下载不完整可以删除重新下载