Building Device-side MindSpore Lite

This chapter introduces how to quickly compile MindSpore Lite, which includes the following modules:

Modules in MindSpore Lite:

Module	Support Platform	Description
converter	Linux, Windows	Model Conversion Tool
runtime(cpp, java)	Linux, Windows, Android, iOS, OpenHarmony(OHOS)	Model Inference Framework(Windows platform does not support java version runtime)
benchmark	Linux, Windows, Android, OpenHarmony(OHOS)	Benchmarking Tool
benchmark_train	Linux, Android	Performance and Accuracy Validation
cropper	Linux	Static library crop tool for libmindspore-lite.a
minddata	Linux, Android	Image Processing Library
codegen	Linux	Model inference code generation tool
obfuscator	Linux	Model Obfuscation Tool

Linux Environment Compilation

Environment Requirements

The compilation environment supports Linux x86_64 only. Ubuntu 18.04.02 LTS is recommended.
Compilation dependencies of cpp:
- GCC >= 7.3.0
- CMake >= 3.18.3
- Git >= 2.28.0
- Android_NDK >= r20
  - Configure environment variables: export ANDROID_NDK=NDK path.
- OpenHarmony_NDK
  - Download OpenHarmony NDK：Go to OpenHarmony daily build web site，select “modality” as “ohos-sdk”，then select any successfully built package to download. After downloading, the files in the unzipped package that begin with ‘native’ are OpenHarmony NDK.
  - Configure environment variables: export OHOS_NDK=OHOS NDK path, export TOOLCHAIN_NAME=ohos.
Compilation dependency of the Java API module (optional). If the JAVA_HOME environment variable is not set, this module will not be compiled:
- Gradle >= 6.6.1
  - Configure environment variables: export GRADLE_HOME=GRADLE path and export GRADLE_USER_HOME=GRADLE path.
  - Add the bin directory to the PATH: export PATH=${GRADLE_HOME}/bin:$PATH.
- Maven >= 3.3.1
  - Configure environment variables: export MAVEN_HOME=MAVEN path.
  - Add the bin directory to the PATH: export PATH=${MAVEN_HOME}/bin:$PATH.
- OpenJDK 1.8 to 1.15
  - Configure environment variables: export JAVA_HOME=JDK path.
  - Add the bin directory to the PATH: export PATH=${JAVA_HOME}/bin:$PATH.
- Android SDK
  - Create a new directory, configure environment variablesexport ANDROID_SDK_ROOT=new directory.
  - Download SDK Tools, uncompress and go to directory cmdline-tools/bin, create SDK through sdkmanager: ./sdkmanager --sdk_root=${ANDROID_SDK_ROOT} "cmdline-tools;latest".
  - Accept the license through sdkmanager under the ${ANDROID_SDK_ROOT} directory: yes | ./sdkmanager --licenses.
Compilation dependency of the Python API module (optional). If Python3 or NumPy is not installed, this module will not be compiled:
- Python >= 3.7.0
- NumPy >= 1.17.0 (If the installation with pip fails, please upgrade the pip version first: python -m pip install -U pip)
- wheel >= 0.32.0 (If the installation with pip fails, please upgrade the pip version first: python -m pip install -U pip)

Gradle is recommended to use the gradle-6.6.1-complete version. If you configure other versions, gradle will automatically download gradle-6.6.1-complete by gradle wrapper mechanism.

You can also directly use the Docker compilation image that has been configured with the above dependencies.

Download the docker image: docker pull swr.cn-south-1.myhuaweicloud.com/mindspore-build/mindspore-lite:ubuntu18.04.2-20210530

Create a container: docker run -tid --net=host --name=docker01 swr.cn-south-1.myhuaweicloud.com/mindspore-build/mindspore-lite:ubuntu18.04.2-20210530

Enter the container: docker exec -ti -u 0 docker01 bash

Compilation Options

The script build.sh in the root directory of MindSpore can be used to compile MindSpore Lite.

Instructions for parameters of `build.sh`

Parameter	Parameter Description	Value Range	Defaults
-I	Selects an applicable architecture.	arm64, arm32, or x86_64	None
-A	Compile AAR package (including arm32 and arm64).	on, off	off
-d	If this parameter is set, the debug version is compiled. Otherwise, the release version is compiled.	None	None
-i	If this parameter is set, incremental compilation is performed. Otherwise, full compilation is performed.	None	None
-j[n]	Sets the number of threads used during compilation. Otherwise, the number of threads is set to 8 by default.	Integer	8
-a	Whether to enable AddressSanitizer	on, off	off

When compiling the x86_64 version, if the JAVA_HOME environment variable is configured and Gradle is installed, the JAR package will be compiled at the same time.

When the -I parameter changes, such as -I x86_64 is converted to -I arm64, adding -i for parameter compilation does not take effect.

When compiling the AAR package, the -A on parameter must be added, and there is no need to add the -I parameter.

Module build compilation options

The construction of modules is controlled by environment variables. Users can control the modules built by compiling by declaring relevant environment variables. After the compilation option is modified, in order to make the option effective, the -i parameter cannot be added for incremental compilation when compiling with the build.sh script.

General module compilation options

Option	Parameter Description	Value Range	Defaults
MSLITE_GPU_BACKEND	Set the GPU backend, only opencl is valid when the target OS is not OpenHarmony and `-I arm64`, and only tensorrt is valid when `-I x86_64`	opencl, tensorrt, off	opencl when `-I arm64`, off when `-I x86_64`
MSLITE_ENABLE_NPU	Whether to compile NPU operator, only valid when the target OS is not OpenHarmony `-I arm64` or `-I arm32`	on, off	off
MSLITE_ENABLE_TRAIN	Whether to compile the training version	on, off	on
MSLITE_ENABLE_SSE	Whether to enable SSE instruction set, only valid when `-I x86_64`	on, off	off
MSLITE_ENABLE_AVX	Whether to enable AVX instruction set, only valid when `-I x86_64`	on, off	off
MSLITE_ENABLE_AVX512	Whether to enable AVX512 instruction set, only valid when `-I x86_64`	on, off	off
MSLITE_ENABLE_CONVERTER	Whether to compile the model conversion tool, only valid when `-I x86_64`	on, off	on
MSLITE_ENABLE_TOOLS	Whether to compile supporting tools	on, off	on
MSLITE_ENABLE_TESTCASES	Whether to compile test cases	on, off	off
MSLITE_ENABLE_MODEL_ENCRYPTION	Whether to support model encryption and decryption	on, off	off
MSLITE_ENABLE_MODEL_PRE_INFERENCE	Whether to enable pre-inference during model compilation	on, off	off
MSLITE_ENABLE_GITEE_MIRROR	Whether to enable download third_party from gitee mirror	on, off	off

For TensorRT and NPU compilation environment configuration, refer to Application Specific Integrated Circuit Integration Instructions.

When the AVX instruction set is enabled, the CPU of the running environment needs to support both AVX and FMA features.

The compilation time of the model conversion tool is long. If it is not necessary, it is recommended to use MSLITE_ENABLE_CONVERTER to turn off the compilation of the conversion tool to speed up the compilation.

The version supported by the OpenSSL encryption library is 1.1.1k, which needs to be downloaded and compiled by the user. For the compilation, please refer to: https://github.com/openssl/openssl#build-and-install. In addition, the path of libcrypto.so.1.1 should be added to LD_LIBRARY_PATH.

When pre-inference during model compilation is enabled, for the non-encrypted model, the inference framework will create a child process for pre-inference when Build interface is called. After the child process returns successfully, the main precess will formally execute the process of graph compilation.

At present, OpenHarmony only supports CPU reasoning, not GPU reasoning.

Runtime feature compilation options

If the user is sensitive to the package size of the framework, the following options can be configured to reduce the package size by reducing the function of the runtime model reasoning framework. Then, the user can further reduce the package size by operator reduction through the reduction tool or download.

Option	Parameter Description	Value Range	Defaults
MSLITE_STRING_KERNEL	Whether to support string data reasoning model, such as smart_reply.tflite	on,off	on
MSLITE_ENABLE_CONTROLFLOW	Whether to support control flow model	on,off	on
MSLITE_ENABLE_WEIGHT_DECODE	Whether to support weight quantitative model	on,off	on
MSLITE_ENABLE_CUSTOM_KERNEL	Whether to support southbound operator registration	on,off	on
MSLITE_ENABLE_DELEGATE	Whether to support Delegate mechanism	on,off	on
MSLITE_ENABLE_FP16	Whether to support FP16 operator	on,off	off when `-I x86_64`, on when `-I arm64`, when `-I arm32`, if the Android_NDK version is greater than r21e, it is on, otherwise it is off
MSLITE_ENABLE_INT8	Whether to support INT8 operator	on,off	on

Since the implementation of NPU and TensorRT depends on the Delegate mechanism, the Delegate mechanism cannot be turned off when using NPU or TensorRT. If the Delegate mechanism is turned off, the related functions must also be turned off.

Compilation Example

First, download source code from the MindSpore code repository.

git clone https://gitee.com/mindspore/mindspore.git -b r2.0

Then, run the following commands in the root directory of the source code to compile MindSpore Lite of different versions:

Compile the x86_64 architecture version and set the number of threads at the same time.
```
bash build.sh -I x86_64 -j32
```
Compile the ARM64 architecture version without compiling training-related code.
```
export MSLITE_ENABLE_TRAIN=off
bash build.sh -I arm64 -j32
```
Or modify mindspore/lite/CMakeLists.txt to set MSLITE_ENABLE_TRAIN to off and execute the command:
```
bash build.sh -I arm64 -j32
```
Compile the AAR package containing aarch64 and aarch32.
```
bash build.sh -A on -j32
```

Compile the aarch32 or aarch64 package of the OpenHarmony OS:

Compile aarch32 package

export OHOS_NDK=OHOS NDK path
export TOOLCHAIN_NAME=ohos
bash build.sh -I arm32 -j32

Compile aarch64 package

export OHOS_NDK=OHOS NDK path
export TOOLCHAIN_NAME=ohos
bash build.sh -I arm64 -j32

Finally, the following files will be generated in the output/ directory:

mindspore-lite-{version}-{os}-{arch}.tar.gz: Contains runtime, and related tools.
mindspore-lite-maven-{version}.zip: The AAR package which contains runtime (java).
mindspore-lite-{version}-{python}-{os}-{arch}.whl: The Whl package which contains runtime (Python).

version: Version of the output, consistent with that of the MindSpore.

python: Python version of the output, for example, Python 3.7 is cp37-cp37m.

os: Operating system on which the output will be deployed.

arch: System architecture on which the output will be deployed.

To experience the Python API, you need to move to the ‘output/’ directory and use the following command to install the Whl installation package.

pip install `mindspore-lite-{version}-{python}-{os}-{arch}.whl`

After installation, you can use the following command to check whether the installation is successful: If no error is reported, the installation is successful.

python -c "import mindspore_lite"

After successful installation, you can use the command of pip show mindspore_lite to check the installation location of the Python module of MindSpot Lite.

Directory Structure

When the compilation option is -I x86_64:

mindspore-lite-{version}-linux-x64
├── runtime
│   ├── include
│   ├── lib
│   │   ├── libminddata-lite.a         # Static library of image processing
│   │   ├── libminddata-lite.so        # Dynamic library of image processing
│   │   ├── libmindspore-lite.a        # Static library of inference framework in MindSpore Lite
│   │   ├── libmindspore-lite-jni.so   # Dynamic library of inference framework jni in MindSpore Lite
│   │   ├── libmindspore-lite.so       # Dynamic library of inference framework in MindSpore Lite
│   │   ├── libmindspore-lite-train.a  # Static library of training framework in MindSpore Lite
│   │   ├── libmindspore-lite-train.so # Dynamic library of training framework in MindSpore Lite
│   │   ├── libmsdeobfuscator-lite.so  # The files of obfuscated model loading dynamic library, need to open the `MSLITE_ENABLE_MODEL_OBF` option.
│   │   └── mindspore-lite-java.jar    # Jar of inference framework in MindSpore Lite
│   └── third_party
│       └── libjpeg-turbo
└── tools
    ├── benchmark       # Benchmarking tool
    ├── benchmark_train # Training model benchmark tool
    ├── codegen         # Code generation tool
    ├── converter       # Model conversion tool
    ├── obfuscator      # Model obfuscation tool
    └── cropper         # Static library crop tool

When the compilation option is -I arm64 or -I arm32:

mindspore-lite-{version}-android-{arch}
├── runtime
│   ├── include
│   ├── lib
│   │   ├── libminddata-lite.a         # Static library of image processing
│   │   ├── libminddata-lite.so        # Dynamic library of image processing
│   │   ├── libmindspore-lite.a        # Static library of inference framework in MindSpore Lite
│   │   ├── libmindspore-lite.so       # Dynamic library of inference framework in MindSpore Lite
│   │   ├── libmindspore-lite-train.a  # Static library of training framework in MindSpore Lite
│   │   ├── libmindspore-lite-train.so # Dynamic library of training framework in MindSpore Lite
│   │   └── libmsdeobfuscator-lite.so  # The files of obfuscated model loading dynamic library, need to open the `MSLITE_ENABLE_MODEL_OBF` option.
│   └── third_party
│       ├── hiai_ddk
│       └── libjpeg-turbo
└── tools
    ├── benchmark       # Benchmarking tool
    ├── benchmark_train # Training model benchmark tool
    └── codegen         # Code generation tool

When the compilation option is -A on:

mindspore-lite-maven-{version}
└── mindspore
    └── mindspore-lite
        └── {version}
            └── mindspore-lite-{version}.aar # MindSpore Lite runtime aar

When the compilation option is -I arm64 or -I arm32，and specifiesTOOLCHAIN_NAME=ohos：

mindspore-lite-{version}-ohos-{arch}
├── runtime
│   ├── include
│   └── lib
│       ├── libmindspore-lite.a  # Static library of inference framework in MindSpore Lite
│       └── libmindspore-lite.so # Dynamic library of training framework in MindSpore Lite
└── tools
    └── benchmark                # Benchmarking tool

Windows Environment Compilation

Environment Requirements

System environment: Windows 7, Windows 10; 64-bit.
MinGW compilation dependencies:
- CMake >= 3.18.3
- Compile 64-bit: MinGW-W64 x86_64 = GCC-7.3.0
- Compile 32-bit: MinGW-W64 i686 = GCC-7.3.0
Visual Studio compilation dependencies:
- Visual Studio = 2017, cmake is included.
- Compile 64-bit: Enter the start menu, click “x64 Native Tools Command Prompt for VS 2017”, or open the cmd window and execute call "C:\Program Files (x86)\Microsoft Visual Studio\2017\Profession\VC\Auxiliary\Build\vcvars64.bat".
- Compile 32-bit: Enter the start menu, click “x64_x86 Cross Tools Command Prompt for VS 2017”, or open the cmd window and execute call "C:\Program Files (x86)\Microsoft Visual Studio\2017\Profession\VC\Auxiliary\Build\vcvarsamd64_x86.bat".

Compilation Options

The script build.bat in the root directory of MindSpore can be used to compile MindSpore Lite.

The compilation parameter of `build.bat`

Parameter	Parameter Description	Mandatory or Not
lite	Set this parameter to compile the MindSpore Lite project.	Yes
[n]	Set the number of threads used during compilation, otherwise the default is set to 6 threads.	No

The options of `mindspore/lite/CMakeLists.txt`

Option	Parameter Description	Value Range	Defaults
MSLITE_ENABLE_SSE	Whether to enable SSE instruction set	on, off	off
MSLITE_ENABLE_AVX	Whether to enable AVX instruction set (This option does not currently support the Visual Studio compiler)	on, off	off
MSLITE_ENABLE_AVX512	Whether to enable AVX512 instruction set (This option does not currently support the Visual Studio compiler)	on, off	off
MSLITE_ENABLE_CONVERTER	Whether to compile the model conversion tool (This option does not currently support the Visual Studio compiler)	on, off	on
MSLITE_ENABLE_TOOLS	Whether to compile supporting tools	on, off	on
MSLITE_ENABLE_TESTCASES	Whether to compile test cases	on, off	off
MSLITE_ENABLE_GITEE_MIRROR	Whether to enable download third_party from gitee mirror	on, off	off

The above options can be modified by setting the environment variable with the same name or the file mindspore/lite/CMakeLists.txt.

The compilation time of the model conversion tool is long. If it is not necessary, it is recommended to use MSLITE_ENABLE_CONVERTER to turn off the compilation of the conversion tool to speed up the compilation.

Compilation Example

First, use the git tool to download the source code from the MindSpore code repository.

git clone https://gitee.com/mindspore/mindspore.git -b r2.0

Then, use the cmd tool to compile MindSpore Lite in the root directory of the source code and execute the following commands.

Turn on SSE instruction set optimization and compile with 8 threads.

set MSLITE_ENABLE_SSE=on
call build.bat lite 8

Finally, the following files will be generated in the output/ directory:

mindspore-lite-{version}-win-x64.zip: Contains model inference framework and related tool.

version: Version of the output, consistent with that of the MindSpore.

Directory Structure

When the compiler is MinGW:

mindspore-lite-{version}-win-x64
├── runtime
│   ├── include
│   └── lib
│       ├── libgcc_s_seh-1.dll      # Dynamic library of MinGW
│       ├── libmindspore-lite.a     # Static library of inference framework in MindSpore Lite
│       ├── libmindspore-lite.dll   # Dynamic library of inference framework in MindSpore Lite
│       ├── libmindspore-lite.dll.a # Link file of dynamic library of inference framework in MindSpore Lite
│       ├── libssp-0.dll            # Dynamic library of MinGW
│       ├── libstdc++-6.dll         # Dynamic library of MinGW
│       └── libwinpthread-1.dll     # Dynamic library of MinGW
└── tools
    ├── benchmark # Benchmarking tool
    └── converter # Model conversion tool

When the compiler is Visual Studio:

mindspore-lite-{version}-win-x64
├── runtime
│   ├── include
│   └── lib
│       ├── libmindspore-lite.dll     # Dynamic library of inference framework in MindSpore Lite
│       ├── libmindspore-lite.dll.lib # Import library of dynamic library of inference framework in MindSpore Lite
│       └── libmindspore-lite.lib     # Static library of inference framework in MindSpore Lite
└── tools
    └── benchmark # Benchmarking tool

When linking the static library compiled by MinGW, you need to add -Wl, --whole-archive mindspore-lite -Wl, --no-whole-archive in the link options.

When linking the static library compiled by Visual Studio, you need to add /WHOLEARCHIVE:libmindspore-lite.lib in “Property Pages -> Linker -> Command Line -> Additional Options”.

When using the Visual Studio compiler, std::ios::binary must be added to read the model stream, otherwise the problem of incomplete reading of the model file will occur.

Currently, MindSpore Lite is not supported on Windows.

macOS Environment Compilation

Environment Requirements

System environment: macOS 10.15.4 and above ; 64-bit.
Compilation dependencies are:
- CMake >= 3.18.3
- Xcode == 11.4.1
- Git >= 2.28.0

The compilation script will execute git clone to obtain the code of the third-party dependent libraries.

Compilation Options

The script build.sh in the root directory of MindSpore can be used to compile MindSpore Lite.

The compilation parameter of `build.sh`

Parameter	Parameter Description	Value Range	Defaults
-I	Selects an applicable architecture.	arm64, arm32	None
-j[n]	Sets the number of threads used during compilation. Otherwise, the number of threads is set to 8 by default.	Integer	8

The options of `mindspore/lite/CMakeLists.txt`

Option	Parameter Description	Value Range	Defaults
MSLITE_ENABLE_COREML	Whether to enable CoreML backend	on, off	off

Compilation Example

First, use the git tool to download the source code from the MindSpore code repository.

git clone https://gitee.com/mindspore/mindspore.git -b r2.0

Then, use the cmd tool to compile MindSpore Lite in the root directory of the source code and execute the following commands.

Compile the ARM64 architecture version
```
bash build.sh -I arm64 -j8
```
Compile the ARM32 architecture version
```
bash build.sh -I arm32 -j8
```

Finally, the following files will be generated in the output/ directory:

mindspore-lite-{version}-{os}-{arch}.tar.gz: Contains model inference framework.

version: Version of the output, consistent with that of the MindSpore.

os: Operating system on which the output will be deployed.

arch: System architecture on which the output will be deployed.

Directory Structure

mindspore-lite.framework
└── runtime
    ├── Headers        # The inference framework header files
    ├── Info.plist     # The configured files
    └── mindspore-lite # The static library

Currently, device-side training and model conversion are not supported on macOS.

Building Device-side MindSpore Lite

Linux Environment Compilation

Environment Requirements

Compilation Options

Instructions for parameters of build.sh

Module build compilation options

Compilation Example

Directory Structure

Windows Environment Compilation

Environment Requirements

Compilation Options

The compilation parameter of build.bat

The options of mindspore/lite/CMakeLists.txt

Compilation Example

Directory Structure

macOS Environment Compilation

Environment Requirements

Compilation Options

The compilation parameter of build.sh

The options of mindspore/lite/CMakeLists.txt

Compilation Example

Directory Structure

Instructions for parameters of `build.sh`

The compilation parameter of `build.bat`

The options of `mindspore/lite/CMakeLists.txt`

The compilation parameter of `build.sh`

The options of `mindspore/lite/CMakeLists.txt`