Building MindSpore Lite

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

Module	Support Platform	Description
converter	Linux	Model Conversion Tool
runtime	Linux、Android	Model Inference Framework
benchmark	Linux、Android	Benchmarking Tool
timeprofiler	Linux、Android	Performance Analysis Tool
imageprocess	Linux、Android	Image Processing Library

Linux Environment Compilation

Environment Requirements

• The compilation environment supports Linux x86_64 only. Ubuntu 18.04.02 LTS is recommended.

• Compilation dependencies of runtime、benchmark and timeprofiler:

  • CMake >= 3.14.1

  • GCC >= 7.3.0

  • Android_NDK r20b

  • Git >= 2.28.0

• Compilation dependencies of converter:

  • CMake >= 3.14.1

  • GCC >= 7.3.0

  • Android_NDK r20b

  • Git >= 2.28.0

  • Autoconf >= 2.69

  • Libtool >= 2.4.6

  • LibreSSL >= 3.1.3

  • Automake >= 1.11.6

  • Libevent >= 2.0

  • M4 >= 1.4.18

  • OpenSSL >= 1.1.1

  • Python >= 3.7.5

• To install and use Android_NDK, you need to configure environment variables. The command example is export ANDROID_NDK={$NDK_PATH}/android-ndk-r20b.

• In the build.sh script, run the git clone command to obtain the code in the third-party dependency library. Ensure that the network settings of Git are correct.

Compilation Options

MindSpore Lite provides a compilation script build.sh for one-click compilation, located in the root directory of MindSpore. This script can be used to compile the code of training and inference. The following describes the compilation options of MindSpore Lite.

Parameter	Parameter Description	Value Range	Mandatory or Not
-I	Selects an applicable architecture. This option is required when compile MindSpore Lite.	arm64, arm32, or x86_64	Yes
-d	If this parameter is set, the debug version is compiled. Otherwise, the release version is compiled.	None	No
-i	If this parameter is set, incremental compilation is performed. Otherwise, full compilation is performed.	None	No
-j[n]	Sets the number of threads used during compilation. Otherwise, the number of threads is set to 8 by default.	Integer	No
-e	In the Arm architecture, select the backend operator and set the gpu parameter. The built-in GPU operator of the framework is compiled at the same time.	GPU	No
-h	Displays the compilation help information.	None	No
-n	Specifies to compile the lightweight image processing module.	lite_cv	No

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

Compilation Example

First, download source code from the MindSpore code repository.

git clone https://gitee.com/mindspore/mindspore.git -b r1.0

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

• Debug version of the x86_64 architecture:

  bash build.sh -I x86_64 -d
  

• Release version of the x86_64 architecture, with the number of threads set:

  bash build.sh -I x86_64 -j32
  

• Release version of the Arm 64-bit architecture in incremental compilation mode, with the number of threads set:

  bash build.sh -I arm64 -i -j32
  

• Release version of the Arm 64-bit architecture in incremental compilation mode, with the built-in GPU operator compiled:

  bash build.sh -I arm64 -e gpu
  

• Compile ARM64 with image preprocessing module:

  bash build.sh -I arm64 -n lite_cv
  

Output Description

After the compilation is complete, go to the mindspore/output directory of the source code to view the file generated after compilation. The file is divided into three parts.

• mindspore-lite-{version}-converter-{os}.tar.gz：Contains model conversion tool.

• mindspore-lite-{version}-runtime-{os}-{device}.tar.gz：Contains model inference framework、benchmarking tool and performance analysis tool.

• mindspore-lite-{version}-minddata-{os}-{device}.tar.gz：Contains image processing library ImageProcess.

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

device: Currently divided into cpu (built-in CPU operator) and gpu (built-in CPU and GPU operator).

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

Execute the decompression command to obtain the compiled output:

tar -xvf mindspore-lite-{version}-converter-{os}.tar.gz
tar -xvf mindspore-lite-{version}-runtime-{os}-{device}.tar.gz
tar -xvf mindspore-lite-{version}-minddata-{os}-{device}.tar.gz

Description of Converter’s Directory Structure

The conversion tool is only available under the -I x86_64 compilation option, and the content includes the following parts:

|
├── mindspore-lite-{version}-converter-{os} 
│   └── converter # Model conversion Ttool
│   └── third_party # Header files and libraries of third party libraries
│       ├── protobuf # Dynamic library of Protobuf
        ├── flatbuffers # Dynamic library of Flatbuffers

Description of Runtime and Other tools’ Directory Structure

The inference framework can be obtained under -I x86_64, -I arm64 and -I arm32 compilation options, and the content includes the following parts:

• When the compilation option is -I x86_64:

  |
  ├── mindspore-lite-{version}-runtime-x86-cpu 
  │   └── benchmark # Benchmarking Tool
  │   └── lib # Inference framework dynamic library
  │       ├── libmindspore-lite.so # Dynamic library of infernece framework in MindSpore Lite
  │   └── third_party # Header files and libraries of third party libraries
  │       ├── flatbuffers # Header files of FlatBuffers
  │   └── include # Header files of inference framework
  │   └── time_profiler # Model network layer time-consuming analysis tool
  
  

• When the compilation option is -I arm64:

  |
  ├── mindspore-lite-{version}-runtime-arm64-cpu
  │   └── benchmark # Benchmarking Tool
  │   └── lib # Inference framework dynamic library
  │       ├── libmindspore-lite.so # Dynamic library of infernece framework in MindSpore Lite
  │       ├── libmindspore-lite-fp16.so # Operator performance optimization library support float16 in MindSpore Lite
  │       ├── libmindspore-lite-optimize.so # Operator performance optimization library support dotprod instruction in MindSpore Lite  
  │   └── third_party # Header files and libraries of third party libraries
  │       ├── flatbuffers # Header files of FlatBuffers
  │   └── include # Header files of inference framework
  │   └── time_profiler # Model network layer time-consuming analysis tool
    
  

• When the compilation option is -I arm32:

  |
  ├── mindspore-lite-{version}-runtime-arm32-cpu
  │   └── benchmark # Benchmarking Tool
  │   └── lib # Inference framework dynamic library
  │       ├── libmindspore-lite.so # Dynamic library of infernece framework in MindSpore Lite
  │   └── third_party # Header files and libraries of third party libraries
  │       ├── flatbuffers # Header files of FlatBuffers
  │   └── include # Header files of inference framework
  │   └── time_profiler # Model network layer time-consuming analysis tool
    
  

1. libmindspore-lite-optimize.so only exists in the output package of runtime-arm64 and is only used on ARMv8.2 and CPUs that support dotprod instruction.

2. libmindspore-lite-fp16.so only exists in the output package of runtime-arm64 and is only used on ARMv8.2 and CPUs that support fp16.

3. Compile ARM64 to get the inference framework output of arm64-cpu by default, if you add -e gpu, you will get the inference framework output of arm64-gpu, and the package name is mindspore-lite-{version}-runtime-arm64-gpu.tar.gz, compiling ARM32 is in the same way.

4. Before running the tools in the converter, benchmark or time_profiler directory, you need to configure environment variables, and configure the path where the dynamic libraries of MindSpore Lite and Protobuf are located to the path where the system searches for dynamic libraries. Take the compiled under version 0.7.0-beta as an example: configure converter: export LD_LIBRARY_PATH=./output/mindspore-lite-0.7.0-converter-ubuntu/third_party/protobuf/lib:./output/mindspore-lite-0.7.0-converter-ubuntu/third_party/flatbuffers/lib:${LD_LIBRARY_PATH}; configure benchmark and timeprofiler: export LD_LIBRARY_PATH= ./output/mindspore-lite-0.7.0-runtime-x86-cpu/lib:${LD_LIBRARY_PATH}.

Description of Imageprocess’s Directory Structure

The image processing library is only available under the -I arm64 -n lite_cv compilation option, and the content includes the following parts:

|
├── mindspore-lite-{version}-minddata-{os}-{device}
│   └── benchmark # Benchmarking Tool 
│   └── include # Head file(Only show files related to image processing)
│       ├── lite_cv # Image processing library header file
│           ├── image_process.h # Image processing function header file
│           ├── lite_mat.h # Image data class structure header file
│   └── lib # Dynamic library(Only show files related to image processing)
│       ├── libminddata-lite.so # Image processing dynamic library
│   └── third_party # Third-party Iibrary header files and libraries
│       ├── flatbuffers # Header files of FlatBuffers
│   └── time_profiler # Model network layer time-consuming analysis tool