C++ code generator for C++ data class serialization using clang's python binding

Copyright Qingfeng Xia 2020-2021 License: BSD 3-clause

Introduction

Class definition in C++ header is parsed by clang, and glue code will be generated automatically.

This project is not completed, as only HDF5 serialization (no deserialization) has been implemented.

Modular design

Module is selected from CMake "ENABLE_*" options

Implemented C++ class serialization file format

HDF5: official cpp API "H5Cpp.h"

HDF5 is most complicated file format, if this format is supported, other serialization data format like json will be fairly straight-forwrard.

Potential module (serialization file format) to support

• yaml:
• json.hpp header only C++ json: extensible to decode/encode user types
• toml11: header only lib: https://github.com/ToruNiina/toml11#converting-a-table
• xml:

Tabular data/DataFrame

• csv: header only https://github.com/vincentlaucsb/csv-parser#single-header

A more advanced C++ lib for data table as in R Table or Pandas.DataFrame

• xframe, towards a C++ dataframe
• https://github.com/hosseinmoein/DataFrame

Third-party code included

Some header only libraries are downloaded and copied into this repository for the moment. git submodule add -b master https://github.com/ToruNiina/toml11.git

• json.hpp: MIT licensed, download as a copy
• toml.hpp: git submodule
• csv.hpp: download as a copy
• eigen3-hdf5.hpp: MIT licensed, download as a copy, with modification

Module code structure

• hdf5/HDF5IO.h: helper functions to ease HDF5 IO

• hdf5/HDF5_TypeTraits.h:

• demo/CodeGen_Types.h: input testing class def

• demo/hdf5 : demo the usage of code generator for hdf5 IO

• code_generator/hdf5_generator : an independent python code generation script

• tests/ : unit tests

HDF5

Tutorial for HDF5

Usage: h5type_generator.py input_header.h output_header_filename.h NameSpaceName

The generated header file output_header_filename.h declares a serial of H5::CompType <class_name>_h5type and init_h5types(); in the original input header's same namespace. init_h5types(); insert field type def into those complex type declared.

Demo

In the folder <../demo/>, there are 3 files

• CodeGen_types.h: input header files, 2 classes defined.
• CodeGen_types_hdf5.h generated output header files, H5::CompType instanced declared and initialized for the 2 classes in the input headers
• CodeGen_demo_hdf5.cpp : how to use API in HDF5IO.h with CodeGen_types_hdf5.h
  • init_h5types();
  • init a `std::vector
  • write WriteVector<ComplexData>

header, class and field requirement:

• c-style struct/ C++ trivially-copyable data class with public member will be saved
• all classes in the input_header must be in a single namespace
• generate header-only, but it can be split into h and cpp in the future field type supported
• all built-in scalar types like int, double
• 1D fixed-size C-style array e.g. int[3]
• std::string

=== non-trivially-copyable class need more tests ===

• std::vector, std::array
• For 2D matrix, using Eigen::Matrix HDF5_Eigen

=== yet completed or tested ===

• Non-trivial C++ class with the help of the generated serializer function
• string types: C string char* and std::string hdf5 string type is special

HDF5 support for Variable length member is highly challenging

https://stackoverflow.com/questions/35477590/reading-a-hdf5-dataset-with-compound-data-type-containing-multiple-sets-with-var

https://github.com/dguest/hdf5-ntuples/blob/master/include/h5container.hh

How to write std::vector<EERAModel::particle> A solution would be declare a new struct to hold several hvl_t for all varLen fields: "vector, T&, T* pointer ", c_str is not nessary, the length can be detected by \0 NUL char. inside the <clsss_name>serialize(), fill the vlen at the runtime. then write into a sibling data set.

To read: h5.py

To get the type that the VarLenType is based on, I can run DataType::getSuper().getClass(). Then to actually construct the type (for example, if it is a CompType), then I can use DataType::getSuper().getClass().getId() in the CompType constructor.

Install dependencies

HDF5 C++ official API. <H5Cpp.h> see A third-party header, "Eigen_HDF5.h" has been copied into this repo.

The currently workflow for HDF5 IO (manual workflow)

• copy all classes to written into HDF5, e.g. particle or later more general result c++ class type, into a header file EERA_types.h, as input to the hdf5_generator.py

• hdf5_generator.py is a python code generator, aiming to generate H5:CompType instance for almost any C++ class see more in <code_generator/Readme.md>

• HDF5IO.h read and write any class with the generated H5:CompType instance in the generated *_types_hdf5.h

HDF5IO.h is a head-only lib, just put include/ and third-party/ of this repo as include_directories() in specific model project cmake, it should work.

The workflow can be automated by cmake.

HDF5 C++ libraries

1. Official C++ API There are official C API and C++ wrapper H5CPP. H5CPP is now MPI capable given c++17 capable MPI compiler

   HDF5 v1.8 has significantly difference with HDF5 v1.10 https://support.hdfgroup.org/HDF5/doc/ADGuide/Changes.html

   For Eigen::Matrix support https://github.com/garrison/eigen3-hdf5 does not work with HDF5 1.10 https://github.com/Gjacquenot/eigen3-hdf5 works with HDF5 1.10

2. HighFive Header-only https://github.com/BlueBrain/HighFive with T[][], boost::ublas, Eigen::Matrix, XTensor support

3. Eigen, Xtensor

HDF5 file viewer on Ubuntu 18.04/20.04

Ubuntu 18.04 's hdfview from official repository has bug, it can not view the data.

pip install h5pyViewer but it is python2 only, not updated since 2015.

On ubuntu 18.04 pip3 install vitables also have some problem, pip3 can not install PyQt5, even after PyQt5 has been installed from system package manager, pip3 seems does not find it, so I download vitables.3.0.2 source code, comment out 2 lines in setup.py and install it. On ubuntu 18.04, vitables can be installed by apt-get

Command line tool h5dump is working to check h5 data structure.

Installation

This is C++ API is designed to be header-only, while dependency libraries need to be installed, see guide below. This API can be used as a git submodule, or just copy this repo folder into specific model project, and then include this folder in the project root CMakeLists.txt.

Or git clone this repo as the sibling folder of model repo,it is just header include dictionary to be sorted out in CMake

note: as git module is used, add --recursivewhen clone this repo, i.e. git clone --recursive this_repo. read more on using submodule: https://www.vogella.com/tutorials/GitSubmodules/article.html

Install clang's python binding

The python module clang is clang C API's official python binding. Install either from

1. using pip pip3 install clang, you will also need to install libclang-6 To ensure the shared lib can be found, set the path by Config.set_library_file("/usr/lib/llvm-6.0/lib/libclang.so.1") in clang_util.py

2. using linux software manager command line: apt install python3-clang then there may be no need to Config.set_library_file()

Install HDF5 dependency on Ubuntu 18.04/20.04

sudo apt install libhdf5-dev optional: sudo apt install libeigen3-dev

RHEL/Centos 7

Centos versions are older than Ubuntu, sometimes versions cause trouble. poco-devel-1.6.1-3.el7.x86_64.rpm hdf5-devel-1.8.12-11.el7.x86_64.rpm yum install epel-release -y && yum install cmake3 hdf5-devel -y

optional: yum install install eigen3-devel

This project's cmake can download and build latest HDF5 lib in local build folder.

MacOS

homebrew maybe is the way to go. The dependencies name and version can be checked online https://formulae.brew.sh/formula/

If brew is not installed yet, run /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install.sh)"

Install dependencies: brew update && brew install eigen hdf5

On macos-latest (?), the default HDF5 version is 1.12. It is possible to install hdf5@1.10, but cmake failed to find it, version 1.12 is used.

cmake could not find OpenSSL installation, however, networking code has been turn off, will be removed later.

build with cmake

Only 2 demo cpp to test out platform cmake configuration.

Future plan:

• generate unit test
• serializer and deserializer functions
• only generate h5 types for a selected
• template class
• filter class by hint in comment @to_hdf5
• QT types, which has property meta data
• for private fields: inject code into original class, or derive class to expose

https://github.com/cool-RR/PySnooper

Note on license and copyright

This repo starts as a personal trial of code_generator in the author's non-office hours when participating the BSD 3-clause project https://github.com/ScottishCovidResponse/data_pipeline_api_cpp/. This code_generator was not a completed project yet, but it will be developed in the author's personal github to be some kind of usefulness.