pydantic-bind 1.0.5

C++/pybind generation from Pydantic classes

pydantic-bind

Table of Contents

1. Overview
2. Getting Started
3. Why Not Protobufs ?
4. No Copy
5. Supported Types
6. Inheritance
7. Msgpack
8. Namespaces
9. Generated Code
10. Other Languages

Overview

Python is the language of choice for finance, data science etc. Python calling C++ (and increasingly, Rust) is a common pattern, leveraging packages such as pybind11 .

A common problem is a how best to represent data to be shared between python and C++ code. One would like idiomatic representations in each language and this may be necessary to fully utilise certain python packages. E.g., FastAPI is a popular way to create REST services, using Open API definitions derived from pydantic classes. Therefore, a data model authored using pydantic classes, or native python dataclasses, from which sensible C++ structs and appropriate marshalling can automatically be generated, is desirable.

This package provides such tools: a cmake rule allows you to generate C++ structs (with msgpack serialisation) and corresponding pybind11 bindings.

Python functions allow you to naviagte between the C++ pybind11 objects and the native python objects. There is also an option for all python operations to be directed to an owned pybind11 object (see No Copy).

Note that the typcal python developer experience is now somewhat changed, in that it's necessary to build/install the project. I personally use JetBrains CLion, in place of PyCharm for such projects.

For an example of the kind of behaviour-less object model this package is intended to help, please see (the rather nascent) fin-data-model

Getting Started

pydantic_bind adds a custom cmake rule: pydantic_bind_add_package(<package path>)

This rule will do the following:

• scan for sub-packages
• scan each sub-package for all .py files
• add custom steps for generating .cpp/.h files from any of the following, encounted in the .py files:
  • dataclasses
  • classes derived from pydantic's BaseModel
  • enums

C++ directory and namespace structure will match the python package structure (see Namespaces).

You can create an instance of the pybind11 class from your original using get_pybind_instance(), e.g.,

my_class.py:

from dataclasses import dataclass

@dataclass
clas MyClass:
    my_int: int
    my_string: str | None

CMakeLists.txt:

cmake_minimum_required(VERSION 3.9)
project(my_project)

set(CMAKE_CXX_STANDARD 20)

find_package(python3 REQUIRED COMPONENTS Interpreter Development)
find_package(pydantic_bind REQUIRED COMPONENTS HINTS "${python3_SITELIB}")

pydantic_bind_add_package(my_package)

my_util.py

from pydantic_bind import get_pybind_value
from my_package.my_class imnport MyClass

orig = MyClass(my_int=123, my_string="hello")
generated = get_pybind_value(orig)

print(f"my_int: {orig.my_int}, {generated.my_int}")

Why Not Protobufs?

I personally find protobufs to be a PITA to use: they have poor to no variant support, the generated code is ugly and idiosyncratic, they're large and painful to copy around etc.

AVRO is more friendly but generates python classes dynamically, which confuses IDEs like Pycharm. I do think a good solution is something like pydantic_avro where one can define the classes using pydantic, generate the AVRO schema and then the generateed C++ etc. I might well try and converge this project with that approach.

I was inspired to some degree by this blog.

No Copy

One annoyance of multi-language representations of data objects is that you often end up copying data around where you'd prefer to share a single copy. This is the raison d'etre for Protobufs and its ilk. In this project I've created implementations of BaseModel and dataclass which allow python to use the underlying C++ data representation, rather than holding its own copy.

Deriving from this BaseModel will give you equivalent functionality of as pydantic's BaseModel. The annotations are re-written using computed_field, with property getters and setters operating on the generated pybind class, which is instantiated behind the scenes in __init__. Note that this will make some operations (especially those that access dict) less efficient. I've also plumbed the computed fields into the JSON schema, so these objects can be used with FastAPI.

dataclass works similarly, adding properties to the dataclass, so that the exisitng get and set functionality works seamless in accessing the generated pybind11 class (also set via a shimmed __init__).

Using regular dataclass or BaseModel as members of classes defined with the pydantic_bind versions is very inefficient and not recommended.

Supported Types

The following python -> C++ mappings are supported (there are likely others I should consider):

• bool --> bool
• float --> double
• int --> int
• str --> std::string
• datetime.date --> std::chrono::system_clock::time_point
• datetime.datetime --> std::chrono::system_clock::time_point
• datetime.time --> std::chrono::system_clock::time_point
• datetime.timedelta --> std::chrono::duration
• pydantic.BaseModel --> struct
• pydantic_bind.BaseModel --> struct
• dataclass --> struct
• pydantic_bind.dataclass --> struct
• Enum --> enum class

Inheritance

I have tested single inheritance (see Generated Code). Multiple inheritance may work ... or it may not. I'd generally advise against using it for data classes.

Msgpack

A rather rudimentary msgpack implementation is added to the generated C++ structs, using a slightly modified version of cpppack. It wasn't clear to me whether this package is maintained or accepting submissions, so I copied and slightly modified msgpack.h (also, I couldn't work out how to add to my project with my rather rudimentary cmake skillz!) Changes include:

• Fixing includes
• Support for std::optional
• Support for std::variant
• Support for enums

A likely future enhancement will be to use cereal and add a mgspack adaptor.

The no-copy python objects add to_msg_pack() and from_msg_pack() (the latter being a class method), to access this functionality.

Namespaces

Directory structure and namespaces in the generated C++ match the python package and module names.

cmake requires unique target names and pybind11 requires that the filename (minus the OS-speicific qualifiers) matches the module name.

Generated Code

Code is generated into a directory structure underneath <top level>/generated.

Headers are installed to <top level>/include.

Compiled pybind11 modules are installed into <original module path>/__pybind__.

For C++ usage, you need only the headers, the compiled code is for pybind/python usage only.

For the example below, common_object_model/common_object_model/v1/common/__pybind__/foo.cpython-311-darwin.so will be installed (obviously with corresponding qualifiers for Linux/Windows). get_pybind_value() searches this directory.

Imports/includes should work seamlessly (the python import scheme will be copied). I have tested this but not completely rigorously.

common_object_model/common_object_model/v1/common/foo.py:

from dataclasses import dataclass
import datetime as dt
from enum import Enum, auto
from typing import Union

from pydantic_bind import BaseModel


class Weekday(Enum):
    MONDAY = auto()
    TUESDAY = auto()
    WEDNESDAY = auto()
    THURSDAY = auto()
    FRIDAY = auto()
    SATURDAY = auto()
    SUNDAY = auto()


@dataclass
class DCFoo:
    my_int: int
    my_string: str | None


class Foo(BaseModel):
    my_bool: bool = True
    my_day: Weekday = Weekday.SUNDAY


class Bar(Foo):
    my_int: int = 123
    my_string: str
    my_optional_string: str | None = None


class Baz(BaseModel):
    my_variant: Union[str, float] = 123.
    my_date: dt.date
    my_foo: Foo
    my_dc_foo: DCFoo

will generate the following files:

common_object_model/generated/common_object_model/v1/common/foo.h:

#ifndef COMMON_OBJECT_MODEL_FOO_H
#define COMMON_OBJECT_MODEL_FOO_H

#include <string>
#include <optional>
#include <variant>
#include <msgpack/msgpack.h>
#include <chrono>

namespace common_object_model::v1::common
{
    enum Weekday { MONDAY = 1, TUESDAY = 2, WEDNESDAY = 3, THURSDAY = 4, FRIDAY = 5, SATURDAY = 6, SUNDAY = 7
    };

    struct DCFoo
    {
        DCFoo() :
            my_string(), my_int()
        {
        }
    
        DCFoo(std::optional<std::string> my_string, int my_int) :
            my_string(my_string), my_int(my_int)
        {
        }

        std::optional<std::string> my_string;
        int my_int;
    
        MSGPACK_DEFINE(my_string, my_int);
    };

    struct Foo
    {
        Foo(bool my_bool=true, Weekday my_day=SUNDAY) :
            my_bool(my_bool), my_day(my_day)
        {
        }

        bool my_bool;
        Weekday my_day;
    
        MSGPACK_DEFINE(my_bool, my_day);
    };

    struct Bar : public Foo
    {
        Bar() :
            Foo(),
            my_string(), my_int(123), my_optional_string(std::nullopt)
        {
        }
    
        Bar(std::string my_string, bool my_bool=true, Weekday my_day=SUNDAY, int my_int=123, std::optional<std::string>
            my_optional_string=std::nullopt) :
            Foo(my_bool, my_day),
            my_string(std::move(my_string)), my_int(my_int), my_optional_string(my_optional_string)
        {
        }

        std::string my_string;
        int my_int;
        std::optional<std::string> my_optional_string;
    
        MSGPACK_DEFINE(my_string, my_bool, my_day, my_int, my_optional_string);
    };

    struct Baz
    {
        Baz() :
            my_dc_foo(), my_foo(), my_date(), my_variant(123.0)
        {
        }
    
        Baz(DCFoo my_dc_foo, Foo my_foo, std::chrono::system_clock::time_point my_date, std::variant<std::string, double>
            my_variant=123.0) :
            my_dc_foo(std::move(my_dc_foo)), my_foo(std::move(my_foo)), my_date(my_date),
            my_variant(my_variant)
        {
        }

        DCFoo my_dc_foo;
        Foo my_foo;
        std::chrono::system_clock::time_point my_date;
        std::variant<std::string, double> my_variant;
    
        MSGPACK_DEFINE(my_dc_foo, my_foo, my_date, my_variant);
    };
} // common_object_model

#endif // COMMON_OBJECT_MODEL_FOO_H

common_object_model/generated/common_object_model/v1/common/foo.cpp:

#include <pybind11/pybind11.h>
#include <pybind11/stl.h>
#include <pybind11/chrono.h>

#include "foo.h"

namespace py = pybind11;
using namespace common_object_model::v1::common;


PYBIND11_MODULE(common_object_model_v1_common_foo, m)
{
    py::enum_<Weekday>(m, "Weekday").value("MONDAY", Weekday::MONDAY)
        .value("TUESDAY", Weekday::TUESDAY)
        .value("WEDNESDAY", Weekday::WEDNESDAY)
        .value("THURSDAY", Weekday::THURSDAY)
        .value("FRIDAY", Weekday::FRIDAY)
        .value("SATURDAY", Weekday::SATURDAY)
        .value("SUNDAY", Weekday::SUNDAY);

    py::class_<DCFoo>(m, "DCFoo")
        .def(py::init<>())
        .def(py::init<std::optional<std::string>, int>(), py::arg("my_string"), py::arg("my_int"))
        .def("to_msg_pack", &DCFoo::to_msg_pack)
        .def_static("from_msg_pack", &DCFoo::from_msg_pack<Baz>)
        .def_readwrite("my_string", &DCFoo::my_string)
        .def_readwrite("my_int", &DCFoo::my_int);

    py::class_<Foo>(m, "Foo")
        .def(py::init<bool, Weekday>(), py::arg("my_bool")=true, py::arg("my_day")=SUNDAY)
        .def("to_msg_pack", &Foo::to_msg_pack)
        .def_static("from_msg_pack", &Foo::from_msg_pack<Baz>)
        .def_readwrite("my_bool", &Foo::my_bool)
        .def_readwrite("my_day", &Foo::my_day);

    py::class_<Bar>(m, "Bar")
        .def(py::init<>())
        .def(py::init<std::string, bool, Weekday, int, std::optional<std::string>>(), py::arg("my_string"), py::arg("my_bool")=true,
            py::arg("my_day")=SUNDAY, py::arg("my_int")=123, py::arg("my_optional_string")=std::nullopt)
        .def("to_msg_pack", &Bazr:to_msg_pack)
        .def_static("from_msg_pack", &Bar::from_msg_pack<Baz>)
        .def_readwrite("my_string", &Bar::my_string)
        .def_readwrite("my_int", &Bar::my_int)
        .def_readwrite("my_optional_string", &Bar::my_optional_string);

    py::class_<Baz>(m, "Baz")
        .def(py::init<>())
        .def(py::init<DCFoo, Foo, std::chrono::system_clock::time_point, std::variant<std::string, double>>(), py::arg("my_dc_foo"),
            py::arg("my_foo"), py::arg("my_date"), py::arg("my_variant")=123.0)
        .def("to_msg_pack", &Baz::to_msg_pack)
        .def_static("from_msg_pack", &Baz::from_msg_pack<Baz>)
        .def_readwrite("my_dc_foo", &Baz::my_dc_foo)
        .def_readwrite("my_foo", &Baz::my_foo)
        .def_readwrite("my_date", &Baz::my_date)
        .def_readwrite("my_variant", &Baz::my_variant);
}

Other languages

When time allows, I will look at adding support for Rust. There is limited value in generating Java or C# classes; calling those VM-based lanagues in-process from python has never worked well, in my experience.