pydantic-bind Table of Contents Overview Getting Started Why Not Protobufs ? No Copy Supported Types Inheritance Msgpack Namespaces Generated Code 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() 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 /generated. Headers are installed to /include. Compiled pybind11 modules are installed into /__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 #include #include #include #include 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 my_string, int my_int) : my_string(my_string), my_int(my_int) { } std::optional 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 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 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 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 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 #include #include #include "foo.h" namespace py = pybind11; using namespace common_object_model::v1::common; PYBIND11_MODULE(common_object_model_v1_common_foo, m) { py::enum_(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_(m, "DCFoo") .def(py::init<>()) .def(py::init, 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) .def_readwrite("my_string", &DCFoo::my_string) .def_readwrite("my_int", &DCFoo::my_int); py::class_(m, "Foo") .def(py::init(), 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) .def_readwrite("my_bool", &Foo::my_bool) .def_readwrite("my_day", &Foo::my_day); py::class_(m, "Bar") .def(py::init<>()) .def(py::init>(), 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) .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_(m, "Baz") .def(py::init<>()) .def(py::init>(), 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) .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.
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Latest version: 1.0.5 Released: 2023-10-27