MultiGen: Multi-Language Code Generator

Documentation

MultiGen is a Python-to-multiple-languages code generator that translates Python code to C, C++, Rust, Go, Haskell, OCaml, and LLVM IR while preserving semantics and performance characteristics.

Key Features

• Multi-Language Support: Generate code for C, C++, Rust, Go, Haskell, OCaml, and LLVM IR
• Universal Preference System: Customize code generation for each backend with language-specific preferences
• Advanced Python Support: Object-oriented programming, comprehensions, string methods, augmented assignment
• Modern Libraries: C++ STL, Rust standard library, Go standard library, Haskell containers, OCaml standard library
• Clean Architecture: Extensible backend system with abstract interfaces for adding new target languages
• Type-Safe Generation: Leverages Python type annotations for accurate and safe code translation
• Runtime Libraries: Enhanced C backend with 50KB+ runtime libraries providing Python-like semantics
• CLI Interface: Simple command-line tool with conversion, building, validation (mgen check), and batch processing
• Production-Ready: 1353 passing tests ensuring translation accuracy and code quality
• LLVM Backend: Native compilation via LLVM IR with O0-O3 optimization levels

Supported Languages

Language	Status	Extension	Build System	Advanced Features	Benchmarks
C	Production	.c	Makefile / gcc	OOP, STC containers, string methods, comprehensions	7/7 (100%)
C++	Production	.cpp	Makefile / g++	OOP, STL containers, string methods, comprehensions	7/7 (100%)
Rust	Production	.rs	Cargo / rustc	OOP, ownership-aware, string methods, comprehensions	7/7 (100%)
Go	Production	.go	go.mod / go build	OOP, defer pattern, string methods, comprehensions	7/7 (100%)
Haskell	Production	.hs	Cabal / ghc	Pure functional, comprehensions, type safety	7/7 (100%)
OCaml	Production	.ml	dune / ocamlc	Functional, pattern matching, mutable refs	7/7 (100%)
LLVM	Production	.ll	llvmlite / clang	Native compilation, O0-O3 optimization, multi-platform	7/7 (100%)

Benchmark Results

% make benchmark # ran on m1 macbook air 
================================================================================
BENCHMARK SUMMARY
================================================================================
Total: 7 benchmarks × 7 backends = 49 runs
Success: 49 | Failed: 0

Backend      Success  Compile (s)  Run (s)      Binary (KB)  LOC
--------------------------------------------------------------------------------
c            7/7       0.390        0.275189     94.9         76
cpp          7/7       0.435        0.251988     36.1         51
go           7/7       0.190        0.265097     2365.4       38
haskell      7/7       0.156        0.024035     19944.6      65
llvm         7/7       0.310        0.251354     49.0         321
ocaml        7/7       0.234        0.271373     826.3        27
rust         7/7       0.266        0.250707     443.0        37
===============================================================================

Quick Start

Installation

Install from pypi

pip install multigen

Install from source

git clone https://github.com/shakfu/multigen
cd multigen
pip install -e .

Optional Dependencies

MultiGen has zero required dependencies for core functionality (C, C++, Rust, Go, Haskell, OCaml backends). Optional features can be installed as needed:

# LLVM backend support (native compilation, WebAssembly)
pip install multigen[llvm]

# Z3 theorem prover (formal verification)
pip install multigen[z3]

# All optional dependencies
pip install multigen[all]

Basic Usage

# List available backends
multigen backends

# Convert Python to C (with advanced features)
multigen --target c convert my_script.py

# Convert Python to C++ (with STL support)
multigen --target cpp convert my_script.py

# Convert Python to Rust with build
multigen --target rust build my_script.py

# Convert Python to Go (with enhanced features)
multigen --target go convert my_script.py

# Convert Python to Haskell (with functional programming features)
multigen --target haskell convert my_script.py

# Convert Python to OCaml (with functional programming and pattern matching)
multigen --target ocaml convert my_script.py

# Batch convert all Python files
multigen --target cpp batch --source-dir ./examples

Backend Preferences

Customize code generation for each target language with the --prefer flag:

# Haskell with native comprehensions (idiomatic)
multigen --target haskell convert my_script.py --prefer use_native_comprehensions=true

# C with custom settings
multigen --target c convert my_script.py --prefer use_stc_containers=false --prefer indent_size=2

# C++ with modern features
multigen --target cpp convert my_script.py --prefer cpp_standard=c++20 --prefer use_modern_cpp=true

# Rust with specific edition
multigen --target rust convert my_script.py --prefer rust_edition=2018 --prefer clone_strategy=explicit

# Go with version targeting
multigen --target go convert my_script.py --prefer go_version=1.19 --prefer use_generics=false

# OCaml with functional programming preferences
multigen --target ocaml convert my_script.py --prefer prefer_immutable=true --prefer use_pattern_matching=true

# Multiple preferences
multigen --target haskell build my_script.py \
  --prefer use_native_comprehensions=true \
  --prefer camel_case_conversion=false \
  --prefer strict_data_types=true

Preference System

MultiGen features a preference system that allows you to choose between cross-language consistency (default) and language-specific idiomatic optimizations.

Design Philosophy

• Default (Consistent): Uses runtime library functions for predictable behavior across all languages
• Idiomatic (Optimized): Uses native language features for better performance and familiarity

Available Preference Categories

Backend	Key Preferences	Description
Haskell	use_native_comprehensions, camel_case_conversion, strict_data_types	Native vs runtime comprehensions, naming, type system
C	use_stc_containers, brace_style, indent_size	Container choice, code style, memory management
C++	cpp_standard, use_modern_cpp, use_stl_containers	Language standard, modern features, STL usage
Rust	rust_edition, clone_strategy, use_iterators	Edition targeting, ownership patterns, functional style
Go	go_version, use_generics, naming_convention	Version compatibility, language features, Go idioms
OCaml	prefer_immutable, use_pattern_matching, curried_functions	Functional style, pattern matching, function curry style

Example: Haskell Comprehensions

Python Source:

def filter_numbers(numbers):
    return [x * 2 for x in numbers if x > 5]

Default (Runtime Consistency):

filterNumbers numbers = listComprehensionWithFilter numbers (\x -> x > 5) (\x -> x * 2)

Native (Idiomatic Haskell):

filterNumbers numbers = [x * 2 | x <- numbers, x > 5]

Example: OCaml Functional Programming

Python Source:

def process_items(items):
    return [item.upper() for item in items if len(item) > 3]

Default (Runtime Consistency):

let process_items items =
  list_comprehension_with_filter items (fun item -> len item > 3) (fun item -> upper item)

Functional (Idiomatic OCaml):

let process_items items =
  List.filter (fun item -> String.length item > 3) items
  |> List.map String.uppercase_ascii

For complete preference documentation, see PREFERENCES.md.

Examples

Simple Functions

Python Input:

def add(x: int, y: int) -> int:
    return x + y

def main() -> None:
    result = add(5, 3)
    print(result)

Generated C++:

#include <iostream>
#include <vector>
#include <unordered_map>
#include "runtime/multigen_cpp_runtime.hpp"

using namespace std;
using namespace multigen;

int add(int x, int y) {
    return (x + y);
}

void main() {
    int result = add(5, 3);
    cout << result << endl;
}

Generated C:

#include <stdio.h>
#include "multigen_runtime.h"

int add(int x, int y) {
    return (x + y);
}

void main() {
    int result = add(5, 3);
    printf("%d\n", result);
}

Generated Go:

package main

import "multigen"

func add(x int, y int) int {
    return (x + y)
}

func main() {
    result := add(5, 3)
    multigen.Print(result)
}

Generated Rust:

// Include MultiGen Rust runtime
mod multigen_rust_runtime;
use multigen_rust_runtime::*;

fn add(x: i32, y: i32) -> i32 {
    (x + y)
}

fn main() {
    let mut result = add(5, 3);
    print_value(result);
}

Generated Haskell:

module Main where

import MultiGenRuntime
import qualified Data.Map as Map
import qualified Data.Set as Set
import Data.Map (Map)
import Data.Set (Set)

add :: Int -> Int -> Int
add x y = (x + y)

main :: IO ()
main = printValue (add 5 3)

Generated OCaml:

(* Generated OCaml code from Python *)

open Mgen_runtime

let add x y =
  (x + y)

let main () =
  let result = add 5 3 in
  print_value result

let () = print_value "Generated OCaml code executed successfully"

Advanced Features (Object-Oriented Programming)

Python Input:

class Calculator:
    def __init__(self, name: str):
        self.name: str = name
        self.total: int = 0

    def add(self, value: int) -> None:
        self.total += value

    def get_result(self) -> str:
        return self.name.upper() + ": " + str(self.total)

def process() -> list:
    calc = Calculator("math")
    calc.add(10)
    return [calc.get_result() for _ in range(2)]

Generated C++:

#include <iostream>
#include <string>
#include <vector>
#include "runtime/multigen_cpp_runtime.hpp"

using namespace std;
using namespace multigen;

class Calculator {
public:
    std::string name;
    int total;

    Calculator(std::string name) {
        this->name = name;
        this->total = 0;
    }

    void add(int value) {
        this->total += value;
    }

    std::string get_result() {
        return (StringOps::upper(this->name) + (": " + to_string(this->total)));
    }
};

std::vector<std::string> process() {
    Calculator calc("math");
    calc.add(10);
    return list_comprehension(Range(2), [&](auto _) {
        return calc.get_result();
    });
}

Generated Go:

package main

import "multigen"

type Calculator struct {
    Name string
    Total int
}

func NewCalculator(name string) Calculator {
    obj := Calculator{}
    obj.Name = name
    obj.Total = 0
    return obj
}

func (obj *Calculator) Add(value int) {
    obj.Total += value
}

func (obj *Calculator) GetResult() string {
    return (multigen.StrOps.Upper(obj.Name) + (": " + multigen.ToStr(obj.Total)))
}

func process() []interface{} {
    calc := NewCalculator("math")
    calc.Add(10)
    return multigen.Comprehensions.ListComprehension(multigen.NewRange(2), func(item interface{}) interface{} {
        _ := item.(int)
        return calc.GetResult()
    })
}

Generated Rust:

use std::collections::{HashMap, HashSet};

// Include MultiGen Rust runtime
mod multigen_rust_runtime;
use multigen_rust_runtime::*;

#[derive(Clone)]
struct Calculator {
    name: String,
    total: i32,
}

impl Calculator {
    fn new(name: String) -> Self {
        Calculator {
            name: name,
            total: 0,
        }
    }

    fn add(&mut self, value: i32) {
        self.total += value;
    }

    fn get_result(&mut self) -> String {
        ((StrOps::upper(&self.name) + ": ".to_string()) + to_string(self.total))
    }
}

fn process() -> Vec<String> {
    let mut calc = Calculator::new("math".to_string());
    calc.add(10);
    Comprehensions::list_comprehension(new_range(2).collect(), |_| calc.get_result())
}

Generated Haskell:

{-# LANGUAGE OverloadedStrings #-}
{-# LANGUAGE FlexibleInstances #-}

module Main where

import MultiGenRuntime
import qualified Data.Map as Map
import qualified Data.Set as Set
import Data.Map (Map)
import Data.Set (Set)

data Calculator = Calculator
  { name :: String
  , total :: Int
  } deriving (Show, Eq)

newCalculator :: String -> Calculator
newCalculator name = Calculator { name = name, total = 0 }

add :: Calculator -> Int -> ()
add obj value = ()  -- Haskell immutable approach

getResult :: Calculator -> String
getResult obj = (upper (name obj)) + ": " + (toString (total obj))

process :: [String]
process =
  let calc = newCalculator "math"
  in listComprehension (rangeList (range 2)) (\_ -> getResult calc)

Generated OCaml:

(* Generated OCaml code from Python *)

open Mgen_runtime

type calculator = {
  name : string;
  total : int;
}

let create_calculator name =
  {
    name = name;
    total = 0;
  }

let calculator_add (calculator_obj : calculator) value =
  (* Functional update creating new record *)
  { calculator_obj with total = calculator_obj.total + value }

let calculator_get_result (calculator_obj : calculator) =
  (calculator_obj.name ^ ": " ^ string_of_int calculator_obj.total)

let process () =
  let calc = create_calculator "math" in
  let updated_calc = calculator_add calc 10 in
  list_comprehension (range_list (range 2)) (fun _ -> calculator_get_result updated_calc)

Architecture

MultiGen follows a clean, extensible architecture with well-defined components:

7-Phase Translation Pipeline

1. Validation: Verify Python source compatibility
2. Analysis: Analyze code structure and dependencies
3. Python Optimization: Apply Python-level optimizations
4. Mapping: Map Python constructs to target language equivalents
5. Target Optimization: Apply target language-specific optimizations
6. Generation: Generate target language code
7. Build: Compile/build using target language toolchain

Frontend (Language-Agnostic)

• Type Inference: Analyzes Python type annotations and infers types
• Static Analysis: Validates code compatibility and detects unsupported features
• AST Processing: Parses and transforms Python abstract syntax tree

Backends (Language-Specific)

Each backend implements abstract interfaces:

• AbstractEmitter: Code generation for target language
• AbstractFactory: Factory for backend components
• AbstractBuilder: Build system integration
• AbstractContainerSystem: Container and collection handling

Runtime Libraries (C Backend)

• Error Handling (multigen_error_handling.h/.c): Python-like exception system
• Memory Management (multigen_memory_ops.h/.c): Safe allocation and cleanup
• Python Operations (multigen_python_ops.h/.c): Python built-ins and semantics
• String Operations (multigen_string_ops.h/.c): String methods with memory safety
• STC Integration (multigen_stc_bridge.h/.c): Smart Template Container bridge

CLI Commands

Convert

Convert Python files to target language:

multigen --target <language> convert <input.py>
multigen --target rust convert example.py

Build

Convert and compile/build the result:

multigen --target <language> build <input.py>
multigen --target go build --makefile example.py  # Generate build file
multigen --target c build example.py              # Direct compilation

Batch

Process multiple files:

multigen --target <language> batch --source-dir <dir>
multigen --target rust batch --source-dir ./src --build

Backends

List available language backends:

multigen backends

Check

Validate Python files against the supported subset without converting:

multigen check my_script.py              # Validate a file
multigen check --report my_script.py     # Full feature support report
multigen check file1.py file2.py         # Validate multiple files

Clean

Clean build artifacts:

multigen clean

Development

Running Tests

make test           # Run all 1353 tests
make lint           # Run code linting with ruff
make typecheck      # Run type checking with mypy

Test Organization

MultiGen maintains a test suite organized into focused modules:

• test_backend_c_*.py: C backend tests (191 tests total)
  • Core functionality, OOP, comprehensions, string methods, runtime libraries
• test_backend_cpp_*.py: C++ backend tests (104 tests)
  • STL integration, modern C++ features, OOP support
• test_backend_rust_*.py: Rust backend tests (176 tests)
  • Ownership patterns, memory safety, standard library
• test_backend_go_*.py: Go backend tests (95 tests)
  • Go idioms, standard library, concurrency patterns
• test_backend_haskell_*.py: Haskell backend tests (93 tests)
  • Functional programming, type safety, comprehensions
• test_backend_ocaml_*.py: OCaml backend tests (51 tests)
  • Functional programming, pattern matching, immutability
• test_backend_llvm_*.py: LLVM backend tests (130 tests)
  • Native compilation, optimization levels, IR generation

Adding New Backends

To add support for a new target language:

1. Create backend directory: src/multigen/backends/mylang/
2. Implement required abstract interfaces:
   • MyLangBackend(LanguageBackend): Main backend class
   • MyLangFactory(AbstractFactory): Component factory
   • MyLangEmitter(AbstractEmitter): Code generation
   • MyLangBuilder(AbstractBuilder): Build system integration
   • MyLangContainerSystem(AbstractContainerSystem): Container handling
   • MyLangPreferences(BasePreferences): Language-specific preferences
3. Register backend in src/multigen/backends/registry.py
4. Add tests in tests/test_backend_mylang_*.py
5. Update documentation

See existing backends (C, C++, Rust, Go, Haskell, OCaml, LLVM) for implementation examples.

Contributing

1. Fork the repository
2. Create a feature branch
3. Add tests for new functionality
4. Ensure all tests pass
5. Submit a pull request

License

MIT License - see LICENSE file for details.

Advanced Features

Supported Python Features

All backends support core Python features:

• Object-Oriented Programming: Classes, methods, constructors, instance variables, method calls
• Augmented Assignment: All operators (+=, -=, *=, /=, //=, %=, |=, ^=, &=, <<=, >>=)
• String Operations: upper(), lower(), strip(), find(), replace(), split()
• Comprehensions: List, dict, and set comprehensions with range iteration and conditional filtering
• Control Structures: if/elif/else, while loops, for loops with range()
• Built-in Functions: abs(), bool(), len(), min(), max(), sum()
• Type Inference: Automatic type detection from annotations and assignments
• Slicing: List slicing (arr[1:3], arr[1:], arr[:2]) and string slicing (s[1:3])
• F-String Format Specs: f"{x:.2f}", f"{n:x}", f"{n:d}" with precision and radix formatting
• Exception Handling: try/except/else/finally, raise, 6 exception types
• Context Managers: with open(...) as f: for file I/O
• Generators: yield, yield from, generator expressions (eager collection)

Container Support by Language

• C: STC (Smart Template Container) library with optimized C containers (864KB integrated library)
• C++: STL containers (std::vector, std::unordered_map, std::unordered_set)
• Rust: Standard library collections (Vec, HashMap, HashSet) with memory safety
• Go: Standard library containers with idiomatic Go patterns
• Haskell: Standard library containers with type-safe functional operations
• OCaml: Standard library with immutable data structures and pattern matching

Test Coverage

MultiGen maintains test coverage ensuring translation accuracy:

• 1353 total tests across all components and backends
• 49/49 benchmarks passing (100%) across all 7 backends
• Comprehensive backend coverage testing all major Python features
• Test categories: basics, OOP, comprehensions, string methods, augmented assignment, control flow, integration, exception handling, context managers, generators, slicing, f-string format specs
• All tests passing with zero regressions (100%)

Development Roadmap

Completed Milestones

• Multi-language backend system with C, C++, Rust, Go, Haskell, and OCaml support
• Advanced C runtime integration with 50KB+ of runtime libraries
• Sophisticated Python-to-C conversion with complete function and control flow support
• Object-oriented programming support across all backends
• Advanced Python language features: comprehensions, string methods, augmented assignment
• Complete STC library integration (864KB Smart Template Container library)
• Architecture consolidation with unified C backend module
• Professional test organization with 1353 tests in focused, single-responsibility files
• Universal preference system with language-specific customization
• Production-ready code generation with clean, efficient output
• 7 production-ready backends (C++, C, Rust, Go, Haskell, OCaml, LLVM) with 100% benchmark success
• Exception handling (try/except/else/finally/raise) across all backends
• Context managers (with statement) across all backends
• Generator/yield support (eager collection) across all backends
• List and string slicing across 6/7 backends
• F-string format specifications across all backends
• mgen check CLI command for validation without conversion

Future Development

• Advanced Frontend Analysis: Integrate optimization detection and static analysis engine
• STC Performance Optimization: Container specialization and memory layout optimization
• Formal Verification: Theorem proving and memory safety proofs integration
• Cross-Language Runtime: Extend runtime concepts to other backends (C++, Rust, Go)
• Performance Benchmarking: Comprehensive performance analysis across all target languages
• IDE Integration: Language server protocol support for MultiGen syntax
• Web Interface: Online code conversion tool
• Plugin System: External backend support and extensibility