transpilers

Hybrid algorithmic + LLM source-to-source transpiler. N-to-M across many languages through a single shared IR pipeline.

10 frontends: Python, C, C++, Java, C#, TypeScript, JavaScript, Fortran, Visual Basic, and Assembly (via PyGhidra → C-like pseudocode). 5 targets: Rust, C, Mojo, Python, Fortran.

It is built for migrating large C++ codebases (EnergyPlus → Mojo and Python is the main driver), so besides the translator itself it ships batch drivers, a verification stack, a migration-progress tool, an NVIDIA fine-tuning toolchain for a C++/Python → Mojo model, and the machinery to make that model's drafts usable: a deterministic post-pass, best-of-n selection against an oracle, and a bit-exactness evaluation harness.

Contents

• Requirements · Quick start · Command-line tools
• How it works · Fidelity dial · Status
• Verification and reporting
• Migrating a large codebase
• Using the local fine-tuned model
• Mojo fine-tuning (NVIDIA) · Documentation
• Layout · Development · Roadmap · Licence

Requirements

• Python 3.14 or newer and uv. just and direnv are optional conveniences.
• Target toolchains only for --verify and the end-to-end matrix: rustc, a C compiler, gfortran, mojo. Cells whose toolchain is missing report SKIP, not PASS.
• API keys, only for the LLM paths:
  • ANTHROPIC_API_KEY for transpile --infer-with-llm and --llm-rename.
  • An OpenRouter key (OPENROUTER_API_KEY) for transpile-llm and the stateless runner's openrouter backend. transpile-llm --endpoint URL talks to a local OpenAI-compatible server instead (Ollama, LM Studio, llama.cpp, vLLM) and needs no key.
• Optional extras: clang for --ir-augment; the codebase-memory-mcp CLI for --cbm; uv sync --group api for transpile-api.
• A CUDA build of PyTorch and an NVIDIA GPU only for fine-tuning (see Mojo fine-tuning).

Quick start

direnv allow      # one-time: auto-activates .venv on cd
just setup        # uv sync
just example      # transpile examples/add.py to Rust and verify it
just check        # lint + full test suite

Without direnv/just:

uv sync
uv run transpile examples/add.py --target rust            # print the Rust translation
uv run transpile examples/add.py --target rust --verify   # also compile it with rustc
uv run pytest

Command-line tools

uv sync installs these entry points:

Command	What it does
transpile	Translate one file through the strict HIR → MIR → LIR pipeline. Choose --source/--target, --verify, --fidelity, --path.
transpile-levels	Run at object · file · module · folder granularity, with --engine {strict|lift}.
transpile-llm	Batch C++/Fortran → Mojo from a JSON config, through OpenRouter or a local server (--endpoint). Repair loop, parallel workers, --postpass, best-of-n with --samples/--verify-cmd, --domain-prompts.
transpile-postpass	Deterministic fixes for LLM-drafted Mojo kernels: C++/pre-1.0 syntax, Array1D loops, glibc-exact maths, nc_mul guards. See below.
transpile-build-config	Scan a source tree and write the JSON config that transpile-llm consumes.
transpile-comments	Strip C++ comments from the sources referenced by a config, or from a single file.
transpile-api	HTTP API server. Needs uv sync --group api.

Useful transpile options beyond the basics:

Option	Effect
--path python_pivot	Route C++ → Python → target, with Python as a shared IR (C++ sources only).
--ir-augment	Compile C/C++ to LLVM IR to pre-fill type holes without LLM calls (needs clang).
--trace-types	Run Python under sys.settrace and use the recorded runtime types to fill holes.
--infer-with-llm, --llm-rename	Ask the LLM for what algorithmic inference could not resolve, or to rename opaque locals (Ghidra local_10).
-I DIR, --include-impls	C/C++: inline local #include "X.h" headers (and their sibling .cpp) into one translation unit.
--cbm REPO_ROOT	C++: recover the cross-file class/method/macro surface from a codebase-memory-mcp graph.
--provenance FILE	Write a JSON sidecar mapping every LIR node to its originating HIR node.

Examples:

uv run transpile examples/example.cpp --path python_pivot --target rust
uv run transpile-levels examples/example.cpp --level file --engine lift

Run any command with --help for the full option list.

How it works

Why hybrid

Pure-algorithmic transpilers can't infer types or map idioms; pure-LLM ones (TransCoder et al.) produce unverifiable output. So the work is split:

Algorithmic	LLM
Parse, symbol resolution, dataflow, emission, verification	Type-hole filling, idiom mapping, naming, comments

Three rules the codebase enforces:

1. LLMs operate on typed holes, never free-form text. Every call declares a response shape and a validator.
2. Every LLM output is verified: parse, compile, and behaviourally (tests).
3. All LLM calls are cached by hash(prompt + model + temperature), so the pipeline is reproducible.

Promoted LLM answers land in stdlib_maps/ as data, so the system bends from LLM-heavy toward algorithmic-heavy over time.

Three-tier IR

Frontend  →  HIR (source-faithful)  →  MIR (normalised, typed)  →  LIR (target-shaped)  →  Backend  →  verify

The key invariant: HIR → MIR may not invent types it doesn't know. Missing information becomes an UnknownT hole that a later pass (algorithmic inference first, LLM fallback) must explicitly fill. The pipeline refuses constructs it can't model rather than emit wrong code.

The never-refuse lift

A second engine, the never-refuse lift (transpilers.lift), does whole-file C++ → Python 1:1, emitting # TODO[lift] stubs for gaps (about 96% mechanical on EnergyPlus). Run any granularity (object · file · module · folder/repo) with transpile-levels --engine {strict|lift}: strict is the verified HIR pipeline, lift never refuses.

Fidelity dial

transpile --fidelity {structural|idiomatic} (default structural) controls how faithful the output must be to the source's architecture. Under structural, --verify additionally runs the structural-fidelity verifier: the output's module/function/struct skeleton and control-flow nesting must be isomorphic to the source's. Idiom mapping is allowed only at the statement/expression level, for example foreach → indexed loop, Rust's struct+impl split, or Fortran methods as free functions. Added, dropped, merged or renamed functions and flattened control flow fail the gate. idiomatic skips the skeleton gate, permitting parent-level rewrites. It applies to the direct path only.

Status

These figures were recorded in earlier runs and have not been re-run for this README; re-run the commands in Verification and reporting to reproduce them.

Verified end-to-end (compile + run + output match) on examples/algorithms/ (18 Python files): Rust, Mojo, Python, Fortran all 18/18; C 17/18 (needs dynamic-array growth for sieve.py).

Real-world corpus (examples/samples/, transpile-only → Rust): JavaScript 26/27, C++ 78/170, Python 32/57, Java 27/74, C 20/70.

The end-to-end Mojo and C++ numbers are compile-and-run results that require those toolchains to be installed. The engine itself (parse → IR → emit, type inference, the verifiers, and all pure-Python paths) is exercised by the pytest suite, which runs without any target toolchain. Mojo target verification is gated on a working mojo install; where it is absent, the matrix reports those cells as SKIP rather than PASS, so the Mojo figures are not reproducible in a toolchain-less CI.

Verification and reporting

End-to-end matrix: compile every output and verify it matches Python's stdout.

uv run python scripts/run_matrix.py examples/algorithms
uv run python scripts/transpile_matrix.py examples/samples/Python rust   # transpile only

Failure taxonomy: classify every verify-gate failure over a corpus into parse · unresolved-symbol · unfilled-UnknownT-hole · type-inference-miss · target-compile-error · output-mismatch · structural-divergence · timeout and roll it up per (source, target) pair (CSV + markdown):

uv run python scripts/failure_taxonomy.py examples/samples --targets rust,mojo --csv out.csv

src/transpilers/verify/behavioral.py additionally classifies semantic divergences in verified output, for example floored versus truncated integer division.

Migrating a large codebase

For whole-repository migrations such as EnergyPlus:

1. Plan the batch: transpile-build-config <source_dir> -o config.json (use --exclude, --min-lines/--max-lines, --tier, --sort).
2. Transpile: transpile-llm config.json (add --repair for the compile-driven repair loop, --dry-run or --status to inspect first, --resolve to retry entries marked done but missing their .mojo). --endpoint URL --model NAME uses a local server instead of OpenRouter; --postpass applies the deterministic fixes to each draft; --samples N --verify-cmd CMD keeps the first of up to N drafts that a verifier accepts. --domain-prompts adds GoogleTest or Eigen guidance when those sources are detected. See Using the local fine-tuned model.
3. Stateless one-shot runner: transpile-stateless produces a Python and a Mojo port per file in one completion. Backends: claude, lmstudio (load-balanced across endpoints) and openrouter (retries with backoff, and model failover via OPENROUTER_FAILOVER). --chunk compresses or splits oversize files and stitches the result. See stateless transpilation/0_inference.md to serve the fine-tuned model locally.
4. Track progress: scripts/migration_status.py compares the upstream repository with its port (lines of code, file/function counts, migration-map status, test results), optionally across two git revisions; see docs/migration_status.md. tools/migraph/ renders the migration as an interactive graph you can scrub through git history.
5. Benchmark models: benchmarks/ holds transpilation-bench, a 40-task C++/Python → Mojo suite (benchmarks/README.md), and benchmarks/epmojo_eval/, which scores drafts by whether they are bit-for-bit identical to the verbatim C++ (benchmarks/epmojo_eval/README.md).

Using the local fine-tuned model

The repo ships the LoRA adapter (data/sft/cpp_mojo/adapter_15b_v2, on Qwen/Qwen2.5-Coder-1.5B-Instruct), not the base weights. To serve it, merge the adapter into the base and convert it to GGUF for Ollama, llama.cpp or LM Studio (stateless transpilation/0_inference.md has the recipe). Then:

uv run transpile-llm config.json --endpoint http://localhost:11434/v1 \
    --model <the name you gave the model> --postpass

Things worth knowing, all measured on this adapter:

• It answers with one fenced mojo block, which transpile-llm accepts when there are no <<<FILE>>> markers (exactly one closed mojo fence; several fences are refused as ambiguous).
• Leave --domain-prompts off for it. With the EnergyPlusMojo guidance the model often copies the guidance's example function instead of translating the routine.
• Raw drafts are rarely bit-exact; the post-pass is what makes them usable. It is transpilers.repair.mojo_postpass, also available as transpile-postpass FILE, and every rule can be switched off (--no-syntax, --no-structs, --no-arrays, --no-mut, --no-libm, --no-fma):
  • C++ and pre-1.0 Mojo habits become Mojo 1.0 (// comments, fn, let, alias, &&, const, quoted imports);
  • a field-less struct with one method is lifted to a function named after the C++ routine;
  • Array1D parameters become List only when every use is the canonical for i in range(a.l(), a.u() + 1) loop with the variable used solely as x[i]; anything else is reported, never guessed;
  • std.math calls (exp, pow, log, ...) are routed to glibc, pow(x, 2) becomes nc_mul(x, x), ObjexxFCL's pow_N and max/min follow the upstream spelling (pass --source the C++ text);
  • mut is added to List-like parameters the body index-assigns;
  • products that feed an add or subtract are wrapped in nc_mul, including products bound to variables, because Mojo fuses a*b + c into an FMA that the oracle build (-ffp-contract=off) does not. Anything it cannot fix (C++ braces, unknown types, no @export entry) is listed in the report.
• Best-of-n when a verifier exists. --samples N --temperature 0.8 --verify-cmd CMD draws up to N drafts per file and keeps the first that CMD accepts with exit status 0 ({target} and {source} are substituted). benchmarks/epmojo_eval/verify_one.py is such a verifier for routines extracted from EnergyPlusMojo: it accepts a draft only if it is bit-exact.

Measured with the harness in benchmarks/epmojo_eval/ (3,000 edge-case inputs per routine, bit for bit against the verbatim C++, no guidance, temperature 0 unless stated):

Set	Raw output	With the post-pass
Development, 40 routines (31 unique)	8 exact	39 exact
Held-out, 21 unique routines	1 exact	16 exact (19 after the struct rule, which was written after seeing that pattern)
The 3 routines greedy decoding fails, best-of-8 with the oracle		2 recovered

Read these numbers narrowly: the routines are short, stateless, numeric Real64 → Real64 functions (52 unique in total); the development set was used while writing the rules, so the held-out 16 of 21 is the fair figure; routines with arrays, state, output parameters or callees were not measured; and the post-pass is meant for model drafts, not for rewriting existing kernels. Best-of-n needs an oracle for the routine.

Mojo fine-tuning (NVIDIA)

A toolchain-free data and fine-tune stack bootstraps the C++/Python → Mojo translator adapter. It runs on NVIDIA GPUs with a CUDA build of PyTorch.

• Run or retrain the adapter: RUN.md covers setup and the LLaMA-Factory recipe. scripts/sft/train_3b_cuda_local.sh is the local two-phase 3B run for an NVIDIA GPU; scripts/sft/cloud_train.sh and tools/cloud/ are for a rented NVIDIA box (scripts/sft/cloud_bundle.md).
• Shipped adapter: data/sft/cpp_mojo/adapter_15b_v2 (LoRA on Qwen/Qwen2.5-Coder-1.5B-Instruct), plus the training pairs in data/sft/.
• Algorithm dataset generator: scripts/sft/build_algorithm_pairs.py emits verified C/C++/Python → Mojo (plus Rust/C bonus) pairs into data/sft/algorithms/ from the seed corpus in examples/, with a rejects channel for unmappable constructs.
• Record/replay shims: scripts/sft/record_replay.py with bool/int normalisation so captured fixtures replay deterministically.
• Psychrometric shims: scripts/sft/psychro_shims.py provides a real PsyPsatFnTemp implementation (replacing the earlier pvstar stub) for EnergyPlus-flavoured ports.
• God-object slicing: scripts/sft/god_object_slice.py emits per-module sub-state Mojo structs from a god-object slice.
• Data flywheel: just flywheel pipes verified repairs back into stdlib_maps/ and the SFT corpus (docs/data_flywheel.md).

Documentation

Topic	Where
Running and retraining the Mojo model	RUN.md, docs/fine_tuning_guide.md, docs/finetune_qwen7b.md
Cloud/NVIDIA training bundle	tools/cloud/README.md, scripts/sft/cloud_bundle.md
Migration status report	docs/migration_status.md
Data flywheel	docs/data_flywheel.md
LLVM IR grounding and IR-pair datasets	docs/llvm_ir_pipeline.md, docs/ir_pair_pipeline.md
Multi-LLM pipeline	docs/multi_llm_pipeline.md
Migrating Topologic	docs/topologic_migration.md
Strategy and research	docs/north_star_strategy.md, docs/transpilation-methods-research.md, docs/related_projects.md
Benchmarks	benchmarks/README.md
Bit-exactness evaluation harness	benchmarks/epmojo_eval/README.md
Post-pass rules	src/transpilers/repair/mojo_postpass.py (module docstring)

Layout

src/transpilers/   frontends · ir (hir/mir/lir) · passes · backends · llm · verify · repair · rag · graph · cli
scripts/           dataset builders · sft/ (fine-tune + eval) · rag/ · migration_status.py
data/              datasets · the shipped Mojo LoRA adapter · codebase RAG index
examples/          verified corpus (algorithms/ + per-language samples)
tests/             engine test suite (Mojo-gated and GPU/ML cases skip automatically)
benchmarks/        transpilation-bench (40-task C++/Python → Mojo) · epmojo_eval/ (bit-exactness harness)
tools/             migraph (migration dashboard) · cloud (portable NVIDIA training bundle)
stateless transpilation/   workspace for the one-shot runner (manifest, out/) and its review/verify scripts
deploy/            Dockerfile for the HTTP API

Development

just --list       # every recipe
just test         # pytest
just lint         # ruff check src tests
just fmt          # ruff format src tests
just check        # lint + test (what CI should run)

The pytest suite needs no target toolchain; Mojo-gated and ML/torch tests skip automatically when their dependencies are absent. Core tooling: uv (Python dependencies), just (task runner), direnv (shell activation) and the Hugging Face stack (Transformers / PEFT / LLaMA-Factory) for fine-tuning.

Roadmap

Tracked in GitHub Issues. Highlights: dynamic array growth on Fortran (done on C), OptionT/RefT in the type lattice, and statement-expression sequencing in HIR (to lift postfix i++/i-- out of expression contexts).

Licence

Copyright (c) 2026 Bartlomiej Tokarzewski. All Rights Reserved. No copying, distribution or modification without prior written permission; see LICENSE.