Togeari

A machine that runs thousands of KIR jobs at once, out of order, on pluggable engines, and checks recorded runs against the reference interpreter.

A KIR program describes one job as a plain sequential run: call this node, branch on that value, loop over these items. Togeari runs many such jobs together the way an out-of-order CPU core runs a thread. Inside a job it overlaps everything the sequential run cannot tell apart; across jobs it batches calls to the same engine and keeps every engine unit busy. kohakunode's reference interpreter defines what each job must produce, and the conformance checker replays recorded runs through it.

$ python examples/parallel/machine.py
part 1: one city, one job
  conformance: 1/1 jobs match the reference
  Kyoto   weather           |###############        |  0.00s
  Kyoto   events            |##########             |  0.00s
  Kyoto   traffic           |############           |  0.00s
  Kyoto   survey north      |#####                  |  0.00s
  Kyoto   survey harbor     |#####                  |  0.00s
  Kyoto   survey old town   |#####                  |  0.00s
  Kyoto   survey west       |#####                  |  0.00s
  Kyoto   survey station    |#####                  |  0.00s
  Kyoto   alert             |               ####### |  0.30s
  summary: storm, 15 events, traffic 0.5, storm warning for Kyoto, district scores [5, 2, 6, 8, 9]
  strands at once (peak): 3
  calls in flight at once (peak): 8
  (a strand ends once it has fetched its code; its calls stay in flight after it)
  one job: 0.45s; the same calls one after another: 1.40s

One job of a sequentially written program (examples/parallel): the two parallel arms, an independent lookup and five district surveys all run at once; the surveys do not wait for the storm branch to be decided, and alert starts the moment weather returns.

When to use it

• Many jobs, each a small program of calls to slow things: models on a GPU, HTTP services, heavy Python functions. You write one job as plain sequential code; the machine overlaps everything that sequential code cannot tell apart, inside a job and across jobs.
• You need the results to be exactly what the sequential code gives. Every recorded job can be replayed through the kohakunode reference interpreter, and a mismatch names the call.
• Calls of one kind should be batched across jobs (a GPU model wants 64 items at once even though each job sends one), and several engine units should share the work and fail over.

It is not a workflow scheduler for long-lived tasks or a cluster manager: one machine is one Python process with one dispatcher thread, and engines bring their own workers, processes or servers.

Concepts

The machine is built like an out-of-order CPU core, with jobs as threads:

flowchart LR
    src[("source")] --> adm["admission"] --> jobs["resident jobs<br/>(strands, slots, window)"]
    jobs -->|"ready call"| lanes["lanes<br/>(queue, form, pick unit)"]
    lanes --> units["engine units<br/>(threads, processes, GPU, HTTP)"]
    units -->|"result via inbox"| jobs
    jobs -->|"retired in order"| sink[("sink")]
    jobs -.->|"recorded calls"| ref["kohakunode reference<br/>(togeari conform)"]

• Out of order, exactly. Each job is a thread; each statement an instruction. Writes are renamed to value slots, statements issue as soon as their inputs exist, and a per-job window retires them in program order. Failures are precise: they are taken in program order, younger work is squashed, and @try behaves as in the sequential run.
• Run-ahead without guessing. A branch whose condition is still being computed parks; the code after its reconvergence point keeps going. each runs one strand per item. Nothing runs on a path the sequential run would not take.
• One job, many strands; many jobs, one machine. A job is never split into sub-jobs: its strands share one window and one order. Thousands of jobs, of any number of programs, share one code memory and one dispatcher.
• Lanes and multi-issue. A lane holds interchangeable units of one engine kind (call, batch or stream). It queues ready calls from every job, forms batches, picks units by a policy, multi-issues to every unit with free capacity, retries on unit errors, fails over, times out, and takes units in and out while running.
• Engines are contracts. The machine defines what an engine provides and how it behaves (B1–B7: submit never blocks, exactly one result per item, honest capacity, item errors apart from unit errors). check_engine and togeari units test any engine against them. Builtins: PythonCallEngine (threads or processes), WebCallEngine (any HTTP service), TorchEngine (models sharing devices under one scheduler).
• Effects stay in order. ordered nodes run when the sequential run would reach them; mutates nodes change values in place without older readers seeing it.
• Plugins at every seam. Admission, submit, results, faults, the sink, unit state, the event bus, queries and commands. Counters, the run report, the trace, the conformance recorder, the HTTP inspector and the budget are ordinary plugins.
• Inspectable and supervised. Queries and commands go through the dispatcher's inbox and see one consistent state. A failing job fails alone, a failing plugin is disabled, and a dead dispatcher answers every caller instead of hanging.
• Proof. Every node call of a recorded job is kept in retire order; togeari conform replays it through kohakunode, and --reexecute runs chosen nodes again to catch a result delivered to the wrong statement.
• Two cores, one protocol. The dispatcher is written twice: in Python, the reference, and in Rust (togeari._rs, in every wheel), which makes the same decisions in the same order 1.9–5.1× faster and leaves the GIL to engine threads while it waits. The Rust core runs by default; core="python" runs the reference (speed).

Install

pip install togeari              # core (kohakunode >= 0.2.2)
pip install "togeari[web]"       # WebCallEngine (httpx)
pip install "togeari[torch]"     # TorchEngine
pip install "togeari[vault]"     # KohakuVault store, source and sink
pip install "togeari[report]"    # zstd-compressed run report events

Wheels cover CPython 3.10–3.14 on Linux x86_64 / aarch64, Windows x64 and macOS arm64; each carries the native extension togeari._rs, so the Rust core runs with nothing more to install. Elsewhere pip builds the sdist, which needs a Rust toolchain (rustup). From a clone (Windows PowerShell or bash), also with a Rust toolchain to build the extension:

git clone https://github.com/KohakuBlueleaf/Togeari && cd Togeari
uv venv -p 3.12
uv pip install -e ".[web,vault,dev]"     # add torch from the index matching your driver
togeari doctor

togeari doctor prints versions, the core a machine runs, devices and any leftover worker processes. A machine runs the native Rust core when the extension is built and the Python core, the reference, otherwise (core="auto"); Machine(core="python" | "rust") (or TOGEARI_CORE=python|rust) picks one, and asking for "rust" without the extension is a construction error (native core). Both make the same decisions in the same order.

Quick start

A machine is a program, its node library, and where each node runs:

from pathlib import Path

from kohakunode import NodeLibrary

from togeari import Inline, Lane, Machine, Use
from togeari.engines.python import PythonCallEngine
from togeari.io import Iterable, Memory

lib = NodeLibrary("demo")


@lib.node(outputs=["y"])
def slow_double(x: int) -> int:
    return 2 * x


@lib.node(outputs=["total"])
def add_up(ys: list) -> int:
    return sum(ys)


PROGRAM = """
@def (xs: list)main(total):
    ys = []
    (xs)each(x, `one`)
    one:
        (x)slow_double(y)
        (ys, y)append(ys)
    (ys)add_up(total)
"""

work = Lane([PythonCallEngine(workers=4, mode="thread")], name="work")
machine = Machine(
    program=PROGRAM,
    entry="main",
    libraries=[lib],
    bind={"slow_double": Use(work), "add_up": Inline(), "kir.std.*": Inline()},
)
with machine:
    sink = Memory()
    machine.run(Iterable([{"xs": [1, 2, 3]}, {"xs": [4, 5]}]), sink)
    print(sorted(r.outputs["total"] for r in sink.records))

It prints [12, 18]. Records reach the sink as jobs finish, so their order is not the input order. Keys are strings (Iterable without key= gives "0", "1", …), so sort by a value the job carries, or give key=. A str program is KIR text; a Path is a file.

For a machine module (a file with build() returning the machine, a source and a sink), with the module's folder on PYTHONPATH so the CLI can import its node library:

togeari check   examples/hello/machine.py              # build, start no unit, print every problem
togeari run     examples/hello/machine.py --report runs/hello
togeari conform runs/hello --lib nodes:lib             # replay every recorded job
togeari conform runs/hello --lib nodes:lib --reexecute compose
togeari decode  examples/hello/hello.kir --entry greet --lib nodes:lib
togeari units   examples/hello/machine.py              # engine behaviour checks on every unit

Examples

Their output on the release tree (the default core, Rust):

$ python examples/hello/machine.py
Ayu: 3 cards ['Hello, Ayu!', 'Konnichiwa, Ayu!', 'Bonjour, Ayu!']
Bao: 4 cards ['Ni hao, Bao!', 'Hola, Bao!', 'Hallo, Bao!', 'Hello, Bao!']
Chi: 1 cards ['Konnichiwa, Chi!']
Dev: 3 cards ['Bonjour, Dev!', '?, Dev!', 'Hello, Dev!']
Eli: 0 cards []
conformance: 5/5 jobs match the reference
5 jobs, 38 node calls in 1.04s

$ python examples/multi_unit/machine.py
40 jobs in 2.10s, wrong results: none
conformance: 40/40 jobs match the reference
lane work: retries 1, units down 1, back up 1
  unit slow    state up    items   84  errors {}
  unit medium  state up    items  171  errors {}
  unit fast    state up    items  225  errors {'SimOutage': 1}

$ python examples/trace_replay/machine.py
60 documents in 1.71s, kept 41
one job at a time, one call at a time: about 40.4s
  lane fetch  submitted   60  batch sizes {}
  lane score  submitted  243  batch sizes {7: 8, 5: 3, 6: 11, 4: 10, 3: 3, 10: 1, 11: 1, 8: 3, 9: 1, 2: 1, 1: 1}
  lane parse  submitted  243  batch sizes {}
conformance: 60/60 jobs match the reference

$ python examples/mixed/machine.py          # an HTTP registry, 8 worker processes, a network on cuda:0
st003: calm/strong    ['calm/strong', 'calm/strong', 'calm/strong', 'calm/strong', 'calm/strong', 'pulse/strong', 'calm/strong']
st004: calm/weak      ['calm/weak', 'calm/weak', 'calm/weak', 'calm/weak', 'calm/weak', 'calm/weak', 'calm/weak', 'calm/weak']
...
... 120 stations, 0 failed
conformance: 120/120 jobs match the reference
ordered log: 0 problem(s)
120 jobs, 5634 node calls in 3.27s

$ PYTHONPATH=examples/mixed togeari conform runs/mixed/report --reexecute features --reexecute classify --reexecute majority
run 20261022-014140-77768-7bd5a8: 120/120 conform; 0 with weak values; 0 weak digests
120/120 jobs conform; 0 with weak values; 0 weak digests

In mixed, twelve stations are unknown to the registry: their lookup fails inside @try, the fault is taken in program order, and the job continues with gain 1.0. Each station's note lines reach the survey log in window order, followed by its verdict, while the windows themselves are computed out of order.

Builtin parts

The main knobs (the full table, with the debug knobs and the Machine arguments, is in writing a machine):

Every knob can change while the machine runs (machine.command("set_knob", ...)).

Speed: the Python and Rust cores

The Rust core makes the Python core's decisions 1.9–5.1× faster. Measured in one run of scripts/studies/p8/s8.py --scale full on one workstation (CPython 3.13, one GPU): every measurement runs each variant once per round in a rotating order; medians of five rounds (ceiling) or three (the rest). Every table, the method and the rule that chose the defaults: the cores compared.

Dispatcher ceiling (simulated engines that answer at once, so the dispatcher is the only cost):

Scaling (dispatcher µs per resident job or per each item; lower is better):

GIL contention (an engine fed from its own thread, items/s, while the dispatcher runs a chain workload paced at 100, 300 and 1000 jobs/s, or at its ceiling):

At its ceiling the Python core runs about 25,000 calls/s and the Rust core about 40,000–47,000, so the ceiling column compares engines next to a dispatcher doing more work on Rust.

gil_offer_us. The default, None, gives the fastest dispatcher, and below its ceiling the dispatcher waits for events often enough, releasing the GIL each time, that engine threads run nearly as if it were idle: a torch network in a worker process keeps 6,884 items/s with the dispatcher at 1000 jobs/s, against 895 on the Python core. At the ceiling the dispatcher never waits, so other threads get the GIL only at the interpreter's switch interval (562 items/s). gil_offer_us=300 offers it at most every 300 µs of the dispatcher's time and keeps 1,970 items/s there, at a cost of up to 18% to the dispatcher (inline chains with RunReport, whose threads take each offer); 0 offers at every chance and costs up to 45% on those chains.

Measured

All on one desktop machine with one GPU for the device rows; each block names its method and the blocks below the Python core's. Full tables: docs/performance.

Dispatcher ceiling, Python core (scripts/benches/dispatcher_ceiling.py: simulated engines that answer at once, so the dispatcher is the only cost; median of five rounds):

With the process's own collector threshold (gc_threshold=None), interleaved in the same rounds: 69,551, 26,872 and 37,393 node calls/s (the default is 6–14% ahead).

Cost does not grow with load (scripts/benches/scaling.py, dispatcher µs per job or per each item; the minimum of three runs):

A loop iteration costs the same at any count too: 66–87 µs from 250 to 8,000 iterations, for jump loops and repeated mutates alike (tests/scaling).

One job against its calls one after another (examples/parallel, regenerated by scripts/docs/plots.py): one job 0.45 s against 1.40 s; four jobs 0.71 s against 4.71 s.

Engines (study S1, scripts/studies/s1/run.py --scale full, 33 configurations, one run each):

The builtin defaults follow these numbers: PythonCallEngine(mode="thread", transport="shm") with shared memory from 1 MiB, TorchEngine(schedule= "priority_streams", isolation="thread").

examples/mixed (120 jobs, three engines, every job recorded; one run each): 5,634 node calls in 4.07 s (29 jobs/s) on the Python core and 3.27 s (37 jobs/s) on the Rust core; 120 / 120 jobs conform in replay and in re-execution.

How measured:

• The ceiling rows are the median of five interleaved rounds; the scaling rows the minimum of three runs; S1 and examples/mixed one run each. S1 shared the machine with short example runs during its first 9 minutes (M1 and the first M2 configurations).
• The benches record nothing and publish events to no subscriber; the examples record every job (record=1.0) and run RunReport where shown, so their times include recording (a digest and a copy per call) and event writing.
• "dispatcher busy" is the dispatcher thread's busy fraction: its time outside the inbox wait over the run's wall time.
• tests/scaling fails when the cost at 4N is above twice the cost at N.
• S1: each phase is a 1 s warm-up then a 2.5 s window; raw JSON and logs in runs/s1/full/.

Architecture

Packages under src/togeari/:

flowchart LR
    examples["machine modules, examples, CLI"] --> machine["core.construct: Machine"]
    machine --> program["core.program: code memory, decode tables, bind"]
    machine --> runtime["core.runtime: dispatcher"]
    runtime --> lanes["core.lanes"]
    lanes --> policies["core.policies"]
    lanes --> contracts["core.contracts: engines, results, work items"]
    runtime --> plugin["core.plugin + core.observe: hooks, bus"]
    engines["engines: python, web, torch"] --> contracts
    plugins["plugins: counters, report, recorder, inspector, budget"] --> plugin
    conformance["conformance"] --> kohakunode["kohakunode reference"]
    program --> kohakunode

src/togeari-rust/ is the Rust core (togeari._rs): the dispatcher's state, stages and builtin policies, and native lanes, with modules mirroring core/. core.construct picks the core a machine runs; everything around the dispatcher (engines, plugins, stores, the CLI) is the same Python for both.

A statement's path through the machine:

flowchart LR
    fetch["fetch: rename writes to slots"] --> wait{"inputs filled?"}
    wait -->|"no"| park["wait on the slots"]
    park --> wait
    wait -->|"yes"| issue["issue: policy order"]
    issue --> inline["inline node: run on the dispatcher"]
    issue --> lane["lane queue"]
    lane --> form["form a batch / pick a unit"] --> engine["engine unit"]
    engine -->|"done -> inbox"| writeback["writeback: fill slots, wake waiters"]
    inline --> writeback
    writeback --> retire["retire in program order"]
    retire -->|"fault taken"| squash["squash younger; @try handler"]
    retire --> sink["job ends: record to the sink"]

Inside one job: strands share one window and one order key space:

flowchart LR
    s0["strand 0: main"] -->|"branch condition pending: park"| s1["strand 1: code after the branch, reading phis"]
    s0 -->|"each over 5 items"| it["5 iteration strands, started in index order"]
    s0 --> win[("one window, retired in key order")]
    s1 --> win
    it --> win

One call across the threads (the dispatcher is the only thread that touches job state):

sequenceDiagram
    participant D as dispatcher
    participant L as lane
    participant E as engine thread / process
    participant I as inbox
    D->>D: fetch, rename, inputs ready
    D->>L: enqueue the work item
    L->>E: submit (returns at once)
    E-->>I: done(results)
    I->>D: next cycle drains the inbox
    D->>D: writeback: fill the slot, wake waiters
    D->>D: retire in program order

A fault inside @try, taken in program order:

flowchart LR
    call["(x)parse(y) fails"] --> mark["marked failed at once"]
    mark --> squash["younger statements squashed<br/>(finished results kept for reuse)"]
    squash --> head{"oldest unfinished?"}
    head -->|"yes"| take["fault taken"]
    take --> handler["handler strand runs<br/>err bound to the failure"]
    handler --> refetch["code after the try fetched again"]

A lane:

flowchart LR
    q[("bucketed queue")] --> former["former: batch when full or waited"]
    former --> sel["selector: unit order, admits"]
    sel --> u1["unit A"]
    sel --> u2["unit B"]
    sel --> u3["unit C (down: health polls)"]
    u1 -->|"UnitError"| retry["back to the queue after backoff"] --> q

Where plugins hook in:

flowchart LR
    src["source item"] -->|"pre_admit, vote_admit"| job["job admitted"]
    job -->|"on_job_start"| run["statements run"]
    run -->|"pre_submit"| unit["engine unit"]
    unit -->|"post_result"| wb["writeback"]
    wb --> run
    run -->|"on_fault"| run
    run -->|"on_job_end, pre_sink"| sink["sink"]
    bus[("event bus")] -->|"on_event (own thread)"| plugins["plugins"]
    lanes["lanes"] -->|"on_unit_state"| plugins
    plugins -->|"contribute_queries / commands / policies"| inbox["dispatcher inbox"]

Documentation

Checks

python -m pytest tests -q -m "not gpu"                      # CPU only (seeds default 20)
python -m pytest tests -q -m gpu                            # the tests on cuda:0, on their own
TOGEARI_SEEDS=300 python -m pytest tests/machine/generated -q
python scripts/benches/dispatcher_ceiling.py
python scripts/benches/scaling.py
python scripts/studies/s1/run.py --scale full               # thread vs process, transports, GPU sharing
python scripts/studies/p8/s8.py --scale full                # the cores compared, every gil_offer_us
TOGEARI_CORE=python python -m pytest tests -q -m "not gpu"  # the suite on the reference core

The generated suite runs programs from kohakunode.testing.progen (switch, parallel, ordered and mutates nodes with aliases, each with exits, @try around each, failing nodes) under 12 configurations (windows 1 / 64 / 256, injected item errors and unit outages, early forming, every selector, run-ahead off, every mutates mode, a content store, a pass-through plugin on every hook), several programs in one machine with one loaded mid-run, snapshot resume and positional reruns; every job is re-executed against the reference.

Contributing

Open an issue first for a feature. The code conventions are in CLAUDE.md: black and ruff at line length 100, no imports inside functions, nested folders, short comments that say what the code does, docs that describe what exists, and every check able to fail on a broken implementation. Run the checks above before a pull request.

License

Apache-2.0.