tract_core/

lanes.rs

//! Serving many streams through one state.
//!
//! A prepared model serves one stream at a time: `spawn()` hands out a state,
//! and each `run()` on it is one turn of that stream. [`LanedRunnable::wrap`]
//! makes one state serve many streams at once, by batching the turns that
//! happen to be ready into a single `run()` on a single state. Callers see
//! nothing of it: they still spawn a state each and run it per turn.
//!
//! This is the one corner of `core` where threads, queues and a promise per
//! caller appear, so its plumbing carries more comment than the rest of the
//! crate: the types are small, and what they mean is where the bugs are.
//!
//! # Two batch axes, and the words for them
//!
//! `doc/lexicon.md` is the reference; the four words this module lives on:
//!
//! - **lane** -- where one stream's state sits inside the shared state: axis 0
//!   of a laned op state's buffers, of extent `max_lanes`, addressed through
//!   the turn's [`Seating`].
//! - **seat** -- a position in one turn's batch: axis 0 of the turn's input and
//!   output tensors, of extent the turn's occupancy.
//! - **turn** -- one `run()` on the state. It has one seating, and its seats
//!   are the streams it serves.
//! - **call** -- one `run()` on a [`LanedStateHandle`]. Usually one seat of one
//!   turn, but a caller feeding several at once asks for several seats, and is
//!   answered over as many turns as they took to seat.
//!
//! One lane takes at most one seat per turn: a stream's state is sequential.
//!
//! # The actors
//!
//! ```text
//!     caller thread                     caller thread
//!      (stream A)                        (stream B)
//!          |                                 |
//!   LanedStateHandle                  LanedStateHandle
//!    lane 0, cloned                    lane 1, cloned
//!          |                                 |
//!          |   Request::{Spawn, Call, Drop}  |
//!          +---------------+-----------------+
//!                          |   one mpsc queue, cloned per handle
//!                          v
//!       +--------------------------------------------+
//!       |        worker thread "tract-lanes"         |
//!       |                                            |
//!       |  Worker  state   the one state, laned      |
//!       |          lanes   which of them are taken   |
//!       |          queued  seats waiting, and the    |
//!       |                  calls they answer         |
//!       +--------------------------------------------+
//!                          |
//!                          |   one answer channel per call
//!                          v
//!                    back to the callers
//! ```
//!
//! The worker owns the state and the [`LaneTable`] both, and is the only thread
//! which touches either. That is not tidiness: taking a lane **resets** it,
//! which writes the state -- device memory for a state on a GPU -- so it has to
//! happen where the state lives. Hence a handle asks for a lane rather than
//! taking one, and `Lease` only knows how to send it back.
//!
//! # The life of a handle, in requests
//!
//! - `Request::Spawn` -- `LanedRunnable::spawn` asks for a lane and blocks
//!   until the worker resets one and answers, or until it answers that every
//!   lane is taken.
//! - `Request::Call` -- `LanedStateHandle::run` sends its inputs and a
//!   one-shot channel, then blocks on that channel.
//! - `Request::Drop` -- the last clone of a handle dropped sends its lane
//!   back, and the next stream can have it.
//!
//! # The life of a turn
//!
//! 1. The worker blocks until a request arrives. If it put a seat in the queue,
//!    the turn **lingers** for [`TRACT_TURN_LINGER_US`], so that streams whose
//!    pulses land within a hair of each other share a turn instead of taking
//!    one each. Zero by default.
//! 2. Everything else pending is drained, so the turn sees every seat ready.
//! 3. `fill` seats the head of the queue: at most [`TRACT_MAX_SEATS`] seats, at
//!    most one per lane.
//! 4. `run_turn` stacks the batched inputs along axis 0 in seat order, checks
//!    that the shared ones agree across seats, publishes the seating, and runs
//!    the state **once**.
//! 5. Its outputs are sliced back per seat, borrowed lanes go back to the
//!    table, and each seat is handed to its call's `Completer`, which answers
//!    the caller once its last seat has landed.
//!
//! A turn that fails fails every seat of it, and a seat that fails fails its
//! whole call and drops that call's seats still queued.
//!
//! # A call asking for several seats
//!
//! A beam decoder hands a stateless model its k hypotheses in one `run()`. Such
//! a call **explodes** into one `Seat` per slice of its batched inputs; the
//! queue is therefore a queue of seats, not of calls, and a call wider than the
//! free lanes is split at the turn boundary rather than held until it fits
//! whole -- so a wide call cannot starve the one-seat calls behind it. Its first
//! seat sits in its caller's own lane and the rest **borrow** free lanes, which
//! resets them, so no seat ever reads what another caller left.
//!
//! With `max_lanes` 4, A holding lane 0 and B lane 1, A calling for 5 seats and
//! B for one:
//!
//! ```text
//!   A: run([5, ..])                      B: run([1, ..])
//!         |                                    |
//!         | explode                            | explode
//!         v                                    v
//!      A0 A1 A2 A3 A4                          B0
//!         |                                    |
//!         +----------------+-------------------+
//!                          v
//!            queue: A0 A1 A2 A3 A4 B0
//!
//!   turn 1                        lane 0  lane 1  lane 2  lane 3
//!     A0 -> its own lane          [ A0 ]  [ B0 ]  [ A1 ]  [ A2 ]
//!     A1 -> borrows lane 2          |       |       |       |
//!     A2 -> borrows lane 3          +-------+---+---+-------+
//!     A3 -> nothing free, waits                 |  one run(), occupancy 4
//!     A4 -> waits                               v
//!     B0 -> its own lane              B answered; A has 3 of 5 seats
//!
//!   turn 2                        lane 0  lane 1  lane 2  lane 3
//!     A3 -> its own lane          [ A3 ]    --    [ A4 ]    --
//!     A4 -> borrows lane 2          |               |
//!                                   +-------+-------+
//!                                           |  one run(), occupancy 2
//!                                           v
//!                                 A's 5 seats stack back into one [5, ..]
//! ```
//!
//! A one-seat call pays nothing for any of this: `explode` does not slice it and
//! `assemble` hands its outputs straight back.
//!
//! # What a laned model must satisfy
//!
//! - **The batch axis is axis 0**, on at least one input and one output, and it
//!   is one symbol for all of them. It is the only position where a seat's
//!   values are a contiguous run, which is what makes stacking a memcpy and
//!   slicing a view, and the only one whose seats are independent under a
//!   liquid schedule. A model wanting it elsewhere gets it moved by a graph
//!   edit, never by a per-turn transpose here.
//! - **An input or output whose axis 0 is not a symbol is shared**: one value of
//!   it serves the whole turn, so seats disagreeing about it fail the turn, and
//!   a shared output is handed back to every seat.
//! - **Axis 0 of every stateful node** is that batch axis. `wrap` walks the I/O
//!   facts and resets every lane once, which fails a state that cannot serve
//!   several streams; the interior is otherwise on trust.
//!
//! # Traps
//!
//! - `Seat::lane` is the lane the seat **sits in**, not the lane its caller
//!   holds: `fill` overwrites it when it borrows, or a turn ends up seating one
//!   lane twice.
//! - The knobs are read at `wrap` and are process-wide, so tests which set
//!   [`TRACT_TURN_LINGER_US`] have to serialize.
//! - Dropping the runnable closes the queue and **joins** the worker. The worker
//!   holds per-thread device state whose destructors must not run against
//!   libraries already tearing themselves down.
//!
//! # Not here
//!
//! Admission is not a policy yet: `spawn` fails when every lane is taken rather
//! than waiting for one. A caller holding a lane for the life of a handle is
//! what makes that felt, and what a server wants of it is still open.

use std::collections::{HashMap, VecDeque};
use std::fmt::Debug;
use std::sync::Mutex;
use std::sync::atomic::{AtomicU64, Ordering};
use std::sync::mpsc::{Receiver, Sender, channel};
use std::thread;
use std::time::{Duration, Instant};

use metrics::{Counter, Gauge, Histogram, Label, Unit};

use crate::internal::*;

/// The lanes of one laned state: which are taken, and which of them a turn
/// seats.
///
/// Plain data. Taking a lane does not touch the state's buffers, and clearing
/// what a stream left in a lane it gave up is the table's caller's, since it
/// writes the state -- device memory for a state on a GPU -- and must run where
/// the state lives. So a lane handed to a new stream carries the previous one's
/// history until that caller resets it.
#[derive(Debug, Clone, PartialEq, Eq)]
pub struct LaneTable {
    /// One flag per lane, true while a stream holds it. A lane's index is its
    /// [`LaneId`], and the length is the state's fixed lane count.
    taken: Vec<bool>,
}

impl LaneTable {
    pub fn new(max_lanes: usize) -> TractResult<LaneTable> {
        ensure!(max_lanes > 0, "A laned state needs at least one lane");
        Ok(LaneTable { taken: vec![false; max_lanes] })
    }

    /// The extent of the lane axis of the state's per-lane buffers, fixed for
    /// the life of the state.
    pub fn max_lanes(&self) -> usize {
        self.taken.len()
    }

    pub fn taken(&self) -> usize {
        self.taken.iter().filter(|t| **t).count()
    }

    /// The lowest free lane, `None` when every lane is taken -- whether that
    /// blocks the new stream or fails it is the caller's policy. Lowest first,
    /// so that a turn seating every lane seats a run of consecutive lanes.
    pub fn take(&mut self) -> Option<LaneId> {
        let lane = self.taken.iter().position(|t| !t)?;
        self.taken[lane] = true;
        Some(LaneId(lane))
    }

    /// Hand `lane` back, for [`LaneTable::take`] to give to another stream.
    pub fn give_back(&mut self, lane: LaneId) -> TractResult<()> {
        ensure!(self.is_taken(lane), "Lane {} is not taken, so it can not be given back", lane.0);
        self.taken[lane.0] = false;
        Ok(())
    }

    pub fn is_taken(&self, lane: LaneId) -> bool {
        self.taken.get(lane.0).copied().unwrap_or(false)
    }

    /// Seat `lanes`, in that order: seat `ix` of the coming turn carries the
    /// `ix`th of them. Every one must be taken, so that a stream which ended
    /// can not be seated by a stale handle of it.
    pub fn seat(&self, lanes: impl IntoIterator<Item = LaneId>) -> TractResult<Seating> {
        let lanes: Vec<LaneId> = lanes.into_iter().collect();
        for lane in &lanes {
            ensure!(self.is_taken(*lane), "Seating lane {}, which no stream took", lane.0);
        }
        Seating::new(self.max_lanes(), lanes)
    }
}

crate::declare_knob!(
    scopable TRACT_MAX_SEATS,
    usize,
    256,
    "Most streams a laned runtime serves in one turn, clamped to the state's lanes."
);

crate::declare_knob!(
    scopable TRACT_AUTOBATCH_LANES,
    Option<usize>,
    None,
    "Sessions a deployment config's subnet is autobatched for; unset, it is not autobatched."
);

crate::declare_knob!(
    scopable TRACT_TURN_LINGER_US,
    usize,
    0,
    "How long a laned runtime waits for more streams once one is ready to run."
);

/// A model prepared to serve many streams at once: one state, one lane per
/// stream, and turns seating whoever is ready.
///
/// `spawn` hands out a [`LanedStateHandle`] per stream, each holding a lane, and
/// every `run` on a handle is a request to the worker thread which owns the
/// state and the [`LaneTable`] both. The worker takes the turns queued at that
/// moment, at most one per lane and at most [`TRACT_MAX_SEATS`] of them,
/// concatenates their inputs along axis 0, publishes the seating and runs the
/// state once, then hands each stream back its own seat.
///
/// A stream feeds one seat per turn: axis 0 carries streams, not data. Inputs and
/// outputs whose axis 0 is a symbol are the batched ones; the rest are shared,
/// so one value of such an input serves the whole turn and every seat must feed
/// the same one, and such an output is handed back to every stream.
#[derive(Clone)]
pub struct LanedRunnable {
    shared: Arc<Shared>,
}

struct Shared {
    /// [`std::sync::mpsc::Sender`] is not `Sync`, and a `Runnable` is: handles
    /// take their own clone of it, under the lock, once. `None` once the
    /// runnable is being dropped, which is what closes the queue.
    requests: Mutex<Option<Sender<Request>>>,
    /// Joined when the runnable is dropped, so the worker is gone before
    /// whatever the caller does next.
    worker: Mutex<Option<thread::JoinHandle<()>>>,
    /// The one-stream model the worker spawned its state from.
    inner: Arc<dyn Runnable>,
    /// `inner`'s model and plan, carried so that a laned runnable answers
    /// [`Runnable::typed_model`] and [`Runnable::typed_plan`] like any other.
    model: Option<Arc<TypedModel>>,
    plan: Option<Arc<TypedSimplePlan>>,
    /// The symbol axis 0 of the batched tensors carries: the turn's occupancy,
    /// never a stream's own shapes.
    batch: Symbol,
    /// Lanes the state was reset for at `wrap`, hence the most streams that can
    /// hold a handle at once and the widest a turn can be.
    max_lanes: usize,
    /// Turns and seats served, shared with the worker which is what counts them.
    counts: Arc<Counts>,
}

/// The worker owns per-thread device state -- a CUDA stream, its cuBLAS and
/// cuDNN handles -- whose destructors run as the thread exits. Nothing joined
/// it before, so a process that returned from `main` while the worker was
/// still winding down ran those destructors against libraries already tearing
/// themselves down in their own `atexit` handlers, and segfaulted in
/// `cudnnDestroy` about one run in fifteen. Closing the queue is what stops
/// the worker, so the sender goes first and the join waits for at most the
/// turn in flight.
impl Drop for Shared {
    fn drop(&mut self) {
        if let Ok(mut requests) = self.requests.lock() {
            requests.take();
        }
        let worker = self.worker.lock().ok().and_then(|mut worker| worker.take());
        if let Some(worker) = worker {
            let _ = worker.join();
        }
    }
}

/// What the worker has served, for whoever tunes the turn policy: mean
/// occupancy is `seats / turns`.
#[derive(Debug, Default)]
struct Counts {
    /// Turns run since the model was prepared.
    turns: AtomicU64,
    /// Seats filled over those turns, so `seats / turns` is mean occupancy.
    seats: AtomicU64,
}

/// What the worker reports through the `metrics` facade, labelled `runnable`
/// with the model's `tract.name` when it has one. tract installs no recorder:
/// the host does, before the runnable is wrapped, which is when the handles
/// bind to it.
struct Meters {
    turns: Counter,
    seats: Counter,
    seats_per_turn: Histogram,
    lanes_taken: Gauge,
    queue_wait: Histogram,
    turn: Histogram,
}

impl Meters {
    fn new(name: Option<&str>) -> Meters {
        metrics::describe_counter!(
            "tract_autobatch_turns_total",
            "Turns an autobatched runnable ran"
        );
        metrics::describe_counter!(
            "tract_autobatch_seats_total",
            "Seats an autobatched runnable filled over its turns"
        );
        metrics::describe_histogram!(
            "tract_autobatch_seats_per_turn",
            "Seats one turn of an autobatched runnable filled"
        );
        metrics::describe_gauge!(
            "tract_autobatch_lanes_taken",
            "Lanes held by the streams of an autobatched runnable"
        );
        metrics::describe_histogram!(
            "tract_autobatch_queue_wait_seconds",
            Unit::Seconds,
            "From a seat queued to the start of the turn seating it, linger included"
        );
        metrics::describe_histogram!(
            "tract_autobatch_turn_seconds",
            Unit::Seconds,
            "One turn of an autobatched runnable, stacking and slicing included"
        );
        let labels: Vec<Label> =
            name.map(|name| Label::new("runnable", name.to_string())).into_iter().collect();
        Meters {
            turns: metrics::counter!("tract_autobatch_turns_total", labels.clone()),
            seats: metrics::counter!("tract_autobatch_seats_total", labels.clone()),
            seats_per_turn: metrics::histogram!("tract_autobatch_seats_per_turn", labels.clone()),
            lanes_taken: metrics::gauge!("tract_autobatch_lanes_taken", labels.clone()),
            queue_wait: metrics::histogram!("tract_autobatch_queue_wait_seconds", labels.clone()),
            turn: metrics::histogram!("tract_autobatch_turn_seconds", labels),
        }
    }
}

impl LanedRunnable {
    /// Serve `max_lanes` streams through `inner`, which must be prepared from a
    /// model carrying a batch axis: at least one input and one output with a
    /// symbol on axis 0, and one symbol for all of them.
    pub fn wrap(inner: Arc<dyn Runnable>, max_lanes: usize) -> TractResult<LanedRunnable> {
        let model = inner.typed_model().cloned();
        let plan = inner.typed_plan().cloned();
        let mut symbols: Vec<Symbol> = vec![];
        let mut facts: Vec<(String, TypedFact)> = vec![];
        let mut batch_in: Vec<bool> = vec![];
        for ix in 0..inner.input_count() {
            let fact = inner.input_fact(ix)?;
            let symbol = batch_symbol(fact);
            batch_in.push(symbol.is_some());
            symbols.extend(symbol);
            facts.push((format!("input {ix}"), fact.clone()));
        }
        let mut batch_out: Vec<bool> = vec![];
        for ix in 0..inner.output_count() {
            let fact = inner.output_fact(ix)?;
            let symbol = batch_symbol(fact);
            batch_out.push(symbol.is_some());
            symbols.extend(symbol);
            facts.push((format!("output {ix}"), fact.clone()));
        }
        symbols.sort();
        symbols.dedup();
        if symbols.len() != 1 {
            bail!(off_axis_zero(&facts, &symbols));
        }
        ensure!(batch_out.iter().any(|b| *b), "A laned model must batch one output at least");
        let batch = symbols.remove(0);
        let counts = Arc::new(Counts::default());
        let meters = Meters::new(model.as_ref().and_then(|model| model.name()));
        let max_seats = TRACT_MAX_SEATS.get().min(max_lanes);
        let linger = Duration::from_micros(TRACT_TURN_LINGER_US.get() as u64);
        let lanes = LaneTable::new(max_lanes)?;
        let (requests, queue) = channel::<Request>();
        let (spawned, ready) = channel::<TractResult<()>>();
        let worker_counts = counts.clone();
        let worker_inner = inner.clone();
        let worker_thread = thread::Builder::new().name("tract-lanes".into()).spawn(move || {
            let state = match worker_inner.spawn().and_then(|mut state| {
                let every: Vec<LaneId> = (0..max_lanes).map(LaneId).collect();
                state.reset_lanes(&every).context("Preparing a laned model")?;
                Ok(state)
            }) {
                Ok(state) => {
                    let _ = spawned.send(Ok(()));
                    state
                }
                Err(e) => {
                    let _ = spawned.send(Err(e));
                    return;
                }
            };
            Worker {
                state,
                lanes,
                queued: Queue::default(),
                batch_in,
                batch_out,
                max_seats,
                linger,
                counts: worker_counts,
                meters,
            }
            .work(queue);
        })?;
        ready.recv().map_err(|_| format_err!("The laned worker died spawning the state"))??;
        Ok(LanedRunnable {
            shared: Arc::new(Shared {
                requests: Mutex::new(Some(requests)),
                worker: Mutex::new(Some(worker_thread)),
                inner,
                model,
                plan,
                batch,
                max_lanes,
                counts,
            }),
        })
    }

    pub fn max_lanes(&self) -> usize {
        self.shared.max_lanes
    }

    /// The model as it was prepared, serving one stream at a time: what a turn
    /// of one seat has to agree with.
    pub fn inner(&self) -> &Arc<dyn Runnable> {
        &self.shared.inner
    }

    /// The symbol axis 0 of the batched tensors carries. A stream feeds one seat
    /// per turn, so it stands for the turn's occupancy, never for a stream's
    /// own shapes.
    pub fn batch_symbol(&self) -> &Symbol {
        &self.shared.batch
    }

    /// Turns run and seats filled since the model was prepared: how wide the
    /// turns the queue actually offers are.
    pub fn turns_and_seats(&self) -> (u64, u64) {
        (
            self.shared.counts.turns.load(Ordering::Relaxed),
            self.shared.counts.seats.load(Ordering::Relaxed),
        )
    }

    fn request(&self) -> TractResult<Sender<Request>> {
        self.shared
            .requests
            .lock()
            .map_err(|_| format_err!("Poisoned laned sender"))?
            .clone()
            .context("The laned runnable is gone")
    }
}

/// The symbol axis 0 of `fact` carries, or `None` for a tensor every seat
/// shares. A stored fact can claim a symbol on an axis of extent one, so this
/// says how the caller talks, not what the graph does with it.
fn batch_symbol(fact: &TypedFact) -> Option<Symbol> {
    match fact.shape.dims().first() {
        Some(TDim::Sym(sym)) => Some(sym.clone()),
        _ => None,
    }
}

/// Why `facts` do not carry one batch symbol on axis 0: where the symbols they
/// do carry sit, and that putting one on axis 0 is a graph edit.
fn off_axis_zero(facts: &[(String, TypedFact)], symbols: &[Symbol]) -> TractError {
    let elsewhere: Vec<String> = facts
        .iter()
        .filter_map(|(what, fact)| {
            let dims = fact.shape.dims();
            let axis = dims.iter().skip(1).position(|dim| matches!(dim, TDim::Sym(_)))? + 1;
            Some(format!("{what} carries {} on axis {axis}", dims[axis]))
        })
        .collect();
    let elsewhere = elsewhere.join(", ");
    if symbols.is_empty() {
        let found = if elsewhere.is_empty() {
            "none of them carries a symbol at all".to_string()
        } else {
            elsewhere
        };
        format_err!(
            "A laned model carries its batch symbol on axis 0 of the inputs and outputs it \
             batches, and {found}. Axis 0 is where a seat's values are a contiguous run, so move \
             the batch there as a graph edit -- Batchify, or an AddAxis/MoveAxis the optimiser \
             can absorb -- rather than have every turn transpose"
        )
    } else {
        let symbols: Vec<String> = symbols.iter().map(|s| s.to_string()).collect();
        format_err!(
            "A laned model carries one batch symbol on axis 0, this one carries {}. One symbol \
             has to stand for the whole turn's occupancy, so share it across the batched inputs \
             and outputs in the export",
            symbols.join(" and ")
        )
    }
}

impl Debug for LanedRunnable {
    fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result {
        write!(f, "LanedRunnable({} lanes)", self.shared.max_lanes)
    }
}

impl Runnable for LanedRunnable {
    fn spawn(&self) -> TractResult<Box<dyn State>> {
        let requests = self.request()?;
        let (taken, lane) = channel();
        requests
            .send(Request::Spawn(taken))
            .map_err(|_| format_err!("The laned worker is gone"))?;
        let lane = lane.recv().map_err(|_| format_err!("The laned worker dropped a lane"))??;
        Ok(Box::new(LanedStateHandle {
            lease: Arc::new(Lease { lane, requests }),
            runnable: self.clone(),
        }))
    }

    fn typed_plan(&self) -> Option<&Arc<TypedSimplePlan>> {
        self.shared.plan.as_ref()
    }

    fn typed_model(&self) -> Option<&Arc<TypedModel>> {
        self.shared.model.as_ref()
    }
}

/// One stream's view of a [`LanedRunnable`]: the lane it holds, and the queue to
/// the worker. Cloning it shares the lane -- clones are the same stream, and the
/// lane goes back to the table once the last of them is dropped.
#[derive(Clone, Debug)]
pub struct LanedStateHandle {
    /// The lane this stream holds, shared by the handle's clones: the last of
    /// them dropped is what gives the lane back.
    lease: Arc<Lease>,
    /// The runnable the handle came from, for [`State::runnable`].
    runnable: LanedRunnable,
}

#[derive(Debug)]
struct Lease {
    /// The lane the worker handed this stream at spawn.
    lane: LaneId,
    /// Where to send the lane back, which is all `Drop` needs.
    requests: Sender<Request>,
}

impl Drop for Lease {
    fn drop(&mut self) {
        let _ = self.requests.send(Request::Drop(self.lane));
    }
}

impl State for LanedStateHandle {
    fn run(&mut self, inputs: TVec<TValue>) -> TractResult<TVec<TValue>> {
        let (done, outputs) = channel();
        self.lease
            .requests
            .send(Request::Call(Call { leased: self.lease.lane, inputs, done }))
            .map_err(|_| format_err!("The laned worker is gone"))?;
        outputs.recv().map_err(|_| format_err!("The laned worker dropped a turn"))?
    }

    fn runnable(&self) -> &dyn Runnable {
        &self.runnable
    }
}

/// What a handle asks the worker for, one variant per event of the handle's
/// life: a lane when it is spawned, a turn per call, and its lane back when it
/// is dropped.
enum Request {
    /// A new stream wants a lane; the worker answers with one, reset, or with
    /// the error that every lane is taken.
    Spawn(Sender<TractResult<LaneId>>),
    /// A stream wants a turn, or several at once.
    Call(Call),
    /// A stream is over and its lane goes back to the table.
    Drop(LaneId),
}

/// One `run()` on a handle. Its inputs carry one seat or several, and it is
/// answered once every one of them has been served -- over as many turns as
/// the free lanes took to seat them all.
struct Call {
    /// The lane the caller holds for the life of its handle, which this call's
    /// first seat sits in. It is a reservation, not a home: a call asking for
    /// several seats borrows the rest.
    leased: LaneId,
    /// What the caller fed, batched inputs still stacked as they came.
    inputs: TVec<TValue>,
    /// Where the assembled answer goes; the caller blocks on the other end.
    done: Sender<TractResult<TVec<TValue>>>,
}

/// One seat of one call, waiting for a lane: the lane it sits in -- its
/// caller's own until a turn borrows another for it -- its place in the call,
/// and the slice of the call's inputs it feeds.
struct Seat {
    /// The call this seat answers, keying its [`Completer`].
    call: u64,
    /// The lane the seat sits in: the call's leased one, overwritten by `fill`
    /// with a borrowed one when that lane already took a seat this turn.
    lane: LaneId,
    /// Its place in the call, so the answer stacks back in the order asked.
    ix: usize,
    /// One seat's slice of the call's inputs, shared inputs whole.
    inputs: TVec<TValue>,
    /// When its call reached the worker.
    queued: Instant,
}

/// The half of a call's answer the worker holds: its seats' outputs as they
/// land, in the order it asked for them, and where to send them once the last
/// of them has. The caller holds the other half and blocks on it.
struct Completer {
    /// One slot per seat the call asked for, filled as its seats land -- over
    /// several turns when the call was wider than the free lanes.
    served: Vec<Option<TVec<TValue>>>,
    /// The call's own answer channel, moved here from the [`Call`].
    done: Sender<TractResult<TVec<TValue>>>,
}

impl Call {
    /// The inputs of each seat of this call: the batched ones sliced along
    /// axis 0, the shared ones as they came. Every batched input says how many
    /// seats the call asks for, so they must agree, and a call with none asks
    /// for one.
    fn explode(&self, batch_in: &[bool]) -> TractResult<Vec<TVec<TValue>>> {
        ensure!(
            self.inputs.len() == batch_in.len(),
            "A call feeds {} inputs, the model takes {}",
            self.inputs.len(),
            batch_in.len()
        );
        let mut seats: Option<usize> = None;
        for (ix, input) in self.inputs.iter().enumerate().filter(|(ix, _)| batch_in[*ix]) {
            ensure!(
                input.rank() > 0,
                "Input {ix} carries the batch axis, so it can not be a scalar"
            );
            let asked = input.shape()[0];
            ensure!(
                seats.is_none_or(|seats| seats == asked),
                "A call asks for as many seats as its batched inputs carry, and input {ix} carries \
                 {asked} against {} before it",
                seats.unwrap_or(0)
            );
            seats = Some(asked);
        }
        let seats = seats.unwrap_or(1);
        ensure!(seats > 0, "A call asks for one seat at least");
        if seats == 1 {
            return Ok(vec![self.inputs.clone()]);
        }
        (0..seats)
            .map(|seat| {
                self.inputs
                    .iter()
                    .zip(batch_in)
                    .map(|(input, is_batched)| {
                        if *is_batched {
                            Ok(input.slice(0, seat, seat + 1)?.into_tvalue())
                        } else {
                            Ok(input.clone())
                        }
                    })
                    .collect()
            })
            .collect()
    }
}

impl Completer {
    /// Whether every seat the call asked for has landed.
    fn is_full(&self) -> bool {
        self.served.iter().all(Option::is_some)
    }

    /// Answer the call with its seats' outputs: the batched ones stacked back
    /// along axis 0 in the order it asked for its seats, the shared ones as the
    /// first seat got them. Every seat must have landed.
    fn answer(self, batch_out: &[bool]) {
        let served: Vec<TVec<TValue>> =
            self.served.into_iter().map(|outputs| outputs.unwrap()).collect();
        let answer = if served.len() == 1 {
            Ok(served.into_iter().next().unwrap())
        } else {
            let first = &served[0];
            (0..first.len())
                .map(|ix| {
                    if batch_out.get(ix).copied().unwrap_or(false) {
                        let seats: TVec<&Tensor> =
                            served.iter().map(|outputs| &*outputs[ix]).collect();
                        Ok(Tensor::stack_tensors(0, &seats)?.into_tvalue())
                    } else {
                        Ok(first[ix].clone())
                    }
                })
                .collect()
        };
        let _ = self.done.send(answer);
    }
}

/// The seats the worker has to seat, and the calls they answer. A call explodes
/// into seats as it arrives, so what a turn picks from is a queue of seats: it
/// fills to the free lanes from the head, and a call wider than they are is
/// split at the boundary rather than held until it fits whole.
#[derive(Default)]
struct Queue {
    /// Seats waiting for a lane, oldest first: what a turn fills from.
    seats: VecDeque<Seat>,
    /// The calls with seats still to land, by call id.
    completers: HashMap<u64, Completer>,
    /// The id the next call gets, so seats of different calls never collide.
    calls: u64,
}

impl Queue {
    fn push(&mut self, call: Call, batch_in: &[bool]) {
        let id = self.calls;
        self.calls += 1;
        let queued = Instant::now();
        match call.explode(batch_in) {
            Ok(seats) => {
                self.completers
                    .insert(id, Completer { served: vec![None; seats.len()], done: call.done });
                for (ix, inputs) in seats.into_iter().enumerate() {
                    self.seats.push_back(Seat { call: id, lane: call.leased, ix, inputs, queued });
                }
            }
            Err(e) => {
                let _ = call.done.send(Err(e));
            }
        }
    }

    /// Hand `seat` its turn's outputs, and answer the call when that was the
    /// last seat it was waiting for. A failed seat fails the whole call at
    /// once, and drops the seats of it still queued.
    fn serve(&mut self, seat: Seat, outputs: TractResult<TVec<TValue>>, batch_out: &[bool]) {
        if !self.completers.contains_key(&seat.call) {
            return;
        }
        let outputs = match outputs {
            Ok(outputs) => outputs,
            Err(e) => {
                let completer = self.completers.remove(&seat.call).unwrap();
                self.seats.retain(|queued| queued.call != seat.call);
                let _ = completer.done.send(Err(e));
                return;
            }
        };
        let completer = self.completers.get_mut(&seat.call).unwrap();
        completer.served[seat.ix] = Some(outputs);
        if completer.is_full() {
            self.completers.remove(&seat.call).unwrap().answer(batch_out);
        }
    }
}

/// The worker thread's world: the one state it runs, the lanes it hands to
/// streams, the seats it has still to run, and what it was set up with.
///
/// It is built on the calling thread and moved to the worker, which is the only
/// thread to touch it afterwards -- `state` and `lanes` go together because
/// taking a lane resets it, and a reset writes the state.
struct Worker {
    /// The one state every turn runs, spawned from the model as prepared.
    state: Box<dyn State>,
    /// Which lanes streams hold, and what a turn's seating is drawn from.
    lanes: LaneTable,
    /// Seats waiting for a lane, and the calls they answer.
    queued: Queue,
    /// One flag per input, true where axis 0 carries the batch symbol: those
    /// are sliced per seat, the rest serve the whole turn.
    batch_in: Vec<bool>,
    /// The same per output: batched ones are sliced back per seat, shared ones
    /// handed to every seat of the turn.
    batch_out: Vec<bool>,
    /// The widest turn to run, [`TRACT_MAX_SEATS`] clamped to the state's lanes.
    max_seats: usize,
    /// How long a turn waits for latecomers once its first seat is queued
    /// ([`TRACT_TURN_LINGER_US`]), zero to run as soon as one is ready.
    linger: Duration,
    /// Turns and seats served, shared with the runnable the caller reads them
    /// from.
    counts: Arc<Counts>,
    meters: Meters,
}

impl Worker {
    /// Serve `queue` until it closes, which is what dropping the runnable does.
    fn work(mut self, queue: Receiver<Request>) {
        loop {
            // The linger belongs to the turn a request opens, not to the
            // request: taking or giving back a lane must not delay the turns
            // behind it, and turns left waiting by a full one have lingered
            // already.
            while self.queued.seats.is_empty() {
                match queue.recv() {
                    Ok(request) => self.serve(request),
                    Err(_) => return,
                }
                if !self.queued.seats.is_empty() && !self.linger.is_zero() {
                    thread::sleep(self.linger);
                }
            }
            while let Ok(request) = queue.try_recv() {
                self.serve(request);
            }
            let (seated, borrowed) = self.fill();
            if seated.is_empty() {
                continue;
            }
            self.counts.turns.fetch_add(1, Ordering::Relaxed);
            self.counts.seats.fetch_add(seated.len() as u64, Ordering::Relaxed);
            let started = Instant::now();
            self.meters.turns.increment(1);
            self.meters.seats.increment(seated.len() as u64);
            self.meters.seats_per_turn.record(seated.len() as f64);
            for seat in &seated {
                self.meters.queue_wait.record(started.duration_since(seat.queued));
            }
            let served = self.run_turn(&seated);
            self.meters.turn.record(started.elapsed());
            for lane in borrowed {
                let _ = self.lanes.give_back(lane);
            }
            match served {
                Ok(per_seat) => {
                    for (seat, outputs) in seated.into_iter().zip(per_seat) {
                        self.queued.serve(seat, Ok(outputs), &self.batch_out);
                    }
                }
                Err(e) => {
                    let e = format!("{e:#}");
                    for seat in seated {
                        self.queued.serve(
                            seat,
                            Err(format_err!("Laned turn failed: {e}")),
                            &self.batch_out,
                        );
                    }
                }
            }
        }
    }

    /// Hand out a lane or take it back there and then; queue the seats of a
    /// call for the coming turns. Taking a lane resets it, which is why it
    /// happens here rather than in the handle: it writes the state.
    fn serve(&mut self, request: Request) {
        match request {
            Request::Spawn(taken) => {
                let lane = match self.lanes.take() {
                    None => Err(format_err!(
                        "Every one of the {} lanes is taken",
                        self.lanes.max_lanes()
                    )),
                    Some(lane) => match self.state.reset_lanes(&[lane]) {
                        Ok(()) => Ok(lane),
                        Err(e) => {
                            let _ = self.lanes.give_back(lane);
                            Err(e)
                        }
                    },
                };
                self.meters.lanes_taken.set(self.lanes.taken() as f64);
                let _ = taken.send(lane);
            }
            Request::Call(call) => self.queued.push(call, &self.batch_in),
            Request::Drop(lane) => {
                let _ = self.lanes.give_back(lane);
                self.meters.lanes_taken.set(self.lanes.taken() as f64);
            }
        }
    }

    /// Seat the head of the queue, and the lanes borrowed to do it. A seat sits
    /// in the lane its call leased, and a call's second and later seats of one
    /// turn in lanes borrowed from whatever is free -- which resets them, so a
    /// seat is always served by a lane holding nothing of another caller's.
    fn fill(&mut self) -> (Vec<Seat>, Vec<LaneId>) {
        let mut seated: Vec<Seat> = vec![];
        let mut taken: Vec<LaneId> = vec![];
        let mut borrowed: Vec<LaneId> = vec![];
        let mut waiting: VecDeque<Seat> = VecDeque::new();
        while let Some(mut seat) = self.queued.seats.pop_front() {
            if seated.len() >= self.max_seats {
                waiting.push_back(seat);
                continue;
            }
            let lane = if taken.contains(&seat.lane) {
                match self.lanes.take() {
                    Some(lane) => match self.state.reset_lanes(&[lane]) {
                        Ok(()) => {
                            borrowed.push(lane);
                            Some(lane)
                        }
                        Err(_) => {
                            let _ = self.lanes.give_back(lane);
                            None
                        }
                    },
                    None => None,
                }
            } else {
                Some(seat.lane)
            };
            match lane {
                Some(lane) => {
                    seat.lane = lane;
                    taken.push(lane);
                    seated.push(seat);
                }
                None => waiting.push_back(seat),
            }
        }
        self.queued.seats = waiting;
        (seated, borrowed)
    }

    /// Run `seated` as one turn: their batched inputs stacked along axis 0 in
    /// seat order, their shared ones checked to agree, and the outputs sliced
    /// back per seat.
    fn run_turn(&mut self, seated: &[Seat]) -> TractResult<Vec<TVec<TValue>>> {
        let seating = self.lanes.seat(seated.iter().map(|seat| seat.lane))?;
        let mut batched: TVec<TValue> = tvec!();
        for (ix, is_batched) in self.batch_in.iter().enumerate() {
            if *is_batched {
                let seats: TVec<&Tensor> = seated.iter().map(|seat| &*seat.inputs[ix]).collect();
                for (seat, input) in seats.iter().enumerate() {
                    ensure!(
                        input.rank() > 0 && input.shape()[0] == 1,
                        "Seat {seat} feeds {:?} of input {ix}, which a turn seats one at a time",
                        input.shape()
                    );
                }
                batched.push(Tensor::stack_tensors(0, &seats)?.into_tvalue());
            } else {
                let shared = &seated[0].inputs[ix];
                for (seat, turn) in seated.iter().enumerate().skip(1) {
                    ensure!(
                        turn.inputs[ix] == *shared,
                        "Input {ix} carries no batch axis, so one value of it serves the whole \
                         turn, and seats 0 and {seat} feed it different ones"
                    );
                }
                batched.push(shared.clone());
            }
        }
        self.state.seat(seating)?;
        let outputs = self.state.run(batched)?;
        let mut per_seat: Vec<TVec<TValue>> = seated.iter().map(|_| tvec!()).collect();
        for (ix, output) in outputs.into_iter().enumerate() {
            if self.batch_out.get(ix).copied().unwrap_or(false) {
                ensure!(
                    output.shape()[0] == seated.len(),
                    "The turn fills {} seats, output {ix} carries {:?}",
                    seated.len(),
                    output.shape()
                );
                for (seat, outputs) in per_seat.iter_mut().enumerate() {
                    outputs.push(output.slice(0, seat, seat + 1)?.into_tvalue());
                }
            } else {
                for outputs in per_seat.iter_mut() {
                    outputs.push(output.clone());
                }
            }
        }
        Ok(per_seat)
    }
}

// The suite is disabled on Wasm because a laned runnable spawns a thread.
#[cfg(all(test, not(target_family = "wasm")))]
mod laned_test {
    use super::*;
    use crate::ops::math::{add, mul};

    /// `[BATCH, 3] * 2`, prepared on the cpu runtime: stateless, so its lanes
    /// address nothing and only the seating of the batch axis is exercised.
    fn doubler(max_lanes: usize) -> TractResult<LanedRunnable> {
        let mut model = TypedModel::default();
        model.set_name("doubler");
        let batch = model.symbols.sym("B");
        let input = model.add_source("input", f32::fact(dims!(batch, 3)))?;
        let two = model.add_const("two", tensor2(&[[2f32]]))?;
        let doubled = model.wire_node("doubled", mul(), &[input, two])?;
        model.select_output_outlets(&doubled)?;
        let inner = DefaultRuntime.prepare(model)?;
        LanedRunnable::wrap(inner.into(), max_lanes)
    }

    fn turn(handle: &mut Box<dyn State>, stream: usize, turn: usize) -> TractResult<()> {
        let input = tensor2(&[[stream as f32, turn as f32, 1.]]);
        let output = handle.run(tvec!(input.into_tvalue()))?;
        assert_eq!(&*output[0], &tensor2(&[[2. * stream as f32, 2. * turn as f32, 2.]]));
        Ok(())
    }

    /// `TRACT_TURN_LINGER_US` is process-wide, so the tests which widen the
    /// turns hold this while they build their runnable and run their streams.
    static LINGER: Mutex<()> = Mutex::new(());

    /// A dropped handle hands its lane back through the queue, so the lane is
    /// free at some point after the drop rather than at it.
    fn spawn_once_free(runnable: &LanedRunnable) -> TractResult<Box<dyn State>> {
        for _ in 0..100 {
            if let Ok(handle) = runnable.spawn() {
                return Ok(handle);
            }
            std::thread::sleep(Duration::from_millis(10));
        }
        runnable.spawn()
    }

    #[test]
    fn one_stream_at_a_time() -> TractResult<()> {
        let runnable = doubler(2)?;
        let mut handle = runnable.spawn()?;
        for t in 0..4 {
            turn(&mut handle, 0, t)?;
        }
        Ok(())
    }

    /// What the worker reported to `recorder`, by metric name, for the
    /// runnable named `doubler`.
    fn reported(
        recorder: &metrics_util::debugging::DebuggingRecorder,
    ) -> HashMap<String, metrics_util::debugging::DebugValue> {
        recorder
            .snapshotter()
            .snapshot()
            .into_vec()
            .into_iter()
            .filter(|(key, ..)| {
                key.key().labels().any(|l| l.key() == "runnable" && l.value() == "doubler")
            })
            .map(|(key, _, _, value)| (key.key().name().to_string(), value))
            .collect()
    }

    #[test]
    fn the_worker_reports_what_it_serves() -> TractResult<()> {
        use metrics_util::debugging::{DebugValue, DebuggingRecorder};
        let recorder = DebuggingRecorder::new();
        let runnable = metrics::with_local_recorder(&recorder, || doubler(4))?;
        let mut handles: Vec<Box<dyn State>> =
            (0..2).map(|_| runnable.spawn()).collect::<TractResult<_>>()?;
        for t in 0..3 {
            for (stream, handle) in handles.iter_mut().enumerate() {
                turn(handle, stream, t)?;
            }
        }
        let (turns, seats) = runnable.turns_and_seats();
        let reported = reported(&recorder);
        let samples = |name: &str| match &reported[name] {
            DebugValue::Histogram(samples) => samples.iter().map(|s| s.0).collect::<Vec<f64>>(),
            other => panic!("{name} is {other:?}"),
        };
        assert_eq!(reported["tract_autobatch_turns_total"], DebugValue::Counter(turns));
        assert_eq!(reported["tract_autobatch_seats_total"], DebugValue::Counter(seats));
        assert_eq!(reported["tract_autobatch_lanes_taken"], DebugValue::Gauge(2f64.into()));
        let per_turn = samples("tract_autobatch_seats_per_turn");
        assert_eq!(per_turn.len() as u64, turns);
        assert_eq!(per_turn.iter().sum::<f64>() as u64, seats);
        assert_eq!(samples("tract_autobatch_queue_wait_seconds").len() as u64, seats);
        let runs = samples("tract_autobatch_turn_seconds");
        assert_eq!(runs.len() as u64, turns);
        assert!(runs.iter().all(|run| *run > 0.));
        Ok(())
    }

    #[test]
    fn every_stream_gets_its_own_seat() -> TractResult<()> {
        let runnable = doubler(8)?;
        let streams: Vec<_> = (0..8)
            .map(|stream| {
                let runnable = runnable.clone();
                std::thread::spawn(move || -> TractResult<()> {
                    let mut handle = runnable.spawn()?;
                    for t in 0..32 {
                        turn(&mut handle, stream, t)?;
                    }
                    Ok(())
                })
            })
            .collect();
        for stream in streams {
            stream.join().unwrap()?;
        }
        Ok(())
    }

    #[test]
    fn a_turn_seats_the_streams_that_are_ready() -> TractResult<()> {
        let _linger = LINGER.lock().unwrap_or_else(|e| e.into_inner());
        TRACT_TURN_LINGER_US.set(20_000);
        let runnable = doubler(8);
        TRACT_TURN_LINGER_US.clear();
        let runnable = runnable?;
        let streams: Vec<_> = (0..8)
            .map(|stream| {
                let runnable = runnable.clone();
                std::thread::spawn(move || -> TractResult<()> {
                    let mut handle = runnable.spawn()?;
                    for t in 0..4 {
                        turn(&mut handle, stream, t)?;
                    }
                    Ok(())
                })
            })
            .collect();
        for stream in streams {
            stream.join().unwrap()?;
        }
        let (turns, seats) = runnable.turns_and_seats();
        assert!(seats > turns, "{seats} seats over {turns} turns, none of them shared");
        Ok(())
    }

    /// `[B, 3] * 2 + bias`, `bias` carrying no batch axis: the shape of a
    /// shared input, which one value of serves the whole turn.
    fn biased(max_lanes: usize) -> TractResult<LanedRunnable> {
        let mut model = TypedModel::default();
        let batch = model.symbols.sym("B");
        let input = model.add_source("input", f32::fact(dims!(batch, 3)))?;
        let bias = model.add_source("bias", f32::fact(dims!(1, 1)))?;
        let two = model.add_const("two", tensor2(&[[2f32]]))?;
        let doubled = model.wire_node("doubled", mul(), &[input, two])?;
        let biased = model.wire_node("biased", add(), &[doubled[0], bias])?;
        model.select_output_outlets(&biased)?;
        let inner = DefaultRuntime.prepare(model)?;
        LanedRunnable::wrap(inner.into(), max_lanes)
    }

    /// One turn per stream, all of them at once, the `stream`th feeding
    /// `biases[stream]`. Every lane is taken before any turn is queued, so the
    /// linger has the turns to seat together rather than a `spawn` to serve.
    fn biased_turns(runnable: &LanedRunnable, biases: &[f32]) -> TractResult<Vec<TractResult<()>>> {
        let handles: Vec<Box<dyn State>> =
            biases.iter().map(|_| runnable.spawn()).collect::<TractResult<_>>()?;
        let streams: Vec<_> = handles
            .into_iter()
            .zip(biases.iter().copied())
            .map(|(mut handle, bias)| {
                std::thread::spawn(move || -> TractResult<()> {
                    handle.run(tvec!(
                        tensor2(&[[1f32, 2., 3.]]).into_tvalue(),
                        tensor2(&[[bias]]).into_tvalue()
                    ))?;
                    Ok(())
                })
            })
            .collect();
        Ok(streams.into_iter().map(|stream| stream.join().unwrap()).collect())
    }

    #[test]
    fn seats_agreeing_on_a_shared_input_share_a_turn() -> TractResult<()> {
        let _linger = LINGER.lock().unwrap_or_else(|e| e.into_inner());
        TRACT_TURN_LINGER_US.set(100_000);
        let runnable = biased(2);
        TRACT_TURN_LINGER_US.clear();
        let runnable = runnable?;
        let served = biased_turns(&runnable, &[7., 7.])?;
        assert!(served.iter().all(|s| s.is_ok()), "{served:?}");
        assert_eq!(runnable.turns_and_seats(), (1, 2));
        Ok(())
    }

    #[test]
    fn seats_disagreeing_on_a_shared_input_fail_the_turn() -> TractResult<()> {
        let _linger = LINGER.lock().unwrap_or_else(|e| e.into_inner());
        TRACT_TURN_LINGER_US.set(100_000);
        let runnable = biased(2);
        TRACT_TURN_LINGER_US.clear();
        let runnable = runnable?;
        let served = biased_turns(&runnable, &[7., 8.])?;
        assert_eq!(runnable.turns_and_seats(), (1, 2));
        for stream in &served {
            let error = format!("{:#}", stream.as_ref().unwrap_err());
            assert!(error.contains("seats 0 and 1 feed it different ones"), "{error}");
        }
        Ok(())
    }

    /// A call feeding `seats` seats of the doubler at once: seat `s` feeds
    /// `[s, s + 1, s + 2]`, and the call is answered in that order.
    fn wide_call(handle: &mut Box<dyn State>, seats: usize) -> TractResult<()> {
        let fed: Vec<f32> =
            (0..seats).flat_map(|s| [s as f32, s as f32 + 1., s as f32 + 2.]).collect();
        let doubled: Vec<f32> = fed.iter().map(|x| 2. * x).collect();
        let output = handle.run(tvec!(Tensor::from_shape(&[seats, 3], &fed)?.into_tvalue()))?;
        assert_eq!(&*output[0], &Tensor::from_shape(&[seats, 3], &doubled)?);
        Ok(())
    }

    #[test]
    fn a_call_asks_for_as_many_seats_as_it_feeds() -> TractResult<()> {
        let runnable = doubler(4)?;
        let mut handle = runnable.spawn()?;
        wide_call(&mut handle, 3)?;
        assert_eq!(runnable.turns_and_seats(), (1, 3));
        Ok(())
    }

    #[test]
    fn a_call_wider_than_the_lanes_is_split_across_turns() -> TractResult<()> {
        let runnable = doubler(2)?;
        let mut handle = runnable.spawn()?;
        wide_call(&mut handle, 5)?;
        let (turns, seats) = runnable.turns_and_seats();
        assert_eq!(seats, 5);
        assert_eq!(turns, 3, "5 seats over 2 lanes are 3 turns, got {turns}");
        Ok(())
    }

    #[test]
    fn a_seat_borrows_a_free_lane_only() -> TractResult<()> {
        let runnable = doubler(2)?;
        let mut handle = runnable.spawn()?;
        let mut other = runnable.spawn()?;
        turn(&mut other, 1, 0)?;
        wide_call(&mut handle, 4)?;
        let (turns, seats) = runnable.turns_and_seats();
        assert_eq!(seats, 5);
        assert_eq!(turns, 5, "another stream holds the second lane, so each seat is a turn");
        Ok(())
    }

    /// `[B, 3] + [B, 3]`: two batched inputs, which a call has to feed the same
    /// number of seats of.
    fn adder(max_lanes: usize) -> TractResult<LanedRunnable> {
        let mut model = TypedModel::default();
        let batch = model.symbols.sym("B");
        let left = model.add_source("left", f32::fact(dims!(batch, 3)))?;
        let right = model.add_source("right", f32::fact(dims!(batch, 3)))?;
        let sum = model.wire_node("sum", add(), &[left, right])?;
        model.select_output_outlets(&sum)?;
        let inner = DefaultRuntime.prepare(model)?;
        LanedRunnable::wrap(inner.into(), max_lanes)
    }

    #[test]
    fn a_call_whose_batched_inputs_disagree_on_seats_fails() -> TractResult<()> {
        let runnable = adder(4)?;
        let mut handle = runnable.spawn()?;
        let error = handle
            .run(tvec!(
                tensor2(&[[1f32, 2., 3.], [4., 5., 6.]]).into_tvalue(),
                tensor2(&[[7f32, 8., 9.]]).into_tvalue()
            ))
            .unwrap_err();
        let error = format!("{error:#}");
        assert!(error.contains("input 1 carries 1 against 2"), "{error}");
        Ok(())
    }

    #[test]
    fn a_batch_off_axis_zero_says_which_axis_it_sits_on() -> TractResult<()> {
        let mut model = TypedModel::default();
        let batch = model.symbols.sym("B");
        let input = model.add_source("input", f32::fact(dims!(3, batch)))?;
        let two = model.add_const("two", tensor2(&[[2f32]]))?;
        let doubled = model.wire_node("doubled", mul(), &[input, two])?;
        model.select_output_outlets(&doubled)?;
        let inner = DefaultRuntime.prepare(model)?;
        let error = format!("{:#}", LanedRunnable::wrap(inner.into(), 2).unwrap_err());
        assert!(error.contains("input 0 carries B on axis 1"), "{error}");
        assert!(error.contains("Batchify"), "{error}");
        Ok(())
    }

    #[test]
    fn two_batch_symbols_are_named() -> TractResult<()> {
        let mut model = TypedModel::default();
        let left_batch = model.symbols.sym("L");
        let right_batch = model.symbols.sym("R");
        let left = model.add_source("left", f32::fact(dims!(left_batch, 3)))?;
        let right = model.add_source("right", f32::fact(dims!(right_batch, 3)))?;
        let sum = model.wire_node("sum", add(), &[left, right])?;
        model.select_output_outlets(&sum)?;
        let inner = DefaultRuntime.prepare(model)?;
        let error = format!("{:#}", LanedRunnable::wrap(inner.into(), 2).unwrap_err());
        assert!(error.contains("L and R"), "{error}");
        Ok(())
    }

    #[test]
    fn a_dropped_stream_gives_its_lane_back() -> TractResult<()> {
        let runnable = doubler(1)?;
        let mut handle = runnable.spawn()?;
        turn(&mut handle, 0, 0)?;
        assert!(runnable.spawn().is_err());
        let clone = dyn_clone::clone_box(&*handle);
        drop(handle);
        assert!(runnable.spawn().is_err());
        drop(clone);
        let mut handle = spawn_once_free(&runnable)?;
        turn(&mut handle, 1, 0)?;
        Ok(())
    }
}

#[cfg(test)]
mod lane_table_test {
    use super::*;

    #[test]
    fn takes_the_lowest_free_lane() -> TractResult<()> {
        let mut table = LaneTable::new(3)?;
        assert_eq!(table.take(), Some(LaneId(0)));
        assert_eq!(table.take(), Some(LaneId(1)));
        table.give_back(LaneId(0))?;
        assert_eq!(table.take(), Some(LaneId(0)));
        assert_eq!(table.taken(), 2);
        Ok(())
    }

    #[test]
    fn runs_out_of_lanes() -> TractResult<()> {
        let mut table = LaneTable::new(1)?;
        assert_eq!(table.take(), Some(LaneId(0)));
        assert_eq!(table.take(), None);
        Ok(())
    }

    #[test]
    fn gives_back_a_taken_lane_only() -> TractResult<()> {
        let mut table = LaneTable::new(2)?;
        assert!(table.give_back(LaneId(0)).is_err());
        table.take();
        table.give_back(LaneId(0))?;
        assert!(table.give_back(LaneId(0)).is_err());
        assert!(table.give_back(LaneId(7)).is_err());
        Ok(())
    }

    #[test]
    fn seats_taken_lanes_in_order() -> TractResult<()> {
        let mut table = LaneTable::new(4)?;
        table.take();
        table.take();
        table.take();
        table.give_back(LaneId(1))?;
        let seating = table.seat([LaneId(2), LaneId(0)])?;
        assert_eq!(seating.max_lanes(), 4);
        assert_eq!(seating.occupancy(), 2);
        assert_eq!(seating.address(0), (Some(0), Some(2)));
        assert_eq!(seating.address(1), (Some(1), Some(0)));
        assert!(table.seat([LaneId(0), LaneId(1)]).is_err());
        assert!(table.seat([LaneId(0), LaneId(0)]).is_err());
        Ok(())
    }
}