cortiq_engine/

linear_core.rs

//! Linear-attention cores, selected by `arch.linear_core.kind`
//! (descriptor-driven operators — Patent 15 claim 8).
//!
//! Two tracks (owner decision 2026-07-04):
//!
//! * `gated_delta_net` — the faithful vendor operator (Qwen3.5 /
//!   Qwen3-Next). Default for models that ship GDN weights: conversion
//!   carries the tensors 1:1 and needs no training. Port of the
//!   validated `gated_delta_net` (vmfcore/rust/src/forward.rs) against
//!   the numpy/torch oracle (vmfcore/gdn_layer.py).
//!
//! * `vmf_phase` — the canonical core: token carries a phase θ; kernel
//!   φ(θ) = [cos θ; sin θ] gives a linear factorization; the recurrent
//!   state S[head][p2, dv] uses decay exp(−exp(A_log)).
//!   Noise-robust and simpler than vendor recurrences. Exotic operators
//!   are folded onto it at CONVERT time (`--linear-core vmf_phase`) and
//!   quality is restored by the offline heal — the research track and
//!   the production mechanism for Patent-15 skills (mask→heal→compress).
//!
//! Both cores implement the same contract: `*_forward` (one position,
//! advances the state) and `*_pair` (fused two positions; lane 1
//! commits, lane 2 is tentative in `scratch` for speculative verify).
//! State lives in the layer's `linear_state: Vec<f32>` and is resized
//! lazily by the core itself.

use crate::pool::Pool;
use crate::qtensor::QTensor;
use std::sync::OnceLock;
use std::sync::atomic::{AtomicU64, Ordering};

static PERF_GDN_FORWARD_CALLS: AtomicU64 = AtomicU64::new(0);
static PERF_GDN_FORWARD_NS: AtomicU64 = AtomicU64::new(0);
static PERF_GDN_BATCH_CALLS: AtomicU64 = AtomicU64::new(0);
static PERF_GDN_BATCH_NS: AtomicU64 = AtomicU64::new(0);
static PERF_GDN_STEP_CALLS: AtomicU64 = AtomicU64::new(0);
static PERF_GDN_STEP_NS: AtomicU64 = AtomicU64::new(0);

fn perf_enabled() -> bool {
    static ON: OnceLock<bool> = OnceLock::new();
    *ON.get_or_init(|| std::env::var("CMF_PERF_PROFILE").as_deref() == Ok("1"))
}

/// Weights of one vmf_phase layer (`model.layers.{i}.vmf_attn.*`).
pub struct VmfPhaseWeights {
    /// [nh·nphase, hidden] — query phase projection
    pub thq: QTensor,
    /// [nh·nphase, hidden] — key phase projection
    pub thk: QTensor,
    /// [nh·dv, hidden]
    pub v_proj: QTensor,
    /// [hidden, nh·dv]
    pub out_proj: QTensor,
    /// Per-component decay exp(−exp(A_log)), len nh·2·nphase (precomputed).
    pub decay: Vec<f64>,
    /// Short causal depthwise conv before the projections (embryo genomes
    /// born with `--conv-k`): flat `[hidden·k]` taps, identity-initialised
    /// at training start. The projections were TRAINED on the conv output —
    /// running without it scrambles the layer (measured: train-val ppl 60
    /// exported to runtime ppl 93 539). None = pre-conv genomes, untouched.
    pub conv: Option<Vec<f32>>,
    /// Selective-write input gate κ (hybrid_k core, stage 71): weight
    /// [nh, hidden] + bias [nh]; κ_h = σ(W_k·x + b)_h multiplies the
    /// state WRITE (S = decay·S + κ·φk⊗v). None = classic phase core,
    /// bit-identical to the pre-κ kernel. Measured at mechanism level:
    /// knee ×2–6 earlier, restores correlated-noise robustness, LM
    /// crossover vs softmax at SEQ 512 (experiments/lc_final_merged.json).
    pub k_gate: Option<(QTensor, Vec<f32>)>,
}

#[derive(Clone, Copy)]
pub struct VmfPhaseCfg {
    pub num_heads: usize,
    pub nphase: usize,
    pub value_head_dim: usize,
    pub hidden_size: usize,
    /// Phase-mass correction: scales the phase toward zero —
    /// θ_eff = θ/(1+mass) — which widens the phase kernel.
    /// Measured (experiments/vmf_native_core*.py) to restore noise
    /// robustness when the phase projection is FIXED (exactly CMF's
    /// fold-before-heal regime: thq/thk are init, not trained) — recall
    /// 3%→91% at moderate noise; redundant once the projection is
    /// healed. 0.0 = massless Goldstone (bit-identical to prior kernel).
    /// Set via CMF_PHASE_MASS. Validated at mechanism level, not yet LM.
    pub phase_mass: f32,
}

impl VmfPhaseCfg {
    pub fn state_len(&self) -> usize {
        self.num_heads * 2 * self.nphase * self.value_head_dim
    }
}

/// One recurrent step for one head-set given projected phases/values.
/// `state` is S[nh][p2, dv] stored f32 (per-element math in f64 — the
/// storage halves, each step's arithmetic keeps the old precision).
fn phase_step(
    thq: &[f32],
    thk: &[f32],
    v: &[f32],
    decay: &[f64],
    kap: Option<&[f32]>,
    cfg: &VmfPhaseCfg,
    state: &mut [f32],
    out: &mut [f32],
) {
    let (nh, nph, dv) = (cfg.num_heads, cfg.nphase, cfg.value_head_dim);
    // Phase-mass correction: θ_eff = θ/(1+mass). mass=0 → factor 1 → no-op.
    let mscale = 1.0f64 / (1.0 + cfg.phase_mass as f64);
    let p2 = 2 * nph;
    for h in 0..nh {
        let s = &mut state[h * p2 * dv..(h + 1) * p2 * dv];
        let thk_h = &thk[h * nph..(h + 1) * nph];
        let thq_h = &thq[h * nph..(h + 1) * nph];
        let vt = &v[h * dv..(h + 1) * dv];
        let ot = &mut out[h * dv..(h + 1) * dv];
        let dec = &decay[h * p2..(h + 1) * p2];
        // Selective write (hybrid_k): κ scales what enters the recurrent state.
        let kh = kap.map_or(1.0f64, |k| k[h] as f64);
        for f in 0..p2 {
            // φ(θ) = [cos·nph, sin·nph], θ scaled by the correction factor.
            let (fk, fq) = if f < nph {
                (
                    (thk_h[f] as f64 * mscale).cos(),
                    (thq_h[f] as f64 * mscale).cos(),
                )
            } else {
                (
                    (thk_h[f - nph] as f64 * mscale).sin(),
                    (thq_h[f - nph] as f64 * mscale).sin(),
                )
            };
            let fkw = fk * kh;
            let row = &mut s[f * dv..(f + 1) * dv];
            let dcf = dec[f];
            for d in 0..dv {
                // S = decay·S + κ·φk⊗v (f64 math, f32 cell)
                let cell = dcf * row[d] as f64 + fkw * vt[d] as f64;
                row[d] = cell as f32;
                ot[d] += (fq * cell) as f32; // o = Σ φq·S
            }
        }
    }
}

/// κ_h = σ(W_k·x + b)_h — the per-head write gate (None when the layer
/// has no k_gate tensors: classic phase core).
fn kappa_of(x: &[f32], w: &VmfPhaseWeights, nh: usize, pool: Option<&Pool>) -> Option<Vec<f32>> {
    let (kw, kb) = w.k_gate.as_ref()?;
    let mut k = vec![0.0f32; nh];
    kw.matvec(x, &mut k, pool);
    for (v, b) in k.iter_mut().zip(kb) {
        *v = 1.0 / (1.0 + (-(*v + b)).exp());
    }
    Some(k)
}

/// Causal depthwise conv over the mixer input, with the last k−1 inputs
/// ringed at the TAIL of the layer state (oldest first). Returns the
/// convolved input; a layer without conv taps passes through untouched.
fn conv_in(
    x: &[f32],
    w: &VmfPhaseWeights,
    cfg: &VmfPhaseCfg,
    state: &mut Vec<f32>,
) -> Option<Vec<f32>> {
    let taps = w.conv.as_ref()?;
    let h = cfg.hidden_size;
    let k = taps.len() / h.max(1);
    if k < 2 || taps.len() != h * k {
        return None;
    }
    let ring = (k - 1) * h;
    let base = cfg.state_len();
    if state.len() != base + ring {
        // The phase part resets alongside — a fresh sequence either way.
        let mut ns = vec![0f32; base + ring];
        let n = state.len().min(base);
        ns[..n].copy_from_slice(&state[..n]);
        *state = ns;
    }
    let mut y = vec![0.0f32; h];
    for c in 0..h {
        // taps j = 0..k−2 read the ring (oldest first), tap k−1 reads x.
        let mut acc = taps[c * k + k - 1] * x[c];
        for j in 0..k - 1 {
            acc += taps[c * k + j] * state[base + j * h + c];
        }
        y[c] = acc;
    }
    conv_ring_push(x, h, base, state);
    Some(y)
}

/// Rotate the conv ring at `base`: drop the oldest input, append `x`.
fn conv_ring_push(x: &[f32], h: usize, base: usize, state: &mut [f32]) {
    let ring = state.len() - base;
    state.copy_within(base + h.., base);
    let at = base + ring - h;
    state[at..at + h].copy_from_slice(&x[..h]);
}

/// Forward one position through a vmf_phase layer, advancing `state`.
pub fn vmf_phase_forward(
    x: &[f32],
    w: &VmfPhaseWeights,
    cfg: &VmfPhaseCfg,
    state: &mut Vec<f32>,
    pool: Option<&Pool>,
) -> Vec<f32> {
    if w.conv.is_none() && state.len() != cfg.state_len() {
        *state = vec![0f32; cfg.state_len()];
    }
    let xc = conv_in(x, w, cfg, state);
    let x = xc.as_deref().unwrap_or(x);
    let (nh, nph, dv) = (cfg.num_heads, cfg.nphase, cfg.value_head_dim);

    let mut thq = vec![0.0f32; nh * nph];
    w.thq.matvec(x, &mut thq, pool);
    let mut thk = vec![0.0f32; nh * nph];
    w.thk.matvec(x, &mut thk, pool);
    let mut v = vec![0.0f32; nh * dv];
    w.v_proj.matvec(x, &mut v, pool);

    let kap = kappa_of(x, w, nh, pool);
    let mut o = vec![0.0f32; nh * dv];
    phase_step(&thq, &thk, &v, &w.decay, kap.as_deref(), cfg, state, &mut o);

    let mut out = vec![0.0f32; cfg.hidden_size];
    w.out_proj.matvec(&o, &mut out, pool);
    out
}

/// Fused two-position forward (speculative verify). Lane 1 commits into
/// `state` (its token is always committed); lane 2's tentative state
/// goes into `scratch` — the caller swaps it in on draft acceptance and
/// simply drops it on rejection.
#[allow(clippy::too_many_arguments)]
pub fn vmf_phase_pair(
    x1: &[f32],
    x2: &[f32],
    w: &VmfPhaseWeights,
    cfg: &VmfPhaseCfg,
    state: &mut Vec<f32>,
    scratch: &mut Vec<f32>,
    pool: Option<&Pool>,
) -> (Vec<f32>, Vec<f32>) {
    if w.conv.is_none() && state.len() != cfg.state_len() {
        *state = vec![0f32; cfg.state_len()];
    }
    // Lane 1 commits its ring advance into the real state; lane 2 works on
    // the tentative copy exactly like the phase state itself.
    let xc1 = conv_in(x1, w, cfg, state);
    let x1 = xc1.as_deref().unwrap_or(x1);
    let (xc2, x2raw) = if w.conv.is_some() {
        let mut tmp = state.clone();
        (conv_in(x2, w, cfg, &mut tmp), Some(x2))
    } else {
        (None, None)
    };
    let x2 = xc2.as_deref().unwrap_or(x2);
    let (nh, nph, dv) = (cfg.num_heads, cfg.nphase, cfg.value_head_dim);

    let mut thq1 = vec![0.0f32; nh * nph];
    let mut thq2 = vec![0.0f32; nh * nph];
    w.thq.matvec2(x1, x2, &mut thq1, &mut thq2, pool);
    let mut thk1 = vec![0.0f32; nh * nph];
    let mut thk2 = vec![0.0f32; nh * nph];
    w.thk.matvec2(x1, x2, &mut thk1, &mut thk2, pool);
    let mut v1 = vec![0.0f32; nh * dv];
    let mut v2 = vec![0.0f32; nh * dv];
    w.v_proj.matvec2(x1, x2, &mut v1, &mut v2, pool);

    // Lane 1 commits into the real state.
    let kap1 = kappa_of(x1, w, nh, pool);
    let mut o1 = vec![0.0f32; nh * dv];
    phase_step(
        &thq1,
        &thk1,
        &v1,
        &w.decay,
        kap1.as_deref(),
        cfg,
        state,
        &mut o1,
    );

    // Lane 2 runs on a copy — tentative until the draft is verified.
    let kap2 = kappa_of(x2, w, nh, pool);
    scratch.clear();
    scratch.extend_from_slice(state);
    let mut o2 = vec![0.0f32; nh * dv];
    phase_step(
        &thq2,
        &thk2,
        &v2,
        &w.decay,
        kap2.as_deref(),
        cfg,
        scratch,
        &mut o2,
    );
    // The scratch copy above re-took state's ring (advanced only through
    // x1); commit x2's advance so an accepted draft leaves a correct ring.
    if let Some(xr) = x2raw {
        conv_ring_push(xr, cfg.hidden_size, cfg.state_len(), scratch);
    }

    let mut out1 = vec![0.0f32; cfg.hidden_size];
    let mut out2 = vec![0.0f32; cfg.hidden_size];
    w.out_proj.matvec2(&o1, &o2, &mut out1, &mut out2, pool);
    (out1, out2)
}

// ───────────────────────── GatedDeltaNet (faithful vendor operator) ─────────────────────────

/// Weights of one GatedDeltaNet layer (`model.layers.{i}.linear_attn.*`,
/// names 1:1 with the source model — no fold, no training).
pub struct GdnWeights {
    /// [2·nk·dk + nv·dv, hidden] — fused q/k/v projection
    pub in_proj_qkv: QTensor,
    /// [nv·dv, hidden] — output-gate projection z
    pub in_proj_z: QTensor,
    /// [nv, hidden] — decay modulation a
    pub in_proj_a: QTensor,
    /// [nv, hidden] — write-strength b (β = σ(b))
    pub in_proj_b: QTensor,
    /// [c_dim · kk] — depthwise causal conv taps, flattened [c][tap]
    pub conv1d: Vec<f32>,
    /// [nv]
    pub a_log: Vec<f32>,
    /// [nv]
    pub dt_bias: Vec<f32>,
    /// [dv] — gated RMSNorm weight (plain x̂·w, validated by the oracle)
    pub norm: Vec<f32>,
    /// [hidden, nv·dv]
    pub out_proj: QTensor,
}

#[derive(Clone, Copy)]
pub struct GdnCfg {
    pub num_v_heads: usize,
    pub num_k_heads: usize,
    pub key_head_dim: usize,
    pub value_head_dim: usize,
    pub conv_kernel: usize,
    pub hidden_size: usize,
    pub rms_eps: f64,
    /// Qwen4-exp trains the output gate as sigmoid(z); older GDN families
    /// use SiLU(z). This is part of the operator, not a sampling option.
    pub output_gate_sigmoid: bool,
}

impl GdnCfg {
    pub fn conv_dim(&self) -> usize {
        2 * self.num_k_heads * self.key_head_dim + self.num_v_heads * self.value_head_dim
    }

    /// Packed state: [conv ring (kk−1)·c_dim | S nv·dk·dv], one Vec<f64>
    /// so the speculative scratch-swap moves ring and recurrent state together.
    pub fn state_len(&self) -> usize {
        (self.conv_kernel - 1) * self.conv_dim()
            + self.num_v_heads * self.key_head_dim * self.value_head_dim
    }
}

fn softplus(x: f64) -> f64 {
    if x > 20.0 { x } else { x.exp().ln_1p() }
}

fn sigmoid(x: f64) -> f64 {
    1.0 / (1.0 + (-x).exp())
}

fn silu(x: f64) -> f64 {
    x / (1.0 + (-x).exp())
}

/// `*mut f32` that may cross worker threads; safety comes from the
/// disjoint (head, element) ranges each worker writes.
#[derive(Clone, Copy)]
struct SendMutF32(*mut f32);
unsafe impl Send for SendMutF32 {}
unsafe impl Sync for SendMutF32 {}

/// One recurrent step given the raw (pre-conv) projections of this
/// position. Advances the packed state (conv ring + S) and writes the
/// gated per-head output into `of` [nv·dv].
///
/// The recurrent-state math runs in f32 (the vendor operator's own dtype —
/// `mamba_ssm_dtype: float32` in the source configs; the old f64 was
/// over-precision at 4× the traffic and no SIMD). The two S passes are
/// element-wise in `dj` with no cross-lane reduction, so LLVM
/// auto-vectorizes them (fmla on NEON, FMA on AVX2). Heads are
/// independent given the conv output and run across the pool — on a
/// Qwen3.5-27B this loop is 48 heads × 128×128 × 48 layers per token,
/// the single biggest serial block in the hybrid's decode.
#[allow(clippy::too_many_arguments)]
fn gdn_step(
    qkv: &[f32],
    z: &[f32],
    a: &[f32],
    b: &[f32],
    w: &GdnWeights,
    cfg: &GdnCfg,
    state: &mut [f32],
    of: &mut [f32],
    pool: Option<&Pool>,
) {
    let perf_t0 = perf_enabled().then(std::time::Instant::now);
    let (nv, nk, dk, dv, kk) = (
        cfg.num_v_heads,
        cfg.num_k_heads,
        cfg.key_head_dim,
        cfg.value_head_dim,
        cfg.conv_kernel,
    );
    let c_dim = cfg.conv_dim();
    let (kd, rep) = (nk * dk, nv / nk);
    let (ring, s_all) = state.split_at_mut((kk - 1) * c_dim);

    // Depthwise causal conv over [ring…, current] + SiLU. Taps are
    // ordered oldest→newest; tap kk−1 multiplies the current position.
    // (Tiny: c_dim × kk — f64 accumulation kept.)
    let mut cq = vec![0f32; c_dim];
    for c in 0..c_dim {
        let taps = &w.conv1d[c * kk..(c + 1) * kk];
        let mut acc = qkv[c] as f64 * taps[kk - 1] as f64;
        for j in 0..kk - 1 {
            acc += ring[j * c_dim + c] as f64 * taps[j] as f64;
        }
        cq[c] = silu(acc) as f32;
    }
    // Ring shift: drop the oldest position, append the raw current one.
    if kk > 1 {
        ring.copy_within(c_dim.., 0);
        let tail = (kk - 2) * c_dim;
        ring[tail..tail + c_dim].copy_from_slice(&qkv[..c_dim]);
    }

    let cq = &cq;
    let s_ptr = SendMutF32(s_all.as_mut_ptr());
    let of_ptr = SendMutF32(of.as_mut_ptr());
    let head_range = |h0: usize, h1: usize| {
        // Rebind the Sync wrappers whole — edition-2021 disjoint capture
        // would otherwise grab the raw `.0` fields and lose Send/Sync.
        let (s_ptr, of_ptr) = (s_ptr, of_ptr);
        // Per-worker scratch, recycled across calls (thread-local freelists).
        let mut kv = crate::attention::take_buf(dv);
        let mut delta = crate::attention::take_buf(dv);
        let mut o = crate::attention::take_buf(dv);
        let mut kf = crate::attention::take_buf(dk);
        let mut qf = crate::attention::take_buf(dk);
        for h in h0..h1 {
            let ko = h / rep; // source q/k head (GQA)
            let (qs, ks) = (ko * dk, kd + ko * dk);
            // l2-normalize q and k; q additionally scaled by 1/√dk.
            let (mut nq, mut nkn) = (0f64, 0f64);
            for d in 0..dk {
                nq += (cq[qs + d] as f64) * (cq[qs + d] as f64);
                nkn += (cq[ks + d] as f64) * (cq[ks + d] as f64);
            }
            let invq = (1.0 / ((nq + 1e-6).sqrt() * (dk as f64).sqrt())) as f32;
            let invk = (1.0 / (nkn + 1e-6).sqrt()) as f32;
            for d in 0..dk {
                qf[d] = cq[qs + d] * invq;
                kf[d] = cq[ks + d] * invk;
            }

            let g = (-(w.a_log[h] as f64).exp() * softplus(a[h] as f64 + w.dt_bias[h] as f64)).exp()
                as f32;
            let beta = sigmoid(b[h] as f64) as f32;

            // SAFETY: disjoint per-head S and output slices per worker.
            let s = unsafe { std::slice::from_raw_parts_mut(s_ptr.0.add(h * dk * dv), dk * dv) };
            let oh = unsafe { std::slice::from_raw_parts_mut(of_ptr.0.add(h * dv), dv) };
            let vt = &cq[2 * kd + h * dv..2 * kd + (h + 1) * dv];

            // S ← g·S;  kv = kᵀS;  S += k ⊗ β(v − kv);  o = qᵀS —
            // algebraically regrouped so S is READ twice and WRITTEN
            // once: kv over S_old (then ×g), one fused update+query pass.
            kv[..dv].fill(0.0);
            for di in 0..dk {
                let kfd = kf[di];
                let row = &s[di * dv..(di + 1) * dv];
                for dj in 0..dv {
                    kv[dj] += row[dj] * kfd; // elementwise in dj → SIMD
                }
            }
            for dj in 0..dv {
                delta[dj] = (vt[dj] - g * kv[dj]) * beta;
            }
            o[..dv].fill(0.0);
            for di in 0..dk {
                let kfd = kf[di];
                let qfd = qf[di];
                let row = &mut s[di * dv..(di + 1) * dv];
                for dj in 0..dv {
                    let cell = g * row[dj] + kfd * delta[dj];
                    row[dj] = cell;
                    o[dj] += qfd * cell; // elementwise in dj → SIMD
                }
            }
            // Gated RMSNorm per head: x̂·w·silu(z) (oracle-validated form).
            let ss: f64 = o[..dv].iter().map(|&v| (v as f64) * (v as f64)).sum();
            let inv = 1.0 / (ss / dv as f64 + cfg.rms_eps).sqrt();
            for dj in 0..dv {
                let gate = if cfg.output_gate_sigmoid {
                    sigmoid(z[h * dv + dj] as f64)
                } else {
                    silu(z[h * dv + dj] as f64)
                };
                oh[dj] = ((o[dj] as f64 * inv) * w.norm[dj] as f64 * gate) as f32;
            }
        }
        crate::attention::recycle_buf(&mut kv);
        crate::attention::recycle_buf(&mut delta);
        crate::attention::recycle_buf(&mut o);
        crate::attention::recycle_buf(&mut kf);
        crate::attention::recycle_buf(&mut qf);
    };
    match pool {
        Some(pool) if nv >= 4 => pool.run(&|widx, n| {
            let chunk = nv.div_ceil(n);
            let h0 = (widx * chunk).min(nv);
            let h1 = (h0 + chunk).min(nv);
            if h0 < h1 {
                head_range(h0, h1);
            }
        }),
        _ => head_range(0, nv),
    }
    if let Some(t0) = perf_t0 {
        PERF_GDN_STEP_CALLS.fetch_add(1, Ordering::Relaxed);
        PERF_GDN_STEP_NS.fetch_add(t0.elapsed().as_nanos() as u64, Ordering::Relaxed);
    }
}

/// Forward one position through a GatedDeltaNet layer, advancing `state`.
pub fn gdn_forward(
    x: &[f32],
    w: &GdnWeights,
    cfg: &GdnCfg,
    state: &mut Vec<f32>,
    pool: Option<&Pool>,
) -> Vec<f32> {
    let perf_t0 = perf_enabled().then(std::time::Instant::now);
    if state.len() != cfg.state_len() {
        *state = vec![0f32; cfg.state_len()];
    }
    let (c_dim, vd) = (cfg.conv_dim(), cfg.num_v_heads * cfg.value_head_dim);

    let mut qkv = vec![0.0f32; c_dim];
    let mut z = vec![0.0f32; vd];
    let mut a = vec![0.0f32; cfg.num_v_heads];
    let mut b = vec![0.0f32; cfg.num_v_heads];
    // D5: two heavy projections (the GDN mixer is ~half a hybrid layer's
    // bytes) — one GPU submission; a/b are tiny and stay on CPU. The
    // Batch probe arbitrates GPU vs the fused-CPU dispatch per machine.
    let cpu_projs = |qkv: &mut Vec<f32>, z: &mut Vec<f32>, a: &mut Vec<f32>, b: &mut Vec<f32>| {
        QTensor::matvec_many(
            [&w.in_proj_qkv, &w.in_proj_z, &w.in_proj_a, &w.in_proj_b],
            x,
            [
                qkv.as_mut_slice(),
                z.as_mut_slice(),
                a.as_mut_slice(),
                b.as_mut_slice(),
            ],
            pool,
        );
    };
    let mut done = false;
    if crate::gpu::enabled_here() && gdn_projs_eligible(w) {
        match crate::gpu::probe_arm(crate::gpu::OpClass::Batch) {
            crate::gpu::ProbeArm::Gpu => {
                let t0 = std::time::Instant::now();
                if gdn_projs_gpu(w, x, &mut qkv, &mut z) {
                    crate::gpu::probe_record(crate::gpu::OpClass::Batch, true, t0.elapsed());
                    w.in_proj_a.matvec(x, &mut a, pool);
                    w.in_proj_b.matvec(x, &mut b, pool);
                    done = true;
                } else {
                    crate::gpu::probe_note_decline(crate::gpu::OpClass::Batch);
                }
            }
            crate::gpu::ProbeArm::CpuTimed => {
                let t0 = std::time::Instant::now();
                crate::gpu::cpu_scope(|| cpu_projs(&mut qkv, &mut z, &mut a, &mut b));
                crate::gpu::probe_record(crate::gpu::OpClass::Batch, false, t0.elapsed());
                done = true;
            }
            crate::gpu::ProbeArm::Cpu => {
                crate::gpu::cpu_scope(|| cpu_projs(&mut qkv, &mut z, &mut a, &mut b));
                done = true;
            }
        }
    }
    if !done {
        cpu_projs(&mut qkv, &mut z, &mut a, &mut b);
    }

    let mut of = vec![0.0f32; vd];
    gdn_step(&qkv, &z, &a, &b, w, cfg, state, &mut of, pool);

    let mut out = vec![0.0f32; cfg.hidden_size];
    w.out_proj.matvec(&of, &mut out, pool);
    if let Some(t0) = perf_t0 {
        PERF_GDN_FORWARD_CALLS.fetch_add(1, Ordering::Relaxed);
        PERF_GDN_FORWARD_NS.fetch_add(t0.elapsed().as_nanos() as u64, Ordering::Relaxed);
    }
    out
}

/// Batched GDN forward (prefill-GEMM): the qkv/z/a/b and out_proj
/// projections are matmat over the batch (a weight row once per chunk),
/// the gdn_step recurrence runs sequentially over positions (state is the
/// same as the sequential path; the math is elementwise identical).
pub fn gdn_forward_batch(
    xs: &[f32],
    b: usize,
    w: &GdnWeights,
    cfg: &GdnCfg,
    state: &mut Vec<f32>,
    pool: Option<&Pool>,
) -> Vec<f32> {
    let perf_t0 = perf_enabled().then(std::time::Instant::now);
    if state.len() != cfg.state_len() {
        *state = vec![0f32; cfg.state_len()];
    }
    let (c_dim, vd) = (cfg.conv_dim(), cfg.num_v_heads * cfg.value_head_dim);
    let nv = cfg.num_v_heads;

    let mut qkv = vec![0.0f32; b * c_dim];
    w.in_proj_qkv.matmat(xs, b, &mut qkv, pool);
    let mut z = vec![0.0f32; b * vd];
    w.in_proj_z.matmat(xs, b, &mut z, pool);
    let mut a = vec![0.0f32; b * nv];
    w.in_proj_a.matmat(xs, b, &mut a, pool);
    let mut bb = vec![0.0f32; b * nv];
    w.in_proj_b.matmat(xs, b, &mut bb, pool);

    let mut of = vec![0.0f32; b * vd];
    for bi in 0..b {
        gdn_step(
            &qkv[bi * c_dim..(bi + 1) * c_dim],
            &z[bi * vd..(bi + 1) * vd],
            &a[bi * nv..(bi + 1) * nv],
            &bb[bi * nv..(bi + 1) * nv],
            w,
            cfg,
            state,
            &mut of[bi * vd..(bi + 1) * vd],
            pool,
        );
    }
    let mut out = vec![0.0f32; b * cfg.hidden_size];
    w.out_proj.matmat(&of, b, &mut out, pool);
    if std::env::var("CMF_GDN_TRACE").is_ok() {
        let n = |v: &[f32]| v.iter().map(|x| x * x).sum::<f32>().sqrt();
        eprintln!(
            "gdn-batch b={b}: |x0|={:.5} |qkv0|={:.5} |z0|={:.5} |a0|={:.5} |b0|={:.5} |of0|={:.5} |out0|={:.5} |state|={:.5}",
            n(&xs[..cfg.hidden_size]),
            n(&qkv[..c_dim]),
            n(&z[..vd]),
            n(&a[..nv]),
            n(&bb[..nv]),
            n(&of[..vd]),
            n(&out[..cfg.hidden_size]),
            n(state)
        );
    }
    if let Some(t0) = perf_t0 {
        PERF_GDN_BATCH_CALLS.fetch_add(1, Ordering::Relaxed);
        PERF_GDN_BATCH_NS.fetch_add(t0.elapsed().as_nanos() as u64, Ordering::Relaxed);
    }
    out
}

/// Aggregate host-side GDN costs for the bounded warm-decode profile.
pub fn perf_report() {
    if !perf_enabled() {
        return;
    }
    let fc = PERF_GDN_FORWARD_CALLS.load(Ordering::Relaxed);
    let bc = PERF_GDN_BATCH_CALLS.load(Ordering::Relaxed);
    let sc = PERF_GDN_STEP_CALLS.load(Ordering::Relaxed);
    eprintln!(
        "[perf-gdn] forward_calls={} forward_ms={:.3} forward_ms_per_call={:.3} batch_calls={} batch_ms={:.3} step_calls={} step_ms={:.3} step_ms_per_call={:.3}",
        fc,
        PERF_GDN_FORWARD_NS.load(Ordering::Relaxed) as f64 / 1e6,
        PERF_GDN_FORWARD_NS.load(Ordering::Relaxed) as f64 / 1e6 / fc.max(1) as f64,
        bc,
        PERF_GDN_BATCH_NS.load(Ordering::Relaxed) as f64 / 1e6,
        sc,
        PERF_GDN_STEP_NS.load(Ordering::Relaxed) as f64 / 1e6,
        PERF_GDN_STEP_NS.load(Ordering::Relaxed) as f64 / 1e6 / sc.max(1) as f64,
    );
}

/// GDN qkv+z GPU eligibility: q1 mixers offload by default (the CPU q1
/// kernel is compute-bound); q8 stays opt-in via CMF_GPU_GDN=1 (measured
/// neutral). The probe in `gdn_forward` still arbitrates either way.
fn gdn_projs_eligible(w: &GdnWeights) -> bool {
    // Any tile-embedded-scale layout, not just q1: the batched matvec now has
    // kernels for all of them, and the probe arbitrates whether it pays.
    // `CMF_GPU_GDN=0` forces the CPU projections back — the switch exists so
    // the two arms can be compared inside ONE run: this machine throttles far
    // enough that measurements minutes apart are not comparable.
    if std::env::var("CMF_GPU_GDN")
        .map(|v| v == "0")
        .unwrap_or(false)
    {
        return false;
    }
    w.in_proj_qkv.is_q1()
        || w.in_proj_qkv.q4t_parts().is_some()
        || w.in_proj_qkv.q4tp_parts().is_some()
        || std::env::var("CMF_GPU_GDN")
            .map(|v| v == "1")
            .unwrap_or(false)
}

/// GDN qkv+z on GPU in a single submission (independent matvecs of one input).
fn gdn_projs_gpu(w: &GdnWeights, x: &[f32], qkv: &mut [f32], z: &mut [f32]) -> bool {
    use crate::gpu::matvec_batch;
    use crate::qtensor::QTensor;
    if !crate::gpu::enabled_here() {
        return false;
    }
    fn part<'a>(
        t: &'a QTensor,
        x: &[f32],
    ) -> Option<(
        std::sync::Arc<cortiq_core::CmfModel>,
        crate::gpu::BatchJob<'a>,
    )> {
        use crate::gpu::BatchJob;
        use crate::qtensor::prescale;
        use cortiq_core::TensorDtype;
        match t {
            QTensor::Mapped {
                model,
                idx,
                dtype: dt @ (TensorDtype::Q8Row | TensorDtype::Q8_2f),
                rows,
                cols,
                row_scale,
                col_field,
                ..
            } => Some((
                model.clone(),
                BatchJob {
                    idx: *idx,
                    rows: *rows,
                    cols: *cols,
                    row_scale,
                    xs: prescale(x, col_field, *dt).into_owned(),
                    layout: crate::gpu::BatchLayout::Q8,
                },
            )),
            QTensor::Mapped {
                model,
                idx,
                dtype: TensorDtype::Q1,
                rows,
                cols,
                ..
            } => Some((
                model.clone(),
                BatchJob {
                    idx: *idx,
                    rows: *rows,
                    cols: *cols,
                    row_scale: &[],
                    xs: x.to_vec(),
                    layout: crate::gpu::BatchLayout::Q1,
                },
            )),
            // q4t/q4tp: same tile-embedded-scale contract as q1, different
            // stride. Missing here, a hybrid model's GDN projections fell to
            // the CPU on every layer while its experts rode the device.
            QTensor::Mapped {
                model,
                idx,
                dtype: dt @ (TensorDtype::Q4Tiled | TensorDtype::Q4TiledP),
                rows,
                cols,
                ..
            } => Some((
                model.clone(),
                BatchJob {
                    idx: *idx,
                    rows: *rows,
                    cols: *cols,
                    row_scale: &[],
                    xs: x.to_vec(),
                    layout: if *dt == TensorDtype::Q4TiledP {
                        crate::gpu::BatchLayout::Q4tp
                    } else {
                        crate::gpu::BatchLayout::Q4t
                    },
                },
            )),
            _ => None,
        }
    }
    let Some((model, jq)) = part(&w.in_proj_qkv, x) else {
        return false;
    };
    let Some((_, jz)) = part(&w.in_proj_z, x) else {
        return false;
    };
    matvec_batch(&model, &[jq, jz], &mut [qkv, z])
}

/// Fused two-position forward (speculative verify): lane 1 commits into
/// `state`, lane 2 is tentative in `scratch` (ring + S move together).
#[allow(clippy::too_many_arguments)]
pub fn gdn_pair(
    x1: &[f32],
    x2: &[f32],
    w: &GdnWeights,
    cfg: &GdnCfg,
    state: &mut Vec<f32>,
    scratch: &mut Vec<f32>,
    pool: Option<&Pool>,
) -> (Vec<f32>, Vec<f32>) {
    if state.len() != cfg.state_len() {
        *state = vec![0f32; cfg.state_len()];
    }
    let (c_dim, vd, nv) = (
        cfg.conv_dim(),
        cfg.num_v_heads * cfg.value_head_dim,
        cfg.num_v_heads,
    );

    let mut qkv1 = vec![0.0f32; c_dim];
    let mut qkv2 = vec![0.0f32; c_dim];
    w.in_proj_qkv.matvec2(x1, x2, &mut qkv1, &mut qkv2, pool);
    let mut z1 = vec![0.0f32; vd];
    let mut z2 = vec![0.0f32; vd];
    w.in_proj_z.matvec2(x1, x2, &mut z1, &mut z2, pool);
    let mut a1 = vec![0.0f32; nv];
    let mut a2 = vec![0.0f32; nv];
    w.in_proj_a.matvec2(x1, x2, &mut a1, &mut a2, pool);
    let mut b1 = vec![0.0f32; nv];
    let mut b2 = vec![0.0f32; nv];
    w.in_proj_b.matvec2(x1, x2, &mut b1, &mut b2, pool);

    let mut of1 = vec![0.0f32; vd];
    gdn_step(&qkv1, &z1, &a1, &b1, w, cfg, state, &mut of1, pool);

    scratch.clear();
    scratch.extend_from_slice(state);
    let mut of2 = vec![0.0f32; vd];
    gdn_step(&qkv2, &z2, &a2, &b2, w, cfg, scratch, &mut of2, pool);

    let mut out1 = vec![0.0f32; cfg.hidden_size];
    let mut out2 = vec![0.0f32; cfg.hidden_size];
    w.out_proj.matvec2(&of1, &of2, &mut out1, &mut out2, pool);
    (out1, out2)
}

// ───────────────────────── ShortConv (LFM2 gated short convolution) ─────────────────────────

/// Weights of one LFM2 short-convolution mixer
/// (`model.layers.{i}.short_conv.*`, renamed from the vendor `conv.*` at
/// convert time). No recurrent mixer state — the only state is the causal
/// conv ring (the last `kernel−1` gated inputs per channel).
pub struct ShortConvWeights {
    /// [3·hidden, hidden] — fused (B, C, x) projection.
    pub in_proj: QTensor,
    /// [hidden · kernel] depthwise conv taps, flattened `[channel][tap]`
    /// (the source `[hidden, 1, kernel]` with the singleton group axis
    /// dropped). Tap `kernel−1` multiplies the current position.
    pub conv: Vec<f32>,
    /// [hidden, hidden] — output projection.
    pub out_proj: QTensor,
}

#[derive(Clone, Copy)]
pub struct ShortConvCfg {
    pub hidden_size: usize,
    /// Conv kernel width `L` (`conv_L_cache`; LFM2 uses 3).
    pub kernel: usize,
}

impl ShortConvCfg {
    /// Conv ring: the last `kernel−1` gated inputs per channel.
    pub fn state_len(&self) -> usize {
        (self.kernel - 1) * self.hidden_size
    }
}

/// One position through the gated conv, given the fused projection
/// `bcx = in_proj·x` [3·hidden] = [B | C | x]. Advances the conv ring and
/// writes the gated conv output `y = C ⊙ conv(B ⊙ x)` [hidden] into `y`.
///
/// The conv is PyTorch's causal depthwise `Conv1d(padding=kernel−1)`
/// truncated to the current length: for tap `k`, weight `w[c][k]` pairs
/// with the input `kernel−1−k` steps in the past, so `w[c][kernel−1]` is
/// the current position. The ring holds `in[t−1] … in[t−(kernel−1)]` at
/// slots `0 … kernel−2`.
fn short_conv_step(
    bcx: &[f32],
    conv: &[f32],
    cfg: &ShortConvCfg,
    ring_state: &mut [f32],
    y: &mut [f32],
) {
    let (h, k) = (cfg.hidden_size, cfg.kernel);
    let ring = k - 1;
    let (bg, cg, xg) = (&bcx[0..h], &bcx[h..2 * h], &bcx[2 * h..3 * h]);
    for c in 0..h {
        let bx = bg[c] * xg[c];
        let wc = &conv[c * k..(c + 1) * k];
        // Current tap, then the past taps read from the channel's ring.
        let mut acc = wc[k - 1] * bx;
        let rc = &mut ring_state[c * ring..c * ring + ring];
        for s in 0..ring {
            acc += wc[k - 2 - s] * rc[s];
        }
        y[c] = cg[c] * acc;
        // Shift newest-in-front: slot 0 becomes the just-seen input.
        for s in (1..ring).rev() {
            rc[s] = rc[s - 1];
        }
        if ring > 0 {
            rc[0] = bx;
        }
    }
}

/// Forward one position through a short-conv layer, advancing `state`.
pub fn short_conv_forward(
    x: &[f32],
    w: &ShortConvWeights,
    cfg: &ShortConvCfg,
    state: &mut Vec<f32>,
    pool: Option<&Pool>,
) -> Vec<f32> {
    if state.len() != cfg.state_len() {
        *state = vec![0f32; cfg.state_len()];
    }
    let h = cfg.hidden_size;
    let mut bcx = vec![0.0f32; 3 * h];
    w.in_proj.matvec(x, &mut bcx, pool);
    let mut y = vec![0.0f32; h];
    short_conv_step(&bcx, &w.conv, cfg, state, &mut y);
    let mut out = vec![0.0f32; h];
    w.out_proj.matvec(&y, &mut out, pool);
    out
}

/// Batched short-conv forward (prefill-GEMM): in_proj/out_proj are matmat
/// over the chunk (a weight row streamed once), the conv walks the
/// positions in order — the chunk is contiguous, so the ring state is
/// exactly the sequential path's and the math is elementwise identical.
pub fn short_conv_forward_batch(
    xs: &[f32],
    b: usize,
    w: &ShortConvWeights,
    cfg: &ShortConvCfg,
    state: &mut Vec<f32>,
    pool: Option<&Pool>,
) -> Vec<f32> {
    if state.len() != cfg.state_len() {
        *state = vec![0f32; cfg.state_len()];
    }
    let h = cfg.hidden_size;
    let mut bcx = vec![0.0f32; b * 3 * h];
    w.in_proj.matmat(xs, b, &mut bcx, pool);
    let mut y = vec![0.0f32; b * h];
    for bi in 0..b {
        short_conv_step(
            &bcx[bi * 3 * h..(bi + 1) * 3 * h],
            &w.conv,
            cfg,
            state,
            &mut y[bi * h..(bi + 1) * h],
        );
    }
    let mut out = vec![0.0f32; b * h];
    w.out_proj.matmat(&y, b, &mut out, pool);
    out
}

/// Fused two-position forward (speculative verify). Lane 1 commits into
/// `state`; lane 2's tentative ring goes into `scratch` — swapped in on
/// draft acceptance, dropped on rejection. LFM2 ships no MTP head, so this
/// is exercised only by the pair-fusion micro-benchmark; kept correct.
#[allow(clippy::too_many_arguments)]
pub fn short_conv_pair(
    x1: &[f32],
    x2: &[f32],
    w: &ShortConvWeights,
    cfg: &ShortConvCfg,
    state: &mut Vec<f32>,
    scratch: &mut Vec<f32>,
    pool: Option<&Pool>,
) -> (Vec<f32>, Vec<f32>) {
    if state.len() != cfg.state_len() {
        *state = vec![0f32; cfg.state_len()];
    }
    let h = cfg.hidden_size;
    let mut bcx1 = vec![0.0f32; 3 * h];
    let mut bcx2 = vec![0.0f32; 3 * h];
    w.in_proj.matvec2(x1, x2, &mut bcx1, &mut bcx2, pool);

    let mut y1 = vec![0.0f32; h];
    short_conv_step(&bcx1, &w.conv, cfg, state, &mut y1);
    scratch.clear();
    scratch.extend_from_slice(state);
    let mut y2 = vec![0.0f32; h];
    short_conv_step(&bcx2, &w.conv, cfg, scratch, &mut y2);

    let mut out1 = vec![0.0f32; h];
    let mut out2 = vec![0.0f32; h];
    w.out_proj.matvec2(&y1, &y2, &mut out1, &mut out2, pool);
    (out1, out2)
}

// ─── Kimi Delta Attention (KDA) ─────────────────────────────────────────
//
// Kimi Linear / Kimi-K3 linear mixer (reference: FLA naive_recurrent_kda
// + moonshotai modeling_kimi.py). Differences from GatedDeltaNet above:
// separate q/k/v projections each behind its OWN causal depthwise short
// convolution; the delta-rule decay is a PER-CHANNEL vector (diagonal)
// instead of a per-head scalar; the decay pre-activation comes from a
// low-rank projection f_b(f_a(x)); and the output gate norm uses
// sigmoid, not SiLU.

pub struct KdaWeights {
    /// [nh·dk, hidden]
    pub q_proj: QTensor,
    /// [nh·dk, hidden]
    pub k_proj: QTensor,
    /// [nh·dv, hidden]
    pub v_proj: QTensor,
    /// [nh·dk × kk] — depthwise taps, oldest→newest (see GdnWeights.conv1d)
    pub conv_q: Vec<f32>,
    pub conv_k: Vec<f32>,
    /// [nh·dv × kk]
    pub conv_v: Vec<f32>,
    /// [rank, hidden] — low-rank decay projection, stage 1
    pub f_a: QTensor,
    /// [nh·dk, rank] — stage 2
    pub f_b: QTensor,
    /// [nh·dk]
    pub dt_bias: Vec<f32>,
    /// [nh] per-head (Kimi-Linear-48B) | [dk] per-dim (Kimi-K3) |
    /// [nh·dk] full — broadcast resolved by length.
    pub a_log: Vec<f32>,
    /// [nh, hidden] — β = σ(b_proj·x) per head
    pub b_proj: QTensor,
    /// Output gate: full-rank g_proj (K3) or low-rank g_b(g_a(x)) (48B).
    pub gate: KdaOutGate,
    /// [dv] — gated RMSNorm weight (per head over head_v_dim)
    pub o_norm: Vec<f32>,
    /// [hidden, nh·dv]
    pub o_proj: QTensor,
    /// Some(lb): log-decay = lb·σ(exp(A)·(f+bias)) (K3, lb=−5);
    /// None: −exp(A)·softplus(f+bias) (Kimi-Linear-48B).
    pub gate_lower_bound: Option<f32>,
}

pub enum KdaOutGate {
    /// [nh·dv, hidden]
    Full(QTensor),
    /// g_a [rank, hidden], g_b [nh·dv, rank]
    LowRank(QTensor, QTensor),
}

#[derive(Clone, Copy)]
pub struct KdaCfg {
    pub num_heads: usize,
    pub head_k_dim: usize,
    pub head_v_dim: usize,
    pub conv_kernel: usize,
    pub hidden_size: usize,
    pub rms_eps: f64,
}

impl KdaCfg {
    /// Packed state: [q ring | k ring | v ring | S nh·dk·dv], one Vec —
    /// same single-buffer convention as GdnCfg::state_len.
    pub fn state_len(&self) -> usize {
        let (nh, dk, dv, kk) = (
            self.num_heads,
            self.head_k_dim,
            self.head_v_dim,
            self.conv_kernel,
        );
        (kk - 1) * (2 * nh * dk + nh * dv) + nh * dk * dv
    }
}

/// Depthwise causal conv over [ring…, current] + SiLU, then ring shift.
/// Taps oldest→newest, tap kk−1 multiplies the current position.
fn kda_conv(raw: &[f32], taps: &[f32], ring: &mut [f32], kk: usize, out: &mut [f32]) {
    let c_dim = raw.len();
    for c in 0..c_dim {
        let t = &taps[c * kk..(c + 1) * kk];
        let mut acc = raw[c] as f64 * t[kk - 1] as f64;
        for j in 0..kk - 1 {
            acc += ring[j * c_dim + c] as f64 * t[j] as f64;
        }
        out[c] = silu(acc) as f32;
    }
    if kk > 1 {
        ring.copy_within(c_dim.., 0);
        let tail = (kk - 2) * c_dim;
        ring[tail..tail + c_dim].copy_from_slice(raw);
    }
}

/// Per-channel log-decay for head-channel (h, d): resolves the A_log
/// broadcast by length and applies the configured gate formula.
#[inline]
fn kda_log_decay(w: &KdaWeights, cfg: &KdaCfg, h: usize, d: usize, f: f32) -> f64 {
    let (nh, dk) = (cfg.num_heads, cfg.head_k_dim);
    let a = if w.a_log.len() == nh {
        w.a_log[h] as f64
    } else if w.a_log.len() == dk {
        w.a_log[d] as f64
    } else {
        w.a_log[h * dk + d] as f64
    };
    let raw = f as f64 + w.dt_bias[h * dk + d] as f64;
    match w.gate_lower_bound {
        Some(lb) => lb as f64 * sigmoid(a.exp() * raw),
        None => -a.exp() * softplus(raw),
    }
}

/// One recurrent step given this position's raw (pre-conv) projections.
/// Advances the packed state and writes the gated per-head output into
/// `of` [nh·dv]. Recurrence (FLA naive_recurrent_kda):
///   S ← Diag(exp(g))·S;  S += β·k ⊗ (v − kᵀS);  o = qᵀS
/// with q,k L2-normalized per head and q additionally scaled by 1/√dk —
/// regrouped into two S passes like gdn_step (per-channel decay folds
/// into the k readout of the first pass).
#[allow(clippy::too_many_arguments)]
fn kda_step(
    xq: &[f32],
    xk: &[f32],
    xv: &[f32],
    f: &[f32],
    b: &[f32],
    gate_out: &[f32],
    w: &KdaWeights,
    cfg: &KdaCfg,
    state: &mut [f32],
    of: &mut [f32],
    pool: Option<&Pool>,
) {
    let (nh, dk, dv, kk) = (
        cfg.num_heads,
        cfg.head_k_dim,
        cfg.head_v_dim,
        cfg.conv_kernel,
    );
    let (kd, vd) = (nh * dk, nh * dv);
    let ring_q_len = (kk - 1) * kd;
    let ring_v_len = (kk - 1) * vd;
    let (ring_q, rest) = state.split_at_mut(ring_q_len);
    let (ring_k, rest) = rest.split_at_mut(ring_q_len);
    let (ring_v, s_all) = rest.split_at_mut(ring_v_len);

    let mut cq = vec![0f32; kd];
    let mut ck = vec![0f32; kd];
    let mut cv = vec![0f32; vd];
    kda_conv(xq, &w.conv_q, ring_q, kk, &mut cq);
    kda_conv(xk, &w.conv_k, ring_k, kk, &mut ck);
    kda_conv(xv, &w.conv_v, ring_v, kk, &mut cv);

    let (cq, ck, cv) = (&cq, &ck, &cv);
    let s_ptr = SendMutF32(s_all.as_mut_ptr());
    let of_ptr = SendMutF32(of.as_mut_ptr());
    let head_range = |h0: usize, h1: usize| {
        let (s_ptr, of_ptr) = (s_ptr, of_ptr);
        let mut kv = crate::attention::take_buf(dv);
        let mut delta = crate::attention::take_buf(dv);
        let mut o = crate::attention::take_buf(dv);
        let mut kf = crate::attention::take_buf(dk);
        let mut qf = crate::attention::take_buf(dk);
        let mut gd = crate::attention::take_buf(dk);
        for h in h0..h1 {
            let qs = h * dk;
            // l2-normalize q and k; q additionally scaled by 1/√dk.
            let (mut nq, mut nkn) = (0f64, 0f64);
            for d in 0..dk {
                nq += (cq[qs + d] as f64) * (cq[qs + d] as f64);
                nkn += (ck[qs + d] as f64) * (ck[qs + d] as f64);
            }
            let invq = (1.0 / ((nq + 1e-6).sqrt() * (dk as f64).sqrt())) as f32;
            let invk = (1.0 / (nkn + 1e-6).sqrt()) as f32;
            for d in 0..dk {
                qf[d] = cq[qs + d] * invq;
                kf[d] = ck[qs + d] * invk;
                gd[d] = kda_log_decay(w, cfg, h, d, f[qs + d]).exp() as f32;
            }
            let beta = sigmoid(b[h] as f64) as f32;

            // SAFETY: disjoint per-head S and output slices per worker.
            let s = unsafe { std::slice::from_raw_parts_mut(s_ptr.0.add(h * dk * dv), dk * dv) };
            let oh = unsafe { std::slice::from_raw_parts_mut(of_ptr.0.add(h * dv), dv) };
            let vt = &cv[h * dv..(h + 1) * dv];

            // Pass 1: kv = kᵀ(Diag(gd)·S_old) — decay folded into k.
            kv[..dv].fill(0.0);
            for di in 0..dk {
                let kg = kf[di] * gd[di];
                let row = &s[di * dv..(di + 1) * dv];
                for dj in 0..dv {
                    kv[dj] += row[dj] * kg;
                }
            }
            for dj in 0..dv {
                delta[dj] = (vt[dj] - kv[dj]) * beta;
            }
            // Pass 2: S[di,:] = gd[di]·row + k[di]·delta;  o += q[di]·row.
            o[..dv].fill(0.0);
            for di in 0..dk {
                let (kfd, qfd, gdd) = (kf[di], qf[di], gd[di]);
                let row = &mut s[di * dv..(di + 1) * dv];
                for dj in 0..dv {
                    let cell = gdd * row[dj] + kfd * delta[dj];
                    row[dj] = cell;
                    o[dj] += qfd * cell;
                }
            }
            // Gated RMSNorm per head: x̂·w·σ(gate) — sigmoid, not SiLU.
            let ss: f64 = o[..dv].iter().map(|&v| (v as f64) * (v as f64)).sum();
            let inv = 1.0 / (ss / dv as f64 + cfg.rms_eps).sqrt();
            for dj in 0..dv {
                oh[dj] = ((o[dj] as f64 * inv)
                    * w.o_norm[dj] as f64
                    * sigmoid(gate_out[h * dv + dj] as f64)) as f32;
            }
        }
        crate::attention::recycle_buf(&mut kv);
        crate::attention::recycle_buf(&mut delta);
        crate::attention::recycle_buf(&mut o);
        crate::attention::recycle_buf(&mut kf);
        crate::attention::recycle_buf(&mut qf);
        crate::attention::recycle_buf(&mut gd);
    };
    match pool {
        Some(pool) if nh >= 4 => pool.run(&|widx, n| {
            let chunk = nh.div_ceil(n);
            let h0 = (widx * chunk).min(nh);
            let h1 = (h0 + chunk).min(nh);
            if h0 < h1 {
                head_range(h0, h1);
            }
        }),
        _ => head_range(0, nh),
    }
}

/// Project one position's raw q/k/v/f/β/gate inputs (shared by the
/// single and batched forwards; `bi` selects the row when batched).
fn kda_gate_out(w: &KdaWeights, x: &[f32], vd: usize, pool: Option<&Pool>) -> Vec<f32> {
    let mut g = vec![0.0f32; vd];
    match &w.gate {
        KdaOutGate::Full(gp) => gp.matvec(x, &mut g, pool),
        KdaOutGate::LowRank(ga, gb) => {
            let mut low = vec![0.0f32; ga.rows()];
            ga.matvec(x, &mut low, pool);
            gb.matvec(&low, &mut g, pool);
        }
    }
    g
}

/// Forward one position through a KDA layer, advancing `state`.
pub fn kda_forward(
    x: &[f32],
    w: &KdaWeights,
    cfg: &KdaCfg,
    state: &mut Vec<f32>,
    pool: Option<&Pool>,
) -> Vec<f32> {
    if state.len() != cfg.state_len() {
        *state = vec![0f32; cfg.state_len()];
    }
    let (nh, dk, dv) = (cfg.num_heads, cfg.head_k_dim, cfg.head_v_dim);
    let (kd, vd) = (nh * dk, nh * dv);

    let mut xq = vec![0.0f32; kd];
    let mut xk = vec![0.0f32; kd];
    let mut xv = vec![0.0f32; vd];
    let mut fl = vec![0.0f32; w.f_a.rows()];
    let mut b = vec![0.0f32; nh];
    QTensor::matvec_many(
        [&w.q_proj, &w.k_proj, &w.v_proj, &w.f_a],
        x,
        [
            xq.as_mut_slice(),
            xk.as_mut_slice(),
            xv.as_mut_slice(),
            fl.as_mut_slice(),
        ],
        pool,
    );
    w.b_proj.matvec(x, &mut b, pool);
    let mut f = vec![0.0f32; kd];
    w.f_b.matvec(&fl, &mut f, pool);
    let gate_out = kda_gate_out(w, x, vd, pool);

    let mut of = vec![0.0f32; vd];
    kda_step(
        &xq, &xk, &xv, &f, &b, &gate_out, w, cfg, state, &mut of, pool,
    );

    let mut out = vec![0.0f32; cfg.hidden_size];
    w.o_proj.matvec(&of, &mut out, pool);
    out
}

/// Batched KDA forward (prefill-GEMM): projections as matmat over the
/// chunk, the recurrence sequential per position — elementwise identical
/// to the single-position path.
pub fn kda_forward_batch(
    xs: &[f32],
    bsz: usize,
    w: &KdaWeights,
    cfg: &KdaCfg,
    state: &mut Vec<f32>,
    pool: Option<&Pool>,
) -> Vec<f32> {
    if state.len() != cfg.state_len() {
        *state = vec![0f32; cfg.state_len()];
    }
    let (nh, dk, dv, hs) = (
        cfg.num_heads,
        cfg.head_k_dim,
        cfg.head_v_dim,
        cfg.hidden_size,
    );
    let (kd, vd) = (nh * dk, nh * dv);

    let mut xq = vec![0.0f32; bsz * kd];
    w.q_proj.matmat(xs, bsz, &mut xq, pool);
    let mut xk = vec![0.0f32; bsz * kd];
    w.k_proj.matmat(xs, bsz, &mut xk, pool);
    let mut xv = vec![0.0f32; bsz * vd];
    w.v_proj.matmat(xs, bsz, &mut xv, pool);
    let rank = w.f_a.rows();
    let mut fl = vec![0.0f32; bsz * rank];
    w.f_a.matmat(xs, bsz, &mut fl, pool);
    let mut f = vec![0.0f32; bsz * kd];
    w.f_b.matmat(&fl, bsz, &mut f, pool);
    let mut b = vec![0.0f32; bsz * nh];
    w.b_proj.matmat(xs, bsz, &mut b, pool);
    let mut gate_out = vec![0.0f32; bsz * vd];
    match &w.gate {
        KdaOutGate::Full(gp) => gp.matmat(xs, bsz, &mut gate_out, pool),
        KdaOutGate::LowRank(ga, gb) => {
            let mut low = vec![0.0f32; bsz * ga.rows()];
            ga.matmat(xs, bsz, &mut low, pool);
            gb.matmat(&low, bsz, &mut gate_out, pool);
        }
    }

    let mut of = vec![0.0f32; bsz * vd];
    for bi in 0..bsz {
        let mut oh = vec![0.0f32; vd];
        kda_step(
            &xq[bi * kd..(bi + 1) * kd],
            &xk[bi * kd..(bi + 1) * kd],
            &xv[bi * vd..(bi + 1) * vd],
            &f[bi * kd..(bi + 1) * kd],
            &b[bi * nh..(bi + 1) * nh],
            &gate_out[bi * vd..(bi + 1) * vd],
            w,
            cfg,
            state,
            &mut oh,
            pool,
        );
        of[bi * vd..(bi + 1) * vd].copy_from_slice(&oh);
    }

    let mut out = vec![0.0f32; bsz * hs];
    w.o_proj.matmat(&of, bsz, &mut out, pool);
    out
}

#[cfg(test)]
mod tests {
    #[test]
    fn kda_forward_matches_naive_reference() {
        // Small deterministic KDA layer; the oracle is a literal port of
        // FLA naive_recurrent_kda + naive_kda_gate + the modeling glue
        // (conv→silu, low-rank decay, sigmoid-gated output norm), coded
        // straight from the reference — a different shape from the fused
        // two-pass production kernel.
        let (nh, dk, dv, kk, hs, rank) = (2usize, 4usize, 4usize, 3usize, 6usize, 3usize);
        let synth = |rows: usize, cols: usize, salt: usize| -> QTensor {
            QTensor::from_f32(
                (0..rows * cols)
                    .map(|i| (((i * 31 + salt * 17) % 101) as f32 / 101.0 - 0.5) * 0.6)
                    .collect(),
                rows,
                cols,
            )
        };
        let vecf = |n: usize, salt: usize| -> Vec<f32> {
            (0..n)
                .map(|i| (((i * 13 + salt * 7) % 89) as f32 / 89.0 - 0.5) * 0.8)
                .collect()
        };
        for (label, a_log, lb) in [
            ("per-head standard", vecf(nh, 40), None),
            ("per-dim lower-bound", vecf(dk, 41), Some(-5.0f32)),
        ] {
            let w = KdaWeights {
                q_proj: synth(nh * dk, hs, 1),
                k_proj: synth(nh * dk, hs, 2),
                v_proj: synth(nh * dv, hs, 3),
                conv_q: vecf(nh * dk * kk, 4),
                conv_k: vecf(nh * dk * kk, 5),
                conv_v: vecf(nh * dv * kk, 6),
                f_a: synth(rank, hs, 7),
                f_b: synth(nh * dk, rank, 8),
                dt_bias: vecf(nh * dk, 9),
                a_log: a_log.clone(),
                b_proj: synth(nh, hs, 10),
                gate: KdaOutGate::LowRank(synth(rank, hs, 11), synth(nh * dv, rank, 12)),
                o_norm: (0..dv).map(|i| 1.0 + 0.1 * i as f32).collect(),
                o_proj: synth(hs, nh * dv, 13),
                gate_lower_bound: lb,
            };
            let cfg = KdaCfg {
                num_heads: nh,
                head_k_dim: dk,
                head_v_dim: dv,
                conv_kernel: kk,
                hidden_size: hs,
                rms_eps: 1e-6,
            };
            let xs: Vec<Vec<f32>> = (0..6)
                .map(|t| {
                    (0..hs)
                        .map(|i| ((t * hs + i) as f32 * 0.37).sin() * 0.5)
                        .collect()
                })
                .collect();

            // Production path.
            let mut state = Vec::new();
            let got: Vec<Vec<f32>> = xs
                .iter()
                .map(|x| kda_forward(x, &w, &cfg, &mut state, None))
                .collect();

            // Oracle.
            let mv = |t: &QTensor, x: &[f32]| -> Vec<f32> {
                let mut o = vec![0.0f32; t.rows()];
                t.matvec(x, &mut o, None);
                o
            };
            let mut hist: Vec<(Vec<f32>, Vec<f32>, Vec<f32>)> = Vec::new(); // raw xq/xk/xv
            let mut s_state = vec![0f64; nh * dk * dv];
            let mut want: Vec<Vec<f32>> = Vec::new();
            for x in &xs {
                let (xq, xk, xv) = (mv(&w.q_proj, x), mv(&w.k_proj, x), mv(&w.v_proj, x));
                hist.push((xq, xk, xv));
                // conv over the raw history, taps oldest→newest.
                let conv = |sel: fn(&(Vec<f32>, Vec<f32>, Vec<f32>)) -> &Vec<f32>,
                            taps: &[f32],
                            n: usize|
                 -> Vec<f32> {
                    (0..n)
                        .map(|c| {
                            let t = &taps[c * kk..(c + 1) * kk];
                            let mut acc = 0f64;
                            for j in 0..kk {
                                let idx = hist.len() as i64 - (kk as i64 - j as i64);
                                if idx >= 0 {
                                    acc += sel(&hist[idx as usize])[c] as f64 * t[j] as f64;
                                }
                            }
                            silu(acc)
                        })
                        .map(|v| v as f32)
                        .collect()
                };
                let cq = conv(|h| &h.0, &w.conv_q, nh * dk);
                let ck = conv(|h| &h.1, &w.conv_k, nh * dk);
                let cv = conv(|h| &h.2, &w.conv_v, nh * dv);
                let f = mv(&w.f_b, &mv(&w.f_a, x));
                let bb = mv(&w.b_proj, x);
                let gate_out = match &w.gate {
                    KdaOutGate::LowRank(ga, gb) => mv(gb, &mv(ga, x)),
                    KdaOutGate::Full(g) => mv(g, x),
                };
                let mut of = vec![0f32; nh * dv];
                for h in 0..nh {
                    // l2norm + scale.
                    let q: Vec<f64> = {
                        let sl = &cq[h * dk..(h + 1) * dk];
                        let n: f64 = sl.iter().map(|&v| (v as f64) * (v as f64)).sum();
                        let inv = 1.0 / ((n + 1e-6).sqrt() * (dk as f64).sqrt());
                        sl.iter().map(|&v| v as f64 * inv).collect()
                    };
                    let k: Vec<f64> = {
                        let sl = &ck[h * dk..(h + 1) * dk];
                        let n: f64 = sl.iter().map(|&v| (v as f64) * (v as f64)).sum();
                        let inv = 1.0 / (n + 1e-6).sqrt();
                        sl.iter().map(|&v| v as f64 * inv).collect()
                    };
                    let v: Vec<f64> = cv[h * dv..(h + 1) * dv].iter().map(|&v| v as f64).collect();
                    // gate: g = −exp(A)·softplus(f+bias) | lb·σ(exp(A)·(f+bias))
                    let g: Vec<f64> = (0..dk)
                        .map(|d| {
                            let a = if w.a_log.len() == nh {
                                w.a_log[h] as f64
                            } else {
                                w.a_log[d] as f64
                            };
                            let raw = f[h * dk + d] as f64 + w.dt_bias[h * dk + d] as f64;
                            match w.gate_lower_bound {
                                Some(lb) => lb as f64 * sigmoid(a.exp() * raw),
                                None => -a.exp() * softplus(raw),
                            }
                        })
                        .collect();
                    let beta = sigmoid(bb[h] as f64);
                    let s = &mut s_state[h * dk * dv..(h + 1) * dk * dv];
                    // S = Diag(exp(g))·S
                    for di in 0..dk {
                        for dj in 0..dv {
                            s[di * dv + dj] *= g[di].exp();
                        }
                    }
                    // kv = kᵀS; S += β·k⊗(v−kv); o = qᵀS
                    let mut kv = vec![0f64; dv];
                    for di in 0..dk {
                        for dj in 0..dv {
                            kv[dj] += k[di] * s[di * dv + dj];
                        }
                    }
                    for di in 0..dk {
                        for dj in 0..dv {
                            s[di * dv + dj] += beta * k[di] * (v[dj] - kv[dj]);
                        }
                    }
                    let mut o = vec![0f64; dv];
                    for di in 0..dk {
                        for dj in 0..dv {
                            o[dj] += q[di] * s[di * dv + dj];
                        }
                    }
                    // sigmoid-gated RMSNorm
                    let ss: f64 = o.iter().map(|&v| v * v).sum();
                    let inv = 1.0 / (ss / dv as f64 + cfg.rms_eps).sqrt();
                    for dj in 0..dv {
                        of[h * dv + dj] = (o[dj]
                            * inv
                            * w.o_norm[dj] as f64
                            * sigmoid(gate_out[h * dv + dj] as f64))
                            as f32;
                    }
                }
                want.push(mv(&w.o_proj, &of));
            }

            for (t, (g, e)) in got.iter().zip(&want).enumerate() {
                for (i, (a, b)) in g.iter().zip(e.iter()).enumerate() {
                    assert!((a - b).abs() < 2e-4, "{label}: t={t} i={i}: {a} vs {b}");
                }
            }
        }

        // Batched prefill must equal the sequential singles bit-close.
        let w = KdaWeights {
            q_proj: synth(nh * dk, hs, 1),
            k_proj: synth(nh * dk, hs, 2),
            v_proj: synth(nh * dv, hs, 3),
            conv_q: vecf(nh * dk * kk, 4),
            conv_k: vecf(nh * dk * kk, 5),
            conv_v: vecf(nh * dv * kk, 6),
            f_a: synth(rank, hs, 7),
            f_b: synth(nh * dk, rank, 8),
            dt_bias: vecf(nh * dk, 9),
            a_log: vecf(nh, 40),
            b_proj: synth(nh, hs, 10),
            gate: KdaOutGate::LowRank(synth(rank, hs, 11), synth(nh * dv, rank, 12)),
            o_norm: (0..dv).map(|i| 1.0 + 0.1 * i as f32).collect(),
            o_proj: synth(hs, nh * dv, 13),
            gate_lower_bound: None,
        };
        let cfg = KdaCfg {
            num_heads: nh,
            head_k_dim: dk,
            head_v_dim: dv,
            conv_kernel: kk,
            hidden_size: hs,
            rms_eps: 1e-6,
        };
        let xs: Vec<f32> = (0..5 * hs).map(|i| (i as f32 * 0.29).cos() * 0.4).collect();
        let mut st1 = Vec::new();
        let seq: Vec<f32> = (0..5)
            .flat_map(|t| kda_forward(&xs[t * hs..(t + 1) * hs], &w, &cfg, &mut st1, None))
            .collect();
        let mut st2 = Vec::new();
        let bat = kda_forward_batch(&xs, 5, &w, &cfg, &mut st2, None);
        for (i, (a, b)) in seq.iter().zip(&bat).enumerate() {
            assert!((a - b).abs() < 1e-5, "batch i={i}: {a} vs {b}");
        }
        assert_eq!(st1, st2, "state must match after the chunk");
    }

    use super::*;

    fn tiny() -> (VmfPhaseWeights, VmfPhaseCfg) {
        let cfg = VmfPhaseCfg {
            num_heads: 2,
            nphase: 3,
            value_head_dim: 4,
            hidden_size: 8,
            phase_mass: 0.0,
        };
        let synth = |rows: usize, cols: usize, salt: usize| {
            QTensor::from_f32(
                (0..rows * cols)
                    .map(|i| (((i * 13 + salt * 7) % 97) as f32 / 97.0 - 0.5) * 0.4)
                    .collect(),
                rows,
                cols,
            )
        };
        let w = VmfPhaseWeights {
            thq: synth(cfg.num_heads * cfg.nphase, cfg.hidden_size, 1),
            thk: synth(cfg.num_heads * cfg.nphase, cfg.hidden_size, 2),
            v_proj: synth(cfg.num_heads * cfg.value_head_dim, cfg.hidden_size, 3),
            out_proj: synth(cfg.hidden_size, cfg.num_heads * cfg.value_head_dim, 4),
            decay: (0..cfg.num_heads * 2 * cfg.nphase)
                .map(|i| 0.9 + 0.005 * (i % 10) as f64)
                .collect(),
            conv: None,
            k_gate: None,
        };
        (w, cfg)
    }

    #[test]
    fn state_persists_and_changes_output() {
        let (w, cfg) = tiny();
        let x: Vec<f32> = (0..8).map(|i| (i as f32 * 0.3).sin()).collect();
        let mut state = Vec::new();
        let o1 = vmf_phase_forward(&x, &w, &cfg, &mut state, None);
        let o2 = vmf_phase_forward(&x, &w, &cfg, &mut state, None);
        // Same input, evolved state → different output.
        assert!(o1.iter().zip(&o2).any(|(a, b)| (a - b).abs() > 1e-6));
        assert_eq!(state.len(), cfg.state_len());
    }

    /// Phase-mass correction: mass=0 is bit-identical to the unscaled kernel; mass>0
    /// changes the output (phase narrowed → kernel widened). Guards the
    /// no-op default and that the knob is actually wired.
    #[test]
    fn phase_mass_zero_is_noop_and_positive_shifts() {
        let (w, cfg0) = tiny();
        let mut cfg_m = cfg0;
        cfg_m.phase_mass = 1.0;
        let x: Vec<f32> = (0..8).map(|i| (i as f32 * 0.4).sin()).collect();

        let mut s0 = Vec::new();
        let base = vmf_phase_forward(&x, &w, &cfg0, &mut s0, None);
        // Re-run with mass=0 → must be bit-identical.
        let mut s0b = Vec::new();
        let base2 = vmf_phase_forward(&x, &w, &cfg0, &mut s0b, None);
        assert_eq!(base, base2, "mass=0 must be deterministic/no-op");
        // mass=1 → output differs (θ halved before cos/sin).
        let mut sm = Vec::new();
        let massed = vmf_phase_forward(&x, &w, &cfg_m, &mut sm, None);
        assert!(
            base.iter().zip(&massed).any(|(a, b)| (a - b).abs() > 1e-5),
            "mass>0 must change the output"
        );
        assert!(massed.iter().all(|v| v.is_finite()));
    }

    /// κ write gate (hybrid_k): saturated-open gate (bias ≫ 0 → κ→1)
    /// matches the gateless kernel within fp tolerance; a closed gate
    /// (bias ≪ 0 → κ→0) writes nothing — the state stays zero and the
    /// output collapses to the empty-state readout.
    #[test]
    fn kappa_gate_open_matches_none_and_closed_writes_nothing() {
        let (mut w, cfg) = tiny();
        let x: Vec<f32> = (0..8).map(|i| (i as f32 * 0.3).sin()).collect();

        let mut s_none = Vec::new();
        let base1 = vmf_phase_forward(&x, &w, &cfg, &mut s_none, None);
        let base2 = vmf_phase_forward(&x, &w, &cfg, &mut s_none, None);

        // Open gate: W=0, bias=+20 → κ = σ(20) ≈ 1 − 2e−9.
        w.k_gate = Some((
            QTensor::from_f32(
                vec![0.0; cfg.num_heads * cfg.hidden_size],
                cfg.num_heads,
                cfg.hidden_size,
            ),
            vec![20.0; cfg.num_heads],
        ));
        let mut s_open = Vec::new();
        let o1 = vmf_phase_forward(&x, &w, &cfg, &mut s_open, None);
        let o2 = vmf_phase_forward(&x, &w, &cfg, &mut s_open, None);
        for (a, b) in base1.iter().zip(&o1).chain(base2.iter().zip(&o2)) {
            assert!(
                (a - b).abs() < 1e-5,
                "open κ must match gateless: {a} vs {b}"
            );
        }

        // Closed gate: bias=−20 → κ ≈ 0 → nothing is written.
        w.k_gate = Some((
            QTensor::from_f32(
                vec![0.0; cfg.num_heads * cfg.hidden_size],
                cfg.num_heads,
                cfg.hidden_size,
            ),
            vec![-20.0; cfg.num_heads],
        ));
        let mut s_closed = Vec::new();
        let oc = vmf_phase_forward(&x, &w, &cfg, &mut s_closed, None);
        assert!(
            s_closed.iter().all(|&v| v.abs() < 1e-7),
            "closed κ: state must stay empty"
        );
        assert!(
            oc.iter().all(|&v| v.abs() < 1e-6),
            "closed κ: empty-state readout"
        );
    }

    #[test]
    fn pair_matches_two_singles_bitexact() {
        let (w, cfg) = tiny();
        let x1: Vec<f32> = (0..8).map(|i| (i as f32 * 0.2).cos()).collect();
        let x2: Vec<f32> = (0..8).map(|i| (i as f32 * 0.5).sin()).collect();

        // Reference: two sequential singles.
        let mut s_ref = Vec::new();
        let r1 = vmf_phase_forward(&x1, &w, &cfg, &mut s_ref, None);
        let r2 = vmf_phase_forward(&x2, &w, &cfg, &mut s_ref, None);

        // Pair: lane1 commits, lane2 tentative in scratch.
        let mut s = Vec::new();
        let mut scratch = Vec::new();
        let (p1, p2) = vmf_phase_pair(&x1, &x2, &w, &cfg, &mut s, &mut scratch, None);
        assert_eq!(r1, p1, "lane 1 must be bit-identical");
        assert_eq!(r2, p2, "lane 2 must be bit-identical");
        // Accepting the draft = swapping scratch in → equals s_ref.
        std::mem::swap(&mut s, &mut scratch);
        assert_eq!(s, s_ref, "accepted state must equal sequential state");
    }

    #[test]
    fn rejected_draft_leaves_state_at_lane1() {
        let (w, cfg) = tiny();
        let x1: Vec<f32> = (0..8).map(|i| (i as f32 * 0.7).sin()).collect();
        let x2 = vec![0.5f32; 8];

        let mut s_ref = Vec::new();
        let _ = vmf_phase_forward(&x1, &w, &cfg, &mut s_ref, None);

        let mut s = Vec::new();
        let mut scratch = Vec::new();
        let _ = vmf_phase_pair(&x1, &x2, &w, &cfg, &mut s, &mut scratch, None);
        // Reject: state must be exactly the post-lane1 state.
        assert_eq!(s, s_ref);
    }

    // ───────────── GatedDeltaNet ─────────────

    fn tiny_gdn() -> (GdnWeights, GdnCfg) {
        let cfg = GdnCfg {
            num_v_heads: 4,
            num_k_heads: 2,
            key_head_dim: 3,
            value_head_dim: 5,
            conv_kernel: 4,
            hidden_size: 8,
            rms_eps: 1e-6,
            output_gate_sigmoid: false,
        };
        let c_dim = cfg.conv_dim();
        let vd = cfg.num_v_heads * cfg.value_head_dim;
        let synth = |rows: usize, cols: usize, salt: usize| {
            QTensor::from_f32(
                (0..rows * cols)
                    .map(|i| (((i * 13 + salt * 7) % 97) as f32 / 97.0 - 0.5) * 0.4)
                    .collect(),
                rows,
                cols,
            )
        };
        let vecf = |n: usize, salt: usize| -> Vec<f32> {
            (0..n)
                .map(|i| (((i * 11 + salt * 5) % 89) as f32 / 89.0 - 0.5) * 0.6)
                .collect()
        };
        let w = GdnWeights {
            in_proj_qkv: synth(c_dim, cfg.hidden_size, 1),
            in_proj_z: synth(vd, cfg.hidden_size, 2),
            in_proj_a: synth(cfg.num_v_heads, cfg.hidden_size, 3),
            in_proj_b: synth(cfg.num_v_heads, cfg.hidden_size, 4),
            conv1d: vecf(c_dim * cfg.conv_kernel, 5),
            a_log: (0..cfg.num_v_heads).map(|i| 0.2 + 0.3 * i as f32).collect(),
            dt_bias: vecf(cfg.num_v_heads, 6),
            norm: vec![1.0; cfg.value_head_dim],
            out_proj: synth(cfg.hidden_size, vd, 7),
        };
        (w, cfg)
    }

    #[test]
    fn gdn_state_persists_and_changes_output() {
        let (w, cfg) = tiny_gdn();
        let x: Vec<f32> = (0..8).map(|i| (i as f32 * 0.3).sin()).collect();
        let mut state = Vec::new();
        let o1 = gdn_forward(&x, &w, &cfg, &mut state, None);
        let o2 = gdn_forward(&x, &w, &cfg, &mut state, None);
        assert!(o1.iter().zip(&o2).any(|(a, b)| (a - b).abs() > 1e-6));
        assert_eq!(state.len(), cfg.state_len());
    }

    #[test]
    fn gdn_pair_matches_two_singles_bitexact() {
        let (w, cfg) = tiny_gdn();
        let x1: Vec<f32> = (0..8).map(|i| (i as f32 * 0.2).cos()).collect();
        let x2: Vec<f32> = (0..8).map(|i| (i as f32 * 0.5).sin()).collect();

        let mut s_ref = Vec::new();
        let r1 = gdn_forward(&x1, &w, &cfg, &mut s_ref, None);
        let r2 = gdn_forward(&x2, &w, &cfg, &mut s_ref, None);

        let mut s = Vec::new();
        let mut scratch = Vec::new();
        let (p1, p2) = gdn_pair(&x1, &x2, &w, &cfg, &mut s, &mut scratch, None);
        assert_eq!(r1, p1, "lane 1 must be bit-identical");
        assert_eq!(r2, p2, "lane 2 must be bit-identical");
        std::mem::swap(&mut s, &mut scratch);
        assert_eq!(s, s_ref, "accepted state must equal sequential state");
    }

    #[test]
    fn gdn_rejected_draft_leaves_state_at_lane1() {
        let (w, cfg) = tiny_gdn();
        let x1: Vec<f32> = (0..8).map(|i| (i as f32 * 0.7).sin()).collect();
        let x2 = vec![0.5f32; 8];

        let mut s_ref = Vec::new();
        let _ = gdn_forward(&x1, &w, &cfg, &mut s_ref, None);

        let mut s = Vec::new();
        let mut scratch = Vec::new();
        let _ = gdn_pair(&x1, &x2, &w, &cfg, &mut s, &mut scratch, None);
        assert_eq!(s, s_ref);
    }

    /// The conv ring must give the same result as an explicit causal
    /// conv over the whole sequence (oracle semantics: zero left-pad,
    /// tap kk−1 on the current position).
    #[test]
    fn gdn_conv_ring_matches_explicit_causal_conv() {
        let (w, cfg) = tiny_gdn();
        let seq: Vec<Vec<f32>> = (0..6)
            .map(|t| (0..8).map(|i| ((t * 8 + i) as f32 * 0.17).sin()).collect())
            .collect();

        // Reference: recompute position t from scratch each time with a
        // fresh state built by replaying the prefix.
        let mut s_inc = Vec::new();
        for (t, x) in seq.iter().enumerate() {
            let inc = gdn_forward(x, &w, &cfg, &mut s_inc, None);
            let mut s_replay = Vec::new();
            let mut replay = Vec::new();
            for xr in &seq[..=t] {
                replay = gdn_forward(xr, &w, &cfg, &mut s_replay, None);
            }
            assert_eq!(inc, replay, "position {t}: ring must equal replay");
        }
    }

    fn tiny_short_conv() -> (ShortConvWeights, ShortConvCfg) {
        let cfg = ShortConvCfg {
            hidden_size: 8,
            kernel: 3,
        };
        let synth = |rows: usize, cols: usize, salt: usize| {
            QTensor::from_f32(
                (0..rows * cols)
                    .map(|i| (((i * 11 + salt * 5) % 89) as f32 / 89.0 - 0.5) * 0.5)
                    .collect(),
                rows,
                cols,
            )
        };
        let w = ShortConvWeights {
            in_proj: synth(3 * cfg.hidden_size, cfg.hidden_size, 1),
            conv: (0..cfg.hidden_size * cfg.kernel)
                .map(|i| ((i * 7 % 13) as f32 / 13.0 - 0.5) * 0.8)
                .collect(),
            out_proj: synth(cfg.hidden_size, cfg.hidden_size, 2),
        };
        (w, cfg)
    }

    /// The incremental conv ring must equal a from-scratch causal replay
    /// of the prefix at every position — the decode/prefill contract.
    #[test]
    fn short_conv_ring_matches_explicit_causal_conv() {
        let (w, cfg) = tiny_short_conv();
        let seq: Vec<Vec<f32>> = (0..6)
            .map(|t| (0..8).map(|i| ((t * 8 + i) as f32 * 0.19).cos()).collect())
            .collect();
        let mut s_inc = Vec::new();
        for (t, x) in seq.iter().enumerate() {
            let inc = short_conv_forward(x, &w, &cfg, &mut s_inc, None);
            let mut s_replay = Vec::new();
            let mut replay = Vec::new();
            for xr in &seq[..=t] {
                replay = short_conv_forward(xr, &w, &cfg, &mut s_replay, None);
            }
            assert_eq!(inc, replay, "position {t}: ring must equal replay");
            assert_eq!(s_inc.len(), cfg.state_len());
        }
    }

    /// The batched prefill path (matmat + sequential conv over the chunk)
    /// must reproduce the position-by-position decode path exactly.
    #[test]
    fn short_conv_batch_matches_sequential() {
        let (w, cfg) = tiny_short_conv();
        let b = 5;
        let xs: Vec<f32> = (0..b * cfg.hidden_size)
            .map(|i| (i as f32 * 0.13).sin() * 0.6)
            .collect();

        let mut s_seq = Vec::new();
        let mut seq_out = vec![0.0f32; b * cfg.hidden_size];
        for bi in 0..b {
            let o = short_conv_forward(
                &xs[bi * cfg.hidden_size..(bi + 1) * cfg.hidden_size],
                &w,
                &cfg,
                &mut s_seq,
                None,
            );
            seq_out[bi * cfg.hidden_size..(bi + 1) * cfg.hidden_size].copy_from_slice(&o);
        }

        let mut s_batch = Vec::new();
        let batch_out = short_conv_forward_batch(&xs, b, &w, &cfg, &mut s_batch, None);
        assert_eq!(
            seq_out, batch_out,
            "batch conv must match sequential decode"
        );
        assert_eq!(s_seq, s_batch, "ring state must match after the chunk");
    }
}